From f0054b509861ed618fbcc4425f821226336569da Mon Sep 17 00:00:00 2001
From: UnforgettableCassini <66637599+AdaBatu@users.noreply.github.com>
Date: Thu, 22 Oct 2026 23:36:49 +0100
Subject: [PATCH 1/4] WIP: integrate Missile DATCOM; prediction discrepancies
 unresolved

DATCOM currently generates shit values. Trying to solve it.

Add optional whole-vehicle native DATCOM aerodynamics, adaptive flight grids,
calibration APIs and measured-flight comparison notebooks. Keep ordinary
RocketPy equations unchanged. Mark predictions as experimental and unresolved.

Validation: 314 relevant tests passed; both comparison notebooks executed;
strict documentation build passed.
---
 data/rockets/ast_her/README.md                |    23 +
 data/rockets/ast_her/atmosphere.json          |   561 +
 data/rockets/ast_her/configuration.json       |   171 +
 data/rockets/ast_her/flight_conditions.json   |    71 +
 data/rockets/ast_her/performance.csv          |    14 +
 data/rockets/ast_her/thrust_curve.csv         |    12 +
 docs/examples/datcom/README.md                |    41 +
 docs/examples/datcom/asteria_comparison.ipynb |   182 +
 .../datcom/comparison_data/asteria_cd.csv     |   152 +
 .../comparison_data/asteria_drag_curve.csv    |  7500 ++
 .../comparison_data/asteria_histories.csv     |   602 +
 .../comparison_data/asteria_metrics.csv       |     6 +
 .../datcom/comparison_data/asteria_model.py   |   202 +
 .../comparison_data/asteria_provenance.json   |   255 +
 .../datcom/comparison_data/model_geometry.py  |   137 +
 .../datcom/comparison_data/model_sources.py   |   393 +
 .../comparison_data/prepare_comparisons.py    |   325 +
 .../comparison_data/reproduce_rockets.py      |   406 +
 .../datcom/comparison_data/rocket_metrics.csv |    79 +
 .../rockets/andromeda_altitude.csv            |   602 +
 .../comparison_data/rockets/andromeda_cd.csv  |   152 +
 .../rockets/andromeda_coast_for005.dat        |   220 +
 .../rockets/andromeda_powered_for005.dat      |   220 +
 .../rockets/andromeda_provenance.json         |   377 +
 .../rockets/andromeda_result.json             |    49 +
 .../rockets/andromeda_telemetry.csv           |  1140 +
 .../rockets/andromeda_velocity.csv            |  1140 +
 .../rockets/astra_altitude.csv                |   602 +
 .../comparison_data/rockets/astra_cd.csv      |   152 +
 .../rockets/astra_coast_for005.dat            |   220 +
 .../rockets/astra_powered_for005.dat          |   220 +
 .../rockets/astra_provenance.json             |   377 +
 .../comparison_data/rockets/astra_result.json |    49 +
 .../rockets/astra_telemetry.csv               |    63 +
 .../rockets/bella_lui_altitude.csv            |   602 +
 .../comparison_data/rockets/bella_lui_cd.csv  |   152 +
 .../rockets/bella_lui_coast_for005.dat        |   235 +
 .../rockets/bella_lui_powered_for005.dat      |   235 +
 .../rockets/bella_lui_provenance.json         |   384 +
 .../rockets/bella_lui_result.json             |    49 +
 .../rockets/bella_lui_telemetry.csv           |   574 +
 .../rockets/bella_lui_velocity.csv            |   574 +
 .../rockets/camoes_altitude.csv               |   502 +
 .../comparison_data/rockets/camoes_cd.csv     |   152 +
 .../rockets/camoes_result.json                |    26 +
 .../rockets/camoes_telemetry.csv              | 14347 ++++
 .../rockets/cavour_altitude.csv               |   602 +
 .../comparison_data/rockets/cavour_cd.csv     |   152 +
 .../rockets/cavour_coast_for005.dat           |   220 +
 .../rockets/cavour_powered_for005.dat         |   220 +
 .../rockets/cavour_provenance.json            |   377 +
 .../rockets/cavour_result.json                |    49 +
 .../rockets/cavour_telemetry.csv              |   405 +
 .../rockets/cavour_velocity.csv               |   405 +
 .../rockets/defiance_altitude.csv             |   502 +
 .../comparison_data/rockets/defiance_cd.csv   |   152 +
 .../rockets/defiance_result.json              |    20 +
 .../rockets/erebus_altitude.csv               |   602 +
 .../comparison_data/rockets/erebus_cd.csv     |   152 +
 .../rockets/erebus_coast_for005.dat           |   235 +
 .../rockets/erebus_powered_for005.dat         |   235 +
 .../rockets/erebus_provenance.json            |   377 +
 .../rockets/erebus_result.json                |    49 +
 .../rockets/erebus_telemetry.csv              |   747 +
 .../rockets/genesis_altitude.csv              |   602 +
 .../comparison_data/rockets/genesis_cd.csv    |   152 +
 .../rockets/genesis_coast_for005.dat          |   220 +
 .../rockets/genesis_powered_for005.dat        |   220 +
 .../rockets/genesis_provenance.json           |   378 +
 .../rockets/genesis_result.json               |    49 +
 .../rockets/genesis_telemetry.csv             | 17934 +++++
 .../rockets/halcyon_altitude.csv              |   602 +
 .../comparison_data/rockets/halcyon_cd.csv    |   152 +
 .../rockets/halcyon_coast_for005.dat          |   235 +
 .../rockets/halcyon_powered_for005.dat        |   235 +
 .../rockets/halcyon_provenance.json           |   382 +
 .../rockets/halcyon_result.json               |    49 +
 .../rockets/halcyon_telemetry.csv             | 65883 ++++++++++++++++
 .../comparison_data/rockets/hedy_result.json  |    11 +
 .../comparison_data/rockets/inventory.json    |   487 +
 .../rockets/juno3_altitude.csv                |   602 +
 .../comparison_data/rockets/juno3_cd.csv      |   152 +
 .../rockets/juno3_coast_for005.dat            |   235 +
 .../rockets/juno3_powered_for005.dat          |   235 +
 .../rockets/juno3_provenance.json             |   384 +
 .../comparison_data/rockets/juno3_result.json |    49 +
 .../rockets/juno3_telemetry.csv               |   612 +
 .../rockets/juno3_velocity.csv                |   612 +
 .../rockets/lince_altitude.csv                |   602 +
 .../comparison_data/rockets/lince_cd.csv      |   152 +
 .../rockets/lince_coast_for005.dat            |   235 +
 .../rockets/lince_powered_for005.dat          |   235 +
 .../rockets/lince_provenance.json             |   382 +
 .../comparison_data/rockets/lince_result.json |    49 +
 .../rockets/lince_telemetry.csv               | 44455 +++++++++++
 .../rockets/ndrt_2020_altitude.csv            |   502 +
 .../comparison_data/rockets/ndrt_2020_cd.csv  |   152 +
 .../rockets/ndrt_2020_result.json             |    26 +
 .../rockets/ndrt_2020_telemetry.csv           |  1820 +
 .../rockets/prometheus_2022_altitude.csv      |   602 +
 .../rockets/prometheus_2022_cd.csv            |   152 +
 .../rockets/prometheus_2022_coast_for005.dat  |   220 +
 .../prometheus_2022_powered_for005.dat        |   220 +
 .../rockets/prometheus_2022_provenance.json   |   376 +
 .../rockets/prometheus_2022_result.json       |    49 +
 .../rockets/prometheus_2022_telemetry.csv     |  5713 ++
 .../rockets/prometheus_2022_velocity.csv      |  7082 ++
 .../comparison_data/rockets/results.csv       |    17 +
 .../rockets/valetudo_altitude.csv             |   602 +
 .../comparison_data/rockets/valetudo_cd.csv   |   152 +
 .../rockets/valetudo_coast_for005.dat         |   220 +
 .../rockets/valetudo_powered_for005.dat       |   220 +
 .../rockets/valetudo_provenance.json          |   382 +
 .../rockets/valetudo_result.json              |    41 +
 .../rockets/valkyrie_result.json              |    13 +
 .../rockets/valkyrie_telemetry.csv            | 12579 +++
 docs/examples/datcom/rocket_comparison.ipynb  |  1404 +
 docs/examples/index.rst                       |     3 +-
 docs/reference/classes/Rocket.rst             |     3 +-
 .../classes/aero_surfaces/DatcomFins.rst      |     5 +
 .../aero_surfaces/DatcomFreeFormFins.rst      |     5 +
 .../reference/classes/aero_surfaces/index.rst |     2 +
 docs/user/rocket/datcom.rst                   |   530 +
 docs/user/rocket/rocket.rst                   |     5 +-
 rocketpy/__init__.py                          |     2 +
 rocketpy/plots/datcom_plots.py                |    50 +
 rocketpy/plots/rocket_plots.py                |     4 +
 rocketpy/rocket/__init__.py                   |     2 +
 rocketpy/rocket/aero_surface/__init__.py      |     1 +
 rocketpy/rocket/aero_surface/datcom_fins.py   |   416 +
 rocketpy/rocket/aero_surface/datcom_tail.py   |    67 +
 rocketpy/rocket/datcom.py                     |   988 +
 rocketpy/rocket/datcom_calibration.py         |   426 +
 rocketpy/rocket/datcom_conditions.py          |    67 +
 rocketpy/rocket/datcom_rocket.py              |   656 +
 rocketpy/rocket/rocket.py                     |    41 +-
 rocketpy/simulation/datcom.py                 |   144 +
 rocketpy/simulation/flight.py                 |    50 +-
 tests/conftest.py                             |     1 +
 tests/fixtures/surfaces/data/datcom/README.md |     8 +
 .../fixtures/surfaces/data/datcom/for006.dat  |   260 +
 tests/fixtures/surfaces/datcom_fixtures.py    |    58 +
 tests/integration/test_datcom.py              |   550 +
 .../rocket/aero_surface/test_body_profiles.py |    73 +
 tests/unit/rocket/test_datcom.py              |   607 +
 tests/unit/rocket/test_datcom_atmosphere.py   |   174 +
 tests/unit/rocket/test_datcom_calibration.py  |   106 +
 tests/unit/rocket/test_datcom_extensions.py   |   423 +
 tests/unit/rocket/test_datcom_wind_tunnel.py  |   300 +
 149 files changed, 215182 insertions(+), 19 deletions(-)
 create mode 100644 data/rockets/ast_her/README.md
 create mode 100644 data/rockets/ast_her/atmosphere.json
 create mode 100644 data/rockets/ast_her/configuration.json
 create mode 100644 data/rockets/ast_her/flight_conditions.json
 create mode 100644 data/rockets/ast_her/performance.csv
 create mode 100644 data/rockets/ast_her/thrust_curve.csv
 create mode 100644 docs/examples/datcom/README.md
 create mode 100644 docs/examples/datcom/asteria_comparison.ipynb
 create mode 100644 docs/examples/datcom/comparison_data/asteria_cd.csv
 create mode 100644 docs/examples/datcom/comparison_data/asteria_drag_curve.csv
 create mode 100644 docs/examples/datcom/comparison_data/asteria_histories.csv
 create mode 100644 docs/examples/datcom/comparison_data/asteria_metrics.csv
 create mode 100644 docs/examples/datcom/comparison_data/asteria_model.py
 create mode 100644 docs/examples/datcom/comparison_data/asteria_provenance.json
 create mode 100644 docs/examples/datcom/comparison_data/model_geometry.py
 create mode 100644 docs/examples/datcom/comparison_data/model_sources.py
 create mode 100644 docs/examples/datcom/comparison_data/prepare_comparisons.py
 create mode 100644 docs/examples/datcom/comparison_data/reproduce_rockets.py
 create mode 100644 docs/examples/datcom/comparison_data/rocket_metrics.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/andromeda_altitude.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/andromeda_cd.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/andromeda_coast_for005.dat
 create mode 100644 docs/examples/datcom/comparison_data/rockets/andromeda_powered_for005.dat
 create mode 100644 docs/examples/datcom/comparison_data/rockets/andromeda_provenance.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/andromeda_result.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/andromeda_telemetry.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/andromeda_velocity.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/astra_altitude.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/astra_cd.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/astra_coast_for005.dat
 create mode 100644 docs/examples/datcom/comparison_data/rockets/astra_powered_for005.dat
 create mode 100644 docs/examples/datcom/comparison_data/rockets/astra_provenance.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/astra_result.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/astra_telemetry.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/bella_lui_altitude.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/bella_lui_cd.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/bella_lui_coast_for005.dat
 create mode 100644 docs/examples/datcom/comparison_data/rockets/bella_lui_powered_for005.dat
 create mode 100644 docs/examples/datcom/comparison_data/rockets/bella_lui_provenance.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/bella_lui_result.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/bella_lui_telemetry.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/bella_lui_velocity.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/camoes_altitude.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/camoes_cd.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/camoes_result.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/camoes_telemetry.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/cavour_altitude.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/cavour_cd.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/cavour_coast_for005.dat
 create mode 100644 docs/examples/datcom/comparison_data/rockets/cavour_powered_for005.dat
 create mode 100644 docs/examples/datcom/comparison_data/rockets/cavour_provenance.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/cavour_result.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/cavour_telemetry.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/cavour_velocity.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/defiance_altitude.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/defiance_cd.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/defiance_result.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/erebus_altitude.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/erebus_cd.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/erebus_coast_for005.dat
 create mode 100644 docs/examples/datcom/comparison_data/rockets/erebus_powered_for005.dat
 create mode 100644 docs/examples/datcom/comparison_data/rockets/erebus_provenance.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/erebus_result.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/erebus_telemetry.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/genesis_altitude.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/genesis_cd.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/genesis_coast_for005.dat
 create mode 100644 docs/examples/datcom/comparison_data/rockets/genesis_powered_for005.dat
 create mode 100644 docs/examples/datcom/comparison_data/rockets/genesis_provenance.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/genesis_result.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/genesis_telemetry.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/halcyon_altitude.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/halcyon_cd.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/halcyon_coast_for005.dat
 create mode 100644 docs/examples/datcom/comparison_data/rockets/halcyon_powered_for005.dat
 create mode 100644 docs/examples/datcom/comparison_data/rockets/halcyon_provenance.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/halcyon_result.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/halcyon_telemetry.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/hedy_result.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/inventory.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/juno3_altitude.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/juno3_cd.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/juno3_coast_for005.dat
 create mode 100644 docs/examples/datcom/comparison_data/rockets/juno3_powered_for005.dat
 create mode 100644 docs/examples/datcom/comparison_data/rockets/juno3_provenance.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/juno3_result.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/juno3_telemetry.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/juno3_velocity.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/lince_altitude.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/lince_cd.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/lince_coast_for005.dat
 create mode 100644 docs/examples/datcom/comparison_data/rockets/lince_powered_for005.dat
 create mode 100644 docs/examples/datcom/comparison_data/rockets/lince_provenance.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/lince_result.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/lince_telemetry.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/ndrt_2020_altitude.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/ndrt_2020_cd.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/ndrt_2020_result.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/ndrt_2020_telemetry.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/prometheus_2022_altitude.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/prometheus_2022_cd.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/prometheus_2022_coast_for005.dat
 create mode 100644 docs/examples/datcom/comparison_data/rockets/prometheus_2022_powered_for005.dat
 create mode 100644 docs/examples/datcom/comparison_data/rockets/prometheus_2022_provenance.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/prometheus_2022_result.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/prometheus_2022_telemetry.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/prometheus_2022_velocity.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/results.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/valetudo_altitude.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/valetudo_cd.csv
 create mode 100644 docs/examples/datcom/comparison_data/rockets/valetudo_coast_for005.dat
 create mode 100644 docs/examples/datcom/comparison_data/rockets/valetudo_powered_for005.dat
 create mode 100644 docs/examples/datcom/comparison_data/rockets/valetudo_provenance.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/valetudo_result.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/valkyrie_result.json
 create mode 100644 docs/examples/datcom/comparison_data/rockets/valkyrie_telemetry.csv
 create mode 100644 docs/examples/datcom/rocket_comparison.ipynb
 create mode 100644 docs/reference/classes/aero_surfaces/DatcomFins.rst
 create mode 100644 docs/reference/classes/aero_surfaces/DatcomFreeFormFins.rst
 create mode 100644 docs/user/rocket/datcom.rst
 create mode 100644 rocketpy/plots/datcom_plots.py
 create mode 100644 rocketpy/rocket/aero_surface/datcom_fins.py
 create mode 100644 rocketpy/rocket/aero_surface/datcom_tail.py
 create mode 100644 rocketpy/rocket/datcom.py
 create mode 100644 rocketpy/rocket/datcom_calibration.py
 create mode 100644 rocketpy/rocket/datcom_conditions.py
 create mode 100644 rocketpy/rocket/datcom_rocket.py
 create mode 100644 rocketpy/simulation/datcom.py
 create mode 100644 tests/fixtures/surfaces/data/datcom/README.md
 create mode 100644 tests/fixtures/surfaces/data/datcom/for006.dat
 create mode 100644 tests/fixtures/surfaces/datcom_fixtures.py
 create mode 100644 tests/integration/test_datcom.py
 create mode 100644 tests/unit/rocket/aero_surface/test_body_profiles.py
 create mode 100644 tests/unit/rocket/test_datcom.py
 create mode 100644 tests/unit/rocket/test_datcom_atmosphere.py
 create mode 100644 tests/unit/rocket/test_datcom_calibration.py
 create mode 100644 tests/unit/rocket/test_datcom_extensions.py
 create mode 100644 tests/unit/rocket/test_datcom_wind_tunnel.py

diff --git a/data/rockets/ast_her/README.md b/data/rockets/ast_her/README.md
new file mode 100644
index 000000000..5c022e2d9
--- /dev/null
+++ b/data/rockets/ast_her/README.md
@@ -0,0 +1,23 @@
+# Asteria AST-HER DATCOM example inputs
+
+**Flight status: Asteria / AST-HER has not flown.** All Asteria apogees and trajectories in this example are simulated predictions, not flight measurements or flight validation.
+
+Snapshot from the local `Asteria-Flight-Dynamics` repository on 2026-10-21.
+`configuration.json` merges its current input and visualization JSON files;
+the provenance section records source paths and SHA-256 hashes of data files.
+
+The configured `input/thrust-curves/opt-ThrustCurve.csv` does not exist, so
+`thrust_curve.csv` copies its existing `Optimized_ThrustCurve.csv`. The saved
+atmosphere is for 2026-10-20 15:30 UTC. No network request is necessary.
+
+See `docs/examples/datcom/asteria_comparison.ipynb` for the executed comparison
+of ordinary RocketPy with native DATCOM. Both retain these motor/tank mass and
+inertia inputs and the saved atmosphere. Asteria's supplied ordinary drag curve
+is snapshotted in `docs/examples/datcom/comparison_data/asteria_drag_curve.csv`
+with its hash recorded. The configured ogive tail is native-DATCOM only;
+ordinary RocketPy uses an explicitly noted conical-tail approximation.
+
+The native executable is user supplied. DAMP, powered nozzle-annulus/coast
+full-base drag and adaptive Mach/alpha/Reynolds schedules are used. No Asteria
+hold-down clamp, PROTUB or recovery model is included. AST-HER has not flown;
+these are simulation comparisons, not measured-flight validation.
diff --git a/data/rockets/ast_her/atmosphere.json b/data/rockets/ast_her/atmosphere.json
new file mode 100644
index 000000000..aa01be6d6
--- /dev/null
+++ b/data/rockets/ast_her/atmosphere.json
@@ -0,0 +1,561 @@
+{
+    "signature": {
+        "module": "rocketpy.environment.environment",
+        "name": "Environment"
+    },
+    "date": [
+        2026,
+        10,
+        20,
+        15,
+        30
+    ],
+    "latitude": 39.3897,
+    "longitude": -8.288964,
+    "elevation": 160.0,
+    "datum": "WGS84",
+    "timezone": "Europe/Lisbon",
+    "max_expected_height": 2600,
+    "atmospheric_model_type": "custom_atmosphere",
+    "temperature": {
+        "source": [
+            [
+                136.80293813362803,
+                291.3
+            ],
+            [
+                572.5514612155088,
+                287.0
+            ],
+            [
+                1024.9649066367328,
+                282.5
+            ],
+            [
+                1997.626299887351,
+                280.4
+            ],
+            [
+                3087.796155669465,
+                276.4
+            ],
+            [
+                4321.630155412766,
+                268.9
+            ],
+            [
+                5736.06091381566,
+                259.9
+            ],
+            [
+                7406.10131086553,
+                249.1
+            ],
+            [
+                9443.981599141402,
+                233.9
+            ],
+            [
+                12121.724655689648,
+                218.1
+            ]
+        ],
+        "inputs": [
+            "Height Above Sea Level (m)"
+        ],
+        "outputs": [
+            "temperature (C)"
+        ],
+        "interpolation": "linear",
+        "extrapolation": "constant",
+        "title": "Temperature Profile",
+        "signature": {
+            "module": "rocketpy.mathutils.function",
+            "name": "Function"
+        }
+    },
+    "wind_velocity_x": {
+        "source": [
+            [
+                170.0,
+                3.6
+            ],
+            [
+                185.0,
+                4.1
+            ],
+            [
+                210.0,
+                4.5
+            ],
+            [
+                260.0,
+                4.9
+            ],
+            [
+                310.0,
+                5.0
+            ],
+            [
+                360.0,
+                5.1
+            ],
+            [
+                410.0,
+                5.2
+            ],
+            [
+                460.0,
+                5.3
+            ],
+            [
+                510.0,
+                5.4
+            ],
+            [
+                560.0,
+                5.4
+            ],
+            [
+                610.0,
+                5.5
+            ],
+            [
+                660.0,
+                5.4
+            ],
+            [
+                710.0,
+                5.4
+            ],
+            [
+                760.0,
+                5.4
+            ],
+            [
+                810.0,
+                5.4
+            ],
+            [
+                860.0,
+                5.4
+            ],
+            [
+                910.0,
+                5.3
+            ],
+            [
+                960.0,
+                5.2
+            ],
+            [
+                1010.0,
+                5.2
+            ],
+            [
+                1060.0,
+                5.0
+            ],
+            [
+                1110.0,
+                4.8
+            ],
+            [
+                1160.0,
+                4.6
+            ],
+            [
+                1260.0,
+                4.1
+            ],
+            [
+                1360.0,
+                3.7
+            ],
+            [
+                1460.0,
+                3.3
+            ],
+            [
+                1560.0,
+                3.4
+            ],
+            [
+                1660.0,
+                3.7
+            ],
+            [
+                1760.0,
+                4.0
+            ],
+            [
+                1860.0,
+                4.3
+            ],
+            [
+                1960.0,
+                4.7
+            ],
+            [
+                2060.0,
+                5.0
+            ],
+            [
+                2160.0,
+                5.4
+            ],
+            [
+                2260.0,
+                5.8
+            ],
+            [
+                2360.0,
+                6.2
+            ],
+            [
+                2460.0,
+                6.6
+            ],
+            [
+                2560.0,
+                7.0
+            ],
+            [
+                3087.796155669465,
+                9.2
+            ],
+            [
+                4321.630155412766,
+                11.0
+            ],
+            [
+                5736.06091381566,
+                14.6
+            ],
+            [
+                7406.10131086553,
+                17.9
+            ],
+            [
+                9443.981599141402,
+                20.0
+            ],
+            [
+                12121.724655689648,
+                9.1
+            ]
+        ],
+        "inputs": [
+            "Height Above Sea Level (m)"
+        ],
+        "outputs": [
+            "wind_velocity_x (m/s)"
+        ],
+        "interpolation": "spline",
+        "extrapolation": "constant",
+        "title": "Wind Velocity X Profile",
+        "signature": {
+            "module": "rocketpy.mathutils.function",
+            "name": "Function"
+        }
+    },
+    "wind_velocity_y": {
+        "source": [
+            [
+                170.0,
+                -3.0
+            ],
+            [
+                185.0,
+                -3.5
+            ],
+            [
+                210.0,
+                -3.8
+            ],
+            [
+                260.0,
+                -4.3
+            ],
+            [
+                310.0,
+                -4.4
+            ],
+            [
+                360.0,
+                -4.5
+            ],
+            [
+                410.0,
+                -4.6
+            ],
+            [
+                460.0,
+                -4.8
+            ],
+            [
+                510.0,
+                -4.9
+            ],
+            [
+                560.0,
+                -5.0
+            ],
+            [
+                610.0,
+                -5.0
+            ],
+            [
+                660.0,
+                -5.0
+            ],
+            [
+                710.0,
+                -5.1
+            ],
+            [
+                760.0,
+                -5.1
+            ],
+            [
+                810.0,
+                -5.1
+            ],
+            [
+                860.0,
+                -5.1
+            ],
+            [
+                910.0,
+                -5.1
+            ],
+            [
+                960.0,
+                -5.1
+            ],
+            [
+                1010.0,
+                -5.0
+            ],
+            [
+                1060.0,
+                -5.0
+            ],
+            [
+                1110.0,
+                -4.9
+            ],
+            [
+                1160.0,
+                -4.7
+            ],
+            [
+                1260.0,
+                -4.5
+            ],
+            [
+                1360.0,
+                -4.2
+            ],
+            [
+                1460.0,
+                -4.0
+            ],
+            [
+                1560.0,
+                -4.0
+            ],
+            [
+                1660.0,
+                -4.1
+            ],
+            [
+                1760.0,
+                -4.3
+            ],
+            [
+                1860.0,
+                -4.4
+            ],
+            [
+                1960.0,
+                -4.6
+            ],
+            [
+                2060.0,
+                -4.7
+            ],
+            [
+                2160.0,
+                -4.8
+            ],
+            [
+                2260.0,
+                -4.8
+            ],
+            [
+                2360.0,
+                -4.9
+            ],
+            [
+                2460.0,
+                -4.9
+            ],
+            [
+                2560.0,
+                -4.9
+            ],
+            [
+                3087.796155669465,
+                -4.7
+            ],
+            [
+                4321.630155412766,
+                -5.3
+            ],
+            [
+                5736.06091381566,
+                -5.6
+            ],
+            [
+                7406.10131086553,
+                -7.8
+            ],
+            [
+                9443.981599141402,
+                -4.2
+            ],
+            [
+                12121.724655689648,
+                -11.5
+            ]
+        ],
+        "inputs": [
+            "Height Above Sea Level (m)"
+        ],
+        "outputs": [
+            "wind_velocity_y (m/s)"
+        ],
+        "interpolation": "spline",
+        "extrapolation": "constant",
+        "title": "Wind Velocity Y Profile",
+        "signature": {
+            "module": "rocketpy.mathutils.function",
+            "name": "Function"
+        }
+    },
+    "gravity": {
+        "source": [
+            [
+                136.80293813362803,
+                9.80622876725276
+            ],
+            [
+                572.5514612155088,
+                9.804887225878863
+            ],
+            [
+                1024.9649066367328,
+                9.803494669384405
+            ],
+            [
+                1997.626299887351,
+                9.800501761984199
+            ],
+            [
+                3087.796155669465,
+                9.79714890626424
+            ],
+            [
+                4321.630155412766,
+                9.793356281621554
+            ],
+            [
+                5736.06091381566,
+                9.789011237805909
+            ],
+            [
+                7406.10131086553,
+                9.783884698700536
+            ],
+            [
+                9443.981599141402,
+                9.777634451481962
+            ],
+            [
+                12121.724655689648,
+                9.769430824875885
+            ]
+        ],
+        "inputs": [
+            "Height Above Sea Level (m)"
+        ],
+        "outputs": [
+            "gravity (m/s^2)"
+        ],
+        "interpolation": "spline",
+        "extrapolation": "constant",
+        "title": "Gravity Profile",
+        "signature": {
+            "module": "rocketpy.mathutils.function",
+            "name": "Function"
+        }
+    },
+    "pressure": {
+        "source": [
+            [
+                136.80293813362803,
+                100000
+            ],
+            [
+                572.5514612155088,
+                95000
+            ],
+            [
+                1024.9649066367328,
+                90000
+            ],
+            [
+                1997.626299887351,
+                80000
+            ],
+            [
+                3087.796155669465,
+                70000
+            ],
+            [
+                4321.630155412766,
+                60000
+            ],
+            [
+                5736.06091381566,
+                50000
+            ],
+            [
+                7406.10131086553,
+                40000
+            ],
+            [
+                9443.981599141402,
+                30000
+            ],
+            [
+                12121.724655689648,
+                20000
+            ]
+        ],
+        "inputs": [
+            "Height Above Sea Level (m)"
+        ],
+        "outputs": [
+            "pressure (Pa)"
+        ],
+        "interpolation": "spline",
+        "extrapolation": "constant",
+        "title": "Pressure Profile",
+        "signature": {
+            "module": "rocketpy.mathutils.function",
+            "name": "Function"
+        }
+    }
+}
\ No newline at end of file
diff --git a/data/rockets/ast_her/configuration.json b/data/rockets/ast_her/configuration.json
new file mode 100644
index 000000000..dfe224df3
--- /dev/null
+++ b/data/rockets/ast_her/configuration.json
@@ -0,0 +1,171 @@
+{
+  "rocket": {
+    "center_of_mass_without_motor": 3.16,
+    "center_of_mass_with_motor": 3.27,
+    "coordinate_system_orientation": "nose_to_tail",
+    "drag_curve": "input_data/drag_curve.CSV",
+    "inertia": [
+      81.7,
+      82.7,
+      0.237
+    ],
+    "radius": 0.0895,
+    "rocket_dry_mass": 47.25,
+    "rocket_fuel_volume_for_mass_and_xle": 26.2,
+    "rocket_length_without_boattail_and_nosecone": 4.329,
+    "mass-cost-kg-per-liter-fuel": 0.36,
+    "extra_length_meter_per_liter_fuel": 0.0445
+  },
+  "motors": {
+    "position": 5.262,
+    "clamp_force": 2000,
+    "clamp_force_mode": "fixed",
+    "max_impulse": 40960,
+    "lox_density": 1141,
+    "lox_density_gas": 1.429,
+    "eth_density": 789,
+    "eth_density_gas": 1.59,
+    "pressurant_density_300bar": 303,
+    "pressurant_density_40bar_cold": 95,
+    "pressurant_density_60bar": 60,
+    "ullage_volume": 0.2,
+    "pressurant_tank_volume": 0.0068,
+    "fuel_tank_volume": 0.0128,
+    "oxidizer_tank_volume": 0.0131,
+    "maximum_thrust": 2700,
+    "minimum_thrust": 1750,
+    "throttle_rate": 800,
+    "throttle_rate_up": 1000,
+    "of": 1.5,
+    "reference_pressure": 101325,
+    "thrust_source": "input/thrust-curves/opt-ThrustCurve.csv",
+    "isp_degradation_factor": 0.74,
+    "tank_optimization": false,
+    "ramp_up_time": 1.5,
+    "tank_radius": 0.0845,
+    "prnt_tank_radius": 0.0775,
+    "tank_spherical_caps": false,
+    "numeric_stab_mass": 0.18,
+    "lox_tank_height": 0.52,
+    "lox_tank_position": 1.07,
+    "eth_tank_height": 0.62,
+    "eth_tank_position": 2.09,
+    "pressure_tank_height": 0.50051981,
+    "pressure_tank_position": 3.05,
+    "nozzle_exit_radius": 0.0413,
+    "nozzle_position": 0.0,
+    "motor_dry_inertia": [
+      3900,
+      3970,
+      9304
+    ],
+    "casing_diameter": 100,
+    "center_of_dry_mass_position": 0,
+    "coordinate_system_orientation": "nozzle_to_combustion_chamber",
+    "dry_inertia_lxx": 65.035,
+    "dry_inertia_lyy": 64.931,
+    "dry_inertia_lzz": 0.744,
+    "dry_mass": 0,
+    "nozzle_radius": 0.051115125,
+    "throat_radius": 0.034076749999999996
+  },
+  "nosecones": {
+    "base_radius": 0.0895,
+    "kind": "Von Karman",
+    "length": 0.81,
+    "name": "Nose Cone with tip",
+    "position": 0.0
+  },
+  "fins": {
+    "SECTYP": "HEX",
+    "XLE": [
+      4.37,
+      4.652
+    ],
+    "SSPAN": [
+      0.0895,
+      0.235
+    ],
+    "CHORD": [
+      0.53,
+      0.2
+    ],
+    "ZUPPER": [
+      0.005622641509433962,
+      0.01192
+    ],
+    "LMAXU": [
+      0.023962264150943394,
+      0.0508
+    ],
+    "LFLATU": [
+      0.9041509433962265,
+      0.7968
+    ],
+    "cant_angle": 0.0,
+    "NPANEL": 4,
+    "ZLOWER": [
+      0.0,
+      0.0149
+    ],
+    "LER": 0.0025,
+    "position": 4.37
+  },
+  "tails": {
+    "DAFT": 0.167,
+    "LAFT": 0.125,
+    "position": 5.137
+  },
+  "rail_buttons": {
+    "angular_position": 0.0,
+    "lower_position": 4.3,
+    "name": "Rail Button",
+    "upper_position": 2.4
+  },
+  "flight": {
+    "minimum_rail_exit_velocity": 31.8,
+    "velocity_safety_factor": 1.01,
+    "heading": 144.0,
+    "inclination": 75.0,
+    "rail_length": 15,
+    "apogee_expectation": 850
+  },
+  "environment": {
+    "timezone": "Europe/Lisbon",
+    "atmospheric_model": "Forecast",
+    "weather_file": "train-weather-data/EuroC_pressure_levels_reanalysis_2021-2024.nc",
+    "weather_dictionary": "ECMWF",
+    "year": 2026,
+    "month": 10,
+    "day": 20,
+    "hour": 15,
+    "minute": 30,
+    "forecast_file": "input/forecast_sweep/environment_20261020T1530Z.json",
+    "elevation": 160.0,
+    "latitude": 39.3897,
+    "longitude": -8.288964,
+    "max_height": 2600
+  },
+  "provenance": {
+    "repository": "Asteria-Flight-Dynamics",
+    "snapshot_date": "2026-10-21",
+    "configuration_files": [
+      "input/input_parameters.json",
+      "input/input_visualization_parameters.json"
+    ],
+    "data_files": {
+      "thrust_curve.csv": "input/thrust-curves/Optimized_ThrustCurve.csv",
+      "performance.csv": "input/throttle_performance_MARK_VI_b.csv",
+      "atmosphere.json": "input/forecast_sweep/environment_20261020T1530Z.json",
+      "flight_conditions.json": "input/datcom_flight_conditions.json"
+    },
+    "sha256": {
+      "thrust_curve.csv": "610081e56c5c465daf7a69b2f9ea947a0d7e4869520436b5b6ecba7b342fb08b",
+      "performance.csv": "bd24c698dae2f932a33e8192b1b6e3ee1836b4d278ca84af7bc3d49ede6d9e28",
+      "atmosphere.json": "4102eabfe54ca1929d95237c2c08f8dd0daf4b2247035d093f2008a3217f8be1",
+      "flight_conditions.json": "cfbfc05db1fec48c23134b077be5c2997559e2a7121fe0d1e2ab9e35aea0ec06"
+    },
+    "thrust_note": "Configured input/thrust-curves/opt-ThrustCurve.csv is absent; use existing Optimized_ThrustCurve.csv. No thrust optimization is performed.",
+    "simulation_note": "Native RocketPy Flight; Asteria hold-down clamp and PROTUB aerocover are excluded. Configured dry mass and dry inertias are preserved, including nonzero motor inertia with zero motor dry mass."
+  }
+}
diff --git a/data/rockets/ast_her/flight_conditions.json b/data/rockets/ast_her/flight_conditions.json
new file mode 100644
index 000000000..907fec340
--- /dev/null
+++ b/data/rockets/ast_her/flight_conditions.json
@@ -0,0 +1,71 @@
+{
+  "source": "Zero-clamp replay of opt-out/ga_best_params.json using configured forecast and saved aerodynamic tables",
+  "altitude_reference": "metres above sea level",
+  "unreached_mach_policy": "Use burnout altitude above the maximum simulated Mach; these points are outside this trajectory.",
+  "trajectory": {
+    "burnout_s": 7.594817071602963,
+    "apogee_asl_m": 3059.248509712067,
+    "apogee_time_s": 27.877298335405182,
+    "release_s": 0.34044468170422326
+  },
+  "mach": [
+    0.1,
+    0.2,
+    0.3,
+    0.4,
+    0.5,
+    0.6,
+    0.7,
+    0.8,
+    0.9,
+    1.0,
+    1.1,
+    1.2,
+    1.3,
+    1.4,
+    1.5,
+    1.6
+  ],
+  "power_on": {
+    "altitude_m": [
+      132,
+      182,
+      263,
+      373,
+      511,
+      676,
+      866,
+      895,
+      895,
+      895,
+      895,
+      895,
+      895,
+      895,
+      895,
+      895
+    ],
+    "max_simulated_mach": 0.7140579159791377
+  },
+  "power_off": {
+    "altitude_m": [
+      3043,
+      2884,
+      2626,
+      2287,
+      1884,
+      1439,
+      966,
+      895,
+      895,
+      895,
+      895,
+      895,
+      895,
+      895,
+      895,
+      895
+    ],
+    "max_simulated_mach": 0.7140579159791377
+  }
+}
diff --git a/data/rockets/ast_her/performance.csv b/data/rockets/ast_her/performance.csv
new file mode 100644
index 000000000..5bb13f17b
--- /dev/null
+++ b/data/rockets/ast_her/performance.csv
@@ -0,0 +1,14 @@
+Throttle_value,Mass_flow_rate_kg_per_s,Pressure_MPa,Specific_impulse_sea_level_Ns_per_kg,Specific_impulse_sea_level_s,Thrust_sea_level_kN,Specific_impulse_optimum_expansion_Ns_per_kg,Specific_impulse_optimum_expansion_s,Thrust_optimum_expansion_kN,Specific_impulse_vacuum_Ns_per_kg,Specific_impulse_vacuum_s,Thrust_vacuum_kN
+0.4300,0.7045,1.1079,1647.612,168.0097,1.1607,2182.639,222.5673,1.5376,2592.997,264.4121,1.8267
+0.5070,0.8306,1.3087,1776.245,181.1267,1.4754,2187.418,223.0546,1.8169,2597.330,264.8540,2.1573
+0.5840,0.9567,1.5097,1871.479,190.8378,1.7905,2191.537,223.4746,2.0968,2601.029,265.2312,2.4885
+0.6610,1.0829,1.7111,1945.086,198.3436,2.1063,2194.951,223.8227,2.3769,2604.093,265.5436,2.8200
+0.7380,1.2090,1.9126,2003.609,204.3113,2.4224,2197.992,224.1329,2.6575,2606.802,265.8199,3.1517
+0.8150,1.3352,2.1144,2051.223,209.1666,2.7388,2200.898,224.4291,2.9386,2609.357,266.0804,3.4840
+0.8920,1.4613,2.3164,2090.868,213.2093,3.0555,2203.445,224.6889,3.2200,2611.592,266.3083,3.8164
+0.9690,1.5875,2.5186,2124.358,216.6243,3.3724,2205.854,224.9346,3.5018,2613.689,266.5222,4.1492
+1.0000,1.6383,2.6000,2136.437,217.8560,3.5001,2206.746,225.0255,3.6152,2614.464,266.6012,4.2832
+1.0460,1.7136,2.7209,2153.083,219.5534,3.6896,2208.018,225.1552,3.7837,2615.567,266.7137,4.4821
+1.1230,1.8398,2.9233,2177.981,222.0923,4.0070,2210.092,225.3667,4.0661,2617.355,266.8960,4.8153
+1.2000,1.9659,3.1259,2199.804,224.3176,4.3246,2211.969,225.5581,4.3486,2618.971,267.0607,5.1487
+
diff --git a/data/rockets/ast_her/thrust_curve.csv b/data/rockets/ast_her/thrust_curve.csv
new file mode 100644
index 000000000..5294d94a2
--- /dev/null
+++ b/data/rockets/ast_her/thrust_curve.csv
@@ -0,0 +1,12 @@
+0.000000000,0.000000000
+0.100000000,270.000000000
+0.200000000,540.000000000
+0.300000000,810.000000000
+0.400000000,1080.000000000
+0.500000000,1350.000000000
+0.600000000,1620.000000000
+0.700000000,1890.000000000
+0.800000000,2160.000000000
+0.900000000,2430.000000000
+1.000000000,2700.000000000
+4.000000000,2700.000000000
diff --git a/docs/examples/datcom/README.md b/docs/examples/datcom/README.md
new file mode 100644
index 000000000..6e9251139
--- /dev/null
+++ b/docs/examples/datcom/README.md
@@ -0,0 +1,41 @@
+# DATCOM comparisons
+
+**Work in progress:** DATCOM currently produces poor flight predictions for several comparison rockets. The discrepancies are unresolved and are being investigated; these results should not be treated as validated predictions.
+
+Only two example notebooks are included:
+
+- [Recorded rockets](rocket_comparison.ipynb): DATCOM, ordinary RocketPy and
+  recorded altitude, upward vertical velocity, CD and stability margin where
+  available, with signed percentage errors. Apogee-fitted constant CD is
+  explicitly inferred calibration, not a measurement or native correction.
+- [AST-HER / Asteria](asteria_comparison.ipynb): the same configuration, tanks
+  and atmosphere using ordinary RocketPy's supplied Asteria drag curve versus
+  native DATCOM. **AST-HER has not flown.** Percentages are model differences.
+
+Missing measurements are shown as unavailable. Scalar CD comparisons use Mach
+0.6 and zero incidence; static margin uses loaded ignition and Mach zero
+(DATCOM uses its lowest positive Mach). NDRT's measured stability conditions
+are unspecified. Velocity uses recorded vertical/altimeter channels, never a
+numerical derivative presented as a measurement. Fitted CD uses one constant
+for powered and coast drag in the ordinary source model; it is not unique.
+
+Source models and raw flight records stay in their original repository folders.
+`comparison_data/` contains reproduction scripts, source hashes, reference
+notes, QA masks, fitted residuals, values and model histories. Unspecified
+construction assumes carbon fibre with vinyl, without inventing a measured
+roughness or fin section. The recorded-rocket DATCOM models retain their
+explicit smooth 2% HEX / 5% bevel / sharp-edge assumptions. AST-HER uses its
+saved Asteria section inputs. All native runs include DAMP and use adaptive
+20-point Mach grids and signed alpha coverage from rail through apogee.
+
+Reproduce from the repository environment:
+
+```sh
+python docs/examples/datcom/comparison_data/reproduce_rockets.py
+python docs/examples/datcom/comparison_data/prepare_comparisons.py
+```
+
+Set `DATCOM_EXECUTABLE` to a native executable when the sibling Asteria copy is
+unavailable. The latter command fits effective CD against the recorded apogees;
+those are calibration results, not independent validation. Notebook execution
+reads the saved comparison outputs and requires no new native runs.
diff --git a/docs/examples/datcom/asteria_comparison.ipynb b/docs/examples/datcom/asteria_comparison.ipynb
new file mode 100644
index 000000000..cd7415201
--- /dev/null
+++ b/docs/examples/datcom/asteria_comparison.ipynb
@@ -0,0 +1,182 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "867cc490",
+   "metadata": {},
+   "source": [
+    "# AST-HER: ordinary RocketPy versus native DATCOM\n",
+    "\n",
+    "**Work in progress:** DATCOM currently produces poor flight predictions for several comparison rockets. The discrepancies are unresolved and are being investigated; these results should not be treated as validated predictions.\n",
+    "\n",
+    "**AST-HER / Asteria has not flown. No measured altitude, velocity, CD or stability margin is available.** Percentages here are `100 × (DATCOM − RocketPy) / |RocketPy|`: model differences, not flight prediction errors.\n",
+    "\n",
+    "Both use the saved current Asteria configuration, the same three liquid/pressurant tanks, thrust, mass/inertia, saved pressure/temperature/wind profile and launch conditions. Ordinary RocketPy uses Asteria's supplied `input/drag_curve.CSV`, copied with its hash recorded; this curve is not labelled as a measured wind-tunnel result. Native DATCOM derives its coefficients from the configured section geometry, with DAMP and powered nozzle-annulus/coast full-base drag. Its 20-point Mach and signed alpha grids and actual-atmosphere Reynolds schedules adapt from the ordinary flight, then twice from DATCOM flights. Scope is rail through apogee, without Asteria's hold-down clamp or PROTUB.\n",
+    "\n",
+    "The configured ogive boattail is used by DATCOM. Ordinary RocketPy retains its conical-tail approximation because ogive tails are DATCOM-only; this geometric/model difference is explicit and contributes to the stability comparison. Nose, fin planform and mass/CG histories otherwise match. Unspecified construction is carbon fibre with vinyl; no numerical roughness is inferred from that label. Native AST-HER fin upper/lower thickness, bevel and leading-edge radius use the saved Asteria inputs.\n",
+    "\n",
+    "The table compares apogee AGL, peak upward vertical velocity, loaded ignition Mach-zero static margin (DATCOM uses its lowest positive Mach), and zero-incidence powered/coast CD at Mach 0.6. Stability curves use each trajectory's actual Mach and CG. This comparison does not establish which model is more accurate.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "04b4d181",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-22T22:26:57.241980Z",
+     "iopub.status.busy": "2026-10-22T22:26:57.241736Z",
+     "iopub.status.idle": "2026-10-22T22:26:58.286568Z",
+     "shell.execute_reply": "2026-10-22T22:26:58.286233Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "</style>\n",
+       "<table id=\"T_81c76\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_81c76_level0_col0\" class=\"col_heading level0 col0\" >metric</th>\n",
+       "      <th id=\"T_81c76_level0_col1\" class=\"col_heading level0 col1\" >unit</th>\n",
+       "      <th id=\"T_81c76_level0_col2\" class=\"col_heading level0 col2\" >measured</th>\n",
+       "      <th id=\"T_81c76_level0_col3\" class=\"col_heading level0 col3\" >rocketpy</th>\n",
+       "      <th id=\"T_81c76_level0_col4\" class=\"col_heading level0 col4\" >datcom</th>\n",
+       "      <th id=\"T_81c76_level0_col5\" class=\"col_heading level0 col5\" >datcom_vs_rocketpy_pct</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c76_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_81c76_row0_col0\" class=\"data row0 col0\" >Apogee AGL</td>\n",
+       "      <td id=\"T_81c76_row0_col1\" class=\"data row0 col1\" >m</td>\n",
+       "      <td id=\"T_81c76_row0_col2\" class=\"data row0 col2\" >—</td>\n",
+       "      <td id=\"T_81c76_row0_col3\" class=\"data row0 col3\" >1030.111</td>\n",
+       "      <td id=\"T_81c76_row0_col4\" class=\"data row0 col4\" >1042.707</td>\n",
+       "      <td id=\"T_81c76_row0_col5\" class=\"data row0 col5\" >1.223</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c76_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+       "      <td id=\"T_81c76_row1_col0\" class=\"data row1 col0\" >Peak upward vertical velocity</td>\n",
+       "      <td id=\"T_81c76_row1_col1\" class=\"data row1 col1\" >m/s</td>\n",
+       "      <td id=\"T_81c76_row1_col2\" class=\"data row1 col2\" >—</td>\n",
+       "      <td id=\"T_81c76_row1_col3\" class=\"data row1 col3\" >132.974</td>\n",
+       "      <td id=\"T_81c76_row1_col4\" class=\"data row1 col4\" >133.089</td>\n",
+       "      <td id=\"T_81c76_row1_col5\" class=\"data row1 col5\" >0.086</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c76_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+       "      <td id=\"T_81c76_row2_col0\" class=\"data row2 col0\" >Initial static margin</td>\n",
+       "      <td id=\"T_81c76_row2_col1\" class=\"data row2 col1\" >cal</td>\n",
+       "      <td id=\"T_81c76_row2_col2\" class=\"data row2 col2\" >—</td>\n",
+       "      <td id=\"T_81c76_row2_col3\" class=\"data row2 col3\" >2.093</td>\n",
+       "      <td id=\"T_81c76_row2_col4\" class=\"data row2 col4\" >3.857</td>\n",
+       "      <td id=\"T_81c76_row2_col5\" class=\"data row2 col5\" >84.287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c76_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+       "      <td id=\"T_81c76_row3_col0\" class=\"data row3 col0\" >Powered CD at Mach 0.6</td>\n",
+       "      <td id=\"T_81c76_row3_col1\" class=\"data row3 col1\" >1</td>\n",
+       "      <td id=\"T_81c76_row3_col2\" class=\"data row3 col2\" >—</td>\n",
+       "      <td id=\"T_81c76_row3_col3\" class=\"data row3 col3\" >0.544</td>\n",
+       "      <td id=\"T_81c76_row3_col4\" class=\"data row3 col4\" >0.420</td>\n",
+       "      <td id=\"T_81c76_row3_col5\" class=\"data row3 col5\" >-22.774</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c76_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+       "      <td id=\"T_81c76_row4_col0\" class=\"data row4 col0\" >Coast CD at Mach 0.6</td>\n",
+       "      <td id=\"T_81c76_row4_col1\" class=\"data row4 col1\" >1</td>\n",
+       "      <td id=\"T_81c76_row4_col2\" class=\"data row4 col2\" >—</td>\n",
+       "      <td id=\"T_81c76_row4_col3\" class=\"data row4 col3\" >0.544</td>\n",
+       "      <td id=\"T_81c76_row4_col4\" class=\"data row4 col4\" >0.447</td>\n",
+       "      <td id=\"T_81c76_row4_col5\" class=\"data row4 col5\" >-17.809</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x169e88ad0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "import json\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from IPython.display import display\n",
+    "ROOT = next(p for p in (Path.cwd(), *Path.cwd().parents) if (p / \"rocketpy\").is_dir())\n",
+    "DATA = ROOT / \"docs/examples/datcom/comparison_data\"\n",
+    "plt.rcParams.update({\"axes.grid\": True, \"grid.alpha\": .2})\n",
+    "def show(frame):\n",
+    "    display(frame.style.format(precision=3, na_rep=\"—\"))\n",
+    "metrics = pd.read_csv(DATA / \"asteria_metrics.csv\")\n",
+    "show(metrics[[\"metric\",\"unit\",\"measured\",\"rocketpy\",\"datcom\",\"datcom_vs_rocketpy_pct\"]])\n",
+    "provenance = json.loads((DATA / \"asteria_provenance.json\").read_text())\n",
+    "assert provenance[\"flight_status\"] == \"AST-HER has not flown\" and provenance[\"mass_cg_match\"]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "26068b7a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-22T22:26:58.287917Z",
+     "iopub.status.busy": "2026-10-22T22:26:58.287779Z",
+     "iopub.status.idle": "2026-10-22T22:26:58.543346Z",
+     "shell.execute_reply": "2026-10-22T22:26:58.543015Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x700 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "history = pd.read_csv(DATA / \"asteria_histories.csv\")\n",
+    "cd = pd.read_csv(DATA / \"asteria_cd.csv\")\n",
+    "fig, axes = plt.subplots(2,2,figsize=(12,7))\n",
+    "for prefix,label,color in [(\"rocketpy\",\"RocketPy\",\"tab:blue\"),(\"datcom\",\"DATCOM\",\"tab:orange\")]:\n",
+    "    for axis,column in [(axes[0,0],\"altitude_agl_m\"),(axes[0,1],\"vz_m_s\"),(axes[1,1],\"margin_cal\")]:\n",
+    "        axis.plot(history.time_s,history[f\"{prefix}_{column}\"],label=label,color=color)\n",
+    "    for phase,style in [(\"powered\",\"--\"),(\"coast\",\"-\")]:\n",
+    "        axes[1,0].plot(cd.mach,cd[f\"{prefix}_{phase}_cd\"],style,label=f\"{label} {phase}\",color=color)\n",
+    "for axis,title,xlabel,ylabel in [(axes[0,0],\"Altitude\",\"Time (s)\",\"Altitude AGL (m)\"),(axes[0,1],\"Vertical velocity\",\"Time (s)\",\"Upward velocity (m/s)\"),(axes[1,0],\"Zero-incidence drag\",\"Mach\",\"CD\"),(axes[1,1],\"Stability margin\",\"Time (s)\",\"Margin (cal)\")]:\n",
+    "    axis.set(title=title,xlabel=xlabel,ylabel=ylabel);axis.legend(fontsize=8)\n",
+    "fig.suptitle(\"AST-HER — simulation comparison; has not flown\")\n",
+    "fig.tight_layout();plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/datcom/comparison_data/asteria_cd.csv b/docs/examples/datcom/comparison_data/asteria_cd.csv
new file mode 100644
index 000000000..2bf3d6dd0
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/asteria_cd.csv
@@ -0,0 +1,152 @@
+mach,rocketpy_powered_cd,datcom_powered_cd,rocketpy_coast_cd,datcom_coast_cd
+0.01,,0.751,,0.789
+0.017933333333333332,0.8245539964533333,0.7218053333333334,0.8245539964533333,0.7591706666666667
+0.025866666666666663,0.7777966665733334,0.6926106666666667,0.7777966665733334,0.7293413333333334
+0.0338,0.74686188382,0.663416,0.74686188382,0.6995119999999999
+0.04173333333333333,0.7241976964,0.6342213333333333,0.7241976964,0.6696826666666666
+0.049666666666666665,0.7066911779999999,0.6050266666666666,0.7066911779999999,0.6398533333333333
+0.05759999999999999,0.6929428922800001,0.5758319999999999,0.6929428922800001,0.610024
+0.06553333333333332,0.6811572971333334,0.5616049999999999,0.6811572971333334,0.5954666666666667
+0.07346666666666665,0.67094321948,0.5538700000000001,0.67094321948,0.5875333333333334
+0.08139999999999999,0.6619851944599999,0.546135,0.6619851944599999,0.5796000000000001
+0.08933333333333332,0.6541282504,0.5384,0.6541282504,0.5716666666666668
+0.09726666666666665,0.6472090871133334,0.530665,0.6472090871133334,0.5637333333333334
+0.10519999999999997,0.6409048382800001,0.52501,0.6409048382800001,0.55788
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diff --git a/docs/examples/datcom/comparison_data/asteria_drag_curve.csv b/docs/examples/datcom/comparison_data/asteria_drag_curve.csv
new file mode 100644
index 000000000..8007084fd
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/asteria_drag_curve.csv
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diff --git a/docs/examples/datcom/comparison_data/asteria_histories.csv b/docs/examples/datcom/comparison_data/asteria_histories.csv
new file mode 100644
index 000000000..ecce5a1e9
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/asteria_histories.csv
@@ -0,0 +1,602 @@
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diff --git a/docs/examples/datcom/comparison_data/asteria_metrics.csv b/docs/examples/datcom/comparison_data/asteria_metrics.csv
new file mode 100644
index 000000000..03a1e9141
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/asteria_metrics.csv
@@ -0,0 +1,6 @@
+metric,unit,rocketpy,datcom,datcom_vs_rocketpy_pct,measured
+Apogee AGL,m,1030.1105580450696,1042.7065175727942,1.222777441639756,
+Peak upward vertical velocity,m/s,132.97414243164462,133.08909320010036,0.08644595584801947,
+Initial static margin,cal,2.093144236271212,3.857394061067111,84.2870641317478,
+Powered CD at Mach 0.6,1,0.543857573,0.42,-22.773898746464646,
+Coast CD at Mach 0.6,1,0.543857573,0.447,-17.809363665880223,
diff --git a/docs/examples/datcom/comparison_data/asteria_model.py b/docs/examples/datcom/comparison_data/asteria_model.py
new file mode 100644
index 000000000..93f6757b1
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/asteria_model.py
@@ -0,0 +1,202 @@
+"""Build AST-HER from the saved current Asteria configuration, without plots."""
+
+import json
+from pathlib import Path
+import numpy as np
+from scipy.integrate import trapezoid
+from rocketpy import (
+    Rocket,
+    Environment,
+    LiquidMotor,
+    MassFlowRateBasedTank,
+    CylindricalTank,
+    Fluid,
+    Function,
+)
+
+ROOT = next(p for p in Path(__file__).resolve().parents if (p / "rocketpy").is_dir())
+DATA = ROOT / "data/rockets/ast_her"
+
+
+def build(native, executable):
+    cfg = json.loads((DATA / "configuration.json").read_text())
+    r, m, f = cfg["rocket"], cfg["motors"], cfg["fins"]
+    thrust = np.loadtxt(DATA / "thrust_curve.csv", delimiter=",")
+    performance = np.genfromtxt(DATA / "performance.csv", delimiter=",", names=True)
+    isp = np.interp(
+        thrust[:, 1],
+        performance["Thrust_sea_level_kN"] * 1000,
+        performance["Specific_impulse_sea_level_s"] * m["isp_degradation_factor"],
+    )
+    mdot = thrust[:, 1] / (isp * 9.80665)
+    fuel_flow = mdot / (1 + m["of"])
+    oxidizer_flow = mdot - fuel_flow
+    fuel_gas_flow = fuel_flow * m["pressurant_density_60bar"] / m["eth_density"]
+    oxidizer_gas_flow = (
+        oxidizer_flow * m["pressurant_density_40bar_cold"] / m["lox_density"]
+    )
+
+    def curve(values):
+        return np.column_stack((thrust[:, 0], values))
+
+    fuel_liquid = Fluid("Ethanol", m["eth_density"])
+    oxidizer_liquid = Fluid("LOX", m["lox_density"])
+    nitrogen_fuel = Fluid("Nitrogen, fuel tank", m["pressurant_density_60bar"])
+    nitrogen_oxidizer = Fluid(
+        "Nitrogen, oxidizer tank", m["pressurant_density_40bar_cold"]
+    )
+    nitrogen_supply = Fluid("Nitrogen, supply tank", m["pressurant_density_300bar"])
+    burn = (float(thrust[0, 0]), float(thrust[-1, 0]))
+    motor = LiquidMotor(
+        thrust_source=thrust,
+        burn_time=burn,
+        dry_mass=0,
+        dry_inertia=m["motor_dry_inertia"],
+        center_of_dry_mass_position=m["center_of_dry_mass_position"],
+        nozzle_position=m["nozzle_position"],
+        nozzle_radius=m["nozzle_exit_radius"],
+        reference_pressure=m["reference_pressure"],
+        interpolation_method="linear",
+        coordinate_system_orientation="nozzle_to_combustion_chamber",
+    )
+    tanks = []
+    for label, height_key, position_key, fluid, gas, liquid_out, gas_in in [
+        (
+            "Fuel",
+            "eth_tank_height",
+            "eth_tank_position",
+            fuel_liquid,
+            nitrogen_fuel,
+            fuel_flow,
+            fuel_gas_flow,
+        ),
+        (
+            "Oxidizer",
+            "lox_tank_height",
+            "lox_tank_position",
+            oxidizer_liquid,
+            nitrogen_oxidizer,
+            oxidizer_flow,
+            oxidizer_gas_flow,
+        ),
+    ]:
+        tank = MassFlowRateBasedTank(
+            name=label,
+            geometry=CylindricalTank(
+                m["tank_radius"], m[height_key], spherical_caps=m["tank_spherical_caps"]
+            ),
+            flux_time=burn,
+            initial_liquid_mass=trapezoid(liquid_out, x=thrust[:, 0])
+            + m["numeric_stab_mass"],
+            initial_gas_mass=0.001,
+            liquid_mass_flow_rate_in=0,
+            liquid_mass_flow_rate_out=curve(liquid_out),
+            gas_mass_flow_rate_in=curve(gas_in),
+            gas_mass_flow_rate_out=0,
+            liquid=fluid,
+            gas=gas,
+        )
+        motor.add_tank(tank, position=m[position_key])
+        tanks.append(tank)
+    pr = m["prnt_tank_radius"]
+    supply_tank = MassFlowRateBasedTank(
+        name="Pressurant",
+        geometry=CylindricalTank(
+            pr,
+            m["pressurant_tank_volume"] / (np.pi * pr**2) + 0.667 * pr,
+            spherical_caps=True,
+        ),
+        flux_time=burn,
+        initial_liquid_mass=0,
+        initial_gas_mass=m["pressurant_tank_volume"] * m["pressurant_density_300bar"],
+        liquid_mass_flow_rate_in=0,
+        liquid_mass_flow_rate_out=0,
+        gas_mass_flow_rate_in=0,
+        gas_mass_flow_rate_out=curve(fuel_gas_flow + oxidizer_gas_flow),
+        liquid=nitrogen_supply,
+        gas=nitrogen_supply,
+    )
+    motor.add_tank(supply_tank, position=m["pressure_tank_position"])
+    tanks.append(supply_tank)
+    print(
+        f"Burn duration: {burn[1]:.3f} s; total impulse: {motor.total_impulse:.1f} N s"
+    )
+
+    drag = Function(
+        str(Path(__file__).with_name("asteria_drag_curve.csv")),
+        interpolation="linear",
+        extrapolation="constant",
+    )
+    rocket = Rocket(
+        radius=r["radius"],
+        mass=r["rocket_dry_mass"],
+        inertia=r["inertia"],
+        center_of_mass_without_motor=r["center_of_mass_without_motor"],
+        power_on_drag=drag,
+        power_off_drag=drag,
+        coordinate_system_orientation=r["coordinate_system_orientation"],
+    )
+    rocket.add_motor(motor, position=m["position"])
+    rocket.add_nose(
+        length=cfg["nosecones"]["length"],
+        kind=cfg["nosecones"]["kind"],
+        position=cfg["nosecones"]["position"],
+    )
+    geometry = dict(
+        n=f["NPANEL"],
+        root_chord=f["CHORD"][0],
+        tip_chord=f["CHORD"][1],
+        span=f["SSPAN"][1] - f["SSPAN"][0],
+        position=f["XLE"][0],
+        sweep_length=f["XLE"][1] - f["XLE"][0],
+        cant_angle=f["cant_angle"],
+    )
+    if native:
+        rocket.add_datcom_hex_fins(
+            **geometry,
+            path=executable,
+            upper_thickness_to_chord=f["ZUPPER"],
+            lower_thickness_to_chord=f["ZLOWER"],
+            max_thickness_position=f["LMAXU"],
+            flat_length=f["LFLATU"],
+            leading_edge_radius=f["LER"],
+            datcom_options={"beta": [-10, 0, 10], "powered_base_drag": "nozzle"},
+        )
+    else:
+        rocket.add_trapezoidal_fins(**geometry)
+    rocket.add_tail(
+        top_radius=r["radius"],
+        bottom_radius=cfg["tails"]["DAFT"] / 2,
+        length=cfg["tails"]["LAFT"],
+        position=cfg["tails"]["position"],
+        kind="ogive" if native else "conical",
+    )
+    rocket.set_rail_buttons(
+        upper_button_position=cfg["rail_buttons"]["upper_position"],
+        lower_button_position=cfg["rail_buttons"]["lower_position"],
+        angular_position=cfg["rail_buttons"]["angular_position"],
+    )
+    saved = json.loads((DATA / "atmosphere.json").read_text())
+    e = cfg["environment"]
+    env = Environment(
+        latitude=e["latitude"],
+        longitude=e["longitude"],
+        elevation=e["elevation"],
+        timezone=e["timezone"],
+    )
+    env.set_atmospheric_model(
+        type="custom_atmosphere",
+        pressure=saved["pressure"]["source"],
+        temperature=saved["temperature"]["source"],
+        wind_u=saved["wind_velocity_x"]["source"],
+        wind_v=saved["wind_velocity_y"]["source"],
+    )
+    launch = dict(
+        rail_length=cfg["flight"]["rail_length"],
+        inclination=cfg["flight"]["inclination"],
+        heading=cfg["flight"]["heading"],
+        terminate_on_apogee=True,
+        max_time=90,
+        max_time_step=0.1,
+    )
+    return rocket, env, launch
diff --git a/docs/examples/datcom/comparison_data/asteria_provenance.json b/docs/examples/datcom/comparison_data/asteria_provenance.json
new file mode 100644
index 000000000..94298b35c
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/asteria_provenance.json
@@ -0,0 +1,255 @@
+{
+  "flight_status": "AST-HER has not flown",
+  "atmosphere": {
+    "mach": [
+      0.01,
+      0.06,
+      0.1,
+      0.14,
+      0.18,
+      0.22,
+      0.26,
+      0.3,
+      0.34,
+      0.41,
+      0.6,
+      0.8,
+      0.9,
+      0.95,
+      0.98,
+      1.0,
+      1.02,
+      1.05,
+      1.1,
+      1.2
+    ],
+    "alpha": [
+      -50.28,
+      -25.14,
+      -12.57,
+      -5.03,
+      -2.51,
+      -1.26,
+      -0.63,
+      -0.25,
+      -0.13,
+      0.0,
+      0.13,
+      0.25,
+      0.63,
+      1.26,
+      2.51,
+      5.03,
+      12.57,
+      25.14,
+      50.28
+    ],
+    "coverage": {
+      "scope": "rail through apogee",
+      "minimum_dynamic_pressure_pa": 100.0,
+      "excluded_low_q_samples": 70,
+      "phases": {
+        "power_on": {
+          "mach_min": 0.01326309325167047,
+          "mach_max": 0.4047552904143389,
+          "loaded_alpha_max_abs_deg": 19.449816544852585
+        },
+        "power_off": {
+          "mach_min": 0.061542900733664994,
+          "mach_max": 0.4047619718868462,
+          "loaded_alpha_max_abs_deg": 41.98003189129927
+        }
+      }
+    },
+    "reynolds": {
+      "power_on": [
+        226365.51566415376,
+        1357557.0430917242,
+        2261026.4992785784,
+        3162210.117638871,
+        4060294.5106000057,
+        4954474.477554425,
+        5843955.459693362,
+        6728037.408058656,
+        7605951.410190682,
+        9128960.108378526,
+        13359453.817139307,
+        17812605.08951908,
+        20039180.72570896,
+        21152468.543803904,
+        21820441.23466087,
+        22265756.361898847,
+        22711071.489136823,
+        23379044.17999379,
+        24492331.998088732,
+        26718907.634278614
+      ],
+      "power_off": [
+        208264.83796614473,
+        1249589.0277968682,
+        2089419.927100325,
+        2941793.4916008483,
+        3810728.550639786,
+        4691644.538806526,
+        5587672.480639468,
+        6503721.591567147,
+        7441356.987433695,
+        9128946.847815478,
+        13359434.411437286,
+        17812579.215249714,
+        20039151.61715593,
+        21152437.818109035,
+        21820409.5386809,
+        22265724.019062143,
+        22711038.499443386,
+        23379010.22001525,
+        24492296.420968357,
+        26718868.822874572
+      ]
+    },
+    "altitude_m_asl": {
+      "power_on": [
+        160.0820665324356,
+        166.50685905994376,
+        176.01232657491323,
+        189.97807322214837,
+        208.16483095016986,
+        230.48225137008208,
+        256.86810984239617,
+        287.0999912159633,
+        321.18007198587213,
+        384.4550069360988,
+        384.4550069360988,
+        384.4550069360988,
+        384.4550069360988,
+        384.4550069360988,
+        384.4550069360988,
+        384.4550069360988,
+        384.4550069360988,
+        384.4550069360988,
+        384.4550069360988,
+        384.4550069360988
+      ],
+      "power_off": [
+        1202.5695560014856,
+        1202.5695560014856,
+        1172.2721301731524,
+        1119.193540606277,
+        1048.513572737287,
+        961.7803554046286,
+        859.54439362034,
+        743.1269561149522,
+        614.5573018947733,
+        384.47454624703414,
+        384.47454624703414,
+        384.47454624703414,
+        384.47454624703414,
+        384.47454624703414,
+        384.47454624703414,
+        384.47454624703414,
+        384.47454624703414,
+        384.47454624703414,
+        384.47454624703414,
+        384.47454624703414
+      ]
+    },
+    "pressure_pa": {
+      "power_on": [
+        99732.88345036532,
+        99659.16211584343,
+        99550.09155092515,
+        99389.8414765421,
+        99181.1573759123,
+        98925.07595236471,
+        98622.31120303525,
+        98275.41523239893,
+        97884.363066245,
+        97158.31430648435,
+        97158.31430648435,
+        97158.31430648435,
+        97158.31430648435,
+        97158.31430648435,
+        97158.31430648435,
+        97158.31430648435,
+        97158.31430648435,
+        97158.31430648435,
+        97158.31430648435,
+        97158.31430648435
+      ],
+      "power_off": [
+        88174.03414387404,
+        88174.03414387404,
+        88485.52410367475,
+        89031.22880559046,
+        89757.89451227363,
+        90698.30540926204,
+        91828.20067231177,
+        93114.82730425234,
+        94535.7582637696,
+        97158.09010250086,
+        97158.09010250086,
+        97158.09010250086,
+        97158.09010250086,
+        97158.09010250086,
+        97158.09010250086,
+        97158.09010250086,
+        97158.09010250086,
+        97158.09010250086,
+        97158.09010250086,
+        97158.09010250086
+      ]
+    },
+    "temperature_k": {
+      "power_on": [
+        291.07027976731416,
+        291.00687941962536,
+        290.91307873379566,
+        290.7752636698262,
+        290.5957953432846,
+        290.3755653190337,
+        290.1151876346103,
+        289.81685709986306,
+        289.4805522369707,
+        288.85615030357656,
+        288.85615030357656,
+        288.85615030357656,
+        288.85615030357656,
+        288.85615030357656,
+        288.85615030357656,
+        288.85615030357656,
+        288.85615030357656,
+        288.85615030357656,
+        288.85615030357656,
+        288.85615030357656
+      ],
+      "power_off": [
+        282.11654717021355,
+        282.11654717021355,
+        282.1819600617717,
+        282.296558049174,
+        282.44915784757745,
+        283.12847486833584,
+        284.1453806050806,
+        285.3033445738271,
+        286.58218243739265,
+        288.85595748815075,
+        288.85595748815075,
+        288.85595748815075,
+        288.85595748815075,
+        288.85595748815075,
+        288.85595748815075,
+        288.85595748815075,
+        288.85595748815075,
+        288.85595748815075,
+        288.85595748815075,
+        288.85595748815075
+      ]
+    }
+  },
+  "ordinary_drag_source": "Asteria-Flight-Dynamics/input/drag_curve.CSV",
+  "ordinary_drag_sha256": "b9d8bbc2da905e90c8ecd0b4ab9cb4fd245c4ad69ebc41a881fb9b07bdd620bb",
+  "tail_note": "ordinary RocketPy conical approximation; DATCOM configured ogive",
+  "mass_cg_match": true,
+  "source_configuration": "data/rockets/ast_her/configuration.json",
+  "scope": "rail to apogee; standard RocketPy rail; no Asteria hold-down clamp or PROTUB"
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/model_geometry.py b/docs/examples/datcom/comparison_data/model_geometry.py
new file mode 100644
index 000000000..3a2172577
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/model_geometry.py
@@ -0,0 +1,137 @@
+"""Geometry adapters for the two comparison notebooks."""
+
+import ast
+import numpy as np
+from rocketpy.mathutils.vector_matrix import Matrix, Vector
+from rocketpy.rocket.aero_surface import TrapezoidalFins
+
+MACH = [0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.2, 1.5, 2.0]
+
+ALPHA = [
+    -20,
+    -10,
+    -5,
+    -2,
+    -1,
+    -0.5,
+    -0.25,
+    -0.1,
+    -0.05,
+    0,
+    0.05,
+    0.1,
+    0.25,
+    0.5,
+    1,
+    2,
+    5,
+    10,
+    20,
+]
+
+BETA = [-20, -5, 0, 5, 20]
+
+
+class LocalPaths(ast.NodeTransformer):
+    def __init__(self, root):
+        self.root = root
+
+    def visit_Constant(self, node):
+        if isinstance(node.value, str) and node.value.startswith("../../data/"):
+            return ast.copy_location(
+                ast.Constant(str(self.root / node.value[6:])), node
+            )
+        return node
+
+
+def attach_datcom(rocket, executable, schedules):
+    """Replace fin definitions by identical planforms, assuming 2% HEX sections."""
+    options = dict(
+        mach=MACH,
+        alpha=ALPHA,
+        beta=BETA,
+        altitude=schedules,
+        powered_base_drag="nozzle",
+    )
+    originals = [
+        (s, p)
+        for s, p, _ in rocket.aerodynamic_surfaces
+        if isinstance(s, TrapezoidalFins)
+    ]
+    if not originals:
+        raise ValueError("Source rocket has no supported trapezoidal fin set")
+    geometry = []
+    for fin, position in originals:
+        rocket.aerodynamic_surfaces.remove(fin)
+        replacement = rocket.add_datcom_hex_fins(
+            n=fin.n,
+            root_chord=fin.root_chord,
+            tip_chord=fin.tip_chord,
+            span=fin.span,
+            sweep_length=fin.sweep_length,
+            cant_angle=fin.cant_angle,
+            position=position,
+            path=executable,
+            thickness_to_chord=0.02,
+            max_thickness_position=0.05,
+            flat_length=0.9,
+            datcom_options=options,
+        )
+        geometry.append(
+            dict(
+                n=fin.n,
+                root_chord_m=fin.root_chord,
+                tip_chord_m=fin.tip_chord,
+                span_m=fin.span,
+                sweep_m=fin.sweep_length,
+                cant_deg=fin.cant_angle,
+                position_m=list(position),
+                assumed_section="symmetric HEX, 2% thickness, 5% edge bevels",
+            )
+        )
+        assert replacement.root_chord == fin.root_chord
+    return geometry
+
+
+def envelope_diagnostics(flight, model, mach_grid=None, alpha_grid=None):
+    """Count sampled envelope excursions at meaningful pressure (q >= 100 Pa).
+
+    Also report solver/probe flags separately; these include pad/near-apogee
+    flow. Sampling is uniform in time and is diagnostic, not an accuracy bound.
+    """
+    solutions = np.asarray(flight.solution)
+    mach_grid = MACH if mach_grid is None else mach_grid
+    alpha_grid = ALPHA if alpha_grid is None else alpha_grid
+    samples = np.linspace(0, flight.apogee_time, 501)
+    counts = dict(samples=0, outside_mach=0, outside_alpha=0, outside_beta=0)
+    max_angles = np.zeros(2)
+    for time in samples:
+        state = [
+            np.interp(time, solutions[:, 0], solutions[:, j]) for j in range(1, 14)
+        ]
+        rotation = Matrix.transformation(state[6:10])
+        offset = model.reference_offset(time)
+        z = state[2] + (rotation @ offset).z
+        wind = Vector([flight.env.wind_velocity_x(z), flight.env.wind_velocity_y(z), 0])
+        velocity = rotation.transpose @ (Vector(state[3:6]) - wind) + (
+            Vector(state[10:13]) ^ offset
+        )
+        speed = abs(velocity)
+        if 0.5 * flight.env.density(z) * speed**2 < 100:
+            continue
+        alpha = np.degrees(np.arctan2(velocity.y, velocity.z))
+        beta = np.degrees(np.arctan2(velocity.x, velocity.z))
+        mach = speed / flight.env.speed_of_sound(z)
+        counts["samples"] += 1
+        counts["outside_mach"] += int(not mach_grid[0] <= mach <= mach_grid[-1])
+        counts["outside_alpha"] += int(not alpha_grid[0] <= alpha <= alpha_grid[-1])
+        counts["outside_beta"] += int(not BETA[0] <= beta <= BETA[-1])
+        max_angles = np.maximum(max_angles, np.abs([alpha, beta]))
+    return dict(
+        counts,
+        max_abs_alpha_deg=float(max_angles[0]),
+        max_abs_beta_deg=float(max_angles[1]),
+        solver_mach_excursion=bool(model.outside_mach_envelope),
+        solver_alpha_excursion=bool(model.outside_alpha_envelope),
+        solver_beta_excursion=bool(model.outside_beta_envelope),
+    )
diff --git a/docs/examples/datcom/comparison_data/model_sources.py b/docs/examples/datcom/comparison_data/model_sources.py
new file mode 100644
index 000000000..e70a5cab9
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/model_sources.py
@@ -0,0 +1,393 @@
+"""Read all source notebooks, their end-of-notebook flight evidence and models.
+
+Only trusted local repository notebooks are supported. Construction stops at
+first Flight; plots, installs, online weather and later recovery runs are not
+executed. Source notebooks are never rewritten.
+"""
+
+import ast
+import hashlib
+import importlib.util
+import json
+from pathlib import Path
+import re
+import sys
+
+import nbformat
+import numpy as np
+import pandas as pd
+
+ROOT = next(
+    p
+    for p in Path(__file__).resolve().parents
+    if (p / "rocketpy/rocket/datcom.py").is_file()
+)
+sys.path.insert(0, str(ROOT))
+spec = importlib.util.spec_from_file_location(
+    "datcom_example_helpers",
+    Path(__file__).with_name("model_geometry.py"),
+)
+h = importlib.util.module_from_spec(spec)
+spec.loader.exec_module(h)
+
+
+class SourceUnavailable(ValueError):
+    """A faithful offline comparison needs additional data or geometry support."""
+
+
+def _tree(source):
+    return ast.parse(
+        "\n".join(
+            line
+            for line in source.splitlines()
+            if not line.lstrip().startswith(("%", "!"))
+        )
+    )
+
+
+def _literal(node, scope):
+    if isinstance(node, ast.Name):
+        return scope[node.id]
+    if isinstance(node, ast.BinOp) and isinstance(node.op, ast.Add):
+        return _literal(node.left, scope) + _literal(node.right, scope)
+    if isinstance(node, (ast.List, ast.Tuple)):
+        return [_literal(item, scope) for item in node.elts]
+    return ast.literal_eval(node)
+
+
+def flight_reference(path):
+    """Read telemetry AGL and all reported/saved references with cell provenance."""
+    path = Path(path).resolve()
+    notebook = nbformat.read(path, 4)
+    scope = {}
+    recorded = None
+    reported_cell = None
+    saved = []
+    measured_summaries = []
+    telemetry = None
+    notes = []
+    for index, cell in enumerate(notebook.cells):
+        if cell.cell_type == "markdown":
+            plain = re.sub(r"[`*$]", "", cell.source)
+            match = re.search(
+                r"(?:official\s+)?recorded\s+apogee(?:\s+altitude)?(?:\s+of)?\s*:?\s*([\d,.]+)\s*m",
+                plain,
+                re.I,
+            )
+            if not match:
+                match = re.search(
+                    r"Measured Altitude[^\n]*?or\s*([\d,.]+)\s*m", plain, re.I
+                )
+            if match:
+                recorded = float(match[1].replace(",", ""))
+                reported_cell = index
+            continue
+        for output in cell.get("outputs", []):
+            text = output.get("text", "") or output.get("data", {}).get(
+                "text/plain", ""
+            )
+            for match in re.finditer(
+                r"Apogee Altitude:\s*[-+\d.eE]+\s*m\s*\(ASL\)\s*\|\s*([-+\d.eE]+)\s*m\s*\(AGL\)",
+                text,
+            ):
+                saved.append(dict(cell=index, agl_m=float(match[1])))
+            if "apogee" in cell.source.lower():
+                for match in re.finditer(
+                    r"(?:Actual apogee|Real apogee|Real data|Actual):\s*([\d.]+)\s*m",
+                    text,
+                    re.I,
+                ):
+                    measured_summaries.append(dict(cell=index, agl_m=float(match[1])))
+        for node in _tree(cell.source).body:
+            if not isinstance(node, ast.Assign):
+                continue
+            if len(node.targets) == 1 and isinstance(node.targets[0], ast.Name):
+                try:
+                    scope[node.targets[0].id] = _literal(node.value, scope)
+                except (ValueError, KeyError, TypeError):
+                    pass
+            call = node.value
+            if telemetry is not None or not isinstance(call, ast.Call):
+                continue
+            if isinstance(call.func, ast.Name) and call.func.id == "FlightDataImporter":
+                options = {kw.arg: kw.value for kw in call.keywords}
+                try:
+                    mapping = _literal(options["columns_map"], scope)
+                    time_col = next(
+                        key for key, value in mapping.items() if value == "time"
+                    )
+                    altitude_col = next(
+                        key for key, value in mapping.items() if value == "altitude"
+                    )
+                    paths = _literal(options["paths"], scope)
+                except (KeyError, ValueError, StopIteration):
+                    continue
+                for filename in [paths] if isinstance(paths, str) else paths:
+                    filename = ROOT / filename.replace("../../", "")
+                    if not filename.exists():
+                        notes.append(
+                            f"Referenced telemetry file is absent: {filename.relative_to(ROOT)} (cell {index})."
+                        )
+                        continue
+                    table = pd.read_csv(filename)
+                    if time_col not in table or altitude_col not in table:
+                        continue
+                    telemetry = dict(
+                        path=str(filename.relative_to(ROOT)),
+                        cell=index,
+                        time=table[time_col].to_numpy(float),
+                        altitude=table[altitude_col].to_numpy(float),
+                        conversion="metres AGL, notebook columns_map",
+                    )
+                    break
+            elif (
+                path.stem in ("bella_lui_flight_sim", "ndrt_2020_flight_sim")
+                and isinstance(call.func, ast.Attribute)
+                and call.func.attr == "loadtxt"
+            ):
+                filename = ROOT / _literal(call.args[0], scope).replace("../../", "")
+                options = {kw.arg: _literal(kw.value, scope) for kw in call.keywords}
+                values = np.loadtxt(filename, **options)
+                if path.stem.startswith("bella_lui"):
+                    values = values[:573]
+                    altitude = values[:, 1]
+                    conversion = "metres AGL, first 573 rows as used in source cell 35"
+                else:
+                    altitude = values[:, 1] * 0.3048
+                    conversion = "feet AGL multiplied by exactly 0.3048; source uses division by 3.281"
+                    notes.append(
+                        "NDRT source uses approximate ft conversion /3.281; comparison uses exact 0.3048 m/ft."
+                    )
+                telemetry = dict(
+                    path=str(filename.relative_to(ROOT)),
+                    cell=index,
+                    time=values[:, 0],
+                    altitude=altitude,
+                    conversion=conversion,
+                )
+    name = path.name.removesuffix("_flight_sim.ipynb")
+    if name == "valetudo":
+        recorded = float(scope["actual_data"]["apogee"])
+        reported_cell = next(
+            i
+            for i, c in enumerate(notebook.cells)
+            if c.cell_type == "code" and "actual_data =" in c.source
+        )
+        notes.append(
+            "Valetudo flight recordings were lost; only recovered scalar apogee/drift values remain."
+        )
+    if telemetry is not None:
+        mask = np.isfinite(telemetry["time"]) & np.isfinite(telemetry["altitude"])
+        time, alt = telemetry["time"][mask], telemetry["altitude"][mask]
+        order = np.argsort(time, kind="stable")
+        time, alt = time[order], alt[order]
+        time, keep = np.unique(time, return_index=True)
+        alt = alt[keep]
+        telemetry["time"], telemetry["altitude"] = time, alt
+        telemetry["sha256"] = hashlib.sha256(
+            (ROOT / telemetry["path"]).read_bytes()
+        ).hexdigest()
+        raw_max = float(alt.max())
+        clean = np.ones(len(time), dtype=bool)
+        previous = 0
+        for i in range(1, len(time)):
+            if abs(alt[i] - alt[previous]) / (time[i] - time[previous]) > 2000:
+                clean[i] = False
+            else:
+                previous = i
+        telemetry["qa_valid"] = clean
+        telemetry["raw_max_agl_m"] = raw_max
+        telemetry["qa_max_agl_m"] = float(alt[clean].max())
+        telemetry["qa_rejected_count"] = int((~clean).sum())
+        if (~clean).any():
+            notes.append(
+                f"Telemetry QA flags {int((~clean).sum())} samples with altitude jumps above 2000 m/s; raw maximum {raw_max:.3f} m is retained separately."
+            )
+        actual = recorded if recorded is not None else telemetry["qa_max_agl_m"]
+        reference_kind = (
+            "notebook reported apogee"
+            if recorded is not None
+            else "telemetry AGL maximum (QA)"
+        )
+        if recorded is not None and abs(raw_max - recorded) > 0.01:
+            notes.append(
+                f"Telemetry maximum {raw_max:.3f} m differs from notebook reported value {recorded:.3f} m; both retained."
+            )
+    else:
+        actual = measured_summaries[0]["agl_m"] if measured_summaries else recorded
+        reference_kind = (
+            "saved measured-apogee summary"
+            if measured_summaries
+            else "notebook reported apogee"
+        )
+    if name == "astra":
+        notes.append(
+            "Source fin trailing edge overhangs the nozzle-inferred body base by 1.216 mm. Native DATCOM accepts this; the integration now validates root-chord centre attachment instead of rejecting all overhang. Geometry is unchanged."
+        )
+    if name == "defiance":
+        notes.append(
+            "Source fin root centre is z=-0.025 m, aft of the tail base z=0.0403 m; native root attachment is outside the declared body."
+        )
+    if name == "ndrt_2020":
+        notes.append(
+            "Source transition is internal at z=1.2..1.327 m with fins near z=3.05 m. The current DATCOM builder represents only an aft tail, not the required downstream smaller cylinder."
+        )
+    if name == "hedy":
+        notes.append(
+            "Hedy end comparison uses simulated ASL 5406.918 m against measured AGL 5231.53 m; saved simulated AGL is 5246.918 m, a 160 m datum discrepancy."
+        )
+    if name == "prometheus_2022":
+        notes.append(
+            "Prometheus prose says June 2022; source environment and local reanalysis are June 2023. Both reruns preserve source conditions."
+        )
+    if name == "valkyrie":
+        notes.append(
+            "Valkyrie prose says 2025 but source environment says 2026 and fetches live Windy weather; no archived profile is supplied."
+        )
+    if name == "juno3":
+        notes.append(
+            "Source fin airfoil is an empirical lift curve (cell 10), not a supplied section contour. The generic 2% HEX DATCOM assumption does not reproduce that curve; source-drag substitution therefore still has a lateral-model discrepancy."
+        )
+    if name == "lince":
+        notes.append(
+            "Lince has three saved trajectories. Compare first/pre-deployment ascent only; retain all saved apogees separately."
+        )
+    return dict(
+        rocket=name,
+        source_notebook=str(path.relative_to(ROOT)),
+        source_sha256=hashlib.sha256(path.read_bytes()).hexdigest(),
+        actual_agl_m=actual,
+        reference_kind=reference_kind,
+        reported_agl_m=recorded,
+        reported_cell=reported_cell,
+        saved_first_agl_m=saved[0]["agl_m"] if saved else None,
+        saved_apogees=saved,
+        measured_summaries=measured_summaries,
+        telemetry=telemetry,
+        notes=notes,
+    )
+
+
+class Construction(ast.NodeTransformer):
+    """Strip display-only statements and reject online model inputs."""
+
+    def visit_Call(self, node):
+        node = self.generic_visit(node)
+        if isinstance(node.func, ast.Name) and node.func.id in {
+            "SolidMotor",
+            "HybridMotor",
+            "GenericMotor",
+            "LiquidMotor",
+        }:
+            for keyword in node.keywords:
+                if keyword.arg == "thrust_source":
+                    keyword.value = ast.Call(
+                        func=ast.Name(id="_example_thrust", ctx=ast.Load()),
+                        args=[keyword.value],
+                        keywords=[],
+                    )
+        if isinstance(node.func, ast.Attribute):
+            if node.func.attr in {"draw", "info", "all_info"}:
+                return (
+                    node.func.value
+                    if isinstance(node.func.value, ast.Call)
+                    else ast.copy_location(ast.Constant(None), node)
+                )
+            if node.func.attr == "set_atmospheric_model":
+                kind = next(
+                    (
+                        kw.value.value
+                        for kw in node.keywords
+                        if kw.arg == "type" and isinstance(kw.value, ast.Constant)
+                    ),
+                    "",
+                )
+                if kind.lower() in {"windy", "forecast", "ensemble"}:
+                    raise SourceUnavailable(
+                        "Source requests live weather with no archived profile; not replaced by invented weather."
+                    )
+            if node.func.attr == "set_elevation" and any(
+                isinstance(a, ast.Constant) and a.value == "Open-Elevation"
+                for a in node.args
+            ):
+                raise SourceUnavailable(
+                    "Source requests online elevation; an explicit archived input is needed."
+                )
+        return node
+
+    def visit_Expr(self, node):
+        if isinstance(node.value, ast.Call):
+            if any(
+                part in ast.unparse(node.value.func) for part in (".plots.", ".prints.")
+            ):
+                return None
+            value = node.value
+            while isinstance(value, ast.Call) and isinstance(value.func, ast.Attribute):
+                value = value.func.value
+            root = value
+            while isinstance(root, ast.Attribute):
+                root = root.value
+            if isinstance(root, ast.Name) and root.id in {"plt", "display", "print"}:
+                return None
+            if isinstance(node.value.func, ast.Attribute) and node.value.func.attr in {
+                "compare_plots",
+                "show",
+                "savefig",
+            }:
+                return None
+        return self.generic_visit(node)
+
+
+def build_model(path):
+    """Execute only trusted pre-Flight construction; preserve physical inputs."""
+    path = Path(path).resolve()
+    notebook = nbformat.read(path, 4)
+    adaptations = []
+
+    def thrust_source(value):
+        if isinstance(value, str) and value.endswith(".eng"):
+            from rocketpy.motors.motor import Motor
+
+            _, _, points = Motor.import_eng(value)
+            if len(points) > 1 and points[1][0] == 0:
+                adaptations.append(
+                    "Read original .eng numeric samples directly: source already has t=0; avoid the importer's extra duplicate zero-time row. No measured sample or motor equation changed."
+                )
+                return np.asarray(points[1:], float)
+        return value
+
+    namespace = {"__name__": "__datcom_source__", "_example_thrust": thrust_source}
+    executed = []
+    for index, cell in enumerate(notebook.cells):
+        if cell.cell_type != "code":
+            continue
+        tree = _tree(cell.source)
+        for node in tree.body:
+            call = node.value if isinstance(node, (ast.Assign, ast.Expr)) else None
+            if (
+                isinstance(call, ast.Call)
+                and isinstance(call.func, ast.Name)
+                and call.func.id == "Flight"
+            ):
+                options = {
+                    kw.arg: eval(
+                        compile(ast.Expression(kw.value), str(path), "eval"), namespace
+                    )
+                    for kw in call.keywords
+                }
+                rocket, env = options.pop("rocket"), options.pop("environment")
+                # Both comparisons stop at apogee. Recovery is irrelevant to
+                # supported passive ascent cases and is deliberately omitted.
+                rocket.parachutes = []
+                rocket._example_adaptations = adaptations
+                options.update(terminate_on_apogee=True)
+                options.setdefault("max_time", 180)
+                options["max_time_step"] = 0.1
+                return rocket, env, options, executed
+            module = ast.Module(body=[node], type_ignores=[])
+            module = h.LocalPaths(ROOT).visit(module)
+            module = Construction().visit(module)
+            ast.fix_missing_locations(module)
+            exec(compile(module, str(path), "exec"), namespace)
+            executed.append(index)
+    raise SourceUnavailable("No first Flight construction found")
diff --git a/docs/examples/datcom/comparison_data/prepare_comparisons.py b/docs/examples/datcom/comparison_data/prepare_comparisons.py
new file mode 100644
index 000000000..b9cf4c8e8
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/prepare_comparisons.py
@@ -0,0 +1,325 @@
+"""Prepare only the requested measured/model comparison values and curves.
+
+Run reproduce_rockets.py first. Apogee-fitted constant CD is an explicitly
+inferred one-parameter calibration, not measured CD or a DATCOM correction.
+"""
+
+import contextlib
+import hashlib
+import io
+import json
+import os
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from scipy.optimize import brentq
+
+import model_sources as c
+from rocketpy import Flight, Function
+from rocketpy.rocket.datcom import run_datcom
+from asteria_model import build
+
+OUT = Path(__file__).resolve().parent
+ROCKETS = OUT / "rockets"
+EXE = Path(
+    os.environ.get(
+        "DATCOM_EXECUTABLE", c.ROOT.parent / "Asteria-Flight-Dynamics/works/dat0/datcom"
+    )
+)
+VELOCITY = {
+    "andromeda": ("t(s)", "vz(m/s)"),
+    "bella_lui": ("time aprox (s)", "v (m/s)"),
+    "cavour": ("ts", "velocity[m/s]"),
+    "juno3": ("time", "velocity"),
+    "prometheus_2022": ("time", "speed"),
+}
+
+
+def error(predicted, measured):
+    return (
+        100 * (predicted - measured) / abs(measured)
+        if pd.notna(predicted) and pd.notna(measured) and measured != 0
+        else np.nan
+    )
+
+
+def fit_cd(rocket, env, launch, target):
+    if rocket.air_brakes:
+        return np.nan, np.nan, "not fitted: active air-brake/controller model"
+    evaluations = {}
+
+    def residual(cd):
+        rocket.power_on_drag = rocket.power_off_drag = Function(float(cd))
+        rocket.power_on_drag_7d = rocket.power_off_drag_7d = Function(
+            lambda alpha, beta, mach, reynolds, q, r, p: float(cd),
+            inputs=["alpha", "beta", "mach", "reynolds", "q", "r", "p"],
+        )
+        with contextlib.redirect_stdout(io.StringIO()):
+            flight = Flight(rocket, env, **launch)
+        height = float(flight.apogee - env.elevation)
+        evaluations[float(cd)] = height
+        return height - target
+
+    low, high = 0.05, 2.0
+    if residual(low) * residual(high) > 0:
+        return np.nan, np.nan, "target not bracketed by constant CD in [0.05, 2]"
+    value = brentq(residual, low, high, xtol=1e-5)
+    mismatch = residual(value)
+    if abs(mismatch) > 0.5:
+        raise AssertionError("Apogee CD fit did not reproduce target within 0.5 m")
+    return (
+        value,
+        mismatch,
+        "inferred: single constant CD, same powered/coast, ordinary lateral model",
+    )
+
+
+def rockets():
+    results = pd.read_csv(ROCKETS / "results.csv")
+    metrics = []
+    for _, row in results.iterrows():
+        name = row.rocket
+        print("VALUES", name, flush=True)
+        ref = c.flight_reference(c.ROOT / f"docs/examples/{name}_flight_sim.ipynb")
+        measured_v = np.nan
+        telemetry = ref["telemetry"]
+        if telemetry and name in VELOCITY:
+            tcol, vcol = VELOCITY[name]
+            table = pd.read_csv(c.ROOT / telemetry["path"])
+            if name == "bella_lui":
+                table = table.iloc[:573]
+            t, v = table[tcol].to_numpy(float), table[vcol].to_numpy(float)
+            accepted = (
+                np.interp(t, telemetry["time"], telemetry["qa_valid"].astype(float))
+                > 0.99
+            )
+            end = telemetry["time"][
+                np.argmax(
+                    np.where(telemetry["qa_valid"], telemetry["altitude"], -np.inf)
+                )
+            ]
+            accepted &= np.isfinite(t) & np.isfinite(v) & (t >= 0) & (t <= end)
+            measured_v = float(np.max(v[accepted]))
+            pd.DataFrame(dict(time_s=t, measured_vz_m_s=v, qa_valid=accepted)).to_csv(
+                ROCKETS / f"{name}_velocity.csv", index=False
+            )
+        for metric, unit, measured, suffix, condition in [
+            ("Apogee AGL", "m", row.actual_agl_m, "agl_m", ref["reference_kind"]),
+            (
+                "Peak upward vertical velocity",
+                "m/s",
+                measured_v,
+                "peak_vertical_velocity_m_s",
+                "recorded ascent velocity channel; not total airspeed",
+            ),
+            (
+                "Initial static margin",
+                "cal",
+                2.875 if name == "ndrt_2020" else np.nan,
+                "initial_static_margin_cal",
+                "Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",
+            ),
+            (
+                "Coast CD at Mach 0.6",
+                "1",
+                np.nan,
+                "coast_cd_mach06",
+                "zero alpha/beta; measured CD unavailable",
+            ),
+        ]:
+            ordinary = row.get("ordinary_" + suffix, np.nan)
+            native = row.get("datcom_" + suffix, np.nan)
+            metrics.append(
+                dict(
+                    rocket=name,
+                    metric=metric,
+                    unit=unit,
+                    measured=measured,
+                    rocketpy=ordinary,
+                    datcom=native,
+                    rocketpy_error_pct=error(ordinary, measured),
+                    datcom_error_pct=error(native, measured),
+                    condition=condition,
+                    availability=row.reason if pd.notna(row.reason) else "",
+                )
+            )
+        # Saved archived values remain identified; do not invent fresh models.
+        if name in {"hedy", "valkyrie"}:
+            for record in metrics:
+                if record["rocket"] == name and record["metric"] == "Apogee AGL":
+                    record["rocketpy"] = row.saved_first_agl_m
+                    record["rocketpy_error_pct"] = error(
+                        row.saved_first_agl_m, row.actual_agl_m
+                    )
+                    record["condition"] += (
+                        "; archived RocketPy output, not fresh simulation"
+                    )
+        if row.status in {"paired untuned comparison", "ordinary only"}:
+            with contextlib.redirect_stdout(io.StringIO()):
+                rocket, env, launch, _ = c.build_model(c.ROOT / ref["source_notebook"])
+            grid = np.linspace(0.01, max(1.2, row.ordinary_max_mach * 1.15), 151)
+            curves = pd.DataFrame(dict(mach=grid))
+            for state, label in [(True, "powered"), (False, "coast")]:
+                function = (
+                    rocket.power_on_drag_7d if state else rocket.power_off_drag_7d
+                )
+                curves[f"rocketpy_{label}_cd"] = [
+                    function(0, 0, m, 1e6, 0, 0, 0) for m in grid
+                ]
+                path = ROCKETS / f"{name}_{label}_for005.dat"
+                if path.exists():
+                    parsed = run_datcom(EXE, path.read_text())
+                    zero = np.flatnonzero(parsed[5] == 0)[0]
+                    curves[f"datcom_{label}_cd"] = np.interp(
+                        grid, parsed[4], parsed[1][:, zero]
+                    )
+            curves.to_csv(ROCKETS / f"{name}_cd.csv", index=False)
+            cd, mismatch, status = fit_cd(rocket, env, launch, row.actual_agl_m)
+            metrics.append(
+                dict(
+                    rocket=name,
+                    metric="Apogee-fitted constant CD",
+                    unit="1",
+                    measured=np.nan,
+                    rocketpy=np.nan,
+                    datcom=np.nan,
+                    inferred_cd=cd,
+                    fit_apogee_residual_m=mismatch,
+                    rocketpy_error_pct=np.nan,
+                    datcom_error_pct=np.nan,
+                    condition=status,
+                    availability="",
+                )
+            )
+        pd.DataFrame(metrics).to_csv(OUT / "rocket_metrics.csv", index=False)
+
+    frame = pd.DataFrame(metrics)
+    fits = (
+        frame[frame.metric == "Apogee-fitted constant CD"]
+        .set_index("rocket")
+        .inferred_cd
+    )
+    cd_rows = frame.metric == "Coast CD at Mach 0.6"
+    frame.loc[cd_rows, "inferred_cd"] = frame.loc[cd_rows, "rocket"].map(fits)
+    for label in ["rocketpy", "datcom"]:
+        frame.loc[cd_rows, f"{label}_vs_fitted_cd_pct"] = [
+            error(predicted, inferred)
+            for predicted, inferred in zip(
+                frame.loc[cd_rows, label], frame.loc[cd_rows, "inferred_cd"]
+            )
+        ]
+    frame.to_csv(OUT / "rocket_metrics.csv", index=False)
+
+
+def asteria():
+    with contextlib.redirect_stdout(io.StringIO()):
+        ordinary, env, launch = build(False, EXE)
+        native, native_env, native_launch = build(True, EXE)
+        baseline = Flight(ordinary, env, **launch)
+        atmosphere = native.set_datcom_atmosphere(baseline)
+        flight = Flight(native, native_env, **native_launch)
+        for _ in range(2):
+            atmosphere = native.set_datcom_atmosphere(flight)
+            flight = Flight(native, native_env, **native_launch)
+    times = np.linspace(0, max(baseline.apogee_time, flight.apogee_time), 601)
+    histories = pd.DataFrame(dict(time_s=times))
+    for label, trajectory in [("rocketpy", baseline), ("datcom", flight)]:
+        active = times <= trajectory.apogee_time
+        histories[f"{label}_altitude_agl_m"] = np.where(
+            active, trajectory.z(times) - env.elevation, np.nan
+        )
+        histories[f"{label}_vz_m_s"] = np.where(active, trajectory.vz(times), np.nan)
+        histories[f"{label}_margin_cal"] = np.where(
+            active, trajectory.stability_margin(times), np.nan
+        )
+    histories.to_csv(OUT / "asteria_histories.csv", index=False)
+    grid = np.linspace(0.01, atmosphere["mach"][-1], 151)
+    curves = pd.DataFrame(dict(mach=grid))
+    for powered, label in [(True, "powered"), (False, "coast")]:
+        function = ordinary.power_on_drag_7d if powered else ordinary.power_off_drag_7d
+        curves[f"rocketpy_{label}_cd"] = [function(0, 0, m, 1e6, 0, 0, 0) for m in grid]
+        curves[f"datcom_{label}_cd"] = [
+            native._datcom_model.drag_coefficient(powered, 0, 0, m, 0, 0, 0, 0)
+            for m in grid
+        ]
+    curves.to_csv(OUT / "asteria_cd.csv", index=False)
+    rows = []
+    for metric, unit, ordinary_value, native_value in [
+        (
+            "Apogee AGL",
+            "m",
+            baseline.apogee - env.elevation,
+            flight.apogee - env.elevation,
+        ),
+        (
+            "Peak upward vertical velocity",
+            "m/s",
+            np.max(baseline.vz(times[times <= baseline.apogee_time])),
+            np.max(flight.vz(times[times <= flight.apogee_time])),
+        ),
+        (
+            "Initial static margin",
+            "cal",
+            ordinary.static_margin(0),
+            native.static_margin(0),
+        ),
+        (
+            "Powered CD at Mach 0.6",
+            "1",
+            ordinary.power_on_drag_7d(0, 0, 0.6, 1e6, 0, 0, 0),
+            native._datcom_model.drag_coefficient(True, 0, 0, 0.6, 0, 0, 0, 0),
+        ),
+        (
+            "Coast CD at Mach 0.6",
+            "1",
+            ordinary.power_off_drag_7d(0, 0, 0.6, 1e6, 0, 0, 0),
+            native._datcom_model.drag_coefficient(False, 0, 0, 0.6, 0, 0, 0, 0),
+        ),
+    ]:
+        rows.append(
+            dict(
+                metric=metric,
+                unit=unit,
+                rocketpy=float(ordinary_value),
+                datcom=float(native_value),
+                datcom_vs_rocketpy_pct=error(native_value, ordinary_value),
+                measured=np.nan,
+            )
+        )
+    pd.DataFrame(rows).to_csv(OUT / "asteria_metrics.csv", index=False)
+    samples = np.linspace(0, ordinary.motor.burn_out_time, 101)
+    np.testing.assert_allclose(
+        ordinary.total_mass(samples), native.total_mass(samples), atol=1e-10
+    )
+    np.testing.assert_allclose(
+        ordinary.center_of_mass(samples), native.center_of_mass(samples), atol=1e-10
+    )
+    (OUT / "asteria_provenance.json").write_text(
+        json.dumps(
+            dict(
+                flight_status="AST-HER has not flown",
+                atmosphere=atmosphere,
+                ordinary_drag_source="Asteria-Flight-Dynamics/input/drag_curve.CSV",
+                ordinary_drag_sha256=hashlib.sha256(
+                    (OUT / "asteria_drag_curve.csv").read_bytes()
+                ).hexdigest(),
+                tail_note="ordinary RocketPy conical approximation; DATCOM configured ogive",
+                mass_cg_match=True,
+                source_configuration="data/rockets/ast_her/configuration.json",
+                scope="rail to apogee; standard RocketPy rail; no Asteria hold-down clamp or PROTUB",
+            ),
+            indent=2,
+        )
+    )
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--asteria-only", action="store_true")
+    args = parser.parse_args()
+    if not args.asteria_only:
+        rockets()
+    asteria()
diff --git a/docs/examples/datcom/comparison_data/reproduce_rockets.py b/docs/examples/datcom/comparison_data/reproduce_rockets.py
new file mode 100644
index 000000000..bb4bb7ecb
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/reproduce_rockets.py
@@ -0,0 +1,406 @@
+"""Untuned ordinary/DATCOM ascent comparisons and full notebook evidence audit."""
+
+import argparse
+import contextlib
+import io
+import json
+import faulthandler
+import os
+from pathlib import Path
+import subprocess
+import sys
+
+import numpy as np
+import pandas as pd
+
+os.environ.setdefault("MPLBACKEND", "Agg")
+import model_sources as c
+
+from rocketpy import Flight  # noqa: E402
+from rocketpy.rocket.datcom import datcom_decks  # noqa: E402
+
+OUT = Path(__file__).resolve().parent / "rockets"
+OUT.mkdir(exist_ok=True)
+EXE = Path(
+    os.environ.get(
+        "DATCOM_EXECUTABLE", c.ROOT.parent / "Asteria-Flight-Dynamics/works/dat0/datcom"
+    )
+)
+
+
+def finalize_result(result, record):
+    """Use one shared measured-ascent overlap for both resampled histories."""
+    result["notes"] = " | ".join(record["notes"])
+    telemetry = record["telemetry"]
+    if telemetry:
+        result["telemetry_qa_max_agl_m"] = telemetry["qa_max_agl_m"]
+        result["telemetry_qa_rejected_count"] = telemetry["qa_rejected_count"]
+        for label in ("ordinary", "datcom"):
+            if f"{label}_agl_m" in result:
+                result[f"{label}_vs_telemetry_pct"] = 100 * (
+                    result[f"{label}_agl_m"] / telemetry["qa_max_agl_m"] - 1
+                )
+        path = OUT / f"{record['rocket']}_altitude.csv"
+        if path.exists():
+            history = pd.read_csv(path)
+            measured = pd.read_csv(OUT / f"{record['rocket']}_telemetry.csv")
+            measured = measured[measured.qa_valid]
+            labels = [
+                label for label in ("ordinary", "datcom") if f"{label}_agl_m" in history
+            ]
+            upper = min(
+                [float(measured.loc[measured.altitude_agl_m.idxmax(), "time_s"])]
+                + [
+                    float(
+                        history.loc[history[f"{label}_agl_m"].notna(), "time_s"].max()
+                    )
+                    for label in labels
+                ]
+            )
+            lower = max(0, float(measured.time_s.min()))
+            if labels and upper > lower:
+                time = np.linspace(lower, upper, 301)
+                observed = np.interp(time, measured.time_s, measured.altitude_agl_m)
+                for label in labels:
+                    mask = history[f"{label}_agl_m"].notna()
+                    predicted = np.interp(
+                        time, history.time_s[mask], history[f"{label}_agl_m"][mask]
+                    )
+                    result[f"{label}_ascent_altitude_rmse_m"] = float(
+                        np.sqrt(np.mean((predicted - observed) ** 2))
+                    )
+                result["rmse_start_s"], result["rmse_end_s"] = lower, upper
+    if result.get("status") == "paired untuned comparison":
+        result["within_sampled_envelope"] = all(
+            result[key] == 0
+            for key in ["outside_mach", "outside_alpha", "outside_beta"]
+        )
+    return result
+
+
+def summarize(flight, reference, prefix):
+    result = {
+        f"{prefix}_agl_m": float(flight.apogee - flight.env.elevation),
+        f"{prefix}_apogee_time_s": float(flight.apogee_time),
+    }
+    result[f"{prefix}_peak_vertical_velocity_m_s"] = float(
+        np.max(flight.vz(np.linspace(0, flight.apogee_time, 2001)))
+    )
+    result[f"{prefix}_initial_static_margin_cal"] = float(
+        flight.rocket.static_margin(0)
+    )
+    if prefix == "datcom":
+        result[f"{prefix}_coast_cd_mach06"] = float(
+            flight.rocket._datcom_model.drag_coefficient(False, 0, 0, 0.6, 0, 0, 0, 0)
+        )
+    else:
+        result[f"{prefix}_coast_cd_mach06"] = float(
+            flight.rocket.power_off_drag_7d(0, 0, 0.6, 1e6, 0, 0, 0)
+        )
+    time = np.linspace(0, flight.apogee_time, 501)
+    result[f"{prefix}_max_mach"] = float(np.max(flight.mach_number(time)))
+    telemetry = reference["telemetry"]
+    if telemetry:
+        mask = telemetry["qa_valid"]
+        t = telemetry["time"][mask]
+        height = telemetry["altitude"][mask]
+        upper = min(flight.apogee_time, float(t[np.argmax(height)]))
+        lower = max(0, float(t.min()))
+        if upper > lower:
+            sample = np.linspace(lower, upper, 301)
+            result[f"{prefix}_ascent_altitude_rmse_m"] = float(
+                np.sqrt(
+                    np.mean(
+                        (
+                            flight.z(sample)
+                            - flight.env.elevation
+                            - np.interp(sample, t, height)
+                        )
+                        ** 2
+                    )
+                )
+            )
+    return result
+
+
+def one(name, refinements=1):
+    faulthandler.dump_traceback_later(30)
+    source = c.ROOT / f"docs/examples/{name}_flight_sim.ipynb"
+    ref = c.flight_reference(source)
+    row = {
+        key: ref[key]
+        for key in [
+            "rocket",
+            "actual_agl_m",
+            "reported_agl_m",
+            "saved_first_agl_m",
+            "reference_kind",
+        ]
+    }
+    row["telemetry_max_agl_m"] = (
+        ref["telemetry"]["raw_max_agl_m"] if ref["telemetry"] else None
+    )
+    row["notes"] = " | ".join(ref["notes"])
+    row["status"] = "not run"
+    row["reason"] = ""
+    if name == "hedy" and not (c.ROOT / "data/rockets/tust").exists():
+        row.update(
+            status="archived comparison only",
+            reason="Source data/rockets/tust is absent; CoolProp is not installed, and the negative-span fin root cutout requires geometry support.",
+        )
+    else:
+        try:
+            np.random.seed(0)
+            print("BUILD SOURCE", name, flush=True)
+            with contextlib.redirect_stdout(io.StringIO()):
+                rocket, env, launch, cells = c.build_model(source)
+            print("ORDINARY FLIGHT", name, flush=True)
+            with contextlib.redirect_stdout(io.StringIO()):
+                baseline = Flight(rocket, env, **launch)
+            row.update(summarize(baseline, ref, "ordinary"))
+            row["status"] = "ordinary only"
+            row["source_adaptations"] = " | ".join(rocket._example_adaptations)
+            times = np.linspace(0, baseline.apogee_time, 501)
+            history = pd.DataFrame(
+                dict(
+                    time_s=times,
+                    ordinary_agl_m=baseline.z(times) - env.elevation,
+                    ordinary_vz_m_s=baseline.vz(times),
+                    ordinary_margin_cal=baseline.stability_margin(times),
+                )
+            )
+            history.to_csv(OUT / f"{name}_altitude.csv", index=False)
+            if rocket.air_brakes:
+                row["reason"] = (
+                    "Source active air brakes override complete rocket drag. Combining their whole-vehicle CD with DATCOM would double-count base drag; no controlled DATCOM result claimed."
+                )
+            else:
+                np.random.seed(0)
+                print("BUILD DATCOM", name, flush=True)
+                with contextlib.redirect_stdout(io.StringIO()):
+                    other, other_env, other_launch, _ = c.build_model(source)
+                c.h.attach_datcom(other, EXE, 0)
+                atmosphere = other.set_datcom_atmosphere(baseline)
+                grid = atmosphere["mach"]
+                samples = np.linspace(0, rocket.motor.burn_out_time, 101)
+                np.testing.assert_allclose(
+                    rocket.total_mass(samples),
+                    other.total_mass(samples),
+                    atol=1e-10,
+                    rtol=0,
+                )
+                np.testing.assert_allclose(
+                    rocket.center_of_mass(samples),
+                    other.center_of_mass(samples),
+                    atol=1e-10,
+                    rtol=0,
+                )
+                with contextlib.redirect_stdout(io.StringIO()):
+                    print("NATIVE FLIGHT", name, file=sys.stderr, flush=True)
+                    native = Flight(other, other_env, **other_launch)
+                iterations = [
+                    {
+                        "pass": 0,
+                        "apogee_agl_m": float(native.apogee - env.elevation),
+                        "reference": "ordinary trajectory",
+                    }
+                ]
+                for iteration in range(refinements):
+                    atmosphere = other.set_datcom_atmosphere(native)
+                    grid = atmosphere["mach"]
+                    with contextlib.redirect_stdout(io.StringIO()):
+                        native = Flight(other, other_env, **other_launch)
+                    iterations.append(
+                        {
+                            "pass": iteration + 1,
+                            "apogee_agl_m": float(native.apogee - env.elevation),
+                            "reference": "previous DATCOM trajectory",
+                        }
+                    )
+                schedules = atmosphere["altitude_m_asl"]
+                decks, _, _, _ = datcom_decks(other)
+                for powered, deck in decks.items():
+                    (
+                        OUT / f"{name}_{'powered' if powered else 'coast'}_for005.dat"
+                    ).write_text(deck)
+                row["atmosphere_refinements"] = refinements
+                row["atmosphere_last_apogee_change_m"] = (
+                    iterations[-1]["apogee_agl_m"] - iterations[-2]["apogee_agl_m"]
+                    if refinements
+                    else None
+                )
+                row.update(summarize(native, ref, "datcom"))
+                row.update(
+                    c.h.envelope_diagnostics(
+                        native,
+                        other._datcom_model,
+                        mach_grid=grid,
+                        alpha_grid=atmosphere["alpha"],
+                    )
+                )
+                row["status"] = "paired untuned comparison"
+                row["reason"] = ""
+                row["mass_cg_match"] = True
+                time = np.linspace(
+                    0, max(native.apogee_time, baseline.apogee_time), 601
+                )
+                ordinary = np.where(
+                    time <= baseline.apogee_time,
+                    baseline.z(time) - env.elevation,
+                    np.nan,
+                )
+                datcom = np.where(
+                    time <= native.apogee_time, native.z(time) - env.elevation, np.nan
+                )
+                pd.DataFrame(
+                    dict(
+                        time_s=time,
+                        ordinary_agl_m=ordinary,
+                        datcom_agl_m=datcom,
+                        ordinary_vz_m_s=np.where(
+                            time <= baseline.apogee_time, baseline.vz(time), np.nan
+                        ),
+                        datcom_vz_m_s=np.where(
+                            time <= native.apogee_time, native.vz(time), np.nan
+                        ),
+                        ordinary_margin_cal=np.where(
+                            time <= baseline.apogee_time,
+                            baseline.stability_margin(time),
+                            np.nan,
+                        ),
+                        datcom_margin_cal=np.where(
+                            time <= native.apogee_time,
+                            native.stability_margin(time),
+                            np.nan,
+                        ),
+                    )
+                ).to_csv(OUT / f"{name}_altitude.csv", index=False)
+                metadata = dict(
+                    construction_cells=sorted(set(cells)),
+                    calibration=other.datcom_calibration,
+                    source_adaptations=rocket._example_adaptations,
+                    mach=grid,
+                    alpha=atmosphere["alpha"],
+                    beta=c.h.BETA,
+                    altitude_schedules_m_asl=schedules,
+                    atmosphere=atmosphere,
+                    atmosphere_iterations=iterations,
+                    atmosphere_method="RocketPy reference ascent rho*a*M/mu; native REN",
+                    source_notebook=ref["source_notebook"],
+                    source_sha256=ref["source_sha256"],
+                    construction_assumption="carbon fibre and vinyl when unspecified; no numerical roughness inferred",
+                    fin_assumption="2% HEX, 5% bevels, sharp edges, auto body attachment",
+                    powered_base_drag="nozzle",
+                    damp=True,
+                )
+                (OUT / f"{name}_provenance.json").write_text(
+                    json.dumps(metadata, indent=2)
+                )
+        except Exception as error:
+            row["reason"] = f"{type(error).__name__}: {error}"
+            if row["status"] == "not run":
+                row["status"] = "unavailable source model"
+    for label in ["ordinary", "datcom"]:
+        if f"{label}_agl_m" in row:
+            row[f"{label}_error_m"] = row[f"{label}_agl_m"] - row["actual_agl_m"]
+            row[f"{label}_error_pct"] = (
+                100 * row[f"{label}_error_m"] / row["actual_agl_m"]
+            )
+    (OUT / f"{name}_result.json").write_text(json.dumps(row, indent=2, allow_nan=False))
+    print(json.dumps(row), flush=True)
+    faulthandler.cancel_dump_traceback_later()
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--case")
+    parser.add_argument("--only", nargs="*")
+    parser.add_argument(
+        "--refinements",
+        type=int,
+        default=1,
+        choices=range(6),
+        help="DATCOM-trajectory atmosphere updates after the ordinary seed (default 1)",
+    )
+    args = parser.parse_args()
+    if args.case:
+        one(args.case, args.refinements)
+        return
+    inventory = []
+    for path in sorted((c.ROOT / "docs/examples").glob("*flight_sim.ipynb")):
+        record = c.flight_reference(path)
+        telemetry = record.pop("telemetry")
+        record["telemetry"] = (
+            {
+                key: value
+                for key, value in telemetry.items()
+                if key not in ["time", "altitude", "qa_valid"]
+            }
+            if telemetry
+            else None
+        )
+        if telemetry:
+            pd.DataFrame(
+                dict(
+                    time_s=telemetry["time"],
+                    altitude_agl_m=telemetry["altitude"],
+                    qa_valid=telemetry["qa_valid"],
+                )
+            ).to_csv(OUT / f"{record['rocket']}_telemetry.csv", index=False)
+        inventory.append(record)
+    (OUT / "inventory.json").write_text(json.dumps(inventory, indent=2))
+    results = []
+    for record in inventory:
+        name = record["rocket"]
+        if args.only is None or name in args.only:
+            print("RUN", name, flush=True)
+            try:
+                with (OUT / f"{name}_execution.log").open("w") as log:
+                    subprocess.run(
+                        [
+                            sys.executable,
+                            str(Path(__file__).resolve()),
+                            "--case",
+                            name,
+                            "--refinements",
+                            str(args.refinements),
+                        ],
+                        cwd=c.ROOT,
+                        stdout=log,
+                        stderr=subprocess.STDOUT,
+                        timeout=90,
+                        check=True,
+                    )
+            except subprocess.TimeoutExpired:
+                (OUT / f"{name}_result.json").write_text(
+                    json.dumps(
+                        dict(
+                            rocket=name,
+                            status="source simulation timed out",
+                            reason="Source model did not complete within 90 s; inputs/equations not silently changed.",
+                            actual_agl_m=record["actual_agl_m"],
+                            reported_agl_m=record["reported_agl_m"],
+                            saved_first_agl_m=record["saved_first_agl_m"],
+                        )
+                    )
+                )
+            except subprocess.CalledProcessError:
+                print("CHECK LOG", name, flush=True)
+        path = OUT / f"{name}_result.json"
+        if path.exists():
+            result = finalize_result(json.loads(path.read_text()), record)
+            path.write_text(json.dumps(result, indent=2, allow_nan=False))
+            results.append(result)
+            pd.DataFrame(results).to_csv(OUT / "results.csv", index=False)
+            print(
+                name,
+                result["status"],
+                result.get("ordinary_agl_m"),
+                result.get("datcom_agl_m"),
+                result["reason"],
+                flush=True,
+            )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/examples/datcom/comparison_data/rocket_metrics.csv b/docs/examples/datcom/comparison_data/rocket_metrics.csv
new file mode 100644
index 000000000..435cd0f27
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rocket_metrics.csv
@@ -0,0 +1,79 @@
+rocket,metric,unit,measured,rocketpy,datcom,rocketpy_error_pct,datcom_error_pct,condition,availability,inferred_cd,fit_apogee_residual_m,rocketpy_vs_fitted_cd_pct,datcom_vs_fitted_cd_pct
+andromeda,Apogee AGL,m,3443.0,3615.880567942097,4170.733199067169,5.02121893529181,21.13660177366161,notebook reported apogee,,,,,
+andromeda,Peak upward vertical velocity,m/s,324.10908,343.1543305235018,352.6406104190726,5.876185426061438,8.80306420883752,recorded ascent velocity channel; not total airspeed,,,,,
+andromeda,Initial static margin,cal,,4.158769051891837,4.585569601745527,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,,
+andromeda,Coast CD at Mach 0.6,1,,0.4984983643122677,0.349,,,zero alpha/beta; measured CD unavailable,,0.5913883886236182,,-15.707109929489876,-40.986328660890116
+andromeda,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.5913883886236182,0.0240281656274419,,
+astra,Apogee AGL,m,3250.0,3043.3679728130164,3695.9340692825294,-6.357908528830264,13.721048285616291,notebook reported apogee,,,,,
+astra,Peak upward vertical velocity,m/s,,306.4797793783156,318.5471261391443,,,recorded ascent velocity channel; not total airspeed,,,,,
+astra,Initial static margin,cal,,3.16256293861153,3.477476916191179,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,,
+astra,Coast CD at Mach 0.6,1,,0.6538147999999999,0.4265,,,zero alpha/beta; measured CD unavailable,,0.5989080138236664,,9.167816243731115,-28.787060757952673
+astra,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.5989080138236664,0.0032524125458621,,
+bella_lui,Apogee AGL,m,458.971,460.57389565932874,465.8765076067215,0.3492368056650066,1.5045629477072564,telemetry AGL maximum (QA),,,,,
+bella_lui,Peak upward vertical velocity,m/s,90.0,86.17870117421616,86.47349849471236,-4.245887584204269,-3.918335005875156,recorded ascent velocity channel; not total airspeed,,,,,
+bella_lui,Initial static margin,cal,,2.776966165584537,3.303739452169553,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,,
+bella_lui,Coast CD at Mach 0.6,1,,0.43,0.3457692307692308,,,zero alpha/beta; measured CD unavailable,,0.4457593658055402,,-3.53539757421836,-22.431415401808856
+bella_lui,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.4457593658055402,0.0052149262372722,,
+camoes,Apogee AGL,m,3015.0,3004.10382797431,,-0.3613987404872236,,notebook reported apogee,Source active air brakes override complete rocket drag. Combining their whole-vehicle CD with DATCOM would double-count base drag; no controlled DATCOM result claimed.,,,,
+camoes,Peak upward vertical velocity,m/s,,327.4402911475654,,,,recorded ascent velocity channel; not total airspeed,Source active air brakes override complete rocket drag. Combining their whole-vehicle CD with DATCOM would double-count base drag; no controlled DATCOM result claimed.,,,,
+camoes,Initial static margin,cal,,3.7616831285769776,,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",Source active air brakes override complete rocket drag. Combining their whole-vehicle CD with DATCOM would double-count base drag; no controlled DATCOM result claimed.,,,,
+camoes,Coast CD at Mach 0.6,1,,0.7036,,,,zero alpha/beta; measured CD unavailable,Source active air brakes override complete rocket drag. Combining their whole-vehicle CD with DATCOM would double-count base drag; no controlled DATCOM result claimed.,,,,
+camoes,Apogee-fitted constant CD,1,,,,,,not fitted: active air-brake/controller model,,,,,
+cavour,Apogee AGL,m,2789.0,2818.1348894226226,3251.608548140568,1.0446356910226806,16.58689667051158,notebook reported apogee,,,,,
+cavour,Peak upward vertical velocity,m/s,314.0,304.6396378918068,319.05542973964634,-2.9810070408258644,1.61000947122495,recorded ascent velocity channel; not total airspeed,,,,,
+cavour,Initial static margin,cal,,4.279860429594885,5.465305853006995,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,,
+cavour,Coast CD at Mach 0.6,1,,0.583,0.44825,,,zero alpha/beta; measured CD unavailable,,0.6086474636527143,,-4.213845482702675,-26.353098177738378
+cavour,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.6086474636527143,0.0013786057097604,,
+defiance,Apogee AGL,m,9308.318,9243.891367330603,,-0.6921404347100731,,notebook reported apogee,ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,,,,
+defiance,Peak upward vertical velocity,m/s,,438.71139566437614,,,,recorded ascent velocity channel; not total airspeed,ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,,,,
+defiance,Initial static margin,cal,,7.9601902493275025,,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,,,,
+defiance,Coast CD at Mach 0.6,1,,0.668,,,,zero alpha/beta; measured CD unavailable,ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,0.6574401678467228,,1.6062042859144585,
+defiance,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.6574401678467228,-0.0629210059214528,,
+erebus,Apogee AGL,m,3020.0,2751.1556122225365,3253.422625182214,-8.902132045611372,7.729225999411052,notebook reported apogee,,,,,
+erebus,Peak upward vertical velocity,m/s,,294.1707251884576,307.31659427636407,,,recorded ascent velocity channel; not total airspeed,,,,,
+erebus,Initial static margin,cal,,2.9374083445614505,4.450320086266694,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,,
+erebus,Coast CD at Mach 0.6,1,,0.531,0.376,,,zero alpha/beta; measured CD unavailable,,0.4428682177698397,,19.900227357467035,-15.098897389062893
+erebus,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.4428682177698397,-0.0107670600518758,,
+genesis,Apogee AGL,m,2916.0,3044.6857862664306,3297.2391270686403,4.41309280749076,13.074044138156388,notebook reported apogee,,,,,
+genesis,Peak upward vertical velocity,m/s,,290.6363275291183,299.2620317383252,,,recorded ascent velocity channel; not total airspeed,,,,,
+genesis,Initial static margin,cal,,3.37721309359005,3.7682700796928534,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,,
+genesis,Coast CD at Mach 0.6,1,,0.55,0.47475,,,zero alpha/beta; measured CD unavailable,,0.6089918859113056,,-9.686809837052115,-22.043296309346353
+genesis,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.6089918859113056,-0.0033379047545167,,
+halcyon,Apogee AGL,m,3450.0,3214.238895757538,3530.231782008296,-6.8336551954336775,2.3255588987911824,notebook reported apogee,,,,,
+halcyon,Peak upward vertical velocity,m/s,,275.5714950426811,280.94965050790404,,,recorded ascent velocity channel; not total airspeed,,,,,
+halcyon,Initial static margin,cal,,3.0192578745448033,4.410132986423363,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,,
+halcyon,Coast CD at Mach 0.6,1,,0.426,0.319,,,zero alpha/beta; measured CD unavailable,,0.3446774638716062,,23.593807153776314,-7.449707788604117
+halcyon,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.3446774638716062,0.0023908790494715,,
+hedy,Apogee AGL,m,5231.53,5246.918,,0.2941395729356406,,"saved measured-apogee summary; archived RocketPy output, not fresh simulation","Source data/rockets/tust is absent; CoolProp is not installed, and the negative-span fin root cutout requires geometry support.",,,,
+hedy,Peak upward vertical velocity,m/s,,,,,,recorded ascent velocity channel; not total airspeed,"Source data/rockets/tust is absent; CoolProp is not installed, and the negative-span fin root cutout requires geometry support.",,,,
+hedy,Initial static margin,cal,,,,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach","Source data/rockets/tust is absent; CoolProp is not installed, and the negative-span fin root cutout requires geometry support.",,,,
+hedy,Coast CD at Mach 0.6,1,,,,,,zero alpha/beta; measured CD unavailable,"Source data/rockets/tust is absent; CoolProp is not installed, and the negative-span fin root cutout requires geometry support.",,,,
+juno3,Apogee AGL,m,3213.0,3008.581569789987,3056.4921836297026,-6.362229387177496,-4.871080497052518,notebook reported apogee,,,,,
+juno3,Peak upward vertical velocity,m/s,268.761,241.2427490025272,243.89859046993095,-10.238930126570752,-9.250750492098582,recorded ascent velocity channel; not total airspeed,,,,,
+juno3,Initial static margin,cal,,3.220648297149346,4.0067340523842745,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,,
+juno3,Coast CD at Mach 0.6,1,,0.368,0.3745,,,zero alpha/beta; measured CD unavailable,,0.2640930601135738,,39.344820284842314,41.80607390400393
+juno3,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.2640930601135738,-0.0003520752607073,,
+lince,Apogee AGL,m,3587.0,3285.089381541881,4254.30425999591,-8.416800068528554,18.60340841917786,notebook reported apogee,,,,,
+lince,Peak upward vertical velocity,m/s,,317.77081353823803,342.53953112405384,,,recorded ascent velocity channel; not total airspeed,,,,,
+lince,Initial static margin,cal,,2.9203959364705625,3.962666916625163,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,,
+lince,Coast CD at Mach 0.6,1,,0.65562,0.38175,,,zero alpha/beta; measured CD unavailable,,0.5781559898205425,,13.398461927809832,-33.9711069812675
+lince,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.5781559898205425,-0.0160574872520555,,
+ndrt_2020,Apogee AGL,m,1316.736,1293.5184123380943,,-1.7632682376653923,,notebook reported apogee,ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,,,,
+ndrt_2020,Peak upward vertical velocity,m/s,,171.66118644734854,,,,recorded ascent velocity channel; not total airspeed,ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,,,,
+ndrt_2020,Initial static margin,cal,2.875,4.016381564609376,,39.70022833423917,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,,,,
+ndrt_2020,Coast CD at Mach 0.6,1,,0.44,,,,zero alpha/beta; measured CD unavailable,ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,0.4171896002390634,,5.467633840312778,
+ndrt_2020,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.4171896002390634,-0.0019144055324886,,
+prometheus_2022,Apogee AGL,m,3898.37,4191.288687549948,4229.19676461329,7.513875993041916,8.486284385866142,notebook reported apogee,,,,,
+prometheus_2022,Peak upward vertical velocity,m/s,317.88,322.07071634939507,324.05991606871703,1.318332814079236,1.944103456875876,recorded ascent velocity channel; not total airspeed,,,,,
+prometheus_2022,Initial static margin,cal,,1.0415367273926033,1.7889093089472907,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,,
+prometheus_2022,Coast CD at Mach 0.6,1,,0.351875,0.33475,,,zero alpha/beta; measured CD unavailable,,0.4026032258387881,,-12.60005449114332,-16.85362199903439
+prometheus_2022,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.4026032258387881,-0.0677661625295513,,
+valetudo,Apogee AGL,m,860.0,815.0526424633565,943.6190073224012,-5.226436922865522,9.723140386325722,saved measured-apogee summary,,,,,
+valetudo,Peak upward vertical velocity,m/s,,123.31112741231496,126.09986587804391,,,recorded ascent velocity channel; not total airspeed,,,,,
+valetudo,Initial static margin,cal,,2.951130333277495,4.156583455241121,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,,
+valetudo,Coast CD at Mach 0.6,1,,1.128,0.4313333333333333,,,zero alpha/beta; measured CD unavailable,,0.809545397450946,,39.33746069729839,-46.71906792485103
+valetudo,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.809545397450946,-0.0149988657269659,,
+valkyrie,Apogee AGL,m,2098.02,2247.849,,7.141447650642043,,"notebook reported apogee; archived RocketPy output, not fresh simulation",SourceUnavailable: Source requests live weather with no archived profile; not replaced by invented weather.,,,,
+valkyrie,Peak upward vertical velocity,m/s,,,,,,recorded ascent velocity channel; not total airspeed,SourceUnavailable: Source requests live weather with no archived profile; not replaced by invented weather.,,,,
+valkyrie,Initial static margin,cal,,,,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",SourceUnavailable: Source requests live weather with no archived profile; not replaced by invented weather.,,,,
+valkyrie,Coast CD at Mach 0.6,1,,,,,,zero alpha/beta; measured CD unavailable,SourceUnavailable: Source requests live weather with no archived profile; not replaced by invented weather.,,,,
diff --git a/docs/examples/datcom/comparison_data/rockets/andromeda_altitude.csv b/docs/examples/datcom/comparison_data/rockets/andromeda_altitude.csv
new file mode 100644
index 000000000..161622f4b
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/andromeda_altitude.csv
@@ -0,0 +1,602 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/andromeda_cd.csv b/docs/examples/datcom/comparison_data/rockets/andromeda_cd.csv
new file mode 100644
index 000000000..b01bf4e5d
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/andromeda_cd.csv
@@ -0,0 +1,152 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/andromeda_coast_for005.dat b/docs/examples/datcom/comparison_data/rockets/andromeda_coast_for005.dat
new file mode 100644
index 000000000..77e8b8886
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/andromeda_coast_for005.dat
@@ -0,0 +1,220 @@
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.9,0.95,0.98,1.,
+    1.02,1.04,1.05,1.1,1.2,1.21,
+  REN = 149949.,1504124.07,2899554.88,4368584.72,5761180.69,7448342.92,
+    9266600.03,10995016.5,13068357.8,15295103.4,17924892.7,19297637.9,
+    20106566.5,20638379.4,21167539.7,21601265.1,21808969.6,22847491.9,
+    24924536.6,25132241.1,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -20.,$
+ $REFQ
+  XCG = 1.0998,
+  SREF = 0.0178367771,
+  LREF = 0.1507,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.3,
+  DNOSE = 0.1507,
+  LCENTR = 2.1996,
+  DCENTR = 0.1507,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.145,2.265,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.185,
+  CHORD = 0.23,0.11,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.9,0.95,0.98,1.,
+    1.02,1.04,1.05,1.1,1.2,1.21,
+  REN = 149949.,1504124.07,2899554.88,4368584.72,5761180.69,7448342.92,
+    9266600.03,10995016.5,13068357.8,15295103.4,17924892.7,19297637.9,
+    20106566.5,20638379.4,21167539.7,21601265.1,21808969.6,22847491.9,
+    24924536.6,25132241.1,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -5.,$
+ $REFQ
+  XCG = 1.0998,
+  SREF = 0.0178367771,
+  LREF = 0.1507,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.3,
+  DNOSE = 0.1507,
+  LCENTR = 2.1996,
+  DCENTR = 0.1507,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.145,2.265,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.185,
+  CHORD = 0.23,0.11,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.9,0.95,0.98,1.,
+    1.02,1.04,1.05,1.1,1.2,1.21,
+  REN = 149949.,1504124.07,2899554.88,4368584.72,5761180.69,7448342.92,
+    9266600.03,10995016.5,13068357.8,15295103.4,17924892.7,19297637.9,
+    20106566.5,20638379.4,21167539.7,21601265.1,21808969.6,22847491.9,
+    24924536.6,25132241.1,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 0.,$
+ $REFQ
+  XCG = 1.0998,
+  SREF = 0.0178367771,
+  LREF = 0.1507,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.3,
+  DNOSE = 0.1507,
+  LCENTR = 2.1996,
+  DCENTR = 0.1507,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.145,2.265,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.185,
+  CHORD = 0.23,0.11,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.9,0.95,0.98,1.,
+    1.02,1.04,1.05,1.1,1.2,1.21,
+  REN = 149949.,1504124.07,2899554.88,4368584.72,5761180.69,7448342.92,
+    9266600.03,10995016.5,13068357.8,15295103.4,17924892.7,19297637.9,
+    20106566.5,20638379.4,21167539.7,21601265.1,21808969.6,22847491.9,
+    24924536.6,25132241.1,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 5.,$
+ $REFQ
+  XCG = 1.0998,
+  SREF = 0.0178367771,
+  LREF = 0.1507,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.3,
+  DNOSE = 0.1507,
+  LCENTR = 2.1996,
+  DCENTR = 0.1507,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.145,2.265,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.185,
+  CHORD = 0.23,0.11,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.9,0.95,0.98,1.,
+    1.02,1.04,1.05,1.1,1.2,1.21,
+  REN = 149949.,1504124.07,2899554.88,4368584.72,5761180.69,7448342.92,
+    9266600.03,10995016.5,13068357.8,15295103.4,17924892.7,19297637.9,
+    20106566.5,20638379.4,21167539.7,21601265.1,21808969.6,22847491.9,
+    24924536.6,25132241.1,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 20.,$
+ $REFQ
+  XCG = 1.0998,
+  SREF = 0.0178367771,
+  LREF = 0.1507,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.3,
+  DNOSE = 0.1507,
+  LCENTR = 2.1996,
+  DCENTR = 0.1507,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.145,2.265,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.185,
+  CHORD = 0.23,0.11,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
diff --git a/docs/examples/datcom/comparison_data/rockets/andromeda_powered_for005.dat b/docs/examples/datcom/comparison_data/rockets/andromeda_powered_for005.dat
new file mode 100644
index 000000000..0b20e0c94
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/andromeda_powered_for005.dat
@@ -0,0 +1,220 @@
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.9,0.95,0.98,1.,
+    1.02,1.04,1.05,1.1,1.2,1.21,
+  REN = 219446.036,2194460.36,4169474.69,6136879.27,7876866.71,9822097.84,
+    11750500.5,13447259.4,15331750.6,17180725.7,19184259.8,20152898.2,
+    20714804.,21077841.2,21422042.,21720479.6,21929330.3,22973584.2,
+    25062091.8,25270942.6,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -20.,$
+ $REFQ
+  XCG = 1.2437,
+  SREF = 0.0178367771,
+  LREF = 0.1507,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.3,
+  DNOSE = 0.1507,
+  LCENTR = 2.1996,
+  DCENTR = 0.1507,
+  BASE = .FALSE.,
+  DEXIT = 0.05625,$
+ $FINSET1
+  XLE = 2.145,2.265,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.185,
+  CHORD = 0.23,0.11,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.9,0.95,0.98,1.,
+    1.02,1.04,1.05,1.1,1.2,1.21,
+  REN = 219446.036,2194460.36,4169474.69,6136879.27,7876866.71,9822097.84,
+    11750500.5,13447259.4,15331750.6,17180725.7,19184259.8,20152898.2,
+    20714804.,21077841.2,21422042.,21720479.6,21929330.3,22973584.2,
+    25062091.8,25270942.6,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -5.,$
+ $REFQ
+  XCG = 1.2437,
+  SREF = 0.0178367771,
+  LREF = 0.1507,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.3,
+  DNOSE = 0.1507,
+  LCENTR = 2.1996,
+  DCENTR = 0.1507,
+  BASE = .FALSE.,
+  DEXIT = 0.05625,$
+ $FINSET1
+  XLE = 2.145,2.265,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.185,
+  CHORD = 0.23,0.11,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.9,0.95,0.98,1.,
+    1.02,1.04,1.05,1.1,1.2,1.21,
+  REN = 219446.036,2194460.36,4169474.69,6136879.27,7876866.71,9822097.84,
+    11750500.5,13447259.4,15331750.6,17180725.7,19184259.8,20152898.2,
+    20714804.,21077841.2,21422042.,21720479.6,21929330.3,22973584.2,
+    25062091.8,25270942.6,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 0.,$
+ $REFQ
+  XCG = 1.2437,
+  SREF = 0.0178367771,
+  LREF = 0.1507,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.3,
+  DNOSE = 0.1507,
+  LCENTR = 2.1996,
+  DCENTR = 0.1507,
+  BASE = .FALSE.,
+  DEXIT = 0.05625,$
+ $FINSET1
+  XLE = 2.145,2.265,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.185,
+  CHORD = 0.23,0.11,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.9,0.95,0.98,1.,
+    1.02,1.04,1.05,1.1,1.2,1.21,
+  REN = 219446.036,2194460.36,4169474.69,6136879.27,7876866.71,9822097.84,
+    11750500.5,13447259.4,15331750.6,17180725.7,19184259.8,20152898.2,
+    20714804.,21077841.2,21422042.,21720479.6,21929330.3,22973584.2,
+    25062091.8,25270942.6,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 5.,$
+ $REFQ
+  XCG = 1.2437,
+  SREF = 0.0178367771,
+  LREF = 0.1507,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.3,
+  DNOSE = 0.1507,
+  LCENTR = 2.1996,
+  DCENTR = 0.1507,
+  BASE = .FALSE.,
+  DEXIT = 0.05625,$
+ $FINSET1
+  XLE = 2.145,2.265,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.185,
+  CHORD = 0.23,0.11,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.9,0.95,0.98,1.,
+    1.02,1.04,1.05,1.1,1.2,1.21,
+  REN = 219446.036,2194460.36,4169474.69,6136879.27,7876866.71,9822097.84,
+    11750500.5,13447259.4,15331750.6,17180725.7,19184259.8,20152898.2,
+    20714804.,21077841.2,21422042.,21720479.6,21929330.3,22973584.2,
+    25062091.8,25270942.6,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 20.,$
+ $REFQ
+  XCG = 1.2437,
+  SREF = 0.0178367771,
+  LREF = 0.1507,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.3,
+  DNOSE = 0.1507,
+  LCENTR = 2.1996,
+  DCENTR = 0.1507,
+  BASE = .FALSE.,
+  DEXIT = 0.05625,$
+ $FINSET1
+  XLE = 2.145,2.265,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.185,
+  CHORD = 0.23,0.11,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
diff --git a/docs/examples/datcom/comparison_data/rockets/andromeda_provenance.json b/docs/examples/datcom/comparison_data/rockets/andromeda_provenance.json
new file mode 100644
index 000000000..aa4763b14
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/andromeda_provenance.json
@@ -0,0 +1,377 @@
+{
+  "construction_cells": [
+    2,
+    4,
+    7,
+    8,
+    10,
+    11,
+    12
+  ],
+  "calibration": {
+    "roughness_height": 0.0,
+    "leading_edge_radius": null
+  },
+  "source_adaptations": [],
+  "mach": [
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+    1.05,
+    1.1,
+    1.2,
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+  ],
+  "alpha": [
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+    0.5,
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+    2.0,
+    5.0,
+    10.0,
+    20.0
+  ],
+  "beta": [
+    -20,
+    -5,
+    0,
+    5,
+    20
+  ],
+  "altitude_schedules_m_asl": {
+    "power_on": [
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+    ],
+    "power_off": [
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+    ],
+    "coverage": {
+      "scope": "rail through apogee",
+      "minimum_dynamic_pressure_pa": 100.0,
+      "excluded_low_q_samples": 65,
+      "phases": {
+        "power_on": {
+          "mach_min": 0.004442720561375454,
+          "mach_max": 1.0333536919726816,
+          "loaded_alpha_max_abs_deg": 4.788819421043279
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+        "power_off": {
+          "mach_min": 0.06751270466949837,
+          "mach_max": 1.0232619787075985,
+          "loaded_alpha_max_abs_deg": 2.4055104204132585
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+    "reynolds": {
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+    },
+    "pressure_pa": {
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+  },
+  "atmosphere_iterations": [
+    {
+      "pass": 0,
+      "apogee_agl_m": 4177.624111696815,
+      "reference": "ordinary trajectory"
+    },
+    {
+      "pass": 1,
+      "apogee_agl_m": 4170.733199067169,
+      "reference": "previous DATCOM trajectory"
+    }
+  ],
+  "atmosphere_method": "RocketPy reference ascent rho*a*M/mu; native REN",
+  "source_notebook": "docs/examples/andromeda_flight_sim.ipynb",
+  "source_sha256": "af60f6b41668aa41f90885324cd7e316c87053660da8f2eb8a755c6e29a83a0d",
+  "construction_assumption": "carbon fibre and vinyl when unspecified; no numerical roughness inferred",
+  "fin_assumption": "2% HEX, 5% bevels, sharp edges, auto body attachment",
+  "powered_base_drag": "nozzle",
+  "damp": true
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/andromeda_result.json b/docs/examples/datcom/comparison_data/rockets/andromeda_result.json
new file mode 100644
index 000000000..0fdb611fe
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/andromeda_result.json
@@ -0,0 +1,49 @@
+{
+  "rocket": "andromeda",
+  "actual_agl_m": 3443.0,
+  "reported_agl_m": 3443.0,
+  "saved_first_agl_m": 3614.946,
+  "reference_kind": "notebook reported apogee",
+  "telemetry_max_agl_m": 3438.549384,
+  "notes": "Telemetry maximum 3438.549 m differs from notebook reported value 3443.000 m; both retained.",
+  "status": "paired untuned comparison",
+  "reason": "",
+  "ordinary_agl_m": 3615.880567942097,
+  "ordinary_apogee_time_s": 25.843083317328176,
+  "ordinary_peak_vertical_velocity_m_s": 343.1543305235018,
+  "ordinary_initial_static_margin_cal": 4.158769051891837,
+  "ordinary_coast_cd_mach06": 0.4984983643122677,
+  "ordinary_max_mach": 1.0050934520481516,
+  "ordinary_ascent_altitude_rmse_m": 164.47060925045412,
+  "source_adaptations": "",
+  "atmosphere_refinements": 1,
+  "atmosphere_last_apogee_change_m": -6.890912629645754,
+  "datcom_agl_m": 4170.733199067169,
+  "datcom_apogee_time_s": 28.13329482306857,
+  "datcom_peak_vertical_velocity_m_s": 352.6406104190726,
+  "datcom_initial_static_margin_cal": 4.585569601745527,
+  "datcom_coast_cd_mach06": 0.349,
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+  "datcom_ascent_altitude_rmse_m": 438.15234503512323,
+  "samples": 497,
+  "outside_mach": 0,
+  "outside_alpha": 0,
+  "outside_beta": 0,
+  "max_abs_alpha_deg": 4.455101471012898,
+  "max_abs_beta_deg": 3.9438542480866237,
+  "solver_mach_excursion": true,
+  "solver_alpha_excursion": true,
+  "solver_beta_excursion": true,
+  "mass_cg_match": true,
+  "ordinary_error_m": 172.88056794209706,
+  "ordinary_error_pct": 5.02121893529181,
+  "datcom_error_m": 727.7331990671692,
+  "datcom_error_pct": 21.13660177366161,
+  "telemetry_qa_max_agl_m": 3438.549384,
+  "telemetry_qa_rejected_count": 0,
+  "ordinary_vs_telemetry_pct": 5.157150999989746,
+  "datcom_vs_telemetry_pct": 21.293392454216665,
+  "rmse_start_s": 0,
+  "rmse_end_s": 25.83574241251797,
+  "within_sampled_envelope": true
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/andromeda_telemetry.csv b/docs/examples/datcom/comparison_data/rockets/andromeda_telemetry.csv
new file mode 100644
index 000000000..5e27291b8
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/andromeda_telemetry.csv
@@ -0,0 +1,1140 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/andromeda_velocity.csv b/docs/examples/datcom/comparison_data/rockets/andromeda_velocity.csv
new file mode 100644
index 000000000..3c19b1361
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/andromeda_velocity.csv
@@ -0,0 +1,1140 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/astra_altitude.csv b/docs/examples/datcom/comparison_data/rockets/astra_altitude.csv
new file mode 100644
index 000000000..feff703e3
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/astra_altitude.csv
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diff --git a/docs/examples/datcom/comparison_data/rockets/astra_cd.csv b/docs/examples/datcom/comparison_data/rockets/astra_cd.csv
new file mode 100644
index 000000000..d6174282f
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/astra_cd.csv
@@ -0,0 +1,152 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/astra_coast_for005.dat b/docs/examples/datcom/comparison_data/rockets/astra_coast_for005.dat
new file mode 100644
index 000000000..ad3914bd1
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/astra_coast_for005.dat
@@ -0,0 +1,220 @@
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.93,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 157289.538,1580173.94,3046183.02,4589511.86,6048926.96,7814972.44,
+    9730029.44,11584413.2,13822039.1,16172291.4,17517934.3,18889393.3,
+    19630434.3,20052594.2,20685834.,21107993.9,21530153.8,22163393.6,
+    23218793.3,25329592.6,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -20.,$
+ $REFQ
+  XCG = 1.1257,
+  SREF = 0.00693977817,
+  LREF = 0.094,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.27,
+  DNOSE = 0.094,
+  LCENTR = 1.53678361,
+  DCENTR = 0.094,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 1.608,1.69,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.117,
+  CHORD = 0.2,0.118,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.93,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 157289.538,1580173.94,3046183.02,4589511.86,6048926.96,7814972.44,
+    9730029.44,11584413.2,13822039.1,16172291.4,17517934.3,18889393.3,
+    19630434.3,20052594.2,20685834.,21107993.9,21530153.8,22163393.6,
+    23218793.3,25329592.6,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -5.,$
+ $REFQ
+  XCG = 1.1257,
+  SREF = 0.00693977817,
+  LREF = 0.094,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.27,
+  DNOSE = 0.094,
+  LCENTR = 1.53678361,
+  DCENTR = 0.094,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 1.608,1.69,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.117,
+  CHORD = 0.2,0.118,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.93,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 157289.538,1580173.94,3046183.02,4589511.86,6048926.96,7814972.44,
+    9730029.44,11584413.2,13822039.1,16172291.4,17517934.3,18889393.3,
+    19630434.3,20052594.2,20685834.,21107993.9,21530153.8,22163393.6,
+    23218793.3,25329592.6,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 0.,$
+ $REFQ
+  XCG = 1.1257,
+  SREF = 0.00693977817,
+  LREF = 0.094,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.27,
+  DNOSE = 0.094,
+  LCENTR = 1.53678361,
+  DCENTR = 0.094,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 1.608,1.69,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.117,
+  CHORD = 0.2,0.118,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.93,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 157289.538,1580173.94,3046183.02,4589511.86,6048926.96,7814972.44,
+    9730029.44,11584413.2,13822039.1,16172291.4,17517934.3,18889393.3,
+    19630434.3,20052594.2,20685834.,21107993.9,21530153.8,22163393.6,
+    23218793.3,25329592.6,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 5.,$
+ $REFQ
+  XCG = 1.1257,
+  SREF = 0.00693977817,
+  LREF = 0.094,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.27,
+  DNOSE = 0.094,
+  LCENTR = 1.53678361,
+  DCENTR = 0.094,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 1.608,1.69,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.117,
+  CHORD = 0.2,0.118,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.93,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 157289.538,1580173.94,3046183.02,4589511.86,6048926.96,7814972.44,
+    9730029.44,11584413.2,13822039.1,16172291.4,17517934.3,18889393.3,
+    19630434.3,20052594.2,20685834.,21107993.9,21530153.8,22163393.6,
+    23218793.3,25329592.6,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 20.,$
+ $REFQ
+  XCG = 1.1257,
+  SREF = 0.00693977817,
+  LREF = 0.094,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.27,
+  DNOSE = 0.094,
+  LCENTR = 1.53678361,
+  DCENTR = 0.094,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 1.608,1.69,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.117,
+  CHORD = 0.2,0.118,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
diff --git a/docs/examples/datcom/comparison_data/rockets/astra_powered_for005.dat b/docs/examples/datcom/comparison_data/rockets/astra_powered_for005.dat
new file mode 100644
index 000000000..d04cd3e1a
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/astra_powered_for005.dat
@@ -0,0 +1,220 @@
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.93,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 219386.533,2193865.33,4168344.12,6139882.07,7879897.39,9824968.77,
+    11752604.7,13447754.4,15329098.,17173988.8,18181243.9,19173588.6,
+    19712408.5,20136331.3,20772215.4,21196138.2,21620060.9,22255945.1,
+    23315752.,25435365.8,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -20.,$
+ $REFQ
+  XCG = 1.19567582,
+  SREF = 0.00693977817,
+  LREF = 0.094,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.27,
+  DNOSE = 0.094,
+  LCENTR = 1.53678361,
+  DCENTR = 0.094,
+  BASE = .FALSE.,
+  DEXIT = 0.05625,$
+ $FINSET1
+  XLE = 1.608,1.69,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.117,
+  CHORD = 0.2,0.118,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.93,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 219386.533,2193865.33,4168344.12,6139882.07,7879897.39,9824968.77,
+    11752604.7,13447754.4,15329098.,17173988.8,18181243.9,19173588.6,
+    19712408.5,20136331.3,20772215.4,21196138.2,21620060.9,22255945.1,
+    23315752.,25435365.8,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -5.,$
+ $REFQ
+  XCG = 1.19567582,
+  SREF = 0.00693977817,
+  LREF = 0.094,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.27,
+  DNOSE = 0.094,
+  LCENTR = 1.53678361,
+  DCENTR = 0.094,
+  BASE = .FALSE.,
+  DEXIT = 0.05625,$
+ $FINSET1
+  XLE = 1.608,1.69,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.117,
+  CHORD = 0.2,0.118,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.93,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 219386.533,2193865.33,4168344.12,6139882.07,7879897.39,9824968.77,
+    11752604.7,13447754.4,15329098.,17173988.8,18181243.9,19173588.6,
+    19712408.5,20136331.3,20772215.4,21196138.2,21620060.9,22255945.1,
+    23315752.,25435365.8,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 0.,$
+ $REFQ
+  XCG = 1.19567582,
+  SREF = 0.00693977817,
+  LREF = 0.094,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.27,
+  DNOSE = 0.094,
+  LCENTR = 1.53678361,
+  DCENTR = 0.094,
+  BASE = .FALSE.,
+  DEXIT = 0.05625,$
+ $FINSET1
+  XLE = 1.608,1.69,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.117,
+  CHORD = 0.2,0.118,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.93,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 219386.533,2193865.33,4168344.12,6139882.07,7879897.39,9824968.77,
+    11752604.7,13447754.4,15329098.,17173988.8,18181243.9,19173588.6,
+    19712408.5,20136331.3,20772215.4,21196138.2,21620060.9,22255945.1,
+    23315752.,25435365.8,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 5.,$
+ $REFQ
+  XCG = 1.19567582,
+  SREF = 0.00693977817,
+  LREF = 0.094,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.27,
+  DNOSE = 0.094,
+  LCENTR = 1.53678361,
+  DCENTR = 0.094,
+  BASE = .FALSE.,
+  DEXIT = 0.05625,$
+ $FINSET1
+  XLE = 1.608,1.69,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.117,
+  CHORD = 0.2,0.118,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.93,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 219386.533,2193865.33,4168344.12,6139882.07,7879897.39,9824968.77,
+    11752604.7,13447754.4,15329098.,17173988.8,18181243.9,19173588.6,
+    19712408.5,20136331.3,20772215.4,21196138.2,21620060.9,22255945.1,
+    23315752.,25435365.8,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 20.,$
+ $REFQ
+  XCG = 1.19567582,
+  SREF = 0.00693977817,
+  LREF = 0.094,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.27,
+  DNOSE = 0.094,
+  LCENTR = 1.53678361,
+  DCENTR = 0.094,
+  BASE = .FALSE.,
+  DEXIT = 0.05625,$
+ $FINSET1
+  XLE = 1.608,1.69,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.117,
+  CHORD = 0.2,0.118,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
diff --git a/docs/examples/datcom/comparison_data/rockets/astra_provenance.json b/docs/examples/datcom/comparison_data/rockets/astra_provenance.json
new file mode 100644
index 000000000..72ec739f6
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/astra_provenance.json
@@ -0,0 +1,377 @@
+{
+  "construction_cells": [
+    2,
+    4,
+    6,
+    7,
+    9,
+    10,
+    11
+  ],
+  "calibration": {
+    "roughness_height": 0.0,
+    "leading_edge_radius": null
+  },
+  "source_adaptations": [],
+  "mach": [
+    0.01,
+    0.1,
+    0.19,
+    0.28,
+    0.36,
+    0.45,
+    0.54,
+    0.62,
+    0.71,
+    0.8,
+    0.85,
+    0.9,
+    0.93,
+    0.95,
+    0.98,
+    1.0,
+    1.02,
+    1.05,
+    1.1,
+    1.2
+  ],
+  "alpha": [
+    -20.0,
+    -10.0,
+    -5.0,
+    -2.0,
+    -1.0,
+    -0.5,
+    -0.25,
+    -0.1,
+    -0.05,
+    0.0,
+    0.05,
+    0.1,
+    0.25,
+    0.5,
+    1.0,
+    2.0,
+    5.0,
+    10.0,
+    20.0
+  ],
+  "beta": [
+    -20,
+    -5,
+    0,
+    5,
+    20
+  ],
+  "altitude_schedules_m_asl": {
+    "power_on": [
+      160.00568944515416,
+      165.78636542818458,
+      180.54643056026123,
+      203.59906006701164,
+      230.04232054401405,
+      267.02948827625613,
+      313.24015916727825,
+      362.74009962388857,
+      428.68845350807993,
+      506.66196320223213,
+      556.0137722339433,
+      610.3539647328778,
+      679.4960103431445,
+      679.4960103431445,
+      679.4960103431445,
+      679.4960103431445,
+      679.4960103431445,
+      679.4960103431445,
+      679.4960103431445,
+      679.4960103431445
+    ],
+    "power_off": [
+      3860.4871134854307,
+      3813.0390294657245,
+      3670.477362258977,
+      3448.6470863795735,
+      3194.048605336086,
+      2855.4970030519735,
+      2475.5029439805,
+      2115.2832720851375,
+      1692.7166340345893,
+      1260.331693422656,
+      1027.8217136333735,
+      814.1689892423002,
+      736.6187205015143,
+      736.6187205015143,
+      736.6187205015143,
+      736.6187205015143,
+      736.6187205015143,
+      736.6187205015143,
+      736.6187205015143,
+      736.6187205015143
+    ]
+  },
+  "atmosphere": {
+    "mach": [
+      0.01,
+      0.1,
+      0.19,
+      0.28,
+      0.36,
+      0.45,
+      0.54,
+      0.62,
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+      0.95,
+      0.98,
+      1.0,
+      1.02,
+      1.05,
+      1.1,
+      1.2
+    ],
+    "alpha": [
+      -20.0,
+      -10.0,
+      -5.0,
+      -2.0,
+      -1.0,
+      -0.5,
+      -0.25,
+      -0.1,
+      -0.05,
+      0.0,
+      0.05,
+      0.1,
+      0.25,
+      0.5,
+      1.0,
+      2.0,
+      5.0,
+      10.0,
+      20.0
+    ],
+    "coverage": {
+      "scope": "rail through apogee",
+      "minimum_dynamic_pressure_pa": 100.0,
+      "excluded_low_q_samples": 103,
+      "phases": {
+        "power_on": {
+          "mach_min": 0.009843518362358866,
+          "mach_max": 0.9284111887033256,
+          "loaded_alpha_max_abs_deg": 8.3453781841579
+        },
+        "power_off": {
+          "mach_min": 0.038252507906137496,
+          "mach_max": 0.9190326723181211,
+          "loaded_alpha_max_abs_deg": 9.203367616092846
+        }
+      }
+    },
+    "reynolds": {
+      "power_on": [
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+        2193865.3250798206,
+        4168344.1176516586,
+        6139882.06600433,
+        7879897.387916496,
+        9824968.765156282,
+        11752604.692631569,
+        13447754.426314173,
+        15329098.025643794,
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+        19712408.490047555,
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+        20772215.398114625,
+        21196138.161341455,
+        21620060.924568284,
+        22255945.06940853,
+        23315751.977475602,
+        25435365.793609746
+      ],
+      "power_off": [
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+        17517934.34453064,
+        18889393.314571533,
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+  },
+  "atmosphere_iterations": [
+    {
+      "pass": 0,
+      "apogee_agl_m": 3700.48711348543,
+      "reference": "ordinary trajectory"
+    },
+    {
+      "pass": 1,
+      "apogee_agl_m": 3695.9340692825294,
+      "reference": "previous DATCOM trajectory"
+    }
+  ],
+  "atmosphere_method": "RocketPy reference ascent rho*a*M/mu; native REN",
+  "source_notebook": "docs/examples/astra_flight_sim.ipynb",
+  "source_sha256": "7e708a54ec4c1d3c2f7c4ec015ac1d537431a33dde79a4b91bb5e1602097731a",
+  "construction_assumption": "carbon fibre and vinyl when unspecified; no numerical roughness inferred",
+  "fin_assumption": "2% HEX, 5% bevels, sharp edges, auto body attachment",
+  "powered_base_drag": "nozzle",
+  "damp": true
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/astra_result.json b/docs/examples/datcom/comparison_data/rockets/astra_result.json
new file mode 100644
index 000000000..50155a075
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/astra_result.json
@@ -0,0 +1,49 @@
+{
+  "rocket": "astra",
+  "actual_agl_m": 3250.0,
+  "reported_agl_m": 3250.0,
+  "saved_first_agl_m": 3043.066,
+  "reference_kind": "notebook reported apogee",
+  "telemetry_max_agl_m": 3249.46,
+  "notes": "Telemetry maximum 3249.460 m differs from notebook reported value 3250.000 m; both retained. | Source fin trailing edge overhangs the nozzle-inferred body base by 1.216 mm. Native DATCOM accepts this; the integration now validates root-chord centre attachment instead of rejecting all overhang. Geometry is unchanged.",
+  "status": "paired untuned comparison",
+  "reason": "",
+  "ordinary_agl_m": 3043.3679728130164,
+  "ordinary_apogee_time_s": 24.008756329078313,
+  "ordinary_peak_vertical_velocity_m_s": 306.4797793783156,
+  "ordinary_initial_static_margin_cal": 3.16256293861153,
+  "ordinary_coast_cd_mach06": 0.6538147999999999,
+  "ordinary_max_mach": 0.892926042224684,
+  "ordinary_ascent_altitude_rmse_m": 222.49037923280986,
+  "source_adaptations": "",
+  "atmosphere_refinements": 1,
+  "atmosphere_last_apogee_change_m": -4.5530442029007645,
+  "datcom_agl_m": 3695.9340692825294,
+  "datcom_apogee_time_s": 26.805544357641757,
+  "datcom_peak_vertical_velocity_m_s": 318.5471261391443,
+  "datcom_initial_static_margin_cal": 3.477476916191179,
+  "datcom_coast_cd_mach06": 0.4265,
+  "datcom_max_mach": 0.92839198452266,
+  "datcom_ascent_altitude_rmse_m": 215.54458729050853,
+  "samples": 480,
+  "outside_mach": 0,
+  "outside_alpha": 0,
+  "outside_beta": 0,
+  "max_abs_alpha_deg": 8.95582043567763,
+  "max_abs_beta_deg": 9.383306642057704,
+  "solver_mach_excursion": true,
+  "solver_alpha_excursion": true,
+  "solver_beta_excursion": true,
+  "mass_cg_match": true,
+  "ordinary_error_m": -206.63202718698358,
+  "ordinary_error_pct": -6.357908528830264,
+  "datcom_error_m": 445.93406928252944,
+  "datcom_error_pct": 13.721048285616291,
+  "telemetry_qa_max_agl_m": 3249.46,
+  "telemetry_qa_rejected_count": 0,
+  "ordinary_vs_telemetry_pct": -6.342346949554189,
+  "datcom_vs_telemetry_pct": 13.739946615207742,
+  "rmse_start_s": 0,
+  "rmse_end_s": 23.990962200089378,
+  "within_sampled_envelope": true
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/astra_telemetry.csv b/docs/examples/datcom/comparison_data/rockets/astra_telemetry.csv
new file mode 100644
index 000000000..1ed9d8c05
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/astra_telemetry.csv
@@ -0,0 +1,63 @@
+time_s,altitude_agl_m,qa_valid
+0.0,0.36,True
+1.0,202.23,True
+2.0,501.42,True
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diff --git a/docs/examples/datcom/comparison_data/rockets/bella_lui_altitude.csv b/docs/examples/datcom/comparison_data/rockets/bella_lui_altitude.csv
new file mode 100644
index 000000000..be4b8fc91
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/bella_lui_altitude.csv
@@ -0,0 +1,602 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/bella_lui_cd.csv b/docs/examples/datcom/comparison_data/rockets/bella_lui_cd.csv
new file mode 100644
index 000000000..d35a61fb2
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/bella_lui_cd.csv
@@ -0,0 +1,152 @@
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+0.8588666666666666,0.43,0.3601006666666665,0.43,0.39751199999999987
+0.8667999999999999,0.43,0.3677959999999999,0.43,0.4051279999999999
+0.8747333333333333,0.43,0.37549133333333323,0.43,0.4127439999999999
+0.8826666666666666,0.43,0.3831866666666666,0.43,0.4203599999999999
+0.8905999999999998,0.43,0.39088199999999984,0.43,0.4279759999999998
+0.8985333333333332,0.43,0.3985773333333332,0.43,0.4355919999999998
+0.9064666666666665,0.43,0.4115106666666664,0.43,0.4494159999999997
+0.9143999999999999,0.43,0.4256319999999998,0.43,0.4646479999999998
+0.9223333333333332,0.43,0.43975333333333316,0.43,0.4798799999999998
+0.9302666666666666,0.43,0.45387466666666654,0.43,0.4951119999999999
+0.9381999999999999,0.43,0.4679959999999999,0.43,0.5103439999999999
+0.9461333333333332,0.43,0.48211733333333306,0.43,0.5255759999999998
+0.9540666666666665,0.43,0.49699777777777754,0.43,0.5415399999999998
+0.9619999999999999,0.43,0.5125999999999998,0.43,0.5581999999999998
+0.9699333333333332,0.43,0.528202222222222,0.43,0.5748599999999998
+0.9778666666666666,0.43,0.5438044444444443,0.43,0.5915199999999998
+0.9857999999999999,0.43,0.5593099999999999,0.43,0.6084699999999998
+0.9937333333333332,0.43,0.5747799999999998,0.43,0.6255266666666665
+1.0016666666666665,0.43,0.5899999999999996,0.43,0.6422499999999997
+1.0095999999999998,0.43,0.6042799999999997,0.43,0.6577199999999996
+1.0175333333333332,0.43,0.6185599999999997,0.43,0.6731899999999997
+1.0254666666666665,0.43,0.6328399999999997,0.43,0.6885688888888886
+1.0333999999999999,0.43,0.6471199999999997,0.43,0.7039066666666663
+1.0413333333333332,0.43,0.6613999999999998,0.43,0.7192444444444441
+1.0492666666666666,0.43,0.6756799999999998,0.43,0.7345822222222219
+1.0572,0.43,0.678152,0.43,0.736864
+1.0651333333333333,0.43,0.6794213333333333,0.43,0.737816
+1.0730666666666666,0.43,0.6806906666666667,0.43,0.738768
+1.081,0.43,0.68196,0.43,0.7397199999999999
+1.088933333333333,0.43,0.6832293333333334,0.43,0.740672
+1.0968666666666664,0.43,0.6844986666666667,0.43,0.741624
+1.1047999999999998,0.43,0.68572,0.43,0.7425759999999999
+1.1127333333333331,0.43,0.68691,0.43,0.743528
+1.1206666666666665,0.43,0.6881,0.43,0.7444799999999999
+1.1285999999999998,0.43,0.68929,0.43,0.745432
+1.1365333333333332,0.43,0.69048,0.43,0.7463839999999999
+1.1444666666666665,0.43,0.69167,0.43,0.747336
+1.1523999999999999,0.43,0.69286,0.43,0.748288
+1.1603333333333332,0.43,0.69405,0.43,0.74924
+1.1682666666666666,0.43,0.69524,0.43,0.750192
+1.1762,0.43,0.69643,0.43,0.751144
+1.1841333333333333,0.43,0.69762,0.43,0.752096
+1.1920666666666666,0.43,0.6988099999999999,0.43,0.753048
+1.2,0.43,0.7,0.43,0.754
diff --git a/docs/examples/datcom/comparison_data/rockets/bella_lui_coast_for005.dat b/docs/examples/datcom/comparison_data/rockets/bella_lui_coast_for005.dat
new file mode 100644
index 000000000..2a145920e
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/bella_lui_coast_for005.dat
@@ -0,0 +1,235 @@
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.03,0.06,0.1,0.14,0.18,0.22,0.26,0.4,0.53,0.66,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 217562.995,652917.53,1307544.93,2185916.55,3074026.83,3975270.48,
+    4897468.72,5832418.09,8972950.9,11889159.9,14805369.,17945901.8,
+    20189139.5,21310758.4,21983729.7,22432377.3,22881024.8,23553996.1,
+    24675615.,26918852.7,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -20.,$
+ $REFQ
+  XCG = 1.54204584,
+  SREF = 0.0191134497,
+  LREF = 0.156,$
+ $AXIBOD
+  TNOSE = OGIVE,
+  LNOSE = 0.242,
+  DNOSE = 0.156,
+  LCENTR = 2.3856,
+  DCENTR = 0.156,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.05,
+  DAFT = 0.135,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.292,2.447,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.2,
+  CHORD = 0.28,0.125,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.03,0.06,0.1,0.14,0.18,0.22,0.26,0.4,0.53,0.66,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 217562.995,652917.53,1307544.93,2185916.55,3074026.83,3975270.48,
+    4897468.72,5832418.09,8972950.9,11889159.9,14805369.,17945901.8,
+    20189139.5,21310758.4,21983729.7,22432377.3,22881024.8,23553996.1,
+    24675615.,26918852.7,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -5.,$
+ $REFQ
+  XCG = 1.54204584,
+  SREF = 0.0191134497,
+  LREF = 0.156,$
+ $AXIBOD
+  TNOSE = OGIVE,
+  LNOSE = 0.242,
+  DNOSE = 0.156,
+  LCENTR = 2.3856,
+  DCENTR = 0.156,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.05,
+  DAFT = 0.135,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.292,2.447,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.2,
+  CHORD = 0.28,0.125,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.03,0.06,0.1,0.14,0.18,0.22,0.26,0.4,0.53,0.66,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 217562.995,652917.53,1307544.93,2185916.55,3074026.83,3975270.48,
+    4897468.72,5832418.09,8972950.9,11889159.9,14805369.,17945901.8,
+    20189139.5,21310758.4,21983729.7,22432377.3,22881024.8,23553996.1,
+    24675615.,26918852.7,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 0.,$
+ $REFQ
+  XCG = 1.54204584,
+  SREF = 0.0191134497,
+  LREF = 0.156,$
+ $AXIBOD
+  TNOSE = OGIVE,
+  LNOSE = 0.242,
+  DNOSE = 0.156,
+  LCENTR = 2.3856,
+  DCENTR = 0.156,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.05,
+  DAFT = 0.135,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.292,2.447,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.2,
+  CHORD = 0.28,0.125,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.03,0.06,0.1,0.14,0.18,0.22,0.26,0.4,0.53,0.66,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 217562.995,652917.53,1307544.93,2185916.55,3074026.83,3975270.48,
+    4897468.72,5832418.09,8972950.9,11889159.9,14805369.,17945901.8,
+    20189139.5,21310758.4,21983729.7,22432377.3,22881024.8,23553996.1,
+    24675615.,26918852.7,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 5.,$
+ $REFQ
+  XCG = 1.54204584,
+  SREF = 0.0191134497,
+  LREF = 0.156,$
+ $AXIBOD
+  TNOSE = OGIVE,
+  LNOSE = 0.242,
+  DNOSE = 0.156,
+  LCENTR = 2.3856,
+  DCENTR = 0.156,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.05,
+  DAFT = 0.135,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.292,2.447,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.2,
+  CHORD = 0.28,0.125,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.03,0.06,0.1,0.14,0.18,0.22,0.26,0.4,0.53,0.66,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 217562.995,652917.53,1307544.93,2185916.55,3074026.83,3975270.48,
+    4897468.72,5832418.09,8972950.9,11889159.9,14805369.,17945901.8,
+    20189139.5,21310758.4,21983729.7,22432377.3,22881024.8,23553996.1,
+    24675615.,26918852.7,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 20.,$
+ $REFQ
+  XCG = 1.54204584,
+  SREF = 0.0191134497,
+  LREF = 0.156,$
+ $AXIBOD
+  TNOSE = OGIVE,
+  LNOSE = 0.242,
+  DNOSE = 0.156,
+  LCENTR = 2.3856,
+  DCENTR = 0.156,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.05,
+  DAFT = 0.135,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.292,2.447,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.2,
+  CHORD = 0.28,0.125,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
diff --git a/docs/examples/datcom/comparison_data/rockets/bella_lui_powered_for005.dat b/docs/examples/datcom/comparison_data/rockets/bella_lui_powered_for005.dat
new file mode 100644
index 000000000..af943a0a0
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/bella_lui_powered_for005.dat
@@ -0,0 +1,235 @@
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.03,0.06,0.1,0.14,0.18,0.22,0.26,0.4,0.53,0.66,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 226942.54,680764.355,1361089.51,2266748.06,3169766.8,4068954.55,
+    4962044.48,5840986.88,8986133.67,11906627.1,14827120.5,17972267.3,
+    20218800.7,21342067.5,22016027.5,22465334.2,22914640.8,23588600.9,
+    24711867.6,26958401.,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -20.,$
+ $REFQ
+  XCG = 1.57817594,
+  SREF = 0.0191134497,
+  LREF = 0.156,$
+ $AXIBOD
+  TNOSE = OGIVE,
+  LNOSE = 0.242,
+  DNOSE = 0.156,
+  LCENTR = 2.3856,
+  DCENTR = 0.156,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.05,
+  DAFT = 0.135,
+  DEXIT = 0.0889,$
+ $FINSET1
+  XLE = 2.292,2.447,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.2,
+  CHORD = 0.28,0.125,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.03,0.06,0.1,0.14,0.18,0.22,0.26,0.4,0.53,0.66,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 226942.54,680764.355,1361089.51,2266748.06,3169766.8,4068954.55,
+    4962044.48,5840986.88,8986133.67,11906627.1,14827120.5,17972267.3,
+    20218800.7,21342067.5,22016027.5,22465334.2,22914640.8,23588600.9,
+    24711867.6,26958401.,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -5.,$
+ $REFQ
+  XCG = 1.57817594,
+  SREF = 0.0191134497,
+  LREF = 0.156,$
+ $AXIBOD
+  TNOSE = OGIVE,
+  LNOSE = 0.242,
+  DNOSE = 0.156,
+  LCENTR = 2.3856,
+  DCENTR = 0.156,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.05,
+  DAFT = 0.135,
+  DEXIT = 0.0889,$
+ $FINSET1
+  XLE = 2.292,2.447,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.2,
+  CHORD = 0.28,0.125,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.03,0.06,0.1,0.14,0.18,0.22,0.26,0.4,0.53,0.66,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 226942.54,680764.355,1361089.51,2266748.06,3169766.8,4068954.55,
+    4962044.48,5840986.88,8986133.67,11906627.1,14827120.5,17972267.3,
+    20218800.7,21342067.5,22016027.5,22465334.2,22914640.8,23588600.9,
+    24711867.6,26958401.,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 0.,$
+ $REFQ
+  XCG = 1.57817594,
+  SREF = 0.0191134497,
+  LREF = 0.156,$
+ $AXIBOD
+  TNOSE = OGIVE,
+  LNOSE = 0.242,
+  DNOSE = 0.156,
+  LCENTR = 2.3856,
+  DCENTR = 0.156,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.05,
+  DAFT = 0.135,
+  DEXIT = 0.0889,$
+ $FINSET1
+  XLE = 2.292,2.447,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.2,
+  CHORD = 0.28,0.125,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.03,0.06,0.1,0.14,0.18,0.22,0.26,0.4,0.53,0.66,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 226942.54,680764.355,1361089.51,2266748.06,3169766.8,4068954.55,
+    4962044.48,5840986.88,8986133.67,11906627.1,14827120.5,17972267.3,
+    20218800.7,21342067.5,22016027.5,22465334.2,22914640.8,23588600.9,
+    24711867.6,26958401.,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 5.,$
+ $REFQ
+  XCG = 1.57817594,
+  SREF = 0.0191134497,
+  LREF = 0.156,$
+ $AXIBOD
+  TNOSE = OGIVE,
+  LNOSE = 0.242,
+  DNOSE = 0.156,
+  LCENTR = 2.3856,
+  DCENTR = 0.156,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.05,
+  DAFT = 0.135,
+  DEXIT = 0.0889,$
+ $FINSET1
+  XLE = 2.292,2.447,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.2,
+  CHORD = 0.28,0.125,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.03,0.06,0.1,0.14,0.18,0.22,0.26,0.4,0.53,0.66,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 226942.54,680764.355,1361089.51,2266748.06,3169766.8,4068954.55,
+    4962044.48,5840986.88,8986133.67,11906627.1,14827120.5,17972267.3,
+    20218800.7,21342067.5,22016027.5,22465334.2,22914640.8,23588600.9,
+    24711867.6,26958401.,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 20.,$
+ $REFQ
+  XCG = 1.57817594,
+  SREF = 0.0191134497,
+  LREF = 0.156,$
+ $AXIBOD
+  TNOSE = OGIVE,
+  LNOSE = 0.242,
+  DNOSE = 0.156,
+  LCENTR = 2.3856,
+  DCENTR = 0.156,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.05,
+  DAFT = 0.135,
+  DEXIT = 0.0889,$
+ $FINSET1
+  XLE = 2.292,2.447,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.2,
+  CHORD = 0.28,0.125,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
diff --git a/docs/examples/datcom/comparison_data/rockets/bella_lui_provenance.json b/docs/examples/datcom/comparison_data/rockets/bella_lui_provenance.json
new file mode 100644
index 000000000..7822f3cd1
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/bella_lui_provenance.json
@@ -0,0 +1,384 @@
+{
+  "construction_cells": [
+    2,
+    3,
+    6,
+    9,
+    11,
+    14,
+    15,
+    16,
+    19,
+    21,
+    23,
+    25,
+    26,
+    27
+  ],
+  "calibration": {
+    "roughness_height": 0.0,
+    "leading_edge_radius": null
+  },
+  "source_adaptations": [],
+  "mach": [
+    0.01,
+    0.03,
+    0.06,
+    0.1,
+    0.14,
+    0.18,
+    0.22,
+    0.26,
+    0.4,
+    0.53,
+    0.66,
+    0.8,
+    0.9,
+    0.95,
+    0.98,
+    1.0,
+    1.02,
+    1.05,
+    1.1,
+    1.2
+  ],
+  "alpha": [
+    -20.0,
+    -10.0,
+    -5.0,
+    -2.0,
+    -1.0,
+    -0.5,
+    -0.25,
+    -0.1,
+    -0.05,
+    0.0,
+    0.05,
+    0.1,
+    0.25,
+    0.5,
+    1.0,
+    2.0,
+    5.0,
+    10.0,
+    20.0
+  ],
+  "beta": [
+    -20,
+    -5,
+    0,
+    5,
+    20
+  ],
+  "altitude_schedules_m_asl": {
+    "power_on": [
+      407.09340131132757,
+      408.13022189189536,
+      411.7290199288868,
+      420.25583440373305,
+      433.1783419979694,
+      450.8162446805547,
+      474.1382311923281,
+      515.0856559759113,
+      515.0856559759113,
+      515.0856559759113,
+      515.0856559759113,
+      515.0856559759113,
+      515.0856559759113,
+      515.0856559759113,
+      515.0856559759113,
+      515.0856559759113,
+      515.0856559759113,
+      515.0856559759113,
+      515.0856559759113,
+      515.0856559759113
+    ],
+    "power_off": [
+      872.8764978702117,
+      868.708287443951,
+      853.1139822104624,
+      816.5750232709552,
+      762.8049258656072,
+      692.9111355131549,
+      608.5057120009741,
+      530.1747942544757,
+      530.1747942544757,
+      530.1747942544757,
+      530.1747942544757,
+      530.1747942544757,
+      530.1747942544757,
+      530.1747942544757,
+      530.1747942544757,
+      530.1747942544757,
+      530.1747942544757,
+      530.1747942544757,
+      530.1747942544757,
+      530.1747942544757
+    ]
+  },
+  "atmosphere": {
+    "mach": [
+      0.01,
+      0.03,
+      0.06,
+      0.1,
+      0.14,
+      0.18,
+      0.22,
+      0.26,
+      0.4,
+      0.53,
+      0.66,
+      0.8,
+      0.9,
+      0.95,
+      0.98,
+      1.0,
+      1.02,
+      1.05,
+      1.1,
+      1.2
+    ],
+    "alpha": [
+      -20.0,
+      -10.0,
+      -5.0,
+      -2.0,
+      -1.0,
+      -0.5,
+      -0.25,
+      -0.1,
+      -0.05,
+      0.0,
+      0.05,
+      0.1,
+      0.25,
+      0.5,
+      1.0,
+      2.0,
+      5.0,
+      10.0,
+      20.0
+    ],
+    "coverage": {
+      "scope": "rail through apogee",
+      "minimum_dynamic_pressure_pa": 100.0,
+      "excluded_low_q_samples": 217,
+      "phases": {
+        "power_on": {
+          "mach_min": 0.0037139829466042024,
+          "mach_max": 0.2554077055513765,
+          "loaded_alpha_max_abs_deg": 4.593921574017928
+        },
+        "power_off": {
+          "mach_min": 0.013414250875176899,
+          "mach_max": 0.25255655168448327,
+          "loaded_alpha_max_abs_deg": 1.3575682608186768
+        }
+      }
+    },
+    "reynolds": {
+      "power_on": [
+        226942.53976398875,
+        680764.3545598559,
+        1361089.5145692232,
+        2266748.061953287,
+        3169766.7960955896,
+        4068954.554525353,
+        4962044.48014376,
+        5840986.882925799,
+        8986133.66603969,
+        11906627.10750259,
+        14827120.54896549,
+        17972267.33207938,
+        20218800.748589303,
+        21342067.456844263,
+        22016027.48179724,
+        22465334.165099226,
+        22914640.84840121,
+        23588600.87335419,
+        24711867.581609152,
+        26958400.99811907
+      ],
+      "power_off": [
+        217562.99452975014,
+        652917.5299606564,
+        1307544.9309943363,
+        2185916.548998707,
+        3074026.8299063877,
+        3975270.4824248864,
+        4897468.718985472,
+        5832418.086827123,
+        8972950.90281096,
+        11889159.94622452,
+        14805368.98963808,
+        17945901.80562192,
+        20189139.531324655,
+        21310758.394176025,
+        21983729.711886846,
+        22432377.257027395,
+        22881024.802167945,
+        23553996.119878765,
+        24675614.982730135,
+        26918852.70843287
+      ]
+    },
+    "altitude_m_asl": {
+      "power_on": [
+        407.09340131132757,
+        408.13022189189536,
+        411.7290199288868,
+        420.25583440373305,
+        433.1783419979694,
+        450.8162446805547,
+        474.1382311923281,
+        515.0856559759113,
+        515.0856559759113,
+        515.0856559759113,
+        515.0856559759113,
+        515.0856559759113,
+        515.0856559759113,
+        515.0856559759113,
+        515.0856559759113,
+        515.0856559759113,
+        515.0856559759113,
+        515.0856559759113,
+        515.0856559759113,
+        515.0856559759113
+      ],
+      "power_off": [
+        872.8764978702117,
+        868.708287443951,
+        853.1139822104624,
+        816.5750232709552,
+        762.8049258656072,
+        692.9111355131549,
+        608.5057120009741,
+        530.1747942544757,
+        530.1747942544757,
+        530.1747942544757,
+        530.1747942544757,
+        530.1747942544757,
+        530.1747942544757,
+        530.1747942544757,
+        530.1747942544757,
+        530.1747942544757,
+        530.1747942544757,
+        530.1747942544757,
+        530.1747942544757,
+        530.1747942544757
+      ]
+    },
+    "pressure_pa": {
+      "power_on": [
+        98042.19930848133,
+        98030.01411458036,
+        97987.7193793937,
+        97887.50832100911,
+        97735.63704870436,
+        97528.34827741463,
+        97259.35884290551,
+        96788.11562038508,
+        96788.11562038508,
+        96788.11562038508,
+        96788.11562038508,
+        96788.11562038508,
+        96788.11562038508,
+        96788.11562038508,
+        96788.11562038508,
+        96788.11562038508,
+        96788.11562038508,
+        96788.11562038508,
+        96788.11562038508,
+        96788.11562038508
+      ],
+      "power_off": [
+        92721.69029804153,
+        92768.60562782174,
+        92944.1274700284,
+        93355.39204958809,
+        93960.60186245947,
+        94747.29204568893,
+        95712.99139750746,
+        96614.46235195542,
+        96614.46235195542,
+        96614.46235195542,
+        96614.46235195542,
+        96614.46235195542,
+        96614.46235195542,
+        96614.46235195542,
+        96614.46235195542,
+        96614.46235195542,
+        96614.46235195542,
+        96614.46235195542,
+        96614.46235195542,
+        96614.46235195542
+      ]
+    },
+    "temperature_k": {
+      "power_on": [
+        286.62757438228476,
+        286.6206025678196,
+        286.5964034414889,
+        286.539067225743,
+        286.45217338214076,
+        286.3335721628345,
+        286.2151398380659,
+        286.0149785026208,
+        286.0149785026208,
+        286.0149785026208,
+        286.0149785026208,
+        286.0149785026208,
+        286.0149785026208,
+        286.0149785026208,
+        286.0149785026208,
+        286.0149785026208,
+        286.0149785026208,
+        286.0149785026208,
+        286.0149785026208,
+        286.0149785026208
+      ],
+      "power_off": [
+        283.6005717018348,
+        283.63464971195094,
+        283.7621439689953,
+        284.060875291322,
+        284.5004831172081,
+        285.07191331172965,
+        285.5583177169874,
+        285.9412189906487,
+        285.9412189906487,
+        285.9412189906487,
+        285.9412189906487,
+        285.9412189906487,
+        285.9412189906487,
+        285.9412189906487,
+        285.9412189906487,
+        285.9412189906487,
+        285.9412189906487,
+        285.9412189906487,
+        285.9412189906487,
+        285.9412189906487
+      ]
+    }
+  },
+  "atmosphere_iterations": [
+    {
+      "pass": 0,
+      "apogee_agl_m": 465.87649787021167,
+      "reference": "ordinary trajectory"
+    },
+    {
+      "pass": 1,
+      "apogee_agl_m": 465.8765076067215,
+      "reference": "previous DATCOM trajectory"
+    }
+  ],
+  "atmosphere_method": "RocketPy reference ascent rho*a*M/mu; native REN",
+  "source_notebook": "docs/examples/bella_lui_flight_sim.ipynb",
+  "source_sha256": "1402eee9b084da9307acd9ec84e3f730118d0f6897f25943f2d8a691749061af",
+  "construction_assumption": "carbon fibre and vinyl when unspecified; no numerical roughness inferred",
+  "fin_assumption": "2% HEX, 5% bevels, sharp edges, auto body attachment",
+  "powered_base_drag": "nozzle",
+  "damp": true
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/bella_lui_result.json b/docs/examples/datcom/comparison_data/rockets/bella_lui_result.json
new file mode 100644
index 000000000..f71a83c7b
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/bella_lui_result.json
@@ -0,0 +1,49 @@
+{
+  "rocket": "bella_lui",
+  "actual_agl_m": 458.971,
+  "reported_agl_m": null,
+  "saved_first_agl_m": 460.54,
+  "reference_kind": "telemetry AGL maximum (QA)",
+  "telemetry_max_agl_m": 458.971,
+  "notes": "",
+  "status": "paired untuned comparison",
+  "reason": "",
+  "ordinary_agl_m": 460.57389565932874,
+  "ordinary_apogee_time_s": 10.607916503445066,
+  "ordinary_peak_vertical_velocity_m_s": 86.17870117421616,
+  "ordinary_initial_static_margin_cal": 2.776966165584537,
+  "ordinary_coast_cd_mach06": 0.43,
+  "ordinary_max_mach": 0.2544470871701163,
+  "ordinary_ascent_altitude_rmse_m": 4.730090709810138,
+  "source_adaptations": "",
+  "atmosphere_refinements": 1,
+  "atmosphere_last_apogee_change_m": 9.736509809954441e-06,
+  "datcom_agl_m": 465.8765076067215,
+  "datcom_apogee_time_s": 10.675120658701045,
+  "datcom_peak_vertical_velocity_m_s": 86.47349849471236,
+  "datcom_initial_static_margin_cal": 3.303739452169553,
+  "datcom_coast_cd_mach06": 0.3457692307692308,
+  "datcom_max_mach": 0.2554070587748375,
+  "datcom_ascent_altitude_rmse_m": 5.162903677983363,
+  "samples": 427,
+  "outside_mach": 0,
+  "outside_alpha": 0,
+  "outside_beta": 0,
+  "max_abs_alpha_deg": 4.441613641629917,
+  "max_abs_beta_deg": 2.9112883549730375,
+  "solver_mach_excursion": true,
+  "solver_alpha_excursion": true,
+  "solver_beta_excursion": true,
+  "mass_cg_match": true,
+  "ordinary_error_m": 1.6028956593287376,
+  "ordinary_error_pct": 0.34923680566500664,
+  "datcom_error_m": 6.905507606721471,
+  "datcom_error_pct": 1.5045629477072564,
+  "telemetry_qa_max_agl_m": 458.971,
+  "telemetry_qa_rejected_count": 0,
+  "ordinary_vs_telemetry_pct": 0.3492368056650097,
+  "datcom_vs_telemetry_pct": 1.5045629477072664,
+  "rmse_start_s": 0,
+  "rmse_end_s": 10.53,
+  "within_sampled_envelope": true
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/bella_lui_telemetry.csv b/docs/examples/datcom/comparison_data/rockets/bella_lui_telemetry.csv
new file mode 100644
index 000000000..b9b804a57
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/bella_lui_telemetry.csv
@@ -0,0 +1,574 @@
+time_s,altitude_agl_m,qa_valid
+0.0,0.201,True
+0.06,0.201,True
+0.15,0.67,True
+0.21,1.503,True
+0.27,2.805,True
+0.33,2.805,True
+0.39,4.608,True
+0.45,4.608,True
+0.51,6.841,True
+0.57,9.597,True
+0.66,9.597,True
+0.72,12.831,True
+0.78,16.605,True
+0.84,16.605,True
+0.9,20.856,True
+0.96,20.856,True
+1.02,25.532,True
+1.08,30.737,True
+1.14,30.737,True
+1.17,36.327,True
+1.2,36.327,True
+1.26,36.327,True
+1.32,42.461,True
+1.38,49.067,True
+1.44,49.067,True
+1.5,55.991,True
+1.56,55.991,True
+1.62,63.507,True
+1.71,71.431,True
+1.77,79.631,True
+1.83,79.631,True
+1.89,88.272,True
+1.95,88.272,True
+2.01,97.025,True
+2.07,105.747,True
+2.13,105.747,True
+2.22,114.407,True
+2.28,122.887,True
+2.34,122.887,True
+2.4,131.364,True
+2.46,131.364,True
+2.52,139.715,True
+2.58,147.787,True
+2.64,147.787,True
+2.7,155.827,True
+2.73,155.827,True
+2.76,155.827,True
+2.88,171.468,True
+3.0,179.148,True
+3.06,179.148,True
+3.12,186.55,True
+3.18,194.028,True
+3.27,201.506,True
+3.33,201.506,True
+3.39,208.713,True
+3.45,208.713,True
+3.51,215.782,True
+3.57,222.596,True
+3.63,222.596,True
+3.69,229.398,True
+3.78,236.125,True
+3.84,236.125,True
+3.9,242.748,True
+3.96,242.748,True
+4.02,249.388,True
+4.08,255.93,True
+4.14,255.93,True
+4.2,262.233,True
+4.26,262.233,True
+4.29,268.516,True
+4.32,268.516,True
+4.38,274.587,True
+4.44,274.587,True
+4.5,280.654,True
+4.56,280.654,True
+4.62,286.598,True
+4.68,292.318,True
+4.74,292.318,True
+4.83,298.153,True
+4.89,303.855,True
+4.95,303.855,True
+5.01,309.359,True
+5.07,309.359,True
+5.13,314.835,True
+5.19,320.235,True
+5.25,320.235,True
+5.34,325.426,True
+5.4,330.604,True
+5.46,330.604,True
+5.52,335.567,True
+5.58,340.487,True
+5.64,340.487,True
+5.7,345.187,True
+5.76,345.187,True
+5.85,349.861,True
+5.88,354.414,True
+5.94,354.414,True
+6.0,358.879,True
+6.06,358.879,True
+6.12,363.294,True
+6.18,367.638,True
+6.24,367.638,True
+6.3,371.821,True
+6.33,371.821,True
+6.36,371.821,True
+6.42,375.955,True
+6.48,380.014,True
+6.54,380.014,True
+6.6,383.934,True
+6.66,383.934,True
+6.72,387.734,True
+6.78,391.356,True
+6.84,391.356,True
+6.87,391.356,True
+6.9,394.994,True
+6.96,394.994,True
+7.02,398.509,True
+7.08,398.509,True
+7.14,401.909,True
+7.2,405.229,True
+7.26,405.229,True
+7.41,411.442,True
+7.44,411.442,True
+7.5,414.396,True
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diff --git a/docs/examples/datcom/comparison_data/rockets/bella_lui_velocity.csv b/docs/examples/datcom/comparison_data/rockets/bella_lui_velocity.csv
new file mode 100644
index 000000000..45e908483
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/bella_lui_velocity.csv
@@ -0,0 +1,574 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/camoes_altitude.csv b/docs/examples/datcom/comparison_data/rockets/camoes_altitude.csv
new file mode 100644
index 000000000..3748be73b
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/camoes_altitude.csv
@@ -0,0 +1,502 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/camoes_cd.csv b/docs/examples/datcom/comparison_data/rockets/camoes_cd.csv
new file mode 100644
index 000000000..0019e1bef
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/camoes_cd.csv
@@ -0,0 +1,152 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/camoes_result.json b/docs/examples/datcom/comparison_data/rockets/camoes_result.json
new file mode 100644
index 000000000..ccedf6b54
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/camoes_result.json
@@ -0,0 +1,26 @@
+{
+  "rocket": "camoes",
+  "actual_agl_m": 3015.0,
+  "reported_agl_m": 3015.0,
+  "saved_first_agl_m": 3003.284,
+  "reference_kind": "notebook reported apogee",
+  "telemetry_max_agl_m": 3015.996337890625,
+  "notes": "Telemetry maximum 3015.996 m differs from notebook reported value 3015.000 m; both retained.",
+  "status": "ordinary only",
+  "reason": "Source active air brakes override complete rocket drag. Combining their whole-vehicle CD with DATCOM would double-count base drag; no controlled DATCOM result claimed.",
+  "ordinary_agl_m": 3004.10382797431,
+  "ordinary_apogee_time_s": 23.03691562903868,
+  "ordinary_peak_vertical_velocity_m_s": 327.4402911475654,
+  "ordinary_initial_static_margin_cal": 3.7616831285769776,
+  "ordinary_coast_cd_mach06": 0.7036,
+  "ordinary_max_mach": 0.9672822932315445,
+  "ordinary_ascent_altitude_rmse_m": 133.14444925478682,
+  "source_adaptations": "",
+  "ordinary_error_m": -10.896172025689793,
+  "ordinary_error_pct": -0.36139874048722365,
+  "telemetry_qa_max_agl_m": 3015.996337890625,
+  "telemetry_qa_rejected_count": 0,
+  "ordinary_vs_telemetry_pct": -0.3943144680551014,
+  "rmse_start_s": 0,
+  "rmse_end_s": 23.03691562903868
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/camoes_telemetry.csv b/docs/examples/datcom/comparison_data/rockets/camoes_telemetry.csv
new file mode 100644
index 000000000..bf9b9f3be
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/camoes_telemetry.csv
@@ -0,0 +1,14347 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/cavour_altitude.csv b/docs/examples/datcom/comparison_data/rockets/cavour_altitude.csv
new file mode 100644
index 000000000..8efe1e7f3
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/cavour_altitude.csv
@@ -0,0 +1,602 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/cavour_cd.csv b/docs/examples/datcom/comparison_data/rockets/cavour_cd.csv
new file mode 100644
index 000000000..90e208950
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/cavour_cd.csv
@@ -0,0 +1,152 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/cavour_coast_for005.dat b/docs/examples/datcom/comparison_data/rockets/cavour_coast_for005.dat
new file mode 100644
index 000000000..1d1dc423f
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/cavour_coast_for005.dat
@@ -0,0 +1,220 @@
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 167211.604,1676007.18,3228170.,4846401.67,6351255.19,8134788.81,
+    10021932.6,11760337.7,13782796.6,14963614.6,15950771.7,17245463.7,
+    18567246.6,19748912.8,20372562.7,20788329.3,21204095.9,21827745.8,
+    22867162.2,24945995.2,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -20.,$
+ $REFQ
+  XCG = 1.523,
+  SREF = 0.00849486654,
+  LREF = 0.104,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.52,
+  DNOSE = 0.104,
+  LCENTR = 2.2024,
+  DCENTR = 0.104,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.512,2.642,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.1,
+  CHORD = 0.2,0.07,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 167211.604,1676007.18,3228170.,4846401.67,6351255.19,8134788.81,
+    10021932.6,11760337.7,13782796.6,14963614.6,15950771.7,17245463.7,
+    18567246.6,19748912.8,20372562.7,20788329.3,21204095.9,21827745.8,
+    22867162.2,24945995.2,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -5.,$
+ $REFQ
+  XCG = 1.523,
+  SREF = 0.00849486654,
+  LREF = 0.104,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.52,
+  DNOSE = 0.104,
+  LCENTR = 2.2024,
+  DCENTR = 0.104,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.512,2.642,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.1,
+  CHORD = 0.2,0.07,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 167211.604,1676007.18,3228170.,4846401.67,6351255.19,8134788.81,
+    10021932.6,11760337.7,13782796.6,14963614.6,15950771.7,17245463.7,
+    18567246.6,19748912.8,20372562.7,20788329.3,21204095.9,21827745.8,
+    22867162.2,24945995.2,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 0.,$
+ $REFQ
+  XCG = 1.523,
+  SREF = 0.00849486654,
+  LREF = 0.104,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.52,
+  DNOSE = 0.104,
+  LCENTR = 2.2024,
+  DCENTR = 0.104,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.512,2.642,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.1,
+  CHORD = 0.2,0.07,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 167211.604,1676007.18,3228170.,4846401.67,6351255.19,8134788.81,
+    10021932.6,11760337.7,13782796.6,14963614.6,15950771.7,17245463.7,
+    18567246.6,19748912.8,20372562.7,20788329.3,21204095.9,21827745.8,
+    22867162.2,24945995.2,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 5.,$
+ $REFQ
+  XCG = 1.523,
+  SREF = 0.00849486654,
+  LREF = 0.104,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.52,
+  DNOSE = 0.104,
+  LCENTR = 2.2024,
+  DCENTR = 0.104,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.512,2.642,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.1,
+  CHORD = 0.2,0.07,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 167211.604,1676007.18,3228170.,4846401.67,6351255.19,8134788.81,
+    10021932.6,11760337.7,13782796.6,14963614.6,15950771.7,17245463.7,
+    18567246.6,19748912.8,20372562.7,20788329.3,21204095.9,21827745.8,
+    22867162.2,24945995.2,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 20.,$
+ $REFQ
+  XCG = 1.523,
+  SREF = 0.00849486654,
+  LREF = 0.104,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.52,
+  DNOSE = 0.104,
+  LCENTR = 2.2024,
+  DCENTR = 0.104,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.512,2.642,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.1,
+  CHORD = 0.2,0.07,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
diff --git a/docs/examples/datcom/comparison_data/rockets/cavour_powered_for005.dat b/docs/examples/datcom/comparison_data/rockets/cavour_powered_for005.dat
new file mode 100644
index 000000000..1c237bc42
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/cavour_powered_for005.dat
@@ -0,0 +1,220 @@
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 222100.421,2221004.21,4219406.88,6208481.09,7967954.04,9934912.59,
+    11883274.2,13593972.5,15476239.1,16490617.7,17285641.8,18252553.,
+    19158475.2,19955866.2,20586051.4,21006174.9,21426298.4,22056483.7,
+    23106792.4,25207409.9,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -20.,$
+ $REFQ
+  XCG = 1.71175909,
+  SREF = 0.00849486654,
+  LREF = 0.104,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.52,
+  DNOSE = 0.104,
+  LCENTR = 2.2024,
+  DCENTR = 0.104,
+  BASE = .FALSE.,
+  DEXIT = 0.067,$
+ $FINSET1
+  XLE = 2.512,2.642,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.1,
+  CHORD = 0.2,0.07,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 222100.421,2221004.21,4219406.88,6208481.09,7967954.04,9934912.59,
+    11883274.2,13593972.5,15476239.1,16490617.7,17285641.8,18252553.,
+    19158475.2,19955866.2,20586051.4,21006174.9,21426298.4,22056483.7,
+    23106792.4,25207409.9,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -5.,$
+ $REFQ
+  XCG = 1.71175909,
+  SREF = 0.00849486654,
+  LREF = 0.104,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.52,
+  DNOSE = 0.104,
+  LCENTR = 2.2024,
+  DCENTR = 0.104,
+  BASE = .FALSE.,
+  DEXIT = 0.067,$
+ $FINSET1
+  XLE = 2.512,2.642,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.1,
+  CHORD = 0.2,0.07,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 222100.421,2221004.21,4219406.88,6208481.09,7967954.04,9934912.59,
+    11883274.2,13593972.5,15476239.1,16490617.7,17285641.8,18252553.,
+    19158475.2,19955866.2,20586051.4,21006174.9,21426298.4,22056483.7,
+    23106792.4,25207409.9,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 0.,$
+ $REFQ
+  XCG = 1.71175909,
+  SREF = 0.00849486654,
+  LREF = 0.104,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.52,
+  DNOSE = 0.104,
+  LCENTR = 2.2024,
+  DCENTR = 0.104,
+  BASE = .FALSE.,
+  DEXIT = 0.067,$
+ $FINSET1
+  XLE = 2.512,2.642,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.1,
+  CHORD = 0.2,0.07,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 222100.421,2221004.21,4219406.88,6208481.09,7967954.04,9934912.59,
+    11883274.2,13593972.5,15476239.1,16490617.7,17285641.8,18252553.,
+    19158475.2,19955866.2,20586051.4,21006174.9,21426298.4,22056483.7,
+    23106792.4,25207409.9,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 5.,$
+ $REFQ
+  XCG = 1.71175909,
+  SREF = 0.00849486654,
+  LREF = 0.104,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.52,
+  DNOSE = 0.104,
+  LCENTR = 2.2024,
+  DCENTR = 0.104,
+  BASE = .FALSE.,
+  DEXIT = 0.067,$
+ $FINSET1
+  XLE = 2.512,2.642,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.1,
+  CHORD = 0.2,0.07,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 222100.421,2221004.21,4219406.88,6208481.09,7967954.04,9934912.59,
+    11883274.2,13593972.5,15476239.1,16490617.7,17285641.8,18252553.,
+    19158475.2,19955866.2,20586051.4,21006174.9,21426298.4,22056483.7,
+    23106792.4,25207409.9,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 20.,$
+ $REFQ
+  XCG = 1.71175909,
+  SREF = 0.00849486654,
+  LREF = 0.104,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.52,
+  DNOSE = 0.104,
+  LCENTR = 2.2024,
+  DCENTR = 0.104,
+  BASE = .FALSE.,
+  DEXIT = 0.067,$
+ $FINSET1
+  XLE = 2.512,2.642,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.1,
+  CHORD = 0.2,0.07,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
diff --git a/docs/examples/datcom/comparison_data/rockets/cavour_provenance.json b/docs/examples/datcom/comparison_data/rockets/cavour_provenance.json
new file mode 100644
index 000000000..4a8ffbb6e
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/cavour_provenance.json
@@ -0,0 +1,377 @@
+{
+  "construction_cells": [
+    2,
+    4,
+    6,
+    7,
+    10,
+    11,
+    12
+  ],
+  "calibration": {
+    "roughness_height": 0.0,
+    "leading_edge_radius": null
+  },
+  "source_adaptations": [],
+  "mach": [
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+    1.1,
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+  ],
+  "alpha": [
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+    2.0,
+    5.0,
+    10.0,
+    20.0
+  ],
+  "beta": [
+    -20,
+    -5,
+    0,
+    5,
+    20
+  ],
+  "altitude_schedules_m_asl": {
+    "power_on": [
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+    "coverage": {
+      "scope": "rail through apogee",
+      "minimum_dynamic_pressure_pa": 100.0,
+      "excluded_low_q_samples": 45,
+      "phases": {
+        "power_on": {
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+        "power_off": {
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+    }
+  },
+  "atmosphere_iterations": [
+    {
+      "pass": 0,
+      "apogee_agl_m": 3254.330908520737,
+      "reference": "ordinary trajectory"
+    },
+    {
+      "pass": 1,
+      "apogee_agl_m": 3251.608548140568,
+      "reference": "previous DATCOM trajectory"
+    }
+  ],
+  "atmosphere_method": "RocketPy reference ascent rho*a*M/mu; native REN",
+  "source_notebook": "docs/examples/cavour_flight_sim.ipynb",
+  "source_sha256": "9d407019f0a22f7e19bad163131c44dfc10295b1c2290e06035ef968616b83be",
+  "construction_assumption": "carbon fibre and vinyl when unspecified; no numerical roughness inferred",
+  "fin_assumption": "2% HEX, 5% bevels, sharp edges, auto body attachment",
+  "powered_base_drag": "nozzle",
+  "damp": true
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/cavour_result.json b/docs/examples/datcom/comparison_data/rockets/cavour_result.json
new file mode 100644
index 000000000..7db9a38b7
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/cavour_result.json
@@ -0,0 +1,49 @@
+{
+  "rocket": "cavour",
+  "actual_agl_m": 2789.0,
+  "reported_agl_m": 2789.0,
+  "saved_first_agl_m": 2817.405,
+  "reference_kind": "notebook reported apogee",
+  "telemetry_max_agl_m": 2789.0,
+  "notes": "",
+  "status": "paired untuned comparison",
+  "reason": "",
+  "ordinary_agl_m": 2818.1348894226226,
+  "ordinary_apogee_time_s": 22.52457463326182,
+  "ordinary_peak_vertical_velocity_m_s": 304.6396378918068,
+  "ordinary_initial_static_margin_cal": 4.279860429594885,
+  "ordinary_coast_cd_mach06": 0.583,
+  "ordinary_max_mach": 0.9017549966473172,
+  "ordinary_ascent_altitude_rmse_m": 57.14208739821666,
+  "source_adaptations": "",
+  "atmosphere_refinements": 1,
+  "atmosphere_last_apogee_change_m": -2.7223603801689933,
+  "datcom_agl_m": 3251.608548140568,
+  "datcom_apogee_time_s": 24.312660883196,
+  "datcom_peak_vertical_velocity_m_s": 319.05542973964634,
+  "datcom_initial_static_margin_cal": 5.465305853006995,
+  "datcom_coast_cd_mach06": 0.44825000000000004,
+  "datcom_max_mach": 0.9445859958835042,
+  "datcom_ascent_altitude_rmse_m": 284.6998716927426,
+  "samples": 497,
+  "outside_mach": 0,
+  "outside_alpha": 0,
+  "outside_beta": 0,
+  "max_abs_alpha_deg": 1.9473736165566062,
+  "max_abs_beta_deg": 12.065683729942409,
+  "solver_mach_excursion": true,
+  "solver_alpha_excursion": true,
+  "solver_beta_excursion": true,
+  "mass_cg_match": true,
+  "ordinary_error_m": 29.134889422622564,
+  "ordinary_error_pct": 1.0446356910226806,
+  "datcom_error_m": 462.608548140568,
+  "datcom_error_pct": 16.58689667051158,
+  "telemetry_qa_max_agl_m": 2789.0,
+  "telemetry_qa_rejected_count": 0,
+  "ordinary_vs_telemetry_pct": 1.0446356910226884,
+  "datcom_vs_telemetry_pct": 16.586896670511585,
+  "rmse_start_s": 0,
+  "rmse_end_s": 22.489211316956297,
+  "within_sampled_envelope": true
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/cavour_telemetry.csv b/docs/examples/datcom/comparison_data/rockets/cavour_telemetry.csv
new file mode 100644
index 000000000..a3ec4e0cf
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/cavour_telemetry.csv
@@ -0,0 +1,405 @@
+time_s,altitude_agl_m,qa_valid
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diff --git a/docs/examples/datcom/comparison_data/rockets/cavour_velocity.csv b/docs/examples/datcom/comparison_data/rockets/cavour_velocity.csv
new file mode 100644
index 000000000..67e87a900
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/cavour_velocity.csv
@@ -0,0 +1,405 @@
+time_s,measured_vz_m_s,qa_valid
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diff --git a/docs/examples/datcom/comparison_data/rockets/defiance_altitude.csv b/docs/examples/datcom/comparison_data/rockets/defiance_altitude.csv
new file mode 100644
index 000000000..e2a5ef4af
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/defiance_altitude.csv
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diff --git a/docs/examples/datcom/comparison_data/rockets/defiance_cd.csv b/docs/examples/datcom/comparison_data/rockets/defiance_cd.csv
new file mode 100644
index 000000000..0f4b70a58
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/defiance_cd.csv
@@ -0,0 +1,152 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/defiance_result.json b/docs/examples/datcom/comparison_data/rockets/defiance_result.json
new file mode 100644
index 000000000..f4c4fd61c
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/defiance_result.json
@@ -0,0 +1,20 @@
+{
+  "rocket": "defiance",
+  "actual_agl_m": 9308.318,
+  "reported_agl_m": 9308.318,
+  "saved_first_agl_m": 9238.009,
+  "reference_kind": "notebook reported apogee",
+  "telemetry_max_agl_m": null,
+  "notes": "Source fin root centre is z=-0.025 m, aft of the tail base z=0.0403 m; native root attachment is outside the declared body.",
+  "status": "ordinary only",
+  "reason": "ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip",
+  "ordinary_agl_m": 9243.891367330605,
+  "ordinary_apogee_time_s": 44.18119446865966,
+  "ordinary_peak_vertical_velocity_m_s": 438.71139566437614,
+  "ordinary_initial_static_margin_cal": 7.9601902493275025,
+  "ordinary_coast_cd_mach06": 0.668,
+  "ordinary_max_mach": 1.3681759374499065,
+  "source_adaptations": "",
+  "ordinary_error_m": -64.42663266939417,
+  "ordinary_error_pct": -0.6921404347100536
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/erebus_altitude.csv b/docs/examples/datcom/comparison_data/rockets/erebus_altitude.csv
new file mode 100644
index 000000000..78c5f1773
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/erebus_altitude.csv
@@ -0,0 +1,602 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/erebus_cd.csv b/docs/examples/datcom/comparison_data/rockets/erebus_cd.csv
new file mode 100644
index 000000000..476a17a22
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/erebus_cd.csv
@@ -0,0 +1,152 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/erebus_coast_for005.dat b/docs/examples/datcom/comparison_data/rockets/erebus_coast_for005.dat
new file mode 100644
index 000000000..eee4aafe8
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/erebus_coast_for005.dat
@@ -0,0 +1,235 @@
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.91,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 164074.338,1647285.62,3170281.03,4766400.34,6269640.6,8065525.32,
+    9980063.03,11778654.9,13913918.9,16294492.5,17634541.2,18849684.6,
+    19059125.6,19896889.3,20525212.2,20944094.,21362975.9,21991298.7,
+    23038503.4,25132912.8,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -20.,$
+ $REFQ
+  XCG = 1.1921,
+  SREF = 0.00657554977,
+  LREF = 0.0915,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.391113,
+  DNOSE = 0.0915,
+  LCENTR = 1.7022,
+  DCENTR = 0.0915,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.079119,
+  DAFT = 0.0715,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 1.988313,2.058313,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.08,
+  CHORD = 0.12,0.05,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.91,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 164074.338,1647285.62,3170281.03,4766400.34,6269640.6,8065525.32,
+    9980063.03,11778654.9,13913918.9,16294492.5,17634541.2,18849684.6,
+    19059125.6,19896889.3,20525212.2,20944094.,21362975.9,21991298.7,
+    23038503.4,25132912.8,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -5.,$
+ $REFQ
+  XCG = 1.1921,
+  SREF = 0.00657554977,
+  LREF = 0.0915,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.391113,
+  DNOSE = 0.0915,
+  LCENTR = 1.7022,
+  DCENTR = 0.0915,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.079119,
+  DAFT = 0.0715,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 1.988313,2.058313,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.08,
+  CHORD = 0.12,0.05,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.91,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 164074.338,1647285.62,3170281.03,4766400.34,6269640.6,8065525.32,
+    9980063.03,11778654.9,13913918.9,16294492.5,17634541.2,18849684.6,
+    19059125.6,19896889.3,20525212.2,20944094.,21362975.9,21991298.7,
+    23038503.4,25132912.8,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 0.,$
+ $REFQ
+  XCG = 1.1921,
+  SREF = 0.00657554977,
+  LREF = 0.0915,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.391113,
+  DNOSE = 0.0915,
+  LCENTR = 1.7022,
+  DCENTR = 0.0915,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.079119,
+  DAFT = 0.0715,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 1.988313,2.058313,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.08,
+  CHORD = 0.12,0.05,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.91,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 164074.338,1647285.62,3170281.03,4766400.34,6269640.6,8065525.32,
+    9980063.03,11778654.9,13913918.9,16294492.5,17634541.2,18849684.6,
+    19059125.6,19896889.3,20525212.2,20944094.,21362975.9,21991298.7,
+    23038503.4,25132912.8,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 5.,$
+ $REFQ
+  XCG = 1.1921,
+  SREF = 0.00657554977,
+  LREF = 0.0915,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.391113,
+  DNOSE = 0.0915,
+  LCENTR = 1.7022,
+  DCENTR = 0.0915,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.079119,
+  DAFT = 0.0715,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 1.988313,2.058313,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.08,
+  CHORD = 0.12,0.05,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.91,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 164074.338,1647285.62,3170281.03,4766400.34,6269640.6,8065525.32,
+    9980063.03,11778654.9,13913918.9,16294492.5,17634541.2,18849684.6,
+    19059125.6,19896889.3,20525212.2,20944094.,21362975.9,21991298.7,
+    23038503.4,25132912.8,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 20.,$
+ $REFQ
+  XCG = 1.1921,
+  SREF = 0.00657554977,
+  LREF = 0.0915,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.391113,
+  DNOSE = 0.0915,
+  LCENTR = 1.7022,
+  DCENTR = 0.0915,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.079119,
+  DAFT = 0.0715,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 1.988313,2.058313,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.08,
+  CHORD = 0.12,0.05,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
diff --git a/docs/examples/datcom/comparison_data/rockets/erebus_powered_for005.dat b/docs/examples/datcom/comparison_data/rockets/erebus_powered_for005.dat
new file mode 100644
index 000000000..5ee45f987
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/erebus_powered_for005.dat
@@ -0,0 +1,235 @@
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.91,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 221102.091,2210273.75,4195660.39,6173500.16,7921897.53,9873835.71,
+    11804956.5,13500488.,15382253.3,17224554.9,18215335.3,19053435.3,
+    19229196.,20074435.3,20708364.9,21130984.6,21553604.3,22187533.8,
+    23244083.,25357181.5,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -20.,$
+ $REFQ
+  XCG = 1.30643685,
+  SREF = 0.00657554977,
+  LREF = 0.0915,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.391113,
+  DNOSE = 0.0915,
+  LCENTR = 1.7022,
+  DCENTR = 0.0915,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.079119,
+  DAFT = 0.0715,
+  DEXIT = 0.0405,$
+ $FINSET1
+  XLE = 1.988313,2.058313,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.08,
+  CHORD = 0.12,0.05,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.91,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 221102.091,2210273.75,4195660.39,6173500.16,7921897.53,9873835.71,
+    11804956.5,13500488.,15382253.3,17224554.9,18215335.3,19053435.3,
+    19229196.,20074435.3,20708364.9,21130984.6,21553604.3,22187533.8,
+    23244083.,25357181.5,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -5.,$
+ $REFQ
+  XCG = 1.30643685,
+  SREF = 0.00657554977,
+  LREF = 0.0915,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.391113,
+  DNOSE = 0.0915,
+  LCENTR = 1.7022,
+  DCENTR = 0.0915,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.079119,
+  DAFT = 0.0715,
+  DEXIT = 0.0405,$
+ $FINSET1
+  XLE = 1.988313,2.058313,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.08,
+  CHORD = 0.12,0.05,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.91,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 221102.091,2210273.75,4195660.39,6173500.16,7921897.53,9873835.71,
+    11804956.5,13500488.,15382253.3,17224554.9,18215335.3,19053435.3,
+    19229196.,20074435.3,20708364.9,21130984.6,21553604.3,22187533.8,
+    23244083.,25357181.5,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 0.,$
+ $REFQ
+  XCG = 1.30643685,
+  SREF = 0.00657554977,
+  LREF = 0.0915,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.391113,
+  DNOSE = 0.0915,
+  LCENTR = 1.7022,
+  DCENTR = 0.0915,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.079119,
+  DAFT = 0.0715,
+  DEXIT = 0.0405,$
+ $FINSET1
+  XLE = 1.988313,2.058313,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.08,
+  CHORD = 0.12,0.05,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.91,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 221102.091,2210273.75,4195660.39,6173500.16,7921897.53,9873835.71,
+    11804956.5,13500488.,15382253.3,17224554.9,18215335.3,19053435.3,
+    19229196.,20074435.3,20708364.9,21130984.6,21553604.3,22187533.8,
+    23244083.,25357181.5,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 5.,$
+ $REFQ
+  XCG = 1.30643685,
+  SREF = 0.00657554977,
+  LREF = 0.0915,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.391113,
+  DNOSE = 0.0915,
+  LCENTR = 1.7022,
+  DCENTR = 0.0915,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.079119,
+  DAFT = 0.0715,
+  DEXIT = 0.0405,$
+ $FINSET1
+  XLE = 1.988313,2.058313,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.08,
+  CHORD = 0.12,0.05,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.91,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 221102.091,2210273.75,4195660.39,6173500.16,7921897.53,9873835.71,
+    11804956.5,13500488.,15382253.3,17224554.9,18215335.3,19053435.3,
+    19229196.,20074435.3,20708364.9,21130984.6,21553604.3,22187533.8,
+    23244083.,25357181.5,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 20.,$
+ $REFQ
+  XCG = 1.30643685,
+  SREF = 0.00657554977,
+  LREF = 0.0915,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.391113,
+  DNOSE = 0.0915,
+  LCENTR = 1.7022,
+  DCENTR = 0.0915,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.079119,
+  DAFT = 0.0715,
+  DEXIT = 0.0405,$
+ $FINSET1
+  XLE = 1.988313,2.058313,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.08,
+  CHORD = 0.12,0.05,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
diff --git a/docs/examples/datcom/comparison_data/rockets/erebus_provenance.json b/docs/examples/datcom/comparison_data/rockets/erebus_provenance.json
new file mode 100644
index 000000000..bb80f1296
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/erebus_provenance.json
@@ -0,0 +1,377 @@
+{
+  "construction_cells": [
+    2,
+    4,
+    7,
+    8,
+    11,
+    12,
+    13
+  ],
+  "calibration": {
+    "roughness_height": 0.0,
+    "leading_edge_radius": null
+  },
+  "source_adaptations": [],
+  "mach": [
+    0.01,
+    0.1,
+    0.19,
+    0.28,
+    0.36,
+    0.45,
+    0.54,
+    0.62,
+    0.71,
+    0.8,
+    0.85,
+    0.9,
+    0.91,
+    0.95,
+    0.98,
+    1.0,
+    1.02,
+    1.05,
+    1.1,
+    1.2
+  ],
+  "alpha": [
+    -20.0,
+    -10.0,
+    -5.0,
+    -2.0,
+    -1.0,
+    -0.5,
+    -0.25,
+    -0.1,
+    -0.05,
+    0.0,
+    0.05,
+    0.1,
+    0.25,
+    0.5,
+    1.0,
+    2.0,
+    5.0,
+    10.0,
+    20.0
+  ],
+  "beta": [
+    -20,
+    -5,
+    0,
+    5,
+    20
+  ],
+  "altitude_schedules_m_asl": {
+    "power_on": [
+      160.00095154517743,
+      164.34974556148535,
+      176.17236200922045,
+      196.07696363758254,
+      221.05737466286567,
+      257.9304143164693,
+      304.90308878529163,
+      356.04139138656797,
+      425.59662541003655,
+      510.64816801513143,
+      574.4731104692468,
+      739.5192613290584,
+      764.7149768482109,
+      764.7149768482109,
+      764.7149768482109,
+      764.7149768482109,
+      764.7149768482109,
+      764.7149768482109,
+      764.7149768482109,
+      764.7149768482109
+    ],
+    "power_off": [
+      3418.2660948041307,
+      3375.3198069851596,
+      3235.455152110242,
+      3023.0162896642523,
+      2786.34693227908,
+      2482.2344621305724,
+      2153.6311901024837,
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+    }
+  ],
+  "atmosphere_method": "RocketPy reference ascent rho*a*M/mu; native REN",
+  "source_notebook": "docs/examples/erebus_flight_sim.ipynb",
+  "source_sha256": "85d34d7f05a2c3653864b9fdfda2347bfae916c350d480edb7c030c365fd67b4",
+  "construction_assumption": "carbon fibre and vinyl when unspecified; no numerical roughness inferred",
+  "fin_assumption": "2% HEX, 5% bevels, sharp edges, auto body attachment",
+  "powered_base_drag": "nozzle",
+  "damp": true
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/erebus_result.json b/docs/examples/datcom/comparison_data/rockets/erebus_result.json
new file mode 100644
index 000000000..ef5bbbc78
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/erebus_result.json
@@ -0,0 +1,49 @@
+{
+  "rocket": "erebus",
+  "actual_agl_m": 3020.0,
+  "reported_agl_m": 3020.0,
+  "saved_first_agl_m": 2750.904,
+  "reference_kind": "notebook reported apogee",
+  "telemetry_max_agl_m": 3001.75136,
+  "notes": "Telemetry maximum 3001.751 m differs from notebook reported value 3020.000 m; both retained.",
+  "status": "paired untuned comparison",
+  "reason": "",
+  "ordinary_agl_m": 2751.1556122225365,
+  "ordinary_apogee_time_s": 22.17286643393269,
+  "ordinary_peak_vertical_velocity_m_s": 294.1707251884576,
+  "ordinary_initial_static_margin_cal": 2.9374083445614505,
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+  "ordinary_ascent_altitude_rmse_m": 173.69041364963655,
+  "source_adaptations": "",
+  "atmosphere_refinements": 1,
+  "atmosphere_last_apogee_change_m": -4.843469621916938,
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+  "outside_mach": 0,
+  "outside_alpha": 0,
+  "outside_beta": 0,
+  "max_abs_alpha_deg": 12.023825633907418,
+  "max_abs_beta_deg": 9.12247497249757,
+  "solver_mach_excursion": true,
+  "solver_alpha_excursion": true,
+  "solver_beta_excursion": true,
+  "mass_cg_match": true,
+  "ordinary_error_m": -268.84438777746345,
+  "ordinary_error_pct": -8.902132045611372,
+  "datcom_error_m": 233.42262518221378,
+  "datcom_error_pct": 7.729225999411052,
+  "telemetry_qa_max_agl_m": 3001.75136,
+  "telemetry_qa_rejected_count": 0,
+  "ordinary_vs_telemetry_pct": -8.348317955870389,
+  "datcom_vs_telemetry_pct": 8.38414761914903,
+  "rmse_start_s": 0,
+  "rmse_end_s": 22.14074422411866,
+  "within_sampled_envelope": true
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/erebus_telemetry.csv b/docs/examples/datcom/comparison_data/rockets/erebus_telemetry.csv
new file mode 100644
index 000000000..010bcd322
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/erebus_telemetry.csv
@@ -0,0 +1,747 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/genesis_altitude.csv b/docs/examples/datcom/comparison_data/rockets/genesis_altitude.csv
new file mode 100644
index 000000000..2fa8c0c53
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/genesis_altitude.csv
@@ -0,0 +1,602 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/genesis_cd.csv b/docs/examples/datcom/comparison_data/rockets/genesis_cd.csv
new file mode 100644
index 000000000..ae1f0498e
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/genesis_cd.csv
@@ -0,0 +1,152 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/genesis_coast_for005.dat b/docs/examples/datcom/comparison_data/rockets/genesis_coast_for005.dat
new file mode 100644
index 000000000..e1e206edb
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/genesis_coast_for005.dat
@@ -0,0 +1,220 @@
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.88,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 163701.844,1639225.58,3157761.99,4748676.81,6239121.16,8017171.15,
+    9919543.25,11732390.8,13890873.6,15125507.8,16131105.6,18100455.2,
+    18511829.2,19540264.2,20157325.1,20568699.1,20980073.1,21597134.1,
+    22625569.,24682438.9,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -20.,$
+ $REFQ
+  XCG = 1.477,
+  SREF = 0.00693977817,
+  LREF = 0.094,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.27,
+  DNOSE = 0.094,
+  LCENTR = 2.102,
+  DCENTR = 0.094,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.122,2.212,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.105,
+  CHORD = 0.2,0.11,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.88,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 163701.844,1639225.58,3157761.99,4748676.81,6239121.16,8017171.15,
+    9919543.25,11732390.8,13890873.6,15125507.8,16131105.6,18100455.2,
+    18511829.2,19540264.2,20157325.1,20568699.1,20980073.1,21597134.1,
+    22625569.,24682438.9,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -5.,$
+ $REFQ
+  XCG = 1.477,
+  SREF = 0.00693977817,
+  LREF = 0.094,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.27,
+  DNOSE = 0.094,
+  LCENTR = 2.102,
+  DCENTR = 0.094,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.122,2.212,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.105,
+  CHORD = 0.2,0.11,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.88,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 163701.844,1639225.58,3157761.99,4748676.81,6239121.16,8017171.15,
+    9919543.25,11732390.8,13890873.6,15125507.8,16131105.6,18100455.2,
+    18511829.2,19540264.2,20157325.1,20568699.1,20980073.1,21597134.1,
+    22625569.,24682438.9,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 0.,$
+ $REFQ
+  XCG = 1.477,
+  SREF = 0.00693977817,
+  LREF = 0.094,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.27,
+  DNOSE = 0.094,
+  LCENTR = 2.102,
+  DCENTR = 0.094,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.122,2.212,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.105,
+  CHORD = 0.2,0.11,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.88,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 163701.844,1639225.58,3157761.99,4748676.81,6239121.16,8017171.15,
+    9919543.25,11732390.8,13890873.6,15125507.8,16131105.6,18100455.2,
+    18511829.2,19540264.2,20157325.1,20568699.1,20980073.1,21597134.1,
+    22625569.,24682438.9,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 5.,$
+ $REFQ
+  XCG = 1.477,
+  SREF = 0.00693977817,
+  LREF = 0.094,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.27,
+  DNOSE = 0.094,
+  LCENTR = 2.102,
+  DCENTR = 0.094,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.122,2.212,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.105,
+  CHORD = 0.2,0.11,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.88,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 163701.844,1639225.58,3157761.99,4748676.81,6239121.16,8017171.15,
+    9919543.25,11732390.8,13890873.6,15125507.8,16131105.6,18100455.2,
+    18511829.2,19540264.2,20157325.1,20568699.1,20980073.1,21597134.1,
+    22625569.,24682438.9,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 20.,$
+ $REFQ
+  XCG = 1.477,
+  SREF = 0.00693977817,
+  LREF = 0.094,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.27,
+  DNOSE = 0.094,
+  LCENTR = 2.102,
+  DCENTR = 0.094,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.122,2.212,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.105,
+  CHORD = 0.2,0.11,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
diff --git a/docs/examples/datcom/comparison_data/rockets/genesis_powered_for005.dat b/docs/examples/datcom/comparison_data/rockets/genesis_powered_for005.dat
new file mode 100644
index 000000000..8db37a1dc
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/genesis_powered_for005.dat
@@ -0,0 +1,220 @@
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.88,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 215533.965,2154491.63,4089332.39,6016560.93,7721014.87,9625016.33,
+    11508645.7,13159564.5,14979778.7,15968340.6,16743697.2,18197336.,
+    18610911.9,19644851.4,20265215.1,20678791.,21092366.8,21712730.5,
+    22746670.,24814549.1,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -20.,$
+ $REFQ
+  XCG = 1.58224212,
+  SREF = 0.00693977817,
+  LREF = 0.094,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.27,
+  DNOSE = 0.094,
+  LCENTR = 2.102,
+  DCENTR = 0.094,
+  BASE = .FALSE.,
+  DEXIT = 0.084708,$
+ $FINSET1
+  XLE = 2.122,2.212,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.105,
+  CHORD = 0.2,0.11,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.88,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 215533.965,2154491.63,4089332.39,6016560.93,7721014.87,9625016.33,
+    11508645.7,13159564.5,14979778.7,15968340.6,16743697.2,18197336.,
+    18610911.9,19644851.4,20265215.1,20678791.,21092366.8,21712730.5,
+    22746670.,24814549.1,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -5.,$
+ $REFQ
+  XCG = 1.58224212,
+  SREF = 0.00693977817,
+  LREF = 0.094,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.27,
+  DNOSE = 0.094,
+  LCENTR = 2.102,
+  DCENTR = 0.094,
+  BASE = .FALSE.,
+  DEXIT = 0.084708,$
+ $FINSET1
+  XLE = 2.122,2.212,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.105,
+  CHORD = 0.2,0.11,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.88,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 215533.965,2154491.63,4089332.39,6016560.93,7721014.87,9625016.33,
+    11508645.7,13159564.5,14979778.7,15968340.6,16743697.2,18197336.,
+    18610911.9,19644851.4,20265215.1,20678791.,21092366.8,21712730.5,
+    22746670.,24814549.1,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 0.,$
+ $REFQ
+  XCG = 1.58224212,
+  SREF = 0.00693977817,
+  LREF = 0.094,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.27,
+  DNOSE = 0.094,
+  LCENTR = 2.102,
+  DCENTR = 0.094,
+  BASE = .FALSE.,
+  DEXIT = 0.084708,$
+ $FINSET1
+  XLE = 2.122,2.212,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.105,
+  CHORD = 0.2,0.11,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.88,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 215533.965,2154491.63,4089332.39,6016560.93,7721014.87,9625016.33,
+    11508645.7,13159564.5,14979778.7,15968340.6,16743697.2,18197336.,
+    18610911.9,19644851.4,20265215.1,20678791.,21092366.8,21712730.5,
+    22746670.,24814549.1,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 5.,$
+ $REFQ
+  XCG = 1.58224212,
+  SREF = 0.00693977817,
+  LREF = 0.094,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.27,
+  DNOSE = 0.094,
+  LCENTR = 2.102,
+  DCENTR = 0.094,
+  BASE = .FALSE.,
+  DEXIT = 0.084708,$
+ $FINSET1
+  XLE = 2.122,2.212,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.105,
+  CHORD = 0.2,0.11,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.88,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 215533.965,2154491.63,4089332.39,6016560.93,7721014.87,9625016.33,
+    11508645.7,13159564.5,14979778.7,15968340.6,16743697.2,18197336.,
+    18610911.9,19644851.4,20265215.1,20678791.,21092366.8,21712730.5,
+    22746670.,24814549.1,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 20.,$
+ $REFQ
+  XCG = 1.58224212,
+  SREF = 0.00693977817,
+  LREF = 0.094,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.27,
+  DNOSE = 0.094,
+  LCENTR = 2.102,
+  DCENTR = 0.094,
+  BASE = .FALSE.,
+  DEXIT = 0.084708,$
+ $FINSET1
+  XLE = 2.122,2.212,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.105,
+  CHORD = 0.2,0.11,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
diff --git a/docs/examples/datcom/comparison_data/rockets/genesis_provenance.json b/docs/examples/datcom/comparison_data/rockets/genesis_provenance.json
new file mode 100644
index 000000000..2d9cb8f50
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/genesis_provenance.json
@@ -0,0 +1,378 @@
+{
+  "construction_cells": [
+    2,
+    4,
+    6,
+    7,
+    9,
+    10,
+    11,
+    12
+  ],
+  "calibration": {
+    "roughness_height": 0.0,
+    "leading_edge_radius": null
+  },
+  "source_adaptations": [],
+  "mach": [
+    0.01,
+    0.1,
+    0.19,
+    0.28,
+    0.36,
+    0.45,
+    0.54,
+    0.62,
+    0.71,
+    0.76,
+    0.8,
+    0.88,
+    0.9,
+    0.95,
+    0.98,
+    1.0,
+    1.02,
+    1.05,
+    1.1,
+    1.2
+  ],
+  "alpha": [
+    -20.0,
+    -10.0,
+    -5.0,
+    -2.0,
+    -1.0,
+    -0.5,
+    -0.25,
+    -0.1,
+    -0.05,
+    0.0,
+    0.05,
+    0.1,
+    0.25,
+    0.5,
+    1.0,
+    2.0,
+    5.0,
+    10.0,
+    20.0
+  ],
+  "beta": [
+    -20,
+    -5,
+    0,
+    5,
+    20
+  ],
+  "altitude_schedules_m_asl": {
+    "power_on": [
+      160.0247955790327,
+      165.85298582979988,
+      181.04929787038714,
+      205.15179124522626,
+      232.93308241359887,
+      272.9771469280038,
+      325.5346134980463,
+      385.29120458355766,
+      471.8216168950038,
+      531.3281861136722,
+      586.7329763637598,
+      753.4989633756181,
+      753.4989633756181,
+      753.4989633756181,
+      753.4989633756181,
+      753.4989633756181,
+      753.4989633756181,
+      753.4989633756181,
+      753.4989633756181,
+      753.4989633756181
+    ],
+    "power_off": [
+      3459.3277882005946,
+      3445.733476857217,
+      3305.4835319490408,
+      3089.0056822645884,
+      2843.159132607208,
+      2521.864558999702,
+      2170.2520166922727,
+      1844.8651070835317,
+      1471.808908246068,
+      1262.3759875582102,
+      1094.758172727381,
+      826.3078060534194,
+      826.3078060534194,
+      826.3078060534194,
+      826.3078060534194,
+      826.3078060534194,
+      826.3078060534194,
+      826.3078060534194,
+      826.3078060534194,
+      826.3078060534194
+    ]
+  },
+  "atmosphere": {
+    "mach": [
+      0.01,
+      0.1,
+      0.19,
+      0.28,
+      0.36,
+      0.45,
+      0.54,
+      0.62,
+      0.71,
+      0.76,
+      0.8,
+      0.88,
+      0.9,
+      0.95,
+      0.98,
+      1.0,
+      1.02,
+      1.05,
+      1.1,
+      1.2
+    ],
+    "alpha": [
+      -20.0,
+      -10.0,
+      -5.0,
+      -2.0,
+      -1.0,
+      -0.5,
+      -0.25,
+      -0.1,
+      -0.05,
+      0.0,
+      0.05,
+      0.1,
+      0.25,
+      0.5,
+      1.0,
+      2.0,
+      5.0,
+      10.0,
+      20.0
+    ],
+    "coverage": {
+      "scope": "rail through apogee",
+      "minimum_dynamic_pressure_pa": 100.0,
+      "excluded_low_q_samples": 45,
+      "phases": {
+        "power_on": {
+          "mach_min": 0.008445546978680503,
+          "mach_max": 0.8778690261837045,
+          "loaded_alpha_max_abs_deg": 3.34264443830099
+        },
+        "power_off": {
+          "mach_min": 0.08622143627137899,
+          "mach_max": 0.8671192687171917,
+          "loaded_alpha_max_abs_deg": 0.9042922958196477
+        }
+      }
+    },
+    "reynolds": {
+      "power_on": [
+        215533.9648491429,
+        2154491.6323138163,
+        4089332.3948878096,
+        6016560.934034695,
+        7721014.870123619,
+        9625016.32591977,
+        11508645.723351069,
+        13159564.519515894,
+        14979778.668347498,
+        15968340.590464214,
+        16743697.246702785,
+        18197336.038183723,
+        18610911.857233353,
+        19644851.404857423,
+        20265215.13343187,
+        20678790.9524815,
+        21092366.77153113,
+        21712730.50010558,
+        22746670.047729652,
+        24814549.1429778
+      ],
+      "power_off": [
+        163701.84377514443,
+        1639225.581917429,
+        3157761.9869082803,
+        4748676.8092659265,
+        6239121.163722952,
+        8017171.146508718,
+        9919543.25413897,
+        11732390.761705289,
+        13890873.630701233,
+        15125507.812276108,
+        16131105.613156134,
+        18100455.22995423,
+        18511829.212453194,
+        19540264.16870059,
+        20157325.142449033,
+        20568699.12494799,
+        20980073.10744695,
+        21597134.08119539,
+        22625569.037442792,
+        24682438.94993759
+      ]
+    },
+    "altitude_m_asl": {
+      "power_on": [
+        160.0247955790327,
+        165.85298582979988,
+        181.04929787038714,
+        205.15179124522626,
+        232.93308241359887,
+        272.9771469280038,
+        325.5346134980463,
+        385.29120458355766,
+        471.8216168950038,
+        531.3281861136722,
+        586.7329763637598,
+        753.4989633756181,
+        753.4989633756181,
+        753.4989633756181,
+        753.4989633756181,
+        753.4989633756181,
+        753.4989633756181,
+        753.4989633756181,
+        753.4989633756181,
+        753.4989633756181
+      ],
+      "power_off": [
+        3459.3277882005946,
+        3445.733476857217,
+        3305.4835319490408,
+        3089.0056822645884,
+        2843.159132607208,
+        2521.864558999702,
+        2170.2520166922727,
+        1844.8651070835317,
+        1471.808908246068,
+        1262.3759875582102,
+        1094.758172727381,
+        826.3078060534194,
+        826.3078060534194,
+        826.3078060534194,
+        826.3078060534194,
+        826.3078060534194,
+        826.3078060534194,
+        826.3078060534194,
+        826.3078060534194,
+        826.3078060534194
+      ]
+    },
+    "pressure_pa": {
+      "power_on": [
+        99967.15781001424,
+        99902.17610620405,
+        99732.74405173419,
+        99464.01207522319,
+        99154.26315549118,
+        98707.78982025842,
+        98121.79767197212,
+        97456.452463496,
+        96511.5090337786,
+        95861.67575428587,
+        95256.63538612891,
+        93460.71932792233,
+        93460.71932792233,
+        93460.71932792233,
+        93460.71932792233,
+        93460.71932792233,
+        93460.71932792233,
+        93460.71932792233,
+        93460.71932792233,
+        93460.71932792233
+      ],
+      "power_off": [
+        67790.63384473899,
+        67903.19071104942,
+        69064.4186615012,
+        70909.82597837193,
+        73071.36115338531,
+        75931.86593077898,
+        79187.575133028,
+        82297.6883089028,
+        85997.29926531971,
+        88124.17570899165,
+        89854.75132632445,
+        92678.44197550307,
+        92678.44197550307,
+        92678.44197550307,
+        92678.44197550307,
+        92678.44197550307,
+        92678.44197550307,
+        92678.44197550307,
+        92678.44197550307,
+        92678.44197550307
+      ]
+    },
+    "temperature_k": {
+      "power_on": [
+        303.0835139639264,
+        303.02133133195105,
+        302.8591975239686,
+        302.6020411150784,
+        302.30563455829247,
+        301.87839295852325,
+        301.31764228624513,
+        300.68139822063773,
+        299.7867393060596,
+        299.1714864578953,
+        298.5986428970769,
+        296.9585966879861,
+        296.9585966879861,
+        296.9585966879861,
+        296.9585966879861,
+        296.9585966879861,
+        296.9585966879861,
+        296.9585966879861,
+        296.9585966879861,
+        296.9585966879861
+      ],
+      "power_off": [
+        277.2827322087192,
+        277.35051240348065,
+        278.04978788283694,
+        279.3769018519916,
+        281.1913226204602,
+        283.60294569578207,
+        286.1398402270491,
+        288.1290345758692,
+        290.5355788679618,
+        292.21342229762234,
+        293.6840210927097,
+        296.2485934482804,
+        296.2485934482804,
+        296.2485934482804,
+        296.2485934482804,
+        296.2485934482804,
+        296.2485934482804,
+        296.2485934482804,
+        296.2485934482804,
+        296.2485934482804
+      ]
+    }
+  },
+  "atmosphere_iterations": [
+    {
+      "pass": 0,
+      "apogee_agl_m": 3299.327788200595,
+      "reference": "ordinary trajectory"
+    },
+    {
+      "pass": 1,
+      "apogee_agl_m": 3297.2391270686403,
+      "reference": "previous DATCOM trajectory"
+    }
+  ],
+  "atmosphere_method": "RocketPy reference ascent rho*a*M/mu; native REN",
+  "source_notebook": "docs/examples/genesis_flight_sim.ipynb",
+  "source_sha256": "7ee71c93b82327814be4d0874ad83db8d4eca2877f8421025f718d87b511e854",
+  "construction_assumption": "carbon fibre and vinyl when unspecified; no numerical roughness inferred",
+  "fin_assumption": "2% HEX, 5% bevels, sharp edges, auto body attachment",
+  "powered_base_drag": "nozzle",
+  "damp": true
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/genesis_result.json b/docs/examples/datcom/comparison_data/rockets/genesis_result.json
new file mode 100644
index 000000000..b8a29a821
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/genesis_result.json
@@ -0,0 +1,49 @@
+{
+  "rocket": "genesis",
+  "actual_agl_m": 2916.0,
+  "reported_agl_m": 2916.0,
+  "saved_first_agl_m": 3044.091,
+  "reference_kind": "notebook reported apogee",
+  "telemetry_max_agl_m": 2916.691650390625,
+  "notes": "Telemetry maximum 2916.692 m differs from notebook reported value 2916.000 m; both retained.",
+  "status": "paired untuned comparison",
+  "reason": "",
+  "ordinary_agl_m": 3044.6857862664306,
+  "ordinary_apogee_time_s": 24.170070527773692,
+  "ordinary_peak_vertical_velocity_m_s": 290.6363275291183,
+  "ordinary_initial_static_margin_cal": 3.37721309359005,
+  "ordinary_coast_cd_mach06": 0.55,
+  "ordinary_max_mach": 0.8522776236704187,
+  "ordinary_ascent_altitude_rmse_m": 104.35940713968827,
+  "source_adaptations": "",
+  "atmosphere_refinements": 1,
+  "atmosphere_last_apogee_change_m": -2.088661131954723,
+  "datcom_agl_m": 3297.2391270686403,
+  "datcom_apogee_time_s": 25.178132490879275,
+  "datcom_peak_vertical_velocity_m_s": 299.2620317383252,
+  "datcom_initial_static_margin_cal": 3.7682700796928534,
+  "datcom_coast_cd_mach06": 0.47475,
+  "datcom_max_mach": 0.8776710952854886,
+  "datcom_ascent_altitude_rmse_m": 236.1872905681145,
+  "samples": 497,
+  "outside_mach": 0,
+  "outside_alpha": 0,
+  "outside_beta": 0,
+  "max_abs_alpha_deg": 3.3632935153000667,
+  "max_abs_beta_deg": 11.894942323656261,
+  "solver_mach_excursion": true,
+  "solver_alpha_excursion": true,
+  "solver_beta_excursion": true,
+  "mass_cg_match": true,
+  "ordinary_error_m": 128.68578626643057,
+  "ordinary_error_pct": 4.41309280749076,
+  "datcom_error_m": 381.2391270686403,
+  "datcom_error_pct": 13.074044138156388,
+  "telemetry_qa_max_agl_m": 2916.691650390625,
+  "telemetry_qa_rejected_count": 0,
+  "ordinary_vs_telemetry_pct": 4.38833278309223,
+  "datcom_vs_telemetry_pct": 13.047230296937617,
+  "rmse_start_s": 0,
+  "rmse_end_s": 24.12904363709264,
+  "within_sampled_envelope": true
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/genesis_telemetry.csv b/docs/examples/datcom/comparison_data/rockets/genesis_telemetry.csv
new file mode 100644
index 000000000..7cd182d9a
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/genesis_telemetry.csv
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diff --git a/docs/examples/datcom/comparison_data/rockets/halcyon_altitude.csv b/docs/examples/datcom/comparison_data/rockets/halcyon_altitude.csv
new file mode 100644
index 000000000..ecf8f2500
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/halcyon_altitude.csv
@@ -0,0 +1,602 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/halcyon_cd.csv b/docs/examples/datcom/comparison_data/rockets/halcyon_cd.csv
new file mode 100644
index 000000000..b4a69db44
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/halcyon_cd.csv
@@ -0,0 +1,152 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/halcyon_coast_for005.dat b/docs/examples/datcom/comparison_data/rockets/halcyon_coast_for005.dat
new file mode 100644
index 000000000..f7228786a
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/halcyon_coast_for005.dat
@@ -0,0 +1,235 @@
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.83,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 162663.454,1631584.63,3144691.36,4737245.62,6235651.75,8028800.02,
+    9954979.17,11781258.8,13973017.9,15282448.8,16402581.9,17270278.5,
+    18726808.,19767186.2,20391413.1,20807564.4,21223715.7,21847942.7,
+    22888320.9,24969077.3,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -20.,$
+ $REFQ
+  XCG = 1.86360305,
+  SREF = 0.0182414692,
+  LREF = 0.1524,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.46,
+  DNOSE = 0.1524,
+  LCENTR = 2.842,
+  DCENTR = 0.1524,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.254,
+  DAFT = 0.0992,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 3.293,3.495,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.125,
+  CHORD = 0.247,0.045,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.83,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 162663.454,1631584.63,3144691.36,4737245.62,6235651.75,8028800.02,
+    9954979.17,11781258.8,13973017.9,15282448.8,16402581.9,17270278.5,
+    18726808.,19767186.2,20391413.1,20807564.4,21223715.7,21847942.7,
+    22888320.9,24969077.3,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -5.,$
+ $REFQ
+  XCG = 1.86360305,
+  SREF = 0.0182414692,
+  LREF = 0.1524,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.46,
+  DNOSE = 0.1524,
+  LCENTR = 2.842,
+  DCENTR = 0.1524,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.254,
+  DAFT = 0.0992,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 3.293,3.495,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.125,
+  CHORD = 0.247,0.045,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.83,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 162663.454,1631584.63,3144691.36,4737245.62,6235651.75,8028800.02,
+    9954979.17,11781258.8,13973017.9,15282448.8,16402581.9,17270278.5,
+    18726808.,19767186.2,20391413.1,20807564.4,21223715.7,21847942.7,
+    22888320.9,24969077.3,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 0.,$
+ $REFQ
+  XCG = 1.86360305,
+  SREF = 0.0182414692,
+  LREF = 0.1524,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.46,
+  DNOSE = 0.1524,
+  LCENTR = 2.842,
+  DCENTR = 0.1524,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.254,
+  DAFT = 0.0992,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 3.293,3.495,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.125,
+  CHORD = 0.247,0.045,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.83,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 162663.454,1631584.63,3144691.36,4737245.62,6235651.75,8028800.02,
+    9954979.17,11781258.8,13973017.9,15282448.8,16402581.9,17270278.5,
+    18726808.,19767186.2,20391413.1,20807564.4,21223715.7,21847942.7,
+    22888320.9,24969077.3,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 5.,$
+ $REFQ
+  XCG = 1.86360305,
+  SREF = 0.0182414692,
+  LREF = 0.1524,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.46,
+  DNOSE = 0.1524,
+  LCENTR = 2.842,
+  DCENTR = 0.1524,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.254,
+  DAFT = 0.0992,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 3.293,3.495,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.125,
+  CHORD = 0.247,0.045,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.83,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 162663.454,1631584.63,3144691.36,4737245.62,6235651.75,8028800.02,
+    9954979.17,11781258.8,13973017.9,15282448.8,16402581.9,17270278.5,
+    18726808.,19767186.2,20391413.1,20807564.4,21223715.7,21847942.7,
+    22888320.9,24969077.3,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 20.,$
+ $REFQ
+  XCG = 1.86360305,
+  SREF = 0.0182414692,
+  LREF = 0.1524,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.46,
+  DNOSE = 0.1524,
+  LCENTR = 2.842,
+  DCENTR = 0.1524,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.254,
+  DAFT = 0.0992,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 3.293,3.495,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.125,
+  CHORD = 0.247,0.045,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
diff --git a/docs/examples/datcom/comparison_data/rockets/halcyon_powered_for005.dat b/docs/examples/datcom/comparison_data/rockets/halcyon_powered_for005.dat
new file mode 100644
index 000000000..67917d7ac
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/halcyon_powered_for005.dat
@@ -0,0 +1,235 @@
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.83,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 221427.462,2214274.62,4205351.78,6178647.56,7914933.82,9842636.16,
+    11725892.2,13341570.8,15093662.,16025795.5,16751578.8,17270285.,
+    18726815.1,19767193.7,20391420.8,20807572.3,21223723.7,21847950.9,
+    22888329.5,24969086.7,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -20.,$
+ $REFQ
+  XCG = 2.01748807,
+  SREF = 0.0182414692,
+  LREF = 0.1524,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.46,
+  DNOSE = 0.1524,
+  LCENTR = 2.842,
+  DCENTR = 0.1524,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.254,
+  DAFT = 0.0992,
+  DEXIT = 0.0282,$
+ $FINSET1
+  XLE = 3.293,3.495,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.125,
+  CHORD = 0.247,0.045,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.83,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 221427.462,2214274.62,4205351.78,6178647.56,7914933.82,9842636.16,
+    11725892.2,13341570.8,15093662.,16025795.5,16751578.8,17270285.,
+    18726815.1,19767193.7,20391420.8,20807572.3,21223723.7,21847950.9,
+    22888329.5,24969086.7,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -5.,$
+ $REFQ
+  XCG = 2.01748807,
+  SREF = 0.0182414692,
+  LREF = 0.1524,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.46,
+  DNOSE = 0.1524,
+  LCENTR = 2.842,
+  DCENTR = 0.1524,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.254,
+  DAFT = 0.0992,
+  DEXIT = 0.0282,$
+ $FINSET1
+  XLE = 3.293,3.495,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.125,
+  CHORD = 0.247,0.045,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.83,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 221427.462,2214274.62,4205351.78,6178647.56,7914933.82,9842636.16,
+    11725892.2,13341570.8,15093662.,16025795.5,16751578.8,17270285.,
+    18726815.1,19767193.7,20391420.8,20807572.3,21223723.7,21847950.9,
+    22888329.5,24969086.7,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 0.,$
+ $REFQ
+  XCG = 2.01748807,
+  SREF = 0.0182414692,
+  LREF = 0.1524,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.46,
+  DNOSE = 0.1524,
+  LCENTR = 2.842,
+  DCENTR = 0.1524,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.254,
+  DAFT = 0.0992,
+  DEXIT = 0.0282,$
+ $FINSET1
+  XLE = 3.293,3.495,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.125,
+  CHORD = 0.247,0.045,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.83,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 221427.462,2214274.62,4205351.78,6178647.56,7914933.82,9842636.16,
+    11725892.2,13341570.8,15093662.,16025795.5,16751578.8,17270285.,
+    18726815.1,19767193.7,20391420.8,20807572.3,21223723.7,21847950.9,
+    22888329.5,24969086.7,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 5.,$
+ $REFQ
+  XCG = 2.01748807,
+  SREF = 0.0182414692,
+  LREF = 0.1524,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.46,
+  DNOSE = 0.1524,
+  LCENTR = 2.842,
+  DCENTR = 0.1524,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.254,
+  DAFT = 0.0992,
+  DEXIT = 0.0282,$
+ $FINSET1
+  XLE = 3.293,3.495,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.125,
+  CHORD = 0.247,0.045,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.83,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 221427.462,2214274.62,4205351.78,6178647.56,7914933.82,9842636.16,
+    11725892.2,13341570.8,15093662.,16025795.5,16751578.8,17270285.,
+    18726815.1,19767193.7,20391420.8,20807572.3,21223723.7,21847950.9,
+    22888329.5,24969086.7,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 20.,$
+ $REFQ
+  XCG = 2.01748807,
+  SREF = 0.0182414692,
+  LREF = 0.1524,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.46,
+  DNOSE = 0.1524,
+  LCENTR = 2.842,
+  DCENTR = 0.1524,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.254,
+  DAFT = 0.0992,
+  DEXIT = 0.0282,$
+ $FINSET1
+  XLE = 3.293,3.495,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.125,
+  CHORD = 0.247,0.045,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
diff --git a/docs/examples/datcom/comparison_data/rockets/halcyon_provenance.json b/docs/examples/datcom/comparison_data/rockets/halcyon_provenance.json
new file mode 100644
index 000000000..156f90e97
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/halcyon_provenance.json
@@ -0,0 +1,382 @@
+{
+  "construction_cells": [
+    2,
+    3,
+    5,
+    6,
+    8,
+    10,
+    11,
+    13,
+    14,
+    15
+  ],
+  "calibration": {
+    "roughness_height": 0.0,
+    "leading_edge_radius": null
+  },
+  "source_adaptations": [
+    "Read original .eng numeric samples directly: source already has t=0; avoid the importer's extra duplicate zero-time row. No measured sample or motor equation changed."
+  ],
+  "mach": [
+    0.01,
+    0.1,
+    0.19,
+    0.28,
+    0.36,
+    0.45,
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+  ],
+  "alpha": [
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+    10.0,
+    20.0
+  ],
+  "beta": [
+    -20,
+    -5,
+    0,
+    5,
+    20
+  ],
+  "altitude_schedules_m_asl": {
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+    "coverage": {
+      "scope": "rail through apogee",
+      "minimum_dynamic_pressure_pa": 100.0,
+      "excluded_low_q_samples": 54,
+      "phases": {
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+          "mach_max": 0.8295828111176137,
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+  },
+  "atmosphere_iterations": [
+    {
+      "pass": 0,
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+      "reference": "ordinary trajectory"
+    },
+    {
+      "pass": 1,
+      "apogee_agl_m": 3530.231782008296,
+      "reference": "previous DATCOM trajectory"
+    }
+  ],
+  "atmosphere_method": "RocketPy reference ascent rho*a*M/mu; native REN",
+  "source_notebook": "docs/examples/halcyon_flight_sim.ipynb",
+  "source_sha256": "ee4e85b91244c95bd4ae0381521aa077e817018e386f2afa9384918dbc453f25",
+  "construction_assumption": "carbon fibre and vinyl when unspecified; no numerical roughness inferred",
+  "fin_assumption": "2% HEX, 5% bevels, sharp edges, auto body attachment",
+  "powered_base_drag": "nozzle",
+  "damp": true
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/halcyon_result.json b/docs/examples/datcom/comparison_data/rockets/halcyon_result.json
new file mode 100644
index 000000000..c958d1ef1
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/halcyon_result.json
@@ -0,0 +1,49 @@
+{
+  "rocket": "halcyon",
+  "actual_agl_m": 3450.0,
+  "reported_agl_m": 3450.0,
+  "saved_first_agl_m": 3212.856,
+  "reference_kind": "notebook reported apogee",
+  "telemetry_max_agl_m": 3470.8251953125,
+  "notes": "Telemetry QA flags 5 samples with altitude jumps above 2000 m/s; raw maximum 3470.825 m is retained separately. | Telemetry maximum 3470.825 m differs from notebook reported value 3450.000 m; both retained.",
+  "status": "paired untuned comparison",
+  "reason": "",
+  "ordinary_agl_m": 3214.238895757538,
+  "ordinary_apogee_time_s": 26.371836502851696,
+  "ordinary_peak_vertical_velocity_m_s": 275.5714950426811,
+  "ordinary_initial_static_margin_cal": 3.0192578745448033,
+  "ordinary_coast_cd_mach06": 0.426,
+  "ordinary_max_mach": 0.812113342368714,
+  "ordinary_ascent_altitude_rmse_m": 112.69062830123777,
+  "source_adaptations": "Read original .eng numeric samples directly: source already has t=0; avoid the importer's extra duplicate zero-time row. No measured sample or motor equation changed.",
+  "atmosphere_refinements": 1,
+  "atmosphere_last_apogee_change_m": -1.122512222840669,
+  "datcom_agl_m": 3530.231782008296,
+  "datcom_apogee_time_s": 27.79754660231069,
+  "datcom_peak_vertical_velocity_m_s": 280.94965050790404,
+  "datcom_initial_static_margin_cal": 4.410132986423363,
+  "datcom_coast_cd_mach06": 0.319,
+  "datcom_max_mach": 0.8293761850832471,
+  "datcom_ascent_altitude_rmse_m": 69.46314134752838,
+  "samples": 495,
+  "outside_mach": 0,
+  "outside_alpha": 0,
+  "outside_beta": 0,
+  "max_abs_alpha_deg": 7.051663487140428,
+  "max_abs_beta_deg": 14.276458017233338,
+  "solver_mach_excursion": false,
+  "solver_alpha_excursion": true,
+  "solver_beta_excursion": true,
+  "mass_cg_match": true,
+  "ordinary_error_m": -235.7611042424619,
+  "ordinary_error_pct": -6.8336551954336775,
+  "datcom_error_m": 80.2317820082958,
+  "datcom_error_pct": 2.3255588987911824,
+  "telemetry_qa_max_agl_m": 3470.8251953125,
+  "telemetry_qa_rejected_count": 5,
+  "ordinary_vs_telemetry_pct": -7.39265981765641,
+  "datcom_vs_telemetry_pct": 1.7115983477366425,
+  "rmse_start_s": 0,
+  "rmse_end_s": 26.36134002785797,
+  "within_sampled_envelope": true
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/halcyon_telemetry.csv b/docs/examples/datcom/comparison_data/rockets/halcyon_telemetry.csv
new file mode 100644
index 000000000..54ee07853
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/halcyon_telemetry.csv
@@ -0,0 +1,65883 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/hedy_result.json b/docs/examples/datcom/comparison_data/rockets/hedy_result.json
new file mode 100644
index 000000000..dc4d563ad
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/hedy_result.json
@@ -0,0 +1,11 @@
+{
+  "rocket": "hedy",
+  "actual_agl_m": 5231.53,
+  "reported_agl_m": null,
+  "saved_first_agl_m": 5246.918,
+  "reference_kind": "saved measured-apogee summary",
+  "telemetry_max_agl_m": null,
+  "notes": "Referenced telemetry file is absent: data/rockets/tust/cats_tust/filteredDataInfo.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/gnssInfo.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/flightInfo.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/imu.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/baro.csv (cell 24). | Hedy end comparison uses simulated ASL 5406.918 m against measured AGL 5231.53 m; saved simulated AGL is 5246.918 m, a 160 m datum discrepancy.",
+  "status": "archived comparison only",
+  "reason": "Source data/rockets/tust is absent; CoolProp is not installed, and the negative-span fin root cutout requires geometry support."
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/inventory.json b/docs/examples/datcom/comparison_data/rockets/inventory.json
new file mode 100644
index 000000000..e4d15281d
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/inventory.json
@@ -0,0 +1,487 @@
+[
+  {
+    "rocket": "andromeda",
+    "source_notebook": "docs/examples/andromeda_flight_sim.ipynb",
+    "source_sha256": "af60f6b41668aa41f90885324cd7e316c87053660da8f2eb8a755c6e29a83a0d",
+    "actual_agl_m": 3443.0,
+    "reference_kind": "notebook reported apogee",
+    "reported_agl_m": 3443.0,
+    "reported_cell": 0,
+    "saved_first_agl_m": 3614.946,
+    "saved_apogees": [
+      {
+        "cell": 15,
+        "agl_m": 3614.946
+      }
+    ],
+    "measured_summaries": [],
+    "notes": [
+      "Telemetry maximum 3438.549 m differs from notebook reported value 3443.000 m; both retained."
+    ],
+    "telemetry": {
+      "path": "data/rockets/andromeda/flight_data.csv",
+      "cell": 17,
+      "conversion": "metres AGL, notebook columns_map",
+      "sha256": "ebd3b757555b564c63937b69d2c222d6596355fc6e938d49537af293a9d48d14",
+      "raw_max_agl_m": 3438.549384,
+      "qa_max_agl_m": 3438.549384,
+      "qa_rejected_count": 0
+    }
+  },
+  {
+    "rocket": "astra",
+    "source_notebook": "docs/examples/astra_flight_sim.ipynb",
+    "source_sha256": "7e708a54ec4c1d3c2f7c4ec015ac1d537431a33dde79a4b91bb5e1602097731a",
+    "actual_agl_m": 3250.0,
+    "reference_kind": "notebook reported apogee",
+    "reported_agl_m": 3250.0,
+    "reported_cell": 0,
+    "saved_first_agl_m": 3043.066,
+    "saved_apogees": [
+      {
+        "cell": 14,
+        "agl_m": 3043.066
+      }
+    ],
+    "measured_summaries": [],
+    "notes": [
+      "Telemetry maximum 3249.460 m differs from notebook reported value 3250.000 m; both retained.",
+      "Source fin trailing edge overhangs the nozzle-inferred body base by 1.216 mm. Native DATCOM accepts this; the integration now validates root-chord centre attachment instead of rejecting all overhang. Geometry is unchanged."
+    ],
+    "telemetry": {
+      "path": "data/rockets/astra/flight_data.csv",
+      "cell": 16,
+      "conversion": "metres AGL, notebook columns_map",
+      "sha256": "dd5287b375e84be8961a7c867dc9af764a42752af3109c25086d228978e4acae",
+      "raw_max_agl_m": 3249.46,
+      "qa_max_agl_m": 3249.46,
+      "qa_rejected_count": 0
+    }
+  },
+  {
+    "rocket": "bella_lui",
+    "source_notebook": "docs/examples/bella_lui_flight_sim.ipynb",
+    "source_sha256": "1402eee9b084da9307acd9ec84e3f730118d0f6897f25943f2d8a691749061af",
+    "actual_agl_m": 458.971,
+    "reference_kind": "telemetry AGL maximum (QA)",
+    "reported_agl_m": null,
+    "reported_cell": null,
+    "saved_first_agl_m": 460.54,
+    "saved_apogees": [
+      {
+        "cell": 30,
+        "agl_m": 460.54
+      }
+    ],
+    "measured_summaries": [
+      {
+        "cell": 46,
+        "agl_m": 458.97
+      }
+    ],
+    "notes": [],
+    "telemetry": {
+      "path": "data/rockets/EPFL_Bella_Lui/bella_lui_flight_data_filtered.csv",
+      "cell": 33,
+      "conversion": "metres AGL, first 573 rows as used in source cell 35",
+      "sha256": "ccf8bcecd91d5b912e8f8b3bcf9efe13248453e1a81dc79d2b4daea4e985de2d",
+      "raw_max_agl_m": 458.971,
+      "qa_max_agl_m": 458.971,
+      "qa_rejected_count": 0
+    }
+  },
+  {
+    "rocket": "camoes",
+    "source_notebook": "docs/examples/camoes_flight_sim.ipynb",
+    "source_sha256": "dca32f4603c1acfdce58ffaf6ffce0355767d99da1db2a3ee88c5f60dd50140b",
+    "actual_agl_m": 3015.0,
+    "reference_kind": "notebook reported apogee",
+    "reported_agl_m": 3015.0,
+    "reported_cell": 0,
+    "saved_first_agl_m": 3003.284,
+    "saved_apogees": [
+      {
+        "cell": 21,
+        "agl_m": 3003.284
+      }
+    ],
+    "measured_summaries": [],
+    "notes": [
+      "Telemetry maximum 3015.996 m differs from notebook reported value 3015.000 m; both retained."
+    ],
+    "telemetry": {
+      "path": "data/rockets/camoes/flight_data.csv",
+      "cell": 26,
+      "conversion": "metres AGL, notebook columns_map",
+      "sha256": "8ceb1a196901569e32a6e9f09067feebb5ca1406d686e402f726e715a1be1dcc",
+      "raw_max_agl_m": 3015.996337890625,
+      "qa_max_agl_m": 3015.996337890625,
+      "qa_rejected_count": 0
+    }
+  },
+  {
+    "rocket": "cavour",
+    "source_notebook": "docs/examples/cavour_flight_sim.ipynb",
+    "source_sha256": "9d407019f0a22f7e19bad163131c44dfc10295b1c2290e06035ef968616b83be",
+    "actual_agl_m": 2789.0,
+    "reference_kind": "notebook reported apogee",
+    "reported_agl_m": 2789.0,
+    "reported_cell": 0,
+    "saved_first_agl_m": 2817.405,
+    "saved_apogees": [
+      {
+        "cell": 15,
+        "agl_m": 2817.405
+      }
+    ],
+    "measured_summaries": [],
+    "notes": [],
+    "telemetry": {
+      "path": "data/rockets/polito/altimeter_cavour.csv",
+      "cell": 17,
+      "conversion": "metres AGL, notebook columns_map",
+      "sha256": "f912724676eea0ca9ba031c3779e48f861538814170b050b7bda2887c31f29a8",
+      "raw_max_agl_m": 2789.0,
+      "qa_max_agl_m": 2789.0,
+      "qa_rejected_count": 0
+    }
+  },
+  {
+    "rocket": "defiance",
+    "source_notebook": "docs/examples/defiance_flight_sim.ipynb",
+    "source_sha256": "0348a6fc8de79458cbc4a48fec1ef3489f5c6a511fd7a215289c34b03aea939b",
+    "actual_agl_m": 9308.318,
+    "reference_kind": "notebook reported apogee",
+    "reported_agl_m": 9308.318,
+    "reported_cell": 0,
+    "saved_first_agl_m": 9238.009,
+    "saved_apogees": [
+      {
+        "cell": 10,
+        "agl_m": 9238.009
+      }
+    ],
+    "measured_summaries": [],
+    "notes": [
+      "Source fin root centre is z=-0.025 m, aft of the tail base z=0.0403 m; native root attachment is outside the declared body."
+    ],
+    "telemetry": null
+  },
+  {
+    "rocket": "erebus",
+    "source_notebook": "docs/examples/erebus_flight_sim.ipynb",
+    "source_sha256": "85d34d7f05a2c3653864b9fdfda2347bfae916c350d480edb7c030c365fd67b4",
+    "actual_agl_m": 3020.0,
+    "reference_kind": "notebook reported apogee",
+    "reported_agl_m": 3020.0,
+    "reported_cell": 0,
+    "saved_first_agl_m": 2750.904,
+    "saved_apogees": [
+      {
+        "cell": 16,
+        "agl_m": 2750.904
+      }
+    ],
+    "measured_summaries": [],
+    "notes": [
+      "Telemetry maximum 3001.751 m differs from notebook reported value 3020.000 m; both retained."
+    ],
+    "telemetry": {
+      "path": "data/rockets/erebus11/flight_data_filtered.csv",
+      "cell": 18,
+      "conversion": "metres AGL, notebook columns_map",
+      "sha256": "045f5b9ed473b80095805d3ca7b831ca4b67c5afbe9156d58d1e91df9fc0b4a9",
+      "raw_max_agl_m": 3001.75136,
+      "qa_max_agl_m": 3001.75136,
+      "qa_rejected_count": 0
+    }
+  },
+  {
+    "rocket": "genesis",
+    "source_notebook": "docs/examples/genesis_flight_sim.ipynb",
+    "source_sha256": "7ee71c93b82327814be4d0874ad83db8d4eca2877f8421025f718d87b511e854",
+    "actual_agl_m": 2916.0,
+    "reference_kind": "notebook reported apogee",
+    "reported_agl_m": 2916.0,
+    "reported_cell": 0,
+    "saved_first_agl_m": 3044.091,
+    "saved_apogees": [
+      {
+        "cell": 15,
+        "agl_m": 3044.091
+      }
+    ],
+    "measured_summaries": [],
+    "notes": [
+      "Telemetry maximum 2916.692 m differs from notebook reported value 2916.000 m; both retained."
+    ],
+    "telemetry": {
+      "path": "data/rockets/genesis/flight_data_faraday.csv",
+      "cell": 17,
+      "conversion": "metres AGL, notebook columns_map",
+      "sha256": "d88a7fc53cbd55d1dafeed5d5c6fcfcad27c7cf8e0a15221e67aae97eaa05a74",
+      "raw_max_agl_m": 2916.691650390625,
+      "qa_max_agl_m": 2916.691650390625,
+      "qa_rejected_count": 0
+    }
+  },
+  {
+    "rocket": "halcyon",
+    "source_notebook": "docs/examples/halcyon_flight_sim.ipynb",
+    "source_sha256": "ee4e85b91244c95bd4ae0381521aa077e817018e386f2afa9384918dbc453f25",
+    "actual_agl_m": 3450.0,
+    "reference_kind": "notebook reported apogee",
+    "reported_agl_m": 3450.0,
+    "reported_cell": 0,
+    "saved_first_agl_m": 3212.856,
+    "saved_apogees": [
+      {
+        "cell": 18,
+        "agl_m": 3212.856
+      }
+    ],
+    "measured_summaries": [],
+    "notes": [
+      "Telemetry QA flags 5 samples with altitude jumps above 2000 m/s; raw maximum 3470.825 m is retained separately.",
+      "Telemetry maximum 3470.825 m differs from notebook reported value 3450.000 m; both retained."
+    ],
+    "telemetry": {
+      "path": "data/rockets/astg/altimeter_halcyon.csv",
+      "cell": 20,
+      "conversion": "metres AGL, notebook columns_map",
+      "sha256": "7f1fec2faa53f63a183c97d8a9b692763e7b53d60eab15cc6f42ab646afa85a0",
+      "raw_max_agl_m": 3470.8251953125,
+      "qa_max_agl_m": 3470.8251953125,
+      "qa_rejected_count": 5
+    }
+  },
+  {
+    "rocket": "hedy",
+    "source_notebook": "docs/examples/hedy_flight_sim.ipynb",
+    "source_sha256": "0feb477c3e254afa5ade9bcfb6a44c31f21416e7c1585900a2c90f427b628fe5",
+    "actual_agl_m": 5231.53,
+    "reference_kind": "saved measured-apogee summary",
+    "reported_agl_m": null,
+    "reported_cell": null,
+    "saved_first_agl_m": 5246.918,
+    "saved_apogees": [
+      {
+        "cell": 21,
+        "agl_m": 5246.918
+      }
+    ],
+    "measured_summaries": [
+      {
+        "cell": 26,
+        "agl_m": 5231.53
+      }
+    ],
+    "notes": [
+      "Referenced telemetry file is absent: data/rockets/tust/cats_tust/filteredDataInfo.csv (cell 24).",
+      "Referenced telemetry file is absent: data/rockets/tust/cats_tust/gnssInfo.csv (cell 24).",
+      "Referenced telemetry file is absent: data/rockets/tust/cats_tust/flightInfo.csv (cell 24).",
+      "Referenced telemetry file is absent: data/rockets/tust/cats_tust/imu.csv (cell 24).",
+      "Referenced telemetry file is absent: data/rockets/tust/cats_tust/baro.csv (cell 24).",
+      "Hedy end comparison uses simulated ASL 5406.918 m against measured AGL 5231.53 m; saved simulated AGL is 5246.918 m, a 160 m datum discrepancy."
+    ],
+    "telemetry": null
+  },
+  {
+    "rocket": "juno3",
+    "source_notebook": "docs/examples/juno3_flight_sim.ipynb",
+    "source_sha256": "fd9327a71280f23fb7baedbf50fc8b78fe68845426ffa2d59308f1343d39f112",
+    "actual_agl_m": 3213.0,
+    "reference_kind": "notebook reported apogee",
+    "reported_agl_m": 3213.0,
+    "reported_cell": 0,
+    "saved_first_agl_m": 2976.849,
+    "saved_apogees": [
+      {
+        "cell": 17,
+        "agl_m": 2976.849
+      }
+    ],
+    "measured_summaries": [],
+    "notes": [
+      "Telemetry QA flags 2 samples with altitude jumps above 2000 m/s; raw maximum 10700.590 m is retained separately.",
+      "Telemetry maximum 10700.590 m differs from notebook reported value 3213.000 m; both retained.",
+      "Source fin airfoil is an empirical lift curve (cell 10), not a supplied section contour. The generic 2% HEX DATCOM assumption does not reproduce that curve; source-drag substitution therefore still has a lateral-model discrepancy."
+    ],
+    "telemetry": {
+      "path": "data/rockets/juno3/cots_altimeter.csv",
+      "cell": 18,
+      "conversion": "metres AGL, notebook columns_map",
+      "sha256": "eef81b3bcc4c285667727c1e2c9ca502a67cb374f5e31bc4f87ed76b8a04c1ee",
+      "raw_max_agl_m": 10700.59,
+      "qa_max_agl_m": 3213.375,
+      "qa_rejected_count": 2
+    }
+  },
+  {
+    "rocket": "lince",
+    "source_notebook": "docs/examples/lince_flight_sim.ipynb",
+    "source_sha256": "a6c5f7094cb8c21d19090feb0c39d2b0df7a307b8160bc03e523a8d10127ce14",
+    "actual_agl_m": 3587.0,
+    "reference_kind": "notebook reported apogee",
+    "reported_agl_m": 3587.0,
+    "reported_cell": 0,
+    "saved_first_agl_m": 3284.107,
+    "saved_apogees": [
+      {
+        "cell": 20,
+        "agl_m": 3284.107
+      },
+      {
+        "cell": 22,
+        "agl_m": 528.631
+      },
+      {
+        "cell": 24,
+        "agl_m": 528.631
+      }
+    ],
+    "measured_summaries": [],
+    "notes": [
+      "Telemetry QA flags 69 samples with altitude jumps above 2000 m/s; raw maximum 3668.521 m is retained separately.",
+      "Telemetry maximum 3668.521 m differs from notebook reported value 3587.000 m; both retained.",
+      "Lince has three saved trajectories. Compare first/pre-deployment ascent only; retain all saved apogees separately."
+    ],
+    "telemetry": {
+      "path": "data/rockets/lince/main_data.csv",
+      "cell": 26,
+      "conversion": "metres AGL, notebook columns_map",
+      "sha256": "a9cf47fe2b2e5be03168d36b1c41f0a5651a0c3b6db4ed091e1afc274bd7473e",
+      "raw_max_agl_m": 3668.5205078125,
+      "qa_max_agl_m": 3628.76416015625,
+      "qa_rejected_count": 69
+    }
+  },
+  {
+    "rocket": "ndrt_2020",
+    "source_notebook": "docs/examples/ndrt_2020_flight_sim.ipynb",
+    "source_sha256": "ac6ac18064b7297c2f272035bb9e555902aad816add0b7860f6641ad033591b2",
+    "actual_agl_m": 1316.736,
+    "reference_kind": "notebook reported apogee",
+    "reported_agl_m": 1316.736,
+    "reported_cell": 0,
+    "saved_first_agl_m": 1293.43,
+    "saved_apogees": [
+      {
+        "cell": 28,
+        "agl_m": 1293.43
+      }
+    ],
+    "measured_summaries": [
+      {
+        "cell": 45,
+        "agl_m": 1320.29
+      }
+    ],
+    "notes": [
+      "NDRT source uses approximate ft conversion /3.281; comparison uses exact 0.3048 m/ft.",
+      "Telemetry maximum 1320.357 m differs from notebook reported value 1316.736 m; both retained.",
+      "Source transition is internal at z=1.2..1.327 m with fins near z=3.05 m. The current DATCOM builder represents only an aft tail, not the required downstream smaller cylinder."
+    ],
+    "telemetry": {
+      "path": "data/rockets/NDRT_2020/ndrt_2020_flight_data.csv",
+      "cell": 31,
+      "conversion": "feet AGL multiplied by exactly 0.3048; source uses division by 3.281",
+      "sha256": "5e64b446199988ac183b2f3965d76da27e57496bb2a33f78df73aa23fd18d7b8",
+      "raw_max_agl_m": 1320.3570240000001,
+      "qa_max_agl_m": 1320.3570240000001,
+      "qa_rejected_count": 0
+    }
+  },
+  {
+    "rocket": "prometheus_2022",
+    "source_notebook": "docs/examples/prometheus_2022_flight_sim.ipynb",
+    "source_sha256": "44e5dfd07323771d54dec3bfb596f943620915d81f162a6b8cc25c3ea1438186",
+    "actual_agl_m": 3898.37,
+    "reference_kind": "notebook reported apogee",
+    "reported_agl_m": 3898.37,
+    "reported_cell": 0,
+    "saved_first_agl_m": 4188.81,
+    "saved_apogees": [
+      {
+        "cell": 13,
+        "agl_m": 4188.81
+      }
+    ],
+    "measured_summaries": [
+      {
+        "cell": 17,
+        "agl_m": 3903.77
+      }
+    ],
+    "notes": [
+      "Telemetry QA flags 2 samples with altitude jumps above 2000 m/s; raw maximum 3903.770 m is retained separately.",
+      "Telemetry maximum 3903.770 m differs from notebook reported value 3898.370 m; both retained.",
+      "Prometheus prose says June 2022; source environment and local reanalysis are June 2023. Both reruns preserve source conditions."
+    ],
+    "telemetry": {
+      "path": "data/rockets/prometheus/2022-06-24-serial-5115-flight-0001-TeleMetrum.csv",
+      "cell": 15,
+      "conversion": "metres AGL, notebook columns_map",
+      "sha256": "9fe8d17a25e7944267b34a45ed955303afd5818b3f18479a5f7ffb31a574cecf",
+      "raw_max_agl_m": 3903.77,
+      "qa_max_agl_m": 3903.77,
+      "qa_rejected_count": 2
+    }
+  },
+  {
+    "rocket": "valetudo",
+    "source_notebook": "docs/examples/valetudo_flight_sim.ipynb",
+    "source_sha256": "2b86db6b4846f1badf9d99a7d6825526c889e64303bee4d3c487a72ba3e69c47",
+    "actual_agl_m": 860.0,
+    "reference_kind": "saved measured-apogee summary",
+    "reported_agl_m": 860.0,
+    "reported_cell": 31,
+    "saved_first_agl_m": 815.26,
+    "saved_apogees": [
+      {
+        "cell": 28,
+        "agl_m": 815.26
+      }
+    ],
+    "measured_summaries": [
+      {
+        "cell": 33,
+        "agl_m": 860.0
+      }
+    ],
+    "notes": [
+      "Valetudo flight recordings were lost; only recovered scalar apogee/drift values remain."
+    ],
+    "telemetry": null
+  },
+  {
+    "rocket": "valkyrie",
+    "source_notebook": "docs/examples/valkyrie_flight_sim.ipynb",
+    "source_sha256": "78aacf4df26933a87d28bfc71ad16e94828933672df20dce87545f0ce43267d3",
+    "actual_agl_m": 2098.02,
+    "reference_kind": "notebook reported apogee",
+    "reported_agl_m": 2098.02,
+    "reported_cell": 0,
+    "saved_first_agl_m": 2247.849,
+    "saved_apogees": [
+      {
+        "cell": 11,
+        "agl_m": 2247.849
+      }
+    ],
+    "measured_summaries": [],
+    "notes": [
+      "Telemetry maximum 2059.024 m differs from notebook reported value 2098.020 m; both retained.",
+      "Valkyrie prose says 2025 but source environment says 2026 and fetches live Windy weather; no archived profile is supplied."
+    ],
+    "telemetry": {
+      "path": "data/rockets/valkyrie/flightInfo_merged.csv",
+      "cell": 13,
+      "conversion": "metres AGL, notebook columns_map",
+      "sha256": "0c7808a84fb39c83908ba9caa1d2d3ccb609d6db46c3d660b85977e6917795bc",
+      "raw_max_agl_m": 2059.0244140625,
+      "qa_max_agl_m": 2059.0244140625,
+      "qa_rejected_count": 0
+    }
+  }
+]
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/juno3_altitude.csv b/docs/examples/datcom/comparison_data/rockets/juno3_altitude.csv
new file mode 100644
index 000000000..521e3b7c5
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/juno3_altitude.csv
@@ -0,0 +1,602 @@
+time_s,ordinary_agl_m,datcom_agl_m,ordinary_vz_m_s,datcom_vz_m_s,ordinary_margin_cal,datcom_margin_cal
+0.0,0.0,0.0,0.0,0.0,3.2208143944873804,4.008594996785594
+0.04439166819116532,0.0,0.0,-4.8981676989789975e-21,8.218536570214719e-22,3.220898769786637,4.008679372084851
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+0.1775666727646613,0.0,0.0,1.4056372884378724e-20,-2.3584903926201076e-21,3.2222149163892313,4.009995518687445
+0.22195834095582662,0.0,0.0,7.170826343573719e-12,-1.3448767027134504e-12,3.223243804386108,4.011024431242373
+0.2663500091469919,0.0006597534475076827,0.0006641914908414037,0.054097537002821196,0.05471782949907035,3.225001666373272,4.01278072367125
+0.3107416773381573,0.007259861588408967,0.0073121372674904705,0.2684649213796364,0.26982035151683376,3.2273472849210454,4.015124631408342
+0.3551333455293226,0.02692265518703607,0.027054846647615705,0.6479134123874504,0.6499991538292617,3.2302952091015262,4.018070622687156
+0.3995250137204879,0.06744835348649758,0.067686131562823,1.2060770904664921,1.2084393580049841,3.2338963167367294,4.02167447911225
+0.44391668191165323,0.13630029970977375,0.1366504586010251,1.9195771226546274,1.9221604195361126,3.238068082747441,4.025859195109493
+0.48830835010281853,0.2397299669894437,0.24019915898406907,2.759567285552093,2.7623659834681735,3.242707581070291,4.030527702339769
+0.5327000182939838,0.3823647339602303,0.38295472841628,3.6675542854096963,3.670574297706572,3.247603677721762,4.035470861630046
+0.5770916864851492,0.5680630563697378,0.5687937382426753,4.7397761910597636,4.743034311154648,3.253110898979304,4.041053454859275
+0.6214833546763145,0.8051504682532595,0.8060311491376524,5.962503190983279,5.966006820395907,3.2591829788715883,4.047234413299398
+0.6658750228674798,1.099258380442052,1.1002968711743506,7.307006058808756,7.310788533554351,3.2657173486569917,4.053917360316112
+0.7102666910586451,1.4553672133254167,1.456578719511981,8.753305608798728,8.757389184258917,3.272644648124984,4.061021748247254
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diff --git a/docs/examples/datcom/comparison_data/rockets/juno3_cd.csv b/docs/examples/datcom/comparison_data/rockets/juno3_cd.csv
new file mode 100644
index 000000000..0157db690
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/juno3_cd.csv
@@ -0,0 +1,152 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/juno3_coast_for005.dat b/docs/examples/datcom/comparison_data/rockets/juno3_coast_for005.dat
new file mode 100644
index 000000000..8afda782f
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/juno3_coast_for005.dat
@@ -0,0 +1,235 @@
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 143857.55,863145.299,1438575.5,2591980.51,3799447.16,5066740.16,
+    6408022.21,7839759.61,9370571.41,11008799.5,12644648.1,13669889.9,
+    15378626.1,16232994.2,16745615.1,17087362.3,17429109.6,17941730.4,
+    18796098.5,20504834.8,
+  NALPHA = 19.,
+  ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14,
+    0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3,
+  BETA = -20.,$
+ $REFQ
+  XCG = 1.477,
+  SREF = 0.0134782179,
+  LREF = 0.131,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.565,
+  DNOSE = 0.131,
+  LCENTR = 2.138,
+  DCENTR = 0.131,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.068,
+  DAFT = 0.107,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.405,2.485,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.13,
+  CHORD = 0.2,0.12,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = 0.,0.,0.,0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 143857.55,863145.299,1438575.5,2591980.51,3799447.16,5066740.16,
+    6408022.21,7839759.61,9370571.41,11008799.5,12644648.1,13669889.9,
+    15378626.1,16232994.2,16745615.1,17087362.3,17429109.6,17941730.4,
+    18796098.5,20504834.8,
+  NALPHA = 19.,
+  ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14,
+    0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3,
+  BETA = -5.,$
+ $REFQ
+  XCG = 1.477,
+  SREF = 0.0134782179,
+  LREF = 0.131,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.565,
+  DNOSE = 0.131,
+  LCENTR = 2.138,
+  DCENTR = 0.131,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.068,
+  DAFT = 0.107,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.405,2.485,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.13,
+  CHORD = 0.2,0.12,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = 0.,0.,0.,0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 143857.55,863145.299,1438575.5,2591980.51,3799447.16,5066740.16,
+    6408022.21,7839759.61,9370571.41,11008799.5,12644648.1,13669889.9,
+    15378626.1,16232994.2,16745615.1,17087362.3,17429109.6,17941730.4,
+    18796098.5,20504834.8,
+  NALPHA = 19.,
+  ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14,
+    0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3,
+  BETA = 0.,$
+ $REFQ
+  XCG = 1.477,
+  SREF = 0.0134782179,
+  LREF = 0.131,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.565,
+  DNOSE = 0.131,
+  LCENTR = 2.138,
+  DCENTR = 0.131,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.068,
+  DAFT = 0.107,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.405,2.485,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.13,
+  CHORD = 0.2,0.12,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = 0.,0.,0.,0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 143857.55,863145.299,1438575.5,2591980.51,3799447.16,5066740.16,
+    6408022.21,7839759.61,9370571.41,11008799.5,12644648.1,13669889.9,
+    15378626.1,16232994.2,16745615.1,17087362.3,17429109.6,17941730.4,
+    18796098.5,20504834.8,
+  NALPHA = 19.,
+  ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14,
+    0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3,
+  BETA = 5.,$
+ $REFQ
+  XCG = 1.477,
+  SREF = 0.0134782179,
+  LREF = 0.131,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.565,
+  DNOSE = 0.131,
+  LCENTR = 2.138,
+  DCENTR = 0.131,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.068,
+  DAFT = 0.107,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.405,2.485,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.13,
+  CHORD = 0.2,0.12,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = 0.,0.,0.,0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 143857.55,863145.299,1438575.5,2591980.51,3799447.16,5066740.16,
+    6408022.21,7839759.61,9370571.41,11008799.5,12644648.1,13669889.9,
+    15378626.1,16232994.2,16745615.1,17087362.3,17429109.6,17941730.4,
+    18796098.5,20504834.8,
+  NALPHA = 19.,
+  ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14,
+    0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3,
+  BETA = 20.,$
+ $REFQ
+  XCG = 1.477,
+  SREF = 0.0134782179,
+  LREF = 0.131,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.565,
+  DNOSE = 0.131,
+  LCENTR = 2.138,
+  DCENTR = 0.131,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.068,
+  DAFT = 0.107,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.405,2.485,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.13,
+  CHORD = 0.2,0.12,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = 0.,0.,0.,0.,$
+DAMP
+NEXT CASE
diff --git a/docs/examples/datcom/comparison_data/rockets/juno3_powered_for005.dat b/docs/examples/datcom/comparison_data/rockets/juno3_powered_for005.dat
new file mode 100644
index 000000000..6041235f3
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/juno3_powered_for005.dat
@@ -0,0 +1,235 @@
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 180330.781,1081600.98,1801573.56,3237190.09,4664267.08,6079651.89,
+    7482495.11,8871912.88,10242729.,11580608.8,12762486.5,13797282.7,
+    15521943.,16384273.2,16901671.3,17246603.4,17591535.4,18108933.5,
+    18971263.7,20695924.,
+  NALPHA = 19.,
+  ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14,
+    0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3,
+  BETA = -20.,$
+ $REFQ
+  XCG = 1.65017802,
+  SREF = 0.0134782179,
+  LREF = 0.131,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.565,
+  DNOSE = 0.131,
+  LCENTR = 2.138,
+  DCENTR = 0.131,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.068,
+  DAFT = 0.107,
+  DEXIT = 0.067,$
+ $FINSET1
+  XLE = 2.405,2.485,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.13,
+  CHORD = 0.2,0.12,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = 0.,0.,0.,0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 180330.781,1081600.98,1801573.56,3237190.09,4664267.08,6079651.89,
+    7482495.11,8871912.88,10242729.,11580608.8,12762486.5,13797282.7,
+    15521943.,16384273.2,16901671.3,17246603.4,17591535.4,18108933.5,
+    18971263.7,20695924.,
+  NALPHA = 19.,
+  ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14,
+    0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3,
+  BETA = -5.,$
+ $REFQ
+  XCG = 1.65017802,
+  SREF = 0.0134782179,
+  LREF = 0.131,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.565,
+  DNOSE = 0.131,
+  LCENTR = 2.138,
+  DCENTR = 0.131,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.068,
+  DAFT = 0.107,
+  DEXIT = 0.067,$
+ $FINSET1
+  XLE = 2.405,2.485,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.13,
+  CHORD = 0.2,0.12,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = 0.,0.,0.,0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 180330.781,1081600.98,1801573.56,3237190.09,4664267.08,6079651.89,
+    7482495.11,8871912.88,10242729.,11580608.8,12762486.5,13797282.7,
+    15521943.,16384273.2,16901671.3,17246603.4,17591535.4,18108933.5,
+    18971263.7,20695924.,
+  NALPHA = 19.,
+  ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14,
+    0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3,
+  BETA = 0.,$
+ $REFQ
+  XCG = 1.65017802,
+  SREF = 0.0134782179,
+  LREF = 0.131,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.565,
+  DNOSE = 0.131,
+  LCENTR = 2.138,
+  DCENTR = 0.131,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.068,
+  DAFT = 0.107,
+  DEXIT = 0.067,$
+ $FINSET1
+  XLE = 2.405,2.485,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.13,
+  CHORD = 0.2,0.12,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = 0.,0.,0.,0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 180330.781,1081600.98,1801573.56,3237190.09,4664267.08,6079651.89,
+    7482495.11,8871912.88,10242729.,11580608.8,12762486.5,13797282.7,
+    15521943.,16384273.2,16901671.3,17246603.4,17591535.4,18108933.5,
+    18971263.7,20695924.,
+  NALPHA = 19.,
+  ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14,
+    0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3,
+  BETA = 5.,$
+ $REFQ
+  XCG = 1.65017802,
+  SREF = 0.0134782179,
+  LREF = 0.131,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.565,
+  DNOSE = 0.131,
+  LCENTR = 2.138,
+  DCENTR = 0.131,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.068,
+  DAFT = 0.107,
+  DEXIT = 0.067,$
+ $FINSET1
+  XLE = 2.405,2.485,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.13,
+  CHORD = 0.2,0.12,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = 0.,0.,0.,0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 180330.781,1081600.98,1801573.56,3237190.09,4664267.08,6079651.89,
+    7482495.11,8871912.88,10242729.,11580608.8,12762486.5,13797282.7,
+    15521943.,16384273.2,16901671.3,17246603.4,17591535.4,18108933.5,
+    18971263.7,20695924.,
+  NALPHA = 19.,
+  ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14,
+    0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3,
+  BETA = 20.,$
+ $REFQ
+  XCG = 1.65017802,
+  SREF = 0.0134782179,
+  LREF = 0.131,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.565,
+  DNOSE = 0.131,
+  LCENTR = 2.138,
+  DCENTR = 0.131,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.068,
+  DAFT = 0.107,
+  DEXIT = 0.067,$
+ $FINSET1
+  XLE = 2.405,2.485,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.13,
+  CHORD = 0.2,0.12,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = 0.,0.,0.,0.,$
+DAMP
+NEXT CASE
diff --git a/docs/examples/datcom/comparison_data/rockets/juno3_provenance.json b/docs/examples/datcom/comparison_data/rockets/juno3_provenance.json
new file mode 100644
index 000000000..c649e17dc
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/juno3_provenance.json
@@ -0,0 +1,384 @@
+{
+  "construction_cells": [
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9,
+    10,
+    11,
+    12,
+    13,
+    14,
+    15
+  ],
+  "calibration": {
+    "roughness_height": 0.0,
+    "leading_edge_radius": null
+  },
+  "source_adaptations": [],
+  "mach": [
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+    0.06,
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+    0.98,
+    1.0,
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+    1.05,
+    1.1,
+    1.2
+  ],
+  "alpha": [
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+    0.72,
+    1.43,
+    2.86,
+    5.73,
+    14.32,
+    28.65,
+    57.3
+  ],
+  "beta": [
+    -20,
+    -5,
+    0,
+    5,
+    20
+  ],
+  "altitude_schedules_m_asl": {
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+    "coverage": {
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+      "minimum_dynamic_pressure_pa": 100.0,
+      "excluded_low_q_samples": 262,
+      "phases": {
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+          "mach_max": 0.7313022533685333,
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+        299.51107139936414,
+        299.51107139936414,
+        299.51107139936414,
+        299.51107139936414
+      ]
+    }
+  },
+  "atmosphere_iterations": [
+    {
+      "pass": 0,
+      "apogee_agl_m": 3056.53079550082,
+      "reference": "ordinary trajectory"
+    },
+    {
+      "pass": 1,
+      "apogee_agl_m": 3056.4921836297026,
+      "reference": "previous DATCOM trajectory"
+    }
+  ],
+  "atmosphere_method": "RocketPy reference ascent rho*a*M/mu; native REN",
+  "source_notebook": "docs/examples/juno3_flight_sim.ipynb",
+  "source_sha256": "fd9327a71280f23fb7baedbf50fc8b78fe68845426ffa2d59308f1343d39f112",
+  "construction_assumption": "carbon fibre and vinyl when unspecified; no numerical roughness inferred",
+  "fin_assumption": "2% HEX, 5% bevels, sharp edges, auto body attachment",
+  "powered_base_drag": "nozzle",
+  "damp": true
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/juno3_result.json b/docs/examples/datcom/comparison_data/rockets/juno3_result.json
new file mode 100644
index 000000000..95aebc9d8
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/juno3_result.json
@@ -0,0 +1,49 @@
+{
+  "rocket": "juno3",
+  "actual_agl_m": 3213.0,
+  "reported_agl_m": 3213.0,
+  "saved_first_agl_m": 2976.849,
+  "reference_kind": "notebook reported apogee",
+  "telemetry_max_agl_m": 10700.59,
+  "notes": "Telemetry QA flags 2 samples with altitude jumps above 2000 m/s; raw maximum 10700.590 m is retained separately. | Telemetry maximum 10700.590 m differs from notebook reported value 3213.000 m; both retained. | Source fin airfoil is an empirical lift curve (cell 10), not a supplied section contour. The generic 2% HEX DATCOM assumption does not reproduce that curve; source-drag substitution therefore still has a lateral-model discrepancy.",
+  "status": "paired untuned comparison",
+  "reason": "",
+  "ordinary_agl_m": 3008.581569789987,
+  "ordinary_apogee_time_s": 26.491920030008096,
+  "ordinary_peak_vertical_velocity_m_s": 241.2427490025272,
+  "ordinary_initial_static_margin_cal": 3.2206482971493466,
+  "ordinary_coast_cd_mach06": 0.368,
+  "ordinary_max_mach": 0.7308763878428423,
+  "ordinary_ascent_altitude_rmse_m": 229.55884227968258,
+  "source_adaptations": "",
+  "atmosphere_refinements": 1,
+  "atmosphere_last_apogee_change_m": -0.03861187111760955,
+  "datcom_agl_m": 3056.4921836297026,
+  "datcom_apogee_time_s": 26.635000914699194,
+  "datcom_peak_vertical_velocity_m_s": 243.89859046993098,
+  "datcom_initial_static_margin_cal": 4.0067340523842745,
+  "datcom_coast_cd_mach06": 0.37450000000000006,
+  "datcom_max_mach": 0.7312932218217086,
+  "datcom_ascent_altitude_rmse_m": 209.292777372015,
+  "samples": 486,
+  "outside_mach": 0,
+  "outside_alpha": 0,
+  "outside_beta": 0,
+  "max_abs_alpha_deg": 43.59333257202937,
+  "max_abs_beta_deg": 5.4737412566148915,
+  "solver_mach_excursion": false,
+  "solver_alpha_excursion": true,
+  "solver_beta_excursion": true,
+  "mass_cg_match": true,
+  "ordinary_error_m": -204.41843021001296,
+  "ordinary_error_pct": -6.362229387177496,
+  "datcom_error_m": -156.5078163702974,
+  "datcom_error_pct": -4.871080497052518,
+  "telemetry_qa_max_agl_m": 3213.375,
+  "telemetry_qa_rejected_count": 2,
+  "ordinary_vs_telemetry_pct": -6.373156889874753,
+  "datcom_vs_telemetry_pct": -4.882182016425018,
+  "rmse_start_s": 0,
+  "rmse_end_s": 26.3,
+  "within_sampled_envelope": true
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/juno3_telemetry.csv b/docs/examples/datcom/comparison_data/rockets/juno3_telemetry.csv
new file mode 100644
index 000000000..a01db0661
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/juno3_telemetry.csv
@@ -0,0 +1,612 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/juno3_velocity.csv b/docs/examples/datcom/comparison_data/rockets/juno3_velocity.csv
new file mode 100644
index 000000000..c1e436def
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/juno3_velocity.csv
@@ -0,0 +1,612 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/lince_altitude.csv b/docs/examples/datcom/comparison_data/rockets/lince_altitude.csv
new file mode 100644
index 000000000..85ad55046
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/lince_altitude.csv
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diff --git a/docs/examples/datcom/comparison_data/rockets/lince_cd.csv b/docs/examples/datcom/comparison_data/rockets/lince_cd.csv
new file mode 100644
index 000000000..48175ca90
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/lince_cd.csv
@@ -0,0 +1,152 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/lince_coast_for005.dat b/docs/examples/datcom/comparison_data/rockets/lince_coast_for005.dat
new file mode 100644
index 000000000..02550c431
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/lince_coast_for005.dat
@@ -0,0 +1,235 @@
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 149204.324,1492043.24,2873657.3,4324474.37,5692174.73,7356706.4,
+    9148737.57,10840013.6,12861518.2,14055449.5,15060627.8,16338920.1,
+    17623820.6,18910372.2,19684782.8,20192746.2,20596601.1,21202383.5,
+    22212020.8,24231295.5,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -20.,$
+ $REFQ
+  XCG = 1.048417,
+  SREF = 0.00849486654,
+  LREF = 0.104,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.28,
+  DNOSE = 0.104,
+  LCENTR = 1.829417,
+  DCENTR = 0.104,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.022,
+  DAFT = 0.09704,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 1.939417,2.066917,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.0825,
+  CHORD = 0.17,0.0425,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 149204.324,1492043.24,2873657.3,4324474.37,5692174.73,7356706.4,
+    9148737.57,10840013.6,12861518.2,14055449.5,15060627.8,16338920.1,
+    17623820.6,18910372.2,19684782.8,20192746.2,20596601.1,21202383.5,
+    22212020.8,24231295.5,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -5.,$
+ $REFQ
+  XCG = 1.048417,
+  SREF = 0.00849486654,
+  LREF = 0.104,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.28,
+  DNOSE = 0.104,
+  LCENTR = 1.829417,
+  DCENTR = 0.104,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.022,
+  DAFT = 0.09704,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 1.939417,2.066917,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.0825,
+  CHORD = 0.17,0.0425,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 149204.324,1492043.24,2873657.3,4324474.37,5692174.73,7356706.4,
+    9148737.57,10840013.6,12861518.2,14055449.5,15060627.8,16338920.1,
+    17623820.6,18910372.2,19684782.8,20192746.2,20596601.1,21202383.5,
+    22212020.8,24231295.5,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 0.,$
+ $REFQ
+  XCG = 1.048417,
+  SREF = 0.00849486654,
+  LREF = 0.104,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.28,
+  DNOSE = 0.104,
+  LCENTR = 1.829417,
+  DCENTR = 0.104,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.022,
+  DAFT = 0.09704,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 1.939417,2.066917,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.0825,
+  CHORD = 0.17,0.0425,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 149204.324,1492043.24,2873657.3,4324474.37,5692174.73,7356706.4,
+    9148737.57,10840013.6,12861518.2,14055449.5,15060627.8,16338920.1,
+    17623820.6,18910372.2,19684782.8,20192746.2,20596601.1,21202383.5,
+    22212020.8,24231295.5,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 5.,$
+ $REFQ
+  XCG = 1.048417,
+  SREF = 0.00849486654,
+  LREF = 0.104,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.28,
+  DNOSE = 0.104,
+  LCENTR = 1.829417,
+  DCENTR = 0.104,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.022,
+  DAFT = 0.09704,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 1.939417,2.066917,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.0825,
+  CHORD = 0.17,0.0425,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 149204.324,1492043.24,2873657.3,4324474.37,5692174.73,7356706.4,
+    9148737.57,10840013.6,12861518.2,14055449.5,15060627.8,16338920.1,
+    17623820.6,18910372.2,19684782.8,20192746.2,20596601.1,21202383.5,
+    22212020.8,24231295.5,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 20.,$
+ $REFQ
+  XCG = 1.048417,
+  SREF = 0.00849486654,
+  LREF = 0.104,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.28,
+  DNOSE = 0.104,
+  LCENTR = 1.829417,
+  DCENTR = 0.104,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.022,
+  DAFT = 0.09704,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 1.939417,2.066917,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.0825,
+  CHORD = 0.17,0.0425,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
diff --git a/docs/examples/datcom/comparison_data/rockets/lince_powered_for005.dat b/docs/examples/datcom/comparison_data/rockets/lince_powered_for005.dat
new file mode 100644
index 000000000..556b170b1
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/lince_powered_for005.dat
@@ -0,0 +1,235 @@
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 221434.247,2214342.47,4201653.58,6172522.61,7907065.96,9833944.82,
+    11727020.,13371551.7,15164177.2,16130432.5,16885444.,17823447.5,
+    18746011.8,19635388.2,20149632.8,20241009.8,20738066.9,21348010.1,
+    22364582.,24397725.8,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -20.,$
+ $REFQ
+  XCG = 1.21534549,
+  SREF = 0.00849486654,
+  LREF = 0.104,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.28,
+  DNOSE = 0.104,
+  LCENTR = 1.829417,
+  DCENTR = 0.104,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.022,
+  DAFT = 0.09704,
+  DEXIT = 0.067,$
+ $FINSET1
+  XLE = 1.939417,2.066917,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.0825,
+  CHORD = 0.17,0.0425,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 221434.247,2214342.47,4201653.58,6172522.61,7907065.96,9833944.82,
+    11727020.,13371551.7,15164177.2,16130432.5,16885444.,17823447.5,
+    18746011.8,19635388.2,20149632.8,20241009.8,20738066.9,21348010.1,
+    22364582.,24397725.8,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -5.,$
+ $REFQ
+  XCG = 1.21534549,
+  SREF = 0.00849486654,
+  LREF = 0.104,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.28,
+  DNOSE = 0.104,
+  LCENTR = 1.829417,
+  DCENTR = 0.104,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.022,
+  DAFT = 0.09704,
+  DEXIT = 0.067,$
+ $FINSET1
+  XLE = 1.939417,2.066917,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.0825,
+  CHORD = 0.17,0.0425,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 221434.247,2214342.47,4201653.58,6172522.61,7907065.96,9833944.82,
+    11727020.,13371551.7,15164177.2,16130432.5,16885444.,17823447.5,
+    18746011.8,19635388.2,20149632.8,20241009.8,20738066.9,21348010.1,
+    22364582.,24397725.8,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 0.,$
+ $REFQ
+  XCG = 1.21534549,
+  SREF = 0.00849486654,
+  LREF = 0.104,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.28,
+  DNOSE = 0.104,
+  LCENTR = 1.829417,
+  DCENTR = 0.104,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.022,
+  DAFT = 0.09704,
+  DEXIT = 0.067,$
+ $FINSET1
+  XLE = 1.939417,2.066917,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.0825,
+  CHORD = 0.17,0.0425,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 221434.247,2214342.47,4201653.58,6172522.61,7907065.96,9833944.82,
+    11727020.,13371551.7,15164177.2,16130432.5,16885444.,17823447.5,
+    18746011.8,19635388.2,20149632.8,20241009.8,20738066.9,21348010.1,
+    22364582.,24397725.8,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 5.,$
+ $REFQ
+  XCG = 1.21534549,
+  SREF = 0.00849486654,
+  LREF = 0.104,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.28,
+  DNOSE = 0.104,
+  LCENTR = 1.829417,
+  DCENTR = 0.104,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.022,
+  DAFT = 0.09704,
+  DEXIT = 0.067,$
+ $FINSET1
+  XLE = 1.939417,2.066917,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.0825,
+  CHORD = 0.17,0.0425,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.76,0.8,0.85,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 221434.247,2214342.47,4201653.58,6172522.61,7907065.96,9833944.82,
+    11727020.,13371551.7,15164177.2,16130432.5,16885444.,17823447.5,
+    18746011.8,19635388.2,20149632.8,20241009.8,20738066.9,21348010.1,
+    22364582.,24397725.8,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 20.,$
+ $REFQ
+  XCG = 1.21534549,
+  SREF = 0.00849486654,
+  LREF = 0.104,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.28,
+  DNOSE = 0.104,
+  LCENTR = 1.829417,
+  DCENTR = 0.104,
+  BASE = .FALSE.,
+  TAFT = CONE,
+  LAFT = 0.022,
+  DAFT = 0.09704,
+  DEXIT = 0.067,$
+ $FINSET1
+  XLE = 1.939417,2.066917,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.0825,
+  CHORD = 0.17,0.0425,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,-0.,$
+DAMP
+NEXT CASE
diff --git a/docs/examples/datcom/comparison_data/rockets/lince_provenance.json b/docs/examples/datcom/comparison_data/rockets/lince_provenance.json
new file mode 100644
index 000000000..6e6f2f72b
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/lince_provenance.json
@@ -0,0 +1,382 @@
+{
+  "construction_cells": [
+    2,
+    4,
+    6,
+    7,
+    9,
+    10,
+    11,
+    12,
+    13,
+    14,
+    16,
+    17
+  ],
+  "calibration": {
+    "roughness_height": 0.0,
+    "leading_edge_radius": null
+  },
+  "source_adaptations": [],
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+    20.0
+  ],
+  "beta": [
+    -20,
+    -5,
+    0,
+    5,
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+  ],
+  "altitude_schedules_m_asl": {
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+    "coverage": {
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+      "excluded_low_q_samples": 30,
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+  "atmosphere_iterations": [
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+      "reference": "ordinary trajectory"
+    },
+    {
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+      "apogee_agl_m": 4254.30425999591,
+      "reference": "previous DATCOM trajectory"
+    }
+  ],
+  "atmosphere_method": "RocketPy reference ascent rho*a*M/mu; native REN",
+  "source_notebook": "docs/examples/lince_flight_sim.ipynb",
+  "source_sha256": "a6c5f7094cb8c21d19090feb0c39d2b0df7a307b8160bc03e523a8d10127ce14",
+  "construction_assumption": "carbon fibre and vinyl when unspecified; no numerical roughness inferred",
+  "fin_assumption": "2% HEX, 5% bevels, sharp edges, auto body attachment",
+  "powered_base_drag": "nozzle",
+  "damp": true
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/lince_result.json b/docs/examples/datcom/comparison_data/rockets/lince_result.json
new file mode 100644
index 000000000..e147e9e19
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/lince_result.json
@@ -0,0 +1,49 @@
+{
+  "rocket": "lince",
+  "actual_agl_m": 3587.0,
+  "reported_agl_m": 3587.0,
+  "saved_first_agl_m": 3284.107,
+  "reference_kind": "notebook reported apogee",
+  "telemetry_max_agl_m": 3668.5205078125,
+  "notes": "Telemetry QA flags 69 samples with altitude jumps above 2000 m/s; raw maximum 3668.521 m is retained separately. | Telemetry maximum 3668.521 m differs from notebook reported value 3587.000 m; both retained. | Lince has three saved trajectories. Compare first/pre-deployment ascent only; retain all saved apogees separately.",
+  "status": "paired untuned comparison",
+  "reason": "",
+  "ordinary_agl_m": 3285.089381541881,
+  "ordinary_apogee_time_s": 24.728341685630557,
+  "ordinary_peak_vertical_velocity_m_s": 317.77081353823803,
+  "ordinary_initial_static_margin_cal": 2.9203959364705625,
+  "ordinary_coast_cd_mach06": 0.65562,
+  "ordinary_max_mach": 0.936438877536046,
+  "ordinary_ascent_altitude_rmse_m": 180.22020526338466,
+  "source_adaptations": "",
+  "atmosphere_refinements": 1,
+  "atmosphere_last_apogee_change_m": -6.875566491943573,
+  "datcom_agl_m": 4254.30425999591,
+  "datcom_apogee_time_s": 28.7853935112826,
+  "datcom_peak_vertical_velocity_m_s": 342.53953112405384,
+  "datcom_initial_static_margin_cal": 3.962666916625163,
+  "datcom_coast_cd_mach06": 0.38175,
+  "datcom_max_mach": 1.0102057901013621,
+  "datcom_ascent_altitude_rmse_m": 299.53187203569485,
+  "samples": 498,
+  "outside_mach": 0,
+  "outside_alpha": 0,
+  "outside_beta": 0,
+  "max_abs_alpha_deg": 3.100325929162926,
+  "max_abs_beta_deg": 3.920227060235464,
+  "solver_mach_excursion": true,
+  "solver_alpha_excursion": true,
+  "solver_beta_excursion": true,
+  "mass_cg_match": true,
+  "ordinary_error_m": -301.9106184581192,
+  "ordinary_error_pct": -8.416800068528554,
+  "datcom_error_m": 667.3042599959099,
+  "datcom_error_pct": 18.60340841917786,
+  "telemetry_qa_max_agl_m": 3628.76416015625,
+  "telemetry_qa_rejected_count": 69,
+  "ordinary_vs_telemetry_pct": -9.470849122351643,
+  "datcom_vs_telemetry_pct": 17.23837847353311,
+  "rmse_start_s": 0,
+  "rmse_end_s": 24.7074627638509,
+  "within_sampled_envelope": true
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/lince_telemetry.csv b/docs/examples/datcom/comparison_data/rockets/lince_telemetry.csv
new file mode 100644
index 000000000..d92d6c80e
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/lince_telemetry.csv
@@ -0,0 +1,44455 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/ndrt_2020_altitude.csv b/docs/examples/datcom/comparison_data/rockets/ndrt_2020_altitude.csv
new file mode 100644
index 000000000..adca3abe9
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/ndrt_2020_altitude.csv
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diff --git a/docs/examples/datcom/comparison_data/rockets/ndrt_2020_cd.csv b/docs/examples/datcom/comparison_data/rockets/ndrt_2020_cd.csv
new file mode 100644
index 000000000..cd2a87b93
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/ndrt_2020_cd.csv
@@ -0,0 +1,152 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/ndrt_2020_result.json b/docs/examples/datcom/comparison_data/rockets/ndrt_2020_result.json
new file mode 100644
index 000000000..87938728b
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/ndrt_2020_result.json
@@ -0,0 +1,26 @@
+{
+  "rocket": "ndrt_2020",
+  "actual_agl_m": 1316.736,
+  "reported_agl_m": 1316.736,
+  "saved_first_agl_m": 1293.43,
+  "reference_kind": "notebook reported apogee",
+  "telemetry_max_agl_m": 1320.3570240000001,
+  "notes": "NDRT source uses approximate ft conversion /3.281; comparison uses exact 0.3048 m/ft. | Telemetry maximum 1320.357 m differs from notebook reported value 1316.736 m; both retained. | Source transition is internal at z=1.2..1.327 m with fins near z=3.05 m. The current DATCOM builder represents only an aft tail, not the required downstream smaller cylinder.",
+  "status": "ordinary only",
+  "reason": "ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip",
+  "ordinary_agl_m": 1293.5184123380945,
+  "ordinary_apogee_time_s": 16.64978369813489,
+  "ordinary_peak_vertical_velocity_m_s": 171.66118644734854,
+  "ordinary_initial_static_margin_cal": 4.016381564609376,
+  "ordinary_coast_cd_mach06": 0.44,
+  "ordinary_max_mach": 0.52460108376313,
+  "ordinary_ascent_altitude_rmse_m": 42.9525631238651,
+  "source_adaptations": "",
+  "ordinary_error_m": -23.217587661905554,
+  "ordinary_error_pct": -1.763268237665375,
+  "telemetry_qa_max_agl_m": 1320.3570240000001,
+  "telemetry_qa_rejected_count": 0,
+  "ordinary_vs_telemetry_pct": -2.0326783721419828,
+  "rmse_start_s": 0.04375,
+  "rmse_end_s": 16.64978369813489
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/ndrt_2020_telemetry.csv b/docs/examples/datcom/comparison_data/rockets/ndrt_2020_telemetry.csv
new file mode 100644
index 000000000..2c062f363
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/ndrt_2020_telemetry.csv
@@ -0,0 +1,1820 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/prometheus_2022_altitude.csv b/docs/examples/datcom/comparison_data/rockets/prometheus_2022_altitude.csv
new file mode 100644
index 000000000..1562e6f38
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/prometheus_2022_altitude.csv
@@ -0,0 +1,602 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/prometheus_2022_cd.csv b/docs/examples/datcom/comparison_data/rockets/prometheus_2022_cd.csv
new file mode 100644
index 000000000..e92d9698e
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/prometheus_2022_cd.csv
@@ -0,0 +1,152 @@
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+1.1047999999999998,0.44549520000000004,0.5226480000000001,0.43676000000000004,0.5526000000000002
+1.1127333333333331,0.4450906,0.5187606666666669,0.4363633333333333,0.5486333333333335
+1.1206666666666665,0.444686,0.5148733333333335,0.43596666666666667,0.5446666666666669
+1.1285999999999998,0.4442814,0.5109860000000002,0.43557,0.5407000000000002
+1.1365333333333332,0.4438768,0.5070986666666668,0.43517333333333336,0.5367333333333335
+1.1444666666666665,0.44347220000000004,0.5032113333333335,0.4347766666666667,0.5327666666666668
+1.1523999999999999,0.4430676,0.49932400000000005,0.43438,0.5288
+1.1603333333333332,0.44266300000000003,0.49543666666666675,0.43398333333333333,0.5248333333333334
+1.1682666666666666,0.4422584,0.4915493333333334,0.4335866666666667,0.5208666666666667
+1.1762,0.4418538,0.48766200000000004,0.43319,0.5169
+1.1841333333333333,0.4414492,0.4837746666666667,0.4327933333333333,0.5129333333333334
+1.1920666666666666,0.4410446,0.47988733333333333,0.43239666666666665,0.5089666666666667
+1.2,0.44064,0.476,0.432,0.505
diff --git a/docs/examples/datcom/comparison_data/rockets/prometheus_2022_coast_for005.dat b/docs/examples/datcom/comparison_data/rockets/prometheus_2022_coast_for005.dat
new file mode 100644
index 000000000..aedf89fbb
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/prometheus_2022_coast_for005.dat
@@ -0,0 +1,220 @@
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.95,0.97,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 130974.759,1309747.59,2515951.85,3777991.72,4965660.68,6394086.74,
+    7931776.03,9398718.29,11171653.6,13073952.5,14188445.5,15337010.5,
+    16512927.3,16899317.2,17073537.,17421976.5,17770416.,18293075.3,
+    19164174.2,20906371.8,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -20.,$
+ $REFQ
+  XCG = 1.39412619,
+  SREF = 0.0153279012,
+  LREF = 0.1397,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.742,
+  DNOSE = 0.1397,
+  LCENTR = 1.487,
+  DCENTR = 0.1397,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 1.956,2.022,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.13,
+  CHORD = 0.268,0.136,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.95,0.97,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 130974.759,1309747.59,2515951.85,3777991.72,4965660.68,6394086.74,
+    7931776.03,9398718.29,11171653.6,13073952.5,14188445.5,15337010.5,
+    16512927.3,16899317.2,17073537.,17421976.5,17770416.,18293075.3,
+    19164174.2,20906371.8,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -5.,$
+ $REFQ
+  XCG = 1.39412619,
+  SREF = 0.0153279012,
+  LREF = 0.1397,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.742,
+  DNOSE = 0.1397,
+  LCENTR = 1.487,
+  DCENTR = 0.1397,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 1.956,2.022,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.13,
+  CHORD = 0.268,0.136,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.95,0.97,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 130974.759,1309747.59,2515951.85,3777991.72,4965660.68,6394086.74,
+    7931776.03,9398718.29,11171653.6,13073952.5,14188445.5,15337010.5,
+    16512927.3,16899317.2,17073537.,17421976.5,17770416.,18293075.3,
+    19164174.2,20906371.8,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 0.,$
+ $REFQ
+  XCG = 1.39412619,
+  SREF = 0.0153279012,
+  LREF = 0.1397,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.742,
+  DNOSE = 0.1397,
+  LCENTR = 1.487,
+  DCENTR = 0.1397,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 1.956,2.022,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.13,
+  CHORD = 0.268,0.136,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.95,0.97,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 130974.759,1309747.59,2515951.85,3777991.72,4965660.68,6394086.74,
+    7931776.03,9398718.29,11171653.6,13073952.5,14188445.5,15337010.5,
+    16512927.3,16899317.2,17073537.,17421976.5,17770416.,18293075.3,
+    19164174.2,20906371.8,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 5.,$
+ $REFQ
+  XCG = 1.39412619,
+  SREF = 0.0153279012,
+  LREF = 0.1397,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.742,
+  DNOSE = 0.1397,
+  LCENTR = 1.487,
+  DCENTR = 0.1397,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 1.956,2.022,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.13,
+  CHORD = 0.268,0.136,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.95,0.97,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 130974.759,1309747.59,2515951.85,3777991.72,4965660.68,6394086.74,
+    7931776.03,9398718.29,11171653.6,13073952.5,14188445.5,15337010.5,
+    16512927.3,16899317.2,17073537.,17421976.5,17770416.,18293075.3,
+    19164174.2,20906371.8,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 20.,$
+ $REFQ
+  XCG = 1.39412619,
+  SREF = 0.0153279012,
+  LREF = 0.1397,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.742,
+  DNOSE = 0.1397,
+  LCENTR = 1.487,
+  DCENTR = 0.1397,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 1.956,2.022,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.13,
+  CHORD = 0.268,0.136,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
diff --git a/docs/examples/datcom/comparison_data/rockets/prometheus_2022_powered_for005.dat b/docs/examples/datcom/comparison_data/rockets/prometheus_2022_powered_for005.dat
new file mode 100644
index 000000000..4052f33f1
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/prometheus_2022_powered_for005.dat
@@ -0,0 +1,220 @@
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.95,0.97,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 188291.497,1881563.25,3568623.41,5244520.08,6721167.22,8362587.5,
+    9976551.33,11382896.5,12923429.4,14407197.7,15200094.4,15969747.9,
+    16726333.9,16976222.2,17151234.8,17501260.,17851285.2,18376323.,
+    19251386.,21001512.,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -20.,$
+ $REFQ
+  XCG = 1.49563653,
+  SREF = 0.0153279012,
+  LREF = 0.1397,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.742,
+  DNOSE = 0.1397,
+  LCENTR = 1.487,
+  DCENTR = 0.1397,
+  BASE = .FALSE.,
+  DEXIT = 0.054,$
+ $FINSET1
+  XLE = 1.956,2.022,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.13,
+  CHORD = 0.268,0.136,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.95,0.97,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 188291.497,1881563.25,3568623.41,5244520.08,6721167.22,8362587.5,
+    9976551.33,11382896.5,12923429.4,14407197.7,15200094.4,15969747.9,
+    16726333.9,16976222.2,17151234.8,17501260.,17851285.2,18376323.,
+    19251386.,21001512.,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = -5.,$
+ $REFQ
+  XCG = 1.49563653,
+  SREF = 0.0153279012,
+  LREF = 0.1397,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.742,
+  DNOSE = 0.1397,
+  LCENTR = 1.487,
+  DCENTR = 0.1397,
+  BASE = .FALSE.,
+  DEXIT = 0.054,$
+ $FINSET1
+  XLE = 1.956,2.022,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.13,
+  CHORD = 0.268,0.136,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.95,0.97,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 188291.497,1881563.25,3568623.41,5244520.08,6721167.22,8362587.5,
+    9976551.33,11382896.5,12923429.4,14407197.7,15200094.4,15969747.9,
+    16726333.9,16976222.2,17151234.8,17501260.,17851285.2,18376323.,
+    19251386.,21001512.,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 0.,$
+ $REFQ
+  XCG = 1.49563653,
+  SREF = 0.0153279012,
+  LREF = 0.1397,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.742,
+  DNOSE = 0.1397,
+  LCENTR = 1.487,
+  DCENTR = 0.1397,
+  BASE = .FALSE.,
+  DEXIT = 0.054,$
+ $FINSET1
+  XLE = 1.956,2.022,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.13,
+  CHORD = 0.268,0.136,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.95,0.97,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 188291.497,1881563.25,3568623.41,5244520.08,6721167.22,8362587.5,
+    9976551.33,11382896.5,12923429.4,14407197.7,15200094.4,15969747.9,
+    16726333.9,16976222.2,17151234.8,17501260.,17851285.2,18376323.,
+    19251386.,21001512.,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 5.,$
+ $REFQ
+  XCG = 1.49563653,
+  SREF = 0.0153279012,
+  LREF = 0.1397,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.742,
+  DNOSE = 0.1397,
+  LCENTR = 1.487,
+  DCENTR = 0.1397,
+  BASE = .FALSE.,
+  DEXIT = 0.054,$
+ $FINSET1
+  XLE = 1.956,2.022,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.13,
+  CHORD = 0.268,0.136,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.1,0.19,0.28,0.36,0.45,0.54,0.62,0.71,0.8,0.85,0.9,0.95,0.97,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 188291.497,1881563.25,3568623.41,5244520.08,6721167.22,8362587.5,
+    9976551.33,11382896.5,12923429.4,14407197.7,15200094.4,15969747.9,
+    16726333.9,16976222.2,17151234.8,17501260.,17851285.2,18376323.,
+    19251386.,21001512.,
+  NALPHA = 19.,
+  ALPHA = -20.,-10.,-5.,-2.,-1.,-0.5,-0.25,-0.1,-0.05,0.,0.05,0.1,0.25,0.5,
+    1.,2.,5.,10.,20.,
+  BETA = 20.,$
+ $REFQ
+  XCG = 1.49563653,
+  SREF = 0.0153279012,
+  LREF = 0.1397,$
+ $AXIBOD
+  TNOSE = KARMAN,
+  LNOSE = 0.742,
+  DNOSE = 0.1397,
+  LCENTR = 1.487,
+  DCENTR = 0.1397,
+  BASE = .FALSE.,
+  DEXIT = 0.054,$
+ $FINSET1
+  XLE = 1.956,2.022,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.13,
+  CHORD = 0.268,0.136,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
diff --git a/docs/examples/datcom/comparison_data/rockets/prometheus_2022_provenance.json b/docs/examples/datcom/comparison_data/rockets/prometheus_2022_provenance.json
new file mode 100644
index 000000000..784afafc2
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/prometheus_2022_provenance.json
@@ -0,0 +1,376 @@
+{
+  "construction_cells": [
+    2,
+    4,
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+  "atmosphere_iterations": [
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+    },
+    {
+      "pass": 1,
+      "apogee_agl_m": 4229.19676461329,
+      "reference": "previous DATCOM trajectory"
+    }
+  ],
+  "atmosphere_method": "RocketPy reference ascent rho*a*M/mu; native REN",
+  "source_notebook": "docs/examples/prometheus_2022_flight_sim.ipynb",
+  "source_sha256": "44e5dfd07323771d54dec3bfb596f943620915d81f162a6b8cc25c3ea1438186",
+  "construction_assumption": "carbon fibre and vinyl when unspecified; no numerical roughness inferred",
+  "fin_assumption": "2% HEX, 5% bevels, sharp edges, auto body attachment",
+  "powered_base_drag": "nozzle",
+  "damp": true
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/prometheus_2022_result.json b/docs/examples/datcom/comparison_data/rockets/prometheus_2022_result.json
new file mode 100644
index 000000000..cb0288a5d
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/prometheus_2022_result.json
@@ -0,0 +1,49 @@
+{
+  "rocket": "prometheus_2022",
+  "actual_agl_m": 3898.37,
+  "reported_agl_m": 3898.37,
+  "saved_first_agl_m": 4188.81,
+  "reference_kind": "notebook reported apogee",
+  "telemetry_max_agl_m": 3903.77,
+  "notes": "Telemetry QA flags 2 samples with altitude jumps above 2000 m/s; raw maximum 3903.770 m is retained separately. | Telemetry maximum 3903.770 m differs from notebook reported value 3898.370 m; both retained. | Prometheus prose says June 2022; source environment and local reanalysis are June 2023. Both reruns preserve source conditions.",
+  "status": "paired untuned comparison",
+  "reason": "",
+  "ordinary_agl_m": 4191.288687549948,
+  "ordinary_apogee_time_s": 29.12493947732805,
+  "ordinary_peak_vertical_velocity_m_s": 322.07071634939507,
+  "ordinary_initial_static_margin_cal": 1.0415367273926033,
+  "ordinary_coast_cd_mach06": 0.351875,
+  "ordinary_max_mach": 0.955532649219189,
+  "ordinary_ascent_altitude_rmse_m": 271.52675273853276,
+  "source_adaptations": "",
+  "atmosphere_refinements": 1,
+  "atmosphere_last_apogee_change_m": 0.09233800101083034,
+  "datcom_agl_m": 4229.19676461329,
+  "datcom_apogee_time_s": 29.30969600583538,
+  "datcom_peak_vertical_velocity_m_s": 324.05991606871703,
+  "datcom_initial_static_margin_cal": 1.7889093089472907,
+  "datcom_coast_cd_mach06": 0.33475000000000005,
+  "datcom_max_mach": 0.9614276912301178,
+  "datcom_ascent_altitude_rmse_m": 287.18960219155747,
+  "samples": 497,
+  "outside_mach": 0,
+  "outside_alpha": 0,
+  "outside_beta": 0,
+  "max_abs_alpha_deg": 0.7103949840277883,
+  "max_abs_beta_deg": 0.745924092897197,
+  "solver_mach_excursion": true,
+  "solver_alpha_excursion": true,
+  "solver_beta_excursion": true,
+  "mass_cg_match": true,
+  "ordinary_error_m": 292.9186875499481,
+  "ordinary_error_pct": 7.513875993041916,
+  "datcom_error_m": 330.8267646132899,
+  "datcom_error_pct": 8.486284385866142,
+  "telemetry_qa_max_agl_m": 3903.77,
+  "telemetry_qa_rejected_count": 2,
+  "ordinary_vs_telemetry_pct": 7.365154390498119,
+  "datcom_vs_telemetry_pct": 8.33621767197581,
+  "rmse_start_s": 0,
+  "rmse_end_s": 29.114298032463147,
+  "within_sampled_envelope": true
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/prometheus_2022_telemetry.csv b/docs/examples/datcom/comparison_data/rockets/prometheus_2022_telemetry.csv
new file mode 100644
index 000000000..620c37abe
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/prometheus_2022_telemetry.csv
@@ -0,0 +1,5713 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/prometheus_2022_velocity.csv b/docs/examples/datcom/comparison_data/rockets/prometheus_2022_velocity.csv
new file mode 100644
index 000000000..edf013c3f
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/prometheus_2022_velocity.csv
@@ -0,0 +1,7082 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/results.csv b/docs/examples/datcom/comparison_data/rockets/results.csv
new file mode 100644
index 000000000..0334e6233
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/results.csv
@@ -0,0 +1,17 @@
+rocket,actual_agl_m,reported_agl_m,saved_first_agl_m,reference_kind,telemetry_max_agl_m,notes,status,reason,ordinary_agl_m,ordinary_apogee_time_s,ordinary_peak_vertical_velocity_m_s,ordinary_initial_static_margin_cal,ordinary_coast_cd_mach06,ordinary_max_mach,ordinary_ascent_altitude_rmse_m,source_adaptations,atmosphere_refinements,atmosphere_last_apogee_change_m,datcom_agl_m,datcom_apogee_time_s,datcom_peak_vertical_velocity_m_s,datcom_initial_static_margin_cal,datcom_coast_cd_mach06,datcom_max_mach,datcom_ascent_altitude_rmse_m,samples,outside_mach,outside_alpha,outside_beta,max_abs_alpha_deg,max_abs_beta_deg,solver_mach_excursion,solver_alpha_excursion,solver_beta_excursion,mass_cg_match,ordinary_error_m,ordinary_error_pct,datcom_error_m,datcom_error_pct,telemetry_qa_max_agl_m,telemetry_qa_rejected_count,ordinary_vs_telemetry_pct,datcom_vs_telemetry_pct,rmse_start_s,rmse_end_s,within_sampled_envelope
+andromeda,3443.0,3443.0,3614.946,notebook reported apogee,3438.549384,Telemetry maximum 3438.549 m differs from notebook reported value 3443.000 m; both retained.,paired untuned comparison,,3615.880567942097,25.843083317328176,343.1543305235018,4.158769051891837,0.4984983643122677,1.0050934520481516,164.47060925045412,,1.0,-6.890912629645754,4170.733199067169,28.13329482306857,352.6406104190726,4.585569601745527,0.349,1.0331483789732863,438.15234503512323,497.0,0.0,0.0,0.0,4.455101471012898,3.9438542480866237,True,True,True,True,172.88056794209706,5.02121893529181,727.7331990671692,21.13660177366161,3438.549384,0.0,5.157150999989746,21.293392454216665,0.0,25.83574241251797,True
+astra,3250.0,3250.0,3043.066,notebook reported apogee,3249.46,Telemetry maximum 3249.460 m differs from notebook reported value 3250.000 m; both retained. | Source fin trailing edge overhangs the nozzle-inferred body base by 1.216 mm. Native DATCOM accepts this; the integration now validates root-chord centre attachment instead of rejecting all overhang. Geometry is unchanged.,paired untuned comparison,,3043.3679728130164,24.008756329078313,306.4797793783156,3.16256293861153,0.6538147999999999,0.892926042224684,222.49037923280986,,1.0,-4.5530442029007645,3695.9340692825294,26.805544357641757,318.5471261391443,3.477476916191179,0.4265,0.92839198452266,215.54458729050853,480.0,0.0,0.0,0.0,8.95582043567763,9.383306642057704,True,True,True,True,-206.63202718698358,-6.357908528830264,445.93406928252944,13.721048285616291,3249.46,0.0,-6.342346949554189,13.739946615207742,0.0,23.990962200089378,True
+bella_lui,458.971,,460.54,telemetry AGL maximum (QA),458.971,,paired untuned comparison,,460.57389565932874,10.607916503445066,86.17870117421616,2.776966165584537,0.43,0.2544470871701163,4.730090709810138,,1.0,9.736509809954441e-06,465.8765076067215,10.675120658701045,86.47349849471236,3.303739452169553,0.3457692307692308,0.2554070587748375,5.162903677983363,427.0,0.0,0.0,0.0,4.441613641629917,2.9112883549730375,True,True,True,True,1.6028956593287376,0.34923680566500664,6.905507606721471,1.5045629477072564,458.971,0.0,0.3492368056650097,1.5045629477072664,0.0,10.53,True
+camoes,3015.0,3015.0,3003.284,notebook reported apogee,3015.996337890625,Telemetry maximum 3015.996 m differs from notebook reported value 3015.000 m; both retained.,ordinary only,Source active air brakes override complete rocket drag. Combining their whole-vehicle CD with DATCOM would double-count base drag; no controlled DATCOM result claimed.,3004.10382797431,23.03691562903868,327.4402911475654,3.7616831285769776,0.7036,0.9672822932315445,133.14444925478682,,,,,,,,,,,,,,,,,,,,,-10.896172025689793,-0.36139874048722365,,,3015.996337890625,0.0,-0.3943144680551014,,0.0,23.03691562903868,
+cavour,2789.0,2789.0,2817.405,notebook reported apogee,2789.0,,paired untuned comparison,,2818.1348894226226,22.52457463326182,304.6396378918068,4.279860429594885,0.583,0.9017549966473172,57.14208739821666,,1.0,-2.7223603801689933,3251.608548140568,24.312660883196,319.05542973964634,5.465305853006995,0.44825000000000004,0.9445859958835042,284.6998716927426,497.0,0.0,0.0,0.0,1.9473736165566062,12.065683729942409,True,True,True,True,29.134889422622564,1.0446356910226806,462.608548140568,16.58689667051158,2789.0,0.0,1.0446356910226884,16.586896670511585,0.0,22.489211316956297,True
+defiance,9308.318,9308.318,9238.009,notebook reported apogee,,"Source fin root centre is z=-0.025 m, aft of the tail base z=0.0403 m; native root attachment is outside the declared body.",ordinary only,ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,9243.891367330605,44.18119446865966,438.71139566437614,7.9601902493275025,0.668,1.3681759374499065,,,,,,,,,,,,,,,,,,,,,,-64.42663266939417,-0.6921404347100536,,,,,,,,,
+erebus,3020.0,3020.0,2750.904,notebook reported apogee,3001.75136,Telemetry maximum 3001.751 m differs from notebook reported value 3020.000 m; both retained.,paired untuned comparison,,2751.1556122225365,22.17286643393269,294.1707251884576,2.9374083445614505,0.531,0.8634835538004397,173.69041364963655,,1.0,-4.843469621916938,3253.422625182214,24.419938482483815,307.31659427636407,4.450320086266694,0.376,0.9025725207744214,119.78288536278599,498.0,0.0,0.0,0.0,12.023825633907418,9.12247497249757,True,True,True,True,-268.84438777746345,-8.902132045611372,233.42262518221378,7.729225999411052,3001.75136,0.0,-8.348317955870389,8.38414761914903,0.0,22.14074422411866,True
+genesis,2916.0,2916.0,3044.091,notebook reported apogee,2916.691650390625,Telemetry maximum 2916.692 m differs from notebook reported value 2916.000 m; both retained.,paired untuned comparison,,3044.6857862664306,24.170070527773692,290.6363275291183,3.37721309359005,0.55,0.8522776236704187,104.35940713968827,,1.0,-2.088661131954723,3297.2391270686403,25.178132490879275,299.2620317383252,3.7682700796928534,0.47475,0.8776710952854886,236.1872905681145,497.0,0.0,0.0,0.0,3.3632935153000667,11.894942323656261,True,True,True,True,128.68578626643057,4.41309280749076,381.2391270686403,13.074044138156388,2916.691650390625,0.0,4.38833278309223,13.047230296937617,0.0,24.12904363709264,True
+halcyon,3450.0,3450.0,3212.856,notebook reported apogee,3470.8251953125,Telemetry QA flags 5 samples with altitude jumps above 2000 m/s; raw maximum 3470.825 m is retained separately. | Telemetry maximum 3470.825 m differs from notebook reported value 3450.000 m; both retained.,paired untuned comparison,,3214.238895757538,26.371836502851696,275.5714950426811,3.0192578745448033,0.426,0.812113342368714,112.69062830123777,Read original .eng numeric samples directly: source already has t=0; avoid the importer's extra duplicate zero-time row. No measured sample or motor equation changed.,1.0,-1.122512222840669,3530.231782008296,27.79754660231069,280.94965050790404,4.410132986423363,0.319,0.8293761850832471,69.46314134752838,495.0,0.0,0.0,0.0,7.051663487140428,14.276458017233338,False,True,True,True,-235.7611042424619,-6.8336551954336775,80.2317820082958,2.3255588987911824,3470.8251953125,5.0,-7.39265981765641,1.7115983477366425,0.0,26.36134002785797,True
+hedy,5231.53,,5246.918,saved measured-apogee summary,,"Referenced telemetry file is absent: data/rockets/tust/cats_tust/filteredDataInfo.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/gnssInfo.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/flightInfo.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/imu.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/baro.csv (cell 24). | Hedy end comparison uses simulated ASL 5406.918 m against measured AGL 5231.53 m; saved simulated AGL is 5246.918 m, a 160 m datum discrepancy.",archived comparison only,"Source data/rockets/tust is absent; CoolProp is not installed, and the negative-span fin root cutout requires geometry support.",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+juno3,3213.0,3213.0,2976.849,notebook reported apogee,10700.59,"Telemetry QA flags 2 samples with altitude jumps above 2000 m/s; raw maximum 10700.590 m is retained separately. | Telemetry maximum 10700.590 m differs from notebook reported value 3213.000 m; both retained. | Source fin airfoil is an empirical lift curve (cell 10), not a supplied section contour. The generic 2% HEX DATCOM assumption does not reproduce that curve; source-drag substitution therefore still has a lateral-model discrepancy.",paired untuned comparison,,3008.581569789987,26.491920030008096,241.2427490025272,3.2206482971493466,0.368,0.7308763878428423,229.55884227968258,,1.0,-0.03861187111760955,3056.4921836297026,26.635000914699194,243.89859046993098,4.0067340523842745,0.37450000000000006,0.7312932218217086,209.292777372015,486.0,0.0,0.0,0.0,43.59333257202937,5.4737412566148915,False,True,True,True,-204.41843021001296,-6.362229387177496,-156.5078163702974,-4.871080497052518,3213.375,2.0,-6.373156889874753,-4.882182016425018,0.0,26.3,True
+lince,3587.0,3587.0,3284.107,notebook reported apogee,3668.5205078125,Telemetry QA flags 69 samples with altitude jumps above 2000 m/s; raw maximum 3668.521 m is retained separately. | Telemetry maximum 3668.521 m differs from notebook reported value 3587.000 m; both retained. | Lince has three saved trajectories. Compare first/pre-deployment ascent only; retain all saved apogees separately.,paired untuned comparison,,3285.089381541881,24.728341685630557,317.77081353823803,2.9203959364705625,0.65562,0.936438877536046,180.22020526338466,,1.0,-6.875566491943573,4254.30425999591,28.7853935112826,342.53953112405384,3.962666916625163,0.38175,1.0102057901013621,299.53187203569485,498.0,0.0,0.0,0.0,3.100325929162926,3.920227060235464,True,True,True,True,-301.9106184581192,-8.416800068528554,667.3042599959099,18.60340841917786,3628.76416015625,69.0,-9.470849122351643,17.23837847353311,0.0,24.7074627638509,True
+ndrt_2020,1316.736,1316.736,1293.43,notebook reported apogee,1320.3570240000001,"NDRT source uses approximate ft conversion /3.281; comparison uses exact 0.3048 m/ft. | Telemetry maximum 1320.357 m differs from notebook reported value 1316.736 m; both retained. | Source transition is internal at z=1.2..1.327 m with fins near z=3.05 m. The current DATCOM builder represents only an aft tail, not the required downstream smaller cylinder.",ordinary only,ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,1293.5184123380945,16.64978369813489,171.66118644734854,4.016381564609376,0.44,0.52460108376313,42.9525631238651,,,,,,,,,,,,,,,,,,,,,-23.217587661905554,-1.763268237665375,,,1320.3570240000001,0.0,-2.0326783721419828,,0.04375,16.64978369813489,
+prometheus_2022,3898.37,3898.37,4188.81,notebook reported apogee,3903.77,Telemetry QA flags 2 samples with altitude jumps above 2000 m/s; raw maximum 3903.770 m is retained separately. | Telemetry maximum 3903.770 m differs from notebook reported value 3898.370 m; both retained. | Prometheus prose says June 2022; source environment and local reanalysis are June 2023. Both reruns preserve source conditions.,paired untuned comparison,,4191.288687549948,29.12493947732805,322.07071634939507,1.0415367273926033,0.351875,0.955532649219189,271.52675273853276,,1.0,0.09233800101083034,4229.19676461329,29.30969600583538,324.05991606871703,1.7889093089472907,0.33475000000000005,0.9614276912301178,287.18960219155747,497.0,0.0,0.0,0.0,0.7103949840277883,0.745924092897197,True,True,True,True,292.9186875499481,7.513875993041916,330.8267646132899,8.486284385866142,3903.77,2.0,7.365154390498119,8.33621767197581,0.0,29.114298032463147,True
+valetudo,860.0,860.0,815.26,saved measured-apogee summary,,Valetudo flight recordings were lost; only recovered scalar apogee/drift values remain.,paired untuned comparison,,815.0526424633565,13.977332377135696,123.31112741231497,2.951130333277495,1.128,0.3680218288920357,,,1.0,0.38337486952264044,943.6190073224011,15.242452283301883,126.09986587804391,4.156583455241121,0.43133333333333335,0.37639511692688415,,448.0,0.0,0.0,0.0,15.80193704194847,5.1181201273022525,True,True,True,True,-44.947357536643494,-5.226436922865522,83.6190073224011,9.72314038632571,,,,,,,True
+valkyrie,2098.02,2098.02,2247.849,notebook reported apogee,2059.0244140625,Telemetry maximum 2059.024 m differs from notebook reported value 2098.020 m; both retained. | Valkyrie prose says 2025 but source environment says 2026 and fetches live Windy weather; no archived profile is supplied.,unavailable source model,SourceUnavailable: Source requests live weather with no archived profile; not replaced by invented weather.,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2059.0244140625,0.0,,,,,
diff --git a/docs/examples/datcom/comparison_data/rockets/valetudo_altitude.csv b/docs/examples/datcom/comparison_data/rockets/valetudo_altitude.csv
new file mode 100644
index 000000000..de9a87d2c
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/valetudo_altitude.csv
@@ -0,0 +1,602 @@
+time_s,ordinary_agl_m,datcom_agl_m,ordinary_vz_m_s,datcom_vz_m_s,ordinary_margin_cal,datcom_margin_cal
+0.0,0.0,0.0,0.0,0.0,2.951130333277495,4.156583455241121
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+0.050808174277672945,4.547473508864641e-13,4.547473508864641e-13,-1.8627942358965036e-09,-1.8629823534474762e-09,2.951272712432416,4.156725834396042
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+0.3810613070825471,-1.4438228390645236e-11,-1.4551915228366852e-11,6.339308242140476e-08,6.339948428328726e-08,2.952831198252275,4.1582843202159
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diff --git a/docs/examples/datcom/comparison_data/rockets/valetudo_cd.csv b/docs/examples/datcom/comparison_data/rockets/valetudo_cd.csv
new file mode 100644
index 000000000..09ef6fe7f
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/valetudo_cd.csv
@@ -0,0 +1,152 @@
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+0.9223333333333332,1.4762333333333328,0.40884666666666664,1.4822333333333328,0.4510799999999999
+0.9302666666666666,1.5004799999999998,0.41376533333333326,1.5064799999999998,0.4567919999999999
+0.9381999999999999,1.5147599999999999,0.41868399999999995,1.52076,0.46250399999999997
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+1.081,1.7192,0.49824,1.7283,0.558
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+1.1047999999999998,1.72592,0.499096,1.7354399999999999,0.557952
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+1.1444666666666665,1.71398,0.49988933333333335,1.7229800000000002,0.5575553333333334
+1.1523999999999999,1.7066000000000001,0.500048,1.7156,0.5574760000000001
+1.1603333333333332,1.6987,0.5002066666666667,1.7077,0.5573966666666668
+1.1682666666666666,1.69156,0.5003653333333333,1.70056,0.5573173333333333
+1.1762,1.68442,0.500524,1.69342,0.557238
+1.1841333333333333,1.67728,0.5006826666666667,1.68628,0.5571586666666667
+1.1920666666666666,1.67014,0.5008413333333334,1.67914,0.5570793333333334
+1.2,1.663,0.501,1.672,0.557
diff --git a/docs/examples/datcom/comparison_data/rockets/valetudo_coast_for005.dat b/docs/examples/datcom/comparison_data/rockets/valetudo_coast_for005.dat
new file mode 100644
index 000000000..92e5d79d7
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/valetudo_coast_for005.dat
@@ -0,0 +1,220 @@
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.06,0.1,0.14,0.17,0.24,0.28,0.31,0.34,0.38,0.59,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 201178.395,1208477.86,2020836.78,2843131.67,3468624.39,4966263.57,
+    5851154.81,6529024.87,7160865.99,8003320.81,12426208.6,16849096.5,
+    18955233.5,20008302.,20640143.2,21061370.6,21482598.,22114439.1,
+    23167507.6,25273644.7,
+  NALPHA = 19.,
+  ALPHA = -20.57,-10.28,-5.14,-2.06,-1.03,-0.51,-0.26,-0.1,-0.05,0.,0.05,
+    0.1,0.26,0.51,1.03,2.06,5.14,10.28,20.57,
+  BETA = -20.,$
+ $REFQ
+  XCG = 1.13407314,
+  SREF = 0.00514028175,
+  LREF = 0.0809,$
+ $AXIBOD
+  TNOSE = OGIVE,
+  LNOSE = 0.274,
+  DNOSE = 0.0809,
+  LCENTR = 1.884,
+  DCENTR = 0.0809,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.04,2.08,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.077,
+  CHORD = 0.058,0.018,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.06,0.1,0.14,0.17,0.24,0.28,0.31,0.34,0.38,0.59,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 201178.395,1208477.86,2020836.78,2843131.67,3468624.39,4966263.57,
+    5851154.81,6529024.87,7160865.99,8003320.81,12426208.6,16849096.5,
+    18955233.5,20008302.,20640143.2,21061370.6,21482598.,22114439.1,
+    23167507.6,25273644.7,
+  NALPHA = 19.,
+  ALPHA = -20.57,-10.28,-5.14,-2.06,-1.03,-0.51,-0.26,-0.1,-0.05,0.,0.05,
+    0.1,0.26,0.51,1.03,2.06,5.14,10.28,20.57,
+  BETA = -5.,$
+ $REFQ
+  XCG = 1.13407314,
+  SREF = 0.00514028175,
+  LREF = 0.0809,$
+ $AXIBOD
+  TNOSE = OGIVE,
+  LNOSE = 0.274,
+  DNOSE = 0.0809,
+  LCENTR = 1.884,
+  DCENTR = 0.0809,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.04,2.08,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.077,
+  CHORD = 0.058,0.018,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.06,0.1,0.14,0.17,0.24,0.28,0.31,0.34,0.38,0.59,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 201178.395,1208477.86,2020836.78,2843131.67,3468624.39,4966263.57,
+    5851154.81,6529024.87,7160865.99,8003320.81,12426208.6,16849096.5,
+    18955233.5,20008302.,20640143.2,21061370.6,21482598.,22114439.1,
+    23167507.6,25273644.7,
+  NALPHA = 19.,
+  ALPHA = -20.57,-10.28,-5.14,-2.06,-1.03,-0.51,-0.26,-0.1,-0.05,0.,0.05,
+    0.1,0.26,0.51,1.03,2.06,5.14,10.28,20.57,
+  BETA = 0.,$
+ $REFQ
+  XCG = 1.13407314,
+  SREF = 0.00514028175,
+  LREF = 0.0809,$
+ $AXIBOD
+  TNOSE = OGIVE,
+  LNOSE = 0.274,
+  DNOSE = 0.0809,
+  LCENTR = 1.884,
+  DCENTR = 0.0809,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.04,2.08,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.077,
+  CHORD = 0.058,0.018,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.06,0.1,0.14,0.17,0.24,0.28,0.31,0.34,0.38,0.59,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 201178.395,1208477.86,2020836.78,2843131.67,3468624.39,4966263.57,
+    5851154.81,6529024.87,7160865.99,8003320.81,12426208.6,16849096.5,
+    18955233.5,20008302.,20640143.2,21061370.6,21482598.,22114439.1,
+    23167507.6,25273644.7,
+  NALPHA = 19.,
+  ALPHA = -20.57,-10.28,-5.14,-2.06,-1.03,-0.51,-0.26,-0.1,-0.05,0.,0.05,
+    0.1,0.26,0.51,1.03,2.06,5.14,10.28,20.57,
+  BETA = 5.,$
+ $REFQ
+  XCG = 1.13407314,
+  SREF = 0.00514028175,
+  LREF = 0.0809,$
+ $AXIBOD
+  TNOSE = OGIVE,
+  LNOSE = 0.274,
+  DNOSE = 0.0809,
+  LCENTR = 1.884,
+  DCENTR = 0.0809,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.04,2.08,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.077,
+  CHORD = 0.058,0.018,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.06,0.1,0.14,0.17,0.24,0.28,0.31,0.34,0.38,0.59,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 201178.395,1208477.86,2020836.78,2843131.67,3468624.39,4966263.57,
+    5851154.81,6529024.87,7160865.99,8003320.81,12426208.6,16849096.5,
+    18955233.5,20008302.,20640143.2,21061370.6,21482598.,22114439.1,
+    23167507.6,25273644.7,
+  NALPHA = 19.,
+  ALPHA = -20.57,-10.28,-5.14,-2.06,-1.03,-0.51,-0.26,-0.1,-0.05,0.,0.05,
+    0.1,0.26,0.51,1.03,2.06,5.14,10.28,20.57,
+  BETA = 20.,$
+ $REFQ
+  XCG = 1.13407314,
+  SREF = 0.00514028175,
+  LREF = 0.0809,$
+ $AXIBOD
+  TNOSE = OGIVE,
+  LNOSE = 0.274,
+  DNOSE = 0.0809,
+  LCENTR = 1.884,
+  DCENTR = 0.0809,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 2.04,2.08,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.077,
+  CHORD = 0.058,0.018,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
diff --git a/docs/examples/datcom/comparison_data/rockets/valetudo_powered_for005.dat b/docs/examples/datcom/comparison_data/rockets/valetudo_powered_for005.dat
new file mode 100644
index 000000000..6e2e79d50
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/valetudo_powered_for005.dat
@@ -0,0 +1,220 @@
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.06,0.1,0.14,0.17,0.24,0.28,0.31,0.34,0.38,0.59,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 219350.475,1315688.72,2191873.9,3066947.72,3722406.66,5247924.85,
+    6115702.5,6531057.32,7231716.15,8229426.14,12777266.9,17325107.7,
+    19490746.1,20573565.4,21223256.9,21656384.6,22089512.3,22739203.8,
+    23822023.,25987661.5,
+  NALPHA = 19.,
+  ALPHA = -20.57,-10.28,-5.14,-2.06,-1.03,-0.51,-0.26,-0.1,-0.05,0.,0.05,
+    0.1,0.26,0.51,1.03,2.06,5.14,10.28,20.57,
+  BETA = -20.,$
+ $REFQ
+  XCG = 1.2220811,
+  SREF = 0.00514028175,
+  LREF = 0.0809,$
+ $AXIBOD
+  TNOSE = OGIVE,
+  LNOSE = 0.274,
+  DNOSE = 0.0809,
+  LCENTR = 1.884,
+  DCENTR = 0.0809,
+  BASE = .FALSE.,
+  DEXIT = 0.043284,$
+ $FINSET1
+  XLE = 2.04,2.08,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.077,
+  CHORD = 0.058,0.018,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.06,0.1,0.14,0.17,0.24,0.28,0.31,0.34,0.38,0.59,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 219350.475,1315688.72,2191873.9,3066947.72,3722406.66,5247924.85,
+    6115702.5,6531057.32,7231716.15,8229426.14,12777266.9,17325107.7,
+    19490746.1,20573565.4,21223256.9,21656384.6,22089512.3,22739203.8,
+    23822023.,25987661.5,
+  NALPHA = 19.,
+  ALPHA = -20.57,-10.28,-5.14,-2.06,-1.03,-0.51,-0.26,-0.1,-0.05,0.,0.05,
+    0.1,0.26,0.51,1.03,2.06,5.14,10.28,20.57,
+  BETA = -5.,$
+ $REFQ
+  XCG = 1.2220811,
+  SREF = 0.00514028175,
+  LREF = 0.0809,$
+ $AXIBOD
+  TNOSE = OGIVE,
+  LNOSE = 0.274,
+  DNOSE = 0.0809,
+  LCENTR = 1.884,
+  DCENTR = 0.0809,
+  BASE = .FALSE.,
+  DEXIT = 0.043284,$
+ $FINSET1
+  XLE = 2.04,2.08,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.077,
+  CHORD = 0.058,0.018,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.06,0.1,0.14,0.17,0.24,0.28,0.31,0.34,0.38,0.59,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 219350.475,1315688.72,2191873.9,3066947.72,3722406.66,5247924.85,
+    6115702.5,6531057.32,7231716.15,8229426.14,12777266.9,17325107.7,
+    19490746.1,20573565.4,21223256.9,21656384.6,22089512.3,22739203.8,
+    23822023.,25987661.5,
+  NALPHA = 19.,
+  ALPHA = -20.57,-10.28,-5.14,-2.06,-1.03,-0.51,-0.26,-0.1,-0.05,0.,0.05,
+    0.1,0.26,0.51,1.03,2.06,5.14,10.28,20.57,
+  BETA = 0.,$
+ $REFQ
+  XCG = 1.2220811,
+  SREF = 0.00514028175,
+  LREF = 0.0809,$
+ $AXIBOD
+  TNOSE = OGIVE,
+  LNOSE = 0.274,
+  DNOSE = 0.0809,
+  LCENTR = 1.884,
+  DCENTR = 0.0809,
+  BASE = .FALSE.,
+  DEXIT = 0.043284,$
+ $FINSET1
+  XLE = 2.04,2.08,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.077,
+  CHORD = 0.058,0.018,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.06,0.1,0.14,0.17,0.24,0.28,0.31,0.34,0.38,0.59,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 219350.475,1315688.72,2191873.9,3066947.72,3722406.66,5247924.85,
+    6115702.5,6531057.32,7231716.15,8229426.14,12777266.9,17325107.7,
+    19490746.1,20573565.4,21223256.9,21656384.6,22089512.3,22739203.8,
+    23822023.,25987661.5,
+  NALPHA = 19.,
+  ALPHA = -20.57,-10.28,-5.14,-2.06,-1.03,-0.51,-0.26,-0.1,-0.05,0.,0.05,
+    0.1,0.26,0.51,1.03,2.06,5.14,10.28,20.57,
+  BETA = 5.,$
+ $REFQ
+  XCG = 1.2220811,
+  SREF = 0.00514028175,
+  LREF = 0.0809,$
+ $AXIBOD
+  TNOSE = OGIVE,
+  LNOSE = 0.274,
+  DNOSE = 0.0809,
+  LCENTR = 1.884,
+  DCENTR = 0.0809,
+  BASE = .FALSE.,
+  DEXIT = 0.043284,$
+ $FINSET1
+  XLE = 2.04,2.08,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.077,
+  CHORD = 0.058,0.018,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
+DIM M
+ $FLTCON
+  NMACH = 20.,
+  MACH = 0.01,0.06,0.1,0.14,0.17,0.24,0.28,0.31,0.34,0.38,0.59,0.8,0.9,0.95,
+    0.98,1.,1.02,1.05,1.1,1.2,
+  REN = 219350.475,1315688.72,2191873.9,3066947.72,3722406.66,5247924.85,
+    6115702.5,6531057.32,7231716.15,8229426.14,12777266.9,17325107.7,
+    19490746.1,20573565.4,21223256.9,21656384.6,22089512.3,22739203.8,
+    23822023.,25987661.5,
+  NALPHA = 19.,
+  ALPHA = -20.57,-10.28,-5.14,-2.06,-1.03,-0.51,-0.26,-0.1,-0.05,0.,0.05,
+    0.1,0.26,0.51,1.03,2.06,5.14,10.28,20.57,
+  BETA = 20.,$
+ $REFQ
+  XCG = 1.2220811,
+  SREF = 0.00514028175,
+  LREF = 0.0809,$
+ $AXIBOD
+  TNOSE = OGIVE,
+  LNOSE = 0.274,
+  DNOSE = 0.0809,
+  LCENTR = 1.884,
+  DCENTR = 0.0809,
+  BASE = .FALSE.,
+  DEXIT = 0.043284,$
+ $FINSET1
+  XLE = 2.04,2.08,
+  NPANEL = 3.,
+  PHIF = 0.,120.,240.,
+  GAM = 0.,0.,0.,
+  SSPAN = 0.,0.077,
+  CHORD = 0.058,0.018,
+  SECTYP = HEX,
+  ZUPPER = 0.01,0.01,
+  ZLOWER = 0.01,0.01,
+  LER = 0.,0.,
+  LMAXU = 0.05,0.05,
+  LFLATU = 0.9,0.9,
+  LMAXL = 0.05,0.05,
+  LFLATL = 0.9,0.9,$
+ $DEFLCT
+  DELTA1 = -0.,-0.,-0.,$
+DAMP
+NEXT CASE
diff --git a/docs/examples/datcom/comparison_data/rockets/valetudo_provenance.json b/docs/examples/datcom/comparison_data/rockets/valetudo_provenance.json
new file mode 100644
index 000000000..077e813aa
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/valetudo_provenance.json
@@ -0,0 +1,382 @@
+{
+  "construction_cells": [
+    2,
+    3,
+    6,
+    9,
+    11,
+    14,
+    15,
+    18,
+    20,
+    22,
+    24,
+    25
+  ],
+  "calibration": {
+    "roughness_height": 0.0,
+    "leading_edge_radius": null
+  },
+  "source_adaptations": [],
+  "mach": [
+    0.01,
+    0.06,
+    0.1,
+    0.14,
+    0.17,
+    0.24,
+    0.28,
+    0.31,
+    0.34,
+    0.38,
+    0.59,
+    0.8,
+    0.9,
+    0.95,
+    0.98,
+    1.0,
+    1.02,
+    1.05,
+    1.1,
+    1.2
+  ],
+  "alpha": [
+    -20.57,
+    -10.28,
+    -5.14,
+    -2.06,
+    -1.03,
+    -0.51,
+    -0.26,
+    -0.1,
+    -0.05,
+    0.0,
+    0.05,
+    0.1,
+    0.26,
+    0.51,
+    1.03,
+    2.06,
+    5.14,
+    10.28,
+    20.57
+  ],
+  "beta": [
+    -20,
+    -5,
+    0,
+    5,
+    20
+  ],
+  "altitude_schedules_m_asl": {
+    "power_on": [
+      668.2451556812705,
+      671.7128099059793,
+      676.4398801362132,
+      682.4573101603798,
+      687.6171073922815,
+      702.7939823289616,
+      715.1655825779246,
+      1110.517002272916,
+      1006.3620428081623,
+      808.9085724369791,
+      808.9085724369791,
+      808.9085724369791,
+      808.9085724369791,
+      808.9085724369791,
+      808.9085724369791,
+      808.9085724369791,
+      808.9085724369791,
+      808.9085724369791,
+      808.9085724369791,
+      808.9085724369791
+    ],
+    "power_off": [
+      1611.2356324528785,
+      1598.6589757835636,
+      1562.761599621697,
+      1509.558428492431,
+      1458.7423406044002,
+      1306.149554864665,
+      1199.3282219926384,
+      1113.9130697237874,
+      1113.9130697237874,
+      1113.9130697237874,
+      1113.9130697237874,
+      1113.9130697237874,
+      1113.9130697237874,
+      1113.9130697237874,
+      1113.9130697237874,
+      1113.9130697237874,
+      1113.9130697237874,
+      1113.9130697237874,
+      1113.9130697237874,
+      1113.9130697237874
+    ]
+  },
+  "atmosphere": {
+    "mach": [
+      0.01,
+      0.06,
+      0.1,
+      0.14,
+      0.17,
+      0.24,
+      0.28,
+      0.31,
+      0.34,
+      0.38,
+      0.59,
+      0.8,
+      0.9,
+      0.95,
+      0.98,
+      1.0,
+      1.02,
+      1.05,
+      1.1,
+      1.2
+    ],
+    "alpha": [
+      -20.57,
+      -10.28,
+      -5.14,
+      -2.06,
+      -1.03,
+      -0.51,
+      -0.26,
+      -0.1,
+      -0.05,
+      0.0,
+      0.05,
+      0.1,
+      0.26,
+      0.51,
+      1.03,
+      2.06,
+      5.14,
+      10.28,
+      20.57
+    ],
+    "coverage": {
+      "scope": "rail through apogee",
+      "minimum_dynamic_pressure_pa": 100.0,
+      "excluded_low_q_samples": 178,
+      "phases": {
+        "power_on": {
+          "mach_min": -1.9191314516862737e-08,
+          "mach_max": 0.3763335399829133,
+          "loaded_alpha_max_abs_deg": 0.2699795295850288
+        },
+        "power_off": {
+          "mach_min": 0.036696416799674426,
+          "mach_max": 0.3089734070463784,
+          "loaded_alpha_max_abs_deg": 16.145273736494154
+        }
+      }
+    },
+    "reynolds": {
+      "power_on": [
+        219350.47485501677,
+        1315688.718430272,
+        2191873.8988514375,
+        3066947.720272081,
+        3722406.6638718154,
+        5247924.847114815,
+        6115702.495601609,
+        6531057.316529517,
+        7231716.151987152,
+        8229426.142495456,
+        12777266.905453471,
+        17325107.66841149,
+        19490746.126962923,
+        20573565.35623864,
+        21223256.893804073,
+        21656384.58551436,
+        22089512.277224645,
+        22739203.814790078,
+        23822023.044065796,
+        25987661.50261723
+      ],
+      "power_off": [
+        201178.39532979444,
+        1208477.8567453611,
+        2020836.781157696,
+        2843131.667754018,
+        3468624.3909234786,
+        4966263.571934043,
+        5851154.811952052,
+        6529024.874529703,
+        7160865.991419674,
+        8003320.813939636,
+        12426208.632169433,
+        16849096.45039923,
+        18955233.506699137,
+        20008302.03484909,
+        20640143.151739057,
+        21061370.56299904,
+        21482597.974259023,
+        22114439.09114899,
+        23167507.619298946,
+        25273644.67559885
+      ]
+    },
+    "altitude_m_asl": {
+      "power_on": [
+        668.2451556812705,
+        671.7128099059793,
+        676.4398801362132,
+        682.4573101603798,
+        687.6171073922815,
+        702.7939823289616,
+        715.1655825779246,
+        1110.517002272916,
+        1006.3620428081623,
+        808.9085724369791,
+        808.9085724369791,
+        808.9085724369791,
+        808.9085724369791,
+        808.9085724369791,
+        808.9085724369791,
+        808.9085724369791,
+        808.9085724369791,
+        808.9085724369791,
+        808.9085724369791,
+        808.9085724369791
+      ],
+      "power_off": [
+        1611.2356324528785,
+        1598.6589757835636,
+        1562.761599621697,
+        1509.558428492431,
+        1458.7423406044002,
+        1306.149554864665,
+        1199.3282219926384,
+        1113.9130697237874,
+        1113.9130697237874,
+        1113.9130697237874,
+        1113.9130697237874,
+        1113.9130697237874,
+        1113.9130697237874,
+        1113.9130697237874,
+        1113.9130697237874,
+        1113.9130697237874,
+        1113.9130697237874,
+        1113.9130697237874,
+        1113.9130697237874,
+        1113.9130697237874
+      ]
+    },
+    "pressure_pa": {
+      "power_on": [
+        93551.28836024513,
+        93512.25460509399,
+        93459.06553487212,
+        93391.39283150285,
+        93333.39690780263,
+        93162.97908987939,
+        93024.24802697696,
+        88678.40569794553,
+        89806.98193867871,
+        91978.48908145505,
+        91978.48908145505,
+        91978.48908145505,
+        91978.48908145505,
+        91978.48908145505,
+        91978.48908145505,
+        91978.48908145505,
+        91978.48908145505,
+        91978.48908145505,
+        91978.48908145505,
+        91978.48908145505
+      ],
+      "power_off": [
+        83412.35687651065,
+        83541.43937139772,
+        83910.77053732627,
+        84460.59127968387,
+        84988.47161807785,
+        86589.740416014,
+        87725.20469352888,
+        88641.80184357554,
+        88641.80184357554,
+        88641.80184357554,
+        88641.80184357554,
+        88641.80184357554,
+        88641.80184357554,
+        88641.80184357554,
+        88641.80184357554,
+        88641.80184357554,
+        88641.80184357554,
+        88641.80184357554,
+        88641.80184357554,
+        88641.80184357554
+      ]
+    },
+    "temperature_k": {
+      "power_on": [
+        283.81389767331984,
+        283.79139679407814,
+        283.76072382914987,
+        283.72167799079756,
+        283.68819715132986,
+        283.58971760057636,
+        283.5094408875045,
+        280.9440886418185,
+        281.619928276765,
+        282.90116233436873,
+        282.90116233436873,
+        282.90116233436873,
+        282.90116233436873,
+        282.90116233436873,
+        282.90116233436873,
+        282.90116233436873,
+        282.90116233436873,
+        282.90116233436873,
+        282.90116233436873,
+        282.90116233436873
+      ],
+      "power_off": [
+        277.6950307191735,
+        277.7766380002469,
+        278.0095685272756,
+        278.3547927195635,
+        278.6845276305475,
+        279.67467013686485,
+        280.36781130828604,
+        280.9220522741021,
+        280.9220522741021,
+        280.9220522741021,
+        280.9220522741021,
+        280.9220522741021,
+        280.9220522741021,
+        280.9220522741021,
+        280.9220522741021,
+        280.9220522741021,
+        280.9220522741021,
+        280.9220522741021,
+        280.9220522741021,
+        280.9220522741021
+      ]
+    }
+  },
+  "atmosphere_iterations": [
+    {
+      "pass": 0,
+      "apogee_agl_m": 943.2356324528785,
+      "reference": "ordinary trajectory"
+    },
+    {
+      "pass": 1,
+      "apogee_agl_m": 943.6190073224011,
+      "reference": "previous DATCOM trajectory"
+    }
+  ],
+  "atmosphere_method": "RocketPy reference ascent rho*a*M/mu; native REN",
+  "source_notebook": "docs/examples/valetudo_flight_sim.ipynb",
+  "source_sha256": "2b86db6b4846f1badf9d99a7d6825526c889e64303bee4d3c487a72ba3e69c47",
+  "construction_assumption": "carbon fibre and vinyl when unspecified; no numerical roughness inferred",
+  "fin_assumption": "2% HEX, 5% bevels, sharp edges, auto body attachment",
+  "powered_base_drag": "nozzle",
+  "damp": true
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/valetudo_result.json b/docs/examples/datcom/comparison_data/rockets/valetudo_result.json
new file mode 100644
index 000000000..d011f2d77
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/valetudo_result.json
@@ -0,0 +1,41 @@
+{
+  "rocket": "valetudo",
+  "actual_agl_m": 860.0,
+  "reported_agl_m": 860.0,
+  "saved_first_agl_m": 815.26,
+  "reference_kind": "saved measured-apogee summary",
+  "telemetry_max_agl_m": null,
+  "notes": "Valetudo flight recordings were lost; only recovered scalar apogee/drift values remain.",
+  "status": "paired untuned comparison",
+  "reason": "",
+  "ordinary_agl_m": 815.0526424633565,
+  "ordinary_apogee_time_s": 13.977332377135696,
+  "ordinary_peak_vertical_velocity_m_s": 123.31112741231497,
+  "ordinary_initial_static_margin_cal": 2.951130333277495,
+  "ordinary_coast_cd_mach06": 1.128,
+  "ordinary_max_mach": 0.3680218288920357,
+  "source_adaptations": "",
+  "atmosphere_refinements": 1,
+  "atmosphere_last_apogee_change_m": 0.38337486952264044,
+  "datcom_agl_m": 943.6190073224011,
+  "datcom_apogee_time_s": 15.242452283301883,
+  "datcom_peak_vertical_velocity_m_s": 126.09986587804391,
+  "datcom_initial_static_margin_cal": 4.156583455241121,
+  "datcom_coast_cd_mach06": 0.43133333333333335,
+  "datcom_max_mach": 0.37639511692688415,
+  "samples": 448,
+  "outside_mach": 0,
+  "outside_alpha": 0,
+  "outside_beta": 0,
+  "max_abs_alpha_deg": 15.80193704194847,
+  "max_abs_beta_deg": 5.1181201273022525,
+  "solver_mach_excursion": true,
+  "solver_alpha_excursion": true,
+  "solver_beta_excursion": true,
+  "mass_cg_match": true,
+  "ordinary_error_m": -44.947357536643494,
+  "ordinary_error_pct": -5.226436922865522,
+  "datcom_error_m": 83.6190073224011,
+  "datcom_error_pct": 9.72314038632571,
+  "within_sampled_envelope": true
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/valkyrie_result.json b/docs/examples/datcom/comparison_data/rockets/valkyrie_result.json
new file mode 100644
index 000000000..f80941c95
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/valkyrie_result.json
@@ -0,0 +1,13 @@
+{
+  "rocket": "valkyrie",
+  "actual_agl_m": 2098.02,
+  "reported_agl_m": 2098.02,
+  "saved_first_agl_m": 2247.849,
+  "reference_kind": "notebook reported apogee",
+  "telemetry_max_agl_m": 2059.0244140625,
+  "notes": "Telemetry maximum 2059.024 m differs from notebook reported value 2098.020 m; both retained. | Valkyrie prose says 2025 but source environment says 2026 and fetches live Windy weather; no archived profile is supplied.",
+  "status": "unavailable source model",
+  "reason": "SourceUnavailable: Source requests live weather with no archived profile; not replaced by invented weather.",
+  "telemetry_qa_max_agl_m": 2059.0244140625,
+  "telemetry_qa_rejected_count": 0
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/rockets/valkyrie_telemetry.csv b/docs/examples/datcom/comparison_data/rockets/valkyrie_telemetry.csv
new file mode 100644
index 000000000..7987b559c
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/rockets/valkyrie_telemetry.csv
@@ -0,0 +1,12579 @@
+time_s,altitude_agl_m,qa_valid
+0.0,0.3442473709583282,True
+0.01,0.4096522331237793,True
+0.02,0.4844495952129364,True
+0.03,0.5686082243919373,True
+0.04,0.6620555520057678,True
+0.05,0.7647705674171448,True
+0.06,0.8767398595809937,True
+0.07,0.9979233145713806,True
+0.08,1.1282371282577517,True
+0.09,1.2677185535430908,True
+0.1,1.4164103269577026,True
+0.11,1.5742592811584473,True
+0.12,1.741287112236023,True
+0.13,1.9174764156341553,True
+0.14,2.1028378009796143,True
+0.15,2.297421455383301,True
+0.16,2.501166582107544,True
+0.17,2.714061737060547,True
+0.18,2.936115980148315,True
+0.19,3.167316436767578,True
+0.2,3.4076409339904785,True
+0.21,3.6570794582366943,True
+0.22,3.9157540798187256,True
+0.23,4.183582305908203,True
+0.24,4.460573673248291,True
+0.25,4.746795177459717,True
+0.26,5.042205810546875,True
+0.27,5.346819877624512,True
+0.28,5.660666465759277,True
+0.29,5.983789920806885,True
+0.3,6.316145896911621,True
+0.31,6.657732963562012,True
+0.32,7.008546352386475,True
+0.33,7.368549346923828,True
+0.34,7.737792015075684,True
+0.35,8.116194725036621,True
+0.36,8.503837585449219,True
+0.37,8.900636672973633,True
+0.38,9.306602478027344,True
+0.39,9.7217378616333,True
+0.4,10.146217346191406,True
+0.41,10.57990837097168,True
+0.42,11.022735595703123,True
+0.43,11.474719047546388,True
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diff --git a/docs/examples/datcom/rocket_comparison.ipynb b/docs/examples/datcom/rocket_comparison.ipynb
new file mode 100644
index 000000000..0a17f84a0
--- /dev/null
+++ b/docs/examples/datcom/rocket_comparison.ipynb
@@ -0,0 +1,1404 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "89141cba",
+   "metadata": {},
+   "source": [
+    "# Recorded rockets: DATCOM, RocketPy and measurements\n",
+    "\n",
+    "**Work in progress:** DATCOM currently produces poor flight predictions for several comparison rockets. The discrepancies are unresolved and are being investigated; these results should not be treated as validated predictions.\n",
+    "\n",
+    "Compare **apogee AGL**, **peak upward vertical velocity**, **CD** and **initial static margin**. Signed measured-reference error is `100 × (prediction − measurement) / |measurement|`; positive means overprediction. Missing data are **—**, never zero. Velocity means the vertical/altimeter ascent channel, not total airspeed. Only five records provide explicit velocity channels. Recorded CD is unavailable; NDRT alone explicitly reports a measured stability margin, with unspecified Mach and loading. Its stability comparison is conditional.\n",
+    "\n",
+    "**Apogee-fitted CD is inferred, not measured.** It is one constant coefficient used for both powered and coast drag in the ordinary RocketPy model, fitted to reach the recorded apogee while retaining that source model's mass, atmosphere and lateral aerodynamics. This is one possible effective-loss calibration, not a unique physical CD curve or a DATCOM correction. CD percentages below compare zero-incidence coast CD at Mach 0.6 against that fitted constant; they are not measurement errors. Controlled air-brake cases are not fitted this way.\n",
+    "\n",
+    "Both models retain source geometry, motor, mass, atmosphere and launch inputs. DATCOM uses DAMP, powered nozzle-annulus/coast full-base drag and adaptive 20-point Mach grids containing 0.1 and 1.0, with dense transonic points. Signed alpha grids retain at least ±20° and update with the reference trajectory. The ordinary flight seeds actual-atmosphere Reynolds schedules; one DATCOM trajectory update follows. Scope is rail through apogee. Unspecified construction is carbon fibre with vinyl; it supplies no measured roughness or fin section. The untuned comparisons assume smooth 2% HEX sections with 5% bevels and sharp edges; native coefficients are not fitted to apogee.\n",
+    "\n",
+    "Source notebook and data hashes, measurement provenance, masks, fitting residuals and availability reasons are saved in `comparison_data/`. Hedy/Valkyrie apogees use archived ordinary outputs where fresh reproduction is unavailable. **AST-HER has not flown** and is compared separately.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d9806fcc",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-22T22:26:49.293282Z",
+     "iopub.status.busy": "2026-10-22T22:26:49.293114Z",
+     "iopub.status.idle": "2026-10-22T22:26:50.248546Z",
+     "shell.execute_reply": "2026-10-22T22:26:50.248230Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import json\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from IPython.display import display\n",
+    "ROOT = next(p for p in (Path.cwd(), *Path.cwd().parents) if (p / \"rocketpy\").is_dir())\n",
+    "DATA = ROOT / \"docs/examples/datcom/comparison_data\"\n",
+    "plt.rcParams.update({\"axes.grid\": True, \"grid.alpha\": .2})\n",
+    "def show(frame):\n",
+    "    display(frame.style.format(precision=3, na_rep=\"—\"))\n",
+    "metrics = pd.read_csv(DATA / \"rocket_metrics.csv\")\n",
+    "status = pd.read_csv(DATA / \"rockets/results.csv\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "330878d5",
+   "metadata": {},
+   "source": [
+    "## Apogee above launch site (m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bc74a0fd",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-22T22:26:50.250187Z",
+     "iopub.status.busy": "2026-10-22T22:26:50.250036Z",
+     "iopub.status.idle": "2026-10-22T22:26:50.296816Z",
+     "shell.execute_reply": "2026-10-22T22:26:50.296487Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "</style>\n",
+       "<table id=\"T_6acf3\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_6acf3_level0_col0\" class=\"col_heading level0 col0\" >rocket</th>\n",
+       "      <th id=\"T_6acf3_level0_col1\" class=\"col_heading level0 col1\" >measured</th>\n",
+       "      <th id=\"T_6acf3_level0_col2\" class=\"col_heading level0 col2\" >rocketpy</th>\n",
+       "      <th id=\"T_6acf3_level0_col3\" class=\"col_heading level0 col3\" >datcom</th>\n",
+       "      <th id=\"T_6acf3_level0_col4\" class=\"col_heading level0 col4\" >rocketpy_error_pct</th>\n",
+       "      <th id=\"T_6acf3_level0_col5\" class=\"col_heading level0 col5\" >datcom_error_pct</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6acf3_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_6acf3_row0_col0\" class=\"data row0 col0\" >andromeda</td>\n",
+       "      <td id=\"T_6acf3_row0_col1\" class=\"data row0 col1\" >3443.000</td>\n",
+       "      <td id=\"T_6acf3_row0_col2\" class=\"data row0 col2\" >3615.881</td>\n",
+       "      <td id=\"T_6acf3_row0_col3\" class=\"data row0 col3\" >4170.733</td>\n",
+       "      <td id=\"T_6acf3_row0_col4\" class=\"data row0 col4\" >5.021</td>\n",
+       "      <td id=\"T_6acf3_row0_col5\" class=\"data row0 col5\" >21.137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6acf3_level0_row1\" class=\"row_heading level0 row1\" >5</th>\n",
+       "      <td id=\"T_6acf3_row1_col0\" class=\"data row1 col0\" >astra</td>\n",
+       "      <td id=\"T_6acf3_row1_col1\" class=\"data row1 col1\" >3250.000</td>\n",
+       "      <td id=\"T_6acf3_row1_col2\" class=\"data row1 col2\" >3043.368</td>\n",
+       "      <td id=\"T_6acf3_row1_col3\" class=\"data row1 col3\" >3695.934</td>\n",
+       "      <td id=\"T_6acf3_row1_col4\" class=\"data row1 col4\" >-6.358</td>\n",
+       "      <td id=\"T_6acf3_row1_col5\" class=\"data row1 col5\" >13.721</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6acf3_level0_row2\" class=\"row_heading level0 row2\" >10</th>\n",
+       "      <td id=\"T_6acf3_row2_col0\" class=\"data row2 col0\" >bella_lui</td>\n",
+       "      <td id=\"T_6acf3_row2_col1\" class=\"data row2 col1\" >458.971</td>\n",
+       "      <td id=\"T_6acf3_row2_col2\" class=\"data row2 col2\" >460.574</td>\n",
+       "      <td id=\"T_6acf3_row2_col3\" class=\"data row2 col3\" >465.877</td>\n",
+       "      <td id=\"T_6acf3_row2_col4\" class=\"data row2 col4\" >0.349</td>\n",
+       "      <td id=\"T_6acf3_row2_col5\" class=\"data row2 col5\" >1.505</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6acf3_level0_row3\" class=\"row_heading level0 row3\" >15</th>\n",
+       "      <td id=\"T_6acf3_row3_col0\" class=\"data row3 col0\" >camoes</td>\n",
+       "      <td id=\"T_6acf3_row3_col1\" class=\"data row3 col1\" >3015.000</td>\n",
+       "      <td id=\"T_6acf3_row3_col2\" class=\"data row3 col2\" >3004.104</td>\n",
+       "      <td id=\"T_6acf3_row3_col3\" class=\"data row3 col3\" >—</td>\n",
+       "      <td id=\"T_6acf3_row3_col4\" class=\"data row3 col4\" >-0.361</td>\n",
+       "      <td id=\"T_6acf3_row3_col5\" class=\"data row3 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6acf3_level0_row4\" class=\"row_heading level0 row4\" >20</th>\n",
+       "      <td id=\"T_6acf3_row4_col0\" class=\"data row4 col0\" >cavour</td>\n",
+       "      <td id=\"T_6acf3_row4_col1\" class=\"data row4 col1\" >2789.000</td>\n",
+       "      <td id=\"T_6acf3_row4_col2\" class=\"data row4 col2\" >2818.135</td>\n",
+       "      <td id=\"T_6acf3_row4_col3\" class=\"data row4 col3\" >3251.609</td>\n",
+       "      <td id=\"T_6acf3_row4_col4\" class=\"data row4 col4\" >1.045</td>\n",
+       "      <td id=\"T_6acf3_row4_col5\" class=\"data row4 col5\" >16.587</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6acf3_level0_row5\" class=\"row_heading level0 row5\" >25</th>\n",
+       "      <td id=\"T_6acf3_row5_col0\" class=\"data row5 col0\" >defiance</td>\n",
+       "      <td id=\"T_6acf3_row5_col1\" class=\"data row5 col1\" >9308.318</td>\n",
+       "      <td id=\"T_6acf3_row5_col2\" class=\"data row5 col2\" >9243.891</td>\n",
+       "      <td id=\"T_6acf3_row5_col3\" class=\"data row5 col3\" >—</td>\n",
+       "      <td id=\"T_6acf3_row5_col4\" class=\"data row5 col4\" >-0.692</td>\n",
+       "      <td id=\"T_6acf3_row5_col5\" class=\"data row5 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6acf3_level0_row6\" class=\"row_heading level0 row6\" >30</th>\n",
+       "      <td id=\"T_6acf3_row6_col0\" class=\"data row6 col0\" >erebus</td>\n",
+       "      <td id=\"T_6acf3_row6_col1\" class=\"data row6 col1\" >3020.000</td>\n",
+       "      <td id=\"T_6acf3_row6_col2\" class=\"data row6 col2\" >2751.156</td>\n",
+       "      <td id=\"T_6acf3_row6_col3\" class=\"data row6 col3\" >3253.423</td>\n",
+       "      <td id=\"T_6acf3_row6_col4\" class=\"data row6 col4\" >-8.902</td>\n",
+       "      <td id=\"T_6acf3_row6_col5\" class=\"data row6 col5\" >7.729</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6acf3_level0_row7\" class=\"row_heading level0 row7\" >35</th>\n",
+       "      <td id=\"T_6acf3_row7_col0\" class=\"data row7 col0\" >genesis</td>\n",
+       "      <td id=\"T_6acf3_row7_col1\" class=\"data row7 col1\" >2916.000</td>\n",
+       "      <td id=\"T_6acf3_row7_col2\" class=\"data row7 col2\" >3044.686</td>\n",
+       "      <td id=\"T_6acf3_row7_col3\" class=\"data row7 col3\" >3297.239</td>\n",
+       "      <td id=\"T_6acf3_row7_col4\" class=\"data row7 col4\" >4.413</td>\n",
+       "      <td id=\"T_6acf3_row7_col5\" class=\"data row7 col5\" >13.074</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6acf3_level0_row8\" class=\"row_heading level0 row8\" >40</th>\n",
+       "      <td id=\"T_6acf3_row8_col0\" class=\"data row8 col0\" >halcyon</td>\n",
+       "      <td id=\"T_6acf3_row8_col1\" class=\"data row8 col1\" >3450.000</td>\n",
+       "      <td id=\"T_6acf3_row8_col2\" class=\"data row8 col2\" >3214.239</td>\n",
+       "      <td id=\"T_6acf3_row8_col3\" class=\"data row8 col3\" >3530.232</td>\n",
+       "      <td id=\"T_6acf3_row8_col4\" class=\"data row8 col4\" >-6.834</td>\n",
+       "      <td id=\"T_6acf3_row8_col5\" class=\"data row8 col5\" >2.326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6acf3_level0_row9\" class=\"row_heading level0 row9\" >45</th>\n",
+       "      <td id=\"T_6acf3_row9_col0\" class=\"data row9 col0\" >hedy</td>\n",
+       "      <td id=\"T_6acf3_row9_col1\" class=\"data row9 col1\" >5231.530</td>\n",
+       "      <td id=\"T_6acf3_row9_col2\" class=\"data row9 col2\" >5246.918</td>\n",
+       "      <td id=\"T_6acf3_row9_col3\" class=\"data row9 col3\" >—</td>\n",
+       "      <td id=\"T_6acf3_row9_col4\" class=\"data row9 col4\" >0.294</td>\n",
+       "      <td id=\"T_6acf3_row9_col5\" class=\"data row9 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6acf3_level0_row10\" class=\"row_heading level0 row10\" >49</th>\n",
+       "      <td id=\"T_6acf3_row10_col0\" class=\"data row10 col0\" >juno3</td>\n",
+       "      <td id=\"T_6acf3_row10_col1\" class=\"data row10 col1\" >3213.000</td>\n",
+       "      <td id=\"T_6acf3_row10_col2\" class=\"data row10 col2\" >3008.582</td>\n",
+       "      <td id=\"T_6acf3_row10_col3\" class=\"data row10 col3\" >3056.492</td>\n",
+       "      <td id=\"T_6acf3_row10_col4\" class=\"data row10 col4\" >-6.362</td>\n",
+       "      <td id=\"T_6acf3_row10_col5\" class=\"data row10 col5\" >-4.871</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6acf3_level0_row11\" class=\"row_heading level0 row11\" >54</th>\n",
+       "      <td id=\"T_6acf3_row11_col0\" class=\"data row11 col0\" >lince</td>\n",
+       "      <td id=\"T_6acf3_row11_col1\" class=\"data row11 col1\" >3587.000</td>\n",
+       "      <td id=\"T_6acf3_row11_col2\" class=\"data row11 col2\" >3285.089</td>\n",
+       "      <td id=\"T_6acf3_row11_col3\" class=\"data row11 col3\" >4254.304</td>\n",
+       "      <td id=\"T_6acf3_row11_col4\" class=\"data row11 col4\" >-8.417</td>\n",
+       "      <td id=\"T_6acf3_row11_col5\" class=\"data row11 col5\" >18.603</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6acf3_level0_row12\" class=\"row_heading level0 row12\" >59</th>\n",
+       "      <td id=\"T_6acf3_row12_col0\" class=\"data row12 col0\" >ndrt_2020</td>\n",
+       "      <td id=\"T_6acf3_row12_col1\" class=\"data row12 col1\" >1316.736</td>\n",
+       "      <td id=\"T_6acf3_row12_col2\" class=\"data row12 col2\" >1293.518</td>\n",
+       "      <td id=\"T_6acf3_row12_col3\" class=\"data row12 col3\" >—</td>\n",
+       "      <td id=\"T_6acf3_row12_col4\" class=\"data row12 col4\" >-1.763</td>\n",
+       "      <td id=\"T_6acf3_row12_col5\" class=\"data row12 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6acf3_level0_row13\" class=\"row_heading level0 row13\" >64</th>\n",
+       "      <td id=\"T_6acf3_row13_col0\" class=\"data row13 col0\" >prometheus_2022</td>\n",
+       "      <td id=\"T_6acf3_row13_col1\" class=\"data row13 col1\" >3898.370</td>\n",
+       "      <td id=\"T_6acf3_row13_col2\" class=\"data row13 col2\" >4191.289</td>\n",
+       "      <td id=\"T_6acf3_row13_col3\" class=\"data row13 col3\" >4229.197</td>\n",
+       "      <td id=\"T_6acf3_row13_col4\" class=\"data row13 col4\" >7.514</td>\n",
+       "      <td id=\"T_6acf3_row13_col5\" class=\"data row13 col5\" >8.486</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6acf3_level0_row14\" class=\"row_heading level0 row14\" >69</th>\n",
+       "      <td id=\"T_6acf3_row14_col0\" class=\"data row14 col0\" >valetudo</td>\n",
+       "      <td id=\"T_6acf3_row14_col1\" class=\"data row14 col1\" >860.000</td>\n",
+       "      <td id=\"T_6acf3_row14_col2\" class=\"data row14 col2\" >815.053</td>\n",
+       "      <td id=\"T_6acf3_row14_col3\" class=\"data row14 col3\" >943.619</td>\n",
+       "      <td id=\"T_6acf3_row14_col4\" class=\"data row14 col4\" >-5.226</td>\n",
+       "      <td id=\"T_6acf3_row14_col5\" class=\"data row14 col5\" >9.723</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6acf3_level0_row15\" class=\"row_heading level0 row15\" >74</th>\n",
+       "      <td id=\"T_6acf3_row15_col0\" class=\"data row15 col0\" >valkyrie</td>\n",
+       "      <td id=\"T_6acf3_row15_col1\" class=\"data row15 col1\" >2098.020</td>\n",
+       "      <td id=\"T_6acf3_row15_col2\" class=\"data row15 col2\" >2247.849</td>\n",
+       "      <td id=\"T_6acf3_row15_col3\" class=\"data row15 col3\" >—</td>\n",
+       "      <td id=\"T_6acf3_row15_col4\" class=\"data row15 col4\" >7.141</td>\n",
+       "      <td id=\"T_6acf3_row15_col5\" class=\"data row15 col5\" >—</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x15c280ad0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show(metrics[metrics.metric == 'Apogee AGL'][['rocket', 'measured', 'rocketpy', 'datcom', 'rocketpy_error_pct', 'datcom_error_pct']])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9993ba7",
+   "metadata": {},
+   "source": [
+    "## Peak upward vertical velocity (m/s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6346434f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-22T22:26:50.298327Z",
+     "iopub.status.busy": "2026-10-22T22:26:50.298145Z",
+     "iopub.status.idle": "2026-10-22T22:26:50.303444Z",
+     "shell.execute_reply": "2026-10-22T22:26:50.303163Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "</style>\n",
+       "<table id=\"T_40020\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_40020_level0_col0\" class=\"col_heading level0 col0\" >rocket</th>\n",
+       "      <th id=\"T_40020_level0_col1\" class=\"col_heading level0 col1\" >measured</th>\n",
+       "      <th id=\"T_40020_level0_col2\" class=\"col_heading level0 col2\" >rocketpy</th>\n",
+       "      <th id=\"T_40020_level0_col3\" class=\"col_heading level0 col3\" >datcom</th>\n",
+       "      <th id=\"T_40020_level0_col4\" class=\"col_heading level0 col4\" >rocketpy_error_pct</th>\n",
+       "      <th id=\"T_40020_level0_col5\" class=\"col_heading level0 col5\" >datcom_error_pct</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_40020_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
+       "      <td id=\"T_40020_row0_col0\" class=\"data row0 col0\" >andromeda</td>\n",
+       "      <td id=\"T_40020_row0_col1\" class=\"data row0 col1\" >324.109</td>\n",
+       "      <td id=\"T_40020_row0_col2\" class=\"data row0 col2\" >343.154</td>\n",
+       "      <td id=\"T_40020_row0_col3\" class=\"data row0 col3\" >352.641</td>\n",
+       "      <td id=\"T_40020_row0_col4\" class=\"data row0 col4\" >5.876</td>\n",
+       "      <td id=\"T_40020_row0_col5\" class=\"data row0 col5\" >8.803</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_40020_level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
+       "      <td id=\"T_40020_row1_col0\" class=\"data row1 col0\" >astra</td>\n",
+       "      <td id=\"T_40020_row1_col1\" class=\"data row1 col1\" >—</td>\n",
+       "      <td id=\"T_40020_row1_col2\" class=\"data row1 col2\" >306.480</td>\n",
+       "      <td id=\"T_40020_row1_col3\" class=\"data row1 col3\" >318.547</td>\n",
+       "      <td id=\"T_40020_row1_col4\" class=\"data row1 col4\" >—</td>\n",
+       "      <td id=\"T_40020_row1_col5\" class=\"data row1 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_40020_level0_row2\" class=\"row_heading level0 row2\" >11</th>\n",
+       "      <td id=\"T_40020_row2_col0\" class=\"data row2 col0\" >bella_lui</td>\n",
+       "      <td id=\"T_40020_row2_col1\" class=\"data row2 col1\" >90.000</td>\n",
+       "      <td id=\"T_40020_row2_col2\" class=\"data row2 col2\" >86.179</td>\n",
+       "      <td id=\"T_40020_row2_col3\" class=\"data row2 col3\" >86.473</td>\n",
+       "      <td id=\"T_40020_row2_col4\" class=\"data row2 col4\" >-4.246</td>\n",
+       "      <td id=\"T_40020_row2_col5\" class=\"data row2 col5\" >-3.918</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_40020_level0_row3\" class=\"row_heading level0 row3\" >16</th>\n",
+       "      <td id=\"T_40020_row3_col0\" class=\"data row3 col0\" >camoes</td>\n",
+       "      <td id=\"T_40020_row3_col1\" class=\"data row3 col1\" >—</td>\n",
+       "      <td id=\"T_40020_row3_col2\" class=\"data row3 col2\" >327.440</td>\n",
+       "      <td id=\"T_40020_row3_col3\" class=\"data row3 col3\" >—</td>\n",
+       "      <td id=\"T_40020_row3_col4\" class=\"data row3 col4\" >—</td>\n",
+       "      <td id=\"T_40020_row3_col5\" class=\"data row3 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_40020_level0_row4\" class=\"row_heading level0 row4\" >21</th>\n",
+       "      <td id=\"T_40020_row4_col0\" class=\"data row4 col0\" >cavour</td>\n",
+       "      <td id=\"T_40020_row4_col1\" class=\"data row4 col1\" >314.000</td>\n",
+       "      <td id=\"T_40020_row4_col2\" class=\"data row4 col2\" >304.640</td>\n",
+       "      <td id=\"T_40020_row4_col3\" class=\"data row4 col3\" >319.055</td>\n",
+       "      <td id=\"T_40020_row4_col4\" class=\"data row4 col4\" >-2.981</td>\n",
+       "      <td id=\"T_40020_row4_col5\" class=\"data row4 col5\" >1.610</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_40020_level0_row5\" class=\"row_heading level0 row5\" >26</th>\n",
+       "      <td id=\"T_40020_row5_col0\" class=\"data row5 col0\" >defiance</td>\n",
+       "      <td id=\"T_40020_row5_col1\" class=\"data row5 col1\" >—</td>\n",
+       "      <td id=\"T_40020_row5_col2\" class=\"data row5 col2\" >438.711</td>\n",
+       "      <td id=\"T_40020_row5_col3\" class=\"data row5 col3\" >—</td>\n",
+       "      <td id=\"T_40020_row5_col4\" class=\"data row5 col4\" >—</td>\n",
+       "      <td id=\"T_40020_row5_col5\" class=\"data row5 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_40020_level0_row6\" class=\"row_heading level0 row6\" >31</th>\n",
+       "      <td id=\"T_40020_row6_col0\" class=\"data row6 col0\" >erebus</td>\n",
+       "      <td id=\"T_40020_row6_col1\" class=\"data row6 col1\" >—</td>\n",
+       "      <td id=\"T_40020_row6_col2\" class=\"data row6 col2\" >294.171</td>\n",
+       "      <td id=\"T_40020_row6_col3\" class=\"data row6 col3\" >307.317</td>\n",
+       "      <td id=\"T_40020_row6_col4\" class=\"data row6 col4\" >—</td>\n",
+       "      <td id=\"T_40020_row6_col5\" class=\"data row6 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_40020_level0_row7\" class=\"row_heading level0 row7\" >36</th>\n",
+       "      <td id=\"T_40020_row7_col0\" class=\"data row7 col0\" >genesis</td>\n",
+       "      <td id=\"T_40020_row7_col1\" class=\"data row7 col1\" >—</td>\n",
+       "      <td id=\"T_40020_row7_col2\" class=\"data row7 col2\" >290.636</td>\n",
+       "      <td id=\"T_40020_row7_col3\" class=\"data row7 col3\" >299.262</td>\n",
+       "      <td id=\"T_40020_row7_col4\" class=\"data row7 col4\" >—</td>\n",
+       "      <td id=\"T_40020_row7_col5\" class=\"data row7 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_40020_level0_row8\" class=\"row_heading level0 row8\" >41</th>\n",
+       "      <td id=\"T_40020_row8_col0\" class=\"data row8 col0\" >halcyon</td>\n",
+       "      <td id=\"T_40020_row8_col1\" class=\"data row8 col1\" >—</td>\n",
+       "      <td id=\"T_40020_row8_col2\" class=\"data row8 col2\" >275.571</td>\n",
+       "      <td id=\"T_40020_row8_col3\" class=\"data row8 col3\" >280.950</td>\n",
+       "      <td id=\"T_40020_row8_col4\" class=\"data row8 col4\" >—</td>\n",
+       "      <td id=\"T_40020_row8_col5\" class=\"data row8 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_40020_level0_row9\" class=\"row_heading level0 row9\" >46</th>\n",
+       "      <td id=\"T_40020_row9_col0\" class=\"data row9 col0\" >hedy</td>\n",
+       "      <td id=\"T_40020_row9_col1\" class=\"data row9 col1\" >—</td>\n",
+       "      <td id=\"T_40020_row9_col2\" class=\"data row9 col2\" >—</td>\n",
+       "      <td id=\"T_40020_row9_col3\" class=\"data row9 col3\" >—</td>\n",
+       "      <td id=\"T_40020_row9_col4\" class=\"data row9 col4\" >—</td>\n",
+       "      <td id=\"T_40020_row9_col5\" class=\"data row9 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_40020_level0_row10\" class=\"row_heading level0 row10\" >50</th>\n",
+       "      <td id=\"T_40020_row10_col0\" class=\"data row10 col0\" >juno3</td>\n",
+       "      <td id=\"T_40020_row10_col1\" class=\"data row10 col1\" >268.761</td>\n",
+       "      <td id=\"T_40020_row10_col2\" class=\"data row10 col2\" >241.243</td>\n",
+       "      <td id=\"T_40020_row10_col3\" class=\"data row10 col3\" >243.899</td>\n",
+       "      <td id=\"T_40020_row10_col4\" class=\"data row10 col4\" >-10.239</td>\n",
+       "      <td id=\"T_40020_row10_col5\" class=\"data row10 col5\" >-9.251</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_40020_level0_row11\" class=\"row_heading level0 row11\" >55</th>\n",
+       "      <td id=\"T_40020_row11_col0\" class=\"data row11 col0\" >lince</td>\n",
+       "      <td id=\"T_40020_row11_col1\" class=\"data row11 col1\" >—</td>\n",
+       "      <td id=\"T_40020_row11_col2\" class=\"data row11 col2\" >317.771</td>\n",
+       "      <td id=\"T_40020_row11_col3\" class=\"data row11 col3\" >342.540</td>\n",
+       "      <td id=\"T_40020_row11_col4\" class=\"data row11 col4\" >—</td>\n",
+       "      <td id=\"T_40020_row11_col5\" class=\"data row11 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_40020_level0_row12\" class=\"row_heading level0 row12\" >60</th>\n",
+       "      <td id=\"T_40020_row12_col0\" class=\"data row12 col0\" >ndrt_2020</td>\n",
+       "      <td id=\"T_40020_row12_col1\" class=\"data row12 col1\" >—</td>\n",
+       "      <td id=\"T_40020_row12_col2\" class=\"data row12 col2\" >171.661</td>\n",
+       "      <td id=\"T_40020_row12_col3\" class=\"data row12 col3\" >—</td>\n",
+       "      <td id=\"T_40020_row12_col4\" class=\"data row12 col4\" >—</td>\n",
+       "      <td id=\"T_40020_row12_col5\" class=\"data row12 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_40020_level0_row13\" class=\"row_heading level0 row13\" >65</th>\n",
+       "      <td id=\"T_40020_row13_col0\" class=\"data row13 col0\" >prometheus_2022</td>\n",
+       "      <td id=\"T_40020_row13_col1\" class=\"data row13 col1\" >317.880</td>\n",
+       "      <td id=\"T_40020_row13_col2\" class=\"data row13 col2\" >322.071</td>\n",
+       "      <td id=\"T_40020_row13_col3\" class=\"data row13 col3\" >324.060</td>\n",
+       "      <td id=\"T_40020_row13_col4\" class=\"data row13 col4\" >1.318</td>\n",
+       "      <td id=\"T_40020_row13_col5\" class=\"data row13 col5\" >1.944</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_40020_level0_row14\" class=\"row_heading level0 row14\" >70</th>\n",
+       "      <td id=\"T_40020_row14_col0\" class=\"data row14 col0\" >valetudo</td>\n",
+       "      <td id=\"T_40020_row14_col1\" class=\"data row14 col1\" >—</td>\n",
+       "      <td id=\"T_40020_row14_col2\" class=\"data row14 col2\" >123.311</td>\n",
+       "      <td id=\"T_40020_row14_col3\" class=\"data row14 col3\" >126.100</td>\n",
+       "      <td id=\"T_40020_row14_col4\" class=\"data row14 col4\" >—</td>\n",
+       "      <td id=\"T_40020_row14_col5\" class=\"data row14 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_40020_level0_row15\" class=\"row_heading level0 row15\" >75</th>\n",
+       "      <td id=\"T_40020_row15_col0\" class=\"data row15 col0\" >valkyrie</td>\n",
+       "      <td id=\"T_40020_row15_col1\" class=\"data row15 col1\" >—</td>\n",
+       "      <td id=\"T_40020_row15_col2\" class=\"data row15 col2\" >—</td>\n",
+       "      <td id=\"T_40020_row15_col3\" class=\"data row15 col3\" >—</td>\n",
+       "      <td id=\"T_40020_row15_col4\" class=\"data row15 col4\" >—</td>\n",
+       "      <td id=\"T_40020_row15_col5\" class=\"data row15 col5\" >—</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x15c18fd90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show(metrics[metrics.metric == 'Peak upward vertical velocity'][['rocket', 'measured', 'rocketpy', 'datcom', 'rocketpy_error_pct', 'datcom_error_pct']])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a47a191",
+   "metadata": {},
+   "source": [
+    "## CD and the apogee-fitted effective coefficient"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bc0b298b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-22T22:26:50.304636Z",
+     "iopub.status.busy": "2026-10-22T22:26:50.304545Z",
+     "iopub.status.idle": "2026-10-22T22:26:50.309449Z",
+     "shell.execute_reply": "2026-10-22T22:26:50.309159Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "</style>\n",
+       "<table id=\"T_9e847\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_9e847_level0_col0\" class=\"col_heading level0 col0\" >rocket</th>\n",
+       "      <th id=\"T_9e847_level0_col1\" class=\"col_heading level0 col1\" >measured</th>\n",
+       "      <th id=\"T_9e847_level0_col2\" class=\"col_heading level0 col2\" >rocketpy</th>\n",
+       "      <th id=\"T_9e847_level0_col3\" class=\"col_heading level0 col3\" >datcom</th>\n",
+       "      <th id=\"T_9e847_level0_col4\" class=\"col_heading level0 col4\" >inferred_cd</th>\n",
+       "      <th id=\"T_9e847_level0_col5\" class=\"col_heading level0 col5\" >rocketpy_vs_fitted_cd_pct</th>\n",
+       "      <th id=\"T_9e847_level0_col6\" class=\"col_heading level0 col6\" >datcom_vs_fitted_cd_pct</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9e847_level0_row0\" class=\"row_heading level0 row0\" >3</th>\n",
+       "      <td id=\"T_9e847_row0_col0\" class=\"data row0 col0\" >andromeda</td>\n",
+       "      <td id=\"T_9e847_row0_col1\" class=\"data row0 col1\" >—</td>\n",
+       "      <td id=\"T_9e847_row0_col2\" class=\"data row0 col2\" >0.498</td>\n",
+       "      <td id=\"T_9e847_row0_col3\" class=\"data row0 col3\" >0.349</td>\n",
+       "      <td id=\"T_9e847_row0_col4\" class=\"data row0 col4\" >0.591</td>\n",
+       "      <td id=\"T_9e847_row0_col5\" class=\"data row0 col5\" >-15.707</td>\n",
+       "      <td id=\"T_9e847_row0_col6\" class=\"data row0 col6\" >-40.986</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9e847_level0_row1\" class=\"row_heading level0 row1\" >8</th>\n",
+       "      <td id=\"T_9e847_row1_col0\" class=\"data row1 col0\" >astra</td>\n",
+       "      <td id=\"T_9e847_row1_col1\" class=\"data row1 col1\" >—</td>\n",
+       "      <td id=\"T_9e847_row1_col2\" class=\"data row1 col2\" >0.654</td>\n",
+       "      <td id=\"T_9e847_row1_col3\" class=\"data row1 col3\" >0.426</td>\n",
+       "      <td id=\"T_9e847_row1_col4\" class=\"data row1 col4\" >0.599</td>\n",
+       "      <td id=\"T_9e847_row1_col5\" class=\"data row1 col5\" >9.168</td>\n",
+       "      <td id=\"T_9e847_row1_col6\" class=\"data row1 col6\" >-28.787</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9e847_level0_row2\" class=\"row_heading level0 row2\" >13</th>\n",
+       "      <td id=\"T_9e847_row2_col0\" class=\"data row2 col0\" >bella_lui</td>\n",
+       "      <td id=\"T_9e847_row2_col1\" class=\"data row2 col1\" >—</td>\n",
+       "      <td id=\"T_9e847_row2_col2\" class=\"data row2 col2\" >0.430</td>\n",
+       "      <td id=\"T_9e847_row2_col3\" class=\"data row2 col3\" >0.346</td>\n",
+       "      <td id=\"T_9e847_row2_col4\" class=\"data row2 col4\" >0.446</td>\n",
+       "      <td id=\"T_9e847_row2_col5\" class=\"data row2 col5\" >-3.535</td>\n",
+       "      <td id=\"T_9e847_row2_col6\" class=\"data row2 col6\" >-22.431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9e847_level0_row3\" class=\"row_heading level0 row3\" >18</th>\n",
+       "      <td id=\"T_9e847_row3_col0\" class=\"data row3 col0\" >camoes</td>\n",
+       "      <td id=\"T_9e847_row3_col1\" class=\"data row3 col1\" >—</td>\n",
+       "      <td id=\"T_9e847_row3_col2\" class=\"data row3 col2\" >0.704</td>\n",
+       "      <td id=\"T_9e847_row3_col3\" class=\"data row3 col3\" >—</td>\n",
+       "      <td id=\"T_9e847_row3_col4\" class=\"data row3 col4\" >—</td>\n",
+       "      <td id=\"T_9e847_row3_col5\" class=\"data row3 col5\" >—</td>\n",
+       "      <td id=\"T_9e847_row3_col6\" class=\"data row3 col6\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9e847_level0_row4\" class=\"row_heading level0 row4\" >23</th>\n",
+       "      <td id=\"T_9e847_row4_col0\" class=\"data row4 col0\" >cavour</td>\n",
+       "      <td id=\"T_9e847_row4_col1\" class=\"data row4 col1\" >—</td>\n",
+       "      <td id=\"T_9e847_row4_col2\" class=\"data row4 col2\" >0.583</td>\n",
+       "      <td id=\"T_9e847_row4_col3\" class=\"data row4 col3\" >0.448</td>\n",
+       "      <td id=\"T_9e847_row4_col4\" class=\"data row4 col4\" >0.609</td>\n",
+       "      <td id=\"T_9e847_row4_col5\" class=\"data row4 col5\" >-4.214</td>\n",
+       "      <td id=\"T_9e847_row4_col6\" class=\"data row4 col6\" >-26.353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9e847_level0_row5\" class=\"row_heading level0 row5\" >28</th>\n",
+       "      <td id=\"T_9e847_row5_col0\" class=\"data row5 col0\" >defiance</td>\n",
+       "      <td id=\"T_9e847_row5_col1\" class=\"data row5 col1\" >—</td>\n",
+       "      <td id=\"T_9e847_row5_col2\" class=\"data row5 col2\" >0.668</td>\n",
+       "      <td id=\"T_9e847_row5_col3\" class=\"data row5 col3\" >—</td>\n",
+       "      <td id=\"T_9e847_row5_col4\" class=\"data row5 col4\" >0.657</td>\n",
+       "      <td id=\"T_9e847_row5_col5\" class=\"data row5 col5\" >1.606</td>\n",
+       "      <td id=\"T_9e847_row5_col6\" class=\"data row5 col6\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9e847_level0_row6\" class=\"row_heading level0 row6\" >33</th>\n",
+       "      <td id=\"T_9e847_row6_col0\" class=\"data row6 col0\" >erebus</td>\n",
+       "      <td id=\"T_9e847_row6_col1\" class=\"data row6 col1\" >—</td>\n",
+       "      <td id=\"T_9e847_row6_col2\" class=\"data row6 col2\" >0.531</td>\n",
+       "      <td id=\"T_9e847_row6_col3\" class=\"data row6 col3\" >0.376</td>\n",
+       "      <td id=\"T_9e847_row6_col4\" class=\"data row6 col4\" >0.443</td>\n",
+       "      <td id=\"T_9e847_row6_col5\" class=\"data row6 col5\" >19.900</td>\n",
+       "      <td id=\"T_9e847_row6_col6\" class=\"data row6 col6\" >-15.099</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9e847_level0_row7\" class=\"row_heading level0 row7\" >38</th>\n",
+       "      <td id=\"T_9e847_row7_col0\" class=\"data row7 col0\" >genesis</td>\n",
+       "      <td id=\"T_9e847_row7_col1\" class=\"data row7 col1\" >—</td>\n",
+       "      <td id=\"T_9e847_row7_col2\" class=\"data row7 col2\" >0.550</td>\n",
+       "      <td id=\"T_9e847_row7_col3\" class=\"data row7 col3\" >0.475</td>\n",
+       "      <td id=\"T_9e847_row7_col4\" class=\"data row7 col4\" >0.609</td>\n",
+       "      <td id=\"T_9e847_row7_col5\" class=\"data row7 col5\" >-9.687</td>\n",
+       "      <td id=\"T_9e847_row7_col6\" class=\"data row7 col6\" >-22.043</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9e847_level0_row8\" class=\"row_heading level0 row8\" >43</th>\n",
+       "      <td id=\"T_9e847_row8_col0\" class=\"data row8 col0\" >halcyon</td>\n",
+       "      <td id=\"T_9e847_row8_col1\" class=\"data row8 col1\" >—</td>\n",
+       "      <td id=\"T_9e847_row8_col2\" class=\"data row8 col2\" >0.426</td>\n",
+       "      <td id=\"T_9e847_row8_col3\" class=\"data row8 col3\" >0.319</td>\n",
+       "      <td id=\"T_9e847_row8_col4\" class=\"data row8 col4\" >0.345</td>\n",
+       "      <td id=\"T_9e847_row8_col5\" class=\"data row8 col5\" >23.594</td>\n",
+       "      <td id=\"T_9e847_row8_col6\" class=\"data row8 col6\" >-7.450</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9e847_level0_row9\" class=\"row_heading level0 row9\" >48</th>\n",
+       "      <td id=\"T_9e847_row9_col0\" class=\"data row9 col0\" >hedy</td>\n",
+       "      <td id=\"T_9e847_row9_col1\" class=\"data row9 col1\" >—</td>\n",
+       "      <td id=\"T_9e847_row9_col2\" class=\"data row9 col2\" >—</td>\n",
+       "      <td id=\"T_9e847_row9_col3\" class=\"data row9 col3\" >—</td>\n",
+       "      <td id=\"T_9e847_row9_col4\" class=\"data row9 col4\" >—</td>\n",
+       "      <td id=\"T_9e847_row9_col5\" class=\"data row9 col5\" >—</td>\n",
+       "      <td id=\"T_9e847_row9_col6\" class=\"data row9 col6\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9e847_level0_row10\" class=\"row_heading level0 row10\" >52</th>\n",
+       "      <td id=\"T_9e847_row10_col0\" class=\"data row10 col0\" >juno3</td>\n",
+       "      <td id=\"T_9e847_row10_col1\" class=\"data row10 col1\" >—</td>\n",
+       "      <td id=\"T_9e847_row10_col2\" class=\"data row10 col2\" >0.368</td>\n",
+       "      <td id=\"T_9e847_row10_col3\" class=\"data row10 col3\" >0.374</td>\n",
+       "      <td id=\"T_9e847_row10_col4\" class=\"data row10 col4\" >0.264</td>\n",
+       "      <td id=\"T_9e847_row10_col5\" class=\"data row10 col5\" >39.345</td>\n",
+       "      <td id=\"T_9e847_row10_col6\" class=\"data row10 col6\" >41.806</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9e847_level0_row11\" class=\"row_heading level0 row11\" >57</th>\n",
+       "      <td id=\"T_9e847_row11_col0\" class=\"data row11 col0\" >lince</td>\n",
+       "      <td id=\"T_9e847_row11_col1\" class=\"data row11 col1\" >—</td>\n",
+       "      <td id=\"T_9e847_row11_col2\" class=\"data row11 col2\" >0.656</td>\n",
+       "      <td id=\"T_9e847_row11_col3\" class=\"data row11 col3\" >0.382</td>\n",
+       "      <td id=\"T_9e847_row11_col4\" class=\"data row11 col4\" >0.578</td>\n",
+       "      <td id=\"T_9e847_row11_col5\" class=\"data row11 col5\" >13.398</td>\n",
+       "      <td id=\"T_9e847_row11_col6\" class=\"data row11 col6\" >-33.971</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9e847_level0_row12\" class=\"row_heading level0 row12\" >62</th>\n",
+       "      <td id=\"T_9e847_row12_col0\" class=\"data row12 col0\" >ndrt_2020</td>\n",
+       "      <td id=\"T_9e847_row12_col1\" class=\"data row12 col1\" >—</td>\n",
+       "      <td id=\"T_9e847_row12_col2\" class=\"data row12 col2\" >0.440</td>\n",
+       "      <td id=\"T_9e847_row12_col3\" class=\"data row12 col3\" >—</td>\n",
+       "      <td id=\"T_9e847_row12_col4\" class=\"data row12 col4\" >0.417</td>\n",
+       "      <td id=\"T_9e847_row12_col5\" class=\"data row12 col5\" >5.468</td>\n",
+       "      <td id=\"T_9e847_row12_col6\" class=\"data row12 col6\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9e847_level0_row13\" class=\"row_heading level0 row13\" >67</th>\n",
+       "      <td id=\"T_9e847_row13_col0\" class=\"data row13 col0\" >prometheus_2022</td>\n",
+       "      <td id=\"T_9e847_row13_col1\" class=\"data row13 col1\" >—</td>\n",
+       "      <td id=\"T_9e847_row13_col2\" class=\"data row13 col2\" >0.352</td>\n",
+       "      <td id=\"T_9e847_row13_col3\" class=\"data row13 col3\" >0.335</td>\n",
+       "      <td id=\"T_9e847_row13_col4\" class=\"data row13 col4\" >0.403</td>\n",
+       "      <td id=\"T_9e847_row13_col5\" class=\"data row13 col5\" >-12.600</td>\n",
+       "      <td id=\"T_9e847_row13_col6\" class=\"data row13 col6\" >-16.854</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9e847_level0_row14\" class=\"row_heading level0 row14\" >72</th>\n",
+       "      <td id=\"T_9e847_row14_col0\" class=\"data row14 col0\" >valetudo</td>\n",
+       "      <td id=\"T_9e847_row14_col1\" class=\"data row14 col1\" >—</td>\n",
+       "      <td id=\"T_9e847_row14_col2\" class=\"data row14 col2\" >1.128</td>\n",
+       "      <td id=\"T_9e847_row14_col3\" class=\"data row14 col3\" >0.431</td>\n",
+       "      <td id=\"T_9e847_row14_col4\" class=\"data row14 col4\" >0.810</td>\n",
+       "      <td id=\"T_9e847_row14_col5\" class=\"data row14 col5\" >39.337</td>\n",
+       "      <td id=\"T_9e847_row14_col6\" class=\"data row14 col6\" >-46.719</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9e847_level0_row15\" class=\"row_heading level0 row15\" >77</th>\n",
+       "      <td id=\"T_9e847_row15_col0\" class=\"data row15 col0\" >valkyrie</td>\n",
+       "      <td id=\"T_9e847_row15_col1\" class=\"data row15 col1\" >—</td>\n",
+       "      <td id=\"T_9e847_row15_col2\" class=\"data row15 col2\" >—</td>\n",
+       "      <td id=\"T_9e847_row15_col3\" class=\"data row15 col3\" >—</td>\n",
+       "      <td id=\"T_9e847_row15_col4\" class=\"data row15 col4\" >—</td>\n",
+       "      <td id=\"T_9e847_row15_col5\" class=\"data row15 col5\" >—</td>\n",
+       "      <td id=\"T_9e847_row15_col6\" class=\"data row15 col6\" >—</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x15c06e210>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show(metrics[metrics.metric == 'Coast CD at Mach 0.6'][['rocket', 'measured', 'rocketpy', 'datcom', 'inferred_cd', 'rocketpy_vs_fitted_cd_pct', 'datcom_vs_fitted_cd_pct']])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "feab3ba7",
+   "metadata": {},
+   "source": [
+    "## Initial static margin (calibers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ead14610",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-22T22:26:50.310538Z",
+     "iopub.status.busy": "2026-10-22T22:26:50.310465Z",
+     "iopub.status.idle": "2026-10-22T22:26:50.315133Z",
+     "shell.execute_reply": "2026-10-22T22:26:50.314884Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "</style>\n",
+       "<table id=\"T_b6a74\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_b6a74_level0_col0\" class=\"col_heading level0 col0\" >rocket</th>\n",
+       "      <th id=\"T_b6a74_level0_col1\" class=\"col_heading level0 col1\" >measured</th>\n",
+       "      <th id=\"T_b6a74_level0_col2\" class=\"col_heading level0 col2\" >rocketpy</th>\n",
+       "      <th id=\"T_b6a74_level0_col3\" class=\"col_heading level0 col3\" >datcom</th>\n",
+       "      <th id=\"T_b6a74_level0_col4\" class=\"col_heading level0 col4\" >rocketpy_error_pct</th>\n",
+       "      <th id=\"T_b6a74_level0_col5\" class=\"col_heading level0 col5\" >datcom_error_pct</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b6a74_level0_row0\" class=\"row_heading level0 row0\" >2</th>\n",
+       "      <td id=\"T_b6a74_row0_col0\" class=\"data row0 col0\" >andromeda</td>\n",
+       "      <td id=\"T_b6a74_row0_col1\" class=\"data row0 col1\" >—</td>\n",
+       "      <td id=\"T_b6a74_row0_col2\" class=\"data row0 col2\" >4.159</td>\n",
+       "      <td id=\"T_b6a74_row0_col3\" class=\"data row0 col3\" >4.586</td>\n",
+       "      <td id=\"T_b6a74_row0_col4\" class=\"data row0 col4\" >—</td>\n",
+       "      <td id=\"T_b6a74_row0_col5\" class=\"data row0 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b6a74_level0_row1\" class=\"row_heading level0 row1\" >7</th>\n",
+       "      <td id=\"T_b6a74_row1_col0\" class=\"data row1 col0\" >astra</td>\n",
+       "      <td id=\"T_b6a74_row1_col1\" class=\"data row1 col1\" >—</td>\n",
+       "      <td id=\"T_b6a74_row1_col2\" class=\"data row1 col2\" >3.163</td>\n",
+       "      <td id=\"T_b6a74_row1_col3\" class=\"data row1 col3\" >3.477</td>\n",
+       "      <td id=\"T_b6a74_row1_col4\" class=\"data row1 col4\" >—</td>\n",
+       "      <td id=\"T_b6a74_row1_col5\" class=\"data row1 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b6a74_level0_row2\" class=\"row_heading level0 row2\" >12</th>\n",
+       "      <td id=\"T_b6a74_row2_col0\" class=\"data row2 col0\" >bella_lui</td>\n",
+       "      <td id=\"T_b6a74_row2_col1\" class=\"data row2 col1\" >—</td>\n",
+       "      <td id=\"T_b6a74_row2_col2\" class=\"data row2 col2\" >2.777</td>\n",
+       "      <td id=\"T_b6a74_row2_col3\" class=\"data row2 col3\" >3.304</td>\n",
+       "      <td id=\"T_b6a74_row2_col4\" class=\"data row2 col4\" >—</td>\n",
+       "      <td id=\"T_b6a74_row2_col5\" class=\"data row2 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b6a74_level0_row3\" class=\"row_heading level0 row3\" >17</th>\n",
+       "      <td id=\"T_b6a74_row3_col0\" class=\"data row3 col0\" >camoes</td>\n",
+       "      <td id=\"T_b6a74_row3_col1\" class=\"data row3 col1\" >—</td>\n",
+       "      <td id=\"T_b6a74_row3_col2\" class=\"data row3 col2\" >3.762</td>\n",
+       "      <td id=\"T_b6a74_row3_col3\" class=\"data row3 col3\" >—</td>\n",
+       "      <td id=\"T_b6a74_row3_col4\" class=\"data row3 col4\" >—</td>\n",
+       "      <td id=\"T_b6a74_row3_col5\" class=\"data row3 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b6a74_level0_row4\" class=\"row_heading level0 row4\" >22</th>\n",
+       "      <td id=\"T_b6a74_row4_col0\" class=\"data row4 col0\" >cavour</td>\n",
+       "      <td id=\"T_b6a74_row4_col1\" class=\"data row4 col1\" >—</td>\n",
+       "      <td id=\"T_b6a74_row4_col2\" class=\"data row4 col2\" >4.280</td>\n",
+       "      <td id=\"T_b6a74_row4_col3\" class=\"data row4 col3\" >5.465</td>\n",
+       "      <td id=\"T_b6a74_row4_col4\" class=\"data row4 col4\" >—</td>\n",
+       "      <td id=\"T_b6a74_row4_col5\" class=\"data row4 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b6a74_level0_row5\" class=\"row_heading level0 row5\" >27</th>\n",
+       "      <td id=\"T_b6a74_row5_col0\" class=\"data row5 col0\" >defiance</td>\n",
+       "      <td id=\"T_b6a74_row5_col1\" class=\"data row5 col1\" >—</td>\n",
+       "      <td id=\"T_b6a74_row5_col2\" class=\"data row5 col2\" >7.960</td>\n",
+       "      <td id=\"T_b6a74_row5_col3\" class=\"data row5 col3\" >—</td>\n",
+       "      <td id=\"T_b6a74_row5_col4\" class=\"data row5 col4\" >—</td>\n",
+       "      <td id=\"T_b6a74_row5_col5\" class=\"data row5 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b6a74_level0_row6\" class=\"row_heading level0 row6\" >32</th>\n",
+       "      <td id=\"T_b6a74_row6_col0\" class=\"data row6 col0\" >erebus</td>\n",
+       "      <td id=\"T_b6a74_row6_col1\" class=\"data row6 col1\" >—</td>\n",
+       "      <td id=\"T_b6a74_row6_col2\" class=\"data row6 col2\" >2.937</td>\n",
+       "      <td id=\"T_b6a74_row6_col3\" class=\"data row6 col3\" >4.450</td>\n",
+       "      <td id=\"T_b6a74_row6_col4\" class=\"data row6 col4\" >—</td>\n",
+       "      <td id=\"T_b6a74_row6_col5\" class=\"data row6 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b6a74_level0_row7\" class=\"row_heading level0 row7\" >37</th>\n",
+       "      <td id=\"T_b6a74_row7_col0\" class=\"data row7 col0\" >genesis</td>\n",
+       "      <td id=\"T_b6a74_row7_col1\" class=\"data row7 col1\" >—</td>\n",
+       "      <td id=\"T_b6a74_row7_col2\" class=\"data row7 col2\" >3.377</td>\n",
+       "      <td id=\"T_b6a74_row7_col3\" class=\"data row7 col3\" >3.768</td>\n",
+       "      <td id=\"T_b6a74_row7_col4\" class=\"data row7 col4\" >—</td>\n",
+       "      <td id=\"T_b6a74_row7_col5\" class=\"data row7 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b6a74_level0_row8\" class=\"row_heading level0 row8\" >42</th>\n",
+       "      <td id=\"T_b6a74_row8_col0\" class=\"data row8 col0\" >halcyon</td>\n",
+       "      <td id=\"T_b6a74_row8_col1\" class=\"data row8 col1\" >—</td>\n",
+       "      <td id=\"T_b6a74_row8_col2\" class=\"data row8 col2\" >3.019</td>\n",
+       "      <td id=\"T_b6a74_row8_col3\" class=\"data row8 col3\" >4.410</td>\n",
+       "      <td id=\"T_b6a74_row8_col4\" class=\"data row8 col4\" >—</td>\n",
+       "      <td id=\"T_b6a74_row8_col5\" class=\"data row8 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b6a74_level0_row9\" class=\"row_heading level0 row9\" >47</th>\n",
+       "      <td id=\"T_b6a74_row9_col0\" class=\"data row9 col0\" >hedy</td>\n",
+       "      <td id=\"T_b6a74_row9_col1\" class=\"data row9 col1\" >—</td>\n",
+       "      <td id=\"T_b6a74_row9_col2\" class=\"data row9 col2\" >—</td>\n",
+       "      <td id=\"T_b6a74_row9_col3\" class=\"data row9 col3\" >—</td>\n",
+       "      <td id=\"T_b6a74_row9_col4\" class=\"data row9 col4\" >—</td>\n",
+       "      <td id=\"T_b6a74_row9_col5\" class=\"data row9 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b6a74_level0_row10\" class=\"row_heading level0 row10\" >51</th>\n",
+       "      <td id=\"T_b6a74_row10_col0\" class=\"data row10 col0\" >juno3</td>\n",
+       "      <td id=\"T_b6a74_row10_col1\" class=\"data row10 col1\" >—</td>\n",
+       "      <td id=\"T_b6a74_row10_col2\" class=\"data row10 col2\" >3.221</td>\n",
+       "      <td id=\"T_b6a74_row10_col3\" class=\"data row10 col3\" >4.007</td>\n",
+       "      <td id=\"T_b6a74_row10_col4\" class=\"data row10 col4\" >—</td>\n",
+       "      <td id=\"T_b6a74_row10_col5\" class=\"data row10 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b6a74_level0_row11\" class=\"row_heading level0 row11\" >56</th>\n",
+       "      <td id=\"T_b6a74_row11_col0\" class=\"data row11 col0\" >lince</td>\n",
+       "      <td id=\"T_b6a74_row11_col1\" class=\"data row11 col1\" >—</td>\n",
+       "      <td id=\"T_b6a74_row11_col2\" class=\"data row11 col2\" >2.920</td>\n",
+       "      <td id=\"T_b6a74_row11_col3\" class=\"data row11 col3\" >3.963</td>\n",
+       "      <td id=\"T_b6a74_row11_col4\" class=\"data row11 col4\" >—</td>\n",
+       "      <td id=\"T_b6a74_row11_col5\" class=\"data row11 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b6a74_level0_row12\" class=\"row_heading level0 row12\" >61</th>\n",
+       "      <td id=\"T_b6a74_row12_col0\" class=\"data row12 col0\" >ndrt_2020</td>\n",
+       "      <td id=\"T_b6a74_row12_col1\" class=\"data row12 col1\" >2.875</td>\n",
+       "      <td id=\"T_b6a74_row12_col2\" class=\"data row12 col2\" >4.016</td>\n",
+       "      <td id=\"T_b6a74_row12_col3\" class=\"data row12 col3\" >—</td>\n",
+       "      <td id=\"T_b6a74_row12_col4\" class=\"data row12 col4\" >39.700</td>\n",
+       "      <td id=\"T_b6a74_row12_col5\" class=\"data row12 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b6a74_level0_row13\" class=\"row_heading level0 row13\" >66</th>\n",
+       "      <td id=\"T_b6a74_row13_col0\" class=\"data row13 col0\" >prometheus_2022</td>\n",
+       "      <td id=\"T_b6a74_row13_col1\" class=\"data row13 col1\" >—</td>\n",
+       "      <td id=\"T_b6a74_row13_col2\" class=\"data row13 col2\" >1.042</td>\n",
+       "      <td id=\"T_b6a74_row13_col3\" class=\"data row13 col3\" >1.789</td>\n",
+       "      <td id=\"T_b6a74_row13_col4\" class=\"data row13 col4\" >—</td>\n",
+       "      <td id=\"T_b6a74_row13_col5\" class=\"data row13 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b6a74_level0_row14\" class=\"row_heading level0 row14\" >71</th>\n",
+       "      <td id=\"T_b6a74_row14_col0\" class=\"data row14 col0\" >valetudo</td>\n",
+       "      <td id=\"T_b6a74_row14_col1\" class=\"data row14 col1\" >—</td>\n",
+       "      <td id=\"T_b6a74_row14_col2\" class=\"data row14 col2\" >2.951</td>\n",
+       "      <td id=\"T_b6a74_row14_col3\" class=\"data row14 col3\" >4.157</td>\n",
+       "      <td id=\"T_b6a74_row14_col4\" class=\"data row14 col4\" >—</td>\n",
+       "      <td id=\"T_b6a74_row14_col5\" class=\"data row14 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b6a74_level0_row15\" class=\"row_heading level0 row15\" >76</th>\n",
+       "      <td id=\"T_b6a74_row15_col0\" class=\"data row15 col0\" >valkyrie</td>\n",
+       "      <td id=\"T_b6a74_row15_col1\" class=\"data row15 col1\" >—</td>\n",
+       "      <td id=\"T_b6a74_row15_col2\" class=\"data row15 col2\" >—</td>\n",
+       "      <td id=\"T_b6a74_row15_col3\" class=\"data row15 col3\" >—</td>\n",
+       "      <td id=\"T_b6a74_row15_col4\" class=\"data row15 col4\" >—</td>\n",
+       "      <td id=\"T_b6a74_row15_col5\" class=\"data row15 col5\" >—</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x15c06e210>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show(metrics[metrics.metric == 'Initial static margin'][['rocket', 'measured', 'rocketpy', 'datcom', 'rocketpy_error_pct', 'datcom_error_pct']])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d171f26",
+   "metadata": {},
+   "source": [
+    "## Trajectory, CD and stability comparisons\n",
+    "\n",
+    "Blue is ordinary RocketPy; orange is native DATCOM; black is recorded data where available. Dotted CD is the inferred constant needed for the recorded apogee. CD curves are zero-incidence values, not time histories. Stability curves follow each simulated Mach and moving CG; the scalar table uses Mach-zero loaded ignition (DATCOM clamps to its lowest positive Mach). NDRT's recorded stability appears as an annotation because its measurement conditions are unknown.\n",
+    "\n",
+    "Recorded altitude keeps its original time and datum. QA flags the two extreme Juno altitude jumps; raw data and masks remain saved. Velocity uses explicitly supplied channels during the QA ascent, without differentiating altitude or fitting a time shift. Reported scalar apogees and telemetry maxima can differ; reported/recovered apogees remain the preferred scalar references."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c96f85d4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-22T22:26:50.316257Z",
+     "iopub.status.busy": "2026-10-22T22:26:50.316180Z",
+     "iopub.status.idle": "2026-10-22T22:26:54.997247Z",
+     "shell.execute_reply": "2026-10-22T22:26:54.996374Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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jfv36bN68mWHDhnHgwAHOnj3L6dOngehhFBLL2dmZChUq6HOh1WpZuXIlzZo1I1u2bEB0LgCGDh0aJx99+vQBPnw+FHPOFV9+7O3t0el0+Pn54efnh1ar/ejxJ0X37t0xNzdn4cKFQPRQEebm5nTv3j1Z9yNEZmH86SZCCGEYS5cuRVEUNm7cyMaNG+OsX758ORMnTkStVqdYDGZmZgCEh4djamqqX/7fE6WYwpOPjw958+bVL4+KitKfPMXIkSMHLi4uTJo0Kd592tvbJ0vsQgghhEgd5ubmtGvXjt9//52XL1+ydOlSLC0t9ROvQOJ//39ur5uY3kHPnj0jX7588ba5fv06V65cwdvbmy5duuiX37t377P23a1bN/r06cOtW7d48OABL1++pFu3bvr1OXLkAGDkyJG0bNky3m0UK1Ys3uUx51wvX76Ms+7FixcYGRlha2uLSqVCrVbz7NmzzzqW/7K2tqZLly4sXryYoUOHsmzZMtq3b4+NjU2y7keIzEKKUkKINEmr1bJ8+XIKFy7M4sWL46zfuXMnM2bMYPfu3TRp0iTO+pgCUkLv8JmamsbbNmaWvqtXr+pnvAHYsWNHrHYxs/WtWrUKNzc3/fL169fHmVGvSZMm7Nq1i8KFC2Nra5ug+IQQQgiRtvXo0YOFCxfy888/s2vXLrp27YqFhYV+fXL8/k/M+U39+vVRq9UsWLCAKlWqxNsmpvD17xtvAL/99luS4ovRrl07Bg8ejLe3Nw8ePCBv3rzUr19fv75YsWI4OTlx5coV/eQ0CVWsWDHy5s3L6tWrGTp0qP4Y3r9/z6ZNm/Qz8gHUqFGDDRs2MGnSJH0hLCH+nWdzc/M46/v378/8+fNp3bo1/v7+9OvXL1HHIIT4PylKCSHSpN27d/PixQumTp2qL/j8W6lSpZg7dy5LliyJtyhVqlQpABYtWoSlpSVmZmY4OjrGeZQuRunSpTl8+DA7duwgT548WFpaUqxYMRo3bky2bNno0aMHEyZMwNjYGG9vb54+fRrr8yVKlKBjx47MmjULjUZD3bp1uX79OtOnT8fKyipW2wkTJrB//37c3d3p378/xYoVIywsjEePHrFr1y4WLlyY7F3NhRBCCJGyypcvj4uLC7NmzUJRlFiP7kHy/P4vXLgw5ubmrFq1ihIlSpA1a1bs7e3j7WVdsGBBRo0axY8//khoaCjt2rXD2tqamzdv8ubNG8aPH0/x4sUpXLgwI0aMQFEUsmXLxo4dO9i/f/9n5cLGxoYWLVrg7e2Nv78/Q4cOjTX+E0QXvho1akSDBg3o2rUrefPm5d27d9y6dYuLFy9+cKZlIyMjpk2bRocOHWjSpAm9evUiPDycn3/+GX9/f3766Sd925kzZ1KtWjUqVarEiBEjKFKkCK9evWL79u389ttvWFpaxruP0qVLAzB16lQaNWqEWq3GxcUFExMTAIoWLUrDhg3ZvXs31apVo0yZMp+VLyEyNcOOsy6EEPFr3ry5YmJi8tHZUtq2basYGxsrPj4+cWbfUxRFmTVrluLo6Kio1epYM9XEN/ve5cuXlapVqyoWFhYKoNSoUUO/7uzZs4q7u7uSJUsWJW/evMq4ceOUxYsXx5mBJjw8XBkyZIiSM2dOxczMTKlcubJy6tQppUCBAnFm9nv9+rXSv39/xdHRUdFoNEq2bNkUNzc3ZfTo0UpwcHASMiaEEEIIQ/v1118VQClZsmS86xPy+z9m9r2ff/453m2sWbNGKV68uKLRaGKd/3xoxuA//vhDqVChgmJmZqZkzZpVKVu2bKzZ+27evKnUq1dPsbS0VGxtbZU2bdooT548iXNuldDZ92Ls27dPAWLNQPhfV65cUTw9PZWcOXMqGo1GyZ07t1K7dm39rMuKEv/seIqiKFu3blUqVaqkmJmZKVmyZFHq1KmjnDhxIs4+bt68qbRp00bJnj27YmJiouTPn1/p2rWrEhYW9sHth4eHKz179lTs7OwUlUoV73F7e3srgLJ27doE5UMIET+VoihK6pfChBBCCCGEEEKI9Clmpr9Hjx591iyJQmR28vieEEIIIYQQQgjxCeHh4Vy8eJGzZ8+yZcsWZs6cKQUpIT6T9JQSQgghhBBCCCE+4dGjRzg6OmJlZUX79u2ZO3duis4CLURmIEUpIYQQQgghhBBCCJHqjD7dRAghhBBCCCGEEEKI5CVFKSGEEEIIIYQQQgiR6mSgc0Cn0/HixQssLS1RqVSGDkcIIYQQaZCiKAQFBWFvb4+RUea9ryfnTUIIIYT4lISeN0lRCnjx4gX58uUzdBhCCCGESAeePn2Kg4ODocMwGDlvEkIIIURCfeq8SYpSgKWlJRCdLCsrq2TfvqIoBAQEYG1tLXcUU4jkOOVJjlOe5Dh1SJ5TXkbNcWBgIPny5dOfN2RWct6U/kmOU57kOHVInlOe5DjlZdQcJ/S8SYpSoP+Ht7KySrGTK0VRsLKyylBfsrREcpzyJMcpT3KcOiTPKS+j5zgjHlNiyHlT+ic5TnmS49QheU55kuOUl9Fz/KljyrwDIgghhBBCCCGEEEIIg5GeUp+g1WqJjIz8rG0oikJERARhYWEZsvL5IRqNBrVabegwhBBCCCGEEEKIj0qOa/+kyAj1gs+59pei1EcEBwfz7NkzFEX57G3pdDrevn2bDFGlHyqVCgcHB7JmzWroUIQQQgghhBBCiHgl57V/UqT3esHnXPtLUeoDtFotz549w8LCAjs7u8+qWCqKglarRa1Wp9vKZ2IpisLr16959uwZTk5O0mNKCCGEEEIIIUSak5zX/kmR3usFn3vtL0WpD4iMjERRFOzs7DA3N/+sbaX3L1lS2dnZ8ejRIyIjI6UoJYQQQgghhBAizUnOa/+kyAj1gs+59pei1Cek1y9FWiC5E0IIAYCiQGQIhAdBeDBEhUJUOESGQlTYh/+MCgdFC7p/XooWdFH/ef/PMkUbvS+VGtosM+zxCpHG3b9/n59++gkTExPy5ctHzpw59RcRpUuXxs3NzcARCiFE6pPr16T7nNxJUUoIIYQQn6aNhFA/CHn7z+vd//8e5v9PwemfV1gglqH+EPkewgOjlym61InTSE5thPiYrVu30rVrVwICAj7Ypn379kybNo28efOmYmRCCCEyIzlzS8OMjY0pVaoUkZGRFCpUiBUrVmBjY5OobXh5eZEjRw769ev3ybaPHj3i7NmzeHp6AnD48GGaN29OwYIFCQ8Pp23btowbNy4phyKEECItigyDYB8IevXPn/+8gl/B+zexC1DhH76A/S8VEF/HbUVlhM44Czq1KVq1GVq1KVFGpkQZmRGp0hBpZEqkypQIlQnhmBKlMkarMkaLETrFCK1KjRYjtKiJ0oFOpUaLGksLU2oWz/PPzo2SJTVCZBRHjhxhxYoV6HQ6/P392bJlCwAVK1akbt26PH/+nNevX6MoCuHh4Rw6dIjVq1ezbds2xo4dy8CBAzExMTHwUQghRMZmZmaWaa/9pSiVhtnY2HD58mUAOnXqxLx58xg9enSK7e/Ro0esX79e/8UEqFu3Lhs3buT9+/e4urrSpEkT6dIthBDpQVggBDyDgKfg/yT670Ev/190CvKJ7uGUCAoqIk2sCNPYEmxkhU+kOfcDjHkeYkSwzpRgrYaXgVE8f/ue4Cgj3uuMCVWMCdOqCYuIIjwsDF14CEr4++g/tcGgBKLotKDTofzzOJ6i0/3zeJ7un3VaFEUX3eaf99ZV22JTrQMAzvZW1KxYPQWSKET69ujRI5o2bUpgYGCs5YMGDWLkyJHkyJEjziMXFy5coF+/fpw+fZrhw4ezcuVK9u3bR+7cuVMzdCGEyFRsbGy4dOkSKpUq0137S1EqARRFITRS+1mfjx64TIn1i99ck/CBzKpWrcqVK1cAePDgAd27d+fdu3cULFgQb29vsmXLxu3bt+nduzd+fn6Ymppy4MCBWNuYMmUKt2/fZtmyZUyZMoWtW7cSHh5Onz596N27N6NHj+bGjRu4uroyYMAAHB0d9Z/NkiULbm5u3L9/nwEDBrB06VKKFi2KoigUL16cM2fOJLqSK4QQIokUJfpRuncPwf9xdOEp4Bn4P/3n708hLGE9m7RGJrxRZed6gAXX3hrzIswEf60576JMeOYXziv/9wS/DyMCNVGKGm1EKNqgN2iD76JEhqXwgX5YxYI2fFmvKEZGKuwsTQ0WhxBpVVRUFB06dCAwMBA3NzfatGkDQOXKlfniiy8++Piem5sbJ06cYMWKFXz//fdcu3aNWrVqcfDgQfLkyZOahyCEEKnuc6/9Pyah1/+Z7dpfilIJEBqppeTYvcm+3ZsTGmBh8ul/Aq1Wy/79++nevTsA/fv3p0+fPnh6ejJ16lS8vLyYPXs2HTt2ZMqUKdSrV4+goKBYMwdMnjyZO3fusGzZMvbt24evry/nzp0jIiKCatWq0aRJEyZNmsTcuXPZuHEjEN2FL8bbt285ffo0Y8aMoWvXrvzxxx9MnDiRQ4cO4erqKgUpIYRIbooS/Qjduwf/et3//98/UnSK1Co89NcRRFYCjbPzONySi74qbrwM43WIjveKCaFaYwIDAwnz80UbdCfJYZpnscTSNhvGamOMVGBpZU3+go4YG6kwNzPB2MgIE40aWxsbrK2tsfnnT2tra8zMzFCr1RgbG3/wz/hearUaS0tLsmTJkuS4hcjofvzxR06ePImVlRUbNmyIdcGhKMpHP2tkZESXLl2oVq0atWrV4vbt29SqVQsvLy+MjIyws7OjRo0aGBnJ47JCiIwlpa79IWHX/5nx2l+KUmmYv78/rq6uPHv2DGdnZxo0aADAuXPn2LFjBxD9WJ+HhweBgYH4+/tTr149ACwtLfXbWbRoEcWLF2ft2rUYGRmxf/9+duzYwZEjRwAICAjg/v378cbw119/UbZsWYyMjBg5ciTOzs7kz5+fSpUq8eOPP7J8+XK6dOmSkmkQQoiMLSoc3t6D17fh9Z3o17sH0b2gIoLi/YhWp/DAT4e/UTbeqXNwLyQLZ55Fcu15ME99/PB77YtOGwW8B14lKAyzLJbkciiIja0tJsZqTIyNyJ07N4UdC1LAIS8aTfQpQ5YsWcibNy/29vbY29vHWxhSFIWAgACsra1lJhshUkl4eDjNmzfX31338fEBYOHChbEKUolRuHBhDh8+TK1atbhz5w7t2rXTr+vYsSNLly5Fo9F8fvBCCJHJ+fv7U7Zs2Ux57S9FqQQw16i5OaFBkj///8f31HEe3/uYmDGlQkJCqFevHvPnz6d///6xtqEo/38k8EMn/mXKlOHKlSv4+vqSO3duFEXBy8uLzp07x2r37+pojJjnSv/N0tISNzc3du3axYkTJ1i6dOlHj0MIIQQQ8R7e3P2n8HQbXt+N/tPvoX5mOkVR+Hf/hbAoFUtvmXH0mREBijnvIk159i6U1z4+aCPDiS46PY13d2oTM0zMzDFSqTAzM6NAYSeKlyiBQ57cZDU1RmOsxsHBgSJFilCkSBGyZ88uBSQh0rGNGzeyZ8+eWMu6d+8eq5CUKBHv4cERCvlc5fC4BoxcfhyfwEgUjTknL91i5cqVBAYGsm7dOszMzJLhCIQQwvA+99r/U9v+kJgxpUJDQzPdtb8UpRJApVIl6DG7D4kuSqniFKUSysLCgl9//ZVWrVrRp08fypcvz6ZNm2jdujWrV6+mevXqWFlZYWtry/79++N04atUqRIdO3bkyy+/ZP/+/dStW5epU6fSpk0bzM3NuXPnDgUKFMDS0pKgoPjvyv9X165d6dSpE+3bt0et/nhxTQghMhWdLrrQ5HMNXl0Hn+vw6gYEPInTNFKrcOGllr+eqPnrsZpzj4MJCY/6T6vAOJ8DUJuYYmpmgZEKzC0sKF6yFOXKuuJe0Q23cuVwdHSUR2uEyETmzZsHwODBg+nUqRMajYbixYsnfkMh7wg/PAMuemMaFQyAI7C21v+bbC9iylcbdWzfvp0ihQthY5sNY2NjvvnmG/r06ZMMRyOEEIbxudf+nyszXvtLUSqdKF++PKVLl2bTpk3Mnj2bbt26MWHCBAoUKMDy5csBWLFiBd988w1Dhw7FzMws1mBnDRo0wM/Pj2bNmrFnzx6uX79OxYoVURSFnDlzsmPHDlxcXIiMjIx3sLP/ql27NlqtNk7FVQghMpXwIHh1E15d+6f4dD36feT7WM0e+es48CCKv56oeRluSpDOlLdhRjx/+ZqoyIiP7sImZx5qNGpBzmzW2FhoKOZYgCpVKlOsWDG5KSCEAODixYucOnUKjUbDsGHDyJUrV+I3oihEnFmC7q/xmEVFF8Of6Ow4oyuBD9nQEIW96i2lVA9pWtSH3e3NaLomhOcvXvL8xUsA+vbti4+PD+PHj5eel0IIkUSZ7dpfpXxqpMNMIDAwEGtrawICArCysgIgLCyMhw8f4ujo+Nldkj/0+F56du/ePdq3b8/Zs2c/2CY5c/gpMn5JypMcpzzJcepIcp7Dg+DlFXh+EV5chBeXo3tE/Wu713117Po7ij/vaTn9TEuUNvbjePExzWJFYZcKlK9clfp1alG5dDGsLTT62GxtbdNdj6eM+l2O73whM0rpPGTU709K6tGjB0uXLqVdu3asXr36k+3j5Pj9W/xX98Dm+SEAbunysSZrVwpXbYlbwezkyGpKaKSWx2/fc+mJPzdu3SD3qyPUCT+A8voBAEcea5lwJByI7q01ffr0TP3vJ9/j1CF5TnmZIceped0an7ReL0jqtX9Czxekp5RItMWLFzNhwgR+//13Q4cihBApIzIs+vG7FxfhxaXoQtSbu4CCoihceKnj5NMoonTwXmXB+ddmHP07AP+gkHg3pzJSk6uwMy4Vq1G8iCM5rczIaW1B5YoVcHZ2TndFJyFE2vHu3Tt9Iapv376J/rzie4vApa2wCXtOuKJhoXFHCjUfwvgyDnEujhxzZKFmsZxQrygvAxqy/twzLpw5QruwtYx3PIedhYrvdocxc+ZMQt75MG/JCvn5JoQQ6VhqXPtLUUokWs+ePenZs6ehwxBCiOShKPDmb3h6Bp6diy5C+d4E3f/HdorUKhx7omX1bWO23Y7gTWD4vzbw/78baUzJW7ICFarXpmH9erg45sEhmwU21lbxzlInhBCfa9myZYSFheHq6oq7u3uiPhv19AKR3s2w1gXzRGfH+iLT6OX5JZZmn55RL4+1OQPqOhFRszDrztej41/76Oy2hiWaU/TcHsZC79WEPL7AknV/YmxXOKmHJ4QQwoBS49pfilJCCCEyl8gQ1M9Ow9Xr8PQsPDsLoX761dd9tay9HsnhpyoCtaYEK2Y8fxNERFiovo1KY0au4m7kyGaLlbmGAva5aN3Mg0b16ugHmhRCiJQWHh7Or7/+CkT3kkrMYx/Ki8tEbmyPhe49F3VOPKi3mKHVXRMdg4mxEZ0qF6CNW3dWnq7DYc1eRhn9yk/b7vDHoTvcdC1GHocCqO2c6NT9G1q2bJnofQghhMi4pCglhBAiYwt4Ft0L6unZ6D99rmGpiyIoXGHL7UjWXI/k3juFcEwIijT6zyN44cTMfmdhZUuVWvXxbN2KDi08yJLFwiCHI4QQMX777TeePn2Kg4MDHTt2TPDnIp5fwWRDeyyU91xQivHecy2tSxX6rFjMNGp6Vi9Eu4pfs+xEXaqaTOXkxqWcf6GFFw+AB2z9cy+zp03ku+9Hf9a+hBBCZBxSlBJCCJFxKAr4P4ZHx/95nYCAJ9x8rWXFlUi23I4iMFxBhxHvQhUitbp/fTgMACNjDU5u1anbsDHF8+cmt5UZefLkpnLlyjLbnRAizXj//j2TJk0CYMyYMQkenFcJfEmId2ts/ylIhXmu4wvnD8+6lFhZTI3pV9uJTpUXMLFeS45vW05tzuH7/BmLL0XSf9gP+F3bR6vvJoGFLfnz58fS0jLZ9i+EECJ9kaKUEEKI9EtRwO/R/4tQj09AwFMAdt6NZOi+cHzfK/iF/XcOPC0A5jkccK3VhDq1a1M8jzX5sllQxqU01tbWqXscQgiRSHPmzMHX15dChQrRrVu3hH0oKpxXi1qSO+o19xR7wlqvomoyFqT+zdpCw889G/OmbR3mH7xHxNkdfGP5K4uO+jBuxVHGragOgKVlVrZs2UqdOnVSJA4hhBBpm0yHkYYZGxtTtmxZSpYsiZubW5wR7xVFIX/+/AwbNgyA0aNH4+rqSsmSJbGwsMDV1RVXV1cOHTrEli1bKFOmDM7OzpQqVYr58+cDoNPpGD16NEWKFKFIkSJ4enry9u1bAB49eoRKpWLatGn6fZ45cwaVSoW3t3fqJEEIIf7L7xFc/AM2fwO/lILZrtxe+i3fT/fGccJNckwLwmZaCF+uCeXOW110QcpITdailanWexKD5m/l19U7OXvpGu99n3By/QJ+7N2GDs3q80X1alKQEkKkef7+/vrzMy8vLzSaTw9MDvBs/ffkDr6Jn5KVy1Xm4l6qSEqGCUCOrKaMberMlKEDyNZ/L1Xqe5DbSoOdhQorUwgKCsajUUO2b1yT4rEIIURaZWZmRrly5TLltb8UpdIwGxsbLl26xM2bN9m8eTMLFixg0aJF+vWnTp0iT548bN68GYBJkyZx+fJldu3aRcmSJbl8+TKXL1/GxsaG4cOHs3XrVm7cuMG5c+ewsIgeC2XWrFncvHmTmzdvcu/ePSpUqBDrbpuTkxPbt2/Xv1+/fj2lS5dOpQwIIQQQ6g83t8POQSizXDjzvTNdu/ekUI/FFJxwi/y/BFNi3numn4rgkb/C21CFgNDomfPKNmrPT6v3cer6ffxvneTYglHM6N2Uzo2qUr6Mc6IGBRYipTx69IgVK1bw448/MnLkSGbOnMmhQ4cICwtL9LaioqL44YcfcHR0xNzcnEKFCjFhwgR0uv8/qqooCl5eXtjb22Nubk7NmjW5ceNGch6SSGEzZ87Ez8+PkiVL0r59+wR9xv/ydhzuLgdgZ6Gx1K3slpIhxmFvY86UVi6sWb2Otr+docXAsRwdXIIWxY0Jj4yi5VftWT22A4QHp2pcQgiRFtjY2HDx4sVMee0vj+8lhKJAZMin233s81otqNXw7wsgjUXs9x9RoEABZsyYwaBBg/jmm28AWLduHb1792b16tWcOXOGSpUqxfvZGTNmMHr0aBwdo7tnm5ub07VrV/26kydPYmJiAsCQIUNYuHAhf//9NxqNBisrK3LkyMGDBw9wdHTk8OHD1K5dO4mJEEKIBNBGwrNzcP8Q3D8ILy4SHK5l9bVIFpyP4LKP7j8fUEBlRPYSlfmiSRtqViyDi4MNxfLnJk+ePAY5BCESYvXq1cyePZuzZ8+SM2dO8ubNi7m5Oe/eveP+/fuYmZnRoUMHhg8fToECBRK0zalTp7Jw4UKWL1+Os7Mz58+fp1u3blhbWzNgwAAApk2bxsyZM/H29qZo0aJMnDiRevXqcefOHRnbJx14/fo1v/zyCwATJkxI0Fh3uvfvYHt/ADabNqVNux6EhRim+FMgexZ+aVuWOzWL8Mu+xuRqto0WJnPZcjWQjj+uxv/aXvp8Pw4q9ASNzGYqhEhln3vt/zEJvP7PbNf+UpRKiMgQmGyf5I+r+ECiR70AkywJ3k65cuW4c+cOEH2Xc8eOHYwfP57IyEjWrVv3wS/mzZs3+f777+MsDwwMJDQ0NNaJrpGRES4uLty6dQsXFxcA2rRpw/r166lRowZubm4YG8vXRgiRjBQF3t6LLkDdP4j/raMsO+vHsSdadApoFTj0SMf7iOhilEqtwaJEdYq6N6ZM4TyUsremfiVnSjsVNOxxCJEI5cqVw8jIiK5du7J+/Xry588fa314eDinTp1i7dq1lC9fnvnz59OmTZtPbvfUqVM0a9YMDw8PAAoWLMiaNWs4f/48EH3+MGvWLEaPHk3Lli0BWL58Obly5WL16tX06tUrzjbDw8MJDw/Xvw8MDNRvS1H+O17b54vZbkpsOyOYOnUqwcHBlCtXjhYtWiQoTw9W9KWIzo/7ij0unWdiYmxEqIFzXDRXVhZ0qsDVZ07MKNSIMosGcOXCRfpufUtQ0PcMbTAbo+pDwK0rGJsaLM6kku9x6pA8p7zMkOOYY1MUBSXiPaopeVNmPyOff/L6PyaWsmXLcufOHX3ud+zYgZeXFxEREaxdu5aKFSvGiR2ir/2HDh0a598r5to/f/78+nUqlQoXFxdu3rypv/Zv3bo169ato0aNGpQrVw61Wp2gf/9YOfxPTJ8i1YV05N//qMeOHaNkyZLY2NjQsmVLKlSowIwZM+J9FEVRlEQt/6+mTZvSsGFDXr16haenJ3v27Pm8AxFCiMgweHwc7u4j7OZudp69z9nnWl6HKKy/EUlIZNyPmNvlo2qTtnzdvSt1XAuTPWv6u0gRIsaPP/6oLxzFx9TUlJo1a1KzZk0mTpzIw4cPE7TdatWqsXDhQu7evUvRokW5cuUKx48fZ9asWQA8fPgQHx8f6tevH2tfNWrU4OTJk/EWpaZMmcL48ePjLA8ICEixolRwcHQvHnnENrbIyEiWLl0KwPfff68vEH5M4NWdFPHZhVZRcbXsBGpl0RAQEJBmclzAUsVsz1Kcr7SJ74b9wO19qxhxIJw99x/y/eXB1Cszk8jKA4hwbgNqE4PGmhjyPU4dkueUlxlyHBERgU6nQ6vVolW0KVYk0Wq10U9QfWz9P6KiovTLjh07RokSJbC0tKRZs2b89NNPTJs2DZVKhVarRVEU/Wd1Op3+WP4tKipK3/7fFEXRt1cUBQ8PDzw8PPDx8aF169bs3bs33u3FF7tOpyMoKEh/Iyshv6NAilIJo7GI7tWURDFfErVaHfs/ssYiUdu5fPkyxYsXB6Kf7zx9+jQFCxYE4NWrV5w8eZKqVavG+VzJkiW5dOmSvvoZw9raGjMzMx49eqTfjk6n48qVK5QoUULfztLSkpw5c7Jr1y6mT58uRSkhRNIEPIe/98Hf+9DdO8TR+0GsvBrJhpuRBIbHbqrJUYCcbg0omi8nTjmzUreKK6096mfYkyGR+XysIPVfOXLkIEeOHAlqO3z4cAICAihevDhqtRqtVsukSZNo164dAD4+PgDkypUr1udy5crF48eP493myJEjGTx4sP59YGAg+fLlw9raGisrqwQfR0LFFLqsra3l//x/7N+/Hz8/P3LmzEnr1q0/+eieEhaAcngMADuytqLpl60wMlKlyRzXcbHmxu4/6DfSiYXTf+TwIy2HH4VS6q/7fO8+FE/3+ZjWGQFl2oJR2r+ESYs5zogkzykvM+Q4LCyMt2/folarUZtaRPdoSgHqTzy+9++f6deuXdP/Lt+4cSNnzpyhSJHoySlevXrFmTNnqFq1qr7GEPNZZ2dnrl69StmyZWNtO1u2bJiZmfH06dNY1/5Xr17F2dlZvx0bGxty5crFnj17mDFjBvv27cPIyOiTv2/UajVGRkZYWlpiZmYGJLyImWZ+ok+ZMoVRo0YxYMAA/d08RVEYP348ixYtws/Pj0qVKjFv3jycnZ31nwsPD2fo0KGsWbOG0NBQ6tSpw/z583FwcEi+4FSqRD1mF8eHxpRKhKdPnzJ06FD69euHTqdj69at3Llzh+zZswOwaNEi1q9fH29RasiQIbRr144aNWpQsGBBwsLC8Pb2pnfv3gwaNIhBgwaxbt06TExMmDFjBs7Ozjg5OfHo0SP9NkaPHs2TJ08SNG6BEEIAoNPCs3MEXdnBm8t74c0d/MIUNtyIZNW1SJ4G/r+HhbFldrIWrYxDruzUr1ef7m2aUCKPFUZGGfPkR4h/u3jxIhqNRj+Y6LZt21i2bBklS5bEy8tLP/ZDQqxbt46VK1eyevVqnJ2duXz5MgMHDsTe3p4uXbro2/33RPFjvadNTU0xNY3bM1GlUqXYBUrMtjPqBVBSbdy4EYCWLVsmaDiFexvH4qR7xyMlNy4dp6JW/3+Oo7SYY5VKxfyp4xjRtxuDxk5m29o/uO4bSpetYYw6cIvKy7qDyXdYFyzL95PmUNy5lKFD/qi0mOOMSPKc8jJ6jmOOS6VSoTIyAtOsqbr/f/c6VqlUPH36lO+//55+/fqhKArbtm2Lc+2/YcMGqlWrFit2+P+1f82aNeO99h88eLD+2n/mzJk4OztTtGhR/bW/SqXSX/sbGxsn+N8+Vg7/E9OnpImi1Llz51i0aFGcnjwJGYhz4MCB7Nixg7Vr15I9e3aGDBlCkyZNuHDhQrovoPj7++Pq6kpERATm5uZ8++239OjRg0OHDuHk5KT/UgI0a9aMH3/8UT/w5b+VK1eOyZMn07RpU6KiojA2NqZPnz4ADBo0iDdv3lCiRAlUKhWurq4sW7YszjbKly9P+fLlU+5ghRAZQ2Ro9ADlt3fy/OwOfj7oy6ILEfwzGV4sKtMs2JetSaMWnnRp0ZAKjtkxNU7fP7eFSIpevXoxYsQISpcuzYMHD2jbti0tWrRgw4YNhISE6G/WJcT333/PiBEjaNu2LQClS5fm8ePHTJkyhS5dupA7d24gusfUvycC8PX1jdN7SqQtUVFRbNmyBYge8+NT/B9exvH+CgCuuYzmyzwJ622XFuTPn59N3gt5O3MKA72msd77N54H+bHpVhQQAFcOs/2vsuxf8iOurYeCOk1c0gghRJL5+/tTtmzZzHntrxhYUFCQ4uTkpOzfv1+pUaOGMmDAAEVRFEWn0ym5c+dWfvrpJ33bsLAwxdraWlm4cKGiKIri7++vaDQaZe3atfo2z58/V4yMjJQ9e/YkOIaAgAAFUAICAvTLQkNDlZs3byqhoaGfeYTRxxIZGanodLrP3lZ6kpw5/BSdTqf4+flluhynJslxyku3OQ55pyiX1yjK2g6KMjG38nBAVqW3m0YxUaNA9MvY2Fgx0pgqRqYWSoFyNZT+k+cr91++M0i46TbP6UhGzXF85wvJwcrKSrl3756iKIry008/KfXr11cURVGOHz+uODg4JGpb2bJlU+bPnx9r2eTJkxUnJydFUf5/fjV16lT9+vDw8FjnV5+SUnmIkVG/P5/rr7/+UgAlR44cSmRk5Mcb63TKvanVFWWclXJsYgMlIkr7n9XpK8dB70OUAZPnK0W/7K00rV9VKZNHowCKjRnK6cHFFOXyWkXRRhk6zFjSW47TK8lzyssMOU7N69b4ZIR6QXw5TOj5gsFvK/Tt2xcPDw/q1q3LxIkT9csTMhDnhQsXiIyMjNXG3t6eUqVKcfLkSRo0aBDvPhMyi4wSz+jxySE5t5XWpVQOP7Sv1NhPZiY5TnnpKseBz+H2Lt5f2cof24/wLCC6K9STAIV1NyKJip4oDzOHkhRv3I0WTRpRq3hOKhS0jdUbyhDHmq7ynE5l1Byn1PEo/wwyCvDXX3/RpEkTAPLly8ebN28Sta0vv/ySSZMmkT9/fpydnbl06RIzZ86ke/fuQHRX+oEDBzJ58mScnJxwcnJi8uTJWFhY0L59++Q9MJGsNmzYAECLFi0++ejeg4PLKBxyhVDFBJsW09H867G99CirhTmzRn7LtCgd688/ZdGfpyngPYDHz15Rb+4dtr7pTi23aahqDodSrcBIet0KIUR6YdCi1Nq1a7l48SLnzp2Lsy4hA3H6+PhgYmKCra1tnDYxn49PQmaRiTUC/ydGmk+ImJPNzCS+EfhTipIJZoUwNMlxykvrOTbye4jvqbWE3j6A+u1t9t6L4qcTEfi+j3uhnqu4G+2/7k/3lvVxsDHTLw97H0xYagYdj7Se54wgo+Y4obPIJFb58uWZOHEidevW5ciRIyxYsACIvkGX2Efq5syZw5gxY+jTpw++vr7Y29vTq1cvxo4dq28zbNgwQkND6dOnj37Mzn379umHRhBpj1arZfPmzcCnH92LCvHH5vgEAI7k7kLDkml73KXEMDE2omPlArR2c8D7C2dG9u6A/4OrNFwZwh/vb9H23dcoR6ahqjEcSrWU4pQQQqQDBitKPX36lAEDBrBv3z796OzxScxAnAltk5BZZGKNwJ9MY1Ol9zGuEiu+EfhTSkwxMSPPCmFokuOUlyZz/O4h3NyKcn0zo1adY+qJiDhNzKxzYFHMHYdsWSiU04o+3dpTt0Z1AwSbMGkyzxlMRs1xSh3LrFmz6NChA1u3bmX06NH62XU2btyIu7t7orZlaWnJrFmzPjoOlUqlwsvLCy8vr8+IWqQ0RVHw8vLi1q1bBAUF8fr1a7Jly0atWrU++rk760bjrPjxmDxUbD8ulaJNXWYaNb3rlaLdlVPUbdqG84d20W5TKJffGONV7S5mm3uiHP2nOOXcQopTQgiRhhmsKHXhwgV8fX1xc3PTL9NqtRw9epS5c+dy584d4OMDcebOnZuIiAj8/Pxi9Zby9fX96ElcQmaRiW/0+KT6d3f/jHRy/inJmcOE7i8jzwqRFkiOU15ayLH/o6vM8BrK8VNnISK6t0tIpMLZ59E9Ps3MzYhUjNFYZqN2m24M7vMN1YrlSleDlKeFPGd0GTHHyX0sd+/epWjRori4uHDt2rU463/++edMd0NL/N/hw4eZMGFCrGWtW7dGo9F88DN+Dy9R7PFqAP52G0td64zd+806qwVn/trBdwMGMn/uHKYeCWLR5azMqKumq/Md2NQD5chUKU4JIUQaZrCiVJ06deKcgHXr1o3ixYszfPhwChUqRO7cudm/fz9ly5YFoh+pO3LkCFOnTgXAzc0NjUbD/v378fT0BODly5dcv36dadOmpe4BCSFEeub/lLBLG5g7fz6Td97H7wPP2BVr/h09e/elXslclM5rjZFRxik4CJHaypYtS/78+WnatCnNmzenSpUqsdandC9jkbbFPK5Xu3ZtWrZsiZmZGa1atfrwBxQFvw39sUXHCRN3anm0S6VIDcvIyIi5s3/FvXIlhg0fzovnz+m+Ccafzsm3pcKwUF0ny/5OtKxWApuGo6Q4JYQQaYzBilKWlpaUKhX7GfcsWbKQPXt2/fJPDcRpbW1Njx49GDJkCNmzZydbtmwMHTqU0qVLU7du3VQ/JiGESFeCX8ONzWivrGfFrpOMPRTO08Donp0Fc5hTomJVbhmXxNrGlrL5bWleoxzN6qbdx/KESG/evn3L/v372bZtGy1atEBRFJo0aUKzZs2oX7++FKUyMZ1Ox5YtW4DoKbxjBr//mIeHvCkUcpUQxRSr5j+jzkQ3DVQqFR06dKB58+bMmDGDqVOn8vi5LyOe/7/N9JNX2PuiGw5FfkZVYxiUbAFG6XsAeCGEyAgMPvvexyRkIM5ffvkFY2NjPD09CQ0NpU6dOnh7e2eI7u7GxsaUKlWKyMhIChUqxIoVK7CxsUnx/T569IjWrVtz/vz5BH8mR44ciZ4hSAhhABHveXTAmx8mTuPkzWeAwvtI9IOVW1pmJVv19hSr247GZfIyt3QeCtllNWzMQmRQZmZmfPnll3z55ZcoisKpU6fYvn07I0aMoF27dtStW5dmzZrRpEkTcubMaehwRSo6f/48z58/J2vWrAm60aoN8cf6WPQkPkdyd6FRBhrcPDGyZMnC2LFj6dmzJzNmzODRk6c8eP2eGxdOceuNH1WWhrCnww1KvOqGYjcV41ojoGRzKU4JIQzOzMws0177p6mi1OHDh2O9T8hAnGZmZsyZM4c5c+akWFyKohASEvJZn9dqtajV6ljjUVhYWHx0fAobGxsuX74MQKdOnZg3bx6jR49OchwfEhObECLjURSFIwcP4nPtEDw5xaXzZ/j15HvC/zOpqNosC0Xqd6bXt31o5lZQClFCpDKVSoW7uzvu7u789NNP/P3332zfvh1vb2++/fZbZs6cSd++fQ0dpkglMY/uNW7cOEE95u6uG00JxY9H5KFiuzEpHV6aZ29vz4wZM/Tvr96+R736DXn+9D6lF7z/Z+k56hf2ZPU3rmT3GA0lmklxSgjx2df+H/Ox638bGxsuXbqESqXKdNf+aaoolVaFhISQNWvyX6AFBweTJUuWBLWtWrUqV65cAeDVq1f06tWLZ8+eYWZmxuLFiylevDgvXrzgm2++4enTp6jVajZs2IC9vT3ffPMNly9fxtzcnEWLFuHq6oqXlxc+Pj7cu3ePkiVL0q9fP9q3b49araZ69f8/nhMcHEyfPn24desWiqLw66+/UrVqVV69ekXbtm0JCgqiYcOGyZ4bIcRnUhSuHdzId4OGcuTakzirnQrkxqJyW8xyF6V6UTs6N65G6YK5DRCoECI+Tk5ODBkyhCFDhvD27VvevXtn6JBEKlEURV+Uatmy5Sfb+z+6gtM/g5vfcxtLXRurFI0vPXIpXoRbl8/SvGUrjh05rF++776WmjMusfdVZ+wLl4Iaw6FEUylOCZGJpdS1PyT8+j+zXftLUSod0Gq17N+/n+7duwPRY22NGTMGNzc3zp07x8CBA9mzZw/9+/enWbNmfP3114SHh6PVapk3bx6WlpZcu3aN06dP06VLF/0X/Nq1axw6dAgTExMaN27MqFGjaNmyJcOGDdPve+LEibRo0YI//viDZ8+e4eHhwZUrVxg/fjxffvklgwcPZv78+QbJixAiHoEv8T++lHE/z2HekVdoFTA3BjcHE95hzVu1HbZVvqJL+za0KOtAsdwZe2YmIdKL58+fc+LECXx9fdHpdPrlKpWK7777juzZsxswOpGabt68yd9//60/P/soReHVxiHYoOOkpkqmGdw8KbJly8bRw4fw8/PDNyCUGZuO4e31Ldd9/XBfGspfna5TxLcL5CwpxSkhhMFkxmv/zy5KhYeHY2pqmhyxpFkWFhYEBwcn+fMfe3zvY/z9/XF1deXZs2c4OzvToEEDAA4ePMitW7fitD9+/Djr168H0P+bHD9+XP9Fq1y5MqGhoQQEBADQrFkzTExMALhw4QJ//vknAB07duTgwYMA7N+/n127djF+fPQ4BW/fviUiIoKTJ0/yww8/6NuPHTs2CZkRQiSHV8+f8vbCNri9g1MnjjHqQJh+jCj34jnJW7sjD3LWprlLfpqXzYtbfluZNU+INGTZsmX07t0bExMTsmfPHutcIaYoJTI2rVbLuXPneP/+PRs3bgSgXr16scZRjc+jU1soFnyOcMUYy6ZTMtXg5klla2uLra0ti4Z8RY+6LjRq2IDHPk8ptzSKXR0sqMZN2NAF7EpAje//GXMqbT3qIoRIOZ977f+pbX+Iv78/ZcuWzZTX/okuSu3du5c1a9Zw7Ngxnjx5gk6nw8LCgnLlylG/fn26deuGvb19sgZpaCqVKsGP2cXnQ0WpT4kZUyokJIR69eoxf/58+vfvD0R/kf77LGhCtq0oir7dv/9TfOiziqKwc+dO8ufP/8HtCCEMw+/2CX4Y+h0Ldl1CUWKvy5YtG9ka9qVckxY0d7WnupMdJsZyx1eItGjs2LGMHTuWkSNHYiQ9MzKluXPnMnDgwFjLPvXonhIVjuZA9EXC8RxtqFO6bEqFl2FVKlOCm5fOUqdefW5ev0aN5Sp6tarO9/aXsQy5QfYN3VDZ/QRffA/OLUEtD5kIkdF97rV/UsWMKRUaGprprv0TfOazdetWihUrRpcuXTAyMuL7779n8+bN7N27lyVLllCjRg3++usvChUqRO/evXn9+nWKBZ3ZWFhY8OuvvzJjxgyioqKoUaMGv/32GxA9ZfC1a9cAqF69OkuWLAGie7CFhIRQrVo1Vq+OHmfg7NmzZMmSBSuruGMNlCtXjm3btgHo2wPUrVuXefPm6d/HdP+rWrUq69ati9NeCJFyrl27xuhhg/m+Y30G1slLMbfqzP8zuiBla25EVgtTzKyzU7Z1P+bvOselpWOZ064sdUrkkoKUEGlYSEgIbdu2lYJUJrZ27VoAChQoQKlSpfDw8MDT0/Ojn7m5fSZ5tc95o1jj/NWE1AgzQ8qdOzcnjh2levXq6MJDWbB6H4Wm+2L3czBlFoXy5P5t2Pw1yryKcHk1aKMMHbIQIgPLjNf+CT77mTx5MtOnT+fFixcsXbqU3r178+WXX1K3bl08PT2ZMGEChw4d4v79+1hZWfHHH38ke7CZWfny5SldujSbNm1izpw57N+/HxcXF0qVKqXvevfrr7+ydetWXFxccHd3x8fHh759++Lv74+Liwv9+vVj2bJl8W5/1qxZTJw4kUqVKum79UH03VtfX19Kly5NyZIlWbx4MQDjxo1j27ZtuLm5yeCrQqSw0PfvGfVte8qVLcPkn39h+qr9/HrwBa9DFPLlyEL99t0o67WXKTuucf/RUy5umMNXlQqR1VTu6AqRHvTo0YMNGzYYOgxhIL6+vpw5cwaAEydOcO3aNXbu3PnRgXbDAnzJdzV65unLTt+RO2fOVIk1o7KxsWHv3r20bds2Vm+Aaz5RlF6s47iPKap392Hrtyhz3eDiHxAVYcCIhRAZWWa79lcpyn8f+sh8AgMDsba2JiAgQF9JDAsL4+HDhzg6OiZoKt6PSerje+ldcubwUxRFISAgAGtr60yV49QkOU55cXIc/JqDS7zoNXEx995En/x6OBmTM4cNt3X5eJXdjRYde/NVFUfKF7CVf5cEku9yysuoOY7vfCE5aLVamjRpQmhoKKVLl0aj0cRaP3PmzGTbV3JIqTzEyKjfnw9Zvnw5Xbt2pWzZsly8eDFBn7n6W09cXm7grqog+Yafw9zM5NMf+pfMluPEiLk0evr0KfUbNuLOrZsYmVrwVeOq/FrqJnZGQdHtrPOhqj4YXDuAcdzxdSXHqUPynPIyQ45T87o1PhmhXhBfDhN6viC30YUQIo1QFIUnT55g/uYakVc28vOK3XhfCgfA3tKI1g0qcq9IR8IKuTHUzYGGpXJjYSI/xoVI7yZPnszevXspVqwYQJyBzkXGtnPnTgCaNGmSoPZvHlzC+cVGUIGvuxdFE1mQEh8X838uf/78nDpxnObNm3P06FHWbNnPjssudKtXnR9ynyRnwFPYOQjl6HRU1QZB2U6gSf2LWSGESO+SdDWjKAobN27k0KFDcaYuBti8eXOyBCeEEJnFrYun6Nm1AyevPYy1XAW4urli6TEax+ouDC3nQL5sH5+5UwiRvsycOZOlS5fStWtXQ4ciUllERAR79+4FEliUUhTebhpCDpXCKVN3qtZtnrIBZnK2trbs3buXSZMm8fPPPxP88CrzltxkdfnG1M4bSQP1eUpaPqZywBA4Oh1VtYFQrguYyO9pIYRIqCQVpQYMGMCiRYuoVasWuXLlytB38eTpxqST3AnxYbdu3eLwoUPw7iGPL+zjlx1XidCCsRFYaFREosY0e15q9BjHoI5NqFYkh0z1LUQGZWpqStWqVQ0dhjCAY8eOERQURM6cOSlfvvwn2z88uYli7y8Qrhhj03Rqhj4HTyvMzMz48ccf6datG4MGDWL79u28PbOdDUDMSHCd3CxZ2vglxntGoBybicr9Oyjf3ZBhCyGSQK5fk+5zcpekotTKlSvZvHkzjRs3TvKO0zqNRoNKpeL169fY2dl91i/9jPCMaGIpisLr169RqVRxxsYQIrM7e+II1WrWITJKG2t5tSKWuDTw5K5DC5pXKkYbNwdyWsmjAEJkdAMGDGDOnDnMnj3b0KGIVBbz6J6Hh8cnZ19UosIxPTgWgOM5PKnj7JLi8Yn/K1SoENu2bWP37t3Mnj2b4OBgwiOjOHf2DCsuBHHC35E/W0NxfGH/GDgxC9OyPaF6PzBL/rHXhBDJJzmv/ZMivdcLPvfaP0lFKWtrawoVKpSUj6YbarUaBwcHnj17xqNHjz57ezqdLtNN9axSqXBwcECtVhs6FCHShncPObtyAp7jVhAZpaVMLiMK2hrjo8qBtmg9Srbsi0fpHMx2KYBanbl+XgiRmZ09e5aDBw+yc+dOnJ2d45zQybAIGZOiKOzYsQNI2KN7N7bOoJT2Oa8Va0p9NSGlwxMf0KhRIxo1aqR/v3XrVtq3b8+D+w8pMTV6mYlaxbflw/g56Ce4uAgq94VK34CZtYGiFkJ8THJf+ydFeq8XfM61f5KKUl5eXowfP56lS5dibm6elE2kC1mzZsXJyYnIyMjP2o6iKAQFBWFpaZkuK59JpdFopCAlhKKg+/svdiwcz4xNpzn2JLp3VAFbDR4dOnMxZzM6VSxBm/IO2GU1JSAgACN5TE+ITMXGxoaWLVsaOgyRyu7evcv9+/fRaDTUq1fvo21D/X0pcH0OAFeLfkednHapEaJIgObNm3Po0CFatmzJixcvAIjQKvx6JoJzvsbsbPMO20MTUU7NQVXpW6jcG8xtDRy1EOK/kuvaPykyQr3gc679k1SUatOmDWvWrCFnzpwULFgwzh29hE5nmx6o1erPLqwoikJ4eDhmZmbp9ksmhEi4Fy9eMHTQAK6ePwkh7/B/H87zoOjnrI2NVBR0dqVcp7HUa+rOj052+iKUPMcuROa0bNkyQ4cgDCDm0b2aNWtiaWn50bZ31v2AKyHcVTlStfWA1AhPJEKlSpV49OgR7969A2DnvgP0+aYnJx+GUOh3W9a0taVhzjdw5CeU0/NRVfwGqvQFi2wGjlwI8W/Jce2fFJm9XpCkolTXrl25cOECHTt2zPADnQshxKfEFJMURWHt77/Sb+go/ILDYrUxN9WQt3w9WvUaRp/GFcifXWbmEUKIzCymKPXll19+tN2bxzdwfrERVPCm6liKmpqkRngikTQaDbly5QKgR6f2lHdxprFHE148f4bH0ggaN2/Kz0WvUjz8KRybjnJmIaoKPaFKP8gqPd+EEJlXkopSf/75J3v37qVatWrJHY8QQqQbiqIwZ84cxo4dS0BAQKx1bnmM+LZGHo4YVeSRbRV6tG6Cp3tRzE3kkVYhBDRs2JCxY8fi7u7+0XZBQUHMnz+frFmz0rdv31SKTqQ0f39/jh07BkQPcv4xLzcNJ4dKy3mTClSp0yI1whPJwMXFhcOHDtKtWzdOnDjBnxvWcbxMPfJpLKlofJ8BLgGUjpiFcuY3VG5doWp/sLI3dNhCCJHqklSUypcvH1ZWMouEECLzevXqFd26dmX3nj2xlpuooV3VAuiqfM1Vl4YMqOpIufy20qNUCBFLmzZt8PT0xNLSkqZNm1K+fHns7e0xMzPDz8+Pmzdvcvz4cXbt2kWTJk34+eefDR2ySEZ79+5Fq9VSsmTJj04e9OjifkoHHkOrqDD3mCy/S9IZOzs7/vrrL/r06cOyZcvwv7QXf+Aa4H1Ww88t8jKo5Ds4swDl/BJUrh2g2kCwLWjQuIUQIjUlqSg1Y8YMhg0bxsKFCylYsGAyhySEEGnb7m0b6dqtO75+QZgZw/R6ZjRzNudPbWX2WjanbPVatKuYj5xWZoYOVQiRRvXo0YNOnTqxceNG1q1bx++//46/vz8QPYNNyZIladCgARcuXKBYsWKGDVYku5hH9z46655Oh3bPaABOWjehepmKqRGaSGampqYsWbKEhg0b6sfdPXHqNMePHmHwhsd4V69Nj3zPKG70lOLvlpDv4h+oXDyh2mCwK2rg6IUQIuUlqSjVsWNHQkJCKFy4MBYWFnEGOo8Z5E8IITKCZ8+eERAQgBLwkt+njWT2tvMAlMppxPyWdpzL5sHQHC3wrOHKWhd7TIzT73SuQojUY2JiQvv27Wnfvj0AAQEBhIaGkj179jjnViLjiIqKYteuXcDHi1I3/1pOyYg7BCtmFGz9Y2qFJ1KASqXC09MTT09PIPo7MGDAAObPn8/VYweIGbo+i6ma233A4coalCtrUZVsBl8MhdylDRe8EEKksCQVpWbNmpXMYQghRNoTGhrKwIEDWbRoUZx1nSvakqNmZ1YVb0OXL4ozqFA2eaxCCPFZrK2tsba2NnQYIoWdPn2ad+/eYWtrS5UqVeJto40II9vpyQCcz9uJmvkdUzNEkcKMjY2ZO3cuLi4uLF68mPCISB49eUaQ/1sq7irMvKZWtLC4Aje3Rr+KNoouTjmUN3ToQgiR7JJUlOrSpUtyxyGEEGnK3Tt3aNO8MVdvP0AF5LCILjhlsTCjWN02FPAcQtdqhShkl9WwgQohhEjTgoODuXz5sn6m1mXLlgHQqFEjjI3jPxW/tuVnXHW+vMIWV8/RqRarSD0qlYpevXrRq1cvAK5evUq5cuV4eecKLX+GXMXKMruZLa3NL2B0dzfc3Q2FasIX30OBqiA3woQQGUSCi1Lv378nS5YsCd5wYtsLIURa8OLZM6aO/Jbf1v5JeJSCnYWKP1pYEOJYg82mzXGvVov2FfNjYyFTcgshhPi0hg0bcuLEiTjLP/ToXoi/L4VuLQDgdokB1LCxTdH4RNrg4uLCmDFjGD9+PIqi8OrOJbqvLMKvLUYz0OYozY1PoHlwGB4chvxVoPpQKFJHilNCiHQvwQOfFClShMmTJ/PixYsPtlEUhf3799OoUSNmz56dLAEKIURK0el03L9/n7///ptrly4ysH1DChfKz+yVOwmPUqjtqGFETw/WlluOquVvLB3Rgz41i0hBSgghRIJERUVx5swZIPpculixYhQrVoxGjRrRtGnTeD9zZ/1YrHjPPVVBKrfom5rhCgMbN24ckZGRnDhxAjs7O96/uMe1VT/RY8V9sv1uS+3t2XkbroYnp2BVK1hUE27tBJ3O0KELIUSSJbin1OHDh/nhhx8YP348rq6u8U5dfOrUKTQaDSNHjuSbb75JybiFEOKzPHjwgNatW3Pp0qU46yo5aHD7ogaR1QZTvk55BjnlkPGihBBCJNrjx4+JiorCzMyMO3fuYGT08fvBb5/cotTz9aCC1+4/UMREboJkNmq1Gnd3d06fPo2Hhwe3b98G/+hJpA69gHxP8/KDZ1kG5jiDxcvLsK4D2JWIHnPKuQUYqQ17AEIIkUgJ7ilVrFgxNmzYwP3792nbti0vXrxg48aN/P777xw+fJi8efPy+++/8+jRI7799lvUavmBKIRIm/bs2UP58m5cunQJjRqsTaNfVfKb0q2dB5V+3M/XP21g0beN+KKonRSkhBApqmvXrhw9ejTZtvf8+XM6duxI9uzZsbCwwNXVlQsXLujXK4qCl5cX9vb2mJubU7NmTW7cuJFs+xf/9/fffwPRvaQ+VZACeL5pFBqVlgsaNyrXbZ3S4Yk0rFChQly4cIHdu3ezbds2Vq5cSZ48eQh98xyv5cco+Gc5au6yZ+QhLb6PbsCmHjC3AlxcAVERhg5fCCESLNEDnTs4ODBo0CAGDRqUEvEIIUSK0el0TPYazdiJP6EoUCmvmo2e5kRlzcNi3ZcoLm3pXqsEhWXwciFEKgoKCqJ+/frky5ePbt260aVLF/LmzZukbfn5+VG1alVq1arF7t27yZkzJ/fv38fGxkbfZtq0acycORNvb2+KFi3KxIkTqVevHnfu3MHS0jKZjkoA3Lt3D4guSn3K42vHcQk4iE5RYdroR7khIrCwsKBhw4b69zVr1qR58+acP3+e1xf2cgQ4Aiy8nIXtbc2ozn3Y3g+OTIWqA6BsJ9CYGSx+IYRIiCTNvieEEOlNwPN7dG7jwfZTdwHo5aahd/2i/GLcErtKnnxXvQi5rOTETQiR+jZt2sTbt29ZuXIl3t7ejBs3jrp169KjRw+aNWuGRqNJ8LamTp1Kvnz59DO8ARQsWFD/d0VRmDVrFqNHj6Zly5YALF++nFy5crF69Wr9TGAiecT0lHJycvpk2/d//gDAGcu6VClXNUXjEulT3rx5OXr0KIsXL+bVq1coisIfq9by7PEDaiyLosEXbjSxvIOt0QPqPB9MrqM/g/t34NYNTOWGmxAibZKilBAiQwoJCWHAgAFs27oFXUQIoaGhhESCiRpGNspHQIVvOVGtNWMrF8TaIuEXfEIIkRKyZ8/OgAEDGDBgAJcuXWLp0qV06tSJrFmz0rFjR/r06ZOgwsb27dtp0KABbdq04ciRI+TNm5c+ffrw9ddfA/Dw4UN8fHyoX7++/jOmpqbUqFGDkydPxluUCg8PJzw8XP8+MDAQiC5wKYryuYceR8x2U2LbqS2mp1ThwoU/ejx3Tm6jZNglwhVj8jSfkOLHnpFynFalVI7NzMzo16+f/v2QIUPw9PTkwIED7Dlwkj3/LM9mYcS2r55TLfgHlGMzofK3UPFrMLNJ1ngMTb7LKU9ynPIyao4TejwGLUotWLCABQsW8OjRIwCcnZ0ZO3YsjRo1AqIPYvz48SxatAg/Pz8qVarEvHnzcHZ21m8jPDycoUOHsmbNGkJDQ6lTpw7z58/HwcHBEIckhEgDHj58SItmTbhy7Was5bmtNNRs9w35OwymXaUCmJvI2HdCiLTl5cuX7Nu3j3379qFWq2ncuDE3btygZMmSTJs27ZPDJzx48IAFCxYwePBgRo0axdmzZ+nfvz+mpqZ07twZHx8fAHLlyhXrc7ly5eLx48fxbnPKlCmMHz8+zvKAgIAUK0oFBwcDpPtH2O7cuQOAvb09AQEB8bZRdFo0B6PzezpbM8rk+HDb5JKRcpxWpVaOjYyMWLt2LbNmzeLUqVMEhUVx+94D3vk+o+Yf4UxrYslg13dwaBLKidmEl+lMeNnuKBY5Uiym1CTf5ZQnOU55GTXHMTexPkWlGLAct2PHDtRqtf45++XLl/Pzzz9z6dIlnJ2dmTp1KpMmTYo15sHRo0djjXnw7bffsmPHDry9vcmePTtDhgzh3bt3XLhwIcGDrQcGBmJtbU1AQABWVlbJfpyKohAQEIC1tXWG+pKlJZLjlJeWc+zj48OQIUOi70hrI7hz+xYB78PJmUXF0qZmhFo7ssOkMTVb9qSdexHMNGmzGJWWc5yRSJ5TXkbNcUqdL0RGRrJ9+3aWLVvGvn37cHFxoWfPnnTo0EF/vrN27Vq+/fZb/Pz8ProtExMTypcvz8mTJ/XL+vfvz7lz5zh16hQnT56katWqvHjxgjx58ujbfP311zx9+pQ9e/bE2WZ8PaXy5cuHv7+/nDd9RFRUFBYWFkRFRfH48WPy5csXb7tre5bicmYwwYo5wb3Pkyt30sYTS4yMkuO0zJA5fv/+PS2+as9fu3YAULhYMWbXiqJxrtfRsRmbRY835f4d2ORP1diSm3yXU57kOOVl1BwHBgZiY2PzyfOmZO0p5efnx44dO+jcuXOC2n/55Zex3k+aNIkFCxZw+vRpSpYs+ckxDwICAliyZAkrVqygbt26AKxcuZJ8+fLx119/0aBBg+Q8PCFEGnTp0iWaNm3Ks2fPYi2vYG/EmNal2J+7K5Vqt+R3t3yYGCd4wlEhhEg1efLkQafT0a5dO86ePYurq2ucNg0aNIg1WPnHtlWyZMlYy0qUKMGmTZsAyJ07NxBdzP93UcrX1zdO76kYpqammJqaxlmuUqlS7OQ5Ztvp8eTcy8uLFStWoNVqiYqKwszMDAcHh3iPRRsZTvaz0wC4lK8T1fOkXk//9Jzj9MJQOc6aNSt7d2xlzJgxTJkyhft37uBx1winUqWZVTOSxtmewbnfUS4sQ1XaE6oNBLtiqRpjcpLvcsqTHKe8jJjjhB5Lshalnjx5Qrdu3RJclPo3rVbLhg0beP/+PVWqVEnQmAcXLlwgMjIyVht7e3tKlSrFyZMnP1iUkrERMh7JccpLazlWFIUVK1bw7bffEhoaSrHsaibXMcFEDQ+NCnK3eE/CG7RmWtm8aNRG+s+kZWktxxmV5DnlZdQcp9Tx/PLLL7Rp0wYzsw9PtmBra8vDhw8/ua2qVavqHxmLcffuXQoUKACAo6MjuXPnZv/+/ZQtWxaAiIgIjhw5wtSpUz/jKAREz6Q4adIkoqKi9MuqVq2KkVH8N0Uub5uNm+LDG6xxaTMqtcIUmYCRkRGTJk2iffv2/PDDD2zdupW/r13B44YxZWs15NfKb6hufBOurEa5sgZViSZQbTDkLWfo0IUQmYzBBzq/du0aVapUISwsjKxZs7JlyxZKliyp73b+sTEPfHx8MDExwdbWNk6bmDET4iNjI2Q8kuOUl5ZyfPbsWX4YNYJzFy4B0KCwmrWtLbhjUoI1Fh2pWL0h40rkwNhIRUhwkEFjTYy0lOOMTPKc8jJqjhM6NkJiHTp0iObNm8cpSr1//57vvvuOpUuXJnhbgwYNwt3dncmTJ+Pp6cnZs2dZtGgRixYtAqL/PQYOHMjkyZNxcnLCycmJyZMnY2FhQfv27ZP1uDKjQ4cOERUVhaOjI6tWrUKlUsXb8w0g7H0AjtfnAHC76LdUs7aNt50Qn8PZ2ZktW7Zw9uxZRo4cycGDB7l0YCdfvq5DtXo/8J35Hhqoz8OtHdGvwrWji1MFq0EG+vkthEi7DF6UKlasGJcvX8bf359NmzbRpUsXjhw5ol//35NZRVE+eYL7qTYjR45k8ODB+vcxYyNYW1un2NgIQIZ7RjQtkRynPEPlODIykiNHjvD+/XsURWHd6pWs27gZgCwaGFnNlAbuJZhs1ZXq9Vszo3QejIzS53dAvsepQ/Kc8jJqjlPqWJYvX85PP/2kHz8qRmhoKH/88UeiilIVKlRgy5YtjBw5kgkTJuDo6MisWbPo0KGDvs2wYcMIDQ2lT58++olk9u3bF2f/IvH27t0LQKNGjahSpcpH217bOIUKBPCM3JRv+fEB7IX4XBUrVuSvv/7i119/ZejQoQRcPcCRJ5fZrzbDWDGnfvEsrK79FvP7B+H+QXCoCNUHQ9GGUpwSQqQogxelTExM9AOdly9fnnPnzvHrr78yfPhw4ONjHuTOnZuIiAj8/Pxi9Zby9fXF3d39g/uUsREyJslxykvtHL9+/ZoWLVpw4sSJ2HEA3ctq6FyjELtzduNpg/ZMcXVAnU6LUf8m3+PUIXlOeRkxx8l9LIGBgfrHHIOCgmL1lNJqtezatYucOXMmertNmjShSZMmH1yvUqnw8vLCy8srKWGLj4gpSv17aIn4BL59SYmH3gA8LTsYh488uilEconpKVmmTBk8PT158+YNABHA1pOQ+2kxJrVy4mvrU5g+Owtr2kJOZ6g2CJxbgNrgl45CiAwoUT9ZZs+e/dH1z58//6xgIPruanh4eILGPHBzc0Oj0bB//348PT2B6KmUr1+/zrRp0z47FiGE4dy8eZMmTZrw8OFDrLKYUSK7gpEuirxWKjpXzcvZAj3wqdeZH93yYayWAcyFEOmPjY2NvnBXtGjROOtVKlW8ww2ItOn+/fvcv38fY2NjatWq9dG2dzeMozyh3DUqTMUmPVMpQiGi1apVi/v373Pt2jUg+rvbr18/Ap/eYcjSt4zJVZD8qld8ke0dE2tcx3pzTzg0EaoOgDLtQSNFVCFE8klUUeqXX375ZJv8+RM+reioUaNo1KgR+fLlIygoiLVr13L48GH27NmToDEPrK2t6dGjB0OGDCF79uxky5aNoUOHUrp0af1sfEKI9Gfv3r14enoSGBhIoewa/myrpngONU91DizVfEVAne6Mqegos+kJIdK1Q4cOoSgKtWvXZtOmTWTLlk2/zsTEhAIFCmBvb2/ACEVixPSScnd3/+hwEK+f3sXl5SZQQVC1H1Cr1akVohB6VlZWVK1aFYgejL98+fI0b96cv//+m4jAN/gDV4G1d63Z5mmKO49g5yA4PBWq9IXy3cBUHvkVQny+RBWlEjLrS2K8evWKTp068fLlS6ytrXFxcWHPnj3Uq1cPSNiYB7/88gvGxsZ4enoSGhpKnTp18Pb2ll/wQqRT8+fNo/+A/mi1OqrnV7P5K1OizLMzxag1uWt/w/AqhTHTyP9vIUT6V6NGDSD6/Cp//vwZ6lHHzCimKPWh2Z9jPN30A3aqKK6auFKuVovUCE2ITypZsiTnz59n27ZthIaGEh4RwY+TpvDa5wXVlpjS0qMmc1zukCfYB/aPgWMzoFJvqNQLLLJ9egdCCPEBKiWjzdecBIGBgVhbWxMQEJBiA50HBARkuAFf0xLJccpLqRzv3r2bn3/+mZCQEMKD/bh84y4AXcpomOaRjVUmrchavQ8dqhXDwiRjj2Ug3+PUIXlOeRk1x8l5vnD16lVKlSqFkZERV69e/WhbFxeXz9pXcpPzprgiIyPJli0bwcHBnD9/Hjc3t3jbPb11lrxr62OkUrjRZBvO5WumbqD/SI85Tm8yQo5fv35Nu3btOHDgAADW5ZvSvU5h+pnuopDqZXQjTZboXlNV+oJV6vfszAh5Tuskxykvo+Y4oecLibrCCw0N5cCBA/rBM0eOHEl4eLh+vVqt5scff4wzpbEQQsRnwYIF9OvXD51OF2v5j7UtyOXegg1VBtCztiuWZhoDRSiEECnD1dUVHx8fcubMiaurKyqVivjuE6pUKrRarQEiFIlx6tQpgoODsbOz04+FGh//HT+QT6VwLksNKhioICVEQtnZ2bF3717GjBnDlClTCDi/nZX+LizLVY4iquc0tr7PiDKBmJ+aC2cXQZl20eNOZS9s6NCFEOlIoopSf/zxBzt37tQXpebOnYuzszPm5uYA3L59G3t7ewYNkmlthRBxXb16lcOHDwPRA5n/9ttvAHRxNaFVcTVaRcVDm4pE1f6BZo2qktNSCtxCiIzp4cOH2NnZ6f8u0reYR/fq1auHkVH84x3ePbuH0iFniFTUZP/yx9QMT4gkU6vVTJ48mYoVK9KlSxde37sK965yHjgPTD9qxYAa2ZhQ9i3GF5ejXFqByrkFVBsMuUsZOnwhRDqQqKLUqlWr4hScVq9eTaFChQBYuXIl8+bNk6KUECKOFStW0L17d6KiomItn1DTlB++MOEvnRtnC/Wjw5cNKZgji4GiFEKI1FGgQIF4/y7Sp0+NJ6XodLB/HADnsjXBvXiZVItNiOTQvHlzzp8/z2+//UZISAiKorB955+8ePaUKTsDmXsqJ9/XsWNMiadwfVP0y6kBVB8C+SsZOnwhRBqWqKLU3bt3Y01ZbGZmFutuUMWKFenbt2/yRSeESNd0Oh3+/v4sWLCAH374AQB3l8LY616iIYLWJTUULFaCSTl706yZJ6MdrA0csRBCpL4pU6aQK1cuunfvHmv50qVLef36NcOHDzdQZCIhXr9+zcWLFwH0k/X81/WDqykdeZsQxRTH1hNSMzwhko2TkxPTp0/Xv//llzAWLFjAjz9OxO+tL2PX+7LItSqzGlrQ3PQc6r/3wt97Ib87VBsETvUgA42XI4RIHomaTz0gIABj4//XsV6/fk3BggX173U6XawxpoQQmVdUVBTVqlUje/bs+oLUgJp2HGvuy4ZWxvzYojC3KngR0nEPP/T9mtJSkBJCZFK//fYbxYsXj7Pc2dmZhQsXGiAikRj79+9HURRcXFzIkydPnPW6qEisT04B4KJ9O/LkLZjKEQqRMszMzBg0aBAPHz5g2LBhqFQqnl0+QacVj3G93Y3R90px/BlEPToBq9vAgqpwdT1ooz69cSFEppGonlIODg5cv36dYsWKxbv+6tWrODg4JEtgQoj0bfny5Zw6dQqArGZqptbW0KdCOG8VK5abtKdY436MdMmHkZHcMRNCZG4+Pj7xFjPs7Ox4+fKlASISifGpR/cu75xPOd0z/BRLnNuMSc3QhEgV1tbWTJ06lVq1atGhQwfePb/L9XV3uQ5MBgo75GDfVwqFuAGbv4YDP4L7d1C2I5hYGDp8IYSBJaoo1bhxY8aOHYuHh0ecGfZCQ0MZP348Hh4eyRqgECJ9UBSF5cuXc/DgQQD27N4FwM/1zBhcRUMkGpbhgabWUPpVK4WJcaI6agohRIaVL18+Tpw4gaOjY6zlJ06cwN4+9adYFwmnKAr79u0D4i9KhYe9J++VXwG4Ubgn1bLlSNX4hEhNDRs25MKFCwwYMID79++jAPcfPOT+szcU+82Wb1rWYELBS2QPeAK7v4cjP0Gl3lChJ1hkM3T4QggDSVRRatSoUaxfv55ixYrRr18/ihYtikql4vbt28ydO5eoqChGjRqVUrEKIdIonU7H4MGD+fXXX2Mtd7RR8V1FDXuUyjws8z0dG9XE2kJjoCiFECJt6tmzJwMHDiQyMpLatWsDcODAAYYNG8aQIUMMHJ34mGvXruHj44OFhQXVqlWLs/7K5hlUVN7iQw7cWn9vgAiFSF0FCxZk27Zt+vd3796lefPm3Lp1iwWrdrOxbk++djPlG+M/yR/iC4cmwfFZ4NYVqvQBa3nqRojMJlFFqVy5cnHy5Em+/fZbRowYgaIoAKhUKurVq8f8+fPJlStXigQqhEhbXr58yatXrwCYPn06q1atAqBPVRsKWYRipIJCRQoxp+Bw2rRoTePsMqOeEELEZ9iwYbx7944+ffoQEREBRI/VMnz4cEaOHGng6MTHxDy6V7NmTUxNTWOtex/4jqJ3FwHwqFQ/KlvI70GR+RQtWpQzZ87QrVs3Nm3ahO/ehawNacqqilPw0FzkW+MdOPMETs+Ds7+By1dQdQDYxT9cjBAi40lUUQrA0dGRPXv28O7dO+7duwdAkSJFyJYtustlVFRUrMHQhRAZz7x58+jfvz86nU6/zNhIhXczMzq46Hih5GGtVQ/ytuxNM0d5VEEIIT5GpVIxdepUxowZw61btzA3N8fJySlOkUOkPR8bT+r6xilUIognqry4NZPZqUXmZWlpyYYNG5g6dSqjRo3i/rHtcGw784B5ag0ly1dmYZ0IqmtuweVV0a9iHtEz9uWrYOjwhRApLMnVo2zZslGxYkX9+5s3b7J48WJWrVql7z0hhMh4pk2bxogRIwDIldMOVeR7shuHMb2+GdULZ+E341bkbTiEgeUKyyDmQgiRCFmzZiVPnjyoVCopSKVhx48fZ8+ePSiKwrFjx4C4RSn/1y9xfrwCVPCq/BDya0wMEaoQaYZKpWLEiBGUK1eOTp064evrG71CG8nNM8do4FuOms1G0D/rAeqrz2N050+48ycUqApVB4JTPVDJeaUQGdFndWkKDg5m7dq1LFmyhHPnzlG5cmX9xaoQImNRFIXJkyfz888/AzCsYz28ilzFXDEGsrJVqcFG91F0qV0BM43asMEKIUQ6otPpmDhxIjNmzCA4OBiI7lkwZMgQRo8ejZGRTAyRVkRGRtK0aVP8/Pz0ywoWLEjRokVjtbuzaTyVVKHcUxfCrWHXVI5SiLSrfv36PHv2jICAACB6/Lzu3bsT8vAiR5Y94qCpFcZKVsrlVrGwRhAlOQGPT0CuUtHFKecWoJancoTISJL0P/r48eMsXryYTZs24ejoyM2bNzly5AhVq1ZN7viEEGmAoigMHTqUX375BYBxDXPjVfgMKHBFV4gTRYfh2bwlObLKnX0hhEis0aNHs2TJEn766SeqVq2KoiicOHECLy8vwsLCmDRpkqFDFP84e/Ysfn5+WFtb06VLF1QqFV999RWqf/XgeP38Pq4vN4IKgqqOwkgtN2qE+DeNRkOOHNHDO3z11VcUK1aMZs2a8eTJE+Ad4cCx1+B83Yjqro4sqemPE9dhc084OAHc+4NrBzCxMOhxCCGSR6KKUtOmTWPp0qUEBwfTrl07jh8/TpkyZdBoNNja2qZUjEIIAwgLCyMkJARFURg9ejS//fYbALMbmvFdpRBeK1ass+5BTc8B9HGQ//9CCJFUy5cvZ/HixTRt2lS/rEyZMuTNm5c+ffpIUSoN2bdvHwANGzaMM+NsjEebxlFBFckNTSlca7ZKzfCESJdcXV25efMmZ86cQafTERYWxtx589m7ZzfHLt2n2DUNDau7svKLl2TzfwK7hsLhKVDpW6jQA5DepEKkZ4kqSo0aNYrhw4czYcIE1HLXR4gMa926dfTs2VP/GAmACljc1IxOruasNmqEdaMx9C1fLNbdYSGEEIn37t07ihcvHmd58eLFeffunQEiEh8SU5SqX79+vOuf37tK2bd/Rv/SrDMOlTx6KUSCZMmShdq1a+vfN2nShGPHjjFi5EhOnjjB7kPncLEbS9ciAfQ23olDyGs4NBGO/4JZ6XZQYxBYOxjwCIQQSZWo35QTJkxgw4YNODo6Mnz4cK5fv55ScQkhDMTb25v27dvHKkjZmsHqVuYUK1OOP8qspNnw5XhUKC4FKSGESAZlypRh7ty5cZbPnTuXMmXKGCAiER9/f3/Onj0LQL169eJt82rbWIxVOi6ZVca5cvyFKyFEwlSvXp3jx47Rt2/07JURRxez6thjnHcUpf7FmtzU5kMV+R6zi4vhV1fY2hde3zFs0EKIREt0T6lRo0Zx5MgRli5dSuXKlSlcuDCKosQa8FEIkX5cv36drVu3EhUVxZs3b5g3bx4AndztWVI7CCMVPFPs2Gn/Hc4NPOnumEeKUUIIkYymTZuGh4cHf/31F1WqVEGlUnHy5EmePn3Krl27DB2e+MfBgwfR6XQUL16cfPnyxVn/4NpJygUdAsCysVcqRydExqRSqZgyZQrbtm3j2bNnvPbZCsD+S1Dh3hc0b9yK7yz2UplbcHll9Kt4k+hB0fNVMGjsQoiESdJA5zVq1KBGjRrMnTuXVatWsWzZMmrUqEHFihVp3bo1gwcPTu44hRDJLDw8nEmTJjFlyhSioqJirfuukim/1g0iDFPWmrbGqfko+pRw0M+UIoQQIvnUqFGDu3fvMm/ePG7fvo2iKLRs2ZI+ffpgb29v6PDEP/bv3w98uJdU8K5xAJyzrE0FlyqpFpcQGZ2lpSV79+5l48aN6HQ6/Pz8mD9/PiG3jrI3+AW7Hd3JTXbqZ73PT2VfYHZ7J9zeCQWqQbWBUKQuyA1VIdIslaIoSnJs6Nq1ayxZsoTVq1fj6+ubHJtMNYGBgVhbWxMQEICVlVWyb19RFAICArC2tpYeJilEcpw4p0+fpkePHty8eROIHhujgI0a3eOTVM8dTucyGg5TnldVJ9CqjjsatZHkOBVIjlOH5DnlZdQcp/T5QnqRWc+bChcuzIMHD9ixYwdNmjSJte72mT0U3/0VkYqalx2PkN+ptIGiTJi0muOMRHKcso4dO0abNm149epVrOWWufIzubUTvXJcQsM/N11zOkPV/lCqFag1Bog2fZPvcsrLqDlO6PlCknpKxad06dLMmjWLn3/+Obk2KYRIZoqiMHnyZMaMGYOiKOTMmZM5P42lauhe8voegRLwTLFnscMgmn3Vk1pWZoYOWQghMqSrV68muK2Li0sKRiIS4v79+zx48ACNRkPNmjVjrVN0OpS/JgBwIVsTKqfxgpQQGUH16tW5ePEiv/zyC76+vhgbG7N16zbevXrCwCXvmOPxNUOLv6S98UGy+N6ALb3gwI9QpQ+U6wymloY+BCHEP5KtKBVDo5HqsxBpkaIojB07lokTJwLQqUMHRjdxpODt8ZgSTqSiZoNpcwq19OLr4vkNG6wQQmRwrq6uqFQqPtVhXaVSodVqUykq8W8RERGMGDGC58+f8+zZMwCqVKlC1qxZY7W7fmQTpSNvEKZoKNjKywCRCpE52dvbM23aNH0PEy8vL9q0acOZM2e4u+kXJtRqz+zys+ikOUQ34z3YBT6DvaPgyFQo3wMq9QbLXIY+DCEyvWQvSgkh0o7Xr19z48YNAHbs2MHMmTMB+GnEt/SwPk6O2zsAOKOU5GHFCbRuUAcTY5m+WgghUtrDhw8NHYL4hI0bN/LLL7/EWubh4RHrvU6rJcvxSQBczN0Gd4dCqRafECK2fPnycfToUYYOHcqcOXN4dmg12d4/ZnXRWvzi35LqWZ4wyekGTmE+cHwmnJoLZdqCe3/I4WTo8IXItKQoJUQGdfjwYTw8PAgJCYm13KutG8NNV0EYvFas2JGrDw3aDaCSrYWBIhVCiMynQIEChg5BfELMwOYeHh40bNgQS0tLvvrqq1htLu9ZRjntQ4IUc4q3HmuIMIUQ/2JiYsLs2bNxd3enZ8+eXD17As6eAGADsKOgK22a9WKA1UHc+Bsu/oFycQWq4h5QdQDkq2jYAxAiE5KilBAZ0JEjR/QFKQcHB6ysrDCKCKKvcxC9i/2NTlGxVdOAHE0n0t1F7gwJIYShrVixgoULF/Lw4UNOnTpFgQIFmDVrFo6OjjRr1szQ4WU6iqLoi1IDBgyId8a9qIhwcp6fDsC1Ap1xt8uTqjEKIT6sbdu2lClThvHjx/Pu3TsUReHkyZOEPLrMlpUv2VO1M06mZWlifI7vCj4ga8yMffkqRxenijYEI3l6QIjUkOT/affv3+eHH36gXbt2+tn29uzZo39USAhhGEePHqVx48aEhITQsGFDbh7/k309c3KtQwC9XXVcVxxZU8abxsNX84UUpIQQwuAWLFjA4MGDady4Mf7+/voxpGxsbJg1a5Zhg8ukbt++zfPnzzE1NaVatWrxtrm0Yx4OykveYUXpViNSOUIhxKeUKFGCtWvXsm/fPvbv38/p06dxcnIi+O0rXm//mZMbFjNqzRXs5pkw8XEZIjCGp6dhbTuYXwku/gFR4YY+DCEyvCQVpY4cOULp0qU5c+YMmzdvJjg4GIieSWbcuHHJGqAQIuGOHTumL0g1qF+PeT3cMF1Wi7wBFwlRTFll/Q1Z+h6hQ8vmmGnUhg5XCCEEMGfOHH7//XdGjx6NWv3/n83ly5fn2rVrSd7ulClTUKlUDBw4UL9MURS8vLywt7fH3NycmjVryg3FePz1119A9Axf5ubmcdaHhQRT4NocAO449cLSOluqxieESLzSpUtz/vx5evfuTfny5Slfvjw5c+UmLPAtY/84hdPxxsyPbEIwFvDmLmz/Dma5wPFfINTf0OELkWElqSg1YsQIJk6cyP79+zExMdEvr1WrFqdOnUq24IQQCXf8+HEaNWrE+/fvqVOtIouqvaTQ9TmYEMUJXDlWfwftB07DMae1oUMVQgjxLw8fPqRs2bJxlpuamvL+/fskbfPcuXMsWrQIFxeXWMunTZvGzJkzmTt3LufOnSN37tzUq1ePoKCgJO0no4p5dK9u3brxrr+yZQY5eYcPOSjbclBqhiaE+AxWVlYsWLCAc+fOce7cOe7euU2rVq1QdFE8ObCSMdueUiHgJyZGdsBHyQbBPvCXF/xSCvaOhoDnhj4EITKcJBWlrl27RosWLeIst7Oz4+3bt58dlBDi0wICAvD09KRw4cIULlyYunXr8v79e6o552XbF7fJr33CG8WKlXnHUmLoPhpUrYRKpTJ02EIIIf7D0dGRy5cvx1m+e/duSpYsmejtBQcH06FDB37//XdsbW31yxVFYdasWYwePZqWLVtSqlQpli9fTkhICKtXr/6cQ8hQIiMjOXz4MEC8Y0kFB/pR9O/fAXhcuj9m5llSMzwhRDKytrZmw4YNzJ49G41Gw7sbx/DfOI5Nr/JR/t7XtH3QhKsR9hARFD1b368usOVbeHXT0KELkWEkaaBzGxsbXr58iaOjY6zlly5dIm/evAnezpQpU9i8eTO3b9/G3Nwcd3d3pk6dSrFixfRtFEVh/PjxLFq0CD8/PypVqsS8efNwdnbWtwkPD2fo0KGsWbOG0NBQ6tSpw/z583FwcEjK4QmR5r1//x4PDw9OnDgRa3mNQmbsahaIhUbFLuM6ZG8xjY7ORQwUpRBCiIT4/vvv6du3L2FhYSiKwtmzZ1mzZg1Tpkxh8eLFid5e37598fDwoG7dukycOFG//OHDh/j4+FC/fn39MlNTU2rUqMHJkyfp1atXvNsLDw8nPPz/46oEBgYC0edoiqIkOr5PidluSmw7IU6fPk1QUBDZs2enTJkyceK4uvEn3Aniicqecl/2Nlicn8PQOc4MJMepI7ny3K9fPypWrMhXX33F48eP8VkyCoB1wCabPDRq2ZFReS9QmVtwZTVcWY3iVB/c+0OBqpCBb/zKdznlZdQcJ/R4klSUat++PcOHD2fDhg2oVCp0Oh0nTpxg6NChdO7cOcHbOXLkCH379qVChQpERUUxevRo6tevz82bN8mSJfquU0w3c29vb4oWLcrEiROpV68ed+7cwdLSEoCBAweyY8cO1q5dS/bs2RkyZAhNmjThwoULscZmECI9Cw0NJSwsjMjISNq3b8+JEyewtrbGe/4v6C56UzDoAq65jXhCHs6UHEOzFu1k3CghhEgHunXrRlRUFMOGDSMkJIT27duTN29efv31V9q2bZuoba1du5aLFy9y7ty5OOt8fHwAyJUrV6zluXLl4vHjxx/c5pQpUxg/fnyc5QEBASlWlIoZr9QQPXz//PNPAL744os4jzUG+b+h1OM/QAXPSvXFKiQUCE31GD+XoXOcGUiOU0dy5rlYsWIcOnSIUaNGceHCBQBev36Nv/9Ldq1YzIlaPSltX52Wxkdpn+0WOf7eB3/vIypXGcLL9yKycEMwynjn3vJdTnkZNccxN7E+RaUk4WwiMjKSrl27snbtWhRFwdjYGK1WS/v27fH29k5yIej169fkzJmTI0eO8MUXX6AoCvb29gwcOJDhw4cD0XfrcuXKxdSpU+nVqxcBAQHY2dmxYsUKvvrqKwBevHhBvnz52LVrFw0aNPjkfgMDA7G2tiYgIAArK6skxf4xiqIQEBCAtbV1hvqSpSUZOceRkZGMGzeO6dOnExkZqV+eNWtW1v7Ul5pvlpNFCSFSUbMtS2tc2k+kqEPOZI8jI+c4rZAcpw7Jc8rLqDlO6fMFgDdv3qDT6ciZM/E/x58+fUr58uXZt28fZcqUAaBmzZq4uroya9YsTp48SdWqVXnx4gV58uTRf+7rr7/m6dOn7NmzJ97txtdTKl++fPj7+2fI86Zq1apx8uRJFi1aRM+ePWOtO/37QKq88OahUUHyj7qAUTq9+WnoHGcGkuPUkdJ5fvv2LZ07d2b37t2xlhsZm/B1Yxd+LfsIUyKiY7F1hCr9wLU9aOJOkJBeyXc55WXUHAcGBmJjY/PJ86Yk9ZTSaDSsWrWKCRMmcOnSJXQ6HWXLlsXJ6fOmlw8ICAAgW7boGUwS0s38woULREZGxmpjb29PqVKlOHnyZLxFqczWDT0zyKg5vn//Ph06dODs2bOxlufJlZNfWufH4/UCAK4phXno/hMt6tbFyEgl3+N0SnKcOiTPKS+j5jiljmf8+PF07NiRwoULkyNHjiRv58KFC/j6+uLm5qZfptVqOXr0KHPnzuXOnTtAdI+pfxelfH194/Se+jdTU1NMTU3jLFepVCl28hyz7dQ+OQ8ICODMmTMA1K9fP9b+37x6hsvzNaCCgCrDUBsn6TQ6zTBUjjMTyXHqSMk858iRg507dzJt2jR+++03IiIiCA+P4O3bN/y2/TybntZlRP3c9DQ7gLXfQ9g1BA5PgUq9oEJPsMgYM3PKdznlZcQcJ/RYPuu3acwAy8lBURQGDx5MtWrVKFWqFJCwbuY+Pj6YmJjEGsgzpk3M5/8rs3VDzwwyYo7Xr1/P0KFDCQoKwtramlmzZuHRuBFvT3jjcHkGWVV3CVM0bLXpQtmWg/nCOgtBQQnrIpkUGTHHaY3kOHVInlNeRs1xQruhJ9amTZuYMGECFSpUoGPHjnz11VfY2dklejt16tTh2rVrsZZ169aN4sWLM3z4cAoVKkTu3LnZv3+/fra/iIgIjhw5wtSpU5PlWNK7I0eOoNVqKVKkCAUKFIi17u9NE6miCudvYyfK1GlnoAiFEKnNyMiIESNGMGLECCC62D9hwgQmTJjAm0t/Mf5tCRZ6jKOD7XV6Gu/CIeQNHJoEx3+Bcp2hch+wLfCJvQiReSW4KDV48OAEb3TmzJmJDqRfv35cvXqV48ePx1n33xNaRVE+eZL7sTYjR46MdTwx3dCtra1TrBs6kOG646UlGS3HEyZMwMvLC4h+jGDlypXkNovk1cq2FA+8CCq4RHH86s3E071KqhxzRstxWiQ5Th2S55SXUXOcUsdy9epVbty4wapVq5g5cyaDBw+mbt26dOzYkebNm2NhYZGg7VhaWupv7MXIkiUL2bNn1y8fOHAgkydPxsnJCScnJyZPnoyFhQXt27dP9uNKj/bv3w/EnXXv1fMHlHu1EVQQVm0kKqMkTWAthMgA1Go148ePp1KlSnTs2BG/J7dQrR3FvvZjWG42lUZG5+hhvAsX7WM0ZxainP0dlXMLqNof8pQxdPhCpDkJLkpdunQp1vsLFy6g1Wr1M+XdvXsXtVodq8t4Qn333Xds376do0ePxpoxL3fu3MDHu5nnzp2biIgI/Pz8YvWW8vX1xd3dPd79ZaZu6JlJRsnxnDlz9AWpH374gXFjxvDiwFx0Z34iP+GEKKbsyvUNtTqNpqxl6j6vnlFynJZJjlOH5DnlZcQcp+SxODs7M3nyZCZPnsyJEydYvXo1AwcOpHfv3snaQ2vYsGGEhobSp08f/azG+/bt008ek9l9qCj1aPN4KqkiuakpRakvWhgiNCFEGtO4cWMuXLhAq1atuHTpEqfmDQJgwT8vIyM1Xi2KMKbUS7i+MfrlWCN6xr4idTL0jH1CJEaCb/McOnRI//ryyy+pWbMmz5494+LFi1y8eJGnT59Sq1YtPDw8ErxzRVHo168fmzdv5uDBgzg6OsZa7+joqO9mHiOmm3lMwcnNzQ2NRhOrzcuXL7l+/foHi1JCpFWrVq2if//+AHh5eTG6T0dezK5D/jPjMSecc6pSnG/8J637TCR7KhekhBBCpI4sWbJgbm6OiYlJrAkukuLw4cPMmjVL/16lUuHl5cXLly8JCwvjyJEjcXpXZVZPnz7lzp07GBkZUatWLf3y5w9uU/bNjug3tX+QXlJCCD1HR0dOnDjB119/jdF/fjbodFq8dr2g7tsRbI2qQpSigodHYFUrWOAOl1ZBVPgHtixE5pGk36ozZsxgypQpsXom2draMnHiRGbMmJHg7fTt25eVK1eyevVqLC0t8fHxwcfHh9DQ6Kl1VSqVvpv5li1buH79Ol27do3Vzdza2poePXowZMgQDhw4wKVLl+jYsSOlS5embt26STk8IQxi586ddOnSBYD+331HZ1cTVL9VI3/wVYIVMzblGULRoQf5olIFA0cqhBAiuT18+JBJkyZRsmRJypcvz8WLF/Hy8vrg+Jgi+f31118AVKhQARsbG/3y59u8MFFpuWZajpJVGhkoOiFEWmVubs6iRYsIDg4mMDCQwMBA/Pz8cHV1RRcaxIG5o2gxaS+mU8JocbgAQYop+N6EbX1glgscmwmhfoY+DCEMJkkDnQcGBvLq1SucnZ1jLff19SUoKCjB21mwIHrmsJo1a8ZavmzZMrp27QokrJv5/9i777Aori6Aw79l6V1QQZRiL6ggooK999571yjGrkmM+lliib1GjTH2GqOJLUXsvcaKvXdRVEDpy3x/bNiIoKKyLOB5n2cfmbuzM2cOi3v3zJ07M2bMwNjYmJYtWxIZGUm1atVYunQp6gx6m17x+dm3bx8tWrRAo9HQumkDBrkcw/3URQCOqryJbziLZj7ehg1SCCGEXvj7+3Ps2DGKFStGly5daNu2LTlz5jR0WJ+d5C7du335NCVf/AUqMKs1ylChCSEyAAuLxFcxLF26lAoVKui+H8fHRvP73nO43PGmf6Pi9LXdi9PLR7BzDOyb+u+k6L1lUnTx2fmoolSTJk3o0qUL06ZNw8/PD4AjR44wdOhQmjZtmuLtpOROdwnDzBPm2EmOubk5c+bMYc6cOSnetxCGFB4ezpYtW4iMjCQqKopvv/2WqKgoqpUuzE9FDmEdFUuYYskO137UaDcYGwtTQ4cshBBCT6pUqcKiRYuSnOwTaSc+Pl43Uur1otSTrWNwVymctiyLt09lA0UnhMiIvLy8CA4O1t2NdvPmzfTp04eXN08zftZZxhsZoUahan5L1jQIx/HofDj2IxRpDGX7Qk4fwx6AEGnko4pSCxYsYMiQIbRv314314GxsTHdunVjypQpqRqgEJlJXFwcixYtYtSoUQQHByd6rnQeO7ZUv4eFWsUxI2+MGs+lafFiBopUCCFEWpkwYYKhQ/jsnTt3jidPnmBlZaU74Xr93FF8w3cBYFtXRkkJIT6cubk55ubmAHTt2hVfX19atWrFpUuXQBOPBgi8FI7bAzumNHUjwP02BG3UPtzLa4tT+WuCzGUnMrGPKkpZWloyb948pkyZwvXr11EUhXz58mFlZZXa8QmRKSiKwh9//MHQoUO5eFF7WV6ePHnw9PQk8slt8sdfZWKVeBRjczbl7Ev1Dt9gZW5i4KiFEEKIzOnIkSNMmDCBmJgYAB48eABApUqVMDXVjk4O+3M0ACetK1OyqJ9B4hRCZC7FixcnKChI93/O3bt3adu+A7duXKfP8gvMqdyY7/wjaGx8BOPbB+D2AchaAPy/hOKtwMTcwEcgROr7qKJUAisrK4oXL55asQiRKV24cIF+/fqxc+dOABwdHRk1ahRdWjfk0aoA8r24BZhwRlWIqPo/0Kikr0HjFUIIITK7ESNG6D6XX9egQQMALv+zlxIRh9AoKrI2GJPW4QkhMjEjIyNy5coFQK5cuThz6h969OjBL7/8wqVdG+j2oCzf1xpPd+sDtDfehfXTK7ClH+z6Dkp/AaW6gaWDgY9CiNTzUUWpKlWqoFKp3vr8rl27PjogITKTS5cuUb58eZ4/f46pqSn9+/fn22+/5eW5bcTPK0c+JZxoxZjtTt2p2GkMdlZy9kMIIYTQp8jISA4cOADAzJkzcXDQfrmzs7Ojbt26AERvHwvAP1lqUaqgt0HiFEJ8HmxtbVm7di0VK1Zk4MCBhF06xPnbp+lrZMqXSjyF3LKyuGYMpXkCu8fB/mlQoj34B4BDHkOHL8Qn+6iilLe3d6Ll2NhYTp8+zfnz53W3tBfic/fo0SPq1KnD8+fPKV26NGvXrsU1my03lvWgwJPtAFwiN09rzqZBuYoGjlYIIYT4PBw4cIDo6Ghy5cpFv379kpxoDTr8J8WjThCrqMnZaLRhghRCfFZUKhV9+vShdOnStGzZklu3bgERAARdCsPvuhn+5csy1/8pJbgNx3+C44ugcAMo2w9cSxk0fiE+xUcVpWbMmJFs++jRo3V3FxDic/by5Uvq1avHrVu3yJcvH1u3biX21mHCVvSnQPwz4hQj/nJoi1/n7ylkZ2PocIUQQqSxs2fPpnhdmSohdQUGBgJQvXr1JAUpJT4e1a5xAJzKWp/SuQuneXxCiM9XqVKluHLlClevXkVRFJ4/f87w4cPZt28fh3bvpsw5NyrUbssIjyCqqM/Axc3ah6ufdlL0gnVlUnSR4XzSnFJvat++PaVLl2bq1KmpuVkhMpS4uDhatmzJP//8Q9asWdm2aQNPNn1LkXu/AHCDnNyuNI16VWq/8zJYIYQQmZe3tzcqlQpFUd77WaDRaNIoqs/Djh07AKhRo0aS587t30Tx2PNEKya4N5E77gkh0p6JiQlFihTRLe/Zs4fly5czZMgQnj69w66VCzhevAY+lUcSYLOPJsYHMbl7BNYdAYe8UPZL8GoDJhYGPAohUi5Vy6iHDx/W3fJSiM+Roij07t2bP//8EwsLC1bOn4zVxra6gtTfNk2w6HuAKlXrSEFKCCE+Yzdv3uTGjRvcvHmTDRs2kDt3bubNm8epU6c4deoU8+bNI2/evGzYsMHQoWYqT58+5dSpUwBUq1Yt0XNKfDxm+ycAcMqpKU658qZ5fEII8SaVSkWnTp24dOkSPXr0ACD8bCBHF39Hp8UXcf3Tj5lR9QjDCp5dh60DYYYn7PkeXj01cPRCvN9HjZRq2rRpomVFUXj48CEnTpxg5MiRqRKYEBnF06dP2b17N4qicOjQIRYtWoSRkRFT+zah6vnBmKAhWMnC2VLfU7NeKylGCSGEwN3dXfdzixYtmD17tm6SbdBesufq6srIkSNp3LixASLMnBLuuFe8eHGcnJwSPXd65xpKxF0hQjEjfzPpzwoh0hdHR0cWLlxI586d6d27t/Yy8JdhRD28xsj7BZjbYCTtHS7QzfhPXCOewJ6JcGAGeLcFvz6QNZ+hD0GIZH1UUcrW1jbRF2sjIyMKFizI2LFjqVmzZqoFJ0R6d/PmTfz8/AgODk7U/r+GeQmw2ArAIVN/crRfSHU3N0OEKIQQIp07d+4cuXPnTtKeO3duLly4YICIMq+ES/eqV6+eqD1eo8Hu8GQAzuZshZ+Ta5rHJoQQKVG2bFlOnDjB4cOHuXHjBoMHD+bZ/Ssoq4bxg3MeJkVb4Grjxuxq8dTOcg9OLEY5sQRVoXraeadcy4CcJBfpyEcVpZYuXZrKYQiR8bx48YJ69eoRHByMq6srefPmJerFY1rnvE9/r8e8UszYn3cIVdsMxtREbehwhRBCpFOFCxdm3Lhx/Pzzz7ppEKKjoxk3bhyFC8tE26lFUZREk5y/7p+/luIbf4twLCgio6SEEOmciYkJFStWpGLFilStWpUWLVpw7NgxXoVrL0++CtS7bkPZBi35X8Hr1FD/A5e2ah+5SmmLU4Xqg5F8RxGG91FFqTx58nD8+HEcHR0Ttb948QIfHx9u3LiRKsEJkd48evSI27dvAzBixAguXrxIzpw52b/rL179/R1FnvwDQJAqP3FNfqS2V0lDhiuEECIDWLBgAQ0aNMDV1RUvLy8Azpw5g0qlYuvWrQaOLvO4fv06t2/f1n2ZSxAXG0P2E9MACHLriJ9jdkOFKIQQH8zNzY39+/ezY8cOwsPDiY+PZ9q0aZw8eZID63+mTbnWFC83ie4mf9HM+ABm947DLx0hiwf4f6m9vM/UytCHIT5jH1WUunXrVrJ3gomOjub+/fufHJQQ6dG2bdto2rQpMTExujYrKyuWzBiN2eomuMc/QqOo2Jm1A35dJ2FrZWnAaIUQQmQUpUuX5ubNm6xcuZJLly6hKAqtWrWibdu2WFnJF4XUkjBKqmzZsony+s/WhZRW7vMcGzybfWOo8IQQ4qOZmpommpewadOmDBo0iHnz5hFycC33X91icq2BTI9rSQfj7XRSB2L//Bb8MQR2jwffrlC6J9g4G+4gxGfrg4pSmzdv1v38999/Y2dnp1vWaDTs3LkTDw+PVAtOiPTi3LlztG7dmpiYGJydnbGwsMDOzpaA2gWpen4IapXCPbJxq+IMalZrYOhwhRBCZBCxsbEULFiQrVu30rNnT0OHk6klzCdVo0YNXVtMdBS5zs4G4HLervjZORgkNiGESE1mZmb88MMPlC9fnh49enDl9BGcH/XHo2hJJj55xRhjLzqWy8kA+714RD6G/dPg4Gwo1gL8+4BzUUMfgviMfFBRKuHuLwm3pXydiYkJHh4eTJs2LdWCEyI9CA4OpkGDBrx8+ZLKlSvz999/E/XsAY+Xtid/1F8A7LOoRr7O8yn/xp18hBBCiHcxMTEhOjpa7syqZxqNhl27dgGJ55M6tWkOZZTHhGCPV9OhhgpPCCH0ok2bNnh7e9O8eXMuXLjAo0fbdM/9eM2RdXUH0dAjhu7G2yjFFTizWvvIUxn8+0K+aoYLXnw2PqgoFR8fD2jvBnP8+HGyZs2ql6CESC+ioqJo3Lgxt2/fJl++fGzYsIE7xzaTbUd/8vOSl4oFRzxHUqV5AGoj+UIhhBDiw/Xt25dJkyaxaNEijI0/amYF8R4nT57kxYsX2NnZUbKkdr7HqIiX5L4wD4Brhb6gjJWNIUMUQgi9KFy4MMeOHWPjxo2EhYWhKAoLFiwgKCiI4HUj2VWyMqtfWWGvKsBgP2P65rqE+sYeuLEHshUCvwBwrwXYvWdPQnycj+r53Lx5M7XjECLdURSF7t27c/jwYezt7dn820bubByB971VAFw2ygvNl1C9iJeBIxVCCJGRHT16lJ07d7J9+3aKFSuWZB6pjRs3GiiyzCPh0r2qVavqCn+nfpuBP894RFa8Gw8wYHRCCKFfVlZWdOjQQbfcpUsXvvzyS5YuXcq149pRpC+Br+5lZ123iXSz2ENr4z1YP7mEaks/bC2zQukeUKo7WMnAFJG6UlyUmj17Nj179sTc3JzZs2e/c91+/fp9cmBCGNqECRNYtWoVarWaxfNmYLq5G4VjLgOwJ0tzSnabhY21tYGjFEIIkdHZ29vTrFkzQ4eRqSVMcp5w6d6r8BcUuPoTAHeKfYmzudycRAjx+bCysmLJkiW0adOGixcvAjBjxgxu377Ns52LGeuQn2nFJtDO+gRdjf/CJeIp7JkIB2aAV2vw6wPZChj4KERmoVIURUnJirlz5+bEiRM4OjqSO3fut29QpeLGjRupFmBaCAsLw87OjtDQUGxtbVN9+4qiEBoaip2dncwZoSepkeMHDx7wxRdfcPXqVRRF4cqVKwCMHtCZwXZ/YU0ELxQrzvtOpFz9jp/d71Lex/onOU4bkmf9y6w51nd/IaPIaP2mV69e4eDgQExMDJcvX6ZAgQIcXvYt/jd/4L7KmezDzmJiapYKkWccmfVvND2RHKcNyXPq2bFjR6IbQWR1ykGRdiO5b+JCHaNjdDf+Ay+j177nF6itnRTdowJI7j9JZn0fp7S/kOKRUq9fsieX74nMJioqiqZNm3L06NFE7e2reTLKTnvZRJC6EBZtllE+XyFDhCiEEEKIj7B//35iYmJwc3Mjf/78hD5/iufNpQA8LDGAnJ9ZQUoIIZJTvXp1tmzZwtmzZ1m2bBlXrlzh4Ky+1KjfhO3Poln2Mgelipbja/cLVFf/g9GVv+DKX+BcHPy/BM8mYGxq6MMQGZDRx7xo7NixREREJGmPjIxk7NixnxyUEGlJURR69erF0aNHyZIlC9u2beP3tYv5s08BVpS/C8CebO3IM2QveaQgJYQQQg9+/fVXWrZsiZ+fHz4+Poke4tMkzCdVvXp1VCoVFzZMxJZX3DJypUTdHgaOTggh0o/69evz7bffcuLECdq2bYtGo+GvTb9yZf8Wwk/9ye5V82l/wJWqkZNZHleDKEzh0Vn4rSfM8tJe3hf53NCHITKYjypKjRkzhpcvXyZpj4iIYMyYMZ8clBD6dvfuXUaOHMngwYNp164dy5YtQ61W88svv+CuukuNi19TO+sjQhRbDpf9icp95mFhYW7osIUQQmRCs2fPpkuXLmTPnp1Tp05RunRpHB0duXHjBnXq1DF0eBnSkSNHaNGiBY0bN2b58uUA1KhRg+dPHlL87koAnpceglrudiiEEEnY2NiwcuVKNm/ezLhx4xgxYgQtWrRAiY/nyZ7l3Nk6n0F/hOGyoQiNDhXhkcYOwh/AjtEw3RP++AqeydVVImU+6pNYUZRkr3U8c+YMDg4OnxyUEPr07NkzqlSpwvXr1xO1T5o4nqwXF+MZsg2AM8bFceiwDH/3PIYIUwghxGdi3rx5LFy4kDZt2rBs2TK++uor8uTJw//+9z+ePXtm6PAypK+++or9+/frls3MzKhWrRqXNozCXxXFNXVevGt2eMcWhBDi86ZSqWjQoAH169fXzXdUr149AgICuHnhtG69zech8JIn7RvVZ1CWfRSKvQvHfoTjP0GheuDfF9zKGO5ARLr3QUWpLFmyoFKpUKlUFChQIFFhSqPR8PLlS3r16pXqQQqRWuLi4mjVqhXXr1/H3d2d1q1bA+DhbEedsOW4v7qHRlGx36Ub/l0mYmYq10ULIYTQrzt37lC2bFkALCwsCA8PB6BDhw74+fkxd+5cQ4aX4YSFhXH48GEApk+fjrW1NV5eXqhiI/B+uB5UEFHua1RGagNHKoQQGUunTp0oV64cW7ZsIS4ujpcvXzJ9+nRe3g1i+dL7rHLzIrvKhBKWwcwuH0aui1vg4hbIVUo7KXqhBqCWEaoisQ96R8ycORNFUejatStjxozBzs5O95ypqSkeHh74+/unepBCpJavvvqKHTt2YGlpyebNmylerBhnNs2i4KlxmKtiCSYLd6rMoXLlBoYOVQghxGfC2dmZkJAQ3N3dcXd358iRI3h5eXHz5k1SeJNk8Zrdu3cTFxdH/vz5GThwoK79yA/d8FPFcMm4MMUqtzBghEIIkXHly5cv0f+t7dq1o1WrVpw+fRou7OUWcAvYet6KQQ0LM7bQVUzvHYf1ncHeDcr0Bp8OYGZjkPhF+vNBRalOnToBkDt3bsqWLYuJiYleghIitWk0Gr799ltmzJgBwPLlyymQx5UzM5vhFboTVPCPqS8unZfi6+Jq4GiFEEJ8TqpWrcqWLVvw8fGhW7duDBw4kF9//ZUTJ07QtGlTQ4eX4Wzfvh2AmjVr6toe3r6CT/DvoIK4yt+iMvqoaVWFEEK8oUCBAhw+fJi1a9fy7NkzFEVh+cpVnD19iknrjrK0TAO+qpKFzqY7cXhxB/4eBnsmQslOUKYX2OUy9CEIA0txUSosLEz3c4kSJYiMjCQyMjLZdW1tbT89MiFSycuXL2nXrh2bN28G4Pvvv6dUQWeeTffHK/4hsYqaQ+4BlOs4GmOZ8FQIIUQaW7hwIfHx8QD06tULBwcHDhw4QIMGDT54WoSJEyeyceNGLl26hIWFBWXLlmXSpEkULFhQt46iKIwZM4aFCxfy/PlzypQpww8//ICnp2eqHpehJFeUuvv7GHKo4ggy9aJo+YaGCk0IITIlc3NzOnfurFv+8ssvGTJkCHPnzuXx0S2MfVyUuXVG0SrLRbqr/yBv9EM4NAcOzwPPJlD2S3ApYbgDEAaV4m/g9vb2yU5u/rqECdA1Gs0nByZEarh9+zYNGzbk7NmzmJmZ8fOiRRQxvYvz+gaYqjQ8IBtPas+nkn8NQ4cqhBDiM2VkZITRayN3WrZsScuWLT9qW3v37qVPnz6UKlWKuLg4hg8fTs2aNblw4QJWVlYATJ48menTp7N06VIKFCjAuHHjqFGjBpcvX8bGJmNfTnHjxg2uXbuGsbExlStXBuDutfP4PPsDVKCuPtKwAQohxGfAzMyMOXPmUKlSJbp160borfMoa77hlyJ+zA3LhZuFA9/5RdHU4Tqc/1X7cC8H/l9Cgdogo1k/KykuSu3evTvVd75v3z6mTJnCyZMnefjwIb/99huNGzfWPZ+SM3nR0dEMGTKENWvWEBkZSbVq1Zg3bx65cskwwM9RXFwcvXv3Zts27R30QkNDiYyMxMnJiTUrl5L19GyKvTwIKjhuUY7cXZfglc3JwFELIYT4nJUrV45KlSpRuXJlypUrpysefYy//vor0fKSJUvInj07J0+epGLFiiiKwsyZMxk+fLju0sBly5bh5OTE6tWr+eKLLz7pWAwtMDAQAH9/f93I/cebR+GqiueMRWm8SstJKCGESCvNmzenRIkStGzZkn/++YewQ38AcAFocd6GEnXb8m3huzRUH8b49kG4fRAc8oJ/AHi1BVNLwx6ASBMpLkpVqlQpReudPn06xTt/9eoVXl5edOnShWbNmiV5PiVn8gYMGMCWLVtYu3Ytjo6ODB48mPr163Py5EnUarmryufm66+/5ueff07U5uXlxZzxX5P/4Jc4K0+IVow5mn8Q5dsMw0gtVXghhBCGVb9+ffbu3cvcuXOJioqiZMmSuiJV+fLlsba2/uhth4aGAuDg4ADAzZs3efToUaJL28zMzKhUqRKHDh1KtigVHR1NdHS0bjlhSgdFUfQyEXvCdj9m2wmX7tWoUQNFUbh54QQ+/84daVnrfzJx/L8+JcciZSTHaUPyrH+fmuM8efJw8OBBVq1axdOnTwF08yae3PAjvco2Y2LZaXQ120lb9S7snl2HbYNRdo0D325QugdYZ+5BBJn1fZzS41EpqXDkoaGhrFq1ikWLFnHmzJmPunxPpVIlGimlKAouLi4MGDCAr7/+GtB2ipycnJg0aRJffPEFoaGhZMuWjRUrVtCqVSsAHjx4gKurK3/88Qe1atVKdl/Jda5cXV158eKFXubDUhSF0NBQ7Ozs3nsJpPg4iqKwePFievToAcBPP/2Er68vRiqIPPMrJa//gLEqnruqHITVW0iRkhUMHHHGI+9j/ZMcpw3Js/5l1hyHhYVhb29PaGioXvoLGo2G48ePs2fPHvbs2cOuXbtQqVSJ+iwfQlEUGjVqxPPnz9m/fz8Ahw4doly5cty/fx8XFxfduj179uT27dv8/fffSbYzevRoxowZk6T99u3beus3vXz5Emtr6w96/8TFxZE3b17CwsLYsWMHJUuW5NqP7fCNPMBJy3Lk7bk61WPNqD42xyLlJMdpQ/Ksf/rIcXR0NKNHj2bBggUA5C5cHKdGXxOqmNFCvZeuxn/irgrW7l9tSkzBRkT7dCc+a6FU2X96k1nfx2FhYbi7u7+33/RJszrv2rWLxYsXs3HjRtzd3WnWrFmSUSofKyVn8k6ePElsbGyidVxcXChatCiHDh16a1Fq4sSJyXauQkND9XbG7+XLlwCZ6k2Wnpw/f56+ffsCMHDgQJo3b05YyENC1wdQJuofUMFhi8rkbD2TnHZZdGeORcrJ+1j/JMdpQ/Ksf5k1x6/f9EUfrl69ypkzZzhz5gxnz57F1taWChU+/iTKl19+ydmzZzlw4ECS5978vSTMC5qcYcOGMWjQIN1ywsk8Ozs7vRWlgBQVNRVFoXXr1uzZsweNRkNYWBhZsmShUqVK3Dx/GN/IA8QrKhzqj8HOzi7VY82oPiTH4uNIjtOG5Fn/9JXjefPmUbNmTbp27crNi2d5/rAPpSpUZ8XTV8yMLkjd0nUYnO0wvlzB7MJ6zC6sR8lTRTvvVN6qkIl+35n1fZzSY/ngotS9e/dYunQpixcv5tWrV7Rs2ZLY2Fg2bNhAkSJFPjjQt3n06BEATk6Jh+o5OTlx+/Zt3TqmpqZkyZIlyToJr09Oeu5ciQ/3/PlzOnfuTFRUFNWrV2fy5MlcPbKV7Dv648YLIhVTThb5mrLNB8otoD+BvI/1T3KcNiTP+pdZc6yvY2nVqhX79u0jPj6eihUrUrFiRYYNG0bx4sU/ept9+/Zl8+bN7Nu3L9E8m87OzoC2D5UjRw5de3BwcJI+VwIzMzPMzMyStKtUKr3lJGHb79v+uXPnWL9+faK2li1bYmxsTMTfYwH4x646vkVK6SXOjCylORYfT3KcNiTP+qevHDdp0gQfHx9atWrF0aNHCdzyq+653y5Zs7d2f8oUaEc34z+ooz6O+sZuuLEbshUCv95QvBWYWKRqTIaSGd/HeilK1a1blwMHDlC/fn3mzJlD7dq1UavVumF3+vAhZ/JSuk567lyJDxMfH0+HDh24fv06rq6urFi2lFNLBlLq/nIArhu5E9/0Z8oXlc5oapD3sf5JjtOG5Fn/MmOO9XUs69evJ2vWrHTu3JkqVapQoUKFj55HSlEU+vbty2+//caePXvInTt3oudz586Ns7MzgYGBlCihvf12TEwMe/fuZdKkSZ98LGkt4XLDqlWrMnv2bIyNjcmfPz+XjgXiFXmMOMUI54ajDRukEEKIt3J3d2f//v2sWbOG4GDtJXsbNmzgyJEjPNw4nqv+VekUZYyxphitCsGEQlewfnIJtvSHnWO1806V6g42mXveqczsg4pS27dvp1+/fvTu3Zv8+fPrKyYgZWfynJ2diYmJ4fnz54lGSwUHB1O2bFm9xicMR6PRcP/+fQB+/PFH/vjjD8zNzZkzdTzhS5pQKvYyAIeyNMar21ysrDP27a2FEEJkbs+ePWPfvn3s2bOHESNGEBQUhJeXF5UrV6Zy5crUqVMnxdvq06cPq1evZtOmTdjY2OhGjtvZ2WFhYYFKpWLAgAFMmDCB/Pnzkz9/fiZMmIClpSVt27bV1yHqTUJRqlGjRonuzhy3YxwA/zjUpXS+ogaJTQghRMqYmJjQsWNH3XL//v0ZPnw4U6ZMIejwLl37nLOwyqcWfaq70dN8J7kinsK+yXBwJhRrAX4B4Cz/52c0H3Qt0/79+wkPD8fX15cyZcowd+5cnjx5opfAXj+TlyDhTF5CwalkyZKYmJgkWufhw4ecP39eilKZVHBwMMWKFcPd3R13d3cmTJgAwLcBbal6YSj5Yy8TqlhxrPQsyvZfJgUpIYQQ6Z69vT0NGzZk+vTpnDx5kqCgIIoUKcL06dOpX7/+B21r/vz5hIaGUrlyZXLkyKF7rFu3TrfOV199xYABAwgICMDX15f79++zfft23Z2NM4pXr17pJnB/fR7R8wc2UzTmNDGKMW5NRhsoOiGEEB/LxMSEyZMns3//fr7//nu+//57evbsCcCzf/5m7tq9lH30Fb1j+nMyPj9oYuD0KlhQDpY1gMt/QXy8gY9CpNQHjZTy9/fH39+fWbNmsXbtWhYvXsygQYOIj48nMDAQV1fXD+rQvHz5kmvXrumWb968yenTp3FwcMDNze29Z/Ls7Ozo1q0bgwcPxtHREQcHB4YMGUKxYsWoXr36hxyayABiY2Np2bIlFy9eRK1WY2JigomJCW3K52akjfb644vGhbFpt4zSuQsaOFohhBAiZZ49e8bevXt1d90LCgrCwcGBRo0aUaVKlQ/aVkpu2KJSqRg9ejSjR4/+yIjTh7179xITE4ObmxsFChQAQImPx3iP9oTVqeyNKeOm35H9Qggh9Kd8+fKUL19et9y4cWM6duzI0ztXiFk9lHvdh9PG8juKxF75d96pYxjf3Ac394FjPu28U15twNTKgEch3uej7r5naWlJ165d6dq1K5cvX+bnn3/m+++/55tvvqFGjRps3rw5Rds5ceJEos5WwuTjnTp1YunSpXz11VdERkYSEBDA8+fPKVOmTJIzeTNmzMDY2JiWLVsSGRlJtWrVWLp0KWq1+mMOTaRjQ4YMYe/evdjY2HDs2DGMIx5gviWAXMot4hUV+53a49dtWrLzhQkhhBDpVbZs2ciaNSsVKlSgR48eVK5cmaJF5fKD90m4dK9WrVq6+b7O7lmPV9xFIhVT8jb5nyHDE0IIkcrq1KnD6dOnadOmDfv372frzG+oXrM29+PN6fw0HnWcJ34OYSyvFIx9yDXYNhh2fge+XaF0D7B1MfQhiGSolJScUksBjUbDli1bWLx4cYqLUulFWFgYdnZ2hIaG6u3ue6GhoZnuLkT6dufOHYYOHcr9+/eJi4vj6NGjAGxYv55coYcoeWcxapXCI7LyqOoMPIpXlBzrkbyP9U9ynDYkz/qXWXOsr/7C+fPnM1QRKr30mwoVKsTly5f59ddfadasGUp8PNfH+5JPc50jzu3w6zUv1WPLLDLr32h6IjlOG5Jn/UuPOY6Li2PMmDGMHz8+2RHCTtU606uMNV2M/8JdpZ08XTEyRlW0mXbeKRfvNI743dJjjlNDSvsLHzVSKjlqtZrGjRvTuHHj1Nqk+IyFhYVRr149zp8/n6h9QJ+eFL80kXxx10AFx2xqUKDzfLwcshIaGmqgaIUQQoiPl1CQCg4O5vLly6hUKgoUKED27NkNHFn6dfv2bS5fvoxaraZatWoAnApciY/mOq8Ucwo2G2ngCIUQQuiLsbEx3333HfXr19fNLQhw7do1fvzxR14cXMeKiCpMC3ejYJZcTCsXTiWzq3B2nfbhXk5bnCpYB4zkCitDS7WilBCpRaPR0KZNG86fP0+OHDmYOXMmaiMjQi/tpG3seszjYgnFisu+YylVrxsqlSpFc2gIIYQQ6VFYWBh9+vRhzZo1xP87MataraZVq1b88MMP2NnZGTjC9Cfh0r0yZcpgb29PvEZDlqNTATjn2ga/bDne9XIhhBCZQJkyZShTpoxuWVEUrl27xs6dO7lx+A8ATgI1L7rh16gPA3KcpoH6CMa3D8Ltg5Alt3beKe92YGZtoKMQUpQS6Up8fDwDBgzgjz/+wNzcnE2bNpHT0YoX63pRJO4MqOCsWUmyd/iZ0rlyGzpcIYQQ4pN1796d06dPs23bNvz9/VGpVBw6dIj+/fvTo0cPfvnlF0OHmO68Pp8UwD9/LcU3/jbhWFC46XBDhiaEEMJAVCoV69atY8WKFcTExBAbG8vcuXN59OgOh1fO4FwBP4w0hSlkGszkMi8oy0348yvYNR5KdoIyX4BdLkMfxmdHilIi3Xj27BkdO3Zk27ZtACxdsoT4K3+S5cocXFQxRChmnC44AL9W32CkNjJwtEIIIUTq2LZtG3///XeiOwzVqlWLn376idq1axswsvQpLi6OnTt3Ato8aeLiyHZyOgBBbh3wc8hmyPCEEEIYkKOjIwMGDNAt9+jRgw4dOrB9+3aen9sDwEGg/DkTateowFzfu+SJfgyHZqMc/gFVkUbg/yXkKmmI8D9LUpQS6cKJEydo3rw5t2/fxtzcnO9GfEWJa9MoEHdFOzrK1JssreZTNm8RQ4cqhBBCpCpHR8dkL9Gzs7MjS5YsBogofTt69CihoaE4ODjg6+vLqW0/4Rt/j1CsKNL0G0OHJ4QQIh3Jnj07f/75Jxs3buTOnTsABAYG8tdff/HnX7sodqs8Teq0IMBqF2XVFyBoo/bhWkY771Sh+qCWsok+SXaFwS1cuJC+ffsSExNDnjx5GN2hPK1i5mCq0hCmWHKh2FeUbtJfRkcJIYTIlEaMGMGgQYNYvnw5OXJo50J69OgRQ4cOZeRImbD7TQmX7lWvXh0lXoPTqVkAXPTojJ+9oyFDE0IIkQ4ZGRnRvHlz3fKAAQOYMWMG33zzDRGXDrD1+S321RlCUWdjuhr/RSP1IUzuHoW7R8HeDcr0ghIdwDz17zgrpCglDGzBggX07t0bgOoV/ZlW7gXF+R1UcMrCH5f28/HLKXNHCSGEyLzmz5/PtWvXcHd3x83NDYA7d+5gZmbGkydP+PHHH3Xr/vPPP4YKM914fT6pf7b+SGnlAc+xpVjToQaOTAghREZgZGTE4MGDKV++PK1ateL27dtErBqKXalK9AiNpheF6VjKlpFuZ8ny4g78/S3sngg+HbXzTmVxN/QhZCpSlBIGs379egICAgDoUq0gP5c7j0ql4hm2XPcdhW/drqiMZHSUEEKIzK1x48aGDiHDCAkJ4fjx4wBUqVwJsxV1ALictwt+tnKpoxBCiJQrU6YMp06donv37mzcuJHzh3bonpt5Uc3KSh3o6mdPN+O/yR9zH478gHJ0PqpC9bXzTrmWBpXKgEeQOUhRSqSZp0+fMmvWLJ4+fUpcXBzLli1DURS6lbTgp3IPUDDiqGMjCrWdQinH7IYOVwghhEgTo0aNMnQIGcaOHTtQFAVPT0+e/LOJ0spjnmKPl4ySEkII8RGyZMnCr7/+yp9//snNmzcB2LVrFxs3buTJ7qUsvlOMOcYOZFOpaZY7gimlnqC6uBkuboacJbXzThVpBGoTAx9JxiVFKZEmnj59StWqVTl37lyi9uZFjPmxrjHXjAsQX28afj6VDBShEEIIIdI73XxS1aridv4HAK4V6I6flY0hwxJCCJGBqVQq6tatq1sOCAhgwYIFDBw4kCfXtd9fbwPTL8HyK0WZ0DAXHW2PY3b/JGzoBoGjoExP8OkEFvaGOYgMTIpSQu+ePn1KtWrVOHfuHNmzZaWJdxZcYu/gYqOiYXF7jhcdTKmmA1Aby9tRCCHE58PIyAhVMsP+bW1tKViwIF999RVNmzY1QGTpk6IouqJUHqsInHlKMA54Nx5o4MiEEEJkJiqVit69e1O1alV27tyJoigEBwczadIknl4/z5eLHzO1/pf0cr9DB+MdZA27B4H/gz2ToER78OsFDnkMfRgZhlQBRKpTFIVRo0axePFiNBoNr169Ijw8HEdbCwJbxFA822M0ijnHHRuhbj0Bv+w5DR2yEEIIkeZ+++23ZNtfvHjBsWPHaN++PcuWLaNFixZpHFn6FBQUxIMHDzA3N6eRKhCAm4W/oIyltYEjE0IIkRkVLFiQggUL6pabNm1KixYtuHr1KtfWjmNR/Z7MLziDRsZH6Kb+k0Kxd+HYjyjHFqIqWBf8+4B7WZl36j2kKCVSlUajoXfv3vz000+J2nPYGLGzvRGFs8EZc19sGn6PX5FSBopSCCGEMLxGjRq99blOnTpRpEgRpk6dKkWpfy1fvhwA7wKuuJs85hFZ8W7Uz8BRCSGE+Fx4eXlx8uRJevbsydq1a7mwaT7FS57kd9MszA91pIhrfib4hlDd+Axc3qZ95PACvz7g2QSMTQ19COmSFKVEqomLi6NLly6sXLlSe5vNrk2obXyErLyggKMRD03dOVNhFMUrN0v2cgUhhBBC/KdmzZqMGDHC0GGkCyEhIcybNw+Avp4vALjtGYCzuaUBoxJCCPG5sbGxYfXq1VSpUoV+/fpx9uQx3XPHL0CT66Xwrvs/etkcpJnxPswfnoHfesKOUVC6B5TsApYOBjyC9EeKUiJVvHr1itatW7N161bUaiNmN3EiIKd2aH0w2ThTpA8lm/TD2ESqw0IIIURKREZGYm5ubugw0oWZM2fy6tUr8rs70yb/Kx6onPBp1MfQYQkhhPgMqVQqevbsSaVKldi+fTvx8fGEhIQwZcoUXl47zrlVtxmQ05MvY/JT1Cac+RVeUJSHsHMs7J0C3m3BrzdkzW/oQ0kXpCglPtmTJ0+oV68ex48fx8xYxS/NzWhY8BXPsOVK/h54Nxkk8z0IIYQQH+inn36iRIkShg7D4F68eMHs2bMBGF46BpVKxb3ifXExlYKdEEIIw3lzzqkWLVrQsmVLLl26BCHBABwAvC9koXHDOozJdwHPuNtw4mftI38t8A8Aj4oGOoL0QYpS4oOEhoZSrVo1Ll68qGuLiYkmLk6Dg4WKLW0sKOpqy2GPThRr9g1+NvaGC1YIIYRIxwYNGpRse2hoKCdOnOD69evs378/jaNKf3744QfCwsLI7ZKVDoWjuadywad+L0OHJYQQQiRSrFgxjh8/zq+//kpYWBiKovDTTz8RFBTEhrW/sKtsS6qXb0sPk+1UU/+D0dW/4erf4OSJafHOULoDmFgY+jDSnBSlxAcZMWIEJ0+eTNKeN4uKtW2zEluiI0qTr/F3yGaA6IQQQoiM49SpU8m229raUrt2bQICAnB3d0/jqNKXly9fMmPGDACG+WkwUql46N2PXDIdgBBCiHTI2tqazp0765Z79OjBgAED+Omnn3h+cB0nQq8TVHMA2Uzb0kX9Fy3U+7B8HIRl4FCUg5OgVDfw7QY2ToY7iDQmRSnxTg8fPmTo0KFcuXIFRVF0BanlLe2pkCMOgKfYEeXTlcJNBmElI6OEEEKIFNm9e7ehQ0j3FixYQEhICLmcHOjiGcs9lQsl6nY3dFhCCCFEilhaWrJw4UKqVq1Kz549uXn+BA4P+mHu6cOQkAgGqj3pU92DXjZ7yRnxFPZOQtk/HVWx5uAXADmKG/oQ9E6KUuKtdu7cSdu2bQkODk7U3r64CR0Kx3NPlZMHRb/Aq94XmMndb4QQQgjxiaKjo9m4cSMRERFYWFgwdepUAIaWAWMjFQ+L9ZZRUkIIITKc1q1b4+vrS+vWrTl58iTP9u/QPbfSsiarK8+kttFxuhr/SUmuwpk12od7ee2k6AXrgJHagEegP1KUEknExMQwbtw4xo0bh6IoFHQyZ2IlFaZqMFWryJK/JKfL9qZY5RbkMpa3kBBCCCFSR3h4OG3btk3U5pzVnt7FY3lIdrxlLikhhBAZVL58+Th48CBbtmzhxYsXhIWFMXToUG4d3Y6v6iV/RatYGgq2Rq508jZhnOdtjG8fgNsHIIsHlOkF3u3A3NbQh5KqpKIgEjlz+jStWzbj0tUbAHQrYcKcOiYoxuacy1oH5+r9cC9c0sBRCiGEECIzMjExoWrVqsTFxWFsbIyRkYqeOc5joo7kTpEvyGFqZugQhRBCiI9mZmZG8+bNdcvPnj1j/PjxnDhySNcWAUy6BkuuVmZYLWc6m+/F/vkt+Osb2DUefDpAmS+0hapMQIpSn7ktW7bw9ddfc/vWLZT4OKJjYolXwMFCxby65pQqnp8z+VpQuE4vymTJauhwhRBCCJGJ2dnZsWPHDkJDQ7Gzs+PYr9MoE3ScYBzwbtjH0OEJIYQQqWrMmDH4+PgQHByMhYUFKpWKM2fOMGPGDILP7OF/wbmZVfdr2jjdpqv6T/LFPIAj81COLkBVsK523in3sqBSGfpQPpoUpT4j0dHR7N27l6ioKBRFYf7cWfy9I+kkq3ULmtGnYyMK1OtL7mJlyWNkZIBohRBCCPE508TFkevCQgBuFuxGGZm/UgghRCajVqtp0qSJ7mSM6t/iUp06dWjfvj2PH94kdvW3HGw7mDVZJ1HR6Dxd1X9SSX0WLm3VPpyLa4tTRZuCccYbUSxFqc9AfHw8a9euZfjw4dy6dSvRc8ZGMMjPlE4+5ty0LI7asyGVWwRgbmFlmGCFEEIIIYCgfRvxUh4ThhXFG/Y3dDhCCCFEmqlevTpnzpyhQ4cOBAYGsu/n7/As5sUejZpfX8XhVqQhI0qE08zkAOaPzsLvvWDHKCjVHUp2Aetshj6EFJOiVCam0WhYtWIZE8aN4fL1OwA4WanInUU78snZ2oj2tUviVqkdOSu1pohcnieEEEKIdEI5sRiAC9nr42dlY+BohBBCiLTl5OTEX3/9xaRJkxg5ciRB587onrt0+yz97lbg+2qT6GB1lI7G23F++Rh2j4d9U6F4C+3oKSdPAx5BykhRKhMIDQ1lzZo1nD17lujICMKe3kXz4iGnL97gZkg0ADam8E15M7qXseNeltLE5atJwYotsXN0MnD0QgghhBCJBd+7TvGIo6CCnNUDDB2OEEIIYRBGRkYMGzaMZs2acf78eQCCgoL47rvveHZ+P/EPLjLR2pH/xRiTJ7sH82rEU9n6DpxaqX3krqQtTuWvCel0Wh4pSmVQcXFx7Nj+N4t+nMu2v3cSFR2b7HpZzKF9mWzUrV8PV//mZClVnexy5xohhBDiszZv3jymTJnCw4cP8fT0ZObMmVSoUMHQYek83r+EAiqF82beFC3gbehwhBBCCIMqUKAABQoUAKBp06bUrl2b1q1bc+PGDXj2FICLj6D6TUfKNuzIV7kvU0d9HPXNvXBzLzjkhTK9wLstmFkb8lCSyDRFqfTeufpYkZGR7N61k62bNnDz6iU0UWFoIkI5de0RzyM0uvUKZzWiUUFjzIzhhcqOl+Y5yJ6vOD36fk3uAkUNeARCCCGESE/WrVvHgAEDmDdvHuXKlePHH3+kTp06XLhwATc3N0OHR0x0FEUeb9b+XKKLgaMRQggh0p9SpUpx9uxZDh8+TGxsLBEREXz77bdcuXKFA2vmcbF0IzxKjqK7/THaqHdj++w6/DkUdo2Dkh2hdE+wN/xnPoBKURTF0EF8qnXr1tGhQ4dEnatFixaluHMVFhaGnZ0doaGh2Nrapnp8iqIkmk1fURQiIyJ4+iSYB3dvcv3qJW7euMaVS5e4dPkq127fJzomFhSFmDgNcfHJb9fRQkWdQhZUK12Ygj5lMfcojUeJamTJliPVjyG9ezPHIvVJjvVPcpw2JM/6l1lzrO/+QlopU6YMPj4+zJ8/X9dWuHBhGjduzMSJE9/7en3n4fDK0fhfm8ETsmD/7WVMZIR3qsusf6PpieQ4bUie9U9yrH+pleOXL1/Sp08fli9fDoCJqSnZS9XDongtWme5QFf1X+Q2eqTdp8oIVeEG4NcHXEuDHn63Ke0vZIqRUtOnT6dbt250794dgJkzZ/L3338zf/78ZDtX0dHRREdH65bDwsIA7ZshtWt0M78byugJM/5dUohX4FWMwofsJaeNiop5zPBwyUq0qQMaC0eKlPCjXotO5HDLh+qNa0MzQZ3xgyX87j7HY08rkmP9kxynDcmz/mXWHGeG44mJieHkyZN88803idpr1qzJoUOHkn1NWvab7l49S4mrc0EFN4r2pbSJaabIe3qTWf9G0xPJcdqQPOuf5Fj/UivHVlZWLF26lDZt2vDdd99x6NAh7h/8DeOjW1lbti4rykyiqvE5uqr/pLw6CC5sggubUBrN017Wl8pSejwZvij1MZ2riRMnMmbMmCTtoaGhqf7HFvEyjLDotwx1AqxMwMXOGBc7U3I6WpEnV3Y8PDywypoTI+tsWDu5U7hEOewdnZMUnwDCwsNTNd6MSlEUXr58CSAVfD2RHOuf5DhtSJ71L7PmOKEYk5E9ffoUjUaDk1PiG504OTnx6NGjZF+Tlv2mWA1cNi8O8XHkr9KZ0NDQVN2+0Mqsf6PpieQ4bUie9U9yrH+pnWM/Pz+2bt3K/v37mTJlCgcOHMDd5CXf9yjFquM5mfasMsVqGWN+ejEmN3YQ5lIB9PB5m9J+U4YvSn1M52rYsGEMGjRItxwWFoarqyt2dnapPgy9fa8h+JTyJyZOg5W1DSZm5tjY2mFra4ejU07s7B2TLTaJD5PQKZZhpfojOdY/yXHakDzrX2bNcWY+FkVR3np8adlvsrMrTvzQ7Tx6cA/7LFkyVc7Tk8z6N5qeSI7ThuRZ/yTH+qevHDdo0IAGDRqwf/9+bG1t8crnQsl8LsTHKxgZqSCfH8RFYWdsnmr7fF1KjyXDF6USfEjnyszMDDOzpPMTqFSqVP9Dc89TALfc+eU63DSQ8PuTHOuP5Fj/JMdpQ/Ksf5kxx5nhWLJmzYparU5y4i44ODjJCb4EadlvAjBSq7Gytc9075/0JjP+jaY3kuO0IXnWP8mx/ukzxxUrVky0rFa/tg8Ti1TfX4KUHkuGH6LzMZ0rIYQQQojPkampKSVLliQwMDBRe2BgIGXLljVQVEIIIYT4XGX4opR0roQQQgghUm7QoEEsWrSIxYsXc/HiRQYOHMidO3fo1auXoUMTQgghxGcmU1y+N2jQIDp06ICvry/+/v4sXLhQOldCCCGEEMlo1aoVISEhjB07locPH1K0aFH++OMP3N3dDR2aEEIIIT4zmaIoJZ0rIYQQQoiUCwgIICAgwNBhCCGEEOIzlymKUiCdKyGEEEIIIYQQQoiMJNMUpT5Fwi0Yw8LC9Lb9sLAwuWOBHkmO9U9yrH+S47Qheda/zJrjhH5CQr/hcyX9poxPcqx/kuO0IXnWP8mx/mXWHKe03yRFKSA8PBwAV1dXA0cihBBCiPQuPDwcOzs7Q4dhMNJvEkIIIURKva/fpFI+99N9QHx8PA8ePMDGxkYvlcmwsDBcXV25e/cutra2qb59ITlOC5Jj/ZMcpw3Js/5l1hwrikJ4eDguLi4YGWX4Gxh/NOk3ZXySY/2THKcNybP+SY71L7PmOKX9JhkpBRgZGZErVy6978fW1jZTvcnSI8mx/kmO9U9ynDYkz/qXGXP8OY+QSiD9psxDcqx/kuO0IXnWP8mx/mXGHKek3/T5nuYTQgghhBBCCCGEEAYjRSkhhBBCCCGEEEIIkeakKJUGzMzMGDVqFGZmZoYOJdOSHOuf5Fj/JMdpQ/Ksf5Jj8Snk/aN/kmP9kxynDcmz/kmO9e9zz7FMdC6EEEIIIYQQQggh0pyMlBJCCCGEEEIIIYQQaU6KUkIIIYQQQgghhBAizUlRSgghhBBCCCGEEEKkOSlKCSGEEEIIIYQQQog0J0UpIYQQQgghhBBCCJHmpCilZ/PmzSN37tyYm5tTsmRJ9u/fb+iQMo2JEydSqlQpbGxsyJ49O40bN+by5cuGDitTmzhxIiqVigEDBhg6lEzn/v37tG/fHkdHRywtLfH29ubkyZOGDivTiIuLY8SIEeTOnRsLCwvy5MnD2LFjiY+PN3RoGdq+ffto0KABLi4uqFQqfv/990TPK4rC6NGjcXFxwcLCgsqVKxMUFGSYYEWGIP0m/ZK+U9qTvpN+SL9Jv6TfpB/Sb0qeFKX0aN26dQwYMIDhw4dz6tQpKlSoQJ06dbhz546hQ8sU9u7dS58+fThy5AiBgYHExcVRs2ZNXr16ZejQMqXjx4+zcOFCihcvbuhQMp3nz59Trlw5TExM+PPPP7lw4QLTpk3D3t7e0KFlGpMmTWLBggXMnTuXixcvMnnyZKZMmcKcOXMMHVqG9urVK7y8vJg7d26yz0+ePJnp06czd+5cjh8/jrOzMzVq1CA8PDyNIxUZgfSb9E/6TmlL+k76If0m/ZN+k35Iv+ktFKE3pUuXVnr16pWorVChQso333xjoIgyt+DgYAVQ9u7da+hQMp3w8HAlf/78SmBgoFKpUiWlf//+hg4pU/n666+V8uXLGzqMTK1evXpK165dE7U1bdpUad++vYEiynwA5bffftMtx8fHK87Ozsr333+va4uKilLs7OyUBQsWGCBCkd5JvyntSd9Jf6TvpD/Sb9I/6Tfpn/Sb/iMjpfQkJiaGkydPUrNmzUTtNWvW5NChQwaKKnMLDQ0FwMHBwcCRZD59+vShXr16VK9e3dChZEqbN2/G19eXFi1akD17dkqUKMFPP/1k6LAylfLly7Nz506uXLkCwJkzZzhw4AB169Y1cGSZ182bN3n06FGiz0EzMzMqVaokn4MiCek3GYb0nfRH+k76I/0m/ZN+U9r7nPtNxoYOILN6+vQpGo0GJyenRO1OTk48evTIQFFlXoqiMGjQIMqXL0/RokUNHU6msnbtWv755x+OHz9u6FAyrRs3bjB//nwGDRrEt99+y7Fjx+jXrx9mZmZ07NjR0OFlCl9//TWhoaEUKlQItVqNRqNh/PjxtGnTxtChZVoJn3XJfQ7evn3bECGJdEz6TWlP+k76I30n/ZJ+k/5Jvyntfc79JilK6ZlKpUq0rChKkjbx6b788kvOnj3LgQMHDB1KpnL37l369+/P9u3bMTc3N3Q4mVZ8fDy+vr5MmDABgBIlShAUFMT8+fOlc5VK1q1bx8qVK1m9ejWenp6cPn2aAQMG4OLiQqdOnQwdXqYmn4PiQ8j7Je1I30k/pO+kf9Jv0j/pNxnO5/g5KEUpPcmaNStqtTrJ2b3g4OAk1U/xafr27cvmzZvZt28fuXLlMnQ4mcrJkycJDg6mZMmSujaNRsO+ffuYO3cu0dHRqNVqA0aYOeTIkYMiRYokaitcuDAbNmwwUESZz9ChQ/nmm29o3bo1AMWKFeP27dtMnDhROld64uzsDGjP/OXIkUPXLp+DIjnSb0pb0nfSH+k76Z/0m/RP+k1p73PuN8mcUnpiampKyZIlCQwMTNQeGBhI2bJlDRRV5qIoCl9++SUbN25k165d5M6d29AhZTrVqlXj3LlznD59Wvfw9fWlXbt2nD59WjpVqaRcuXJJbsl95coV3N3dDRRR5hMREYGRUeKPPLVaLbc21qPcuXPj7Oyc6HMwJiaGvXv3yuegSEL6TWlD+k76J30n/ZN+k/5Jvyntfc79JhkppUeDBg2iQ4cO+Pr64u/vz8KFC7lz5w69evUydGiZQp8+fVi9ejWbNm3CxsZGd3bVzs4OCwsLA0eXOdjY2CSZZ8LKygpHR0eZfyIVDRw4kLJlyzJhwgRatmzJsWPHWLhwIQsXLjR0aJlGgwYNGD9+PG5ubnh6enLq1CmmT59O165dDR1ahvby5UuuXbumW7558yanT5/GwcEBNzc3BgwYwIQJE8ifPz/58+dnwoQJWFpa0rZtWwNGLdIr6Tfpn/Sd9E/6Tvon/Sb9k36Tfki/6S0Md+O/z8MPP/yguLu7K6ampoqPj4/ccjcVAck+lixZYujQMjW5rbF+bNmyRSlatKhiZmamFCpUSFm4cKGhQ8pUwsLClP79+ytubm6Kubm5kidPHmX48OFKdHS0oUPL0Hbv3p3s/8OdOnVSFEV7e+NRo0Ypzs7OipmZmVKxYkXl3Llzhg1apGvSb9Iv6TsZhvSdUp/0m/RL+k36If2m5KkURVHSsggmhBBCCCGEEEIIIYTMKSWEEEIIIYQQQggh0pwUpYQQQgghhBBCCCFEmpOilBBCCCGEEEIIIYRIc1KUEkIIIYQQQgghhBBpTopSQgghhBBCCCGEECLNSVFKCCGEEEIIIYQQQqQ5KUoJIYQQQgghhBBCiDQnRSkhhBBCCCGEEEIIkeakKCWEyFRGjx6Nt7e3wfY/cuRIevbsmaJ1hwwZQr9+/fQckRBCCCFE8qTfJIQwNJWiKIqhgxBCiJRQqVTvfL5Tp07MnTuX6OhoHB0d0yiq/zx+/Jj8+fNz9uxZPDw83rt+cHAwefPm5ezZs+TOnVv/AQohhBDisyH9JiFERiBFKSFEhvHo0SPdz+vWreN///sfly9f1rVZWFhgZ2dniNAAmDBhAnv37uXvv/9O8WuaNWtGvnz5mDRpkh4jE0IIIcTnRvpNQoiMQC7fE0JkGM7OzrqHnZ0dKpUqSdubw9A7d+5M48aNmTBhAk5OTtjb2zNmzBji4uIYOnQoDg4O5MqVi8WLFyfa1/3792nVqhVZsmTB0dGRRo0acevWrXfGt3btWho2bJio7ddff6VYsWJYWFjg6OhI9erVefXqle75hg0bsmbNmk/OjRBCCCHE66TfJITICKQoJYTI9Hbt2sWDBw/Yt28f06dPZ/To0dSvX58sWbJw9OhRevXqRa9evbh79y4AERERVKlSBWtra/bt28eBAwewtramdu3axMTEJLuP58+fc/78eXx9fXVtDx8+pE2bNnTt2pWLFy+yZ88emjZtyusDVEuXLs3du3e5ffu2fpMghBBCCJEC0m8SQqQlKUoJITI9BwcHZs+eTcGCBenatSsFCxYkIiKCb7/9lvz58zNs2DBMTU05ePAgoD1zZ2RkxKJFiyhWrBiFCxdmyZIl3Llzhz179iS7j9u3b6MoCi4uLrq2hw8fEhcXR9OmTfHw8KBYsWIEBARgbW2tWydnzpwA7z2bKIQQQgiRFqTfJIRIS8aGDkAIIfTN09MTI6P/avBOTk4ULVpUt6xWq3F0dCQ4OBiAkydPcu3aNWxsbBJtJyoqiuvXrye7j8jISADMzc11bV5eXlSrVo1ixYpRq1YtatasSfPmzcmSJYtuHQsLC0B7llEIIYQQwtCk3ySESEtSlBJCZHomJiaJllUqVbJt8fHxAMTHx1OyZElWrVqVZFvZsmVLdh9Zs2YFtMPRE9ZRq9UEBgZy6NAhtm/fzpw5cxg+fDhHjx7V3TXm2bNn79yuEEIIIURakn6TECItyeV7QgjxBh8fH65evUr27NnJly9fosfb7lKTN29ebG1tuXDhQqJ2lUpFuXLlGDNmDKdOncLU1JTffvtN9/z58+cxMTHB09NTr8ckhBBCCKEP0m8SQnwKKUoJIcQb2rVrR9asWWnUqBH79+/n5s2b7N27l/79+3Pv3r1kX2NkZET16tU5cOCAru3o0aNMmDCBEydOcOfOHTZu3MiTJ08oXLiwbp39+/dToUIF3XB0IYQQQoiMRPpNQohPIUUpIYR4g6WlJfv27cPNzY2mTZtSuHBhunbtSmRkJLa2tm99Xc+ePVm7dq1uOLutrS379u2jbt26FChQgBEjRjBt2jTq1Kmje82aNWvo0aOH3o9JCCGEEEIfpN8khPgUKuX1e2wKIYT4aIqi4Ofnx4ABA2jTps1719+2bRtDhw7l7NmzGBvLFH9CCCGE+HxIv0kIATJSSgghUo1KpWLhwoXExcWlaP1Xr16xZMkS6VgJIYQQ4rMj/SYhBMhIKSGEEEIIIYQQQghhADJSSgghhBBCCCGEEEKkOSlKCSGEEEIIIYQQQog0J0UpIYQQQgghhBBCCJHmpCglhBBCCCGEEEIIIdKcFKWEEEIIIYQQQgghRJqTopQQQgghhBBCCCGESHNSlBJCCCGEEEIIIYQQaU6KUkIIIYQQQgghhBAizUlRSgghhBBCCCGEEEKkOSlKCSGEEEIIIYQQQog0J0UpIYQQQgghhBBCCJHmpCglhBBCCCGEEEIIIdKcFKWEEEIIIYQQQgghRJqTopQQQgghhBBCCCGESHNSlBJCJLF06VJUKpXuYW5ujrOzM1WqVGHixIkEBwcbOsR05dSpU1SqVAk7OztUKhUzZ84EYOfOnfj6+mJlZYVKpeL333/X5fbWrVsftI/Ro0ejUqlSP/jXREREMHr0aPbs2aPX/QghhBCZ0dGjR2nSpAlubm6YmZnh5OSEv78/gwcPTrTevHnzWLp06SftS6VS8eWXX753vT179qBSqRJ9tifXp6hcuTKVK1fWLUufQKtz5854eHgYOgwhMjVjQwcghEi/lixZQqFChYiNjSU4OJgDBw4wadIkpk6dyrp166hevbqhQ0wXunbtyqtXr1i7di1ZsmTBw8MDRVFo2bIlBQoUYPPmzVhZWVGwYEHi4uI4fPgwOXLk+KB9dO/endq1a+vpCLQiIiIYM2YMQKKOqRBCCCHebdu2bTRs2JDKlSszefJkcuTIwcOHDzlx4gRr165l2rRpunXnzZtH1qxZ6dy5s97j8vHx4fDhwxQpUuSd682bNy/RsvQJtEaOHEn//v0NHYYQmZoUpYQQb1W0aFF8fX11y82aNWPgwIGUL1+epk2bcvXqVZycnN76+oiICCwtLdMiVIM6f/48PXr0oE6dOrq2+/fv8+zZM5o0aUK1atUSrZ8tW7YP3keuXLnIlSvXJ8cqhBBCiNQ3efJkcufOzd9//42x8X9fsVq3bs3kyZMNFpetrS1+fn7vXe99RavMIjIyEgsLixSvnzdvXj1GI4QAuXxPCPGB3NzcmDZtGuHh4fz444+69s6dO2Ntbc25c+eoWbMmNjY2umJMYGAgjRo1IleuXJibm5MvXz6++OILnj59mmT7mzZtonjx4piZmZEnTx5mzZr1QZeu/fXXX1SrVg07OzssLS0pXLgwEydOTLTO5s2b8ff3x9LSEhsbG2rUqMHhw4eTbOvq1au0bduW7NmzY2ZmRuHChfnhhx90zydcihcXF8f8+fN1lzuOHj1aV0D6+uuvUalUuqHfb7t8731xvy0H69atw9/fHysrK6ytralVqxanTp1KtE7C7+batWvUrVsXa2trXF1dGTx4MNHR0QDcunVLVywbM2aM7ljS4iyuEEIIkdGFhISQNWvWRAWpBEZG/33l8vDwICgoiL179+o+axP6CFFRUQwePBhvb2/s7OxwcHDA39+fTZs2vXW/P/74IwUKFMDMzIwiRYqwdu3aRM8nd/lecl6/fO9dfYL9+/ejUqlYs2ZNkm0sX74clUrF8ePH37qfhH7Qrl276NGjB46Ojtja2tKxY0devXrFo0ePaNmyJfb29uTIkYMhQ4YQGxubaBtjxoyhTJkyODg4YGtri4+PDz///DOKoiRaz8PDg/r167Nx40ZKlCiBubm5bvRXUFAQNWvWxNLSkmzZstGnTx+2bduWJFfJXb6XcOnkihUrKFy4MJaWlnh5ebF169Z35lgIkTwZKSWE+GB169ZFrVazb9++RO0xMTE0bNiQL774gm+++Ya4uDgArl+/jr+/P927d8fOzo5bt24xffp0ypcvz7lz5zAxMQG0hZmmTZtSsWJF1q1bR1xcHFOnTuXx48cpiuvnn3+mR48eVKpUiQULFpA9e3auXLnC+fPndeusXr2adu3aUbNmTdasWUN0dDSTJ0+mcuXK7Ny5k/LlywNw4cIFypYtqyvCOTs78/fff9OvXz+ePn3KqFGjqFevHocPH8bf35/mzZvr5ozIlSsXXl5eNG3alL59+9K2bVvMzMw+Ke7kTJgwgREjRtClSxdGjBhBTEwMU6ZMoUKFChw7dizRWc/Y2FgaNmxIt27dGDx4MPv27eO7777Dzs6O//3vf+TIkYO//vqL2rVr061bN7p37w583KguIYQQ4nPj7+/PokWL6NevH+3atcPHx0fXv3ndb7/9RvPmzbGzs9NdMpfQR4iOjubZs2cMGTKEnDlzEhMTw44dO2jatClLliyhY8eOiba1efNmdu/ezdixY7GysmLevHm0adMGY2Njmjdv/tHH8q4+Qd68eSlRogQ//PADbdq0SfS6uXPnUqpUKUqVKvXefXTv3p2mTZuydu1aTp06xbfffktcXByXL1+madOm9OzZkx07djBp0iRcXFwYNGiQ7rW3bt3iiy++wM3NDYAjR47Qt29f7t+/z//+979E+/nnn3+4ePEiI0aMIHfu3FhZWfHw4UMqVaqElZUV8+fPJ3v27KxZsyZFc3Ql2LZtG8ePH2fs2LFYW1szefJkmjRpwuXLl8mTJ0+KtyOEABQhhHjDkiVLFEA5fvz4W9dxcnJSChcurFvu1KmTAiiLFy9+57bj4+OV2NhY5fbt2wqgbNq0SfdcqVKlFFdXVyU6OlrXFh4erjg6Oirv++8qPDxcsbW1VcqXL6/Ex8cnu45Go1FcXFyUYsWKKRqNJtFrs2fPrpQtW1bXVqtWLSVXrlxKaGhoom18+eWXirm5ufLs2TNdG6D06dMn0Xo3b95UAGXKlCmJ2hNye/PmzRTHrSiKMmrUqEQ5uHPnjmJsbKz07ds3SR6cnZ2Vli1b6toSfje//PJLonXr1q2rFCxYULf85MkTBVBGjRr11jiEEEIIkdTTp0+V8uXLK4ACKCYmJkrZsmWViRMnKuHh4YnW9fT0VCpVqvTebcbFxSmxsbFKt27dlBIlSiR6DlAsLCyUR48eJVq/UKFCSr58+XRtu3fvVgBl9+7durY3+xSKoiiVKlVKFNO7+gQJfZlTp07p2o4dO6YAyrJly955TAmvfbP/0rhxYwVQpk+fnqjd29tb8fHxeev2NBqNEhsbq4wdO1ZxdHRM1Jdyd3dX1Gq1cvny5USvGTp0qKJSqZSgoKBE7bVq1UqSq06dOinu7u6J1gMUJycnJSwsTNf26NEjxcjISJk4ceI7j18IkZRcvieE+CjKG0OkEzRr1ixJW3BwML169cLV1RVjY2NMTExwd3cH4OLFiwC8evWKEydO0LhxY0xNTXWvtba2pkGDBu+N59ChQ4SFhREQEPDWS/0uX77MgwcP6NChQ6Kh9NbW1jRr1owjR44QERFBVFQUO3fupEmTJlhaWhIXF6d71K1bl6ioKI4cOfLemFIiJXEn5++//yYuLo6OHTsmis/c3JxKlSolGaavUqmS5LF48eLcvn07NQ5DCCGE+Kw5Ojqyf/9+jh8/zvfff0+jRo24cuUKw4YNo1ixYslOWZCc9evXU65cOaytrXV9pp9//lnXX3pdtWrVEs3tqVaradWqFdeuXePevXupdmxvatOmDdmzZ080pcGcOXPIli0brVq1StE26tevn2i5cOHCANSrVy9J+5t9lV27dlG9enXs7OxQq9WYmJjwv//9j5CQkCR3iC5evDgFChRI1LZ3716KFi2aZB6tN0d+vUuVKlWwsbHRLTs5OZE9e3bpVwnxEaQoJYT4YK9evSIkJAQXF5dE7ZaWltja2iZqi4+Pp2bNmmzcuJGvvvqKnTt3cuzYMV1RJzIyEoDnz5+jKEqyE6e/azL1BE+ePAF452TgISEhAMne+c7FxYX4+HieP39OSEgIcXFxzJkzBxMTk0SPunXrAqS4c5kacScn4ZLGUqVKJYlx3bp1SeKztLTE3Nw8UZuZmRlRUVGfEL0QQgghXufr68vXX3/N+vXrefDgAQMHDuTWrVspmux848aNtGzZkpw5c7Jy5UoOHz7M8ePH6dq1a7Kf187Ozm9tS+jz6IOZmRlffPEFq1ev5sWLFzx58oRffvmF7t27v3O6gtc5ODgkWk44IZlc++vHfuzYMWrWrAnATz/9xMGDBzl+/DjDhw8H/utXJkiuzxcSEvLR/c0Ejo6OSdrMzMyS7F8I8X4yp5QQ4oNt27YNjUaT5BbByY30OX/+PGfOnGHp0qV06tRJ137t2rVE62XJkgWVSpXs/FGPHj16b0wJcx+968xgQgfi4cOHSZ578OABRkZGZMmSBdCebezQoQN9+vRJdlu5c+d+b0wpkZK4k5M1a1YAfv31V92oMyGEEEKkHyYmJowaNYoZM2a8d55IgJUrV5I7d27WrVuXqE+VcFOSNyXXP0poS65okpp69+7N999/z+LFi4mKiiIuLo5evXrpdZ8Aa9euxcTEhK1btyY62fb7778nu35yfVNHR8eP7m8KIVKfFKWEEB/kzp07DBkyBDs7O7744ov3rp/QGXjzzNnrd+4DsLKywtfXl99//52pU6fqzpi9fPkyRXczKVu2LHZ2dixYsIDWrVsn2wkpWLAgOXPmZPXq1QwZMkS3zqtXr9iwYYPujnygHZZ96tQpihcvnuhywtSWkriTU6tWLYyNjbl+/Xqyl0x+jITfkZzlE0IIIT7Mw4cPkx2Vk3DZ3eujy982okalUmFqapqoL/Do0aO33n1v586dPH78WDfCR6PRsG7dOvLmzfvBI7Df9L4+QY4cOWjRogXz5s0jJiaGBg0a6CYe1yeVSoWxsTFqtVrXFhkZyYoVK1K8jUqVKjF16lQuXLiQ6BK+N+9cKIRIG1KUEkK81fnz53VzFQUHB7N//36WLFmCWq3mt99+S9Gd2QoVKkTevHn55ptvUBQFBwcHtmzZQmBgYJJ1x44dS7169ahVqxb9+/dHo9EwZcoUrK2tefbs2Tv3Y21tzbRp0+jevTvVq1enR48eODk5ce3aNc6cOcPcuXMxMjJi8uTJtGvXjvr16/PFF18QHR3NlClTePHiBd9//71ue7NmzaJ8+fJUqFCB3r174+HhQXh4ONeuXWPLli3s2rXrwxP6kXEnx8PDg7FjxzJ8+HBu3LhB7dq1yZIlC48fP+bYsWNYWVnpbnucUjY2Nri7u7Np0yaqVauGg4MDWbNmTXIrZCGEEEIkVqtWLXLlykWDBg0oVKgQ8fHxnD59mmnTpmFtbU3//v116xYrVoy1a9eybt068uTJg7m5OcWKFaN+/fps3LiRgIAAmjdvzt27d/nuu+/IkSMHV69eTbLPrFmzUrVqVUaOHKm7+96lS5dSpbiSkj5B//79KVOmDABLliz55H2mRL169Zg+fTpt27alZ8+ehISEMHXq1BRfNggwYMAAFi9eTJ06dRg7dixOTk6sXr2aS5cuASSad1QIoX9SlBJCvFWXLl0A7fX89vb2FC5cmK+//pru3bunqCAF2qHrW7ZsoX///nzxxRcYGxtTvXp1duzYkeSMWu3atdmwYQP/+9//aNWqFc7OzgQEBPDgwYMUnQHr1q0bLi4uTJo0ie7du6MoCh4eHokuG2zbti1WVlZMnDiRVq1aoVar8fPzY/fu3ZQtW1a3XpEiRfjnn3/47rvvGDFiBMHBwdjb25M/f37dvFKpJSVxJ2fYsGEUKVKEWbNmsWbNGqKjo3F2dqZUqVIfPYT+559/ZujQoTRs2JDo6Gg6derE0qVLP2pbQgghxOdixIgRbNq0iRkzZvDw4UOio6PJkSMH1atXZ9iwYbqJvAHGjBnDw4cP6dGjB+Hh4bi7u3Pr1i26dOlCcHAwCxYsYPHixeTJk4dvvvmGe/fuJXuiqWHDhnh6ejJixAju3LlD3rx5WbVqVYonG3+f9/UJSpcujYeHBxYWFlSrVi1V9vk+VatWZfHixUyaNIkGDRqQM2dOevToQfbs2enWrVuKtuHi4sLevXsZMGAAvXr1wtLSkiZNmjB27Fg6deqEvb29fg9CCJGISnnbLbSEECIdiI2Nxdvbm5w5c7J9+3ZDhyOEEEIIIYCzZ8/i5eXFDz/8QEBAgKHD+WQ9e/ZkzZo1hISE6HXqBiFEYjJSSgiRrnTr1o0aNWqQI0cOHj16xIIFC7h48SKzZs0ydGhCCCGEEJ+969evc/v2bb799lty5MhB586dDR3SBxs7diwuLi7kyZNHN3/pokWLGDFihBSkhEhjUpQSQqQr4eHhDBkyhCdPnmBiYoKPjw9//PEH1atXN3RoQgghhBCfve+++44VK1ZQuHBh1q9fr7tJTEZiYmLClClTuHfvHnFxceTPn5/p06cnmvtLCJE25PI9IYQQQgghhBBCCJHm5NYCQgghhBBCCCGEECLNSVFKCCGEEEIIIYQQQqQ5KUoJIYQQQgghhBBCiDQnE50D8fHxPHjwABsbG1QqlaHDEUIIIUQ6pCgK4eHhuLi4YGT0+Z7Xk36TEEIIId4npf0mKUoBDx48wNXV1dBhCCGEECIDuHv3Lrly5TJ0GAYj/SYhhBBCpNT7+k1SlAJsbGwAbbJsbW0/eXuKohAaGoqdnZ2cQfwIkr9PI/n7NJK/TyP5+zSSv0+j7/yFhYXh6uqq6zd8rlK73/Qm+TvQP8mx/kmO04bkWf8kx/qXWXOc0n6TFKVA94u3tbVNtaKUoijY2tpmqjdVWpH8fRrJ36eR/H0ayd+nkfx9mrTK3+f+u0ntftOb5O9A/yTH+ic5ThuSZ/2THOtfZs/x+47p850QQQghhBBCCCGEEEIYjIyUEuIzptFoiI2NNXQY6YqiKMTExBAVFZUpz1SkJhMTE9RqtaHDEEIIIYQQQmRQUpQS4jP18uVL7t27h6Iohg4l3YmPjyckJMTQYaR7KpWKXLlyYW1tbehQhBBCCCGEEBmQFKWE+AxpNBru3buHpaUl2bJlkxFBr1EUBY1Gg1qtlry8g6IoPHnyhHv37pE/f34ZMSWEEEIIIYT4YFKUEuIzFBsbi6IoZMuWDQsLC0OHk65IUSrlsmXLxq1bt4iNjZWilDA8TSyE3UcdfAfsKhg6GiEyp3gNRDyDuEjtz0q89qE2AVNrMLUCY3OQz08hhBApJEUpIT5jUnQRn0LePyJNxbyCF3ch9C68uKP9N/Tef23hD1Ep8Vib2sCwu4aOVoiMLV4D947Drf3w+AI8uQzhDyHyOfCey/5VRv8VqHSPf5dNLN947o1lk2Rek7CO2iRNDl0IIUTakqLUR/Dw8CAiIoL79+9jYqL9gNy1axfVqlVj8ODBTJkyxcARplznzp3x9fXlyy+/TPLcy5cvGTFiBFu3bsXc3ByNRkO9evWYOHEiBw8epG7duhQsWJCYmBgAatWqxciRI8mSJUtaH8YH8fb25vDhw1hYWODh4cHWrVspWrToO1+jUqkIDw9Pdu6cdz0nhBAiBRQFIkKSLzYltEU+f/9m1KbEWzpiFBuh/TIr0o339Z2mTp1q4AhT7l19p7R24sQJZsyYwapVqz59Y5pYuLodLmzS/vuuvzljC23xKeGhidGOngLtyKnoMO3jPR6Ex9NuYyS7O2n/XlVjwggfZoO1aTInPdSmYGrFrXATfGfe4un08m8Uvqy49TwO337Lebr1u2QKY68Vu0ws/1s2kpG+QghhSFKU+khubm5s3ryZZs2aAbB48WJ8fX0NHBXExcVhbPzpv1ZFUahfvz758+fn3LlzWFhYEBMTw+LFi4mOjgagSJEinDhxAoDw8HCGDh1KtWrVOH78eLq+lOf06dOGDkEAxsbGFC1alNjYWPLkycOKFSuwt7f/oG2MHj2arFmzpuiLwa1btzh27BgtW7YEYM+ePTRu3BgPDw+io6Np3bo1o0aN+phDSVf27NnD3Llz+fXXXw0dihD/0cRB+IN/C033IPTOa0Wnf9sSvtC+i5kd2LuCXS6wc/33Z9f/frbKRnhYOHYmlvo/JvHBMnvfSR/eF5uvr++nF6Se3YCTy+D0angV/F+7uR3krQo5vCF7Ee3fmKUjWDiAOpmY4jXaEY26x8v/fo59sz0CYl7hEvOS3X6vIFa7DNvAMR+oov5bNz5Ou31NDETGwMt47b4e/JM0hhfxEBsJO8ek/PiNzbUFL7WJ9l8jk39//vdhZPLa8yavPf9mW9J1zGLjwdIajM3AyPgtr0loN33HOiaJ4zMy+pDfsBBCpGvp8xM4A+jatSuLFy+mWbNmhIaGcuTIEdq0aUNk5H+d6qlTp7J+/Xri4uJwdnbmxx9/xNXVlZ07dzJixAiioqKIjY1l8ODBdOnSBYBx48axatUqzMzMANi0aRPu7u5JRuNkzZqVEydO4OHhgYeHBz169GDHjh24uLiwdOlSRo4cya5du4iJiaFQoUIsWLAAe3t77t+/T8eOHXny5Am5c+dGo9Eke3y7du3i2rVrbN++HVNTUwBMTU3p1atXsuvb2NgwZ84c8uXLx19//UW9evUSPb906VK2bt2q+6K8detWpk6dyp49e9izZw8DBgygbNmyHDx4kLi4OObOnUvlypWJi4ujXr16hISEEBkZibe3Nz/99BOWlpYUKFCANWvWULJkSQCWLFnCli1b2LhxI9OnT2fNmjXExcVhYmLCnDlzKFOmDPD2kU3vek3C7zMwMJAnT54wZswY2rRpkyQPV69eZcCAAQQHBxMTE8MXX3xBQEBAsjlLbyJi4t76nJFKhbmJ+qPWfRt7e3tdgbBDhw788MMPDB8+POUBf6Bbt27xyy+/6IpSANWrV+fXX3/l1atXeHt7U79+fXx8fPQWw6dImOdKiHQpJuLfEU53kxabQu9C2ANQkv+8ScTaWVtwSig22bslLkCZ27379XI30XQtpX2nX375hejoaHLmzJmh+k63bt3C19eXXr16sW3bNiIjI1m5ciULFy7kyJEjmJub8/vvv+Pi4sK5c+cICAjg1atXREVF0aFDB4YNGwZoR2LZ2tpy5coV7t69S1BQEMOHD2fdunU4OjpSsWJFdu/ezYkTJ9izZw9DhgzhxIkTuv0HBASwbds2QkNDmT17NnXr1k0S6+jRo3l+7wqzapvBhU3MPRrFiQcalrZ1Zenjwqw5/gQHl9ycX3USM7Pz/PLLL+TJnodHjx7RpkENwsLCiIqKolq1asyaNYvIyEhcXV0JCgrC2TkHAKNGjSI8PJzp06czdOhQ9uzZQ2xsLHZ2dixatIj8+fPrYn769Kk2sC4q+GIfWFv/95qYGOxsrVk0cyL53Zzg+hVY3oEhd6qx//hZXkZEMGdAc6oWzwV37oHJT+DdDmJecvzSXb5ec5awiBji4zUMr2JPs0JG2iKZEq/dZ1yU9pHKVIDeZu1UqV8rUqWkkGWsHRH2+ug2VNq5v15vS/RQvfHvm69Nbt23Lb/WluS1b67zrueTi1uFSUQUWFu/P4Zk961692sS2t8Z93ti5819yPQHQrxOilIfqWLFisyZM4f79++zZcsWWrRokegL4/r167ly5QqHDx9GrVazYsUKvvzySzZt2oSPjw8HDhxArVbz7NkzfHx8qF27Nubm5kydOpWHDx9iYWFBREQERik8E3Lnzh127dqFSqViwoQJWFtbc+zYMQC+++47Ro0axaxZs+jXrx8VK1Zk1KhR3LhxAy8vL2rXrp1keydPnqRkyZK6glRKmJiY4O3tTVBQUJKi1PsEBQWxaNEi5s2bx/z58xk3bhyVK1dGrVazevVqHB0dURSFgIAA5s2bx5AhQ+jcuTNLlizRFaWWLl3KkCFDAG2RY9CgQQAcOXKEbt26cf78+XfG8L7XqFQqDh48yI0bNyhdujTly5fH1dVV97xGo6Ft27asWLGCQoUKERERgZ+fH35+fum20PG6Iv/7+63PVSmYjSVdSuuWS363g8jY5DvlZXI7sO4L/w/ad7ly5Thz5gwAN27coGvXrjx79gwPDw+WLl2Kg4MDly5dolevXjx//hwzMzN27tyZaBsTJ07k0qVLLFmyhIkTJ/L7778THR1NQEAAvXr1Yvjw4QQFBeHt7U3//v3JnTu37rVWVlaULFmS69ev079/fxYuXEjhwoVRFIVChQpx9OjRRKO4Ro8ezc2bN7l9+zYPHjxg8uTJNG7cmMjISHr27Mnp06exsLBg4cKFeHt74+npyfHjxwGws7Nj3759+Pv74+3tzd69e1Gr1QQEBHDx4kUURWHWrFmUK1eO0aNH8+jRI65du0aRIkXo27cvAQEBPHv2DEdHR5YtW0aOHDk4fPgw3bt3x97ePkO810QGoyjaiY3fLDq9/nNEyPu3Y2QCdjlfKza5Ji5A2eXSjiYQmdb7+k6rV6/mypUrHDp0iJcvX7J58+YM1XcCCAkJwd/fn3HjxjFlyhSqV6/Onj17WLhwIQEBAcydO5cJEybg4eHBjh07MDMzIzIykrJly1KjRg3dyLEDBw6wb98+rK2t2bJlC1u3buXMmTNYWFjQvHnztx5TSEgIJUuWZOzYsfz111/0798/aVEq+BKc+xXTR9dQuZpr27IVBFNHGPQnrFzN0Z8HcGbFL7i7u/PNN98wadIkfvzxR+zt7dmyZQvW1tZoNBoaNWrEhg0baN68Oc2aNWPlypUMGTIERVFYvnw5mzdvBuDrr7/WTW+xdu1aBg4cyNatW9/5+0nymlGTta95ZUbI81CKVWrI1MUbOHLkCI0bN+b69evg/gRMVkHjebx48YIvvq/Ktl3nyZEjB0+fPqVkyZKUm3wUZycnbSEqYTSWJk47Eis+VnsZoyb2jeWY/9rjE5bf9hptu6KJJTbyFSbGKlSvr//aOine5pvzeCkaiNPopZiW0aiADHmxdrLFqjeLWKq3tL9RNHzvdlK6LaP/Ynuj3SouDkxMQaVOvK3ktpOojRTs8/XYUuN4PyE3qfI7ef2YUrCuSgUqNapXkaCO/re4bPzGI2WfaxmVFKU+QYcOHVi2bBm///47q1atSjR8+o8//uDMmTO6gsnroxxCQkLo1q0bV65cwdjYmKdPnxIUFESVKlXInz8/7du3p2bNmtSrV49cuXKlKJYuXbroJh3+/fffCQsL041KiomJIW/evADs3r2b2bNnA5AnTx6qVauWOsn4l/KRZ6kLFiyo64j5+/vrOiGKojBjxgy2bdtGXFwcoaGhVKxYEYBOnTpRokQJpk+fzt27d7ly5Qp16tQB4NSpU4wfP56QkBCMjY25cOECMTEx7yyyve813bt3B7R5K1++PPv376dt27a611++fJmgoCBat26tawsPD+fChQtSKHgHjUZDYGAgXbt2BaBfv34EBATQsmVLJk2axOjRo5k9ezbt27dn4sSJ1KhRg/Dw8ER3DZwwYQKXL19myZIlbN++neDgYI4fP05MTAzly5enfv36jB8/PtFlbXv27NG9PiQkhCNHjjBy5Eg6derEypUrGT9+PLt378bb2zvZywovXLjAwYMHefz4MeXKlaNWrVr88MMP2NjYcO7cOY4cOUKnTp04c+YMpUuX5ujRoyiKQvHixTlw4ABFihQBtEWqb775hiZNmrB8+XLu3btHvXr1dEW6c+fOsXv3bkxNTalRowaLFi3C3d2d9evXM2bMGBYsWED37t1ZtmwZvr6+tGrVSk+/KZFpxWu0ExgnmsPpXuLRTrGv3r8dU5vXRji9fondvwUoa6dM36kS7/euvtPvv//OiRMn8PX11Y1Gymh9J2tra92JOR8fH3LlyoW3tzcAJUuWJDAwEIDIyEgCAgI4ffo0RkZG3L17l9OnT+v6Qi1bttSN8Nq9ezctW7bEykr71btTp0589913ye7fysqKRo0aAdr+1PXr1/97MvI57JkExxaievYKBRVK0eaoyg+EX/fAiRNgrO3zlC9fHnd3d9125syZA0B8fDxff/01Bw4cQFEUgoOD8fb2pnnz5nTp0oUePXowZMgQdu/ejaOjI8WKFQNg+/btzJkzh/DwcOLj4wkLC3vv7+ddrzE1NaVDhw4A+Pn54ezszJkzZ3BxcdGtc+jQIW7cuKHrG4K2X3n58mWcnZ3BxEL7sMr63lg+iqIQERqKnZ0dnzwyJl6TuIj1zgJabPKFr4Q7Jb75QNGefHjb87rn3rIOSjLrvm0bb1mHN9vet/xfm6LEo4mLRa02QvWh+329LUkMH3D8H/X++HekXkpGERuYCpDbDOiXCnjnWHCVUTKFqtce6teX1f9e5vvasq5A93qh7rW2Gt9B9kJpcajJkqLUJ+jcuTM+Pj4UKFCA/PnzJ3pOURSGDx9Ot27dkryuV69eNGjQgA0bNqBSqfDx8SEqKgq1Ws2RI0c4dOgQe/bswc/PjzVr1lChQgXUanWi4eJRUYnPjLx+KZqiKMybN4+qVat+9LGVLFmSOXPmvLeQ87rY2FhOnz6d7CV+xsbG74zf3Nxc97NarSYuTnt52OrVq9m7dy/79u3DxsaG2bNns2/fPgBy5syJj48Pmzdv5syZM3To0AFjY2NiYmJo1qwZe/bsoWTJkoSFhWFnZ/fOY/mY17x55zFFUciaNWuGnbPqwthab33O6I1jPTmyeorXfZsXL17g7e3NvXv38PT0pFYt7f6PHz/Oli1bAO2Xl3r16hEWFsaLFy+oUaMGoL1cNMHChQspVKgQa9euxcjIiMDAQLZs2cLevXsBCA0NTdwpf82OHTsoUaIERkZGDBs2DE9PT1xdXfHz82PcuHEsW7aMTp06JfvaJk2aYGpqiqurK4UKFeLy5cscOHCAr776CtB2kiMjIwkNDaVcuXK6DvxXX33F2rVr8fT0xN9fO6IsMDCQP/74gzFjtHNghISE6G4g0KhRI0xNTQkPD+fgwYO6LxsajQYPDw9evHhBbGys7otMu3btWL58eYp+B+IzEhejvZNWyNU3JhG/C2H3U9Yptsr+RtHpjZ/NU+GLl8j03td3GjFiBF26dCH03y/zCZ+1GaHvBOguIQRtf+Zt/Ztvv/0WJycnTp06hbGxMU2bNk0U35uxpfRup2/uT6PRaL9Mn98Af36lG9Wozl6QKOti0GwRqFRERf31zu0kxD19+nRCQkI4evQo5ubmDBo0SBe3v78/Go2GEydOsGTJEt3Jpjt37tCvXz+OHTtGnjx5OHv27Hvz/DGvSa5fVrx4cV2/MUMzUoPRv0U0kZii8DK1in8fuX9dwSq5wla8hv8Kf8mtl1xhTHlLe3LrJ1M0S7bI9/H7VuLjiIyIwMLC4r/C35vH9Elxvt5GCoqG/27vvceSgmNPjZwnu25K4wMUDUq8BuJjUcW/ZYoUJf6/kZb6UGGwfrabQlKU+gQuLi5MnDiRQoWSVhVr167N/PnzadKkCQ4ODsTGxnL+/HlKlCjB8+fPdXMd7Nu3TzciIjw8nPDwcCpUqECFChUICgri1KlTVKhQgbx583L06FFq1qzJxo0befXq7WetGzZsyPTp0/Hz88PS0pKIiAhu3ryJp6cnVatWZfHixYwcOZJbt26xc+dOqldPWmCoWrUquXPnpl+/fsycORNzc3Pi4uKYPXs2PXv2TLL+y5cvGTJkCFmzZtUVF16XN29ezpw5Q1RUFMbGxqxevTpFOX7+/DmOjo7Y2NgQHh7O0qVLyZMnj+75hPkpgoKC+PPPPwF0800kXFqXcHbvXVLymtfzduDAgSTrFCxYEEtLS5YvX07Hjh0BuHbtGg4ODjg4OKToeA3J0jTl/x18yLpvkzCnVEREBDVq1GDevHn069cvUafy9Y742zrkXl5enDlzhuDgYJydnVEUhdGjR+t+BwleHxmVIGFOqdfZ2NhQokQJ/vjjDw4ePMjixYuT3e+b8SQXX0L85cqVY+DAgRgZGTFw4EDmz5/PgQMHKF++vG69rVu34ubmlmQblpaWunWcnZ2TFD2fP3+e4i8r4jOjKHB2HVzcAjf2aC9ReRsjY7DNmfxldfZu2udMzN/+eiFS6F19p4YNGzJr1iwaNWqEWq0mNjaWoKCgDNN3+hDPnz+naNGiGBsbc/nyZQIDA99adKlSpQqjRo1iwIABmJubs2LFig/b2S8d4aL2MjqyFoQ635P36CP+mDOH+Ph4oqOj2bBhAwULFkxR3M7Ozpibm/P48WPWr1+faIRuly5dmD17Ntu2bdONLgsNDcXU1FT3GT137tz37ud9r4mJiWHVqlV06NCBY8eO8ejRI4oXL86TJ09065QtW5arV6+ya9cuXW5Pnz5NkSJFPmh6CiHeSTdHVCYeCawoxISGYmGowt/nQFG0J2NsbVGh/DvSMVb7778FK+3P/y5r3lh+/XlN3GvPxf13s4hERTQl8b8Oed4RnP5JUeoTJUyy+abWrVsTGRlJ5cqVUalUxMXF0a1bN0qUKMH3339PQEAA33//PUWKFNFNph0aGkrz5s159eoVKpWK/Pnz60ZpzJw5kz59+pA9e3aqVKmCo6PjW2P65ptvGDNmDGXKlNF9Wf3666/x9PRk1qxZdOzYkfXr11OgQIG3dqpUKhXbtm1j+PDheHp6YmFhgaIo1KtXT3f27MKFC3h7exMbG4uiKNSqVYudO3cmOxmzv78/tWrVomjRonh4eFCiRAmePXv23vx27NiRTZs2UaRIEXLmzEmFChW4f/++7vlGjRrRu3dv8ufPr7scytbWlrFjx1K6dGnc3Nxo2LDhe/eTkteYmZlRrlw5njx5wpw5cxLNJwXa0WBbtmxh4MCBTJ06FY1GQ7Zs2VLnNs2ZmKWlJbNmzaJZs2YEBATg6+urm59i9erVVKhQAVtbW7JkyUJgYGCSy/fKlClD+/btadCgAYGBgVSvXp1JkybRokULLCwsuHz5Mu7u7rrCZkp06tSJzp0707Zt27dOLv77778zePBggoODuXz5MgUKFKB8+fKsXr2asmXLcuzYMaysrLC1tcXGxoarV6/i5uaGjY0NBQsWZMWKFezfvx/QFsd++OEHJk2aBMCZM2fw8vJKtD9bW1uyZcvGH3/8Qd26dYmNjeXatWsULlwYExMT/vnnH3x8fFizZs3H/ipEZnNwFux47Y6SVtm0d9FKNIn4vwUoG2e5LbpIM2/rO3Xo0IGQkBCqVKlCfHw8iqJkqL7ThxgxYgQdOnRg1apVeHh4vHMUUMOGDTl06BBeXl64uLjg5+fH8+fP37+ThELUxc3awnPFoVB+EBib0ixXtG7UroeHB97e3okmnH+bfv360aJFC7y9vcmZM2eSXHTo0AE3NzeaNWtGlixZAChWrBgtWrTA09MTNzc33ajnd3nfaxwdHbl27RplypTh5cuXrF69Gisrq0RFqSxZsrBlyxaGDh3KwIEDiY2Nxc3Njd9///29+xdCCINImGvKSP1ZzbOpUj52EqBMJOFSrdDQUGxtbT95e0pCpfO1Yeci5SR/nyYl+YuKiuLmzZvkzp070RD9tJQ1a9b/7rYD1K9fnw4dOlCqVCm6dOmiOyu+bNkyHBwcuHz5Mj179uTFixeYm5uzc+dOpk6dStasWfnyyy9Zu3Yt8+fP56+//mLOnDmsWLECRVHInj07W7ZswdTUlFq1avHs2TPdROevzzGVQFEU4uLiyJUrF4GBgRQvXjxJ7AkTkF+6dCnJROc9evTg7NmzmJub8+OPP1KiRAlA+6XCw8OD2bNns3z5coYNG6YrsL58+ZK+ffty4sQJNBoN1apVY86cOYwePVp3fKAdederVy+ePHlCXFwcQ4cOpXPnzrqJzu3s7KhQoQLXr19Pclz6kNz7SP5+P02q5S/yBcwqDlGhULoneLcFZ69MP6eTvt9/qd1fyKj0nQf5fySp8PBwbGxsiI+Pp3v37ri4uDBu3LjkV34VAn8MgaCN2uXsntBkPuT472SH5Fj/JMdpQ/Ksf5Jj/cusOU5pf0GKUkhRKr2R/H2ajFKUSq8SJkHt2LGj7i5Mb3qzWPS5kqJU6ku1/O2eAHsnQbZC0PvQZzMKSopSaUOKUmmvSZMm3Lp1i8jISHx8fFiwYEHS3CsKBP0GfwyFiKfau2RVGAQVv9JNYP7fqpJjfZMcpw3Js/5JjvUvs+Y4pf0FuXxPCCFes2jRIr777jsWLlxo6FCE+DgRz+DwPO3PVb79bApSQmRmv/3227tXCHsI2wbD5W3a5WyFofE8yCl3/xVCCJG+SVFKCCFe0717d7p06fLWuaRAO1JKiHTr4CyICQfn4lCogaGjEULoU3w8nFoB20dCdKj2NuAVBmsfxjKZtxBCiPRPilJCCCFEZhH+GI7+qP256ohMP4eUEJ+1J1dg6wC4fVC77OIDjeaCk6dBwxJCCCE+hBSlhBBCiMziwAyIi4ScvpC/pqGjEULoQ1w0HJgJ+6eCJgZMLKHKcPDrLZfrCiGEyHCkKCWEEEJkBqH34cRi7c9VR2hvKyyEyFxuH4Yt/eHpZe1yvhpQbxpkcTdsXEIIIcRHknH9QgiDMDY2xtvbG09PTxo0aMCLFy8+eBujR49m7ty5KVr31q1b/PLLL7rlPXv2YG9vj7e3N4ULF2bMmDEfvP/U9maMQnyQ/VNBEw3u5SFPZUNHI4RITZEvYMsAWFJbW5CyygbNF0O79VKQEkIIkaFJUUoIYRD29vacPn2aoKAg7O3t+eGHH/S6v+QKPtWrV+f06dOcOHGClStXcvLkSb3G8D5SlBIf7fkt+Ge59ueqw2WUlBCZhaJA0G/wQ2k4uUTb5tMR+hyDos3kb10IIUSGJ0UpIT5ziqIQEROX6g9FUVIcQ7ly5bh37x4AN27coHLlyhQvXpyGDRvy7NkzAC5dukTlypXx8vKidOnShIeHJ9rGxIkT6dSpE/Hx8YwfP55SpUpRvHhxFixYAMDw4cPZsWMH3t7eLFmyJNFrraysKFmyJNevX6dChQpcuXJFl5uCBQsmGcUVFhZGu3btKF68ON7e3hw6dAhFURgwYABFixbF29ubHTt2AOi26ePjg5+fHxcvXgTg3Llz+Pj44O3tjbe3N8HBwe+MUYh32jsZ4uMgb1VwL2voaIQQqeHFXVjTGtZ3hpePwTE/dN4GDeeApYOhoxNCCCFSRbqcU2revHlMmTKFhw8f4unpycyZM6lQoUKy63bu3Jlly5YlaS9SpAhBQUH6DlWIDC8yVkOR//2d6tu9MLYWlqbv/y9Go9EQGBhI165dAejXrx8BAQG0bNmSSZMmMXr0aGbPnk379u2ZOHEiNWrUIDw8HAsLC902JkyYwOXLl1myZAnbt28nODiY48ePExMTQ/ny5alfvz7jx49n7ty5/Prrr4D28r0EISEhHDlyhJEjR9KpUydWrlzJ+PHj2b17N97e3tjb2yeKeezYsXh4eLBq1So0Gg2vXr1iw4YNXLt2jbNnz3Lnzh0qV67MpUuXyJEjBzt27MDMzIxDhw7x7bff8ttvv7Fw4UJ69+5Njx49iIyMRK1WJ4lRiBR5eg3OrNH+XGWEYWMRQnw6TRwcWwi7xkHsKzAygQqDoPwgMDE3dHRCCCFEqkp3Ral169YxYMAA5s2bR7ly5fjxxx+pU6cOFy5cwM3NLcn6s2bN4vvvv9ctx8XF4eXlRYsWLdIybCHEB3rx4gXe3t7cu3cPT09PatWqBcDx48fZsmULAB06dKBevXqEhYXx4sULatSoAYCNjY1uOwsXLqRQoUKsXbsWIyMjAgMD2bJlC3v37gUgNDSU69evJxvDjh07KFGiBEZGRgwbNgxPT09cXV3x8/Nj3LhxLFu2jE6dOiV53a5du9i8eTMAarUaW1tbDhw4QNu2bTEyMsLDw4MCBQpw+fJl3Nzc6NOnD2fPnsXIyIjo6GgA/P39GTt2LCEhIbRs2ZI8efKkUmbFZ2fPRFDioUAdyFXS0NEIIT7F7UOwbQgE/3ti1dUPGsyC7IUMG5cQQgihJ+muKDV9+nS6detG9+7dAZg5cyZ///038+fPZ+LEiUnWt7Ozw87OTrf8+++/8/z5c7p06ZJmMQuRkVmYqLkwtpZetvsuCXNKRUREUKNGDebNm0e/fv1QvTY/hqIoumXVW+bN8PLy4syZMwQHB+Ps7IyiKIwePZqOHTsmWu/1kVEJqlevnmRUko2NDSVKlOCPP/7g4MGDLF68OCWHm0RC7DNnziR37tysWrWKx48f4+fnB0Dbtm0pXbo0W7dupWrVqmzcuPGj9iM+c48vwPkN2p+rfGvYWAwlLhqe3QQjNTjmM3Q0Qnyc8McQ+D84u1a7bJEFqo0Cn05gJLNtCCGEyLzSVVEqJiaGkydP8s033yRqr1mzJocOHUrRNn7++WeqV6+Ou/vb70QSHR2tG60A2vlhQPsl8kPmwXmbhO2kxrY+R5K/T5OS/CU8l/Dv+wpInxLL+563sLBg5syZNG/enN69e+Pr68uvv/5K8+bNWbVqFeXLl8fGxoYsWbKwffv2RJfvKYpC6dKladeuHQ0aNGD79u1Uq1aNyZMn07x5cywsLLh8+TLu7u5YW1sTHh6e5NiTi7FTp0507tyZNm3aYGRklGSdatWqMW/ePMaPH6+7fK9cuXKsWLGCVq1acffuXa5du0b+/PkJDQ0lXz7tF+Xly5fr9nnjxg3y5MlD//79OX/+PEFBQRQuXDhRjOnd6zl88+eMcgzpzQfnb88EVCgoRRqDczHtpMiZlSZWO4ok5BqEXIWQ69p/X9xBpcSjFGuJ0uRHvb7/5H0tUp0mDo4vgt3jIToMUEHJTtqClMwbJYQQ4jOQropST58+RaPR4OTklKjdycmJR48evff1Dx8+5M8//2T16tXvXG/ixInJ3v49NDQ01YpSL1++BN4+ukO8neTv06QkfzExMcTHx6PRaNBoNGkZXiIJ+y5RogRFixZl/fr1TJ8+ne7duzN27Fjc3d1ZvHgxGo2GJUuW0Lt3b4YMGYK5uTnbt29HURTi4+OpXr06ISEhNGrUiG3btnHu3DlKly4NQLZs2fj999/x9PQkJiYGb29v+vbtS+7cuVEUJdnjr1SpEhqNhnbt2iX7/LfffktAQADFihVDrVYzd+5cGjVqxP79+/Hy8kKtVrNgwQJMTEzo0aMHrVq1YtWqVVSrVk133GvXrmX16tUYGxvj7u5Ow4YNMTU1TRRj586d9Zf8VKDRaIiPjyc8PFxX6Je/30/zIflTPz6HzcUtKCojwn2/JD40NC1C1K/oMNTPb2D0/AbqFzeJt8xOjFcH7XOaGOxWNkWlJP2bVEytiY3T8Co0VK/vv4STWEKkituH4Y8h8Pi8dtmlBNSdJpfhCiGE+KyolHR02u/BgwfkzJmTQ4cO4e/vr2sfP348K1as4NKlS+98/cSJE5k2bRoPHjzA1NT0reslN1LK1dWVFy9eYGtr+8nHoSgKoaGh2NnZyZeyjyD5+zQpyV9UVBS3bt3Cw8MDc3OZNPVNly9fpmPHjhw9etTQoaRryb2P5O/303xQ/la3RHV1O0rxltBkYdoEmBoU5b/b2CsKbB0ITy9DyDVUr54kXjVXKegW+F/DyqagNtVepueYHxzzQtb8YJUdVCq9v//CwsKwt7cnNDQ0VfoLGVVYWBh2dnZ6y0Om/3/kZTAEjoIz/55E1V2q11F7GWoayPQ5Tgckx2lD8qx/kmP9y6w5Tml/IV2NlEznwygAAJRASURBVMqaNStqtTrJqKjg4OAko6fepCgKixcvpkOHDu8sSAGYmZlhZmaWpF2lUqXamyBhW5npTZWWJH+f5n35e32eJslxYj/99BPfffcdCxculNy8x9veR/L3+2lSlL+7x+DqdlCpUVX65r8iT3qhKBD+EJ5e/fdyu9cetjmh81bteioV3NgFL+7891prZ22hyTEvqhxeiY+tw2/v3bU+33/ynhafRBMHJ36GXeMhOhRQaQtR1UaBlaOhoxNCCCEMIl0VpUxNTSlZsiSBgYE0adJE1x4YGEijRo3e+dq9e/dy7do1unXrpu8whRCZWPfu3enSpQtqddqcrRbio+wap/23RDvtaCFDiY3Uzu0U+QxyV/yv/YfS8PRK8q+JDk+8XGU4GBlrj8MxH5jZJP86ITKyO0e0d9V7fE67nMMb6k2DXL4GDUsIIYQwtHRVlAIYNGgQHTp0wNfXF39/fxYuXMidO3fo1asXAMOGDeP+/fu6CYMT/Pzzz5QpU4aiRYsaImwhhBAibdzcBzf3gpEJVByadvu9d1L7hfrpVXhyWVt0enEHUMAmBwx+7RJ7mxzaYlUWj39HPeVL/HidV+u0OwYh0trLJ7BjFJxepV02t4dq/4OSndPsUj0hhBAiPUt3RalWrVoREvJ/9u47von6DeD4J0l308Eqq4wis0AptGwQQYaIDPcEUeBHRVTAxVAERKooCIogKIo4kSkqMpRZ9ihDyhIoZZRCgQ66m9zvj2vTlm7ajLbP+/W6Vy+X7909+SZNLk++4wbTpk0jMjKSFi1asG7dOtNsepGRkUREROTYJzY2lpUrVzJ37lxrhCyEEEJYhqKoXX9A/VLrWbf0jm00QMwFNekUfRpuR0Hv6Vn3b3oXLuzMvZ+TB3jUgfRUsMvoPv/YN+p2nX3pxSdEWWJIhwPfqK0aUzImIWgzBO6fIl31hBBCiGxsLikFMGrUKEaNGpXnfUuWLMm1zcPDg8TERDNHJYQQQljZf//AxT1g5wRdXy/58Q4tVY8ZfVpt2WRIyXn/fRPBwUVdr9cJ7J2hamO19VPVJuq6a9XcY1q5Vi15bEKUVRF7Yd3rcDWzq14rdVa9Om2tG5cQQghhg2wyKSWEEEKIOygKbMloudR2OLjXLNp+cZFwap06MPq1MBh9AOwyJvuI2Atha7LK2jmps9pVbaQmnIxpWff1eKdUHoYQ5dbt6/D3FDj8g3rbySOjq94L0lVPCCGEyIfW2gEIISomOzs7Wrduja+vLwEBAXz11Vc57lcUhbp16/LWW28BMGnSJPz9/fH19cXFxQV/f3/8/f3ZsmULq1evplWrVjRv3pwWLVowf/58AIxGI5MmTaJhw4Y0bNiQJ554ghs3bgAQHh6ORqNh5syZpnPu3bsXrVbLd999Z6FaKB1LlizhjTfesHYYwtxOrYMroWDvCp3HFF4+5iJ80xdmN4U/x8Hp9eoYUDfPZZVp8TD0/gCeXQGvHYGJV+ClEHj8W+g+Qf1SLYQomNEA+76CeQFZCanWg+GVQ2oCWRJSQgghRL4kKSWEsApPT09CQ0MJCwtj1apVLFiwgEWLFpnu3717NzVr1mTVqlUAfPDBBxw+fJh169bh6+vL4cOHOXz4MJ6enrz99tusWbOG48ePs3//flxc1O5Gc+bMISwsjLCwMP777z/atm3LCy+8YDpHo0aNWLt2ren2r7/+SsuWLS1UA4UzGAzWDkHYCqMxayypDkGgr1b4PuvegIhd6rp3W3Xa+Rc3qIOPZ2rYEzqNhka91O3y5VmI4rm4Dxbdp/6/JcdCDT8YtgkGzpNurEIIIUQRSFJKCKFKTch/SUsuRtmkYp+6Xr16zJo1y9TCCWDZsmUEBQXh4+PD3r1789131qxZTJo0CR8fHwCcnZ0ZOnSo6b45c+bg4KAOvvz6669z/Phxzpw5A4C7uztVq1bl3LlzKIrC1q1b6d69e57nGTp0KEFBQXTu3BlfX192794NQHR0NP3798fPz4/77ruP8PBw0tLSaNy4MQCnT59Go9EQGRmJ0WjknnvuQVEUoqKiGDRoEIGBgXTp0oWTJ0+azvP6669z3333MXPmTPbt20fXrl1p06YNjz76KLdv3wbgt99+o3HjxnTr1o2QkJBi17koY8LWwLXj4OgBnV4pvPz57WrLKK0dBO2E4X9D13FQt4M6LpQQomQSouG3l2FxL7h6VG1V+OAn8L+tUKedtaMTQgghygwZU0oIoZpRK//7GvWGZ5dn3f64IaTlM7lAvS7wwp/FPn2bNm04deoUoHbd+/3335k6dSppaWksW7aM9u3b57lfWFgYb775Zq7tcXFxJCUlmWbuBNBqtfj5+XHixAn8/PwAePzxx/n111/p1q0bAQEB2Nnl/7YYGRlJSEgIBw8eZOjQofz7779MmTKFrl278vvvv7Ns2TJeffVV1q5dS+3atTl//jwhISG0adOGkJAQmjRpQvPmzdFoNIwZM4Z3332XgIAA9u/fz5gxY1i/fj0AFy9eZMuWLaSlpdGzZ0/Wrl1LpUqV+Pjjj5k3bx6vvfYar7zyCjt37qRGjRp0796dDh06FLvORRlhSIctM9T1ji+Dc6XC97kSChotBL4INVqYNz5hNQsWLGDBggWEh4cD0Lx5cyZPnkzfvn0L3Xfnzp1069aNFi1acPjwYfMGWp4YDXDwW/hnmtoyCsD/Oeg5pWgtGIUQQgiRgySlhBA2QVEU0/qOHTvw9fXF09OTRx55hLZt2zJr1iw0d87wlbFfcbbfacCAATzwwANERUXxxBNP8Ndff+Vb9sknn0Sj0RAYGEhqaioxMTGEhISwbt06AJ544glee+01ADp37kxISAghISG8/fbbhISEcP36dTp37gzA5s2bOXHiRJ7neeyxx9BoNJw6dYqjR4+aWm+lpqZy3333cerUKZo2bUqdOnVM542IiCj0sYoy6thyuHFGTUZ1eKlo+3R+TU0mu3qZNzZhVd7e3nz44Yc0bNgQgO+++46BAwcSGhpK8+bN890vNjaWIUOGcP/99xMVFWWpcMu+SwfU8dkij6i3a7RUZ9Wrm/ePJkIIIYQonCSlhBCqiVfyv09zxzgzb/5XQNm76xV8+PBhmjZtCqhjO+3Zs4f69esDEBUVxa5du0wJnex8fX0JDQ01tXzK5OHhgZOTE+Hh4abjGI1Gjhw5QrNmzUzl3Nzc8PLyYt26dXzyyScFJqXuTHLllfTK3Na5c2d+++03zpw5w1dffcWXX35JdHQ0o0aNMpU9ePAgOl3uMXwyx8RSFIU2bdqwefPmHPeHhoYWKeEmygFDGmz7UF3vPAac3Iu+r1ezwsuIMq1///45bn/wwQcsWLCAPXv2FJiUGjlyJM888ww6nY41a9aYOcpy4PZ1+GcKhGYMYu7ooc5GGfgi6ORSWgghhCgJGVNKCKFycM1/sXcqRtnij1dz8eJF3njjDUaPHo3RaGTNmjWcOnWK8PBwwsPDmTt3Lr/++mue+77++uvMmDHD1H0lOTmZL7/8EoCxY8cyduxYUlNTAXWMqebNm9OoUaMcx5g0aRLBwcF5JoiyW758OYqicOjQIRwcHPDw8KBLly789NNPAKxYsYJ27dSxRDp16sRff/2Fl5cXOp0OZ2dndu7cSWBgIADdunVj4cKFgJosO3bsWK7zNW3alAsXLpi61iQkJPDff//RtGlTTp48yaVLl0hPT2f58uW59hXlxOEf4VY4uFaDdiMKLx/2G1w7afawhO0xGAz88ssvJCQk0LFjx3zLffvtt5w9e5b33nuvyMdOSUkhLi4uxwJq4rxcL4Y0lD0LUD5vY0pIKf7PoIzej9JuBIpWZ/0YZZFFFllkkcWGl6KQn3eEEFYRExODv78/qampODs789JLLzFs2DC2bNlCo0aNqFKliqnswIEDef/99/n0009zHadNmzbMmDGDAQMGkJ6ejp2dnak10tixY4mOjqZZs2ZoNBr8/f359ttvcx0jMDDQlCwqSP369enSpQu3bt1i8eLFAEyZMoWhQ4eydOlSKleuzJIlSwC1pZaHh4epdVf79u2JiYnB0dERgM8//5ygoCC+/PJL0tPTGTJkSK6Z/xwcHPjll18YNWqUaYDzmTNn0rBhQz777DPuv/9+qlevTuvWrQuNXZRBacmwbaa63vV1NelbkIRoWPMypCWos395F/6aFmXfsWPH6NixI8nJyej1elavXo2vr2+eZc+cOcP48ePZsWNHgePn3Sk4OJipU6fm2h4bG1vkC87iUBTF9J5nrVahdhd347z1PXQ31LEO06v5ktR9GoZabcEAxMZaJa7SYgt1XN5JHVuG1LP5SR2bX3mt48wfsQqjUcxxNVHGxMXF4eHhQWxsLO7uxegakQ9FUYiNjcXDw6NcvagsReqvZIpSf8nJyZw/fx4fHx+cnJzyLFNRKYqCwWBAp9PlqL+hQ4fy2GOP8dBDD1kxOtuS1+tI/n9LJkf97VsEf70FbrXg1dDcLRbv9OcbsP8rqOkPI7aAtuI1hjb366+0rxdKQ2pqKhEREcTExLBy5Uq+/vprtm3blisxZTAY6NChA8OGDSMoKAhQk+pr1qwpdKDzlJQUUlJSTLfj4uKoU6cOMTExZqkHq76PxF2Gje+iOb5KjcW5EvR4F9o8D9qCW9OWJfJebX5Sx5Yh9Wx+UsfmV17rOC4uDk9Pz0Kvm6SllBBCCGFr0hJh+yfq+r1vFJ6Qij4DB75R13tPr5AJqYrKwcHBNNB5YGAg+/fvZ+7cuabuwZni4+M5cOAAoaGhjB49GlC7DiuKgp2dHRs3bqRHjx55nsPR0dHUyjM7jUZjtovnzGNb7OI8PQV2z1P/79IS1fERA15A0+MdcKlsmRgszOJ1XAFJHVuG1LP5SR2bX3ms46I+FklKCSFEEWR2yxPCIvYvhoRr4FkXWg8uvPym90AxQJMHwaer+eMTNktRlBytmjK5u7vnGrtu/vz5bN68mRUrVuDj42OpEG3P6Q2wfjzcPKfertMBHpwJNVtZNy4hhBCiApCklBBCCGFLUm9DSMb4ad3Gg51DweXDQ+DUn+osmT1zj/sjyq+JEyfSt29f6tSpQ3x8PL/88gtbt25l/fr1AEyYMIHLly+zdOlStFotLVq0yLG/l5cXTk5OubZXGDfOwoaJcFqtL/TVodf74PcElKNfqoUQQghbJkkpIYQQwoY4hn6DJukmVGkIfk8WXNhohI3vqOuBL0C1xuYPUNiMqKgoBg8eTGRkJB4eHvj5+bF+/Xp69eoFQGRkJBEREVaO0galJsCOWbDrczCkgtYOOrwE974FTrYxVpgQQghRUUhSSgghhLAVSTE4HfxKXb9vAugK+ZhWjNDiUXXmvW7jzR+fsCmZs4Dmp7Bux1OmTGHKlCmlF5CtUxQ4vgo2vqsOaA5wTw944CNJ6AohhBBWIkkpIYQQwlbsXYAmNQ7FyxdN80cKL6+zg06vQPuXCk9gCVGRRYWps1mG71Bve9aFPsHQtJ901RNCCCGsSKbnEUJYhZ2dHa1bt8bX15eAgAC++uqrHPcrikLdunV56623AJg0aRL+/v74+vri4uKCv78//v7+bNmyhdWrV9OqVSuaN29OixYtmD9/PqDOLDVp0iQaNmxIw4YNeeKJJ7hx4wYA4eHhaDQaZs6caTrn3r170Wq1fPfddxaqBTh8+DAbN2602PmEDUu5DfsWqev3vlm8GfQkISVE3pJi4K/x8GUXNSFl56S2Qnx5HzR7SBJSQgghhJVJUkoIYRWenp6EhoYSFhbGqlWrWLBgAYsWLTLdv3v3bmrWrMmqVasA+OCDDzh8+DDr1q3D19eXw4cPc/jwYTw9PXn77bdZs2YNx48fZ//+/bi4uAAwZ84cwsLCCAsL47///qNt27a88MILpnM0atSItWvXmm7/+uuvtGzZ0kI1oJKklDA5tBRN0i0MnvWh2YCCyybcgK97qbOGKYpFwhOiTDEa4dD38HkA7F2gzk7Z9CE1GXXfeLB3tnaEQgghhECSUkIIRVEHfS3tpRhflOvVq8esWbNMLZwAli1bRlBQED4+PuzduzfffWfNmsWkSZNM05k7OzszdOhQ031z5szBwUGdvez111/n+PHjnDlzBlCnSK9atSrnzp1DURS2bt1K9+7d8zzPlStXeOihh2jVqhVt2rTh7NmzJCUlMXjwYFq2bEm7du04fPgwAHv27KFTp060bt2aHj16EBkZCcCWLVto2bIlrVq1IjAwEIPBwOTJk1m6dCn+/v5s2LChyHUmypn0VNg9D4CUgCDQ6gouv+0juLQPNk+XpJQQd7p8EBb3hLWjITEaqjaGwavhqR+hUj1rRyeEEEKIbKS9vxAVXVoizKhV+sedeAUcXItcvE2bNpw6dQpQu+79/vvvTJ06lbS0NJYtW0b79u3z3C8sLIw333wz1/a4uDiSkpKoVy/rC4hWq8XPz48TJ07g5+cHwOOPP86vv/5Kt27dCAgIwM4u77fFV199lYEDBzJixAhSUlIwGAx88cUXuLm5cezYMfbs2cPzzz/PkSNH8PX1ZceOHeh0On766SdmzpzJp59+yuzZs5k9eza9evUiNjYWnU7HtGnT+Pfff/nkk0+KXFeiHDq2HOIuo+hrkNrsEQpswxH9HxzIGOC69/TidfMTojxLiIa/p0DoD4ACDm5w39vQbiTYOVg7OiGEEELkQZJSQgiboGRr7bFjxw58fX3x9PTkkUceoW3btsyaNQtNHmN/KIpSrO13GjBgAA888ABRUVE88cQT/PXXX3mWCwkJ4ddffwXA0dHRtC1zzKsOHTqQlJREbGwsMTExPPfcc5w7d4709HRTYqxz586MHz+eEydO8Pjjj+Ph4VFofKICMBph5xx1vcMosHMsuPzf74ExHRo/AA26mT08IWyeIR32fw1bZkBKrLrN7ynoNRXcalg3NiGEEEIUSJJSQlR09i5qqyZzHLcYDh8+TNOmTQF1bKc9e/ZQv359AKKioti1axedO3fOtZ+vry+hoaGmlk+ZPDw8cHJyIjw83HQco9HIkSNHaNasmamcm5sbXl5erFu3jk8++STfpFRRElyZibDJkyfTr18/Ro4cyZ49exg/fjwA48ePp2/fvvz555+0bdu2wG6JogI5tQ6iT4OjBwQOheQCuuNd2AUn/wCNDnpNs1iIQtis8BBY9xZcO67ertESHvwE6nawblxCCCGEKBJp8y9ERafRqN3sSnspxoxGFy9e5I033mD06NEYjUbWrFnDqVOnCA8PJzw8nLlz55paKd3p9ddfZ8aMGYSHhwOQnJzMl19+CcDYsWMZO3YsqampgDrGVPPmzWnUqFGOY0yaNIng4GB0uvzH8enatSuLF6tdplJSUkhMTKRLly789NNPAOzbtw9XV1fc3d2Ji4vD29sbgB9++MF0jLNnz9KqVSsmTpxIs2bNOH/+PG5ubsTHxxe5rkQ5oygQMltdbzccHN3zL2s0woZJ6nrA81CtifnjE8JWJcXA6iBY0k9NSDlXgn6z4X/bJCElhBBClCGSlBJCWEVMTAz+/v74+voyaNAggoKCGDZsGNu2baNRo0ZUqVLFVHbgwIGsWrUKo9GY6zht2rRhxowZDBgwAF9fX9q1a2e6b+zYsTRp0oRmzZrRsGFD9u7dy7fffpvrGIGBgTzyyCMFxjt37lzWrFmDn58fnTp14urVq7z88svExMTg5+fH6NGjTcd+4403GDNmDF26dDHNBAjw6aef0rx5c/z8/Khbty4dO3ake/fuHDp0iNatW8tA5xVReIg6KLOdE7QPKrjsuc1w5RA46NUp7UWZFBsby5IlSxg2bBj3338/HTt2ZMCAAbz33nvs2rXL2uGVDWc3w4JOcORnQAOBL8Irh6DtsMInCRBCCCGETdEoikzbExcXh4eHB7Gxsbi7F/ArdREpikJsbCweHh5F6vIjcpL6K5mi1F9ycjLnz5/Hx8cHJycnC0do2xRFwWAwoNPp5PVXiLxeR/L/W0zfPwJn/4G2w6HfrILrT1Hg1F+QcF1tKSVyMffrryTXC5GRkUyePJkff/yRGjVq0K5dO2rXro2zszM3b97k33//5eDBg9SrV4/33nuPJ598stTjLy2lfd10p3yfx9QE2DRZHT8KoHIDGPQl1M17IgyRP3mvNj+pY8uQejY/qWPzK691XNTrBRlTSgghhLCGyCNqQkqjg06vFF5eo4GmD5o/LmEWrVq1YsiQIezbt48WLVrkWSYpKYk1a9Ywe/ZsU7dmkSFiL6wJgpvn1NttR6gDmRdjllchhBBC2B5JSgkhhBDWEDJH/dviEahUP/9ySTGgGMGlsgWCEuZy/PhxqlWrVmAZZ2dnnn76aZ5++mmuX79uochsXHoKbA2GnXPV/wO3WjDoC7inh7UjE0IIIUQpkDGlhBBCCEu7cRbC1qjrnccUXHbrh/BZazi2wtxRCTMqLCFV0vLl0tVj8FUPCPlUTUj5PQWjdktCSgghhChHpKWUEEIIYWm7Ple/ZDfqDTXy7soFqMmr/V+BMV1aSpVxa9euLXLZAQMGmDGSMiAtEcd9n8OeuWBMA5cq8NAc8K3g9SKEEEKUQzaZlJo/fz4ff/wxkZGRNG/enDlz5tC1a9d8y6ekpDBt2jR++OEHrl69ire3N5MmTeLFF1+0YNRCCCFEEcRfhcM/qutdxhZc9u8pakKqYU9pHVLGDRo0qEjlNBoNBoPBvMHYsiuh8P0jOCfdVG836Qf954Dey6phCSGEEMI8bC4ptWzZMsaMGcP8+fPp3LkzCxcupG/fvoSFhVG3bt0893niiSeIiopi8eLFNGzYkGvXrpGenm7hyIUQQogi2P0FGFKhTgeo1yn/chF74MRa0Gih1/uWi0+YhdFotHYIZUO1pqDRYnD3RtvjHTStnlIH+RdCCCFEuWRzY0rNnj2bYcOGMXz4cJo1a8acOXOoU6cOCxYsyLP8+vXr2bZtG+vWraNnz57Ur1+fdu3a0alTARf6Qgib8PXXX+Po6EhMTIy1Q8ll2bJlNGvWjIcffpgvv/ySZcuWAbBkyRKuXbtmKjdnzhxSU1OLdewpU6Ywb968AstMnjyZHTt2FFjmxIkTtGrVCn9/f27cuFGsGIojPDycwMBAsx2/Qkm6BQe+Ude7jsu/nKLAhknqeuvBUN3X/LEJYQvsnWHon8QP3QaSkBJCCCHKPZtqKZWamsrBgwcZP358ju29e/dm165dee6zdu1aAgMDmTlzJt9//z2urq4MGDCA999/H2dn5zz3SUlJISUlxXQ7Li4OAEVRUBSlxI8j8zilcayKSOqvZIpSf5n3Wbuef/31VwIDA1m1ahUvvPCC1eLIy5IlS/jmm2/o0KGDaZuiKCxZsoSAgADTIMRz5sxh2LBh2NvbF/nYRXmOpk6daiqbnzVr1vDUU0+Z3jMLey4NBgM6nS7f2wXFm9fx83odyf9vIfZ9hSb1Nkr15tCwl5p8ysZUf8dXo7l8AMXeFe6bkKucyJu5X3+ledyEhAS2bdtGRERErsT2q6++WmrnKZOqNYHYWGtHIYQQQggLsKmkVHR0NAaDgerVq+fYXr16da5evZrnPufOnSMkJAQnJydWr15NdHQ0o0aN4ubNm3zzzTd57hMcHGz6wpddbGxsqSWlbt++DahjQ5CegiY1HsWlaomPXRHkqj9RLEWpv9TUVIxGIwaDwTR2SUJCAgAuLi6m/VJTU0lLS8POzg5HR0fT/pllnZ2d0WrVBpdpaWmkpqai0+lwcnIqNM7o6GjOnTvH119/zYcffsiQIUMAmDZtGufPnyciIoLIyEiCg4MZOHAgSUlJBAUFceTIEZydnVmwYAH+/v5ERUXxzDPPcPv2bXr37s1XX33F1atXSU9P580332TPnj2kpqYyefJkBg4cmO/27D766CNCQkJ44YUXeOqppzAajVSpUoXatWtz4MABHn/8cdzd3Xnuuee4cuUKnTp14p577mHFihX89ddfTJ8+neTkZNq2bcv8+fPRarUsXLiQzz77DG9vb6pVq0blypULHDfmxRdf5NFHH6Vfv340bNiQIUOGsHbtWuzs7Fi9ejVhYWHMnTsXOzs7du3axerVqwkODua3334jJSWFoKAgRo4cybZt2/jwww/x9PTk6tWrDB06lE2bNhEXF4erqytz5sxh1KhRXLp0CScnJxYuXEjTpk05ffo0gwcPRqfT0aVLFxRFyRWvwWDAaDQSHx9vSvTL/28B0hJx3z0fDZDYeiRpGT+IZJdZf06XjuKk0ZIcMJIUo7N8QS8ic7/+4vJ4zu5GaGgoDz74IImJiSQkJFC5cmWio6NxcXHBy8tLklJCCCGEqDgUG3L58mUFUHbt2pVj+/Tp05UmTZrkuU+vXr0UJycnJSYmxrRt5cqVikajURITE/PcJzk5WYmNjTUtFy9eVAAlJiZGMRqNJV4MBoNy8+ZNxWAwKMZDPyjGaVUV47LBpXLsirDkqD8biKesLUWpv8TERCUsLExJTEw0bQMUQImKijJte//99xVAGTZsWI79XVxcFEA5d+6cadvs2bMVQHnmmWeKFOeCBQuUN954QzEYDEr9+vWV69evK0ajUZk8ebISGBioJCcnKxcuXFDq1KmjJCQkKDNnzlSCgoIUo9Go7Nq1S/Hz81OMRqMSFBSkzJ49WzEajcrChQuVKlWqKEajUZk/f74ya9YsxWg0KjExMUrTpk2VpKSkfLdnjy0tLU3p1q2bcvToUVNMn332mWI0GnNsNxqNSr169ZS4uDjFaDQq165dU+6//35TvY4aNUr56aeflIsXLyoNGjRQbt26pcTGxir33HOP6Xj5Lc8//7yydu1a0zm+/vprxWg0Ku+8844yderUXHGtW7dOeeWVVxSj0agkJycrbdu2VSIiIpTNmzcr7u7uyqVLlxSj0ah88803SoMGDUzvd0899ZSyf/9+xWg0Knv37lX69OmjGI1GpW/fvsqKFSsUo9GovPHGG0pAQECRXkfy/1vAsusLRXnPXTHO8VOM6al5lslRf1ePK8bkeOvHXYYWc7/+YmJiFECJjY0t0fVOt27dlBEjRijp6emKXq9Xzp49q0RERCj33nuvsnLlyhId2xJiY2NLpR7yYzQalVu3bilGo9EsxxdSx5YgdWwZUs/mJ3VsfuW1jot6vWBTLaWqVq2KTqfL1Srq2rVruVpPZapZsya1a9fGw8PDtK1Zs2YoisKlS5do1KhRrn0cHR1ztPrIpNFoSu2X1cxjaTzrqAPaXjoo4yIUg6n+pM7uSmH1l7k9rzLZtxVULr+yd67n59dff2XmzJlotVoGDhzImjVrGD58OBqNhocffhhHR0fq1q1rarWzc+dO3nrrLTQaDR07diQpKYm4uDh2797N5MmT0Wg0PP3000ycOBGNRsPff//N8ePHWbp0KaC27rpy5Uq+2xs0aADk7J6TvR7vfKx3Pl6NRsOePXs4evQoHTt2BCApKQlvb2+cnZ25//778fT0BNTp3ovy+s5eZtCgQWg0GgIDA1m7dm2uuP7++2/++OMPtm/fDqgtP8+dO4dGo6Fz587Url3bdMw+ffqY3jM3b97MiRMncp334MGD/Pnnn2g0GgYPHsyWLVvyfK0UVB/y/5tNeirsVscR03R+DXT5d/c01Z+MI3VXzPn6K61jHj58mIULF6LT6dDpdKSkpNCgQQNmzpzJ888/zyOPPFIq5xFCCCGEsHU2lZRycHAgICCATZs28fDDD5u2b9q0KVf3mkydO3dm+fLl3L59G71eD8Dp06fRarV4e3tbJO4C1WoNaCDukjoNuFsNa0ckRJ4yu7y4uLiYtr355puMGTMGO7ucbxWZA31nH7ft5ZdfZsSIEUUaoygqKopdu3bx2GOPAeo4b8ePH2f48OFA7i9+eX0RVBQFjUaTb5dbRVFYtGgR9957b5G2DxkyhKNHj+Ln55dv19/CKIrCQw89lGv/1atXl/jLbGYiXafT5dntT1EUpkyZYuoGmWnr1q05nlMg1+2DBw/met4koVTKjv0KcZdBXwNaPVNAuRVo9fXBQwaWL8/s7e1N/2PVq1cnIiKCZs2a4eHhQUREhJWjE0IIIYSwHJubfW/cuHF8/fXXfPPNN5w4cYKxY8cSERFBUFAQABMmTMjxpeuZZ56hSpUqvPDCC4SFhbF9+3befPNNXnzxxXwHOrcoRzfwaqauXzpg3ViEKICrqyuurq45khEODg64urrmalmYWTZzPClQv2S5uroWaTypFStW8NJLLxEeHk54eDhXrlzh9OnTXL9+HVAH8E5NTeXSpUucOnWKxo0b06VLF3766ScA9u3bh6urK+7u7nTq1Inly5cDauurTD179uTLL780JXAOHz5c4PalS5dy+PBhvvvuuwJjd3NzIz4+Ps/bHTp0YMuWLVy8eBGAGzducOnSJdq1a8c///xDbGwst2/f5vfffzftP2HCBFavXl1onRWmZ8+eLF68mKSkJABOnTpFcnJyoft169aNhQsXAuqU9ceOHQOgTZs2/PbbbwCmehd3yWiAkDnqeseXwT6f/5H4KFj7Cm7f94arRy0WnrC81q1bc+CAek3QvXt3Jk+ezI8//siYMWNo2bKllaMTQgghhLAcm0tKPfnkk8yZM4dp06bh7+/P9u3bWbduHfXq1QMgMjIyx6+Ier2eTZs2ERMTQ2BgIM8++yz9+/fns88+s9ZDyK12gPr3siSlhAA1eTRo0CDTbY1Gw0MPPcTKlSsB9Qtb79696dGjB3PnzsXZ2ZmXX36ZmJgY/Pz8GD16NN9++y0A7733HitXriQwMJALFy7g7u4OwMiRI6lVqxb+/v60aNGCadOmFbi9qIYOHcrQoUNp3749ACNGjKB79+48/PDDeHl5sWDBAgYNGoSfnx+9e/fm2rVr1K5dm9dff5127drx8MMP06VLF9Px/v33X2rUKHkLygcffJB+/frRrl07WrRowUsvvVTgQOqZPv/8czZt2oSfnx8tWrTgzz//BNRZBadPn0779u1xcHAocXwV2sk/4MYZcPKEwAJmmdw5B016EobqraC6JCbKsxkzZlCzZk0A3n//fapUqcJLL73EtWvXTEliIYQQQoiKQKPk1/elAomLi8PDw4PY2FjTF9qSUBSF2NhYPDw81FYnB5fA769B/a4w9I+SB1zO5ao/USxFqb/k5GTOnz+Pj49PkVo2WdKUKVOoWrUqo0ePLlL55ORk7O3t0el0LF++nGXLlrFixYq7Pr+SMcucTqezyOvvwQcfZN26dWY/jznk9TqS/987KAosug8iD8O9b0GPSXmXi7sCc/3BkMLth7/H1a+/1N9dMPfrr7SvF8oqc9eDvI+Yn9Sx+UkdW4bUs/lJHZtfea3jol4v2NSYUuVW7YyxQa6Eqt04tIWPuSOEKJrw8HCefvppDAYDHh4ephZUZUVZTUiJIjq7WU1I2btA+6D8y+2YBYYUlLodSa/b1WLhCes4f/486enpuSZjOXPmDPb29tSvX986gQkhhBBCWJgkpSzBqxnYu0LqbYg+nTXGlBAilylTphSrfNOmTQkNDTVPMEKUVMin6t82z4NrlbzLxETAwYyxzLpPlJlaK4ChQ4fy4osv5kpK7d27l6+//pqtW7daJzAhhBBCCAuzuTGlyiWtLmMWPmSwc2FTpPeuKAl5/RTi4n4I3wFae+hUQHfU7R+DMQ187lW7eYtyLzQ0lM6dO+fa3qFDB9PkC0IIIYQQFYG0lLIU7wC4EKIOdt5msLWjERVc5nTk169fp1q1auWq73JJWXpMqbJKURSuX7+ORqPB3t7e2uHYppDZ6t9WT4KHd/7lqjYG50rQ/R3LxCWsTqPR5JjFM1NsbGyRJigQQgghhCgvJCllKZnjSl06aN04hAB0Oh3e3t5cunSJ8PBwa4djc4xGI1qtNCQtjEajwdvbG51OxsnLJSoMTq0DNNB5TMFlO70CgcPAwUUdGF2Ue127diU4OJiff/7Z9P9jMBgIDg7OMTunEEIIIUR5J0kpS/HOSEpdOw6pCeDgat14RIWn1+tp1KgRaWlp1g7FpiiKQnx8PG5ubtJSqhCZsx6KPOyco/71HQBVGxVYFFATUqLCmDlzJvfeey9NmjSha1e1y+aOHTuIi4tj8+bNVo5OCCGEEMJyJCllKe61wK0WxF+BK4ehfu6xJISwNJ1OJ0mFOyiKQkpKCk5OTpKUEnfnVjgcW6GudxmXf7ktwVDLHxo/IIObVzC+vr4cPXqUefPmceTIEZydnRkyZAijR4+mcuXK1g5PCCGEEMJiJCllSd4BcOKKOq6UJKWEEKJ82vkZKAa4p4eadMrLtROw7SNAgVF7waupJSMUNqBWrVrMmDHD2mEIIYQQQliVDJpiSaZxpWQGPiGEKJfioyD0B3W9oFZSW4MBBZr1l4RUBREREVGs8pcvXzZTJEIIIYQQtkOSUpaUOa7UZRnsXAghyqU988GQAt5toX4+A1ZHHoWw3wAN3DfRouEJ62nbti0jRoxg3759+ZaJjY3lq6++okWLFqxatcqC0QkhhBBCWIckpSyppj9otBB3GeIirR2NEEKI0pQUA/sXq+tdxuU/TtTWYPVvi0eguq9FQhPWd+LECTw8PHjggQeoXr06/fr1Y8SIEbzyyis899xztGnTBi8vL5YsWcLHH3/MK6+8UugxFyxYgJ+fH+7u7ri7u9OxY0f++uuvfMuvWrWKXr16Ua1aNVP5DRs2lObDFEIIIYQoFklKWcLta2qXPUc9VGumbrssXfiEEKJc2f81pMaDl686eHleLh+EU+vUHyjum2DZ+IRVVa5cmU8++YQrV66wYMECGjduTHR0NGfOnAHg2Wef5eDBg+zcuZO+ffsW6Zje3t58+OGHHDhwgAMHDtCjRw8GDhzI8ePH8yy/fft2evXqxbp16zh48CDdu3enf//+hIaGltrjFEIIIYQoDhno3NwuH4KvuoNLFXjzrDrY+bXjapKqWX9rRyeEEKI0pCbCngXqepexoM3nN58tGa2k/J6Eqo0sE5uwKU5OTjzyyCM88sgjJT5W//45ryM++OADFixYwJ49e2jevHmu8nPmzMlxe8aMGfz222/8/vvvtG7dusTxCCGEEEIUlySlzM3LF7T2kHhDnSa8diAcWirjSgkhRHkS+gMkRoNnPWheQLIhYCgkXINub1ksNFExGAwGli9fTkJCAh07dizSPkajkfj4eCpXrlxguZSUFFJSUky34+LiAFAUBUVR7j7ofGQe1xzHFiqpY/OTOrYMqWfzkzo2v/Jax0V9PJKUMjd7J6jREq4cUhNRmYOdXwkFowG0OuvGJ4QQomQMabDrM3W986ugK+CjtdlD0LRf/uNNCVFMx44do2PHjiQnJ6PX61m9ejW+vkUbq2zWrFkkJCTwxBNPFFguODiYqVOn5toeGxtrtqTU7du3AdDI/4pZSB2bn9SxZUg9m5/UsfmV1zrO/BGrMKWSlDpz5gy//fYb4eHhaDQafHx8GDRoEA0aNCiNw5d93oFZSanmD4ODHlJvw/VTMsitEEKUdceWQ+xFcPUC/+cKL1+OLjaE9TVp0oTDhw8TExPDypUref7559m2bVuhiamff/6ZKVOm8Ntvv+Hl5VVg2QkTJjBu3DjT7bi4OOrUqYOHhwfu7u6l8jiyy0x0eXh4lKuLc1sidWx+UseWIfVsflLH5lde67ioj6XESang4GAmT56M0WjEy8sLRVG4fv0648ePZ8aMGbzxxhslPUXZVztA/XvpgNoyqlZrCN+hDnYuSSkhhCi7DOmw/RN1vePLauvYOykK/DpE/YGi7QhwcLFsjKJcc3BwoGHDhgAEBgayf/9+5s6dy8KFC/PdZ9myZQwbNozly5fTs2fPQs/h6OiIo6Njru0ajcZsF8+Zxy5PF+e2RurY/KSOLUPq2fykjs2vPNZxUR9LiWbf27JlC++88w6TJk0iOjqayMhIrl69akpKjR8/nu3bt5fkFOVD7Ywue5FHID01Z5JKCCFE2fXvSrh5FpwrQ9vheZc5txVOrIXNH0DSLYuGJyoeRVFyjP90p59//pmhQ4fy008/0a9fPwtGJoQQQgiRW4laSn355ZcMHz6cKVOm5NheuXJlpk2bxtWrV1mwYAH33ntvSU5T9lW5B5w8ITkGov7NGldKBjsXQoiyy2iA7R+r651Gg6M+dxlFgS0fqOuBL4BHbcvFJ2za6dOn2bp1K9euXcNoNOa4b/LkyUU6xsSJE+nbty916tQhPj6eX375ha1bt7J+/XpA7XZ3+fJlli5dCqgJqSFDhjB37lw6dOjA1atXAXB2dsbDw6MUH50QQgghRNGUKCm1b98+vv/++3zvHzx4MEOGDCnJKcoHjQa6T1K/sHjWA7ea6vZrYZByO+8vMkIIIWzbv6vgxhlwrgTt/pd3mf/+hkv7wc4ZuozLu4yocL766iteeuklqlatSo0aNXI0b9doNEVOSkVFRTF48GAiIyPx8PDAz8+P9evX06tXLwAiIyOJiIgwlV+4cCHp6em8/PLLvPzyy6btzz//PEuWLCmdByeEEEIIUQwlSkpFRUVRv379fO/38fEx/QpX4bW/4wuLe22IuwyRh6F+F6uEJIQQ4i4ZDbB9prre8WVwdMtdJnsrqXbDwa265eITNm369Ol88MEHvP322yU6zuLFiwu8/85E09atW0t0PiGEEEKI0laiMaWSk5NxcHDI9357e3tSU1NLcoryS8aVEkKIsuv4aog+rXbNbjcy7zKn1sGVULB3hc5jLBmdsHG3bt3i8ccft3YYQgghhBBWV+LZ977++mv0+ry7n8XHx5f08OXLxX1qNw7/Z9VxpU6sVWfgE0IIUXYY0mHrh+p6x5fByT13GUXJKtN+JLhWtVx8wuY9/vjjbNy4kaCgIGuHIoQQQghhVSVKStWtW5evvvqq0DIiw6r/wa3zUK1J1ox8l2SwcyGEKFOOLssaS6p9PkkFjQYeWQQhn0KnVywbn7B5DRs25N1332XPnj20bNkSe3v7HPe/+uqrVopMCCGEEMKySpSUCg8PL6UwKgjvQDUpdemgOlOTRgfxV+BWOFSqb+3ohBBCFCY9FbZltIDqMjbvVlKZvJqpiSkh7rBo0SL0ej3btm1j27ZtOe7TaDSSlBJCCCFEhVGiMaU2b96Mr68vcXFxue6LjY2lefPm7NixoySnKF8yW0ddPgAOrlC3o3r75DrrxSSEEKLoDn0HMRGgrw5tR+RdJl3GUhQFO3/+fL7LuXPnrB2eEEIIIYTFlCgpNWfOHEaMGIG7e+5fij08PBg5ciSzZ88uySnKF+/MLnsH1PFGmvZTb5/803oxCSGEKJrURNj+ibp+75vg4JK7jCEdFnaF30ZDwg3LxieEEEIIIUQZU6Lue0eOHOGjjz7K9/7evXvzySeflOQU5UuNlqBzgKSbaje+pv1gwwSI2AUJ0TIQrhBC2LL9X8Ptq+BRF9o8n3eZo8vg+klIuA4PBFs2PmHTxo0bx/vvv4+rqyvjxo0rsKz8oCeEEEKIiqJESamoqKhcg3PmOLidHdevXy/JKcoXO0c1MXX5oDqulN/jUMMPrh6FU39Bm8HWjlAIIURekuPUQcsB7nsb7BxylzGkwbaMH2o6vwaObpaLT9i80NBQ0tLSTOv50Wg0lgpJCCGEEMLqStR9r3bt2hw7dizf+48ePUrNmjWLfdz58+fj4+ODk5MTAQEBBY5LtXXrVjQaTa7l5MmTxT6vRWQfVwqgWX/178k/rBOPEEKIwu1ZoLZyrdII/J7Ku8zhHyHmArh65T/elKiwtmzZgqenp2k9v2Xz5s3WDVQIIYQQwoJKlJR68MEHmTx5MsnJybnuS0pK4r333uOhhx4q1jGXLVvGmDFjmDRpEqGhoXTt2pW+ffsSERFR4H6nTp0iMjLStDRq1KhY57WYtsPgxQ3Qc4p6u2lG/ZzdAinxVgtLCCFEPhJvwu556nr3CaDLo5Fxegps+1hd7zI27/GmhBBCCCGEEDmUqPveO++8w6pVq2jcuDGjR4+mSZMmaDQaTpw4wRdffIHBYGDSpEnFOubs2bMZNmwYw4cPB9TB1Dds2MCCBQsIDs5/fA4vLy/TL5A2rVqTnLe9mkHlBnDzHPz3NzR/2DpxCSGEyNvOuZASB9Vbgm8+79GHlkLcJXCrCYEvWjY+UeY8/PDDeXbT02g0ODk50bBhQ5555hmaNGmSx95CCCGEEOVHiZJS1atXZ9euXbz00ktMmDABRVEA9aKqT58+zJ8/n+rVqxf5eKmpqRw8eJDx48fn2N67d2927dpV4L6tW7cmOTkZX19f3nnnHbp3755v2ZSUFFJSUky34+LiAFAUxfQYSiLzOEU+VtOH0Oz6DOXEH+A7qMTnL+uKXX8iB6m/kpH6K5lyV3+3o2DvQjSA0n0iaDTq7KnZKQrs/1ot0/V1dfzAu3z85a7+LMzc9Vdax/Xw8GDNmjV4enoSEBCAoiiEhoYSExND7969WbZsGR999BH//PMPnTt3LpVzCiGEEELYohIlpQDq1avHunXruHXrFv/99x+KotCoUSMqVapU7GNFR0djMBhyJbKqV6/O1atX89ynZs2aLFq0iICAAFJSUvj++++5//772bp1K/fee2+e+wQHBzN16tRc22NjY0stKXX79m0g7wFL7S7uwv6/9aR7tyetUT903vfhxmdwegOxN6+rM/RVYIXVnyiY1F/JSP2VTHmrP+etwTimJ5FeozW3q3eE2Ng8y2ke/RWHoz+Q0mBAvmWKorzVn6WZu/4yf8QqqRo1avDMM88wb948tFp1JAWj0chrr72Gm5sbv/zyC0FBQbz99tuEhISUyjmFEEIIIWxRiZNSmSpVqkTbtm1L5Vh3XkgqipLvxWWTJk1yNG/v2LEjFy9e5JNPPsk3KTVhwoQc0zHHxcVRp04dPDw8cHd3L3H8mYktDw+PvOMOPYzmyHc4KEkQ+Ay434eir4Hm9lU8wv+CNkNKHENZVmj9iQJJ/ZWM1F/JlKv6i4mAoz8CoOs1BY+Cuoh7eECviTiV8JTlqv6swNz1V1rHXLx4MTt37jQlpAC0Wi2vvPIKnTp1YsaMGYwePZquXbuWyvmEEEIIIWxVqSWlSkPVqlXR6XS5WkVdu3atWN0AO3TowA8//JDv/Y6Ojjg6OubanjlzX2nIPhNgLt5q8k5z6aDaFUSjg06jYeM7aDa9C437gFuNUomjrCqw/kShpP5KRuqvZMpN/W2fCcY0qN8VzT335V3mxll1XMBSfKzlpv6sxJz1V1rHTE9P5+TJkzRu3DjH9pMnT2IwGABwcnKS14AQQgghyr0Szb5X2hwcHAgICGDTpk05tm/atIlOnToV+TihoaHUrFmztMMrPbUD1L83z6qzOgG0fwlqtYbkWPjz9bsej0QIIUQpiP4PDv+srt8/Oe8yt6/Dl13h275Z7+VCFMHgwYMZNmwYn376KSEhIezcuZNPP/2UYcOGMWSI2lp627ZtNG/e3MqRCiGEEEKYl021lAIYN24cgwcPJjAwkI4dO7Jo0SIiIiIICgoC1K53ly9fZunSpYA6O1/9+vVp3rw5qamp/PDDD6xcuZKVK1da82EUzKVy1ox7lw9Bo57qFOMDv4CF98LJPyBsjczEJ4QQ1rJ1BigGaPwA1GmXd5kdn0BaAqQng3Pxx1EUFdenn35K9erVmTlzJlFRUYA6fubYsWN5++23AXWSlwceeMCaYQohhBBCmJ3NJaWefPJJbty4wbRp04iMjKRFixasW7eOevXqARAZGUlERISpfGpqKm+88QaXL1/G2dmZ5s2b8+eff/Lggw9a6yEUjXfbjKTUATUpBVC9OXR9HbZ9BOveBJ9uagJLCCGE5Vz9F/7N+GGj+6S8y9y6APsXq+s9p5Rq9z1RvqWnp/Pjjz8ybNgwJk2aZBo8/c4xLevWrWuN8IQQQgghLMrmklIAo0aNYtSoUXnet2TJkhy333rrLd566y0LRFXKagfC0WVw6UDO7V1fh7C1cP0ErB8PjyyyTnxCCFFRbflA/dv8Yajpl38ZYxo0uE9dhCgiOzs7XnrpJU6cOAHkTkYJIYQQQlQkNjWmVIXinTGu1O2onNvtHGHgPNBo1aTV6Y2Wj00IISqqSwfg1Dr1Pfi+iXmXuXxIfX8GtZWUEMXUvn17QkNDrR2GEEIIIYTV2WRLqQqhhh+8firvWfa8A6HDKNg9D/4YA6P2gJP8kiqEEGa3+X31b6unoVrj3PcrCmx8R133e0qdoEKIYho1ahSvv/46ly5dIiAgAFdX1xz3+/nl00JPCCGEEKKckaSUtejs805IZeo+SR3w/FY4/P0ePPSpxUITQogK6fwOOLcVtPbQ7e28yyTegKRbYOcE979r0fBE+fHkk08C8Oqrr5q2aTQaFEVBo9FgMBisFZoQQgghhEVJUsoWpCWDvVPObQ4uMOBz+K4/HPgGWjwK9btYJz4hhCjvFCWrlVTA81CpXt7lXKvCyB1w9Sh4eFsuPlGunD9/3tohCCGEEELYBElKWZPRCCuGwplNMGo3VKqf836feyFgKBxcAmtfgaCdarJKCCFE6TqzCS7uVVtAdX2j4LI6O6jdxjJxiXIpc0ZhIYQQQoiKTpJS1qTVQuJNSEuEk39Cx5dzl+k1TR3s/OY52DoDek+3fJxCCFGeGY1ZraTajQD3mrnLJN6E0B+g3f9yt2wV4i6FhYURERFBampqju0DBgywUkRCCCGEEJYlSSlra9YfwnfAid/zTko5eajjSf38JOz+Qp2ivHaA5eMUQojy6sRatTuegxt0Hpt3mW0zYe8CCA+BZ3+1bHyi3Dl37hwPP/wwx44dM40lBeq4UoCMKSWEEEKICkNr7QAqvKb91L8Re+D2tbzLNHkAWj4OihF+ewXSU/MuJ4QQoniMBtjygbre8WVwrZK7TPR/sP8rdb1DkOViE+XWa6+9ho+PD1FRUbi4uHD8+HG2b99OYGAgW7dutXZ4QgghhBAWI0kpa/PwzphSXIFT6/Iv98CH4FIFrh2HrcEWC08IIcq1o8sg+jQ4V8q7tSqoM6Aa06FhL7inh2XjE+XS7t27mTZtGtWqVUOr1aLVaunSpQvBwcE5ZuQTQgghhCjvJCllC5r1V/+e+D3/Mq5V4cGP1fWQ2bD9Y/PHJYQQ5Vl6alaSv/MYcHLPXSY8BE7+ARqdjOknSo3BYECv1wNQtWpVrly5AqgDoJ86dcqaoQkhhBBCWJQkpWxB04yk1LltkByXf7kWj0L3d9T1zdNh8wfqNOZCCCGKL3QpxESAvro6gPmdjEbYMFFdDxgKXk0tGp4ov1q0aMHRo0cBaN++PTNnzmTnzp1MmzaNBg0aWDk6IYQQQgjLkYHObUG1xlCnPbQZAo5uBZft9ibYOcCmybB9JqQmqDP06eSpFEKIIktLgu2fqOtd3wAHl9xljv0KkUfUAdDvm2DZ+ES59s4775CQkADA9OnTeeihh+jatStVqlRh2bJlVo5OiMIpioLBqJBuVEgzGNFpNTjotNjp5PduIYQQxSOZjGwSEhJwc3MzzX6TmppKWloadnZ2ODo65igH4OzsjFarfvimpaWRmpqKTqcrVlknp4ypxV/4i8TkFJTERJycnNDpdACkp6eTkpKCVqvF2dlZLdv5NRLTNCgb38Fp1zx0lw/Ao1+Trq+VuyyQlJSE0WjE0dEROzv1KTcYDCQnJxerrEajwcUl64tbcnIyBoMBBwcH7O3ti13WaDSSlJQEgKurq6lsSkoKCQkJODs7m+qyoLLp6enY29vj4OAAqBdKiYmJALi4uOR6PotTtijP/d2+TkzPPZCYmIiiKDme++KUzf46yV428/ks9DVVzOe+pK+T/J7P4r5OCnruS/I6SUhIwMnJKUddZj6fd/s6ye/5LOnrJL/nszhlS/M9IntZc75H3Pl8Fvt1sm0e3LiCS9U6aAKez/F8msrWDkBp3JfEav6gccFFUSzyHpGQkICjo2Ohz2dpvE7K23tE9uOa6z2iNPTp08e03qBBA8LCwrh58yaVKlUq1fMIURKXY5LYf/4mp6Pi+e/abaLiU7hxO4WbCakkpuY9Q6ROq8HRTouDnRZHOy2OdjrTuoOdFgedFkd7nfo3W7ms+7Q46NR9st/nmKPcHcfMXi7bvjqt/C8JIURZID9nZFOrVi2io6NNtz/++GP0ej2jR4/OUc7Lywu9Xk9ERIRp2xdffIFer2fYsGE5ytavXx+9Xs+JEydM25YsWYJer+epp57KKqjV4evri16v59CubZCeAsCyZcvQ6/UMGDAgx3HbvrwQfXA8OyId4eJeWNCFP+a/g16vp2fPnjnK3nvvvej1ejZs2GDatnnzZvR6PR07dsxRtm/fvuj1elavXm3atmfPHvR6Pa1atcpR9tFHH0Wv1/Pjjz+ath07dgy9Xk+jRo1ylB08eDB6vZ5FixaZtp09exa9Xk/t2rVzlA0KCsLb25u5c+eatkVGRqLX6/H09MxRdty4cej1embMmGHaFhsbi16vR6/Xk56ebto+adIk9Ho9kyZNMm1LT083lY2NjTVtnzFjBnq9nnHjxuU4n6enJ3q9nsjISNO2uXPnotfrGTlyZI6ytWvXRq/Xc/bsWdO2RYsWodfrGTx4cI6yjRo1Qq/Xc+zYMdO2H3/8Eb1ez6OPPpqjbKtWrdDr9ezZs8e0bfXq1ej1evr27ZujbMeOHdHr9WzevNm0bcOGDej1eu69994cZXv27Iler+ePP/4wbduxYwd6vZ62bdvmKDtgwAD0en2OX/QPHTqEXq/H19c3R9mnnnoKvV7PkiVLTNtOnDiBXq+nfv36OcoOGzYMvV7PF198YdoWERGBXq/Hy8srR9nRo0ej1+v5+OOs8dWio6NNz2d2b7/9Nnq9nqlTp5q2JSYmmspmfvEEmDp1Kt7e3rz99ts5jpFZ1mrvEZD1HnHokGlbvu8Rbdui1+vZsWOHadsff/xRLt4jRo4ciV6vv/v3iCvn0Pd6C31wPOld3gI7NVGU6z2iaiPSH/8efe8JFn2P8Pb2ZsiQITnKynuEyhbeI8ylcuXKkpASVmU0Kuw9d4Ppf4TRa/Y2On+4mTHLDjN/61k2hkVx5GIMl24l5ZuQAjAYFRJTDcQkphEVl0LEzUT+u3ab41fiCI2IYe/5m2w/fZ2/T0Tx57FIVode5pf9F1m6+wJfh5zniy1n+fTv03y0/iTv/xHGO2v+5a0VR3ntl8ME/XCIF5cc4LnFe3li4W4GfrGTvnN3cP+sbXT5aAvtPviHVtM20mzyeu6ZuI6GE9fhO3k9radtpP2Mv+k6czP3z9rKg3N3MOiLnTyxcDeDF+9l+Hf7GfXjQcb8EsrbK47y7pp/mfZ7GMF/nWDWxlN89s8ZFmw9y9c7zrF0dzg/74tgxcFL/Hb4Mn8di+TvsCi2nb7OrrPRHAi/yZGLMYRdieO/a7e5eCuJKzFJXI9PITYxjcTUdNIMRhQZfkMIIUykpZQtWvEipLwAPd8rvOyAzyHqa7i0H458pm5TjOaNTwghyrLMbnsArZ7Kfb98WRBm8uKLLxap3DfffGPmSITIcvb6bVYevMRvh69wOSbJtF2rAT9vT3xrudPYS08tT2eq6B2p4uqA3skOO60GO50WO60GRYGUdAMp6UZS042kpBtITjOSajCSnGYgNWN7qsFISsb2zHKZ96Xkus9Iaub9ed6XVSYlo0z2t+90o0J6qqHAJJo1Oei02Os02Ntpsddps25ndIN0yFi312mxt7vjtk6Lg90dt3Xq86HTarDXabDTqsfTabXY6bK2mZ43nQZ7bbbyusz7svY1bcvYRz2eup9WWqIJIUqJRpFUPXFxcXh4eHDlyhVq1KhRKt33YmNj8fDwMP26WuSuOSf+xOm3YWo3iuF/k169VeFdczDC1mDSt80iJV1BW7URzs8sgRotgbLXfS85OZkbN25QtWpV6b53l933Ml9/ycnJ5aprjiW676WkpBAdHU2VKlWk+95ddt/LfP2lpKTYXve9q/+ifNmFxFQjDF6NS9Meud8jNryNg06BHu+i6Ktb9D0iNTWV69evU7lyZem+d5fd9zJff6mpqaX+HpF5vRAbG4u7ex6zNRZCq9VSr149WrduXWBLiewtEW1RSeuhMIqimJ5HaT1mHgaDkfVHLvDr4etsO33dtN3N0Y7ezWvQo6kXXRpWxcPF3opRFo+iqGNcZSasUrMlyFLySGzlSIplS3alpBtJMxhJy/ibalDHzUozGEk3KKRmrKtl7rhtUEhNz7qdmm4kzahuK2+0GnIkrewzEl122sy/6jZdZkIrW8LLLkfSLGPfjPt1WvU+rUa9rdWox9JlW3Lc1mhITUlG7+qSkZRDTcRpc+9rp9WgvXP/jPPl2C+zbOb+OvU82c9dkd6b5D3Z/MprHRf1ekGSUpT+xVWJX1QrhsG/K6BaMxi5zdS1pFDntsGq/8Htq6BzhN7vqzNKlbEXdnn9p7QUqb+SkforGZuuP0WB7/pD+A7wHQRPfJe7zNV/4csugAIvboS67S0cog3XXxlg7vor6fXCqFGj+OWXX6hbty4vvvgizz33HJUrVy71OM1NklJll9Go8Ne/V5nz92nOXLsNqJeJPZp48Ugbb+5v5oWTvc7KUZYP2V/HoHZtTCs0qWUkNV3JedugmBJkOZJkmduM6v2pGUkxg1FNnqUZFdINRrXFWMbfzMRaulEhPbOcwWiKLXNb5j6ZxzBW+G+LuWk1qMkzLRlJrZyJssx1rQa0GQkunUaTsY4psaXTgDZju3p/xm1Ntv1N69n212Tsr81+rDvOl7mPJu8y2Y9vKp/t+Jnn0wApyUm46V3QatQEHqjlNJrMv+q6BvV4mozjasjYrsm+PY/90WQco5D9sx1fc8f+mce7sywZ58z3XDmOaZ3PnPL6uVfU6wXpvmeL+s6E89vg+gnY9hHcP7lo+zXoBi/tgt9Gwen18NdbcHYLDPwCXKuYN2YhhLB1J/9UE1I6R3XW0jspCmycBChq0srCCSlR/s2fP59PP/2UVatW8c033zBhwgT69evHsGHD6N27d7EvRBcsWMCCBQsIDw8HoHnz5kyePDnX2GHZbdu2jXHjxnH8+HFq1arFW2+9RVBQUEkeligjdp2NZvofJwiLjAPA1UHHk23rMLSTD3Wr5DEDqSg1moxWP3Y6cKbsJf2MxqxEVppBTVQZjFmJLzUZlpH0unNbZqIr8z7TMbInwbLKZy7pRgWjopYzGI0YMmd8NCim9cxyySmpaHV2OfY3ZJzLoGBK1BkzWtNlPp5c58u+PeMc+daJAqkGIxgAyl9LuIos86NYY7qtueN25v05C+Z3f2HHQ82boSiKqeV8UWMg33MUHsOGsfeid7SNdJBtRCFycq0C/WbDr4MhZA40fQhqtyn6vk//AvsWwcZ34PRf8GVneGQR+Nxb+P5CCFEeReyBNS+p651GQ6V6ucuc2QTntoLOAXpOsWR0ogJxdHTk6aef5umnn+bChQssWbKEUaNGkZaWRlhYWK5B2Avi7e3Nhx9+SMOGDQH47rvvGDhwIKGhoTRv3jxX+fPnz/Pggw8yYsQIfvjhB3bu3MmoUaOoVq1argHzRflxLS6Z6X+eYO2RKwDoHe0Y1qU+j7Wsgnf1KuXqV3lhHlqtBgetBgcbnCPLnC1MFCVngip74urOxFbuJJfawsyoqGUNioKiYDqeeuys+40K2bari5KxLUeZjGSdsZD9M89tKp/nOTIeo5JR3qhk2yfn/qmpaWriL+NYSkb9GBVM60rGPpl/IedttVz2/RSMGfm83OVy3s465x37K0D2chn7lex5z/h754bcJUt2ojxZbgw8W+owJ0kpM1MUhaQ0A4mp6fmW0Wo0OZpKJ6amQ93euDUdhOPJNaSvfomY5zaCnWPeZfPS/HnsvQJx+2Mk2htnUL4bQFL710js9Aboco8PoEGDs0PWcZNSDSj5/KPdWTY5zWB648mLi4NdscoqikJSqgFjQkqBvzsU57jO9rqsMYPSDQX+8lGcsk52OtNAj6npRtKN+UdcnLKOdjrTVMbFKas2+zYQn5BKmi4514ezQ8bgmZll0wz5Hzd72XSD2iw8P5mDbBa3rMGokJKe/5uvnVad1rm4ZY1GheS7LKsoSo76U6e31pnuS0rL/7jFKVvk/2ULli3O/31+ZRVFIT4xFXvndNPrr7TfI4pbVhu+Hcflz6JJSyTNuxOxbUbD7eQcZZ11Cs4bJqEFktsM57ZDzVxlTGXN+B6h0WTUbWIqhgIeW0neI4r6f18W3yOS09JJTzXgrihl4ou2JqP7haIoGAt4DvPTv3//HLc/+OADFixYwJ49e/JMSn355ZfUrVuXOXPmANCsWTMOHDjAJ598UmBSKiUlhZSUFNPtuDi1pY2S8eWmtGUe15YumMuidIORpbsv8OnfZ7idko5GA8+2q8vYXo2p5GJPbGys1LEZyevYMsxdzzqtBh0aymADt1JTFruWmRJX2RJYakIrZwLLlNDKTCAquY8DWemnrISVcsftnPeT7/25tysZ8cTHxePmpiezPVOufYoYA4XGqK452+vM/v5U1ONLUsrMktIM+E7eUHjBPFTiATY5bmZnVGXe+XAj8RS/abUzE5hs9z1P223BZe8cTu7+g3FpLxGu1LyrmIQQoiy5T3uYL+0/RaNJY7uhJf/7bwTJH+3OVe453Sam25/mpqLnvh2tidvxjxWiFaXl+NTeuDra3i/6oCZ4MrvvhYSE8NBDDzFv3jweeOABU7P9u2EwGFi+fDkJCQl07NgxzzK7d++md+/eObb16dOHxYsXmyYCyUtwcDBTp07Ntd1cSQ1FUbh9O3O8o7LxBcjWhF6KI3jDWU5fVwfrb1FTz8Q+9+BbQw/pScTGJkodm5m8ji1D6tn8ynsdazKWu/oEvrM67rJ6FEXB0dGA3sFggTpWj387Ps7M58n6EaswkpSyYbdwp2/Kh1zH866PkYQTE9JHEGJsSbD917TR/scmh7f40XA/n6c/zA08Si9gIYSwIX20+/nc/jMcNAY2GQJ4Oe1VUsmrpaiRoTr1x4M56Y8Sh2uuMkKUhuwDnb/wwgv88ssvVKlSsjEfjx07RseOHUlOTkav17N69Wp8fX3zLHv16lWqV6+eY1v16tVJT08nOjqamjXz/sFqwoQJjBs3znQ7Li6OOnXq4OHhYbaBzoEy9au8rYi+ncJH60+y4uBlADyc7XnrgSY8FVjH1GoTpI4tQerYMqSezU/q2PzKax0X9bFIUsrMnO11hE3rUzrdbRQFLQpOjg6Fl8113D6kXB9C6t/jcTj/N0PtNjLEeSfJAf8jqfWL4FrdprrvxcXG4ejiKt33ilnW1H0vLh43dzfpvncXZRVFyVF/0n1PVazue/HxVK9Syard9xxOrsbtz8/QKAbSmw4k4MH57NLm/5HnnL6d1NBvGN/hVcYqWqu9R2g0EBcbh5OrXrrvcZfd95IScbbR2cMyu8/5+Piwbds2tm3blme5VatWFfmYTZo04fDhw8TExLBy5Uqef/55tm3blm9i6s7PhcwL4YIuHB0dHXF0zD0TcGb3Q3PIPHZ5ujg3J6NRYdmBiwSvO0Fcsvq+/2RgHd7u25TKrg557iN1bH5Sx5Yh9Wx+UsfmVx7rWJJSNkKj0eDiYJfji1Rh8ix7+xr8ORa8mkGPdwoum99xazeC51fC+e2w8V20kYdx2fMpLvvnQ6snoeNoqNak+Mct5bKKopDmoMPD1bHIL2RrxptVtshFzVYW1PqzN6TgoXcqE29qbnm0XCmNsvq7LFtY/bk6Fv24xSlrG6/hkpfNrD8XBztT/Vk83tAf4I/RgAKtnsZuwDwq6wo7rhN0fxsHKFZH6dJ+jzC9/7k4FOP9r8ghlEnF+r93tCPWkGKz731Dhgwp9dgcHBxMA50HBgayf/9+5s6dy8KFC3OVrVGjBlevXs2x7dq1a9jZ2ZW4xZawnv+u3WbiqmPsC78JgG9Nd94f1IKAepWsHJkQQghROElKlRURu+HkH3DqL3U2vlr+d38sn3thxBY4+Tvs/AwuH4BDS9WlUR91Zqr6XbPPNSmEEGXDvq9g3RvqesAL6kymBY3Tc+mgOrupvN8JC1iyZInZz6EoSo5BybPr2LEjv//+e45tGzduJDAwMN/xpITtSkk3sGDrWeZvOUuqwYizvY7XezdmaKf6plaMQgghhK2TT6yywncg+A4CxQC/DoZb4SU7nlarHnP43/DiBjXRhQbObIDv+sOibnB0ORjSSiF4IYSwgF2fZyWkOoyChz4tOCF1cR983UN9z5P3OlEGTZw4kR07dhAeHs6xY8eYNGkSW7du5dlnnwXUsaCGDBliKh8UFMSFCxcYN24cJ06c4JtvvmHx4sW88cYb1noI4i7tD79Jv89CmPP3GVINRu5rUo2NY+9leNcGkpASQghRpkhLqbKk3yy4ehRunoNv+sLza6Fqo5IdU6OBuh3U5cZZ2DMfQn+EyCOwajj8PQU6BEGbIeAkg6ILIWyQosD2j2HLB+rtrq9Dj3cLbv2kKLBhorruWQ900kpElD1RUVEMHjyYyMhIPDw88PPzY/369fTq1QuAyMhIIiIiTOV9fHxYt24dY8eO5YsvvqBWrVp89tlnPProo9Z6CKKYYpPS+Gj9SX7aqz6vVfUOvNe/OQ/51bTZbqtCCCFEQSQpVZa4VoUX/oKlA+H6Sfi2Lwz5Dao3L53jV7lHTXzdNxEOfAP7FkLcJdj4Dmz9CAKeh/ZB4FmndM4nhBAlpSjwzzQIma3e7vEO3Ptm4fsdXwWX9oO9S45x+oQoSxYvXlzg/Xl1F+zWrRuHDh0yU0TCXBRF4a9/r/Le2uNcj1e7Zz7Vtg7j+zbFs7wPLCeEEKJck/a9ZY1bDRj6J1RvCQnXYe2r6pey0uRaBbq9CWP+hQGfQ9UmkBoPu+fB3Fbw4xPw70pITSzd8wohRHEoCqyfkJWQ6v1B0RJSKfGwISMR1XkMuNc0W4hCCFFSV2KSGLH0IKN+PMT1+BQaVHXl5xEd+PBRP0lICSGEKPNsMik1f/58fHx8cHJyIiAggB07dhRpv507d2JnZ4e/v795A7Q216ow9Hdo0B36zzHfAL32Tmq3vVF74Jnl6uDnikEdd2rFi/BJI/Xv0eWQdMs8MQghRF6MRvhjDOxdoN7uN0udpKEotn4I8VegUn3o/Kq5IhRCiBIxGBWW7DxPr9nb+PtEFPY6Da/2aMi617rS8R6ZLVEIIUT5YHPd95YtW8aYMWOYP38+nTt3ZuHChfTt25ewsDDq1q2b736xsbEMGTKE+++/n6ioKAtGbCXOlWDImpzbQn+AGi2hZqvSPZdWC417q8v103DsVzi6DGIi1BZT/64EjQ7qdoQmD0DjB0o+1pUQQuTHkA6/vQxHfwGNFgbMg9bPFm3fq//CnoxE1oOzwN7ZfHEKIcRdOhEZx/hVxzhyMQaAgHqVCH6kJY2ru1k3MCGEEKKU2VxSavbs2QwbNozhw4cDMGfOHDZs2MCCBQsIDg7Od7+RI0fyzDPPoNPpWLNmjYWitSGXD8Hvr6lf0HpOhQ4vmacFVbXG6vgr3Sep47GcWgen1sP1E3AhRF02vgOV74EmfdUEVd2OoLO5l5oQoiwypMHK4RC2Rk2GP7IIWj5WjP1T1aR5tSbQqKfZwhRCiLuRnGbgs3/OsGj7OdKNCm6OdrzdtynPtKuLVisDmQshhCh/bCpTkJqaysGDBxk/fnyO7b1792bXrl357vftt99y9uxZfvjhB6ZPn17oeVJSUkhJSTHdjouLA9RBJJVSGJ8p8zilcawi86wHjfugOfknbJiAcm4LDPwCXKuZ75zebdXl/vfgVjicXq8u4TvR3DyrjkG1ex6Kkwc07KUmqBr2BGfPAg9rlforR6T+Skbqr2TMWn/pybB8KJrT61F0DvDYN9D0oeKNq1erNYzcAakJpT8eXymQ11/JmLv+5HkR5rTzv2gmrT5G+A11zM4Hmtdg6sDmVHd3snJkQgghhPnYVFIqOjoag8FA9erVc2yvXr06V69ezXOfM2fOMH78eHbs2IGdXdEeTnBwMFOnTs21PTY2ttSSUrdv3waw4PS8OujzBQ41O+K8/X00ZzZinN+JxD6fkl6vq/lPr60ETZ9Wl5R47CO2Y3/uH+zOb0abfAv+XQH/rkDRaDF4tSC9VlsMtduTXrstinPlHIeyTv2VH1J/JSP1VzJmq7+0JFx/H4F9xA4UnSMJ/ReRXrMrxMYWaXftzf8wVm6YbYsGUou2ryXJ669kzF1/mT9iCVGabiWkMv3PE6w8dAmAGu5OTB3YnD7Na1g5MiGEEML8bCoplenOC0lFUfK8uDQYDDzzzDNMnTqVxo0bF/n4EyZMYNy4cabbcXFx1KlTBw8PD9zd3e8+8GzxAnh4eFj+S8W9o6FJd5SVw9FeP4F+9XMoXd+w8JTnHuD1DAQ+A0YDyqX9plZUmusnsYs6il3UUQhVp7JWqjVTu/jVbQ/e7VA866lHsUb9lQNWff2VA1J/JWOW+kuJh9UvoonYhWLvCk//jKvPvUXf/8gv8Nso6PU+dHy5dGIyE3n9lYy560+eE1GaFEVh7ZErTPs9jBsJqWg0MLhDPd7s0wQ3J3trhyeEEEJYhE0lpapWrYpOp8vVKuratWu5Wk8BxMfHc+DAAUJDQxk9Wp11yWg0oigKdnZ2bNy4kR49euTaz9HREUdHx1zbNRpNqV1wZh7LKhewNVrAiM2wcRIc+AaNS2XzzdBXGJ0d1OuoLr2mQuxliNgNF3bChV1w/SSa6yfUMakOfqPu41oNfXV/ND6d0NRpp3a3cXC1TvxllFVff+WA1F/JlGr9JcXAD4/C5QPg6I7m2RVqAruoDi2Fta8CCkSfzgyw5HGZkbz+Ssac9SfPiSgtF28m8s6af9l2+joAjavrCX7Ej4B6lawcmRBCCGFZNpWUcnBwICAggE2bNvHwww+btm/atImBAwfmKu/u7s6xY8dybJs/fz6bN29mxYoV+Pj4mD1mm+XgAg99Ci0eg3qdsrYn3VJn7rMWj9rqoMSZAxMnRGckqXbBxX0QeQRNwnXsz22Cc5vUMhqdmmjzbqeOYVWnLVTysfkvlkKIEkq4Ad8PgqtH1fetwavVJHVR7fsK1r2hrrcdAX1nyvuGEMKq0g1Gvt0ZzuxNp0lKM+Cg0/JKj4aM7HYPDnZaa4cnhBBCWJxNJaUAxo0bx+DBgwkMDKRjx44sWrSIiIgIgoKCALXr3eXLl1m6dClarZYWLVrk2N/LywsnJ6dc2yus+p2z1lNuw1f3Q9XGcP9kqO5rvbgyuVaFZv3VBSAtGSXyCMn/bccp+hiai/sh/gpEHlGX/V9l7FdNTVDV9IeaflCzFbjVlC+cQpQX8VGwdKDaitK1Ggxeoyani2r3F7BhorrecTT0ni7vD0IIq/r3ciwTVh3j2GV1PLv2PpWZ8UhL7qmmt3JkQgghhPXYXFLqySef5MaNG0ybNo3IyEhatGjBunXrqFdPHWcoMjKSiIgIK0dZRp3fBrfOw82z6hhPfk9Ct7egyj3WjiyLvRPUaUeKexOcPDzUL5Gxl9RWVJcOwCW1NRUJ1+HUOnXJ5FJVTU5lJqlq+KktqrTyy6MQZUrsJfhugPpe5VYThqyFakUfN5Ads+GfjMksuoxTk/CSkBJCWElSqoFP/z7N4pDzGIwK7k52THywGU8E1kGrlfcmIYQQFZtGkfmNiYuLw8PDg9jY2FIb6Dw2NtY2B6q9fgo2T4cTazM2aKBRb2j/P2jQwyYSOIXWX1qy2p3n0gH1b+QR9XEphtxlHd3V5FT2RFXVxupYV+WUTb/+ygCpv5Ipcf3dPA9LB0BMBHjWVRNSlYvZFXvnXNg0Ge6bqCbey9DzKK+/kjF3/ZX29UJZZe56KE//B9tPX2fSmmNcvJkEQD+/mrzX3xcvNyerxlWe6thWSR1bhtSz+Ukdm195reOiXi+U32/mIm/VmsCT38PlQ7A1GM5shDMb1OWVQ7bVaio/Ga2pqNMua1taEkSFwdWMbn6RRyHqOKTEwYUQdclk5wTVm0P1FuqXXc+64Flf/etatUx9gRWiXIk+o7aQir8ClRvA87+Dh3fxj9P5NajTHup2KP0YhRCiCG7cTmH6nydYHXoZgFoeTrw/qAX3N8s9cY8QQghRkUlSqqKq3QaeXQ7R/6njNMVfzZmQOvS9+oWuaiPrxVgc9s7gHaAumQxpaguqzNZUkUfV9dTbcPmguuQ6jktGkqoeVKqXte5ZV73t5ClJKyHMIeq4OoZUwnWo1hSG/AZuNYq2r6LA3oXg/ww4ZfwKIwkpIYQVKIrCqkOXmf5nGLcS09Bo4PmO9XmjTxP0jnLZLYQQQtxJPh0ruqoNoe9HObfFXobfX1O7w93TA9qNVLv42UDXvmLR2asDI9dooX5ZBTAa1XG1Ig/DtZMQc0HtJnTrAsRHQloiXD+pLnlxdM+ZpLozaeXoZrGHJ0S5ceWwOste0i2o0VId1Ny1atH2NRrhr7fU5HrYGhj6J2h15otVCCHyEXEjkUlrjrHjTDQATWu48eGjfvjX8bRuYEIIIYrFYDCQlpZmsfMpikJqairJyclltvuevb09Ot3dXYNLUkrklpakJqFOr4ezm9Wlkg+0GwH+z4Kzp7UjvHtardoiLK9uiukp6gDLt8LVRFX2hFVMBCRcU7sDRh1Tl7w4V84jYZXR6sqjDji4mPXhCVHmXNwHPzwGKbFQOwCeWwnOlYq2r9EIf7wGh5YCGmj1lCSkhBAWl24w8nXIeeb8fZrkNCMOdlpeu78R/7u3Afa6MvaDnhBCVHC3b9/m0qVLWHrobaPRyI0bNyx6ztKk0Wjw9vZGry/+jLKSlBK5VW0Iz/yiDji8/2sI/V5tXbRhojpI+rMroH5na0dZ+uwc809YAaQmQuzFjCTVhTuSVhfUVh5JN9Ul8nDex3D1ypa0qpdz3cNbjUGIiuL8DvjpSUhLgLqd4JllWd3vCmM0wG8vw5GfQaOFgfPB/2nzxiuEEHc4fDGGiauOERYZB0Cne6rwwcMt8anqauXIhBBCFJfBYODSpUu4uLhQrVo1i7VaUhQFg8GATqcrky2lFEXh+vXrXLp0iUaNGhW7xZQkpUT+KvtAnw+g+0Q4+ivsWwQ3zqrd4TKdWg/GdLinOziU8wswBxd1oPhqTfK+Pzkuo4VVHq2sYi6orawSrqnL5QN5HEADbjXz7hboWRfcvcv1rIGigvnvb/jlWUhPhgbd4akfi/4eYkiD1SPh35Wg0cEji6DlY+aNVwghsolLTuOTDaf4fs8FFAU8nO15p18zHgvwLpNfKIQQQkBaWhqKolCtWjWcnZ0tdt6ynpQCqFatGuHh4aSlpUlSSpiBgysEvgABQ9WklJNH1n3bPoIrh0DnCD73QpMHoPEDdzdjVlnn5J41htWdFAWSY3ImqbKvx0So41nFX1GXiN25j6HRgUftrC6Bd3YTdKshXZdE2XDyT1g+FAyp6vvF49+ps2oW1V9vqQkprT08/i0062+2UIUQIjtFUfjzWCTTfg/jWnwKAA+3rs3EB5tRzU1aOwshRHlQVhND1lSSOpOklCg6jUbt2pfJaIS6HdXuarfC4b9N6vLn6+pgxS0egy5jrBWtbdFo1HFynCtBLf/c9ysKJERnJKnCc7eyiolQv8BntsRiR+5jaO3Bsw541sXZpSZ4NcxIVlVXuw3qvdTzy5ussKZ/V8Kq/6ktLH0HwiNfg51D8Y7RbiSc3gD9ZquJcCGEsICIG4m8+9u/bDt9HQCfqq5MH9SCzg2LODGDEEIIIXKRpJS4e1otPDBD7eJ3/RSc/kvtzndpH1w9BlWyJbAURe2uU69T+e/mdzc0GtBXUxfvgNz3G41wO+qOVlbZxrWKvQTGNLh5Ds3Nc+T7W63WHlyrqQkqvVdWskrvlW17dXVdEliitB3+SR0HSjGC35PqOFB30yXVqym8cqh4rauEEOIupaYb+WrHOT775wwp6UYcdFpeuu8eXrrvHpzspYWyEEKI0mNnZ0eLFi1IS0ujQYMGfP/993h6ehbrGFOmTKFq1aqMHj260LLh4eHs27ePJ554AoCtW7cyaNAg6tevT0pKCk899RTvvffe3TyUIpOklCg5jUb9kujVFLqMhYQbaospjzpZZaJPw4+Pqd38vAPVFlb1OkGdduDoZr3YywqtFtxrqkvd9rnvN6RDfCTEXEC5dYGUq6dxTI5CE3tJTWbdvqZ2HzSmZXURLPSc9nckq+5MYmVbd/KUBJYo2P7F8Oc4db3N8/DQHPV1XRSpCbD8Bej4MjTopm6ThJQQwgL2nrvBpDX/8t+124A6kPn0QS1oUK34swsJIYQQhfH09OTw4cMADB48mC+++IJJkyaZ7Xzh4eH8+uuvpqQUQM+ePVmxYgUJCQn4+/vz0EMPERCQR8OJUiJJKVH6XKuoU7NnFx+pjn0UEwEXdqrLDtRxkmq2UgdTb9TLKuGWCzq7jK57daBeZ5JjY3H08MiZKEpPgYTraoLqdsaA6znWr6sJrIRrkByrJrDiLqtLoed3yEpeuXpltPqqnve6JLAqnt3zYcMEdb19EDzwYdFfA8lx8NMT6jhrkYfhtSNgb7mBJ4UQFdPNhFSC151g+cFLAFRxdeCdh5oxyL+2jDUihBAVgKIoJKUZSv24zvZFH8y8c+fOHDlyBIBz587x4osvcvPmTerXr8+SJUuoXLkyJ0+eJCgoiFu3buHo6Mg///yT4xjBwcGcPHmSb7/9luDgYNasWUNKSgqjRo0iKCiISZMmcfz4cfz9/Xnttdfw8fEx7evq6kpAQABnz57ltdde45tvvqFx48YoikLTpk3Zu3dvsVtx5UWSUsIyGtwHrx1VB0qP2AUXdqmJqZgIdaD07P+Y57bhsucr8OkA3u3UpJW0iig5O0d1APqiDEKflqwmsO5MVpnWM/7evg4psep4V8VKYGVPVt3RbTD7upOHJLDKuu2fwOb31fUuY+H+94r+nCbFwA+PqrNVOnrAUz9JQkoIYVaKorDi4CVmrDvBrcQ0AJ5uV5fxDzTFw8XeytEJIYSwlKQ0A76TN5T6ccOm9cHFofA0jMFgYNOmTbz44osAvPrqq4waNYonnniCjz76iClTpvDZZ5/x3HPPERwcTK9evYiPj88xa+CMGTM4deoU3377LRs3buTatWvs37+f1NRUunTpwkMPPcQHH3zAvHnzWLFiBaB238t048YN9uzZw7vvvsvQoUNZunQp06dPZ8uWLfj7+5dKQgokKSUsKXOg9KoNoc0QdVvsJbiwG+pk65J2fhsOZ/6AM3+ot7X26sDp3m3VpXHvnDMAitJn75TV8qowaclZCauEa1nJqlzr1yAlLiOBdUldCqNzzOpC6FoV7F3AQQ8OLne3Lgkuy1EU2Dwddnyi3u4+Ce59s+jPQeJN+H4QRB5RxzcbvCbvSQKEEKKU/Hctnomr/2Xf+ZsANK3hxgcPtyCgXmUrRyaEEKKiiImJwd/fn0uXLtG8eXP69OkDwP79+/n9998BtVtfv379iIuLIyYmhl691B5Hbm5Zw+IsWrSIpk2b8ssvv6DVatm0aRO///4727ZtAyA2NpazZ8/mGcPff/9N69at0Wq1TJgwgebNm1O3bl3at2/P+++/z3fffcfzzz9fao9ZklLCujy8we/xnNt8B5BktMPpxr9oLu1XW+VcOaQu+xaqAxxnJqXOb1eTHV7NoEqj4s/iJUrO3kntmulZt/CyaUmFdCHMtqTGgyEFYi+qS6nE6lqEJJYrTkYduFUuetJLXnc5KQpsfAf2fKHe7vU+dH616Pvfvq4mpKL+BZeqMOQ3qNHCLKEKIURymoF5m/9j4fazpBkUnO11jOnZiBe7+GCvK+LYd0IIIcoVZ3sdYdP6mOW4BckcUyoxMZFevXoxf/58Xn311Rxd/hRFMd3Orytgq1atOHLkCNeuXaNGjRooisKUKVMYMmRIjnLZW0ZlyhxTKjs3NzcCAgJYt24dO3fu5JtvvinKwy0SSUoJ21PTnxQXH5w8MhJPMRFwaT9cOgDXT0DlBlll9y+GsDXqutZOnfGvWlPw8lUTVY0fkISBLbF3Ll4CK3vCKvEGpCZCWoL6NzWhaOvpSdmOmXFfATRAsTuLau0yEl5FSXrdsW7vpO6vtVPHWNPqMm5n+6vRZZXRZiuTY7v2juPYFX0g8dJiNML57bju/BzNub/VbQ9+Au1GFO84uz9XE1L66jBkrTqJghBClDJFUdgYFsX7f4Rx6Zb6WXF/Uy+mDGhOncouVo5OCCGENWk0miJ1szMXFxcX5s6dy6OPPsqoUaMIDAxk5cqVPPbYY/z000907doVd3d3KlWqxKZNm3J132vfvj3PPfcc/fv3Z9OmTfTs2ZOPPvqIxx9/HGdnZ06dOkW9evVwc3MjPj6+SDENHTqUwYMH88wzz6DTld7ss5KUErZNo4FK9dSl5WO57/fyVQdRv3ZC7Rp2/aS6hK1Rxy6aGJlV9sA36gDeXr5q4sqjjuW/tIuis3fOeu5LwmiEtER1Sb2dkdjKf11JuU1qwi0cNOloUhOylUnISHhlWzemZZwjXR1bKyW25I+7VGnuIsGVPTFWjESZRgPhIWhuhWMPKGjQDPgc2gwuftg93oWUeOjwstrdVwghStm567eZ8nsY209fB6CWhxOT+/vSp3kNGchcCCGETQgMDKRly5asXLmSzz77jBdeeIFp06ZRr149vvvuOwC+//57/ve///HGG2/g5OSUY6DzPn36cOvWLQYOHMj69ev5999/adeuHYqi4OXlxe+//46fnx9paWl5DnR+px49emAwGHK1tiopjaIoSqkesQyKi4vDw8OD2NhY3N3dS3w8RVGIjY3Fw8NDLmzuwl3Vn6Kog2xfOwnXwtQklSEVHlucVebLrnD1aNZtexfwzEh6VG0Mvd/Pui89RR0YvAyS11/JFKv+DGl5J6sy1/NLZmVfT08Go0FNbBnT1XUl+21j1rppuyHnPtjO27ji6E5qk4E4dApCU5wud7evqV31KniiWP5/S8bc9Vfa1wtllbnrwZzPY0JKOvO2/MfXO86RZlBw0GkZca8PL3dvaNVfxC1N3mvMT+rYMqSeza8i1XFycjLnz5/Hx8cHJyfLTbSlKAoGgwGdrugz81nDf//9xzPPPMO+ffty3ZdX3RX1eqHifPqK8k2jyZpZrlHPvMv4PQnVmqgJq+jTamLg+gl1uXE2Z1JqcW+4FQ6V6qtJq8zklWd9qOwDVe6xwIMSNk9nD86e6mJNRuMdCau8klt3JLLyS3AphpzHKOxY2bd71IWm/UhKSsfBoxiTEdw8B98NgIY94aFPZUB6IUSpUxSFP45G8sGfJ7galwzAfU2q8V7/5vhUdbVydEIIIYRt+/rrr5k2bRpfffVVqR9bklKi4ug0OmvdkKaOVXUrHGIuqDP8ZXcrHJJjIPKwumRXpSG8cjDr9uYP1L/Zk1futdUuTUJYglYLaNUkmbUpCiQVoxtj9Bn4rr/aDTc8BJJugYvMdCWEKD2nrsbz3tp/2XNOnVWvTmVn3nuoOfc387LpX6SFEEIIWzF8+HCGDx9ulmNLUkpUTDp7tbVTfi2exv6bkbS6oCatsv+9c5/9X0PSzZzbtHZqq616XWDQF1nbo46rXZT0XtIaRIioMFg6UB3IvlozeH6tJKSEEKUmLjmNuX+fYcmucAxGBUc7LS93b8j/7m2AUyGzHwkhhBDCMiQpJUReHN2genN1KYjRCJ1eyZm4irmoDoB9KzznTIGgtghJvAF2GbPQZW9dVb053NPDbA9JCJsSeRS+H6T+P1RvCUPWgGtVa0clhCgHjEaF1aGXCf7rJNG3UwDo07w67/TzlVn1hBBCCBsjSSkhSkKrha7jcm4zGtSuSDERObsFpiWrySg0kJ4E0afUJVOD+3Impb4bAE7uGUmr+tnGtaqrzkwnRFl1+RB8/7DaRbZWa3hulbSQEkKUiuNXYpn823EOXrgFQIOqrrw3oDndGlezcmRCCCGEyIskpYQobVpd1qDr2dk7wbjjkJ4KsRdzdwus0TKrbMptOL8t/3P4DoInvsu6fWQZuNXIGs9KCFsWHwkp8eDdDp5bAU7FGBRdCCHycCshlVmbTvHT3giMCrg46Hj1/ka82NkHB7uKPaunEEIIYcvkU1oIS7NzUMeluqcHBL4APafA49/mbHGls4enf4EHPoIOo6BJP6jeAhzc1Puzf4lPTYDV/4OlA2BuK3i/Gu4LW8P8DrDkIdg5N6usosDJPyFirzrjYHKcuk0IS2raT01GDV4lCSkhSiA4OJi2bdvi5uaGl5cXgwYN4tSpU4Xu9+OPP9KqVStcXFyoWbMmL7zwAjdu3LBAxKUv3WDk+93h3PfJVn7Yoyak+reqxT+vdyOo2z2SkBJCCFGm2NnZ0bp1a3x9fQkICMg1252iKNStW5e33noLgEmTJuHv74+vry8uLi74+/vj7+/Pli1bWL16Na1ataJ58+a0aNGC+fPnA2A0Gpk0aRINGzakYcOGPPHEE6brgPDwcDQaDTNnzjSdc+/evWg0GpYsWWKex2yWowohSsbOEZr0zb1dUdTZyYzpWdtSbkPDnhmtriLQGFLQJN1UB1+/fhIq+2SVTU2AX57JeUydI7hWU8fzafIg3Pd21rkO/6Rud6kKrlXUcg4ydba4C+d3ZI2jBjJ+mhClYNu2bbz88su0bduW9PR0Jk2aRO/evQkLC8PVNe/36pCQEIYMGcKnn35K//79uXz5MkFBQQwfPpzVq1db+BGUzJ5zN5iy9jgnr8YD0LSGG+/1b07He6pYOTIhhBDi7nh6ehIaGgrAhQsXePjhh1EUhf/9738A7N69m5o1a7Jq1SpmzpzJBx98wAcffEB4eDiPPfYYBw4cACA0NJSRI0eyYcMGfHx8SEpKYtmyZQDMmTOHsLAwwsLCcHBw4OOPP+aFF15g7dq1ADRq1Ii1a9eaEl+//vorLVu2vDPUUiNJKSHKEo0m99g7btXhuZXqutGIkhhN/NXzuGmS0CRGg0edrLJpieDdFhKuQ0I0pN4GQwrEXVKXmq2yyqbEw2+jcsdg56wmqnwHQp8P1G2KArs+y0heVc3661pVklgCzvwNy54FfXUYtlHtaiqEKLH169fnuP3tt9/i5eXFwYMHuffee/PcZ8+ePdSvX59XX30VAB8fH0aOHJnjF9E7paSkkJKSYrodFxcHqL/WKmZobZt53PyOfTkmieB1J/nzWCQAHs72vN6rMU+3q4OdTmuWmMqbwupYlJzUsWVIPZtfRarjzMeoKAqK0ah+dypt9i6FzsKeGUfdunX55JNPGDduHCNGjADgl19+YeTIkfz888/s2bOH9u3b54odYNasWUycOJH69eujKApOTk48//zzKIrCrFmz2LlzJ/b29iiKwrhx4/jyyy85ffo09vb2uLu7U7VqVc6ePYuPjw9bt26le/fuBb4OctTdHbEURpJSQpQnWi24VsNY1QE8PHK/4em9YPjfWbdTEyExOitJpffKus+QCvfcn3H/DbWMIUUdpD32opq0ypQSB5sm5x2TnTP4PQEDPlNvKwr8/R44V85qoZXZEsslI4lVyBu1KENOroPlz6uvp+rNwbmStSMSotyKjY0FoHLl/CcO6NSpE5MmTWLdunX07duXa9eusWLFCvr165fvPsHBwUydOjXP85krKXX79m0ANNk+D5LTDHy39zLf7rlMcroRrQYea12DUV3r4ulsT8Lt+PwOKe6QXx2L0iN1bBlSz+ZXkeo4NTUVo9GIwWDAkByP3cy6pX6O9Lci8vzR3mg0mtYNBoNpvVWrVpw6dQqDwYCiKPzxxx9MnjyZlJQUfvnlFwIDA037KIpi2vf48eOMHTs2x7FA/WEpKSkJb2/vHPe1bNmS48eP07JlSxRF4ZFHHmHZsmV07dqV1q1bo9PpTHWTF4PBgNFoJD4+3vRDVuaPWIWRpJQQFZmDCzjUVbtV3cm1qjrmTyZFUVtWJUSrS/axgIwG8HsqI4GVsSRGQ3qymsTK/gGWHJtznKs7NX9EHWMr85y/vay2DnOuDA56NWZ7F3Xdwxuq+2btm3QL7F3VcbuE9R1fAyuHqd1NfQfCo4vV8dKEEKUu85fOLl260KJFi3zLderUiR9//JEnn3yS5ORk0tPTGTBgAJ9//nm++0yYMIFx47LGPYyLi6NOnTp4eHjg7u5eqo8Dsn5Z9fDwQKPRoCgKG45HMf3PE1yOSQKgnU9l3uvvi2/N0j9/RXBnHYvSJ3VsGVLP5leR6jg5OZkbN26g0+nQmWlIQp1OBzpd/vdl+wug1WpN27Zv346vry9VqlThscceo127dsyePRuNRoNOpzP9zWRnZ5fjdubx7iwHasJRq9WajjNo0CDTj1dPPvkk69evN92fX+xarRY3NzecnJxMxywKSUoJIYpGowFHN3XJPk4VqEmjRxbm3KYo6hhWCddBd0eSqOPorMRVwvWcLbGcPbPKJcfA4R/zjyl7AstogI/qq+taOzU55eCalcRqcB/0fj9r37/GqwkSh4xy9i6mvzqtB3hk6/py+xrYOan3a/N+IxZ3OLYcVgeBYoCWj8OgL0EnHzlCmMvo0aM5evQoISEhBZYLCwvj1VdfZfLkyfTp04fIyEjefPNNgoKCWLx4cZ77ODo64ujomGu7RqMx2xeUzGNfvJnExNXHCPkvGoCaHk5MfLAZD/nVLPdfjswts46lHs1H6tgypJ7Nr6LUcebj02g0aBxcYeKV0j9HHt337mx1nL2ejxw5QtOmTdFoNCxfvpw9e/bg46N+F4uKimL37t107tw5R+wAvr6+HD58mFatWuU4tqenJ05OTly4cIH69esDaiutI0eO4Ovra9rf3d0dLy8v/vrrL2bNmsWGDRsKfA3kqLs7YimMTX5DmD9/Ph9//DGRkZE0b96cOXPm0LVr1zzLhoSE8Pbbb3Py5EkSExOpV68eI0eOZOzYsRaOWgiRg0YDjnp1yc7ZM2ssquwyW2IZszUJ1dqpsxMmRKutoFIT1L7dqQnqUrlBVtnsfb6N6ZASqy6Zspc1GmHvgrzDBpzqdYMm2ZJSn7VWY4Os5FRm0qtOu6yuiQAbJqnnz0xyZU96udWE+p2zysZFqo9Rq7vjrx1otGW2G6P2ehis+h+ggP+zMOBzSeYJYUavvPIKa9euZfv27Xh7exdYNjg4mM6dO/Pmm28C4Ofnh6urK127dmX69OnUrFnTEiEXyqgoLNkVzsz1p0hKM+BgpyXo3gYE3XcPLg42efkqhBCiPNForD427sWLF3njjTcYPXo0RqORNWvWcOrUKapUUSf0WLRoEb/++iudO3fOte/rr7/O008/Tbdu3ahfvz7JycksWbKEoKAgxo4dy9ixY1m2bBkODg7MmjWL5s2b06hRI8LDw03HmDRpEhEREfm2jiotNvepvmzZMsaMGcP8+fPp3LkzCxcupG/fvoSFhVG3bu4uRq6urowePdp0URUSEsLIkSNxdXU1jVAvhCgDMltiZefoBl2KmGB2dIN3o3MnrjLXsw8Qrxih29u5y6QloqQmYKjSOOvNUVHUboiZ0pMzbmdMn37noN2HlqpjbOXFu23OMb2+vh/iLuddtnpLeClbi4cvu0JMRO4klkanJtyyd7Vc9T+4FZ6V3MpMdGl1arfMgV9kld32McRkls2eHNOpXSTvG59V9tgKNd7sibPMdZ0DtHoSAGPlRur4ZE0ehH6z1bHOhBClTlEUXnnlFVavXs3WrVtNv5wWJDExETu7nJd/mRebtjCIraIo7D57g4/XnyD0kvpe2t6nMh896kf9qjJxhhBCiPItJiYGf39/UlNTcXZ25qWXXmLYsGFs2bKFRo0amRJSAAMHDuT999/n008/zXWcNm3aMGPGDAYMGEB6ejp2dnaMGqVOYjV27Fiio6Np1qwZGo0Gf39/vv3221zHCAwMNI1ZZU4axRauQLJp3749bdq0YcGCrFYMzZo1Y9CgQQQHBxfpGI888giurq58//33RSofFxeHh4cHsbGxpTI2gqIoxMbGVoh+t+Yg9VcyUn8lk2f9ZSamUhPVFlNpiep6WoL6C0rtgKwDhHwKyXE5El2m5Fe1ptDvk6yynzSB21fzDqSmP4zclnV7Tks1KZWXqk1g9L6s2190gOsn8i7r7g3jjmfd/qoHXD6Yd1nnSvB2eNbtJQ9B+I68y+oc4d1rWfWXGI6mZqsy29rLWuT/t2TMXX+lfb1QUqNGjeKnn37it99+o0mTJqbtHh4eODs7A+p4UJcvX2bp0qUALFmyhBEjRvDZZ5+Zuu+NGTMGrVbL3r17i3Rec9bDwQs3eXTBbgBcHHRM6NuUZ9vXQ6uV/4fSJO815id1bBlSz+ZXkeo4OTmZ8+fP4+PjYxoXyRIyByjPHM+pLMqr7op6vWBTLaVSU1M5ePAg48ePz7G9d+/e7Nq1q0jHCA0NZdeuXUyfPj3fMuae2rgiTZtpDlJ/JSP1VzL51p+dk7q45DGrVfaynccUdoKs9ddPqrcVo9rlz5iujsFkNACanGWHrFVnsDOmq/cbDVn72DnmLNv3I3VAedNxs5W1d85Ztu1waPpQ1v2msobcx72nB3jUzlkmM2aNDrLVnVLDL/fjFYWS/9+SMXf92drzkvkD3n333Zdj+7fffsvQoUMBiIyMJCIiK6E9dOhQ4uPjmTdvHq+//jqenp706NGDjz76yFJhF6hN3UoE1KvEPVUceaVnM+pUdrF2SEIIIYQwI5tKSkVHR2MwGKhevXqO7dWrV+fq1XxaE2Tw9vbm+vXrpKenM2XKFIYPH55vWXNPbVyRps00B6m/kpH6KxnbqL+M7m6p2cbE0lYybc5TbPbxs1rlX+7OsvUfLHrZli8WWtY26q/skvorGXPXX1GnNraUolyzLFmyJNe2V155hVdeecUMEZWcRqPh1/91ID4+Dg8PZ2uHI4QQQggzs6mkVKY7LyQVRSn04nLHjh3cvn2bPXv2MH78eBo2bMjTTz+dZ1lzT21ckabNNAepv5KR+isZqb+SkforGam/kjF3/clzYhnSVU8IIYQ12VrL6LKgJHVmU0mpqlWrotPpcrWKunbtWq7WU3fKHNyzZcuWREVFMWXKlHyTUpaY2riiTJtpLlJ/JSP1VzJSfyUj9VcyUn8lY876k+dECCGEKL/s7e3RaDRcv36datWqWexzv6yPKaUoCtevX0ej0WBvb1/s/W0qKeXg4EBAQACbNm3i4YcfNm3ftGkTAwcOLPJxFEXJMWaUEEIIIYQQQgghRH50Oh3e3t5cunSJ8PBwi57baDSiLcMzVms0Gry9vU0z+haHTSWlAMaNG8fgwYMJDAykY8eOLFq0iIiICIKCgoDcs8h88cUX1K1bl6ZNmwIQEhLCJ598YrNjJQghhBBCCCGEEML26PV6GjVqRFpamsXOqSgK8fHxuLm5lcmWUqC2MrubhBTYYFLqySef5MaNG0ybNo3IyEhatGjBunXrqFevHpB7Fhmj0ciECRM4f/48dnZ23HPPPXz44YeMHDnSWg9BCCGEEEIIIYQQZZBOp7vrBMvdyOzp5eTkVGaTUiVhc0kpgFGjRjFq1Kg877tzFhlbnkFGCCGEEEIIIYQQQuTNJpNSlpY5UnxpTfWsKApxcXEyUO1dkvorGam/kpH6Kxmpv5KR+isZc9df5nVCRZ+Vp7Svm/I6vvwfmJfUsflJHVuG1LP5SR2bX3mt46JeN0lSCoiPjwegTp06Vo5ECCGEELYuPj4eDw8Pa4dhNXLdJIQQQoiiKuy6SaNU9J/7UMelunLlSqkNLBYXF0edOnW4ePEi7u7upRBhxSL1VzJSfyUj9VcyUn8lI/VXMuauv8yBSGvVqlWmZ8gpqdK+brqT/B+Yn9Sx+UkdW4bUs/lJHZtfea3jol43SUspQKvV4u3tXerHdXd3L1cvKkuT+isZqb+SkforGam/kpH6Kxlz1l9FbiGVyVzXTXeS/wPzkzo2P6ljy5B6Nj+pY/Mrj3VclOumivsznxBCCCGEEEIIIYSwGklKCSGEEEIIIYQQQgiLk6SUGTg6OvLee+/h6Oho7VDKJKm/kpH6Kxmpv5KR+isZqb+SkforH+R5ND+pY/OTOrYMqWfzkzo2v4pexzLQuRBCCCGEEEIIIYSwOGkpJYQQQgghhBBCCCEsTpJSQgghhBBCCCGEEMLiJCklhBBCCCGEEEIIISxOklJCCCGEEEIIIYQQwuIkKXWX5s+fj4+PD05OTgQEBLBjx44Cy2/bto2AgACcnJxo0KABX375pYUitU3Fqb9Vq1bRq1cvqlWrhru7Ox07dmTDhg0WjNb2FPf1l2nnzp3Y2dnh7+9v3gBtXHHrLyUlhUmTJlGvXj0cHR255557+OabbywUre0pbv39+OOPtGrVChcXF2rWrMkLL7zAjRs3LBSt7di+fTv9+/enVq1aaDQa1qxZU+g+8tmRpbj1J58dZdPdfr6JogkODqZt27a4ubnh5eXFoEGDOHXqlLXDKteCg4PRaDSMGTPG2qGUK5cvX+a5556jSpUquLi44O/vz8GDB60dVrmRnp7OO++8g4+PD87OzjRo0IBp06ZhNBqtHVqZVti1jKIoTJkyhVq1auHs7Mx9993H8ePHrROsBUlS6i4sW7aMMWPGMGnSJEJDQ+natSt9+/YlIiIiz/Lnz5/nwQcfpGvXroSGhjJx4kReffVVVq5caeHIbUNx62/79u306tWLdevWcfDgQbp3707//v0JDQ21cOS2obj1lyk2NpYhQ4Zw//33WyhS23Q39ffEE0/wzz//sHjxYk6dOsXPP/9M06ZNLRi17Shu/YWEhDBkyBCGDRvG8ePHWb58Ofv372f48OEWjtz6EhISaNWqFfPmzStSefnsyKm49SefHWXP3X6+iaLbtm0bL7/8Mnv27GHTpk2kp6fTu3dvEhISrB1aubR//34WLVqEn5+ftUMpV27dukXnzp2xt7fnr7/+IiwsjFmzZuHp6Wnt0MqNjz76iC+//JJ58+Zx4sQJZs6cyccff8znn39u7dDKtMKuZWbOnMns2bOZN28e+/fvp0aNGvTq1Yv4+HgLR2phiii2du3aKUFBQTm2NW3aVBk/fnye5d966y2ladOmObaNHDlS6dChg9litGXFrb+8+Pr6KlOnTi3t0MqEu62/J598UnnnnXeU9957T2nVqpUZI7Rtxa2/v/76S/Hw8FBu3LhhifBsXnHr7+OPP1YaNGiQY9tnn32meHt7my3GsgBQVq9eXWAZ+ezIX1HqLy8V+bOjLCiN6wNRPNeuXVMAZdu2bdYOpdyJj49XGjVqpGzatEnp1q2b8tprr1k7pHLj7bffVrp06WLtMMq1fv36KS+++GKObY888ojy3HPPWSmi8ufOaxmj0ajUqFFD+fDDD03bkpOTFQ8PD+XLL7+0QoSWIy2liik1NZWDBw/Su3fvHNt79+7Nrl278txn9+7ducr36dOHAwcOkJaWZrZYbdHd1N+djEYj8fHxVK5c2Rwh2rS7rb9vv/2Ws2fP8t5775k7RJt2N/W3du1aAgMDmTlzJrVr16Zx48a88cYbJCUlWSJkm3I39depUycuXbrEunXrUBSFqKgoVqxYQb9+/SwRcpkmnx2lqyJ/dpQFpXF9IIovNjYWQP4vzODll1+mX79+9OzZ09qhlDuZ12aPP/44Xl5etG7dmq+++sraYZUrXbp04Z9//uH06dMAHDlyhJCQEB588EErR1Z+nT9/nqtXr+b4HHR0dKRbt27l/nPQztoBlDXR0dEYDAaqV6+eY3v16tW5evVqnvtcvXo1z/Lp6elER0dTs2ZNs8Vra+6m/u40a9YsEhISeOKJJ8wRok27m/o7c+YM48ePZ8eOHdjZVex/+bupv3PnzhESEoKTkxOrV68mOjqaUaNGcfPmzQo3rtTd1F+nTp348ccfefLJJ0lOTiY9PZ0BAwZI8+8ikM+O0lWRPzvKgtK4PhDFoygK48aNo0uXLrRo0cLa4ZQrv/zyC4cOHWL//v3WDqVcOnfuHAsWLGDcuHFMnDiRffv28eqrr+Lo6MiQIUOsHV658PbbbxMbG0vTpk3R6XQYDAY++OADnn76aWuHVm5lftbl9Tl44cIFa4RkMRX7G2oJaDSaHLcVRcm1rbDyeW2vKIpbf5l+/vlnpkyZwm+//YaXl5e5wrN5Ra0/g8HAM888w9SpU2ncuLGlwrN5xXn9GY1GNBoNP/74Ix4eHgDMnj2bxx57jC+++AJnZ2ezx2trilN/YWFhvPrqq0yePJk+ffoQGRnJm2++SVBQEIsXL7ZEuGWafHaUDvnsKDvu9vpAFN/o0aM5evQoISEh1g6lXLl48SKvvfYaGzduxMnJydrhlEtGo5HAwEBmzJgBQOvWrTl+/DgLFiyQpFQpWbZsGT/88AM//fQTzZs35/Dhw4wZM4ZatWrx/PPPWzu8cq0ifg5KUqqYqlatik6ny/Wr3bVr13JlNTPVqFEjz/J2dnZUqVLFbLHaorupv0zLli1j2LBhLF++vMI2hS5u/cXHx3PgwAFCQ0MZPXo0oH6QK4qCnZ0dGzdupEePHhaJ3RbczeuvZs2a1K5d25SQAmjWrBmKonDp0iUaNWpk1phtyd3UX3BwMJ07d+bNN98EwM/PD1dXV7p27cr06dOltU8B5LOjdMhnR9lQkusDUXyvvPIKa9euZfv27Xh7e1s7nHLl4MGDXLt2jYCAANM2g8HA9u3bmTdvHikpKeh0OitGWPbVrFkTX1/fHNuaNWtWYScCMYc333yT8ePH89RTTwHQsmVLLly4QHBwsCSlzKRGjRqA2mIq+/VxRfgclDGlisnBwYGAgAA2bdqUY/umTZvo1KlTnvt07NgxV/mNGzcSGBiIvb292WK1RXdTf6D+yj106FB++umnCj0WTXHrz93dnWPHjnH48GHTEhQURJMmTTh8+DDt27e3VOg24W5ef507d+bKlSvcvn3btO306dNotdoKdyF/N/WXmJiIVpvzoybzYjyz1Y/Im3x2lJx8dpQdd3t9IIpHURRGjx7NqlWr2Lx5Mz4+PtYOqdy5//77c117BQYG8uyzz3L48GFJSJWCzp07c+rUqRzbTp8+Tb169awUUfmT3/Wb0Wi0UkTln4+PDzVq1MjxOZiamsq2bdvK/+egFQZXL/N++eUXxd7eXlm8eLESFhamjBkzRnF1dVXCw8MVRVGU8ePHK4MHDzaV/3979x9TVf3Hcfx1Lz8UIn6HoIBU9oO8VBBztX6wJsNSUyO3YK5BrBy1lsmUzdSlf+hYLZc2qlF4a6XcP+zWirZmwwChInNYGvcPRUBJVlY0CSzH+Hz/aJ5vN/iifJVz4/p8bOeP+znvc8/78xn33s/enPM5x48fN5GRkWb16tWmo6PD1NbWmrCwMLNnz55AdSGgJjp+u3fvNqGhoaa6utr09fVZ22+//RaoLgTURMfvn670p+9NdPwGBgZMamqqWb58ufn+++9NU1OTueGGG8wTTzwRqC4E1ETHz+12m9DQUPPaa6+Zzs5O09LSYnJzc828efMC1YWAGRgYMO3t7aa9vd1IMtu2bTPt7e2mp6fHGMNvx4VMdPz47Zh6LvT9gkv31FNPmZiYGNPY2Oj3uRgaGgp0akGNp+9dXl9//bUJDQ01W7ZsMUePHjW7du0ykZGR5r333gt0akGjpKTEzJo1y9TX15uuri7j9XpNYmKiqaysDHRqU9qF5jJVVVUmJibGeL1ec/jwYVNcXGxSUlLMmTNnApz55KIo9X+qrq42s2fPNuHh4SYnJ8fvUbolJSUmLy/PL76xsdFkZ2eb8PBwk5GRYV5//XWbM/53mcj45eXlGUmjtpKSEvsT/5eY6N/f313pRSljJj5+Pp/P5Ofnm4iICJOammoqKiqu6An8RMdvx44d5pZbbjEREREmJSXFrFixwvT29tqcdeB9/vnn436X8dsxvomOH78dU9N43y+4dGN9JiQZt9sd6NSCGkWpy+/jjz82LpfLTJs2zdx8882mpqYm0CkFlTNnzphVq1aZ9PR0M336dHPdddeZ9evXmz///DPQqU1pF5rLjIyMmBdeeMEkJyebadOmmfvuu88cPnw4sEnbwGEM908AAAAAAADAXqwpBQAAAAAAANtRlAIAAAAAAIDtKEoBAAAAAADAdhSlAAAAAAAAYDuKUgAAAAAAALAdRSkAAAAAAADYjqIUAAAAAAAAbEdRCgAAAAAAALajKAUAl2jTpk26/fbbA50GAADAhAR6DrNx40atXLnyomLXrFmjZ599dpIzAmA3ilIAglJpaakcDofKy8tH7Xv66aflcDhUWlpqf2IAAAA2cDgc426lpaVas2aNGhoaApLfjz/+qO3bt+v555+/qPjKykq53W51dXVNcmYA7ERRCkDQSktLk8fj0dmzZ622P/74Q3V1dUpPTw9gZgAAAJOrr6/P2l555RVFR0f7tW3fvl1RUVFKSEgISH61tbW66667lJGRcVHxSUlJKigo0BtvvDG5iQGwFUUpAEErJydH6enp8nq9VpvX61VaWpqys7Ottk8//VT33HOPYmNjlZCQoMWLF6uzs9PvvXp7e1VUVKT4+HhdddVVys3NVVtbm1/Mu+++q4yMDMXExKioqEgDAwOT20EAAID/ITk52dpiYmLkcDhGtf3z9r3S0lItW7ZMW7du1YwZMxQbG6vNmzdreHhYa9euVXx8vFJTU7Vz506/c/3www969NFHFRcXp4SEBC1dulTd3d3j5ufxeLRkyRK/tj179igrK0sRERFKSEhQfn6+BgcHrf1LlixRXV3dJY8NgH8PilIAgtrjjz8ut9ttvd65c6fKysr8YgYHB1VRUaEDBw6ooaFBTqdTDz/8sEZGRiRJv//+u/Ly8nTq1Cl99NFH+vbbb1VZWWntl6TOzk59+OGHqq+vV319vZqamlRVVWVPJwEAAC6Tffv26dSpU2pubta2bdu0adMmLV68WHFxcWpra1N5ebnKy8t18uRJSdLQ0JDuv/9+RUVFqbm5WS0tLYqKitIDDzygc+fOjXmO/v5+HTlyRLm5uVZbX1+fiouLVVZWJp/Pp8bGRhUWFsoYY8XMmzdPJ0+eVE9Pz+QOAgDbhAY6AQCYTI899pjWrVun7u5uORwOtba2yuPxqLGx0Yp55JFH/I6pra1VUlKSOjo65HK5tHv3bp0+fVoHDhxQfHy8JGnOnDl+x4yMjOjtt9/W1VdfbZ23oaFBW7ZsmdwOAgAAXEbx8fHasWOHnE6nbrrpJr344osaGhqy1n5at26dqqqq1NraqqKiInk8HjmdTr311ltyOBySJLfbrdjYWDU2NqqgoGDUOXp6emSM0cyZM622vr4+DQ8Pq7CwULNnz5YkZWVl+R03a9YsSVJ3d7cVA2BqoygFIKglJiZq0aJFeuedd2SM0aJFi5SYmOgX09nZqY0bN+qrr77Szz//bF0BdeLECblcLh06dEjZ2dlWQWosGRkZVkFKklJSUvTTTz9NTqcAAAAmydy5c+V0/veGmhkzZsjlclmvQ0JClJCQYM1zDh48qGPHjvnNg6S/1vH853II551f73P69OlW22233ab58+crKytLCxYsUEFBgZYvX664uDgrJiIiQtJfV2cBCA4UpQAEvbKyMj3zzDOSpOrq6lH7H3roIaWlpenNN9/UzJkzNTIyIpfLZV1yfn4CNJ6wsDC/1w6Hw+/2PgAAgKlgrDnNePOckZER3XHHHdq1a9eo97rmmmvGPMf5fxD29/dbMSEhIfrss8/0xRdfaO/evXr11Ve1fv16tbW16dprr5Uk/frrr+O+L4CphzWlAAS982sanDt3TgsWLPDb98svv8jn82nDhg2aP3++MjMz1d/f7xdz66236tChQ9ZECAAAAH/JycnR0aNHlZSUpDlz5vhtMTExYx5z/fXXKzo6Wh0dHX7tDodDd999tzZv3qz29naFh4frgw8+sPYfOXJEYWFhmjt37qT2CYB9KEoBCHohISHy+Xzy+XwKCQnx23f+KTE1NTU6duyY9u3bp4qKCr+Y4uJiJScna9myZWptbdXx48f1/vvv68svv7SzGwAAAP86K1asUGJiopYuXar9+/erq6tLTU1NWrVqlXp7e8c8xul0Kj8/Xy0tLVZbW1ubtm7dqm+++UYnTpyQ1+vV6dOnlZmZacXs379f995770VdxQ5gaqAoBeCKEB0drejo6FHtTqdTHo9HBw8elMvl0urVq/XSSy/5xYSHh2vv3r1KSkrSwoULlZWVpaqqqlEFLgAAgCtNZGSkmpublZ6ersLCQmVmZqqsrExnz54dc+513sqVK+XxeKzbAKOjo9Xc3KyFCxfqxhtv1IYNG/Tyyy/rwQcftI6pq6vTk08+Oel9AmAfh/n7MzYBAAAAAJhkxhjdeeedeu6551RcXHzB+E8++URr167Vd999p9BQlkYGggVXSgEAAAAAbOVwOFRTU6Ph4eGLih8cHJTb7aYgBQQZrpQCAAAAAACA7bhSCgAAAAAAALajKAUAAAAAAADbUZQCAAAAAACA7ShKAQAAAAAAwHYUpQAAAAAAAGA7ilIAAAAAAACwHUUpAAAAAAAA2I6iFAAAAAAAAGxHUQoAAAAAAAC2+w/7964NMJGqpQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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91Wt5mVwuR3BwMNasWYPQ0FCsX78egYGBsLKyKtXzEJVHLEoRkWTl5+djw4YNqFWrFtasWVNk+/79+7FgwQL8/vvv8Pf3L7K9sIBU0m/8TE1N1fYtXKXv0qVL4go4ALBv3z6VfoWr9W3ZsgWenp5i+08//VRkRT1/f38cOHAAtWrVQuXKlUsUHxEREUlLSEgIVq5cifnz5+PAgQMYMmQIzM3Nxe2l8XmvyXjGz88PhoaGWLFiBXx8fNT2KSx8vfhFGwCsWrXqjeIrNHDgQEycOBERERG4ffs2qlatCj8/P3F7vXr1UKdOHVy8eFFcjKak6tWrh6pVq2Lr1q0IDQ0VX0N2djZ+/vlncUU+AGjbti127tyJb7/9ViyElcSLP+cKFSoU2T527FiEh4ejb9++yMjIwCeffKLRayAi9ViUIiLJ+v3333Hv3j3MnTtXLPi8yN3dHcuWLcPatWvVFqXc3d0BAD/++CMqVaoEMzMz1KhRo8itdIU8PDxw7Ngx7Nu3D46OjqhUqRLq1auHbt26wdraGiEhIZg5cyaMjIwQERGB//77T2V/Nzc3DBo0CIsXL4axsTE6deqEK1eu4Pvvv4elpaVK35kzZyIyMhK+vr4YO3Ys6tWrh2fPnuHOnTs4cOAAVq5cWeqXnhMREVHpat68ORo1aoTFixdDEASVW/eA0vm8r1WrFipUqIAtW7bAzc0NFStWhJOTk9qrqqtXr44vvvgC33zzDZ4+fYqBAwdCLpfj2rVrSEtLQ1hYGOrXr49atWphypQpEAQB1tbW2LdvHyIjI9/qZ2FlZYXevXsjIiICGRkZCA0NVZn/CXhe+OratSs6d+6MIUOGoGrVqnj06BHi4+Nx4cKFYldWNjAwwLx58/DRRx/B398fo0aNglKpxPz585GRkYHvvvtO7Ltw4UK0bt0aXl5emDJlCmrXro379+9j7969WLVqFSpVqqT2HB4eHgCAuXPnomvXrjA0NESjRo1gYmICAKhbty66dOmC33//Ha1bt0bjxo3f6udFRP+ffudZJyIqXq9evQQTE5NXrp7y4YcfCkZGRkJKSkqR1fcEQRAWL14s1KhRQzA0NFRZuUbd6ntxcXFCq1atBHNzcwGA0LZtW3Hb2bNnBV9fX8HCwkKoWrWq8PXXXwtr1qwpsiKNUqkUJk2aJNjZ2QlmZmaCt7e3EBMTI7i6uhZZ2e/BgwfC2LFjhRo1agjGxsaCtbW14OnpKXz55ZdCVlbWG/zEiIiISNd++OEHAYDQoEEDtdtL8nlfuPre/Pnz1R5j27ZtQv369QVjY2OV8U5xKwRv3LhRaNGihWBmZiZUrFhRaNq0qcrqfdeuXRPef/99oVKlSkLlypWFfv36CYmJiUXGUiVdfa/Q4cOHBQAqKxC+7OLFi0L//v0FOzs7wdjYWHBwcBA6dOggrrIsCOpXxxMEQfjll18ELy8vwczMTLCwsBA6duwonDp1qsg5rl27JvTr10+wsbERTExMBBcXF2HIkCHCs2fPij2+UqkUhg8fLlSpUkWQyWRqX3dERIQAQNi+fXuJfh5E9HoyQRAE3ZfCiIiIiIiIiN4dhSv93blz561WSSSi/+Hte0RERERERERqKJVKXLhwAWfPnsWePXuwcOFCFqSIShGvlCIiIiIiIiJS486dO6hRowYsLS0RGBiIZcuWaXXVZ6LyhkUpIiIiIiIiIiLSOYPXdyEiIiKid8GJEyfQo0cPODk5QSaT4ZdffnntPsePH4enpyfMzMxQs2ZNrFy5UvuBEhEREYFFKSIiIqIyIzs7G40bN8ayZctK1D8hIQHdunVDmzZtEBsbiy+++AJjx47Fzz//rOVIiYiIiHj7XokVFBTg3r17qFSpEmQymb7DISIiIi0QBAGPHz+Gk5MTDAze7e/uZDIZ9uzZg169ehXb5/PPP8fevXsRHx8vto0ePRoXL15ETExMsfsplUoolUrxeUFBAR49egQbGxuOk4iIiMoobYyTuPpeCd27dw/Ozs76DoOIiIh04L///kO1atX0HYbWxcTEwM/PT6Wtc+fOWLt2LXJzc4tdYWrOnDkICwvTRYhEREQkMaU5TmJRqoQqVaoEAEhMTIRcLtdzNAQ8r9IqFArI5XJ+KyshzIs0MS/Sw5xIk0KhgIuLi/i5X9alpKTA3t5epc3e3h55eXlIS0uDo6Oj2v2mTp2KiRMnis8Lf27//vsvx0kSwfcYaWJepIl5kR7mRJoUCgVcXV1LdZzEolQJFf5DsLS0hKWlpZ6jIeD5G5UgCLC0tOQblYQwL9LEvEgPcyJNhbMalKecvPxaS/IzMDU1hampaZF2uVzOopREFOaRf9BJC/MiTcyL9DAn0laaOdHrZAkzZsyATCZTeTg4OIjbhwwZUmS7t7e3yjGUSiU+/fRT2NrawsLCAgEBAUhKSlLpk56ejqCgIHGgFBQUhIyMDF28RCIiIiLJcnBwQEpKikpbamoqjIyMYGNjo6eoiIiIqLzQ+5VSDRs2xJEjR8TnhoaGKtu7dOmC9evXi89NTExUto8fPx779u3D9u3bYWNjg0mTJsHf3x/nz58XjxUYGIikpCQcPHgQADBy5EgEBQVh37592npZRESkQ4IgqEy6/C4QBAE5OTl49uwZvwHUE2Nj4yLjjvLGx8enyHjo8OHDaN68ebHzSRERERGVFr0XpYyMjFSujnqZqalpsdsVCgXWrl2LTZs2oVOnTgCAzZs3w9nZGUeOHEHnzp0RHx+PgwcP4vTp0/Dy8gIArF69Gj4+Prh+/Trq1atX+i+KiIh06t69e8jMzNR3GBorKCjAw4cP9R1GuSWTyVCtWjVUrFhR36GUmqysLPzzzz/i84SEBMTFxcHa2houLi6YOnUq7t69i40bNwJ4vtLesmXLMHHiRIwYMQIxMTFYu3Yttm3bpq+XQEREROWI3otSN2/ehJOTE0xNTeHl5YXZs2ejZs2a4vZjx47Bzs4OVlZWaNu2Lb799lvY2dkBAM6fP4/c3FyVVWOcnJzg7u6O6OhodO7cGTExMZDL5WJBCgC8vb0hl8sRHR1dbFHq5aWOC//YKZwDhPSvMBfMh7QwL9JUlvOSl5eHzMxM2NjYvHOTU+fn55f7K3X0RRAEpKWlISkpCbVr1xbz8K7/Gzl37hzat28vPi+cjDw4OBgRERFITk5GYmKiuL1GjRo4cOAAJkyYgOXLl8PJyQlLlizBBx98oPPYiYiIqPzRa1HKy8sLGzduRN26dXH//n3MmjULvr6+uHr1KmxsbNC1a1f069cPrq6uSEhIwFdffYUOHTrg/PnzMDU1RUpKCkxMTFC5cmWV49rb24vzI6SkpIhFrBfZ2dkVmUPhRcUtdaxQKN7yVVNpEQQBWVlZAMrXhLRSx7xIU1nOS05ODgoKCmBubv7O3W5kaGgIAwO9Tu9YrlWuXBmPHz/Go0ePxOkB3vXP+Xbt2r2ysBYREVGkrW3btrhw4YIWoyIiIiJST69Fqa5du4r/7+HhAR8fH9SqVQsbNmzAxIkTMWDAAHG7u7s7mjdvDldXV/z222/o06dPsccVBEHljy51f4C93OdlLy91nJmZCWdnZ64qIyFckUGamBdpKst5efbsGR4+fAhDQ8N38qqjdzHmssLIyAgGBgaoVKkSzMzM9B0OERERUbkjqa9nLSws4OHhgZs3b6rd7ujoCFdXV3G7g4MDcnJykJ6ertIvNTUV9vb2Yp/79+8XOdaDBw/EPuqYmprC0tJS5QGgyGqAfPDBBx986P/xLr4/v0jfsZTnh7qfP2lf9erVYWdnh9zcXLHtzz//hEwmQ2hoqB4j09yQIUOwbNkyyRxv+vTp2LFjBwAgKioKhw8fLq3Q9OLF1zNjxowS/X686mdY2vkiIqK3I6milFKpRHx8PBwdHdVuf/jwIf777z9xu6enJ4yNjREZGSn2SU5OxpUrV+Dr6wvg+aoyCoUCZ8+eFfucOXMGCoVC7ENERPQ2jIyM0KRJEzRs2BA9evRARkaGTs57584dNG/eXKN9bG1ttRSNdFSvXl28XZWky8XFBXv37hWfr1u3TuPfZ23Iy8vTdwhvZebMmeLdBmWhKPXi6yEiorJHr0Wp0NBQHD9+HAkJCThz5gz69u2LzMxMBAcHIysrC6GhoYiJicGdO3dw7Ngx9OjRA7a2tujduzeA57ehhISEYNKkSfjjjz8QGxuLQYMGwcPDQ1yNz83NDV26dMGIESNw+vRpnD59GiNGjIC/vz9X3iMiolJhZWWFuLg4XL16FVZWVli+fLlWzpOfn6+V4xLpw7Bhw7Bu3ToAz+fyOn36NLp06aLS5/vvv0fLli3RrFkzdOvWDf/99x8A4I8//oCPjw+aNm0Kd3d3rF+/Xtxn1qxZcHNzQ5MmTdCkSRP8+++/AJ5fEfdisdLW1hZ37twB8LyQ+e2336J9+/YIDg5Gbm4upkyZgpYtW6JJkyb48MMPxWLz3bt30bFjRzRq1Ag9e/ZEWlqa2tc3fPhwLFiwQHyekJAABwcH5ObmvvL4L8rKysKwYcPg7u4Od3d3lflO7969i759+6JRo0Zo1KgRvvrqKwD/uxIoLi4O69evx6ZNm9CkSRPMnDkTH3/8MebMmSMe4/r163B2dlZbiHvdzyssLAy+vr6oUaMGZs2aJfZbuHAhWrRogaZNm6Jly5Y4c+aMmJdPP/1U5bVZW1sjLS0Nly9fRps2bdCsWTM0aNBAJcbirmx61T4AcPHiRXTs2BH169fHkCFDVBYwKlTSPBARkfbotSiVlJSEgQMHol69eujTpw9MTExw+vRpuLq6wtDQEJcvX0bPnj1Rt25dBAcHo27duoiJiVFZXWnRokXo1asX+vfvj1atWsHc3Bz79u1TmaNjy5Yt8PDwgJ+fH/z8/NCoUSNs2rRJHy+ZiIi0LDs7G9nZ2SqTPefk5CA7O7vIHyWFfQsKCsS23NxcZGdn49mzZ290/latWiEpKQkAcP/+ffTq1QvNmzdH69at8ffffwMA7t27hx49eqBZs2bw9PTErVu38PTpUwQFBcHDwwMtW7ZEXFwcgOe3q4wePRqdOnXChAkTcOPGDTRv3hxeXl4qf6hlZWVh8ODBaNGiBZo3b45Tp06JMbRv3x7NmzfHtGnT1MacmZmJDh06oFmzZmjatCmioqIAPF8Bt0OHDujRowfc3NxU9p87dy7c3d3h4eGBLVu2AHheNBs+fDjc3d3Rt29feHl54cqVKwCA9evXo2XLlmjUqBGmT58uHqe49hdt2rQJHh4e8PDwwPz58wE8v0qscePGCA4OhpubGwYMGFBkgu9p06bhxx9/FJ8PGjQIBw8efFX6SIfee+893L59G3fv3sW2bdvQr18/lfHb1q1bcePGDcTExODChQsYOHAgPvnkEwBAs2bNEBUVhdjYWJw4cQJhYWFITk5Geno6vv/+e1y4cAFxcXGIjo5+5XQNL0pMTMSff/6JLVu2YP78+ahYsSLOnj2LuLg4NGzYEF9//TUAYOzYsXjvvfdw6dIlLFq0CMePH1d7vGHDhqlMLB8REYGPPvoIxsbGrzz+i7755hvk5OTg0qVLOHPmDH755Rfs3LkTwPPfZy8vL1y6dAmXLl3C2LFjVfZt0qQJhg4diqCgIMTFxWH69OkYN24cfvzxR7HAvWzZMowcORJGRppPM5uRkYHo6GicPXsW8+fPx927dwEAQUFB+OuvvxAbG4slS5YgJCQEwPPi0o4dO5CTkwMA2LlzJ9q3bw9bW1tUr14dR44cwYULF3D+/Hn89NNPOHfu3CvP/7p9zpw5g19//RVXr17Fo0eP8MMPPxQ5RknzQERE2qPXic63b99e7LYKFSrg0KFDrz2GmZkZli5diqVLlxbbx9raGps3b36jGImI6N1SsWJFAM/nF6xSpQqA5394TJs2DcOHD8fq1avFvnZ2dnjy5AkSEhJQvXp1AMDy5csxYcIEBAYGisWWksrPz0dkZCSGDRsGABg/fjy++uoreHp64q+//sL48eNx8OBBjB07FgEBARg2bBjy8vJQUFCA5cuXo1KlSrh8+TJOnz6N4OBgXLx4EcDzKwKOHj0KExMTdOvWDV988QX69OmDzz77TDz3rFmz0Lt3b2zcuBFJSUno3r07Ll68iLCwMPTo0QMTJ05EeHi42rgrVKiAX3/9FZUqVUJiYiI++OAD/PXXXwCe/2H3999/w97eHr6+vggICICBgYH4B+CTJ0/QokULtG/fHlFRUXj48CEuX76Mv//+G40aNQIAXLt2DQcOHEBMTAxkMhl69uyJmJgYyOVyte0+Pj5ibHfv3sWMGTPw119/wdzcHL6+vujQoQNsbGwQHx+PHTt2oF69euL527RpI+47ZMgQDB06FCNHjkRWVhbOnDmDDRs2aJRT0q6goCBs2LABv/zyC7Zs2aLyb+6XX37BuXPn4OnpCeD5v6/CotXDhw8REhKCGzduwMjICGlpabh69Srat2+POnXqYNCgQfDz80P37t1RrVq1EsUydOhQcU6xX375BZmZmdi1axeA54XtWrVqAQCOHj2KJUuWAABq1qyJjh07qj2er68vcnNzxdewYcMG7N+//7XHf9GRI0fwww8/wMDAABYWFhg8eDCOHDmCrl27Ijo6WmUKi8L3u1epW7cu3NzcsH//fnTs2BHbt28XC8ea+uijj8Tz1qxZEwkJCahatSpiY2Px7bff4uHDhzAyMsK1a9eQk5ODatWqoWnTpti7dy/69u2L9evXi+9hT58+xZgxYxAXFwcDAwP8999/iIuLe+XtnK/bZ8CAAeLnwbBhwxAeHq7yngmUPA9ERKQ9ei1KERERlQUZGRlo0qQJkpKS0LBhQ3Tu3BnA84mb4+Pji/SPiorCjh07IAgCTE1NIZPJEBUVJf7B5O3tjadPn0KhUAAAevbsCRMTEwDA+fPn8dtvvwF4fqXEn3/+CQCIjIzEgQMHxNt7Hj58iJycHERHR4tXOA0aNEjt1UiCIOCzzz5DVFQUDA0NVRYcadWqFZydnQEAffr0QXR0NADggw8+gJmZGczMzNCxY0f89ddfiI6ORv/+/SGTyeDm5iYWpf744w/ExMSIxYWsrCzcunUL6enpattfLEr99ddf6NixI6ytrQEAffv2RVRUFHr27Il69eqhfv36AICmTZvizp07KkWp2rVrQyaT4datWzhx4gR69+7N1Q4lZsiQIWjWrBnq1q2LOnXqqGwTBAHTpk0Ti7wvGj16NHr06IGff/4ZMpkMzZo1w7Nnz2BoaIjTp08jOjoax44dg7e3N7Zt24Y2bdrA0NBQ5RbYl6+GLCxgFJ47PDwcHTp0eOvXFxERAYVCATs7O7i7u2t0fHWrRb/tZPzjxo3DggULkJSUBD8/v2KvJHvdz+vFFSsNDQ2Rl5eHnJwcfPDBBzh27Bg8PT2RmZkJuVyOnJwcmJiYYOjQoYiIiECTJk3wzz//iCtxf/HFF7C3t0dsbCyMjIzQp0+f116tquk+6n5upZVnIiJ6c5Ka6JyIiOhtZWVlISsrS2VC78mTJyMrK6vIvCSpqanIysqCi4uL2Pbxxx8jKysLa9euLfE5C+eUSkxMRF5ensoVSefPn0dcXJz4AEr2R+WLf4yam5uL7cXtKwgC9u/fL54nKSkJJiYmav+ofdmWLVuQnZ2N2NhYxMbGqtzOWJI/iAvP8fLtc4XPBUHAyJEjxdj++ecfDBo0qNj2V3nx9ZiamortL/8BXSg4OBibNm3Chg0bMHjw4Fcem3TPyckJc+bMwdy5c4tsCwgIQHh4OB49egTg+a21sbGxAID09HS4urpCJpPhxIkT4lWFjx8/xv3799GmTRt89dVXaN26tbhPrVq1xPmNdu/ejezs7GLjCggIwMKFC/HkyRMAwJMnT3D16lUAQIcOHcS5sO7cuYM//vij2OMEBwdj586dWLlyJYYOHVqi47/o/fffx+rVqyEIArKzs7F582Z06tQJFStWROvWrbFo0SKx74MHD4rsX6lSJbG4XcjPzw9JSUmYM2eOeDukOpr8vAo9e/YMubm5YiH75TsZevfujbNnz+K7775DUFCQWCROT09HtWrVYGRkhOvXr6tcAVac1+2zc+dOZGdnIz8/H+vXrxfnm31RSfNARETaw6IUERGVKRYWFrCwsFApnpiYmMDCwkKliPFiXwOD/30cGhsbw8LCQuUqgJIyNzfHDz/8gAULFiAvLw9t27bFqlWrAAAFBQW4fPkyAKBNmzZi0UupVOLJkydo3bo1tm7dCgA4e/YsLCwsYGlpWeQczZo1w6+//goAYn8A6NSpk8oE64V/pLdq1UpcTv3F/i/KzMyEvb09jIyMsGvXLpWrDU6dOoWkpCTk5OTgl19+gY+PD1q3bo3du3dDqVQiPT0dR48eRYsWLeDr64tdu3ZBEARcv35dfL0dOnTAjh07kJ6eDuD5nJIPHz4stv1FLVu2xB9//IH09HQolUrs2bMHrVu3fn0y/r/+/ftj06ZNyM7OFq9SIWkZOnSoytVxhYKCgjBo0CC0a9cOjRs3RpMmTXD06FEAwHfffYfJkyfD29sbERER8PLyAvB8wvQ+ffrAw8MDjRo1Qm5uLoKDgwEAixcvxscff4xWrVrhwoULsLGxKTamKVOmoEmTJvDy8kKjRo3g7e0tFpV/+OEHHDt2DI0aNUJoaKjaYkchR0dHNG/eHPv378fAgQNLdPwXffXVV5DJZPDw8ICXlxcCAgLQt29fAM/nWjt9+jQaNmyIxo0bq50M3N/fH+fPnxcnOgeeF5ZDQkJgb2+v9udeSJOfVyFLS0vMnDkTLVu2xHvvvVfkPdfU1BT9+vXD2rVrVYp006ZNw5o1a9CiRQtMmzatRFcuvW6f9957D7169ULDhg1RuXJllUnWC5U0D0REpD0y4eWvNUmtwsuPMzIyIJfL9R0O4fm35QqFAnK5/K0vZafSw7xIU1nOy7Nnz5CQkIAaNWq8USGpNNja2qqswOXv74+goCC0a9cOo0ePxq1bt5CXl4fBgwdjypQpSE5OxvDhw5GYmAgTExPs3LkTjo6OGDFiBC5dugQzMzOsWrUKTZs2xYwZM2Brayte0XDjxg0EBgbC0NAQnTt3xoEDB3Du3DlkZWXh008/xblz55Cfn4+OHTti6dKluH//Pj788ENkZmbigw8+wMKFC4usFvbgwQN0794dANC6dWts3LgRaWlpOHbsGObMmQMLCwvEx8fjgw8+EFfZmjt3LjZt2gSZTIYpU6bgo48+Qn5+PkaMGIGzZ8+iSZMmiI+Px549e+Di4oKNGzdiwYIFKCgoQKVKlbB9+/ZXtr9o48aNmD9/PgRBQHBwMCZPnow7d+6gb9++4sTGoaGhcHd3x5AhQ1C9enVcuXJFvB2rX79+aNOmTZGJoNX97igUClhZWUGhUKgtCpJ6HCdJz6ve97t3744PP/wQQUFBeoqu/CrLn8fvMuZFepgTadLGOIlFqRLiYEt6+EYlTcyLNJXlvEihKPUmBEEQJ26Wak6OHTuGZcuWiZMAl0R2djYsLCxw584ddOzYETdu3NDrPE55eXlo3Lgxjh8/rnJLJ8CiVGniOEl61L3vnzt3DgMGDIC7uzt2797NOdb0oCx/Hr/LmBfpYU6kSRvjJE50TkRERKWmc+fOePz4MQoKCrBs2TK9/tEbFxeHXr16ISQkpEhBiqg8at68OW7duqXvMIiIiEQsShEREZFa7dq1Q7t27TTaJyoqSjvBvIEmTZrgzp07+g6DiIiIiIrBic6JiKhM4N3opCn+zhARERHpF6+UIiKid5qR0fOPssePH79Tcw68C3NKlWWCIODBgweQyWQwNjbWdzhERERE5RKLUkRE9E4zMjKCpaUlHj58iIcPH+o7HI0UFBTAwIAXLeuLTCZDtWrVONkzERERkZ6wKEVERO88Jycn2NravlO3YwmCgMePH6NSpUq8UkpPjI2NWZAiIiIi0iMWpYiI6J0nk8lgamqq7zA0IggClEolzMzMWJQiIiIionKJRSkiIiIiPbpz5w5OnjyJO3fu4MmTJ6hSpQqaNm0KHx8fmJmZ6Ts8IiIiIq1hUYqIiIhID7Zu3YolS5bg7NmzsLOzQ9WqVVGhQgU8evQIt27dgpmZGT766CN8/vnncHV11Xe4RERERKWORSkiIiIiHWvWrBkMDAwwZMgQ/PTTT3BxcVHZrlQqERMTg+3bt6N58+YIDw9Hv3799BQtERERkXawKEVERESkY9988w26d+9e7HZTU1O0a9cO7dq1w6xZs5CQkKDD6IiIiIh0g0UpIiIiIh17VUHqZba2trC1tdViNERERET6YaDvAIiIiIjKswsXLuDy5cvi819//RW9evXCF198gZycHD1GRkRERKRdLEoRERER6dGoUaNw48YNAMDt27fx4YcfwtzcHDt37sRnn32m5+iIiIiItIdFKSIiIiI9unHjBpo0aQIA2LlzJ9577z1s3boVERER+Pnnn/UbHBEREZEWsShFREREpEeCIKCgoAAAcOTIEXTr1g0A4OzsjLS0NH2GRkRERKRVLEoRERER6VHz5s0xa9YsbNq0CcePHxcnQU9ISIC9vb2eoyMiIiLSHhaliIiIiPRo8eLFuHDhAj755BN8+eWXqF27NgBg165d8PX11XN0RERERNrDohQRERGRHhRObt6oUSNcvnwZCoUCX3/9tbh9/vz52LBhwxsdOzw8HDVq1ICZmRk8PT1x8uTJV/bfsmULGjduDHNzczg6OmLo0KF4+PDhG52biIiIqKT0WpSaMWMGZDKZysPBwUFt31GjRkEmk2Hx4sUq7UqlEp9++ilsbW1hYWGBgIAAJCUlqfRJT09HUFAQ5HI55HI5goKCkJGRoaVXRURERPR6TZs2hZubGz7//HPExMQU2W5mZgZjY2ONj7tjxw6MHz8eX375JWJjY9GmTRt07doViYmJavtHRUVh8ODBCAkJwdWrV7Fz50789ddfGD58uMbnJiIiItKEkb4DaNiwIY4cOSI+NzQ0LNLnl19+wZkzZ+Dk5FRk2/jx47Fv3z5s374dNjY2mDRpEvz9/XH+/HnxWIGBgUhKSsLBgwcBACNHjkRQUBD27dunpVdFRERE9GoPHz5EZGQkfv31V/Tu3RuCIMDf3x89e/aEn58fzMzM3ui4CxcuREhIiFhUWrx4MQ4dOoQVK1Zgzpw5RfqfPn0a1atXx9ixYwEANWrUwKhRozBv3rxiz6FUKqFUKsXnmZmZAJ5P2i4IwhvFTaWrMBfMh7QwL9LEvEgPcyJN2siH3otSRkZGxV4dBQB3797FJ598gkOHDokTfxZSKBRYu3YtNm3ahE6dOgEANm/eDGdnZxw5cgSdO3dGfHw8Dh48iNOnT8PLywsAsHr1avj4+OD69euoV6+e9l4cERERUTHMzMzQo0cP9OjRA4IgICYmBnv37sWUKVMwcOBAdOrUCT179oS/vz/s7OxKdMycnBycP38eU6ZMUWn38/NDdHS02n18fX3x5Zdf4sCBA+jatStSU1Oxa9euIuOuF82ZMwdhYWFF2hUKRYniJO0TBAFZWVkAAJlMpudoqBDzIk3Mi/QwJ9Kkjc95vRelbt68CScnJ5iamsLLywuzZ89GzZo1AQAFBQUICgrC5MmT0bBhwyL7nj9/Hrm5ufDz8xPbnJyc4O7ujujoaHTu3BkxMTGQy+ViQQoAvL29IZfLER0dXWxRit8ASh+r59LEvEgT8yI9zIk06SsfMpkMvr6+8PX1xXfffYebN29i7969iIiIwP/93/9h4cKF+Pjjj197nLS0NOTn5xdZtc/e3h4pKSlq9/H19cWWLVswYMAAPHv2DHl5eQgICMDSpUuLPc/UqVMxceJE8XlmZiacnZ3FqRJI/wp/l+VyOf+gkxDmRZqYF+lhTsoPvRalvLy8sHHjRtStWxf379/HrFmz4Ovri6tXr8LGxgZz586FkZGReDn5y1JSUmBiYoLKlSurtL848EpJSVH77aKdnV2xgzOA3wC+C1g9lybmRZqYF+lhTqRJKp/zderUwaRJkzBp0iQ8fPgQjx490mj/l3+nBEEo9vfs2rVrGDt2LKZPn47OnTsjOTkZkydPxujRo7F27Vq1+5iamsLU1FTtefn7LB0vzttK0sG8SBPzIj3MifRoIxd6LUp17dpV/H8PDw/4+PigVq1a2LBhA9q2bYsffvgBFy5c0PiFvzzwUrf/qwZnAL8BfBewei5NzIs0MS/Sw5zQi+7evYtTp04hNTUVBQUFYrtMJsOnn34KGxubEh3H1tYWhoaGRb54S01NLXL1VKE5c+agVatWmDx5MoDnqwFaWFigTZs2mDVrFhwdHd/wVRERERG92lsVpZRKpdpvyd6UhYUFPDw8cPPmTRgYGCA1NRUuLi7i9vz8fEyaNAmLFy/GnTt34ODggJycHKSnp6tcLZWamgpfX18AgIODA+7fv1/kXA8ePCh2cAbwG8B3Bavn0sS8SBPzIj3MifToIxfr16/H6NGjYWJiAhsbmyJfrH366aclPpaJiQk8PT0RGRmJ3r17i+2RkZHo2bOn2n2ePHkCIyPVIWHhYjG8vZSIiIi0yUCTzocOHcKQIUNQq1YtGBsbw9zcHJUqVULbtm3x7bff4t69e28VjFKpRHx8PBwdHREUFIRLly4hLi5OfDg5OWHy5Mk4dOgQAMDT0xPGxsaIjIwUj5GcnIwrV66IRSkfHx8oFAqcPXtW7HPmzBkoFAqxDxEREZG+TJ8+HdOnT4dCocCdO3eQkJAgPm7fvq3x8SZOnIg1a9Zg3bp1iI+Px4QJE5CYmIjRo0cDeH41+ODBg8X+PXr0wO7du7FixQrcvn0bp06dwtixY9GyZUu1Kx8TERERlZYSXSn1yy+/4PPPP4dCoUC3bt0wefJkVK1aFRUqVMCjR49w5coVHDlyBN988w2GDBmCb775BlWqVHntcUNDQ9GjRw+4uLggNTUVs2bNQmZmJoKDg2FjY1PkUnVjY2M4ODiIk5PL5XKEhIRg0qRJsLGxgbW1NUJDQ+Hh4SGuxufm5oYuXbpgxIgRWLVqFQBg5MiR8Pf358p7REREpHdPnjzBhx9+CAMDjb4rLNaAAQPw8OFDzJw5E8nJyXB3d8eBAwfg6uoK4PkXeImJiWL/IUOG4PHjx1i2bBkmTZoEKysrdOjQAXPnzi2VeIiIiIiKU6Ki1OzZs/H999+je/fuagdM/fv3B/B8PoQffvgBGzduxKRJk1573KSkJAwcOBBpaWmoUqUKvL29cfr0aXHQVBKLFi2CkZER+vfvj6dPn6Jjx46IiIgQLzsHgC1btmDs2LHiKn0BAQFYtmxZic9BREREpC0hISHYuXMnpkyZUmrHHDNmDMaMGaN2W0RERJG2Tz/9VKPbBImIiIhKg0zgZAElkpmZCblcjoyMDE50LhGCIEChUHCSYIlhXqSJeZEe5kSaFAoFrKysoFAoYGlpqZNz5ufnw9/fH0+fPoWHhweMjY1Vti9cuFAncbwNjpOkh+8x0sS8SBPzIj3MiTRpY5yk19X3iIiIiMq72bNn49ChQ+K0Aq9bQZiIiIiorNC4KCUIAnbt2oWjR48WWbYYAHbv3l1qwRERERGVdQsXLsS6deswZMgQfYdCREREpFMaF6XGjRuHH3/8Ee3bt4e9vT2/wSMiIiJ6C6ampmjVqpW+wyAiIiLSOY2LUps3b8bu3bvRrVs3bcRDREREVK6MGzcOS5cuxZIlS/QdChEREZFOaVyUksvlqFmzpjZiISIiIip3zp49iz///BP79+9Hw4YNi0x0zqkRiIiIqKzSuCg1Y8YMhIWFYd26dahQoYI2YiIiIiIqN6ysrNCnTx99h0FERESkcxoXpfr164dt27bBzs4O1atXL/Jt3oULF0otOCIiIqKybv369foOgYiIiEgvNC5KDRkyBOfPn8egQYM40TkREREREREREb0RjYtSv/32Gw4dOoTWrVtrIx4iIiKiMq9Lly6YPn06fH19X9nv8ePHCA8PR8WKFfHxxx/rKDoiIiIi3dC4KOXs7AxLS0ttxEJERERULvTr1w/9+/dHpUqVEBAQgObNm8PJyQlmZmZIT0/HtWvXEBUVhQMHDsDf3x/z58/Xd8hEREREpU7jotSCBQvw2WefYeXKlahevboWQiIiIiIq20JCQhAUFIRdu3Zhx44dWL16NTIyMgAAMpkMDRo0QOfOnXH+/HnUq1dPv8ESERERaYnGRalBgwbhyZMnqFWrFszNzYtMdP7o0aNSC46IiIiorDIxMUFgYCACAwMBAAqFAk+fPoWNjU2R8RURERFRWaRxUWrx4sVaCIOIiIiofJPL5ZDL5foOg4iIiEhnNC5KBQcHayMOIiIiIiIiIiIqRwxK0ik7O1ujg2ran4iIiIiIiIiIypcSFaVq166N2bNn4969e8X2EQQBkZGR6Nq1K5YsWVJqARIRERERERERUdlTotv3jh07hmnTpiEsLAxNmjRRu2xxTEwMjI2NMXXqVIwcOVLbcRMRERERERER0TusREWpevXqYefOnUhKSsLOnTtx4sQJREdH4+nTp7C1tUXTpk2xevVqdOvWDQYGJbr4ioiIiIgADBkyBMOGDcN7772n71CIiIiIdEqjic6rVauGCRMmYMKECdqKh4iIiKhcefz4Mfz8/ODs7IyhQ4ciODgYVatW1XdYRERERFrHy5qIiIiI9Ojnn3/G3bt38cknn2Dnzp2oXr06unbtil27diE3N1ff4RERERFpDYtSRERERHpmY2ODcePGITY2FmfPnkXt2rURFBQEJycnTJgwATdv3tR3iERERESljkUpIiIiIolITk7G4cOHcfjwYRgaGqJbt264evUqGjRogEWLFuk7PCIiIqJSxaIUERERkR7l5ubi559/hr+/P1xdXbFz505MmDABycnJ2LBhAw4fPoxNmzZh5syZ+g6ViIiIqFRpNNH5q6Snp2Pfvn0YPHhwaR2SiIiIqMxzdHREQUEBBg4ciLNnz6JJkyZF+nTu3BlWVlY6j42IiIhIm0rtSqnExEQMHTpUo31mzJgBmUym8nBwcFDZXr9+fVhYWKBy5cro1KkTzpw5o3IMpVKJTz/9FLa2trCwsEBAQACSkpJU+qSnpyMoKAhyuRxyuRxBQUHIyMh449dKREREVFoWLVqEe/fuYfny5WoLUgBQuXJlJCQk6DYwIiIiIi3T++17DRs2RHJysvi4fPmyuK1u3bpYtmwZLl++jKioKFSvXh1+fn548OCB2Gf8+PHYs2cPtm/fjqioKGRlZcHf3x/5+flin8DAQMTFxeHgwYM4ePAg4uLiEBQUpNPXSURERKTO0aNH1a6yl52djWHDhukhIiIiIiLdKLXb9944ACMjlaujXhQYGKjyfOHChVi7di0uXbqEjh07QqFQYO3atdi0aRM6deoEANi8eTOcnZ1x5MgRdO7cGfHx8Th48CBOnz4NLy8vAMDq1avh4+OD69evo169emrPrVQqoVQqxeeZmZkAAEEQIAjCW79uenuFuWA+pIV5kSbmRXqYE2nSRz42bNiA7777DpUqVVJpf/r0KTZu3Ih169bpPCYiIiIiXdB7UermzZtwcnKCqakpvLy8MHv2bNSsWbNIv5ycHPz444+Qy+Vo3LgxAOD8+fPIzc2Fn5+f2M/JyQnu7u6Ijo5G586dERMTA7lcLhakAMDb2xtyuRzR0dHFFqXmzJmDsLCwIu0KheJtXzKVEkEQkJWVBQCQyWR6joYKMS/SxLxID3MiTbr8nM/MzBQLk48fP4aZmZm4LT8/HwcOHICdnd0bHTs8PBzz589HcnIyGjZsiMWLF6NNmzbF9lcqlZg5cyY2b96MlJQUVKtWDV9++SWv1CIiIiKtKnFRasmSJa/cfvfuXY1P7uXlhY0bN6Ju3bq4f/8+Zs2aBV9fX1y9ehU2NjYAgP379+PDDz/EkydP4OjoiMjISNja2gIAUlJSYGJigsqVK6sc197eHikpKWIfdQM6Ozs7sY86U6dOxcSJE8XnmZmZcHZ2FuelIv0r/DZbLpfzDzoJYV6kiXmRHuaErKysxDk169atW2S7TCZT+wXZ6+zYsQPjx49HeHg4WrVqhVWrVqFr1664du0aXFxc1O7Tv39/3L9/H2vXrkXt2rWRmpqKvLw8jc9NREREpIkSF6UWLVr02j7FDXSK07VrV/H/PTw84OPjg1q1amHDhg1iQah9+/aIi4tDWloaVq9ejf79++PMmTOv/OZQEASVAb66wf7LfV5mamoKU1PTIu2Fg0eShhcnySfpYF6kiXmRHuZEenSZi6NHj0IQBHTo0AE///wzrK2txW0mJiZwdXWFk5OTxsdduHAhQkJCMHz4cADA4sWLcejQIaxYsQJz5swp0v/gwYM4fvw4bt++LcZQvXr1N3tRRERERBoocVFKFyu+WFhYwMPDAzdv3lRpq127NmrXrg1vb2/UqVMHa9euxdSpU+Hg4ICcnBykp6erXC2VmpoKX19fAICDgwPu379f5FwPHjyAvb291l8TERERkTpt27YF8HyM5eLiUioFsZycHJw/fx5TpkxRaffz80N0dLTaffbu3YvmzZtj3rx52LRpk7ia8TfffIMKFSqo3Ydzb0of562TJuZFmpgX6WFOpEkb+dD7nFIvUiqViI+Pf+WcB4IgiIMgT09PGBsbIzIyEv379wcAJCcn48qVK5g3bx4AwMfHBwqFAmfPnkXLli0BAGfOnIFCoRALV0RERES6dOnSJbi7u8PAwAAKhUJl9eGXNWrUqMTHTUtLQ35+fpEv3l6c2uBlt2/fRlRUFMzMzLBnzx6kpaVhzJgxePToUbGTrHPuTenjvHXSxLxIE/MiPcyJNGnjc77ERamnT5/ijz/+gL+/P4Dncy69+A2ZoaEhvvnmG5VJOl8nNDQUPXr0gIuLC1JTUzFr1ixkZmYiODgY2dnZ+PbbbxEQEABHR0c8fPgQ4eHhSEpKQr9+/QA8n4cjJCQEkyZNgo2NDaytrREaGgoPDw9xNT43Nzd06dIFI0aMwKpVqwAAI0eOhL+/f7GTnBMRERFpU5MmTcR5L5s0aQKZTKb220eZTIb8/HyNj//yAP5V0xYUFBRAJpNhy5Yt4ryZCxcuRN++fbF8+XK1V0tx7k3p47x10sS8SBPzIj3MSflR4qLUxo0bsX//frEotWzZMjRs2FAcqPz9999wcnLChAkTSnzypKQkDBw4EGlpaahSpQq8vb1x+vRpuLq64tmzZ/j777+xYcMGpKWlwcbGBi1atMDJkyfRsGFD8RiLFi2CkZER+vfvj6dPn6Jjx46IiIiAoaGh2GfLli0YO3asuEpfQEAAli1bVuI4iYiIiEpTQkICqlSpIv5/abG1tYWhoWGRq6JSU1OLnbbA0dERVatWVSkmubm5QRAEJCUloU6dOkX24dyb7wbOWydNzIs0MS/Sw5xIjzZyUeKi1JYtW4oUnLZu3YqaNWsCADZv3ozly5drVJTavn17sdvMzMywe/fu1x7DzMwMS5cuxdKlS4vtY21tjc2bN5c4LiIiIiJtcnV1Vfv/b8vExASenp6IjIxE7969xfbIyEj07NlT7T6tWrXCzp07kZWVhYoVKwIAbty4AQMDA1SrVq3UYiMiIiJ6mUFJO964cUNluWIzMzMYGPxv95YtW+LatWulGx0RERFRGTdnzhy1czetW7cOc+fO1fh4EydOxJo1a7Bu3TrEx8djwoQJSExMxOjRowE8v/Vu8ODBYv/AwEDY2Nhg6NChuHbtGk6cOIHJkydj2LBhxU50TkRERFQaSlyUUigUMDL634VVDx48UFkuuKCgQGWOKSIiIiJ6vVWrVqF+/fpF2hs2bIiVK1dqfLwBAwZg8eLFmDlzJpo0aYITJ07gwIED4hVZycnJSExMFPtXrFgRkZGRyMjIQPPmzfHRRx+hR48eWLJkyZu/KCIiIqISKPHte9WqVcOVK1eKnRz80qVLvMSbiIiISEMpKSlwdHQs0l6lShUkJye/0THHjBmDMWPGqN0WERFRpK1+/fqIjIx8o3MRERERvakSXynVrVs3TJ8+Hc+ePSuy7enTpwgLC0P37t1LNTgiIiKiss7Z2RmnTp0q0n7q1Ck4OTnpISIiIiIi3SjxlVJffPEFfvrpJ9SrVw+ffPIJ6tatC5lMhr///hvLli1DXl4evvjiC23GSkRERFTmDB8+HOPHj0dubi46dOgAAPjjjz/w2WefYdKkSXqOjoiIiEh7SlyUsre3R3R0NP7v//4PU6ZMgSAIAJ4vCfj+++8jPDy82KWGiYiIiEi9zz77DI8ePcKYMWOQk5MD4PmCMp9//jmmTp2q5+iIiIiItKfERSkAqFGjBg4ePIhHjx7hn3/+AQDUrl0b1tbWAIC8vDyVydCJiIiI6NVkMhnmzp2Lr776CvHx8ahQoQLq1KkDU1NTfYdGREREpFVvVEGytrZGy5YtxefXrl3DmjVrsGXLFty/f7/UgiMiIiIqLypWrAhHR0fIZDIWpIiIiKhcKPFE5y/LysrCmjVr4OPjg0aNGuHs2bOYMmVKacZGREREVOYVFBRg5syZkMvlcHV1hYuLC6ysrPDNN9+goKBA3+ERERERaY3GV0pFRUVhzZo1+Pnnn1GjRg1cu3YNx48fR6tWrbQRHxEREVGZ9uWXX2Lt2rX47rvv0KpVKwiCgFOnTmHGjBl49uwZvv32W32HSERERKQVJS5KzZs3D+vWrUNWVhYGDhyIqKgoNG7cGMbGxqhcubI2YyQiIiIqszZs2IA1a9YgICBAbGvcuDGqVq2KMWPGsChFREREZVaJi1JffPEFPv/8c8ycOROGhobajImIiIio3Hj06BHq169fpL1+/fp49OiRHiIiIiIi0o0Szyk1c+ZM7Ny5EzVq1MDnn3+OK1euaDMuIiIionKhcePGWLZsWZH2ZcuWoXHjxnqIiIiIiEg3NLpS6osvvsDx48exbt06eHt7o1atWhAEAenp6dqMkYiIiKjMmjdvHrp3744jR47Ax8cHMpkM0dHR+O+//3DgwAF9h0dERESkNRqvvte2bVts2LABycnJ+L//+z94enqibdu28PX1xcKFC7URo6RkZ2dDEATxeU5ODrKzs6FUKov0y87OVlk1Jzc3F9nZ2Xj27Nkb933y5Amys7ORn58vtuXl5SE7OxtPnz59475Pnz5FdnY28vLyxLb8/HyN+z558kSl77Nnz5CdnY3c3Nw36ltQUCD+fF6kVCqRnZ2NnJycN+orCILYV10+NelbktyXxu+JunyWxu9JYT7f9vfk5Xy+7e9Jcfl829+TF/OpSV9Ncs/3CL5HvNiX7xHv3nuErrVt2xY3btxA7969kZGRgUePHqFPnz64fv062rRpo/N4iIiIiHRGKAWXLl0Sxo0bJ1SpUqU0DidJCoVCACAAEFJTU8X2WbNmCQCE4cOHq/Q3NzcXAAgJCQli26JFiwQAQmBgoEpfW1tbAYBw5coVse3HH38UAAg9e/ZU6evq6ioAEM6ePSu2bd68WQAgdOrUSaVvgwYNBADC0aNHxbY9e/YIAARfX1+Vvs2bNxcACPv37xfbDh8+LAAQGjdurNK3bdu2AgDhp59+EtuioqIEAELt2rVV+nbr1k0AIKxfv15si42NFQAITk5OKn379u0rABCWLVsmtt24cUMAIMjlcpW+wcHBAgAhLCxMKCgoEARBEJKSkgQAgpGRkUrfMWPGCACEr7/+WmxLT08X85mTkyO2h4aGCgCE0NBQsS0nJ0fsm56eLrZ//fXXAgBhzJgxKuczMjISAAhJSUli27x58wQAQnBwsEpfuVwuABBu3Lghti1btkwAIPTt21elr5OTkwBAiI2NFdvWr18vABC6deum0rd27doCACEqKkps++mnnwQAQtu2bVX6Nm7cWAAgHD58WGzbv3+/AEBo3ry5Sl9fX18BgLBnzx6x7ejRowIAoUGDBoIgCEJBQYGQnp4udOrUSQAgbN68Wex79uxZAYDg6uqqctyePXsKAIQff/xRbLty5YoAQLC1tVXpGxgYKAAQFi1aJLYlJCQIAARzc3OVvsOHDxcACLNmzRLbUlNTxXy+aNy4cQIA4YsvvhDbsrKyxL5ZWVli+xdffCEAEMaNG6dyDKm/R7Rr10789yII5eM9Yt68eWKb1N4j/vvvPyE9PV0oKCgoV+8RhaT6HvHPP/8IAASFQiFQyRWOkzIyMvQdCv1/hZ/HL77vk/4xL9LEvEgPcyJNGRkZpT5OKvHte6/i4eGBxYsXY/78+aVxOCIiIqIy7dKlSyXu26hRIy1GQkRERKQ/MkHQw3Xq76DMzEzI5XLcvXsXjo6OkMlkAJ7fcpGbmwsjIyOYmpqK/QtvEahQoQIMDJ7fJZmbm4ucnBwYGhrCzMzsjfo+efIEgiDAzMxMXAUxLy8PSqUSBgYGqFChwhv1ffr0KQoKCmBqagojo+e1yvz8fDx79kyjvjKZDObm5mLfZ8+eIT8/HyYmJjA2Nta4b0FBgXh7iIWFhdhXqVQiNzcXT548QZUqVSCTyV7ZNy8vD8bGxjAxMQHw/PaMwttDzM3Ni+RTk74lyX1p/J6oy2dp/J4U5vNtf08K8ykIAhQKBUxMTCAIwhv/nhSXT01/T16V+7f9PSkun2/6e6LN94hnz57h8ePHcHBwEGMr6+8RL+dTau8RZmZmePz4MeRyOXJzc8vNe8Sb9NXle0RGRgYqV64MhUIBS0tLaIuBgQFkMtlrbxeUyWQqt05KVeE4KSMjA3K5XN/hECB+HsvlcvF9ifSPeZEm5kV6mBNpUigUsLKyKtVxEotSJcTBlvTwjUqamBdpYl6khzmRJm0MttT5999/S9zX1dVVa3GUFo6TpIfvMdLEvEgT8yI9zIk0aWOcVCq37xERERFRyb0LhSYiIiIibdN49T0iIiIiKl2bNm1Cq1at4OTkJF5FtXjxYvz66696joyIiIhIe96oKHXr1i1MmzYNAwcORGpqKgDg4MGDuHr1aqkGR0RERFTWrVixAhMnTkS3bt2QkZEhziFlZWWFxYsX6zc4IiIiIi3SuCh1/PhxeHh44MyZM9i9ezeysrIAPF9F5uuvvy71AImIiIjKsqVLl2L16tX48ssvxcnkAaB58+a4fPmyHiMjIiIi0i6Ni1JTpkzBrFmzEBkZKa5UAwDt27dHTEyMRseaMWMGZDKZysPBwQHA8xWDPv/8c3h4eMDCwgJOTk4YPHgw7t27p3IMpVKJTz/9FLa2trCwsEBAQACSkpJU+qSnpyMoKAhyuRxyuRxBQUHIyMjQ9KUTERERlbqEhAQ0bdq0SLupqam4AiMRERFRWaRxUery5cvo3bt3kfYqVarg4cOHGgfQsGFDJCcni4/CbwSfPHmCCxcu4KuvvsKFCxewe/du3LhxAwEBASr7jx8/Hnv27MH27dsRFRWFrKws+Pv7qyyfHBgYiLi4OBw8eBAHDx5EXFwcgoKCNI6ViIiIqLTVqFEDcXFxRdp///13NGjQQPcBEREREemIxqvvWVlZITk5GTVq1FBpj42NRdWqVTUPwMhIvDrqRXK5HJGRkSptS5cuRcuWLZGYmAgXFxcoFAqsXbsWmzZtQqdOnQAAmzdvhrOzM44cOYLOnTsjPj4eBw8exOnTp+Hl5QUAWL16NXx8fHD9+nXUq1dP45iJiIiISsvkyZPx8ccf49mzZxAEAWfPnsW2bdswZ84crFmzRt/hEREREWmNxkWpwMBAfP7559i5cydkMhkKCgpw6tQphIaGYvDgwRoHcPPmTTg5OcHU1BReXl6YPXs2atasqbavQqGATCaDlZUVAOD8+fPIzc2Fn5+f2MfJyQnu7u6Ijo5G586dERMTA7lcLhakAMDb2xtyuRzR0dHFFqWUSiWUSqX4PDMzEwAgCAIEQdD4dVLpK8wF8yEtzIs0MS/Sw5xIkz7yMXToUOTl5eGzzz7DkydPEBgYiKpVq+KHH37Ahx9+qPN4iIiIiHRF46LUt99+iyFDhqBq1aoQBAENGjRAfn4+AgMDMW3aNI2O5eXlhY0bN6Ju3bq4f/8+Zs2aBV9fX1y9ehU2NjYqfZ89e4YpU6YgMDAQlpaWAICUlBSYmJigcuXKKn3t7e2RkpIi9rGzsytybjs7O7GPOnPmzEFYWFiRdoVCodFrJO0RBEGcaF8mk+k5GirEvEgT8yI9zIk06etzfsSIERgxYgTS0tJQUFCgduxCREREVNZoXJQyNjbGli1bMHPmTMTGxqKgoABNmzZFnTp1ND55165dxf/38PCAj48PatWqhQ0bNmDixInittzcXHz44YcoKChAeHj4a48rCILKAF/dYP/lPi+bOnWqSgyZmZlwdnYWJ0sn/Sv8Nlsul/MPOglhXqSJeZEe5oQKhYWFYdCgQahVqxZsbW31HQ4RERGRzmhclCpUq1Yt1KpVqzRjgYWFBTw8PHDz5k2xLTc3F/3790dCQgL+/PNP8SopAHBwcEBOTg7S09NVrpZKTU2Fr6+v2Of+/ftFzvXgwQPY29sXG4upqSlMTU2LtBeuEkjS8OLKjSQdzIs0MS/Sw5xIjz5y8fPPP2PmzJlo0aIFBg0ahAEDBqBKlSo6j4OIiIhI10pUlHrxiqHXWbhw4RsHo1QqER8fjzZt2gD4X0Hq5s2bOHr0aJFb+jw9PWFsbIzIyEj0798fAJCcnIwrV65g3rx5AAAfHx8oFAqcPXsWLVu2BACcOXMGCoVCLFwRERER6culS5dw9epVbNmyBQsXLsTEiRPRqVMnDBo0CL169YK5ubm+QyQiIiLSihIVpWJjY1Wenz9/Hvn5+eIk4Tdu3IChoSE8PT01OnloaCh69OgBFxcXpKamYtasWcjMzERwcDDy8vLQt29fXLhwAfv370d+fr44B5S1tTVMTEwgl8sREhKCSZMmwcbGBtbW1ggNDYWHh4e4Gp+bmxu6dOmCESNGYNWqVQCAkSNHwt/fnyvvERERkSQ0bNgQs2fPxuzZs3Hq1Cls3boV48ePx+jRo8XFVoiIiIjKGoOSdDp69Kj46NGjB9q1a4ekpCRcuHABFy5cwH///Yf27duje/fuGp08KSkJAwcORL169dCnTx+YmJjg9OnTcHV1RVJSEvbu3YukpCQ0adIEjo6O4iM6Olo8xqJFi9CrVy/0798frVq1grm5Ofbt2wdDQ0Oxz5YtW+Dh4QE/Pz/4+fmhUaNG2LRpk0axEhEREemChYUFKlSoABMTE+Tm5r7RMcLDw1GjRg2YmZnB09MTJ0+eLNF+p06dgpGREZo0afJG5yUiIiLShEzQcO3jqlWr4vDhw2jYsKFK+5UrV+Dn54d79+6VaoBSkZmZCblcjoyMDE50LhGCIEChUHCSYIlhXqSJeZEe5kSaFAoFrKysoFAoVOax1LaEhARs3boVW7ZswY0bN/Dee+8hMDAQ/fr103jcsWPHDgQFBSE8PBytWrXCqlWrsGbNGly7dg0uLi7F7qdQKNCsWTPUrl0b9+/fR1xcXInPyXGS9PA9RpqYF2liXqSHOZEmbYyTSnSl1IsyMzPVThyempqKx48fl0pQREREROWFj48PateujZ07d2Lo0KH4999/8eeff2L48OFvVOBZuHAhQkJCMHz4cLi5uWHx4sVwdnbGihUrXrnfqFGjEBgYCB8fnzd9KUREREQa0Xj1vd69e2Po0KFYsGABvL29AQCnT5/G5MmT0adPn1IPkIiIiKgsa9++PdasWVPkKvQ3kZOTg/Pnz2PKlCkq7X5+firTH7xs/fr1uHXrFjZv3oxZs2a99jxKpRJKpVJ8XjjvlSAI0PAifNKSwlwwH9LCvEgT8yI9zIk0aSMfGhelVq5cidDQUAwaNEic58DIyAghISGYP39+qQdIREREVJbNnj271I6VlpaG/Px82Nvbq7Tb29uLC8a87ObNm5gyZQpOnjwJI6OSDQ3nzJmDsLCwIu0KhULzoEkrBEFAVlYWAPDWFwlhXqSJeZEe5kSatPE5r3FRytzcHOHh4Zg/fz5u3boFQRBQu3ZtWFhYlHpwRERERKS5lwfwgiCoHdTn5+cjMDAQYWFhqFu3bomPP3XqVEycOFF8npmZCWdnZ8jlcs4pJRGF32ZzPhZpYV6kiXmRHuak/NC4KFXIwsICjRo1Ks1YiIiIiOgt2NrawtDQsMhVUampqUWungKAx48f49y5c4iNjcUnn3wCACgoKIAgCDAyMsLhw4fRoUOHIvuZmprC1NS0SLtMJuMfDxJSmA/mRFqYF2liXqSHOZEebeRC46JU+/btXxnIn3/++VYBEREREdGbMTExgaenJyIjI9G7d2+xPTIyEj179izS39LSEpcvX1ZpCw8Px59//oldu3ahRo0aWo+ZiIiIyi+Ni1JNmjRReZ6bm4u4uDhcuXIFwcHBpRUXEREREb2BiRMnIigoCM2bN4ePjw9+/PFHJCYmYvTo0QCe33p39+5dbNy4EQYGBnB3d1fZ387ODmZmZkXaiYiIiEqbxkWpRYsWqW2fMWOGOBEZERERERXv0qVLJe6r6XQJAwYMwMOHDzFz5kwkJyfD3d0dBw4cgKurKwAgOTkZiYmJGh2TiIiISBtkQimt6ffPP/+gZcuWePToUWkcTnIyMzMhl8uRkZHBCTwlQhAEKBQKTn4nMcyLNDEv0sOcSJNCoYCVlRUUCgUsLS21dh4DAwPIZLJiJyB/UX5+vtbiKC0cJ0kP32OkiXmRJuZFepgTadLGOMmgVI4CICYmBmZmZqV1OCIiIqIyKyEhAbdv30ZCQgJ+/vln1KhRA+Hh4YiNjUVsbCzCw8NRq1Yt/Pzzz/oOlYiIiEhrNL59r0+fPirPBUFAcnIyzp07h6+++qrUAiMiIiIqqwpvpQOAfv36YcmSJejWrZvY1qhRIzg7O+Orr75Cr1699BAhERERkfZpXJSytLRUuXzOwMAA9erVw8yZM+Hn51eqwRERERGVdZcvX1a7yl2NGjVw7do1PUREREREpBsaF6UiIiK0EAYRERFR+eTm5oZZs2Zh7dq14lQISqUSs2bNgpubm56jIyIiItIejYtSNWvWxF9//QUbGxuV9oyMDDRr1gy3b98uteCIiIiIyrqVK1eiR48ecHZ2RuPGjQEAFy9ehEwmw/79+/UcHREREZH2aFyUunPnjtpVYJRKJe7evVsqQRERERGVFy1btkRCQgI2b96Mv//+G4IgYMCAAQgMDISFhYW+wyMiIiLSmhIXpfbu3Sv+/6FDh1SW+83Pz8cff/yB6tWrl2pwRERERGVZbm4u6tWrh/3792PkyJH6DoeIiIhIp0pclCpc+UUmkyE4OFhlm7GxMapXr44FCxaUanBEREREZZmxsTGUSqXKIjJERERE5YVBSTsWFBSgoKAALi4uSE1NFZ8XFBRAqVTi+vXr8Pf312asRERERGXOp59+irlz5yIvL0/foRARERHplMZzSiUkJGgjDiIiIqJy6cyZM/jjjz9w+PBheHh4FJlHavfu3XqKjIiIiEi7SlSUWrJkCUaOHAkzMzMsWbLklX3Hjh1bKoERERERlQdWVlb44IMP9B0GERERkc6VqCi1aNEifPTRRzAzM8OiRYuK7SeTyViUIiIiItLA+vXr9R0CERERkV6UqCj14i17vH2PiIiIiIiIiIjeVoknOi80c+ZMPHnypEj706dPMXPmzFIJioiIiKg82bVrF/r37w9vb280a9ZM5UFERERUVmlclAoLC0NWVlaR9idPniAsLKxUgiIiIiIqL5YsWYKhQ4fCzs4OsbGxaNmyJWxsbHD79m107dpV3+ERERERaY3GRSlBECCTyYq0X7x4EdbW1hoda8aMGZDJZCoPBwcHcfvu3bvRuXNn2NraQiaTIS4ursgxlEolPv30U9ja2sLCwgIBAQFISkpS6ZOeno6goCDI5XLI5XIEBQUhIyNDo1iJiIiItCE8PBw//vgjli1bBhMTE3z22WeIjIzE2LFjoVAo9B0eERERkdaUuChVuXJlWFtbQyaToW7durC2thYfcrkc77//Pvr3769xAA0bNkRycrL4uHz5srgtOzsbrVq1wnfffVfs/uPHj8eePXuwfft2REVFISsrC/7+/sjPzxf7BAYGIi4uDgcPHsTBgwcRFxeHoKAgjWMlIiIiKm2JiYnw9fUFAFSoUAGPHz8GAAQFBWHbtm36DI2IiIhIq0o00TkALF68GIIgYNiwYQgLC4NcLhe3mZiYoHr16vDx8dE8ACMjlaujXlRYOLpz547a7QqFAmvXrsWmTZvQqVMnAMDmzZvh7OyMI0eOoHPnzoiPj8fBgwdx+vRpeHl5AQBWr14NHx8fXL9+HfXq1VN7bKVSCaVSKT7PzMwE8PxKMUEQNH6dVPoKc8F8SAvzIk3Mi/QwJ9Kkj3w4ODjg4cOHcHV1haurK06fPo3GjRsjISGBvx9ERERUppW4KBUcHAwAqFGjBnx9fWFsbFwqAdy8eRNOTk4wNTWFl5cXZs+ejZo1a5Zo3/PnzyM3Nxd+fn5im5OTE9zd3REdHY3OnTsjJiYGcrlcLEgBgLe3N+RyOaKjo4stSs2ZM0ftHFm8jF46BEEQ5zdTd0sp6QfzIk3Mi/QwJ9Kkj8/5Dh06YN++fWjWrBlCQkIwYcIE7Nq1C+fOnUOfPn10Hg8RERGRrpSoKFV4lRAANG3aFE+fPsXTp0/V9rW0tCzxyb28vLBx40bUrVsX9+/fx6xZs+Dr64urV6/CxsbmtfunpKTAxMQElStXVmm3t7dHSkqK2MfOzq7IvnZ2dmIfdaZOnYqJEyeKzzMzM+Hs7CzOS0X6V/jtsVwu5x90EsK8SBPzIj3MCRX68ccfUVBQAAAYPXo0rK2tERUVhR49emD06NF6jo6IiIhIe0pUlLKysnrtgLlwAvQX53J6nRdXlPHw8ICPjw9q1aqFDRs2qBSENPXyZOzqYi9uwvZCpqamMDU1LdJeOCE7ScOLk+STdDAv0sS8SA9zIj36yIWBgQEMDP43zWf//v3faJ5OIiIiondNiYpSR48e1XYcAAALCwt4eHjg5s2bJerv4OCAnJwcpKenq1wtlZqaKk4Y6uDggPv37xfZ98GDB7C3ty+dwImIiIjeUKtWrdC2bVu0a9cOrVq1goWFhb5DIiIiItKJEhWl2rZtW6KDxcXFvU0sUCqViI+PR5s2bUrU39PTE8bGxoiMjBS/UUxOTsaVK1cwb948AICPjw8UCgXOnj2Lli1bAgDOnDkDhUIhFq6IiIiI9MXf3x/Hjx/HsmXL8OzZM3h6eopFqtatW6NixYr6DpGIiIhIK0o80XlxFAoFtmzZgjVr1uDixYsa3b4XGhqKHj16wMXFBampqZg1axYyMzPFSdUfPXqExMRE3Lt3DwBw/fp1AM+vfnJwcIBcLkdISAgmTZoEGxsbWFtbIzQ0FB4eHuJqfG5ubujSpQtGjBiBVatWAQBGjhwJf3//Yic5JyIiItKVqVOnYurUqcjPz8dff/2FY8eO4dixY1i4cCFkMpnKasBEREREZYnB67uo9+eff2LQoEFwdHTE0qVL0a1bN5w7d06jYyQlJWHgwIGoV68e+vTpAxMTE5w+fRqurq4AgL1796Jp06bo3r07AODDDz9E06ZNsXLlSvEYixYtQq9evdC/f3+0atUK5ubm2LdvHwwNDcU+W7ZsgYeHB/z8/ODn54dGjRph06ZNb/rSiYiIiErdzZs3cfHiRVy8eBGXLl2CpaUlunXrpu+wiIiIiLRGJhQu/1MCSUlJiIiIwLp165CdnY3+/ftj5cqVuHjxIho0aKDNOPUuMzMTcrkcGRkZXH1PIgRBgEKh4MpVEsO8SBPzIj3MiTQpFApYWVlBoVBotKLw2xgwYABOnDiBgoICvPfee3jvvffQtm1bNGrUSCfnLw0cJ0kP32OkiXmRJuZFepgTadLGOKnEV0p169YNDRo0wLVr17B06VLcu3cPS5cuLZUgiIiIiMqrnTt3Ij8/H8HBwRg2bBiGDh36ThWkiIiIiN5UieeUOnz4MMaOHYv/+7//Q506dbQZExEREVG58ejRI5w4cQLHjh3DtGnTcPXqVTRu3Bjt2rVDu3bt0LVrV32HSERERKQVJb5S6uTJk3j8+DGaN28OLy8vLFu2DA8ePNBmbERERERlnpWVFQICArBw4UKcP38eV69eRYMGDbBw4UL4+/vrOzwiIiIirSlxUcrHxwerV69GcnIyRo0ahe3bt6Nq1aooKChAZGQkHj9+rM04iYiIiMqkR48eYc+ePRg3bhwaN26MevXq4bfffkPPnj2xZMmSNzpmeHg4atSoATMzM3h6euLkyZPF9t29ezfef/99VKlSBZaWlvDx8cGhQ4fe9OUQERERlZjGq++Zm5tj2LBhiIqKwuXLlzFp0iR89913sLOzQ0BAgDZiJCIiIiqzqlSpgtGjR+Pu3bsYMWIE4uLikJqail27duHjjz/W+Hg7duzA+PHj8eWXXyI2NhZt2rRB165dkZiYqLb/iRMn8P777+PAgQM4f/482rdvjx49eiA2NvZtXxoRERHRK2m0+l5x8vPzsW/fPqxbtw579+4tjbgkh6vKSA9XZJAm5kWamBfpYU6kSR+r7125cgXu7u6ldjwvLy80a9YMK1asENvc3NzQq1cvzJkzp0THaNiwIQYMGIDp06er3a5UKqFUKsXnmZmZcHZ2Rnp6OsdJEsH3GGliXqSJeZEe5kSaFAoFKleuXKrjpBJPdP4qhoaG6NWrF3r16lUahyMiIiIqNwoLUqmpqbh+/TpkMhnq1q0LOzs7jY+Vk5OD8+fPY8qUKSrtfn5+iI6OLtExCgoK8PjxY1hbWxfbZ86cOQgLCyvSrlAoNAuYtEYQBGRlZQEA/6CTEOZFmpgX6WFOpEkbn/OlUpQiIiIiojeTmZmJjz/+GNu2bUNBQQGA51/4DRgwAMuXL9foyqO0tDTk5+fD3t5epd3e3h4pKSklOsaCBQuQnZ2N/v37F9tn6tSpmDhxosprcHZ2hlwu55VSElF4MwSvMpAW5kWamBfpYU7KDxaliIiIiPRo+PDhiIuLw2+//QYfHx/IZDJER0dj3LhxGDFiBH766SeNj/nyAF4QhBIN6rdt24YZM2bg119/feWVWqampjA1NVV7Xv7xIB2F+WBOpIV5kSbmRXqYE+nRRi5YlCIiIiLSo99++w2HDh1C69atxbbOnTtj9erV6NKli0bHsrW1haGhYZGrolJTU4tcPfWyHTt2ICQkBDt37kSnTp00Oi8RERHRm9B49T0iIiIiKj02NjZqb3mTy+WoXLmyRscyMTGBp6cnIiMjVdojIyPh6+tb7H7btm3DkCFDsHXrVnTv3l2jcxIRERG9KRaliIiIiPRo2rRpmDhxIpKTk8W2lJQUTJ48GV999ZXGx5s4cSLWrFmDdevWIT4+HhMmTEBiYiJGjx4N4Pl8UIMHDxb7b9u2DYMHD8aCBQvg7e2NlJQUpKSkcNJyIiIi0jrevkdERESkRytWrMA///wDV1dXuLi4AAASExNhamqKBw8eYNWqVWLfCxcuvPZ4AwYMwMOHDzFz5kwkJyfD3d0dBw4cgKurKwAgOTkZiYmJYv9Vq1YhLy8PH3/8MT7++GOxPTg4GBEREaX0KomIiIiKYlGKiIiISI969epV6sccM2YMxowZo3bby4WmY8eOlfr5iYiIiEqCRSkiIiIiPfr666/1HQIRERGRXnBOKSIiIiIiIiIi0jkWpYiIiIj0wMDAAIaGhkUelStXhre3N3bv3q3vEImIiIi0irfvEREREenBnj171LZnZGTg7NmzGDRoEDZs2IB+/frpODIiIiIi3WBRioiIiEgPevbsWey24OBgNGjQAN9//z2LUkRERFRm8fY9IiIiIgny8/PDjRs39B0GERERkdawKEVEREQkQU+fPoWZmZm+wyAiIiLSGhaliIiIiCRo9erVaNq0qb7DICIiItIazilFREREpAcTJ05U265QKHDu3DncunULJ0+e1HFURERERLqj1yulZsyYAZlMpvJwcHAQtwuCgBkzZsDJyQkVKlRAu3btcPXqVZVjKJVKfPrpp7C1tYWFhQUCAgKQlJSk0ic9PR1BQUGQy+WQy+UICgpCRkaGLl4iERERkVqxsbFqH2lpaejSpQuuXr3KK6WIiIioTNP7lVINGzbEkSNHxOeGhobi/8+bNw8LFy5EREQE6tati1mzZuH999/H9evXUalSJQDA+PHjsW/fPmzfvh02NjaYNGkS/P39cf78efFYgYGBSEpKwsGDBwEAI0eORFBQEPbt26fDV0pERET0P0ePHtV3CERERER6pfeilJGRkcrVUYUEQcDixYvx5Zdfok+fPgCADRs2wN7eHlu3bsWoUaOgUCiwdu1abNq0CZ06dQIAbN68Gc7Ozjhy5Ag6d+6M+Ph4HDx4EKdPn4aXlxeA53M0+Pj44Pr166hXr57uXiwREREREREREQGQQFHq5s2bcHJygqmpKby8vDB79mzUrFkTCQkJSElJgZ+fn9jX1NQUbdu2RXR0NEaNGoXz588jNzdXpY+TkxPc3d0RHR2Nzp07IyYmBnK5XCxIAYC3tzfkcjmio6OLLUoplUoolUrxeWZmJoDnxTJBEEr7x0BvoDAXzIe0MC/SxLxID3MiTcwHERERke7otSjl5eWFjRs3om7durh//z5mzZoFX19fXL16FSkpKQAAe3t7lX3s7e3x77//AgBSUlJgYmKCypUrF+lTuH9KSgrs7OyKnNvOzk7so86cOXMQFhZWpF2hUGj2IklrBEFAVlYWAEAmk+k5GirEvEgT8yI9zIk08XOeiIiISHf0WpTq2rWr+P8eHh7w8fFBrVq1sGHDBnh7ewMoOlAXBOG1g/eX+6jr/7rjTJ06VWVVnMzMTDg7O4uTpZP+FX6bLZfL+QedhDAv0sS8SA9zQkRERETlnd5v33uRhYUFPDw8cPPmTfTq1QvA8yudHB0dxT6pqani1VMODg7IyclBenq6ytVSqamp8PX1Ffvcv3+/yLkePHhQ5CqsF5mamsLU1LRIe+EqgSQNL67cSNLBvEgT8yI9zIn0MBdEREREumOg7wBepFQqER8fD0dHR9SoUQMODg6IjIwUt+fk5OD48eNiwcnT0xPGxsYqfZKTk3HlyhWxj4+PDxQKBc6ePSv2OXPmDBQKhdiHiIiIiIiIiIh0S69XSoWGhqJHjx5wcXFBamoqZs2ahczMTAQHB0Mmk2H8+PGYPXs26tSpgzp16mD27NkwNzdHYGAggOe3PISEhGDSpEmwsbGBtbU1QkND4eHhIa7G5+bmhi5dumDEiBFYtWoVAGDkyJHw9/fnyntERERERERERHqi16JUUlISBg4ciLS0NFSpUgXe3t44ffo0XF1dAQCfffYZnj59ijFjxiA9PR1eXl44fPgwKlWqJB5j0aJFMDIyQv/+/fH06VN07NgRERERMDQ0FPts2bIFY8eOFVfpCwgIwLJly3T7YomIiIiIiIiISCQTuPZxiWRmZkIulyMjI4MTnUuEIAhQKBScJFhimBdpYl6khzmRJoVCASsrKygUClhaWuo7nHcGx0nSw/cYaWJepIl5kR7mRJq0MU6S1JxSRERERERERERUPrAoRUREREREREREOseiFBERERERERER6RyLUkREREREREREpHMsShERERERERERkc6xKEVERERERERERDrHohQREREREREREekci1JERERERERERKRzLEoREREREREREZHOsShFREREVMaEh4ejRo0aMDMzg6enJ06ePPnK/sePH4enpyfMzMxQs2ZNrFy5UkeREhERUXnGohQRERFRGbJjxw6MHz8eX375JWJjY9GmTRt07doViYmJavsnJCSgW7duaNOmDWJjY/HFF19g7Nix+Pnnn3UcOREREZU3RvoO4F0hCAIAIDMzEzKZTM/REPA8J4X5YE6kg3mRJuZFepgTacrMzATwv8/9d9HChQsREhKC4cOHAwAWL16MQ4cOYcWKFZgzZ06R/itXroSLiwsWL14MAHBzc8O5c+fw/fff44MPPlB7DqVSCaVSKT5XKBQq/yX9EwRBzAffY6SDeZEm5kV6mBNpKsxJaY6TWJQqoYcPHwIAXFxc9BwJERERadvDhw8hl8v1HYbGcnJycP78eUyZMkWl3c/PD9HR0Wr3iYmJgZ+fn0pb586dsXbtWuTm5sLY2LjIPnPmzEFYWFiRdldX17eInoiIiN4FpTlOYlGqhKytrQEAiYmJ7+QgtSzKzMyEs7Mz/vvvP1haWuo7HPr/mBdpYl6khzmRJoVCARcXF/Fz/12TlpaG/Px82Nvbq7Tb29sjJSVF7T4pKSlq++fl5SEtLQ2Ojo5F9pk6dSomTpwoPs/IyICrqyvHSRLC9xhpYl6kiXmRHuZEmrQxTmJRqoQMDJ5PvyWXy/mPQmIsLS2ZEwliXqSJeZEe5kSaCj/331Uv3+ogCMIrb39Q119deyFTU1OYmpoWaec4SXr4HiNNzIs0MS/Sw5xIU2mOk97tERcRERERiWxtbWFoaFjkqqjU1NQiV0MVcnBwUNvfyMgINjY2WouViIiIiEUpIiIiojLCxMQEnp6eiIyMVGmPjIyEr6+v2n18fHyK9D98+DCaN2+udj4pIiIiotLColQJmZqa4uuvv1Z7qTrpB3MiTcyLNDEv0sOcSFNZyMvEiROxZs0arFu3DvHx8ZgwYQISExMxevRoAM/ngxo8eLDYf/To0fj3338xceJExMfHY926dVi7di1CQ0NLfM6y8HMra5gTaWJepIl5kR7mRJq0kReZ8C6veUxERERERYSHh2PevHlITk6Gu7s7Fi1ahPfeew8AMGTIENy5cwfHjh0T+x8/fhwTJkzA1atX4eTkhM8//1wsYhERERFpC4tSRERERERERESkc7x9j4iIiIiIiIiIdI5FKSIiIiIiIiIi0jkWpYiIiIiIiIiISOdYlCIiIiIiIiIiIp1jUeoF4eHhqFGjBszMzODp6YmTJ0++sv/x48fh6ekJMzMz1KxZEytXrtRRpOWHJjnZvXs33n//fVSpUgWWlpbw8fHBoUOHdBht+aHpv5VCp06dgpGREZo0aaLdAMspTfOiVCrx5ZdfwtXVFaampqhVqxbWrVuno2jLB01zsmXLFjRu3Bjm5uZwdHTE0KFD8fDhQx1FWz6cOHECPXr0gJOTE2QyGX755ZfX7sPPe46RpIrjJGniOEl6OEaSJo6TpEVvYySBBEEQhO3btwvGxsbC6tWrhWvXrgnjxo0TLCwshH///Vdt/9u3bwvm5ubCuHHjhGvXrgmrV68WjI2NhV27duk48rJL05yMGzdOmDt3rnD27Fnhxo0bwtSpUwVjY2PhwoULOo68bNM0L4UyMjKEmjVrCn5+fkLjxo11E2w58iZ5CQgIELy8vITIyEghISFBOHPmjHDq1CkdRl22aZqTkydPCgYGBsIPP/wg3L59Wzh58qTQsGFDoVevXjqOvGw7cOCA8OWXXwo///yzAEDYs2fPK/vz855jJKniOEmaOE6SHo6RpInjJOnR1xiJRan/r2XLlsLo0aNV2urXry9MmTJFbf/PPvtMqF+/vkrbqFGjBG9vb63FWN5omhN1GjRoIISFhZV2aOXam+ZlwIABwrRp04Svv/6agy0t0DQvv//+uyCXy4WHDx/qIrxySdOczJ8/X6hZs6ZK25IlS4Rq1appLcbyriQDLn7ec4wkVRwnSRPHSdLDMZI0cZwkbbocI/H2PQA5OTk4f/48/Pz8VNr9/PwQHR2tdp+YmJgi/Tt37oxz584hNzdXa7GWF2+Sk5cVFBTg8ePHsLa21kaI5dKb5mX9+vW4desWvv76a22HWC69SV727t2L5s2bY968eahatSrq1q2L0NBQPH36VBchl3lvkhNfX18kJSXhwIEDEAQB9+/fx65du9C9e3ddhEzFKO+f9xwjSRPHSdLEcZL0cIwkTRwnlQ2l9XlvVNqBvYvS0tKQn58Pe3t7lXZ7e3ukpKSo3SclJUVt/7y8PKSlpcHR0VFr8ZYHb5KTly1YsADZ2dno37+/NkIsl94kLzdv3sSUKVNw8uRJGBnxLUcb3iQvt2/fRlRUFMzMzLBnzx6kpaVhzJgxePToEedMKAVvkhNfX19s2bIFAwYMwLNnz5CXl4eAgAAsXbpUFyFTMcr75z3HSNLEcZI0cZwkPRwjSRPHSWVDaX3e80qpF8hkMpXngiAUaXtdf3Xt9OY0zUmhbdu2YcaMGdixYwfs7Oy0FV65VdK85OfnIzAwEGFhYahbt66uwiu3NPn3UlBQAJlMhi1btqBly5bo1q0bFi5ciIiICH4TWIo0ycm1a9cwduxYTJ8+HefPn8fBgweRkJCA0aNH6yJUegV+3nOMJFUcJ0kTx0nSwzGSNHGc9O4rjc97luMB2NrawtDQsEhVNjU1tUjlr5CDg4Pa/kZGRrCxsdFarOXFm+Sk0I4dOxASEoKdO3eiU6dO2gyz3NE0L48fP8a5c+cQGxuLTz75BMDzD3pBEGBkZITDhw+jQ4cOOom9LHuTfy+Ojo6oWrUq5HK52Obm5gZBEJCUlIQ6depoNeay7k1yMmfOHLRq1QqTJ08GADRq1AgWFhZo06YNZs2axatL9KS8f95zjCRNHCdJE8dJ0sMxkjRxnFQ2lNbnPa+UAmBiYgJPT09ERkaqtEdGRsLX11ftPj4+PkX6Hz58GM2bN4exsbHWYi0v3iQnwPNv/oYMGYKtW7fy/mIt0DQvlpaWuHz5MuLi4sTH6NGjUa9ePcTFxcHLy0tXoZdpb/LvpVWrVrh37x6ysrLEths3bsDAwADVqlXTarzlwZvk5MmTJzAwUP1YNjQ0BPC/b51I98r75z3HSNLEcZI0cZwkPRwjSRPHSWVDqX3eazQtehlWuCTl2rVrhWvXrgnjx48XLCwshDt37giCIAhTpkwRgoKCxP6Fyx9OmDBBuHbtmrB27Voud1zKNM3J1q1bBSMjI2H58uVCcnKy+MjIyNDXSyiTNM3Ly7iqjHZompfHjx8L1apVE/r27StcvXpVOH78uFCnTh1h+PDh+noJZY6mOVm/fr1gZGQkhIeHC7du3RKioqKE5s2bCy1bttTXSyiTHj9+LMTGxgqxsbECAGHhwoVCbGysuAQ1P++L4hhJmjhOkiaOk6SHYyRp4jhJevQ1RmJR6gXLly8XXF1dBRMTE6FZs2bC8ePHxW3BwcFC27ZtVfofO3ZMaNq0qWBiYiJUr15dWLFihY4jLvs0yUnbtm0FAEUewcHBug+8jNP038qLONjSHk3zEh8fL3Tq1EmoUKGCUK1aNWHixInCkydPdBx12aZpTpYsWSI0aNBAqFChguDo6Ch89NFHQlJSko6jLtuOHj36ys8Kft6rxzGSNHGcJE0cJ0kPx0jSxHGStOhrjCQTBF7rRkREREREREREusU5pYiIiIiIiIiISOdYlCIiIiIiIiIiIp1jUYqIiIiIiIiIiHSORSkiIiIiIiIiItI5FqWIiIiIiIiIiEjnWJQiIiIiIiIiIiKdY1GKiIiIiIiIiIh0jkUpIiIiIiIiIiLSORaliKjMmTFjBpo0aaK383/11VcYOXJkifqGhoZi7NixWo6IiIiI6DmOk4hISmSCIAj6DoKIqKRkMtkrtwcHB2PZsmVQKpWwsbHRUVT/c//+fdSpUweXLl1C9erVX9s/NTUVtWrVwqVLl1CjRg3tB0hERERlFsdJRPSuYVGKiN4pKSkp4v/v2LED06dPx/Xr18W2ChUqQC6X6yM0AMDs2bNx/PhxHDp0qMT7fPDBB6hduzbmzp2rxciIiIiorOM4iYjeNbx9j4jeKQ4ODuJDLpdDJpMVaXv5svQhQ4agV69emD17Nuzt7WFlZYWwsDDk5eVh8uTJsLa2RrVq1bBu3TqVc929excDBgxA5cqVYWNjg549e+LOnTuvjG/79u0ICAhQadu1axc8PDxQoUIF2NjYoFOnTsjOzha3BwQEYNu2bW/9syEiIqLyjeMkInrXsChFROXCn3/+iXv37uHEiRNYuHAhZsyYAX9/f1SuXBlnzpzB6NGjMXr0aPz3338AgCdPnqB9+/aoWLEiTpw4gaioKFSsWBFdunRBTk6O2nOkp6fjypUraN68udiWnJyMgQMHYtiwYYiPj8exY8fQp08fvHiRasuWLfHff//h33//1e4PgYiIiEgNjpOISF9YlCKicsHa2hpLlixBvXr1MGzYMNSrVw9PnjzBF198gTp16mDq1KkwMTHBqVOnADz/Js/AwABr1qyBh4cH3NzcsH79eiQmJuLYsWNqz/Hvv/9CEAQ4OTmJbcnJycjLy0OfPn1QvXp1eHh4YMyYMahYsaLYp2rVqgDw2m8XiYiIiLSB4yQi0hcjfQdARKQLDRs2hIHB/+rw9vb2cHd3F58bGhrCxsYGqampAIDz58/jn3/+QaVKlVSO8+zZM9y6dUvtOZ4+fQoAMDMzE9saN26Mjh07wsPDA507d4afnx/69u2LypUri30qVKgA4Pm3jkRERES6xnESEekLi1JEVC4YGxurPJfJZGrbCgoKAAAFBQXw9PTEli1bihyrSpUqas9ha2sL4Pnl6YV9DA0NERkZiejoaBw+fBhLly7Fl19+iTNnzoiryDx69OiVxyUiIiLSJo6TiEhfePseEZEazZo1w82bN2FnZ4fatWurPIpbtaZWrVqwtLTEtWvXVNplMhlatWqFsLAwxMbGwsTEBHv27BG3X7lyBcbGxmjYsKFWXxMRERFRaeA4iYhKC4tSRERqfPTRR7C1tUXPnj1x8uRJJCQk4Pjx4xg3bhySkpLU7mNgYIBOnTohKipKbDtz5gxmz56Nc+fOITExEbt378aDBw/g5uYm9jl58iTatGkjXp5OREREJGUcJxFRaWFRiohIDXNzc5w4cQIuLi7o06cP3NzcMGzYMDx9+hSWlpbF7jdy5Ehs375dvLzd0tISJ06cQLdu3VC3bl1MmzYNCxYsQNeuXcV9tm3bhhEjRmj9NRERERGVBo6TiKi0yIQX19skIqK3IggCvL29MX78eAwcOPC1/X/77TdMnjwZly5dgpERp/kjIiKisovjJCJ6Ga+UIiIqRTKZDD/++CPy8vJK1D87Oxvr16/nQIuIiIjKPI6TiOhlvFKKiIiIiIiIiIh0jldKERERERERERGRzrEoRUREREREREREOseiFBERERERERER6RyLUkREREREREREpHMsShERERERERERkc6xKEVERERERERERDrHohQREREREREREekci1JERERERERERKRzLEoREREREREREZHOsShFREREREREREQ6x6IUERERERERERHpHItSRERERERERESkcyxKERERERERERGRzrEoRUREREREREREOseiFBGpFRERAZlMJj7MzMzg4OCA9u3bY86cOUhNTdV3iJISGxuLtm3bQi6XQyaTYfHixQCAP/74A82bN4eFhQVkMhl++eUX8Wd7584djc4xY8YMyGSy0g/+BU+ePMGMGTNw7NgxrZ6HiIjoXXfmzBn07t0bLi4uMDU1hb29PXx8fDBp0iSVfuHh4YiIiHirc8lkMnzyySev7Xfs2DHIZDKVz3F144d27dqhXbt24nN+/j83ZMgQVK9eXd9hEJUrRvoOgIikbf369ahfvz5yc3ORmpqKqKgozJ07F99//z127NiBTp066TtESRg2bBiys7Oxfft2VK5cGdWrV4cgCOjfvz/q1q2LvXv3wsLCAvXq1UNeXh5iYmLg6Oio0TmGDx+OLl26aOkVPPfkyROEhYUBgMpglYiIiP7nt99+Q0BAANq1a4d58+bB0dERycnJOHfuHLZv344FCxaIfcPDw2Fra4shQ4ZoPa5mzZohJiYGDRo0eGW/8PBwlef8/H/uq6++wrhx4/QdBlG5wqIUEb2Su7s7mjdvLj7/4IMPMGHCBLRu3Rp9+vTBzZs3YW9vX+z+T548gbm5uS5C1asrV65gxIgR6Nq1q9h29+5dPHr0CL1790bHjh1V+lepUkXjc1SrVg3VqlV761iJiIjo7cybNw81atTAoUOHYGT0vz+pPvzwQ8ybN09vcVlaWsLb2/u1/V5XtCornj59igoVKpS4f61atbQYDRGpw9v3iEhjLi4uWLBgAR4/foxVq1aJ7UOGDEHFihVx+fJl+Pn5oVKlSmIxJjIyEj179kS1atVgZmaG2rVrY9SoUUhLSyty/F9//RWNGjWCqakpatasiR9++EGjW9cOHjyIjh07Qi6Xw9zcHG5ubpgzZ45Kn71798LHxwfm5uaoVKkS3n//fcTExBQ51s2bNxEYGAg7OzuYmprCzc0Ny5cvF7cX3oqXl5eHFStWiLc7zpgxQywgff7555DJZOLl4MXdvve6uIv7GezYsQM+Pj6wsLBAxYoV0blzZ8TGxqr0KczNP//8g27duqFixYpwdnbGpEmToFQqAQB37twRi2VhYWHia9HFN7tERETvkocPH8LW1lalIFXIwOB/f2JVr14dV69exfHjx8XP1cLxwLNnzzBp0iQ0adIEcrkc1tbW8PHxwa+//lrseVetWoW6devC1NQUDRo0wPbt21W2q7t9T50Xb9971ef/yZMnIZPJsG3btiLH2LhxI2QyGf76669iz1M45vnzzz8xYsQI2NjYwNLSEoMHD0Z2djZSUlLQv39/WFlZwdHREaGhocjNzVU5RlhYGLy8vGBtbQ1LS0s0a9YMa9euhSAIKv2qV68Of39/7N69G02bNoWZmZl49dfVq1fh5+cHc3NzVKlSBR9//DF+++23Ij8rdbfvFd46uWnTJri5ucHc3ByNGzfG/v37X/kzJqKS4ZVSRPRGunXrBkNDQ5w4cUKlPScnBwEBARg1ahSmTJmCvLw8AMCtW7fg4+OD4cOHQy6X486dO1i4cCFat26Ny5cvw9jYGMDzwkyfPn3w3nvvYceOHcjLy8P333+P+/fvlyiutWvXYsSIEWjbti1WrlwJOzs73LhxA1euXBH7bN26FR999BH8/Pywbds2KJVKzJs3D+3atcMff/yB1q1bAwCuXbsGX19fsQjn4OCAQ4cOYezYsUhLS8PXX3+N7t27IyYmBj4+Pujbt684j0S1atXQuHFj9OnTB59++ikCAwNhamr6VnGrM3v2bEybNg1Dhw7FtGnTkJOTg/nz56NNmzY4e/asyjehubm5CAgIQEhICCZNmoQTJ07gm2++gVwux/Tp0+Ho6IiDBw+iS5cuCAkJwfDhwwG82VVdREREZZmPjw/WrFmDsWPH4qOPPkKzZs3EscyL9uzZg759+0Iul4u3zBWOB5RKJR49eoTQ0FBUrVoVOTk5OHLkCPr06YP169dj8ODBKsfau3cvjh49ipkzZ8LCwgLh4eEYOHAgjIyM0Ldv3zd+La/6/K9VqxaaNm2K5cuXY+DAgSr7LVu2DC1atECLFi1ee47hw4ejT58+2L59O2JjY/HFF18gLy8P169fR58+fTBy5EgcOXIEc+fOhZOTEyZOnCjue+fOHYwaNQouLi4AgNOnT+PTTz/F3bt3MX36dJXzXLhwAfHx8Zg2bRpq1KgBCwsLJCcno23btrCwsMCKFStgZ2eHbdu2lWiOrkK//fYb/vrrL8ycORMVK1bEvHnz0Lt3b1y/fh01a9Ys8XGISA2BiEiN9evXCwCEv/76q9g+9vb2gpubm/g8ODhYACCsW7fulccuKCgQcnNzhX///VcAIPz666/ithYt/l979x0fVZ3ucfw7KSQUM0CQQCihIyTURJC2XIpBQBBFwbJ00IguJYKAIAiWWJBVUXBVyurSLgosKruSK1VQETZgAZUekYSQAJPQAkl+9w9u5jomQEBm5kzyeb9evHR+8ztnnpkHZp7Xc875nVtNjRo1THZ2tnMsKyvLhIaGmqt9ZWVlZZmQkBDTvn17k5eXV+ic3NxcEx4ebpo0aWJyc3Ndtq1cubJp27atc6xbt26mevXqxuFwuOzj8ccfN8HBwebEiRPOMUnmsccec5l38OBBI8m88sorLuP5n+3BgweLHLcxxkybNs3lM0hOTjYBAQHmL3/5S4HPoUqVKqZfv37Osfzc/Pd//7fL3B49epiGDRs6Hx8/ftxIMtOmTbtsHAAAlHTp6emmffv2RpKRZAIDA03btm1NQkKCycrKcpkbGRlpOnbseNV95uTkmIsXL5phw4aZFi1auDwnyZQuXdqkpqa6zL/llltMvXr1nGPr1683ksz69eudY7+vH4wxpmPHji4xXen3P79uSUpKco5t27bNSDJ///vfr/ie8rf9fa3Sp08fI8nMmjXLZbx58+amZcuWl91fbm6uuXjxopkxY4YJDQ11qZsiIiKMv7+/+emnn1y2GT9+vLHZbOaHH35wGe/WrVuBz2rQoEEmIiLCZZ4kExYWZjIzM51jqampxs/PzyQkJFzx/QO4Oi7fA3DdzO9Om87Xt2/fAmNpaWmKi4tTjRo1FBAQoMDAQEVEREiS9uzZI0k6c+aMtm/frj59+qhUqVLObcuVK6devXpdNZ6tW7cqMzNTI0eOvOylfj/99JOOHj2qAQMGuJxeX65cOfXt21dfffWVzp49q/Pnz+vzzz/X3XffrTJlyignJ8f5p0ePHjp//ry++uqrq8ZUFEWJuzCfffaZcnJyNHDgQJf4goOD1bFjxwKn7ttstgKfY9OmTXX48OEb8TYAACgxQkNDtXnzZn3zzTd68cUXddddd+nnn3/WpEmT1KRJk0KXJyjM8uXL1a5dO5UrV85ZH82bN89ZG/1Wly5dXNbx9Pf3V//+/bVv3z4dOXLkhr2333vggQdUuXJll+ULZs+erZtvvln9+/cv0j7uvPNOl8eNGjWSJPXs2bPA+O/rknXr1qlr166y2+3y9/dXYGCgpk6dqoyMjAJ3g27atKkaNGjgMrZx40ZFRUUVWEfr92d+XUmnTp100003OR+HhYWpcuXK1FDADUBTCsB1OXPmjDIyMhQeHu4yXqZMGYWEhLiM5eXlKTY2VitWrNCTTz6pzz//XNu2bXM2dc6dOydJOnnypIwxhS6cfqXF1PMdP35ckq64GHhGRoYkFXrnu/DwcOXl5enkyZPKyMhQTk6OZs+ercDAQJc/PXr0kKQiF5w3Iu7C5F/SeOuttxaIcdmyZQXiK1OmjIKDg13GgoKCdP78+T8QPQAAJVdMTIwmTJig5cuX6+jRoxo7dqwOHTpUpMXOV6xYoX79+qlatWr6xz/+oS+//FLffPONhg4dWuhvc5UqVS47ll/fuENQUJAeeeQRLV68WKdOndLx48f13//93xo+fPgVlyb4rYoVK7o8zj/4WNj4b9/7tm3bFBsbK0l69913tWXLFn3zzTeaPHmypP+vIfMVVt9lZGRcd22ZLzQ0tMBYUFBQgdcHcO1YUwrAdfn000+Vm5tb4LbBhZ3p8/3332vXrl1auHChBg0a5Bzft2+fy7wKFSrIZrMVun5UamrqVWPKX/voSkcL84uKlJSUAs8dPXpUfn5+qlChgqRLRyAHDBigxx57rNB91a5d+6oxFUVR4i5MpUqVJEkffvih86wzAADgHYGBgZo2bZr++te/XnVNSEn6xz/+odq1a2vZsmUu9VP+DUh+r7BaKH+ssKbJjfToo4/qxRdf1Pz583X+/Hnl5OQoLi7Ora8pSUuXLlVgYKA++eQTlwNrq1atKnR+YXVoaGjoddeWANyPphSAa5acnKxx48bJbrfrkUceuer8/ALh90fTfnvnPkkqW7asYmJitGrVKs2cOdN5FO306dNFusNJ27ZtZbfb9fbbb+v+++8vtDBp2LChqlWrpsWLF2vcuHHOOWfOnNFHH33kvCOfdOlU7aSkJDVt2tTlcsIbrShxF6Zbt24KCAjQ/v37C71k8nrk54gjfwAAXF5KSkqhZ+XkX3b32zPJL3dGjc1mU6lSpVx+91NTUy97973PP/9cx44dc57hk5ubq2XLlqlu3brXfLb1713t979q1aq67777NGfOHF24cEG9evVyLjzuTjabTQEBAfL393eOnTt3Th988EGR99GxY0fNnDlTu3fvdrmE7/d3LgTgHTSlAFzR999/71yrKC0tTZs3b9aCBQvk7++vlStXFunObLfccovq1q2riRMnyhijihUr6uOPP1ZiYmKBuTNmzFDPnj3VrVs3jR49Wrm5uXrllVdUrlw5nThx4oqvU65cOb366qsaPny4unbtqhEjRigsLEz79u3Trl279Oabb8rPz08vv/yyHnroId1555165JFHlJ2drVdeeUWnTp3Siy++6Nzf66+/rvbt26tDhw569NFHVatWLWVlZWnfvn36+OOPtW7dumv/QK8z7sLUqlVLM2bM0OTJk3XgwAHdcccdqlChgo4dO6Zt27apbNmyzlshF9VNN92kiIgI/fOf/1SXLl1UsWJFVapUqcDtkQEAKMm6deum6tWrq1evXrrllluUl5ennTt36tVXX1W5cuU0evRo59wmTZpo6dKlWrZsmerUqaPg4GA1adJEd955p1asWKGRI0fq3nvv1S+//KJnn31WVatW1d69ewu8ZqVKldS5c2c9/fTTzrvv/fjjjzekuVKU3//Ro0erdevWkqQFCxb84dcsip49e2rWrFl68MEH9fDDDysjI0MzZ84s8mWDkjRmzBjNnz9f3bt314wZMxQWFqbFixfrxx9/lCSXNUYBeB5NKQBXNGTIEEmXrvEvX768GjVqpAkTJmj48OFFakhJl05n//jjjzV69Gg98sgjCggIUNeuXfU///M/BY6y3XHHHfroo480depU9e/fX1WqVNHIkSN19OjRIh0VGzZsmMLDw/XSSy9p+PDhMsaoVq1aLpcNPvjggypbtqwSEhLUv39/+fv767bbbtP69evVtm1b57zGjRvrP//5j5599llNmTJFaWlpKl++vOrXr+9cV+pGKUrchZk0aZIaN26s119/XUuWLFF2draqVKmiW2+99bpPq583b57Gjx+v3r17Kzs7W4MGDdLChQuva18AABRHU6ZM0T//+U/99a9/VUpKirKzs1W1alV17dpVkyZNci7kLUnTp09XSkqKRowYoaysLEVEROjQoUMaMmSI0tLS9Pbbb2v+/PmqU6eOJk6cqCNHjhR6UKl3796KjIzUlClTlJycrLp162rRokVFXmz8aq72+9+qVSvVqlVLpUuXVpcuXW7Ia15N586dNX/+fL300kvq1auXqlWrphEjRqhy5coaNmxYkfYRHh6ujRs3asyYMYqLi1OZMmV09913a8aMGRo0aJDKly/v3jcB4Ips5nK3zwIAi7h48aKaN2+uatWqae3atd4OBwAAoMT59ttv1axZM7311lsaOXKkt8P5wx5++GEtWbJEGRkZbl2mAcCVcaYUAMsZNmyYbr/9dlWtWlWpqal6++23tWfPHr3++uveDg0AAKBE2b9/vw4fPqynnnpKVatW1eDBg70d0jWbMWOGwsPDVadOHedape+9956mTJlCQwrwMppSACwnKytL48aN0/HjxxUYGKiWLVtqzZo16tq1q7dDAwAAKFGeffZZffDBB2rUqJGWL1/uvCGMLwkMDNQrr7yiI0eOKCcnR/Xr19esWbNc1v4C4B1cvgcAAAAAAACP87lbDWzatEm9evVSeHi4bDabVq1addVtNm7cqOjoaAUHB6tOnTp6++233R8oAACAh1EnAQAAX+JzTakzZ86oWbNml71F+u8dPHhQPXr0UIcOHZSUlKSnnnpKo0aN0kcffeTmSAEAADyLOgkAAPgSn758z2azaeXKlerTp89l50yYMEGrV6/Wnj17nGNxcXHatWuXvvzySw9ECQAA4HnUSQAAwOqK/ULnX375pWJjY13GunXrpnnz5unixYsKDAwsdLvs7GxlZ2c7H+fl5enEiRMKDQ2VzWZza8wAAMA7jDHKyspSeHi4/Px87oTya0adBAAAisoddVKxb0qlpqYqLCzMZSwsLEw5OTlKT09X1apVC90uISFB06dP90SIAADAYn755RdVr17d22G4HXUSAAC4VjeyTir2TSlJBY7Y5V+xeKUjeZMmTVJ8fLzzscPhUM2aNXX48GHZ7Xb3BIprYoyRw+GQ3W7nqKyFkBdrIi/WQ06syeFwKCIiQjfddJO3Q/EY6qTiie8YayIv1kRerIecWJM76qRi35SqUqWKUlNTXcbS0tIUEBCg0NDQy24XFBSkoKCgAuN2u51iyyLyi2a+qKyFvFgTebEecmJtJSUn1EnFF98x1kRerIm8WA85sbYbmZNiv1hCmzZtlJiY6DK2du1axcTEXHadBAAAgJKAOgkAAHiTzzWlTp8+rZ07d2rnzp2SLt3KeOfOnUpOTpZ06XTygQMHOufHxcXp8OHDio+P1549ezR//nzNmzdP48aN80b4AAAAbkOdBAAAfInPXb63fft2derUyfk4fz2DQYMGaeHChUpJSXEWXpJUu3ZtrVmzRmPHjtVbb72l8PBwvfHGG+rbt6/HYwcAAHAn6iQAAOBLbCb/Yk1cUWZmpux2u06dOsVaCRbB4nfWRF6sibxYDzmxJofDofLly8vhcCgkJMTb4fgM6iTr4TvGmsiLNZEX6yEn1uSOOsnnLt8DAAAAAACA76MpBQAAAAAAAI+jKeUmtWrVUuXKlXXx4kXn2Lp162Sz2Xxu8dDBgwfrzTffLPS506dPa8yYMapXr56ioqLUqFEjjRs3ThcvXtSGDRtUpkwZtWjRQpGRkYqMjFR8fLxOnjzp4Xdw7Zo3b65z585JupTL77///qrb2Gw2nT59+pqfAwCgpCkpdZKnbd++XQ899JC3w7huR48edVkTrSj106FDh1SpUqVrfg4AYA00pdyoZs2aWr16tfPx/PnzFRMT48WILsnJybkh+zHG6M4779SZM2f03Xff6fvvv9euXbtUr149ZWdnS5IaN26spKQk/fDDD/rqq6909uxZdenSRbm5uTckBnfZuXOnSpcu7e0wAAAotop7neQOV4stJiZGixYt8lA0N154eLjWr1/v7TAAAB5EU8qNhg4dqvnz50u6tCDYV199pTvuuMNlzsyZM9WqVSu1bNlSPXr00C+//CJJ+vzzz9WmTRu1aNFCUVFRWrBggXOb5557To0aNVLz5s3VvHlzHT58WFLBo0mVKlXSoUOHJF06Ivn888+rU6dOGjRokC5evKiJEyeqVatWat68ue6//36dOnVKkvTrr7+qS5cuatq0qe666y6lp6cX+v7WrVunffv26a233nI2cEqVKqW4uDiVK1euwPybbrpJs2fPVkZGhv79738XeH7hwoW69957nY8/+eQT/dd//ZckacOGDWrevLlGjhypZs2aKTIyUtu3b5d0qUDr1q2bYmJiFBkZqYceekhnz56VJDVo0EA7duxw7nPBggW65557JEmzZs3SrbfeqhYtWqhVq1b6+uuvnfMud2TuSttIl/LZrl07NWjQQEuWLCn0c9u7d6969uypW2+9Vc2aNdOcOXMKnQcAQHFW3Ouk/LN0pkyZohYtWuiWW27R9u3b9fDDD6tp06Zq1aqVjh49Kkn67rvv1KFDB7Vs2VKNGzdWQkKCcz+DBw/WqFGjdMcdd6hZs2aSpMmTJ6tevXpq3bq1xo8f72zmbdiwwfn/hw4dUt26dTV16lRFR0erXr16WrNmTaGxPvPMMy5nqL355psaPHiwpEv1Wbdu3fTAAw+oSZMmiomJ0YEDByRJqamp6tSpk6KjoxUZGalRo0bJGKOzZ88qNDRUqampzn1OmzbNeTfI8ePH69Zbb1Xz5s3VsWNH7d271+UzK8zltsk3btw4tW7dWpGRkVq3bl2h+/jmm2/UuXNnxcTEqGXLlvroo48KnQcA8CCDInE4HEaSOXXqVJHmR0REmO+++87ccsst5siRI2bu3Llm4sSJZtq0aeaJJ54wxhizaNEiM2LECJOTk2OMMeb99983vXv3NsYYc+LECed4RkaGiYiIMEePHjUnTpwwdrvdnD171hhjzJkzZ8y5c+eMMcZIMllZWc4YQkNDzcGDB53xPPzwwyYvL88YY8zzzz9vnn32WefcGTNmmFGjRhljjLnnnnvMM888Y4wxZv/+/aZcuXJm9uzZBd7jSy+95Iy3MOvXrzfR0dEFxnv37m1eeumlAuMLFiwwffv2dT7++OOPTceOHZ37CggIMN98840xxpi5c+ea2NhYc/LkSZObm2vS09ONMcbk5eWZuLg488orrzjf52OPPebc55/+9CezevVqY4wxaWlpzvEvv/zSREZGOh//9rPMz2VRtvnt5xYaGmqSk5Nd9peTk2NiYmLMnj17jDGX8tekSROzY8eOy36OviYvL8+cPHnS+XcN1kBerIecWNOpU6eMJONwOLwdik+hTipYJx08eNBIMp988okxxpiXX37Z2O12k5SUZIwx5tFHHzWTJk0yxhiTmZlpzp8/b4wx5uzZs6Z58+bOmmfQoEGmRYsWzthXr15tmjZtak6fPm1yc3PN3Xff7ay3flt7HThwwEgyK1euNMYY869//cs0aNCg0Hz89nM3xpjZs2ebQYMGGWMu1Wd2u90cOnTIGGPMhAkTzMMPP2yMMebcuXPOuHJyckzPnj3N8uXLjTHGjBgxwlmP5eXlmVq1aplvv/3WGGPM8ePHna+1ZMkS07NnT+dnFhoa6nzutzm70jaSzMKFC40xl+qzsLAwc/r0aZf9nTx50rRo0cIcPXrUub+aNWualJSUQj8Td+G735rIi/WQE2tyR50U4JVOWAkyYMAA/f3vf9eqVau0aNEil1OqV61ape3btys6OlqSlJubK39/f0lSRkaGhg0bpp9//lkBAQFKT0/XDz/8oE6dOql+/fr685//rNjYWPXs2VPVq1cvUixDhgxx3k5z1apVyszM1IcffihJunDhgurWrStJWr9+vd544w1JUp06ddSlS5cb82H8H2PMdW3XsGFD59G/Nm3aaObMmc79/fWvf9Wnn36qnJwcORwO/elPf5IkDRo0SC1atNCsWbP0yy+/6Oeff1b37t0lSUlJSXr++eeVkZGhgIAA7d69WxcuXFCpUqUuG8PVthk+fLikS59b+/bttXnzZj344IPO7X/66Sf98MMPuv/++51jWVlZ2r17t1q2bHldnwsAAL6quNdJ5cqVU8+ePSVJLVu2VPXq1dW8eXNJUnR0tBITEyVJ586d08iRI7Vz5075+fnpl19+0c6dO511T79+/Zxnoa9fv179+vVT2bJlJV2qdZ599tlCX79s2bK66667JF2qnfbv31+kz+L32rdvr4iICOd+Zs+eLUnKy8vThAkT9MUXX8gYo7S0NDVv3lz33nuvhgwZohEjRmjcuHFav369QkND1aRJE0nS2rVrNXv2bGVlZSkvL0+ZmZlXjeFK25QqVUoDBgyQJN12222qUqWKdu3apfDwcOecrVu36sCBA846ULpUQ/7000+qUqXKdX0uAIA/jqaUmw0ePFgtW7ZUgwYNVL9+fZfnjDGaMmWKhg4dWmC7uLg49erVSx999JFsNptatmyp8+fPy9/fX1999ZW2bt2qDRs26LbbbtOSJUvUoUMH+fv7u6zVdP78eZd9/vaSOmOM5syZo86dO1/3e4uOjtbs2bOv2sj5rYsXL2rnzp2Ki4sr8FxAQMAV4w8ODnb+v7+/v3NdhcWLF2vjxo3atGmTbrrpJr3xxhvatGmTJKlatWpq2bKlVq9erV27dmnAgAEKCAjQhQsX1LdvX23YsEHR0dHKzMyU3W6/4nu5nm3yi9t8xhhVqlRJO3fuvPqHBQBAMVec6yRJCgoKcv6/v7//ZWuZp556SmFhYUpKSlJAQIDuuecel/h+H9vv64uivv7l1vS83hps1qxZysjI0Ndff63g4GDFx8c7t23Tpo1yc3O1fft2LViwwJnH5ORkjRo1Stu2bVOdOnX07bffXvVzvp5tCqvBmjZt6qwRAQDWwJpSbhYeHq6EhAS99NJLBZ7r3bu35syZoxMnTki61LBJSkqSJJ08eVIRERGy2WzatGmTdu3aJenSWTXHjh1Thw4d9PTTT6t9+/bOberWretc42jFihU6c+bMZePq3bu3Zs2a5Vx76ezZs/rhhx8kSZ07d3au8XDo0CF9/vnnhe6jc+fOql27tkaNGuUsQHJycjRr1qxC12M6ffq0/vKXv6hSpUrq1q1bgefr1q2rXbt26fz588rJydHixYsvG/9vnTx5UqGhobrpppuUlZWlhQsXujyfv2bF+++/71wf4fz587p48aJq1KghSc4jfldSlG1++7l98cUXat++vcvzDRs2VJkyZfT+++87x/bt2+f8OwAAQElSnOuka3Hy5ElVr15dAQEB+umnn5xnUBWmU6dOWr58uc6ePau8vDx98MEHf/j169atq+3btysvL09nz54t8lpLJ0+eVJUqVRQcHKxjx45p+fLlLs8PGTJEb7zxhj799FM98MADki6tH1aqVClVqVJFxpgi3bnwattcuHDBeZbdtm3blJqaqqZNm7rMadu2rfbu3euy3tTOnTt14cKFIr1XAIB7cKaUBwwZMqTQ8QEDBigjI0P/9V//JZvNppycHA0bNkwtWrTQiy++qJEjR+rFF19U48aN1bp1a0mXfpTvvfdenTlzRjabTfXr19egQYMkSa+99poee+wxVa5cWZ06dVJoaOhlY5o4caKmT5+u1q1bO48kTZgwQZGRkXr99dc1cOBALV++XA0aNFDXrl0L3YfNZtOnn36qyZMnKzIyUqVLl5YxRj179nQeUdu9e7eaN2+uixcvyhijbt266fPPP3eefv9bbdq0Ubdu3RQVFaVatWqpRYsWRWrWDBw4UKtXr1bjxo1VrVo1dejQQb/++qvz+bvuukuPPvqo6tevr8aNG0uSQkJCNGPGDLVq1Uo1a9ZU7969r/o6RdkmKChI7dq10/HjxzV79mxnAytfQECAPv74Y40dO1YzZ85Ubm6ubr75Zp++Uw4AAH9Eca2TrsWUKVM0YMAALVq0SLVq1briWUC9e/fW1q1b1axZM4WHh+u2227TyZMn/9Dr9+3bVx9++KEaN26sWrVqqXnz5jp37txVtxs1apTuu+8+NW/eXNWqVSvwWQwYMEA1a9ZU3759VaFCBUlSkyZNdN999ykyMlI1a9bU7bffftXXudo2oaGh2rdvn1q3bq3Tp09r8eLFKlu2rI4fP+6cU6FCBX388ccaP368xo4dq4sXL6pmzZpatWpVET4hAIC72Mz1LvBTwuRfqnXq1CnZ7XZvhwNdOg3b4XDIbrcX+TR2uB95sSbyYj3kxJocDofKly8vh8OhkJAQb4fjM6iTPCsrK0s33XST8vLyNHz4cIWHh+u5555zmcN3jDWRF2siL9ZDTqzJHXUSZ0oBAAAAPmTgwIE6dOiQzp07p5YtW+rJJ5/0dkgAAFwXmlIAAACAD1m5cqW3QwAA4IZgoXMAAAAAAAB4HE0pAAAAAAAAeBxNKQAAAAAAAHgcTSkAAAAAAAB4HE0pAAAAAAAAeBxNKQAAAAAAAHgcTSkAAAAAAAB4HE0pAAAAAAAAeBxNKQAAAAAAAHgcTSkAAAAAAAB4HE0pAAAAAAAAeBxNKQAAAAAAAHgcTSkAAAAAAAB4HE0pAAAAAAAAeBxNKQAAAAAAAHgcTSkAAAAAAAB4nM82pebMmaPatWsrODhY0dHR2rx58xXnL1q0SM2aNVOZMmVUtWpVDRkyRBkZGR6KFgAAwHOokwAAgC/wyabUsmXLNGbMGE2ePFlJSUnq0KGDunfvruTk5ELnf/HFFxo4cKCGDRumH374QcuXL9c333yj4cOHezhyAAAA96JOAgAAvsInm1KzZs3SsGHDNHz4cDVq1EivvfaaatSooblz5xY6/6uvvlKtWrU0atQo1a5dW+3bt9cjjzyi7du3ezhyAAAA96JOAgAAviLA2wFcqwsXLmjHjh2aOHGiy3hsbKy2bt1a6DZt27bV5MmTtWbNGnXv3l1paWn68MMP1bNnz8u+TnZ2trK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+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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27fHjx2zevBkTE5NknzJlygC8VS4HIYQQQmS9du3aYWdnp5+2tnXrVh4/fpwswXl6f+/nz58/Q3V6+vQpoEuA/iparZZGjRrx+++/M3ToUHbv3s2JEyc4duwYoEufkF5lypShUqVK+rbQaDQsW7aMVq1a4eDgAOjaAmDIkCEp2qNv377Aq5+DXj5rpdY+7u7uaLVawsLCCAsLQ6PRvPb+30avXr2wsLBgzpw5gC5FhIWFBb169crU6wiRWxm/uYgQQrxbFi5ciKIorF27lrVr16bY/+uvvzJ69GjUarXB6mBubg5AfHw8ZmZm+u3/fWB6GXgKCQmhQIEC+u1JSUn6h6iXnJycKFeuHD/99FOq13R3d8+UugshhBDCsCwsLOjcuTO//PILjx49YuHChdjY2OgXXIH0/97P6Kibl6ODHjx4gIeHR6plLl26xPnz51m8eDHdu3fXb79582aGrt2zZ0/69u3L1atXuX37No8ePaJnz576/U5OTgB88803vPfee6meo0SJEqluf/ms9ejRoxT7Hj58iJGREfny5UOlUqFWq3nw4EGG7uW/7Ozs6N69O/Pnz2fIkCEsWrSILl26YG9vn6nXESK3kqCUECJH0Wg0/Prrr3h7ezN//vwU+7ds2cKkSZPYtm0bLVq0SLH/ZQAprW/6zMzMUi37cpW+Cxcu6Fe+Adi8eXOyci9X61u+fDn+/v767atXr06xol6LFi3YunUr3t7e5MuXL031E0IIIcS7qXfv3syZM4cJEyawdetWevTogaWlpX5/ZvzeT89zTaNGjVCr1cyePZtq1aqlWuZl4OvfL9wA5s6d+1b1e6lz58588cUXLF68mNu3b1OgQAEaNWqk31+iRAmKFSvG+fPn9YvSpFWJEiUoUKAAK1asYMiQIfp7ePHiBevWrdOvyAdQu3Zt1qxZw08//aQPhKXFv9vZwsIixf4BAwYwa9Ys2rVrR3h4OJ999lm67kGIvEyCUkKIHGXbtm08fPiQcePG6QM+/1a2bFlmzJjBggULUg1KlS1bFoB58+ZhY2ODubk5Xl5eKabSveTj48O+ffvYvHkz+fPnx8bGhhIlStCsWTMcHBzo3bs3o0aNwtjYmMWLF3P//v1kx5cqVYquXbsydepUTExMaNCgAZcuXWLixInY2tomKztq1CiCgoIICAhgwIABlChRgri4OIKDg9m6dStz5szJ9CHnQgghhDCMihUrUq5cOaZOnYqiKMmm7kHm/N739vbGwsKC5cuXU6pUKaytrXF3d091dHXhwoX59ttv+fHHH4mNjaVz587Y2dlx5coVnj17xsiRIylZsiTe3t58/fXXKIqCg4MDmzdvJigoKENtYW9vT5s2bVi8eDHh4eEMGTIkWf4n0AW+mjZtSuPGjenRowcFChQgNDSUq1evcubMmVeusGxkZMT48eN5//33adGiBX369CE+Pp4JEyYQHh7O//73P33ZyZMnU6NGDapUqcLXX39N0aJFefz4MZs2bWLu3LnY2Nikeg0fHx8Axo0bR9OmTVGr1ZQrVw5TU1MAihcvTpMmTdi2bRs1atSgfPnyGWovIfKU7M2zLoQQ6dO6dWvF1NT0taumdOrUSTE2NlZCQkJSrL6nKIoydepUxcvLS1Gr1clWrElt9b1z584p1atXVywtLRVAqV27tn7fiRMnlICAAMXKykopUKCAMnz4cGX+/PkpVqKJj49XBg8erLi4uCjm5uZK1apVlaNHjyqenp4pVvZ7+vSpMmDAAMXLy0sxMTFRHBwcFH9/f2XYsGFKdHT0W7SYEEIIIbLLtGnTFEApXbp0qvvT8nv/5ep7EyZMSPUcK1euVEqWLKmYmJgke+551UrBS5YsUSpVqqSYm5sr1tbWip+fX7LV+65cuaI0bNhQsbGxUfLly6e0b99euXfvXopnqrSuvvfSzp07FSDZCoT/df78eaVDhw6Ki4uLYmJiori5uSn16tXTr7asKKmvjqcoirJhwwalSpUqirm5uWJlZaXUr19fOXz4cIprXLlyRWnfvr3i6OiomJqaKoUKFVJ69OihxMXFvfL88fHxyocffqg4OzsrKpUq1ftevHixAiirVq1KU3sIIXRUiqIoWR8KE0IIIYQQQgghcoeXK/0FBwdnaJVEIfIamb4nhBBCCCGEEEKkU3x8PGfOnOHEiROsX7+eyZMnS0BKiHSSkVJCCCGEEEIIIUQ6BQcH4+Xlha2tLV26dGHGjBkGXf1ZiNxIglJCCCGEEEIIIYQQIssZvbmIEEIIIYQQQgghhBCZS4JSQgghhBBCCCGEECLLSaLzNNJqtTx8+BAbGxtUKlV2V0cIIYQQ2URRFKKionB3d8fISN7v/Zs8LwkhhBAC0v68JEGpNHr48CEeHh7ZXQ0hhBBCvCPu379PwYIFs7sa7xR5XhJCCCHEv73peUmCUmlkY2MD6BrU1tY2U8+tKAoRERHY2dnJW0UDkTY2LGlfw5L2NSxpX8PLbW0cGRmJh4eH/tlA/EOel3I2aWPDkvY1LGlfw5L2Nbzc1sZpfV6SoFQavfxS2NraGuQhS1EUbG1tc8WX710kbWxY0r6GJe1rWNK+hpdb2zg33UtmkeelnE3a2LCkfQ1L2tewpH0NL7e28ZvuRRIhCCGEEEIIIYQQQogsJyOlMoFGoyExMfGtj1cUhYSEBOLi4nJVRPRNTExMUKvV2V0NIYQQQgghhBB5XEb79RmVk+MCGenbS1Aqg6Kjo3nw4AGKomToPFqtlufPn2dSrXIGlUpFwYIFsba2zu6qCCGEEEIIIYTIozKrX59ROTUukJG+vQSlMkCj0fDgwQMsLS1xdnZ+62imoihoNBrUanWOi4i+LUVRePr0KQ8ePKBYsWIyYkoIIYQQQgghRJbLrH59RuXUuEBG+/YSlMqAxMREFEXB2dkZCwuLtz5PTv3yZZSzszPBwcEkJiZKUEoIIcRbURQlT/3uFEIIIUTmyqx+fUbl5LhARvr2kug8E+S0L8y7QtpNCCFERhw4cIAKFSpw7Nix7K6KENkqPDycr776ihkzZjB37lxCQkJSlElMTGT27NmcOXOGHTt2oNFosqGmQgjx7pL+6dvLSNtJUEoIIYQQOUpiYiKDBg2idu3anDt3juHDh2d3lYTIFoqi8OWXX5I/f37Gjx9P//79+eSTT2jcuDEJCQnJys6YMYO+ffvi7+9PkyZNGDBgwCvPu379ej7++GMePXqU7flVhBBC5G4SlMrhjI2N8fX1pUyZMrRs2ZLw8PB0n2PEiBHMmDEjTWWDg4NZvXq1/s/79u3D3t4eX19fSpUqxciRI9N9fSGEECKtoqKiCAwMZOrUqQB8/PHHLF++PHsrJUQ22b59OxMnTiQuLi7Z9gsXLuDt7c3ly5dRFIWZM2fyxRdfJCsza9YsWrVqRUxMjH7bvn378Pb2pm3btvzyyy+4u7tTrlw5oqKisuR+hBAirzI2NsbPz4/y5csTGBiYp/r1EpTK4ezt7Tl37hyXL1/G3t6emTNnGvR6//3yAjRo0IBz585x6tQpli1bxunTpw1aByGEENlLo9EQExPD06dPuXXrFufOnePq1aupjqjQarU8fPiQBw8e8PDhQx4/fsyzZ88ICwsjKioqTVOIEhISuHz5MsuXLycgIIDt27djaWnJ+vXrmTt3Lk5OToa4TSHeaSdPnqR3794AfPDBByQmJnLz5k3Wrl2LhYUFDx48oGzZspiamvLZZ58BuukVixYtol+/fgBs2rSJpUuXAhAXF0f37t0JDQ1Ndp1Lly7xwQcfpNguhBAi89jb23P27FnOnz+f5/r1kug8kyiKQmzi283N/yehWcpkrRYmaU9yVr16dc6fPw/A7du36dWrF6GhoRQuXJjFixfj4ODAtWvX+OSTTwgLC8PMzIzdu3cnO8fYsWO5du0aixYtYuzYsWzYsIH4+Hj69u3LJ598wrBhw7h8+TK+vr58/vnneHl56Y+1srLC39+fW7du8fnnn7Nw4UKKFy+OoiiULFmS48ePY29v/1ZtJIQQInNpNBpCQ0N59uyZ/hMaGsqLFy+Iiori+fPnPH/+nNDQ0GT/HR4eTlJSUqrnrFu3LuvXr8fOzo6kpCTGjBnD1KlTCQsLe21drKyssLKyQqvVotVq0Wg0+p8ajYbExES0Wq2+vIuLC1u2bKFSpUqZ2iZC5BR//fUX3bt359GjR5QsWZIpU6ZgbGyMt7c33t7eVKlShfLlyxMaGprs7+u8efPo0aMHPXr0wNHRkVGjRvHJJ59w+PBh1q1bpx819e2339KvXz82bdrEp59+yoYNGwgNDWX//v3ZdctCCJElMtKvf5O09u0DAgK4cOECkDf69RKUyiSxiRpK/7Aj0897ZVRjLE3f/L9Jo9EQFBREr169ABgwYAB9+/alQ4cOjBs3jhEjRvDzzz/TtWtXxo4dS8OGDYmKikq2usCYMWO4fv06ixYtYufOnTx58oSTJ0+SkJBAjRo1aNGiBT/99BMzZsxg7dq1gG6Y30vPnz/n2LFjfP/99/To0YMlS5YwevRo9u7di6+vrwSkhBDCgBRFITo6mocPH3L37l0ePHiQLOD078/z588JCwvLlFwx1tbWWFtbExISwt69e5kyZQojRoygb9++/PLLLwCo1WrUarU+yPRfL1684MWLF6+9jo2NDaVLl6Z69ep8/fXXODs7Z7juQuQk0dHR9OvXjytXrnDq1CkALCwsOHLkCPny5UtWtmDBgmzbto0//viD58+f4+rqynfffZesM9S3b1/GjBlDUlKSfrSUWq1m4cKFdOvWDZVKxSeffIKDgwMdO3bkwIEDXL16lVKlSmXdTQshRBYzVL8e0ta312g07Nq1K0/16yUolcOFh4fj6+vLgwcPKFOmDI0bNwZ0Q7o3b94MQLdu3WjevDmRkZGEh4fTsGFDQPeA/9K8efMoWbIkq1atwsjIiKCgIDZv3qx/IxYREcGtW7dSrcOuXbvw8/PDyMiIb775hjJlylCoUCGqVKnCjz/+yK+//kr37t0N2QxCCJGrRUdH8+jRI0JCQvRBp3v37hESEkJISIh+379zw6RVvnz5cHR0xMnJCQcHB2xsbLC2tsbR0RFHR0ccHBz0/+3o6Ii9vT3m5uaYmZlhbm6OsbHuUWLOnDl8+umnbNy4kSZNmvDLL79gZGTE3Llz+eCDD4iJicHOzg6VSqUfBZWUlMSLFy+IjIwkOjoaIyMjfQDr3/9tamqKq6urrIoj8rR58+axZMmSZNu6deuWIiD1UuXKlalcufIrz+fq6sqqVauYO3cuQUFBFClShJMnT2JklDy7R4cOHVi6dClbtmxh7ty5+nxuQgghMk94eDh+fn55sl8vQalMYmGi5sqoxm917D/T91IO57MwUb/22Jc5pWJiYmjYsCGzZs1iwIAByc6jKP9MC3zVA3358uU5f/48T548wc3NDUVRGDFiBB988EGycv+OoL7UoEEDfYT1JRsbG/z9/dm6dSuHDx9m4cKFr70PIYTIqxRF4fnz5wQHB3P37l3u3LnDrVu3uHXrFsHBwTx69Ijo6Og0n8/GxgZPT088PDxwdnbGyckJJycnfeDp3x8HBwd9UCmj2rVrR79+/Th37hzDhg0DoGvXrnz44YcpRmQZGRlhZGSEiYkJFhYWkhNKiDdITExk3rx5ybZ99NFHjBgxIkPnbdu2LW3btuXu3bs4OjpiZWVFREREinIffvghW7ZsYdq0aVSpUoXOnTtn6LpCCPGuyki/Pi3nfpWXOaWioqJo2rRpnurXS1Aqk6hUqjRNs0uNLiilSjUolVaWlpZMmzaNtm3b0rdvXypWrMi6deto164dK1asoGbNmtja2pIvXz6CgoJSDPOrUqUKXbt2pWXLlgQFBdGgQQPGjRtH+/btsbCw4Pr163h6emJjY5PmFVh69OhBt27d6NKlC2r164NrQgiRm8XHx3Pr1i1u375NcHCwPuj0MvCUlhFOlpaW5M+fn/z58+Pp6UmhQoVwd3fHzc2N/Pnz4+bmhqurK9bW1llwRyk5OTlRq1Yt9u3bx549ewDo06dPttRFiNxEq9XSrFkzrl+/DsDXX39Nt27dKF26dKZdw9PTE+CVU3oDAwMZOHAgU6dOZfbs2RKUEkLkWhnp12cGS0tLpk6dSrt27fJMv16CUrlIxYoV8fHxYd26dfz888/07NmTUaNG4enpya+//grA0qVL+fjjjxkyZAjm5ubJEqI1btyYsLAwWrVqxfbt27l06RKVK1dGURRcXFzYvHkz5cqVIzExMdWEaP9Vr149NBpNiqisEELkRomJidy+fZvbt29z584dbty4wY0bN7h+/TrBwcHJEnWnxs3NDS8vLzw9PfXJir28vHB3dyd//vxYW1u/89PXBg0apH/zVqtWLapV8oeYUEiIxij0MbxQQ8ILSIzR/fz3fyfGQFIcaBIhKV73UxMPmoR/bUv4Z7tWA4oCivbVn+oDoNKH2dsoQmTQrl272LVrF5aWlqxZs4ZmzZpleR1UKpU+KHX48GGeP3+Oo6NjltdDCCHygrzWr1cpmZHl9C3Nnj2b2bNnExwcDECZMmX44YcfaNq0KaB7WzNy5EjmzZtHWFgYVapUYebMmZQpU0Z/jvj4eIYMGcLKlSuJjY2lfv36zJo1i4IFC+rLhIWFMWDAADZt2gTo3vZMnz49XQm6IiMjsbOzIyIiAltbW0C3dO6dO3fw8vLC3Nz8rdvhddP3crKbN2/SpUsXTpw4ker+zGq/tFAUhYiICH0+E5G5pH0NS9rXsNLbvtHR0Vy7do1r165x9epVrl69yrVr17h58yaJiYmvPM7W1hZvb+9kQSdvb28KFy5MoUKFDP7v4FtTFIiLgJjn8OIpvHgGsWG6bXHhf/+MgNhwlNhwao49yoW/YjjWx4HS+RKyt+71f4CagzP9tKk9EwgdQ7ZNXv23sE2bNmzYsIEBAwYwbdo0g17rTW1crlw5Ll68yIIFC/RJeEXa5dXvcFaR9jWs3Ny+WdkvfZ13NS7wpn49pN6GaX0myNaRUgULFuR///sfRYsWBeDXX3+lVatWnD17ljJlyjB+/HgmT57M4sWLKV68OKNHj6Zhw4Zcv35dn8xr4MCBbN68mVWrVuHo6MjgwYNp0aIFp0+f1g8t69KlCw8ePGD79u0AfPzxx3Tr1k2fMExkvvnz5zNq1Cj9yktCCJHTREREcO7cOX3g6WXw6f79+688xsrKSh94KlasGCVKlNB/3qlE3YqiCzJFPYKoEIh8CC+ewIu/A08xz3T/HfNMF4TSvjrY9m8qYHdnFXFJVtiZ/xOQUtSmKMYWqMxsUJlagoklmFrDf//b2AzUZqA2RVGboDUyRaMyQaM2IQljNCoTklQmaFUmaFXGaFUqFHQfLUYoKhWO1hZYm5uCyghsCxioAYXIGs+ePWPLli3AuzEdtnPnzly8eJHvvvuOtm3bYmdnl91VEkIIYUBZ0a/P1pFSqXFwcGDChAn06tULd3d3Bg4cyFdffQXoRkW5uroybtw4+vTpQ0REBM7OzixdupSOHTsC8PDhQzw8PNi6dSuNGzfm6tWrlC5dmmPHjlGlShUAjh07RrVq1bh27RolSpRItR7x8fHEx8fr/xwZGYmHhwfh4eHJRkoFBwdTuHDhDEdUX0ZE85LMbL83yc2R/XeBtK9hSfsaVlJSEufOnSM4OJhLly5x6dIlLl68yJ9//vnKY1xcXChVqhQlS5akZMmSlCpVilKlSlGgQIEUK1dlOU0iRP4FEfd1waaXQafoEIh8pAtERT9GpUnfKCbF1BqsnMDSCSwdwNwOzO1IMLElTm1NjJENL1SWRGJFhGJFuNaSMK0FkRpzopNURLyIQYMxsYkaYhM0xCRqiPv7Z2yChvgkDYkahSSNlkSNQoLm9dMdX2V6Zz9alMv/VsemVWRkJPb29jJSKhUyUipzzZs3jz59+uDn58eZM2cMfr03tXFcXBw+Pj7cvHmTX375hQ8/lOmx6ZEXv8NZSdrXsHJz+8pIqYzLsSOl/k2j0bBmzRpevHhBtWrVuHPnDiEhITRq1EhfxszMjNq1a3PkyBH69OnD6dOnSUxMTFbG3d2dsmXLcuTIERo3bszRo0exs7PTB6QAqlatip2dHUeOHHllUGrs2LGMHDkyxfaIiAh9EsiEhAT9stYajSZD9/+mXCO5kUajQavVEhUVlSwAaAiKouhXr8ppf8FzAmlfw5L2zTyxsbFcvXqVS5cucfbsWc6fP8+1a9eIjY1NtbyHhwelSpWiePHiyT6vWoI9rQkjMyQpDqOoRxhFPsAo6oHuZ+RfGEX9hVHkA1TRIaiUtP1O0Vo6obVyQbFyJcnSmVhje6LV9kSobAnFjqeKDY811jxMsOZZvBFhMYlERCUR/SyJqHgN0fFJaFN9tZUIRPz9yTzGRiqMjVQYGYGRSoUK3U8jFfD3z6T42FRXD8tMkZGRBj2/EC+9XAXp5ctXg4v8C+NbRyD2IcSG6oLcxuZgXwicimHuWpYOHTowZswYjh49KkEpIYQQGZbtQamLFy9SrVo14uLisLa2Zv369ZQuXZojR44A4Orqmqy8q6srd+/eBSAkJARTU9MUnQNXV1dCQkL0ZVxcXFJc18XFRV8mNd988w1ffPGF/s8vR0rZ2dklGyn1/Plz1Gp1poxyymsjpdRqNUZGRtjY2GTJSCkgV0b23wXSvoYl7ft2Xk6/O3v2LKdOneLMmTPcuHEj1ZcA5ubmlClTBh8fH8qWLYuPjw9+fn44OTllQ83RJf4OuwPPb0HoHQi9BaG3IfQ2qqhHbzxcUZuBXUGwK0CSpRvRpk6EGzvxFAceae24m2hPcJw1D6M1PI6M59nTeKLikl5VGSD0tdczNlJha2GCrbkxNubG2JqbYGNujLW5MRYmxliYGGGkTcLexhJLU2MsTNW6j4kaS1M15iZqzIyNMFUbYWJshLGRChO1ESbqlz+N/g5GvRvff/l7KLKCVqvl+PHjADRp0sRwF4p+CmeXwMW1qJ5c4bXrd6qMqBpRCIBjhw8Yrk5CCCHyjGwPSpUoUYJz584RHh7OunXr6N69O/v379fv/++Dn6Iob3wY/G+Z1Mq/6TxmZmaYmZml2K5SqfTH/ftnRh5Q/z2DMi896GZW+6Xnell1rbxI2tewpH1f78WLF1y8eJFTp05x7Ngxjh07xq1bt1It6+TkRPny5fH396dixYqUK1cOJycnHBwcsrZ9NYm6gNPTa/D85t+Bp78DUdGvfmkC6PIw2RdCY1uQFxYFCDNx5SFO3NU4cSPOnuvRFjyKTODJ7Xii41MLNsX+/UlOpYJ8lqY4WJni8PfPfFamOP7rp52lCXZ/B6B0wScTzE2MXtt2uW3If264B/Huu337NpGRkZiZmVG6dOnMv0BsGBycBMfn6Va0BDQYcUPx4Lq2AM8UOxIwwYJ4vIyeUNL4IW7ax1S1uA3Ales3CZ/ZEPtafaB0K1CbZH4dhRBC5HrZHpQyNTXVJzqvWLEiJ0+eZNq0afo8UiEhIeTP/09uiCdPnuhHT7m5uZGQkEBYWFiy0VJPnjwhICBAX+bx48cprvv06dMUo7CEEEK8+7RaLdevX+fw4cMcOXKEY8eOcf369VRHQBUqVAg/Pz8qVqxIhQoV8PX1JX/+/MmCCi8DJgaTGAfP/4Sn13WfZ3//fH7r9QnELfKhOHgTY12Ip6YFeaDKz80kZ67GOXI90pS/wuN4ej+1qc9xf3/+YWWqxtXWHGcbM1xszXG1McPF1uyfbTZmOFiZYWdhgvodGY0kRF539uxZQLfinYlJJgd8buxA2dQfVbTuGfmctgjLNQ0I0vgTjg0qFdiYGaNSqYiKS0SbBCSAG8+paXqRgg4zeRAay8agQ3R/egJ2fg+VPwL/Hrqcc0IIIUQaZXtQ6r8URSE+Ph4vLy/c3NwICgrCz88P0OVw2r9/P+PGjQPA398fExMTgoKC6NChAwCPHj3i0qVLjB8/HoBq1aoRERHBiRMnqFy5MgDHjx8nIiJCH7jKyYyNjfHx8SE+Ph4LCws++eQTPvroI/1+RVHw9PSkU6dOjB8/nmHDhvHHH3+QkJBAcHAwxYsXB2DKlCmEh4czYsQIkpKSUKlU9O3bl759+6LVavn+++/57bffAKhQoQKzZ8/G0dGR4OBgvLy8GDduHEOHDgV07Vu1alUWLVpEjx49srxNhBC5y/Pnz9m1axdnz57l7NmznDx5krCwsBTl3Nzc8PPzo2rVqlStWhV/f38cHR2zrqKaJN1op5CL8PgyPLmqC0CFBcOrcjyZWKF1Kk60jRdPTQpyT+XGjQRnzr1w4Gq4mgd3YklKkbQp+SgncxMjCthb4G5vQcF8FrjbWVAgnwX57SxwtdUFoazN3rlf90KIN3iZ2LxChQqZd1KtFvb8CIcmowJuat0ZnfQ+h1V+1CnhyrelXCjuYExpT1dMjXVpJTRahb/CYjn/IJzTd8MIulKQaJ+nsP9XvjlqQaCvNfmiHsLukbB/PPh2hiqfgHPqeVuFEEKklJf79dn6lPrtt9/StGlTPDw8iIqKYtWqVezbt4/t27ejUqkYOHAgY8aMoVixYhQrVowxY8ZgaWlJly5dAF1+ld69ezN48GAcHR1xcHBgyJAh+Pj40KBBAwBKlSpFkyZN+Oijj5g7dy4AH3/8MS1atHhlkvOcxN7eXv8m7e7du7Rp0wZFUfj4448BOHr0KPnz5+f3339n/Pjx/PTTT/z0008EBwfTrl07Tp06BejexvXp04cdO3bg5eVFbGys/ss6depUrly5wpUrVzA1NWXChAn07NmTTZs2AVCsWDE2bdqk//KuXr0aHx+frG4KIUQuoNVquXbtGocOHeLQoUMcOXKE27dv89+FYi0sLKhcuTLVq1enWrVqVKxYETc3t6yraGyYLvAUcgke/ysIlRSXenlzOxIdihNmVYQHxoW4lpSfky9cORVqzoPgeFKug/vPCChTYyM8HSzxdLTEw8GSgvksKWBvTgF7SwrksyCfpYlMJ8uhgoODOXjwIMHBwcTExODs7Iyfnx/VqlXL1tV/xLvhZT6pTAtKJbyA9X3g6mYAFiU1ZrLyPp2qF2NczSK42prrR46aqP9ZRVRtpKKQoyWFHC1pWd6d4S1Lc6h5EepV/I1HT8Lwvfc97dxC6GO2neJJwXBqoe5TtAFU/RS86+vmBgshhHgle3t7zpw5g0aj4cGDB7z33nt5pl+frUGpx48f061bNx49eoSdnR3lypVj+/btNGzYEIChQ4cSGxtL3759CQsLo0qVKuzcuRMbGxv9OaZMmYKxsTEdOnQgNjaW+vXrs3jx4mRJw5cvX86AAQP0q/QFBgYyY8aMzL0ZRYHEmLc/VqMBtTrlL20TyzT/Ivf09GTSpEkMGjRI/+X97bff+OSTT1ixYgXHjx9Ptgrhv02aNIlhw4bh5eUF6Dp8L6OhkyZN4siRI5iamgIwePBg5syZw59//omJiQm2trY4OTlx+/ZtvLy82LdvH/Xq1XuLhhBC5DXx8fGcOnWKw4cPc+jQIQ4fPkxoaMqk2uXKlSMgIABfX1/8/f0pX7585k9neZXIR/DwrO4TckEXiIp8kGpRxcSKRKeSPLMsSrC6MJeT8nMiypnTz00JvZ3aVD1d8MnazBhPR0sKO1pRyNGSwo6WFHKworCTJa425u9Mgm+ROVasWMHPP//MiRMncHFxoUCBAlhYWBAaGsqtW7cwNzfn/fff56uvvsLT0zO7qyuyQXx8PEePHgWgZs2aGT9hwguUpe+hun+MBEXN14kf8bBwa/5oW55CjpbpOpVKpaKmT2Hq163Njh078Ld4zn7LhqyLrklVo6t8ZLKdeqrTqG7ugpu7wKmELjhVriOYpu9aQgiR5TLSr3+TNPbt81q/PluDUgsWLHjtfpVKxYgRIxgxYsQry5ibmzN9+nSmT5/+yjIODg4sW7bsbauZNokxMMb9rQ5V8Zr/Ed8+BFOrNJ+rQoUKXL9+HdAN8du8eTMjR44kMTGR33777ZVf3itXrvDll1+m2B4ZGUlsbGyyh2IjIyPKlSvH1atXKVeuHADt27dn9erV1K5dG39/f4yNZaqIECIlrVbLhQsX2LdvHzt37mTfvn3ExiZPuG1hYUHVqlWpUaMG1atXp0KFCjg7O2dNBaOf/B2AOvdPIOoVSce1doWItCvOA1NvLmk8OBKdn/1PrYi4k9pUPV1AqoC9BUVdrPF2tqaoi+5TxNkKRytTGe2UR1SoUAEjIyN69OjB6tWrKVSoULL9L4MRq1atomLFisyaNYv27dtnU21Fdjl58iRxcXG4uLhQsmTJjJ0sMRbtik4Y3T9GhGLJhwlDqFa3BRMaFM9QDrm6deuyY8cOlIeXOTxtJBvPPWTBQVt6Py5NIdVjeqp30NlkP+bPrsOWgbrpff49dbmnbN/umVkIIQwuA/36N0pH3z4v9eslcpDL/HuKy8GDByldujT29va89957VKpUiUmTJqVrNcK0rHYIutFnTZo04fHjx3To0IHt27dn7EaEELmCRqPRB6H27dvHwYMHU+SDcnZ2pkaNGvqPn59f1oyCiouEv07Bg9NY3jsJTy9B5F8piikqIxIdSvDEphQ3jLw5HefOvnAXrjxRoaRYR0OL2khFEScrfdDpZRCqiLMVlqbyazev+/HHH2nevPkr95uZmVGnTh3q1KnD6NGjuXPnThbWTrwr9u3bB0CtWrUyFrDWJKGs7o5R8AGiFXN6a76hW8e2tPItkOE61qlTB4D9+/djrIIOFT1o71+QA38+Y/7B24z805XJSe1or95PH/MgXGND4NBkOPIzlGmjGz1VwD/D9RBCiNwoL/Xr5ek4s5hY6iKfb0FRFDQaDWq1OuUXxSR9w5zPnTunf6O2evVqjh07RuHChQHddMkjR45QvXr1FMeVLl2as2fP6iOkL9nZ2WFubk5wcLD+PFqtlvPnz1OqVCl9ORsbG1xcXNi6dSsTJ06UoJQQeZRGo+HcuXPs27eP/fv3c+DAgRQr21lbW1OzZk3q1q1L06ZNKVOmjOFHCSkKPPsTHpyA+yfgwUldDigUVIDpy2KoSMxXlCc2pbiq8uZwjAfbnrrw+C+jVE/rbGNGSTcbSuW3paSbDSXcbCjqYo2ZsTrV8kK8LiD1X05OTjg5ORmwNuJdtW7dOgB96om3pez4FtWfO4hVTPk46Us+7tKBRmUyJ/+ev78/+fLlIywsjOPHjxMQEIBKpaJ2cWdqF3fmWkgk8w/eYek5Kxa/aExDo9N8ar4TX+1luLhG9/GoogtOlWwJamOOHDnCqVOn6NOnD2ZmZplSTyGESJcM9OvTdO40ykv9eglKZRaVKl3T7JJ5XU6pdLh//z5Dhgzhs88+Q6vVsmHDBq5fv65ffWrevHmsXr061S/v4MGD6dy5M7Vr16Zw4cLExcWxePFiPvnkEwYNGsSgQYP47bffMDU1ZdKkSZQpU4ZixYoRHBysP8ewYcO4d+9esnxeQojc7/Hjx2zbto0tW7awa9euFEEoGxsbatasqR/94efnZ/ihwPFR8NdpuH/yn0BUXHiKYom2hXhiW5aL2sKcSCjC1mfOhDwygUfJy5moVRR3taF0fltK/isA5WQtnSbx9s6cOYOJiYk+iejGjRtZtGgRpUuXZsSIEfqcDyJvuXHjBufOncPY2Jj33nvv7U90ZimqE7pFfgYl9eP9ju9nWkAKdCtFNW7cmFWrVvHHH3+kWNW6pJstE9uXZ2jjEiw5epdlx83YEVOJMqo79DHbQXPVEdT3jxN35xi/B9sy6aSKMzd0+fp+/PFHvvjiC7755ptMq68QQqRJRvr1mSSv9eslKJXDhYeH4+vrS0JCAhYWFnz66af07t2bvXv3UqxYsWTLobdq1Yoff/yRKVOmpDhPhQoVGDNmDIGBgSQlJWFsbEzfvn0BGDRoEM+ePaNUqVKoVCp8fX1ZtGhRinNUrFiRihUrGu5mhRDvhLCwMPbv38+ePXvYu3cvly5dSrbf1taWWrVqUbt2berUqYOvr6/hg1AvnsHdI39/DulWw1OS53ZSjM2JzOfDn2alOBJfhI3P3Ln1xBqeJD+VqdqIkvltKFvADp+/P8VdbTA1Tn20lBBvq0+fPnz99df4+Phw+/ZtOnXqRJs2bVizZg0xMTFMnTo1u6sossGSJUsAaNCgQbLnuHR5eh3NH0NQA5MS21ErsBfNy+XPvEr+rXnz5qxatYotW7bw008/pVrGxdacIY1L0LeuN+vO/MWCg5b0f1KQXjsiMX54ntDwSBI1UcmOefbsGd9++y2tW7dO9gZfCCFyq/DwcPz8/PJkv16l/HedbZGqyMhI7OzsiIiIwNbWFoC4uDju3LmDl5dXhpZufu30vVwss9ovLV4ucWxnZ5en2jirSPsaVna3r6IoXLp0ic2bN7NlyxaOHTvGf391+Pv707x5c5o3b46/v7/h36xEPoK7h//+HIGn11IUSbQuyEMbH85RnKDIQuwMdSZBSR4cMzU20o16craggpezBKAMJLu/w5kttWeCt2FnZ8eZM2fw9vZm3Lhx7Nmzhx07dnD48GE6derE/fv3M7HWWSOz2iY1ue17lJr4+Hg8PDx4+vQpa9eupW3btuk/SWIcsXPqYvH8Cgc0PuytNJvhgWlb0ju9bfzkyRNcXV0BePr06Runm2q1Wjp27MTatWuSbTc2MaZnZXuGVojj6QstAQt1K19N+uZTvvhpZoZmErxL8sJ3ODtJ+xpWbm7frOyXvk5Ojguk1oZpfSaQkVJCCCFSeP78Obt27SIoKIigoCDu3buXbH/JkiWpV68e9erVo3bt2obPexN+H4IP6UZB3T0CobdTFHlhX5w/zctxMKEYvz/35M4zW3iWvEwhB0v8Ctnj52GPX6F8lMpvi4lalWsfssS7TVEUtFrdiL5du3bRokULADw8PHj27NnrDhW51MaNG3n69CkFCxakVatWb3WOxJ3DsXh+hWeKLWsKfceU5mUyuZb/cHFxoUyZMly+fJkDBw68drrh8ePH6dWrF1euXNFvcy9YiNJNuhLsEsBOjUKU0RU+ddzBpEZHGLwzjhmz59Db8SR2dfuDT3swsTDYvQghhMgeEpQSQgiBoihcuXKFLVu2sHnzZo4eParvLAOYm5tTv359WrZsSfPmzSlYsKBhKxQTCsEH4fZ+uL0PQm8lr6/KiCj7Ulwz9WF3bFF+f+bB0xCbZGUsTdWUL2ivC0IVyoevhz3ONilzQMmAYZFdKlasyOjRo2nQoAH79+9n9uzZANy5c0c/+kTkLatWrQKgW7dubzft+d4xTE7OAeAnk/6M6lIPY7VhR37WqVOHy5cvs3fv3mRBKUVRmDx5Mu7u7iQlJdGvXz+ionTT9Lp160a3bt1o2LAhAKEvElh54h5Lj1rwQWQZXArXw0j1PXfCFSr8dJqv9/WhfcXvsa/5EVT6EGwyLzeWEEKI7CVBKSGEyKPi4uLYs2cPGzduZNu2bSmmCpUtW5ZGjRrRoEEDatWqhZWVAZM+JsbCvWO6ANSd/fDwHPBPsEhRqQmzL8NlEx92RBdlY6gHUbHJVzBxtzOnkpcDlQo74O+Zj+KuNqiNZOSTeHdNnTqV999/nw0bNjBs2DCKFi0KwNq1a1MkjRa5X2RkJFu3bgWgc+fO6T9BUgIv1vXDCvhNU4f23XqRz8rwyfLr16/PzJkzWbFiBT/88APOzs48e/aMuXPn8t133yUrW7RoUdatW5diVSgHK1P61S3Kx7WKsONyCIsO5yO4zTCebRrP7bAEPt4Sx8db7jGg8o8MrzsZh8rtdav2ufsa/P6EEEIYlgSlhBAiDwkPD2fr1q1s2LCBbdu2ER0drd9nZmZG3bp19aOhPD09DVcRrRZCzsOtPbrRUPeOgSY+WZEom6JcNPPlj+jibAovkiII5e1sReW/g1CVvRwomC/ty+wKkZ1u3LhB8eLFKVeuHBcvXkyxf8KECbKSbR60Y8cO4uPjKVGiBGXLlk338YmHpmEVcZNnii3Bfl/T0dvA06r/1rJlS8qXL8/58+fp06cPAQEBDBs2jISEhGTlOnfuzMSJE3F3d3/luUzURrQo506Lcu5caFmaIc5qguaO1O//+UQC4fEKv5qvgguroFCALjhVsjkYyd8ZIYTIiSQoJYQQudyDBw/YuHEjGzduZO/evSQlJen3FShQgFatWtGiRQtq166NpaUBAzsxobog1M1dcHM3vEi+7F2chSvXLf3ZGVuKNaFFeBKXT7/PSAVl3W2pVNiBKl4OVCzsgJN1yql4QuQEfn5+FCpUiMDAQFq3bk21atWS7c/OJKsi+/zxxx8AtGjRIv357UJvw/4JAEw37smXzStldvVeydjYmGnTplGnTh3Wr1/P+vXr9fvUajWlSpWiTZs2jBo1Kl3nLVfQnp1zRnCgVyAT569i8y+6+1tyPpFbiU7sbhuL2b0jcO8I2BeCyn2gQjcwt8vU+xNCCGFYEpQSQohc5mV+qA0bNrBhwwZOnTqVbH+ZMmVo1aoVrVu3xt/fHyMjA+Ub0Wrh0TldEOrPIPjrFCj/5KnSGFtxx9afvYllWfO8CDfi8kPYPx2xEq42BBR1JMDbiSpFHLA1NzFMPYXIYs+fPycoKIiNGzfSpk0bFEWhRYsWtGrVikaNGklQKg/SarVs27YNgGbNmqX7+Kgt32GjxHNQU5aAdp9ibZa1j/i1atWiePHi3LhxA9AlQF+7di3ly5fP8CqMtSpXoFblCsTNGEPPAV+xau5kDl95REF1LfpUz8cAp1O4hN+DncNg7xjuujVm9J5I8nuXwc/Pj+rVq+Ps7CwLWQghxDtKglI5nLGxMWXLliUxMZEiRYqwdOlS7O3tDX7d4OBg2rVrl6Kz+zpOTk6ympAQBqIoCmfOnGHVqlVs2LCBmzdv6vepVCoCAgJo3bo1rVq1olixYoarSGyYbhTUzV26z4unyXaHWRfluLoCq8JKcDi6GInR//wa8nCwoLq3EwFFnahWxDHVpORC5Abm5ua0bNmSli1boigKR48eZdOmTXz99dd07tyZBg0a6Ecwuri4ZHd1RRa4evUqT548wdLSkho1aqTv4AensLn9B1pFxW7PzxlRNr9hKvkaKpWKRYsW8fXXX/PTTz9Rs2bNTL+GuakxK+dMopCdMePHj+fZxQP8dBF+Asr7+jCgTCS/HnrAgbuL/z7i92THb926laZNm2Z6vYQQIjPk5X69BKUyiaIoxMTEvPWxGo0GtVqd4i2OpaXla9/s2Nvbc+7cOUC3ksnMmTMZNmzYW9XjdV7WTwjx7lAUhZMnT7JkyRK2bdvG7du39fvMzMxo0KABrVu3pmXLloZdySv0DlzfBte3wt0joGj0u5KMrfjTyp+tcWVZG1GKR3GO+n1O1qYEeDtR/e/RUB4OkhNK5D0vg8YBAQH873//488//2TTpk0sXryYTz/9lMmTJ9OvX7/srqYwsGPHjgFQuXJlTE3TkZxcUYjY9C12wO/aWvRo3dwwFUyDgIAADhw4YPDrfP7558yaNStZTsTz5y7S+9zrj/vh268kKCWEeKOM9Ovf5HV9e3t7e86ePYtGo6Fnz555ql8vQalMEhMTg7W1daafNzo6Os0rXlWvXp3z588D8PjxY/r06cODBw8wNzdn/vz5lCxZkocPH/Lxxx9z//591Go1a9aswd3dnY8//phz585hYWHBvHnz8PX1ZcSIEYSEhHDz5k1Kly7NZ599RpcuXVCr1cnegEVHR9O3b1+uXr2KoihMmzaN6tWr8/jxYzp16kRUVBRNmjTJ9LYRIi/SarUcPXqUtWvX8vvvv3Pv3j39PnNzcwIDA2nfvj2NGzfGxsbGUJWAh2fh+h+6YNSTK8l2h1p5c9SoAqvCS3Is7p/RUGojFZU981G7uDO1iztTOr8tRrI6nhDJFCtWjMGDBzN48GCeP39OaGjoG48ZO3Ysv//+O9euXcPCwoKAgADGjRtHiRIl9GUURWHkyJHMmzePsLAwqlSpwsyZMylTpoy+THx8PEOGDGHlypXExsZSv359Zs2aRcGCBQ1yr+IfR48eBaBq1arpOk57fQd2T44Tr5hwv/zntHMy4Cqp7wh3d3fOnz9PbGwsK1eu5NatW+zfvx8za1tU+QqR4Nee/PaW1Hy+jkYcZ8mxJ6y8lMSpcxfZ+VUNGvUaBsUaSWJ0IUSqDNWvh7T37QMCArhw4QKQN/r1EpTKJTQaDUFBQfTq1QuAgQMH8v333+Pv78/JkycZOHAg27dvZ8CAAbRq1YqPPvqI+Ph4NBoNM2fOxMbGhosXL3Ls2DG6d++uD25dvHiRvXv3YmpqSrNmzfj222957733GDp0qP7ao0ePpk2bNixZsoQHDx7QvHlzzp8/z8iRI2nZsiVffPEFs2bNypZ2ESI30Gq1HD58mK1bt7J+/XoePXqk32dlZUVgYCCdO3emdu3aGc7d8UqJcXBnv2401PXtEB2i36Wo1ARbl2dznB9rostyP+6fUVnudubULuFC7eLOBBR1lLxQQvzHX3/9xeHDh3ny5Ala7T8511QqFf3798fR0fE1R+vs37+ffv36UalSJZKSkhg2bBiNGjXiypUr+off8ePHM3nyZBYvXkzx4sUZPXo0DRs25Pr16/oA9sCBA9m8eTOrVq3C0dGRwYMH06JFC06fPv3OvVXNbV6OlEpXUEpRiNr6A3bAcpryQZN0TvvLwYoUKQLonkH/635oDCtO3OO3ky5sfNGDui3PEWg6m01nQugw7TBXkjrgXtAT/HtAhQ/AWqbICiHeHRqNhl27duWpfr1KURTlbQ+Oj4/HzCxv5PyIjIzEzs6OiIgIfacvLi6OO3fu4OXlhZmZWbZM33s59/TBgweUKVOG3bt3Y2xsjKurK/nzJ88pcO7cOdzc3Hj48GGyxMatW7dm6NChBAQEAFC8eHFOnjzJlClTsLS01H9RXV1dCQkJQaVSceHCBXr16sWpU6fw9/cnMTFRf85nz55x+/ZtKleuzNatW3F3dycyMpIiRYokm3v67/YzdFJXRVGIiIjAzs5OEl0agLSvYVy7do1ly5axdOnSZCOibG1tCQwMpF27djRq1AgLCwvDVCA+Cv7cCVc26hKVJ/7zb1yisRUXzCuxJtqHrXE+RKJ7o2SqNqJKEQdqF3emTglnvJ2t3/nvhHx/DS+3tXFqzwRvY9GiRXzyySeYmpri6OiYrG1UKlWyKbnp8fTpU1xcXNi/fz+1atVCURTc3d0ZOHAgX331FaB7hnN1dWXcuHH06dOHiIgInJ2dWbp0KR07dgTg4cOHeHh4sHXrVho3bpyma2dW26Qmt32PXtq/fz916tRBrVbz8OHDNOcRS7qyBePV7xOtmLOq+h982KhihuuSm9o4PknDtoshLD12l5M3QwhdNpAXT+7zY0Nbvgv4u5CRCZQOhEofQqFqYOB7zk3t+y6S9jWs3Ny+mdWvf5PX9e1zcr8eUu/bp/WZIF0jpXbs2MHKlSs5ePAg9+7dQ6vVYmlpSYUKFWjUqBE9e/bE3d09PafMNVQqVZqn2f3X64JSb/Iyp1RMTAwNGzZk1qxZDBgwACDVN5tpOb+iKPpy/14e/lXHKorCli1bKFSo0CvPI4RImwcPHvDbb7+xfPlyzp49q99uY2ND27Ztad++PfXr1zfcC4G4CN1IqCsb4dZuSIrT74o2c+WwuhIrInw4GleShGjdqCdHK1Pal3ShfilXahZzwiqLV30SIqf64Ycf+OGHH/jmm28ydRXMiIgIABwcHAC4c+cOISEhNGrUSF/GzMyM2rVrc+TIEfr06cPp06dJTExMVsbd3Z2yZcty5MiRVwal4uPjiY+P1/85MjIS0D0DZOC9Z6penjOzz5tdwsLCCAoKYtSoUQB8+OGHODs7p+3+FIXIHT/hAKwxakrn2uUzpV1yUxubqo1o5etOK193rjyM5IuoD9j5y0+MvuTC84pt6W6yC1/tn3BpHVxah+JSWjd6qlwHMLc3SJ1yU/u+i6R9DSs3t+/Le3r58999YENd679e5pSKioqiadOmzJw5U9+vP3XqVLJ+/ct+dmr/P/697d8/LSws9H9+eex/yyiKwubNm1Pt17/q3Okp8ypp6jls2LCBr776ioiICJo1a8aXX35JgQIFsLCwIDQ0lEuXLrFr1y5+/PFHevTowY8//oizs3OaKiAyh6WlJdOmTaNt27b07duX2rVrM3fuXPr27YtWq+Xy5cv4+PhQs2ZNFixYkGyYX40aNVixYgUBAQGcOHECKyurVCOZFSpUYOPGjbRu3ZoVK1botzdo0ICZM2cybtw4AM6fP0/58uWpXr06v/32GwMHDkxWXgiR3OPHj1mzZg2//fYbhw4d0m83NjamcePGdOvWjVq1auHm5maYQG9MqC431JWNcHsvaBL0u0LNC7JNW5WVUb5civMCdNcv4WpD/VK6QJSvhz1qyQ0lRLrFxMTQqVOnTA1IKYrCF198QY0aNShbtiwAISG66bb/XfDA1dWVu3fv6suYmpqSL1++FGVeHp+asWPHMnLkyBTbIyIiDBKUepncOqe/9IqOjqZp06ZcunQJ0L14GDx4sD6g+CaqW7txiLhCjGJGYsUPSYx9QURsxuuVm9r43wpYwfQhH1BqwVjiH90k6L4Z6wuMpIwqmPfVQbxnfATzJ1dg21CUnd+TWLwF8T6d0eSvmKmjp3Jr+74rpH0NKze3b0JCAlqtFo1Gg0ajefMBBqLRaDA3N2fSpEl07NiRPn36UKtWLWbPns2nn36arF9fo0YNfvnlFz788EN9vz4gIIDly5dTpUoVTpw4gaWlJVZWViiKor8/AD8/P9avX0+rVq1Yvny5fpBMvXr1mDFjBmPHjgX+6ddXq1aNlStX8vnnn7Ns2TJ9Xf9bd61WS1RUlP5l1csXVW+SpqDUmDFjmDhxIs2bN0/1walDhw6ALi/CtGnTWLJkCYMHD05TBUTmqVixIj4+Pqxbt47p06fzySefMGfOHJKSkvjggw/w8fFh2rRpfPjhh0yfPh0TExPWrFlDv379+OijjyhXrhzm5uYsWrQo1fNPnTqVLl26MHbs2GRvTH/44Qf69++Pj48PGo2G+vXrM336dIYPH06nTp1YunQpbdu2zapmECJHSExMZOvWrSxcuJA//vgj2T/sNWrUoEuXLrRv3x4nJyf9cOlMFRMKVzfrAlF39oM2Sb/rqbknWxIr81uMP9fiPAAVxkYqang7Ur+UCw1KucpKeUJkgt69e7NmzRq+/vrrTDvnZ599xoULF5IFuF/6byciLSOa31Tmm2++4YsvvtD/OTIyEg8PD+zs7AwyfQ/IFVNHpkyZog9IAXz99dcULVo0bQcrCqEHpwKw1qgx7zesgqVp5oxQzU1t/F92dnZ89tln/Pzzz9z5fSIbD51n640CjLxYhLFx79NGfZD3jfdSQnMP06vrML26DsW5JFToDuU7gUW+N1/kDXJz+74LpH0NKze3b1xcHM+fP0etVmdrDsWX165SpQo+Pj5s2LCB6dOn8+mnn/LLL7+QlJREt27d8PX1Zdq0aXz00UfMmjULExMTVq9ezWeffcbHH39MhQoV9P36l7OxjIyM9OefOnUq77//PuPHj6dRo0aoVCrUajXDhw9nwIAB+Pn56YNU06dPZ8SIEXTu3JkVK1bw3nvvJavrv+tuZGSEjY2NfvpeWr8nGcoplZe8KadURnIiZWT6Xk4mOaVyD2nftLt8+TKLFi1i6dKlPHnyRL+9cuXKdOrUifbt26dY6SrT2jc+Wpeo/OJa3dS8fwWiQsy92ZhQkTWx/txUdNe3MFFTt6QzTcrmp04J51ybpFy+v4aX29o4s/ImaTQaWrRoQWxsLD4+PpiYJP87Nnny5HSdr3///mzYsIEDBw7g5eWl33779m28vb05c+YMfn5++u2tWrXC3t6eX3/9lT179lC/fn1CQ0OTjZYqX748rVu3TnU0VGokp9SbKYpC0aJFuX37NiNHjqRo0aJ07NgxzR2hpFsHMF7akjjFhLU1t9G1QaVMrVtuaONX0Wg0+Pn5cfHiRcaNG8fQoUMJj0lg/dm/WHH8Hn8+icJXdYvO6j20Mj6KOX9PTVWbQelWuul9ngFvPXoqt7dvdpP2Nazc3L5Z2S99nZwcF8iynFJCCCHSLzw8nFWrVrFo0SJOnDih3+7q6soHH3xAz549KVWqlGEunhQPN3fDxTW6KXpJ/8zveGhelHXxlVgf58/tOF0+QBtzY9qUcqVJWTdqF3fG3ERW3BLCUMaMGcOOHTsoUaIEkPyNYnoeRhVFoX///qxfv559+/YlC0gBeHl54ebmRlBQkD4olZCQwP79+/VT7/39/TExMSEoKEg/Av7Ro0dcunSJ8ePHZ+g+RXInTpzg9u3bWFlZMXjw4HTnJH28cxIFgC1GdXmvlt8by4t/qNVq+vfvz8cff8xXX31FdHQ0JUuWpGPr1vQIKMyZe2EsP16QHy4UZ3RcV1qpD9PFeC+lNcFwcbXu41hMF5wq3xms3rw6phBCiNdLd1BKURTWrl3L3r17UyxfDPD7779nWuWEECKn0mq17N27l4ULF/L7778TF6dLGG5sbEyLFi3o1asXTZo0STEyInMuroHgQ3BprW56Xtw/U/+emRZkbUIV1sRX5VZcAQAcrEzpVNqVxmXdqO7thKlx5uW3EUK82uTJk1m4cCE9evTI0Hn69evHihUr2LhxIzY2NvocUHZ2dlhYWKBSqRg4cCBjxoyhWLFiFCtWjDFjxmBpaUmXLl30ZXv37s3gwYNxdHTEwcGBIUOG4OPjQ4MGDTJ6q+Jf1q9fD0BgYGC6A1KaJzco8HgfANqqfTNt2l5e0rVrV1avXq3PhwvQqVMnVq5cib+nA/6eDgxvUYb1Zx+w4oQLyx43wEd1h87q3bQxPorF8z9h5zDYPRJKtdQFqArXNPjKfUIIkVul+zfZ559/zrx586hbty6urq45bliZIcgMyLcj7SZyo+DgYBYvXszixYv1CYQBypQpQ69evejatWual/tOF0WBR+fgwmq49DtE/5OYONLEic1JVVkVV5WLfycrd7Yx44OybjQp60blwg4YqyUQJURWMzMzo3r16hk+z+zZswGoU6dOsu2LFi3SB7yGDh1KbGwsffv2JSwsjCpVqrBz505sbGz05adMmYKxsTEdOnQgNjaW+vXrs3jx4mzNr5EbbdmyBdAFpdLrwbaJeAL78adF3ZqZXLO8wcLCgp07d/LVV18xYcIEAFatWsWmTZvYvn07AQEB2Fma0KO6F93/Hj214rgHIy8U5ae4rrRUH+V94z34aG7rV+7DwRv8u0P5LmAtiz0JkVNJ//TtZaTt0p1TysHBgWXLltGsWbO3vmhOlNp8SI1Gw59//omlpSXOzs5vHaDLyXNH35aiKDx9+pSYmBiKFStm8Afe3DwH+l2Q19s3JiaG9evXs3DhQvbs2aPfbmdnR+fOnenVqxcVK1bM0L8Rr2zfqBC48BucWwlPr+o3x6pt2UkVVsZW4YS2JFqMsLc0oWnZ/LQsn58qXo6yYt7f8vr3NyvktjbOrLxJY8eO5dGjR/z888+ZWLvsJTmlXu9lfi+1Ws3Tp09TrHb4OsqLZyRMKIUZCawuO4cO7Tpnev1yQxunlaIoXL58mcmTJydb5MfR0ZHKlSvTvn17evbsqd/+ODSK30/dYcmOE9yLhnx3d9G7aBjvWZ7F0/Lv3FNGxlCiKfh1A+/6oDZOcc280r7ZQdrXsHJz+2ZWvz6jcmpc4FV9e4PllLKzs6NIkSJvX+NcRK1WU7BgQR48eEBwcHCGzqXVajN1SeicQKVSUbBgQXkDK3IkRVE4ceIEixYtYuXKlfolT1UqFfXr16dnz560adMGCwuLzL94Yixc+wPOr4Rbe0DRTaNOMjLjgFEllsdU4YC2PIkYY21mTOvSrrT0dadGUSdMZESUEO+MEydOsGfPHrZs2UKZMmVSTOeVlAi5z4gRIwCoV69eugJSAPd3TqcQCVxSilC/cRsD1C5vUalUlC1bll9++YX+/fvTo0cPLly4wPPnz9m2bRvbtm1j/PjxJCQk8OTJE6Kjo5Md/xD4Ygd8ATi7OFPJJYlVLRKxubpZt7qttRv4dtYFqBy9s+UehRBpk5n9+ozKqXGBjPTt0x2UGjFiBCNHjmThwoWG6WzlMNbW1hQrVozExMS3PoeiKERFRWFjY5OjIqIZZWJiIgEpkeM8fvyYpUuXsmjRIq5cuaLfXrhwYXr27En37t3x9PTM/AsrCtw7pgtEXd4A8f/kibpqUppfYwLYqqlCJFaYGRvRsIwLgeXdqVPCRZKVC/GOsre31y+tLHK/W7dusXTpUgB++umn9B2cGIfdxcUAXC3cjbI22bc6VG6jVqvx8/Pj9OnThIeHs2/fPiZOnMjx48e5du1ams7x9MlTtj4Bl3v5sFDiiImJpV+lOCZETcbo0BQoFAB+XaF0+qdsCiGyRmb06zMqJ8cFMtK3T3dQqn379qxcuRIXFxcKFy6c4q3emTNn3qoiOZlarc5QcEVRFOLj4zE3N89xXz4h8orTp08zbdo0Vq1apf9lZWFhQdu2benVqxe1a9c2zFuNiAdwdjk251agCg/Wb35u7MKq+OqsSapBcFx+1EYqapZworVvARqUdsXaTJLfCvGu+/eUIZH7vcwlVbduXSpVqpSuYx8eWoK7NpyHigNVmvc2RPXyPGNjY5ycnGjXrh1t2rRh48aNXL9+naNHj+Lp6UmBAgXw9fUlMTGRNWvW4Ovry4sXL/D39+fQoUNMmDCBuMgw4v4+3+RjCUw+lsBnlU1xsdzL4FuHsbAaikXxFlC5F3hUluToQrxjMtqvz6i8GhdId6+lR48enD59mq5du0qicyFErhYVFcXixYtZsGAB58+f12+vXLkyvXv3pmPHjtjZ2WX+hTWJcGM7nP4Vbu5ChYIaiDeyYIe2MisTanAsrhQKRpTOb8t3FQoQ6OuOi7w5F0KId9Yff/wBQIsWLdJ3oKKgOjYLgGPOHXjPxQC/d0QyarX6taMYW7ZsmezPzZo1o1u3bty4cQMXVzcWrN7Mgim60XAzTiQAMPVEEp/6x1PzyjKqnFqJfcESutFT5TuDtQEWQBFCiBwi3UGpP/74gx07dlCjRg1D1EcIIbLd+fPnmTNnDsuWLdPnkDA2NqZjx458/vnn6X7DnWbPb8GZJXBuBbx4ot98Tl2WpbE12KatTAzmONuY8ZFfAdr4FaBU/sxNJCyEMKwmTZrwww8/EBAQ8NpyUVFRzJo1C2tra/r165dFtROGcurUKfbt2weQ7sWCnp3fRv74O0Qr5hRv2tcAtROZoVSpUpQqVQqAalWr8P3nH1HWx4foqCgAQmM0/HRQAyRQ2lnN0d7XsX32A+waCcWb6AJUxRqlSI4uhBC5Xbr/1fPw8Mj01VSEECK7aTQaNm3axNSpUzlw4IB+e4kSJRgwYAAdO3bE0dEx8y+cGAfXtsDpxRB8UL85wsieVQk1WampQ7CSH3NjIxr7uPGef0GqeztiLAnLhciR2rdvT4cOHbCxsSEwMJCKFSvi7u6Oubk5YWFhXLlyhUOHDrF161ZatGihX7Je5Gw9e/YkMTGRVq1aUaJEiXQdG7FnCk7AAeumNPM2QM5CYRCenp5s37aN1atXU6dOHbYfOM6Wbdt5eP08V55q8JxrRL8AOwLzP6ey8gdc/wOsXaF8J/DtCs7Fs/sWhBAiS6gURVHSc8Aff/zB9OnTmTNnDoULFzZQtd49ssRxziZtbFg5uX0fPXrEggULmDdvHvfv3wf+Gbbft29fateubZh7enJVNyrq/EqIDQNAQcURVXmWxNdht7YCSRhT2cuB9v4FqeZhQQEXxxzXvjlBTv7+5hS5rY0z+kyQkJDA2rVr+e233zh48CDh4eGAbuWa0qVL07hxYz766KN0By/eBfK8lNLLNgHdYhkuLmmfqhV97xzWC2ujUVScCNxNNX9/Q1UTyLltnFMoisKOXXvo3Lkz4c+f6rf371SH4cVv4aj6ZxETClTUrd5X5j2wdMiG2uY88v01LGlfw8ttbZzWZ4J0j5Tq2rUrMTExeHt7Y2lpmSLReWhoaPprK4QQWezSpUtMnz6dhQsXkpSUBICjoyMff/wxffv2pWDBgpl/0aQEuLoJTs6He0f1m58aObM8oSark+rwECecbcz4sEJBOlQsSBFna/0vKCFE7mBqakqXLl3o0qULABEREcTGxuLo6JjiuUrkfC9XcHNzc0tXQArgr62TKAEcMgmgVoUKBqidyGpVK/lz784tJkyYwI8//gjA9FX7WFy+Ic2rl6a3wznqqs+j/usU/HUKtn8DJZpC+S5QtD6o5d8IIUTuku6g1NSpUw1QDSGEMDytVsv27duZPHkyu3fv1m+vXr06n376KW3btsXc3ADJwiMe6Kbnnf5VnytKg5q9ij/LEmtzQFseVEbUK+XCiIoe1C3pgolMzxMiz7CzszPMogninXDlyhUAfb6htEoKf0iRkK0AxFfumyvemgsda2trRo0aRdeuXfUjIqPOB7Hmyn721O6GvXNNOnk8oZPZEUpxD65s1H2snMGng24ElZtPNt+FEEJkjnQHpbp3726IegghhMHExsaydOlSpkyZon9jrVarCQwMZNCgQdSsWTPzL6oocHufblTU9a2gaAF4pnJgaUJdVmrq8YR8eDpaMriiB+38C+JqK6vnCSFEbnP16lUASpcuna7j7mybSjGSOEcJatVtaoiqiWxWvHhx+vXrx4YNG1AUhYcPH/Jk1wKeAFPdPJlTowcVvJ1oZ3yI1urDOL54Csdm6j6uPrrglE8HsHbO7lsRQoi3lqag1IsXL7CyskrzSdNbXgghDCEkJISZM2cye/Zsnj9/DoCtrS0fffQR/fv3x9PTAAljY8N1eaJOzofnN/WbTyhlWJTYgCCtP0bGpjT3y0+Hih5U8XLAyEjefgshRG71NiOllPhoXG+sAOB+yV74mqgNUjeR/WbMmMGMGTNQFIUhQ4YwefJkACJD7hK5diT3q9ZlZsV2DLtfiCr5IulT8BYN1GcwfXwRdlyEnd9DsYbg20W3ip+xWTbfkRBCpE+aglJFixalf//+9OjRA3d391TLKIrCrl27mDx5MrVq1eKbb77J1IoKIURaXbhwgSlTprBixQoSEhIAKFy4MJ9//jm9evUyzAqijy/D8blwcQ0kxgAQo7JkTWJ1lmoaclMpSGFHS76q4kk7/4LkszLN/DoIIYR4pyiKwqlTpwDw8Un7dKv7exdQSIninuJC1aYfGKp64h2iUqmYMGECDRs25OHDh4waNYq7d+9y+dheOLYXgE0qFYcrBeLsP4r37G/Q3vggvka34MZ23cfcHnza6fJPFagAMuVTCJEDpCkotW/fPr777jtGjhyJr69vqssXHz16FBMTE7755hs+/vhjQ9dbCCGS0Wq17Nixg8mTJ7Nr1y799oCAAL744gtatWqFsXG6Zyy/6aLw5044Ngvu7NdvvoUHixIbsF5TgzgjSxqUdmF4VU+qezvJqCghhMhDrl+/TkhICGZmZlSuXDltB2k1mJ+eC8AZ9860trM0YA3Fu8TIyIgmTZoA0KtXLyZOnMjSpUu5fPkyGo0GFIXnJzby/MRGxqhUTCtZExenOnQuoeFD54u8ePKcYtG/YHZyPjgVh/KdoVxHsCuQzXcmhBCvlqYeWokSJVizZg0PHjxgzZo1HDhwgCNHjhAbG4uTkxN+fn788ssvNGvWDCMjSc4rhMg6sbGxLFu2jClTpujzdhgZGdGuXTsGDRpE1apVM/+i8dG6KXrHZkPoLQC0GLFdU5HFSY05oZTExcac3pUL0bmyB/ntLDK/DkKIHK9Hjx706tWLWrVqZXdVhIHs2bMH0C2okdaFNJ6e3ohL4l9EKJaUbtbXkNUT77ghQ4YwZMgQHj58SHR0NJMmTWLevHm6nYrCi6sHuAOMOQhj/j7Gv6gzB99XsHh2A3aPRNk9CpVXTV1wqlRLMJdFFYQQ75Z0DRsoWLAggwYNYtCgQYaqjxBCpMnjx4+ZNWsWs2bN4tmzZwDY2Njo80UVLlw48y8afh9OzIMzv0JcBADRWLE8qQ5LkhrxF85UL+rI7CqeNCjtKivoCSFeKyoqikaNGuHh4UHPnj3p3r07BQrIiIbcZO9e3bSrunXrpvmYmP3TADhg25KWHm4GqZfIWV6mT5k7dy4DBw5k0qRJbNy4Uf/882+nbz7FYW4R3K0UvMwiGFklDrfQfXjfOQBbvoASTaF8J/CuD8aSSkAIkf0yeS6LEEIY1qNHjxg3bhxz584lLi4OAE9PTz7//HN69+6d+fmiFAUenISjM+HqZlA0ANwlP/MTG7NOUwuVqRXtAzzoVs0Tb2frzL2+ECLXWrduHc+fP2fZsmUsXryY4cOH06BBA3r37k2rVq0wMTHJ7iqKDNBqtezbtw9Ie1Aq+s5xPKPPkaiocarf34C1EzlVqVKlmD9/Pr/88gsxMTFYWVmRlJTE77//zqxZs9i/fz9xIbe5DdwGdl8BtdqIbxo480X5GPJd2QBXNoCFA5RpoxtB5VFZ8k8JIbKNBKWEEDnCnTt3mDRpEgsWLNAHo6pUqcLgwYNp06aNAfJFaeDqJjgyHf46rd98RFuG+UlN2av1xcPBmsEBhWlfsSC25tJ5FEKkn6OjI59//jmff/45Z8+eZeHChXTr1g1ra2u6du1K3759KVasWHZXU7yFy5cv8+zZM6ysrKhUqVKajnm8fRLWwD7TWjQoX9awFRQ5mkql0q92bmxsTIcOHWjevDkLFizg6NGjrFq1Sl9Wo9Eyesdjxu23pH3tUkys9IT8saFwaoHuk68w+HTQBaicimbTHQkh8iqZWyKEeKedO3eOLl26UKxYMWbOnElcXBwBAQHs3LmTo0eP0r59+8wNSCXGwsn5MN0f1vSAv06TgAm/JdWhSfz/6JIwjDivhsz7oDJ7h9Shdw0vCUgJITLs0aNH7Ny5k507d6JWq2nWrBmXL1+mdOnSTJkyJburJ97Cy3xSNWrUwNT0zdOkkp4H4/k4CABNlb6oZOSKSCcrKysGDBjAypUrURSFGzdusGXLFpo0aYKjoxOJcTGs2HGawvOMaXy7C+s0NXmhmENYMBwYDzP8YV5dODYHop9m9+0IIfIIGSklhHgnHTt2jBEjRrBjxw79tkaNGjF06FDq1auX+Q/rMaFwcgEcnwMxuhwN4Vjza1IjliQ1JNo4H20qFmBq9cKUdMvkKYJCiDwpMTGRTZs2sWjRInbu3Em5cuUYNGgQ77//PjY2NgCsWrWKTz/9VPJ55jCKorBo0SJA97srLe5tGUcRtJygLHVq1zdk9UQeUaxYMYoVK0bz5s3RaDTMnTuXYcOGEf7kDjuXzuFY4bIYu1WnS52StDE+TC31BYwfnoGHZ1B2fIvKu55u9FTJZmBqld23I4TIpTItKBUWFsbmzZv54IMPMuuUQog86OTJkwwfPpxt27YBupX0OnbsyJdffomfn1/mXzD8PhybBad/hcQXAPylODMvqRmrNbWxs7WnVzVPOlcuhIOVJAQVQmSe/Pnzo9Vq6dy5MydOnMDX1zdFmcaNG2Nvb5/ldRMZs3v3bs6fP4+lpSU9evR4Y3lt5GMK3FkLwF9l+1LZRG3gGoq8Rq1W07dvX9q3b8+3337LggULiAy+BMGXWHG/MJurd8POrQutrS/TRn0IX6NbcDNI9zGx0q3cV64DeNUGtYxrEEJknkz7F+XevXv07NlTglJCiLdy9uxZhg8fzubNmwHdw1P37t0ZNmwYRYoUyfwLhlyCIz/DxbX65OVXtJ7MSWrJH9oqlMifj//VLkIzn/yyip4QwiCmTJlC+/btMTc3f2WZfPnycefOnSyslcgojUbDl19+CUCvXr1wcHB44zHBW8ZThATOK8Wo16y9oaso8jBnZ2d++eUX+vXrx4QJE1ixYgWhfwUTuvpHABJqB7Kyyve4ah7RRHuQ961PUzjxMVxYpftYu+oSpJdtBwUrSoJ0IUSGZWtPa+zYsVSqVAkbGxtcXFxo3bo1169fT1ZGURRGjBiBu7s7FhYW1KlTh8uXLycrEx8fT//+/XFycsLKyorAwEAePHiQrExYWBjdunXDzs4OOzs7unXrRnh4uKFvUQjxBhcuXOC9996jQoUKbN68GSMjI7p37861a9dYsGBB5gek7h6BZe1gTnW48BsoGg5pytAt4WuaJYwhzDuQX3sH8MeAGrTyLSABKSGEwezdu5fExMQU21+8eEGvXr2yoUYiMyxfvpxz585hb2/PDz/88MbySkwobjeWA3Cr5CfYWcqoXGF4vr6+LF++nLVr11KiRAn99mv7N/Hn+Pc4NKkfI2ZtpNrVzrSK+YFfkxoSqlhD9GNdqoMFDWBaedg1Eh5fycY7EULkdNna29q/fz/9+vXj2LFjBAUFkZSURKNGjXjx4oW+zPjx45k8eTIzZszg5MmTuLm50bBhQ6KiovRlBg4cyPr161m1ahWHDh0iOjqaFi1aoNFo9GW6dOnCuXPn2L59O9u3b+fcuXN069YtS+9XCPGPy5cv06FDB8qXL8/69etRqVS8//77XL16lcWLF1O0aCau/qIocGsvLGoGi5rCzSA0GLFFU5UW8aPpofkOp/JN2TqgFkt7V6FGMSdJMCuEMLhff/2V2NjYFNtjY2NZsmRJNtRIZJSiKEycOBGAoUOH4uzs/MZjgrdOwZJYrimFqNNSnk1F1mrbti1nzpxh0qRJtG+ffJReUnwsIWtHsWvOKEYd0FD+7hf0TPiS9ZrqvFDMIPwuHJoMs6vBrGpwYCKEyshOIUT6ZOuE4O3btyf786JFi3BxceH06dPUqlULRVGYOnUqw4YN47333gN0D3Curq6sWLGCPn36EBERwYIFC1i6dCkNGjQAYNmyZXh4eLBr1y4aN27M1atX2b59O8eOHaNKlSoA/PLLL1SrVo3r168nezvwUnx8PPHx8fo/R0ZGArqHDUVRMrUdXp4zs88r/iFtbFjpad9r167x448/smrVKhRFQaVS0aFDB77//ntKly6tP18mVQz+3AkHJqD66xQAiRizJqkWczQtCTUtQKcaHswNKIy7vUXmXjsTyffXsKR9DS+3tXFG7yMyMlLfHlFRUcmm72k0GrZu3YqLi0tGqymywcmTJ7l48SJWVlZ88sknbyyvxEXidHkhAFe8P6KktZmhqyhECpaWlnzxxReAbgGGw4cPc/jwYZYtW8a1a9eIiYrk9sENcHADD9r3Ylrx9/k6pgcNTC4SqD5CXaNzmD65AnuuwJ4foUBF8Gmnm+Zn45a9NyeEeOelOSj1888/v3b/X3/9leHKREREAOjn3t+5c4eQkJBkq5aYmZlRu3Ztjhw5Qp8+fTh9+jSJiYnJyri7u1O2bFmOHDlC48aNOXr0KHZ2dvqAFEDVqlWxs7PjyJEjqQalxo4dy8iRI1OtoyGCUtHR0QAyOsNApI0NKy3te+vWLcaPH8/atWvRarUABAYGMnToUMqUKQP8829AxiukxeTmdsxOTMf4qW5IeRwmrEyqx9ykFmis89OlYn7a+rpha24MJBARkZA51zYA+f4alrSv4eW2Nn75oupt2dvbo1KpUKlUFC9ePMV+lUqV6jOIePcdPHgQgPr165MvX743lr+7cwaFlWhuK/mpEdjb0NUT4o1MTEyoU6cOderUYdiwYRw8eJAmTZoQExMDwK41C4GF2NjacbJKPfbkq4Sx50c0MTlNoNERAtSXUf91Cv46pVvBr3ANXf6p0oFg8ea/E0KIvCfNQakpU6a8sUyhQoXeuiKKovDFF19Qo0YNypYtC0BISAgArq6uycq6urpy9+5dfRlTU9MUv/hdXV31x4eEhKT6xtHFxUVf5r+++eYb/RsD0D2Aenh4YGdnh61t5i4H/zLIZWdnlyse1t9F0saG9br2vX37NqNHj2bp0qX6KbWtWrVi+PDhqa40lSHaJLi8Hg5OQvX0GgAxihlLNA2Zn9QcK4f8fF7HmzZ+7pgZ55yVjeT7a1jSvoaX29o4o/ewd+9eFEWhXr16rFu3LlkibFNTUzw9PXF3d89oNUU2OHLkCADVq1d/c+HEWOzPzQPgvGdP2thbGbJqQryVmjVrEh4eztOnT5k2bRr79u3jxIkTREVGcDJoPQA+vv4c9/ZlWYg7LuU709LmKm1Mj+Nv9CfcOQB3DqD8MRhV0Qa6EVQlmoKpfN+FEDppDkoZeuWXzz77jAsXLnDo0KEU+/778Pdyys/r/LdMauVfdx4zMzPMzFIOoX75ZjOzvTxvbnhYf1dJGxvWf9v37t27jB49msWLF5OUlARA8+bNGTlyJP7+/pl7cU0inF+ly2sQehuASMWSRZrGLEpqgotrfr6vW5TmPvkxzqGJy+X7a1jSvoaXm9o4o/dQu3ZtQPdsVahQoVzRJkL3XHn48GEAAgIC3lj+3u65FNKG8ZfiROVWnxq6ekK8NRMTE9zd3Rk3bhygW4xh/fr1HDhwgN9++42L507DudMAhBxey1VzC2aXrk0+5zL09jOhtckxSnEfbmyDG9tQTCxRlWimC1B51wdjSe4vRF6WrTmlXurfvz+bNm3iwIEDFCxYUL/dzU03BzkkJIT8+fPrtz958kQ/esrNzY2EhATCwsKSjZZ68uSJ/oHAzc2Nx48fp7ju06dPU4zCEkJkzP379xkzZgwLFizQryrVpEkTRo4cSeXKlTP3Ypok3Qp6+8fpkm0CoYo1C5KasVTTkMIF3RlXtygNS7liZCSdPiFE9rtw4QJly5bFyMiIiIgILl68+Mqy5cqVy8KaiYy6fv06jx8/xtTUlIoVK76+sCYRy5MzATjp3o3Wjpk7Cl8IQ7KysqJr16507dqVESNGMHPmTGJjY1m8eDFhYWEkxsUSemY7ocDka8WZV/9TirpYEKg+QkvVYQrzFC6thUtrUcztUZUOhDLvQeGaoH4nuqdCiCyU5r/1sbGx7N69mxYtWgC66W3/TgSuVqv58ccfkyXrfBNFUejfvz/r169n3759eHl5Jdvv5eWFm5sbQUFB+Pn5AZCQkMD+/fv1kXp/f39MTEwICgqiQ4cOADx69IhLly4xfvx4AKpVq0ZERAQnTpzQd4qPHz9OREREmt5kCSHeLDg4mNmzZ7N48WISEnT5mRo0aMDIkSMz/++ZVgOX1sG+/0HoLQCeKbbMTWrBck0DfLzcmVmvKDWKyip6Qoh3i6+vrz6tgK+vLyqVKtVclSqVKtkqwuLdt3nzZgDq1KnzxufhB/sXU1DzhKeKHX6tPsuK6glhEO7u7vz0008AdO7cmfnz5/Pbb7/p84Q+u3uDZwsHEWJhya7YGAZZ2lGz48d0c79HC/UxXOLC4cwSOLMExdLpnwCVZwAY5ZxUC0KIt5fmoNSSJUvYsmWLPig1Y8YMypQpg4WFbsWqa9eu4e7uzqBBg9J88X79+rFixQo2btyIjY2NPr+TnZ0dFhYWqFQqBg4cyJgxYyhWrBjFihVjzJgxWFpa0qVLF33Z3r17M3jwYBwdHXFwcGDIkCH4+PjoV+MrVaoUTZo04aOPPmLu3LkAfPzxx7Ro0SLVJOdCiLR7+vQpw4cPZ968efoOVO3atRk1ahS1atXK3ItptXBlgy4Y9ew6oBsZNTepJUs0DalSwoMldYtSsbDD688jhBDZ5M6dOzg7O+v/W+QeL4NSgYGBry+o1WB8dCoAR1w608rNycA1EyJrVKpUiUqVKjF9+nQ0Gg3R0dEMGTKEJUuWEBerS5SuiYlg36KJXC9Zge9KdKCCm4ouTjdooj6JQ8wzOLVQ97F2hdKtdCv4eVQFo5yZfkEI8WYqJY1LydWqVYtBgwbRpk0bAGxsbDh//jxFihQBYNmyZcycOZOjR4+m/eKvGMGwaNEievToAehGU40cOZK5c+cSFhZGlSpVmDlzpj4ZOkBcXBxffvklK1asIDY2lvr16zNr1iw8PDz0ZUJDQxkwYACbNm0CdA8MM2bMwN7ePk11jYyMxM7OjoiICIMkOo+IiMg1CWDfRdLGmS8+Pp5p06bx008/6Veiaty4Md98840+X0qmURS4tgX2joUnlwGIUCyZl9SCxZrGBJQuzOf1i1G2gF3mXvcdId9fw5L2Nbzc1saGfCbI6fLa89LL/KQ3b96kePHiKIpCcHAwnp6erzzm4eEVuAd9SrhixbMPT1PUI/8ry2a1d7GNc5O82r6PHj3i4MGDzJ8/n6CgoGT7TM3Nqfr+lzzO50Nlkz9pbnScJuoT2Kli/ilkkx9Kt4ay70GBiq8MUOXV9s0q0r6Gl9vaOK3PBGkOSrm5ubF792790u3Ozs6cPHmSwoULA3Djxg0qVaqUeUu6v2Py2kNWbiNtnHmSkpJYt24d3333HTdv3gSgQoUKjBw5kubNm2du+yoK3NgBe3+CkAsARCkWzE9qxkJNU6qUKszABsVzbTDqJfn+Gpa0r+HltjbOrGeCsWPH4urqSq9evZJtX7hwIU+fPuWrr77KaFWzXF56XlqyZAnDhg3jxx9/ZPfu3SxbtoymTZuydevWVx+kKPz1P38KxN9ii0N3Wgz4OesqnAbvWhvnNtK+MGfOHKZOncr169eTbbfPlw9Ht4LEqiwwqdiWOgUSaKE+TiOjk9iqYv8paFsQyrTWTfErUAH+1Y7SvoYl7Wt4ua2N0/pMkObpexERERgb/1P86dOnyfZrtdpkOaaEELnPjh07+OKLL7hy5QqgC1b/73//o2vXrkRFRWXuxW7vg90/wl+nAIhWzFmkacL8pGb4lyzC8gbFKFfQPnOvKYQQWWju3LmsWLEixfYyZcrQqVOnHBmUyisOHz5M9+7dAejZs6d++5AhQ1573ONTGykQf4toxRzv5oMNWkch3kWffPIJn3zyCXfu3CEkJIRhw4Zx5swZwsPCCA8LA0B19SRHyvpxulB5hjgNpq5rFIGmp2hodBrryAdwdIbuY++pm95Xpg3kL5/NdyaEeFtpDkoVLFiQS5cuvTIH04ULF5KtnCeEyD2uXr3K4MGD2bZtGwAODg7079+fwYMHY2Njk2qS3rf28CzsGqELSgGxiim/ahozN6k55Ut482uD4vh62Gfe9YQQIpv8d3Xhl5ydnXn06FE21EikRVJSEp9++mmybY6OjkycOJF69eq9+kBFIW6PbhGeA3ataOb96il+QuR2Xl5eeHl5sWfPHl68eMGePXswMjJi3bp1LFq0iD8vnoGLZwDYWdibe+8NZMiLltSwCKad9TnqG53FMvwuHJ6q+zgUgdJtMPJsALZVk42gEkK829IclGrWrBk//PADzZs3T7GiSGxsrH7qjhAi93j+/DkjR45k1qxZaDQajI2N6d+/P99//z358uXL5Ivdgj0/wuX1ACQoapZrGjArqRWlihdjQYNiVCiUydcUQohs5OHhweHDh1OsPnz48GHc3d2zqVbiTbZv387FixfJly8fx44d49ChQ7Rq1QpHR8fXHvf00i48Yy8Tp5hQoNnrR1QJkZdYWVnRsmVLAJo3b07fvn3Zv38/S5cu5fLlyzwMvsXDyf0BuKs2ZnfZADQFOhBY1pZA05PUMzqHRehtVIcmYXtoEopjMd3oqbLvgUup7Lw1IUQapDko9e2337J69WpKlCjBZ599RvHixVGpVFy7do0ZM2aQlJTEt99+a8i6CiGySGJiIrNnz2bEiBGE/T2UOjAwkAkTJlC8ePHMvVjkI9g/DuXMElSKBq2iYoO2OpOT2uFVtDRzGhTH31OCUUKI3OfDDz9k4MCBJCYm6kfY7N69m6FDhzJ4sEztelft3r0bgA4dOlC8ePE0/16M2jEGZ2C/dVMal8zk36VC5CIVK1akYsWKDB48mLCwML788kuWL19OUlISSUlJ/HX+AJw/wLytsDOgORY1Z1Pf6CzNjI5RT30es+d/woHxuo9zSd0qfqVbgUtpGUElxDsozUEpV1dXjhw5wqeffsrXX3+tn66jUqlo2LAhs2bNwtXV1WAVFUIYnqIobN26lcGDB+sTUPr4+DBlyhTq16+fuReLDYfDU1GOzUGVFIsK2K3xY0JSRyw9yjGxSUmqFnn9W2chhMjJhg4dSmhoKH379iUhIQEAc3NzvvrqK7755ptsrp14lZdBqddO1fuPpxeDKBJ9hgRFjWPjLw1VNSFynXz58jF//nzmz5+Poihs27aN7777jrNnzwIQfOQP1Me3c0NlxHxLW6xLVad6YXP6Fn1MbfVFzJ5eg/3XYP84cCz2T4DKzUcCVEK8I9K8+t6/hYaG6lfdKlq0KA4ODoBujv2/k6HnJnlpNZncSNr4zS5dusQXX3yhX6rX2dmZ0aNH07t3b9Rq9WuPTVf7JsbCiXkoByejigsH4LS2GP9L7EykSyWGNC5Bg1Iu8v/pX+T7a1jSvoaX29o4s58JoqOjuXr1KhYWFhQrVgwzM7NMqGX2yM3PS4qicPjwYWrWrAnA48ePcXFxScuBBE+oQeGYS+ywbEmjL5e+s38PsruNcztp38yRkJDA+PHj2bx5MydOnEi1jIdfbWxqvE8j61s0U5+gltEFzFSJ/xTI5/VPgMrdTwJUaSDfX8PLbW2c6avv/ZuDgwOVK1fW//nKlSvMnz+f5cuX8/jx47c5pRAimzx9+pThw4czd+5ctFotpqamDBw4kG+//RY7O7vMu5BWA+dXoewZjSrqISrgurYgE5I6ct2uOl80KkFg+QKojXL+P8BCCJEe1tbW5M+fH5VKlaMDUrlZZGQkLVu25MCBAwA0btw4bQEpIOTMFgrHXCJOMcGt5Xe5oqMhRHYyNTXlu+++47vvvuPmzZsEBQWxb98+7O3tuXPnDnv37uX+2f3YB19gZ5GS/BanxcLnU5p7aWmqPk5do3OYh93RJ0lX7DxQlW4FpVtDAX8wMsruWxQiT3nrYU3R0dGsWrWKBQsWcPLkSapWrcrXX3+dmXUTQhhQQkICM2bMYNSoUURERADw3nvvMX78eLy9vTP3Yrf3oez8DlXIRVTAX4ojU5LaccC8Pp81LcGsSoUwNZYHACFE3qLVahk9ejSTJk0iOjoaABsbGwYPHsywYcMwko7RO+Ojjz7SB6SqV6/OihUr0nagopC4cxQAe+1a07RUSUNVUYg8qWjRohQtWpRPP/1UP8okODiYXr16cfbsWcJPH9UVvHycfZVrcLhgWeIKdqGW5W2aG5+kntFZLCPuw9EZcHQGim0BVKUCdSOoPKpIgEqILJDuoNShQ4eYP38+69atw8vLiytXrrB//36qV69uiPoJITKZoihs2rSJIUOG6Kfh+vn5MWXKFGrXrp25F3t6HXZ+D3/uQAVEKpbMSGrFOpPm9GpQilHVC2Npmjun/AohxJsMGzaMBQsW8L///Y/q1avrp4eNGDGCuLg4fvrpp+yuogBCQkJYt24doFsZMSAgIM3HPjiyGo/4G0Qr5ni1HmaoKgoh/qV8+fIcP36cbdu2ERsby8GDB5k5cyYXTxyCE4eAOUQXLcHxsrUY7BZInXxPaao+Tn2js9hE/gXHZ8Px2SjWbqhKtdQFqDwDwOj16SyEEG8nzb3B8ePHs3DhQqKjo+ncuTOHDh2ifPnymJiYZP7S8EIIg7h27RoDBgzQ541yc3Pjp59+onv37m/MG5Uu0U9h31iU04tRKRoSFTXLNA2YS1ta1yjP7tpFsLc0zbzrCSFEDvTrr78yf/58AgMD9dvKly9PgQIF6Nu3rwSl3hErV65Eo9FQpUqVdAWk0GpQ7RsDwAHH9jQr4mWgGgoh/svExET/b2vHjh3p2rUrW7ZsYenSpdy7d497N69z76ZuUZ/nLm6crNqMvvGF8HVKpLfXExoancY2OgRO/gInf0GxckZVsoUuQFW4JqjlpaoQmSXN4xG//fZb2rZty927d5kwYQLly5c3ZL2EEJkoKiqKr776inLlyhEUFISZmRnffPMNN27coFevXpkXkEqMhYOTUX72hVMLUCkadmr8aZI4jovlvuX3IYF83bSkBKSEEALdwjElS6aczlWyZElCQ0PTda4DBw7QsmVL3N3dUalUbNiwIdl+RVEYMWIE7u7uWFhYUKdOHS5fvpysTHx8PP3798fJyQkrKysCAwN58OBBuu8rt1m6dCkA3bp1S9dxt3fOpkBiMOGKFSXbyGqKQmSnqlWrMnr0aIKDg3nw4AHDhg2jatWqADx/EsLZTQt5tGMe25Yv4utdiZT/81O6vhjIb0l1CFOsUb14CqcXwdLWKBOLwcbP4M9dkJSQzXcmRM6X5qDUqFGjWLNmDV5eXnz11VdcunTJkPUSQmQCRVFYsWIFJUuWZPz48SQmJtKiRQsuX77MmDFjsLGxyaQLaTG5tgFmVILdI1ElRHNRW5hOCd/xq+cYpvXrwOQOvrjbW2TO9YQQIhcoX748M2bMSLF9xowZ6X759+LFi1eeD3Qj3idPnsyMGTM4efIkbm5uNGzYkKioKH2ZgQMHsn79elatWsWhQ4eIjo6mRYsWaDSa9N1YLnL58mXOnj2LiYkJHTt2TPNxmtgI8h2fAMDhgr0p4lHAUFUUQqSDSqWiQIECjB49miNHjhAcHMyUKVNo2bIlzZs3x8jIiJvHdhK8cjhbFs7k+92xVLz5MR0jB7IiqS7PFRtUsaFwdiksb4sysSis/wSubdW9nBVCpJtKURQlPQfs37+fhQsXsm7dOry9vbl8+XKeyCmVm5c4zgvyYhtfuHCB/v376xOzent7M3XqVFq0aJG5F3pwGmXbUFR/nQLgoeLAhMSOXHVqwlfNS1OnuHOeaXNDyYvf36wk7Wt4ua2NM+uZYP/+/TRv3pxChQpRrVo1VCoVR44c4f79+2zdupWaNWu+1XlVKhXr16+ndevWgK793d3dGThwIF999RWgGxXl6urKuHHj6NOnDxERETg7O7N06VJ98OXhw4d4eHiwdetWGjdunOq14uPjiY+P1/85MjISDw8Pwv/P3p2Hx3S9ARz/zkz2lSSSCJJYgwixFLGrfV9LKbUv1RZVbSm1VfnZ16qqWmtra1+K2KldUWLft0SIyL7O3N8fkakQBDMZSd7P8+TJ3Dvn3vvek8ucee+55zx+nC3aSyNGjGDs2LG0aNGCtWvXZni7C78NosTVBVwnL/YDj+HsaGfEKA0nu/1bfddI/RqXIer3yJEjDBs2jKNHj+onoAAwN7egaOW6xOUpQcViLjTRHKOe6ijumsj/jm9uA0XrQfFmUKw+WBr2/0BTk+vX+LJbHUdGRpIrV65Xtpde+2HYmjVrUrNmTWbPns2yZctYuHAhNWvWpGLFirRt25ZBgwa9VeBCiLcTHh7OyJEj+fHHH9HpdFhbWzNs2DC+/PJLrKysDHegqPuwczScWoYKiFas+Cm5ORusW/JZUz8mly+ARp31/zMVQghjqVmzJpcuXeLHH3/kwoULKIpC69at6devHx4eHgY7zvXr1wkJCaF+/fr6dZaWltSsWZODBw/Sp08fTpw4QVJSUpoyHh4elCpVioMHD74wKTV+/HhGjx793PqIiAhe877nKymKov+SmFmN9aNHjwJQrVo1/Uy1rxL/4DqFry4B4GzxL6iONsPbmpop6jgnkfo1LkPUb/HixVm9ejVRUVFs3bqVv/76i82bN5OYmMi5/VuALVwHVgEqM3PK12vJJ+VUNNAcI19SGJxbD+fWo6jNSfasRlKRBiQVqo9i42yw8zQVuX6NL7vVcWRk5KsL8QZJqVT29vb07duXvn37cubMGf3sMZKUEsI0tFotv/zyC8OHDycsLAyADz74gMmTJ+Pp6Wm4AyUnwpGf0O2diDox5T/N1drqzKAjbWpXYFuNQjKjnhBCZJCHh4fRBzQPCQkBwM3NLc16Nzc3bt68qS9jYWHx3OQ1bm5u+u3TM3To0DRtv9SeUo6OjkbpKQVk6h3kS5cuAVChQgUcHR0ztE3Iku9xJ5kT6tLUb90DC/OsM2OXKeo4J5H6NS5D1q+joyM9e/akZ8+eKIrCunXr2Lp1K8uXLycmJibleMlJHP/rD0Zc8WWSfxvyOtvQxvkaDdXHKEww5jd2Y35jN4rqW/AMgBLNoHgTcCzw1udqCnL9Gl92q+OMnoNBvjn6+fkxffp0Jk2aZIjdCSFe0969e+nfvz///vsvACVLlmTmzJnUqVPHsAe6tA3d1qGoH11FDZzSFWZ0cheKlqvFgsp5KZxPHtUTQoiXSf1/OiNKly5t0GM/+/+zoiiv/D/7VWUsLS2xtLRM91jG+DxI3W9mfNbExMRw48YNAEqVKpWhY94/u5vij3aiVVQk1h2LZRa8SZOZdZwTSf0alzHqV6VS0bp1a1q3bs3MmTM5efIkS5Ys4bfffiM6Opq7l4PgchCX1Gpu+1Xge10ekmMtaFQ6D1+Xeoif+gbc/DvlZ+sQ8CibkqAq0Rxcihoszswg16/xZac6ztSkVCpzc3ND7k4I8Qq3bt1i8ODB/PHHHwDkzp2bMWPG0LdvX8zMDPjP++FllK1DUV0JRA2EKrmYkPQhtws04/vmfvh6OGSZRxOEEMKU/P39UalUr3y0TaVSGWyAcXd3dyClN1TevHn160NDQ/W9p9zd3UlMTCQ8PDxNb6nQ0FCqVKlikDiymvPnzwPg6uqKi4vLqzfQ6YjfmDJm1167htQOqGHM8IQQJmBlZUVAQAABAQFMnz6do0ePsmrVKv766y+uXbvGldNH9WV/uwp7b9cjb9VeNLG9QAPNMd5TXUR97yTcOwk7x6C4+KAq0SwlSZW3DGSDRIQQryvr3b4RQhAbG8uUKVMYP348cXFxqNVq+vTpw5gxYzLWcM6o+EjYOwHl8FxUSjKJioYF2sastv2QAW3K0cQvb4a+XAkhhEhx/fr1TD9mwYIFcXd3JzAwkLJlywKQmJjI3r17mTBhAgDly5fH3NycwMBA2rVrB0BwcDBnz55l4sSJmR7zuyAoKAhI6X2cEVd3LqBwwkWiFGsKtPkhW9zlFkK8mKWlJdWrV9dPSrF371527tzJokWLCA0NJSEhgdvHA7l9PJDgwsXZUL4BYZF+NCyk0N7pMlXUZ7F4eBH2X4T9k1EcC6Aq0RxKNIUClUCddR79FeJtSFJKiCxEp9OxePFihgwZQmhoKAA1atRg5syZrz19+EspCpz5A+22YWhiQlEBO7RlmUwXGteuxsYahbDKQmNkCCHEu8LLy8so+42OjubKlSv65evXr3Pq1CmcnJzw9PRk4MCBjBs3jqJFi1K0aFHGjRuHjY0NHTt2BFLGr+jRowdffvklzs7OODk5MXjwYPz8/Khbt65RYn7X7d69G0hJ2L2KNj4ax4PjADiY92MaFCps1NiEEO+e1AnBxowZA6RMlNClSxcuXLjA7aspPwDz1GpWuOYnSe1EnSpl+aR4ODXV/2ITcRsO/wiHf0SxzYOqeJOUHlTeNcDMwpSnJoRRSVJKiCzizJkzfPLJJ/z9998AeHt7M378eNq3b2/Yu7GhF9Bt/hL1zQNogKu6vHyf3JncZZqwsKEPeR2tDXcsIYTI4ZYuXcrcuXO5fv06hw4dwsvLi+nTp1OwYEFatGiR4f0cP36c2rVr65dTBx/v0qULixYt4uuvvyYuLo5+/foRHh5OpUqV2L59O/b29vptpk2bhpmZGe3atSMuLo46deqwaNEiNJqcdxNCq9WyadMmAJo1a/bK8ufX/EApJYw7Sh4qfDjM2OEJIbKAihUrEhQUxIULF/j111+5dOkS169fJygoiIiQWwBs/PMql6o05LEugOIuaj7xi6eh+SkcYx7AiUVwYhGKpQOqYg1TElRF6oCFrWlPTLxzzp07h6enJ3Z2dqYO5Y2olDd47ubq1assXLiQq1evMmPGDFxdXdm6dSsFChTA19fXGHGaXGRkJI6OjkRERBhlNpmIiIhsM8r+uygr13F0dDSjR49m2rRpaLVabG1tGTVqFAMGDDDsOG4J0bB3ArpDc1ArycQpFsxKbsmRvB8xrHkZynnmfuGmWbl+swKpX+OS+jW+7FbHhmoT/PTTT4wYMYKBAwfyww8/cPbsWQoVKsSiRYtYvHixvqdOVpJd2ks7duygXr165M6dm9DQ0JeO0xjz8Bbq2RWwJoGdvhOo80Ffo8ZmTNnt3+q7RurXuLJC/ep0Oo4fP05sbCwbN25k2rRpzw2D4eDtR9HCnvQsA23s/iWP6r9xWxUza1SF30+Zxa9YQ7B1zrTYs0L9ZnVvUscHDx6katWqlCxZUv/Y+bsio20C9evueO/evfj5+XHkyBHWrFlDdHTKlPD//vsvI0eOfPOIhRBpKIrC2rVrKVmyJJMnT0ar1dK6dWvOnz/P4MGDDZeQUhQIWod21ntwcCZqJZnt2vJ8oJlOwVYj+KNfzZcmpIQQQryZWbNm8csvvzBs2LA0vZEqVKjAmTNnTBhZzvbw4UP69+8PwIcffvjKiUOur/wGaxI4oy5B9Za9MiNEIUQWpVarqVixIrVq1WLKlCns2rWLggULpikTeeMMJ3Zu5tMZ2yi22J6aVzrzaVBpfjlnzaPIGLi4Gdb3Q5lcBGVhYzj0IzzK/PEKxbth5cqVQEpvqazqtR/fGzJkCGPHjmXQoEFpunzXrl2bGTNmGDQ4IXKqGzdu8Nlnn7F582YgZZDa2bNn07hxY8MeKOwqus2DUV/bhQa4pcvDaG1XClRqxbJ6xXC0lhk1hRDCWK5fv64fePxplpaWxMTEmCAiERUVRfny5bl16xZOTk76sWFeJOT8QUo93AJA7PvfYyHjLQohXkOtWrW4cuUKsbGxTJgwATMzMx4+fMjGjRu5efMmj2+eY9/Nc+x7Ur43UKqYJ19U1NC9cBjc/DvlZ9u3KG6+qHyapPSikpn8cgyDzrhuIq99BmfOnGH58uXPrc+TJw9hYWEGCUqInEqr1TJt2jRGjBhBXFwc5ubmfP3113z77bfY2NgY7kBJcbB/KroD01HrEklQzJirbc4Rj48Z1rIcvh6OhjuWEEKIdBUsWJBTp049N/j5X3/9leEZ34RhrV69mlu3buHg4MBff/318hltFYXo9V8DcMCmDlWr5swB4YUQb0etVmNnZ8f333+vXzdlyhT+/PNPunTpQnJyMv7+/ly/fp2IiAjOXrpFj0sw1LcyVQrZMsgvmgCzi5jdD4L7QbBvIopDPlTFm6YkqLyqgEZuNGdXTyelFEXJko9WvnZSKleuXAQHBz/XzfDkyZPky5fPYIEJkdNcuHCBzz77jJ07dwIpM3jMnTuX4sWLG/ZAV3aSvGEgZpG3UAN7taWZbtGLTs3fp3+5fFnyPzIhhMiKvvrqKz799FPi4+NRFIWjR4+yYsUKxo8fz/z5800dXo60ZMkSAL755hsqVqz40rKX9yyjaPwZ4hQL3FqNl89PIYTBWFhY0LFjR0qWLElYWBjvv/8+27dvp2vXroSEhAAQGnSYdUGwaZsVKgsrVInx5LJWM7SKGZXy3qRyxFxUR39GsXRE5dMwJUFVuA5YZs3BsEX6nk5KRUdHp3maLat47aRUx44d+eabb/jjjz9QqVTodDr+/vtvBg8ezMcff2yMGIXI1hRFYf78+Xz++eckJCRgbW3NzJkz6dGjh2EbuNEP0G0dgvrsn5gB9xQnxiZ/jGvFD1hU30ce1RNCiEzWrVs3kpOT+frrr4mNjaVjx47ky5ePGTNm8OGHH5o6vBwnKCiI3bt3o1Kp+Oijj15aVpcYh/3+lEf7Drp/RJ2iPpkRohAih/H399e/btCgAcHBwQBs3bqVlStXsnr16pQxnhPjAQhNhC/+SinfoXYJhpYJp7jdY8z/XQX/rkLRWKIqVCslQeXTCOxcM/mMhKElJyfrX4eHh+eMpNQPP/xA165dyZcvH4qiULJkSbRaLR07dmT48OHGiFGIbCsmJoZ+/frp78zWr1+fWbNmUaxYMcMdRFHg5FKStw7HLDECraJisbYBu/L24ttWFSnpYdjZkYQQQmRcr1696NWrFw8fPkSn0+HqKl8QTGXcuHEAtG7d+rlHKp91Zs1Eyujuc1/JjX/7EZkRnhBC6DVs2JCGDRsyY8YMrl69ys2bN5k/fz5btmzRl1mx+zwrdoOVnQPVS3lQ0iKUAf5xFNRug8vbUDaqoEBFVMWbQPGm4FzYhGck3lRUVJT+dXh4OJ6eniaM5s28dlLK3NycZcuWMWbMGE6ePIlOp6Ns2bIULVrUGPEJkW1duHCBtm3bEhQUhFqt5ocffuDrr79GrX7tSTFf7OFlktf3x+z2QcyAIJ0X4zSf0LJZU5aWzy+PGgghhAmNHj2aTp06Ubhw4ZePXSSMbvr06foxU7/++uuXlo0Ou0fhCz8BcL7kQGo5ORk9PiGESI+joyPlypWjXLlytGrVitjYWHbv3s2iRYtYs2YNOp2O+OhIAg9HEgj8dMKBHq1r8EGeGxRIukERjsDtIxA4AsWlOKrijVMSVB5lwZDfSYTRPJ2UevTokQkjeXNvPFR74cKFKVxYsqlCvImVK1fSq1cvoqOjcXd3Z+XKldSsWdNwB0hOQDkwDd2+yZjpkohVLJmW3IaIMj2Z1cQPJ1sLwx1LCCHEG1m9ejVjxozhvffeo1OnTrRv3548efKYOqwc5/LlywwZMgSAiRMnvnIsqUsrv6UccVxUF6ZK688yI0QhhMgQGxsbmjRpQpMmTQCIjY1l5syZ3L59mz///JPQ0FB+WrqJn56Uz+fhRrMSVowq9xi3hxfgwAU4MBWdnTvq4k9m8vOuDmby3eFdFRkZqX8dHh5uwkjeXIaSUoMGDcrwDqdOnfrGwQiR3YWGhjJgwABWrlwJpEwDu2LFCtzd3Q13kJsHSVr3OebhV9AAe7RlmOfwKZ+3rktAYWfDHUcIIcRb+ffffwkKCmLZsmVMnTqVQYMGUbduXTp16kTLli0NO+uqeKEhQ4aQkJBAvXr1GDx48EvL3jl/lDKh60AFMbW+x8I860/FLYTIvmxsbPRJ97Zt2/LFF19w+vRp/ft3791n7j2YuxPye3nRtIQ1w0o/JD8hcPxXOP4rOgt71MXqQ7FGqFwrgqPM0v0uefbxvawoQ5+kJ0+eTLN84sQJtFotPj4pgzpeunQJjUZD+fLlDR+hENmATqdj8eLFDB48mEePHqFWq/n2228ZOXJkmhkT3krcY7TbR6A5uRhz4IHiwDhdV7xrdGZh7cJYmmkMcxwhhBAG4+vry7hx4xg3bhx///03y5cvZ+DAgfTt2zfN3U9hHJcuXWLt2rVAyo3Vlz7WrihErf+K/CqFYzbVea9Gk0yKUggh3l7t2rU5deoUFy9e5MCBA8yaNStNgurOzZvMvQnzdlrili8f7cu78EmhOxD2mPwxf2JzdjX2KjPwqgLFG6cMlJ7b23QnJIAc9Pje7t279a+nTp2Kvb09ixcvJnfu3EBKRq5bt25Ur17dOFEKkYUdOnSIAQMGcOzYMSBlFo1ff/2VcuXKGe4gF7eSuL4/FrH3AVieXJvdBT5laOsACuWRaV+FECIrsLW1xdraGgsLizSNTGE88+bNQ1EUmjZtSqlSpV5a9uzOZZSKP0WCYo5rm0mZFKEQQhiWj48PPj4+dOvWDZVKhaIoBAUF8eOPP7Jx40bu3btH8I1rTL9xjelPtnFytGV2Kxc6eIXDjX0pP1uHoMtTAnXxxuDTGDzKyThUJpAdHt977atmypQpjB8/Xp+QAsidOzdjx45lypQpBg1OiKzszp07dOrUiSpVqnDs2DHs7e2ZOHEiR48eNVxCKiaMpD96wIr2WMTe57rOjZ7qMVi1ns283nUlISWEEO+469ev88MPP1CyZEkqVKjAP//8w6hRowgJCTF1aNmeoiisW7cOgK5du760bFJCLE5/fw/AMY+P8CpcwsjRCSGEcanValQqFWq1Gj8/P+bOncvVq1fZsWMHHTt2xPGpx/QeRcTQcdFNLCZqaXqoDEvueXHkrg71g/OwfwrMr4NucjHY8Dlc/AsSY014ZjlLjnl872mRkZHcv38fX1/fNOtDQ0Plrp4QgFarZebMmQwfPpzY2FhUKhXdunXjhx9+MOzYUUFrSdwwCIuER2gVFb9om3DTbwCTmvqTWwYyF0KId15AQABHjx7Fz8+Pbt260bFjR/Lly2fqsHKMc+fOcfXqVSwtLWnQoMFLy578fRwVlRAekJvSHUZlToBCCJHJrKysqFOnDnXq1EGr1bJ8+XLy5MnD8uXLWbp0KUlxMWzevp/NT8rncXMjj0UCeTSxdCl9l2YPF+HyzxJ0ZlaoCtdG5dMYijUEO1eTnld2lpHH9/744w9GjhxJ+/btGTlyZGaFlmGvnZRq1aoV3bp1Y8qUKVSuXBmAw4cP89VXX9G6dWuDByhEVnL+/Hm6d+/O4cOHAahatSozZsww7HhrUfdJ3DgIi0ubsAAu6vIzxfpzPm7blr5FZUpxIYTIKmrXrs38+fOfu9EnMse2bdsAeP/997Gze3HP4rCQW/he/QWAq2W+orJD7heWFUKI7EKj0dC5c2cAGjZsyNSpU/npp58YO3YsiYmJADy4f58HT8rvvQEqoJmvLW2LRvJR0hZUF/9CQQX5KqAq3ijlMb88xeFl4/eJDNNqtcTExOiXo6Oj0y13/Phxzp8//872wn7tpNTcuXMZPHgwnTp1IikpKWUnZmb06NGDSZPk+XqRMyUlJTF58mRGjRpFYmIi9vb2TJkyhZ49e7580NTXoSgop1eStPkbLJIiSFI0/KRtTnTFgUxvWAobC5kBSAghspJx48aZOoQc7e+//wagZs2aLy13fdXXVCCei2bFeK9538wITQgh3jnOzs58/vnndOjQgfv372NlZcXVq1fRaDTs37+fRYsWExHxmA1BMWwIgl5bksllpaJlUWjhcxDHw4cIyD8aJbc3ap8nA6V7VQGNualPLct6Ngn1oqRU6sR1ZcuWNXpMb+K1v8Xa2NgwZ84cJk2axNWrV1EUhSJFimBra2uM+IR45927d4+2bdty6NAhABo3bszcuXMpUKCA4Q4ScZeEdf2xvL4DC+CszpvZ9gPp3b4l5Tzljq0QQgjxOhRF0Selqlat+sJyV07to0L4XwDo6v8PjUZmshVC5GyFCxemSJEiAPqnQdq0acP06dPZunUrq1at4vfffyc2Npb7ifDzCfj5REpnlvF1bfimyg10h+egPvITOksH1EXrpySoitQF61ymOq0s6dnhk57uNZVKUZTsl5RKZWtrS+nSpQ0ZixBZzoEDB2jbti3379/H0dGRWbNm0alTJ8P2jjr5G0lbhmCZHE2CYsYsbRvMqg9kRh0fLM2kcSyEEEK8rmvXrnH//n0sLCyoUKFCumUUnY7kzd8AcMyhHu9VrJOZIQohRJbTsGFDGjZsyJQpU7hx4wYXLlzgp5/mcvd+KNcvX2TojliG7kgpWz6fBX3LJdL80e+4nv0TRWUG3lVTxqHyaQi5vU16LlnBs0mp9HpK3bt3j4cPH6LRaF45y6ypvHZSqnbt2i/9wr1r1663CkiIrEBRFObMmcPAgQNJTk7Gz8+PtWvXUrhwYcMdJCqEuNWfYX0jEAvgH10RfnX6ks8/bEpxdwfDHUcIIYTIYfbu3QtAhQoVsLKySrfMP1t+pXzSOWIVSzzbT8zM8IQQIktzcnLCycmJcuXK0bFjRxRFoV27dvz555/6MifuJtLrLny6VUNVb2u+rxZPVWUvXN8LW79Bl6ck6uKNU8ah8igLarUJz+jdFBubdpbD9JJSp06dAqB48eJYW1tnRliv7bWTUv7+/mmWk5KSOHXqFGfPnqVLly6GikuId1ZcXByffPIJixcvBqB9+/b8+uuvBn2EVTm7hsT1A7FOiiBBMWOG0h6nOl8ws3oRNGoZGFAIIYR4Gzt2pNyqr1Mn/d5PcTFR5D8+HoDT3t0IyFco02ITQojsRqVS8fvvv/PgQcqw6A8ePGD69On8tXUrd+/cYfflaKpdUVG5fCnKOTymZb5H1OMcPDgH+yejtXFFU7xRymN+BWuChY2Jz+jd8GxSKr3H986fPw/wzvaSgjdISk2bNi3d9aNGjXrhwFpCZBe3bt2idevWnDhxArVazYQJE/jyyy8N97he7CPi1g3E+tJ6LEkZO+rXPN8woEMLvF1k3DYhhMjq/v333wyXlWESjENRFHbu3AlA3bp10y1zavlwAggjmDyUbf9dZoYnhBDZkkqlwtXVFQBXV1d++eUXEhIS2Lp1K0uXLmX16tUcPn6Gw8AcwCGXI7nMk3E2S2BwwF06xi6Gfxaj01iiKlQLlU9DKNoAHPOZ9LxMKS4uDgAXFxcePnyYbj7m0qVLABQrVixTY3sdBpuuq1OnTlSsWJHJkycbapdCvFNOnDhB48aNCQ0NxdnZmZUrV76wMfsmlItbiV/zKdYJD0lW1MxVWmFT5xsmVy8mvaOEECKb8Pf3R6VSoSjKK29oaLXaTIoqZzl//jyhoaHY2NhQuXLl596/efEU5e8sBRWEVBlFXhs7E0QphBDZn6WlJS1atKBFixb88ccfbNmyhfv37/PXX38R+TiCSOAW8NGaZKYG5aZ87hgC3KL4OHkrqsvbANC6+aHxaQTFGua4x/xSk1J58uTh4cOHJCcnk5iYiIWFhb5MjkpKHTp06IXP5AuR1QUGBtK6dWuio6MpXbo069evx9vb2zA7j48kbuPXWAetwBq4rMvHXKev+KTjBxRxlYawEEJkJ9evX9e/PnnyJIMHD+arr74iICAASGlPTZkyhYkTZQwjYzl27BiQMp7U0w13SBncPGrNALxUWk5bV8K/bkdThCiEEDnOBx98wAcffACkJFJu376NWq1m165djBs3jhMX73ECmAf03pyEnYUKK7WWx/F/453rEGPf/4EW5T3Q+DRISVAVqg2W2fu7VGpSytnZWb8uOjoaJycn/XK2TEq1bt06zbKiKAQHB3P8+HG++066N4vsJTk5mVmzZvH111+TnJzM+++/z9q1a3FwMMxA48q1PcT90RebuGB0ioqFusYk1/qWCbVKYqbJOVl+IYTIKby8vPSvP/jgA2bOnEnjxo3160qXLk2BAgX47rvvaNmypQkizP6eTko968SWX6mQcIp4xZw87WagykF33IUQ4l1RrFgxfRKldu3aNG7cmHXr1hEWFsby5cuJi4sjPPm/8ucf6mjzexwN/7lBLqt51Cm4gG7lbVEXqoGqWMOU2fxyeZrobIwnNSnl4OCAhYUFiYmJxMTE6JNSUVFRBAcHA1C0aFGTxfkqr52UcnBwSNPdXK1W4+Pjw5gxY6hfv75BgxPClHbs2MGAAQM4d+4cAB9++CGLFi3C0tLy7XeeFE/c1hFYn/gZG+CWLg8/5vqS7h074eNu//b7F0II8c47c+YMBQsWfG59wYIF9Z89wvCOHz8OwHvvvZdmfWTEI7yO/wDAKe8eVC5YItNjE0II8byAgAB9j+JJkyZx584d1Go1u3fvZumKVdwNecDdaxfZeiXlsfeVZ5NZeyGZtiX+wtFqK02LmaFxL/nfY375K4BaY8pTMojUpJS1tTW2trYkJiamGVfqypUrQMqYU7lz5zZJjBnx2kmpRYsWGezg+/btY9KkSZw4cYLg4GDWrl2b5q6goiiMHj2aefPmER4eTqVKlfjxxx/x9fXVl0lISGDw4MGsWLGCuLg46tSpw5w5c8ifP7++THh4OP3792fDhg0ANG/enFmzZpErVy6DnYvIPq5evcqXX37J+vXrgZTukOPGjaNnz56oDXHH9H4QUcu7Yh+R0pVyubYOj6uNZGxdP8yld5QQQuQYJUqUYOzYsfz666/6IRASEhIYO3YsJUpIQsQYEhMT9dNjP9tT6vySL6hEOLdVHpTtMNIE0QkhhHiV3Llz6xMsvr6+fPbZZwAcPXqUnXv28c+l26xdMpctlxPZcjmlO1UBBxUNCv/DFwFncbaejLOzM2bFG0KxBlC4DlgZ5imYzPZ0UsrOzo7w8PA0M/Bdu3YNgCJFipgkvox67W/AhQoVIiws7Ln1jx8/plCh15suNyYmhjJlyjB79ux03584cSJTp05l9uzZHDt2DHd3d+rVq0dUVJS+zMCBA1m7di0rV67kwIEDREdH07Rp0zSDg3bs2JFTp06xdetWtm7dyqlTp+jcufNrxSqyv8jISL755htKlizJ+vXr0Wg09O/fn8uXL9O7d++3T0jpdCTsn03S3FrYR1zigeLACNsRlPlkIf0alJGElBBC5DBz585lx44dFChQgLp161K3bl3y589PYGAgc+fONXV42dLhw4dJSEjA1dWVwoUL69cHHdxCpbB1AETWnYSllUw3LoQQWUnFihUZ+vVg/pg/g5MnjtP+ww+pVP19LKysuR2pMP9kEr5zYnCfEk3lWbfZt2EpM77swM/tPIie1xgO/wSPrpn6NF7Lsz2lgDQ9pVLHsTTYWMhG8to9pW7cuJHubDAJCQncvXv3tfbVqFEjGjVqlO57iqIwffp0hg0bph/HavHixbi5ubF8+XL69OlDREQEv/76K0uXLtXPgvbbb79RoEABduzYQYMGDTh//jxbt27l8OHDVKpUCYBffvmFgIAALl68iI+Pz2vFLLIfrVbLokWLGDZsGPfv3wegfv36TJ06NU2vvLcSGUzkyp443DsAwE5tWYLeG8ewxpWwNMv6XUeFEEK8vooVK3L9+nV+++03Lly4gKIotG/fno4dO+obl8KwduzYAUCdOnX0w1HExUThGDgIgKNOzalYtanJ4hNCCPH2/Pz8WLliBQD3799n06ZNTJ/1I2dPnwTgRLCOmotin5ROYNbhbUypv5dqnt9g5V4MTfEnj/kVqAQag80NZ3DP9pQC0vSUunHjBkC6QwW8SzJcw6mPvgFs27YNR0dH/bJWq2Xnzp0GzcBdv36dkJCQNONUWVpaUrNmTQ4ePEifPn04ceIESUlJacp4eHhQqlQpDh48SIMGDTh06BCOjo76hBRA5cqVcXR05ODBgy9MSiUkJJCQkKBfjoyMBFKSZYqiGOw8n96nofcr/vOiOj569CifffaZfnyJokWLMmXKFJo0aaKfsvttJQdtIGnd5zgkRxCnWDDbvCvVO3/D54Wc9bFldXING5fUr3FJ/RpfdqtjQ5xHUlISPj4+bNq0id69exsgKpERO3fuBNDfzAQ4tfQbApRgQnGi+MfTTRSZEEIIY3Bzc6NHjx706NEDRVG4d+8e7Tt05O/9+7B3cSc+JoqgBzE0XBZLLisVnfz+Jbf1GTqUmkax/M5oitVLSVAVqQPW79a4TDmup1TqWE8qlYouXbqkec/c3Bxvb2+mTJlisMBCQkKAlIvoaW5ubty8eVNfxsLC4rlBu9zc3PTbh4SE4Orq+tz+XV1d9WXSM378eEaPHv3c+oiICKMkpVIvnqcHkReG82wdJycn87///Y+pU6eiKAr29vZ888039OrVCwsLC30S8q0kxqDdPgLnK39iDpzVebPWazhdm9TG3sqMiIiItz/GO0KuYeOS+jUuqV/jy251bIjPCHNzcxISErJFfWQVkZGRHDlyBEjpKQVw6Z+9VAxeDiq4V20c/rmcX7YLIYQQWZhKpSJfvnzs3b2Lf/75h9KlS/Mw7BGde/Rh346/eByfzOxjSQB8vy+RAg6xqFQL8M61iAGVrahVLQCncs1TklQupp/N7lU9pVKTUtmmp5ROpwNSTujYsWO4uLgYLainPdtYUxTllQ24Z8ukV/5V+xk6dCiDBg3SL0dGRlKgQAEcHR1xcDDsQGipSS5HR0dpnBrJ03V88+ZNPvroIw4dOgTARx99xKRJk3B3dzfc8e4cJ3pFd3LF3kKnqFikao5rqzEM9/d69cZZkFzDxiX1a1xSv8aX3erYUOfw+eefM2HCBObPn4+Z2bv7eEB2sXfvXrRaLUWKFMHLy4vY6AisN/ZFo1I4bl+HCnU7mDpEIYQQmUCj0ehnYM3nkZddf20gMTGRefPmcSroIrv3/821oJPcjkxpv9yK0LLvZgy51u9gQfO/aVViOMm5C2Hm0yhlsHSvKqAxz/TzeFlPKUVRst/je6lSs23GlpogCAkJIW/evPr1oaGh+t5T7u7uJCYmEh4enqa3VGhoKFWqVNGXSR0n6GkPHjx4rhfW0ywtLbG0tHxuvUqlMkqDOnW/2aGx/q5SqVSsXr2aXr16ERERgYODA/PmzaN9+/aGO4hOS/SuSVgfmIADOu4pTizMM5QenT/G3dHKcMd5B8k1bFxSv8Yl9Wt82amODXUOR44cYefOnWzfvh0/P7/nxpFas2aNQY4jUjw9nhTAmYWfU0m5RyhOFOkyx5ShCSGEMDELCwv9TH6KonDu3DkehEdy+OJdFi/8lSvH9/I4Po7Wv8fhahvPp++dp2qBS8AMvPLYU7hSQ1TFGkDRemCbOR14YmNTxsVKr6dUaGgocXFxqFQqChQokCnxvKkMJaVmzpxJ7969sbKyYubMmS8t279/f4MEVrBgQdzd3QkMDKRs2bJAyjS+e/fuZcKECQCUL18ec3NzAgMDadeuHQDBwcGcPXuWiRMnAhAQEEBERARHjx6lYsWKQEojMCIiQp+4EtlfbGwsAwcOZPHixUDKuGLLly83bNY4KoSwpV1xDk3pgbVZF0B47QkMrVkatTrrfwkTQghhWLly5aJNmzamDiPH2L59O5CSlDoVuIxKYesBCK0znVIuhustLYQQImtTqVT6Ca9qVYMhPdqSkJBInwGDWfzzLEJjFEbu+W/8abUqlhY+y2jv+zsf+Jqj5CuPxqdhSi8q99JgpBtyL+spldpLKl++fOl2tnmXZCgpNW3aND766COsrKyYNm3aC8upVKrXSkpFR0dz5coV/fL169c5deoUTk5OeHp6MnDgQMaNG0fRokUpWrQo48aNw8bGho4dOwIpjwH06NGDL7/8EmdnZ5ycnBg8eDB+fn76ASxLlChBw4YN6dWrFz///DMAvXv3pmnTpjLzXg5x584dWrRowT///INKpWLIkCGMHj0ac3PDdbFMvLCNpD9745z8mFjFkrm2fWnSeTA+eQ37qKcQQojsY+HChaYOIce4dOkSFy5cwNzcnPJlSpB7ecrsz4fdOlC5egsTRyeEEOJdZ2lpwaK5M/nfqG9ZvXo18xcs5FFkDJExcTwOvsnaC8msvZBMjw1xqFS7cbTcw6fvjaCxvztlajaDog2gUC2wtDNYTE8npVKHGHr8+DGQdcaTggwmpZ5+ZM+Qj+8dP36c2rVr65dTx3Dq0qULixYt4uuvvyYuLo5+/foRHh5OpUqV2L59O/b29vptpk2bhpmZGe3atSMuLo46deqwaNEiNBqNvsyyZcvo37+/fpa+5s2bM3v2bIOdh3h3HT16lJYtWxIcHIyzszMrVqygXr16hjtAciLhm0aQ+9RPWADndZ7s8ZvAp60aYGmmeeXmQgghhDC+9etTekXVrFmD2NWfUohIrmoKUrbbVBNHJoQQIitxd3fn008/5dNPP9WvW7dhIwuW/8HmP1cQk5QMQHSiwre7Evh2103qrfuJNiXm06OCDeqC1VD7NIJi9cGp0FvF8nRSKn/+/ADcunULyDoz78EbjCk1ZswYBg8ejI2NTZr1cXFxTJo0iREjRmR4X7Vq1XrpTHYqlYpRo0YxatSoF5axsrJi1qxZzJo164VlnJyc+O233zIcl8geVq5cSbdu3YiPj6dUqVIsW7YMPz8/g+1feXSdR0s64/z4DACrVA1xbz+ZT3zf7Wd2hRBCvDv+/PNPfv/9d27dukViYmKa9/755x8TRZX9pCalSjsnUCrhX2IVS8za/oqllc0rthRCCCFermXzZrRs3ozw8Bk8evSI249imbN4JYFrV/D43nUCr2kJvKal7+Z4uvv/xcxGu7G1+Ibk3EUwK94QitYHz4DXHiz96aRUavLp5s2bAFlmkHMA9etuMHr0aP1zik+LjY1l9OjRBglKiLeh0+kYMWIEHTp0ID4+nqZNm/L333/j6elpsGPEnvyD+NlVcX58hgjFhunOI6j9xWJqSkJKCCFEBs2cOZNu3brh6urKyZMnqVixIs7Ozly7do1GjRqZOrxsIzQ0lIMHDwIwIO8pAM5XmoBXifImjEoIIUR2kzt3bgoXLkyt9/z4ffYPhN+9xtYdu2n9cR/MLVMmvVpwKol+++xJUjSYhV+BQ7NhSXO0EwrCqo+xCPodokMzdLynk1JeXimzvKcmpbJST6nXTkopipLurDOnT5/GycnJIEEJ8aYSExPp2LEj33//PQBfffUV69atS/PI59sdIJaHy/tis74n1roYTuiKsSHgd/p/OghXh+w9u54QQgjDmjNnDvPmzWP27NlYWFjw9ddfExgYSP/+/YmIiDB1eNnGpk2bUBSFMu5meDqqOez+EeUbdzN1WEIIIXKABnVqsXrxXMLDHvLll18CcCjGk49dVtIvaQB/JNfggeKAJika1YUN2AR+hWpKMbQ/14I9/4O7/4BOl+6+n05KpXbAiIyM5PHjx1mqp1SGH9/LnTu3firnYsWKpUlMabVaoqOj6du3r1GCFCIj4uPjadu2LZs3b8bc3Jx58+bRtWtXgJc+JppR2pBzPF7SCZfYq+gUFUvN21C60//o7J3nrfcthBAi57l165Z+JmBra2uioqIA6Ny5M5UrV5bxLw1AURR+nJ0yc3Sb4hrOWvpTocd00wYlhBAix7G1taVt27ZMmTKFuId3WPF5fR5E1WTPxVBGXQgh7PJRArTHqa0+SWn1dTTBJyH4JOwZT7KNK5pi9VAVawCFaoNVyqDmz86+5+LiwsOHD7l69aq+x1S2SkpNnz4dRVHo3r07o0ePxtHRUf+ehYUF3t7eBAQEGCVIIV4lJiaGli1bsmPHDqysrFi3bh0NGjQw2P4jDy/FctuXOCsJhCq5+L3Ad3zcqQsOVoabwU8IIUTO4u7uTlhYGF5eXnh5eXH48GHKlCnD9evXDXIzRcCaP3/nn5OnsdRA0wr58OixHDNzC1OHJYQQIgcqVqwYkDI7fExMDHnsbfmgQgE+qFCAJG15jl1/xJ//3ub7q9fxfnyI99Unqa4+g11sKJxaBqeWoVObo3hWQePTgLjYGCAlKQUpj+o9fPiQbdu2kZiYiL29PQUKvPvDy2Q4KdWlSxcgJdNWpUoVzM3ly7h4N9y8eZNmzZpx5swZ7Ozs2LRpEzVr1jTMzpPiCV41gLxXVgJwQCnNo/qz+LRKmXQfYxVCCCEy6v3332fjxo2UK1eOHj168MUXX/Dnn39y/PhxWrduberwsrzDB/fT7eOPAPionB2un2zCyTWfiaMSQgiRUzk5OeHs7ExYWBiXL1/G399f/97F8+f4edIk6tWrx+jBH3HrUQN2XQjls/N3UW4cpAb/UFt9kkKEwI29cGPvf0mpw9NA/QHeXp4cP36c33//HYAyZcqgVr/2iE2ZLkNJqcjISP3rsmXLEhcXp+8q9iwHBwfDRCZEBly6dIm6dety+/ZtXF1dWb9+PZUrVzbIvrVh1wlb0J68MRfRKSp+s+5A1W7/o5qb46s3FkIIIV5h3rx56J6ME9G3b1+cnJw4cOAAzZo1kyER3tLVS+dp1qAOUfFaAgqYM3D2BvIVKmHqsIQQQuRwxYoV49ChQ1y6dEmflLp69SqVKlUiNjaWJUuW8Ndff9G2bVs+bt6cblULEpNQmb+vPGTehVCuXDhF6dijVFdOkKw7DID1maVwdRkVohX+JGW8b0jJ3WQFGUpK5cqV65W9QlIHQNdqtQYJTIhXOXPmDPXq1eP+/fsUL16cwMBA8ufPb5B9R55aj2b9J7gqMYQp9qwrNJqPOnbFylxjkP0LIYQQarU6zR3Mdu3a0a5dOxNGlGLOnDlMmjSJ4OBgfH19mT59OtWrVzd1WBl2PegIM/s14WF0Ej4uZkxf/Cd+FWubOiwhhBCCokWLcujQIa5cuaJf9/vvvxMbG6tfXr58OcuXL6d27dosW7aMvHnzUt/Xnfq+7iiKH0H3WrD52GXgPQA2q2rSUDlDA+8whjx1rDKPt8KuH6BYQ/AoC+9or6kMJaV2795t7DiEeC1//fUXHTt25PHjx5QuXZrAwEBcXV3ffsfaZO6tGYpH0DwATirFCKn/Ez2qVnj7fQshhBBPqVq1KjVr1qRWrVpUrVoVW1tbU4fEqlWrGDhwIHPmzKFq1ar8/PPPNGrUiHPnzuln9nlXxcfFcHLV9+Q+OY8lR8MBGDDseyrWbm7iyIQQQogUuXPnBtBPbgKwY8cOAGbOnEn+/PlZsmQJW7duZffu3fj7+/Pbb79Rr149AFQqFaXyOZJ4X80QwMUlD7bt5jLmQgihHMFMM4DkJx2Fqjveg30TYd9Ekq1d/hssvfD7YPXuPP2ToaRURsfnOXXq1NvEIsQrabVaxowZw/fff4+iKAQEBLBp0yacnJzeet+6iGBCFnTEI+IfAP60aEGZrtNp5PH2+xZCCCGe1bRpU/bu3cvs2bOJj4+nfPny+iRVtWrVsLOzy/SYpk6dSo8ePejZsyeQMtHNtm3b+Omnnxg/fnymx/O0y+fPEHLvDtY21jjlcsTKyhJFp+NxyHWiLuym6J01FIp+RLXfYnkcD36lfOn9+VcmjVkIIYR4Wuqg5KnDIcXGxnLgwAEA6tWrh7u7Oy1btuTixYu0b9+ef//9lwYNGjBhwgS++uq/z7RLly4BULy4D23K56dN+fwkacvRL/I2vy9dSPGGnfnZXkNt7UlqqP/FIe4hnF4Bp1egU5mhFKiMpnhDKNoAXIqCCcdLzvBA5y8SERHBsmXLmD9/PqdPn5bH94TRhIeH89FHH/HXX38B8MknnzBt2jQsLS3fet9R53ej+6MbHrpwohRr/sw/lPZdPsXG4q3/iQghhBDpGjp0KEOHDkWr1XLs2DH27NnDnj17mDp1KiqVioSEhEyNJzExkRMnTjBkyJA06+vXr8/BgwfT3SYhISFNnKnjkCqKYvAZBHu1rcu+8w8AsDGH4dUtGVLNgvxPGtJanULlVUlceaTDy8uLLX9tRa1Wy0yGryH17yZ1ZhxSv8Yl9WtcUr+GYWVlBaQkoxRF4ezZsyQmJuLm5kbRokWJjIxEURR8fHw4dOgQX3zxBfPmzePrr7/m2rVrjBo1CldXVy5cuACkPA6Y+jcxU6uYN3U886am3ES6/SiW3RdD+eJ8MEk3DlJN+Yf31Scpor4Htw6k/GwfjuLfCVrMNvi5ZvRaeeNv3Lt27WLBggWsWbMGLy8v2rRpw6+//vqmuxPipc6cOUOrVq24evUq1tbW/Pzzz3Tu3Pntd6zTcW/zeNxOTEaDjguKJ1dr/kjX2tVldj0hhBCZ4vLly5w+fZrTp0/z77//4uDgYJIxnB4+fIhWq8XNzS3Nejc3N0JCQtLdZvz48YwePfq59REREQb/4qJRq7HUQJIOYpPg210JxGjN+KRWXoJtfFh1xY7jd1Zhb2/P6tWrsbOzIyIiwqAxZHeKohAdHQ0g7SAjkPo1Lqlf45L6NayoqCgiIiK4ePEiAJ6enkRGRj5XxxMmTCA6Oprly5czd+5c9u7dy7p16wgKCtJv96LPOgcNtCiZmxYlcxOX5MOxm635+Wo4166c53++t/EOP4TZnUPE5S5OohE+L5+eMO9lXispdefOHRYtWsSCBQuIiYmhXbt2JCUlsXr1akqWLPlGgQrxMoqisHjxYj799FNiY2Px9vZm7dq1aabPfON9x4Zze8HHeD7cB8BWs9p4fjyXJp7ub71vIYQQ4lXat2/Pvn370Ol01KhRgxo1ajB06FBKly5t0rie/bKROplNeoYOHcqgQYP0y5GRkRQoUABHR0eDz8i84997RERE4ODgwMyZMxk0aBATDyYy8M9DlM2dmzaFCgEwduxYypUrZ9Bj5xSpiURHR0f50mkEUr/GJfVrXFK/hpE6ppRWq8XR0ZGwsDAAvL29cXRMGefp2Tr+6aefsLOzY968eZw/fx4fHx/9e6VLl9Zv9zKOQDMXJ5qVL4iipMzKp1KpIDEaa8DawvBDBmT0OslwUqpx48YcOHCApk2bMmvWLBo2bIhGo2Hu3LlvHKQQL/P48WP69u3LqlWrgJRnbFesWIGzs/Nb7zv6+lHil3XGMzmEBMWcP9wG0KLbN9hbW7z1voUQQoiM+OOPP3BxcaFr167Url2b6tWrm2QcqVQuLi5oNJrnekWFhoY+13sqlaWlZbqP0atUKqN8aVGpVKjVar744guWLFnCqVOn+O233yhZsiS3bt3C0dGRXr16yRemt5D6t5M6NA6pX+OS+jUuqd+3Z2NjA6SMKaVSqbhz5w4A+fPnT1O/T9exg4MDP//8M7Vq1aJfv348fvxY/56vr+9r/z3SlLe0f/OTeZ3jvESG5wTcvn07PXv2ZPTo0TRp0gSNRvPGwQnxKgcOHKBMmTKsWrUKMzMzxo0bx19//fX2CSlF4W7gbCwWN8IlOYSbihvbAn7jo0+GS0JKCCFEpnr06BHz588nOTmZ4cOH4+LiQqVKlfjmm2/04ydmJgsLC8qXL09gYGCa9YGBgVSpUiXT43mVLl26APDFF1/QqFEjANq1a6cfRFYIIYR41zw70HlqUqpAgQKv3LZDhw6Eh4ezZcsW+vbty+zZs9P0msqqMtxTav/+/SxYsIAKFSpQvHhxOnfuTPv27Y0Zm8iBFEVhwoQJDBs2DJ1OR+HChVm+fDkVK1Z8+30nxnBj/qcUvLcJgL3qSjh1/IXmRbzeet9CCCHE68qVKxfNmzenefPmAFy9epWxY8cydepUJk+ebJLJYwYNGkTnzp2pUKECAQEBzJs3j1u3btG3b99Mj+VVevTowbp169i7dy86nQ4fHx+GDRtm6rCEEEKIF3o2KXX79m0gY0mpVI0aNdLfjMkOMpyUCggIICAggBkzZrBy5UoWLFjAoEGD0Ol0BAYGUqBAAeztjdf1S2R/iqIwcOBAZs6cCcDHH3/M7NmzDXJdxd47T9yiDymYdJNkRc0a557U7zGWXLZvP3OfEEII8SYePXrE3r179bPuBQUF4eTkRIsWLahdu7ZJYmrfvj1hYWGMGTOG4OBgSpUqxZYtW/Dyevdu4Njb27N7925Onz5NQkIC5cuXx8xMZs0VQgjx7npRUip//vwmi8nUXvuT28bGhu7du9O9e3cuXrzIr7/+yv/+9z+GDBlCvXr12LBhgzHiFNmcoigMGDCAWbNmoVKpmDFjBp9//rlB9h184Ddy7fgSD+K5r+TicLnJfNC8rTwLLYQQwqTy5MmDi4sL1atXp1evXtSqVYtSpUqZOiz69etHv379TB1GhqhUKoNMfiKEEEJkhtSkVHx8PFqtlnv37gGv11Mqu3mr20k+Pj5MnDiR8ePHs3HjRhYsWGCouEQO8mxCav78+XTv3v3td5ycyJXfBlDkxnIAjqtKoWn3Ky1KFn/7fQshhBBv6fTp0+9EEkoIIYQQmcPKygpI6SkVHh6uf1T/RROK5AQG6eOs0Who2bIlLVu2NMTuRA6SlJRE7969WbRokUETUvEPbxL664cUiTsHwHr7Dvi1H0nBfO5vvW8hhBDCEFITUqGhoVy8eBGVSkWxYsVwdXU1cWRCCCGEMIanH9+LiYkBUhJVZmZmKIpiytBMRh68FyYTExNDu3bt2LJlCxqNhvnz59O1a9e33m/w8Y3Ybv4ETyWKx4ote31/oEmbLkRHRb590EIIIYSBREZG8umnn7JixQp0Oh2QcqOvffv2/Pjjjzg6Opo4QiGEEEIY0tNJqejoaABsbW1NGZLJqU0dgMiZHj58yPvvv8+WLVuwtrZm3bp1b5+Q0mm5tHIobhs746BEEURhrrTaQot23dCoZfwoIYQQ75aePXty5MgRNm/ezOPHj4mIiGDTpk0cP36cXr16mTo8IYQQQhhYej2l7OzsTBmSyUlPKZHpbty4QYMGDbh06RJOTk5s2rSJgICAt9pnwuNg7vzaiWJRx0EF222aUKbnT7g5yV1mIYQQ76bNmzezbds2qlWrpl/XoEEDfvnlFxo2bGjCyIQQQghhDKlJqaSkJCIiIgDpKSVJKZGpDh48SKtWrQgNDcXT05Nt27ZRvPjbDTx+98QWrDd9QmHlMbGKJTuLDKVRxwGYaaQjoBBCiHeXs7Nzuo/oOTo6kjt3bhNEJIQQQghjSk1KAYSFhQHSU0q+tYtMs2TJEmrXrk1oaCj+/v4cPHjwrRJSSnIi55cOIt/GDjgpj7mMJ2car6FZ5y8kISWEEOKdN3z4cAYNGkRwcLB+XUhICF999RXfffedCSMTQgghhDGkzr4HKUPagPSUkp5Swui0Wi3ffvstEydOBKB169YsWbLkrf7xRQZfIWxxZ0rEp8yut8O2CX7df6Sos9xZFkIIkTX89NNPXLlyBS8vLzw9PQG4desWlpaWPHjwgJ9//llf9p9//jFVmEIIIYQwEI1Gg7m5OUlJSTx48ACQnlKSlBJGFRUVRadOndiwYQMAw4YNY8yYMajVb96T6dqeJbju+YaCxBKh2HK41CjqtemFWgYzF0IIkYW0bNnS1CEIIYQQIpNZW1uTlJQkPaWekKSUMJqbN2/SrFkzzpw5g6WlJQsWLKBjx45vvL+kmHAuLf4M39BNAJxRF0fVdj4NSvoZKmQhhBAi04wcOdLUIQghhBAik1lbWxMZGSk9pZ6QpJQwir179/LBBx/w4MED3N3dWbduHZUqVXrj/d0+sRXLzZ/hq3uATlGxw+UjAnpMxt7G+tUbCyGEEEIIIYQQ74DUwc5Te0rl9KSUjAYtDOrRo0f06tWLWrVq8eDBA/z9/Tl69OgbJ6S0CTGcmd+XAhvb46p7wG3cOFRjKfU//1ESUkIIIbIktVqNRqN57id37txUrlyZNWvWmDpEIYQQQhhJalIqtaeUPL4nhAEoisKyZcsYNGiQ/h9Xz549mT59+hv/I7t3dj/K2j74ae8CsNu+KaW6zKSqi7PB4hZCCCEy29q1a9Nd//jxY44ePUqnTp1YvHgxH3zwQSZHJoQQQghjk55SaUlSSry1R48e8eGHHxIYGAhAyZIl+fnnn6lWrdob7S8xJoLzy7+m1J1VaFQK95XcXKw0nlqNPkSlksHMhRBCZG0tWrR44XtdunShZMmSTJ48WZJSQgghRDb0bFIqp/eUksf3xFu5d+8eNWrUIDAwECsrK3744QdOnjz5xgmpy/tW8nhyOcrcXYlGpXDApg7JfQ9So3EHSUgJIYTIEerXr8+lS5dMHYYQQgghjMDKygqA5ORkQHpKSU8p8cYePnxIzZo1uXLlCh4eHmzbto1SpUq90b4i7t/g9rLPKRW5D4A7uHEjYCxV638gySghhBA5SlxcnL7BKoQQQojsxcbGJs1yTu8pJUkp8Ubu3LlDy5YtuXLlCl5eXuzevZuCBQu+9n4SY6MI+uN7il9fSCkSSVI0HHD9kLKdxlHNMZfhAxdCCCHecb/88gtly5Y1dRhCCCGEMAIHB4c0y9JTSojXtH37dj7++GPu37+Pk5MTW7Zsee2ElKLTErT1F9yOTaSsEgbAWU1JaDqF2mWrGCNsIYQQ4p0waNCgdNdHRERw/Phxrl69yv79+zM5KiGEEEJkBnt7+zTL0lNKiAwICwtjxYoVLF68mOPHjwPg5+fHunXrKFSoUMZ3pChcPrwJ9a4xlEpKGS/jLq5cL/sNAU27o9HIMGdCCCGyt5MnT6a73sHBgYYNG9KvXz+8vLwyOSohhBBCZIZnk1LSU0qIl3j8+DETJ05k+vTpxMXFAWBmZsYnn3zCuHHjXusf0JWjf6Hd+QM+CWcAiFasOe7ZnQoffks125z9D1EIIUTOsXv3blOHIIQQQggTefbxPekpJUQ67t+/z7x585g2bRrh4eEAlClThm7dutGhQwdcXV0ztB9Fp+PSoQ3o9k+jRPwpABIUc446t6BQ6xHUyi93goUQQgghhBBC5AzP9pTKlSuXaQJ5R0hSSqQRERHB6NGj+fHHH0lMTATA19eXcePG0axZswzPhJecGM+ZrQvIffpnfLQ3AEhUNBxzaoZni++o7l3EWKcghBBCCCGEEEK8k55NSjk5OZkokneDJKWEXmBgIJ06dSI0NBSASpUq0b9/f9q3b49Go8nQPh6F3OLK9rkUvLaCsjwCIEax5FSe5uRv/BVVC/kYLX4hhBBCCCGEEOJd9vTje3Z2dlhYWJgwGtOTpFQOd/HiRdauXcuOHTvYuXMnAMWLF2fatGk0bNgwQ/vQJSdxbv8atMcX4xt9iIoqHQChOHHJuyMlmvanqoub0c5BCCGEEEIIIYTICp7uKZXTe0mBJKVyrOvXrzNkyBD++OMPFEUBQK1W07t3b6ZNm4aVldVLt1d0Oq6fPcSDwysoeG8LpQhLeUMF58xKEunbCf9G3almZW3sUxFCCCGEEEIIIbIESUqlJUmpHCY0NJSFCxfyv//9j8ePHwPQsGFDGjZsSJMmTShS5MVjPSk6HdeDDvPg8Cry39tKISWEQk/eC8ee865NcK3Zm5K+5Y1/IkIIIYQQQgghRBbz9ON7kpSSpFS2pSgKV69eJSwsDAcHB06ePMnatWtZv349SUlJQMqYUfPmzaN06dIv3E9UxCOuHN5E0sVteD86SCEe6RNRcYoFQXYBKL6t8KvdnirWNplwZkIIIYQQQgghRNb0dE8pZ2dnE0bybpCkVDYTFRXF3LlzmTVrFrdv3063TMWKFenTpw+dOnV6blC1yMdh3Di5m5grB3B8cJyiCecoq9Lq349TLDhnVwldyZaUqPEBFewdjXo+QgghhBBCCCFEdvF0UurZmfhyohyVlJozZw6TJk0iODgYX19fpk+fTvXq1U0dlkHcu3ePefPmMWvWLB49Spn1ztLSkjx58hAdHY2npydNmjThgw8+oGzZsgDERD3m2ul9RFz/B0LO4BJxFu/k65RWKf/tWAW3VB7cc6mGtW9DilVsSHkbW1OcohBCCCGEEEIIkaU9nYgyNzc3YSTvhhyTlFq1ahUDBw5kzpw5VK1alZ9//plGjRpx7tw5PD09TR3eG4mKimL79u389ttvbNy4Ea02pUdT0aJFGTp0KO3btSM5KZ7w+7d5fO8ycSGXSTg0h1Pbb+Ecf4t8umCKP52AAlDBHZU7wQ7+KJ6V8fCvj2dhX7JmDQkhhBBCCCGEEO+OpxNRZmY5JiXzQjmmBqZOnUqPHj3o2bMnANOnT2fbtm389NNPjB8/3qSx/TpjLPduXcPayhIzjRoHW2sKe+bF1socRdGRmJjIhau3uXT9LrdDHnA7+CG3Q8K4G/oIrVan349/QWc+rJyXZsWtyXVrHOqJX+GgSsIB8ErvwCp4QG7uWRUh1qkkFvnL4ulfm/we3uTPrJMXQgghhBBCCCFyoGeH08mJckRSKjExkRMnTjBkyJA06+vXr8/BgwfT3SYhIYGEhAT9cmRkJJAygLiiKOlu86Z+njGJ4zei0qwzV4OrrQozNdyPUYhPTn/bwrlVtCxuTjd/c3xdk4BbkJqnUqX8ilKseaBxI8I6P/H2nqidCmOTtyh5i5XDxTU/Ls/s09Dn9y5I/btlx3N7F0j9GpfUr3FJ/Rpfdqvj7HIeQgghhDAteXwvhySlHj58iFarxc3NLc16Nzc3QkJC0t1m/PjxjB49+rn1ERERBm+MenvmB+6SrIMkrcKDqERCIxO4G/XfcXLZmlOqQC48XezIn8cOTxd7PF3tcXZ2QmdhzyMLO/Zb2KGytMPMOheWud2wze2Bg0terKxtcQLSm2wyIiLCoOfyrlIUhejoaABUKpWJo8l+pH6NS+rXuKR+jS+71XHqjSohhBBCiDdRuXJlDh8+TOfOnU0disnliKRUqmcbwoqivLBxPHToUAYNGqRfjoyMpECBAjg6OuLg4GDQuFbtPktERASOjo6oVCoUReHmzZuEh4eTmJiIs7MzhQsXzhYNeVNJTSSm1rEwLKlf45L6NS6pX+PLbnWcHc5BCCGEEKazd+9eHjx4QL58+UwdisnliKSUi4sLGo3muV5RoaGhz/WeSmVpaYmlpeVz61UqlVEao6n7Tf0pWLAgBQsWNPhxcrKn61cYntSvcUn9GpfUr/FlpzrODucghBBCCNOxsLCQhNQTalMHkBksLCwoX748gYGBadYHBgZSpUoVE0UlhBBCCCGEEEIIkXPliJ5SAIMGDaJz585UqFCBgIAA5s2bx61bt+jbt6+pQxNCCCGEEEIIIYTIcXJMUqp9+/aEhYUxZswYgoODKVWqFFu2bMHLy8vUoQkhhBBCCCGEEELkODkmKQXQr18/+vXrZ+owhBBCCCGEEEIIIXK8HDGmlBBCCCGEEEIIIYR4t+SonlJvI3U668jISKPsOzIyMtvMSvQukjo2Lqlf45L6NS6pX+PLbnWc2hZIbRuI/0h7KWuTOjYuqV/jkvo1Lqlf48tudZzR9pIkpTIoKioKgAIFCpg4EiGEEEK8C6KionB0dDR1GO8UaS8JIYQQ4mmvai+pFLnNlyE6nY579+5hb29v8KxlZGQkBQoU4Pbt2zg4OBh03yKF1LFxSf0al9SvcUn9Gl92q2NFUYiKisLDwwO1WkZCeJq0l7I2qWPjkvo1Lqlf45L6Nb7sVscZbS9JT6kMUqvV5M+f36jHcHBwyBYX37tM6ti4pH6NS+rXuKR+jS871bH0kEqftJeyB6lj45L6NS6pX+OS+jW+7FTHGWkvye09IYQQQgghhBBCCJHpJCklhBBCCCGEEEIIITKdJKXeAZaWlowcORJLS0tTh5JtSR0bl9SvcUn9GpfUr/FJHQtDkOvI+KSOjUvq17ikfo1L6tf4cmody0DnQgghhBBCCCGEECLTSU8pIYQQQgghhBBCCJHpJCklhBBCCCGEEEIIITKdJKWEEEIIIYQQQgghRKaTpJQQQgghhBBCCCGEyHSSlBJCCCGEEEIIIYQQmU6SUu+AOXPmULBgQaysrChfvjz79+83dUjZwqhRo1CpVGl+3N3dTR1WlrVv3z6aNWuGh4cHKpWKdevWpXlfURRGjRqFh4cH1tbW1KpVi6CgINMEm0W9qo67du363DVduXJl0wSbxYwfP5733nsPe3t7XF1dadmyJRcvXkxTRq7ht5OROpZrWLwNaS8Zh7SXDE/aTMYl7SXjkjaTcUl76XmSlDKxVatWMXDgQIYNG8bJkyepXr06jRo14tatW6YOLVvw9fUlODhY/3PmzBlTh5RlxcTEUKZMGWbPnp3u+xMnTmTq1KnMnj2bY8eO4e7uTr169YiKisrkSLOuV9UxQMOGDdNc01u2bMnECLOuvXv38umnn3L48GECAwNJTk6mfv36xMTE6MvINfx2MlLHINeweDPSXjIuaS8ZlrSZjEvaS8YlbSbjkvZSOhRhUhUrVlT69u2bZl3x4sWVIUOGmCii7GPkyJFKmTJlTB1GtgQoa9eu1S/rdDrF3d1d+d///qdfFx8frzg6Oipz5841QYRZ37N1rCiK0qVLF6VFixYmiSe7CQ0NVQBl7969iqLINWwMz9axosg1LN6ctJeMR9pLxiVtJuOS9pLxSZvJuKS9pCjSU8qEEhMTOXHiBPXr10+zvn79+hw8eNBEUWUvly9fxsPDg4IFC/Lhhx9y7do1U4eULV2/fp2QkJA017KlpSU1a9aUa9nA9uzZg6urK8WKFaNXr16EhoaaOqQsKSIiAgAnJydArmFjeLaOU8k1LF6XtJeMT9pLmUc+bzKHfNYYjrSZjEvaS/L4nkk9fPgQrVaLm5tbmvVubm6EhISYKKrso1KlSixZsoRt27bxyy+/EBISQpUqVQgLCzN1aNlO6vUq17JxNWrUiGXLlrFr1y6mTJnCsWPHeP/990lISDB1aFmKoigMGjSIatWqUapUKUCuYUNLr45BrmHxZqS9ZFzSXspc8nljfPJZYzjSZjIuaS+lMDN1AAJUKlWaZUVRnlsnXl+jRo30r/38/AgICKBw4cIsXryYQYMGmTCy7EuuZeNq3769/nWpUqWoUKECXl5ebN68mdatW5swsqzls88+499//+XAgQPPvSfXsGG8qI7lGhZvQ/59Goe0l0xDrmfjkc8aw5E2k3FJeymF9JQyIRcXFzQazXMZ5dDQ0Ocyz+Lt2dra4ufnx+XLl00dSraTOkuPXMuZK2/evHh5eck1/Ro+//xzNmzYwO7du8mfP79+vVzDhvOiOk6PXMMiI6S9lLmkvWRc8nmT+eSz5s1Im8m4pL30H0lKmZCFhQXly5cnMDAwzfrAwECqVKlioqiyr4SEBM6fP0/evHlNHUq2U7BgQdzd3dNcy4mJiezdu1euZSMKCwvj9u3bck1ngKIofPbZZ6xZs4Zdu3ZRsGDBNO/LNfz2XlXH6ZFrWGSEtJcyl7SXjEs+bzKffNa8HmkzGZe0l54nj++Z2KBBg+jcuTMVKlQgICCAefPmcevWLfr27Wvq0LK8wYMH06xZMzw9PQkNDWXs2LFERkbSpUsXU4eWJUVHR3PlyhX98vXr1zl16hROTk54enoycOBAxo0bR9GiRSlatCjjxo3DxsaGjh07mjDqrOVldezk5MSoUaNo06YNefPm5caNG3z77be4uLjQqlUrE0adNXz66acsX76c9evXY29vr7+75+joiLW1NSqVSq7ht/SqOo6OjpZrWLwxaS8Zj7SXDE/aTMYl7SXjkjaTcUl7KR2mmPJPpPXjjz8qXl5eioWFhVKuXLk000GKN9e+fXslb968irm5ueLh4aG0bt1aCQoKMnVYWdbu3bsV4LmfLl26KIqSMj3syJEjFXd3d8XS0lKpUaOGcubMGdMGncW8rI5jY2OV+vXrK3ny5FHMzc0VT09PpUuXLsqtW7dMHXaWkF69AsrChQv1ZeQafjuvqmO5hsXbkvaScUh7yfCkzWRc0l4yLmkzGZe0l56nUhRFMU66SwghhBBCCCGEEEKI9MmYUkIIIYQQQgghhBAi00lSSgghhBBCCCGEEEJkOklKCSGEEEIIIYQQQohMJ0kpIYQQQgghhBBCCJHpJCklhBBCCCGEEEIIITKdJKWEEEIIIYQQQgghRKaTpJQQQgghhBBCCCGEyHSSlBJCCCGEEEIIIYQQmU6SUkKIbG/UqFH4+/ub7PjfffcdvXv3zlDZwYMH079/fyNHJIQQQgiRlrSXhBCmoFIURTF1EEII8aZUKtVL3+/SpQuzZ88mISEBZ2fnTIrqP/fv36do0aL8+++/eHt7v7J8aGgohQsX5t9//6VgwYLGD1AIIYQQ2Z60l4QQ7ypJSgkhsrSQkBD961WrVjFixAguXryoX2dtbY2jo6MpQgNg3Lhx7N27l23btmV4mzZt2lCkSBEmTJhgxMiEEEIIkVNIe0kI8a6Sx/eEEFmau7u7/sfR0RGVSvXcume7o3ft2pWWLVsybtw43NzcyJUrF6NHjyY5OZmvvvoKJycn8ufPz4IFC9Ic6+7du7Rv357cuXPj7OxMixYtuHHjxkvjW7lyJc2bN0+z7s8//8TPzw9ra2ucnZ2pW7cuMTEx+vebN2/OihUr3rpuhBBCCCFA2ktCiHeXJKWEEDnSrl27uHfvHvv27WPq1KmMGjWKpk2bkjt3bo4cOULfvn3p27cvt2/fBiA2NpbatWtjZ2fHvn37OHDgAHZ2djRs2JDExMR0jxEeHs7Zs2epUKGCfl1wcDAdOnSge/funD9/nj179tC6dWue7rRasWJFbt++zc2bN41bCUIIIYQQLyHtJSGEsUlSSgiRIzk5OTFz5kx8fHzo3r07Pj4+xMbG8u2331K0aFGGDh2KhYUFf//9N5ByB0+tVjN//nz8/PwoUaIECxcu5NatW+zZsyfdY9y8eRNFUfDw8NCvCw4OJjk5mdatW+Pt7Y2fnx/9+vXDzs5OXyZfvnwAr7yrKIQQQghhTNJeEkIYm5mpAxBCCFPw9fVFrf4vL+/m5kapUqX0yxqNBmdnZ0JDQwE4ceIEV65cwd7ePs1+4uPjuXr1arrHiIuLA8DKykq/rkyZMtSpUwc/Pz8aNGhA/fr1adu2Lblz59aXsba2BlLuNgohhBBCmIq0l4QQxiZJKSFEjmRubp5mWaVSpbtOp9MBoNPpKF++PMuWLXtuX3ny5En3GC4uLkBKt/TUMhqNhsDAQA4ePMj27duZNWsWw4YN48iRI/rZYx49evTS/QohhBBCZAZpLwkhjE0e3xNCiAwoV64cly9fxtXVlSJFiqT5edFsNYULF8bBwYFz586lWa9SqahatSqjR4/m5MmTWFhYsHbtWv37Z8+exdzcHF9fX6OekxBCCCGEIUl7SQjxuiQpJYQQGfDRRx/h4uJCixYt2L9/P9evX2fv3r0MGDCAO3fupLuNWq2mbt26HDhwQL/uyJEjjBs3juPHj3Pr1i3WrFnDgwcPKFGihL7M/v37qV69ur5buhBCCCFEViDtJSHE65KklBBCZICNjQ379u3D09OT1q1bU6JECbp3705cXBwODg4v3K53796sXLlS363dwcGBffv20bhxY4oVK8bw4cOZMmUKjRo10m+zYsUKevXqZfRzEkIIIYQwJGkvCSFel0p5el5NIYQQBqUoCpUrV2bgwIF06NDhleU3b97MV199xb///ouZmQz7J4QQQojsT9pLQuRc0lNKCCGMSKVSMW/ePJKTkzNUPiYmhoULF0oDSwghhBA5hrSXhMi5pKeUEEIIIYQQQgghhMh00lNKCCGEEEIIIYQQQmQ6SUoJIYQQQgghhBBCiEwnSSkhhBBCCCGEEEIIkekkKSWEEEIIIYQQQgghMp0kpYQQQgghhBBCCCFEppOklBBCCCGEEEIIIYTIdJKUEkIIIYQQQgghhBCZTpJSQgghhBBCCCGEECLTSVJKCCGEEEIIIYQQQmQ6SUoJIYQQQgghhBBCiEwnSSkhhBBCCCGEEEIIkekkKSWEEEIIIYQQQgghMp0kpYQQQgghhBBCCCFEppOklBBCCCGEEEIIIYTIdJKUEkJk2KJFi1CpVPofKysr3N3dqV27NuPHjyc0NNTUIb5TTp48Sc2aNXF0dESlUjF9+nQAdu7cSYUKFbC1tUWlUrFu3Tp93d64ceO1jjFq1ChUKpXhg39KbGwso0aNYs+ePUY9jhBCCJFdHDlyhFatWuHp6YmlpSVubm4EBATw5Zdfpik3Z84cFi1a9FbHUqlUfPbZZ68st2fPHlQqVZrP8/TaEbVq1aJWrVr6ZWkHpOjatSve3t6mDkOIbMfM1AEIIbKehQsXUrx4cZKSkggNDeXAgQNMmDCByZMns2rVKurWrWvqEN8J3bt3JyYmhpUrV5I7d268vb1RFIV27dpRrFgxNmzYgK2tLT4+PiQnJ3Po0CHy5s37Wsfo2bMnDRs2NNIZpIiNjWX06NEAaRqpQgghhHje5s2bad68ObVq1WLixInkzZuX4OBgjh8/zsqVK5kyZYq+7Jw5c3BxcaFr165Gj6tcuXIcOnSIkiVLvrTcnDlz0ixLOyDFd999x4ABA0wdhhDZjiSlhBCvrVSpUlSoUEG/3KZNG7744guqVatG69atuXz5Mm5ubi/cPjY2Fhsbm8wI1aTOnj1Lr169aNSokX7d3bt3efToEa1ataJOnTppyufJk+e1j5E/f37y58//1rEKIYQQwjAmTpxIwYIF2bZtG2Zm/33d+vDDD5k4caLJ4nJwcKBy5cqvLPeqpFV2ERcXh7W1dYbLFy5c2IjRCJFzyeN7QgiD8PT0ZMqUKURFRfHzzz/r13ft2hU7OzvOnDlD/fr1sbe31ydjAgMDadGiBfnz58fKyooiRYrQp08fHj58+Nz+169fT+nSpbG0tKRQoULMmDHjtR5d27p1K3Xq1MHR0REbGxtKlCjB+PHj05TZsGEDAQEB2NjYYG9vT7169Th06NBz+7p8+TIdO3bE1dUVS0tLSpQowY8//qh/P/VRvOTkZH766Sf9446jRo3SJ5C++eYbVCqVvhv4ix7fe1XcL6qDVatWERAQgK2tLXZ2djRo0ICTJ0+mKZP6t7ly5QqNGzfGzs6OAgUK8OWXX5KQkADAjRs39Mmy0aNH688lM+7oCiGEEFlRWFgYLi4uaRJSqdTq/75+eXt7ExQUxN69e/Wfr6ntgvj4eL788kv8/f1xdHTEycmJgIAA1q9f/8Lj/vzzzxQrVgxLS0tKlizJypUr07yf3uN76Xn68b2XtQP279+PSqVixYoVz+1jyZIlqFQqjh079sLjpLZ9du3aRa9evXB2dsbBwYGPP/6YmJgYQkJCaNeuHbly5SJv3rwMHjyYpKSkNPsYPXo0lSpVwsnJCQcHB8qVK8evv/6Koihpynl7e9O0aVPWrFlD2bJlsbKy0vf+CgoKon79+tjY2JAnTx4+/fRTNm/e/Fxdpff4Xuqjk0uXLqVEiRLY2NhQpkwZNm3a9NI6FkL8R3pKCSEMpnHjxmg0Gvbt25dmfWJiIs2bN6dPnz4MGTKE5ORkAK5evUpAQAA9e/bE0dGRGzduMHXqVKpVq8aZM2cwNzcHUhIzrVu3pkaNGqxatYrk5GQmT57M/fv3MxTXr7/+Sq9evahZsyZz587F1dWVS5cucfbsWX2Z5cuX89FHH1G/fn1WrFhBQkICEydOpFatWuzcuZNq1aoBcO7cOapUqaJPwrm7u7Nt2zb69+/Pw4cPGTlyJE2aNOHQoUMEBATQtm1b/fgR+fPnp0yZMrRu3ZrPP/+cjh07Ymlp+VZxp2fcuHEMHz6cbt26MXz4cBITE5k0aRLVq1fn6NGjae6AJiUl0bx5c3r06MGXX37Jvn37+P7773F0dGTEiBHkzZuXrVu30rBhQ3r06EHPnj2BN+vVJYQQQuQEAQEBzJ8/n/79+/PRRx9Rrlw5fZvmaWvXrqVt27Y4OjrqH5lLbRckJCTw6NEjBg8eTL58+UhMTGTHjh20bt2ahQsX8vHHH6fZ14YNG9i9ezdjxozB1taWOXPm0KFDB8zMzGjbtu0bn8vL2gGFCxembNmy/Pjjj3To0CHNdrNnz+a9997jvffee+UxevbsSevWrVm5ciUnT57k22+/JTk5mYsXL9K6dWt69+7Njh07mDBhAh4eHgwaNEi/7Y0bN+jTpw+enp4AHD58mM8//5y7d+8yYsSINMf5559/OH/+PMOHD6dgwYLY2toSHBxMzZo1sbW15aeffsLV1ZUVK1ZkaIyuVJs3b+bYsWOMGTMGOzs7Jk6cSKtWrbh48SKFChXK8H6EyLEUIYTIoIULFyqAcuzYsReWcXNzU0qUKKFf7tKliwIoCxYseOm+dTqdkpSUpNy8eVMBlPXr1+vfe++995QCBQooCQkJ+nVRUVGKs7Oz8qr/xqKiohQHBwelWrVqik6nS7eMVqtVPDw8FD8/P0Wr1abZ1tXVValSpYp+XYMGDZT8+fMrERERafbx2WefKVZWVsqjR4/06wDl008/TVPu+vXrCqBMmjQpzfrUur1+/XqG41YURRk5cmSaOrh165ZiZmamfP7558/Vg7u7u9KuXTv9utS/ze+//56mbOPGjRUfHx/98oMHDxRAGTly5AvjEEIIIUSKhw8fKtWqVVMABVDMzc2VKlWqKOPHj1eioqLSlPX19VVq1qz5yn0mJycrSUlJSo8ePZSyZcumeQ9QrK2tlZCQkDTlixcvrhQpUkS/bvfu3Qqg7N69W7/u2XaEoihKzZo108T0snZAavvl5MmT+nVHjx5VAGXx4sUvPafUbZ9ts7Rs2VIBlKlTp6ZZ7+/vr5QrV+6F+9NqtUpSUpIyZswYxdnZOU37ycvLS9FoNMrFixfTbPPVV18pKpVKCQoKSrO+QYMGz9VVly5dFC8vrzTlAMXNzU2JjIzUrwsJCVHUarUyfvz4l56/ECKFPL4nhDAo5Znu0qnatGnz3LrQ0FD69u1LgQIFMDMzw9zcHC8vLwDOnz8PQExMDMePH6dly5ZYWFjot7Wzs6NZs2avjOfgwYNERkbSr1+/Fz7qd/HiRe7du0fnzp3TdKu3s7OjTZs2HD58mNjYWOLj49m5cyetWrXCxsaG5ORk/U/jxo2Jj4/n8OHDr4wpIzISd3q2bdtGcnIyH3/8cZr4rKysqFmz5nNd9lUq1XP1WLp0aW7evGmI0xBCCCFyHGdnZ/bv38+xY8f43//+R4sWLbh06RJDhw7Fz88v3WEK0vPHH39QtWpV7Ozs9O2kX3/9Vd9GelqdOnXSjOep0Who3749V65c4c6dOwY7t2d16NABV1fXNMMYzJo1izx58tC+ffsM7aNp06ZplkuUKAFAkyZNnlv/bPtk165d1K1bF0dHRzQaDebm5owYMYKwsLDnZoUuXbo0xYoVS7Nu7969lCpV6rlxtJ7t+fUytWvXxt7eXr/s5uaGq6urtKWEyCBJSgkhDCYmJoawsDA8PDzSrLexscHBwSHNOp1OR/369VmzZg1ff/01O3fu5OjRo/qkTlxcHADh4eEoipLuwOkvG0w91YMHDwBeOhh4WFgYQLoz33l4eKDT6QgPDycsLIzk5GRmzZqFubl5mp/GjRsDZLihaYi405P6SON77733XIyrVq16Lj4bGxusrKzSrLO0tCQ+Pv4tohdCCCFEhQoV+Oabb/jjjz+4d+8eX3zxBTdu3MjQYOdr1qyhXbt25MuXj99++41Dhw5x7Ngxunfvnu5ntLu7+wvXpbZzjMHS0pI+ffqwfPlyHj9+zIMHD/j999/p2bPnS4coeJqTk1Oa5dSbkOmtf/rcjx49Sv369QH45Zdf+Pvvvzl27BjDhg0D/mtLpkqvnRcWFvbGbcxUzs7Oz62ztLR87vhCiPTJmFJCCIPZvHkzWq32uemC0+vpc/bsWU6fPs2iRYvo0qWLfv2VK1fSlMudOzcqlSrd8aNCQkJeGVPq2Ecvu0uY2pgIDg5+7r179+6hVqvJnTs3kHLnsXPnznz66afp7qtgwYKvjCkjMhJ3elxcXAD4888/9b3OhBBCCGFa5ubmjBw5kmnTpr1ybEiA3377jYIFC7Jq1ao07ajUiUielV6bKHVdekkTQ/rkk0/43//+x4IFC4iPjyc5OZm+ffsa9ZgAK1euxNzcnE2bNqW5wbZu3bp0y6fXHnV2dn7jNqYQwjAkKSWEMIhbt24xePBgHB0d6dOnzyvLpzYMnr2L9vTMfQC2trZUqFCBdevWMXnyZP3ds+jo6AzNbFKlShUcHR2ZO3cuH374YboNEh8fH/Lly8fy5csZPHiwvkxMTAyrV6/Wz8gHKV20T548SenSpdM8TmhoGYk7PQ0aNMDMzIyrV6+m+8jkm0j9G8kdPyGEEOLVgoOD0+2Vk/rY3dM9yl/Uo0alUmFhYZHm8z8kJOSFs+/t3LmT+/fv63v4aLVaVq1aReHChV+71/WzXtUOyJs3Lx988AFz5swhMTGRZs2a6QceNyaVSoWZmRkajUa/Li4ujqVLl2Z4HzVr1mTy5MmcO3cuzSN8z85cKIQwHklKCSFe29mzZ/VjFYWGhrJ//34WLlyIRqNh7dq1GZqZrXjx4hQuXJghQ4agKApOTk5s3LiRwMDA58qOGTOGJk2a0KBBAwYMGIBWq2XSpEnY2dnx6NGjlx7Hzs6OKVOm0LNnT+rWrUuvXr1wc3PjypUrnD59mtmzZ6NWq5k4cSIfffQRTZs2pU+fPiQkJDBp0iQeP37M//73P/3+ZsyYQbVq1ahevTqffPIJ3t7eREVFceXKFTZu3MiuXbtev0LfMO70eHt7M2bMGIYNG8a1a9do2LAhuXPn5v79+xw9ehRbW1v9FMgZZW9vj5eXF+vXr6dOnTo4OTnh4uLy3LTIQgghhEi5QZQ/f36aNWtG8eLF0el0nDp1iilTpmBnZ8eAAQP0Zf38/Fi5ciWrVq2iUKFCWFlZ4efnR9OmTVmzZg39+vWjbdu23L59m++//568efNy+fLl547p4uLC+++/z3fffaeffe/ChQsGSa5kpB0wYMAAKlWqBMDChQvf+pgZ0aRJE6ZOnUrHjh3p3bs3YWFhTJ48OcOPDQIMHDiQBQsW0KhRI8aMGYObmxvLly/nwoULAGnGGhVCGIckpYQQr61bt25AyrP9uXLlokSJEnzzzTf07NkzQwkpSOnGvnHjRgYMGECfPn0wMzOjbt267Nix47m7aw0bNmT16tWMGDGC9u3b4+7uTr9+/bh3716G7ob16NEDDw8PJkyYQM+ePVEUBW9v7zSPDXbs2BFbW1vGjx9P+/bt0Wg0VK5cmd27d1OlShV9uZIlS/LPP//w/fffM3z4cEJDQ8mVKxdFixbVjytlKBmJOz1Dhw6lZMmSzJgxgxUrVpCQkIC7uzvvvffeG3en//XXX/nqq69o3rw5CQkJdOnShUWLFr3RvoQQQojsbPjw4axfv55p06YRHBxMQkICefPmpW7dugwdOlQ/kDfA6NGjCQ4OplevXkRFReHl5cWNGzfo1q0boaGhzJ07lwULFlCoUCGGDBnCnTt30r251Lx5c3x9fRk+fDi3bt2icOHCLFu2LMODjb/Kq9oBFStWxNvbG2tra+rUqWOQY77K+++/z4IFC5gwYQLNmjUjX7589OrVC1dXV3r06JGhfXh4eLB3714GDhxI3759sbGxoVWrVowZM4YuXbqQK1cu456EEAKV8qKpsoQQ4h2WlJSEv78/+fLlY/v27aYORwghhBAix/r3338pU6YMP/74I/369TN1OG+td+/erFixgrCwMKMO1yCEkJ5SQogsokePHtSrV4+8efMSEhLC3LlzOX/+PDNmzDB1aEIIIYQQOdLVq1e5efMm3377LXnz5qVr166mDum1jRkzBg8PDwoVKqQfs3T+/PkMHz5cElJCZAJJSgkhsoSoqCgGDx7MgwcPMDc3p1y5cmzZsoW6deuaOjQhhBBCiBzp+++/Z+nSpZQoUYI//vhDPzFMVmJubs6kSZO4c+cOycnJFC1alKlTp6YZ+0sIYTzy+J4QQgghhBBCCCGEyHQynYAQQgghhBBCCCGEyHSSlBJCCCGEyGZGjRqFSqVK8+Pu7v7C8nv27HmuvEql0k+LLoQQQghhDDKmlBBCCCFENuTr68uOHTv0yxqN5pXbXLx4EQcHB/1ynjx5jBKbEEIIIQRIUirDdDod9+7dw97eHpVKZepwhBBCCGEiiqIQFRWFh4cHavW72+nczMzspb2j0uPq6kquXLkyXD4hIYGEhAT9sk6n49GjRzg7O0t7SQghhMjBMtpekqRUBt27d48CBQqYOgwhhBBCvCNu375N/vz5TR3GC12+fBkPDw8sLS2pVKkS48aNo1ChQi/dpmzZssTHx1OyZEmGDx9O7dq1X1p+/PjxjB492pBhCyGEECIbeVV7SWbfy6CIiAhy5crF7du303RrfxOKohAREYGjo6PcRXxNUndvTuruzUndvTmpuzcndffmjF13kZGRFChQgMePH+Po6Gjw/RvCX3/9RWxsLMWKFeP+/fuMHTuWCxcuEBQUhLOz83PlL168yL59+yhfvjwJCQksXbqUuXPnsmfPHmrUqPHC4zzbUyoiIgJPT09u3br11u2lZ8m/CeOTOjYuqV/jkvo1Lqlf48tudRwZGYmnp+cr20vSUyqDUi8KBwcHgySlFEXBwcEhW1xsmUnq7s1J3b05qbs3J3X35qTu3lxm1d27/Hdp1KiR/rWfnx8BAQEULlyYxYsXM2jQoOfK+/j44OPjo18OCAjg9u3bTJ48+aVJKUtLSywtLZ9b7+joaJSkVOq+3+W6z8qkjo1L6te4pH6NS+rX+LJbHaeew6vO5d0dCEEIIYQQQhiEra0tfn5+XL58OcPbVK5c+bXKCyGEEEK8LukpJYRIQ1GUNI9iiJQ6SUxMJD4+PlvctTAmc3PzDM3wJYTIXAkJCZw/f57q1atneJuTJ0+SN29eI0YlhBBCiJxOklJCiDTu3btHZGSkqcN45+h0OsLCwkwdxjtPpVKRP39+7OzsTB2KEDna4MGDadasGZ6enoSGhjJ27FgiIyPp0qULAEOHDuXu3bssWbIEgOnTp+Pt7Y2vry+JiYn89ttvrF69mtWrV5vyNIQQQgiRzUlSSgihl5ycTGRkJM7OzgYfCyQrUxQFrVaLRqORnlIvoSgKDx484M6dOxQtWlR6TAlhQnfu3KFDhw48fPiQPHnyULlyZQ4fPoyXlxcAwcHB3Lp1S18+MTGRwYMHc/fuXaytrfH19WXz5s00btzYVKcghBBCiBxAklJCCL3k5GQA7O3tsbKyMnE07w5JSmVcnjx5uHHjBklJSZKUekNxiVo0ahXmGrnWxJtbuXLlS99ftGhRmuWvv/6ar7/+2ogRCZGFKcqTHy3otCm/Fd2T17qnXj9ZVpuDmQVoLMHMEtTyeSiEEC8iSSkhxHMk8SLelFw7byc6IZn6U/fiYm/Jun5VTB2OEEK8HkUBXTJoE5/8JKXzOukF65+81iW9YtuM7iPpSRLpSdJIn1DSPbVe+3xCSZdOWUX3dvWi0qQkpzQWOKjNwdzqybLlf8krjfnz655ObGksXvA7tdyz27xsW0tQy3xXQoh3gySljMTb25vY2Fju3r2Lubk5ALt27aJOnToMGjSI7777zsQRZlzXrl2pUKECn3322XPvRUdHM3z4cDZt2oSVlRVarZYmTZowfvx4/v77bxo3boyPjw+JiYkANGjQgO+++47cuXNn9mm8Fn9/fw4dOoS1tTXe3t5s2rSJUqVKvXQblUpFVFRUumPpvOw9IYRItT0ohHsR8dyLiOdxbJJMkSuyvZe1l7788ksmT55s4ggz7mXtpcx2/Phxpk2bxrJlywyzw6gQuHUI7gfBw0vw+BYkRKX8JCf8lxTSJRnkcPeidHy0Jo7dXWwBUI2OJGqoPXYWL77xceOxjgrzYnj4tf1rvWdQKg2o1M/Xg6KFpFhUSbG8M7du1GbPJMUs0kloWYDZkwSamdVTP5Zgbp3O+pe998x6jQXIjSwhBJKUMipPT082bNhAmzZtAFiwYAEVKlQwcVQpj2iZmb39n15RFJo2bUrRokU5c+YM1tbWJCYmsmDBAv3sbSVLluT48eMAREVF8dVXX1GnTh2OHTv2Tj/ac+rUKVOHkGOZmZlRqlQpkpKSKFSoEEuXLiVXrlyvtY9Ro0bh4uKSoS8GN27c4OjRo7Rr1w6APXv20LJlS7y9vUlISODDDz9kxIgRb3Iq75Q9e/Ywe/Zs/vzzT1OHIl7i573X9K8v3o+khLO5CaMRInNk9/aSMbwqtgoVKrx9Qur+OTjzB5xbD4+uvvl+NKm9eMz/e602S2f9f789NObsDnjqfWbDe73Azjbl0bhn96cxh/sRsHgEfLDov2OoNCk9gu6GwpK+0G3Lf4kjtfrJ+0+W07xWP3mteea16r/Xz233VIJFUZ4k6hIgOfHJ7wSU5ASiH4dhZ22BSvtkvTbpSWIv8cnvp7f5b9v/3k9M+/qV26YeJzHt30WXnPKTFPPmf9u3onq9JNbT682snvQ2s0qb5FKbYR6fBA65nro2LECTzvWmNn/mmtRIkkwIE3k3P2mzie7du7NgwQLatGlDREQEhw8fpkOHDsTGxurLTJ48md9//53k5GTc3d35+eefKVCgADt37mT48OHEx8eTlJTEl19+Sbdu3QAYO3Ysy5Ytw9LSEoD169fj5eX1XG8cFxcXjh8/jre3N97e3vTq1YsdO3bg4eHBokWL+O6779i1axeJiYkUL16cuXPnkitXLu7evcvHH3/MgwcPKFiwIFqtNt3z27VrF1euXGH79u1YWFgAYGFhQd++fdMtb29vz6xZsyhSpAhbt26lSZMmad5ftGgRmzZt0n9p3rRpE5MnT2bPnj3s2bOHgQMHEhAQwP79+1EUhcWLF1OhQgWSk5Np0qQJYWFhxMXF4e/vzy+//IKNjQ3FihVjxYoVlC9fHoCFCxeyceNG1qxZw9SpU1mxYgXJycmYm5sza9YsKlWqBLy4Z9PLtkn9ewYGBvLgwQNGjx5Nhw4dnquHy5cvM3DgQEJDQ0lMTKRPnz7069cv3ToztdjE5Be+p1apsDLXvFHZl8mVK5c+Kdi5c2d+/PFHhg0blrGA38CNGzf4/fff9UkpgLp16/Lnn38SExODv78/TZo0wd/f32gxvI3Usa5E9nDtYbT+9eZ/QyhRu4AJoxEic7yovRQXF6cvk9peSkhIIF++fFmqvXTjxg0qVKhA37592bx5M3Fxcfz222/MmzePw4cPY2Vlxbp16/Dw8ODMmTP069ePmJgY4uPj6dy5M0OHDgVSemI5ODhw6dIlbt++TVBQEMOGDWPVqlU4OztTo0YNdu/ezfHjx9mzZw+DBw/m+PHj+uP369ePzZs3ExERwcyZM9MdxH7UyJE8vvYP02vFw51jzD6ayPF7Wha1tGHR9TysOJOIk3Mezt58iKWVFb/Pm0qhwkUIefiYDr0HEhkdQ3xCInVq12bGjOnEJSRRwNOToKATuLu7AzBy5EiioqKYOnUqX331FXv27CEpKQlHR0fmz59P0aJF9TE/fPjwSWSzod5osLN74TbcuAGasQxe+Df79+8nOjqaWbNm8f7774PmRkqSyivlsehjx47xzTdfERkZiU6nY9iwYfqkqEGoVE96GVmA5VPrFQWtRQQ4OmZ+AuQFibKMJbbiU95Likv5nRz/1M9rrv8vIEiOS/mJf2yQU1QBtm+ztT5p9UzC87lEqPmLy6aX8EpT7pnkrD7p+UyC87mEqDrte09vk2Yf6e1P/ZJkq1qSccLkJCllRDVq1GDWrFncvXuXjRs38sEHH6T58rh8+XIuXbrEoUOH0Gg0LF26lM8++4z169dTrlw5Dhw4gEaj4dGjR5QrV46GDRtiZWXF5MmTCQ4OxtramtjYWNQZfCb81q1b7Nq1C5VKxbhx47Czs+Po0aMAfP/994wcOZIZM2bQv39/atSowciRI7l27RplypShYcOGz+3vxIkTlC9fXp+Qyghzc3P8/f0JCgp6Lin1KkFBQfzyyy+MHz+eFStWMGzYMLZt24ZGo2H58uU4OzujKAr9+vVjzpw5DB48mK5du7Jw4UJ9UmrRokUMHjwYSEl4DBo0CIDDhw/To0cPzp49+9IYXrWNSqXi77//5tq1a1SsWJFq1apRoMB/Xyq1Wi0dO3Zk6dKlFC9enNjYWCpXrkzlypUpV67ca9VHZig5YtsL36vtk4eF3Srql8t/v4O4pPQb5JUKOrGqT8BrH79q1aqcPn0agGvXrtG9e3cePXqEt7c3ixYtwsnJiQsXLtC3b1/Cw8OxtLRk586dafYxfvx4Lly4wMKFCxk/fjzr1q0jISGBfv360bdvX4YNG0ZQUBD+/v4MGDCAggUL6re1tbWlfPnyXL16lQEDBrBgwQJ8fHxQFIXixYtz5MiRNL24Ro0axfXr17l58yb37t1j4sSJtGzZkri4OHr37s2pU6ewtrZm3rx5+Pv74+vry7FjxwBwdHRk3759BAQE4O/vz969e9FoNPTr14/z58+jKAozZsygatWqjBo1ipCQEK5cuULJkiX5/PPP6devH48ePcLZ2ZnFixeTN29eDh06RM+ePcmVK9c7eX2JtE7eCidJq+iXr4RGv6S0ENlHRttLBw8eJDo6mg0bNmSp9hJAWFgYAQEBjB07lkmTJlG3bl327NnDvHnz6NevH7Nnz2bcuHF4e3uzY8cOLC0tiYuLo0qVKtSrV0/fc+zAgQPs27cPOzs7Nm7cyKZNmzh9+jTW1ta0bdv2hecUFhZG+fLlGTNmDFu3bmXAgAHPJ6UubYPj8zF/HIaqiFXKF2vXomBuD9+shVXrOLJ+IKfX7sPLy4shQ4YwYeF6fv75Z3LZx7Pxr+3Y2dmh1Wpp0aIFq9dtoG3btrRp04bffvuNwYMHoygKS5YsYcOGDQB88803TJo0CUgZnP+LL75g06ZNL/37vGybsLAw/Pz8mDx5MocPH6Zly5ZcvZq2l9fjx4/p06cPmzdvJm/evDx8+JDy5ctTtWpVfeIsW3pRoiwzKUpK8uuNk1tPvZ/0TBldEoo2EW1CHBqVDpX2mbHNnh2rTPfszVTlSY+yBJNUjWmpMpzkckAFmvR6Eab2PkynB+LTPROfTZo93ftQnzxTpfzmyW8VT71+9r10luE1yqqerDdGWfV/5TOy3yfMYmLA1u6pdao0v/5bfub99Na9cPmZdQ4ekNs7Y5eLEUhSysg6d+7M4sWLWbduHcuWLUvTlXr9+vUcP35cnzB5usdDWFgYPXr04NKlS5iZmfHw4UOCgoKoXbs2RYsWpVOnTtSvX58mTZqQP3/+DMXSrVs3/SDE69atIzIyUt8rKTExkcKFCwOwe/duZs6cCUChQoWoU6eOYSrjCUVRXl0oHT4+PlSoUIGIiAgCAgKYMmWKfn/Tpk1j8+bNJCcnExERQY0aNQDo0qULZcuWZerUqdy+fZtLly7RqFEjAE6ePMkPP/xAWFgYZmZmnDt3jsTExJcm2V61Tc+ePYGUeqtWrRr79++nY8eO+u0vXrxIUFAQH374oX5dVFQU586dk6TBM7RaLYGBgXTv3h2A/v37069fP9q1a8eECRMYNWoUM2fOpFOnTowfP5569eoRFRWFtbW1fh/jxo3j4sWLLFy4kO3btxMaGsqxY8dITEykWrVqNG3alB9++CHNY2179uzRbx8WFsbhw4cZPnw4H3/8MUuWLOGHH35g9+7d+Pv7p/tY4blz5/j777+5f/8+VatWpUGDBvz444/Y29tz5swZDh8+TJcuXTh9+jQVK1bkyJEjKIpC6dKlOXDgACVLlgRSklRDhgyhVatWLFmyhDt37tCkSRN9ku7MmTPs3r0bCwsL6tWrx/z58/Hy8uKPP/5g9OjRzJ07l549e+p7FLZv395IfylhKMuP3kqzfPdx3AtKCpH9vKy9tG7dOo4fP06FChX0vZGyWnvJzs5OfzOuXLly5M+fX98Dt3z58gQGBgIQFxdHv379OHXqFGq1mtu3b3Pq1Cl9Uqpdu3b6Hl67d++mXbt22Nqm9A3p0qUL33//fbrHt7W1pUWLFgAEBASkTdQ8vgWbv4TL21HFxqNoLFGqD0ZVqQ8s+h2OHwfrXABUq1YNLy8v/X5mzZoFgE6n45tvvuHAgQMoikJoaCj+/v60bduWbt260atXLwYPHszu3btxdnbGz88PgO3btzNr1iyioqLQ6XRERka+8u/zsm0sLCzo3LkzAJUrV8bd3Z3Tp0/j4eGhL3Pw4EGuXbumbw9CSlvy4sWL2Tsp9S5QqZ48bmekrJiiEB0RgWNGeqLpdOkPyq9LbxD9dAbfT3dA/3S2fW7g/mf2pzw74L7u+QH60wzGrzxT7qkZIJ/bhxbIyPcuJZ0k3fOepE+EEakAk4xAXHVgSm9UE5GklJF17dqVcuXKUaxYsZSuxU9RFIXhw4frv3Q/rW/fvjRr1ozVq1ejUqkoV64c8fHxaDQaDh8+zMGDB9mzZw+VK1dmxYoVVK9eHY1Gk6breHx8fJp9Pv0omqIozJkzJ6VL8xsqX748s2bNemUi52lJSUmcOnUq3Uf8zMzMXhq/lZWV/rVGoyE5OeU/z+XLl7N371727duHvb09M2fOZN++fQDky5ePcuXKsWHDBk6fPk3nzp0xMzMjMTGRNm3asGfPHsqXL09kZCSOjo4vPZc32ebZmcgURcHFxSXLjFl1bkyDF76nfubcTnxXN8NlX+bx48f4+/tz584dfH19adAgJYZjx46xceNGIOXLS5MmTYiMjOTx48fUq1cPSHlENNW8efMoXrw4K1euRK1WExgYyMaNG9m7dy8AERERz909TbVjxw7Kli2LWq1m6NCh+Pr6ki9fPqpWrcrYsWNZvHgxXbp0SXfbVq1aYWFhQYECBShevDgXL17kwIED+qnWK1euTFxcHBEREVStWlXfgP/6669ZuXIlvr6+BASk9CoLDAxky5YtjB6d8iERFhamnzSgRYsWWFhYEBUVxd9//63/sqHVavH29ubx48ckJSXpv8h89NFHLFmyJMN/B5H5Dl4JA8BcoyJJq/AoJvEVWwiRfWSkvdStWzcinnzhTP18zQrtJUD/CCGktGFe1Kb59ttvcXNz4+TJk5iZmdG6des08T0bW0ZnPH32ePrzP/MnbPoCEiJBbY7GqyxxVkXg/eGgUmW4LTZ16lTCwsI4cuQIVlZWDBo0SL9tQEAAWq2W48ePs3DhQn2799atW/Tv35+jR49SqFAh/v3331fW85tsk15brHTp0vq2osih1GpQP+k5lp0pyjMJq2cTYLrnZ5p8wXuKLpnoqCjsbKxRobz1/tJNqJEa75PfaZaVtMsvLftkxsyXvq+k8/plZV+w77eJg6fLKCiAVpuMRqNBlaYjh5Lm13/L6ZVRXr6c3job51ddSUYlSSkj8/DwYPz48RQvXvy595o1a8bMmTNp2bIlTk5OJCUlcfbsWcqWLUt4eLh+3IN9+/bpe0dERUURFRVF9erVqV69OkFBQZw8eZLq1atTuHBhjhw5Qv369VmzZg0xMS8euLB58+ZMnTqVypUrY2NjQ2xsLNevX8fX15f333+fBQsW8N1333Hjxg127txJ3brPJxzef/99ChYsSP/+/Zk+fTpWVlYkJyczc+ZMevfu/Vz56OhoBg8ejIuLiz7R8LTChQtz+vRp4uPjMTMzY/ny5QlG3hEAALPmSURBVBmq4/DwcJydnbG3tycqKopFixZRqFAh/fupY1UEBQXx119/AejHnkh9tC71Tt/LZGSbp+vtwIEDz5Xx8fHBxsaGJUuW8PHHHwNw5coVnJyccHJyytD5ZiYbi4z/F/E6ZV8mdUyp2NhY6tWrx5w5c+jfv3+aRuXTDfEXNcjLlCnD6dOnCQ0Nxd3dHUVRGDVqlL7eUz3dMypV6phSTx/P3t6e8uXLs2XLFv7++28WLFiQ7nGfjSe9+FLjr1q1Kl988QVqtZovvviCn376iQMHDlCtWjV9uU2bNuHp6fncPmxsbPRl3N3dn0t0hoeHZ/jLijC95GQd9570jKpT3JWtQfeJTUz/cVghsqOXtZeaN2/OjBkzaNGiBRqNhqSkJIKCgrJMe+l1hIeHU6pUKczMzLh48SKBgYEvTLrUrl2bkSNHMnDgQKysrFi6dOnrHWzD5/DPk5sV+d+Dlj9RePsxtsyahU6nIyEhgdWrV+Pj45OhuN3d3bGysuL+/fv88ccfaXroduvWjZkzZ7J582Z977KIiAgsLCz0n9GzZ89+5XFetU1iYiLLli2jc+fOHD16lJCQEEqXLs2DBw/0ZapUqcLly5fZtWuXvm5PnTpFyZIlX2tICiGyhNQxqNA8mTTgLSgKWhsTjYmWU7xOb79sRGabzgTdunXT93x4WufOnenUqRO1atWiTJky+Pv7s3v3bgD+97//8dVXX1G5cmUWLVqkH0w7IiKC1q1b4+fnR+nSpUlKStL32Jg+fTqffvopVatW5Z9//sHZ+cUZzyFDhuDv70+lSpUoXbo0lStX1n+pnTFjBnv27KF06dIMHjz4hQ0slUrF5s2bsbCwwNfXl1KlSlGmTBlCQkL0d9LOnTunHzunYsWKWFtbs3PnznQHZv4/e/cd31T1PnD8c5O0TWdaKJRVStmUvbcoIIgDEDcqgoAi+gMBF+IARPgKDpwgiOJCQQUURRRlKBuh7A2FsksLdDfz/v64adrSFtrSNG153q/XfSW599x7T07S9ObJOc/p2LEjvXv3pkmTJtx2222u7vHXMmjQIFJSUoiKimLAgAF07do1x/Z+/fqxefNmqlat6hoaFRQUxOTJk2nXrh033XRTjl8w81OQfXx8fOjcuTO9evXiww8/zJFPCrTeYMuWLWPRokU0a9aMxo0bM2zYsBzJXIXGz8+P999/n3feeQebzUabNm346aefAK13XNeuXQkKCiIkJMQ17CE5Odn1q2379u2ZMWMGd911F5cvX6Znz57MmzfP1dYHDx4kIyPDFcwsiMcee4zhw4fTv3//fJOLL126FIvFwqlTpzh48CD169enS5curiDrli1b8Pf3JygoiIYNG3L48GHS09MJDAykQYMGfP3113Tu3BnQgmMff/yx69iZX7ayCwoKolKlSixfvhzQeiPu37+fkJAQvLy82L59OwDfffddgZ6j8IwVe8+5fr968TbtS7kKnE3MyHcfIcqba10v3XLLLXTp0oWWLVuWqeulwnjllVf47LPPaNu2La+88spVewH17duX3r1707x5c2655Rbq1KmjfZG5lrSL2u32r7ScJt1ehCErILQe99xzD5UqVaJx48YMGDCgwJN8jBo1ig0bNtCiRQsef/zxXG3x6KOP8t1339GrVy9CQkIAaNq0Kffddx+NGzfm5ptvzvMHmCtda5+KFSty5MgR2rdvz5AhQ1iwYIFreGOmkJAQli1bxhtvvEHz5s2JioripZdewuFwFOi5CiGEKF6KWtQEPzeYzKFaiYmJBAUFXdexVFXN1f1cFIy0XdEVpO0yMjKIiYkhMjIyRxf9khQaGpptth248847efTRR2nbti1Dhgxx/Sr+5ZdfUqFCBQ4ePMgTTzzB5cuXMRqN/P3337z99tuEhobyzDPP8P333zNr1ixWrFjBhx9+yNdff42qqlSuXJlly5bh7e1N7969uXjxoivRefYcU6C1nd1uR6fTUbVqVVauXEmzZs1y1T0zAfmBAwdyJTofPnw4u3btwmg08umnn9KyZUtA+1JRq1YtPvjgA7766ivGjx/P6dOnAa134f/93//x33//Ybfb6dGjBx9++CETJ050PT/QetuNGDGCCxcuYLPZeP755xk8eLAr0bnJZKJr164cPXo0x/NyhyvfQ/I3WzDvrTzI+38foVZFP9Y8fwuR439DVWHqXfV4qFM9abtCcvf7rjivCcobd7aNfJ7klpycTGBgIA6Hg2HDhlGtWjWmTJmS/w6Jp+Cr/pBwGLwD4b75UC8rgCRt7F7Svu4l7ete0r7uV97auKDXBBKUKiAJSpUO0nZFV1aCUqVRZlAqJiaGhx9+2DUL05WuDBbdiCQoVTSDPt/CP4cu8OqdUQztEkm7N/8iLtnMCz0jeapHI2m7QpKglOdIUKpk3X333Rw/fpz09HRatWrF7Nmz82/3y7Ew/064fAKCasDDiyCscY4i0sbuJe3rXtK+7iXt637lrY0Lek0gOaWEEKIA5s2bx5tvvsncuXM9XRVRzphtdrbEaEnOu9QNdd0ujj5NulWGkwgh8rdkyZKCFbx0Ar68UwtMhUTCY8sgOPza+wkhhBBuJkEpIYQogKFDh/LEE09c9VeLiRMnllyFRLmxNPoMGVYHFf29qR+mzawVUVHLgXLykuSbE0Jcp9R4+PpuLSBVoY4WkDJV93SthBBCCECCUkIIIYRHLdwaC4Cft94V9KwVqs2uePhCmsfqJYQoByypsOB+uHgUTOEw+FcIqubpWgkhhBAuZXb2vU8++cSVs6R169b8+++/Vy3/7bff0rx5c/z8/KhatSpDhgwhISGhhGorhBBC5G3/WW32x8yhewA2u5bucf+5FI/USQhRDjjs8MMQOL0NfEPgkcUSkBJCCFHqlMmg1MKFC3n22WeZMGEC0dHRdO3alT59+hAbG5tn+XXr1jFo0CCGDh3K3r17+eGHH9i6dSvDhg0r4ZoLIYQQWWITUkm32gF4qH3W1OatI4IBcKhwMcXiiaoJIcq6VW/A4T/AYISBP0Cl+p6ukRBCCJFLmQxKvfvuuwwdOpRhw4bRqFEjZs6cSXh4OLNmzcqz/KZNm6hVqxajRo0iMjKSLl268OSTT/Lff/+VcM2FENdiMBho0aIFjRs35q677uLy5cuFPsbEiRP56KOPClT2+PHjLFq0yPV4zZo1BAcH06JFCxo1asSkSZMKff7idmUdRfnx9aYTAHjrdTSrEexan5lTCmDr8YslXS0hRFm35ydY9552v9/HEN7Ws/URQggh8lHmglIWi4Vt27bRq1evHOt79erFhg0b8tynU6dOnDp1iuXLl6OqKufPn+fHH3/kjjvuyPc8ZrOZpKSkHAto0zTKIkt5Xjz9Pg8ODiY6Opo9e/YQHBzMRx995NbzxcTEsGjRohzrevbsSXR0NFu3buWbb75h27Ztrs8GT7RJXnUszYun30NlaVl9MA6AOpX9c7Whr5cegB2nLnu8nrLkXoQoteIOwNKntfudRkHTez1bHyGEEOIqylyi8/j4eOx2O2FhYTnWh4WFce7cuTz36dSpE99++y0PPPAAGRkZ2Gw2+vbty4cffpjveaZNm5ZnD4nExMTrvhhVVZWUFC1PyNVm8hK5SdsVXUHazmKx4HA4sNlsJKeb3VIPXy/9NV87u10bztShQwd27dqF3W7n2LFjDB8+nIsXL1KrVi3mzZtHhQoVOHDgAE8//TSXLl3Cx8eHP//8E1VVcTgc2O123nrrLQ4cOMC8efN46623+PnnnzGbzYwYMYInn3ySCRMmsG/fPlq0aMH//d//ERkZiaqq2O12jEYjrVq14vDhw4wePZq5c+dSv359VFWlSZMmrF+/nuDgYFe9k5KSePrpp9mzZw86nY4PP/yQjh07Mm7cOFatWoVer2f69On06NGDo0ePMnToUFJTU/H29uazzz6jUaNG7N69m8cffxyHwwHA77//nquOgwcPdstrUxzsdjsOh4Pk5GTMZrP8zV7D8XgtkXnXSBOJiYk5tpl89aRb7ew9eTHXNnF17n7fZf5QJUSpY7PA4mFgS4faN0PPiZ6ukRBCCHFVZS4olenKi0xVVfO98Ny3bx+jRo3itddeo3fv3pw9e5bnn3+eESNGMG/evDz3GT9+PGPHjnU9TkpKIjw8HJPJRFBQ0HXVPTOoZTKZ5EtaIUnbFV1B2i4jI4OEhAQsDoXWb/7tlnrsndQLP2/9Vcvo9XrsdjurVq1iyJAh6PV6xo4dy8iRI7n//vt56623eOONN/jggw8YPHgwU6dO5dZbbyU5ORlfX18URUGn0/HWW29x6NAh5s+fz59//smFCxfYunUrFouFrl270rdvX958800+/vhjfvjhB0AbvqcoCnq9noSEBDZv3syrr77KoEGD+Pbbb5kyZQqrVq2iRYsWVKxYMUe933zzTSIjI1mwYAF2u53U1FSWLl3KsWPH2LlzJ7Gxsdxyyy3s37+fGjVq8Ndff+Hj48OGDRt47bXXWLx4MfPmzeOpp55i+PDhpKeno9frc9WxNNPr9eh0OgIDAzEajfI3exXbT1zC5tDa5/Fu9TEFGXNsrxHiz7kkC2eTrZhMJk9Uscxy9/tO3sui1Fr9JpzbDb4V4O5PQXf1/7dCCCGEp5W5oFRoaCh6vT5Xr6i4uLhcvacyTZs2jc6dO/P8888D0KxZM/z9/enatStTpkyhatWqufbx8fHBx8cn13pFUYrlYjTzOHJhW3jSdkV3rbbLXO/Otr3Wa3f58mVatmzJqVOnaNy4MbfddhuKorB161aWLVuGoigMGjSIO+64g+TkZC5fvuwazpsZMFYUhblz59KwYUO+//57dDodf/31F7/++iv//PMPoPV6PHbsWK7nrCgKf/31F61atUKn0zF+/HgaN25M9erV6dy5M1OmTOGrr77isccey/U8Vq9ezS+//IKiKBgMBkwmE+vXr2fgwIHo9XoiIyOpX78+hw4dombNmjz99NPs2rULnU6H2WxGURQ6derE5MmTuXjxIvfffz+1a9cukdeluGSv65X3y0L9S9KhOK0nT/2wAKqYfHNtr1PJn/9OXOJCikXargjc+b6T10OUSsfXw/r3tft9P4DAKp6tjxBCCFEAZS4o5e3tTevWrVm5ciV33323a/3KlSvp169fnvukpaVhMOR8qnq99suR5IUQIjdfLx37Jvd207Gv/qttcHAwO3bsIC0tjVtvvZVPPvmEUaNG5fgSmL1nZH5fDps3b87OnTuJi4ujSpUqqKrKxIkTGTRoUI5ya9asybVvz549+fHHH3OcLzAwkNatW7N8+XLWr1/P559/XtCnnENm3WfOnElkZCTffvst58+fp0OHDgAMHDiQdu3a8euvv9K9e3cWL15cpPOI0m/dkXgAbm+a+4cRgDa1Qlj43yl0Ev8QQlyLNR1+eQZQoeUj0OguT9dICCGEKJAyl+gcYOzYsXz22Wd8/vnn7N+/nzFjxhAbG8uIESMAbehd9i+ed911F4sXL2bWrFkcO3aM9evXM2rUKNq1a0e1atU89TSEKLUURcHP2+CWpaA9DPz8/Hj//fd55513sNlstGnThp9++gmABQsW0LVrV4KCgggJCWHlypUAJCcnY7PZAGjfvj0zZsxwzeDXs2dP5s2bR3p6OgAHDx4kIyODwMBAkpOTC1Snxx57jOHDh9O/f39XYDu7nj17umYBtdvtJCUl0aVLF77//nscDgcnTpzgyJEj1K9fn6SkJKpVq4aiKHz99deuYxw7dow6derw7LPPcuutt7Jv375C1VGUDQ6HygZnUKprvdA8y3SrXxmAxHQrZpu9xOomhCiD/nkbLh6DwKrQe5qnayOEEEIUWJnrKQXwwAMPkJCQwOTJkzl79ixNmjRh+fLlREREAHD27FliY2Nd5QcPHkxycjIfffQR48aNIzg4mO7du/PWW2956ikIIQqgTZs2NG3alJ9++okPPviAIUOGMHnyZCIiIvjyyy8B+Prrr3niiSd47rnnMBqN/P13Vi6s3r17c+nSJfr168eKFSvYs2cP7dq1Q1VVKleuzLJly2jWrBlWq5UWLVowevRoIiMj861P9+7dsdvtuXpbZXr11VcZMWIETZs2Ra/XM2vWLAYMGMC6deto1qwZBoOBuXPnYjQaGTFiBPfccw/ffvstPXv2dB1j4cKFfPPNN3h5eREREcHdd9+Nt7d3jjoOGTKkmFpYeMovO89wKc2Kt0FHsxrBeZYJDfDGoFOwOVTiUyxUD849xE8IIYjbD+tnavf7TAfj9eU+FUIIIUqSosr4tQJJSkrCZNJmRyqOROeJiYmS+LcIpO2KriBtl5GRQUxMDJGRkRiNxjzL3IgyZ+OLiYnh4YcfZsuWLZ6uUql15XtI/mbz9tjnm1l7KJ7QAG/+e+XWPMuoqkrUaytItzr4a8xN1A0LLOFall3uft8V5zVBeePOtpHPkzw4HPDFbXByMzS4HR5cANfRNtLG7iXt617Svu4l7et+5a2NC3pNUCaH7wkhREmbN28ePXr04I033vB0VUQ5sONkIgBta1W4arl0qwOAExfT3F4nIUQZtH2+FpDyDoDbZ1xXQEoIIYTwBAlKCSFEAQwdOpQTJ07Qu7d7EsCLG8flNAuJ6VYA7mtT46plM79fZjiDU0II4ZJ2Ef6apN3v/gqYrv55IoQQQpRGEpQSQgghStCCLVrOQ50CN9evdNWymX0eJNG5ECKXNdMg4zJUbgxth3u6NkIIIUSRSFBKCCGEKEF/7DkHQHgFP3S6q/8bzgxKSU8pIUQOcfth6zzt/m3TQF8m5y4SQgghJCglhBBClKSD55MB6Fo39JplM5NcSk8pIYSLqsKK8aDaodFdULubp2skhBBCFJkEpYQQQogSEpeU4er19EiHiGuW1zm7Splt0lNKFM7EiRNRFCXHUqVKlavus3btWlq3bo3RaKR27drMnj27hGorCuX4Oji2GvTecKtMviGEEKJsk6CUEKJUMRgMtGzZkqioKFq3bs3cuXNzbFdVlZo1a/LCCy8AMGHCBFq0aEFUVBR+fn60aNGCFi1asHr1apYsWULz5s1p3LgxTZo04ZNPPgHA4XAwYcIE6tatS926dbn//vtJSEgA4Pjx4yiKwvTp013n3Lx5M15eXsyfP79kGqGYzJ8/n+eee87T1RDZbI65CECDsEAaVs1/atxM0lNKXI/GjRtz9uxZ17J79+58y8bExHD77bfTtWtXoqOjefnllxk1ahQ//fRTCdZYFMg/M7TbVo9BhUjP1kUIIYS4ThKUEkKUKsHBwURHR7Nv3z4WL17MrFmzmDNnjmv7xo0bqVq1KosXLwbgzTffZMeOHSxfvpyoqCh27NjBjh07CA4O5sUXX2Tp0qXs3buXrVu34ufnB8DMmTPZt28f+/bt48iRI7Rt25YhQ4a4zlGvXj1++eUX1+NFixbRpEmTEmqBa7PbJUBRVq0/Eg9A13rXHroHEOKr5YkJ9vV2W51E+WUwGKhSpYprqVQp/8T6s2fPpmbNmsycOZNGjRoxbNgwHn/8cd5+++0SrLG4ppNbIGYt6AzQebSnayOEEEJcN8mKKITInyU1/22KHryMBSyrAy/fQp8+IiKCd955hzFjxvDEE08AsHDhQkaMGMGCBQvYvHkz7du3z3Pfd955hwkTJhAZqf2K7Ovry+DBg13bNmzYgLe39kV/3LhxzJ49m8OHD+Pl5UVQUBChoaEcO3aMyMhI1q5dyy233JLneQYPHozRaGT37t1cunSJefPm0bFjR+Lj4xkyZAgnTpygQoUKzJ8/n+rVq9O4cWMOHTrEoUOHaNCgAWfOnCEsLIx69epx5MgR4uLiePLJJzl16hRGo5HPPvuMhg0bMnjwYCpWrMi2bdvo3bs3PXr0YNy4caSmphIZGcmXX35JQEAAP//8M88//zxVq1alXr16BAcHF7rdhXs4HA7WHLwAQOcCBqWqmoycS7ZQOdDHnVUT5dThw4epVq0aPj4+tG/fnqlTp1K7du08y27cuJFevXrlWNe7d2/mzZuH1WrFy8srz/3MZjNms9n1OCkpCdB6taqqWkzPhBzHLO7jlin/vI0CqM0fAlMNLb9UMZI2di9pX/eS9nUvaV/3K29tXNDnIUEpIUT+plbLf1u9XvDwD1mPZ9QFa1reZSO6wJDfilSFVq1acfDgQUD7YFu2bBmTJk3CarWycOHCfINS+/bt4/nnn8+1PikpifT0dCIisvL56HQ6mjVrxv79+2nWrBkA9913H4sWLaJbt260atUKgyH/j8uzZ8+ybt06tm3bxuDBg9mzZw8TJ06ka9euLFu2jIULFzJq1Ch++eUXqlevTkxMDOvWraNVq1asW7eOBg0a0LhxYxRF4dlnn+XVV1+ldevWbN26lWeffZYVK1YAcPLkSVavXo3VaqVnz5788ssvhISEMGPGDD766CNGjx7N//3f/7F+/XqqVKnCLbfcQocOHYrU7qL4bTiawLmkDADaRIQUaB+DM6mUxS45pUThtG/fnq+++or69etz/vx5pkyZQqdOndi7dy8VK1bMVf7cuXOEhYXlWBcWFobNZiM+Pp6qVavmeZ5p06YxadKkXOsTExPdEpRKSUkBsoa23kj0cXsIPPwHqqIjufkwHImJxX6OG72N3U3a172kfd1L2tf9ylsbZ/5QdS0SlBJClGrZv9T8+++/REVFERwczIABA2jbti3vvPNOnh/aqqoWav2V+vbty2233cb58+e5//77+f333/Mt+8ADD6AoCm3atMFisXD58mXWrVvH8uXLAbj//vsZPVobZtG5c2fWrVvHunXrePHFF1m3bh0XLlygc+fOAKxatYr9+/fneZ57770XRVE4ePAgu3btcvXeslgs3HzzzRw8eJCGDRsSHh7uOm9sbOw1n6soGQv/OwlAoNFAoDHvXidXynyrplls7qqWKKf69Onjut+0aVM6duxInTp1+PLLLxk7dmye+1z52Zj5+Xu1z8zx48fnOF5SUhLh4eGYTCaCgq6dN60wMutjMpnKxcV6of3xqXbb5B4CI5q75RQ3fBu7mbSve0n7upe0r/uVtzYu6HOQoJQQIn8vn8l/m6LP+fj5I1cpW/T0dTt27KBhw4aAlttp06ZN1KpVC4Dz58+zYcMGV0Anu6ioKKKjo109nzKZTCaMRiPHjx93HcfhcLBz504aNWrkKhcYGEjlypVZvnw5M2bMuGpQ6soP3Lw+gDPXde7cmZ9//pnDhw8zd+5cZs+eTXx8PCNHjnSV3bZtG3q9PtcxMnNiqapKq1atWLVqVY7t0dHR5eIfWHm15ZiW5Lx5DVOB9zlyQet9+N/xSzzU7tqz9QmRH39/f5o2bcrhw4fz3F6lShXOnTuXY11cXBwGgyHPnlWZfHx88PHJPbw0c8a/4pZ9NsEbStwB2L8MAKXruKyItRvcsG1cQqR93Uva172kfd2vPLVxQZ+DJDovLcrJuFFRznj7579kzyd1zbKFzycF2nC15557jmeeeQaHw8HSpUs5ePAgx48f5/jx47z//vssWrQoz33HjRvH1KlTOX78OAAZGRmu6c3HjBnDmDFjsFgsgJZjqnHjxtSrVy/HMSZMmMC0adPyDBBl98MPP6CqKtu3b8fb2xuTyUSXLl1YsGABAD/++CPt2rUDoFOnTvz+++9UrlwZvV6Pr68v69evp02bNgB069aNTz/Vfg13OBx5zpbVsGFDTpw4wY4dOwBITU3lyJEjNGzYkAMHDnDq1ClsNhs//PBDrn2FZ1hsDs4na3l37mp+lWGxV9DL8D1RTMxmM/v37893GF7Hjh1ZuXJljnV//vknbdq0yTeflChB694FVGh0F1RudM3iQgghRFkhQSlPczhgx3fw6U2QftnTtRHC4y5fvkyLFi2Iioqif//+jBgxgqFDh7J27Vrq1auX4xf7fv36sXjxYhyO3F/YW7VqxdSpU+nbty9RUVGuoBBoQakGDRrQqFEj6taty+bNm/niiy9yHaNNmzYMGDDgmnWuVasWXbp04ZFHHmHu3LkATJw4kTVr1tCsWTM+/vhj3n//fUDrqWUymVy9u9q3b0/16tVdPQ0+/PBDVq5cSbNmzWjSpAm//ZY7F5e3tzfff/89I0eOpFmzZnTs2JEjR47g6+vLBx98QI8ePejevTstW7a8Zt1FyVgSfcp1v3+L6gXeT+/8L221SVBKFM5zzz3H2rVriYmJYfPmzdx7770kJSXx2GOPAdqwu0GDBrnKjxgxghMnTjB27Fj279/P559/zrx583juuec89RREpovHYLfzR4au8noIIYQoXxS1vKR2d7OkpCRMJhOJiYnXnSNBVVUSExO1saIOO8zuDBcOQJcx0HNi8VS4nMrRduWgS2NJKkjbZWRkEBMTQ2RkJEajMc8yNyJVVbHb7ej1+lxtN3jwYO69917uvPNOD9WudLnyPSR/s5pHPtvEuiMJVA70YcuEngXaR1VVuvzvb04nmunRsDLzBrd1cy3LD3e/74rzmsBdHnzwQf755x/i4+OpVKkSHTp04I033iAqKgrQPruOHz/OmjVrXPusXbuWMWPGsHfvXqpVq8aLL77IiBEjCnVed7bNDft58sv/wfavck8w4gY3bBuXEGlf95L2dS9pX/crb21c0GsCySnlaXoD9Hgdvn8INs2Cdk9AUMGHdgghhCj9dp7SZslqH1mhUPtlDt+zyvA9UUjff//9VbfPnz8/17pu3bqxfft2N9VIFMnlk1qPeoCbcs8oK4QQQpR1MnyvNGjQB2p2BFsGrJ7q6doIIQph/vz50ktKXFWaxUaaxQ7Ag+3CC7WvwRWUkk7NQtyQNnwADitE3gTh7a5dXgghhChjJChVGigK9Jyk3d/xrTbDihBCiHJhS8xF7A6V6sG+dKoTWqh9M3tK2fLImyaEKOeSz8O2L7X7kktKCCFEOSVBqdKiZntoeCeoDvh7kqdrI4QQopisOxwPQJe6oYXOD1A50BuAED/vYq+XEKKU2/gh2M1Qo53WU0oIIYQohyQoVZr0nAiKHg4uhxMbPV0bIYQQxWDZrjMAdKpT8Rolc6tf2R+A8Ap+xVonIUQpl3YRtn6u3b/pea1XvRBCCFEOSVCqNAmtB60e1e6vfA1kYkQhhCjTDpxN4nySGYDG1Qo/E1lmTimbJDoX4sayaRZYU6FKM6h3q6drI4QQQriNBKVKm5vHg5cfnNoCB371dG2EKHEGg4GWLVsSFRVF69atmTt3bo7tqqpSs2ZNXnjhBQAmTJhAixYtiIqKws/PjxYtWtCiRQtWr17NkiVLaN68OY0bN6ZJkyZ88sknADgcDiZMmEDdunWpW7cu999/PwkJCQAcP34cRVGYPn2665ybN2/Gy8srz9mq3GXHjh38+eefJXY+4R7fbo4FwOilo25YYKH3z+wckWa1F2e1hBClWUYibP5Uuy+9pIQQQpRzEpQqbQKrQIeR2v2/JoHd5tn6CFHCgoODiY6OZt++fSxevJhZs2YxZ84c1/aNGzdStWpVFi9eDMCbb77Jjh07WL58OVFRUezYsYMdO3YQHBzMiy++yNKlS9m7dy9bt27Fz08bAjVz5kz27dvHvn37OHLkCG3btmXIkCGuc9SrV49ffvnF9XjRokU0adKkhFpAI0Gp8uGfwxcAaFCEgBTA7jMpAGyNuVhsdRJClHJb5oI5ESo11PKNCiGEEOWYBKVKo86jwbcCJByG6K89XRtxI1JVsKS6ZynEsNSIiAjeeecdVw8ngIULFzJixAgiIyPZvHlzvvu+8847TJgwgcjISAB8fX0ZPHiwa9vMmTPx9taSR48bN469e/dy+PBhAIKCgggNDeXYsWOoqsratWu55ZZb8jzPmTNnuPPOO2nevDmtWrXi6NGjpKen8+ijj9K0aVPatWvHjh07ANi0aROdOnWiZcuWdO/enbNnzwKwevVqmjZtSvPmzWnTpg12u53XXnuNr776ihYtWvDHH38UuM1E6eFwODh5MQ2A3k2qFOkYXs7he3aHDOcW4oZgSYWNH2v3uz4HOrlUF0IIUb4ZPF0BkQdjEHR7AVa8BGv+B83uB29/T9dK3EisafBWbfcc++UzhXo/t2rVioMHDwLa0L1ly5YxadIkrFYrCxcupH379nnut2/fPp5//vlc65OSkkhPTyciIsK1TqfT0axZM/bv30+zZs0AuO+++1i0aBHdunWjVatWGAx5f1yOGjWKfv36MXz4cMxmM3a7nY8//pjAwEB2797Npk2beOyxx9i5cydRUVH8+++/6PV6FixYwPTp03nvvfd49913effdd7n11ltJTExEr9czefJk9uzZw9tvv13gthKly5pD8WTGkga2q1mkY3jpnTmlJCglxI3hvy8g/SKERELjuz1dGyGEEMLt5OeX0iL5HFw6nvW4zeMQHAEp52DTJ/nuJkR5p2brWfXvv/8SFRVFcHAwAwYMYMmSJTm2X7mfkkcejvzWX6lv374sW7aMRYsWcf/99+dbbt26dQwdOhQAHx8f/Pz8WLduHY888ggAHTp0ID09ncTERC5dusTdd99NkyZNmDx5Mvv27QOgc+fOvPTSS3zwwQekpaVds26ibPhx20kATL5eBPt5F+kYXnrt37T0lBLiBmDNgA0faPe7jgW9/HYshBCi/JP/dqXB/l9hyZNQoy08ukRLaGnwge6vwuJhsO59aD0E/EM9XVNxo/Dy03o0uevYhbBjxw4aNmwIaLmdNm3aRK1atQA4f/48GzZsoHPnzrn2i4qKIjo62tXzKZPJZMJoNHL8+HHXcRwOBzt37qRRo0aucoGBgVSuXJnly5czY8YMfv/99zzrV5AAV2Yg7LXXXuOOO+7gySefZNOmTbz00ksAvPTSS/Tp04fffvuNtm3bXnVYoig7tp24BEDzcFORj5EZlHJIUEqI8i/6a0g5D0E1oNmDnq6NEEIIUSKkp1RpENYY7BY4thoOZvvi2+QeqNocLMnwjwzhESVIUbQhdu5YCjGL0MmTJ3nuued45plncDgcLF26lIMHD3L8+HGOHz/O+++/z6JFi/Lcd9y4cUydOpXjx48DkJGRwezZswEYM2YMY8aMwWKxAFqOqcaNG1OvXr0cx5gwYQLTpk1Dr9fnW8euXbsyb948AMxmM2lpaXTp0oUFCxYAsGXLFvz9/QkKCiIpKYkaNWoA8M0337iOcfToUZo3b87LL79Mo0aNiImJITAwkOTk5AK3lShdrHYHKWZtooohnWoV+TjeBmdOqULkYhNClEE2M6x7T7vf5VkwFK13pRBCCFHWSFCqNKgQCR2f1u7/8bJ2YQJacsuek7T7Wz+DizGeqZ8QJejy5cu0aNGCqKgo+vfvz4gRIxg6dChr166lXr16VKxY0VW2X79+LF68GIfDkes4rVq1YurUqfTt25eoqCjatWvn2jZmzBgaNGhAo0aNqFu3Lps3b+aLL77IdYw2bdowYMCAq9b3/fffZ+nSpTRr1oxOnTpx7tw5nn76aS5fvkyzZs145plnXMd+7rnnePbZZ+nSpYtrJkCA9957j8aNG9OsWTNq1qxJx44dueWWW9i+fTstW7aUROdl0K5Tl0k12wn28+Km+pWLfJxiTXRuTdeGB4lSKTExkfnz5zN06FB69OhBx44d6du3L6+//jobNmzwdPWEu0V/A0mnIbAatBrk6doIIYQQJUZR80vIInJISkrCZDKRmJhIUFDQdR1LVVUSExMxmUxZQ3/MyfBha63b9q2TtRn4Mn3VX+tF1fQ+uOez6zp3WZdn24kCKUjbZWRkEBMTQ2RkJEajsYRrWHqpqordbkev18v77hqufA/dqH+zM/86xMy/DnNH06p8/HCrIh1DVVXe/X0vH/5zgurBvqx/qXvRKpNwFNa/Dzu/A0UHtbpAvV5Q/zYIibj2/mWQqqoknY8lKDgExXh9/7PzUpzXBGfPnuW1117j22+/pUqVKrRr147q1avj6+vLxYsX2bNnD9u2bSMiIoLXX3+dBx54oJiehXsUZ9tcqdx+ntgs8GErSDwJfaZD+yc9VpVy28alhLSve0n7upe0r/uVtzYu6DVBmc0p9cknnzBjxgzOnj1L48aNmTlzJl27ds23vNlsZvLkyXzzzTecO3eOGjVqMGHCBB5//PESrPVV+ARCz4mw9ClYO0PLJRAYpm27dRJ8uhp2/wAdn4FqLTxZUyGEENfw9cYTADSqen1fyutU8gWgUqBP4Xc+vR3Wz4R9vwDZfn868pe2/P4ChHeA5g9os3z5hlxXXT3CZoFLMZBwBOIPa7fOxZR6AfWOd6HtUE/X8qqaN2/OoEGD2LJlC02aNMmzTHp6OkuXLuXdd991DWsW5ciOb7WAVEAVaPWYp2sjhBBClKgyGZRauHAhzz77LJ988gmdO3fm008/pU+fPuzbt4+aNfOedvv+++/n/PnzzJs3j7p16xIXF4fNZivhml9Dswdhy1w4sx1WTYZ+H2vrqzbXeknt/gH+mgiDlnqylkIIIa7ifFIGCalavrK2ta4v0GPQaaPsrfbcQ1TzpKpwbI2WmyZmbdb6er21PDXGYDiyEg79CSfWw8lN2vL7i1C/NzR/COreWrry2agqJJ/NFng6CgmHtfuXT4Cau21cvy0muWnChmK0d+9eKlWqdNUyvr6+PPTQQzz00ENcuHChhGomSoQlFda+pd3v8ix4SS9lIYQQN5YyGZR69913GTp0KMOGDQNg5syZ/PHHH8yaNYtp06blKr9ixQrWrl3LsWPHqFChAoBr1q1SRaeDPm/B/DvAt4J2IZ7Zba/7K7B3qTaM7+gqqFPEYRxCCCHc6ttNWi8pg06hfe2K1yh9dV567X/ANYNSDjvs/wXWzYSzO7R1ih6a3qsNBw9rnFU2LEpbl3RG+7Fj50KI2wv7l2mLbwVoMgCaPaDNCltS3cczkpy9nJxBp+xBKGtq/vt5B0DFutoSWg8q1kWtWIdEQyVMlaqXTN2vw7UCUtdbXpRyGz/Wgq7BNaFNKem9L4QQQpSgMheUslgsbNu2zTWVeqZevXrlmwj0l19+oU2bNkyfPp2vv/4af39/+vbtyxtvvIGvr2+e+5jNZsxms+txUlISoI3zvN40XJnHyPM4NdrCs7shICyzsHYbHAFth6Jsno268nWI7KblBrnBXLXtxFUVpO0yt0kb50/a5equfA/diH+zK/efB6BWRb/ret6qqnI0Pg2AmPjUvI9ly4Cd38OGD1AuHtP2M/hCq0e1CTSCIzIPlnvfwKrQaZS2nNsNuxbC7h9QUs5rk2ts/Qy1Qm1oer8WoKoQWeTn4mK3ar2b8hhup6Scz3c3VdFDSK2s4FPFuhBaFyrW0/5fXhE4U1UVNTHRbe+74jzuL7/8UuCyffv2LbbzilIg+bwWSAbo8ToYijBMVwghhCjjylxQKj4+HrvdTlhYWI71YWFhnDt3Ls99jh07xrp16zAajSxZsoT4+HhGjhzJxYsX+fzzz/PcZ9q0aUyaNCnX+sRiuMhVVZWUlBSAfBKYGSExMddapcWTBEV/g3JuF6lbvsbasP911aMsunbbifwUpO0sFgsOhwO73Y7dbi/J6pV6ec3wJ3Kz2+04HA6Sk5Mxm8035N/skTjt+XaspSV2LCpVVbWcSYDDoeY8ljkJn90L8Nn+Gbo0bTiXw8eEpcVgzM0fQ/Vz9tAq6Pl9a0L756HtWAwn1+O9fzFeR1Zoga61/4O1/8NWtTWWRvdgrX8nqtF0tYqjpF1Ad+kY+ksx6C4dRX85Bt2lY+gSY1Ec+Q+dd/hVwhFSG3tIpHYbXBtHSG0cpnDQ5zGk0AE4fzTKWQX3vu+S8jhnUfXv379A5RRFkc/l8mbNNK0XYPXW0OQeT9dGCCGE8IgyF5TKdOVFpqqq+V54OhwOFEXh22+/xWTSLqTfffdd7r33Xj7++OM8e0uNHz+esWPHuh4nJSURHh6OyWQqltn3gGtn1T+7U5sxqd9H4OUHJhN0fhZWT8Fv0zvQ+sEb7le1AredyKUgbZeRkUFCQgJ6vR69Xl+S1SsTpE2uTa/Xo9PpCAwMdM2+BzfO3+yuU4lY7dpzHtqtHiaTf5GPpaoqQQHa/ycVrQ1JOQ+bZsN/81DMzh68QdWh49MorQbh4x3Adf9XCLkLmt0FlhTU/b9qPahi1mI4uw3D2W2oaydqs/dVjtIm6fAOgNS4bMPujrrqlufz8vKDinW0Xk6u27pQsQ6K0YQeuN6/NHe/74rzmBLwvkHFHYDtX2n3e00puWGyQgghRClT5oJSoaGh6PX6XL2i4uLicvWeylS1alWqV6/uCkgBNGrUCFVVOXXqFPXq1cu1j4+PDz4+uS/tFUUplovRzOPkeyy7DRYN0oY5VGoIN7+ore84ErZ+hnI5FrZ9AR2euu66lDXXbDuRr2u1XeZ6T7fvZ599xtNPP8358+cJDg72WD0yZe8duWjRIiZOnEjDhg3p3bs3ISEhPPDAA8yfP5/bb7+dypUrA1quu5EjR+LtXfCE0RMnTiQ0NJRnnnkm3zKvvfYat95661VnG92/fz8PPvggiqLw999/U7Hi9eU1ys/x48e59957+e+//1zr8noP3Uh/s99viQXA10tPrdCA6z6e0aCFZ2pyDuXXMbBjAdidQ8tDG0CXZ1Ga3OuexOQ+gdDiIW1JOqvln9q1EOX8Hjjwq7bkR9FpOXIyA06hmcPu6qEEVtVyKLqZO993N8J7WbjZX6+DaoeGd0JEJ0/XRgghhPCYMheU8vb2pnXr1qxcuZK7777btX7lypX069cvz306d+7MDz/8QEpKCgEB2peEQ4cOodPpqFGjRonUu9D0Bug5EX4cos2i1PJhMNUAb3+4ZTwsGw1rp0OLgXC1YRRClEGLFi2iTZs2LFmyhCFDhni6Ojl88cUXfPHFF3To0CHH+vnz59OmTZscQalhw4YVKihVEJMnT75mmZ9//pmHHnooV+69/Njt9hy9wK58LAru4HltyFijqoHFcryKaUf5yOsD+ug2wzZncLRGO+gyBurfViLBHQCCqkLnUdpyzhmUSokDczJYUsCvgivoRMW6Wv6pG6wnb3FJTU1l7dq1xMbGYrFYcmwbNWqUh2olilXMP3BohTYZQc+Jnq5NoWVY7VxKs2B3qHgbdPh7G/D10qPTSbBWCCFE4ZW5oBTA2LFjefTRR2nTpg0dO3Zkzpw5xMbGMmLECEAbenf69Gm++krrFj1w4EDeeOMNhgwZwqRJk4iPj+f555/n8ccfzzfReanQ+G7YMhdiN8DK1+Heedr6Fo9os7XEH4L1H0CPVz1bT1FupaZqM175+fm5egZYLBasVisGgyFHb8LMsr6+vugyp7G3WrFYLOj1eozGgk1zHR8fz7Fjx5g/fz5vvvmmKyg1ceJEYmJiOHHiBGfOnGH69On079+f9PR0nnjiCXbs2IGvry9z5syhRYsWnD9/ngceeICUlBR69+7Np59+Snx8PDabjXHjxrFx40YsFgsTJ06kf//++a7P7n//+x/r1q1j8ODBDBw4EIfDQWhoKNWrV+e///7j3nvvxWQyMWjQIM6cOUOnTp2oU6cOS5YsYfny5UyePJmMjAzatWvH7Nmz0el0zJ49m/fee4/w8HAqVapEaGjoVdtn8ODB3Hvvvdx5553UqlWLwYMH8/PPP2MwGPjll1/Yu3cvM2fOxGAwsGHDBn755RfefPNNli5ditlsZuTIkYwYMYI1a9YwdepUgoODOXfuHI8//jh//PEHSUlJ+Pv78+GHH/Lkk09y6tQpjEYjn332GQ0bNuTQoUMMHDgQvV5/1d5aNyJVVTl5SUtMPqZn/es7mMMO62bS7d+p6PTOHEz1emnBqJodCzfUx+GA4//A7h9BZ4DaN0PkTVogqSiqNNGW0shu03oYm0rpD07XEB0dze23305aWhqpqalUqFCB+Ph4/Pz8qFy5sgSlygOHA/58RbvfZog2Y2Qplmq2sfbQBbaduER07CWOJ6RxMdWSZ1lfLz3+Pnr8vA34eevx89bj76Pd9/c24Oej3fpe8TiznGt9tv18DDrpmSiEEOVcmQxKPfDAAyQkJDB58mTOnj1LkyZNWL58ORER2ixDZ8+eJTY21lU+ICCAlStX8n//93+0adOGihUrcv/99zNlyhRPPYWCURTo8z/4tBvs+RHaDoOIjlovqh6vw8KHteBU22Har9hCFLPMnoVxcXGuachnzJjBK6+8wrBhw5g7d66rbOXKlUlLSyMmJoZatWoB8PHHHzNmzBgGDhzIt99+W6Bz/vTTTwwYMIDOnTtz4MABEhISXMPP9u3bx/r16zl//jydO3emd+/efPzxxwQGBrJ79242bdrEY489xs6dO5k0aRL9+/fn2WefzVHPzz77jFq1avH++++TlJRE+/bt6dOnD1988UWe67MH3l566SX++OMPPvroI5o0acLEiRMBuPvuu2nTpo1rfWY7bdiwgYCAAOLj43n33XdZs2YNRqORZ555hkWLFtG1a1dmzJjB9u3b0el0tGzZks6dOxfqNapZsybR0dG89tprfPbZZ7z66quMGDHCNQxwxYoVxMXFsXXrViwWC126dOHOO+8EYPPmzezfv59q1aoxf/58tmzZQnR0NEFBQTz00EO8+uqrtG7dmq1bt/Lss8+yYsUKnn32WV5++WUGDBjACy+8UKi6lneHzqdwIdmM0UtHu9pFDPgAXI6FJSNQTqxHAf60t+Zd232seLgIw7VPboXFw+FSTNa6bV8AClRvBd1fgTrdi15XTzGnaD/MxB923jrvXzwKdgs8+a+WvL2MGTNmDHfddRezZs0iODiYTZs24eXlxSOPPMLo0aM9XT1RHHYv0nKGegdCt4L1ZvWEbScu8e3mWFbsOUe6NXeCfYNOQadTsNodrsk90612Z9m8g1ZFoVPIGcDKFvS6MoClLQZXYCxngEx77Oulx+glwS4hhChNymRQCmDkyJGMHDkyz23z58/Pta5hw4asXLnSzbVyg6rNodUg2P4lrHgRhq/Rhms0vAPC28PJzdrMSHe97+maClEsFi5cyPTp01EUhX79+rFkyRKGDRsGaMEfb29vwsPDadiwIQcPHmTdunWu4EiHDh1IT08nMTGRDRs28OqrWi/CBx98kPHjxwPaUN+9e/fy5ZdfAloPr9OnT+e7vnbt2tf9nDZu3MiuXbtcQ/7S09OpXr06RqORHj16uPLdFWW698xhy61bt85zavmVK1eybNky1q5dC2gziB49ehTQhjZXq1bNVbZ3796uiRxWrVrF/v37cx1v27Zt/PbbbwA88sgjrFq1qtB1Lq9W7D0LQLvIivgYijj8cdcP8Ns4MCeiegdwovXLPLE6AijglyeHHdISIEAbRkpwTUg8qX0BbnYfGIxwbA3E7YPT27ThQ5kun9Ry3ITUKlrdi5vDAUmntYBTwhFodBcEOd+vW+fCXxPz3s9g1BLCl8Gg1I4dO/j0009dk02YzWZq167N9OnTeeyxxxgwYICnqyiuhzkl633bdSwEVPJodfKy7cQlZvy+j80nsmbujKjox031KtEqIpgGYUFUCzZi8vVCURRUVSXD6iDVYiPNbNduLXbSLDZSzc5bi530Kx6nmTPX2/PcN8OqTQDgUCHZbCPZbAPMxfY8FQV8DDpXkMrXS4+Plx5fLx3GHOtyljFm2559Xdb+zsfeeoyGrACYDG0UQoj8ldmg1A2l+6uwd4n2y9q+Jdq0wYoCPSfBF7fB9q+hw9NQ6TqHiwhxhcwp1f38/Fzrnn/+eZ599lkMhpwfH3FxcQA5hsQ+/fTTDB8+vMD5ic6fP8+GDRu49957ATCbzezdu9cVlLryV828fuXMnIkze3LyK7fPmTOHm266qUDrBw0axK5du2jSpAlff/11gZ5HXue88847+fzzz3OsX7JkyXX/UpvZk0uv1+c5XbyqqkycOJFBgwblWL9mzZocryuQ6/G2bdtyvXbyy3L+vtxwAoBQ/yLkEUu/DMuf05KJg5Y36u5PwRIIq7cAYHeo6PP7YpN4CqK/0ZaQWjDYmYQ8MAwe/hHC22k5CTMln4ODv0NEtp55Gz+CzbOhWkuI6g+N+5dsgCpuP+z7Oav3U8IRsKZlbQ+sClHOwG1offCvrN2G1nPeOu+bwrX/kYmJeZ+nFPPy8nL9jYWFhREbG0ujRo0wmUw5eoCLMmrde5B8Vvu76pD3D6uekpRhZdry/Xy35SSg9YQa0Ko6D7arScvw4KtOkOLrrcfXWw/XP7eDi92hkuYMUqWaM4NVVwSwzDbSrPZsj7VbV6Dryn3NNmwO7dpAVSHD6nAGv6zFV/F8+Bh0rgCVlw58fbzw1uvw8dLhY9DhY9C2eWfed673zrYta9Hn2M/7ivVZx83az6AvofyDQghRBBKUKgsCKsGtk8Fhg0bZkrlHdIQGt8PB5fD3JHiwYMOjhCgof//c09l7e3vnmbw7r7JeXl54eXkV+Hw//vgjTz31FO+99x6gBVRq1arFhQsXAFi6dCnjxo0jLi6OgwcPUr9+fbp06cKCBQvo1KkTW7Zswd/fn6CgIDp16sQPP/zAqFGjWLRokescPXv2ZPbs2XTu3Bm9Xs+OHTto0aJFvuu/+uorVFXNM+CTXWBgIMnJybkeBwQE0KFDB0aPHs3JkycJDw8nISGB9PR02rVrx/PPP09iYiJ6vZ5ly5a5huiMHz+edu3a5ZjQoSh69uzJW2+9xX333Yevry8HDx50DXW+mm7duvHpp58ycuRIHA4He/fupWnTprRq1Yqff/6Z/v37s2DBguuqW3mSkmFz5Vm5pWEhez8cXw9LntR6NCl66PYCdH0OdHoMcQmuYla7A70uW5DQboVDf2g9aY/8BarWswBLqhbk8g3WHte5Jfc5A6to+WyyS7+kzZp3Jlpb/nodqrbQglNR/bXk5UWlqlpi9PhDkHA4K/DUaRTU7qaVuXAQ1kzLuZ/OABXqaMGmzOcD2v++hndc/XxlUMuWLfnvv/+oX78+t9xyC6+99hrx8fF8/fXXNG3a1NPVE9fj0nHY8KF2v9cU8CpYnsWSsCXmIqO+i+ZcUgYAdzerzNjbGhNewe8ae7qPXqcQaPQi0Fjwa4iCsNodZFjtzoCUnQznkMPMx+nOdZll0rOVMV9RJj3bMa48TobVjtWe9Tlktjkw2xzZalJ8vb4KQqeQK9jlpdcCWNnvexl0eOsV7bFzvbZNyfnY4CyvV5z75D6Ol17JsT7n+bLtp5eeZELc6CQoVVZc+eUhU4/XtRlcDvwKsZuhZvuSrZcQxWjRokU5ZpdTFIU777yTn376CdC+sPXq1YszZ87w/vvv4+vr6+qN1axZM4xGI1988QUAr7/+Og888ABfffUVt99+u2tY2pNPPsmxY8do0aIFqqpSv359Fi9enO/6gho8eDCDBw8mODiYzZs3M3z4cG655RYaNWrEkiVLmDVrFv3798dqteLl5cXcuXNp1aoV48aNo127dtSsWZMuXbq4jrdnz54iDee70u23386ePXto164dqqpSuXJlli1bds39PvzwQ0aMGMHs2bOx2WwMGjSIpk2bMnPmTAYOHMi0adPo3bv3ddevvPhhm9a7QAH6NC5gjj+7VQvC/PsuoGq9JwZ8BuFtte2qiiHbhbrV7sDo5QxKbf8aVr2hDVPLVKsrtHpMG+ZWlC+8A+ZArzfhwDLYuxSO/wtnd2jL5jkwdt+1E6zbrdri7fwye3IrrHhJC0KZ8+i5FNktKyhVtRm0fETr8VTR2fspJAL0eXwpLac99qZOneoKbr/xxhs89thjPPXUU9StWzdXT0tRhqgqrBgPdrM2yUDDOz1dI5fvt8TyytI92BwqtSr68b97mtGwgh6TqRRPBHQdMoMjgSUQE7TZHWTYnIEsix2zTQtsJVxOwsvoi8WuYrbaMdscWJxBK7NNe2y2OrDYtfKZ67PKOFzHstgdzjK5j5M9KOZQs+f8Kn30OsUVxPLS6zDoFQy6zFslxzqvbNu89DrXdr1OK6varfj5Gl3bDPor98l+7Jzr8jp25v56Xc7zGZzr9Iq2j16v3dfrtEWnSO9yIQpKUfMb4yJySEpKwmQykZiY6PpyW1SqqpKYmIjJZCrah5U1Q5uC2985S9fPz0D01xDeAR5fUW4v1qEY2u4GVpC2y8jIICYmhsjIyALPlldSJk6c6EreXRAZGRl4eXmh1+v54YcfWLhwIT/++GORzp3ZU0qv15fY++72229n+fLlJXKu4nTle+hG+Zu9b/YGth6/RPVgX9a/VIDE4fFHYPEwrUcSaLOq9vkf+AS6iqiqyoWES7R7eyPeWPljVGciqzl7Ye38Xutd5V8JWgzUglEV6xTvk0qNh/3LYN9SCGsCvd/U1ttt8M3d4FcRvPzA4KMNB4w/pPUGufUN6OgcmnR2J3zqHBKr6LQcV9mH2kV0dsvsY+5+3xXnNUF54862KbOfJ3t+gh8fB50XPPkPhEV5ukaoqsq03w8w559jANzZrCrT722Gr5e+bLZxGVGS72GHQ80VtMq6dWC1ObDaVSx2OxabitXuwGrXAltWuwOLXXXd1x5nbbM6y5vtmcfRtltt2jmzH8dqV7Xz2bMvN8bXz8wJAQy6rGCV676ioHcGwXQKWmDrinJ57atTFGdATIdeAb1Od/XzXHE8vaIdQ+cMnGW/f+U2vU5BUTL30b5iZqSnEeDvj16vQ5dtm85Zt+z39YqC4jyOTlHQ68h2vJzbdDqyHS/r8ZXblHIe7Cuz/+fyUdBrAukpVdac2Kh9EQlrAg85h87c8rI21ffJTVqOkIa3e7aOQpQCx48f56GHHsJut2MymVw9qMqKshiQupHtPZMEQKc6Fa9eUFVh23z442UtX5IxWJuoonH/PIsbU04ywfAN9+j/wbLrWaj2vLYhqh94+UL9PmAoQg6rgvAP1XrpthmSczjciXUQ80/++108lnU/tD7c96V2W6F2qRqyVBrFxMRgs9moVy9noO7w4cN4eXm5ZjYVZUhqAix3zlTadVypCUhN/nUfX6w/DsC4W+vzTPe6V83HKMoenU7BqNM7e9gW7zDI6+VwqFgdWnDKassKeFnsDmx2LeBlc6jYXLdaeZtdW2fN3JZtfWbAKzUtHYOXDzbH1fZxrrOr2B3aOaxXbMvrnPZs5WwOB3aHiuMqfzI2hwoOtRjnoxSZFGdATXHeV7Ldz1p/xfaC7uMMwClk9XjLbx+c++iu2Adnuez767QCWcdCcT0X160KNpsNg5cBBSXPviaZwSolxzpyrMse0FKuKJO59tGOEXSrXzom3JCgVFnjG6IltL18Ao6u1nKFBFWDDk/Bune1WV3q9QK9vLSifJk4cWKhyjds2JDo6Gj3VEaIbE5dSiPNog2JeLDdVWZ8S42HX0bBQW32QiK7Qf9ZYKqeu6zdCuvfJ3DtWww3aJezScf/BpxBKS9fLTBVUrJfyVRpBgPmQtpFsKVrvXf9Q7UeTxXrZc2Ql1nPfAJuIrfBgwfz+OOP5wpKbd68mc8++4w1a9Z4pmKi6Fa8CGnxUDlKC0p5mKqqTPltvysg9dY9TXmgbdmbqVKUbTqdgo9Oj48B8Cm+43qil4mqasEqmzNoZVdV7HbtsUN1rrc71zsDYPYrFptDxZH9GFcezxkccx0vr3JXHMehasE8u8PhPIbqPAY4VG273aGiOh9nBtiu3GZ3HktVwa6qWK020OlybNMWLdiYeV/bX6t/9m05zpnHfgVvd60+2dYU+2tb3nVvWNnTVXCRyEVZU7khtBuuzZC0YjyMWKcFoDqPhm1fQPxB2LkAWg269rGEyIf8UiqK6kZ873y7SZsVzUuv0DoiJO9Ch/6En5+G1DjQe0OP17RZU3V5zIh0dhf8PBLl3G4A1tkb87m9D8O6D6eTu55EYfhVgGb3e7oW5VJ0dDSdO3fOtb5Dhw4FHrqcn2nTpvHyyy8zevRoZs6cmWeZNWvWcMstuRPj79+/n4YNG17X+W9IB1doM2oqOuj3kft6NRbCrLVHmbcuBoD/DZCAlBDXS3EOpzMUbKLpMs3dQT81W/DLoao4HFlBssz7dmcZFRW0jmioZAW6Mi9DHa5y2ddnlivcPg5VC4KpzsBZ5rnVPPbBud3h0NZl7pPXubW1Wc89c0VqWhp+fr5k7wulkjVzaPbbvI6R40rcddistZn7tqmVzzWrB0hQqiy6+SXYtQgu7NcCUe2Ga7MS3fS8NiRk1ZtQpzuYani6pqKMMRi0j4Tk5ORyMY65uHgip1RZpKoqFy5cQFGUQs26WNadS0oHoEV4cO6NllT48xX4z5mkulIjuGcuVMlnJrVtX8JvY8FhQ/UNIe2m13j058qoKDzikPdeeacoSo5ZPDMlJiZecwbQq9m6dStz5syhWbNmBSp/8ODBHLkfKlUqHd37y5SMRPh1jHa/49NQvbVn6wP8tuss01ccBGDiXVFX79kphBAlzDXEjhv3eqe85ZQqKAlKlUW+IdB9Avw2DlZNgSb3aL9ctx0GWz/T8nl8dis88lOpyF0gyg6DwUBQUBAJCQkkJCRce4cbiMPhQJdXrxaRg6Io1KhRA73+BvjJ0GnnSW1WuWFda+fccHobLH4CEo5ojzuM1GZMvVpepRptAUUbmtdnBla7D/yyHlTIsDjy30+UC127dmXatGl89913rr8hu93OtGnTcszOWRgpKSk8/PDDzJ07lylTphRon8qVKxMcHFygsmazGbM5a3r7pCQtv5r2i3fx9pzMPGaZ6JH5xwSU5DOoFWrDzeNz/qztAdGxlxmzaAcAQzrV4rFOtfJsxzLVxmWQtK97Sfu6l7Sv+5W3Ni7o85CgVFnVajBsnQdx+7QpxW+foc2ANOgX+OYebRjfF7fBg99BrdxDAYTIT7Vq1QgNDS03H4bFQVVVkpOTCQwMvKF+tSiKzBkPbxSnL6dzLD4VnQIdM5Oc221ajr81/wPVDoHVoP8nWg7AK1nS4Pg6qN9LexwWBSM3avmZVBUSE3HmvcRsk6BUeTd9+nRuuukmGjRoQNeuXQH4999/SUpKYtWqVUU65tNPP80dd9xBz549CxyUatmyJRkZGURFRfHKK6/kOaQv07Rp05g0aVKu9YmJiW4JSqWkpACle/Ylw9GVBER/jYpCSvdp2NOsQKLH6nM53crIb3dgsTnoVjeEZ7pUIzEx7/qUlTYuq6R93Uva172kfd2vvLVx5g9V1yJBqbJKb4DbpsFX/SDpDDgcWm6S4HB4fAV895A2G99X/aDFQ9BljDbzkRDXoCgKPj7FmHWyHFBVFbPZjNFoLBf/IETx+WbTCQAaVzMRZPTSeqoufgJObdUKNL4b7nhX6816pZh/4Zf/g8uxMHwVVGuhrQ/NmeTa6KUnzWJHr5P3XnkXFRXFrl27+Oijj9i5cye+vr4MGjSIZ555hgoV8ngPXcP333/P9u3b2bp1a4HKV61alTlz5tC6dWvMZjNff/01PXr0YM2aNdx000157jN+/HjGjh3repyUlER4eDgmk+mq0z8XRWaQq1QPa0i9AH+/pN3v+AwBjXt7tDoOh8rYJf9xLslCrYp+fPhwGwKN+Q+vLhNtXIZJ+7qXtK97Sfu6X3lr44I+BwlKlWW1b4Yn1mZ9kcnkVwEGLYUlI2DfUtj+FUR/ow3z6zoOKjcq+boKIUQ59MuOMwAYDYqWD2rFeLCmgk8Q3P62lhD8yn/IGUnw1+tZeaaCqoMlJd9zhAUZiYlPJSxIgsU3gmrVqjF16tTrPs7JkycZPXo0f/75J0bjVYaMZtOgQQMaNGjgetyxY0dOnjzJ22+/nW9QysfHJ88fMrTcIMV/QZ153FJ5sa6qsOxZ12x7SvdXcv/9l7B564+x6uAFvA06Pn64FUG+1062XqrbuByQ9nUvaV/3kvZ1v/LUxgV9DpIgpay7MiCVycsX7v8ShqyAureC6tBmgPmkA3z/MJzeXqLVFEKI8sZmc3DmcjoVSOJtx3RYNkoLSEV0gafWQ/MHcn8hPbwSPumYFZBqPQRGboJa+ecL8tJrx7AVZq5kUWbExsYWqvzp06cLVG7btm3ExcXRunVrDAYDBoOBtWvX8sEHH2AwGAqcOL1Dhw4cPny4UHW8YUV/Awd/A50XDJhz9fxxJWDfmSRm/KElNn/9rigaVzN5tD5CCCFEXiQoVV6kxMHK18Fmzrk+oiM88qPWo6pRX0CBA7/C3Fvg67vh+HqPVFcIIcq63/ee42ZdNH/4vEjEhTXaF9FbJ8Njv0BwHrNa/fYcfHsvJJ2CkFrw2DK4ayYYrz7EyUuv/au22iWnVHnUtm1bhg8fzpYtW/Itk5iYyNy5c2nSpAmLFy8u0HF79OjB7t272bFjh2tp06YNDz/8MDt27Chw7rfo6GiqVq1aoLI3tEvHYYVz2F73V/KfYbOEWGwOnvthJ1a7yq1RYQyUmfaEEEKUUjJ8rxSw2h2uLx1F4nDAF7dDwmFt6F7n0bnLVGsBD3wNFw7Cuvdg1yI4ukpbanaErs9B3R4e72YuhBBlgiWVgL+e5wvvX7XHlRrBPXOv/kW0UgNAgQ5PaV9avf0LdKrYhDQAtp24xM0NKl9nxUVps3//fqZOncptt92Gl5cXbdq0oVq1ahiNRi5dusS+ffvYu3cvbdq0YcaMGfTp06dAxw0MDKRJkyY51vn7+1OxYkXX+vHjx3P69Gm++uorAGbOnEmtWrVo3LgxFouFb775hp9++omffvqpeJ90eeOww5KntGG4NTtBp//zdI34ePUR9p1NIsTPi6l3Ny0Xw0CEEEKUT9JTqhR4+8+DdJuxmhd+3MnlNEvhD6DTQVdnktG1M7ReU/mp1ADung2jtkObx0HvDbEb4dt7YE432PezFuQSQgiRt1PbYHZXbknWAlIrTffAE2tyB6SSzsKZ6KzHbYbCiH+1SSoKGJACUNGG7WVYCzbcSpQtFSpU4O233+bMmTPMmjWL+vXrEx8f7xoy9/DDD7Nt2zbWr19f4IBUQZ09ezbH8EGLxcJzzz1Hs2bN6Nq1K+vWreO3335jwIABxXrecmfDhxC7AbwD4O5ZoPPsDKT7zybx8eojAEzu14RKgZKPTgghROklPaU8zO5QWRp9mvNJZk4kpPHDf6doWsPExw+3IjzEr+AHavYgbJkLZ7bD35Oh30dXLx9SC+58D256ATZ+pOU3ObsTFg3Spi9vdCc0ukv7xU8vbxMhhMBug3/fgbVvgWrnrFqBcdYRPHbrYzlzx6iqNsHEn6+CbzCM3KgFoXS6Ig3p0Tl7OFjsklOqPDMajQwYMMCtAaA1a9bkeDx//vwcj1944QVeeOEFt52/XDq7E1ZN0e7f9j/t+sqDVFXl1aV7sDlUbmtchTubydBLIYQQpZv0lPIwvU7h52c6061+JfSKggrsOpVI97fXsO7whYIfSKeDPm9p96O/hjX/074YXUtQVej9Jjy7RwtQGU2QfAa2zIEv74K3asHXA+Cft+HEBrBmFOVpCiFE2ZZwFL64DdZMBdVOTFhvbjP/j01qE26Nyjak7uIx7bNz2SgwJ4J/KKRdvK5T63TOoJRNekoJUapYUuHHoeCwQsM7oeUjnq4Ri7ef5r8Tl/Dz1vN63ygZtieEEKLUky4wpUCVIF++fLwdFpuD9/8+zKw1R7DaVR6dt4VX7mjE0K61C3ag8HZaYOmf6bBmGsQfgn6fFGz2F/+K0H0CdB0HMWth3y/aDDLpl+Do39oCoPeB6q0hopOWRD28PfgEFv3JCyFEaZbZ62nFeG1mPR8T3PE2P5xuSuKJY3StE4pOp9Nyymz6BFa9CbZ0MPhCj1eh/YjrHsqjz+wpZZOh1UKUKivGa/k8A6tC3w89npczMd3KtN/3AzCqRz2qmnw9Wh8hhBCiIEosKHX48GF+/vlnjh8/jqIoREZG0r9/f2rXLmDA5QbgbdDxfO8G3NG0CvfO3kiaxc4bv+0n1WJnVI96BTtI9wkQHA6/joG0hMJ/GfIyQv3e2uL4AM7v1XpIxW7QblMvaPdjN8C/gKKDKs2cQapOWtJ0/9BCP3chhCh1Ui5oPZ4OLtce1+oK/WdBcDjr/lkHQP+W1SEjCb7qpw2fBoi8Ce76ACpEFks19M6eUlYZvidE6bHvZ9j+JaDA3Z9qE8142Pt/HSY+xUKdSv483rl4Pn+EEEIIdyuRoNS0adN47bXXcDgcVK5cGVVVuXDhAi+99BJTp07lueeeK4lqlBlR1UxseKk7t3+wjjOX03l35SG8DTqevKl2wbphtxoEFetB5Uag99LWxR2AuL3QqF/Bc0Tp9FC1mbZ0GKH1GEg4CifWa8nRT2yAyyfg7A5t2fSJtl9oA60XVURnLUgVHF6UZhBCCM859Af8/LQWiNd7Q/dXoeMzoNNxOc3C7tOJAHSuGwo+PlpPCR8T9J4CLR8t1h4Tehm+J0TpkngKfhml3e/yLNTu5tHqAJy8mMbXm44D8PpdjfE2SIYOIYQQZYPbg1KrV6/mlVde4dVXX2X06NGEhIQAcPHiRWbOnMlLL71Eu3btuOmmm9xdlTIl2M+bdS/czP9+P8icf4/xv98PcD4pgwl9GmEoyIVGRMecj9e9C7sWgqkmdBypfWnyCShcpRQFQutqS+vHtHWJp50BqvVwYiNc2A/xB7Vl23ytjKmmVp+azkBVaD2Pd3EXQog8WVLhjwmw7QvtceUoGDAXqjRxFZm99igtOExqYE2qmJzDo+98D1SHlqevmAUZDZxNBF9vz87oJYRAG6q7+AnIuAzVWsEtEzxdIwDeXXkIq12lS91QbqpfydPVEUIIIQrM7UGp2bNnM2zYMCZOnJhjfYUKFZg8eTLnzp1j1qxZEpTKg06n4+U7GhEa6M3U5Qf4Yv1x/thzjl//rysVArwLd7BKDcAvFBJjYcVLWs6pNkOh/ZMQWKXolTRVh6b3agtoCX0ze1Gd2KDNSpMYC7titaAYaPWI6KjN7BfRSZuNysPTJwshBKe2weLhcPGo9rjjM1oPqex5+Syp1Iueygvey1itdAOcM6UFhrmtWs1qBHPwfAr1wiR/343g0KFDrFmzhri4OByOnHnEXnvtNQ/VSrise1f7Ic7LH+75LKtHugftPZPI0h2nAXjxtoYero0QQghROG4PSm3ZsoWvv/463+2PPvoogwYNcnc1yrQnbqrD5TQrn6w5ypnEDDr972++GdaeNrUKkb+g6zjoMBJ2fgcbPtK+dK17FzZ8CB2fhlsnFU9l/SpAwzu0BcCcAqe2aL2oYjfCqa2QFg/7l2kLgHeglqQ9opOWOL1KE/ANKZ76CCHEtVgztAki1s0E1Q6B1eDuWVD75pzljq6GZaO4xxILClQx+YPdVvAh0UVk0DtzStkkp1R5N3fuXJ566ilCQ0OpUqVKjiH7iqJIUMrTTm6F1dO0+3e8DRXreLY+TtNXHERV4a7m1Whaw+Tp6gghhBCF4vag1Pnz56lVq1a+2yMjIzl37py7q1HmvXBbQ0IDfHjj131k2BzcN3sjL9/RkOFdC3FB5OULbR6HVoPh0O+w/gM4uQmM2S5gVOeXnuIaXucTAHW6awuAzQxndmTlpYrdBOaknDP8AQTV0HJiVYiEkEgIqaXdD65ZPPUSQgiAmH9h2eis3lFN7oE73skZGE+/BH+8Aju+AeCUGsrL1qHMeHSs2wNSAAadNmTb5pDZ98q7KVOm8Oabb/Liiy96uiriSmkX4YfBWuC6yT3Q/CFP1wiADUfjWXvoAgadwrhb63u6OkIIIUShuf1qOiMjA2/v/IeaeXl5YbFY3F2NcuHxLpE0Dw/m4c82kWF18OZvB1i5L46vhrTF6F2Il1Kny+rNdHILhGa7iNn3M/w9CRrcDg36QHiH4v3SZfCBmu21BbTcDOf3ZuWlOhMNl2Mh6ZS2XEEBgvwrQ4XaWYGqkFpZgauAypKvSghxdTYLHFoB0d/A4T+0dQFV4PYZENU3Z9nT2+G7ByHlPKCwNmQAT529A51PAGFBxlyHdoe9Z7Sk6puOJZTI+YTnXLp0ifvuu8/T1RBXcji0ob1Jp6BCHbhzZqm41lBVlbf/OAjAwPY1qRXq7+EaCSGEEIVXIrPvffbZZwQE5J1UOzk5uSSqUG60jghhw4s9GDBrPccT0tgSc5Eu01ez9OnO1AjxK/wBw9vlfLz1M7h4DDZ+pC1e/lCjtTasLrw9RHYDQyHzWV1N9hn+2j+prctIhPP74MIBuHTcucTAxeNgTkSXGgepcVovryt5+eUMUmUPWgWHa0ExIcSN6dxuiP4Wdi+CtGwBntZDoOdE8A3OvU+F2oCiBe/7fsRL36aQRgYdqgeVUKXB7tB6sKZbZPa98u6+++7jzz//ZMSIEZ6uisju33fgyF9gMML9X4Gx5P7+r2bj0QS2x17G26DjmVvqero6QgghRJG4PShVs2ZN5s6de80youAqBHiz5vlbeO3nPXy98QTxKRZ6vfcPT99Sl6FdIjF6XUfS8Ie+g6Or4ODvWk+C9EsQ84+2KHoYfwpwBqVObdMuzCrWLd5fDI0mLRH6lTMIAmraRVJO7iHAcgHl8nG4GJMVuEo8BdY0iNunLbkoYKrhDFJFXBG4itSG65SCXz6FEMUo7SLs/kHrFXVuV9b6gCrQ/EFo+Yg2I2gmVdW+fNbtqX0e+AbDoKUQEolF8eZc4u+AlrulpHjpM4fvSU6p8q5u3bq8+uqrbNq0iaZNm+LllTOJ9qhRozxUsxvY0dWw+k3t/h3v5piJ09M+WHUYgIfahlO5hHpuCiGEEMXN7UGp48ePu/sUN6zJ/ZrwaIcIxi/ezX8nLjHjj4PM+ecY97epwfg+DdE585AUik8gRPXTFocdLhyEk5u1xZIK3tl6Yy1/Ds5sB98Kzp5U7bTbai1zlitOviHYw5qByZQ7gGSzaEP/MntWZQarMgNX1lRIPKktx//N47mbtGBVZu+qoOrazISB1bTbgLDi7SUmhHAPu00Lru/4Rguw251DxPXe2rDkFo9oee6uHJp86TgsexaOrYb+s6DFQG195UYArN13jsyw0N0tq5fEMwHAy5noXIJS5d+cOXMICAhg7dq1rF27Nsc2RVEkKFXSks7AT8MAFVo+Ci0f9nSNXLYev8imYxfx0is82a10JFwXQgghisLtQalVq1bxzDPPsGnTJoKCcnZ3TkxMpFOnTsyePZuuXbu6uyrlUr2wQH4Y0ZGfd5zhjV/3kZBqYe6/MXy98QRDOkfyfO/6RQtOgTa0LixKW9oMybnN4dCSmBuMkH5RS5x+6Hfnfgao10vrdVWSDN4QWldbrqSqkHohZ5AqM3B1MQZSzoE5UetJkb03xZX8QiGoKgRWdQasquYMXAVWBf9Qre2EECUr/rDWI2rn99rfdKYqzbQeUU3v02YIvZLNAptnw5ppWm9Lg1ELwl/hm02xANzXugZ+hcnjd50Mzp5Sdkl0Xu7FxMR4ugoik90KPz6uzRgc1lTLOVeKfLjqCAD3tq5BtWBfD9dGCCGEKDq3X1XPnDmT4cOH5wpIAZhMJp588knefffdQgelPvnkE2bMmMHZs2dp3LgxM2fOLNAx1q9fT7du3WjSpAk7duwo1DlLK0VR6N+yOp3qVOSZBdvZcvwSGTYHs9Ye5fP1MfRrUY0JtzfC5FecuaB08Ngy7cvcuV1ZvaliN2tfBvXZzqWq8OlN2hCZzB5VYU1LZNYqF0XRkqAHVM6dRwvAkgaXT+QMWiWfheRzzuUsOKzaxWlavJabJt9z6Z0BquxBqysCV4FVZLigEMUhIwn2LtZyRZ3akrXeryI0vV/r2VClaf77H10Fv78I8Ye0xxGdoe+HuaZ6P3AuibWHLqBT4JnuJZu7xdVTyi49pYQoMSte0iZh8QmC+7/UZjAuJXaevMw/hy6g1yk81U1ySQkhhCjb3B4V2LlzJ2+99Va+23v16sXbb79dqGMuXLiQZ599lk8++YTOnTvz6aef0qdPH/bt23fV/FSJiYkMGjSIHj16cP78+UKdsyyoHGRk0YhOxFxI4bkfd7LtxGXMNgeL/jvFD/+d4t7WNRjXqwFVTMWYd8DgDTXaaEvHp7UAVOJJsJmzyiQczeqBtOcnbZ2XH1R3JlCvfxuEty2+OhWFt582RMc5TCcXh0PrEZYZqEo6kxWsct2ehZQ4bbropNPacjUG4xWBq6pZi3+oFrTyDdZufYIkgCVEJodDG4K741vY9wvY0rX1ih7q3QotHtY+V6413PafGbBqinbfvxL0nKRN855H79JXlu4B4LYmVYioWLIzXHm5ekpJUKo8Gjt2LG+88Qb+/v6MHTv2qmXffffdEqrVDW7rPG3iFxS4+9NcQWpP+2i11kuqX4tq1KzopnQJQgghRAlxe1Dq/PnzuRJ15qiAwcCFCxcKdcx3332XoUOHMmzYMEDrjfXHH38wa9Yspk2blu9+Tz75JAMHDkSv17N06dJCnbMsiawUwE9PdeZEQiqv/byXdYcvYFfhh22nWBJ9mj5Nq3Jvq+p0rRda9KF9+VEUCL4iMGiqAYN+gZNbtN5Up7ZoM+wd/1dbVHtWUCo1HnYs0IJDofW1vE4l2aMqPzqdFijyD716rwu7TZsZMPksJJ29ordVtkBW+iWwZWTlvboWRa8lgPcNyRms8g0BY3De6zO3SR4sUV5cOg47voOdC7T8cZlCG2g9opo9CIFhee+rqlqvh8CqWt44gBrttL+tdk/AzS/lPfsesPPkJf47fgmAfs1LLpdUJpOv9j/Ux6uYP69FqRAdHY3VanXdz48iP0yUjJh/4fcXtPs9XoWGt3u2PlfYfzaJlfvOoygw8mbpJSWEEKLsc/u3/erVq7N7927q1s37H+euXbuoWrVqgY9nsVjYtm0bL730Uo71vXr1YsOGDfnu98UXX3D06FG++eYbpkyZcs3zmM1mzOas3j5JSUkAqKqKql7fr9WZx7je41xLzQp+zB/SlgyLjW82x7JyfxxbYi6ybOcZlu08g9FLxx1Nq/Jyn0ZUCHBj4MLgA5E3aQuA6tCGysRuhlObofYt2hdGgDPRKCtfde2qKnoth5MpHILDUVs8ihrSVGs7u0U7lqEUzTij02f1drra5Fy2jDx6WmXrcZV2UQtcpV9CsWVogbv0i9pSSKqXvzNIZcLfKxACKqHmG9TKFvjyDpTeWU4l9TdbHl1321nTtN5QO75FyTZBgeoTBE3u0XpFVW+d9V698jyJp7QcUzsXoFw8htr+KbjN+eNFrS7w7C4t+J3Xvk6ZvaQCjQZ6NQ4rsfdBZru1DA9m0X+niAwNkPdgAbn7b7Y4j7t69eo87wsPuBgDiwaBw6Z9vnS5es81T8jsJXVH06rUrRzg4doIIYQQ18/tQanbb7+d1157jT59+mA05gwepKen8/rrr3PnnXcW+Hjx8fHY7XbCwnL+Gh4WFsa5c+fy3Ofw4cO89NJL/PvvvxgMBXvK06ZNY9KkSbnWJyYmFktQKiUlBSi5Xz7va1aR+5pV5MD5FD7bcIq/DiaQYXXw0/bT/LT9NBEhRm6LCuXhttUJMpZAzyTvqlC3v7YAJCYCoLfq8Kl3B/qEQ+gST6DYLdqXysRTELuRtCrtSfHSejl4nVhLwNLHcPhVwhFYHUeQcwmsjiMoHHtYU1T/yu5/LkWlCwFTCJiirl7OloGSkYiScRnFrN3qst3XliTn9svZyiahoKJYU7WZB5NOkX+fxdxURY9qNKEag1F9TFn3nY8d2e5fWS5HTrFywBN/s+VFkdpOVdGf3Yb3vh/wPvQrikXbX0XBVrMzlqj7sNa9LSsg7fzRwMWWgdfRP/He+wOG2H9RnHPmqV5+mG12MpyfN5oA1+dPXk5cTGP3ae34D7epSuJVyha3zLazmDMAyDBbSvT8ZZm7/2aTrnzPibIv7SJ8e6/240/VFtD3o1L3w8yRuBSW7z4LlHxuOyGEEMJd3B59eOWVV1i8eDH169fnmWeeoUGDBiiKwv79+/n444+x2+1MmDCh0Me98iJTVdU8LzztdjsDBw5k0qRJ1K9fv8DHHz9+fI7cDklJSYSHh2MymfJM2l4YmUEtk8lU4l9w25tMtK9fnZgLKUz5bT//HI7H5lA5cSmDT9ef4tP1p2hSPYjhXWpzc8NKBBkLE8YoBqbu0Ki7dl91oKach8sntTxVl0/iW+8mAgwBWtvZLgOgS7uALu0CnN+R41Bq/1lQ7SHtwcnNsH4mmGpCcLiz51VN7davYqm78MzJBOQzJOlqHHZUc7Krx5Wafom0hNP4KWaUjERtfcYlSL/sLHMZMi5D2kUUuxlFtaMUtXeWwVcbbmgMct46F5+gvO9fWdbgW6peE0/+zZZ1hWq75LNar6YdC1ASDmcdI6QWNB8ILR7CYAq/+j8uVYWPe+bcP6KT1qMqqh8+3gH4FLDu+84kct/nOwHwNuh4vk/j4h/yfBWutgt0tptOj8lkKrHzl2Xu/pt11+fA3XffneexFUXBaDRSt25dBg4cSIMGDdxy/huWNR2+exASjkBQDXjoey3XZCnzyeojqCr0igqjYZXruxYVQgghSgu3B6XCwsLYsGEDTz31FOPHj3ddKCqKQu/evfnkk09y9Xq6mtDQUPR6fa5eUXFxcXkeJzk5mf/++4/o6GieeeYZABwOB6qqYjAY+PPPP+nevXuu/Xx8fPDxyf3VRVGUYrkYzTyOp77g1q4cyOdD2mGzOfhiQww/bj/N4fPJOFTYczqJ0Qt34KVXaFLNROVAHx7tGEGXepVKtpKKHoKqaQvttXWqipKYqLVd68cgqq+WW8YZtMq6H4tSsW5WYCNuPxz8Pe/zePnBgLnQyNlj72IMnPpPy03jX1lLgOwbkmfy41JNbwC/EG0BUFVslRJRCvIlzZqeLVh1SQtWZd6/2vqMREBFsaVDSro2E2NR6LyuHtC6cnFtC9Lu+wRqQymLkaf/Zsuyq7adzQwHl2uz5x39WxuSC9rfZVR/aPkwSs1O+f/9pcTBvp+hzePaa64oUL837E2HFg9Bi4EoFWoXus7rj1xg0OdbsTtUdAp8+GBL9PrifU8VhKIoHInTevzsP5sk779CcOffrLteB5PJxNKlSwkODqZ169aoqkp0dDSXL1+mV69eLFy4kLfeeou///6bzp07u6UONxy7FX4cqv14ZTTBIz9qaQNKmRMJqfy88wwgvaSEEEKULyWSQToiIoLly5dz6dIljhw5gqqq1KtXj5CQkEIfy9vbm9atW7Ny5Uruvvtu1/qVK1fSr1+/XOWDgoLYvXt3jnWffPIJq1at4scffyQyMrLwT6gcMRh0DL+pDsNvqoPD4WDx9tMcjkvhr/3nOXohleiTlwH4Y995DDqF8Ap+dK5Tkfva1KB5eOFfv2KlKKjO/EdqleaoaLNTORwOnF9rcWTYUFFRqrRHf+tbeKWcRkk8iZKo9b7SpZwHaxoZhkBUiw0FMBxZjWH5mJzn0hnALxQCKsFt/9Ny0YA2s+DpbVrgKqCyFsTyq1DsAZES5+WrLYW9MHfYwZwEGUlagCpzMV/xOCNJC2jltV11gMMKaQnaUuTn4K8Fp3ItQQVb5x0AXkat11ZZfz1LG1WFszu12fN2/6AFNTPV7Kj1amrcX3sd8mK3wqE/tP0P/6nlf6lYB+o4f2C4eTzcOrnQr5vdobLz1GX+PRTPnH+OYneoGHQK3wxtT4c6FYv2XItB5qR7Zpvj6gVFmVelShUGDhzIRx995OqV53A4GD16NIGBgXz//feMGDGCF198kXXr1nm4tuWA3QY/DYODv4HeBx5ckP8svB42a432mdStfiWa1Qj2dHWEEEKIYlOi05qFhITQtm3b6z7O2LFjefTRR2nTpg0dO3Zkzpw5xMbGMmLECEAbenf69Gm++uordDodTZo0ybF/5cqVMRqNudbf6HQ6Hfe2CQdg/O2NOHohhQ/+Psyag3EkptuwOVRi4lOJiU/lm82x6BToWi+UdpEVublBJaKqBhXo12OLzcHeM4kcOJfM8fgU4pItpGRYSTHbSbXY0ClQq6I/KWY7aRYbe04nkmF1oGbmhVHBoaoUfnb0cOfSwbXGGytVlQTOz0sggz8A6KM7zmOGRlTiMqFKIiYlTfvSm3IOUs7x4JyNbHJoeV0e1v/Fm16f5ziLXVW4SCDxqomJ1sFsVrUL3EjlLK2Uw6R4hXCRYBIUE2et/qTb9WS2WmbzKYqCAoRX8MNLr6BTFC6mWkjOsKEooGSWcd7X6RSiqgbh561Hr1M4n2QmPsWMTgGdou2vKIDqwKA30CLchL+PF3odnLmcwdnEdAw6BYNeh16n4KVXMOh0GPQKTaub8PfRPiouplq4kGzGz1vvXAwEGA0YdFrFwyv44eulB4K4lGbkQnKwq130BgX/UAOBPgYCjQYqBxkx+XrhrdehOF9XVBUsKTmCVIo5UcuPZU5CMWcPauUT9LJbtBNm5tIqam+t7HRemAxGbTiHwaj14vFy3hqMWUE8L18tiJVjWz5l89zm55HZJh0OByoKqqpid6iYbQ7nfXDgwOFwBnxR0SsKRi89qgoqkJBidm1DBZtDSy7tUMGgUwjy9eJyYjq+GTpiT50k7PgvhJ9YTHDyIdf5U41hxNboi7nJg2QEReJQVQ5sjcdij3Mdy+ZwYLx4gEbnltHq8p8E2C+79j9ujGLp30c5uDHn0DZVVfH11uf4Arfr1GUS052fN2YbqWYbSRk20iw27A4Vqz3rgyUsyIcvhrQlqqpnh8wZDc7ghCQ5L/fmzZvH+vXrcwwT1el0/N///R+dOnVi6tSpPPPMM3Tt2tWDtSwnHHb4eSTsW6r1zn3gm6wfnEqZ05fT+Wn7KQBG9ZBeUkIIIcqXkv/2UwweeOABEhISmDx5MmfPnqVJkyYsX76ciIgIAM6ePUtsbOw1jiKupU6lAN5/sCUAZy+n8/3WWFYfvMDh8ymkW+04VFh7KJ61h+KZ8cdB/L31VAr0IcgZaNApcCHFwqU0Cza7itFLR3KGrUC/9u84WTLJfC14cUKtkmPd7472/G5p73rsjZWKJFFRSSRUSWSfo6ZrW7waxHp7Y0Kd20JIQa+oVCKJSkrORLiddXuY4vVFzgp4wUVDAPGqiddsQ9jk0JKe1yeWDrp9JF30Jwk/klQ/HPijU/1Iwp9UfICcAcANRwveq+jg+eQCl12+uxiCOtctEJ0SiI+hJjoF0q12LdimU9ApoJAVoIswGajqa8NPTQNzMilJlwhQ0gkgjQDSCSTd+TidMB8rIYYMjI40vG0p6Kwp+Klp+Kvp+JLhOrvisILFCpaCt1tRWdFjVr2xoceGHjs6bBiwoQOdAVVnwO5cn2ZTsKLHrmaW07sWRW9Ab/DGjg4rOpIyVK2sc7Gix6rqsKl6bM597dn2d51bzXrsQMGODgc6VJTcj1XtsbZNhw4HNZU4auvO0Fg5wU26XXgrdgDMqhd/OlqzyH4z6zOa4Lisgz3ngfN5tkuUcpzlPi+7HsepwSy2d+FH+00cyagBlwHyfq8uiT5T4PYPMhroUi+Um+pVom+Lavh5e/7fpLcrKOXhigi3s9lsHDhwIFcOzAMHDmC3a387RqNRhnFeL4cdlo2CXQu1VAH3zYf6vTxdq3zNWXsUq12lY+2KtI6o4OnqCCGEEMXK81fbRTRy5EhGjhyZ57b58+dfdd+JEycyceLE4q9UOVY12JcxtzZgzK1actWLKRbWHorjYpqVTccSWHc4nlSLndSEtHyPkWLO+VjrcaHD11uP0UuPj0GPn7eOEH8futYNxd/HgL+PnrOJGeidPX5QwJyRQdUKQYQG+lApwIdKgVruL0VRSDFbUVVnkAJAl9mrCPSKjgDnzIKqqpJm0Xp/OVRAxdnzQ8Xh0PpkmXy1JO8qkJRu1XqPOHuDqCo4UFHVzqjqswQGGjEDZx02kuLPYE+Owys9nudDW2DzCkJVIST2MpcOH8LPchF9Wjz6jAQU1UEFJYUKSgrjejXkfEhLrHaVmkf30nrvl/m25eJ609kf3BW7qhJ5aRMdz3+Lt38FbN6BZOgDOGf24bzFSDJ+7PNpSoK+Mja7A9WShpdOpV6Nqvh463E4VI4npHLyYhp2B9hVFbvDgd3h7CWjqjSsEoiftwEVldiENE5dTsdmV51lVBxqZh82qOjv7foCndkDxcXZ46Wo36sdqhaMynqs5vktfd8FO/sA8HEuofkfNP+3KwoOfLBixIIRC76K8xYzPooVX8zOxxaMzm1G533XNsWCT+b+rm3aOl8la39fxeI6rxd2vJT0vCulAvZsj5Urbq8sa832uJSMQNzlqM2P9m4sUzuRjDaduU4Hvl46woKM6BQFvWqn5uUtVCaBH1VtSN4xJZKDagSnlCr8ZbyVlPCbcSh6GgDW04lYsgW7szeH0UtP4+pZPZ32ndHK+nrr8fXWE2T0okqQkeohvtStFMCtUWH4eJWSxnLyNmj1ud6ZX0Xp9+ijjzJ06FBefvll2rZti6IobNmyhalTpzJo0CAA1q5dS+PGjT1c0zLMboUlT8Ken0DRwT3ZckqWQnFJGXy39SQA/ye9pIQQQpRDZTYoJTyrQoA3d7eqAcDQLpGkmW3MXXeMdYfjybDataCFqlLR34eaFf1oWCWQDrUrEmg0EOjjRYCPHr2+8InDVVUlMTEx3xmVKvh7F/hYgYWYWTA0oKDzdUH1Cg2APGZGqvswdH8467HDrk1BnRoHKXG0rd5KS7IKENAC1P5X5GFK1PIwOWwM6NwYamm9qtiyAY5ug5SsQ0dlP+8D30Kj9logbvNX+K8YBRf0OROJBztvOz4DNZ3DGy+dgNiNYLRklfMO1XIt+QSAoeBtkheb3YHF7sBsdZBhs5Nhsedb1ugclmi2OohPNhN7KY0Us400s400qx3VgTPAqBJewQ+TrxcOVSUhxcKRuBQt0OYc9ml3vjftdgc1K/pTwd8bm0PlQrKZA2fzn+a9aqCBiMomFCApw6YFN4C89giv4EeNED8S0YJzu0/n3/OvWrAvNUN88caKUTFzOu4iwV42DIqKXrWhw4FetaOoNir66ggL0KM47NjtVs5cTEan2tGpdq2sakeHdj/QC0J89SgOG6rDysWkNHSqHcW5Xa86MOod+BlU/Azgo3OgqHawW7HbrSgOO4rDhqLatJkYHdotqtYvCtUBzseK6nzssDvXO1wJy1VTDSxBtfCuGoVSpzvNwqJoBkzOqzHij2h5onZ+D/oz4GPizecma8MdAey9aKA30CPf1iyffL20z0qJSZV/7733HmFhYUyfPp3z57Veg2FhYYwZM4YXX3wRgF69enHbbbd5sppllzUDfhgMh37XckXe8xk0vvuau3nS3H+PYbE5aB0RQsfansttJ4QQQriLBKVEsfDzMTC6R31G96h/7cJCo9NridMDKkHYFb961++V91ACVdVmxtNnC6jV6Q4DPsuZODz7YqruKqqYnSEU1Z41a152zR7Mun9qq/Zrcn7u/hSaO8sfXwcrX9eCVd6Zi7/zcaA2G1rVZlrZtItw4QAGb38M3gH4eQeAbwAE+Wcl1bqK8Ap+tIwo2ST71wqGekrrQpT12JQOqkp6YiLeJlPer685GfYu0WbfO7kpa71vCDS9T3u/ZwalPJBvqzTwlpxSNwSbzca3337L0KFDmTBhAklJ2ud1UFBQjnI1a9bMa3dxLeYU+H4gxKzVcvjd/3WpHrIHWs6+bzZp6Sie6V63VP3/EUIIIYrLjXmFL0RZpShasu3sKtbRlgKwNHsE346Po2RPFO5KEn45K3AEWlCgTvcrEoqngM05tMzbP6ts8jk4/V/+Jw6qlnXsU//BgvvyenJaMOu2qdBKG6bCuT2w6o2s3llXBrzCO0CYs1+YJQ0ST2nt4+VcDD4FCnQJD1r/AfwzXbuv6KBuT232vQZ9rrs3XnkR4JxoQC/v5XLNYDDw1FNPsX//fiB3MEpch+RzWkDq9Dbtf8hD30Nk6U8WP29dDOlWO81qmLi5fiVPV0cIIYRwCwlKCXEjURQtWOPtD0FVr162bg9tuZLDrs2QZzBmrYvoDA9+55w5Lxksqc77KdptpYZZZXV6qFAnq4wlc9yhqiUSV7IN60w+C4dW5F/H2/6XFZQ6tws+733F89VlBahueh7aP6GtvxgDK15ytkW2IJa3v9YrJ7wDhDtnCjUnQ8y/GMw2CArWpg3Xe4HeW1v8KoK/c0iFwwG2DOc2+XjN5XIs7PhOGyJau5u2rvmD2uxXLQZqPfWu9b68AdWupOXe8jfKe6q8a9++PdHR0a6JW0QxOLsTvnsIkk5rP7Y8/CPUaOPpWl1TYpqVrzaeAOCZW6SXlBBCiPJLrnCFEIWj02flvsoUVLXgwYS6PWDU9qzHDgdY05wBqlTwyzazUKWGcNcHWdvMydnup0DFbElf7VatXtZ0sDsTh6uOrMCXI1vW79QLVw923fxyVlDqcizK9w85U3LnofNouNWZIenyCfighXZf0WUFrvReWjCr1aNwi3MGubSLsOCBbNudtwYfbXryWl208qA9pxXjtaCiosu9VG0Oze7Pas/Vb+ZRzrlvxboQ1Ter/pvnAGrOMplLYDWo1zOr7N6l4LDlXQe/Cln5yACOr9fqfSkG3zN74eIBiN2knavhnVlBqYp14Okt0qPtKgw6rW2sBZi5VJRtI0eOZNy4cZw6dYrWrVvj7++fY3uzZs3y2VPkoqqw7QtY8bLWwze0vtZDqoA9iz3tiw0xpJhtNKwSSM9GYZ6ujhBCCOE2EpQSQniWTqcNxfPJI+wTHA6tHyvYcSK7wkta7g3sNi3QZU3TAljWdAionO24EVqwy1UmTStjTdXuV2mSVVbvjVq9NXZLOnocKHaLFgCzW7TFO1u97dkCX6qz15QtI2udOVs2ems6nNqS//Mx+GQFpWwZ2per/DS9Lysopdrh37fzL9vgjpxBqT/Ga4GmvNS+OWdQ6pdRYM4naXt4exj6Z9bjHx+HlHMoaHMgukTelDuxsASkrsrgnBTCmsdsk6J8eeCBBwAYNWqUa52iKKiqiqIo2O35Twghskk8Db+N0xKagzYs+J554Bvs0WoVVHKGlS/WHwe0XFI6nXxGCiGEKL8kKCWEKH/0BtAHaTMM5iUwrODBrtB6MOxvUpyJzq8aQKlYF8afyhm0yn7fN1svMN8QeHCBtt5myV0+e/J7g1HrveWa9e6KpUr23hMKtHtC6yVwZTnUK8oCUf1ds+hpi5p1mz04BxDRSet1lv3YmXXKPkQzs938Q1FNNTAH1sSnWhOU2jdDiAxLKqxUsxY0tEhPqXIvJibGbceeNm0aL7/8MqNHj2bmzJn5llu7di1jx45l7969VKtWjRdeeIERI0a4rV7FKu0irHsPtszJGkrdcyK0f0r7AaSM+HrTCRLTrdSp5E+fJjKkWQghRPkmQSkhhCguOh34BBasrLcfNLyjYGW9fOHmFwtWVm+A22cUrCzAvfMKXnbg9wUvO/hX7VZVyUhMxOdaAT2RL4M+q90sNodrNj5R/rgrl9TWrVuZM2fONYf/xcTEcPvttzN8+HC++eYb1q9fz8iRI6lUqRL33HOPW+p23dIuQuxG2P8r7F2c1Ts1ojP0mZ47uF7KpVlsfPavFpx8+pa66KWXlBBCiHJOglJCCCFEKWb0ygpCpVtseBu8PVgbURL27dtHbGwsFoslx/q+ffvms0f+UlJSePjhh5k7dy5Tpky5atnZs2dTs2ZNV0+qRo0a8d9///H222+XjqDUHy8TcOwfcFjAmqENJ864YkhxlWbQ/VWod2uZDIQv2BzLxVQLNSv40bd5NU9XRwghhHA7CUoJIYQQpZi/d9a/6gybA9NVyoqy7dixY9x9993s3r3blUsKcM28VpScUk8//TR33HEHPXv2vGZQauPGjfTq1SvHut69ezNv3jysViteXl659jGbzZjNZtfjpKQkAFRVddW/2Fw6jiFuT67VaoXaUKcHNL0XarTLCkYV9/ndzGy1M+efYwA8dXMd9Dql+NvwGjJft5I+741C2te9pH3dS9rX/cpbGxf0eUhQSgghhCjFjIacPaVE+TV69GgiIyP566+/qF27Nlu2bCEhIYFx48bx9ttXmcAgH99//z3bt29n69atBSp/7tw5wsJyzvQWFhaGzWYjPj6eqlVz5zeaNm0akyZNyrU+MTGx2C+qdS2ewBxxF8bAYPDyQ/XywxFUA7z8sgo5g2Jl0YL/zhCXbKZKkDc96wSQmJjPxBJupKoqKSnapBxKGexpVtpJ+7qXtK97Sfu6X3lr46QC/k+WoJQQQghRimXPIZVmldnXyrONGzeyatUqKlWqhE6nQ6fT0aVLF6ZNm8aoUaOIjo4u8LFOnjzJ6NGj+fPPPzEajQXe78qL4Ct7a11p/PjxjB071vU4KSmJ8PBwTCYTQUH5TDZRRGpQN+yJifibTOXiYj27NIuNzzedBuD/etQntEKIR+qR+XqbymEblwbSvu4l7ete0r7uV97auKDPQYJSQgghRCmmyzZrmNkqM/CVZ3a7nYCAAABCQ0M5c+YMDRo0ICIigoMHDxbqWNu2bSMuLo7WrVvnOP4///zDRx99hNlsRq/X59inSpUqnDt3Lse6uLg4DAYDFStWzPM8Pj4++Pj45FqvKIpbLqgzj1seLtaz+3pTLPEpFsIr+HJ/m3CPPr/y2salhbSve0n7upe0r/uVpzaWoJQQQghRTiiACljsEpQqz5o0acKuXbuoXbs27du3Z/r06Xh7ezNnzhxq165dqGP16NGD3bt351g3ZMgQGjZsyIsvvpgrIAXQsWNHli1blmPdn3/+SZs2bfLMJyWKR3KGlU/XHgVgdI/6eOllhk0hhBA3DglKCSGEEKVcoNFAUoaNiv65e6SI8uOVV14hNTUVgClTpnDnnXfStWtXKlasyMKFCwt1rMDAQJo0aZJjnb+/PxUrVnStHz9+PKdPn+arr74CYMSIEXz00UeMHTuW4cOHs3HjRubNm8d3331XDM9O5GfeuhgupVmpXcmf/i1kxj0hhBA3FglKCSGEEKVcZl4pm0N6SpVnvXv3dt2vXbs2+/bt4+LFi4SEhLilG//Zs2eJjY11PY6MjGT58uWMGTOGjz/+mGrVqvHBBx9wzz33FPu5heZ8UgafrtVm3BvTsz4G6SUlhBDiBiNBKSGEEKKUMzjzSllt5WOKYFFwFSpUKLZjrVmzJsfj+fPn5yrTrVs3tm/fXmznFFf3zp8HSbfaaVkzmDub5Z7dUAghhCjvJCglhBBClHKJ6VYAjlxIpmkNk4drI4rb448/XqByn3/+uZtrIkrSntOJ/LDtFACv3hlVLpLaCiGEEIUlQSkhhBCilMsctpeUbvNwTYQ7zJ8/n4iICFq2bOmaDlqUb6qq8uZv+1FVuKt5NVrVDPF0lYQQQgiPkKBUIaWmphIYGOj6NctisWC1WjEYDDmmRM5MVOrr6+uazttqtWKxWHJM732tsnq9HqPR6CqblpaGqqoYjUbXzDk2mw2z2YxOp8PX17dIZdPT03E4HPj4+GAwaG8Lu91ORkZGocoqioKfn5+rbEZGBna7HW9vb9fMPYUp63A4SE9PB7QErZnMZjN2ux0vLy+8vb2vWdZms+Uoq6oqaWlpAPj5+eV6PQtTtiCvfVHeJwV57Qv7PklNTcXb2ztHu2e+ntf7Prny9bze90l+r2dh3ydXe+0LWlZV1RxfFK/1ehb1fZLf61mWPyMyj2u32zGbzSX6GXHl61lWPyMMBgM4339mq92tnxFXe+3L0meEzWbDarW61hX3Z0RxGzFiBN9//z3Hjh3j8ccf55FHHinWYXui9PlrfxwbjyXgbdDx4m0NPF0dIYQQwnNUUSCJiYkq2ozcalxcnGv9lClTVEAdNmxYjvJ+fn4qoMbExLjWvffeeyqgDhw4UL106ZLqcDhUVVXV0NBQFVD37NnjKjtnzhwVUPv165fjuBERESqgbtmyxbXum2++UQG1Z8+eOcpGRUWpgLp69WrXuiVLlqiA2qlTpxxl27RpowLqr7/+6lr3559/qoDavHnzHGW7deumAuqiRYtc69atW6cCat26dXOUvf3221VA/eKLL1zroqOjVUCtVq1ajrL33nuvCqgfffSRa92hQ4dUQDWZTKqqqqrD4VAvXbqkPvbYYyqgTp8+3VX21KlTKqAaDIYcxx05cqQKqK+//rpr3aVLl1yvp8Vica1/7rnnVEB97rnnXOssFour7KVLl1zrX3/9dRVQR44cmeN8BoNBBdRTp0651k2fPl0F1MceeyxHWZPJpALqoUOHXOs++ugjFVDvvffeHGWrVaumAmp0dLRr3RdffKEC6u23356jbN26dVVAXbdunWvdwoULVUDt1q1bjrLNmzdXAfXPP/90rfv1119VQG3Tpk2Osp06dVIBdcmSJa51q1evVgE1KioqR9mePXuqgPrNN9+41m3ZskUF1IiIiBxl+/XrpwLqnDlzXOv27NmjAmpoaGiOsgMHDlQB9b333nOti4mJUQHVz88vR9lhw4apgDplyhTXuri4ONfrmd3o0aNVQH355Zdd61JSUlxlT5065fqbffnll1VAHT16dI5jFOdnRHZl+TMi82/233//LZHPiEzl4TPC4XCoQUFBKqBGPjVXjXjxV/WDvw+57TNi0aJF5eozYurUqa6/2eL+jMi8JkhMTFSLS0ZGhrpgwQK1Z8+eqp+fn3rfffepK1ascD2HssIdbZMp8/OkrLXJlcxWu3rzjNVqxIu/qm/9vt/T1cmhvLRxaSXt617Svu4l7et+5a2NC3pNIFN8CCGEEKVcZu8rs1Vm3yuvfHx8eOihh1i5ciX79u2jcePGjBw5koiICFJSUjxdPVGMvtl0gpj4VEIDvHnq5jqero4QQgjhUYqqSvKCgkhKSsJkMnHmzBmqVKly3cP3zGYzJpMJRVHK9NCckh6+p6oqiYmJGI1GGb5XhLJxcXGEhITI8L0iDN+zWq0EBwejKIoM3yvk8L3ExEQCAgJk+N5VXs+8yqqqypkzZzCZTLSf/g+pFpUnbork+VvryfC9q7yemcP30tPTCQ0NRVGUYv+MyMjIwGQykZiYSFBQEMUtNjaW+fPnM3/+fCwWCwcOHCAgIKDYz+MOmddL7mibzGuAzOunsuhymoVuM9aQmG5l2oCmPNSupqerlEN5aOPSTNrXvaR93Uva1/3KWxsX9JpAglIFVJwXWeXtzVaSpO2KTtqu6KTtik7aruiyt13zSX+SlGFjcKdaTOzb2NNVK/Xc/b5zR+DFbDazePFiPv/8c9atW8edd97JkCFDuO2223LloizNJCh1da8u3cPXm07QICyQ5aO7oteVrudRHtq4NJP2dS9pX/eS9nW/8tbGBb0mkETnQgghRCnnpdeCEg75HalcGjlyJN9//z01a9ZkyJAhfP/991SsWNHT1RLFbM/pRL7ZfAKAiX0bl7qAlBBCCOEJEpQSQgghSrnm4cGsOhBHk2omT1dFuMHs2bOpWbMmkZGRrF27lrVr1+ZZbvHixSVcM1FcHA6VV3/eg6pC3+bV6FhHgo5CCCEESFBKCCGEKPUMzh4VFrskOi+PBg0aVC666Yv8/bj9FNGxl/H31jPhjkaero4QQghRakhQSgghhCjlMofv2SQoVS7Nnz/f01UQbpSYZuWt3w8AMLpnPcKCjNfYQwghhLhxlJ3MmUIIIcQNKiZem7Fvx6lED9dECFFY/1txgIRUC3UrBzCkc6SnqyOEEEKUKhKUEkIIIUq5NIsNgIupZg/XRAhRGBuOxPPdllgApvRv4ur1KIQQQghNmf3P+MknnxAZGYnRaKR169b8+++/+ZZdvHgxt956K5UqVSIoKIiOHTvyxx9/lGBthRBCiKLLnKXLZpfZ94QoK9IsNl5cvAuARztE0KG2JDcXQgghrlQmg1ILFy7k2WefZcKECURHR9O1a1f69OlDbGxsnuX/+ecfbr31VpYvX862bdu45ZZbuOuuu4iOji7hmgshhBCFZ9BJTikhypoZfxzk5MV0qgf78mKfhp6ujhBCCFEqlcmg1LvvvsvQoUMZNmwYjRo1YubMmYSHhzNr1qw8y8+cOZMXXniBtm3bUq9ePaZOnUq9evVYtmxZCddcCCGEKDyDPnP2PekpJURZsO3EReZvOA7A1AFNCfCRuYWEEEKIvJS5/5AWi4Vt27bx0ksv5Vjfq1cvNmzYUKBjOBwOkpOTqVChQr5lzGYzZnNW7o6kpCQAVFVFVa/vS0HmMa73ODciabuik7YrOmm7opO2K7rsbWdwDd9zSFsWgLvfd/IaiKvJsNp54cddqCrc27oG3epX8nSVhBBCiFKrzAWl4uPjsdvthIWF5VgfFhbGuXPnCnSMd955h9TUVO6///58y0ybNo1JkyblWp+YmFgsQamUlBQAFEW5rmPdaKTtik7aruik7YpO2q7ocrQd2rA9i9VGYqLMwHct7n7fZf5QJURe3l15iKMXUqkU6MOrd0R5ujpCCCFEqVbmglKZrrzIVFW1QBee3333HRMnTuTnn3+mcuXK+ZYbP348Y8eOdT1OSkoiPDwck8lEUFBQ0StO1i+sJpNJvqQVkrRd0UnbFZ20XdFJ2xVd9rbz9fYGQNHpMJlMnqxWmeDu9528l0V+Nh5NYO6/xwCYdndTTH5eHq6REEIIUbqVuaBUaGgoer0+V6+ouLi4XL2nrrRw4UKGDh3KDz/8QM+ePa9a1sfHBx8fn1zrFUUplovRzOPIhW3hSdsVnbRd0UnbFZ20XdFltluXeqFsPJZA8/BgaccCcuf7Tl4DkZekDCvP/bATVYWH2oXTM+rq16VCCCGEKIOJzr29vWndujUrV67MsX7lypV06tQp3/2+++47Bg8ezIIFC7jjjjvcXU0hhBCi2HjpM3NKSS4jIUqriT/v5fTldGpW8OMVGbYnhBBCFEiZ6ykFMHbsWB599FHatGlDx44dmTNnDrGxsYwYMQLQht6dPn2ar776CtACUoMGDeL999+nQ4cOrl5Wvr6+MgxCCCFEqWfQab8hWewOD9dECJGX33adZXH0aXQKvPdAc/xltj0hhBCiQMrkf8wHHniAhIQEJk+ezNmzZ/+/vTuPi6rc/wD+OTPsCoMiCIgg5cKqIKSSkeWalUtWLnVVMuty1Z8pqTfFUlvUm2lquWQhaplobjeNa3JVMDcuFpiCkYKEKKiQyACyzvP7A5kcQYWR2eTzfr2mmDPPOed7vhznHL7nOc+Bn58fYmNj4eHhAQDIzc1Fdna2uv0XX3yBqqoqTJ48GZMnT1ZPHz9+PDZs2KDv8ImIiBolq6AEAPBbntLAkRDRna4UlSFy92kAwKSnOiLI4+5PdyYiIiJNJlmUAoBJkyZh0qRJ9X52Z6EpPj5e9wERERHpSHFZFQCgsLTCwJEQ0e2EEJi5/VcUllbCr50dpvbrZOiQiIiITIrJjSlFRETU3FiY1Ryuq1UcU4rImHxz4g8c/v0aLM1k+HRkgPrfKhERETUMj5xERERGzlxec7hWCRaliIxFxrVifBR7FgDwzmAvdGpra+CIiIiITA+LUkREREbO0vxWUYrjnBMZhfKqarwVk4yyShWe6NgG40M6GDokIiIik8SiFBERkZGzvNVTqpo9pYiMwpJ96ThzqQj2Nub45OVukMkkQ4dERERkkkx2oHMiIqLmwtyMt+8RGYtD6Vfx1ZELAIAlL3WDs8LKwBEREVFTqq6uRmVlpd7XK4RARUUFysrKIEmmdbHD3Nwccrlcq3lZlCIiIjJylmY1B3nWpIgM62pRGWZsOwUACHu8Awb4tDVwRERE1JSKi4uRk5MDYaCTLpVKhYKCAoOs+0FIkgQ3Nze0bNmy0fOyKEVERGTkAtwUAIB2rawNHAlR86VSCURsO4WCkgp4u9jhncFehg6JiIiaUHV1NXJycmBjYwNHR0e991YSQqC6uhpyudykekoJIXDt2jXk5OSgU6dOje4xxaIUERGRkbO4NdB5VTW7SlHDrFmzBmvWrEFWVhYAwNfXF++99x4GDx5cb/v4+Hg8/fTTdaafPXsWXl4svgDAF4czceR8PqzN5fhsTCCszLW7TYGIiIxTZWUlhBBwdHSEtbX+LwSaalEKABwdHZGVlYXKykoWpYiIiB42ZrLaohQfv0cN4+bmhsWLF6Njx44AgI0bN2LYsGFITk6Gr6/vXedLT0+HnZ2d+r2jo6POYzUFv2Rfx9L96QCA+UN90NGp8bcnEBGRaTC1gpAxeJCcsShFRERk5K6XVgAA/iypMHAkZCqGDBmi8f6jjz7CmjVrcOLEiXsWpZycnGBvb9/g9ZSXl6O8vFz9vqioCEDN1d6mHo+jdpn6Hucjv7gck775BVUqgee7uuDlIDeDjTWia4bKcXPB/OoW86tbzSG/tdtmDNtp6PU3Vn25a+g2sChFRERk5Gp7SJVXsacUNV51dTW+++47lJSUICQk5J5tAwMDUVZWBh8fH8ydO7feW/put2jRIixYsKDO9Bs3buikKFVcXAxAf1exq1UCk7emIq+oDJ4O1pjdz11deHsYGSLHzQnzq1vMr241h/xWVFRApVKhuroa1dXVBonBysoKvr6+qKqqgqenJzZs2NCoi0UA8P7778PBwQGTJ0++b9usrCwkJSXh5ZdfBgAkJCTgxRdfhIeHByoqKjBy5Ei8++67911OdXU1VCoVlEql+mJVQ4+XLEoREREZudqxa0zrmhkZ2unTpxESEoKysjK0bNkSu3btgo+PT71tXVxcsG7dOgQFBaG8vBxff/01+vXrh/j4eDz55JN3Xcfs2bMRERGhfl9UVIT27dtDoVBo3AbYFGqLXAqFQm9/EC35MR3/++MGbCzk+GJsMFydbPWyXkMxRI6bE+ZXt5hf3WoO+S0rK0NBQQHkcnmjx0VqKvb29khJSQEAjBs3DmvXrkVkZGSjliFJEmQyWYO24eLFi9ixYwdGjx4NAJDJZOjfv7/6YlZgYCCGDBmCoKCgey5HLpdDJpPB1tYWVlZW6jgagkUpIiIiI8cBlUkbXbp0QUpKCgoLC7Fjxw6MHz8eCQkJ9RamunTpgi5duqjfh4SE4OLFi/jkk0/uWZSytLSEpaVlnemSJOnkj5ba5erjD6L9qXlYHZ8BAPjXi13R2blpi2zGSp85bo6YX91ifnXrYc9v7XbV/v9mZdP3lrI2v/sg5rf3MJYkCb1798apU6cgSRIyMzMxYcIE/Pnnn+jQoQM2bNiA1q1b47fffkN4eDiuX78OS0tLHDhwQOP3tGjRIvz222+Ijo7GokWLsHv3bpSXl2PSpEkIDw/H3LlzkZqaisDAQLz11lvw9PRUr79ly5YICgpCZmYmpk2bhvXr16Nz584QQsDLywuJiYnqXly35+7OPN4Pi1JERERGjkUp0oaFhYV6oPPg4GAkJSVhxYoV+OKLLxo0f69evfDNN9/oMkSjlXmtGG9/dwoA8FrvDhjSzdXAERERkT7drKyGz3s/Nvly094fBBuL+5dhqqurERcXhwkTJgAApk6dikmTJmHkyJH417/+hfnz52PlypX429/+hkWLFmHAgAFQKpUaTw1cuHAh0tPTER0djf379+Pq1atISkpCRUUFnnjiCTz//PP46KOP8Pnnn2P79u0Aap7GW6ugoAAnTpzAu+++i7CwMGzatAkffvghDh06hICAgEbfVng3LEoREREZOevbilKVVSqYm8kMGA2ZKiGExqDk95OcnAwXFxcdRmScbpRWYuLGk1CWVSHIoxVmD/Y2dEhERNRMFBYWIjAwEDk5OfD19cWgQYMAAElJSdizZw8AYOzYsXjuuedQVFSEwsJCDBgwAABga/vXLebr1q2Dl5cXYmJiIJPJEBcXhz179iAhIQFAzdiPGRkZ9cbw3//+F4GBgZDJZJg9ezZ8fX3h7u6Onj174oMPPsDGjRsxfvz4JttmFqWIiIiMnJXFX0Wom5VVMDezMGA0ZArmzJmDwYMHo3379lAqlYiJiUF8fDz27dsHoGYsqEuXLmHTpk0AgOXLl6NDhw7w9fVFRUUFvvnmG+zYsQM7duww5GboXWW1CpO//QWZ+SVwVVhh7d+CYMEiMBFRs2NtLkfa+4N0stx7sbe3R3JyMm7evIkBAwZg9erVmDp1qsatcEKI+94i161bN5w6dQpXr16Fs7MzhBCYP38+xo0bp9Hu9p5Rtfr376/uOVXL1tYWQUFBiI2NxdGjR7F+/fqGbG6D8ChLRERk5KzN/rqGVFbJJ/DR/V25cgVjx45Fly5d0K9fPyQmJmLfvn3qq6m5ubnIzs5Wt6+oqMCMGTPQtWtXhIaG4siRI/jhhx8wYsQIQ22CQXywNw1HzufDxkKOr8Y/BkfbuuNlERHRw0+SJNhYmDX5q6HjLNnY2GDFihVYunQpqqqqEBwcrL5Q9O233yI0NBR2dnZo1aoV4uLiAABKpRJVVVUAgJ49e2LJkiUYMmQICgsL0b9/f0RFReHmzZsAgPT0dJSVlcHW1hZKpbJBMYWFheGNN97A8OHDm3QgePaUIiIiMnItrf468JvLH87BRalpRUVF3fPzDRs2aLyfNWsWZs2apcOIjF/00QvYdPwPAMCnowLg49o8BjYnIiLjFBwcDH9/f+zYsQMrV67Ea6+9hvfffx8eHh7YuHEjAODrr7/Gm2++iRkzZsDKygoHDhxQzz9o0CBcv34dw4YNw759+3DmzBn06NEDQgg4OTlhz5496Nq1KyorKxEQEKAx0Hl9+vbti+rq6jq9rR4Ui1JERERGzlz+V8fmSpW4R0si0sbeXy/j/b1pAIB/PuOFQb7OBo6IiIiao7y8PI33e/fuVf9cOx7U7bp06VJn+vz589U/jx49GqNHjwZw9wtQBw8e1Hj/1FNP1RtbRkYGPDw80LVr13tuQ2Px9j0iIiIjJ0mSuodUVTWLUkRN6VhGPiK2noIQwLgQD4T3ecTQIRERERmVr776Cn379sUHH3zQ5MtmUYqIiMgE1HaQulJUZthAiB4iaZeL8PdNP6OiWoXBfs6YN8S3weN9EBERNRcTJ05Edna2+mmATYlFKSIiIhNQfasqVVhaaeBIiB4Ov19R4m9RiVCWV6GHZ2t8OioAchkLUkRERPrEohQREZEJqP1TubyKT98jelAZ14rxypeJ+LOkAv7tFPhyXDCs7vOYbiIiImp6LEoRERGZgNo7isoqqw0bCJGJy8ovwStfnkB+cTm8Xezw9es9oLA2N3RYREREzRKfvkdERGQCJEkChGBRiugBpF0uwrj1/0N+cTm6tLXF5ok9YW9jYeiwiIiImi32lCIiIjIBtbfv3WRRikgrSVl/YtS648gvLoeXsy2+mdgTrVuwIEVERMbDysoK3bt3h4+PD4KCgvDll19qfC6EgLu7O2bNmgUAiIyMREBAAHx8fGBjY4OAgAAEBATg0KFD2LVrF7p16wZfX1/4+flh9erVAACVSoXIyEh07NgRHTt2xMiRI1FQUAAAyMrKgiRJ+Pjjj9XrTExMhCRJ2LBhg062mUUpIiIiE9DSqqZz83/O5Bk4EiLT89+0K/jbV4lQllXhsQ6tsPXvIXC0tTR0WERERBrs7e3xyy+/IC0tDTt37sSaNWuwbt069efHjx+Hi4sLdu7cCQD46KOPkJKSgtjYWPj4+CAlJQUpKSmwt7fHP//5T+zevRupqalISkqCjY0NAGD58uVIS0tDWloazp8/j8ceewyvvfaaeh2dOnXC999/r36/bds2+Pv762ybWZQiIiIyAcMD2gEAfs0pRGU1BzsnagghBFbHn8cbX59EeZUK/bycsGlCT44hRURE9yYEUFHS9C8hGhyCh4cHli5dqu7hBABbt25FeHg4PD09kZiYeNd5ly5disjISHh6egIArK2tERYWpv5s+fLlsLCo6S389ttvIzU1FefOnQMA2NnZoU2bNsjMzIQQAvHx8ejbt29jM9hgHFOKiIjIBMwY2AXfp1zCn6WViEu7gmf9XQwdEpFRKyqrxJydp7H311wAwCs93bFgqC/M5bwmS0RE91FZCix0bfrlzrkMWLRocPPu3bsjPT0dQM2Flj179mDBggWorKzE1q1b0bNnz3rnS0tLw8yZM+tMLyoqws2bN+Hh4aGeJpPJ0LVrV5w9exZdu3YFALz88svYtm0b+vTpg6CgIJiZ6a50xKMyERGRCWhpZYZXe9WcQHx9/A8DR0Nk3I5l5GPw8p+w99dcmMkkfDjcDwtf8GdBioiITIq4rWfVTz/9BB8fH9jb22PEiBHYtWuXxud3zifVPrq5AdPvNHToUOzZswfbtm3DyJEjtd+ABjDZnlKrV6/GkiVLkJubC19fXyxfvhyhoaF3bZ+QkICIiAikpqbC1dUVs2bNQnh4uB4jJiIiejBjerhj1aHzOJ5ZgBOZBej1iIOhQyIyKhf/LMWncb9jZ/IlAIB7axt8OqobgjxaGzgyIiIyKeY2Nb2adLHcRkhJSYGXlxeAmrGdTpw4gQ4dOgAArly5gmPHjqF379515vPx8UFycrK651MthUIBKysrZGVlqZejUqlw6tQpeHt7q9vZ2trCyckJsbGx+OSTT7Bv375Gxd0YJnm5aOvWrZg2bRoiIyORnJyM0NBQDB48GNnZ2fW2v3DhAp599lmEhoYiOTkZc+bMwdSpU7Fjxw49R05ERKQ9V3trPOrYEgCwYE+qgaMhMg5CCJy5dAPv7PgVfZfGqwtSY3q44z9vhbIgRUREjSdJNbfZNfWrAb2Ual28eBEzZszAlClToFKpsHv3bqSnpyMrKwtZWVlYsWIFtm3bVu+8b7/9NhYuXIisrCwAQFlZGdauXQsAmD59OqZPn46KigoANWNM+fr6olOnThrLiIyMxKJFiyCXy7VIYMOZZE+pZcuW4fXXX8fEiRMB1Iwe/+OPP2LNmjVYtGhRnfZr166Fu7s7li9fDgDw9vbGyZMn8cknn+DFF1/UZ+hEREQPZFhgO3zyYzrO5ioxe8evUKHmj3IBwNdVAWtzGYQALl4vxaXrZRAQEAKo6dx962ch4OOqQAsLOQSAS4U3kV1Qql4ObrWvndfbxQ62VuYQEMgrLMOFgpKaturl4tbPAl3a2kJhYwEIgSvKcmRcLVYv59ai1W07OdmidQsLCCGQX1yO368U31q3ULetjeURxxZwaGEJAYHrJRVIv1J8a5nitvXXvHdv3QIKSwnDurujn3dbnf9OSD9OZBbg/OU/IZkXoqxSBWVZFbIKSnAy6zouFd5Utwvt1AazBnnB301hwGiJiIgar7CwEIGBgaioqIC1tTX+8Y9/4PXXX8ehQ4fQqVMnODj81Ut+2LBh+OCDD/Dpp5/WWU737t2xcOFCDB06FFVVVTAzM8OkSZMA1BSl8vPz4e3tDUmSEBAQgOjo6DrLCA4ORnBwsO429hZJ3O0mRCNVUVEBGxsbfPfdd3jhhRfU09966y2kpKQgISGhzjxPPvkkAgMDsWLFCvW0Xbt2YeTIkSgtLYW5ed0nsJSXl6O8vFz9vqioCO3bt0dhYSHs7OweaBuEELhx4wYUCkWD7uekvzB32mPutMfcaY+5097dcldVpYL3vB9RpTKpw7dBzBjYGZOf7tjkyy0qKoK9vT1u3LjxwOcED5uioiIoFAqd5GZC9P9wMP1avZ9ZyGUY4NsW40M6oIcne0Zpi9/ZusX86hbzq1vNIb9lZWW4cOECPD09YWVlpff1CyFQXV0NuVxucjmuL3cNPScwuZ5S+fn5qK6uRtu2mlc+27Zti7y8vHrnycvLq7d9VVUV8vPz4eJS9wlGixYtwoIFC+pMv3Hjxl0HE2soIQSKi4sBwOR2NkNj7rTH3GmPudMec6e9e+VuypPu2Pi/S+reR9Kt/3R0bAFrMxkkCbiqrMDV4gr155KkbgkJQCcnG7SwqDnpuaasQG7RXxdiJKm2ZW3bFrC1MoMEIL+4AjmFZeqV/rXsW20dbaCwMYcEoKCkEtl/3lQvTKqd49byH21jg9YtzCFJEq6XVuJCQemtxUp/rf9WW08HG7RpaQEJQFFZFc5fK4F0W7vaZUqSBI9WVrC3BPycLHHjxg0tfwN3V1RU1OTLpPvza6dAWUUlbG0sYW0uh7WFGdxb28DLxRY9PVvDxsLkTmuJiIiaPZM9et95gn6/UeTra1/f9FqzZ89GRESE+n1tTymFQtEkPaUAPNRVZl1h7rTH3GmPudMec6e9e+XurUEKvDXI1xBhmQRdX83lvmwY0wd0xo0bbfl9QkRE9BAxuaJUmzZtIJfL6/SKunr1ap3eULWcnZ3rbW9mZqZxT+btLC0tYWlpWWe6JElNciJUuxyeVDUec6c95k57zJ32mDvtMXfa02Xu+PsgIiJ6eJnYCEdG4UFyZnJFKQsLCwQFBSEuLk5jTKm4uDgMGzas3nlCQkKwZ88ejWn79+9HcHBwveNJEREREREREVHzYW5eM6TAtWvX4OjoqPeLUKY6ppQQAteuXYMkSVrVV0yuKAUAERERGDt2LIKDgxESEoJ169YhOzsb4eHhAGpuvbt06RI2bdoEAAgPD8fnn3+OiIgIvPHGGzh+/DiioqKwZcsWQ24GERERERERERkBuVwONzc35OTkICsryyAxqFQqyGQyg6z7QUiSBDc3N8jl8kbPa5JFqVGjRqGgoADvv/8+cnNz4efnh9jYWHh4eAAAcnNzkZ2drW7v6emJ2NhYTJ8+HatWrYKrqytWrlyJF1980VCbQERERERERERGpGXLlujUqRMqKyv1vm4hBJRKJWxtbU2qpxRQ08tMm4IUYKJFKQCYNGkSJk2aVO9nGzZsqDOtT58++OWXX3QcFRERERERERGZKrlcrnWB5UEIIVBeXg4rKyuTK0o9CNPrF0ZERERERERERCbPZHtK6VvtaPJFRUVNsqyioiI+UUkLzJ32mDvtMXfaY+60x9xpT9e5qz0X4NN56mrK86X6ls1/E7rFHOsW86tbzK9uMb+697DluKHnSyxKNZBSqQQAtG/f3sCREBERkTFQKpVQKBSGDsOo8HyJiIiIbne/8yVJ8DJfg6hUKly+fLlJBh0rKipC+/btcfHiRdjZ2TVRhM0Dc6c95k57zJ32mDvtMXfa03XuagcidXV1Nckn5OhSU54v3Yn/JnSPOdYt5le3mF/dYn5172HLcUPPl9hTqoFkMhnc3NyadJl2dnYPxc5mCMyd9pg77TF32mPutMfcaU+XuWMPqfrp4nzpTvw3oXvMsW4xv7rF/OoW86t7D1OOG3K+xMt7RERERERERESkdyxKERERERERERGR3rEoZQCWlpaYN28eLC0tDR2KyWHutMfcaY+50x5zpz3mTnvM3cOJv1fdY451i/nVLeZXt5hf3WuuOeZA50REREREREREpHfsKUVERERERERERHrHohQREREREREREekdi1JERERERERERKR3LEoREREREREREZHesSilI6tXr4anpyesrKwQFBSEn3766Z7tExISEBQUBCsrKzzyyCNYu3atniI1Po3J3c6dOzFgwAA4OjrCzs4OISEh+PHHH/UYrXFp7H5X6+jRozAzM0NAQIBuAzRijc1deXk5IiMj4eHhAUtLSzz66KNYv369nqI1Lo3N3ebNm9GtWzfY2NjAxcUFr732GgoKCvQUrfE4fPgwhgwZAldXV0iShN27d993Hh4rajQ2dzxWPBy0PcbRvc2fPx+SJGm8nJ2dDR2WSbvfd5QQAvPnz4erqyusra3x1FNPITU11TDBmqD75TcsLKzOPt2rVy/DBGuCFi1ahMceewy2trZwcnLC8OHDkZ6ertGG+7D2GpLf5rYPsyilA1u3bsW0adMQGRmJ5ORkhIaGYvDgwcjOzq63/YULF/Dss88iNDQUycnJmDNnDqZOnYodO3boOXLDa2zuDh8+jAEDBiA2NhY///wznn76aQwZMgTJycl6jtzwGpu7Wjdu3MC4cePQr18/PUVqfLTJ3ciRI3HgwAFERUUhPT0dW7ZsgZeXlx6jNg6Nzd2RI0cwbtw4vP7660hNTcV3332HpKQkTJw4Uc+RG15JSQm6deuGzz//vEHteaz4S2Nzx2OF6dP2GEcN4+vri9zcXPXr9OnThg7JpN3vO+rjjz/GsmXL8PnnnyMpKQnOzs4YMGAAlEqlniM1TQ05BjzzzDMa+3RsbKweIzRtCQkJmDx5Mk6cOIG4uDhUVVVh4MCBKCkpUbfhPqy9huQXaGb7sKAm16NHDxEeHq4xzcvLS7zzzjv1tp81a5bw8vLSmPb3v/9d9OrVS2cxGqvG5q4+Pj4+YsGCBU0dmtHTNnejRo0Sc+fOFfPmzRPdunXTYYTGq7G5+89//iMUCoUoKCjQR3hGrbG5W7JkiXjkkUc0pq1cuVK4ubnpLEZTAEDs2rXrnm14rKhfQ3JXn+Z6rDBVTXF+QPVrzsd/fbjzO0qlUglnZ2exePFi9bSysjKhUCjE2rVrDRChaavvGDB+/HgxbNgwg8TzMLp69aoAIBISEoQQ3Ieb2p35FaL57cPsKdXEKioq8PPPP2PgwIEa0wcOHIhjx47VO8/x48frtB80aBBOnjyJyspKncVqbLTJ3Z1UKhWUSiVat26tixCNlra5i46ORkZGBubNm6frEI2WNrn7/vvvERwcjI8//hjt2rVD586dMWPGDNy8eVMfIRsNbXL3+OOPIycnB7GxsRBC4MqVK9i+fTuee+45fYRs0nisaDrN9Vhhqpri/IDu7dy5c3B1dYWnpydGjx6NzMxMQ4f00Lpw4QLy8vI09mdLS0v06dOH+3MTio+Ph5OTEzp37ow33ngDV69eNXRIJuvGjRsAoD5mch9uWnfmt1Zz2odZlGpi+fn5qK6uRtu2bTWmt23bFnl5efXOk5eXV2/7qqoq5Ofn6yxWY6NN7u60dOlSlJSUYOTIkboI0Whpk7tz587hnXfewebNm2FmZqaPMI2SNrnLzMzEkSNHcObMGezatQvLly/H9u3bMXnyZH2EbDS0yd3jjz+OzZs3Y9SoUbCwsICzszPs7e3x2Wef6SNkk8ZjRdNprscKU9UU5wd0dz179sSmTZvw448/4ssvv0ReXh4ef/zxZjnWnz7U7rPcn3Vn8ODB2Lx5Mw4ePIilS5ciKSkJffv2RXl5uaFDMzlCCEREROCJJ56An58fAO7DTam+/ALNbx9uvn+J6pgkSRrvhRB1pt2vfX3Tm4PG5q7Wli1bMH/+fPz73/+Gk5OTrsIzag3NXXV1NV555RUsWLAAnTt31ld4Rq0x+51KpYIkSdi8eTMUCgUAYNmyZXjppZewatUqWFtb6zxeY9KY3KWlpWHq1Kl47733MGjQIOTm5mLmzJkIDw9HVFSUPsI1aTxWPDgeK0yXtucHdG+DBw9W/+zv74+QkBA8+uij2LhxIyIiIgwY2cON+7PujBo1Sv2zn58fgoOD4eHhgR9++AEjRowwYGSmZ8qUKfj1119x5MiROp9xH35wd8tvc9uHWZRqYm3atIFcLq9TJb569WqdanItZ2fnetubmZnBwcFBZ7EaG21yV2vr1q14/fXX8d1336F///66DNMoNTZ3SqUSJ0+eRHJyMqZMmQKgptAihICZmRn279+Pvn376iV2Q9Nmv3NxcUG7du3UBSkA8Pb2hhACOTk56NSpk05jNhba5G7RokXo3bs3Zs6cCQDo2rUrWrRogdDQUHz44YdwcXHRedymiseKB9fcjxWm6kHOD6jxWrRoAX9/f5w7d87QoTyUap9smJeXp3HM4/6sOy4uLvDw8OA+3Uj/93//h++//x6HDx+Gm5ubejr34aZxt/zW52Hfh3n7XhOzsLBAUFAQ4uLiNKbHxcXh8ccfr3eekJCQOu3379+P4OBgmJub6yxWY6NN7oCaq95hYWH49ttvm+24NI3NnZ2dHU6fPo2UlBT1Kzw8HF26dEFKSgp69uypr9ANTpv9rnfv3rh8+TKKi4vV037//XfIZLL7HlQeJtrkrrS0FDKZ5qFHLpcD+KvXD9WPx4oHw2OF6dL2/IC0U15ejrNnz/IigY54enrC2dlZY3+uqKhAQkIC92cdKSgowMWLF7lPN5AQAlOmTMHOnTtx8OBBeHp6anzOffjB3C+/9Xno92F9j6zeHMTExAhzc3MRFRUl0tLSxLRp00SLFi1EVlaWEEKId955R4wdO1bdPjMzU9jY2Ijp06eLtLQ0ERUVJczNzcX27dsNtQkG09jcffvtt8LMzEysWrVK5Obmql+FhYWG2gSDaWzu7tScn77T2NwplUrh5uYmXnrpJZGamioSEhJEp06dxMSJEw21CQbT2NxFR0cLMzMzsXr1apGRkSGOHDkigoODRY8ePQy1CQajVCpFcnKySE5OFgDEsmXLRHJysvjjjz+EEDxW3Etjc8djhem733cNae/tt98W8fHxIjMzU5w4cUI8//zzwtbWlrl9APf7jlq8eLFQKBRi586d4vTp02LMmDHCxcVFFBUVGThy03Cv/CqVSvH222+LY8eOiQsXLohDhw6JkJAQ0a5dO+a3gf7xj38IhUIh4uPjNY6ZpaWl6jbch7V3v/w2x32YRSkdWbVqlfDw8BAWFhaie/fudR7x2KdPH4328fHxIjAwUFhYWIgOHTqINWvW6Dli49GY3PXp00cAqPMaP368/gM3Ao3d727XnItSQjQ+d2fPnhX9+/cX1tbWws3NTURERGgcrJuTxuZu5cqVwsfHR1hbWwsXFxfx6quvipycHD1HbXiHDh265/cXjxV319jc8VjxcLjXdw1pb9SoUcLFxUWYm5sLV1dXMWLECJGammrosEza/b6jVCqVmDdvnnB2dhaWlpbiySefFKdPnzZs0CbkXvktLS0VAwcOFI6OjsLc3Fy4u7uL8ePHi+zsbEOHbTLqyy0AER0drW7DfVh798tvc9yHJSF4vwQREREREREREekXx5QiIiIiIiIiIiK9Y1GKiIiIiIiIiIj0jkUpIiIiIiIiIiLSOxaliIiIiIiIiIhI71iUIiIiIiIiIiIivWNRioiIiIiIiIiI9I5FKSIiIiIiIiIi0jsWpYiIiIiIiIiISO9YlCIi0oH58+cjICDA0GEQERERNYihz13effddvPnmmw1qO2PGDEydOlXHERGRPrAoRUTNRlhYGCRJQnh4eJ3PJk2aBEmSEBYWpv/AiIiIiHRIkqR7vsLCwjBjxgwcOHDAIPFduXIFK1aswJw5cxrUftasWYiOjsaFCxd0HBkR6RqLUkTUrLRv3x4xMTG4efOmelpZWRm2bNkCd3d3A0ZGREREpBu5ubnq1/Lly2FnZ6cxbcWKFWjZsiUcHBwMEl9UVBRCQkLQoUOHBrV3cnLCwIEDsXbtWt0GRkQ6x6IUETUr3bt3h7u7O3bu3KmetnPnTrRv3x6BgYHqafv27cMTTzwBe3t7ODg44Pnnn0dGRobGsnJycjB69Gi0bt0aLVq0QHBwMBITEzXafP311+jQoQMUCgVGjx4NpVKp2w0kIiIiuoOzs7P6pVAoIElSnWl33r4XFhaG4cOHY+HChWjbti3s7e2xYMECVFVVYebMmWjdujXc3Nywfv16jXVdunQJo0aNQqtWreDg4IBhw4YhKyvrnvHFxMRg6NChGtO2b98Of39/WFtbw8HBAf3790dJSYn686FDh2LLli0PnBsiMiwWpYio2XnttdcQHR2tfr9+/XpMmDBBo01JSQkiIiKQlJSEAwcOQCaT4YUXXoBKpQIAFBcXo0+fPrh8+TK+//57nDp1CrNmzVJ/DgAZGRnYvXs39u7di7179yIhIQGLFy/Wz0YSERERPaCDBw/i8uXLOHz4MJYtW4b58+fj+eefR6tWrZCYmIjw8HCEh4fj4sWLAIDS0lI8/fTTaNmyJQ4fPowjR46gZcuWeOaZZ1BRUVHvOq5fv44zZ84gODhYPS03NxdjxozBhAkTcPbsWcTHx2PEiBEQQqjb9OjRAxcvXsQff/yh2yQQkU6ZGToAIiJ9Gzt2LGbPno2srCxIkoSjR48iJiYG8fHx6jYvvviixjxRUVFwcnJCWloa/Pz88O233+LatWtISkpC69atAQAdO3bUmEelUmHDhg2wtbVVr/fAgQP46KOPdLuBRERERE2gdevWWLlyJWQyGbp06YKPP/4YpaWl6rGfZs+ejcWLF+Po0aMYPXo0YmJiIJPJ8NVXX0GSJABAdHQ07O3tER8fj4EDB9ZZxx9//AEhBFxdXdXTcnNzUVVVhREjRsDDwwMA4O/vrzFfu3btAABZWVnqNkRkeliUIqJmp02bNnjuueewceNGCCHw3HPPoU2bNhptMjIy8O677+LEiRPIz89X94DKzs6Gn58fUlJSEBgYqC5I1adDhw7qghQAuLi44OrVq7rZKCIiIqIm5uvrC5nsr5tr2rZtCz8/P/V7uVwOBwcH9fnNzz//jPPnz2uc/wA143feOQxCrdpxPq2srNTTunXrhn79+sHf3x+DBg3CwIED8dJLL6FVq1bqNtbW1gBqemcRkeliUYqImqUJEyZgypQpAIBVq1bV+XzIkCFo3749vvzyS7i6ukKlUsHPz0/d9bz2ROhezM3NNd5LkqRxex8RERGRMavvXOZe5zcqlQpBQUHYvHlznWU5OjrWu47aC4PXr19Xt5HL5YiLi8OxY8ewf/9+fPbZZ4iMjERiYiI8PT0BAH/++ec9l0tEpoFjShFRs1Q7tkFFRQUGDRqk8VlBQQHOnj2LuXPnol+/fvD29sb169c12nTt2hUpKSnqEyIiIiKi5q579+44d+4cnJyc0LFjR42XQqGod55HH30UdnZ2SEtL05guSRJ69+6NBQsWIDk5GRYWFti1a5f68zNnzsDc3By+vr463SYi0i0WpYioWZLL5Th79izOnj0LuVyu8Vnt02LWrVuH8+fP4+DBg4iIiNBoM2bMGDg7O2P48OE4evQoMjMzsWPHDhw/flyfm0FERERkNF599VW0adMGw4YNw08//YQLFy4gISEBb731FnJycuqdRyaToX///jhy5Ih6WmJiIhYuXIiTJ08iOzsbO3fuxLVr1+Dt7a1u89NPPyE0NLRBvdeJyHixKEVEzZadnR3s7OzqTJfJZIiJicHPP/8MPz8/TJ8+HUuWLNFoY2Fhgf3798PJyQnPPvss/P39sXjx4joFLiIiIqLmwsbGBocPH4a7uztGjBgBb29vTJgwATdv3qz3nKvWm2++iZiYGPVtgHZ2djh8+DCeffZZdO7cGXPnzsXSpUsxePBg9TxbtmzBG2+8ofNtIiLdksTtz9UkIiIiIiIi0iMhBHr16oVp06ZhzJgx923/ww8/YObMmfj1119hZsZhkolMGXtKERERERERkcFIkoR169ahqqqqQe1LSkoQHR3NghTRQ4A9pYiIiIiIiIiISO/YU4qIiIiIiIiIiPSORSkiIiIiIiIiItI7FqWIiIiIiIiIiEjvWJQiIiIiIiIiIiK9Y1GKiIiIiIiIiIj0jkUpIiIiIiIiIiLSOxaliIiIiIiIiIhI71iUIiIiIiIiIiIivWNRioiIiIiIiIiI9O7/AaWsDHPVsxJcAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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FIaLf4kgpIlJ5AQEB0NfXR7du3QAAhQoVQufOnXH+/Hk8ffpUydH9n9OnTwP4seLOz7p06QItLa1UbYcPH0bDhg1hYWGBpKQk+U/KUP+zZ8/mTtBERESULR4eHvjw4QMOHjwIAEhKSsKWLVvg7OyMcuXKAcj6532bNm2gra2d7ZhCQkKQnJz829E70dHR8PT0hKWlJbS0tKCtrQ0rKysAwMOHD7N83v79+yM+Ph47duyQtwUGBkJXVxc9evQAADx79gyPHj2SXyf9/Hq0bNkSkZGRePz4cbrHf/z4Mf7991/07t0bGhr/9ytsoUKF0LFjR1y+fBnfvn3Dt2/fcPbsWXTp0gVFihTJ8vNIj4aGBoYPH47Dhw8jIiICwI+RZseOHcPQoUMVuvohUX7AohQRqbRnz57h3LlzaNWqFQRBQExMDGJiYuRDrDOaZFQZPn78CAAoWrRoqnYtLS2YmJikanv37h0OHToEbW3tVD9VqlQBgGzN6UBERES5p1OnThCLxfLb1o4cOYJ3796lmuA8q5/3xYoV+6OY3r9/D+DHBOgZkclkcHV1xd69ezF+/Hj8888/uHr1Ki5fvgzgx7QJWVWlShXUqlVL/lokJydjy5YtaNu2LYyNjQH8eC0AYOzYsWlej6FDhwLI+Pon5RorvdfHwsICMpkMnz9/xufPn5GcnPzL558d/fv3h76+PlavXg3gx9QQ+vr66N+/f46ehyg/0vp9FyIi5dmwYQMEQcDu3buxe/fuNNs3btyI2bNnQ1NTU2Ex6OnpAQCkUil0dXXl7f+9cEopPEVFRaF48eLy9qSkJPnFVApTU1NUrVoVf//9d7rntLCwyJHYiYiISDH09fXRvXt3rFu3DpGRkdiwYQMMDAzkC60AWf+8/9NRNymjg968eQNLS8t0+9y7dw+3b99GUFAQ+vbtK29/9uzZH527X79+GDp0KB4+fIgXL14gMjIS/fr1k283NTUFAEyaNAkdOnRI9xgVKlRItz3lGisyMjLNtn///RcaGhooXLgwRCIRNDU18ebNmz96Lv8lFovRt29frF+/HmPHjkVgYCB69OgBIyOjHD0PUX7EohQRqazk5GRs3LgRZcqUwfr169NsP3z4MBYtWoSjR4/Czc0tzfaUAlJmv/HT1dVNt2/KKn137tyRr4ADAIcOHUrVL2W1vuDgYNjZ2cnbd+7cmWZFPTc3Nxw5cgRlypRB4cKFMxUfERERqRYPDw+sXr0aCxYswJEjR+Du7o4CBQrIt+fE531WrmdcXV2hqamJVatWwdHRMd0+KYWvn79oA4A1a9ZkK74U3bt3h5eXF4KCgvDixQsUL14crq6u8u0VKlRAuXLlcPv2bfliNJlVoUIFFC9eHFu3bsXYsWPlz+Hr16/Ys2ePfEU+AHBxccGuXbvw999/ywthmfHz66yvr59m+8iRI+Hv749OnTohJiYGw4cPz9JzIKL0sShFRCrr6NGj+PfffzFv3jx5wednNjY2WLFiBQICAtItStnY2AAA1q5dCwMDA+jp6cHa2jrNrXQpbG1tcebMGRw6dAjFihWDgYEBKlSogJYtW8LY2BgeHh6YOXMmtLS0EBQUhNevX6fav1KlSujVqxf8/Pygra2NJk2a4N69e1i4cCEMDQ1T9Z05cyZCQkLg5OSEkSNHokKFCvj+/TtevnyJI0eOYPXq1Tk+9JyIiIhylr29PapWrQo/Pz8IgpDq1j0gZz7vy5QpA319fQQHB6NSpUooVKgQLCws0h1VXapUKUyePBmzZs1CfHw8unfvDrFYjAcPHuDDhw/w8fFBxYoVUaZMGUycOBGCIMDY2BiHDh1CSEjIH70WRkZGaN++PYKCghATE4OxY8emmv8J+FH4atGiBZo1awZ3d3cUL14cnz59wsOHD3Hjxo0MV1bW0NDA/Pnz0bNnT7i5uWHw4MGQSqVYsGABYmJiMHfuXHnfxYsXo169enBwcMDEiRNRtmxZvHv3DgcPHsSaNWtgYGCQ7jlsbW0BAPPmzUOLFi2gqamJqlWrQkdHBwBQvnx5NG/eHEePHkW9evVQrVq1P3q9iOj/U+4860REGWvXrp2go6Pzy9VTunXrJmhpaQlRUVFpVt8TBEHw8/MTrK2tBU1NzVQr16S3+t6tW7eEunXrCgUKFBAACC4uLvJtV69eFZycnISCBQsKxYsXF2bMmCGsX78+zYo0UqlUGDNmjGBmZibo6ekJderUES5duiRYWVmlWdnv/fv3wsiRIwVra2tBW1tbMDY2Fuzs7IQpU6YIX758ycYrRkRERLlt6dKlAgChcuXK6W7PzOd9yup7CxYsSPcY27ZtEypWrChoa2unut7JaIXgTZs2CbVq1RL09PSEQoUKCTVq1Ei1et+DBw+Epk2bCgYGBkLhwoWFzp07CxEREWmupTK7+l6KEydOCABSrUD4X7dv3xa6dOkimJmZCdra2kLRokWFRo0ayVdZFoT0V8cTBEHYv3+/4ODgIOjp6QkFCxYUGjduLFy8eDHNOR48eCB07txZMDExEXR0dISSJUsK7u7uwvfv3zM8vlQqFQYMGCAUKVJEEIlE6T7voKAgAYCwffv2TL0eRPR7IkEQhNwvhRERERERERGpj5SV/l6+fPlHqyQS0f/h7XtERERERERE6ZBKpbhx4wauXr2Kffv2YfHixSxIEeUgjpQiIiIiIiIiSsfLly9hbW0NQ0ND9OjRAytWrFDoqs9E+Q2LUkRERERERERElOs0ft+FiIiIiNTBuXPn0Lp1a1hYWEAkEmH//v2/3efs2bOws7ODnp4eSpcujdWrVys+UCIiIiKwKEVERESUZ3z9+hXVqlXDihUrMtU/PDwcLVu2hLOzM27evInJkydj5MiR2LNnj4IjJSIiIuLte5kmk8nw77//wsDAACKRSNnhEBERkQIIgoC4uDhYWFhAQ0O9v7sTiUTYt28f2rVrl2GfCRMm4ODBg3j48KG8zdPTE7dv38alS5cy3E8qlUIqlcofy2QyfPr0CSYmJrxOIiIiyqMUcZ3E1fcy6d9//4WlpaWywyAiIqJc8Pr1a5QoUULZYSjcpUuX4OrqmqqtWbNmCAgIQGJiYoYrTPn6+sLHxyc3QiQiIiIVk5PXSSxKZZKBgQGAHy++oaFhjh5bEARIJBKIxWJ+u6hmmDv1xdypL+ZOPalL3mJjY2FpaSn/3M/roqKiYG5unqrN3NwcSUlJ+PDhA4oVK5bufpMmTYKXl5f8sUQiQcmSJfHq1SuIxWKFxkyZoy7vufyGeVFNzIvqYU5Uk0QigZWVVY5eJ7EolUkpbwRDQ0OFFKUEQYChoSHfcGqGuVNfzJ36Yu7Uk7rlTR1izCn/fa4pMzv86jXQ1dWFrq5umnaxWMyilIpIySN/oVMtzItqYl5UD3Oi2nIyJ+o9WQIRERERZVvRokURFRWVqi06OhpaWlowMTFRUlRERESUX3CkVA5ITk5GYmJitvcXBAEJCQn4/v17vqoCa2trQ1NTU9lhEBER5VuOjo44dOhQqrYTJ07A3t4+w/mkiIiIiHIKi1J/6MuXL3jz5g3+dBFDmUyGjx8/5lBU6kEkEqFEiRIoVKiQskMhIiLKE758+YJnz57JH4eHh+PWrVswNjZGyZIlMWnSJLx9+xabNm0C8GOlvRUrVsDLywsDBw7EpUuXEBAQgG3btinrKRAREVE+wqLUH0hOTsabN29QoEABFClSJNujnARBQHJyMjQ1NfPNSClBEPD+/Xu8efMG5cqV44gpIiKiHHD9+nU0bNhQ/jhlMvK+ffsiKCgIkZGRiIiIkG+3trbGkSNHMHr0aKxcuRIWFhZYtmwZOnbsmOuxExERUf7DotQfSExMhCAIKFKkCPT19bN9nPxYlAKAIkWK4OXLl0hMTGRRioiIKAc0aNDgl6O3g4KC0rS5uLjgxo0bCoyKiIiIKH0sSuWA/FRIykl83YiI8oaUuRG/ffuG+Ph4+c/Pj799+4YPHz5AJBLh+/fvSEhIQGJiovwnKSkpw8fJycny1ftkMpn875s2bUKBAgWU/fSJiIiIKJtYlCIiIsrnZDIZPn36hOjoaHz+/BmxsbGQSCSZ+knpm5ycnOtxBwQE5Po5KWeUKlUK3759w9u3b+UTqp86dQqNGzfGmDFjsHDhQiVHmHnu7u6wt7fH8OHDVeJ406dPR5UqVdClSxdcuHAB2traaNasWY7Epgwpz6dr167w9vbGly9ffvvv41evYU7ni4iI/gyLUmpOS0sLNjY2SExMROnSpbF582YYGRkp/LwvX75Ep06dcP369UzvY2pqig8fPigwKiIiShEfH4/o6Gj5z7t37zL8+4cPH3KsqKShoYECBQpAX19f/pPyWFtbG4aGhtDX14eOjg60tbXlP1paWhk+Trm9XUNDAyKRSP6jp6eXIzGTcpQsWRIHDx6Uz1+1YcMG2NvbKzkqICkpCVpa6nuJPHPmTAA/RjBeuHABiYmJal2USnk+RESUNyn1E9fX1xd79+7Fo0ePoK+vDycnJ8ybNw8VKlSQ9xEEAT4+Pli7di0+f/4MBwcHrFy5ElWqVJH3kUqlGDt2LLZt24b4+Hg0btwY/v7+KFGihLzP58+fMXLkSBw8eBAA0KZNGyxfvjxXCjiKZGRkhFu3bgEAevfujZUrV2LKlCk5fp6UOa+IiEh5ZDIZoqOjERERgdevXyMyMjLDYlNcXFyWj1+4cGEULlwYYrH4lz+GhoZp2goVKiQvPKV3e7YgCJBIJBCLxbx9mwAA/fv3x4YNG9CxY0dIJBJcvnwZ3bt3R3x8vLzPwoULsXPnTiQlJaFo0aJYs2YNLC0t8c8//2Dq1Kn4/v07EhMTMWbMGPTr1w8AMHv2bAQHB0NXVxcAcODAAVhZWUEkEiEuLk6+6q+pqSmuX7+OUqVKoVSpUhg4cCBOnjwJCwsLBAUFYdq0aTh16hQSEhJQsWJFrF69GkZGRnj79i369OmD9+/fw9raOsOC7oABA1CpUiWMGTMGwI+VEB0dHfH69WsAyPD4P/vy5QtGjhyJq1evAgA6d+6MGTNmAADevn2Lv/76C0+ePAEAtG3bFrNmzZKPBKpbty4CAwMhCAL++ecfdOjQAe/evUOJEiUwadIkAMDjx4/RpEkThIeHpynE/e716tevH44fP47IyEh4eHhg6tSpAIDFixdj27ZtSEpKgra2NpYvXw4HBwfMnj0b7969w/Lly+XPrWTJknjy5AkiIyMxdOhQfP36Fd+/f0fv3r3lMWY0sunu3bsZ7gMAt2/fRuPGjfH27VvUqVMHa9askf+bSJGYmJipPBARkeIotSh19uxZDBs2DLVq1UJSUhKmTJkCV1dXPHjwAAULFgQAzJ8/H4sXL0ZQUBDKly+P2bNno2nTpnj8+DEMDAwAAKNGjcKhQ4ewfft2mJiYYMyYMXBzc0NYWJi8kNKjRw+8efMGx44dAwAMGjQIvXv3xqFDh3LkuQiCgG/fvmV734wmOi9QoECmL97r1q2L27dvAwDevXuHwYMH482bN9DT08P69etRsWJF/Pvvvxg0aBBev34NTU1N7Nq1CxYWFhg0aBBu3boFfX19rF27FtWrV4e3tzeioqLw7NkzVK5cGcOHD0ePHj2gqakJZ2dn+Xm/fPmCoUOH4uHDhxAEAUuXLkXdunXx7t07dOvWDXFxcWjevHm2XhsiovxEIpHIC04pf/789zdv3iAhISHTx9PR0YG5uTnMzMzkf6b8/PzY3Nwcpqam8tuoiHJD/fr1sXz5crx9+xaHDh1C586dU30BtnXrVjx58gSXLl2CpqYmNm/ejOHDh+PAgQOoWbMmLly4AE1NTXz69Ak1a9ZE8+bNoaenh4ULFyIyMhL6+vr49u0bNDQ0MhVPREQETp06BZFIhDlz5qBQoULyYtCsWbMwY8YMLF26FCNHjkT9+vUxY8YMvHjxAtWqVUv3Oqd///4YPHiwvCgVFBSEnj17Qltb+5fH/9msWbOQkJCAO3fuID4+HvXq1UPlypXRuXNn9OrVCy1btsTu3bsBAO/fv0+1b/Xq1dGvXz8kJiZi0aJFAIAnT56gWbNmGD9+PDQ1NbFixQoMGjQoWyPDYmJiEBoaivfv36Ns2bLo168fihcvjt69e8tXfbx8+TI8PDxw7949uLu7o2bNmli0aBF0dHSwa9cuNGzYEKamptDV1cXJkyehq6uL+Ph4ODk5oWnTpr8cOVeqVKlf7nPlyhWEhoZCX18f7du3x9KlSzF+/PhUx1iwYEGm8kBERIqj1KJUSoEoRWBgIMzMzBAWFob69etDEAT4+flhypQp6NChAwBg48aNMDc3x9atWzF48GBIJBIEBARg8+bNaNKkCQBgy5YtsLS0xMmTJ9GsWTM8fPgQx44dw+XLl+Hg4AAAWLduHRwdHfH48eNUI7Oy69u3b/JvknLSly9f5AW6X0lOTkZISAj69+8P4Eehbtq0abCzs8O1a9cwatQoHDt2DCNHjkTbtm0xcOBASKVSJCcnY+XKlTAwMMDdu3dx+fJl9O3bV17cunv3Lk6fPg0dHR20bNkSkydPRocOHVJ9qM+ePRvt27fHpk2b8ObNG7Rq1Qq3b9+Gj48PWrduDS8vL/j7++f4a0NEpG4EQUBkZCSePXuG58+fp/n59OnTb4+hoaEBCwsLWFpaonjx4r8sNhkaGnJUEqm03r17Y+PGjdi/fz+Cg4MRHBws37Z//35cv34ddnZ2AFKP2v748SM8PDzw5MkTaGlp4cOHD7h//z4aNmyIcuXKoVevXnB1dUWrVq1SjZz/lX79+snfL/v370dsbKy84JOQkIAyZcoAAE6fPo1ly5YBAEqXLo3GjRunezwnJyckJibKn8PGjRtx+PDh3x7/ZydPnsTSpUuhoaGBggULok+fPjh58iRatGiB0NBQhISEyPsWKVLkt8+xfPnyqFSpEg4fPozGjRtj+/btuHfvXqZen//q2bOn/LylS5dGeHg4ihcvjps3b+Lvv//Gx48foaWlhQcPHiAhIQElSpRAjRo1cPDgQXTq1AmBgYHy68n4+HgMHToUt27dgoaGBl6/fo1bt279sij1u326du0qvzbv378//P390xSlMpsHIiJSHJW6YV4ikQAAjI2NAfwY5hwVFQVXV1d5H11dXbi4uCA0NBSDBw9GWFgYEhMTU/WxsLCAjY0NQkND0axZM1y6dAlisVhekAKAOnXqQCwWIzQ0NN2ilFQqhVQqlT+OjY0FAPmKPyl//29bTvvdsWNiYlC9enW8efMGVapUgaurKwRBwKlTp/Dw4cM0x7pw4QJ27NgBQRCgo6MDALhw4QLGjRsHQRDg4OCA+Ph4xMTEQBAEtGnTBtra2hAEAWFhYTh8+DAEQUDPnj1x6tQpCIKAkJAQHDlyBD4+PgB+XChKpVKEhoZiypQp8v7Tp09P9Vxy4/VTtJTY1TX+/Iy5U1+qnjtBEPD27Vs8evQIjx8/xvPnz/HixQv5nz/fmpQeExMTWFpaomTJkihRooT87yl/WlhYZGlUg6q8TqqetxSqHl9ekzJ6pnz58ihXrlyqbYIgYOrUqfIv3H7m6emJ1q1bY8+ePRCJRKhZsya+f/8OTU1NXL58GaGhoThz5gzq1KmDbdu2wdnZGZqamqlutfv+/XuqY/785aIgCPD390ejRo3++PkFBQVBIpHAzMwMNjY2WTq+IAhpCst/Wmj+66+/sGjRIrx58waurq4wNzdPt9/vXq+f53TT1NREUlISEhIS0LFjR5w5cwZ2dnaIjY2FWCxGQkICdHR00K9fPwQFBaF69ep49uwZWrRoAQCYPHkyzM3NcfPmTWhpaaFDhw5pzvdfWd0no9uKcyLPRESUfSpTlBIEAV5eXqhXr578AzsqKgoA0nxYmpub49WrV/I+Ojo6KFy4cJo+KftHRUXBzMwszTnNzMzkff7L19dXXmT5mUQikV+wJiQkQCaTITk5Gbq6uoiJicnCM05NJpOlO7xcV1f3l5PPGhkZ4fr16/j27RuaN2+OFStWYMSIEQB+DFv+eRh8cnIyRCKRfGntFCm3D6ac5+clt/X09OTtKfv+fIzk5GTIZDLs378fJUuWTPOcUl6flGP8/FxS9o2Li0tVAFQngiDgy5cvAP78IpFyF3OnvlQld9+/f8eLFy/w9OlTPH36FE+ePJH//evXrxnup6mpiRIlSsDa2jrVT8o8Lb8bdfurY6syVcnb76R8CUW5w8LCAr6+vqhYsWKabW3atMHSpUvRrl07GBsbIzExEffu3UONGjXw+fNn+TxR586dk4/wjouLQ1xcHJydneHs7Iz79+/j5s2bcHZ2RpkyZXDlyhW4urpi7969v3wvtWnTBosXL0adOnVQoEABfPv2DeHh4ahSpQoaNWqEDRs2YNq0aXj58iX++ecf+Wj9/+rbty+qV6+Od+/eyee8+t3xf9a0aVOsW7cOTk5O+PbtG7Zs2YJJkyahUKFCqFevHpYsWYJx48YB+HH73n9HSxkYGCA8PDxVm6urK0aPHg1fX1/s2rUrw9cgK69XipQ5viwtLQFAPn9Uivbt22PkyJGYO3cuevfuLb9O/fz5M2xsbKClpYXHjx8jJCTkt4Wi3+2za9cujBo1Cnp6eggMDEw3R5nNAxERKY7KFKWGDx+OO3fu4MKFC2m2/ffiNb1vjf7rv30y+nYko+NMmjRJfj888OMi1dLSUj7BK/Djg/fjx4/Q1NSElpaWvD07/mQicU1NTRgYGGDp0qXo1KkThg8fDhcXF6xfvx5Dhw6FTCbD/fv3YWtrC2dnZwQFBaW6fa9evXrYsWMHnJ2dcfXqVRQsWBCFCxeWr3SUElfNmjVx+PBhtGvXDjt27IBIJIKmpiaaNGmC1atXY968eQB+TCxZrVo11K1bF7t378aoUaOwY8cOeaw/x62hoQEDAwO1XUEppbjHiXvVD3OnvnI7d9++fcPDhw9x79493Lt3D48ePcKjR48QHh4OmUyW7j6ampooW7YsypcvjzJlyqBs2bIoU6YMypQpAysrq3w5d5O6vOdUOba86udizc969+6Njx8/okGDBhCJREhKSoKHhwdq1KiBuXPnYujQoZg7dy4qV64sHw0vkUjQqVMnfP36FSKRCOXKlUPfvn0BAH5+fhg2bBjMzMzQsGFDmJiYZBjTxIkT4ePjAwcHB/m/iQkTJqBKlSpYunQp+vTpg127dqF8+fIZFqQAoFixYrC3t8fhw4exbt26TB3/Z9OmTcOIESNga2sL4MdE5506dQIAbN68GSNGjECVKlWgpaWFdu3apflC1c3NDe7u7qhevTo6dOiA6dOnQyQSwcPDA1u3boWjo2OGsWfl9UphaGiImTNnonbt2ihZsiTatGmTaruuri46d+4Mf3//VCP6p06dit69eyM4OBilSpXK1Mil3+1Tv359tGvXDq9fv0adOnXkX9r+LLN5ICIixREJKjBOfcSIEdi/fz/OnTsHa2trefuLFy9QpkwZ3LhxAzVq1JC3t23bFkZGRti4cSNOnTqFxo0b49OnT6lGS1WrVk3+4bxhwwZ4eXmlGclkZGSEJUuWZHgx9LOU4ccSiSRVUSo8PBzW1tZ/VFT51UTnv2NqaooPHz7IH7u5uaF3795o0KABPD098fz5cyQlJaFPnz6YOHEiIiMjMWDAALx+/Rra2trYtWsXihUrhoEDB+LOnTvQ09PDmjVrUKNGDXh7e8PU1FS+2smTJ0/kE503a9YMR44cwfXr1/HlyxeMGDEC169fR3JyMho3bozly5fLJzqPjY1Fx44dsXjx4lSx5tTrp0xcTUp9MXfqS1G5S0pKwtOnT3Hv3j3cvXtXXoR69uxZhrd0icViVKxYMc1P6dKl5bdI0w/q8p5L7/Oefi/ldYuJiYFYLFZ2OIRfv+datWqFbt26oXfv3kqKLv9Sl/8L8xvmRfUwJ6pJIpHAyMgoR6+TlFqUEgQBI0aMwL59+3DmzJl05xKwsLDA6NGj5RMTJiQkwMzMDPPmzZNPdF6kSBFs2bIFXbp0AQBERkaiRIkSOHLkiHyi88qVK+PKlSuoXbs2gB+3ttWpUwePHj3K1ETnqlqUUmcsSpEyMXfqKydy9/37d9y9exdhYWG4ceMGbty4gbt372a4sp2pqSlsbW1hY2ODypUry4tP5ubm/PeTSerynmNRKntYlFI96b3nrl+/jq5du8LGxgZ79+7N9ih9yj51+b8wv2FeVA9zopoUUZRS6u17w4YNw9atW3HgwAEYGBjI53cSi8XQ19eHSCTCqFGjMGfOHJQrVw7lypXDnDlzUKBAAfTo0UPe18PDA2PGjIGJiQmMjY0xduxY2NrayodTV6pUCc2bN8fAgQOxZs0aAMCgQYPg5uaWIyvvERGR6vry5Qtu374tLz7duHED9+/fT3e+voIFC8LGxgY2NjbyIpSNjU2GEwETEakTe3t7PH/+XNlhEBERySm1KLVq1SoAQIMGDVK1BwYGwt3dHQAwfvx4+ZKvnz9/hoODA06cOAEDAwN5/yVLlkBLSwtdunRBfHw8GjdujKCgoFTf/gQHB2PkyJHyVfratGmDFStWKPYJEhFRrvr27Rtu3LiBK1euyAtQjx8/Tvf2uyJFiqBmzZqws7NDzZo1UaNGDZQqVSrdRSeIiIiIiCjnKbUolZk7B0UiEby9veHt7Z1hHz09PSxfvjzNCh8/MzY2xpYtW7IT5m+pwLRcaomvGxH9CZlMhmfPnuHy5cu4cuUKLl++jDt37qQ7Aqp48eKoWbNmqp/ixYtzODgRERERkRKpzOp76khbWxsikUi+BG92f7nJj3NKCYKA9+/fQyQS5ctVqIgo6z5+/CgvPoWGhiIsLCzNAhbAj9WuHBwcUKtWLfkIKN5+R0RERESkeliU+gOampooUaIE3rx5g5cvX/7RsWQyWb67ZUQkEqFEiRKcZJOI0hAEAS9evMCFCxdw4cIFXLx4MdXy4Sn09PRgb28PBwcH1KlTBw4ODihRokS+KfATEREREakzFqX+UKFChVCuXDkkJiZm+xiCICAuLg4GBgb56hcpbW1tFqSICACQlJSEW7dupSpCpSx+8bMKFSrAwcEBVatWRYMGDVC1alWOtiQiIiIiUlMsSuUATU3NPyquCIIAqVQKPT29fFWUIqL8SyqV4vLlyzh9+jTOnz+PK1eu4OvXr6n66OjowN7eHvXq1UO9evXg5OQEExMTLhFMRERERJRHsChFREQKl5iYiGvXruH06dM4ffo0Ll68iO/fv6fqU7hwYTg5OcmLUPb29tDT01NSxES55+XLlzh//jxevnyJb9++oUiRIqhRowYcHR35HiAiIqI8jUUpIiLKccnJybh58yZOnz6NU6dO4fz582lGQpmbm6NRo0aoX78+nJ2dUalSpXw3tx7lb1u3bsWyZctw9epVmJmZoXjx4tDX18enT5/w/Plz6OnpoWfPnpgwYQKsrKyUHS4RERFRjmNRioiI/pggCHj06BFOnDiBU6dO4ezZs5BIJKn6GBsbo2HDhmjYsCEaNWqEihUr8vY7yrdq1qwJDQ0NuLu7Y+fOnShZsmSq7VKpFJcuXcL27dthb28Pf39/dO7cWUnREhERESkGi1JERJQtnz9/xsmTJ3H8+HGcOHECr1+/TrXd0NAQLi4u8iKUra0tR0IR/X+zZs1Cq1atMtyuq6uLBg0aoEGDBpg9ezbCw8NzMToiIiKi3MGiFBERZUpSUhKuXbuG48eP4/jx47h69SpkMpl8u66uLpydndG0aVM0bNgQNWrUgJYWP2aI0vOrgtR/mZqawtTUVIHREBERESkHf1sgIqIMRUREyEdCnTx5EjExMam2V6pUCc2aNUOzZs1Qv359FChQQDmBEqmxGzduQFtbG7a2tgCAAwcOIDAwEJUrV4a3tzd0dHSUHCERERGRYrAoRUREcomJiTh37hwOHz6MY8eO4dGjR6m2Fy5cGE2aNEGzZs3g6uoKS0tLJUVKlHcMHjwYEydOhK2tLV68eIFu3bqhffv22LVrF759+wY/Pz9lh0hERESkECxKERHlc58+fcLRo0dx8OBBHDt2DLGxsfJtGhoacHBwkI+GqlWrFjQ1NZUYLVHe8+TJE1SvXh0AsGvXLtSvXx9bt27FxYsX0a1bNxaliIiIKM9iUYqIKB968uQJDh48iEOHDuHixYtITk6WbzMzM4ObmxtatmyJxo0bw8jISHmBEuUDgiDI52c7efIk3NzcAACWlpb48OGDMkMjIiIiUigWpYiI8oHk5GRcvHhRXoh68uRJqu22trZo3bo1Wrdujdq1a3OVPKJcZG9vj9mzZ6NJkyY4e/YsVq1aBQAIDw+Hubm5kqMjIiIiUhwWpYiI8iipVIqTJ09i3759OHDgQKoRF9ra2nBxcUGbNm3QunVrlCpVSnmBEuVzfn5+6NmzJ/bv348pU6agbNmyAIDdu3fDyclJydERERERKQ6LUkREeUhcXByOHj2KvXv34siRI4iLi5NvK1y4MFq1aoU2bdqgWbNmMDQ0VGKkRPTkyROUL18eVatWxd27d9NsX7BgQbbncPP398eCBQsQGRmJKlWqwM/PD87Ozhn2Dw4Oxvz58/H06VOIxWI0b94cCxcuhImJSbbOT0RERJQZLEoREam52NhYHDx4EDt37sSJEycglUrl2ywsLNCuXTt06NAB9evXh7a2thIjJaKf1ahRAyVLlkSbNm3Qrl07ODo6ptqup6eXrePu2LEDo0aNgr+/P+rWrYs1a9agRYsWePDgAUqWLJmm/4ULF9CnTx8sWbIErVu3xtu3b+Hp6YkBAwZg37592YqBiIiIKDNYlCIiUkNfvnzBoUOHsHPnThw9ejRVIaps2bLo0KEDOnTogFq1anF+KCIV9fHjR4SEhODAgQNo3749BEGAm5sb2rZtC1dX12wXpRYvXgwPDw8MGDAAwI/bA48fP45Vq1bB19c3Tf/Lly+jVKlSGDlyJADA2toagwcPxvz58zM8h1QqTfX/TsqqnYIgQBCEbMVNOSslF8yHamFeVBPzonqYE9WkiHywKEVEpCa+fv2KI0eOYMeOHfjf//6H79+/y7dVqFABXbt2RadOnWBjYwORSKTESIkoM/T09OQLDAiCgEuXLuHgwYOYOHEiunfvjiZNmqBt27Zwc3ODmZlZpo6ZkJCAsLAwTJw4MVW7q6srQkND093HyckJU6ZMwZEjR9CiRQtER0dj9+7daNWqVYbn8fX1hY+PT5p2iUSSqThJ8QRBwJcvXwCAnwkqhHlRTcyL6mFOVJMiPudZlCIiUmHx8fE4evQodu7ciUOHDuHbt2/ybWXLlkXXrl3RpUsX2Nra8gObSI2JRCI4OTnByckJc+fOxdOnT3Hw4EEEBQVhyJAhWLx4MYYNG/bb43z48AHJyclpVu0zNzdHVFRUuvs4OTkhODgYXbt2xffv35GUlIQ2bdpg+fLlGZ5n0qRJ8PLykj+OjY2FpaUlxGIxxGJxJp81KVLKt9lisZifDyqEeVFNzIvqYU7yDxaliIhUTFJSEk6ePIktW7bgwIED8m+JgB+31aQUoqpXr84PaaI8qly5chgzZgzGjBmDjx8/4tOnT1na/7//NwiCkOH/Fw8ePMDIkSMxffp0NGvWDJGRkRg3bhw8PT0REBCQ7j66urrQ1dVN97z8f0l1pOSDOVEtzItqYl5UD3OiehSRCxaliIhUgCAIuHHjBjZv3oxt27YhOjpavq1kyZLo0qULunbtCjs7O34wE+VBb9++xcWLFxEdHQ2ZTCZvF4lEGDFiRKZXwTM1NYWmpmaaUVHR0dFpRk+l8PX1Rd26dTFu3DgAQNWqVVGwYEE4Oztj9uzZKFasWDafFREREdGv/VFRSiqVpvstGRERZc7Lly8RHByMLVu24NGjR/J2U1NTdOvWDT179oSDgwMLUUR5WGBgIDw9PaGjowMTE5NU7/eUolRm6ejowM7ODiEhIWjfvr28PSQkBG3btk13n2/fvkFLK/UloaamJgDFTGhKRERElCJLRanjx49j27ZtOH/+PCIiIiCTyVCgQAHUrFkTrq6u6NevHywsLBQVKxFRnvD582fs2rULmzdvxoULF+Ttenp6aNeuHXr16gVXV1doa2srMUoiyi3Tp0/H9OnTMWnSpBxZLdPLywu9e/eGvb09HB0dsXbtWkRERMDT0xPAj/mg3r59i02bNgEAWrdujYEDB2LVqlXy2/dGjRqF2rVr87qOiIiIFCpTRan9+/djwoQJkEgkaNmyJcaNG4fixYtDX18fnz59wr1793Dy5EnMmjUL7u7umDVrFooUKaLo2ImI1EZycjJOnjyJDRs2YP/+/UhISADwYxREo0aN0KtXL3To0AGGhoZKjpSIctu3b9/QrVu3HClIAUDXrl3x8eNHzJw5E5GRkbCxscGRI0dgZWUFAIiMjERERIS8v7u7O+Li4rBixQqMGTMGRkZGaNSoEebNm5cj8RARERFlRCRkYlx27dq1MW3aNLRq1eqXF0xv377F0qVLYW5ujjFjxuRooMoWGxsLsVgMiUSS4780CoIAiUTClQXUEHOnvnIrd+Hh4QgMDERQUBBev34tb7e1tUXv3r3Ro0cPFC9eXGHnz4v4vlNP6pI3RX7eZ2T8+PEwNjbGxIkTc+V8ipDyusXExHD1PRWhLu+5/IZ5UU3Mi+phTlSTRCKBkZFRjl4nZWqk1NWrVzN1sOLFi2P+/Pl/FBARkbqLj4/Hvn37EBAQgFOnTsnbCxcujJ49e6J///6oUaOGEiMkIlXi6+sLNzc3HDt2DLa2tmlu3V28eLGSIiMiIiJSLK6+R0SUA1JWz9uwYQO2bt2KmJgYAD9uz2vSpAn69++Pdu3aQU9PT7mBEpHKmTNnDo4fP44KFSoAQJqJzomIiIjyqiwXpQRBwO7du3H69Ok0yxYDwN69e3MsOCIiVffp0yds2bIFAQEBuHPnjrzdysoK/fr1g7u7u3weFyKi9CxevBgbNmyAu7u7skMhIiIiylVZLkr99ddfWLt2LRo2bAhzc3N+g0dE+Y4gCLh69SpWr16N7du34/v37wAAXV1dtG/fHh4eHmjUqFGOTVpMRHmbrq4u6tatq+wwiIiIiHJdlotSW7Zswd69e9GyZUtFxENEpLK+fv2KrVu3YtWqVbh586a8vWrVqhg4cCB69OgBY2NjJUZIROror7/+wvLly7Fs2TJlh0JERESUq7JclBKLxShdurQiYiEiUkn379/HqlWrsHnzZsTGxgL4MbKhS5cuGDJkCOrUqcNRo0SUbVevXsWpU6dw+PBhVKlSJc1E55wagYiIiPKqLBelvL294ePjgw0bNkBfX18RMRERKZ1UKsWePXuwevVqnD9/Xt5epkwZeHp6wt3dHaampkqMkIjyCiMjI3To0EHZYRARERHluiwXpTp37oxt27bBzMwMpUqVSvNt3o0bNzJ9rHPnzmHBggUICwtDZGQk9u3bh3bt2sm3u7u7Y+PGjan2cXBwwOXLl+WPpVIpxo4di23btiE+Ph6NGzeGv78/SpQoIe/z+fNnjBw5EgcPHgQAtGnTBsuXL4eRkVEWnjkR5Qdv376Fv78/1q1bh/fv3wMANDU10aZNGwwZMgSNGzfmXFFElKMCAwOVHQIRERGRUmS5KOXu7o6wsDD06tXrjyc6//r1K6pVq4Z+/fqhY8eO6fZp3rx5qos1HR2dVNtHjRqFQ4cOYfv27TAxMcGYMWPg5uaGsLAwaGpqAgB69OiBN2/e4NixYwCAQYMGoXfv3jh06FC2YyeivEMQBFy+fBnLli3D7t27kZSUBAAoXrw4Bg4ciAEDBqB48eJKjpKIiIiIiChvyXJR6n//+x+OHz+OevXq/fHJW7RogRYtWvyyj66uLooWLZruNolEgoCAAGzevBlNmjQB8GMidktLS5w8eRLNmjXDw4cPcezYMVy+fBkODg4AgHXr1sHR0RGPHz9GhQoV0j22VCqFVCqVP06ZR0YQBAiCkOXn+ispx8zp45LiMXfqSxAESKVSbNmyBcuXL8e1a9fk2+rXr48RI0agbdu20NLSkvcn1cD3nXpSl7zlVnzNmzfH9OnT4eTk9Mt+cXFx8Pf3R6FChTBs2LBciY2IiIgot2S5KGVpaQlDQ0NFxJKuM2fOwMzMDEZGRnBxccHff/8NMzMzAEBYWBgSExPh6uoq729hYQEbGxuEhoaiWbNmuHTpEsRisbwgBQB16tSBWCxGaGhohkUpX19f+Pj4pGmXSCQKKUp9+fIFADhZspph7tTT+/fvERgYiICAAERHRwP4MQqzU6dOGDx4MKpWrQrgx2hOUj1836kndclbypdQita5c2d06dIFBgYGaNOmDezt7WFhYQE9PT18/vwZDx48wIULF3DkyBG4ublhwYIFuRIXERERUW7KclFq0aJFGD9+PFavXo1SpUopIKT/06JFC3Tu3BlWVlYIDw/HtGnT0KhRI4SFhUFXVxdRUVHQ0dFB4cKFU+1nbm6OqKgoAEBUVJS8iPUzMzMzeZ/0TJo0CV5eXvLHsbGxsLS0hFgszvGiXEqRSywWq/SFOqXF3KmXmzdvYtmyZdi2bRsSEhIAAMWKFcOQIUMwaNCgdP+vINXD9516Upe85VZsHh4e6N27N3bv3o0dO3Zg3bp1iImJkcdQuXJlNGvWDGFhYRl+gUZERESk7rJclOrVqxe+ffuGMmXKoECBAmkmOv/06VOOBde1a1f5321sbGBvbw8rKyv873//++UqNYIgpLqoTO8C8799/ktXVxe6urpp2kUikUIuWFOOq8oX6pQ+5k61yWQyHD58GIsWLcK5c+fk7bVr18aAAQPQp0+fdN/rpNr4vlNP6pC33IxNR0cHPXr0QI8ePQD8GI0dHx8PExOTNNdXRERERHlRlotSfn5+Cggjc4oVKwYrKys8ffoUAFC0aFEkJCTg8+fPqUZLRUdHy+doKFq0KN69e5fmWO/fv4e5uXnuBE5EuS5lvqiFCxfi0aNHAAAtLS107twZf/31F2rXrg2JRJJm8QQiImURi8UQi8XKDoOIiIgo12S5KNW3b19FxJEpHz9+xOvXr1GsWDEAgJ2dHbS1tRESEoIuXboAACIjI3Hv3j3Mnz8fAODo6AiJRIKrV6+idu3aAIArV65AIpH8dnJRIlI/nz9/xurVq7Fs2TL5LbqGhobw9PTEyJEj5avoqfpky0RERERERHldpopSX79+RcGCBTN90Mz2//LlC549eyZ/HB4ejlu3bsHY2BjGxsbw9vZGx44dUaxYMbx8+RKTJ0+Gqakp2rdvD+DHN4oeHh4YM2YMTExMYGxsjLFjx8LW1la+Gl+lSpXQvHlzDBw4EGvWrAEADBo0CG5ubpyjgSgPefXqFfz8/LBu3Tr5BOUlSpTAqFGjMHDgwFxdoIGIiIiIiIh+TyMzncqWLYs5c+bg33//zbCPIAgICQlBixYtsGzZskyd/Pr166hRowZq1KgBAPDy8kKNGjUwffp0aGpq4u7du2jbti3Kly+Pvn37onz58rh06RIMDAzkx1iyZAnatWuHLl26oG7duihQoAAOHToETU1NeZ/g4GDY2trC1dUVrq6uqFq1KjZv3pypGIlItd26dQs9e/ZEmTJl4Ofnh69fv8LW1habNm3CixcvMGbMGBakiIiIiIiIVJBIyMQ9LI8fP8bUqVNx8OBBVK9ePd1liy9dugRtbW1MmjQJgwYNSlUUygtiY2MhFoshkUgUsvqeRCJR+RWJKC3mTnkuXryIv//+G0ePHpW3NW7cGOPGjYOrq+tv88HcqS/mTj2pS94U+Xmfl6W8bjExMZwXS0Woy3suv2FeVBPzonqYE9UkkUhgZGSUo9dJmbp9r0KFCti1axfevHmDXbt24dy5cwgNDUV8fDxMTU1Ro0YNrFu3Di1btoSGRqYGXxERZZkgCDhx4gTmzJkjX0lPQ0MDXbp0wbhx41CzZk0lR0hElHXu7u7o378/6tevr+xQiIiIiHJVliY6L1GiBEaPHo3Ro0crKh4iojRkMhn279+POXPmICwsDMCPpdTd3d0xfvx4lClTRskREhFlX1xcHFxdXWFpaYl+/fqhb9++8kUZiIiIiPIyDmsiIpWVmJiITZs2wcbGBh07dkRYWBgKFCiA0aNH48WLF1izZg0LUkSk9vbs2YO3b99i+PDh2LVrF0qVKoUWLVpg9+7dSExMVHZ4RERERArDohQRqZzExEQEBASgQoUK6Nu3Lx4+fAixWIypU6fi1atXWLx4MUcREFGeYmJigr/++gs3b97E1atXUbZsWfTu3RsWFhYYPXo0nj59quwQiYiIiHIci1JEpDISExMRGBiIihUrYsCAAQgPD4eZmRnmzp2LiIgIzJo1C6ampsoOk4hIYSIjI3HixAmcOHECmpqaaNmyJe7fv4/KlStjyZIlyg6PiIiIKEdlaU4pIiJFSEpKwpYtWzB79mw8f/4cAGBmZoYJEybA09MTBQoUUHKERESKk5iYiIMHDyIwMBAnTpxA1apVMXr0aPTs2RMGBgYAgO3bt2PIkCGc15OIiIjylBwrSn3+/BmHDh1Cnz59cuqQRJTHJSUlITg4GLNmzZIXo4oUKYLx48djyJAhKFiwoJIjJCJSvGLFikEmk6F79+64evUqqlevnqZPs2bNYGRklOuxERERESlSjhWlIiIi0K9fPxaliOi3kpKSsG3bNsyaNUs+T4qpqSnGjx+PoUOHshhFRPnKkiVL0LlzZ+jp6WXYp3DhwggPD8/FqIiIiIgUj3NKEVGuSU5OxpYtW1ClShX06dMHT58+hampKebNm4fw8HCMGzeOBSkiyndOnz6d7ip7X79+Rf/+/ZUQEREREVHuYFGKiBROEATs2bMHNjY26N27N548eQITExP4+voiPDwc48ePR6FChZQdJhGRUmzcuBHx8fFp2uPj47Fp0yYlRERERESUO1iUIiKFOnnyJGrXro1OnTrh0aNHMDY2xpw5cxAeHo6JEyeyGEVE+VZsbCwkEgkEQUBcXBxiY2PlP58/f8aRI0dgZmaWrWP7+/vD2toaenp6sLOzw/nz53/ZXyqVYsqUKbCysoKuri7KlCmDDRs2ZOvcRERERJmV6Tmlli1b9svtb9++/eNgiCjvuHr1KiZNmoRTp04BAAoWLIgxY8ZgzJgxMDQ0VHJ0RETKZ2RkBJFIBJFIhPLly6fZLhKJ4OPjk+Xj7tixA6NGjYK/vz/q1q2LNWvWoEWLFnjw4AFKliyZ7j5dunTBu3fvEBAQgLJlyyI6OhpJSUlZPjcRERFRVmS6KLVkyZLf9snoQoeI8o+HDx9i6tSp2Lt3LwBAR0cHQ4YMweTJk7P9jT8RUV50+vRpCIKARo0aYc+ePTA2NpZv09HRgZWVFSwsLLJ83MWLF8PDwwMDBgwAAPj5+eH48eNYtWoVfH190/Q/duwYzp49ixcvXshjKFWqVPaeFBEREVEWZLooxRVfiOhXIiIi4O3tjY0bN0Imk0FDQwO9e/eGt7c3f7khIkqHi4sLgB/XWCVLloRIJPrjYyYkJCAsLAwTJ05M1e7q6orQ0NB09zl48CDs7e0xf/58bN68GQULFkSbNm0wa9Ys6Ovrp7uPVCqFVCqVP46NjQXwYw5BQRD++HnQn0vJBfOhWpgX1cS8qB7mRDUpIh+ZLkoREaXn/fv3mDNnDvz9/ZGQkAAAaNeuHWbPno0qVaooOToiItV0584d2NjYQENDAxKJBHfv3s2wb9WqVTN93A8fPiA5ORnm5uap2s3NzREVFZXuPi9evMCFCxegp6eHffv24cOHDxg6dCg+ffqU4bxSvr6+6d5aKJFIMh0rKZYgCPjy5QsA5EjBk3IG86KamBfVw5yoJkV8zme6KBUfH49//vkHbm5uAIBJkyal+oZMU1MTs2bNgp6eXo4HSUSq59u3b1iyZAnmzp0r/8Bo2LAh5syZgzp16ig5OiIi1Va9enVERUXBzMwM1atXh0gkSvfbR5FIhOTk5Cwf/78X8IIgZHhRL5PJIBKJEBwcDLFYDODHLYCdOnXCypUr0x0tNWnSJHh5eckfx8bGwtLSEmKxWH4MUq6Uf09isZi/0KkQ5kU1MS+qhznJPzJdlNq0aRMOHz4sL0qtWLECVapUkV+oPHr0CBYWFhg9erRiIiUilSCTyRAcHIzJkyfjzZs3AICaNWti7ty5aNKkCT80iIgyITw8HEWKFJH/PaeYmppCU1Mzzaio6OjoNKOnUhQrVgzFixdPVUyqVKkSBEHAmzdvUK5cuTT76OrqQldXN017ysTtpBpS8sGcqBbmRTUxL6qHOVE9ishFpotSwcHBaQpOW7duRenSpQEAW7ZswcqVK1mUIsrDzpw5gzFjxuDGjRsAfixu4Ovri27dukFDQ0PJ0RERqQ8rK6t0//6ndHR0YGdnh5CQELRv317eHhISgrZt26a7T926dbFr1y58+fIFhQoVAgA8efIEGhoaKFGiRI7FRkRERPRfmf4t8smTJ6mWK9bT00v1S2jt2rXx4MGDnI2OiFTCo0eP0KZNGzRs2BA3btyAoaEh5s6di8ePH6NHjx4sSBER/QFfX990527asGED5s2bl+XjeXl5Yf369diwYQMePnyI0aNHIyIiAp6engB+3HrXp08fef8ePXrAxMQE/fr1w4MHD3Du3DmMGzcO/fv3z3CicyIiIqKckOnfJCUSCbS0/m9g1fv371OtqCWTyVLNMUVE6u/9+/cYPnw4bGxscOjQIWhqamLYsGF49uwZJkyYwDnkiIhywJo1a1CxYsU07VWqVMHq1auzfLyuXbvCz88PM2fORPXq1XHu3DkcOXJEPiIrMjISERER8v6FChVCSEgIYmJiYG9vj549e6J169ZYtmxZ9p8UERERUSZk+va9EiVK4N69e6hQoUK62+/cucMh3kR5xPfv37F06VLMmTNHvsx3mzZtMG/evHR/cSIiouyLiopCsWLF0rQXKVIEkZGR2Trm0KFDMXTo0HS3BQUFpWmrWLEiQkJCsnUuIiIiouzK9Eipli1bYvr06fj+/XuabfHx8fDx8UGrVq1yNDgiyl2CIGDnzp2oWLEiJk6ciNjYWNSsWROnTp3CgQMHWJAiIlIAS0tLXLx4MU37xYsXYWFhoYSIiIiIiHJHpkdKTZ48GTt37kSFChUwfPhwlC9fHiKRCI8ePcKKFSuQlJSEyZMnKzJWIlKg27dv46+//sLZs2cB/BgdOWfOHPTs2ZNzRhERKdCAAQMwatQoJCYmolGjRgCAf/75B+PHj8eYMWOUHB0RERGR4mS6KGVubo7Q0FAMGTIEEydOhCAIAH4sCdi0aVP4+/tnuNQwEamuDx8+YPr06VizZg1kMhn09fUxYcIEjBs3DgUKFFB2eEREed748ePx6dMnDB06FAkJCQB+LCgzYcIETJo0ScnRERERESlOpotSAGBtbY1jx47h06dPePbsGQCgbNmyMDY2BgAkJSWlmgydiFRXUlISVq1ahRkzZuDz588AgC5dumDBggUoWbKkkqMjIso/RCIR5s2bh2nTpuHhw4fQ19dHuXLloKurq+zQiIiIiBQqWxUkY2Nj1K5dW/74wYMHWL9+PYKDg/Hu3bscC46IFOPUqVP466+/cO/ePQBAtWrVsHTpUri4uCg5MiKi/KtQoUIoVqwYRCIRC1JERESUL2R7opgvX75g/fr1cHR0RNWqVXH16lVMnDgxJ2Mjohz26tUrdOzYEY0bN8a9e/dgbGyMVatWISwsjAUpIiIlkclkmDlzJsRiMaysrFCyZEkYGRlh1qxZkMlkyg6PiIiISGGyPFLqwoULWL9+Pfbs2QNra2s8ePAAZ8+eRd26dRURHxHlAKlUikWLFmH27NmIj4+HpqYmhgwZAh8fH/ntt0REpBxTpkxBQEAA5s6di7p160IQBFy8eBHe3t74/v07/v77b2WHSERERKQQmS5KzZ8/Hxs2bMCXL1/QvXt3XLhwAdWqVYO2tjYKFy6syBiJ6A+cPHkSw4YNw5MnTwAALi4uWL58OWxtbZUcGRERAcDGjRuxfv16tGnTRt5WrVo1FC9eHEOHDmVRioiIiPKsTBelJk+ejAkTJmDmzJnQ1NRUZExElAPevn0LLy8v7Ny5E8CPFTQXLVqEHj16QCQSKTk6IiJK8enTJ1SsWDFNe8WKFfHp0yclRERERESUOzI9p9TMmTOxa9cuWFtbY8KECfIJkolItSQmJmLRokWoWLEidu7cCQ0NDYwcORKPHz9Gz549WZAiIlIx1apVw4oVK9K0r1ixAtWqVVNCRERERES5I0sjpSZPnoyzZ89iw4YNqFOnDsqUKQNBEOTLyRORcp07dw5Dhw7F/fv3AQCOjo7w9/dH9erVlRsYERFlaP78+WjVqhVOnjwJR0dHiEQihIaG4vXr1zhy5IiywyMiIiJSmCyvvufi4oKNGzciMjISQ4YMgZ2dHVxcXODk5ITFixcrIkYi+o2PHz+iX79+cHFxwf3792FqaoqAgABcuHCBBSkiIhXn4uKCJ0+eoH379oiJicGnT5/QoUMHPH78GM7OzsoOj4iIiEhhslyUSmFgYABPT09cuXIFN2/eRO3atTF37twsHePcuXNo3bo1LCwsIBKJsH///lTbBUGAt7c3LCwsoK+vjwYNGshHgKSQSqUYMWIETE1NUbBgQbRp0wZv3rxJ1efz58/o3bs3xGIxxGIxevfujZiYmOw8bSKVIggCNm/ejIoVKyIoKAgikQiDBw/G48eP0b9/f2hoZPstTkREucjCwgJ///039uzZg71792L27NmwsLBQdlhERERECpXp2/d+xdbWFn5+fliwYEGW9vv69SuqVauGfv36oWPHjmm2z58/H4sXL0ZQUBDKly+P2bNno2nTpnj8+DEMDAwAAKNGjcKhQ4ewfft2mJiYYMyYMXBzc0NYWJh8QvYePXrgzZs3OHbsGABg0KBB6N27Nw4dOvSHz5xIeZ4+fYohQ4bgn3/+AQDY2Nhg7dq1cHR0VHJkRET0O3fu3Ml036pVqyowEiIiIiLlyZGiVAptbe0s9W/RogVatGiR7jZBEODn54cpU6agQ4cOAH4smWxubo6tW7di8ODBkEgkCAgIwObNm9GkSRMAwJYtW2BpaYmTJ0+iWbNmePjwIY4dO4bLly/DwcEBALBu3To4Ojri8ePHqFChQrrnl0qlkEql8sexsbHyuARByNLz/J2UY+b0cUnxlJG7hIQELFiwALNnz4ZUKoWenh6mTZuGsWPHQltbm/+OMonvO/XF3KkndclbbsVXvXp1iESi355PJBIhOTk5V2IiIiIiym05WpTKSeHh4YiKioKrq6u8TVdXFy4uLggNDcXgwYMRFhaGxMTEVH0sLCxgY2OD0NBQNGvWDJcuXYJYLJYXpACgTp06EIvFCA0NzbAo5evrCx8fnzTtEolEIUWpL1++AABXRlMzuZ27y5cvY/To0Xj06BEAoGHDhli0aBGsra3x7ds3hZ8/L+H7Tn0xd+pJXfKW8iWUooWHh+fKeYiIiIhUmcoWpaKiogAA5ubmqdrNzc3x6tUreR8dHR0ULlw4TZ+U/aOiomBmZpbm+GZmZvI+6Zk0aRK8vLzkj2NjY2FpaQmxWAxDQ8PsPakMpBS5xGKxSl+oU1q5lbuYmBhMnDgRa9euBQAUKVIEixcvRo8ePfhvJpv4vlNfzJ16Upe85VZsVlZWuXIeIiIiIlWmskWpFP+9OBQE4bcXjP/tk17/3x1HV1cXurq66cajiAvWlOOq8oU6pU/RuTt48CA8PT0RGRkJAOjfvz/mz58PExMThZwvP+H7Tn0xd+pJHfKmrNg2b96M1atXIzw8HJcuXYKVlRX8/PxgbW2Ntm3bKiUmIiIiIkXL1tJcz58/x9SpU9G9e3dER0cDAI4dO5ZmZbw/UbRoUQBIM5opOjpaPnqqaNGiSEhIwOfPn3/Z5927d2mO//79+zSjsIhUyfv379G9e3e0bdsWkZGRKFeuHM6cOYOAgAAWpIiI8pBVq1bBy8sLLVu2RExMjHwOKSMjI/j5+Sk3OCIiIiIFynJR6uzZs7C1tcWVK1ewd+9e+fwQd+7cwYwZM3IsMGtraxQtWhQhISHytoSEBJw9exZOTk4AADs7O2hra6fqExkZiXv37sn7ODo6QiKR4OrVq/I+V65cgUQikfchUiWCIGD79u2oXLkytm/fDg0NDUyYMAG3b9+Gi4uLssMjIqIctnz5cqxbtw5TpkyRrxwMAPb29rh7964SIyMiIiJSrCzfvjdx4kTMnj0bXl5eMDAwkLc3bNgQS5cuzdKxvnz5gmfPnskfh4eH49atWzA2NkbJkiUxatQozJkzB+XKlUO5cuUwZ84cFChQAD169ADwY14KDw8PjBkzBiYmJjA2NsbYsWNha2srX42vUqVKaN68OQYOHIg1a9YAAAYNGgQ3N7cMJzknUpZ///0XQ4YMwcGDBwEAtra22LBhA+zt7ZUcGRERKUp4eDhq1KiRpl1XVxdfv35VQkREREREuSPLRam7d+9i69atadqLFCmCjx8/ZulY169fR8OGDeWPUyYW79u3L4KCgjB+/HjEx8dj6NCh+Pz5MxwcHHDixIlUxbAlS5ZAS0sLXbp0QXx8PBo3boygoKBU3zQGBwdj5MiR8lX62rRpgxUrVmQpViJFEgQBgYGB8PLygkQigba2NqZOnYqJEydCR0dH2eEREZECWVtb49atW2kmPz969CgqV66spKiIiIiIFC/LRSkjIyNERkbC2to6VfvNmzdRvHjxLB2rQYMG8tV40iMSieDt7Q1vb+8M++jp6WH58uVYvnx5hn2MjY2xZcuWLMVGlFtevnyJQYMGyW9DrVWrFjZs2AAbGxslR0ZERLlh3LhxGDZsGL5//w5BEHD16lVs27YNvr6+WL9+vbLDIyIiIlKYLBelevTogQkTJmDXrl0QiUSQyWS4ePEixo4diz59+igiRqI8SSaTwd/fHxMnTsTXr1+hp6eHWbNmYdSoUdDSUvmFMYmIKIf069cPSUlJGD9+PL59+4YePXqgePHiWLp0Kbp166bs8IiIiIgUJsu/+f79999wd3dH8eLFIQgCKleujOTkZPTo0QNTp05VRIxEec6TJ0/g4eGBCxcuAACcnZ0REBCAcuXKKTkyIiJShoEDB2LgwIH48OEDZDIZzMzMlB0SERERkcJlefU9bW1tBAcH48mTJ9i5cye2bNmCR48eYfPmzanmcSKitGQyGZYuXYpq1arhwoULKFSoEFauXIkzZ86wIEVElE/5+Pjg+fPnAABTU1MWpIiIiCjfyPY9QmXKlEGZMmVyMhaiPO3Vq1fo168fTp8+DQBo2rQp1q1bl2ZiWyIiyl/27NmDmTNnolatWujVqxe6du2KIkWKKDssIiIiIoXLVFEqZVW8zFi8eHG2gyHKi1JW1hs1ahTi4uJQoEABLFq0CIMHD4ZIJFJ2eEREpGR37tzB/fv3ERwcjMWLF8PLywtNmjRBr1690K5dOxQoUEDZIRIREREpRKaKUjdv3kz1OCwsDMnJyahQoQKAH/PjaGpqws7OLucjJFJjUVFRGDhwIA4fPgwAqFu3LjZu3MhRhkRElEqVKlUwZ84czJkzBxcvXsTWrVsxatQoeHp6IjY2VtnhERERESlEpuaUOn36tPyndevWaNCgAd68eYMbN27gxo0beP36NRo2bIhWrVopOl4itbF7927Y2Njg8OHD0NHRwbx583D27FkWpIiI6JcKFiwIfX196OjoIDExMVvH8Pf3h7W1NfT09GBnZ4fz589nar+LFy9CS0sL1atXz9Z5iYiIiLIiyxOdL1q0CL6+vihcuLC8rXDhwpg9ezYWLVqUo8ERqaNPnz6hZ8+e6Ny5Mz5+/Ijq1avj+vXrGD9+PBcDICKidIWHh+Pvv/9G5cqVYW9vjxs3bsDb2xtRUVFZPtaOHTswatQoTJkyBTdv3oSzszNatGiBiIiIX+4nkUjQp08fNG7cOLtPg4iIiChLslyUio2Nxbt379K0R0dHIy4uLkeCIlJXx44dg62tLbZu3QoNDQ1MnToVV65cga2trbJDIyIiFeXo6IiyZcti165d6NevH169eoVTp05hwIABEIvFWT7e4sWL4eHhgQEDBqBSpUrw8/ODpaUlVq1a9cv9Bg8ejB49esDR0TG7T4WIiIgoS7K8+l779u3Rr18/LFq0CHXq1AEAXL58GePGjUOHDh1yPEAidfDlyxeMGzcOa9asAQCUL18emzZtgoODg5IjIyIiVdewYUOsX78eVapU+eNjJSQkICwsDBMnTkzV7urqitDQ0Az3CwwMxPPnz7FlyxbMnj37t+eRSqWQSqXyxynzXgmCAEEQshk95aSUXDAfqoV5UU3Mi+phTlSTIvKR5aLU6tWrMXbsWPTq1Us+z4GWlhY8PDywYMGCHA+QSNWFhoZixIgRePHiBQBg5MiR8PX15WpJRESUKXPmzMmxY3348AHJyckwNzdP1W5ubp7hrYBPnz7FxIkTcf78eWhpZe7S0NfXFz4+PmnaJRJJ1oMmhRAEAV++fAEArvarQpgX1cS8qB7mRDUp4nM+y0WpAgUKwN/fHwsWLMDz588hCALKli2LggUL5nhwRKpMKpVi6tSpWLRoEQRBQMmSJREYGIhGjRopOzQiIsrn/nsBLwhCuhf1ycnJ6NGjB3x8fFC+fPlMH3/SpEnw8vKSP46NjYWlpSXEYnG2bjmknJfybbZYLOYvdCqEeVFNzIvqYU7yjywXpVIULFgQVatWzclYiNTGo0eP0L17d9y6dQsA4O7uDj8/P16IExGRUpmamkJTUzPNqKjo6Og0o6cAIC4uDtevX8fNmzcxfPhwAIBMJoMgCNDS0sKJEyfS/bJFV1cXurq6adpFIhF/eVAhKflgTlQL86KamBfVw5yoHkXkIstFqYYNG/4ykFOnTv1RQESqTBAErF27FqNHj0Z8fDxMTEywbNkydO/enf9ZEhGR0uno6MDOzg4hISFo3769vD0kJARt27ZN09/Q0BB3795N1ebv749Tp05h9+7dsLa2VnjMRERElH9luShVvXr1VI8TExNx69Yt3Lt3D3379s2puIhUzocPHzBgwAAcOHAAANC0aVMEBgby1lUiIlIpXl5e6N27N+zt7eHo6Ii1a9ciIiICnp6eAH7cevf27Vts2rQJGhoasLGxSbW/mZkZ9PT00rQTERER5bQsF6WWLFmSbru3t7d8IjKivCYkJAR9+/ZFZGQktLW1MXfuXIwaNQoikYiTuhIRUZbduXMn032zOl1C165d8fHjR8ycORORkZGwsbHBkSNHYGVlBQCIjIxERERElo5JREREpAgiIYfW9Hv27Blq166NT58+5cThVE5sbCzEYjEkEgkMDQ1z9NiCIEAikXASNxUklUoxZcoULFq0CABQsWJFbNu2TT5ikLlTX8yd+mLu1JO65E2Rn/c/09DQgEgkynAC8p8lJycrLI6ckvK6xcTEcH5FFaEu77n8hnlRTcyL6mFOVJNEIoGRkVGOXidp5MhRAFy6dAl6eno5dTgipXv48CHq1KkjL0h5enoiLCwszS2sREREWRUeHo4XL14gPDwce/bsgbW1Nfz9/XHz5k3cvHkT/v7+KFOmDPbs2aPsUImIiIgUJsu373Xo0CHVY0EQEBkZievXr2PatGk5FhiRsgiCgDVr1sDLy0s+mXlAQEC6E8QSERFlR8qtdADQuXNnLFu2DC1btpS3Va1aFZaWlpg2bRratWunhAiJiIiIFC/LRSlDQ8NUw+c0NDRQoUIFzJw5E66urjkaHFFuS28y840bN6JYsWJKjoyIiPKqu3fvprvKnbW1NR48eKCEiIiIiIhyR5aLUkFBQQoIg0j5QkJC0KdPH0RFRUFHRwe+vr4YNWoUNDRy7C5XIiKiNCpVqoTZs2cjICBAPhWCVCrF7NmzUalSJSVHR0RERKQ4WS5KlS5dGteuXYOJiUmq9piYGNSsWRMvXrzIseCIcoNUKsXkyZOxePFiAGknMyciIlKk1atXo3Xr1rC0tES1atUAALdv34ZIJMLhw4eVHB0RERGR4mS5KPXy5ct0V4GRSqV4+/ZtjgRFlFuePHmCbt264ebNmwCAIUOGYOHChShQoICSIyMiovyidu3aCA8Px5YtW/Do0SMIgoCuXbuiR48eKFiwoLLDIyIiIlKYTBelDh48KP/78ePHUy33m5ycjH/++QelSpXK0eCIFGnLli3w9PTE169fYWJigg0bNqBNmzbKDouIiPKRxMREVKhQAYcPH8agQYOUHQ4RERFRrsp0USpl5ReRSIS+ffum2qatrY1SpUph0aJFORockSJ8/foVw4cPl8+P5uLiguDgYBQvXly5gRERUb6jra0NqVSaahEZIiIiovwi0zM4y2QyyGQylCxZEtHR0fLHMpkMUqkUjx8/hpubmyJjJfpjd+7cgb29PYKCgqChoYEZM2bgn3/+YUGKiIiUZsSIEZg3bx6SkpKUHQoRERFRrsrynFLh4eGKiINIoQRBwNq1azFq1Ch8//4dxYoVw9atW9GgQQNlh0ZERPnclStX8M8//+DEiROwtbVNM4/U3r17lRQZERERkWJlqii1bNkyDBo0CHp6eli2bNkv+44cOTJHAiPKKRKJBAMHDsSuXbsAAC1atMDGjRtRpEgRJUdGREQEGBkZoWPHjsoOg4iIiCjXZaootWTJEvTs2RN6enpYsmRJhv1EIhGLUqRSrl69im7duiE8PBxaWlrw9fWFl5cXNDQyfecqERGRQgUGBio7BCIiIiKlyFRR6udb9nj7HqkDQRCwZMkSTJgwAUlJSShVqhS2b98OBwcHZYdGRERERERERMjCROcpZs6ciW/fvqVpj4+Px8yZM3MkKKI/8fnzZ7Rv3x5jxoxBUlISOnXqhJs3b7IgRUREKmv37t3o0qUL6tSpg5o1a6b6ISIiIsqrslyU8vHxwZcvX9K0f/v2DT4+PjkSFFF2hYWFwc7ODgcOHICOjg5WrlyJnTt3wsjISNmhERERpWvZsmXo168fzMzMcPPmTdSuXRsmJiZ48eIFWrRooezwiIiIiBQmy0UpQRAgEonStN++fRvGxsY5EhRRVgmCgFWrVsHJyQnh4eGwtrZGaGgohg4dmu6/VyIiIlXh7++PtWvXYsWKFdDR0cH48eMREhKCkSNHQiKRKDs8IiIiIoXJdFGqcOHCMDY2hkgkQvny5WFsbCz/EYvFaNq0Kbp06ZKjwXl7e0MkEqX6KVq0qHy7IAjw9vaGhYUF9PX10aBBA9y/fz/VMaRSKUaMGAFTU1MULFgQbdq0wZs3b3I0TlKuuLg49OzZE0OHDkVCQgLatm0rHzFFRESk6iIiIuDk5AQA0NfXR1xcHACgd+/e2LZtmzJDIyIiIlKoTE10DgB+fn4QBAH9+/eHj48PxGKxfJuOjg5KlSoFR0fHHA+wSpUqOHnypPyxpqam/O/z58/H4sWLERQUhPLly2P27Nlo2rQpHj9+DAMDAwDAqFGjcOjQIWzfvh0mJiYYM2YM3NzcEBYWlupYpJ7u3buHTp064fHjx9DU1MS8efPg5eXF0VFERKQ2ihYtio8fP8LKygpWVla4fPkyqlWrhvDwcAiCoOzwiIiIiBQm00Wpvn37AgCsra3h5OQEbW1thQX1My0trVSjo1IIggA/Pz9MmTIFHTp0AABs3LgR5ubm2Lp1KwYPHgyJRIKAgABs3rwZTZo0AQBs2bIFlpaWOHnyJJo1a5bheaVSKaRSqfxxbGys/Lw5fYGYckxeeGbNpk2bMGTIEMTHx6N48eLYvn076tatCwC59loyd+qLuVNfzJ16Upe8KSO+Ro0a4dChQ6hZsyY8PDwwevRo7N69G9evX5df4xARERHlRZkqSqUUZACgRo0aiI+PR3x8fLp9DQ0Ncyay/+/p06ewsLCArq4uHBwcMGfOHJQuXRrh4eGIioqCq6urvK+uri5cXFwQGhqKwYMHIywsDImJian6WFhYwMbGBqGhob8sSvn6+qY7cbtEIlFIUSpl8niO8Pm979+/Y8KECdi0aROAHxfza9asgampaa7PvcHcqS/mTn0xd+pJXfL28zVPblm7di1kMhkAwNPTE8bGxrhw4QJat24NT0/PXI+HiIiIKLdkqihlZGT02wvIlAnQk5OTcyQwAHBwcMCmTZtQvnx5vHv3DrNnz4aTkxPu37+PqKgoAIC5uXmqfczNzfHq1SsAQFRUFHR0dFC4cOE0fVL2z8ikSZPg5eUlfxwbGwtLS0uIxeIcL7ylFLnEYrFKX6irglevXqFTp04ICwuDSCTCjBkzMGXKFKXdisncqS/mTn0xd+pJXfKmjNg0NDSgofF/03x26dIlx+fpJCIiIlJFmSpKnT59WtFxpOvnZZBtbW3h6OiIMmXKYOPGjahTpw6AtBePGa0OmNU+urq60NXVTdOeMuF6Tvt5MndKX0hICLp3746PHz/CxMQE27ZtQ9OmTZUdFnOnxpg79cXcqSd1yJsyYqtbty5cXFzQoEED1K1bFwULFsz1GIiIiIiUIVNFKRcXl0wd7NatW38Sy28VLFgQtra2ePr0Kdq1awfgx2ioYsWKyftER0fLR08VLVoUCQkJ+Pz5c6rRUtHR0fJVbkj1CYKAefPmYcqUKZDJZLCzs8OePXtgZWWl7NCIiIj+mJubG86ePYsVK1bg+/fvsLOzkxep6tWrh0KFCik7RCIiIiKF0Ph9l1+TSCTw9/dHzZo1YWdnlxMxZUgqleLhw4coVqwYrK2tUbRoUYSEhMi3JyQk4OzZs/KCk52dHbS1tVP1iYyMxL1791iUUhOxsbHo2LEjJk2aBJlMhv79++PChQssSBERUZ4xadIkHDt2DJ8/f8a5c+fQtm1b3Lp1C23atIGJiYmywyMiIiJSmEyvvvdfp06dwoYNG7B3715YWVmhY8eOCAgIyMnYMHbsWLRu3RolS5ZEdHQ0Zs+ejdjYWPTt2xcikQijRo3CnDlzUK5cOZQrVw5z5sxBgQIF0KNHDwA/5q3w8PDAmDFjYGJiAmNjY4wdOxa2trby1fhIdT18+BDt27fH48ePoaOjg+XLl2PgwIEqfdsHERFRdj19+hS3b9/G7du3cefOHRgaGsLZ2VnZYREREREpTJaKUm/evEFQUBA2bNiAr1+/okuXLkhMTMSePXtQuXLlHA/uzZs36N69Oz58+IAiRYqgTp06uHz5snyUzPjx4xEfH4+hQ4fi8+fPcHBwwIkTJ2BgYCA/xpIlS6ClpYUuXbogPj4ejRs3RlBQkNImxqbM2bNnD9zd3fHlyxcUL14ce/bsgYODg7LDIiIiynFdu3bFuXPnIJPJUL9+fdSvXx+TJk1C1apVlR0aERERkUKJhJTlcH6jZcuWuHDhAtzc3NCzZ080b94cmpqa0NbWxu3btxVSlFIlsbGxEIvFkEgkCll9TyKRqPyKRLkhOTkZU6dOxdy5cwEADRo0wI4dO2BmZqbkyNLH3Kkv5k59MXfqSV3ypsjP+4xoaGjA1NQU7u7uaNiwIZydndVuHqmU1y0mJgZisVjZ4RDU5z2X3zAvqol5UT3MiWqSSCQwMjLK0eukTI+UOnHiBEaOHIkhQ4agXLlyOXJyop/FxsaiR48e+N///gcAGDNmDObOnQstrWzfZUpERKTyPn36hHPnzuHMmTOYOnUq7t+/j2rVqqFBgwZo0KBBqtWIiYiIiPKSTE90fv78ecTFxcHe3h4ODg5YsWIF3r9/r8jYKB95/vw5HB0d8b///Q96enoIDg7GwoULWZAiIqI8z8jICG3atMHixYsRFhaG+/fvo3Llyli8eDHc3NyUHR4RERGRwmS6KOXo6Ih169YhMjISgwcPxvbt21G8eHHIZDKEhIQgLi5OkXFSHnbq1CnUrl0bDx48gIWFBc6dOyefrJ6IiCiv+/TpE/bt24e//voL1apVQ4UKFfC///0Pbdu2xbJly7J1TH9/f1hbW0NPTw92dnY4f/58hn337t2Lpk2bokiRIjA0NISjoyOOHz+e3adDRERElGmZLkqlKFCgAPr3748LFy7g7t278luszMzM0KZNG0XESHmYv78/XF1d8enTJ9SuXRvXrl1DrVq1lB0WERFRrilSpAg8PT3x9u1bDBw4ELdu3UJ0dDR2796NYcOGZfl4O3bswKhRozBlyhTcvHkTzs7OaNGiBSIiItLtf+7cOTRt2hRHjhxBWFgYGjZsiNatW+PmzZt/+tSIiIiIfinTE53/SnJyMg4dOoQNGzbg4MGDORGXyuFE5zkrMTERI0eOxOrVqwEAPXv2xLp166Cvr6/kyLImP+Yur2Du1Bdzp57UJW/KmOj83r17sLGxybHjOTg4oGbNmli1apW8rVKlSmjXrh18fX0zdYwqVaqga9eumD59errbpVIppFKp/HFsbCwsLS3x+fNnTnSuItTlPZffMC+qiXlRPcyJapJIJChcuLByJjr/FU1NTbRr1w7t2rXLicNRHvfhwwd07twZZ86cgUgkwty5czFu3Dj+Z0NERPlSSkEqOjoajx8/hkgkQvny5bO18mxCQgLCwsIwceLEVO2urq4IDQ3N1DFkMhni4uJgbGycYR9fX1/4+PikaZdIJFkLmBRGEAR8+fIFAHiNpUKYF9XEvKge5kQ1KeJznrNIU666d+8e2rRpg/DwcBgYGGDr1q2cxJWIiPK12NhYDBs2DNu2bYNMJgPw4wu/rl27YuXKlVkaefThwwckJyfD3Nw8Vbu5uTmioqIydYxFixbh69ev6NKlS4Z9Jk2aBC8vr1TPwdLSEmKxmCOlVETKzRAcZaBamBfVxLyoHuYk/2BRinLNwYMH0bNnT3z58gWlS5fGwYMHUaVKFWWHRUREpFQDBgzArVu38L///Q+Ojo4QiUQIDQ3FX3/9hYEDB2Lnzp1ZPuZ/L+AFQcjURf22bdvg7e2NAwcO/HKklq6uLnR1ddM9L395UB0p+WBOVAvzopqYF9XDnKgeReSCRSlSOEEQMG/ePEyePBmCIKBhw4bYtWsXTExMlB0aERGR0v3vf//D8ePHUa9ePXlbs2bNsG7dOjRv3jxLxzI1NYWmpmaaUVHR0dFpRk/9144dO+Dh4YFdu3ahSZMmWTovERERUXZkefU9oqyIj49Hr169MGnSJAiCgKFDh+L48eMsSBEREf1/JiYm6d7yJhaLUbhw4SwdS0dHB3Z2dggJCUnVHhISAicnpwz327ZtG9zd3bF161a0atUqS+ckIiIiyi4WpUhh3r59CxcXF2zduhVaWlpYtWoVVq5cCW1tbWWHRkREpDKmTp0KLy8vREZGytuioqIwbtw4TJs2LcvH8/Lywvr167FhwwY8fPgQo0ePRkREBDw9PQH8mA+qT58+8v7btm1Dnz59sGjRItSpUwdRUVGIioripOVERESkcLx9jxTi6tWraNeuHSIjI2FsbIzdu3ejYcOGyg6LiIhI5axatQrPnj2DlZUVSpYsCQCIiIiArq4u3r9/jzVr1sj73rhx47fH69q1Kz5+/IiZM2ciMjISNjY2OHLkCKysrAAAkZGRiIiIkPdfs2YNkpKSMGzYMAwbNkze3rdvXwQFBeXQsyQiIiJKi0UpynFBQUHw9PSEVCpFlSpVcPDgQZQuXVrZYREREamkdu3a5fgxhw4diqFDh6a77b+FpjNnzuT4+YmIiIgyg0UpyjHfv3/HyJEjsW7dOgBA69atsWXLFhgaGio5MiIiItU1Y8YMZYdAREREpBScU4pyRHh4OOrWrYt169ZBJBJh5syZ2L9/PwtSRERERERERJQuFqXojx09ehR2dna4ceMGTExMcOzYMUybNg0aGvznRURElBENDQ1oamqm+SlcuDDq1KmDvXv3KjtEIiIiIoXi7XuUbQkJCZgyZQoWLlwIAKhVqxZ2794tn6SViIiIMrZv375022NiYnD16lX06tULGzduROfOnXM5MiIiIqLcwaIUZcuTJ0/Qo0cPhIWFAfgxoerixYuhq6ur5MiIiIjUQ9u2bTPc1rdvX1SuXBkLFy5kUYqIiIjyLN5fRVkiCAICAwNRs2ZNhIWFwdjYGPv27cPKlStZkCIiIspBrq6uePLkibLDICIiIlIYjpSiTHv//j2GDRuGXbt2AQAaNGiAzZs3o0SJEkqOjIiIKO+Jj4+Hnp6essMgIiIiUhiOlKLfEgQBO3fuROXKlbFr1y5oaWnB19cXJ0+eZEGKiIhIQdatW4caNWooOwwiIiIiheFIKfqld+/eYejQofIVgGxtbREYGAg7OzslR0ZERKTevLy80m2XSCS4fv06nj9/jvPnz+dyVERERES5h0UpSpdMJkNgYCDGjx+PT58+QUtLC5MnT8aUKVOgo6Oj7PCIiIjU3s2bN9NtNzQ0RPPmzTF06FBYWVnlclREREREuYdFKUrj5s2bGDp0KC5fvgwAqFatGoKCglC9enXlBkZERJSHnD59WtkhEBERESkV55Qiuc+fP2PEiBGwt7fH5cuXUahQISxatAjXrl1jQYqIiIiIiIiIchRHShG+f/+O5cuXY86cOYiJiQEAdO/eHQsXLoSFhYVygyMiIiIiIiKiPIlFqXwsOTkZwcHBmDZtGiIiIgD8mMjcz88PjRo1UnJ0RERERERERJSXsSiVDyUmJmLbtm2YM2cOHj9+DACwtLTErFmz0KtXL2hqaio5QiIiIiIiIiLK61iUykfi4+OxadMmzJ07Fy9fvgQAFC5cGJMmTcLw4cOhr6+v3ACJiIiIiIiIKN9gUSofePXqFVatWoX169fj48ePAAAzMzOMGTMGQ4YMgYGBgZIjJCIiIiIiIqL8hkWpPCoxMRHHjx9HQEAADh48CJlMBgAoWbIkxowZgwEDBqBAgQJKjpKIiIiIiIiI8isWpfIQmUyGa9euYcuWLdi+fTs+fPgg39a4cWMMHz4cbm5u0NJi2omIiIiIiIhIuVidUHPfvn3DP//8g0OHDuHw4cOIjIyUbzMzM0OPHj0wcOBAVK5cWYlREhERERERERGllq+KUv7+/liwYAEiIyNRpUoV+Pn5wdnZWdlhZcn79+9x9epVnD9/HufOncP169eRmJgo316oUCG0bt0avXv3RtOmTTkqioiIiIiIiIhUUr6pWOzYsQOjRo2Cv78/6tatizVr1qBFixZ48OABSpYsqezw5JKTkyGRSPD27VtEREQgIiICz58/x927d3Hnzh1ERUWl2cfKygqtW7dG69at4eLiAl1dXSVETkRERERERESUefmmKLV48WJ4eHhgwIABAAA/Pz8cP34cq1atgq+vb5r+UqkUUqlU/jg2NhYAIAgCBEHIsbgePnyIDh06QCKRIC4uDl+/fv3tPuXKlUO9evXg7OyM+vXrw9raGiKRSL49J+OjX0v598DXXP0wd+qLuVNP6pI3VY+PiIiIKC/JF0WphIQEhIWFYeLEianaXV1dERoamu4+vr6+8PHxSdMukUhy9II1Pj4ejx8/TtNubGyMEiVKoESJErC0tESlSpVQuXJlVKpUCYUKFUrVN6VgRrlPEAR8+fIFAFIVBkn1MXfqi7lTT+qSN36mEhEREeWefFGU+vDhA5KTk2Fubp6q3dzcPN3b4QBg0qRJ8PLykj+OjY2FpaUlxGIxDA0Ncyy2ypUr49SpU9DQ0ICFhQWMjIxgYGDAW/DUREqBUiwWq/QvWZQWc6e+mDv1pC55U+XYiIiIiPKafFGUSvHfC01BEDK8+NTV1U23MCQSiXL0glVfXx8NGjSARCJR+Qt1Sl/KvwnmTv0wd+qLuVNP6pA3VY6NiIiIKK/RUHYAucHU1BSampppRkVFR0enGT1FRERERERERESKly+KUjo6OrCzs0NISEiq9pCQEDg5OSkpKiIiIiIiIiKi/Cvf3L7n5eWF3r17w97eHo6Ojli7di0iIiLg6emp7NCIiIiIiIiIiPKdfDFSCgC6du0KPz8/zJw5E9WrV8e5c+dw5MgRWFlZKTs0IiIiohzl7+8Pa2tr6Onpwc7ODufPn/9l/7Nnz8LOzg56enooXbo0Vq9enUuREhERUX6Wb4pSADB06FC8fPkSUqkUYWFhqF+/vrJDIiIiIspRO3bswKhRozBlyhTcvHkTzs7OaNGiBSIiItLtHx4ejpYtW8LZ2Rk3b97E5MmTMXLkSOzZsyeXIyciIqL8Jt/cvvenUpayjo2NVcixY2NjVX5FIkqLuVNfzJ36Yu7Uk7rkLeVzPuVzXx0tXrwYHh4eGDBgAADAz88Px48fx6pVq+Dr65um/+rVq1GyZEn4+fkBACpVqoTr169j4cKF6NixY7rnkEqlkEql8scSiSTVn6R8giDI86HK77n8hnlRTcyL6mFOVFNKTnLyOolFqUyKi4sDAFhaWio5EiIiIlK0uLg4iMViZYeRZQkJCQgLC8PEiRNTtbu6uiI0NDTdfS5dugRXV9dUbc2aNUNAQAASExOhra2dZh9fX1/4+Pikaee0CERERHnfx48fc+w6iUWpTLKwsMDr169hYGCQ45Xa2NhYWFpa4vXr1zA0NMzRY5NiMXfqi7lTX8ydelKXvAmCgLi4OFhYWCg7lGz58OEDkpOTYW5unqrd3NwcUVFR6e4TFRWVbv+kpCR8+PABxYoVS7PPpEmT4OXlJX8cExMDKysrREREqGUxLy9Sl/dcfsO8qCbmRfUwJ6pJIpGgZMmSMDY2zrFjsiiVSRoaGihRooRCz2FoaMg3nJpi7tQXc6e+mDv1pA55ywtFlf9+gSYIwi+/VEuvf3rtKXR1daGrq5umXSwWq3x+8xt1eM/lR8yLamJeVA9zopo0NHJuevJ8NdE5ERERUV5mamoKTU3NNKOioqOj04yGSlG0aNF0+2tpacHExERhsRIRERGxKEVERESUR+jo6MDOzg4hISGp2kNCQuDk5JTuPo6Ojmn6nzhxAvb29unOJ0VERESUU1iUUgG6urqYMWNGusPgSbUxd+qLuVNfzJ16Yt5yj5eXF9avX48NGzbg4cOHGD16NCIiIuDp6Qngx3xQffr0kff39PTEq1ev4OXlhYcPH2LDhg0ICAjA2LFjM31O5lf1MCeqiXlRTcyL6mFOVJMi8iIS1HnNYyIiIiJKw9/fH/Pnz0dkZCRsbGywZMkS1K9fHwDg7u6Oly9f4syZM/L+Z8+exejRo3H//n1YWFhgwoQJ8iIWERERkaKwKEVERERERERERLmOt+8REREREREREVGuY1GKiIiIiIiIiIhyHYtSRERERERERESU61iUIiIiIiIiIiKiXMeilJL5+/vD2toaenp6sLOzw/nz55UdEv2Gt7c3RCJRqp+iRYsqOyxKx7lz59C6dWtYWFhAJBJh//79qbYLggBvb29YWFhAX18fDRo0wP3795UTLKXyu9y5u7uneR/WqVNHOcFSKr6+vqhVqxYMDAxgZmaGdu3a4fHjx6n68L2nfrJ6vXL27FnY2dlBT08PpUuXxurVq3Mp0vwlK3nZu3cvmjZtiiJFisDQ0BCOjo44fvx4Lkabf2T3+v7ixYvQ0tJC9erVFRtgPpTVnEilUkyZMgVWVlbQ1dVFmTJlsGHDhlyKNv/Ial6Cg4NRrVo1FChQAMWKFUO/fv3w8ePHXIo27/vd9Xd6cuLznkUpJdqxYwdGjRqFKVOm4ObNm3B2dkaLFi0QERGh7NDoN6pUqYLIyEj5z927d5UdEqXj69evqFatGlasWJHu9vnz52Px4sVYsWIFrl27hqJFi6Jp06aIi4vL5Ujpv36XOwBo3rx5qvfhkSNHcjFCysjZs2cxbNgwXL58GSEhIUhKSoKrqyu+fv0q78P3nnrJ6vVKeHg4WrZsCWdnZ9y8eROTJ0/GyJEjsWfPnlyOPG/Lal7OnTuHpk2b4siRIwgLC0PDhg3RunVr3Lx5M5cjz9uye30vkUjQp08fNG7cOJcizT+yk5MuXbrgn3/+QUBAAB4/foxt27ahYsWKuRh13pfVvFy4cAF9+vSBh4cH7t+/j127duHatWsYMGBALkeed2Xm+vtnOfZ5L5DS1K5dW/D09EzVVrFiRWHixIlKiogyY8aMGUK1atWUHQZlEQBh37598scymUwoWrSoMHfuXHnb9+/fBbFYLKxevVoJEVJG/ps7QRCEvn37Cm3btlVKPJQ10dHRAgDh7NmzgiDwvaeOsnq9Mn78eKFixYqp2gYPHizUqVNHYTHmRzlxHVm5cmXBx8cnp0PL17Kbl65duwpTp07ldaYCZDUnR48eFcRisfDx48fcCC/fympeFixYIJQuXTpV27Jly4QSJUooLMb8LL3r7//Kqc97jpRSkoSEBISFhcHV1TVVu6urK0JDQ5UUFWXW06dPYWFhAWtra3Tr1g0vXrxQdkiUReHh4YiKikr1HtTV1YWLiwvfg2rizJkzMDMzQ/ny5TFw4EBER0crOyRKh0QiAQAYGxsD4HtP3WTneuXSpUtp+jdr1gzXr19HYmKiwmLNT3LiOlImkyEuLk7+3qQ/l928BAYG4vnz55gxY4aiQ8x3spOTgwcPwt7eHvPnz0fx4sVRvnx5jB07FvHx8bkRcr6Qnbw4OTnhzZs3OHLkCARBwLt377B79260atUqN0KmdOTU5z2LUkry4cMHJCcnw9zcPFW7ubk5oqKilBQVZYaDgwM2bdqE48ePY926dYiKioKTkxPvZ1YzKe8zvgfVU4sWLRAcHIxTp05h0aJFuHbtGho1agSpVKrs0OgngiDAy8sL9erVg42NDQC+99RNdq5XoqKi0u2flJSEDx8+KCzW/CQnriMXLVqEr1+/okuXLooIMV/KTl6ePn2KiRMnIjg4GFpaWrkRZr6SnZy8ePECFy5cwL1797Bv3z74+flh9+7dGDZsWG6EnC9kJy9OTk4IDg5G165doaOjg6JFi8LIyAjLly/PjZApHTn1ec//+ZRMJBKleiwIQpo2Ui0tWrSQ/93W1haOjo4oU6YMNm7cCC8vLyVGRtnB96B66tq1q/zvNjY2sLe3h5WVFf73v/+hQ4cOSoyMfjZ8+HDcuXMHFy5cSLON7z31ktV8pdc/vXb6M9l9H23btg3e3t44cOAAzMzMFBVevpXZvCQnJ6NHjx7w8fFB+fLlcyu8fCkr7xWZTAaRSITg4GCIxWIAwOLFi9GpUyesXLkS+vr6Co83v8hKXh48eICRI0di+vTpaNasGSIjIzFu3Dh4enoiICAgN8KldOTE5z2LUkpiamoKTU3NNJXg6OjoNNVGUm0FCxaEra0tnj59quxQKAtSVkyMiopCsWLF5O18D6qnYsWKwcrKiu9DFTJixAgcPHgQ586dQ4kSJeTtfO+pl+xcrxQtWjTd/lpaWjAxMVFYrPnJn1xH7tixAx4eHti1axeaNGmiyDDznazmJS4uDtevX8fNmzcxfPhwAD8KIoIgQEtLCydOnECjRo1yJfa8KjvvlWLFiqF48eLyghQAVKpUCYIg4M2bNyhXrpxCY84PspMXX19f1K1bF+PGjQMAVK1aFQULFoSzszNmz56d6pqCckdOfd7z9j0l0dHRgZ2dHUJCQlK1h4SEwMnJSUlRUXZIpVI8fPiQ/xGqGWtraxQtWjTVezAhIQFnz57le1ANffz4Ea9fv+b7UAUIgoDhw4dj7969OHXqFKytrVNt53tPvWTnesXR0TFN/xMnTsDe3h7a2toKizU/ye515LZt2+Du7o6tW7dyHhYFyGpeDA0NcffuXdy6dUv+4+npiQoVKuDWrVtwcHDIrdDzrOy8V+rWrYt///0XX758kbc9efIEGhoaqb5koezLTl6+ffsGDY3U5QtNTU38v/buP77m+v//+P3YZsPsYJjNj83vMD+3iHhLNKEkeRN9/KaWym9vREK9Kcm7EnpXfrzr7denQiqf4u33r4j3lPAuv5dsxrCNMbY9v3/47rw7bRg557zO3K6Xyy45z/N8vV6Pcx7bOY8er1/Sf4/OgXvdse/7W7osOu6oJUuWGD8/PzN37lyzf/9+M3ToUFOsWDFz7NgxT4eGGxgxYoTZsGGDOXL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+      "text/plain": [
+       "<Figure size 1200x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for name in status.rocket:\n",
+    "    fig, axes = plt.subplots(2, 2, figsize=(12, 6))\n",
+    "    history_path = DATA / \"rockets\" / f\"{name}_altitude.csv\"\n",
+    "    if history_path.exists():\n",
+    "        history = pd.read_csv(history_path)\n",
+    "        for prefix, label, color in [(\"ordinary\", \"RocketPy\", \"tab:blue\"), (\"datcom\", \"DATCOM\", \"tab:orange\")]:\n",
+    "            for axis, suffix in [(axes[0,0], \"agl_m\"), (axes[0,1], \"vz_m_s\"), (axes[1,1], \"margin_cal\")]:\n",
+    "                column = f\"{prefix}_{suffix}\"\n",
+    "                if column in history:\n",
+    "                    axis.plot(history.time_s, history[column], label=label, color=color)\n",
+    "    measured_path = DATA / \"rockets\" / f\"{name}_telemetry.csv\"\n",
+    "    if measured_path.exists():\n",
+    "        measured = pd.read_csv(measured_path)\n",
+    "        measured = measured[measured.qa_valid]\n",
+    "        end = measured.loc[measured.altitude_agl_m.idxmax(), \"time_s\"]\n",
+    "        measured = measured[(measured.time_s >= 0) & (measured.time_s <= end)]\n",
+    "        axes[0,0].plot(measured.time_s, measured.altitude_agl_m, color=\"black\", label=\"Recorded\")\n",
+    "    else:\n",
+    "        value = metrics[(metrics.rocket==name) & (metrics.metric==\"Apogee AGL\")].measured.iloc[0]\n",
+    "        axes[0,0].axhline(value, color=\"black\", linestyle=\":\", label=\"Recorded apogee only\")\n",
+    "    velocity_path = DATA / \"rockets\" / f\"{name}_velocity.csv\"\n",
+    "    if velocity_path.exists():\n",
+    "        velocity = pd.read_csv(velocity_path); velocity = velocity[velocity.qa_valid]\n",
+    "        axes[0,1].plot(velocity.time_s, velocity.measured_vz_m_s, color=\"black\", label=\"Recorded\")\n",
+    "    else:\n",
+    "        axes[0,1].text(.02,.96,\"Measured velocity unavailable\", transform=axes[0,1].transAxes, va=\"top\", fontsize=8)\n",
+    "    cd_path = DATA / \"rockets\" / f\"{name}_cd.csv\"\n",
+    "    if cd_path.exists():\n",
+    "        cd = pd.read_csv(cd_path)\n",
+    "        for prefix, label, color in [(\"rocketpy\", \"RocketPy\", \"tab:blue\"), (\"datcom\", \"DATCOM\", \"tab:orange\")]:\n",
+    "            for phase, style in [(\"powered\", \"--\"), (\"coast\", \"-\")]:\n",
+    "                column = f\"{prefix}_{phase}_cd\"\n",
+    "                if column in cd:\n",
+    "                    axes[1,0].plot(cd.mach, cd[column], style, color=color, label=f\"{label} {phase}\")\n",
+    "    fit = metrics[(metrics.rocket==name) & (metrics.metric==\"Apogee-fitted constant CD\")]\n",
+    "    if not fit.empty and pd.notna(fit.inferred_cd.iloc[0]):\n",
+    "        axes[1,0].axhline(fit.inferred_cd.iloc[0], color=\"black\", linestyle=\":\", label=\"Apogee-fitted, inferred\")\n",
+    "    axes[1,0].text(.02,.96,\"Measured CD unavailable\",transform=axes[1,0].transAxes, va=\"top\", fontsize=8)\n",
+    "    if name == \"ndrt_2020\":\n",
+    "        axes[1,1].text(.02,.96,\"Recorded: 2.875 cal; conditions unspecified\", transform=axes[1,1].transAxes, va=\"top\",fontsize=8)\n",
+    "    else:\n",
+    "        axes[1,1].text(.02,.96,\"Measured margin unavailable\",transform=axes[1,1].transAxes, va=\"top\",fontsize=8)\n",
+    "    for axis,title,xlabel,ylabel in [(axes[0,0],\"Altitude\",\"Time (s)\",\"Altitude AGL (m)\"), (axes[0,1],\"Vertical velocity\",\"Time (s)\",\"Upward velocity (m/s)\"), (axes[1,0],\"Drag coefficient\",\"Mach\",\"CD\"), (axes[1,1],\"Stability margin\",\"Time (s)\",\"Margin (cal)\")]:\n",
+    "        axis.set(title=title,xlabel=xlabel,ylabel=ylabel)\n",
+    "        if axis.get_legend_handles_labels()[0]: axis.legend(fontsize=7)\n",
+    "    fig.suptitle(name.replace(\"_\",\" \").upper())\n",
+    "    fig.tight_layout(); plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d270f1f9",
+   "metadata": {},
+   "source": [
+    "## Availability and calibration residuals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b9cbc7a4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-22T22:26:55.001119Z",
+     "iopub.status.busy": "2026-10-22T22:26:55.000873Z",
+     "iopub.status.idle": "2026-10-22T22:26:55.016035Z",
+     "shell.execute_reply": "2026-10-22T22:26:55.015626Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "</style>\n",
+       "<table id=\"T_0a17a\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_0a17a_level0_col0\" class=\"col_heading level0 col0\" >rocket</th>\n",
+       "      <th id=\"T_0a17a_level0_col1\" class=\"col_heading level0 col1\" >status</th>\n",
+       "      <th id=\"T_0a17a_level0_col2\" class=\"col_heading level0 col2\" >reason</th>\n",
+       "      <th id=\"T_0a17a_level0_col3\" class=\"col_heading level0 col3\" >notes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a17a_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_0a17a_row0_col0\" class=\"data row0 col0\" >andromeda</td>\n",
+       "      <td id=\"T_0a17a_row0_col1\" class=\"data row0 col1\" >paired untuned comparison</td>\n",
+       "      <td id=\"T_0a17a_row0_col2\" class=\"data row0 col2\" >—</td>\n",
+       "      <td id=\"T_0a17a_row0_col3\" class=\"data row0 col3\" >Telemetry maximum 3438.549 m differs from notebook reported value 3443.000 m; both retained.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a17a_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+       "      <td id=\"T_0a17a_row1_col0\" class=\"data row1 col0\" >astra</td>\n",
+       "      <td id=\"T_0a17a_row1_col1\" class=\"data row1 col1\" >paired untuned comparison</td>\n",
+       "      <td id=\"T_0a17a_row1_col2\" class=\"data row1 col2\" >—</td>\n",
+       "      <td id=\"T_0a17a_row1_col3\" class=\"data row1 col3\" >Telemetry maximum 3249.460 m differs from notebook reported value 3250.000 m; both retained. | Source fin trailing edge overhangs the nozzle-inferred body base by 1.216 mm. Native DATCOM accepts this; the integration now validates root-chord centre attachment instead of rejecting all overhang. Geometry is unchanged.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a17a_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+       "      <td id=\"T_0a17a_row2_col0\" class=\"data row2 col0\" >bella_lui</td>\n",
+       "      <td id=\"T_0a17a_row2_col1\" class=\"data row2 col1\" >paired untuned comparison</td>\n",
+       "      <td id=\"T_0a17a_row2_col2\" class=\"data row2 col2\" >—</td>\n",
+       "      <td id=\"T_0a17a_row2_col3\" class=\"data row2 col3\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a17a_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+       "      <td id=\"T_0a17a_row3_col0\" class=\"data row3 col0\" >camoes</td>\n",
+       "      <td id=\"T_0a17a_row3_col1\" class=\"data row3 col1\" >ordinary only</td>\n",
+       "      <td id=\"T_0a17a_row3_col2\" class=\"data row3 col2\" >Source active air brakes override complete rocket drag. Combining their whole-vehicle CD with DATCOM would double-count base drag; no controlled DATCOM result claimed.</td>\n",
+       "      <td id=\"T_0a17a_row3_col3\" class=\"data row3 col3\" >Telemetry maximum 3015.996 m differs from notebook reported value 3015.000 m; both retained.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a17a_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+       "      <td id=\"T_0a17a_row4_col0\" class=\"data row4 col0\" >cavour</td>\n",
+       "      <td id=\"T_0a17a_row4_col1\" class=\"data row4 col1\" >paired untuned comparison</td>\n",
+       "      <td id=\"T_0a17a_row4_col2\" class=\"data row4 col2\" >—</td>\n",
+       "      <td id=\"T_0a17a_row4_col3\" class=\"data row4 col3\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a17a_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+       "      <td id=\"T_0a17a_row5_col0\" class=\"data row5 col0\" >defiance</td>\n",
+       "      <td id=\"T_0a17a_row5_col1\" class=\"data row5 col1\" >ordinary only</td>\n",
+       "      <td id=\"T_0a17a_row5_col2\" class=\"data row5 col2\" >ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip</td>\n",
+       "      <td id=\"T_0a17a_row5_col3\" class=\"data row5 col3\" >Source fin root centre is z=-0.025 m, aft of the tail base z=0.0403 m; native root attachment is outside the declared body.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a17a_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+       "      <td id=\"T_0a17a_row6_col0\" class=\"data row6 col0\" >erebus</td>\n",
+       "      <td id=\"T_0a17a_row6_col1\" class=\"data row6 col1\" >paired untuned comparison</td>\n",
+       "      <td id=\"T_0a17a_row6_col2\" class=\"data row6 col2\" >—</td>\n",
+       "      <td id=\"T_0a17a_row6_col3\" class=\"data row6 col3\" >Telemetry maximum 3001.751 m differs from notebook reported value 3020.000 m; both retained.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a17a_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+       "      <td id=\"T_0a17a_row7_col0\" class=\"data row7 col0\" >genesis</td>\n",
+       "      <td id=\"T_0a17a_row7_col1\" class=\"data row7 col1\" >paired untuned comparison</td>\n",
+       "      <td id=\"T_0a17a_row7_col2\" class=\"data row7 col2\" >—</td>\n",
+       "      <td id=\"T_0a17a_row7_col3\" class=\"data row7 col3\" >Telemetry maximum 2916.692 m differs from notebook reported value 2916.000 m; both retained.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a17a_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+       "      <td id=\"T_0a17a_row8_col0\" class=\"data row8 col0\" >halcyon</td>\n",
+       "      <td id=\"T_0a17a_row8_col1\" class=\"data row8 col1\" >paired untuned comparison</td>\n",
+       "      <td id=\"T_0a17a_row8_col2\" class=\"data row8 col2\" >—</td>\n",
+       "      <td id=\"T_0a17a_row8_col3\" class=\"data row8 col3\" >Telemetry QA flags 5 samples with altitude jumps above 2000 m/s; raw maximum 3470.825 m is retained separately. | Telemetry maximum 3470.825 m differs from notebook reported value 3450.000 m; both retained.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a17a_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+       "      <td id=\"T_0a17a_row9_col0\" class=\"data row9 col0\" >hedy</td>\n",
+       "      <td id=\"T_0a17a_row9_col1\" class=\"data row9 col1\" >archived comparison only</td>\n",
+       "      <td id=\"T_0a17a_row9_col2\" class=\"data row9 col2\" >Source data/rockets/tust is absent; CoolProp is not installed, and the negative-span fin root cutout requires geometry support.</td>\n",
+       "      <td id=\"T_0a17a_row9_col3\" class=\"data row9 col3\" >Referenced telemetry file is absent: data/rockets/tust/cats_tust/filteredDataInfo.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/gnssInfo.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/flightInfo.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/imu.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/baro.csv (cell 24). | Hedy end comparison uses simulated ASL 5406.918 m against measured AGL 5231.53 m; saved simulated AGL is 5246.918 m, a 160 m datum discrepancy.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a17a_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
+       "      <td id=\"T_0a17a_row10_col0\" class=\"data row10 col0\" >juno3</td>\n",
+       "      <td id=\"T_0a17a_row10_col1\" class=\"data row10 col1\" >paired untuned comparison</td>\n",
+       "      <td id=\"T_0a17a_row10_col2\" class=\"data row10 col2\" >—</td>\n",
+       "      <td id=\"T_0a17a_row10_col3\" class=\"data row10 col3\" >Telemetry QA flags 2 samples with altitude jumps above 2000 m/s; raw maximum 10700.590 m is retained separately. | Telemetry maximum 10700.590 m differs from notebook reported value 3213.000 m; both retained. | Source fin airfoil is an empirical lift curve (cell 10), not a supplied section contour. The generic 2% HEX DATCOM assumption does not reproduce that curve; source-drag substitution therefore still has a lateral-model discrepancy.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a17a_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
+       "      <td id=\"T_0a17a_row11_col0\" class=\"data row11 col0\" >lince</td>\n",
+       "      <td id=\"T_0a17a_row11_col1\" class=\"data row11 col1\" >paired untuned comparison</td>\n",
+       "      <td id=\"T_0a17a_row11_col2\" class=\"data row11 col2\" >—</td>\n",
+       "      <td id=\"T_0a17a_row11_col3\" class=\"data row11 col3\" >Telemetry QA flags 69 samples with altitude jumps above 2000 m/s; raw maximum 3668.521 m is retained separately. | Telemetry maximum 3668.521 m differs from notebook reported value 3587.000 m; both retained. | Lince has three saved trajectories. Compare first/pre-deployment ascent only; retain all saved apogees separately.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a17a_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
+       "      <td id=\"T_0a17a_row12_col0\" class=\"data row12 col0\" >ndrt_2020</td>\n",
+       "      <td id=\"T_0a17a_row12_col1\" class=\"data row12 col1\" >ordinary only</td>\n",
+       "      <td id=\"T_0a17a_row12_col2\" class=\"data row12 col2\" >ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip</td>\n",
+       "      <td id=\"T_0a17a_row12_col3\" class=\"data row12 col3\" >NDRT source uses approximate ft conversion /3.281; comparison uses exact 0.3048 m/ft. | Telemetry maximum 1320.357 m differs from notebook reported value 1316.736 m; both retained. | Source transition is internal at z=1.2..1.327 m with fins near z=3.05 m. The current DATCOM builder represents only an aft tail, not the required downstream smaller cylinder.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a17a_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
+       "      <td id=\"T_0a17a_row13_col0\" class=\"data row13 col0\" >prometheus_2022</td>\n",
+       "      <td id=\"T_0a17a_row13_col1\" class=\"data row13 col1\" >paired untuned comparison</td>\n",
+       "      <td id=\"T_0a17a_row13_col2\" class=\"data row13 col2\" >—</td>\n",
+       "      <td id=\"T_0a17a_row13_col3\" class=\"data row13 col3\" >Telemetry QA flags 2 samples with altitude jumps above 2000 m/s; raw maximum 3903.770 m is retained separately. | Telemetry maximum 3903.770 m differs from notebook reported value 3898.370 m; both retained. | Prometheus prose says June 2022; source environment and local reanalysis are June 2023. Both reruns preserve source conditions.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a17a_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
+       "      <td id=\"T_0a17a_row14_col0\" class=\"data row14 col0\" >valetudo</td>\n",
+       "      <td id=\"T_0a17a_row14_col1\" class=\"data row14 col1\" >paired untuned comparison</td>\n",
+       "      <td id=\"T_0a17a_row14_col2\" class=\"data row14 col2\" >—</td>\n",
+       "      <td id=\"T_0a17a_row14_col3\" class=\"data row14 col3\" >Valetudo flight recordings were lost; only recovered scalar apogee/drift values remain.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0a17a_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
+       "      <td id=\"T_0a17a_row15_col0\" class=\"data row15 col0\" >valkyrie</td>\n",
+       "      <td id=\"T_0a17a_row15_col1\" class=\"data row15 col1\" >unavailable source model</td>\n",
+       "      <td id=\"T_0a17a_row15_col2\" class=\"data row15 col2\" >SourceUnavailable: Source requests live weather with no archived profile; not replaced by invented weather.</td>\n",
+       "      <td id=\"T_0a17a_row15_col3\" class=\"data row15 col3\" >Telemetry maximum 2059.024 m differs from notebook reported value 2098.020 m; both retained. | Valkyrie prose says 2025 but source environment says 2026 and fetches live Windy weather; no archived profile is supplied.</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x15c775e50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "</style>\n",
+       "<table id=\"T_1460b\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_1460b_level0_col0\" class=\"col_heading level0 col0\" >rocket</th>\n",
+       "      <th id=\"T_1460b_level0_col1\" class=\"col_heading level0 col1\" >inferred_cd</th>\n",
+       "      <th id=\"T_1460b_level0_col2\" class=\"col_heading level0 col2\" >fit_apogee_residual_m</th>\n",
+       "      <th id=\"T_1460b_level0_col3\" class=\"col_heading level0 col3\" >condition</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1460b_level0_row0\" class=\"row_heading level0 row0\" >4</th>\n",
+       "      <td id=\"T_1460b_row0_col0\" class=\"data row0 col0\" >andromeda</td>\n",
+       "      <td id=\"T_1460b_row0_col1\" class=\"data row0 col1\" >0.591</td>\n",
+       "      <td id=\"T_1460b_row0_col2\" class=\"data row0 col2\" >0.024</td>\n",
+       "      <td id=\"T_1460b_row0_col3\" class=\"data row0 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1460b_level0_row1\" class=\"row_heading level0 row1\" >9</th>\n",
+       "      <td id=\"T_1460b_row1_col0\" class=\"data row1 col0\" >astra</td>\n",
+       "      <td id=\"T_1460b_row1_col1\" class=\"data row1 col1\" >0.599</td>\n",
+       "      <td id=\"T_1460b_row1_col2\" class=\"data row1 col2\" >0.003</td>\n",
+       "      <td id=\"T_1460b_row1_col3\" class=\"data row1 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1460b_level0_row2\" class=\"row_heading level0 row2\" >14</th>\n",
+       "      <td id=\"T_1460b_row2_col0\" class=\"data row2 col0\" >bella_lui</td>\n",
+       "      <td id=\"T_1460b_row2_col1\" class=\"data row2 col1\" >0.446</td>\n",
+       "      <td id=\"T_1460b_row2_col2\" class=\"data row2 col2\" >0.005</td>\n",
+       "      <td id=\"T_1460b_row2_col3\" class=\"data row2 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1460b_level0_row3\" class=\"row_heading level0 row3\" >19</th>\n",
+       "      <td id=\"T_1460b_row3_col0\" class=\"data row3 col0\" >camoes</td>\n",
+       "      <td id=\"T_1460b_row3_col1\" class=\"data row3 col1\" >—</td>\n",
+       "      <td id=\"T_1460b_row3_col2\" class=\"data row3 col2\" >—</td>\n",
+       "      <td id=\"T_1460b_row3_col3\" class=\"data row3 col3\" >not fitted: active air-brake/controller model</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1460b_level0_row4\" class=\"row_heading level0 row4\" >24</th>\n",
+       "      <td id=\"T_1460b_row4_col0\" class=\"data row4 col0\" >cavour</td>\n",
+       "      <td id=\"T_1460b_row4_col1\" class=\"data row4 col1\" >0.609</td>\n",
+       "      <td id=\"T_1460b_row4_col2\" class=\"data row4 col2\" >0.001</td>\n",
+       "      <td id=\"T_1460b_row4_col3\" class=\"data row4 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1460b_level0_row5\" class=\"row_heading level0 row5\" >29</th>\n",
+       "      <td id=\"T_1460b_row5_col0\" class=\"data row5 col0\" >defiance</td>\n",
+       "      <td id=\"T_1460b_row5_col1\" class=\"data row5 col1\" >0.657</td>\n",
+       "      <td id=\"T_1460b_row5_col2\" class=\"data row5 col2\" >-0.063</td>\n",
+       "      <td id=\"T_1460b_row5_col3\" class=\"data row5 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1460b_level0_row6\" class=\"row_heading level0 row6\" >34</th>\n",
+       "      <td id=\"T_1460b_row6_col0\" class=\"data row6 col0\" >erebus</td>\n",
+       "      <td id=\"T_1460b_row6_col1\" class=\"data row6 col1\" >0.443</td>\n",
+       "      <td id=\"T_1460b_row6_col2\" class=\"data row6 col2\" >-0.011</td>\n",
+       "      <td id=\"T_1460b_row6_col3\" class=\"data row6 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1460b_level0_row7\" class=\"row_heading level0 row7\" >39</th>\n",
+       "      <td id=\"T_1460b_row7_col0\" class=\"data row7 col0\" >genesis</td>\n",
+       "      <td id=\"T_1460b_row7_col1\" class=\"data row7 col1\" >0.609</td>\n",
+       "      <td id=\"T_1460b_row7_col2\" class=\"data row7 col2\" >-0.003</td>\n",
+       "      <td id=\"T_1460b_row7_col3\" class=\"data row7 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1460b_level0_row8\" class=\"row_heading level0 row8\" >44</th>\n",
+       "      <td id=\"T_1460b_row8_col0\" class=\"data row8 col0\" >halcyon</td>\n",
+       "      <td id=\"T_1460b_row8_col1\" class=\"data row8 col1\" >0.345</td>\n",
+       "      <td id=\"T_1460b_row8_col2\" class=\"data row8 col2\" >0.002</td>\n",
+       "      <td id=\"T_1460b_row8_col3\" class=\"data row8 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1460b_level0_row9\" class=\"row_heading level0 row9\" >53</th>\n",
+       "      <td id=\"T_1460b_row9_col0\" class=\"data row9 col0\" >juno3</td>\n",
+       "      <td id=\"T_1460b_row9_col1\" class=\"data row9 col1\" >0.264</td>\n",
+       "      <td id=\"T_1460b_row9_col2\" class=\"data row9 col2\" >-0.000</td>\n",
+       "      <td id=\"T_1460b_row9_col3\" class=\"data row9 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1460b_level0_row10\" class=\"row_heading level0 row10\" >58</th>\n",
+       "      <td id=\"T_1460b_row10_col0\" class=\"data row10 col0\" >lince</td>\n",
+       "      <td id=\"T_1460b_row10_col1\" class=\"data row10 col1\" >0.578</td>\n",
+       "      <td id=\"T_1460b_row10_col2\" class=\"data row10 col2\" >-0.016</td>\n",
+       "      <td id=\"T_1460b_row10_col3\" class=\"data row10 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1460b_level0_row11\" class=\"row_heading level0 row11\" >63</th>\n",
+       "      <td id=\"T_1460b_row11_col0\" class=\"data row11 col0\" >ndrt_2020</td>\n",
+       "      <td id=\"T_1460b_row11_col1\" class=\"data row11 col1\" >0.417</td>\n",
+       "      <td id=\"T_1460b_row11_col2\" class=\"data row11 col2\" >-0.002</td>\n",
+       "      <td id=\"T_1460b_row11_col3\" class=\"data row11 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1460b_level0_row12\" class=\"row_heading level0 row12\" >68</th>\n",
+       "      <td id=\"T_1460b_row12_col0\" class=\"data row12 col0\" >prometheus_2022</td>\n",
+       "      <td id=\"T_1460b_row12_col1\" class=\"data row12 col1\" >0.403</td>\n",
+       "      <td id=\"T_1460b_row12_col2\" class=\"data row12 col2\" >-0.068</td>\n",
+       "      <td id=\"T_1460b_row12_col3\" class=\"data row12 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1460b_level0_row13\" class=\"row_heading level0 row13\" >73</th>\n",
+       "      <td id=\"T_1460b_row13_col0\" class=\"data row13 col0\" >valetudo</td>\n",
+       "      <td id=\"T_1460b_row13_col1\" class=\"data row13 col1\" >0.810</td>\n",
+       "      <td id=\"T_1460b_row13_col2\" class=\"data row13 col2\" >-0.015</td>\n",
+       "      <td id=\"T_1460b_row13_col3\" class=\"data row13 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x15c775e50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show(status[[\"rocket\", \"status\", \"reason\", \"notes\"]])\n",
+    "show(metrics[metrics.metric==\"Apogee-fitted constant CD\"][[\"rocket\",\"inferred_cd\",\"fit_apogee_residual_m\",\"condition\"]])\n",
+    "assert metrics[metrics.metric==\"Coast CD at Mach 0.6\"].measured.isna().all()\n",
+    "fits = metrics[metrics.metric==\"Apogee-fitted constant CD\"].dropna(subset=[\"inferred_cd\"])\n",
+    "assert (fits.fit_apogee_residual_m.abs() <= .5).all()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/index.rst b/docs/examples/index.rst
index bd7506c30..a89524967 100644
--- a/docs/examples/index.rst
+++ b/docs/examples/index.rst
@@ -113,5 +113,6 @@ In the next sections you will find the simulations of the rockets listed above.
    defiance_flight_sim.ipynb
    hedy_flight_sim.ipynb
    valkyrie_flight_sim.ipynb
-
+   datcom/rocket_comparison.ipynb
+   datcom/asteria_comparison.ipynb
 
diff --git a/docs/reference/classes/Rocket.rst b/docs/reference/classes/Rocket.rst
index 636ca9cad..09bbec846 100644
--- a/docs/reference/classes/Rocket.rst
+++ b/docs/reference/classes/Rocket.rst
@@ -2,4 +2,5 @@ Rocket Class
 ------------
 
 .. autoclass:: rocketpy.Rocket
-   :members:
\ No newline at end of file
+   :members:
+   :inherited-members:
diff --git a/docs/reference/classes/aero_surfaces/DatcomFins.rst b/docs/reference/classes/aero_surfaces/DatcomFins.rst
new file mode 100644
index 000000000..7adf4e0c8
--- /dev/null
+++ b/docs/reference/classes/aero_surfaces/DatcomFins.rst
@@ -0,0 +1,5 @@
+DATCOM Fins Class
+-----------------
+
+.. autoclass:: rocketpy.DatcomFins
+   :members:
diff --git a/docs/reference/classes/aero_surfaces/DatcomFreeFormFins.rst b/docs/reference/classes/aero_surfaces/DatcomFreeFormFins.rst
new file mode 100644
index 000000000..c4f87ecd9
--- /dev/null
+++ b/docs/reference/classes/aero_surfaces/DatcomFreeFormFins.rst
@@ -0,0 +1,5 @@
+DATCOM Free Form Fins Class
+---------------------------
+
+.. autoclass:: rocketpy.DatcomFreeFormFins
+   :members:
diff --git a/docs/reference/classes/aero_surfaces/index.rst b/docs/reference/classes/aero_surfaces/index.rst
index 1662c965e..cb24af943 100644
--- a/docs/reference/classes/aero_surfaces/index.rst
+++ b/docs/reference/classes/aero_surfaces/index.rst
@@ -10,6 +10,8 @@ AeroSurface Classes
    Tail
    Fins
    TrapezoidalFins
+   DatcomFins
+   DatcomFreeFormFins
    EllipticalFins
    FreeFormFins
    TubeFins
diff --git a/docs/user/rocket/datcom.rst b/docs/user/rocket/datcom.rst
new file mode 100644
index 000000000..8ecc903bf
--- /dev/null
+++ b/docs/user/rocket/datcom.rst
@@ -0,0 +1,530 @@
+Missile DATCOM Aerodynamics
+===========================
+
+When construction or finish is unspecified, assume **carbon-fibre construction
+with a vinyl finish**. Explicit material details, measured surface properties
+and supplied geometry take precedence. This is a construction assumption,
+not a measured roughness or fin-section specification: it does not determine
+one universal roughness height, thickness, bevel or leading-edge radius.
+Document any additional assumptions and use the calibration methods below
+to set or fit native aerodynamic inputs.
+
+Build the rocket, motor and tanks normally, using :class:`rocketpy.Rocket`,
+``add_motor``, ``add_nose`` and ``add_tail``. Select whole-vehicle DATCOM
+aerodynamics by adding one of these fin sets:
+
+* ``add_datcom_trapezoidal_fins``: trapezoidal planform with a symmetric HEX
+  section, a flat middle 90% of the chord and 5% edge bevels.
+* ``add_datcom_arc_fins``: trapezoidal planform with a symmetric circular-arc
+  airfoil section.
+* ``add_datcom_hex_fins``: trapezoidal planform with a configurable symmetric
+  hexagonal or diamond airfoil section.
+
+ARC and HEX describe the airfoil cross section, not the planform. Root chord,
+tip chord, exposed span, sweep and placement follow ``add_trapezoidal_fins``.
+For example, after defining ``motor`` and ``environment``::
+
+    from rocketpy import Flight, Rocket
+
+    rocket = Rocket(
+        radius=0.0635,
+        mass=14.426,                    # excludes the entire motor, kg
+        inertia=(6.321, 6.321, 0.034),   # excludes the motor, kg m²
+        center_of_mass_without_motor=0,
+        power_on_drag=0,                # replaced by DATCOM
+        power_off_drag=0,
+        coordinate_system_orientation="tail_to_nose",
+    )
+    rocket.add_motor(motor, position=-1.373)
+    rocket.add_nose(length=0.55, kind="von karman", position=1.2)
+    fins = rocket.add_datcom_hex_fins(
+        n=4,
+        root_chord=0.25,
+        tip_chord=0.10,
+        span=0.10,
+        position=-0.9,
+        sweep_length=0.10,
+        path="/absolute/path/to/native/datcom",
+        thickness_to_chord=(0.02, 0.03),
+        max_thickness_position=0.25,
+        flat_length=0.5,
+        leading_edge_radius=0,
+        datcom_options={
+            "mach": [0.1, 0.3, 0.5, 0.7, 0.9, 1.1, 1.5, 2.0],
+            "alpha": [-20, -10, -5, -2, -1, -0.5, -0.25, -0.1, -0.05, 0,
+                      0.05, 0.1, 0.25, 0.5, 1, 2, 5, 10, 20],
+            "altitude": 0,
+            "timeout": 60,
+        },
+    )
+    rocket.add_tail(
+        top_radius=rocket.radius, bottom_radius=0.045,
+        length=0.10, position=-1.2, kind="ogive",
+    )
+    flight = Flight(rocket, environment, rail_length=5)
+
+The nose, fins and tail retain their original geometry for drawing and export.
+Flight uses one DATCOM model for all aerodynamic forces and moments, replacing
+their component models and the rocket drag inputs. Geometry is read from the
+assembled objects, including surfaces and motor added after the DATCOM fins.
+Adding a :class:`rocketpy.DatcomFins` instance with ``add_surfaces`` also selects
+DATCOM aerodynamics.
+
+Body Profile Selection
+----------------------
+
+After adding a DATCOM fin set, select the actual body geometry::
+
+    rocket.add_nose(length=0.81, position=0, kind="von karman")
+    rocket.add_tail(top_radius=0.0895, bottom_radius=0.0835,
+                    length=0.125, position=5.137, kind="ogive")
+
+``NoseCone`` and ``add_nose`` use their existing ``kind`` argument.
+Supported DATCOM nose profiles are conical, tangent-ogive, von Karman,
+L-V Haack and power series. Other existing RocketPy nose kinds retain
+their ordinary component behavior.
+
+``Tail`` and ``add_tail`` accept ``kind="conical"`` (the backward-compatible
+default) or ``kind="ogive"``. The ogive is a circular arc tangent to the upstream
+cylinder, joining the given radii over the given axial length in meters.
+Ogive geometry, drawing and DATCOM ``TAFT`` follow that profile, including
+fin attachment to its local radius. Ogive tails have no component aerodynamic
+model; the whole-vehicle DATCOM tables supply their loads and CP. Add a DATCOM
+fin set before calling ``add_tail(kind="ogive")``; ordinary rockets raise
+``ValueError``. The kind is selected at construction and is read-only.
+Conical tails use the original :class:`rocketpy.Tail` without any changes to
+its equations or serialization. Profile selection survives serialization.
+
+The default DATCOM alpha grid has 19 points over ±20 degrees, with 13 points
+within ±2 degrees. Explicit ``datcom_options["alpha"]`` overrides it. Grid
+convergence and the actual flight-incidence envelope still need checking.
+
+Executable and Tables
+---------------------
+
+Supply an executable for the current operating system and processor, with
+execute permission where required. RocketPy does not distribute DATCOM or
+automatically run a Windows executable on another platform.
+The integration targets Asteria's revision 3/99 input/output convention.
+Its body incidence angles are the projected angles
+``alpha=atan(v_down/v_forward)`` and ``beta=atan(v_right/v_forward)``.
+The native regression suite checks this using an axisymmetric-body force
+ratio. Verify angle definitions before substituting another DATCOM revision.
+
+Flight automatically prepares powered and coast databases in two executable
+invocations. Each runs in a separate temporary
+directory containing a copy of the executable and a generated ``for005.dat``.
+RocketPy parses ``for006.dat`` and removes the directory on success or failure.
+Every deck includes ``DAMP``; missing or invalid damping tables raise an error.
+No ``PROTUB`` or plume-interaction calculations are included.
+Fixed-sideslip cases share each invocation when a beta grid is requested;
+they do not require additional executable copies or persistent output files.
+
+By default the powered case omits ``DEXIT`` to exclude all base drag, preserving
+an explicit zero-powered-base-drag assumption. For a motor whose nozzle is
+smaller than the rocket base, this also removes the exposed annular base drag.
+To model that known annulus instead, configure::
+
+    datcom_options={"powered_base_drag": "nozzle"}
+
+This writes ``DEXIT=2*motor.nozzle_radius`` for the powered case. The nozzle
+must have positive radius and fit inside the aft body. ``"full"`` writes
+``DEXIT=0.`` for a full-base diagnostic; ``"none"`` is the default.
+The coast case always uses ``DEXIT=0.`` for full base drag. ``BASE`` stays false:
+that flag controls jet/plume interaction, not the annular base-area calculation.
+Nozzle mode therefore requires no plume-flow inputs. Powered/coast selection
+switches at ignition and burnout independently of propellant fraction.
+The flown-rocket comparison example uses nozzle mode; AST-HER retains its
+explicit zero-powered-base-drag assumption.
+
+Call ``rocket.prepare_datcom()`` explicitly to inspect drag or stability before
+creating a Flight. It returns the complete aerodynamic model. Identical decks
+and executable metadata reuse the in-memory result. A geometry, reference-CG,
+flight-condition or executable change rebuilds both cases. Re-add a modified
+motor to the rocket after changing its tanks, following the normal mass-property
+workflow.
+
+Section Inputs and Flight Conditions
+------------------------------------
+
+``thickness_to_chord`` is the total symmetric section thickness divided by
+chord. ``max_thickness_position`` locates the beginning of the maximum-thickness
+region of HEX sections; ``flat_length`` gives its length. Both are fractions
+of chord. Set ``flat_length=0`` for a diamond section. ARC uses thickness and
+leading-edge radius. Section inputs accept a scalar or one value per span
+station; ``leading_edge_radius`` is in meters. A zero tip chord is supported.
+
+Use ``upper_thickness_to_chord`` and ``lower_thickness_to_chord`` for separate
+upper/lower dimensions. ``lower_max_thickness_position`` and
+``lower_flat_length`` default to their upper-surface equivalents. Thickness
+may be zero on one surface, provided the total thickness is positive.
+
+The trapezoidal helper also accepts ``section_kind="NACA", naca="0012"``.
+Four-digit sections can omit the series prefix. Other supported designations
+include ``"5-23012"``, ``"6-64-005"`` and the manual's modified-series formats.
+The fin-set number is inserted into the NACA control card after sorting.
+For custom sections, use ``section_kind="USER"`` with
+``airfoil_coordinates={"x": [...], "upper": [...], "lower": [...]}``, or
+``x``, ``mean`` and ``thickness`` arrays. Supply 3 to 50 increasing chord
+stations from zero to one. All coordinate values are fractions of chord.
+
+This DATCOM version includes camber effects on normal force and pitch moment
+only for NACA and USER sections at Mach 0.8 and below. HEX/ARC camber affects
+drag calculations only.
+
+Multi-station Planforms and Panel Placement
+-------------------------------------------
+
+``add_datcom_fins`` creates a :class:`rocketpy.DatcomFreeFormFins` object from
+2 to 10 exposed ``span_stations``, ``chords`` and ``leading_edges``. Span
+stations begin at zero on the body; leading edges begin at zero relative to
+``position``. Chords and distances are in meters. For example::
+
+    fins = rocket.add_datcom_fins(
+        n=4, position=-0.9, path="/path/to/datcom",
+        span_stations=[0, 0.05, 0.10],
+        chords=[0.25, 0.20, 0],
+        leading_edges=[0, 0.04, 0.10],
+        section_kind="HEX", thickness_to_chord=[0.02, 0.025, 0.03],
+    )
+
+All stations share one airfoil section type, with proportions allowed to vary
+by station. The example has a triangular tip. NACA and USER designate a common
+normalized section for the entire fin set.
+
+``panel_angles`` specifies each panel's azimuth using DATCOM PHIF: degrees
+clockwise from the top vertical when viewed forward from behind the rocket.
+``dihedral`` specifies each panel's constant GAM in degrees; this tilts the
+span direction away from its radial ray. ``incidence`` specifies fixed panel
+angles in DATCOM's convention, where positive incidence produces a negative
+roll increment. Alternatively use RocketPy's ``cant_angle`` convention, with
+the opposite roll-forcing sign. Do not specify both. Incidence and dihedral
+accept a scalar or one value per panel. Time-varying actuators and trailing-edge
+flaps are not part of this interface.
+
+``auto_body_radius=True`` uses DATCOM's ``SSPAN(1)=0`` convention to mount the
+root at the interpolated body radius at root mid-chord. This supports roots
+on the selected boattail profile as well as the cylindrical body. After preparation,
+rocket drawings use that attachment radius and the actual panel placement.
+Fixed-incidence drawings use the default half-root-chord hinge and zero skew.
+
+``datcom_options`` accepts:
+
+* ``mach``: 2 to 20 positive, increasing Mach values. Without a reference
+  flight, the default is a provisional 20-point grid; see adaptive coverage below.
+* ``alpha``: 2 to 20 increasing angles in degrees, including negative angles,
+  exactly one zero and positive angles.
+* ``beta``: ``[0]`` or an increasing signed sideslip grid containing zero, in
+  degrees. The default is ``[0]`` for the symmetric configuration and
+  ``[-5, 0, 5]`` for nonstandard panel placements, dihedral, incidence, or
+  fin sets with fewer than three panels.
+* ``altitude``: meters above sea level, either a scalar or one value per Mach.
+  These specify a Mach/altitude schedule, not an independent altitude grid.
+  Separate schedules can be provided as
+  ``{"power_on": [...], "power_off": [...]}``.
+* ``timeout``: maximum execution time in seconds for each case.
+* ``reynolds``: optional positive Reynolds number per meter, either a scalar,
+  one value per Mach, or separate ``power_on``/``power_off`` schedules. It
+  selects native ``MACH``/``REN`` conditions instead of ``MACH``/``ALT``.
+  The API uses inverse meters, not Reynolds number based on diameter or chord.
+* ``body_length``: optional cylindrical section length in meters, excluding the
+  nose and boattail. With a tail, its position determines this length and an
+  explicit value must agree. Without a tail, the nozzle position supplies the
+  inferred aft end unless ``body_length`` is specified.
+
+Atmosphere from a preliminary flight
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+With ``altitude`` alone, DATCOM derives atmospheric properties using its 1962
+Standard Atmosphere; altitude defaults to sea level. It does not automatically
+read a RocketPy Environment. To use the actual launch atmosphere, first run an
+ordinary RocketPy model with the intended environment, then configure the
+DATCOM rocket from that flight::
+
+    atmosphere = datcom_rocket.set_datcom_atmosphere(ordinary_flight)
+    datcom_flight = Flight(rocket=datcom_rocket, environment=env, **launch_options)
+
+    # Optional explicit refinement using the resulting DATCOM trajectory:
+    atmosphere = datcom_rocket.set_datcom_atmosphere(datcom_flight)
+    refined_flight = Flight(rocket=datcom_rocket, environment=env, **launch_options)
+
+Use separate rocket instances when retaining ordinary and DATCOM results.
+Both must have the same motor burn interval. The method does not create flights
+or run the executable; the next Flight regenerates native tables.
+
+Adaptive coverage uses all **20 Mach slots**, always including **0.1 and 1.0**.
+Fixed points from 0.8 to 1.2 concentrate resolution near Mach 1; the remaining
+points adapt to the reached regime. The upper bound includes 15% headroom plus
+0.02 Mach and is at least 1.2. Mach zero uses the positive 0.01 endpoint. Values
+are spaced by at least 0.01 to survive the native two-decimal output format.
+One shared grid covers the union of powered and coast flight conditions, with
+separate Reynolds schedules for the two native cases.
+
+Alpha adapts too: 19 signed points stay concentrated near zero, retaining at
+least ±20 degrees and extending beyond observed incidence by 15% plus 2 degrees.
+Only samples with dynamic pressure at least ``minimum_dynamic_pressure=100`` Pa
+set the extent; no loaded samples retain ±20 degrees. This excludes ambiguous
+pad/near-apogee flow directions from grid selection. Set a lower threshold if
+those low-load conditions matter. The returned ``coverage`` report records the
+scope, excluded sample count and separate phase Mach/loaded-alpha maxima.
+
+``samples=1000`` supplies uniform samples, augmented by actual solver states
+and burn boundaries. The default covers the rail, powered ascent and coast to
+apogee; descent and parachute flight are excluded. Adaptive envelopes
+approaching 90 degrees are rejected: the current forward-flight load mapping
+cannot faithfully represent tail-first flow. Extending a grid does not validate DATCOM's high-incidence methodology.
+
+Explicit ``mach=[...]`` and ``alpha=[...]`` arguments fix custom grids instead,
+for controlled studies or tunnel conditions; custom Mach grids may omit the
+adaptive anchors. Every call without those arguments recalculates both grids
+from the new reference flight. The example reproduction pipeline applies this
+again after its initial DATCOM simulation. No measured apogee selects grid points.
+
+At each sampled altitude in meters ASL, the reference Environment supplies
+actual density, speed of sound and dynamic viscosity. Reynolds number per meter
+is ``rho * Mach * speed_of_sound / dynamic_viscosity``. Native ``REN`` replaces
+``ALT``, bypassing DATCOM's standard-atmosphere Reynolds calculation. Pressure
+(Pa), temperature (K), altitude and Reynolds schedules are returned for provenance;
+pressure and temperature are not separately sent to DATCOM. Flight forces still
+use the simulated flight's actual density and relative airspeed.
+
+Separate powered and unpowered ascent schedules assign one atmospheric state
+per Mach. Duplicate sampled Mach values retain the earliest sample. Outside
+the reached Mach range, atmospheric state is clamped to its endpoint, but speed
+is recomputed at the requested Mach. This avoids clamping Reynolds number to a
+different speed. Multiple crossings, descent and separated stages require a
+richer database than this approximation. No convergence is assumed: repeat
+explicitly with the DATCOM trajectory if needed, comparing apogee and schedules.
+Existing ordinary RocketPy equations are unchanged.
+
+All DATCOM fin sets must use the same executable and options. At most four fin
+sets with 1 to 8 panels each are supported. Sets are automatically numbered
+from nose to tail. Overlapping axial planform envelopes are rejected;
+the root chord center must lie on the body's axial extent. A trailing edge
+may extend aft of the base, as verified with the native 3/99 executable.
+Separation below four exposed semispans triggers a warning about DATCOM's
+upstream vortex-roll-up approximation. Existing ordinary
+trapezoidal fin sets are included with a default symmetric HEX section of 2%
+thickness and 25% edge bevels. Other aerodynamic surface types are rejected
+rather than omitted. Exactly one unblunted conical, tangent-ogive, von Karman,
+L-V Haack or power-series nose is required. An optional conical or tangent-ogive boattail must
+connect to the cylindrical body. Off-axis geometry is unsupported.
+
+Explicit Calibration
+--------------------
+
+After adding DATCOM fins, attach calibration to the whole rocket::
+
+    rocket.add_datcom_calibration(
+        roughness_height=30.48e-6,  # equivalent sand roughness, meters
+        leading_edge_radius=0.0005,  # optional radius, meters
+    )
+    rocket.prepare_datcom()
+
+``roughness_height`` is an equivalent sand roughness height, not the measured
+arithmetic mean roughness (Ra). Carbon-fibre construction with a vinyl wrap
+does not determine this value: seams, finish, exposed edges and other losses
+can differ between vehicles. A fit to apogee is an effective calibration and
+does not identify the real surface finish. No material-specific value is
+silently selected.
+
+The carbon-fibre/vinyl assumption applies only where more specific information
+is missing. It does not supersede supplied masses, inertias, motor data,
+geometry, measured coefficients or calibration values. Fitted values represent
+effective aerodynamic losses and must not be described as measured material
+properties. In the four-rocket apogee example, physically bounded leading-edge
+radius sweeps did not reach the targets; separate roughness fits did. A shared
+GENESIS roughness did not fit all four rockets, and Andromeda's much larger
+fitted roughness indicates that the fit can absorb other missing losses.
+
+The conversion uses dimensionless DATCOM ``RHR``, with equivalent sand
+roughness equal to three times RHR in microinches. For example,
+30.48 micrometers produces ``RHR=400``. This avoids a verified discrepancy:
+the local 3/99 executable treats ``ROUGH`` as inches even with ``DIM M``,
+whereas the 5/97 Missile DATCOM guide Table 4 specifies centimeters for metric
+``ROUGH``. The RHR/roughness conversion was checked against that executable;
+other native versions should be independently verified.
+
+``leading_edge_radius`` overrides ``LER`` at every station of every supported
+fin set without modifying the stored fin definitions. ``None`` retains the
+individual fin radii. NACA sections define their own edge shape and reject an
+override. Use physically compatible values and choose fit bounds from the
+actual fin section rather than from the material label alone.
+
+Calling ``add_datcom_calibration`` replaces the previous settings; omitted
+roughness defaults to zero and omitted radius to ``None``. Call without
+arguments to reset. ``rocket.datcom_calibration`` returns a copy of the current
+settings. Changes invalidate prepared native tables, which the next
+``prepare_datcom`` or Flight rebuilds. Rocket serialization preserves the
+settings. The method requires DATCOM fins and does not fit values automatically.
+No multiplier or correction is applied to RocketPy's ordinary aerodynamics;
+all calibrated aerodynamic properties still come from native DATCOM.
+
+Wind-Tunnel Coefficient Fitting
+-------------------------------
+
+Mach, alpha and measured CD are sufficient for a roughness fit::
+
+    report = rocket.fit_datcom_calibration("wind_tunnel.csv")
+    rocket.prepare_datcom()
+
+For example, a CSV header can be ``mach,alpha,CD``. A column mapping with
+numeric vectors is also supported. Headers are case-insensitive. Alpha and
+beta are degrees and default to zero when omitted. The default reference area
+is the rocket frontal area; supply ``reference_area`` in square meters when
+the measurements use another normalization. The fit uses static coefficients
+at zero body rates and zero incidence rate.
+
+Optional measured columns are ``CL``, ``CA``, ``CN``, ``CY``, ``CM``, ``CLN``
+and ``CLL``, together with ``beta``, ``power_on`` and
+``reynolds_per_meter``. Blank coefficient cells are allowed if each row has at
+least one finite measurement. Unknown columns are rejected. Body coefficients
+use DATCOM's axes and signs. ``CL`` is pitch-plane lift. By default ``CD``
+opposes the full flow, including sideslip; ``drag_axis="stability"`` selects
+the pitch-plane drag projection. Native reporting conventions can differ at
+nonzero beta, so match the experiment's convention explicitly.
+
+Moment measurements require ``reference_length`` in meters and
+``moment_reference`` in the rocket's axial position coordinates. Native moments
+are transferred to that axial reference before comparison. An optional
+``lateral_reference_length`` specifies the yaw/roll normalization length;
+otherwise it equals ``reference_length``. Off-axis moment references are not
+supported. Positive ``weights={"CD": ..., "CM": ...}`` supplies coefficient-wise
+least-squares weights; for a known uncertainty sigma, use ``1/sigma**2``.
+
+The default fitted parameter is ``roughness_height``, bounded to 0--300
+micrometers. To fit an edge radius, supply explicit physically justified
+bounds in meters::
+
+    report = rocket.fit_datcom_calibration(
+        "wind_tunnel.csv",
+        parameters=("roughness_height", "leading_edge_radius"),
+        bounds={
+            "roughness_height": (0, 100e-6),
+            "leading_edge_radius": (0, 0.001),
+        },
+        reference_area=rocket.area,
+        reference_length=2 * rocket.radius,
+        moment_reference=rocket.center_of_dry_mass_position,
+        max_evaluations=200,
+    )
+
+``model_scale`` is the tunnel-model length divided by the full-rocket length,
+defaulting to 1. Reference areas and lengths are supplied for the model and
+converted to full-scale equivalents. The default area is the scaled model
+frontal area. ``moment_reference`` always remains in full-rocket axial
+coordinates. Reported fitted dimensions are full-scale equivalents, not
+measured model-surface properties.
+
+``reynolds_per_meter`` supplies tunnel conditions, with one positive value per
+Mach and power state. It is converted to full-scale Reynolds number per meter
+by multiplying by ``model_scale``. These conditions affect only the fitting
+probes; Flight keeps the original rocket options. Without this column, the
+probes use the existing DATCOM atmospheric conditions. Nonzero measured beta
+requires a beta table; measurements outside the native Mach/angle envelope
+are rejected. ``power_on=False`` is the default, or use a per-row column
+containing 0 or 1 to match the measured base/nozzle condition.
+
+Successful fits apply their settings through ``add_datcom_calibration``.
+The JSON-compatible report contains initial and final coefficient RMSE,
+weighted RMSE, residual rows, references, fitted inputs, bounds, boundary
+flags and native evaluation count. Failed data validation, execution or
+optimization leaves calibration and the prepared model unchanged. The fit
+does not replace any aerodynamic coefficient; damping remains native DATCOM.
+Static measurements do not identify forced-oscillation damping derivatives,
+and this interface does not ingest measured dynamic derivatives.
+
+A drag-only curve may not uniquely identify two fitted parameters. A bounded
+numerical fit is not guaranteed to be a unique or global solution; use
+additional channels and independent test conditions to assess predictive
+accuracy. Matching the fitted measurements, or a single apogee, is not an
+independent validation. Real measured coefficients and their reference conditions
+are required for wind-tunnel validation.
+
+Accuracy of Apogee Predictions
+------------------------------
+
+Ordinary RocketPy can predict apogee more accurately than RocketPy using
+DATCOM when its supplied drag curve better represents the real vehicle.
+In the four untuned comparisons, ordinary RocketPy was closer to the
+notebook-reported flight altitude for Cavour, GENESIS and Andromeda; DATCOM
+was closer for EREBUS. This does not establish a general ranking. A successful
+fit to those same apogees or coefficients is calibration; predictive accuracy
+must be assessed using independent data. Asteria / AST-HER has not flown and
+its results are simulation predictions, not flight validation.
+
+The :doc:`recorded-flight comparison </examples/datcom/rocket_comparison>`
+compares altitude, upward vertical velocity, CD and stability margin between
+ordinary RocketPy, DATCOM and measured records where available, including
+percentage errors. Apogee-fitted constant CD is explicitly inferred calibration,
+not measured CD. Missing measurements and unsupported source configurations
+remain visible; archived outputs are labelled. No inferred CD replaces the
+native DATCOM predictions shown in that comparison.
+
+CG, Damping and Interpolation
+-----------------------------
+
+RocketPy derives the filled CG from the dry rocket, dry motor, and the motor's
+propellant masses and positions, including liquid tanks. The input
+``center_of_mass_without_motor`` still excludes the entire motor; it is not
+the assembled empty-tank CG. DATCOM uses the filled CG at ignition for the
+powered run and the assembled empty CG for coast. Physical moments are
+transferred to the instantaneous CG throughout the burn. Coefficients are not
+blended by fuel fraction, which would mix base drag with CG migration.
+
+Static coefficients and all eleven DAMP derivatives interpolate linearly in
+Mach, signed angle of attack, and sideslip when a beta grid is available.
+With only ``beta=[0]``, sideslip uses the first-order lateral derivatives.
+The full beta database bypasses that expansion to avoid counting it twice.
+The reported ``CD`` is DATCOM's stability-axis drag, which differs from
+wind-axis drag at nonzero sideslip. Flight applies the body-axis force
+coefficients directly, including during the constrained rail phase.
+The parser retains DATCOM's per-degree units; the load model
+converts body rates in rad/s to the required nondimensional rates. ``CNAD`` and
+``CMAD`` use a simultaneous incidence-rate/acceleration solve within each ODE
+evaluation, including local rotation and wind shear. Adaptive solver call
+history does not determine incidence rate.
+
+Mach and angle are clamped to the generated envelope. The returned model records
+``outside_mach_envelope``, ``outside_alpha_envelope`` and, for full beta grids,
+``outside_beta_envelope`` excursions. Low-speed
+damping is disabled below 1 m/s to avoid dividing by vanishing airspeed.
+Reverse axial drag opposes tail-first motion, but the bounded continuation
+outside the small-angle envelope is not validated descent aerodynamics.
+The 3 DOF solver uses the force coefficients and retains its usual simplified
+attitude behavior; use 6 DOF to simulate rotational damping.
+
+``cp_position``, ``static_margin`` and ``stability_margin`` use the coast
+small-angle DATCOM derivatives after preparation. Before preparation they
+are undefined (NaN), rather than displaying component-model results. CP is
+also undefined when the normal-force slope vanishes.
+
+The parser is adapted from the Asteria-Flight-Dynamics DATCOM implementation.
+Deck definitions follow the Missile DATCOM user's guide, Tables 5, 10 and 11.
+
+The executed :doc:`AST-HER comparison </examples/datcom/asteria_comparison>`
+uses the saved Asteria configuration and atmosphere. It compares ordinary
+RocketPy using the supplied Asteria drag curve with native DATCOM. AST-HER has
+not flown: percentages are model-to-model differences, not measured-flight
+errors. The ordinary conical-tail approximation and native ogive tail are
+explicit. Mass and CG histories are checked to match.
+
+
+Interpreting disagreement with source flight examples
+-----------------------------------------------------
+
+Ordinary RocketPy's whole-vehicle drag curves are supplied independently of its
+nose and fin normal-force models. Replacing them with native DATCOM therefore
+changes the drag model even with identical mass and planform. The
+:doc:`comparison notebook </examples/datcom/rocket_comparison>` keeps supplied
+CD, native CD, inferred apogee-fitted CD and measured values separate. One
+constant fitted to apogee does not uniquely identify a physical drag curve.
+
+In particular, a constant thickness/chord ratio describes a thickness taper.
+For a measured constant-thickness plate with nonzero root and tip chords, supply
+separate ratios, for example ``thickness_to_chord=(t / root_chord, t / tip_chord)``
+with all lengths in meters. Match bevels, section contour and edge radius to
+hardware. Smooth-surface roughness zero is an assumption, not a measured vinyl
+finish. Carbon fibre and vinyl do not determine these missing numerical inputs.
+Use measured geometry or the calibration API; an apogee-fitted roughness is an
+effective loss calibration, not an identified material property.
diff --git a/docs/user/rocket/rocket.rst b/docs/user/rocket/rocket.rst
index 956175409..43ac3761a 100644
--- a/docs/user/rocket/rocket.rst
+++ b/docs/user/rocket/rocket.rst
@@ -16,6 +16,7 @@ Rocket Usage
 .. toctree::
     :maxdepth: 3
     :caption: Generic Surfaces and Custom Aerodynamic Coefficients
-    
+
     Generic Surfaces and Custom Aerodynamic Coefficients <generic_surface.rst>
-    
\ No newline at end of file
+    Missile DATCOM Aerodynamics <datcom.rst>
+
diff --git a/rocketpy/__init__.py b/rocketpy/__init__.py
index 27b7c034a..ea0f75fd2 100644
--- a/rocketpy/__init__.py
+++ b/rocketpy/__init__.py
@@ -36,6 +36,8 @@
     AeroSurface,
     AirBrakes,
     Components,
+    DatcomFins,
+    DatcomFreeFormFins,
     EllipticalFin,
     EllipticalFins,
     Fin,
diff --git a/rocketpy/plots/datcom_plots.py b/rocketpy/plots/datcom_plots.py
new file mode 100644
index 000000000..446093158
--- /dev/null
+++ b/rocketpy/plots/datcom_plots.py
@@ -0,0 +1,50 @@
+"""Geometry projections for DATCOM-specific panel placement."""
+
+import numpy as np
+
+from rocketpy.rocket.aero_surface.nose_cone import NoseCone
+
+
+def draw_datcom_fins(surface, ax, position, drawn_surfaces, vis_args, plane, csys):
+    """Draw a true-view panel outline at PHIF/GAM and fixed incidence.
+
+    The incidence hinge is DATCOM's default half-root-chord line; SKEW is zero.
+    Root attachment radius is set from the assembled body during preparation.
+    """
+    aft = np.asarray(surface.shape_vec[0])
+    span = np.asarray(surface.shape_vec[1])
+    radius = getattr(surface, "_datcom_attachment_radius", surface.rocket_radius)
+    hinge = surface.root_chord / 2
+    component = 0 if plane == "xz" else 1
+    for phi, gam, delta in zip(
+        surface.panel_angles, surface.dihedral, surface.datcom_incidence
+    ):
+        phi, gam, delta = np.radians([phi, gam, delta])
+        root = radius * np.array([np.sin(phi), np.cos(phi)])
+        direction = np.array([np.sin(phi + gam), np.cos(phi + gam)])
+        tangent = np.array([np.cos(phi + gam), -np.sin(phi + gam)])
+        transverse = root[:, None] + direction[:, None] * span
+        transverse -= tangent[:, None] * ((aft - hinge) * np.sin(delta))
+        axial = position - csys * (hinge + (aft - hinge) * np.cos(delta))
+        x = np.append(axial, axial[0])
+        y = np.append(transverse[component], transverse[component, 0])
+        ax.plot(x, y, color=vis_args["fins"], linewidth=vis_args["line_width"])
+    body = getattr(surface, "_datcom_drawing_body", None)
+    if body is not None:
+        # Fins are overlays, not diameter transitions. In particular a fin on
+        # the boattail must not introduce a fictitious cylindrical tube there.
+        if not body["has_tail"]:
+            for index, (component, origin, body_radius, end) in enumerate(
+                drawn_surfaces
+            ):
+                if isinstance(component, NoseCone):
+                    drawn_surfaces[index] = (
+                        component,
+                        origin,
+                        body_radius,
+                        body["end"],
+                    )
+        return
+    drawn_surfaces.append(
+        (surface, position, radius, position - csys * surface.root_chord)
+    )
diff --git a/rocketpy/plots/rocket_plots.py b/rocketpy/plots/rocket_plots.py
index 5f55eaaab..80193b213 100644
--- a/rocketpy/plots/rocket_plots.py
+++ b/rocketpy/plots/rocket_plots.py
@@ -319,6 +319,10 @@ def _draw_tail(self, ax, surface, position, drawn_surfaces, vis_args):
     def _draw_fins(self, ax, surface, position, drawn_surfaces, vis_args, plane):
         """Draws the fins and saves the position of the points of interest
         for the tubes."""
+        if hasattr(surface, "draw_on_rocket"):
+            return surface.draw_on_rocket(
+                ax, position, drawn_surfaces, vis_args, plane, self.rocket._csys
+            )
         num_fins = surface.n
         x_fin = -self.rocket._csys * surface.shape_vec[0] + position
         y_fin = surface.shape_vec[1] + surface.rocket_radius
diff --git a/rocketpy/rocket/__init__.py b/rocketpy/rocket/__init__.py
index 3fa907f2d..d5c4c593d 100644
--- a/rocketpy/rocket/__init__.py
+++ b/rocketpy/rocket/__init__.py
@@ -2,6 +2,8 @@
 from rocketpy.rocket.aero_surface import (
     AeroSurface,
     AirBrakes,
+    DatcomFins,
+    DatcomFreeFormFins,
     EllipticalFin,
     EllipticalFins,
     Fin,
diff --git a/rocketpy/rocket/aero_surface/__init__.py b/rocketpy/rocket/aero_surface/__init__.py
index ec8aed537..bb5162d58 100644
--- a/rocketpy/rocket/aero_surface/__init__.py
+++ b/rocketpy/rocket/aero_surface/__init__.py
@@ -1,5 +1,6 @@
 from rocketpy.rocket.aero_surface.aero_surface import AeroSurface
 from rocketpy.rocket.aero_surface.air_brakes import AirBrakes
+from rocketpy.rocket.aero_surface.datcom_fins import DatcomFins, DatcomFreeFormFins
 from rocketpy.rocket.aero_surface.fins import (
     EllipticalFin,
     EllipticalFins,
diff --git a/rocketpy/rocket/aero_surface/datcom_fins.py b/rocketpy/rocket/aero_surface/datcom_fins.py
new file mode 100644
index 000000000..4be7d6e9a
--- /dev/null
+++ b/rocketpy/rocket/aero_surface/datcom_fins.py
@@ -0,0 +1,416 @@
+"""Missile DATCOM fin planforms, airfoil sections and panel placement.
+
+Planform distances are meters. Airfoil coordinates and section dimensions are
+fractions of chord. PHIF, GAM and DEFLCT inputs use DATCOM's degree conventions.
+"""
+
+import re
+from pathlib import Path
+
+import numpy as np
+
+from .fins.free_form_fins import FreeFormFins
+from .fins.trapezoidal_fins import TrapezoidalFins
+
+
+def _station_values(value, name, count):
+    """Normalize a scalar or one finite value per station/panel."""
+    values = np.asarray(value, dtype=float)
+    if values.ndim == 0:
+        values = np.repeat(values, count)
+    if values.shape != (count,) or not np.isfinite(values).all():
+        raise ValueError(f"{name} must be a finite scalar or have {count} values")
+    return tuple(values.tolist())
+
+
+def _station_pair(value, name):
+    """Backward-compatible root/tip section normalization."""
+    return _station_values(value, name, 2)
+
+
+def _planform(stations, chords, leading_edges):
+    """Validate a DATCOM spanwise planform and create its closed outline."""
+    stations = np.asarray(stations, float)
+    if (
+        stations.ndim != 1
+        or not 2 <= len(stations) <= 10
+        or not np.isfinite(stations).all()
+        or stations[0] != 0
+        or np.any(np.diff(stations) <= 0)
+    ):
+        raise ValueError("span_stations needs 2..10 increasing values starting at zero")
+    chords = np.asarray(_station_values(chords, "chords", len(stations)))
+    edges = np.asarray(_station_values(leading_edges, "leading_edges", len(stations)))
+    if np.any(chords[:-1] <= 0) or chords[-1] < 0:
+        raise ValueError(
+            "Chords must be positive except for an optional zero tip chord"
+        )
+    if edges[0] != 0:
+        raise ValueError(
+            "The root leading_edges value must be zero (relative to position)"
+        )
+    points = list(zip(edges, stations)) + list(
+        zip((edges + chords)[::-1], stations[::-1])
+    )
+    return tuple(stations), tuple(chords), tuple(edges), points
+
+
+class _DatcomFinSettings:
+    """Shared section metadata for existing RocketPy planform classes."""
+
+    def _configure_datcom(
+        self,
+        n,
+        count,
+        *,
+        path,
+        section_type="HEX",
+        thickness_to_chord=0.02,
+        upper_thickness_to_chord=None,
+        lower_thickness_to_chord=None,
+        max_thickness_position=0.25,
+        flat_length=0.5,
+        lower_max_thickness_position=None,
+        lower_flat_length=None,
+        leading_edge_radius=0,
+        naca=None,
+        airfoil_coordinates=None,
+        panel_angles=None,
+        dihedral=0,
+        incidence=None,
+        cant_angle=0,
+        auto_body_radius=True,
+        datcom_options=None,
+    ):
+        if not isinstance(n, (int, np.integer)) or not 1 <= n <= 8:
+            raise ValueError("DATCOM fin sets require an integer n between 1 and 8")
+        section_type = section_type.upper()
+        if section_type not in ("HEX", "ARC", "NACA", "USER"):
+            raise ValueError("section_type must be HEX, ARC, NACA or USER")
+        total = _station_values(thickness_to_chord, "thickness_to_chord", count)
+        upper = _station_values(
+            np.asarray(total) / 2
+            if upper_thickness_to_chord is None
+            else upper_thickness_to_chord,
+            "upper_thickness_to_chord",
+            count,
+        )
+        lower = _station_values(
+            upper if lower_thickness_to_chord is None else lower_thickness_to_chord,
+            "lower_thickness_to_chord",
+            count,
+        )
+        maximum = _station_values(
+            max_thickness_position, "max_thickness_position", count
+        )
+        flat = _station_values(flat_length, "flat_length", count)
+        lower_maximum = _station_values(
+            maximum
+            if lower_max_thickness_position is None
+            else lower_max_thickness_position,
+            "lower_max_thickness_position",
+            count,
+        )
+        lower_flat = _station_values(
+            flat if lower_flat_length is None else lower_flat_length,
+            "lower_flat_length",
+            count,
+        )
+        radius = _station_values(leading_edge_radius, "leading_edge_radius", count)
+        if any(r < 0 for r in radius):
+            raise ValueError("leading_edge_radius cannot be negative")
+        if section_type in ("HEX", "ARC"):
+            if any(
+                top < 0 or bottom < 0 or not 0 < top + bottom < 1
+                for top, bottom in zip(upper, lower)
+            ):
+                raise ValueError(
+                    "Section upper/lower thickness must be nonnegative with total between 0 and 1"
+                )
+            if section_type == "HEX":
+                for maxima, flats in ((maximum, flat), (lower_maximum, lower_flat)):
+                    if any(
+                        not (0 < m < 1 and 0 <= f < 1 and m + f < 1)
+                        for m, f in zip(maxima, flats)
+                    ):
+                        raise ValueError(
+                            "HEX requires 0 < maximum, 0 <= flat, maximum + flat < 1"
+                        )
+        if section_type == "NACA":
+            if not isinstance(naca, str):
+                raise ValueError(
+                    "NACA sections require naca, e.g. '0012' or '6-64-005'"
+                )
+            if re.fullmatch(r"\d{4}(?:\.\d+)?", naca):
+                naca = "4-" + naca
+            if not re.fullmatch(r"[1456S]-[0-9.AS-]+(?: A=0\.\d+)?", naca):
+                raise ValueError(
+                    "Invalid NACA designation; omit the NACA and fin-set prefix"
+                )
+        elif naca is not None:
+            raise ValueError("naca requires section_type='NACA'")
+        coordinates = None
+        if section_type == "USER":
+            coordinates = self._validate_airfoil_coordinates(airfoil_coordinates)
+        elif airfoil_coordinates is not None:
+            raise ValueError("airfoil_coordinates requires section_type='USER'")
+        angles = _station_values(
+            np.arange(n) * 360 / n if panel_angles is None else panel_angles,
+            "panel_angles",
+            n,
+        )
+        angles = tuple(float(a % 360) for a in angles)
+        if len(set(round(a, 8) for a in angles)) != n:
+            raise ValueError("panel_angles must not repeat the same azimuth")
+        gam = _station_values(dihedral, "dihedral", n)
+        if any(abs(a) >= 90 for a in gam):
+            raise ValueError("dihedral must lie strictly between -90 and 90 degrees")
+        if not np.isfinite(cant_angle):
+            raise ValueError("cant_angle must be finite")
+        if incidence is not None and cant_angle != 0:
+            raise ValueError(
+                "Use either DATCOM incidence or RocketPy cant_angle, not both"
+            )
+        delta = (
+            None if incidence is None else _station_values(incidence, "incidence", n)
+        )
+        if any(abs(a) >= 90 for a in (delta or (-cant_angle,))):
+            raise ValueError(
+                "incidence/cant must lie strictly between -90 and 90 degrees"
+            )
+        self.datcom_path = str(Path(path).expanduser().resolve())
+        self.datcom_options = dict(datcom_options or {})
+        self.section_type = section_type
+        self.thickness_to_chord = tuple(
+            top + bottom for top, bottom in zip(upper, lower)
+        )
+        self.upper_thickness_to_chord = upper
+        self.lower_thickness_to_chord = lower
+        self.max_thickness_position = maximum
+        self.flat_length = flat
+        self.lower_max_thickness_position = lower_maximum
+        self.lower_flat_length = lower_flat
+        self.leading_edge_radius = radius
+        self.naca = naca
+        self.airfoil_coordinates = coordinates
+        self.panel_angles = angles
+        self.dihedral = gam
+        self.incidence = delta
+        self.auto_body_radius = bool(auto_body_radius)
+
+    @staticmethod
+    def _validate_airfoil_coordinates(coordinates):
+        if not isinstance(coordinates, dict):
+            raise ValueError("USER sections require an airfoil_coordinates dictionary")
+        keys = set(coordinates)
+        if keys not in ({"x", "upper", "lower"}, {"x", "mean", "thickness"}):
+            raise ValueError("USER needs x/upper/lower or x/mean/thickness")
+        result = {k: np.asarray(v, float) for k, v in coordinates.items()}
+        x = result["x"]
+        if (
+            x.ndim != 1
+            or not 3 <= len(x) <= 50
+            or x[0] != 0
+            or x[-1] != 1
+            or np.any(np.diff(x) <= 0)
+        ):
+            raise ValueError(
+                "USER x needs 3..50 increasing chord fractions from 0 to 1"
+            )
+        if any(v.shape != x.shape or not np.isfinite(v).all() for v in result.values()):
+            raise ValueError("USER coordinates must have matching finite arrays")
+        thickness = result.get(
+            "thickness", result.get("upper", 0) - result.get("lower", 0)
+        )
+        if np.any(thickness < 0) or not np.any(thickness > 0):
+            raise ValueError("USER section thickness must be nonnegative and nonzero")
+        return {k: tuple(v.tolist()) for k, v in result.items()}
+
+    @property
+    def datcom_incidence(self):
+        """DATCOM positive incidence gives negative roll; RocketPy cant is opposite."""
+        return (
+            self.incidence
+            if self.incidence is not None
+            else (-self.cant_angle,) * self.n
+        )
+
+    def _datcom_settings_dict(self):
+        names = (
+            "section_type",
+            "thickness_to_chord",
+            "upper_thickness_to_chord",
+            "lower_thickness_to_chord",
+            "max_thickness_position",
+            "flat_length",
+            "lower_max_thickness_position",
+            "lower_flat_length",
+            "leading_edge_radius",
+            "naca",
+            "airfoil_coordinates",
+            "panel_angles",
+            "dihedral",
+            "incidence",
+            "cant_angle",
+            "auto_body_radius",
+            "datcom_options",
+        )
+        return {
+            "path": self.datcom_path,
+            **{name: getattr(self, name) for name in names},
+        }
+
+    def draw_on_rocket(self, ax, position, drawn_surfaces, vis_args, plane, csys):
+        """Project actual PHIF/GAM/DEFLCT placement into a Rocket drawing."""
+        from rocketpy.plots.datcom_plots import draw_datcom_fins
+
+        return draw_datcom_fins(
+            self, ax, position, drawn_surfaces, vis_args, plane, csys
+        )
+
+    @classmethod
+    def from_dict(cls, data):
+        return cls(**data)
+
+
+class DatcomFins(_DatcomFinSettings, TrapezoidalFins):
+    """A trapezoidal or triangular planform with a HEX, ARC, NACA or USER section.
+
+    Dimensions and position follow TrapezoidalFins. Section inputs accept scalar
+    or root/tip values; thicknesses are chord fractions and radius is meters.
+    NACA uses a designation such as ``0012`` or ``6-64-005``. USER accepts
+    ``airfoil_coordinates={"x": ..., "upper": ..., "lower": ...}`` or mean and
+    thickness arrays instead of upper/lower. PHIF panel_angles and GAM dihedral
+    use degrees. Fixed incidence follows DATCOM's negative-roll convention;
+    cant_angle follows RocketPy's positive-roll convention. All execution and
+    geometry inputs survive serialization. See the DATCOM user guide page.
+    """
+
+    def __init__(
+        self,
+        n,
+        root_chord,
+        tip_chord,
+        span,
+        rocket_radius,
+        *,
+        path,
+        sweep_length=None,
+        sweep_angle=None,
+        name="DATCOM Fins",
+        **section_options,
+    ):
+        for value, label in (
+            (root_chord, "root_chord"),
+            (span, "span"),
+            (rocket_radius, "rocket_radius"),
+        ):
+            if not np.isfinite(value) or value <= 0:
+                raise ValueError(f"DATCOM {label} must be positive and finite")
+        if not np.isfinite(tip_chord) or tip_chord < 0:
+            raise ValueError("DATCOM tip_chord must be finite and nonnegative")
+        self._configure_datcom(n, 2, path=path, **section_options)
+        super().__init__(
+            n=n,
+            root_chord=root_chord,
+            tip_chord=tip_chord,
+            span=span,
+            rocket_radius=rocket_radius,
+            sweep_length=sweep_length,
+            sweep_angle=sweep_angle,
+            cant_angle=section_options.get("cant_angle", 0),
+            name=name,
+        )
+
+    @property
+    def datcom_span_stations(self):
+        return (0.0, self.span)
+
+    @property
+    def datcom_chords(self):
+        return (self.root_chord, self.tip_chord)
+
+    @property
+    def datcom_leading_edges(self):
+        return (0.0, self.sweep_length)
+
+    def evaluate_shape(self):
+        _, _, _, points = _planform(
+            self.datcom_span_stations, self.datcom_chords, self.datcom_leading_edges
+        )
+        self.shape_vec = [np.array([p[i] for p in points]) for i in (0, 1)]
+        return self.shape_vec
+
+    def to_dict(self, **kwargs):
+        return dict(
+            n=self.n,
+            root_chord=self.root_chord,
+            tip_chord=self.tip_chord,
+            span=self.span,
+            rocket_radius=self.rocket_radius,
+            sweep_length=self.sweep_length,
+            name=self.name,
+            **self._datcom_settings_dict(),
+        )
+
+
+class DatcomFreeFormFins(_DatcomFinSettings, FreeFormFins):
+    """Piecewise-linear planform with 2..10 exposed span stations.
+
+    Span stations start at zero at the body surface. Chords and leading_edges
+    are meters; leading_edges start at zero relative to the root position.
+    All cross sections belong to one type, with proportions varying by station.
+    Section options and panel placement are identical to DatcomFins.
+    """
+
+    def __init__(
+        self,
+        n,
+        span_stations,
+        chords,
+        leading_edges,
+        rocket_radius,
+        *,
+        path,
+        name="DATCOM Free Form Fins",
+        **section_options,
+    ):
+        stations, chords, edges, points = _planform(
+            span_stations, chords, leading_edges
+        )
+        self._datcom_stations = stations
+        self._datcom_chords = chords
+        self._datcom_edges = edges
+        if not np.isfinite(rocket_radius) or rocket_radius <= 0:
+            raise ValueError("rocket_radius must be positive and finite")
+        self._configure_datcom(n, len(stations), path=path, **section_options)
+        super().__init__(
+            n,
+            points,
+            rocket_radius,
+            cant_angle=section_options.get("cant_angle", 0),
+            name=name,
+        )
+
+    @property
+    def datcom_span_stations(self):
+        return self._datcom_stations
+
+    @property
+    def datcom_chords(self):
+        return self._datcom_chords
+
+    @property
+    def datcom_leading_edges(self):
+        return self._datcom_edges
+
+    def to_dict(self, **kwargs):
+        return dict(
+            n=self.n,
+            span_stations=self.datcom_span_stations,
+            chords=self.datcom_chords,
+            leading_edges=self.datcom_leading_edges,
+            rocket_radius=self.rocket_radius,
+            name=self.name,
+            **self._datcom_settings_dict(),
+        )
diff --git a/rocketpy/rocket/aero_surface/datcom_tail.py b/rocketpy/rocket/aero_surface/datcom_tail.py
new file mode 100644
index 000000000..674faac51
--- /dev/null
+++ b/rocketpy/rocket/aero_surface/datcom_tail.py
@@ -0,0 +1,67 @@
+"""Ogive geometry for whole-vehicle DATCOM, without component aerodynamics."""
+
+import numpy as np
+from scipy.integrate import quad
+
+from rocketpy.mathutils import Function
+from rocketpy.rocket.aero_surface.tail import Tail
+
+
+class DatcomTail(Tail):
+    """A tangent-ogive afterbody usable only with DATCOM aerodynamics.
+
+    The inherited component lift and CP are disabled placeholders. Native
+    DATCOM supplies the whole vehicle's forces, moments and pressure center.
+    ``kind`` is read-only; construct a new surface to change its profile.
+    """
+
+    @property
+    def kind(self):
+        return "ogive"
+
+    def radius_at(self, x):
+        """Radius (m) at distance x (m) aft of the upstream cylinder joint."""
+        x = np.asarray(x)
+        change = self.bottom_radius - self.top_radius
+        if change == 0:
+            return np.full_like(x, self.top_radius, dtype=float)
+        rho = (self.length**2 + change**2) / (2 * abs(change))
+        sagitta = x**2 / (rho + np.sqrt(np.maximum(0, rho**2 - x**2)))
+        return self.top_radius + np.sign(change) * sagitta
+
+    def evaluate_geometrical_parameters(self):
+        change = self.bottom_radius - self.top_radius
+        if self.length <= 0 or abs(change) > self.length:
+            raise ValueError(
+                "Ogive tail needs positive length and radius change <= length"
+            )
+        if change == 0:
+            self.slant_length = self.length
+            self.surface_area = 2 * np.pi * self.top_radius * self.length
+        else:
+            rho = (self.length**2 + change**2) / (2 * abs(change))
+            angle = np.arcsin(self.length / rho)
+            self.slant_length = rho * angle
+            self.surface_area = quad(
+                lambda theta: 2 * np.pi * self.radius_at(rho * np.sin(theta)) * rho,
+                0,
+                angle,
+            )[0]
+        self.evaluate_shape()
+
+    def evaluate_shape(self):
+        x = np.linspace(0, self.length, 100)
+        self.shape_vec = [x, self.radius_at(x)]
+
+    def evaluate_lift_coefficient(self):
+        self.clalpha = Function(0, "Mach", "DATCOM-only component lift slope")
+        self.cl = Function(
+            lambda alpha, mach: 0, ["Alpha (rad)", "Mach"], "DATCOM-only component lift"
+        )
+
+    def evaluate_center_of_pressure(self):
+        self.cpx = self.cpy = self.cpz = 0
+        self.cp = (0, 0, 0)
+
+    def compute_forces_and_moments(self, *args, **kwargs):
+        raise ValueError("Ogive tails require whole-vehicle DATCOM aerodynamics")
diff --git a/rocketpy/rocket/datcom.py b/rocketpy/rocket/datcom.py
new file mode 100644
index 000000000..72ad311c4
--- /dev/null
+++ b/rocketpy/rocket/datcom.py
@@ -0,0 +1,988 @@
+"""Missile DATCOM decks, isolated execution and complete aerodynamic tables.
+
+The for006 parser is adapted from Asteria-Flight-Dynamics/classes/datcom/datcom.py.
+Geometry and execution are independent of Asteria's project configuration.
+"""
+
+import json
+import math
+import re
+import shutil
+import subprocess
+import tempfile
+import warnings
+from pathlib import Path
+
+import numpy as np
+from scipy.interpolate import RegularGridInterpolator
+
+from rocketpy.mathutils.vector_matrix import Vector
+from rocketpy.rocket.aero_surface.datcom_fins import (
+    DatcomFins,
+    DatcomFreeFormFins,
+    _station_values,
+)
+from rocketpy.rocket.aero_surface.fins.trapezoidal_fins import TrapezoidalFins
+from rocketpy.rocket.aero_surface.generic_surface import GenericSurface
+from rocketpy.rocket.aero_surface.nose_cone import NoseCone
+from rocketpy.rocket.aero_surface.tail import Tail
+from rocketpy.rocket.datcom_conditions import adaptive_mach_grid
+
+BODY_KEYS = ("CN", "CM", "CA", "CY", "CLN", "CLL")
+LATERAL_KEYS = ("CYB", "CLNB", "CLLB")
+DAMPING_KEYS = (
+    "CNQ",
+    "CMQ",
+    "CAQ",
+    "CNAD",
+    "CMAD",
+    "CYR",
+    "CLNR",
+    "CLLR",
+    "CYP",
+    "CLNP",
+    "CLLP",
+)
+
+
+class DatcomResult(tuple):
+    """Backward-compatible six-array result with body-axis and lateral tables."""
+
+    def __new__(
+        cls, values, body=None, derivatives=None, damping=None, beta=None, sideslip=None
+    ):
+        result = super().__new__(cls, values)
+        result.body = {} if body is None else body
+        result.derivatives = {} if derivatives is None else derivatives
+        result.damping = {} if damping is None else damping
+        result.beta = np.array([0.0]) if beta is None else np.asarray(beta)
+        result.sideslip = sideslip
+        return result
+
+
+def zero_alpha_index(alpha):
+    indices = np.flatnonzero(np.isclose(alpha, 0.0, atol=1e-12, rtol=0))
+    if len(indices) != 1:
+        raise ValueError("DATCOM tables must contain exactly one zero-angle row")
+    return int(indices[0])
+
+
+def parse_datcom_output(base_dir, n_alpha_expected=None, _output=None):
+    """Read every angle row, including signed alpha and lateral derivatives.
+
+    The public tuple retains CL, CD, CM, XCP, Mach, alpha (degrees here).
+    Body coefficients and derivatives (per degree) are retained as attributes.
+    Never truncate a table to a hardcoded row count.
+    """
+    output = Path(base_dir, "for006.dat").read_text() if _output is None else _output
+    lines = output.splitlines()
+    if n_alpha_expected is None:
+        declared = re.search(r"\bNALPHA\s*=\s*([0-9.]+)", output)
+        if declared is not None:
+            n_alpha_expected = int(float(declared[1]))
+    starts = [i for i, line in enumerate(lines) if "----- LONGITUDINAL -----" in line]
+    if not starts:
+        raise RuntimeError("No LONGITUDINAL blocks found in for006.dat")
+
+    # Multiple fixed-BETA cases can share one executable invocation. Preserve
+    # complete body/rate channels at each beta instead of imposing symmetry on
+    # arbitrary panel layouts. The six-array view remains the beta=0 slice.
+    beta_blocks = {}
+    for index, start in enumerate(starts):
+        mach_start = next(
+            (i for i in range(start - 1, -1, -1) if "MACH NO" in lines[i]), None
+        )
+        beta_line = next(
+            (line for line in reversed(lines[:start]) if "SIDESLIP =" in line),
+            "SIDESLIP = 0",
+        )
+        beta = float(beta_line.split("=")[1].split()[0])
+        stop = starts[index + 1] if index + 1 < len(starts) else len(lines)
+        if mach_start is not None:
+            beta_blocks.setdefault(beta, []).extend(lines[mach_start:stop])
+    if len(beta_blocks) > 1:
+        tables = {
+            beta: parse_datcom_output(base_dir, n_alpha_expected, "\n".join(block))
+            for beta, block in sorted(beta_blocks.items())
+        }
+        if 0.0 not in tables:
+            raise RuntimeError("DATCOM sideslip database must include beta=0")
+        zero = tables[0.0]
+        if any(
+            not np.array_equal(t[4], zero[4]) or not np.array_equal(t[5], zero[5])
+            for t in tables.values()
+        ):
+            raise RuntimeError("DATCOM sideslip cases have different Mach/alpha grids")
+        return DatcomResult(
+            tuple(zero),
+            zero.body,
+            zero.derivatives,
+            zero.damping,
+            beta=list(tables),
+            sideslip=tables,
+        )
+
+    def read_table(block, columns):
+        header = next(
+            (i for i, line in enumerate(block) if line.split() == columns), None
+        )
+        if header is None:
+            raise RuntimeError(f"DATCOM missing {' '.join(columns)} table")
+        rows = []
+        for line in block[header + 1 :]:
+            fields = line.split()
+            if not fields and not rows:
+                continue
+            if len(fields) != len(columns):
+                # Fixed-width output can join a negative value to its preceding
+                # column, e.g. CL/CD=-0.000 followed by X-C.P.=-28096.705.
+                number = r"[-+]?(?:\d+(?:\.\d*)?|\.\d+)(?:[EeDd][-+]?\d+)?"
+                fields = re.findall(number, line)
+                if len(fields) != len(columns) or re.sub(number, "", line).strip():
+                    break
+            try:
+                rows.append([float(value.replace("D", "E")) for value in fields])
+            except ValueError as exc:
+                raise RuntimeError(
+                    "DATCOM returned a nonnumeric coefficient row"
+                ) from exc
+        if not rows:
+            raise RuntimeError("DATCOM returned an empty coefficient table")
+        return np.asarray(rows)
+
+    mach, alpha_rows, lift_rows = [], [], []
+    body_rows, derivative_rows, damping_rows = [], [], []
+    for index, start in enumerate(starts):
+        stop = starts[index + 1] if index + 1 < len(starts) else len(lines)
+        block = lines[start:stop]
+        mach_line = next(
+            (line for line in reversed(lines[:start]) if "MACH NO" in line), None
+        )
+        if mach_line is None:
+            raise RuntimeError("DATCOM missing Mach reference")
+        mach.append(float(mach_line.split("=")[1].split()[0]))
+        body = read_table(block, ["ALPHA", "CN", "CM", "CA", "CY", "CLN", "CLL"])
+        lift = read_table(block, ["ALPHA", "CL", "CD", "CL/CD", "X-C.P."])
+        derivatives = read_table(block, ["ALPHA", "CNA", "CMA", "CYB", "CLNB", "CLLB"])
+        if not np.array_equal(body[:, 0], lift[:, 0]) or not np.array_equal(
+            body[:, 0], derivatives[:, 0]
+        ):
+            raise RuntimeError("DATCOM coefficient tables have different angle grids")
+        if n_alpha_expected is not None and len(body) != n_alpha_expected:
+            raise RuntimeError("DATCOM angle count does not match the requested count")
+        alpha_rows.append(body[:, 0])
+        body_rows.append(body[:, 1:])
+        lift_rows.append(lift[:, 1:])
+        derivative_rows.append(derivatives[:, 1:])
+        if "DYNAMIC DERIVATIVES" in "\n".join(block):
+            longitudinal = read_table(
+                block, ["ALPHA", "CNQ", "CMQ", "CAQ", "CNAD", "CMAD"]
+            )
+            lateral = read_table(
+                block, ["ALPHA", "CYR", "CLNR", "CLLR", "CYP", "CLNP", "CLLP"]
+            )
+            if not (
+                np.array_equal(body[:, 0], longitudinal[:, 0])
+                and np.array_equal(body[:, 0], lateral[:, 0])
+            ):
+                raise RuntimeError("DATCOM damping angle grid mismatch")
+            if "DYNAMIC DERIVATIVES (PER DEGREE)" not in "\n".join(block):
+                raise RuntimeError("Expected DATCOM dynamic derivatives per degree")
+            damping_rows.append(np.column_stack([longitudinal[:, 1:], lateral[:, 1:]]))
+    alpha = alpha_rows[0]
+    if any(not np.array_equal(row, alpha) for row in alpha_rows[1:]):
+        raise RuntimeError("DATCOM Mach blocks have different angle grids")
+    mach = np.asarray(mach)
+    if np.any(np.diff(mach) <= 0) or np.any(np.diff(alpha) <= 0):
+        raise RuntimeError("DATCOM Mach and angle grids must be strictly increasing")
+    body = np.asarray(body_rows)
+    lift = np.asarray(lift_rows)
+    derivatives = np.asarray(derivative_rows)
+    if not all(np.isfinite(values).all() for values in (body, lift, derivatives)):
+        raise RuntimeError("DATCOM returned nonfinite coefficient tables")
+    if damping_rows and len(damping_rows) != len(mach):
+        raise RuntimeError("Incomplete DATCOM damping Mach grid")
+    damping = (
+        dict(
+            zip(
+                (
+                    "CNQ",
+                    "CMQ",
+                    "CAQ",
+                    "CNAD",
+                    "CMAD",
+                    "CYR",
+                    "CLNR",
+                    "CLLR",
+                    "CYP",
+                    "CLNP",
+                    "CLLP",
+                ),
+                np.moveaxis(np.asarray(damping_rows), 2, 0),
+            )
+        )
+        if damping_rows
+        else {}
+    )
+    if any(not np.isfinite(v).all() for v in damping.values()):
+        raise RuntimeError("Nonfinite DATCOM damping coefficients")
+    if not damping:
+        raise RuntimeError(
+            "DATCOM output lacks DAMP derivatives; include DAMP in the deck"
+        )
+    return DatcomResult(
+        (lift[:, :, 0], lift[:, :, 1], body[:, :, 1], lift[:, :, 3], mach, alpha),
+        dict(zip(("CN", "CM", "CA", "CY", "CLN", "CLL"), np.moveaxis(body, 2, 0))),
+        dict(
+            zip(("CNA", "CMA", "CYB", "CLNB", "CLLB"), np.moveaxis(derivatives, 2, 0))
+        ),
+        damping,
+        beta=list(beta_blocks),
+    )
+
+
+def _flight_conditions(options):
+    """Validate the supported single-altitude-per-Mach DATCOM flight envelope."""
+    unknown = set(options) - {
+        "mach",
+        "alpha",
+        "beta",
+        "altitude",
+        "timeout",
+        "body_length",
+        "powered_base_drag",
+        "reynolds",
+    }
+    if unknown:
+        raise ValueError(f"Unknown DATCOM options: {sorted(unknown)}")
+    mach = np.asarray(options.get("mach", adaptive_mach_grid(2)), float)
+    alpha = np.asarray(
+        options.get(
+            "alpha",
+            [
+                -20,
+                -10,
+                -5,
+                -2,
+                -1,
+                -0.5,
+                -0.25,
+                -0.1,
+                -0.05,
+                0,
+                0.05,
+                0.1,
+                0.25,
+                0.5,
+                1,
+                2,
+                5,
+                10,
+                20,
+            ],
+        ),
+        float,
+    )
+    for grid, name in ((mach, "mach"), (alpha, "alpha")):
+        if (
+            grid.ndim != 1
+            or not 2 <= len(grid) <= 20
+            or not np.isfinite(grid).all()
+            or np.any(np.diff(grid) <= 0)
+        ):
+            raise ValueError(f"DATCOM {name} needs 2..20 finite, increasing values")
+    if mach[0] <= 0:
+        raise ValueError("DATCOM Mach values must be positive")
+    if not alpha[0] < 0 < alpha[-1] or np.count_nonzero(alpha == 0) != 1:
+        raise ValueError(
+            "DATCOM alpha must include negative angles, zero and positive angles"
+        )
+    raw_altitude = options.get("altitude", 0)
+    if isinstance(raw_altitude, dict):
+        if set(raw_altitude) != {"power_on", "power_off"}:
+            raise ValueError("altitude schedules need power_on and power_off")
+        schedules = {True: raw_altitude["power_on"], False: raw_altitude["power_off"]}
+    else:
+        schedules = {True: raw_altitude, False: raw_altitude}
+    altitude = {}
+    for powered, values in schedules.items():
+        values = np.asarray(values, float)
+        if values.ndim == 0:
+            values = np.full(len(mach), float(values))
+        if values.shape != mach.shape or not np.isfinite(values).all():
+            raise ValueError(
+                "DATCOM altitude must be a finite scalar or one value per Mach"
+            )
+        altitude[powered] = values
+    timeout = float(options.get("timeout", 60))
+    if not math.isfinite(timeout) or timeout <= 0:
+        raise ValueError("DATCOM timeout must be positive and finite")
+    return mach, alpha, altitude, timeout
+
+
+def _namelist(name, items):
+    """Wrap Fortran namelist lines below the legacy 80-column input limit."""
+    lines = [f" ${name}"]
+    for key, values in items:
+        if isinstance(values, str):
+            tokens = [values]
+        else:
+            tokens = [f"{float(v):.9G}" for v in np.atleast_1d(values)]
+            tokens = [
+                token
+                if "." in token
+                else (token.replace("E", ".E") if "E" in token else token + ".")
+                for token in tokens
+            ]
+        line = f"  {key} = "
+        for token in tokens:
+            if len(line) + len(token) + 1 > 76:
+                lines.append(line)
+                line = "    "
+            line += token + ","
+        lines.append(line)
+    lines[-1] += "$"
+    return "\n".join(lines)
+
+
+def datcom_decks(rocket, *, calibration=None, flight_conditions=None):
+    """Build powered/coast decks from assembled geometry, without executing.
+
+    DATCOM x is measured aft from the nose tip, in meters. The powered moment
+    reference is the filled CG at ignition; the coast reference is the empty
+    CG (including the dry motor). No plume or protuberance model is requested.
+    """
+    surfaces = list(rocket.aerodynamic_surfaces)
+    datcom_fins = [
+        s for s, _, _ in surfaces if isinstance(s, (DatcomFins, DatcomFreeFormFins))
+    ]
+    if not datcom_fins:
+        raise ValueError("Add a DATCOM fin set before preparing DATCOM aerodynamics")
+    first = datcom_fins[0]
+    options = dict(first.datcom_options)
+    options.update(flight_conditions or {})
+    calibration = rocket.datcom_calibration if calibration is None else calibration
+    edge_radius = calibration["leading_edge_radius"]
+    powered_base_drag = options.get("powered_base_drag", "none")
+    if powered_base_drag not in ("none", "nozzle", "full"):
+        raise ValueError("powered_base_drag must be 'none', 'nozzle' or 'full'")
+    mach, alpha, altitude, timeout = _flight_conditions(options)
+
+    def canonical(value):
+        return json.dumps(
+            value, sort_keys=True, default=lambda v: np.asarray(v).tolist()
+        )
+
+    for fins in datcom_fins[1:]:
+        if fins.datcom_path != first.datcom_path or canonical(
+            fins.datcom_options
+        ) != canonical(first.datcom_options):
+            raise ValueError(
+                "DATCOM fin sets must use the same path and datcom_options"
+            )
+    noses = [(s, p) for s, p, _ in surfaces if isinstance(s, NoseCone)]
+    tails = [(s, p) for s, p, _ in surfaces if isinstance(s, Tail)]
+    if len(noses) != 1 or len(tails) > 1:
+        raise ValueError("DATCOM requires exactly one nose and at most one tail")
+    if any(
+        not isinstance(s, (NoseCone, Tail, TrapezoidalFins, DatcomFreeFormFins))
+        for s, _, _ in surfaces
+    ):
+        raise ValueError(
+            "DATCOM supports a nose, DATCOM or trapezoidal fins and a conical or ogive tail"
+        )
+    if any(abs(p.x) > 1e-12 or abs(p.y) > 1e-12 for _, p, _ in surfaces):
+        raise ValueError("DATCOM geometry must be centered on the rocket axis")
+    nose, tip = noses[0]
+    nose_types = {
+        "conical": "CONE",
+        "ogive": "OGIVE",
+        "tangent": "OGIVE",
+        "tangentogive": "OGIVE",
+        "vonkarman": "KARMAN",
+        "lvhaack": "HAACK",
+        "powerseries": "POWER",
+    }
+    kind = nose.kind.replace(" ", "").lower()
+    if kind not in nose_types or nose.bluffness:
+        raise ValueError(
+            "DATCOM requires an unblunted conical, ogive, Haack, Karman or power nose"
+        )
+    if not math.isclose(nose.base_radius, rocket.radius):
+        raise ValueError("DATCOM nose base radius must match the rocket radius")
+
+    def aft(position):
+        return (tip.z - position) * rocket._csys
+
+    if tails:
+        tail, position = tails[0]
+        body_length = aft(position.z) - nose.length
+        if not math.isclose(tail.top_radius, rocket.radius):
+            raise ValueError("DATCOM tail top radius must match the rocket radius")
+        if tail.bottom_radius <= 0 or tail.bottom_radius > tail.top_radius:
+            raise ValueError("DATCOM tail must be a boattail with positive base radius")
+        aft_length = tail.length
+        aft_diameter = 2 * tail.bottom_radius
+        if "body_length" in options and not math.isclose(
+            float(options["body_length"]), body_length
+        ):
+            raise ValueError(
+                "DATCOM body_length conflicts with the existing tail position"
+            )
+    else:
+        body_length = float(
+            options.get("body_length", aft(rocket.nozzle_position) - nose.length)
+        )
+        aft_length, aft_diameter = 0, 2 * rocket.radius
+    if not math.isfinite(body_length) or body_length <= 0:
+        raise ValueError(
+            "DATCOM cylindrical body length must be positive; add a tail or set body_length"
+        )
+    if aft_length < 0 or not math.isfinite(aft_length):
+        raise ValueError("DATCOM tail length must be finite and nonnegative")
+    total_length = nose.length + body_length + aft_length
+
+    def body_radius(x):
+        if x < nose.length:
+            return float(np.interp(x, nose.shape_vec[0], nose.shape_vec[1]))
+        if aft_length > 0 and x > nose.length + body_length:
+            if hasattr(tail, "radius_at"):
+                return float(tail.radius_at(x - nose.length - body_length))
+            fraction = (x - nose.length - body_length) / aft_length
+            return rocket.radius + fraction * (aft_diameter / 2 - rocket.radius)
+        return rocket.radius
+
+    fin_items, naca_cards, deflections = [], [], []
+    all_fins = [
+        (s, p)
+        for s, p, _ in surfaces
+        if isinstance(s, (TrapezoidalFins, DatcomFreeFormFins))
+    ]
+    all_fins.sort(key=lambda pair: aft(pair[1].z))
+    if len(all_fins) > 4:
+        raise ValueError("This DATCOM integration supports at most four fin sets")
+    previous_end, previous_span = None, None
+    for index, (fins, position) in enumerate(all_fins, 1):
+        is_datcom = isinstance(fins, (DatcomFins, DatcomFreeFormFins))
+        spans = np.asarray(
+            fins.datcom_span_stations if is_datcom else [0, fins.span], float
+        )
+        chords = np.asarray(
+            fins.datcom_chords if is_datcom else [fins.root_chord, fins.tip_chord],
+            float,
+        )
+        edges = np.asarray(
+            fins.datcom_leading_edges if is_datcom else [0, fins.sweep_length], float
+        )
+        xle = aft(position.z) + edges
+        if (
+            len(spans) > 10
+            or len(spans) < 2
+            or np.any(np.diff(spans) <= 0)
+            or not np.isfinite(np.concatenate((spans, chords, edges))).all()
+            or np.any(chords[:-1] <= 0)
+            or chords[-1] < 0
+        ):
+            raise ValueError("Invalid DATCOM fin span stations/chords/leading edges")
+        # Automatic attachment samples the body at the exposed root chord
+        # centre (Missile DATCOM guide, p. 13). A fin may trail past the base;
+        # requiring every trailing edge inside the body rejects valid cases.
+        root_center = float(xle[0] + chords[0] / 2)
+        if np.any(xle < 0) or not 0 <= root_center <= total_length + 1e-9:
+            raise ValueError(
+                "DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip"
+            )
+        if not isinstance(fins.n, (int, np.integer)) or not 1 <= fins.n <= 8:
+            raise ValueError("DATCOM requires 1..8 panels per fin set")
+        start, finish = float(xle.min()), float((xle + chords).max())
+        if previous_end is not None:
+            gap = start - previous_end
+            if gap < -1e-9:
+                raise ValueError("DATCOM fin-set planforms must not overlap")
+            if gap < 4 * previous_span:
+                warnings.warn(
+                    "DATCOM fin sets are separated by less than four exposed semispans; "
+                    "the upstream vortex-roll-up approximation may be inaccurate.",
+                    UserWarning,
+                    stacklevel=2,
+                )
+        previous_end, previous_span = finish, float(spans[-1])
+        attachment = body_radius(float(xle[0] + chords[0] / 2))
+        automatic = getattr(fins, "auto_body_radius", True)
+        if not automatic and not math.isclose(fins.rocket_radius, attachment):
+            raise ValueError(
+                "DATCOM fin root radius must match the body at root mid-chord"
+            )
+        if is_datcom:
+            fins._datcom_attachment_radius = attachment
+            fins._datcom_drawing_body = {
+                "has_tail": bool(tails),
+                "end": tip.z - rocket._csys * total_length,
+            }
+        span_input = spans if automatic else spans + fins.rocket_radius
+        count = len(spans)
+        section = getattr(fins, "section_type", "HEX")
+        if edge_radius is not None and section == "NACA":
+            raise ValueError("NACA sections define their own leading-edge radius")
+        radius = (
+            getattr(fins, "leading_edge_radius", 0)
+            if edge_radius is None
+            else edge_radius
+        )
+        items = [
+            ("XLE", xle),
+            ("NPANEL", fins.n),
+            (
+                "PHIF",
+                _station_values(
+                    getattr(fins, "panel_angles", np.arange(fins.n) * 360 / fins.n),
+                    "panel_angles",
+                    fins.n,
+                ),
+            ),
+            ("GAM", _station_values(getattr(fins, "dihedral", 0), "dihedral", fins.n)),
+            ("SSPAN", span_input),
+            ("CHORD", chords),
+            ("SECTYP", section),
+        ]
+        if section in ("HEX", "ARC"):
+            items.extend(
+                [
+                    (
+                        "ZUPPER",
+                        _station_values(
+                            getattr(fins, "upper_thickness_to_chord", 0.01),
+                            "upper thickness",
+                            count,
+                        ),
+                    ),
+                    (
+                        "ZLOWER",
+                        _station_values(
+                            getattr(fins, "lower_thickness_to_chord", 0.01),
+                            "lower thickness",
+                            count,
+                        ),
+                    ),
+                    (
+                        "LER",
+                        _station_values(
+                            radius,
+                            "leading_edge_radius",
+                            count,
+                        ),
+                    ),
+                ]
+            )
+            if section == "HEX":
+                items.extend(
+                    [
+                        (
+                            "LMAXU",
+                            _station_values(
+                                getattr(fins, "max_thickness_position", 0.25),
+                                "max_thickness_position",
+                                count,
+                            ),
+                        ),
+                        (
+                            "LFLATU",
+                            _station_values(
+                                getattr(fins, "flat_length", 0.5), "flat_length", count
+                            ),
+                        ),
+                        (
+                            "LMAXL",
+                            _station_values(
+                                getattr(fins, "lower_max_thickness_position", 0.25),
+                                "lower_max_thickness_position",
+                                count,
+                            ),
+                        ),
+                        (
+                            "LFLATL",
+                            _station_values(
+                                getattr(fins, "lower_flat_length", 0.5),
+                                "lower_flat_length",
+                                count,
+                            ),
+                        ),
+                    ]
+                )
+        elif section == "NACA":
+            naca_cards.append(f"NACA-{index}-{fins.naca}")
+        elif section == "USER":
+            coordinates = fins.airfoil_coordinates
+            items.append(("XCORD", coordinates["x"]))
+            for key, variable in (
+                ("upper", "YUPPER"),
+                ("lower", "YLOWER"),
+                ("mean", "MEAN"),
+                ("thickness", "THICK"),
+            ):
+                if key in coordinates:
+                    items.append((variable, coordinates[key]))
+            items.append(
+                (
+                    "LER",
+                    _station_values(radius, "leading_edge_radius", count),
+                )
+            )
+        else:
+            raise ValueError("Unsupported DATCOM fin section")
+        fin_items.append(_namelist(f"FINSET{index}", items))
+        incidence = fins.datcom_incidence if is_datcom else (-fins.cant_angle,) * fins.n
+        deflections.append(
+            (f"DELTA{index}", _station_values(incidence, "incidence", fins.n))
+        )
+
+    filled_cg = float(rocket.center_of_mass(rocket.motor.burn_start_time))
+    empty_cg = float(rocket.center_of_dry_mass_position)
+    if not all(
+        math.isfinite(cg) and 0 <= aft(cg) <= total_length
+        for cg in (filled_cg, empty_cg)
+    ):
+        raise ValueError("DATCOM filled and empty CG must lie inside the body")
+    needs_beta_grid = any(
+        s.n < 3
+        or not np.allclose(
+            getattr(s, "panel_angles", np.arange(s.n) * 360 / s.n),
+            np.arange(s.n) * 360 / s.n,
+        )
+        or np.any(getattr(s, "dihedral", 0))
+        or np.any(getattr(s, "datcom_incidence", -s.cant_angle))
+        for s, _ in all_fins
+    )
+    beta_values = np.asarray(
+        options.get("beta", [-5, 0, 5] if needs_beta_grid else [0]), float
+    )
+    if (
+        beta_values.ndim != 1
+        or not 1 <= len(beta_values) <= 20
+        or not np.isfinite(beta_values).all()
+        or np.any(np.diff(beta_values) <= 0)
+        or np.count_nonzero(beta_values == 0) != 1
+        or (len(beta_values) > 1 and not beta_values[0] < 0 < beta_values[-1])
+    ):
+        raise ValueError(
+            "DATCOM beta must be [0] or increasing signed angles including zero"
+        )
+    exit_diameter = None
+    if powered_base_drag == "nozzle":
+        exit_diameter = 2 * float(rocket.motor.nozzle_radius)
+        if not math.isfinite(exit_diameter) or not 0 < exit_diameter <= aft_diameter:
+            raise ValueError(
+                "Powered nozzle diameter must be positive and fit inside the aft body"
+            )
+    elif powered_base_drag == "full":
+        exit_diameter = 0
+    decks = {}
+    reynolds = options.get("reynolds")
+    if reynolds is not None:
+        if not isinstance(reynolds, dict):
+            reynolds = {"power_on": reynolds, "power_off": reynolds}
+        if set(reynolds) != {"power_on", "power_off"}:
+            raise ValueError("reynolds schedules need power_on and power_off")
+        ren = {}
+        for powered, label in ((True, "power_on"), (False, "power_off")):
+            values = np.asarray(reynolds[label], float)
+            if values.ndim == 0:
+                values = np.full(len(mach), float(values))
+            if (
+                values.shape != mach.shape
+                or not np.isfinite(values).all()
+                or np.any(values <= 0)
+            ):
+                raise ValueError(
+                    "DATCOM reynolds needs positive Reynolds numbers per meter, one per Mach"
+                )
+            ren[powered] = values
+    for powered, cg in ((True, filled_cg), (False, empty_cg)):
+        body = [
+            ("TNOSE", nose_types[kind]),
+            ("LNOSE", nose.length),
+            ("DNOSE", 2 * rocket.radius),
+            ("LCENTR", body_length),
+            ("DCENTR", 2 * rocket.radius),
+            ("BASE", ".FALSE."),
+        ]
+        if kind == "powerseries":
+            body.append(("POWER", nose.power))
+        if aft_length > 0 and not math.isclose(aft_diameter, 2 * rocket.radius):
+            body.extend(
+                [
+                    (
+                        "TAFT",
+                        "OGIVE"
+                        if getattr(tail, "kind", "conical") == "ogive"
+                        else "CONE",
+                    ),
+                    ("LAFT", aft_length),
+                    ("DAFT", aft_diameter),
+                ]
+            )
+        if not powered:
+            body.append(("DEXIT", 0))
+        elif exit_diameter is not None:
+            body.append(("DEXIT", exit_diameter))
+        reference_items = [
+            ("XCG", aft(cg)),
+            ("SREF", rocket.area),
+            ("LREF", 2 * rocket.radius),
+        ]
+        if calibration["roughness_height"] > 0:
+            # RHR is independent of DIM; the local 3/99 binary disagrees with
+            # the 5/97 guide's metric ROUGH note. k = 3 RHR microinches.
+            reference_items.append(
+                ("RHR", calibration["roughness_height"] / (3e-6 * 0.0254))
+            )
+        cases = []
+        for beta in beta_values:
+            cases.append(
+                "\n".join(
+                    [
+                        "DIM M",
+                        _namelist(
+                            "FLTCON",
+                            [
+                                ("NMACH", len(mach)),
+                                ("MACH", mach),
+                                ("ALT", altitude[powered])
+                                if reynolds is None
+                                else ("REN", ren[powered]),
+                                ("NALPHA", len(alpha)),
+                                ("ALPHA", alpha),
+                                ("BETA", beta),
+                            ],
+                        ),
+                        _namelist("REFQ", reference_items),
+                        _namelist("AXIBOD", body),
+                        *fin_items,
+                        _namelist("DEFLCT", deflections),
+                        *naca_cards,
+                        "DAMP",
+                        "NEXT CASE",
+                        "",
+                    ]
+                )
+            )
+        decks[powered] = "".join(cases)
+    return decks, {True: filled_cg, False: empty_cg}, timeout, first.datcom_path
+
+
+def run_datcom(path, deck, timeout=60):
+    """Copy and run a user-supplied native executable; always remove all files."""
+    source = Path(path).expanduser().resolve()
+    if not source.is_file():
+        raise FileNotFoundError(f"DATCOM executable not found: {source}")
+    with tempfile.TemporaryDirectory(prefix="rocketpy-datcom-") as directory:
+        executable = Path(directory, source.name)
+        shutil.copy2(source, executable)
+        Path(directory, "for005.dat").write_text(deck, encoding="ascii")
+        try:
+            result = subprocess.run(
+                [str(executable)],
+                cwd=directory,
+                capture_output=True,
+                text=True,
+                timeout=timeout,
+                errors="replace",
+                check=False,
+            )
+        except subprocess.TimeoutExpired as exc:
+            raise RuntimeError(
+                f"DATCOM exceeded its {timeout:g} second timeout"
+            ) from exc
+        except OSError as exc:
+            raise RuntimeError(
+                "Cannot execute DATCOM; provide a native executable with execute permission"
+            ) from exc
+        if result.returncode:
+            raise RuntimeError(
+                f"DATCOM failed ({result.returncode}): {result.stderr[-2000:]}"
+            )
+        output = Path(directory, "for006.dat")
+        if not output.is_file():
+            raise RuntimeError("DATCOM did not produce for006.dat")
+        # Some DATCOM builds return success even after rejecting an input deck.
+        raw = output.read_text(errors="replace")
+        if re.search(r"\*\*\s*ERROR\s*\*\*|\bFATAL\b", raw, re.IGNORECASE):
+            raise RuntimeError("DATCOM reported an input error in for006.dat")
+        parsed = parse_datcom_output(directory)
+        # A successful exit is insufficient: old DATCOM builds can fall back
+        # to defaults after malformed inputs. Verify the requested table grid.
+        for variable, actual in (("MACH", parsed[4]), ("ALPHA", parsed[5])):
+            requested = re.search(rf"\b{variable}\s*=\s*([\dEe+.,\s-]+)", deck)
+            if requested:
+                expected = np.array(
+                    [float(v) for v in re.split(r"[\s,]+", requested[1].strip(" ,\n"))]
+                )
+                if actual.shape != expected.shape or not np.allclose(
+                    actual, expected, atol=0.005, rtol=0
+                ):
+                    raise RuntimeError(f"DATCOM returned a different {variable} grid")
+        requested_beta = [
+            float(match[1]) for match in re.finditer(r"\bBETA\s*=\s*([-+\d.eE]+)", deck)
+        ]
+        if requested_beta:
+            expected_beta = np.unique(requested_beta)
+            if parsed.beta.shape != expected_beta.shape or not np.allclose(
+                parsed.beta, expected_beta, atol=0.005, rtol=0
+            ):
+                raise RuntimeError("DATCOM returned a different BETA grid")
+        return parsed
+
+
+class DatcomSurface(GenericSurface):
+    """Whole-vehicle body-axis loads, including all eleven DAMP derivatives.
+
+    DATCOM uses forward/right/down axes. In RocketPy the force channels map to
+    (CY, -CN, -CA) and moments to (CM, CLN, CLL). Static lateral derivatives and
+    DAMP derivatives in for006 are per degree. Rates are nondimensionalized by
+    diameter/(2 V). Sideslip uses full tables when available, otherwise a
+    linear expansion about beta=0.
+
+    Loads are evaluated at each case's reference CG and transferred to the dry
+    CG. Flight explicitly transfers their physical torque to the instantaneous
+    wet CG. No fuel-fraction blending of powered and coast base drag is used.
+    """
+
+    def __init__(self, rocket, tables, references):
+        super().__init__(rocket.area, 2 * rocket.radius, {}, name="DATCOM vehicle")
+        self.tables = tables
+        self.references = references
+        self.dry_reference = float(rocket.center_of_dry_mass_position)
+        self.csys = rocket._csys
+        self.burn_start = rocket.motor.burn_start_time
+        self.burn_out = rocket.motor.burn_out_time
+        self._interpolators = {}
+        self.outside_alpha_envelope = False
+        self.outside_mach_envelope = False
+        self.outside_beta_envelope = False
+        for powered, table in tables.items():
+            mach, alpha = table[4], np.radians(table[5])
+            zero_alpha_index(alpha)
+            if not alpha[0] < 0 < alpha[-1]:
+                raise ValueError("DATCOM needs signed alpha tables")
+
+            def channels(t):
+                arrays = [t.body[k] for k in BODY_KEYS]
+                arrays += [t.derivatives[k] for k in LATERAL_KEYS]
+                arrays += [t.damping[k] for k in DAMPING_KEYS]
+                arrays += [t[1]]
+                values = np.stack(arrays, axis=-1)
+                if (
+                    values.shape != (len(mach), len(alpha), 21)
+                    or not np.isfinite(values).all()
+                ):
+                    raise ValueError("DATCOM tables are incomplete or nonfinite")
+                return values
+
+            if table.sideslip:
+                values = np.stack(
+                    [channels(table.sideslip[float(beta)]) for beta in table.beta],
+                    axis=2,
+                )
+                grid = (mach, alpha, np.radians(table.beta))
+            else:
+                values = channels(table)
+                grid = (mach, alpha)
+            self._interpolators[powered] = RegularGridInterpolator(grid, values)
+
+    def is_powered(self, time):
+        return self.burn_start <= time < self.burn_out
+
+    def reference_offset(self, time):
+        """Case reference relative to dry CG, in RocketPy body axes (meters)."""
+        return Vector(
+            [
+                0,
+                0,
+                (self.references[self.is_powered(time)] - self.dry_reference)
+                * self.csys,
+            ]
+        )
+
+    def coefficients(self, powered, mach, alpha, beta=0):
+        """Interpolate Mach and signed incidence; clamp outside the input grid."""
+        interp = self._interpolators[powered]
+        m, a = interp.grid[:2]
+        point = [np.clip(mach, m[0], m[-1]), np.clip(alpha, a[0], a[-1])]
+        if len(interp.grid) == 3:
+            b = interp.grid[2]
+            point.append(np.clip(beta, b[0], b[-1]))
+        return interp(point)[0]
+
+    def drag_coefficient(self, powered, alpha, beta, mach, reynolds, q, r, p):
+        """DATCOM stability-axis drag for reporting and plots."""
+        return float(self.coefficients(powered, mach, alpha, beta)[-1])
+
+    def compute_forces_and_moments_at_time(
+        self,
+        time,
+        alpha_dot,
+        stream_velocity,
+        stream_speed,
+        stream_mach,
+        rho,
+        cp,
+        omega,
+        reynolds,
+    ):
+        """Return body loads at dry CG for a deterministic RHS evaluation."""
+        powered = self.is_powered(time)
+        velocity = -stream_velocity
+        speed = abs(velocity)
+        if speed < 1e-12:
+            return (0.0,) * 6
+        alpha = math.atan2(velocity.y, abs(velocity.z))
+        beta = math.atan2(velocity.x, abs(velocity.z))
+        interp = self._interpolators[powered]
+        m, a = interp.grid[:2]
+        physical_alpha = math.atan2(velocity.y, velocity.z)
+        self.outside_alpha_envelope |= not a[0] <= physical_alpha <= a[-1]
+        self.outside_mach_envelope |= not m[0] <= stream_mach <= m[-1]
+        if len(interp.grid) == 3:
+            b = interp.grid[2]
+            self.outside_beta_envelope |= not b[0] <= beta <= b[-1]
+        values = self.coefficients(powered, stream_mach, alpha, beta)
+        cn, cm, ca, cy, cln, cll, cyb, clnb, cllb = values[:9]
+        beta_deg = math.degrees(beta)
+        if len(interp.grid) == 2:
+            cy += cyb * beta_deg
+            cln += clnb * beta_deg
+            cll += cllb * beta_deg
+        if speed >= 1:
+            cnq, cmq, caq, cnad, cmad, cyr, clnr, cllr, cyp, clnp, cllp = values[9:20]
+            factor = math.degrees(self.reference_length / (2 * speed))
+            q, r, p = (rate * factor for rate in omega)
+            adot = alpha_dot * factor
+            cn += cnq * q + cnad * adot
+            cm += cmq * q + cmad * adot
+            ca += caq * q
+            cy += cyr * r + cyp * p
+            cln += clnr * r + clnp * p
+            cll += cllr * r + cllp * p
+        q_area = 0.5 * rho * speed**2 * self.reference_area
+        # CA is already a body-axis coefficient. Applying another cosine here
+        # would project it twice. Only reverse its direction for tail-first flow.
+        axial_direction = math.copysign(1.0, velocity.z)
+        forces = Vector([cy, -cn, -ca * axial_direction]) * q_area
+        moments = Vector([cm, cln, cll]) * (q_area * self.reference_length)
+        moments += cp ^ forces
+        return (*forces, *moments)
+
+    def compute_forces_and_moments(self, *args, **kwargs):
+        raise RuntimeError(
+            "DATCOM loads require Flight to supply time and incidence rate"
+        )
+
+    def small_angle_cp(self, mach):
+        """Coast small-angle CP in the user's axial frame, in meters."""
+        table = self.tables[False]
+        zero = zero_alpha_index(table[5])
+        cna = np.interp(mach, table[4], table.derivatives["CNA"][:, zero])
+        cma = np.interp(mach, table[4], table.derivatives["CMA"][:, zero])
+        if abs(cna) < 1e-12:
+            return float("nan")
+        return self.references[False] + self.csys * self.reference_length * cma / cna
diff --git a/rocketpy/rocket/datcom_calibration.py b/rocketpy/rocket/datcom_calibration.py
new file mode 100644
index 000000000..15136245f
--- /dev/null
+++ b/rocketpy/rocket/datcom_calibration.py
@@ -0,0 +1,426 @@
+"""Fit native DATCOM inputs without replacing aerodynamic coefficients."""
+
+from collections.abc import Mapping
+from pathlib import Path
+
+import numpy as np
+from scipy.optimize import minimize, minimize_scalar
+
+COEFFICIENTS = ("CD", "CL", "CA", "CN", "CY", "CM", "CLN", "CLL")
+INPUTS = {"MACH", "ALPHA", "BETA", "POWER_ON", "REYNOLDS_PER_METER"}
+
+
+def _measurements(data, power_on):
+    """Normalize finite column data, preserving partial measured channels."""
+    if isinstance(data, (str, Path)):
+        table = np.genfromtxt(data, delimiter=",", names=True, encoding="utf-8-sig")
+        if not table.dtype.names:
+            raise ValueError("Wind-tunnel CSV requires a header")
+        data = {key: np.atleast_1d(table[key]) for key in table.dtype.names}
+    if not isinstance(data, Mapping):
+        raise ValueError("Wind-tunnel data must be a CSV path or column mapping")
+    columns = {}
+    for key, value in data.items():
+        name = str(key).strip().upper()
+        if name in columns:
+            raise ValueError(f"Duplicate wind-tunnel column: {name}")
+        if name not in INPUTS | set(COEFFICIENTS):
+            raise ValueError(f"Unsupported wind-tunnel column: {key}")
+        array = np.asarray(value, float)
+        if array.ndim != 1 or not len(array):
+            raise ValueError(f"Wind-tunnel column {key} must be a nonempty vector")
+        columns[name] = array
+    if "MACH" not in columns or not set(columns) & set(COEFFICIENTS):
+        raise ValueError("Wind-tunnel data needs mach and at least one coefficient")
+    count = len(columns["MACH"])
+    if any(len(value) != count for value in columns.values()):
+        raise ValueError("Wind-tunnel columns must have equal lengths")
+    if not np.isfinite(columns["MACH"]).all() or np.any(columns["MACH"] <= 0):
+        raise ValueError("Wind-tunnel Mach numbers must be finite and positive")
+    for key, default in (("ALPHA", 0), ("BETA", 0), ("POWER_ON", float(power_on))):
+        columns.setdefault(key, np.full(count, default))
+        if not np.isfinite(columns[key]).all():
+            raise ValueError(f"Wind-tunnel {key} must be finite")
+    if not np.isin(columns["POWER_ON"], [0, 1]).all():
+        raise ValueError("Wind-tunnel power_on must be 0 or 1")
+    if "REYNOLDS_PER_METER" in columns:
+        ren = columns["REYNOLDS_PER_METER"]
+        if not np.isfinite(ren).all() or np.any(ren <= 0):
+            raise ValueError(
+                "Tunnel Reynolds number per meter must be finite and positive"
+            )
+    measured = [key for key in COEFFICIENTS if key in columns]
+    if any(np.isinf(columns[key]).any() for key in measured):
+        raise ValueError("Measured coefficients cannot be infinite")
+    if any(not np.isfinite(columns[key]).any() for key in measured):
+        raise ValueError("Every coefficient column must contain a measurement")
+    if not np.any(
+        np.column_stack([np.isfinite(columns[key]) for key in measured]), axis=1
+    ).all():
+        raise ValueError("Every row needs at least one finite measured coefficient")
+    return columns, measured
+
+
+def _positive(value, name):
+    try:
+        number = float(value) if np.ndim(value) == 0 else float("nan")
+    except (ValueError, TypeError, OverflowError):
+        number = float("nan")
+    if not np.isfinite(number) or number <= 0:
+        raise ValueError(f"{name} must be a finite positive scalar")
+    return number
+
+
+def _predictions(
+    model, rocket, columns, measured, area, length, lateral_length, origin, drag_axis
+):
+    """Transform native coefficients to the tunnel's axes and references."""
+    predicted = {key: [] for key in measured}
+    for i, mach in enumerate(columns["MACH"]):
+        state = bool(columns["POWER_ON"][i])
+        alpha, beta = np.radians([columns["ALPHA"][i], columns["BETA"][i]])
+        grid = model._interpolators[state].grid
+        values = [mach, alpha] + ([beta] if len(grid) == 3 else [])
+        if any(
+            value < axis[0] - 1e-12 or value > axis[-1] + 1e-12
+            for value, axis in zip(values, grid)
+        ):
+            raise ValueError(
+                "Wind-tunnel measurements exceed the DATCOM Mach/angle envelope"
+            )
+        if len(grid) == 2 and beta != 0:
+            raise ValueError("Nonzero tunnel beta requires a DATCOM beta table")
+        cn, cm, ca, cy, cln, cll = model.coefficients(state, mach, alpha, beta)[:6]
+        lift = cn * np.cos(alpha) - ca * np.sin(alpha)
+        if drag_axis == "wind":
+            # Unit flow = (tan(beta), tan(alpha), 1) / norm in RocketPy axes.
+            cd = (ca + cn * np.tan(alpha) - cy * np.tan(beta)) / np.sqrt(
+                1 + np.tan(alpha) ** 2 + np.tan(beta) ** 2
+            )
+        else:
+            cd = ca * np.cos(alpha) + cn * np.sin(alpha)
+        result = {"CD": cd, "CL": lift, "CA": ca, "CN": cn, "CY": cy}
+        result = {key: value * rocket.area / area for key, value in result.items()}
+        if any(key in measured for key in ("CM", "CLN", "CLL")):
+            # M_new = M_old + (r_old - r_new) x F. In RocketPy axes
+            # F=(CY,-CN,-CA), so the axial shift adds (d*CN,d*CY,0).
+            shift = (
+                (model.references[state] - origin) * rocket._csys / (2 * rocket.radius)
+            )
+            scale = rocket.area * (2 * rocket.radius) / area
+            result.update(
+                CM=(cm + shift * cn) * scale / length,
+                CLN=(cln + shift * cy) * scale / lateral_length,
+                CLL=cll * scale / lateral_length,
+            )
+        for key in measured:
+            predicted[key].append(float(result[key]))
+    return predicted
+
+
+def _tunnel_conditions(columns, original_mach, model_scale=1):
+    """Use measured Reynolds conditions only for fitting, never for Flight."""
+    if "REYNOLDS_PER_METER" not in columns:
+        return None
+    mach = np.unique(columns["MACH"])
+    if len(mach) == 1:
+        other = np.asarray(original_mach)[np.asarray(original_mach) != mach[0]]
+        mach = np.sort([mach[0], other[np.argmin(abs(other - mach[0]))]])
+    if len(mach) > 20:
+        raise ValueError("Tunnel Reynolds schedule supports at most 20 Mach values")
+    schedules = {}
+    for state, label in ((True, "power_on"), (False, "power_off")):
+        mask = columns["POWER_ON"] == state
+        # The unused state still needs a valid deck; it is not evaluated.
+        if not mask.any():
+            mask = np.ones(len(columns["MACH"]), dtype=bool)
+        levels = np.unique(columns["MACH"][mask])
+        reynolds = []
+        for value in levels:
+            values = columns["REYNOLDS_PER_METER"][mask & (columns["MACH"] == value)]
+            if not np.allclose(values, values[0], rtol=1e-8, atol=0):
+                raise ValueError(
+                    "Tunnel data needs one Reynolds number per Mach and power state"
+                )
+            reynolds.append(values[0] * model_scale)
+        schedules[label] = np.interp(mach, levels, reynolds).tolist()
+    # ALT is unused with REN but must have the new grid's shape for validation.
+    return {"mach": mach.tolist(), "reynolds": schedules, "altitude": 0}
+
+
+def fit_datcom_calibration(
+    rocket,
+    data,
+    *,
+    reference_area=None,
+    parameters=("roughness_height",),
+    bounds=None,
+    reference_length=None,
+    moment_reference=None,
+    lateral_reference_length=None,
+    model_scale=1.0,
+    power_on=False,
+    weights=None,
+    drag_axis="wind",
+    max_evaluations=100,
+):
+    """Bounded coefficient fit; publish calibration only after successful runs."""
+    from rocketpy.rocket.datcom import DatcomSurface, datcom_decks, run_datcom
+
+    if not rocket._has_datcom_fins():
+        raise ValueError("Wind-tunnel calibration requires DATCOM fins")
+    if not isinstance(power_on, (bool, np.bool_)):
+        raise ValueError("power_on must be boolean; row values may be 0 or 1")
+    columns, measured = _measurements(data, power_on)
+    model_scale = _positive(model_scale, "model_scale")
+    measured_area = _positive(
+        rocket.area * model_scale**2 if reference_area is None else reference_area,
+        "reference_area",
+    )
+    area = measured_area / model_scale**2
+    length = lateral_length = origin = None
+    if set(measured) & {"CM", "CLN", "CLL"}:
+        length = _positive(reference_length, "reference_length") / model_scale
+        lateral_length = (
+            _positive(
+                reference_length
+                if lateral_reference_length is None
+                else lateral_reference_length,
+                "lateral_reference_length",
+            )
+            / model_scale
+        )
+        if (
+            moment_reference is None
+            or np.ndim(moment_reference) != 0
+            or not np.isfinite(moment_reference)
+        ):
+            raise ValueError(
+                "Moment measurements require a finite moment_reference in rocket coordinates"
+            )
+        origin = float(moment_reference)
+    if drag_axis not in ("wind", "stability"):
+        raise ValueError("drag_axis must be 'wind' or 'stability'")
+    if isinstance(parameters, str):
+        parameters = (parameters,)
+    parameters = tuple(parameters)
+    if (
+        not parameters
+        or len(set(parameters)) != len(parameters)
+        or set(parameters) - {"roughness_height", "leading_edge_radius"}
+    ):
+        raise ValueError(
+            "Fit roughness_height and/or leading_edge_radius without duplicates"
+        )
+    if "leading_edge_radius" in parameters and any(
+        getattr(s, "section_type", None) == "NACA"
+        for s, _, _ in rocket.aerodynamic_surfaces
+    ):
+        raise ValueError("NACA sections define their own leading-edge radius")
+    limits = dict(bounds or {})
+    if set(limits) - set(parameters):
+        raise ValueError("bounds contains parameters which are not being fitted")
+    limits.setdefault("roughness_height", (0, 300e-6))
+    for name in parameters:
+        pair = np.asarray(limits.get(name, []), float)
+        if (
+            pair.shape != (2,)
+            or not np.isfinite(pair).all()
+            or not 0 <= pair[0] < pair[1]
+        ):
+            raise ValueError(
+                f"{name} requires finite nonnegative (lower, upper) bounds"
+            )
+        limits[name] = pair
+    weights = dict(weights or {})
+    if set(weights) - set(measured):
+        raise ValueError("weights names must correspond to measured coefficients")
+    weight = {key: _positive(weights.get(key, 1), f"weight {key}") for key in measured}
+    if (
+        isinstance(max_evaluations, bool)
+        or not isinstance(max_evaluations, (int, np.integer))
+        or max_evaluations < 3
+    ):
+        raise ValueError("max_evaluations must be an integer of at least 3")
+    observation_count = sum(int(np.isfinite(columns[key]).sum()) for key in measured)
+    if observation_count < len(parameters):
+        raise ValueError(
+            "Not enough measured coefficients for the requested parameters"
+        )
+    original = rocket.datcom_calibration
+    # Native geometry setup is evaluated without publishing any trial model or
+    # changing the rocket's calibration, cached model, or reporting functions.
+    first = next(
+        s for s, _, _ in rocket.aerodynamic_surfaces if hasattr(s, "datcom_options")
+    )
+    original_mach = np.asarray(
+        first.datcom_options.get("mach", [0.1, 0.3, 0.5, 0.7, 0.9, 1.1, 1.5, 2])
+    )
+    conditions = _tunnel_conditions(columns, original_mach, model_scale)
+    states = sorted(set(bool(value) for value in columns["POWER_ON"]))
+    cache = {}
+
+    def evaluate(settings):
+        key = (settings["roughness_height"], settings["leading_edge_radius"])
+        if key in cache:
+            return cache[key]
+        if len(cache) >= max_evaluations:
+            raise RuntimeError(
+                "Wind-tunnel fit exceeded max_evaluations; calibration was not changed"
+            )
+        decks, references, timeout, path = datcom_decks(
+            rocket, calibration=settings, flight_conditions=conditions
+        )
+        tables = {state: run_datcom(path, decks[state], timeout) for state in states}
+        model = DatcomSurface(rocket, tables, references)
+        predictions = _predictions(
+            model,
+            rocket,
+            columns,
+            measured,
+            area,
+            length,
+            lateral_length,
+            origin,
+            drag_axis,
+        )
+        residuals = np.concatenate(
+            [
+                (np.asarray(predictions[key]) - columns[key])[np.isfinite(columns[key])]
+                * np.sqrt(weight[key])
+                for key in measured
+            ]
+        )
+        cache[key] = (float(np.mean(residuals**2)), predictions)
+        return cache[key]
+
+    initial_cost, initial_prediction = evaluate(original)
+
+    def settings_for(point):
+        settings = dict(original)
+        for name, value in zip(parameters, np.atleast_1d(point)):
+            low, high = limits[name]
+            settings[name] = float(low + value * (high - low))
+        return settings
+
+    def objective(point):
+        return evaluate(settings_for(point))[0]
+
+    if len(parameters) == 1:
+        result = minimize_scalar(
+            objective,
+            bounds=(0, 1),
+            method="bounded",
+            options={"xatol": 1e-4, "maxiter": max_evaluations - 3},
+        )
+        best_point = [result.x]
+    else:
+        start = [
+            np.clip(
+                ((original[name] or 0) - limits[name][0]) / np.ptp(limits[name]), 0, 1
+            )
+            for name in parameters
+        ]
+        result = minimize(
+            objective,
+            start,
+            method="Powell",
+            bounds=[(0, 1)] * len(parameters),
+            options={"maxfev": max_evaluations - 1, "xtol": 1e-4, "ftol": 1e-6},
+        )
+        best_point = result.x
+    if not result.success:
+        raise RuntimeError(
+            f"Wind-tunnel fit failed; calibration was not changed: {result.message}"
+        )
+    # Explicitly compare endpoints: rounded native coefficients can hide a
+    # boundary optimum from an interior bounded search.
+    candidates = [settings_for(best_point)]
+    if len(parameters) == 1:
+        candidates += [settings_for([0]), settings_for([1])]
+    fitted = min(candidates, key=lambda settings: evaluate(settings)[0])
+    final_cost, prediction = evaluate(fitted)
+    # Retain an existing calibration if it is within bounds and already better.
+    if (
+        all(original[name] is not None for name in parameters)
+        and all(
+            limits[name][0] <= (original[name] or 0) <= limits[name][1]
+            for name in parameters
+        )
+        and initial_cost < final_cost
+    ):
+        fitted, final_cost, prediction = original, initial_cost, initial_prediction
+    residual_rows = []
+    rmse = {}
+    for key in measured:
+        mask = np.isfinite(columns[key])
+        delta = np.asarray(prediction[key])[mask] - columns[key][mask]
+        rmse[key] = dict(
+            initial=float(
+                np.sqrt(
+                    np.mean(
+                        (np.asarray(initial_prediction[key])[mask] - columns[key][mask])
+                        ** 2
+                    )
+                )
+            ),
+            fitted=float(np.sqrt(np.mean(delta**2))),
+        )
+        for i in np.flatnonzero(mask):
+            residual_rows.append(
+                dict(
+                    row=int(i),
+                    mach=float(columns["MACH"][i]),
+                    alpha_deg=float(columns["ALPHA"][i]),
+                    beta_deg=float(columns["BETA"][i]),
+                    power_on=bool(columns["POWER_ON"][i]),
+                    coefficient=key,
+                    observed=float(columns[key][i]),
+                    predicted=prediction[key][i],
+                    residual=float(prediction[key][i] - columns[key][i]),
+                )
+            )
+    report = dict(
+        success=True,
+        improved=bool(final_cost < initial_cost - 1e-16),
+        observation_count=observation_count,
+        calibration=dict(fitted),
+        initial_calibration=original,
+        parameters=list(parameters),
+        bounds={key: limits[key].tolist() for key in parameters},
+        reference_area=area,
+        measured_reference_area=measured_area,
+        model_scale=model_scale,
+        reference_length=length,
+        lateral_reference_length=lateral_length,
+        moment_reference=origin,
+        drag_axis=drag_axis,
+        reynolds_source="measurements"
+        if conditions is not None
+        else "rocket DATCOM options",
+        weighted_rmse_initial=float(np.sqrt(initial_cost)),
+        weighted_rmse_fitted=float(np.sqrt(final_cost)),
+        rmse=rmse,
+        residuals=residual_rows,
+        native_evaluations=len(cache),
+        native_runs=len(cache) * len(states),
+        at_bound={
+            name: bool(
+                np.isclose(
+                    fitted[name],
+                    limits[name][0],
+                    rtol=0,
+                    atol=np.ptp(limits[name]) * 1e-4,
+                )
+                or np.isclose(
+                    fitted[name],
+                    limits[name][1],
+                    rtol=0,
+                    atol=np.ptp(limits[name]) * 1e-4,
+                )
+            )
+            for name in parameters
+        },
+    )
+    rocket.add_datcom_calibration(**fitted)
+    return report
diff --git a/rocketpy/rocket/datcom_conditions.py b/rocketpy/rocket/datcom_conditions.py
new file mode 100644
index 000000000..ab5a3b9da
--- /dev/null
+++ b/rocketpy/rocket/datcom_conditions.py
@@ -0,0 +1,67 @@
+"""Native-size DATCOM flight grids, independent of ordinary RocketPy models."""
+
+import numpy as np
+
+MAX_MACH_POINTS = 20
+
+
+def adaptive_mach_grid(peak_mach, margin=0.15):
+    """Twenty positive Mach points with fixed anchors and transonic resolution.
+
+    Mach output has two decimal places in the supported native build, so points
+    are separated by at least 0.01. Zero speed uses the 0.01 endpoint. The upper
+    bound includes 15% headroom plus 0.02 Mach, and is at least 1.2. Spare points
+    bisect gaps, prioritizing the reached flight regime over unreached speeds.
+    """
+    if not np.isfinite(peak_mach) or peak_mach < 0:
+        raise ValueError("Reference peak Mach must be finite and nonnegative")
+    if not np.isfinite(margin) or margin < 0:
+        raise ValueError("Mach margin must be finite and nonnegative")
+    upper = np.ceil(max(1.2, peak_mach * (1 + margin) + 0.02) * 100) / 100
+    reached = max(0.01, np.ceil(peak_mach * 100) / 100)
+    selected = {
+        0.01,
+        0.1,
+        0.8,
+        0.9,
+        0.95,
+        0.98,
+        1.0,
+        1.02,
+        1.05,
+        1.1,
+        1.2,
+        upper,
+        reached,
+    }
+    while len(selected) < MAX_MACH_POINTS:
+        values = np.array(sorted(selected))
+        gaps = np.diff(values)
+        middle = (values[:-1] + values[1:]) / 2
+        priorities = gaps * np.where(middle <= reached, 4, 1)
+        priorities[gaps < 0.0199] = -1
+        index = int(np.argmax(priorities))
+        selected.add(float(np.round(middle[index], 2)))
+    return np.array(sorted(selected))
+
+
+def adaptive_alpha_grid(alpha_degrees):
+    """Signed 19-point grid, retaining ±20° minimum and dense small angles.
+
+    Extend symmetrically beyond observed, dynamically meaningful incidence by
+    15% plus 2 degrees. Tail-first flow is outside the current forward-flight
+    DATCOM load mapping and is rejected instead of silently folded or clipped.
+    """
+    alpha = np.asarray(alpha_degrees, float)
+    if not alpha.size or not np.isfinite(alpha).all():
+        raise ValueError("Reference flight needs finite loaded alpha samples")
+    peak = float(np.max(np.abs(alpha)))
+    extent = max(20, np.ceil((peak * 1.15 + 2) * 100) / 100)
+    if extent >= 90:
+        raise ValueError(
+            "Adaptive DATCOM alpha envelope reaches tail-first flow (90 degrees)"
+        )
+    positive = np.round(
+        extent * np.array([0.0025, 0.005, 0.0125, 0.025, 0.05, 0.1, 0.25, 0.5, 1]), 2
+    )
+    return np.concatenate((-positive[::-1], [0.0], positive))
diff --git a/rocketpy/rocket/datcom_rocket.py b/rocketpy/rocket/datcom_rocket.py
new file mode 100644
index 000000000..368dfa495
--- /dev/null
+++ b/rocketpy/rocket/datcom_rocket.py
@@ -0,0 +1,656 @@
+"""DATCOM-specific Rocket assembly and preparation, isolated from core models."""
+
+import numpy as np
+
+from rocketpy.mathutils import Function
+from rocketpy.mathutils.vector_matrix import Vector
+from rocketpy.rocket.aero_surface.datcom_fins import DatcomFins, DatcomFreeFormFins
+
+
+class DatcomRocketMixin:
+    """Optional DATCOM behavior shared by Rocket and its subclasses."""
+
+    def set_datcom_atmosphere(
+        self,
+        flight,
+        *,
+        mach=None,
+        alpha=None,
+        samples=1000,
+        minimum_dynamic_pressure=100,
+    ):
+        """Adapt native Mach/alpha grids and Reynolds schedules to a flight.
+
+        The default Mach grid uses all 20 native slots, always includes 0.1
+        and 1.0, concentrates points near Mach 1, and extends above the reference
+        peak by 15% plus 0.02. Both powered and coast samples determine coverage.
+        Mach zero uses the positive 0.01 endpoint. Explicit ``mach`` retains a
+        custom 2..20-point grid instead. The initial no-flight grid is provisional.
+
+        Adaptive alpha has 19 signed points concentrated around zero, retaining
+        at least ±20 degrees and extending beyond observed incidence by 15% plus
+        2 degrees. Only samples at ``minimum_dynamic_pressure`` (Pa, default
+        100) or greater determine its extent; low-load pad/apogee directions do
+        not force extreme grids. With no loaded samples it retains ±20 degrees.
+        Explicit ``alpha`` fixes a custom native grid (degrees). Tail-first
+        adaptive envelopes are rejected because the current load model assumes
+        forward flight. The returned coverage report identifies excluded samples.
+
+        ``flight`` may be an ordinary or previous DATCOM Flight with the same
+        motor burn interval. Sampling includes ``samples`` uniform points,
+        solver states and burn boundaries, from rail to apogee. Descent is
+        excluded; the current load model assumes forward-flight aerodynamics.
+
+        At sampled altitude (meters ASL), the reference Environment supplies
+        density, sound speed and dynamic viscosity. Native Reynolds per meter
+        is rho*a*Mach/mu; REN replaces ALT, bypassing the standard atmosphere.
+        Return Mach, alpha, coverage, Reynolds, altitude, pressure (Pa) and
+        temperature (K) schedules for provenance. No Flight or native run is
+        started here; the next Flight regenerates the tables. Call again with
+        that result to refine both the envelope and atmospheric schedules.
+
+        Each phase still uses one atmospheric state per Mach. Repeated Mach
+        retains the earliest sample; unreached Mach clamps atmospheric state
+        while recomputing speed. This does not resolve multiple ascent/descent
+        crossings at different altitudes or guarantee iterative convergence.
+        Ordinary equations are unchanged; failed validation preserves settings.
+        """
+        from rocketpy.rocket.datcom import _flight_conditions
+        from rocketpy.rocket.datcom_conditions import (
+            adaptive_alpha_grid,
+            adaptive_mach_grid,
+        )
+
+        fins = [
+            s
+            for s, _, _ in self.aerodynamic_surfaces
+            if isinstance(s, (DatcomFins, DatcomFreeFormFins))
+        ]
+        if not fins:
+            raise ValueError("Add DATCOM fins before setting their atmosphere")
+        if not isinstance(samples, (int, np.integer)) or samples < 3:
+            raise ValueError("samples must be an integer of at least 3")
+        if not np.allclose(self.motor.burn_time, flight.rocket.motor.burn_time):
+            raise ValueError("Reference flight must use the same motor burn interval")
+        if not np.isfinite(minimum_dynamic_pressure) or minimum_dynamic_pressure < 0:
+            raise ValueError("minimum_dynamic_pressure must be finite and nonnegative")
+        end = float(flight.apogee_time)
+        if not np.isfinite(end) or end <= 0:
+            raise ValueError("Reference flight must contain a positive flight end time")
+        time = np.linspace(0, end, samples)
+        # Include actual solver states and phase boundaries, not just uniform
+        # samples: short burns and rail/burnout transitions must not disappear.
+        extra = list(self.motor.burn_time)
+        if hasattr(flight, "solution"):
+            extra.extend(np.asarray(flight.solution)[:, 0])
+        time = np.unique(np.concatenate((time, np.asarray(extra))))
+        time = time[(time >= 0) & (time <= end)]
+        trajectory_mach = np.asarray(flight.mach_number(time), float)
+        altitude = np.asarray(flight.z(time), float)
+        env = flight.env
+        dynamic_pressure = (
+            0.5
+            * np.asarray(env.density(altitude))
+            * (trajectory_mach * env.speed_of_sound(altitude)) ** 2
+        )
+        loaded = np.isfinite(dynamic_pressure) & (
+            dynamic_pressure >= minimum_dynamic_pressure
+        )
+        options = dict(fins[0].datcom_options)
+        options["mach"] = (
+            adaptive_mach_grid(float(np.max(trajectory_mach))) if mach is None else mach
+        )
+        if alpha is None:
+            observed_alpha = np.asarray(flight.partial_angle_of_attack(time), float)
+            options["alpha"] = adaptive_alpha_grid(
+                observed_alpha[loaded] if np.any(loaded) else [0.0]
+            )
+        else:
+            observed_alpha = None
+            options["alpha"] = alpha
+        # The old ALT schedule belongs to the old Mach grid. REN replaces it.
+        options.pop("altitude", None)
+        grid, alpha_grid, _, _ = _flight_conditions(options)
+        start, stop = self.motor.burn_time
+        powered = (time >= start) & (time < stop)
+        report = {
+            "mach": grid.tolist(),
+            "alpha": alpha_grid.tolist(),
+            "coverage": {
+                "scope": "rail through apogee",
+                "minimum_dynamic_pressure_pa": float(minimum_dynamic_pressure),
+                "excluded_low_q_samples": int(np.count_nonzero(~loaded)),
+                "phases": {},
+            },
+            "reynolds": {},
+            "altitude_m_asl": {},
+            "pressure_pa": {},
+            "temperature_k": {},
+        }
+        for state, label in [(True, "power_on"), (False, "power_off")]:
+            indices = np.flatnonzero(
+                (powered == state)
+                & np.isfinite(trajectory_mach)
+                & np.isfinite(altitude)
+            )
+            if not len(indices):
+                raise ValueError(f"Reference ascent has no {label} samples")
+            phase_loaded = indices[loaded[indices]]
+            phase = {
+                "mach_min": float(np.min(trajectory_mach[indices])),
+                "mach_max": float(np.max(trajectory_mach[indices])),
+                "loaded_alpha_max_abs_deg": (
+                    float(np.max(np.abs(observed_alpha[phase_loaded])))
+                    if observed_alpha is not None and len(phase_loaded)
+                    else None
+                ),
+            }
+            report["coverage"]["phases"][label] = phase
+            order = indices[np.argsort(trajectory_mach[indices], kind="stable")]
+            unique, first = np.unique(trajectory_mach[order], return_index=True)
+            z = altitude[order[first]]
+            env = flight.env
+            # Interpolate Re/M, then apply target Mach: clamped endpoints retain
+            # atmospheric state rather than incorrectly retaining endpoint speed.
+            factor = (
+                np.asarray(env.density(z))
+                * np.asarray(env.speed_of_sound(z))
+                / np.asarray(env.dynamic_viscosity(z))
+            )
+            ren = grid * np.interp(grid, unique, factor)
+            if not np.all(np.isfinite(ren) & (ren > 0)):
+                raise ValueError(
+                    "Environment must supply finite positive Reynolds numbers"
+                )
+            report["reynolds"][label] = ren.tolist()
+            report["altitude_m_asl"][label] = np.interp(grid, unique, z).tolist()
+            for key, function in [
+                ("pressure_pa", env.pressure),
+                ("temperature_k", env.temperature),
+            ]:
+                report[key][label] = np.interp(grid, unique, function(z)).tolist()
+        options.update(mach=grid.tolist(), reynolds=report["reynolds"])
+        options.pop("altitude", None)
+        self._invalidate_datcom()
+        for surface in fins:
+            surface.datcom_options = dict(options)
+        self.evaluate_center_of_pressure()
+        self.evaluate_stability_margin()
+        self._invalidate_static_margin()
+        return report
+
+    @property
+    def datcom_calibration(self):
+        """Return a copy of the explicit whole-rocket calibration inputs."""
+        return dict(
+            getattr(
+                self,
+                "_datcom_calibration",
+                {"roughness_height": 0.0, "leading_edge_radius": None},
+            )
+        )
+
+    def add_datcom_calibration(self, *, roughness_height=0.0, leading_edge_radius=None):
+        """Set native DATCOM surface/edge calibration, replacing previous values.
+
+        Parameters
+        ----------
+        roughness_height : float, optional
+            Nonnegative equivalent sand roughness height in meters, not Ra.
+            Converted to DATCOM RHR using k = 3 RHR microinches, avoiding the
+            version-dependent metric ROUGH convention. Default is smooth.
+        leading_edge_radius : float or None, optional
+            Nonnegative radius in meters, overriding LER at every station of
+            every supported fin set. None retains each fin's own radius.
+            NACA sections define their own edge geometry and reject overrides.
+
+        Returns
+        -------
+        dict
+            Copy of the stored calibration. Call without arguments to reset.
+
+        Notes
+        -----
+        Add DATCOM fins first. This method sets inputs; it does not fit them or
+        alter mass, planform or ordinary RocketPy aerodynamics. A fitted
+        roughness is an effective calibration, not a measured material finish.
+        Changes invalidate the prepared tables; Flight regenerates them.
+        """
+        if not self._has_datcom_fins():
+            raise ValueError(
+                "DATCOM calibration requires a DATCOM fin set; add it first"
+            )
+        values = {}
+        for name, value in (
+            ("roughness_height", roughness_height),
+            ("leading_edge_radius", leading_edge_radius),
+        ):
+            if name == "leading_edge_radius" and value is None:
+                values[name] = None
+                continue
+            if np.ndim(value) != 0:
+                raise ValueError(f"{name} must be a finite nonnegative scalar")
+            try:
+                number = float(value)
+            except (TypeError, ValueError, OverflowError) as error:
+                raise ValueError(
+                    f"{name} must be a finite nonnegative scalar"
+                ) from error
+            if (
+                isinstance(value, (bool, np.bool_))
+                or not np.isfinite(number)
+                or number < 0
+            ):
+                raise ValueError(f"{name} must be a finite nonnegative scalar")
+            values[name] = number
+        if leading_edge_radius is not None and any(
+            getattr(s, "section_type", None) == "NACA"
+            for s, _, _ in self.aerodynamic_surfaces
+        ):
+            raise ValueError("NACA sections define their own leading-edge radius")
+        if values != self.datcom_calibration:
+            self._invalidate_datcom()
+            self._datcom_calibration = values
+            self.evaluate_center_of_pressure()
+            self.evaluate_stability_margin()
+            self._invalidate_static_margin()
+        return self.datcom_calibration
+
+    def fit_datcom_calibration(
+        self,
+        data,
+        *,
+        reference_area=None,
+        parameters=("roughness_height",),
+        bounds=None,
+        reference_length=None,
+        moment_reference=None,
+        lateral_reference_length=None,
+        model_scale=1.0,
+        power_on=False,
+        weights=None,
+        drag_axis="wind",
+        max_evaluations=100,
+    ):
+        """Fit native calibration inputs to static wind-tunnel coefficients.
+
+        ``data`` is a CSV path or a column mapping. Required columns are
+        ``mach`` and at least one of CD, CL, CA, CN, CY, CM, CLN, CLL. Optional
+        ``alpha``/``beta`` are degrees (default zero); ``power_on`` is 0 or 1;
+        ``reynolds_per_meter`` specifies tunnel Reynolds number per meter.
+
+        ``reference_area`` is the measurement area in m², defaulting to the
+        rocket frontal area. Moment measurements
+        also require ``reference_length`` (m) and ``moment_reference`` (m in
+        this rocket's axial position convention). An optional lateral length
+        scales yaw/roll coefficients; it defaults to reference_length.
+        ``model_scale`` is tunnel-model size divided by full-rocket size.
+        Reference areas/lengths and tunnel Reynolds numbers are converted to
+        full-scale equivalents; moment_reference stays in rocket coordinates.
+
+        Fit roughness_height and/or leading_edge_radius, with bounds in meters.
+        Roughness defaults to 0..300 micrometers. Fitting edge radius requires
+        explicit physically justified bounds. ``weights`` maps coefficient
+        names to positive least-squares weights. CD is wind-axis drag by
+        default; drag_axis='stability' selects the pitch-plane convention.
+
+        Returns a JSON-compatible report with residuals, initial/final RMSE,
+        fitted inputs, bounds and native evaluation count. Successful fits
+        are applied through add_datcom_calibration. Failed fits preserve the
+        existing settings and prepared model. No measured coefficient or
+        derivative replaces the native DATCOM tables.
+        """
+        from rocketpy.rocket.datcom_calibration import fit_datcom_calibration
+
+        return fit_datcom_calibration(
+            self,
+            data,
+            reference_area=reference_area,
+            parameters=parameters,
+            bounds=bounds,
+            reference_length=reference_length,
+            moment_reference=moment_reference,
+            lateral_reference_length=lateral_reference_length,
+            model_scale=model_scale,
+            power_on=power_on,
+            weights=weights,
+            drag_axis=drag_axis,
+            max_evaluations=max_evaluations,
+        )
+
+    def _create_datcom_or_conical_tail(self, top, bottom, length, radius, name, kind):
+        from rocketpy.rocket.aero_surface.tail import Tail
+
+        key = kind.replace(" ", "").replace("-", "").lower()
+        if key in ("cone", "conical"):
+            return Tail(top, bottom, length, radius, name)
+        if key not in ("ogive", "tangent", "tangentogive"):
+            raise ValueError("Tail kind must be 'conical' or 'ogive'")
+        if not self._has_datcom_fins():
+            raise ValueError("Ogive tails require a DATCOM fin set; add it first")
+        from rocketpy.rocket.aero_surface.datcom_tail import DatcomTail
+
+        return DatcomTail(top, bottom, length, radius, name)
+
+    def _validate_datcom_tail(self, surface):
+        from rocketpy.rocket.aero_surface.datcom_tail import DatcomTail
+
+        if isinstance(surface, DatcomTail) and not self._has_datcom_fins():
+            raise ValueError("Ogive tails require a DATCOM fin set; add it first")
+
+    def _set_undefined_datcom_static_margin(self):
+        """Keep an undefined CP out of the ordinary finite-data spline fit."""
+        if self._has_datcom_fins() and not np.isfinite(self.cp_position(0)):
+            self._static_margin.set_source(lambda time: float("nan"))
+            self._static_margin_dirty = False
+            return True
+        return False
+
+    def _evaluate_datcom_center_of_pressure(self):
+        if self._has_datcom_fins():
+            model = self._datcom_model
+            if model is None:
+                self.cp_position.set_source(lambda mach: float("nan"))
+                self.total_lift_coeff_der.set_source(lambda mach: float("nan"))
+            else:
+                self.cp_position.set_source(lambda mach: model.small_angle_cp(mach))
+                table = model.tables[False]
+                zero = int(np.flatnonzero(table[5] == 0)[0])
+                self.total_lift_coeff_der.set_source(
+                    lambda mach: float(
+                        np.interp(mach, table[4], table.derivatives["CNA"][:, zero])
+                    )
+                    * 180
+                    / np.pi
+                )
+            return True
+
+        return False
+
+    def _has_datcom_fins(self):
+        return any(
+            isinstance(s, (DatcomFins, DatcomFreeFormFins))
+            for s, _, _ in self.aerodynamic_surfaces
+        )
+
+    @property
+    def _flight_aerodynamic_surfaces(self):
+        """Active flight models; original geometry remains available for drawing."""
+        if self._datcom_model is not None:
+            return [
+                (
+                    self._datcom_model,
+                    Vector([0, 0, self.center_of_dry_mass_position]),
+                    1,
+                )
+            ]
+        return self.aerodynamic_surfaces
+
+    def prepare_datcom(self):
+        """Generate powered/coast DATCOM tables from the current assembled rocket.
+
+        Flight calls this automatically. Call it explicitly before inspecting
+        DATCOM drag or stability plots. Identical geometry, CG references,
+        conditions and executable reuse the in-memory model. Changes rebuild
+        both cases. No executable or generated output is kept in the repository.
+
+        Returns
+        -------
+        DatcomSurface or None
+            Whole-vehicle aerodynamic model, or None without DATCOM fins.
+        """
+        if not self._has_datcom_fins():
+            for surface, _, _ in self.aerodynamic_surfaces:
+                self._validate_datcom_tail(surface)
+            self._invalidate_datcom()
+            return None
+
+        from pathlib import Path  # pylint: disable=import-outside-toplevel
+
+        from rocketpy.rocket.datcom import (  # pylint: disable=import-outside-toplevel
+            DatcomSurface,
+            datcom_decks,
+            run_datcom,
+        )
+
+        decks, references, timeout, path = datcom_decks(self)
+        stat = Path(path).stat()
+        signature = (
+            tuple(decks.items()),
+            tuple(references.items()),
+            path,
+            stat.st_size,
+            stat.st_mtime_ns,
+            self.motor.burn_start_time,
+            self.motor.burn_out_time,
+        )
+        if signature == self._datcom_signature:
+            return self._datcom_model
+        # Publish only after both runs succeed, so failed cases cannot leave a
+        # half-populated aerodynamic database attached to the rocket.
+        tables = {
+            powered: run_datcom(path, deck, timeout) for powered, deck in decks.items()
+        }
+        model = DatcomSurface(self, tables, references)
+        self._invalidate_datcom()
+        self._datcom_model = model
+        self._datcom_signature = signature
+        names = [
+            "power_on_drag_7d",
+            "power_off_drag_7d",
+            "power_on_drag_by_mach",
+            "power_off_drag_by_mach",
+            "power_on_drag",
+            "power_off_drag",
+        ]
+        self._datcom_original_drag = {name: getattr(self, name) for name in names}
+        for powered, prefix in ((True, "power_on"), (False, "power_off")):
+            # Function counts callable arguments, so bind the powered selector
+            # through a closure factory rather than an eighth default argument.
+            def make_coefficient(state):
+                return (
+                    lambda alpha, beta, mach, reynolds, q, r, p: model.drag_coefficient(
+                        state, alpha, beta, mach, reynolds, q, r, p
+                    )
+                )
+
+            drag = Function(
+                make_coefficient(powered),
+                inputs=["alpha", "beta", "mach", "reynolds", "q", "r", "p"],
+            )
+
+            def make_curve(state):
+                return lambda mach: model.drag_coefficient(
+                    state, 0, 0, mach, 0, 0, 0, 0
+                )
+
+            curve = Function(
+                make_curve(powered), inputs="Mach", outputs="Drag Coefficient"
+            )
+            setattr(self, f"{prefix}_drag_7d", drag)
+            setattr(self, f"{prefix}_drag_by_mach", curve)
+            setattr(self, f"{prefix}_drag", curve)
+        self.surfaces_cp_to_cdm[model] = Vector([0, 0, 0])
+        self.evaluate_center_of_pressure()
+        self.evaluate_stability_margin()
+        self._invalidate_static_margin()
+        return model
+
+    def _invalidate_datcom(self):
+        """Discard whole-vehicle tables when assembly changes."""
+        if self._datcom_model is not None:
+            self.surfaces_cp_to_cdm.pop(self._datcom_model, None)
+        if self._datcom_original_drag is not None:
+            for name, value in self._datcom_original_drag.items():
+                setattr(self, name, value)
+        self._datcom_model = None
+        self._datcom_signature = None
+        self._datcom_original_drag = None
+
+    def add_datcom_trapezoidal_fins(
+        self,
+        n,
+        root_chord,
+        tip_chord,
+        span,
+        position,
+        *,
+        path,
+        thickness_to_chord=0.02,
+        leading_edge_radius=0,
+        sweep_length=None,
+        sweep_angle=None,
+        name="DATCOM Trapezoidal Fins",
+        datcom_options=None,
+        **section_options,
+    ):
+        """Add a trapezoidal planform with a beveled HEX section by default.
+
+        Uses the same geometry/position convention as add_trapezoidal_fins.
+        The section has a flat middle 90% of each chord and 5% edge bevels.
+        ``thickness_to_chord`` is full thickness/chord, scalar or root/tip pair.
+        ``path`` is the native DATCOM executable; ``datcom_options`` accepts
+        mach, alpha (degrees), altitude (meters), timeout (seconds), and optional
+        cylindrical body_length (meters). All existing nose, tail and supported
+        fins are included in the DATCOM model and their Barrowman loads disabled.
+        DAMP is mandatory. No protuberance or plume calculations are requested.
+        See :class:`rocketpy.DatcomFins` for section inputs and limitations.
+        """
+        return self._add_datcom_fins(
+            n,
+            root_chord,
+            tip_chord,
+            span,
+            position,
+            path=path,
+            thickness_to_chord=thickness_to_chord,
+            max_thickness_position=section_options.pop("max_thickness_position", 0.05),
+            flat_length=section_options.pop("flat_length", 0.9),
+            leading_edge_radius=leading_edge_radius,
+            sweep_length=sweep_length,
+            sweep_angle=sweep_angle,
+            name=name,
+            datcom_options=datcom_options,
+            **section_options,
+        )
+
+    def add_datcom_arc_fins(
+        self,
+        n,
+        root_chord,
+        tip_chord,
+        span,
+        position,
+        *,
+        path,
+        thickness_to_chord=0.02,
+        leading_edge_radius=0,
+        sweep_length=None,
+        sweep_angle=None,
+        name="DATCOM Arc Fins",
+        datcom_options=None,
+        **section_options,
+    ):
+        """Add trapezoidal-planform fins with a circular ARC section.
+
+        Geometry and DATCOM settings follow add_datcom_trapezoidal_fins.
+        ARC describes the airfoil cross section, not a curved planform.
+        """
+        return self._add_datcom_fins(
+            n,
+            root_chord,
+            tip_chord,
+            span,
+            position,
+            path=path,
+            section_type="ARC",
+            thickness_to_chord=thickness_to_chord,
+            leading_edge_radius=leading_edge_radius,
+            sweep_length=sweep_length,
+            sweep_angle=sweep_angle,
+            name=name,
+            datcom_options=datcom_options,
+            **section_options,
+        )
+
+    def add_datcom_hex_fins(
+        self,
+        n,
+        root_chord,
+        tip_chord,
+        span,
+        position,
+        *,
+        path,
+        thickness_to_chord=0.02,
+        max_thickness_position=0.25,
+        flat_length=0.5,
+        leading_edge_radius=0,
+        sweep_length=None,
+        sweep_angle=None,
+        name="DATCOM Hex Fins",
+        datcom_options=None,
+        **section_options,
+    ):
+        """Add trapezoidal-planform fins with a HEX airfoil section.
+
+        Geometry and DATCOM settings follow add_datcom_trapezoidal_fins.
+        ``max_thickness_position`` locates the start of maximum thickness;
+        ``flat_length`` is its extent, both as scalar or root/tip chord fractions.
+        A zero flat_length gives a diamond section. HEX describes the airfoil
+        cross section, not a six-sided planform.
+        """
+        return self._add_datcom_fins(
+            n,
+            root_chord,
+            tip_chord,
+            span,
+            position,
+            path=path,
+            thickness_to_chord=thickness_to_chord,
+            max_thickness_position=max_thickness_position,
+            flat_length=flat_length,
+            leading_edge_radius=leading_edge_radius,
+            sweep_length=sweep_length,
+            sweep_angle=sweep_angle,
+            name=name,
+            datcom_options=datcom_options,
+            **section_options,
+        )
+
+    def add_datcom_fins(
+        self,
+        n,
+        position,
+        *,
+        span_stations,
+        chords,
+        leading_edges,
+        path,
+        name="DATCOM Free Form Fins",
+        **section_options,
+    ):
+        """Add a 2..10-station DATCOM planform with any supported airfoil section.
+
+        span_stations are exposed distances from the body surface in meters.
+        leading_edges are aft distances from position; their first value is zero.
+        Chords are meters and the final chord can be zero for a triangular tip.
+        Section and panel-placement options follow DatcomFins.
+        """
+        fins = DatcomFreeFormFins(
+            n,
+            span_stations,
+            chords,
+            leading_edges,
+            self.radius,
+            path=path,
+            name=name,
+            **section_options,
+        )
+        self.add_surfaces(fins, position)
+        return fins
+
+    def _add_datcom_fins(self, n, root_chord, tip_chord, span, position, **kwargs):
+        fins = DatcomFins(n, root_chord, tip_chord, span, self.radius, **kwargs)
+        self.add_surfaces(fins, position)
+        return fins
diff --git a/rocketpy/rocket/rocket.py b/rocketpy/rocket/rocket.py
index f973677fd..b701fa61c 100644
--- a/rocketpy/rocket/rocket.py
+++ b/rocketpy/rocket/rocket.py
@@ -35,6 +35,7 @@
 from rocketpy.rocket.aero_surface.fins.trapezoidal_fin import TrapezoidalFin
 from rocketpy.rocket.aero_surface.generic_surface import GenericSurface
 from rocketpy.rocket.components import Components
+from rocketpy.rocket.datcom_rocket import DatcomRocketMixin
 from rocketpy.rocket.parachute import Parachute
 from rocketpy.tools import (
     deprecated,
@@ -46,7 +47,7 @@
 
 
 # pylint: disable=too-many-instance-attributes, too-many-public-methods, too-many-instance-attributes
-class Rocket:
+class Rocket(DatcomRocketMixin):
     """Keeps rocket information.
 
     Attributes
@@ -373,6 +374,9 @@ def __init__(  # pylint: disable=too-many-statements
         self.air_brakes = []
         self.sensors = Components()
         self.aerodynamic_surfaces = Components()
+        self._datcom_model = None
+        self._datcom_signature = None
+        self._datcom_original_drag = None
         self.surfaces_cp_to_cdm = {}
         self.rail_buttons = Components()
 
@@ -661,6 +665,9 @@ def evaluate_center_of_pressure(self):
             See :doc:`Positions and Coordinate Systems </user/positions>`
             for more information.
         """
+        if self._evaluate_datcom_center_of_pressure():
+            return self.cp_position
+
         # Re-Initialize total lift coefficient derivative and center of pressure position
         self.total_lift_coeff_der.set_source(lambda mach: 0)
         self.cp_position.set_source(lambda mach: 0)
@@ -774,6 +781,9 @@ def evaluate_static_margin(self):
             Static margin is defined as the distance between the center of
             pressure and the center of mass, divided by the rocket's diameter.
         """
+        if self._set_undefined_datcom_static_margin():
+            return self._static_margin
+
         # Calculate static margin; fold _csys into the source so we do not
         # rebind a property when multiplying.
         self._static_margin.set_source(
@@ -1140,6 +1150,7 @@ def add_motor(self, motor, position):  # pylint: disable=too-many-statements
                     "Only one motor per rocket is currently supported. "
                     "Overwriting previous motor."
                 )
+        self._invalidate_datcom()
         self.motor = motor
         self.motor_position = position
         _ = self._csys * self.motor._csys
@@ -1175,6 +1186,7 @@ def __add_single_surface(self, surface, position):
         """Adds a single aerodynamic surface to the rocket. Makes checks for
         rail buttons case, and position type.
         """
+        self._validate_datcom_tail(surface)
         if isinstance(surface, (TrapezoidalFin, EllipticalFin, FreeFormFin)):
             # TODO: the leading edge position should be recomputed whenever cant
             # angle of the fin changes, but currently it is only computed at the
@@ -1238,6 +1250,7 @@ def add_surfaces(self, surfaces, positions):
         -------
         None
         """
+        self._invalidate_datcom()
         if isinstance(surfaces, Iterable):
             if isinstance(positions, Iterable):
                 if len(surfaces) != len(positions):
@@ -1276,7 +1289,15 @@ def _add_controllers(self, controllers):
             self._controllers.append(controllers)
 
     def add_tail(
-        self, top_radius, bottom_radius, length, position, radius=None, name="Tail"
+        self,
+        top_radius,
+        bottom_radius,
+        length,
+        position,
+        radius=None,
+        name="Tail",
+        *,
+        kind="conical",
     ):
         """Create a new tail or rocket diameter change, storing its
         parameters as part of the aerodynamic_surfaces list. Its
@@ -1305,6 +1326,10 @@ def add_tail(
             be used.
         name : string
             Tail name. Default is "Tail".
+        kind : string, optional
+            Tail profile, ``"conical"`` (default) or ``"ogive"``. Ogive tails
+            require a DATCOM fin set to be added first; otherwise ValueError is
+            raised. Conical tails retain the original component model.
 
         See Also
         --------
@@ -1318,7 +1343,9 @@ def add_tail(
         # Modify reference radius if not provided
         radius = self.radius if radius is None else radius
         # Create tail, adds it to the rocket and returns it
-        tail = Tail(top_radius, bottom_radius, length, radius, name)
+        tail = self._create_datcom_or_conical_tail(
+            top_radius, bottom_radius, length, radius, name, kind
+        )
         self.add_surfaces(tail, position)
         return tail
 
@@ -2247,6 +2274,9 @@ def to_dict(self, **kwargs):
 
         power_off_drag = self.power_off_drag_7d
         power_on_drag = self.power_on_drag_7d
+        if self._datcom_original_drag is not None:
+            power_off_drag = self._datcom_original_drag["power_off_drag_7d"]
+            power_on_drag = self._datcom_original_drag["power_on_drag_7d"]
 
         rocket_dict = {
             "radius": self.radius,
@@ -2270,6 +2300,8 @@ def to_dict(self, **kwargs):
             "_controllers": self._controllers,
             "sensors": self.sensors,
         }
+        if hasattr(self, "_datcom_calibration"):
+            rocket_dict["datcom_calibration"] = self.datcom_calibration
 
         if kwargs.get("include_outputs", False):
             thrust_to_weight = self.thrust_to_weight
@@ -2378,6 +2410,9 @@ def from_dict(cls, data):
         for surface, position, *_ in data["aerodynamic_surfaces"]:
             rocket.add_surfaces(surfaces=surface, positions=position)
 
+        if "datcom_calibration" in data:
+            rocket.add_datcom_calibration(**data["datcom_calibration"])
+
         for button, position, *_ in data["rail_buttons"]:
             rocket.set_rail_buttons(
                 upper_button_position=position[2] + button.buttons_distance,
diff --git a/rocketpy/simulation/datcom.py b/rocketpy/simulation/datcom.py
new file mode 100644
index 000000000..9b6f0e0f6
--- /dev/null
+++ b/rocketpy/simulation/datcom.py
@@ -0,0 +1,144 @@
+"""DATCOM damping and load coupling without modifying ordinary flight models."""
+
+import math
+from functools import wraps
+
+from rocketpy.mathutils.vector_matrix import Matrix, Vector
+
+
+def datcom_derivative(method):
+    """Wrap an RHS with a local implicit incidence-rate solve when DATCOM is active."""
+
+    @wraps(method)
+    def evaluate(self, time, state, post_processing=False):
+        if self.rocket._datcom_model is None or getattr(
+            self, "_datcom_evaluating", False
+        ):
+            return method(self, time, state, post_processing)
+
+        def derivative(t, u, post):
+            return method(self, t, u, post)
+
+        return self._evaluate_datcom_derivative(
+            derivative, time, state, post_processing
+        )
+
+    return evaluate
+
+
+class DatcomFlightMixin:
+    """Time-dependent loads and deterministic unsteady aerodynamic coupling."""
+
+    def _datcom_surface_loads(self, surface, time, *args):
+        """Supply time and the instantaneous incidence rate to DATCOM only."""
+        if surface is self.rocket._datcom_model:
+            loads = surface.compute_forces_and_moments_at_time(
+                time, getattr(self, "_datcom_alpha_dot", 0.0), *args
+            )
+            if any(
+                brake.override_rocket_drag and brake.deployment_level > 0
+                for brake in self.rocket.air_brakes
+            ):
+                # The air-brake loop already supplied the overriding axial load.
+                # Keep DATCOM's lateral forces and intrinsic moments.
+                return (*loads[:2], 0.0, *loads[3:])
+            return loads
+        return surface.compute_forces_and_moments(*args)
+
+    def _surface_cp_at_time(self, surface, time):
+        """Aerodynamic reference relative to dry CG in body axes, in meters."""
+        if surface is self.rocket._datcom_model:
+            return surface.reference_offset(time)
+        return self.rocket.surfaces_cp_to_cdm[surface]
+
+    def _evaluate_datcom_derivative(self, derivative, time, state, post_processing):
+        """Solve the affine CNAD/CMAD acceleration coupling within one RHS call.
+
+        The rate is never differenced across adaptive ODE evaluations. Probe
+        evaluations do not append post-processing records. Scratch state belongs
+        to this Flight rather than its shared rocket, and is cleared on errors.
+        """
+        self._datcom_evaluating = True
+        self._datcom_alpha_dot = 0.0
+        try:
+            base = derivative(time, state, False)
+            intercept = self._datcom_incidence_rate(time, state, base)
+            self._datcom_alpha_dot = 1.0
+            probe = derivative(time, state, False)
+            response = self._datcom_incidence_rate(time, state, probe) - intercept
+            denominator = 1 - response
+            if not math.isfinite(denominator) or abs(denominator) < 1e-10:
+                raise RuntimeError("Singular DATCOM alpha-dot aerodynamic coupling")
+            self._datcom_alpha_dot = intercept / denominator
+            if not math.isfinite(self._datcom_alpha_dot):
+                raise RuntimeError("Nonfinite DATCOM alpha-dot aerodynamic coupling")
+            return derivative(time, state, post_processing)
+        finally:
+            self._datcom_alpha_dot = 0.0
+            self._datcom_evaluating = False
+
+    def _datcom_incidence_rate(self, time, state, derivative):
+        """Local alpha rate (rad/s), including rotation, acceleration and shear."""
+        rotation = Matrix.transformation(state[6:10])
+        inverse = rotation.transpose
+        omega = Vector(state[10:13])
+        omega_dot = Vector(derivative[10:13])
+        reference = self.rocket._datcom_model.reference_offset(time)
+        z = state[2] + (rotation @ reference).z
+        wind = Vector([self.env.wind_velocity_x(z), self.env.wind_velocity_y(z), 0])
+        relative = inverse @ (Vector(state[3:6]) - wind)
+        velocity = relative + (omega ^ reference)
+        speed = abs(velocity)
+        axial = abs(velocity.z)
+        denominator = axial**2 + velocity.y**2
+        if speed < 1 or denominator < 1e-16:
+            return 0.0
+        z_dot = state[5] + (rotation @ (omega ^ reference)).z
+        wind_dot = Vector(
+            [
+                self.env.wind_velocity_x.differentiate(z, dx=0.1) * z_dot,
+                self.env.wind_velocity_y.differentiate(z, dx=0.1) * z_dot,
+                0,
+            ]
+        )
+        acceleration = (
+            inverse @ (Vector(derivative[3:6]) - wind_dot)
+            - (omega ^ relative)
+            + (omega_dot ^ reference)
+        )
+        axial_dot = math.copysign(1.0, velocity.z) * acceleration.z
+        return (axial * acceleration.y - velocity.y * axial_dot) / denominator
+
+    def _datcom_rail_force(self, time, velocity, speed, mach, rho, reynolds, fallback):
+        """Constrained axial load uses body-axis CA rather than stability-axis CD."""
+        if self.rocket._datcom_model is None:
+            return fallback
+        loads = self.rocket._datcom_model.compute_forces_and_moments_at_time(
+            time,
+            0.0,
+            velocity,
+            speed,
+            mach,
+            rho,
+            Vector.zeros(),
+            Vector.zeros(),
+            reynolds,
+        )
+        return loads[2]
+
+    def _datcom_moment_at_cm(self, time, forces, moments):
+        """Transfer a physical dry-CG moment to the current CG for legacy EOM."""
+        if self.rocket._datcom_model is None:
+            return moments
+        cm_to_dry = Vector([0, 0, self.rocket.com_to_cdm_function(time)])
+        return Vector(moments) + (cm_to_dry ^ Vector(forces))
+
+    def _datcom_angular_acceleration(self, inertia, T20, T21, r_cm, forces, moments):
+        """Transfer aerodynamic torque explicitly within generalized EOM conventions."""
+        if self.rocket._datcom_model is None:
+            return inertia.inverse @ (T21 + (T20 ^ r_cm))
+        aero_force, aero_moment = Vector(forces), Vector(moments)
+        moment_at_cm = aero_moment + (r_cm ^ aero_force)
+        return inertia.inverse @ (
+            T21 - aero_moment + ((T20 - aero_force) ^ r_cm) + moment_at_cm
+        )
diff --git a/rocketpy/simulation/flight.py b/rocketpy/simulation/flight.py
index 43790110c..70b8f63c8 100644
--- a/rocketpy/simulation/flight.py
+++ b/rocketpy/simulation/flight.py
@@ -26,6 +26,7 @@
     quaternions_to_precession,
     quaternions_to_spin,
 )
+from .datcom import DatcomFlightMixin, datcom_derivative
 
 logger = logging.getLogger(__name__)
 
@@ -41,7 +42,7 @@
 
 # pylint: disable=too-many-public-methods
 # pylint: disable=too-many-instance-attributes
-class Flight:
+class Flight(DatcomFlightMixin):
     """Keeps all flight information and has a method to simulate flight.
 
     Attributes
@@ -653,6 +654,7 @@ def __init__(  # pylint: disable=too-many-arguments,too-many-statements
         # Save arguments
         self.env = environment
         self.rocket = rocket
+        self.rocket.prepare_datcom()
         # Warn about an aerodynamically unstable rocket now that it is fully
         # assembled and about to be simulated. Doing it here (instead of on every
         # add_surfaces call during construction) avoids spurious warnings for
@@ -1998,6 +2000,9 @@ def udot_rail1(self, t, u, post_processing=False):
             0,
         )
         R3 = -0.5 * rho * (free_stream_speed**2) * self.rocket.area * (drag_coeff)
+        R3 = self._datcom_rail_force(
+            t, stream_velocity_body, free_stream_speed, mach, rho, reynolds, R3
+        )
 
         # Calculate Linear acceleration
         a3 = (R3 + net_thrust) / total_mass_at_t - (
@@ -2018,6 +2023,7 @@ def udot_rail1(self, t, u, post_processing=False):
 
         return [vx, vy, vz, ax, ay, az, 0, 0, 0, 0, 0, 0, 0]
 
+    @datcom_derivative
     def udot_rail2(self, t, u, post_processing=False):
         """Calculates the derivative of the u state vector with respect to time
         for the intermediate 3-DOF "tip-off" rail phase: the upper rail button
@@ -2165,6 +2171,7 @@ def _response(f_r, tau):
         u_dot[10:13] = [w_dot.x, w_dot.y, w_dot.z]
         return u_dot
 
+    @datcom_derivative
     def u_dot(self, t, u, post_processing=False):  # pylint: disable=too-many-locals,too-many-statements
         """Calculates derivative of u state vector with respect to time
         when rocket is flying in 6 DOF motion during ascent out of rail
@@ -2311,6 +2318,8 @@ def u_dot(self, t, u, post_processing=False):  # pylint: disable=too-many-locals
                 omega2,
                 omega3,
             )
+        if self.rocket._datcom_model is not None:
+            drag_coeff = 0.0
         R3 = -0.5 * rho * (free_stream_speed**2) * self.rocket.area * drag_coeff
         for air_brakes in self.rocket.air_brakes:
             if air_brakes.deployment_level > 0:
@@ -2338,9 +2347,9 @@ def u_dot(self, t, u, post_processing=False):  # pylint: disable=too-many-locals
         # Calculate lift and moment for each component of the rocket
         velocity_in_body_frame = Vector([vx_b, vy_b, vz_b])
         w = Vector([omega1, omega2, omega3])
-        for aero_surface, _, _ref_factor in self.rocket.aerodynamic_surfaces:
+        for aero_surface, _, _ref_factor in self.rocket._flight_aerodynamic_surfaces:
             # Component cp relative to CDM in body frame
-            comp_cp = self.rocket.surfaces_cp_to_cdm[aero_surface]
+            comp_cp = self._surface_cp_at_time(aero_surface, t)
             # Component absolute velocity in body frame
             comp_vb = velocity_in_body_frame + (w ^ comp_cp)
             # Wind velocity at component altitude
@@ -2364,7 +2373,9 @@ def u_dot(self, t, u, post_processing=False):  # pylint: disable=too-many-locals
                 / comp_dynamic_viscosity
             )
             # Forces and moments
-            X, Y, Z, M, N, L = aero_surface.compute_forces_and_moments(
+            X, Y, Z, M, N, L = self._datcom_surface_loads(
+                aero_surface,
+                t,
                 comp_stream_velocity,
                 comp_stream_speed,
                 comp_stream_mach,
@@ -2382,6 +2393,8 @@ def u_dot(self, t, u, post_processing=False):  # pylint: disable=too-many-locals
         # Off center moment
         M3 += self.rocket.cp_eccentricity_x * R2 - self.rocket.cp_eccentricity_y * R1
 
+        M1, M2, M3 = self._datcom_moment_at_cm(t, [R1, R2, R3], [M1, M2, M3])
+
         # Calculate derivatives
         # Angular acceleration
         alpha1 = (
@@ -2511,6 +2524,7 @@ def u_dot(self, t, u, post_processing=False):  # pylint: disable=too-many-locals
             )
         return u_dot
 
+    @datcom_derivative
     def u_dot_generalized_3dof(self, t, u, post_processing=False):
         """Calculates derivative of u state vector with respect to time when the
         rocket is flying in 3 DOF motion in space and significant mass variation
@@ -2576,6 +2590,8 @@ def u_dot_generalized_3dof(self, t, u, post_processing=False):
             )
 
         R1, R2 = 0, 0
+        if self.rocket._datcom_model is not None:
+            cd = 0.0
         R3 = -0.5 * rho * free_stream_speed**2 * self.rocket.area * cd
 
         for air_brake in self.rocket.air_brakes:
@@ -2594,8 +2610,8 @@ def u_dot_generalized_3dof(self, t, u, post_processing=False):
         # Velocity in body frame
         vb_body = Kt @ v
 
-        for surface, _, _ref_factor in self.rocket.aerodynamic_surfaces:
-            cp = self.rocket.surfaces_cp_to_cdm[surface]
+        for surface, _, _ref_factor in self.rocket._flight_aerodynamic_surfaces:
+            cp = self._surface_cp_at_time(surface, t)
             vb_component = vb_body + (w ^ cp)
 
             comp_z = z + (K @ cp).z
@@ -2616,8 +2632,8 @@ def u_dot_generalized_3dof(self, t, u, post_processing=False):
                 / comp_dynamic_viscosity
             )
 
-            fx, fy, fz, *_ = surface.compute_forces_and_moments(
-                rel_velocity, rel_speed, rel_mach, rho, cp, w, reynolds
+            fx, fy, fz, *_ = self._datcom_surface_loads(
+                surface, t, rel_velocity, rel_speed, rel_mach, rho, cp, w, reynolds
             )
             R1 += fx
             R2 += fy
@@ -2731,6 +2747,7 @@ def u_dot_generalized_3dof(self, t, u, post_processing=False):
 
         return u_dot
 
+    @datcom_derivative
     def u_dot_generalized(self, t, u, post_processing=False):  # pylint: disable=too-many-locals,too-many-statements
         """Calculates derivative of u state vector with respect to time when the
         rocket is flying in 6 DOF motion in space and significant mass variation
@@ -2846,6 +2863,8 @@ def u_dot_generalized(self, t, u, post_processing=False):  # pylint: disable=too
                 omega2,
                 omega3,
             )
+        if self.rocket._datcom_model is not None:
+            drag_coeff = 0.0
         R3 += -0.5 * rho * (free_stream_speed**2) * self.rocket.area * drag_coeff
         for air_brakes in self.rocket.air_brakes:
             if air_brakes.deployment_level > 0:
@@ -2866,9 +2885,9 @@ def u_dot_generalized(self, t, u, post_processing=False):  # pylint: disable=too
         # Get rocket velocity in body frame
         velocity_in_body_frame = Kt @ v
         # Calculate lift and moment for each component of the rocket
-        for aero_surface, _, _ref_factor in self.rocket.aerodynamic_surfaces:
+        for aero_surface, _, _ref_factor in self.rocket._flight_aerodynamic_surfaces:
             # Component cp relative to CDM in body frame
-            comp_cp = self.rocket.surfaces_cp_to_cdm[aero_surface]
+            comp_cp = self._surface_cp_at_time(aero_surface, t)
             # Component absolute velocity in body frame
             comp_vb = velocity_in_body_frame + (w ^ comp_cp)
             # Wind velocity at component altitude
@@ -2892,7 +2911,9 @@ def u_dot_generalized(self, t, u, post_processing=False):  # pylint: disable=too
                 / comp_dynamic_viscosity
             )
             # Forces and moments
-            X, Y, Z, M, N, L = aero_surface.compute_forces_and_moments(
+            X, Y, Z, M, N, L = self._datcom_surface_loads(
+                aero_surface,
+                t,
                 comp_stream_velocity,
                 comp_stream_speed,
                 comp_stream_mach,
@@ -2949,7 +2970,12 @@ def u_dot_generalized(self, t, u, post_processing=False):  # pylint: disable=too
         )
 
         # Angular velocity derivative
-        w_dot = I_CM.inverse @ (T21 + (T20 ^ r_CM))
+        if self.rocket._datcom_model is None:
+            w_dot = I_CM.inverse @ (T21 + (T20 ^ r_CM))
+        else:
+            w_dot = self._datcom_angular_acceleration(
+                I_CM, T20, T21, r_CM, [R1, R2, R3], [M1, M2, M3]
+            )
 
         # Euler parameters derivative
         e_dot = [
diff --git a/tests/conftest.py b/tests/conftest.py
index 621ddc2a5..e96825d80 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -35,6 +35,7 @@ def isolate_atmosphere_cache(monkeypatch, tmp_path):
     "tests.fixtures.parachutes.parachute_fixtures",
     "tests.fixtures.rockets.rocket_fixtures",
     "tests.fixtures.surfaces.surface_fixtures",
+    "tests.fixtures.surfaces.datcom_fixtures",
     "tests.fixtures.units.numerical_fixtures",
     "tests.fixtures.monte_carlo.monte_carlo_fixtures",
     "tests.fixtures.monte_carlo.stochastic_fixtures",
diff --git a/tests/fixtures/surfaces/data/datcom/README.md b/tests/fixtures/surfaces/data/datcom/README.md
new file mode 100644
index 000000000..1bc55f25c
--- /dev/null
+++ b/tests/fixtures/surfaces/data/datcom/README.md
@@ -0,0 +1,8 @@
+`for006.dat` is native Missile DATCOM revision 3/99 output for a simple,
+unblunted von Karman nose, cylindrical body, four HEX fins and a conical
+boattail. The echoed deck specifies the geometry, two Mach points and three
+signed incidence angles. DAMP and full base drag are enabled; protuberances
+and plume interaction are absent.
+
+This fixture checks parsing and coefficient-channel completeness. It is not
+an independently validated aerodynamic reference. No executable is included.
diff --git a/tests/fixtures/surfaces/data/datcom/for006.dat b/tests/fixtures/surfaces/data/datcom/for006.dat
new file mode 100644
index 000000000..54aadb19b
--- /dev/null
+++ b/tests/fixtures/surfaces/data/datcom/for006.dat
@@ -0,0 +1,260 @@
+1         ***** THE USAF AUTOMATED MISSILE DATCOM * REV 3/99 *****
+               AERODYNAMIC METHODS FOR MISSILE CONFIGURATIONS     
+
+    CONERR - INPUT ERROR CHECKING
+
+    ERROR CODES - N* DENOTES THE NUMBER OF OCCURENCES OF EACH ERROR
+    A - UNKNOWN VARIABLE NAME
+    B - MISSING EQUAL SIGN FOLLOWING VARIABLE NAME
+    C - NON-ARRAY VARIABLE HAS AN ARRAY ELEMENTDESIGNATION - (N)
+    D - NON-ARRAY VARIABLE HAS MULTIPLE VALUES ASSIGNED
+    E - ASSIGNED VALUES EXCEED ARRAY DIMENSION
+    F - SYNTAX ERROR
+
+    ************************* INPUT DATA CARDS *************************
+
+   1 DIM M                                                                           
+   2  $FLTCON                                                                        
+   3   NMACH = 2.,                                                                   
+   4   MACH = 0.2,0.6,                                                               
+   5   ALT = 0.,0.,                                                                  
+   6   NALPHA = 3.,                                                                  
+   7   ALPHA = -2.,0.,2.,$                                                           
+   8  $REFQ                                                                          
+   9   XCG = 1.2,                                                                    
+  10   SREF = 0.012667687,                                                           
+  11   LREF = 0.127,$                                                                
+  12  $AXIBOD                                                                        
+  13   TNOSE = KARMAN,                                                               
+               ** SUBSTITUTING NUMERIC FOR NAME KARMAN
+  14   LNOSE = 0.55,                                                                 
+  15   DNOSE = 0.127,                                                                
+  16   LCENTR = 1.85,                                                                
+  17   DCENTR = 0.127,                                                               
+  18   BASE = .FALSE.,                                                               
+  19   TAFT = CONE,                                                                  
+               ** SUBSTITUTING NUMERIC FOR NAME CONE
+  20   LAFT = 0.1,                                                                   
+  21   DAFT = 0.09,                                                                  
+  22   DEXIT = 0.,$                                                                  
+  23  $FINSET1                                                                       
+  24   XLE = 2.1,2.25,                                                               
+  25   NPANEL = 4.,                                                                  
+  26   PHIF = 0.,90.,180.,270.,                                                      
+  27   SSPAN = 0.0635,0.1635,                                                        
+  28   CHORD = 0.25,0.1,                                                             
+  29   SECTYP = HEX,                                                                 
+               ** SUBSTITUTING NUMERIC FOR NAME HEX
+  30   ZUPPER = 0.01,0.01,                                                           
+  31   ZLOWER = 0.01,0.01,                                                           
+  32   LER = 0.,0.,                                                                  
+  33   LMAXU = 0.25,0.25,                                                            
+  34   LFLATU = 0.5,0.5,$                                                            
+  35 DAMP                                                                            
+  36 NEXT CASE                                                                       
+1         ***** THE USAF AUTOMATED MISSILE DATCOM * REV 3/99 *****     CASE   1
+               AERODYNAMIC METHODS FOR MISSILE CONFIGURATIONS          PAGE   1
+    CASE INPUTS
+    FOLLOWING ARE THE CARDS INPUT FOR THIS CASE
+
+ DIM M                                                                           
+  $FLTCON                                                                        
+   NMACH = 2.,                                                                   
+   MACH = 0.2,0.6,                                                               
+   ALT = 0.,0.,                                                                  
+   NALPHA = 3.,                                                                  
+   ALPHA = -2.,0.,2.,$                                                           
+  $REFQ                                                                          
+   XCG = 1.2,                                                                    
+   SREF = 0.012667687,                                                           
+   LREF = 0.127,$                                                                
+  $AXIBOD                                                                        
+   TNOSE = 4.,                                                                   
+   LNOSE = 0.55,                                                                 
+   DNOSE = 0.127,                                                                
+   LCENTR = 1.85,                                                                
+   DCENTR = 0.127,                                                               
+   BASE = .FALSE.,                                                               
+   TAFT = 0.,                                                                    
+   LAFT = 0.1,                                                                   
+   DAFT = 0.09,                                                                  
+   DEXIT = 0.,$                                                                  
+  $FINSET1                                                                       
+   XLE = 2.1,2.25,                                                               
+   NPANEL = 4.,                                                                  
+   PHIF = 0.,90.,180.,270.,                                                      
+   SSPAN = 0.0635,0.1635,                                                        
+   CHORD = 0.25,0.1,                                                             
+   SECTYP = 0.,                                                                  
+   ZUPPER = 0.01,0.01,                                                           
+   ZLOWER = 0.01,0.01,                                                           
+   LER = 0.,0.,                                                                  
+   LMAXU = 0.25,0.25,                                                            
+   LFLATU = 0.5,0.5,$                                                            
+ DAMP                                                                            
+ NEXT CASE                                                                       
+    THE BOUNDARY LAYER IS ASSUMED TO BE TURBULENT
+    THE INPUT UNITS ARE IN METERS, THE SCALE FACTOR IS   1.0000
+1         ***** THE USAF AUTOMATED MISSILE DATCOM * REV 3/99 *****     CASE   1
+               AERODYNAMIC METHODS FOR MISSILE CONFIGURATIONS          PAGE   2
+                   STATIC AERODYNAMICS FOR BODY-FIN SET 1
+
+       ******* FLIGHT CONDITIONS AND REFERENCE QUANTITIES *******
+     MACH NO  =       0.20                REYNOLDS NO = 4.637E+06 /M
+     ALTITUDE =        0.0 M         DYNAMIC PRESSURE =   2837.11 N/M**2
+     SIDESLIP =       0.00 DEG                   ROLL =      0.00 DEG     
+     REF AREA =      0.013 M**2         MOMENT CENTER =     1.200 M
+     REF LENGTH =     0.13 M           LAT REF LENGTH =      0.13 M
+
+                   ----- LONGITUDINAL -----     -- LATERAL DIRECTIONAL --
+         ALPHA       CN        CM        CA        CY       CLN       CLL
+
+         -2.00    -0.430     2.212     0.368    -0.000     0.000     0.000
+          0.00     0.000     0.000     0.368     0.000    -0.000     0.000
+          2.00     0.430    -2.212     0.368     0.000    -0.000    -0.000
+
+         ALPHA       CL        CD      CL/CD     X-C.P.
+
+         -2.00    -0.417     0.382    -1.090    -5.145
+          0.00     0.000     0.368     0.000    -5.145
+          2.00     0.417     0.382     1.090    -5.145
+
+    X-C.P. MEAS. FROM MOMENT CENTER IN REF. LENGTHS, NEG. AFT OF MOMENT CENTER
+1         ***** THE USAF AUTOMATED MISSILE DATCOM * REV 3/99 *****     CASE   1
+               AERODYNAMIC METHODS FOR MISSILE CONFIGURATIONS          PAGE   3
+                   STATIC AERODYNAMICS FOR BODY-FIN SET 1
+
+       ******* FLIGHT CONDITIONS AND REFERENCE QUANTITIES *******
+     MACH NO  =       0.20                REYNOLDS NO = 4.637E+06 /M
+     ALTITUDE =        0.0 M         DYNAMIC PRESSURE =   2837.11 N/M**2
+     SIDESLIP =       0.00 DEG                   ROLL =      0.00 DEG     
+     REF AREA =      0.013 M**2         MOMENT CENTER =     1.200 M
+     REF LENGTH =     0.13 M           LAT REF LENGTH =      0.13 M
+
+                   ---------- DERIVATIVES (PER DEGREE) ----------
+         ALPHA       CNA         CMA         CYB         CLNB        CLLB
+         -2.00      0.2149     -1.1058     -0.2064      1.0389      0.0001
+          0.00      0.2149     -1.1058     -0.2071      1.0437     -0.0000
+          2.00      0.2149     -1.1058     -0.2064      1.0389     -0.0001
+
+    PANEL DEFLECTION ANGLES (DEGREES)
+    SET    FIN 1   FIN 2   FIN 3   FIN 4   FIN 5   FIN 6   FIN 7   FIN 8
+      1    0.00    0.00    0.00    0.00
+1         ***** THE USAF AUTOMATED MISSILE DATCOM * REV 3/99 *****     CASE   1
+               AERODYNAMIC METHODS FOR MISSILE CONFIGURATIONS          PAGE   4
+                        BODY + 1 FIN SET DYNAMIC DERIVATIVES
+
+       ******* FLIGHT CONDITIONS AND REFERENCE QUANTITIES *******
+     MACH NO  =       0.20                REYNOLDS NO = 4.637E+06 /M
+     ALTITUDE =        0.0 M         DYNAMIC PRESSURE =   2837.11 N/M**2
+     SIDESLIP =       0.00 DEG                   ROLL =      0.00 DEG     
+     REF AREA =      0.013 M**2         MOMENT CENTER =     1.200 M
+     REF LENGTH =     0.13 M           LAT REF LENGTH =      0.13 M
+
+                    ------------ DYNAMIC DERIVATIVES (PER DEGREE) -----------
+         ALPHA       CNQ        CMQ        CAQ       CNAD       CMAD
+         -2.00      4.335    -32.199      0.000      0.848     -1.721
+          0.00      4.485    -33.380      0.000      0.848     -1.721
+          2.00      4.565    -34.018      0.000      0.848     -1.721
+
+    PITCH RATE DERIVATIVES NON-DIMENSIONALIZED BY Q*LREF/2*V
+1         ***** THE USAF AUTOMATED MISSILE DATCOM * REV 3/99 *****     CASE   1
+               AERODYNAMIC METHODS FOR MISSILE CONFIGURATIONS          PAGE   5
+                        BODY + 1 FIN SET DYNAMIC DERIVATIVES
+
+       ******* FLIGHT CONDITIONS AND REFERENCE QUANTITIES *******
+     MACH NO  =       0.20                REYNOLDS NO = 4.637E+06 /M
+     ALTITUDE =        0.0 M         DYNAMIC PRESSURE =   2837.11 N/M**2
+     SIDESLIP =       0.00 DEG                   ROLL =      0.00 DEG     
+     REF AREA =      0.013 M**2         MOMENT CENTER =     1.200 M
+     REF LENGTH =     0.13 M           LAT REF LENGTH =      0.13 M
+
+                    ------------ DYNAMIC DERIVATIVES (PER DEGREE) -----------
+         ALPHA       CYR       CLNR       CLLR        CYP       CLNP       CLLP
+         -2.00      4.471    -33.273      0.000     -0.000      0.000     -0.259
+          0.00      4.485    -33.380      0.000     -0.000      0.000     -0.243
+          2.00      4.471    -33.273      0.000     -0.000      0.000     -0.259
+
+    YAW AND ROLL RATE DERIVATIVES NON-DIMENSIONALIZED BY R*LATREF/2*V
+1         ***** THE USAF AUTOMATED MISSILE DATCOM * REV 3/99 *****     CASE   1
+               AERODYNAMIC METHODS FOR MISSILE CONFIGURATIONS          PAGE   6
+                   STATIC AERODYNAMICS FOR BODY-FIN SET 1
+
+       ******* FLIGHT CONDITIONS AND REFERENCE QUANTITIES *******
+     MACH NO  =       0.60                REYNOLDS NO = 1.391E+07 /M
+     ALTITUDE =        0.0 M         DYNAMIC PRESSURE =  25533.95 N/M**2
+     SIDESLIP =       0.00 DEG                   ROLL =      0.00 DEG     
+     REF AREA =      0.013 M**2         MOMENT CENTER =     1.200 M
+     REF LENGTH =     0.13 M           LAT REF LENGTH =      0.13 M
+
+                   ----- LONGITUDINAL -----     -- LATERAL DIRECTIONAL --
+         ALPHA       CN        CM        CA        CY       CLN       CLL
+
+         -2.00    -0.433     2.226     0.320    -0.000     0.000     0.000
+          0.00     0.000     0.000     0.320     0.000    -0.000     0.000
+          2.00     0.433    -2.226     0.320     0.000    -0.000    -0.000
+
+         ALPHA       CL        CD      CL/CD     X-C.P.
+
+         -2.00    -0.422     0.335    -1.261    -5.137
+          0.00     0.000     0.320     0.000    -5.137
+          2.00     0.422     0.335     1.261    -5.137
+
+    X-C.P. MEAS. FROM MOMENT CENTER IN REF. LENGTHS, NEG. AFT OF MOMENT CENTER
+1         ***** THE USAF AUTOMATED MISSILE DATCOM * REV 3/99 *****     CASE   1
+               AERODYNAMIC METHODS FOR MISSILE CONFIGURATIONS          PAGE   7
+                   STATIC AERODYNAMICS FOR BODY-FIN SET 1
+
+       ******* FLIGHT CONDITIONS AND REFERENCE QUANTITIES *******
+     MACH NO  =       0.60                REYNOLDS NO = 1.391E+07 /M
+     ALTITUDE =        0.0 M         DYNAMIC PRESSURE =  25533.95 N/M**2
+     SIDESLIP =       0.00 DEG                   ROLL =      0.00 DEG     
+     REF AREA =      0.013 M**2         MOMENT CENTER =     1.200 M
+     REF LENGTH =     0.13 M           LAT REF LENGTH =      0.13 M
+
+                   ---------- DERIVATIVES (PER DEGREE) ----------
+         ALPHA       CNA         CMA         CYB         CLNB        CLLB
+         -2.00      0.2167     -1.1132     -0.2084      1.0481      0.0001
+          0.00      0.2167     -1.1132     -0.2091      1.0530     -0.0000
+          2.00      0.2167     -1.1132     -0.2084      1.0481     -0.0001
+
+    PANEL DEFLECTION ANGLES (DEGREES)
+    SET    FIN 1   FIN 2   FIN 3   FIN 4   FIN 5   FIN 6   FIN 7   FIN 8
+      1    0.00    0.00    0.00    0.00
+1         ***** THE USAF AUTOMATED MISSILE DATCOM * REV 3/99 *****     CASE   1
+               AERODYNAMIC METHODS FOR MISSILE CONFIGURATIONS          PAGE   8
+                        BODY + 1 FIN SET DYNAMIC DERIVATIVES
+
+       ******* FLIGHT CONDITIONS AND REFERENCE QUANTITIES *******
+     MACH NO  =       0.60                REYNOLDS NO = 1.391E+07 /M
+     ALTITUDE =        0.0 M         DYNAMIC PRESSURE =  25533.95 N/M**2
+     SIDESLIP =       0.00 DEG                   ROLL =      0.00 DEG     
+     REF AREA =      0.013 M**2         MOMENT CENTER =     1.200 M
+     REF LENGTH =     0.13 M           LAT REF LENGTH =      0.13 M
+
+                    ------------ DYNAMIC DERIVATIVES (PER DEGREE) -----------
+         ALPHA       CNQ        CMQ        CAQ       CNAD       CMAD
+         -2.00      4.251    -32.131      0.000      0.848     -1.869
+          0.00      4.350    -32.919      0.000      0.848     -1.869
+          2.00      4.377    -33.127      0.000      0.848     -1.869
+
+    PITCH RATE DERIVATIVES NON-DIMENSIONALIZED BY Q*LREF/2*V
+1         ***** THE USAF AUTOMATED MISSILE DATCOM * REV 3/99 *****     CASE   1
+               AERODYNAMIC METHODS FOR MISSILE CONFIGURATIONS          PAGE   9
+                        BODY + 1 FIN SET DYNAMIC DERIVATIVES
+
+       ******* FLIGHT CONDITIONS AND REFERENCE QUANTITIES *******
+     MACH NO  =       0.60                REYNOLDS NO = 1.391E+07 /M
+     ALTITUDE =        0.0 M         DYNAMIC PRESSURE =  25533.95 N/M**2
+     SIDESLIP =       0.00 DEG                   ROLL =      0.00 DEG     
+     REF AREA =      0.013 M**2         MOMENT CENTER =     1.200 M
+     REF LENGTH =     0.13 M           LAT REF LENGTH =      0.13 M
+
+                    ------------ DYNAMIC DERIVATIVES (PER DEGREE) -----------
+         ALPHA       CYR       CLNR       CLLR        CYP       CLNP       CLLP
+         -2.00      4.339    -32.827      0.000     -0.000      0.000     -0.267
+          0.00      4.350    -32.919      0.000     -0.000      0.000     -0.250
+          2.00      4.339    -32.827      0.000     -0.000      0.000     -0.267
+
+    YAW AND ROLL RATE DERIVATIVES NON-DIMENSIONALIZED BY R*LATREF/2*V
+    *** END OF JOB ***
diff --git a/tests/fixtures/surfaces/datcom_fixtures.py b/tests/fixtures/surfaces/datcom_fixtures.py
new file mode 100644
index 000000000..256df7762
--- /dev/null
+++ b/tests/fixtures/surfaces/datcom_fixtures.py
@@ -0,0 +1,58 @@
+"""Deterministic geometry and coefficient tables for DATCOM regression tests."""
+
+import numpy as np
+import pytest
+
+from rocketpy.rocket.datcom import BODY_KEYS, DAMPING_KEYS, DatcomResult
+
+
+@pytest.fixture
+def datcom_table_factory():
+    """Make complete signed tables without requiring an external executable."""
+
+    def make(values=None):
+        values = values or {}
+        mach = np.array([0.2, 0.6])
+        alpha = np.array([-2.0, 0.0, 2.0])
+        zeros = np.zeros((2, 3))
+        body = {key: zeros + values.get(key, 0) for key in BODY_KEYS}
+        derivatives = {
+            key: zeros + values.get(key, 0)
+            for key in ("CNA", "CMA", "CYB", "CLNB", "CLLB")
+        }
+        damping = {key: zeros + values.get(key, 0) for key in DAMPING_KEYS}
+        return DatcomResult(
+            (
+                zeros.copy(),
+                zeros + values.get("CD", 0.4),
+                body["CM"],
+                zeros.copy(),
+                mach,
+                alpha,
+            ),
+            body,
+            derivatives,
+            damping,
+        )
+
+    return make
+
+
+@pytest.fixture
+def datcom_rocket(calisto, tmp_path):
+    """An assembled rocket with a fake executable, nose, DATCOM fins and tail."""
+    executable = tmp_path / "datcom"
+    executable.write_text("not executed by unit tests")
+    calisto.add_nose(0.55, "von karman", position=1.2)
+    calisto.add_datcom_hex_fins(
+        4,
+        root_chord=0.25,
+        tip_chord=0.1,
+        span=0.1,
+        position=-0.9,
+        sweep_length=0.1,
+        path=executable,
+        datcom_options={"mach": [0.2, 0.6], "alpha": [-2, 0, 2]},
+    )
+    calisto.add_tail(calisto.radius, 0.045, 0.1, position=-1.2)
+    return calisto
diff --git a/tests/integration/test_datcom.py b/tests/integration/test_datcom.py
new file mode 100644
index 000000000..24e0a3179
--- /dev/null
+++ b/tests/integration/test_datcom.py
@@ -0,0 +1,550 @@
+"""DATCOM text parsing, isolated executable lifecycle and flight integration."""
+
+import os
+import shutil
+import subprocess
+from pathlib import Path
+from types import SimpleNamespace
+from unittest.mock import patch
+
+import numpy as np
+import pytest
+
+from rocketpy import DatcomFins, Environment, Flight
+from rocketpy.rocket.datcom import DAMPING_KEYS, parse_datcom_output, run_datcom
+
+SAMPLE = Path(__file__).parents[1] / "fixtures/surfaces/data/datcom/for006.dat"
+
+
+@pytest.mark.slow
+def test_datcom_native_fin_overhang(datcom_rocket):
+    """The native executable accepts aft overhang with an attached root centre."""
+    # Arrange
+    executable = os.environ.get("DATCOM_EXECUTABLE")
+    if not executable:
+        pytest.skip("Set DATCOM_EXECUTABLE to the target 3/99 executable")
+    datcom_rocket.fins[0].datcom_path = executable
+    datcom_rocket.fins[0].root_chord = 0.45
+    # Act
+    model = datcom_rocket.prepare_datcom()
+    # Assert
+    for state in (True, False):
+        assert model.coefficients(state, 0.6, 0)[2] > 0
+        assert set(model.tables[state].damping) == set(DAMPING_KEYS)
+
+
+@pytest.mark.slow
+def test_datcom_native_wind_tunnel_fit(datcom_rocket):
+    """A drag-only synthetic tunnel sweep recovers native coefficients."""
+    # Arrange
+    executable = os.environ.get("DATCOM_EXECUTABLE")
+    if not executable:
+        pytest.skip("Set DATCOM_EXECUTABLE to the target 3/99 executable")
+    rocket = datcom_rocket
+    rocket.fins[0].datcom_path = executable
+    rocket.fins[0].datcom_options["reynolds"] = [2e7, 4e7]
+    rocket.add_datcom_calibration(roughness_height=30.48e-6)
+    known = rocket.prepare_datcom()
+    mach = [0.2, 0.2, 0.2, 0.6, 0.6, 0.6]
+    alpha = [-2, 0, 2, -2, 0, 2]
+    drag = []
+    for m, a in zip(mach, alpha):
+        radians = np.radians(a)
+        values = known.coefficients(False, m, radians)
+        drag.append(values[2] * np.cos(radians) + values[0] * np.sin(radians))
+    rocket.add_datcom_calibration()
+    del rocket.fins[0].datcom_options["reynolds"]
+    # Act
+    report = rocket.fit_datcom_calibration(
+        {
+            "mach": mach,
+            "alpha": alpha,
+            "CD": drag,
+            "reynolds_per_meter": [2e7] * 3 + [4e7] * 3,
+        },
+        bounds={"roughness_height": (0, 100e-6)},
+    )
+    model = rocket.prepare_datcom()
+    # Assert
+    assert report["weighted_rmse_fitted"] < 0.001
+    assert report["weighted_rmse_fitted"] < report["weighted_rmse_initial"]
+    assert report["calibration"]["roughness_height"] == pytest.approx(
+        30.48e-6, abs=5e-6
+    )
+    assert set(model.tables[False].damping) == set(DAMPING_KEYS)
+    assert "reynolds" not in rocket.fins[0].datcom_options
+
+
+@pytest.mark.slow
+def test_datcom_native_calibration_roughness(datcom_rocket):
+    """Public calibration changes native drag and reset restores the baseline."""
+    # Arrange
+    executable = os.environ.get("DATCOM_EXECUTABLE")
+    if not executable:
+        pytest.skip("Set DATCOM_EXECUTABLE to the target 3/99 executable")
+    datcom_rocket.fins[0].datcom_path = executable
+    baseline = datcom_rocket.prepare_datcom()
+    # Act
+    datcom_rocket.add_datcom_calibration(roughness_height=30.48e-6)
+    calibrated = datcom_rocket.prepare_datcom()
+    datcom_rocket.add_datcom_calibration()
+    reset = datcom_rocket.prepare_datcom()
+    # Assert
+    for powered in (True, False):
+        assert (
+            calibrated.coefficients(powered, 0.6, 0)[2]
+            > baseline.coefficients(powered, 0.6, 0)[2]
+        )
+        np.testing.assert_array_equal(
+            reset.tables[powered].body["CA"], baseline.tables[powered].body["CA"]
+        )
+
+
+def test_parse_datcom_output_complete_signed_tables():
+    """A real for006 sample supplies static, lateral and all dynamic channels."""
+    # Arrange
+    directory = SAMPLE.parent
+    # Act
+    result = parse_datcom_output(directory)
+    # Assert
+    np.testing.assert_allclose(result[4], [0.2, 0.6])
+    np.testing.assert_allclose(result[5], [-2, 0, 2])
+    assert result[0].shape == (2, 3)
+    assert set(result.damping) == set(DAMPING_KEYS)
+    assert result.body["CA"][0, 1] > 0
+    assert result.damping["CMQ"][0, 1] < 0
+    assert result.damping["CLLP"][0, 1] < 0
+    for values in result.damping.values():
+        assert values.shape == (2, 3)
+        assert np.isfinite(values).all()
+
+
+def test_parse_datcom_output_joined_fixed_width_columns(tmp_path):
+    """An overflowing negative CP field must not truncate a valid alpha table."""
+    # Arrange
+    lines = SAMPLE.read_text().splitlines()
+    header = next(
+        i
+        for i, line in enumerate(lines)
+        if line.split() == ["ALPHA", "CL", "CD", "CL/CD", "X-C.P."]
+    )
+    row = next(i for i in range(header + 1, len(lines)) if len(lines[i].split()) == 5)
+    fields = lines[row].split()
+    fields[-1] = "-28096.705"
+    lines[row] = " ".join(fields[:-1]) + fields[-1]
+    (tmp_path / "for006.dat").write_text("\n".join(lines))
+    # Act
+    result = parse_datcom_output(tmp_path)
+    # Assert
+    assert result[3][0, 0] == pytest.approx(-28096.705)
+    assert result[5] == pytest.approx([-2, 0, 2])
+
+
+@pytest.mark.parametrize(
+    "replacement,message",
+    [
+        (("DYNAMIC DERIVATIVES", "MISSING DERIVATIVES"), "lacks DAMP"),
+        (("ALPHA       CNQ", "ALPHA       BAD"), "CNQ"),
+        (("NALPHA = 3.", "NALPHA = 4."), "angle count"),
+    ],
+)
+def test_parse_datcom_output_rejects_incomplete_data(replacement, message, tmp_path):
+    """Missing damping or truncated grids fail instead of silently returning partial data."""
+    # Arrange
+    text = SAMPLE.read_text()
+    old, new = replacement
+    assert old in text
+    (tmp_path / "for006.dat").write_text(text.replace(old, new))
+    # Act / Assert
+    with pytest.raises(RuntimeError, match=message):
+        parse_datcom_output(tmp_path)
+
+
+@pytest.mark.parametrize(
+    "failure", [None, "exit", "timeout", "missing", "input", "grid"]
+)
+def test_run_datcom_temp_directory_cleanup(failure, tmp_path):
+    """Executable/deck copies and all outputs are removed on success and every failure."""
+    # Arrange
+    source = tmp_path / "native datcom"
+    source.write_bytes(b"executable placeholder")
+    source.chmod(0o755)
+    working_directories = []
+    deck = "DIM M\nDAMP\nNEXT CASE\n"
+    if failure == "grid":
+        deck = "DIM M\n $FLTCON MACH = .2,.7,$\nDAMP\nNEXT CASE\n"
+
+    def execute(argv, cwd, **kwargs):
+        directory = Path(cwd)
+        working_directories.append(directory)
+        assert Path(argv[0]).read_bytes() == source.read_bytes()
+        assert Path(argv[0]).stat().st_mode & 0o111
+        assert directory != source.parent
+        assert (directory / "for005.dat").read_text() == deck
+        assert kwargs["timeout"] == 2
+        if failure == "timeout":
+            raise subprocess.TimeoutExpired(argv, 2)
+        if failure != "missing":
+            shutil.copyfile(SAMPLE, directory / "for006.dat")
+        if failure == "input":
+            (directory / "for006.dat").write_text("** ERROR ** 1*F")
+        return SimpleNamespace(
+            returncode=1 if failure == "exit" else 0, stderr="failure"
+        )
+
+    # Act
+    with patch("rocketpy.rocket.datcom.subprocess.run", side_effect=execute):
+        if failure:
+            with pytest.raises(RuntimeError):
+                run_datcom(source, deck, timeout=2)
+        else:
+            parsed = run_datcom(source, deck, timeout=2)
+            assert parsed[0].shape == (2, 3)
+    # Assert
+    assert len(working_directories) == 1
+    assert not working_directories[0].exists()
+    assert source.read_bytes() == b"executable placeholder"
+    assert sorted(p.name for p in tmp_path.iterdir()) == [source.name]
+
+
+@pytest.mark.parametrize(
+    "mode,equations",
+    [
+        ("6 DOF", "standard"),
+        ("6 DOF", "solid_propulsion"),
+        ("3 DOF", "standard"),
+    ],
+)
+def test_datcom_flight_uses_full_model(mode, equations, datcom_rocket):
+    """Each supported free-flight solver consumes DATCOM and produces finite states."""
+    # Arrange
+    rocket = datcom_rocket
+    tables = parse_datcom_output(SAMPLE.parent)
+    # Act
+    with patch("rocketpy.rocket.datcom.run_datcom", return_value=tables) as run:
+        flight = Flight(
+            rocket,
+            Environment(),
+            rail_length=5,
+            initial_solution=[0, 0, 0, 1000, 0, 0, 100, 1, 0, 0, 0, 0.05, 0, 0],
+            max_time=0.02,
+            max_time_step=0.01,
+            simulation_mode=mode,
+            equations_of_motion=equations,
+        )
+    # Assert
+    assert run.call_count == 2
+    assert np.isfinite(np.asarray(flight.solution)).all()
+    assert flight.solution[-1][0] == pytest.approx(0.02)
+    assert len(rocket.aerodynamic_surfaces) == 3
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("powered_base_drag", ["none", "nozzle"])
+@pytest.mark.parametrize(
+    "method",
+    [
+        "add_datcom_trapezoidal_fins",
+        "add_datcom_arc_fins",
+        "add_datcom_hex_fins",
+    ],
+)
+def test_datcom_native_executable(method, powered_base_drag, calisto):
+    """Optional real executable test, enabled with DATCOM_EXECUTABLE and --runslow."""
+    # Arrange
+    executable = os.environ.get("DATCOM_EXECUTABLE")
+    if not executable:
+        pytest.skip("Set DATCOM_EXECUTABLE to a native DATCOM executable")
+    rocket = calisto
+    rocket.add_nose(0.55, "von karman", 1.2)
+    fins = getattr(rocket, method)(
+        4,
+        0.25,
+        0.1,
+        0.1,
+        -0.9,
+        path=executable,
+        datcom_options={"powered_base_drag": powered_base_drag},
+    )
+    rocket.add_tail(rocket.radius, 0.045, 0.1, -1.2)
+    # Act
+    model = rocket.prepare_datcom()
+    flight = Flight(
+        rocket,
+        Environment(),
+        rail_length=5,
+        max_time=30,
+        max_time_step=0.1,
+        terminate_on_apogee=True,
+    )
+    # Assert
+    assert isinstance(fins, DatcomFins)
+    assert model.tables[True].damping["CMQ"][0, 3] < 0
+    assert rocket.power_off_drag(0.3) > rocket.power_on_drag(0.3)
+    assert model.references[True] != pytest.approx(model.references[False])
+    assert np.isfinite(np.asarray(flight.solution)).all()
+    assert flight.t_final > rocket.motor.burn_out_time
+    assert flight.apogee > 100
+
+
+def test_parse_datcom_multiple_sideslip_cases(tmp_path):
+    """Repeated Mach grids at different sideslip angles form one complete database."""
+    # Arrange
+    text = SAMPLE.read_text()
+    assert "SIDESLIP =       0.00" in text
+    blocks = [
+        text.replace("SIDESLIP =       0.00", f"SIDESLIP =       {b:.2f}")
+        for b in [-5, 0, 5]
+    ]
+    (tmp_path / "for006.dat").write_text("\n".join(blocks))
+    # Act
+    parsed = parse_datcom_output(tmp_path)
+    # Assert
+    assert parsed.beta == pytest.approx([-5, 0, 5])
+    assert list(parsed.sideslip) == [-5, 0, 5]
+    for table in parsed.sideslip.values():
+        assert table[0].shape == (2, 3)
+        assert set(table.damping) == set(DAMPING_KEYS)
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize(
+    "feature",
+    [
+        "multi_hex",
+        "arc_asymmetric",
+        "naca4",
+        "naca6",
+        "naca1",
+        "naca5",
+        "naca_modified",
+        "naca_supersonic",
+        "fifty_point_user",
+        "rocketpy_cant",
+        "user_surfaces",
+        "user_mean",
+        "one_panel_triangle",
+        "dihedral_incidence",
+        "eight_panels_ten_stations",
+        "boattail",
+    ],
+)
+def test_datcom_native_extended_fin_definitions(feature, calisto):
+    """The installed native executable accepts every supported geometry/section family."""
+    # Arrange
+    executable = os.environ.get("DATCOM_EXECUTABLE")
+    if not executable:
+        pytest.skip("Set DATCOM_EXECUTABLE to a native DATCOM executable")
+    rocket = calisto
+    rocket.add_nose(0.55, "von karman", 1.2)
+    rocket.add_tail(rocket.radius, 0.045, 0.1, -1.2)
+    kwargs = dict(
+        n=4,
+        position=-0.9,
+        span_stations=[0, 0.05, 0.1],
+        chords=[0.25, 0.2, 0.1],
+        leading_edges=[0, 0.04, 0.1],
+        path=executable,
+        datcom_options={"mach": [0.2, 0.6], "alpha": [-2, 0, 2]},
+    )
+    if feature == "arc_asymmetric":
+        kwargs.update(
+            section_type="ARC",
+            upper_thickness_to_chord=[0.01, 0.02, 0.025],
+            lower_thickness_to_chord=[0, 0.01, 0.02],
+        )
+    elif feature.startswith("naca"):
+        designations = {
+            "naca4": "0012",
+            "naca6": "6-64-005",
+            "naca1": "1-16-212.25",
+            "naca5": "5-23012",
+            "naca_modified": "4-0012-62",
+            "naca_supersonic": "S-3-25-5-50",
+        }
+        kwargs.update(section_type="NACA", naca=designations[feature])
+    elif feature == "fifty_point_user":
+        x = np.linspace(0, 1, 50)
+        upper = 0.03 * np.sin(np.pi * x)
+        upper[[0, -1]] = 0
+        kwargs.update(
+            section_type="USER",
+            airfoil_coordinates={"x": x, "upper": upper, "lower": -upper},
+        )
+    elif feature == "rocketpy_cant":
+        kwargs.update(cant_angle=0.5)
+    elif feature == "user_surfaces":
+        kwargs.update(
+            section_type="USER",
+            airfoil_coordinates={
+                "x": [0, 0.25, 0.5, 0.75, 1],
+                "upper": [0, 0.03, 0.04, 0.03, 0],
+                "lower": [0, -0.02, -0.03, -0.02, 0],
+            },
+        )
+    elif feature == "user_mean":
+        kwargs.update(
+            section_type="USER",
+            airfoil_coordinates={
+                "x": [0, 0.25, 0.5, 0.75, 1],
+                "mean": [0, 0.005, 0.005, 0.005, 0],
+                "thickness": [0, 0.05, 0.07, 0.05, 0],
+            },
+        )
+    elif feature == "one_panel_triangle":
+        kwargs.update(n=1, chords=[0.25, 0.15, 0])
+    elif feature == "dihedral_incidence":
+        kwargs.update(n=2, panel_angles=[20, 200], dihedral=[5, -5], incidence=[1, -1])
+    elif feature == "eight_panels_ten_stations":
+        kwargs.update(
+            n=8,
+            span_stations=np.linspace(0, 0.1, 10),
+            chords=np.linspace(0.25, 0.1, 10),
+            leading_edges=np.linspace(0, 0.1, 10),
+        )
+    elif feature == "boattail":
+        kwargs.update(
+            position=-1.21,
+            span_stations=[0, 0.025, 0.05],
+            chords=[0.04, 0.03, 0.02],
+            leading_edges=[0, 0.005, 0.01],
+        )
+    fins = rocket.add_datcom_fins(**kwargs)
+    # Act
+    model = rocket.prepare_datcom()
+    # Assert
+    for powered in (True, False):
+        table = model.tables[powered]
+        assert table[0].shape == (2, 3)
+        assert np.isfinite(table[1]).all()
+        assert set(table.damping) == set(DAMPING_KEYS)
+    if feature in ("one_panel_triangle", "dihedral_incidence"):
+        assert len(model.tables[True].beta) == 3
+        b = model.tables[True].sideslip[5]
+        assert model.coefficients(True, 0.2, 0, np.radians(5))[3] == pytest.approx(
+            b.body["CY"][0, 1]
+        )
+    if feature == "rocketpy_cant":
+        assert model.coefficients(True, 0.2, 0)[5] > 0
+    assert len(fins.datcom_span_stations) <= 10
+
+
+@pytest.mark.slow
+def test_datcom_native_body_axis_angle_convention(calisto):
+    """The target 3/99 executable uses projected atan/atan body incidence angles."""
+    # Arrange
+    import re
+
+    from rocketpy.rocket.datcom import datcom_decks
+
+    executable = os.environ.get("DATCOM_EXECUTABLE")
+    if not executable:
+        pytest.skip("Set DATCOM_EXECUTABLE to the target 3/99 executable")
+    calisto.add_nose(0.55, "von karman", 1.2)
+    calisto.add_datcom_hex_fins(
+        4,
+        0.25,
+        0.1,
+        0.1,
+        -0.9,
+        path=executable,
+        datcom_options={
+            "mach": [0.3, 0.6],
+            "alpha": [-20, 0, 20],
+            "beta": [-30, 0, 30],
+        },
+    )
+    calisto.add_tail(calisto.radius, 0.045, 0.1, -1.2)
+    deck = datcom_decks(calisto)[0][True]
+    # Isolate the axisymmetric body; BUILD enables body-only damping output.
+    deck = re.sub(r"\s*\$FINSET1.*?\$", "", deck, flags=re.S)
+    deck = re.sub(r"\s*\$DEFLCT.*?\$", "", deck, flags=re.S)
+    deck = deck.replace("DAMP", "BUILD\nDAMP")
+    # Act
+    table = run_datcom(executable, deck).sideslip[30]
+    ratio = abs(table.body["CY"][0, 2] / table.body["CN"][0, 2])
+    # Assert
+    expected = np.tan(np.radians(30)) / np.tan(np.radians(20))
+    assert ratio == pytest.approx(expected, rel=1e-3)
+
+
+def test_datcom_tipoff_alpha_dot_is_self_consistent(
+    datcom_rocket, datcom_table_factory
+):
+    """The implicit solve includes the rail constraint rather than the free-flight acceleration."""
+    # Arrange
+    rocket = datcom_rocket
+    rocket.set_rail_buttons(
+        upper_button_position=0.1, lower_button_position=-0.6, angular_position=0
+    )
+    with patch(
+        "rocketpy.rocket.datcom.run_datcom",
+        return_value=datcom_table_factory({"CNAD": 0.5, "CMAD": -0.2, "CA": 0.4}),
+    ):
+        model = rocket.prepare_datcom()
+    state = [0, 0, 1000, 0, 0, 20, 1, 0, 0, 0, 0.2, 0, 0]
+    flight = Flight(
+        rocket,
+        Environment(),
+        rail_length=5,
+        inclination=90,
+        initial_solution=[0, *state],
+        max_time=0.001,
+        max_time_step=0.001,
+        use_udot_rail2=True,
+    )
+    # Act
+    with patch.object(
+        model,
+        "compute_forces_and_moments_at_time",
+        wraps=model.compute_forces_and_moments_at_time,
+    ) as loads:
+        derivative = flight.udot_rail2(0.5, state)
+    # Assert
+    assert loads.call_count == 3
+    used_alpha_dot = loads.call_args.args[1]
+    assert used_alpha_dot == pytest.approx(
+        flight._datcom_incidence_rate(0.5, state, derivative), rel=1e-8, abs=1e-10
+    )
+    assert flight._datcom_evaluating is False
+    assert np.isfinite(derivative).all()
+
+
+@pytest.mark.slow
+def test_datcom_native_small_leading_edge_radius(datcom_rocket):
+    """A measured ten-micrometre edge radius must survive native input parsing."""
+    # Arrange
+    executable = os.environ.get("DATCOM_EXECUTABLE")
+    if not executable:
+        pytest.skip("Set DATCOM_EXECUTABLE to the target 3/99 executable")
+    datcom_rocket.fins[0].datcom_path = executable
+    datcom_rocket.add_datcom_calibration(leading_edge_radius=1e-5)
+    # Act
+    model = datcom_rocket.prepare_datcom()
+    # Assert
+    assert np.isfinite(model.coefficients(False, 0.6, 0)).all()
+
+
+@pytest.mark.slow
+def test_datcom_native_maximum_adaptive_grid(datcom_rocket):
+    """All 20 adaptive Mach points, including the transonic cluster, survive I/O."""
+    # Arrange
+    from rocketpy.rocket.datcom_conditions import (
+        adaptive_alpha_grid,
+        adaptive_mach_grid,
+    )
+
+    executable = os.environ.get("DATCOM_EXECUTABLE")
+    if not executable:
+        pytest.skip("Set DATCOM_EXECUTABLE to the target 3/99 executable")
+    datcom_rocket.fins[0].datcom_path = executable
+    mach = adaptive_mach_grid(3)
+    alpha = adaptive_alpha_grid([-35, 40])
+    datcom_rocket.fins[0].datcom_options.update(mach=mach, alpha=alpha)
+    # Act
+    model = datcom_rocket.prepare_datcom()
+    # Assert
+    for state in (True, False):
+        assert len(model.tables[state][4]) == 20
+        assert model.tables[state][4] == pytest.approx(mach, abs=0.005)
+        assert model.tables[state][5] == pytest.approx(alpha, abs=0.005)
diff --git a/tests/unit/rocket/aero_surface/test_body_profiles.py b/tests/unit/rocket/aero_surface/test_body_profiles.py
new file mode 100644
index 000000000..991de9379
--- /dev/null
+++ b/tests/unit/rocket/aero_surface/test_body_profiles.py
@@ -0,0 +1,73 @@
+"""DATCOM-only ogive tails preserve the original ordinary Tail behavior."""
+
+import json
+
+import numpy as np
+import pytest
+
+from rocketpy import Tail
+from rocketpy._encoders import RocketPyDecoder, RocketPyEncoder
+from rocketpy.rocket.aero_surface.datcom_tail import DatcomTail
+
+
+def test_ogive_tail_requires_datcom(calisto):
+    """An ordinary rocket rejects ogive tails before modifying its assembly."""
+    # Arrange
+    count = len(calisto.aerodynamic_surfaces)
+    # Act / Assert
+    with pytest.raises(ValueError, match="DATCOM fin set"):
+        calisto.add_tail(0.06, 0.04, 0.2, position=-1, kind="ogive")
+    assert len(calisto.aerodynamic_surfaces) == count
+    with pytest.raises(ValueError, match="DATCOM fin set"):
+        calisto.add_surfaces(DatcomTail(0.06, 0.04, 0.2, 0.06), -1)
+    with pytest.raises(ValueError, match="Tail kind"):
+        calisto.add_tail(0.06, 0.04, 0.2, position=-1, kind="unknown")
+
+
+def test_datcom_ogive_geometry_and_serialization(datcom_rocket):
+    """Ogive shape survives serialization and has no component aero model."""
+    # Arrange / Act
+    tail = datcom_rocket.add_tail(0.0635, 0.045, 0.1, position=-1.2, kind="ogive")
+    restored = json.loads(json.dumps(tail, cls=RocketPyEncoder), cls=RocketPyDecoder)
+    # Assert
+    assert isinstance(tail, DatcomTail)
+    assert restored.kind == tail.kind == "ogive"
+    assert tail.radius_at([0, 0.1]) == pytest.approx([0.0635, 0.045])
+    assert tail.radius_at(0.05) > (0.0635 + 0.045) / 2
+    np.testing.assert_allclose(restored.shape_vec, tail.shape_vec)
+    assert tail.clalpha(0) == 0
+    with pytest.raises(ValueError, match="DATCOM"):
+        tail.compute_forces_and_moments()
+    with pytest.raises(AttributeError):
+        tail.kind = "conical"
+
+
+def test_conical_tail_preserves_original_model(calisto):
+    """The ordinary helper constructs exactly the original Tail class and equations."""
+    # Arrange
+    top, bottom, length = 0.06, 0.04, 0.2
+    r = top / bottom
+    # Act
+    tail = calisto.add_tail(top, bottom, length, -1, kind="conical")
+    # Assert
+    assert type(tail) is Tail
+    assert tail.cpz == pytest.approx((length / 3) * (1 + (1 - r) / (1 - r**2)))
+    assert "kind" not in tail.to_dict()
+
+
+def test_datcom_ogive_rejects_invalid_dimensions():
+    """A tangent ogive cannot overhang its upstream cylinder joint."""
+    # Arrange / Act / Assert
+    with pytest.raises(ValueError, match="radius change"):
+        DatcomTail(0.09, 0.01, 0.02, 0.09)
+
+
+def test_removing_datcom_fins_rejects_ogive_before_flight(datcom_rocket):
+    """An assembled ogive cannot silently fall back to ordinary aerodynamics."""
+    # Arrange
+    datcom_rocket.add_tail(0.0635, 0.045, 0.1, -1.2, kind="ogive")
+    for fin in list(datcom_rocket.fins):
+        datcom_rocket.aerodynamic_surfaces.remove(fin)
+    # Act / Assert
+    with pytest.raises(ValueError, match="DATCOM fin set"):
+        datcom_rocket.prepare_datcom()
diff --git a/tests/unit/rocket/test_datcom.py b/tests/unit/rocket/test_datcom.py
new file mode 100644
index 000000000..bcf46d223
--- /dev/null
+++ b/tests/unit/rocket/test_datcom.py
@@ -0,0 +1,607 @@
+"""DATCOM geometry, interpolation, units and reference-frame regressions."""
+
+import json
+import re
+from types import SimpleNamespace
+from unittest.mock import patch
+
+import numpy as np
+import pytest
+
+from rocketpy import DatcomFins, EmptyMotor, Environment, Flight, Function, Rocket
+from rocketpy._encoders import RocketPyDecoder, RocketPyEncoder
+from rocketpy.mathutils.vector_matrix import Vector
+from rocketpy.rocket.datcom import (
+    DAMPING_KEYS,
+    DatcomSurface,
+    datcom_decks,
+    parse_datcom_output,
+)
+
+
+def _deck_value(deck, name):
+    return float(re.search(rf"\b{name}\s*=\s*([-+\d.eE]+)", deck)[1])
+
+
+def test_datcom_fin_trailing_edge_can_extend_past_base(datcom_rocket):
+    """Root-centre attachment permits aft fin overhang without extending the body."""
+    # Arrange
+    rocket = datcom_rocket
+    original_body = _deck_value(datcom_decks(rocket)[0][False], "LCENTR")
+    fin, position, _ = next(
+        s for s in rocket.aerodynamic_surfaces if s[0] is rocket.fins[0]
+    )
+    fin.root_chord = 0.45
+    # Act
+    decks, _, _, _ = datcom_decks(rocket)
+    # Assert
+    assert _deck_value(decks[False], "LCENTR") == pytest.approx(original_body)
+    assert _deck_value(decks[False], "CHORD") == pytest.approx(0.45)
+
+
+def test_datcom_fin_root_center_cannot_be_beyond_body(datcom_rocket):
+    """A fin with its root centre behind the body has no valid attachment."""
+    # Arrange
+    datcom_rocket.fins[0].root_chord = 1.0
+    # Act / Assert
+    with pytest.raises(ValueError, match="root chord centers"):
+        datcom_decks(datcom_rocket)
+
+
+@pytest.mark.parametrize("mode", ["none", "nozzle", "full"])
+def test_datcom_powered_base_drag_policy(datcom_rocket, mode):
+    """Nozzle mode includes the real base annulus without enabling plume models."""
+    # Arrange
+    datcom_rocket.fins[0].datcom_options["powered_base_drag"] = mode
+    # Act
+    decks, _, _, _ = datcom_decks(datcom_rocket)
+    # Assert
+    assert _deck_value(decks[False], "DEXIT") == 0
+    assert "BASE = .FALSE." in decks[True]
+    if mode == "none":
+        assert "DEXIT" not in decks[True]
+    else:
+        expected = 2 * datcom_rocket.motor.nozzle_radius if mode == "nozzle" else 0
+        assert _deck_value(decks[True], "DEXIT") == pytest.approx(expected)
+
+
+@pytest.mark.parametrize("mode", ["bad", True])
+def test_datcom_powered_base_drag_rejects_invalid_policy(datcom_rocket, mode):
+    """Ambiguous policy values cannot silently change the base-drag model."""
+    # Arrange
+    datcom_rocket.fins[0].datcom_options["powered_base_drag"] = mode
+    # Act / Assert
+    with pytest.raises(ValueError, match="powered_base_drag"):
+        datcom_decks(datcom_rocket)
+
+
+def test_datcom_powered_nozzle_must_fit_base(datcom_rocket):
+    """A nozzle outside the body cannot produce a valid annular base area."""
+    # Arrange
+    datcom_rocket.fins[0].datcom_options["powered_base_drag"] = "nozzle"
+    datcom_rocket.motor.nozzle_radius = 1
+    # Act / Assert
+    with pytest.raises(ValueError, match="nozzle diameter"):
+        datcom_decks(datcom_rocket)
+
+
+@pytest.mark.parametrize(
+    "method,section",
+    [
+        ("add_datcom_trapezoidal_fins", "HEX"),
+        ("add_datcom_arc_fins", "ARC"),
+        ("add_datcom_hex_fins", "HEX"),
+    ],
+)
+def test_add_datcom_fins_retains_geometry(method, section, calisto_motorless, tmp_path):
+    """The public methods preserve planform dimensions without executing DATCOM."""
+    # Arrange
+    rocket = calisto_motorless
+    # Act
+    with patch("rocketpy.rocket.datcom.run_datcom") as run:
+        fins = getattr(rocket, method)(
+            4,
+            0.3,
+            0.1,
+            0.12,
+            position=-1,
+            path=tmp_path / "datcom",
+            sweep_angle=30,
+            thickness_to_chord=(0.02, 0.03),
+        )
+    # Assert
+    run.assert_not_called()
+    assert isinstance(fins, DatcomFins)
+    assert fins.section_type == section
+    assert fins.sweep_length == pytest.approx(0.12 * np.tan(np.radians(30)))
+    assert fins.thickness_to_chord == pytest.approx((0.02, 0.03))
+    assert rocket.aerodynamic_surfaces[0].component is fins
+    assert np.isnan(rocket.cp_position(0))
+
+
+@pytest.mark.parametrize("orientation", ["tail_to_nose", "nose_to_tail"])
+def test_datcom_decks_cg_geometry_and_base_drag(orientation, cesaroni_m1670, tmp_path):
+    """Both rocket orientations produce the same nose-to-tail DATCOM geometry."""
+    # Arrange
+    sign = 1 if orientation == "tail_to_nose" else -1
+    rocket = Rocket(
+        0.0635, 14, (6, 6, 0.04), 0.9, 0.8, 0, coordinate_system_orientation=orientation
+    )
+    rocket.add_motor(cesaroni_m1670, -1.373 * sign)
+    rocket.add_nose(0.55, "von karman", 1.2 * sign)
+    rocket.add_datcom_hex_fins(
+        4, 0.25, 0.1, 0.1, -0.9 * sign, sweep_length=0.1, path=tmp_path / "datcom"
+    )
+    rocket.add_tail(0.0635, 0.045, 0.1, -1.2 * sign)
+    # Act
+    decks, references, _, _ = datcom_decks(rocket)
+    # Assert
+    for powered, deck in decks.items():
+        assert _deck_value(deck, "LNOSE") == pytest.approx(0.55)
+        assert _deck_value(deck, "LCENTR") == pytest.approx(1.85)
+        assert _deck_value(deck, "XLE") == pytest.approx(2.1)
+        assert _deck_value(deck, "XCG") == pytest.approx(
+            (1.2 * sign - references[powered]) * sign
+        )
+        assert "DAMP\n" in deck
+        assert "PROTUB" not in deck
+        assert "BASE = .FALSE." in deck
+        assert max(map(len, deck.splitlines())) < 80
+    assert "DEXIT" not in decks[True]
+    assert _deck_value(decks[False], "DEXIT") == 0
+    assert references[True] == pytest.approx(
+        rocket.center_of_mass(rocket.motor.burn_start_time)
+    )
+    assert references[False] == pytest.approx(rocket.center_of_dry_mass_position)
+    assert references[True] != pytest.approx(references[False])
+
+
+def test_datcom_decks_filled_tank_cg(calisto_liquid_modded, tmp_path):
+    """The ignition reference follows existing liquid tank masses and positions."""
+    # Arrange
+    rocket = calisto_liquid_modded
+    rocket.add_nose(0.55, "ogive", 3)
+    rocket.add_datcom_arc_fins(4, 0.25, 0.1, 0.1, -2, path=tmp_path / "datcom")
+    rocket.add_tail(rocket.radius, 0.04, 0.1, -3)
+    time = rocket.motor.burn_start_time
+    mass = rocket.motor.propellant_mass(time)
+    expected = (
+        rocket.dry_mass * rocket.center_of_dry_mass_position
+        + mass * rocket.center_of_propellant_position(time)
+    ) / (rocket.dry_mass + mass)
+    # Act
+    _, references, _, _ = datcom_decks(rocket)
+    # Assert
+    assert references[True] == pytest.approx(expected, rel=1e-10)
+    assert references[True] != pytest.approx(references[False])
+
+
+def test_prepare_datcom_disables_component_models_and_rebuilds(
+    datcom_rocket, datcom_table_factory
+):
+    """One complete model replaces component loads and geometry edits regenerate cases."""
+    # Arrange
+    rocket = datcom_rocket
+    original = tuple(rocket.aerodynamic_surfaces)
+    # Act
+    with patch(
+        "rocketpy.rocket.datcom.run_datcom", return_value=datcom_table_factory()
+    ) as run:
+        model = rocket.prepare_datcom()
+        same = rocket.prepare_datcom()
+        assert run.call_count == 2
+        original[1].component.tip_chord = 0.11
+        rebuilt = rocket.prepare_datcom()
+    # Assert
+    assert same is model
+    assert rebuilt is not model
+    assert run.call_count == 4
+    assert tuple(rocket.aerodynamic_surfaces) == original
+    assert [s for s, _, _ in rocket._flight_aerodynamic_surfaces] == [rebuilt]
+    assert model not in rocket.surfaces_cp_to_cdm
+    assert rocket.power_on_drag(0.3) == pytest.approx(0.4)
+
+
+def test_prepare_datcom_failed_run_is_atomic(datcom_rocket, datcom_table_factory):
+    """A failed coast run never installs the powered half of a database."""
+    # Arrange
+    rocket = datcom_rocket
+    original_drag = rocket.power_on_drag_7d
+    # Act / Assert
+    with patch(
+        "rocketpy.rocket.datcom.run_datcom",
+        side_effect=[datcom_table_factory(), RuntimeError("failed")],
+    ):
+        with pytest.raises(RuntimeError, match="failed"):
+            rocket.prepare_datcom()
+    assert rocket._datcom_model is None
+    assert rocket.power_on_drag_7d is original_drag
+
+
+@pytest.mark.parametrize(
+    "options,message",
+    [
+        ({"mach": [0.5, 0.2]}, "increasing"),
+        ({"alpha": [0, 1, 2]}, "negative"),
+        ({"altitude": [0]}, "one value per Mach"),
+        ({"timeout": 0}, "timeout"),
+        ({"unknown": 1}, "Unknown"),
+    ],
+)
+def test_datcom_decks_invalid_conditions(options, message, datcom_rocket):
+    """Bad grids fail before launching an external process."""
+    # Arrange
+    datcom_rocket.fins[0].datcom_options.update(options)
+    # Act / Assert
+    with pytest.raises(ValueError, match=message):
+        datcom_decks(datcom_rocket)
+
+
+@pytest.mark.parametrize(
+    "field,value",
+    [
+        ("thickness_to_chord", 0),
+        ("flat_length", 0.9),
+        ("leading_edge_radius", -1),
+        ("thickness_to_chord", [1, 2, 3]),
+    ],
+)
+def test_datcom_fins_invalid_section(field, value, tmp_path):
+    """Invalid or mismatched section stations are rejected."""
+    # Arrange
+    kwargs = {field: value}
+    # Act / Assert
+    with pytest.raises(ValueError):
+        DatcomFins(4, 0.3, 0.1, 0.1, 0.06, path=tmp_path / "datcom", **kwargs)
+
+
+def test_datcom_fins_serialization(datcom_rocket):
+    """Saved rockets retain section dimensions and deferred execution settings."""
+    # Arrange
+    fins = datcom_rocket.fins[0]
+    # Act
+    restored = json.loads(json.dumps(fins, cls=RocketPyEncoder), cls=RocketPyDecoder)
+    # Assert
+    assert isinstance(restored, DatcomFins)
+    assert restored.to_dict() == fins.to_dict()
+
+
+def test_datcom_fins_direct_inclusion(datcom_rocket):
+    """Adding a constructed DATCOM surface also selects the whole-vehicle model."""
+    # Arrange
+    fins = DatcomFins.from_dict(datcom_rocket.fins[0].to_dict())
+    rocket = Rocket(0.0635, 14, (6, 6, 0.04), 0, 0, 0)
+    # Act
+    rocket.add_surfaces(fins, positions=-0.9)
+    # Assert
+    assert rocket._has_datcom_fins()
+    assert rocket.fins[0] is fins
+
+
+def test_datcom_prepared_rocket_serialization(datcom_rocket, datcom_table_factory):
+    """Rocket export preserves inputs and geometry without embedding live model closures."""
+    # Arrange
+    with patch(
+        "rocketpy.rocket.datcom.run_datcom", return_value=datcom_table_factory()
+    ):
+        datcom_rocket.prepare_datcom()
+    # Act
+    restored = json.loads(
+        json.dumps(datcom_rocket, cls=RocketPyEncoder), cls=RocketPyDecoder
+    )
+    # Assert
+    assert isinstance(restored, Rocket)
+    assert restored._has_datcom_fins()
+    assert restored._datcom_model is None
+    assert restored.fins[0].to_dict() == datcom_rocket.fins[0].to_dict()
+
+
+@pytest.mark.parametrize(
+    "key,axis,index,sign",
+    [
+        ("CNQ", 0, 1, -1),
+        ("CMQ", 0, 3, 1),
+        ("CAQ", 0, 2, -1),
+        ("CNAD", None, 1, -1),
+        ("CMAD", None, 3, 1),
+        ("CYR", 1, 0, 1),
+        ("CLNR", 1, 4, 1),
+        ("CLLR", 1, 5, 1),
+        ("CYP", 2, 0, 1),
+        ("CLNP", 2, 4, 1),
+        ("CLLP", 2, 5, 1),
+    ],
+)
+@pytest.mark.parametrize("direction", [-1, 1])
+def test_datcom_damping_units_axes(
+    key, axis, index, sign, direction, datcom_rocket, datcom_table_factory
+):
+    """Every DAMP channel has the correct per-degree rate scaling and body axis."""
+    # Arrange
+    table = datcom_table_factory({key: 2})
+    model = DatcomSurface(
+        datcom_rocket, {True: table, False: table}, {True: 0, False: 0}
+    )
+    omega = [0, 0, 0]
+    if axis is not None:
+        omega[axis] = direction
+    expected = np.zeros(6)
+    scale = 0.5 * 1.2 * 100**2 * model.reference_area
+    if index >= 3:
+        scale *= model.reference_length
+    expected[index] = (
+        sign * 2 * direction * np.degrees(model.reference_length / 200) * scale
+    )
+    # Act
+    result = model.compute_forces_and_moments_at_time(
+        0.5,
+        direction if axis is None else 0,
+        Vector([0, 0, -100]),
+        100,
+        0.3,
+        1.2,
+        Vector.zeros(),
+        Vector(omega),
+        1e6,
+    )
+    # Assert
+    np.testing.assert_allclose(result, expected, atol=1e-10)
+
+
+def test_datcom_interpolation_and_power_boundaries(datcom_rocket, datcom_table_factory):
+    """Mach/alpha interpolate, while base-drag state switches at ignition/burnout."""
+    # Arrange
+    on = datcom_table_factory({"CA": 1})
+    on.body["CN"][:] = [[0, 2, 4], [4, 6, 8]]
+    off = datcom_table_factory({"CA": 2})
+    model = DatcomSurface(datcom_rocket, {True: on, False: off}, {True: 0, False: 0})
+    model.burn_start, model.burn_out = 1, 3
+    # Act
+    values = model.coefficients(True, 0.4, np.radians(-1))
+    states = [model.is_powered(t) for t in [0, 1, 2, 3, 4]]
+    # Assert
+    assert values[0] == pytest.approx(3)
+    assert states == [False, True, True, False, False]
+    assert model.coefficients(False, 0.4, 0)[2] == 2
+    assert model.coefficients(True, 5, 1)[0] == 8  # bounded envelope
+
+
+@pytest.mark.parametrize("speed", [0, 1e-12, 0.5, 100, -100])
+def test_datcom_low_speed_and_reverse_flow(speed, datcom_rocket, datcom_table_factory):
+    """Loads remain finite near rest and axial drag opposes reverse motion."""
+    # Arrange
+    table = datcom_table_factory({"CA": 1, "CMQ": -2})
+    model = DatcomSurface(
+        datcom_rocket, {True: table, False: table}, {True: 0, False: 0}
+    )
+    # Act
+    loads = model.compute_forces_and_moments_at_time(
+        0.5,
+        0,
+        Vector([0, 0, -speed]),
+        abs(speed),
+        0.3,
+        1.2,
+        Vector.zeros(),
+        Vector([1, 0, 0]),
+        0,
+    )
+    # Assert
+    assert np.isfinite(loads).all()
+    assert loads[2] * speed <= 0
+
+
+def test_datcom_moment_reference_transfer(datcom_rocket, datcom_table_factory):
+    """Moments are transferred from the case reference to dry CG exactly once."""
+    # Arrange
+    table = datcom_table_factory({"CN": 1, "CM": 2})
+    model = DatcomSurface(
+        datcom_rocket, {True: table, False: table}, {True: 0.5, False: 0}
+    )
+    cp = Vector([0, 0, 0.7])
+    scale = 0.5 * 1.2 * 100**2 * model.reference_area
+    # Act
+    loads = model.compute_forces_and_moments_at_time(
+        0.5,
+        0,
+        Vector([0, 0, -100]),
+        100,
+        0.3,
+        1.2,
+        cp,
+        Vector.zeros(),
+        0,
+    )
+    # Assert
+    assert loads[1] == pytest.approx(-scale)
+    assert loads[3] == pytest.approx(scale * (2 * model.reference_length + 0.7))
+
+
+def test_datcom_axial_coefficient_is_not_projected_twice(
+    datcom_rocket, datcom_table_factory
+):
+    """CA already acts along the body axis, including at nonzero incidence."""
+    # Arrange
+    table = datcom_table_factory({"CA": 0.4, "CYB": -0.1})
+    model = DatcomSurface(
+        datcom_rocket, {True: table, False: table}, {True: 0, False: 0}
+    )
+    velocity = Vector([1, 2, 100])
+    speed = abs(velocity)
+    scale = 0.5 * 1.2 * speed**2 * model.reference_area
+    beta = np.arctan2(1, 100)
+    # Act
+    loads = model.compute_forces_and_moments_at_time(
+        0.5,
+        0,
+        -velocity,
+        speed,
+        0.3,
+        1.2,
+        Vector.zeros(),
+        Vector.zeros(),
+        0,
+    )
+    # Assert
+    assert loads[2] == pytest.approx(-0.4 * scale)
+    assert loads[0] == pytest.approx(-0.1 * np.degrees(beta) * scale)
+
+
+def test_datcom_rail_uses_body_axial_coefficient(datcom_rocket, datcom_table_factory):
+    """The constrained rail phase uses CA rather than the reporting CD curve."""
+    # Arrange
+    rocket = datcom_rocket
+    with patch(
+        "rocketpy.rocket.datcom.run_datcom",
+        return_value=datcom_table_factory({"CA": 0.4, "CD": 0.8}),
+    ):
+        rocket.prepare_datcom()
+    flight = object.__new__(Flight)
+    flight.rocket, flight.env = rocket, Environment()
+    state = [0, 0, 1000, 0, 0, 20, 1, 0, 0, 0, 0, 0, 0]
+    time = 0.5
+    thrust = max(
+        rocket.motor.thrust(time)
+        + rocket.motor.pressure_thrust(flight.env.pressure(1000)),
+        0,
+    )
+    force = -0.5 * flight.env.density(1000) * 20**2 * rocket.area * 0.4
+    expected = (thrust + force) / rocket.total_mass(time) - flight.env.gravity(1000)
+    # Act
+    derivative = flight.udot_rail1(time, state)
+    # Assert
+    assert expected > 0
+    assert derivative[5] == pytest.approx(expected)
+
+
+@pytest.mark.parametrize("time", [0.2, 1.0, 2.0])
+@pytest.mark.parametrize("rocket_fixture", ["calisto", "calisto_nose_to_tail"])
+def test_datcom_generalized_torque_uses_current_cg(
+    time, rocket_fixture, request, tmp_path, datcom_table_factory
+):
+    """A fixed DATCOM reference transfers to the actual CG throughout the burn."""
+    # Arrange
+    rocket = request.getfixturevalue(rocket_fixture)
+    sign = rocket._csys
+    path = tmp_path / "datcom"
+    path.touch()
+    rocket.add_nose(0.55, "von karman", 1.2 * sign)
+    rocket.add_datcom_hex_fins(
+        4,
+        0.25,
+        0.1,
+        0.1,
+        -0.9 * sign,
+        path=path,
+        datcom_options={"mach": [0.2, 0.6], "alpha": [-2, 0, 2]},
+    )
+    rocket.add_tail(rocket.radius, 0.045, 0.1, -1.2 * sign)
+    table = datcom_table_factory({"CN": 1, "CM": 0.5})
+    with patch("rocketpy.rocket.datcom.run_datcom", return_value=table):
+        model = rocket.prepare_datcom()
+    flight = object.__new__(Flight)
+    flight.rocket, flight.env = rocket, Environment()
+    state = [0, 0, 1000, 0, 0, 100, 1, 0, 0, 0, 0, 0, 0]
+    cg = rocket.center_of_mass(time)
+    offset = (model.references[model.is_powered(time)] - cg) * rocket._csys
+    q_area = 0.5 * flight.env.density(1000) * 100**2 * rocket.area
+    inertia = (
+        rocket.I_11(time)
+        - rocket.total_mass(time) * rocket.com_to_cdm_function(time) ** 2
+    )
+    expected = q_area * (0.5 * model.reference_length + offset) / inertia
+    # Act
+    derivative = flight.u_dot_generalized(time, state)
+    # Assert
+    assert derivative[10] == pytest.approx(expected, rel=1e-9)
+
+
+def test_datcom_unsteady_coupling_is_history_independent(
+    datcom_rocket, datcom_table_factory
+):
+    """CNAD feeds back through acceleration; repeated RHS evaluations are deterministic."""
+    # Arrange
+    rocket = datcom_rocket
+    rocket.add_motor(EmptyMotor(), position=0)
+    with patch(
+        "rocketpy.rocket.datcom.run_datcom",
+        return_value=datcom_table_factory({"CNAD": 1}),
+    ):
+        model = rocket.prepare_datcom()
+    # Put both case references at dry CG to isolate the analytic CNAD feedback.
+    model.references = {True: model.dry_reference, False: model.dry_reference}
+    flight = object.__new__(Flight)
+    flight.rocket, flight.env = rocket, Environment()
+    state = [0, 0, 1000, 0, 0, 100, 1, 0, 0, 0, 0.2, 0, 0]
+    time = 0.5
+    q_area = 0.5 * flight.env.density(1000) * 100**2 * rocket.area
+    expected = 0.2 / (
+        1
+        + q_area
+        * np.degrees(model.reference_length / 200)
+        / (rocket.total_mass(time) * 100)
+    )
+    # Act
+    first = flight.u_dot_generalized(time, state)
+    flight.u_dot_generalized(0.1, state)
+    repeated = flight.u_dot_generalized(time, state)
+    # Assert
+    assert flight._datcom_incidence_rate(time, state, first) == pytest.approx(
+        expected, rel=1e-8
+    )
+    np.testing.assert_allclose(first, repeated, atol=1e-12)
+    assert flight._datcom_alpha_dot == 0
+    assert flight._datcom_evaluating is False
+
+
+@pytest.mark.parametrize(
+    "override,expected_cd", [(None, 0.4), (False, 0.6), (True, 0.2)]
+)
+def test_datcom_no_duplicate_drag_or_component_loads(
+    override, expected_cd, datcom_rocket, datcom_table_factory
+):
+    """Full-vehicle axial force is applied once despite nonzero original rocket drag."""
+    # Arrange
+    rocket = datcom_rocket
+    if override is not None:
+        rocket.air_brakes.append(
+            SimpleNamespace(
+                deployment_level=1,
+                override_rocket_drag=override,
+                drag_coefficient=Function(lambda level, mach: 0.2),
+                reference_area=rocket.area,
+            )
+        )
+    with patch(
+        "rocketpy.rocket.datcom.run_datcom",
+        return_value=datcom_table_factory({"CA": 0.4, "CD": 0.4}),
+    ):
+        rocket.prepare_datcom()
+    flight = object.__new__(Flight)
+    flight.rocket, flight.env = rocket, Environment()
+    flight._Flight__post_processed_variables = []
+    state = [0, 0, 1000, 0, 0, 100, 1, 0, 0, 0, 0, 0, 0]
+    expected = -0.5 * flight.env.density(1000) * 100**2 * rocket.area * expected_cd
+    # Act
+    with patch.object(
+        rocket.nosecones[0],
+        "compute_forces_and_moments",
+        side_effect=AssertionError("component loads used"),
+    ):
+        flight.u_dot_generalized(0.5, state, post_processing=True)
+    # Assert
+    assert len(flight._Flight__post_processed_variables) == 1
+    assert flight._Flight__post_processed_variables[0][9] == pytest.approx(expected)
+
+
+def test_datcom_namelist_scientific_notation_uppercase():
+    """Legacy CONERR rejects lowercase exponents for small physical inputs."""
+    # Arrange
+    from rocketpy.rocket.datcom import _namelist
+
+    # Act
+    text = _namelist("FINSET1", [("LER", [1e-5, 1.25e-5])])
+    # Assert
+    assert "1.E-05" in text
+    assert "1.25E-05" in text
+    assert "e" not in text
diff --git a/tests/unit/rocket/test_datcom_atmosphere.py b/tests/unit/rocket/test_datcom_atmosphere.py
new file mode 100644
index 000000000..c56b4ef31
--- /dev/null
+++ b/tests/unit/rocket/test_datcom_atmosphere.py
@@ -0,0 +1,174 @@
+"""Atmosphere transfer uses actual reference-flight conditions, not ISA."""
+
+from types import SimpleNamespace
+
+import numpy as np
+import pytest
+
+from rocketpy.rocket.datcom import datcom_decks
+
+
+def _flight(rocket, density=1.5):
+    stop = rocket.motor.burn_time[1]
+    return SimpleNamespace(
+        rocket=rocket,
+        apogee_time=2 * stop,
+        mach_number=lambda t: np.where(t < stop, t / stop, 2 - t / stop),
+        z=lambda t: 1000 + t * 100,
+        partial_angle_of_attack=lambda t: np.zeros_like(t),
+        env=SimpleNamespace(
+            density=lambda z: np.full_like(z, density),
+            speed_of_sound=lambda z: np.full_like(z, 300),
+            dynamic_viscosity=lambda z: np.full_like(z, 2e-5),
+            pressure=lambda z: np.full_like(z, 90000),
+            temperature=lambda z: np.full_like(z, 280),
+        ),
+    )
+
+
+@pytest.mark.parametrize("density", [0.8, 1.5])
+def test_set_datcom_atmosphere_actual_environment(datcom_rocket, density):
+    """Both native phases use rho*a*M/mu, including unreached Mach values."""
+    # Arrange
+    flight = _flight(datcom_rocket, density)
+    grid = [0.1, 0.5, 2.0]
+    # Act
+    report = datcom_rocket.set_datcom_atmosphere(flight, mach=grid)
+    decks, _, _, _ = datcom_decks(datcom_rocket)
+    # Assert
+    for state in ("power_on", "power_off"):
+        assert report["reynolds"][state] == pytest.approx(
+            density * 300 * np.array(grid) / 2e-5
+        )
+        assert report["temperature_k"][state] == pytest.approx([280] * 3)
+        assert report["pressure_pa"][state] == pytest.approx([90000] * 3)
+    assert all("REN =" in deck and "ALT =" not in deck for deck in decks.values())
+
+
+def test_set_datcom_atmosphere_failure_preserves_options(datcom_rocket):
+    """An invalid atmosphere cannot partially overwrite the native settings."""
+    # Arrange
+    fins = [
+        s
+        for s, _, _ in datcom_rocket.aerodynamic_surfaces
+        if hasattr(s, "datcom_options")
+    ]
+    before = [dict(s.datcom_options) for s in fins]
+    flight = _flight(datcom_rocket, density=-1)
+    # Act / Assert
+    with pytest.raises(ValueError, match="positive Reynolds"):
+        datcom_rocket.set_datcom_atmosphere(flight)
+    assert [s.datcom_options for s in fins] == before
+
+
+def test_set_datcom_atmosphere_separates_power_phases(datcom_rocket):
+    """The same Mach at different altitudes has different native Reynolds values."""
+    # Arrange
+    flight = _flight(datcom_rocket)
+    flight.env.density = lambda z: 2 - z / 10000
+    stop = datcom_rocket.motor.burn_time[1]
+    # Act
+    report = datcom_rocket.set_datcom_atmosphere(flight, mach=[0.25, 0.5], samples=1001)
+    # Assert
+    for phase, t in [("power_on", 0.5 * stop), ("power_off", 1.5 * stop)]:
+        expected = (2 - (1000 + 100 * t) / 10000) * 300 * 0.5 / 2e-5
+        assert report["reynolds"][phase][1] == pytest.approx(expected)
+    assert report["reynolds"]["power_on"][1] > report["reynolds"]["power_off"][1]
+
+
+def test_set_datcom_atmosphere_requires_both_phases(datcom_rocket):
+    """A reference ending before burnout cannot supply a coast atmosphere."""
+    # Arrange
+    flight = _flight(datcom_rocket)
+    flight.apogee_time = datcom_rocket.motor.burn_time[1] / 2
+    # Act / Assert
+    with pytest.raises(ValueError, match="no power_off samples"):
+        datcom_rocket.set_datcom_atmosphere(flight)
+
+
+@pytest.mark.parametrize("peak", [0.0, 0.2, 0.95, 1.5, 3.0, 8.0])
+def test_adaptive_mach_grid_native_limit_and_regime(peak):
+    """Every flight regime gets 20 distinct points, fixed anchors and headroom."""
+    # Arrange
+    from rocketpy.rocket.datcom_conditions import adaptive_mach_grid
+
+    # Act
+    grid = adaptive_mach_grid(peak)
+    # Assert
+    assert len(grid) == 20
+    assert 0.1 in grid and 1.0 in grid
+    assert grid[0] == pytest.approx(0.01)
+    assert grid[-1] >= peak * 1.15
+    assert np.diff(grid).min() >= 0.0099
+    assert np.count_nonzero((grid >= 0.8) & (grid <= 1.2)) >= 9
+
+
+def test_set_datcom_atmosphere_adapts_mach_and_alpha(datcom_rocket):
+    """Updating from a hotter trajectory expands both grids and rebuilds REN."""
+    # Arrange
+    flight = _flight(datcom_rocket)
+    original_mach = flight.mach_number
+    flight.mach_number = lambda t: 3 * original_mach(t)
+    flight.partial_angle_of_attack = lambda t: 40 * original_mach(t)
+    # Act
+    report = datcom_rocket.set_datcom_atmosphere(flight)
+    decks, _, _, _ = datcom_decks(datcom_rocket)
+    # Assert
+    assert len(report["mach"]) == 20
+    assert report["mach"][-1] >= 3.45
+    assert 0.1 in report["mach"] and 1 in report["mach"]
+    assert report["alpha"][-1] >= 48
+    assert report["alpha"][0] == pytest.approx(-report["alpha"][-1])
+    assert 0 in report["alpha"]
+    for phase in ("power_on", "power_off"):
+        assert len(report["reynolds"][phase]) == 20
+        assert report["coverage"]["phases"][phase]["mach_max"] > 2.9
+    assert all("NMACH = 20." in deck for deck in decks.values())
+
+
+def test_set_datcom_atmosphere_ignores_unloaded_incidence(datcom_rocket):
+    """An undefined pad-flow direction cannot force a tail-first alpha table."""
+    # Arrange
+    flight = _flight(datcom_rocket)
+    flight.partial_angle_of_attack = lambda t: np.where(t == 0, 180, 3)
+    # Act
+    report = datcom_rocket.set_datcom_atmosphere(flight)
+    # Assert
+    assert report["alpha"][-1] == pytest.approx(20)
+    assert report["coverage"]["excluded_low_q_samples"] > 0
+
+
+def test_set_datcom_atmosphere_loaded_tail_first_rejected(datcom_rocket):
+    """A tumbling ascent cannot silently fold loaded tail-first flow."""
+    # Arrange
+    flight = _flight(datcom_rocket)
+    flight.mach_number = lambda t: np.full_like(t, 0.5)
+    flight.partial_angle_of_attack = lambda t: np.full_like(t, 180)
+    # Act / Assert
+    with pytest.raises(ValueError, match="tail-first"):
+        datcom_rocket.set_datcom_atmosphere(flight)
+
+
+def test_set_datcom_atmosphere_explicit_alpha_preserved(datcom_rocket):
+    """Explicit native grids remain available for controlled tunnel conditions."""
+    # Arrange
+    flight = _flight(datcom_rocket)
+    # Act
+    report = datcom_rocket.set_datcom_atmosphere(
+        flight, mach=[0.1, 1.0], alpha=[-5, 0, 5]
+    )
+    # Assert
+    assert report["alpha"] == [-5, 0, 5]
+    assert report["mach"] == [0.1, 1.0]
+
+
+def test_datcom_provisional_grid_uses_twenty_points():
+    """Even before a reference flight, the default uses all native Mach slots."""
+    # Arrange
+    from rocketpy.rocket.datcom import _flight_conditions
+
+    # Act
+    mach, _, _, _ = _flight_conditions({})
+    # Assert
+    assert len(mach) == 20
+    assert 0.1 in mach and 1.0 in mach
diff --git a/tests/unit/rocket/test_datcom_calibration.py b/tests/unit/rocket/test_datcom_calibration.py
new file mode 100644
index 000000000..46b2ee9ae
--- /dev/null
+++ b/tests/unit/rocket/test_datcom_calibration.py
@@ -0,0 +1,106 @@
+"""Explicit native DATCOM calibration and prepared-model lifecycle."""
+
+import json
+import re
+from unittest.mock import patch
+
+import numpy as np
+import pytest
+
+from rocketpy._encoders import RocketPyDecoder, RocketPyEncoder
+from rocketpy.rocket.datcom import datcom_decks
+
+
+def test_datcom_calibration_native_units_and_geometry(datcom_rocket):
+    """Sand roughness uses dimensionless RHR while LER remains in metres."""
+    # Arrange
+    rocket = datcom_rocket
+    original = rocket.fins[0].leading_edge_radius
+    # Act
+    rocket.add_datcom_calibration(roughness_height=30.48e-6, leading_edge_radius=0.0005)
+    decks, _, _, _ = datcom_decks(rocket)
+    # Assert
+    for deck in decks.values():
+        assert float(re.search(r"RHR\s*=\s*([-+\d.eE]+)", deck)[1]) == pytest.approx(
+            400
+        )
+        assert "LER = 0.0005,0.0005," in deck
+        assert "ROUGH =" not in deck
+    assert rocket.fins[0].leading_edge_radius == original
+
+
+@pytest.mark.parametrize("field", ["roughness_height", "leading_edge_radius"])
+@pytest.mark.parametrize("value", [-1, np.nan, np.inf, True, "invalid", [0.001]])
+def test_datcom_calibration_rejects_invalid_values_atomically(
+    datcom_rocket, field, value
+):
+    """Invalid replacement preserves the previous calibration and native deck."""
+    # Arrange
+    datcom_rocket.add_datcom_calibration(roughness_height=10e-6)
+    before = datcom_decks(datcom_rocket)[0]
+    # Act / Assert
+    with pytest.raises(ValueError, match=field):
+        datcom_rocket.add_datcom_calibration(**{field: value})
+    assert datcom_decks(datcom_rocket)[0] == before
+
+
+def test_datcom_calibration_requires_datcom_fins(calisto_motorless):
+    """Calibration cannot silently affect an ordinary RocketPy rocket."""
+    # Arrange
+    rocket = calisto_motorless
+    # Act / Assert
+    with pytest.raises(ValueError, match="DATCOM fin set"):
+        rocket.add_datcom_calibration(roughness_height=10e-6)
+
+
+def test_datcom_calibration_cache_and_reset(datcom_rocket, datcom_table_factory):
+    """Changed inputs rebuild both cases; identical inputs reuse the model."""
+    # Arrange
+    rocket = datcom_rocket
+    original = datcom_decks(rocket)[0]
+    # Act
+    with patch(
+        "rocketpy.rocket.datcom.run_datcom", return_value=datcom_table_factory()
+    ) as run:
+        first = rocket.prepare_datcom()
+        rocket.add_datcom_calibration(roughness_height=20e-6)
+        assert rocket._datcom_model is None
+        calibrated = rocket.prepare_datcom()
+        rocket.add_datcom_calibration(roughness_height=20e-6)
+        unchanged = rocket.prepare_datcom()
+        rocket.add_datcom_calibration()
+        reset = rocket.prepare_datcom()
+    # Assert
+    assert first is not calibrated
+    assert unchanged is calibrated
+    assert reset is not calibrated
+    assert run.call_count == 6
+    assert datcom_decks(rocket)[0] == original
+
+
+def test_datcom_calibration_serialization_and_copy(datcom_rocket):
+    """Saved rockets retain calibration; editing a returned dict cannot change it."""
+    # Arrange
+    datcom_rocket.add_datcom_calibration(
+        roughness_height=20e-6, leading_edge_radius=0.0005
+    )
+    # Act
+    returned = datcom_rocket.datcom_calibration
+    returned["roughness_height"] = 1
+    restored = json.loads(
+        json.dumps(datcom_rocket, cls=RocketPyEncoder), cls=RocketPyDecoder
+    )
+    # Assert
+    assert restored.datcom_calibration == datcom_rocket.datcom_calibration
+    assert restored.datcom_calibration["roughness_height"] == pytest.approx(20e-6)
+    assert restored._datcom_model is None
+    assert datcom_decks(restored)[0] == datcom_decks(datcom_rocket)[0]
+
+
+def test_datcom_calibration_naca_radius_is_not_ignored(datcom_rocket):
+    """A NACA airfoil defines its own leading edge and rejects an override."""
+    # Arrange
+    datcom_rocket.fins[0].section_type = "NACA"
+    # Act / Assert
+    with pytest.raises(ValueError, match="NACA"):
+        datcom_rocket.add_datcom_calibration(leading_edge_radius=0.001)
diff --git a/tests/unit/rocket/test_datcom_extensions.py b/tests/unit/rocket/test_datcom_extensions.py
new file mode 100644
index 000000000..b1eab6e49
--- /dev/null
+++ b/tests/unit/rocket/test_datcom_extensions.py
@@ -0,0 +1,423 @@
+"""DATCOM spanwise geometry, section definitions and asymmetric layout behavior."""
+
+import json
+import re
+from unittest.mock import patch
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pytest
+
+from rocketpy import DatcomFins, DatcomFreeFormFins, Rocket
+from rocketpy._encoders import RocketPyDecoder, RocketPyEncoder
+from rocketpy.mathutils.vector_matrix import Vector
+from rocketpy.rocket.datcom import (
+    DatcomResult,
+    DatcomSurface,
+    datcom_decks,
+    parse_datcom_output,
+)
+
+
+def _values(deck, variable):
+    text = re.search(rf"\b{variable}\s*=\s*([\dEe+.,\s-]+)", deck)[1]
+    return [float(v) for v in re.split(r"[\s,]+", text.strip(" ,\n"))]
+
+
+def test_datcom_default_alpha_dense_near_zero():
+    """Default incidence spans ±20 degrees with most points within ±2 degrees."""
+    # Arrange
+    from rocketpy.rocket.datcom import _flight_conditions
+
+    # Act
+    _, alpha, _, _ = _flight_conditions({})
+    # Assert
+    assert alpha[[0, -1]] == pytest.approx([-20, 20])
+    assert len(alpha) <= 20
+    assert np.count_nonzero(abs(alpha) <= 2) > len(alpha) / 2
+    assert np.count_nonzero(alpha == 0) == 1
+    assert np.diff(alpha).min() > 0
+
+
+@pytest.mark.parametrize("profile,card", [("conical", "CONE"), ("ogive", "OGIVE")])
+def test_datcom_tail_kind_export(datcom_rocket, profile, card):
+    """The assembled tail's actual profile selects the DATCOM afterbody card."""
+    # Arrange
+    from rocketpy import Tail
+
+    tail = next(
+        s for s, _, _ in datcom_rocket.aerodynamic_surfaces if isinstance(s, Tail)
+    )
+    if profile == "ogive":
+        datcom_rocket.aerodynamic_surfaces.remove(tail)
+        tail = datcom_rocket.add_tail(
+            tail.top_radius, tail.bottom_radius, tail.length, -1.2, kind="ogive"
+        )
+    # Act
+    decks, _, _, _ = datcom_decks(datcom_rocket)
+    # Assert
+    assert all(f"TAFT = {card}" in deck for deck in decks.values())
+
+
+@pytest.mark.parametrize("count", [1, 2, 4, 8])
+def test_datcom_fins_panel_count_and_triangular_tip(count, tmp_path):
+    """DATCOM accepts 1..8 panels and a zero tip chord without inherited loads."""
+    # Arrange / Act
+    fins = DatcomFins(count, 0.3, 0, 0.1, 0.06, path=tmp_path / "datcom")
+    # Assert
+    assert fins.n == count
+    assert fins.tip_chord == 0
+    assert fins.panel_angles == pytest.approx(np.arange(count) * 360 / count)
+    assert np.isfinite(fins.shape_vec).all()
+
+
+def test_datcom_multi_station_geometry_and_serialization(tmp_path):
+    """The drawn multi-segment outline and serialized section stations agree."""
+    # Arrange
+    fins = DatcomFreeFormFins(
+        4,
+        [0, 0.06, 0.12],
+        [0.3, 0.22, 0.04],
+        [0, 0.02, 0.1],
+        0.06,
+        path=tmp_path / "datcom",
+        thickness_to_chord=[0.02, 0.025, 0.03],
+        panel_angles=[10, 100, 190, 280],
+        dihedral=[1, 2, 3, 4],
+    )
+    # Act
+    restored = json.loads(json.dumps(fins, cls=RocketPyEncoder), cls=RocketPyDecoder)
+    # Assert
+    assert isinstance(restored, DatcomFreeFormFins)
+    assert restored.to_dict() == fins.to_dict()
+    assert fins.datcom_span_stations == pytest.approx([0, 0.06, 0.12])
+    np.testing.assert_allclose(fins.shape_vec[0], [0, 0.02, 0.1, 0.14, 0.24, 0.3])
+    assert fins.upper_thickness_to_chord == pytest.approx([0.01, 0.0125, 0.015])
+
+
+@pytest.mark.parametrize(
+    "section,options",
+    [
+        ("NACA", {"naca": "0012"}),
+        ("NACA", {"naca": "6-64-005"}),
+        (
+            "USER",
+            {
+                "airfoil_coordinates": {
+                    "x": [0, 0.5, 1],
+                    "upper": [0, 0.03, 0],
+                    "lower": [0, -0.02, 0],
+                }
+            },
+        ),
+        (
+            "USER",
+            {
+                "airfoil_coordinates": {
+                    "x": [0, 0.5, 1],
+                    "mean": [0, 0.005, 0],
+                    "thickness": [0, 0.05, 0],
+                }
+            },
+        ),
+    ],
+)
+def test_datcom_airfoil_sections_generate_decks(section, options, datcom_rocket):
+    """NACA control cards and USER coordinate namelists use the requested section."""
+    # Arrange
+    old = datcom_rocket.fins[0]
+    datcom_rocket.aerodynamic_surfaces.remove(old)
+    datcom_rocket.add_datcom_trapezoidal_fins(
+        4,
+        0.25,
+        0.1,
+        0.1,
+        -0.9,
+        path=old.datcom_path,
+        section_type=section,
+        datcom_options=old.datcom_options,
+        **options,
+    )
+    # Act
+    deck = datcom_decks(datcom_rocket)[0][True]
+    # Assert
+    assert f"SECTYP = {section}" in deck
+    if section == "NACA":
+        assert "NACA-1-" in deck
+        assert "ZUPPER" not in deck
+    else:
+        assert _values(deck, "XCORD") == pytest.approx([0, 0.5, 1])
+        assert ("YUPPER" in deck) == ("upper" in options["airfoil_coordinates"])
+
+
+def test_datcom_asymmetric_hex_and_cant_sign(datcom_rocket):
+    """Separate upper/lower geometry and RocketPy-positive cant survive the deck."""
+    # Arrange
+    old = datcom_rocket.fins[0]
+    datcom_rocket.aerodynamic_surfaces.remove(old)
+    fins = datcom_rocket.add_datcom_hex_fins(
+        4,
+        0.25,
+        0.1,
+        0.1,
+        -0.9,
+        path=old.datcom_path,
+        datcom_options=old.datcom_options,
+        upper_thickness_to_chord=[0.01, 0.02],
+        lower_thickness_to_chord=[0, 0.015],
+        lower_max_thickness_position=0.2,
+        lower_flat_length=0.4,
+        cant_angle=0.5,
+    )
+    # Act
+    deck = datcom_decks(datcom_rocket)[0][True]
+    # Assert
+    assert _values(deck, "ZUPPER") == pytest.approx([0.01, 0.02])
+    assert _values(deck, "ZLOWER") == pytest.approx([0, 0.015])
+    assert _values(deck, "LMAXL") == pytest.approx([0.2, 0.2])
+    assert _values(deck, "DELTA1") == pytest.approx([-0.5] * 4)
+    assert fins.thickness_to_chord == pytest.approx([0.01, 0.035])
+    assert deck.count("NEXT CASE") == 3
+
+
+def test_datcom_fin_sets_sorted_with_matching_naca_and_deflection(datcom_rocket):
+    """Sorting moves NACA cards and DELTA arrays along with their fin geometry."""
+    # Arrange
+    old = datcom_rocket.fins[0]
+    datcom_rocket.add_datcom_trapezoidal_fins(
+        4,
+        0.12,
+        0.06,
+        0.06,
+        0.4,
+        path=old.datcom_path,
+        datcom_options=old.datcom_options,
+        section_type="NACA",
+        naca="0012",
+        incidence=2,
+    )
+    # Act
+    deck = datcom_decks(datcom_rocket)[0][True]
+    first = deck.split("$FINSET1", 1)[1].split("$FINSET2", 1)[0]
+    # Assert
+    assert _values(first, "XLE")[0] == pytest.approx(0.8)
+    assert "NACA-1-4-0012" in deck
+    assert _values(deck, "DELTA1") == pytest.approx([2] * 4)
+    assert _values(deck, "DELTA2") == pytest.approx([0] * 4)
+
+
+def test_datcom_fin_sets_overlap_rejected(datcom_rocket):
+    """Axially overlapping planform envelopes fail before executing DATCOM."""
+    # Arrange
+    old = datcom_rocket.fins[0]
+    datcom_rocket.add_datcom_hex_fins(
+        4, 0.25, 0.1, 0.1, -0.8, path=old.datcom_path, datcom_options=old.datcom_options
+    )
+    # Act / Assert
+    with pytest.raises(ValueError, match="overlap"):
+        datcom_decks(datcom_rocket)
+
+
+@pytest.mark.parametrize("profile", ["conical", "ogive"])
+def test_datcom_fins_automatic_boattail_attachment(datcom_rocket, profile):
+    """SSPAN=0 attaches to the interpolated body radius at root mid-chord."""
+    # Arrange
+    from rocketpy import Tail
+
+    tail = next(
+        s for s, _, _ in datcom_rocket.aerodynamic_surfaces if isinstance(s, Tail)
+    )
+    if profile == "ogive":
+        datcom_rocket.aerodynamic_surfaces.remove(tail)
+        tail = datcom_rocket.add_tail(
+            tail.top_radius, tail.bottom_radius, tail.length, -1.2, kind="ogive"
+        )
+    old = datcom_rocket.fins[0]
+    datcom_rocket.aerodynamic_surfaces.remove(old)
+    fins = datcom_rocket.add_datcom_arc_fins(
+        4,
+        0.04,
+        0.02,
+        0.05,
+        -1.21,
+        path=old.datcom_path,
+        datcom_options=old.datcom_options,
+    )
+    # Act
+    deck = datcom_decks(datcom_rocket)[0][True]
+    # Assert
+    assert _values(deck, "SSPAN") == pytest.approx([0, 0.05])
+    expected = (
+        float(tail.radius_at(0.03))
+        if profile == "ogive"
+        else 0.0635 + 0.3 * (0.045 - 0.0635)
+    )
+    assert fins._datcom_attachment_radius == pytest.approx(expected)
+
+
+def test_datcom_projection_respects_panel_angles_and_dihedral(tmp_path):
+    """The rocket drawing projects actual PHIF/GAM positions instead of equal spacing."""
+    # Arrange
+    fins = DatcomFins(
+        2,
+        0.3,
+        0.1,
+        0.1,
+        0.06,
+        path=tmp_path / "datcom",
+        panel_angles=[0, 90],
+        dihedral=[10, 0],
+    )
+    fig, ax = plt.subplots()
+    records = []
+    # Act
+    fins.draw_on_rocket(ax, 0, records, {"fins": "black", "line_width": 1}, "xz", 1)
+    # Assert
+    assert ax.lines[0].get_ydata()[1] == pytest.approx(0.1 * np.sin(np.radians(10)))
+    assert ax.lines[1].get_ydata()[0] == pytest.approx(0.06)
+    assert len(records) == 1
+    plt.close(fig)
+
+
+@pytest.mark.parametrize(
+    "options",
+    [
+        {"section_type": "NACA"},
+        {"section_type": "NACA", "naca": "0012\nDAMP"},
+        {
+            "section_type": "USER",
+            "airfoil_coordinates": {
+                "x": [0, 0.5, 0.4],
+                "upper": [0, 0.1, 0],
+                "lower": [0, 0, 0],
+            },
+        },
+        {"panel_angles": [0, 0, 90, 180]},
+        {"dihedral": 90},
+        {"incidence": [0, 1]},
+        {"incidence": 1, "cant_angle": 1},
+    ],
+)
+def test_datcom_invalid_section_or_panel_definition(options, tmp_path):
+    """Unsupported or inconsistent definitions are rejected without a native run."""
+    # Arrange / Act / Assert
+    with pytest.raises(ValueError):
+        DatcomFins(4, 0.3, 0.1, 0.1, 0.06, path=tmp_path / "datcom", **options)
+
+
+def test_datcom_full_beta_interpolation_does_not_add_derivatives_twice(
+    datcom_rocket, datcom_table_factory
+):
+    """Full beta data already contains the side force and must bypass CYB expansion."""
+    # Arrange
+    tables = {b: datcom_table_factory({"CY": b, "CYB": 100}) for b in [-5, 0, 5]}
+    zero = tables[0]
+    grid = DatcomResult(
+        tuple(zero),
+        zero.body,
+        zero.derivatives,
+        zero.damping,
+        beta=[-5, 0, 5],
+        sideslip=tables,
+    )
+    model = DatcomSurface(datcom_rocket, {True: grid, False: grid}, {True: 0, False: 0})
+    velocity = Vector([100 * np.tan(np.radians(2.5)), 0, 100])
+    speed = abs(velocity)
+    # Act
+    loads = model.compute_forces_and_moments_at_time(
+        0.5, 0, -velocity, speed, 0.4, 1.2, Vector.zeros(), Vector.zeros(), 0
+    )
+    # Assert
+    assert loads[0] == pytest.approx(0.5 * 1.2 * speed**2 * model.reference_area * 2.5)
+
+
+def test_datcom_separate_power_altitude_schedules(datcom_rocket):
+    """Powered and coast decks retain different altitude schedules at the same Mach."""
+    # Arrange
+    datcom_rocket.fins[0].datcom_options["altitude"] = {
+        "power_on": [100, 200],
+        "power_off": [900, 500],
+    }
+    # Act
+    decks = datcom_decks(datcom_rocket)[0]
+    # Assert
+    assert _values(decks[True], "ALT") == pytest.approx([100, 200])
+    assert _values(decks[False], "ALT") == pytest.approx([900, 500])
+
+
+def test_datcom_incidence_rate_uses_body_projected_alpha(
+    datcom_rocket, datcom_table_factory
+):
+    """Local alpha-dot follows atan(v_down/v_forward), even with lateral velocity."""
+    # Arrange
+    from rocketpy import Environment, Flight
+
+    rocket = datcom_rocket
+    table = datcom_table_factory()
+    model = DatcomSurface(
+        rocket,
+        {True: table, False: table},
+        {
+            True: rocket.center_of_dry_mass_position,
+            False: rocket.center_of_dry_mass_position,
+        },
+    )
+    rocket._datcom_model = model
+    flight = object.__new__(Flight)
+    flight.rocket, flight.env = rocket, Environment()
+    velocity = Vector([7, 8, 100])
+    omega = Vector([0.2, 0.3, 0.1])
+    acceleration = Vector([1, 2, 3])
+    state = [0, 0, 1000, *velocity, 1, 0, 0, 0, *omega]
+    derivative = [*velocity, *acceleration, 0, 0, 0, 0, 0, 0, 0]
+    body_acceleration = acceleration - (omega ^ velocity)
+    expected = (velocity.z * body_acceleration.y - velocity.y * body_acceleration.z) / (
+        velocity.z**2 + velocity.y**2
+    )
+    # Act
+    rate = flight._datcom_incidence_rate(0.5, state, derivative)
+    # Assert
+    assert rate == pytest.approx(expected, rel=1e-12)
+
+
+def test_datcom_zero_normal_slope_has_undefined_cp(datcom_rocket, datcom_table_factory):
+    """A zero normal-force slope has no CP and must not fabricate static stability."""
+    # Arrange
+    table = datcom_table_factory({"CNA": 0, "CMA": 0})
+    model = DatcomSurface(
+        datcom_rocket, {True: table, False: table}, {True: 0, False: 0}
+    )
+    # Act
+    cp = model.small_angle_cp(0.4)
+    datcom_rocket._datcom_model = model
+    datcom_rocket.evaluate_center_of_pressure()
+    # Assert
+    assert np.isnan(cp)
+    assert np.isnan(datcom_rocket.static_margin(0))
+
+
+def test_datcom_boattail_fin_drawing_preserves_body_anchors(datcom_rocket):
+    """Fins on a boattail remain overlays and do not create cylindrical body segments."""
+    # Arrange
+    old = datcom_rocket.fins[0]
+    datcom_rocket.aerodynamic_surfaces.remove(old)
+    fins = datcom_rocket.add_datcom_arc_fins(
+        4,
+        0.04,
+        0.02,
+        0.05,
+        -1.21,
+        path=old.datcom_path,
+        datcom_options=old.datcom_options,
+    )
+    datcom_decks(datcom_rocket)
+    nose, tail = datcom_rocket.nosecones[0], datcom_rocket.tails[0]
+    records = [(nose, 0.65, 0.0635, 0.65), (tail, -1.2, 0.045, -1.3)]
+    original = list(records)
+    fig, ax = plt.subplots()
+    # Act
+    fins.draw_on_rocket(ax, -1.21, records, {"fins": "black", "line_width": 1}, "xz", 1)
+    # Assert
+    assert records == original
+    assert len(ax.lines) == 4
+    plt.close(fig)
diff --git a/tests/unit/rocket/test_datcom_wind_tunnel.py b/tests/unit/rocket/test_datcom_wind_tunnel.py
new file mode 100644
index 000000000..32ba0f1db
--- /dev/null
+++ b/tests/unit/rocket/test_datcom_wind_tunnel.py
@@ -0,0 +1,300 @@
+"""Wind-tunnel coefficient fits, units, frames and failure atomicity."""
+
+import json
+import re
+from unittest.mock import patch
+
+import numpy as np
+import pytest
+
+from rocketpy.mathutils.vector_matrix import Vector
+from rocketpy.rocket.datcom import DatcomSurface, datcom_decks
+from rocketpy.rocket.datcom_calibration import _measurements, _predictions
+
+
+def _linear_native(factory):
+    """A deterministic native surrogate with independently identifiable inputs."""
+
+    def run(path, deck, timeout):
+        rough = re.search(r"RHR\s*=\s*([-+\d.eE]+)", deck)
+        height = float(rough[1]) * 3e-6 * 0.0254 if rough else 0
+        radius = float(re.search(r"LER\s*=\s*([-+\d.eE]+)", deck)[1])
+        return factory(
+            {"CA": 0.2 + 10000 * height, "CN": 0.3 + 500 * radius, "CMQ": -0.2}
+        )
+
+    return run
+
+
+@pytest.mark.parametrize("source", ["mapping", "csv"])
+def test_wind_tunnel_drag_only_fit(
+    datcom_rocket, datcom_table_factory, tmp_path, source
+):
+    """Mach-alpha-CD alone recovers a known input and applies it explicitly."""
+    # Arrange
+    data = {"mach": [0.2, 0.6], "alpha": [0, 0], "CD": [0.7, 0.7]}
+    if source == "csv":
+        path = tmp_path / "tunnel.csv"
+        path.write_text("mach,alpha,CD\n0.2,0,0.7\n0.6,0,0.7\n")
+        data = path
+    # Act
+    with patch(
+        "rocketpy.rocket.datcom.run_datcom",
+        side_effect=_linear_native(datcom_table_factory),
+    ):
+        report = datcom_rocket.fit_datcom_calibration(data)
+    # Assert
+    assert report["calibration"]["roughness_height"] == pytest.approx(50e-6, abs=1e-8)
+    assert report["weighted_rmse_fitted"] < 1e-5
+    assert report["weighted_rmse_initial"] == pytest.approx(0.5)
+    assert report["reference_area"] == datcom_rocket.area
+    assert datcom_rocket.datcom_calibration == report["calibration"]
+    assert datcom_rocket._datcom_model is None
+    json.dumps(report, allow_nan=False)
+
+
+def test_wind_tunnel_two_parameter_fit(datcom_rocket, datcom_table_factory):
+    """Independent axial/normal measurements constrain roughness and radius."""
+    # Arrange
+    data = {"mach": [0.2, 0.6], "CA": [0.7, 0.7], "CN": [0.55, 0.55]}
+    # Act
+    with patch(
+        "rocketpy.rocket.datcom.run_datcom",
+        side_effect=_linear_native(datcom_table_factory),
+    ):
+        report = datcom_rocket.fit_datcom_calibration(
+            data,
+            parameters=("roughness_height", "leading_edge_radius"),
+            bounds={"roughness_height": (0, 100e-6), "leading_edge_radius": (0, 0.001)},
+            max_evaluations=150,
+        )
+    # Assert
+    assert report["calibration"]["roughness_height"] == pytest.approx(50e-6, abs=1e-8)
+    assert report["calibration"]["leading_edge_radius"] == pytest.approx(
+        0.0005, abs=1e-7
+    )
+    assert report["weighted_rmse_fitted"] < 1e-5
+
+
+@pytest.mark.parametrize("orientation", [1, -1])
+def test_wind_tunnel_force_moment_references(
+    datcom_rocket, datcom_table_factory, orientation
+):
+    """Moment transfer matches an independent force/cross-product calculation."""
+    # Arrange
+    rocket = datcom_rocket
+    rocket._csys = orientation
+    table = datcom_table_factory(
+        {"CA": 0.4, "CN": 0.2, "CY": 0.1, "CM": 0.03, "CLN": 0.04, "CLL": 0.05}
+    )
+    model = DatcomSurface(rocket, {False: table}, {False: 0.4})
+    origin = -0.2
+    diameter = 2 * rocket.radius
+    area, length, lateral = 2 * rocket.area, 3 * diameter, 4 * diameter
+    columns, measured = _measurements(
+        {
+            "mach": [0.2],
+            "alpha": [1],
+            "CD": [0],
+            "CL": [0],
+            "CA": [0],
+            "CN": [0],
+            "CY": [0],
+            "CM": [0],
+            "CLN": [0],
+            "CLL": [0],
+        },
+        False,
+    )
+    force = Vector([0.1, -0.2, -0.4]) * rocket.area
+    moment = Vector([0.03, 0.04, 0.05]) * (rocket.area * diameter)
+    moment += Vector([0, 0, (0.4 - origin) * orientation]) ^ force
+    flow = Vector([0, np.sin(np.radians(1)), np.cos(np.radians(1))])
+    # Act
+    values = _predictions(
+        model, rocket, columns, measured, area, length, lateral, origin, "wind"
+    )
+    # Assert
+    assert values["CD"][0] == pytest.approx(
+        -sum(force[i] * flow[i] for i in range(3)) / area
+    )
+    assert values["CA"][0] == pytest.approx(0.2)
+    assert values["CM"][0] == pytest.approx(moment[0] / (area * length))
+    assert values["CLN"][0] == pytest.approx(moment[1] / (area * lateral))
+    assert values["CLL"][0] == pytest.approx(moment[2] / (area * lateral))
+
+
+def test_wind_tunnel_reference_area_and_missing_channels(
+    datcom_rocket, datcom_table_factory
+):
+    """Area scaling and blank partial channels are honored in weighted residuals."""
+    # Arrange
+    data = {"mach": [0.2, 0.6], "CA": [0.35, np.nan], "CN": [np.nan, 0.15]}
+    # Act
+    with patch(
+        "rocketpy.rocket.datcom.run_datcom",
+        side_effect=_linear_native(datcom_table_factory),
+    ):
+        report = datcom_rocket.fit_datcom_calibration(
+            data, reference_area=2 * datcom_rocket.area, weights={"CA": 2}
+        )
+    # Assert
+    assert report["calibration"]["roughness_height"] == pytest.approx(50e-6, abs=1e-8)
+    assert len(report["residuals"]) == 2
+    assert report["rmse"]["CN"]["fitted"] == pytest.approx(0)
+
+
+@pytest.mark.parametrize(
+    "data,extra,message",
+    [
+        ({"mach": [0.2]}, {}, "at least one coefficient"),
+        ({"mach": [0.2], "CD": [np.inf]}, {}, "infinite"),
+        ({"mach": [0.2], "CD": [np.nan]}, {}, "contain a measurement"),
+        ({"mach": [0.2], "alpha": [0, 1], "CD": [0.4]}, {}, "equal lengths"),
+        ({"mach": [0.2], "CM": [0]}, {}, "reference_length"),
+        ({"mach": [0.2], "CM": [0]}, {"reference_length": 1}, "moment_reference"),
+        (
+            {"mach": [0.2], "CD": [0.4]},
+            {"parameters": ["leading_edge_radius"]},
+            "bounds",
+        ),
+        ({"mach": [0.2], "CD": [0.4]}, {"weights": {"CD": 0}}, "weight"),
+        ({"mach": [0.2], "CD": [0.4]}, {"drag_axis": "body"}, "drag_axis"),
+        ({"mach": [0.2], "CD": [0.4], "power_on": [2]}, {}, "power_on"),
+        ({"mach": [0.2], "CD": [0.4], "reynolds_per_meter": [-1]}, {}, "Reynolds"),
+    ],
+)
+def test_wind_tunnel_invalid_data_is_atomic(datcom_rocket, data, extra, message):
+    """Bad measurements cannot change calibration or start native execution."""
+    # Arrange
+    datcom_rocket.add_datcom_calibration(roughness_height=10e-6)
+    before = datcom_rocket.datcom_calibration
+    # Act / Assert
+    with patch("rocketpy.rocket.datcom.run_datcom") as run:
+        with pytest.raises(ValueError, match=message):
+            datcom_rocket.fit_datcom_calibration(data, **extra)
+    run.assert_not_called()
+    assert datcom_rocket.datcom_calibration == before
+
+
+@pytest.mark.parametrize("failure", ["native", "envelope", "budget", "beta"])
+def test_wind_tunnel_failed_fit_preserves_prepared_model(
+    datcom_rocket, datcom_table_factory, failure
+):
+    """Execution failures and unsupported measurements preserve the live model."""
+    # Arrange
+    with patch(
+        "rocketpy.rocket.datcom.run_datcom", return_value=datcom_table_factory()
+    ):
+        original = datcom_rocket.prepare_datcom()
+    settings = datcom_rocket.datcom_calibration
+    data = {"mach": [0.9 if failure == "envelope" else 0.2], "CD": [0.7]}
+    if failure == "beta":
+        data["beta"] = [1]
+    extra = {"max_evaluations": 3} if failure == "budget" else {}
+    native = (
+        RuntimeError("native failed")
+        if failure == "native"
+        else _linear_native(datcom_table_factory)
+    )
+    # Act / Assert
+    with patch("rocketpy.rocket.datcom.run_datcom", side_effect=native):
+        with pytest.raises((RuntimeError, ValueError)):
+            datcom_rocket.fit_datcom_calibration(data, **extra)
+    assert datcom_rocket._datcom_model is original
+    assert datcom_rocket.datcom_calibration == settings
+
+
+def test_wind_tunnel_reynolds_is_probe_only(datcom_rocket, datcom_table_factory):
+    """Tunnel REN replaces ALT only during probes and never changes Flight options."""
+    # Arrange
+    native = _linear_native(datcom_table_factory)
+    original = dict(datcom_rocket.fins[0].datcom_options)
+    decks = []
+
+    def run(path, deck, timeout):
+        decks.append(deck)
+        return native(path, deck, timeout)
+
+    # Act
+    with patch("rocketpy.rocket.datcom.run_datcom", side_effect=run):
+        report = datcom_rocket.fit_datcom_calibration(
+            {"mach": [0.2, 0.6], "CD": [0.7, 0.7], "reynolds_per_meter": [1e6, 2e6]}
+        )
+    # Assert
+    assert all(
+        "REN = 1000000.,2000000.," in deck and "ALT =" not in deck for deck in decks
+    )
+    assert datcom_rocket.fins[0].datcom_options == original
+    assert "ALT =" in datcom_decks(datcom_rocket)[0][False]
+    assert report["reynolds_source"] == "measurements"
+
+
+def test_wind_tunnel_scale_model_references(datcom_rocket, datcom_table_factory):
+    """A half-size tunnel model preserves coefficients and equivalent geometry."""
+    # Arrange
+    scale = 0.5
+    data = {"mach": [0.2, 0.6], "CD": [0.7, 0.7], "reynolds_per_meter": [2e6, 4e6]}
+    native = _linear_native(datcom_table_factory)
+    decks = []
+
+    def run(path, deck, timeout):
+        decks.append(deck)
+        return native(path, deck, timeout)
+
+    # Act
+    with patch("rocketpy.rocket.datcom.run_datcom", side_effect=run):
+        report = datcom_rocket.fit_datcom_calibration(
+            data, reference_area=datcom_rocket.area * scale**2, model_scale=scale
+        )
+    # Assert
+    assert report["calibration"]["roughness_height"] == pytest.approx(50e-6, abs=1e-8)
+    assert report["reference_area"] == datcom_rocket.area
+    assert report["model_scale"] == scale
+    assert all("REN = 1000000.,2000000.," in deck for deck in decks)
+
+
+@pytest.mark.parametrize("axis", ["wind", "stability"])
+def test_wind_tunnel_drag_axes_with_sideslip(datcom_rocket, datcom_table_factory, axis):
+    """Wind-axis CD includes lateral force; stability drag is the pitch projection."""
+    # Arrange
+    table = datcom_table_factory({"CA": 0.4, "CN": 0.2, "CY": 0.1})
+    table.beta = np.array([-5.0, 0.0, 5.0])
+    table.sideslip = {angle: table for angle in table.beta}
+    model = DatcomSurface(datcom_rocket, {False: table}, {False: 0})
+    columns, measured = _measurements(
+        {"mach": [0.2], "alpha": [1], "beta": [3], "CD": [0]}, False
+    )
+    alpha, beta = np.radians([1, 3])
+    force = Vector([0.1, -0.2, -0.4])
+    flow = Vector([np.tan(beta), np.tan(alpha), 1])
+    flow = flow / abs(flow)
+    expected = (
+        -sum(force[i] * flow[i] for i in range(3))
+        if axis == "wind"
+        else 0.4 * np.cos(alpha) + 0.2 * np.sin(alpha)
+    )
+    # Act
+    result = _predictions(
+        model,
+        datcom_rocket,
+        columns,
+        measured,
+        datcom_rocket.area,
+        None,
+        None,
+        None,
+        axis,
+    )
+    # Assert
+    assert result["CD"][0] == pytest.approx(expected)
+
+
+@pytest.mark.parametrize("ren", [0, -1, np.nan, [1], [1, 2, 3], {"power_on": 1}])
+def test_datcom_reynolds_schedule_validation(datcom_rocket, ren):
+    """Invalid native Reynolds conditions cannot silently select an atmosphere."""
+    # Arrange
+    datcom_rocket.fins[0].datcom_options["reynolds"] = ren
+    # Act / Assert
+    with pytest.raises(ValueError, match="reynolds"):
+        datcom_decks(datcom_rocket)

From cc7dfc7b5f3a334b8baa46a1b7526dbf765e6c22 Mon Sep 17 00:00:00 2001
From: UnforgettableCassini <66637599+AdaBatu@users.noreply.github.com>
Date: Sat, 24 Oct 2026 21:16:47 +0100
Subject: [PATCH 2/4] Fix DATCOM damping parsing and improve flight comparisons

Read total-vehicle damping when PART output includes component tables,
preserve native diagnostics, and allow a measured nozzle exit diameter.
Add the manual worked-example regression and reproducible native audits.

Preserve source notebooks' drag edits in the example loader, regenerate
Bella Lui and Juno comparisons, and add apogee bars, error percentiles,
and explanations for unavailable cases. Keep prediction discrepancies
and unverified fin-section assumptions explicit.

Validation: 327 targeted tests passed; recorded-rocket notebook executed;
strict Sphinx build and changed-file Ruff checks passed. Native audits
include 33 fin-section probes and 15 sensitivity flights.

Refs RocketPy-Team/RocketPy#1205
---
 docs/examples/datcom/README.md                |   96 +
 .../comparison_data/apogee_comparison.csv     |   17 +
 .../comparison_data/apogee_comparison.png     |  Bin 0 -> 490181 bytes
 .../comparison_data/apogee_comparison.svg     | 8146 +++++++++++++++++
 .../apogee_error_percentiles.csv              |    3 +
 .../comparison_data/audit_datcom_usage.py     |  295 +
 .../comparison_data/audit_fin_parameters.py   |  199 +
 .../comparison_data/datcom_usage_audit.json   |  951 ++
 .../comparison_data/fin_parameter_audit.json  | 1014 ++
 .../datcom/comparison_data/model_sources.py   |   47 +
 .../comparison_data/plot_apogee_comparison.py |  206 +
 .../datcom/comparison_data/rocket_metrics.csv |   46 +-
 .../rockets/bella_lui_altitude.csv            | 1192 +--
 .../comparison_data/rockets/bella_lui_cd.csv  |  240 +-
 .../rockets/bella_lui_provenance.json         |    7 +-
 .../rockets/bella_lui_result.json             |   20 +-
 .../rockets/juno3_altitude.csv                | 1200 +--
 .../comparison_data/rockets/juno3_cd.csv      |  302 +-
 .../rockets/juno3_coast_for005.dat            |   60 +-
 .../rockets/juno3_powered_for005.dat          |   60 +-
 .../rockets/juno3_provenance.json             |  471 +-
 .../comparison_data/rockets/juno3_result.json |   42 +-
 .../comparison_data/rockets/results.csv       |    4 +-
 docs/examples/datcom/rocket_comparison.ipynb  | 1881 ++--
 docs/user/rocket/datcom.rst                   |   44 +
 rocketpy/rocket/datcom.py                     |   66 +-
 tests/fixtures/surfaces/data/datcom/README.md |    8 +
 .../data/datcom/manual_example_for005.dat     |   16 +
 tests/integration/test_datcom.py              |   54 +-
 tests/unit/rocket/test_datcom.py              |   64 +
 30 files changed, 14105 insertions(+), 2646 deletions(-)
 create mode 100644 docs/examples/datcom/comparison_data/apogee_comparison.csv
 create mode 100644 docs/examples/datcom/comparison_data/apogee_comparison.png
 create mode 100644 docs/examples/datcom/comparison_data/apogee_comparison.svg
 create mode 100644 docs/examples/datcom/comparison_data/apogee_error_percentiles.csv
 create mode 100644 docs/examples/datcom/comparison_data/audit_datcom_usage.py
 create mode 100644 docs/examples/datcom/comparison_data/audit_fin_parameters.py
 create mode 100644 docs/examples/datcom/comparison_data/datcom_usage_audit.json
 create mode 100644 docs/examples/datcom/comparison_data/fin_parameter_audit.json
 create mode 100644 docs/examples/datcom/comparison_data/plot_apogee_comparison.py
 create mode 100644 tests/fixtures/surfaces/data/datcom/manual_example_for005.dat

diff --git a/docs/examples/datcom/README.md b/docs/examples/datcom/README.md
index 6e9251139..12bae109f 100644
--- a/docs/examples/datcom/README.md
+++ b/docs/examples/datcom/README.md
@@ -8,6 +8,8 @@ Only two example notebooks are included:
   recorded altitude, upward vertical velocity, CD and stability margin where
   available, with signed percentage errors. Apogee-fitted constant CD is
   explicitly inferred calibration, not a measurement or native correction.
+  Apogee bars cover all 16 rockets: green is the closer paired prediction and
+  red is the farther one, with recorded apogee as the dashed reference.
 - [AST-HER / Asteria](asteria_comparison.ipynb): the same configuration, tanks
   and atmosphere using ordinary RocketPy's supplied Asteria drag curve versus
   native DATCOM. **AST-HER has not flown.** Percentages are model differences.
@@ -39,3 +41,97 @@ Set `DATCOM_EXECUTABLE` to a native executable when the sibling Asteria copy is
 unavailable. The latter command fits effective CD against the recorded apogees;
 those are calibration results, not independent validation. Notebook execution
 reads the saved comparison outputs and requires no new native runs.
+
+The [apogee bar chart](comparison_data/apogee_comparison.png) is also exported
+as [SVG](comparison_data/apogee_comparison.svg), with values in
+`comparison_data/apogee_comparison.csv`. Rebuild only these exports with
+`python docs/examples/datcom/comparison_data/plot_apogee_comparison.py`.
+Labels show signed percentage error. P50/P75/P90/P95 in
+`comparison_data/apogee_error_percentiles.csv` are empirical absolute-error
+percentiles across the same paired rockets, not per-flight uncertainty.
+Missing DATCOM results remain unavailable, and archived RocketPy values are
+marked. Colour ranking is withheld when the competing prediction is absent.
+
+The supplied CD group includes RASAero, constants, curves of unspecified
+provenance and Juno's explicitly CFD-scaled curve. It is not universally CFD.
+RocketPy has no separate built-in geometry-to-CD predictor. The example loader
+now preserves legacy post-construction `power_on_drag`/`power_off_drag` edits
+in the seven-input solver coefficient; this corrects stale curve use for
+Juno and Bella Lui without changing any RocketPy equations. The two affected
+ordinary/DATCOM comparisons and the associated metrics have been regenerated.
+
+The [manual audit](comparison_data/datcom_usage_audit.json) records an
+independent reproduction of the guide's worked example, `PART` invariance for
+all 11 native comparison models, source nozzle dimensions and base-drag
+sensitivity flights. Reproduce it with:
+
+```sh
+python docs/examples/datcom/comparison_data/audit_datcom_usage.py
+```
+
+The audit found and corrected component damping being selected instead of
+total-vehicle damping when parsing `PART` output. These comparison decks do
+not request `PART`, so their existing predictions are unaffected. GENESIS and
+Bella Lui have source nozzle exit diameters larger than their thrust files'
+listed motor case diameters; those inputs need hardware verification.
+GENESIS remains 10.5% above recorded apogee even with full base drag during
+the burn (3,223 m versus 2,916 m). This bounds the nozzle-area contribution;
+it does not establish a measured nozzle correction. Fin section, bevel,
+edge radius and surface finish remain assumptions. The guide also describes
+approximate airfoil properties after its crest-critical-Mach warning, which
+all 11 native grids produce. Parsed native diagnostics are now retained.
+Some warnings concern unflown grid points. The guide also assumes sharp
+trailing edges in its airfoil section solver and approximates blunt sections
+as ideal airfoils. Neither a carbon-fibre label nor USER coordinates establish
+that these approximations describe the real fin edges.
+
+Explicit trailing-edge sweep and running Mach 0.6 alone versus in a larger
+grid produce identical CA/CN in all 11 controlled checks. Equivalent metre
+and inch decks show a small native transonic unit sensitivity: Andromeda's
+CA at Mach 0.9 is 0.396 versus 0.404, localized to body pressure/wave drag in
+the partial output. Its Mach 0.3/0.6 values agree. The audit records this
+remaining numerical discrepancy; it does not silently select the result
+closer to the recorded flight.
+Running the affected full-flight models with equivalent inch decks changes
+apogee by -12.06 m for Andromeda, -0.78 m for Astra, -0.01 m for GENESIS and
++0.09 m for Valetudo. These changes are much smaller than their flight errors.
+
+The [fin-parameter audit](comparison_data/fin_parameter_audit.json) tests
+additional section assumptions without replacing the baseline comparisons.
+Reproduce its 33 coefficient probes and 15 flight runs with:
+
+```sh
+python docs/examples/datcom/comparison_data/audit_fin_parameters.py
+```
+
+Trailing-edge thickness is a substantial missing hardware input. In the
+controlled GENESIS run at Mach 0.6, changing a sharp USER section to a section
+with a full-thickness trailing edge raises zero-incidence CD from 0.472 to
+0.586. Native partial output attributes the increase to trailing-edge drag:
+0.0285 per fin, or 0.114 for four fins. Doubling the HEX total thickness/chord
+from 2% to 4% gives only 0.474 at that speed; 0.5/1 mm leading-edge-radius
+probes give 0.472. Thickness and bevels have larger transonic effects.
+
+| Rocket | Recorded apogee AGL (m) | Baseline HEX (m) | Sharp USER control (m) | Blunt USER hypothesis (m) |
+| --- | ---: | ---: | ---: | ---: |
+| GENESIS | 2916 | 3297 (+13.1%) | 3325 (+14.0%) | 3025 (+3.7%) |
+| Astra | 3250 | 3696 (+13.7%) | 3773 (+16.1%) | 3378 (+3.9%) |
+| Andromeda | 3443 | 4171 (+21.1%) | 4293 (+24.7%) | 3974 (+15.4%) |
+
+The USER controls distinguish edge changes from differences between native
+HEX and USER methods. The blunt hypothesis retains 2% thickness/chord,
+which tapers in physical thickness: for GENESIS it is 4 mm at the root and
+2.2 mm at the tip. It is not a measured constant-thickness plate. A separate
+4 mm plate / 5 mm bevel HEX probe gives GENESIS 3284 m, so that change alone
+does not close the gap. All flight variants stay within the sampled Mach,
+alpha and beta grids at dynamic pressure above 100 Pa.
+
+Ordinary RocketPy's fin inputs describe planform and lift; whole-vehicle drag
+comes from the separately supplied drag curve. Its optional `airfoil` input
+is a lift curve, not cross-section coordinates. DATCOM additionally needs
+thickness, bevels and edge geometry. `ZUPPER`/`ZLOWER` describe each half of
+the section, while our `thickness_to_chord` is their sum. `LMAX`/`LFLAT` are
+chord fractions, `LER` is a dimensional radius, and `SSPAN` is normally from
+the centreline; our `SSPAN(1)=0` convention correctly requests exposed-span
+automatic body attachment. These conversions were checked. The hardware
+section values remain unknown, and no fit or default change is applied.
diff --git a/docs/examples/datcom/comparison_data/apogee_comparison.csv b/docs/examples/datcom/comparison_data/apogee_comparison.csv
new file mode 100644
index 000000000..5ce64c760
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/apogee_comparison.csv
@@ -0,0 +1,17 @@
+rocket,measured,rocketpy,datcom,condition,availability,rocketpy_error_pct,datcom_error_pct,closer_prediction,paired,rocketpy_archived,drag_provenance
+andromeda,3443.0,3615.880567942097,4170.733199067169,notebook reported apogee,,5.02121893529181,21.13660177366161,RocketPy + supplied CD,True,False,Supplied CD; CFD provenance not established
+astra,3250.0,3043.3679728130164,3695.9340692825294,notebook reported apogee,,-6.357908528830264,13.721048285616291,RocketPy + supplied CD,True,False,Supplied CD; CFD provenance not established
+bella_lui,458.971,460.5523116828233,465.8765076067215,telemetry AGL maximum (QA),,0.3445341171497323,1.5045629477072564,RocketPy + supplied CD,True,False,Supplied CD; CFD provenance not established
+camoes,3015.0,3004.10382797431,,notebook reported apogee,Source active air brakes override complete rocket drag. Combining their whole-vehicle CD with DATCOM would double-count base drag; no controlled DATCOM result claimed.,-0.36139874048722365,,unavailable comparison,False,False,Supplied CD; CFD provenance not established
+cavour,2789.0,2818.1348894226226,3251.608548140568,notebook reported apogee,,1.0446356910226806,16.58689667051158,RocketPy + supplied CD,True,False,RASAero II (source cell 10)
+defiance,9308.318,9243.891367330603,,notebook reported apogee,ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,-0.6921404347100731,,unavailable comparison,False,False,Supplied CD; CFD provenance not established
+erebus,3020.0,2751.1556122225365,3253.422625182214,notebook reported apogee,,-8.902132045611372,7.729225999411052,DATCOM,True,False,Supplied CD; CFD provenance not established
+genesis,2916.0,3044.6857862664306,3297.2391270686403,notebook reported apogee,,4.41309280749076,13.074044138156388,RocketPy + supplied CD,True,False,Supplied CD; CFD provenance not established
+halcyon,3450.0,3214.238895757538,3530.231782008296,notebook reported apogee,,-6.8336551954336775,2.3255588987911824,DATCOM,True,False,Supplied CD; CFD provenance not established
+hedy,5231.53,5246.918,,"saved measured-apogee summary; archived RocketPy output, not fresh simulation","Source data/rockets/tust is absent; CoolProp is not installed, and the negative-span fin root cutout requires geometry support.",0.29413957293564064,,unavailable comparison,False,True,Supplied CD; CFD provenance not established
+juno3,3213.0,2987.2806591791605,3056.5496067381873,notebook reported apogee,,-7.025189568031109,-4.8692932854594675,DATCOM,True,False,RASAero II curve scaled using a CFD value (source cell 14)
+lince,3587.0,3285.089381541881,4254.30425999591,notebook reported apogee,,-8.416800068528554,18.60340841917786,RocketPy + supplied CD,True,False,Supplied CD; CFD provenance not established
+ndrt_2020,1316.736,1293.5184123380943,,notebook reported apogee,ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,-1.7632682376653923,,unavailable comparison,False,False,Supplied CD; CFD provenance not established
+prometheus_2022,3898.37,4191.288687549948,4229.19676461329,notebook reported apogee,,7.513875993041916,8.486284385866142,RocketPy + supplied CD,True,False,Supplied CD; CFD provenance not established
+valetudo,860.0,815.0526424633565,943.6190073224012,saved measured-apogee summary,,-5.226436922865522,9.723140386325722,RocketPy + supplied CD,True,False,RASAero (source input filenames)
+valkyrie,2098.02,2247.849,,"notebook reported apogee; archived RocketPy output, not fresh simulation",SourceUnavailable: Source requests live weather with no archived profile; not replaced by invented weather.,7.141447650642043,,unavailable comparison,False,True,Supplied CD; CFD provenance not established
diff --git a/docs/examples/datcom/comparison_data/apogee_comparison.png b/docs/examples/datcom/comparison_data/apogee_comparison.png
new file mode 100644
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+     <use xlink:href="#DejaVuSans-39" transform="translate(83.789062 0)"/>
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+       <use xlink:href="#DejaVuSans-6f" transform="translate(64.982422 0)"/>
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+       <use xlink:href="#DejaVuSans-79" transform="translate(396.464844 0)"/>
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+      <!-- supplied CD -->
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+       <use xlink:href="#DejaVuSans-73"/>
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+      <!-- (archived) -->
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+     <g id="text_175">
+      <!-- DATCOM -->
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+       <use xlink:href="#DejaVuSans-44"/>
+       <use xlink:href="#DejaVuSans-41" transform="translate(75.251953 0)"/>
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+      <!-- 0 -->
+      <g transform="translate(1008.658125 999.471219) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-30"/>
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+     </g>
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+      <!-- 500 -->
+      <g transform="translate(995.933125 974.342116) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-35"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
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+      <!-- 1000 -->
+      <g transform="translate(989.570625 949.213013) scale(0.1 -0.1)">
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+      <!-- 1500 -->
+      <g transform="translate(989.570625 924.08391) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-31"/>
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+      <!-- 2000 -->
+      <g transform="translate(989.570625 898.954808) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-32"/>
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+      <!-- 2500 -->
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diff --git a/docs/examples/datcom/comparison_data/apogee_error_percentiles.csv b/docs/examples/datcom/comparison_data/apogee_error_percentiles.csv
new file mode 100644
index 000000000..826fb2aa9
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/apogee_error_percentiles.csv
@@ -0,0 +1,3 @@
+model,paired_rockets,P50_absolute_error_pct,P75_absolute_error_pct,P90_absolute_error_pct,P95_absolute_error_pct
+RocketPy + supplied CD,11,6.357908528830264,7.269532780536513,8.416800068528554,8.659466057069963
+DATCOM,11,9.723140386325722,15.153972478063936,18.60340841917786,19.870005096419735
diff --git a/docs/examples/datcom/comparison_data/audit_datcom_usage.py b/docs/examples/datcom/comparison_data/audit_datcom_usage.py
new file mode 100644
index 000000000..ef2c1b0e2
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/audit_datcom_usage.py
@@ -0,0 +1,295 @@
+"""Recheck manual contracts without adding another example notebook or fitting.
+
+The nozzle alternatives below are sensitivity bounds/hypotheses, not measured
+hardware corrections. No source input, source result or measured target changes.
+"""
+
+import contextlib
+import hashlib
+import io
+import json
+import os
+import re
+from pathlib import Path
+from unittest.mock import patch
+
+import numpy as np
+import pandas as pd
+
+import model_sources as c
+from rocketpy import Flight
+from rocketpy.rocket.datcom import DAMPING_KEYS, _namelist, run_datcom
+
+OUT = Path(__file__).resolve().parent
+EXE = Path(
+    os.environ.get(
+        "DATCOM_EXECUTABLE", c.ROOT.parent / "Asteria-Flight-Dynamics/works/dat0/datcom"
+    )
+)
+
+
+def conditions(deck, mach):
+    """Controlled conditions for sweep/grid/unit probes, not a flight fit."""
+    return re.sub(
+        r"\$FLTCON.*?\$",
+        lambda _: _namelist(
+            "FLTCON",
+            [
+                ("NMACH", len(mach)),
+                ("MACH", mach),
+                ("REN", np.asarray(mach) * 2e7),
+                ("NALPHA", 3),
+                ("ALPHA", [-2, 0, 2]),
+                ("BETA", 0),
+            ],
+        ),
+        deck,
+        flags=re.S,
+    )
+
+
+def variants(deck):
+    """Independent equivalent deck encodings recommended/defined by the guide."""
+    dimensional = {
+        "XCG",
+        "LREF",
+        "LNOSE",
+        "DNOSE",
+        "LCENTR",
+        "DCENTR",
+        "LAFT",
+        "DAFT",
+        "DEXIT",
+        "XLE",
+        "SSPAN",
+        "CHORD",
+        "LER",
+    }
+
+    def rewrite(match, *, inches):
+        fields = []
+        for field in re.finditer(r"(\w+)\s*=\s*(.*?)(?=\b\w+\s*=|$)", match[2], re.S):
+            key, raw = field[1], field[2].strip(" ,\n")
+            try:
+                value = np.array([float(v) for v in raw.split(",")])
+            except ValueError:
+                value = raw
+            fields.append((key, value))
+        values = dict(fields)
+        if inches:
+            for key, value in fields:
+                if key in dimensional:
+                    values[key] = value / 0.0254
+                elif key == "SREF":
+                    values[key] = value / 0.0254**2
+                elif key == "REN":
+                    # FLTCON uses /ft in all English geometry units (Table 3).
+                    values[key] = value * 0.3048
+        elif match[1].startswith("FINSET"):
+            te = values["XLE"] + values["CHORD"]
+            values["SWEEP"] = np.degrees(
+                np.arctan2(np.diff(te), np.diff(values["SSPAN"]))
+            )
+            values["STA"] = 1
+            values["XLE"] = values["XLE"][:1]
+        return _namelist(match[1], list(values.items()))
+
+    return {
+        "explicit_trailing_edge_sweep": re.sub(
+            r"\$(\w+)\s*(.*?)\$",
+            lambda m: rewrite(m, inches=False),
+            deck,
+            flags=re.S,
+        ),
+        "inch_geometry_feet_reynolds": re.sub(
+            r"\$(\w+)\s*(.*?)\$",
+            lambda m: rewrite(m, inches=True),
+            deck,
+            flags=re.S,
+        ).replace("DIM M", "DIM IN"),
+    }
+
+
+def main():
+    report = {
+        "manual": "Missile DATCOM Users Guide, revision 5/97",
+        "native": "local revision 3/99 executable",
+        "executable_sha256": hashlib.sha256(EXE.read_bytes()).hexdigest(),
+        "controlled_conditions": {
+            "mach": [0.3, 0.6, 0.9, 1.2, 3.0],
+            "alpha_deg": [-2, 0, 2],
+            "beta_deg": 0,
+            "reynolds_per_meter": "Mach * 2e7",
+            "note": "encoding probes use controlled conditions, not flight fits",
+        },
+        "manual_reference": {},
+        "part_checks": [],
+        "encoding_checks": [],
+        "nozzle_inputs": [],
+        "nozzle_sensitivity": [],
+        "unit_sensitivity": [],
+    }
+    sample = c.ROOT / "tests/fixtures/surfaces/data/datcom/manual_example_for005.dat"
+    worked = run_datcom(EXE, sample.read_text())
+    report["manual_reference"] = dict(
+        source="Figures 19-21, printed pp. 65-67",
+        mach=float(worked[4][0]),
+        published_ca_alpha0=0.466,
+        native_ca_alpha0=float(worked.body["CA"][0, 0]),
+        published_cn_alpha4=1.179,
+        native_cn_alpha4=float(worked.body["CN"][0, 1]),
+    )
+    results = pd.read_csv(OUT / "rockets/results.csv")
+    for _, row in results[results.status == "paired untuned comparison"].iterrows():
+        name = row.rocket
+        print("AUDIT", name, flush=True)
+        # Exact final zero-beta case, same geometry/conditions. PART only changes
+        # diagnostic output; all force/moment and dynamic tables must agree.
+        deck = (OUT / "rockets" / f"{name}_coast_for005.dat").read_text().split(
+            "NEXT CASE"
+        )[2] + "NEXT CASE\n"
+        plain = run_datcom(EXE, deck)
+        partial = run_datcom(EXE, deck.replace("NEXT CASE", "PART\nNEXT CASE"))
+        max_body = max(
+            float(np.max(np.abs(plain.body[key] - partial.body[key])))
+            for key in plain.body
+        )
+        max_damp = max(
+            float(np.max(np.abs(plain.damping[key] - partial.damping[key])))
+            for key in DAMPING_KEYS
+        )
+        assert max_body == 0 and max_damp == 0
+        report["part_checks"].append(
+            dict(
+                rocket=name,
+                max_body_difference=max_body,
+                max_damping_difference=max_damp,
+                diagnostics=partial.diagnostics,
+            )
+        )
+        controlled = conditions(deck, [0.3, 0.6, 0.9, 1.2, 3.0])
+        baseline = run_datcom(EXE, controlled)
+        for encoding, equivalent in variants(controlled).items():
+            other = run_datcom(EXE, equivalent)
+            max_ca = float(np.max(np.abs(other.body["CA"] - baseline.body["CA"])))
+            max_cn = float(np.max(np.abs(other.body["CN"] - baseline.body["CN"])))
+            report["encoding_checks"].append(
+                dict(
+                    rocket=name,
+                    encoding=encoding,
+                    max_ca_difference=max_ca,
+                    max_cn_difference=max_cn,
+                    zero_alpha_ca_reference=baseline.body["CA"][:, 1].tolist(),
+                    zero_alpha_ca_alternative=other.body["CA"][:, 1].tolist(),
+                )
+            )
+        alone = run_datcom(EXE, conditions(deck, [0.6]))
+        report["encoding_checks"].append(
+            dict(
+                rocket=name,
+                encoding="Mach 0.6 alone versus larger Mach grid",
+                max_ca_difference=float(
+                    np.max(np.abs(alone.body["CA"][0] - baseline.body["CA"][1]))
+                ),
+                max_cn_difference=float(
+                    np.max(np.abs(alone.body["CN"][0] - baseline.body["CN"][1]))
+                ),
+            )
+        )
+        with contextlib.redirect_stdout(io.StringIO()):
+            rocket, env, launch, _ = c.build_model(
+                c.ROOT / f"docs/examples/{name}_flight_sim.ipynb"
+            )
+        description = [
+            part for part in (rocket.motor.description_eng_file or []) if part
+        ]
+        case_diameter = float(description[1]) / 1000 if len(description) >= 7 else None
+        report["nozzle_inputs"].append(
+            dict(
+                rocket=name,
+                motor_description=description,
+                source_nozzle_radius_m=rocket.motor.nozzle_radius,
+                assumed_datcom_exit_diameter_m=2 * rocket.motor.nozzle_radius,
+                listed_motor_case_diameter_m=case_diameter,
+                exceeds_listed_case=bool(
+                    case_diameter and 2 * rocket.motor.nozzle_radius > case_diameter
+                ),
+                note="case diameter is not a measured nozzle exit diameter",
+            )
+        )
+        # Broad bound: full powered base drag. It isolates base-area uncertainty;
+        # it is not asserted to describe the actual powered jet or flown rocket.
+        provenance = json.loads(
+            (OUT / "rockets" / f"{name}_provenance.json").read_text()
+        )
+        c.h.attach_datcom(rocket, EXE, 0)
+        options = dict(
+            mach=provenance["mach"],
+            alpha=provenance["alpha"],
+            beta=provenance["beta"],
+            reynolds=provenance["atmosphere"]["reynolds"],
+            powered_base_drag="full",
+        )
+        alternatives = [("full powered base (bound)", None)]
+        if name == "genesis":
+            alternatives += [
+                ("exit equal to listed case diameter (upper bound)", 0.075),
+                ("source radius treated as diameter (unverified hypothesis)", 0.042354),
+            ]
+        for label, diameter in alternatives:
+            trial = dict(options)
+            if diameter is not None:
+                trial.update(powered_base_drag="nozzle", nozzle_exit_diameter=diameter)
+            for fin in rocket.fins:
+                if hasattr(fin, "datcom_options"):
+                    fin.datcom_options = dict(trial)
+            with contextlib.redirect_stdout(io.StringIO()):
+                flight = Flight(rocket, env, **launch)
+            altitude = float(flight.apogee - env.elevation)
+            report["nozzle_sensitivity"].append(
+                dict(
+                    rocket=name,
+                    scenario=label,
+                    assumed_exit_diameter_m=diameter,
+                    source_native_apogee_agl_m=row.datcom_agl_m,
+                    alternative_apogee_agl_m=altitude,
+                    recorded_apogee_agl_m=row.actual_agl_m,
+                    alternative_error_pct=100 * (altitude / row.actual_agl_m - 1),
+                    fitted=False,
+                    motor_nozzle_radius_unchanged_m=rocket.motor.nozzle_radius,
+                )
+            )
+        if name in ("andromeda", "astra", "genesis", "valetudo"):
+            # Equivalent units isolate the native transonic sensitivity from
+            # nozzle assumptions. Keep the saved flight's original nozzle mode.
+            for fin in rocket.fins:
+                if hasattr(fin, "datcom_options"):
+                    fin.datcom_options = dict(options, powered_base_drag="nozzle")
+            rocket._invalidate_datcom()
+
+            def in_inches(path, input_deck, timeout):
+                equivalent = variants(input_deck)["inch_geometry_feet_reynolds"]
+                return run_datcom(path, equivalent, timeout)
+
+            with (
+                patch("rocketpy.rocket.datcom.run_datcom", side_effect=in_inches),
+                contextlib.redirect_stdout(io.StringIO()),
+            ):
+                flight = Flight(rocket, env, **launch)
+            altitude = float(flight.apogee - env.elevation)
+            report["unit_sensitivity"].append(
+                dict(
+                    rocket=name,
+                    metric_apogee_agl_m=row.datcom_agl_m,
+                    inch_apogee_agl_m=altitude,
+                    change_m=altitude - row.datcom_agl_m,
+                    recorded_apogee_agl_m=row.actual_agl_m,
+                    fitted=False,
+                )
+            )
+        (OUT / "datcom_usage_audit.json").write_text(json.dumps(report, indent=2))
+    return report
+
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/examples/datcom/comparison_data/audit_fin_parameters.py b/docs/examples/datcom/comparison_data/audit_fin_parameters.py
new file mode 100644
index 000000000..3110a0000
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/audit_fin_parameters.py
@@ -0,0 +1,199 @@
+"""Probe unspecified fin sections without changing the baseline comparisons.
+
+All alternative dimensions below are sensitivity assumptions, not measurements
+or fitted geometry. Native tables are regenerated; no CD multiplier is applied.
+"""
+
+import contextlib
+import hashlib
+import io
+import json
+import re
+from pathlib import Path
+from unittest.mock import patch
+
+import numpy as np
+import pandas as pd
+
+import audit_datcom_usage as audit
+from rocketpy import Flight
+from rocketpy.rocket.datcom import _namelist, run_datcom
+
+OUT = Path(__file__).resolve().parent
+
+
+def fin_fields(deck, label):
+    def change(match):
+        values = {}
+        for field in re.finditer(r"(\w+)\s*=\s*(.*?)(?=\b\w+\s*=|$)", match[2], re.S):
+            key, raw = field[1], field[2].strip(" ,\n")
+            try:
+                val = np.array([float(v) for v in raw.split(",")])
+            except ValueError:
+                val = raw
+            values[key] = val
+        if not match[1].startswith("FINSET"):
+            return match[0]
+        chord = values["CHORD"]
+        n = len(chord)
+        if label.startswith("ratio_"):
+            total = float(label[6:])
+            values.update(ZUPPER=np.full(n, total / 2), ZLOWER=np.full(n, total / 2))
+        elif label.startswith("bevel_"):
+            bevel = float(label[6:])
+            values.update(
+                LMAXU=np.full(n, bevel),
+                LMAXL=np.full(n, bevel),
+                LFLATU=np.full(n, 1 - 2 * bevel),
+                LFLATL=np.full(n, 1 - 2 * bevel),
+            )
+        elif label.startswith("radius_"):
+            values["LER"] = np.full(n, float(label[7:]))
+        elif label == "constant_4mm_5mm_bevel":
+            values.update(
+                ZUPPER=0.002 / chord,
+                ZLOWER=0.002 / chord,
+                LMAXU=0.005 / chord,
+                LMAXL=0.005 / chord,
+                LFLATU=1 - 0.01 / chord,
+                LFLATL=1 - 0.01 / chord,
+            )
+        elif label.startswith("user_"):
+            for k in ["ZUPPER", "ZLOWER", "LMAXU", "LMAXL", "LFLATU", "LFLATL"]:
+                values.pop(k, None)
+            x = np.array([0, 0.05, 0.25, 0.5, 0.75, 0.95, 1])
+            upper = np.full(7, 0.01)
+            if label == "user_sharp_both":
+                upper[[0, -1]] = 0
+            elif label == "user_blunt_TE":
+                upper[0] = 0
+            else:
+                raise ValueError(f"Unknown USER section variant: {label}")
+            values.update(SECTYP="USER", XCORD=x, YUPPER=upper, YLOWER=-upper)
+        return _namelist(match[1], list(values.items()))
+
+    return re.sub(r"\$(\w+)\s*(.*?)\$", change, deck, flags=re.S)
+
+
+LABELS = [
+    "baseline",
+    "ratio_0.01",
+    "ratio_0.04",
+    "ratio_0.08",
+    "bevel_0.25",
+    "bevel_0.45",
+    "radius_0.0005",
+    "radius_0.001",
+    "constant_4mm_5mm_bevel",
+    "user_sharp_both",
+    "user_blunt_TE",
+]
+FLIGHT_LABELS = [
+    "baseline",
+    "constant_4mm_5mm_bevel",
+    "ratio_0.04",
+    "user_sharp_both",
+    "user_blunt_TE",
+]
+
+
+def main():
+    report = {
+        "fitted": False,
+        "hardware_geometry_verified": False,
+        "executable_sha256": hashlib.sha256(audit.EXE.read_bytes()).hexdigest(),
+        "conditions": {
+            "mach": [0.3, 0.6, 0.9, 1.05, 1.2],
+            "alpha_deg": [-2, 0, 2],
+            "beta_deg": 0,
+            "reynolds_per_meter": "Mach * 2e7",
+        },
+        "interpretation": {
+            "baseline": "HEX total thickness/chord 2%, 5% leading/trailing bevels, LER=0",
+            "ratio_*": "HEX total thickness/chord; all other inputs unchanged",
+            "bevel_*": "HEX bevel length/chord at both edges; total thickness/chord remains 2%",
+            "radius_*": "leading-edge radius in metres; all other inputs unchanged",
+            "constant_4mm_5mm_bevel": "4 mm total plate thickness, 5 mm bevel length at both edges; ratios vary with spanwise chord",
+            "user_sharp_both": "USER profile, total thickness/chord 2%, 5% leading/trailing bevels; control for the section-method change",
+            "user_blunt_TE": "same USER profile, 5% leading bevel, full 2% thickness/chord at trailing edge",
+            "scope": "Only fin section inputs change. Flight grids, mass, motor, nozzle, atmosphere and planform retain saved comparison inputs.",
+            "limitation": "USER uses one normalized section across the span, so the blunt USER plate tapers in physical thickness. Native airfoil-section properties for blunt trailing edges are approximate (guide 4.2.2).",
+        },
+        "coefficients": [],
+        "flights": [],
+    }
+    results = pd.read_csv(OUT / "rockets/results.csv").set_index("rocket")
+    for name in ["genesis", "andromeda", "astra"]:
+        deck = (OUT / "rockets" / f"{name}_coast_for005.dat").read_text().split(
+            "NEXT CASE"
+        )[2] + "NEXT CASE\n"
+        controlled = audit.conditions(deck, report["conditions"]["mach"])
+        controlled = controlled.replace("NEXT CASE", "PART\nNEXT CASE")
+        for label in LABELS:
+            try:
+                table = run_datcom(audit.EXE, fin_fields(controlled, label))
+                record = dict(
+                    rocket=name,
+                    variant=label,
+                    ca_zero=table.body["CA"][:, 1].tolist(),
+                    cn_alpha2=table.body["CN"][:, 2].tolist(),
+                    diagnostics=table.diagnostics,
+                )
+            except RuntimeError as exc:
+                record = dict(rocket=name, variant=label, error=str(exc))
+            report["coefficients"].append(record)
+        with contextlib.redirect_stdout(io.StringIO()):
+            rocket, env, launch, _ = audit.c.build_model(
+                audit.c.ROOT / f"docs/examples/{name}_flight_sim.ipynb"
+            )
+            audit.c.h.attach_datcom(rocket, audit.EXE, 0)
+        provenance = json.loads(
+            (OUT / "rockets" / f"{name}_provenance.json").read_text()
+        )
+        options = dict(
+            mach=provenance["mach"],
+            alpha=provenance["alpha"],
+            beta=provenance["beta"],
+            reynolds=provenance["atmosphere"]["reynolds"],
+            powered_base_drag="nozzle",
+        )
+        for fin in rocket.fins:
+            if hasattr(fin, "datcom_options"):
+                fin.datcom_options = dict(options)
+        for label in FLIGHT_LABELS:
+            rocket._invalidate_datcom()
+
+            def native(path, input_deck, timeout):
+                return run_datcom(path, fin_fields(input_deck, label), timeout)
+
+            with (
+                patch("rocketpy.rocket.datcom.run_datcom", side_effect=native),
+                contextlib.redirect_stdout(io.StringIO()),
+            ):
+                flight = Flight(rocket, env, **launch)
+            model = rocket.prepare_datcom()
+            apogee = float(flight.apogee - env.elevation)
+            measured = float(results.loc[name, "actual_agl_m"])
+            record = dict(
+                rocket=name,
+                variant=label,
+                apogee_agl_m=apogee,
+                recorded_agl_m=measured,
+                error_pct=100 * (apogee / measured - 1),
+                coast_cd_mach06=float(
+                    model.drag_coefficient(False, 0, 0, 0.6, 0, 0, 0, 0)
+                ),
+                envelope=audit.c.h.envelope_diagnostics(
+                    flight, model, provenance["mach"], provenance["alpha"]
+                ),
+            )
+            report["flights"].append(record)
+            print(json.dumps(record), flush=True)
+        (OUT / "fin_parameter_audit.json").write_text(
+            json.dumps(report, indent=2) + "\n"
+        )
+    return report
+
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/examples/datcom/comparison_data/datcom_usage_audit.json b/docs/examples/datcom/comparison_data/datcom_usage_audit.json
new file mode 100644
index 000000000..472ea6b40
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/datcom_usage_audit.json
@@ -0,0 +1,951 @@
+{
+  "manual": "Missile DATCOM Users Guide, revision 5/97",
+  "native": "local revision 3/99 executable",
+  "executable_sha256": "96dfeae8a1ffca2eb82c44be5245f45da0dddfcfbcb5250653f07cfeefa5c22b",
+  "controlled_conditions": {
+    "mach": [
+      0.3,
+      0.6,
+      0.9,
+      1.2,
+      3.0
+    ],
+    "alpha_deg": [
+      -2,
+      0,
+      2
+    ],
+    "beta_deg": 0,
+    "reynolds_per_meter": "Mach * 2e7",
+    "note": "encoding probes use controlled conditions, not flight fits"
+  },
+  "manual_reference": {
+    "source": "Figures 19-21, printed pp. 65-67",
+    "mach": 2.36,
+    "published_ca_alpha0": 0.466,
+    "native_ca_alpha0": 0.466,
+    "published_cn_alpha4": 1.179,
+    "native_cn_alpha4": 1.179
+  },
+  "part_checks": [
+    {
+      "rocket": "andromeda",
+      "max_body_difference": 0.0,
+      "max_damping_difference": 0.0,
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "astra",
+      "max_body_difference": 0.0,
+      "max_damping_difference": 0.0,
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "bella_lui",
+      "max_body_difference": 0.0,
+      "max_damping_difference": 0.0,
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "cavour",
+      "max_body_difference": 0.0,
+      "max_damping_difference": 0.0,
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "erebus",
+      "max_body_difference": 0.0,
+      "max_damping_difference": 0.0,
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "genesis",
+      "max_body_difference": 0.0,
+      "max_damping_difference": 0.0,
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "halcyon",
+      "max_body_difference": 0.0,
+      "max_damping_difference": 0.0,
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "juno3",
+      "max_body_difference": 0.0,
+      "max_damping_difference": 0.0,
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "lince",
+      "max_body_difference": 0.0,
+      "max_damping_difference": 0.0,
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "prometheus_2022",
+      "max_body_difference": 0.0,
+      "max_damping_difference": 0.0,
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "valetudo",
+      "max_body_difference": 0.0,
+      "max_damping_difference": 0.0,
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    }
+  ],
+  "encoding_checks": [
+    {
+      "rocket": "andromeda",
+      "encoding": "explicit_trailing_edge_sweep",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0,
+      "zero_alpha_ca_reference": [
+        0.373,
+        0.345,
+        0.396,
+        0.689,
+        0.383
+      ],
+      "zero_alpha_ca_alternative": [
+        0.373,
+        0.345,
+        0.396,
+        0.689,
+        0.383
+      ]
+    },
+    {
+      "rocket": "andromeda",
+      "encoding": "inch_geometry_feet_reynolds",
+      "max_ca_difference": 0.008000000000000007,
+      "max_cn_difference": 0.0,
+      "zero_alpha_ca_reference": [
+        0.373,
+        0.345,
+        0.396,
+        0.689,
+        0.383
+      ],
+      "zero_alpha_ca_alternative": [
+        0.373,
+        0.345,
+        0.404,
+        0.689,
+        0.383
+      ]
+    },
+    {
+      "rocket": "andromeda",
+      "encoding": "Mach 0.6 alone versus larger Mach grid",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0
+    },
+    {
+      "rocket": "astra",
+      "encoding": "explicit_trailing_edge_sweep",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0,
+      "zero_alpha_ca_reference": [
+        0.463,
+        0.423,
+        0.503,
+        0.747,
+        0.378
+      ],
+      "zero_alpha_ca_alternative": [
+        0.463,
+        0.423,
+        0.503,
+        0.747,
+        0.378
+      ]
+    },
+    {
+      "rocket": "astra",
+      "encoding": "inch_geometry_feet_reynolds",
+      "max_ca_difference": 0.0020000000000000018,
+      "max_cn_difference": 0.0,
+      "zero_alpha_ca_reference": [
+        0.463,
+        0.423,
+        0.503,
+        0.747,
+        0.378
+      ],
+      "zero_alpha_ca_alternative": [
+        0.463,
+        0.423,
+        0.505,
+        0.747,
+        0.378
+      ]
+    },
+    {
+      "rocket": "astra",
+      "encoding": "Mach 0.6 alone versus larger Mach grid",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0
+    },
+    {
+      "rocket": "bella_lui",
+      "encoding": "explicit_trailing_edge_sweep",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0,
+      "zero_alpha_ca_reference": [
+        0.377,
+        0.348,
+        0.441,
+        0.757,
+        0.499
+      ],
+      "zero_alpha_ca_alternative": [
+        0.377,
+        0.348,
+        0.441,
+        0.757,
+        0.499
+      ]
+    },
+    {
+      "rocket": "bella_lui",
+      "encoding": "inch_geometry_feet_reynolds",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0,
+      "zero_alpha_ca_reference": [
+        0.377,
+        0.348,
+        0.441,
+        0.757,
+        0.499
+      ],
+      "zero_alpha_ca_alternative": [
+        0.377,
+        0.348,
+        0.441,
+        0.757,
+        0.499
+      ]
+    },
+    {
+      "rocket": "bella_lui",
+      "encoding": "Mach 0.6 alone versus larger Mach grid",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0
+    },
+    {
+      "rocket": "cavour",
+      "encoding": "explicit_trailing_edge_sweep",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0,
+      "zero_alpha_ca_reference": [
+        0.485,
+        0.446,
+        0.482,
+        0.537,
+        0.311
+      ],
+      "zero_alpha_ca_alternative": [
+        0.485,
+        0.446,
+        0.482,
+        0.537,
+        0.311
+      ]
+    },
+    {
+      "rocket": "cavour",
+      "encoding": "inch_geometry_feet_reynolds",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0,
+      "zero_alpha_ca_reference": [
+        0.485,
+        0.446,
+        0.482,
+        0.537,
+        0.311
+      ],
+      "zero_alpha_ca_alternative": [
+        0.485,
+        0.446,
+        0.482,
+        0.537,
+        0.311
+      ]
+    },
+    {
+      "rocket": "cavour",
+      "encoding": "Mach 0.6 alone versus larger Mach grid",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0
+    },
+    {
+      "rocket": "erebus",
+      "encoding": "explicit_trailing_edge_sweep",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0,
+      "zero_alpha_ca_reference": [
+        0.408,
+        0.373,
+        0.411,
+        0.485,
+        0.274
+      ],
+      "zero_alpha_ca_alternative": [
+        0.408,
+        0.373,
+        0.411,
+        0.485,
+        0.274
+      ]
+    },
+    {
+      "rocket": "erebus",
+      "encoding": "inch_geometry_feet_reynolds",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0,
+      "zero_alpha_ca_reference": [
+        0.408,
+        0.373,
+        0.411,
+        0.485,
+        0.274
+      ],
+      "zero_alpha_ca_alternative": [
+        0.408,
+        0.373,
+        0.411,
+        0.485,
+        0.274
+      ]
+    },
+    {
+      "rocket": "erebus",
+      "encoding": "Mach 0.6 alone versus larger Mach grid",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0
+    },
+    {
+      "rocket": "genesis",
+      "encoding": "explicit_trailing_edge_sweep",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0,
+      "zero_alpha_ca_reference": [
+        0.516,
+        0.472,
+        0.535,
+        0.687,
+        0.395
+      ],
+      "zero_alpha_ca_alternative": [
+        0.516,
+        0.472,
+        0.535,
+        0.687,
+        0.395
+      ]
+    },
+    {
+      "rocket": "genesis",
+      "encoding": "inch_geometry_feet_reynolds",
+      "max_ca_difference": 0.0020000000000000018,
+      "max_cn_difference": 0.0,
+      "zero_alpha_ca_reference": [
+        0.516,
+        0.472,
+        0.535,
+        0.687,
+        0.395
+      ],
+      "zero_alpha_ca_alternative": [
+        0.516,
+        0.472,
+        0.537,
+        0.687,
+        0.395
+      ]
+    },
+    {
+      "rocket": "genesis",
+      "encoding": "Mach 0.6 alone versus larger Mach grid",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0
+    },
+    {
+      "rocket": "halcyon",
+      "encoding": "explicit_trailing_edge_sweep",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0,
+      "zero_alpha_ca_reference": [
+        0.348,
+        0.317,
+        0.345,
+        0.462,
+        0.282
+      ],
+      "zero_alpha_ca_alternative": [
+        0.348,
+        0.317,
+        0.345,
+        0.462,
+        0.282
+      ]
+    },
+    {
+      "rocket": "halcyon",
+      "encoding": "inch_geometry_feet_reynolds",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0,
+      "zero_alpha_ca_reference": [
+        0.348,
+        0.317,
+        0.345,
+        0.462,
+        0.282
+      ],
+      "zero_alpha_ca_alternative": [
+        0.348,
+        0.317,
+        0.345,
+        0.462,
+        0.282
+      ]
+    },
+    {
+      "rocket": "halcyon",
+      "encoding": "Mach 0.6 alone versus larger Mach grid",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0
+    },
+    {
+      "rocket": "juno3",
+      "encoding": "explicit_trailing_edge_sweep",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0,
+      "zero_alpha_ca_reference": [
+        0.399,
+        0.366,
+        0.433,
+        0.609,
+        0.281
+      ],
+      "zero_alpha_ca_alternative": [
+        0.399,
+        0.366,
+        0.433,
+        0.609,
+        0.281
+      ]
+    },
+    {
+      "rocket": "juno3",
+      "encoding": "inch_geometry_feet_reynolds",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0,
+      "zero_alpha_ca_reference": [
+        0.399,
+        0.366,
+        0.433,
+        0.609,
+        0.281
+      ],
+      "zero_alpha_ca_alternative": [
+        0.399,
+        0.366,
+        0.433,
+        0.609,
+        0.281
+      ]
+    },
+    {
+      "rocket": "juno3",
+      "encoding": "Mach 0.6 alone versus larger Mach grid",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0
+    },
+    {
+      "rocket": "lince",
+      "encoding": "explicit_trailing_edge_sweep",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0,
+      "zero_alpha_ca_reference": [
+        0.408,
+        0.375,
+        0.407,
+        0.551,
+        0.33
+      ],
+      "zero_alpha_ca_alternative": [
+        0.408,
+        0.375,
+        0.407,
+        0.551,
+        0.33
+      ]
+    },
+    {
+      "rocket": "lince",
+      "encoding": "inch_geometry_feet_reynolds",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0,
+      "zero_alpha_ca_reference": [
+        0.408,
+        0.375,
+        0.407,
+        0.551,
+        0.33
+      ],
+      "zero_alpha_ca_alternative": [
+        0.408,
+        0.375,
+        0.407,
+        0.551,
+        0.33
+      ]
+    },
+    {
+      "rocket": "lince",
+      "encoding": "Mach 0.6 alone versus larger Mach grid",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0
+    },
+    {
+      "rocket": "prometheus_2022",
+      "encoding": "explicit_trailing_edge_sweep",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0,
+      "zero_alpha_ca_reference": [
+        0.352,
+        0.326,
+        0.313,
+        0.502,
+        0.247
+      ],
+      "zero_alpha_ca_alternative": [
+        0.352,
+        0.326,
+        0.313,
+        0.502,
+        0.247
+      ]
+    },
+    {
+      "rocket": "prometheus_2022",
+      "encoding": "inch_geometry_feet_reynolds",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0,
+      "zero_alpha_ca_reference": [
+        0.352,
+        0.326,
+        0.313,
+        0.502,
+        0.247
+      ],
+      "zero_alpha_ca_alternative": [
+        0.352,
+        0.326,
+        0.313,
+        0.502,
+        0.247
+      ]
+    },
+    {
+      "rocket": "prometheus_2022",
+      "encoding": "Mach 0.6 alone versus larger Mach grid",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0
+    },
+    {
+      "rocket": "valetudo",
+      "encoding": "explicit_trailing_edge_sweep",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0,
+      "zero_alpha_ca_reference": [
+        0.471,
+        0.434,
+        0.437,
+        0.559,
+        0.325
+      ],
+      "zero_alpha_ca_alternative": [
+        0.471,
+        0.434,
+        0.437,
+        0.559,
+        0.325
+      ]
+    },
+    {
+      "rocket": "valetudo",
+      "encoding": "inch_geometry_feet_reynolds",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0030000000000000027,
+      "zero_alpha_ca_reference": [
+        0.471,
+        0.434,
+        0.437,
+        0.559,
+        0.325
+      ],
+      "zero_alpha_ca_alternative": [
+        0.471,
+        0.434,
+        0.437,
+        0.559,
+        0.325
+      ]
+    },
+    {
+      "rocket": "valetudo",
+      "encoding": "Mach 0.6 alone versus larger Mach grid",
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0
+    }
+  ],
+  "nozzle_inputs": [
+    {
+      "rocket": "andromeda",
+      "motor_description": [],
+      "source_nozzle_radius_m": 0.028125,
+      "assumed_datcom_exit_diameter_m": 0.05625,
+      "listed_motor_case_diameter_m": null,
+      "exceeds_listed_case": false,
+      "note": "case diameter is not a measured nozzle exit diameter"
+    },
+    {
+      "rocket": "astra",
+      "motor_description": [
+        "4263-L1350-CS-P",
+        "75",
+        "486",
+        "P",
+        "2.0245",
+        "3.5707",
+        "CTI"
+      ],
+      "source_nozzle_radius_m": 0.028125,
+      "assumed_datcom_exit_diameter_m": 0.05625,
+      "listed_motor_case_diameter_m": 0.075,
+      "exceeds_listed_case": false,
+      "note": "case diameter is not a measured nozzle exit diameter"
+    },
+    {
+      "rocket": "bella_lui",
+      "motor_description": [
+        "K828FJ",
+        "54.0",
+        "579.00",
+        "6-10-14-18",
+        "1.45000",
+        "2.25500",
+        "AT"
+      ],
+      "source_nozzle_radius_m": 0.04445,
+      "assumed_datcom_exit_diameter_m": 0.0889,
+      "listed_motor_case_diameter_m": 0.054,
+      "exceeds_listed_case": true,
+      "note": "case diameter is not a measured nozzle exit diameter"
+    },
+    {
+      "rocket": "cavour",
+      "motor_description": [
+        "L995-RL",
+        "75",
+        "486",
+        "0",
+        "1.996",
+        "3.591",
+        "CTI"
+      ],
+      "source_nozzle_radius_m": 0.0335,
+      "assumed_datcom_exit_diameter_m": 0.067,
+      "listed_motor_case_diameter_m": 0.075,
+      "exceeds_listed_case": false,
+      "note": "case diameter is not a measured nozzle exit diameter"
+    },
+    {
+      "rocket": "erebus",
+      "motor_description": [],
+      "source_nozzle_radius_m": 0.02025,
+      "assumed_datcom_exit_diameter_m": 0.0405,
+      "listed_motor_case_diameter_m": null,
+      "exceeds_listed_case": false,
+      "note": "case diameter is not a measured nozzle exit diameter"
+    },
+    {
+      "rocket": "genesis",
+      "motor_description": [
+        "L995-RL",
+        "75",
+        "486",
+        "0",
+        "1.996",
+        "3.591",
+        "CTI"
+      ],
+      "source_nozzle_radius_m": 0.042354,
+      "assumed_datcom_exit_diameter_m": 0.084708,
+      "listed_motor_case_diameter_m": 0.075,
+      "exceeds_listed_case": true,
+      "note": "case diameter is not a measured nozzle exit diameter"
+    },
+    {
+      "rocket": "halcyon",
+      "motor_description": [],
+      "source_nozzle_radius_m": 0.0141,
+      "assumed_datcom_exit_diameter_m": 0.0282,
+      "listed_motor_case_diameter_m": null,
+      "exceeds_listed_case": false,
+      "note": "case diameter is not a measured nozzle exit diameter"
+    },
+    {
+      "rocket": "juno3",
+      "motor_description": [],
+      "source_nozzle_radius_m": 0.0335,
+      "assumed_datcom_exit_diameter_m": 0.067,
+      "listed_motor_case_diameter_m": null,
+      "exceeds_listed_case": false,
+      "note": "case diameter is not a measured nozzle exit diameter"
+    },
+    {
+      "rocket": "lince",
+      "motor_description": [
+        "\ufeff0",
+        "0"
+      ],
+      "source_nozzle_radius_m": 0.0335,
+      "assumed_datcom_exit_diameter_m": 0.067,
+      "listed_motor_case_diameter_m": null,
+      "exceeds_listed_case": false,
+      "note": "case diameter is not a measured nozzle exit diameter"
+    },
+    {
+      "rocket": "prometheus_2022",
+      "motor_description": [
+        "M1520-BS",
+        "98",
+        "548",
+        "P",
+        "3.737",
+        "6.718",
+        "CTI"
+      ],
+      "source_nozzle_radius_m": 0.027,
+      "assumed_datcom_exit_diameter_m": 0.054,
+      "listed_motor_case_diameter_m": 0.098,
+      "exceeds_listed_case": false,
+      "note": "case diameter is not a measured nozzle exit diameter"
+    },
+    {
+      "rocket": "valetudo",
+      "motor_description": [],
+      "source_nozzle_radius_m": 0.021641999999999998,
+      "assumed_datcom_exit_diameter_m": 0.043283999999999996,
+      "listed_motor_case_diameter_m": null,
+      "exceeds_listed_case": false,
+      "note": "case diameter is not a measured nozzle exit diameter"
+    }
+  ],
+  "nozzle_sensitivity": [
+    {
+      "rocket": "andromeda",
+      "scenario": "full powered base (bound)",
+      "assumed_exit_diameter_m": null,
+      "source_native_apogee_agl_m": 4170.733199067169,
+      "alternative_apogee_agl_m": 4155.710032872648,
+      "recorded_apogee_agl_m": 3443.0,
+      "alternative_error_pct": 20.700262354709498,
+      "fitted": false,
+      "motor_nozzle_radius_unchanged_m": 0.028125
+    },
+    {
+      "rocket": "astra",
+      "scenario": "full powered base (bound)",
+      "assumed_exit_diameter_m": null,
+      "source_native_apogee_agl_m": 3695.9340692825294,
+      "alternative_apogee_agl_m": 3669.239301757876,
+      "recorded_apogee_agl_m": 3250.0,
+      "alternative_error_pct": 12.899670823319266,
+      "fitted": false,
+      "motor_nozzle_radius_unchanged_m": 0.028125
+    },
+    {
+      "rocket": "bella_lui",
+      "scenario": "full powered base (bound)",
+      "assumed_exit_diameter_m": null,
+      "source_native_apogee_agl_m": 465.8765076067215,
+      "alternative_apogee_agl_m": 464.3511095001862,
+      "recorded_apogee_agl_m": 458.971,
+      "alternative_error_pct": 1.1722112072846125,
+      "fitted": false,
+      "motor_nozzle_radius_unchanged_m": 0.04445
+    },
+    {
+      "rocket": "cavour",
+      "scenario": "full powered base (bound)",
+      "assumed_exit_diameter_m": null,
+      "source_native_apogee_agl_m": 3251.608548140568,
+      "alternative_apogee_agl_m": 3204.985152018102,
+      "recorded_apogee_agl_m": 2789.0,
+      "alternative_error_pct": 14.915208032201566,
+      "fitted": false,
+      "motor_nozzle_radius_unchanged_m": 0.0335
+    },
+    {
+      "rocket": "erebus",
+      "scenario": "full powered base (bound)",
+      "assumed_exit_diameter_m": null,
+      "source_native_apogee_agl_m": 3253.422625182214,
+      "alternative_apogee_agl_m": 3232.7619663411715,
+      "recorded_apogee_agl_m": 3020.0,
+      "alternative_error_pct": 7.045098223217594,
+      "fitted": false,
+      "motor_nozzle_radius_unchanged_m": 0.02025
+    },
+    {
+      "rocket": "genesis",
+      "scenario": "full powered base (bound)",
+      "assumed_exit_diameter_m": null,
+      "source_native_apogee_agl_m": 3297.2391270686403,
+      "alternative_apogee_agl_m": 3223.251514478237,
+      "recorded_apogee_agl_m": 2916.0,
+      "alternative_error_pct": 10.53674603834831,
+      "fitted": false,
+      "motor_nozzle_radius_unchanged_m": 0.042354
+    },
+    {
+      "rocket": "genesis",
+      "scenario": "exit equal to listed case diameter (upper bound)",
+      "assumed_exit_diameter_m": 0.075,
+      "source_native_apogee_agl_m": 3297.2391270686403,
+      "alternative_apogee_agl_m": 3281.110730740635,
+      "recorded_apogee_agl_m": 2916.0,
+      "alternative_error_pct": 12.520944126907917,
+      "fitted": false,
+      "motor_nozzle_radius_unchanged_m": 0.042354
+    },
+    {
+      "rocket": "genesis",
+      "scenario": "source radius treated as diameter (unverified hypothesis)",
+      "assumed_exit_diameter_m": 0.042354,
+      "source_native_apogee_agl_m": 3297.2391270686403,
+      "alternative_apogee_agl_m": 3241.721673242308,
+      "recorded_apogee_agl_m": 2916.0,
+      "alternative_error_pct": 11.17015340337133,
+      "fitted": false,
+      "motor_nozzle_radius_unchanged_m": 0.042354
+    },
+    {
+      "rocket": "halcyon",
+      "scenario": "full powered base (bound)",
+      "assumed_exit_diameter_m": null,
+      "source_native_apogee_agl_m": 3530.231782008296,
+      "alternative_apogee_agl_m": 3527.7657672110763,
+      "recorded_apogee_agl_m": 3450.0,
+      "alternative_error_pct": 2.2540802090166956,
+      "fitted": false,
+      "motor_nozzle_radius_unchanged_m": 0.0141
+    },
+    {
+      "rocket": "juno3",
+      "scenario": "full powered base (bound)",
+      "assumed_exit_diameter_m": null,
+      "source_native_apogee_agl_m": 3056.4921836297026,
+      "alternative_apogee_agl_m": 3038.7362605522867,
+      "recorded_apogee_agl_m": 3213.0,
+      "alternative_error_pct": -5.423708043813047,
+      "fitted": false,
+      "motor_nozzle_radius_unchanged_m": 0.0335
+    },
+    {
+      "rocket": "lince",
+      "scenario": "full powered base (bound)",
+      "assumed_exit_diameter_m": null,
+      "source_native_apogee_agl_m": 4254.30425999591,
+      "alternative_apogee_agl_m": 4198.898835085533,
+      "recorded_apogee_agl_m": 3587.0,
+      "alternative_error_pct": 17.05879105340211,
+      "fitted": false,
+      "motor_nozzle_radius_unchanged_m": 0.0335
+    },
+    {
+      "rocket": "prometheus_2022",
+      "scenario": "full powered base (bound)",
+      "assumed_exit_diameter_m": null,
+      "source_native_apogee_agl_m": 4229.19676461329,
+      "alternative_apogee_agl_m": 4204.01504615547,
+      "recorded_apogee_agl_m": 3898.37,
+      "alternative_error_pct": 7.840329321112938,
+      "fitted": false,
+      "motor_nozzle_radius_unchanged_m": 0.027
+    },
+    {
+      "rocket": "valetudo",
+      "scenario": "full powered base (bound)",
+      "assumed_exit_diameter_m": null,
+      "source_native_apogee_agl_m": 943.6190073224012,
+      "alternative_apogee_agl_m": 936.5094014874567,
+      "recorded_apogee_agl_m": 860.0,
+      "alternative_error_pct": 8.896442033425188,
+      "fitted": false,
+      "motor_nozzle_radius_unchanged_m": 0.021641999999999998
+    }
+  ],
+  "unit_sensitivity": [
+    {
+      "rocket": "andromeda",
+      "metric_apogee_agl_m": 4170.733199067169,
+      "inch_apogee_agl_m": 4158.67688441827,
+      "change_m": -12.056314648899388,
+      "recorded_apogee_agl_m": 3443.0,
+      "fitted": false
+    },
+    {
+      "rocket": "astra",
+      "metric_apogee_agl_m": 3695.9340692825294,
+      "inch_apogee_agl_m": 3695.155893462332,
+      "change_m": -0.778175820197248,
+      "recorded_apogee_agl_m": 3250.0,
+      "fitted": false
+    },
+    {
+      "rocket": "genesis",
+      "metric_apogee_agl_m": 3297.2391270686403,
+      "inch_apogee_agl_m": 3297.2284116874466,
+      "change_m": -0.010715381193676876,
+      "recorded_apogee_agl_m": 2916.0,
+      "fitted": false
+    },
+    {
+      "rocket": "valetudo",
+      "metric_apogee_agl_m": 943.6190073224012,
+      "inch_apogee_agl_m": 943.7086282024495,
+      "change_m": 0.08962088004830093,
+      "recorded_apogee_agl_m": 860.0,
+      "fitted": false
+    }
+  ]
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/fin_parameter_audit.json b/docs/examples/datcom/comparison_data/fin_parameter_audit.json
new file mode 100644
index 000000000..cc8114880
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/fin_parameter_audit.json
@@ -0,0 +1,1014 @@
+{
+  "fitted": false,
+  "hardware_geometry_verified": false,
+  "executable_sha256": "96dfeae8a1ffca2eb82c44be5245f45da0dddfcfbcb5250653f07cfeefa5c22b",
+  "conditions": {
+    "mach": [
+      0.3,
+      0.6,
+      0.9,
+      1.05,
+      1.2
+    ],
+    "alpha_deg": [
+      -2,
+      0,
+      2
+    ],
+    "beta_deg": 0,
+    "reynolds_per_meter": "Mach * 2e7"
+  },
+  "interpretation": {
+    "baseline": "HEX total thickness/chord 2%, 5% leading/trailing bevels, LER=0",
+    "ratio_*": "HEX total thickness/chord; all other inputs unchanged",
+    "bevel_*": "HEX bevel length/chord at both edges; total thickness/chord remains 2%",
+    "radius_*": "leading-edge radius in metres; all other inputs unchanged",
+    "constant_4mm_5mm_bevel": "4 mm total plate thickness, 5 mm bevel length at both edges; ratios vary with spanwise chord",
+    "user_sharp_both": "USER profile, total thickness/chord 2%, 5% leading/trailing bevels; control for the section-method change",
+    "user_blunt_TE": "same USER profile, 5% leading bevel, full 2% thickness/chord at trailing edge",
+    "scope": "Only fin section inputs change. Flight grids, mass, motor, nozzle, atmosphere and planform retain saved comparison inputs.",
+    "limitation": "USER uses one normalized section across the span, so the blunt USER plate tapers in physical thickness. Native airfoil-section properties for blunt trailing edges are approximate (guide 4.2.2)."
+  },
+  "coefficients": [
+    {
+      "rocket": "genesis",
+      "variant": "baseline",
+      "ca_zero": [
+        0.516,
+        0.472,
+        0.535,
+        0.766,
+        0.687
+      ],
+      "cn_alpha2": [
+        0.72,
+        0.741,
+        0.774,
+        0.913,
+        0.978
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "genesis",
+      "variant": "ratio_0.01",
+      "ca_zero": [
+        0.514,
+        0.47,
+        0.472,
+        0.611,
+        0.59
+      ],
+      "cn_alpha2": [
+        0.726,
+        0.745,
+        0.773,
+        0.912,
+        0.976
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "genesis",
+      "variant": "ratio_0.04",
+      "ca_zero": [
+        0.519,
+        0.474,
+        0.637,
+        1.016,
+        1.061
+      ],
+      "cn_alpha2": [
+        0.707,
+        0.731,
+        0.778,
+        0.917,
+        0.981
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "genesis",
+      "variant": "ratio_0.08",
+      "ca_zero": [
+        0.526,
+        0.48,
+        0.85,
+        1.539,
+        1.832
+      ],
+      "cn_alpha2": [
+        0.676,
+        0.598,
+        0.796,
+        0.932,
+        0.992
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "genesis",
+      "variant": "bevel_0.25",
+      "ca_zero": [
+        0.516,
+        0.472,
+        0.475,
+        0.614,
+        0.602
+      ],
+      "cn_alpha2": [
+        0.729,
+        0.747,
+        0.772,
+        0.911,
+        0.975
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "genesis",
+      "variant": "bevel_0.45",
+      "ca_zero": [
+        0.515,
+        0.471,
+        0.465,
+        0.593,
+        0.586
+      ],
+      "cn_alpha2": [
+        0.731,
+        0.749,
+        0.776,
+        0.914,
+        0.977
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "genesis",
+      "variant": "radius_0.0005",
+      "ca_zero": [
+        0.516,
+        0.472,
+        0.526,
+        0.759,
+        0.666
+      ],
+      "cn_alpha2": [
+        0.72,
+        0.741,
+        0.774,
+        0.913,
+        0.978
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "genesis",
+      "variant": "radius_0.001",
+      "ca_zero": [
+        0.516,
+        0.472,
+        0.536,
+        0.799,
+        0.701
+      ],
+      "cn_alpha2": [
+        0.72,
+        0.741,
+        0.774,
+        0.913,
+        0.978
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "genesis",
+      "variant": "constant_4mm_5mm_bevel",
+      "ca_zero": [
+        0.517,
+        0.473,
+        0.57,
+        0.852,
+        0.907
+      ],
+      "cn_alpha2": [
+        0.718,
+        0.739,
+        0.775,
+        0.914,
+        0.978
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "genesis",
+      "variant": "user_sharp_both",
+      "ca_zero": [
+        0.516,
+        0.472,
+        0.454,
+        0.563,
+        0.563
+      ],
+      "cn_alpha2": [
+        0.701,
+        0.725,
+        0.78,
+        0.919,
+        0.982
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "genesis",
+      "variant": "user_blunt_TE",
+      "ca_zero": [
+        0.627,
+        0.586,
+        0.587,
+        0.743,
+        0.733
+      ],
+      "cn_alpha2": [
+        0.736,
+        0.75,
+        0.769,
+        0.91,
+        0.976
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "andromeda",
+      "variant": "baseline",
+      "ca_zero": [
+        0.373,
+        0.345,
+        0.396,
+        0.637,
+        0.689
+      ],
+      "cn_alpha2": [
+        0.546,
+        0.574,
+        0.633,
+        0.787,
+        0.748
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "andromeda",
+      "variant": "ratio_0.01",
+      "ca_zero": [
+        0.372,
+        0.344,
+        0.358,
+        0.545,
+        0.597
+      ],
+      "cn_alpha2": [
+        0.554,
+        0.58,
+        0.632,
+        0.785,
+        0.746
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "andromeda",
+      "variant": "ratio_0.04",
+      "ca_zero": [
+        0.375,
+        0.346,
+        0.457,
+        0.787,
+        0.96
+      ],
+      "cn_alpha2": [
+        0.529,
+        0.56,
+        0.639,
+        0.794,
+        0.752
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "andromeda",
+      "variant": "ratio_0.08",
+      "ca_zero": [
+        0.378,
+        0.35,
+        0.585,
+        1.102,
+        1.443
+      ],
+      "cn_alpha2": [
+        0.495,
+        0.422,
+        0.66,
+        0.815,
+        0.766
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "andromeda",
+      "variant": "bevel_0.25",
+      "ca_zero": [
+        0.373,
+        0.345,
+        0.361,
+        0.552,
+        0.584
+      ],
+      "cn_alpha2": [
+        0.555,
+        0.581,
+        0.636,
+        0.795,
+        0.758
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "andromeda",
+      "variant": "bevel_0.45",
+      "ca_zero": [
+        0.372,
+        0.344,
+        0.356,
+        0.538,
+        0.571
+      ],
+      "cn_alpha2": [
+        0.558,
+        0.583,
+        0.64,
+        0.799,
+        0.76
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "andromeda",
+      "variant": "radius_0.0005",
+      "ca_zero": [
+        0.373,
+        0.345,
+        0.39,
+        0.63,
+        0.651
+      ],
+      "cn_alpha2": [
+        0.546,
+        0.574,
+        0.633,
+        0.787,
+        0.748
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "andromeda",
+      "variant": "radius_0.001",
+      "ca_zero": [
+        0.373,
+        0.345,
+        0.396,
+        0.653,
+        0.64
+      ],
+      "cn_alpha2": [
+        0.546,
+        0.574,
+        0.633,
+        0.787,
+        0.748
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "andromeda",
+      "variant": "constant_4mm_5mm_bevel",
+      "ca_zero": [
+        0.373,
+        0.345,
+        0.409,
+        0.671,
+        0.848
+      ],
+      "cn_alpha2": [
+        0.545,
+        0.573,
+        0.634,
+        0.788,
+        0.749
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "andromeda",
+      "variant": "user_sharp_both",
+      "ca_zero": [
+        0.373,
+        0.345,
+        0.348,
+        0.517,
+        0.556
+      ],
+      "cn_alpha2": [
+        0.522,
+        0.554,
+        0.641,
+        0.795,
+        0.753
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "andromeda",
+      "variant": "user_blunt_TE",
+      "ca_zero": [
+        0.436,
+        0.41,
+        0.424,
+        0.62,
+        0.653
+      ],
+      "cn_alpha2": [
+        0.566,
+        0.586,
+        0.627,
+        0.783,
+        0.747
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "astra",
+      "variant": "baseline",
+      "ca_zero": [
+        0.463,
+        0.423,
+        0.503,
+        0.764,
+        0.747
+      ],
+      "cn_alpha2": [
+        0.801,
+        0.828,
+        0.871,
+        1.034,
+        1.091
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "astra",
+      "variant": "ratio_0.01",
+      "ca_zero": [
+        0.461,
+        0.422,
+        0.427,
+        0.577,
+        0.582
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+        0.809,
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+        0.869,
+        1.031,
+        1.086
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+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "astra",
+      "variant": "ratio_0.04",
+      "ca_zero": [
+        0.466,
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+        0.63,
+        1.078,
+        1.323
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+        0.784,
+        0.814,
+        0.877,
+        1.04,
+        1.096
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "astra",
+      "variant": "ratio_0.08",
+      "ca_zero": [
+        0.474,
+        0.433,
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+        1.702,
+        2.321
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+        0.897,
+        1.058,
+        1.108
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "astra",
+      "variant": "bevel_0.25",
+      "ca_zero": [
+        0.463,
+        0.423,
+        0.431,
+        0.584,
+        0.581
+      ],
+      "cn_alpha2": [
+        0.811,
+        0.836,
+        0.874,
+        1.041,
+        1.101
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "astra",
+      "variant": "bevel_0.45",
+      "ca_zero": [
+        0.461,
+        0.422,
+        0.42,
+        0.559,
+        0.557
+      ],
+      "cn_alpha2": [
+        0.814,
+        0.838,
+        0.879,
+        1.044,
+        1.102
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "astra",
+      "variant": "radius_0.0005",
+      "ca_zero": [
+        0.463,
+        0.423,
+        0.49,
+        0.749,
+        0.67
+      ],
+      "cn_alpha2": [
+        0.801,
+        0.828,
+        0.871,
+        1.034,
+        1.091
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "astra",
+      "variant": "radius_0.001",
+      "ca_zero": [
+        0.463,
+        0.423,
+        0.502,
+        0.795,
+        0.685
+      ],
+      "cn_alpha2": [
+        0.801,
+        0.828,
+        0.871,
+        1.034,
+        1.091
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "astra",
+      "variant": "constant_4mm_5mm_bevel",
+      "ca_zero": [
+        0.464,
+        0.424,
+        0.537,
+        0.848,
+        1.1
+      ],
+      "cn_alpha2": [
+        0.8,
+        0.826,
+        0.872,
+        1.036,
+        1.092
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "astra",
+      "variant": "user_sharp_both",
+      "ca_zero": [
+        0.463,
+        0.423,
+        0.405,
+        0.521,
+        0.528
+      ],
+      "cn_alpha2": [
+        0.776,
+        0.808,
+        0.879,
+        1.042,
+        1.097
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    },
+    {
+      "rocket": "astra",
+      "variant": "user_blunt_TE",
+      "ca_zero": [
+        0.589,
+        0.553,
+        0.557,
+        0.725,
+        0.721
+      ],
+      "cn_alpha2": [
+        0.822,
+        0.839,
+        0.866,
+        1.031,
+        1.09
+      ],
+      "diagnostics": [
+        "*** CREST CRITICAL MACH NUMBER EXCEEDED ***"
+      ]
+    }
+  ],
+  "flights": [
+    {
+      "rocket": "genesis",
+      "variant": "baseline",
+      "apogee_agl_m": 3297.2391270686403,
+      "recorded_agl_m": 2916.0,
+      "error_pct": 13.074044138156381,
+      "coast_cd_mach06": 0.47475,
+      "envelope": {
+        "samples": 497,
+        "outside_mach": 0,
+        "outside_alpha": 0,
+        "outside_beta": 0,
+        "max_abs_alpha_deg": 3.3632935153000667,
+        "max_abs_beta_deg": 11.894942323656261,
+        "solver_mach_excursion": true,
+        "solver_alpha_excursion": true,
+        "solver_beta_excursion": true
+      }
+    },
+    {
+      "rocket": "genesis",
+      "variant": "constant_4mm_5mm_bevel",
+      "apogee_agl_m": 3284.445319296054,
+      "recorded_agl_m": 2916.0,
+      "error_pct": 12.635299015639712,
+      "coast_cd_mach06": 0.47575,
+      "envelope": {
+        "samples": 497,
+        "outside_mach": 0,
+        "outside_alpha": 0,
+        "outside_beta": 0,
+        "max_abs_alpha_deg": 3.3733455727548263,
+        "max_abs_beta_deg": 11.929502107377205,
+        "solver_mach_excursion": true,
+        "solver_alpha_excursion": true,
+        "solver_beta_excursion": true
+      }
+    },
+    {
+      "rocket": "genesis",
+      "variant": "ratio_0.04",
+      "apogee_agl_m": 3262.3800930121533,
+      "recorded_agl_m": 2916.0,
+      "error_pct": 11.878604012762462,
+      "coast_cd_mach06": 0.47775,
+      "envelope": {
+        "samples": 497,
+        "outside_mach": 0,
+        "outside_alpha": 0,
+        "outside_beta": 0,
+        "max_abs_alpha_deg": 3.3905954160176144,
+        "max_abs_beta_deg": 11.988789509827408,
+        "solver_mach_excursion": true,
+        "solver_alpha_excursion": true,
+        "solver_beta_excursion": true
+      }
+    },
+    {
+      "rocket": "genesis",
+      "variant": "user_sharp_both",
+      "apogee_agl_m": 3324.546069789132,
+      "recorded_agl_m": 2916.0,
+      "error_pct": 14.010496220477787,
+      "coast_cd_mach06": 0.47475,
+      "envelope": {
+        "samples": 497,
+        "outside_mach": 0,
+        "outside_alpha": 0,
+        "outside_beta": 0,
+        "max_abs_alpha_deg": 3.3435832985348406,
+        "max_abs_beta_deg": 11.827153631278193,
+        "solver_mach_excursion": true,
+        "solver_alpha_excursion": true,
+        "solver_beta_excursion": true
+      }
+    },
+    {
+      "rocket": "genesis",
+      "variant": "user_blunt_TE",
+      "apogee_agl_m": 3024.861516258556,
+      "recorded_agl_m": 2916.0,
+      "error_pct": 3.7332481570149456,
+      "coast_cd_mach06": 0.5892499999999999,
+      "envelope": {
+        "samples": 496,
+        "outside_mach": 0,
+        "outside_alpha": 0,
+        "outside_beta": 0,
+        "max_abs_alpha_deg": 2.622012554187905,
+        "max_abs_beta_deg": 9.325502913670954,
+        "solver_mach_excursion": true,
+        "solver_alpha_excursion": true,
+        "solver_beta_excursion": true
+      }
+    },
+    {
+      "rocket": "andromeda",
+      "variant": "baseline",
+      "apogee_agl_m": 4170.733199067169,
+      "recorded_agl_m": 3443.0,
+      "error_pct": 21.13660177366161,
+      "coast_cd_mach06": 0.349,
+      "envelope": {
+        "samples": 497,
+        "outside_mach": 0,
+        "outside_alpha": 0,
+        "outside_beta": 0,
+        "max_abs_alpha_deg": 4.455101471012898,
+        "max_abs_beta_deg": 3.9438542480866237,
+        "solver_mach_excursion": true,
+        "solver_alpha_excursion": true,
+        "solver_beta_excursion": true
+      }
+    },
+    {
+      "rocket": "andromeda",
+      "variant": "constant_4mm_5mm_bevel",
+      "apogee_agl_m": 4141.06812408761,
+      "recorded_agl_m": 3443.0,
+      "error_pct": 20.274996342945407,
+      "coast_cd_mach06": 0.34974999999999995,
+      "envelope": {
+        "samples": 497,
+        "outside_mach": 0,
+        "outside_alpha": 0,
+        "outside_beta": 0,
+        "max_abs_alpha_deg": 4.486375620785864,
+        "max_abs_beta_deg": 3.971563960600419,
+        "solver_mach_excursion": true,
+        "solver_alpha_excursion": true,
+        "solver_beta_excursion": true
+      }
+    },
+    {
+      "rocket": "andromeda",
+      "variant": "ratio_0.04",
+      "apogee_agl_m": 4048.9345866324375,
+      "recorded_agl_m": 3443.0,
+      "error_pct": 17.599029527517796,
+      "coast_cd_mach06": 0.35075,
+      "envelope": {
+        "samples": 497,
+        "outside_mach": 0,
+        "outside_alpha": 0,
+        "outside_beta": 0,
+        "max_abs_alpha_deg": 4.559609206365785,
+        "max_abs_beta_deg": 4.036452839759637,
+        "solver_mach_excursion": true,
+        "solver_alpha_excursion": true,
+        "solver_beta_excursion": true
+      }
+    },
+    {
+      "rocket": "andromeda",
+      "variant": "user_sharp_both",
+      "apogee_agl_m": 4293.400625847303,
+      "recorded_agl_m": 3443.0,
+      "error_pct": 24.699408244185395,
+      "coast_cd_mach06": 0.349,
+      "envelope": {
+        "samples": 497,
+        "outside_mach": 0,
+        "outside_alpha": 0,
+        "outside_beta": 0,
+        "max_abs_alpha_deg": 4.3662635785117505,
+        "max_abs_beta_deg": 3.865144322950249,
+        "solver_mach_excursion": true,
+        "solver_alpha_excursion": true,
+        "solver_beta_excursion": true
+      }
+    },
+    {
+      "rocket": "andromeda",
+      "variant": "user_blunt_TE",
+      "apogee_agl_m": 3973.7324070571403,
+      "recorded_agl_m": 3443.0,
+      "error_pct": 15.414824486120837,
+      "coast_cd_mach06": 0.4145,
+      "envelope": {
+        "samples": 497,
+        "outside_mach": 0,
+        "outside_alpha": 0,
+        "outside_beta": 0,
+        "max_abs_alpha_deg": 4.692713888966549,
+        "max_abs_beta_deg": 4.154398064195836,
+        "solver_mach_excursion": true,
+        "solver_alpha_excursion": true,
+        "solver_beta_excursion": true
+      }
+    },
+    {
+      "rocket": "astra",
+      "variant": "baseline",
+      "apogee_agl_m": 3695.9340692825294,
+      "recorded_agl_m": 3250.0,
+      "error_pct": 13.721048285616288,
+      "coast_cd_mach06": 0.4265,
+      "envelope": {
+        "samples": 480,
+        "outside_mach": 0,
+        "outside_alpha": 0,
+        "outside_beta": 0,
+        "max_abs_alpha_deg": 8.95582043567763,
+        "max_abs_beta_deg": 9.383306642057704,
+        "solver_mach_excursion": true,
+        "solver_alpha_excursion": true,
+        "solver_beta_excursion": true
+      }
+    },
+    {
+      "rocket": "astra",
+      "variant": "constant_4mm_5mm_bevel",
+      "apogee_agl_m": 3670.881628801916,
+      "recorded_agl_m": 3250.0,
+      "error_pct": 12.950203963135888,
+      "coast_cd_mach06": 0.4275,
+      "envelope": {
+        "samples": 480,
+        "outside_mach": 0,
+        "outside_alpha": 0,
+        "outside_beta": 0,
+        "max_abs_alpha_deg": 9.018873270720917,
+        "max_abs_beta_deg": 9.416729726485588,
+        "solver_mach_excursion": true,
+        "solver_alpha_excursion": true,
+        "solver_beta_excursion": true
+      }
+    },
+    {
+      "rocket": "astra",
+      "variant": "ratio_0.04",
+      "apogee_agl_m": 3612.9640744403036,
+      "recorded_agl_m": 3250.0,
+      "error_pct": 11.168125367393955,
+      "coast_cd_mach06": 0.4305,
+      "envelope": {
+        "samples": 480,
+        "outside_mach": 0,
+        "outside_alpha": 0,
+        "outside_beta": 0,
+        "max_abs_alpha_deg": 9.196325621082272,
+        "max_abs_beta_deg": 9.49519236826517,
+        "solver_mach_excursion": true,
+        "solver_alpha_excursion": true,
+        "solver_beta_excursion": true
+      }
+    },
+    {
+      "rocket": "astra",
+      "variant": "user_sharp_both",
+      "apogee_agl_m": 3773.2657531954837,
+      "recorded_agl_m": 3250.0,
+      "error_pct": 16.100484713707196,
+      "coast_cd_mach06": 0.4265,
+      "envelope": {
+        "samples": 481,
+        "outside_mach": 0,
+        "outside_alpha": 0,
+        "outside_beta": 0,
+        "max_abs_alpha_deg": 9.558042379340797,
+        "max_abs_beta_deg": 9.285909509335255,
+        "solver_mach_excursion": true,
+        "solver_alpha_excursion": true,
+        "solver_beta_excursion": true
+      }
+    },
+    {
+      "rocket": "astra",
+      "variant": "user_blunt_TE",
+      "apogee_agl_m": 3377.964850221871,
+      "recorded_agl_m": 3250.0,
+      "error_pct": 3.937380006826796,
+      "coast_cd_mach06": 0.557,
+      "envelope": {
+        "samples": 478,
+        "outside_mach": 0,
+        "outside_alpha": 0,
+        "outside_beta": 0,
+        "max_abs_alpha_deg": 8.974078435616935,
+        "max_abs_beta_deg": 9.983150218071039,
+        "solver_mach_excursion": true,
+        "solver_alpha_excursion": true,
+        "solver_beta_excursion": true
+      }
+    }
+  ]
+}
diff --git a/docs/examples/datcom/comparison_data/model_sources.py b/docs/examples/datcom/comparison_data/model_sources.py
index e70a5cab9..b8a03e2df 100644
--- a/docs/examples/datcom/comparison_data/model_sources.py
+++ b/docs/examples/datcom/comparison_data/model_sources.py
@@ -35,6 +35,43 @@ class SourceUnavailable(ValueError):
     """A faithful offline comparison needs additional data or geometry support."""
 
 
+def _freeze_mach_drag(function):
+    """Capture a solver source before legacy Mach-only aliases are reassigned."""
+    from rocketpy import Function
+
+    return Function(lambda mach: function(0, 0, mach, 0, 0, 0, 0))
+
+
+def _drag_assignment(node, namespace):
+    """Find legacy drag writes in a trusted example, without changing equations."""
+    targets = (
+        node.targets
+        if isinstance(node, ast.Assign)
+        else [node.target]
+        if isinstance(node, ast.AugAssign)
+        else []
+    )
+    writes = []
+    for target in targets:
+        if isinstance(target, ast.Attribute) and target.attr in {
+            "power_off_drag",
+            "power_on_drag",
+        }:
+            owner = eval(
+                compile(ast.Expression(target.value), "<drag owner>", "eval"), namespace
+            )
+            # The initial aliases call self.*_7d dynamically. Freeze them before
+            # an expression such as alias *= factor, or rebinding *_7d would
+            # make that expression recursively call itself.
+            for name in ("power_off_drag", "power_on_drag"):
+                if getattr(owner, name) is getattr(owner, name + "_by_mach"):
+                    setattr(
+                        owner, name, _freeze_mach_drag(getattr(owner, name + "_7d"))
+                    )
+            writes.append((owner, target.attr))
+    return writes
+
+
 def _tree(source):
     return ast.parse(
         "\n".join(
@@ -388,6 +425,16 @@ def thrust_source(value):
             module = h.LocalPaths(ROOT).visit(module)
             module = Construction().visit(module)
             ast.fix_missing_locations(module)
+            drag_writes = _drag_assignment(node, namespace)
             exec(compile(module, str(path), "exec"), namespace)
+            for owner, name in drag_writes:
+                setattr(
+                    owner,
+                    name + "_7d",
+                    owner._Rocket__process_drag_input(getattr(owner, name), name),
+                )
+                adaptations.append(
+                    f"Source cell {index}: preserve the notebook's post-construction {name} update in the seven-input solver coefficient."
+                )
             executed.append(index)
     raise SourceUnavailable("No first Flight construction found")
diff --git a/docs/examples/datcom/comparison_data/plot_apogee_comparison.py b/docs/examples/datcom/comparison_data/plot_apogee_comparison.py
new file mode 100644
index 000000000..63092ae86
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/plot_apogee_comparison.py
@@ -0,0 +1,206 @@
+"""Plot measured apogees and the two supported aerodynamic comparisons."""
+
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+from matplotlib.lines import Line2D
+from matplotlib.patches import Patch
+import numpy as np
+import pandas as pd
+
+OUT = Path(__file__).resolve().parent
+GREEN, RED, NEUTRAL = "#25834a", "#c93b3b", "#7d8995"
+NAMES = {
+    "bella_lui": "Bella Lui",
+    "camoes": "Camões",
+    "juno3": "Juno III",
+    "ndrt_2020": "NDRT 2020",
+    "prometheus_2022": "Prometheus 2022",
+}
+
+
+def comparison_data(directory=OUT):
+    """Keep all rockets; compare errors only where both predictions exist."""
+    directory = Path(directory)
+    metrics = pd.read_csv(directory / "rocket_metrics.csv")
+    frame = metrics.loc[
+        metrics.metric == "Apogee AGL",
+        ["rocket", "measured", "rocketpy", "datcom", "condition", "availability"],
+    ].copy()
+    if frame.rocket.duplicated().any():
+        raise ValueError("Duplicate apogee records")
+    for key in ("rocketpy", "datcom"):
+        frame[f"{key}_error_pct"] = (
+            100 * (frame[key] - frame.measured) / frame.measured.abs()
+        )
+    valid_measurement = np.isfinite(frame.measured) & (frame.measured > 0)
+    paired = valid_measurement & np.isfinite(frame.rocketpy) & np.isfinite(frame.datcom)
+    frame["closer_prediction"] = "unavailable comparison"
+    a = frame.rocketpy_error_pct.abs()
+    b = frame.datcom_error_pct.abs()
+    tie = paired & np.isclose(a, b, atol=1e-9, rtol=0)
+    frame.loc[paired & ~tie & (a < b), "closer_prediction"] = "RocketPy + supplied CD"
+    frame.loc[paired & ~tie & (b < a), "closer_prediction"] = "DATCOM"
+    frame.loc[tie, "closer_prediction"] = "tie"
+    frame["paired"] = paired
+    frame["rocketpy_archived"] = frame.condition.fillna("").str.contains(
+        "archived", case=False
+    )
+    # The first-flight Juno source explicitly identifies CFD scaling; Cavour
+    # names RASAero II and Valetudo identifies RASAero in its input filenames.
+    frame["drag_provenance"] = frame.rocket.map(
+        {
+            "juno3": "RASAero II curve scaled using a CFD value (source cell 14)",
+            "cavour": "RASAero II (source cell 10)",
+            "valetudo": "RASAero (source input filenames)",
+        }
+    ).fillna("Supplied CD; CFD provenance not established")
+    return frame.reset_index(drop=True)
+
+
+def error_percentiles(frame):
+    """Empirical error quantiles across the same paired rockets, not flight CIs."""
+    paired = frame.loc[frame.paired]
+    records = []
+    for key, label in [("rocketpy", "RocketPy + supplied CD"), ("datcom", "DATCOM")]:
+        errors = paired[f"{key}_error_pct"].abs()
+        records.append(
+            {
+                "model": label,
+                "paired_rockets": len(errors),
+                **{
+                    f"P{int(q * 100)}_absolute_error_pct": errors.quantile(q)
+                    for q in [0.5, 0.75, 0.9, 0.95]
+                },
+            }
+        )
+    return pd.DataFrame(records)
+
+
+def plot_apogees(frame):
+    """Each panel uses its own metre scale and the recorded apogee reference."""
+    columns = 4
+    rows = int(np.ceil(len(frame) / columns))
+    fig, axes = plt.subplots(rows, columns, figsize=(18, 3.8 * rows), squeeze=False)
+    for ax, (_, row) in zip(axes.flat, frame.iterrows()):
+        heights = np.array([row.rocketpy, row.datcom], dtype=float)
+        finite = heights[np.isfinite(heights)]
+        maximum = max([row.measured, *finite])
+        ax.set_ylim(0, maximum * 1.40)
+        ax.axhline(row.measured, color="#242424", ls="--", lw=1.5, zorder=3)
+        for index, (key, label) in enumerate(
+            [("rocketpy", "RocketPy + supplied CD"), ("datcom", "DATCOM")]
+        ):
+            value = heights[index]
+            if not np.isfinite(value):
+                ax.text(
+                    index,
+                    0.09,
+                    "Unavailable",
+                    ha="center",
+                    va="bottom",
+                    color=NEUTRAL,
+                    transform=ax.get_xaxis_transform(),
+                    fontsize=10,
+                )
+                continue
+            winner = row.closer_prediction
+            color = (
+                NEUTRAL
+                if not row.paired or winner == "tie"
+                else GREEN
+                if winner == label
+                else RED
+            )
+            ax.bar(
+                index,
+                value,
+                width=0.57,
+                color=color,
+                edgecolor="#333333",
+                linewidth=0.6,
+                hatch="//" if key == "datcom" else "",
+                zorder=2,
+            )
+            error = row[f"{key}_error_pct"]
+            ax.text(
+                index,
+                value + maximum * 0.035,
+                f"{value:,.0f} m\n{error:+.1f}%",
+                ha="center",
+                va="bottom",
+                fontsize=10,
+                bbox=dict(facecolor="white", edgecolor="none", alpha=0.9, pad=1),
+                zorder=4,
+            )
+        title = NAMES.get(row.rocket, row.rocket.replace("_", " ").title())
+        ax.set_title(
+            f"{title}  ·  recorded {row.measured:,.0f} m",
+            fontsize=11,
+            fontweight="bold",
+            pad=9,
+        )
+        labels = ["RocketPy\nsupplied CD", "DATCOM"]
+        if row.rocketpy_archived:
+            labels[0] += "\n(archived)"
+        ax.set_xticks([0, 1], labels)
+        ax.set_xlim(-0.65, 1.65)
+        ax.set_ylabel("Apogee AGL (m)")
+        ax.yaxis.grid(True, alpha=0.16, zorder=0)
+        ax.spines[["top", "right"]].set_visible(False)
+    for ax in axes.flat[len(frame) :]:
+        ax.set_visible(False)
+    fig.suptitle(
+        "Recorded rockets: apogee with supplied CD versus DATCOM",
+        fontsize=19,
+        fontweight="bold",
+        y=0.995,
+    )
+    fig.legend(
+        handles=[
+            Patch(facecolor=GREEN, label="Closer to recorded apogee"),
+            Patch(facecolor=RED, label="Farther from recorded apogee"),
+            Patch(facecolor=NEUTRAL, label="No paired result / tie"),
+            Line2D([0], [0], color="#242424", linestyle="--", label="Recorded apogee"),
+        ],
+        loc="upper center",
+        bbox_to_anchor=(0.5, 0.974),
+        ncol=4,
+        frameon=False,
+        fontsize=10,
+    )
+    fig.text(
+        0.5,
+        0.008,
+        "Labels: altitude and signed error 100 × (prediction − recorded) / |recorded|. Green/red rank only available paired predictions.\n"
+        "Each panel has its own vertical scale. Supplied CD is not universally CFD-derived. Archived outputs and missing DATCOM cases remain identified.",
+        ha="center",
+        va="bottom",
+        fontsize=10,
+    )
+    fig.tight_layout(rect=(0, 0.045, 1, 0.945), h_pad=2.3)
+    return fig
+
+
+def save_apogees(figure, directory=OUT):
+    """Export raster/vector charts with whitespace-clean SVG markup."""
+    directory = Path(directory)
+    figure.savefig(directory / "apogee_comparison.png", dpi=170, bbox_inches="tight")
+    svg = directory / "apogee_comparison.svg"
+    figure.savefig(svg, bbox_inches="tight")
+    svg.write_text(
+        "\n".join(line.rstrip() for line in svg.read_text().splitlines()) + "\n"
+    )
+
+
+def main():
+    frame = comparison_data()
+    frame.to_csv(OUT / "apogee_comparison.csv", index=False)
+    error_percentiles(frame).to_csv(OUT / "apogee_error_percentiles.csv", index=False)
+    figure = plot_apogees(frame)
+    save_apogees(figure)
+    plt.close(figure)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/examples/datcom/comparison_data/rocket_metrics.csv b/docs/examples/datcom/comparison_data/rocket_metrics.csv
index 435cd0f27..28f045bf8 100644
--- a/docs/examples/datcom/comparison_data/rocket_metrics.csv
+++ b/docs/examples/datcom/comparison_data/rocket_metrics.csv
@@ -3,18 +3,18 @@ andromeda,Apogee AGL,m,3443.0,3615.880567942097,4170.733199067169,5.021218935291
 andromeda,Peak upward vertical velocity,m/s,324.10908,343.1543305235018,352.6406104190726,5.876185426061438,8.80306420883752,recorded ascent velocity channel; not total airspeed,,,,,
 andromeda,Initial static margin,cal,,4.158769051891837,4.585569601745527,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,,
 andromeda,Coast CD at Mach 0.6,1,,0.4984983643122677,0.349,,,zero alpha/beta; measured CD unavailable,,0.5913883886236182,,-15.707109929489876,-40.986328660890116
-andromeda,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.5913883886236182,0.0240281656274419,,
+andromeda,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.5913883886236182,0.024028165627441922,,
 astra,Apogee AGL,m,3250.0,3043.3679728130164,3695.9340692825294,-6.357908528830264,13.721048285616291,notebook reported apogee,,,,,
 astra,Peak upward vertical velocity,m/s,,306.4797793783156,318.5471261391443,,,recorded ascent velocity channel; not total airspeed,,,,,
 astra,Initial static margin,cal,,3.16256293861153,3.477476916191179,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,,
 astra,Coast CD at Mach 0.6,1,,0.6538147999999999,0.4265,,,zero alpha/beta; measured CD unavailable,,0.5989080138236664,,9.167816243731115,-28.787060757952673
-astra,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.5989080138236664,0.0032524125458621,,
-bella_lui,Apogee AGL,m,458.971,460.57389565932874,465.8765076067215,0.3492368056650066,1.5045629477072564,telemetry AGL maximum (QA),,,,,
-bella_lui,Peak upward vertical velocity,m/s,90.0,86.17870117421616,86.47349849471236,-4.245887584204269,-3.918335005875156,recorded ascent velocity channel; not total airspeed,,,,,
+astra,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.5989080138236664,0.003252412545862171,,
+bella_lui,Apogee AGL,m,458.971,460.5523116828233,465.8765076067215,0.3445341171497323,1.5045629477072564,telemetry AGL maximum (QA),,,,,
+bella_lui,Peak upward vertical velocity,m/s,90.0,86.17634365334754,86.47349849471236,-4.248507051836062,-3.9183350058751563,recorded ascent velocity channel; not total airspeed,,,,,
 bella_lui,Initial static margin,cal,,2.776966165584537,3.303739452169553,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,,
-bella_lui,Coast CD at Mach 0.6,1,,0.43,0.3457692307692308,,,zero alpha/beta; measured CD unavailable,,0.4457593658055402,,-3.53539757421836,-22.431415401808856
-bella_lui,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.4457593658055402,0.0052149262372722,,
-camoes,Apogee AGL,m,3015.0,3004.10382797431,,-0.3613987404872236,,notebook reported apogee,Source active air brakes override complete rocket drag. Combining their whole-vehicle CD with DATCOM would double-count base drag; no controlled DATCOM result claimed.,,,,
+bella_lui,Coast CD at Mach 0.6,1,,0.46,0.3457692307692308,,,zero alpha/beta; measured CD unavailable,,0.4457593658055402,,3.194690967115249,-22.431415401808856
+bella_lui,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.4457593658055402,0.005214926237272266,,
+camoes,Apogee AGL,m,3015.0,3004.10382797431,,-0.36139874048722365,,notebook reported apogee,Source active air brakes override complete rocket drag. Combining their whole-vehicle CD with DATCOM would double-count base drag; no controlled DATCOM result claimed.,,,,
 camoes,Peak upward vertical velocity,m/s,,327.4402911475654,,,,recorded ascent velocity channel; not total airspeed,Source active air brakes override complete rocket drag. Combining their whole-vehicle CD with DATCOM would double-count base drag; no controlled DATCOM result claimed.,,,,
 camoes,Initial static margin,cal,,3.7616831285769776,,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",Source active air brakes override complete rocket drag. Combining their whole-vehicle CD with DATCOM would double-count base drag; no controlled DATCOM result claimed.,,,,
 camoes,Coast CD at Mach 0.6,1,,0.7036,,,,zero alpha/beta; measured CD unavailable,Source active air brakes override complete rocket drag. Combining their whole-vehicle CD with DATCOM would double-count base drag; no controlled DATCOM result claimed.,,,,
@@ -23,56 +23,56 @@ cavour,Apogee AGL,m,2789.0,2818.1348894226226,3251.608548140568,1.04463569102268
 cavour,Peak upward vertical velocity,m/s,314.0,304.6396378918068,319.05542973964634,-2.9810070408258644,1.61000947122495,recorded ascent velocity channel; not total airspeed,,,,,
 cavour,Initial static margin,cal,,4.279860429594885,5.465305853006995,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,,
 cavour,Coast CD at Mach 0.6,1,,0.583,0.44825,,,zero alpha/beta; measured CD unavailable,,0.6086474636527143,,-4.213845482702675,-26.353098177738378
-cavour,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.6086474636527143,0.0013786057097604,,
+cavour,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.6086474636527143,0.0013786057097604498,,
 defiance,Apogee AGL,m,9308.318,9243.891367330603,,-0.6921404347100731,,notebook reported apogee,ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,,,,
 defiance,Peak upward vertical velocity,m/s,,438.71139566437614,,,,recorded ascent velocity channel; not total airspeed,ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,,,,
 defiance,Initial static margin,cal,,7.9601902493275025,,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,,,,
 defiance,Coast CD at Mach 0.6,1,,0.668,,,,zero alpha/beta; measured CD unavailable,ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,0.6574401678467228,,1.6062042859144585,
-defiance,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.6574401678467228,-0.0629210059214528,,
+defiance,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.6574401678467228,-0.06292100592145289,,
 erebus,Apogee AGL,m,3020.0,2751.1556122225365,3253.422625182214,-8.902132045611372,7.729225999411052,notebook reported apogee,,,,,
 erebus,Peak upward vertical velocity,m/s,,294.1707251884576,307.31659427636407,,,recorded ascent velocity channel; not total airspeed,,,,,
 erebus,Initial static margin,cal,,2.9374083445614505,4.450320086266694,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,,
 erebus,Coast CD at Mach 0.6,1,,0.531,0.376,,,zero alpha/beta; measured CD unavailable,,0.4428682177698397,,19.900227357467035,-15.098897389062893
-erebus,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.4428682177698397,-0.0107670600518758,,
+erebus,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.4428682177698397,-0.010767060051875887,,
 genesis,Apogee AGL,m,2916.0,3044.6857862664306,3297.2391270686403,4.41309280749076,13.074044138156388,notebook reported apogee,,,,,
 genesis,Peak upward vertical velocity,m/s,,290.6363275291183,299.2620317383252,,,recorded ascent velocity channel; not total airspeed,,,,,
 genesis,Initial static margin,cal,,3.37721309359005,3.7682700796928534,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,,
 genesis,Coast CD at Mach 0.6,1,,0.55,0.47475,,,zero alpha/beta; measured CD unavailable,,0.6089918859113056,,-9.686809837052115,-22.043296309346353
-genesis,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.6089918859113056,-0.0033379047545167,,
+genesis,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.6089918859113056,-0.003337904754516785,,
 halcyon,Apogee AGL,m,3450.0,3214.238895757538,3530.231782008296,-6.8336551954336775,2.3255588987911824,notebook reported apogee,,,,,
 halcyon,Peak upward vertical velocity,m/s,,275.5714950426811,280.94965050790404,,,recorded ascent velocity channel; not total airspeed,,,,,
 halcyon,Initial static margin,cal,,3.0192578745448033,4.410132986423363,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,,
 halcyon,Coast CD at Mach 0.6,1,,0.426,0.319,,,zero alpha/beta; measured CD unavailable,,0.3446774638716062,,23.593807153776314,-7.449707788604117
-halcyon,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.3446774638716062,0.0023908790494715,,
-hedy,Apogee AGL,m,5231.53,5246.918,,0.2941395729356406,,"saved measured-apogee summary; archived RocketPy output, not fresh simulation","Source data/rockets/tust is absent; CoolProp is not installed, and the negative-span fin root cutout requires geometry support.",,,,
+halcyon,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.3446774638716062,0.0023908790494715504,,
+hedy,Apogee AGL,m,5231.53,5246.918,,0.29413957293564064,,"saved measured-apogee summary; archived RocketPy output, not fresh simulation","Source data/rockets/tust is absent; CoolProp is not installed, and the negative-span fin root cutout requires geometry support.",,,,
 hedy,Peak upward vertical velocity,m/s,,,,,,recorded ascent velocity channel; not total airspeed,"Source data/rockets/tust is absent; CoolProp is not installed, and the negative-span fin root cutout requires geometry support.",,,,
 hedy,Initial static margin,cal,,,,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach","Source data/rockets/tust is absent; CoolProp is not installed, and the negative-span fin root cutout requires geometry support.",,,,
 hedy,Coast CD at Mach 0.6,1,,,,,,zero alpha/beta; measured CD unavailable,"Source data/rockets/tust is absent; CoolProp is not installed, and the negative-span fin root cutout requires geometry support.",,,,
-juno3,Apogee AGL,m,3213.0,3008.581569789987,3056.4921836297026,-6.362229387177496,-4.871080497052518,notebook reported apogee,,,,,
-juno3,Peak upward vertical velocity,m/s,268.761,241.2427490025272,243.89859046993095,-10.238930126570752,-9.250750492098582,recorded ascent velocity channel; not total airspeed,,,,,
+juno3,Apogee AGL,m,3213.0,2987.2806591791605,3056.5496067381873,-7.025189568031109,-4.8692932854594675,notebook reported apogee,,,,,
+juno3,Peak upward vertical velocity,m/s,268.761,240.91733213924152,243.90063624160936,-10.36001051520068,-9.2499893058854,recorded ascent velocity channel; not total airspeed,,,,,
 juno3,Initial static margin,cal,,3.220648297149346,4.0067340523842745,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,,
-juno3,Coast CD at Mach 0.6,1,,0.368,0.3745,,,zero alpha/beta; measured CD unavailable,,0.2640930601135738,,39.344820284842314,41.80607390400393
-juno3,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.2640930601135738,-0.0003520752607073,,
+juno3,Coast CD at Mach 0.6,1,,0.3799999999999999,0.3745,,,zero alpha/beta; measured CD unavailable,,0.2640930601135738,,43.88867312021757,41.80607390400393
+juno3,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.2640930601135738,-0.00035207526070735184,,
 lince,Apogee AGL,m,3587.0,3285.089381541881,4254.30425999591,-8.416800068528554,18.60340841917786,notebook reported apogee,,,,,
 lince,Peak upward vertical velocity,m/s,,317.77081353823803,342.53953112405384,,,recorded ascent velocity channel; not total airspeed,,,,,
 lince,Initial static margin,cal,,2.9203959364705625,3.962666916625163,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,,
 lince,Coast CD at Mach 0.6,1,,0.65562,0.38175,,,zero alpha/beta; measured CD unavailable,,0.5781559898205425,,13.398461927809832,-33.9711069812675
-lince,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.5781559898205425,-0.0160574872520555,,
+lince,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.5781559898205425,-0.01605748725205558,,
 ndrt_2020,Apogee AGL,m,1316.736,1293.5184123380943,,-1.7632682376653923,,notebook reported apogee,ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,,,,
 ndrt_2020,Peak upward vertical velocity,m/s,,171.66118644734854,,,,recorded ascent velocity channel; not total airspeed,ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,,,,
 ndrt_2020,Initial static margin,cal,2.875,4.016381564609376,,39.70022833423917,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,,,,
 ndrt_2020,Coast CD at Mach 0.6,1,,0.44,,,,zero alpha/beta; measured CD unavailable,ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,0.4171896002390634,,5.467633840312778,
-ndrt_2020,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.4171896002390634,-0.0019144055324886,,
+ndrt_2020,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.4171896002390634,-0.0019144055324886722,,
 prometheus_2022,Apogee AGL,m,3898.37,4191.288687549948,4229.19676461329,7.513875993041916,8.486284385866142,notebook reported apogee,,,,,
-prometheus_2022,Peak upward vertical velocity,m/s,317.88,322.07071634939507,324.05991606871703,1.318332814079236,1.944103456875876,recorded ascent velocity channel; not total airspeed,,,,,
+prometheus_2022,Peak upward vertical velocity,m/s,317.88,322.07071634939507,324.05991606871703,1.3183328140792359,1.944103456875876,recorded ascent velocity channel; not total airspeed,,,,,
 prometheus_2022,Initial static margin,cal,,1.0415367273926033,1.7889093089472907,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,,
-prometheus_2022,Coast CD at Mach 0.6,1,,0.351875,0.33475,,,zero alpha/beta; measured CD unavailable,,0.4026032258387881,,-12.60005449114332,-16.85362199903439
-prometheus_2022,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.4026032258387881,-0.0677661625295513,,
+prometheus_2022,Coast CD at Mach 0.6,1,,0.351875,0.33475,,,zero alpha/beta; measured CD unavailable,,0.40260322583878816,,-12.600054491143334,-16.853621999034402
+prometheus_2022,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.40260322583878816,-0.06776616252955137,,
 valetudo,Apogee AGL,m,860.0,815.0526424633565,943.6190073224012,-5.226436922865522,9.723140386325722,saved measured-apogee summary,,,,,
 valetudo,Peak upward vertical velocity,m/s,,123.31112741231496,126.09986587804391,,,recorded ascent velocity channel; not total airspeed,,,,,
 valetudo,Initial static margin,cal,,2.951130333277495,4.156583455241121,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",,,,,
 valetudo,Coast CD at Mach 0.6,1,,1.128,0.4313333333333333,,,zero alpha/beta; measured CD unavailable,,0.809545397450946,,39.33746069729839,-46.71906792485103
-valetudo,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.809545397450946,-0.0149988657269659,,
+valetudo,Apogee-fitted constant CD,1,,,,,,"inferred: single constant CD, same powered/coast, ordinary lateral model",,0.809545397450946,-0.01499886572696596,,
 valkyrie,Apogee AGL,m,2098.02,2247.849,,7.141447650642043,,"notebook reported apogee; archived RocketPy output, not fresh simulation",SourceUnavailable: Source requests live weather with no archived profile; not replaced by invented weather.,,,,
 valkyrie,Peak upward vertical velocity,m/s,,,,,,recorded ascent velocity channel; not total airspeed,SourceUnavailable: Source requests live weather with no archived profile; not replaced by invented weather.,,,,
 valkyrie,Initial static margin,cal,,,,,,"Mach 0, loaded ignition; recorded NDRT conditions unspecified; DATCOM uses lowest positive Mach",SourceUnavailable: Source requests live weather with no archived profile; not replaced by invented weather.,,,,
diff --git a/docs/examples/datcom/comparison_data/rockets/bella_lui_altitude.csv b/docs/examples/datcom/comparison_data/rockets/bella_lui_altitude.csv
index be4b8fc91..d51a1c787 100644
--- a/docs/examples/datcom/comparison_data/rockets/bella_lui_altitude.csv
+++ b/docs/examples/datcom/comparison_data/rockets/bella_lui_altitude.csv
@@ -1,601 +1,601 @@
 time_s,ordinary_agl_m,datcom_agl_m,ordinary_vz_m_s,datcom_vz_m_s,ordinary_margin_cal,datcom_margin_cal
 0.0,0.0,0.0,0.0,0.0,2.776971022136796,3.303739452169553
-0.017791867764501743,0.003696398276531454,0.0036944534694498543,0.6014028276131008,0.6006410540510206,2.77853690477481,3.3053041782635804
-0.035583735529003485,0.02322054997523537,0.023208033261482797,1.603237263909207,1.602329870072509,2.780943810014526,3.3077032064385725
-0.05337560329350523,0.06079899468693384,0.0608276627180544,2.6045801009199483,2.6026855703540677,2.7833552164907625,3.310103181066158
-0.07116747105800697,0.11517968124741174,0.11513770901586895,3.492607815989455,3.490449964023141,2.7855402608799897,3.312418126880381
-0.08895933882250871,0.18481689446042537,0.18473227208517073,4.329702126518269,4.328260980652765,2.787627794820488,3.3148782439729856
-0.10675120658701046,0.269151475557976,0.26903721841688366,5.148060093845161,5.146765030697776,2.789676992386094,3.3172921518581564
-0.1245430743515122,0.3679152433368813,0.3677600830889105,5.951499482959596,5.950125545628171,2.7917027805440466,3.3196768635461362
-0.14233494211601394,0.48085213489082435,0.4806704835941673,6.743136159871589,6.741813981446059,2.79371273636203,3.3220390617491553
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-0.4981722974060488,5.641637406839436,5.640793376608997,22.182638025130274,22.181426475300494,2.833983636089807,3.363659488078909
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 10.639536923172042,,465.8704931833389,,0.3460706998925722,,3.5359229411359943
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diff --git a/docs/examples/datcom/comparison_data/rockets/bella_lui_cd.csv b/docs/examples/datcom/comparison_data/rockets/bella_lui_cd.csv
index d35a61fb2..cff88a1a2 100644
--- a/docs/examples/datcom/comparison_data/rockets/bella_lui_cd.csv
+++ b/docs/examples/datcom/comparison_data/rockets/bella_lui_cd.csv
@@ -1,8 +1,8 @@
 mach,rocketpy_powered_cd,datcom_powered_cd,rocketpy_coast_cd,datcom_coast_cd
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@@ -34,119 +34,119 @@ mach,rocketpy_powered_cd,datcom_powered_cd,rocketpy_coast_cd,datcom_coast_cd
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diff --git a/docs/examples/datcom/comparison_data/rockets/bella_lui_provenance.json b/docs/examples/datcom/comparison_data/rockets/bella_lui_provenance.json
index 7822f3cd1..6894dd8b4 100644
--- a/docs/examples/datcom/comparison_data/rockets/bella_lui_provenance.json
+++ b/docs/examples/datcom/comparison_data/rockets/bella_lui_provenance.json
@@ -19,7 +19,12 @@
     "roughness_height": 0.0,
     "leading_edge_radius": null
   },
-  "source_adaptations": [],
+  "source_adaptations": [
+    "Source cell 25: preserve the notebook's post-construction power_off_drag update in the seven-input solver coefficient.",
+    "Source cell 25: preserve the notebook's post-construction power_on_drag update in the seven-input solver coefficient.",
+    "Source cell 25: preserve the notebook's post-construction power_off_drag update in the seven-input solver coefficient.",
+    "Source cell 25: preserve the notebook's post-construction power_on_drag update in the seven-input solver coefficient."
+  ],
   "mach": [
     0.01,
     0.03,
diff --git a/docs/examples/datcom/comparison_data/rockets/bella_lui_result.json b/docs/examples/datcom/comparison_data/rockets/bella_lui_result.json
index f71a83c7b..425d75dfa 100644
--- a/docs/examples/datcom/comparison_data/rockets/bella_lui_result.json
+++ b/docs/examples/datcom/comparison_data/rockets/bella_lui_result.json
@@ -8,14 +8,14 @@
   "notes": "",
   "status": "paired untuned comparison",
   "reason": "",
-  "ordinary_agl_m": 460.57389565932874,
-  "ordinary_apogee_time_s": 10.607916503445066,
-  "ordinary_peak_vertical_velocity_m_s": 86.17870117421616,
+  "ordinary_agl_m": 460.5523116828233,
+  "ordinary_apogee_time_s": 10.607630761324499,
+  "ordinary_peak_vertical_velocity_m_s": 86.17634365334754,
   "ordinary_initial_static_margin_cal": 2.776966165584537,
-  "ordinary_coast_cd_mach06": 0.43,
-  "ordinary_max_mach": 0.2544470871701163,
-  "ordinary_ascent_altitude_rmse_m": 4.730090709810138,
-  "source_adaptations": "",
+  "ordinary_coast_cd_mach06": 0.46,
+  "ordinary_max_mach": 0.25444012331502597,
+  "ordinary_ascent_altitude_rmse_m": 4.732669828684498,
+  "source_adaptations": "Source cell 25: preserve the notebook's post-construction power_off_drag update in the seven-input solver coefficient. | Source cell 25: preserve the notebook's post-construction power_on_drag update in the seven-input solver coefficient. | Source cell 25: preserve the notebook's post-construction power_off_drag update in the seven-input solver coefficient. | Source cell 25: preserve the notebook's post-construction power_on_drag update in the seven-input solver coefficient.",
   "atmosphere_refinements": 1,
   "atmosphere_last_apogee_change_m": 9.736509809954441e-06,
   "datcom_agl_m": 465.8765076067215,
@@ -35,13 +35,13 @@
   "solver_alpha_excursion": true,
   "solver_beta_excursion": true,
   "mass_cg_match": true,
-  "ordinary_error_m": 1.6028956593287376,
-  "ordinary_error_pct": 0.34923680566500664,
+  "ordinary_error_m": 1.5813116828232978,
+  "ordinary_error_pct": 0.3445341171497323,
   "datcom_error_m": 6.905507606721471,
   "datcom_error_pct": 1.5045629477072564,
   "telemetry_qa_max_agl_m": 458.971,
   "telemetry_qa_rejected_count": 0,
-  "ordinary_vs_telemetry_pct": 0.3492368056650097,
+  "ordinary_vs_telemetry_pct": 0.3445341171497418,
   "datcom_vs_telemetry_pct": 1.5045629477072664,
   "rmse_start_s": 0,
   "rmse_end_s": 10.53,
diff --git a/docs/examples/datcom/comparison_data/rockets/juno3_altitude.csv b/docs/examples/datcom/comparison_data/rockets/juno3_altitude.csv
index 521e3b7c5..2ad8fe6de 100644
--- a/docs/examples/datcom/comparison_data/rockets/juno3_altitude.csv
+++ b/docs/examples/datcom/comparison_data/rockets/juno3_altitude.csv
@@ -1,602 +1,602 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/juno3_cd.csv b/docs/examples/datcom/comparison_data/rockets/juno3_cd.csv
index 0157db690..bb5b55243 100644
--- a/docs/examples/datcom/comparison_data/rockets/juno3_cd.csv
+++ b/docs/examples/datcom/comparison_data/rockets/juno3_cd.csv
@@ -1,152 +1,152 @@
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diff --git a/docs/examples/datcom/comparison_data/rockets/juno3_coast_for005.dat b/docs/examples/datcom/comparison_data/rockets/juno3_coast_for005.dat
index 8afda782f..6ec3725a3 100644
--- a/docs/examples/datcom/comparison_data/rockets/juno3_coast_for005.dat
+++ b/docs/examples/datcom/comparison_data/rockets/juno3_coast_for005.dat
@@ -3,13 +3,13 @@ DIM M
   NMACH = 20.,
   MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95,
     0.98,1.,1.02,1.05,1.1,1.2,
-  REN = 143857.55,863145.299,1438575.5,2591980.51,3799447.16,5066740.16,
-    6408022.21,7839759.61,9370571.41,11008799.5,12644648.1,13669889.9,
-    15378626.1,16232994.2,16745615.1,17087362.3,17429109.6,17941730.4,
-    18796098.5,20504834.8,
+  REN = 143858.879,863153.275,1438588.79,2592011.19,3799499.47,5066821.03,
+    6408126.21,7839884.7,9370731.18,11008904.1,12644649.5,13669891.3,
+    15378627.7,16232995.9,16745616.9,17087364.1,17429111.4,17941732.3,
+    18796100.5,20504837.,
   NALPHA = 19.,
-  ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14,
-    0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3,
+  ALPHA = -57.45,-28.72,-14.36,-5.75,-2.87,-1.44,-0.72,-0.29,-0.14,0.,0.14,
+    0.29,0.72,1.44,2.87,5.75,14.36,28.72,57.45,
   BETA = -20.,$
  $REFQ
   XCG = 1.477,
@@ -50,13 +50,13 @@ DIM M
   NMACH = 20.,
   MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95,
     0.98,1.,1.02,1.05,1.1,1.2,
-  REN = 143857.55,863145.299,1438575.5,2591980.51,3799447.16,5066740.16,
-    6408022.21,7839759.61,9370571.41,11008799.5,12644648.1,13669889.9,
-    15378626.1,16232994.2,16745615.1,17087362.3,17429109.6,17941730.4,
-    18796098.5,20504834.8,
+  REN = 143858.879,863153.275,1438588.79,2592011.19,3799499.47,5066821.03,
+    6408126.21,7839884.7,9370731.18,11008904.1,12644649.5,13669891.3,
+    15378627.7,16232995.9,16745616.9,17087364.1,17429111.4,17941732.3,
+    18796100.5,20504837.,
   NALPHA = 19.,
-  ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14,
-    0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3,
+  ALPHA = -57.45,-28.72,-14.36,-5.75,-2.87,-1.44,-0.72,-0.29,-0.14,0.,0.14,
+    0.29,0.72,1.44,2.87,5.75,14.36,28.72,57.45,
   BETA = -5.,$
  $REFQ
   XCG = 1.477,
@@ -97,13 +97,13 @@ DIM M
   NMACH = 20.,
   MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95,
     0.98,1.,1.02,1.05,1.1,1.2,
-  REN = 143857.55,863145.299,1438575.5,2591980.51,3799447.16,5066740.16,
-    6408022.21,7839759.61,9370571.41,11008799.5,12644648.1,13669889.9,
-    15378626.1,16232994.2,16745615.1,17087362.3,17429109.6,17941730.4,
-    18796098.5,20504834.8,
+  REN = 143858.879,863153.275,1438588.79,2592011.19,3799499.47,5066821.03,
+    6408126.21,7839884.7,9370731.18,11008904.1,12644649.5,13669891.3,
+    15378627.7,16232995.9,16745616.9,17087364.1,17429111.4,17941732.3,
+    18796100.5,20504837.,
   NALPHA = 19.,
-  ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14,
-    0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3,
+  ALPHA = -57.45,-28.72,-14.36,-5.75,-2.87,-1.44,-0.72,-0.29,-0.14,0.,0.14,
+    0.29,0.72,1.44,2.87,5.75,14.36,28.72,57.45,
   BETA = 0.,$
  $REFQ
   XCG = 1.477,
@@ -144,13 +144,13 @@ DIM M
   NMACH = 20.,
   MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95,
     0.98,1.,1.02,1.05,1.1,1.2,
-  REN = 143857.55,863145.299,1438575.5,2591980.51,3799447.16,5066740.16,
-    6408022.21,7839759.61,9370571.41,11008799.5,12644648.1,13669889.9,
-    15378626.1,16232994.2,16745615.1,17087362.3,17429109.6,17941730.4,
-    18796098.5,20504834.8,
+  REN = 143858.879,863153.275,1438588.79,2592011.19,3799499.47,5066821.03,
+    6408126.21,7839884.7,9370731.18,11008904.1,12644649.5,13669891.3,
+    15378627.7,16232995.9,16745616.9,17087364.1,17429111.4,17941732.3,
+    18796100.5,20504837.,
   NALPHA = 19.,
-  ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14,
-    0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3,
+  ALPHA = -57.45,-28.72,-14.36,-5.75,-2.87,-1.44,-0.72,-0.29,-0.14,0.,0.14,
+    0.29,0.72,1.44,2.87,5.75,14.36,28.72,57.45,
   BETA = 5.,$
  $REFQ
   XCG = 1.477,
@@ -191,13 +191,13 @@ DIM M
   NMACH = 20.,
   MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95,
     0.98,1.,1.02,1.05,1.1,1.2,
-  REN = 143857.55,863145.299,1438575.5,2591980.51,3799447.16,5066740.16,
-    6408022.21,7839759.61,9370571.41,11008799.5,12644648.1,13669889.9,
-    15378626.1,16232994.2,16745615.1,17087362.3,17429109.6,17941730.4,
-    18796098.5,20504834.8,
+  REN = 143858.879,863153.275,1438588.79,2592011.19,3799499.47,5066821.03,
+    6408126.21,7839884.7,9370731.18,11008904.1,12644649.5,13669891.3,
+    15378627.7,16232995.9,16745616.9,17087364.1,17429111.4,17941732.3,
+    18796100.5,20504837.,
   NALPHA = 19.,
-  ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14,
-    0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3,
+  ALPHA = -57.45,-28.72,-14.36,-5.75,-2.87,-1.44,-0.72,-0.29,-0.14,0.,0.14,
+    0.29,0.72,1.44,2.87,5.75,14.36,28.72,57.45,
   BETA = 20.,$
  $REFQ
   XCG = 1.477,
diff --git a/docs/examples/datcom/comparison_data/rockets/juno3_powered_for005.dat b/docs/examples/datcom/comparison_data/rockets/juno3_powered_for005.dat
index 6041235f3..5f9ece0f0 100644
--- a/docs/examples/datcom/comparison_data/rockets/juno3_powered_for005.dat
+++ b/docs/examples/datcom/comparison_data/rockets/juno3_powered_for005.dat
@@ -3,13 +3,13 @@ DIM M
   NMACH = 20.,
   MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95,
     0.98,1.,1.02,1.05,1.1,1.2,
-  REN = 180330.781,1081600.98,1801573.56,3237190.09,4664267.08,6079651.89,
-    7482495.11,8871912.88,10242729.,11580608.8,12762486.5,13797282.7,
-    15521943.,16384273.2,16901671.3,17246603.4,17591535.4,18108933.5,
-    18971263.7,20695924.,
+  REN = 180330.77,1081600.99,1801573.49,3237190.02,4664266.97,6079651.55,
+    7482494.39,8871910.05,10242724.8,11580593.,12759964.6,13794556.3,
+    15518875.9,16381035.7,16898331.5,17243195.4,17588059.3,18105355.2,
+    18967515.,20691834.5,
   NALPHA = 19.,
-  ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14,
-    0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3,
+  ALPHA = -57.45,-28.72,-14.36,-5.75,-2.87,-1.44,-0.72,-0.29,-0.14,0.,0.14,
+    0.29,0.72,1.44,2.87,5.75,14.36,28.72,57.45,
   BETA = -20.,$
  $REFQ
   XCG = 1.65017802,
@@ -50,13 +50,13 @@ DIM M
   NMACH = 20.,
   MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95,
     0.98,1.,1.02,1.05,1.1,1.2,
-  REN = 180330.781,1081600.98,1801573.56,3237190.09,4664267.08,6079651.89,
-    7482495.11,8871912.88,10242729.,11580608.8,12762486.5,13797282.7,
-    15521943.,16384273.2,16901671.3,17246603.4,17591535.4,18108933.5,
-    18971263.7,20695924.,
+  REN = 180330.77,1081600.99,1801573.49,3237190.02,4664266.97,6079651.55,
+    7482494.39,8871910.05,10242724.8,11580593.,12759964.6,13794556.3,
+    15518875.9,16381035.7,16898331.5,17243195.4,17588059.3,18105355.2,
+    18967515.,20691834.5,
   NALPHA = 19.,
-  ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14,
-    0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3,
+  ALPHA = -57.45,-28.72,-14.36,-5.75,-2.87,-1.44,-0.72,-0.29,-0.14,0.,0.14,
+    0.29,0.72,1.44,2.87,5.75,14.36,28.72,57.45,
   BETA = -5.,$
  $REFQ
   XCG = 1.65017802,
@@ -97,13 +97,13 @@ DIM M
   NMACH = 20.,
   MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95,
     0.98,1.,1.02,1.05,1.1,1.2,
-  REN = 180330.781,1081600.98,1801573.56,3237190.09,4664267.08,6079651.89,
-    7482495.11,8871912.88,10242729.,11580608.8,12762486.5,13797282.7,
-    15521943.,16384273.2,16901671.3,17246603.4,17591535.4,18108933.5,
-    18971263.7,20695924.,
+  REN = 180330.77,1081600.99,1801573.49,3237190.02,4664266.97,6079651.55,
+    7482494.39,8871910.05,10242724.8,11580593.,12759964.6,13794556.3,
+    15518875.9,16381035.7,16898331.5,17243195.4,17588059.3,18105355.2,
+    18967515.,20691834.5,
   NALPHA = 19.,
-  ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14,
-    0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3,
+  ALPHA = -57.45,-28.72,-14.36,-5.75,-2.87,-1.44,-0.72,-0.29,-0.14,0.,0.14,
+    0.29,0.72,1.44,2.87,5.75,14.36,28.72,57.45,
   BETA = 0.,$
  $REFQ
   XCG = 1.65017802,
@@ -144,13 +144,13 @@ DIM M
   NMACH = 20.,
   MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95,
     0.98,1.,1.02,1.05,1.1,1.2,
-  REN = 180330.781,1081600.98,1801573.56,3237190.09,4664267.08,6079651.89,
-    7482495.11,8871912.88,10242729.,11580608.8,12762486.5,13797282.7,
-    15521943.,16384273.2,16901671.3,17246603.4,17591535.4,18108933.5,
-    18971263.7,20695924.,
+  REN = 180330.77,1081600.99,1801573.49,3237190.02,4664266.97,6079651.55,
+    7482494.39,8871910.05,10242724.8,11580593.,12759964.6,13794556.3,
+    15518875.9,16381035.7,16898331.5,17243195.4,17588059.3,18105355.2,
+    18967515.,20691834.5,
   NALPHA = 19.,
-  ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14,
-    0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3,
+  ALPHA = -57.45,-28.72,-14.36,-5.75,-2.87,-1.44,-0.72,-0.29,-0.14,0.,0.14,
+    0.29,0.72,1.44,2.87,5.75,14.36,28.72,57.45,
   BETA = 5.,$
  $REFQ
   XCG = 1.65017802,
@@ -191,13 +191,13 @@ DIM M
   NMACH = 20.,
   MACH = 0.01,0.06,0.1,0.18,0.26,0.34,0.42,0.5,0.58,0.66,0.74,0.8,0.9,0.95,
     0.98,1.,1.02,1.05,1.1,1.2,
-  REN = 180330.781,1081600.98,1801573.56,3237190.09,4664267.08,6079651.89,
-    7482495.11,8871912.88,10242729.,11580608.8,12762486.5,13797282.7,
-    15521943.,16384273.2,16901671.3,17246603.4,17591535.4,18108933.5,
-    18971263.7,20695924.,
+  REN = 180330.77,1081600.99,1801573.49,3237190.02,4664266.97,6079651.55,
+    7482494.39,8871910.05,10242724.8,11580593.,12759964.6,13794556.3,
+    15518875.9,16381035.7,16898331.5,17243195.4,17588059.3,18105355.2,
+    18967515.,20691834.5,
   NALPHA = 19.,
-  ALPHA = -57.3,-28.65,-14.32,-5.73,-2.86,-1.43,-0.72,-0.29,-0.14,0.,0.14,
-    0.29,0.72,1.43,2.86,5.73,14.32,28.65,57.3,
+  ALPHA = -57.45,-28.72,-14.36,-5.75,-2.87,-1.44,-0.72,-0.29,-0.14,0.,0.14,
+    0.29,0.72,1.44,2.87,5.75,14.36,28.72,57.45,
   BETA = 20.,$
  $REFQ
   XCG = 1.65017802,
diff --git a/docs/examples/datcom/comparison_data/rockets/juno3_provenance.json b/docs/examples/datcom/comparison_data/rockets/juno3_provenance.json
index c649e17dc..3c28afdef 100644
--- a/docs/examples/datcom/comparison_data/rockets/juno3_provenance.json
+++ b/docs/examples/datcom/comparison_data/rockets/juno3_provenance.json
@@ -19,7 +19,10 @@
     "roughness_height": 0.0,
     "leading_edge_radius": null
   },
-  "source_adaptations": [],
+  "source_adaptations": [
+    "Source cell 14: preserve the notebook's post-construction power_on_drag update in the seven-input solver coefficient.",
+    "Source cell 14: preserve the notebook's post-construction power_off_drag update in the seven-input solver coefficient."
+  ],
   "mach": [
     0.01,
     0.06,
@@ -43,12 +46,12 @@
     1.2
   ],
   "alpha": [
-    -57.3,
-    -28.65,
-    -14.32,
-    -5.73,
-    -2.86,
-    -1.43,
+    -57.45,
+    -28.72,
+    -14.36,
+    -5.75,
+    -2.87,
+    -1.44,
     -0.72,
     -0.29,
     -0.14,
@@ -56,12 +59,12 @@
     0.14,
     0.29,
     0.72,
-    1.43,
-    2.86,
-    5.73,
-    14.32,
-    28.65,
-    57.3
+    1.44,
+    2.87,
+    5.75,
+    14.36,
+    28.72,
+    57.45
   ],
   "beta": [
     -20,
@@ -72,48 +75,48 @@
   ],
   "altitude_schedules_m_asl": {
     "power_on": [
-      1480.0301125983062,
-      1484.9372871144653,
-      1493.3371400612946,
-      1517.3836013704854,
-      1551.8151814892797,
-      1596.3918233423174,
-      1646.8431660544736,
-      1701.6803675864062,
-      1767.236330637347,
-      1859.907597197272,
-      2104.830486187533,
-      2104.830486187533,
-      2104.830486187533,
-      2104.830486187533,
-      2104.830486187533,
-      2104.830486187533,
-      2104.830486187533,
-      2104.830486187533,
-      2104.830486187533,
-      2104.830486187533
+      1480.0308852969265,
+      1484.9372496431731,
+      1493.3376469402508,
+      1517.3839151518675,
+      1551.8155145727126,
+      1596.3926006677943,
+      1646.844470666282,
+      1701.6847061018198,
+      1767.242131664186,
+      1859.927079933004,
+      2107.6257314026493,
+      2107.6257314026493,
+      2107.6257314026493,
+      2107.6257314026493,
+      2107.6257314026493,
+      2107.6257314026493,
+      2107.6257314026493,
+      2107.6257314026493,
+      2107.6257314026493,
+      2107.6257314026493
     ],
     "power_off": [
-      4536.53079550082,
-      4536.53079550082,
-      4536.53079550082,
-      4523.974967429359,
-      4337.060931776755,
-      4084.9181693858245,
-      3774.6552006990096,
-      3413.450629964633,
-      3008.816974053171,
-      2569.666227996419,
-      2235.3283230909747,
-      2235.3283230909747,
-      2235.3283230909747,
-      2235.3283230909747,
-      2235.3283230909747,
-      2235.3283230909747,
-      2235.3283230909747,
-      2235.3283230909747,
-      2235.3283230909747,
-      2235.3283230909747
+      4536.41273537114,
+      4536.41273537114,
+      4536.41273537114,
+      4523.823594739011,
+      4336.885689777673,
+      4084.710172073903,
+      3774.443967172133,
+      3413.2360749388536,
+      3008.5865451299906,
+      2569.5363596972693,
+      2235.326834077212,
+      2235.326834077212,
+      2235.326834077212,
+      2235.326834077212,
+      2235.326834077212,
+      2235.326834077212,
+      2235.326834077212,
+      2235.326834077212,
+      2235.326834077212,
+      2235.326834077212
     ]
   },
   "atmosphere": {
@@ -140,12 +143,12 @@
       1.2
     ],
     "alpha": [
-      -57.3,
-      -28.65,
-      -14.32,
-      -5.73,
-      -2.86,
-      -1.43,
+      -57.45,
+      -28.72,
+      -14.36,
+      -5.75,
+      -2.87,
+      -1.44,
       -0.72,
       -0.29,
       -0.14,
@@ -153,224 +156,224 @@
       0.14,
       0.29,
       0.72,
-      1.43,
-      2.86,
-      5.73,
-      14.32,
-      28.65,
-      57.3
+      1.44,
+      2.87,
+      5.75,
+      14.36,
+      28.72,
+      57.45
     ],
     "coverage": {
       "scope": "rail through apogee",
       "minimum_dynamic_pressure_pa": 100.0,
-      "excluded_low_q_samples": 262,
+      "excluded_low_q_samples": 251,
       "phases": {
         "power_on": {
-          "mach_min": 0.03058696725034305,
-          "mach_max": 0.7313022533685333,
-          "loaded_alpha_max_abs_deg": 48.08409144146688
+          "mach_min": 0.03058698441914193,
+          "mach_max": 0.7312885446754361,
+          "loaded_alpha_max_abs_deg": 48.21001727672974
         },
         "power_off": {
-          "mach_min": 0.17230685542875226,
-          "mach_max": 0.7180316852531115,
-          "loaded_alpha_max_abs_deg": 0.12903752556338202
+          "mach_min": 0.17228838342692263,
+          "mach_max": 0.718017794863697,
+          "loaded_alpha_max_abs_deg": 0.1286228981726786
         }
       }
     },
     "reynolds": {
       "power_on": [
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     "altitude_m_asl": {
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     "pressure_pa": {
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   },
   "atmosphere_iterations": [
     {
       "pass": 0,
-      "apogee_agl_m": 3056.53079550082,
+      "apogee_agl_m": 3056.41273537114,
       "reference": "ordinary trajectory"
     },
     {
       "pass": 1,
-      "apogee_agl_m": 3056.4921836297026,
+      "apogee_agl_m": 3056.5496067381873,
       "reference": "previous DATCOM trajectory"
     }
   ],
diff --git a/docs/examples/datcom/comparison_data/rockets/juno3_result.json b/docs/examples/datcom/comparison_data/rockets/juno3_result.json
index 95aebc9d8..bea7a2b2f 100644
--- a/docs/examples/datcom/comparison_data/rockets/juno3_result.json
+++ b/docs/examples/datcom/comparison_data/rockets/juno3_result.json
@@ -8,41 +8,41 @@
   "notes": "Telemetry QA flags 2 samples with altitude jumps above 2000 m/s; raw maximum 10700.590 m is retained separately. | Telemetry maximum 10700.590 m differs from notebook reported value 3213.000 m; both retained. | Source fin airfoil is an empirical lift curve (cell 10), not a supplied section contour. The generic 2% HEX DATCOM assumption does not reproduce that curve; source-drag substitution therefore still has a lateral-model discrepancy.",
   "status": "paired untuned comparison",
   "reason": "",
-  "ordinary_agl_m": 3008.581569789987,
-  "ordinary_apogee_time_s": 26.491920030008096,
-  "ordinary_peak_vertical_velocity_m_s": 241.2427490025272,
+  "ordinary_agl_m": 2987.2806591791605,
+  "ordinary_apogee_time_s": 26.377738526215797,
+  "ordinary_peak_vertical_velocity_m_s": 240.91733213924152,
   "ordinary_initial_static_margin_cal": 3.2206482971493466,
-  "ordinary_coast_cd_mach06": 0.368,
-  "ordinary_max_mach": 0.7308763878428423,
-  "ordinary_ascent_altitude_rmse_m": 229.55884227968258,
-  "source_adaptations": "",
+  "ordinary_coast_cd_mach06": 0.37999999999999995,
+  "ordinary_max_mach": 0.7298289434237538,
+  "ordinary_ascent_altitude_rmse_m": 236.31153566230557,
+  "source_adaptations": "Source cell 14: preserve the notebook's post-construction power_on_drag update in the seven-input solver coefficient. | Source cell 14: preserve the notebook's post-construction power_off_drag update in the seven-input solver coefficient.",
   "atmosphere_refinements": 1,
-  "atmosphere_last_apogee_change_m": -0.03861187111760955,
-  "datcom_agl_m": 3056.4921836297026,
-  "datcom_apogee_time_s": 26.635000914699194,
-  "datcom_peak_vertical_velocity_m_s": 243.89859046993098,
+  "atmosphere_last_apogee_change_m": 0.13687136704720615,
+  "datcom_agl_m": 3056.5496067381873,
+  "datcom_apogee_time_s": 26.635240533339015,
+  "datcom_peak_vertical_velocity_m_s": 243.90063624160936,
   "datcom_initial_static_margin_cal": 4.0067340523842745,
   "datcom_coast_cd_mach06": 0.37450000000000006,
-  "datcom_max_mach": 0.7312932218217086,
-  "datcom_ascent_altitude_rmse_m": 209.292777372015,
+  "datcom_max_mach": 0.7312940789165373,
+  "datcom_ascent_altitude_rmse_m": 209.27254149468092,
   "samples": 486,
   "outside_mach": 0,
   "outside_alpha": 0,
   "outside_beta": 0,
-  "max_abs_alpha_deg": 43.59333257202937,
-  "max_abs_beta_deg": 5.4737412566148915,
+  "max_abs_alpha_deg": 43.59273692070459,
+  "max_abs_beta_deg": 5.480286417702728,
   "solver_mach_excursion": false,
   "solver_alpha_excursion": true,
   "solver_beta_excursion": true,
   "mass_cg_match": true,
-  "ordinary_error_m": -204.41843021001296,
-  "ordinary_error_pct": -6.362229387177496,
-  "datcom_error_m": -156.5078163702974,
-  "datcom_error_pct": -4.871080497052518,
+  "ordinary_error_m": -225.71934082083953,
+  "ordinary_error_pct": -7.025189568031109,
+  "datcom_error_m": -156.45039326181268,
+  "datcom_error_pct": -4.8692932854594675,
   "telemetry_qa_max_agl_m": 3213.375,
   "telemetry_qa_rejected_count": 2,
-  "ordinary_vs_telemetry_pct": -6.373156889874753,
-  "datcom_vs_telemetry_pct": -4.882182016425018,
+  "ordinary_vs_telemetry_pct": -7.036039703453212,
+  "datcom_vs_telemetry_pct": -4.880395013399075,
   "rmse_start_s": 0,
   "rmse_end_s": 26.3,
   "within_sampled_envelope": true
diff --git a/docs/examples/datcom/comparison_data/rockets/results.csv b/docs/examples/datcom/comparison_data/rockets/results.csv
index 0334e6233..2cc112e72 100644
--- a/docs/examples/datcom/comparison_data/rockets/results.csv
+++ b/docs/examples/datcom/comparison_data/rockets/results.csv
@@ -1,7 +1,7 @@
 rocket,actual_agl_m,reported_agl_m,saved_first_agl_m,reference_kind,telemetry_max_agl_m,notes,status,reason,ordinary_agl_m,ordinary_apogee_time_s,ordinary_peak_vertical_velocity_m_s,ordinary_initial_static_margin_cal,ordinary_coast_cd_mach06,ordinary_max_mach,ordinary_ascent_altitude_rmse_m,source_adaptations,atmosphere_refinements,atmosphere_last_apogee_change_m,datcom_agl_m,datcom_apogee_time_s,datcom_peak_vertical_velocity_m_s,datcom_initial_static_margin_cal,datcom_coast_cd_mach06,datcom_max_mach,datcom_ascent_altitude_rmse_m,samples,outside_mach,outside_alpha,outside_beta,max_abs_alpha_deg,max_abs_beta_deg,solver_mach_excursion,solver_alpha_excursion,solver_beta_excursion,mass_cg_match,ordinary_error_m,ordinary_error_pct,datcom_error_m,datcom_error_pct,telemetry_qa_max_agl_m,telemetry_qa_rejected_count,ordinary_vs_telemetry_pct,datcom_vs_telemetry_pct,rmse_start_s,rmse_end_s,within_sampled_envelope
 andromeda,3443.0,3443.0,3614.946,notebook reported apogee,3438.549384,Telemetry maximum 3438.549 m differs from notebook reported value 3443.000 m; both retained.,paired untuned comparison,,3615.880567942097,25.843083317328176,343.1543305235018,4.158769051891837,0.4984983643122677,1.0050934520481516,164.47060925045412,,1.0,-6.890912629645754,4170.733199067169,28.13329482306857,352.6406104190726,4.585569601745527,0.349,1.0331483789732863,438.15234503512323,497.0,0.0,0.0,0.0,4.455101471012898,3.9438542480866237,True,True,True,True,172.88056794209706,5.02121893529181,727.7331990671692,21.13660177366161,3438.549384,0.0,5.157150999989746,21.293392454216665,0.0,25.83574241251797,True
 astra,3250.0,3250.0,3043.066,notebook reported apogee,3249.46,Telemetry maximum 3249.460 m differs from notebook reported value 3250.000 m; both retained. | Source fin trailing edge overhangs the nozzle-inferred body base by 1.216 mm. Native DATCOM accepts this; the integration now validates root-chord centre attachment instead of rejecting all overhang. Geometry is unchanged.,paired untuned comparison,,3043.3679728130164,24.008756329078313,306.4797793783156,3.16256293861153,0.6538147999999999,0.892926042224684,222.49037923280986,,1.0,-4.5530442029007645,3695.9340692825294,26.805544357641757,318.5471261391443,3.477476916191179,0.4265,0.92839198452266,215.54458729050853,480.0,0.0,0.0,0.0,8.95582043567763,9.383306642057704,True,True,True,True,-206.63202718698358,-6.357908528830264,445.93406928252944,13.721048285616291,3249.46,0.0,-6.342346949554189,13.739946615207742,0.0,23.990962200089378,True
-bella_lui,458.971,,460.54,telemetry AGL maximum (QA),458.971,,paired untuned comparison,,460.57389565932874,10.607916503445066,86.17870117421616,2.776966165584537,0.43,0.2544470871701163,4.730090709810138,,1.0,9.736509809954441e-06,465.8765076067215,10.675120658701045,86.47349849471236,3.303739452169553,0.3457692307692308,0.2554070587748375,5.162903677983363,427.0,0.0,0.0,0.0,4.441613641629917,2.9112883549730375,True,True,True,True,1.6028956593287376,0.34923680566500664,6.905507606721471,1.5045629477072564,458.971,0.0,0.3492368056650097,1.5045629477072664,0.0,10.53,True
+bella_lui,458.971,,460.54,telemetry AGL maximum (QA),458.971,,paired untuned comparison,,460.5523116828233,10.607630761324499,86.17634365334754,2.776966165584537,0.46,0.25444012331502597,4.732669828684498,Source cell 25: preserve the notebook's post-construction power_off_drag update in the seven-input solver coefficient. | Source cell 25: preserve the notebook's post-construction power_on_drag update in the seven-input solver coefficient. | Source cell 25: preserve the notebook's post-construction power_off_drag update in the seven-input solver coefficient. | Source cell 25: preserve the notebook's post-construction power_on_drag update in the seven-input solver coefficient.,1.0,9.736509809954441e-06,465.8765076067215,10.675120658701045,86.47349849471236,3.303739452169553,0.3457692307692308,0.2554070587748375,5.162903677983363,427.0,0.0,0.0,0.0,4.441613641629917,2.9112883549730375,True,True,True,True,1.5813116828232978,0.3445341171497323,6.905507606721471,1.5045629477072564,458.971,0.0,0.3445341171497418,1.5045629477072664,0.0,10.53,True
 camoes,3015.0,3015.0,3003.284,notebook reported apogee,3015.996337890625,Telemetry maximum 3015.996 m differs from notebook reported value 3015.000 m; both retained.,ordinary only,Source active air brakes override complete rocket drag. Combining their whole-vehicle CD with DATCOM would double-count base drag; no controlled DATCOM result claimed.,3004.10382797431,23.03691562903868,327.4402911475654,3.7616831285769776,0.7036,0.9672822932315445,133.14444925478682,,,,,,,,,,,,,,,,,,,,,-10.896172025689793,-0.36139874048722365,,,3015.996337890625,0.0,-0.3943144680551014,,0.0,23.03691562903868,
 cavour,2789.0,2789.0,2817.405,notebook reported apogee,2789.0,,paired untuned comparison,,2818.1348894226226,22.52457463326182,304.6396378918068,4.279860429594885,0.583,0.9017549966473172,57.14208739821666,,1.0,-2.7223603801689933,3251.608548140568,24.312660883196,319.05542973964634,5.465305853006995,0.44825000000000004,0.9445859958835042,284.6998716927426,497.0,0.0,0.0,0.0,1.9473736165566062,12.065683729942409,True,True,True,True,29.134889422622564,1.0446356910226806,462.608548140568,16.58689667051158,2789.0,0.0,1.0446356910226884,16.586896670511585,0.0,22.489211316956297,True
 defiance,9308.318,9308.318,9238.009,notebook reported apogee,,"Source fin root centre is z=-0.025 m, aft of the tail base z=0.0403 m; native root attachment is outside the declared body.",ordinary only,ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,9243.891367330605,44.18119446865966,438.71139566437614,7.9601902493275025,0.668,1.3681759374499065,,,,,,,,,,,,,,,,,,,,,,-64.42663266939417,-0.6921404347100536,,,,,,,,,
@@ -9,7 +9,7 @@ erebus,3020.0,3020.0,2750.904,notebook reported apogee,3001.75136,Telemetry maxi
 genesis,2916.0,2916.0,3044.091,notebook reported apogee,2916.691650390625,Telemetry maximum 2916.692 m differs from notebook reported value 2916.000 m; both retained.,paired untuned comparison,,3044.6857862664306,24.170070527773692,290.6363275291183,3.37721309359005,0.55,0.8522776236704187,104.35940713968827,,1.0,-2.088661131954723,3297.2391270686403,25.178132490879275,299.2620317383252,3.7682700796928534,0.47475,0.8776710952854886,236.1872905681145,497.0,0.0,0.0,0.0,3.3632935153000667,11.894942323656261,True,True,True,True,128.68578626643057,4.41309280749076,381.2391270686403,13.074044138156388,2916.691650390625,0.0,4.38833278309223,13.047230296937617,0.0,24.12904363709264,True
 halcyon,3450.0,3450.0,3212.856,notebook reported apogee,3470.8251953125,Telemetry QA flags 5 samples with altitude jumps above 2000 m/s; raw maximum 3470.825 m is retained separately. | Telemetry maximum 3470.825 m differs from notebook reported value 3450.000 m; both retained.,paired untuned comparison,,3214.238895757538,26.371836502851696,275.5714950426811,3.0192578745448033,0.426,0.812113342368714,112.69062830123777,Read original .eng numeric samples directly: source already has t=0; avoid the importer's extra duplicate zero-time row. No measured sample or motor equation changed.,1.0,-1.122512222840669,3530.231782008296,27.79754660231069,280.94965050790404,4.410132986423363,0.319,0.8293761850832471,69.46314134752838,495.0,0.0,0.0,0.0,7.051663487140428,14.276458017233338,False,True,True,True,-235.7611042424619,-6.8336551954336775,80.2317820082958,2.3255588987911824,3470.8251953125,5.0,-7.39265981765641,1.7115983477366425,0.0,26.36134002785797,True
 hedy,5231.53,,5246.918,saved measured-apogee summary,,"Referenced telemetry file is absent: data/rockets/tust/cats_tust/filteredDataInfo.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/gnssInfo.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/flightInfo.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/imu.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/baro.csv (cell 24). | Hedy end comparison uses simulated ASL 5406.918 m against measured AGL 5231.53 m; saved simulated AGL is 5246.918 m, a 160 m datum discrepancy.",archived comparison only,"Source data/rockets/tust is absent; CoolProp is not installed, and the negative-span fin root cutout requires geometry support.",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-juno3,3213.0,3213.0,2976.849,notebook reported apogee,10700.59,"Telemetry QA flags 2 samples with altitude jumps above 2000 m/s; raw maximum 10700.590 m is retained separately. | Telemetry maximum 10700.590 m differs from notebook reported value 3213.000 m; both retained. | Source fin airfoil is an empirical lift curve (cell 10), not a supplied section contour. The generic 2% HEX DATCOM assumption does not reproduce that curve; source-drag substitution therefore still has a lateral-model discrepancy.",paired untuned comparison,,3008.581569789987,26.491920030008096,241.2427490025272,3.2206482971493466,0.368,0.7308763878428423,229.55884227968258,,1.0,-0.03861187111760955,3056.4921836297026,26.635000914699194,243.89859046993098,4.0067340523842745,0.37450000000000006,0.7312932218217086,209.292777372015,486.0,0.0,0.0,0.0,43.59333257202937,5.4737412566148915,False,True,True,True,-204.41843021001296,-6.362229387177496,-156.5078163702974,-4.871080497052518,3213.375,2.0,-6.373156889874753,-4.882182016425018,0.0,26.3,True
+juno3,3213.0,3213.0,2976.849,notebook reported apogee,10700.59,"Telemetry QA flags 2 samples with altitude jumps above 2000 m/s; raw maximum 10700.590 m is retained separately. | Telemetry maximum 10700.590 m differs from notebook reported value 3213.000 m; both retained. | Source fin airfoil is an empirical lift curve (cell 10), not a supplied section contour. The generic 2% HEX DATCOM assumption does not reproduce that curve; source-drag substitution therefore still has a lateral-model discrepancy.",paired untuned comparison,,2987.2806591791605,26.377738526215797,240.91733213924152,3.2206482971493466,0.37999999999999995,0.7298289434237538,236.31153566230557,Source cell 14: preserve the notebook's post-construction power_on_drag update in the seven-input solver coefficient. | Source cell 14: preserve the notebook's post-construction power_off_drag update in the seven-input solver coefficient.,1.0,0.13687136704720615,3056.5496067381873,26.635240533339015,243.90063624160936,4.0067340523842745,0.37450000000000006,0.7312940789165373,209.27254149468092,486.0,0.0,0.0,0.0,43.59273692070459,5.480286417702728,False,True,True,True,-225.71934082083953,-7.025189568031109,-156.45039326181268,-4.8692932854594675,3213.375,2.0,-7.036039703453212,-4.880395013399075,0.0,26.3,True
 lince,3587.0,3587.0,3284.107,notebook reported apogee,3668.5205078125,Telemetry QA flags 69 samples with altitude jumps above 2000 m/s; raw maximum 3668.521 m is retained separately. | Telemetry maximum 3668.521 m differs from notebook reported value 3587.000 m; both retained. | Lince has three saved trajectories. Compare first/pre-deployment ascent only; retain all saved apogees separately.,paired untuned comparison,,3285.089381541881,24.728341685630557,317.77081353823803,2.9203959364705625,0.65562,0.936438877536046,180.22020526338466,,1.0,-6.875566491943573,4254.30425999591,28.7853935112826,342.53953112405384,3.962666916625163,0.38175,1.0102057901013621,299.53187203569485,498.0,0.0,0.0,0.0,3.100325929162926,3.920227060235464,True,True,True,True,-301.9106184581192,-8.416800068528554,667.3042599959099,18.60340841917786,3628.76416015625,69.0,-9.470849122351643,17.23837847353311,0.0,24.7074627638509,True
 ndrt_2020,1316.736,1316.736,1293.43,notebook reported apogee,1320.3570240000001,"NDRT source uses approximate ft conversion /3.281; comparison uses exact 0.3048 m/ft. | Telemetry maximum 1320.357 m differs from notebook reported value 1316.736 m; both retained. | Source transition is internal at z=1.2..1.327 m with fins near z=3.05 m. The current DATCOM builder represents only an aft tail, not the required downstream smaller cylinder.",ordinary only,ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip,1293.5184123380945,16.64978369813489,171.66118644734854,4.016381564609376,0.44,0.52460108376313,42.9525631238651,,,,,,,,,,,,,,,,,,,,,-23.217587661905554,-1.763268237665375,,,1320.3570240000001,0.0,-2.0326783721419828,,0.04375,16.64978369813489,
 prometheus_2022,3898.37,3898.37,4188.81,notebook reported apogee,3903.77,Telemetry QA flags 2 samples with altitude jumps above 2000 m/s; raw maximum 3903.770 m is retained separately. | Telemetry maximum 3903.770 m differs from notebook reported value 3898.370 m; both retained. | Prometheus prose says June 2022; source environment and local reanalysis are June 2023. Both reruns preserve source conditions.,paired untuned comparison,,4191.288687549948,29.12493947732805,322.07071634939507,1.0415367273926033,0.351875,0.955532649219189,271.52675273853276,,1.0,0.09233800101083034,4229.19676461329,29.30969600583538,324.05991606871703,1.7889093089472907,0.33475000000000005,0.9614276912301178,287.18960219155747,497.0,0.0,0.0,0.0,0.7103949840277883,0.745924092897197,True,True,True,True,292.9186875499481,7.513875993041916,330.8267646132899,8.486284385866142,3903.77,2.0,7.365154390498119,8.33621767197581,0.0,29.114298032463147,True
diff --git a/docs/examples/datcom/rocket_comparison.ipynb b/docs/examples/datcom/rocket_comparison.ipynb
index 0a17f84a0..7fb5736dc 100644
--- a/docs/examples/datcom/rocket_comparison.ipynb
+++ b/docs/examples/datcom/rocket_comparison.ipynb
@@ -15,7 +15,11 @@
     "\n",
     "Both models retain source geometry, motor, mass, atmosphere and launch inputs. DATCOM uses DAMP, powered nozzle-annulus/coast full-base drag and adaptive 20-point Mach grids containing 0.1 and 1.0, with dense transonic points. Signed alpha grids retain at least ±20° and update with the reference trajectory. The ordinary flight seeds actual-atmosphere Reynolds schedules; one DATCOM trajectory update follows. Scope is rail through apogee. Unspecified construction is carbon fibre with vinyl; it supplies no measured roughness or fin section. The untuned comparisons assume smooth 2% HEX sections with 5% bevels and sharp edges; native coefficients are not fitted to apogee.\n",
     "\n",
-    "Source notebook and data hashes, measurement provenance, masks, fitting residuals and availability reasons are saved in `comparison_data/`. Hedy/Valkyrie apogees use archived ordinary outputs where fresh reproduction is unavailable. **AST-HER has not flown** and is compared separately.\n"
+    "Source notebook and data hashes, measurement provenance, masks, fitting residuals and availability reasons are saved in `comparison_data/`. Hedy/Valkyrie apogees use archived ordinary outputs where fresh reproduction is unavailable. **AST-HER has not flown** and is compared separately.\n",
+    "\n",
+    "**Manual audit:** `comparison_data/datcom_usage_audit.json` records independent guide-example, equivalent-deck and nozzle-sensitivity checks. A corrected `PART` damping-parser bug does not change these saved runs, which omit `PART`. GENESIS and Bella Lui nozzle inputs need hardware verification; full powered base drag still leaves GENESIS 10.5% above its recorded apogee. The source motor radii are not established nozzle measurements. Fin section and finish assumptions remain unverified.\n",
+    "\n",
+    "**Fin-section sensitivity:** Additional native runs in `comparison_data/fin_parameter_audit.json` test the unspecified trailing-edge shape. A blunt USER hypothesis gives GENESIS 3025 m and Astra 3378 m, versus recorded 2916 m and 3250 m; a sharp USER control separates the edge effect from HEX/USER method differences. These are unverified geometry assumptions, without fitting, and do not replace the baseline results below.\n"
    ]
   },
   {
@@ -24,10 +28,10 @@
    "id": "d9806fcc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-22T22:26:49.293282Z",
-     "iopub.status.busy": "2026-10-22T22:26:49.293114Z",
-     "iopub.status.idle": "2026-10-22T22:26:50.248546Z",
-     "shell.execute_reply": "2026-10-22T22:26:50.248230Z"
+     "iopub.execute_input": "2026-10-24T20:16:02.174240Z",
+     "iopub.status.busy": "2026-10-24T20:16:02.174181Z",
+     "iopub.status.idle": "2026-10-24T20:16:02.841588Z",
+     "shell.execute_reply": "2026-10-24T20:16:02.841355Z"
     }
    },
    "outputs": [],
@@ -61,10 +65,10 @@
    "id": "bc74a0fd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-22T22:26:50.250187Z",
-     "iopub.status.busy": "2026-10-22T22:26:50.250036Z",
-     "iopub.status.idle": "2026-10-22T22:26:50.296816Z",
-     "shell.execute_reply": "2026-10-22T22:26:50.296487Z"
+     "iopub.execute_input": "2026-10-24T20:16:02.842670Z",
+     "iopub.status.busy": "2026-10-24T20:16:02.842608Z",
+     "iopub.status.idle": "2026-10-24T20:16:02.872899Z",
+     "shell.execute_reply": "2026-10-24T20:16:02.872711Z"
     }
    },
    "outputs": [
@@ -73,168 +77,168 @@
       "text/html": [
        "<style type=\"text/css\">\n",
        "</style>\n",
-       "<table id=\"T_6acf3\">\n",
+       "<table id=\"T_9a942\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_6acf3_level0_col0\" class=\"col_heading level0 col0\" >rocket</th>\n",
-       "      <th id=\"T_6acf3_level0_col1\" class=\"col_heading level0 col1\" >measured</th>\n",
-       "      <th id=\"T_6acf3_level0_col2\" class=\"col_heading level0 col2\" >rocketpy</th>\n",
-       "      <th id=\"T_6acf3_level0_col3\" class=\"col_heading level0 col3\" >datcom</th>\n",
-       "      <th id=\"T_6acf3_level0_col4\" class=\"col_heading level0 col4\" >rocketpy_error_pct</th>\n",
-       "      <th id=\"T_6acf3_level0_col5\" class=\"col_heading level0 col5\" >datcom_error_pct</th>\n",
+       "      <th id=\"T_9a942_level0_col0\" class=\"col_heading level0 col0\" >rocket</th>\n",
+       "      <th id=\"T_9a942_level0_col1\" class=\"col_heading level0 col1\" >measured</th>\n",
+       "      <th id=\"T_9a942_level0_col2\" class=\"col_heading level0 col2\" >rocketpy</th>\n",
+       "      <th id=\"T_9a942_level0_col3\" class=\"col_heading level0 col3\" >datcom</th>\n",
+       "      <th id=\"T_9a942_level0_col4\" class=\"col_heading level0 col4\" >rocketpy_error_pct</th>\n",
+       "      <th id=\"T_9a942_level0_col5\" class=\"col_heading level0 col5\" >datcom_error_pct</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_6acf3_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
-       "      <td id=\"T_6acf3_row0_col0\" class=\"data row0 col0\" >andromeda</td>\n",
-       "      <td id=\"T_6acf3_row0_col1\" class=\"data row0 col1\" >3443.000</td>\n",
-       "      <td id=\"T_6acf3_row0_col2\" class=\"data row0 col2\" >3615.881</td>\n",
-       "      <td id=\"T_6acf3_row0_col3\" class=\"data row0 col3\" >4170.733</td>\n",
-       "      <td id=\"T_6acf3_row0_col4\" class=\"data row0 col4\" >5.021</td>\n",
-       "      <td id=\"T_6acf3_row0_col5\" class=\"data row0 col5\" >21.137</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6acf3_level0_row1\" class=\"row_heading level0 row1\" >5</th>\n",
-       "      <td id=\"T_6acf3_row1_col0\" class=\"data row1 col0\" >astra</td>\n",
-       "      <td id=\"T_6acf3_row1_col1\" class=\"data row1 col1\" >3250.000</td>\n",
-       "      <td id=\"T_6acf3_row1_col2\" class=\"data row1 col2\" >3043.368</td>\n",
-       "      <td id=\"T_6acf3_row1_col3\" class=\"data row1 col3\" >3695.934</td>\n",
-       "      <td id=\"T_6acf3_row1_col4\" class=\"data row1 col4\" >-6.358</td>\n",
-       "      <td id=\"T_6acf3_row1_col5\" class=\"data row1 col5\" >13.721</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6acf3_level0_row2\" class=\"row_heading level0 row2\" >10</th>\n",
-       "      <td id=\"T_6acf3_row2_col0\" class=\"data row2 col0\" >bella_lui</td>\n",
-       "      <td id=\"T_6acf3_row2_col1\" class=\"data row2 col1\" >458.971</td>\n",
-       "      <td id=\"T_6acf3_row2_col2\" class=\"data row2 col2\" >460.574</td>\n",
-       "      <td id=\"T_6acf3_row2_col3\" class=\"data row2 col3\" >465.877</td>\n",
-       "      <td id=\"T_6acf3_row2_col4\" class=\"data row2 col4\" >0.349</td>\n",
-       "      <td id=\"T_6acf3_row2_col5\" class=\"data row2 col5\" >1.505</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6acf3_level0_row3\" class=\"row_heading level0 row3\" >15</th>\n",
-       "      <td id=\"T_6acf3_row3_col0\" class=\"data row3 col0\" >camoes</td>\n",
-       "      <td id=\"T_6acf3_row3_col1\" class=\"data row3 col1\" >3015.000</td>\n",
-       "      <td id=\"T_6acf3_row3_col2\" class=\"data row3 col2\" >3004.104</td>\n",
-       "      <td id=\"T_6acf3_row3_col3\" class=\"data row3 col3\" >—</td>\n",
-       "      <td id=\"T_6acf3_row3_col4\" class=\"data row3 col4\" >-0.361</td>\n",
-       "      <td id=\"T_6acf3_row3_col5\" class=\"data row3 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6acf3_level0_row4\" class=\"row_heading level0 row4\" >20</th>\n",
-       "      <td id=\"T_6acf3_row4_col0\" class=\"data row4 col0\" >cavour</td>\n",
-       "      <td id=\"T_6acf3_row4_col1\" class=\"data row4 col1\" >2789.000</td>\n",
-       "      <td id=\"T_6acf3_row4_col2\" class=\"data row4 col2\" >2818.135</td>\n",
-       "      <td id=\"T_6acf3_row4_col3\" class=\"data row4 col3\" >3251.609</td>\n",
-       "      <td id=\"T_6acf3_row4_col4\" class=\"data row4 col4\" >1.045</td>\n",
-       "      <td id=\"T_6acf3_row4_col5\" class=\"data row4 col5\" >16.587</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6acf3_level0_row5\" class=\"row_heading level0 row5\" >25</th>\n",
-       "      <td id=\"T_6acf3_row5_col0\" class=\"data row5 col0\" >defiance</td>\n",
-       "      <td id=\"T_6acf3_row5_col1\" class=\"data row5 col1\" >9308.318</td>\n",
-       "      <td id=\"T_6acf3_row5_col2\" class=\"data row5 col2\" >9243.891</td>\n",
-       "      <td id=\"T_6acf3_row5_col3\" class=\"data row5 col3\" >—</td>\n",
-       "      <td id=\"T_6acf3_row5_col4\" class=\"data row5 col4\" >-0.692</td>\n",
-       "      <td id=\"T_6acf3_row5_col5\" class=\"data row5 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6acf3_level0_row6\" class=\"row_heading level0 row6\" >30</th>\n",
-       "      <td id=\"T_6acf3_row6_col0\" class=\"data row6 col0\" >erebus</td>\n",
-       "      <td id=\"T_6acf3_row6_col1\" class=\"data row6 col1\" >3020.000</td>\n",
-       "      <td id=\"T_6acf3_row6_col2\" class=\"data row6 col2\" >2751.156</td>\n",
-       "      <td id=\"T_6acf3_row6_col3\" class=\"data row6 col3\" >3253.423</td>\n",
-       "      <td id=\"T_6acf3_row6_col4\" class=\"data row6 col4\" >-8.902</td>\n",
-       "      <td id=\"T_6acf3_row6_col5\" class=\"data row6 col5\" >7.729</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6acf3_level0_row7\" class=\"row_heading level0 row7\" >35</th>\n",
-       "      <td id=\"T_6acf3_row7_col0\" class=\"data row7 col0\" >genesis</td>\n",
-       "      <td id=\"T_6acf3_row7_col1\" class=\"data row7 col1\" >2916.000</td>\n",
-       "      <td id=\"T_6acf3_row7_col2\" class=\"data row7 col2\" >3044.686</td>\n",
-       "      <td id=\"T_6acf3_row7_col3\" class=\"data row7 col3\" >3297.239</td>\n",
-       "      <td id=\"T_6acf3_row7_col4\" class=\"data row7 col4\" >4.413</td>\n",
-       "      <td id=\"T_6acf3_row7_col5\" class=\"data row7 col5\" >13.074</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6acf3_level0_row8\" class=\"row_heading level0 row8\" >40</th>\n",
-       "      <td id=\"T_6acf3_row8_col0\" class=\"data row8 col0\" >halcyon</td>\n",
-       "      <td id=\"T_6acf3_row8_col1\" class=\"data row8 col1\" >3450.000</td>\n",
-       "      <td id=\"T_6acf3_row8_col2\" class=\"data row8 col2\" >3214.239</td>\n",
-       "      <td id=\"T_6acf3_row8_col3\" class=\"data row8 col3\" >3530.232</td>\n",
-       "      <td id=\"T_6acf3_row8_col4\" class=\"data row8 col4\" >-6.834</td>\n",
-       "      <td id=\"T_6acf3_row8_col5\" class=\"data row8 col5\" >2.326</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6acf3_level0_row9\" class=\"row_heading level0 row9\" >45</th>\n",
-       "      <td id=\"T_6acf3_row9_col0\" class=\"data row9 col0\" >hedy</td>\n",
-       "      <td id=\"T_6acf3_row9_col1\" class=\"data row9 col1\" >5231.530</td>\n",
-       "      <td id=\"T_6acf3_row9_col2\" class=\"data row9 col2\" >5246.918</td>\n",
-       "      <td id=\"T_6acf3_row9_col3\" class=\"data row9 col3\" >—</td>\n",
-       "      <td id=\"T_6acf3_row9_col4\" class=\"data row9 col4\" >0.294</td>\n",
-       "      <td id=\"T_6acf3_row9_col5\" class=\"data row9 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6acf3_level0_row10\" class=\"row_heading level0 row10\" >49</th>\n",
-       "      <td id=\"T_6acf3_row10_col0\" class=\"data row10 col0\" >juno3</td>\n",
-       "      <td id=\"T_6acf3_row10_col1\" class=\"data row10 col1\" >3213.000</td>\n",
-       "      <td id=\"T_6acf3_row10_col2\" class=\"data row10 col2\" >3008.582</td>\n",
-       "      <td id=\"T_6acf3_row10_col3\" class=\"data row10 col3\" >3056.492</td>\n",
-       "      <td id=\"T_6acf3_row10_col4\" class=\"data row10 col4\" >-6.362</td>\n",
-       "      <td id=\"T_6acf3_row10_col5\" class=\"data row10 col5\" >-4.871</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6acf3_level0_row11\" class=\"row_heading level0 row11\" >54</th>\n",
-       "      <td id=\"T_6acf3_row11_col0\" class=\"data row11 col0\" >lince</td>\n",
-       "      <td id=\"T_6acf3_row11_col1\" class=\"data row11 col1\" >3587.000</td>\n",
-       "      <td id=\"T_6acf3_row11_col2\" class=\"data row11 col2\" >3285.089</td>\n",
-       "      <td id=\"T_6acf3_row11_col3\" class=\"data row11 col3\" >4254.304</td>\n",
-       "      <td id=\"T_6acf3_row11_col4\" class=\"data row11 col4\" >-8.417</td>\n",
-       "      <td id=\"T_6acf3_row11_col5\" class=\"data row11 col5\" >18.603</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6acf3_level0_row12\" class=\"row_heading level0 row12\" >59</th>\n",
-       "      <td id=\"T_6acf3_row12_col0\" class=\"data row12 col0\" >ndrt_2020</td>\n",
-       "      <td id=\"T_6acf3_row12_col1\" class=\"data row12 col1\" >1316.736</td>\n",
-       "      <td id=\"T_6acf3_row12_col2\" class=\"data row12 col2\" >1293.518</td>\n",
-       "      <td id=\"T_6acf3_row12_col3\" class=\"data row12 col3\" >—</td>\n",
-       "      <td id=\"T_6acf3_row12_col4\" class=\"data row12 col4\" >-1.763</td>\n",
-       "      <td id=\"T_6acf3_row12_col5\" class=\"data row12 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6acf3_level0_row13\" class=\"row_heading level0 row13\" >64</th>\n",
-       "      <td id=\"T_6acf3_row13_col0\" class=\"data row13 col0\" >prometheus_2022</td>\n",
-       "      <td id=\"T_6acf3_row13_col1\" class=\"data row13 col1\" >3898.370</td>\n",
-       "      <td id=\"T_6acf3_row13_col2\" class=\"data row13 col2\" >4191.289</td>\n",
-       "      <td id=\"T_6acf3_row13_col3\" class=\"data row13 col3\" >4229.197</td>\n",
-       "      <td id=\"T_6acf3_row13_col4\" class=\"data row13 col4\" >7.514</td>\n",
-       "      <td id=\"T_6acf3_row13_col5\" class=\"data row13 col5\" >8.486</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6acf3_level0_row14\" class=\"row_heading level0 row14\" >69</th>\n",
-       "      <td id=\"T_6acf3_row14_col0\" class=\"data row14 col0\" >valetudo</td>\n",
-       "      <td id=\"T_6acf3_row14_col1\" class=\"data row14 col1\" >860.000</td>\n",
-       "      <td id=\"T_6acf3_row14_col2\" class=\"data row14 col2\" >815.053</td>\n",
-       "      <td id=\"T_6acf3_row14_col3\" class=\"data row14 col3\" >943.619</td>\n",
-       "      <td id=\"T_6acf3_row14_col4\" class=\"data row14 col4\" >-5.226</td>\n",
-       "      <td id=\"T_6acf3_row14_col5\" class=\"data row14 col5\" >9.723</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_6acf3_level0_row15\" class=\"row_heading level0 row15\" >74</th>\n",
-       "      <td id=\"T_6acf3_row15_col0\" class=\"data row15 col0\" >valkyrie</td>\n",
-       "      <td id=\"T_6acf3_row15_col1\" class=\"data row15 col1\" >2098.020</td>\n",
-       "      <td id=\"T_6acf3_row15_col2\" class=\"data row15 col2\" >2247.849</td>\n",
-       "      <td id=\"T_6acf3_row15_col3\" class=\"data row15 col3\" >—</td>\n",
-       "      <td id=\"T_6acf3_row15_col4\" class=\"data row15 col4\" >7.141</td>\n",
-       "      <td id=\"T_6acf3_row15_col5\" class=\"data row15 col5\" >—</td>\n",
+       "      <th id=\"T_9a942_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_9a942_row0_col0\" class=\"data row0 col0\" >andromeda</td>\n",
+       "      <td id=\"T_9a942_row0_col1\" class=\"data row0 col1\" >3443.000</td>\n",
+       "      <td id=\"T_9a942_row0_col2\" class=\"data row0 col2\" >3615.881</td>\n",
+       "      <td id=\"T_9a942_row0_col3\" class=\"data row0 col3\" >4170.733</td>\n",
+       "      <td id=\"T_9a942_row0_col4\" class=\"data row0 col4\" >5.021</td>\n",
+       "      <td id=\"T_9a942_row0_col5\" class=\"data row0 col5\" >21.137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9a942_level0_row1\" class=\"row_heading level0 row1\" >5</th>\n",
+       "      <td id=\"T_9a942_row1_col0\" class=\"data row1 col0\" >astra</td>\n",
+       "      <td id=\"T_9a942_row1_col1\" class=\"data row1 col1\" >3250.000</td>\n",
+       "      <td id=\"T_9a942_row1_col2\" class=\"data row1 col2\" >3043.368</td>\n",
+       "      <td id=\"T_9a942_row1_col3\" class=\"data row1 col3\" >3695.934</td>\n",
+       "      <td id=\"T_9a942_row1_col4\" class=\"data row1 col4\" >-6.358</td>\n",
+       "      <td id=\"T_9a942_row1_col5\" class=\"data row1 col5\" >13.721</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9a942_level0_row2\" class=\"row_heading level0 row2\" >10</th>\n",
+       "      <td id=\"T_9a942_row2_col0\" class=\"data row2 col0\" >bella_lui</td>\n",
+       "      <td id=\"T_9a942_row2_col1\" class=\"data row2 col1\" >458.971</td>\n",
+       "      <td id=\"T_9a942_row2_col2\" class=\"data row2 col2\" >460.552</td>\n",
+       "      <td id=\"T_9a942_row2_col3\" class=\"data row2 col3\" >465.877</td>\n",
+       "      <td id=\"T_9a942_row2_col4\" class=\"data row2 col4\" >0.345</td>\n",
+       "      <td id=\"T_9a942_row2_col5\" class=\"data row2 col5\" >1.505</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9a942_level0_row3\" class=\"row_heading level0 row3\" >15</th>\n",
+       "      <td id=\"T_9a942_row3_col0\" class=\"data row3 col0\" >camoes</td>\n",
+       "      <td id=\"T_9a942_row3_col1\" class=\"data row3 col1\" >3015.000</td>\n",
+       "      <td id=\"T_9a942_row3_col2\" class=\"data row3 col2\" >3004.104</td>\n",
+       "      <td id=\"T_9a942_row3_col3\" class=\"data row3 col3\" >—</td>\n",
+       "      <td id=\"T_9a942_row3_col4\" class=\"data row3 col4\" >-0.361</td>\n",
+       "      <td id=\"T_9a942_row3_col5\" class=\"data row3 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9a942_level0_row4\" class=\"row_heading level0 row4\" >20</th>\n",
+       "      <td id=\"T_9a942_row4_col0\" class=\"data row4 col0\" >cavour</td>\n",
+       "      <td id=\"T_9a942_row4_col1\" class=\"data row4 col1\" >2789.000</td>\n",
+       "      <td id=\"T_9a942_row4_col2\" class=\"data row4 col2\" >2818.135</td>\n",
+       "      <td id=\"T_9a942_row4_col3\" class=\"data row4 col3\" >3251.609</td>\n",
+       "      <td id=\"T_9a942_row4_col4\" class=\"data row4 col4\" >1.045</td>\n",
+       "      <td id=\"T_9a942_row4_col5\" class=\"data row4 col5\" >16.587</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9a942_level0_row5\" class=\"row_heading level0 row5\" >25</th>\n",
+       "      <td id=\"T_9a942_row5_col0\" class=\"data row5 col0\" >defiance</td>\n",
+       "      <td id=\"T_9a942_row5_col1\" class=\"data row5 col1\" >9308.318</td>\n",
+       "      <td id=\"T_9a942_row5_col2\" class=\"data row5 col2\" >9243.891</td>\n",
+       "      <td id=\"T_9a942_row5_col3\" class=\"data row5 col3\" >—</td>\n",
+       "      <td id=\"T_9a942_row5_col4\" class=\"data row5 col4\" >-0.692</td>\n",
+       "      <td id=\"T_9a942_row5_col5\" class=\"data row5 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9a942_level0_row6\" class=\"row_heading level0 row6\" >30</th>\n",
+       "      <td id=\"T_9a942_row6_col0\" class=\"data row6 col0\" >erebus</td>\n",
+       "      <td id=\"T_9a942_row6_col1\" class=\"data row6 col1\" >3020.000</td>\n",
+       "      <td id=\"T_9a942_row6_col2\" class=\"data row6 col2\" >2751.156</td>\n",
+       "      <td id=\"T_9a942_row6_col3\" class=\"data row6 col3\" >3253.423</td>\n",
+       "      <td id=\"T_9a942_row6_col4\" class=\"data row6 col4\" >-8.902</td>\n",
+       "      <td id=\"T_9a942_row6_col5\" class=\"data row6 col5\" >7.729</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9a942_level0_row7\" class=\"row_heading level0 row7\" >35</th>\n",
+       "      <td id=\"T_9a942_row7_col0\" class=\"data row7 col0\" >genesis</td>\n",
+       "      <td id=\"T_9a942_row7_col1\" class=\"data row7 col1\" >2916.000</td>\n",
+       "      <td id=\"T_9a942_row7_col2\" class=\"data row7 col2\" >3044.686</td>\n",
+       "      <td id=\"T_9a942_row7_col3\" class=\"data row7 col3\" >3297.239</td>\n",
+       "      <td id=\"T_9a942_row7_col4\" class=\"data row7 col4\" >4.413</td>\n",
+       "      <td id=\"T_9a942_row7_col5\" class=\"data row7 col5\" >13.074</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9a942_level0_row8\" class=\"row_heading level0 row8\" >40</th>\n",
+       "      <td id=\"T_9a942_row8_col0\" class=\"data row8 col0\" >halcyon</td>\n",
+       "      <td id=\"T_9a942_row8_col1\" class=\"data row8 col1\" >3450.000</td>\n",
+       "      <td id=\"T_9a942_row8_col2\" class=\"data row8 col2\" >3214.239</td>\n",
+       "      <td id=\"T_9a942_row8_col3\" class=\"data row8 col3\" >3530.232</td>\n",
+       "      <td id=\"T_9a942_row8_col4\" class=\"data row8 col4\" >-6.834</td>\n",
+       "      <td id=\"T_9a942_row8_col5\" class=\"data row8 col5\" >2.326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9a942_level0_row9\" class=\"row_heading level0 row9\" >45</th>\n",
+       "      <td id=\"T_9a942_row9_col0\" class=\"data row9 col0\" >hedy</td>\n",
+       "      <td id=\"T_9a942_row9_col1\" class=\"data row9 col1\" >5231.530</td>\n",
+       "      <td id=\"T_9a942_row9_col2\" class=\"data row9 col2\" >5246.918</td>\n",
+       "      <td id=\"T_9a942_row9_col3\" class=\"data row9 col3\" >—</td>\n",
+       "      <td id=\"T_9a942_row9_col4\" class=\"data row9 col4\" >0.294</td>\n",
+       "      <td id=\"T_9a942_row9_col5\" class=\"data row9 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9a942_level0_row10\" class=\"row_heading level0 row10\" >49</th>\n",
+       "      <td id=\"T_9a942_row10_col0\" class=\"data row10 col0\" >juno3</td>\n",
+       "      <td id=\"T_9a942_row10_col1\" class=\"data row10 col1\" >3213.000</td>\n",
+       "      <td id=\"T_9a942_row10_col2\" class=\"data row10 col2\" >2987.281</td>\n",
+       "      <td id=\"T_9a942_row10_col3\" class=\"data row10 col3\" >3056.550</td>\n",
+       "      <td id=\"T_9a942_row10_col4\" class=\"data row10 col4\" >-7.025</td>\n",
+       "      <td id=\"T_9a942_row10_col5\" class=\"data row10 col5\" >-4.869</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9a942_level0_row11\" class=\"row_heading level0 row11\" >54</th>\n",
+       "      <td id=\"T_9a942_row11_col0\" class=\"data row11 col0\" >lince</td>\n",
+       "      <td id=\"T_9a942_row11_col1\" class=\"data row11 col1\" >3587.000</td>\n",
+       "      <td id=\"T_9a942_row11_col2\" class=\"data row11 col2\" >3285.089</td>\n",
+       "      <td id=\"T_9a942_row11_col3\" class=\"data row11 col3\" >4254.304</td>\n",
+       "      <td id=\"T_9a942_row11_col4\" class=\"data row11 col4\" >-8.417</td>\n",
+       "      <td id=\"T_9a942_row11_col5\" class=\"data row11 col5\" >18.603</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9a942_level0_row12\" class=\"row_heading level0 row12\" >59</th>\n",
+       "      <td id=\"T_9a942_row12_col0\" class=\"data row12 col0\" >ndrt_2020</td>\n",
+       "      <td id=\"T_9a942_row12_col1\" class=\"data row12 col1\" >1316.736</td>\n",
+       "      <td id=\"T_9a942_row12_col2\" class=\"data row12 col2\" >1293.518</td>\n",
+       "      <td id=\"T_9a942_row12_col3\" class=\"data row12 col3\" >—</td>\n",
+       "      <td id=\"T_9a942_row12_col4\" class=\"data row12 col4\" >-1.763</td>\n",
+       "      <td id=\"T_9a942_row12_col5\" class=\"data row12 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9a942_level0_row13\" class=\"row_heading level0 row13\" >64</th>\n",
+       "      <td id=\"T_9a942_row13_col0\" class=\"data row13 col0\" >prometheus_2022</td>\n",
+       "      <td id=\"T_9a942_row13_col1\" class=\"data row13 col1\" >3898.370</td>\n",
+       "      <td id=\"T_9a942_row13_col2\" class=\"data row13 col2\" >4191.289</td>\n",
+       "      <td id=\"T_9a942_row13_col3\" class=\"data row13 col3\" >4229.197</td>\n",
+       "      <td id=\"T_9a942_row13_col4\" class=\"data row13 col4\" >7.514</td>\n",
+       "      <td id=\"T_9a942_row13_col5\" class=\"data row13 col5\" >8.486</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9a942_level0_row14\" class=\"row_heading level0 row14\" >69</th>\n",
+       "      <td id=\"T_9a942_row14_col0\" class=\"data row14 col0\" >valetudo</td>\n",
+       "      <td id=\"T_9a942_row14_col1\" class=\"data row14 col1\" >860.000</td>\n",
+       "      <td id=\"T_9a942_row14_col2\" class=\"data row14 col2\" >815.053</td>\n",
+       "      <td id=\"T_9a942_row14_col3\" class=\"data row14 col3\" >943.619</td>\n",
+       "      <td id=\"T_9a942_row14_col4\" class=\"data row14 col4\" >-5.226</td>\n",
+       "      <td id=\"T_9a942_row14_col5\" class=\"data row14 col5\" >9.723</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9a942_level0_row15\" class=\"row_heading level0 row15\" >74</th>\n",
+       "      <td id=\"T_9a942_row15_col0\" class=\"data row15 col0\" >valkyrie</td>\n",
+       "      <td id=\"T_9a942_row15_col1\" class=\"data row15 col1\" >2098.020</td>\n",
+       "      <td id=\"T_9a942_row15_col2\" class=\"data row15 col2\" >2247.849</td>\n",
+       "      <td id=\"T_9a942_row15_col3\" class=\"data row15 col3\" >—</td>\n",
+       "      <td id=\"T_9a942_row15_col4\" class=\"data row15 col4\" >7.141</td>\n",
+       "      <td id=\"T_9a942_row15_col5\" class=\"data row15 col5\" >—</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x15c280ad0>"
+       "<pandas.io.formats.style.Styler at 0x117e7cad0>"
       ]
      },
      "metadata": {},
@@ -245,6 +249,253 @@
     "show(metrics[metrics.metric == 'Apogee AGL'][['rocket', 'measured', 'rocketpy', 'datcom', 'rocketpy_error_pct', 'datcom_error_pct']])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "007172ad",
+   "metadata": {},
+   "source": [
+    "## Apogee bars and percentage errors\n",
+    "\n",
+    "Each panel compares **RocketPy + supplied CD** with **DATCOM**, using the recorded apogee as the dashed black reference. **Green** marks the smaller absolute percentage error; **red** marks the larger error. Grey indicates that the competing result is unavailable, so no winner can be ranked. Panel heights are metres AGL, and each panel has its own vertical scale. Every rocket is retained; missing predictions are labelled, and archived ordinary outputs are identified.\n",
+    "\n",
+    "Supplied CD is not universally CFD-derived: Juno III explicitly applies CFD scaling to a RASAero II curve, Cavour identifies RASAero II, and other notebooks supply constants or curves with different/unspecified provenance. RocketPy requires a supplied drag coefficient; there is no separate built-in geometry-to-CD prediction to plot.\n",
+    "\n",
+    "Bar labels show signed error `100 × (prediction − recorded) / |recorded|`. The percentile table describes **absolute percentage errors across the same paired rockets**, not per-flight uncertainty or Monte Carlo confidence intervals. The example loader preserves post-construction drag edits in the solver's seven-input coefficient, including Juno's CFD scaling and Bella Lui's updated curve."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "03aa12aa",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-10-24T20:16:02.874084Z",
+     "iopub.status.busy": "2026-10-24T20:16:02.873994Z",
+     "iopub.status.idle": "2026-10-24T20:16:03.915086Z",
+     "shell.execute_reply": "2026-10-24T20:16:03.914899Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x1520 with 16 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "</style>\n",
+       "<table id=\"T_65d3c\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_65d3c_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
+       "      <th id=\"T_65d3c_level0_col1\" class=\"col_heading level0 col1\" >Paired rockets</th>\n",
+       "      <th id=\"T_65d3c_level0_col2\" class=\"col_heading level0 col2\" >P50 (%)</th>\n",
+       "      <th id=\"T_65d3c_level0_col3\" class=\"col_heading level0 col3\" >P75 (%)</th>\n",
+       "      <th id=\"T_65d3c_level0_col4\" class=\"col_heading level0 col4\" >P90 (%)</th>\n",
+       "      <th id=\"T_65d3c_level0_col5\" class=\"col_heading level0 col5\" >P95 (%)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_65d3c_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_65d3c_row0_col0\" class=\"data row0 col0\" >RocketPy + supplied CD</td>\n",
+       "      <td id=\"T_65d3c_row0_col1\" class=\"data row0 col1\" >11</td>\n",
+       "      <td id=\"T_65d3c_row0_col2\" class=\"data row0 col2\" >6.358</td>\n",
+       "      <td id=\"T_65d3c_row0_col3\" class=\"data row0 col3\" >7.270</td>\n",
+       "      <td id=\"T_65d3c_row0_col4\" class=\"data row0 col4\" >8.417</td>\n",
+       "      <td id=\"T_65d3c_row0_col5\" class=\"data row0 col5\" >8.659</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_65d3c_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+       "      <td id=\"T_65d3c_row1_col0\" class=\"data row1 col0\" >DATCOM</td>\n",
+       "      <td id=\"T_65d3c_row1_col1\" class=\"data row1 col1\" >11</td>\n",
+       "      <td id=\"T_65d3c_row1_col2\" class=\"data row1 col2\" >9.723</td>\n",
+       "      <td id=\"T_65d3c_row1_col3\" class=\"data row1 col3\" >15.154</td>\n",
+       "      <td id=\"T_65d3c_row1_col4\" class=\"data row1 col4\" >18.603</td>\n",
+       "      <td id=\"T_65d3c_row1_col5\" class=\"data row1 col5\" >19.870</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x120fe9bd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "</style>\n",
+       "<table id=\"T_8721a\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_8721a_level0_col0\" class=\"col_heading level0 col0\" >rocket</th>\n",
+       "      <th id=\"T_8721a_level0_col1\" class=\"col_heading level0 col1\" >drag_provenance</th>\n",
+       "      <th id=\"T_8721a_level0_col2\" class=\"col_heading level0 col2\" >closer_prediction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8721a_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_8721a_row0_col0\" class=\"data row0 col0\" >andromeda</td>\n",
+       "      <td id=\"T_8721a_row0_col1\" class=\"data row0 col1\" >Supplied CD; CFD provenance not established</td>\n",
+       "      <td id=\"T_8721a_row0_col2\" class=\"data row0 col2\" >RocketPy + supplied CD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8721a_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+       "      <td id=\"T_8721a_row1_col0\" class=\"data row1 col0\" >astra</td>\n",
+       "      <td id=\"T_8721a_row1_col1\" class=\"data row1 col1\" >Supplied CD; CFD provenance not established</td>\n",
+       "      <td id=\"T_8721a_row1_col2\" class=\"data row1 col2\" >RocketPy + supplied CD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8721a_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+       "      <td id=\"T_8721a_row2_col0\" class=\"data row2 col0\" >bella_lui</td>\n",
+       "      <td id=\"T_8721a_row2_col1\" class=\"data row2 col1\" >Supplied CD; CFD provenance not established</td>\n",
+       "      <td id=\"T_8721a_row2_col2\" class=\"data row2 col2\" >RocketPy + supplied CD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8721a_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+       "      <td id=\"T_8721a_row3_col0\" class=\"data row3 col0\" >camoes</td>\n",
+       "      <td id=\"T_8721a_row3_col1\" class=\"data row3 col1\" >Supplied CD; CFD provenance not established</td>\n",
+       "      <td id=\"T_8721a_row3_col2\" class=\"data row3 col2\" >unavailable comparison</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8721a_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+       "      <td id=\"T_8721a_row4_col0\" class=\"data row4 col0\" >cavour</td>\n",
+       "      <td id=\"T_8721a_row4_col1\" class=\"data row4 col1\" >RASAero II (source cell 10)</td>\n",
+       "      <td id=\"T_8721a_row4_col2\" class=\"data row4 col2\" >RocketPy + supplied CD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8721a_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+       "      <td id=\"T_8721a_row5_col0\" class=\"data row5 col0\" >defiance</td>\n",
+       "      <td id=\"T_8721a_row5_col1\" class=\"data row5 col1\" >Supplied CD; CFD provenance not established</td>\n",
+       "      <td id=\"T_8721a_row5_col2\" class=\"data row5 col2\" >unavailable comparison</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8721a_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+       "      <td id=\"T_8721a_row6_col0\" class=\"data row6 col0\" >erebus</td>\n",
+       "      <td id=\"T_8721a_row6_col1\" class=\"data row6 col1\" >Supplied CD; CFD provenance not established</td>\n",
+       "      <td id=\"T_8721a_row6_col2\" class=\"data row6 col2\" >DATCOM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8721a_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+       "      <td id=\"T_8721a_row7_col0\" class=\"data row7 col0\" >genesis</td>\n",
+       "      <td id=\"T_8721a_row7_col1\" class=\"data row7 col1\" >Supplied CD; CFD provenance not established</td>\n",
+       "      <td id=\"T_8721a_row7_col2\" class=\"data row7 col2\" >RocketPy + supplied CD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8721a_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+       "      <td id=\"T_8721a_row8_col0\" class=\"data row8 col0\" >halcyon</td>\n",
+       "      <td id=\"T_8721a_row8_col1\" class=\"data row8 col1\" >Supplied CD; CFD provenance not established</td>\n",
+       "      <td id=\"T_8721a_row8_col2\" class=\"data row8 col2\" >DATCOM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8721a_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+       "      <td id=\"T_8721a_row9_col0\" class=\"data row9 col0\" >hedy</td>\n",
+       "      <td id=\"T_8721a_row9_col1\" class=\"data row9 col1\" >Supplied CD; CFD provenance not established</td>\n",
+       "      <td id=\"T_8721a_row9_col2\" class=\"data row9 col2\" >unavailable comparison</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8721a_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
+       "      <td id=\"T_8721a_row10_col0\" class=\"data row10 col0\" >juno3</td>\n",
+       "      <td id=\"T_8721a_row10_col1\" class=\"data row10 col1\" >RASAero II curve scaled using a CFD value (source cell 14)</td>\n",
+       "      <td id=\"T_8721a_row10_col2\" class=\"data row10 col2\" >DATCOM</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8721a_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
+       "      <td id=\"T_8721a_row11_col0\" class=\"data row11 col0\" >lince</td>\n",
+       "      <td id=\"T_8721a_row11_col1\" class=\"data row11 col1\" >Supplied CD; CFD provenance not established</td>\n",
+       "      <td id=\"T_8721a_row11_col2\" class=\"data row11 col2\" >RocketPy + supplied CD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8721a_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
+       "      <td id=\"T_8721a_row12_col0\" class=\"data row12 col0\" >ndrt_2020</td>\n",
+       "      <td id=\"T_8721a_row12_col1\" class=\"data row12 col1\" >Supplied CD; CFD provenance not established</td>\n",
+       "      <td id=\"T_8721a_row12_col2\" class=\"data row12 col2\" >unavailable comparison</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8721a_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
+       "      <td id=\"T_8721a_row13_col0\" class=\"data row13 col0\" >prometheus_2022</td>\n",
+       "      <td id=\"T_8721a_row13_col1\" class=\"data row13 col1\" >Supplied CD; CFD provenance not established</td>\n",
+       "      <td id=\"T_8721a_row13_col2\" class=\"data row13 col2\" >RocketPy + supplied CD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8721a_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
+       "      <td id=\"T_8721a_row14_col0\" class=\"data row14 col0\" >valetudo</td>\n",
+       "      <td id=\"T_8721a_row14_col1\" class=\"data row14 col1\" >RASAero (source input filenames)</td>\n",
+       "      <td id=\"T_8721a_row14_col2\" class=\"data row14 col2\" >RocketPy + supplied CD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8721a_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
+       "      <td id=\"T_8721a_row15_col0\" class=\"data row15 col0\" >valkyrie</td>\n",
+       "      <td id=\"T_8721a_row15_col1\" class=\"data row15 col1\" >Supplied CD; CFD provenance not established</td>\n",
+       "      <td id=\"T_8721a_row15_col2\" class=\"data row15 col2\" >unavailable comparison</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x120fe82d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "sys.path.insert(0, str(DATA))\n",
+    "from plot_apogee_comparison import comparison_data, error_percentiles, plot_apogees, save_apogees\n",
+    "\n",
+    "apogee_bars = comparison_data(DATA)\n",
+    "apogee_bars.to_csv(DATA / \"apogee_comparison.csv\", index=False)\n",
+    "fig = plot_apogees(apogee_bars)\n",
+    "save_apogees(fig, DATA)\n",
+    "plt.show()\n",
+    "plt.close(fig)\n",
+    "\n",
+    "percentiles = error_percentiles(apogee_bars)\n",
+    "percentiles.to_csv(DATA / \"apogee_error_percentiles.csv\", index=False)\n",
+    "show(percentiles.rename(columns={\"model\": \"Model\", \"paired_rockets\": \"Paired rockets\", **{f\"P{p}_absolute_error_pct\": f\"P{p} (%)\" for p in [50, 75, 90, 95]}}))\n",
+    "show(apogee_bars[[\"rocket\", \"drag_provenance\", \"closer_prediction\"]])\n",
+    "assert set(apogee_bars.rocket) == set(status.rocket)\n",
+    "assert (apogee_bars.measured > 0).all()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7aab47e",
+   "metadata": {},
+   "source": [
+    "### Why some DATCOM results are unavailable\n",
+    "\n",
+    "These gaps reflect missing inputs or current integration limits. They do not represent zero predicted apogee or a failed accuracy comparison.\n",
+    "\n",
+    "| Rocket | Why DATCOM is unavailable |\n",
+    "| --- | --- |\n",
+    "| **Camões** | Its air-brake controller overrides whole-vehicle drag. Coupling it with DATCOM needs an incremental air-brake drag model to avoid double-counting drag. |\n",
+    "| **Defiance** | The fin root centre lies behind the inferred DATCOM body end. The body extent and fin attachment need reconciling before generating a valid model. This does not establish that the real rocket's geometry is invalid. |\n",
+    "| **NDRT 2020** | Its internal diameter transition is followed by another cylindrical body section. The current DATCOM builder supports an aft boattail, rather than this body arrangement. |\n",
+    "| **Hedy** | Required source files under `data/rockets/tust` are missing. Its fin cutout also needs additional geometry support. |\n",
+    "| **Valkyrie** | The source requests live weather, and no archived launch-atmosphere profile is available for a reproducible comparison. |\n",
+    "\n",
+    "**Hedy and Valkyrie's RocketPy apogees are archived notebook outputs, not fresh simulations.** Their bars are labelled accordingly. Grey bars indicate that the competing result is unavailable, so no closer/farther ranking is assigned. The same availability limits apply to the trajectory panels below.\n",
+    "\n",
+    "Missing data or geometry/controller support must be supplied before adding these DATCOM predictions; silently substituting inputs would change the comparison."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "f9993ba7",
@@ -255,14 +506,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "6346434f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-22T22:26:50.298327Z",
-     "iopub.status.busy": "2026-10-22T22:26:50.298145Z",
-     "iopub.status.idle": "2026-10-22T22:26:50.303444Z",
-     "shell.execute_reply": "2026-10-22T22:26:50.303163Z"
+     "iopub.execute_input": "2026-10-24T20:16:03.915921Z",
+     "iopub.status.busy": "2026-10-24T20:16:03.915843Z",
+     "iopub.status.idle": "2026-10-24T20:16:03.918946Z",
+     "shell.execute_reply": "2026-10-24T20:16:03.918757Z"
     }
    },
    "outputs": [
@@ -271,168 +522,168 @@
       "text/html": [
        "<style type=\"text/css\">\n",
        "</style>\n",
-       "<table id=\"T_40020\">\n",
+       "<table id=\"T_bcd12\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_40020_level0_col0\" class=\"col_heading level0 col0\" >rocket</th>\n",
-       "      <th id=\"T_40020_level0_col1\" class=\"col_heading level0 col1\" >measured</th>\n",
-       "      <th id=\"T_40020_level0_col2\" class=\"col_heading level0 col2\" >rocketpy</th>\n",
-       "      <th id=\"T_40020_level0_col3\" class=\"col_heading level0 col3\" >datcom</th>\n",
-       "      <th id=\"T_40020_level0_col4\" class=\"col_heading level0 col4\" >rocketpy_error_pct</th>\n",
-       "      <th id=\"T_40020_level0_col5\" class=\"col_heading level0 col5\" >datcom_error_pct</th>\n",
+       "      <th id=\"T_bcd12_level0_col0\" class=\"col_heading level0 col0\" >rocket</th>\n",
+       "      <th id=\"T_bcd12_level0_col1\" class=\"col_heading level0 col1\" >measured</th>\n",
+       "      <th id=\"T_bcd12_level0_col2\" class=\"col_heading level0 col2\" >rocketpy</th>\n",
+       "      <th id=\"T_bcd12_level0_col3\" class=\"col_heading level0 col3\" >datcom</th>\n",
+       "      <th id=\"T_bcd12_level0_col4\" class=\"col_heading level0 col4\" >rocketpy_error_pct</th>\n",
+       "      <th id=\"T_bcd12_level0_col5\" class=\"col_heading level0 col5\" >datcom_error_pct</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_40020_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
-       "      <td id=\"T_40020_row0_col0\" class=\"data row0 col0\" >andromeda</td>\n",
-       "      <td id=\"T_40020_row0_col1\" class=\"data row0 col1\" >324.109</td>\n",
-       "      <td id=\"T_40020_row0_col2\" class=\"data row0 col2\" >343.154</td>\n",
-       "      <td id=\"T_40020_row0_col3\" class=\"data row0 col3\" >352.641</td>\n",
-       "      <td id=\"T_40020_row0_col4\" class=\"data row0 col4\" >5.876</td>\n",
-       "      <td id=\"T_40020_row0_col5\" class=\"data row0 col5\" >8.803</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_40020_level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
-       "      <td id=\"T_40020_row1_col0\" class=\"data row1 col0\" >astra</td>\n",
-       "      <td id=\"T_40020_row1_col1\" class=\"data row1 col1\" >—</td>\n",
-       "      <td id=\"T_40020_row1_col2\" class=\"data row1 col2\" >306.480</td>\n",
-       "      <td id=\"T_40020_row1_col3\" class=\"data row1 col3\" >318.547</td>\n",
-       "      <td id=\"T_40020_row1_col4\" class=\"data row1 col4\" >—</td>\n",
-       "      <td id=\"T_40020_row1_col5\" class=\"data row1 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_40020_level0_row2\" class=\"row_heading level0 row2\" >11</th>\n",
-       "      <td id=\"T_40020_row2_col0\" class=\"data row2 col0\" >bella_lui</td>\n",
-       "      <td id=\"T_40020_row2_col1\" class=\"data row2 col1\" >90.000</td>\n",
-       "      <td id=\"T_40020_row2_col2\" class=\"data row2 col2\" >86.179</td>\n",
-       "      <td id=\"T_40020_row2_col3\" class=\"data row2 col3\" >86.473</td>\n",
-       "      <td id=\"T_40020_row2_col4\" class=\"data row2 col4\" >-4.246</td>\n",
-       "      <td id=\"T_40020_row2_col5\" class=\"data row2 col5\" >-3.918</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_40020_level0_row3\" class=\"row_heading level0 row3\" >16</th>\n",
-       "      <td id=\"T_40020_row3_col0\" class=\"data row3 col0\" >camoes</td>\n",
-       "      <td id=\"T_40020_row3_col1\" class=\"data row3 col1\" >—</td>\n",
-       "      <td id=\"T_40020_row3_col2\" class=\"data row3 col2\" >327.440</td>\n",
-       "      <td id=\"T_40020_row3_col3\" class=\"data row3 col3\" >—</td>\n",
-       "      <td id=\"T_40020_row3_col4\" class=\"data row3 col4\" >—</td>\n",
-       "      <td id=\"T_40020_row3_col5\" class=\"data row3 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_40020_level0_row4\" class=\"row_heading level0 row4\" >21</th>\n",
-       "      <td id=\"T_40020_row4_col0\" class=\"data row4 col0\" >cavour</td>\n",
-       "      <td id=\"T_40020_row4_col1\" class=\"data row4 col1\" >314.000</td>\n",
-       "      <td id=\"T_40020_row4_col2\" class=\"data row4 col2\" >304.640</td>\n",
-       "      <td id=\"T_40020_row4_col3\" class=\"data row4 col3\" >319.055</td>\n",
-       "      <td id=\"T_40020_row4_col4\" class=\"data row4 col4\" >-2.981</td>\n",
-       "      <td id=\"T_40020_row4_col5\" class=\"data row4 col5\" >1.610</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_40020_level0_row5\" class=\"row_heading level0 row5\" >26</th>\n",
-       "      <td id=\"T_40020_row5_col0\" class=\"data row5 col0\" >defiance</td>\n",
-       "      <td id=\"T_40020_row5_col1\" class=\"data row5 col1\" >—</td>\n",
-       "      <td id=\"T_40020_row5_col2\" class=\"data row5 col2\" >438.711</td>\n",
-       "      <td id=\"T_40020_row5_col3\" class=\"data row5 col3\" >—</td>\n",
-       "      <td id=\"T_40020_row5_col4\" class=\"data row5 col4\" >—</td>\n",
-       "      <td id=\"T_40020_row5_col5\" class=\"data row5 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_40020_level0_row6\" class=\"row_heading level0 row6\" >31</th>\n",
-       "      <td id=\"T_40020_row6_col0\" class=\"data row6 col0\" >erebus</td>\n",
-       "      <td id=\"T_40020_row6_col1\" class=\"data row6 col1\" >—</td>\n",
-       "      <td id=\"T_40020_row6_col2\" class=\"data row6 col2\" >294.171</td>\n",
-       "      <td id=\"T_40020_row6_col3\" class=\"data row6 col3\" >307.317</td>\n",
-       "      <td id=\"T_40020_row6_col4\" class=\"data row6 col4\" >—</td>\n",
-       "      <td id=\"T_40020_row6_col5\" class=\"data row6 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_40020_level0_row7\" class=\"row_heading level0 row7\" >36</th>\n",
-       "      <td id=\"T_40020_row7_col0\" class=\"data row7 col0\" >genesis</td>\n",
-       "      <td id=\"T_40020_row7_col1\" class=\"data row7 col1\" >—</td>\n",
-       "      <td id=\"T_40020_row7_col2\" class=\"data row7 col2\" >290.636</td>\n",
-       "      <td id=\"T_40020_row7_col3\" class=\"data row7 col3\" >299.262</td>\n",
-       "      <td id=\"T_40020_row7_col4\" class=\"data row7 col4\" >—</td>\n",
-       "      <td id=\"T_40020_row7_col5\" class=\"data row7 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_40020_level0_row8\" class=\"row_heading level0 row8\" >41</th>\n",
-       "      <td id=\"T_40020_row8_col0\" class=\"data row8 col0\" >halcyon</td>\n",
-       "      <td id=\"T_40020_row8_col1\" class=\"data row8 col1\" >—</td>\n",
-       "      <td id=\"T_40020_row8_col2\" class=\"data row8 col2\" >275.571</td>\n",
-       "      <td id=\"T_40020_row8_col3\" class=\"data row8 col3\" >280.950</td>\n",
-       "      <td id=\"T_40020_row8_col4\" class=\"data row8 col4\" >—</td>\n",
-       "      <td id=\"T_40020_row8_col5\" class=\"data row8 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_40020_level0_row9\" class=\"row_heading level0 row9\" >46</th>\n",
-       "      <td id=\"T_40020_row9_col0\" class=\"data row9 col0\" >hedy</td>\n",
-       "      <td id=\"T_40020_row9_col1\" class=\"data row9 col1\" >—</td>\n",
-       "      <td id=\"T_40020_row9_col2\" class=\"data row9 col2\" >—</td>\n",
-       "      <td id=\"T_40020_row9_col3\" class=\"data row9 col3\" >—</td>\n",
-       "      <td id=\"T_40020_row9_col4\" class=\"data row9 col4\" >—</td>\n",
-       "      <td id=\"T_40020_row9_col5\" class=\"data row9 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_40020_level0_row10\" class=\"row_heading level0 row10\" >50</th>\n",
-       "      <td id=\"T_40020_row10_col0\" class=\"data row10 col0\" >juno3</td>\n",
-       "      <td id=\"T_40020_row10_col1\" class=\"data row10 col1\" >268.761</td>\n",
-       "      <td id=\"T_40020_row10_col2\" class=\"data row10 col2\" >241.243</td>\n",
-       "      <td id=\"T_40020_row10_col3\" class=\"data row10 col3\" >243.899</td>\n",
-       "      <td id=\"T_40020_row10_col4\" class=\"data row10 col4\" >-10.239</td>\n",
-       "      <td id=\"T_40020_row10_col5\" class=\"data row10 col5\" >-9.251</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_40020_level0_row11\" class=\"row_heading level0 row11\" >55</th>\n",
-       "      <td id=\"T_40020_row11_col0\" class=\"data row11 col0\" >lince</td>\n",
-       "      <td id=\"T_40020_row11_col1\" class=\"data row11 col1\" >—</td>\n",
-       "      <td id=\"T_40020_row11_col2\" class=\"data row11 col2\" >317.771</td>\n",
-       "      <td id=\"T_40020_row11_col3\" class=\"data row11 col3\" >342.540</td>\n",
-       "      <td id=\"T_40020_row11_col4\" class=\"data row11 col4\" >—</td>\n",
-       "      <td id=\"T_40020_row11_col5\" class=\"data row11 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_40020_level0_row12\" class=\"row_heading level0 row12\" >60</th>\n",
-       "      <td id=\"T_40020_row12_col0\" class=\"data row12 col0\" >ndrt_2020</td>\n",
-       "      <td id=\"T_40020_row12_col1\" class=\"data row12 col1\" >—</td>\n",
-       "      <td id=\"T_40020_row12_col2\" class=\"data row12 col2\" >171.661</td>\n",
-       "      <td id=\"T_40020_row12_col3\" class=\"data row12 col3\" >—</td>\n",
-       "      <td id=\"T_40020_row12_col4\" class=\"data row12 col4\" >—</td>\n",
-       "      <td id=\"T_40020_row12_col5\" class=\"data row12 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_40020_level0_row13\" class=\"row_heading level0 row13\" >65</th>\n",
-       "      <td id=\"T_40020_row13_col0\" class=\"data row13 col0\" >prometheus_2022</td>\n",
-       "      <td id=\"T_40020_row13_col1\" class=\"data row13 col1\" >317.880</td>\n",
-       "      <td id=\"T_40020_row13_col2\" class=\"data row13 col2\" >322.071</td>\n",
-       "      <td id=\"T_40020_row13_col3\" class=\"data row13 col3\" >324.060</td>\n",
-       "      <td id=\"T_40020_row13_col4\" class=\"data row13 col4\" >1.318</td>\n",
-       "      <td id=\"T_40020_row13_col5\" class=\"data row13 col5\" >1.944</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_40020_level0_row14\" class=\"row_heading level0 row14\" >70</th>\n",
-       "      <td id=\"T_40020_row14_col0\" class=\"data row14 col0\" >valetudo</td>\n",
-       "      <td id=\"T_40020_row14_col1\" class=\"data row14 col1\" >—</td>\n",
-       "      <td id=\"T_40020_row14_col2\" class=\"data row14 col2\" >123.311</td>\n",
-       "      <td id=\"T_40020_row14_col3\" class=\"data row14 col3\" >126.100</td>\n",
-       "      <td id=\"T_40020_row14_col4\" class=\"data row14 col4\" >—</td>\n",
-       "      <td id=\"T_40020_row14_col5\" class=\"data row14 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_40020_level0_row15\" class=\"row_heading level0 row15\" >75</th>\n",
-       "      <td id=\"T_40020_row15_col0\" class=\"data row15 col0\" >valkyrie</td>\n",
-       "      <td id=\"T_40020_row15_col1\" class=\"data row15 col1\" >—</td>\n",
-       "      <td id=\"T_40020_row15_col2\" class=\"data row15 col2\" >—</td>\n",
-       "      <td id=\"T_40020_row15_col3\" class=\"data row15 col3\" >—</td>\n",
-       "      <td id=\"T_40020_row15_col4\" class=\"data row15 col4\" >—</td>\n",
-       "      <td id=\"T_40020_row15_col5\" class=\"data row15 col5\" >—</td>\n",
+       "      <th id=\"T_bcd12_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
+       "      <td id=\"T_bcd12_row0_col0\" class=\"data row0 col0\" >andromeda</td>\n",
+       "      <td id=\"T_bcd12_row0_col1\" class=\"data row0 col1\" >324.109</td>\n",
+       "      <td id=\"T_bcd12_row0_col2\" class=\"data row0 col2\" >343.154</td>\n",
+       "      <td id=\"T_bcd12_row0_col3\" class=\"data row0 col3\" >352.641</td>\n",
+       "      <td id=\"T_bcd12_row0_col4\" class=\"data row0 col4\" >5.876</td>\n",
+       "      <td id=\"T_bcd12_row0_col5\" class=\"data row0 col5\" >8.803</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bcd12_level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
+       "      <td id=\"T_bcd12_row1_col0\" class=\"data row1 col0\" >astra</td>\n",
+       "      <td id=\"T_bcd12_row1_col1\" class=\"data row1 col1\" >—</td>\n",
+       "      <td id=\"T_bcd12_row1_col2\" class=\"data row1 col2\" >306.480</td>\n",
+       "      <td id=\"T_bcd12_row1_col3\" class=\"data row1 col3\" >318.547</td>\n",
+       "      <td id=\"T_bcd12_row1_col4\" class=\"data row1 col4\" >—</td>\n",
+       "      <td id=\"T_bcd12_row1_col5\" class=\"data row1 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bcd12_level0_row2\" class=\"row_heading level0 row2\" >11</th>\n",
+       "      <td id=\"T_bcd12_row2_col0\" class=\"data row2 col0\" >bella_lui</td>\n",
+       "      <td id=\"T_bcd12_row2_col1\" class=\"data row2 col1\" >90.000</td>\n",
+       "      <td id=\"T_bcd12_row2_col2\" class=\"data row2 col2\" >86.176</td>\n",
+       "      <td id=\"T_bcd12_row2_col3\" class=\"data row2 col3\" >86.473</td>\n",
+       "      <td id=\"T_bcd12_row2_col4\" class=\"data row2 col4\" >-4.249</td>\n",
+       "      <td id=\"T_bcd12_row2_col5\" class=\"data row2 col5\" >-3.918</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bcd12_level0_row3\" class=\"row_heading level0 row3\" >16</th>\n",
+       "      <td id=\"T_bcd12_row3_col0\" class=\"data row3 col0\" >camoes</td>\n",
+       "      <td id=\"T_bcd12_row3_col1\" class=\"data row3 col1\" >—</td>\n",
+       "      <td id=\"T_bcd12_row3_col2\" class=\"data row3 col2\" >327.440</td>\n",
+       "      <td id=\"T_bcd12_row3_col3\" class=\"data row3 col3\" >—</td>\n",
+       "      <td id=\"T_bcd12_row3_col4\" class=\"data row3 col4\" >—</td>\n",
+       "      <td id=\"T_bcd12_row3_col5\" class=\"data row3 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bcd12_level0_row4\" class=\"row_heading level0 row4\" >21</th>\n",
+       "      <td id=\"T_bcd12_row4_col0\" class=\"data row4 col0\" >cavour</td>\n",
+       "      <td id=\"T_bcd12_row4_col1\" class=\"data row4 col1\" >314.000</td>\n",
+       "      <td id=\"T_bcd12_row4_col2\" class=\"data row4 col2\" >304.640</td>\n",
+       "      <td id=\"T_bcd12_row4_col3\" class=\"data row4 col3\" >319.055</td>\n",
+       "      <td id=\"T_bcd12_row4_col4\" class=\"data row4 col4\" >-2.981</td>\n",
+       "      <td id=\"T_bcd12_row4_col5\" class=\"data row4 col5\" >1.610</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bcd12_level0_row5\" class=\"row_heading level0 row5\" >26</th>\n",
+       "      <td id=\"T_bcd12_row5_col0\" class=\"data row5 col0\" >defiance</td>\n",
+       "      <td id=\"T_bcd12_row5_col1\" class=\"data row5 col1\" >—</td>\n",
+       "      <td id=\"T_bcd12_row5_col2\" class=\"data row5 col2\" >438.711</td>\n",
+       "      <td id=\"T_bcd12_row5_col3\" class=\"data row5 col3\" >—</td>\n",
+       "      <td id=\"T_bcd12_row5_col4\" class=\"data row5 col4\" >—</td>\n",
+       "      <td id=\"T_bcd12_row5_col5\" class=\"data row5 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bcd12_level0_row6\" class=\"row_heading level0 row6\" >31</th>\n",
+       "      <td id=\"T_bcd12_row6_col0\" class=\"data row6 col0\" >erebus</td>\n",
+       "      <td id=\"T_bcd12_row6_col1\" class=\"data row6 col1\" >—</td>\n",
+       "      <td id=\"T_bcd12_row6_col2\" class=\"data row6 col2\" >294.171</td>\n",
+       "      <td id=\"T_bcd12_row6_col3\" class=\"data row6 col3\" >307.317</td>\n",
+       "      <td id=\"T_bcd12_row6_col4\" class=\"data row6 col4\" >—</td>\n",
+       "      <td id=\"T_bcd12_row6_col5\" class=\"data row6 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bcd12_level0_row7\" class=\"row_heading level0 row7\" >36</th>\n",
+       "      <td id=\"T_bcd12_row7_col0\" class=\"data row7 col0\" >genesis</td>\n",
+       "      <td id=\"T_bcd12_row7_col1\" class=\"data row7 col1\" >—</td>\n",
+       "      <td id=\"T_bcd12_row7_col2\" class=\"data row7 col2\" >290.636</td>\n",
+       "      <td id=\"T_bcd12_row7_col3\" class=\"data row7 col3\" >299.262</td>\n",
+       "      <td id=\"T_bcd12_row7_col4\" class=\"data row7 col4\" >—</td>\n",
+       "      <td id=\"T_bcd12_row7_col5\" class=\"data row7 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bcd12_level0_row8\" class=\"row_heading level0 row8\" >41</th>\n",
+       "      <td id=\"T_bcd12_row8_col0\" class=\"data row8 col0\" >halcyon</td>\n",
+       "      <td id=\"T_bcd12_row8_col1\" class=\"data row8 col1\" >—</td>\n",
+       "      <td id=\"T_bcd12_row8_col2\" class=\"data row8 col2\" >275.571</td>\n",
+       "      <td id=\"T_bcd12_row8_col3\" class=\"data row8 col3\" >280.950</td>\n",
+       "      <td id=\"T_bcd12_row8_col4\" class=\"data row8 col4\" >—</td>\n",
+       "      <td id=\"T_bcd12_row8_col5\" class=\"data row8 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bcd12_level0_row9\" class=\"row_heading level0 row9\" >46</th>\n",
+       "      <td id=\"T_bcd12_row9_col0\" class=\"data row9 col0\" >hedy</td>\n",
+       "      <td id=\"T_bcd12_row9_col1\" class=\"data row9 col1\" >—</td>\n",
+       "      <td id=\"T_bcd12_row9_col2\" class=\"data row9 col2\" >—</td>\n",
+       "      <td id=\"T_bcd12_row9_col3\" class=\"data row9 col3\" >—</td>\n",
+       "      <td id=\"T_bcd12_row9_col4\" class=\"data row9 col4\" >—</td>\n",
+       "      <td id=\"T_bcd12_row9_col5\" class=\"data row9 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bcd12_level0_row10\" class=\"row_heading level0 row10\" >50</th>\n",
+       "      <td id=\"T_bcd12_row10_col0\" class=\"data row10 col0\" >juno3</td>\n",
+       "      <td id=\"T_bcd12_row10_col1\" class=\"data row10 col1\" >268.761</td>\n",
+       "      <td id=\"T_bcd12_row10_col2\" class=\"data row10 col2\" >240.917</td>\n",
+       "      <td id=\"T_bcd12_row10_col3\" class=\"data row10 col3\" >243.901</td>\n",
+       "      <td id=\"T_bcd12_row10_col4\" class=\"data row10 col4\" >-10.360</td>\n",
+       "      <td id=\"T_bcd12_row10_col5\" class=\"data row10 col5\" >-9.250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bcd12_level0_row11\" class=\"row_heading level0 row11\" >55</th>\n",
+       "      <td id=\"T_bcd12_row11_col0\" class=\"data row11 col0\" >lince</td>\n",
+       "      <td id=\"T_bcd12_row11_col1\" class=\"data row11 col1\" >—</td>\n",
+       "      <td id=\"T_bcd12_row11_col2\" class=\"data row11 col2\" >317.771</td>\n",
+       "      <td id=\"T_bcd12_row11_col3\" class=\"data row11 col3\" >342.540</td>\n",
+       "      <td id=\"T_bcd12_row11_col4\" class=\"data row11 col4\" >—</td>\n",
+       "      <td id=\"T_bcd12_row11_col5\" class=\"data row11 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bcd12_level0_row12\" class=\"row_heading level0 row12\" >60</th>\n",
+       "      <td id=\"T_bcd12_row12_col0\" class=\"data row12 col0\" >ndrt_2020</td>\n",
+       "      <td id=\"T_bcd12_row12_col1\" class=\"data row12 col1\" >—</td>\n",
+       "      <td id=\"T_bcd12_row12_col2\" class=\"data row12 col2\" >171.661</td>\n",
+       "      <td id=\"T_bcd12_row12_col3\" class=\"data row12 col3\" >—</td>\n",
+       "      <td id=\"T_bcd12_row12_col4\" class=\"data row12 col4\" >—</td>\n",
+       "      <td id=\"T_bcd12_row12_col5\" class=\"data row12 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bcd12_level0_row13\" class=\"row_heading level0 row13\" >65</th>\n",
+       "      <td id=\"T_bcd12_row13_col0\" class=\"data row13 col0\" >prometheus_2022</td>\n",
+       "      <td id=\"T_bcd12_row13_col1\" class=\"data row13 col1\" >317.880</td>\n",
+       "      <td id=\"T_bcd12_row13_col2\" class=\"data row13 col2\" >322.071</td>\n",
+       "      <td id=\"T_bcd12_row13_col3\" class=\"data row13 col3\" >324.060</td>\n",
+       "      <td id=\"T_bcd12_row13_col4\" class=\"data row13 col4\" >1.318</td>\n",
+       "      <td id=\"T_bcd12_row13_col5\" class=\"data row13 col5\" >1.944</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bcd12_level0_row14\" class=\"row_heading level0 row14\" >70</th>\n",
+       "      <td id=\"T_bcd12_row14_col0\" class=\"data row14 col0\" >valetudo</td>\n",
+       "      <td id=\"T_bcd12_row14_col1\" class=\"data row14 col1\" >—</td>\n",
+       "      <td id=\"T_bcd12_row14_col2\" class=\"data row14 col2\" >123.311</td>\n",
+       "      <td id=\"T_bcd12_row14_col3\" class=\"data row14 col3\" >126.100</td>\n",
+       "      <td id=\"T_bcd12_row14_col4\" class=\"data row14 col4\" >—</td>\n",
+       "      <td id=\"T_bcd12_row14_col5\" class=\"data row14 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bcd12_level0_row15\" class=\"row_heading level0 row15\" >75</th>\n",
+       "      <td id=\"T_bcd12_row15_col0\" class=\"data row15 col0\" >valkyrie</td>\n",
+       "      <td id=\"T_bcd12_row15_col1\" class=\"data row15 col1\" >—</td>\n",
+       "      <td id=\"T_bcd12_row15_col2\" class=\"data row15 col2\" >—</td>\n",
+       "      <td id=\"T_bcd12_row15_col3\" class=\"data row15 col3\" >—</td>\n",
+       "      <td id=\"T_bcd12_row15_col4\" class=\"data row15 col4\" >—</td>\n",
+       "      <td id=\"T_bcd12_row15_col5\" class=\"data row15 col5\" >—</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x15c18fd90>"
+       "<pandas.io.formats.style.Styler at 0x117b6e490>"
       ]
      },
      "metadata": {},
@@ -453,14 +704,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "bc0b298b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-22T22:26:50.304636Z",
-     "iopub.status.busy": "2026-10-22T22:26:50.304545Z",
-     "iopub.status.idle": "2026-10-22T22:26:50.309449Z",
-     "shell.execute_reply": "2026-10-22T22:26:50.309159Z"
+     "iopub.execute_input": "2026-10-24T20:16:03.919698Z",
+     "iopub.status.busy": "2026-10-24T20:16:03.919644Z",
+     "iopub.status.idle": "2026-10-24T20:16:03.922619Z",
+     "shell.execute_reply": "2026-10-24T20:16:03.922382Z"
     }
    },
    "outputs": [
@@ -469,185 +720,185 @@
       "text/html": [
        "<style type=\"text/css\">\n",
        "</style>\n",
-       "<table id=\"T_9e847\">\n",
+       "<table id=\"T_db5fe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_9e847_level0_col0\" class=\"col_heading level0 col0\" >rocket</th>\n",
-       "      <th id=\"T_9e847_level0_col1\" class=\"col_heading level0 col1\" >measured</th>\n",
-       "      <th id=\"T_9e847_level0_col2\" class=\"col_heading level0 col2\" >rocketpy</th>\n",
-       "      <th id=\"T_9e847_level0_col3\" class=\"col_heading level0 col3\" >datcom</th>\n",
-       "      <th id=\"T_9e847_level0_col4\" class=\"col_heading level0 col4\" >inferred_cd</th>\n",
-       "      <th id=\"T_9e847_level0_col5\" class=\"col_heading level0 col5\" >rocketpy_vs_fitted_cd_pct</th>\n",
-       "      <th id=\"T_9e847_level0_col6\" class=\"col_heading level0 col6\" >datcom_vs_fitted_cd_pct</th>\n",
+       "      <th id=\"T_db5fe_level0_col0\" class=\"col_heading level0 col0\" >rocket</th>\n",
+       "      <th id=\"T_db5fe_level0_col1\" class=\"col_heading level0 col1\" >measured</th>\n",
+       "      <th id=\"T_db5fe_level0_col2\" class=\"col_heading level0 col2\" >rocketpy</th>\n",
+       "      <th id=\"T_db5fe_level0_col3\" class=\"col_heading level0 col3\" >datcom</th>\n",
+       "      <th id=\"T_db5fe_level0_col4\" class=\"col_heading level0 col4\" >inferred_cd</th>\n",
+       "      <th id=\"T_db5fe_level0_col5\" class=\"col_heading level0 col5\" >rocketpy_vs_fitted_cd_pct</th>\n",
+       "      <th id=\"T_db5fe_level0_col6\" class=\"col_heading level0 col6\" >datcom_vs_fitted_cd_pct</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_9e847_level0_row0\" class=\"row_heading level0 row0\" >3</th>\n",
-       "      <td id=\"T_9e847_row0_col0\" class=\"data row0 col0\" >andromeda</td>\n",
-       "      <td id=\"T_9e847_row0_col1\" class=\"data row0 col1\" >—</td>\n",
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-       "      <td id=\"T_9e847_row0_col6\" class=\"data row0 col6\" >-40.986</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_9e847_level0_row1\" class=\"row_heading level0 row1\" >8</th>\n",
-       "      <td id=\"T_9e847_row1_col0\" class=\"data row1 col0\" >astra</td>\n",
-       "      <td id=\"T_9e847_row1_col1\" class=\"data row1 col1\" >—</td>\n",
-       "      <td id=\"T_9e847_row1_col2\" class=\"data row1 col2\" >0.654</td>\n",
-       "      <td id=\"T_9e847_row1_col3\" class=\"data row1 col3\" >0.426</td>\n",
-       "      <td id=\"T_9e847_row1_col4\" class=\"data row1 col4\" >0.599</td>\n",
-       "      <td id=\"T_9e847_row1_col5\" class=\"data row1 col5\" >9.168</td>\n",
-       "      <td id=\"T_9e847_row1_col6\" class=\"data row1 col6\" >-28.787</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_9e847_level0_row2\" class=\"row_heading level0 row2\" >13</th>\n",
-       "      <td id=\"T_9e847_row2_col0\" class=\"data row2 col0\" >bella_lui</td>\n",
-       "      <td id=\"T_9e847_row2_col1\" class=\"data row2 col1\" >—</td>\n",
-       "      <td id=\"T_9e847_row2_col2\" class=\"data row2 col2\" >0.430</td>\n",
-       "      <td id=\"T_9e847_row2_col3\" class=\"data row2 col3\" >0.346</td>\n",
-       "      <td id=\"T_9e847_row2_col4\" class=\"data row2 col4\" >0.446</td>\n",
-       "      <td id=\"T_9e847_row2_col5\" class=\"data row2 col5\" >-3.535</td>\n",
-       "      <td id=\"T_9e847_row2_col6\" class=\"data row2 col6\" >-22.431</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_9e847_level0_row3\" class=\"row_heading level0 row3\" >18</th>\n",
-       "      <td id=\"T_9e847_row3_col0\" class=\"data row3 col0\" >camoes</td>\n",
-       "      <td id=\"T_9e847_row3_col1\" class=\"data row3 col1\" >—</td>\n",
-       "      <td id=\"T_9e847_row3_col2\" class=\"data row3 col2\" >0.704</td>\n",
-       "      <td id=\"T_9e847_row3_col3\" class=\"data row3 col3\" >—</td>\n",
-       "      <td id=\"T_9e847_row3_col4\" class=\"data row3 col4\" >—</td>\n",
-       "      <td id=\"T_9e847_row3_col5\" class=\"data row3 col5\" >—</td>\n",
-       "      <td id=\"T_9e847_row3_col6\" class=\"data row3 col6\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_9e847_level0_row4\" class=\"row_heading level0 row4\" >23</th>\n",
-       "      <td id=\"T_9e847_row4_col0\" class=\"data row4 col0\" >cavour</td>\n",
-       "      <td id=\"T_9e847_row4_col1\" class=\"data row4 col1\" >—</td>\n",
-       "      <td id=\"T_9e847_row4_col2\" class=\"data row4 col2\" >0.583</td>\n",
-       "      <td id=\"T_9e847_row4_col3\" class=\"data row4 col3\" >0.448</td>\n",
-       "      <td id=\"T_9e847_row4_col4\" class=\"data row4 col4\" >0.609</td>\n",
-       "      <td id=\"T_9e847_row4_col5\" class=\"data row4 col5\" >-4.214</td>\n",
-       "      <td id=\"T_9e847_row4_col6\" class=\"data row4 col6\" >-26.353</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_9e847_level0_row5\" class=\"row_heading level0 row5\" >28</th>\n",
-       "      <td id=\"T_9e847_row5_col0\" class=\"data row5 col0\" >defiance</td>\n",
-       "      <td id=\"T_9e847_row5_col1\" class=\"data row5 col1\" >—</td>\n",
-       "      <td id=\"T_9e847_row5_col2\" class=\"data row5 col2\" >0.668</td>\n",
-       "      <td id=\"T_9e847_row5_col3\" class=\"data row5 col3\" >—</td>\n",
-       "      <td id=\"T_9e847_row5_col4\" class=\"data row5 col4\" >0.657</td>\n",
-       "      <td id=\"T_9e847_row5_col5\" class=\"data row5 col5\" >1.606</td>\n",
-       "      <td id=\"T_9e847_row5_col6\" class=\"data row5 col6\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_9e847_level0_row6\" class=\"row_heading level0 row6\" >33</th>\n",
-       "      <td id=\"T_9e847_row6_col0\" class=\"data row6 col0\" >erebus</td>\n",
-       "      <td id=\"T_9e847_row6_col1\" class=\"data row6 col1\" >—</td>\n",
-       "      <td id=\"T_9e847_row6_col2\" class=\"data row6 col2\" >0.531</td>\n",
-       "      <td id=\"T_9e847_row6_col3\" class=\"data row6 col3\" >0.376</td>\n",
-       "      <td id=\"T_9e847_row6_col4\" class=\"data row6 col4\" >0.443</td>\n",
-       "      <td id=\"T_9e847_row6_col5\" class=\"data row6 col5\" >19.900</td>\n",
-       "      <td id=\"T_9e847_row6_col6\" class=\"data row6 col6\" >-15.099</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_9e847_level0_row7\" class=\"row_heading level0 row7\" >38</th>\n",
-       "      <td id=\"T_9e847_row7_col0\" class=\"data row7 col0\" >genesis</td>\n",
-       "      <td id=\"T_9e847_row7_col1\" class=\"data row7 col1\" >—</td>\n",
-       "      <td id=\"T_9e847_row7_col2\" class=\"data row7 col2\" >0.550</td>\n",
-       "      <td id=\"T_9e847_row7_col3\" class=\"data row7 col3\" >0.475</td>\n",
-       "      <td id=\"T_9e847_row7_col4\" class=\"data row7 col4\" >0.609</td>\n",
-       "      <td id=\"T_9e847_row7_col5\" class=\"data row7 col5\" >-9.687</td>\n",
-       "      <td id=\"T_9e847_row7_col6\" class=\"data row7 col6\" >-22.043</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_9e847_level0_row8\" class=\"row_heading level0 row8\" >43</th>\n",
-       "      <td id=\"T_9e847_row8_col0\" class=\"data row8 col0\" >halcyon</td>\n",
-       "      <td id=\"T_9e847_row8_col1\" class=\"data row8 col1\" >—</td>\n",
-       "      <td id=\"T_9e847_row8_col2\" class=\"data row8 col2\" >0.426</td>\n",
-       "      <td id=\"T_9e847_row8_col3\" class=\"data row8 col3\" >0.319</td>\n",
-       "      <td id=\"T_9e847_row8_col4\" class=\"data row8 col4\" >0.345</td>\n",
-       "      <td id=\"T_9e847_row8_col5\" class=\"data row8 col5\" >23.594</td>\n",
-       "      <td id=\"T_9e847_row8_col6\" class=\"data row8 col6\" >-7.450</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_9e847_level0_row9\" class=\"row_heading level0 row9\" >48</th>\n",
-       "      <td id=\"T_9e847_row9_col0\" class=\"data row9 col0\" >hedy</td>\n",
-       "      <td id=\"T_9e847_row9_col1\" class=\"data row9 col1\" >—</td>\n",
-       "      <td id=\"T_9e847_row9_col2\" class=\"data row9 col2\" >—</td>\n",
-       "      <td id=\"T_9e847_row9_col3\" class=\"data row9 col3\" >—</td>\n",
-       "      <td id=\"T_9e847_row9_col4\" class=\"data row9 col4\" >—</td>\n",
-       "      <td id=\"T_9e847_row9_col5\" class=\"data row9 col5\" >—</td>\n",
-       "      <td id=\"T_9e847_row9_col6\" class=\"data row9 col6\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_9e847_level0_row10\" class=\"row_heading level0 row10\" >52</th>\n",
-       "      <td id=\"T_9e847_row10_col0\" class=\"data row10 col0\" >juno3</td>\n",
-       "      <td id=\"T_9e847_row10_col1\" class=\"data row10 col1\" >—</td>\n",
-       "      <td id=\"T_9e847_row10_col2\" class=\"data row10 col2\" >0.368</td>\n",
-       "      <td id=\"T_9e847_row10_col3\" class=\"data row10 col3\" >0.374</td>\n",
-       "      <td id=\"T_9e847_row10_col4\" class=\"data row10 col4\" >0.264</td>\n",
-       "      <td id=\"T_9e847_row10_col5\" class=\"data row10 col5\" >39.345</td>\n",
-       "      <td id=\"T_9e847_row10_col6\" class=\"data row10 col6\" >41.806</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_9e847_level0_row11\" class=\"row_heading level0 row11\" >57</th>\n",
-       "      <td id=\"T_9e847_row11_col0\" class=\"data row11 col0\" >lince</td>\n",
-       "      <td id=\"T_9e847_row11_col1\" class=\"data row11 col1\" >—</td>\n",
-       "      <td id=\"T_9e847_row11_col2\" class=\"data row11 col2\" >0.656</td>\n",
-       "      <td id=\"T_9e847_row11_col3\" class=\"data row11 col3\" >0.382</td>\n",
-       "      <td id=\"T_9e847_row11_col4\" class=\"data row11 col4\" >0.578</td>\n",
-       "      <td id=\"T_9e847_row11_col5\" class=\"data row11 col5\" >13.398</td>\n",
-       "      <td id=\"T_9e847_row11_col6\" class=\"data row11 col6\" >-33.971</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_9e847_level0_row12\" class=\"row_heading level0 row12\" >62</th>\n",
-       "      <td id=\"T_9e847_row12_col0\" class=\"data row12 col0\" >ndrt_2020</td>\n",
-       "      <td id=\"T_9e847_row12_col1\" class=\"data row12 col1\" >—</td>\n",
-       "      <td id=\"T_9e847_row12_col2\" class=\"data row12 col2\" >0.440</td>\n",
-       "      <td id=\"T_9e847_row12_col3\" class=\"data row12 col3\" >—</td>\n",
-       "      <td id=\"T_9e847_row12_col4\" class=\"data row12 col4\" >0.417</td>\n",
-       "      <td id=\"T_9e847_row12_col5\" class=\"data row12 col5\" >5.468</td>\n",
-       "      <td id=\"T_9e847_row12_col6\" class=\"data row12 col6\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_9e847_level0_row13\" class=\"row_heading level0 row13\" >67</th>\n",
-       "      <td id=\"T_9e847_row13_col0\" class=\"data row13 col0\" >prometheus_2022</td>\n",
-       "      <td id=\"T_9e847_row13_col1\" class=\"data row13 col1\" >—</td>\n",
-       "      <td id=\"T_9e847_row13_col2\" class=\"data row13 col2\" >0.352</td>\n",
-       "      <td id=\"T_9e847_row13_col3\" class=\"data row13 col3\" >0.335</td>\n",
-       "      <td id=\"T_9e847_row13_col4\" class=\"data row13 col4\" >0.403</td>\n",
-       "      <td id=\"T_9e847_row13_col5\" class=\"data row13 col5\" >-12.600</td>\n",
-       "      <td id=\"T_9e847_row13_col6\" class=\"data row13 col6\" >-16.854</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_9e847_level0_row14\" class=\"row_heading level0 row14\" >72</th>\n",
-       "      <td id=\"T_9e847_row14_col0\" class=\"data row14 col0\" >valetudo</td>\n",
-       "      <td id=\"T_9e847_row14_col1\" class=\"data row14 col1\" >—</td>\n",
-       "      <td id=\"T_9e847_row14_col2\" class=\"data row14 col2\" >1.128</td>\n",
-       "      <td id=\"T_9e847_row14_col3\" class=\"data row14 col3\" >0.431</td>\n",
-       "      <td id=\"T_9e847_row14_col4\" class=\"data row14 col4\" >0.810</td>\n",
-       "      <td id=\"T_9e847_row14_col5\" class=\"data row14 col5\" >39.337</td>\n",
-       "      <td id=\"T_9e847_row14_col6\" class=\"data row14 col6\" >-46.719</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_9e847_level0_row15\" class=\"row_heading level0 row15\" >77</th>\n",
-       "      <td id=\"T_9e847_row15_col0\" class=\"data row15 col0\" >valkyrie</td>\n",
-       "      <td id=\"T_9e847_row15_col1\" class=\"data row15 col1\" >—</td>\n",
-       "      <td id=\"T_9e847_row15_col2\" class=\"data row15 col2\" >—</td>\n",
-       "      <td id=\"T_9e847_row15_col3\" class=\"data row15 col3\" >—</td>\n",
-       "      <td id=\"T_9e847_row15_col4\" class=\"data row15 col4\" >—</td>\n",
-       "      <td id=\"T_9e847_row15_col5\" class=\"data row15 col5\" >—</td>\n",
-       "      <td id=\"T_9e847_row15_col6\" class=\"data row15 col6\" >—</td>\n",
+       "      <th id=\"T_db5fe_level0_row0\" class=\"row_heading level0 row0\" >3</th>\n",
+       "      <td id=\"T_db5fe_row0_col0\" class=\"data row0 col0\" >andromeda</td>\n",
+       "      <td id=\"T_db5fe_row0_col1\" class=\"data row0 col1\" >—</td>\n",
+       "      <td id=\"T_db5fe_row0_col2\" class=\"data row0 col2\" >0.498</td>\n",
+       "      <td id=\"T_db5fe_row0_col3\" class=\"data row0 col3\" >0.349</td>\n",
+       "      <td id=\"T_db5fe_row0_col4\" class=\"data row0 col4\" >0.591</td>\n",
+       "      <td id=\"T_db5fe_row0_col5\" class=\"data row0 col5\" >-15.707</td>\n",
+       "      <td id=\"T_db5fe_row0_col6\" class=\"data row0 col6\" >-40.986</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_db5fe_level0_row1\" class=\"row_heading level0 row1\" >8</th>\n",
+       "      <td id=\"T_db5fe_row1_col0\" class=\"data row1 col0\" >astra</td>\n",
+       "      <td id=\"T_db5fe_row1_col1\" class=\"data row1 col1\" >—</td>\n",
+       "      <td id=\"T_db5fe_row1_col2\" class=\"data row1 col2\" >0.654</td>\n",
+       "      <td id=\"T_db5fe_row1_col3\" class=\"data row1 col3\" >0.426</td>\n",
+       "      <td id=\"T_db5fe_row1_col4\" class=\"data row1 col4\" >0.599</td>\n",
+       "      <td id=\"T_db5fe_row1_col5\" class=\"data row1 col5\" >9.168</td>\n",
+       "      <td id=\"T_db5fe_row1_col6\" class=\"data row1 col6\" >-28.787</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_db5fe_level0_row2\" class=\"row_heading level0 row2\" >13</th>\n",
+       "      <td id=\"T_db5fe_row2_col0\" class=\"data row2 col0\" >bella_lui</td>\n",
+       "      <td id=\"T_db5fe_row2_col1\" class=\"data row2 col1\" >—</td>\n",
+       "      <td id=\"T_db5fe_row2_col2\" class=\"data row2 col2\" >0.460</td>\n",
+       "      <td id=\"T_db5fe_row2_col3\" class=\"data row2 col3\" >0.346</td>\n",
+       "      <td id=\"T_db5fe_row2_col4\" class=\"data row2 col4\" >0.446</td>\n",
+       "      <td id=\"T_db5fe_row2_col5\" class=\"data row2 col5\" >3.195</td>\n",
+       "      <td id=\"T_db5fe_row2_col6\" class=\"data row2 col6\" >-22.431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_db5fe_level0_row3\" class=\"row_heading level0 row3\" >18</th>\n",
+       "      <td id=\"T_db5fe_row3_col0\" class=\"data row3 col0\" >camoes</td>\n",
+       "      <td id=\"T_db5fe_row3_col1\" class=\"data row3 col1\" >—</td>\n",
+       "      <td id=\"T_db5fe_row3_col2\" class=\"data row3 col2\" >0.704</td>\n",
+       "      <td id=\"T_db5fe_row3_col3\" class=\"data row3 col3\" >—</td>\n",
+       "      <td id=\"T_db5fe_row3_col4\" class=\"data row3 col4\" >—</td>\n",
+       "      <td id=\"T_db5fe_row3_col5\" class=\"data row3 col5\" >—</td>\n",
+       "      <td id=\"T_db5fe_row3_col6\" class=\"data row3 col6\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_db5fe_level0_row4\" class=\"row_heading level0 row4\" >23</th>\n",
+       "      <td id=\"T_db5fe_row4_col0\" class=\"data row4 col0\" >cavour</td>\n",
+       "      <td id=\"T_db5fe_row4_col1\" class=\"data row4 col1\" >—</td>\n",
+       "      <td id=\"T_db5fe_row4_col2\" class=\"data row4 col2\" >0.583</td>\n",
+       "      <td id=\"T_db5fe_row4_col3\" class=\"data row4 col3\" >0.448</td>\n",
+       "      <td id=\"T_db5fe_row4_col4\" class=\"data row4 col4\" >0.609</td>\n",
+       "      <td id=\"T_db5fe_row4_col5\" class=\"data row4 col5\" >-4.214</td>\n",
+       "      <td id=\"T_db5fe_row4_col6\" class=\"data row4 col6\" >-26.353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_db5fe_level0_row5\" class=\"row_heading level0 row5\" >28</th>\n",
+       "      <td id=\"T_db5fe_row5_col0\" class=\"data row5 col0\" >defiance</td>\n",
+       "      <td id=\"T_db5fe_row5_col1\" class=\"data row5 col1\" >—</td>\n",
+       "      <td id=\"T_db5fe_row5_col2\" class=\"data row5 col2\" >0.668</td>\n",
+       "      <td id=\"T_db5fe_row5_col3\" class=\"data row5 col3\" >—</td>\n",
+       "      <td id=\"T_db5fe_row5_col4\" class=\"data row5 col4\" >0.657</td>\n",
+       "      <td id=\"T_db5fe_row5_col5\" class=\"data row5 col5\" >1.606</td>\n",
+       "      <td id=\"T_db5fe_row5_col6\" class=\"data row5 col6\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_db5fe_level0_row6\" class=\"row_heading level0 row6\" >33</th>\n",
+       "      <td id=\"T_db5fe_row6_col0\" class=\"data row6 col0\" >erebus</td>\n",
+       "      <td id=\"T_db5fe_row6_col1\" class=\"data row6 col1\" >—</td>\n",
+       "      <td id=\"T_db5fe_row6_col2\" class=\"data row6 col2\" >0.531</td>\n",
+       "      <td id=\"T_db5fe_row6_col3\" class=\"data row6 col3\" >0.376</td>\n",
+       "      <td id=\"T_db5fe_row6_col4\" class=\"data row6 col4\" >0.443</td>\n",
+       "      <td id=\"T_db5fe_row6_col5\" class=\"data row6 col5\" >19.900</td>\n",
+       "      <td id=\"T_db5fe_row6_col6\" class=\"data row6 col6\" >-15.099</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_db5fe_level0_row7\" class=\"row_heading level0 row7\" >38</th>\n",
+       "      <td id=\"T_db5fe_row7_col0\" class=\"data row7 col0\" >genesis</td>\n",
+       "      <td id=\"T_db5fe_row7_col1\" class=\"data row7 col1\" >—</td>\n",
+       "      <td id=\"T_db5fe_row7_col2\" class=\"data row7 col2\" >0.550</td>\n",
+       "      <td id=\"T_db5fe_row7_col3\" class=\"data row7 col3\" >0.475</td>\n",
+       "      <td id=\"T_db5fe_row7_col4\" class=\"data row7 col4\" >0.609</td>\n",
+       "      <td id=\"T_db5fe_row7_col5\" class=\"data row7 col5\" >-9.687</td>\n",
+       "      <td id=\"T_db5fe_row7_col6\" class=\"data row7 col6\" >-22.043</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_db5fe_level0_row8\" class=\"row_heading level0 row8\" >43</th>\n",
+       "      <td id=\"T_db5fe_row8_col0\" class=\"data row8 col0\" >halcyon</td>\n",
+       "      <td id=\"T_db5fe_row8_col1\" class=\"data row8 col1\" >—</td>\n",
+       "      <td id=\"T_db5fe_row8_col2\" class=\"data row8 col2\" >0.426</td>\n",
+       "      <td id=\"T_db5fe_row8_col3\" class=\"data row8 col3\" >0.319</td>\n",
+       "      <td id=\"T_db5fe_row8_col4\" class=\"data row8 col4\" >0.345</td>\n",
+       "      <td id=\"T_db5fe_row8_col5\" class=\"data row8 col5\" >23.594</td>\n",
+       "      <td id=\"T_db5fe_row8_col6\" class=\"data row8 col6\" >-7.450</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_db5fe_level0_row9\" class=\"row_heading level0 row9\" >48</th>\n",
+       "      <td id=\"T_db5fe_row9_col0\" class=\"data row9 col0\" >hedy</td>\n",
+       "      <td id=\"T_db5fe_row9_col1\" class=\"data row9 col1\" >—</td>\n",
+       "      <td id=\"T_db5fe_row9_col2\" class=\"data row9 col2\" >—</td>\n",
+       "      <td id=\"T_db5fe_row9_col3\" class=\"data row9 col3\" >—</td>\n",
+       "      <td id=\"T_db5fe_row9_col4\" class=\"data row9 col4\" >—</td>\n",
+       "      <td id=\"T_db5fe_row9_col5\" class=\"data row9 col5\" >—</td>\n",
+       "      <td id=\"T_db5fe_row9_col6\" class=\"data row9 col6\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_db5fe_level0_row10\" class=\"row_heading level0 row10\" >52</th>\n",
+       "      <td id=\"T_db5fe_row10_col0\" class=\"data row10 col0\" >juno3</td>\n",
+       "      <td id=\"T_db5fe_row10_col1\" class=\"data row10 col1\" >—</td>\n",
+       "      <td id=\"T_db5fe_row10_col2\" class=\"data row10 col2\" >0.380</td>\n",
+       "      <td id=\"T_db5fe_row10_col3\" class=\"data row10 col3\" >0.374</td>\n",
+       "      <td id=\"T_db5fe_row10_col4\" class=\"data row10 col4\" >0.264</td>\n",
+       "      <td id=\"T_db5fe_row10_col5\" class=\"data row10 col5\" >43.889</td>\n",
+       "      <td id=\"T_db5fe_row10_col6\" class=\"data row10 col6\" >41.806</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_db5fe_level0_row11\" class=\"row_heading level0 row11\" >57</th>\n",
+       "      <td id=\"T_db5fe_row11_col0\" class=\"data row11 col0\" >lince</td>\n",
+       "      <td id=\"T_db5fe_row11_col1\" class=\"data row11 col1\" >—</td>\n",
+       "      <td id=\"T_db5fe_row11_col2\" class=\"data row11 col2\" >0.656</td>\n",
+       "      <td id=\"T_db5fe_row11_col3\" class=\"data row11 col3\" >0.382</td>\n",
+       "      <td id=\"T_db5fe_row11_col4\" class=\"data row11 col4\" >0.578</td>\n",
+       "      <td id=\"T_db5fe_row11_col5\" class=\"data row11 col5\" >13.398</td>\n",
+       "      <td id=\"T_db5fe_row11_col6\" class=\"data row11 col6\" >-33.971</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_db5fe_level0_row12\" class=\"row_heading level0 row12\" >62</th>\n",
+       "      <td id=\"T_db5fe_row12_col0\" class=\"data row12 col0\" >ndrt_2020</td>\n",
+       "      <td id=\"T_db5fe_row12_col1\" class=\"data row12 col1\" >—</td>\n",
+       "      <td id=\"T_db5fe_row12_col2\" class=\"data row12 col2\" >0.440</td>\n",
+       "      <td id=\"T_db5fe_row12_col3\" class=\"data row12 col3\" >—</td>\n",
+       "      <td id=\"T_db5fe_row12_col4\" class=\"data row12 col4\" >0.417</td>\n",
+       "      <td id=\"T_db5fe_row12_col5\" class=\"data row12 col5\" >5.468</td>\n",
+       "      <td id=\"T_db5fe_row12_col6\" class=\"data row12 col6\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_db5fe_level0_row13\" class=\"row_heading level0 row13\" >67</th>\n",
+       "      <td id=\"T_db5fe_row13_col0\" class=\"data row13 col0\" >prometheus_2022</td>\n",
+       "      <td id=\"T_db5fe_row13_col1\" class=\"data row13 col1\" >—</td>\n",
+       "      <td id=\"T_db5fe_row13_col2\" class=\"data row13 col2\" >0.352</td>\n",
+       "      <td id=\"T_db5fe_row13_col3\" class=\"data row13 col3\" >0.335</td>\n",
+       "      <td id=\"T_db5fe_row13_col4\" class=\"data row13 col4\" >0.403</td>\n",
+       "      <td id=\"T_db5fe_row13_col5\" class=\"data row13 col5\" >-12.600</td>\n",
+       "      <td id=\"T_db5fe_row13_col6\" class=\"data row13 col6\" >-16.854</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_db5fe_level0_row14\" class=\"row_heading level0 row14\" >72</th>\n",
+       "      <td id=\"T_db5fe_row14_col0\" class=\"data row14 col0\" >valetudo</td>\n",
+       "      <td id=\"T_db5fe_row14_col1\" class=\"data row14 col1\" >—</td>\n",
+       "      <td id=\"T_db5fe_row14_col2\" class=\"data row14 col2\" >1.128</td>\n",
+       "      <td id=\"T_db5fe_row14_col3\" class=\"data row14 col3\" >0.431</td>\n",
+       "      <td id=\"T_db5fe_row14_col4\" class=\"data row14 col4\" >0.810</td>\n",
+       "      <td id=\"T_db5fe_row14_col5\" class=\"data row14 col5\" >39.337</td>\n",
+       "      <td id=\"T_db5fe_row14_col6\" class=\"data row14 col6\" >-46.719</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_db5fe_level0_row15\" class=\"row_heading level0 row15\" >77</th>\n",
+       "      <td id=\"T_db5fe_row15_col0\" class=\"data row15 col0\" >valkyrie</td>\n",
+       "      <td id=\"T_db5fe_row15_col1\" class=\"data row15 col1\" >—</td>\n",
+       "      <td id=\"T_db5fe_row15_col2\" class=\"data row15 col2\" >—</td>\n",
+       "      <td id=\"T_db5fe_row15_col3\" class=\"data row15 col3\" >—</td>\n",
+       "      <td id=\"T_db5fe_row15_col4\" class=\"data row15 col4\" >—</td>\n",
+       "      <td id=\"T_db5fe_row15_col5\" class=\"data row15 col5\" >—</td>\n",
+       "      <td id=\"T_db5fe_row15_col6\" class=\"data row15 col6\" >—</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x15c06e210>"
+       "<pandas.io.formats.style.Styler at 0x117c87250>"
       ]
      },
      "metadata": {},
@@ -668,14 +919,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "ead14610",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-22T22:26:50.310538Z",
-     "iopub.status.busy": "2026-10-22T22:26:50.310465Z",
-     "iopub.status.idle": "2026-10-22T22:26:50.315133Z",
-     "shell.execute_reply": "2026-10-22T22:26:50.314884Z"
+     "iopub.execute_input": "2026-10-24T20:16:03.923618Z",
+     "iopub.status.busy": "2026-10-24T20:16:03.923553Z",
+     "iopub.status.idle": "2026-10-24T20:16:03.926854Z",
+     "shell.execute_reply": "2026-10-24T20:16:03.926677Z"
     }
    },
    "outputs": [
@@ -684,168 +935,168 @@
       "text/html": [
        "<style type=\"text/css\">\n",
        "</style>\n",
-       "<table id=\"T_b6a74\">\n",
+       "<table id=\"T_097ea\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_b6a74_level0_col0\" class=\"col_heading level0 col0\" >rocket</th>\n",
-       "      <th id=\"T_b6a74_level0_col1\" class=\"col_heading level0 col1\" >measured</th>\n",
-       "      <th id=\"T_b6a74_level0_col2\" class=\"col_heading level0 col2\" >rocketpy</th>\n",
-       "      <th id=\"T_b6a74_level0_col3\" class=\"col_heading level0 col3\" >datcom</th>\n",
-       "      <th id=\"T_b6a74_level0_col4\" class=\"col_heading level0 col4\" >rocketpy_error_pct</th>\n",
-       "      <th id=\"T_b6a74_level0_col5\" class=\"col_heading level0 col5\" >datcom_error_pct</th>\n",
+       "      <th id=\"T_097ea_level0_col0\" class=\"col_heading level0 col0\" >rocket</th>\n",
+       "      <th id=\"T_097ea_level0_col1\" class=\"col_heading level0 col1\" >measured</th>\n",
+       "      <th id=\"T_097ea_level0_col2\" class=\"col_heading level0 col2\" >rocketpy</th>\n",
+       "      <th id=\"T_097ea_level0_col3\" class=\"col_heading level0 col3\" >datcom</th>\n",
+       "      <th id=\"T_097ea_level0_col4\" class=\"col_heading level0 col4\" >rocketpy_error_pct</th>\n",
+       "      <th id=\"T_097ea_level0_col5\" class=\"col_heading level0 col5\" >datcom_error_pct</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_b6a74_level0_row0\" class=\"row_heading level0 row0\" >2</th>\n",
-       "      <td id=\"T_b6a74_row0_col0\" class=\"data row0 col0\" >andromeda</td>\n",
-       "      <td id=\"T_b6a74_row0_col1\" class=\"data row0 col1\" >—</td>\n",
-       "      <td id=\"T_b6a74_row0_col2\" class=\"data row0 col2\" >4.159</td>\n",
-       "      <td id=\"T_b6a74_row0_col3\" class=\"data row0 col3\" >4.586</td>\n",
-       "      <td id=\"T_b6a74_row0_col4\" class=\"data row0 col4\" >—</td>\n",
-       "      <td id=\"T_b6a74_row0_col5\" class=\"data row0 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b6a74_level0_row1\" class=\"row_heading level0 row1\" >7</th>\n",
-       "      <td id=\"T_b6a74_row1_col0\" class=\"data row1 col0\" >astra</td>\n",
-       "      <td id=\"T_b6a74_row1_col1\" class=\"data row1 col1\" >—</td>\n",
-       "      <td id=\"T_b6a74_row1_col2\" class=\"data row1 col2\" >3.163</td>\n",
-       "      <td id=\"T_b6a74_row1_col3\" class=\"data row1 col3\" >3.477</td>\n",
-       "      <td id=\"T_b6a74_row1_col4\" class=\"data row1 col4\" >—</td>\n",
-       "      <td id=\"T_b6a74_row1_col5\" class=\"data row1 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b6a74_level0_row2\" class=\"row_heading level0 row2\" >12</th>\n",
-       "      <td id=\"T_b6a74_row2_col0\" class=\"data row2 col0\" >bella_lui</td>\n",
-       "      <td id=\"T_b6a74_row2_col1\" class=\"data row2 col1\" >—</td>\n",
-       "      <td id=\"T_b6a74_row2_col2\" class=\"data row2 col2\" >2.777</td>\n",
-       "      <td id=\"T_b6a74_row2_col3\" class=\"data row2 col3\" >3.304</td>\n",
-       "      <td id=\"T_b6a74_row2_col4\" class=\"data row2 col4\" >—</td>\n",
-       "      <td id=\"T_b6a74_row2_col5\" class=\"data row2 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b6a74_level0_row3\" class=\"row_heading level0 row3\" >17</th>\n",
-       "      <td id=\"T_b6a74_row3_col0\" class=\"data row3 col0\" >camoes</td>\n",
-       "      <td id=\"T_b6a74_row3_col1\" class=\"data row3 col1\" >—</td>\n",
-       "      <td id=\"T_b6a74_row3_col2\" class=\"data row3 col2\" >3.762</td>\n",
-       "      <td id=\"T_b6a74_row3_col3\" class=\"data row3 col3\" >—</td>\n",
-       "      <td id=\"T_b6a74_row3_col4\" class=\"data row3 col4\" >—</td>\n",
-       "      <td id=\"T_b6a74_row3_col5\" class=\"data row3 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b6a74_level0_row4\" class=\"row_heading level0 row4\" >22</th>\n",
-       "      <td id=\"T_b6a74_row4_col0\" class=\"data row4 col0\" >cavour</td>\n",
-       "      <td id=\"T_b6a74_row4_col1\" class=\"data row4 col1\" >—</td>\n",
-       "      <td id=\"T_b6a74_row4_col2\" class=\"data row4 col2\" >4.280</td>\n",
-       "      <td id=\"T_b6a74_row4_col3\" class=\"data row4 col3\" >5.465</td>\n",
-       "      <td id=\"T_b6a74_row4_col4\" class=\"data row4 col4\" >—</td>\n",
-       "      <td id=\"T_b6a74_row4_col5\" class=\"data row4 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b6a74_level0_row5\" class=\"row_heading level0 row5\" >27</th>\n",
-       "      <td id=\"T_b6a74_row5_col0\" class=\"data row5 col0\" >defiance</td>\n",
-       "      <td id=\"T_b6a74_row5_col1\" class=\"data row5 col1\" >—</td>\n",
-       "      <td id=\"T_b6a74_row5_col2\" class=\"data row5 col2\" >7.960</td>\n",
-       "      <td id=\"T_b6a74_row5_col3\" class=\"data row5 col3\" >—</td>\n",
-       "      <td id=\"T_b6a74_row5_col4\" class=\"data row5 col4\" >—</td>\n",
-       "      <td id=\"T_b6a74_row5_col5\" class=\"data row5 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b6a74_level0_row6\" class=\"row_heading level0 row6\" >32</th>\n",
-       "      <td id=\"T_b6a74_row6_col0\" class=\"data row6 col0\" >erebus</td>\n",
-       "      <td id=\"T_b6a74_row6_col1\" class=\"data row6 col1\" >—</td>\n",
-       "      <td id=\"T_b6a74_row6_col2\" class=\"data row6 col2\" >2.937</td>\n",
-       "      <td id=\"T_b6a74_row6_col3\" class=\"data row6 col3\" >4.450</td>\n",
-       "      <td id=\"T_b6a74_row6_col4\" class=\"data row6 col4\" >—</td>\n",
-       "      <td id=\"T_b6a74_row6_col5\" class=\"data row6 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b6a74_level0_row7\" class=\"row_heading level0 row7\" >37</th>\n",
-       "      <td id=\"T_b6a74_row7_col0\" class=\"data row7 col0\" >genesis</td>\n",
-       "      <td id=\"T_b6a74_row7_col1\" class=\"data row7 col1\" >—</td>\n",
-       "      <td id=\"T_b6a74_row7_col2\" class=\"data row7 col2\" >3.377</td>\n",
-       "      <td id=\"T_b6a74_row7_col3\" class=\"data row7 col3\" >3.768</td>\n",
-       "      <td id=\"T_b6a74_row7_col4\" class=\"data row7 col4\" >—</td>\n",
-       "      <td id=\"T_b6a74_row7_col5\" class=\"data row7 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b6a74_level0_row8\" class=\"row_heading level0 row8\" >42</th>\n",
-       "      <td id=\"T_b6a74_row8_col0\" class=\"data row8 col0\" >halcyon</td>\n",
-       "      <td id=\"T_b6a74_row8_col1\" class=\"data row8 col1\" >—</td>\n",
-       "      <td id=\"T_b6a74_row8_col2\" class=\"data row8 col2\" >3.019</td>\n",
-       "      <td id=\"T_b6a74_row8_col3\" class=\"data row8 col3\" >4.410</td>\n",
-       "      <td id=\"T_b6a74_row8_col4\" class=\"data row8 col4\" >—</td>\n",
-       "      <td id=\"T_b6a74_row8_col5\" class=\"data row8 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b6a74_level0_row9\" class=\"row_heading level0 row9\" >47</th>\n",
-       "      <td id=\"T_b6a74_row9_col0\" class=\"data row9 col0\" >hedy</td>\n",
-       "      <td id=\"T_b6a74_row9_col1\" class=\"data row9 col1\" >—</td>\n",
-       "      <td id=\"T_b6a74_row9_col2\" class=\"data row9 col2\" >—</td>\n",
-       "      <td id=\"T_b6a74_row9_col3\" class=\"data row9 col3\" >—</td>\n",
-       "      <td id=\"T_b6a74_row9_col4\" class=\"data row9 col4\" >—</td>\n",
-       "      <td id=\"T_b6a74_row9_col5\" class=\"data row9 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b6a74_level0_row10\" class=\"row_heading level0 row10\" >51</th>\n",
-       "      <td id=\"T_b6a74_row10_col0\" class=\"data row10 col0\" >juno3</td>\n",
-       "      <td id=\"T_b6a74_row10_col1\" class=\"data row10 col1\" >—</td>\n",
-       "      <td id=\"T_b6a74_row10_col2\" class=\"data row10 col2\" >3.221</td>\n",
-       "      <td id=\"T_b6a74_row10_col3\" class=\"data row10 col3\" >4.007</td>\n",
-       "      <td id=\"T_b6a74_row10_col4\" class=\"data row10 col4\" >—</td>\n",
-       "      <td id=\"T_b6a74_row10_col5\" class=\"data row10 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b6a74_level0_row11\" class=\"row_heading level0 row11\" >56</th>\n",
-       "      <td id=\"T_b6a74_row11_col0\" class=\"data row11 col0\" >lince</td>\n",
-       "      <td id=\"T_b6a74_row11_col1\" class=\"data row11 col1\" >—</td>\n",
-       "      <td id=\"T_b6a74_row11_col2\" class=\"data row11 col2\" >2.920</td>\n",
-       "      <td id=\"T_b6a74_row11_col3\" class=\"data row11 col3\" >3.963</td>\n",
-       "      <td id=\"T_b6a74_row11_col4\" class=\"data row11 col4\" >—</td>\n",
-       "      <td id=\"T_b6a74_row11_col5\" class=\"data row11 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b6a74_level0_row12\" class=\"row_heading level0 row12\" >61</th>\n",
-       "      <td id=\"T_b6a74_row12_col0\" class=\"data row12 col0\" >ndrt_2020</td>\n",
-       "      <td id=\"T_b6a74_row12_col1\" class=\"data row12 col1\" >2.875</td>\n",
-       "      <td id=\"T_b6a74_row12_col2\" class=\"data row12 col2\" >4.016</td>\n",
-       "      <td id=\"T_b6a74_row12_col3\" class=\"data row12 col3\" >—</td>\n",
-       "      <td id=\"T_b6a74_row12_col4\" class=\"data row12 col4\" >39.700</td>\n",
-       "      <td id=\"T_b6a74_row12_col5\" class=\"data row12 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b6a74_level0_row13\" class=\"row_heading level0 row13\" >66</th>\n",
-       "      <td id=\"T_b6a74_row13_col0\" class=\"data row13 col0\" >prometheus_2022</td>\n",
-       "      <td id=\"T_b6a74_row13_col1\" class=\"data row13 col1\" >—</td>\n",
-       "      <td id=\"T_b6a74_row13_col2\" class=\"data row13 col2\" >1.042</td>\n",
-       "      <td id=\"T_b6a74_row13_col3\" class=\"data row13 col3\" >1.789</td>\n",
-       "      <td id=\"T_b6a74_row13_col4\" class=\"data row13 col4\" >—</td>\n",
-       "      <td id=\"T_b6a74_row13_col5\" class=\"data row13 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b6a74_level0_row14\" class=\"row_heading level0 row14\" >71</th>\n",
-       "      <td id=\"T_b6a74_row14_col0\" class=\"data row14 col0\" >valetudo</td>\n",
-       "      <td id=\"T_b6a74_row14_col1\" class=\"data row14 col1\" >—</td>\n",
-       "      <td id=\"T_b6a74_row14_col2\" class=\"data row14 col2\" >2.951</td>\n",
-       "      <td id=\"T_b6a74_row14_col3\" class=\"data row14 col3\" >4.157</td>\n",
-       "      <td id=\"T_b6a74_row14_col4\" class=\"data row14 col4\" >—</td>\n",
-       "      <td id=\"T_b6a74_row14_col5\" class=\"data row14 col5\" >—</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b6a74_level0_row15\" class=\"row_heading level0 row15\" >76</th>\n",
-       "      <td id=\"T_b6a74_row15_col0\" class=\"data row15 col0\" >valkyrie</td>\n",
-       "      <td id=\"T_b6a74_row15_col1\" class=\"data row15 col1\" >—</td>\n",
-       "      <td id=\"T_b6a74_row15_col2\" class=\"data row15 col2\" >—</td>\n",
-       "      <td id=\"T_b6a74_row15_col3\" class=\"data row15 col3\" >—</td>\n",
-       "      <td id=\"T_b6a74_row15_col4\" class=\"data row15 col4\" >—</td>\n",
-       "      <td id=\"T_b6a74_row15_col5\" class=\"data row15 col5\" >—</td>\n",
+       "      <th id=\"T_097ea_level0_row0\" class=\"row_heading level0 row0\" >2</th>\n",
+       "      <td id=\"T_097ea_row0_col0\" class=\"data row0 col0\" >andromeda</td>\n",
+       "      <td id=\"T_097ea_row0_col1\" class=\"data row0 col1\" >—</td>\n",
+       "      <td id=\"T_097ea_row0_col2\" class=\"data row0 col2\" >4.159</td>\n",
+       "      <td id=\"T_097ea_row0_col3\" class=\"data row0 col3\" >4.586</td>\n",
+       "      <td id=\"T_097ea_row0_col4\" class=\"data row0 col4\" >—</td>\n",
+       "      <td id=\"T_097ea_row0_col5\" class=\"data row0 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_097ea_level0_row1\" class=\"row_heading level0 row1\" >7</th>\n",
+       "      <td id=\"T_097ea_row1_col0\" class=\"data row1 col0\" >astra</td>\n",
+       "      <td id=\"T_097ea_row1_col1\" class=\"data row1 col1\" >—</td>\n",
+       "      <td id=\"T_097ea_row1_col2\" class=\"data row1 col2\" >3.163</td>\n",
+       "      <td id=\"T_097ea_row1_col3\" class=\"data row1 col3\" >3.477</td>\n",
+       "      <td id=\"T_097ea_row1_col4\" class=\"data row1 col4\" >—</td>\n",
+       "      <td id=\"T_097ea_row1_col5\" class=\"data row1 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_097ea_level0_row2\" class=\"row_heading level0 row2\" >12</th>\n",
+       "      <td id=\"T_097ea_row2_col0\" class=\"data row2 col0\" >bella_lui</td>\n",
+       "      <td id=\"T_097ea_row2_col1\" class=\"data row2 col1\" >—</td>\n",
+       "      <td id=\"T_097ea_row2_col2\" class=\"data row2 col2\" >2.777</td>\n",
+       "      <td id=\"T_097ea_row2_col3\" class=\"data row2 col3\" >3.304</td>\n",
+       "      <td id=\"T_097ea_row2_col4\" class=\"data row2 col4\" >—</td>\n",
+       "      <td id=\"T_097ea_row2_col5\" class=\"data row2 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_097ea_level0_row3\" class=\"row_heading level0 row3\" >17</th>\n",
+       "      <td id=\"T_097ea_row3_col0\" class=\"data row3 col0\" >camoes</td>\n",
+       "      <td id=\"T_097ea_row3_col1\" class=\"data row3 col1\" >—</td>\n",
+       "      <td id=\"T_097ea_row3_col2\" class=\"data row3 col2\" >3.762</td>\n",
+       "      <td id=\"T_097ea_row3_col3\" class=\"data row3 col3\" >—</td>\n",
+       "      <td id=\"T_097ea_row3_col4\" class=\"data row3 col4\" >—</td>\n",
+       "      <td id=\"T_097ea_row3_col5\" class=\"data row3 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_097ea_level0_row4\" class=\"row_heading level0 row4\" >22</th>\n",
+       "      <td id=\"T_097ea_row4_col0\" class=\"data row4 col0\" >cavour</td>\n",
+       "      <td id=\"T_097ea_row4_col1\" class=\"data row4 col1\" >—</td>\n",
+       "      <td id=\"T_097ea_row4_col2\" class=\"data row4 col2\" >4.280</td>\n",
+       "      <td id=\"T_097ea_row4_col3\" class=\"data row4 col3\" >5.465</td>\n",
+       "      <td id=\"T_097ea_row4_col4\" class=\"data row4 col4\" >—</td>\n",
+       "      <td id=\"T_097ea_row4_col5\" class=\"data row4 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_097ea_level0_row5\" class=\"row_heading level0 row5\" >27</th>\n",
+       "      <td id=\"T_097ea_row5_col0\" class=\"data row5 col0\" >defiance</td>\n",
+       "      <td id=\"T_097ea_row5_col1\" class=\"data row5 col1\" >—</td>\n",
+       "      <td id=\"T_097ea_row5_col2\" class=\"data row5 col2\" >7.960</td>\n",
+       "      <td id=\"T_097ea_row5_col3\" class=\"data row5 col3\" >—</td>\n",
+       "      <td id=\"T_097ea_row5_col4\" class=\"data row5 col4\" >—</td>\n",
+       "      <td id=\"T_097ea_row5_col5\" class=\"data row5 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_097ea_level0_row6\" class=\"row_heading level0 row6\" >32</th>\n",
+       "      <td id=\"T_097ea_row6_col0\" class=\"data row6 col0\" >erebus</td>\n",
+       "      <td id=\"T_097ea_row6_col1\" class=\"data row6 col1\" >—</td>\n",
+       "      <td id=\"T_097ea_row6_col2\" class=\"data row6 col2\" >2.937</td>\n",
+       "      <td id=\"T_097ea_row6_col3\" class=\"data row6 col3\" >4.450</td>\n",
+       "      <td id=\"T_097ea_row6_col4\" class=\"data row6 col4\" >—</td>\n",
+       "      <td id=\"T_097ea_row6_col5\" class=\"data row6 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_097ea_level0_row7\" class=\"row_heading level0 row7\" >37</th>\n",
+       "      <td id=\"T_097ea_row7_col0\" class=\"data row7 col0\" >genesis</td>\n",
+       "      <td id=\"T_097ea_row7_col1\" class=\"data row7 col1\" >—</td>\n",
+       "      <td id=\"T_097ea_row7_col2\" class=\"data row7 col2\" >3.377</td>\n",
+       "      <td id=\"T_097ea_row7_col3\" class=\"data row7 col3\" >3.768</td>\n",
+       "      <td id=\"T_097ea_row7_col4\" class=\"data row7 col4\" >—</td>\n",
+       "      <td id=\"T_097ea_row7_col5\" class=\"data row7 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_097ea_level0_row8\" class=\"row_heading level0 row8\" >42</th>\n",
+       "      <td id=\"T_097ea_row8_col0\" class=\"data row8 col0\" >halcyon</td>\n",
+       "      <td id=\"T_097ea_row8_col1\" class=\"data row8 col1\" >—</td>\n",
+       "      <td id=\"T_097ea_row8_col2\" class=\"data row8 col2\" >3.019</td>\n",
+       "      <td id=\"T_097ea_row8_col3\" class=\"data row8 col3\" >4.410</td>\n",
+       "      <td id=\"T_097ea_row8_col4\" class=\"data row8 col4\" >—</td>\n",
+       "      <td id=\"T_097ea_row8_col5\" class=\"data row8 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_097ea_level0_row9\" class=\"row_heading level0 row9\" >47</th>\n",
+       "      <td id=\"T_097ea_row9_col0\" class=\"data row9 col0\" >hedy</td>\n",
+       "      <td id=\"T_097ea_row9_col1\" class=\"data row9 col1\" >—</td>\n",
+       "      <td id=\"T_097ea_row9_col2\" class=\"data row9 col2\" >—</td>\n",
+       "      <td id=\"T_097ea_row9_col3\" class=\"data row9 col3\" >—</td>\n",
+       "      <td id=\"T_097ea_row9_col4\" class=\"data row9 col4\" >—</td>\n",
+       "      <td id=\"T_097ea_row9_col5\" class=\"data row9 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_097ea_level0_row10\" class=\"row_heading level0 row10\" >51</th>\n",
+       "      <td id=\"T_097ea_row10_col0\" class=\"data row10 col0\" >juno3</td>\n",
+       "      <td id=\"T_097ea_row10_col1\" class=\"data row10 col1\" >—</td>\n",
+       "      <td id=\"T_097ea_row10_col2\" class=\"data row10 col2\" >3.221</td>\n",
+       "      <td id=\"T_097ea_row10_col3\" class=\"data row10 col3\" >4.007</td>\n",
+       "      <td id=\"T_097ea_row10_col4\" class=\"data row10 col4\" >—</td>\n",
+       "      <td id=\"T_097ea_row10_col5\" class=\"data row10 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_097ea_level0_row11\" class=\"row_heading level0 row11\" >56</th>\n",
+       "      <td id=\"T_097ea_row11_col0\" class=\"data row11 col0\" >lince</td>\n",
+       "      <td id=\"T_097ea_row11_col1\" class=\"data row11 col1\" >—</td>\n",
+       "      <td id=\"T_097ea_row11_col2\" class=\"data row11 col2\" >2.920</td>\n",
+       "      <td id=\"T_097ea_row11_col3\" class=\"data row11 col3\" >3.963</td>\n",
+       "      <td id=\"T_097ea_row11_col4\" class=\"data row11 col4\" >—</td>\n",
+       "      <td id=\"T_097ea_row11_col5\" class=\"data row11 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_097ea_level0_row12\" class=\"row_heading level0 row12\" >61</th>\n",
+       "      <td id=\"T_097ea_row12_col0\" class=\"data row12 col0\" >ndrt_2020</td>\n",
+       "      <td id=\"T_097ea_row12_col1\" class=\"data row12 col1\" >2.875</td>\n",
+       "      <td id=\"T_097ea_row12_col2\" class=\"data row12 col2\" >4.016</td>\n",
+       "      <td id=\"T_097ea_row12_col3\" class=\"data row12 col3\" >—</td>\n",
+       "      <td id=\"T_097ea_row12_col4\" class=\"data row12 col4\" >39.700</td>\n",
+       "      <td id=\"T_097ea_row12_col5\" class=\"data row12 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_097ea_level0_row13\" class=\"row_heading level0 row13\" >66</th>\n",
+       "      <td id=\"T_097ea_row13_col0\" class=\"data row13 col0\" >prometheus_2022</td>\n",
+       "      <td id=\"T_097ea_row13_col1\" class=\"data row13 col1\" >—</td>\n",
+       "      <td id=\"T_097ea_row13_col2\" class=\"data row13 col2\" >1.042</td>\n",
+       "      <td id=\"T_097ea_row13_col3\" class=\"data row13 col3\" >1.789</td>\n",
+       "      <td id=\"T_097ea_row13_col4\" class=\"data row13 col4\" >—</td>\n",
+       "      <td id=\"T_097ea_row13_col5\" class=\"data row13 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_097ea_level0_row14\" class=\"row_heading level0 row14\" >71</th>\n",
+       "      <td id=\"T_097ea_row14_col0\" class=\"data row14 col0\" >valetudo</td>\n",
+       "      <td id=\"T_097ea_row14_col1\" class=\"data row14 col1\" >—</td>\n",
+       "      <td id=\"T_097ea_row14_col2\" class=\"data row14 col2\" >2.951</td>\n",
+       "      <td id=\"T_097ea_row14_col3\" class=\"data row14 col3\" >4.157</td>\n",
+       "      <td id=\"T_097ea_row14_col4\" class=\"data row14 col4\" >—</td>\n",
+       "      <td id=\"T_097ea_row14_col5\" class=\"data row14 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_097ea_level0_row15\" class=\"row_heading level0 row15\" >76</th>\n",
+       "      <td id=\"T_097ea_row15_col0\" class=\"data row15 col0\" >valkyrie</td>\n",
+       "      <td id=\"T_097ea_row15_col1\" class=\"data row15 col1\" >—</td>\n",
+       "      <td id=\"T_097ea_row15_col2\" class=\"data row15 col2\" >—</td>\n",
+       "      <td id=\"T_097ea_row15_col3\" class=\"data row15 col3\" >—</td>\n",
+       "      <td id=\"T_097ea_row15_col4\" class=\"data row15 col4\" >—</td>\n",
+       "      <td id=\"T_097ea_row15_col5\" class=\"data row15 col5\" >—</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x15c06e210>"
+       "<pandas.io.formats.style.Styler at 0x117c87250>"
       ]
      },
      "metadata": {},
@@ -870,14 +1121,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "c96f85d4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-22T22:26:50.316257Z",
-     "iopub.status.busy": "2026-10-22T22:26:50.316180Z",
-     "iopub.status.idle": "2026-10-22T22:26:54.997247Z",
-     "shell.execute_reply": "2026-10-22T22:26:54.996374Z"
+     "iopub.execute_input": "2026-10-24T20:16:03.927621Z",
+     "iopub.status.busy": "2026-10-24T20:16:03.927568Z",
+     "iopub.status.idle": "2026-10-24T20:16:05.964838Z",
+     "shell.execute_reply": "2026-10-24T20:16:05.964633Z"
     }
    },
    "outputs": [
@@ -903,7 +1154,7 @@
     },
     {
      "data": {
-      "image/png": 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QgC1btjB8+HAOHjzIuXPnOHPmDBA9jEJiOTs7U7FiRX0utFotq1atonnz5mTLlg2IzgXAsGHD4uSjb9++wIfPh2LOueLLj4ODAzqdDl9fX3x9fdFqtR89/qTo0aMH5ubmLFq0CIgeKsLc3JwePXok636EyCyMP91ECCEMY9myZSiKwqZNm9i0aVOc9StWrGDSpEmo1eoUi8HMzAyA8PBwTE1N9cv/e6IUU3jy9vYmT548+uVRUVH6k6cYOXLkwMXFhcmTJ8e7TwcHh2SJXQghhBCpw9zcnPbt2/P777/z6tUrli1bhqWlpX7iFUj87//P7XUT0zvo+fPn5MuXL942N27c4OrVq3h5edG1a1f98vv373/Wvrt3707fvn25ffs2Dx8+5NWrV3Tv3l2/PkeOHACMGjWKVq1axbuNYsWKxbs85pzr1atXcda9fPkSIyMjbG1tUalUqNVqnj9//lnH8l/W1tZ07dqVJUuWMGzYMJYvX06HDh2wsbFJ1v0IkVlIUUoIkSZptVpWrFhB4cKFWbJkSZz1u3btYubMmezZs4emTZvGWR9TQEroHT5TU9N428bM0nft2jX9jDcAO3fujNUuZra+1atXU758ef3yDRs2xJlRr2nTpuzevZvChQtja2uboPiEEEIIkbb17NmTRYsW8fPPP7N79266deuGhYWFfn1y/P5PzPlNgwYNUKvVLFy4kKpVq8bbJqbw9e8bbwC//fZbkuKL0b59e4YMGYKXlxcPHz4kT548NGjQQL++WLFiODk5cfXqVf3kNAlVrFgx8uTJw5o1axg2bJj+GIKDg9m8ebN+Rj6AmjVrsnHjRiZPnqwvhCXEv/Nsbm4eZ/2AAQNYsGABbdq0wc/Pj/79+yfqGIQQ/ydFKSFEmrRnzx5evnzJtGnT9AWffytVqhTz5s1j6dKl8RalSpUqBcDixYuxtLTEzMwMR0fHOI/SxShdujRHjhxh586d5M6dG0tLS4oVK0aTJk3Ili0bPXv2ZOLEiRgbG+Pl5cWzZ89ifb5EiRJ06tSJ2bNno9FoqFevHjdu3GDGjBlYWVnFajtx4kQOHDiAm5sbAwYMoFixYoSFhfH48WN2797NokWLkr2ruRBCCCFSVoUKFXBxcWH27NkoihLr0T1Int//hQsXxtzcnNWrV1OiRAmyZs2Kg4NDvL2sCxYsyOjRo/nxxx8JDQ2lffv2WFtbc+vWLd6+fcuECRMoXrw4hQsXZuTIkSiKQrZs2di5cycHDhz4rFzY2NjQsmVLvLy88PPzY9iwYbHGf4Lowlfjxo1p2LAh3bp1I0+ePLx//57bt29z6dKlD860bGRkxPTp0+nYsSNNmzald+/ehIeH8/PPP+Pn58dPP/2kbztr1iyqV69O5cqVGTlyJEWKFOH169fs2LGD3377DUtLy3j3Ubp0aQCmTZtG48aNUavVuLi4YGJiAkDRokVp1KgRe/bsoXr16pQpU+az8iVEpmbYcdaFECJ+LVq0UExMTD46W0q7du0UY2NjxdvbO87se4qiKLNnz1YcHR0VtVoda6aa+Gbfu3LlilKtWjXFwsJCAZSaNWvq1507d05xc3NTsmTJouTJk0cZP368smTJkjgz0ISHhytDhw5VcubMqZiZmSlVqlRRTp8+rRQoUCDOzH5v3rxRBgwYoDg6OioajUbJli2bUr58eWXMmDFKUFBQEjImhBBCCEP79ddfFUApWbJkvOsT8vs/Zva9n3/+Od5trF27VilevLii0Whinf98aMbgP/74Q6lYsaJiZmamZM2aVSlbtmys2ftu3bql1K9fX7G0tFRsbW2Vtm3bKk+fPo1zbpXQ2fdi7N+/XwFizUD4X1evXlU8PDyUnDlzKhqNRsmVK5dSp04d/azLihL/7HiKoijbtm1TKleurJiZmSlZsmRR6tatq5w8eTLOPm7duqW0bdtWyZ49u2JiYqLkz59f6datmxIWFvbB7YeHhyu9evVS7OzsFJVKFe9xe3l5KYCybt26BOVDCBE/laIoSuqXwoQQQgghhBBCiPQpZqa/x48ff9YsiUJkdvL4nhBCCCGEEEII8Qnh4eFcunSJc+fOsXXrVmbNmiUFKSE+k/SUEkIIIYQQQgghPuHx48c4OjpiZWVFhw4dmDdvXorOAi1EZiBFKSGEEEIIIYQQQgiR6ow+3UQIIYQQQgghhBBCiOQlRSkhhBBCCCGEEEIIkepkoHNAp9Px8uVLLC0tUalUhg5HCCGEEGmQoigEBgbi4OCAkVHmva8n501CCCGE+JSEnjdJUQp4+fIl+fLlM3QYQgghhEgHnj17Rt68eQ0dhsHIeZMQQgghEupT501SlAIsLS2B6GRZWVkl+/YVRcHf3x9ra2u5o5hCJMcpT3Kc8iTHqUPynPIyao4DAgLIly+f/rwhs5LzpvRPcpzyJMepQ/Kc8iTHKS+j5jih501SlAL9P7yVlVWKnVwpioKVlVWG+pKlJZLjlCc5TnmS49QheU55GT3HGfGYEkPOm9I/yXHKkxynDslzypMcp7yMnuNPHVPmHRBBCCGEEEIIIYQQQhiM9JT6BK1WS2Rk5GdtQ1EUIiIiCAsLy5CVzw/RaDSo1WpDhyGEEEIIIYQQQnxUclz7J0VGqBd8zrW/FKU+IigoiOfPn6MoymdvS6fT8e7du2SIKv1QqVTkzZuXrFmzGjoUIYQQQgghhBAiXsl57Z8U6b1e8DnX/lKU+gCtVsvz58+xsLDAzs7usyqWiqKg1WpRq9XptvKZWIqi8ObNG54/f46Tk5P0mBJCCCGEEEIIkeYk57V/UqT3esHnXvtLUeoDIiMjURQFOzs7zM3NP2tb6f1LllR2dnY8fvyYyMhIKUoJIYQQQgghhEhzkvPaPykyQr3gc679pSj1Cen1S5EWSO6EEEIAoCgQGQLhgRAeBFGhEBUOkaEQFfbhP6PCQdGC7p+XogVd1H/e/7NM0UbvS6WGtssNe7xCpHEPHjzgp59+wsTEhHz58pEzZ079RUTp0qUpX768gSMUQojUJ9evSfc5uZOilBBCCCE+TRsJob4Q8u6f1/v//z3M75+C0z+vsAAsQ/0gMhjCA6KXKbrUidNITm2E+Jht27bRrVs3/P39P9imQ4cOTJ8+nTx58qRiZEIIITIjOXNLw4yNjSlVqhSRkZEUKlSIlStXYmNjk6hteHp6kiNHDvr37//Jto8fP+bcuXN4eHgAcOTIEVq0aEHBggUJDw+nXbt2jB8/PimHIoQQIi2KDIMgbwh8/c+f/7yCXkPw29gFqPAPX8D+lwqIr+O2ojJCZ5wFnbEpWiMztGpToozMiDIyJcrIhEiVCREqUyL++TNKpUGL+p+XETqM0GKEFjVRigqdKnq5pbkZNYvn+mfnRsmSGiEyiqNHj7Jy5Up0Oh1+fn5s3boVgEqVKlGvXj1evHjBmzdvUBSF8PBwDh8+zJo1a9i+fTvjxo1j0KBBmJiYGPgohBAiYzMzM8u01/5SlErDbGxsuHLlCgCdO3dm/vz5jBkzJsX29/jxYzZs2KD/YgLUq1ePTZs2ERwcjKurK02bNpUu3UIIkR6EBYD/c/B/Bn5Po/8e+Or/RadA7+geTomgoCLSxJowjQ3BRlZ4R5pz39+YlyFqgnQmBOlMeOUfyfN3wQRrjQjWGhOiGBOmVRMWEUV4WBi68BCU8ODoP7VBoASg6LSg06H88zieotP983ie7p91WhRFF93mn/fW1dphU70jAM4OVtSsVCMFkihE+vb48WOaNWtGQEBArOWDBw9m1KhR5MiRI84jFxcvXqR///6cOXOGESNGsGrVKvbv30+uXLlSM3QhhMhUbGxsuHz5MiqVKtNd+0tRKgEURSE0UvtZn48euEyJ9YvfXJPwgcyqVavG1atXAXj48CE9evTg/fv3FCxYEC8vL7Jly8adO3fo06cPvr6+mJqacvDgwVjbmDp1Knfu3GH58uVMnTqVbdu2ER4eTt++fenTpw9jxozh5s2buLq6MnDgQBwdHfWfzZIlC+XLl+fBgwcMHDiQZcuWUbRoURRFoXjx4pw9ezbRlVwhhBBJpCjRj9K9fwR+T6ILT/7Pwe/ZP39/BmEJ69mkNTLhnVF2rvtZcP2dMa/CTPDVmfM+0pTnfmG89g0mKDiMCNREKWq0EaFoA9+iDbqHEhmWwgf6YRUL2OBezwm1SoWdpanB4hAirYqKiqJjx44EBARQvnx52rZtC0CVKlX44osvPvj4Xvny5Tl58iQrV67k+++/5/r169SuXZtDhw6RO3fu1DwEIYRIdZ977f8xCb3+z2zX/lKUSoDQSC0lx+1L9u3emtgQC5NP/xNotVoOHDhAjx49ABgwYAB9+/bFw8ODadOm4enpyZw5c+jUqRNTp06lfv36BAYGxpo5YMqUKdy9e5fly5ezf/9+fHx8OH/+PBEREVSvXp2mTZsyefJk5s2bx6ZNm4DoLnwx3r17x5kzZxg7dizdunXjjz/+YNKkSRw+fBhXV1cpSAkhRHJTlOhH6N4//Nfrwf///pGiU6RW4ZGfjkCyEmCcnScRVlx+DTe9w/AJ1hGsmBCqNSYgIIAwXx+0gXeTHKZZFkusbLNhrDbGSAVZrazIX8ARY7UR5qYmaNRGmBirsbW1wdraGhub6D+tra0xMzNDrVZjbGz8wT/je6nVaiwtLcmSJUuS4xYio/vxxx85deoUVlZWbNy4MdYFh6IoH/2skZERXbt2pXr16tSuXZs7d+5Qu3ZtPD09MTIyws7Ojpo1a2JkJI/LCiEylpS69oeEXf9nxmt/KUqlYX5+fri6uvL8+XOcnZ1p2LAhAOfPn2fnzp1A9GN97u7uBAQE4OfnR/369QGwtLTUb2fx4sUUL16cdevWYWRkxIEDB9i5cydHjx4FwN/fnwcPHsQbw19//UXZsmUxMjJi1KhRODs7kz9/fipXrsyPP/7IihUr6Nq1a0qmQQghMraocHh3H97cgTd3o1/vH0b3gooIjPcjWp3CQ18dfkbZeK/OwYPQLJx9Fsm1F8E8836P7xsfdNooIBh4naAwTLNYYp+3ILa2tpgYq9EYG5HbPheFCxWkQN48aDTRpwxZsmQhT548ODg44ODgEG9hSFEU/P39sba2lplshEgl4eHhtGjRQn933dvbG4BFixbFKkglRuHChTly5Ai1a9fm7t27tG/fXr+uU6dOLFu2DI1G8/nBCyFEJufn50fZsmUz5bW/FKUSwFyj5tbEhkn+/P8f31PHeXzvY2LGlAoJCaF+/fosWLCAAQMGxNqGovz/kcAPnfiXKVOGq1ev4uPjQ65cuVAUBU9PT7p06RKr3b+rozFiniv9N0tLS8qXL8/u3bs5efIky5Yt++hxCCGEACKC4e29fwpPd+DNveg/fR/pZ6ZTFIV/918Ii1Kx7LYZx14Y4a8z532UKc/fhfLG2xttZDjRRadn8e5ObWKGiZk5RioVZmZmFCjsRLHiJciXOxdZzYzRGKvJmzcvRYoUoUiRImTPnl0KSEKkY5s2bWLv3r2xlvXo0SNWISlRIoLh4VEKeV/jyPiGjFpxAu+ASBSNOacu32bVqlUEBASwfv16zMzMkuEIhBDC8D732v9T2/6QmDGlQkNDM921vxSlEkClUiXoMbsPiS5KqeIUpRLKwsKCX3/9ldatW9O3b18qVKjA5s2badOmDWvWrKFGjRpYWVlha2vLgQMH4nThq1y5Mp06deLLL7/kwIED1KtXj2nTptG2bVvMzc25e/cuBQoUwNLSksDA+O/K/1e3bt3o3LkzHTp0QK3+eHFNCCEyFZ0uutDkfR1e3wDvG/D6Jvg/jdM0Uqtw8ZWWv56q+euJmvNPgggJj/pPq4A4nwNQm5hiamaBkQrMzS0oVrIU5cq64la5PBXKlcPR0VEerREiE5k/fz4AQ4YMoXPnzmg0GooXL574DYW8J/zITLjkhWlUEACOwLra/2+yo4gpX23SsWPHDooULoSNbTaMjY355ptv6Nu3bzIcjRBCGMbnXvt/rsx47S9FqXSiQoUKlC5dms2bNzNnzhy6d+/OxIkTKVCgACtWrABg5cqVfPPNNwwbNgwzM7NYg501bNgQX19fmjdvzt69e7lx4waVKlVCURRy5szJzp07cXFxITIyMt7Bzv6rTp06aLXaOBVXIYTIVMID4fUteH39n+LTjej3kcGxmj3203HwYRR/PVXzKtyUIJ0pb8OMePHqDVGRER/dhY1dbr5o3BL7bFZYW5hQvFABqlatQrFixeSmgBACgEuXLnH69Gk0Gg3Dhw/H3t4+8RtRFCLOLkX31wTMoqKL4U91dpzVlcCbbGiIwkH1jlKqRzQr6s2eDmY0WxvCi5evePHyFQD9+vXD29ubCRMmSM9LIYRIosx27a9SPjXSYSYQEBCAtbU1/v7+WFlZARAWFsajR49wdHT87C7JH3p8Lz27f/8+HTp04Ny5cx9sk5w5/BQZvyTlSY5TnuQ4dSQ5z+GB8OoqvLgELy/ByyvRPaL+td0bPjp2/x3Fn/e1nHmuJUob+3G8+JhmsaKQS0UqVqlG/Tq1qFy6GLZZTPSx2draprseTxn1uxzf+UJmlNJ5yKjfn5TUs2dPli1bRvv27VmzZs0n28fJcfA7fNf0wPbFEQBu6/Kx1rIbhd1aUb5gdrJnNSE0QsuT9yFcfurHzds3yf36KHXDD6K8eQjA0SdaJh4NB6J7a82YMSNT//vJ9zh1SJ5TXmbIcWpet8YnrdcLknrtn9DzBekpJRJtyZIlTJw4kd9//93QoQghRMqIDIt+/O7lJXh5OboQ9fYeoKAoChdf6Tj1LIooHYSosnD+rSnH7vnjFxgS7+ZURmrsCztTulI1ihUpRE5LU3LZZKFKpYo4Ozunu6KTECLteP/+vb4Q1a9fv0R/XvG5TcCy1tiGvSBc0fCbphOFvhzKBJe8cS6OCtllpXaxnFC/KC/9GrHhwjMunjlGh/B1THA8j52Fiu/2hDFr1ixC3nszf+lK+fkmhBDpWGpc+0tRSiRar1696NWrl6HDEEKI5KEo8PZveHYWnp+PLkL53ALd/8d2itQqHH+qZc0dY7bfieBtQPi/NvD/vxtpTMlTsgIVqtelYYN6uBRyIJ+tObY21vHOUieEEJ9r+fLlhIWF4erqipubW6I+G/XsIpFezbHWBfFUZ8cmp+l83fZLLM0+PaOeg405g+oVJbxWYTacr0engwfoUn4tSzWn6bUjjEVeawh9cpEl6//E2K5wUg9PCCGEAaXGtb8UpYQQQmQukSGon5+Bazfg2Tl4fg5CffWrb/hoWXcjkiPPVARoTQlSzHjxNpCIsFB9G5XGDPvi5cmezRZrM2Py58lFm2buNGlQVz/QpBBCpLTw8HB+/fVXILqXVGIe+1BeXiFyUwcsdMFc0jnxqP5ShtQok+gYTI3VdK5akLYVerDqTF2OaPYx2uhXftp+lxWH73LTtRi58xZAbedE5x7f0KpVq0TvQwghRMYlRSkhhBAZm//z6F5Qz85F/+l9HUtdFIHhClvvRLL2RiT33ytEYEJApNF/HsELJ2b2OwsrW6rWboBHm1Z0aNmUrFksDHI4QggR47fffuPZs2fkzZuXTp06JfhzES+uYrKxAxZKMBeVYgR7rKN1qUKfFYuZRk2vGoVoX+lrlp+sRzWTaZzatIwLL7Xw8iHwkG1/7mPO9El89/2Yz9qXEEKIjEOKUkIIITIORQG/J/D4xD+vk+D/lFtvtKy8GsnWO1EEhCvoMOJ9qEKkVvevD4cBYGSswal8Deo2bEKJArnIZWVG7ty5qFKlisx2J4RIM4KDg5k8eTIAY8eOTfDgvErAK0K82mD7T0EqzGM9Xzh/eNalxMpiakz/Ok50rrKQH+u14uT2FdRRncfnxXOWXI5kwPAf8L2+n9bfTQYLW/Lnz4+lpWWy7V8IIUT6IkUpIYQQ6ZeigO/j/xehnpwE/2cA7LoXybD94fgEK/iG/XcOPC0A5jny4lqrKXXr1qZYbhvyZ7OgjEtprK2tU/c4hBAikebOnYuPjw+FChWie/fuCftQVDivF7ciV9Qb7isOhLVZTbVkLEj9m7WFhhlfN+FNu7osOPw3EWd38Y3lryw+5s34lccYv7IGAJaWWdm6dRt169ZNkTiEEEKkbTIdRhpmbGxM2bJlKVmyJOXLl48z4r2iKOTPn5/hw4cDMGbMGFxdXSlZsiQWFha4urri6urK4cOH2bp1K2XKlMHZ2ZlSpUqxYMECAHQ6HWPGjKFIkSIUKVIEDw8P3r17B8Djx49RqVRMnz5dv8+zZ8+iUqnw8vJKnSQIIcR/+T6GS3/Alm/gl1Iwx5U7y77l+xleOE68RY7pgdhMD+HLtaHcfaeLLkgZqclatArVvpnMwPlbmb16F2cvXSfY5ymnNi7kxz4edGregC9qVJeClBAizfPz89Ofn3l6eqLRfHpgcoDnG74nV9AtfJWsXKk6D7dSRVIyTADsLE0Z36wUU78fSLYBe6nSwJ1cVhrsLFRYmUJgYBDujRuxY9PaFI9FCCHSKjMzM8qVK5cpr/2lKJWG2djYcPnyZW7dusWWLVtYuHAhixcv1q8/ffo0uXPnZsuWLQBMnjyZK1eusHv3bkqWLMmVK1e4cuUKNjY2jBgxgm3btnHz5k3Onz+PhUX0WCizZ8/m1q1b3Lp1i/v371OxYsVYd9ucnJzYsWOH/v2GDRsoXbp0KmVACCGAUD+4tQN2DUaZ7cLZ753p1qMXhXouoeDE2+T/JYgS84OZcTqCx34K70IV/EOjZ85zbdSeKav3c/L6A3xvneTEb6P55dvmdG1SjYquzokaFFiIlPL48WNWrlzJjz/+yKhRo5g1axaHDx8mLCws0duKiorihx9+wNHREXNzcwoVKsTEiRPR6f7/qKqiKHh6euLg4IC5uTm1atXi5s2byXlIIoXNmjULX19fSpYsSYcOHRL0Gb8rO8h7bwUAuwqNo16V8ikZYhwONuZMbV2GtavX89WiM7QcNI5jQ0rQsrgx4ZFRtPqqA2vGdYTwoFSNSwgh0gIbGxsuXbqUKa/95fG9hFAUiAz5dLuPfV6rBbUa/n0BpLGI/f4jChQowMyZMxk8eDDffPMNAOvXr6dPnz6sWbOGs2fPUrly5Xg/O3PmTMaMGYOjY3T3bHNzc7p166Zfd+rUKUxMTAAYOnQoixYt4u+//0aj0WBlZUWOHDl4+PAhjo6OHDlyhDp16iQxEUIIkQDaSHh+Hh4chgeH4OUlgsK1rLkeycILEVzx1v3nAwqojMhevDI1mralViVXSuexpkTB3OTOndsghyBEQqxZs4Y5c+Zw7tw5cubMSZ48eTA3N+f9+/c8ePAAMzMzOnbsyIgRIyhQoECCtjlt2jQWLVrEihUrcHZ25sKFC3Tv3h1ra2sGDhwIwPTp05k1axZeXl4ULVqUSZMmUb9+fe7evStj+6QDb9684ZdffgFg4sSJCRrrThf8HnYMAGCLaTPatu9JWIhhij8Fc2Rhdvty3PV2Yta+Jtg3305Lk3lsvRZApx/X4Hd9H32/Hw8Ve4FGZjMVQqSyz732/5gEXv9ntmt/KUolRGQITHFI8sdVfCDRo1+CSZYEb6dcuXLcvXsXiL7LuXPnTiZMmEBkZCTr16//4Bfz1q1bfP/993GWBwQEEBoaGutE18jICBcXF27fvo2LiwsAbdu2ZcOGDdSsWZPy5ctjbCxfGyFEMlIUeHc/ugD14BB+t4+x/Jwvx59q0SmgVeDwYx3BEdHFKJVag0WJGji5NcG1cG5KOljRsFIpXIoWNOxxCJEI5cqVw8jIiG7durFhwwby588fa314eDinT59m3bp1VKhQgQULFtC2bdtPbvf06dM0b94cd3d3AAoWLMjatWu5cOECEH3+MHv2bMaMGUOrVq0AWLFiBfb29qxZs4bevXvH2WZ4eDjh4eH69wEBAfptKcp/x2v7fDHbTYltZwTTpk0jKCiIcuXK0bJlywTl6eHKfhTR+fJAccClyyxMjI0INXCOi9pnZVGXilx95sTMQo0ps3ggVy9eot+2dwQGfs/3DeegqjEUyncDY1ODxZlU8j1OHZLnlJcZchxzbIqioEQEo5qaJ2X2M+rFJ6//Y2IpW7Ysd+/e1ed+586deHp6EhERwbp166hUqVKc2CH62n/YsGFx/r1irv3z58+vX6dSqXBxceHWrVv6a/82bdqwfv16atasSbly5VCr1Qn694+Vw//E9ClSXUhH/v2Pevz4cUqWLImNjQ2tWrWiYsWKzJw5M95HURRFSdTy/2rWrBmNGjXi9evXeHh4sHfv3s87ECGEiAyDJyfg3n7Cbu1h17kHnHuh5U2IwoabkYRExv2IeY68VPuyPb16dKOua2FyZE1/FylCxPjxxx/1haP4mJqaUqtWLWrVqsWkSZN49OhRgrZbvXp1Fi1axL179yhatChXr17lxIkTzJ49G4BHjx7h7e1NgwYNYu2rZs2anDp1Kt6i1NSpU5kwYUKc5f7+/ilWlAoKiu7FI4/YxhYZGcmyZcsA+P777/UFwo8JuLaLIt670SoqrpWdSO0sGvz9/dNMjgtaqZjrUYoLlTfz3fAfuLN/NSMPhrP3wSO+vzKE+mVmEVllIBHObUFtYtBYE0O+x6lD8pzyMkOOIyIi0Ol0aLVatIo2xYokWq02+gmqj63/R1RUlH7Z8ePHKVGiBJaWljRv3pyffvqJ6dOno1Kp0Gq1KIqi/6xOp9Mfy79FRUXp2/+boij69oqi4O7ujru7O97e3rRp04Z9+/bFu734YtfpdAQGBupvZCXkdxRIUSphNBbRvZqSKOZLolarY/9H1lgkajtXrlyhePHiQPTznWfOnKFgwYIAvH79mlOnTlGtWrU4nytZsiSXL1/WVz9jWFtbY2ZmxuPHj/Xb0el0XL16lRIlSujbWVpakjNnTnbv3s2MGTOkKCWESBr/F/D3fvh7P7r7hzn2IJBV1yLZeCuSgPDYTTU5CmBXviFF89pRJKclDaq60qZpgwx7MiQyn48VpP4rR44c5MiRI0FtR4wYgb+/P8WLF0etVqPVapk8eTLt27cHwNvbGwB7e/tYn7O3t+fJkyfxbnPUqFEMGTJE/z4gIIB8+fJhbW2NlZVVgo8joWIKXdbW1vJ//j8OHDiAr68vOXPmpE2bNp98dE8J80c5MhaAnVlb0+zL1hgZqdJkjuu6WHNzzx/0H+XEohk/cuSxliOPQyn11wO+dxuGh9sCTOuOhDLtwCjtX8KkxRxnRJLnlJcZchwWFsa7d+9Qq9WoTS2iezSlAPUnHt/798/069ev63+Xb9q0ibNnz1KkSPTkFK9fv+bs2bNUq1ZNX2OI+ayzszPXrl2jbNmysbadLVs2zMzMePbsWaxr/2vXruHs7Kzfjo2NDfb29uzdu5eZM2eyf/9+jIyMPvn7Rq1WY2RkhKWlJWZmZkDCi5hp5if61KlTGT16NAMHDtTfzVMUhQkTJrB48WJ8fX2pXLky8+fPx9nZWf+58PBwhg0bxtq1awkNDaVu3bosWLCAvHnzJl9wKlWiHrOL40NjSiXCs2fPGDZsGP3790en07Ft2zbu3r1L9uzZAVi8eDEbNmyItyg1dOhQ2rdvT82aNSlYsCBhYWF4eXnRp08fBg8ezODBg1m/fj0mJibMnDkTZ2dnnJycePz4sX4bY8aM4enTpwkat0AIIQDQaeH5eQKv7uTtlX3w9i6+YQobb0ay+nokzwL+38PC2DI7WYpWIW/O7DRoUJ8ebZtSMrc1RkYZ8+RHiH+7dOkSGo1GP5jo9u3bWb58OSVLlsTT01M/9kNCrF+/nlWrVrFmzRqcnZ25cuUKgwYNwsHBga5du+rb/fdE8WO9p01NTTE1jdszUaVSpdgFSsy2M+oFUFJt2rQJgFatWiVoOIX7m8bhpHvPYyUXLp2moVb/f46jtJhjlUrFgmnjGdmvO4PHTmH7+j+44RNK121hjD54myrLe4DJd1gXLMv3k+dS3LmUoUP+qLSY44xI8pzyMnqOY45LpVKhMjIC06ypuv9/9zpWqVQ8e/aM77//nv79+6MoCtu3b49z7b9x40aqV68eK3b4/7V/rVq14r32HzJkiP7af9asWTg7O1O0aFH9tb9KpdJf+xsbGyf43z5WDv8T06ekiaLU+fPnWbx4cZyePAkZiHPQoEHs3LmTdevWkT17doYOHUrTpk25ePFiui+g+Pn54erqSkREBObm5nz77bf07NmTw4cP4+TkpP9SAjRv3pwff/xRP/Dlv5UrV44pU6bQrFkzoqKiMDY2pm/fvgAMHjyYt2/fUqJECVQqFa6urixfvjzONipUqECFChVS7mCFEBlDZGj0AOV3dvHi3E5+PuTD4osR/DMZXiwq0yw4uNaicUsPurRsREXH7Jhp0vfPbSGSonfv3owcOZLSpUvz8OFD2rVrR8uWLdm4cSMhISH6m3UJ8f333zNy5EjatWsHQOnSpXny5AlTp06la9eu5MqVC4juMfXviQB8fHzi9J4SaUtUVBRbt24Fosf8+BS/R1dwfLASgOsuY/gyd8J626UF+fPnZ/OKRbz7ZSoDx09n44rfeBHoy+bbUYA/XD3Cjr/KcmDpj7i2GQbqNHFJI4QQSebn50fZsmUz57W/YmCBgYGKk5OTcuDAAaVmzZrKwIEDFUVRFJ1Op+TKlUv56aef9G3DwsIUa2trZdGiRYqiKIqfn5+i0WiUdevW6du8ePFCMTIyUvbu3ZvgGPz9/RVA8ff31y8LDQ1Vbt26pYSGhn7mEUYfS2RkpKLT6T57W+lJcubwU3Q6neLr65vpcpyaJMcpL93mOOS9olxZqyjrOirKpFzKo4FZlT7lNYqJGgWiX8bGxoqRxlQxMrVQ8pf9Qvlu8gLl75fvDBJuus1zOpJRcxzf+UJysLKyUu7fv68oiqL89NNPSoMGDRRFUZQTJ04oefPmTdS2smXLpixYsCDWsilTpihOTk6Kovz//GratGn69eHh4bHOrz4lpfIQI6N+fz7XX3/9pQBKjhw5lMjIyI831umU+9NqKMp4K+X4pIZKRJT2P6vTV44Dg0OUAVMWKEW/7KM0a1BNKZNbowCKjRnKmSHFFOXKOkXRRhk6zFjSW47TK8lzyssMOU7N69b4ZIR6QXw5TOj5gsFvK/Tr1w93d3fq1avHpEmT9MsTMhDnxYsXiYyMjNXGwcGBUqVKcerUKRo2bBjvPhMyi4wSz+jxySE5t5XWpVQOP7Sv1NhPZiY5TnnpKscBL+DOboKvbuOPHUd57h/dFeqpv8L6m5FERU+Uh1nekhRr0p2W7o2oXTwnlQpmw/RfvaEMcazpKs/pVEbNcUodj/LPIKMAf/31F02bNgUgX758vH37NlHb+vLLL5k8eTL58+fH2dmZy5cvM2vWLHr06AFEd6UfNGgQU6ZMwcnJCScnJ6ZMmYKFhQUdOnRI3gMTyWrjxo0AtGzZ8pOP7j08tJzCIVcJVUywaTkDzb8e20uPslqY8+uobwmP0rLh/DMW7z5DAa9BPHn+mvrz7rLtbQ9ql5+OqtYIKNUajKTXrRBCpBcGLUqtW7eOS5cucf78+TjrEjIQp7e3NyYmJtja2sZpE/P5+CRkFplYI/B/YqT5hIg52cxM4huBP6UomWBWCEOTHKe8tJ5jI99H+JxeR+idg6jf3WHf/Sh+OhmBT3DcC3X7YuVp//V39GjVgHy25vrlYSFBhKVm0PFI63nOCDJqjhM6i0xiVahQgUmTJlGvXj2OHj3KwoULgegbdIl9pG7u3LmMHTuWvn374uPjg4ODA71792bcuHH6NsOHDyc0NJS+ffvqx+zcv3+/fmgEkfZotVq2bNkCfPrRvagQP2xOTATgqH1XGpVM2+MuJYapsZrOVQvStkI+ln9RilG9O+L/6BqNVoXwR/Bt2r3/GuXodFQ1R0CpVlKcEkKIdMBgRalnz54xcOBA9u/frx+dPT6JGYgzoW0SMotMrBH4k2lsqvQ+xlVixTcCf0qJKSZm5FkhDE1ynPLSZI7fP4Jb21BubGH06vNMOxkRp4mZdQ4sirmR1zYLhXJa0bd7B+rVqmGAYBMmTeY5g8moOU6pY5k9ezYdO3Zk27ZtjBkzRj+7zqZNm3Bzc0vUtiwtLZk9e/ZHx6FSqVR4enri6en5GVGLlKYoCp6enty+fZvAwEDevHlDtmzZqF279kc/d3f9GJwVX56Qm4odxn20bXplplHzbf1StL96mvrN23Lh8G7abw7lyltjPKvfw2xLL5Rj/xSnnFtKcUoIIdIwgxWlLl68iI+PD+XLl9cv02q1HDt2jHnz5nH37l3g4wNx5sqVi4iICHx9fWP1lvLx8fnoSVxCZpGJb/T4pPp3d/+MdHL+KcmZw4TuLyPPCpEWSI5TXlrIsd/ja8z0HMaJ0+cgIrq3S0ikwrkX0T0+zczNiFSM0Vhmo06b7gzp9w3Vi9ljapx+TvrTQp4zuoyY4+Q+lnv37lG0aFFcXFy4fv16nPU///xzpruhJf7vyJEjTJw4MdayNm3aoNFoPvgZ30eXKfZkDQB/lx9HPRurFI3R0GwsLTj7106+GziIBfPmMu1oIIuvZGVmPTXdnO/C5p4oR6dJcUoIIdIwgxWl6tatG+cErHv37hQvXpwRI0ZQqFAhcuXKxYEDByhbtiwQ/Ujd0aNHmTZtGgDly5dHo9Fw4MABPDw8AHj16hU3btxg+vTpqXtAQgiRnvk9I+zyRuYtWMCUXQ/w/cAzdkVbfEevPn2pXzIXLnmsMTLKOAUHIVJb2bJlyZ8/P82aNaNFixZUrVo11vqU7mUs0raYx/Xq1KlDq1atMDMzo3Xr1h/+gKLgu3EAtug4aeJGbff2qRSpYRkZGTFvzq+4VanM8OEjePnyBT02w4QzOfm2VBgWqhtkOdCZVtVLYNNotBSnhBAijTFYUcrS0pJSpWI/454lSxayZ8+uX/6pgTitra3p2bMnQ4cOJXv27GTLlo1hw4ZRunRp6tWrl+rHJIQQ6UrQG7i5Be3VDazcfYpxh8N5FhDds7NgDnNKVKrGbeOSWNvYUja/Lc2/KEeL+mn3sTwh0pt3795x4MABtm/fTsuWLVEUhaZNm9K8eXMaNGggRalMTKfTsXXrViB6Cu+Ywe8/5tFhLwqFXCNEMcWy+c+oM9FNA5VKRceOHWnRogUzZ85k2rRpPHnhw8gX/28z49RV9r3sTt4iP6OqORxKtgSj9D0AvBBCZAQGn33vYxIyEOcvv/yCsbExHh4ehIaGUrduXby8vDJEd3djY2NKlSpFZGQkhQoVYuXKldjY2KT4fh8/fkybNm24cOFCgj+TI0eORM8QJIQwgIhgHh/04odJ0zl16zmgEByJfrByS8usZKvRgaJ129G4TF7muuSmsF1Ww8YsRAZlZmbGl19+yZdffomiKJw+fZodO3YwcuRI2rdvT7169WjevDlNmzYlZ86chg5XpKILFy7w4sULsmbNmqAbrdoQP6yPR0/iczRXVxo7Z5zBzRMjS5YsjBs3jl69ejFz5kwePX3GwzfB3Lp4mttvfam6LIS9HW9S4nV3FLtpGNceCSVbSHFKCGFwZmZmmfbaP00VpY4cORLrfUIG4jQzM2Pu3LnMnTs3xeJSFIWQkJDP+rxWq0WtVscaj8LCwuKj41PY2Nhw5coVADp37sz8+fMZM2ZMkuP4kJjYhBAZj6IoHD10CO/rh+HpaS5fOMuvp4IJ/8+komqzLBRp0IVv+nxLiwqOFJJClBCpSqVS4ebmhpubGz/99BN///03O3bswMvLi2+//ZZZs2bRr18/Q4cpUknMo3tNmjRJUI+5e+vHUELx5TG5qdR+bEqHl+Y5ODgwc+ZM/ftrd+5Tr0FDXjx7SOmFwf8sPU+Dwh6s+caV7O5joERzKU4JIT772v9jPnb9b2Njw+XLl1GpVJnu2j9NFaXSqpCQELJmTf4LtKCgILJkyZKgttWqVePq1asAvH79mt69e/P8+XPMzMxYsmQJxYsX5+XLl3zzzTc8e/YMtVrNxo0bcXBw4JtvvuHKlSuYm5uzePFiXF1d8fT0xNvbm/v371OyZEn69+9Phw4dUKvV1Kjx/8dzgoKC6Nu3L7dv30ZRFH799VeqVavG69evadeuHYGBgTRq1CjZcyOE+EyKwvVDm/hu8DCOXn8aZ7VTgVxYVGmHqX1RahS1o0uTarg45o5nQ0IIQ3BycmLo0KEMHTqUd+/e8f79e0OHJFKJoij6olSrVq0+2d7v8VWc/jW4ef0MPrh5UrgUL8KdK+dp0ao1x48e0S/f/0BLrZmX2fe6Cw6FS0HNEVCimRSnhMjEUuraHxJ+/Z/Zrv2lKJUOaLVaDhw4QI8ePYDosbbGjh1L+fLlOX/+PIMGDWLv3r0MGDCA5s2b8/XXXxMeHo5Wq2X+/PlYWlpy/fp1zpw5Q9euXfVf8OvXr3P48GFMTExo0qQJo0ePplWrVgwfPly/70mTJtGyZUv++OMPnj9/jru7O1evXmXChAl8+eWXDBkyhAULFhgkL0KIeAS8wu/EMsb/PJf5R1+jVcDcGMrnNeE91rxT22FT5Su6dmhLy3J5KJ5LLl6ESAtevHjByZMn8fHxQafT6ZerVCq+++47smfPbsDoRGq6desWf//9t/787KMUhdebhmKDjlOaqtTJJIObJ0W2bNk4duQwvr6+vPYPZdbm43h5fssNH1/cloXyV+cbFPHpCjlLSnFKCGEwmfHa/7OLUuHh4ZiamiZHLGmWhYUFQUFBSf78xx7f+xg/Pz9cXV15/vw5zs7ONGzYEIBDhw5x+/btOO1PnDjBhg0bAPT/JidOnNB/0apUqUJoaCj+/v4ANG/eHBMTEwAuXrzIn3/+CUCnTp04dOgQAAcOHGD37t1MmBA9TsG7d++IiIjg1KlT/PDDD/r248aNS0JmhBDJ4fWLZ7y7uB3u7OT0yeOMPhimHyPKrXhO8tTpxAO7OjRzyU8LVwcqFswms+YJkYYsX76cPn36YGJiQvbs2WOdK8QUpUTGptVqOX/+PMHBwWzatAmA+vXrxxpHNT6PT2+lWNB5whVjLJtNzVSDmyeVra0ttra2LB76FT3qudCkUUOeeD+j3LIodne0oDq3YGNXsCsBNb//Z8yptPWoixAi5Xzutf+ntv0hfn5+lC1bNlNe+ye6KLVv3z7Wrl3L8ePHefr0KTqdDgsLC8qVK0eDBg3o3r07Dg4OyRqkoalUqgQ/ZhefDxWlPiVmTKmQkBDq16/PggULGDBgABD9Rfrvs6AJ2baiKPp2//5P8aHPKorCrl27yJ8//we3I4QwDN87J/lh2Hcs3H0ZRYm9Llu2bNg27Idr0xa0cM1DzaJ2mBjLHV8h0qJx48Yxbtw4Ro0ahZH0zMiU5s2bx6BBg2It+9Sje0pUOJqD0RcJJ7K3pW7psikVXoZVpUwJbl0+R936Dbh14zo1V6jo3boG3ztcwTLkJtk3dkdl9xN88T04twK1PGQiREb3udf+SRUzplRoaGimu/ZP8JnPtm3bKFasGF27dsXIyIjvv/+eLVu2sG/fPpYuXUrNmjX566+/KFSoEH369OHNmzcpFnRmY2Fhwa+//srMmTOJioqiZs2a/Pbbb0D0lMHXr18HoEaNGixduhSI7sEWEhJC9erVWbMmepyBc+fOkSVLFqys4j6uU65cObZv3w6gbw9Qr1495s+fr38f0/2vWrVqrF+/Pk57IUTKuX79OmOGD+H7Tg0YVDcPxcrXYMGf0QUpW3MjslqYYmadHdc2/Zj353kuLxvL/A7lqF/SXgpSQqRhISEhtGvXTgpSmdi6desAKFCgAKVKlcLd3R0PD4+PfubWjlnk0b7grWKNc7uJqRFmhpQrVy5OHj9GjRo10IWHsnDNfgrN8MHu5yDKLA7l6YM7sOVrmF8JrqwBbZShQxZCZGCZ8do/wWc/U6ZMYcaMGbx8+ZJly5bRp08fvvzyS+rVq4eHhwcTJ07k8OHDPHjwACsrK/74449kDzYzq1ChAqVLl2bz5s3MnTuXAwcO4OLiQqlSpfRd73799Ve2bduGi4sLbm5ueHt7069fP/z8/HBxcaF///4sX7483u3Pnj2bSZMmUblyZX23Poi+e+vj40Pp0qUpWbIkS5YsAWD8+PFs376d8uXLy+CrQqSw0OBgRn/bgXJlyzDl51+YsfoAvx56yZsQhXw5stCgQ3fKjN/H5B3X+fvRUy5vnEf7KoWwNNMYOnQhRAL07NmTjRs3GjoMYSA+Pj6cPXsWgJMnT3L9+nV27dr10YF2w/x9yHcteubpy079yZUzZ6rEmlHZ2Niwb98+2rVrF6s3wHXvKEov0XHC2xTeP4Bt36LMKw+X/oCoCANGLITIyDLbtb9KUf770EfmExAQgLW1Nf7+/vpKYlhYGI8ePcLR0TFBU/F+TFIf30vvkjOHn6IoCv7+/lhbW2eqHKcmyXHKi5PjoDccWupJ70lLuP82+uTX3cmYnDlsuKPLx+vs5WneqTftqhSiYkFb+XdJIPkup7yMmuP4zheSg1arpWnTpoSGhlK6dGk0mtgF5VmzZiXbvpJDSuUhRkb9/nzIihUr6NatG2XLluXSpUsJ+sy133rh8moj91QFyTfiPOZmJp/+0L9kthwnRsyl0bNnz2jQqDF3b9/CyNSCr5pU49dSt7AzCoxuZ50PVY0h4NoRjOOOrys5Th2S55SXGXKcmtet8ckI9YL4cpjQ8wV5MFoIIdIIRVF4+vQp5m+vE3l1Ez+v3IPX5XAAHCyNaNOwEveLdCLEsRxDy+ejUalcZDGVH+NCpHdTpkxh3759FCtWDCDOQOciY9u1axcATZs2TVD7tw8v4/xyE6jAx82TooksSImPi/k/lz9/fk6fPEGLFi04duwYa7ceYOdlF7o3qMEPuU6R0/8Z7BqMcmwGquqDoWxn0KT+xawQQqR3SbqaURSFTZs2cfjw4ThTFwNs2bIlWYITQojM4val0/Tq1pFT1x/FWq4CXMu7Yuk+hoLVXRhWPi/5sn185k4hRPoya9Ysli1bRrdu3QwdikhlERER7Nu3D0hgUUpReLd5KDlUCqdN3ahWr0XKBpjJ2drasm/fPiZPnszPP/9M0ONrzF96izUVmlAnTyQN1RcoafmEKv5D4dgMVNUHQbmuYCK/p4UQIqGSVJQaOHAgixcvpnbt2tjb22fou3jydGPSSe6E+LDbt29z5PBheP+IJxf388vOa0RowdgILDQqIlFjmj0PX/Qcx+COTanhZCdTfQuRQZmamlKtWjVDhyEM4Pjx4wQGBpIzZ04qVKjwyfaPTm2mWPBFwhVjrJtNy9Dn4GmFmZkZP/74I927d2fw4MHs2LGDd2d3sBGIGQmuc3lLljV5hfHekSjHZ6Fy+w4q9DBk2EKIJJDr16T7nNwlqSi1atUqtmzZQpMmTZK847ROo9GgUql48+YNdnZ2n/VLPyM8I5pYiqLw5s0bVCpVnLExhMjszp08SvVadYmM0sZaXr2IJS4NPbiXpyXNKxelbYV82FvJowBCZHQDBw5k7ty5zJkzx9ChiFQW8+ieu7v7J2dfVKLCMT00DoATOTyo6+yS4vGJ/ytUqBDbt29nz549zJkzh6CgIMIjozh/7iwrLwZy0s+RP9tAcXzgwFg4ORvTsr2gRn8wS/6x14QQySc5r/2TIr3XCz732j9JRSlra2sKFSqUlI+mG2q1mrx58/L8+XMeP3782dvT6XSZbqpnlUpF3rx5UavVhg5FiLTh/SPOrZqIx/iVREZpKWNvREFbY7xVOYgqWp+SLfviXtqOOWUKoFZnrp8XQmRm586d49ChQ+zatQtnZ+c4J3QyLELGpCgKO3fuBBL26N7NbTMppX3BG8WaUl9NTOnwxAc0btyYxo0b699v27aNDh068PDBI0pMi15molbxbYUwfg78CS4thir9oPI3YGZtoKiFEB+T3Nf+SZHe6wWfc+2fpKKUp6cnEyZMYNmyZZibmydlE+lC1qxZcXJyIjIy8rO2oygKgYGBWFpapsvKZ1JpNBopSAmhKOj+/oudiyYwc/MZjj+N7h1VwFaDe8cuXLRrTufKJWhbPi92lqb4+/tjJI/pCZGp2NjY0KpVK0OHIVLZvXv3ePDgARqNhvr163+0baifDwVuzAXgWtHvqJvTLjVCFAnQokULDh8+TKtWrXj58iUAEVqFX89GcN7HmF1t32N7eBLK6bmoKn8LVfqAua2BoxZC/FdyXfsnRUaoF3zOtX+SilJt27Zl7dq15MyZk4IFC8a5o5fQ6WzTA7Va/dmFFUVRCA8Px8zMLN1+yYQQCffy5UuGDR7ItQunIOQ9fsHhvAiMfs7a2EhFQWdXynUeR71mbvzoZKcvQslz7EJkTsuXLzd0CMIAYh7dq1WrFpaWlh9te3f9D7gSwj2VI9XaDEyN8EQiVK5cmcePH/P+/XsAdu07SN/evTj1KIRCv9uytp0tjXK+haM/oZxZgKrSN1C1H1hkM3DkQoh/S45r/6TI7PWCJBWlunXrxsWLF+nUqVOGH+hcCCE+JaaYpCgK637/lf7DRuMbFBarjbmphjwV6tPqm+/p616RAtmzGCJUIYQQaURMUerLL7/8aLu3T27i/HITqOBttXEUNTVJjfBEImk0Guzt7QHo2aUDFco408S9KS9fPMd9WQRNWjTj56LXKB7+DI7PQDm7CFXFXlC1P2SVnm9CiMwrSUWpP//8k3379lG9evXkjkcIIdINRVGYO3cu48aNw9/fP9a68rmN+LZmbo4aVeKxbVV6tHHnK7dimJvII61CCGjUqBHjxo3Dzc3to+0CAwNZsGABWbNmpV+/fqkUnUhpfn5+HD9+HIge5PxjXm0eQQ6VlgsmFalat2VqhCeSgYuLC0cOH6J79+6cPHmSPzeu50SZ+uTTWFLJ+AEDXfwpHTEb5exvqMp3g2oDwMrB0GELIUSqS1JRKl++fFhZySwSQojM6/Xr13Tv1o09e/fGWm6ihvbVCqCr+jVXXRoxwM2R8gVspUepECKWtm3b4uHhgaWlJc2aNaNChQo4ODhgZmaGr68vt27d4sSJE+zevZumTZvy888/GzpkkYz27duHVqulZMmSH5086PGlA5QOOI5WUWHuPkV+l6QzdnZ2/PXXX/Tt25fly5fjd3kffsB1wOuchp9b5mFwyfdwdiHKhaWoXDtC9UFgW9CgcQshRGpKUlFq5syZDB8+nEWLFlGwYMFkDkkIIdK2Pds30a17D3x8AzEzhhn1zWjubM6f2irstWyBa41adKiUn5xWZoYOVQiRRvXs2ZPOnTuzadMm1q9fz++//46fnx8QPYNNyZIladiwIRcvXqRYsWKGDVYku5hH9z46655Oh3bvGABOWTelRplKqRGaSGampqYsXbqURo0a6cfdPXn6DCeOHWXIxid41ahDz3zPKW70jOLvl5Lv0h+oXDyg+hCwK2rg6IUQIuUlqSjVqVMnQkJCKFy4MBYWFnEGOo8Z5E8IITKC58+f4+/vj+L/it+nj2LO9gsAlMppxIJWdpzP5s6wHC1p+4Ur68rkxtRYHtETQnyaiYkJHTp0oEOHDgD4+/sTGhpK9uzZ45xbiYwjKiqK3bt3Ax8vSt36awUlI+4SpJhRsM2PqRWeSAEqlQoPDw88PDyA6O/AwIEDWbBgAdeOHyRm6Pospmru9IW8V9eiXF2HqmRz+GIY5CptuOCFECKFJakoNXv27GQOQwgh0p7Q0FAGDRrE4sWL46zrUsmWHLW6sKpYW7rWLMbgQtnlsQohxGextrbG2tra0GGIFHbmzBnev3+Pra0tVatWjbeNNiKMbGemAHAhT2dq5XdMzRBFCjM2NmbevHm4uLiwZMkSwiMiePz0OYF+76m0uzDzm1nR0uIq3NoW/SraOLo4lbeCoUMXQohkl6SiVNeuXZM7DiGESFPu3b1L2xZNuHbnISogh0V0wSmLhRnF6rUlv8cQulUvTGG7rIYNVAghRJoWFBTElStX9DO1Ll++HIDGjRtjbBz/qfj1rT/jqvPhNba4eoxJtVhF6lGpVPTu3ZvevXsDcO3aNcqVK8eru1dp9TPYFyvLnOa2tDG/iNG9PXBvDxSqBV98DwWqgdwIE0JkEAkuSgUHB5MlS8KnME9seyGESAtePn/OtFHf8tu6PwmPUrCzUPFHSwtCHGuy2bQFbtVq06FSfmyzyJTcQgghPq1Ro0acPHkyzvIPPboX4udDodsLAbhTYiA1bWxTND6RNri4uDB27FgmTJiAoii8vnuZHquK8GvLMQyyOUYL45NoHh6Bh0cgf1WoMQyK1JXilBAi3TNKaMMiRYowZcoUXr58+cE2iqJw4MABGjduzJw5c5IlQCGESCk6nY4HDx7w999/c/3yJQZ1aEThQvmZs2oX4VEKdRw1jOzlztpyK6Dlbywf2ZN+tYtIQUoIIUSCREVFcfbsWSD6XLpYsWIUK1aMxo0b06xZs3g/c3fDOKwI5r6qIFVa9kvNcIWBjR8/nsjISE6ePImdnR3BL+9zffVP9Fz5gGy/21JnR3behavh6WlY3RoW14Lbu0CnM3ToQgiRZAnuKXXkyBF++OEHJkyYgKura7xTF58+fRqNRsOoUaP45ptvUjJuIYT4LA8fPqRNmzZcvnw5zrrKeTWU/6Im4dUGU75eRQY75ZDxooQQQiTakydPiIqKwszMjLt372Jk9PH7we+e3qbUiw2ggjduP1DERG6CZDZqtRo3NzfOnDmDu7s7d+7cAb/oSaQOv4R8z/Lwg0dZBuU4i8WrK7C+I9iViB5zyrklGMlkK0KI9CXBPaWKFSvGxo0befDgAe3atePly5ds2rSJ33//nSNHjpAnTx5+//13Hj9+zLfffotaLT8QhRBp0969e6lQoTyXL19GowZr0+hX1fymdG/vTqUf99Nr6gaW9G1CzaJ2UpASQqSobt26cezYsWTb3osXL+jUqRPZs2fHwsICV1dXLl68qF+vKAqenp44ODhgbm5OrVq1uHnzZrLtX/zf33//DUT3kvpUQQrgxebRaFRaLmrKU6Vem5QOT6RhhQoV4uLFi+zZs4ft27ezatUqcufOTejbF3iuOE7BP8tRa7cDow5r8Xl8Ezb3hHkV4dJKiIowdPhCCJFgiR7oPG/evAwePJjBgwenRDxCCJFidDodUzzHMG7STygKVM6jZpOHOVFZc7NE9yU6l6/oUaskRXLK4OVCiNQTGBhIgwYNyJcvH927d6dr167kyZMnSdvy9fWlWrVq1K5dmz179pAzZ04ePHiAjY2Nvs306dOZNWsWXl5eFC1alEmTJlG/fn3u3r2LpaVlMh2VALh//z4QXZT6lCfXT+DifwidosK08Y9yQ0RgYWFBo0aN9O9r1apFixYtuHDhAm8u7uMocBRYdCULO9qZUYMHsKM/HJ0G1QZC2c6gMTNY/EIIkRBJmn1PCCHSG/8X9+nS1p0dp+8B0Lu8hj4NivKLcSuyV2pLvxpFyG1tbuAohRCZ0ebNm3n37h2rVq3Cy8uL8ePHU69ePXr27Enz5s3RaDQJ3ta0adPIly+ffoY3gIIFC+r/rigKs2fPZsyYMbRq1QqAFStWYG9vz5o1a/QzgYnkEdNTysnJ6ZNtg//8AYCzlvWoWq5aisYl0qc8efJw7NgxlixZwuvXr1EUhT9Wr+P5k4fUXB5Fwy/K09TyLrZGD6n7Ygj2x34Gt++gfHcwlRtuQoi0SYpSQogMKSQkhIEDB7J921Z0ESGEhoYSEgkmahjVOB9+Fb/luFtrxroVxMZCxuwQQhhW9uzZGThwIAMHDuTy5cssW7aMzp07kzVrVjp16kTfvn0TVNjYsWMHDRs2pG3bthw9epQ8efLQt29fvv76awAePXqEt7c3DRo00H/G1NSUmjVrcurUqXiLUuHh4YSHh+vfBwQEANEFLkVRPvfQ44jZbkpsO7XF9JQqXLjwR4/n7qntlAy7TLhiTO4WE1P82DNSjtOqlMqxmZkZ/fv3178fOnQoHh4eHDx4kL0HT7H3n+XZLIzY/tULqgf9gHJ8FlT5Fip9DWY2yRqPocl3OeVJjlNeRs1xQo/HoEWphQsXsnDhQh4/fgyAs7Mz48aNo3HjxkD0QUyYMIHFixfj6+tL5cqVmT9/Ps7OzvpthIeHM2zYMNauXUtoaCh169ZlwYIF5M2b1xCHJIRIAx49ekTL5k25ev1WrOW5rDTUbP8NeTsMZniVAliYSF1eCJG2vHr1iv3797N//37UajVNmjTh5s2blCxZkunTp39y+ISHDx+ycOFChgwZwujRozl37hwDBgzA1NSULl264O3tDYC9vX2sz9nb2/PkyZN4tzl16lQmTJgQZ7m/v3+KFaWCgoIA0v0jbHfv3gXAwcEBf3//eNsoOi2aQ9H5PZOtOWVyfLhtcslIOU6rUivHRkZGrFu3jtmzZ3P69GkCw6K4c/8h732eU+uPcKY3tWSI63s4PBnl5BzCy3QhvGwPFIscKRZTapLvcsqTHKe8jJrjmJtYn6JSDFiO27lzJ2q1Wv+c/YoVK/j555+5fPkyzs7OTJs2jcmTJ8ca8+DYsWOxxjz49ttv2blzJ15eXmTPnp2hQ4fy/v17Ll68mODB1gMCArC2tsbf3x8rK6tkP05FUfD398fa2jpDfcnSEslxykvLOfb29mbo0KHRd6S1Edy9cxv/4HByZlGxrJkZodaO7DBpQs2WvehYrQhmmrQ5EUNaznFGInlOeRk1xyl1vhAZGcmOHTtYvnw5+/fvx8XFhV69etGxY0f9+c66dev49ttv8fX1/ei2TExMqFChAqdOndIvGzBgAOfPn+f06dOcOnWKatWq8fLlS3Lnzq1v8/XXX/Ps2TP27t0bZ5vx9ZTKly8ffn5+ct70EVFRUVhYWBAVFcWTJ0/Ily9fvO2u712Gy9khBCnmBPW5gH2upI0nlhgZJcdpmSFzHBwcTMuvOvDX7p0AFC5WjDm1o2hi/yY6NmOz6PGm3L4Dm/ypGltyk+9yypMcp7yMmuOAgABsbGw+ed6UrN0EfH192blzJ126dElQ+y+//DLW+8mTJ7Nw4ULOnDlDyZIlPznmgb+/P0uXLmXlypXUq1cPgFWrVpEvXz7++usvGjZsmJyHJ4RIgy5fvkyzZs14/vx5rOUVHYwY26YUB+y7UalOK36vkBdT47RZjBJCZG65c+dGp9PRvn17zp07h6ura5w2DRs2jDVY+ce2VbJkyVjLSpQowebNmwHIlSsXEF3M/3dRysfHJ07vqRimpqaYmprGWa5SqVLs5Dlm2+nx5NzT05OVK1ei1WqJiorCzMyMvHnzxnss2shwsp+bDsDlfJ2pkTv1evqn5xynF4bKcdasWdm3cxtjx45l6tSpPLh7F/d7RjiVKs3sWpE0yfYczv+OcnE5qtIeUH0Q2BVL1RiTk3yXU57kOOVlxBwn9FiStSj19OlTunfvnuCi1L9ptVo2btxIcHAwVatWTdCYBxcvXiQyMjJWGwcHB0qVKsWpU6c+WJSSsREyHslxyktrOVYUhZUrV/Ltt98SGhpKsexqptQ1wUQNj4wKcq94L0IbtGZ6ubxo1Eb6z6RlaS3HGZXkOeVl1Byn1PH88ssvtG3bFjOzD8+SZWtry6NHjz65rWrVqukfGYtx7949ChQoAICjoyO5cuXiwIEDlC1bFoCIiAiOHj3KtGnTPuMoBETPpDh58mSioqL0y6pVq4aRkVG87a9sn0N5xZu3WOPSdnRqhSkyASMjIyZPnkyHDh344Ycf2LZtG39fv4r7TWPK1m7Er1XeUsP4Flxdg3J1LaoSTaH6EMhTztChCyEyGYMPqHL9+nWqVq1KWFgYWbNmZevWrZQsWVLf7fxjYx54e3tjYmKCra1tnDYxYybER8ZGyHgkxykvLeX43Llz/DB6JOcvXgagYWE169pYcNekBGssOlGpRkPGl7DD2EhFSFCgQWNNjLSU44xM8pzyMmqOEzo2QmIdPnyYFi1axClKBQcH891337Fs2bIEb2vw4MG4ubkxZcoUPDw8OHfuHIsXL2bx4sVA9L/HoEGDmDJlCk5OTjg5OTFlyhQsLCzo0KFDsh5XZnT48GGioqJwdHRk9erVqFSqeHu+AYQF++N4Yy4Ad4p+S3Vr23jbCfE5nJ2d2bp1K+fOnWPUqFEcOnSIywd38aVPXao3+IHvzPfSUH0Bbu+MfhWuE12cKlgdMtDPbyFE2mXwolSxYsW4cuUKfn5+bN68ma5du3L06FH9+v+ezCqK8skT3E+1GTVqFEOGDNG/jxkbwdraOsXGRgAy3DOiaYnkOOUZKseRkZEcPXqU4OBgFEVh/ZpVrN+0BYAsGhhV3ZSGbiWYbNmVLxq2YWZpB9RG6fM7IN/j1CF5TnkZNccpdSwrVqzgp59+0o8fFSM0NJQ//vgjUUWpihUrsnXrVkaNGsXEiRNxdHRk9uzZdOzYUd9m+PDhhIaG0rdvX/1EMvv374+zf5F4+/btA6Bx48ZUrVr1o21vbJpKBfx5Ti4qtPr4APZCfK5KlSrx119/8euvvzJs2DD8rx/k6LMrHFCbYayY06B4FtbUeYf5g0Pw4BDkrQQ1hkDRRlKcEkKkKIMXpUxMTPQDnVeoUIHz58/z66+/MmLECODjYx7kypWLiIgIfH19Y/WW8vHxwc3N7YP7lLERMibJccpL7Ry/efOGli1bcvLkydhxAD3KauhSsxB7cnbnaYMO/FQ2b7otRv2bfI9Th+Q55WXEHCf3sQQEBOgfcwwMDIzVU0qr1bJ7925y5syZ6O02bdqUpk2bfnC9SqXC09MTT0/PpIQtPiKmKPXvoSXiE/DuFcUfeQHwrOwQ8n7k0U0hkktMT8kyZcrg4eHB27dvAYgAtp2CXM+KMbm1E19bn8b0+TlY2w5yOkP1weDcEtQGv3QUQmRAifrJMmfOnI+uf/HixWcFA9F3V8PDwxM05kH58uXRaDQcOHAADw8PIHoq5Rs3bjB9+vTPjkUIYTi3bt2iadOmPHr0CKssZpTIrmCkiyKPlYou1fJwrkBPXtXvzMTy+fVjRgkhRHpiY2OjL9wVLVo0znqVShXvcAMibXrw4AEPHjzA2NiY2rVrf7TtvY3jqUAo94wKU6lpr1SKUIhotWvX5sGDB1y/fh2I/u7279+fgGd3GbrsHWPtC5Jf9Zovsr1nUs0bWG/pBYcnQbWBUKYDaKSIKoRIPokqSv3yyy+fbJM/f8KnFR09ejSNGzcmX758BAYGsm7dOo4cOcLevXsTNOaBtbU1PXv2ZOjQoWTPnp1s2bIxbNgwSpcurZ+NTwiR/uzbtw8PDw8CAgIolF3Dn+3UFM+h5pkuL8s0X+Ffpzs/VHaU2fSEEOna4cOHURSFOnXqsHnzZrJly6ZfZ2JiQoECBXBwcDBghCIxYnpJubm5fXQ4iDfP7uHyajOoILD6D6jV8rtMpD4rKyuqVasGRA/GX6FCBVq0aMHff/9NRMBb/IBrwLp71mz3MMWNx7BrMByZBlX7QYXuYCqP/AohPl+iilIJmfUlMV6/fk3nzp159eoV1tbWuLi4sHfvXurXrw8kbMyDX375BWNjYzw8PAgNDaVu3bp4eXnJL3gh0qkF8+czYOAAtFodNfKr2fKVKVHm2Zlq1Ab72l8zwq0IZhr5/y2ESP9q1qwJRJ9f5c+fP0M96pgZxRSlPjT7c4xnm3/AThXFNRNXytVumRqhCfFJJUuW5MKFC2zfvp3Q0FDCIyL4cfJU3ni/pPpSU1q512Kuy11yB3nDgbFwfCZU7gOVe4NFtk/vQAghPkClZLT5mpMgICAAa2tr/P39U2ygc39//ww34GtaIjlOeSmV4z179vDzzz8TEhJCeJAvV27eA6BrGQ3T3bOx2qQ1War3pVONYliYZOyxDOR7nDokzykvo+Y4Oc8Xrl27RqlSpTAyMuLatWsfbevi4vJZ+0puct4UV2RkJNmyZSMoKIgLFy5Qvnz5eNs9u32OPOsaYKRSuNl0O84VaqVuoP9IjzlObzJCjt+8eUP79u05ePAgANYVmtGjbmH6m+6mkOpVdCNNluheU1X7gVXq9+zMCHlO6yTHKS+j5jih5wuJusILDQ3l4MGD+sEzR40aRXh4uH69Wq3mxx9/jDOlsRBCxGfhwoX0798fnU4Xa/mPdSywd2vJhqoD6VnHFSszjYEiFEKIlOHq6oq3tzc5c+bE1dUVlUpFfPcJVSoVWq3WABGKxDh9+jRBQUHY2dnpx0KNj9/OH8inUjifpSYVDVSQEiKh7Ozs2LdvH2PHjmXq1Kn4X9jBKj8XltuXo4jqBU2sHzCyTADmp+fBucVQpn30uFPZCxs6dCFEOpKootQff/zBrl279EWpefPm4ezsjLm5OQB37tzBwcGBwYNlWlshRFzXrl3jyJEjQPRA5r/99hsAXV1NaF1cjVZR8cimElF1xtC8UXVyWkmBWwiRMT169Ag7Ozv930X6FvPoXv369TEyin/yjXvn9lI65CyRiprsX/6YmuEJkWRqtZopU6ZQqVIlunbtypv71+D+NS4AF4AZx6wYWDMbE8u+w/jSCpTLK1E5t4TqQyBXKUOHL4RIBxJVlFq9enWcgtOaNWsoVKgQAKtWrWL+/PlSlBJCxLFy5Up69OhBVFRUrOUTa5nywxcm/KUrz7lC/en4ZSMK5shioCiFECJ1FChQIN6/i/TpU+NJKTodHBgPwPlsTXErXibVYhMiObRo0YILFy7w22+/ERISgqIo7Nj1Jy+fP2PqrgDmnc7J93XtGFviGdzYHP1yagg1hkL+yoYOXwiRhiWqKHXv3r1YUxabmZnFuhtUqVIl+vXrl3zRCSHSNZ1Oh5+fHwsXLuSHH34AwM2lMA66V2iIoE1JDQWLlWBSzj60aO7BmLzWBo5YCCFS39SpU7G3t6dHjx6xli9btow3b94wYsQIA0UmEuLNmzdcunQJQD9Zz3/dPLSGUpF3CFFMcWwzMTXDEyLZODk5MWPGDP37X34JY+HChUz8cRJ+73wYt8GHxa7VmN3Igham51H/vQ/+3gf53aD6YHCqDxlovBwhRPKIv3/xB/j7+2Ns/P861ps3byhYsKD+vU6nizXGlBAi84qKiqJ69epkz55dX5AaWMuO4y182NjamB9bFuZ2xfEEd9rD2H5fU1oKUkKITOq3336jePHicZY7OzuzaNEiA0QkEuPAgQMoioKLiwu5c+eOs14XFYnVqakAXHJoT+48BVM5QiFShpmZGYMHD+bxo4cMHz4clUrF8ysn6fzHE1zvdGfM/VKceA5Rj0/CmrawsBpc2wDaqE9vXAiRaSSqp1TevHm5ceMGxYoVi3f9tWvXyJs3b7IEJoRI31asWMHp06cByGqmZlodDX0rhvNOsWKFSQeKNu7PqDL5MDKSO2ZCiMzN29s73mKGnZ0dr169MkBEIjE+9eje1V0LKat7jq9iiXPbsakZmhCpwtrammnTplG7dm06duzI+5f3uLH+HjeAKUDhvDnY/5VCIW7Clq/h4I/g9h2U7QQmFoYOXwhhYIkqSjVp0oRx48bh7u4eZ4a90NBQJkyYgLu7e7IGKIRIHxRFYcWKFRw6dAiAvXt2A/BzfTOGVNUQiYbluGNcexj9q5fCxDhRHTWFECLDypcvHydPnsTR0THW8pMnT+LgkPpTrIuEUxSF/fv3A/EXpcLDgnG4OhuAm4V7UT1bjtQMT4hU1ahRIy5evMjAgQN58OABCvDg4SMePH9Lsd9s+aZVTSYWvEx2/6ew53s4+hNU7gMVe4FFNkOHL4QwkEQVpUaPHs2GDRsoVqwY/fv3p2jRoqhUKu7cucO8efOIiopi9OjRKRWrECKN0ul0DBkyhF9//TXWckcbFd9V0rBXqcLDMt/TuXEtrC00BopSCCHSpl69ejFo0CAiIyOpU6cOAAcPHmT48OEMHTrUwNGJj7l+/Tre3t5YWFhQvXr1OOuvbplJJeUd3uSgfJvvDRChEKmrYMGCbN++Xf/+3r17tGjRgtu3b7Nw9R421evF1+VN+cb4T/KH+MDhyXBiNpTvBlX7grU8dSNEZpOoopS9vT2nTp3i22+/ZeTIkSiKAoBKpaJ+/fosWLAAe3v7FAlUCJG2vHr1itevXwMwY8YMVq9eDUDfajYUsgjFSAWFihRiToEReLRqQ5PsMqOeEELEZ/jw4bx//56+ffsSEREBRI/VMmLECEaNGmXg6MTHxDy6V6tWLUxNTWOtCw54T9F7iwF4XKo/VSzk96DIfIoWLcrZs2fp3r07mzdvxmffItYFN2N15am4ay7xrfFOnHkKZ+bDud/A5SuoNhDs4h8uRgiR8SSqKAXg6OjI3r17ef/+Pffv3wegSJEiZMsW3eUyKioq1mDoQoiMZ/78+QwYMACdTqdfZmykwqu5GR1ddLxUcrPOqicOrfrQ3FEeVRBCiI9RqVRMmzaNsWPHcvv2bczNzXFycopT5BBpz8fGk7qxeSqVCeSpKg/lm8vs1CLzsrS0ZOPGjUybNo3Ro0fz4MQOOLGD+cB8tYaSFaqwqG4ENTS34crq6Fcx9+gZ+/JVNHT4QogUluTqUbZs2ahUqZL+/a1bt1iyZAmrV6/W954QQmQ806dPZ+TIkQDY57RDFRlMduMwZjQwo0bhLPxm3Jo8jYYyqFxhGcRcCCESIWvWrOTOnRuVSiUFqTTsxIkT7N27F0VROH78OBC3KOX/9hXOT1YC8LrCUPJrTFI9TiHSEpVKxciRIylXrhydO3fGx8cneoU2kltnj9PQpxy1mo9kQNaDNFBfwOjun3D3TyhQDaoNAqf6oJLzSiEyos/q0hQUFMS6detYunQp58+fp0qVKvqLVSFExqIoClOmTOHnn38GYHin+ngWuYa5YgxkZZtSk41uo+hapxJmGrVhgxVCiHREp9MxadIkZs6cSVBQEBDds2Do0KGMGTMGIyOZGCKtiIyMpFmzZvj6+uqXFSxYkKJFi8Zqd2fTBCoTyn11Ico36pbKUQqRdjVo0IDnz5/j7+8PRI+f16NHD0IeXeLo8sccNLVCo2SlXC4Vi2oGUpKT8OQk2JeKLk45twS1PJUjREaSpP/RJ06cYMmSJWzevBlHR0du3brF0aNHqVatWnLHJ4RIAxRFYdiwYfzyyy8AjG+UC8/CZ0GBq7pCnHAajkeLVthZyp19IYRIrDFjxrB06VJ++uknqlWrhqIonDx5Ek9PT8LCwpg8ebKhQxT/OHfuHL6+vlhbW9O1a1dUKhVfffUVqn/14Hjz4gGurzaBCgKrjcZILTdqhPg3jUZDjhzRwzt89dVXFCtWjObNm/P06VPgPRHA8TfgfMOIGq6OLK3lhxM3YEsvODQR3AaAa0cwsTDocQghkkeiilLTp09n2bJlBAUF0b59e06cOEGZMmXQaDTY2tqmVIxCCAMICwsjJCQERVEYM2YMv/32GwBzGpnxXeUQ3ihWrLfuSc22A+mXT/7/CyFEUq1YsYIlS5bQrFkz/bIyZcqQJ08e+vbtK0WpNGT//v0ANGrUKM6MszEebx5PRVUkNzWlcK3VOjXDEyJdcnV15datW5w9exadTkdYWBhz5y9g/949HL/8gGLXNTSq4cqqL16Rze8p7B4GR6ZC5W+hYk9AepMKkZ4lqig1evRoRowYwcSJE1HLXR8hMqz169fTq1cv/WMkACpgSTMzOruas8aoMVaNxtKvYrFYd4eFEEIk3vv37ylevHic5cWLF+f9+/cGiEh8SExRqkGDBvGuf3H/GmXf/Rn9S7PueFTy6KUQCZIlSxbq1Kmjf9+0aVOOHz/OyFGjOHXyJHsOn8fFbhzdivjTx3gXeUPewOFJcOIXzEq3h5qDwTqvAY9ACJFUifpNOXHiRDZu3IijoyMjRozgxo0bKRWXEMJAvLy86NChQ6yClK0ZrGltTrEy5fijzCqaj1hB00rFpSAlhBDJoEyZMsybNy/O8nnz5lGmTBkDRCTi4+fnx7lz5wCoX79+vG1ebx+HsUrHZbMqOFeJv3AlhEiYGjVqcOL4cfr1i569MuLYElYfe4LzzqI0uFSLW9p8qCKDMbu0BH51hW394M1dwwYthEi0RPeUGj16NEePHmXZsmVUqVKFwoULoyhKrAEfhRDpx40bN9i2bRtRUVG8ffuW+fPnA9DZzYGldQIxUsFzxY6dub/DuZEHPRxzSzFKCCGS0fTp03F3d+evv/6iatWqqFQqTp06xbNnz9i9e7ehwxP/OHToEDqdjuLFi5MvX7446x9eP0W5wMMAWDbxTOXohMiYVCoVU6dOZfv27Tx//pw33tsAOHAZKt7/ghZNWvOdxT6qcBuurIp+FW8aPSh6vooGjV0IkTBJGui8Zs2a1KxZk3nz5rF69WqWL19OzZo1qVSpEm3atGHIkCHJHacQIpmFh4czefJkpk6dSlRUVKx131U25dd6gYRhyjrTNhRpMZp+JfLqZ0oRQgiRfGrWrMm9e/eYP38+d+7cQVEUWrVqRd++fXFwcDB0eOIfBw4cAD7cSyp493gAzlvWoaJL1VSLS4iMztLSkn379rFp0yZ0Oh2+vr4sWLCAkNvH2Bf0kj2ObuQiOw2yPuCnsi8xu7ML7uyCAtWh+iAoUg/khqoQaZZKURQlOTZ0/fp1li5dypo1a/Dx8UmOTaaagIAArK2t8ff3x8rKKtm3rygK/v7+WFtbSw+TFCI5TpwzZ87Qs2dPbt26BUSPjVHAVo3u8Slq5AqnSxkNR6jA62oTaV3XDY3aSHKcCiTHqUPynPIyao5T+nwhvcis502FCxfm4cOH7Ny5k6ZNm8Zad/fsXort+YpIRc2rTkfJ71TaQFEmTFrNcUYiOU5Zx48fp23btrx+/TrWckv7/Exp40TvHJfR8M9N15zOUG0AlGoNao0Bok3f5Luc8jJqjhN6vpCknlLxKV26NLNnz+bnn39Ork0KIZKZoihMmTKFsWPHoigKOXPmZO5P46gWuo88PkehODxXHFiSdzDNv+pFbSszQ4cshBAZ0rVr1xLc1sXFJQUjEQnx4MEDHj58iEajoVatWrHWKTodur8mAnAxW1OqpPGClBAZQY0aNbh06RK//PILPj4+GBsbs23bdt6/fsqgpe+Z6/41w4q/ooPxIbL43IStveHgj1C1L5TrAqaWhj4EIcQ/kq0oFUOjkeqzEGmRoiiMGzeOSZMmAdC5Y0fGNHWk4J0JmBJOpKJmo2kLCrXy5Ovi+Q0brBBCZHCurq6oVCo+1WFdpVKh1WpTKSrxbxEREYwcOZIXL17w/PlzAKpWrUrWrFljtbtxdDOlI28Spmgo2NrTAJEKkTk5ODgwffp0fQ8TT09P2rZty9mzZ7m3+Rcm1u7AnAqz6aw5THfjvdgFPId9o+HoNKjQEyr3AUt7Qx+GEJleshelhBBpx5s3b7h58yYAO3fuZNasWQD8NPJbelqfIMednQCcVUrysNIE2jSsh4mxTF8thBAp7dGjR4YOQXzCpk2b+OWXX2Itc3d3j/Vep9WS5cRkAC7laotb3kKpFp8QIrZ8+fJx7Ngxhg0bxty5c3l+eA3Zgp+wumhtfvFrRY0sT5nsdBOnMG84MQtOz4My7cBtAORwMnT4QmRaUpQSIoM6cuQI7u7uhISExFru2a48I0xXQxi8UazYkbMvDdsPoHK2LAaKVAghMp8CBQoYOgTxCTEDm7u7u9OoUSMsLS356quvYrW5snc55bSPCFTMKd5mnCHCFEL8i4mJCXPmzMHNzY1evXpx7dxJOHcSgI3AzoKutG3em4FWhyjP33DpD5RLK1EVd4dqAyFfJcMegBCZkBSlhMiAjh49qi9I5c2bFysrK4wiAunnHEifYn+jU1Rs0zQkR7NJ9HSRO0NCCGFoK1euZNGiRTx69IjTp09ToEABZs+ejaOjI82bNzd0eJmOoij6otTAgQPjnXEvKiKcnBdmAHC9QBfc7HKnaoxCiA9r164dZcqUYcKECbx//x5FUTh16hQhj6+wddUr9lbrgpNpWZoan+e7gg/JGjNjX74q0cWpoo3ASJ4eECI1JPl/2oMHD/jhhx9o3769fra9vXv36h8VEkIYxrFjx2jSpAkhISE0atSIWyf+ZH+vnFzv6E8fVx03FEfWlvGiyYg1fCEFKSGEMLiFCxcyZMgQmjRpgp+fn34MKRsbG2bPnm3Y4DKpO3fu8OLFC0xNTalevXq8bS7vnE9e5RXvsaJ065GpHKEQ4lNKlCjBunXr2L9/PwcOHODMmTM4OTkR9O41b3b8zKmNSxi99ip2802Y9KQMERjDszOwrj0sqAyX/oCocEMfhhAZXpKKUkePHqV06dKcPXuWLVu2EBQUBETPJDN+/PhkDVAIkXDHjx/XF6QaNqjP/J7lMV1emzz+lwhRTFlt/Q1Z+h2lY6sWmGnUhg5XCCEEMHfuXH7//XfGjBmDWv3/n80VKlTg+vXrSd7u1KlTUalUDBo0SL9MURQ8PT1xcHDA3NycWrVqyQ3FePz1119A9Axf5ubmcdaHhQRR4PpcAO469cbSOluqxieESLzSpUtz4cIF+vTpQ4UKFahQoQI57XMRFvCOcX+cxulEExZENiUIC3h7D3Z8B7Nd4MQvEOpn6PCFyLCSVJQaOXIkkyZN4sCBA5iYmOiX165dm9OnTydbcEKIhDtx4gSNGzcmODiYutUrsbj6KwrdmIsJUZzElWP1d9Jh0HQcc1obOlQhhBD/8ujRI8qWLRtnuampKcHBwUna5vnz51m8eDEuLi6xlk+fPp1Zs2Yxb948zp8/T65cuahfvz6BgYFJ2k9GFfPoXr169eJdf3XrTHLyHm9yULbV4NQMTQjxGaysrFi4cCHnz5/n/Pnz3Lt7h9atW6Poonh6cBVjtz+jov9PTIrsiLeSDYK84S9P+KUU7BsD/i8MfQhCZDhJKkpdv36dli1bxlluZ2fHu3fvPjsoIcSn+fv74+HhQeHChSlcuDD16tUjODiY6s552P7FHfJrn/JWsWJVnnGUGLafRtUro1KpDB22EEKI/3B0dOTKlStxlu/Zs4eSJUsmentBQUF07NiR33//HVtbW/1yRVGYPXs2Y8aMoVWrVpQqVYoVK1YQEhLCmjVrPucQMpTIyEiOHDkCEO9YUkEBvhT9+3cAnpQegJm5TBQiRHplbW3Nxo0bmTNnDhqNhvc3j+O3aTybX+ejwv2vafewKdciHCAiMHq2vl9dYOu38PqWoUMXIsNI0kDnNjY2vHr1CkdHx1jLL1++TJ48eRK8nalTp7Jlyxbu3LmDubk5bm5uTJs2jWLFiunbKIrChAkTWLx4Mb6+vlSuXJn58+fj7OysbxMeHs6wYcNYu3YtoaGh1K1blwULFpA3b96kHJ4QaV5wcDDu7u6cPHky1vKahczY3TwAC42K3cZ1yd5yOp2cixgoSiGEEAnx/fff069fP8LCwlAUhXPnzrF27VqmTp3KkiVLEr29fv364e7uTr169Zg0aZJ++aNHj/D29qZBgwb6ZaamptSsWZNTp07Ru3fveLcXHh5OePj/x1UJCAgAos/RFEVJdHyfErPdlNh2Qpw5c4bAwECyZ89OmTJl4sRxbdNPuBHIU5UD5b7sY7A4P4ehc5wZSI5TR3LluX///lSqVImvvvqKJ0+e4L10NADrgc02uWncqhOj81ykCrfh6hq4ugbFqQG4DYAC1SAD3/iV73LKy6g5TujxJKko1aFDB0aMGMHGjRtRqVTodDpOnjzJsGHD6NKlS4K3c/ToUfr160fFihWJiopizJgxNGjQgFu3bpElS/Rdp5hu5l5eXhQtWpRJkyZRv3597t69i6WlJQCDBg1i586drFu3juzZszN06FCaNm3KxYsXY43NIER6FhoaSlhYGJGRkXTo0IGTJ09ibW2N14Jf0F3yomDgRVxzGfGU3JwtOZbmLdvLuFFCCJEOdO/enaioKIYPH05ISAgdOnQgT548/Prrr7Rr1y5R21q3bh2XLl3i/PnzcdZ5e3sDYG9vH2u5vb09T548+eA2p06dyoQJE+Is9/f3T7GiVMx4pYbo4fvnn38C8MUXX8R5rDHQ7y2lnvwBKnheqh9WIaFAaKrH+LkMnePMQHKcOpIzz8WKFePw4cOMHj2aixcvAvDmzRv8/F6xe+USTtbuRWmHGrQyPkaHbLfJ8fd++Hs/UfZlCK/Qm8jCjcAo4517y3c55WXUHMfcxPoUlZKEs4nIyEi6devGunXrUBQFY2NjtFotHTp0wMvLK8mFoDdv3pAzZ06OHj3KF198gaIoODg4MGjQIEaMGAFE362zt7dn2rRp9O7dG39/f+zs7Fi5ciVfffUVAC9fviRfvnzs3r2bhg0bfnK/AQEBWFtb4+/vj5WVVZJi/xhFUfD398fa2jpDfcnSkoyc48jISMaPH8+MGTOIjIzUL8+aNSvrfupHrbcryKKEEKmo2Z6lDaU7/EixvPYf2WLSZOQcpxWS49QheU55GTXHKX2+APD27Vt0Oh05c+ZM9GefPXtGhQoV2L9/P2XKlAGgVq1auLq6Mnv2bE6dOkW1atV4+fIluXPn1n/u66+/5tmzZ+zduzfe7cbXUypfvnz4+fllyPOm6tWrc+rUKRYvXkyvXr1irTvz+yCqvvTikVFB8o++iFE6vflp6BxnBpLj1JHSeX737h1dunRhz549sZYbGZvwdRMXfi37GFMiomOxdYSq/cG1A2jiTpCQXsl3OeVl1BwHBARgY2PzyfOmJPWU0mg0rF69mokTJ3L58mV0Oh1ly5bFyenzppf39/cHIFu26BlMEtLN/OLFi0RGRsZq4+DgQKlSpTh16lS8RanM1g09M8ioOX7w4AEdO3bk3LlzsZbnts/JL23y4/5mIQDXlcI8cvuJFvXqoTZSyfc4nZIcpw7Jc8rLqDlOqeOZMGECnTp1onDhwuTIkSPJ27l48SI+Pj6UL19ev0yr1XLs2DHmzZvH3bt3gegeU/8uSvn4+MTpPfVvpqammJqaxlmuUqlS7OQ5ZtupfXLu7+/P2bNnAWjQoEGs/b99/RyXF2tBBf5Vh6M2TtJpdJphqBxnJpLj1JGSec6RIwe7du1i+vTp/Pbbb0RERBAeHsG7d2/5bccFNj+rx8gGuehldhBr30eweygcmQqVe0PFXmCRMWbmlO9yysuIOU7osXzWb9OYAZaTg6IoDBkyhOrVq1OqVCkgYd3Mvb29MTExiTWQZ0ybmM//V2brhp4ZZMQcb9iwgWHDhhEYGIi1tTWzZ8/GvUlj3p30Iu+VmWRV3SNM0bDNpitlWw3hC+ssBAUmrItkUmTEHKc1kuPUIXlOeRk1xwnthp5YmzdvZuLEiVSsWJFOnTrx1VdfYWdnl+jt1K1bl+vXr8da1r17d4oXL86IESMoVKgQuXLl4sCBA/rZ/iIiIjh69CjTpk1LlmNJ744ePYpWq6VIkSIUKFAg1rq/N0+iqiqcv42dKFO3vYEiFEKkNiMjI0aOHMnIkSOB6GL/xIkTmThxIm8v/8WEtyVY1HQ8HW1v0Mt4N3lD3sLhyXDiFyjXBar0BdsCn9iLEJlXgotSQ4YMSfBGZ82alehA+vfvz7Vr1zhx4kScdf89oVUU5ZMnuR9rM2rUqFjHE9MN3draOsW6oQMZrjteWpLRcjxx4kQ8PT2B6McIVq1aRS6zSF6vakfxgEuggssU4339X/Bwq5oqx5zRcpwWSY5Th+Q55WXUHKfUsVy7do2bN2+yevVqZs2axZAhQ6hXrx6dOnWiRYsWWFhYJGg7lpaW+ht7MbJkyUL27Nn1ywcNGsSUKVNwcnLCycmJKVOmYGFhQYcOHZL9uNKjAwcOAHFn3Xv94iHlXm8CFYRVH4XKKEkTWAshMgC1Ws2ECROoXLkynTp1wvfZbVTrRrOvw1hWmE2jsdF5ehrvxkX7BM3ZRXDud3BuCdUGQO4yhg5fiDQnwUWpy5cvx3p/8eJFtFqtfqa8e/fuoVarY3UZT6jvvvuOHTt2cOzYsVgz5uXKlQv4eDfzXLlyERERga+vb6zeUj4+Pri5ucW7v8zUDT0zySg5njt3rr4g9cMPPzB+7FheHpyH7uxP5CecEMWUP3N+Te0uP1DWMnWfV88oOU7LJMepQ/Kc8jJijlPyWJydnZkyZQpTpkzh5MmTrFmzhkGDBtGnT59k7aE1fPhwQkND6du3r35W4/379+snj8nsPlSUerxlApVVkdzSlKLUFy0NEZoQIo1p0qQJFy9epHXr1ly+fJkz8wcDsPCfl5GRGs+WRRhb6hXc2BT9cqwZPWNfkboZesY+IRIjwbd5Dh8+rH99+eWX1KpVi+fPn3Pp0iUuXbrEs2fPqF27Nu7u7gneuaIo9O/fny1btnDo0CEcHR1jrXd0dNR3M48R0808puBUvnx5NBpNrDavXr3ixo0bHyxKCZFWrV69mgEDBgDg6enJmL6deDmnLvnPTsCccM6rSnG+8Z+07TeZHKlckBJCCJE6smTJgrm5OSYmJrEmuEiKI0eOMHv2bP17lUqFp6cnr169IiwsjKNHj8bpXZVZPXv2jLt372JkZETt2rX1y188vEPZtzuj39T5QXpJCSH0HB0dOXnyJF9//TVG//nZoNNp8dz9knrvRrItqipRigoeHYXVrWGhG1xeDVHhH9iyEJlHkn6rzpw5k6lTp8bqmWRra8ukSZOYOXNmgrfTr18/Vq1axZo1a7C0tMTb2xtvb29CQ6On1lWpVPpu5lu3buXGjRt069YtVjdza2trevbsydChQzl48CCXL1+mU6dOlC5dmnr16iXl8IQwiF27dtG1a1cABnz3HV1cTVD9Vp38QdcIUszYnHsoRYcdomaVigaOVAghRHJ79OgRkydPpmTJklSoUIFLly7h6en5wfExRfL766+/AKhYsSI2Njb65S+2e2Ki0nLdtBwlqzY2UHRCiLTK3NycxYsXExQUREBAAAEBAfj6+uLq6oouNJCD80bTcvI+TKeG0fJIAQIVU/C5Bdv7wmwXOD4LQn0NfRhCGEySBjoPCAjg9evXODs7x1ru4+NDYGBggrezcGH0zGG1atWKtXz58uV069YNSFg3819++QVjY2M8PDwI/R979x0WxdUFcPi3LL0LKohSLNgVRFSwF+y9t9hLFGPXJEb9LLHEEmOLGmPsNUYTWxLF3mus2HsXRQWUvsz3x4aNCCoqywKe93n2kbk7O3PmsLh3z9y5ExVFjRo1WLx4MepMepte8enZu3cvLVu2RKPR0KZZQwa5HMX95AUAjqi8SWg0g+Y+3oYNUgghhF74+/tz9OhRSpQoQZcuXWjXrh25c+c2dFifnJQu3bt16RSln/8NKjCrPcpQoQkhMgELi6RXMSxevJhKlSrpvh8nxMXwx56zuNz2pn/jkvS13YPTi4ewYwzsnfrvpOi9ZVJ08cn5oKJU06ZN6dKlC99//z1+fn4AHD58mKFDh9KsWbNUbyc1d7pLHGaeOMdOSszNzZk1axazZs1K9b6FMKSIiAg2bdpEVFQU0dHRfPPNN0RHR1OjbBF+LnoQ6+g4whVLtrv2o2b7wdhYmBo6ZCGEEHpSrVo1FixYkOxkn0g/CQkJupFSrxalHm8eg7tK4ZRlebx9qhooOiFEZuTl5UVISIjubrQbN26kT58+vLhxivEzzjDeyAg1CtU9LVnVMALHI3Ph6E9QtAmU7wu5fQx7AEKkkw8qSs2bN48hQ4bw2Wef6eY6MDY2plu3bkyZMiVNAxQiK4mPj2fBggWMGjWKkJCQJM+VzWfHpoC7WKhVHDXyxqjJbJqVLGGgSIUQQqSXCRMmGDqET97Zs2d5/PgxVlZWuhOu184ewTdiJwC29WSUlBDi/Zmbm2Nubg5A165d8fX1pXXr1ly8eBE0CWiAoIsRuN23Y0ozNwLdb0Hweu3DvaK2OOVZC2QuO5GFfVBRytLSkjlz5jBlyhSuXbuGoigUKFAAKyurtI5PiCxBURT+/PNPhg4dyoUL2svy8uXLR7FixYh6fAvPhCtMrJaAYmzOhtx9CejwNVbmJgaOWgghhMiaDh8+zIQJE4iNjQXg/v37AFSpUgVTU+3o5PC/RgNwwroqpYv7GSROIUTWUrJkSYKDg3X/59y5c4e2n3Xg1vVr9Fl6nllVm/CtfyRNjA9jfGs/3NoP2QuC/xdQsjWYmBv4CIRIex9UlEpkZWVFyZIl0yoWIbKk8+fP069fP3bs2AGAo6Mjo0aNokubRjxcEUiB5zcBE06rChPd4Ecal/Y1aLxCCCFEVjdixAjd5/KrGjZsCMClf/ZQKvIgGkVF9oZj0js8IUQWZmRkRJ48eQDIkycPZ07+Q48ePfj111+5uHMd3e6X57va4+luvZ/PjHdi/eQybOoHO7+Fsp9DmW5g6WDgoxAi7XxQUapatWqoVKo3Pr9z584PDkiIrOTixYtUrFiRZ8+eYWpqSv/+/fnmm294cXYLCXMqUECJIEYxZptTdyp3GoOdlZz9EEIIIfQpKiqK/fv3AzB9+nQcHLRf7uzs7KhXrx4AMdvGAvBPttqUKeRtkDiFEJ8GW1tbVq9eTeXKlRk4cCDhFw9y7tYp+hqZ8oWSQGG37CyqFUsZHsOucbDveyj1GfgHgkM+Q4cvxEf7oKKUt7d3kuW4uDhOnTrFuXPndLe0F+JT9/DhQ+rWrcuzZ88oW7Ysq1evxjWHLdeX9KDg420AXCQvT2rNpGGFygaOVgghhPg07N+/n5iYGPLkyUO/fv2SnWgNPvQXJaOPE6eoyd14tGGCFEJ8UlQqFX369KFs2bK0atWKmzdvApEABF8Mp9w1M/wrlme2/xNKcQuO/QzHFkCRhlC+H7iWMWj8QnyMDypK/fDDDym2jx49Wnd3ASE+ZS9evKB+/frcvHmTAgUKsHnzZuJuHiJ8WX8KJjwlXjHib4d2+HX+jsJ2NoYOVwghRDo7c+ZMqteVqRLSVlBQEAABAQHJClJKQgKqneMAOJm9AWXzFkn3+IQQn64yZcpw+fJlrly5gqIoPHv2jOHDh7N3714O7tpFubNuVKrTjhEewVRTn4YLG7UPVz/tpOiF6smk6CLT+ag5pV732WefUbZsWaZOnZqWmxUiU4mPj6dVq1b8888/ZM+enS0b1vF4wzcUvfsrANfJza0q31O/Wp23XgYrhBAi6/L29kalUqEoyjs/CzQaTTpF9WnYvn07ADVr1kz23Nl9GygZd44YxQT3pnLHPSFE+jMxMaFo0aK65d27d7N06VKGDBnCkye32bl8HsdK1sSn6kgCbfbS1PgAJncOw5rD4JAfyn8BXm3BxMKARyFE6qVpGfXQoUO6W14K8SlSFIXevXvz119/YWFhwfK5k7Fa305XkNpq0xSLvvupVr2uFKSEEOITduPGDa5fv86NGzdYt24defPmZc6cOZw8eZKTJ08yZ84c8ufPz7p16wwdapby5MkTTp48CUCNGjWSPKckJGC2bwIAJ52a4ZQnf7rHJ4QQr1OpVHTq1ImLFy/So0cPACLOBHFk4bd0WngB17/8mB5dn3Cs4Ok12DwQfigGu7+Dl08MHL0Q7/ZBI6WaNWuWZFlRFB48eMDx48cZOXJkmgQmRGbx5MkTdu3ahaIoHDx4kAULFmBkZMTUvk2pfm4wJmgIUbJxpsx31KrfWopRQgghcHd31/3csmVLZs6cqZtkG7SX7Lm6ujJy5EiaNGligAizpsQ77pUsWRInJ6ckz53asYpS8ZeJVMzwbC79WSFExuLo6Mj8+fPp3LkzvXv31l4G/iKc6AdXGXGvILMbjuQzh/N0M/4L18jHsHsi7P8BvNuBXx/IXsDQhyBEij6oKGVra5vki7WRkRGFChVi7Nix1KpVK82CEyKju3HjBn5+foSEhCRp/1+j/ARabAbgoKk/uT6bT4CbmyFCFEIIkcGdPXuWvHnzJmvPmzcv58+fN0BEWVfipXsBAQFJ2hM0GuwOTQbgTO7W+Dm5pntsQgiRGuXLl+f48eMcOnSI69evM3jwYJ7eu8zDFcOY7ZyPSTEWuNq4MbNGAnWy3YXjC1GOL0JVuL523inXciAnyUUG8kFFqcWLF6dxGEJkPs+fP6d+/fqEhITg6upK/vz5iX7+iDa579Hf6xEvFTP25h9CjbaDMTVRGzpcIYQQGVSRIkUYN24cv/zyi24ahJiYGMaNG0eRIjLRdlpRFCXJJOev+ufvxfgm3CQCC4rKKCkhRAZnYmJC5cqVqVy5MtWrV6dly5YcPXqUlxHay5OvAPWv2VC+YSv+V+gaNdX/wMXN2keeMtriVOEGYCTfUYThfVBRKl++fBw7dgxHR8ck7c+fP8fHx4fr16+nSXBCZDQPHz7k1q1bAIwYMYILFy6QO3du9u38m5dbv6Xo438ACFZ5Et/0J+p6lTZkuEIIITKBefPm0bBhQ1xdXfHy8gLg9OnTqFQqNm/ebODoso5r165x69Yt3Ze5RPFxseQ8/j0AwW4d8XPMaagQhRDivbm5ubFv3z62b99OREQECQkJfP/995w4cYL9a3+hTYXWeFWYRHeTv2luvB+zu8fg146QzQP8v9Be3mdqZejDEJ+wDypK3bx5M8U7wcTExHDv3r2PDkqIjGjLli00a9aM2NhYXZuVlRWLfhiN2cqmuCc8RKOo2JG9A35dJ2FrZWnAaIUQQmQWZcuW5caNGyxfvpyLFy+iKAqtW7emXbt2WFnJF4W0kjhKqnz58kny+s/m+ZRV7vEMG4o1/9pQ4QkhxAczNTVNMi9hs2bNGDRoEHPmzOHpgTXcfXmLSbUGMi2+FR2Mt9FJHYT9s5vw5xDYNR58u0LZnmDjbLiDEJ+s9ypKbdy4Uffz1q1bsbOz0y1rNBp27NiBh4dHmgUnREZx9uxZ2rRpQ2xsLM7OzlhYWGBnZ0tgnUJUPzcEtUrhLjm4WfkHatVoaOhwhRBCZBJxcXEUKlSIzZs307NnT0OHk6UlzidVs2ZNXVtsTDR5zswE4FL+rvjZORgkNiGESEtmZmb8+OOPVKxYkR49enDl1GGcH/bHo1hpJj55yRhjLzpWyM1A+z24Rz2Cfd/DgZlQoiX49wHn4oY+BPEJea+iVOLdXxJvS/kqExMTPDw8+P7779MsOCEygpCQEBo2bMiLFy+oWrUqW7duJfrpfR4t/gzP6L8B2GtRgwKd51LxtTv5CCGEEG9jYmJCTEyM3JlVzzQaDTt37gSSzid1csMsyimPCMUer2ZDDRWeEELoRdu2bfH29qZFixacP3+ehw+36J776aoja+oNopFHLN2Nt1CGy3B6pfaRryr494UCNQwXvPhkvFdRKiEhAdDeDebYsWNkz55dL0EJkVFER0fTpEkTbt26RYECBVi3bh23j24kx/b+ePKCF4oFh4uNpFqLQNRG8oVCCCHE++vbty+TJk1iwYIFGBt/0MwK4h1OnDjB8+fPsbOzo3Rp7XyP0ZEvyHt+DgBXC39OOSsbQ4YohBB6UaRIEY4ePcr69esJDw9HURTmzZtHcHAwIWtGsrN0VVa+tMJeVZDBfsb0zXMR9fXdcH035CgMfoHgXhuwe8eehPgwH9TzuXHjRlrHIUSGoygK3bt359ChQ9jb27Px9/XcXj8C77srALholB9Vi0UEFPUycKRCCCEysyNHjrBjxw62bdtGiRIlks0jtX79egNFlnUkXrpXvXp1XeHv5O8/4M9THpId7yYDDBidEELol5WVFR06dNAtd+nShS+++ILFixdz9Zh2FOkL4Mu7OVnTbSLdLHbTxng31o8votrUD1vL7FC2B5TpDlYyMEWkrVQXpWbOnEnPnj0xNzdn5syZb123X79+Hx2YEIY2YcIEVqxYgVqtZuGcHzDd2I0isZcA2JWtBb7dZmBjbW3gKIUQQmR29vb2NG/e3NBhZGmJk5wnXrr3MuI5Ba/8DMDtEl/gbC43JxFCfDqsrKxYtGgRbdu25cKFCwD88MMP3Lp1i6c7FjLGoQDfl5hAe+vjdDX+G5fIJ7B7Iuz/AbzagF8fyFHQwEchsgqVoihKalbMmzcvx48fx9HRkbx58755gyoV169fT7MA00N4eDh2dnaEhYVha2ub5ttXFIWwsDDs7Oxkzgg9SYsc379/n88//5wrV66gKAqXL18GYPSAzgy2+xtrInmuWHHWdwIVG3T65H6X8j7WP8lx+pA8619WzbG++wuZRWbrN718+RIHBwdiY2O5dOkSBQsW5NCSb/C/8SP3VM7kHHYGE1OzNIg888iqf6MZieQ4fUie08727duT3Agiu1MuirQfyX0TF+oaHaW78Z94Gb3yPb9gHe2k6B6VQHL/UbLq+zi1/YVUj5R69ZI9uXxPZDXR0dE0a9aMI0eOJGn/rEYxRtlpL5sIVhfGou0SKhUobIgQhRBCCPEB9u3bR2xsLG5ubnh6ehL27AnFbiwG4EGpAeT+xApSQgiRkoCAADZt2sSZM2dYsmQJly9f5uCMvgQ0aMq2pzEseZGLMsUr8JX7eQLU/2B0+W+4/Dc4lwT/L6BYUzA2NfRhiEzI6ENeNHbsWCIjI5O1R0VFMXbs2I8OSoj0pCgKvXr14siRI2TLlo0tW7bwx+qF/NWnIMsq3gFgV4725Buyh3xSkBJCCKEHv/32G61atcLPzw8fH58kD/FxEueTCggIQKVScX7dRGx5yU0jV0rV62Hg6IQQIuNo0KAB33zzDcePH6ddu3ZoNBq2bviNy/s2EXHyL3atmEv7fa5Uj5rM0viaRGMKD8/A7z1hhpf28r6oZ4Y+DJHJfFBRasyYMbx48SJZe2RkJGPGjPnooITQtzt37jBy5EgGDx5M+/btWbJkCWq1ml9//RV31R1qXviKOtkfEqrYcqj8z1TrMwcLC3NDhy2EECILmjlzJl26dCFnzpycPHmSsmXL4ujoyPXr16lbt66hw8uUDh8+TMuWLWnSpAlLly4FoGbNmjx7/ICSd5YD8KzsENRyt0MhhEjGxsaG5cuXs3HjRsaNG8eIESNo2bIlSkICT/Ys5dbmuQz6MxyXdUVpfLAoDzV2EHEfto+GacXgzy/hqVxdJVLngz6JFUVJ8VrH06dP4+Dg8NFBCaFPT58+pVq1aly7di1J+6SJ48l+YSHFQrcAcNq4JA4dluDvns8QYQohhPhEzJkzh/nz59O2bVuWLFnCl19+Sb58+fjf//7H06dPDR1epvTll1+yb98+3bKZmRk1atTg4rpR+KuiuarOj3etDm/ZghBCfNpUKhUNGzakQYMGuvmO6tevT2BgIDfPn9Ktt/EcBF0sxmeNGzAo214Kx92Boz/BsZ+hcH3w7wtu5Qx3ICLDe6+iVLZs2VCpVKhUKgoWLJikMKXRaHjx4gW9evVK8yCFSCvx8fG0bt2aa9eu4e7uTps2bQDwcLajbvhS3F/eRaOo2OvSjfJdJmJmKtdFCyGE0K/bt29Tvnx5ACwsLIiIiACgQ4cO+Pn5MXv2bEOGl+mEh4dz6NAhAKZNm4a1tTVeXl6o4iLxfrAWVBBZ4StURmoDRyqEEJlLp06dqFChAps2bSI+Pp4XL14wbdo0XtwJZunie6xw8yKnyoRSliHMrBhOngub4MImyFNGOyl64YaglhGqIqn3ekdMnz4dRVHo2rUrY8aMwc7OTvecqakpHh4e+Pv7p3mQQqSVL7/8ku3bt2NpacnGjRspWaIEpzfMoNDJcZir4gghG7erzaJa1YaGDlUIIcQnwtnZmdDQUNzd3XF3d+fw4cN4eXlx48YNUnmTZPGKXbt2ER8fj6enJwMHDtS1H/6xG36qWC4aF6FE1ZYGjFAIITKvAgUKJPm/tX379rRu3ZpTp07B+T3cBG4Cm89ZMahREcYWvoLp3WOwtjPYu0G53uDTAcxsDBK/yHjeqyjVqVMnAPLmzUv58uUxMTHRS1BCpDWNRsM333zDDz/8AMDSpUspmM+V09Ob4xW2A1Twj6kvLp0X4+viauBohRBCfEqqV6/Opk2b8PHxoVu3bgwcOJDffvuN48eP06xZM0OHl+ls27YNgFq1aunaHty6jE/IH6CC+KrfoDL6oGlVhRBCvKZgwYIcOnSI1atX8/TpUxRFYenyFZw5dZJJa46wuFxDvqyWjc6mO3B4fhu2DoPdE6F0JyjXC+zyGPoQhIGluigVHh6u+7lUqVJERUURFRWV4rq2trYfH5kQaeTFixe0b9+ejRs3AvDdd99RppAzT6f545XwgDhFzUH3QCp0HI2xTHgqhBAinc2fP5+EhAQAevXqhYODA/v376dhw4bvPS3CxIkTWb9+PRcvXsTCwoLy5cszadIkChUqpFtHURTGjBnD/PnzefbsGeXKlePHH3+kWLFiaXpchpJSUerOH2PIpYon2NSL4hUbGSo0IYTIkszNzencubNu+YsvvmDIkCHMnj2bR0c2MfZRcWbXHUXrbBforv6T/DEP4OAsODQHijWF8l+ASynDHYAwqFR/A7e3t09xcvNXJU6ArtFoPjowIdLCrVu3aNSoEWfOnMHMzIxfFiygqOkdnNc2xFSl4T45eFxnLlX8axo6VCGEEJ8oIyMjjF4ZudOqVStatWr1Qdvas2cPffr0oUyZMsTHxzN8+HBq1arF+fPnsbKyAmDy5MlMmzaNxYsXU7BgQcaNG0fNmjW5dOkSNjaZ+3KK69evc/XqVYyNjalatSoAd66ew+fpn6ACdcBIwwYohBCfADMzM2bNmkWVKlXo1q0bYTfPoaz6ml+L+jE7PA9uFg586xdNM4drcO437cO9Avh/AQXrgIxm/aSkuii1a9euNN/53r17mTJlCidOnODBgwf8/vvvNGnSRPd8as7kxcTEMGTIEFatWkVUVBQ1atRgzpw55MkjwwA/RfHx8fTu3ZstW7R30AsLCyMqKgonJydWLV9M9lMzKfHiAKjgmEUFPLouxCuHs4GjFkII8SmrUKECVapUoWrVqlSoUEFXPPoQf//9d5LlRYsWkTNnTk6cOEHlypVRFIXp06czfPhw3aWBS5YswcnJiZUrV/L5559/1LEYWlBQEAD+/v66kfuPNo7CVZXAaYuyeJWVk1BCCJFeWrRoQalSpWjVqhX//PMP4Qf/BOA80PKcDaXqteObIndopD6E8a0DcOsAOOQH/0DwagemloY9AJEuUl2UqlKlSqrWO3XqVKp3/vLlS7y8vOjSpQvNmzdP9nxqzuQNGDCATZs2sXr1ahwdHRk8eDANGjTgxIkTqNVyV5VPzVdffcUvv/ySpM3Ly4tZ47/C88AXOCuPiVGMOeI5iIpth2Gkliq8EEIIw2rQoAF79uxh9uzZREdHU7p0aV2RqmLFilhbW3/wtsPCwgBwcHAA4MaNGzx8+DDJpW1mZmZUqVKFgwcPpliUiomJISYmRrecOKWDoih6mYg9cbsfsu3ES/dq1qyJoijcOH8cn3/njrSs/T+ZOP5fH5NjkTqS4/Qheda/j81xvnz5OHDgACtWrODJkycAunkTT6z7iV7lmzOx/Pd0NdtBO/VO7J5egy2DUXaOA99uULYHWDul5SFlOFn1fZza41EpaXDkYWFhrFixggULFnD69OkPunxPpVIlGSmlKAouLi4MGDCAr776CtB2ipycnJg0aRKff/45YWFh5MiRg2XLltG6dWsA7t+/j6urK3/++Se1a9dOcV8pda5cXV15/vy5XubDUhSFsLAw7Ozs3nkJpPgwiqKwcOFCevToAcDPP/+Mr68vRiqIOv0bpa/9iLEqgTuqXITXn0/R0pUMHHHmI+9j/ZMcpw/Js/5l1RyHh4djb29PWFiYXvoLGo2GY8eOsXv3bnbv3s3OnTtRqVRJ+izvQ1EUGjduzLNnz9i3bx8ABw8epEKFCty7dw8XFxfduj179uTWrVts3bo12XZGjx7NmDFjkrXfunVLb/2mFy9eYG1t/V7vn/j4ePLnz094eDjbt2+ndOnSXP2pPb5R+zlhWYH8PVemeayZ1YfmWKSe5Dh9SJ71Tx85jomJYfTo0cybNw+AvIVL4tTkK8IUM1qq99DV+C/cVSHa/atNiS3UmBif7iRkL5wm+89osur7ODw8HHd393f2mz5qVuedO3eycOFC1q9fj7u7O82bN082SuVDpeZM3okTJ4iLi0uyjouLC8WLF+fgwYNvLEpNnDgxxc5VWFiY3s74vXjxAiBLvckyknPnztG3b18ABg4cSIsWLQgPfUDY2kDKRf8DKjhkUZXcbaaT2y6b7syxSD15H+uf5Dh9SJ71L6vm+NWbvujDlStXOH36NKdPn+bMmTPY2tpSqdKHn0T54osvOHPmDPv370/23Ou/l8R5QVMybNgwBg0apFtOPJlnZ2ent6IUkKqipqIotGnTht27d6PRaAgPDydbtmxUqVKFG+cO4Ru1nwRFhUODMdjZ2aV5rJnV++RYfBjJcfqQPOufvnI8Z84catWqRdeuXblx8QzP5vXBt1IAyx6/ZHpsIeqVrcvgHIfw5TJm59didn4tSr5q2nmn8leHLPT7zqrv49Qey3sXpe7evcvixYtZuHAhL1++pFWrVsTFxbFu3TqKFi363oG+ycOHDwFwcko6VM/JyYlbt27p1jE1NSVbtmzJ1kl8fUoycudKvL9nz57RuXNnoqOjCQgIYPLkyVw5vJmc2/vjxnOiFFNOFP2K8i0Gyi2gP4K8j/VPcpw+JM/6l1VzrK9jad26NXv37iUhIYHKlStTuXJlhg0bRsmSJT94m3379mXjxo3s3bs3yTybzs7aeRQfPnxIrly5dO0hISHJ+lyJzMzMMDMzS9auUqn0lpPEbb9r+2fPnmXt2rVJ2lq1aoWxsTGRW8cC8I9dAL5Fy+glzswstTkWH05ynD4kz/qnrxw3bdoUHx8fWrduzZEjR9i+6Tfdc79ftGZPnf6UK9iebsZ/Uld9DPX1XXB9F+QoDH69oWRrMLFI05gMJSu+j/VSlKpXrx779++nQYMGzJo1izp16qBWq3XD7vThfc7kpXadjNy5Eu8nISGBDh06cO3aNVxdXVm2ZDEnFw2kzL2lAFwzcieh2S9ULC6d0bQg72P9kxynD8mz/mXFHOvrWNauXUv27Nnp3Lkz1apVo1KlSh88j5SiKPTt25fff/+d3bt3kzdv3iTP582bF2dnZ4KCgihVSnv77djYWPbs2cOkSZM++ljSW+LlhtWrV2fmzJkYGxvj6enJxaNBeEUdJV4xwrnRaMMGKYQQ4o3c3d3Zt28fq1atIiREe8neunXrOHz4MA/Wj+eKf3U6RRtjrClB68IwofBlrB9fhE39YcdY7bxTZbqDTdaedyore6+i1LZt2+jXrx+9e/fG09NTXzEBqTuT5+zsTGxsLM+ePUsyWiokJITy5cvrNT5hOBqNhnv37gHw008/8eeff2Jubs6sqeOJWNSUMnGXADiYrQle3WZjZZ25b28thBAia3v69Cl79+5l9+7djBgxguDgYLy8vKhatSpVq1albt26qd5Wnz59WLlyJRs2bMDGxkY3ctzOzg4LCwtUKhUDBgxgwoQJeHp64unpyYQJE7C0tKRdu3b6OkS9SSxKNW7cOMndmeO3jwPgH4d6lC1Q3CCxCSGESB0TExM6duyoW+7fvz/Dhw9nypQpBB/aqWufdQZW+NSmT4AbPc13kCfyCeydDAemQ4mW4BcIzvJ/fmbzXtcy7du3j4iICHx9fSlXrhyzZ8/m8ePHegns1TN5iRLP5CUWnEqXLo2JiUmSdR48eMC5c+ekKJVFhYSEUKJECdzd3XF3d2fChAkAfBPYjurnh+IZd4kwxYqjZWdQvv8SKUgJIYTI8Ozt7WnUqBHTpk3jxIkTBAcHU7RoUaZNm0aDBg3ea1tz584lLCyMqlWrkitXLt1jzZo1unW+/PJLBgwYQGBgIL6+vty7d49t27bp7mycWbx8+VI3gfur84ie27+R4rGniFWMcWs62kDRCSGE+FAmJiZMnjyZffv28d133/Hdd9/Rs2dPAJ7+s5XZq/dQ/uGX9I7tz4kET9DEwqkVMK8CLGkIl/6GhAQDH4VIrfcaKeXv74+/vz8zZsxg9erVLFy4kEGDBpGQkEBQUBCurq7v1aF58eIFV69e1S3fuHGDU6dO4eDggJub2zvP5NnZ2dGtWzcGDx6Mo6MjDg4ODBkyhBIlShAQEPA+hyYygbi4OFq1asWFCxdQq9WYmJhgYmJC24p5GWmjvf74gnERbNovoWzeQgaOVgghhEidp0+fsmfPHt1d94KDg3FwcKBx48ZUq1btvbaVmhu2qFQqRo8ezejRoz8w4oxhz549xMbG4ubmRsGCBQFQEhIw3q09YXUyZxPKuel3ZL8QQgj9qVixIhUrVtQtN2nShI4dO/Lk9mViVw7lbrfhtLX6lqJxl/+dd+ooxjf2wo294FhAO++UV1swtTLgUYh3+aC771laWtK1a1e6du3KpUuX+OWXX/juu+/4+uuvqVmzJhs3bkzVdo4fP56ks5U4+XinTp1YvHgxX375JVFRUQQGBvLs2TPKlSuX7EzeDz/8gLGxMa1atSIqKooaNWqwePFi1Gr1hxyayMCGDBnCnj17sLGx4ejRoxhH3sd8UyB5lJskKCr2OX2GX7fvU5wvTAghhMiocuTIQfbs2alUqRI9evSgatWqFC8ulx+8S+Kle7Vr19bN93Vm91q84i8QpZiSv+n/DBmeEEKINFa3bl1OnTpF27Zt2bdvH5tnfE1ArTrcSzCn85ME1PHF8HMIZ2mVEOxDr8KWwbDjW/DtCmV7gK2LoQ9BpEClpOaUWipoNBo2bdrEwoULU12UyijCw8Oxs7MjLCxMb3ffCwsLy3J3IdK327dvM3ToUO7du0d8fDxHjhwBYN3ateQJO0jp2wtRqxQekp2H1X/Ao2RlybEeyftY/yTH6UPyrH9ZNcf66i+cO3cuUxWhMkq/qXDhwly6dInffvuN5s2boyQkcG28LwU01zjs3B6/XnPSPLasIqv+jWYkkuP0IXnWv4yY4/j4eMaMGcP48eNTHCHsVKMzvcpZ08X4b9xV2snTMTKG4s218065eKdvwO+QEXOcFlLbX/igkVIpUavVNGnShCZNmqTVJsUnLDw8nPr163Pu3Lkk7QP69KTkxYkUiL8KKjhqU5OCnefi5ZCdsLAwA0UrhBBCfLjEglRISAiXLl1CpVJRsGBBcubMaeDIMq5bt25x6dIl1Go1NWrUAOBk0HJ8NNd4qZhTqPlIA0cohBBCX4yNjfn2229p0KCBbm5BgKtXr/LTTz/x/MAalkVW4/sINwply8P3FSKoYnYFzqzRPtwraItTheqCkVxhZWhpVpQSIq1oNBratm3LuXPnyJUrF9OnT0dtZETYxR20i1uLeXwcYVhxyXcsZep3Q6VSpWoODSGEECIjCg8Pp0+fPqxatYqEfydmVavVtG7dmh9//BE7OzsDR5jxJF66V65cOezt7UnQaMh2ZCoAZ13b4pcj19teLoQQIgsoV64c5cqV0y0risLVq1fZsWMH1w/9CcAJoNYFN/wa92FArlM0VB/G+NYBuHUAsuXVzjvl3R7MrA10FEKKUiJDSUhIYMCAAfz555+Ym5uzYcMGcjta8XxNL4rGnwYVnDErTc4Ov1A2T15DhyuEEEJ8tO7du3Pq1Cm2bNmCv78/KpWKgwcP0r9/f3r06MGvv/5q6BAznFfnkwL45+/F+CbcIgILijQbbsjQhBBCGIhKpWLNmjUsW7aM2NhY4uLimD17Ng8f3ubQ8h84U9APtaYIhU1DmFzuOeW5AX99CTvHQ+lOUO5zsMtj6MP45EhRSmQYT58+pWPHjmzZsgWAxYsWkXD5L7JdnoWLKpZIxYxThQbg1/prjNRGBo5WCCGESBtbtmxh69atSe4wVLt2bX7++Wfq1KljwMgypvj4eHbs2AFo86SJjyfHiWkABLt1wM8hhyHDE0IIYUCOjo4MGDBAt9yjRw86dOjAtm3beH52NwAHgIpnTahTsxKzfe+QL+YRHJwJh36Eoo3B/wvIU9oQ4X+SpCglMoTjx4/TokULbt26hbm5Od+O+JJSV7+nYPxl7egoU2+ytZ5L+fxFDR2qEEIIkaYcHR1TvETPzs6ObNmyGSCijO3IkSOEhYXh4OCAr68vJ7f8jG/CXcKwomizrw0dnhBCiAwkZ86c/PXXX6xfv57bt28DsG1bEFu3/s1ff++kxM2KNK3bkkCrnZRXn4fg9dqHazntvFOFG4Bayib6JNkVBjd//nz69u1LbGws+fLlY3SHirSOnYWpSkO4Ysn5El9Stml/GR0lhBAiSxoxYgSDBg1i6dKl5MqlnQvp4cOHDB06lJEjZcLu1yVeuhcQEICSoMHp5AwALnh0xs/e0ZChCSGEyICMjIxo0aKFbnnAgAH88MMPfP3110Re3M/mZzfZW3cIxZ2N6Wr8N43VBzG5cwTuHAF7NyjXC0p1APO0v+OskKKUMLB58+bRu3dvAAIq+/N9heeU5A9QwUkLf1w+m4tfbpk7SgghRNY1d+5crl69iru7O25ubgDcvn0bMzMzHj9+zE8//aRb959//jFUmBnGq/NJ/bP5J8oq93mGLSWaDTVwZEIIITIDIyMjBg8eTMWKFWndujW3bt0icsVQ7MpUoUdYDL0oQscytox0O0O257dh6zewayL4dNTOO5XN3dCHkKVIUUoYzNq1awkMDASgS41C/FLhHCqViqfYcs13FL71uqIyktFRQgghsrYmTZoYOoRMIzQ0lGPHjgFQrWoVzJbVBeBS/i742cqljkIIIVKvXLlynDx5ku7du7N+/XrOHdyue276BTXLq3Sgq5893Yy34hl7Dw7/iHJkLqrCDbTzTrmWBZXKgEeQNUhRSqSbJ0+eMGPGDJ48eUJ8fDxLlixBURS6lbbg5wr3UTDiiGNjCrebQhnHnIYOVwghhEgXo0aNMnQImcb27dtRFIVixYrx+J8NlFUe8QR7vGSUlBBCiA+QLVs2fvvtN/766y9u3LgBwM6dO1m/fj2Pdy1m4e0SzDJ2IIdKTfO8kUwp8xjVhY1wYSPkLq2dd6poY1CbGPhIMi8pSol08eTJE6pXr87Zs2eTtLcoasxP9Yy5alyQhPrf4+dTxUARCiGEECKj080nVaM6bud+BOBqwe74WdkYMiwhhBCZmEqlol69errlwMBA5s2bx8CBA3l8Tfv99RYw7SIsvVycCY3y0NH2GGb3TsC6bhA0Csr1BJ9OYGFvmIPIxKQoJfTuyZMn1KhRg7Nnz5IzR3aaemfDJe42LjYqGpW051jxwZRpNgC1sbwdhRBCfDqMjIxQpTDs39bWlkKFCvHll1/SrFkzA0SWMSmKoitK5bOKxJknhOCAd5OBBo5MCCFEVqJSqejduzfVq1dnx44dKIpCSEgIkyZN4sm1c3yx8BFTG3xBL/fbdDDeTvbwuxD0P9g9CUp9Bn69wCGfoQ8j05AqgEhziqIwatQoFi5ciEaj4eXLl0REROBoa0FQy1hK5niERjHnmGNj1G0m4Jczt6FDFkIIIdLd77//nmL78+fPOXr0KJ999hlLliyhZcuW6RxZxhQcHMz9+/cxNzensSoIgBtFPqecpbWBIxNCCJEVFSpUiEKFCumWmzVrRsuWLbly5QpXV4/j5wY9mVvoBxobH6ab+i8Kx92Boz+hHJ2PqlA98O8D7uVl3ql3kKKUSFMajYbevXvz888/J2nPZWPEjs+MKJIDTpv7YtPoO/yKljFQlEIIIYThNW7c+I3PderUiaJFizJ16lQpSv1r6dKlAHgXdMXd5BEPyY53434GjkoIIcSnwsvLixMnTtCzZ09Wr17NhQ1zKVn6BH+YZGNuuCNFXT2Z4BtKgPFpuLRF+8jlBX59oFhTMDY19CFkSFKUEmkmPj6eLl26sHz5cu1tNrs2pY7xYbLznIKORjwwded0pVGUrNo8xcsVhBBCCPGfWrVqMWLECEOHkSGEhoYyZ84cAPoWew7ArWKBOJtbGjAqIYQQnxobGxtWrlxJtWrV6NevH2dOHNU9d+w8NL1WBu96/6OXzQGaG+/F/MFp+L0nbB8FZXtA6S5g6WDAI8h4pCgl0sTLly9p06YNmzdvRq02YmZTJwJza4fWh5CD00X7ULppP4xNpDoshBBCpEZUVBTm5uaGDiNDmD59Oi9fvsTT3Zm2ni+5r3LCp3EfQ4clhBDiE6RSqejZsydVqlRh27ZtJCQkEBoaypQpU3hx9RhnV9xiQO6ifBHrSXGbCOZWek5xHsCOsbBnCni3A7/ekN3T0IeSIUhRSny0x48fU79+fY4dO4aZsYpfW5jRqNBLnmLLZc8eeDcdJPM9CCGEEO/p559/plSpUoYOw+CeP3/OzJkzARheNhaVSsXdkn1xMZWCnRBCCMN5fc6pli1b0qpVKy5evAihIQDsB7zPZ6NJo7qMKXCeYvG34Pgv2odnbfAPBI/KBjqCjEGKUuK9hIWFUaNGDS5cuKBri42NIT5eg4OFik1tLSjuasshj06UaP41fjb2hgtWCCGEyMAGDRqUYntYWBjHjx/n2rVr7Nu3L52jynh+/PFHwsPDyeuSnQ5FYrircsGnQS9DhyWEEEIkUaJECY4dO8Zvv/1GeHg4iqLw888/ExwczLrVv7LTvxUBldrRw2QbNdT/YHRlK1zZCk7FMC3ZGcp2ABMLQx9GupOilHgvI0aM4MSJE8na82dTsbpdduJKdURp+hX+DjkMEJ0QQgiReZw8eTLFdltbW+rUqUNgYCDu7u7pHFXG8uLFC3744QcAhvlpMFKpeODdjzwyHYAQQogMyNrams6dO+uWe/TowYABA/j55595dnANx8Ovcq7WQHKatqOL+m9aqvdi+SgYy6ChKAcmQZlu4NsNbJwMdxDpTIpS4q0ePHjA0KFDuXz5Moqi6ApSS1vZUylXPABPsCPapytFmg7CSkZGCSGEEKmya9cuQ4eQ4c2bN4/Q0FDyODnQpVgcd1UulKrX3dBhCSGEEKliaWnJ/PnzqV69Oj179uTGuRM43O+HeVEfhjyNZKC6GH0CPOhtsweXyCewZxLKvmmoSrQAv0DIVdLQh6B3UpQSb7Rjxw7atWtHSEhIkvbPSprQoUgCd1W5uV/8c7zqf46Z3P1GCCGEEB8pJiaG9evXExkZiYWFBVOnTgVgaDkwNlLxoERvGSUlhBAi02nTpg2+vr60adOGEydO8HT/dt1zyy1rsbLqdOoYHaOr8V+U5gqcXqV9uFfUTopeqC4YqQ14BPojRSmRTGxsLOPGjWPcuHEoikIhJ3MmVlFhqgZTtYpsnqU5Vb43Jaq2JI+xvIWEEEIIkTYiIiJo165dkjbn7Pb0LhnHA3LiLXNJCSGEyKQKFCjAgQMH2LRpE8+fPyc8PJyhQ4dy88g2fFUv+CtaxeJwsDVypZO3CeOK3cL41n64tR+yeUC5XuDdHsxtDX0oaUoqCiKJ06dO0aZVcy5euQ5At1ImzKprgmJsztnsdXEO6Id7kdIGjlIIIYQQWZGJiQnVq1cnPj4eY2NjjIxU9Mx1DhN1FLeLfk4uUzNDhyiEEEJ8MDMzM1q0aKFbfvr0KePHj+f44YO6tkhg0lVYdKUqw2o709l8D/bPbsLfX8PO8eDTAcp9ri1UZQFSlPrEbdq0ia+++opbN2+iJMQTExtHggIOFirm1DOnTElPThdoSZG6vSiXLbuhwxVCCCFEFmZnZ8f27dsJCwvDzs6Oo799T7ngY4TggHejPoYOTwghhEhTY8aMwcfHh5CQECwsLFCpVJw+fZoffviBkNO7GRmSlxn1vqKt0y26qv+iQOx9ODwH5cg8VIXqaeedci8PKpWhD+WDSVHqExITE8OePXuIjo5GURTmzp7B1u3JJ1mtV8iMPh0bU7B+X/KWKE8+IyMDRCuEEEKIT5kmPp485+cDcKNQN8rJ/JVCCCGyGLVaTdOmTXUnY1T/Fpfq1q3LZ599xqMHN4hf+Q372w1mVfZJVDY6R1f1X1RRn4GLm7UP55La4lTxZmCc+UYUS1HqE5CQkMDq1asZPnw4N2/eTPKcsREM8jOlk485NyxLoi7WiKotAzG3sDJMsEIIIYQQQPDe9XgpjwjHipKN+hs6HCGEECLdBAQEcPr0aTp06EBQUBD7fvmWYiW82K1R89vLeNyKNmJEqQiamxzA/OEZ+KMXbB8FZbpD6S5gncPQh5BqUpTKwjQaDSuWLWHCuDFcunYbACcrFXmzaUc+OVsb8Vmd0rhVaU/uKm0oKpfnCSGEECKDUI4vBOB8zgb4WdkYOBohhBAifTk5OfH3338zadIkRo4cSfDZ07rnLt46Q787lfiuxnd0sDpCR+NtOL94BLvGw96pULKldvSUUzEDHkHqSFEqCwgLC2PVqlWcOXOGmKhIwp/cQfP8AacuXOdGaAwANqbwdUUzupez4262ssQXqEWhyq2wc3QycPRCCCGEEEmF3L1GycgjoILcAYGGDkcIIYQwCCMjI4YNG0bz5s05d+4cAMHBwXz77bc8PbePhPsXmGjtyP9ijcmX04M5NROoan0bTi7XPvJW0RanPGtBBp2WR4pSmVR8fDzbt21lwU+z2bJ1B9ExcSmul80cPiuXg3oN6uPq34JsZQLIKXeuEUIIIT5pc+bMYcqUKTx48IBixYoxffp0KlWqZOiwdB7tW0RBlcI5M2+KF/Q2dDhCCCGEQRUsWJCCBQsC0KxZM+rUqUObNm24fv06PH0CwIWHEHDDkfKNOvJl3kvUVR9DfWMP3NgDDvmhXC/wbgdm1oY8lGSyTFEqo3euPlRUVBS7du5g84Z13LhyEU10OJrIME5efcizSI1uvSLZjWhcyBgzY3iusuOFeS5yFihJj75fkbdgcQMegRBCCCEykjVr1jBgwADmzJlDhQoV+Omnn6hbty7nz5/Hzc3N0OERGxNN0UcbtT+X6mLgaIQQQoiMp0yZMpw5c4ZDhw4RFxdHZGQk33zzDZcvX2b/qjmcL9OYvL6j6G5/lLbGu7B9eg3+Ggo7x0HpjlC2J9gb/jMfQKUoimLoID7WmjVr6NChQ5LO1YIFC1LduQoPD8fOzo6wsDBsbW3TPD5FUZLMpq8oClGRkTx5HML9Oze4duUiN65f5fLFi1y8dIWrt+4RExsHikJsvIb4hJS362ihom5hC2qULUIhn/KYe5TFo1QNsuXIlebHkNG9nmOR9iTH+ic5Th+SZ/3LqjnWd38hvZQrVw4fHx/mzp2raytSpAhNmjRh4sSJ73y9vvNwaPlo/K/+wGOyYf/NJUxkhHeay6p/oxmJ5Dh9SJ71T3Ksf2mV4xcvXtCnTx+WLl0KgImpKTnL1MeiZG3aZDtPV/Xf5DV6qN2nyghVkYbg1wdcy4Iefrep7S9kiZFS06ZNo1u3bnTv3h2A6dOns3XrVubOnZti5yomJoaYmBjdcnh4OKB9M6R1jW76t0MZPeGHf5cUEhR4GavwPnvJbaOicj4zPFyyE2PqgMbCkaKl/KjfshO53Aqgeu3a0CxQZ3xvib+7T/HY04vkWP8kx+lD8qx/WTXHWeF4YmNjOXHiBF9//XWS9lq1anHw4MEUX5Oe/aY7V85Q6spsUMH14n0pa2KaJfKe0WTVv9GMRHKcPiTP+ic51r+0yrGVlRWLFy+mbdu2fPvttxw8eJB7B37H+MhmVvvXY5nfJKobn6Wr+i8qqoPh/AY4vwGl8RztZX1pLLXHk+mLUh/SuZo4cSJjxoxJ1h4WFpbmf2yRL8IJj3nDUCfAygRc7IxxsTMlt6MV+fLkxMPDA6vsuTGyzoG1kztFSlXA3tE5WfEJIDwiIk3jzawUReHFixcAUsHXE8mx/kmO04fkWf+yao4TizGZ2ZMnT9BoNDg5Jb3RiZOTEw8fPkzxNenZb4rTwCXzkpAQj2e1zoSFhaXp9oVWVv0bzUgkx+lD8qx/kmP9S+sc+/n5sXnzZvbt28eUKVPYv38/7qYvmNC9DCuO5WbK0yqUqGOC+amFmFzfTrhLJdDD521q+02Zvij1IZ2rYcOGMWjQIN1yeHg4rq6u2NnZpfkw9M96DcGnjD+x8RqsrG0wMTPHxtYOW1s7HJ1yY2fvmGKxSbyfxE6xDCvVH8mx/kmO04fkWf+yao6z8rEoivLG40vPfpOdXUkShm7j4f272GfLlqVynpFk1b/RjERynD4kz/onOdY/feW4YcOGNGzYkH379mFra4uXpwtlPF3QJCiojVRQwA/io7EzNk+zfb4qtceS6YtSid6nc2VmZoaZWfL5CVQqVZr/obnnK4hbXk+5DjcdJP7+JMf6IznWP8lx+pA8619WzHFWOJbs2bOjVquTnbgLCQlJdoIvUXr2mwCM1GqsbO2z3Psno8mKf6MZjeQ4fUie9U9yrH/6zHHlypWTLBurX9mHiUWa7y9Rao8l0w/R+ZDOlRBCCCHEp8jU1JTSpUsTFBSUpD0oKIjy5csbKCohhBBCfKoyfVFKOldCCCGEEKk3aNAgFixYwMKFC7lw4QIDBw7k9u3b9OrVy9ChCSGEEOITkyUu3xs0aBAdOnTA19cXf39/5s+fL50rIYQQQogUtG7dmtDQUMaOHcuDBw8oXrw4f/75J+7u7oYOTQghhBCfmCxRlJLOlRBCCCFE6gUGBhIYGGjoMIQQQgjxicsSRSmQzpUQQgghhBBCCCFEZpJlilIfI/EWjOHh4Xrbfnh4uNyxQI8kx/onOdY/yXH6kDzrX1bNcWI/IbHf8KmSflPmJznWP8lx+pA865/kWP+yao5T22+SohQQEREBgKurq4EjEUIIIURGFxERgZ2dnaHDMBjpNwkhhBAitd7Vb1Ipn/rpPiAhIYH79+9jY2Ojl8pkeHg4rq6u3LlzB1tb2zTfvpAcpwfJsf5JjtOH5Fn/smqOFUUhIiICFxcXjIwy/Q2MP5j0mzI/ybH+SY7Th+RZ/yTH+pdVc5zafpOMlAKMjIzIkyeP3vdja2ubpd5kGZHkWP8kx/onOU4fkmf9y4o5/pRHSCWSflPWITnWP8lx+pA865/kWP+yYo5T02/6dE/zCSGEEEIIIYQQQgiDkaKUEEIIIYQQQgghhEh3UpRKB2ZmZowaNQozMzNDh5JlSY71T3Ksf5Lj9CF51j/JsfgY8v7RP8mx/kmO04fkWf8kx/r3qedYJjoXQgghhBBCCCGEEOlORkoJIYQQQgghhBBCiHQnRSkhhBBCCCGEEEIIke6kKCWEEEIIIYQQQggh0p0UpYQQQgghhBBCCCFEupOilBBCCCGEEEIIIYRId1KU0rM5c+aQN29ezM3NKV26NPv27TN0SFnGxIkTKVOmDDY2NuTMmZMmTZpw6dIlQ4eVpU2cOBGVSsWAAQMMHUqWc+/ePT777DMcHR2xtLTE29ubEydOGDqsLCM+Pp4RI0aQN29eLCwsyJcvH2PHjiUhIcHQoWVqe/fupWHDhri4uKBSqfjjjz+SPK8oCqNHj8bFxQULCwuqVq1KcHCwYYIVmYL0m/RL+k7pT/pO+iH9Jv2SfpN+SL8pZVKU0qM1a9YwYMAAhg8fzsmTJ6lUqRJ169bl9u3bhg4tS9izZw99+vTh8OHDBAUFER8fT61atXj58qWhQ8uSjh07xvz58ylZsqShQ8lynj17RoUKFTAxMeGvv/7i/PnzfP/999jb2xs6tCxj0qRJzJs3j9mzZ3PhwgUmT57MlClTmDVrlqFDy9RevnyJl5cXs2fPTvH5yZMnM23aNGbPns2xY8dwdnamZs2aREREpHOkIjOQfpP+Sd8pfUnfST+k36R/0m/SD+k3vYEi9KZs2bJKr169krQVLlxY+frrrw0UUdYWEhKiAMqePXsMHUqWExERoXh6eipBQUFKlSpVlP79+xs6pCzlq6++UipWrGjoMLK0+vXrK127dk3S1qxZM+Wzzz4zUERZD6D8/vvvuuWEhATF2dlZ+e6773Rt0dHRip2dnTJv3jwDRCgyOuk3pT/pO+mP9J30R/pN+if9Jv2TftN/ZKSUnsTGxnLixAlq1aqVpL1WrVocPHjQQFFlbWFhYQA4ODgYOJKsp0+fPtSvX5+AgABDh5Ilbdy4EV9fX1q2bEnOnDkpVaoUP//8s6HDylIqVqzIjh07uHz5MgCnT59m//791KtXz8CRZV03btzg4cOHST4HzczMqFKlinwOimSk32QY0nfSH+k76Y/0m/RP+k3p71PuNxkbOoCs6smTJ2g0GpycnJK0Ozk58fDhQwNFlXUpisKgQYOoWLEixYsXN3Q4Wcrq1av5559/OHbsmKFDybKuX7/O3LlzGTRoEN988w1Hjx6lX79+mJmZ0bFjR0OHlyV89dVXhIWFUbhwYdRqNRqNhvHjx9O2bVtDh5ZlJX7WpfQ5eOvWLUOEJDIw6TelP+k76Y/0nfRL+k36J/2m9Pcp95ukKKVnKpUqybKiKMnaxMf74osvOHPmDPv37zd0KFnKnTt36N+/P9u2bcPc3NzQ4WRZCQkJ+Pr6MmHCBABKlSpFcHAwc+fOlc5VGlmzZg3Lly9n5cqVFCtWjFOnTjFgwABcXFzo1KmTocPL0uRzULwPeb+kH+k76Yf0nfRP+k36J/0mw/kUPwelKKUn2bNnR61WJzu7FxISkqz6KT5O37592bhxI3v37iVPnjyGDidLOXHiBCEhIZQuXVrXptFo2Lt3L7NnzyYmJga1Wm3ACLOGXLlyUbRo0SRtRYoUYd26dQaKKOsZOnQoX3/9NW3atAGgRIkS3Lp1i4kTJ0rnSk+cnZ0B7Zm/XLly6drlc1CkRPpN6Uv6TvojfSf9k36T/km/Kf19yv0mmVNKT0xNTSldujRBQUFJ2oOCgihfvryBospaFEXhiy++YP369ezcuZO8efMaOqQsp0aNGpw9e5ZTp07pHr6+vrRv355Tp05JpyqNVKhQIdktuS9fvoy7u7uBIsp6IiMjMTJK+pGnVqvl1sZ6lDdvXpydnZN8DsbGxrJnzx75HBTJSL8pfUjfSf+k76R/0m/SP+k3pb9Pud8kI6X0aNCgQXTo0AFfX1/8/f2ZP38+t2/fplevXoYOLUvo06cPK1euZMOGDdjY2OjOrtrZ2WFhYWHg6LIGGxubZPNMWFlZ4ejoKPNPpKGBAwdSvnx5JkyYQKtWrTh69Cjz589n/vz5hg4ty2jYsCHjx4/Hzc2NYsWKcfLkSaZNm0bXrl0NHVqm9uLFC65evapbvnHjBqdOncLBwQE3NzcGDBjAhAkT8PT0xNPTkwkTJmBpaUm7du0MGLXIqKTfpH/Sd9I/6Tvpn/Sb9E/6Tfoh/aY3MNyN/z4NP/74o+Lu7q6YmpoqPj4+csvdNASk+Fi0aJGhQ8vS5LbG+rFp0yalePHiipmZmVK4cGFl/vz5hg4pSwkPD1f69++vuLm5Kebm5kq+fPmU4cOHKzExMYYOLVPbtWtXiv8Pd+rUSVEU7e2NR40apTg7OytmZmZK5cqVlbNnzxo2aJGhSb9Jv6TvZBjSd0p70m/SL+k36Yf0m1KmUhRFSc8imBBCCCGEEEIIIYQQMqeUEEIIIYQQQgghhEh3UpQSQgghhBBCCCGEEOlOilJCCCGEEEIIIYQQIt1JUUoIIYQQQgghhBBCpDspSgkhhBBCCCGEEEKIdCdFKSGEEEIIIYQQQgiR7qQoJYQQQgghhBBCCCHSnRSlhBBCCCGEEEIIIUS6k6KUECJLGT16NN7e3gbb/8iRI+nZs2eq1h0yZAj9+vXTc0RCCCGEECmTfpMQwtBUiqIohg5CCCFSQ6VSvfX5Tp06MXv2bGJiYnB0dEynqP7z6NEjPD09OXPmDB4eHu9cPyQkhPz583PmzBny5s2r/wCFEEII8cmQfpMQIjOQopQQItN4+PCh7uc1a9bwv//9j0uXLunaLCwssLOzM0RoAEyYMIE9e/awdevWVL+mefPmFChQgEmTJukxMiGEEEJ8aqTfJITIDOTyPSFEpuHs7Kx72NnZoVKpkrW9Pgy9c+fONGnShAkTJuDk5IS9vT1jxowhPj6eoUOH4uDgQJ48eVi4cGGSfd27d4/WrVuTLVs2HB0dady4MTdv3nxrfKtXr6ZRo0ZJ2n777TdKlCiBhYUFjo6OBAQE8PLlS93zjRo1YtWqVR+dGyGEEEKIV0m/SQiRGUhRSgiR5e3cuZP79++zd+9epk2bxujRo2nQoAHZsmXjyJEj9OrVi169enHnzh0AIiMjqVatGtbW1uzdu5f9+/djbW1NnTp1iI2NTXEfz54949y5c/j6+uraHjx4QNu2benatSsXLlxg9+7dNGvWjFcHqJYtW5Y7d+5w69Yt/SZBCCGEECIVpN8khEhPUpQSQmR5Dg4OzJw5k0KFCtG1a1cKFSpEZGQk33zzDZ6engwbNgxTU1MOHDgAaM/cGRkZsWDBAkqUKEGRIkVYtGgRt2/fZvfu3Snu49atWyiKgouLi67twYMHxMfH06xZMzw8PChRogSBgYFYW1vr1smdOzfAO88mCiGEEEKkB+k3CSHSk7GhAxBCCH0rVqwYRkb/1eCdnJwoXry4blmtVuPo6EhISAgAJ06c4OrVq9jY2CTZTnR0NNeuXUtxH1FRUQCYm5vr2ry8vKhRowYlSpSgdu3a1KpVixYtWpAtWzbdOhYWFoD2LKMQQgghhKFJv0kIkZ6kKCWEyPJMTEySLKtUqhTbEhISAEhISKB06dKsWLEi2bZy5MiR4j6yZ88OaIejJ66jVqsJCgri4MGDbNu2jVmzZjF8+HCOHDmiu2vM06dP37pdIYQQQoj0JP0mIUR6ksv3hBDiNT4+Ply5coWcOXNSoECBJI833aUmf/782Nracv78+STtKpWKChUqMGbMGE6ePImpqSm///677vlz585hYmJCsWLF9HpMQgghhBD6IP0mIcTHkKKUEEK8pn379mTPnp3GjRuzb98+bty4wZ49e+jfvz93795N8TVGRkYEBASwf/9+XduRI0eYMGECx48f5/bt26xfv57Hjx9TpEgR3Tr79u2jUqVKuuHoQgghhBCZifSbhBAfQ4pSQgjxGktLS/bu3YubmxvNmjWjSJEidO3alaioKGxtbd/4up49e7J69WrdcHZbW1v27t1LvXr1KFiwICNGjOD777+nbt26utesWrWKHj166P2YhBBCCCH0QfpNQoiPoVJevcemEEKID6YoCn5+fgwYMIC2bdu+c/0tW7YwdOhQzpw5g7GxTPEnhBBCiE+H9JuEECAjpYQQIs2oVCrmz59PfHx8qtZ/+fIlixYtko6VEEIIIT450m8SQoCMlBJCCCGEEEIIIYQQBiAjpYQQQgghhBBCCCFEupOilBBCCCGEEEIIIYRId1KUEkIIIYQQQgghhBDpTopSQgghhBBCCCGEECLdSVFKCCGEEEIIIYQQQqQ7KUoJIYQQQgghhBBCiHQnRSkhhBBCCCGEEEIIke6kKCWEEEIIIYQQQggh0p0UpYQQQgghhBBCCCFEupOilBBCCCGEEEIIIYRId1KUEkIIIYQQQgghhBDpTopSQgghhBBCCCGEECLdSVFKCCGEEEIIIYQQQqQ7KUoJIYQQQgghhBBCiHQnRSkhRDKLFy9GpVLpHubm5jg7O1OtWjUmTpxISEiIoUPMUE6ePEmVKlWws7NDpVIxffp0AHbs2IGvry9WVlaoVCr++OMPXW5v3rz5XvsYPXo0KpUq7YN/RWRkJKNHj2b37t163Y8QQgiRFR05coSmTZvi5uaGmZkZTk5O+Pv7M3jw4CTrzZkzh8WLF3/UvlQqFV988cU719u9ezcqlSrJZ3tKfYqqVatStWpV3bL0CbQ6d+6Mh4eHocMQIkszNnQAQoiMa9GiRRQuXJi4uDhCQkLYv38/kyZNYurUqaxZs4aAgABDh5ghdO3alZcvX7J69WqyZcuGh4cHiqLQqlUrChYsyMaNG7GysqJQoULEx8dz6NAhcuXK9V776N69O3Xq1NHTEWhFRkYyZswYgCQdUyGEEEK83ZYtW2jUqBFVq1Zl8uTJ5MqViwcPHnD8+HFWr17N999/r1t3zpw5ZM+enc6dO+s9Lh8fHw4dOkTRokXfut6cOXOSLEufQGvkyJH079/f0GEIkaVJUUoI8UbFixfH19dXt9y8eXMGDhxIxYoVadasGVeuXMHJyemNr4+MjMTS0jI9QjWoc+fO0aNHD+rWratru3fvHk+fPqVp06bUqFEjyfo5cuR4733kyZOHPHnyfHSsQgghhEh7kydPJm/evGzduhVj4/++YrVp04bJkycbLC5bW1v8/Pzeud67ilZZRVRUFBYWFqleP3/+/HqMRggBcvmeEOI9ubm58f333xMREcFPP/2ka+/cuTPW1tacPXuWWrVqYWNjoyvGBAUF0bhxY/LkyYO5uTkFChTg888/58mTJ8m2v2HDBkqWLImZmRn58uVjxowZ73Xp2t9//02NGjWws7PD0tKSIkWKMHHixCTrbNy4EX9/fywtLbGxsaFmzZocOnQo2bauXLlCu3btyJkzJ2ZmZhQpUoQff/xR93zipXjx8fHMnTtXd7nj6NGjdQWkr776CpVKpRv6/abL994V95tysGbNGvz9/bGyssLa2pratWtz8uTJJOsk/m6uXr1KvXr1sLa2xtXVlcGDBxMTEwPAzZs3dcWyMWPG6I4lPc7iCiGEEJldaGgo2bNnT1KQSmRk9N9XLg8PD4KDg9mzZ4/uszaxjxAdHc3gwYPx9vbGzs4OBwcH/P392bBhwxv3+9NPP1GwYEHMzMwoWrQoq1evTvJ8SpfvpeTVy/fe1ifYt28fKpWKVatWJdvG0qVLUalUHDt27I37SewH7dy5kx49euDo6IitrS0dO3bk5cuXPHz4kFatWmFvb0+uXLkYMmQIcXFxSbYxZswYypUrh4ODA7a2tvj4+PDLL7+gKEqS9Tw8PGjQoAHr16+nVKlSmJub60Z/BQcHU6tWLSwtLcmRIwd9+vRhy5YtyXKV0uV7iZdOLlu2jCJFimBpaYmXlxebN29+a46FECmTkVJCiPdWr1491Go1e/fuTdIeGxtLo0aN+Pzzz/n666+Jj48H4Nq1a/j7+9O9e3fs7Oy4efMm06ZNo2LFipw9exYTExNAW5hp1qwZlStXZs2aNcTHxzN16lQePXqUqrh++eUXevToQZUqVZg3bx45c+bk8uXLnDt3TrfOypUrad++PbVq1WLVqlXExMQwefJkqlatyo4dO6hYsSIA58+fp3z58roinLOzM1u3bqVfv348efKEUaNGUb9+fQ4dOoS/vz8tWrTQzRmRJ08evLy8aNasGX379qVdu3aYmZl9VNwpmTBhAiNGjKBLly6MGDGC2NhYpkyZQqVKlTh69GiSs55xcXE0atSIbt26MXjwYPbu3cu3336LnZ0d//vf/8iVKxd///03derUoVu3bnTv3h34sFFdQgghxKfG39+fBQsW0K9fP9q3b4+Pj4+uf/Oq33//nRYtWmBnZ6e7ZC6xjxATE8PTp08ZMmQIuXPnJjY2lu3bt9OsWTMWLVpEx44dk2xr48aN7Nq1i7Fjx2JlZcWcOXNo27YtxsbGtGjR4oOP5W19gvz581OqVCl+/PFH2rZtm+R1s2fPpkyZMpQpU+ad++jevTvNmjVj9erVnDx5km+++Yb4+HguXbpEs2bN6NmzJ9u3b2fSpEm4uLgwaNAg3Wtv3rzJ559/jpubGwCHDx+mb9++3Lt3j//9739J9vPPP/9w4cIFRowYQd68ebGysuLBgwdUqVIFKysr5s6dS86cOVm1alWq5uhKtGXLFo4dO8bYsWOxtrZm8uTJNG3alEuXLpEvX75Ub0cIAShCCPGaRYsWKYBy7NixN67j5OSkFClSRLfcqVMnBVAWLlz41m0nJCQocXFxyq1btxRA2bBhg+65MmXKKK6urkpMTIyuLSIiQnF0dFTe9d9VRESEYmtrq1SsWFFJSEhIcR2NRqO4uLgoJUqUUDQaTZLX5syZUylfvryurXbt2kqePHmUsLCwJNv44osvFHNzc+Xp06e6NkDp06dPkvVu3LihAMqUKVOStCfm9saNG6mOW1EUZdSoUUlycPv2bcXY2Fjp27dvsjw4OzsrrVq10rUl/m5+/fXXJOvWq1dPKVSokG758ePHCqCMGjXqjXEIIYQQIrknT54oFStWVAAFUExMTJTy5csrEydOVCIiIpKsW6xYMaVKlSrv3GZ8fLwSFxendOvWTSlVqlSS5wDFwsJCefjwYZL1CxcurBQoUEDXtmvXLgVQdu3apWt7vU+hKIpSpUqVJDG9rU+Q2Jc5efKkru3o0aMKoCxZsuStx5T42tf7L02aNFEAZdq0aUnavb29FR8fnzduT6PRKHFxccrYsWMVR0fHJH0pd3d3Ra1WK5cuXUrymqFDhyoqlUoJDg5O0l67du1kuerUqZPi7u6eZD1AcXJyUsLDw3VtDx8+VIyMjJSJEye+9fiFEMnJ5XtCiA+ivDZEOlHz5s2TtYWEhNCrVy9cXV0xNjbGxMQEd3d3AC5cuADAy5cvOX78OE2aNMHU1FT3Wmtraxo2bPjOeA4ePEh4eDiBgYFvvNTv0qVL3L9/nw4dOiQZSm9tbU3z5s05fPgwkZGRREdHs2PHDpo2bYqlpSXx8fG6R7169YiOjubw4cPvjCk1UhN3SrZu3Up8fDwdO3ZMEp+5uTlVqlRJNkxfpVIly2PJkiW5detWWhyGEEII8UlzdHRk3759HDt2jO+++47GjRtz+fJlhg0bRokSJVKcsiAla9eupUKFClhbW+v6TL/88ouuv/SqGjVqJJnbU61W07p1a65evcrdu3fT7Nhe17ZtW3LmzJlkSoNZs2aRI0cOWrdunaptNGjQIMlykSJFAKhfv36y9tf7Kjt37iQgIAA7OzvUajUmJib873//IzQ0NNkdokuWLEnBggWTtO3Zs4fixYsnm0fr9ZFfb1OtWjVsbGx0y05OTuTMmVP6VUJ8AClKCSHe28uXLwkNDcXFxSVJu6WlJba2tknaEhISqFWrFuvXr+fLL79kx44dHD16VFfUiYqKAuDZs2coipLixOlvm0w90ePHjwHeOhl4aGgoQIp3vnNxcSEhIYFnz54RGhpKfHw8s2bNwsTEJMmjXr16AKnuXKZF3ClJvKSxTJkyyWJcs2ZNsvgsLS0xNzdP0mZmZkZ0dPRHRC+EEEKIV/n6+vLVV1+xdu1a7t+/z8CBA7l582aqJjtfv349rVq1Infu3CxfvpxDhw5x7NgxunbtmuLntbOz8xvbEvs8+mBmZsbnn3/OypUref78OY8fP+bXX3+le/fub52u4FUODg5JlhNPSKbU/uqxHz16lFq1agHw888/c+DAAY4dO8bw4cOB//qViVLq84WGhn5wfzORo6NjsjYzM7Nk+xdCvJvMKSWEeG9btmxBo9Eku0VwSiN9zp07x+nTp1m8eDGdOnXStV+9ejXJetmyZUOlUqU4f9TDhw/fGVPi3EdvOzOY2IF48OBBsufu37+PkZER2bJlA7RnGzt06ECfPn1S3FbevHnfGVNqpCbulGTPnh2A3377TTfqTAghhBAZh4mJCaNGjeKHH3545zyRAMuXLydv3rysWbMmSZ8q8aYkr0upf5TYllLRJC317t2b7777joULFxIdHU18fDy9evXS6z4BVq9ejYmJCZs3b05ysu2PP/5Icf2U+qaOjo4f3N8UQqQ9KUoJId7L7du3GTJkCHZ2dnz++efvXD+xM/D6mbNX79wHYGVlha+vL3/88QdTp07VnTF78eJFqu5mUr58eezs7Jg3bx5t2rRJsRNSqFAhcufOzcqVKxkyZIhunZcvX7Ju3TrdHflAOyz75MmTlCxZMsnlhGktNXGnpHbt2hgbG3Pt2rUUL5n8EIm/IznLJ4QQQryfBw8epDgqJ/Gyu1dHl79pRI1KpcLU1DRJX+Dhw4dvvPvejh07ePTokW6Ej0ajYc2aNeTPn/+9R2C/7l19gly5ctGyZUvmzJlDbGwsDRs21E08rk8qlQpjY2PUarWuLSoqimXLlqV6G1WqVGHq1KmcP38+ySV8r9+5UAiRPqQoJYR4o3PnzunmKgoJCWHfvn0sWrQItVrN77//nqo7sxUuXJj8+fPz9ddfoygKDg4ObNq0iaCgoGTrjh07lvr161O7dm369++PRqNhypQpWFtb8/Tp07fux9ramu+//57u3bsTEBBAjx49cHJy4urVq5w+fZrZs2djZGTE5MmTad++PQ0aNODzzz8nJiaGKVOm8Pz5c7777jvd9mbMmEHFihWpVKkSvXv3xsPDg4iICK5evcqmTZvYuXPn+yf0A+NOiYeHB2PHjmX48OFcv36dOnXqkC1bNh49esTRo0exsrLS3fY4tWxsbHB3d2fDhg3UqFEDBwcHsmfPnuxWyEIIIYRIqnbt2uTJk4eGDRtSuHBhEhISOHXqFN9//z3W1tb0799ft26JEiVYvXo1a9asIV++fJibm1OiRAkaNGjA+vXrCQwMpEWLFty5c4dvv/2WXLlyceXKlWT7zJ49O9WrV2fkyJG6u+9dvHgxTYorqekT9O/fn3LlygGwaNGij95natSvX59p06bRrl07evbsSWhoKFOnTk31ZYMAAwYMYOHChdStW5exY8fi5OTEypUruXjxIkCSeUeFEPonRSkhxBt16dIF0F7Pb29vT5EiRfjqq6/o3r17qgpSoB26vmnTJvr378/nn3+OsbExAQEBbN++PdkZtTp16rBu3Tr+97//0bp1a5ydnQkMDOT+/fupOgPWrVs3XFxcmDRpEt27d0dRFDw8PJJcNtiuXTusrKyYOHEirVu3Rq1W4+fnx65duyhfvrxuvaJFi/LPP//w7bffMmLECEJCQrC3t8fT01M3r1RaSU3cKRk2bBhFixZlxowZrFq1ipiYGJydnSlTpswHD6H/5ZdfGDp0KI0aNSImJoZOnTqxePHiD9qWEEII8akYMWIEGzZs4IcffuDBgwfExMSQK1cuAgICGDZsmG4ib4AxY8bw4MEDevToQUREBO7u7ty8eZMuXboQEhLCvHnzWLhwIfny5ePrr7/m7t27KZ5oatSoEcWKFWPEiBHcvn2b/Pnzs2LFilRPNv4u7+oTlC1bFg8PDywsLKhRo0aa7PNdqlevzsKFC5k0aRINGzYkd+7c9OjRg5w5c9KtW7dUbcPFxYU9e/YwYMAAevXqhaWlJU2bNmXs2LF06tQJe3t7/R6EECIJlfKmW2gJIUQGEBcXh7e3N7lz52bbtm2GDkcIIYQQQgBnzpzBy8uLH3/8kcDAQEOH89F69uzJqlWrCA0N1evUDUKIpGSklBAiQ+nWrRs1a9YkV65cPHz4kHnz5nHhwgVmzJhh6NCEEEIIIT55165d49atW3zzzTfkypWLzp07Gzqk9zZ27FhcXFzIly+fbv7SBQsWMGLECClICZHOpCglhMhQIiIiGDJkCI8fP8bExAQfHx/+/PNPAgICDB2aEEIIIcQn79tvv2XZsmUUKVKEtWvX6m4Sk5mYmJgwZcoU7t69S3x8PJ6enkybNi3J3F9CiPQhl+8JIYQQQgghhBBCiHQntxYQQgghhBBCCCGEEOlOilJCCCGEEEIIIYQQIt1JUUoIIYQQQgghhBBCpDuZ6BxISEjg/v372NjYoFKpDB2OEEIIITIgRVGIiIjAxcUFI6NP97ye9JuEEEII8S6p7TdJUQq4f/8+rq6uhg5DCCGEEJnAnTt3yJMnj6HDMBjpNwkhhBAitd7Vb5KiFGBjYwNok2Vra/vR21MUhbCwMOzs7OQM4geQ/H0cyd/Hkfx9HMnfx5H8fRx95y88PBxXV1ddv+FTldb9ptfJ34H+SY71T3KcPiTP+ic51r+smuPU9pukKAW6X7ytrW2aFaUURcHW1jZLvanSi+Tv40j+Po7k7+NI/j6O5O/jpFf+PvXfTVr3m14nfwf6JznWP8lx+pA865/kWP+yeo7fdUyf7oQIQgghhBBCCCGEEMJgZKSUEJ8wjUZDXFycocPIUBRFITY2lujo6Cx5piItmZiYoFarDR2GEEIIIYQQIpOSopQQn6gXL15w9+5dFEUxdCgZTkJCAqGhoYYOI8NTqVTkyZMHa2trQ4cihBBCCCGEyISkKCXEJ0ij0XD37l0sLS3JkSOHjAh6haIoaDQa1Gq15OUtFEXh8ePH3L17F09PTxkxJYQQQgghhHhvUpQS4hMUFxeHoijkyJEDCwsLQ4eToUhRKvVy5MjBzZs3iYuLk6KUMDxNHITfQx1yG+wqGToaIbKmBA1EPoX4KO3PSoL2oTYBU2swtQJjc5DPTyGEEKkkRSkhPmFSdBEfQ94/Il3FvoTndyDsDjy/rf037O5/bREPUCkJWJvawLA7ho5WiMwtQQN3j8HNffDoPDy+BBEPIOoZ8I7L/lVG/xWodI9/l00sX3vutWWTFF6TuI7aJF0OXQghRPqSotQH8PDwIDIyknv37mFiov2A3LlzJzVq1GDw4MFMmTLFwBGmXufOnfH19eWLL75I9tyLFy8YMWIEmzdvxtzcHI1GQ/369Zk4cSIHDhygXr16FCpUiNjYWABq167NyJEjyZYtW3ofxnvx9vbm0KFDWFhY4OHhwebNmylevPhbX6NSqYiIiEhx7py3PSeEECIVFAUiQ1MuNiW2RT1792bUpiRYOmIUF6n9MisyjHf1naZOnWrgCFPvbX2n9Hb8+HF++OEHVqxY8fEb08TBlW1wfoP237f9zRlbaItPiQ9NrHb0FGhHTsWEax/vcD8igfbro9jVSfv3qhoTTsQwG6xNUzjpoTYFUytuRpjgO/0mT6ZVfK3wZcXNZ/H49lvKk83fplAYe6XYZWL537KRjPQVQghDkqLUB3Jzc2Pjxo00b94cgIULF+Lr62vgqCA+Ph5j44//tSqKQoMGDfD09OTs2bNYWFgQGxvLwoULiYmJAaBo0aIcP34cgIiICIYOHUqNGjU4duxYhr6U59SpU4YOQQDGxsYUL16cuLg48uXLx7Jly7C3t3+vbYwePZrs2bOn6ovBzZs3OXr0KK1atQJg9+7dNGnSBA8PD2JiYmjTpg2jRo36kEPJUHbv3s3s2bP57bffDB2KEP/RxEPE/X8LTXch7PYrRad/2xK/0L6NmR3Yu4JdHrBz/fdn1/9+tspBRHgEdiaW+j8m8d6yet9JH94Vm6+v78cXpJ5ehxNL4NRKeBnyX7u5HeSvDrm8IWdR7d+YpSNYOIA6hZgSNNoRjbrHi/9+jnu9PRJiX+IS+4Jdfi8hTrsMW8CxAKii/1s3IV67fU0sRMXCiwTtvu7/kzyG5wkQFwU7xqT++I3NtQUvtYn2XyOTf3/+92Fk8srzJq88/3pb8nXM4hLA0hqMzcDI+A2vSWw3fcs6JknjMzJ6n9+wEEJkaBnzEzgT6Nq1KwsXLqR58+aEhYVx+PBh2rZtS1TUf53qqVOnsnbtWuLj43F2duann37C1dWVHTt2MGLECKKjo4mLi2Pw4MF06dIFgHHjxrFixQrMzMwA2LBhA+7u7slG42TPnp3jx4/j4eGBh4cHPXr0YPv27bi4uLB48WJGjhzJzp07iY2NpXDhwsybNw97e3vu3btHx44defz4MXnz5kWj0aR4fDt37uTq1ats27YNU1NTAExNTenVq1eK69vY2DBr1iwKFCjA33//Tf369ZM8v3jxYjZv3qz7orx582amTp3K7t272b17NwMGDKB8+fIcOHCA+Ph4Zs+eTdWqVYmPj6d+/fqEhoYSFRWFt7c3P//8M5aWlhQsWJBVq1ZRunRpABYtWsSmTZtYv34906ZNY9WqVcTHx2NiYsKsWbMoV64c8OaRTW97TeLvMygoiMePHzNmzBjatm2bLA9XrlxhwIABhISEEBsby+eff05gYGCKOctoImPj3/ickUqFuYn6g9Z9E3t7e12BsEOHDvz4448MHz489QG/p5s3b/Lrr7/qilIAAQEB/Pbbb7x8+RJvb28aNGiAj4+P3mL4GInzXAmRIcVG/jvC6U7yYlPYHQi/D0rKnzdJWDtrC06JxSZ7t6QFKHO7t79e7iaaoaW27/Trr78SExND7ty5M1Xf6ebNm/j6+tKrVy+2bNlCVFQUy5cvZ/78+Rw+fBhzc3P++OMPXFxcOHv2LIGBgbx8+ZLo6Gg6dOjAsGHDAO1ILFtbWy5fvsydO3cIDg5m+PDhrFmzBkdHRypXrsyuXbs4fvw4u3fvZsiQIRw/fly3/8DAQLZs2UJYWBgzZ86kXr16yWIdPXo0z+5eZkYdMzi/gdlHojl+X8Pidq4sflSEVcce4+CSl3MrTmBmdo5ff/2VfDnz8fDhQ9o2rEl4eDjR0dHUqFGDGTNmEBUVhaurK8HBwTg75wJg1KhRREREMG3aNIYOHcru3buJi4vDzs6OBQsW4OnpqYv5yZMn2sC6qODzvWBt/d9rYmOxs7VmwfSJeLo5wbXLsLQDQ27XYN+xM7yIjGTWgBZUL5kHbt8Fk5/Buz3EvuDYxTt8teoM4ZGxJCRoGF7NnuaFjbRFMiVBu8/4aO0jjakAvc3aqVK/UqRKTSHLWDsi7NXRbai0c3+92pbkoXrt39dfm9K6b1p+pS3Za19f523PpxS3CpPIaLC2fncMKe5b9fbXJLa/Ne53xM7r+5DpD4R4lRSlPlDlypWZNWsW9+7dY9OmTbRs2TLJF8a1a9dy+fJlDh06hFqtZtmyZXzxxRds2LABHx8f9u/fj1qt5unTp/j4+FCnTh3Mzc2ZOnUqDx48wMLCgsjISIxSeSbk9u3b7Ny5E5VKxYQJE7C2tubo0aMAfPvtt4waNYoZM2bQr18/KleuzKhRo7h+/TpeXl7UqVMn2fZOnDhB6dKldQWp1DAxMcHb25vg4OBkRal3CQ4OZsGCBcyZM4e5c+cybtw4qlatilqtZuXKlTg6OqIoCoGBgcyZM4chQ4bQuXNnFi1apCtKLV68mCFDhgDaIsegQYMAOHz4MN26dePcuXNvjeFdr1GpVBw4cIDr169TtmxZKlasiKurq+55jUZDu3btWLZsGYULFyYyMhI/Pz/8/PwybKHjVUX/t/WNz1UrlINFXcrqlkt/u52ouJQ75eXyOrDmc//32neFChU4ffo0ANevX6dr1648ffoUDw8PFi9ejIODAxcvXqRXr148e/YMMzMzduzYkWQbEydO5OLFiyxatIiJEyfyxx9/EBMTQ2BgIL169WL48OEEBwfj7e1N//79yZs3r+61VlZWlC5dmmvXrtG/f3/mz59PkSJFUBSFwoULc+TIkSSjuEaPHs2NGze4desW9+/fZ/LkyTRp0oSoqCh69uzJqVOnsLCwYP78+Xh7e1OsWDGOHTsGgJ2dHXv37sXf3x9vb2/27NmDWq0mMDCQCxcuoCgKM2bMoEKFCowePZqHDx9y9epVihYtSt++fQkMDOTp06c4OjqyZMkScuXKxaFDh+jevTv29vaZ4r0mMhlF0U5s/HrR6dWfI0PfvR0jE7DL/UqxyTVpAcouj3Y0gciy3tV3WrlyJZcvX+bgwYO8ePGCjRs3Zqq+E0BoaCj+/v6MGzeOKVOmEBAQwO7du5k/fz6BgYHMnj2bCRMm4OHhwfbt2zEzMyMqKory5ctTs2ZN3cix/fv3s3fvXqytrdm0aRObN2/m9OnTWFhY0KJFizceU2hoKKVLl2bs2LH8/fff9O/fP3lRKuQinP0N04dXUbmaa9tyFAJTRxj0FyxfyZFfBnB62a+4u7vz9ddfM2nSJH766Sfs7e3ZtGkT1tbWaDQaGjduzLp162jRogXNmzdn+fLlDBkyBEVRWLp0KRs3bgTgq6++0k1vsXr1agYOHMjmzZvf+vtJ9ppRk7WveWlG6LMwSlRpxNSF6zh8+DBNmjTh2rVr4P4YTFZAkzk8f/6cz7+rzpad58iVKxdPnjyhdOnSVJh8BGcnJ20hKnE0liZeOxIrIU57GaMm7rXl2P/aExKX3/QabbuiiSMu6iUmxipUr67/yjqp3ubr83gpGojX6KWYltmogEx5sXaKxarXi1iqN7S/VjR853ZSuy2j/2J7rd0qPh5MTEGlTrqtlLaTpI1U7PPV2NLieD8iN2nyO3n1mFKxrkoFKjWql1Ggjvm3uGz82iN1n2uZlRSlPkKHDh1YsmQJf/zxBytWrEgyfPrPP//k9OnTuoLJq6McQkND6datG5cvX8bY2JgnT54QHBxMtWrV8PT05LPPPqNWrVrUr1+fPHnypCqWLl266CYd/uOPPwgPD9eNSoqNjSV//vwA7Nq1i5kzZwKQL18+atSokTbJ+JfygWepCxUqpOuI+fv76zohiqLwww8/sGXLFuLj4wkLC6Ny5coAdOrUiVKlSjFt2jTu3LnD5cuXqVu3LgAnT55k/PjxhIaGYmxszPnz54mNjX1rke1dr+nevTugzVvFihXZt28f7dq1073+0qVLBAcH06ZNG11bREQE58+fl0LBW2g0GoKCgujatSsA/fr1IzAwkFatWjFp0iRGjx7NzJkz+eyzz5g4cSI1a9YkIiIiyV0DJ0yYwKVLl1i0aBHbtm0jJCSEY8eOERsbS8WKFWnQoAHjx49Pclnb7t27da8PDQ3l8OHDjBw5kk6dOrF8+XLGjx/Prl278Pb2TvGywvPnz3PgwAEePXpEhQoVqF27Nj/++CM2NjacPXuWw4cP06lTJ06fPk3ZsmU5cuQIiqJQsmRJ9u/fT9GiRQFtkerrr7+madOmLF26lLt371K/fn1dke7s2bPs2rULU1NTatasyYIFC3B3d2ft2rWMGTOGefPm0b17d5YsWYKvry+tW7fW029KZFkJGu0ExknmcLqbdLRT3Mt3b8fU5pURTq9eYvdvAcraKct3qsS7va3v9Mcff3D8+HF8fX11o5EyW9/J2tpad2LOx8eHPHny4O3tDUDp0qUJCgoCICoqisDAQE6dOoWRkRF37tzh1KlTur5Qq1atdCO8du3aRatWrbCy0n717tSpE99++22K+7eysqJx48aAtj917dq1/56Mega7J8HR+aievkRBhVK8BaqKA+G33XD8OBhr+zwVK1bE3d1dt51Zs2YBkJCQwFdffcX+/ftRFIWQkBC8vb1p0aIFXbp0oUePHgwZMoRdu3bh6OhIiRIlANi2bRuzZs0iIiKChIQEwsPD3/n7edtrTE1N6dChAwB+fn44Oztz+vRpXFxcdOscPHiQ69ev6/qGoO1XXrp0CWdnZzCx0D6ssr8zlg+iKESGhWFnZ8dHj4xJ0CQtYr21gBaXcuEr8U6Jrz9QtCcf3vS87rk3rIOSwrpv2sYb1uH1tnct/9emKAlo4uNQq41Qve9+X21LFsN7HP8HvT/+HamXmlHEBqYC5DYD+qUC3joWXGWUQqHqlYf61WX1v5f5vrKsK9C9Wqh7pa3mt5CzcHocaoqkKPUROnfujI+PDwULFsTT0zPJc4qiMHz4cLp165bsdb169aJhw4asW7cOlUqFj48P0dHRqNVqDh8+zMGDB9m9ezd+fn6sWrWKSpUqoVarkwwXj45Oembk1UvRFEVhzpw5VK9e/YOPrXTp0syaNeudhZxXxcXFcerUqRQv8TM2Nn5r/Obm5rqf1Wo18fHay8NWrlzJnj172Lt3LzY2NsycOZO9e/cCkDt3bnx8fNi4cSOnT5+mQ4cOGBsbExsbS/Pmzdm9ezelS5cmPDwcOzu7tx7Lh7zm9TuPKYpC9uzZM+2cVefH1n7jc0avHeuJkQGpXvdNnj9/jre3N3fv3qVYsWLUrq3d/7Fjx9i0aROg/fJSv359wsPDef78OTVr1gS0l4smmj9/PoULF2b16tUYGRkRFBTEpk2b2LNnDwBhYWFJO+Wv2L59O6VKlcLIyIhhw4ZRrFgxXF1d8fPzY9y4cSxZsoROnTql+NqmTZtiamqKq6srhQsX5tKlS+zfv58vv/wS0HaSo6KiCAsLo0KFCroO/Jdffsnq1aspVqwY/v7aEWVBQUH8+eefjBmjnQMjNDRUdwOBxo0bY2pqSkREBAcOHNB92dBoNHh4ePD8+XPi4uJ0X2Tat2/P0qVLU/U7EJ+Q+FjtnbRCr7w2ifgdCL+Xuk6xVc7Xik6v/WyeBl+8RJb3rr7TiBEj6NKlC2H/fplP/KzNDH0nQHcJIWj7M2/q33zzzTc4OTlx8uRJjI2NadasWZL4Xo8ttXc7fX1/Go1G+2X63Dr460vdqEZ1zkJEW5eA5gtApSI6+u+3bicx7mnTphEaGsqRI0cwNzdn0KBBurj9/f3RaDQcP36cRYsW6U423b59m379+nH06FHy5cvHmTNn3pnnD3lNSv2ykiVL6vqNmZqRGoz+LaKJpBSFF2lV/PvA/esKVikVthI0/Ff4S2m9lApjyhvaU1o/haJZikW+D9+3khBPVGQkFhYW/xX+Xj+mj4rz1TZSUTT8d3vvPJZUHHta5DzFdVMbH6BoUBI0kBCHKuENU6QoCf+NtNSHSoP1s91UkqLUR3BxcWHixIkULpy8qlinTh3mzp1L06ZNcXBwIC4ujnPnzlGqVCmePXumm+tg7969uhERERERREREUKlSJSpVqkRwcDAnT56kUqVK5M+fnyNHjlCrVi3Wr1/Py5dvPmvdqFEjpk2bhp+fH5aWlkRGRnLjxg2KFStG9erVWbhwISNHjuTmzZvs2LGDgIDkBYbq1auTN29e+vXrx/Tp0zE3Nyc+Pp6ZM2fSs2fPZOu/ePGCIUOGkD17dl1x4VX58+fn9OnTREdHY2xszMqVK1OV42fPnuHo6IiNjQ0REREsXryYfPny6Z5PnJ8iODiYv/76C0A330TipXWJZ/feJjWveTVv+/fvT7ZOoUKFsLS0ZOnSpXTs2BGAq1ev4uDggIODQ6qO15AsTVP/38H7rPsmiXNKRUZGUrNmTebMmUO/fv2SdCpf7Yi/qUPu5eXF6dOnCQkJwdnZGUVRGD16tO53kOjVkVGJEueUepWNjQ2lSpXizz//5MCBAyxcuDDF/b4eT0rxJcZfoUIFBg4ciJGREQMHDmTu3Lns37+fihUr6tbbvHkzbm5uybZhaWmpW8fZ2TlZ0fPZs2ep/rIiPjGKAmfWwIVNcH239hKVNzEyBtvcKV9WZ++mfc7E/M2vFyKV3tZ3atSoETNmzKBx48ao1Wri4uIIDg7ONH2n9/Hs2TOKFy+OsbExly5dIigo6I1Fl2rVqjFq1CgGDBiAubk5y5Yte7+d/doRLmgvoyN7Iaj7HfmPPOTPWbNISEggJiaGdevWUahQoVTF7ezsjLm5OY8ePWLt2rVJRuh26dKFmTNnsmXLFt3osrCwMExNTXWf0bNnz37nft71mtjYWFasWEGHDh04evQoDx8+pGTJkjx+/Fi3Tvny5bly5Qo7d+7U5fbUqVMULVr0vaanEOKtdHNEZeGRwIpCbFgYFoYq/H0KFEV7MsbWFhXKvyMd47T//luw0v7877LmteVXn9fEv/Jc/H83i0hSRFOS/uuQ7y3B6Z8UpT5S4iSbr2vTpg1RUVFUrVoVlUpFfHw83bp1o1SpUnz33XcEBgby3XffUbRoUd1k2mFhYbRo0YKXL1+iUqnw9PTUjdKYPn06ffr0IWfOnFSrVg1HR8c3xvT1118zZswYypUrp/uy+tVXX1GsWDFmzJhBx44dWbt2LQULFnxjp0qlUrFlyxaGDx9OsWLFsLCwQFEU6tevrzt7dv78eby9vYmLi0NRFGrXrs2OHTtSnIzZ39+f2rVrU7x4cTw8PChVqhRPnz59Z347duzIhg0bKFq0KLlz56ZSpUrcu3dP93zjxo3p3bs3np6eusuhbG1tGTt2LGXLlsXNzY1GjRq9cz+peY2ZmRkVKlTg8ePHzJo1K8l8UqAdDbZp0yYGDhzI1KlT0Wg05MiRI21u05yFWVpaMmPGDJo3b05gYCC+vr66+SlWrlxJpUqVsLW1JVu2bAQFBSW7fK9cuXJ89tlnNGzYkKCgIAICApg0aRItW7bEwsKCS5cu4e7uritspkanTp3o3Lkz7dq1e+Pk4n/88QeDBw8mJCSES5cuUbBgQSpWrMjKlSspX748R48excrKCltbW2xsbLhy5Qpubm7Y2NhQqFAhli1bxr59+wBtcezHH39k0qRJAJw+fRovL68k+7O1tSVHjhz8+eef1KtXj7i4OK5evUqRIkUwMTHhn3/+wcfHh1WrVn3or0JkNQdmwPZX7ihplUN7F60kk4j/W4CycZbboot086a+U4cOHQgNDaVatWokJCSgKEqm6ju9jxEjRtChQwdWrFiBh4fHW0cBNWrUiIMHD+Ll5YWLiwt+fn48e/bs3TtJLERd2KgtPFceChUHgbEpzfPE6Ebtenh44O3tnWTC+Tfp168fLVu2xNvbm9y5cyfLRYcOHXBzc6N58+Zky5YNgBIlStCyZUuKFSuGm5ubbtTz27zrNY6Ojly9epVy5crx4sULVq5ciZWVVZKiVLZs2di0aRNDhw5l4MCBxMXF4ebmxh9//PHO/QshhEEkzjVlpP6k5tlUKR86CVAWknipVlhYGLa2th+9PSWx0vnKsHORepK/j5Oa/EVHR3Pjxg3y5s2bZIh+esqePft/d9sBGjRoQIcOHShTpgxdunTRnRVfsmQJDg4OXLp0iZ49e/L8+XPMzc3ZsWMHU6dOJXv27HzxxResXr2auXPn8vfffzNr1iyWLVuGoijkzJmTTZs2YWpqSu3atXn69KluovNX55hKpCgK8fHx5MmTh6CgIEqWLJks9sQJyC9evJhsovMePXpw5swZzM3N+emnnyhVqhSg/VLh4eHBzJkzWbp0KcOGDdMVWF+8eEHfvn05fvw4Go2GGjVqMGvWLEaPHq07PtCOvOvVqxePHz8mPj6eoUOH0rlzZ91E53Z2dlSqVIlr164lOy59SOl9JH+/HyfN8hf1HGaUhOgwKNsTvNuBs1eWn9NJ3++/tO4vZFb6zoP8P5JcREQENjY2JCQk0L17d1xcXBg3blzKK78MhT+HQPB67XLOYtB0LuT672SH5Fj/JMfpQ/Ksf5Jj/cuqOU5tf0GKUkhRKqOR/H2czFKUyqgSJ0Ht2LGj7i5Mr3u9WPSpkqJU2kuz/O2aAHsmQY7C0PvgJzMKSopS6UOKUumvadOm3Lx5k6ioKHx8fJg3b17y3CsKBP8Ofw6FyCfau2RVGgSVv9RNYP7fqpJjfZMcpw/Js/5JjvUvq+Y4tf0FuXxPCCFesWDBAr799lvmz59v6FCE+DCRT+HQHO3P1b75ZApSQmRlv//++9tXCH8AWwbDpS3a5RxFoMkcyC13/xVCCJGxSVFKCCFe0b17d7p06fLGuaRAO1JKiAzrwAyIjQDnklC4oaGjEULoU0ICnFwG20ZCTJj2NuCVBmsfxjKZtxBCiIxPilJCCCFEVhHxCI78pP25+ogsP4eUEJ+0x5dh8wC4dUC77OIDjWeDUzGDhiWEEEK8DylKCSGEEFnF/h8gPgpy+4JnLUNHI4TQh/gY2D8d9k0FTSyYWEK14eDXWy7XFUIIkelIUUoIIYTICsLuwfGF2p+rj9DeVlgIkbXcOgSb+sOTS9rlAjWh/veQzd2wcQkhhBAfSMb1CyEMwtjYGG9vb4oVK0bDhg15/vz5e29j9OjRzJ49O1Xr3rx5k19//VW3vHv3buzt7fH29qZIkSKMGTPmvfef1l6PUYj3sm8qaGLAvSLkq2roaIQQaSnqOWwaAIvqaAtSVjmgxUJov1YKUkIIITI1KUoJIQzC3t6eU6dOERwcjL29PT/++KNe95dSwScgIIBTp05x/Phxli9fzokTJ/Qaw7tIUUp8sGc34Z+l2p+rD5dRUkJkFYoCwb/Dj2XhxCJtm09H6HMUijeXv3UhhBCZnhSlhPjEKYpCZGx8mj8URUl1DBUqVODu3bsAXL9+napVq1KyZEkaNWrE06dPAbh48SJVq1bFy8uLsmXLEhERkWQbEydOpFOnTiQkJDB+/HjKlClDyZIlmTdvHgDDhw9n+/bteHt7s2jRoiSvtbKyonTp0ly7do1KlSpx+fJlXW4KFSqUbBRXeHg47du3p2TJknh7e3Pw4EEURWHAgAEUL14cb29vtm/fDqDbpo+PD35+fly4cAGAs2fP4uPjg7e3N97e3oSEhLw1RiHeas9kSIiH/NXBvbyhoxFCpIXnd2BVG1jbGV48AkdP6LwFGs0CSwdDRyeEEEKkiQw5p9ScOXOYMmUKDx48oFixYkyfPp1KlSqluG7nzp1ZsmRJsvaiRYsSHBys71CFyPSi4jQU/d/WNN/u+bG1sTR9938xGo2GoKAgunbtCkC/fv0IDAykVatWTJo0idGjRzNz5kw+++wzJk6cSM2aNYmIiMDCwkK3jQkTJnDp0iUWLVrEtm3bCAkJ4dixY8TGxlKxYkUaNGjA+PHjmT17Nr/99hugvXwvUWhoKIcPH2bkyJF06tSJ5cuXM378eHbt2oW3tzf29vZJYh47diweHh6sWLECjUbDy5cvWbduHVevXuXMmTPcvn2bqlWrcvHiRXLlysX27dsxMzPj4MGDfPPNN/z+++/Mnz+f3r1706NHD6KiolCr1cliFCJVnlyF06u0P1cbYdhYhBAfTxMPR+fDznEQ9xKMTKDSIKg4CEzMDR2dEEIIkaYyXFFqzZo1DBgwgDlz5lChQgV++ukn6taty/nz53Fzc0u2/owZM/juu+90y/Hx8Xh5edGyZcv0DFsI8Z6eP3+Ot7c3d+/epVixYtSuXRuAY8eOsWnTJgA6dOhA/fr1CQ8P5/nz59SsWRMAGxsb3Xbmz59P4cKFWb16NUZGRgQFBbFp0yb27NkDQFhYGNeuXUsxhu3bt1OqVCmMjIwYNmwYxYoVw9XVFT8/P8aNG8eSJUvo1KlTstft3LmTjRs3AqBWq7G1tWX//v20a9cOIyMjPDw8KFiwIJcuXcLNzY0+ffpw5swZjIyMiImJAcDf35+xY8cSGhpKq1atyJcvXxplVnxydk8EJQEK1oU8pQ0djRDiY9w6CFuGQMi/J1Zd/aDhDMhZ2LBxCSGEEHqS4YpS06ZNo1u3bnTv3h2A6dOns3XrVubOncvEiROTrW9nZ4ednZ1u+Y8//uDZs2d06dIl3WIWIjOzMFFzfmxtvWz3bRLnlIqMjKRmzZrMmTOHfv36oXplfgxFUXTLqjfMm+Hl5cXp06cJCQnB2dkZRVEYPXo0HTt2TLLeqyOjEgUEBCQblWRjY0OpUqX4888/OXDgAAsXLkzN4SaTGPv06dPJmzcvK1as4NGjR/j5+QHQrl07ypYty+bNm6levTrr16//oP2IT9yj83Bunfbnat8YNhZDiY+BpzfASA2OBQwdjRAfJuIRBP0PzqzWLltkgxqjwKcTGMlsG0IIIbKuDFWUio2N5cSJE3z99ddJ2mvVqsXBgwdTtY1ffvmFgIAA3N3ffCeSmJgY3WgF0M4PA9ovke8zD86bJG4nLbb1KZL8fZzU5C/xucR/31VA+phY3vW8hYUF06dPp0WLFvTu3RtfX19+++03WrRowYoVK6hYsSI2NjZky5aNbdu2Jbl8T1EUypYtS/v27WnYsCHbtm2jRo0aTJ48mRYtWmBhYcGlS5dwd3fH2tqaiIiIZMeeUoydOnWic+fOtG3bFiMjo2Tr1KhRgzlz5jB+/Hjd5XsVKlRg2bJltG7dmjt37nD16lU8PT0JCwujQAHtF+WlS5fq9nn9+nXy5ctH//79OXfuHMHBwRQpUiRJjBndqzl8/efMcgwZzXvnb/cEVCgoRZuAcwntpMhZlSZOO4ok9CqEXoHQa9p/n99GpSSglGiF0vQnvb7/5H0t0pwmHo4tgF3jISYcUEHpTtqClMwbJYQQ4hOQoYpST548QaPR4OTklKTdycmJhw8fvvP1Dx484K+//mLlypVvXW/ixIkp3v49LCwszYpSL168AN48ukO8meTv46Qmf7GxsSQkJKDRaNBoNOkZXhKJ+y5VqhTFixdn7dq1TJs2je7duzN27Fjc3d1ZuHAhGo2GRYsW0bt3b4YMGYK5uTnbtm1DURQSEhIICAggNDSUxo0bs2XLFs6ePUvZsmUByJEjB3/88QfFihUjNjYWb29v+vbtS968eVEUJcXjr1KlChqNhvbt26f4/DfffENgYCAlSpRArVYze/ZsGjduzL59+/Dy8kKtVjNv3jxMTEzo0aMHrVu3ZsWKFdSoUUN33KtXr2blypUYGxvj7u5Oo0aNMDU1TRJj586d9Zf8NKDRaEhISCAiIkJX6Je/34/zPvlTPzqLzYVNKCojIny/ICEsLD1C1K+YcNTPrmP07Drq5zdIsMxJrFcH7XOaWOyWN0OlJP+bVEytiYvX8DIsTK/vv8STWEKkiVuH4M8h8OicdtmlFNT7Xi7DFUII8UlRKRnotN/9+/fJnTs3Bw8exN/fX9c+fvx4li1bxsWLF9/6+okTJ/L9999z//59TE1N37heSiOlXF1def78Oba2th99HIqiEBYWhp2dnXwp+wCSv4+TmvxFR0dz8+ZNPDw8MDeXSVNfd+nSJTp27MiRI0cMHUqGltL7SP5+P8575W9lK1RXtqGUbAVN56dPgGlBUf67jb2iwOaB8OQShF5F9fJx0lXzlIFuQf81LG8GalPtZXqOnuCYH7J7glVOUKn0/v4LDw/H3t6esLCwNOkvZFbh4eHY2dnpLQ9Z/v+RFyEQNApO/3sSVXepXkftZajpIMvnOAOQHKcPybP+SY71L6vmOLX9hQw1Uip79oFWOigAAJqHSURBVOyo1epko6JCQkKSjZ56naIoLFy4kA4dOry1IAVgZmaGmZlZsnaVSpVmb4LEbWWlN1V6kvx9nHfl79V5miTHSf388898++23zJ8/X3LzDm96H8nf78dJVf7uHIUr20ClRlXl6/+KPBmFokDEA3hy5d/L7V552OaGzpu166lUcH0nPL/932utnbWFJsf8qHJ5JT22Dr+/c9f6fP/Je1p8FE08HP8Fdo6HmDBApS1E1RgFVo6Gjk4IIYQwiAxVlDI1NaV06dIEBQXRtGlTXXtQUBCNGzd+62v37NnD1atX6datm77DFEJkYd27d6dLly6o1elztlqID7JznPbfUu21o4UMJS5KO7dT1FPIW/m/9h/LwpPLKb8mJiLpcrXhYGSsPQ7HAmBmk/LrhMjMbh/W3lXv0Vntci5vqP895PE1aFhCCCGEoWWoohTAoEGD6NChA76+vvj7+zN//nxu375Nr169ABg2bBj37t3TTRic6JdffqFcuXIUL17cEGELIYQQ6ePGXrixB4xMoPLQ9Nvv3RPaL9RPrsDjS9qi0/PbgAI2uWDwK5fY2+TSFquyefw76qlA0servNqk3zEIkd5ePIbto+DUCu2yuT3U+B+U7pxul+oJIYQQGVmGK0q1bt2a0NBQxo4dy4MHDyj+f/buOzyK6mvg+Hd307PJBggJJZTQCRACCb0JUkSqvYIg8oKIClgRRUQliqLgT0FQEbGiNFERROm9hRo6hFBCCpDed+f9Y5JNQgoJye6mnM/zzJOZ2Tszd282m9mz957bujVr1641z6YXERFBeHh4nmPi4uJYsWIF8+bNs0WVhRBCCOtQFHXoD6gfaj3ql925TUaIvagGnWJOQ2Ik9H8v5/ENb8HFHfmPczKAoR5kpoNd1vD5Bxer+3X2ZVc/ISoSYybsX6z2akzLmoSg/Ui4e4YM1RNCCCFyKXdBKYAJEyYwYcKEAh9bsmRJvn0Gg4Hk5GQL10oIIYSwsbP/waXdYOcEPV4q/fkOLlXPGXNa7dlkTMv7+F1vgIOLut6gK9g7g2cztfeTZ3N13dUzf04rV8/S102Iiip8D6x9Ca5lD9Vrq86qV6+DbeslhBBClEPlMiglhBBCiFsoCmzK6rnU4Rlwr1284+Ij4NRaNTF6VChM3A92WZN9hO+B0NU5Ze2c1FntPJuqASdTRs5jfd4sk6chRKWVGA3/zoBDP6jbToasoXqjZaieEEIIUQitrSsghKia7OzsaNeuHX5+fgQGBvLVV1/leVxRFOrXr8+rr74KwLRp0wgICMDPzw8XFxcCAgIICAhg06ZNrFq1irZt29KqVStat27N/PnzATCZTEybNo0mTZrQpEkTHn74Ya5fvw5AWFgYGo2G2bNnm6+5Z88etFot3333nZVaoWwsWbKEl19+2dbVEJZ2ai1cDQF7V+g26fblYy/B4oHwSQv4awqcXqfmgLpxPqdM6/ug//vwxHJ48TC8cRWe3Q4PfQu9p6ofqoUQRTMZYe9X8HlgTkCq3Qh4/qAaQJaAlBBCCFEoCUoJIWzCw8ODkJAQQkNDWblyJQsWLGDRokXmx3ft2kXt2rVZuXIlAO+//z6HDh1i7dq1+Pn5cejQIQ4dOoSHhwevvfYaq1ev5vjx4+zbtw8XF3W40dy5cwkNDSU0NJSzZ8/SoUMHRo8ebb5G06ZNWbNmjXn7119/pU2bNlZqgdszGo22roIoL0ymnFxSnceDvubtj1n7MoTvVNd9OqjTzj+9Xk0+nq1JX+g6EZr2U/fLh2chSubSXlh0l/r3lhoHtfxhzAYY9rkMYxVCCCGKQYJSQghVelLhS0ZqCcqmlPjSDRo0YM6cOeYeTgDLli1j/Pjx+Pr6smfPnkKPnTNnDtOmTcPX1xcAZ2dnRo0aZX5s7ty5ODioyZdfeukljh8/zpkzZwBwd3fH09OT8+fPoygKmzdvpnfv3gVeZ9SoUYwfP55u3brh5+fHrl27AIiJiWHIkCH4+/tz1113ERYWRkZGBs2aNQPg9OnTaDQaIiIiMJlMNG7cGEVRiIyMZPjw4QQFBdG9e3dOnjxpvs5LL73EXXfdxezZs9m7dy89evSgffv2PPDAAyQmJgLw+++/06xZM3r16sX27dtL3OaiggldDVHHwdEAXZ+/ffkLW9WeUVo7GL8DnvkXekyB+p3VvFBCiNJJioHfn4Nv+sG1I2qvwns/hv/bDPU62rp2QgghRIUhOaWEEKpZdQp/rGl/eOK3nO2PmkBGIZMLNOgOo/8q8eXbt2/PqVOnAHXo3h9//ME777xDRkYGy5Yto1OnTgUeFxoayiuvvJJvf3x8PCkpKeaZOwG0Wi3+/v6cOHECf39/AB566CF+/fVXevXqRWBgIHZ2hb8tRkREsH37dg4cOMCoUaM4duwYM2bMoEePHvzxxx8sW7aMF154gTVr1lC3bl0uXLjA9u3bad++Pdu3b6d58+a0atUKjUbDpEmTeOuttwgMDGTfvn1MmjSJdevWAXDp0iU2bdpERkYGffv2Zc2aNVSrVo2PPvqIzz//nBdffJHnn3+eHTt2UKtWLXr37k3nzp1L3OaigjBmwqZZ6nqX58C52u2PuRoCGi0EPQ21Wlu2fsJmFixYwIIFCwgLCwOgVatWTJ8+nYEDB9722B07dtCrVy9at27NoUOHLFvRysRkhAPfwn8z1Z5RAAFPQt8ZxevBKIQQQog8JCglhCgXFEUxr2/btg0/Pz88PDy4//776dChA3PmzEFz6wxfWceVZP+thg4dyj333ENkZCQPP/wwf//9d6FlH3nkETQaDUFBQaSnpxMbG8v27dtZu3YtAA8//DAvvvgiAN26dWP79u1s376d1157je3btxMdHU23bt0A2LhxIydOnCjwOg8++CAajYZTp05x5MgRc++t9PR07rrrLk6dOkWLFi2oV6+e+brh4eG3fa6igjr6G1w/owajOj9bvGO6vagGk129LFs3YVM+Pj588MEHNGnSBIDvvvuOYcOGERISQqtWrQo9Li4ujpEjR3L33XcTGRlprepWfJf3q/nZIg6r27XaqLPq1S/4SxMhhBBC3J4EpYQQqjeuFv6Y5pY8M6+cLaLsnY0KPnToEC1atADU3E67d++mYcOGAERGRrJz505zQCc3Pz8/QkJCzD2fshkMBpycnAgLCzOfx2QycfjwYVq2bGku5+bmhpeXF2vXruXjjz8uMih1a5CroKBX9r5u3brx+++/c+bMGb766iu+/PJLYmJimDBhgrnsgQMH0Ony5/DJzomlKArt27dn48aNeR4PCQkpVsBNVALGDNjygbrebRI4uRf/WK+Wty8jKrQhQ4bk2X7//fdZsGABu3fvLjIoNW7cOB5//HF0Oh2rV6+2cC0rgcRo+G8GhGQlMXc0qLNRBj0NOrmVFkIIIUpDckoJIVQOroUv9k4lKFvyfDWXLl3i5ZdfZuLEiZhMJlavXs2pU6cICwsjLCyMefPm8euvvxZ47EsvvcSsWbPMw1dSU1P58ssvAZg8eTKTJ08mPT0dUHNMtWrViqZNm+Y5x7Rp0wgODi4wQJTbb7/9hqIoHDx4EAcHBwwGA927d+enn34CYPny5XTsqOYS6dq1K3///TdeXl7odDqcnZ3ZsWMHQUFBAPTq1YuFCxcCarDs6NGj+a7XokULLl68aB5ak5SUxNmzZ2nRogUnT57k8uXLZGZm8ttvv+U7VlQSh36Em2HgWhM6jr19+dDfIeqkxaslyh+j0cgvv/xCUlISXbp0KbTct99+y7lz53j77beLfe60tDTi4+PzLKAGziv1YsxA2b0A5X/tzQEpJeBxlIn7UDqORdHqbF9HWWSRRRZZZCnHS3HI1ztCCJuIjY0lICCA9PR0nJ2defbZZxkzZgybNm2iadOm1KhRw1x22LBhvPvuu3z66af5ztO+fXtmzZrF0KFDyczMxM7OztwbafLkycTExNCyZUs0Gg0BAQF8++23+c4RFBRkDhYVpWHDhnTv3p2bN2/yzTffADBjxgxGjRrF0qVLqV69OkuWLAHUnloGg8Hcu6tTp07Exsbi6OgIwP/+9z/Gjx/Pl19+SWZmJiNHjsw385+DgwO//PILEyZMMCc4nz17Nk2aNOGzzz7j7rvvxtvbm3bt2t227qICykiFLbPV9R4vqUHfoiTFwOrnICNJnf3L5/avaVHxHT16lC5dupCamoper2fVqlX4+fkVWPbMmTO8/vrrbNu2rcj8ebcKDg7mnXfeybc/Li6u2DecJaEoivk9z1a9Qu0u7cJ589vorqu5DjNr+pHSeybGOh3ACMTF2aReZaU8tHFlJ21sHdLOlidtbHmVtY2zv8S6HY1iibuJCiY+Ph6DwUBcXBzu7iUYGlEIRVGIi4vDYDBUqheVtUj7lU5x2i81NZULFy7g6+uLk5NTgWWqKkVRMBqN6HS6PO03atQoHnzwQQYPHmzD2pUvBb2O5O+3dPK0395F8Per4FYHXgjJ32PxVn+9DPu+gtoBMHYTaKteZ2hLv/7K+n6hLKSnpxMeHk5sbCwrVqzg66+/ZsuWLfkCU0ajkc6dOzNmzBjGjx8PqEH11atX3zbReVpaGmlpaebt+Ph46tWrR2xsrEXawabvI/FX4J+30BxfqdbFuRr0eQvaPwXaonvTViTyXm150sbWIe1sedLGlldZ2zg+Ph4PD4/b3jdJTykhhBCivMlIhq0fq+s9X759QCrmDOxfrK73f69KBqSqKgcHB3Oi86CgIPbt28e8efPMw4OzJSQksH//fkJCQpg4cSKgDh1WFAU7Ozv++ecf+vTpU+A1HB0dzb08c9NoNBa7ec4+t9VuzjPTYNfn6t9dRrKaHzFwNJo+b4JLdevUwcqs3sZVkLSxdUg7W560seVVxjYu7nORoJQQQhRD9rA8Iaxi3zeQFAUe9aHdiNuX3/A2KEZofi/49rB8/US5pShKnl5N2dzd3fPlrps/fz4bN25k+fLl+Pr6WquK5c/p9bDudbhxXt2u1xnunQ2129q2XkIIIUQVIEEpIYQQojxJT4TtWfnTer0Odg5Flw/bDqf+UmfJ7Js/74+ovN544w0GDhxIvXr1SEhI4JdffmHz5s2sW7cOgKlTp3LlyhWWLl2KVquldevWeY738vLCyckp3/4q4/o5WP8GnFbbC7039HsX/B+GSvRNtRBCCFGeSVBKCCGEKEccQxajSbkBNZqA/yNFFzaZ4J831fWg0VCzmeUrKMqNyMhIRowYQUREBAaDAX9/f9atW0e/fv0AiIiIIDw83Ma1LIfSk2DbHNj5PzCmg9YOOj8LPV8Fp/KRK0wIIYSoKiQoJYQQQpQXKbE4HfhKXb9rKuhu829aMUHrB9SZ93q9bvn6iXIlexbQwtxu2PGMGTOYMWNG2VWovFMUOL4S/nlLTWgO0LgP3POhBHSFEEIIG5GglBBCCFFe7FmAJj0excsPTav7b19eZwddn4dOz94+gCVEVRYZqs5mGbZN3faoDwOCocUgGaonhBBC2JBMzyOEsAk7OzvatWuHn58fgYGBfPXVV3keVxSF+vXr8+qrrwIwbdo0AgIC8PPzw8XFhYCAAAICAti0aROrVq2ibdu2tGrVitatWzN//nxAnVlq2rRpNGnShCZNmvDwww9z/fp1AMLCwtBoNMyePdt8zT179qDVavnuu++s1Apw6NAh/vnnH6tdT5RjaYmwd5G63vOVks2gJwEpIQqWEgt/vw5fdlcDUnZOai/E5/ZCy8ESkBJCCCFsTIJSQgib8PDwICQkhNDQUFauXMmCBQtYtGiR+fFdu3ZRu3ZtVq5cCcD777/PoUOHWLt2LX5+fhw6dIhDhw7h4eHBa6+9xurVqzl+/Dj79u3DxcUFgLlz5xIaGkpoaChnz56lQ4cOjB492nyNpk2bsmbNGvP2r7/+Sps2bazUAioJSgmzg0vRpNzE6NEQWg4tumzSdfi6nzprmKJYpXpCVCgmExz8Hv4XCHsWqLNTthisBqPueh3snW1dQyGEEEIgQSkhhKKoSV/LeinBB+UGDRowZ84ccw8ngGXLljF+/Hh8fX3Zs2dPocfOmTOHadOmmaczd3Z2ZtSoUebH5s6di4ODOnvZSy+9xPHjxzlz5gygTpHu6enJ+fPnURSFzZs307t37wKvc/XqVQYPHkzbtm1p3749586dIyUlhREjRtCmTRs6duzIoUOHANi9ezddu3alXbt29OnTh4iICAA2bdpEmzZtaNu2LUFBQRiNRqZPn87SpUsJCAhg/fr1xW4zUclkpsOuzwFICxwPWl3R5bd8CJf3wsb3JCglxK2uHIBv+sKaiZAcA57NYMQqePRHqNbA1rUTQgghRC7S31+Iqi4jGWbVKfvzvnEVHFyLXbx9+/acOnUKUIfu/fHHH7zzzjtkZGSwbNkyOnXqVOBxoaGhvPLKK/n2x8fHk5KSQoMGOR9AtFot/v7+nDhxAn9/fwAeeughfv31V3r16kVgYCB2dgW/Lb7wwgsMGzaMsWPHkpaWhtFo5IsvvsDNzY2jR4+ye/dunnrqKQ4fPoyfnx/btm1Dp9Px008/MXv2bD799FM++eQTPvnkE/r160dcXBw6nY6ZM2dy7NgxPv7442K3laiEjv4G8VdQ9LVIb3k/RfbhiDkL+7MSXPd/r2TD/ISozJJi4N8ZEPIDoICDG9z1GnQcB3YOtq6dEEIIIQogQSkhRLmg5OrtsW3bNvz8/PDw8OD++++nQ4cOzJkzB00BuT8URSnR/lsNHTqUe+65h8jISB5++GH+/vvvAstt376dX3/9FQBHR0fzvuycV507dyYlJYW4uDhiY2N58sknOX/+PJmZmebAWLdu3Xj99dc5ceIEDz30EAaD4bb1E1WAyQQ75qrrnSeAnWPR5f99G0yZ0OweaNTL4tUTotwzZsK+r2HTLEiLU/f5Pwr93gG3WratmxBCCCGKJEEpIao6exe1V5MlzlsChw4dokWLFoCa22n37t00bNgQgMjISHbu3Em3bt3yHefn50dISIi551M2g8GAk5MTYWFh5vOYTCYOHz5My5YtzeXc3Nzw8vJi7dq1fPzxx4UGpYoT4MoOhE2fPp1BgwYxbtw4du/ezeuvvw7A66+/zsCBA/nrr7/o0KFDkcMSRRVyai3EnAZHAwSNgtQihuNd3Akn/wSNDvrNtFoVhSi3wrbD2lch6ri6XasN3Psx1O9s23oJIYQQolikz78QVZ1Gow6zK+ulBDMaXbp0iZdffpmJEydiMplYvXo1p06dIiwsjLCwMObNm2fupXSrl156iVmzZhEWFgZAamoqX375JQCTJ09m8uTJpKenA2qOqVatWtG0adM855g2bRrBwcHodIXn8enRowfffKMOmUpLSyM5OZnu3bvz008/AbB3715cXV1xd3cnPj4eHx8fAH744QfzOc6dO0fbtm154403aNmyJRcuXMDNzY2EhIRit5WoZBQFtn+irnd8BhzdCy9rMsH6aep64FNQs7nl6ydEeZUSC6vGw5JBakDKuRoM+gT+b4sEpIQQQogKRIJSQgibiI2NJSAgAD8/P4YPH8748eMZM2YMW7ZsoWnTptSoUcNcdtiwYaxcuRKTyZTvPO3bt2fWrFkMHToUPz8/OnbsaH5s8uTJNG/enJYtW9KkSRP27NnDt99+m+8cQUFB3H///UXWd968eaxevRp/f3+6du3KtWvXeO6554iNjcXf35+JEyeaz/3yyy8zadIkunfvbp4JEODTTz+lVatW+Pv7U79+fbp06ULv3r05ePAg7dq1k0TnVVHYdjUps50TdBpfdNnzG+HqQXDQq1PaiwopLi6OJUuWMGbMGO6++266dOnC0KFDefvtt9m5c6etq1cxnNsIC7rC4Z8BDQQ9Dc8fhA5jbj9JgBBCCCHKFY2iyLQ98fHxGAwG4uLicHcv4lvqYlIUhbi4OAwGQ7GG/Ii8pP1Kpzjtl5qayoULF/D19cXJycnKNSzfFEXBaDSi0+nk9XcbBb2O5O+3hL6/H879Bx2egUFzim4/RYFTf0NStNpTSuRj6ddfae4XIiIimD59Oj/++CO1atWiY8eO1K1bF2dnZ27cuMGxY8c4cOAADRo04O233+aRRx4p8/qXlbK+b7pVob/H9CTYMF3NHwVQvREM/xLqFzwRhiicvFdbnrSxdUg7W560seVV1jYu7v2C5JQSQgghbCHisBqQ0uig6/O3L6/RQIt7LV8vYRFt27Zl5MiR7N27l9atWxdYJiUlhdWrV/PJJ5+YhzWLLOF7YPV4uHFe3e4wVk1kXoJZXoUQQghR/khQSgghhLCF7XPVn63vh2oNCy+XEguKCVyqW6FSwlKOHz9OzZo1iyzj7OzMY489xmOPPUZ0dLSValbOZabB5mDYMU/9O3CrA8O/gMZ9bF0zIYQQQpQBySklhBBCWNv1cxC6Wl3vNqnosps/gM/awdHllq6VsKDbBaRKW75SunYUvuoD2z9VA1L+j8KEXRKQEkIIISoR6SklhBBCWNvO/6kfspv2h1oFD+UC1ODVvq/AlCk9pSq4NWvWFLvs0KFDLViTCiAjGce9/4Pd88CUAS41YPBc8Kvi7SKEEEJUQuUyKDV//nw++ugjIiIiaNWqFXPnzqVHjx6Flk9LS2PmzJn88MMPXLt2DR8fH6ZNm8bTTz9txVoLIYQQxZBwDQ79qK53n1x02X9nqAGpJn2ld0gFN3z48GKV02g0GI1Gy1amPLsaAt/fj3PKDXW7+SAYMhf0XjatlhBCCCEso9wFpZYtW8akSZOYP38+3bp1Y+HChQwcOJDQ0FDq169f4DEPP/wwkZGRfPPNNzRp0oSoqCgyMzOtXHMhhBCiGHZ9AcZ0qNcZGnQtvFz4bjixBjRa6Peu9eonLMJkMtm6ChVDzRag0WJ090Hb5000bR9Vk/wLIYQQolIqdzmlPvnkE8aMGcMzzzxDy5YtmTt3LvXq1WPBggUFll+3bh1btmxh7dq19O3bl4YNG9KxY0e6di3iRl8IUS58/fXXODo6Ehsba+uq5LNs2TJatmzJfffdx5dffsmyZcsAWLJkCVFRUeZyc+fOJT09vUTnnjFjBp9//nmRZaZPn862bduKLHPixAnatm1LQEAA169fL1EdSiIsLIygoCCLnb9KSbkJ+xer6z2mFF5OUWD9NHW93Qjw9rN83YQoD+ydYdRfJIzaAhKQEkIIISq9ctVTKj09nQMHDvD666/n2d+/f3927txZ4DFr1qwhKCiI2bNn8/333+Pq6srQoUN59913cXZ2LvCYtLQ00tLSzNvx8fEAKIqCoiilfh7Z5ymLc1VF0n6lU5z2y37M1u3866+/EhQUxMqVKxk9erTN6lGQJUuWsHjxYjp37mzepygKS5YsITAw0JyEeO7cuYwZMwZ7e/tin7s4v6N33nnHXLYwq1ev5tFHHzW/Z97ud2k0GtHpdIVuF1Xfgs5f0OtI/n5vY+9XaNITUbxbQZN+avApF3P7HV+F5sp+FHtXuGtqvnKiYJZ+/ZXleZOSktiyZQvh4eH5AtsvvPBCmV2nQqrZHOLibF0LIYQQQlhBuQpKxcTEYDQa8fb2zrPf29uba9euFXjM+fPn2b59O05OTqxatYqYmBgmTJjAjRs3WLx4cYHHBAcHmz/w5RYXF1dmQanExERAzQ1BZhqa9AQUF89Sn7sqyNd+okSK037p6emYTCaMRqM5d0lSUhIALi4u5uPS09PJyMjAzs4OR0dH8/HZZZ2dndFq1Q6XGRkZpKeno9PpcHJyum09Y2JiOH/+PF9//TUffPABI0eOBGDmzJlcuHCB8PBwIiIiCA4OZtiwYaSkpDB+/HgOHz6Ms7MzCxYsICAggMjISB5//HESExPp378/X331FdeuXSMzM5NXXnmF3bt3k56ezvTp0xk2bFih+3P78MMP2b59O6NHj+bRRx/FZDJRo0YN6taty/79+3nooYdwd3fnySef5OrVq3Tt2pXGjRuzfPly/v77b9577z1SU1Pp0KED8+fPR6vVsnDhQj777DN8fHyoWbMm1atXLzJvzNNPP80DDzzAoEGDaNKkCSNHjmTNmjXY2dmxatUqQkNDmTdvHnZ2duzcuZNVq1YRHBzM77//TlpaGuPHj2fcuHFs2bKFDz74AA8PD65du8aoUaPYsGED8fHxuLq6MnfuXCZMmMDly5dxcnJi4cKFtGjRgtOnTzNixAh0Oh3du3dHUZR89TUajZhMJhISEsyBfvn7LUJGMu675qMBktuNIyPrC5HcstvP6fIRnDRaUgPHkWZylg/oxWTp1198Ab+zOxESEsK9995LcnIySUlJVK9enZiYGFxcXPDy8pKglBBCCCGqDqUcuXLligIoO3fuzLP/vffeU5o3b17gMf369VOcnJyU2NhY874VK1YoGo1GSU5OLvCY1NRUJS4uzrxcunRJAZTY2FjFZDKVejEajcqNGzcUo9GomA7+oJhmeiqmZSPK5NxVYcnTfuWgPhVtKU77JScnK6GhoUpycrJ5H6AASmRkpHnfu+++qwDKmDFj8hzv4uKiAMr58+fN+z755BMFUB5//PFi1XPBggXKyy+/rBiNRqVhw4ZKdHS0YjKZlOnTpytBQUFKamqqcvHiRaVevXpKUlKSMnv2bGX8+PGKyWRSdu7cqfj7+ysmk0kZP3688sknnygmk0lZuHChUqNGDcVkMinz589X5syZo5hMJiU2NlZp0aKFkpKSUuj+3HXLyMhQevXqpRw5csRcp88++0wxmUx59ptMJqVBgwZKfHy8YjKZlKioKOXuu+82t+uECROUn376Sbl06ZLSqFEj5ebNm0pcXJzSuHFj8/kKW5566illzZo15mt8/fXXislkUt58803lnXfeyVevtWvXKs8//7xiMpmU1NRUpUOHDkp4eLiyceNGxd3dXbl8+bJiMpmUxYsXK40aNTK/3z366KPKvn37FJPJpOzZs0cZMGCAYjKZlIEDByrLly9XTCaT8vLLLyuBgYHFeh3J328Ry84vFOVtd8U0118xZaYXWCZP+107rphSE2xf7wq0WPr1FxsbqwBKXFxcqe53evXqpYwdO1bJzMxU9Hq9cu7cOSU8PFzp2bOnsmLFilKd2xri4uLKpB0KYzKZlJs3byomk8ki5xfSxtYgbWwd0s6WJ21seZW1jYt7v1Cuekp5enqi0+ny9YqKiorK13sqW+3atalbty4Gg8G8r2XLliiKwuXLl2natGm+YxwdHfP0+sim0WjK7JvV7HNpPOqpCW0vH5C8CCVgbj9psztyu/bL3l9Qmdz7iipXWNlb1wvz66+/Mnv2bLRaLcOGDWP16tU888wzaDQa7rvvPhwdHalfv765186OHTt49dVX0Wg0dOnShZSUFOLj49m1axfTp09Ho9Hw2GOP8cYbb6DRaPj33385fvw4S5cuBdTeXVevXi10f6NGjYC8w3Nyt+Otz/XW56vRaNi9ezdHjhyhS5cuAKSkpODj44OzszN33303Hh4egDrde3Fe37nLDB8+HI1GQ1BQEGvWrMlXr3///Zc///yTrVu3AmrPz/Pnz6PRaOjWrRt169Y1n3PAgAHm98yNGzdy4sSJfNc9cOAAf/31FxqNhhEjRrBp06YCXytFtYf8/eaSmQ671Dximm4vgq7w4Z7m9pM8UnfEkq+/sjrnoUOHWLhwITqdDp1OR1paGo0aNWL27Nk89dRT3H///WVyHSGEEEKI8q5cBaUcHBwIDAxkw4YN3Hfffeb9GzZsyDe8Jlu3bt347bffSExMRK/XA3D69Gm0Wi0+Pj5WqXeR6rQDNBB/WZ0G3K2WrWskRIGyh7y4uLiY973yyitMmjQJO7u8bxXZib5z52177rnnGDt2bLFyFEVGRrJz504efPBBQM3zdvz4cZ555hkg/we/gj4IKoqCRqMpdMitoigsWrSInj17Fmv/yJEjOXLkCP7+/oUO/b0dRVEYPHhwvuNXrVpV6g+z2YF0nU5X4LA/RVGYMWOGeRhkts2bN+f5nQL5tg8cOJDv9yYBpTJ29FeIvwL6WtD28SLKLUerbwgGSSxfmdnb25v/xry9vQkPD6dly5YYDAbCw8NtXDshhBBCCOspd7PvTZkyha+//prFixdz4sQJJk+eTHh4OOPHjwdg6tSpeT50Pf7449SoUYPRo0cTGhrK1q1beeWVV3j66acLTXRuVY5u4NVSXb+837Z1EaIIrq6uuLq65glGODg44Orqmq9nYXbZ7HxSoH7IcnV1LVY+qeXLl/Pss88SFhZGWFgYV69e5fTp00RHRwNqAu/09HQuX77MqVOnaNasGd27d+enn34CYO/evbi6uuLu7k7Xrl357bffALX3Vba+ffvy5ZdfmgM4hw4dKnL/0qVLOXToEN99912RdXdzcyMhIaHA7c6dO7Np0yYuXboEwPXr17l8+TIdO3bkv//+Iy4ujsTERP744w/z8VOnTmXVqlW3bbPb6du3L9988w0pKSkAnDp1itTU1Nse16tXLxYuXAioU9YfPXoUgPbt2/P7778DmNtd3CGTEbbPVde7PAf2hfyNJETCmudx+74/XDtiteoJ62vXrh3796v3BL1792b69On8+OOPTJo0iTZt2ti4dkIIIYQQ1lPuglKPPPIIc+fOZebMmQQEBLB161bWrl1LgwYNAIiIiMjzLaJer2fDhg3ExsYSFBTEE088wZAhQ/jss89s9RTyqxuo/rwiQSkhQA0eDR8+3Lyt0WgYPHgwK1asANQPbP3796dPnz7MmzcPZ2dnnnvuOWJjY/H392fixIl8++23ALz99tusWLGCoKAgLl68iLu7OwDjxo2jTp06BAQE0Lp1a2bOnFnk/uIaNWoUo0aNolOnTgCMHTuW3r17c9999+Hl5cWCBQsYPnw4/v7+9O/fn6ioKOrWrctLL71Ex44due++++jevbv5fMeOHaNWrdL3oLz33nsZNGgQHTt2pHXr1jz77LNFJlLP9r///Y8NGzbg7+9P69at+euvvwB1VsH33nuPTp064eDgUOr6VWkn/4TrZ8DJA4KKmGVyx1w0mSkYvduCtwQmKrNZs2ZRu3ZtAN59911q1KjBs88+S1RUlDlILIQQQghRFWiUwsa+VCHx8fEYDAbi4uLMH2hLQ1EU4uLiMBgMaq+TA0vgjxehYQ8Y9WfpK1zJ5Ws/USLFab/U1FQuXLiAr69vsXo2WdOMGTPw9PRk4sSJxSqfmpqKvb09Op2O3377jWXLlrF8+fI7vr6SNcucTqezyuvv3nvvZe3atRa/jiUU9DqSv99bKAosugsiDkHPV6HPtILLxV+FeQFgTCPxvu9x9R8i7XcHLP36K+v7hYrK0u0g7yOWJ21sedLG1iHtbHnSxpZXWdu4uPcL5SqnVKVVNys3yNUQdRiH9vY5d4QQxRMWFsZjjz2G0WjEYDCYe1BVFBU1ICWK6dxGNSBl7wKdxhdebtscMKah1O9CZv0eVquesI0LFy6QmZmZbzKWM2fOYG9vT8OGDW1TMSGEEEIIK5OglDV4tQR7V0hPhJjTOTmmhBD5zJgxo0TlW7RoQUhIiGUqI0Rpbf9U/dn+KXCtUXCZ2HA4kJXLrPcbMlNrFTBq1CiefvrpfEGpPXv28PXXX7N582bbVEwIIYQQwsrKXU6pSkmry5qFD0l2LsoVGb0rSkNeP7dxaR+EbQOtPXQtYjjq1o/AlAG+PdVh3qLSCwkJoVu3bvn2d+7c2Tz5ghBCCCFEVSA9pazFJxAubleTnbcfYevaiCouezry6OhoatasWanGLpeWtXNKVVSKohAdHY1Go8He3t7W1Smftn+i/mz7CBh8Ci/n2Qycq0HvN61TL2FzGo0mzyye2eLi4oo1QYEQQgghRGUhQSlryc4rdfmAbeshBKDT6fDx8eHy5cuEhYXZujrljslkQquVjqS3o9Fo8PHxQaeTPHn5RIbCqbWABrpNKrps1+chaAw4uKiJ0UWl16NHD4KDg/n555/Nfz9Go5Hg4OA8s3MKIYQQQlR2EpSyFp+soFTUcUhPAgdX29ZHVHl6vZ6mTZuSkZFh66qUK4qikJCQgJubm/SUuo3sWQ9FAXbMVX/6DQXPpkUWBdSAlKgyZs+eTc+ePWnevDk9eqhDNrdt20Z8fDwbN260ce2EEEIIIaxHglLW4l4H3OpAwlW4egga5s8lIYS16XQ6CSrcQlEU0tLScHJykqCUuDM3w+DocnW9+5TCy20KhjoB0OweSW5exfj5+XHkyBE+//xzDh8+jLOzMyNHjmTixIlUr17d1tUTQgghhLAaCUpZk08gnLiq5pWSoJQQQlROOz4DxQiN+6hBp4JEnYAtHwIKTNgDXi2sWUNRDtSpU4dZs2bZuhpCCCGEEDYlSVOsyZxXSmbgE0KISikhEkJ+UNeL6iW1ORhQoOUQCUhVEeHh4SUqf+XKFQvVRAghhBCi/JCglDVl55W6IsnOhRCiUto9H4xp4NMBGhaSsDriCIT+DmjgrjesWj1hOx06dGDs2LHs3bu30DJxcXF89dVXtG7dmpUrV1qxdkIIIYQQtiFBKWuqHQAaLcRfgfgIW9dGCCFEWUqJhX3fqOvdpxSeJ2pzsPqz9f3g7WeVqgnbO3HiBAaDgXvuuQdvb28GDRrE2LFjef7553nyySdp3749Xl5eLFmyhI8++ojnn3/+tudcsGAB/v7+uLu74+7uTpcuXfj7778LLb9y5Ur69etHzZo1zeXXr19flk9TCCGEEKJEJChlDYlR6pA9Rz3UbKnuuyJD+IQQolLZ9zWkJ4CXn5q8vCBXDsCpteoXFHdNtW79hE1Vr16djz/+mKtXr7JgwQKaNWtGTEwMZ86cAeCJJ57gwIED7Nixg4EDBxbrnD4+PnzwwQfs37+f/fv306dPH4YNG8bx48cLLL9161b69evH2rVrOXDgAL1792bIkCGEhISU2fMUQgghhCgJSXRuaVcOwle9waUGvHJOTXYedVwNUrUcYuvaCSGEKAvpybB7gbrefTJoC/nOZ1NWLyn/R8CzqXXqJsoVJycn7r//fu6///5Sn2vIkLz3Ee+//z4LFixg9+7dtGrVKl/5uXPn5tmeNWsWv//+O3/88Qft2rUrdX2EEEIIIUpKglKW5uUHWntIvq5OE143CA4ulbxSQghRmYT8AMkx4NEAWhURbAgcBUlR0OtVq1VNVA1Go5HffvuNpKQkunTpUqxjTCYTCQkJVK9evchyaWlppKWlmbfj4+MBUBQFRVHuvNKFyD6vJc4tVNLGlidtbB3SzpYnbWx5lbWNi/t8JChlafZOUKsNXD2oBqKyk51fDQGTEbQ629ZPCCFE6RgzYOdn6nq3F0BXxL/WloOhxaDC800JUUJHjx6lS5cupKamotfrWbVqFX5+xctVNmfOHJKSknj44YeLLBccHMw777yTb39cXJzFglKJiYkAaORvxSKkjS1P2tg6pJ0tT9rY8iprG2d/iXU7ZRKUOnPmDL///jthYWFoNBp8fX0ZPnw4jRo1KovTV3w+QTlBqVb3gYMe0hMh+pQkuRVCiIru6G8QdwlcvSDgyduXr0Q3G8L2mjdvzqFDh4iNjWXFihU89dRTbNmy5baBqZ9//pkZM2bw+++/4+XlVWTZqVOnMmXKFPN2fHw89erVw2Aw4O7uXibPI7fsQJfBYKhUN+flibSx5UkbW4e0s+VJG1teZW3j4j6XUgelgoODmT59OiaTCS8vLxRFITo6mtdff51Zs2bx8ssvl/YSFV/dQPXn5f1qz6g67SBsm5rsXIJSQghRcRkzYevH6nqX59TesbdSFPh1pPoFRYex4OBi3TqKSs3BwYEmTZoAEBQUxL59+5g3bx4LFy4s9Jhly5YxZswYfvvtN/r27Xvbazg6OuLo6Jhvv0ajsdjNc/a5K9PNeXkjbWx50sbWIe1sedLGllcZ27i4z6VUs+9t2rSJN998k2nTphETE0NERATXrl0zB6Vef/11tm7dWppLVA51s4bsRRyGzPS8QSohhBAV17EVcOMcOFeHDs8UXOb8ZjixBja+Dyk3rVo9UfUoipIn/9Otfv75Z0aNGsVPP/3EoEGDrFgzIYQQQoj8StVT6ssvv+SZZ55hxowZefZXr16dmTNncu3aNRYsWEDPnj1Lc5mKr0ZjcPKA1FiIPJaTV0qSnQshRMVlMsLWj9T1rhPBUZ+/jKLApvfV9aDRYKhrvfqJcu306dNs3ryZqKgoTCZTnsemT59erHO88cYbDBw4kHr16pGQkMAvv/zC5s2bWbduHaAOu7ty5QpLly4F1IDUyJEjmTdvHp07d+batWsAODs7YzAYyvDZCSGEEEIUT6mCUnv37uX7778v9PERI0YwcuTI0lyictBooPc09QOLRwNwq63ujwqFtMSCP8gIIYQo346thOtnwLkadPy/gsuc/Rcu7wM7Z+g+peAyosr56quvePbZZ/H09KRWrVp5urdrNJpiB6UiIyMZMWIEERERGAwG/P39WbduHf369QMgIiKC8PBwc/mFCxeSmZnJc889x3PPPWfe/9RTT7FkyZKyeXJCCCGEECVQqqBUZGQkDRs2LPRxX19f87dwVV6nWz6wuNeF+CsQcQgadrdJlYQQQtwhkxG2zlbXuzwHjm75y+TuJdXxGXDztl79RLn23nvv8f777/Paa6+V6jzffPNNkY/fGmjavHlzqa4nhBBCCFHWSpVTKjU1FQcHh0Ift7e3Jz09vTSXqLwkr5QQQlRcx1dBzGl1aHbHcQWXObUWroaAvSt0m2TN2oly7ubNmzz00EO2roYQQgghhM2Veva9r7/+Gr2+4OFnCQkJpT195XJprzqMI+AJNa/UiTXqDHxCCCEqDmMmbP5AXe/yHDi55y+jKDllOo0DV0/r1U+Uew899BD//PMP48ePt3VVhBBCCCFsqlRBqfr16/PVV1/dtozIsvL/4OYFqNk8Z0a+y5LsXAghKpQjy3JySXUqJKig0cD9i2D7p9D1eevWT5R7TZo04a233mL37t20adMGe3v7PI+/8MILNqqZEEIIIYR1lSooFRYWVkbVqCJ8gtSg1OUD6kxNGh0kXIWbYVCtoa1rJ4QQ4nYy02FLVg+o7pML7iWVzaulGpgS4haLFi1Cr9ezZcsWtmzZkucxjUYjQSkhhBBCVBmlyim1ceNG/Pz8iI+Pz/dYXFwcrVq1Ytu2baW5ROWS3Tvqyn5wcIX6XdTtk2ttVychhBDFd/A7iA0HvTd0GFtwmUzJpSiKduHChUKX8+fP27p6QgghhBBWU6qg1Ny5cxk7dizu7vm/KTYYDIwbN45PPvmkNJeoXHyyh+ztV/ONtBikbp/8y3Z1EkIIUTzpybD1Y3W95yvg4JK/jDETFvaA3ydC0nXr1k8IIYQQQogKplTD9w4fPsyHH35Y6OP9+/fn448/Ls0lKrzriWnsv3gTZ3sdPRu1AZ0DpNxQh/G1GATrp0L4TkiKkUS4QghRnu37GhKvgaE+tH+q4DJHlkH0SUiKhnuCrVs/Ua5NmTKFd999F1dXV6ZMmVJkWflCTwghhBBVRamCUpGRkfmSc+Y5uZ0d0dHRpblEhffnkQjeXnOc5t56ek7uBbXawJUDal4p/4eglj9cOwKn/ob2I2xdXSGEEAVJjVeTlgPc9RrYOeQvY8yALVlf1HR7ERzdrFc/Ue6FhISQkZFhXi+MRqOxVpWEEEIIIWyuVMP36taty9GjRwt9/MiRI9SuXbvE550/fz6+vr44OTkRGBhYZF6qzZs3o9Fo8i0nT54s8XUtweCsBu1ORyZiMpny5pUCaDlE/XnyTxvUTgghRLHsXqD2cq3RFPwfLbjMoR8h9iK4ehWeb0pUWZs2bcLDw8O8XtiyceNG21ZUCCGEEMKKShWUuvfee5k+fTqpqan5HktJSeHtt99m8ODBJTrnsmXLmDRpEtOmTSMkJIQePXowcOBAwsPDizzu1KlTREREmJemTZuW6LqW0relNwAKsPfCTegwBp5eD31nqAVaZLXPuU2QlmCTOgohhChC8g3Y9bm63nsq6AroZJyZBls+Ute7Ty4435QQQgghhBAij1IN33vzzTdZuXIlzZo1Y+LEiTRv3hyNRsOJEyf44osvMBqNTJs2rUTn/OSTTxgzZgzPPPMMoCZTX79+PQsWLCA4uPD8HF5eXuZvIMsTvZMdzvY6UjKMbDgRSefBfnkLeLWE6o3gxnk4+y+0us82FRVCCFGwHfMgLR6824BfIe/RB5dC/GVwqw1BT1u3fqLCue+++wocpqfRaHBycqJJkyY8/vjjNG/e3Aa1E0IIIYSwnlIFpby9vdm5cyfPPvssU6dORVEUQL2pGjBgAPPnz8fb27vY50tPT+fAgQO8/vrrefb379+fnTt3Fnlsu3btSE1Nxc/PjzfffJPevXsXWjYtLY20tDTzdnx8PACKopifQ2lknyf7XHU9nDgbncSBizcKPn+LwWh2foZy4k/wG17q61d0t7afKBlpv9KR9iudStd+iZGwZyEaQOn9Bmg06uypuSkK7PtaLdPjJbBzzF+mmCpd+1mZpduvrM5rMBhYvXo1Hh4eBAYGoigKISEhxMbG0r9/f5YtW8aHH37If//9R7du3crkmkIIIYQQ5VGpglIADRo0YO3atdy8eZOzZ8+iKApNmzalWrVqJT5XTEwMRqMxXyDL29uba9euFXhM7dq1WbRoEYGBgaSlpfH9999z9913s3nzZnr27FngMcHBwbzzzjv59sfFxZVZUCoxMRFQA3TNvVw4G53E+egk4uLisLu0E/uz68j06URG00HofO7Cjc/g9HribkSrM/RVYbe2nygZab/SkfYrncrWfs6bg3HMTCGzVjsSvbtAXFyB5TQP/IrDkR9IazS00DLFUdnaz9os3X7ZX2KVVq1atXj88cf5/PPP0WrVTAomk4kXX3wRNzc3fvnlF8aPH89rr73G9u3by+SaQgghhBDlUamDUtmqVatGhw4dyuRct95IKopS6M1l8+bN83Rv79KlC5cuXeLjjz8uNCg1derUPNMxx8fHU69ePQwGA+7u7qWuf3Zgy2AwoNFo6NuqDn8djyYhNRM3Nze0MYfQHP4OByUFgh4H97tQ9LXQJF7DEPY3tB9Z6jpUZLe2nygZab/SkfYrnUrVfrHhcORHAHT9ZmAoaoi4wQD93sCplJesVO1nA5Zuv7I65zfffMOOHTvMASkArVbL888/T9euXZk1axYTJ06kR48eZXI9IYQQQojyqsyCUmXB09MTnU6Xr1dUVFRUiYYBdu7cmR9++KHQxx0dHXF0dMy3P3vmvrKQeybAfn61gMMowL6wWDr7qME7zeUD6lAQjQ66ToR/3kSz4S1oNgDcapVJPSqq3O0nSk7ar3Sk/Uqn0rTf1tlgyoCGPdA0vqvgMtfPqXkBy/C5Vpr2sxFLtl9ZnTMzM5OTJ0/SrFmzPPtPnjyJ0WgEwMnJSV4DQgghhKj0SjX7XllzcHAgMDCQDRs25Nm/YcMGunbtWuzzhISEULt27bKu3h3TO9lRr7ozAJEJqVA3UH3gxjl1VieATs9CnXaQGgd/vXTH+UiEEEKUgZizcOhndf3u6QWXSYyGL3vAtwNz3suFKIYRI0YwZswYPv30U7Zv386OHTv49NNPGTNmDCNHqr2lt2zZQqtWrWxcUyGEEEIIyypXPaUApkyZwogRIwgKCqJLly4sWrSI8PBwxo8fD6hD765cucLSpUsBdXa+hg0b0qpVK9LT0/nhhx9YsWIFK1assOXTyKd7k5r8vDecExEJDAuomzPj3pWD0LSvOsX4sC9gYU84+SeErpaZ+IQQwlY2zwLFCM3ugXodCy6z7WPISILMVHAueR5FUXV9+umneHt7M3v2bCIjIwE1f+bkyZN57bXXAHWSl3vuuceW1RRCCCGEsLhyF5R65JFHuH79OjNnziQiIoLWrVuzdu1aGjRoAEBERATh4eHm8unp6bz88stcuXIFZ2dnWrVqxV9//cW9995rq6dQoDZ1DfwMHLuSlQDXp0NWUGq/GpQC8G4FPV6CLR/C2lfAtxe4VLdZnYUQokq6dgyOZX2x0XtawWVuXoR936jrfWeU6fA9UbllZmby448/MmbMGKZNm2ZOnn5rTsv69evbonpCCCGEEFZV7oJSABMmTGDChAkFPrZkyZI826+++iqvvvqqFWpVOs289QDsDbuByWRCWzcIjiyDy/vzFuzxEoSugegTsO51uH+RDWorhBBV2Kb31Z+t7oPa/oWXMWVAo7vURYhisrOz49lnn+XEiRNA/mCUEEIIIURVUq5ySlVmLWq5AZCeaWLvhZvgk5VXKjEyb0E7Rxj2OWi0atDq9D9WrqkQQlRhl/fDqbXqe/BdbxRc5spB9f0Z1F5SQpRQp06dCAkJsXU1hBBCCCFsrlz2lKqM9E72ONvrSMkwsuFEJJ0H+sNLpwqeZc8nCDpPgF2fw5+TYMJucJJvUoUQwuI2vqv+bPsY1GyW/3FFgX/eVNf9H1UnqBCihCZMmMBLL73E5cuXCQwMxNXVNc/j/v6F9NATQgghhKhkJChlRXU8nDkXncjBizdBZ19wQCpb72lqwvObYfDv2zD4U6vVUwghqqQL2+D8ZtDaQ6/XCi6TfB1SboKdE9z9llWrJyqPRx55BIAXXnjBvE+j0aAoChqNBqPRaKuqCSGEEEJYlQSlrKhNXQPnohO5EJOU94GMVLB3yrvPwQWG/g++GwL7F0PrB6Bhd+tVVgghqhJFyeklFfgUVGtQcDlXTxi3Da4dAYOP9eonKpULFy7YugpCCCGEEOWCBKWs6K7mNVl96AqxKRncSEynuosdLB8FZzbAhF1QrWHeA3x7QuAoOLAE1jwP43eowSohhBBl68wGuLRH7QHV4+Wiy+rsoG5769RLVErZMwoLIYQQQlR1kujciga3qY1Oq04bHvz3CdBqIfkGZCTDyb8KPqjfTHCrAzfOw+ZZVqytEEJUESZTTi+pjmPBvXb+Msk3YMdnas9WIcpIaGgo69atY82aNXkWIYQQQoiqQnpKWZGdnZYeTT3ZfCqaTaei1NwRLYdA2DY48Qd0eS7/QU4GNZ/Uz4/Ari/UKcrrBlq/8kIIUVmdWKMOx3Nwg26TCy6zZTbsWQBh2+GJX61bP1HpnD9/nvvuu4+jR4+ac0mBmlcKkJxSQgghhKgyJChlZTOGtqL/J1uJSUzn8OU4AloMgr9fhfDdkBgFeq/8BzW/B9o8BEd/g9+fh//bDHYOVq+7EHcqMj6Vk9cSMCkKRy7HFlrO3cmehp45s1AduxyHCaXAsq4OdjT20pu3Q6/Ek6mYCizrYq+jibebeftkRDzpxoLLOuq0+LjpcHVNA42G09cSSM0s+AOig05Li9o5M2OejUwgOaPgsnYaLX51c8qei0okKT2zwLJaNLT2MZi3w2KSiE/NKLAsgL+Ph3n9YkwScUWUbV3bgFanfvC9dCOZm8nphZb1q23ALqvslZspXE9KK7RsC293HOy1oCicu3aDpMwo0BRctpm3G072OgAi41KJTCi891GTmnpcHNV/VdHxaUTEpxRatpGnHr2TWjYmIY2rcYWXbVjDFXdnezAZCfz7HdyB3bUeZc/u68D1PGX9HKK4e+9XaIEdng+y/7/ThZ63XjUXqrmq78/xKRmEXU8qtGwdgzOebo4AJKZmcj4mERRITU3FyckpT/vVdnemprtaNjktk7PRiYWe19vNCW+DmqcwNcPI6ciEQsvW1DtS28MZgPQMEycj4wstW8PVkbrV1LKZRoXQiLhCy1ZzcaBedXW4ucmocKyIsgYnexrk+rsvzXuEFmhUzZ572xkKOLr8ePHFF/H19eXff/+lUaNG7N27l+vXr/PSSy/x8ccf27p6QgghhBBWI0EpK2tYw5XB/rVZGXKF73ddJODhtuqU4ldD4NRaNYdUQe75AM5thKjjsDkY+r5t1XoLURImk4lvd4Tx875L3EhK50ZS4YEPIWzpAe1Wejuc56ai55nTnUg8fSZfmYX2n6DVZbLJ2JbRm1yB/GVE+WGn1XBvu4a2rkaRdu3axcaNG6lZsyZarRatVkv37t0JDg7mhRdeICQkxNZVFEIIIYSwCglK2cCTXRqwMuQKfxy+ygt3N6FByyFqUOrEH4UHpVw94d6PYPnTsP0TNeF5z1esWm8hbkdRFL7efp7P/jtLQmpOLyCNRu3x4mSv42xU4T08XBx01MnqtQFwLjoRpeCOUjjb68y9NgDOxyRiKrjzE452WnOvDYCw60lkGgs+sb2dBh+DIzqdDtAQfiOZ9MyCT2yn09CwRk6vjUs3kkkrpKxWq/bkyXblZgophfSq0migcc2csldjU0hOL3w4T5NcPcYi4lJISiu8bKOarmizhghFxqfm+T3dytfT1ZwHLzohjbiUwntgNajhgr1OCyhExacSn5pJYV2l6ld3xsFO7Sl1u6ClTzVnc6+qm8npXE8svGxdD2ecHdSysSnpxCQUXra2hxMGe4VXY1eBCb5RhmK0d8P5lnIdCGWAbj9GtPxWfTy+RleuxRXes8vL3RF3J3sAktIziYgtvKynmwMezmqvqpR0I1diUwAFRVFfA7nbr4begWouatnUDCOXbxbeC6y6qwPVs3prpWcaCb9ReFkPF3s89WoPrAyjiYvXkwsta3C2p2ZWzy6jSck/k2wubk52eLurvbVMisL56MLLujrqqG3IafnSvEdoNeBb49bfYvljNBrR69W/W09PT65evUrz5s1p0KABp06dsnHthBBCCCGsR4JSNtCungc1XB24npTOzD9C+WbQEPhvJpzfAqnx4ORe8IGtH4Dr52HTe7DxPchMh95vZH96EcJmFEXhm+0XmPffmTxBjsY1XRnYujZjezbC4GxvwxoWn6IoxMXFYTAYzPldRPFVqPbb9zX8FQl6b15+YQ4v3zq7qckEX82CCNAFjWb+4MctXqUK1X7lUHb7lXetW7fmyJEjNGrUiE6dOjF79mwcHBxYtGgRjRo1snX1hBBCCCGsRoJSNqDRaOjVrCYrQ66w5XQ0qY+3w6leJ2g/Ehzdij641ytqPqkN02HrbEhPUmfo08mvUtjGrnPXeem3Q1zN1SOkubcbsx9sQ9t61WxYMyGKkJECW7Ny9/R4We19equjv0LEYTUB+l1TrVs/Uam9+eabJCWpvcfee+89Bg8eTI8ePahRowbLli2zce2EuD1FUTCaFDJNChlGEzqtBgedFjudTOwthBCiZCSSkUtSUhJubm7mb6fT09PJyMjAzs4OR0fHPOUAnJ2d0WrVf74ZGRmkp6ej0+mKVfbFu+qxYt8FMu0cePbHg3w7+m+SU9NQkpNxcnLKGjoEmZmZpKWlodVqcXbOGpLQ7UWSMzQo/7yJ087P0V3ZDw98Taa+Tv6yQEpKCiaTCUdHR+zs1F+50WgkNTW1RGU1Gg0uLjkf3FJTUzEajTg4OGBvb1/isiaTiZQUdViJq2vOEKi0tDSSkpJwdnY2t2VRZTMzM7G3t8fBQR2uoigKycnqEBQXF5d8v8+SlC3O7/5OXydOTk7mssnJySiKkud3X5KyuV8nuctm/z5v+5oq4e8+JSWF7acjWbTjEvvC1cTIislIk+qOvH9/azo3q3tHv/uSvk6K+t2X5nWSlJSEk5NTnrbM/n3e6euksN9naV8nhf0+S1K2LN8jcpe15HvErb/PEr9OtnwO16/i4lkPTeBTeX6f5rJ1A1GaDSS5ZgBoXHBRFKu8RyQlJeHo6Hjb32dZvE4s+R5Rmv8ld/oekfu8lnqPKAsDBgwwrzdq1IjQ0FBu3LhBtWrVpIecKDeuxKaw78INzkQlcDYqkcj4NK4npXE9Mb3QIeU6rQZHOy0Odloc7bQ42unM6w52Whx0WhztderP3PvNj2lx0N1yjN0tZXW6rHLaAsrp1G2dFq1W/paEEKIikK8zcqlTpw4xMTHm7Y8++gi9Xs/EiRPzlPPy8kKv1xMeHm7e98UXX6DX6xkzZkyesg0bNkSv13PixAnzviVLltCwlicOWz4DYNOpaDaevo6fnx96vZ6DO7dApjrL1bJly9Dr9QwdOjTPeTs8txB9cALbIhzh0h5Y0J0/57+JXq+nb9++ecr27NkTvV7P+vXrzfs2btyIXq+nS5cuecoOHDgQvV7PqlWrzPt2796NXq+nbdu2eco+8MAD6PV6fvzxR/O+o0ePotfradq0aZ6yI0aMQK/Xs2jRIvO+c+fOodfrqVu3bp6y48ePx8fHh3nz5pn3RUREoNfr8fDwyFN2ypQp6PV6Zs2aZd4XFxeHXq9Hr9eTmZkzlGzatGno9XqmTZtm3peZmWkum3vIx6xZs9Dr9UyZMiXP9Tw8PNDr9URERJj3zZs3D71ez7hx4/KUrVu3Lnq9nnPnzpn3LVq0CL1ez4gRI/KUbdq0KXq9nqNHj5r3/fjjj+j1eh544IE8Zdu2bYter2f37t3mfatWrUKv1zNw4MA8Zbt06YJer2fjxo3mfevXr0ev19OzZ888Zfv27Yter+fPP/8079u2bRt6vZ4OHTqY9y3efgGv1t3oH+DLpr9/x0GnZWSXBnzc242NU+/h0f7d8pz30UcfRa/Xs2TJEvO+EydOoNfradiwYZ6yY8aMQa/X88UXX5j3hYeHo9fr8fLKOzPlxIkT0ev1fPTRR+Z9MTEx5t9nbq+99hp6vZ533nnHvC85OdlcNvuDJ8A777yDj48Pr732Wp5zZJe11nuEXq/n0UcfzVPW/B5x8KB5X6HvER06oNfr2bZtm3nfn3/+WSneI8aNG4der7/z94ir59H3exV9cAKZ3V8FOzVQlO89wrMpmQ99j77/VKu+R/j4+DBy5Mg8ZSvSewTA0KFD0ev1eXr9HDx4EL1ej5+fX56yFe09wlKqV68uASlhUyaTwp7z13nvz1D6fbKFbh9sZNKyQ3yx6Rzrj0dy6FIsl24UnePQaFJITjcSm5xBZHwa4TeSORuVyPGr8YSEx7Lnwg22no7m3xOR/HU0gpUhV/hl3yWW7rrI19sv8MWmc3z672k+XHeSmX+G8ubqY7y6/Agv/nKI8T8c5Okl+3nymz089OUuhn2xg4HzttFnzha6f7iJju//R9t3/qHFW+to9MZamk5bS6vp62g38x86zfqXnrM30feTLdw7bxv3zd/BIwt3MeKbPTzz3X6e+/Egk5cd4rXlR3hr9TFm/hFK8N8nmPPPKT777wwLNp/j623nWborjJ/3hrP8wGV+P3SFv49G8G9oJFtOR7PzXAz7w25w+FIsoVfjORuVyKWbKVyNTVHzMiZnkJyeSYbRhFJYwkwhhKiCpKeUDfnVcSfcxZ6byRlM/CnnQybLn4a00cWbYW/o/yDya7i8Dw6rQS6UQrI9C1EKi7dfYO6/p4lPzTQnCe/SqAYLX7mLOh7O7Nu3z8Y1FKKYsoftAbR9NP/j8mFBWMjTTz9drHKLFy+2cE2EyHE2KpEVBy+z5tDVrAkXVFoN+Pt44FfHnWZeeup4OFND70gNVwf0TnbYaTXY6bTYaTUoCqRlGknLNJGeaSIt00hqhol0o4nUDCPpWfvTjSbSsvZnl0s3H6MuOY+ZSM96PK2I43M/lvvtO8OokGE0klREEM2WHHRa7HUa7O202Ou0Ods6ddveTotD7m2dFge7W7azHtfpNNhrtdhlbef+3djlesy8T5tVTqfBLvuxW/bZ6zTotHnPZ96nlZ5oQoiyo1EkVE98fDwGg4GrV69Sq1atMhm+l52oNvvb1cLKnohK4b75OwHo5evG/KBInH4fow6jeOZfMr3b3n5oDibYHEzmljmkZSpoPZvi/PgSqNUGqHjD91JTU7l+/Tqenp7lZvieyWRi8+kYjl6IBKB1A08cs57Hxeg4YuKS0ep02DvkvE7SUtTz+tX3xNlBLXv5RjyRN5LylU1PTUFRFFr41MDVSa3b1RsJRNxILLRss7rVcXNW91+LTeRKTAIarRZ7R0dSU1NxcnIiIy0NxWSiSZ1qGFzU4T1RcUlcio5Ho9Xi4Jgz5Cc9LRXFZKKRt4FqburvbueZKNaEhKkzfdnllG1czZ53h/nRsUktqw7NscbwvbS0NGJiYqhRo4YM37vD4XvZ739paWnlb/jetWMoX3YnOd0EI1bh0qJP/veI9a/hoFOgz1soem+rDvFNT08nOjqa6tWry/C9Oxy+l/36S09PL/P3iOz7hbi4ONzdC5mUpAharZYGDRrQrl27IntK5O6JWB6Vth1uRxL+W57RaGLd4YssC4li65mcHsBujnYMaF2L3s296N7EE4NLxZikBNTXTYZRKTrgZQ5sGW8JfuUcowazTFmLer6MzFu2sxb1fEqux/Mek240kmmEdGPl+8JYq0ENVGlzBa9yBbRyB8XM+3IFwLKDXHZatZw2K1imy/qZs53/cV3udY2G9LRU9K4u2Om06LSYj8l+XJd1TZ0m61idet6827c8nr2uVYN+OduaKheQk/dky6usbVzc+wUJSlH2N1clfVG99OshVhy8gr1Ow+/Pdcdv52Q4thxqtoRxW8xDS27r/BZY+X+QeA10jtD/Xej4fxVudr7y9Ef53U61m/aZqESMpir/p0Izbz0fPuBPu/qVN4F5eXr9VUTluv0UBb4bAmHbwG84PPxd/jLXjsGX3QEFnv4H6neychXLcftVAJZuv9LeL0yYMIFffvmF+vXr8/TTT/Pkk09SvXr1Mq+npUlQquIymRT+PnaNuf+e5kxUIqDeJt7dwov72/vQp4UXTvY6G9eycsj9OgbMSeEzMvMGttTgVk4gLD0rsJUd6DJv5wqE5dk2msg0qonnM7LWM0zqz0zzz5zHMk3q8ebyJoXMrGtkmkxZ+7P2ZT0mt8D5FRxA06DR5ASwNBo1z5pWo0GbZ12TtQ7arGCYVqNBqy2kjHldvZZWg3qMtrDz5tqfr3z+Mmqdc61n1Sm7/hrUL5v0ri5otRo0qMdqNOoEXlqNWkarBQ3qcVpNrp9kl8v985Zjs8oX69jsa2gLOTZrn1ajAQ15jtVm/U8xH5N1/uz/NNn1srbK+n+vuPcLMnyvHPjoQX+i4tPYdjaGMd/tY83TM6l5YQtEn4AtH8Ld04t3oka94Nmd8PsEOL0O/n4Vzm2CYV+Aaw3LPolKaMySvfx3MjrPPkc7tTdD/eou6LK+JbmemE58akah56lX3QW7rLI3ktKJSym8bN1qzjhkzVwTm5zBzeT0QsvW8XA21ycuJYMbSTlllVwJmQFqG5zMN5kJqZnEJKYVel5vdydcHNSyWo0Ge52GAa1r8XjH+ni4OBR6nBDl3sm/1ICUzlGdtfRWigL/TAMUNWhl5YCUqPzmz5/Pp59+ysqVK1m8eDFTp05l0KBBjBkzhv79+5f4RnTBggUsWLCAsLAwAFq1asX06dPz5Q7LbcuWLUyZMoXjx49Tp04dXn31VcaPH1+apyUqiJ3nYnjvzxOERqgTlLg66Hi0Qz2e6upL/RoFzEAqyowm637KXqeFCngrZcqaaTE7oJWZFczKMOYKYmUFwHICXfn3ZZfPHTjLMJowKer5jUYFY66ZHbOvazTl7DOaTHkeyzQppKWlo9HZYTQp6rmM2eVNGBXUY4xKznXynTP3tgmTCfVnEcE487Wt92sQNpIdvFLX8wawQA2SYV7PfYymwOPN5XIdo9FoUBTFHEjT3FI25/agoHPm1ENzyzmLsn5yT/SO5SMcVD5qUcVptVo+f7w99y/YwbnoJJ746SzL7/4Q9zVPw/a50GIw1G1fvJO51oDHfoG9i+CfN+H03/BlN7h/Efj2vP3xAoBJv4SYA1J1PZwZ3q4uo7s1wFPvdJsjba+yRtqFKJXw3bD6WXW960So1iB/mTMb4Pxm0DlA3xnWrJ2oQhwdHXnsscd47LHHuHjxIkuWLGHChAlkZGQQGhqaLwl7UXx8fPjggw9o0qQJAN999x3Dhg0jJCSEVq1a5St/4cIF7r33XsaOHcsPP/zAjh07mDBhAjVr1syXMF9UHlHxqbz31wnWHL4KgN7RjjHdG/Jgmxr4eNeQewVxW1qtBgetBodyOEeWJe97FSVv4CpfoExRA2nZPcxMCubAmCnrWHU9a392mazHTSbFfFze8gpGEzllss6RU76I82Y9nrN+a50ouIyimJ+v0ZT13LO2FUVNGaCzszNfl6w6K5Brn/pTQcGUVX+yHleUnJ9KQftyH5u1reS+hinvsTnH5RyTva+sZV/XvJG/RNlf1ArK04A5CUqVEwYXexaP6sDg/23ndGQid/9t4GfvATSJXM+Nn8ayquMPmLQONPN2U79lASJiU7hRaE+avgT0a0GrnZNxiT+H8t1QDjUczQHfcSja/PkBGtfUm3vSRMenEZWYWmhdfT315p401xPTuRafUmjZhtVdcXVSX2Y3k9K5Gld42frVXXFzsgMFrsbEcjP9ek4I+BY+Hi7mPAfxKRlculn4zEh1DM5Uc1W/lkpMzeTijaRCy9Y2OHP0chyrD6k3bt0a1+DHsZ0LLS+EqADOb4GfH4WMZGjQHXq8nL+MMVMN5AN0GgfVfa1bR1ElaTQa87ejJlPJc84MGTIkz/b777/PggUL2L17d4FBqS+//JL69eszd+5cAFq2bMn+/fv5+OOPiwxKpaWlkZaW0x8gPl7taaNkfYgpa9nnLU83zBVRptHE0l0X+fTfMySmZaLRwBMd6zO5XzOqudgTFxcnbWxB8jq2Dku3c3YOq6qson3hnTuglR3oyg5gmQNaYA6GZb90FHICNYr5XLec95b92efOf4xiXldyHV/QPiVrZ3xCAm56N8i6L8ipU861bj2eQupRnL8HZ3udxd+fint+CUqVIw1quPLagOa8+ftxohPSeCjhPjY47mJHvBdz/z5GAiXvWu3MNKbbfc9jdptoF7YY5fxWpmQ8S5hS2wLPoHKZOrAF43o1tnU1hBClcWYDLHsSMlOhcR945EdwKOC99OASiDkFztULDloJUUbS0tLMw/e2b9/O4MGD+fzzz7nnnnvMCe/vhNFo5LfffiMpKYkuXboUWGbXrl30798/z74BAwbwzTffmCcCKUhwcDDvvPNOvv2WCmooikJiYna+o/L/Aag8CrkcT/D6c5yOVr+0a11bzxsDGuNXSw+ZKcTFJUsbW5i8jq1D2tnyKnMbayi0D0TJTlLUdjEoioKjowm9o7GM2vj250hMiC+D6xQt+0us25GgVDnzZJeGXItP45vtF0gyeTA0czYxeIBOHYJer7qzOVp/Iymd+JTMQs9Vt5oz9jo93zKZI4ntmJr5Je21Z9ng8Cq/mPryhel+bqAmX6zj4YSDXU4uo9jkwvMe1TY44Wifncsok5tJhec98jY44pwrl9H1xMLLerk7mntgxaekcyOp8OdW080RV0e1bHKakaiEwkd0e+rVqYsBUtKNRMYXXra6qwM19A481aUhD3eoV2g5IUQFcOIP+G00mDKg+b3w0JKCJ44wmWDPQnX9rqng7GHNWooqJHei89GjR/PLL79Qo0bpcj4ePXqULl26kJqail6vZ9WqVfj5+RVY9tq1a3h7e+fZ5+3tTWZmJjExMdSuXfAXVlOnTmXKlCnm7fj4eOrVq4fBYLBYonOgwnwrX57EJKbx4bqTLD9wBQCDsz2v3tOcR4Pq5ZkxTNrY8qSNrUPa2fKkjS2vsrZxcZ+LBKXKoZcHNOflAc3zP6AooJhAeyezovSCm6Ng7cvYn/mHEbr1jHDeAZ2fVWfo03uVttplpqJ1ERVClENHl6uzkSpGNWn5A1+DrpCpzbVaGPMP7PsagkZbtZqiaskePufr68uWLVvYsmVLgeVWrlxZ7HM2b96cQ4cOERsby4oVK3jqqafYsmVLoYGpW/+vZt8IF/X/1tHREUfH/AHd7OGHlpB9brkPKB6TSWHZ/ksErz1BfKr6pd4jQfV4bWALqrsWnFlb2tjypI2tQ9rZ8qSNLa8ytrEEpSqbxCj4czJ4tYQ+b97ZOao1gCd+gwtb4Z+3IOIQbP0IdnwGbR+BLhOhZgHBMCGEqEhCfoDfJwIKtH0Mhn4Outv8u3OuBj1fsUr1RNU1cuTIMr/ZdHBwMCc6DwoKYt++fcybN4+FCxfmK1urVi2uXbuWZ19UVBR2dnal7rElbOdsVCJvrDzK3rAbAPjVdufd4a0JbFDNxjUTQgghbk+CUhVF+C44+Sec+ludja9OwJ2fy7cnjN0EJ/9QA1JX9sPBperSdIA6M1XDHrnnnhRCiIph71ewNisnVOBoGPSJ2hOqMJcPqLObyvudsIIlS5ZY/BqKouRJSp5bly5d+OOPP/Ls++effwgKCio0n5Qov9IyjSzYfI75m86RbjThbK/jpf7NGNW1IXa68jdLmhBCCFEQ+Y9VUfgNU4egKEb4dQTcDCvd+bRa9ZzP/AtPr1cDXWjgzHr4bggs6gVHfgNj4bmlhBCiXNn5v5yAVOcJMPjTogNSl/bC133U9zx5rxMV0BtvvMG2bdsICwvj6NGjTJs2jc2bN/PEE08Aai6okSNHmsuPHz+eixcvMmXKFE6cOMHixYv55ptvePllSe5f0ewLu8Ggz7Yz998zpBtN3NW8Jv9M7skzPRpJQEoIIUSFIj2lKpJBc+DaEbhxHhYPhKfWgGfT0p1To4H6ndXl+jnYPR9CfoSIw7DyGfh3BnQeD+1HgpOhTJ6GEEKUKUVRhyJvel/d7vES9Hmr6N5PigLr31DXPRoUnm9KiHIsMjKSESNGEBERgcFgwN/fn3Xr1tGvXz8AIiIiCA8PN5f39fVl7dq1TJ48mS+++II6derw2Wef8cADD9jqKYgSikvJ4MN1J/lpj/p79dQ78PaQVgz2r12p8pAIIYSoOiQoVZG4esLov2HpMIg+Cd8OhJG/g3ersjl/jcZq4OuuN2D/Yti7EOIvwz9vwuYPIfAp6DQePGRWOiFEOaEo8N9M2P6Jut3nzeLlhjq+Ei7vA3uXO8/TJ4SNffPNN0U+XtBwwV69enHw4EEL1UhYiqIo/H3sGm+vOU501ozDj3aox+sDW+DhUnAicyGEEKIikP69FY1bLRj1F3i3gaRoWPOC+qGsLLnWgF6vwKRjMPR/4Nkc0hNg1+cwry38+DAcWwHpyWV7XSGEKAlFgXVTcwJS/d8vXkAqLQHWZwWiuk0C99oWq6IQQpTW1dgUxi49wIQfDxKdkEYjT1d+HtuZDx7wl4CUEEKICq9cBqXmz5+Pr68vTk5OBAYGsm3btmIdt2PHDuzs7AgICLBsBW3N1RNG/QGNesOQuZZL0GvvpA7bm7AbHv9NTX6uGNW8U8ufho+bqj+P/AYpNy1TByGEKIjJBH9Ogj0L1O1Bc9RJGopj8weQcBWqNYRuL1iqhkIIUSpGk8KSHRfo98kW/j0Rib1Owwt9mrD2xR50aSyzJQohhKgcyt3wvWXLljFp0iTmz59Pt27dWLhwIQMHDiQ0NJT69esXelxcXBwjR47k7rvvJjIy0oo1thHnajBydd59IT9ArTZQu23ZXkurhWb91SX6NBz9FY4sg9hwtcfUsRWg0UH9LtD8Hmh2T+lzXQkhRGGMmfD7c3DkF9BoYejn0O6J4h177Rjszgpk3TsH7J0tV08hhLhDJyLieX3lUQ5figUgsEE1gu9vQzNvN9tWTAghhChj5S4o9cknnzBmzBieeeYZAObOncv69etZsGABwcHBhR43btw4Hn/8cXQ6HatXr7ZSbcuRKwfhjxfVD2h934HOz1qmB1XNZmr+ld7T1Hwsp9bCqXUQfQIubleXf96E6o2h+UA1QFW/C+jK3UtNCFERGTNgxTMQuloNht+/CNo8WILj09Wgec3m0LSvxaophBB3IjXDyGf/nWHR1vNkmhTcHO14bWALHu9YH61WEpkLIYSofMpVpCA9PZ0DBw7w+uuv59nfv39/du7cWehx3377LefOneOHH37gvffeu+110tLSSEtLM2/Hx8cDahJJpQzyM2WfpyzOVWweDaDZADQn/4L1U1HOb4JhX4BrTctd06eDutz9NtwMg9Pr1CVsB5ob59QcVLs+R3EyQJN+aoCqSV9w9ijytDZpv0pE2q90pP1Kx6Ltl5kKv41Cc3odis4BHlwMLQaXLK9enXYwbhukJ5V9Pr4yIK+/0rF0+8nvRVjSjrMxTFt1lLDras7Oe1rV4p1hrfB2d7JxzYQQQgjLKVdBqZiYGIxGI97e3nn2e3t7c+3atQKPOXPmDK+//jrbtm3Dzq54Tyc4OJh33nkn3/64uLgyC0olJiYCWHF6Xh0M+AKH2l1w3voumjP/YJrfleQBn5LZoIflL6+tBi0eU5e0BOzDt2J//j/sLmxEm3oTji2HY8tRNFqMXq3JrNMBY91OZNbtgOJcPc+pbNN+lYe0X+lI+5WOxdovIwXXP8ZiH74NRedI0pBFZNbuAXFxxTpce+MspupNcu3RQHrxjrUmef2VjqXbL/tLLCHK0s2kdN776wQrDl4GoJa7E+8Ma8WAVrVsXDMhhBDC8spVUCrbrTeSiqIUeHNpNBp5/PHHeeedd2jWrFmxzz916lSmTJli3o6Pj6devXoYDAbc3d3vvOK56gtgMBis/6Gi50Ro3htlxTNoo0+gX/UkSo+XrTzluQG8Hoegx8FkRLm8z9yLShN9ErvII9hFHoEQdSprpWZLdYhf/U7g0xHFo4F6Flu0XyVg09dfJSDtVzoWab+0BFj1NJrwnSj2rvDYz7j69iz+8Yd/gd8nQL93octzZVMnC5HXX+lYuv3kdyLKkqIorDl8lZl/hHI9KR2NBkZ0bsArA5rj5mRv6+oJIYQQVlGuglKenp7odLp8vaKioqLy9Z4CSEhIYP/+/YSEhDBxojrrkslkQlEU7Ozs+Oeff+jTp0++4xwdHXF0dMy3X6PRlNkNZ/a5bHIDW6s1jN0I/0yD/YvRuFS33Ax9t6OzgwZd1KXfOxB3BcJ3wcUdcHEnRJ9EE31CzUl1YLF6jGtN9N4BaHy7oqnXUR1u4+Bqm/pXUDZ9/VUC0n6lU6btlxILPzwAV/aDozuaJ5arAeziOrgU1rwAKBBzOruCpa+XBcnrr3Qs2X7yOxFl5dKNZN5cfYwtp6MBaOatJ/h+fwIbVLNxzYQQQgjrKldBKQcHBwIDA9mwYQP33Xefef+GDRsYNmxYvvLu7u4cPXo0z7758+ezceNGli9fjq+vr8XrXG45uMDgT6H1g9Cga87+lJvqzH22YqirJiXOTkycFJMVpNoJl/ZCxGE0SdHYn98A5zeoZTQ6NdDm01HNYVWvA1TzLfcfLIUQpZR0Hb4fDteOqO9bI1apQeri2vsVrH1ZXe8wFgbOlvcNIYRNZRpNfLsjjE82nCYlw4iDTsvzfZowrldjHOy0tq6eEEIIYXXlKigFMGXKFEaMGEFQUBBdunRh0aJFhIeHM378eEAdenflyhWWLl2KVquldevWeY738vLCyckp3/4qq2G3nPW0RPjqbvBsBndPB28/29Urm6sntByiLgAZqSgRh0k9uxWnmKNoLu2DhKsQcVhd9n2VdVxNNUBVOwBq+0PttuBWWz5wClFZJETC0mFqL0rXmjBitRqcLq5dX8D6N9T1LhOh/3vy/iCEsKljV+KYuvIoR6+o+ew6+VZn1v1taFxTb+OaCSGEELZT7oJSjzzyCNevX2fmzJlERETQunVr1q5dS4MGap6hiIgIwsPDbVzLCurCFrh5AW6cU3M8+T8CvV6FGo1tXbMc9k5QryNp7s1xMhjUD5Fxl9VeVJf3w2W1NxVJ0XBqrbpkc/FUg1PZQapa/mqPKq188yhEhRJ3Gb4bqr5XudWGkWugZvHzBrLtE/gvazKL7lPUILwEpIQQNpKSbuTTf0/zzfYLGE0K7k52vHFvSx4OqodWK+9NQgghqjaNIvMbEx8fj8FgIC4urswSncfFxZXPRLXRp2Dje3BiTdYODTTtD53+Dxr1KRcBnNu2X0aqOpzn8n71Z8Rh9XkpxvxlHd3V4FTuQJVnMzXXVSVVrl9/FYC0X+mUuv1uXIClQyE2HDzqqwGp6iUcir1jHmyYDne9oQbeK9DvUV5/pWPp9ivr+4WKytLtUJn+Draejmba6qNcupECwCD/2rw9xA8vNyeb1qsytXF5JW1sHdLOlidtbHmVtY2Le79QeT+Zi4LVbA6PfA9XDsLmYDjzD5xZry7PHyxfvaYKk9Wbinodc/ZlpEBkKFzLGuYXcQQij0NaPFzcri7Z7JzAuxV4t1Y/7HrUB4+G6k9Xzwr1AVaISiXmjNpDKuEqVG8ET/0BBp+Sn6fbi1CvE9TvXPZ1FEKIYriemMZ7f51gVcgVAOoYnHh3eGvubpl/4h4hhBCiKpOgVFVVtz088RvEnFXzNCVcyxuQOvi9+oHOs6nt6lgS9s7gE6gu2YwZag+q7N5UEUfU9fREuHJAXfKdxyUrSNUAqjXIWfeor247eUjQSghLiDyu5pBKioaaLWDk7+BWq3jHKgrsWQgBj4NT1rcwEpASQtiAoiisPHiF9/4K5WZyBhoNPNWlIS8PaI7eUW67hRBCiFvJf8eqzrMJDPww7764K/DHi+pwuMZ9oOM4dYhfORjaVyI6ezUxcq3W6odVAJNJzasVcQiiTkLsRXWY0M2LkBABGckQfVJdCuLonjdIdWvQytHNak9PiErj6iF1lr2Um1CrjZrU3NWzeMeaTPD3q2pwPXQ1jPoLtDrL1VUIIQoRfj2ZaauPsu1MDAAtarnxwQP+BNTzsG3FhBBClIjRaCQjI8Nq11MUhfT0dFJTUyvs8D17e3t0uju7B5eglMgvI0UNQp1eB+c2qks1X+g4FgKeAGcPW9fwzmm1ao+wgoYpZqapCZZvhqmBqtwBq9hwSIpShwNGHlWXgjhXLyBgldXrylAPHFws+vSEqHAu7YUfHoS0OKgbCE+uAOdqxTvWZII/X4SDSwENtH1UAlJCCKvLNJr4evsF5v57mtQMEw52Wl68uyn/17MR9roK9oWeEEJUcYmJiVy+fBlrp942mUxcv37dqtcsSxqNBh8fH/T6ks8oK0EpkZ9nE3j8FzXh8L6vIeR7tXfR+jfUJOlPLIeG3Wxdy7Jn51h4wAogPRniLmUFqS7eErS6qPbySLmhLhGHCj6Hq1euoFWDvOsGH7UOQlQVF7bBT49ARhLU7wqPL8sZfnc7JiP8/hwc/hk0Whg2HwIes2x9hRDiFocuxfLGyqOERsQD0LVxDd6/rw2+nq42rpkQQoiSMhqNXL58GRcXF2rWrGm1XkuKomA0GtHpdBWyp5SiKERHR3P58mWaNm1a4h5TEpQShavuCwPeh95vwJFfYe8iuH5OHQ6X7dQ6MGVC497gUMlvwBxc1ETxNZsX/HhqfFYPqwJ6WcVeVHtZJUWpy5X9BZxAA261Cx4W6FEf3H0q9ayBooo5+y/88gRkpkKj3vDoj8V/DzFmwKpxcGwFaHRw/yJo86Bl6yuEELnEp2bw8fpTfL/7IooCBmd73hzUkgcDfSrkBwohhBCQkZGBoijUrFkTZ2dnq123ogelAGrWrElYWBgZGRkSlBIW4OAKQaMhcJQalHIy5Dy25UO4ehB0juDbE5rfA83uubMZsyo6J/ecHFa3UhRIjc0bpMq9Hhuu5rNKuKou4bvyn0OjA0PdnCGBtw4TdKslQ5dExXDyL/htFBjT1feLh75TZ9Usrr9fVQNSWnt46FtoOcRiVRVCiNwUReGvoxHM/COUqIQ0AO5rV5c37m1JTTfp7SyEEJVBRQ0M2VJp2kyCUqL4NBp1aF82kwnqd1GHq90Mg7Mb1OWvl9Rkxa0fhO6TbFXb8kWjUfPkOFeDOgH5H1cUSIrJClKF5e9lFRuufoDP7onFtvzn0NqDRz3wqI+zS23wapIVrPJWhw3qvdTry5ussKVjK2Dl/6k9LP2Gwf1fg51Dyc7RcRycXg+DPlED4UIIYQXh15N56/djbDkdDYCvpyvvDW9NtybFnJhBCCGEEPlIUErcOa0W7pmlDvGLPgWn/1aH813eC9eOQo1cASxFUYfrNOha+Yf53QmNBvQ11cUnMP/jJhMkRt7SyypXXqu4y2DKgBvn0dw4T6Hf1WrtwbWmGqDSe+UEq/ReufZ7q+sSwBJl7dBPah4oxQT+j6h5oO5kSKpXC3j+YMl6VwkhxB1KzzTx1bbzfPbfGdIyTTjotDx7V2OevasxTvbSQ1kIIUTZsbOzo3Xr1mRkZNCoUSO+//57PDw8SnSOGTNm4OnpycSJE29bNiwsjL179/Lwww8DsHnzZoYPH07Dhg1JS0vj0Ucf5e23376Tp1JsEpQSpafRqB8SvVpA98mQdF3tMWWol1Mm5jT8+KA6zM8nSO1h1aAr1OsIjm62q3tFodWCe211qd8p/+PGTEiIgNiLKDcvknbtNI6pkWjiLqvBrMQodfigKSNniOBtr2l/S7Dq1iBWrnUnDwlgiaLt+wb+mqKut38KBs9VX9fFkZ4Ev42GLs9Bo17qPglICSGsYM/560xbfYyzUYmAmsj8veGtaVSz5LMLCSGEELfj4eHBoUOHABgxYgRffPEF06ZNs9j1wsLC+PXXX81BKYC+ffuyfPlykpKSCAgIYPDgwQQGFtBxooxIUEqUPdca6tTsuSVEqLmPYsPh4g512YaaJ6l2WzWZetN+NqlupaCzyxq6Vw8adCM1Lg5HgyFvoCgzDZKi1QBVYlbC9Tzr0WoAKykKUuPUAFb8FXW57fUdcoJXrl5Zvb68C16XAFbVs2s+rJ+qrncaD/d8UPzXQGo8/PSwmmct4hC8eBjsrZd4UghRNd1ISid47Ql+O3AZgBquDrw5uCXDA+pKrhEhhKgCFEUhJcNY5ud1ti9+MvNu3bpx+PBhAM6fP8/TTz/NjRs3aNiwIUuWLKF69eqcPHmS8ePHc/PmTRwdHfnvv//ynCM4OJiTJ0/y7bffEhwczOrVq0lLS2PChAmMHz+eadOmcfz4cQICAnjxxRfx9fU1H+vq6kpgYCDnzp3jxRdfZPHixTRr1gxFUWjRogV79uwpcS+ugkhQSlhHo7vgxSNqovTwnXBxpxqYig1XE6Xn/sM8vwWX3V+Bb2fw6agGraRXROnZOaoJ6IuThD4jVQ1g3RqsMq9n/UyMhrQ4Nd9ViQJYuYNVtwwbzL3uZJAAVkW39WPY+K663n0y3P128X+nKbHwwwPqbJWOBnj0JwlICSEsSlEUlh+4zKy1J7iZnAHAYx3r8/o9LTC42Nu4dkIIIawlJcOI3/T1ZX7e0JkDcHG4fRjGaDSyYcMGnn76aQBeeOEFJkyYwMMPP8yHH37IjBkz+Oyzz3jyyScJDg6mX79+JCQk5Jk1cNasWZw6dYpvv/2Wf/75h6ioKPbt20d6ejrdu3dn8ODBvP/++3z++ecsX74cUIfvZbt+/Tq7d+/mrbfeYtSoUSxdupT33nuPTZs2ERAQUCYBKZCglLCm7ETpnk2g/Uh1X9xluLgL6uUaknZhCw5n/oQzf6rbWns1cbpPB3Vp1j/vDICi7Nk75fS8up2M1JyAVVJUTrAq33oUpMVnBbAuq8vt6BxzhhC6eoK9CzjowcHlztYlwGU9igIb34NtH6vbvadBz1eK/ztIvgHfD4eIw2p+sxGrC54kQAghysjZqATeWHWMvRduANCilhvv39eawAbVbVwzIYQQVUVsbCwBAQFcvnyZVq1aMWDAAAD27dvHH3/8AajD+gYNGkR8fDyxsbH066eOOHJzy0mLs2jRIlq0aMEvv/yCVqtlw4YN/PHHH2zZsgWAuLg4zp07V2Ad/v33X9q1a4dWq2Xq1Km0atWK+vXr06lTJ959912+++47nnrqqTJ7zhKUErZl8AH/h/Lu8xtKiskOp+vH0Fzep/bKuXpQXfYuVBMcZwelLmxVgx1eLaFG05LP4iVKz95JHZrpUf/2ZTNSbjOEMNeSngDGNIi7pC5lUlfXYgSxXHEy6cCtevGDXvK6y0tR4J83YfcX6na/d6HbC8U/PjFaDUhFHgMXTxj5O9RqbZGqCiFEaoaRzzeeZeHWc2QYFZztdUzq25Snu/tirytm7jshhBCVirO9jtCZAyxy3qJk55RKTk6mX79+zJ8/nxdeeCHPkD9FUczbhQ0FbNu2LYcPHyYqKopatWqhKAozZsxg5MiRecrl7hmVLTunVG5ubm4EBgaydu1aduzYweLFi4vzdItFglKi/KkdQJqLL06GrMBTbDhc3geX90P0CajeKKfsvm8gdLW6rrVTZ/yr2QK8/NRAVbN7JGBQntg7lyyAlTtglXwd0pMhI0n9mZ5UvPXMlFznzHqsCBqgxINFtXZZAa/iBL1uWbd3Uo/X2qk51rS6rO1cPzW6nDLaXGXy7Nfech674icSLysmE1zYiuuO/6E5/6+6796PoePYkp1n1//UgJTeG0auUSdREEKIMqYoCv+ERvLun6Fcvqn+r7i7hRczhraiXnUXG9dOCCGELWk0mmINs7MUFxcX5s2bxwMPPMCECRMICgpixYoVPPjgg/z000/06NEDd3d3qlWrxoYNG/IN3+vUqRNPPvkkQ4YMYcOGDfTt25cPP/yQhx56CGdnZ06dOkWDBg1wc3MjISGhWHUaNWoUI0aM4PHHH0enK7vZZyUoJco3jQaqNVCXNg/mf9zLT02iHnVCHRoWfVJdQleruYveiMgpu3+xmsDby08NXBnqWf9Duyg+e+ec331pmEyQkawu6YlZga3C15W0RNKTbuKgyUSTnpSrTFJWwCvXuikj6xqZam6ttLjSP+8ypbmDAFfuwFgJAmUaDYRtR3MzDHtAQYNm6P+g/YiSV7vPW5CWAJ2fU4f7CiFEGTsfnciMP0LZejoagDoGJ6YP8WNAq1qSyFwIIUS5EBQURJs2bVixYgWfffYZo0ePZubMmTRo0IDvvvsOgO+//57/+7//4+WXX8bJySlPovMBAwZw8+ZNhg0bxrp16zh27BgdO3ZEURS8vLz4448/8Pf3JyMjo8BE57fq06cPRqMxX2+r0tIoiqKU6RkroPj4eAwGA3Fxcbi7u5f6fIqiEBcXh8FgkBubO3BH7acoapLtqJMQFaoGqYzp8OA3OWW+7AHXjuRs27uAR1bQw7MZ9H8357HMNDUxeAUkr7/SKVH7GTMKDlZlrxcWzMq9npkKJqMa2DJlqutK7m1Tzrp5vzHvMZSft3HF0Z305sNw6DoeTUmG3CVGqUP1qnigWP5+S8fS7VfW9wsVlaXbwZK/x6S0TD7fdJavt50nw6jgoNMytqcvz/VuYtNvxK1N3mssT9rYOqSdLa8qtXFqaioXLlzA19cXJyfrTbSlKApGoxGdrvgz89nC2bNnefzxx9m7d2++xwpqu+LeL1Sd/76ictNocmaWa9q34DL+j0DN5mrAKua0GhiIPqEu18/lDUp90x9uhkG1hmrQKjt45dEQqvtCjcZWeFKi3NPZg7OHutiSyXRLwKqg4NYtgazCAlyKMe85bneu3PsN9aHFIFJSMnEwlGAyghvn4buh0KQvDP5UEtILIcqcoij8eSSC9/86wbX4VADual6Tt4e0wtfT1ca1E0IIIcq3r7/+mpkzZ/LVV1+V+bklKCWqjq4Tc9aNGWquqpthEHtRneEvt5thkBoLEYfUJbcaTeD5AznbG99Xf+YOXrnXVYc0CWENWi2gVYNktqYokFKCYYwxZ+C7Ieow3LDtkHITXGSmKyFE2Tl1LYG31xxj93l1Vr161Z15e3Ar7m7pVa6/kRZCCCHKi2eeeYZnnnnGIueWoJSomnT2am+nwno8TT6WFbS6qAatcv+89Zh9X0PKjbz7tHZqr60G3WH4Fzn7I4+rQ5T0XtIbRIjIUFg6TE1kX7MlPLVGAlJCiDITn5rBvH/PsGRnGEaTgqOdlud6N+H/ejbC6TazHwkhhBDCOiQoJURBHN3Au5W6FMVkgq7P5w1cxV5SE2DfDMs7UyCoPUKSr4Nd1ix0uXtXebeCxn0s9pSEKFcijsD3w9W/B+82MHI1uHraulZCiErAZFJYFXKF4L9PEpOYBsCAVt68OchPZtUTQgghyhkJSglRGlot9JiSd5/JqA5Fig3POywwI1UNRqGBzBSIOaUu2RrdlTco9d1QcHLPClo1zJXXqr46M50QFdWVg/D9feoQ2Trt4MmV0kNKCFEmjl+NY/rvxzlw8SYAjTxdeXtoK3o1q2njmgkhhBCiIBKUEqKsaXU5Sddzs3eCKcchMx3iLuUfFlirTU7ZtES4sKXwa/gNh4e/y9k+vAzcauXksxKiPEuIgLQE8OkITy4HpxIkRRdCiALcTEpnzoZT/LQnHJMCLg46Xri7KU9388XBrmrP6imEEEKUZ/JfWghrs3NQ81I17gNBo6HvDHjo27w9rnT28NgvcM+H0HkCNB8E3q3BwU19PPeH+PQkWPV/sHQozGsL79bEfWE7mN8ZlgyGHfNyyioKnPwLwveoMw6mxqv7hLCmFoPUYNSIlRKQEqIUgoOD6dChA25ubnh5eTF8+HBOnTp12+N+/PFH2rZti4uLC7Vr12b06NFcv37dCjUue5lGE9/vCuOujzfzw241IDWkbR3+e6kX43s1loCUEEKICsXOzo527drh5+dHYGBgvtnuFEWhfv36vPrqqwBMmzaNgIAA/Pz8cHFxISAggICAADZt2sSqVato27YtrVq1onXr1syfPx8Ak8nEtGnTaNKkCU2aNOHhhx823weEhYWh0WiYPXu2+Zp79uxBo9GwZMkSyzxni5xVCFE6do7QfGD+/Yqizk5myszZl5YITfpm9boKR2NMQ5NyQ02+Hn0SqvvmlE1Pgl8ez3tOnSO41lTz+TS/F+56Ledah35S97t4gmsNtZyDTJ0t7sCFbTl51EDypwlRBrZs2cJzzz1Hhw4dyMzMZNq0afTv35/Q0FBcXQt+r96+fTsjR47k008/ZciQIVy5coXx48fzzDPPsGrVKis/g9LZff46M9Yc5+S1BABa1HLj7SGt6NK4ho1rJoQQQtwZDw8PQkJCALh48SL33XcfiqLwf//3fwDs2rWL2rVrs3LlSmbPns3777/P+++/T1hYGA8++CD79+8HICQkhHHjxrF+/Xp8fX1JSUlh2bJlAMydO5fQ0FBCQ0NxcHDgo48+YvTo0axZswaApk2bsmbNGnPg69dff6VNmza3VrXMSFBKiIpEo8mfe8fNG55coa6bTCjJMSRcu4CbJgVNcgwY6uWUzUgGnw6QFA1JMZCeCMY0iL+sLrXb5pRNS4DfJ+Svg52zGqjyGwYD3lf3KQrs/CwreOWZ89PVU4JYAs78C8ueAL03jPlHHWoqhCi1devW5dn+9ttv8fLy4sCBA/Ts2bPAY3bv3k3Dhg154YUXAPD19WXcuHF5vhG9VVpaGmlpaebt+Ph4QP22VrFAb9vs8xZ27iuxKQSvPclfRyMAMDjb81K/ZjzWsR52Oq1F6lTZ3K6NRelJG1uHtLPlVaU2zn6OiqKgmEzqZ6eyZu9y21nYs+tRv359Pv74Y6ZMmcLYsWMB+OWXXxg3bhw///wzu3fvplOnTvnqDjBnzhzeeOMNGjZsiKIoODk58dRTT6EoCnPmzGHHjh3Y29ujKApTpkzhyy+/5PTp09jb2+Pu7o6npyfnzp3D19eXzZs307t37yJfB3na7pa63I4EpYSoTLRacK2JydMBDIb8b3h6L3jm35zt9GRIjskJUum9ch4zpkPju7Mev66WMaapSdrjLqlBq2xp8bBhesF1snMG/4dh6GfqtqLAv2+Dc/WcHlrZPbFcsoJYt3mjFhXIybXw21Pq68m7FThXs3WNhKi04uLiAKhevfCJA7p27cq0adNYu3YtAwcOJCoqiuXLlzNo0KBCjwkODuadd94p8HqWCkolJiYCoMn1/yA1w8h3e67w7e4rpGaa0GrgwXa1mNCjPh7O9iQlJhR2SnGLwtpYlB1pY+uQdra8qtTG6enpmEwmjEYjxtQE7GbXL/NrZL4aXuCX9iaTybxuNBrN623btuXUqVMYjUYUReHPP/9k+vTppKWl8csvvxAUFGQ+RlEU87HHjx9n8uTJec4F6hdLKSkp+Pj45HmsTZs2HD9+nDZt2qAoCvfffz/Lli2jR48etGvXDp1OZ26bghiNRkwmEwkJCeYvsrK/xLodCUoJUZU5uIBDfXVY1a1cPdWcP9kURe1ZlRSjLrlzAZmM4P9oVgAra0mOgcxUNYiV+x9YalzePFe3anW/mmMr+5q/P6f2DnOuDg56tc72Luq6wQe8/XKOTbkJ9q5q3i5he8dXw4ox6nBTv2HwwDdqvjQhRJnL/qaze/futG7dutByXbt25ccff+SRRx4hNTWVzMxMhg4dyv/+979Cj5k6dSpTpuTkPYyPj6devXoYDAbc3d3L9HlAzjerBoMBjUaDoiisPx7Je3+d4EpsCgAdfavz9hA//GqX/fWrglvbWJQ9aWPrkHa2vKrUxqmpqVy/fh2dTofOQikJdTod6HSFP5brJ4BWqzXv27p1K35+ftSoUYMHH3yQjh078sknn6DRaNDpdOaf2ezs7PJsZ5/v1nKgBhy1Wq35PMOHDzd/efXII4+wbt068+OF1V2r1eLm5oaTk5P5nMUhQSkhRPFoNODopi6581SBGjS6f2HefYqi5rBKigbdLUGiLhNzAldJ0Xl7Yjl75JRLjYVDPxZep9wBLJMRPmyormvt1OCUg2tOEKvRXdD/3Zxj/35dDZA4ZJWzdzH/1GkNYMg19CUxCuyc1Me1Bb8Ri1sc/Q1WjQfFCG0eguFfgk7+5QhhKRMnTuTIkSNs3769yHKhoaG88MILTJ8+nQEDBhAREcErr7zC+PHj+eabbwo8xtHREUdHx3z7NRqNxT6gZJ/70o0U3lh1lO1nYwCobXDijXtbMti/dqX/cGRp2W0s7Wg50sbWIe1seVWljbOfn0ajQePgCm9cLftrFDB879Zex7nb+fDhw7Ro0QKNRsNvv/3G7t278fVVP4tFRkaya9cuunXrlqfuAH5+fhw6dIi2bdvmObeHhwdOTk5cvHiRhg0bAmovrcOHD+Pn52c+3t3dHS8vL/7++2/mzJnD+vXri3wN5Gm7W+pyO+XyE8L8+fP56KOPiIiIoFWrVsydO5cePXoUWHb79u289tprnDx5kuTkZBo0aMC4ceOYPHmylWsthMhDowFHvbrk5uyRk4sqt+yeWKZcXUK1durshEkxai+o9CR1bHd6krpUb5RTNveYb1MmpMWpS7bcZU0m2LOg4GoDTg16QfNcQanP2ql1g5zgVHbQq17HnKGJAOunqdfPDnLlDnq51YaG3XLKxkeoz1Gru+WnHWi0FXYYozY6FFb+H6BAwBMw9H8SzBPCgp5//nnWrFnD1q1b8fHxKbJscHAw3bp145VXXgHA398fV1dXevTowXvvvUft2rWtUeXbMikKS3aGMXvdKVIyjDjYaRnfsxHj72qMi0O5vH0VQghRmWg0Ns+Ne+nSJV5++WUmTpyIyWRi9erVnDp1iho11Ak9Fi1axK+//kq3bt3yHfvSSy/x2GOP0atXLxo2bEhqaipLlixh/PjxTJ48mcmTJ7Ns2TIcHByYM2cOrVq1omnTpoSFhZnPMW3aNMLDwwvtHVVWyt1/9WXLljFp0iTmz59Pt27dWLhwIQMHDiQ0NJT69fMPMXJ1dWXixInmm6rt27czbtw4XF1dzRnqhRAVQHZPrNwc3aB7MQPMjm7wVkz+wFX2eu4E8YoJer2Wv0xGMkp6EsYazXLeHBVFHYaYLTM1aztr+vRbk3YfXKrm2CqIT4e8Ob2+vhvirxRc1rsNPJurx8OXPSA2PH8QS6NTA265h1qu/D+4GZYT3MoOdGl16rDMYV/klN3yEcRml80dHNOpQyTvej2n7NHlan1zB86y13UO0PYRAEzVm6r5yZrfC4M+UXOdCSHKnKIoPP/886xatYrNmzebvzktSnJyMnZ2eW//sm82y0MSW0VR2HXuOh+tO0HIZfW9tJNvdT58wJ+GnjJxhhBCiMotNjaWgIAA0tPTcXZ25tlnn2XMmDFs2rSJpk2bmgNSAMOGDePdd9/l008/zXee9u3bM2vWLIYOHUpmZiZ2dnZMmKBOYjV58mRiYmJo2bIlGo2GgIAAvv3223znCAoKMuessiSNUh7uQHLp1KkT7du3Z8GCnF4MLVu2ZPjw4QQHBxfrHPfffz+urq58//33xSofHx+PwWAgLi6uTHIjKIpCXFxclRh3awnSfqUj7Vc6BbZfdmAqPVntMZWRrK5nJKnfoNQNzDnB9k8hNT5PoMsc/KrZAgZ9nFP24+aQeK3gitQOgHFbcrbntlGDUgXxbA4T9+Zsf9EZok8UXNbdB6Ycz9n+qg9cOVBwWedq8FpYzvaSwRC2reCyOkd4Kyqn/ZLD0NRuW2F7e9mK/P2WjqXbr6zvF0prwoQJ/PTTT/z+++80b97cvN9gMODs7Ayo+aCuXLnC0qVLAViyZAljx47ls88+Mw/fmzRpElqtlj179hTrupZshwMXb/DAgl0AuDjomDqwBU90aoBWK38PZUneayxP2tg6pJ0tryq1cWpqKhcuXMDX19ecF8kashOUZ+dzqogKarvi3i+Uq55S6enpHDhwgNdffz3P/v79+7Nz585inSMkJISdO3fy3nvvFVrG0lMbV6VpMy1B2q90pP1Kp9D2s3NSF5cCZrXKXbbbpNtdIGf9pZPqtmJSh/yZMtUcTCYjoMlbduQadQY7U6b6uMmYc4ydY96yAz9UE8qbz5urrL1z3rIdnoEWg3MeN5c15j9v4z5gqJu3THadNTrI1XZKLf/8z1fclvz9lo6l26+8/V6yv8C766678uz/9ttvGTVqFAARERGEh+cEtEeNGkVCQgKff/45L730Eh4eHvTp04cPP/zQWtUuUvv61QhsUI3GNRx5vm9L6lV3sXWVhBBCCGFB5SooFRMTg9FoxNvbO89+b29vrl0rpDdBFh8fH6Kjo8nMzGTGjBk888wzhZa19NTGVWnaTEuQ9isdab/SKR/tlzXcLT1XTixtNfPuAsXlzp/VtvByt5ZteG/xy7Z5+rZly0f7VVzSfqVj6fYr7tTG1lKce5YlS5bk2/f888/z/PPPW6BGpafRaPj1/zqTkBCPweBs6+oIIYQQwsLKVVAq2603koqi3Pbmctu2bSQmJrJ7925ef/11mjRpwmOPPVZgWUtPbVyVps20BGm/0pH2Kx1pv9KR9isdab/SsXT7ye/EOmSonhBCCFsqbz2jK4LStFm5Ckp5enqi0+ny9YqKiorK13vqVtnJPdu0aUNkZCQzZswoNChljamNq8q0mZYi7Vc60n6lI+1XOtJ+pSPtVzqWbD/5nQghhBCVl729PRqNhujoaGrWrGm1//sVPaeUoihER0ej0Wiwt7cv8fHlKijl4OBAYGAgGzZs4L777jPv37BhA8OGDSv2eRRFyZMzSgghhBBCCCGEEKIwOp0OHx8fLl++TFhYmFWvbTKZ0FbgGas1Gg0+Pj7mGX1LolwFpQCmTJnCiBEjCAoKokuXLixatIjw8HDGjx8P5J9F5osvvqB+/fq0aNECgO3bt/Pxxx+X21wJQgghhBBCCCGEKH/0ej1NmzYlIyPDatdUFIWEhATc3NwqZE8pUHuZ3UlACsphUOqRRx7h+vXrzJw5k4iICFq3bs3atWtp0KABkH8WGZPJxNSpU7lw4QJ2dnY0btyYDz74gHHjxtnqKQghhBBCCCGEEKIC0ul0dxxguRPZI72cnJwqbFCqNMpdUApgwoQJTJgwocDHbp1FpjzPICOEEEIIIYQQQgghClYug1LWlp0pvqymelYUhfj4eElUe4ek/UpH2q90pP1KR9qvdKT9SsfS7Zd9n1DVZ+Up6/umgs4vfweWJW1sedLG1iHtbHnSxpZXWdu4uPdNEpQCEhISAKhXr56NayKEEEKI8i4hIQGDwWDratiM3DcJIYQQorhud9+kUar6132oeamuXr1aZonF4uPjqVevHpcuXcLd3b0Mali1SPuVjrRf6Uj7lY60X+lI+5WOpdsvOxFpnTp1KvQMOaVV1vdNt5K/A8uTNrY8aWPrkHa2PGljy6usbVzc+ybpKQVotVp8fHzK/Lzu7u6V6kVlbdJ+pSPtVzrSfqUj7Vc60n6lY8n2q8o9pLJZ6r7pVvJ3YHnSxpYnbWwd0s6WJ21seZWxjYtz31R1v+YTQgghhBBCCCGEEDYjQSkhhBBCCCGEEEIIYXUSlLIAR0dH3n77bRwdHW1dlQpJ2q90pP1KR9qvdKT9Skfar3Sk/SoH+T1anrSx5UkbW4e0s+VJG1teVW9jSXQuhBBCCCGEEEIIIaxOekoJIYQQQgghhBBCCKuToJQQQgghhBBCCCGEsDoJSgkhhBBCCCGEEEIIq5OglBBCCCGEEEIIIYSwOglK3aH58+fj6+uLk5MTgYGBbNu2rcjyW7ZsITAwECcnJxo1asSXX35ppZqWTyVpv5UrV9KvXz9q1qyJu7s7Xbp0Yf369VasbflT0tdfth07dmBnZ0dAQIBlK1jOlbT90tLSmDZtGg0aNMDR0ZHGjRuzePFiK9W2/Clp+/3444+0bdsWFxcXateuzejRo7l+/bqValt+bN26lSFDhlCnTh00Gg2rV6++7THyvyNHSdtP/ndUTHf6/00UT3BwMB06dMDNzQ0vLy+GDx/OqVOnbF2tSi04OBiNRsOkSZNsXZVK5cqVKzz55JPUqFEDFxcXAgICOHDggK2rVWlkZmby5ptv4uvri7OzM40aNWLmzJmYTCZbV61Cu929jKIozJgxgzp1/r+9ew+Kqvz/AP5erqIGCsRFENG8gIKGMqYCkaGY4i0ylUwlNCMlUUbwnjqTSjg6qZiOhtSkgKNimWkDgwHiFQm8AOMFECXxTop4Idjn94fD/lrhqy6y5yzL+zWzf/Dsc9j3+Qz4PH44e7YDzMzM8N577yE/P1+esBJiU6oRdu3ahTlz5mDx4sXIzc2Fj48Phg8fjqtXrzY4v6SkBCNGjICPjw9yc3OxaNEizJ49G3v37pU4uW7QtH6ZmZkYOnQoDh48iJycHAwePBijRo1Cbm6uxMl1g6b1q3P//n1MmTIFfn5+EiXVTY2p3/jx45GWloa4uDhcuHABiYmJcHFxkTC17tC0fllZWZgyZQqmTZuG/Px87N69G9nZ2Zg+fbrEyeVXVVWFPn36IDY29pXmc+1Qp2n9uHY0P41d3+jVZWRkYNasWThx4gRSU1NRU1MDf39/VFVVyR1NL2VnZ2Pr1q3o3bu33FH0SkVFBby8vGBsbIxDhw6hoKAAa9euRbt27eSOpje+/fZbbNmyBbGxsSgsLERMTAzWrFmDjRs3yh2tWXvZXiYmJgbr1q1DbGwssrOzYWdnh6FDh6KyslLipBITpLH+/fuL0NBQtTEXFxexYMGCBudHRUUJFxcXtbEvvvhCDBgwQGsZdZmm9WtIz549xYoVK5o6WrPQ2PpNmDBBLFmyRCxbtkz06dNHiwl1m6b1O3TokLCwsBB3796VIp7O07R+a9asEV26dFEb27Bhg3B0dNRaxuYAgNi3b98L53Dt+N9epX4NaclrR3PQFPsD0sytW7cEAJGRkSF3FL1TWVkpunXrJlJTU4Wvr68IDw+XO5LemD9/vvD29pY7hl4LCAgQISEhamOBgYHi008/lSmR/nl+L6NUKoWdnZ2Ijo5WjT158kRYWFiILVu2yJBQOrxSSkPV1dXIycmBv7+/2ri/vz+OHTvW4DHHjx+vN3/YsGE4ffo0/v33X61l1UWNqd/zlEolKisrYWlpqY2IOq2x9YuPj0dRURGWLVum7Yg6rTH1279/Pzw9PRETEwMHBwd0794d8+bNw+PHj6WIrFMaU79BgwahrKwMBw8ehBACN2/exJ49exAQECBF5GaNa0fTaslrR3PQFPsD0tz9+/cBgL8XWjBr1iwEBARgyJAhckfRO3V7s48//hg2Njbw8PDAtm3b5I6lV7y9vZGWloaLFy8CAM6cOYOsrCyMGDFC5mT6q6SkBDdu3FBbB01NTeHr66v366CR3AGamzt37qC2tha2trZq47a2trhx40aDx9y4caPB+TU1Nbhz5w7s7e21llfXNKZ+z1u7di2qqqowfvx4bUTUaY2p36VLl7BgwQIcOXIERkYt+1e+MfUrLi5GVlYWWrVqhX379uHOnTuYOXMm7t271+LuK9WY+g0aNAg7d+7EhAkT8OTJE9TU1GD06NG8/PsVcO1oWi157WgOmmJ/QJoRQiAiIgLe3t5wc3OTO45eSUpKwl9//YXs7Gy5o+il4uJibN68GREREVi0aBFOnTqF2bNnw9TUFFOmTJE7nl6YP38+7t+/DxcXFxgaGqK2thYrV65EUFCQ3NH0Vt1a19A6WFpaKkckybTs/6G+BoVCofa1EKLe2MvmNzTeUmhavzqJiYlYvnw5fv31V9jY2Ggrns571frV1tbik08+wYoVK9C9e3ep4uk8TX7+lEolFAoFdu7cCQsLCwDAunXrMG7cOGzatAlmZmZaz6trNKlfQUEBZs+eja+//hrDhg1DeXk5IiMjERoairi4OCniNmtcO5oG147mo7H7A9JcWFgYzp49i6ysLLmj6JVr164hPDwcKSkpaNWqldxx9JJSqYSnpydWrVoFAPDw8EB+fj42b97MplQT2bVrF3bs2IGEhAT06tULeXl5mDNnDjp06ICpU6fKHU+vtcR1kE0pDVlbW8PQ0LDeX+1u3bpVr6tZx87OrsH5RkZGsLKy0lpWXdSY+tXZtWsXpk2bht27d7fYS6E1rV9lZSVOnz6N3NxchIWFAXi2kAshYGRkhJSUFLz//vuSZNcFjfn5s7e3h4ODg6ohBQCurq4QQqCsrAzdunXTamZd0pj6rV69Gl5eXoiMjAQA9O7dG23atIGPjw+++eYbXu3zAlw7mgbXjubhdfYHpLmvvvoK+/fvR2ZmJhwdHeWOo1dycnJw69Yt9OvXTzVWW1uLzMxMxMbG4unTpzA0NJQxYfNnb2+Pnj17qo25urq22A8C0YbIyEgsWLAAEydOBAC4u7ujtLQUq1evZlNKS+zs7AA8u2Lqv/vjlrAO8p5SGjIxMUG/fv2QmpqqNp6amopBgwY1eMzAgQPrzU9JSYGnpyeMjY21llUXNaZ+wLO/cgcHByMhIaFF34tG0/qZm5vj3LlzyMvLUz1CQ0PRo0cP5OXl4Z133pEquk5ozM+fl5cXrl+/jocPH6rGLl68CAMDgxa3kW9M/R49egQDA/Wlpm4zXnfVDzWMa8fr49rRfDR2f0CaEUIgLCwMycnJOHz4MDp37ix3JL3j5+dXb+/l6emJSZMmIS8vjw2pJuDl5YULFy6ojV28eBGdOnWSKZH++V/7N6VSKVMi/de5c2fY2dmprYPV1dXIyMjQ/3VQhpurN3tJSUnC2NhYxMXFiYKCAjFnzhzRpk0bceXKFSGEEAsWLBCTJ09WzS8uLhatW7cWc+fOFQUFBSIuLk4YGxuLPXv2yHUKstK0fgkJCcLIyEhs2rRJlJeXqx7//POPXKcgK03r97yW/ul7mtavsrJSODo6inHjxon8/HyRkZEhunXrJqZPny7XKchK0/rFx8cLIyMj8f3334uioiKRlZUlPD09Rf/+/eU6BdlUVlaK3NxckZubKwCIdevWidzcXFFaWiqE4NrxMprWj2tH8/Oyf1/o9X355ZfCwsJCpKenq/1ePHr0SO5oeo2fvte0Tp06JYyMjMTKlSvFpUuXxM6dO0Xr1q3Fjh075I6mN6ZOnSocHBzEgQMHRElJiUhOThbW1tYiKipK7mjN2sv2MtHR0cLCwkIkJyeLc+fOiaCgIGFvby8ePHggc3LtYlOqkTZt2iQ6deokTExMRN++fdU+Snfq1KnC19dXbX56errw8PAQJiYmwtnZWWzevFnixLpFk/r5+voKAPUeU6dOlT64jtD05++/WnpTSgjN61dYWCiGDBkizMzMhKOjo4iIiGjRG3hN67dhwwbRs2dPYWZmJuzt7cWkSZNEWVmZxKnl9+eff77w3zKuHS+maf24djRPL/r3hV5fQ78TAER8fLzc0fQam1JN77fffhNubm7C1NRUuLi4iK1bt8odSa88ePBAhIeHCycnJ9GqVSvRpUsXsXjxYvH06VO5ozVrL9vLKJVKsWzZMmFnZydMTU3Fu+++K86dOydvaAkohOD7J4iIiIiIiIiISFq8pxQREREREREREUmOTSkiIiIiIiIiIpIcm1JERERERERERCQ5NqWIiIiIiIiIiEhybEoREREREREREZHk2JQiIiIiIiIiIiLJsSlFRERERERERESSY1OKiIiIiIiIiIgkx6YUEdFrWr58Od5++225YxARERFpRO49zNKlSzFjxoxXmjtv3jzMnj1by4mISGpsShGRXgoODoZCoUBoaGi952bOnAmFQoHg4GDpgxERERFJQKFQvPARHByMefPmIS0tTZZ8N2/exPr167Fo0aJXmh8VFYX4+HiUlJRoORkRSYlNKSLSWx07dkRSUhIeP36sGnvy5AkSExPh5OQkYzIiIiIi7SovL1c9vvvuO5ibm6uNrV+/Hm3btoWVlZUs+eLi4jBw4EA4Ozu/0nwbGxv4+/tjy5Yt2g1GRJJiU4qI9Fbfvn3h5OSE5ORk1VhycjI6duwIDw8P1dgff/wBb29vtGvXDlZWVhg5ciSKiorUvldZWRkmTpwIS0tLtGnTBp6enjh58qTanJ9//hnOzs6wsLDAxIkTUVlZqd0TJCIiIvof7OzsVA8LCwsoFIp6Y8+/fS84OBhjx47FqlWrYGtri3bt2mHFihWoqalBZGQkLC0t4ejoiO3bt6u91t9//40JEyagffv2sLKywpgxY3DlypUX5ktKSsLo0aPVxvbs2QN3d3eYmZnBysoKQ4YMQVVVler50aNHIzEx8bVrQ0S6g00pItJrn332GeLj41Vfb9++HSEhIWpzqqqqEBERgezsbKSlpcHAwAAffvghlEolAODhw4fw9fXF9evXsX//fpw5cwZRUVGq5wGgqKgIv/zyCw4cOIADBw4gIyMD0dHR0pwkERERURM5fPgwrl+/jszMTKxbtw7Lly/HyJEj0b59e5w8eRKhoaEIDQ3FtWvXAACPHj3C4MGD0bZtW2RmZiIrKwtt27bFBx98gOrq6gZfo6KiAufPn4enp6dqrLy8HEFBQQgJCUFhYSHS09MRGBgIIYRqTv/+/XHt2jWUlpZqtwhEJBkjuQMQEWnT5MmTsXDhQly5cgUKhQJHjx5FUlIS0tPTVXM++ugjtWPi4uJgY2ODgoICuLm5ISEhAbdv30Z2djYsLS0BAF27dlU7RqlU4scff8Qbb7yhet20tDSsXLlSuydIRERE1IQsLS2xYcMGGBgYoEePHoiJicGjR49U935auHAhoqOjcfToUUycOBFJSUkwMDDADz/8AIVCAQCIj49Hu3btkJ6eDn9//3qvUVpaCiEEOnTooBorLy9HTU0NAgMD0alTJwCAu7u72nEODg4AgCtXrqjmEFHzxqYUEek1a2trBAQE4KeffoIQAgEBAbC2tlabU1RUhKVLl+LEiRO4c+eO6gqoq1evws3NDXl5efDw8FA1pBri7OysakgBgL29PW7duqWdkyIiIiLSkl69esHA4P/fUGNraws3NzfV14aGhrCyslLtc3JycnD58mW1fRDw7D6ez98OoU7d/T5btWqlGuvTpw/8/Pzg7u6OYcOGwd/fH+PGjUP79u1Vc8zMzAA8uzqLiPQDm1JEpPdCQkIQFhYGANi0aVO950eNGoWOHTti27Zt6NChA5RKJdzc3FSXnNdtgF7E2NhY7WuFQqH29j4iIiKi5qChPc2L9jlKpRL9+vXDzp07632vN998s8HXqPsDYUVFhWqOoaEhUlNTcezYMaSkpGDjxo1YvHgxTp48ic6dOwMA7t2798LvS0TND+8pRUR6r+6eBtXV1Rg2bJjac3fv3kVhYSGWLFkCPz8/uLq6oqKiQm1O7969kZeXp9oIEREREdEzffv2xaVLl2BjY4OuXbuqPSwsLBo85q233oK5uTkKCgrUxhUKBby8vLBixQrk5ubCxMQE+/btUz1//vx5GBsbo1evXlo9JyKSDptSRKT3DA0NUVhYiMLCQhgaGqo9V/cpMVu3bsXly5dx+PBhREREqM0JCgqCnZ0dxo4di6NHj6K4uBh79+7F8ePHpTwNIiIiIp0zadIkWFtbY8yYMThy5AhKSkqQkZGB8PBwlJWVNXiMgYEBhgwZgqysLNXYyZMnsWrVKpw+fRpXr15FcnIybt++DVdXV9WcI0eOwMfH55WuYiei5oFNKSJqEczNzWFubl5v3MDAAElJScjJyYGbmxvmzp2LNWvWqM0xMTFBSkoKbGxsMGLECLi7uyM6Orpeg4uIiIiopWndujUyMzPh5OSEwMBAuLq6IiQkBI8fP25w71VnxowZSEpKUr0N0NzcHJmZmRgxYgS6d++OJUuWYO3atRg+fLjqmMTERHz++edaPyciko5C/PczNomIiIiIiIi0TAiBAQMGYM6cOQgKCnrp/N9//x2RkZE4e/YsjIx4a2QifcErpYiIiIiIiEhSCoUCW7duRU1NzSvNr6qqQnx8PBtSRHqGV0oREREREREREZHkeKUUERERERERERFJjk0pIiIiIiIiIiKSHJtSREREREREREQkOTaliIiIiIiIiIhIcmxKERERERERERGR5NiUIiIiIiIiIiIiybEpRUREREREREREkmNTioiIiIiIiIiIJMemFBERERERERERSe7/AAJQiJvwpyECAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 1200x600 with 4 Axes>"
       ]
@@ -983,7 +1234,7 @@
     },
     {
      "data": {
-      "image/png": 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27fHjx2zevBkTE5NknzJlygC8VS4HIYQQQmS9du3aYWdnp5+2tnXrVh4/fpwswXl6f+/nz58/Q3V6+vQpoEuA/iparZZGjRrx+++/M3ToUHbv3s2JEyc4duwYoEufkF5lypShUqVK+rbQaDQsW7aMVq1a4eDgAOjaAmDIkCEp2qNv377Aq5+DXj5rpdY+7u7uaLVawsLCCAsLQ6PRvPb+30avXr2wsLBgzpw5gC5FhIWFBb169crU6wiRWxm/uYgQQrxbFi5ciKIorF27lrVr16bY/+uvvzJ69GjUarXB6mBubg5AfHw8ZmZm+u3/fWB6GXgKCQmhQIEC+u1JSUn6h6iXnJycKFeuHD/99FOq13R3d8+UugshhBDCsCwsLOjcuTO//PILjx49YuHChdjY2OgXXIH0/97P6Kibl6ODHjx4gIeHR6plLl26xPnz51m8eDHdu3fXb79582aGrt2zZ0/69u3L1atXuX37No8ePaJnz576/U5OTgB88803vPfee6meo0SJEqluf/ms9ejRoxT7Hj58iJGREfny5UOlUqFWq3nw4EGG7uW/7Ozs6N69O/Pnz2fIkCEsWrSILl26YG9vn6nXESK3kqCUECJH0Wg0/Prrr3h7ezN//vwU+7ds2cKkSZPYtm0bLVq0SLH/ZQAprW/6zMzMUi37cpW+Cxcu6Fe+Adi8eXOyci9X61u+fDn+/v767atXr06xol6LFi3YunUr3t7e5MuXL031E0IIIcS7qXfv3syZM4cJEyawdetWevTogaWlpX5/ZvzeT89zTaNGjVCr1cyePZtq1aqlWuZl4OvfL9wA5s6d+1b1e6lz58588cUXLF68mNu3b1OgQAEaNWqk31+iRAmKFSvG+fPn9YvSpFWJEiUoUKAAK1asYMiQIfp7ePHiBevWrdOvyAdQu3Zt1qxZw08//aQPhKXFv9vZwsIixf4BAwYwa9Ys2rVrR3h4OJ999lm67kGIvEyCUkKIHGXbtm08fPiQcePG6QM+/1a2bFlmzJjBggULUg1KlS1bFoB58+ZhY2ODubk5Xl5eKabSveTj48O+ffvYvHkz+fPnx8bGhhIlStCsWTMcHBzo3bs3o0aNwtjYmMWLF3P//v1kx5cqVYquXbsydepUTExMaNCgAZcuXWLixInY2tomKztq1CiCgoIICAhgwIABlChRgri4OIKDg9m6dStz5szJ9CHnQgghhDCMihUrUq5cOaZOnYqiKMmm7kHm/N739vbGwsKC5cuXU6pUKaytrXF3d091dHXhwoX59ttv+fHHH4mNjaVz587Y2dlx5coVnj17xsiRIylZsiTe3t58/fXXKIqCg4MDmzdvJigoKENtYW9vT5s2bVi8eDHh4eEMGTIkWf4n0AW+mjZtSuPGjenRowcFChQgNDSUq1evcubMmVeusGxkZMT48eN5//33adGiBX369CE+Pp4JEyYQHh7O//73P33ZyZMnU6NGDapUqcLXX39N0aJFefz4MZs2bWLu3LnY2Nikeg0fHx8Axo0bR9OmTVGr1ZQrVw5TU1MAihcvTpMmTdi2bRs1atSgfPnyGWovIfKU7M2zLoQQ6dO6dWvF1NT0taumdOrUSTE2NlZCQkJSrL6nKIoydepUxcvLS1Gr1clWrElt9b1z584p1atXVywtLRVAqV27tn7fiRMnlICAAMXKykopUKCAMnz4cGX+/PkpVqKJj49XBg8erLi4uCjm5uZK1apVlaNHjyqenp4pVvZ7+vSpMmDAAMXLy0sxMTFRHBwcFH9/f2XYsGFKdHT0W7SYEEIIIbLLtGnTFEApXbp0qvvT8nv/5ep7EyZMSPUcK1euVEqWLKmYmJgke+551UrBS5YsUSpVqqSYm5sr1tbWip+fX7LV+65cuaI0bNhQsbGxUfLly6e0b99euXfvXopnqrSuvvfSzp07FSDZCoT/df78eaVDhw6Ki4uLYmJiori5uSn16tXTr7asKKmvjqcoirJhwwalSpUqirm5uWJlZaXUr19fOXz4cIprXLlyRWnfvr3i6OiomJqaKoUKFVJ69OihxMXFvfL88fHxyocffqg4OzsrKpUq1ftevHixAiirVq1KU3sIIXRUiqIoWR8KE0IIIYQQQgghcoeXK/0FBwdnaJVEIfIamb4nhBBCCCGEEEKkU3x8PGfOnOHEiROsX7+eyZMnS0BKiHSSkVJCCCGEEEIIIUQ6BQcH4+Xlha2tLV26dGHGjBkGXf1ZiNxIglJCCCGEEEIIIYQQIssZvbmIEEIIIYQQQgghhBCZS4JSQgghhBBCCCGEECLLSaLzNNJqtTx8+BAbGxtUKlV2V0cIIYQQ2URRFKKionB3d8fISN7v/Zs8LwkhhBAC0v68JEGpNHr48CEeHh7ZXQ0hhBBCvCPu379PwYIFs7sa7xR5XhJCCCHEv73peUmCUmlkY2MD6BrU1tY2U8+tKAoRERHY2dnJW0UDkTY2LGlfw5L2NSxpX8PLbW0cGRmJh4eH/tlA/EOel3I2aWPDkvY1LGlfw5L2Nbzc1sZpfV6SoFQavfxS2NraGuQhS1EUbG1tc8WX710kbWxY0r6GJe1rWNK+hpdb2zg33UtmkeelnE3a2LCkfQ1L2tewpH0NL7e28ZvuRRIhCCGEEEIIIYQQQogsJyOlMoFGoyExMfGtj1cUhYSEBOLi4nJVRPRNTExMUKvV2V0NIYQQQgghhBB5XEb79RmVk+MCGenbS1Aqg6Kjo3nw4AGKomToPFqtlufPn2dSrXIGlUpFwYIFsba2zu6qCCGEEEIIIYTIozKrX59ROTUukJG+vQSlMkCj0fDgwQMsLS1xdnZ+62imoihoNBrUanWOi4i+LUVRePr0KQ8ePKBYsWIyYkoIIYQQQgghRJbLrH59RuXUuEBG+/YSlMqAxMREFEXB2dkZCwuLtz5PTv3yZZSzszPBwcEkJiZKUEoIIcRbURQlT/3uFEIIIUTmyqx+fUbl5LhARvr2kug8E+S0L8y7QtpNCCFERhw4cIAKFSpw7Nix7K6KENkqPDycr776ihkzZjB37lxCQkJSlElMTGT27NmcOXOGHTt2oNFosqGmQgjx7pL+6dvLSNtJUEoIIYQQOUpiYiKDBg2idu3anDt3juHDh2d3lYTIFoqi8OWXX5I/f37Gjx9P//79+eSTT2jcuDEJCQnJys6YMYO+ffvi7+9PkyZNGDBgwCvPu379ej7++GMePXqU7flVhBBC5G4SlMrhjI2N8fX1pUyZMrRs2ZLw8PB0n2PEiBHMmDEjTWWDg4NZvXq1/s/79u3D3t4eX19fSpUqxciRI9N9fSGEECKtoqKiCAwMZOrUqQB8/PHHLF++PHsrJUQ22b59OxMnTiQuLi7Z9gsXLuDt7c3ly5dRFIWZM2fyxRdfJCsza9YsWrVqRUxMjH7bvn378Pb2pm3btvzyyy+4u7tTrlw5oqKisuR+hBAirzI2NsbPz4/y5csTGBiYp/r1EpTK4ezt7Tl37hyXL1/G3t6emTNnGvR6//3yAjRo0IBz585x6tQpli1bxunTpw1aByGEENlLo9EQExPD06dPuXXrFufOnePq1aupjqjQarU8fPiQBw8e8PDhQx4/fsyzZ88ICwsjKioqTVOIEhISuHz5MsuXLycgIIDt27djaWnJ+vXrmTt3Lk5OToa4TSHeaSdPnqR3794AfPDBByQmJnLz5k3Wrl2LhYUFDx48oGzZspiamvLZZ58BuukVixYtol+/fgBs2rSJpUuXAhAXF0f37t0JDQ1Ndp1Lly7xwQcfpNguhBAi89jb23P27FnOnz+f5/r1kug8kyiKQmzi283N/yehWcpkrRYmaU9yVr16dc6fPw/A7du36dWrF6GhoRQuXJjFixfj4ODAtWvX+OSTTwgLC8PMzIzdu3cnO8fYsWO5du0aixYtYuzYsWzYsIH4+Hj69u3LJ598wrBhw7h8+TK+vr58/vnneHl56Y+1srLC39+fW7du8fnnn7Nw4UKKFy+OoiiULFmS48ePY29v/1ZtJIQQInNpNBpCQ0N59uyZ/hMaGsqLFy+Iiori+fPnPH/+nNDQ0GT/HR4eTlJSUqrnrFu3LuvXr8fOzo6kpCTGjBnD1KlTCQsLe21drKyssLKyQqvVotVq0Wg0+p8ajYbExES0Wq2+vIuLC1u2bKFSpUqZ2iZC5BR//fUX3bt359GjR5QsWZIpU6ZgbGyMt7c33t7eVKlShfLlyxMaGprs7+u8efPo0aMHPXr0wNHRkVGjRvHJJ59w+PBh1q1bpx819e2339KvXz82bdrEp59+yoYNGwgNDWX//v3ZdctCCJElMtKvf5O09u0DAgK4cOECkDf69RKUyiSxiRpK/7Aj0897ZVRjLE3f/L9Jo9EQFBREr169ABgwYAB9+/alQ4cOjBs3jhEjRvDzzz/TtWtXxo4dS8OGDYmKikq2usCYMWO4fv06ixYtYufOnTx58oSTJ0+SkJBAjRo1aNGiBT/99BMzZsxg7dq1gG6Y30vPnz/n2LFjfP/99/To0YMlS5YwevRo9u7di6+vrwSkhBDCgBRFITo6mocPH3L37l0ePHiQLOD078/z588JCwvLlFwx1tbWWFtbExISwt69e5kyZQojRoygb9++/PLLLwCo1WrUarU+yPRfL1684MWLF6+9jo2NDaVLl6Z69ep8/fXXODs7Z7juQuQk0dHR9OvXjytXrnDq1CkALCwsOHLkCPny5UtWtmDBgmzbto0//viD58+f4+rqynfffZesM9S3b1/GjBlDUlKSfrSUWq1m4cKFdOvWDZVKxSeffIKDgwMdO3bkwIEDXL16lVKlSmXdTQshRBYzVL8e0ta312g07Nq1K0/16yUolcOFh4fj6+vLgwcPKFOmDI0bNwZ0Q7o3b94MQLdu3WjevDmRkZGEh4fTsGFDQPeA/9K8efMoWbIkq1atwsjIiKCgIDZv3qx/IxYREcGtW7dSrcOuXbvw8/PDyMiIb775hjJlylCoUCGqVKnCjz/+yK+//kr37t0N2QxCCJGrRUdH8+jRI0JCQvRBp3v37hESEkJISIh+379zw6RVvnz5cHR0xMnJCQcHB2xsbLC2tsbR0RFHR0ccHBz0/+3o6Ii9vT3m5uaYmZlhbm6OsbHuUWLOnDl8+umnbNy4kSZNmvDLL79gZGTE3Llz+eCDD4iJicHOzg6VSqUfBZWUlMSLFy+IjIwkOjoaIyMjfQDr3/9tamqKq6urrIoj8rR58+axZMmSZNu6deuWIiD1UuXKlalcufIrz+fq6sqqVauYO3cuQUFBFClShJMnT2JklDy7R4cOHVi6dClbtmxh7ty5+nxuQgghMk94eDh+fn55sl8vQalMYmGi5sqoxm917D/T91IO57MwUb/22Jc5pWJiYmjYsCGzZs1iwIAByc6jKP9MC3zVA3358uU5f/48T548wc3NDUVRGDFiBB988EGycv+OoL7UoEEDfYT1JRsbG/z9/dm6dSuHDx9m4cKFr70PIYTIqxRF4fnz5wQHB3P37l3u3LnDrVu3uHXrFsHBwTx69Ijo6Og0n8/GxgZPT088PDxwdnbGyckJJycnfeDp3x8HBwd9UCmj2rVrR79+/Th37hzDhg0DoGvXrnz44YcpRmQZGRlhZGSEiYkJFhYWkhNKiDdITExk3rx5ybZ99NFHjBgxIkPnbdu2LW3btuXu3bs4OjpiZWVFREREinIffvghW7ZsYdq0aVSpUoXOnTtn6LpCCPGuyki/Pi3nfpWXOaWioqJo2rRpnurXS1Aqk6hUqjRNs0uNLiilSjUolVaWlpZMmzaNtm3b0rdvXypWrMi6deto164dK1asoGbNmtja2pIvXz6CgoJSDPOrUqUKXbt2pWXLlgQFBdGgQQPGjRtH+/btsbCw4Pr163h6emJjY5PmFVh69OhBt27d6NKlC2r164NrQgiRm8XHx3Pr1i1u375NcHCwPuj0MvCUlhFOlpaW5M+fn/z58+Pp6UmhQoVwd3fHzc2N/Pnz4+bmhqurK9bW1llwRyk5OTlRq1Yt9u3bx549ewDo06dPttRFiNxEq9XSrFkzrl+/DsDXX39Nt27dKF26dKZdw9PTE+CVU3oDAwMZOHAgU6dOZfbs2RKUEkLkWhnp12cGS0tLpk6dSrt27fJMv16CUrlIxYoV8fHxYd26dfz888/07NmTUaNG4enpya+//grA0qVL+fjjjxkyZAjm5ubJEqI1btyYsLAwWrVqxfbt27l06RKVK1dGURRcXFzYvHkz5cqVIzExMdWEaP9Vr149NBpNiqisEELkRomJidy+fZvbt29z584dbty4wY0bN7h+/TrBwcHJEnWnxs3NDS8vLzw9PfXJir28vHB3dyd//vxYW1u/89PXBg0apH/zVqtWLapV8oeYUEiIxij0MbxQQ8ILSIzR/fz3fyfGQFIcaBIhKV73UxMPmoR/bUv4Z7tWA4oCivbVn+oDoNKH2dsoQmTQrl272LVrF5aWlqxZs4ZmzZpleR1UKpU+KHX48GGeP3+Oo6NjltdDCCHygrzWr1cpmZHl9C3Nnj2b2bNnExwcDECZMmX44YcfaNq0KaB7WzNy5EjmzZtHWFgYVapUYebMmZQpU0Z/jvj4eIYMGcLKlSuJjY2lfv36zJo1i4IFC+rLhIWFMWDAADZt2gTo3vZMnz49XQm6IiMjsbOzIyIiAltbW0C3dO6dO3fw8vLC3Nz8rdvhddP3crKbN2/SpUsXTpw4ker+zGq/tFAUhYiICH0+E5G5pH0NS9rXsNLbvtHR0Vy7do1r165x9epVrl69yrVr17h58yaJiYmvPM7W1hZvb+9kQSdvb28KFy5MoUKFDP7v4FtTFIiLgJjn8OIpvHgGsWG6bXHhf/+MgNhwlNhwao49yoW/YjjWx4HS+RKyt+71f4CagzP9tKk9EwgdQ7ZNXv23sE2bNmzYsIEBAwYwbdo0g17rTW1crlw5Ll68yIIFC/RJeEXa5dXvcFaR9jWs3Ny+WdkvfZ13NS7wpn49pN6GaX0myNaRUgULFuR///sfRYsWBeDXX3+lVatWnD17ljJlyjB+/HgmT57M4sWLKV68OKNHj6Zhw4Zcv35dn8xr4MCBbN68mVWrVuHo6MjgwYNp0aIFp0+f1g8t69KlCw8ePGD79u0AfPzxx3Tr1k2fMExkvvnz5zNq1Cj9yktCCJHTREREcO7cOX3g6WXw6f79+688xsrKSh94KlasGCVKlNB/3qlE3YqiCzJFPYKoEIh8CC+ewIu/A08xz3T/HfNMF4TSvjrY9m8qYHdnFXFJVtiZ/xOQUtSmKMYWqMxsUJlagoklmFrDf//b2AzUZqA2RVGboDUyRaMyQaM2IQljNCoTklQmaFUmaFXGaFUqFHQfLUYoKhWO1hZYm5uCyghsCxioAYXIGs+ePWPLli3AuzEdtnPnzly8eJHvvvuOtm3bYmdnl91VEkIIYUBZ0a/P1pFSqXFwcGDChAn06tULd3d3Bg4cyFdffQXoRkW5uroybtw4+vTpQ0REBM7OzixdupSOHTsC8PDhQzw8PNi6dSuNGzfm6tWrlC5dmmPHjlGlShUAjh07RrVq1bh27RolSpRItR7x8fHEx8fr/xwZGYmHhwfh4eHJRkoFBwdTuHDhDEdUX0ZE85LMbL83yc2R/XeBtK9hSfsaVlJSEufOnSM4OJhLly5x6dIlLl68yJ9//vnKY1xcXChVqhQlS5akZMmSlCpVilKlSlGgQIEUK1dlOU0iRP4FEfd1waaXQafoEIh8pAtERT9GpUnfKCbF1BqsnMDSCSwdwNwOzO1IMLElTm1NjJENL1SWRGJFhGJFuNaSMK0FkRpzopNURLyIQYMxsYkaYhM0xCRqiPv7Z2yChvgkDYkahSSNlkSNQoLm9dMdX2V6Zz9alMv/VsemVWRkJPb29jJSKhUyUipzzZs3jz59+uDn58eZM2cMfr03tXFcXBw+Pj7cvHmTX375hQ8/lOmx6ZEXv8NZSdrXsHJz+8pIqYzLsSOl/k2j0bBmzRpevHhBtWrVuHPnDiEhITRq1EhfxszMjNq1a3PkyBH69OnD6dOnSUxMTFbG3d2dsmXLcuTIERo3bszRo0exs7PTB6QAqlatip2dHUeOHHllUGrs2LGMHDkyxfaIiAh9EsiEhAT9stYajSZD9/+mXCO5kUajQavVEhUVlSwAaAiKouhXr8ppf8FzAmlfw5L2zTyxsbFcvXqVS5cucfbsWc6fP8+1a9eIjY1NtbyHhwelSpWiePHiyT6vWoI9rQkjMyQpDqOoRxhFPsAo6oHuZ+RfGEX9hVHkA1TRIaiUtP1O0Vo6obVyQbFyJcnSmVhje6LV9kSobAnFjqeKDY811jxMsOZZvBFhMYlERCUR/SyJqHgN0fFJaFN9tZUIRPz9yTzGRiqMjVQYGYGRSoUK3U8jFfD3z6T42FRXD8tMkZGRBj2/EC+9XAXp5ctXg4v8C+NbRyD2IcSG6oLcxuZgXwicimHuWpYOHTowZswYjh49KkEpIYQQGZbtQamLFy9SrVo14uLisLa2Zv369ZQuXZojR44A4Orqmqy8q6srd+/eBSAkJARTU9MUnQNXV1dCQkL0ZVxcXFJc18XFRV8mNd988w1ffPGF/s8vR0rZ2dklGyn1/Plz1Gp1poxyymsjpdRqNUZGRtjY2GTJSCkgV0b23wXSvoYl7ft2Xk6/O3v2LKdOneLMmTPcuHEj1ZcA5ubmlClTBh8fH8qWLYuPjw9+fn44OTllQ83RJf4OuwPPb0HoHQi9BaG3IfQ2qqhHbzxcUZuBXUGwK0CSpRvRpk6EGzvxFAceae24m2hPcJw1D6M1PI6M59nTeKLikl5VGSD0tdczNlJha2GCrbkxNubG2JqbYGNujLW5MRYmxliYGGGkTcLexhJLU2MsTNW6j4kaS1M15iZqzIyNMFUbYWJshLGRChO1ESbqlz+N/g5GvRvff/l7KLKCVqvl+PHjADRp0sRwF4p+CmeXwMW1qJ5c4bXrd6qMqBpRCIBjhw8Yrk5CCCHyjGwPSpUoUYJz584RHh7OunXr6N69O/v379fv/++Dn6Iob3wY/G+Z1Mq/6TxmZmaYmZml2K5SqfTH/ftnRh5Q/z2DMi896GZW+6Xnell1rbxI2tewpH1f78WLF1y8eJFTp05x7Ngxjh07xq1bt1It6+TkRPny5fH396dixYqUK1cOJycnHBwcsrZ9NYm6gNPTa/D85t+Bp78DUdGvfmkC6PIw2RdCY1uQFxYFCDNx5SFO3NU4cSPOnuvRFjyKTODJ7Xii41MLNsX+/UlOpYJ8lqY4WJni8PfPfFamOP7rp52lCXZ/B6B0wScTzE2MXtt2uW3If264B/Huu337NpGRkZiZmVG6dOnMv0BsGBycBMfn6Va0BDQYcUPx4Lq2AM8UOxIwwYJ4vIyeUNL4IW7ax1S1uA3Ales3CZ/ZEPtafaB0K1CbZH4dhRBC5HrZHpQyNTXVJzqvWLEiJ0+eZNq0afo8UiEhIeTP/09uiCdPnuhHT7m5uZGQkEBYWFiy0VJPnjwhICBAX+bx48cprvv06dMUo7CEEEK8+7RaLdevX+fw4cMcOXKEY8eOcf369VRHQBUqVAg/Pz8qVqxIhQoV8PX1JX/+/MmCCi8DJgaTGAfP/4Sn13WfZ3//fH7r9QnELfKhOHgTY12Ip6YFeaDKz80kZ67GOXI90pS/wuN4ej+1qc9xf3/+YWWqxtXWHGcbM1xszXG1McPF1uyfbTZmOFiZYWdhgvodGY0kRF539uxZQLfinYlJJgd8buxA2dQfVbTuGfmctgjLNQ0I0vgTjg0qFdiYGaNSqYiKS0SbBCSAG8+paXqRgg4zeRAay8agQ3R/egJ2fg+VPwL/Hrqcc0IIIUQaZXtQ6r8URSE+Ph4vLy/c3NwICgrCz88P0OVw2r9/P+PGjQPA398fExMTgoKC6NChAwCPHj3i0qVLjB8/HoBq1aoRERHBiRMnqFy5MgDHjx8nIiJCH7jKyYyNjfHx8SE+Ph4LCws++eQTPvroI/1+RVHw9PSkU6dOjB8/nmHDhvHHH3+QkJBAcHAwxYsXB2DKlCmEh4czYsQIkpKSUKlU9O3bl759+6LVavn+++/57bffAKhQoQKzZ8/G0dGR4OBgvLy8GDduHEOHDgV07Vu1alUWLVpEjx49srxNhBC5y/Pnz9m1axdnz57l7NmznDx5krCwsBTl3Nzc8PPzo2rVqlStWhV/f38cHR2zrqKaJN1op5CL8PgyPLmqC0CFBcOrcjyZWKF1Kk60jRdPTQpyT+XGjQRnzr1w4Gq4mgd3YklKkbQp+SgncxMjCthb4G5vQcF8FrjbWVAgnwX57SxwtdUFoazN3rlf90KIN3iZ2LxChQqZd1KtFvb8CIcmowJuat0ZnfQ+h1V+1CnhyrelXCjuYExpT1dMjXVpJTRahb/CYjn/IJzTd8MIulKQaJ+nsP9XvjlqQaCvNfmiHsLukbB/PPh2hiqfgHPqeVuFEEKklJf79dn6lPrtt9/StGlTPDw8iIqKYtWqVezbt4/t27ejUqkYOHAgY8aMoVixYhQrVowxY8ZgaWlJly5dAF1+ld69ezN48GAcHR1xcHBgyJAh+Pj40KBBAwBKlSpFkyZN+Oijj5g7dy4AH3/8MS1atHhlkvOcxN7eXv8m7e7du7Rp0wZFUfj4448BOHr0KPnz5+f3339n/Pjx/PTTT/z0008EBwfTrl07Tp06BejexvXp04cdO3bg5eVFbGys/ss6depUrly5wpUrVzA1NWXChAn07NmTTZs2AVCsWDE2bdqk//KuXr0aHx+frG4KIUQuoNVquXbtGocOHeLQoUMcOXKE27dv89+FYi0sLKhcuTLVq1enWrVqVKxYETc3t6yraGyYLvAUcgke/ysIlRSXenlzOxIdihNmVYQHxoW4lpSfky9cORVqzoPgeFKug/vPCChTYyM8HSzxdLTEw8GSgvksKWBvTgF7SwrksyCfpYlMJ8uhgoODOXjwIMHBwcTExODs7Iyfnx/VqlXL1tV/xLvhZT6pTAtKJbyA9X3g6mYAFiU1ZrLyPp2qF2NczSK42prrR46aqP9ZRVRtpKKQoyWFHC1pWd6d4S1Lc6h5EepV/I1HT8Lwvfc97dxC6GO2neJJwXBqoe5TtAFU/RS86+vmBgshhHgle3t7zpw5g0aj4cGDB7z33nt5pl+frUGpx48f061bNx49eoSdnR3lypVj+/btNGzYEIChQ4cSGxtL3759CQsLo0qVKuzcuRMbGxv9OaZMmYKxsTEdOnQgNjaW+vXrs3jx4mRJw5cvX86AAQP0q/QFBgYyY8aMzL0ZRYHEmLc/VqMBtTrlL20TyzT/Ivf09GTSpEkMGjRI/+X97bff+OSTT1ixYgXHjx9Ptgrhv02aNIlhw4bh5eUF6Dp8L6OhkyZN4siRI5iamgIwePBg5syZw59//omJiQm2trY4OTlx+/ZtvLy82LdvH/Xq1XuLhhBC5DXx8fGcOnWKw4cPc+jQIQ4fPkxoaMqk2uXKlSMgIABfX1/8/f0pX7585k9neZXIR/DwrO4TckEXiIp8kGpRxcSKRKeSPLMsSrC6MJeT8nMiypnTz00JvZ3aVD1d8MnazBhPR0sKO1pRyNGSwo6WFHKworCTJa425u9Mgm+ROVasWMHPP//MiRMncHFxoUCBAlhYWBAaGsqtW7cwNzfn/fff56uvvsLT0zO7qyuyQXx8PEePHgWgZs2aGT9hwguUpe+hun+MBEXN14kf8bBwa/5oW55CjpbpOpVKpaKmT2Hq163Njh078Ld4zn7LhqyLrklVo6t8ZLKdeqrTqG7ugpu7wKmELjhVriOYpu9aQgiR5TLSr3+TNPbt81q/PluDUgsWLHjtfpVKxYgRIxgxYsQry5ibmzN9+nSmT5/+yjIODg4sW7bsbauZNokxMMb9rQ5V8Zr/Ed8+BFOrNJ+rQoUKXL9+HdAN8du8eTMjR44kMTGR33777ZVf3itXrvDll1+m2B4ZGUlsbGyyh2IjIyPKlSvH1atXKVeuHADt27dn9erV1K5dG39/f4yNZaqIECIlrVbLhQsX2LdvHzt37mTfvn3ExiZPuG1hYUHVqlWpUaMG1atXp0KFCjg7O2dNBaOf/B2AOvdPIOoVSce1doWItCvOA1NvLmk8OBKdn/1PrYi4k9pUPV1AqoC9BUVdrPF2tqaoi+5TxNkKRytTGe2UR1SoUAEjIyN69OjB6tWrKVSoULL9L4MRq1atomLFisyaNYv27dtnU21Fdjl58iRxcXG4uLhQsmTJjJ0sMRbtik4Y3T9GhGLJhwlDqFa3BRMaFM9QDrm6deuyY8cOlIeXOTxtJBvPPWTBQVt6Py5NIdVjeqp30NlkP+bPrsOWgbrpff49dbmnbN/umVkIIQwuA/36N0pH3z4v9eslcpDL/HuKy8GDByldujT29va89957VKpUiUmTJqVrNcK0rHYIutFnTZo04fHjx3To0IHt27dn7EaEELmCRqPRB6H27dvHwYMHU+SDcnZ2pkaNGvqPn59f1oyCiouEv07Bg9NY3jsJTy9B5F8piikqIxIdSvDEphQ3jLw5HefOvnAXrjxRoaRYR0OL2khFEScrfdDpZRCqiLMVlqbyazev+/HHH2nevPkr95uZmVGnTh3q1KnD6NGjuXPnThbWTrwr9u3bB0CtWrUyFrDWJKGs7o5R8AGiFXN6a76hW8e2tPItkOE61qlTB4D9+/djrIIOFT1o71+QA38+Y/7B24z805XJSe1or95PH/MgXGND4NBkOPIzlGmjGz1VwD/D9RBCiNwoL/Xr5ek4s5hY6iKfb0FRFDQaDWq1OuUXxSR9w5zPnTunf6O2evVqjh07RuHChQHddMkjR45QvXr1FMeVLl2as2fP6iOkL9nZ2WFubk5wcLD+PFqtlvPnz1OqVCl9ORsbG1xcXNi6dSsTJ06UoJQQeZRGo+HcuXPs27eP/fv3c+DAgRQr21lbW1OzZk3q1q1L06ZNKVOmjOFHCSkKPPsTHpyA+yfgwUldDigUVIDpy2KoSMxXlCc2pbiq8uZwjAfbnrrw+C+jVE/rbGNGSTcbSuW3paSbDSXcbCjqYo2ZsTrV8kK8LiD1X05OTjg5ORmwNuJdtW7dOgB96om3pez4FtWfO4hVTPk46Us+7tKBRmUyJ/+ev78/+fLlIywsjOPHjxMQEIBKpaJ2cWdqF3fmWkgk8w/eYek5Kxa/aExDo9N8ar4TX+1luLhG9/GoogtOlWwJamOOHDnCqVOn6NOnD2ZmZplSTyGESJcM9OvTdO40ykv9eglKZRaVKl3T7JJ5XU6pdLh//z5Dhgzhs88+Q6vVsmHDBq5fv65ffWrevHmsXr061S/v4MGD6dy5M7Vr16Zw4cLExcWxePFiPvnkEwYNGsSgQYP47bffMDU1ZdKkSZQpU4ZixYoRHBysP8ewYcO4d+9esnxeQojc7/Hjx2zbto0tW7awa9euFEEoGxsbatasqR/94efnZ/ihwPFR8NdpuH/yn0BUXHiKYom2hXhiW5aL2sKcSCjC1mfOhDwygUfJy5moVRR3taF0fltK/isA5WQtnSbx9s6cOYOJiYk+iejGjRtZtGgRpUuXZsSIEfqcDyJvuXHjBufOncPY2Jj33nvv7U90ZimqE7pFfgYl9eP9ju9nWkAKdCtFNW7cmFWrVvHHH3+kWNW6pJstE9uXZ2jjEiw5epdlx83YEVOJMqo79DHbQXPVEdT3jxN35xi/B9sy6aSKMzd0+fp+/PFHvvjiC7755ptMq68QQqRJRvr1mSSv9eslKJXDhYeH4+vrS0JCAhYWFnz66af07t2bvXv3UqxYsWTLobdq1Yoff/yRKVOmpDhPhQoVGDNmDIGBgSQlJWFsbEzfvn0BGDRoEM+ePaNUqVKoVCp8fX1ZtGhRinNUrFiRihUrGu5mhRDvhLCwMPbv38+ePXvYu3cvly5dSrbf1taWWrVqUbt2berUqYOvr6/hg1AvnsHdI39/DulWw1OS53ZSjM2JzOfDn2alOBJfhI3P3Ln1xBqeJD+VqdqIkvltKFvADp+/P8VdbTA1Tn20lBBvq0+fPnz99df4+Phw+/ZtOnXqRJs2bVizZg0xMTFMnTo1u6sossGSJUsAaNCgQbLnuHR5eh3NH0NQA5MS21ErsBfNy+XPvEr+rXnz5qxatYotW7bw008/pVrGxdacIY1L0LeuN+vO/MWCg5b0f1KQXjsiMX54ntDwSBI1UcmOefbsGd9++y2tW7dO9gZfCCFyq/DwcPz8/PJkv16l/HedbZGqyMhI7OzsiIiIwNbWFoC4uDju3LmDl5dXhpZufu30vVwss9ovLV4ucWxnZ5en2jirSPsaVna3r6IoXLp0ic2bN7NlyxaOHTvGf391+Pv707x5c5o3b46/v7/h36xEPoK7h//+HIGn11IUSbQuyEMbH85RnKDIQuwMdSZBSR4cMzU20o16craggpezBKAMJLu/w5kttWeCt2FnZ8eZM2fw9vZm3Lhx7Nmzhx07dnD48GE6derE/fv3M7HWWSOz2iY1ue17lJr4+Hg8PDx4+vQpa9eupW3btuk/SWIcsXPqYvH8Cgc0PuytNJvhgWlb0ju9bfzkyRNcXV0BePr06Runm2q1Wjp27MTatWuSbTc2MaZnZXuGVojj6QstAQt1K19N+uZTvvhpZoZmErxL8sJ3ODtJ+xpWbm7frOyXvk5Ojguk1oZpfSaQkVJCCCFSeP78Obt27SIoKIigoCDu3buXbH/JkiWpV68e9erVo3bt2obPexN+H4IP6UZB3T0CobdTFHlhX5w/zctxMKEYvz/35M4zW3iWvEwhB0v8Ctnj52GPX6F8lMpvi4lalWsfssS7TVEUtFrdiL5du3bRokULADw8PHj27NnrDhW51MaNG3n69CkFCxakVatWb3WOxJ3DsXh+hWeKLWsKfceU5mUyuZb/cHFxoUyZMly+fJkDBw68drrh8ePH6dWrF1euXNFvcy9YiNJNuhLsEsBOjUKU0RU+ddzBpEZHGLwzjhmz59Db8SR2dfuDT3swsTDYvQghhMgeEpQSQgiBoihcuXKFLVu2sHnzZo4eParvLAOYm5tTv359WrZsSfPmzSlYsKBhKxQTCsEH4fZ+uL0PQm8lr6/KiCj7Ulwz9WF3bFF+f+bB0xCbZGUsTdWUL2ivC0IVyoevhz3ONilzQMmAYZFdKlasyOjRo2nQoAH79+9n9uzZANy5c0c/+kTkLatWrQKgW7dubzft+d4xTE7OAeAnk/6M6lIPY7VhR37WqVOHy5cvs3fv3mRBKUVRmDx5Mu7u7iQlJdGvXz+ionTT9Lp160a3bt1o2LAhAKEvElh54h5Lj1rwQWQZXArXw0j1PXfCFSr8dJqv9/WhfcXvsa/5EVT6EGwyLzeWEEKI7CVBKSGEyKPi4uLYs2cPGzduZNu2bSmmCpUtW5ZGjRrRoEEDatWqhZWVAZM+JsbCvWO6ANSd/fDwHPBPsEhRqQmzL8NlEx92RBdlY6gHUbHJVzBxtzOnkpcDlQo74O+Zj+KuNqiNZOSTeHdNnTqV999/nw0bNjBs2DCKFi0KwNq1a1MkjRa5X2RkJFu3bgWgc+fO6T9BUgIv1vXDCvhNU4f23XqRz8rwyfLr16/PzJkzWbFiBT/88APOzs48e/aMuXPn8t133yUrW7RoUdatW5diVSgHK1P61S3Kx7WKsONyCIsO5yO4zTCebRrP7bAEPt4Sx8db7jGg8o8MrzsZh8rtdav2ufsa/P6EEEIYlgSlhBAiDwkPD2fr1q1s2LCBbdu2ER0drd9nZmZG3bp19aOhPD09DVcRrRZCzsOtPbrRUPeOgSY+WZEom6JcNPPlj+jibAovkiII5e1sReW/g1CVvRwomC/ty+wKkZ1u3LhB8eLFKVeuHBcvXkyxf8KECbKSbR60Y8cO4uPjKVGiBGXLlk338YmHpmEVcZNnii3Bfl/T0dvA06r/1rJlS8qXL8/58+fp06cPAQEBDBs2jISEhGTlOnfuzMSJE3F3d3/luUzURrQo506Lcu5caFmaIc5qguaO1O//+UQC4fEKv5qvgguroFCALjhVsjkYyd8ZIYTIiSQoJYQQudyDBw/YuHEjGzduZO/evSQlJen3FShQgFatWtGiRQtq166NpaUBAzsxobog1M1dcHM3vEi+7F2chSvXLf3ZGVuKNaFFeBKXT7/PSAVl3W2pVNiBKl4OVCzsgJN1yql4QuQEfn5+FCpUiMDAQFq3bk21atWS7c/OJKsi+/zxxx8AtGjRIv357UJvw/4JAEw37smXzStldvVeydjYmGnTplGnTh3Wr1/P+vXr9fvUajWlSpWiTZs2jBo1Kl3nLVfQnp1zRnCgVyAT569i8y+6+1tyPpFbiU7sbhuL2b0jcO8I2BeCyn2gQjcwt8vU+xNCCGFYEpQSQohc5mV+qA0bNrBhwwZOnTqVbH+ZMmVo1aoVrVu3xt/fHyMjA+Ub0Wrh0TldEOrPIPjrFCj/5KnSGFtxx9afvYllWfO8CDfi8kPYPx2xEq42BBR1JMDbiSpFHLA1NzFMPYXIYs+fPycoKIiNGzfSpk0bFEWhRYsWtGrVikaNGklQKg/SarVs27YNgGbNmqX7+Kgt32GjxHNQU5aAdp9ibZa1j/i1atWiePHi3LhxA9AlQF+7di3ly5fP8CqMtSpXoFblCsTNGEPPAV+xau5kDl95REF1LfpUz8cAp1O4hN+DncNg7xjuujVm9J5I8nuXwc/Pj+rVq+Ps7CwLWQghxDtKglI5nLGxMWXLliUxMZEiRYqwdOlS7O3tDX7d4OBg2rVrl6Kz+zpOTk6ympAQBqIoCmfOnGHVqlVs2LCBmzdv6vepVCoCAgJo3bo1rVq1olixYoarSGyYbhTUzV26z4unyXaHWRfluLoCq8JKcDi6GInR//wa8nCwoLq3EwFFnahWxDHVpORC5Abm5ua0bNmSli1boigKR48eZdOmTXz99dd07tyZBg0a6Ecwuri4ZHd1RRa4evUqT548wdLSkho1aqTv4AensLn9B1pFxW7PzxlRNr9hKvkaKpWKRYsW8fXXX/PTTz9Rs2bNTL+GuakxK+dMopCdMePHj+fZxQP8dBF+Asr7+jCgTCS/HnrAgbuL/z7i92THb926laZNm2Z6vYQQIjPk5X69BKUyiaIoxMTEvPWxGo0GtVqd4i2OpaXla9/s2Nvbc+7cOUC3ksnMmTMZNmzYW9XjdV7WTwjx7lAUhZMnT7JkyRK2bdvG7du39fvMzMxo0KABrVu3pmXLloZdySv0DlzfBte3wt0joGj0u5KMrfjTyp+tcWVZG1GKR3GO+n1O1qYEeDtR/e/RUB4OkhNK5D0vg8YBAQH873//488//2TTpk0sXryYTz/9lMmTJ9OvX7/srqYwsGPHjgFQuXJlTE3TkZxcUYjY9C12wO/aWvRo3dwwFUyDgIAADhw4YPDrfP7558yaNStZTsTz5y7S+9zrj/vh268kKCWEeKOM9Ovf5HV9e3t7e86ePYtGo6Fnz555ql8vQalMEhMTg7W1daafNzo6Os0rXlWvXp3z588D8PjxY/r06cODBw8wNzdn/vz5lCxZkocPH/Lxxx9z//591Go1a9aswd3dnY8//phz585hYWHBvHnz8PX1ZcSIEYSEhHDz5k1Kly7NZ599RpcuXVCr1cnegEVHR9O3b1+uXr2KoihMmzaN6tWr8/jxYzp16kRUVBRNmjTJ9LYRIi/SarUcPXqUtWvX8vvvv3Pv3j39PnNzcwIDA2nfvj2NGzfGxsbGUJWAh2fh+h+6YNSTK8l2h1p5c9SoAqvCS3Is7p/RUGojFZU981G7uDO1iztTOr8tRrI6nhDJFCtWjMGDBzN48GCeP39OaGjoG48ZO3Ysv//+O9euXcPCwoKAgADGjRtHiRIl9GUURWHkyJHMmzePsLAwqlSpwsyZMylTpoy+THx8PEOGDGHlypXExsZSv359Zs2aRcGCBQ1yr+IfR48eBaBq1arpOk57fQd2T44Tr5hwv/zntHMy4Cqp7wh3d3fOnz9PbGwsK1eu5NatW+zfvx8za1tU+QqR4Nee/PaW1Hy+jkYcZ8mxJ6y8lMSpcxfZ+VUNGvUaBsUaSWJ0IUSqDNWvh7T37QMCArhw4QKQN/r1EpTKJTQaDUFBQfTq1QuAgQMH8v333+Pv78/JkycZOHAg27dvZ8CAAbRq1YqPPvqI+Ph4NBoNM2fOxMbGhosXL3Ls2DG6d++uD25dvHiRvXv3YmpqSrNmzfj222957733GDp0qP7ao0ePpk2bNixZsoQHDx7QvHlzzp8/z8iRI2nZsiVffPEFs2bNypZ2ESI30Gq1HD58mK1bt7J+/XoePXqk32dlZUVgYCCdO3emdu3aGc7d8UqJcXBnv2401PXtEB2i36Wo1ARbl2dznB9rostyP+6fUVnudubULuFC7eLOBBR1lLxQQvzHX3/9xeHDh3ny5Ala7T8511QqFf3798fR0fE1R+vs37+ffv36UalSJZKSkhg2bBiNGjXiypUr+off8ePHM3nyZBYvXkzx4sUZPXo0DRs25Pr16/oA9sCBA9m8eTOrVq3C0dGRwYMH06JFC06fPv3OvVXNbV6OlEpXUEpRiNr6A3bAcpryQZN0TvvLwYoUKQLonkH/635oDCtO3OO3ky5sfNGDui3PEWg6m01nQugw7TBXkjrgXtAT/HtAhQ/AWqbICiHeHRqNhl27duWpfr1KURTlbQ+Oj4/HzCxv5PyIjIzEzs6OiIgIfacvLi6OO3fu4OXlhZmZWbZM33s59/TBgweUKVOG3bt3Y2xsjKurK/nzJ88pcO7cOdzc3Hj48GGyxMatW7dm6NChBAQEAFC8eHFOnjzJlClTsLS01H9RXV1dCQkJQaVSceHCBXr16sWpU6fw9/cnMTFRf85nz55x+/ZtKleuzNatW3F3dycyMpIiRYokm3v67/YzdFJXRVGIiIjAzs5OEl0agLSvYVy7do1ly5axdOnSZCOibG1tCQwMpF27djRq1AgLCwvDVCA+Cv7cCVc26hKVJ/7zb1yisRUXzCuxJtqHrXE+RKJ7o2SqNqJKEQdqF3emTglnvJ2t3/nvhHx/DS+3tXFqzwRvY9GiRXzyySeYmpri6OiYrG1UKlWyKbnp8fTpU1xcXNi/fz+1atVCURTc3d0ZOHAgX331FaB7hnN1dWXcuHH06dOHiIgInJ2dWbp0KR07dgTg4cOHeHh4sHXrVho3bpyma2dW26Qmt32PXtq/fz916tRBrVbz8OHDNOcRS7qyBePV7xOtmLOq+h982KhihuuSm9o4PknDtoshLD12l5M3QwhdNpAXT+7zY0Nbvgv4u5CRCZQOhEofQqFqYOB7zk3t+y6S9jWs3Ny+mdWvf5PX9e1zcr8eUu/bp/WZIF0jpXbs2MHKlSs5ePAg9+7dQ6vVYmlpSYUKFWjUqBE9e/bE3d09PafMNVQqVZqn2f3X64JSb/Iyp1RMTAwNGzZk1qxZDBgwACDVN5tpOb+iKPpy/14e/lXHKorCli1bKFSo0CvPI4RImwcPHvDbb7+xfPlyzp49q99uY2ND27Ztad++PfXr1zfcC4G4CN1IqCsb4dZuSIrT74o2c+WwuhIrInw4GleShGjdqCdHK1Pal3ShfilXahZzwiqLV30SIqf64Ycf+OGHH/jmm28ydRXMiIgIABwcHAC4c+cOISEhNGrUSF/GzMyM2rVrc+TIEfr06cPp06dJTExMVsbd3Z2yZcty5MiRVwal4uPjiY+P1/85MjIS0D0DZOC9Z6penjOzz5tdwsLCCAoKYtSoUQB8+OGHODs7p+3+FIXIHT/hAKwxakrn2uUzpV1yUxubqo1o5etOK193rjyM5IuoD9j5y0+MvuTC84pt6W6yC1/tn3BpHVxah+JSWjd6qlwHMLc3SJ1yU/u+i6R9DSs3t+/Le3r58999YENd679e5pSKioqiadOmzJw5U9+vP3XqVLJ+/ct+dmr/P/697d8/LSws9H9+eex/yyiKwubNm1Pt17/q3Okp8ypp6jls2LCBr776ioiICJo1a8aXX35JgQIFsLCwIDQ0lEuXLrFr1y5+/PFHevTowY8//oizs3OaKiAyh6WlJdOmTaNt27b07duX2rVrM3fuXPr27YtWq+Xy5cv4+PhQs2ZNFixYkGyYX40aNVixYgUBAQGcOHECKyurVCOZFSpUYOPGjbRu3ZoVK1botzdo0ICZM2cybtw4AM6fP0/58uWpXr06v/32GwMHDkxWXgiR3OPHj1mzZg2//fYbhw4d0m83NjamcePGdOvWjVq1auHm5maYQG9MqC431JWNcHsvaBL0u0LNC7JNW5WVUb5civMCdNcv4WpD/VK6QJSvhz1qyQ0lRLrFxMTQqVOnTA1IKYrCF198QY0aNShbtiwAISG66bb/XfDA1dWVu3fv6suYmpqSL1++FGVeHp+asWPHMnLkyBTbIyIiDBKUepncOqe/9IqOjqZp06ZcunQJ0L14GDx4sD6g+CaqW7txiLhCjGJGYsUPSYx9QURsxuuVm9r43wpYwfQhH1BqwVjiH90k6L4Z6wuMpIwqmPfVQbxnfATzJ1dg21CUnd+TWLwF8T6d0eSvmKmjp3Jr+74rpH0NKze3b0JCAlqtFo1Gg0ajefMBBqLRaDA3N2fSpEl07NiRPn36UKtWLWbPns2nn36arF9fo0YNfvnlFz788EN9vz4gIIDly5dTpUoVTpw4gaWlJVZWViiKor8/AD8/P9avX0+rVq1Yvny5fpBMvXr1mDFjBmPHjgX+6ddXq1aNlStX8vnnn7Ns2TJ9Xf9bd61WS1RUlP5l1csXVW+SpqDUmDFjmDhxIs2bN0/1walDhw6ALi/CtGnTWLJkCYMHD05TBUTmqVixIj4+Pqxbt47p06fzySefMGfOHJKSkvjggw/w8fFh2rRpfPjhh0yfPh0TExPWrFlDv379+OijjyhXrhzm5uYsWrQo1fNPnTqVLl26MHbs2GRvTH/44Qf69++Pj48PGo2G+vXrM336dIYPH06nTp1YunQpbdu2zapmECJHSExMZOvWrSxcuJA//vgj2T/sNWrUoEuXLrRv3x4nJyf9cOlMFRMKVzfrAlF39oM2Sb/rqbknWxIr81uMP9fiPAAVxkYqang7Ur+UCw1KucpKeUJkgt69e7NmzRq+/vrrTDvnZ599xoULF5IFuF/6byciLSOa31Tmm2++4YsvvtD/OTIyEg8PD+zs7AwyfQ/IFVNHpkyZog9IAXz99dcULVo0bQcrCqEHpwKw1qgx7zesgqVp5oxQzU1t/F92dnZ89tln/Pzzz9z5fSIbD51n640CjLxYhLFx79NGfZD3jfdSQnMP06vrML26DsW5JFToDuU7gUW+N1/kDXJz+74LpH0NKze3b1xcHM+fP0etVmdrDsWX165SpQo+Pj5s2LCB6dOn8+mnn/LLL7+QlJREt27d8PX1Zdq0aXz00UfMmjULExMTVq9ezWeffcbHH39MhQoV9P36l7OxjIyM9OefOnUq77//PuPHj6dRo0aoVCrUajXDhw9nwIAB+Pn56YNU06dPZ8SIEXTu3JkVK1bw3nvvJavrv+tuZGSEjY2NfvpeWr8nGcoplZe8KadURnIiZWT6Xk4mOaVyD2nftLt8+TKLFi1i6dKlPHnyRL+9cuXKdOrUifbt26dY6SrT2jc+Wpeo/OJa3dS8fwWiQsy92ZhQkTWx/txUdNe3MFFTt6QzTcrmp04J51ybpFy+v4aX29o4s/ImaTQaWrRoQWxsLD4+PpiYJP87Nnny5HSdr3///mzYsIEDBw7g5eWl33779m28vb05c+YMfn5++u2tWrXC3t6eX3/9lT179lC/fn1CQ0OTjZYqX748rVu3TnU0VGokp9SbKYpC0aJFuX37NiNHjqRo0aJ07NgxzR2hpFsHMF7akjjFhLU1t9G1QaVMrVtuaONX0Wg0+Pn5cfHiRcaNG8fQoUMJj0lg/dm/WHH8Hn8+icJXdYvO6j20Mj6KOX9PTVWbQelWuul9ngFvPXoqt7dvdpP2Nazc3L5Z2S99nZwcF8iynFJCCCHSLzw8nFWrVrFo0SJOnDih3+7q6soHH3xAz549KVWqlGEunhQPN3fDxTW6KXpJ/8zveGhelHXxlVgf58/tOF0+QBtzY9qUcqVJWTdqF3fG3ERW3BLCUMaMGcOOHTsoUaIEkPyNYnoeRhVFoX///qxfv559+/YlC0gBeHl54ebmRlBQkD4olZCQwP79+/VT7/39/TExMSEoKEg/Av7Ro0dcunSJ8ePHZ+g+RXInTpzg9u3bWFlZMXjw4HTnJH28cxIFgC1GdXmvlt8by4t/qNVq+vfvz8cff8xXX31FdHQ0JUuWpGPr1vQIKMyZe2EsP16QHy4UZ3RcV1qpD9PFeC+lNcFwcbXu41hMF5wq3xms3rw6phBCiNdLd1BKURTWrl3L3r17UyxfDPD7779nWuWEECKn0mq17N27l4ULF/L7778TF6dLGG5sbEyLFi3o1asXTZo0STEyInMuroHgQ3BprW56Xtw/U/+emRZkbUIV1sRX5VZcAQAcrEzpVNqVxmXdqO7thKlx5uW3EUK82uTJk1m4cCE9evTI0Hn69evHihUr2LhxIzY2NvocUHZ2dlhYWKBSqRg4cCBjxoyhWLFiFCtWjDFjxmBpaUmXLl30ZXv37s3gwYNxdHTEwcGBIUOG4OPjQ4MGDTJ6q+Jf1q9fD0BgYGC6A1KaJzco8HgfANqqfTNt2l5e0rVrV1avXq3PhwvQqVMnVq5cib+nA/6eDgxvUYb1Zx+w4oQLyx43wEd1h87q3bQxPorF8z9h5zDYPRJKtdQFqArXNPjKfUIIkVul+zfZ559/zrx586hbty6urq45bliZIcgMyLcj7SZyo+DgYBYvXszixYv1CYQBypQpQ69evejatWual/tOF0WBR+fgwmq49DtE/5OYONLEic1JVVkVV5WLfycrd7Yx44OybjQp60blwg4YqyUQJURWMzMzo3r16hk+z+zZswGoU6dOsu2LFi3SB7yGDh1KbGwsffv2JSwsjCpVqrBz505sbGz05adMmYKxsTEdOnQgNjaW+vXrs3jx4mzNr5EbbdmyBdAFpdLrwbaJeAL78adF3ZqZXLO8wcLCgp07d/LVV18xYcIEAFatWsWmTZvYvn07AQEB2Fma0KO6F93/Hj214rgHIy8U5ae4rrRUH+V94z34aG7rV+7DwRv8u0P5LmAtiz0JkVNJ//TtZaTt0p1TysHBgWXLltGsWbO3vmhOlNp8SI1Gw59//omlpSXOzs5vHaDLyXNH35aiKDx9+pSYmBiKFStm8Afe3DwH+l2Q19s3JiaG9evXs3DhQvbs2aPfbmdnR+fOnenVqxcVK1bM0L8Rr2zfqBC48BucWwlPr+o3x6pt2UkVVsZW4YS2JFqMsLc0oWnZ/LQsn58qXo6yYt7f8vr3NyvktjbOrLxJY8eO5dGjR/z888+ZWLvsJTmlXu9lfi+1Ws3Tp09TrHb4OsqLZyRMKIUZCawuO4cO7Tpnev1yQxunlaIoXL58mcmTJydb5MfR0ZHKlSvTvn17evbsqd/+ODSK30/dYcmOE9yLhnx3d9G7aBjvWZ7F0/Lv3FNGxlCiKfh1A+/6oDZOcc280r7ZQdrXsHJz+2ZWvz6jcmpc4FV9e4PllLKzs6NIkSJvX+NcRK1WU7BgQR48eEBwcHCGzqXVajN1SeicQKVSUbBgQXkDK3IkRVE4ceIEixYtYuXKlfolT1UqFfXr16dnz560adMGCwuLzL94Yixc+wPOr4Rbe0DRTaNOMjLjgFEllsdU4YC2PIkYY21mTOvSrrT0dadGUSdMZESUEO+MEydOsGfPHrZs2UKZMmVSTOeVlAi5z4gRIwCoV69eugJSAPd3TqcQCVxSilC/cRsD1C5vUalUlC1bll9++YX+/fvTo0cPLly4wPPnz9m2bRvbtm1j/PjxJCQk8OTJE6Kjo5Md/xD4Ygd8ATi7OFPJJYlVLRKxubpZt7qttRv4dtYFqBy9s+UehRBpk5n9+ozKqXGBjPTt0x2UGjFiBCNHjmThwoWG6WzlMNbW1hQrVozExMS3PoeiKERFRWFjY5OjIqIZZWJiIgEpkeM8fvyYpUuXsmjRIq5cuaLfXrhwYXr27En37t3x9PTM/AsrCtw7pgtEXd4A8f/kibpqUppfYwLYqqlCJFaYGRvRsIwLgeXdqVPCRZKVC/GOsre31y+tLHK/W7dusXTpUgB++umn9B2cGIfdxcUAXC3cjbI22bc6VG6jVqvx8/Pj9OnThIeHs2/fPiZOnMjx48e5du1ams7x9MlTtj4Bl3v5sFDiiImJpV+lOCZETcbo0BQoFAB+XaF0+qdsCiGyRmb06zMqJ8cFMtK3T3dQqn379qxcuRIXFxcKFy6c4q3emTNn3qoiOZlarc5QcEVRFOLj4zE3N89xXz4h8orTp08zbdo0Vq1apf9lZWFhQdu2benVqxe1a9c2zFuNiAdwdjk251agCg/Wb35u7MKq+OqsSapBcFx+1EYqapZworVvARqUdsXaTJLfCvGu+/eUIZH7vcwlVbduXSpVqpSuYx8eWoK7NpyHigNVmvc2RPXyPGNjY5ycnGjXrh1t2rRh48aNXL9+naNHj+Lp6UmBAgXw9fUlMTGRNWvW4Ovry4sXL/D39+fQoUNMmDCBuMgw4v4+3+RjCUw+lsBnlU1xsdzL4FuHsbAaikXxFlC5F3hUluToQrxjMtqvz6i8GhdId6+lR48enD59mq5du0qicyFErhYVFcXixYtZsGAB58+f12+vXLkyvXv3pmPHjtjZ2WX+hTWJcGM7nP4Vbu5ChYIaiDeyYIe2MisTanAsrhQKRpTOb8t3FQoQ6OuOi7w5F0KId9Yff/wBQIsWLdJ3oKKgOjYLgGPOHXjPxQC/d0QyarX6taMYW7ZsmezPzZo1o1u3bty4cQMXVzcWrN7Mgim60XAzTiQAMPVEEp/6x1PzyjKqnFqJfcESutFT5TuDtQEWQBFCiBwi3UGpP/74gx07dlCjRg1D1EcIIbLd+fPnmTNnDsuWLdPnkDA2NqZjx458/vnn6X7DnWbPb8GZJXBuBbx4ot98Tl2WpbE12KatTAzmONuY8ZFfAdr4FaBU/sxNJCyEMKwmTZrwww8/EBAQ8NpyUVFRzJo1C2tra/r165dFtROGcurUKfbt2weQ7sWCnp3fRv74O0Qr5hRv2tcAtROZoVSpUpQqVQqAalWr8P3nH1HWx4foqCgAQmM0/HRQAyRQ2lnN0d7XsX32A+waCcWb6AJUxRqlSI4uhBC5Xbr/1fPw8Mj01VSEECK7aTQaNm3axNSpUzlw4IB+e4kSJRgwYAAdO3bE0dEx8y+cGAfXtsDpxRB8UL85wsieVQk1WampQ7CSH3NjIxr7uPGef0GqeztiLAnLhciR2rdvT4cOHbCxsSEwMJCKFSvi7u6Oubk5YWFhXLlyhUOHDrF161ZatGihX7Je5Gw9e/YkMTGRVq1aUaJEiXQdG7FnCk7AAeumNPM2QM5CYRCenp5s37aN1atXU6dOHbYfOM6Wbdt5eP08V55q8JxrRL8AOwLzP6ey8gdc/wOsXaF8J/DtCs7Fs/sWhBAiS6gURVHSc8Aff/zB9OnTmTNnDoULFzZQtd49ssRxziZtbFg5uX0fPXrEggULmDdvHvfv3wf+Gbbft29fateubZh7enJVNyrq/EqIDQNAQcURVXmWxNdht7YCSRhT2cuB9v4FqeZhQQEXxxzXvjlBTv7+5hS5rY0z+kyQkJDA2rVr+e233zh48CDh4eGAbuWa0qVL07hxYz766KN0By/eBfK8lNLLNgHdYhkuLmmfqhV97xzWC2ujUVScCNxNNX9/Q1UTyLltnFMoisKOXXvo3Lkz4c+f6rf371SH4cVv4aj6ZxETClTUrd5X5j2wdMiG2uY88v01LGlfw8ttbZzWZ4J0j5Tq2rUrMTExeHt7Y2lpmSLReWhoaPprK4QQWezSpUtMnz6dhQsXkpSUBICjoyMff/wxffv2pWDBgpl/0aQEuLoJTs6He0f1m58aObM8oSark+rwECecbcz4sEJBOlQsSBFna/0vKCFE7mBqakqXLl3o0qULABEREcTGxuLo6JjiuUrkfC9XcHNzc0tXQArgr62TKAEcMgmgVoUKBqidyGpVK/lz784tJkyYwI8//gjA9FX7WFy+Ic2rl6a3wznqqs+j/usU/HUKtn8DJZpC+S5QtD6o5d8IIUTuku6g1NSpUw1QDSGEMDytVsv27duZPHkyu3fv1m+vXr06n376KW3btsXc3ADJwiMe6Kbnnf5VnytKg5q9ij/LEmtzQFseVEbUK+XCiIoe1C3pgolMzxMiz7CzszPMogninXDlyhUAfb6htEoKf0iRkK0AxFfumyvemgsda2trRo0aRdeuXfUjIqPOB7Hmyn721O6GvXNNOnk8oZPZEUpxD65s1H2snMGng24ElZtPNt+FEEJkjnQHpbp3726IegghhMHExsaydOlSpkyZon9jrVarCQwMZNCgQdSsWTPzL6oocHufblTU9a2gaAF4pnJgaUJdVmrq8YR8eDpaMriiB+38C+JqK6vnCSFEbnP16lUASpcuna7j7mybSjGSOEcJatVtaoiqiWxWvHhx+vXrx4YNG1AUhYcPH/Jk1wKeAFPdPJlTowcVvJ1oZ3yI1urDOL54Csdm6j6uPrrglE8HsHbO7lsRQoi3lqag1IsXL7CyskrzSdNbXgghDCEkJISZM2cye/Zsnj9/DoCtrS0fffQR/fv3x9PTAAljY8N1eaJOzofnN/WbTyhlWJTYgCCtP0bGpjT3y0+Hih5U8XLAyEjefgshRG71NiOllPhoXG+sAOB+yV74mqgNUjeR/WbMmMGMGTNQFIUhQ4YwefJkACJD7hK5diT3q9ZlZsV2DLtfiCr5IulT8BYN1GcwfXwRdlyEnd9DsYbg20W3ip+xWTbfkRBCpE+aglJFixalf//+9OjRA3d391TLKIrCrl27mDx5MrVq1eKbb77J1IoKIURaXbhwgSlTprBixQoSEhIAKFy4MJ9//jm9evUyzAqijy/D8blwcQ0kxgAQo7JkTWJ1lmoaclMpSGFHS76q4kk7/4LkszLN/DoIIYR4pyiKwqlTpwDw8Un7dKv7exdQSIninuJC1aYfGKp64h2iUqmYMGECDRs25OHDh4waNYq7d+9y+dheOLYXgE0qFYcrBeLsP4r37G/Q3vggvka34MZ23cfcHnza6fJPFagAMuVTCJEDpCkotW/fPr777jtGjhyJr69vqssXHz16FBMTE7755hs+/vhjQ9dbCCGS0Wq17Nixg8mTJ7Nr1y799oCAAL744gtatWqFsXG6Zyy/6aLw5044Ngvu7NdvvoUHixIbsF5TgzgjSxqUdmF4VU+qezvJqCghhMhDrl+/TkhICGZmZlSuXDltB2k1mJ+eC8AZ9860trM0YA3Fu8TIyIgmTZoA0KtXLyZOnMjSpUu5fPkyGo0GFIXnJzby/MRGxqhUTCtZExenOnQuoeFD54u8ePKcYtG/YHZyPjgVh/KdoVxHsCuQzXcmhBCvlqYeWokSJVizZg0PHjxgzZo1HDhwgCNHjhAbG4uTkxN+fn788ssvNGvWDCMjSc4rhMg6sbGxLFu2jClTpujzdhgZGdGuXTsGDRpE1apVM/+i8dG6KXrHZkPoLQC0GLFdU5HFSY05oZTExcac3pUL0bmyB/ntLDK/DkKIHK9Hjx706tWLWrVqZXdVhIHs2bMH0C2okdaFNJ6e3ohL4l9EKJaUbtbXkNUT77ghQ4YwZMgQHj58SHR0NJMmTWLevHm6nYrCi6sHuAOMOQhj/j7Gv6gzB99XsHh2A3aPRNk9CpVXTV1wqlRLMJdFFYQQ75Z0DRsoWLAggwYNYtCgQYaqjxBCpMnjx4+ZNWsWs2bN4tmzZwDY2Njo80UVLlw48y8afh9OzIMzv0JcBADRWLE8qQ5LkhrxF85UL+rI7CqeNCjtKivoCSFeKyoqikaNGuHh4UHPnj3p3r07BQrIiIbcZO9e3bSrunXrpvmYmP3TADhg25KWHm4GqZfIWV6mT5k7dy4DBw5k0qRJbNy4Uf/882+nbz7FYW4R3K0UvMwiGFklDrfQfXjfOQBbvoASTaF8J/CuD8aSSkAIkf0yeS6LEEIY1qNHjxg3bhxz584lLi4OAE9PTz7//HN69+6d+fmiFAUenISjM+HqZlA0ANwlP/MTG7NOUwuVqRXtAzzoVs0Tb2frzL2+ECLXWrduHc+fP2fZsmUsXryY4cOH06BBA3r37k2rVq0wMTHJ7iqKDNBqtezbtw9Ie1Aq+s5xPKPPkaiocarf34C1EzlVqVKlmD9/Pr/88gsxMTFYWVmRlJTE77//zqxZs9i/fz9xIbe5DdwGdl8BtdqIbxo480X5GPJd2QBXNoCFA5RpoxtB5VFZ8k8JIbKNBKWEEDnCnTt3mDRpEgsWLNAHo6pUqcLgwYNp06aNAfJFaeDqJjgyHf46rd98RFuG+UlN2av1xcPBmsEBhWlfsSC25tJ5FEKkn6OjI59//jmff/45Z8+eZeHChXTr1g1ra2u6du1K3759KVasWHZXU7yFy5cv8+zZM6ysrKhUqVKajnm8fRLWwD7TWjQoX9awFRQ5mkql0q92bmxsTIcOHWjevDkLFizg6NGjrFq1Sl9Wo9Eyesdjxu23pH3tUkys9IT8saFwaoHuk68w+HTQBaicimbTHQkh8iqZWyKEeKedO3eOLl26UKxYMWbOnElcXBwBAQHs3LmTo0eP0r59+8wNSCXGwsn5MN0f1vSAv06TgAm/JdWhSfz/6JIwjDivhsz7oDJ7h9Shdw0vCUgJITLs0aNH7Ny5k507d6JWq2nWrBmXL1+mdOnSTJkyJburJ97Cy3xSNWrUwNT0zdOkkp4H4/k4CABNlb6oZOSKSCcrKysGDBjAypUrURSFGzdusGXLFpo0aYKjoxOJcTGs2HGawvOMaXy7C+s0NXmhmENYMBwYDzP8YV5dODYHop9m9+0IIfIIGSklhHgnHTt2jBEjRrBjxw79tkaNGjF06FDq1auX+Q/rMaFwcgEcnwMxuhwN4Vjza1IjliQ1JNo4H20qFmBq9cKUdMvkKYJCiDwpMTGRTZs2sWjRInbu3Em5cuUYNGgQ77//PjY2NgCsWrWKTz/9VPJ55jCKorBo0SJA97srLe5tGUcRtJygLHVq1zdk9UQeUaxYMYoVK0bz5s3RaDTMnTuXYcOGEf7kDjuXzuFY4bIYu1WnS52StDE+TC31BYwfnoGHZ1B2fIvKu55u9FTJZmBqld23I4TIpTItKBUWFsbmzZv54IMPMuuUQog86OTJkwwfPpxt27YBupX0OnbsyJdffomfn1/mXzD8PhybBad/hcQXAPylODMvqRmrNbWxs7WnVzVPOlcuhIOVJAQVQmSe/Pnzo9Vq6dy5MydOnMDX1zdFmcaNG2Nvb5/ldRMZs3v3bs6fP4+lpSU9evR4Y3lt5GMK3FkLwF9l+1LZRG3gGoq8Rq1W07dvX9q3b8+3337LggULiAy+BMGXWHG/MJurd8POrQutrS/TRn0IX6NbcDNI9zGx0q3cV64DeNUGtYxrEEJknkz7F+XevXv07NlTglJCiLdy9uxZhg8fzubNmwHdw1P37t0ZNmwYRYoUyfwLhlyCIz/DxbX65OVXtJ7MSWrJH9oqlMifj//VLkIzn/yyip4QwiCmTJlC+/btMTc3f2WZfPnycefOnSyslcgojUbDl19+CUCvXr1wcHB44zHBW8ZThATOK8Wo16y9oaso8jBnZ2d++eUX+vXrx4QJE1ixYgWhfwUTuvpHABJqB7Kyyve4ah7RRHuQ961PUzjxMVxYpftYu+oSpJdtBwUrSoJ0IUSGZWtPa+zYsVSqVAkbGxtcXFxo3bo1169fT1ZGURRGjBiBu7s7FhYW1KlTh8uXLycrEx8fT//+/XFycsLKyorAwEAePHiQrExYWBjdunXDzs4OOzs7unXrRnh4uKFvUQjxBhcuXOC9996jQoUKbN68GSMjI7p37861a9dYsGBB5gek7h6BZe1gTnW48BsoGg5pytAt4WuaJYwhzDuQX3sH8MeAGrTyLSABKSGEwezdu5fExMQU21+8eEGvXr2yoUYiMyxfvpxz585hb2/PDz/88MbySkwobjeWA3Cr5CfYWcqoXGF4vr6+LF++nLVr11KiRAn99mv7N/Hn+Pc4NKkfI2ZtpNrVzrSK+YFfkxoSqlhD9GNdqoMFDWBaedg1Eh5fycY7EULkdNna29q/fz/9+vXj2LFjBAUFkZSURKNGjXjx4oW+zPjx45k8eTIzZszg5MmTuLm50bBhQ6KiovRlBg4cyPr161m1ahWHDh0iOjqaFi1aoNFo9GW6dOnCuXPn2L59O9u3b+fcuXN069YtS+9XCPGPy5cv06FDB8qXL8/69etRqVS8//77XL16lcWLF1O0aCau/qIocGsvLGoGi5rCzSA0GLFFU5UW8aPpofkOp/JN2TqgFkt7V6FGMSdJMCuEMLhff/2V2NjYFNtjY2NZsmRJNtRIZJSiKEycOBGAoUOH4uzs/MZjgrdOwZJYrimFqNNSnk1F1mrbti1nzpxh0qRJtG+ffJReUnwsIWtHsWvOKEYd0FD+7hf0TPiS9ZrqvFDMIPwuHJoMs6vBrGpwYCKEyshOIUT6ZOuE4O3btyf786JFi3BxceH06dPUqlULRVGYOnUqw4YN47333gN0D3Curq6sWLGCPn36EBERwYIFC1i6dCkNGjQAYNmyZXh4eLBr1y4aN27M1atX2b59O8eOHaNKlSoA/PLLL1SrVo3r168nezvwUnx8PPHx8fo/R0ZGArqHDUVRMrUdXp4zs88r/iFtbFjpad9r167x448/smrVKhRFQaVS0aFDB77//ntKly6tP18mVQz+3AkHJqD66xQAiRizJqkWczQtCTUtQKcaHswNKIy7vUXmXjsTyffXsKR9DS+3tXFG7yMyMlLfHlFRUcmm72k0GrZu3YqLi0tGqymywcmTJ7l48SJWVlZ88sknbyyvxEXidHkhAFe8P6KktZmhqyhECpaWlnzxxReAbgGGw4cPc/jwYZYtW8a1a9eIiYrk9sENcHADD9r3Ylrx9/k6pgcNTC4SqD5CXaNzmD65AnuuwJ4foUBF8Gmnm+Zn45a9NyeEeOelOSj1888/v3b/X3/9leHKREREAOjn3t+5c4eQkJBkq5aYmZlRu3Ztjhw5Qp8+fTh9+jSJiYnJyri7u1O2bFmOHDlC48aNOXr0KHZ2dvqAFEDVqlWxs7PjyJEjqQalxo4dy8iRI1OtoyGCUtHR0QAyOsNApI0NKy3te+vWLcaPH8/atWvRarUABAYGMnToUMqUKQP8829AxiukxeTmdsxOTMf4qW5IeRwmrEyqx9ykFmis89OlYn7a+rpha24MJBARkZA51zYA+f4alrSv4eW2Nn75oupt2dvbo1KpUKlUFC9ePMV+lUqV6jOIePcdPHgQgPr165MvX743lr+7cwaFlWhuK/mpEdjb0NUT4o1MTEyoU6cOderUYdiwYRw8eJAmTZoQExMDwK41C4GF2NjacbJKPfbkq4Sx50c0MTlNoNERAtSXUf91Cv46pVvBr3ANXf6p0oFg8ea/E0KIvCfNQakpU6a8sUyhQoXeuiKKovDFF19Qo0YNypYtC0BISAgArq6uycq6urpy9+5dfRlTU9MUv/hdXV31x4eEhKT6xtHFxUVf5r+++eYb/RsD0D2Aenh4YGdnh61t5i4H/zLIZWdnlyse1t9F0saG9br2vX37NqNHj2bp0qX6KbWtWrVi+PDhqa40lSHaJLi8Hg5OQvX0GgAxihlLNA2Zn9QcK4f8fF7HmzZ+7pgZ55yVjeT7a1jSvoaX29o4o/ewd+9eFEWhXr16rFu3LlkibFNTUzw9PXF3d89oNUU2OHLkCADVq1d/c+HEWOzPzQPgvGdP2thbGbJqQryVmjVrEh4eztOnT5k2bRr79u3jxIkTREVGcDJoPQA+vv4c9/ZlWYg7LuU709LmKm1Mj+Nv9CfcOQB3DqD8MRhV0Qa6EVQlmoKpfN+FEDppDkoZeuWXzz77jAsXLnDo0KEU+/778Pdyys/r/LdMauVfdx4zMzPMzFIOoX75ZjOzvTxvbnhYf1dJGxvWf9v37t27jB49msWLF5OUlARA8+bNGTlyJP7+/pl7cU0inF+ly2sQehuASMWSRZrGLEpqgotrfr6vW5TmPvkxzqGJy+X7a1jSvoaXm9o4o/dQu3ZtQPdsVahQoVzRJkL3XHn48GEAAgIC3lj+3u65FNKG8ZfiROVWnxq6ekK8NRMTE9zd3Rk3bhygW4xh/fr1HDhwgN9++42L507DudMAhBxey1VzC2aXrk0+5zL09jOhtckxSnEfbmyDG9tQTCxRlWimC1B51wdjSe4vRF6WrTmlXurfvz+bNm3iwIEDFCxYUL/dzU03BzkkJIT8+fPrtz958kQ/esrNzY2EhATCwsKSjZZ68uSJ/oHAzc2Nx48fp7ju06dPU4zCEkJkzP379xkzZgwLFizQryrVpEkTRo4cSeXKlTP3Ypok3Qp6+8fpkm0CoYo1C5KasVTTkMIF3RlXtygNS7liZCSdPiFE9rtw4QJly5bFyMiIiIgILl68+Mqy5cqVy8KaiYy6fv06jx8/xtTUlIoVK76+sCYRy5MzATjp3o3Wjpk7Cl8IQ7KysqJr16507dqVESNGMHPmTGJjY1m8eDFhYWEkxsUSemY7ocDka8WZV/9TirpYEKg+QkvVYQrzFC6thUtrUcztUZUOhDLvQeGaoH4nuqdCiCyU5r/1sbGx7N69mxYtWgC66W3/TgSuVqv58ccfkyXrfBNFUejfvz/r169n3759eHl5Jdvv5eWFm5sbQUFB+Pn5AZCQkMD+/fv1kXp/f39MTEwICgqiQ4cOADx69IhLly4xfvx4AKpVq0ZERAQnTpzQd4qPHz9OREREmt5kCSHeLDg4mNmzZ7N48WISEnT5mRo0aMDIkSMz/++ZVgOX1sG+/0HoLQCeKbbMTWrBck0DfLzcmVmvKDWKyip6Qoh3i6+vrz6tgK+vLyqVKtVclSqVKtkqwuLdt3nzZgDq1KnzxufhB/sXU1DzhKeKHX6tPsuK6glhEO7u7vz0008AdO7cmfnz5/Pbb7/p84Q+u3uDZwsHEWJhya7YGAZZ2lGz48d0c79HC/UxXOLC4cwSOLMExdLpnwCVZwAY5ZxUC0KIt5fmoNSSJUvYsmWLPig1Y8YMypQpg4WFbsWqa9eu4e7uzqBBg9J88X79+rFixQo2btyIjY2NPr+TnZ0dFhYWqFQqBg4cyJgxYyhWrBjFihVjzJgxWFpa0qVLF33Z3r17M3jwYBwdHXFwcGDIkCH4+PjoV+MrVaoUTZo04aOPPmLu3LkAfPzxx7Ro0SLVJOdCiLR7+vQpw4cPZ968efoOVO3atRk1ahS1atXK3ItptXBlgy4Y9ew6oBsZNTepJUs0DalSwoMldYtSsbDD688jhBDZ5M6dOzg7O+v/W+QeL4NSgYGBry+o1WB8dCoAR1w608rNycA1EyJrVKpUiUqVKjF9+nQ0Gg3R0dEMGTKEJUuWEBerS5SuiYlg36KJXC9Zge9KdKCCm4ouTjdooj6JQ8wzOLVQ97F2hdKtdCv4eVQFo5yZfkEI8WYqJY1LydWqVYtBgwbRpk0bAGxsbDh//jxFihQBYNmyZcycOZOjR4+m/eKvGMGwaNEievToAehGU40cOZK5c+cSFhZGlSpVmDlzpj4ZOkBcXBxffvklK1asIDY2lvr16zNr1iw8PDz0ZUJDQxkwYACbNm0CdA8MM2bMwN7ePk11jYyMxM7OjoiICIMkOo+IiMg1CWDfRdLGmS8+Pp5p06bx008/6Veiaty4Md98840+X0qmURS4tgX2joUnlwGIUCyZl9SCxZrGBJQuzOf1i1G2gF3mXvcdId9fw5L2Nbzc1saGfCbI6fLa89LL/KQ3b96kePHiKIpCcHAwnp6erzzm4eEVuAd9SrhixbMPT1PUI/8ry2a1d7GNc5O82r6PHj3i4MGDzJ8/n6CgoGT7TM3Nqfr+lzzO50Nlkz9pbnScJuoT2Kli/ilkkx9Kt4ay70GBiq8MUOXV9s0q0r6Gl9vaOK3PBGkOSrm5ubF792790u3Ozs6cPHmSwoULA3Djxg0qVaqUeUu6v2Py2kNWbiNtnHmSkpJYt24d3333HTdv3gSgQoUKjBw5kubNm2du+yoK3NgBe3+CkAsARCkWzE9qxkJNU6qUKszABsVzbTDqJfn+Gpa0r+HltjbOrGeCsWPH4urqSq9evZJtX7hwIU+fPuWrr77KaFWzXF56XlqyZAnDhg3jxx9/ZPfu3SxbtoymTZuydevWVx+kKPz1P38KxN9ii0N3Wgz4OesqnAbvWhvnNtK+MGfOHKZOncr169eTbbfPlw9Ht4LEqiwwqdiWOgUSaKE+TiOjk9iqYv8paFsQyrTWTfErUAH+1Y7SvoYl7Wt4ua2N0/pMkObpexERERgb/1P86dOnyfZrtdpkOaaEELnPjh07+OKLL7hy5QqgC1b/73//o2vXrkRFRWXuxW7vg90/wl+nAIhWzFmkacL8pGb4lyzC8gbFKFfQPnOvKYQQWWju3LmsWLEixfYyZcrQqVOnHBmUyisOHz5M9+7dAejZs6d++5AhQ1573ONTGykQf4toxRzv5oMNWkch3kWffPIJn3zyCXfu3CEkJIRhw4Zx5swZwsPCCA8LA0B19SRHyvpxulB5hjgNpq5rFIGmp2hodBrryAdwdIbuY++pm95Xpg3kL5/NdyaEeFtpDkoVLFiQS5cuvTIH04ULF5KtnCeEyD2uXr3K4MGD2bZtGwAODg7079+fwYMHY2Njk2qS3rf28CzsGqELSgGxiim/ahozN6k55Ut482uD4vh62Gfe9YQQIpv8d3Xhl5ydnXn06FE21EikRVJSEp9++mmybY6OjkycOJF69eq9+kBFIW6PbhGeA3ataOb96il+QuR2Xl5eeHl5sWfPHl68eMGePXswMjJi3bp1LFq0iD8vnoGLZwDYWdibe+8NZMiLltSwCKad9TnqG53FMvwuHJ6q+zgUgdJtMPJsALZVk42gEkK829IclGrWrBk//PADzZs3T7GiSGxsrH7qjhAi93j+/DkjR45k1qxZaDQajI2N6d+/P99//z358uXL5Ivdgj0/wuX1ACQoapZrGjArqRWlihdjQYNiVCiUydcUQohs5OHhweHDh1OsPnz48GHc3d2zqVbiTbZv387FixfJly8fx44d49ChQ7Rq1QpHR8fXHvf00i48Yy8Tp5hQoNnrR1QJkZdYWVnRsmVLAJo3b07fvn3Zv38/S5cu5fLlyzwMvsXDyf0BuKs2ZnfZADQFOhBY1pZA05PUMzqHRehtVIcmYXtoEopjMd3oqbLvgUup7Lw1IUQapDko9e2337J69WpKlCjBZ599RvHixVGpVFy7do0ZM2aQlJTEt99+a8i6CiGySGJiIrNnz2bEiBGE/T2UOjAwkAkTJlC8ePHMvVjkI9g/DuXMElSKBq2iYoO2OpOT2uFVtDRzGhTH31OCUUKI3OfDDz9k4MCBJCYm6kfY7N69m6FDhzJ4sEztelft3r0bgA4dOlC8ePE0/16M2jEGZ2C/dVMal8zk36VC5CIVK1akYsWKDB48mLCwML788kuWL19OUlISSUlJ/HX+AJw/wLytsDOgORY1Z1Pf6CzNjI5RT30es+d/woHxuo9zSd0qfqVbgUtpGUElxDsozUEpV1dXjhw5wqeffsrXX3+tn66jUqlo2LAhs2bNwtXV1WAVFUIYnqIobN26lcGDB+sTUPr4+DBlyhTq16+fuReLDYfDU1GOzUGVFIsK2K3xY0JSRyw9yjGxSUmqFnn9W2chhMjJhg4dSmhoKH379iUhIQEAc3NzvvrqK7755ptsrp14lZdBqddO1fuPpxeDKBJ9hgRFjWPjLw1VNSFynXz58jF//nzmz5+Poihs27aN7777jrNnzwIQfOQP1Me3c0NlxHxLW6xLVad6YXP6Fn1MbfVFzJ5eg/3XYP84cCz2T4DKzUcCVEK8I9K8+t6/hYaG6lfdKlq0KA4ODoBujv2/k6HnJnlpNZncSNr4zS5dusQXX3yhX6rX2dmZ0aNH07t3b9Rq9WuPTVf7JsbCiXkoByejigsH4LS2GP9L7EykSyWGNC5Bg1Iu8v/pX+T7a1jSvoaX29o4s58JoqOjuXr1KhYWFhQrVgwzM7NMqGX2yM3PS4qicPjwYWrWrAnA48ePcXFxScuBBE+oQeGYS+ywbEmjL5e+s38PsruNcztp38yRkJDA+PHj2bx5MydOnEi1jIdfbWxqvE8j61s0U5+gltEFzFSJ/xTI5/VPgMrdTwJUaSDfX8PLbW2c6avv/ZuDgwOVK1fW//nKlSvMnz+f5cuX8/jx47c5pRAimzx9+pThw4czd+5ctFotpqamDBw4kG+//RY7O7vMu5BWA+dXoewZjSrqISrgurYgE5I6ct2uOl80KkFg+QKojXL+P8BCCJEe1tbW5M+fH5VKlaMDUrlZZGQkLVu25MCBAwA0btw4bQEpIOTMFgrHXCJOMcGt5Xe5oqMhRHYyNTXlu+++47vvvuPmzZsEBQWxb98+7O3tuXPnDnv37uX+2f3YB19gZ5GS/BanxcLnU5p7aWmqPk5do3OYh93RJ0lX7DxQlW4FpVtDAX8wMsruWxQiT3nrYU3R0dGsWrWKBQsWcPLkSapWrcrXX3+dmXUTQhhQQkICM2bMYNSoUURERADw3nvvMX78eLy9vTP3Yrf3oez8DlXIRVTAX4ojU5LaccC8Pp81LcGsSoUwNZYHACFE3qLVahk9ejSTJk0iOjoaABsbGwYPHsywYcMwko7RO+Ojjz7SB6SqV6/OihUr0nagopC4cxQAe+1a07RUSUNVUYg8qWjRohQtWpRPP/1UP8okODiYXr16cfbsWcJPH9UVvHycfZVrcLhgWeIKdqGW5W2aG5+kntFZLCPuw9EZcHQGim0BVKUCdSOoPKpIgEqILJDuoNShQ4eYP38+69atw8vLiytXrrB//36qV69uiPoJITKZoihs2rSJIUOG6Kfh+vn5MWXKFGrXrp25F3t6HXZ+D3/uQAVEKpbMSGrFOpPm9GpQilHVC2Npmjun/AohxJsMGzaMBQsW8L///Y/q1avrp4eNGDGCuLg4fvrpp+yuogBCQkJYt24doFsZMSAgIM3HPjiyGo/4G0Qr5ni1HmaoKgoh/qV8+fIcP36cbdu2ERsby8GDB5k5cyYXTxyCE4eAOUQXLcHxsrUY7BZInXxPaao+Tn2js9hE/gXHZ8Px2SjWbqhKtdQFqDwDwOj16SyEEG8nzb3B8ePHs3DhQqKjo+ncuTOHDh2ifPnymJiYZP7S8EIIg7h27RoDBgzQ541yc3Pjp59+onv37m/MG5Uu0U9h31iU04tRKRoSFTXLNA2YS1ta1yjP7tpFsLc0zbzrCSFEDvTrr78yf/58AgMD9dvKly9PgQIF6Nu3rwSl3hErV65Eo9FQpUqVdAWk0GpQ7RsDwAHH9jQr4mWgGgoh/svExET/b2vHjh3p2rUrW7ZsYenSpdy7d497N69z76ZuUZ/nLm6crNqMvvGF8HVKpLfXExoancY2OgRO/gInf0GxckZVsoUuQFW4JqjlpaoQmSXN4xG//fZb2rZty927d5kwYQLly5c3ZL2EEJkoKiqKr776inLlyhEUFISZmRnffPMNN27coFevXpkXkEqMhYOTUX72hVMLUCkadmr8aZI4jovlvuX3IYF83bSkBKSEEALdwjElS6aczlWyZElCQ0PTda4DBw7QsmVL3N3dUalUbNiwIdl+RVEYMWIE7u7uWFhYUKdOHS5fvpysTHx8PP3798fJyQkrKysCAwN58OBBuu8rt1m6dCkA3bp1S9dxt3fOpkBiMOGKFSXbyGqKQmSnqlWrMnr0aIKDg3nw4AHDhg2jatWqADx/EsLZTQt5tGMe25Yv4utdiZT/81O6vhjIb0l1CFOsUb14CqcXwdLWKBOLwcbP4M9dkJSQzXcmRM6X5qDUqFGjWLNmDV5eXnz11VdcunTJkPUSQmQCRVFYsWIFJUuWZPz48SQmJtKiRQsuX77MmDFjsLGxyaQLaTG5tgFmVILdI1ElRHNRW5hOCd/xq+cYpvXrwOQOvrjbW2TO9YQQIhcoX748M2bMSLF9xowZ6X759+LFi1eeD3Qj3idPnsyMGTM4efIkbm5uNGzYkKioKH2ZgQMHsn79elatWsWhQ4eIjo6mRYsWaDSa9N1YLnL58mXOnj2LiYkJHTt2TPNxmtgI8h2fAMDhgr0p4lHAUFUUQqSDSqWiQIECjB49miNHjhAcHMyUKVNo2bIlzZs3x8jIiJvHdhK8cjhbFs7k+92xVLz5MR0jB7IiqS7PFRtUsaFwdiksb4sysSis/wSubdW9nBVCpJtKURQlPQfs37+fhQsXsm7dOry9vbl8+XKeyCmVm5c4zgvyYhtfuHCB/v376xOzent7M3XqVFq0aJG5F3pwGmXbUFR/nQLgoeLAhMSOXHVqwlfNS1OnuHOeaXNDyYvf36wk7Wt4ua2NM+uZYP/+/TRv3pxChQpRrVo1VCoVR44c4f79+2zdupWaNWu+1XlVKhXr16+ndevWgK793d3dGThwIF999RWgGxXl6urKuHHj6NOnDxERETg7O7N06VJ98OXhw4d4eHiwdetWGjdunOq14uPjiY+P1/85MjISDw8Pwv/P3p2Hx3S9ARz/zkz2lSSSCJJYgwixFLGrfV9LKbUv1RZVbSm1VfnZ16qqWmtra1+K2KldUWLft0SIyL7O3N8fkakQBDMZSd7P8+TJ3Dvn3vvek8ucee+55zx+nC3aSyNGjGDs2LG0aNGCtWvXZni7C78NosTVBVwnL/YDj+HsaGfEKA0nu/1bfddI/RqXIer3yJEjDBs2jKNHj+onoAAwN7egaOW6xOUpQcViLjTRHKOe6ijumsj/jm9uA0XrQfFmUKw+WBr2/0BTk+vX+LJbHUdGRpIrV65Xtpde+2HYmjVrUrNmTWbPns2yZctYuHAhNWvWpGLFirRt25ZBgwa9VeBCiLcTHh7OyJEj+fHHH9HpdFhbWzNs2DC+/PJLrKysDHegqPuwczScWoYKiFas+Cm5ORusW/JZUz8mly+ARp31/zMVQghjqVmzJpcuXeLHH3/kwoULKIpC69at6devHx4eHgY7zvXr1wkJCaF+/fr6dZaWltSsWZODBw/Sp08fTpw4QVJSUpoyHh4elCpVioMHD74wKTV+/HhGjx793PqIiAhe877nKymKov+SmFmN9aNHjwJQrVo1/Uy1rxL/4DqFry4B4GzxL6iONsPbmpop6jgnkfo1LkPUb/HixVm9ejVRUVFs3bqVv/76i82bN5OYmMi5/VuALVwHVgEqM3PK12vJJ+VUNNAcI19SGJxbD+fWo6jNSfasRlKRBiQVqo9i42yw8zQVuX6NL7vVcWRk5KsL8QZJqVT29vb07duXvn37cubMGf3sMZKUEsI0tFotv/zyC8OHDycsLAyADz74gMmTJ+Pp6Wm4AyUnwpGf0O2diDox5T/N1drqzKAjbWpXYFuNQjKjnhBCZJCHh4fRBzQPCQkBwM3NLc16Nzc3bt68qS9jYWHx3OQ1bm5u+u3TM3To0DRtv9SeUo6OjkbpKQVk6h3kS5cuAVChQgUcHR0ztE3Iku9xJ5kT6tLUb90DC/OsM2OXKeo4J5H6NS5D1q+joyM9e/akZ8+eKIrCunXr2Lp1K8uXLycmJibleMlJHP/rD0Zc8WWSfxvyOtvQxvkaDdXHKEww5jd2Y35jN4rqW/AMgBLNoHgTcCzw1udqCnL9Gl92q+OMnoNBvjn6+fkxffp0Jk2aZIjdCSFe0969e+nfvz///vsvACVLlmTmzJnUqVPHsAe6tA3d1qGoH11FDZzSFWZ0cheKlqvFgsp5KZxPHtUTQoiXSf1/OiNKly5t0GM/+/+zoiiv/D/7VWUsLS2xtLRM91jG+DxI3W9mfNbExMRw48YNAEqVKpWhY94/u5vij3aiVVQk1h2LZRa8SZOZdZwTSf0alzHqV6VS0bp1a1q3bs3MmTM5efIkS5Ys4bfffiM6Opq7l4PgchCX1Gpu+1Xge10ekmMtaFQ6D1+Xeoif+gbc/DvlZ+sQ8CibkqAq0Rxcihoszswg16/xZac6ztSkVCpzc3ND7k4I8Qq3bt1i8ODB/PHHHwDkzp2bMWPG0LdvX8zMDPjP++FllK1DUV0JRA2EKrmYkPQhtws04/vmfvh6OGSZRxOEEMKU/P39UalUr3y0TaVSGWyAcXd3dyClN1TevHn160NDQ/W9p9zd3UlMTCQ8PDxNb6nQ0FCqVKlikDiymvPnzwPg6uqKi4vLqzfQ6YjfmDJm1167htQOqGHM8IQQJmBlZUVAQAABAQFMnz6do0ePsmrVKv766y+uXbvGldNH9WV/uwp7b9cjb9VeNLG9QAPNMd5TXUR97yTcOwk7x6C4+KAq0SwlSZW3DGSDRIQQryvr3b4RQhAbG8uUKVMYP348cXFxqNVq+vTpw5gxYzLWcM6o+EjYOwHl8FxUSjKJioYF2sastv2QAW3K0cQvb4a+XAkhhEhx/fr1TD9mwYIFcXd3JzAwkLJlywKQmJjI3r17mTBhAgDly5fH3NycwMBA2rVrB0BwcDBnz55l4sSJmR7zuyAoKAhI6X2cEVd3LqBwwkWiFGsKtPkhW9zlFkK8mKWlJdWrV9dPSrF371527tzJokWLCA0NJSEhgdvHA7l9PJDgwsXZUL4BYZF+NCyk0N7pMlXUZ7F4eBH2X4T9k1EcC6Aq0RxKNIUClUCddR79FeJtSFJKiCxEp9OxePFihgwZQmhoKAA1atRg5syZrz19+EspCpz5A+22YWhiQlEBO7RlmUwXGteuxsYahbDKQmNkCCHEu8LLy8so+42OjubKlSv65evXr3Pq1CmcnJzw9PRk4MCBjBs3jqJFi1K0aFHGjRuHjY0NHTt2BFLGr+jRowdffvklzs7OODk5MXjwYPz8/Khbt65RYn7X7d69G0hJ2L2KNj4ax4PjADiY92MaFCps1NiEEO+e1AnBxowZA6RMlNClSxcuXLjA7aspPwDz1GpWuOYnSe1EnSpl+aR4ODXV/2ITcRsO/wiHf0SxzYOqeJOUHlTeNcDMwpSnJoRRSVJKiCzizJkzfPLJJ/z9998AeHt7M378eNq3b2/Yu7GhF9Bt/hL1zQNogKu6vHyf3JncZZqwsKEPeR2tDXcsIYTI4ZYuXcrcuXO5fv06hw4dwsvLi+nTp1OwYEFatGiR4f0cP36c2rVr65dTBx/v0qULixYt4uuvvyYuLo5+/foRHh5OpUqV2L59O/b29vptpk2bhpmZGe3atSMuLo46deqwaNEiNJqcdxNCq9WyadMmAJo1a/bK8ufX/EApJYw7Sh4qfDjM2OEJIbKAihUrEhQUxIULF/j111+5dOkS169fJygoiIiQWwBs/PMql6o05LEugOIuaj7xi6eh+SkcYx7AiUVwYhGKpQOqYg1TElRF6oCFrWlPTLxzzp07h6enJ3Z2dqYO5Y2olDd47ubq1assXLiQq1evMmPGDFxdXdm6dSsFChTA19fXGHGaXGRkJI6OjkRERBhlNpmIiIhsM8r+uygr13F0dDSjR49m2rRpaLVabG1tGTVqFAMGDDDsOG4J0bB3ArpDc1ArycQpFsxKbsmRvB8xrHkZynnmfuGmWbl+swKpX+OS+jW+7FbHhmoT/PTTT4wYMYKBAwfyww8/cPbsWQoVKsSiRYtYvHixvqdOVpJd2ks7duygXr165M6dm9DQ0JeO0xjz8Bbq2RWwJoGdvhOo80Ffo8ZmTNnt3+q7RurXuLJC/ep0Oo4fP05sbCwbN25k2rRpzw2D4eDtR9HCnvQsA23s/iWP6r9xWxUza1SF30+Zxa9YQ7B1zrTYs0L9ZnVvUscHDx6katWqlCxZUv/Y+bsio20C9evueO/evfj5+XHkyBHWrFlDdHTKlPD//vsvI0eOfPOIhRBpKIrC2rVrKVmyJJMnT0ar1dK6dWvOnz/P4MGDDZeQUhQIWod21ntwcCZqJZnt2vJ8oJlOwVYj+KNfzZcmpIQQQryZWbNm8csvvzBs2LA0vZEqVKjAmTNnTBhZzvbw4UP69+8PwIcffvjKiUOur/wGaxI4oy5B9Za9MiNEIUQWpVarqVixIrVq1WLKlCns2rWLggULpikTeeMMJ3Zu5tMZ2yi22J6aVzrzaVBpfjlnzaPIGLi4Gdb3Q5lcBGVhYzj0IzzK/PEKxbth5cqVQEpvqazqtR/fGzJkCGPHjmXQoEFpunzXrl2bGTNmGDQ4IXKqGzdu8Nlnn7F582YgZZDa2bNn07hxY8MeKOwqus2DUV/bhQa4pcvDaG1XClRqxbJ6xXC0lhk1hRDCWK5fv64fePxplpaWxMTEmCAiERUVRfny5bl16xZOTk76sWFeJOT8QUo93AJA7PvfYyHjLQohXkOtWrW4cuUKsbGxTJgwATMzMx4+fMjGjRu5efMmj2+eY9/Nc+x7Ur43UKqYJ19U1NC9cBjc/DvlZ9u3KG6+qHyapPSikpn8cgyDzrhuIq99BmfOnGH58uXPrc+TJw9hYWEGCUqInEqr1TJt2jRGjBhBXFwc5ubmfP3113z77bfY2NgY7kBJcbB/KroD01HrEklQzJirbc4Rj48Z1rIcvh6OhjuWEEKIdBUsWJBTp049N/j5X3/9leEZ34RhrV69mlu3buHg4MBff/318hltFYXo9V8DcMCmDlWr5swB4YUQb0etVmNnZ8f333+vXzdlyhT+/PNPunTpQnJyMv7+/ly/fp2IiAjOXrpFj0sw1LcyVQrZMsgvmgCzi5jdD4L7QbBvIopDPlTFm6YkqLyqgEZuNGdXTyelFEXJko9WvnZSKleuXAQHBz/XzfDkyZPky5fPYIEJkdNcuHCBzz77jJ07dwIpM3jMnTuX4sWLG/ZAV3aSvGEgZpG3UAN7taWZbtGLTs3fp3+5fFnyPzIhhMiKvvrqKz799FPi4+NRFIWjR4+yYsUKxo8fz/z5800dXo60ZMkSAL755hsqVqz40rKX9yyjaPwZ4hQL3FqNl89PIYTBWFhY0LFjR0qWLElYWBjvv/8+27dvp2vXroSEhAAQGnSYdUGwaZsVKgsrVInx5LJWM7SKGZXy3qRyxFxUR39GsXRE5dMwJUFVuA5YZs3BsEX6nk5KRUdHp3maLat47aRUx44d+eabb/jjjz9QqVTodDr+/vtvBg8ezMcff2yMGIXI1hRFYf78+Xz++eckJCRgbW3NzJkz6dGjh2EbuNEP0G0dgvrsn5gB9xQnxiZ/jGvFD1hU30ce1RNCiEzWrVs3kpOT+frrr4mNjaVjx47ky5ePGTNm8OGHH5o6vBwnKCiI3bt3o1Kp+Oijj15aVpcYh/3+lEf7Drp/RJ2iPpkRohAih/H399e/btCgAcHBwQBs3bqVlStXsnr16pQxnhPjAQhNhC/+SinfoXYJhpYJp7jdY8z/XQX/rkLRWKIqVCslQeXTCOxcM/mMhKElJyfrX4eHh+eMpNQPP/xA165dyZcvH4qiULJkSbRaLR07dmT48OHGiFGIbCsmJoZ+/frp78zWr1+fWbNmUaxYMcMdRFHg5FKStw7HLDECraJisbYBu/L24ttWFSnpYdjZkYQQQmRcr1696NWrFw8fPkSn0+HqKl8QTGXcuHEAtG7d+rlHKp91Zs1Eyujuc1/JjX/7EZkRnhBC6DVs2JCGDRsyY8YMrl69ys2bN5k/fz5btmzRl1mx+zwrdoOVnQPVS3lQ0iKUAf5xFNRug8vbUDaqoEBFVMWbQPGm4FzYhGck3lRUVJT+dXh4OJ6eniaM5s28dlLK3NycZcuWMWbMGE6ePIlOp6Ns2bIULVrUGPEJkW1duHCBtm3bEhQUhFqt5ocffuDrr79GrX7tSTFf7OFlktf3x+z2QcyAIJ0X4zSf0LJZU5aWzy+PGgghhAmNHj2aTp06Ubhw4ZePXSSMbvr06foxU7/++uuXlo0Ou0fhCz8BcL7kQGo5ORk9PiGESI+joyPlypWjXLlytGrVitjYWHbv3s2iRYtYs2YNOp2O+OhIAg9HEgj8dMKBHq1r8EGeGxRIukERjsDtIxA4AsWlOKrijVMSVB5lwZDfSYTRPJ2UevTokQkjeXNvPFR74cKFKVxYsqlCvImVK1fSq1cvoqOjcXd3Z+XKldSsWdNwB0hOQDkwDd2+yZjpkohVLJmW3IaIMj2Z1cQPJ1sLwx1LCCHEG1m9ejVjxozhvffeo1OnTrRv3548efKYOqwc5/LlywwZMgSAiRMnvnIsqUsrv6UccVxUF6ZK688yI0QhhMgQGxsbmjRpQpMmTQCIjY1l5syZ3L59mz///JPQ0FB+WrqJn56Uz+fhRrMSVowq9xi3hxfgwAU4MBWdnTvq4k9m8vOuDmby3eFdFRkZqX8dHh5uwkjeXIaSUoMGDcrwDqdOnfrGwQiR3YWGhjJgwABWrlwJpEwDu2LFCtzd3Q13kJsHSVr3OebhV9AAe7RlmOfwKZ+3rktAYWfDHUcIIcRb+ffffwkKCmLZsmVMnTqVQYMGUbduXTp16kTLli0NO+uqeKEhQ4aQkJBAvXr1GDx48EvL3jl/lDKh60AFMbW+x8I860/FLYTIvmxsbPRJ97Zt2/LFF19w+vRp/ft3791n7j2YuxPye3nRtIQ1w0o/JD8hcPxXOP4rOgt71MXqQ7FGqFwrgqPM0v0uefbxvawoQ5+kJ0+eTLN84sQJtFotPj4pgzpeunQJjUZD+fLlDR+hENmATqdj8eLFDB48mEePHqFWq/n2228ZOXJkmhkT3krcY7TbR6A5uRhz4IHiwDhdV7xrdGZh7cJYmmkMcxwhhBAG4+vry7hx4xg3bhx///03y5cvZ+DAgfTt2zfN3U9hHJcuXWLt2rVAyo3Vlz7WrihErf+K/CqFYzbVea9Gk0yKUggh3l7t2rU5deoUFy9e5MCBA8yaNStNgurOzZvMvQnzdlrili8f7cu78EmhOxD2mPwxf2JzdjX2KjPwqgLFG6cMlJ7b23QnJIAc9Pje7t279a+nTp2Kvb09ixcvJnfu3EBKRq5bt25Ur17dOFEKkYUdOnSIAQMGcOzYMSBlFo1ff/2VcuXKGe4gF7eSuL4/FrH3AVieXJvdBT5laOsACuWRaV+FECIrsLW1xdraGgsLizSNTGE88+bNQ1EUmjZtSqlSpV5a9uzOZZSKP0WCYo5rm0mZFKEQQhiWj48PPj4+dOvWDZVKhaIoBAUF8eOPP7Jx40bu3btH8I1rTL9xjelPtnFytGV2Kxc6eIXDjX0pP1uHoMtTAnXxxuDTGDzKyThUJpAdHt977atmypQpjB8/Xp+QAsidOzdjx45lypQpBg1OiKzszp07dOrUiSpVqnDs2DHs7e2ZOHEiR48eNVxCKiaMpD96wIr2WMTe57rOjZ7qMVi1ns283nUlISWEEO+469ev88MPP1CyZEkqVKjAP//8w6hRowgJCTF1aNmeoiisW7cOgK5du760bFJCLE5/fw/AMY+P8CpcwsjRCSGEcanValQqFWq1Gj8/P+bOncvVq1fZsWMHHTt2xPGpx/QeRcTQcdFNLCZqaXqoDEvueXHkrg71g/OwfwrMr4NucjHY8Dlc/AsSY014ZjlLjnl872mRkZHcv38fX1/fNOtDQ0Plrp4QgFarZebMmQwfPpzY2FhUKhXdunXjhx9+MOzYUUFrSdwwCIuER2gVFb9om3DTbwCTmvqTWwYyF0KId15AQABHjx7Fz8+Pbt260bFjR/Lly2fqsHKMc+fOcfXqVSwtLWnQoMFLy578fRwVlRAekJvSHUZlToBCCJHJrKysqFOnDnXq1EGr1bJ8+XLy5MnD8uXLWbp0KUlxMWzevp/NT8rncXMjj0UCeTSxdCl9l2YPF+HyzxJ0ZlaoCtdG5dMYijUEO1eTnld2lpHH9/744w9GjhxJ+/btGTlyZGaFlmGvnZRq1aoV3bp1Y8qUKVSuXBmAw4cP89VXX9G6dWuDByhEVnL+/Hm6d+/O4cOHAahatSozZsww7HhrUfdJ3DgIi0ubsAAu6vIzxfpzPm7blr5FZUpxIYTIKmrXrs38+fOfu9EnMse2bdsAeP/997Gze3HP4rCQW/he/QWAq2W+orJD7heWFUKI7EKj0dC5c2cAGjZsyNSpU/npp58YO3YsiYmJADy4f58HT8rvvQEqoJmvLW2LRvJR0hZUF/9CQQX5KqAq3ijlMb88xeFl4/eJDNNqtcTExOiXo6Oj0y13/Phxzp8//872wn7tpNTcuXMZPHgwnTp1IikpKWUnZmb06NGDSZPk+XqRMyUlJTF58mRGjRpFYmIi9vb2TJkyhZ49e7580NTXoSgop1eStPkbLJIiSFI0/KRtTnTFgUxvWAobC5kBSAghspJx48aZOoQc7e+//wagZs2aLy13fdXXVCCei2bFeK9538wITQgh3jnOzs58/vnndOjQgfv372NlZcXVq1fRaDTs37+fRYsWExHxmA1BMWwIgl5bksllpaJlUWjhcxDHw4cIyD8aJbc3ap8nA6V7VQGNualPLct6Ngn1oqRU6sR1ZcuWNXpMb+K1v8Xa2NgwZ84cJk2axNWrV1EUhSJFimBra2uM+IR45927d4+2bdty6NAhABo3bszcuXMpUKCA4Q4ScZeEdf2xvL4DC+CszpvZ9gPp3b4l5Tzljq0QQgjxOhRF0Selqlat+sJyV07to0L4XwDo6v8PjUZmshVC5GyFCxemSJEiAPqnQdq0acP06dPZunUrq1at4vfffyc2Npb7ifDzCfj5REpnlvF1bfimyg10h+egPvITOksH1EXrpySoitQF61ymOq0s6dnhk57uNZVKUZTsl5RKZWtrS+nSpQ0ZixBZzoEDB2jbti3379/H0dGRWbNm0alTJ8P2jjr5G0lbhmCZHE2CYsYsbRvMqg9kRh0fLM2kcSyEEEK8rmvXrnH//n0sLCyoUKFCumUUnY7kzd8AcMyhHu9VrJOZIQohRJbTsGFDGjZsyJQpU7hx4wYXLlzgp5/mcvd+KNcvX2TojliG7kgpWz6fBX3LJdL80e+4nv0TRWUG3lVTxqHyaQi5vU16LlnBs0mp9HpK3bt3j4cPH6LRaF45y6ypvHZSqnbt2i/9wr1r1663CkiIrEBRFObMmcPAgQNJTk7Gz8+PtWvXUrhwYcMdJCqEuNWfYX0jEAvgH10RfnX6ks8/bEpxdwfDHUcIIYTIYfbu3QtAhQoVsLKySrfMP1t+pXzSOWIVSzzbT8zM8IQQIktzcnLCycmJcuXK0bFjRxRFoV27dvz555/6MifuJtLrLny6VUNVb2u+rxZPVWUvXN8LW79Bl6ck6uKNU8ah8igLarUJz+jdFBubdpbD9JJSp06dAqB48eJYW1tnRliv7bWTUv7+/mmWk5KSOHXqFGfPnqVLly6GikuId1ZcXByffPIJixcvBqB9+/b8+uuvBn2EVTm7hsT1A7FOiiBBMWOG0h6nOl8ws3oRNGoZGFAIIYR4Gzt2pNyqr1Mn/d5PcTFR5D8+HoDT3t0IyFco02ITQojsRqVS8fvvv/PgQcqw6A8ePGD69On8tXUrd+/cYfflaKpdUVG5fCnKOTymZb5H1OMcPDgH+yejtXFFU7xRymN+BWuChY2Jz+jd8GxSKr3H986fPw/wzvaSgjdISk2bNi3d9aNGjXrhwFpCZBe3bt2idevWnDhxArVazYQJE/jyyy8N97he7CPi1g3E+tJ6LEkZO+rXPN8woEMLvF1k3DYhhMjq/v333wyXlWESjENRFHbu3AlA3bp10y1zavlwAggjmDyUbf9dZoYnhBDZkkqlwtXVFQBXV1d++eUXEhIS2Lp1K0uXLmX16tUcPn6Gw8AcwCGXI7nMk3E2S2BwwF06xi6Gfxaj01iiKlQLlU9DKNoAHPOZ9LxMKS4uDgAXFxcePnyYbj7m0qVLABQrVixTY3sdBpuuq1OnTlSsWJHJkycbapdCvFNOnDhB48aNCQ0NxdnZmZUrV76wMfsmlItbiV/zKdYJD0lW1MxVWmFT5xsmVy8mvaOEECKb8Pf3R6VSoSjKK29oaLXaTIoqZzl//jyhoaHY2NhQuXLl596/efEU5e8sBRWEVBlFXhs7E0QphBDZn6WlJS1atKBFixb88ccfbNmyhfv37/PXX38R+TiCSOAW8NGaZKYG5aZ87hgC3KL4OHkrqsvbANC6+aHxaQTFGua4x/xSk1J58uTh4cOHJCcnk5iYiIWFhb5MjkpKHTp06IXP5AuR1QUGBtK6dWuio6MpXbo069evx9vb2zA7j48kbuPXWAetwBq4rMvHXKev+KTjBxRxlYawEEJkJ9evX9e/PnnyJIMHD+arr74iICAASGlPTZkyhYkTZQwjYzl27BiQMp7U0w13SBncPGrNALxUWk5bV8K/bkdThCiEEDnOBx98wAcffACkJFJu376NWq1m165djBs3jhMX73ECmAf03pyEnYUKK7WWx/F/453rEGPf/4EW5T3Q+DRISVAVqg2W2fu7VGpSytnZWb8uOjoaJycn/XK2TEq1bt06zbKiKAQHB3P8+HG++066N4vsJTk5mVmzZvH111+TnJzM+++/z9q1a3FwMMxA48q1PcT90RebuGB0ioqFusYk1/qWCbVKYqbJOVl+IYTIKby8vPSvP/jgA2bOnEnjxo3160qXLk2BAgX47rvvaNmypQkizP6eTko968SWX6mQcIp4xZw87WagykF33IUQ4l1RrFgxfRKldu3aNG7cmHXr1hEWFsby5cuJi4sjPPm/8ucf6mjzexwN/7lBLqt51Cm4gG7lbVEXqoGqWMOU2fxyeZrobIwnNSnl4OCAhYUFiYmJxMTE6JNSUVFRBAcHA1C0aFGTxfkqr52UcnBwSNPdXK1W4+Pjw5gxY6hfv75BgxPClHbs2MGAAQM4d+4cAB9++CGLFi3C0tLy7XeeFE/c1hFYn/gZG+CWLg8/5vqS7h074eNu//b7F0II8c47c+YMBQsWfG59wYIF9Z89wvCOHz8OwHvvvZdmfWTEI7yO/wDAKe8eVC5YItNjE0II8byAgAB9j+JJkyZx584d1Go1u3fvZumKVdwNecDdaxfZeiXlsfeVZ5NZeyGZtiX+wtFqK02LmaFxL/nfY375K4BaY8pTMojUpJS1tTW2trYkJiamGVfqypUrQMqYU7lz5zZJjBnx2kmpRYsWGezg+/btY9KkSZw4cYLg4GDWrl2b5q6goiiMHj2aefPmER4eTqVKlfjxxx/x9fXVl0lISGDw4MGsWLGCuLg46tSpw5w5c8ifP7++THh4OP3792fDhg0ANG/enFmzZpErVy6DnYvIPq5evcqXX37J+vXrgZTukOPGjaNnz56oDXHH9H4QUcu7Yh+R0pVyubYOj6uNZGxdP8yld5QQQuQYJUqUYOzYsfz666/6IRASEhIYO3YsJUpIQsQYEhMT9dNjP9tT6vySL6hEOLdVHpTtMNIE0QkhhHiV3Llz6xMsvr6+fPbZZwAcPXqUnXv28c+l26xdMpctlxPZcjmlO1UBBxUNCv/DFwFncbaejLOzM2bFG0KxBlC4DlgZ5imYzPZ0UsrOzo7w8PA0M/Bdu3YNgCJFipgkvox67W/AhQoVIiws7Ln1jx8/plCh15suNyYmhjJlyjB79ux03584cSJTp05l9uzZHDt2DHd3d+rVq0dUVJS+zMCBA1m7di0rV67kwIEDREdH07Rp0zSDg3bs2JFTp06xdetWtm7dyqlTp+jcufNrxSqyv8jISL755htKlizJ+vXr0Wg09O/fn8uXL9O7d++3T0jpdCTsn03S3FrYR1zigeLACNsRlPlkIf0alJGElBBC5DBz585lx44dFChQgLp161K3bl3y589PYGAgc+fONXV42dLhw4dJSEjA1dWVwoUL69cHHdxCpbB1AETWnYSllUw3LoQQWUnFihUZ+vVg/pg/g5MnjtP+ww+pVP19LKysuR2pMP9kEr5zYnCfEk3lWbfZt2EpM77swM/tPIie1xgO/wSPrpn6NF7Lsz2lgDQ9pVLHsTTYWMhG8to9pW7cuJHubDAJCQncvXv3tfbVqFEjGjVqlO57iqIwffp0hg0bph/HavHixbi5ubF8+XL69OlDREQEv/76K0uXLtXPgvbbb79RoEABduzYQYMGDTh//jxbt27l8OHDVKpUCYBffvmFgIAALl68iI+Pz2vFLLIfrVbLokWLGDZsGPfv3wegfv36TJ06NU2vvLcSGUzkyp443DsAwE5tWYLeG8ewxpWwNMv6XUeFEEK8vooVK3L9+nV+++03Lly4gKIotG/fno4dO+obl8KwduzYAUCdOnX0w1HExUThGDgIgKNOzalYtanJ4hNCCPH2/Pz8WLliBQD3799n06ZNTJ/1I2dPnwTgRLCOmotin5ROYNbhbUypv5dqnt9g5V4MTfEnj/kVqAQag80NZ3DP9pQC0vSUunHjBkC6QwW8SzJcw6mPvgFs27YNR0dH/bJWq2Xnzp0GzcBdv36dkJCQNONUWVpaUrNmTQ4ePEifPn04ceIESUlJacp4eHhQqlQpDh48SIMGDTh06BCOjo76hBRA5cqVcXR05ODBgy9MSiUkJJCQkKBfjoyMBFKSZYqiGOw8n96nofcr/vOiOj569CifffaZfnyJokWLMmXKFJo0aaKfsvttJQdtIGnd5zgkRxCnWDDbvCvVO3/D54Wc9bFldXING5fUr3FJ/RpfdqtjQ5xHUlISPj4+bNq0id69exsgKpERO3fuBNDfzAQ4tfQbApRgQnGi+MfTTRSZEEIIY3Bzc6NHjx706NEDRVG4d+8e7Tt05O/9+7B3cSc+JoqgBzE0XBZLLisVnfz+Jbf1GTqUmkax/M5oitVLSVAVqQPW79a4TDmup1TqWE8qlYouXbqkec/c3Bxvb2+mTJlisMBCQkKAlIvoaW5ubty8eVNfxsLC4rlBu9zc3PTbh4SE4Orq+tz+XV1d9WXSM378eEaPHv3c+oiICKMkpVIvnqcHkReG82wdJycn87///Y+pU6eiKAr29vZ888039OrVCwsLC30S8q0kxqDdPgLnK39iDpzVebPWazhdm9TG3sqMiIiItz/GO0KuYeOS+jUuqV/jy251bIjPCHNzcxISErJFfWQVkZGRHDlyBEjpKQVw6Z+9VAxeDiq4V20c/rmcX7YLIYQQWZhKpSJfvnzs3b2Lf/75h9KlS/Mw7BGde/Rh346/eByfzOxjSQB8vy+RAg6xqFQL8M61iAGVrahVLQCncs1TklQupp/N7lU9pVKTUtmmp5ROpwNSTujYsWO4uLgYLainPdtYUxTllQ24Z8ukV/5V+xk6dCiDBg3SL0dGRlKgQAEcHR1xcDDsQGipSS5HR0dpnBrJ03V88+ZNPvroIw4dOgTARx99xKRJk3B3dzfc8e4cJ3pFd3LF3kKnqFikao5rqzEM9/d69cZZkFzDxiX1a1xSv8aX3erYUOfw+eefM2HCBObPn4+Z2bv7eEB2sXfvXrRaLUWKFMHLy4vY6AisN/ZFo1I4bl+HCnU7mDpEIYQQmUCj0ehnYM3nkZddf20gMTGRefPmcSroIrv3/821oJPcjkxpv9yK0LLvZgy51u9gQfO/aVViOMm5C2Hm0yhlsHSvKqAxz/TzeFlPKUVRst/je6lSs23GlpogCAkJIW/evPr1oaGh+t5T7u7uJCYmEh4enqa3VGhoKFWqVNGXSR0n6GkPHjx4rhfW0ywtLbG0tHxuvUqlMkqDOnW/2aGx/q5SqVSsXr2aXr16ERERgYODA/PmzaN9+/aGO4hOS/SuSVgfmIADOu4pTizMM5QenT/G3dHKcMd5B8k1bFxSv8Yl9Wt82amODXUOR44cYefOnWzfvh0/P7/nxpFas2aNQY4jUjw9nhTAmYWfU0m5RyhOFOkyx5ShCSGEMDELCwv9TH6KonDu3DkehEdy+OJdFi/8lSvH9/I4Po7Wv8fhahvPp++dp2qBS8AMvPLYU7hSQ1TFGkDRemCbOR14YmNTxsVKr6dUaGgocXFxqFQqChQokCnxvKkMJaVmzpxJ7969sbKyYubMmS8t279/f4MEVrBgQdzd3QkMDKRs2bJAyjS+e/fuZcKECQCUL18ec3NzAgMDadeuHQDBwcGcPXuWiRMnAhAQEEBERARHjx6lYsWKQEojMCIiQp+4EtlfbGwsAwcOZPHixUDKuGLLly83bNY4KoSwpV1xDk3pgbVZF0B47QkMrVkatTrrfwkTQghhWLly5aJNmzamDiPH2L59O5CSlDoVuIxKYesBCK0znVIuhustLYQQImtTqVT6Ca9qVYMhPdqSkJBInwGDWfzzLEJjFEbu+W/8abUqlhY+y2jv+zsf+Jqj5CuPxqdhSi8q99JgpBtyL+spldpLKl++fOl2tnmXZCgpNW3aND766COsrKyYNm3aC8upVKrXSkpFR0dz5coV/fL169c5deoUTk5OeHp6MnDgQMaNG0fRokUpWrQo48aNw8bGho4dOwIpjwH06NGDL7/8EmdnZ5ycnBg8eDB+fn76ASxLlChBw4YN6dWrFz///DMAvXv3pmnTpjLzXg5x584dWrRowT///INKpWLIkCGMHj0ac3PDdbFMvLCNpD9745z8mFjFkrm2fWnSeTA+eQ37qKcQQojsY+HChaYOIce4dOkSFy5cwNzcnPJlSpB7ecrsz4fdOlC5egsTRyeEEOJdZ2lpwaK5M/nfqG9ZvXo18xcs5FFkDJExcTwOvsnaC8msvZBMjw1xqFS7cbTcw6fvjaCxvztlajaDog2gUC2wtDNYTE8npVKHGHr8+DGQdcaTggwmpZ5+ZM+Qj+8dP36c2rVr65dTx3Dq0qULixYt4uuvvyYuLo5+/foRHh5OpUqV2L59O/b29vptpk2bhpmZGe3atSMuLo46deqwaNEiNBqNvsyyZcvo37+/fpa+5s2bM3v2bIOdh3h3HT16lJYtWxIcHIyzszMrVqygXr16hjtAciLhm0aQ+9RPWADndZ7s8ZvAp60aYGmmeeXmQgghhDC+9etTekXVrFmD2NWfUohIrmoKUrbbVBNHJoQQIitxd3fn008/5dNPP9WvW7dhIwuW/8HmP1cQk5QMQHSiwre7Evh2103qrfuJNiXm06OCDeqC1VD7NIJi9cGp0FvF8nRSKn/+/ADcunULyDoz78EbjCk1ZswYBg8ejI2NTZr1cXFxTJo0iREjRmR4X7Vq1XrpTHYqlYpRo0YxatSoF5axsrJi1qxZzJo164VlnJyc+O233zIcl8geVq5cSbdu3YiPj6dUqVIsW7YMPz8/g+1feXSdR0s64/z4DACrVA1xbz+ZT3zf7Wd2hRBCvDv+/PNPfv/9d27dukViYmKa9/755x8TRZX9pCalSjsnUCrhX2IVS8za/oqllc0rthRCCCFermXzZrRs3ozw8Bk8evSI249imbN4JYFrV/D43nUCr2kJvKal7+Z4uvv/xcxGu7G1+Ibk3EUwK94QitYHz4DXHiz96aRUavLp5s2bAFlmkHMA9etuMHr0aP1zik+LjY1l9OjRBglKiLeh0+kYMWIEHTp0ID4+nqZNm/L333/j6elpsGPEnvyD+NlVcX58hgjFhunOI6j9xWJqSkJKCCFEBs2cOZNu3brh6urKyZMnqVixIs7Ozly7do1GjRqZOrxsIzQ0lIMHDwIwIO8pAM5XmoBXifImjEoIIUR2kzt3bgoXLkyt9/z4ffYPhN+9xtYdu2n9cR/MLVMmvVpwKol+++xJUjSYhV+BQ7NhSXO0EwrCqo+xCPodokMzdLynk1JeXimzvKcmpbJST6nXTkopipLurDOnT5/GycnJIEEJ8aYSExPp2LEj33//PQBfffUV69atS/PI59sdIJaHy/tis74n1roYTuiKsSHgd/p/OghXh+w9u54QQgjDmjNnDvPmzWP27NlYWFjw9ddfExgYSP/+/YmIiDB1eNnGpk2bUBSFMu5meDqqOez+EeUbdzN1WEIIIXKABnVqsXrxXMLDHvLll18CcCjGk49dVtIvaQB/JNfggeKAJika1YUN2AR+hWpKMbQ/14I9/4O7/4BOl+6+n05KpXbAiIyM5PHjx1mqp1SGH9/LnTu3firnYsWKpUlMabVaoqOj6du3r1GCFCIj4uPjadu2LZs3b8bc3Jx58+bRtWtXgJc+JppR2pBzPF7SCZfYq+gUFUvN21C60//o7J3nrfcthBAi57l165Z+JmBra2uioqIA6Ny5M5UrV5bxLw1AURR+nJ0yc3Sb4hrOWvpTocd00wYlhBAix7G1taVt27ZMmTKFuId3WPF5fR5E1WTPxVBGXQgh7PJRArTHqa0+SWn1dTTBJyH4JOwZT7KNK5pi9VAVawCFaoNVyqDmz86+5+LiwsOHD7l69aq+x1S2SkpNnz4dRVHo3r07o0ePxtHRUf+ehYUF3t7eBAQEGCVIIV4lJiaGli1bsmPHDqysrFi3bh0NGjQw2P4jDy/FctuXOCsJhCq5+L3Ad3zcqQsOVoabwU8IIUTO4u7uTlhYGF5eXnh5eXH48GHKlCnD9evXDXIzRcCaP3/nn5OnsdRA0wr58OixHDNzC1OHJYQQIgcqVqwYkDI7fExMDHnsbfmgQgE+qFCAJG15jl1/xJ//3ub7q9fxfnyI99Unqa4+g11sKJxaBqeWoVObo3hWQePTgLjYGCAlKQUpj+o9fPiQbdu2kZiYiL29PQUKvPvDy2Q4KdWlSxcgJdNWpUoVzM3ly7h4N9y8eZNmzZpx5swZ7Ozs2LRpEzVr1jTMzpPiCV41gLxXVgJwQCnNo/qz+LRKmXQfYxVCCCEy6v3332fjxo2UK1eOHj168MUXX/Dnn39y/PhxWrduberwsrzDB/fT7eOPAPionB2un2zCyTWfiaMSQgiRUzk5OeHs7ExYWBiXL1/G399f/97F8+f4edIk6tWrx+jBH3HrUQN2XQjls/N3UW4cpAb/UFt9kkKEwI29cGPvf0mpw9NA/QHeXp4cP36c33//HYAyZcqgVr/2iE2ZLkNJqcjISP3rsmXLEhcXp+8q9iwHBwfDRCZEBly6dIm6dety+/ZtXF1dWb9+PZUrVzbIvrVh1wlb0J68MRfRKSp+s+5A1W7/o5qb46s3FkIIIV5h3rx56J6ME9G3b1+cnJw4cOAAzZo1kyER3tLVS+dp1qAOUfFaAgqYM3D2BvIVKmHqsIQQQuRwxYoV49ChQ1y6dEmflLp69SqVKlUiNjaWJUuW8Ndff9G2bVs+bt6cblULEpNQmb+vPGTehVCuXDhF6dijVFdOkKw7DID1maVwdRkVohX+JGW8b0jJ3WQFGUpK5cqV65W9QlIHQNdqtQYJTIhXOXPmDPXq1eP+/fsUL16cwMBA8ufPb5B9R55aj2b9J7gqMYQp9qwrNJqPOnbFylxjkP0LIYQQarU6zR3Mdu3a0a5dOxNGlGLOnDlMmjSJ4OBgfH19mT59OtWrVzd1WBl2PegIM/s14WF0Ej4uZkxf/Cd+FWubOiwhhBCCokWLcujQIa5cuaJf9/vvvxMbG6tfXr58OcuXL6d27dosW7aMvHnzUt/Xnfq+7iiKH0H3WrD52GXgPQA2q2rSUDlDA+8whjx1rDKPt8KuH6BYQ/AoC+9or6kMJaV2795t7DiEeC1//fUXHTt25PHjx5QuXZrAwEBcXV3ffsfaZO6tGYpH0DwATirFCKn/Ez2qVnj7fQshhBBPqVq1KjVr1qRWrVpUrVoVW1tbU4fEqlWrGDhwIHPmzKFq1ar8/PPPNGrUiHPnzuln9nlXxcfFcHLV9+Q+OY8lR8MBGDDseyrWbm7iyIQQQogUuXPnBtBPbgKwY8cOAGbOnEn+/PlZsmQJW7duZffu3fj7+/Pbb79Rr149AFQqFaXyOZJ4X80QwMUlD7bt5jLmQgihHMFMM4DkJx2Fqjveg30TYd9Ekq1d/hssvfD7YPXuPP2ToaRURsfnOXXq1NvEIsQrabVaxowZw/fff4+iKAQEBLBp0yacnJzeet+6iGBCFnTEI+IfAP60aEGZrtNp5PH2+xZCCCGe1bRpU/bu3cvs2bOJj4+nfPny+iRVtWrVsLOzy/SYpk6dSo8ePejZsyeQMtHNtm3b+Omnnxg/fnymx/O0y+fPEHLvDtY21jjlcsTKyhJFp+NxyHWiLuym6J01FIp+RLXfYnkcD36lfOn9+VcmjVkIIYR4Wuqg5KnDIcXGxnLgwAEA6tWrh7u7Oy1btuTixYu0b9+ef//9lwYNGjBhwgS++uq/z7RLly4BULy4D23K56dN+fwkacvRL/I2vy9dSPGGnfnZXkNt7UlqqP/FIe4hnF4Bp1egU5mhFKiMpnhDKNoAXIqCCcdLzvBA5y8SERHBsmXLmD9/PqdPn5bH94TRhIeH89FHH/HXX38B8MknnzBt2jQsLS3fet9R53ej+6MbHrpwohRr/sw/lPZdPsXG4q3/iQghhBDpGjp0KEOHDkWr1XLs2DH27NnDnj17mDp1KiqVioSEhEyNJzExkRMnTjBkyJA06+vXr8/BgwfT3SYhISFNnKnjkCqKYvAZBHu1rcu+8w8AsDGH4dUtGVLNgvxPGtJanULlVUlceaTDy8uLLX9tRa1Wy0yGryH17yZ1ZhxSv8Yl9WtcUr+GYWVlBaQkoxRF4ezZsyQmJuLm5kbRokWJjIxEURR8fHw4dOgQX3zxBfPmzePrr7/m2rVrjBo1CldXVy5cuACkPA6Y+jcxU6uYN3U886am3ES6/SiW3RdD+eJ8MEk3DlJN+Yf31Scpor4Htw6k/GwfjuLfCVrMNvi5ZvRaeeNv3Lt27WLBggWsWbMGLy8v2rRpw6+//vqmuxPipc6cOUOrVq24evUq1tbW/Pzzz3Tu3Pntd6zTcW/zeNxOTEaDjguKJ1dr/kjX2tVldj0hhBCZ4vLly5w+fZrTp0/z77//4uDgYJIxnB4+fIhWq8XNzS3Nejc3N0JCQtLdZvz48YwePfq59REREQb/4qJRq7HUQJIOYpPg210JxGjN+KRWXoJtfFh1xY7jd1Zhb2/P6tWrsbOzIyIiwqAxZHeKohAdHQ0g7SAjkPo1Lqlf45L6NayoqCgiIiK4ePEiAJ6enkRGRj5XxxMmTCA6Oprly5czd+5c9u7dy7p16wgKCtJv96LPOgcNtCiZmxYlcxOX5MOxm635+Wo4166c53++t/EOP4TZnUPE5S5OohE+L5+eMO9lXispdefOHRYtWsSCBQuIiYmhXbt2JCUlsXr1akqWLPlGgQrxMoqisHjxYj799FNiY2Px9vZm7dq1aabPfON9x4Zze8HHeD7cB8BWs9p4fjyXJp7ub71vIYQQ4lXat2/Pvn370Ol01KhRgxo1ajB06FBKly5t0rie/bKROplNeoYOHcqgQYP0y5GRkRQoUABHR0eDz8i84997RERE4ODgwMyZMxk0aBATDyYy8M9DlM2dmzaFCgEwduxYypUrZ9Bj5xSpiURHR0f50mkEUr/GJfVrXFK/hpE6ppRWq8XR0ZGwsDAAvL29cXRMGefp2Tr+6aefsLOzY968eZw/fx4fHx/9e6VLl9Zv9zKOQDMXJ5qVL4iipMzKp1KpIDEaa8DawvBDBmT0OslwUqpx48YcOHCApk2bMmvWLBo2bIhGo2Hu3LlvHKQQL/P48WP69u3LqlWrgJRnbFesWIGzs/Nb7zv6+lHil3XGMzmEBMWcP9wG0KLbN9hbW7z1voUQQoiM+OOPP3BxcaFr167Url2b6tWrm2QcqVQuLi5oNJrnekWFhoY+13sqlaWlZbqP0atUKqN8aVGpVKjVar744guWLFnCqVOn+O233yhZsiS3bt3C0dGRXr16yRemt5D6t5M6NA6pX+OS+jUuqd+3Z2NjA6SMKaVSqbhz5w4A+fPnT1O/T9exg4MDP//8M7Vq1aJfv348fvxY/56vr+9r/z3SlLe0f/OTeZ3jvESG5wTcvn07PXv2ZPTo0TRp0gSNRvPGwQnxKgcOHKBMmTKsWrUKMzMzxo0bx19//fX2CSlF4W7gbCwWN8IlOYSbihvbAn7jo0+GS0JKCCFEpnr06BHz588nOTmZ4cOH4+LiQqVKlfjmm2/04ydmJgsLC8qXL09gYGCa9YGBgVSpUiXT43mVLl26APDFF1/QqFEjANq1a6cfRFYIIYR41zw70HlqUqpAgQKv3LZDhw6Eh4ezZcsW+vbty+zZs9P0msqqMtxTav/+/SxYsIAKFSpQvHhxOnfuTPv27Y0Zm8iBFEVhwoQJDBs2DJ1OR+HChVm+fDkVK1Z8+30nxnBj/qcUvLcJgL3qSjh1/IXmRbzeet9CCCHE68qVKxfNmzenefPmAFy9epWxY8cydepUJk+ebJLJYwYNGkTnzp2pUKECAQEBzJs3j1u3btG3b99Mj+VVevTowbp169i7dy86nQ4fHx+GDRtm6rCEEEKIF3o2KXX79m0gY0mpVI0aNdLfjMkOMpyUCggIICAggBkzZrBy5UoWLFjAoEGD0Ol0BAYGUqBAAeztjdf1S2R/iqIwcOBAZs6cCcDHH3/M7NmzDXJdxd47T9yiDymYdJNkRc0a557U7zGWXLZvP3OfEEII8SYePXrE3r179bPuBQUF4eTkRIsWLahdu7ZJYmrfvj1hYWGMGTOG4OBgSpUqxZYtW/Dyevdu4Njb27N7925Onz5NQkIC5cuXx8xMZs0VQgjx7npRUip//vwmi8nUXvuT28bGhu7du9O9e3cuXrzIr7/+yv/+9z+GDBlCvXr12LBhgzHiFNmcoigMGDCAWbNmoVKpmDFjBp9//rlB9h184Ddy7fgSD+K5r+TicLnJfNC8rTwLLYQQwqTy5MmDi4sL1atXp1evXtSqVYtSpUqZOiz69etHv379TB1GhqhUKoNMfiKEEEJkhtSkVHx8PFqtlnv37gGv11Mqu3mr20k+Pj5MnDiR8ePHs3HjRhYsWGCouEQO8mxCav78+XTv3v3td5ycyJXfBlDkxnIAjqtKoWn3Ky1KFn/7fQshhBBv6fTp0+9EEkoIIYQQmcPKygpI6SkVHh6uf1T/RROK5AQG6eOs0Who2bIlLVu2NMTuRA6SlJRE7969WbRokUETUvEPbxL664cUiTsHwHr7Dvi1H0nBfO5vvW8hhBDCEFITUqGhoVy8eBGVSkWxYsVwdXU1cWRCCCGEMIanH9+LiYkBUhJVZmZmKIpiytBMRh68FyYTExNDu3bt2LJlCxqNhvnz59O1a9e33m/w8Y3Ybv4ETyWKx4ote31/oEmbLkRHRb590EIIIYSBREZG8umnn7JixQp0Oh2QcqOvffv2/Pjjjzg6Opo4QiGEEEIY0tNJqejoaABsbW1NGZLJqU0dgMiZHj58yPvvv8+WLVuwtrZm3bp1b5+Q0mm5tHIobhs746BEEURhrrTaQot23dCoZfwoIYQQ75aePXty5MgRNm/ezOPHj4mIiGDTpk0cP36cXr16mTo8IYQQQhhYej2l7OzsTBmSyUlPKZHpbty4QYMGDbh06RJOTk5s2rSJgICAt9pnwuNg7vzaiWJRx0EF222aUKbnT7g5yV1mIYQQ76bNmzezbds2qlWrpl/XoEEDfvnlFxo2bGjCyIQQQghhDKlJqaSkJCIiIgDpKSVJKZGpDh48SKtWrQgNDcXT05Nt27ZRvPjbDTx+98QWrDd9QmHlMbGKJTuLDKVRxwGYaaQjoBBCiHeXs7Nzuo/oOTo6kjt3bhNEJIQQQghjSk1KAYSFhQHSU0q+tYtMs2TJEmrXrk1oaCj+/v4cPHjwrRJSSnIi55cOIt/GDjgpj7mMJ2car6FZ5y8kISWEEOKdN3z4cAYNGkRwcLB+XUhICF999RXfffedCSMTQgghhDGkzr4HKUPagPSUkp5Swui0Wi3ffvstEydOBKB169YsWbLkrf7xRQZfIWxxZ0rEp8yut8O2CX7df6Sos9xZFkIIkTX89NNPXLlyBS8vLzw9PQG4desWlpaWPHjwgJ9//llf9p9//jFVmEIIIYQwEI1Gg7m5OUlJSTx48ACQnlKSlBJGFRUVRadOndiwYQMAw4YNY8yYMajVb96T6dqeJbju+YaCxBKh2HK41CjqtemFWgYzF0IIkYW0bNnS1CEIIYQQIpNZW1uTlJQkPaWekKSUMJqbN2/SrFkzzpw5g6WlJQsWLKBjx45vvL+kmHAuLf4M39BNAJxRF0fVdj4NSvoZKmQhhBAi04wcOdLUIQghhBAik1lbWxMZGSk9pZ6QpJQwir179/LBBx/w4MED3N3dWbduHZUqVXrj/d0+sRXLzZ/hq3uATlGxw+UjAnpMxt7G+tUbCyGEEEIIIYQQ74DUwc5Te0rl9KSUjAYtDOrRo0f06tWLWrVq8eDBA/z9/Tl69OgbJ6S0CTGcmd+XAhvb46p7wG3cOFRjKfU//1ESUkIIIbIktVqNRqN57id37txUrlyZNWvWmDpEIYQQQhhJalIqtaeUPL4nhAEoisKyZcsYNGiQ/h9Xz549mT59+hv/I7t3dj/K2j74ae8CsNu+KaW6zKSqi7PB4hZCCCEy29q1a9Nd//jxY44ePUqnTp1YvHgxH3zwQSZHJoQQQghjk55SaUlSSry1R48e8eGHHxIYGAhAyZIl+fnnn6lWrdob7S8xJoLzy7+m1J1VaFQK95XcXKw0nlqNPkSlksHMhRBCZG0tWrR44XtdunShZMmSTJ48WZJSQgghRDb0bFIqp/eUksf3xFu5d+8eNWrUIDAwECsrK3744QdOnjz5xgmpy/tW8nhyOcrcXYlGpXDApg7JfQ9So3EHSUgJIYTIEerXr8+lS5dMHYYQQgghjMDKygqA5ORkQHpKSU8p8cYePnxIzZo1uXLlCh4eHmzbto1SpUq90b4i7t/g9rLPKRW5D4A7uHEjYCxV638gySghhBA5SlxcnL7BKoQQQojsxcbGJs1yTu8pJUkp8Ubu3LlDy5YtuXLlCl5eXuzevZuCBQu+9n4SY6MI+uN7il9fSCkSSVI0HHD9kLKdxlHNMZfhAxdCCCHecb/88gtly5Y1dRhCCCGEMAIHB4c0y9JTSojXtH37dj7++GPu37+Pk5MTW7Zsee2ElKLTErT1F9yOTaSsEgbAWU1JaDqF2mWrGCNsIYQQ4p0waNCgdNdHRERw/Phxrl69yv79+zM5KiGEEEJkBnt7+zTL0lNKiAwICwtjxYoVLF68mOPHjwPg5+fHunXrKFSoUMZ3pChcPrwJ9a4xlEpKGS/jLq5cL/sNAU27o9HIMGdCCCGyt5MnT6a73sHBgYYNG9KvXz+8vLwyOSohhBBCZIZnk1LSU0qIl3j8+DETJ05k+vTpxMXFAWBmZsYnn3zCuHHjXusf0JWjf6Hd+QM+CWcAiFasOe7ZnQoffks125z9D1EIIUTOsXv3blOHIIQQQggTefbxPekpJUQ67t+/z7x585g2bRrh4eEAlClThm7dutGhQwdcXV0ztB9Fp+PSoQ3o9k+jRPwpABIUc446t6BQ6xHUyi93goUQQgghhBBC5AzP9pTKlSuXaQJ5R0hSSqQRERHB6NGj+fHHH0lMTATA19eXcePG0axZswzPhJecGM+ZrQvIffpnfLQ3AEhUNBxzaoZni++o7l3EWKcghBBCCCGEEEK8k55NSjk5OZkokneDJKWEXmBgIJ06dSI0NBSASpUq0b9/f9q3b49Go8nQPh6F3OLK9rkUvLaCsjwCIEax5FSe5uRv/BVVC/kYLX4hhBBCCCGEEOJd9vTje3Z2dlhYWJgwGtOTpFQOd/HiRdauXcuOHTvYuXMnAMWLF2fatGk0bNgwQ/vQJSdxbv8atMcX4xt9iIoqHQChOHHJuyMlmvanqoub0c5BCCGEEEIIIYTICp7uKZXTe0mBJKVyrOvXrzNkyBD++OMPFEUBQK1W07t3b6ZNm4aVldVLt1d0Oq6fPcSDwysoeG8LpQhLeUMF58xKEunbCf9G3almZW3sUxFCCCGEEEIIIbIESUqlJUmpHCY0NJSFCxfyv//9j8ePHwPQsGFDGjZsSJMmTShS5MVjPSk6HdeDDvPg8Cry39tKISWEQk/eC8ee865NcK3Zm5K+5Y1/IkIIIYQQQgghRBbz9ON7kpSSpFS2pSgKV69eJSwsDAcHB06ePMnatWtZv349SUlJQMqYUfPmzaN06dIv3E9UxCOuHN5E0sVteD86SCEe6RNRcYoFQXYBKL6t8KvdnirWNplwZkIIIYQQQgghRNb0dE8pZ2dnE0bybpCkVDYTFRXF3LlzmTVrFrdv3063TMWKFenTpw+dOnV6blC1yMdh3Di5m5grB3B8cJyiCecoq9Lq349TLDhnVwldyZaUqPEBFewdjXo+QgghhBBCCCFEdvF0UurZmfhyohyVlJozZw6TJk0iODgYX19fpk+fTvXq1U0dlkHcu3ePefPmMWvWLB49Spn1ztLSkjx58hAdHY2npydNmjThgw8+oGzZsgDERD3m2ul9RFz/B0LO4BJxFu/k65RWKf/tWAW3VB7cc6mGtW9DilVsSHkbW1OcohBCCCGEEEIIkaU9nYgyNzc3YSTvhhyTlFq1ahUDBw5kzpw5VK1alZ9//plGjRpx7tw5PD09TR3eG4mKimL79u389ttvbNy4Ea02pUdT0aJFGTp0KO3btSM5KZ7w+7d5fO8ycSGXSTg0h1Pbb+Ecf4t8umCKP52AAlDBHZU7wQ7+KJ6V8fCvj2dhX7JmDQkhhBBCCCGEEO+OpxNRZmY5JiXzQjmmBqZOnUqPHj3o2bMnANOnT2fbtm389NNPjB8/3qSx/TpjLPduXcPayhIzjRoHW2sKe+bF1socRdGRmJjIhau3uXT9LrdDHnA7+CG3Q8K4G/oIrVan349/QWc+rJyXZsWtyXVrHOqJX+GgSsIB8ErvwCp4QG7uWRUh1qkkFvnL4ulfm/we3uTPrJMXQgghhBBCCCFyoGeH08mJckRSKjExkRMnTjBkyJA06+vXr8/BgwfT3SYhIYGEhAT9cmRkJJAygLiiKOlu86Z+njGJ4zei0qwzV4OrrQozNdyPUYhPTn/bwrlVtCxuTjd/c3xdk4BbkJqnUqX8ilKseaBxI8I6P/H2nqidCmOTtyh5i5XDxTU/Ls/s09Dn9y5I/btlx3N7F0j9GpfUr3FJ/Rpfdqvj7HIeQgghhDAteXwvhySlHj58iFarxc3NLc16Nzc3QkJC0t1m/PjxjB49+rn1ERERBm+MenvmB+6SrIMkrcKDqERCIxO4G/XfcXLZmlOqQC48XezIn8cOTxd7PF3tcXZ2QmdhzyMLO/Zb2KGytMPMOheWud2wze2Bg0terKxtcQLSm2wyIiLCoOfyrlIUhejoaABUKpWJo8l+pH6NS+rXuKR+jS+71XHqjSohhBBCiDdRuXJlDh8+TOfOnU0disnliKRUqmcbwoqivLBxPHToUAYNGqRfjoyMpECBAjg6OuLg4GDQuFbtPktERASOjo6oVCoUReHmzZuEh4eTmJiIs7MzhQsXzhYNeVNJTSSm1rEwLKlf45L6NS6pX+PLbnWcHc5BCCGEEKazd+9eHjx4QL58+UwdisnliKSUi4sLGo3muV5RoaGhz/WeSmVpaYmlpeVz61UqlVEao6n7Tf0pWLAgBQsWNPhxcrKn61cYntSvcUn9GpfUr/FlpzrODucghBBCCNOxsLCQhNQTalMHkBksLCwoX748gYGBadYHBgZSpUoVE0UlhBBCCCGEEEIIkXPliJ5SAIMGDaJz585UqFCBgIAA5s2bx61bt+jbt6+pQxNCCCGEEEIIIYTIcXJMUqp9+/aEhYUxZswYgoODKVWqFFu2bMHLy8vUoQkhhBBCCCGEEELkODkmKQXQr18/+vXrZ+owhBBCCCGEEEIIIXK8HDGmlBBCCCGEEEIIIYR4t+SonlJvI3U668jISKPsOzIyMtvMSvQukjo2Lqlf45L6NS6pX+PLbnWc2hZIbRuI/0h7KWuTOjYuqV/jkvo1Lqlf48tudZzR9pIkpTIoKioKgAIFCpg4EiGEEEK8C6KionB0dDR1GO8UaS8JIYQQ4mmvai+pFLnNlyE6nY579+5hb29v8KxlZGQkBQoU4Pbt2zg4OBh03yKF1LFxSf0al9SvcUn9Gl92q2NFUYiKisLDwwO1WkZCeJq0l7I2qWPjkvo1Lqlf45L6Nb7sVscZbS9JT6kMUqvV5M+f36jHcHBwyBYX37tM6ti4pH6NS+rXuKR+jS871bH0kEqftJeyB6lj45L6NS6pX+OS+jW+7FTHGWkvye09IYQQQgghhBBCCJHpJCklhBBCCCGEEEIIITKdJKXeAZaWlowcORJLS0tTh5JtSR0bl9SvcUn9GpfUr/FJHQtDkOvI+KSOjUvq17ikfo1L6tf4cmody0DnQgghhBBCCCGEECLTSU8pIYQQQgghhBBCCJHpJCklhBBCCCGEEEIIITKdJKWEEEIIIYQQQgghRKaTpJQQQgghhBBCCCGEyHSSlBJCCCGEEEIIIYQQmU6SUu+AOXPmULBgQaysrChfvjz79+83dUjZwqhRo1CpVGl+3N3dTR1WlrVv3z6aNWuGh4cHKpWKdevWpXlfURRGjRqFh4cH1tbW1KpVi6CgINMEm0W9qo67du363DVduXJl0wSbxYwfP5733nsPe3t7XF1dadmyJRcvXkxTRq7ht5OROpZrWLwNaS8Zh7SXDE/aTMYl7SXjkjaTcUl76XmSlDKxVatWMXDgQIYNG8bJkyepXr06jRo14tatW6YOLVvw9fUlODhY/3PmzBlTh5RlxcTEUKZMGWbPnp3u+xMnTmTq1KnMnj2bY8eO4e7uTr169YiKisrkSLOuV9UxQMOGDdNc01u2bMnECLOuvXv38umnn3L48GECAwNJTk6mfv36xMTE6MvINfx2MlLHINeweDPSXjIuaS8ZlrSZjEvaS8YlbSbjkvZSOhRhUhUrVlT69u2bZl3x4sWVIUOGmCii7GPkyJFKmTJlTB1GtgQoa9eu1S/rdDrF3d1d+d///qdfFx8frzg6Oipz5841QYRZ37N1rCiK0qVLF6VFixYmiSe7CQ0NVQBl7969iqLINWwMz9axosg1LN6ctJeMR9pLxiVtJuOS9pLxSZvJuKS9pCjSU8qEEhMTOXHiBPXr10+zvn79+hw8eNBEUWUvly9fxsPDg4IFC/Lhhx9y7do1U4eULV2/fp2QkJA017KlpSU1a9aUa9nA9uzZg6urK8WKFaNXr16EhoaaOqQsKSIiAgAnJydArmFjeLaOU8k1LF6XtJeMT9pLmUc+bzKHfNYYjrSZjEvaS/L4nkk9fPgQrVaLm5tbmvVubm6EhISYKKrso1KlSixZsoRt27bxyy+/EBISQpUqVQgLCzN1aNlO6vUq17JxNWrUiGXLlrFr1y6mTJnCsWPHeP/990lISDB1aFmKoigMGjSIatWqUapUKUCuYUNLr45BrmHxZqS9ZFzSXspc8nljfPJZYzjSZjIuaS+lMDN1AAJUKlWaZUVRnlsnXl+jRo30r/38/AgICKBw4cIsXryYQYMGmTCy7EuuZeNq3769/nWpUqWoUKECXl5ebN68mdatW5swsqzls88+499//+XAgQPPvSfXsGG8qI7lGhZvQ/59Goe0l0xDrmfjkc8aw5E2k3FJeymF9JQyIRcXFzQazXMZ5dDQ0Ocyz+Lt2dra4ufnx+XLl00dSraTOkuPXMuZK2/evHh5eck1/Ro+//xzNmzYwO7du8mfP79+vVzDhvOiOk6PXMMiI6S9lLmkvWRc8nmT+eSz5s1Im8m4pL30H0lKmZCFhQXly5cnMDAwzfrAwECqVKlioqiyr4SEBM6fP0/evHlNHUq2U7BgQdzd3dNcy4mJiezdu1euZSMKCwvj9u3bck1ngKIofPbZZ6xZs4Zdu3ZRsGDBNO/LNfz2XlXH6ZFrWGSEtJcyl7SXjEs+bzKffNa8HmkzGZe0l54nj++Z2KBBg+jcuTMVKlQgICCAefPmcevWLfr27Wvq0LK8wYMH06xZMzw9PQkNDWXs2LFERkbSpUsXU4eWJUVHR3PlyhX98vXr1zl16hROTk54enoycOBAxo0bR9GiRSlatCjjxo3DxsaGjh07mjDqrOVldezk5MSoUaNo06YNefPm5caNG3z77be4uLjQqlUrE0adNXz66acsX76c9evXY29vr7+75+joiLW1NSqVSq7ht/SqOo6OjpZrWLwxaS8Zj7SXDE/aTMYl7SXjkjaTcUl7KR2mmPJPpPXjjz8qXl5eioWFhVKuXLk000GKN9e+fXslb968irm5ueLh4aG0bt1aCQoKMnVYWdbu3bsV4LmfLl26KIqSMj3syJEjFXd3d8XS0lKpUaOGcubMGdMGncW8rI5jY2OV+vXrK3ny5FHMzc0VT09PpUuXLsqtW7dMHXaWkF69AsrChQv1ZeQafjuvqmO5hsXbkvaScUh7yfCkzWRc0l4yLmkzGZe0l56nUhRFMU66SwghhBBCCCGEEEKI9MmYUkIIIYQQQgghhBAi00lSSgghhBBCCCGEEEJkOklKCSGEEEIIIYQQQohMJ0kpIYQQQgghhBBCCJHpJCklhBBCCCGEEEIIITKdJKWEEEIIIYQQQgghRKaTpJQQQgghhBBCCCGEyHSSlBJCCCGEEEIIIYQQmU6SUkKIbG/UqFH4+/ub7PjfffcdvXv3zlDZwYMH079/fyNHJIQQQgiRlrSXhBCmoFIURTF1EEII8aZUKtVL3+/SpQuzZ88mISEBZ2fnTIrqP/fv36do0aL8+++/eHt7v7J8aGgohQsX5t9//6VgwYLGD1AIIYQQ2Z60l4QQ7ypJSgkhsrSQkBD961WrVjFixAguXryoX2dtbY2jo6MpQgNg3Lhx7N27l23btmV4mzZt2lCkSBEmTJhgxMiEEEIIkVNIe0kI8a6Sx/eEEFmau7u7/sfR0RGVSvXcume7o3ft2pWWLVsybtw43NzcyJUrF6NHjyY5OZmvvvoKJycn8ufPz4IFC9Ic6+7du7Rv357cuXPj7OxMixYtuHHjxkvjW7lyJc2bN0+z7s8//8TPzw9ra2ucnZ2pW7cuMTEx+vebN2/OihUr3rpuhBBCCCFA2ktCiHeXJKWEEDnSrl27uHfvHvv27WPq1KmMGjWKpk2bkjt3bo4cOULfvn3p27cvt2/fBiA2NpbatWtjZ2fHvn37OHDgAHZ2djRs2JDExMR0jxEeHs7Zs2epUKGCfl1wcDAdOnSge/funD9/nj179tC6dWue7rRasWJFbt++zc2bN41bCUIIIYQQLyHtJSGEsUlSSgiRIzk5OTFz5kx8fHzo3r07Pj4+xMbG8u2331K0aFGGDh2KhYUFf//9N5ByB0+tVjN//nz8/PwoUaIECxcu5NatW+zZsyfdY9y8eRNFUfDw8NCvCw4OJjk5mdatW+Pt7Y2fnx/9+vXDzs5OXyZfvnwAr7yrKIQQQghhTNJeEkIYm5mpAxBCCFPw9fVFrf4vL+/m5kapUqX0yxqNBmdnZ0JDQwE4ceIEV65cwd7ePs1+4uPjuXr1arrHiIuLA8DKykq/rkyZMtSpUwc/Pz8aNGhA/fr1adu2Lblz59aXsba2BlLuNgohhBBCmIq0l4QQxiZJKSFEjmRubp5mWaVSpbtOp9MBoNPpKF++PMuWLXtuX3ny5En3GC4uLkBKt/TUMhqNhsDAQA4ePMj27duZNWsWw4YN48iRI/rZYx49evTS/QohhBBCZAZpLwkhjE0e3xNCiAwoV64cly9fxtXVlSJFiqT5edFsNYULF8bBwYFz586lWa9SqahatSqjR4/m5MmTWFhYsHbtWv37Z8+exdzcHF9fX6OekxBCCCGEIUl7SQjxuiQpJYQQGfDRRx/h4uJCixYt2L9/P9evX2fv3r0MGDCAO3fupLuNWq2mbt26HDhwQL/uyJEjjBs3juPHj3Pr1i3WrFnDgwcPKFGihL7M/v37qV69ur5buhBCCCFEViDtJSHE65KklBBCZICNjQ379u3D09OT1q1bU6JECbp3705cXBwODg4v3K53796sXLlS363dwcGBffv20bhxY4oVK8bw4cOZMmUKjRo10m+zYsUKevXqZfRzEkIIIYQwJGkvCSFel0p5el5NIYQQBqUoCpUrV2bgwIF06NDhleU3b97MV199xb///ouZmQz7J4QQQojsT9pLQuRc0lNKCCGMSKVSMW/ePJKTkzNUPiYmhoULF0oDSwghhBA5hrSXhMi5pKeUEEIIIYQQQgghhMh00lNKCCGEEEIIIYQQQmQ6SUoJIYQQQgghhBBCiEwnSSkhhBBCCCGEEEIIkekkKSWEEEIIIYQQQgghMp0kpYQQQgghhBBCCCFEppOklBBCCCGEEEIIIYTIdJKUEkIIIYQQQgghhBCZTpJSQgghhBBCCCGEECLTSVJKCCGEEEIIIYQQQmQ6SUoJIYQQQgghhBBCiEwnSSkhhBBCCCGEEEIIkekkKSWEEEIIIYQQQgghMp0kpYQQQgghhBBCCCFEppOklBBCCCGEEEIIIYTIdJKUEkJk2KJFi1CpVPofKysr3N3dqV27NuPHjyc0NNTUIb5TTp48Sc2aNXF0dESlUjF9+nQAdu7cSYUKFbC1tUWlUrFu3Tp93d64ceO1jjFq1ChUKpXhg39KbGwso0aNYs+ePUY9jhBCCJFdHDlyhFatWuHp6YmlpSVubm4EBATw5Zdfpik3Z84cFi1a9FbHUqlUfPbZZ68st2fPHlQqVZrP8/TaEbVq1aJWrVr6ZWkHpOjatSve3t6mDkOIbMfM1AEIIbKehQsXUrx4cZKSkggNDeXAgQNMmDCByZMns2rVKurWrWvqEN8J3bt3JyYmhpUrV5I7d268vb1RFIV27dpRrFgxNmzYgK2tLT4+PiQnJ3Po0CHy5s37Wsfo2bMnDRs2NNIZpIiNjWX06NEAaRqpQgghhHje5s2bad68ObVq1WLixInkzZuX4OBgjh8/zsqVK5kyZYq+7Jw5c3BxcaFr165Gj6tcuXIcOnSIkiVLvrTcnDlz0ixLOyDFd999x4ABA0wdhhDZjiSlhBCvrVSpUlSoUEG/3KZNG7744guqVatG69atuXz5Mm5ubi/cPjY2Fhsbm8wI1aTOnj1Lr169aNSokX7d3bt3efToEa1ataJOnTppyufJk+e1j5E/f37y58//1rEKIYQQwjAmTpxIwYIF2bZtG2Zm/33d+vDDD5k4caLJ4nJwcKBy5cqvLPeqpFV2ERcXh7W1dYbLFy5c2IjRCJFzyeN7QgiD8PT0ZMqUKURFRfHzzz/r13ft2hU7OzvOnDlD/fr1sbe31ydjAgMDadGiBfnz58fKyooiRYrQp08fHj58+Nz+169fT+nSpbG0tKRQoULMmDHjtR5d27p1K3Xq1MHR0REbGxtKlCjB+PHj05TZsGEDAQEB2NjYYG9vT7169Th06NBz+7p8+TIdO3bE1dUVS0tLSpQowY8//qh/P/VRvOTkZH766Sf9446jRo3SJ5C++eYbVCqVvhv4ix7fe1XcL6qDVatWERAQgK2tLXZ2djRo0ICTJ0+mKZP6t7ly5QqNGzfGzs6OAgUK8OWXX5KQkADAjRs39Mmy0aNH688lM+7oCiGEEFlRWFgYLi4uaRJSqdTq/75+eXt7ExQUxN69e/Wfr6ntgvj4eL788kv8/f1xdHTEycmJgIAA1q9f/8Lj/vzzzxQrVgxLS0tKlizJypUr07yf3uN76Xn68b2XtQP279+PSqVixYoVz+1jyZIlqFQqjh079sLjpLZ9du3aRa9evXB2dsbBwYGPP/6YmJgYQkJCaNeuHbly5SJv3rwMHjyYpKSkNPsYPXo0lSpVwsnJCQcHB8qVK8evv/6Koihpynl7e9O0aVPWrFlD2bJlsbKy0vf+CgoKon79+tjY2JAnTx4+/fRTNm/e/Fxdpff4Xuqjk0uXLqVEiRLY2NhQpkwZNm3a9NI6FkL8R3pKCSEMpnHjxmg0Gvbt25dmfWJiIs2bN6dPnz4MGTKE5ORkAK5evUpAQAA9e/bE0dGRGzduMHXqVKpVq8aZM2cwNzcHUhIzrVu3pkaNGqxatYrk5GQmT57M/fv3MxTXr7/+Sq9evahZsyZz587F1dWVS5cucfbsWX2Z5cuX89FHH1G/fn1WrFhBQkICEydOpFatWuzcuZNq1aoBcO7cOapUqaJPwrm7u7Nt2zb69+/Pw4cPGTlyJE2aNOHQoUMEBATQtm1b/fgR+fPnp0yZMrRu3ZrPP/+cjh07Ymlp+VZxp2fcuHEMHz6cbt26MXz4cBITE5k0aRLVq1fn6NGjae6AJiUl0bx5c3r06MGXX37Jvn37+P7773F0dGTEiBHkzZuXrVu30rBhQ3r06EHPnj2BN+vVJYQQQuQEAQEBzJ8/n/79+/PRRx9Rrlw5fZvmaWvXrqVt27Y4OjrqH5lLbRckJCTw6NEjBg8eTL58+UhMTGTHjh20bt2ahQsX8vHHH6fZ14YNG9i9ezdjxozB1taWOXPm0KFDB8zMzGjbtu0bn8vL2gGFCxembNmy/Pjjj3To0CHNdrNnz+a9997jvffee+UxevbsSevWrVm5ciUnT57k22+/JTk5mYsXL9K6dWt69+7Njh07mDBhAh4eHgwaNEi/7Y0bN+jTpw+enp4AHD58mM8//5y7d+8yYsSINMf5559/OH/+PMOHD6dgwYLY2toSHBxMzZo1sbW15aeffsLV1ZUVK1ZkaIyuVJs3b+bYsWOMGTMGOzs7Jk6cSKtWrbh48SKFChXK8H6EyLEUIYTIoIULFyqAcuzYsReWcXNzU0qUKKFf7tKliwIoCxYseOm+dTqdkpSUpNy8eVMBlPXr1+vfe++995QCBQooCQkJ+nVRUVGKs7Oz8qr/xqKiohQHBwelWrVqik6nS7eMVqtVPDw8FD8/P0Wr1abZ1tXVValSpYp+XYMGDZT8+fMrERERafbx2WefKVZWVsqjR4/06wDl008/TVPu+vXrCqBMmjQpzfrUur1+/XqG41YURRk5cmSaOrh165ZiZmamfP7558/Vg7u7u9KuXTv9utS/ze+//56mbOPGjRUfHx/98oMHDxRAGTly5AvjEEIIIUSKhw8fKtWqVVMABVDMzc2VKlWqKOPHj1eioqLSlPX19VVq1qz5yn0mJycrSUlJSo8ePZSyZcumeQ9QrK2tlZCQkDTlixcvrhQpUkS/bvfu3Qqg7N69W7/u2XaEoihKzZo108T0snZAavvl5MmT+nVHjx5VAGXx4sUvPafUbZ9ts7Rs2VIBlKlTp6ZZ7+/vr5QrV+6F+9NqtUpSUpIyZswYxdnZOU37ycvLS9FoNMrFixfTbPPVV18pKpVKCQoKSrO+QYMGz9VVly5dFC8vrzTlAMXNzU2JjIzUrwsJCVHUarUyfvz4l56/ECKFPL4nhDAo5Znu0qnatGnz3LrQ0FD69u1LgQIFMDMzw9zcHC8vLwDOnz8PQExMDMePH6dly5ZYWFjot7Wzs6NZs2avjOfgwYNERkbSr1+/Fz7qd/HiRe7du0fnzp3TdKu3s7OjTZs2HD58mNjYWOLj49m5cyetWrXCxsaG5ORk/U/jxo2Jj4/n8OHDr4wpIzISd3q2bdtGcnIyH3/8cZr4rKysqFmz5nNd9lUq1XP1WLp0aW7evGmI0xBCCCFyHGdnZ/bv38+xY8f43//+R4sWLbh06RJDhw7Fz88v3WEK0vPHH39QtWpV7Ozs9O2kX3/9Vd9GelqdOnXSjOep0Who3749V65c4c6dOwY7t2d16NABV1fXNMMYzJo1izx58tC+ffsM7aNp06ZplkuUKAFAkyZNnlv/bPtk165d1K1bF0dHRzQaDebm5owYMYKwsLDnZoUuXbo0xYoVS7Nu7969lCpV6rlxtJ7t+fUytWvXxt7eXr/s5uaGq6urtKWEyCBJSgkhDCYmJoawsDA8PDzSrLexscHBwSHNOp1OR/369VmzZg1ff/01O3fu5OjRo/qkTlxcHADh4eEoipLuwOkvG0w91YMHDwBeOhh4WFgYQLoz33l4eKDT6QgPDycsLIzk5GRmzZqFubl5mp/GjRsDZLihaYi405P6SON77733XIyrVq16Lj4bGxusrKzSrLO0tCQ+Pv4tohdCCCFEhQoV+Oabb/jjjz+4d+8eX3zxBTdu3MjQYOdr1qyhXbt25MuXj99++41Dhw5x7Ngxunfvnu5ntLu7+wvXpbZzjMHS0pI+ffqwfPlyHj9+zIMHD/j999/p2bPnS4coeJqTk1Oa5dSbkOmtf/rcjx49Sv369QH45Zdf+Pvvvzl27BjDhg0D/mtLpkqvnRcWFvbGbcxUzs7Oz62ztLR87vhCiPTJmFJCCIPZvHkzWq32uemC0+vpc/bsWU6fPs2iRYvo0qWLfv2VK1fSlMudOzcqlSrd8aNCQkJeGVPq2Ecvu0uY2pgIDg5+7r179+6hVqvJnTs3kHLnsXPnznz66afp7qtgwYKvjCkjMhJ3elxcXAD4888/9b3OhBBCCGFa5ubmjBw5kmnTpr1ybEiA3377jYIFC7Jq1ao07ajUiUielV6bKHVdekkTQ/rkk0/43//+x4IFC4iPjyc5OZm+ffsa9ZgAK1euxNzcnE2bNqW5wbZu3bp0y6fXHnV2dn7jNqYQwjAkKSWEMIhbt24xePBgHB0d6dOnzyvLpzYMnr2L9vTMfQC2trZUqFCBdevWMXnyZP3ds+jo6AzNbFKlShUcHR2ZO3cuH374YboNEh8fH/Lly8fy5csZPHiwvkxMTAyrV6/Wz8gHKV20T548SenSpdM8TmhoGYk7PQ0aNMDMzIyrV6+m+8jkm0j9G8kdPyGEEOLVgoOD0+2Vk/rY3dM9yl/Uo0alUmFhYZHm8z8kJOSFs+/t3LmT+/fv63v4aLVaVq1aReHChV+71/WzXtUOyJs3Lx988AFz5swhMTGRZs2a6QceNyaVSoWZmRkajUa/Li4ujqVLl2Z4HzVr1mTy5MmcO3cuzSN8z85cKIQwHklKCSFe29mzZ/VjFYWGhrJ//34WLlyIRqNh7dq1GZqZrXjx4hQuXJghQ4agKApOTk5s3LiRwMDA58qOGTOGJk2a0KBBAwYMGIBWq2XSpEnY2dnx6NGjlx7Hzs6OKVOm0LNnT+rWrUuvXr1wc3PjypUrnD59mtmzZ6NWq5k4cSIfffQRTZs2pU+fPiQkJDBp0iQeP37M//73P/3+ZsyYQbVq1ahevTqffPIJ3t7eREVFceXKFTZu3MiuXbtev0LfMO70eHt7M2bMGIYNG8a1a9do2LAhuXPn5v79+xw9ehRbW1v9FMgZZW9vj5eXF+vXr6dOnTo4OTnh4uLy3LTIQgghhEi5QZQ/f36aNWtG8eLF0el0nDp1iilTpmBnZ8eAAQP0Zf38/Fi5ciWrVq2iUKFCWFlZ4efnR9OmTVmzZg39+vWjbdu23L59m++//568efNy+fLl547p4uLC+++/z3fffaeffe/ChQsGSa5kpB0wYMAAKlWqBMDChQvf+pgZ0aRJE6ZOnUrHjh3p3bs3YWFhTJ48OcOPDQIMHDiQBQsW0KhRI8aMGYObmxvLly/nwoULAGnGGhVCGIckpYQQr61bt25AyrP9uXLlokSJEnzzzTf07NkzQwkpSOnGvnHjRgYMGECfPn0wMzOjbt267Nix47m7aw0bNmT16tWMGDGC9u3b4+7uTr9+/bh3716G7ob16NEDDw8PJkyYQM+ePVEUBW9v7zSPDXbs2BFbW1vGjx9P+/bt0Wg0VK5cmd27d1OlShV9uZIlS/LPP//w/fffM3z4cEJDQ8mVKxdFixbVjytlKBmJOz1Dhw6lZMmSzJgxgxUrVpCQkIC7uzvvvffeG3en//XXX/nqq69o3rw5CQkJdOnShUWLFr3RvoQQQojsbPjw4axfv55p06YRHBxMQkICefPmpW7dugwdOlQ/kDfA6NGjCQ4OplevXkRFReHl5cWNGzfo1q0boaGhzJ07lwULFlCoUCGGDBnCnTt30r251Lx5c3x9fRk+fDi3bt2icOHCLFu2LMODjb/Kq9oBFStWxNvbG2tra+rUqWOQY77K+++/z4IFC5gwYQLNmjUjX7589OrVC1dXV3r06JGhfXh4eLB3714GDhxI3759sbGxoVWrVowZM4YuXbqQK1cu456EEAKV8qKpsoQQ4h2WlJSEv78/+fLlY/v27aYORwghhBAix/r3338pU6YMP/74I/369TN1OG+td+/erFixgrCwMKMO1yCEkJ5SQogsokePHtSrV4+8efMSEhLC3LlzOX/+PDNmzDB1aEIIIYQQOdLVq1e5efMm3377LXnz5qVr166mDum1jRkzBg8PDwoVKqQfs3T+/PkMHz5cElJCZAJJSgkhsoSoqCgGDx7MgwcPMDc3p1y5cmzZsoW6deuaOjQhhBBCiBzp+++/Z+nSpZQoUYI//vhDPzFMVmJubs6kSZO4c+cOycnJFC1alKlTp6YZ+0sIYTzy+J4QQgghhBBCCCGEyHQynYAQQgghhBBCCCGEyHSSlBJCCCGEyGZGjRqFSqVK8+Pu7v7C8nv27HmuvEql0k+LLoQQQghhDDKmlBBCCCFENuTr68uOHTv0yxqN5pXbXLx4EQcHB/1ynjx5jBKbEEIIIQRIUirDdDod9+7dw97eHpVKZepwhBBCCGEiiqIQFRWFh4cHavW72+nczMzspb2j0uPq6kquXLkyXD4hIYGEhAT9sk6n49GjRzg7O0t7SQghhMjBMtpekqRUBt27d48CBQqYOgwhhBBCvCNu375N/vz5TR3GC12+fBkPDw8sLS2pVKkS48aNo1ChQi/dpmzZssTHx1OyZEmGDx9O7dq1X1p+/PjxjB492pBhCyGEECIbeVV7SWbfy6CIiAhy5crF7du303RrfxOKohAREYGjo6PcRXxNUndvTuruzUndvTmpuzcndffmjF13kZGRFChQgMePH+Po6Gjw/RvCX3/9RWxsLMWKFeP+/fuMHTuWCxcuEBQUhLOz83PlL168yL59+yhfvjwJCQksXbqUuXPnsmfPHmrUqPHC4zzbUyoiIgJPT09u3br11u2lZ8m/CeOTOjYuqV/jkvo1Lqlf48tudRwZGYmnp+cr20vSUyqDUi8KBwcHgySlFEXBwcEhW1xsmUnq7s1J3b05qbs3J3X35qTu3lxm1d27/Hdp1KiR/rWfnx8BAQEULlyYxYsXM2jQoOfK+/j44OPjo18OCAjg9u3bTJ48+aVJKUtLSywtLZ9b7+joaJSkVOq+3+W6z8qkjo1L6te4pH6NS+rX+LJbHaeew6vO5d0dCEEIIYQQQhiEra0tfn5+XL58OcPbVK5c+bXKCyGEEEK8LukpJYRIQ1GUNI9iiJQ6SUxMJD4+PlvctTAmc3PzDM3wJYTIXAkJCZw/f57q1atneJuTJ0+SN29eI0YlhBBCiJxOklJCiDTu3btHZGSkqcN45+h0OsLCwkwdxjtPpVKRP39+7OzsTB2KEDna4MGDadasGZ6enoSGhjJ27FgiIyPp0qULAEOHDuXu3bssWbIEgOnTp+Pt7Y2vry+JiYn89ttvrF69mtWrV5vyNIQQQgiRzUlSSgihl5ycTGRkJM7OzgYfCyQrUxQFrVaLRqORnlIvoSgKDx484M6dOxQtWlR6TAlhQnfu3KFDhw48fPiQPHnyULlyZQ4fPoyXlxcAwcHB3Lp1S18+MTGRwYMHc/fuXaytrfH19WXz5s00btzYVKcghBBCiBxAklJCCL3k5GQA7O3tsbKyMnE07w5JSmVcnjx5uHHjBklJSZKUekNxiVo0ahXmGrnWxJtbuXLlS99ftGhRmuWvv/6ar7/+2ogRCZGFKcqTHy3otCm/Fd2T17qnXj9ZVpuDmQVoLMHMEtTyeSiEEC8iSSkhxHMk8SLelFw7byc6IZn6U/fiYm/Jun5VTB2OEEK8HkUBXTJoE5/8JKXzOukF65+81iW9YtuM7iPpSRLpSdJIn1DSPbVe+3xCSZdOWUX3dvWi0qQkpzQWOKjNwdzqybLlf8krjfnz655ObGksXvA7tdyz27xsW0tQy3xXQoh3gySljMTb25vY2Fju3r2Lubk5ALt27aJOnToMGjSI7777zsQRZlzXrl2pUKECn3322XPvRUdHM3z4cDZt2oSVlRVarZYmTZowfvx4/v77bxo3boyPjw+JiYkANGjQgO+++47cuXNn9mm8Fn9/fw4dOoS1tTXe3t5s2rSJUqVKvXQblUpFVFRUumPpvOw9IYRItT0ohHsR8dyLiOdxbJJMkSuyvZe1l7788ksmT55s4ggz7mXtpcx2/Phxpk2bxrJlywyzw6gQuHUI7gfBw0vw+BYkRKX8JCf8lxTSJRnkcPeidHy0Jo7dXWwBUI2OJGqoPXYWL77xceOxjgrzYnj4tf1rvWdQKg2o1M/Xg6KFpFhUSbG8M7du1GbPJMUs0kloWYDZkwSamdVTP5Zgbp3O+pe998x6jQXIjSwhBJKUMipPT082bNhAmzZtAFiwYAEVKlQwcVQpj2iZmb39n15RFJo2bUrRokU5c+YM1tbWJCYmsmDBAv3sbSVLluT48eMAREVF8dVXX1GnTh2OHTv2Tj/ac+rUKVOHkGOZmZlRqlQpkpKSKFSoEEuXLiVXrlyvtY9Ro0bh4uKSoS8GN27c4OjRo7Rr1w6APXv20LJlS7y9vUlISODDDz9kxIgRb3Iq75Q9e/Ywe/Zs/vzzT1OHIl7i573X9K8v3o+khLO5CaMRInNk9/aSMbwqtgoVKrx9Qur+OTjzB5xbD4+uvvl+NKm9eMz/e602S2f9f789NObsDnjqfWbDe73Azjbl0bhn96cxh/sRsHgEfLDov2OoNCk9gu6GwpK+0G3Lf4kjtfrJ+0+W07xWP3mteea16r/Xz233VIJFUZ4k6hIgOfHJ7wSU5ASiH4dhZ22BSvtkvTbpSWIv8cnvp7f5b9v/3k9M+/qV26YeJzHt30WXnPKTFPPmf9u3onq9JNbT682snvQ2s0qb5FKbYR6fBA65nro2LECTzvWmNn/mmtRIkkwIE3k3P2mzie7du7NgwQLatGlDREQEhw8fpkOHDsTGxurLTJ48md9//53k5GTc3d35+eefKVCgADt37mT48OHEx8eTlJTEl19+Sbdu3QAYO3Ysy5Ytw9LSEoD169fj5eX1XG8cFxcXjh8/jre3N97e3vTq1YsdO3bg4eHBokWL+O6779i1axeJiYkUL16cuXPnkitXLu7evcvHH3/MgwcPKFiwIFqtNt3z27VrF1euXGH79u1YWFgAYGFhQd++fdMtb29vz6xZsyhSpAhbt26lSZMmad5ftGgRmzZt0n9p3rRpE5MnT2bPnj3s2bOHgQMHEhAQwP79+1EUhcWLF1OhQgWSk5Np0qQJYWFhxMXF4e/vzy+//IKNjQ3FihVjxYoVlC9fHoCFCxeyceNG1qxZw9SpU1mxYgXJycmYm5sza9YsKlWqBLy4Z9PLtkn9ewYGBvLgwQNGjx5Nhw4dnquHy5cvM3DgQEJDQ0lMTKRPnz7069cv3ToztdjE5Be+p1apsDLXvFHZl8mVK5c+Kdi5c2d+/PFHhg0blrGA38CNGzf4/fff9UkpgLp16/Lnn38SExODv78/TZo0wd/f32gxvI3Usa5E9nDtYbT+9eZ/QyhRu4AJoxEic7yovRQXF6cvk9peSkhIIF++fFmqvXTjxg0qVKhA37592bx5M3Fxcfz222/MmzePw4cPY2Vlxbp16/Dw8ODMmTP069ePmJgY4uPj6dy5M0OHDgVSemI5ODhw6dIlbt++TVBQEMOGDWPVqlU4OztTo0YNdu/ezfHjx9mzZw+DBw/m+PHj+uP369ePzZs3ExERwcyZM9MdxH7UyJE8vvYP02vFw51jzD6ayPF7Wha1tGHR9TysOJOIk3Mezt58iKWVFb/Pm0qhwkUIefiYDr0HEhkdQ3xCInVq12bGjOnEJSRRwNOToKATuLu7AzBy5EiioqKYOnUqX331FXv27CEpKQlHR0fmz59P0aJF9TE/fPjwSWSzod5osLN74TbcuAGasQxe+Df79+8nOjqaWbNm8f7774PmRkqSyivlsehjx47xzTdfERkZiU6nY9iwYfqkqEGoVE96GVmA5VPrFQWtRQQ4OmZ+AuQFibKMJbbiU95Likv5nRz/1M9rrv8vIEiOS/mJf2yQU1QBtm+ztT5p9UzC87lEqPmLy6aX8EpT7pnkrD7p+UyC87mEqDrte09vk2Yf6e1P/ZJkq1qSccLkJCllRDVq1GDWrFncvXuXjRs38sEHH6T58rh8+XIuXbrEoUOH0Gg0LF26lM8++4z169dTrlw5Dhw4gEaj4dGjR5QrV46GDRtiZWXF5MmTCQ4OxtramtjYWNQZfCb81q1b7Nq1C5VKxbhx47Czs+Po0aMAfP/994wcOZIZM2bQv39/atSowciRI7l27RplypShYcOGz+3vxIkTlC9fXp+Qyghzc3P8/f0JCgp6Lin1KkFBQfzyyy+MHz+eFStWMGzYMLZt24ZGo2H58uU4OzujKAr9+vVjzpw5DB48mK5du7Jw4UJ9UmrRokUMHjwYSEl4DBo0CIDDhw/To0cPzp49+9IYXrWNSqXi77//5tq1a1SsWJFq1apRoMB/Xyq1Wi0dO3Zk6dKlFC9enNjYWCpXrkzlypUpV67ca9VHZig5YtsL36vtk4eF3Srql8t/v4O4pPQb5JUKOrGqT8BrH79q1aqcPn0agGvXrtG9e3cePXqEt7c3ixYtwsnJiQsXLtC3b1/Cw8OxtLRk586dafYxfvx4Lly4wMKFCxk/fjzr1q0jISGBfv360bdvX4YNG0ZQUBD+/v4MGDCAggUL6re1tbWlfPnyXL16lQEDBrBgwQJ8fHxQFIXixYtz5MiRNL24Ro0axfXr17l58yb37t1j4sSJtGzZkri4OHr37s2pU6ewtrZm3rx5+Pv74+vry7FjxwBwdHRk3759BAQE4O/vz969e9FoNPTr14/z58+jKAozZsygatWqjBo1ipCQEK5cuULJkiX5/PPP6devH48ePcLZ2ZnFixeTN29eDh06RM+ePcmVK9c7eX2JtE7eCidJq+iXr4RGv6S0ENlHRttLBw8eJDo6mg0bNmSp9hJAWFgYAQEBjB07lkmTJlG3bl327NnDvHnz6NevH7Nnz2bcuHF4e3uzY8cOLC0tiYuLo0qVKtSrV0/fc+zAgQPs27cPOzs7Nm7cyKZNmzh9+jTW1ta0bdv2hecUFhZG+fLlGTNmDFu3bmXAgAHPJ6UubYPj8zF/HIaqiFXKF2vXomBuD9+shVXrOLJ+IKfX7sPLy4shQ4YwYeF6fv75Z3LZx7Pxr+3Y2dmh1Wpp0aIFq9dtoG3btrRp04bffvuNwYMHoygKS5YsYcOGDQB88803TJo0CUgZnP+LL75g06ZNL/37vGybsLAw/Pz8mDx5MocPH6Zly5ZcvZq2l9fjx4/p06cPmzdvJm/evDx8+JDy5ctTtWpVfeIsW3pRoiwzKUpK8uuNk1tPvZ/0TBldEoo2EW1CHBqVDpX2mbHNnh2rTPfszVTlSY+yBJNUjWmpMpzkckAFmvR6Eab2PkynB+LTPROfTZo93ftQnzxTpfzmyW8VT71+9r10luE1yqqerDdGWfV/5TOy3yfMYmLA1u6pdao0v/5bfub99Na9cPmZdQ4ekNs7Y5eLEUhSysg6d+7M4sWLWbduHcuWLUvTlXr9+vUcP35cnzB5usdDWFgYPXr04NKlS5iZmfHw4UOCgoKoXbs2RYsWpVOnTtSvX58mTZqQP3/+DMXSrVs3/SDE69atIzIyUt8rKTExkcKFCwOwe/duZs6cCUChQoWoU6eOYSrjCUVRXl0oHT4+PlSoUIGIiAgCAgKYMmWKfn/Tpk1j8+bNJCcnExERQY0aNQDo0qULZcuWZerUqdy+fZtLly7RqFEjAE6ePMkPP/xAWFgYZmZmnDt3jsTExJcm2V61Tc+ePYGUeqtWrRr79++nY8eO+u0vXrxIUFAQH374oX5dVFQU586dk6TBM7RaLYGBgXTv3h2A/v37069fP9q1a8eECRMYNWoUM2fOpFOnTowfP5569eoRFRWFtbW1fh/jxo3j4sWLLFy4kO3btxMaGsqxY8dITEykWrVqNG3alB9++CHNY2179uzRbx8WFsbhw4cZPnw4H3/8MUuWLOGHH35g9+7d+Pv7p/tY4blz5/j777+5f/8+VatWpUGDBvz444/Y29tz5swZDh8+TJcuXTh9+jQVK1bkyJEjKIpC6dKlOXDgACVLlgRSklRDhgyhVatWLFmyhDt37tCkSRN9ku7MmTPs3r0bCwsL6tWrx/z58/Hy8uKPP/5g9OjRzJ07l549e+p7FLZv395IfylhKMuP3kqzfPdx3AtKCpH9vKy9tG7dOo4fP06FChX0vZGyWnvJzs5OfzOuXLly5M+fX98Dt3z58gQGBgIQFxdHv379OHXqFGq1mtu3b3Pq1Cl9Uqpdu3b6Hl67d++mXbt22Nqm9A3p0qUL33//fbrHt7W1pUWLFgAEBASkTdQ8vgWbv4TL21HFxqNoLFGqD0ZVqQ8s+h2OHwfrXABUq1YNLy8v/X5mzZoFgE6n45tvvuHAgQMoikJoaCj+/v60bduWbt260atXLwYPHszu3btxdnbGz88PgO3btzNr1iyioqLQ6XRERka+8u/zsm0sLCzo3LkzAJUrV8bd3Z3Tp0/j4eGhL3Pw4EGuXbumbw9CSlvy4sWL2Tsp9S5QqZ48bmekrJiiEB0RgWNGeqLpdOkPyq9LbxD9dAbfT3dA/3S2fW7g/mf2pzw74L7u+QH60wzGrzxT7qkZIJ/bhxbIyPcuJZ0k3fOepE+EEakAk4xAXHVgSm9UE5GklJF17dqVcuXKUaxYsZSuxU9RFIXhw4frv3Q/rW/fvjRr1ozVq1ejUqkoV64c8fHxaDQaDh8+zMGDB9mzZw+VK1dmxYoVVK9eHY1Gk6breHx8fJp9Pv0omqIozJkzJ6VL8xsqX748s2bNemUi52lJSUmcOnUq3Uf8zMzMXhq/lZWV/rVGoyE5OeU/z+XLl7N371727duHvb09M2fOZN++fQDky5ePcuXKsWHDBk6fPk3nzp0xMzMjMTGRNm3asGfPHsqXL09kZCSOjo4vPZc32ebZmcgURcHFxSXLjFl1bkyDF76nfubcTnxXN8NlX+bx48f4+/tz584dfH19adAgJYZjx46xceNGIOXLS5MmTYiMjOTx48fUq1cPSHlENNW8efMoXrw4K1euRK1WExgYyMaNG9m7dy8AERERz909TbVjxw7Kli2LWq1m6NCh+Pr6ki9fPqpWrcrYsWNZvHgxXbp0SXfbVq1aYWFhQYECBShevDgXL17kwIED+qnWK1euTFxcHBEREVStWlXfgP/6669ZuXIlvr6+BASk9CoLDAxky5YtjB6d8iERFhamnzSgRYsWWFhYEBUVxd9//63/sqHVavH29ubx48ckJSXpv8h89NFHLFmyJMN/B5H5Dl4JA8BcoyJJq/AoJvEVWwiRfWSkvdStWzcinnzhTP18zQrtJUD/CCGktGFe1Kb59ttvcXNz4+TJk5iZmdG6des08T0bW0ZnPH32ePrzP/MnbPoCEiJBbY7GqyxxVkXg/eGgUmW4LTZ16lTCwsI4cuQIVlZWDBo0SL9tQEAAWq2W48ePs3DhQn2799atW/Tv35+jR49SqFAh/v3331fW85tsk15brHTp0vq2osih1GpQP+k5lp0pyjMJq2cTYLrnZ5p8wXuKLpnoqCjsbKxRobz1/tJNqJEa75PfaZaVtMsvLftkxsyXvq+k8/plZV+w77eJg6fLKCiAVpuMRqNBlaYjh5Lm13/L6ZVRXr6c3job51ddSUYlSSkj8/DwYPz48RQvXvy595o1a8bMmTNp2bIlTk5OJCUlcfbsWcqWLUt4eLh+3IN9+/bpe0dERUURFRVF9erVqV69OkFBQZw8eZLq1atTuHBhjhw5Qv369VmzZg0xMS8euLB58+ZMnTqVypUrY2NjQ2xsLNevX8fX15f333+fBQsW8N1333Hjxg127txJ3brPJxzef/99ChYsSP/+/Zk+fTpWVlYkJyczc+ZMevfu/Vz56OhoBg8ejIuLiz7R8LTChQtz+vRp4uPjMTMzY/ny5QlG3hEAALPmSURBVBmq4/DwcJydnbG3tycqKopFixZRqFAh/fupY1UEBQXx119/AejHnkh9tC71Tt/LZGSbp+vtwIEDz5Xx8fHBxsaGJUuW8PHHHwNw5coVnJyccHJyytD5ZiYbi4z/F/E6ZV8mdUyp2NhY6tWrx5w5c+jfv3+aRuXTDfEXNcjLlCnD6dOnCQ0Nxd3dHUVRGDVqlL7eUz3dMypV6phSTx/P3t6e8uXLs2XLFv7++28WLFiQ7nGfjSe9+FLjr1q1Kl988QVqtZovvviCn376iQMHDlCtWjV9uU2bNuHp6fncPmxsbPRl3N3dn0t0hoeHZ/jLijC95GQd9570jKpT3JWtQfeJTUz/cVghsqOXtZeaN2/OjBkzaNGiBRqNhqSkJIKCgrJMe+l1hIeHU6pUKczMzLh48SKBgYEvTLrUrl2bkSNHMnDgQKysrFi6dOnrHWzD5/DPk5sV+d+Dlj9RePsxtsyahU6nIyEhgdWrV+Pj45OhuN3d3bGysuL+/fv88ccfaXroduvWjZkzZ7J582Z977KIiAgsLCz0n9GzZ89+5XFetU1iYiLLli2jc+fOHD16lJCQEEqXLs2DBw/0ZapUqcLly5fZtWuXvm5PnTpFyZIlX2tICiGyhNQxqNA8mTTgLSgKWhsTjYmWU7xOb79sRGabzgTdunXT93x4WufOnenUqRO1atWiTJky+Pv7s3v3bgD+97//8dVXX1G5cmUWLVqkH0w7IiKC1q1b4+fnR+nSpUlKStL32Jg+fTqffvopVatW5Z9//sHZ+cUZzyFDhuDv70+lSpUoXbo0lStX1n+pnTFjBnv27KF06dIMHjz4hQ0slUrF5s2bsbCwwNfXl1KlSlGmTBlCQkL0d9LOnTunHzunYsWKWFtbs3PnznQHZv4/e/cd31T1PnD8c5O0TWdaKJRVStmUvbcoIIgDEDcqgoAi+gMBF+IARPgKDpwgiOJCQQUURRRlKBuh7A2FsksLdDfz/v64adrSFtrSNG153q/XfSW599x7T07S9ObJOc/p2LEjvXv3pkmTJtx2222u7vHXMmjQIFJSUoiKimLAgAF07do1x/Z+/fqxefNmqlat6hoaFRQUxOTJk2nXrh033XRTjl8w81OQfXx8fOjcuTO9evXiww8/zJFPCrTeYMuWLWPRokU0a9aMxo0bM2zYsBzJXIXGz8+P999/n3feeQebzUabNm346aefAK13XNeuXQkKCiIkJMQ17CE5Odn1q2379u2ZMWMGd911F5cvX6Znz57MmzfP1dYHDx4kIyPDFcwsiMcee4zhw4fTv3//fJOLL126FIvFwqlTpzh48CD169enS5curiDrli1b8Pf3JygoiIYNG3L48GHS09MJDAykQYMGfP3113Tu3BnQgmMff/yx69iZX7ayCwoKolKlSixfvhzQeiPu37+fkJAQvLy82L59OwDfffddgZ6j8IwVe8+5fr968TbtS7kKnE3MyHcfIcqba10v3XLLLXTp0oWWLVuWqeulwnjllVf47LPPaNu2La+88spVewH17duX3r1707x5c2655Rbq1KmjfZG5lrSL2u32r7ScJt1ehCErILQe99xzD5UqVaJx48YMGDCgwJN8jBo1ig0bNtCiRQsef/zxXG3x6KOP8t1339GrVy9CQkIAaNq0Kffddx+NGzfm5ptvzvMHmCtda5+KFSty5MgR2rdvz5AhQ1iwYIFreGOmkJAQli1bxhtvvEHz5s2JioripZdewuFwFOi5CiGEKF6KWtQEPzeYzKFaiYmJBAUFXdexVFXN1f1cFIy0XdEVpO0yMjKIiYkhMjIyRxf9khQaGpptth248847efTRR2nbti1Dhgxx/Sr+5ZdfUqFCBQ4ePMgTTzzB5cuXMRqN/P3337z99tuEhobyzDPP8P333zNr1ixWrFjBhx9+yNdff42qqlSuXJlly5bh7e1N7969uXjxoivRefYcU6C1nd1uR6fTUbVqVVauXEmzZs1y1T0zAfmBAwdyJTofPnw4u3btwmg08umnn9KyZUtA+1JRq1YtPvjgA7766ivGjx/P6dOnAa134f/93//x33//Ybfb6dGjBx9++CETJ050PT/QetuNGDGCCxcuYLPZeP755xk8eLAr0bnJZKJr164cPXo0x/NyhyvfQ/I3WzDvrTzI+38foVZFP9Y8fwuR439DVWHqXfV4qFM9abtCcvf7rjivCcobd7aNfJ7klpycTGBgIA6Hg2HDhlGtWjWmTJmS/w6Jp+Cr/pBwGLwD4b75UC8rgCRt7F7Svu4l7ete0r7uV97auKDXBBKUKiAJSpUO0nZFV1aCUqVRZlAqJiaGhx9+2DUL05WuDBbdiCQoVTSDPt/CP4cu8OqdUQztEkm7N/8iLtnMCz0jeapHI2m7QpKglOdIUKpk3X333Rw/fpz09HRatWrF7Nmz82/3y7Ew/064fAKCasDDiyCscY4i0sbuJe3rXtK+7iXt637lrY0Lek0gOaWEEKIA5s2bx5tvvsncuXM9XRVRzphtdrbEaEnOu9QNdd0ujj5NulWGkwgh8rdkyZKCFbx0Ar68UwtMhUTCY8sgOPza+wkhhBBuJkEpIYQogKFDh/LEE09c9VeLiRMnllyFRLmxNPoMGVYHFf29qR+mzawVUVHLgXLykuSbE0Jcp9R4+PpuLSBVoY4WkDJV93SthBBCCECCUkIIIYRHLdwaC4Cft94V9KwVqs2uePhCmsfqJYQoByypsOB+uHgUTOEw+FcIqubpWgkhhBAuZXb2vU8++cSVs6R169b8+++/Vy3/7bff0rx5c/z8/KhatSpDhgwhISGhhGorhBBC5G3/WW32x8yhewA2u5bucf+5FI/USQhRDjjs8MMQOL0NfEPgkcUSkBJCCFHqlMmg1MKFC3n22WeZMGEC0dHRdO3alT59+hAbG5tn+XXr1jFo0CCGDh3K3r17+eGHH9i6dSvDhg0r4ZoLIYQQWWITUkm32gF4qH3W1OatI4IBcKhwMcXiiaoJIcq6VW/A4T/AYISBP0Cl+p6ukRBCCJFLmQxKvfvuuwwdOpRhw4bRqFEjZs6cSXh4OLNmzcqz/KZNm6hVqxajRo0iMjKSLl268OSTT/Lff/+VcM2FENdiMBho0aIFjRs35q677uLy5cuFPsbEiRP56KOPClT2+PHjLFq0yPV4zZo1BAcH06JFCxo1asSkSZMKff7idmUdRfnx9aYTAHjrdTSrEexan5lTCmDr8YslXS0hRFm35ydY9552v9/HEN7Ws/URQggh8lHmglIWi4Vt27bRq1evHOt79erFhg0b8tynU6dOnDp1iuXLl6OqKufPn+fHH3/kjjvuyPc8ZrOZpKSkHAto0zTKIkt5Xjz9Pg8ODiY6Opo9e/YQHBzMRx995NbzxcTEsGjRohzrevbsSXR0NFu3buWbb75h27Ztrs8GT7RJXnUszYun30NlaVl9MA6AOpX9c7Whr5cegB2nLnu8nrLkXoQoteIOwNKntfudRkHTez1bHyGEEOIqylyi8/j4eOx2O2FhYTnWh4WFce7cuTz36dSpE99++y0PPPAAGRkZ2Gw2+vbty4cffpjveaZNm5ZnD4nExMTrvhhVVZWUFC1PyNVm8hK5SdsVXUHazmKx4HA4sNlsJKeb3VIPXy/9NV87u10bztShQwd27dqF3W7n2LFjDB8+nIsXL1KrVi3mzZtHhQoVOHDgAE8//TSXLl3Cx8eHP//8E1VVcTgc2O123nrrLQ4cOMC8efN46623+PnnnzGbzYwYMYInn3ySCRMmsG/fPlq0aMH//d//ERkZiaqq2O12jEYjrVq14vDhw4wePZq5c+dSv359VFWlSZMmrF+/nuDgYFe9k5KSePrpp9mzZw86nY4PP/yQjh07Mm7cOFatWoVer2f69On06NGDo0ePMnToUFJTU/H29uazzz6jUaNG7N69m8cffxyHwwHA77//nquOgwcPdstrUxzsdjsOh4Pk5GTMZrP8zV7D8XgtkXnXSBOJiYk5tpl89aRb7ew9eTHXNnF17n7fZf5QJUSpY7PA4mFgS4faN0PPiZ6ukRBCCHFVZS4olenKi0xVVfO98Ny3bx+jRo3itddeo3fv3pw9e5bnn3+eESNGMG/evDz3GT9+PGPHjnU9TkpKIjw8HJPJRFBQ0HXVPTOoZTKZ5EtaIUnbFV1B2i4jI4OEhAQsDoXWb/7tlnrsndQLP2/9Vcvo9XrsdjurVq1iyJAh6PV6xo4dy8iRI7n//vt56623eOONN/jggw8YPHgwU6dO5dZbbyU5ORlfX18URUGn0/HWW29x6NAh5s+fz59//smFCxfYunUrFouFrl270rdvX958800+/vhjfvjhB0AbvqcoCnq9noSEBDZv3syrr77KoEGD+Pbbb5kyZQqrVq2iRYsWVKxYMUe933zzTSIjI1mwYAF2u53U1FSWLl3KsWPH2LlzJ7Gxsdxyyy3s37+fGjVq8Ndff+Hj48OGDRt47bXXWLx4MfPmzeOpp55i+PDhpKeno9frc9WxNNPr9eh0OgIDAzEajfI3exXbT1zC5tDa5/Fu9TEFGXNsrxHiz7kkC2eTrZhMJk9Uscxy9/tO3sui1Fr9JpzbDb4V4O5PQXf1/7dCCCGEp5W5oFRoaCh6vT5Xr6i4uLhcvacyTZs2jc6dO/P8888D0KxZM/z9/enatStTpkyhatWqufbx8fHBx8cn13pFUYrlYjTzOHJhW3jSdkV3rbbLXO/Otr3Wa3f58mVatmzJqVOnaNy4MbfddhuKorB161aWLVuGoigMGjSIO+64g+TkZC5fvuwazpsZMFYUhblz59KwYUO+//57dDodf/31F7/++iv//PMPoPV6PHbsWK7nrCgKf/31F61atUKn0zF+/HgaN25M9erV6dy5M1OmTOGrr77isccey/U8Vq9ezS+//IKiKBgMBkwmE+vXr2fgwIHo9XoiIyOpX78+hw4dombNmjz99NPs2rULnU6H2WxGURQ6derE5MmTuXjxIvfffz+1a9cukdeluGSv65X3y0L9S9KhOK0nT/2wAKqYfHNtr1PJn/9OXOJCikXargjc+b6T10OUSsfXw/r3tft9P4DAKp6tjxBCCFEAZS4o5e3tTevWrVm5ciV33323a/3KlSvp169fnvukpaVhMOR8qnq99suR5IUQIjdfLx37Jvd207Gv/qttcHAwO3bsIC0tjVtvvZVPPvmEUaNG5fgSmL1nZH5fDps3b87OnTuJi4ujSpUqqKrKxIkTGTRoUI5ya9asybVvz549+fHHH3OcLzAwkNatW7N8+XLWr1/P559/XtCnnENm3WfOnElkZCTffvst58+fp0OHDgAMHDiQdu3a8euvv9K9e3cWL15cpPOI0m/dkXgAbm+a+4cRgDa1Qlj43yl0Ev8QQlyLNR1+eQZQoeUj0OguT9dICCGEKJAyl+gcYOzYsXz22Wd8/vnn7N+/nzFjxhAbG8uIESMAbehd9i+ed911F4sXL2bWrFkcO3aM9evXM2rUKNq1a0e1atU89TSEKLUURcHP2+CWpaA9DPz8/Hj//fd55513sNlstGnThp9++gmABQsW0LVrV4KCgggJCWHlypUAJCcnY7PZAGjfvj0zZsxwzeDXs2dP5s2bR3p6OgAHDx4kIyODwMBAkpOTC1Snxx57jOHDh9O/f39XYDu7nj17umYBtdvtJCUl0aVLF77//nscDgcnTpzgyJEj1K9fn6SkJKpVq4aiKHz99deuYxw7dow6derw7LPPcuutt7Jv375C1VGUDQ6HygZnUKprvdA8y3SrXxmAxHQrZpu9xOomhCiD/nkbLh6DwKrQe5qnayOEEEIUWJnrKQXwwAMPkJCQwOTJkzl79ixNmjRh+fLlREREAHD27FliY2Nd5QcPHkxycjIfffQR48aNIzg4mO7du/PWW2956ikIIQqgTZs2NG3alJ9++okPPviAIUOGMHnyZCIiIvjyyy8B+Prrr3niiSd47rnnMBqN/P13Vi6s3r17c+nSJfr168eKFSvYs2cP7dq1Q1VVKleuzLJly2jWrBlWq5UWLVowevRoIiMj861P9+7dsdvtuXpbZXr11VcZMWIETZs2Ra/XM2vWLAYMGMC6deto1qwZBoOBuXPnYjQaGTFiBPfccw/ffvstPXv2dB1j4cKFfPPNN3h5eREREcHdd9+Nt7d3jjoOGTKkmFpYeMovO89wKc2Kt0FHsxrBeZYJDfDGoFOwOVTiUyxUD849xE8IIYjbD+tnavf7TAfj9eU+FUIIIUqSosr4tQJJSkrCZNJmRyqOROeJiYmS+LcIpO2KriBtl5GRQUxMDJGRkRiNxjzL3IgyZ+OLiYnh4YcfZsuWLZ6uUql15XtI/mbz9tjnm1l7KJ7QAG/+e+XWPMuoqkrUaytItzr4a8xN1A0LLOFall3uft8V5zVBeePOtpHPkzw4HPDFbXByMzS4HR5cANfRNtLG7iXt617Svu4l7et+5a2NC3pNUCaH7wkhREmbN28ePXr04I033vB0VUQ5sONkIgBta1W4arl0qwOAExfT3F4nIUQZtH2+FpDyDoDbZ1xXQEoIIYTwBAlKCSFEAQwdOpQTJ07Qu7d7EsCLG8flNAuJ6VYA7mtT46plM79fZjiDU0II4ZJ2Ef6apN3v/gqYrv55IoQQQpRGEpQSQgghStCCLVrOQ50CN9evdNWymX0eJNG5ECKXNdMg4zJUbgxth3u6NkIIIUSRSFBKCCGEKEF/7DkHQHgFP3S6q/8bzgxKSU8pIUQOcfth6zzt/m3TQF8m5y4SQgghJCglhBBClKSD55MB6Fo39JplM5NcSk8pIYSLqsKK8aDaodFdULubp2skhBBCFJkEpYQQQogSEpeU4er19EiHiGuW1zm7Splt0lNKFM7EiRNRFCXHUqVKlavus3btWlq3bo3RaKR27drMnj27hGorCuX4Oji2GvTecKtMviGEEKJsk6CUEKJUMRgMtGzZkqioKFq3bs3cuXNzbFdVlZo1a/LCCy8AMGHCBFq0aEFUVBR+fn60aNGCFi1asHr1apYsWULz5s1p3LgxTZo04ZNPPgHA4XAwYcIE6tatS926dbn//vtJSEgA4Pjx4yiKwvTp013n3Lx5M15eXsyfP79kGqGYzJ8/n+eee87T1RDZbI65CECDsEAaVs1/atxM0lNKXI/GjRtz9uxZ17J79+58y8bExHD77bfTtWtXoqOjefnllxk1ahQ//fRTCdZYFMg/M7TbVo9BhUjP1kUIIYS4ThKUEkKUKsHBwURHR7Nv3z4WL17MrFmzmDNnjmv7xo0bqVq1KosXLwbgzTffZMeOHSxfvpyoqCh27NjBjh07CA4O5sUXX2Tp0qXs3buXrVu34ufnB8DMmTPZt28f+/bt48iRI7Rt25YhQ4a4zlGvXj1++eUX1+NFixbRpEmTEmqBa7PbJUBRVq0/Eg9A13rXHroHEOKr5YkJ9vV2W51E+WUwGKhSpYprqVQp/8T6s2fPpmbNmsycOZNGjRoxbNgwHn/8cd5+++0SrLG4ppNbIGYt6AzQebSnayOEEEJcN8mKKITInyU1/22KHryMBSyrAy/fQp8+IiKCd955hzFjxvDEE08AsHDhQkaMGMGCBQvYvHkz7du3z3Pfd955hwkTJhAZqf2K7Ovry+DBg13bNmzYgLe39kV/3LhxzJ49m8OHD+Pl5UVQUBChoaEcO3aMyMhI1q5dyy233JLneQYPHozRaGT37t1cunSJefPm0bFjR+Lj4xkyZAgnTpygQoUKzJ8/n+rVq9O4cWMOHTrEoUOHaNCgAWfOnCEsLIx69epx5MgR4uLiePLJJzl16hRGo5HPPvuMhg0bMnjwYCpWrMi2bdvo3bs3PXr0YNy4caSmphIZGcmXX35JQEAAP//8M88//zxVq1alXr16BAcHF7rdhXs4HA7WHLwAQOcCBqWqmoycS7ZQOdDHnVUT5dThw4epVq0aPj4+tG/fnqlTp1K7du08y27cuJFevXrlWNe7d2/mzZuH1WrFy8srz/3MZjNms9n1OCkpCdB6taqqWkzPhBzHLO7jlin/vI0CqM0fAlMNLb9UMZI2di9pX/eS9nUvaV/3K29tXNDnIUEpIUT+plbLf1u9XvDwD1mPZ9QFa1reZSO6wJDfilSFVq1acfDgQUD7YFu2bBmTJk3CarWycOHCfINS+/bt4/nnn8+1PikpifT0dCIisvL56HQ6mjVrxv79+2nWrBkA9913H4sWLaJbt260atUKgyH/j8uzZ8+ybt06tm3bxuDBg9mzZw8TJ06ka9euLFu2jIULFzJq1Ch++eUXqlevTkxMDOvWraNVq1asW7eOBg0a0LhxYxRF4dlnn+XVV1+ldevWbN26lWeffZYVK1YAcPLkSVavXo3VaqVnz5788ssvhISEMGPGDD766CNGjx7N//3f/7F+/XqqVKnCLbfcQocOHYrU7qL4bTiawLmkDADaRIQUaB+DM6mUxS45pUThtG/fnq+++or69etz/vx5pkyZQqdOndi7dy8VK1bMVf7cuXOEhYXlWBcWFobNZiM+Pp6qVavmeZ5p06YxadKkXOsTExPdEpRKSUkBsoa23kj0cXsIPPwHqqIjufkwHImJxX6OG72N3U3a172kfd1L2tf9ylsbZ/5QdS0SlBJClGrZv9T8+++/REVFERwczIABA2jbti3vvPNOnh/aqqoWav2V+vbty2233cb58+e5//77+f333/Mt+8ADD6AoCm3atMFisXD58mXWrVvH8uXLAbj//vsZPVobZtG5c2fWrVvHunXrePHFF1m3bh0XLlygc+fOAKxatYr9+/fneZ57770XRVE4ePAgu3btcvXeslgs3HzzzRw8eJCGDRsSHh7uOm9sbOw1n6soGQv/OwlAoNFAoDHvXidXynyrplls7qqWKKf69Onjut+0aVM6duxInTp1+PLLLxk7dmye+1z52Zj5+Xu1z8zx48fnOF5SUhLh4eGYTCaCgq6dN60wMutjMpnKxcV6of3xqXbb5B4CI5q75RQ3fBu7mbSve0n7upe0r/uVtzYu6HOQoJQQIn8vn8l/m6LP+fj5I1cpW/T0dTt27KBhw4aAlttp06ZN1KpVC4Dz58+zYcMGV0Anu6ioKKKjo109nzKZTCaMRiPHjx93HcfhcLBz504aNWrkKhcYGEjlypVZvnw5M2bMuGpQ6soP3Lw+gDPXde7cmZ9//pnDhw8zd+5cZs+eTXx8PCNHjnSV3bZtG3q9PtcxMnNiqapKq1atWLVqVY7t0dHR5eIfWHm15ZiW5Lx5DVOB9zlyQet9+N/xSzzU7tqz9QmRH39/f5o2bcrhw4fz3F6lShXOnTuXY11cXBwGgyHPnlWZfHx88PHJPbw0c8a/4pZ9NsEbStwB2L8MAKXruKyItRvcsG1cQqR93Uva172kfd2vPLVxQZ+DJDovLcrJuFFRznj7579kzyd1zbKFzycF2nC15557jmeeeQaHw8HSpUs5ePAgx48f5/jx47z//vssWrQoz33HjRvH1KlTOX78OAAZGRmu6c3HjBnDmDFjsFgsgJZjqnHjxtSrVy/HMSZMmMC0adPyDBBl98MPP6CqKtu3b8fb2xuTyUSXLl1YsGABAD/++CPt2rUDoFOnTvz+++9UrlwZvV6Pr68v69evp02bNgB069aNTz/Vfg13OBx5zpbVsGFDTpw4wY4dOwBITU3lyJEjNGzYkAMHDnDq1ClsNhs//PBDrn2FZ1hsDs4na3l37mp+lWGxV9DL8D1RTMxmM/v37893GF7Hjh1ZuXJljnV//vknbdq0yTeflChB694FVGh0F1RudM3iQgghRFkhQSlPczhgx3fw6U2QftnTtRHC4y5fvkyLFi2Iioqif//+jBgxgqFDh7J27Vrq1auX4xf7fv36sXjxYhyO3F/YW7VqxdSpU+nbty9RUVGuoBBoQakGDRrQqFEj6taty+bNm/niiy9yHaNNmzYMGDDgmnWuVasWXbp04ZFHHmHu3LkATJw4kTVr1tCsWTM+/vhj3n//fUDrqWUymVy9u9q3b0/16tVdPQ0+/PBDVq5cSbNmzWjSpAm//ZY7F5e3tzfff/89I0eOpFmzZnTs2JEjR47g6+vLBx98QI8ePejevTstW7a8Zt1FyVgSfcp1v3+L6gXeT+/8L221SVBKFM5zzz3H2rVriYmJYfPmzdx7770kJSXx2GOPAdqwu0GDBrnKjxgxghMnTjB27Fj279/P559/zrx583juuec89RREpovHYLfzR4au8noIIYQoXxS1vKR2d7OkpCRMJhOJiYnXnSNBVVUSExO1saIOO8zuDBcOQJcx0HNi8VS4nMrRduWgS2NJKkjbZWRkEBMTQ2RkJEajMc8yNyJVVbHb7ej1+lxtN3jwYO69917uvPNOD9WudLnyPSR/s5pHPtvEuiMJVA70YcuEngXaR1VVuvzvb04nmunRsDLzBrd1cy3LD3e/74rzmsBdHnzwQf755x/i4+OpVKkSHTp04I033iAqKgrQPruOHz/OmjVrXPusXbuWMWPGsHfvXqpVq8aLL77IiBEjCnVed7bNDft58sv/wfavck8w4gY3bBuXEGlf95L2dS9pX/crb21c0GsCySnlaXoD9Hgdvn8INs2Cdk9AUMGHdgghhCj9dp7SZslqH1mhUPtlDt+zyvA9UUjff//9VbfPnz8/17pu3bqxfft2N9VIFMnlk1qPeoCbcs8oK4QQQpR1MnyvNGjQB2p2BFsGrJ7q6doIIQph/vz50ktKXFWaxUaaxQ7Ag+3CC7WvwRWUkk7NQtyQNnwADitE3gTh7a5dXgghhChjJChVGigK9Jyk3d/xrTbDihBCiHJhS8xF7A6V6sG+dKoTWqh9M3tK2fLImyaEKOeSz8O2L7X7kktKCCFEOSVBqdKiZntoeCeoDvh7kqdrI4QQopisOxwPQJe6oYXOD1A50BuAED/vYq+XEKKU2/gh2M1Qo53WU0oIIYQohyQoVZr0nAiKHg4uhxMbPV0bIYQQxWDZrjMAdKpT8Rolc6tf2R+A8Ap+xVonIUQpl3YRtn6u3b/pea1XvRBCCFEOSVCqNAmtB60e1e6vfA1kYkQhhCjTDpxN4nySGYDG1Qo/E1lmTimbJDoX4sayaRZYU6FKM6h3q6drI4QQQriNBKVKm5vHg5cfnNoCB371dG2EKHEGg4GWLVsSFRVF69atmTt3bo7tqqpSs2ZNXnjhBQAmTJhAixYtiIqKws/PjxYtWtCiRQtWr17NkiVLaN68OY0bN6ZJkyZ88sknADgcDiZMmEDdunWpW7cu999/PwkJCQAcP34cRVGYPn2665ybN2/Gy8srz9mq3GXHjh38+eefJXY+4R7fbo4FwOilo25YYKH3z+wckWa1F2e1hBClWUYibP5Uuy+9pIQQQpRzEpQqbQKrQIeR2v2/JoHd5tn6CFHCgoODiY6OZt++fSxevJhZs2YxZ84c1/aNGzdStWpVFi9eDMCbb77Jjh07WL58OVFRUezYsYMdO3YQHBzMiy++yNKlS9m7dy9bt27Fz08bAjVz5kz27dvHvn37OHLkCG3btmXIkCGuc9SrV49ffvnF9XjRokU0adKkhFpAI0Gp8uGfwxcAaFCEgBTA7jMpAGyNuVhsdRJClHJb5oI5ESo11PKNCiGEEOWYBKVKo86jwbcCJByG6K89XRtxI1JVsKS6ZynEsNSIiAjeeecdVw8ngIULFzJixAgiIyPZvHlzvvu+8847TJgwgcjISAB8fX0ZPHiwa9vMmTPx9taSR48bN469e/dy+PBhAIKCgggNDeXYsWOoqsratWu55ZZb8jzPmTNnuPPOO2nevDmtWrXi6NGjpKen8+ijj9K0aVPatWvHjh07ANi0aROdOnWiZcuWdO/enbNnzwKwevVqmjZtSvPmzWnTpg12u53XXnuNr776ihYtWvDHH38UuM1E6eFwODh5MQ2A3k2qFOkYXs7he3aHDOcW4oZgSYWNH2v3uz4HOrlUF0IIUb4ZPF0BkQdjEHR7AVa8BGv+B83uB29/T9dK3EisafBWbfcc++UzhXo/t2rVioMHDwLa0L1ly5YxadIkrFYrCxcupH379nnut2/fPp5//vlc65OSkkhPTyciIsK1TqfT0axZM/bv30+zZs0AuO+++1i0aBHdunWjVatWGAx5f1yOGjWKfv36MXz4cMxmM3a7nY8//pjAwEB2797Npk2beOyxx9i5cydRUVH8+++/6PV6FixYwPTp03nvvfd49913effdd7n11ltJTExEr9czefJk9uzZw9tvv13gthKly5pD8WTGkga2q1mkY3jpnTmlJCglxI3hvy8g/SKERELjuz1dGyGEEMLt5OeX0iL5HFw6nvW4zeMQHAEp52DTJ/nuJkR5p2brWfXvv/8SFRVFcHAwAwYMYMmSJTm2X7mfkkcejvzWX6lv374sW7aMRYsWcf/99+dbbt26dQwdOhQAHx8f/Pz8WLduHY888ggAHTp0ID09ncTERC5dusTdd99NkyZNmDx5Mvv27QOgc+fOvPTSS3zwwQekpaVds26ibPhx20kATL5eBPt5F+kYXnrt37T0lBLiBmDNgA0faPe7jgW9/HYshBCi/JP/dqXB/l9hyZNQoy08ukRLaGnwge6vwuJhsO59aD0E/EM9XVNxo/Dy03o0uevYhbBjxw4aNmwIaLmdNm3aRK1atQA4f/48GzZsoHPnzrn2i4qKIjo62tXzKZPJZMJoNHL8+HHXcRwOBzt37qRRo0aucoGBgVSuXJnly5czY8YMfv/99zzrV5AAV2Yg7LXXXuOOO+7gySefZNOmTbz00ksAvPTSS/Tp04fffvuNtm3bXnVYoig7tp24BEDzcFORj5EZlHJIUEqI8i/6a0g5D0E1oNmDnq6NEEIIUSKkp1RpENYY7BY4thoOZvvi2+QeqNocLMnwjwzhESVIUbQhdu5YCjGL0MmTJ3nuued45plncDgcLF26lIMHD3L8+HGOHz/O+++/z6JFi/Lcd9y4cUydOpXjx48DkJGRwezZswEYM2YMY8aMwWKxAFqOqcaNG1OvXr0cx5gwYQLTpk1Dr9fnW8euXbsyb948AMxmM2lpaXTp0oUFCxYAsGXLFvz9/QkKCiIpKYkaNWoA8M0337iOcfToUZo3b87LL79Mo0aNiImJITAwkOTk5AK3lShdrHYHKWZtooohnWoV+TjeBmdOqULkYhNClEE2M6x7T7vf5VkwFK13pRBCCFHWSFCqNKgQCR2f1u7/8bJ2YQJacsuek7T7Wz+DizGeqZ8QJejy5cu0aNGCqKgo+vfvz4gRIxg6dChr166lXr16VKxY0VW2X79+LF68GIfDkes4rVq1YurUqfTt25eoqCjatWvn2jZmzBgaNGhAo0aNqFu3Lps3b+aLL77IdYw2bdowYMCAq9b3/fffZ+nSpTRr1oxOnTpx7tw5nn76aS5fvkyzZs145plnXMd+7rnnePbZZ+nSpYtrJkCA9957j8aNG9OsWTNq1qxJx44dueWWW9i+fTstW7aUROdl0K5Tl0k12wn28+Km+pWLfJxiTXRuTdeGB4lSKTExkfnz5zN06FB69OhBx44d6du3L6+//jobNmzwdPWEu0V/A0mnIbAatBrk6doIIYQQJUZR80vIInJISkrCZDKRmJhIUFDQdR1LVVUSExMxmUxZQ3/MyfBha63b9q2TtRn4Mn3VX+tF1fQ+uOez6zp3WZdn24kCKUjbZWRkEBMTQ2RkJEajsYRrWHqpqordbkev18v77hqufA/dqH+zM/86xMy/DnNH06p8/HCrIh1DVVXe/X0vH/5zgurBvqx/qXvRKpNwFNa/Dzu/A0UHtbpAvV5Q/zYIibj2/mWQqqoknY8lKDgExXh9/7PzUpzXBGfPnuW1117j22+/pUqVKrRr147q1avj6+vLxYsX2bNnD9u2bSMiIoLXX3+dBx54oJiehXsUZ9tcqdx+ntgs8GErSDwJfaZD+yc9VpVy28alhLSve0n7upe0r/uVtzYu6DVBmc0p9cknnzBjxgzOnj1L48aNmTlzJl27ds23vNlsZvLkyXzzzTecO3eOGjVqMGHCBB5//PESrPVV+ARCz4mw9ClYO0PLJRAYpm27dRJ8uhp2/wAdn4FqLTxZUyGEENfw9cYTADSqen1fyutU8gWgUqBP4Xc+vR3Wz4R9vwDZfn868pe2/P4ChHeA5g9os3z5hlxXXT3CZoFLMZBwBOIPa7fOxZR6AfWOd6HtUE/X8qqaN2/OoEGD2LJlC02aNMmzTHp6OkuXLuXdd991DWsW5ciOb7WAVEAVaPWYp2sjhBBClKgyGZRauHAhzz77LJ988gmdO3fm008/pU+fPuzbt4+aNfOedvv+++/n/PnzzJs3j7p16xIXF4fNZivhml9Dswdhy1w4sx1WTYZ+H2vrqzbXeknt/gH+mgiDlnqylkIIIa7ifFIGCalavrK2ta4v0GPQaaPsrfbcQ1TzpKpwbI2WmyZmbdb6er21PDXGYDiyEg79CSfWw8lN2vL7i1C/NzR/COreWrry2agqJJ/NFng6CgmHtfuXT4Cau21cvy0muWnChmK0d+9eKlWqdNUyvr6+PPTQQzz00ENcuHChhGomSoQlFda+pd3v8ix4SS9lIYQQN5YyGZR69913GTp0KMOGDQNg5syZ/PHHH8yaNYtp06blKr9ixQrWrl3LsWPHqFChAoBr1q1SRaeDPm/B/DvAt4J2IZ7Zba/7K7B3qTaM7+gqqFPEYRxCCCHc6ttNWi8pg06hfe2K1yh9dV567X/ANYNSDjvs/wXWzYSzO7R1ih6a3qsNBw9rnFU2LEpbl3RG+7Fj50KI2wv7l2mLbwVoMgCaPaDNCltS3cczkpy9nJxBp+xBKGtq/vt5B0DFutoSWg8q1kWtWIdEQyVMlaqXTN2vw7UCUtdbXpRyGz/Wgq7BNaFNKem9L4QQQpSgMheUslgsbNu2zTWVeqZevXrlmwj0l19+oU2bNkyfPp2vv/4af39/+vbtyxtvvIGvr2+e+5jNZsxms+txUlISoI3zvN40XJnHyPM4NdrCs7shICyzsHYbHAFth6Jsno268nWI7KblBrnBXLXtxFUVpO0yt0kb50/a5equfA/diH+zK/efB6BWRb/ret6qqnI0Pg2AmPjUvI9ly4Cd38OGD1AuHtP2M/hCq0e1CTSCIzIPlnvfwKrQaZS2nNsNuxbC7h9QUs5rk2ts/Qy1Qm1oer8WoKoQWeTn4mK3ar2b8hhup6Scz3c3VdFDSK2s4FPFuhBaFyrW0/5fXhE4U1UVNTHRbe+74jzuL7/8UuCyffv2LbbzilIg+bwWSAbo8ToYijBMVwghhCjjylxQKj4+HrvdTlhYWI71YWFhnDt3Ls99jh07xrp16zAajSxZsoT4+HhGjhzJxYsX+fzzz/PcZ9q0aUyaNCnX+sRiuMhVVZWUlBSAfBKYGSExMddapcWTBEV/g3JuF6lbvsbasP911aMsunbbifwUpO0sFgsOhwO73Y7dbi/J6pV6ec3wJ3Kz2+04HA6Sk5Mxm8035N/skTjt+XaspSV2LCpVVbWcSYDDoeY8ljkJn90L8Nn+Gbo0bTiXw8eEpcVgzM0fQ/Vz9tAq6Pl9a0L756HtWAwn1+O9fzFeR1Zoga61/4O1/8NWtTWWRvdgrX8nqtF0tYqjpF1Ad+kY+ksx6C4dRX85Bt2lY+gSY1Ec+Q+dd/hVwhFSG3tIpHYbXBtHSG0cpnDQ5zGk0AE4fzTKWQX3vu+S8jhnUfXv379A5RRFkc/l8mbNNK0XYPXW0OQeT9dGCCGE8IgyF5TKdOVFpqqq+V54OhwOFEXh22+/xWTSLqTfffdd7r33Xj7++OM8e0uNHz+esWPHuh4nJSURHh6OyWQqltn3gGtn1T+7U5sxqd9H4OUHJhN0fhZWT8Fv0zvQ+sEb7le1AredyKUgbZeRkUFCQgJ6vR69Xl+S1SsTpE2uTa/Xo9PpCAwMdM2+BzfO3+yuU4lY7dpzHtqtHiaTf5GPpaoqQQHa/ycVrQ1JOQ+bZsN/81DMzh68QdWh49MorQbh4x3Adf9XCLkLmt0FlhTU/b9qPahi1mI4uw3D2W2oaydqs/dVjtIm6fAOgNS4bMPujrrqlufz8vKDinW0Xk6u27pQsQ6K0YQeuN6/NHe/74rzmBLwvkHFHYDtX2n3e00puWGyQgghRClT5oJSoaGh6PX6XL2i4uLicvWeylS1alWqV6/uCkgBNGrUCFVVOXXqFPXq1cu1j4+PDz4+uS/tFUUplovRzOPkeyy7DRYN0oY5VGoIN7+ore84ErZ+hnI5FrZ9AR2euu66lDXXbDuRr2u1XeZ6T7fvZ599xtNPP8358+cJDg72WD0yZe8duWjRIiZOnEjDhg3p3bs3ISEhPPDAA8yfP5/bb7+dypUrA1quu5EjR+LtXfCE0RMnTiQ0NJRnnnkm3zKvvfYat95661VnG92/fz8PPvggiqLw999/U7Hi9eU1ys/x48e59957+e+//1zr8noP3Uh/s99viQXA10tPrdCA6z6e0aCFZ2pyDuXXMbBjAdidQ8tDG0CXZ1Ga3OuexOQ+gdDiIW1JOqvln9q1EOX8Hjjwq7bkR9FpOXIyA06hmcPu6qEEVtVyKLqZO993N8J7WbjZX6+DaoeGd0JEJ0/XRgghhPCYMheU8vb2pnXr1qxcuZK7777btX7lypX069cvz306d+7MDz/8QEpKCgEB2peEQ4cOodPpqFGjRonUu9D0Bug5EX4cos2i1PJhMNUAb3+4ZTwsGw1rp0OLgXC1YRRClEGLFi2iTZs2LFmyhCFDhni6Ojl88cUXfPHFF3To0CHH+vnz59OmTZscQalhw4YVKihVEJMnT75mmZ9//pmHHnooV+69/Njt9hy9wK58LAru4HltyFijqoHFcryKaUf5yOsD+ug2wzZncLRGO+gyBurfViLBHQCCqkLnUdpyzhmUSokDczJYUsCvgivoRMW6Wv6pG6wnb3FJTU1l7dq1xMbGYrFYcmwbNWqUh2olilXMP3BohTYZQc+Jnq5NoWVY7VxKs2B3qHgbdPh7G/D10qPTSbBWCCFE4ZW5oBTA2LFjefTRR2nTpg0dO3Zkzpw5xMbGMmLECEAbenf69Gm++krrFj1w4EDeeOMNhgwZwqRJk4iPj+f555/n8ccfzzfReanQ+G7YMhdiN8DK1+Heedr6Fo9os7XEH4L1H0CPVz1bT1FupaZqM175+fm5egZYLBasVisGgyFHb8LMsr6+vugyp7G3WrFYLOj1eozGgk1zHR8fz7Fjx5g/fz5vvvmmKyg1ceJEYmJiOHHiBGfOnGH69On079+f9PR0nnjiCXbs2IGvry9z5syhRYsWnD9/ngceeICUlBR69+7Np59+Snx8PDabjXHjxrFx40YsFgsTJ06kf//++a7P7n//+x/r1q1j8ODBDBw4EIfDQWhoKNWrV+e///7j3nvvxWQyMWjQIM6cOUOnTp2oU6cOS5YsYfny5UyePJmMjAzatWvH7Nmz0el0zJ49m/fee4/w8HAqVapEaGjoVdtn8ODB3Hvvvdx5553UqlWLwYMH8/PPP2MwGPjll1/Yu3cvM2fOxGAwsGHDBn755RfefPNNli5ditlsZuTIkYwYMYI1a9YwdepUgoODOXfuHI8//jh//PEHSUlJ+Pv78+GHH/Lkk09y6tQpjEYjn332GQ0bNuTQoUMMHDgQvV5/1d5aNyJVVTl5SUtMPqZn/es7mMMO62bS7d+p6PTOHEz1emnBqJodCzfUx+GA4//A7h9BZ4DaN0PkTVogqSiqNNGW0shu03oYm0rpD07XEB0dze23305aWhqpqalUqFCB+Ph4/Pz8qFy5sgSlygOHA/58RbvfZog2Y2Qplmq2sfbQBbaduER07CWOJ6RxMdWSZ1lfLz3+Pnr8vA34eevx89bj76Pd9/c24Oej3fpe8TiznGt9tv18DDrpmSiEEOVcmQxKPfDAAyQkJDB58mTOnj1LkyZNWL58ORER2ixDZ8+eJTY21lU+ICCAlStX8n//93+0adOGihUrcv/99zNlyhRPPYWCURTo8z/4tBvs+RHaDoOIjlovqh6vw8KHteBU22Har9hCFLPMnoVxcXGuachnzJjBK6+8wrBhw5g7d66rbOXKlUlLSyMmJoZatWoB8PHHHzNmzBgGDhzIt99+W6Bz/vTTTwwYMIDOnTtz4MABEhISXMPP9u3bx/r16zl//jydO3emd+/efPzxxwQGBrJ79242bdrEY489xs6dO5k0aRL9+/fn2WefzVHPzz77jFq1avH++++TlJRE+/bt6dOnD1988UWe67MH3l566SX++OMPPvroI5o0acLEiRMBuPvuu2nTpo1rfWY7bdiwgYCAAOLj43n33XdZs2YNRqORZ555hkWLFtG1a1dmzJjB9u3b0el0tGzZks6dOxfqNapZsybR0dG89tprfPbZZ7z66quMGDHCNQxwxYoVxMXFsXXrViwWC126dOHOO+8EYPPmzezfv59q1aoxf/58tmzZQnR0NEFBQTz00EO8+uqrtG7dmq1bt/Lss8+yYsUKnn32WV5++WUGDBjACy+8UKi6lneHzqdwIdmM0UtHu9pFDPgAXI6FJSNQTqxHAf60t+Zd232seLgIw7VPboXFw+FSTNa6bV8AClRvBd1fgTrdi15XTzGnaD/MxB923jrvXzwKdgs8+a+WvL2MGTNmDHfddRezZs0iODiYTZs24eXlxSOPPMLo0aM9XT1RHHYv0nKGegdCt4L1ZvWEbScu8e3mWFbsOUe6NXeCfYNOQadTsNodrsk90612Z9m8g1ZFoVPIGcDKFvS6MoClLQZXYCxngEx77Oulx+glwS4hhChNymRQCmDkyJGMHDkyz23z58/Pta5hw4asXLnSzbVyg6rNodUg2P4lrHgRhq/Rhms0vAPC28PJzdrMSHe97+maClEsFi5cyPTp01EUhX79+rFkyRKGDRsGaMEfb29vwsPDadiwIQcPHmTdunWu4EiHDh1IT08nMTGRDRs28OqrWi/CBx98kPHjxwPaUN+9e/fy5ZdfAloPr9OnT+e7vnbt2tf9nDZu3MiuXbtcQ/7S09OpXr06RqORHj16uPLdFWW698xhy61bt85zavmVK1eybNky1q5dC2gziB49ehTQhjZXq1bNVbZ3796uiRxWrVrF/v37cx1v27Zt/PbbbwA88sgjrFq1qtB1Lq9W7D0LQLvIivgYijj8cdcP8Ns4MCeiegdwovXLPLE6AijglyeHHdISIEAbRkpwTUg8qX0BbnYfGIxwbA3E7YPT27ThQ5kun9Ry3ITUKlrdi5vDAUmntYBTwhFodBcEOd+vW+fCXxPz3s9g1BLCl8Gg1I4dO/j0009dk02YzWZq167N9OnTeeyxxxgwYICnqyiuhzkl633bdSwEVPJodfKy7cQlZvy+j80nsmbujKjox031KtEqIpgGYUFUCzZi8vVCURRUVSXD6iDVYiPNbNduLXbSLDZSzc5bi530Kx6nmTPX2/PcN8OqTQDgUCHZbCPZbAPMxfY8FQV8DDpXkMrXS4+Plx5fLx3GHOtyljFm2559Xdb+zsfeeoyGrACYDG0UQoj8ldmg1A2l+6uwd4n2y9q+Jdq0wYoCPSfBF7fB9q+hw9NQ6TqHiwhxhcwp1f38/Fzrnn/+eZ599lkMhpwfH3FxcQA5hsQ+/fTTDB8+vMD5ic6fP8+GDRu49957ATCbzezdu9cVlLryV828fuXMnIkze3LyK7fPmTOHm266qUDrBw0axK5du2jSpAlff/11gZ5HXue88847+fzzz3OsX7JkyXX/UpvZk0uv1+c5XbyqqkycOJFBgwblWL9mzZocryuQ6/G2bdtyvXbyy3L+vtxwAoBQ/yLkEUu/DMuf05KJg5Y36u5PwRIIq7cAYHeo6PP7YpN4CqK/0ZaQWjDYmYQ8MAwe/hHC22k5CTMln4ODv0NEtp55Gz+CzbOhWkuI6g+N+5dsgCpuP+z7Oav3U8IRsKZlbQ+sClHOwG1offCvrN2G1nPeOu+bwrX/kYmJeZ+nFPPy8nL9jYWFhREbG0ujRo0wmUw5eoCLMmrde5B8Vvu76pD3D6uekpRhZdry/Xy35SSg9YQa0Ko6D7arScvw4KtOkOLrrcfXWw/XP7eDi92hkuYMUqWaM4NVVwSwzDbSrPZsj7VbV6Dryn3NNmwO7dpAVSHD6nAGv6zFV/F8+Bh0rgCVlw58fbzw1uvw8dLhY9DhY9C2eWfed673zrYta9Hn2M/7ivVZx83az6AvofyDQghRBBKUKgsCKsGtk8Fhg0bZkrlHdIQGt8PB5fD3JHiwYMOjhCgof//c09l7e3vnmbw7r7JeXl54eXkV+Hw//vgjTz31FO+99x6gBVRq1arFhQsXAFi6dCnjxo0jLi6OgwcPUr9+fbp06cKCBQvo1KkTW7Zswd/fn6CgIDp16sQPP/zAqFGjWLRokescPXv2ZPbs2XTu3Bm9Xs+OHTto0aJFvuu/+uorVFXNM+CTXWBgIMnJybkeBwQE0KFDB0aPHs3JkycJDw8nISGB9PR02rVrx/PPP09iYiJ6vZ5ly5a5huiMHz+edu3a5ZjQoSh69uzJW2+9xX333Yevry8HDx50DXW+mm7duvHpp58ycuRIHA4He/fupWnTprRq1Yqff/6Z/v37s2DBguuqW3mSkmFz5Vm5pWEhez8cXw9LntR6NCl66PYCdH0OdHoMcQmuYla7A70uW5DQboVDf2g9aY/8BarWswBLqhbk8g3WHte5Jfc5A6to+WyyS7+kzZp3Jlpb/nodqrbQglNR/bXk5UWlqlpi9PhDkHA4K/DUaRTU7qaVuXAQ1kzLuZ/OABXqaMGmzOcD2v++hndc/XxlUMuWLfnvv/+oX78+t9xyC6+99hrx8fF8/fXXNG3a1NPVE9fj0nHY8KF2v9cU8CpYnsWSsCXmIqO+i+ZcUgYAdzerzNjbGhNewe8ae7qPXqcQaPQi0Fjwa4iCsNodZFjtzoCUnQznkMPMx+nOdZll0rOVMV9RJj3bMa48TobVjtWe9Tlktjkw2xzZalJ8vb4KQqeQK9jlpdcCWNnvexl0eOsV7bFzvbZNyfnY4CyvV5z75D6Ol17JsT7n+bLtp5eeZELc6CQoVVZc+eUhU4/XtRlcDvwKsZuhZvuSrZcQxWjRokU5ZpdTFIU777yTn376CdC+sPXq1YszZ87w/vvv4+vr6+qN1axZM4xGI1988QUAr7/+Og888ABfffUVt99+u2tY2pNPPsmxY8do0aIFqqpSv359Fi9enO/6gho8eDCDBw8mODiYzZs3M3z4cG655RYaNWrEkiVLmDVrFv3798dqteLl5cXcuXNp1aoV48aNo127dtSsWZMuXbq4jrdnz54iDee70u23386ePXto164dqqpSuXJlli1bds39PvzwQ0aMGMHs2bOx2WwMGjSIpk2bMnPmTAYOHMi0adPo3bv3ddevvPhhm9a7QAH6NC5gjj+7VQvC/PsuoGq9JwZ8BuFtte2qiiHbhbrV7sDo5QxKbf8aVr2hDVPLVKsrtHpMG+ZWlC+8A+ZArzfhwDLYuxSO/wtnd2jL5jkwdt+1E6zbrdri7fwye3IrrHhJC0KZ8+i5FNktKyhVtRm0fETr8VTR2fspJAL0eXwpLac99qZOneoKbr/xxhs89thjPPXUU9StWzdXT0tRhqgqrBgPdrM2yUDDOz1dI5fvt8TyytI92BwqtSr68b97mtGwgh6TqRRPBHQdMoMjgSUQE7TZHWTYnIEsix2zTQtsJVxOwsvoi8WuYrbaMdscWJxBK7NNe2y2OrDYtfKZ67PKOFzHstgdzjK5j5M9KOZQs+f8Kn30OsUVxPLS6zDoFQy6zFslxzqvbNu89DrXdr1OK6varfj5Gl3bDPor98l+7Jzr8jp25v56Xc7zGZzr9Iq2j16v3dfrtEWnSO9yIQpKUfMb4yJySEpKwmQykZiY6PpyW1SqqpKYmIjJZCrah5U1Q5uC2985S9fPz0D01xDeAR5fUW4v1qEY2u4GVpC2y8jIICYmhsjIyALPlldSJk6c6EreXRAZGRl4eXmh1+v54YcfWLhwIT/++GORzp3ZU0qv15fY++72229n+fLlJXKu4nTle+hG+Zu9b/YGth6/RPVgX9a/VIDE4fFHYPEwrUcSaLOq9vkf+AS6iqiqyoWES7R7eyPeWPljVGciqzl7Ye38Xutd5V8JWgzUglEV6xTvk0qNh/3LYN9SCGsCvd/U1ttt8M3d4FcRvPzA4KMNB4w/pPUGufUN6OgcmnR2J3zqHBKr6LQcV9mH2kV0dsvsY+5+3xXnNUF54862KbOfJ3t+gh8fB50XPPkPhEV5ukaoqsq03w8w559jANzZrCrT722Gr5e+bLZxGVGS72GHQ80VtMq6dWC1ObDaVSx2OxabitXuwGrXAltWuwOLXXXd1x5nbbM6y5vtmcfRtltt2jmzH8dqV7Xz2bMvN8bXz8wJAQy6rGCV676ioHcGwXQKWmDrinJ57atTFGdATIdeAb1Od/XzXHE8vaIdQ+cMnGW/f+U2vU5BUTL30b5iZqSnEeDvj16vQ5dtm85Zt+z39YqC4jyOTlHQ68h2vJzbdDqyHS/r8ZXblHIe7Cuz/+fyUdBrAukpVdac2Kh9EQlrAg85h87c8rI21ffJTVqOkIa3e7aOQpQCx48f56GHHsJut2MymVw9qMqKshiQupHtPZMEQKc6Fa9eUFVh23z442UtX5IxWJuoonH/PIsbU04ywfAN9+j/wbLrWaj2vLYhqh94+UL9PmAoQg6rgvAP1XrpthmSczjciXUQ80/++108lnU/tD7c96V2W6F2qRqyVBrFxMRgs9moVy9noO7w4cN4eXm5ZjYVZUhqAix3zlTadVypCUhN/nUfX6w/DsC4W+vzTPe6V83HKMoenU7BqNM7e9gW7zDI6+VwqFgdWnDKassKeFnsDmx2LeBlc6jYXLdaeZtdW2fN3JZtfWbAKzUtHYOXDzbH1fZxrrOr2B3aOaxXbMvrnPZs5WwOB3aHiuMqfzI2hwoOtRjnoxSZFGdATXHeV7Ldz1p/xfaC7uMMwClk9XjLbx+c++iu2Adnuez767QCWcdCcT0X160KNpsNg5cBBSXPviaZwSolxzpyrMse0FKuKJO59tGOEXSrXzom3JCgVFnjG6IltL18Ao6u1nKFBFWDDk/Bune1WV3q9QK9vLSifJk4cWKhyjds2JDo6Gj3VEaIbE5dSiPNog2JeLDdVWZ8S42HX0bBQW32QiK7Qf9ZYKqeu6zdCuvfJ3DtWww3aJezScf/BpxBKS9fLTBVUrJfyVRpBgPmQtpFsKVrvXf9Q7UeTxXrZc2Ql1nPfAJuIrfBgwfz+OOP5wpKbd68mc8++4w1a9Z4pmKi6Fa8CGnxUDlKC0p5mKqqTPltvysg9dY9TXmgbdmbqVKUbTqdgo9Oj48B8Cm+43qil4mqasEqmzNoZVdV7HbtsUN1rrc71zsDYPYrFptDxZH9GFcezxkccx0vr3JXHMehasE8u8PhPIbqPAY4VG273aGiOh9nBtiu3GZ3HktVwa6qWK020OlybNMWLdiYeV/bX6t/9m05zpnHfgVvd60+2dYU+2tb3nVvWNnTVXCRyEVZU7khtBuuzZC0YjyMWKcFoDqPhm1fQPxB2LkAWg269rGEyIf8UiqK6kZ873y7SZsVzUuv0DoiJO9Ch/6En5+G1DjQe0OP17RZU3V5zIh0dhf8PBLl3G4A1tkb87m9D8O6D6eTu55EYfhVgGb3e7oW5VJ0dDSdO3fOtb5Dhw4FHrqcn2nTpvHyyy8zevRoZs6cmWeZNWvWcMstuRPj79+/n4YNG17X+W9IB1doM2oqOuj3kft6NRbCrLVHmbcuBoD/DZCAlBDXS3EOpzMUbKLpMs3dQT81W/DLoao4HFlBssz7dmcZFRW0jmioZAW6Mi9DHa5y2ddnlivcPg5VC4KpzsBZ5rnVPPbBud3h0NZl7pPXubW1Wc89c0VqWhp+fr5k7wulkjVzaPbbvI6R40rcddistZn7tqmVzzWrB0hQqiy6+SXYtQgu7NcCUe2Ga7MS3fS8NiRk1ZtQpzuYani6pqKMMRi0j4Tk5ORyMY65uHgip1RZpKoqFy5cQFGUQs26WNadS0oHoEV4cO6NllT48xX4z5mkulIjuGcuVMlnJrVtX8JvY8FhQ/UNIe2m13j058qoKDzikPdeeacoSo5ZPDMlJiZecwbQq9m6dStz5syhWbNmBSp/8ODBHLkfKlUqHd37y5SMRPh1jHa/49NQvbVn6wP8tuss01ccBGDiXVFX79kphBAlzDXEjhv3eqe85ZQqKAlKlUW+IdB9Avw2DlZNgSb3aL9ctx0GWz/T8nl8dis88lOpyF0gyg6DwUBQUBAJCQkkJCRce4cbiMPhQJdXrxaRg6Io1KhRA73+BvjJ0GnnSW1WuWFda+fccHobLH4CEo5ojzuM1GZMvVpepRptAUUbmtdnBla7D/yyHlTIsDjy30+UC127dmXatGl89913rr8hu93OtGnTcszOWRgpKSk8/PDDzJ07lylTphRon8qVKxMcHFygsmazGbM5a3r7pCQtv5r2i3fx9pzMPGaZ6JH5xwSU5DOoFWrDzeNz/qztAdGxlxmzaAcAQzrV4rFOtfJsxzLVxmWQtK97Sfu6l7Sv+5W3Ni7o85CgVFnVajBsnQdx+7QpxW+foc2ANOgX+OYebRjfF7fBg99BrdxDAYTIT7Vq1QgNDS03H4bFQVVVkpOTCQwMvKF+tSiKzBkPbxSnL6dzLD4VnQIdM5Oc221ajr81/wPVDoHVoP8nWg7AK1nS4Pg6qN9LexwWBSM3avmZVBUSE3HmvcRsk6BUeTd9+nRuuukmGjRoQNeuXQH4999/SUpKYtWqVUU65tNPP80dd9xBz549CxyUatmyJRkZGURFRfHKK6/kOaQv07Rp05g0aVKu9YmJiW4JSqWkpACle/Ylw9GVBER/jYpCSvdp2NOsQKLH6nM53crIb3dgsTnoVjeEZ7pUIzEx7/qUlTYuq6R93Uva172kfd2vvLVx5g9V1yJBqbJKb4DbpsFX/SDpDDgcWm6S4HB4fAV895A2G99X/aDFQ9BljDbzkRDXoCgKPj7FmHWyHFBVFbPZjNFoLBf/IETx+WbTCQAaVzMRZPTSeqoufgJObdUKNL4b7nhX6816pZh/4Zf/g8uxMHwVVGuhrQ/NmeTa6KUnzWJHr5P3XnkXFRXFrl27+Oijj9i5cye+vr4MGjSIZ555hgoV8ngPXcP333/P9u3b2bp1a4HKV61alTlz5tC6dWvMZjNff/01PXr0YM2aNdx000157jN+/HjGjh3repyUlER4eDgmk+mq0z8XRWaQq1QPa0i9AH+/pN3v+AwBjXt7tDoOh8rYJf9xLslCrYp+fPhwGwKN+Q+vLhNtXIZJ+7qXtK97Sfu6X3lr44I+BwlKlWW1b4Yn1mZ9kcnkVwEGLYUlI2DfUtj+FUR/ow3z6zoOKjcq+boKIUQ59MuOMwAYDYqWD2rFeLCmgk8Q3P62lhD8yn/IGUnw1+tZeaaCqoMlJd9zhAUZiYlPJSxIgsU3gmrVqjF16tTrPs7JkycZPXo0f/75J0bjVYaMZtOgQQMaNGjgetyxY0dOnjzJ22+/nW9QysfHJ88fMrTcIMV/QZ153FJ5sa6qsOxZ12x7SvdXcv/9l7B564+x6uAFvA06Pn64FUG+1062XqrbuByQ9nUvaV/3kvZ1v/LUxgV9DpIgpay7MiCVycsX7v8ShqyAureC6tBmgPmkA3z/MJzeXqLVFEKI8sZmc3DmcjoVSOJtx3RYNkoLSEV0gafWQ/MHcn8hPbwSPumYFZBqPQRGboJa+ecL8tJrx7AVZq5kUWbExsYWqvzp06cLVG7btm3ExcXRunVrDAYDBoOBtWvX8sEHH2AwGAqcOL1Dhw4cPny4UHW8YUV/Awd/A50XDJhz9fxxJWDfmSRm/KElNn/9rigaVzN5tD5CCCFEXiQoVV6kxMHK18Fmzrk+oiM88qPWo6pRX0CBA7/C3Fvg67vh+HqPVFcIIcq63/ee42ZdNH/4vEjEhTXaF9FbJ8Njv0BwHrNa/fYcfHsvJJ2CkFrw2DK4ayYYrz7EyUuv/au22iWnVHnUtm1bhg8fzpYtW/Itk5iYyNy5c2nSpAmLFy8u0HF79OjB7t272bFjh2tp06YNDz/8MDt27Chw7rfo6GiqVq1aoLI3tEvHYYVz2F73V/KfYbOEWGwOnvthJ1a7yq1RYQyUmfaEEEKUUjJ8rxSw2h2uLx1F4nDAF7dDwmFt6F7n0bnLVGsBD3wNFw7Cuvdg1yI4ukpbanaErs9B3R4e72YuhBBlgiWVgL+e5wvvX7XHlRrBPXOv/kW0UgNAgQ5PaV9avf0LdKrYhDQAtp24xM0NKl9nxUVps3//fqZOncptt92Gl5cXbdq0oVq1ahiNRi5dusS+ffvYu3cvbdq0YcaMGfTp06dAxw0MDKRJkyY51vn7+1OxYkXX+vHjx3P69Gm++uorAGbOnEmtWrVo3LgxFouFb775hp9++omffvqpeJ90eeOww5KntGG4NTtBp//zdI34ePUR9p1NIsTPi6l3Ny0Xw0CEEEKUT9JTqhR4+8+DdJuxmhd+3MnlNEvhD6DTQVdnktG1M7ReU/mp1ADung2jtkObx0HvDbEb4dt7YE432PezFuQSQgiRt1PbYHZXbknWAlIrTffAE2tyB6SSzsKZ6KzHbYbCiH+1SSoKGJACUNGG7WVYCzbcSpQtFSpU4O233+bMmTPMmjWL+vXrEx8f7xoy9/DDD7Nt2zbWr19f4IBUQZ09ezbH8EGLxcJzzz1Hs2bN6Nq1K+vWreO3335jwIABxXrecmfDhxC7AbwD4O5ZoPPsDKT7zybx8eojAEzu14RKgZKPTgghROklPaU8zO5QWRp9mvNJZk4kpPHDf6doWsPExw+3IjzEr+AHavYgbJkLZ7bD35Oh30dXLx9SC+58D256ATZ+pOU3ObsTFg3Spi9vdCc0ukv7xU8vbxMhhMBug3/fgbVvgWrnrFqBcdYRPHbrYzlzx6iqNsHEn6+CbzCM3KgFoXS6Ig3p0Tl7OFjsklOqPDMajQwYMMCtAaA1a9bkeDx//vwcj1944QVeeOEFt52/XDq7E1ZN0e7f9j/t+sqDVFXl1aV7sDlUbmtchTubydBLIYQQpZv0lPIwvU7h52c6061+JfSKggrsOpVI97fXsO7whYIfSKeDPm9p96O/hjX/074YXUtQVej9Jjy7RwtQGU2QfAa2zIEv74K3asHXA+Cft+HEBrBmFOVpCiFE2ZZwFL64DdZMBdVOTFhvbjP/j01qE26Nyjak7uIx7bNz2SgwJ4J/KKRdvK5T63TOoJRNekoJUapYUuHHoeCwQsM7oeUjnq4Ri7ef5r8Tl/Dz1vN63ygZtieEEKLUky4wpUCVIF++fLwdFpuD9/8+zKw1R7DaVR6dt4VX7mjE0K61C3ag8HZaYOmf6bBmGsQfgn6fFGz2F/+K0H0CdB0HMWth3y/aDDLpl+Do39oCoPeB6q0hopOWRD28PfgEFv3JCyFEaZbZ62nFeG1mPR8T3PE2P5xuSuKJY3StE4pOp9Nyymz6BFa9CbZ0MPhCj1eh/YjrHsqjz+wpZZOh1UKUKivGa/k8A6tC3w89npczMd3KtN/3AzCqRz2qmnw9Wh8hhBCiIEosKHX48GF+/vlnjh8/jqIoREZG0r9/f2rXLmDA5QbgbdDxfO8G3NG0CvfO3kiaxc4bv+0n1WJnVI96BTtI9wkQHA6/joG0hMJ/GfIyQv3e2uL4AM7v1XpIxW7QblMvaPdjN8C/gKKDKs2cQapOWtJ0/9BCP3chhCh1Ui5oPZ4OLtce1+oK/WdBcDjr/lkHQP+W1SEjCb7qpw2fBoi8Ce76ACpEFks19M6eUlYZvidE6bHvZ9j+JaDA3Z9qE8142Pt/HSY+xUKdSv483rl4Pn+EEEIIdyuRoNS0adN47bXXcDgcVK5cGVVVuXDhAi+99BJTp07lueeeK4lqlBlR1UxseKk7t3+wjjOX03l35SG8DTqevKl2wbphtxoEFetB5Uag99LWxR2AuL3QqF/Bc0Tp9FC1mbZ0GKH1GEg4CifWa8nRT2yAyyfg7A5t2fSJtl9oA60XVURnLUgVHF6UZhBCCM859Af8/LQWiNd7Q/dXoeMzoNNxOc3C7tOJAHSuGwo+PlpPCR8T9J4CLR8t1h4Tehm+J0TpkngKfhml3e/yLNTu5tHqAJy8mMbXm44D8PpdjfE2SIYOIYQQZYPbg1KrV6/mlVde4dVXX2X06NGEhIQAcPHiRWbOnMlLL71Eu3btuOmmm9xdlTIl2M+bdS/czP9+P8icf4/xv98PcD4pgwl9GmEoyIVGRMecj9e9C7sWgqkmdBypfWnyCShcpRQFQutqS+vHtHWJp50BqvVwYiNc2A/xB7Vl23ytjKmmVp+azkBVaD2Pd3EXQog8WVLhjwmw7QvtceUoGDAXqjRxFZm99igtOExqYE2qmJzDo+98D1SHlqevmAUZDZxNBF9vz87oJYRAG6q7+AnIuAzVWsEtEzxdIwDeXXkIq12lS91QbqpfydPVEUIIIQrM7UGp2bNnM2zYMCZOnJhjfYUKFZg8eTLnzp1j1qxZEpTKg06n4+U7GhEa6M3U5Qf4Yv1x/thzjl//rysVArwLd7BKDcAvFBJjYcVLWs6pNkOh/ZMQWKXolTRVh6b3agtoCX0ze1Gd2KDNSpMYC7titaAYaPWI6KjN7BfRSZuNysPTJwshBKe2weLhcPGo9rjjM1oPqex5+Syp1Iueygvey1itdAOcM6UFhrmtWs1qBHPwfAr1wiR/343g0KFDrFmzhri4OByOnHnEXnvtNQ/VSrise1f7Ic7LH+75LKtHugftPZPI0h2nAXjxtoYero0QQghROG4PSm3ZsoWvv/463+2PPvoogwYNcnc1yrQnbqrD5TQrn6w5ypnEDDr972++GdaeNrUKkb+g6zjoMBJ2fgcbPtK+dK17FzZ8CB2fhlsnFU9l/SpAwzu0BcCcAqe2aL2oYjfCqa2QFg/7l2kLgHeglqQ9opOWOL1KE/ANKZ76CCHEtVgztAki1s0E1Q6B1eDuWVD75pzljq6GZaO4xxILClQx+YPdVvAh0UVk0DtzStkkp1R5N3fuXJ566ilCQ0OpUqVKjiH7iqJIUMrTTm6F1dO0+3e8DRXreLY+TtNXHERV4a7m1Whaw+Tp6gghhBCF4vag1Pnz56lVq1a+2yMjIzl37py7q1HmvXBbQ0IDfHjj131k2BzcN3sjL9/RkOFdC3FB5OULbR6HVoPh0O+w/gM4uQmM2S5gVOeXnuIaXucTAHW6awuAzQxndmTlpYrdBOaknDP8AQTV0HJiVYiEkEgIqaXdD65ZPPUSQgiAmH9h2eis3lFN7oE73skZGE+/BH+8Aju+AeCUGsrL1qHMeHSs2wNSAAadNmTb5pDZ98q7KVOm8Oabb/Liiy96uiriSmkX4YfBWuC6yT3Q/CFP1wiADUfjWXvoAgadwrhb63u6OkIIIUShuf1qOiMjA2/v/IeaeXl5YbFY3F2NcuHxLpE0Dw/m4c82kWF18OZvB1i5L46vhrTF6F2Il1Kny+rNdHILhGa7iNn3M/w9CRrcDg36QHiH4v3SZfCBmu21BbTcDOf3ZuWlOhMNl2Mh6ZS2XEEBgvwrQ4XaWYGqkFpZgauAypKvSghxdTYLHFoB0d/A4T+0dQFV4PYZENU3Z9nT2+G7ByHlPKCwNmQAT529A51PAGFBxlyHdoe9Z7Sk6puOJZTI+YTnXLp0ifvuu8/T1RBXcji0ob1Jp6BCHbhzZqm41lBVlbf/OAjAwPY1qRXq7+EaCSGEEIVXIrPvffbZZwQE5J1UOzk5uSSqUG60jghhw4s9GDBrPccT0tgSc5Eu01ez9OnO1AjxK/wBw9vlfLz1M7h4DDZ+pC1e/lCjtTasLrw9RHYDQyHzWV1N9hn+2j+prctIhPP74MIBuHTcucTAxeNgTkSXGgepcVovryt5+eUMUmUPWgWHa0ExIcSN6dxuiP4Wdi+CtGwBntZDoOdE8A3OvU+F2oCiBe/7fsRL36aQRgYdqgeVUKXB7tB6sKZbZPa98u6+++7jzz//ZMSIEZ6uisju33fgyF9gMML9X4Gx5P7+r2bj0QS2x17G26DjmVvqero6QgghRJG4PShVs2ZN5s6de80youAqBHiz5vlbeO3nPXy98QTxKRZ6vfcPT99Sl6FdIjF6XUfS8Ie+g6Or4ODvWk+C9EsQ84+2KHoYfwpwBqVObdMuzCrWLd5fDI0mLRH6lTMIAmraRVJO7iHAcgHl8nG4GJMVuEo8BdY0iNunLbkoYKrhDFJFXBG4itSG65SCXz6FEMUo7SLs/kHrFXVuV9b6gCrQ/EFo+Yg2I2gmVdW+fNbtqX0e+AbDoKUQEolF8eZc4u+AlrulpHjpM4fvSU6p8q5u3bq8+uqrbNq0iaZNm+LllTOJ9qhRozxUsxvY0dWw+k3t/h3v5piJ09M+WHUYgIfahlO5hHpuCiGEEMXN7UGp48ePu/sUN6zJ/ZrwaIcIxi/ezX8nLjHjj4PM+ecY97epwfg+DdE585AUik8gRPXTFocdLhyEk5u1xZIK3tl6Yy1/Ds5sB98Kzp5U7bTbai1zlitOviHYw5qByZQ7gGSzaEP/MntWZQarMgNX1lRIPKktx//N47mbtGBVZu+qoOrazISB1bTbgLDi7SUmhHAPu00Lru/4Rguw251DxPXe2rDkFo9oee6uHJp86TgsexaOrYb+s6DFQG195UYArN13jsyw0N0tq5fEMwHAy5noXIJS5d+cOXMICAhg7dq1rF27Nsc2RVEkKFXSks7AT8MAFVo+Ci0f9nSNXLYev8imYxfx0is82a10JFwXQgghisLtQalVq1bxzDPPsGnTJoKCcnZ3TkxMpFOnTsyePZuuXbu6uyrlUr2wQH4Y0ZGfd5zhjV/3kZBqYe6/MXy98QRDOkfyfO/6RQtOgTa0LixKW9oMybnN4dCSmBuMkH5RS5x+6Hfnfgao10vrdVWSDN4QWldbrqSqkHohZ5AqM3B1MQZSzoE5UetJkb03xZX8QiGoKgRWdQasquYMXAVWBf9Qre2EECUr/rDWI2rn99rfdKYqzbQeUU3v02YIvZLNAptnw5ppWm9Lg1ELwl/hm02xANzXugZ+hcnjd50Mzp5Sdkl0Xu7FxMR4ugoik90KPz6uzRgc1lTLOVeKfLjqCAD3tq5BtWBfD9dGCCGEKDq3X1XPnDmT4cOH5wpIAZhMJp588knefffdQgelPvnkE2bMmMHZs2dp3LgxM2fOLNAx1q9fT7du3WjSpAk7duwo1DlLK0VR6N+yOp3qVOSZBdvZcvwSGTYHs9Ye5fP1MfRrUY0JtzfC5FecuaB08Ngy7cvcuV1ZvaliN2tfBvXZzqWq8OlN2hCZzB5VYU1LZNYqF0XRkqAHVM6dRwvAkgaXT+QMWiWfheRzzuUsOKzaxWlavJabJt9z6Z0BquxBqysCV4FVZLigEMUhIwn2LtZyRZ3akrXeryI0vV/r2VClaf77H10Fv78I8Ye0xxGdoe+HuaZ6P3AuibWHLqBT4JnuJZu7xdVTyi49pYQoMSte0iZh8QmC+7/UZjAuJXaevMw/hy6g1yk81U1ySQkhhCjb3B4V2LlzJ2+99Va+23v16sXbb79dqGMuXLiQZ599lk8++YTOnTvz6aef0qdPH/bt23fV/FSJiYkMGjSIHj16cP78+UKdsyyoHGRk0YhOxFxI4bkfd7LtxGXMNgeL/jvFD/+d4t7WNRjXqwFVTMWYd8DgDTXaaEvHp7UAVOJJsJmzyiQczeqBtOcnbZ2XH1R3JlCvfxuEty2+OhWFt582RMc5TCcXh0PrEZYZqEo6kxWsct2ehZQ4bbropNPacjUG4xWBq6pZi3+oFrTyDdZufYIkgCVEJodDG4K741vY9wvY0rX1ih7q3QotHtY+V6413PafGbBqinbfvxL0nKRN855H79JXlu4B4LYmVYioWLIzXHm5ekpJUKo8Gjt2LG+88Qb+/v6MHTv2qmXffffdEqrVDW7rPG3iFxS4+9NcQWpP+2i11kuqX4tq1KzopnQJQgghRAlxe1Dq/PnzuRJ15qiAwcCFCxcKdcx3332XoUOHMmzYMEDrjfXHH38wa9Yspk2blu9+Tz75JAMHDkSv17N06dJCnbMsiawUwE9PdeZEQiqv/byXdYcvYFfhh22nWBJ9mj5Nq3Jvq+p0rRda9KF9+VEUCL4iMGiqAYN+gZNbtN5Up7ZoM+wd/1dbVHtWUCo1HnYs0IJDofW1vE4l2aMqPzqdFijyD716rwu7TZsZMPksJJ29ordVtkBW+iWwZWTlvboWRa8lgPcNyRms8g0BY3De6zO3SR4sUV5cOg47voOdC7T8cZlCG2g9opo9CIFhee+rqlqvh8CqWt44gBrttL+tdk/AzS/lPfsesPPkJf47fgmAfs1LLpdUJpOv9j/Ux6uYP69FqRAdHY3VanXdz48iP0yUjJh/4fcXtPs9XoWGt3u2PlfYfzaJlfvOoygw8mbpJSWEEKLsc/u3/erVq7N7927q1s37H+euXbuoWrVqgY9nsVjYtm0bL730Uo71vXr1YsOGDfnu98UXX3D06FG++eYbpkyZcs3zmM1mzOas3j5JSUkAqKqKql7fr9WZx7je41xLzQp+zB/SlgyLjW82x7JyfxxbYi6ybOcZlu08g9FLxx1Nq/Jyn0ZUCHBj4MLgA5E3aQuA6tCGysRuhlObofYt2hdGgDPRKCtfde2qKnoth5MpHILDUVs8ihrSVGs7u0U7lqEUzTij02f1drra5Fy2jDx6WmXrcZV2UQtcpV9CsWVogbv0i9pSSKqXvzNIZcLfKxACKqHmG9TKFvjyDpTeWU4l9TdbHl1321nTtN5QO75FyTZBgeoTBE3u0XpFVW+d9V698jyJp7QcUzsXoFw8htr+KbjN+eNFrS7w7C4t+J3Xvk6ZvaQCjQZ6NQ4rsfdBZru1DA9m0X+niAwNkPdgAbn7b7Y4j7t69eo87wsPuBgDiwaBw6Z9vnS5es81T8jsJXVH06rUrRzg4doIIYQQ18/tQanbb7+d1157jT59+mA05gwepKen8/rrr3PnnXcW+Hjx8fHY7XbCwnL+Gh4WFsa5c+fy3Ofw4cO89NJL/PvvvxgMBXvK06ZNY9KkSbnWJyYmFktQKiUlBSi5Xz7va1aR+5pV5MD5FD7bcIq/DiaQYXXw0/bT/LT9NBEhRm6LCuXhttUJMpZAzyTvqlC3v7YAJCYCoLfq8Kl3B/qEQ+gST6DYLdqXysRTELuRtCrtSfHSejl4nVhLwNLHcPhVwhFYHUeQcwmsjiMoHHtYU1T/yu5/LkWlCwFTCJiirl7OloGSkYiScRnFrN3qst3XliTn9svZyiahoKJYU7WZB5NOkX+fxdxURY9qNKEag1F9TFn3nY8d2e5fWS5HTrFywBN/s+VFkdpOVdGf3Yb3vh/wPvQrikXbX0XBVrMzlqj7sNa9LSsg7fzRwMWWgdfRP/He+wOG2H9RnHPmqV5+mG12MpyfN5oA1+dPXk5cTGP3ae34D7epSuJVyha3zLazmDMAyDBbSvT8ZZm7/2aTrnzPibIv7SJ8e6/240/VFtD3o1L3w8yRuBSW7z4LlHxuOyGEEMJd3B59eOWVV1i8eDH169fnmWeeoUGDBiiKwv79+/n444+x2+1MmDCh0Me98iJTVdU8LzztdjsDBw5k0qRJ1K9fv8DHHz9+fI7cDklJSYSHh2MymfJM2l4YmUEtk8lU4l9w25tMtK9fnZgLKUz5bT//HI7H5lA5cSmDT9ef4tP1p2hSPYjhXWpzc8NKBBkLE8YoBqbu0Ki7dl91oKach8sntTxVl0/iW+8mAgwBWtvZLgOgS7uALu0CnN+R41Bq/1lQ7SHtwcnNsH4mmGpCcLiz51VN7davYqm78MzJBOQzJOlqHHZUc7Krx5Wafom0hNP4KWaUjERtfcYlSL/sLHMZMi5D2kUUuxlFtaMUtXeWwVcbbmgMct46F5+gvO9fWdbgW6peE0/+zZZ1hWq75LNar6YdC1ASDmcdI6QWNB8ILR7CYAq/+j8uVYWPe+bcP6KT1qMqqh8+3gH4FLDu+84kct/nOwHwNuh4vk/j4h/yfBWutgt0tptOj8lkKrHzl2Xu/pt11+fA3XffneexFUXBaDRSt25dBg4cSIMGDdxy/huWNR2+exASjkBQDXjoey3XZCnzyeojqCr0igqjYZXruxYVQgghSgu3B6XCwsLYsGEDTz31FOPHj3ddKCqKQu/evfnkk09y9Xq6mtDQUPR6fa5eUXFxcXkeJzk5mf/++4/o6GieeeYZABwOB6qqYjAY+PPPP+nevXuu/Xx8fPDxyf3VRVGUYrkYzTyOp77g1q4cyOdD2mGzOfhiQww/bj/N4fPJOFTYczqJ0Qt34KVXaFLNROVAHx7tGEGXepVKtpKKHoKqaQvttXWqipKYqLVd68cgqq+WW8YZtMq6H4tSsW5WYCNuPxz8Pe/zePnBgLnQyNlj72IMnPpPy03jX1lLgOwbkmfy41JNbwC/EG0BUFVslRJRCvIlzZqeLVh1SQtWZd6/2vqMREBFsaVDSro2E2NR6LyuHtC6cnFtC9Lu+wRqQymLkaf/Zsuyq7adzQwHl2uz5x39WxuSC9rfZVR/aPkwSs1O+f/9pcTBvp+hzePaa64oUL837E2HFg9Bi4EoFWoXus7rj1xg0OdbsTtUdAp8+GBL9PrifU8VhKIoHInTevzsP5sk779CcOffrLteB5PJxNKlSwkODqZ169aoqkp0dDSXL1+mV69eLFy4kLfeeou///6bzp07u6UONxy7FX4cqv14ZTTBIz9qaQNKmRMJqfy88wwgvaSEEEKULyWSQToiIoLly5dz6dIljhw5gqqq1KtXj5CQkEIfy9vbm9atW7Ny5Uruvvtu1/qVK1fSr1+/XOWDgoLYvXt3jnWffPIJq1at4scffyQyMrLwT6gcMRh0DL+pDsNvqoPD4WDx9tMcjkvhr/3nOXohleiTlwH4Y995DDqF8Ap+dK5Tkfva1KB5eOFfv2KlKKjO/EdqleaoaLNTORwOnF9rcWTYUFFRqrRHf+tbeKWcRkk8iZKo9b7SpZwHaxoZhkBUiw0FMBxZjWH5mJzn0hnALxQCKsFt/9Ny0YA2s+DpbVrgKqCyFsTyq1DsAZES5+WrLYW9MHfYwZwEGUlagCpzMV/xOCNJC2jltV11gMMKaQnaUuTn4K8Fp3ItQQVb5x0AXkat11ZZfz1LG1WFszu12fN2/6AFNTPV7Kj1amrcX3sd8mK3wqE/tP0P/6nlf6lYB+o4f2C4eTzcOrnQr5vdobLz1GX+PRTPnH+OYneoGHQK3wxtT4c6FYv2XItB5qR7Zpvj6gVFmVelShUGDhzIRx995OqV53A4GD16NIGBgXz//feMGDGCF198kXXr1nm4tuWA3QY/DYODv4HeBx5ckP8svB42a432mdStfiWa1Qj2dHWEEEKIYlOi05qFhITQtm3b6z7O2LFjefTRR2nTpg0dO3Zkzpw5xMbGMmLECEAbenf69Gm++uordDodTZo0ybF/5cqVMRqNudbf6HQ6Hfe2CQdg/O2NOHohhQ/+Psyag3EkptuwOVRi4lOJiU/lm82x6BToWi+UdpEVublBJaKqBhXo12OLzcHeM4kcOJfM8fgU4pItpGRYSTHbSbXY0ClQq6I/KWY7aRYbe04nkmF1oGbmhVHBoaoUfnb0cOfSwbXGGytVlQTOz0sggz8A6KM7zmOGRlTiMqFKIiYlTfvSm3IOUs7x4JyNbHJoeV0e1v/Fm16f5ziLXVW4SCDxqomJ1sFsVrUL3EjlLK2Uw6R4hXCRYBIUE2et/qTb9WS2WmbzKYqCAoRX8MNLr6BTFC6mWkjOsKEooGSWcd7X6RSiqgbh561Hr1M4n2QmPsWMTgGdou2vKIDqwKA30CLchL+PF3odnLmcwdnEdAw6BYNeh16n4KVXMOh0GPQKTaub8PfRPiouplq4kGzGz1vvXAwEGA0YdFrFwyv44eulB4K4lGbkQnKwq130BgX/UAOBPgYCjQYqBxkx+XrhrdehOF9XVBUsKTmCVIo5UcuPZU5CMWcPauUT9LJbtBNm5tIqam+t7HRemAxGbTiHwaj14vFy3hqMWUE8L18tiJVjWz5l89zm55HZJh0OByoKqqpid6iYbQ7nfXDgwOFwBnxR0SsKRi89qgoqkJBidm1DBZtDSy7tUMGgUwjy9eJyYjq+GTpiT50k7PgvhJ9YTHDyIdf5U41hxNboi7nJg2QEReJQVQ5sjcdij3Mdy+ZwYLx4gEbnltHq8p8E2C+79j9ujGLp30c5uDHn0DZVVfH11uf4Arfr1GUS052fN2YbqWYbSRk20iw27A4Vqz3rgyUsyIcvhrQlqqpnh8wZDc7ghCQ5L/fmzZvH+vXrcwwT1el0/N///R+dOnVi6tSpPPPMM3Tt2tWDtSwnHHb4eSTsW6r1zn3gm6wfnEqZ05fT+Wn7KQBG9ZBeUkIIIcqXkv/2UwweeOABEhISmDx5MmfPnqVJkyYsX76ciIgIAM6ePUtsbOw1jiKupU6lAN5/sCUAZy+n8/3WWFYfvMDh8ymkW+04VFh7KJ61h+KZ8cdB/L31VAr0IcgZaNApcCHFwqU0Cza7itFLR3KGrUC/9u84WTLJfC14cUKtkmPd7472/G5p73rsjZWKJFFRSSRUSWSfo6ZrW7waxHp7Y0Kd20JIQa+oVCKJSkrORLiddXuY4vVFzgp4wUVDAPGqiddsQ9jk0JKe1yeWDrp9JF30Jwk/klQ/HPijU/1Iwp9UfICcAcANRwveq+jg+eQCl12+uxiCOtctEJ0SiI+hJjoF0q12LdimU9ApoJAVoIswGajqa8NPTQNzMilJlwhQ0gkgjQDSCSTd+TidMB8rIYYMjI40vG0p6Kwp+Klp+Kvp+JLhOrvisILFCpaCt1tRWdFjVr2xoceGHjs6bBiwoQOdAVVnwO5cn2ZTsKLHrmaW07sWRW9Ab/DGjg4rOpIyVK2sc7Gix6rqsKl6bM597dn2d51bzXrsQMGODgc6VJTcj1XtsbZNhw4HNZU4auvO0Fg5wU26XXgrdgDMqhd/OlqzyH4z6zOa4Lisgz3ngfN5tkuUcpzlPi+7HsepwSy2d+FH+00cyagBlwHyfq8uiT5T4PYPMhroUi+Um+pVom+Lavh5e/7fpLcrKOXhigi3s9lsHDhwIFcOzAMHDmC3a387RqNRhnFeL4cdlo2CXQu1VAH3zYf6vTxdq3zNWXsUq12lY+2KtI6o4OnqCCGEEMXK81fbRTRy5EhGjhyZ57b58+dfdd+JEycyceLE4q9UOVY12JcxtzZgzK1actWLKRbWHorjYpqVTccSWHc4nlSLndSEtHyPkWLO+VjrcaHD11uP0UuPj0GPn7eOEH8futYNxd/HgL+PnrOJGeidPX5QwJyRQdUKQYQG+lApwIdKgVruL0VRSDFbUVVnkAJAl9mrCPSKjgDnzIKqqpJm0Xp/OVRAxdnzQ8Xh0PpkmXy1JO8qkJRu1XqPOHuDqCo4UFHVzqjqswQGGjEDZx02kuLPYE+Owys9nudDW2DzCkJVIST2MpcOH8LPchF9Wjz6jAQU1UEFJYUKSgrjejXkfEhLrHaVmkf30nrvl/m25eJ609kf3BW7qhJ5aRMdz3+Lt38FbN6BZOgDOGf24bzFSDJ+7PNpSoK+Mja7A9WShpdOpV6Nqvh463E4VI4npHLyYhp2B9hVFbvDgd3h7CWjqjSsEoiftwEVldiENE5dTsdmV51lVBxqZh82qOjv7foCndkDxcXZ46Wo36sdqhaMynqs5vktfd8FO/sA8HEuofkfNP+3KwoOfLBixIIRC76K8xYzPooVX8zOxxaMzm1G533XNsWCT+b+rm3aOl8la39fxeI6rxd2vJT0vCulAvZsj5Urbq8sa832uJSMQNzlqM2P9m4sUzuRjDaduU4Hvl46woKM6BQFvWqn5uUtVCaBH1VtSN4xJZKDagSnlCr8ZbyVlPCbcSh6GgDW04lYsgW7szeH0UtP4+pZPZ32ndHK+nrr8fXWE2T0okqQkeohvtStFMCtUWH4eJWSxnLyNmj1ud6ZX0Xp9+ijjzJ06FBefvll2rZti6IobNmyhalTpzJo0CAA1q5dS+PGjT1c0zLMboUlT8Ken0DRwT3ZckqWQnFJGXy39SQA/ye9pIQQQpRDZTYoJTyrQoA3d7eqAcDQLpGkmW3MXXeMdYfjybDataCFqlLR34eaFf1oWCWQDrUrEmg0EOjjRYCPHr2+8InDVVUlMTEx3xmVKvh7F/hYgYWYWTA0oKDzdUH1Cg2APGZGqvswdH8467HDrk1BnRoHKXG0rd5KS7IKENAC1P5X5GFK1PIwOWwM6NwYamm9qtiyAY5ug5SsQ0dlP+8D30Kj9logbvNX+K8YBRf0OROJBztvOz4DNZ3DGy+dgNiNYLRklfMO1XIt+QSAoeBtkheb3YHF7sBsdZBhs5Nhsedb1ugclmi2OohPNhN7KY0Us400s400qx3VgTPAqBJewQ+TrxcOVSUhxcKRuBQt0OYc9ml3vjftdgc1K/pTwd8bm0PlQrKZA2fzn+a9aqCBiMomFCApw6YFN4C89giv4EeNED8S0YJzu0/n3/OvWrAvNUN88caKUTFzOu4iwV42DIqKXrWhw4FetaOoNir66ggL0KM47NjtVs5cTEan2tGpdq2sakeHdj/QC0J89SgOG6rDysWkNHSqHcW5Xa86MOod+BlU/Azgo3OgqHawW7HbrSgOO4rDhqLatJkYHdotqtYvCtUBzseK6nzssDvXO1wJy1VTDSxBtfCuGoVSpzvNwqJoBkzOqzHij2h5onZ+D/oz4GPizecma8MdAey9aKA30CPf1iyffL20z0qJSZV/7733HmFhYUyfPp3z57Veg2FhYYwZM4YXX3wRgF69enHbbbd5sppllzUDfhgMh37XckXe8xk0vvuau3nS3H+PYbE5aB0RQsfansttJ4QQQriLBKVEsfDzMTC6R31G96h/7cJCo9NridMDKkHYFb961++V91ACVdVmxtNnC6jV6Q4DPsuZODz7YqruKqqYnSEU1Z41a152zR7Mun9qq/Zrcn7u/hSaO8sfXwcrX9eCVd6Zi7/zcaA2G1rVZlrZtItw4QAGb38M3gH4eQeAbwAE+Wcl1bqK8Ap+tIwo2ST71wqGekrrQpT12JQOqkp6YiLeJlPer685GfYu0WbfO7kpa71vCDS9T3u/ZwalPJBvqzTwlpxSNwSbzca3337L0KFDmTBhAklJ2ud1UFBQjnI1a9bMa3dxLeYU+H4gxKzVcvjd/3WpHrIHWs6+bzZp6Sie6V63VP3/EUIIIYrLjXmFL0RZpShasu3sKtbRlgKwNHsE346Po2RPFO5KEn45K3AEWlCgTvcrEoqngM05tMzbP6ts8jk4/V/+Jw6qlnXsU//BgvvyenJaMOu2qdBKG6bCuT2w6o2s3llXBrzCO0CYs1+YJQ0ST2nt4+VcDD4FCnQJD1r/AfwzXbuv6KBuT232vQZ9rrs3XnkR4JxoQC/v5XLNYDDw1FNPsX//fiB3MEpch+RzWkDq9Dbtf8hD30Nk6U8WP29dDOlWO81qmLi5fiVPV0cIIYRwCwlKCXEjURQtWOPtD0FVr162bg9tuZLDrs2QZzBmrYvoDA9+55w5Lxksqc77KdptpYZZZXV6qFAnq4wlc9yhqiUSV7IN60w+C4dW5F/H2/6XFZQ6tws+733F89VlBahueh7aP6GtvxgDK15ytkW2IJa3v9YrJ7wDhDtnCjUnQ8y/GMw2CArWpg3Xe4HeW1v8KoK/c0iFwwG2DOc2+XjN5XIs7PhOGyJau5u2rvmD2uxXLQZqPfWu9b68AdWupOXe8jfKe6q8a9++PdHR0a6JW0QxOLsTvnsIkk5rP7Y8/CPUaOPpWl1TYpqVrzaeAOCZW6SXlBBCiPJLrnCFEIWj02flvsoUVLXgwYS6PWDU9qzHDgdY05wBqlTwyzazUKWGcNcHWdvMydnup0DFbElf7VatXtZ0sDsTh6uOrMCXI1vW79QLVw923fxyVlDqcizK9w85U3LnofNouNWZIenyCfighXZf0WUFrvReWjCr1aNwi3MGubSLsOCBbNudtwYfbXryWl208qA9pxXjtaCiosu9VG0Oze7Pas/Vb+ZRzrlvxboQ1Ter/pvnAGrOMplLYDWo1zOr7N6l4LDlXQe/Cln5yACOr9fqfSkG3zN74eIBiN2knavhnVlBqYp14Okt0qPtKgw6rW2sBZi5VJRtI0eOZNy4cZw6dYrWrVvj7++fY3uzZs3y2VPkoqqw7QtY8bLWwze0vtZDqoA9iz3tiw0xpJhtNKwSSM9GYZ6ujhBCCOE2EpQSQniWTqcNxfPJI+wTHA6tHyvYcSK7wkta7g3sNi3QZU3TAljWdAionO24EVqwy1UmTStjTdXuV2mSVVbvjVq9NXZLOnocKHaLFgCzW7TFO1u97dkCX6qz15QtI2udOVs2ems6nNqS//Mx+GQFpWwZ2per/DS9Lysopdrh37fzL9vgjpxBqT/Ga4GmvNS+OWdQ6pdRYM4naXt4exj6Z9bjHx+HlHMoaHMgukTelDuxsASkrsrgnBTCmsdsk6J8eeCBBwAYNWqUa52iKKiqiqIo2O35Twghskk8Db+N0xKagzYs+J554Bvs0WoVVHKGlS/WHwe0XFI6nXxGCiGEKL8kKCWEKH/0BtAHaTMM5iUwrODBrtB6MOxvUpyJzq8aQKlYF8afyhm0yn7fN1svMN8QeHCBtt5myV0+e/J7g1HrveWa9e6KpUr23hMKtHtC6yVwZTnUK8oCUf1ds+hpi5p1mz04BxDRSet1lv3YmXXKPkQzs938Q1FNNTAH1sSnWhOU2jdDiAxLKqxUsxY0tEhPqXIvJibGbceeNm0aL7/8MqNHj2bmzJn5llu7di1jx45l7969VKtWjRdeeIERI0a4rV7FKu0irHsPtszJGkrdcyK0f0r7AaSM+HrTCRLTrdSp5E+fJjKkWQghRPkmQSkhhCguOh34BBasrLcfNLyjYGW9fOHmFwtWVm+A22cUrCzAvfMKXnbg9wUvO/hX7VZVyUhMxOdaAT2RL4M+q90sNodrNj5R/rgrl9TWrVuZM2fONYf/xcTEcPvttzN8+HC++eYb1q9fz8iRI6lUqRL33HOPW+p23dIuQuxG2P8r7F2c1Ts1ojP0mZ47uF7KpVlsfPavFpx8+pa66KWXlBBCiHJOglJCCCFEKWb0ygpCpVtseBu8PVgbURL27dtHbGwsFoslx/q+ffvms0f+UlJSePjhh5k7dy5Tpky5atnZs2dTs2ZNV0+qRo0a8d9///H222+XjqDUHy8TcOwfcFjAmqENJ864YkhxlWbQ/VWod2uZDIQv2BzLxVQLNSv40bd5NU9XRwghhHA7CUoJIYQQpZi/d9a/6gybA9NVyoqy7dixY9x9993s3r3blUsKcM28VpScUk8//TR33HEHPXv2vGZQauPGjfTq1SvHut69ezNv3jysViteXl659jGbzZjNZtfjpKQkAFRVddW/2Fw6jiFuT67VaoXaUKcHNL0XarTLCkYV9/ndzGy1M+efYwA8dXMd9Dql+NvwGjJft5I+741C2te9pH3dS9rX/cpbGxf0eUhQSgghhCjFjIacPaVE+TV69GgiIyP566+/qF27Nlu2bCEhIYFx48bx9ttXmcAgH99//z3bt29n69atBSp/7tw5wsJyzvQWFhaGzWYjPj6eqlVz5zeaNm0akyZNyrU+MTGx2C+qdS2ewBxxF8bAYPDyQ/XywxFUA7z8sgo5g2Jl0YL/zhCXbKZKkDc96wSQmJjPxBJupKoqKSnapBxKGexpVtpJ+7qXtK97Sfu6X3lr46QC/k+WoJQQQghRimXPIZVmldnXyrONGzeyatUqKlWqhE6nQ6fT0aVLF6ZNm8aoUaOIjo4u8LFOnjzJ6NGj+fPPPzEajQXe78qL4Ct7a11p/PjxjB071vU4KSmJ8PBwTCYTQUH5TDZRRGpQN+yJifibTOXiYj27NIuNzzedBuD/etQntEKIR+qR+XqbymEblwbSvu4l7ete0r7uV97auKDPQYJSQgghRCmmyzZrmNkqM/CVZ3a7nYCAAABCQ0M5c+YMDRo0ICIigoMHDxbqWNu2bSMuLo7WrVvnOP4///zDRx99hNlsRq/X59inSpUqnDt3Lse6uLg4DAYDFStWzPM8Pj4++Pj45FqvKIpbLqgzj1seLtaz+3pTLPEpFsIr+HJ/m3CPPr/y2salhbSve0n7upe0r/uVpzaWoJQQQghRTiiACljsEpQqz5o0acKuXbuoXbs27du3Z/r06Xh7ezNnzhxq165dqGP16NGD3bt351g3ZMgQGjZsyIsvvpgrIAXQsWNHli1blmPdn3/+SZs2bfLMJyWKR3KGlU/XHgVgdI/6eOllhk0hhBA3DglKCSGEEKVcoNFAUoaNiv65e6SI8uOVV14hNTUVgClTpnDnnXfStWtXKlasyMKFCwt1rMDAQJo0aZJjnb+/PxUrVnStHz9+PKdPn+arr74CYMSIEXz00UeMHTuW4cOHs3HjRubNm8d3331XDM9O5GfeuhgupVmpXcmf/i1kxj0hhBA3FglKCSGEEKVcZl4pm0N6SpVnvXv3dt2vXbs2+/bt4+LFi4SEhLilG//Zs2eJjY11PY6MjGT58uWMGTOGjz/+mGrVqvHBBx9wzz33FPu5heZ8UgafrtVm3BvTsz4G6SUlhBDiBiNBKSGEEKKUMzjzSllt5WOKYFFwFSpUKLZjrVmzJsfj+fPn5yrTrVs3tm/fXmznFFf3zp8HSbfaaVkzmDub5Z7dUAghhCjvJCglhBBClHKJ6VYAjlxIpmkNk4drI4rb448/XqByn3/+uZtrIkrSntOJ/LDtFACv3hlVLpLaCiGEEIUlQSkhhBCilMsctpeUbvNwTYQ7zJ8/n4iICFq2bOmaDlqUb6qq8uZv+1FVuKt5NVrVDPF0lYQQQgiPkKBUIaWmphIYGOj6NctisWC1WjEYDDmmRM5MVOrr6+uazttqtWKxWHJM732tsnq9HqPR6CqblpaGqqoYjUbXzDk2mw2z2YxOp8PX17dIZdPT03E4HPj4+GAwaG8Lu91ORkZGocoqioKfn5+rbEZGBna7HW9vb9fMPYUp63A4SE9PB7QErZnMZjN2ux0vLy+8vb2vWdZms+Uoq6oqaWlpAPj5+eV6PQtTtiCvfVHeJwV57Qv7PklNTcXb2ztHu2e+ntf7Prny9bze90l+r2dh3ydXe+0LWlZV1RxfFK/1ehb1fZLf61mWPyMyj2u32zGbzSX6GXHl61lWPyMMBgM4339mq92tnxFXe+3L0meEzWbDarW61hX3Z0RxGzFiBN9//z3Hjh3j8ccf55FHHinWYXui9PlrfxwbjyXgbdDx4m0NPF0dIYQQwnNUUSCJiYkq2ozcalxcnGv9lClTVEAdNmxYjvJ+fn4qoMbExLjWvffeeyqgDhw4UL106ZLqcDhUVVXV0NBQFVD37NnjKjtnzhwVUPv165fjuBERESqgbtmyxbXum2++UQG1Z8+eOcpGRUWpgLp69WrXuiVLlqiA2qlTpxxl27RpowLqr7/+6lr3559/qoDavHnzHGW7deumAuqiRYtc69atW6cCat26dXOUvf3221VA/eKLL1zroqOjVUCtVq1ajrL33nuvCqgfffSRa92hQ4dUQDWZTKqqqqrD4VAvXbqkPvbYYyqgTp8+3VX21KlTKqAaDIYcxx05cqQKqK+//rpr3aVLl1yvp8Vica1/7rnnVEB97rnnXOssFour7KVLl1zrX3/9dRVQR44cmeN8BoNBBdRTp0651k2fPl0F1MceeyxHWZPJpALqoUOHXOs++ugjFVDvvffeHGWrVaumAmp0dLRr3RdffKEC6u23356jbN26dVVAXbdunWvdwoULVUDt1q1bjrLNmzdXAfXPP/90rfv1119VQG3Tpk2Osp06dVIBdcmSJa51q1evVgE1KioqR9mePXuqgPrNN9+41m3ZskUF1IiIiBxl+/XrpwLqnDlzXOv27NmjAmpoaGiOsgMHDlQB9b333nOti4mJUQHVz88vR9lhw4apgDplyhTXuri4ONfrmd3o0aNVQH355Zdd61JSUlxlT5065fqbffnll1VAHT16dI5jFOdnRHZl+TMi82/233//LZHPiEzl4TPC4XCoQUFBKqBGPjVXjXjxV/WDvw+57TNi0aJF5eozYurUqa6/2eL+jMi8JkhMTFSLS0ZGhrpgwQK1Z8+eqp+fn3rfffepK1ascD2HssIdbZMp8/OkrLXJlcxWu3rzjNVqxIu/qm/9vt/T1cmhvLRxaSXt617Svu4l7et+5a2NC3pNIFN8CCGEEKVcZu8rs1Vm3yuvfHx8eOihh1i5ciX79u2jcePGjBw5koiICFJSUjxdPVGMvtl0gpj4VEIDvHnq5jqero4QQgjhUYqqSvKCgkhKSsJkMnHmzBmqVKly3cP3zGYzJpMJRVHK9NCckh6+p6oqiYmJGI1GGb5XhLJxcXGEhITI8L0iDN+zWq0EBwejKIoM3yvk8L3ExEQCAgJk+N5VXs+8yqqqypkzZzCZTLSf/g+pFpUnbork+VvryfC9q7yemcP30tPTCQ0NRVGUYv+MyMjIwGQykZiYSFBQEMUtNjaW+fPnM3/+fCwWCwcOHCAgIKDYz+MOmddL7mibzGuAzOunsuhymoVuM9aQmG5l2oCmPNSupqerlEN5aOPSTNrXvaR93Uva1/3KWxsX9JpAglIFVJwXWeXtzVaSpO2KTtqu6KTtik7aruiyt13zSX+SlGFjcKdaTOzb2NNVK/Xc/b5zR+DFbDazePFiPv/8c9atW8edd97JkCFDuO2223LloizNJCh1da8u3cPXm07QICyQ5aO7oteVrudRHtq4NJP2dS9pX/eS9nW/8tbGBb0mkETnQgghRCnnpdeCEg75HalcGjlyJN9//z01a9ZkyJAhfP/991SsWNHT1RLFbM/pRL7ZfAKAiX0bl7qAlBBCCOEJEpQSQgghSrnm4cGsOhBHk2omT1dFuMHs2bOpWbMmkZGRrF27lrVr1+ZZbvHixSVcM1FcHA6VV3/eg6pC3+bV6FhHgo5CCCEESFBKCCGEKPUMzh4VFrskOi+PBg0aVC666Yv8/bj9FNGxl/H31jPhjkaero4QQghRakhQSgghhCjlMofv2SQoVS7Nnz/f01UQbpSYZuWt3w8AMLpnPcKCjNfYQwghhLhxlJ3MmUIIIcQNKiZem7Fvx6lED9dECFFY/1txgIRUC3UrBzCkc6SnqyOEEEKUKhKUEkIIIUq5NIsNgIupZg/XRAhRGBuOxPPdllgApvRv4ur1KIQQQghNmf3P+MknnxAZGYnRaKR169b8+++/+ZZdvHgxt956K5UqVSIoKIiOHTvyxx9/lGBthRBCiKLLnKXLZpfZ94QoK9IsNl5cvAuARztE0KG2JDcXQgghrlQmg1ILFy7k2WefZcKECURHR9O1a1f69OlDbGxsnuX/+ecfbr31VpYvX862bdu45ZZbuOuuu4iOji7hmgshhBCFZ9BJTikhypoZfxzk5MV0qgf78mKfhp6ujhBCCFEqlcmg1LvvvsvQoUMZNmwYjRo1YubMmYSHhzNr1qw8y8+cOZMXXniBtm3bUq9ePaZOnUq9evVYtmxZCddcCCGEKDyDPnP2PekpJURZsO3EReZvOA7A1AFNCfCRuYWEEEKIvJS5/5AWi4Vt27bx0ksv5Vjfq1cvNmzYUKBjOBwOkpOTqVChQr5lzGYzZnNW7o6kpCQAVFVFVa/vS0HmMa73ODciabuik7YrOmm7opO2K7rsbWdwDd9zSFsWgLvfd/IaiKvJsNp54cddqCrc27oG3epX8nSVhBBCiFKrzAWl4uPjsdvthIWF5VgfFhbGuXPnCnSMd955h9TUVO6///58y0ybNo1JkyblWp+YmFgsQamUlBQAFEW5rmPdaKTtik7aruik7YpO2q7ocrQd2rA9i9VGYqLMwHct7n7fZf5QJURe3l15iKMXUqkU6MOrd0R5ujpCCCFEqVbmglKZrrzIVFW1QBee3333HRMnTuTnn3+mcuXK+ZYbP348Y8eOdT1OSkoiPDwck8lEUFBQ0StO1i+sJpNJvqQVkrRd0UnbFZ20XdFJ2xVd9rbz9fYGQNHpMJlMnqxWmeDu9528l0V+Nh5NYO6/xwCYdndTTH5eHq6REEIIUbqVuaBUaGgoer0+V6+ouLi4XL2nrrRw4UKGDh3KDz/8QM+ePa9a1sfHBx8fn1zrFUUplovRzOPIhW3hSdsVnbRd0UnbFZ20XdFltluXeqFsPJZA8/BgaccCcuf7Tl4DkZekDCvP/bATVYWH2oXTM+rq16VCCCGEKIOJzr29vWndujUrV67MsX7lypV06tQp3/2+++47Bg8ezIIFC7jjjjvcXU0hhBCi2HjpM3NKSS4jIUqriT/v5fTldGpW8OMVGbYnhBBCFEiZ6ykFMHbsWB599FHatGlDx44dmTNnDrGxsYwYMQLQht6dPn2ar776CtACUoMGDeL999+nQ4cOrl5Wvr6+MgxCCCFEqWfQab8hWewOD9dECJGX33adZXH0aXQKvPdAc/xltj0hhBCiQMrkf8wHHniAhIQEJk+ezNmzZ/+/vTuPi6rc/wD+OTPsCoMiCIgg5cKqIKSSkeWalUtWLnVVMuty1Z8pqTfFUlvUm2lquWQhaplobjeNa3JVMDcuFpiCkYKEKKiQyACyzvP7A5kcQYWR2eTzfr2mmDPPOed7vhznHL7nOc+Bn58fYmNj4eHhAQDIzc1Fdna2uv0XX3yBqqoqTJ48GZMnT1ZPHz9+PDZs2KDv8ImIiBolq6AEAPBbntLAkRDRna4UlSFy92kAwKSnOiLI4+5PdyYiIiJNJlmUAoBJkyZh0qRJ9X52Z6EpPj5e9wERERHpSHFZFQCgsLTCwJEQ0e2EEJi5/VcUllbCr50dpvbrZOiQiIiITIrJjSlFRETU3FiY1Ryuq1UcU4rImHxz4g8c/v0aLM1k+HRkgPrfKhERETUMj5xERERGzlxec7hWCRaliIxFxrVifBR7FgDwzmAvdGpra+CIiIiITA+LUkREREbO0vxWUYrjnBMZhfKqarwVk4yyShWe6NgG40M6GDokIiIik8SiFBERkZGzvNVTqpo9pYiMwpJ96ThzqQj2Nub45OVukMkkQ4dERERkkkx2oHMiIqLmwtyMt+8RGYtD6Vfx1ZELAIAlL3WDs8LKwBEREVFTqq6uRmVlpd7XK4RARUUFysrKIEmmdbHD3Nwccrlcq3lZlCIiIjJylmY1B3nWpIgM62pRGWZsOwUACHu8Awb4tDVwRERE1JSKi4uRk5MDYaCTLpVKhYKCAoOs+0FIkgQ3Nze0bNmy0fOyKEVERGTkAtwUAIB2rawNHAlR86VSCURsO4WCkgp4u9jhncFehg6JiIiaUHV1NXJycmBjYwNHR0e991YSQqC6uhpyudykekoJIXDt2jXk5OSgU6dOje4xxaIUERGRkbO4NdB5VTW7SlHDrFmzBmvWrEFWVhYAwNfXF++99x4GDx5cb/v4+Hg8/fTTdaafPXsWXl4svgDAF4czceR8PqzN5fhsTCCszLW7TYGIiIxTZWUlhBBwdHSEtbX+LwSaalEKABwdHZGVlYXKykoWpYiIiB42ZrLaohQfv0cN4+bmhsWLF6Njx44AgI0bN2LYsGFITk6Gr6/vXedLT0+HnZ2d+r2jo6POYzUFv2Rfx9L96QCA+UN90NGp8bcnEBGRaTC1gpAxeJCcsShFRERk5K6XVgAA/iypMHAkZCqGDBmi8f6jjz7CmjVrcOLEiXsWpZycnGBvb9/g9ZSXl6O8vFz9vqioCEDN1d6mHo+jdpn6Hucjv7gck775BVUqgee7uuDlIDeDjTWia4bKcXPB/OoW86tbzSG/tdtmDNtp6PU3Vn25a+g2sChFRERk5Gp7SJVXsacUNV51dTW+++47lJSUICQk5J5tAwMDUVZWBh8fH8ydO7feW/put2jRIixYsKDO9Bs3buikKFVcXAxAf1exq1UCk7emIq+oDJ4O1pjdz11deHsYGSLHzQnzq1vMr241h/xWVFRApVKhuroa1dXVBonBysoKvr6+qKqqgqenJzZs2NCoi0UA8P7778PBwQGTJ0++b9usrCwkJSXh5ZdfBgAkJCTgxRdfhIeHByoqKjBy5Ei8++67911OdXU1VCoVlEql+mJVQ4+XLEoREREZudqxa0zrmhkZ2unTpxESEoKysjK0bNkSu3btgo+PT71tXVxcsG7dOgQFBaG8vBxff/01+vXrh/j4eDz55JN3Xcfs2bMRERGhfl9UVIT27dtDoVBo3AbYFGqLXAqFQm9/EC35MR3/++MGbCzk+GJsMFydbPWyXkMxRI6bE+ZXt5hf3WoO+S0rK0NBQQHkcnmjx0VqKvb29khJSQEAjBs3DmvXrkVkZGSjliFJEmQyWYO24eLFi9ixYwdGjx4NAJDJZOjfv7/6YlZgYCCGDBmCoKCgey5HLpdDJpPB1tYWVlZW6jgagkUpIiIiI8cBlUkbXbp0QUpKCgoLC7Fjxw6MHz8eCQkJ9RamunTpgi5duqjfh4SE4OLFi/jkk0/uWZSytLSEpaVlnemSJOnkj5ba5erjD6L9qXlYHZ8BAPjXi13R2blpi2zGSp85bo6YX91ifnXrYc9v7XbV/v9mZdP3lrI2v/sg5rf3MJYkCb1798apU6cgSRIyMzMxYcIE/Pnnn+jQoQM2bNiA1q1b47fffkN4eDiuX78OS0tLHDhwQOP3tGjRIvz222+Ijo7GokWLsHv3bpSXl2PSpEkIDw/H3LlzkZqaisDAQLz11lvw9PRUr79ly5YICgpCZmYmpk2bhvXr16Nz584QQsDLywuJiYnqXly35+7OPN4Pi1JERERGjkUp0oaFhYV6oPPg4GAkJSVhxYoV+OKLLxo0f69evfDNN9/oMkSjlXmtGG9/dwoA8FrvDhjSzdXAERERkT7drKyGz3s/Nvly094fBBuL+5dhqqurERcXhwkTJgAApk6dikmTJmHkyJH417/+hfnz52PlypX429/+hkWLFmHAgAFQKpUaTw1cuHAh0tPTER0djf379+Pq1atISkpCRUUFnnjiCTz//PP46KOP8Pnnn2P79u0Aap7GW6ugoAAnTpzAu+++i7CwMGzatAkffvghDh06hICAgEbfVng3LEoREREZOevbilKVVSqYm8kMGA2ZKiGExqDk95OcnAwXFxcdRmScbpRWYuLGk1CWVSHIoxVmD/Y2dEhERNRMFBYWIjAwEDk5OfD19cWgQYMAAElJSdizZw8AYOzYsXjuuedQVFSEwsJCDBgwAABga/vXLebr1q2Dl5cXYmJiIJPJEBcXhz179iAhIQFAzdiPGRkZ9cbw3//+F4GBgZDJZJg9ezZ8fX3h7u6Onj174oMPPsDGjRsxfvz4JttmFqWIiIiMnJXFX0Wom5VVMDezMGA0ZArmzJmDwYMHo3379lAqlYiJiUF8fDz27dsHoGYsqEuXLmHTpk0AgOXLl6NDhw7w9fVFRUUFvvnmG+zYsQM7duww5GboXWW1CpO//QWZ+SVwVVhh7d+CYMEiMBFRs2NtLkfa+4N0stx7sbe3R3JyMm7evIkBAwZg9erVmDp1qsatcEKI+94i161bN5w6dQpXr16Fs7MzhBCYP38+xo0bp9Hu9p5Rtfr376/uOVXL1tYWQUFBiI2NxdGjR7F+/fqGbG6D8ChLRERk5KzN/rqGVFbJJ/DR/V25cgVjx45Fly5d0K9fPyQmJmLfvn3qq6m5ubnIzs5Wt6+oqMCMGTPQtWtXhIaG4siRI/jhhx8wYsQIQ22CQXywNw1HzufDxkKOr8Y/BkfbuuNlERHRw0+SJNhYmDX5q6HjLNnY2GDFihVYunQpqqqqEBwcrL5Q9O233yI0NBR2dnZo1aoV4uLiAABKpRJVVVUAgJ49e2LJkiUYMmQICgsL0b9/f0RFReHmzZsAgPT0dJSVlcHW1hZKpbJBMYWFheGNN97A8OHDm3QgePaUIiIiMnItrf468JvLH87BRalpRUVF3fPzDRs2aLyfNWsWZs2apcOIjF/00QvYdPwPAMCnowLg49o8BjYnIiLjFBwcDH9/f+zYsQMrV67Ea6+9hvfffx8eHh7YuHEjAODrr7/Gm2++iRkzZsDKygoHDhxQzz9o0CBcv34dw4YNw759+3DmzBn06NEDQgg4OTlhz5496Nq1KyorKxEQEKAx0Hl9+vbti+rq6jq9rR4Ui1JERERGzlz+V8fmSpW4R0si0sbeXy/j/b1pAIB/PuOFQb7OBo6IiIiao7y8PI33e/fuVf9cOx7U7bp06VJn+vz589U/jx49GqNHjwZw9wtQBw8e1Hj/1FNP1RtbRkYGPDw80LVr13tuQ2Px9j0iIiIjJ0mSuodUVTWLUkRN6VhGPiK2noIQwLgQD4T3ecTQIRERERmVr776Cn379sUHH3zQ5MtmUYqIiMgE1HaQulJUZthAiB4iaZeL8PdNP6OiWoXBfs6YN8S3weN9EBERNRcTJ05Edna2+mmATYlFKSIiIhNQfasqVVhaaeBIiB4Ov19R4m9RiVCWV6GHZ2t8OioAchkLUkRERPrEohQREZEJqP1TubyKT98jelAZ14rxypeJ+LOkAv7tFPhyXDCs7vOYbiIiImp6LEoRERGZgNo7isoqqw0bCJGJy8ovwStfnkB+cTm8Xezw9es9oLA2N3RYREREzRKfvkdERGQCJEkChGBRiugBpF0uwrj1/0N+cTm6tLXF5ok9YW9jYeiwiIiImi32lCIiIjIBtbfv3WRRikgrSVl/YtS648gvLoeXsy2+mdgTrVuwIEVERMbDysoK3bt3h4+PD4KCgvDll19qfC6EgLu7O2bNmgUAiIyMREBAAHx8fGBjY4OAgAAEBATg0KFD2LVrF7p16wZfX1/4+flh9erVAACVSoXIyEh07NgRHTt2xMiRI1FQUAAAyMrKgiRJ+Pjjj9XrTExMhCRJ2LBhg062mUUpIiIiE9DSqqZz83/O5Bk4EiLT89+0K/jbV4lQllXhsQ6tsPXvIXC0tTR0WERERBrs7e3xyy+/IC0tDTt37sSaNWuwbt069efHjx+Hi4sLdu7cCQD46KOPkJKSgtjYWPj4+CAlJQUpKSmwt7fHP//5T+zevRupqalISkqCjY0NAGD58uVIS0tDWloazp8/j8ceewyvvfaaeh2dOnXC999/r36/bds2+Pv762ybWZQiIiIyAcMD2gEAfs0pRGU1BzsnagghBFbHn8cbX59EeZUK/bycsGlCT44hRURE9yYEUFHS9C8hGhyCh4cHli5dqu7hBABbt25FeHg4PD09kZiYeNd5ly5disjISHh6egIArK2tERYWpv5s+fLlsLCo6S389ttvIzU1FefOnQMA2NnZoU2bNsjMzIQQAvHx8ejbt29jM9hgHFOKiIjIBMwY2AXfp1zCn6WViEu7gmf9XQwdEpFRKyqrxJydp7H311wAwCs93bFgqC/M5bwmS0RE91FZCix0bfrlzrkMWLRocPPu3bsjPT0dQM2Flj179mDBggWorKzE1q1b0bNnz3rnS0tLw8yZM+tMLyoqws2bN+Hh4aGeJpPJ0LVrV5w9exZdu3YFALz88svYtm0b+vTpg6CgIJiZ6a50xKMyERGRCWhpZYZXe9WcQHx9/A8DR0Nk3I5l5GPw8p+w99dcmMkkfDjcDwtf8GdBioiITIq4rWfVTz/9BB8fH9jb22PEiBHYtWuXxud3zifVPrq5AdPvNHToUOzZswfbtm3DyJEjtd+ABjDZnlKrV6/GkiVLkJubC19fXyxfvhyhoaF3bZ+QkICIiAikpqbC1dUVs2bNQnh4uB4jJiIiejBjerhj1aHzOJ5ZgBOZBej1iIOhQyIyKhf/LMWncb9jZ/IlAIB7axt8OqobgjxaGzgyIiIyKeY2Nb2adLHcRkhJSYGXlxeAmrGdTpw4gQ4dOgAArly5gmPHjqF379515vPx8UFycrK651MthUIBKysrZGVlqZejUqlw6tQpeHt7q9vZ2trCyckJsbGx+OSTT7Bv375Gxd0YJnm5aOvWrZg2bRoiIyORnJyM0NBQDB48GNnZ2fW2v3DhAp599lmEhoYiOTkZc+bMwdSpU7Fjxw49R05ERKQ9V3trPOrYEgCwYE+qgaMhMg5CCJy5dAPv7PgVfZfGqwtSY3q44z9vhbIgRUREjSdJNbfZNfWrAb2Ual28eBEzZszAlClToFKpsHv3bqSnpyMrKwtZWVlYsWIFtm3bVu+8b7/9NhYuXIisrCwAQFlZGdauXQsAmD59OqZPn46KigoANWNM+fr6olOnThrLiIyMxKJFiyCXy7VIYMOZZE+pZcuW4fXXX8fEiRMB1Iwe/+OPP2LNmjVYtGhRnfZr166Fu7s7li9fDgDw9vbGyZMn8cknn+DFF1/UZ+hEREQPZFhgO3zyYzrO5ioxe8evUKHmj3IBwNdVAWtzGYQALl4vxaXrZRAQEAKo6dx962ch4OOqQAsLOQSAS4U3kV1Qql4ObrWvndfbxQ62VuYQEMgrLMOFgpKaturl4tbPAl3a2kJhYwEIgSvKcmRcLVYv59ai1W07OdmidQsLCCGQX1yO368U31q3ULetjeURxxZwaGEJAYHrJRVIv1J8a5nitvXXvHdv3QIKSwnDurujn3dbnf9OSD9OZBbg/OU/IZkXoqxSBWVZFbIKSnAy6zouFd5Utwvt1AazBnnB301hwGiJiIgar7CwEIGBgaioqIC1tTX+8Y9/4PXXX8ehQ4fQqVMnODj81Ut+2LBh+OCDD/Dpp5/WWU737t2xcOFCDB06FFVVVTAzM8OkSZMA1BSl8vPz4e3tDUmSEBAQgOjo6DrLCA4ORnBwsO429hZJ3O0mRCNVUVEBGxsbfPfdd3jhhRfU09966y2kpKQgISGhzjxPPvkkAgMDsWLFCvW0Xbt2YeTIkSgtLYW5ed0nsJSXl6O8vFz9vqioCO3bt0dhYSHs7OweaBuEELhx4wYUCkWD7uekvzB32mPutMfcaY+5097dcldVpYL3vB9RpTKpw7dBzBjYGZOf7tjkyy0qKoK9vT1u3LjxwOcED5uioiIoFAqd5GZC9P9wMP1avZ9ZyGUY4NsW40M6oIcne0Zpi9/ZusX86hbzq1vNIb9lZWW4cOECPD09YWVlpff1CyFQXV0NuVxucjmuL3cNPScwuZ5S+fn5qK6uRtu2mlc+27Zti7y8vHrnycvLq7d9VVUV8vPz4eJS9wlGixYtwoIFC+pMv3Hjxl0HE2soIQSKi4sBwOR2NkNj7rTH3GmPudMec6e9e+VuypPu2Pi/S+reR9Kt/3R0bAFrMxkkCbiqrMDV4gr155KkbgkJQCcnG7SwqDnpuaasQG7RXxdiJKm2ZW3bFrC1MoMEIL+4AjmFZeqV/rXsW20dbaCwMYcEoKCkEtl/3lQvTKqd49byH21jg9YtzCFJEq6XVuJCQemtxUp/rf9WW08HG7RpaQEJQFFZFc5fK4F0W7vaZUqSBI9WVrC3BPycLHHjxg0tfwN3V1RU1OTLpPvza6dAWUUlbG0sYW0uh7WFGdxb28DLxRY9PVvDxsLkTmuJiIiaPZM9et95gn6/UeTra1/f9FqzZ89GRESE+n1tTymFQtEkPaUAPNRVZl1h7rTH3GmPudMec6e9e+XurUEKvDXI1xBhmQRdX83lvmwY0wd0xo0bbfl9QkRE9BAxuaJUmzZtIJfL6/SKunr1ap3eULWcnZ3rbW9mZqZxT+btLC0tYWlpWWe6JElNciJUuxyeVDUec6c95k57zJ32mDvtMXfa02Xu+PsgIiJ6eJnYCEdG4UFyZnJFKQsLCwQFBSEuLk5jTKm4uDgMGzas3nlCQkKwZ88ejWn79+9HcHBwveNJEREREREREVHzYW5eM6TAtWvX4OjoqPeLUKY6ppQQAteuXYMkSVrVV0yuKAUAERERGDt2LIKDgxESEoJ169YhOzsb4eHhAGpuvbt06RI2bdoEAAgPD8fnn3+OiIgIvPHGGzh+/DiioqKwZcsWQ24GERERERERERkBuVwONzc35OTkICsryyAxqFQqyGQyg6z7QUiSBDc3N8jl8kbPa5JFqVGjRqGgoADvv/8+cnNz4efnh9jYWHh4eAAAcnNzkZ2drW7v6emJ2NhYTJ8+HatWrYKrqytWrlyJF1980VCbQERERERERERGpGXLlujUqRMqKyv1vm4hBJRKJWxtbU2qpxRQ08tMm4IUYKJFKQCYNGkSJk2aVO9nGzZsqDOtT58++OWXX3QcFRERERERERGZKrlcrnWB5UEIIVBeXg4rKyuTK0o9CNPrF0ZERERERERERCbPZHtK6VvtaPJFRUVNsqyioiI+UUkLzJ32mDvtMXfaY+60x9xpT9e5qz0X4NN56mrK86X6ls1/E7rFHOsW86tbzK9uMb+697DluKHnSyxKNZBSqQQAtG/f3sCREBERkTFQKpVQKBSGDsOo8HyJiIiIbne/8yVJ8DJfg6hUKly+fLlJBh0rKipC+/btcfHiRdjZ2TVRhM0Dc6c95k57zJ32mDvtMXfa03XuagcidXV1Nckn5OhSU54v3Yn/JnSPOdYt5le3mF/dYn5172HLcUPPl9hTqoFkMhnc3NyadJl2dnYPxc5mCMyd9pg77TF32mPutMfcaU+XuWMPqfrp4nzpTvw3oXvMsW4xv7rF/OoW86t7D1OOG3K+xMt7RERERERERESkdyxKERERERERERGR3rEoZQCWlpaYN28eLC0tDR2KyWHutMfcaY+50x5zpz3mTnvM3cOJv1fdY451i/nVLeZXt5hf3WuuOeZA50REREREREREpHfsKUVERERERERERHrHohQREREREREREekdi1JERERERERERKR3LEoREREREREREZHesSilI6tXr4anpyesrKwQFBSEn3766Z7tExISEBQUBCsrKzzyyCNYu3atniI1Po3J3c6dOzFgwAA4OjrCzs4OISEh+PHHH/UYrXFp7H5X6+jRozAzM0NAQIBuAzRijc1deXk5IiMj4eHhAUtLSzz66KNYv369nqI1Lo3N3ebNm9GtWzfY2NjAxcUFr732GgoKCvQUrfE4fPgwhgwZAldXV0iShN27d993Hh4rajQ2dzxWPBy0PcbRvc2fPx+SJGm8nJ2dDR2WSbvfd5QQAvPnz4erqyusra3x1FNPITU11TDBmqD75TcsLKzOPt2rVy/DBGuCFi1ahMceewy2trZwcnLC8OHDkZ6ertGG+7D2GpLf5rYPsyilA1u3bsW0adMQGRmJ5ORkhIaGYvDgwcjOzq63/YULF/Dss88iNDQUycnJmDNnDqZOnYodO3boOXLDa2zuDh8+jAEDBiA2NhY///wznn76aQwZMgTJycl6jtzwGpu7Wjdu3MC4cePQr18/PUVqfLTJ3ciRI3HgwAFERUUhPT0dW7ZsgZeXlx6jNg6Nzd2RI0cwbtw4vP7660hNTcV3332HpKQkTJw4Uc+RG15JSQm6deuGzz//vEHteaz4S2Nzx2OF6dP2GEcN4+vri9zcXPXr9OnThg7JpN3vO+rjjz/GsmXL8PnnnyMpKQnOzs4YMGAAlEqlniM1TQ05BjzzzDMa+3RsbKweIzRtCQkJmDx5Mk6cOIG4uDhUVVVh4MCBKCkpUbfhPqy9huQXaGb7sKAm16NHDxEeHq4xzcvLS7zzzjv1tp81a5bw8vLSmPb3v/9d9OrVS2cxGqvG5q4+Pj4+YsGCBU0dmtHTNnejRo0Sc+fOFfPmzRPdunXTYYTGq7G5+89//iMUCoUoKCjQR3hGrbG5W7JkiXjkkUc0pq1cuVK4ubnpLEZTAEDs2rXrnm14rKhfQ3JXn+Z6rDBVTXF+QPVrzsd/fbjzO0qlUglnZ2exePFi9bSysjKhUCjE2rVrDRChaavvGDB+/HgxbNgwg8TzMLp69aoAIBISEoQQ3Ieb2p35FaL57cPsKdXEKioq8PPPP2PgwIEa0wcOHIhjx47VO8/x48frtB80aBBOnjyJyspKncVqbLTJ3Z1UKhWUSiVat26tixCNlra5i46ORkZGBubNm6frEI2WNrn7/vvvERwcjI8//hjt2rVD586dMWPGDNy8eVMfIRsNbXL3+OOPIycnB7GxsRBC4MqVK9i+fTuee+45fYRs0nisaDrN9Vhhqpri/IDu7dy5c3B1dYWnpydGjx6NzMxMQ4f00Lpw4QLy8vI09mdLS0v06dOH+3MTio+Ph5OTEzp37ow33ngDV69eNXRIJuvGjRsAoD5mch9uWnfmt1Zz2odZlGpi+fn5qK6uRtu2bTWmt23bFnl5efXOk5eXV2/7qqoq5Ofn6yxWY6NN7u60dOlSlJSUYOTIkboI0Whpk7tz587hnXfewebNm2FmZqaPMI2SNrnLzMzEkSNHcObMGezatQvLly/H9u3bMXnyZH2EbDS0yd3jjz+OzZs3Y9SoUbCwsICzszPs7e3x2Wef6SNkk8ZjRdNprscKU9UU5wd0dz179sSmTZvw448/4ssvv0ReXh4ef/zxZjnWnz7U7rPcn3Vn8ODB2Lx5Mw4ePIilS5ciKSkJffv2RXl5uaFDMzlCCEREROCJJ56An58fAO7DTam+/ALNbx9uvn+J6pgkSRrvhRB1pt2vfX3Tm4PG5q7Wli1bMH/+fPz73/+Gk5OTrsIzag3NXXV1NV555RUsWLAAnTt31ld4Rq0x+51KpYIkSdi8eTMUCgUAYNmyZXjppZewatUqWFtb6zxeY9KY3KWlpWHq1Kl47733MGjQIOTm5mLmzJkIDw9HVFSUPsI1aTxWPDgeK0yXtucHdG+DBw9W/+zv74+QkBA8+uij2LhxIyIiIgwY2cON+7PujBo1Sv2zn58fgoOD4eHhgR9++AEjRowwYGSmZ8qUKfj1119x5MiROp9xH35wd8tvc9uHWZRqYm3atIFcLq9TJb569WqdanItZ2fnetubmZnBwcFBZ7EaG21yV2vr1q14/fXX8d1336F///66DNMoNTZ3SqUSJ0+eRHJyMqZMmQKgptAihICZmRn279+Pvn376iV2Q9Nmv3NxcUG7du3UBSkA8Pb2hhACOTk56NSpk05jNhba5G7RokXo3bs3Zs6cCQDo2rUrWrRogdDQUHz44YdwcXHRedymiseKB9fcjxWm6kHOD6jxWrRoAX9/f5w7d87QoTyUap9smJeXp3HM4/6sOy4uLvDw8OA+3Uj/93//h++//x6HDx+Gm5ubejr34aZxt/zW52Hfh3n7XhOzsLBAUFAQ4uLiNKbHxcXh8ccfr3eekJCQOu3379+P4OBgmJub6yxWY6NN7oCaq95hYWH49ttvm+24NI3NnZ2dHU6fPo2UlBT1Kzw8HF26dEFKSgp69uypr9ANTpv9rnfv3rh8+TKKi4vV037//XfIZLL7HlQeJtrkrrS0FDKZ5qFHLpcD+KvXD9WPx4oHw2OF6dL2/IC0U15ejrNnz/IigY54enrC2dlZY3+uqKhAQkIC92cdKSgowMWLF7lPN5AQAlOmTMHOnTtx8OBBeHp6anzOffjB3C+/9Xno92F9j6zeHMTExAhzc3MRFRUl0tLSxLRp00SLFi1EVlaWEEKId955R4wdO1bdPjMzU9jY2Ijp06eLtLQ0ERUVJczNzcX27dsNtQkG09jcffvtt8LMzEysWrVK5Obmql+FhYWG2gSDaWzu7tScn77T2NwplUrh5uYmXnrpJZGamioSEhJEp06dxMSJEw21CQbT2NxFR0cLMzMzsXr1apGRkSGOHDkigoODRY8ePQy1CQajVCpFcnKySE5OFgDEsmXLRHJysvjjjz+EEDxW3Etjc8djhem733cNae/tt98W8fHxIjMzU5w4cUI8//zzwtbWlrl9APf7jlq8eLFQKBRi586d4vTp02LMmDHCxcVFFBUVGThy03Cv/CqVSvH222+LY8eOiQsXLohDhw6JkJAQ0a5dO+a3gf7xj38IhUIh4uPjNY6ZpaWl6jbch7V3v/w2x32YRSkdWbVqlfDw8BAWFhaie/fudR7x2KdPH4328fHxIjAwUFhYWIgOHTqINWvW6Dli49GY3PXp00cAqPMaP368/gM3Ao3d727XnItSQjQ+d2fPnhX9+/cX1tbWws3NTURERGgcrJuTxuZu5cqVwsfHR1hbWwsXFxfx6quvipycHD1HbXiHDh265/cXjxV319jc8VjxcLjXdw1pb9SoUcLFxUWYm5sLV1dXMWLECJGammrosEza/b6jVCqVmDdvnnB2dhaWlpbiySefFKdPnzZs0CbkXvktLS0VAwcOFI6OjsLc3Fy4u7uL8ePHi+zsbEOHbTLqyy0AER0drW7DfVh798tvc9yHJSF4vwQREREREREREekXx5QiIiIiIiIiIiK9Y1GKiIiIiIiIiIj0jkUpIiIiIiIiIiLSOxaliIiIiIiIiIhI71iUIiIiIiIiIiIivWNRioiIiIiIiIiI9I5FKSIiIiIiIiIi0jsWpYiIiIiIiIiISO9YlCIi0oH58+cjICDA0GEQERERNYihz13effddvPnmmw1qO2PGDEydOlXHERGRPrAoRUTNRlhYGCRJQnh4eJ3PJk2aBEmSEBYWpv/AiIiIiHRIkqR7vsLCwjBjxgwcOHDAIPFduXIFK1aswJw5cxrUftasWYiOjsaFCxd0HBkR6RqLUkTUrLRv3x4xMTG4efOmelpZWRm2bNkCd3d3A0ZGREREpBu5ubnq1/Lly2FnZ6cxbcWKFWjZsiUcHBwMEl9UVBRCQkLQoUOHBrV3cnLCwIEDsXbtWt0GRkQ6x6IUETUr3bt3h7u7O3bu3KmetnPnTrRv3x6BgYHqafv27cMTTzwBe3t7ODg44Pnnn0dGRobGsnJycjB69Gi0bt0aLVq0QHBwMBITEzXafP311+jQoQMUCgVGjx4NpVKp2w0kIiIiuoOzs7P6pVAoIElSnWl33r4XFhaG4cOHY+HChWjbti3s7e2xYMECVFVVYebMmWjdujXc3Nywfv16jXVdunQJo0aNQqtWreDg4IBhw4YhKyvrnvHFxMRg6NChGtO2b98Of39/WFtbw8HBAf3790dJSYn686FDh2LLli0PnBsiMiwWpYio2XnttdcQHR2tfr9+/XpMmDBBo01JSQkiIiKQlJSEAwcOQCaT4YUXXoBKpQIAFBcXo0+fPrh8+TK+//57nDp1CrNmzVJ/DgAZGRnYvXs39u7di7179yIhIQGLFy/Wz0YSERERPaCDBw/i8uXLOHz4MJYtW4b58+fj+eefR6tWrZCYmIjw8HCEh4fj4sWLAIDS0lI8/fTTaNmyJQ4fPowjR46gZcuWeOaZZ1BRUVHvOq5fv44zZ84gODhYPS03NxdjxozBhAkTcPbsWcTHx2PEiBEQQqjb9OjRAxcvXsQff/yh2yQQkU6ZGToAIiJ9Gzt2LGbPno2srCxIkoSjR48iJiYG8fHx6jYvvviixjxRUVFwcnJCWloa/Pz88O233+LatWtISkpC69atAQAdO3bUmEelUmHDhg2wtbVVr/fAgQP46KOPdLuBRERERE2gdevWWLlyJWQyGbp06YKPP/4YpaWl6rGfZs+ejcWLF+Po0aMYPXo0YmJiIJPJ8NVXX0GSJABAdHQ07O3tER8fj4EDB9ZZxx9//AEhBFxdXdXTcnNzUVVVhREjRsDDwwMA4O/vrzFfu3btAABZWVnqNkRkeliUIqJmp02bNnjuueewceNGCCHw3HPPoU2bNhptMjIy8O677+LEiRPIz89X94DKzs6Gn58fUlJSEBgYqC5I1adDhw7qghQAuLi44OrVq7rZKCIiIqIm5uvrC5nsr5tr2rZtCz8/P/V7uVwOBwcH9fnNzz//jPPnz2uc/wA143feOQxCrdpxPq2srNTTunXrhn79+sHf3x+DBg3CwIED8dJLL6FVq1bqNtbW1gBqemcRkeliUYqImqUJEyZgypQpAIBVq1bV+XzIkCFo3749vvzyS7i6ukKlUsHPz0/d9bz2ROhezM3NNd5LkqRxex8RERGRMavvXOZe5zcqlQpBQUHYvHlznWU5OjrWu47aC4PXr19Xt5HL5YiLi8OxY8ewf/9+fPbZZ4iMjERiYiI8PT0BAH/++ec9l0tEpoFjShFRs1Q7tkFFRQUGDRqk8VlBQQHOnj2LuXPnol+/fvD29sb169c12nTt2hUpKSnqEyIiIiKi5q579+44d+4cnJyc0LFjR42XQqGod55HH30UdnZ2SEtL05guSRJ69+6NBQsWIDk5GRYWFti1a5f68zNnzsDc3By+vr463SYi0i0WpYioWZLL5Th79izOnj0LuVyu8Vnt02LWrVuH8+fP4+DBg4iIiNBoM2bMGDg7O2P48OE4evQoMjMzsWPHDhw/flyfm0FERERkNF599VW0adMGw4YNw08//YQLFy4gISEBb731FnJycuqdRyaToX///jhy5Ih6WmJiIhYuXIiTJ08iOzsbO3fuxLVr1+Dt7a1u89NPPyE0NLRBvdeJyHixKEVEzZadnR3s7OzqTJfJZIiJicHPP/8MPz8/TJ8+HUuWLNFoY2Fhgf3798PJyQnPPvss/P39sXjx4joFLiIiIqLmwsbGBocPH4a7uztGjBgBb29vTJgwATdv3qz3nKvWm2++iZiYGPVtgHZ2djh8+DCeffZZdO7cGXPnzsXSpUsxePBg9TxbtmzBG2+8ofNtIiLdksTtz9UkIiIiIiIi0iMhBHr16oVp06ZhzJgx923/ww8/YObMmfj1119hZsZhkolMGXtKERERERERkcFIkoR169ahqqqqQe1LSkoQHR3NghTRQ4A9pYiIiIiIiIiISO/YU4qIiIiIiIiIiPSORSkiIiIiIiIiItI7FqWIiIiIiIiIiEjvWJQiIiIiIiIiIiK9Y1GKiIiIiIiIiIj0jkUpIiIiIiIiIiLSOxaliIiIiIiIiIhI71iUIiIiIiIiIiIivWNRioiIiIiIiIiI9O7/AaWsDHPVsxJcAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 1200x600 with 4 Axes>"
       ]
@@ -1102,14 +1353,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "b9cbc7a4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2026-10-22T22:26:55.001119Z",
-     "iopub.status.busy": "2026-10-22T22:26:55.000873Z",
-     "iopub.status.idle": "2026-10-22T22:26:55.016035Z",
-     "shell.execute_reply": "2026-10-22T22:26:55.015626Z"
+     "iopub.execute_input": "2026-10-24T20:16:05.965760Z",
+     "iopub.status.busy": "2026-10-24T20:16:05.965706Z",
+     "iopub.status.idle": "2026-10-24T20:16:05.970256Z",
+     "shell.execute_reply": "2026-10-24T20:16:05.970078Z"
     }
    },
    "outputs": [
@@ -1118,134 +1369,134 @@
       "text/html": [
        "<style type=\"text/css\">\n",
        "</style>\n",
-       "<table id=\"T_0a17a\">\n",
+       "<table id=\"T_2ac72\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_0a17a_level0_col0\" class=\"col_heading level0 col0\" >rocket</th>\n",
-       "      <th id=\"T_0a17a_level0_col1\" class=\"col_heading level0 col1\" >status</th>\n",
-       "      <th id=\"T_0a17a_level0_col2\" class=\"col_heading level0 col2\" >reason</th>\n",
-       "      <th id=\"T_0a17a_level0_col3\" class=\"col_heading level0 col3\" >notes</th>\n",
+       "      <th id=\"T_2ac72_level0_col0\" class=\"col_heading level0 col0\" >rocket</th>\n",
+       "      <th id=\"T_2ac72_level0_col1\" class=\"col_heading level0 col1\" >status</th>\n",
+       "      <th id=\"T_2ac72_level0_col2\" class=\"col_heading level0 col2\" >reason</th>\n",
+       "      <th id=\"T_2ac72_level0_col3\" class=\"col_heading level0 col3\" >notes</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_0a17a_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
-       "      <td id=\"T_0a17a_row0_col0\" class=\"data row0 col0\" >andromeda</td>\n",
-       "      <td id=\"T_0a17a_row0_col1\" class=\"data row0 col1\" >paired untuned comparison</td>\n",
-       "      <td id=\"T_0a17a_row0_col2\" class=\"data row0 col2\" >—</td>\n",
-       "      <td id=\"T_0a17a_row0_col3\" class=\"data row0 col3\" >Telemetry maximum 3438.549 m differs from notebook reported value 3443.000 m; both retained.</td>\n",
+       "      <th id=\"T_2ac72_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_2ac72_row0_col0\" class=\"data row0 col0\" >andromeda</td>\n",
+       "      <td id=\"T_2ac72_row0_col1\" class=\"data row0 col1\" >paired untuned comparison</td>\n",
+       "      <td id=\"T_2ac72_row0_col2\" class=\"data row0 col2\" >—</td>\n",
+       "      <td id=\"T_2ac72_row0_col3\" class=\"data row0 col3\" >Telemetry maximum 3438.549 m differs from notebook reported value 3443.000 m; both retained.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_0a17a_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
-       "      <td id=\"T_0a17a_row1_col0\" class=\"data row1 col0\" >astra</td>\n",
-       "      <td id=\"T_0a17a_row1_col1\" class=\"data row1 col1\" >paired untuned comparison</td>\n",
-       "      <td id=\"T_0a17a_row1_col2\" class=\"data row1 col2\" >—</td>\n",
-       "      <td id=\"T_0a17a_row1_col3\" class=\"data row1 col3\" >Telemetry maximum 3249.460 m differs from notebook reported value 3250.000 m; both retained. | Source fin trailing edge overhangs the nozzle-inferred body base by 1.216 mm. Native DATCOM accepts this; the integration now validates root-chord centre attachment instead of rejecting all overhang. Geometry is unchanged.</td>\n",
+       "      <th id=\"T_2ac72_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+       "      <td id=\"T_2ac72_row1_col0\" class=\"data row1 col0\" >astra</td>\n",
+       "      <td id=\"T_2ac72_row1_col1\" class=\"data row1 col1\" >paired untuned comparison</td>\n",
+       "      <td id=\"T_2ac72_row1_col2\" class=\"data row1 col2\" >—</td>\n",
+       "      <td id=\"T_2ac72_row1_col3\" class=\"data row1 col3\" >Telemetry maximum 3249.460 m differs from notebook reported value 3250.000 m; both retained. | Source fin trailing edge overhangs the nozzle-inferred body base by 1.216 mm. Native DATCOM accepts this; the integration now validates root-chord centre attachment instead of rejecting all overhang. Geometry is unchanged.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_0a17a_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
-       "      <td id=\"T_0a17a_row2_col0\" class=\"data row2 col0\" >bella_lui</td>\n",
-       "      <td id=\"T_0a17a_row2_col1\" class=\"data row2 col1\" >paired untuned comparison</td>\n",
-       "      <td id=\"T_0a17a_row2_col2\" class=\"data row2 col2\" >—</td>\n",
-       "      <td id=\"T_0a17a_row2_col3\" class=\"data row2 col3\" >—</td>\n",
+       "      <th id=\"T_2ac72_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+       "      <td id=\"T_2ac72_row2_col0\" class=\"data row2 col0\" >bella_lui</td>\n",
+       "      <td id=\"T_2ac72_row2_col1\" class=\"data row2 col1\" >paired untuned comparison</td>\n",
+       "      <td id=\"T_2ac72_row2_col2\" class=\"data row2 col2\" >—</td>\n",
+       "      <td id=\"T_2ac72_row2_col3\" class=\"data row2 col3\" >—</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_0a17a_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
-       "      <td id=\"T_0a17a_row3_col0\" class=\"data row3 col0\" >camoes</td>\n",
-       "      <td id=\"T_0a17a_row3_col1\" class=\"data row3 col1\" >ordinary only</td>\n",
-       "      <td id=\"T_0a17a_row3_col2\" class=\"data row3 col2\" >Source active air brakes override complete rocket drag. Combining their whole-vehicle CD with DATCOM would double-count base drag; no controlled DATCOM result claimed.</td>\n",
-       "      <td id=\"T_0a17a_row3_col3\" class=\"data row3 col3\" >Telemetry maximum 3015.996 m differs from notebook reported value 3015.000 m; both retained.</td>\n",
+       "      <th id=\"T_2ac72_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+       "      <td id=\"T_2ac72_row3_col0\" class=\"data row3 col0\" >camoes</td>\n",
+       "      <td id=\"T_2ac72_row3_col1\" class=\"data row3 col1\" >ordinary only</td>\n",
+       "      <td id=\"T_2ac72_row3_col2\" class=\"data row3 col2\" >Source active air brakes override complete rocket drag. Combining their whole-vehicle CD with DATCOM would double-count base drag; no controlled DATCOM result claimed.</td>\n",
+       "      <td id=\"T_2ac72_row3_col3\" class=\"data row3 col3\" >Telemetry maximum 3015.996 m differs from notebook reported value 3015.000 m; both retained.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_0a17a_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
-       "      <td id=\"T_0a17a_row4_col0\" class=\"data row4 col0\" >cavour</td>\n",
-       "      <td id=\"T_0a17a_row4_col1\" class=\"data row4 col1\" >paired untuned comparison</td>\n",
-       "      <td id=\"T_0a17a_row4_col2\" class=\"data row4 col2\" >—</td>\n",
-       "      <td id=\"T_0a17a_row4_col3\" class=\"data row4 col3\" >—</td>\n",
+       "      <th id=\"T_2ac72_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+       "      <td id=\"T_2ac72_row4_col0\" class=\"data row4 col0\" >cavour</td>\n",
+       "      <td id=\"T_2ac72_row4_col1\" class=\"data row4 col1\" >paired untuned comparison</td>\n",
+       "      <td id=\"T_2ac72_row4_col2\" class=\"data row4 col2\" >—</td>\n",
+       "      <td id=\"T_2ac72_row4_col3\" class=\"data row4 col3\" >—</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_0a17a_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
-       "      <td id=\"T_0a17a_row5_col0\" class=\"data row5 col0\" >defiance</td>\n",
-       "      <td id=\"T_0a17a_row5_col1\" class=\"data row5 col1\" >ordinary only</td>\n",
-       "      <td id=\"T_0a17a_row5_col2\" class=\"data row5 col2\" >ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip</td>\n",
-       "      <td id=\"T_0a17a_row5_col3\" class=\"data row5 col3\" >Source fin root centre is z=-0.025 m, aft of the tail base z=0.0403 m; native root attachment is outside the declared body.</td>\n",
+       "      <th id=\"T_2ac72_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+       "      <td id=\"T_2ac72_row5_col0\" class=\"data row5 col0\" >defiance</td>\n",
+       "      <td id=\"T_2ac72_row5_col1\" class=\"data row5 col1\" >ordinary only</td>\n",
+       "      <td id=\"T_2ac72_row5_col2\" class=\"data row5 col2\" >ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip</td>\n",
+       "      <td id=\"T_2ac72_row5_col3\" class=\"data row5 col3\" >Source fin root centre is z=-0.025 m, aft of the tail base z=0.0403 m; native root attachment is outside the declared body.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_0a17a_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
-       "      <td id=\"T_0a17a_row6_col0\" class=\"data row6 col0\" >erebus</td>\n",
-       "      <td id=\"T_0a17a_row6_col1\" class=\"data row6 col1\" >paired untuned comparison</td>\n",
-       "      <td id=\"T_0a17a_row6_col2\" class=\"data row6 col2\" >—</td>\n",
-       "      <td id=\"T_0a17a_row6_col3\" class=\"data row6 col3\" >Telemetry maximum 3001.751 m differs from notebook reported value 3020.000 m; both retained.</td>\n",
+       "      <th id=\"T_2ac72_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+       "      <td id=\"T_2ac72_row6_col0\" class=\"data row6 col0\" >erebus</td>\n",
+       "      <td id=\"T_2ac72_row6_col1\" class=\"data row6 col1\" >paired untuned comparison</td>\n",
+       "      <td id=\"T_2ac72_row6_col2\" class=\"data row6 col2\" >—</td>\n",
+       "      <td id=\"T_2ac72_row6_col3\" class=\"data row6 col3\" >Telemetry maximum 3001.751 m differs from notebook reported value 3020.000 m; both retained.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_0a17a_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
-       "      <td id=\"T_0a17a_row7_col0\" class=\"data row7 col0\" >genesis</td>\n",
-       "      <td id=\"T_0a17a_row7_col1\" class=\"data row7 col1\" >paired untuned comparison</td>\n",
-       "      <td id=\"T_0a17a_row7_col2\" class=\"data row7 col2\" >—</td>\n",
-       "      <td id=\"T_0a17a_row7_col3\" class=\"data row7 col3\" >Telemetry maximum 2916.692 m differs from notebook reported value 2916.000 m; both retained.</td>\n",
+       "      <th id=\"T_2ac72_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+       "      <td id=\"T_2ac72_row7_col0\" class=\"data row7 col0\" >genesis</td>\n",
+       "      <td id=\"T_2ac72_row7_col1\" class=\"data row7 col1\" >paired untuned comparison</td>\n",
+       "      <td id=\"T_2ac72_row7_col2\" class=\"data row7 col2\" >—</td>\n",
+       "      <td id=\"T_2ac72_row7_col3\" class=\"data row7 col3\" >Telemetry maximum 2916.692 m differs from notebook reported value 2916.000 m; both retained.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_0a17a_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
-       "      <td id=\"T_0a17a_row8_col0\" class=\"data row8 col0\" >halcyon</td>\n",
-       "      <td id=\"T_0a17a_row8_col1\" class=\"data row8 col1\" >paired untuned comparison</td>\n",
-       "      <td id=\"T_0a17a_row8_col2\" class=\"data row8 col2\" >—</td>\n",
-       "      <td id=\"T_0a17a_row8_col3\" class=\"data row8 col3\" >Telemetry QA flags 5 samples with altitude jumps above 2000 m/s; raw maximum 3470.825 m is retained separately. | Telemetry maximum 3470.825 m differs from notebook reported value 3450.000 m; both retained.</td>\n",
+       "      <th id=\"T_2ac72_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+       "      <td id=\"T_2ac72_row8_col0\" class=\"data row8 col0\" >halcyon</td>\n",
+       "      <td id=\"T_2ac72_row8_col1\" class=\"data row8 col1\" >paired untuned comparison</td>\n",
+       "      <td id=\"T_2ac72_row8_col2\" class=\"data row8 col2\" >—</td>\n",
+       "      <td id=\"T_2ac72_row8_col3\" class=\"data row8 col3\" >Telemetry QA flags 5 samples with altitude jumps above 2000 m/s; raw maximum 3470.825 m is retained separately. | Telemetry maximum 3470.825 m differs from notebook reported value 3450.000 m; both retained.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_0a17a_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
-       "      <td id=\"T_0a17a_row9_col0\" class=\"data row9 col0\" >hedy</td>\n",
-       "      <td id=\"T_0a17a_row9_col1\" class=\"data row9 col1\" >archived comparison only</td>\n",
-       "      <td id=\"T_0a17a_row9_col2\" class=\"data row9 col2\" >Source data/rockets/tust is absent; CoolProp is not installed, and the negative-span fin root cutout requires geometry support.</td>\n",
-       "      <td id=\"T_0a17a_row9_col3\" class=\"data row9 col3\" >Referenced telemetry file is absent: data/rockets/tust/cats_tust/filteredDataInfo.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/gnssInfo.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/flightInfo.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/imu.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/baro.csv (cell 24). | Hedy end comparison uses simulated ASL 5406.918 m against measured AGL 5231.53 m; saved simulated AGL is 5246.918 m, a 160 m datum discrepancy.</td>\n",
+       "      <th id=\"T_2ac72_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+       "      <td id=\"T_2ac72_row9_col0\" class=\"data row9 col0\" >hedy</td>\n",
+       "      <td id=\"T_2ac72_row9_col1\" class=\"data row9 col1\" >archived comparison only</td>\n",
+       "      <td id=\"T_2ac72_row9_col2\" class=\"data row9 col2\" >Source data/rockets/tust is absent; CoolProp is not installed, and the negative-span fin root cutout requires geometry support.</td>\n",
+       "      <td id=\"T_2ac72_row9_col3\" class=\"data row9 col3\" >Referenced telemetry file is absent: data/rockets/tust/cats_tust/filteredDataInfo.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/gnssInfo.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/flightInfo.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/imu.csv (cell 24). | Referenced telemetry file is absent: data/rockets/tust/cats_tust/baro.csv (cell 24). | Hedy end comparison uses simulated ASL 5406.918 m against measured AGL 5231.53 m; saved simulated AGL is 5246.918 m, a 160 m datum discrepancy.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_0a17a_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
-       "      <td id=\"T_0a17a_row10_col0\" class=\"data row10 col0\" >juno3</td>\n",
-       "      <td id=\"T_0a17a_row10_col1\" class=\"data row10 col1\" >paired untuned comparison</td>\n",
-       "      <td id=\"T_0a17a_row10_col2\" class=\"data row10 col2\" >—</td>\n",
-       "      <td id=\"T_0a17a_row10_col3\" class=\"data row10 col3\" >Telemetry QA flags 2 samples with altitude jumps above 2000 m/s; raw maximum 10700.590 m is retained separately. | Telemetry maximum 10700.590 m differs from notebook reported value 3213.000 m; both retained. | Source fin airfoil is an empirical lift curve (cell 10), not a supplied section contour. The generic 2% HEX DATCOM assumption does not reproduce that curve; source-drag substitution therefore still has a lateral-model discrepancy.</td>\n",
+       "      <th id=\"T_2ac72_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
+       "      <td id=\"T_2ac72_row10_col0\" class=\"data row10 col0\" >juno3</td>\n",
+       "      <td id=\"T_2ac72_row10_col1\" class=\"data row10 col1\" >paired untuned comparison</td>\n",
+       "      <td id=\"T_2ac72_row10_col2\" class=\"data row10 col2\" >—</td>\n",
+       "      <td id=\"T_2ac72_row10_col3\" class=\"data row10 col3\" >Telemetry QA flags 2 samples with altitude jumps above 2000 m/s; raw maximum 10700.590 m is retained separately. | Telemetry maximum 10700.590 m differs from notebook reported value 3213.000 m; both retained. | Source fin airfoil is an empirical lift curve (cell 10), not a supplied section contour. The generic 2% HEX DATCOM assumption does not reproduce that curve; source-drag substitution therefore still has a lateral-model discrepancy.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_0a17a_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
-       "      <td id=\"T_0a17a_row11_col0\" class=\"data row11 col0\" >lince</td>\n",
-       "      <td id=\"T_0a17a_row11_col1\" class=\"data row11 col1\" >paired untuned comparison</td>\n",
-       "      <td id=\"T_0a17a_row11_col2\" class=\"data row11 col2\" >—</td>\n",
-       "      <td id=\"T_0a17a_row11_col3\" class=\"data row11 col3\" >Telemetry QA flags 69 samples with altitude jumps above 2000 m/s; raw maximum 3668.521 m is retained separately. | Telemetry maximum 3668.521 m differs from notebook reported value 3587.000 m; both retained. | Lince has three saved trajectories. Compare first/pre-deployment ascent only; retain all saved apogees separately.</td>\n",
+       "      <th id=\"T_2ac72_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
+       "      <td id=\"T_2ac72_row11_col0\" class=\"data row11 col0\" >lince</td>\n",
+       "      <td id=\"T_2ac72_row11_col1\" class=\"data row11 col1\" >paired untuned comparison</td>\n",
+       "      <td id=\"T_2ac72_row11_col2\" class=\"data row11 col2\" >—</td>\n",
+       "      <td id=\"T_2ac72_row11_col3\" class=\"data row11 col3\" >Telemetry QA flags 69 samples with altitude jumps above 2000 m/s; raw maximum 3668.521 m is retained separately. | Telemetry maximum 3668.521 m differs from notebook reported value 3587.000 m; both retained. | Lince has three saved trajectories. Compare first/pre-deployment ascent only; retain all saved apogees separately.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_0a17a_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
-       "      <td id=\"T_0a17a_row12_col0\" class=\"data row12 col0\" >ndrt_2020</td>\n",
-       "      <td id=\"T_0a17a_row12_col1\" class=\"data row12 col1\" >ordinary only</td>\n",
-       "      <td id=\"T_0a17a_row12_col2\" class=\"data row12 col2\" >ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip</td>\n",
-       "      <td id=\"T_0a17a_row12_col3\" class=\"data row12 col3\" >NDRT source uses approximate ft conversion /3.281; comparison uses exact 0.3048 m/ft. | Telemetry maximum 1320.357 m differs from notebook reported value 1316.736 m; both retained. | Source transition is internal at z=1.2..1.327 m with fins near z=3.05 m. The current DATCOM builder represents only an aft tail, not the required downstream smaller cylinder.</td>\n",
+       "      <th id=\"T_2ac72_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
+       "      <td id=\"T_2ac72_row12_col0\" class=\"data row12 col0\" >ndrt_2020</td>\n",
+       "      <td id=\"T_2ac72_row12_col1\" class=\"data row12 col1\" >ordinary only</td>\n",
+       "      <td id=\"T_2ac72_row12_col2\" class=\"data row12 col2\" >ValueError: DATCOM fin root chord centers must lie within the body's axial extent; leading edges must be aft of the nose tip</td>\n",
+       "      <td id=\"T_2ac72_row12_col3\" class=\"data row12 col3\" >NDRT source uses approximate ft conversion /3.281; comparison uses exact 0.3048 m/ft. | Telemetry maximum 1320.357 m differs from notebook reported value 1316.736 m; both retained. | Source transition is internal at z=1.2..1.327 m with fins near z=3.05 m. The current DATCOM builder represents only an aft tail, not the required downstream smaller cylinder.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_0a17a_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
-       "      <td id=\"T_0a17a_row13_col0\" class=\"data row13 col0\" >prometheus_2022</td>\n",
-       "      <td id=\"T_0a17a_row13_col1\" class=\"data row13 col1\" >paired untuned comparison</td>\n",
-       "      <td id=\"T_0a17a_row13_col2\" class=\"data row13 col2\" >—</td>\n",
-       "      <td id=\"T_0a17a_row13_col3\" class=\"data row13 col3\" >Telemetry QA flags 2 samples with altitude jumps above 2000 m/s; raw maximum 3903.770 m is retained separately. | Telemetry maximum 3903.770 m differs from notebook reported value 3898.370 m; both retained. | Prometheus prose says June 2022; source environment and local reanalysis are June 2023. Both reruns preserve source conditions.</td>\n",
+       "      <th id=\"T_2ac72_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
+       "      <td id=\"T_2ac72_row13_col0\" class=\"data row13 col0\" >prometheus_2022</td>\n",
+       "      <td id=\"T_2ac72_row13_col1\" class=\"data row13 col1\" >paired untuned comparison</td>\n",
+       "      <td id=\"T_2ac72_row13_col2\" class=\"data row13 col2\" >—</td>\n",
+       "      <td id=\"T_2ac72_row13_col3\" class=\"data row13 col3\" >Telemetry QA flags 2 samples with altitude jumps above 2000 m/s; raw maximum 3903.770 m is retained separately. | Telemetry maximum 3903.770 m differs from notebook reported value 3898.370 m; both retained. | Prometheus prose says June 2022; source environment and local reanalysis are June 2023. Both reruns preserve source conditions.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_0a17a_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
-       "      <td id=\"T_0a17a_row14_col0\" class=\"data row14 col0\" >valetudo</td>\n",
-       "      <td id=\"T_0a17a_row14_col1\" class=\"data row14 col1\" >paired untuned comparison</td>\n",
-       "      <td id=\"T_0a17a_row14_col2\" class=\"data row14 col2\" >—</td>\n",
-       "      <td id=\"T_0a17a_row14_col3\" class=\"data row14 col3\" >Valetudo flight recordings were lost; only recovered scalar apogee/drift values remain.</td>\n",
+       "      <th id=\"T_2ac72_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
+       "      <td id=\"T_2ac72_row14_col0\" class=\"data row14 col0\" >valetudo</td>\n",
+       "      <td id=\"T_2ac72_row14_col1\" class=\"data row14 col1\" >paired untuned comparison</td>\n",
+       "      <td id=\"T_2ac72_row14_col2\" class=\"data row14 col2\" >—</td>\n",
+       "      <td id=\"T_2ac72_row14_col3\" class=\"data row14 col3\" >Valetudo flight recordings were lost; only recovered scalar apogee/drift values remain.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_0a17a_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
-       "      <td id=\"T_0a17a_row15_col0\" class=\"data row15 col0\" >valkyrie</td>\n",
-       "      <td id=\"T_0a17a_row15_col1\" class=\"data row15 col1\" >unavailable source model</td>\n",
-       "      <td id=\"T_0a17a_row15_col2\" class=\"data row15 col2\" >SourceUnavailable: Source requests live weather with no archived profile; not replaced by invented weather.</td>\n",
-       "      <td id=\"T_0a17a_row15_col3\" class=\"data row15 col3\" >Telemetry maximum 2059.024 m differs from notebook reported value 2098.020 m; both retained. | Valkyrie prose says 2025 but source environment says 2026 and fetches live Windy weather; no archived profile is supplied.</td>\n",
+       "      <th id=\"T_2ac72_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
+       "      <td id=\"T_2ac72_row15_col0\" class=\"data row15 col0\" >valkyrie</td>\n",
+       "      <td id=\"T_2ac72_row15_col1\" class=\"data row15 col1\" >unavailable source model</td>\n",
+       "      <td id=\"T_2ac72_row15_col2\" class=\"data row15 col2\" >SourceUnavailable: Source requests live weather with no archived profile; not replaced by invented weather.</td>\n",
+       "      <td id=\"T_2ac72_row15_col3\" class=\"data row15 col3\" >Telemetry maximum 2059.024 m differs from notebook reported value 2098.020 m; both retained. | Valkyrie prose says 2025 but source environment says 2026 and fetches live Windy weather; no archived profile is supplied.</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x15c775e50>"
+       "<pandas.io.formats.style.Styler at 0x12325a5d0>"
       ]
      },
      "metadata": {},
@@ -1256,120 +1507,120 @@
       "text/html": [
        "<style type=\"text/css\">\n",
        "</style>\n",
-       "<table id=\"T_1460b\">\n",
+       "<table id=\"T_9cbc9\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_1460b_level0_col0\" class=\"col_heading level0 col0\" >rocket</th>\n",
-       "      <th id=\"T_1460b_level0_col1\" class=\"col_heading level0 col1\" >inferred_cd</th>\n",
-       "      <th id=\"T_1460b_level0_col2\" class=\"col_heading level0 col2\" >fit_apogee_residual_m</th>\n",
-       "      <th id=\"T_1460b_level0_col3\" class=\"col_heading level0 col3\" >condition</th>\n",
+       "      <th id=\"T_9cbc9_level0_col0\" class=\"col_heading level0 col0\" >rocket</th>\n",
+       "      <th id=\"T_9cbc9_level0_col1\" class=\"col_heading level0 col1\" >inferred_cd</th>\n",
+       "      <th id=\"T_9cbc9_level0_col2\" class=\"col_heading level0 col2\" >fit_apogee_residual_m</th>\n",
+       "      <th id=\"T_9cbc9_level0_col3\" class=\"col_heading level0 col3\" >condition</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_1460b_level0_row0\" class=\"row_heading level0 row0\" >4</th>\n",
-       "      <td id=\"T_1460b_row0_col0\" class=\"data row0 col0\" >andromeda</td>\n",
-       "      <td id=\"T_1460b_row0_col1\" class=\"data row0 col1\" >0.591</td>\n",
-       "      <td id=\"T_1460b_row0_col2\" class=\"data row0 col2\" >0.024</td>\n",
-       "      <td id=\"T_1460b_row0_col3\" class=\"data row0 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
+       "      <th id=\"T_9cbc9_level0_row0\" class=\"row_heading level0 row0\" >4</th>\n",
+       "      <td id=\"T_9cbc9_row0_col0\" class=\"data row0 col0\" >andromeda</td>\n",
+       "      <td id=\"T_9cbc9_row0_col1\" class=\"data row0 col1\" >0.591</td>\n",
+       "      <td id=\"T_9cbc9_row0_col2\" class=\"data row0 col2\" >0.024</td>\n",
+       "      <td id=\"T_9cbc9_row0_col3\" class=\"data row0 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1460b_level0_row1\" class=\"row_heading level0 row1\" >9</th>\n",
-       "      <td id=\"T_1460b_row1_col0\" class=\"data row1 col0\" >astra</td>\n",
-       "      <td id=\"T_1460b_row1_col1\" class=\"data row1 col1\" >0.599</td>\n",
-       "      <td id=\"T_1460b_row1_col2\" class=\"data row1 col2\" >0.003</td>\n",
-       "      <td id=\"T_1460b_row1_col3\" class=\"data row1 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
+       "      <th id=\"T_9cbc9_level0_row1\" class=\"row_heading level0 row1\" >9</th>\n",
+       "      <td id=\"T_9cbc9_row1_col0\" class=\"data row1 col0\" >astra</td>\n",
+       "      <td id=\"T_9cbc9_row1_col1\" class=\"data row1 col1\" >0.599</td>\n",
+       "      <td id=\"T_9cbc9_row1_col2\" class=\"data row1 col2\" >0.003</td>\n",
+       "      <td id=\"T_9cbc9_row1_col3\" class=\"data row1 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1460b_level0_row2\" class=\"row_heading level0 row2\" >14</th>\n",
-       "      <td id=\"T_1460b_row2_col0\" class=\"data row2 col0\" >bella_lui</td>\n",
-       "      <td id=\"T_1460b_row2_col1\" class=\"data row2 col1\" >0.446</td>\n",
-       "      <td id=\"T_1460b_row2_col2\" class=\"data row2 col2\" >0.005</td>\n",
-       "      <td id=\"T_1460b_row2_col3\" class=\"data row2 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
+       "      <th id=\"T_9cbc9_level0_row2\" class=\"row_heading level0 row2\" >14</th>\n",
+       "      <td id=\"T_9cbc9_row2_col0\" class=\"data row2 col0\" >bella_lui</td>\n",
+       "      <td id=\"T_9cbc9_row2_col1\" class=\"data row2 col1\" >0.446</td>\n",
+       "      <td id=\"T_9cbc9_row2_col2\" class=\"data row2 col2\" >0.005</td>\n",
+       "      <td id=\"T_9cbc9_row2_col3\" class=\"data row2 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1460b_level0_row3\" class=\"row_heading level0 row3\" >19</th>\n",
-       "      <td id=\"T_1460b_row3_col0\" class=\"data row3 col0\" >camoes</td>\n",
-       "      <td id=\"T_1460b_row3_col1\" class=\"data row3 col1\" >—</td>\n",
-       "      <td id=\"T_1460b_row3_col2\" class=\"data row3 col2\" >—</td>\n",
-       "      <td id=\"T_1460b_row3_col3\" class=\"data row3 col3\" >not fitted: active air-brake/controller model</td>\n",
+       "      <th id=\"T_9cbc9_level0_row3\" class=\"row_heading level0 row3\" >19</th>\n",
+       "      <td id=\"T_9cbc9_row3_col0\" class=\"data row3 col0\" >camoes</td>\n",
+       "      <td id=\"T_9cbc9_row3_col1\" class=\"data row3 col1\" >—</td>\n",
+       "      <td id=\"T_9cbc9_row3_col2\" class=\"data row3 col2\" >—</td>\n",
+       "      <td id=\"T_9cbc9_row3_col3\" class=\"data row3 col3\" >not fitted: active air-brake/controller model</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1460b_level0_row4\" class=\"row_heading level0 row4\" >24</th>\n",
-       "      <td id=\"T_1460b_row4_col0\" class=\"data row4 col0\" >cavour</td>\n",
-       "      <td id=\"T_1460b_row4_col1\" class=\"data row4 col1\" >0.609</td>\n",
-       "      <td id=\"T_1460b_row4_col2\" class=\"data row4 col2\" >0.001</td>\n",
-       "      <td id=\"T_1460b_row4_col3\" class=\"data row4 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
+       "      <th id=\"T_9cbc9_level0_row4\" class=\"row_heading level0 row4\" >24</th>\n",
+       "      <td id=\"T_9cbc9_row4_col0\" class=\"data row4 col0\" >cavour</td>\n",
+       "      <td id=\"T_9cbc9_row4_col1\" class=\"data row4 col1\" >0.609</td>\n",
+       "      <td id=\"T_9cbc9_row4_col2\" class=\"data row4 col2\" >0.001</td>\n",
+       "      <td id=\"T_9cbc9_row4_col3\" class=\"data row4 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1460b_level0_row5\" class=\"row_heading level0 row5\" >29</th>\n",
-       "      <td id=\"T_1460b_row5_col0\" class=\"data row5 col0\" >defiance</td>\n",
-       "      <td id=\"T_1460b_row5_col1\" class=\"data row5 col1\" >0.657</td>\n",
-       "      <td id=\"T_1460b_row5_col2\" class=\"data row5 col2\" >-0.063</td>\n",
-       "      <td id=\"T_1460b_row5_col3\" class=\"data row5 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
+       "      <th id=\"T_9cbc9_level0_row5\" class=\"row_heading level0 row5\" >29</th>\n",
+       "      <td id=\"T_9cbc9_row5_col0\" class=\"data row5 col0\" >defiance</td>\n",
+       "      <td id=\"T_9cbc9_row5_col1\" class=\"data row5 col1\" >0.657</td>\n",
+       "      <td id=\"T_9cbc9_row5_col2\" class=\"data row5 col2\" >-0.063</td>\n",
+       "      <td id=\"T_9cbc9_row5_col3\" class=\"data row5 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1460b_level0_row6\" class=\"row_heading level0 row6\" >34</th>\n",
-       "      <td id=\"T_1460b_row6_col0\" class=\"data row6 col0\" >erebus</td>\n",
-       "      <td id=\"T_1460b_row6_col1\" class=\"data row6 col1\" >0.443</td>\n",
-       "      <td id=\"T_1460b_row6_col2\" class=\"data row6 col2\" >-0.011</td>\n",
-       "      <td id=\"T_1460b_row6_col3\" class=\"data row6 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
+       "      <th id=\"T_9cbc9_level0_row6\" class=\"row_heading level0 row6\" >34</th>\n",
+       "      <td id=\"T_9cbc9_row6_col0\" class=\"data row6 col0\" >erebus</td>\n",
+       "      <td id=\"T_9cbc9_row6_col1\" class=\"data row6 col1\" >0.443</td>\n",
+       "      <td id=\"T_9cbc9_row6_col2\" class=\"data row6 col2\" >-0.011</td>\n",
+       "      <td id=\"T_9cbc9_row6_col3\" class=\"data row6 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1460b_level0_row7\" class=\"row_heading level0 row7\" >39</th>\n",
-       "      <td id=\"T_1460b_row7_col0\" class=\"data row7 col0\" >genesis</td>\n",
-       "      <td id=\"T_1460b_row7_col1\" class=\"data row7 col1\" >0.609</td>\n",
-       "      <td id=\"T_1460b_row7_col2\" class=\"data row7 col2\" >-0.003</td>\n",
-       "      <td id=\"T_1460b_row7_col3\" class=\"data row7 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
+       "      <th id=\"T_9cbc9_level0_row7\" class=\"row_heading level0 row7\" >39</th>\n",
+       "      <td id=\"T_9cbc9_row7_col0\" class=\"data row7 col0\" >genesis</td>\n",
+       "      <td id=\"T_9cbc9_row7_col1\" class=\"data row7 col1\" >0.609</td>\n",
+       "      <td id=\"T_9cbc9_row7_col2\" class=\"data row7 col2\" >-0.003</td>\n",
+       "      <td id=\"T_9cbc9_row7_col3\" class=\"data row7 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1460b_level0_row8\" class=\"row_heading level0 row8\" >44</th>\n",
-       "      <td id=\"T_1460b_row8_col0\" class=\"data row8 col0\" >halcyon</td>\n",
-       "      <td id=\"T_1460b_row8_col1\" class=\"data row8 col1\" >0.345</td>\n",
-       "      <td id=\"T_1460b_row8_col2\" class=\"data row8 col2\" >0.002</td>\n",
-       "      <td id=\"T_1460b_row8_col3\" class=\"data row8 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
+       "      <th id=\"T_9cbc9_level0_row8\" class=\"row_heading level0 row8\" >44</th>\n",
+       "      <td id=\"T_9cbc9_row8_col0\" class=\"data row8 col0\" >halcyon</td>\n",
+       "      <td id=\"T_9cbc9_row8_col1\" class=\"data row8 col1\" >0.345</td>\n",
+       "      <td id=\"T_9cbc9_row8_col2\" class=\"data row8 col2\" >0.002</td>\n",
+       "      <td id=\"T_9cbc9_row8_col3\" class=\"data row8 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1460b_level0_row9\" class=\"row_heading level0 row9\" >53</th>\n",
-       "      <td id=\"T_1460b_row9_col0\" class=\"data row9 col0\" >juno3</td>\n",
-       "      <td id=\"T_1460b_row9_col1\" class=\"data row9 col1\" >0.264</td>\n",
-       "      <td id=\"T_1460b_row9_col2\" class=\"data row9 col2\" >-0.000</td>\n",
-       "      <td id=\"T_1460b_row9_col3\" class=\"data row9 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
+       "      <th id=\"T_9cbc9_level0_row9\" class=\"row_heading level0 row9\" >53</th>\n",
+       "      <td id=\"T_9cbc9_row9_col0\" class=\"data row9 col0\" >juno3</td>\n",
+       "      <td id=\"T_9cbc9_row9_col1\" class=\"data row9 col1\" >0.264</td>\n",
+       "      <td id=\"T_9cbc9_row9_col2\" class=\"data row9 col2\" >-0.000</td>\n",
+       "      <td id=\"T_9cbc9_row9_col3\" class=\"data row9 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1460b_level0_row10\" class=\"row_heading level0 row10\" >58</th>\n",
-       "      <td id=\"T_1460b_row10_col0\" class=\"data row10 col0\" >lince</td>\n",
-       "      <td id=\"T_1460b_row10_col1\" class=\"data row10 col1\" >0.578</td>\n",
-       "      <td id=\"T_1460b_row10_col2\" class=\"data row10 col2\" >-0.016</td>\n",
-       "      <td id=\"T_1460b_row10_col3\" class=\"data row10 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
+       "      <th id=\"T_9cbc9_level0_row10\" class=\"row_heading level0 row10\" >58</th>\n",
+       "      <td id=\"T_9cbc9_row10_col0\" class=\"data row10 col0\" >lince</td>\n",
+       "      <td id=\"T_9cbc9_row10_col1\" class=\"data row10 col1\" >0.578</td>\n",
+       "      <td id=\"T_9cbc9_row10_col2\" class=\"data row10 col2\" >-0.016</td>\n",
+       "      <td id=\"T_9cbc9_row10_col3\" class=\"data row10 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1460b_level0_row11\" class=\"row_heading level0 row11\" >63</th>\n",
-       "      <td id=\"T_1460b_row11_col0\" class=\"data row11 col0\" >ndrt_2020</td>\n",
-       "      <td id=\"T_1460b_row11_col1\" class=\"data row11 col1\" >0.417</td>\n",
-       "      <td id=\"T_1460b_row11_col2\" class=\"data row11 col2\" >-0.002</td>\n",
-       "      <td id=\"T_1460b_row11_col3\" class=\"data row11 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
+       "      <th id=\"T_9cbc9_level0_row11\" class=\"row_heading level0 row11\" >63</th>\n",
+       "      <td id=\"T_9cbc9_row11_col0\" class=\"data row11 col0\" >ndrt_2020</td>\n",
+       "      <td id=\"T_9cbc9_row11_col1\" class=\"data row11 col1\" >0.417</td>\n",
+       "      <td id=\"T_9cbc9_row11_col2\" class=\"data row11 col2\" >-0.002</td>\n",
+       "      <td id=\"T_9cbc9_row11_col3\" class=\"data row11 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1460b_level0_row12\" class=\"row_heading level0 row12\" >68</th>\n",
-       "      <td id=\"T_1460b_row12_col0\" class=\"data row12 col0\" >prometheus_2022</td>\n",
-       "      <td id=\"T_1460b_row12_col1\" class=\"data row12 col1\" >0.403</td>\n",
-       "      <td id=\"T_1460b_row12_col2\" class=\"data row12 col2\" >-0.068</td>\n",
-       "      <td id=\"T_1460b_row12_col3\" class=\"data row12 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
+       "      <th id=\"T_9cbc9_level0_row12\" class=\"row_heading level0 row12\" >68</th>\n",
+       "      <td id=\"T_9cbc9_row12_col0\" class=\"data row12 col0\" >prometheus_2022</td>\n",
+       "      <td id=\"T_9cbc9_row12_col1\" class=\"data row12 col1\" >0.403</td>\n",
+       "      <td id=\"T_9cbc9_row12_col2\" class=\"data row12 col2\" >-0.068</td>\n",
+       "      <td id=\"T_9cbc9_row12_col3\" class=\"data row12 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1460b_level0_row13\" class=\"row_heading level0 row13\" >73</th>\n",
-       "      <td id=\"T_1460b_row13_col0\" class=\"data row13 col0\" >valetudo</td>\n",
-       "      <td id=\"T_1460b_row13_col1\" class=\"data row13 col1\" >0.810</td>\n",
-       "      <td id=\"T_1460b_row13_col2\" class=\"data row13 col2\" >-0.015</td>\n",
-       "      <td id=\"T_1460b_row13_col3\" class=\"data row13 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
+       "      <th id=\"T_9cbc9_level0_row13\" class=\"row_heading level0 row13\" >73</th>\n",
+       "      <td id=\"T_9cbc9_row13_col0\" class=\"data row13 col0\" >valetudo</td>\n",
+       "      <td id=\"T_9cbc9_row13_col1\" class=\"data row13 col1\" >0.810</td>\n",
+       "      <td id=\"T_9cbc9_row13_col2\" class=\"data row13 col2\" >-0.015</td>\n",
+       "      <td id=\"T_9cbc9_row13_col3\" class=\"data row13 col3\" >inferred: single constant CD, same powered/coast, ordinary lateral model</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x15c775e50>"
+       "<pandas.io.formats.style.Styler at 0x12325a5d0>"
       ]
      },
      "metadata": {},
diff --git a/docs/user/rocket/datcom.rst b/docs/user/rocket/datcom.rst
index 8ecc903bf..9852cb8f0 100644
--- a/docs/user/rocket/datcom.rst
+++ b/docs/user/rocket/datcom.rst
@@ -130,6 +130,15 @@ To model that known annulus instead, configure::
 This writes ``DEXIT=2*motor.nozzle_radius`` for the powered case. The nozzle
 must have positive radius and fit inside the aft body. ``"full"`` writes
 ``DEXIT=0.`` for a full-base diagnostic; ``"none"`` is the default.
+If the motor's radius is an unverified placeholder, supply the measured nozzle
+exit diameter independently, in meters::
+
+    datcom_options={"nozzle_exit_diameter": 0.03}
+
+An explicit diameter selects nozzle mode unless ``powered_base_drag`` is also
+specified; combining it with ``"none"`` or ``"full"`` raises an error. This
+changes only the aerodynamic input, leaving the motor's geometry and thrust
+calculations unchanged. Motor case diameter is not nozzle exit diameter.
 The coast case always uses ``DEXIT=0.`` for full base drag. ``BASE`` stays false:
 that flag controls jet/plume interaction, not the annular base-area calculation.
 Nozzle mode therefore requires no plume-flow inputs. Powered/coast selection
@@ -144,6 +153,18 @@ flight-condition or executable change rebuilds both cases. Re-add a modified
 motor to the rocket after changing its tanks, following the normal mass-property
 workflow.
 
+Native approximation and fallback messages are retained in
+``rocket.prepare_datcom().tables[False].diagnostics`` for coast, and
+``tables[True].diagnostics`` for powered flight. Some messages require native
+``PART`` output to be printed. The manual's section 4.2.2 explains that
+``CREST CRITICAL MACH NUMBER EXCEEDED`` selects approximate airfoil properties;
+it does not by itself mean the complete coefficient tables are invalid. These
+messages deserve inspection even when the executable finishes successfully.
+When parsing ``PART`` output, damping must come from the final total-vehicle
+tables, after the component tables (section 4.2.3). The parser selects these
+totals. A native regression independently reproduces the manual's two-fin-set
+worked example (Figures 19--21), including its published CN, CM and CA.
+
 Section Inputs and Flight Conditions
 ------------------------------------
 
@@ -171,6 +192,11 @@ stations from zero to one. All coordinate values are fractions of chord.
 This DATCOM version includes camber effects on normal force and pitch moment
 only for NACA and USER sections at Mach 0.8 and below. HEX/ARC camber affects
 drag calculations only.
+The airfoil section module assumes sharp trailing edges. According to the
+manual's section 4.2.2, non-sharp trailing edges are treated as an ideal airfoil
+approximation. A measured blunt plate supplied as USER coordinates therefore
+still needs validation; supplying its contour does not remove that native
+method limitation.
 
 Multi-station Planforms and Panel Placement
 -------------------------------------------
@@ -528,3 +554,21 @@ hardware. Smooth-surface roughness zero is an assumption, not a measured vinyl
 finish. Carbon fibre and vinyl do not determine these missing numerical inputs.
 Use measured geometry or the calibration API; an apogee-fitted roughness is an
 effective loss calibration, not an identified material property.
+
+The manual audit also found unverified nozzle inputs in the recorded-rocket
+examples: GENESIS supplies an 84.708 mm exit diameter through its motor radius,
+while the thrust-file header lists a 75 mm motor case; Bella Lui similarly
+supplies 88.9 mm for a listed 54 mm case. These dimensions need hardware
+verification, rather than automatic halving or substitution of case diameter.
+For GENESIS, even the full-powered-base-drag sensitivity run reduces apogee
+only from 3297 m to 3223 m, versus the recorded 2916 m. Nozzle-area uncertainty
+alone does not explain that discrepancy. The existing comparison decks omit
+``PART``, so correcting component-versus-total damping parsing does not change
+their saved results.
+
+Equivalent metric/English decks also expose a small native transonic
+sensitivity: for the controlled Andromeda check, CA at Mach 0.9 is 0.396
+versus 0.404, with matching values at Mach 0.3 and 0.6. The difference comes
+from body pressure/wave drag in the native partial output. The audit retains
+this numerical discrepancy; an independent transonic reference is needed to
+decide which prediction is more accurate.
diff --git a/rocketpy/rocket/datcom.py b/rocketpy/rocket/datcom.py
index 72ad311c4..5a110ae26 100644
--- a/rocketpy/rocket/datcom.py
+++ b/rocketpy/rocket/datcom.py
@@ -49,7 +49,14 @@ class DatcomResult(tuple):
     """Backward-compatible six-array result with body-axis and lateral tables."""
 
     def __new__(
-        cls, values, body=None, derivatives=None, damping=None, beta=None, sideslip=None
+        cls,
+        values,
+        body=None,
+        derivatives=None,
+        damping=None,
+        beta=None,
+        sideslip=None,
+        diagnostics=None,
     ):
         result = super().__new__(cls, values)
         result.body = {} if body is None else body
@@ -57,6 +64,7 @@ def __new__(
         result.damping = {} if damping is None else damping
         result.beta = np.array([0.0]) if beta is None else np.asarray(beta)
         result.sideslip = sideslip
+        result.diagnostics = [] if diagnostics is None else list(diagnostics)
         return result
 
 
@@ -76,6 +84,21 @@ def parse_datcom_output(base_dir, n_alpha_expected=None, _output=None):
     """
     output = Path(base_dir, "for006.dat").read_text() if _output is None else _output
     lines = output.splitlines()
+    diagnostic_terms = (
+        "CALCULATIONS IN SUBROUTINE",
+        "ANALYSIS FOR THIS AIRFOIL",
+        "CREST CRITICAL MACH NUMBER EXCEEDED",
+        "HYBRID THEORY INVALID",
+        "SECOND ORDER SHOCK EXPANSION TO BE USED",
+        "* WARNING *",
+    )
+    diagnostics = list(
+        dict.fromkeys(
+            line.strip()
+            for line in lines
+            if any(term in line for term in diagnostic_terms)
+        )
+    )
     if n_alpha_expected is None:
         declared = re.search(r"\bNALPHA\s*=\s*([0-9.]+)", output)
         if declared is not None:
@@ -120,12 +143,12 @@ def parse_datcom_output(base_dir, n_alpha_expected=None, _output=None):
             zero.damping,
             beta=list(tables),
             sideslip=tables,
+            diagnostics=diagnostics,
         )
 
-    def read_table(block, columns):
-        header = next(
-            (i for i, line in enumerate(block) if line.split() == columns), None
-        )
+    def read_table(block, columns, *, last=False):
+        headers = [i for i, line in enumerate(block) if line.split() == columns]
+        header = (headers[-1] if last else headers[0]) if headers else None
         if header is None:
             raise RuntimeError(f"DATCOM missing {' '.join(columns)} table")
         rows = []
@@ -175,11 +198,15 @@ def read_table(block, columns):
         lift_rows.append(lift[:, 1:])
         derivative_rows.append(derivatives[:, 1:])
         if "DYNAMIC DERIVATIVES" in "\n".join(block):
+            # PART prints body-alone damping before the assembled vehicle.
+            # The last pair belongs to the total configuration, like CN/CM/CA.
             longitudinal = read_table(
-                block, ["ALPHA", "CNQ", "CMQ", "CAQ", "CNAD", "CMAD"]
+                block, ["ALPHA", "CNQ", "CMQ", "CAQ", "CNAD", "CMAD"], last=True
             )
             lateral = read_table(
-                block, ["ALPHA", "CYR", "CLNR", "CLLR", "CYP", "CLNP", "CLLP"]
+                block,
+                ["ALPHA", "CYR", "CLNR", "CLLR", "CYP", "CLNP", "CLLP"],
+                last=True,
             )
             if not (
                 np.array_equal(body[:, 0], longitudinal[:, 0])
@@ -238,6 +265,7 @@ def read_table(block, columns):
         ),
         damping,
         beta=list(beta_blocks),
+        diagnostics=diagnostics,
     )
 
 
@@ -251,6 +279,7 @@ def _flight_conditions(options):
         "timeout",
         "body_length",
         "powered_base_drag",
+        "nozzle_exit_diameter",
         "reynolds",
     }
     if unknown:
@@ -363,9 +392,13 @@ def datcom_decks(rocket, *, calibration=None, flight_conditions=None):
     options.update(flight_conditions or {})
     calibration = rocket.datcom_calibration if calibration is None else calibration
     edge_radius = calibration["leading_edge_radius"]
-    powered_base_drag = options.get("powered_base_drag", "none")
+    powered_base_drag = options.get(
+        "powered_base_drag", "nozzle" if "nozzle_exit_diameter" in options else "none"
+    )
     if powered_base_drag not in ("none", "nozzle", "full"):
         raise ValueError("powered_base_drag must be 'none', 'nozzle' or 'full'")
+    if "nozzle_exit_diameter" in options and powered_base_drag != "nozzle":
+        raise ValueError("nozzle_exit_diameter requires powered_base_drag='nozzle'")
     mach, alpha, altitude, timeout = _flight_conditions(options)
 
     def canonical(value):
@@ -669,7 +702,17 @@ def body_radius(x):
         )
     exit_diameter = None
     if powered_base_drag == "nozzle":
-        exit_diameter = 2 * float(rocket.motor.nozzle_radius)
+        try:
+            raw_exit = options.get(
+                "nozzle_exit_diameter", 2 * rocket.motor.nozzle_radius
+            )
+            if np.ndim(raw_exit) != 0:
+                raise ValueError
+            exit_diameter = float(raw_exit)
+        except (TypeError, ValueError, OverflowError) as exc:
+            raise ValueError(
+                "Powered nozzle diameter must be a finite positive scalar"
+            ) from exc
         if not math.isfinite(exit_diameter) or not 0 < exit_diameter <= aft_diameter:
             raise ValueError(
                 "Powered nozzle diameter must be positive and fit inside the aft body"
@@ -836,8 +879,9 @@ def run_datcom(path, deck, timeout=60):
 class DatcomSurface(GenericSurface):
     """Whole-vehicle body-axis loads, including all eleven DAMP derivatives.
 
-    DATCOM uses forward/right/down axes. In RocketPy the force channels map to
-    (CY, -CN, -CA) and moments to (CM, CLN, CLL). Static lateral derivatives and
+    Native force channels map to RocketPy (CY, -CN, -CA), and moments to
+    (CM, CLN, CLL). DATCOM geometry coordinates increase aft from the nose;
+    RocketPy body z points toward the nose. Static lateral derivatives and
     DAMP derivatives in for006 are per degree. Rates are nondimensionalized by
     diameter/(2 V). Sideslip uses full tables when available, otherwise a
     linear expansion about beta=0.
diff --git a/tests/fixtures/surfaces/data/datcom/README.md b/tests/fixtures/surfaces/data/datcom/README.md
index 1bc55f25c..6aaad8ce6 100644
--- a/tests/fixtures/surfaces/data/datcom/README.md
+++ b/tests/fixtures/surfaces/data/datcom/README.md
@@ -6,3 +6,11 @@ and plume interaction are absent.
 
 This fixture checks parsing and coefficient-channel completeness. It is not
 an independently validated aerodynamic reference. No executable is included.
+
+
+`manual_example_for005.dat` independently transcribes the worked two-fin-set
+example in the Missile DATCOM Users Guide, revision 5/97, Figures 19–21
+(printed pages 65–67). It uses inches, Mach 2.36 and the published geometry,
+not RocketPy's deck generator. The optional native regression compares the
+published CN/CM/CA values and verifies that PART retains total-vehicle damping.
+The local 3/99 executable reproduces the published coefficient table.
diff --git a/tests/fixtures/surfaces/data/datcom/manual_example_for005.dat b/tests/fixtures/surfaces/data/datcom/manual_example_for005.dat
new file mode 100644
index 000000000..c37dfe775
--- /dev/null
+++ b/tests/fixtures/surfaces/data/datcom/manual_example_for005.dat
@@ -0,0 +1,16 @@
+DIM IN
+ $FLTCON NALPHA=8.,NMACH=1.,MACH=2.36,REN=3000000.,
+ ALPHA=0.,4.,8.,12.,16.,20.,24.,28.,$
+ $REFQ XCG=18.75,$
+ $AXIBOD LNOSE=11.25,DNOSE=3.75,LCENTR=26.25,DEXIT=0.,$
+ $FINSET1 XLE=15.42,NPANEL=2.,PHIF=90.,270.,SWEEP=0.,STA=1.,
+ CHORD=6.96,0.,SSPAN=1.875,5.355,
+ ZUPPER=0.02238,0.02238,LMAXU=0.238,0.238,
+ LFLATU=0.524,0.524,LER=0.015,0.015,$
+ $FINSET2 XLE=31.915,NPANEL=4.,PHIF=0.,90.,180.,270.,LER=0.015,0.015,
+ SWEEP=0.,STA=1.,SSPAN=1.875,6.26,CHORD=5.585,2.792,
+ ZUPPER=0.02238,0.02238,LMAXU=0.288,0.288,LFLATU=0.428,0.428,$
+PART
+DAMP
+SOSE
+NEXT CASE
diff --git a/tests/integration/test_datcom.py b/tests/integration/test_datcom.py
index 24e0a3179..1b6995d6b 100644
--- a/tests/integration/test_datcom.py
+++ b/tests/integration/test_datcom.py
@@ -161,7 +161,7 @@ def test_parse_datcom_output_rejects_incomplete_data(replacement, message, tmp_p
 
 
 @pytest.mark.parametrize(
-    "failure", [None, "exit", "timeout", "missing", "input", "grid"]
+    "failure", [None, "exit", "timeout", "missing", "input", "grid", "airfoil"]
 )
 def test_run_datcom_temp_directory_cleanup(failure, tmp_path):
     """Executable/deck copies and all outputs are removed on success and every failure."""
@@ -188,18 +188,25 @@ def execute(argv, cwd, **kwargs):
             shutil.copyfile(SAMPLE, directory / "for006.dat")
         if failure == "input":
             (directory / "for006.dat").write_text("** ERROR ** 1*F")
+        if failure == "airfoil":
+            with (directory / "for006.dat").open("a") as stream:
+                stream.write("\n*** ANALYSIS FOR THIS AIRFOIL IMPOSSIBLE.\n")
         return SimpleNamespace(
             returncode=1 if failure == "exit" else 0, stderr="failure"
         )
 
     # Act
     with patch("rocketpy.rocket.datcom.subprocess.run", side_effect=execute):
-        if failure:
+        if failure and failure != "airfoil":
             with pytest.raises(RuntimeError):
                 run_datcom(source, deck, timeout=2)
         else:
             parsed = run_datcom(source, deck, timeout=2)
             assert parsed[0].shape == (2, 3)
+            if failure == "airfoil":
+                assert any(
+                    "AIRFOIL IMPOSSIBLE" in message for message in parsed.diagnostics
+                )
     # Assert
     assert len(working_directories) == 1
     assert not working_directories[0].exists()
@@ -548,3 +555,46 @@ def test_datcom_native_maximum_adaptive_grid(datcom_rocket):
         assert len(model.tables[state][4]) == 20
         assert model.tables[state][4] == pytest.approx(mach, abs=0.005)
         assert model.tables[state][5] == pytest.approx(alpha, abs=0.005)
+
+
+@pytest.mark.slow
+def test_datcom_partial_output_preserves_total_damping(datcom_rocket):
+    """PART adds component tables but must not replace whole-vehicle DAMP data."""
+    # Arrange
+    from rocketpy.rocket.datcom import datcom_decks
+
+    executable = os.environ.get("DATCOM_EXECUTABLE")
+    if not executable:
+        pytest.skip("Set DATCOM_EXECUTABLE to the target 3/99 executable")
+    deck = datcom_decks(datcom_rocket)[0][False]
+    baseline = run_datcom(executable, deck)
+    # Act
+    partial = run_datcom(executable, deck.replace("NEXT CASE", "PART\nNEXT CASE"))
+    # Assert
+    for key in DAMPING_KEYS:
+        assert partial.damping[key] == pytest.approx(baseline.damping[key])
+
+
+@pytest.mark.slow
+def test_datcom_native_manual_worked_example():
+    """The independent published example reproduces its total CN/CM/CA table."""
+    # Arrange
+    executable = os.environ.get("DATCOM_EXECUTABLE")
+    if not executable:
+        pytest.skip("Set DATCOM_EXECUTABLE to the target 3/99 executable")
+    deck = (SAMPLE.parent / "manual_example_for005.dat").read_text()
+    # Act
+    result = run_datcom(executable, deck)
+    # Assert
+    plain = run_datcom(executable, deck.replace("PART\n", ""))
+    for key in DAMPING_KEYS:
+        assert result.damping[key] == pytest.approx(plain.damping[key])
+    assert result.body["CN"][0] == pytest.approx(
+        [0, 1.179, 2.476, 3.992, 5.582, 7.151, 8.601, 10.093], abs=0.002
+    )
+    assert result.body["CA"][0] == pytest.approx(
+        [0.466, 0.465, 0.463, 0.458, 0.453, 0.445, 0.436, 0.426], abs=0.002
+    )
+    assert result.body["CM"][0] == pytest.approx(
+        [0, -1.577, -3.194, -5.056, -6.962, -8.904, -10.792, -12.692], abs=0.002
+    )
diff --git a/tests/unit/rocket/test_datcom.py b/tests/unit/rocket/test_datcom.py
index bcf46d223..8c62b0cd2 100644
--- a/tests/unit/rocket/test_datcom.py
+++ b/tests/unit/rocket/test_datcom.py
@@ -2,6 +2,7 @@
 
 import json
 import re
+from pathlib import Path
 from types import SimpleNamespace
 from unittest.mock import patch
 
@@ -605,3 +606,66 @@ def test_datcom_namelist_scientific_notation_uppercase():
     assert "1.E-05" in text
     assert "1.25E-05" in text
     assert "e" not in text
+
+
+def test_datcom_exit_diameter_override_preserves_motor(datcom_rocket):
+    """Measured aerodynamic exit diameter can correct a legacy motor placeholder."""
+    # Arrange
+    fins = datcom_rocket.fins[0]
+    original = datcom_rocket.motor.nozzle_radius
+    fins.datcom_options["nozzle_exit_diameter"] = 0.03
+    # Act
+    decks, _, _, _ = datcom_decks(datcom_rocket)
+    # Assert
+    assert _deck_value(decks[True], "DEXIT") == pytest.approx(0.03)
+    assert _deck_value(decks[False], "DEXIT") == 0
+    assert datcom_rocket.motor.nozzle_radius == original
+
+
+@pytest.mark.parametrize("diameter", [-0.01, 0, float("nan"), 0.5, None, [0.02, 0.03]])
+def test_datcom_exit_diameter_override_invalid(datcom_rocket, diameter):
+    """A nozzle override must be finite, positive and fit the body base."""
+    # Arrange
+    datcom_rocket.fins[0].datcom_options["nozzle_exit_diameter"] = diameter
+    # Act / Assert
+    with pytest.raises(ValueError, match="Powered nozzle diameter"):
+        datcom_decks(datcom_rocket)
+
+
+@pytest.mark.parametrize("mode", ["none", "full"])
+def test_datcom_exit_diameter_override_conflicting_mode(datcom_rocket, mode):
+    """An explicit nozzle opening cannot be silently ignored by another mode."""
+    # Arrange
+    datcom_rocket.fins[0].datcom_options.update(
+        nozzle_exit_diameter=0.03, powered_base_drag=mode
+    )
+    # Act / Assert
+    with pytest.raises(ValueError, match="requires powered_base_drag='nozzle'"):
+        datcom_decks(datcom_rocket)
+
+
+def test_datcom_partial_component_tables_cannot_replace_total_damping(tmp_path):
+    """Component DAMP rows printed by PART precede the total, which must win."""
+    # Arrange
+    sample = Path(__file__).parents[2] / "fixtures/surfaces/data/datcom/for006.dat"
+    raw = sample.read_text()
+    expected = parse_datcom_output(tmp_path, _output=raw)
+    rows = []
+    for line in raw.splitlines():
+        columns = line.split()
+        if columns in (
+            ["ALPHA", "CNQ", "CMQ", "CAQ", "CNAD", "CMAD"],
+            ["ALPHA", "CYR", "CLNR", "CLLR", "CYP", "CLNP", "CLLP"],
+        ):
+            rows.append(line)
+            rows.extend(
+                f"{alpha} " + " ".join(["999"] * (len(columns) - 1))
+                for alpha in [-2, 0, 2]
+            )
+            rows.append("")
+        rows.append(line)
+    # Act
+    actual = parse_datcom_output(tmp_path, _output="\n".join(rows))
+    # Assert
+    for key in DAMPING_KEYS:
+        assert actual.damping[key] == pytest.approx(expected.damping[key])

From 8dd997413edf80fa8a964aa57d69e8c14fd53179 Mon Sep 17 00:00:00 2001
From: UnforgettableCassini <66637599+AdaBatu@users.noreply.github.com>
Date: Fri, 30 Oct 2026 17:02:07 +0100
Subject: [PATCH 3/4] WIP: More audits with datcom

---
 docs/examples/datcom/README.md                |  46 +++
 .../audit_reference_examples.py               | 294 ++++++++++++++++++
 .../manual_generated_for005.dat               |  55 ++++
 .../reference_example_audit.json              | 226 ++++++++++++++
 tests/integration/test_datcom.py              |  82 ++++-
 5 files changed, 702 insertions(+), 1 deletion(-)
 create mode 100644 docs/examples/datcom/comparison_data/audit_reference_examples.py
 create mode 100644 docs/examples/datcom/comparison_data/manual_generated_for005.dat
 create mode 100644 docs/examples/datcom/comparison_data/reference_example_audit.json

diff --git a/docs/examples/datcom/README.md b/docs/examples/datcom/README.md
index 12bae109f..f12d8b540 100644
--- a/docs/examples/datcom/README.md
+++ b/docs/examples/datcom/README.md
@@ -135,3 +135,49 @@ chord fractions, `LER` is a dimensional radius, and `SSPAN` is normally from
 the centreline; our `SSPAN(1)=0` convention correctly requests exposed-span
 automatic body attachment. These conversions were checked. The hardware
 section values remain unknown, and no fit or default change is applied.
+
+## Published Missile DATCOM examples versus this integration
+
+The [reference-example audit](comparison_data/reference_example_audit.json)
+compares the 1997 guide's worked body/two-fin-set case (Figures 19–21,
+printed pages 65–67) with geometry rebuilt through RocketPy's public API,
+and checks the documented
+[Ansys MissileDatCom wrapper example](https://ansyshelp.ansys.com/public/Views/Secured/ModelCenter/v252/en/ModelCenter_Rem_Exec/ModelCenter_Rem_Exec/mcre_functions/wrappers_components/FileWrapper/examples/ref_fileWrapper_example_missiledatcom.html).
+Run it with:
+
+```sh
+python docs/examples/datcom/comparison_data/audit_reference_examples.py
+```
+
+[The generated manual-case deck](comparison_data/manual_generated_for005.dat)
+is the unmodified output of the geometry generator. The audit adds `SOSE`
+to a copy for the matching-method comparison; no production defaults or
+recorded-rocket predictions change.
+
+| Item | Published examples | Our implementation and result |
+| --- | --- | --- |
+| Geometry and units | Guide: inch dimensions, centreline semispans, trailing-edge sweep and REN per foot | API: metres, exposed span, explicit leading-edge coordinates and REN per metre. With the same body method, all eight published CN/CM/CA rows match at printed precision. |
+| Body method | The guide selects `SOSE`; the Ansys demonstration selects `HYPER` | The generator currently leaves the native default and exposes no method selector. For the guide case at Mach 2.36, default CA at zero incidence is 0.460 versus 0.466 with SOSE; CM at 4 degrees is -1.462 versus -1.577. |
+| Fin thickness arrays | Ansys specifies only `ZUPPER=0.04`; native effective total thickness is 0.066 | Explicit root and tip values of 0.04 give 0.080 total thickness. Our scalar section arguments intentionally repeat at every station, and total thickness is divided between upper/lower surfaces. A single low-level DATCOM array value does not imply that repetition. |
+| Reference quantities | Ansys uses SREF=346.4 square inches and LREF=21 inches for a 20-inch body | We use frontal area and diameter consistently in both native coefficients and physical loads. Different coefficient normalization must be converted before comparison. The Ansys zero-angle CA changes from 0.611 to 0.673 when using frontal area. |
+| Automatic trim | Ansys uses `$TRIM`, which changes panel deflections | Our passive flight model uses specified fixed incidence. In the local reproduction at 10 degrees, trimmed CD is 0.736 and untrimmed CD is 0.711. Trim output is a different table format and is rejected by our passive-table parser. |
+| Surface/atmosphere | Ansys specifies roughness, turbulent flow and sea-level altitude | We default to smooth/turbulent flow and can use actual-atmosphere REN schedules. The example's finish and altitude are inputs for another configuration, not calibration values for our rockets. |
+| Multiple cases | `SAVE` carries selected input namelists into the next case | We repeat complete fixed-beta cases without `SAVE`, avoiding inherited geometry or flow settings. Both are supported native input patterns. |
+
+An additional native numerical sensitivity appears in derivatives. Adding a
+negative-alpha row to the guide's original 0–28 degree schedule changes CNA
+at zero from 0.2802 to 0.2948 per degree and changes some DAMP derivatives,
+even though the positive-angle CN/CM/CA rows remain unchanged. Therefore,
+damping comparisons must also match the alpha schedule. After matching
+method and grids, the generated and independent reference damping tables
+agree within 0.001 at printed precision. An optional native regression now
+checks this complete geometry conversion, rather than only the hand-written
+reference deck and parser.
+
+The missing method selector matters for reproducing supersonic examples.
+However, SOSE produced no CA/CN/CM change in controlled Mach 0.3–1.2 probes
+for GENESIS, Andromeda or Astra. All 11 paired native flight peaks are below
+Mach 1.04, so this finding does not resolve their apogee discrepancies.
+Fin section/edge geometry and surface finish in those models remain assumed.
+Reproducing these examples verifies input conversion and native execution;
+it does not independently validate full-flight accuracy against hardware.
diff --git a/docs/examples/datcom/comparison_data/audit_reference_examples.py b/docs/examples/datcom/comparison_data/audit_reference_examples.py
new file mode 100644
index 000000000..243a929e6
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/audit_reference_examples.py
@@ -0,0 +1,294 @@
+"""Compare published Missile DATCOM examples with RocketPy-generated geometry.
+
+The manual example is recreated through the public Rocket/fin APIs. SOSE is
+added to the raw deck only for the matching-method check; no production default
+or measured-rocket comparison is changed. The Ansys wrapper demonstration is
+reconstructed from its documented numerical inputs to inspect native defaults.
+"""
+
+import hashlib
+import json
+import os
+from pathlib import Path
+import re
+import shutil
+import subprocess
+import tempfile
+
+import numpy as np
+
+import model_sources as sources
+from rocketpy import Rocket
+from rocketpy.rocket.datcom import (
+    DAMPING_KEYS,
+    _namelist,
+    datcom_decks,
+    parse_datcom_output,
+)
+
+OUT = Path(__file__).resolve().parent
+EXE = Path(
+    os.environ.get(
+        "DATCOM_EXECUTABLE",
+        sources.ROOT.parent / "Asteria-Flight-Dynamics/works/dat0/datcom",
+    )
+)
+ANSYS_URL = "https://ansyshelp.ansys.com/public/Views/Secured/ModelCenter/v252/en/ModelCenter_Rem_Exec/ModelCenter_Rem_Exec/mcre_functions/wrappers_components/FileWrapper/examples/ref_fileWrapper_example_missiledatcom.html"
+
+
+def native_text(deck):
+    """Read diagnostic/trim output independently, retaining no executable copy."""
+    with tempfile.TemporaryDirectory(prefix="rocketpy-reference-review-") as directory:
+        binary = Path(directory, "datcom")
+        shutil.copy2(EXE, binary)
+        Path(directory, "for005.dat").write_text(deck, encoding="ascii")
+        subprocess.run(
+            [str(binary)], cwd=directory, capture_output=True, timeout=60, check=True
+        )
+        raw = Path(directory, "for006.dat").read_text()
+    if "** ERROR **" in raw:
+        raise RuntimeError("Native input error in reference probe")
+    return raw
+
+
+def tables(deck):
+    return parse_datcom_output(".", _output=native_text(deck))
+
+
+def manual_model():
+    inch = 0.0254
+    rocket = Rocket(
+        radius=3.75 * inch / 2,
+        mass=1,
+        inertia=(1, 1, 1),
+        power_off_drag=0,
+        power_on_drag=0,
+        center_of_mass_without_motor=18.75 * inch,
+    )
+    rocket.add_nose(length=11.25 * inch, kind="ogive", position=37.5 * inch)
+    options = dict(
+        mach=[2.35, 2.36],
+        alpha=[-4, 0, 4, 8, 12, 16, 20, 24, 28],
+        beta=[0],
+        reynolds=3e6 / 0.3048,
+        body_length=26.25 * inch,
+    )
+    for n, xle, root, tip, span, maximum, flat, angles in [
+        (2, 15.42, 6.96, 0, 3.48, 0.238, 0.524, [90, 270]),
+        (4, 31.915, 5.585, 2.792, 4.385, 0.288, 0.428, [0, 90, 180, 270]),
+    ]:
+        rocket.add_datcom_hex_fins(
+            n=n,
+            root_chord=root * inch,
+            tip_chord=tip * inch,
+            span=span * inch,
+            position=(37.5 - xle) * inch,
+            sweep_length=(root - tip) * inch,
+            path=EXE,
+            thickness_to_chord=0.04476,
+            max_thickness_position=maximum,
+            flat_length=flat,
+            leading_edge_radius=0.015 * inch,
+            panel_angles=angles,
+            datcom_options=options,
+        )
+    return rocket
+
+
+def ansys_deck(upper):
+    return "\n".join(
+        [
+            "DIM IN",
+            "HYPER",
+            _namelist(
+                "FLTCON",
+                [
+                    ("NMACH", 1),
+                    ("MACH", 1),
+                    ("ALT", 0),
+                    ("NALPHA", 2),
+                    ("ALPHA", [0, 10]),
+                    ("BETA", 0),
+                ],
+            ),
+            _namelist(
+                "REFQ",
+                [
+                    ("XCG", 81),
+                    ("LREF", 21),
+                    ("SREF", 346.4),
+                    ("SCALE", 1),
+                    ("ROUGH", 2.5e-4),
+                    ("BLAYER", "TURB"),
+                ],
+            ),
+            _namelist(
+                "AXIBOD",
+                [
+                    ("TNOSE", "CONE"),
+                    ("LNOSE", 20),
+                    ("DNOSE", 20),
+                    ("LCENTR", 140),
+                    ("DCENTR", 20),
+                    ("TAFT", "CONE"),
+                    ("LAFT", 20),
+                    ("DAFT", 20),
+                    ("DEXIT", 16),
+                ],
+            ),
+            _namelist(
+                "FINSET1",
+                [
+                    ("SECTYP", "ARC"),
+                    ("ZUPPER", upper),
+                    ("LMAXU", [0.4, 0.4]),
+                    ("LFLATU", [0, 0]),
+                    ("SSPAN", [10, 15]),
+                    ("CHORD", [20, 10.8]),
+                    ("SWEEP", 30),
+                    ("XLE", 0),
+                    ("STA", 0),
+                    ("NPANEL", 4),
+                    ("PHIF", [45, 135, 225, 315]),
+                ],
+            ),
+            _namelist(
+                "TRIM", [("SET", 1), *[(f"PANL{i}", ".TRUE.") for i in range(1, 5)]]
+            ),
+            "PART",
+            "DAMP",
+            "SAVE",
+            "NEXT CASE",
+            "",
+        ]
+    )
+
+
+def main():
+    reference = (
+        sources.ROOT / "tests/fixtures/surfaces/data/datcom/manual_example_for005.dat"
+    ).read_text()
+    published = tables(reference)
+    deck = datcom_decks(manual_model())[0][False]
+    (OUT / "manual_generated_for005.dat").write_text(deck)
+    generated = tables(deck)
+    sose = tables(deck.replace("NEXT CASE", "SOSE\nPART\nNEXT CASE"))
+    aligned_conditions = _namelist(
+        "FLTCON",
+        [
+            ("NMACH", 2),
+            ("MACH", [2.35, 2.36]),
+            ("REN", [3e6, 3e6]),
+            ("NALPHA", 9),
+            ("ALPHA", [-4, 0, 4, 8, 12, 16, 20, 24, 28]),
+            ("BETA", 0),
+        ],
+    )
+    aligned = tables(
+        re.sub(r"\$FLTCON.*?\$", lambda _: aligned_conditions, reference, flags=re.S)
+    )
+    result = {
+        "executable_sha256": hashlib.sha256(EXE.read_bytes()).hexdigest(),
+        "scope": "Reference-example audit; original flight comparisons and production defaults unchanged",
+        "manual_source": "Missile DATCOM Users Guide, Figures 19-21, printed pp. 65-67",
+        "manual_source_units": "DIM IN; REN per foot",
+        "generated_units": "DIM M; REN per metre; identical physical reference area/length/CG",
+        "manual_comparison": {},
+        "ansys_source": ANSYS_URL,
+        "ansys_sections": [],
+        "low_mach_method_checks": [],
+    }
+    for key in ["CA", "CN", "CM"]:
+        expected = published.body[key][0]
+        matched = sose.body[key][-1, 1:]
+        result["manual_comparison"][key] = dict(
+            alpha_deg=published[5].tolist(),
+            published=expected.tolist(),
+            generated_default=generated.body[key][-1, 1:].tolist(),
+            generated_with_sose=matched.tolist(),
+            max_difference_same_method=float(np.max(np.abs(expected - matched))),
+        )
+        np.testing.assert_allclose(matched, expected, atol=0.002, rtol=0)
+    result["manual_damping_grid_effect"] = {
+        "change": "Add alpha=-4 degrees ahead of the published 0..28 schedule; extra Mach 2.35 alone was checked to have no effect",
+        "cna_at_zero_original_per_degree": float(published.derivatives["CNA"][0, 0]),
+        "cna_at_zero_signed_per_degree": float(aligned.derivatives["CNA"][-1, 1]),
+        "max_dynamic_difference_by_channel": {
+            k: float(
+                np.max(np.abs(published.damping[k][0] - aligned.damping[k][-1, 1:]))
+            )
+            for k in DAMPING_KEYS
+        },
+    }
+    result["manual_damping_max_difference_same_method_and_grid"] = max(
+        float(np.max(np.abs(aligned.damping[k][-1] - sose.damping[k][-1])))
+        for k in DAMPING_KEYS
+    )
+    assert result["manual_damping_max_difference_same_method_and_grid"] <= 0.002
+    for label, upper in [
+        ("root_only_as_published", [0.04]),
+        ("root_and_tip_explicit", [0.04, 0.04]),
+    ]:
+        raw = native_text(ansys_deck(upper))
+        thickness = float(
+            re.search(r"MAXIMUM AIRFOIL THICKNESS\s*=\s*([\d.]+)", raw)[1]
+        )
+        result["ansys_sections"].append(
+            dict(
+                variant=label,
+                zupper=upper,
+                effective_total_thickness_to_chord=thickness,
+                output_kind="trimmed coefficients; unsupported by the passive whole-vehicle table parser",
+            )
+        )
+    untrimmed = re.sub(r"\$TRIM.*?\$", "", ansys_deck([0.04]), flags=re.S)
+    native = tables(untrimmed)
+    normalized = tables(
+        untrimmed.replace("SREF = 346.4", f"SREF = {np.pi * 10**2:.10f}").replace(
+            "LREF = 21.", "LREF = 20."
+        )
+    )
+    result["ansys_reference_normalization"] = dict(
+        area_ratio=346.4 / (np.pi * 10**2),
+        example_ca_alpha0=float(native.body["CA"][0, 0]),
+        frontal_area_ca_alpha0=float(normalized.body["CA"][0, 0]),
+        untrimmed_cd_alpha10=float(native[1][0, 1]),
+        published_trimmed_cd_alpha10=0.736,
+        note="Three-decimal coefficient printing limits normalization comparisons; trimmed and untrimmed CD are different states",
+    )
+    for name in ["genesis", "andromeda", "astra"]:
+        source = (OUT / "rockets" / f"{name}_coast_for005.dat").read_text().split(
+            "NEXT CASE"
+        )[2] + "NEXT CASE\n"
+        # Minimal same-atmosphere subsonic/transonic probe: only method changes.
+        flight = _namelist(
+            "FLTCON",
+            [
+                ("NMACH", 5),
+                ("MACH", [0.3, 0.6, 0.9, 1.05, 1.2]),
+                ("REN", np.array([0.3, 0.6, 0.9, 1.05, 1.2]) * 2e7),
+                ("NALPHA", 3),
+                ("ALPHA", [-2, 0, 2]),
+                ("BETA", 0),
+            ],
+        )
+        source = re.sub(r"\$FLTCON.*?\$", lambda _: flight, source, flags=re.S)
+        a = tables(source)
+        b = tables(source.replace("NEXT CASE", "SOSE\nNEXT CASE"))
+        result["low_mach_method_checks"].append(
+            dict(
+                rocket=name,
+                mach=a[4].tolist(),
+                max_ca_difference=float(np.abs(a.body["CA"] - b.body["CA"]).max()),
+                max_cn_difference=float(np.abs(a.body["CN"] - b.body["CN"]).max()),
+                max_cm_difference=float(np.abs(a.body["CM"] - b.body["CM"]).max()),
+            )
+        )
+    (OUT / "reference_example_audit.json").write_text(
+        json.dumps(result, indent=2) + "\n"
+    )
+    print(json.dumps(result, indent=2))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/examples/datcom/comparison_data/manual_generated_for005.dat b/docs/examples/datcom/comparison_data/manual_generated_for005.dat
new file mode 100644
index 000000000..0ab57f882
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/manual_generated_for005.dat
@@ -0,0 +1,55 @@
+DIM M
+ $FLTCON
+  NMACH = 2.,
+  MACH = 2.35,2.36,
+  REN = 9842519.69,9842519.69,
+  NALPHA = 9.,
+  ALPHA = -4.,0.,4.,8.,12.,16.,20.,24.,28.,
+  BETA = 0.,$
+ $REFQ
+  XCG = 0.47625,
+  SREF = 0.00712557392,
+  LREF = 0.09525,$
+ $AXIBOD
+  TNOSE = OGIVE,
+  LNOSE = 0.28575,
+  DNOSE = 0.09525,
+  LCENTR = 0.66675,
+  DCENTR = 0.09525,
+  BASE = .FALSE.,
+  DEXIT = 0.,$
+ $FINSET1
+  XLE = 0.391668,0.568452,
+  NPANEL = 2.,
+  PHIF = 90.,270.,
+  GAM = 0.,0.,
+  SSPAN = 0.,0.088392,
+  CHORD = 0.176784,0.,
+  SECTYP = HEX,
+  ZUPPER = 0.02238,0.02238,
+  ZLOWER = 0.02238,0.02238,
+  LER = 0.000381,0.000381,
+  LMAXU = 0.238,0.238,
+  LFLATU = 0.524,0.524,
+  LMAXL = 0.238,0.238,
+  LFLATL = 0.524,0.524,$
+ $FINSET2
+  XLE = 0.810641,0.8815832,
+  NPANEL = 4.,
+  PHIF = 0.,90.,180.,270.,
+  GAM = 0.,0.,0.,0.,
+  SSPAN = 0.,0.111379,
+  CHORD = 0.141859,0.0709168,
+  SECTYP = HEX,
+  ZUPPER = 0.02238,0.02238,
+  ZLOWER = 0.02238,0.02238,
+  LER = 0.000381,0.000381,
+  LMAXU = 0.288,0.288,
+  LFLATU = 0.428,0.428,
+  LMAXL = 0.288,0.288,
+  LFLATL = 0.428,0.428,$
+ $DEFLCT
+  DELTA1 = 0.,0.,
+  DELTA2 = 0.,0.,0.,0.,$
+DAMP
+NEXT CASE
diff --git a/docs/examples/datcom/comparison_data/reference_example_audit.json b/docs/examples/datcom/comparison_data/reference_example_audit.json
new file mode 100644
index 000000000..630a15bb3
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/reference_example_audit.json
@@ -0,0 +1,226 @@
+{
+  "executable_sha256": "96dfeae8a1ffca2eb82c44be5245f45da0dddfcfbcb5250653f07cfeefa5c22b",
+  "scope": "Reference-example audit; original flight comparisons and production defaults unchanged",
+  "manual_source": "Missile DATCOM Users Guide, Figures 19-21, printed pp. 65-67",
+  "manual_source_units": "DIM IN; REN per foot",
+  "generated_units": "DIM M; REN per metre; identical physical reference area/length/CG",
+  "manual_comparison": {
+    "CA": {
+      "alpha_deg": [
+        0.0,
+        4.0,
+        8.0,
+        12.0,
+        16.0,
+        20.0,
+        24.0,
+        28.0
+      ],
+      "published": [
+        0.466,
+        0.465,
+        0.463,
+        0.458,
+        0.453,
+        0.445,
+        0.436,
+        0.426
+      ],
+      "generated_default": [
+        0.46,
+        0.459,
+        0.457,
+        0.453,
+        0.447,
+        0.44,
+        0.431,
+        0.421
+      ],
+      "generated_with_sose": [
+        0.466,
+        0.465,
+        0.463,
+        0.458,
+        0.453,
+        0.445,
+        0.436,
+        0.426
+      ],
+      "max_difference_same_method": 0.0
+    },
+    "CN": {
+      "alpha_deg": [
+        0.0,
+        4.0,
+        8.0,
+        12.0,
+        16.0,
+        20.0,
+        24.0,
+        28.0
+      ],
+      "published": [
+        0.0,
+        1.179,
+        2.476,
+        3.992,
+        5.582,
+        7.151,
+        8.601,
+        10.093
+      ],
+      "generated_default": [
+        0.0,
+        1.154,
+        2.427,
+        3.92,
+        5.488,
+        7.038,
+        8.472,
+        9.949
+      ],
+      "generated_with_sose": [
+        0.0,
+        1.179,
+        2.476,
+        3.992,
+        5.582,
+        7.151,
+        8.601,
+        10.093
+      ],
+      "max_difference_same_method": 0.0
+    },
+    "CM": {
+      "alpha_deg": [
+        0.0,
+        4.0,
+        8.0,
+        12.0,
+        16.0,
+        20.0,
+        24.0,
+        28.0
+      ],
+      "published": [
+        0.0,
+        -1.577,
+        -3.194,
+        -5.056,
+        -6.962,
+        -8.904,
+        -10.792,
+        -12.692
+      ],
+      "generated_default": [
+        0.0,
+        -1.462,
+        -2.967,
+        -4.723,
+        -6.531,
+        -8.383,
+        -10.194,
+        -12.031
+      ],
+      "generated_with_sose": [
+        0.0,
+        -1.577,
+        -3.194,
+        -5.056,
+        -6.962,
+        -8.904,
+        -10.792,
+        -12.692
+      ],
+      "max_difference_same_method": 0.0
+    }
+  },
+  "ansys_source": "https://ansyshelp.ansys.com/public/Views/Secured/ModelCenter/v252/en/ModelCenter_Rem_Exec/ModelCenter_Rem_Exec/mcre_functions/wrappers_components/FileWrapper/examples/ref_fileWrapper_example_missiledatcom.html",
+  "ansys_sections": [
+    {
+      "variant": "root_only_as_published",
+      "zupper": [
+        0.04
+      ],
+      "effective_total_thickness_to_chord": 0.066,
+      "output_kind": "trimmed coefficients; unsupported by the passive whole-vehicle table parser"
+    },
+    {
+      "variant": "root_and_tip_explicit",
+      "zupper": [
+        0.04,
+        0.04
+      ],
+      "effective_total_thickness_to_chord": 0.08,
+      "output_kind": "trimmed coefficients; unsupported by the passive whole-vehicle table parser"
+    }
+  ],
+  "low_mach_method_checks": [
+    {
+      "rocket": "genesis",
+      "mach": [
+        0.3,
+        0.6,
+        0.9,
+        1.05,
+        1.2
+      ],
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0,
+      "max_cm_difference": 0.0
+    },
+    {
+      "rocket": "andromeda",
+      "mach": [
+        0.3,
+        0.6,
+        0.9,
+        1.05,
+        1.2
+      ],
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0,
+      "max_cm_difference": 0.0
+    },
+    {
+      "rocket": "astra",
+      "mach": [
+        0.3,
+        0.6,
+        0.9,
+        1.05,
+        1.2
+      ],
+      "max_ca_difference": 0.0,
+      "max_cn_difference": 0.0,
+      "max_cm_difference": 0.0
+    }
+  ],
+  "manual_damping_grid_effect": {
+    "change": "Add alpha=-4 degrees ahead of the published 0..28 schedule; extra Mach 2.35 alone was checked to have no effect",
+    "cna_at_zero_original_per_degree": 0.2802,
+    "cna_at_zero_signed_per_degree": 0.2948,
+    "max_dynamic_difference_by_channel": {
+      "CNQ": 0.08300000000000018,
+      "CMQ": 0.367,
+      "CAQ": 0.0,
+      "CNAD": 0.07300000000000006,
+      "CMAD": 0.04800000000000004,
+      "CYR": 0.04700000000000015,
+      "CLNR": 0.2060000000000004,
+      "CLLR": 0.0,
+      "CYP": 0.0,
+      "CLNP": 0.0,
+      "CLLP": 0.0
+    }
+  },
+  "manual_damping_max_difference_same_method_and_grid": 0.001000000000000334,
+  "ansys_reference_normalization": {
+    "area_ratio": 1.1026254457406508,
+    "example_ca_alpha0": 0.611,
+    "frontal_area_ca_alpha0": 0.673,
+    "untrimmed_cd_alpha10": 0.711,
+    "published_trimmed_cd_alpha10": 0.736,
+    "note": "Three-decimal coefficient printing limits normalization comparisons; trimmed and untrimmed CD are different states"
+  }
+}
diff --git a/tests/integration/test_datcom.py b/tests/integration/test_datcom.py
index 1b6995d6b..31c7bb29c 100644
--- a/tests/integration/test_datcom.py
+++ b/tests/integration/test_datcom.py
@@ -1,6 +1,7 @@
 """DATCOM text parsing, isolated executable lifecycle and flight integration."""
 
 import os
+import re
 import shutil
 import subprocess
 from pathlib import Path
@@ -10,7 +11,7 @@
 import numpy as np
 import pytest
 
-from rocketpy import DatcomFins, Environment, Flight
+from rocketpy import DatcomFins, Environment, Flight, Rocket
 from rocketpy.rocket.datcom import DAMPING_KEYS, parse_datcom_output, run_datcom
 
 SAMPLE = Path(__file__).parents[1] / "fixtures/surfaces/data/datcom/for006.dat"
@@ -598,3 +599,82 @@ def test_datcom_native_manual_worked_example():
     assert result.body["CM"][0] == pytest.approx(
         [0, -1.577, -3.194, -5.056, -6.962, -8.904, -10.792, -12.692], abs=0.002
     )
+
+
+@pytest.mark.slow
+def test_datcom_generated_geometry_matches_manual_example():
+    """Public geometry conversion reproduces the manual with matched methods."""
+    # Arrange
+    from rocketpy.rocket.datcom import _namelist, datcom_decks
+
+    executable = os.environ.get("DATCOM_EXECUTABLE")
+    if not executable:
+        pytest.skip("Set DATCOM_EXECUTABLE to the target 3/99 executable")
+    inch = 0.0254
+    rocket = Rocket(
+        radius=3.75 * inch / 2,
+        mass=1,
+        inertia=(1, 1, 1),
+        power_off_drag=0,
+        power_on_drag=0,
+        center_of_mass_without_motor=18.75 * inch,
+    )
+    rocket.add_nose(11.25 * inch, "ogive", position=37.5 * inch)
+    options = dict(
+        mach=[2.35, 2.36],
+        alpha=[-4, 0, 4, 8, 12, 16, 20, 24, 28],
+        beta=[0],
+        reynolds=3e6 / 0.3048,  # Published REN is per foot, including with DIM IN.
+        body_length=26.25 * inch,
+    )
+    for n, xle, root, tip, span, maximum, flat, panels in [
+        (2, 15.42, 6.96, 0, 3.48, 0.238, 0.524, [90, 270]),
+        (4, 31.915, 5.585, 2.792, 4.385, 0.288, 0.428, [0, 90, 180, 270]),
+    ]:
+        rocket.add_datcom_hex_fins(
+            n,
+            root_chord=root * inch,
+            tip_chord=tip * inch,
+            span=span * inch,
+            position=(37.5 - xle) * inch,
+            sweep_length=(root - tip) * inch,
+            path=executable,
+            thickness_to_chord=0.04476,
+            max_thickness_position=maximum,
+            flat_length=flat,
+            leading_edge_radius=0.015 * inch,
+            panel_angles=panels,
+            datcom_options=options,
+        )
+    reference = (SAMPLE.parent / "manual_example_for005.dat").read_text()
+    # Native numerical derivatives depend on the ALPHA schedule. Match the
+    # signed grid required by the API and retain the independent input geometry.
+    reference_conditions = _namelist(
+        "FLTCON",
+        [
+            ("NMACH", 2),
+            ("MACH", options["mach"]),
+            ("REN", [3e6, 3e6]),
+            ("NALPHA", len(options["alpha"])),
+            ("ALPHA", options["alpha"]),
+            ("BETA", 0),
+        ],
+    )
+    reference = re.sub(
+        r"\$FLTCON.*?\$", lambda _: reference_conditions, reference, flags=re.S
+    )
+    # Act
+    with pytest.warns(UserWarning, match="four exposed semispans"):
+        generated = datcom_decks(rocket)[0][False]
+    # Match the independently published body method. The production options
+    # currently expose no SOSE selector; do not mistake this for a geometry fix.
+    generated = generated.replace("NEXT CASE", "SOSE\nPART\nNEXT CASE")
+    actual = run_datcom(executable, generated)
+    expected = run_datcom(executable, reference)
+    # Assert
+    for key in ("CN", "CM", "CA"):
+        assert actual.body[key][-1] == pytest.approx(expected.body[key][-1], abs=0.002)
+    for key in DAMPING_KEYS:
+        assert actual.damping[key][-1] == pytest.approx(
+            expected.damping[key][-1], abs=0.002
+        )

From 81c58cbc0a917545d59ec8d04ef91f1723e4638d Mon Sep 17 00:00:00 2001
From: UnforgettableCassini <66637599+AdaBatu@users.noreply.github.com>
Date: Sat, 31 Oct 2026 14:40:03 +0100
Subject: [PATCH 4/4] ENH: Add DATCOM uncertainty scenarios and comparison
 charts

---
 CHANGELOG.md                                  |     1 +
 README.md                                     |    40 +
 docs/examples/datcom/README.md                |   103 +
 docs/examples/datcom/asteria_comparison.ipynb |    44 +
 .../comparison_data/andromeda_uncertainty.txt |    29 +
 .../apogee_uncertainty_comparison.csv         |    12 +
 .../apogee_uncertainty_comparison.png         |   Bin 0 -> 681021 bytes
 .../apogee_uncertainty_comparison.svg         |  7939 +++++++++++
 .../apogee_uncertainty_comparison_focus.png   |   Bin 0 -> 239697 bytes
 .../apogee_uncertainty_comparison_focus.svg   |  3751 ++++++
 .../asteria_cd_uncertainty_comparison.csv     |   303 +
 .../asteria_cd_uncertainty_comparison.png     |   Bin 0 -> 195207 bytes
 .../asteria_cd_uncertainty_comparison.svg     |  3835 ++++++
 .../comparison_data/astra_uncertainty.txt     |    29 +
 .../comparison_data/bella_lui_uncertainty.txt |    29 +
 .../comparison_data/cavour_uncertainty.txt    |    29 +
 .../comparison_data/cd_coast_comparison.png   |   Bin 0 -> 654283 bytes
 .../comparison_data/cd_coast_comparison.svg   | 11071 ++++++++++++++++
 .../comparison_data/cd_comparison_focus.png   |   Bin 0 -> 353483 bytes
 .../comparison_data/cd_comparison_focus.svg   |  6647 ++++++++++
 .../comparison_data/cd_powered_comparison.png |   Bin 0 -> 660096 bytes
 .../comparison_data/cd_powered_comparison.svg | 11018 +++++++++++++++
 .../cd_uncertainty_comparison.csv             |  3323 +++++
 .../comparison_data/erebus_uncertainty.txt    |    29 +
 .../comparison_data/genesis_uncertainty.txt   |    29 +
 .../comparison_data/halcyon_uncertainty.txt   |    29 +
 .../comparison_data/juno3_uncertainty.txt     |    29 +
 .../comparison_data/lince_uncertainty.txt     |    29 +
 .../plot_apogee_uncertainty.py                |   302 +
 .../comparison_data/plot_cd_comparison.py     |   267 +
 .../prometheus_2022_uncertainty.txt           |    29 +
 .../stability_uncertainty_comparison.png      |   Bin 0 -> 159171 bytes
 .../stability_uncertainty_comparison.svg      |  2675 ++++
 .../uncertainty_sensitivity.csv               |    34 +
 .../uncertainty_sensitivity.json              |   866 ++
 .../uncertainty_sensitivity.py                |   107 +
 .../comparison_data/valetudo_uncertainty.txt  |    29 +
 docs/examples/datcom/rocket_comparison.ipynb  |   450 +
 docs/user/rocket/datcom.rst                   |   105 +
 rocketpy/prints/flight_prints.py              |    97 +-
 rocketpy/rocket/datcom_uncertainty.py         |   187 +
 rocketpy/simulation/datcom.py                 |    10 +-
 rocketpy/simulation/datcom_uncertainty.py     |   169 +
 rocketpy/simulation/flight.py                 |    22 +-
 tests/integration/test_datcom.py              |   162 +
 tests/unit/rocket/test_datcom_uncertainty.py  |   220 +
 46 files changed, 54071 insertions(+), 8 deletions(-)
 create mode 100644 docs/examples/datcom/comparison_data/andromeda_uncertainty.txt
 create mode 100644 docs/examples/datcom/comparison_data/apogee_uncertainty_comparison.csv
 create mode 100644 docs/examples/datcom/comparison_data/apogee_uncertainty_comparison.png
 create mode 100644 docs/examples/datcom/comparison_data/apogee_uncertainty_comparison.svg
 create mode 100644 docs/examples/datcom/comparison_data/apogee_uncertainty_comparison_focus.png
 create mode 100644 docs/examples/datcom/comparison_data/apogee_uncertainty_comparison_focus.svg
 create mode 100644 docs/examples/datcom/comparison_data/asteria_cd_uncertainty_comparison.csv
 create mode 100644 docs/examples/datcom/comparison_data/asteria_cd_uncertainty_comparison.png
 create mode 100644 docs/examples/datcom/comparison_data/asteria_cd_uncertainty_comparison.svg
 create mode 100644 docs/examples/datcom/comparison_data/astra_uncertainty.txt
 create mode 100644 docs/examples/datcom/comparison_data/bella_lui_uncertainty.txt
 create mode 100644 docs/examples/datcom/comparison_data/cavour_uncertainty.txt
 create mode 100644 docs/examples/datcom/comparison_data/cd_coast_comparison.png
 create mode 100644 docs/examples/datcom/comparison_data/cd_coast_comparison.svg
 create mode 100644 docs/examples/datcom/comparison_data/cd_comparison_focus.png
 create mode 100644 docs/examples/datcom/comparison_data/cd_comparison_focus.svg
 create mode 100644 docs/examples/datcom/comparison_data/cd_powered_comparison.png
 create mode 100644 docs/examples/datcom/comparison_data/cd_powered_comparison.svg
 create mode 100644 docs/examples/datcom/comparison_data/cd_uncertainty_comparison.csv
 create mode 100644 docs/examples/datcom/comparison_data/erebus_uncertainty.txt
 create mode 100644 docs/examples/datcom/comparison_data/genesis_uncertainty.txt
 create mode 100644 docs/examples/datcom/comparison_data/halcyon_uncertainty.txt
 create mode 100644 docs/examples/datcom/comparison_data/juno3_uncertainty.txt
 create mode 100644 docs/examples/datcom/comparison_data/lince_uncertainty.txt
 create mode 100644 docs/examples/datcom/comparison_data/plot_apogee_uncertainty.py
 create mode 100644 docs/examples/datcom/comparison_data/plot_cd_comparison.py
 create mode 100644 docs/examples/datcom/comparison_data/prometheus_2022_uncertainty.txt
 create mode 100644 docs/examples/datcom/comparison_data/stability_uncertainty_comparison.png
 create mode 100644 docs/examples/datcom/comparison_data/stability_uncertainty_comparison.svg
 create mode 100644 docs/examples/datcom/comparison_data/uncertainty_sensitivity.csv
 create mode 100644 docs/examples/datcom/comparison_data/uncertainty_sensitivity.json
 create mode 100644 docs/examples/datcom/comparison_data/uncertainty_sensitivity.py
 create mode 100644 docs/examples/datcom/comparison_data/valetudo_uncertainty.txt
 create mode 100644 rocketpy/rocket/datcom_uncertainty.py
 create mode 100644 rocketpy/simulation/datcom_uncertainty.py
 create mode 100644 tests/unit/rocket/test_datcom_uncertainty.py

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 13c598421..397959aed 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -32,6 +32,7 @@ Attention: The newest changes should be on top -->
 
 ### Added
 
+- ENH: Missile DATCOM sensitivity ascents with a 20% CD floor, larger in-envelope axial-force discrepancies from Wu et al. (2019), and ±2% nose-to-CP distance. Three apogee, speed and stability results include apogee ± the largest scenario excursion. These are engineering assumptions motivated by White (1995), not statistical confidence bounds.
 - ENH: Reproducible Monte Carlo seeding, and an append that continues the same study [#1187](https://github.com/RocketPy-Team/RocketPy/pull/1187) [#1053](https://github.com/RocketPy-Team/RocketPy/issues/1053) [#1075](https://github.com/RocketPy-Team/RocketPy/issues/1075)
 - ENH: 3-DOF single rail button flight phase (tip-off analysis), enabled with the opt-in `Flight(use_udot_rail2=True)`. Between the upper rail button leaving the rail and the lower one following it, the rocket pivots about the lower button under a solved constraint wrench instead of jumping straight to free 6-DOF flight. The window is reported as `between_rails_time`, `between_rails_velocity` and `tip_off_duration`, printed by `Flight.info()` and shaded in the attitude plots. Requires `simulation_mode="6 DOF"` and `equations_of_motion="standard"`. [#920](https://github.com/RocketPy-Team/RocketPy/pull/920)
 - ENH: Support fixed-time parachute deployment triggers [#1133](https://github.com/RocketPy-Team/RocketPy/pull/1133) [#437](https://github.com/RocketPy-Team/RocketPy/issues/437)
diff --git a/README.md b/README.md
index eff904d11..e316a471d 100644
--- a/README.md
+++ b/README.md
@@ -40,6 +40,11 @@ RocketPy is the next-generation trajectory simulation solution for High-Power Ro
 3. **Aerodynamic Models**
    - Optional Barrowman equations for lift coefficients
    - Easy import of drag coefficients from other sources (e.g., CFD simulations)
+   - Experimental Missile DATCOM integration with nominal, upper and lower
+     coefficient sensitivity flights for apogee, ascent speed and stability.
+     Flight reports show apogee ± the largest scenario departure in meters.
+     [Usage, assumptions and limitations](docs/user/rocket/datcom.rst) and
+     [comparison examples](docs/examples/datcom/README.md).
 
 4. **Parachutes with External Trigger Functions**
    - Test the exact code that will fly
@@ -65,6 +70,41 @@ RocketPy is the next-generation trajectory simulation solution for High-Power Ro
 
 These powerful features make RocketPy an indispensable tool for high-power rocket trajectory simulation, catering to enthusiasts, researchers, and engineers in the field of rocketry.
 
+DATCOM flights include two engineering sensitivity ascents around the nominal
+prediction. Static wind-axis **CD varies by at least ±20% at every Mach and
+AoA**. Within Mach 0.8–3.02 and alpha ±6°, larger axial-force discrepancies
+from Wu et al. are used as a CD-error proxy after converting them to the native
+prediction's denominator; outside that envelope the excursion is 20%.
+**CP varies by ±2% of its distance from the nose**, with static pitching/yawing
+moments transferred consistently. Drag is varied along the airflow, preserving
+perpendicular force; native damping derivatives are retained. These replace
+the earlier independent CN/CM-alpha variations. CP uncertainty is not a
+percentage of stability margin. Both powered and coast tables use this rule.
+
+The reference for the design-accuracy discussion is J. T. White (1995),
+*An assessment of missile DATCOM prediction accuracy relative to generic
+Body+Wing+Tail missile pitch aerodynamics*, AIAA 95-1893,
+[doi:10.2514/6.1995-1893](https://doi.org/10.2514/6.1995-1893).
+White evaluates CN/CP for Mach 1.5–5.0; his cited preliminary-design criteria
+concern normal force and CP relative to body length. **He does not establish
+a universal ±20% CD uncertainty or ±2% nose-to-CP uncertainty.** Applying these
+two percentages throughout rocket flight is an explicit engineering assumption,
+not a result validated by that paper.
+
+The larger drag discrepancies adapt Table 2 of Hanzhou Wu, Min Gao, Weidong
+Song, Zhao Jie and Yi Wang (2019), *Accuracy Analysis of Aerodynamic Calculation
+of Two-dimensional Ballistic correction Projectile based on Missile Datcom*,
+**IOP Conference Series: Materials Science and Engineering 612**, 032096,
+[doi:10.1088/1757-899X/612/3/032096](https://doi.org/10.1088/1757-899X/612/3/032096),
+under [CC BY 3.0](https://creativecommons.org/licenses/by/3.0/).
+
+Printed apogee ± uncertainty is the largest departure from nominal in the two
+correlated scenarios, **not a confidence interval or a guaranteed envelope**.
+The comparison discrepancies remain unresolved. See the
+[method and limitations](docs/user/rocket/datcom.rst) and
+[comparison charts](docs/examples/datcom/README.md). Use
+`Flight(..., datcom_uncertainty=False)` to skip the two extra ascents.
+
 ## Validation
 
 RocketPy's features have been validated in our latest [research article published in the Journal of Aerospace Engineering](http://dx.doi.org/10.1061/%28ASCE%29AS.1943-5525.0001331).
diff --git a/docs/examples/datcom/README.md b/docs/examples/datcom/README.md
index f12d8b540..80314cb38 100644
--- a/docs/examples/datcom/README.md
+++ b/docs/examples/datcom/README.md
@@ -2,6 +2,109 @@
 
 **Work in progress:** DATCOM currently produces poor flight predictions for several comparison rockets. The discrepancies are unresolved and are being investigated; these results should not be treated as validated predictions.
 
+DATCOM flights calculate two engineering sensitivity ascents around the nominal
+result. The current rule is **at least ±20% static wind-axis CD at every Mach
+and AoA**, with **±2% nose-to-CP distance**. Within Wu et al.'s Mach 0.8–3.02 /
+alpha ±6° envelope, use a larger native-relative axial-force discrepancy as a
+CD-error proxy. Outside that envelope use the 20% excursion. At zero incidence
+this scales the whole CD-versus-Mach curve; at finite incidence the added force
+follows the relative airflow and preserves perpendicular force. Static pitch
+and yaw moments are transferred consistently; native DAMP terms are retained.
+The earlier independent CN and CM-alpha scaling is no longer active.
+
+The design-accuracy reference is J. T. White (1995), *An assessment of missile
+DATCOM prediction accuracy relative to generic Body+Wing+Tail missile pitch
+aerodynamics*, 13th Applied Aerodynamics Conference, AIAA 95-1893,
+[doi:10.2514/6.1995-1893](https://doi.org/10.2514/6.1995-1893).
+The supplied filename says 2012, but the paper is from 1995. It assesses
+revision 6/93 on 15 configurations and over 900 cases at Mach 1.5–5.0,
+AoA 0–24°, roll 0°/45°, without control deflections. White's cited preliminary
+criteria concern **normal force** and CP relative to **body length**; he does
+not establish universal ±20% CD or ±2% nose-to-CP confidence bounds. The two
+percentages used here are the requested engineering assumptions, extended
+throughout flight. His normal-force bias/scatter and ambiguous CP statistics
+are not applied as a nominal correction or a statistical distribution.
+
+The larger drag discrepancies adapt Table 2 of Hanzhou Wu, Min Gao, Weidong
+Song, Zhao Jie and Yi Wang (2019), *Accuracy Analysis of Aerodynamic Calculation
+of Two-dimensional Ballistic correction Projectile based on Missile Datcom*,
+**IOP Conf. Ser.: Mater. Sci. Eng. 612**, 032096,
+[doi:10.1088/1757-899X/612/3/032096](https://doi.org/10.1088/1757-899X/612/3/032096),
+under [CC BY 3.0](https://creativecommons.org/licenses/by/3.0/).
+For signed measurement-relative error `e`, the native-relative magnitude is
+`abs(e / (1 + e))`, with `e` expressed as a fraction. The drag excursion is
+`max(0.20, converted_Wu_error)` inside the paper's envelope and `0.20` outside.
+CA discrepancy is transferred to CD as an explicit assumption. The raw CN and
+CM-alpha tables remain for provenance but no longer set sensitivity bounds.
+
+CP is measured from the nose: `x_CP_new = x_CP_native * (1 ± 0.02)`.
+The stability-margin change is `±0.02 * x_CP_native / diameter`, in calibers,
+not 2% of the original margin. Coordinate origins and powered/coast CG
+references remain consistent. Upper/lower mean more/less drag with CP
+farther aft/forward; they are two correlated scenarios, not an exhaustive
+search over independent combinations. The nominal prediction is uncorrected.
+
+Printing shows three apogees, peak ascent speeds, apogee freestream speeds
+and stability margins. Apogee **± uncertainty** is the largest absolute
+scenario departure in meters, not a confidence interval or percentile.
+`flight.datcom_uncertainty.cases` exposes the values and
+`flight.datcom_apogee_uncertainty` exposes the scalar. Disable replays using
+`Flight(..., datcom_uncertainty=False)`. See the
+[full method and limitations](../../user/rocket/datcom.rst).
+
+The regenerated [sensitivity results](comparison_data/uncertainty_sensitivity.csv)
+cover all 11 currently supported recorded-rocket DATCOM models, with the
+[machine-readable report](comparison_data/uncertainty_sensitivity.json) and
+[example printed output](comparison_data/genesis_uncertainty.txt). Reproduce
+all of them with
+`python docs/examples/datcom/comparison_data/uncertainty_sensitivity.py`.
+
+The [apogee chart](comparison_data/apogee_uncertainty_comparison.png) compares
+RocketPy's supplied-CD prediction, nominal DATCOM with **asymmetric scenario
+whiskers**, and recorded apogee as a dashed line. Green/red rank nominal
+accuracy. A [compact three-rocket view](comparison_data/apogee_uncertainty_comparison_focus.png)
+and [stability chart](comparison_data/stability_uncertainty_comparison.png)
+are also exported, with [CSV values](comparison_data/apogee_uncertainty_comparison.csv).
+All charts and tables are in [the comparison notebook](rocket_comparison.ipynb);
+the original 16-rocket chart still identifies unavailable cases separately.
+No recorded stability margin is available for the 11 paired DATCOM models,
+so their stability whiskers compare simulations only. Rebuild charts without
+running DATCOM using
+`python docs/examples/datcom/comparison_data/plot_apogee_uncertainty.py`.
+
+CD-versus-Mach charts are exported for all 11 rockets, separately for
+[powered flight](comparison_data/cd_powered_comparison.png) and
+[coast](comparison_data/cd_coast_comparison.png), with a
+[compact three-rocket view](comparison_data/cd_comparison_focus.png).
+Blue is RocketPy's supplied drag curve; orange is nominal DATCOM; the orange
+band uses the active 20%-minimum CD rule at **alpha = beta = 0°**. The dotted
+black line is the **apogee-fitted constant CD**, inferred using ordinary
+RocketPy; it is not measured CD and is not unique. No measured CD records
+are supplied, so these charts do not rank drag-prediction accuracy. Grey
+background marks Mach values above the reference ascent peak, including
+unflown transonic points. Curves retain their saved Mach/atmosphere schedules;
+the 2% CP shift does not directly change zero-incidence static CD.
+The [AST-HER chart](comparison_data/asteria_cd_uncertainty_comparison.png)
+compares the same two models without a fitted or measured CD: **AST-HER has
+not flown**. Its saved RocketPy curve lacks the Mach 0.01 sample; that point
+remains a gap rather than an invented value. Both existing notebooks include
+their corresponding CD charts.
+PNG/SVG exports and the complete
+[rocket curve data](comparison_data/cd_uncertainty_comparison.csv) and
+[AST-HER curve data](comparison_data/asteria_cd_uncertainty_comparison.csv)
+can be regenerated without running DATCOM:
+`python docs/examples/datcom/comparison_data/plot_cd_comparison.py`.
+
+Recorded apogees fall inside the scenario ranges for **4 of 11 rockets**. RocketPy's supplied-CD nominal prediction is closer for **8 of 11**. Inclusion is a diagnostic, not evidence of a calibrated confidence level.
+
+| Rocket | Recorded AGL (m) | RocketPy (m) | DATCOM nominal (m) | DATCOM scenario range (m) |
+| --- | ---: | ---: | ---: | ---: |
+| GENESIS | 2916 | 3045 | 3297 | 3068–3584 |
+| Astra | 3250 | 3043 | 3696 | 3437–4032 |
+| Andromeda | 3443 | 3616 | 4171 | 3794–4832 |
+
+These bars do not cover every recorded apogee. Geometry assumptions, drag modelling and other uncertainties remain unresolved.
+
 Only two example notebooks are included:
 
 - [Recorded rockets](rocket_comparison.ipynb): DATCOM, ordinary RocketPy and
diff --git a/docs/examples/datcom/asteria_comparison.ipynb b/docs/examples/datcom/asteria_comparison.ipynb
index cd7415201..901a9997e 100644
--- a/docs/examples/datcom/asteria_comparison.ipynb
+++ b/docs/examples/datcom/asteria_comparison.ipynb
@@ -161,6 +161,50 @@
     "fig.suptitle(\"AST-HER — simulation comparison; has not flown\")\n",
     "fig.tight_layout();plt.show()\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2579db9d",
+   "metadata": {},
+   "source": [
+    "## CD comparison with engineering bands\n",
+    "\n",
+    "**AST-HER has not flown. No measured or apogee-fitted CD is available.** This compares Asteria's supplied drag curve in RocketPy (blue) with native DATCOM (orange), separately for powered and coast aerodynamics, at **alpha = beta = 0°**.\n",
+    "\n",
+    "Orange bands follow the same **±20% minimum CD** rule, retaining larger converted Wu discrepancies inside its measured Mach range; they are not confidence intervals. Grey backgrounds mark Mach values above the saved reference ascent's peak. The ±2% CP shift does not directly change zero-incidence static CD. Curves retain the saved Mach/atmosphere schedule and show a fixed-incidence comparison, not the actual higher-incidence AST-HER flight history.\n",
+    "\n",
+    "The saved RocketPy curve has no finite CD at Mach 0.01; this sample remains a gap rather than being filled."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7070db41",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1040x520 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "sys.path.insert(0, str(DATA))\n",
+    "from plot_cd_comparison import cd_comparison_data, plot_cd_comparison, save_cd_comparison\n",
+    "\n",
+    "cd_curves = cd_comparison_data(DATA, asteria=True)\n",
+    "cd_curves.to_csv(DATA / \"asteria_cd_uncertainty_comparison.csv\", index=False)\n",
+    "fig = plot_cd_comparison(cd_curves)\n",
+    "save_cd_comparison(fig, \"asteria_cd_uncertainty_comparison\", DATA)\n",
+    "plt.show()\n",
+    "plt.close(fig)\n"
+   ]
   }
  ],
  "metadata": {
diff --git a/docs/examples/datcom/comparison_data/andromeda_uncertainty.txt b/docs/examples/datcom/comparison_data/andromeda_uncertainty.txt
new file mode 100644
index 000000000..dcbd6c226
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/andromeda_uncertainty.txt
@@ -0,0 +1,29 @@
+
+Apogee State
+
+Apogee Time: 28.133 s
+Apogee Altitude: 4330.733 ± 661.117 m (ASL) | 4170.733 ± 661.117 m (AGL)
+Apogee Freestream Speed: 22.295 m/s
+DATCOM nominal: peak ascent speed 354.399 ± 12.539 m/s
+DATCOM coefficient upper: apogee 3793.821 m (AGL) | peak ascent speed 345.640 m/s | apogee freestream speed 19.094 m/s
+DATCOM coefficient lower: apogee 4831.850 m (AGL) | peak ascent speed 366.938 m/s | apogee freestream speed 22.942 m/s
+DATCOM ±: largest departure from nominal in two engineering sensitivity runs; not a confidence interval. Upper/lower mean more/less drag and farther-aft/forward CP, not guaranteed altitude bounds.
+DATCOM sensitivity: CD at least ±20%; nose-to-CP distance ±2%.
+DATCOM nominal: 85.4% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+DATCOM upper: 87.2% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+DATCOM lower: 81.8% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+Apogee X position: 472.035 m
+Apogee Y position: -475.639 m
+Apogee latitude: 39.3844134°
+Apogee longitude: -8.2823204°
+
+Stability Margin
+
+Initial Stability Margin: 4.586 ± 0.257 c at 0.00 s
+Out of Rail Stability Margin: 4.703 ± 0.257 c at 0.52 s
+Maximum Stability Margin: 6.104 c at 3.63 s
+Minimum Stability Margin: 4.586 c at 0.00 s
+DATCOM nominal stability at apogee: 5.545 ± 0.257 c
+DATCOM nominal ascent stability min/max: 4.586 ± 0.257 / 6.104 ± 0.226 c
+DATCOM coefficient upper stability: initial 4.842 c | rail exit 4.960 c | apogee 5.801 c | ascent min/max 4.842 / 6.215 c
+DATCOM coefficient lower stability: initial 4.329 c | rail exit 4.447 c | apogee 5.288 c | ascent min/max 4.329 / 5.878 c
diff --git a/docs/examples/datcom/comparison_data/apogee_uncertainty_comparison.csv b/docs/examples/datcom/comparison_data/apogee_uncertainty_comparison.csv
new file mode 100644
index 000000000..27490b8db
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/apogee_uncertainty_comparison.csv
@@ -0,0 +1,12 @@
+rocket,measured,rocketpy,datcom,condition,availability,rocketpy_error_pct,datcom_error_pct,closer_prediction,paired,rocketpy_archived,drag_provenance,datcom_scenario_low_m,datcom_scenario_high_m,datcom_sensitivity_minus_m,datcom_sensitivity_plus_m,datcom_low_error_pct,datcom_high_error_pct,recorded_within_datcom_scenarios,nominal_outside_paper_pct,rocketpy_stability,recorded_stability,datcom_stability,datcom_stability_low,datcom_stability_high
+genesis,2916.0,3044.6857862664306,3297.2391270686403,notebook reported apogee,,4.41309280749076,13.074044138156388,RocketPy + supplied CD,True,False,Supplied CD; CFD provenance not established,3067.920091987003,3584.4220980788755,229.31903508163714,287.1829710102352,5.209879697771025,22.92256852122343,False,93.006993006993,3.37721309359005,,3.7682700796928534,3.356257418508913,4.180282740876794
+astra,3250.0,3043.3679728130164,3695.9340692825294,notebook reported apogee,,-6.357908528830264,13.721048285616291,RocketPy + supplied CD,True,False,Supplied CD; CFD provenance not established,3436.8854594405384,4031.6782683587103,259.04860984199104,335.74419907618085,5.750321828939642,24.051639026421846,False,90.50949050949052,3.16256293861153,,3.477476916191179,3.153528267890674,3.801425564491684
+andromeda,3443.0,3615.880567942097,4170.733199067169,notebook reported apogee,,5.02121893529181,21.13660177366161,RocketPy + supplied CD,True,False,Supplied CD; CFD provenance not established,3793.820927076927,4831.850019075376,376.91227199024206,661.1168200082066,10.18939666212393,40.338368256618516,False,85.41458541458542,4.158769051891837,,4.585569601745527,4.328801806260052,4.8423373972310015
+bella_lui,458.971,460.5523116828233,465.8765076067215,telemetry AGL maximum (QA),,0.3445341171497323,1.5045629477072564,RocketPy + supplied CD,True,False,Supplied CD; CFD provenance not established,457.81782458524265,474.2391393227877,8.058683021478828,8.362631716066232,-0.25125234813471176,3.326602186802141,True,100.0,2.776966165584537,,3.303739452169553,3.0353344137841964,3.5721444905549093
+cavour,2789.0,2818.1348894226226,3251.608548140568,notebook reported apogee,,1.0446356910226806,16.58689667051158,RocketPy + supplied CD,True,False,RASAero II (source cell 10),2968.7222252341453,3660.194984348673,282.8863229064227,408.58643620810517,6.44396648383454,31.23682267295351,False,89.6103896103896,4.279860429594885,,5.465305853006995,5.02681529548008,5.903796410533909
+erebus,3020.0,2751.1556122225365,3253.422625182214,notebook reported apogee,,-8.902132045611372,7.729225999411052,DATCOM,True,False,Supplied CD; CFD provenance not established,2988.004855920229,3612.9959689910174,265.41776926198463,359.57334380880366,-1.0594418569460529,19.635628112285342,True,90.70929070929073,2.9374083445614505,,4.4503224821337275,4.07575606431388,4.824888899953575
+halcyon,3450.0,3214.238895757538,3530.231782008296,notebook reported apogee,,-6.8336551954336775,2.3255588987911824,DATCOM,True,False,Supplied CD; CFD provenance not established,3334.1995082721137,3757.608831328152,196.0322737361821,227.37704931985627,-3.3565359921126414,8.916198009511644,True,97.30269730269731,3.0192578745448033,,4.410248384571101,4.0572812034891035,4.763215565653098
+juno3,3213.0,2987.2806591791605,3056.5496067381873,notebook reported apogee,,-7.025189568031109,-4.8692932854594675,DATCOM,True,False,RASAero II curve scaled using a CFD value (source cell 14),2907.190741370203,3223.8193393853353,149.35886536798444,167.26973264714798,-9.517872973227426,0.33673636431170717,True,100.0,3.220648297149346,,4.008594996785594,3.6764875206173433,4.340702472953844
+lince,3587.0,3285.089381541881,4254.30425999591,notebook reported apogee,,-8.416800068528554,18.60340841917786,RocketPy + supplied CD,True,False,Supplied CD; CFD provenance not established,3889.101707098463,4849.011561829239,365.2025528974468,594.7073018333294,8.42212732362595,35.182926173103965,False,85.21478521478521,2.9203959364705625,,3.962666916625163,3.6496932911149185,4.275640542135408
+prometheus_2022,3898.37,4191.288687549948,4229.19676461329,notebook reported apogee,,7.513875993041916,8.486284385866142,RocketPy + supplied CD,True,False,Supplied CD; CFD provenance not established,3913.979327730014,4665.186108382249,315.2174368832757,435.98934376895886,0.40040652195698634,19.670172620409264,False,86.91308691308691,1.0415367273926033,,1.7889093089472907,1.539009930708202,2.03880868718638
+valetudo,860.0,815.0526424633565,943.6190073224012,saved measured-apogee summary,,-5.226436922865522,9.723140386325722,RocketPy + supplied CD,True,False,RASAero (source input filenames),921.1450730019072,966.867844154012,22.47393432049398,23.248836831610788,7.1098922095240935,12.426493506280467,False,100.0,2.951130333277495,,4.156583455241121,3.771330378170176,4.5418365323120655
diff --git a/docs/examples/datcom/comparison_data/apogee_uncertainty_comparison.png b/docs/examples/datcom/comparison_data/apogee_uncertainty_comparison.png
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+      <!-- 2000 -->
+      <g transform="translate(762.478125 347.805666) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-32"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(190.869141 0)"/>
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+      <!-- 3000 -->
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+      <!-- 5000 -->
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+    <g transform="translate(127.121354 568.390272) scale(0.11 -0.11)">
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+    <!-- 466 m -->
+    <g transform="translate(257.688021 563.259237) scale(0.11 -0.11)">
+     <use xlink:href="#DejaVuSans-34"/>
+     <use xlink:href="#DejaVuSans-36" transform="translate(63.623047 0)"/>
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+     <use xlink:href="#DejaVuSans-6d" transform="translate(222.65625 0)"/>
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+    <!-- +1.5% -->
+    <g transform="translate(256.710052 575.576831) scale(0.11 -0.11)">
+     <use xlink:href="#DejaVuSans-2b"/>
+     <use xlink:href="#DejaVuSans-31" transform="translate(83.789062 0)"/>
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+     <g id="text_63">
+      <!-- 3000 -->
+      <g transform="translate(391.678125 618.176062) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-33"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
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+     <g id="text_64">
+      <!-- 4000 -->
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+       <use xlink:href="#DejaVuSans-34"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(190.869141 0)"/>
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+    </g>
+    <g id="ytick_35">
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+      <!-- 5000 -->
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+       <use xlink:href="#DejaVuSans-35"/>
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+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
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+     </g>
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+    <g id="text_66">
+     <!-- Apogee above launch site (m) -->
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+      <use xlink:href="#DejaVuSans-70" transform="translate(68.408203 0)"/>
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+    <!-- +16.6% -->
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+       <use xlink:href="#DejaVuSans-6b" transform="translate(181.144531 0)"/>
+       <use xlink:href="#DejaVuSans-65" transform="translate(235.429688 0)"/>
+       <use xlink:href="#DejaVuSans-74" transform="translate(296.953125 0)"/>
+       <use xlink:href="#DejaVuSans-50" transform="translate(336.162109 0)"/>
+       <use xlink:href="#DejaVuSans-79" transform="translate(396.464844 0)"/>
+      </g>
+      <!-- supplied CD -->
+      <g transform="translate(856.243542 785.89225) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-73"/>
+       <use xlink:href="#DejaVuSans-75" transform="translate(52.099609 0)"/>
+       <use xlink:href="#DejaVuSans-70" transform="translate(115.478516 0)"/>
+       <use xlink:href="#DejaVuSans-70" transform="translate(178.955078 0)"/>
+       <use xlink:href="#DejaVuSans-6c" transform="translate(242.431641 0)"/>
+       <use xlink:href="#DejaVuSans-69" transform="translate(270.214844 0)"/>
+       <use xlink:href="#DejaVuSans-65" transform="translate(297.998047 0)"/>
+       <use xlink:href="#DejaVuSans-64" transform="translate(359.521484 0)"/>
+       <use xlink:href="#DejaVuSans-20" transform="translate(422.998047 0)"/>
+       <use xlink:href="#DejaVuSans-43" transform="translate(454.785156 0)"/>
+       <use xlink:href="#DejaVuSans-44" transform="translate(524.609375 0)"/>
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+     </g>
+    </g>
+    <g id="xtick_12">
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+      <path d="M 1016.891458 760.096
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+     <g id="line2d_109">
+      <g>
+       <use xlink:href="#m1ce099ee8e" x="1016.891458" y="760.096" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_72">
+      <!-- DATCOM -->
+      <g transform="translate(995.594583 774.694438) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-44"/>
+       <use xlink:href="#DejaVuSans-41" transform="translate(75.251953 0)"/>
+       <use xlink:href="#DejaVuSans-54" transform="translate(135.910156 0)"/>
+       <use xlink:href="#DejaVuSans-43" transform="translate(191.119141 0)"/>
+       <use xlink:href="#DejaVuSans-4f" transform="translate(260.943359 0)"/>
+       <use xlink:href="#DejaVuSans-4d" transform="translate(339.654297 0)"/>
+      </g>
+      <!-- with sensitivity range -->
+      <g transform="translate(963.106302 785.89225) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-77"/>
+       <use xlink:href="#DejaVuSans-69" transform="translate(81.787109 0)"/>
+       <use xlink:href="#DejaVuSans-74" transform="translate(109.570312 0)"/>
+       <use xlink:href="#DejaVuSans-68" transform="translate(148.779297 0)"/>
+       <use xlink:href="#DejaVuSans-20" transform="translate(212.158203 0)"/>
+       <use xlink:href="#DejaVuSans-73" transform="translate(243.945312 0)"/>
+       <use xlink:href="#DejaVuSans-65" transform="translate(296.044922 0)"/>
+       <use xlink:href="#DejaVuSans-6e" transform="translate(357.568359 0)"/>
+       <use xlink:href="#DejaVuSans-73" transform="translate(420.947266 0)"/>
+       <use xlink:href="#DejaVuSans-69" transform="translate(473.046875 0)"/>
+       <use xlink:href="#DejaVuSans-74" transform="translate(500.830078 0)"/>
+       <use xlink:href="#DejaVuSans-69" transform="translate(540.039062 0)"/>
+       <use xlink:href="#DejaVuSans-76" transform="translate(567.822266 0)"/>
+       <use xlink:href="#DejaVuSans-69" transform="translate(627.001953 0)"/>
+       <use xlink:href="#DejaVuSans-74" transform="translate(654.785156 0)"/>
+       <use xlink:href="#DejaVuSans-79" transform="translate(693.994141 0)"/>
+       <use xlink:href="#DejaVuSans-20" transform="translate(753.173828 0)"/>
+       <use xlink:href="#DejaVuSans-72" transform="translate(784.960938 0)"/>
+       <use xlink:href="#DejaVuSans-61" transform="translate(826.074219 0)"/>
+       <use xlink:href="#DejaVuSans-6e" transform="translate(887.353516 0)"/>
+       <use xlink:href="#DejaVuSans-67" transform="translate(950.732422 0)"/>
+       <use xlink:href="#DejaVuSans-65" transform="translate(1014.208984 0)"/>
+      </g>
+     </g>
+    </g>
+   </g>
+   <g id="matplotlib.axis_12">
+    <g id="ytick_36">
+     <g id="line2d_110">
+      <path d="M 794.928125 760.096
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+     <g id="line2d_111">
+      <g>
+       <use xlink:href="#md5cb123175" x="794.928125" y="760.096" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_73">
+      <!-- 0 -->
+      <g transform="translate(781.565625 763.895219) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-30"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_37">
+     <g id="line2d_112">
+      <path d="M 794.928125 710.888405
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+      <g>
+       <use xlink:href="#md5cb123175" x="794.928125" y="710.888405" style="stroke: #000000; stroke-width: 0.8"/>
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+     </g>
+     <g id="text_74">
+      <!-- 1000 -->
+      <g transform="translate(762.478125 714.687624) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-31"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(190.869141 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_38">
+     <g id="line2d_114">
+      <path d="M 794.928125 661.68081
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+      <g>
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+     </g>
+     <g id="text_75">
+      <!-- 2000 -->
+      <g transform="translate(762.478125 665.480029) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-32"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(190.869141 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_39">
+     <g id="line2d_116">
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+      <g>
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+     </g>
+     <g id="text_76">
+      <!-- 3000 -->
+      <g transform="translate(762.478125 616.272434) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-33"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(190.869141 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_40">
+     <g id="line2d_118">
+      <path d="M 794.928125 563.26562
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+      <g>
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+      </g>
+     </g>
+     <g id="text_77">
+      <!-- 4000 -->
+      <g transform="translate(762.478125 567.064839) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-34"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(190.869141 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_41">
+     <g id="line2d_120">
+      <path d="M 794.928125 514.058025
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+      <g>
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+     </g>
+     <g id="text_78">
+      <!-- 5000 -->
+      <g transform="translate(762.478125 517.857244) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-35"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(190.869141 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="text_79">
+     <!-- Apogee above launch site (m) -->
+     <g transform="translate(756.398437 700.160031) rotate(-90) scale(0.1 -0.1)">
+      <use xlink:href="#DejaVuSans-41"/>
+      <use xlink:href="#DejaVuSans-70" transform="translate(68.408203 0)"/>
+      <use xlink:href="#DejaVuSans-6f" transform="translate(131.884766 0)"/>
+      <use xlink:href="#DejaVuSans-67" transform="translate(193.066406 0)"/>
+      <use xlink:href="#DejaVuSans-65" transform="translate(256.542969 0)"/>
+      <use xlink:href="#DejaVuSans-65" transform="translate(318.066406 0)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(379.589844 0)"/>
+      <use xlink:href="#DejaVuSans-61" transform="translate(411.376953 0)"/>
+      <use xlink:href="#DejaVuSans-62" transform="translate(472.65625 0)"/>
+      <use xlink:href="#DejaVuSans-6f" transform="translate(536.132812 0)"/>
+      <use xlink:href="#DejaVuSans-76" transform="translate(597.314453 0)"/>
+      <use xlink:href="#DejaVuSans-65" transform="translate(656.494141 0)"/>
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+      <use xlink:href="#DejaVuSans-6c" transform="translate(749.804688 0)"/>
+      <use xlink:href="#DejaVuSans-61" transform="translate(777.587891 0)"/>
+      <use xlink:href="#DejaVuSans-75" transform="translate(838.867188 0)"/>
+      <use xlink:href="#DejaVuSans-6e" transform="translate(902.246094 0)"/>
+      <use xlink:href="#DejaVuSans-63" transform="translate(965.625 0)"/>
+      <use xlink:href="#DejaVuSans-68" transform="translate(1020.605469 0)"/>
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+      <use xlink:href="#DejaVuSans-73" transform="translate(1115.771484 0)"/>
+      <use xlink:href="#DejaVuSans-69" transform="translate(1167.871094 0)"/>
+      <use xlink:href="#DejaVuSans-74" transform="translate(1195.654297 0)"/>
+      <use xlink:href="#DejaVuSans-65" transform="translate(1234.863281 0)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(1296.386719 0)"/>
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+      <use xlink:href="#DejaVuSans-6d" transform="translate(1367.1875 0)"/>
+      <use xlink:href="#DejaVuSans-29" transform="translate(1464.599609 0)"/>
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+    </g>
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+   <g id="patch_38">
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+   <g id="patch_39">
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+   <g id="text_80">
+    <!-- Recorded: 3,020 m -->
+    <g style="fill: #333333" transform="translate(904.033906 505.565518) scale(0.1 -0.1)">
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+     <use xlink:href="#DejaVuSans-65" transform="translate(64.982422 0)"/>
+     <use xlink:href="#DejaVuSans-63" transform="translate(126.505859 0)"/>
+     <use xlink:href="#DejaVuSans-6f" transform="translate(181.486328 0)"/>
+     <use xlink:href="#DejaVuSans-72" transform="translate(242.667969 0)"/>
+     <use xlink:href="#DejaVuSans-64" transform="translate(282.03125 0)"/>
+     <use xlink:href="#DejaVuSans-65" transform="translate(345.507812 0)"/>
+     <use xlink:href="#DejaVuSans-64" transform="translate(407.03125 0)"/>
+     <use xlink:href="#DejaVuSans-3a" transform="translate(470.507812 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(504.199219 0)"/>
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+     <use xlink:href="#DejaVuSans-2c" transform="translate(599.609375 0)"/>
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+     <use xlink:href="#DejaVuSans-6d" transform="translate(854.052734 0)"/>
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+    <g transform="translate(252.440677 905.759237) scale(0.11 -0.11)">
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+     <use xlink:href="#DejaVuSans-6d" transform="translate(318.066406 0)"/>
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+    <!-- +2.3% -->
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+     <use xlink:href="#DejaVuSans-2b"/>
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+     <g id="text_100">
+      <!-- 1000 -->
+      <g transform="translate(391.678125 1051.247326) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-31"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(190.869141 0)"/>
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+     </g>
+    </g>
+    <g id="ytick_50">
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+     </g>
+     <g id="text_101">
+      <!-- 2000 -->
+      <g transform="translate(391.678125 996.099434) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-32"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(190.869141 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_51">
+     <g id="line2d_154">
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+      <g>
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+      </g>
+     </g>
+     <g id="text_102">
+      <!-- 3000 -->
+      <g transform="translate(391.678125 940.951541) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-33"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(190.869141 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_52">
+     <g id="line2d_156">
+      <path d="M 424.128125 882.00443
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+     <g id="text_103">
+      <!-- 4000 -->
+      <g transform="translate(391.678125 885.803649) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-34"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(190.869141 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="text_104">
+     <!-- Apogee above launch site (m) -->
+     <g transform="translate(385.598438 1042.660031) rotate(-90) scale(0.1 -0.1)">
+      <use xlink:href="#DejaVuSans-41"/>
+      <use xlink:href="#DejaVuSans-70" transform="translate(68.408203 0)"/>
+      <use xlink:href="#DejaVuSans-6f" transform="translate(131.884766 0)"/>
+      <use xlink:href="#DejaVuSans-67" transform="translate(193.066406 0)"/>
+      <use xlink:href="#DejaVuSans-65" transform="translate(256.542969 0)"/>
+      <use xlink:href="#DejaVuSans-65" transform="translate(318.066406 0)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(379.589844 0)"/>
+      <use xlink:href="#DejaVuSans-61" transform="translate(411.376953 0)"/>
+      <use xlink:href="#DejaVuSans-62" transform="translate(472.65625 0)"/>
+      <use xlink:href="#DejaVuSans-6f" transform="translate(536.132812 0)"/>
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+      <use xlink:href="#DejaVuSans-65" transform="translate(656.494141 0)"/>
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+      <use xlink:href="#DejaVuSans-6c" transform="translate(749.804688 0)"/>
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+      <use xlink:href="#DejaVuSans-6e" transform="translate(902.246094 0)"/>
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+      <use xlink:href="#DejaVuSans-68" transform="translate(1020.605469 0)"/>
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+      <use xlink:href="#DejaVuSans-65" transform="translate(1234.863281 0)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(1296.386719 0)"/>
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+      <use xlink:href="#DejaVuSans-6d" transform="translate(1367.1875 0)"/>
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+   <g id="patch_52">
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+   <g id="patch_54">
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+   <g id="line2d_158">
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+   <g id="text_105">
+    <!-- Recorded: 3,213 m -->
+    <g style="fill: #333333" transform="translate(533.233906 848.065518) scale(0.1 -0.1)">
+     <use xlink:href="#DejaVuSans-52"/>
+     <use xlink:href="#DejaVuSans-65" transform="translate(64.982422 0)"/>
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+     <use xlink:href="#DejaVuSans-65" transform="translate(345.507812 0)"/>
+     <use xlink:href="#DejaVuSans-64" transform="translate(407.03125 0)"/>
+     <use xlink:href="#DejaVuSans-3a" transform="translate(470.507812 0)"/>
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+    <!-- 2,987 m -->
+    <g transform="translate(492.67401 918.803847) scale(0.11 -0.11)">
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+     <use xlink:href="#DejaVuSans-6d" transform="translate(318.066406 0)"/>
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+    <!-- -7.0% -->
+    <g transform="translate(499.567917 931.12144) scale(0.11 -0.11)">
+     <use xlink:href="#DejaVuSans-2d"/>
+     <use xlink:href="#DejaVuSans-37" transform="translate(36.083984 0)"/>
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+    <!-- 3,057 m -->
+    <g transform="translate(623.240677 905.759237) scale(0.11 -0.11)">
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+    <!-- -4.9% -->
+    <g transform="translate(630.134583 918.076831) scale(0.11 -0.11)">
+     <use xlink:href="#DejaVuSans-2d"/>
+     <use xlink:href="#DejaVuSans-34" transform="translate(36.083984 0)"/>
+     <use xlink:href="#DejaVuSans-2e" transform="translate(99.707031 0)"/>
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+       <use xlink:href="#DejaVuSans-4d" transform="translate(339.654297 0)"/>
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+      <!-- 0 -->
+      <g transform="translate(781.565625 1106.395219) scale(0.1 -0.1)">
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+      <!-- 1000 -->
+      <g transform="translate(762.478125 1069.730666) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-31"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(190.869141 0)"/>
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+      <!-- 2000 -->
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+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
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+      <!-- 3000 -->
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+      <!-- 4000 -->
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+      <!-- 5000 -->
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+      <!-- 6000 -->
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+     <use xlink:href="#DejaVuSans-2b"/>
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+      <!-- DATCOM -->
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+       <use xlink:href="#DejaVuSans-41" transform="translate(75.251953 0)"/>
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+      <g transform="translate(20.878125 1410.785948) scale(0.1 -0.1)">
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+      <g transform="translate(20.878125 1372.676677) scale(0.1 -0.1)">
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+       <use xlink:href="#DejaVuSans-6f" transform="translate(64.982422 0)"/>
+       <use xlink:href="#DejaVuSans-63" transform="translate(126.164062 0)"/>
+       <use xlink:href="#DejaVuSans-6b" transform="translate(181.144531 0)"/>
+       <use xlink:href="#DejaVuSans-65" transform="translate(235.429688 0)"/>
+       <use xlink:href="#DejaVuSans-74" transform="translate(296.953125 0)"/>
+       <use xlink:href="#DejaVuSans-50" transform="translate(336.162109 0)"/>
+       <use xlink:href="#DejaVuSans-79" transform="translate(396.464844 0)"/>
+      </g>
+      <!-- supplied CD -->
+      <g transform="translate(485.443542 1470.89225) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-73"/>
+       <use xlink:href="#DejaVuSans-75" transform="translate(52.099609 0)"/>
+       <use xlink:href="#DejaVuSans-70" transform="translate(115.478516 0)"/>
+       <use xlink:href="#DejaVuSans-70" transform="translate(178.955078 0)"/>
+       <use xlink:href="#DejaVuSans-6c" transform="translate(242.431641 0)"/>
+       <use xlink:href="#DejaVuSans-69" transform="translate(270.214844 0)"/>
+       <use xlink:href="#DejaVuSans-65" transform="translate(297.998047 0)"/>
+       <use xlink:href="#DejaVuSans-64" transform="translate(359.521484 0)"/>
+       <use xlink:href="#DejaVuSans-20" transform="translate(422.998047 0)"/>
+       <use xlink:href="#DejaVuSans-43" transform="translate(454.785156 0)"/>
+       <use xlink:href="#DejaVuSans-44" transform="translate(524.609375 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_22">
+     <g id="line2d_209">
+      <path d="M 646.091458 1445.096
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+" clip-path="url(#p4230150eaf)" style="fill: none; stroke: #b0b0b0; stroke-opacity: 0.2; stroke-width: 0.8; stroke-linecap: square"/>
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+     <g id="line2d_210">
+      <g>
+       <use xlink:href="#m1ce099ee8e" x="646.091458" y="1445.096" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_140">
+      <!-- DATCOM -->
+      <g transform="translate(624.794583 1459.694438) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-44"/>
+       <use xlink:href="#DejaVuSans-41" transform="translate(75.251953 0)"/>
+       <use xlink:href="#DejaVuSans-54" transform="translate(135.910156 0)"/>
+       <use xlink:href="#DejaVuSans-43" transform="translate(191.119141 0)"/>
+       <use xlink:href="#DejaVuSans-4f" transform="translate(260.943359 0)"/>
+       <use xlink:href="#DejaVuSans-4d" transform="translate(339.654297 0)"/>
+      </g>
+      <!-- with sensitivity range -->
+      <g transform="translate(592.306302 1470.89225) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-77"/>
+       <use xlink:href="#DejaVuSans-69" transform="translate(81.787109 0)"/>
+       <use xlink:href="#DejaVuSans-74" transform="translate(109.570312 0)"/>
+       <use xlink:href="#DejaVuSans-68" transform="translate(148.779297 0)"/>
+       <use xlink:href="#DejaVuSans-20" transform="translate(212.158203 0)"/>
+       <use xlink:href="#DejaVuSans-73" transform="translate(243.945312 0)"/>
+       <use xlink:href="#DejaVuSans-65" transform="translate(296.044922 0)"/>
+       <use xlink:href="#DejaVuSans-6e" transform="translate(357.568359 0)"/>
+       <use xlink:href="#DejaVuSans-73" transform="translate(420.947266 0)"/>
+       <use xlink:href="#DejaVuSans-69" transform="translate(473.046875 0)"/>
+       <use xlink:href="#DejaVuSans-74" transform="translate(500.830078 0)"/>
+       <use xlink:href="#DejaVuSans-69" transform="translate(540.039062 0)"/>
+       <use xlink:href="#DejaVuSans-76" transform="translate(567.822266 0)"/>
+       <use xlink:href="#DejaVuSans-69" transform="translate(627.001953 0)"/>
+       <use xlink:href="#DejaVuSans-74" transform="translate(654.785156 0)"/>
+       <use xlink:href="#DejaVuSans-79" transform="translate(693.994141 0)"/>
+       <use xlink:href="#DejaVuSans-20" transform="translate(753.173828 0)"/>
+       <use xlink:href="#DejaVuSans-72" transform="translate(784.960938 0)"/>
+       <use xlink:href="#DejaVuSans-61" transform="translate(826.074219 0)"/>
+       <use xlink:href="#DejaVuSans-6e" transform="translate(887.353516 0)"/>
+       <use xlink:href="#DejaVuSans-67" transform="translate(950.732422 0)"/>
+       <use xlink:href="#DejaVuSans-65" transform="translate(1014.208984 0)"/>
+      </g>
+     </g>
+    </g>
+   </g>
+   <g id="matplotlib.axis_22">
+    <g id="ytick_69">
+     <g id="line2d_211">
+      <path d="M 424.128125 1445.096
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+     </g>
+     <g id="line2d_212">
+      <g>
+       <use xlink:href="#md5cb123175" x="424.128125" y="1445.096" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_141">
+      <!-- 0 -->
+      <g transform="translate(410.765625 1448.895219) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-30"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_70">
+     <g id="line2d_213">
+      <path d="M 424.128125 1408.320169
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+     </g>
+     <g id="line2d_214">
+      <g>
+       <use xlink:href="#md5cb123175" x="424.128125" y="1408.320169" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_142">
+      <!-- 200 -->
+      <g transform="translate(398.040625 1412.119388) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-32"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_71">
+     <g id="line2d_215">
+      <path d="M 424.128125 1371.544338
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+     </g>
+     <g id="line2d_216">
+      <g>
+       <use xlink:href="#md5cb123175" x="424.128125" y="1371.544338" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_143">
+      <!-- 400 -->
+      <g transform="translate(398.040625 1375.343557) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-34"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_72">
+     <g id="line2d_217">
+      <path d="M 424.128125 1334.768507
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+     <g id="line2d_218">
+      <g>
+       <use xlink:href="#md5cb123175" x="424.128125" y="1334.768507" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_144">
+      <!-- 600 -->
+      <g transform="translate(398.040625 1338.567726) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-36"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_73">
+     <g id="line2d_219">
+      <path d="M 424.128125 1297.992676
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+     <g id="line2d_220">
+      <g>
+       <use xlink:href="#md5cb123175" x="424.128125" y="1297.992676" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_145">
+      <!-- 800 -->
+      <g transform="translate(398.040625 1301.791895) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-38"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_74">
+     <g id="line2d_221">
+      <path d="M 424.128125 1261.216845
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+     <g id="line2d_222">
+      <g>
+       <use xlink:href="#md5cb123175" x="424.128125" y="1261.216845" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_146">
+      <!-- 1000 -->
+      <g transform="translate(391.678125 1265.016064) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-31"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(190.869141 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_75">
+     <g id="line2d_223">
+      <path d="M 424.128125 1224.441014
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+     </g>
+     <g id="line2d_224">
+      <g>
+       <use xlink:href="#md5cb123175" x="424.128125" y="1224.441014" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_147">
+      <!-- 1200 -->
+      <g transform="translate(391.678125 1228.240233) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-31"/>
+       <use xlink:href="#DejaVuSans-32" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(190.869141 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_76">
+     <g id="line2d_225">
+      <path d="M 424.128125 1187.665183
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+     </g>
+     <g id="line2d_226">
+      <g>
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+     </g>
+     <g id="text_148">
+      <!-- 1400 -->
+      <g transform="translate(391.678125 1191.464402) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-31"/>
+       <use xlink:href="#DejaVuSans-34" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(190.869141 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="text_149">
+     <!-- Apogee above launch site (m) -->
+     <g transform="translate(385.598438 1385.160031) rotate(-90) scale(0.1 -0.1)">
+      <use xlink:href="#DejaVuSans-41"/>
+      <use xlink:href="#DejaVuSans-70" transform="translate(68.408203 0)"/>
+      <use xlink:href="#DejaVuSans-6f" transform="translate(131.884766 0)"/>
+      <use xlink:href="#DejaVuSans-67" transform="translate(193.066406 0)"/>
+      <use xlink:href="#DejaVuSans-65" transform="translate(256.542969 0)"/>
+      <use xlink:href="#DejaVuSans-65" transform="translate(318.066406 0)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(379.589844 0)"/>
+      <use xlink:href="#DejaVuSans-61" transform="translate(411.376953 0)"/>
+      <use xlink:href="#DejaVuSans-62" transform="translate(472.65625 0)"/>
+      <use xlink:href="#DejaVuSans-6f" transform="translate(536.132812 0)"/>
+      <use xlink:href="#DejaVuSans-76" transform="translate(597.314453 0)"/>
+      <use xlink:href="#DejaVuSans-65" transform="translate(656.494141 0)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(718.017578 0)"/>
+      <use xlink:href="#DejaVuSans-6c" transform="translate(749.804688 0)"/>
+      <use xlink:href="#DejaVuSans-61" transform="translate(777.587891 0)"/>
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+      <use xlink:href="#DejaVuSans-6e" transform="translate(902.246094 0)"/>
+      <use xlink:href="#DejaVuSans-63" transform="translate(965.625 0)"/>
+      <use xlink:href="#DejaVuSans-68" transform="translate(1020.605469 0)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(1083.984375 0)"/>
+      <use xlink:href="#DejaVuSans-73" transform="translate(1115.771484 0)"/>
+      <use xlink:href="#DejaVuSans-69" transform="translate(1167.871094 0)"/>
+      <use xlink:href="#DejaVuSans-74" transform="translate(1195.654297 0)"/>
+      <use xlink:href="#DejaVuSans-65" transform="translate(1234.863281 0)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(1296.386719 0)"/>
+      <use xlink:href="#DejaVuSans-28" transform="translate(1328.173828 0)"/>
+      <use xlink:href="#DejaVuSans-6d" transform="translate(1367.1875 0)"/>
+      <use xlink:href="#DejaVuSans-29" transform="translate(1464.599609 0)"/>
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+    </g>
+   </g>
+   <g id="patch_73">
+    <path d="M 481.577458 1445.096
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+   <g id="patch_74">
+    <path d="M 612.144125 1445.096
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+   <g id="patch_75">
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+   <g id="patch_76">
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+   <g id="line2d_227">
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+   <g id="text_150">
+    <!-- Recorded: 860 m -->
+    <g style="fill: #333333" transform="translate(538.004219 1190.565518) scale(0.1 -0.1)">
+     <use xlink:href="#DejaVuSans-52"/>
+     <use xlink:href="#DejaVuSans-65" transform="translate(64.982422 0)"/>
+     <use xlink:href="#DejaVuSans-63" transform="translate(126.505859 0)"/>
+     <use xlink:href="#DejaVuSans-6f" transform="translate(181.486328 0)"/>
+     <use xlink:href="#DejaVuSans-72" transform="translate(242.667969 0)"/>
+     <use xlink:href="#DejaVuSans-64" transform="translate(282.03125 0)"/>
+     <use xlink:href="#DejaVuSans-65" transform="translate(345.507812 0)"/>
+     <use xlink:href="#DejaVuSans-64" transform="translate(407.03125 0)"/>
+     <use xlink:href="#DejaVuSans-3a" transform="translate(470.507812 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(504.199219 0)"/>
+     <use xlink:href="#DejaVuSans-38" transform="translate(535.986328 0)"/>
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+     <use xlink:href="#DejaVuSans-30" transform="translate(663.232422 0)"/>
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+     <use xlink:href="#DejaVuSans-6d" transform="translate(758.642578 0)"/>
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+    <!-- DATCOM scenarios: 921–967 m -->
+    <g style="fill: #333333" transform="translate(502.83 1201.76333) scale(0.1 -0.1)">
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+     <use xlink:href="#DejaVuSans-41" transform="translate(75.251953 0)"/>
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+     <use xlink:href="#DejaVuSans-6e" transform="translate(626.324219 0)"/>
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+     <use xlink:href="#DejaVuSans-6f" transform="translate(819.878906 0)"/>
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+     <use xlink:href="#DejaVuSans-6d" transform="translate(1462.164062 0)"/>
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+    <!-- Recorded apogee outside scenario range -->
+    <g style="fill: #333333" transform="translate(478.160469 1212.961143) scale(0.1 -0.1)">
+     <use xlink:href="#DejaVuSans-52"/>
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+      <!-- with sensitivity range -->
+      <g transform="translate(963.106302 331.70825) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-77"/>
+       <use xlink:href="#DejaVuSans-69" transform="translate(81.787109 0)"/>
+       <use xlink:href="#DejaVuSans-74" transform="translate(109.570312 0)"/>
+       <use xlink:href="#DejaVuSans-68" transform="translate(148.779297 0)"/>
+       <use xlink:href="#DejaVuSans-20" transform="translate(212.158203 0)"/>
+       <use xlink:href="#DejaVuSans-73" transform="translate(243.945312 0)"/>
+       <use xlink:href="#DejaVuSans-65" transform="translate(296.044922 0)"/>
+       <use xlink:href="#DejaVuSans-6e" transform="translate(357.568359 0)"/>
+       <use xlink:href="#DejaVuSans-73" transform="translate(420.947266 0)"/>
+       <use xlink:href="#DejaVuSans-69" transform="translate(473.046875 0)"/>
+       <use xlink:href="#DejaVuSans-74" transform="translate(500.830078 0)"/>
+       <use xlink:href="#DejaVuSans-69" transform="translate(540.039062 0)"/>
+       <use xlink:href="#DejaVuSans-76" transform="translate(567.822266 0)"/>
+       <use xlink:href="#DejaVuSans-69" transform="translate(627.001953 0)"/>
+       <use xlink:href="#DejaVuSans-74" transform="translate(654.785156 0)"/>
+       <use xlink:href="#DejaVuSans-79" transform="translate(693.994141 0)"/>
+       <use xlink:href="#DejaVuSans-20" transform="translate(753.173828 0)"/>
+       <use xlink:href="#DejaVuSans-72" transform="translate(784.960938 0)"/>
+       <use xlink:href="#DejaVuSans-61" transform="translate(826.074219 0)"/>
+       <use xlink:href="#DejaVuSans-6e" transform="translate(887.353516 0)"/>
+       <use xlink:href="#DejaVuSans-67" transform="translate(950.732422 0)"/>
+       <use xlink:href="#DejaVuSans-65" transform="translate(1014.208984 0)"/>
+      </g>
+     </g>
+    </g>
+   </g>
+   <g id="matplotlib.axis_6">
+    <g id="ytick_14">
+     <g id="line2d_39">
+      <path d="M 794.928125 305.912
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+     <g id="line2d_40">
+      <g>
+       <use xlink:href="#mbc59ce5423" x="794.928125" y="305.912" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_30">
+      <!-- 0 -->
+      <g transform="translate(781.565625 309.711219) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-30"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_15">
+     <g id="line2d_41">
+      <path d="M 794.928125 281.147594
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+     <g id="line2d_42">
+      <g>
+       <use xlink:href="#mbc59ce5423" x="794.928125" y="281.147594" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_31">
+      <!-- 1000 -->
+      <g transform="translate(762.478125 284.946813) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-31"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(190.869141 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_16">
+     <g id="line2d_43">
+      <path d="M 794.928125 256.383188
+L 1108.288125 256.383188
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+     </g>
+     <g id="line2d_44">
+      <g>
+       <use xlink:href="#mbc59ce5423" x="794.928125" y="256.383188" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_32">
+      <!-- 2000 -->
+      <g transform="translate(762.478125 260.182407) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-32"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(190.869141 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_17">
+     <g id="line2d_45">
+      <path d="M 794.928125 231.618783
+L 1108.288125 231.618783
+" clip-path="url(#pac4e843f95)" style="fill: none; stroke: #b0b0b0; stroke-opacity: 0.2; stroke-width: 0.8; stroke-linecap: square"/>
+     </g>
+     <g id="line2d_46">
+      <g>
+       <use xlink:href="#mbc59ce5423" x="794.928125" y="231.618783" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_33">
+      <!-- 3000 -->
+      <g transform="translate(762.478125 235.418001) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-33"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(190.869141 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_18">
+     <g id="line2d_47">
+      <path d="M 794.928125 206.854377
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+     </g>
+     <g id="line2d_48">
+      <g>
+       <use xlink:href="#mbc59ce5423" x="794.928125" y="206.854377" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_34">
+      <!-- 4000 -->
+      <g transform="translate(762.478125 210.653595) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-34"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(190.869141 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_19">
+     <g id="line2d_49">
+      <path d="M 794.928125 182.089971
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+     <g id="line2d_50">
+      <g>
+       <use xlink:href="#mbc59ce5423" x="794.928125" y="182.089971" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_35">
+      <!-- 5000 -->
+      <g transform="translate(762.478125 185.88919) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-35"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(190.869141 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_20">
+     <g id="line2d_51">
+      <path d="M 794.928125 157.325565
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+     <g id="line2d_52">
+      <g>
+       <use xlink:href="#mbc59ce5423" x="794.928125" y="157.325565" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_36">
+      <!-- 6000 -->
+      <g transform="translate(762.478125 161.124784) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-36"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(190.869141 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_21">
+     <g id="line2d_53">
+      <path d="M 794.928125 132.561159
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+     <g id="line2d_54">
+      <g>
+       <use xlink:href="#mbc59ce5423" x="794.928125" y="132.561159" style="stroke: #000000; stroke-width: 0.8"/>
+      </g>
+     </g>
+     <g id="text_37">
+      <!-- 7000 -->
+      <g transform="translate(762.478125 136.360378) scale(0.1 -0.1)">
+       <use xlink:href="#DejaVuSans-37"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(127.246094 0)"/>
+       <use xlink:href="#DejaVuSans-30" transform="translate(190.869141 0)"/>
+      </g>
+     </g>
+    </g>
+    <g id="text_38">
+     <!-- Apogee above launch site (m) -->
+     <g transform="translate(756.398437 290.154031) rotate(-90) scale(0.1 -0.1)">
+      <use xlink:href="#DejaVuSans-41"/>
+      <use xlink:href="#DejaVuSans-70" transform="translate(68.408203 0)"/>
+      <use xlink:href="#DejaVuSans-6f" transform="translate(131.884766 0)"/>
+      <use xlink:href="#DejaVuSans-67" transform="translate(193.066406 0)"/>
+      <use xlink:href="#DejaVuSans-65" transform="translate(256.542969 0)"/>
+      <use xlink:href="#DejaVuSans-65" transform="translate(318.066406 0)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(379.589844 0)"/>
+      <use xlink:href="#DejaVuSans-61" transform="translate(411.376953 0)"/>
+      <use xlink:href="#DejaVuSans-62" transform="translate(472.65625 0)"/>
+      <use xlink:href="#DejaVuSans-6f" transform="translate(536.132812 0)"/>
+      <use xlink:href="#DejaVuSans-76" transform="translate(597.314453 0)"/>
+      <use xlink:href="#DejaVuSans-65" transform="translate(656.494141 0)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(718.017578 0)"/>
+      <use xlink:href="#DejaVuSans-6c" transform="translate(749.804688 0)"/>
+      <use xlink:href="#DejaVuSans-61" transform="translate(777.587891 0)"/>
+      <use xlink:href="#DejaVuSans-75" transform="translate(838.867188 0)"/>
+      <use xlink:href="#DejaVuSans-6e" transform="translate(902.246094 0)"/>
+      <use xlink:href="#DejaVuSans-63" transform="translate(965.625 0)"/>
+      <use xlink:href="#DejaVuSans-68" transform="translate(1020.605469 0)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(1083.984375 0)"/>
+      <use xlink:href="#DejaVuSans-73" transform="translate(1115.771484 0)"/>
+      <use xlink:href="#DejaVuSans-69" transform="translate(1167.871094 0)"/>
+      <use xlink:href="#DejaVuSans-74" transform="translate(1195.654297 0)"/>
+      <use xlink:href="#DejaVuSans-65" transform="translate(1234.863281 0)"/>
+      <use xlink:href="#DejaVuSans-20" transform="translate(1296.386719 0)"/>
+      <use xlink:href="#DejaVuSans-28" transform="translate(1328.173828 0)"/>
+      <use xlink:href="#DejaVuSans-6d" transform="translate(1367.1875 0)"/>
+      <use xlink:href="#DejaVuSans-29" transform="translate(1464.599609 0)"/>
+     </g>
+    </g>
+   </g>
+   <g id="patch_17">
+    <path d="M 852.377458 305.912
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+   <g id="patch_18">
+    <path d="M 982.944125 305.912
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+   <g id="patch_19">
+    <path d="M 794.928125 305.912
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+   <g id="patch_20">
+    <path d="M 794.928125 305.912
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+   </g>
+   <g id="line2d_55">
+    <path d="M 794.928125 220.648151
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+   </g>
+   <g id="text_39">
+    <!-- Recorded: 3,443 m -->
+    <g style="fill: #333333" transform="translate(904.033906 137.086838) scale(0.1 -0.1)">
+     <use xlink:href="#DejaVuSans-52"/>
+     <use xlink:href="#DejaVuSans-65" transform="translate(64.982422 0)"/>
+     <use xlink:href="#DejaVuSans-63" transform="translate(126.505859 0)"/>
+     <use xlink:href="#DejaVuSans-6f" transform="translate(181.486328 0)"/>
+     <use xlink:href="#DejaVuSans-72" transform="translate(242.667969 0)"/>
+     <use xlink:href="#DejaVuSans-64" transform="translate(282.03125 0)"/>
+     <use xlink:href="#DejaVuSans-65" transform="translate(345.507812 0)"/>
+     <use xlink:href="#DejaVuSans-64" transform="translate(407.03125 0)"/>
+     <use xlink:href="#DejaVuSans-3a" transform="translate(470.507812 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(504.199219 0)"/>
+     <use xlink:href="#DejaVuSans-33" transform="translate(535.986328 0)"/>
+     <use xlink:href="#DejaVuSans-2c" transform="translate(599.609375 0)"/>
+     <use xlink:href="#DejaVuSans-34" transform="translate(631.396484 0)"/>
+     <use xlink:href="#DejaVuSans-34" transform="translate(695.019531 0)"/>
+     <use xlink:href="#DejaVuSans-33" transform="translate(758.642578 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(822.265625 0)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(854.052734 0)"/>
+    </g>
+    <!-- DATCOM scenarios: 3,794–4,832 m -->
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+     <use xlink:href="#DejaVuSans-44"/>
+     <use xlink:href="#DejaVuSans-41" transform="translate(75.251953 0)"/>
+     <use xlink:href="#DejaVuSans-54" transform="translate(135.910156 0)"/>
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+     <use xlink:href="#DejaVuSans-4f" transform="translate(260.943359 0)"/>
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+     <use xlink:href="#DejaVuSans-63" transform="translate(509.820312 0)"/>
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+     <use xlink:href="#DejaVuSans-6e" transform="translate(626.324219 0)"/>
+     <use xlink:href="#DejaVuSans-61" transform="translate(689.703125 0)"/>
+     <use xlink:href="#DejaVuSans-72" transform="translate(750.982422 0)"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(792.095703 0)"/>
+     <use xlink:href="#DejaVuSans-6f" transform="translate(819.878906 0)"/>
+     <use xlink:href="#DejaVuSans-73" transform="translate(881.060547 0)"/>
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+     <use xlink:href="#DejaVuSans-2c" transform="translate(1062.261719 0)"/>
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+     <use xlink:href="#DejaVuSans-2c" transform="translate(1398.541016 0)"/>
+     <use xlink:href="#DejaVuSans-38" transform="translate(1430.328125 0)"/>
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+     <use xlink:href="#DejaVuSans-32" transform="translate(1557.574219 0)"/>
+     <use xlink:href="#DejaVuSans-20" transform="translate(1621.197266 0)"/>
+     <use xlink:href="#DejaVuSans-6d" transform="translate(1652.984375 0)"/>
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+    <!-- Recorded apogee outside scenario range -->
+    <g style="fill: #333333" transform="translate(848.960469 159.482463) scale(0.1 -0.1)">
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+     <use xlink:href="#DejaVuSans-65" transform="translate(64.982422 0)"/>
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+     <use xlink:href="#DejaVuSans-65" transform="translate(813.232422 0)"/>
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+     <use xlink:href="#DejaVuSans-65" transform="translate(1213.671875 0)"/>
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+     <use xlink:href="#DejaVuSans-61" transform="translate(1538.964844 0)"/>
+     <use xlink:href="#DejaVuSans-72" transform="translate(1600.244141 0)"/>
+     <use xlink:href="#DejaVuSans-69" transform="translate(1641.357422 0)"/>
+     <use xlink:href="#DejaVuSans-6f" transform="translate(1669.140625 0)"/>
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diff --git a/docs/examples/datcom/comparison_data/asteria_cd_uncertainty_comparison.csv b/docs/examples/datcom/comparison_data/asteria_cd_uncertainty_comparison.csv
new file mode 100644
index 000000000..b5fcfb7ed
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/asteria_cd_uncertainty_comparison.csv
@@ -0,0 +1,303 @@
+rocket,phase,mach,alpha_deg,beta_deg,rocketpy_cd,rocketpy_available,datcom_cd,datcom_low_cd,datcom_high_cd,drag_excursion_pct,apogee_fitted_constant_cd,reference_peak_mach,above_reference_peak,within_native_grid
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diff --git a/docs/examples/datcom/comparison_data/asteria_cd_uncertainty_comparison.png b/docs/examples/datcom/comparison_data/asteria_cd_uncertainty_comparison.png
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diff --git a/docs/examples/datcom/comparison_data/astra_uncertainty.txt b/docs/examples/datcom/comparison_data/astra_uncertainty.txt
new file mode 100644
index 000000000..c032acde3
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/astra_uncertainty.txt
@@ -0,0 +1,29 @@
+
+Apogee State
+
+Apogee Time: 26.806 s
+Apogee Altitude: 3855.934 ± 335.744 m (ASL) | 3695.934 ± 335.744 m (AGL)
+Apogee Freestream Speed: 12.707 m/s
+DATCOM nominal: peak ascent speed 319.411 ± 3.857 m/s
+DATCOM coefficient upper: apogee 3436.885 m (AGL) | peak ascent speed 315.999 m/s | apogee freestream speed 11.928 m/s
+DATCOM coefficient lower: apogee 4031.678 m (AGL) | peak ascent speed 323.268 m/s | apogee freestream speed 13.765 m/s
+DATCOM ±: largest departure from nominal in two engineering sensitivity runs; not a confidence interval. Upper/lower mean more/less drag and farther-aft/forward CP, not guaranteed altitude bounds.
+DATCOM sensitivity: CD at least ±20%; nose-to-CP distance ±2%.
+DATCOM nominal: 90.5% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+DATCOM upper: 91.6% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+DATCOM lower: 89.0% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+Apogee X position: 337.021 m
+Apogee Y position: -321.485 m
+Apogee latitude: 39.3868081°
+Apogee longitude: -8.2850414°
+
+Stability Margin
+
+Initial Stability Margin: 3.477 ± 0.324 c at 0.00 s
+Out of Rail Stability Margin: 3.582 ± 0.324 c at 0.47 s
+Maximum Stability Margin: 4.341 c at 3.17 s
+Minimum Stability Margin: 3.477 c at 0.00 s
+DATCOM nominal stability at apogee: 4.223 ± 0.324 c
+DATCOM nominal ascent stability min/max: 3.477 ± 0.324 / 4.341 ± 0.323 c
+DATCOM coefficient upper stability: initial 3.801 c | rail exit 3.906 c | apogee 4.547 c | ascent min/max 3.801 / 4.664 c
+DATCOM coefficient lower stability: initial 3.154 c | rail exit 3.258 c | apogee 3.899 c | ascent min/max 3.154 / 4.018 c
diff --git a/docs/examples/datcom/comparison_data/bella_lui_uncertainty.txt b/docs/examples/datcom/comparison_data/bella_lui_uncertainty.txt
new file mode 100644
index 000000000..65df29810
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/bella_lui_uncertainty.txt
@@ -0,0 +1,29 @@
+
+Apogee State
+
+Apogee Time: 10.675 s
+Apogee Altitude: 872.877 ± 8.363 m (ASL) | 465.877 ± 8.363 m (AGL)
+Apogee Freestream Speed: 4.529 m/s
+DATCOM nominal: peak ascent speed 86.536 ± 0.298 m/s
+DATCOM coefficient upper: apogee 457.818 m (AGL) | peak ascent speed 86.239 m/s | apogee freestream speed 4.549 m/s
+DATCOM coefficient lower: apogee 474.239 m (AGL) | peak ascent speed 86.821 m/s | apogee freestream speed 4.497 m/s
+DATCOM ±: largest departure from nominal in two engineering sensitivity runs; not a confidence interval. Upper/lower mean more/less drag and farther-aft/forward CP, not guaranteed altitude bounds.
+DATCOM sensitivity: CD at least ±20%; nose-to-CP distance ±2%.
+DATCOM nominal: 100.0% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+DATCOM upper: 100.0% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+DATCOM lower: 100.0% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+Apogee X position: -12.872 m
+Apogee Y position: -25.604 m
+Apogee latitude: 47.2132456°
+Apogee longitude: 9.0031655°
+
+Stability Margin
+
+Initial Stability Margin: 3.304 ± 0.268 c at 0.00 s
+Out of Rail Stability Margin: 3.348 ± 0.268 c at 0.36 s
+Maximum Stability Margin: 3.560 c at 2.38 s
+Minimum Stability Margin: 3.304 c at 0.00 s
+DATCOM nominal stability at apogee: 3.536 ± 0.268 c
+DATCOM nominal ascent stability min/max: 3.304 ± 0.268 / 3.560 ± 0.269 c
+DATCOM coefficient upper stability: initial 3.572 c | rail exit 3.616 c | apogee 3.804 c | ascent min/max 3.572 / 3.828 c
+DATCOM coefficient lower stability: initial 3.035 c | rail exit 3.079 c | apogee 3.267 c | ascent min/max 3.035 / 3.291 c
diff --git a/docs/examples/datcom/comparison_data/cavour_uncertainty.txt b/docs/examples/datcom/comparison_data/cavour_uncertainty.txt
new file mode 100644
index 000000000..e0b0c4e33
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/cavour_uncertainty.txt
@@ -0,0 +1,29 @@
+
+Apogee State
+
+Apogee Time: 24.313 s
+Apogee Altitude: 3401.609 ± 408.586 m (ASL) | 3251.609 ± 408.586 m (AGL)
+Apogee Freestream Speed: 24.890 m/s
+DATCOM nominal: peak ascent speed 323.434 ± 8.425 m/s
+DATCOM coefficient upper: apogee 2968.722 m (AGL) | peak ascent speed 316.696 m/s | apogee freestream speed 23.538 m/s
+DATCOM coefficient lower: apogee 3660.195 m (AGL) | peak ascent speed 331.859 m/s | apogee freestream speed 26.943 m/s
+DATCOM ±: largest departure from nominal in two engineering sensitivity runs; not a confidence interval. Upper/lower mean more/less drag and farther-aft/forward CP, not guaranteed altitude bounds.
+DATCOM sensitivity: CD at least ±20%; nose-to-CP distance ±2%.
+DATCOM nominal: 89.6% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+DATCOM upper: 90.8% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+DATCOM lower: 87.5% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+Apogee X position: 549.232 m
+Apogee Y position: -619.317 m
+Apogee latitude: 39.3831209°
+Apogee longitude: -8.2814220°
+
+Stability Margin
+
+Initial Stability Margin: 5.465 ± 0.438 c at 0.00 s
+Out of Rail Stability Margin: 5.701 ± 0.439 c at 0.51 s
+Maximum Stability Margin: 7.540 c at 5.16 s
+Minimum Stability Margin: 5.465 c at 0.00 s
+DATCOM nominal stability at apogee: 7.284 ± 0.438 c
+DATCOM nominal ascent stability min/max: 5.465 ± 0.438 / 7.540 ± 0.444 c
+DATCOM coefficient upper stability: initial 5.904 c | rail exit 6.140 c | apogee 7.722 c | ascent min/max 5.904 / 7.983 c
+DATCOM coefficient lower stability: initial 5.027 c | rail exit 5.263 c | apogee 6.846 c | ascent min/max 5.027 / 7.096 c
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+       <use xlink:href="#DejaVuSans-2e" transform="translate(63.623047 0)"/>
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+      <!-- 0.2 -->
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+       <use xlink:href="#DejaVuSans-30"/>
+       <use xlink:href="#DejaVuSans-2e" transform="translate(63.623047 0)"/>
+       <use xlink:href="#DejaVuSans-32" transform="translate(95.410156 0)"/>
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+     <g id="text_98">
+      <!-- 0.4 -->
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+       <use xlink:href="#DejaVuSans-30"/>
+       <use xlink:href="#DejaVuSans-2e" transform="translate(63.623047 0)"/>
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+     <g id="text_99">
+      <!-- 0.6 -->
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+       <use xlink:href="#DejaVuSans-30"/>
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+      <!-- 0.8 -->
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+       <use xlink:href="#DejaVuSans-30"/>
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+      <!-- 1.0 -->
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diff --git a/docs/examples/datcom/comparison_data/erebus_uncertainty.txt b/docs/examples/datcom/comparison_data/erebus_uncertainty.txt
new file mode 100644
index 000000000..3dfa5fc47
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/erebus_uncertainty.txt
@@ -0,0 +1,29 @@
+
+Apogee State
+
+Apogee Time: 24.420 s
+Apogee Altitude: 3413.423 ± 359.573 m (ASL) | 3253.423 ± 359.573 m (AGL)
+Apogee Freestream Speed: 15.665 m/s
+DATCOM nominal: peak ascent speed 308.520 ± 6.947 m/s
+DATCOM coefficient upper: apogee 2988.005 m (AGL) | peak ascent speed 302.862 m/s | apogee freestream speed 14.486 m/s
+DATCOM coefficient lower: apogee 3612.996 m (AGL) | peak ascent speed 315.467 m/s | apogee freestream speed 17.266 m/s
+DATCOM ±: largest departure from nominal in two engineering sensitivity runs; not a confidence interval. Upper/lower mean more/less drag and farther-aft/forward CP, not guaranteed altitude bounds.
+DATCOM sensitivity: CD at least ±20%; nose-to-CP distance ±2%.
+DATCOM nominal: 90.7% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+DATCOM upper: 91.7% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+DATCOM lower: 89.1% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+Apogee X position: 250.306 m
+Apogee Y position: -372.223 m
+Apogee latitude: 39.3863517°
+Apogee longitude: -8.2860507°
+
+Stability Margin
+
+Initial Stability Margin: 4.450 ± 0.375 c at 0.00 s
+Out of Rail Stability Margin: 4.641 ± 0.375 c at 0.43 s
+Maximum Stability Margin: 5.874 c at 4.70 s
+Minimum Stability Margin: 4.450 c at 0.00 s
+DATCOM nominal stability at apogee: 5.703 ± 0.374 c
+DATCOM nominal ascent stability min/max: 4.450 ± 0.375 / 5.874 ± 0.378 c
+DATCOM coefficient upper stability: initial 4.825 c | rail exit 5.015 c | apogee 6.077 c | ascent min/max 4.825 / 6.252 c
+DATCOM coefficient lower stability: initial 4.076 c | rail exit 4.266 c | apogee 5.328 c | ascent min/max 4.076 / 5.496 c
diff --git a/docs/examples/datcom/comparison_data/genesis_uncertainty.txt b/docs/examples/datcom/comparison_data/genesis_uncertainty.txt
new file mode 100644
index 000000000..05aeadb37
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/genesis_uncertainty.txt
@@ -0,0 +1,29 @@
+
+Apogee State
+
+Apogee Time: 25.178 s
+Apogee Altitude: 3457.239 ± 287.183 m (ASL) | 3297.239 ± 287.183 m (AGL)
+Apogee Freestream Speed: 28.756 m/s
+DATCOM nominal: peak ascent speed 302.763 ± 3.928 m/s
+DATCOM coefficient upper: apogee 3067.920 m (AGL) | peak ascent speed 299.191 m/s | apogee freestream speed 26.985 m/s
+DATCOM coefficient lower: apogee 3584.422 m (AGL) | peak ascent speed 306.692 m/s | apogee freestream speed 30.970 m/s
+DATCOM ±: largest departure from nominal in two engineering sensitivity runs; not a confidence interval. Upper/lower mean more/less drag and farther-aft/forward CP, not guaranteed altitude bounds.
+DATCOM sensitivity: CD at least ±20%; nose-to-CP distance ±2%.
+DATCOM nominal: 93.0% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+DATCOM upper: 93.9% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+DATCOM lower: 91.8% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+Apogee X position: 433.092 m
+Apogee Y position: -650.493 m
+Apogee latitude: 39.3830985°
+Apogee longitude: -8.2833297°
+
+Stability Margin
+
+Initial Stability Margin: 3.768 ± 0.412 c at 0.00 s
+Out of Rail Stability Margin: 3.926 ± 0.412 c at 0.54 s
+Maximum Stability Margin: 5.108 c at 3.63 s
+Minimum Stability Margin: 3.768 c at 0.00 s
+DATCOM nominal stability at apogee: 4.891 ± 0.412 c
+DATCOM nominal ascent stability min/max: 3.768 ± 0.412 / 5.108 ± 0.406 c
+DATCOM coefficient upper stability: initial 4.180 c | rail exit 4.339 c | apogee 5.303 c | ascent min/max 4.180 / 5.514 c
+DATCOM coefficient lower stability: initial 3.356 c | rail exit 3.514 c | apogee 4.480 c | ascent min/max 3.356 / 4.707 c
diff --git a/docs/examples/datcom/comparison_data/halcyon_uncertainty.txt b/docs/examples/datcom/comparison_data/halcyon_uncertainty.txt
new file mode 100644
index 000000000..f468359c0
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/halcyon_uncertainty.txt
@@ -0,0 +1,29 @@
+
+Apogee State
+
+Apogee Time: 27.798 s
+Apogee Altitude: 3660.232 ± 227.377 m (ASL) | 3530.232 ± 227.377 m (AGL)
+Apogee Freestream Speed: 22.288 m/s
+DATCOM nominal: peak ascent speed 284.792 ± 3.112 m/s
+DATCOM coefficient upper: apogee 3334.200 m (AGL) | peak ascent speed 281.754 m/s | apogee freestream speed 21.616 m/s
+DATCOM coefficient lower: apogee 3757.609 m (AGL) | peak ascent speed 287.904 m/s | apogee freestream speed 24.481 m/s
+DATCOM ±: largest departure from nominal in two engineering sensitivity runs; not a confidence interval. Upper/lower mean more/less drag and farther-aft/forward CP, not guaranteed altitude bounds.
+DATCOM sensitivity: CD at least ±20%; nose-to-CP distance ±2%.
+DATCOM nominal: 97.3% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+DATCOM upper: 98.1% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+DATCOM lower: 96.5% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+Apogee X position: 869.078 m
+Apogee Y position: -429.109 m
+Apogee latitude: 39.3848316°
+Apogee longitude: -8.2776994°
+
+Stability Margin
+
+Initial Stability Margin: 4.410 ± 0.353 c at 0.00 s
+Out of Rail Stability Margin: 4.502 ± 0.353 c at 0.67 s
+Maximum Stability Margin: 5.625 c at 9.40 s
+Minimum Stability Margin: 4.404 c at 0.07 s
+DATCOM nominal stability at apogee: 5.426 ± 0.353 c
+DATCOM nominal ascent stability min/max: 4.404 ± 0.353 / 5.625 ± 0.357 c
+DATCOM coefficient upper stability: initial 4.763 c | rail exit 4.855 c | apogee 5.779 c | ascent min/max 4.757 / 5.982 c
+DATCOM coefficient lower stability: initial 4.057 c | rail exit 4.148 c | apogee 5.074 c | ascent min/max 4.051 / 5.268 c
diff --git a/docs/examples/datcom/comparison_data/juno3_uncertainty.txt b/docs/examples/datcom/comparison_data/juno3_uncertainty.txt
new file mode 100644
index 000000000..366486a49
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/juno3_uncertainty.txt
@@ -0,0 +1,29 @@
+
+Apogee State
+
+Apogee Time: 26.635 s
+Apogee Altitude: 4536.550 ± 167.270 m (ASL) | 3056.550 ± 167.270 m (AGL)
+Apogee Freestream Speed: 57.481 m/s
+DATCOM nominal: peak ascent speed 250.806 ± 2.102 m/s
+DATCOM coefficient upper: apogee 2907.191 m (AGL) | peak ascent speed 248.771 m/s | apogee freestream speed 57.843 m/s
+DATCOM coefficient lower: apogee 3223.819 m (AGL) | peak ascent speed 252.908 m/s | apogee freestream speed 56.974 m/s
+DATCOM ±: largest departure from nominal in two engineering sensitivity runs; not a confidence interval. Upper/lower mean more/less drag and farther-aft/forward CP, not guaranteed altitude bounds.
+DATCOM sensitivity: CD at least ±20%; nose-to-CP distance ±2%.
+DATCOM nominal: 100.0% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+DATCOM upper: 100.0% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+DATCOM lower: 100.0% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+Apogee X position: -700.963 m
+Apogee Y position: -917.500 m
+Apogee latitude: 32.9311266°
+Apogee longitude: -106.9194957°
+
+Stability Margin
+
+Initial Stability Margin: 4.009 ± 0.332 c at 0.00 s
+Out of Rail Stability Margin: 4.094 ± 0.332 c at 0.89 s
+Maximum Stability Margin: 5.398 c at 9.44 s
+Minimum Stability Margin: 4.009 c at 0.00 s
+DATCOM nominal stability at apogee: 5.338 ± 0.332 c
+DATCOM nominal ascent stability min/max: 4.009 ± 0.332 / 5.398 ± 0.333 c
+DATCOM coefficient upper stability: initial 4.341 c | rail exit 4.426 c | apogee 5.671 c | ascent min/max 4.341 / 5.731 c
+DATCOM coefficient lower stability: initial 3.676 c | rail exit 3.762 c | apogee 5.006 c | ascent min/max 3.676 / 5.064 c
diff --git a/docs/examples/datcom/comparison_data/lince_uncertainty.txt b/docs/examples/datcom/comparison_data/lince_uncertainty.txt
new file mode 100644
index 000000000..0978c75a9
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/lince_uncertainty.txt
@@ -0,0 +1,29 @@
+
+Apogee State
+
+Apogee Time: 28.785 s
+Apogee Altitude: 4412.304 ± 594.707 m (ASL) | 4254.304 ± 594.707 m (AGL)
+Apogee Freestream Speed: 32.987 m/s
+DATCOM nominal: peak ascent speed 347.094 ± 10.525 m/s
+DATCOM coefficient upper: apogee 3889.102 m (AGL) | peak ascent speed 339.282 m/s | apogee freestream speed 30.711 m/s
+DATCOM coefficient lower: apogee 4849.012 m (AGL) | peak ascent speed 357.619 m/s | apogee freestream speed 35.853 m/s
+DATCOM ±: largest departure from nominal in two engineering sensitivity runs; not a confidence interval. Upper/lower mean more/less drag and farther-aft/forward CP, not guaranteed altitude bounds.
+DATCOM sensitivity: CD at least ±20%; nose-to-CP distance ±2%.
+DATCOM nominal: 85.2% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+DATCOM upper: 86.9% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+DATCOM lower: 82.0% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+Apogee X position: 747.303 m
+Apogee Y position: -780.874 m
+Apogee latitude: 39.3826755°
+Apogee longitude: -8.2802670°
+
+Stability Margin
+
+Initial Stability Margin: 3.963 ± 0.313 c at 0.00 s
+Out of Rail Stability Margin: 4.130 ± 0.313 c at 0.51 s
+Maximum Stability Margin: 6.131 c at 4.72 s
+Minimum Stability Margin: 3.963 c at 0.00 s
+DATCOM nominal stability at apogee: 5.576 ± 0.313 c
+DATCOM nominal ascent stability min/max: 3.963 ± 0.313 / 6.131 ± 0.325 c
+DATCOM coefficient upper stability: initial 4.276 c | rail exit 4.443 c | apogee 5.888 c | ascent min/max 4.276 / 6.399 c
+DATCOM coefficient lower stability: initial 3.650 c | rail exit 3.817 c | apogee 5.263 c | ascent min/max 3.650 / 5.807 c
diff --git a/docs/examples/datcom/comparison_data/plot_apogee_uncertainty.py b/docs/examples/datcom/comparison_data/plot_apogee_uncertainty.py
new file mode 100644
index 000000000..9438f8ac4
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/plot_apogee_uncertainty.py
@@ -0,0 +1,302 @@
+"""Compare recorded apogees with supplied-CD RocketPy and DATCOM scenarios."""
+
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+from matplotlib.lines import Line2D
+from matplotlib.patches import Patch
+import numpy as np
+import pandas as pd
+
+from plot_apogee_comparison import GREEN, NAMES, NEUTRAL, RED, comparison_data
+
+OUT = Path(__file__).resolve().parent
+
+
+def uncertainty_comparison(directory=OUT):
+    """Join complete scenario ascents to matching nominal/measured comparisons.
+
+    All altitudes are meters AGL. Missing scenario runs are not zero-width
+    uncertainties. Reject stale nominal results instead of mixing models.
+    """
+    directory = Path(directory)
+    nominal = comparison_data(directory).set_index("rocket")
+    stability = pd.read_csv(directory / "rocket_metrics.csv")
+    stability = stability.loc[stability.metric == "Initial static margin"].set_index(
+        "rocket"
+    )
+    scenarios = pd.read_csv(directory / "uncertainty_sensitivity.csv")
+    if scenarios.duplicated(["rocket", "case"]).any():
+        raise ValueError("Duplicate DATCOM sensitivity cases")
+    rows = []
+    for name, group in scenarios.groupby("rocket", sort=False):
+        if set(group.case) != {"nominal", "upper", "lower"}:
+            raise ValueError(f"{name}: nominal, upper and lower runs are required")
+        if name not in nominal.index:
+            raise ValueError(f"{name}: no recorded-rocket comparison")
+        row = nominal.loc[name].to_dict()
+        values = group.set_index("case")
+        heights = values.apogee_agl.to_numpy(float)
+        if not np.isfinite(heights).all() or not np.all(heights > 0):
+            raise ValueError(f"{name}: incomplete or invalid scenario apogees")
+        if not np.isclose(
+            values.loc["nominal", "apogee_agl"], row["datcom"], rtol=0, atol=0.001
+        ):
+            raise ValueError(
+                f"{name}: sensitivity and comparison nominal apogees differ"
+            )
+        if not row["paired"]:
+            raise ValueError(
+                f"{name}: both predictions and a recorded apogee are required"
+            )
+        low, high = float(heights.min()), float(heights.max())
+        margins = values.initial_stability_margin.to_numpy(float)
+        rows.append(
+            dict(
+                rocket=name,
+                **row,
+                datcom_scenario_low_m=low,
+                datcom_scenario_high_m=high,
+                datcom_sensitivity_minus_m=row["datcom"] - low,
+                datcom_sensitivity_plus_m=high - row["datcom"],
+                datcom_low_error_pct=100 * (low / row["measured"] - 1),
+                datcom_high_error_pct=100 * (high / row["measured"] - 1),
+                recorded_within_datcom_scenarios=low <= row["measured"] <= high,
+                nominal_outside_paper_pct=100
+                * values.loc["nominal", "outside_paper_fraction"],
+                rocketpy_stability=stability.loc[name, "rocketpy"],
+                recorded_stability=stability.loc[name, "measured"],
+                datcom_stability=values.loc["nominal", "initial_stability_margin"],
+                datcom_stability_low=float(margins.min()),
+                datcom_stability_high=float(margins.max()),
+            )
+        )
+    if not rows:
+        raise ValueError("No complete DATCOM sensitivity comparisons")
+    return pd.DataFrame(rows)
+
+
+def plot_uncertainty_comparison(frame):
+    """Draw asymmetric scenario whiskers on nominal DATCOM predictions."""
+    columns = min(3, len(frame))
+    rows = int(np.ceil(len(frame) / columns))
+    figure, axes = plt.subplots(
+        rows, columns, figsize=(5.2 * columns, 5.4 * rows), squeeze=False
+    )
+    for axis, (_, row) in zip(axes.flat, frame.iterrows()):
+        maximum = max(row.measured, row.rocketpy, row.datcom_scenario_high_m)
+        for index, (key, label) in enumerate(
+            (("rocketpy", "RocketPy + supplied CD"), ("datcom", "DATCOM"))
+        ):
+            color = (
+                NEUTRAL
+                if row.closer_prediction == "tie"
+                else GREEN
+                if row.closer_prediction == label
+                else RED
+            )
+            axis.bar(
+                index,
+                row[key],
+                width=0.52,
+                color=color,
+                edgecolor="#333333",
+                linewidth=0.7,
+                hatch="//" if key == "datcom" else "",
+                zorder=2,
+            )
+            height = row.datcom_scenario_high_m if key == "datcom" else row[key]
+            axis.text(
+                index,
+                height + 0.025 * maximum,
+                f"{row[key]:,.0f} m\n{row[f'{key}_error_pct']:+.1f}%",
+                ha="center",
+                va="bottom",
+                fontsize=11,
+                bbox=dict(facecolor="white", edgecolor="none", alpha=0.95, pad=1),
+                zorder=5,
+            )
+        axis.errorbar(
+            1,
+            row.datcom,
+            yerr=np.array(
+                [[row.datcom_sensitivity_minus_m], [row.datcom_sensitivity_plus_m]]
+            ),
+            fmt="none",
+            ecolor="#202020",
+            elinewidth=2,
+            capsize=7,
+            capthick=2,
+            zorder=4,
+        )
+        axis.axhline(
+            row.measured, color="#202020", linestyle="--", linewidth=1.5, zorder=3
+        )
+        name = NAMES.get(row.rocket, row.rocket.replace("_", " ").title())
+        axis.set_title(name, fontsize=15, fontweight="bold", pad=12)
+        inside = "inside" if row.recorded_within_datcom_scenarios else "outside"
+        axis.text(
+            0.5,
+            0.97,
+            f"Recorded: {row.measured:,.0f} m\nDATCOM scenarios: {row.datcom_scenario_low_m:,.0f}–{row.datcom_scenario_high_m:,.0f} m\nRecorded apogee {inside} scenario range",
+            transform=axis.transAxes,
+            ha="center",
+            va="top",
+            fontsize=10,
+            color="#333333",
+        )
+        axis.set_xticks(
+            [0, 1], ["RocketPy\nsupplied CD", "DATCOM\nwith sensitivity range"]
+        )
+        axis.set_xlim(-0.7, 1.7)
+        axis.set_ylim(0, 1.52 * maximum)
+        axis.set_ylabel("Apogee above launch site (m)")
+        axis.yaxis.grid(True, alpha=0.2, zorder=0)
+        axis.spines[["top", "right"]].set_visible(False)
+    for axis in axes.flat[len(frame) :]:
+        axis.set_visible(False)
+    figure.suptitle(
+        "RocketPy, DATCOM sensitivity and recorded apogees",
+        fontsize=18,
+        fontweight="bold",
+        y=0.99,
+    )
+    figure.legend(
+        handles=[
+            Patch(facecolor=GREEN, label="Closer nominal prediction"),
+            Patch(facecolor=RED, label="Farther nominal prediction"),
+            Line2D([], [], color="#202020", linestyle="--", label="Recorded apogee"),
+            Line2D(
+                [],
+                [],
+                color="#202020",
+                marker="_",
+                markersize=10,
+                label="DATCOM scenario range",
+            ),
+        ],
+        loc="upper center",
+        bbox_to_anchor=(0.5, 0.974 if rows > 1 else 0.925),
+        ncol=4,
+        frameon=False,
+        fontsize=10,
+    )
+    figure.text(
+        0.5,
+        0.015,
+        "Whiskers: CD at least ±20% (larger Wu errors inside its envelope), nose-to-CP distance ±2%; not confidence intervals.\n"
+        "Labels show nominal altitude and signed error versus the recorded apogee. Each panel has its own vertical scale.\n"
+        f"{len(frame)} rockets shown; "
+        f"{frame.nominal_outside_paper_pct.min():.0f}–{frame.nominal_outside_paper_pct.max():.0f}% "
+        "of their nominal ascent time lies outside Wu's test envelope.",
+        ha="center",
+        va="bottom",
+        fontsize=10,
+    )
+    figure.tight_layout(
+        rect=(0, 0.04 if rows > 1 else 0.12, 1, 0.94 if rows > 1 else 0.85), h_pad=2
+    )
+    return figure
+
+
+def plot_stability_comparison(frame):
+    """Show the margin resulting from 2% CP-distance shifts, in calibers."""
+    figure, axis = plt.subplots(figsize=(max(12, 1.3 * len(frame)), 6))
+    position = np.arange(len(frame))
+    axis.bar(
+        position - 0.18,
+        frame.rocketpy_stability,
+        width=0.34,
+        color="#3979b7",
+        label="RocketPy + supplied CD",
+    )
+    axis.bar(
+        position + 0.18,
+        frame.datcom_stability,
+        width=0.34,
+        color="#dc8a28",
+        label="DATCOM",
+        hatch="//",
+        yerr=np.array(
+            [
+                frame.datcom_stability - frame.datcom_stability_low,
+                frame.datcom_stability_high - frame.datcom_stability,
+            ]
+        ),
+        capsize=5,
+        error_kw={"elinewidth": 1.5},
+    )
+    observed = np.isfinite(frame.recorded_stability)
+    if observed.any():
+        axis.scatter(
+            position[observed],
+            frame.loc[observed, "recorded_stability"],
+            color="#222222",
+            marker="D",
+            label="Recorded stability",
+            zorder=4,
+        )
+    axis.set_xticks(
+        position,
+        [NAMES.get(name, name.replace("_", " ").title()) for name in frame.rocket],
+        rotation=25,
+        ha="right",
+    )
+    axis.set_ylabel("Loaded-ignition stability margin (calibers)")
+    axis.set_title(
+        "Stability sensitivity from ±2% nose-to-CP distance",
+        fontsize=16,
+        fontweight="bold",
+    )
+    axis.legend(loc="upper left", frameon=False)
+    axis.yaxis.grid(True, alpha=0.2)
+    axis.set_axisbelow(True)
+    axis.spines[["top", "right"]].set_visible(False)
+    missing = (
+        "No recorded stability margins are available for these rockets."
+        if not observed.any()
+        else "Missing recorded stability values are omitted; their test conditions may differ."
+    )
+    figure.text(
+        0.5,
+        0.015,
+        missing
+        + "\nWhiskers are scenario ranges, not confidence intervals. A 2% CP-distance shift is not a 2% margin change.",
+        ha="center",
+        fontsize=10,
+    )
+    figure.tight_layout(rect=(0, 0.10, 1, 1))
+    return figure
+
+
+def save_uncertainty_comparison(
+    figure, directory=OUT, stem="apogee_uncertainty_comparison"
+):
+    """Export both PNG and SVG for the notebook and standalone viewing."""
+    directory = Path(directory)
+    figure.savefig(directory / f"{stem}.png", dpi=180, bbox_inches="tight")
+    svg = directory / f"{stem}.svg"
+    figure.savefig(svg, bbox_inches="tight")
+    svg.write_text(
+        "\n".join(line.rstrip() for line in svg.read_text().splitlines()) + "\n"
+    )
+
+
+def main():
+    frame = uncertainty_comparison()
+    frame.to_csv(OUT / "apogee_uncertainty_comparison.csv", index=False)
+    figure = plot_uncertainty_comparison(frame)
+    save_uncertainty_comparison(figure)
+    plt.close(figure)
+    figure = plot_stability_comparison(frame)
+    save_uncertainty_comparison(figure, stem="stability_uncertainty_comparison")
+    plt.close(figure)
+    focus = frame[frame.rocket.isin(["genesis", "astra", "andromeda"])]
+    if len(focus):
+        figure = plot_uncertainty_comparison(focus)
+        save_uncertainty_comparison(figure, stem="apogee_uncertainty_comparison_focus")
+        plt.close(figure)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/examples/datcom/comparison_data/plot_cd_comparison.py b/docs/examples/datcom/comparison_data/plot_cd_comparison.py
new file mode 100644
index 000000000..ca65bd4cc
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/plot_cd_comparison.py
@@ -0,0 +1,267 @@
+"""Zero-incidence CD curves and engineering bands from saved comparison data."""
+
+import json
+from pathlib import Path
+import sys
+
+import matplotlib.pyplot as plt
+from matplotlib.lines import Line2D
+from matplotlib.patches import Patch
+import numpy as np
+import pandas as pd
+
+from plot_apogee_comparison import NAMES
+
+OUT = Path(__file__).resolve().parent
+ROOT = next(parent for parent in OUT.parents if (parent / "rocketpy").is_dir())
+sys.path.insert(0, str(ROOT))
+from rocketpy.rocket.datcom_uncertainty import Wu2019Errors  # noqa: E402
+
+BLUE, ORANGE, GREY = "#3979b7", "#d17a19", "#ededed"
+
+
+def cd_comparison_data(directory=OUT, *, asteria=False):
+    """Attach the active zero-AoA drag policy to saved powered/coast curves.
+
+    CD is dimensionless and all curves use alpha=beta=0. Native table limits
+    are retained; values outside those limits are unavailable, not extrapolated.
+    The apogee-fitted constant is inferred using the ordinary RocketPy model.
+    """
+    directory = Path(directory)
+    source = Wu2019Errors()
+    if asteria:
+        names = ["asteria"]
+        metrics = None
+        status = None
+    else:
+        status = pd.read_csv(directory / "rockets/results.csv").set_index("rocket")
+        names = status.index[status.datcom_agl_m.notna()].tolist()
+        metrics = pd.read_csv(directory / "rocket_metrics.csv")
+    records = []
+    for name in names:
+        stem = directory / "asteria" if asteria else directory / "rockets" / name
+        curves = pd.read_csv(stem.with_name(stem.name + "_cd.csv"))
+        provenance = json.loads(
+            stem.with_name(stem.name + "_provenance.json").read_text()
+        )
+        grid = np.asarray(
+            provenance["atmosphere"]["mach"] if asteria else provenance["mach"], float
+        )
+        if asteria:
+            phases = provenance["atmosphere"]["coverage"]["phases"]
+            peak = max(p["mach_max"] for p in phases.values())
+            fitted = float("nan")
+        else:
+            peak = max(
+                status.loc[name, "ordinary_max_mach"],
+                status.loc[name, "datcom_max_mach"],
+            )
+            fit = metrics.loc[
+                (metrics.rocket == name)
+                & (metrics.metric == "Apogee-fitted constant CD"),
+                "inferred_cd",
+            ]
+            if len(fit) != 1:
+                raise ValueError(f"{name}: expected one apogee-fit record")
+            fitted = float(fit.iloc[0])
+        mach = curves.mach.to_numpy(float)
+        if not np.isfinite(mach).all() or not np.all(np.diff(mach) > 0):
+            raise ValueError(f"{name}: Mach samples must be finite and increasing")
+        # Same rule as DatcomUncertaintySurface.drag_fraction at alpha=0.
+        fraction = np.array(
+            [
+                max(0.2, source.fractions(m, 0)[0])
+                if source.mach[0] <= m <= source.mach[-1]
+                else 0.2
+                for m in mach
+            ]
+        )
+        valid = (mach >= grid[0] - 1e-9) & (mach <= grid[-1] + 1e-9)
+        for phase in ("powered", "coast"):
+            rocketpy = curves[f"rocketpy_{phase}_cd"].to_numpy(float)
+            datcom = curves[f"datcom_{phase}_cd"].to_numpy(float)
+            if (
+                not np.isfinite(rocketpy).any()
+                or np.isinf(rocketpy).any()
+                or not np.isfinite(datcom[valid]).all()
+            ):
+                raise ValueError(f"{name}: nonfinite {phase} curve")
+            datcom = np.where(valid, datcom, np.nan)
+            records.append(
+                pd.DataFrame(
+                    dict(
+                        rocket=name,
+                        phase=phase,
+                        mach=mach,
+                        alpha_deg=0,
+                        beta_deg=0,
+                        rocketpy_cd=rocketpy,
+                        rocketpy_available=np.isfinite(rocketpy),
+                        datcom_cd=datcom,
+                        datcom_low_cd=datcom * (1 - fraction),
+                        datcom_high_cd=datcom * (1 + fraction),
+                        drag_excursion_pct=100 * fraction,
+                        apogee_fitted_constant_cd=fitted,
+                        reference_peak_mach=peak,
+                        above_reference_peak=mach > peak,
+                        within_native_grid=valid,
+                    )
+                )
+            )
+    if not records:
+        raise ValueError("No saved CD comparisons")
+    return pd.concat(records, ignore_index=True)
+
+
+def plot_cd_comparison(frame, *, phase=None, rockets=None):
+    """Plot supplied curves and nominal DATCOM with engineering bands."""
+    names = list(frame.rocket.unique()) if rockets is None else list(rockets)
+    phases = [phase] if phase is not None else ["powered", "coast"]
+    panels = [(name, state) for state in phases for name in names]
+    columns = min(3, len(panels))
+    rows = int(np.ceil(len(panels) / columns))
+    figure, axes = plt.subplots(
+        rows, columns, figsize=(5.2 * columns, 4.2 * rows + 1), squeeze=False
+    )
+    has_fit = False
+    for axis, (name, state) in zip(axes.flat, panels):
+        values = frame[(frame.rocket == name) & (frame.phase == state)]
+        if values.empty:
+            raise ValueError(f"{name}: no {state} curve")
+        mach = values.mach.to_numpy()
+        peak = float(values.reference_peak_mach.iloc[0])
+        if peak < mach[-1]:
+            axis.axvspan(max(peak, mach[0]), mach[-1], color=GREY, zorder=0)
+            axis.axvline(peak, color="#999999", linestyle=":", linewidth=1)
+        axis.fill_between(
+            mach,
+            values.datcom_low_cd.to_numpy(),
+            values.datcom_high_cd.to_numpy(),
+            color=ORANGE,
+            alpha=0.22,
+            linewidth=0,
+            zorder=1,
+        )
+        axis.plot(mach, values.rocketpy_cd, color=BLUE, linewidth=2, zorder=3)
+        axis.plot(mach, values.datcom_cd, color=ORANGE, linewidth=2, zorder=3)
+        fitted = float(values.apogee_fitted_constant_cd.iloc[0])
+        if np.isfinite(fitted):
+            axis.axhline(
+                fitted, color="#242424", linestyle=":", linewidth=1.5, zorder=2
+            )
+            has_fit = True
+        title = (
+            "AST-HER / Asteria (has not flown)"
+            if name == "asteria"
+            else NAMES.get(name, name.replace("_", " ").title())
+        )
+        axis.set_title(f"{title} · {state}", fontsize=11, fontweight="bold", pad=10)
+        axis.text(
+            0.03,
+            0.96,
+            f"Reference peak Mach: {peak:.2f}",
+            transform=axis.transAxes,
+            va="top",
+            fontsize=9,
+            bbox=dict(facecolor="white", edgecolor="none", alpha=0.9, pad=2),
+        )
+        maximum = np.nanmax(values[["rocketpy_cd", "datcom_high_cd"]].to_numpy())
+        if np.isfinite(fitted):
+            maximum = max(maximum, fitted)
+        axis.set_ylim(0, maximum * 1.20)
+        axis.set_xlim(mach[0], mach[-1])
+        axis.set_xlabel("Mach number")
+        axis.set_ylabel("Drag coefficient CD")
+        axis.yaxis.grid(True, alpha=0.2)
+        axis.spines[["top", "right"]].set_visible(False)
+    for axis in axes.flat[len(panels) :]:
+        axis.set_visible(False)
+    title = "Zero-incidence CD comparison"
+    if phase is not None:
+        title += f" · {phase}"
+    figure.suptitle(title, fontsize=18, fontweight="bold", y=0.995)
+    handles = [
+        Line2D([], [], color=BLUE, linewidth=2, label="RocketPy + supplied CD"),
+        Line2D([], [], color=ORANGE, linewidth=2, label="Nominal DATCOM"),
+        Patch(facecolor=ORANGE, alpha=0.22, label="DATCOM CD sensitivity range"),
+        Patch(facecolor=GREY, label="Above reference peak Mach"),
+    ]
+    if has_fit:
+        handles.append(
+            Line2D(
+                [],
+                [],
+                color="#242424",
+                linestyle=":",
+                label="Apogee-fitted constant CD (inferred)",
+            )
+        )
+    figure.legend(
+        handles=handles,
+        loc="upper center",
+        bbox_to_anchor=(0.5, 0.965 if rows > 2 else 0.935),
+        ncol=3,
+        frameon=False,
+        fontsize=10,
+    )
+    footer = "Alpha = beta = 0°. DATCOM band: ±20% minimum, larger in-envelope Wu errors; not a confidence interval.\n"
+    footer += (
+        "No measured CD supplied. Apogee-fitted CD is an inferred, nonunique RocketPy calibration.\n"
+        if has_fit
+        else "AST-HER has not flown; no measured or apogee-fitted CD is available.\n"
+    )
+    footer += "Curves retain saved atmosphere schedules. The ±2% CP shift does not directly alter zero-incidence CD."
+    missing = frame.loc[~frame.rocketpy_available, "mach"].unique()
+    if len(missing):
+        samples = ", ".join(f"{mach:g}" for mach in sorted(missing))
+        footer += f"\nMissing saved RocketPy CD samples remain gaps (Mach: {samples})."
+    figure.text(
+        0.5,
+        0.01,
+        footer,
+        ha="center",
+        va="bottom",
+        fontsize=10,
+    )
+    figure.tight_layout(
+        rect=(
+            0,
+            0.035 if rows > 2 else 0.14 if rows == 1 else 0.09,
+            1,
+            0.94 if rows > 2 else 0.85,
+        ),
+        h_pad=2,
+    )
+    return figure
+
+
+def save_cd_comparison(figure, stem, directory=OUT):
+    """Save PNG and whitespace-clean SVG versions of a CD comparison."""
+    directory = Path(directory)
+    figure.savefig(directory / f"{stem}.png", dpi=180, bbox_inches="tight")
+    path = directory / f"{stem}.svg"
+    figure.savefig(path, bbox_inches="tight")
+    path.write_text(
+        "\n".join(line.rstrip() for line in path.read_text().splitlines()) + "\n"
+    )
+
+
+def main():
+    frame = cd_comparison_data()
+    frame.to_csv(OUT / "cd_uncertainty_comparison.csv", index=False)
+    for phase in ("powered", "coast"):
+        figure = plot_cd_comparison(frame, phase=phase)
+        save_cd_comparison(figure, f"cd_{phase}_comparison")
+        plt.close(figure)
+    figure = plot_cd_comparison(frame, rockets=["genesis", "astra", "andromeda"])
+    save_cd_comparison(figure, "cd_comparison_focus")
+    plt.close(figure)
+    frame = cd_comparison_data(asteria=True)
+    frame.to_csv(OUT / "asteria_cd_uncertainty_comparison.csv", index=False)
+    figure = plot_cd_comparison(frame)
+    save_cd_comparison(figure, "asteria_cd_uncertainty_comparison")
+    plt.close(figure)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/examples/datcom/comparison_data/prometheus_2022_uncertainty.txt b/docs/examples/datcom/comparison_data/prometheus_2022_uncertainty.txt
new file mode 100644
index 000000000..37d52d181
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/prometheus_2022_uncertainty.txt
@@ -0,0 +1,29 @@
+
+Apogee State
+
+Apogee Time: 29.310 s
+Apogee Altitude: 5630.197 ± 435.989 m (ASL) | 4229.197 ± 435.989 m (AGL)
+Apogee Freestream Speed: 36.421 m/s
+DATCOM nominal: peak ascent speed 332.297 ± 6.357 m/s
+DATCOM coefficient upper: apogee 3913.979 m (AGL) | peak ascent speed 326.876 m/s | apogee freestream speed 34.971 m/s
+DATCOM coefficient lower: apogee 4665.186 m (AGL) | peak ascent speed 338.653 m/s | apogee freestream speed 38.241 m/s
+DATCOM ±: largest departure from nominal in two engineering sensitivity runs; not a confidence interval. Upper/lower mean more/less drag and farther-aft/forward CP, not guaranteed altitude bounds.
+DATCOM sensitivity: CD at least ±20%; nose-to-CP distance ±2%.
+DATCOM nominal: 86.9% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+DATCOM upper: 88.4% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+DATCOM lower: 84.7% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+Apogee X position: 1543.335 m
+Apogee Y position: 434.145 m
+Apogee latitude: 32.9432797°
+Apogee longitude: -106.8954494°
+
+Stability Margin
+
+Initial Stability Margin: 1.789 ± 0.250 c at 0.00 s
+Out of Rail Stability Margin: 1.838 ± 0.250 c at 0.38 s
+Maximum Stability Margin: 2.647 c at 4.80 s
+Minimum Stability Margin: 1.789 c at 0.00 s
+DATCOM nominal stability at apogee: 2.518 ± 0.250 c
+DATCOM nominal ascent stability min/max: 1.789 ± 0.250 / 2.647 ± 0.236 c
+DATCOM coefficient upper stability: initial 2.039 c | rail exit 2.088 c | apogee 2.768 c | ascent min/max 2.039 / 2.883 c
+DATCOM coefficient lower stability: initial 1.539 c | rail exit 1.588 c | apogee 2.269 c | ascent min/max 1.539 / 2.412 c
diff --git a/docs/examples/datcom/comparison_data/stability_uncertainty_comparison.png b/docs/examples/datcom/comparison_data/stability_uncertainty_comparison.png
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diff --git a/docs/examples/datcom/comparison_data/uncertainty_sensitivity.json b/docs/examples/datcom/comparison_data/uncertainty_sensitivity.json
new file mode 100644
index 000000000..06c4771a8
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/uncertainty_sensitivity.json
@@ -0,0 +1,866 @@
+{
+  "source": "https://doi.org/10.1088/1757-899X/612/3/032096",
+  "interpretation": "CD excursions are at least 20%; larger native-relative Wu CA discrepancies are used inside Mach 0.8\u20133.02 and alpha \u00b16 degrees. Outside that envelope use 20%. Nose-to-CP distance varies by 2%, with static moments transferred consistently. Native damping is unchanged. These are engineering scenarios, not confidence intervals.",
+  "design_reference": "https://doi.org/10.2514/6.1995-1893",
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+      "policy": "CD: at least \u00b120%, larger Wu CA discrepancies inside its envelope; nose-to-CP distance: \u00b12%. Engineering scenarios, not confidence intervals.",
+      "cases": {
+        "nominal": {
+          "apogee": 4536.549606738187,
+          "apogee_agl": 3056.5496067381873,
+          "max_speed": 250.8063284271083,
+          "apogee_speed": 57.48122903189963,
+          "initial_stability_margin": 4.008594996785594,
+          "out_of_rail_stability_margin": 4.094195462883622,
+          "apogee_stability_margin": 5.338310321893494,
+          "min_stability_margin": 4.008594996785594,
+          "max_stability_margin": 5.397745458575622,
+          "outside_paper_fraction": 1.0
+        },
+        "upper": {
+          "apogee": 4387.190741370203,
+          "apogee_agl": 2907.190741370203,
+          "max_speed": 248.7712829023005,
+          "apogee_speed": 57.843074253310824,
+          "initial_stability_margin": 4.340702472953844,
+          "out_of_rail_stability_margin": 4.426376550344119,
+          "apogee_stability_margin": 5.6706177293209015,
+          "min_stability_margin": 4.340702472953844,
+          "max_stability_margin": 5.7311784989082835,
+          "outside_paper_fraction": 1.0
+        },
+        "lower": {
+          "apogee": 4703.819339385335,
+          "apogee_agl": 3223.8193393853353,
+          "max_speed": 252.90841683422335,
+          "apogee_speed": 56.974466806308385,
+          "initial_stability_margin": 3.6764875206173433,
+          "out_of_rail_stability_margin": 3.7620139997171846,
+          "apogee_stability_margin": 5.005978221658281,
+          "min_stability_margin": 3.6764875206173433,
+          "max_stability_margin": 5.064292888954693,
+          "outside_paper_fraction": 1.0
+        }
+      },
+      "errors": {},
+      "apogee_uncertainty_m": 167.26973264714798,
+      "native_envelope": {
+        "nominal": {
+          "samples": 486,
+          "outside_mach": 0,
+          "outside_alpha": 0,
+          "outside_beta": 0,
+          "max_abs_alpha_deg": 43.59273692070459,
+          "max_abs_beta_deg": 5.480286417702728,
+          "solver_mach_excursion": false,
+          "solver_alpha_excursion": true,
+          "solver_beta_excursion": true
+        },
+        "upper": {
+          "samples": 486,
+          "outside_mach": 0,
+          "outside_alpha": 0,
+          "outside_beta": 0,
+          "max_abs_alpha_deg": 45.94846265203522,
+          "max_abs_beta_deg": 5.09075477377063,
+          "solver_mach_excursion": false,
+          "solver_alpha_excursion": true,
+          "solver_beta_excursion": true
+        },
+        "lower": {
+          "samples": 487,
+          "outside_mach": 0,
+          "outside_alpha": 0,
+          "outside_beta": 0,
+          "max_abs_alpha_deg": 46.446439642116566,
+          "max_abs_beta_deg": 5.8681595487055995,
+          "solver_mach_excursion": false,
+          "solver_alpha_excursion": true,
+          "solver_beta_excursion": true
+        }
+      }
+    },
+    "lince": {
+      "policy": "CD: at least \u00b120%, larger Wu CA discrepancies inside its envelope; nose-to-CP distance: \u00b12%. Engineering scenarios, not confidence intervals.",
+      "cases": {
+        "nominal": {
+          "apogee": 4412.30425999591,
+          "apogee_agl": 4254.30425999591,
+          "max_speed": 347.0940731984339,
+          "apogee_speed": 32.98716076124619,
+          "initial_stability_margin": 3.962666916625163,
+          "out_of_rail_stability_margin": 4.12996224993943,
+          "apogee_stability_margin": 5.575817418038545,
+          "min_stability_margin": 3.962666916625163,
+          "max_stability_margin": 6.131460425080713,
+          "outside_paper_fraction": 0.8521478521478522
+        },
+        "upper": {
+          "apogee": 4047.101707098463,
+          "apogee_agl": 3889.101707098463,
+          "max_speed": 339.2818018348115,
+          "apogee_speed": 30.710825671833465,
+          "initial_stability_margin": 4.275640542135408,
+          "out_of_rail_stability_margin": 4.443136776520599,
+          "apogee_stability_margin": 5.888287982276191,
+          "min_stability_margin": 4.275640542135408,
+          "max_stability_margin": 6.399334718246965,
+          "outside_paper_fraction": 0.8691308691308691
+        },
+        "lower": {
+          "apogee": 5007.011561829239,
+          "apogee_agl": 4849.011561829239,
+          "max_speed": 357.6190498314763,
+          "apogee_speed": 35.85280474203033,
+          "initial_stability_margin": 3.6496932911149185,
+          "out_of_rail_stability_margin": 3.8167876689758504,
+          "apogee_stability_margin": 5.2633064922815835,
+          "min_stability_margin": 3.6496932911149185,
+          "max_stability_margin": 5.806755914874441,
+          "outside_paper_fraction": 0.8201798201798202
+        }
+      },
+      "errors": {},
+      "apogee_uncertainty_m": 594.7073018333294,
+      "native_envelope": {
+        "nominal": {
+          "samples": 498,
+          "outside_mach": 0,
+          "outside_alpha": 0,
+          "outside_beta": 0,
+          "max_abs_alpha_deg": 3.100325929162926,
+          "max_abs_beta_deg": 3.920227060235464,
+          "solver_mach_excursion": true,
+          "solver_alpha_excursion": true,
+          "solver_beta_excursion": true
+        },
+        "upper": {
+          "samples": 497,
+          "outside_mach": 0,
+          "outside_alpha": 0,
+          "outside_beta": 0,
+          "max_abs_alpha_deg": 2.404255311909931,
+          "max_abs_beta_deg": 3.040785153597855,
+          "solver_mach_excursion": true,
+          "solver_alpha_excursion": true,
+          "solver_beta_excursion": true
+        },
+        "lower": {
+          "samples": 498,
+          "outside_mach": 0,
+          "outside_alpha": 0,
+          "outside_beta": 0,
+          "max_abs_alpha_deg": 2.8539643185582393,
+          "max_abs_beta_deg": 3.609035646060776,
+          "solver_mach_excursion": true,
+          "solver_alpha_excursion": true,
+          "solver_beta_excursion": true
+        }
+      }
+    },
+    "prometheus_2022": {
+      "policy": "CD: at least \u00b120%, larger Wu CA discrepancies inside its envelope; nose-to-CP distance: \u00b12%. Engineering scenarios, not confidence intervals.",
+      "cases": {
+        "nominal": {
+          "apogee": 5630.19676461329,
+          "apogee_agl": 4229.19676461329,
+          "max_speed": 332.2965115237052,
+          "apogee_speed": 36.42082131487423,
+          "initial_stability_margin": 1.7889093089472907,
+          "out_of_rail_stability_margin": 1.8377492349529205,
+          "apogee_stability_margin": 2.518445209117559,
+          "min_stability_margin": 1.7889093089472907,
+          "max_stability_margin": 2.6467415686542166,
+          "outside_paper_fraction": 0.8691308691308691
+        },
+        "upper": {
+          "apogee": 5314.979327730014,
+          "apogee_agl": 3913.979327730014,
+          "max_speed": 326.87595439526086,
+          "apogee_speed": 34.971275095920255,
+          "initial_stability_margin": 2.0388086871863798,
+          "out_of_rail_stability_margin": 2.087683531105383,
+          "apogee_stability_margin": 2.7681857426437437,
+          "min_stability_margin": 2.0388086871863798,
+          "max_stability_margin": 2.883078435113149,
+          "outside_paper_fraction": 0.8841158841158842
+        },
+        "lower": {
+          "apogee": 6066.186108382249,
+          "apogee_agl": 4665.186108382249,
+          "max_speed": 338.6532645140468,
+          "apogee_speed": 38.2414051393605,
+          "initial_stability_margin": 1.539009930708202,
+          "out_of_rail_stability_margin": 1.587814932959005,
+          "apogee_stability_margin": 2.2687552586846875,
+          "min_stability_margin": 1.539009930708202,
+          "max_stability_margin": 2.4123877488337864,
+          "outside_paper_fraction": 0.8471528471528471
+        }
+      },
+      "errors": {},
+      "apogee_uncertainty_m": 435.98934376895886,
+      "native_envelope": {
+        "nominal": {
+          "samples": 497,
+          "outside_mach": 0,
+          "outside_alpha": 0,
+          "outside_beta": 0,
+          "max_abs_alpha_deg": 0.7103949840277883,
+          "max_abs_beta_deg": 0.745924092897197,
+          "solver_mach_excursion": true,
+          "solver_alpha_excursion": true,
+          "solver_beta_excursion": true
+        },
+        "upper": {
+          "samples": 496,
+          "outside_mach": 0,
+          "outside_alpha": 0,
+          "outside_beta": 0,
+          "max_abs_alpha_deg": 0.6822439088840412,
+          "max_abs_beta_deg": 0.6137104827345524,
+          "solver_mach_excursion": true,
+          "solver_alpha_excursion": true,
+          "solver_beta_excursion": true
+        },
+        "lower": {
+          "samples": 497,
+          "outside_mach": 0,
+          "outside_alpha": 0,
+          "outside_beta": 0,
+          "max_abs_alpha_deg": 0.729535374572074,
+          "max_abs_beta_deg": 0.6988787884025948,
+          "solver_mach_excursion": true,
+          "solver_alpha_excursion": true,
+          "solver_beta_excursion": true
+        }
+      }
+    },
+    "valetudo": {
+      "policy": "CD: at least \u00b120%, larger Wu CA discrepancies inside its envelope; nose-to-CP distance: \u00b12%. Engineering scenarios, not confidence intervals.",
+      "cases": {
+        "nominal": {
+          "apogee": 1611.619007322401,
+          "apogee_agl": 943.6190073224011,
+          "max_speed": 126.83406577404007,
+          "apogee_speed": 12.245755336519384,
+          "initial_stability_margin": 4.156583455241121,
+          "out_of_rail_stability_margin": 4.360037597957584,
+          "apogee_stability_margin": 5.252556214138524,
+          "min_stability_margin": 4.156583455241121,
+          "max_stability_margin": 5.361022277859971,
+          "outside_paper_fraction": 1.0
+        },
+        "upper": {
+          "apogee": 1589.1450730019071,
+          "apogee_agl": 921.1450730019071,
+          "max_speed": 126.41599706040827,
+          "apogee_speed": 11.972107522469505,
+          "initial_stability_margin": 4.5418365323120655,
+          "out_of_rail_stability_margin": 4.7456077885826655,
+          "apogee_stability_margin": 5.637714570036948,
+          "min_stability_margin": 4.5418365323120655,
+          "max_stability_margin": 5.746759492472915,
+          "outside_paper_fraction": 1.0
+        },
+        "lower": {
+          "apogee": 1634.867844154012,
+          "apogee_agl": 966.8678441540119,
+          "max_speed": 127.22896610139178,
+          "apogee_speed": 12.532444379679278,
+          "initial_stability_margin": 3.771330378170176,
+          "out_of_rail_stability_margin": 3.9744673966573947,
+          "apogee_stability_margin": 4.867399635557609,
+          "min_stability_margin": 3.771330378170176,
+          "max_stability_margin": 4.975170519567735,
+          "outside_paper_fraction": 1.0
+        }
+      },
+      "errors": {},
+      "apogee_uncertainty_m": 23.248836831610788,
+      "native_envelope": {
+        "nominal": {
+          "samples": 448,
+          "outside_mach": 0,
+          "outside_alpha": 0,
+          "outside_beta": 0,
+          "max_abs_alpha_deg": 15.80193704194847,
+          "max_abs_beta_deg": 5.1181201273022525,
+          "solver_mach_excursion": true,
+          "solver_alpha_excursion": true,
+          "solver_beta_excursion": true
+        },
+        "upper": {
+          "samples": 446,
+          "outside_mach": 0,
+          "outside_alpha": 0,
+          "outside_beta": 0,
+          "max_abs_alpha_deg": 14.992419060908908,
+          "max_abs_beta_deg": 4.727047873671721,
+          "solver_mach_excursion": true,
+          "solver_alpha_excursion": true,
+          "solver_beta_excursion": true
+        },
+        "lower": {
+          "samples": 450,
+          "outside_mach": 0,
+          "outside_alpha": 0,
+          "outside_beta": 0,
+          "max_abs_alpha_deg": 17.20684807746054,
+          "max_abs_beta_deg": 6.145879075165148,
+          "solver_mach_excursion": true,
+          "solver_alpha_excursion": true,
+          "solver_beta_excursion": true
+        }
+      }
+    }
+  }
+}
\ No newline at end of file
diff --git a/docs/examples/datcom/comparison_data/uncertainty_sensitivity.py b/docs/examples/datcom/comparison_data/uncertainty_sensitivity.py
new file mode 100644
index 000000000..391e7d317
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/uncertainty_sensitivity.py
@@ -0,0 +1,107 @@
+"""Reproduce 20%-minimum CD / 2%-CP ascents with saved comparison conditions.
+
+No apogee fit or nominal comparison data is overwritten. These transferred
+engineering assumptions are not statistical confidence bounds.
+"""
+
+import argparse
+import contextlib
+import hashlib
+import io
+import json
+import os
+from pathlib import Path
+
+import pandas as pd
+
+import model_sources as sources
+from rocketpy import Flight
+
+OUT = Path(__file__).resolve().parent
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--rockets",
+        nargs="+",
+        help="Defaults to all rockets with saved DATCOM results.",
+    )
+    args = parser.parse_args()
+    if args.rockets is None:
+        metrics = pd.read_csv(OUT / "rocket_metrics.csv")
+        args.rockets = metrics.loc[
+            (metrics.metric == "Apogee AGL") & metrics.datcom.notna(), "rocket"
+        ].tolist()
+    executable = Path(
+        os.environ.get(
+            "DATCOM_EXECUTABLE",
+            sources.ROOT.parent / "Asteria-Flight-Dynamics/works/dat0/datcom",
+        )
+    )
+    report = {
+        "source": "https://doi.org/10.1088/1757-899X/612/3/032096",
+        "interpretation": "CD excursions are at least 20%; larger native-relative Wu CA discrepancies are used inside Mach 0.8–3.02 and alpha ±6 degrees. Outside that envelope use 20%. Nose-to-CP distance varies by 2%, with static moments transferred consistently. Native damping is unchanged. These are engineering scenarios, not confidence intervals.",
+        "design_reference": "https://doi.org/10.2514/6.1995-1893",
+        "executable_sha256": hashlib.sha256(executable.read_bytes()).hexdigest(),
+        "rockets": {},
+    }
+    rows = []
+    for name in args.rockets:
+        print(f"SENSITIVITY {name}", flush=True)
+        with contextlib.redirect_stdout(io.StringIO()):
+            rocket, env, launch, _ = sources.build_model(
+                sources.ROOT / f"docs/examples/{name}_flight_sim.ipynb"
+            )
+            sources.h.attach_datcom(rocket, executable, 0)
+        provenance = json.loads(
+            (OUT / "rockets" / f"{name}_provenance.json").read_text()
+        )
+        for fin in rocket.fins:
+            if hasattr(fin, "datcom_options"):
+                fin.datcom_options = dict(
+                    mach=provenance["mach"],
+                    alpha=provenance["alpha"],
+                    beta=provenance["beta"],
+                    reynolds=provenance["atmosphere"]["reynolds"],
+                    powered_base_drag="nozzle",
+                )
+        flight = Flight(rocket, env, **launch)
+        result = flight.datcom_uncertainty
+        if result is None:
+            raise RuntimeError(f"{name}: {flight.datcom_uncertainty_status}")
+        record = {
+            "policy": result.policy,
+            "cases": result.cases,
+            "errors": result.errors,
+            "apogee_uncertainty_m": flight.datcom_apogee_uncertainty,
+        }
+        for case, values in result.cases.items():
+            rows.append(dict(rocket=name, case=case, **values))
+            replay = flight if case == "nominal" else result.flights[case]
+            record.setdefault("native_envelope", {})[case] = (
+                sources.h.envelope_diagnostics(
+                    replay,
+                    replay._datcom_load_model,
+                    provenance["mach"],
+                    provenance["alpha"],
+                )
+            )
+        report["rockets"][name] = record
+        output = io.StringIO()
+        with contextlib.redirect_stdout(output):
+            flight.prints.apogee_conditions()
+            flight.prints.stability_margin()
+        (OUT / f"{name}_uncertainty.txt").write_text(output.getvalue())
+        (OUT / "uncertainty_sensitivity.json").write_text(
+            json.dumps(report, indent=2, allow_nan=False)
+        )
+        pd.DataFrame(rows).to_csv(OUT / "uncertainty_sensitivity.csv", index=False)
+        print(
+            f"{name}: {flight.apogee - env.elevation:.3f} ± {flight.datcom_apogee_uncertainty:.3f} m (AGL)",
+            flush=True,
+        )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/examples/datcom/comparison_data/valetudo_uncertainty.txt b/docs/examples/datcom/comparison_data/valetudo_uncertainty.txt
new file mode 100644
index 000000000..554d860ad
--- /dev/null
+++ b/docs/examples/datcom/comparison_data/valetudo_uncertainty.txt
@@ -0,0 +1,29 @@
+
+Apogee State
+
+Apogee Time: 15.242 s
+Apogee Altitude: 1611.619 ± 23.249 m (ASL) | 943.619 ± 23.249 m (AGL)
+Apogee Freestream Speed: 12.246 m/s
+DATCOM nominal: peak ascent speed 126.834 ± 0.418 m/s
+DATCOM coefficient upper: apogee 921.145 m (AGL) | peak ascent speed 126.416 m/s | apogee freestream speed 11.972 m/s
+DATCOM coefficient lower: apogee 966.868 m (AGL) | peak ascent speed 127.229 m/s | apogee freestream speed 12.532 m/s
+DATCOM ±: largest departure from nominal in two engineering sensitivity runs; not a confidence interval. Upper/lower mean more/less drag and farther-aft/forward CP, not guaranteed altitude bounds.
+DATCOM sensitivity: CD at least ±20%; nose-to-CP distance ±2%.
+DATCOM nominal: 100.0% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+DATCOM upper: 100.0% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+DATCOM lower: 100.0% of ascent time outside Wu Mach 0.8–3.02 / incidence ±6° coverage; 20% CD floor used outside its alpha/Mach envelope.
+Apogee X position: 143.711 m
+Apogee Y position: 109.523 m
+Apogee latitude: -23.3626266°
+Apogee longitude: -48.0099820°
+
+Stability Margin
+
+Initial Stability Margin: 4.157 ± 0.385 c at 0.00 s
+Out of Rail Stability Margin: 4.360 ± 0.386 c at 1.08 s
+Maximum Stability Margin: 5.361 c at 3.62 s
+Minimum Stability Margin: 4.157 c at 0.00 s
+DATCOM nominal stability at apogee: 5.253 ± 0.385 c
+DATCOM nominal ascent stability min/max: 4.157 ± 0.385 / 5.361 ± 0.386 c
+DATCOM coefficient upper stability: initial 4.542 c | rail exit 4.746 c | apogee 5.638 c | ascent min/max 4.542 / 5.747 c
+DATCOM coefficient lower stability: initial 3.771 c | rail exit 3.974 c | apogee 4.867 c | ascent min/max 3.771 / 4.975 c
diff --git a/docs/examples/datcom/rocket_comparison.ipynb b/docs/examples/datcom/rocket_comparison.ipynb
index 7fb5736dc..b8f1d82b6 100644
--- a/docs/examples/datcom/rocket_comparison.ipynb
+++ b/docs/examples/datcom/rocket_comparison.ipynb
@@ -474,6 +474,403 @@
     "assert (apogee_bars.measured > 0).all()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "6c48f5a0",
+   "metadata": {},
+   "source": [
+    "## DATCOM sensitivity bars versus recorded apogees\n",
+    "\n",
+    "The current engineering rule uses **at least ±20% static CD at every Mach/AoA**, with **±2% of nose-to-CP distance**. Larger native-relative axial-force discrepancies from Wu et al. are retained as a CD-error proxy **inside Mach 0.8–3.02 and alpha ±6°**. Outside that envelope use 20%. This replaces the earlier independent CN/CM-alpha variations. At finite incidence, the added drag follows the airflow and preserves perpendicular force; static moments are transferred consistently and native damping is retained.\n",
+    "\n",
+    "Compare **RocketPy + supplied CD**, **nominal DATCOM with asymmetric scenario whiskers**, and the **recorded apogee** (dashed line). Whiskers span nominal and the upper/lower drag-and-CP scenarios; they are **not confidence intervals**. Green/red rank nominal errors. The scenarios correlate drag and CP and do not exhaust independent combinations.\n",
+    "\n",
+    "The design-accuracy discussion is referenced to **J. T. White (1995), AIAA 95-1893**, [doi:10.2514/6.1995-1893](https://doi.org/10.2514/6.1995-1893). White studies supersonic **CN/CP** accuracy and cites criteria based on CN and CP/body length; he does **not** establish universal 20% CD or 2% nose-to-CP uncertainty. Applying these values throughout flight is an explicit engineering assumption. The larger in-envelope errors adapt Wu et al. (2019), [doi:10.1088/1757-899X/612/3/032096](https://doi.org/10.1088/1757-899X/612/3/032096), Table 2 (CC BY 3.0). Measurement-relative errors are converted to native-relative magnitudes before use.\n",
+    "\n",
+    "Sensitivity runs now cover **all 11 currently supported recorded-rocket DATCOM models**. Recorded apogees fall inside the ranges for **4/11**; RocketPy's supplied-CD nominal prediction is closer for **8/11**. These counts do not establish a confidence level. The original chart above retains all 16 rockets and identifies unavailable cases.\n",
+    "\n",
+    "Values are metres AGL. Supplied CD is not universally CFD-derived. Rendering uses saved outputs and requires no native executable runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "27fb2ee1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1560x2160 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "</style>\n",
+       "<table id=\"T_17d5e\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_17d5e_level0_col0\" class=\"col_heading level0 col0\" >Rocket</th>\n",
+       "      <th id=\"T_17d5e_level0_col1\" class=\"col_heading level0 col1\" >Recorded (m)</th>\n",
+       "      <th id=\"T_17d5e_level0_col2\" class=\"col_heading level0 col2\" >RocketPy (m)</th>\n",
+       "      <th id=\"T_17d5e_level0_col3\" class=\"col_heading level0 col3\" >RocketPy error (%)</th>\n",
+       "      <th id=\"T_17d5e_level0_col4\" class=\"col_heading level0 col4\" >DATCOM (m)</th>\n",
+       "      <th id=\"T_17d5e_level0_col5\" class=\"col_heading level0 col5\" >DATCOM error (%)</th>\n",
+       "      <th id=\"T_17d5e_level0_col6\" class=\"col_heading level0 col6\" >DATCOM range (m)</th>\n",
+       "      <th id=\"T_17d5e_level0_col7\" class=\"col_heading level0 col7\" >Range error (%)</th>\n",
+       "      <th id=\"T_17d5e_level0_col8\" class=\"col_heading level0 col8\" >Recorded in range</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_17d5e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_17d5e_row0_col0\" class=\"data row0 col0\" >genesis</td>\n",
+       "      <td id=\"T_17d5e_row0_col1\" class=\"data row0 col1\" >2916.000</td>\n",
+       "      <td id=\"T_17d5e_row0_col2\" class=\"data row0 col2\" >3044.686</td>\n",
+       "      <td id=\"T_17d5e_row0_col3\" class=\"data row0 col3\" >4.413</td>\n",
+       "      <td id=\"T_17d5e_row0_col4\" class=\"data row0 col4\" >3297.239</td>\n",
+       "      <td id=\"T_17d5e_row0_col5\" class=\"data row0 col5\" >13.074</td>\n",
+       "      <td id=\"T_17d5e_row0_col6\" class=\"data row0 col6\" >3,067.9–3,584.4</td>\n",
+       "      <td id=\"T_17d5e_row0_col7\" class=\"data row0 col7\" >+5.2 to +22.9</td>\n",
+       "      <td id=\"T_17d5e_row0_col8\" class=\"data row0 col8\" >No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_17d5e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+       "      <td id=\"T_17d5e_row1_col0\" class=\"data row1 col0\" >astra</td>\n",
+       "      <td id=\"T_17d5e_row1_col1\" class=\"data row1 col1\" >3250.000</td>\n",
+       "      <td id=\"T_17d5e_row1_col2\" class=\"data row1 col2\" >3043.368</td>\n",
+       "      <td id=\"T_17d5e_row1_col3\" class=\"data row1 col3\" >-6.358</td>\n",
+       "      <td id=\"T_17d5e_row1_col4\" class=\"data row1 col4\" >3695.934</td>\n",
+       "      <td id=\"T_17d5e_row1_col5\" class=\"data row1 col5\" >13.721</td>\n",
+       "      <td id=\"T_17d5e_row1_col6\" class=\"data row1 col6\" >3,436.9–4,031.7</td>\n",
+       "      <td id=\"T_17d5e_row1_col7\" class=\"data row1 col7\" >+5.8 to +24.1</td>\n",
+       "      <td id=\"T_17d5e_row1_col8\" class=\"data row1 col8\" >No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_17d5e_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+       "      <td id=\"T_17d5e_row2_col0\" class=\"data row2 col0\" >andromeda</td>\n",
+       "      <td id=\"T_17d5e_row2_col1\" class=\"data row2 col1\" >3443.000</td>\n",
+       "      <td id=\"T_17d5e_row2_col2\" class=\"data row2 col2\" >3615.881</td>\n",
+       "      <td id=\"T_17d5e_row2_col3\" class=\"data row2 col3\" >5.021</td>\n",
+       "      <td id=\"T_17d5e_row2_col4\" class=\"data row2 col4\" >4170.733</td>\n",
+       "      <td id=\"T_17d5e_row2_col5\" class=\"data row2 col5\" >21.137</td>\n",
+       "      <td id=\"T_17d5e_row2_col6\" class=\"data row2 col6\" >3,793.8–4,831.9</td>\n",
+       "      <td id=\"T_17d5e_row2_col7\" class=\"data row2 col7\" >+10.2 to +40.3</td>\n",
+       "      <td id=\"T_17d5e_row2_col8\" class=\"data row2 col8\" >No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_17d5e_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+       "      <td id=\"T_17d5e_row3_col0\" class=\"data row3 col0\" >bella_lui</td>\n",
+       "      <td id=\"T_17d5e_row3_col1\" class=\"data row3 col1\" >458.971</td>\n",
+       "      <td id=\"T_17d5e_row3_col2\" class=\"data row3 col2\" >460.552</td>\n",
+       "      <td id=\"T_17d5e_row3_col3\" class=\"data row3 col3\" >0.345</td>\n",
+       "      <td id=\"T_17d5e_row3_col4\" class=\"data row3 col4\" >465.877</td>\n",
+       "      <td id=\"T_17d5e_row3_col5\" class=\"data row3 col5\" >1.505</td>\n",
+       "      <td id=\"T_17d5e_row3_col6\" class=\"data row3 col6\" >457.8–474.2</td>\n",
+       "      <td id=\"T_17d5e_row3_col7\" class=\"data row3 col7\" >-0.3 to +3.3</td>\n",
+       "      <td id=\"T_17d5e_row3_col8\" class=\"data row3 col8\" >Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_17d5e_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+       "      <td id=\"T_17d5e_row4_col0\" class=\"data row4 col0\" >cavour</td>\n",
+       "      <td id=\"T_17d5e_row4_col1\" class=\"data row4 col1\" >2789.000</td>\n",
+       "      <td id=\"T_17d5e_row4_col2\" class=\"data row4 col2\" >2818.135</td>\n",
+       "      <td id=\"T_17d5e_row4_col3\" class=\"data row4 col3\" >1.045</td>\n",
+       "      <td id=\"T_17d5e_row4_col4\" class=\"data row4 col4\" >3251.609</td>\n",
+       "      <td id=\"T_17d5e_row4_col5\" class=\"data row4 col5\" >16.587</td>\n",
+       "      <td id=\"T_17d5e_row4_col6\" class=\"data row4 col6\" >2,968.7–3,660.2</td>\n",
+       "      <td id=\"T_17d5e_row4_col7\" class=\"data row4 col7\" >+6.4 to +31.2</td>\n",
+       "      <td id=\"T_17d5e_row4_col8\" class=\"data row4 col8\" >No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_17d5e_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+       "      <td id=\"T_17d5e_row5_col0\" class=\"data row5 col0\" >erebus</td>\n",
+       "      <td id=\"T_17d5e_row5_col1\" class=\"data row5 col1\" >3020.000</td>\n",
+       "      <td id=\"T_17d5e_row5_col2\" class=\"data row5 col2\" >2751.156</td>\n",
+       "      <td id=\"T_17d5e_row5_col3\" class=\"data row5 col3\" >-8.902</td>\n",
+       "      <td id=\"T_17d5e_row5_col4\" class=\"data row5 col4\" >3253.423</td>\n",
+       "      <td id=\"T_17d5e_row5_col5\" class=\"data row5 col5\" >7.729</td>\n",
+       "      <td id=\"T_17d5e_row5_col6\" class=\"data row5 col6\" >2,988.0–3,613.0</td>\n",
+       "      <td id=\"T_17d5e_row5_col7\" class=\"data row5 col7\" >-1.1 to +19.6</td>\n",
+       "      <td id=\"T_17d5e_row5_col8\" class=\"data row5 col8\" >Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_17d5e_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+       "      <td id=\"T_17d5e_row6_col0\" class=\"data row6 col0\" >halcyon</td>\n",
+       "      <td id=\"T_17d5e_row6_col1\" class=\"data row6 col1\" >3450.000</td>\n",
+       "      <td id=\"T_17d5e_row6_col2\" class=\"data row6 col2\" >3214.239</td>\n",
+       "      <td id=\"T_17d5e_row6_col3\" class=\"data row6 col3\" >-6.834</td>\n",
+       "      <td id=\"T_17d5e_row6_col4\" class=\"data row6 col4\" >3530.232</td>\n",
+       "      <td id=\"T_17d5e_row6_col5\" class=\"data row6 col5\" >2.326</td>\n",
+       "      <td id=\"T_17d5e_row6_col6\" class=\"data row6 col6\" >3,334.2–3,757.6</td>\n",
+       "      <td id=\"T_17d5e_row6_col7\" class=\"data row6 col7\" >-3.4 to +8.9</td>\n",
+       "      <td id=\"T_17d5e_row6_col8\" class=\"data row6 col8\" >Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_17d5e_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+       "      <td id=\"T_17d5e_row7_col0\" class=\"data row7 col0\" >juno3</td>\n",
+       "      <td id=\"T_17d5e_row7_col1\" class=\"data row7 col1\" >3213.000</td>\n",
+       "      <td id=\"T_17d5e_row7_col2\" class=\"data row7 col2\" >2987.281</td>\n",
+       "      <td id=\"T_17d5e_row7_col3\" class=\"data row7 col3\" >-7.025</td>\n",
+       "      <td id=\"T_17d5e_row7_col4\" class=\"data row7 col4\" >3056.550</td>\n",
+       "      <td id=\"T_17d5e_row7_col5\" class=\"data row7 col5\" >-4.869</td>\n",
+       "      <td id=\"T_17d5e_row7_col6\" class=\"data row7 col6\" >2,907.2–3,223.8</td>\n",
+       "      <td id=\"T_17d5e_row7_col7\" class=\"data row7 col7\" >-9.5 to +0.3</td>\n",
+       "      <td id=\"T_17d5e_row7_col8\" class=\"data row7 col8\" >Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_17d5e_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+       "      <td id=\"T_17d5e_row8_col0\" class=\"data row8 col0\" >lince</td>\n",
+       "      <td id=\"T_17d5e_row8_col1\" class=\"data row8 col1\" >3587.000</td>\n",
+       "      <td id=\"T_17d5e_row8_col2\" class=\"data row8 col2\" >3285.089</td>\n",
+       "      <td id=\"T_17d5e_row8_col3\" class=\"data row8 col3\" >-8.417</td>\n",
+       "      <td id=\"T_17d5e_row8_col4\" class=\"data row8 col4\" >4254.304</td>\n",
+       "      <td id=\"T_17d5e_row8_col5\" class=\"data row8 col5\" >18.603</td>\n",
+       "      <td id=\"T_17d5e_row8_col6\" class=\"data row8 col6\" >3,889.1–4,849.0</td>\n",
+       "      <td id=\"T_17d5e_row8_col7\" class=\"data row8 col7\" >+8.4 to +35.2</td>\n",
+       "      <td id=\"T_17d5e_row8_col8\" class=\"data row8 col8\" >No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_17d5e_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+       "      <td id=\"T_17d5e_row9_col0\" class=\"data row9 col0\" >prometheus_2022</td>\n",
+       "      <td id=\"T_17d5e_row9_col1\" class=\"data row9 col1\" >3898.370</td>\n",
+       "      <td id=\"T_17d5e_row9_col2\" class=\"data row9 col2\" >4191.289</td>\n",
+       "      <td id=\"T_17d5e_row9_col3\" class=\"data row9 col3\" >7.514</td>\n",
+       "      <td id=\"T_17d5e_row9_col4\" class=\"data row9 col4\" >4229.197</td>\n",
+       "      <td id=\"T_17d5e_row9_col5\" class=\"data row9 col5\" >8.486</td>\n",
+       "      <td id=\"T_17d5e_row9_col6\" class=\"data row9 col6\" >3,914.0–4,665.2</td>\n",
+       "      <td id=\"T_17d5e_row9_col7\" class=\"data row9 col7\" >+0.4 to +19.7</td>\n",
+       "      <td id=\"T_17d5e_row9_col8\" class=\"data row9 col8\" >No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_17d5e_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
+       "      <td id=\"T_17d5e_row10_col0\" class=\"data row10 col0\" >valetudo</td>\n",
+       "      <td id=\"T_17d5e_row10_col1\" class=\"data row10 col1\" >860.000</td>\n",
+       "      <td id=\"T_17d5e_row10_col2\" class=\"data row10 col2\" >815.053</td>\n",
+       "      <td id=\"T_17d5e_row10_col3\" class=\"data row10 col3\" >-5.226</td>\n",
+       "      <td id=\"T_17d5e_row10_col4\" class=\"data row10 col4\" >943.619</td>\n",
+       "      <td id=\"T_17d5e_row10_col5\" class=\"data row10 col5\" >9.723</td>\n",
+       "      <td id=\"T_17d5e_row10_col6\" class=\"data row10 col6\" >921.1–966.9</td>\n",
+       "      <td id=\"T_17d5e_row10_col7\" class=\"data row10 col7\" >+7.1 to +12.4</td>\n",
+       "      <td id=\"T_17d5e_row10_col8\" class=\"data row10 col8\" >No</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x11f763a10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from plot_apogee_uncertainty import (\n",
+    "    uncertainty_comparison,\n",
+    "    plot_uncertainty_comparison,\n",
+    "    save_uncertainty_comparison,\n",
+    ")\n",
+    "\n",
+    "uncertainty = uncertainty_comparison(DATA)\n",
+    "uncertainty.to_csv(DATA / \"apogee_uncertainty_comparison.csv\", index=False)\n",
+    "fig = plot_uncertainty_comparison(uncertainty)\n",
+    "save_uncertainty_comparison(fig, DATA)\n",
+    "plt.show()\n",
+    "plt.close(fig)\n",
+    "\n",
+    "summary = uncertainty[[\"rocket\", \"measured\", \"rocketpy\", \"rocketpy_error_pct\", \"datcom\", \"datcom_error_pct\"]].rename(columns={\n",
+    "    \"rocket\": \"Rocket\", \"measured\": \"Recorded (m)\",\n",
+    "    \"rocketpy\": \"RocketPy (m)\", \"rocketpy_error_pct\": \"RocketPy error (%)\",\n",
+    "    \"datcom\": \"DATCOM (m)\", \"datcom_error_pct\": \"DATCOM error (%)\",\n",
+    "})\n",
+    "summary[\"DATCOM range (m)\"] = uncertainty.apply(\n",
+    "    lambda row: f\"{row.datcom_scenario_low_m:,.1f}–{row.datcom_scenario_high_m:,.1f}\", axis=1\n",
+    ")\n",
+    "summary[\"Range error (%)\"] = uncertainty.apply(\n",
+    "    lambda row: f\"{row.datcom_low_error_pct:+.1f} to {row.datcom_high_error_pct:+.1f}\", axis=1\n",
+    ")\n",
+    "summary[\"Recorded in range\"] = uncertainty.recorded_within_datcom_scenarios.map({True: \"Yes\", False: \"No\"})\n",
+    "show(summary)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31f7dee1",
+   "metadata": {},
+   "source": [
+    "## Stability sensitivity from the ±2% CP rule\n",
+    "\n",
+    "The CP shift is **2% of its distance from the nose**, so the corresponding margin change is `±0.02 * nose_to_CP / diameter`, in calibers. It is not 2% of the original margin. The chart compares loaded-ignition RocketPy and DATCOM margins with the two scenario endpoints.\n",
+    "\n",
+    "**No recorded stability margins are available for these 11 paired models.** These are simulation comparisons; the missing measurements are not zero. The existing all-rocket stability table later in this notebook retains separately reported measurements and their stated conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ad85ebb0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1430x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "</style>\n",
+       "<table id=\"T_bcbbe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_bcbbe_level0_col0\" class=\"col_heading level0 col0\" >Rocket</th>\n",
+       "      <th id=\"T_bcbbe_level0_col1\" class=\"col_heading level0 col1\" >RocketPy (cal)</th>\n",
+       "      <th id=\"T_bcbbe_level0_col2\" class=\"col_heading level0 col2\" >DATCOM (cal)</th>\n",
+       "      <th id=\"T_bcbbe_level0_col3\" class=\"col_heading level0 col3\" >Scenario min (cal)</th>\n",
+       "      <th id=\"T_bcbbe_level0_col4\" class=\"col_heading level0 col4\" >Scenario max (cal)</th>\n",
+       "      <th id=\"T_bcbbe_level0_col5\" class=\"col_heading level0 col5\" >Recorded (cal)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bcbbe_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_bcbbe_row0_col0\" class=\"data row0 col0\" >genesis</td>\n",
+       "      <td id=\"T_bcbbe_row0_col1\" class=\"data row0 col1\" >3.377</td>\n",
+       "      <td id=\"T_bcbbe_row0_col2\" class=\"data row0 col2\" >3.768</td>\n",
+       "      <td id=\"T_bcbbe_row0_col3\" class=\"data row0 col3\" >3.356</td>\n",
+       "      <td id=\"T_bcbbe_row0_col4\" class=\"data row0 col4\" >4.180</td>\n",
+       "      <td id=\"T_bcbbe_row0_col5\" class=\"data row0 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bcbbe_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+       "      <td id=\"T_bcbbe_row1_col0\" class=\"data row1 col0\" >astra</td>\n",
+       "      <td id=\"T_bcbbe_row1_col1\" class=\"data row1 col1\" >3.163</td>\n",
+       "      <td id=\"T_bcbbe_row1_col2\" class=\"data row1 col2\" >3.477</td>\n",
+       "      <td id=\"T_bcbbe_row1_col3\" class=\"data row1 col3\" >3.154</td>\n",
+       "      <td id=\"T_bcbbe_row1_col4\" class=\"data row1 col4\" >3.801</td>\n",
+       "      <td id=\"T_bcbbe_row1_col5\" class=\"data row1 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bcbbe_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+       "      <td id=\"T_bcbbe_row2_col0\" class=\"data row2 col0\" >andromeda</td>\n",
+       "      <td id=\"T_bcbbe_row2_col1\" class=\"data row2 col1\" >4.159</td>\n",
+       "      <td id=\"T_bcbbe_row2_col2\" class=\"data row2 col2\" >4.586</td>\n",
+       "      <td id=\"T_bcbbe_row2_col3\" class=\"data row2 col3\" >4.329</td>\n",
+       "      <td id=\"T_bcbbe_row2_col4\" class=\"data row2 col4\" >4.842</td>\n",
+       "      <td id=\"T_bcbbe_row2_col5\" class=\"data row2 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bcbbe_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+       "      <td id=\"T_bcbbe_row3_col0\" class=\"data row3 col0\" >bella_lui</td>\n",
+       "      <td id=\"T_bcbbe_row3_col1\" class=\"data row3 col1\" >2.777</td>\n",
+       "      <td id=\"T_bcbbe_row3_col2\" class=\"data row3 col2\" >3.304</td>\n",
+       "      <td id=\"T_bcbbe_row3_col3\" class=\"data row3 col3\" >3.035</td>\n",
+       "      <td id=\"T_bcbbe_row3_col4\" class=\"data row3 col4\" >3.572</td>\n",
+       "      <td id=\"T_bcbbe_row3_col5\" class=\"data row3 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bcbbe_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+       "      <td id=\"T_bcbbe_row4_col0\" class=\"data row4 col0\" >cavour</td>\n",
+       "      <td id=\"T_bcbbe_row4_col1\" class=\"data row4 col1\" >4.280</td>\n",
+       "      <td id=\"T_bcbbe_row4_col2\" class=\"data row4 col2\" >5.465</td>\n",
+       "      <td id=\"T_bcbbe_row4_col3\" class=\"data row4 col3\" >5.027</td>\n",
+       "      <td id=\"T_bcbbe_row4_col4\" class=\"data row4 col4\" >5.904</td>\n",
+       "      <td id=\"T_bcbbe_row4_col5\" class=\"data row4 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bcbbe_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+       "      <td id=\"T_bcbbe_row5_col0\" class=\"data row5 col0\" >erebus</td>\n",
+       "      <td id=\"T_bcbbe_row5_col1\" class=\"data row5 col1\" >2.937</td>\n",
+       "      <td id=\"T_bcbbe_row5_col2\" class=\"data row5 col2\" >4.450</td>\n",
+       "      <td id=\"T_bcbbe_row5_col3\" class=\"data row5 col3\" >4.076</td>\n",
+       "      <td id=\"T_bcbbe_row5_col4\" class=\"data row5 col4\" >4.825</td>\n",
+       "      <td id=\"T_bcbbe_row5_col5\" class=\"data row5 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bcbbe_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+       "      <td id=\"T_bcbbe_row6_col0\" class=\"data row6 col0\" >halcyon</td>\n",
+       "      <td id=\"T_bcbbe_row6_col1\" class=\"data row6 col1\" >3.019</td>\n",
+       "      <td id=\"T_bcbbe_row6_col2\" class=\"data row6 col2\" >4.410</td>\n",
+       "      <td id=\"T_bcbbe_row6_col3\" class=\"data row6 col3\" >4.057</td>\n",
+       "      <td id=\"T_bcbbe_row6_col4\" class=\"data row6 col4\" >4.763</td>\n",
+       "      <td id=\"T_bcbbe_row6_col5\" class=\"data row6 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bcbbe_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+       "      <td id=\"T_bcbbe_row7_col0\" class=\"data row7 col0\" >juno3</td>\n",
+       "      <td id=\"T_bcbbe_row7_col1\" class=\"data row7 col1\" >3.221</td>\n",
+       "      <td id=\"T_bcbbe_row7_col2\" class=\"data row7 col2\" >4.009</td>\n",
+       "      <td id=\"T_bcbbe_row7_col3\" class=\"data row7 col3\" >3.676</td>\n",
+       "      <td id=\"T_bcbbe_row7_col4\" class=\"data row7 col4\" >4.341</td>\n",
+       "      <td id=\"T_bcbbe_row7_col5\" class=\"data row7 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bcbbe_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+       "      <td id=\"T_bcbbe_row8_col0\" class=\"data row8 col0\" >lince</td>\n",
+       "      <td id=\"T_bcbbe_row8_col1\" class=\"data row8 col1\" >2.920</td>\n",
+       "      <td id=\"T_bcbbe_row8_col2\" class=\"data row8 col2\" >3.963</td>\n",
+       "      <td id=\"T_bcbbe_row8_col3\" class=\"data row8 col3\" >3.650</td>\n",
+       "      <td id=\"T_bcbbe_row8_col4\" class=\"data row8 col4\" >4.276</td>\n",
+       "      <td id=\"T_bcbbe_row8_col5\" class=\"data row8 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bcbbe_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+       "      <td id=\"T_bcbbe_row9_col0\" class=\"data row9 col0\" >prometheus_2022</td>\n",
+       "      <td id=\"T_bcbbe_row9_col1\" class=\"data row9 col1\" >1.042</td>\n",
+       "      <td id=\"T_bcbbe_row9_col2\" class=\"data row9 col2\" >1.789</td>\n",
+       "      <td id=\"T_bcbbe_row9_col3\" class=\"data row9 col3\" >1.539</td>\n",
+       "      <td id=\"T_bcbbe_row9_col4\" class=\"data row9 col4\" >2.039</td>\n",
+       "      <td id=\"T_bcbbe_row9_col5\" class=\"data row9 col5\" >—</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bcbbe_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
+       "      <td id=\"T_bcbbe_row10_col0\" class=\"data row10 col0\" >valetudo</td>\n",
+       "      <td id=\"T_bcbbe_row10_col1\" class=\"data row10 col1\" >2.951</td>\n",
+       "      <td id=\"T_bcbbe_row10_col2\" class=\"data row10 col2\" >4.157</td>\n",
+       "      <td id=\"T_bcbbe_row10_col3\" class=\"data row10 col3\" >3.771</td>\n",
+       "      <td id=\"T_bcbbe_row10_col4\" class=\"data row10 col4\" >4.542</td>\n",
+       "      <td id=\"T_bcbbe_row10_col5\" class=\"data row10 col5\" >—</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x11ff29f90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from plot_apogee_uncertainty import plot_stability_comparison\n",
+    "\n",
+    "fig = plot_stability_comparison(uncertainty)\n",
+    "save_uncertainty_comparison(fig, DATA, stem=\"stability_uncertainty_comparison\")\n",
+    "plt.show()\n",
+    "plt.close(fig)\n",
+    "show(uncertainty[[\"rocket\", \"rocketpy_stability\", \"datcom_stability\", \"datcom_stability_low\", \"datcom_stability_high\", \"recorded_stability\"]].rename(columns={\n",
+    "    \"rocket\": \"Rocket\", \"rocketpy_stability\": \"RocketPy (cal)\",\n",
+    "    \"datcom_stability\": \"DATCOM (cal)\", \"datcom_stability_low\": \"Scenario min (cal)\",\n",
+    "    \"datcom_stability_high\": \"Scenario max (cal)\", \"recorded_stability\": \"Recorded (cal)\",\n",
+    "}))\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "c7aab47e",
@@ -909,6 +1306,59 @@
     "show(metrics[metrics.metric == 'Coast CD at Mach 0.6'][['rocket', 'measured', 'rocketpy', 'datcom', 'inferred_cd', 'rocketpy_vs_fitted_cd_pct', 'datcom_vs_fitted_cd_pct']])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "e89d9ac5",
+   "metadata": {},
+   "source": [
+    "## CD comparison curves with uncertainty\n",
+    "\n",
+    "Separate powered and coast panels compare **RocketPy's supplied CD curve** (blue) with **nominal DATCOM** (orange) for all 11 supported rockets. Orange bands apply the active **±20% minimum** drag rule at **alpha = beta = 0°**, retaining larger converted Wu discrepancies inside its measured Mach range. These are engineering sensitivity bands, not confidence intervals. The ±2% CP shift does not directly change zero-incidence static CD.\n",
+    "\n",
+    "The dotted black line is an **apogee-fitted constant CD**, calibrated in the ordinary RocketPy model to the recorded apogee. It is inferred and nonunique, **not measured CD**. None of the supplied records establishes measured CD, so the chart cannot determine which drag curve is more accurate. The supplied curves are not universally CFD-derived.\n",
+    "\n",
+    "Grey backgrounds identify Mach numbers **above the reference ascent peak** (the greater of the two nominal peak Mach values). Curves use the saved native Mach/atmosphere schedules; native values outside the actual input grid are omitted. In particular, large transonic bands beyond the grey boundary need not affect the simulated ascent. These are fixed-incidence curve comparisons, not drag histories along the flight."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "9f021bec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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v0Q9A/PHHHxr3+eHDB7UWhLVq1VILN336dLU49+7dm81c0Zw+bXocuLi4iPj4eClMQkKC2lAVw4cPl8VTpEgR2XorKytx584djWlK33o+qx4HixcvVlufvifA48eP1Ya0SN+qffLkybL1pUuXVmthm5SUpNY6fuHChbIw6dfr6OiIK1euyMI0a9ZMFqZixYqy9b1791Y7pr/++ktjfsXGxoqkpCTpc7169WTb9evXT22bmzdvqsV/8eJFjfFrcurUKbXtO3TooPX26WWnx4EQQnTo0EFt/6dOndJ6f2FhYWrbt2nTRqSkpGgMn7YnxZo1a9S2TT9sVVJSkihRooQsjL29vSx+Tb1k5s+fL4unfPnyamF27twp24+trW2m156mHgctWrRQO0YvLy9ZGIVCIZ49e6Z1niYlJan14Eg/5JGma7lKlSqy8ze99OHT9jg4efKkbF3jxo0zjCchIUG8f/9e+nzkyBG1uMeNG6e2XePGjWVh9PT0ZL06NB2Tt7e3LI7Tp0+rhdmzZ0+Gac2INr2vtJG+xwEAsX37dlkYTffM5s2bS+tz6jr46aefZOsHDhwohEjtpWZqaiqA1PJX1bOhZMmS0rbp73Vz5syR1n348EGYmJjI1m/ZskUtL1auXCkLY2trK0tfds/ZXr16ycI2a9ZMLUxcXJxar6m0Qxa1b99etk5XV1fcvn1bFse2bdvU0sUeB0RERF8f9jggIiL6il28eFGtNa2BgQG2bt2q1lI7rcOHD8s+P3/+HCYmJmqtPdO2jgQ0t5BMK0+ePOjdu7fGdadPn1ZrQagp7E8//aS2LO1+Hz58iLt372r8e/LkSabp00bv3r1hYGAgfdbX10ehQoVkYdK2wn/06BFu3LghW9+nTx9pMtj0LC0ts5WekydPyj4bGRnB29tbtszR0RFNmjTJNJ703/nFixehq6sr+7719PRw7949WbisvvO6deuiePHismXpW/Om77WQvrVplSpV0KJFC43xm5qaQldXF0Bqq/T05+SiRYvUztvChQurxZPVcaQlsjnZdE7TtP/stL5On78AMGXKlAzjSNuTIv15YmhoiG7dusmW6erqqvVIevbsWZY9krp27Sr7XKBAAdlnKysrNG/eXLaf9BPGZtUDBgB69uyptiz9fVIIgVOnTsmWvX79GvPnz0eTJk1QoEABmJqaQkdHR7o20vfgyGwScZVff/1VOn+zSzU5u0pQUBBq1aqF4cOHY/ny5Th8+LCUJn19fRgaGkph03+PQOp9KatlSUlJOH78eKbp6t+/v+xz+usd0O57+lKsra3RsmVL2TJN98wTJ05I/8+p66Bu3bqy9UePHgWQ2nI/Li4OAFC/fn2UKVMGAHDlyhW8ePECSUlJsvSkjys8PFxtUud27dqp3QvTpy86OlrWS0GTzM7Z9Pny999/q+3T1NQUHz58kIVLe/9NX6bVqVNH7V7www8/ZDnJPBEREeU+vayDEBERUW549eoVWrVqpfbyYMGCBahWrVqm22ozxIYmUVFRma4vWbKk7KV7Wo8fP1Zblv6FPJD68tDS0hKvXr3SuG3NmjXVXm6reHh44NChQ5mmMSuaXoKlH14o7dBJmo6rQoUKn5SGtJ4+fSr7nC9fPujr66uFS//iJb3P9Z1nN78A9TzTNr9evHiB+Ph4rcKml9VxpGVvb6+2TJuXxDlF03dlZ2en9fbp89fY2Fjj96TNts7OzhrPt/Qv9FXbFi1aVGO8FhYWai8CjY2NZZ/z58+v9sIyq3NJE03XgqZlaSsaT548CS8vLzx//jzL+FVUL34zU65cOa3jS8/U1BRTp07F0KFDpWWhoaGyyjMdHR1UqVIFgwcPRvv27aXl6b9HAwMD5MuXT20fGX2PmUl/Lmkafu1LDi+XFRcXF+joqLeHS39OREdHIykpCXp6ejl2HdStWxcKhUKqDDx//jzevHkje5Feq1Yt2NraIiIiAkIIHD16FA4ODrKy3cbGBu7u7tLnj72fA6n3whIlSmS4PrNzNifKkfRlmqZrU0dHBy4uLrKhuoiIiOjrwx4HREREXyEhBLp06YJbt27Jlvfs2VNji/2ckna8bE3y5s372fb9pdjY2Kgty6zF8OdunZ4+/oxajX+udGT1nWc3v3JLVseRVv78+dWOKzw8XK3F+ecQGxuLM2fOyJbZ2NjA2dlZ6zhyu8eEJpp62qR/mZvd3jgZ0XSNZNaLIzExEe3bt89WpUFGcab3qffEIUOGICQkBG3btpWNV6+SkpKC48ePo0OHDhnOaQNkr8dKVtJfG1/j9Z5Wdu6ZOZlPAGBra4tSpUpJn1NSUnDs2DGp5wGQWnFQu3Zt6fPRo0fVekh5enrmWNpyoxxPu8/cLtOIiIgo57DHARER0VfIz88Pf//9t2xZpUqVsHjxYq22T/9ioHjx4ti7d2+W22X1giiz9ZpeRty6dQvVq1eXLYuJiZH1NgBSh5X4Wjk5OaktO3PmjNqkyh8rT548ss8PHjxAYmKiWuvXu3fvZhpP3rx5ZUNUNGjQAMuXL89y/0qlUvvEailv3ry4efOm9Dn9i/KM2NjYwMDAAAkJCdKy8ePHaxyaJr3sTGyso6ODJk2a4M8//5SWxcfHY/78+fD19dU6no/x+++/qw3z0bhxY40tpjOS/px89+4drl27plWvg/TX6YMHD5CQkKDWk+j27dtq234t1+nt27dlE2QDmq8P1bV17NgxtcmnW7dujX79+iFfvnzSsVeqVAnR0dHZSktOvFT39PSEp6cngNSeL7du3cK1a9ewefNmHDx4UAo3depUDB48GDo6OmrfY3x8PB4+fKhWAfU1fY8+Pj7w8fHJ8Xjv3r2LlJQUtWso/Tlha2srfV85eR3UrVsXFy9elD4fOXJE6jViZWWFkiVLynoUHTlyRO2+n37II03l6d9//51pTwKV9HGnl1U5nrbBQo8ePTBhwoQs92liYiLbf9rrTVMepqSkZGtCeCIiIsod7HFARET0ldm9ezcmT54sW2Zvb4/t27dr/ZJX9RJK5dq1a3j8+DFcXV01/rm4uODGjRtITEz86HRXqlRJbUgLTS+uly1bprYsbWvMu3fvQgih8e9Thyn6GE5OTihSpIhs2YoVKzJ86ZG+UiQrVapUkX1+//49Nm7cKFsWFRWFf/75J9N40n/nx44dQ2JiYobfubOzM86cOfNZKg7q1Kkj+3zy5Ens2bNHY9i4uDhp2BNdXV3UqlVLtn737t3IkydPhsdhbW2NsLAwja21MzNy5Ei1F41TpkzBjh07MtxGCIG1a9fiwoUL2dqXyubNmzFx4kTZMoVCgVGjRmUrnvT5CwATJkzIsAVv2nPSw8NDtu7Dhw9Ys2aNbFlycjJWrlwpW2Zvb5/hMEVfmr+/f5bLFAoFKlWqBEDz8CsrV65E3bp14ebmBldXV0RHR2e70uBTJScnaxyqzMPDAz/99BO2bdsmW/fy5Us8e/YMgPr3CGi+t6Zfpqenp1aZ+617+fIldu7cKVum6Z6Z9l6bk9dB+pf+q1evxosXLwCkDr2nUCiQJ08euLm5AUid/yBtjwRNcVSqVEltqK+//vorw/ugq6srFAoFrl69qnFoKW2lL0f+/fdfmJiYZLhPBwcHHDp0SFYxkr5MCwkJwZ07d2TLdu7cyWGKiIiIvgGsOCAiIvqK3LhxA127dlV7AThjxgwkJSVlOGmw6k+ldevWssmThRBo1qwZJk2ahLCwMNy4cQPnzp3D5s2bMWTIEBQsWBANGzb8pBaASqVSrWV4aGgoWrdujZCQEJw7dw5Tp07F+PHjZWEKFSqEhg0bfvR+v4QBAwbIPr98+RJVq1bFokWLcOHCBURGRmL//v0YNGgQmjVrlq2427Vrp/byvm/fvli8eDEuXryI3bt3o27dulmO/d+jRw/ZC6O3b9/C09MT8+bNw8mTJ3Hjxg2Eh4fjzz//xE8//YR8+fKhbdu2ePPmTbbSq41+/fqpvZRv06YNfv31V5w8eRI3b97EsWPHMH36dBQvXlw2lvwvv/wi2y4iIgK1atXCunXrEBERgcjISISFhWHp0qVo27YtHB0dMXbs2GynsXTp0hg+fLhsWVJSEtq0aYP27dtj586duHLlCq5du4YDBw5g4sSJcHNzQ7du3bJ84aW6Vu/cuYNz585hzZo1aNKkCTp06KA2Nvzo0aPVWs9npVq1aqhYsaJs2ZYtW9CoUSPs2bMHN27cwMWLF/Hnn3+ifv36CAwMlMK1bt1arUXygAEDMHv2bERERCA4OBjNmjXDlStXZGH69u2b48O8fKzdu3ejV69eOHbsGI4fP47evXtj9+7dsjCNGjWS5rLQNH/EyJEjcfbsWVy8eBFLlizJ9nWbE96/f498+fKhSZMmmDNnDvbv34/Lly/j5s2bCAsLU7vvAP9r2V2zZk1pwl2V6dOnY+zYsQgPD0doaCi6du2Kffv2ycJ07twZ5ubmn++gckmPHj2wbNmyTO+ZaSfvzsnrwMPDQ9aKP+38CWkrQlX/T05Olt13HR0d1XoLKZVKtQm/ly9fjrZt22Lv3r24cuUKrly5gv3792PmzJnw8PBAwYIFsWnTpowzSQvpj+/Ro0eoUaMGVqxYgTNnzuDGjRs4efIkVq1ahW7dusHR0RE9evSQxdGlSxfZ5+TkZDRo0AA7duzAxYsXsWLFCrVtiIiI6CsliIiI6KtRrVo1AeCj/9LavXu30NXVzdb2ISEhsjg8PDxk67t3755p+l+8eCGKFCmi9f709fXFwYMHPzq/skrfnTt3sjxGbeJJSEgQ9erV0+qY3N3dZdsGBARk+j0JIcS4ceOyjFdPT0/22cXFRS2eJUuWZPucuXPnjiwOFxcX2XpfX1+1/fj6+maZlgkTJmidhpiYGNm23t7e2ToGTfvXRkpKiujZs2e28yz9OZQ+z7T9++mnn0RKSspHpT0iIkKYmZlptZ958+bJtt25c6fQ0dHROp1ly5YVcXFxsji0OQe6d+8uC+Ph4aEWJqtrLyQkRC09xsbGmaZXX19fnDt3Torj3bt3ws7OLtNtTE1N1fIz/bmvzbWcXvrwAQEB0rrY2NhsnS/p8+/kyZPCyMhI6+2dnZ1FVFTURx1TZsfxpaU/r7I6HwCIqlWriqSkJFk8OXEdqFSqVEnjNsePH5fCrF69WmOYzp07a4zz5cuXolixYtk6R9JfPx9zzo4aNSpb+0wfZ0pKiqhdu3aW26Qv07J6viAiIqIvjz0OiIiIviJpWyp+qubNm2Pnzp2wtbXVKryZmdknT15qbW2N4OBgVKtWLcuwtra22LVrl8ZhV742+vr62LlzJ7p16/ZZ4vf19UX79u0zXF+tWjUMHDgwy3h+/vln+Pv7y8abzoytre0nDWuRmYkTJ2LWrFlqY4ZrY/Xq1RgwYIDWLdyzM7FwWgqFAv7+/ggICJD10MmMgYEBzMzMPmp/Ki4uLtiwYQOWLl360a343d3dERISIg1/kh0tW7bE5s2btZoXom7duti3b5/W59SX8Oeff2Z4X9PT00NgYCDKli0rLTMyMoK/v7/avCFp12/cuBHW1tafI7k5wsXFBStWrJAtq1y5MoKCgrSa7LZs2bI4ePCg1uf5t6RSpUqYNGlShtdSkSJFsGXLFrWx/XPyOkg/1BCQel5VqFBB+px+GLbMtgVS50c4ePBghuvTUygUyJcvn1ZhMzN9+nRMmTIFenraTYeYfp8KhQKbNm3KdD6G/v37o0aNGp+UTiIiIvr8WHFARET0HWvevDlu376NP/74A82aNYOTkxMMDQ2hr68POzs7VK1aFf369cP27dvx9OlT2cu2j+Xs7IzQ0FDs3LkT3t7eKFCgAIyNjaGvr488efKgfv36mDt3Lm7duoXGjRt/+kF+Iaampli9ejXOnTuHgQMHonz58rCysoKenh5sbGxQqlQp9O7dG9OmTct23Hp6eti4cSNWr16N6tWrw9TUFCYmJihXrhxmz56Nw4cPa/2yumfPnrh79y5+++031KtXDw4ODlAqlTAwMICDgwNq1aqFoUOH4p9//sHjx4+znEjzUwwfPhx37tzBpEmT4OHhAXt7e+nFe+HChdGmTRssXboUpqamsu0MDAywYMECXL58GcOGDUPlypVhbW0NPT09GBsbw9XVFU2aNMHkyZNx+vRptfHCs8vHxwf37t3DunXr4OPjg+LFi8PGxgZ6enowMTFBoUKF8MMPP+CPP/7AgwcPZC8DM6JQKGBgYABLS0sULFgQHh4e6NevH3bt2oXbt2/D29v7k9IMABUqVMClS5ewfv16tGvXDgUKFICJiQkMDAyQL18+1KpVCxMmTECLFi3Utm3Tpg1u376NGTNmoE6dOrC3t4e+vr50vJ07d8bevXsRHBwsDfnztShXrhwuXbqEAQMGoECBAjAwMICdnR3atWuH06dPo1OnTmrbeHl54cSJE2jbti3s7Oygr68PJycndOnSBeHh4bkyVJGJiQlOnTqFefPmoW3btihTpgwcHR2hr68PpVIJJycnNGzYEL///jsuX76sNtcKkDo/zI0bN7B48WI0adIEjo6OMDAwgJGREfLnz482bdpg48aNCA8PR+HChb/4MX4p48ePx4EDB9CsWTPY2tpCqVTCzc0NY8eOxdmzZzN8oZ5T10G9evXUllWtWlVWWVWgQAGNlZyZVQw4OjoiODgY+/fvR48ePVC8eHGYm5tDV1cX5ubmKF68ONq1a4eFCxfizp07mDJlSmbZpBWFQoGxY8fi1q1bGD9+PGrWrCldM4aGhsiXLx/q1auHX3/9FYcPH8a9e/fU4nBwcMDp06cxceJEFC9eHEqlElZWVqhbty62b9+OhQsXfnI6iYiI6PNTCJHBLGpERERERES56NChQ2q9ku7cuQNXV9fcSRDlOh8fH6xevVr67OHhgUOHDuVegoiIiIi+U+xxQEREREREREREREREElYcEBERERERERERERGRhBUHREREREREREREREQkYcUBERERERERERERERFJODkyERERERERERERERFJ2OOAiIiIiIiIiIiIiIgkrDggIiIiIiIiIiIiIiIJKw6IiIiIiIiIiIiIiEjCigMiIiIiIiIiIiIiIpKw4oCIiIiIiIiIiIiIiCSsOCAiIiIiIiIiIiIiIgkrDoiIiIiIiIiIiIiISMKKAyIiIiIiIiIiIiIikrDigIiIiIiIiIiIiIiIJKw4ICIiIiIiIiIiIiIiCSsOiIiIiIiIiIiIiIhIwooDIiIiIiIiIiIiIiKSsOKAiIiIiIiIiIiIiIgkrDggIiIiIiIiIiIiIiIJKw6IiIiIiIiIiIiIiEjCigMiIiIiIiIiIiIiIpKw4oCIiIiIiIiIiIiIiCSsOCAiIiIiIiIiIiIiIgkrDoiIiIiIiIiIiIiISMKKAyIiIiIiIiIiIiIikrDigIiIiIiIiIiIiIiIJKw4ICIiIiIiIiIiIiIiCSsOiIiIiIiIiIiIiIhIwooDIiIiIiIiIiIiIiKSsOKAiIiIiIiIiIiIiIgkrDggIiIiIiIiIiIiIiIJKw6IiIiIiIiIiIiIiEjCigMiIiIiIiIiIiIiIpKw4oCIiIiIiIiIiIiIiCSsOCAiIiIiIiIiIiIiIgkrDoiIiIiIiIiIiIiISMKKAyIiIiIiIiIiIiIikrDigIiIiIiIiIiIiIiIJKw4ICIiIiIiIiIiIiIiCSsOiIiIiIiIiIiIiIhIwooDIiIiIiIiIiIiIiKSsOKAiIiIiIiIiIiIiIgkrDggIiIiIiIiIiIiIiIJKw6IiIiIiIiIiIiIiEjCigMiIiIiIiIiIiIiIpKw4oCIiIiIiIiIiIiIiCSsOCAiou+Wn58fypYtm9vJ+E9RKBTYuXMnAODu3btQKBSIiIj4pDg9PT0xePDgT04bEclpc20FBgbC0tJS6zgPHToEhUKBV69efVLaiL4F165dQ9WqVWFoaIiyZcvmWLmXHSwjvx7Xr1+Hg4MDYmNjAWT//gkAQgj06dMH1tbWX/xcykz686xSpUrYvn177iWIiIi+CFYcEBFRrvDx8YFCoYBCoYCenh7y58+Pvn37IiYmJreTJsnoB5+np6eUdqVSCTc3N0ybNg3JyclfPpFfMWdnZ0RFRaFUqVKffV8JCQmYOXMm3N3dYWxsDFtbW9SoUQMBAQFITEwEID/n9PX1kSdPHjRo0ACrVq1CSkrKZ0+jJm/vn/mif9n1MXnWsGFD6Orq4sSJEwD+V4GU2Z+fnx8AICQkBE2bNoWNjQ2MjY1RokQJDBs2DI8ePZLiT05Oxrx581CmTBkYGhrC0tISTZo0QVhYmCwdgYGBUCgUKF68uFoaN2/eDIVCAVdX1yzzIKs0qV6UKxQK6OjowMLCAuXKlcPIkSMRFRWlTTbnmu3bt2Py5MnSZ1dXV8yfP18WpkOHDoiMjNQ6zurVqyMqKgoWFhYAPu7FGX0/jh07Bl1dXTRu3Di3k/JZ+Pr6wsTEBNevX0dwcLBauZdRRdp/6WX/52zE4ePjgx9++EGrsE+ePMGAAQNQsGBBKJVKODs7w8vLC8HBwVIYV1dX6X5uZGQEV1dXtG/fHgcPHtRqH2PHjkW/fv1gZmYGIPv3TwAICgpCYGAg9uzZ88WeoT7G+PHjMXr06Fx7fiIioi+DFQdERJRrGjdujKioKNy9excrV67E7t278csvv+R2srTSu3dvREVF4fr16xg4cCDGjRuH2bNn51j8np6eCAwMzLH4coOuri4cHBygp6f3WfeTkJCARo0a4bfffkOfPn1w7NgxnDp1Cv369cPChQtx+fJlKWzac+6ff/5BnTp1MGjQIDRv3hxJSUmfNZ3fquzk2f3793H8+HH0798f/v7+AP5XgaT6GzZsGEqWLClbNnz4cCxbtgz169eHg4MDtm3bhitXrmDp0qV4/fo15syZAyC1Jaa3tzcmTZqEgQMH4urVqzh8+DCcnZ3h6ekp9XZRMTExwbNnz3D8+HHZ8lWrViF//vxZHrs2aVK5fv06Hj9+jNOnT2PUqFE4cOAASpUqhYsXL2Y3y78Ya2tr6QVXRoyMjGBvb691nAYGBnBwcIBCofjU5GVKCMFr9huwatUqDBgwAKGhobh//35uJyfH3bp1CzVr1oSLiwtsbGy+WLlH2XP37l1UqFABBw8exMyZM3Hx4kUEBQWhTp066NevnyzspEmTpOe7NWvWwNLSEvXr18fUqVMz3cfDhw+xa9cu9OjRQ1qW3fsnkHpOOTo6onr16h99LmV0f0xISMh2XBlp1qwZXr9+jX379uVYnERE9BUSREREuaB79+6iZcuWsmVDhw4V1tbW0ufk5GQxceJE4eTkJAwMDIS7u7v4559/ZNs8ePBAdOjQQVhZWQljY2NRoUIFceLECSGEEL6+vsLd3V0Ke/v2bVGoUCHx888/i+TkZBEfHy9GjBgh8ubNK4yNjUXlypVFSEiIEEKIkJAQAUD25+vrK4QQwsPDQwwaNEiWjvr164uqVauKuLg4YWZmJrZs2SJbv2vXLmFsbCzevHmjVf54eHiIgIAArcKq0lupUiVhbGwsLCwsRPXq1cXdu3eFEJrzetCgQcLDw0O2v379+ol+/foJCwsLYW1tLcaOHStSUlKkMC4uLmLSpEmiY8eOwsTERDg6OooFCxbI4gUgduzYIYQQ4s6dOwKAOHfunLT+8uXLokmTJsLExETY29uLLl26iOfPn0vr4+LiRNeuXYWJiYlwcHAQs2fP1pjfac2YMUPo6OiIs2fPqq1LSEgQcXFxGeaDEEIEBwcLAGLFihUZ7uNzibsX/kX/siu7eebn5ye8vb3F1atXhZmZmZT3aaW/LoVIvY4NDAzE4MGDNaYjJiZGCCHExo0bBQCxa9cutTCtW7cWNjY20j4DAgKEhYWF6N+/v/jxxx9l+1IqlWL06NHCxcUlw2PXNk2qe4Xqs8q7d+9E0aJFRY0aNTLcx8uXL0WnTp2Era2tMDQ0FIULFxarVq2S1j98+FC0b99eWFpaCmtra9GiRQtx584dab3q+5k1a5ZwcHAQ1tbW4pdffhEJCQlSmEWLFonChQsLpVIp7O3tRZs2baR1aa8tDw8PtXte2nwUQohr164JAOLq1auy45gzZ45wcXERKSkpsvzI6D46ceJEUapUKbX8KF++vBg/frzGvFLFFRQUJCpUqCD09fXFwYMHxc2bN0WLFi2Evb29MDExERUrVhT79++Xbevi4iKmTp0qevToIUxNTYWzs7NYtmyZLExYWJhwd3cXSqVSVKhQQezYsSPb9y+SU5WH165dEx06dBATJ06UrVd9p3v27BFlypQRSqVSVK5cWVy4cEEWbuvWraJEiRLCwMBAuLi4iNmzZ8vWP378WDRt2lQYGhoKV1dXsW7dOuHi4iLmzZsnhXn16pXo3bu3sLOzE2ZmZqJOnToiIiJCFs+uXbtE+fLlhVKpFAUKFBB+fn4iMTExw+PTdG6nLfdU/0/71717d9G9e3e15arr+nOUkUII8ddff4kKFSoIpVIpbGxsRKtWraR1L1++FF27dhWWlpbCyMhING7cWERGRkrrVfeAoKAgUaxYMWFiYiIaNWokHj9+LPsuNT2DBAQEqB2r6tlmzpw5olSpUsLY2Fjky5dP9O3bV8TGxmq9X19fX7W4Vc9w6TVp0kQ4OTlpLJPS3rvTnzcqEyZMEDo6OuLatWsZ5vGcOXNExYoVZcvS3j9VaXZ3dxdr1qwRLi4uwtzcXHTo0EF6Nkx/bqjKqJSUFDFjxgxRoEABYWhoKMqUKSN7zszo/qh6thsyZIiwsbERtWvXFkLk3Hnm4+MjunbtmmGeEBHRt489DoiI6Ktw+/ZtBAUFQV9fX1r2+++/Y86cOZg9ezYuXLiARo0aoUWLFrhx4wYAIC4uDh4eHnj8+DF27dqF8+fPY+TIkRq7TV+6dAk1atRAu3btsGTJEujo6KBHjx4ICwvDxo0bceHCBbRr1w6NGzfGjRs3UL16dcyfPx/m5uayVtEZMTIyQmJiIkxMTODt7Y2AgADZ+oCAALRt2zbL1r0fIykpCT/88AM8PDxw4cIFHD9+HH369Ml2i9/Vq1dDT08PJ0+exIIFCzBv3jysXLlSFmbWrFkoU6YMzp49izFjxmDIkCHYv3+/VvFHRUXBw8MDZcuWRXh4OIKCgvD06VO0b99eCjNixAiEhIRgx44d+Pfff3Ho0CGcOZP5EDvr1q1D/fr1Ua5cObV1+vr6MDExyXT7unXrwt3dnWP1ZoOmPBNCICAgAF26dEGxYsXg5uaGzZs3axXfli1bkJCQgJEjR2pcrxrqZv369XBzc4OXl5damGHDhuHFixdq52OvXr2wadMmvHv3DkDq0DmNGzdGnjx5ciRNGTEyMsLPP/+MsLAwPHv2TGOY8ePH48qVK/jnn39w9epVLFmyBLa2tgCAd+/eoU6dOjA1NcWRI0cQGhoKU1NTNG7cWNZqNCQkBLdu3UJISAhWr16NwMBAqbdSeHg4Bg4ciEmTJuH69esICgpC7dq1NaZl+/btyJcvn9TaVtMwS0WLFkWFChWwbt062fL169ejU6dOavecjO6jPXv2xJUrV3D69Gkp7IULF3Du3Dn4+Phkmq8jR47E9OnTcfXqVZQpUwZxcXFo2rQpDhw4gHPnzqFRo0bw8vJSa90+Z84cVKxYEefOncMvv/yCvn374tq1awCA2NhYeHl5oXTp0jh79iwmT56MUaNGybbX5v71Jb179w7v3r2DEEJalpCQgHfv3iE+Pl5j2LRlY2JiYrbCfoxNmzahaNGiKFq0KLp06YKAgABZelVGjBiB2bNn4/Tp07C3t0eLFi2kfZ45cwbt27eHt7c3Ll68CD8/P4wfP17WI69bt254/PgxDh06hG3btmH58uWya04IgWbNmuHJkyfYu3cvzpw5g/Lly6NevXp4+fIlAGDfvn3o0qULBg4ciCtXrmDZsmUIDAzMtJV5VFQUSpYsiWHDhml8RnB2dsa2bdsApPZIioqKwu+//47ff/8d1apVk3ouRkVFST2zPkcZ+ffff6N169Zo1qwZzp07h+DgYFSsWFFa7+Pjg/DwcOzatQvHjx+HEAJNmzaVfe/v3r3D7NmzsXbtWhw5cgT379+XjjezZ5AOHTqo9TLr0KEDAEBHRwcLFizApUuXsHr1ahw8eFDtfpvZfocPH4727dtLveKioqJQvXp1teN/+fIlgoKC0K9fP43PA9oMpTZo0CAIIfDXX39lGObIkSOyfM3IrVu3sHPnTuzZswd79uzB4cOH8dtvvwFIfe6dNGkS8uXLh6ioKOkeOW7cOAQEBGDJkiW4fPkyhgwZgi5duuDw4cOyuNPfH4H/PduFhYVh2bJlOXqeVa5cGUePHs3ymImI6NvFPpRERN+hnouO4UVsfNYBc5iNmRKr+qn/aMvInj17YGpqiuTkZHz48AEAMHfuXGn97NmzMWrUKHh7ewMAZsyYgZCQEMyfPx+LFi3C+vXr8fz5c5w+fRrW1tYAgMKFC6vt5/jx42jevDnGjBkj/eC8desWNmzYgIcPHyJv3rwAUn+EBgUFISAgANOmTYOFhQUUCgUcHBwyPIaUlBT8+++/2LdvnzRe8Y8//ojq1avj8ePHyJs3L6Kjo7Fnzx6tX7Bn15s3b/D69Ws0b94chQoVAgCN47pnxdnZGfPmzYNCoUDRokVx8eJFzJs3D71795bC1KhRA6NHjwYAuLm5ISwsDPPmzUODBg2yjH/JkiUoX748pk2bJi1btWoVnJ2dERkZibx588Lf3x9r1qyR4lu9ejXy5cuXabw3btyAp6dnto83rWLFiuHChQufFMd/Tfo8O3DgAN69e4dGjRoBALp06QJ/f3/ZsA0ZuXHjBszNzeHo6JhpuMjIyAzPbdXy9ONJly1bFoUKFcLWrVvRtWtXBAYGYu7cubh9+3aOpCkzxYoVA5A6TIam4Sru37+PcuXKSS+b0s65sHHjRujo6GDlypXSC/mAgABYWlri0KFDaNiwIQDAysoKf/zxB3R1dVGsWDE0a9YMwcHB6N27N+7fvw8TExM0b94cZmZmcHFx0VjBBqQOW6SrqwszM7NM73mdO3fGH3/8Ic2NEBkZiTNnzmDNmjVqYQ0MDDTeR01NTdGoUSMEBASgUqVK0rF5eHigYMGCGe4bSB1GJO39xsbGBu7u7tLnKVOmYMeOHdi1axf69+8vLW/atKk0FN6oUaMwb948HDp0CMWKFcO6deugUCiwYsUKGBoaokSJEnj06JHs3pfV/cvNzS3TdOc01TGfOHECNjY2AICVK1di3rx5aN++veyFd9WqVfH+/XuEhIRI99M///wT06ZNg5eXl6zc9fT0RExMDPbu3YsiRYoASK1UUr3szQ5/f3906dIFQOqQZ3FxcQgODkb9+vVl4Xx9fdXu+Tt27ED79u0xd+5c1KtXD+PHjweQWu5cuXIFs2bNgo+PD65du4YDBw7g9OnT0nW0cuVKKe1AauXaxYsX8ezZMyiVSgCpzxc7d+7E1q1b0adPH0ydOhWjR49G9+7dAQAFCxbE5MmTMXLkSPj6+mo8PtUwMqamptL5HR0dLa3X1dWVnk3s7e1lL6gNDAxgbGwsuy4+Vxk5depUeHt7Y+LEidIy1flz48YN7Nq1C2FhYdJL93Xr1sHZ2Rk7d+5Eu3btAKRWHi1dulR6xujfvz8mTZoEIOtnEFNTU+jp6andV9LO8VCgQAFMnjwZffv2xeLFi6Xlme3X1NQURkZGiI+Pz/SedfPmTQghpPvxx7C2toa9vT3u3r2bYRjVcEhZSUlJQWBgoNSQpGvXrggODsbUqVNhYWEBMzMzacgrAHj79i3mzp2LgwcPolq1agBSz8/Q0FAsW7YMHh4eUtzp749A6nPxzJkzpc8TJkzIsfPMyckJ9+/fR0pKCnR02CaViOh7xLs7EdF36EVsPJ6/+fJ/2a2sqFOnDiIiInDy5EkMGDAAjRo1woABAwCk/hB9/PgxatSoIdumRo0auHr1KgAgIiIC5cqVk36Ya3L//n3Ur18f48aNk7UGPHv2LIQQcHNzg6mpqfR3+PBh3Lp1K8u0L168GKampjA0NESLFi3QpUsX6eVC5cqVUbJkSelF2tq1a5E/f/4MW/oCwLRp02TpOHr0KH7++We1ZZpYW1vDx8dHamn7+++/f9SkrFWrVpW1GK5WrRpu3Lghm/RZ9aM17WfV95GVM2fOICQkRHZMqh/yt27dwq1bt5CQkCDbh7W1NYoWLZppvEKITx5PPSfi+K9Jn2f+/v7o0KGDNB5zx44dcfLkSVy/fj3bcX0KTfH07NkTAQEBOHz4sNRC/UukSdW6OqN4+vbti40bN6Js2bIYOXIkjh07Jq07c+YMbt68CTMzM+l6sba2xocPH2T3qJIlS0JXV1f67OjoKLW2btCgAVxcXFCwYEF07doV69atk3pefCxvb2/cu3dPmvx63bp1KFu2LEqUKJGteHr37o0NGzbgw4cPSExMxLp169CzZ88st0vfovft27cYOXIkSpQoAUtLS5iamuLatWtqPQ5UrW8BSBUZqny6fv26NNm2SuXKlWXbZ3X/Irnr16/j1KlTUsW/np4eOnTogFWrVqmF1XTPV5UrV69e1fgcoCqbrl+/Dj09PZQvX15aX7hwYVhZWUmfz5w5g7i4ONjY2Mi+vzt37kjf3ZkzZzBp0iTZelWPgHfv3qmVx5/D5yojIyIiUK9ePY3rrl69Cj09PVSpUkVaZmNjI/sOAMDY2Fh6eQ/I7zMf+wwSEhKCBg0awMnJCWZmZujWrRtevHiBt2/farVfbWV1H85OPJnF8f79e9k9JCOurq6y3qdZHdOVK1fw4cMHNGjQQHZurFmzRu3eo6nHQ/plOXmeGRkZISUlRa3nEhERfT/Y44CI6DtkY6b8JvZrYmIi9RBYsGAB6tSpg4kTJ0otWQH1H3ppf7gZGRlluQ87OzvkzZsXGzduRK9evWBubg4gtcWXrq4uzpw5I3vpBkCrlwKdO3fG2LFjoVQqkTdvXrU4fvzxR/zxxx8YPXo0AgIC0KNHj0x/cP7888+ybuKdO3dGmzZt0Lp1a2mZk5NThtsHBARg4MCBCAoKwqZNmzBu3Djs378fVatWhY6OjtrwEB879IQm2v4YT0lJgZeXF2bMmKG2ztHRURqCKrvc3Ny0rrzIyNWrV1GgQIFPiuO/Jm2evXz5Ejt37kRiYiKWLFkihUlOTsaqVas0fudpubm54fXr14iKisq0hb+qtXFG6QEga2ms0rlzZ4wcORJ+fn7o1q2bVpNNapumzKjSlLYnQVpNmjTBvXv38Pfff+PAgQOoV68e+vXrh9mzZyMlJUXjsEBA6n1NJe3wbkDq9agaZsbMzAxnz57FoUOH8O+//2LChAnw8/PD6dOntRqeQxNHR0fUqVMH69evR9WqVbFhwwb89NNP2Y7Hy8sLSqUSO3bsgFKpRHx8PNq0aZPldumHGhkxYgT27duH2bNno3DhwjAyMkLbtm3VJgHNLJ80vRBMf8/M6v71pZ0/fx6AvBz88ccf4ePjo1YeqSp50r7U7NKlCzp06KAW9tChQ2ph05ZD2vL390dSUpKs3BJCQF9fHzExMbIX+5qovo+svhtNQx+lX56SkgJHR0fp2NJSXQcpKSmYOHGixmM1NDTEpEmTMh2uMCd8rjIys2elzPIvbb5run7SbpvZM4gm9+7dQ9OmTfHzzz9j8uTJsLa2RmhoKHr16iV7Pslqv9ooUqQIFAoFrl69ih9++CFb26q8ePECz58/z/Q5wdbWFjExMVnGldm9SBPVur///lvtOVDVg0ZF01BM6Zfl5Hn28uVLGBsba/U8TkRE3yZWHBARfYeyM1zQ18TX1xdNmjRB3759kTdvXuTNmxehoaGylvrHjh2TWoKWKVMGK1euxMuXLzPsdWBkZIQ9e/agadOmaNSoEf7991+YmZmhXLlySE5OxrNnz1CrVi2N2xoYGMha26dlYWGhcVgklS5dumDkyJFYsGABLl++LA1/kBFra2vZMRgZGcHe3j7TfaRXrlw5lCtXDmPGjEG1atWkF3t2dna4dOmSLGxERITaj1fVy6W0n4sUKSJ7saQpjLbd/8uXL49t27bB1dVV44vbwoULQ19fHydOnED+/PkBADExMYiMjJR1xU+vU6dO+PXXX3Hu3Dm1YViSkpIQHx+f6TwHBw8exMWLFzFkyBCtjoPU82zdunXIly8fdu7cKQsXHByM6dOnY+rUqZm+rG/bti1Gjx6NmTNnYt68eWrrX716BUtLS3h7e6NTp07YvXu32jwHc+bMgY2NjcZhs6ytrdGiRQts3rwZS5cu1eoYtU1TRt6/f4/ly5ejdu3ashf96dnZ2cHHxwc+Pj6oVauWNN57+fLlsWnTJtjb20sVnh9DT08P9evXR/369eHr6wtLS0scPHhQ4wvSzO55aXXu3BmjRo1Cx44dcevWLalVuSYZxamnp4fu3bsjICAASqUS3t7eMDY2zt7BATh69Ch8fHzQqlUrAKlz32Q2nIgmquGK4uPjpRdx4eHhsjBZ3b++NE15ZWBgAAMDA63C6uvrq5UBmYXNjqSkJKxZswZz5syRhtRSadOmDdatWycbRkrTPV9VrpQoUQKhoaGyOI4dOwY3NzdpeK6kpCScO3dOGibm5s2bePXqlRS+fPnyePLkCfT09DKsxCtfvjyuX7+eYZlrb2+vcbixrKi+j/TXgKbr4nOVkWXKlEFwcLDGYeNKlCiBpKQknDx5Uhqq6MWLF5kOC5eRjJ5BNB1reHg4kpKSMGfOHGmIG23nxElLm3uWtbU1GjVqhEWLFmHgwIFqzwNZ3cuB1LkHdHR0Mq14KFeuXIYV25+iRIkSUCqVuH//fqbfs7Zy8jy7dOmSrLcPERF9fzhUERERfTU8PT1RsmRJadzVESNGYMaMGdi0aROuX7+O0aNHIyIiAoMGDQKQOhSKg4MDfvjhB4SFheH27dvYtm0bjh8/LovXxMQEf//9N/T09NCkSRPExcXBzc0NnTt3Rrdu3bB9+3bcuXMHp0+fxowZM7B3714Aqa2EVWMyR0dHZ2uIDysrK7Ru3RojRoxAw4YNsxyD+FPcuXMHY8aMwfHjx3Hv3j38+++/sh/9devWRXh4ONasWYMbN27A19dXrSIBAB48eIChQ4fi+vXr2LBhAxYuXCjltUpYWBhmzpyJyMhILFq0CFu2bFELk5F+/frh5cuX6NixI06dOoXbt2/j33//Rc+ePZGcnAxTU1P06tULI0aMQHBwMC5dugQfH58sx80dPHgwatSogXr16mHRokU4f/48bt++jc2bN6NKlSqy1nPx8fF48uQJHj16hLNnz2LatGlo2bIlmjdvjm7duml1HP812uSZv78/2rZti1KlSsn+evbsiVevXuHvv//OdB+q+TV+//139OrVC4cPH8a9e/cQFhaGn376SeqF5O3tjVatWqF79+7w9/fH3bt3ceHCBfz000/YtWsXVq5cmWElUWBgIKKjo7Wu6NI2TSrPnj3DkydPcOPGDWzcuBE1atRAdHS0rAdGehMmTMBff/2Fmzdv4vLly9izZ4903Xbu3Bm2trZo2bIljh49ijt37uDw4cMYNGgQHj58qNUx7NmzBwsWLEBERATu3buHNWvWICUlJcOhTVxdXXHkyBE8evRINlZ7eq1bt8abN2/Qt29f1KlTJ9PeUJndR3/88UccPHgQ//zzj1bDFGlSuHBhbN++HRERETh//jw6deqUaetdTVTb9OnTB1evXpV6MAD/a/me1f2L/mfPnj2IiYlBr1691O4Jbdu2hb+/vyz8pEmTZPd8W1tb6QXtsGHDEBwcjMmTJyMyMhKrV6/GH3/8IbX+L1asGOrXr48+ffrg1KlTOHfuHPr06QMjIyPpu6tfvz6qVauGH374Afv27cPdu3dx7NgxjBs3TqogmjBhAtasWQM/Pz9cvnwZV69elVrOfwoXFxcoFArs2bMHz58/R1xcHIDU6+LkyZO4e/cuoqOjkZKS8tnKSF9fX2zYsAG+vr64evUqLl68KI15X6RIEbRs2RK9e/dGaGgozp8/jy5dusDJyQktW7bU6hizegZxdXXFnTt3EBERgejoaMTHx6NQoUJISkrCwoULcfv2baxdu1brSt20XF1dceHCBVy/fh3R0dEZ9qZcvHgxkpOTUblyZWzbtg03btzA1atXsWDBArUhGGNjY/HkyRM8ePAAR44cQZ8+fTBlyhRMnTo108YcjRo1wvHjx3P8fmBmZobhw4djyJAhWL16NW7duoVz585h0aJFWL16dbbjy8nz7OjRo2qVg0RE9J0RREREuaB79+6iZcuWasvXrVsnDAwMxP3790VycrKYOHGicHJyEvr6+sLd3V38888/svB3794Vbdq0Eebm5sLY2FhUrFhRnDx5UgghhK+vr3B3d5fCxsbGiurVq4tatWqJuLg4kZCQICZMmCBcXV2Fvr6+cHBwEK1atRIXLlyQtvn555+FjY2NACB8fX2FEEJ4eHiIQYMGZXmMwcHBAoDYvHlztvPHw8NDBAQEaBX2yZMn4ocffhCOjo7CwMBAuLi4iAkTJojk5GQpzIQJE0SePHmEhYWFGDJkiOjfv7/w8PCQ7e+XX34RP//8szA3NxdWVlZi9OjRIiUlRQrj4uIiJk6cKNq3by+MjY1Fnjx5xPz582VpASB27NghhBDizp07AoA4d+6ctD4yMlK0atVKWFpaCiMjI1GsWDExePBgaT+xsbGiS5cuUvwzZ87UKr8/fPggpk+fLkqXLi0MDQ2FtbW1qFGjhggMDBSJiYlCiNRzDoAAIPT09ISdnZ2oX7++WLVqlSyvvqS4e+Ff9C+7tMmz8PBwAUCcOnVKYxxeXl7Cy8tL+pz+ukxr//79olGjRsLKykoYGhqKYsWKieHDh4vHjx9LYRITE8Xs2bNFyZIlhVKpFObm5qJRo0bi6NGjsrgCAgKEhYVFhsc2b9484eLikmUeZJWmkJAQKY8UCoUwMzMT7u7uYsSIESIqKirTuCdPniyKFy8ujIyMhLW1tWjZsqW4ffu2tD4qKkp069ZN2NraCqVSKQoWLCh69+4tXr9+LYTQfB8dNGiQdG0fPXpUeHh4CCsrK2FkZCTKlCkjNm3aJIVNf20dP35clClTRiiVSqH6mZBRPrZr104AEKtWrZItV+VHTEyMtEzTfVSlVq1aokSJEpnmU0bxCpF6n6lTp44wMjISzs7O4o8//lA7LhcXFzFv3jzZdu7u7rK0hIWFiTJlyggDAwNRoUIFsX79egFAXLt2TQqT1f2LUjVv3lw0bdpU47ozZ84IAOLMmTPSd7p7925RsmRJYWBgICpVqiQiIiJk22zdulWUKFFC6Ovri/z584tZs2bJ1j9+/Fg0adJEKJVK4eLiItavXy/s7e3F0qVLpTBv3rwRAwYMEHnz5hX6+vrC2dlZdO7cWdy/f18KExQUJKpXry6MjIyEubm5qFy5sli+fHmmx5r+PNJU7k2aNEk4ODgIhUIhunfvLoQQ4vr166Jq1arCyMhIABB37twRQny+MnLbtm2ibNmywsDAQNja2orWrVtL616+fCm6du0qLCwshJGRkWjUqJGIjIyU1mu6B+zYsUO6R2T1DPLhwwfRpk0bYWlpKQBIzzZz584Vjo6O0j7XrFkju8az2q8QQjx79kw0aNBAmJqaCgAiJCQkwzx4/Pix6Nevn3BxcREGBgbCyclJtGjRQraNi4uLdD83MDAQ+fPnF+3btxcHDx7MNH+FECIpKUk4OTmJoKCgDPNOU/mXvizSVDalpKSI33//XRQtWlTo6+sLOzs70ahRI3H48GEhRMb3x4zOjZw4zx4+fCj09fXFgwcPsswbIiL6dimEyOYggURERKSVdevWYdCgQXj8+LHG4SO+Jp6enihbtizmz5+fYRhXV1cMHjwYgwcP/mLpIqLvlxACxYoVw08//YShQ4fmdnJk1q1bhx49euD169ccv/szOXToEOrUqYOYmJiPnnNDk4cPH8LZ2VmaN4ToS1m8eDH++usv7Nu3L7eT8tmNGDECr1+/xvLly3M7KURE9Bnl/gCdRERE35l3797hzp07mD59On766aevvtKAiOhLe/bsGdauXYtHjx5pHHv9S1uzZg0KFiwIJycnnD9/HqNGjUL79u1ZafANOHjwIOLi4lC6dGlERUVh5MiRcHV1lc2PRPQl9OnTBzExMYiNjYWZmVluJ+ezsre3/+wThhMRUe5jxQEREVEOmzlzJqZOnYratWtjzJgxuZ0cIqKvTp48eWBra4vly5fDysoqt5ODJ0+eYMKECXjy5AkcHR3Rrl07TJ06NbeTRVpITEzEr7/+itu3b8PMzAzVq1fHunXrsj2pM9Gn0tPTw9ixY3M7GV/EiBEjcjsJRET0BXCoIiIiIiIiIiIiIiIikujkdgKIiIiIiIiIiIiIiOjrwYoDIiIiIiIiIiIiIiKSsOKAiIiIiIiIiIiIiIgkrDggIiIiIiIiIiIiIiIJKw6IiIiIiIiIiIiIiEjCigMiIiIiIiIiIiIiIpKw4oCIiP6TAgMDYWlpmaNx3r17FwqFAhERETkaL9HX7NChQ1AoFHj16lVuJyXHCCHQp08fWFtb85r+RAqFAjt37sztZGToezx/iYiIiIhyAisOiIgoV/j4+EChUOC3336TLd+5cycUCsVn33+HDh0QGRn52feTnqenJxQKBRQKBZRKJZycnODl5YXt27dnuE3RokVhYGCAR48eAfjfi67M/gIDAwEA27Ztg6enJywsLGBqaooyZcpg0qRJePnypRT/+/fv4evri6JFi0KpVMLW1hZt27bF5cuXZenw8/ODQqFA48aN1dI4c+ZMKBQKeHp6fnom/Ye8fPnyi/59rGPHjkFXV1fjd/89CgoKQmBgIPbs2YOoqCiUKlUqt5P0nxEYGAiFQoHixYurrdu8eTMUCgVcXV2/fMKIiIiIiP5jWHFARES5xtDQEDNmzEBMTMwX37eRkRHs7e2/+H4BoHfv3oiKisLNmzexbds2lChRAt7e3ujTp49a2NDQUHz48AHt2rWTKgOqV6+OqKgo6a99+/Zo3LixbFmHDh0wduxYdOjQAZUqVcI///yDS5cuYc6cOTh//jzWrl0LAIiPj0f9+vWxatUqTJ48GZGRkdi7dy+Sk5NRpUoVnDhxQpYeR0dHhISE4OHDh7LlAQEByJ8//+fJMMp1q1atwoABAxAaGor79+/ndnI+WkJCglbhbt26BUdHR1SvXh0ODg7Q09PL9r6EEEhKSsr2dgSYmJjg2bNnOH78uGz5qlWreJ8hIiIiIvpCWHFARES5pn79+nBwcMD06dMzDbdt2zaULFkSSqUSrq6umDNnjmy9q6srpkyZgm7dusHU1BQuLi7466+/8Pz5c7Rs2RKmpqYoXbo0wsPDpW3SD1Xk5+eHsmXLYu3atXB1dYWFhQW8vb0RGxsrhQkKCkLNmjVhaWkJGxsbNG/eHLdu3cr2cRsbG8PBwQHOzs6oWrUqZsyYgWXLlmHFihU4cOCALKy/vz86deqErl27YtWqVRBCwMDAAA4ODtKfkZERlEqlbNnFixcxbdo0zJkzB7NmzUL16tXh6uqKBg0aYNu2bejevTsAYP78+Th+/Dj27NmD9u3bw8XFBZUrV8a2bdtQvHhx9OrVC0IIKT329vZo2LAhVq9eLS07duwYoqOj0axZs2znBX393r59i82bN6Nv375o3ry5VIGVXlhYGNzd3WFoaIgqVarg4sWLsvWZXcdjxoxB1apV1eIsU6YMfH19pc8BAQEoXrw4DA0NUaxYMSxevDjTtHt6eqJ///4YOnQobG1t0aBBAwDAlStX0LRpU5iamiJPnjzo2rUroqOjAaT2hhowYADu378va90uhMDMmTNRsGBBGBkZwd3dHVu3bpX2peoJtG/fPlSsWBFKpRJHjx7Vervg4GBUrFgRxsbGqF69Oq5fvy47ll27dqFixYowNDSEra0tWrduLa1LSEjAyJEj4eTkBBMTE1SpUgWHDh3KNG8UCgWWLFmCJk2awMjICAUKFMCWLVtkYR49eoQOHTrAysoKNjY2aNmyJe7evSutP336NBo0aABbW1tYWFjAw8MDZ8+ezXS/kyZNQp48eTId/klPTw+dOnXCqlWrpGUPHz7EoUOH0KlTJ1nYW7duoWXLlsiTJw9MTU1RqVIltftofHw8Ro4cCWdnZyiVShQpUgT+/v6yMGfOnMk0/4mIiIiI/mtYcUBERLlGV1cX06ZNw8KFC9VasKucOXMG7du3h7e3Ny5evAg/Pz+MHz9e7eXlvHnzUKNGDZw7dw7NmjVD165d0a1bN3Tp0gVnz55F4cKF0a1bN9lL8PRu3bqFnTt3Ys+ePdizZw8OHz4sG0rp7du3GDp0KE6fPo3g4GDo6OigVatWSElJ+eS86N69O6ysrGRDFsXGxmLLli3o0qULGjRogLdv32b5MlBl3bp1MDU1xS+//KJxvarSZP369WjQoAHc3d1l63V0dDBkyBBcuXIF58+fl63r2bOnLP9XrVqFzp07w8DAQKu00bdl06ZNKFq0KIoWLYouXbogICBA43U0YsQIzJ49G6dPn4a9vT1atGiBxMREAFlfx507d8bJkydlFXGXL1/GxYsX0blzZwDAihUrMHbsWEydOhVXr17FtGnTMH78eFklliarV6+Gnp4ewsLCsGzZMkRFRcHDwwNly5ZFeHg4goKC8PTpU7Rv3x4A8Pvvv2PSpEnIly8foqKicPr0aQDAuHHjEBAQgCVLluDy5csYMmQIunTpgsOHD8v2N3LkSEyfPh1Xr15FmTJltN5u7NixmDNnDsLDw6Gnp4eePXtK6/7++2+0bt0azZo1w7lz56RKBpUePXogLCwMGzduxIULF9CuXTs0btwYN27cyDRvxo8fjzZt2uD8+fPo0qULOnbsiKtXrwIA3r17hzp16sDU1BRHjhxBaGgoTE1N0bhxY6nnRmxsLLp3746jR4/ixIkTKFKkCJo2bSqrcFURQmDQoEHw9/dHaGgoypYtm2naevXqhU2bNuHdu3cAUit7GzdujDx58sjCxcXFoWnTpjhw4ADOnTuHRo0awcvLS9Yzplu3bti4cSMWLFiAq1evYunSpTA1NdU6/4mIiIiI/ouy3++aiIi+ejcWNUZS3PMvvl89UzsU6ReUrW1atWqFsmXLwtfXV60FKADMnTsX9erVw/jx4wEAbm5uuHLlCmbNmgUfHx8pXNOmTfHTTz8BACZMmIAlS5agUqVKaNeuHQBg1KhRqFatGp4+fQoHBweNaUlJSUFgYCDMzMwAAF27dkVwcDCmTp0KAGjTpo0svL+/P+zt7XHlypVPHgNdR0cHbm5usta8GzduRJEiRVCyZEkAgLe3N/z9/VGnTp0s47tx4wYKFiwIfX39TMNFRkZmGJ9qjPHIyEjZS77mzZvj559/xpEjR1ChQgVs3rwZoaGhstbB9P3w9/dHly5dAACNGzdGXFwcgoODUb9+fVk4X19fqUX/6tWrkS9fPuzYsQPt27fP8jouVaoUypQpg/Xr10th1q1bh0qVKsHNzQ0AMHnyZMyZM0dqaV+gQAFcuXIFy5Ytk3rQaFK4cGHMnDlT+jxhwgSUL18e06ZNk5atWrUKzs7OiIyMhJubG8zMzKCrqyvdK96+fYu5c+fi4MGDqFatGgCgYMGCCA0NxbJly+Dh4SHFNWnSJCkfsrPd1KlTpc+jR49Gs2bN8OHDBxgaGmLq1Knw9vbGxIkTpfCqyr5bt25hw4YNePjwIfLmzQsAGD58OIKCghAQECA7zvTatWuHH3/8Ucrf/fv3Y+HChVi8eDE2btwIHR0drFy5Upp3JiAgAJaWljh06BAaNmyIunXryuJbtmwZrKyscPjwYTRv3lxanpSUhG7duiE8PBxhYWHIly9fhmlSKVu2LAoVKoStW7eia9euCAwMxNy5c3H79m1ZOHd3d1nF55QpU7Bjxw7s2rUL/fv3R2RkJDZv3oz9+/dL52zBggXV9pdZ/hMRERER/Rex4oCI6DuUFPcciW+icjsZWpsxYwbq1q2LYcOGqa27evUqWrZsKVtWo0YNzJ8/H8nJydDV1QWQOqSJiqpFaunSpdWWPXv2LMOKA1dXV6nSAEgdz//Zs2fS51u3bmH8+PE4ceIEoqOjpZ4G9+/fz5HJU4UQsomh076wBYAuXbqgdu3aePXqlWyYJW3i+tj0AFCLR19fX2p5fvv2bbi5ucnyn74f169fx6lTp6SeMHp6eujQoQNWrVqlVnGgejEOANbW1ihatKjUel2b67hz585YtWoVxo8fDyEENmzYgMGDBwMAnj9/jgcPHqBXr17o3bu3FEdSUhIsLCwyPYa0LfOB1N4PISEhai3OgdRrXFVRkdaVK1fw4cMHqUJAJSEhAeXKlctwf9nZLu015OjoCCD1fpU/f35ERETIjjuts2fPQgihlu74+HjY2Nho3EYl7Xem+qwaQujMmTO4efOm7J4IAB8+fJB6hjx79gwTJkzAwYMH8fTpUyQnJ+Pdu3dq82AMGTIESqUSJ06cgK2tbaZpSqtnz57S/CmqngV//PGHLMzbt28xceJE7NmzB48fP0ZSUhLev38vpSEiIgK6urqyShpNMst/IiIiIqL/IlYcEBF9h/RM7b6p/dauXRuNGjXCr7/+KutFAGh+Aa5pmJS0LetV4TUty2xYofSt8xUKhSy8l5cXnJ2dsWLFCuTNmxcpKSkoVaqU1hOuZiY5ORk3btxApUqVAKS+cDx58iROnz6NUaNGycJt2LABffv2zTQ+Nzc3hIaGIjExMdNeB6qW35pcu3YNAFCkSBG1dT179kSVKlVw6dIlDunxHfP390dSUhKcnJykZUII6OvrIyYmBlZWVplur7rutLmOO3XqhNGjR+Ps2bN4//49Hjx4AG9vbwD/u25XrFiBKlWqyLZTVR5mxMTERPY5JSUFXl5emDFjhlpY1Qvj9FT7//vvv2V5AQBKpTLD/WVnu8zuV0ZGRhrTpQqjq6uLM2fOqOWFpsqRrKTdd4UKFbBu3Tq1MHZ2qfd6Hx8fPH/+HPPnz4eLiwuUSiWqVaumdk9s0KABNmzYgH379klDT2mjc+fOGDlyJPz8/NCtWzeNk1SPGDEC+/btw+zZs1G4cGEYGRmhbdu2Uhoyy7u0slteEBERERF971hxQET0HcrucEFfg99++w1ly5ZVazVbokQJhIaGypYdO3YMbm5uWb4wzEkvXrzA1atXsWzZMtSqVQsA1NL1KVavXo2YmBhpOCR/f3/Url0bixYtkoVbu3Yt/P39s6w46NSpExYsWIDFixdj0KBBautVvRa8vb0xduxYnD9/XjbcR0pKCubNm4cSJUqozX8AACVLlkTJkiVx4cIFtclK6fuQlJSENWvWYM6cOWjYsKFsXZs2bbBu3Tr0799fWnbixAmpdXZMTAwiIyNRrFgxANpdx/ny5UPt2rWxbt06vH//HvXr15d6CuXJkwdOTk64fft2tl48a1K+fHls27YNrq6uGl9Ea1KiRAkolUrcv38/y5brObFdemXKlEFwcDB69Oihtq5cuXJITk7Gs2fPpHuTtk6cOIFu3brJPqt6QpQvXx6bNm2Cvb09zM3NNW5/9OhRLF68GE2bNgUAPHjwQJpkOq0WLVrAy8sLnTp1gq6urlQhlBVra2u0aNECmzdvxtKlSzNMg4+PD1q1agUgdc6DtEO+lS5dGikpKTh8+LBaLxkiIiIiIsoYKw6IiOirULp0aXTu3BkLFy6ULR82bBgqVaqEyZMno0OHDjh+/Dj++OMPLF68+Iumz8rKCjY2Nli+fDkcHR1x//59jB49+qPievfuHZ48eYKkpCQ8evQI27dvx7x589C3b1/UqVMHiYmJWLt2LSZNmqQ2BNKPP/6ImTNnqr3oT69KlSoYOXIkhg0bhkePHqFVq1bImzcvbt68iaVLl6JmzZoYNGgQhgwZgr/++gteXl6YM2cOqlSpgqdPn2LatGm4evUqDhw4kOGQRwcPHkRiYmKWwybRt2nPnj2IiYlBr1691IYDatu2Lfz9/WUVB5MmTYKNjQ3y5MmDsWPHwtbWFj/88AMA7a/jzp07w8/PDwkJCZg3b55snZ+fHwYOHAhzc3M0adIE8fHxCA8PR0xMDIYOHar1cfXr1w8rVqxAx44dMWLECNja2uLmzZvYuHEjVqxYobFC0szMDMOHD8eQIUOQkpKCmjVr4s2bNzh27BhMTU0znGPhY7dLz9fXF/Xq1UOhQoXg7e2NpKQk/PPPPxg5ciTc3NzQuXNndOvWDXPmzEG5cuUQHR2NgwcPonTp0tJLfU22bNmCihUrombNmli3bh1OnTolzTXTuXNnzJo1Cy1btpQmi75//z62b9+OESNGIF++fChcuDDWrl2LihUr4s2bNxgxYkSGLfxbtWqFtWvXomvXrtDT00Pbtm21OvbAwEAsXrw4w2GXChcujO3bt8PLywsKhQLjx4+X9RRwdXVF9+7d0bNnTyxYsADu7u64d+8enj17Jk2ITURERERE6nRyOwFEREQqkydPVhu+pHz58ti8eTM2btyIUqVKYcKECZg0aZLakEafm46ODjZu3IgzZ86gVKlSGDJkCGbNmvVRca1YsQKOjo4oVKgQWrVqhStXrmDTpk3SS9Rdu3bhxYsXUgvatIoUKYLSpUtrnEg6vRkzZmD9+vU4efIkGjVqhJIlS2Lo0KEoU6aM9MLS0NAQBw8eRPfu3fHrr7+icOHCaNy4MXR1dXHixAlUrVo1w/hNTExYafAd8/f3R/369TXOIdCmTRtERETg7Nmz0rLffvsNgwYNQoUKFRAVFYVdu3bBwMAAgPbXcbt27fDixQu8e/dOqnRQ+fHHH7Fy5UoEBgaidOnS8PDwQGBgIAoUKJCt48qbNy/CwsKQnJyMRo0aoVSpUhg0aBAsLCygo5Pxo/HkyZMxYcIETJ8+HcWLF0ejRo2we/fuLPf/sdul5enpiS1btmDXrl0oW7Ys6tati5MnT0rrAwIC0K1bNwwbNgxFixZFixYtcPLkSTg7O2ca78SJE7Fx40aUKVMGq1evxrp161CiRAkAgLGxMY4cOYL8+fOjdevWKF68OHr27In3799LPRBWrVqFmJgYlCtXDl27dsXAgQNhb2+f4f7atm2L1atXo2vXrtK8GVkxMjLKdK6GefPmwcrKCtWrV4eXlxcaNWqE8uXLy8IsWbIEbdu2xS+//IJixYqhd+/eePv2rVb7JyIiIiL6r1IITQNFExERERHRd0uhUGDHjh1qFTREREREREQAexwQEREREREREREREVEarDggIiIiIiIiIiIiIiIJJ0cmIiIiIvqP4WilRERERESUGfY4ICIiIiIiIiIiIiIiCSsOiIiIiIiIiIiIiIhIwooDIiIiIiIiIiIiIiKSsOKAiIiIiIiIiIiIiIgkrDggIiIiIiIiIiIiIiIJKw6IiIiIiIiIiIiIiEjCigMiIiIiIiIiIiIiIpKw4oCIiIiIiIiIiIiIiCSsOCAiIiIiIiIiIiIiIgkrDoiIiIiIiIiIiIiISMKKAyIiIiIiIiIiIiIikrDigIiIiIiIiIiIiIiIJKw4ICIiIiIiIiIiIiIiCSsOiIiIiIiIiIiIiIhIwooDIiIiIiIiIiIiIiKSsOKAiIiIiIiIiIiIiIgkrDggIiIiIiIiIiIiIiIJKw6IiIiIiIiIiIiIiEjCigMiIiIiIiIiIiIiIpKw4oCIiIiIiIiIiIiIiCSsOCAiIiIiIiIiIiIiIgkrDoiIiIiIiIiIiIiISMKKAyIiIiIiIiIiIiIikrDigIiIiIiIiIiIiIiIJKw4ICIiIiIiIiIiIiIiCSsOiIiIiIiIiIiIiIhIwooDIiIiIiIiIiIiIiKSsOKAiIiIiIiIiIiIiIgkrDggIiIiIiIiIiIiIiIJKw6IiIiIiIiIiIiIiEjCigMiIiIiIiIiIiIiIpKw4oCIiIiIiIiIiIiIiCSsOCAiIiIiIiIiIiIiIgkrDoiIiIiIiIiIiIiISMKKAyIiIiIiIiIiIiIikrDigIiIiIiIiIiIiIiIJKw4ICIiIiIiIiIiIiIiCSsOiIiIiIiIiIiIiIhIwooDIiIiIiIiIiIiIiKSsOKAiIiIiIiIiIiIiIgkrDggIiIiIiIiIiIiIiIJKw6IiIiIiIiIiIiIiEjCigMiIiIiIiIiIiIiIpKw4oCIiIiIiIiIiIiIiCSsOCAiIiIiIiIiIiIiIgkrDoiIiIiIiIiIiIiISMKKAyIiIiIiIiIiIiIikrDigIiIiIiIiIiIiIiIJKw4ICIiIiIiIiIiIiIiCSsOiIiIiIiIiIiIiIhIwooD+u7Fx8fD0tISCoUCCoUClSpV+iz7CQwMlPZx6NChz7KP3PI9H9u36u7du9J34ufnl9vJISIiDXLyGeTQoUPw8/ODn58fXr16lXOJpM/O1dUVCoUCnp6euZ0UIiL6BD4+PlKZrvozNjZGiRIlMGrUKMTGxn5UvJ6enlAoFHB1ddW4r5ySNs6c+F3P36RfzqFDh6S8DgwMzO3k0H+IXm4ngOhz27t3L16/fi19Dg8Px61bt1CoUKFcTBURERF973LyGeTQoUOYOHEigNQf/paWljmVTCIiIvpI79+/x9WrV3H16lVcvnwZe/bsye0kERHlGPY4oO/exo0btVr2Jb1//z5X90+5i98/EdF/Q24/g7C8+TTMPyIiykhISAiEEIiIiICJiQmA1AYDb968yeWUfTmurq4QQkAIwR4Hmfjw4UNuJ4Hoo7HigL5rb9++lWr869atCycnJwCaf7Sn7Ua+c+dOlClTBsbGxihbtiwOHjwoC/vixQt06tQJZmZmsLOzw4gRI5CQkKAWZ9ruZIsXL0b//v1hZ2eHvHnzSmH27duH+vXrw9LSEkqlEkWLFsX48eNlP1bTx/PTTz/BwsICDg4OmDlzJgDgjz/+QP78+WFlZYV27drhxYsXsrQ8efIEv/zyC1xdXWFgYAA7Ozt07NgRN2/e/Khje/XqFTp37ozixYvD0tIS+vr6cHBwQNu2bXH58uVMv5fs8vPzk44/NDQUrVu3hqmpKWxtbTFkyBC19J06dQotWrSAra0tDAwMUKBAAQwaNAgvX76UwjRq1EitO+jIkSOhUCjg4OAgLZswYYK07+fPnwMAhBBYunQpKlWqBBMTExgbG6Nq1arYvHmzLB1pu4JGRETAw8MDRkZGGDNmDAAgISEB06dPR6lSpWBkZAQzMzPUrVsXwcHBsnhSUlIwfvx4ODg4wMTEBK1bt0ZUVFSO5C0REX0e2XkGOXDgAOrUqQM7OzsolUo4OTmhcePG2LlzJ4DUZxRVbwMAKFCggKwMS1tOhoSEoHnz5jA1NUXHjh0BAGPGjEHFihVhZ2cHfX19WFpawtPTE7t3787RY077vLJs2TIMHToU9vb2MDExwQ8//IBHjx7Jwj9+/Bh9+vSBs7MzDAwMYG9vj3bt2smeI6ZPny7FeffuXQCpL2ZUy65duwYAOHjwoLRs69at0vbBwcFo1KgRrKyspOesKVOmIDExUQqTdkjGrVu3okuXLrC0tET16tWlMFu2bEGtWrVgbm4OQ0NDuLu7Y8mSJRBCyI5p8+bNKFq0KAwNDVGlShWcOHEix/KXiIi+Pu7u7ihZsiSA1N+J8fHx0jptf+9p6/Tp02jRogVcXV1hamoKpVKJggULYsCAAYiJicmR4wEglYk+Pj7SMk1D5XyuoYrS/o6+fPky6tWrB2NjYzg5OWHKlClqZa8271SKFi2qNmxg+/btoVAoULVqVWlZt27doFAoYGFhgeTkZADaf49ph5wKDg5GhQoVoFQqsXTpUgDAmzdvMGrUKLi5uUGpVMLKygpeXl44e/asLJ7379/jl19+gbW1NSwsLNCrV6//VIUUfWUE0Xds/fr1AoAAIBYuXCj69u0rfb506ZIsrIuLiwAgzM3NhUKhkMIBEKampiI6OloKW6dOHdl6AMLR0VH6f0hIiBBCiJCQEGmZtbW19H8LCwshhBArVqxQ25fqr0aNGiI+Pl4tHhsbG7WwLVq0UFvWsWNHKb2PHj0STk5OGvdjZWUlrl+/nu1ju3Pnjsb4VMf69OnTHPsefX19Mz3+7t27S2H/+ecfoa+vrzFdbm5uIiYmRgghxNSpU6Xljx8/FkIIUaNGDWnZzZs3hRBC1KtXTwAQxYsXl/bh4+OT4bHPnDlTCte9e3eN3/+gQYNEUlKSqF+/vsY4FAqF2LhxoxTPxIkTM/1OfH19cyyviYgoZ2j7DHL37l1haGiosTzo16+fEOJ/zyjp/1xcXIQQ8nIybXnTsmXLTLdXKBTi33//zbFjzup5pVixYuL9+/dCCCEePHggK8vS/hkbG4tTp04JIYQICwuTlq9fv14IIcTYsWOlZStXrhRCCDF58mRpmeoZJCAgIMPnrGbNmomUlBQpnKb8c3d3F0II4efnl2G5/8svv8iOX9MzpKmpqQAgPDw8ciyviYjoy0v7+071u/jChQvSfb5atWpS2Oz83vPw8JCV6+n3pZK2vEr/V6NGjY9KvyaafmenLeMDAgKEEPJ3Ajn5mzRtOjU9T6Tdl7bvVHr37i09YyQmJgohhPSORF9fX7x7904IIUShQoUEANGkSRMhxMd9j8bGxkKpVErh5s2bJ2JjY0Xp0qU1xqNUKsXRo0eleHr06JHp739V/hN9CexxQN+1tK36WrRogRYtWmhcl9abN28wYcIEvHr1CuPGjQMAxMXFISgoCEBqi7aQkBAAQO3atfHkyRNcuHAhy0mL3r9/j61btyIuLg6hoaGIjY3FsGHDIISAnZ0dTp48iRcvXqBdu3YAgLCwMKxevVotHqVSicuXL8tqpXft2gU/Pz/ExMSgWrVqAIBt27YhJSUFQGqr+UePHsHCwgKHDx9GfHw8zpw5A2tra8TExGDs2LHZPjYrKyts27YNDx48wIcPHxAXF4cVK1YAAF6+fIn169dnmh8fO7lP8eLF8fjxY1y7dg1FihQBAKxevRrXr1+HEAL9+vVDYmIiDA0NERQUhFevXmHw4MEAgMjISMyaNUs6PpVjx44hISEB4eHh0rGGhYUhOTkZJ0+elIUPDQ2V0jt27Fi8fv0a0dHRaNWqlZTX6Xt7AEChQoVw/fp1vHnzBgMGDMCGDRtw4MABAMDixYvx9u1bPHz4ENWqVYMQAoMHD0ZycjJev36N2bNnAwAcHBxw7tw5PH36FMWLF9c6z4iI6MvT9hkkPDxc6sJ++vRpxMfH4969e1i/fj1q1qwJILVFn6+vr7TNnTt3IISQWuCnZWVlhfDwcLx9+xYzZswAAMydOxeRkZGIjY1FfHw8Tp06BWNjYwghsGjRoiyPRVPLw6zo6ekhPDwc0dHR+OGHHwAA165dw5o1awCklpeq3nPTp0/H69evpXx59+6dVHZXqlQJxsbGAFLLayC1jE5bXqf9t2jRorC3t0dcXBwGDx4MIQSaNGmChw8f4t27d5g2bRoA4O+//8bevXvV0q2jo4ODBw/i7du32LBhA+7evYvJkycDAHr06IHnz5/jzZs36NevH4DUMvzSpUvSMYn/bwW5efNmvH79Gr/88gvi4uK0zjciIvo21KlTBwqFAmXKlEFcXBysra1lv9+1/b2XHVWqVEFISAiePn2KxMREREdH48cffwSQWg5GRETk2PHltLS9+7IzMXOdOnXw4sULnDp1Cra2tgCAmTNn4tWrV9l6p6L6Pf/u3TucP38e9+7dw6NHj6BQKJCYmIjTp0/j2bNnuHXrliz8x3yP7969Q/369fHgwQO8ePECbdq0wfz583Hx4kXo6upix44d+PDhAyIjI1G4cGHEx8djyJAhAIAbN25IaS5evDhu3bqFO3fuwMbG5mOynejT5VaNBdHn9urVK6mWt3z58kIIIeLj44WZmZkAIAoXLiwLr2qNlydPHpGcnCyEEOLKlStSre60adOEEEJMmjRJWrZ3715p+/Hjx6vV3qetlf/pp59k+wsKCpLWDR8+XFp+48YNaXnr1q3V4hk7dqwU1t7eXgAQBgYGUgu+MWPGSGFVLekzatGn+rO2ts72saWkpIiZM2cKd3d3YWxsrBbnzz//nOn3o6nFQkbStqTcv3+/tPyPP/6Qli9dulRcu3ZN+ty2bVsp3Nu3b6VeCGnPBSMjIwFADBs2TGrR2LJlS+n7Onv2rBTfunXr1PI3o79du3YJIeQtJY4fPy47po4dO2YZz4ULF8SRI0ekzyNHjpS2Dw4O/iytO4iI6NNl5xnk9OnT0v28VatWYu7cuWLfvn3izZs3sjjTloV37tzJcN2GDRvU0rNr1y7h4eEhLC0t1VrlFStWLMvjUYVN2/JQk4yeVy5duiQt9/b2FkII4eDgIAAIe3t76blLiP/1/lMoFOLVq1dCCCHq1q0rAIgKFSqIxMREYWxsLKpWrSocHBxE0aJFRUpKirC0tBQARO/evYUQQuzbty/Lcnbo0KFCCHkLzunTp8uOadmyZVnGs2DBApGUlCQ9a1SuXFna/t27d8LAwEAA7HFARPStS/v7TtNf8eLFpZbr2v7eE0L7HgcxMTGif//+omDBglLZkvYvbev3rNL/pXscpC1rM9t3+nTeuHFDWj58+HBpeVBQULbeqdy/f19atnDhQrFu3TrZ7//p06eL7du3S2HCwsKEEB/3PSoUCuldjEq1atWyjOf169dizZo10ufFixdL2/v7+6vlP9GXwB4H9N3avn27NL5gxYoVcenSJURGRqJixYoAgJs3b+LMmTNq2xUqVAg6OqmXhqGhobRcFdfjx4+lZarxitP/XxN3d3fZ5+joaOn/zs7OGv+vGlM/rbRj8hsZGQEA7OzspLQaGBiopVlTPGmpxv7PzrHNnTsXI0eOxPnz5/Hu3Tu19Z9rQsG0+ZMvXz7p/9HR0RnmqbGxsVRDr8oLAwMDaSzD48ePS60Ye/bsCRsbG4SFhUmtF4H/tTjIKi8ByOZSUEn//Wsbz8eeb0RElHuy8wxSsWJFjB07FkZGRtixYweGDh2KRo0aIU+ePFi5cmW2952+vDl58iRat26Nw4cP49WrV2rjAudGeZ32XycnJ+m5K+12QgipB5+HhwcA4Pz58zh27BjevXuH6tWro1q1arh+/bp0bGnDfunyOjo6Wpo3IW0ZbWRkBGtr6yzjICKib4tqcuSoqCjUq1cPAHD16lXs2rULwMeXQ5np2rUr/vjjD9y+fVvjPISfq0wHkO3eETnlY37/a3qn4uzsjAIFCgCQ//4fPnw4FAqF7Pe/sbExKlWqJNs+M+m/Rzs7Ozg6OsqWaRNPTEwMf//TV4cVB/TdSjsMwPLly1G6dGmULl1aGoonfRgVfX196f+ahuhJO7Fx2kn+0k/4l57qJb+KqpsdADx8+FD6/4MHDzSGUdHT09NqWVp2dnYAUrvvCyHU/lRDGmXn2LZt2wYgtXLl5MmTSEpKwsWLFzNNR1qenp7S/rMz9EHa/Embb7a2thnm6fv376WXD2nDqCoDzpw5I02AXb16dVStWhWXL1/G33//DQAoWLCg9JCiyksg9YFDU152795dLd3pv/+08Tx+/FhjPB4eHh99vhERUe7J7jPIlClTEB0djbCwMAQGBqJq1ap4//49Bg4ciKSkJACan0k0SV/e/PXXX1IcixYtwocPHyCEyFaXd1XZlJ2hBTMrr9P+qyoD02+nUCikNKrK66SkJMyfPx9AanmtmrxYNQxh2rBpy9np06drfP5ZtWqVWrozK683bNigMZ4JEybA1tZWeoZMW0Z/+PAh2y+GiIjo2+Hg4IDWrVtLn2/fvg1A+9972nr//j3++ecfAECpUqVw//59CCGwcOHCHDqS/1E1RlQNpQikDpP4KXx8fKRjTztBcVY+5vd/Ru9UVM8Ix44dQ1hYGMzNzVG9enWUKFECx44dQ2hoKACgatWqUpn+Md9j+meJtPGYmpoiISFBYzwuLi78/U9fHVYc0HcpOjpaehGcmU2bNqm1vMtKjRo1pP//9ttvePr0KS5evAh/f/9sxVO9enWYmZkBSB2jPzw8HDExMdK8CgDQuHHjbMWZkaZNmwIArl+/jvHjx+PFixd4//49zpw5gwEDBmDAgAEAsndsqhYOCoUCZmZmePXqFfz8/HIkvZmZPHkyoqKiEBkZid9//11a7unpCTc3N6kVwe7du3HgwAG8efMG48aNk1oBps1TVQEfHx+Pffv2wc3NDba2tqhevTqEENi3bx8A+XwIqrwEgMGDB+Py5ctISEjA/fv3sXjxYhQrVkyr40gbT58+fXDnzh0kJCTg5s2bmDFjBmrVqgUAKFOmjHSerFmzBhEREXj27BmmTp2qfaYREdEXk91nkIsXL2Ly5MmIjIxEyZIl0bZtW5QrVw5A6kuC2NhYAKlzF6ioxtTXRtoWiWZmZkhKSsLMmTM1zseTk/z9/XHu3Dm8ePEC48ePl5bXqVMHwP/K46dPn2LWrFmIjY3Fli1bpBaAVatWhYWFhfR/pVIJILUiBJBXHKhepLi6ukqtDKtXry5tP2fOHAQHB+PDhw+Ijo7Gjh07ULduXRw5ciTL42jYsCF0dXUBAOPHj8epU6eQkJCAJ0+eYO3atahYsSLu3bsHXV1dqSfj6dOnsWXLFrx58wa+vr4aW4USEdH34cmTJ9i6dav0WdXSXNvfe9pKSkqSWv3r6+vDxMQE169f12quIk1OnTqFoKAg2d+zZ88A/K/F/vHjx/H69Wu8ePECCxYs+Kj9fKpx48bh5cuXCA8PlxowGBkZoWrVqtl+p6L6/X/37l1cuHABVatWhY6ODqpXr46XL1/i1KlTADL+/f8p36Mqnri4OPTt2xdRUVGIj4/H5cuXMXbsWLRt2xYAUK1aNakn5sKFC3H79m3cu3cP8+bNy1a+EeWYnB77iOhrsHjxYmn8t4kTJ6qtb9OmjbQ+NDRUCPG/OQ7Sjj+b0Zh9derUURuPzsbGRm3MvqzG8c9s3NyqVauK+Pj4TONRpTntWIiaxkB+9OiRcHZ2znBfaccu1PbYJk6cqBauUKFCGuP8VGmPSdN8DWn39ffffws9PT2Nx1m4cGHx8uVLKWzacYcBCB8fH7X8BiBWrVolS0+vXr0yHZtQRdO4lCpJSUmiYcOGGcaR9jv18/PL9DvhHAdERF+P7D6DpC9z0v5VqVJF2u7YsWNq6zt37iyEyHz+g0OHDmksQ1RzAqQtbz5V2mPRVF4XK1ZMmpPp3r17Ik+ePBqP28jISJw4cUIWd61ataT1rq6uQgghPnz4IM0lAUB069ZNts3q1avV5nRI+6d6pslq3OXJkydnWu6r8jwkJERtf4aGhtJcUJzjgIjo25bVHAd58+YVMTExQojs/d7Tdo4DTb/V0/4Gz2rs+6zSv2PHDiGEEKNHj5aVY3p6erJ5DT9mjoPsSJtOTc8Tafel7TsVIYS4deuWbL2fn58QQv4cAEAcPHhQ2uZTv0eV2NhY4e7unmE8aZ8RfHx8Mv39zzkO6EtijwP6Lm3atEn6f8eOHdXWd+7cWfq/puGKsrJlyxZ4e3vDxMQENjY2GDhwIKZMmZLtePr06YO///4bderUgbm5OfT19VGkSBH8+uuvCA4Ols1X8Cny5s2L8PBwDBgwAAULFoSBgQGsra1RtmxZDBs2DEOHDs32sY0ePRpDhw5Fnjx5YGpqirZt22LDhg05kt7MbN++HW3btoWpqSmsra0xaNAgLF++XFrftGlTHDlyBM2bN4eVlRX09PSQP39+9O/fH8ePH5e12DQyMkLlypWlz6qWi5UrV5YN/5S2xQEArFixAitXrkTVqlVhamoKIyMjFCpUCO3atcP69eu1Og5dXV3s2bMHs2bNQtmyZWFkZAQTExO4ubmhW7duWLJkiRR2/PjxGDduHOzt7WFsbAwvLy/s2LEjexlHRERfRHafQQoXLow+ffqgZMmSsLCwgFKphKurK/r06YOdO3dKYatVq4Zp06bB2dlZNidAVjw8PLBq1SoUKlQIhoaGqFq1Kv7991+pNf7nMnHiRIwcORL29vYwMjJCixYtsH//fmlOpvz58yM8PBy9evWCk5MT9PT0YGtrizZt2uDEiROoUqWK2nGoqMprpVKJChUqSMvTl9fdunXDwYMH0axZM1hbW0NfXx/58uVDvXr18Pvvv6N8+fJaHcu4ceOwfft21KlTBxYWFjAwMICLiwuaNWuGFStWSMMKeHp6YsOGDShSpAgMDAxQoUIF7Nu3TzbMARERfV/09PSQL18+dO/eHWFhYbC0tASQvd972vrzzz/xww8/wMzMDPb29hg9ejTGjBmTw0eU+vuzT58+sLOzg4GBAby9vREQEJDj+9HGoUOH0KBBAxgZGcHR0RGTJ0+Gr6+vtD4771TSDkEM/O95Iu3IC2nnQgRy7ns0NTVFaGgoxo4di2LFikGpVMLc3BwlSpTAzz//LBtRYPHixejbty8sLS1hbm6Orl27YsWKFdnPPKIcoBAim+O0EBF9YX5+fpg4cSKA1LEV004QTURERF+HQ4cOSUMRBQQEZGsOIyIiIiIgdT6E1atXA0C2h5YmopzFHgdERERERERERERERCRhxQEREREREREREREREUk4VBEREREREREREREREUnY44CIiIiIiIiIiIiIiCSsOCAiIiIiIiIiIiIiIgkrDoiIiIiIiIiIiIiISMKKAyIiIiIiIiIiIiIikrDigIiIiIiIiIiIiIiIJKw4ICIiIiIiIiIiIiIiCSsOiIiIiIiIiIiIiIhIwooDIiIiIiIiIiIiIiKS/OcqDoQQePPmDYQQuZ0UIiIiymV8LiAiIvo6HTlyBF5eXsibNy8UCgV27tyZ5Tbx8fEYO3YsXFxcoFQqUahQIaxatUrrffK5gIiI6H/0cjsBX1psbCwsLCzw+vVrmJub53ZyvgpCCLx+/RoWFhZQKBS5nZyvEvMoc8yfrDGPMvcx+fPu3TvcvHkT9vb2MDAw+MwpzH1CCNjY2OR2Mr47fC5Qx/tV1phHmWP+ZO1bzaMvVfYKIRAXFwdTU9NvKn++pP/Cc8Hbt2/h7u6OHj16oE2bNlpt0759ezx9+hT+/v4oXLgwnj17hqSkJK33yecCdTl9v/oen+F5z8oc8ydrzKPMMX+y9rmeC/5zFQdERPR9ePPmDU6cOIF69ep9Nz86iIiIvmYse+lLatKkCZo0aaJ1+KCgIBw+fBi3b9+GtbU1AMDV1fUzpY4+Fu8jRETfDlYcEBHRN8na2hotW7aEnh6LMiIioi+BZS99zXbt2oWKFSti5syZWLt2LUxMTNCiRQtMnjwZRkZGGreJj49HfHy89PnNmzcAUltucriiVKq8yKn8sLKyQosWLaCnp/fd5HFO59H3hvmTNeZR5pg/WftcecMnPiIi+ibp6OhAX18/t5NBRET0n8Gyl75mt2/fRmhoKAwNDbFjxw5ER0fjl19+wcuXLzOc52D69OmYOHGi2vLXr1/zBdX/Uw0RAiBHhghJTk5Wq7D5Hrx//55DqGSC+ZM15lHmmD9Zs7W1zfE4WXFARETfpNjYWJw8eRKlSpWCiYlJbieHiIjou8eyl75mKSkpUCgUWLduHSwsLAAAc+fORdu2bbFo0SKNvQ7GjBmDoUOHSp/fvHkDZ2dnWFhYcI6D/6eqQMmpOQ5iYmJw+fLl7+o+omoJbWJiwhebGjB/ssY8yhzzJ2vscUBERJRGSkoK4uPjkZKSkttJISIi+k9g2UtfM0dHRzg5OUmVBgBQvHhxCCHw8OFDFClSRG0bpVIJpVKptlyhUPDlVBqq/MiJPBFCICEhAUKI7yqPczKPvkfMn6wxjzLH/MkdOrmdACIioo9hYWGB2rVrw8zMLLeTQkRE9J/Aspe+ZjVq1MDjx4+lYXUAIDIyEjo6OsiXL18upozS4n2EiOjbwYoDIiIiIiIiIvqqxMXFISIiAhEREQCAO3fuICIiAvfv3weQOsxQt27dpPCdOnWCjY0NevTogStXruDIkSMYMWIEevbsmeHkyERERJQxVhwQEdE36cWLF9i+fTtiYmJyOylERET/CSx76UsKDw9HuXLlUK5cOQDA0KFDUa5cOUyYMAEAEBUVJVUiAICpqSn279+PV69eoWLFiujcuTO8vLywYMGCXEk/acb7CBHRt4NzHBAR0TfJ2NgYZcqUgbGxcW4nhYiI6D+BZS99SZ6enplO9hgYGKi2rFixYti/f/9nTBV9Kt5HiIi+Haw4IACAubl5bifhq8c8Ivq6GBkZoXDhwrmdDKLvEsu8rDGP6L+IZS/Rf1NOlnmmpqaoWLFijsX3tbCwsICOzrc3qMerV69yOwlE9BX79u5qREREABISEhAVFYXExMTcTgoREdF/AsteIvpUQggkJydn2puEiIi+Dqw4ICKib9KbN28QFhaGuLi43E4KERHRfwLLXiL6VEIIJCQksOKAiOgbwIqDL+j58+eoW7cuzM3N0a5du9xOzldNoVAgIiIit5MhOXToEKytrXM7GUSUhrW1NZo1awYLC4vcTgrRR+Fzgfb4XED0dWDZS/T5/FeeC3R0dGBoaAiFQvHRcejq6n51zwW2tra5nQwiohzHioNs8PT0hFKphKmpKaytreHh4YHw8HCtt1++fDl0dXXx6tUrbNmy5TOm9L/Fx8cHCoUCK1eulC3fsmULFAoFfvjhh9xJWDp9+vRB0aJFoaOjg/nz52cZ/vHjx2jatClMTEyQP39+rFixQloXHx8PT09P2Nvbw9zcHMWKFcPy5cs/Y+qJvj46OjowMjL6JscSpe8Dnwu+Tt/Kc0Fm5bwm+/fvR/ny5WFmZoYSJUogKChIWsfnAvpSWPYSZYzPBdpTKBSfVHGQHT169ICurq7G5wJdXV20atXqi6QjK48fP0azZs1gZmYGV1fXTJ8LIiMj0bp1a+TNmxfW1taoVasWwsLCZGGEEPjtt99QsGBBmJmZoVixYjh58uTnPgwi+s7wiS+bZsyYgbi4ODx58gRVqlRB69attd72zp07KFmy5Ec/aCclJX3Udv8FRYsWRUBAgGxZQEAAihUrlkspUufu7o7FixejcuXKWoXv2LEjHBwc8OzZM2zZsgUjRozA4cOHAQB6enpYuHAhHj9+jDdv3mD79u0YP348jh49+jkPgeirEhcXh/DwcLx9+za3k0L/YXwu+Dp9C88FmZXz6d2+fRutWrXCxIkT8fr1a8ycORNt2rTB7du3AfC5gL4clr1EmeNzQdZyY6iiokWLYvXq1bJlgYGBX9VzQefOneHg4IAnT55g06ZNGDVqVIbPBa9evULjxo1x/vx5PH/+HN26dUPz5s0RHR0thRk3bhz27t2Lf//9F2/evMG+ffuQP3/+L3U4RPSdYMXBRzIwMED37t3x4MEDPH/+HEBqAbhgwQIUK1YMlpaW8PT0xNWrVwEA7dq1w+rVq7F48WKYmprC398fAHDgwAFUrlwZlpaWKFmyJHbt2iXtw8fHB7169UL79u1hbm6OJUuWIDExERMmTEChQoVgY2ODFi1a4PHjx9I2CoUCS5cuRalSpWBubo4WLVrg9evX0vobN26gRYsWsLOzg7W1texB5tatW/Dy8oKdnR1cXFwwZcoUpKSkaDz+wMBAlC1bFr/++itsbGyQP39+LF68WBZm48aNKFOmDCwtLVGpUiUcO3ZMWrdu3TqUKlUKZmZmyJ8/P8aPH5/hg0NkZCQKFSqEP/74I8Pvo2HDhrh//z6uX78OILW2/vTp02jZsqUs3MiRI+Hi4iK11kvfkuPMmTOoW7curK2tYWdnhwEDBsjWr1y5Es7OzrCxscHIkSMzTI8m/fr1Q7169WBoaJhl2Fu3biE0NBTTp0+HiYkJqlSpgs6dO2PVqlUAUrtmli5dGnp6egD+12Lj5s2b2UoT0bcsOTkZb968yfA+RfQl8bmAzwXZeS7IqpxPLygoCOXLl4eXlxd0dHTQvHlzVK5cGWvWrAHA5wL6clj2EmmHzwWZPxcIIbBx40aULVsW1tbWqFKlitpzQZkyZWBhYQFXV1dMmDAh0+eCIkWKYNGiRRl+Hw0aNFB7LggPD0eLFi1k4UaNGoUCBQrAwsICpUqV0vhcUL9+fdja2iJPnjwYOHCgbP3KlSvh4uICOzs7jBo1KsP0pKd6Lpg2bZr0XNCpUye1RhAqlStXRp8+fWBnZwddXV307t0burq6uHDhAgDg5cuXmDdvHlauXInChQtDoVDAxcUFjo6OWqeJiAhgxcFHe//+Pfz9/WFrawsrKysAwJIlS+Dv74/du3cjOjoarVu3hpeXFxISErBlyxZ07twZv/zyC+Li4tCrVy9cuHAB7dq1w2+//YaXL19i2bJl6Nq1q1SYAcCGDRvQq1cvvHr1Cr169cLYsWMRFhaG0NBQREVFwc3NDd7e3rK0bdq0CcHBwbh//z4ePnyIefPmAQDevn2L+vXro1SpUrh79y6ePHki/QB+//496tevj7p16+LRo0c4evQoNm7cmGFBBQCXLl2CQqFAVFQUNm3ahNGjR+PIkSMAgL1792L48OEIDAzEy5cvMWbMGHh5eeHFixcAUsdH3b59O968eYNdu3Zh+fLlWL9+vdo+Tp06hbp162L69Ono379/hmnR0dFB165dpfSuXr0a7du3V3tJ7+7ujtOnT+PVq1eYMGECunbtijt37gAAHj16hLp166Jt27Z4/Pgx7t27h/bt20vbxsbG4uLFi7hx4wZCQ0OxaNEiHDp0SFpvaWmJ0NDQDNOYHRcuXICjoyPy5MkjLStbtqz0IKDSvHlzGBoaokSJEsiTJ89X082S6EuwsLBA3bp1YWZmlttJIeJzAfhckJ3nAm3LeZWUlBS1FyYpKSl8LqAvjmUvkXb4XJDxc4FCoUBwcDBGjRqFVatWITo6GqNGjULLli1lzwVbt27Fq1evsHPnTqxYsSLD54L69etj6tSp6NevX4Zp0dHRQZcuXRAYGAgAWLNmDdq1a6f2XFCmTBmcPHkSL1++xLhx49C9e3fZc0H9+vXRpk0bPHz4EHfu3JHNRREbG4tLly7h+vXrOHLkCBYvXix7LrC2ts7Wc4G7uzsuXryY4TGldfHiRcTGxqJEiRIAgBMnTkCpVGLv3r1wdnZGoUKFMGbMGCQmJmoVHxGRCisOsmnMmDGwtLSEiYkJNmzYgB07dkituxYtWoRJkyahSJEi0NPTw8CBA/H+/fsMx5FbtmwZfHx8ULduXejo6KBmzZpo3rw5Nm/eLIVp2LAhGjVqJI0nunjxYsydOxf/x96dhzdVpX8A/2Zf2ibdC2WXRRCwqSDDIgqCQHVwVHRUdHABBwRBh3FDfuOAoqiogxu4jowjwzDiPjICiiwioiBFWVTWFrrSNvt+7z2/P0JCQ9M2aZPeLO/nefpAT07ufXOS3vvmnnvO6dy5M5RKJZYsWYIdO3bg5MmTgec89NBDKCgoQGZmJqZMmYI9e/YAAP773/9CoVDgiSeeQFpaGpRKJcaOHRt4LCsrC3/605+gVCrRvXt33HvvvSFPzn5paWlYtGgRlEolRowYgVtuuSVw59srr7yCBx54ABdddBGkUimuu+469O/fH+vXrwcAlJSUoF+/fpBIJDAYDLj55puDTqqA7+66a665Bu+8807QF/Xm3HHHHfjnP/8JjuOwatUq3HHHHU3q3HLLLcjPz4dMJsNNN92E/v37B+5sePfddzFkyBDMnj0barUaWq0Wo0ePDjyXMYalS5dCrVZjwIABGDlyZKBtAd9wwUsuuaTVOMNhs9mQmZkZVJaZmQmr1RpU9t///hd2ux1btmzBlClToNFoorJ/Qggh4aG84CzKC8LPC8I9z/tNmDABu3fvxkcffQSO4/DRRx9hx44dsFgsQfUoLyCEEHFRXnBWS3nBihUrcP/994edF9x0001Npu3ZsGEDrrvuOqxatSqsvOD222/Hu+++C47j8I9//AO33357kzot5QWrV6/GkCFDcPfddzebFzz55JOBvGDEiBH44YcfAo83NDRELS9ozGg0YurUqViwYAE6deoU2JfFYsGePXtw6NAhfPXVV/jf//6HZcuWtbo9QghpjDoOIrR06VKYTCacPHkShYWF2LdvX+CxEydO4NZbb0VmZmbgx2g04tSpUyG3deLECbz66qtB9T/++OOgoYSN56Crq6uD3W7HpZdeGqjfqVMnKJXKoETAf7IAfCdr/8mmrKwMvXv3DrkIUVlZGfbv3x8Uy5///GdUV1c32xaFhYVQKBSB33v06IGKiorAa3vkkUeCtldaWhp4fMOGDRg5ciRyc3Oh1+vx6quvBs3HBwDLly/H2LFjcfnllzcbQ2N9+/ZFr1698Oijj0KpVGLo0KFN6vztb3/DwIEDodfrkZmZif379wf2W1ZWhr59+za7fZ1OB61WG/i9cdtGW3p6etCQUQAwm80h7+6SyWS47LLLUFNTQ4kASSn19fX4+OOPYTKZxA6FpDDKC86ivCD8vCCS8zwA9OvXD++99x4ee+wx5Ofn46233sJNN92EnJycJnUpLyCxROdeQlpGecFZzeUFgiDgxIkTWLhwIbKzswM/paWlgde2YcMGXHLJJcjPz0dWVhZee+21JnnBCy+8gDFjxkScF/z1r39tNi9Yvnw5Bg8ejKysLGRnZ2P//v2BURBlZWXo06dPs9vvyLygcZ0rr7wSo0aNwl//+teg7QHAokWLkJ6eju7du2Pu3LlBU10RQkg4RO042LZtGyZPnozCwkJIJBJ89NFHYT93x44dkMvlMBgMMYuvJV26dMEbb7yBhx56KHBy69atG9577z2YTKbAj8PhwM033xxyG926dcO9994bVN9ms2HlypWBOo0XRsrJyYFWq8WuXbuCnuN0OjFy5MhWY+7RoweOHj0acm7Arl27YsiQIUHbtVgsOHDgQLPbq6ysDBrqVl5eji5dugRe23PPPRe0PbvdjocffhgejwfXXXcdZs6ciYqKCpjNZsyaNatJXP/6179w6NAh3HPPPWEvnHTHHXfgqaeeCnlX4ddff41FixbhnXfegdFohMlkwqBBgwLb7tGjR9zMBXzhhReisrIStbW1gbLS0lIMHjy42ed4vV4cPny4I8IjJC5otVr0798/rHVDCIk1ygsoL4hEW87zv/3tb/HDDz+goaEBn376KQ4fPozLLrus2fqUF5BYoHMvIeGhvKD5vEAikaBr165YtmwZGhoaAj9WqxUPPfQQPB4Prr/+etx11104efIkjEYjZs6c2SSud999Fz///DPmzp0bdl5w22234emnnw452uDrr7/G4sWLsWrVKtTX16OhoaFJXnD06NGw9hOpUHnBvn37MGjQoGafY7FYUFJSggsuuAArV64M6vApKioCgJCdQIQQEglROw7sdjuKiopaXNwuFLPZjGnTpmHcuHExiiw8F110EcaMGYMnn3wSgG/x20cffTQw56DFYsHHH3/cbC/zzJkz8fbbb+Orr74Cz/Nwu93YuXNnYIGkc0mlUsyaNQt//vOfA3cM1NfXY+3atWHFe9VVV8HtduPRRx+F3W6Hx+PBV199BcD3ZbSmpgYrVqyAy+UCz/P45ZdfmkwT0Jjdbsfjjz8Oj8eDXbt2YfXq1bjlllsAAPfccw+WLVuGPXv2gDEGh8OBL774AqdOnYLb7YbL5UJOTg5UKhV27doVcohjdnY2vvzyS3z77beYPXt2WMnA1KlTsXHjRtx1111NHrNYLJDL5cjLy4MgCPj73/+O/fv3Bx6/5ZZb8N133+HVV1+F2+2Gw+HA9u3bW91nuDweD1wuFwRBAMdxcLlc4DguZN3evXtj1KhReOSRR+BwOPDdd99h9erVmD59OgDfxYVNmzbB6XSC4zh89tlnWL16NSZOnBi1eAmJdxqNBueffz5dvCBxg/ICygvC1dp5PpTdu3eD4zhYrVY89thjaGhowG233QaA8gLScejcS0j4KC8InRdIJBLcc889eO6558LOC9asWdNk+9nZ2di0aRN27dqFOXPmhJ0XfP7555gxY0aTx1rLC6ZOnRrzvGDhwoWBvOBf//oX7rzzzpD1/Z0Gffv2xRtvvNGkg6BXr14YN24cHnvsMTgcDlRWVuKVV15pshg0IYS0RtSOg5KSEixZsgTXXXddRM+bOXMmpk6dihEjRsQosvAtXLgQb775Jk6ePIl77rkHt99+O6677jrodDoMGDCgxTn/iouLsWbNGvzf//0f8vLy0KVLF/zlL3+B2+1u9jlLly7FiBEjAouSDRkyBBs3bgwr1vT0dHzxxRfYs2cPunfvjs6dO+OVV14JPLZp0yZ8+eWX6NmzJ3JycjB16tQWhx4OGjQIHMehc+fOuP766/HEE08E5kD87W9/i6eeegp33XUXsrKy0KtXL7zwwgsQBAEZGRl45ZVX8Mc//hE6nQ5PPPEEbrzxxpD7yMrKwhdffIEffvgBf/zjH1tNBjQaDcaPHx9ySN+kSZMwZcoUDB48GIWFhThw4ABGjRoVeLxr16744osv8K9//QsFBQXo2bMn1q1b12q7+qWnp7eYOEyYMAEajQbbt2/HAw88AI1GgyVLlgQeHzhwIFavXh34fc2aNaioqEBeXh6mTJmCZ555JnBnIcdxeOSRR1BQUICcnBw88sgjeP755zF16tSw4yUk0Xm9XtTW1tIiXySuUF5AeYFfa3lBS+d5oGlesGDBAmRnZ6Nr16748ccf8dVXXyEtLQ0A5QWk49C5l5DIUF4QOi8oKSnBk08+iZkzZyInJwe9e/fGiy++GMgLXnrpJcyaNQuZmZl48sknm13DICsrCxs3bsTevXtDjko4V2t5wXXXXYeioiJ07doVBw8ebJIXbNy4Ef/+97/RuXNnnHfeeXj//fdbbVc/nU7XYl6wevVqVFRUoKCgILAoduO8YPDgwYG84MMPP8S3336LDz74AJmZmdDpdNDpdEF5w7vvvguz2YzOnTvjN7/5DSZMmIAHHngg7HgJIQQAJCzcMV0xJpFI8OGHH+Kaa65psd7bb7+NFStWYOfOnViyZAk++ugjlJaWhr0fi8UCvV4Ps9kMnU7XvqCTBGMMjDFIJJKwh7KtWrUKy5cvj6jtE1lb2iiVMMZgNpuh1+upfZpBbdSytrRPdXU1PvnkE4wbNw5ZWVkxjlB8jLGQ85mT9qG8oCnKC1pHeUHL6JzXukRto4469zLGYLPZkJ6enlDt05EoL4gNyguainZewPM8XC4XVCpV0FRLiU4QhIR8PR2xZg0d01tHbdQyap/WxSovkEd9izF0+PBhPPzww9i+fTvk8vBCd7vdQT3yFosFwNmTHwkWbpv466ViG6bia26N/++J2qZ51EYta0v76PV6TJw4ERqNJiXaNRVeIyGEkPiWmZmJSZMmQaPRiB0KISRBSaVSqFQquvhHCCEJIGE6Dniex9SpU7F48WL069cv7OctXboUixcvblJuNpvDugiTKncZRHpBKhU7DhL1tfo7y2LJ3/sL0AJMzaE2allb2ofneQCA0+mMWVzxJjc3V+wQCCGEpDC5XI709HSxwyCEJLhEvDOfEEJSUcJMVWQymZCVlQWZTBYoEwQBjDHIZDJs3LgRl19+eZPnhRpx0K1bN5hMppTpFAiHf+ghaR61UfMSdbh9R6I2allb2sdkMuGHH35Av379oNVqYxyh+BhjSd9xsG3btsACulVVVWFNYei3Y8cOXHbZZRg0aBBNYdhONA1P66iNWkbnvNYlaht11LmXpiRoHU1VFBuUFzQV7XOeIAjwer2Qy+VJ9fdNUxU1j47praM2ahm1T+tSfqoinU6Hn376KahsxYoV2Lx5M9atW4devXqFfJ5KpYJKpWpSTl/0zmrcd0RtEhq1Uev8f1PUPs2jNmpZpO3DcRzq6urQu3dvatMkYbfbUVRUhDvuuANTpkwJ+3lmsxnTpk3DuHHjUFNTE8MICSEktXm9Xpw+fRrnnXee2KEQQhIUYyxwEyjl8IQQEt9E7Tiw2Ww4cuRI4Pfjx4+jtLQU2dnZ6N69OxYsWICKigq88847kEqlGDRoUNDz8/PzoVarm5STyPnvrCDNozYiJL5kZWXhiiuuEDsMEkUlJSUoKSmJ+HkzZ87E1KlTIZPJ8NFHH0U/sBRE57zWURuRVETnXkJSUzTPeYwxOJ3OpJpulO6GJoQkK1HHUe3evRvFxcUoLi4GAMyfPx/FxcV49NFHAQBVVVUoLy8XM0RCCCGExLG3334bR48exV//+lexQyGEEEIIIYQQQpKGqCMOxowZ0+KCs6tWrWrx+YsWLcKiRYuiGxQhhJCE0NDQgA0bNuCSSy6hu35T1OHDh/Hwww9j+/btkMvDS2lCrX0EnJ2/l5xtC2qP5lEbtYzap3WJ2kb19fXYuHEjRo0aFdNzb6K2T0eitiGJinJ4QghJHAmzxgEhhBDSmFqtRs+ePaFUKsUOhYiA53lMnToVixcvRr9+/cJ+3tKlS7F48eIm5WazmS7CnOEfbg/Quj7NoTZqGbVP6xK1jbxeLwoLC8FxXCD+WHE6nQnVNmLIzc0VOwRCIkY5PCGEJA7qOCCEEJKQtFotBg4cKHYYRCRWqxW7d+/G3r17cc899wBAYKE9uVyOjRs34vLLL2/yvAULFmD+/PmB3y0WC7p16wa9Xg+dTtdh8cczfweKXq+ni3bNoDZqGbVP6xK1jdLS0qDRaGK+H/9og7S0tIRqn45End0kUVEOTwghiYM6DgghhCQkr9eLhoYG6HS6sKepIclDp9Php59+CipbsWIFNm/ejHXr1qFXr14hn6dSqaBSqZqUSyQSujjViL89qE2aR23UMmqf1iViG3EcB6PR2CHn3kRsH0JI6yiHJ4SQxCHq4siEEEJIW5nNZmzevBlWq1XsUEiU2Gw2lJaWorS0FABw/PhxlJaWory8HIBvtMC0adMAAFKpFIMGDQr6yc/Ph1qtxqBBg5CWlibWyyCEkKRF515CSHvRcYQQQhIHde8SQghJSJmZmRg/fjwyMjLEDoVEye7duzF27NjA7/4phW677TasWrUKVVVVgU4EQgghHY/OvYSQ9qLjCCGEJA4acUAIISQhyeVyZGZmQiaTiR0KiZIxY8YE5rVu/LNq1SoAwKpVq7Bly5Zmn79o0aLAaAVCCCHRR+de0pG2bduGyZMno7CwEBKJBB999FHYz92xYwfkcjkMBkPM4iNtQ8cRQghJHNRxQAghJCHZ7Xb89NNPcDqdYodCCCGEpAQ695KOZLfbUVRUhJdffjmi55nNZkybNg3jxo2LUWSkPeg4QgghiYOmKiKEEJKQPB4PTp06he7du0Oj0YgdDiGEEJL06NxLOlJJSQlKSkoift7MmTMxdepUyGSyiEYpkI5BxxFCCEkc1HFACCEkIWVlZbXpyyQhhBBC2obOvSTevf322zh69CjeffddLFmypNX6brcbbrc78LvFYgGAwHSJBEHTR0ZDZmYmJk2aFNh2Moh2GyUbap/WURu1jNqndbFqG+o4IIQQQgghhBBCSEI7fPgwHn74YWzfvh1yeXiXOpYuXYrFixc3KTebzXSB6gzGGGw2GwBAIpG0e3s8zwe2l0ycTmdU2idZUfu0jtqoZdQ+rcvNzY36NqnjgBBCSEIyGo3YtGkTRowYAb1eL3Y4hBBCSNKjcy+JVzzPY+rUqVi8eDH69esX9vMWLFiA+fPnB363WCzo1q0b9Ho9dDpdLEJNOP4OFL1eH5WLdqdPn8aOHTswYsSIpGlj/53QaWlpdGEzBGqf1lEbtYzap3U04oAQQghpRKlUorCwEAqFQuxQCCGEkJRA514Sr6xWK3bv3o29e/finnvuAQAIggDGGORyOTZu3IjLL7+8yfNUKhVUKlWTcolEQhenGvG3RzTaRKVSBY4jydTG0WyjZETt0zpqo5ZR+4iDOg4IIYQkpLS0NFx44YVih0EIIYSkDDr3knil0+nw008/BZWtWLECmzdvxrp169CrVy+RIiPnouMIIYQkDuo4IIQQkpA4joPZbEZ6ejpkMpnY4RBCCCFJj869pCPZbDYcOXIk8Pvx48dRWlqK7OxsdO/eHQsWLEBFRQXeeecdSKVSDBo0KOj5+fn5UKvVTcqJuOg4QgghiUMqdgCEEEJIW5hMJmzatAkWi0XsUAghhJCUQOde0pF2796N4uJiFBcXAwDmz5+P4uJiPProowCAqqoqlJeXixkiaQM6jhBCSOKgEQeEEEISkl6vx9ixY5GRkSF2KIQQQkhKoHMv6UhjxoxpcbHHVatWtfj8RYsWYdGiRdENirQbHUcIISRxUMcBIYSQhKRQKJCTkyN2GIQQQkjKoHMvIaS96DhCCCGJg6YqIoQQkpAcDgcOHToEp9MpdiiEEEJISqBzLyGkveg4QgghiYM6DgghhCQkl8uFo0ePwu12ix0KIYQQkhLo3EsIaS86jhBCSOKgqYoIIYQkpOzsbPz2t78VO4wOJXgckCq1YodBCCEkRaXiuTdeMcbAu22QqdLFDoWQiNBxhBBCoo8JfEzyAhpxQAghhCQA3mEE5zCKHQYhhBBC4gBvrYbgsoodBiGEEEJExgQeXMNxCG571LdNHQeEEEISkslkwhdffAGLxSJ2KDHHeC94S4XYYRBCCElxqXTujWe80wTeVit2GIS0CR1HCCEkehgTwBlPQPBEv9MAoI4DQgghCUoulyMnJwdyefLPuseZToIJvNhhEEIISXGpdO6NV4LXCd50UuwwCGkzOo4QQkh0MCacGWlgi9k+6EhNCCEkIaWnp6O4uFjsMGKOd9RDcNNUBIQQQsSXKufeeMUEDlzDCTAmiB0KIW1GxxFCCGm/jug0AGjEASGEkATF8zzsdjt4PnnvxGecG7y5UuwwCCGEEACpce6NV4wxcMYyMN4jdiiEtAsdRwghpH06qtMAoI4DQgghCcpoNOJ///tf0s6PyhjzTVFEdxUSQgiJE8l+7o1nvLWqQy4QEBJrdBwhhJC283UanOiwnIA6DgghhCQknU6H0aNHIz09XexQYkKw1cZsgSNCCCGkLZL93BuveIcRvO202GEQEhV0HCGEkLY522nQcVMZ0xoHhBBCEpJSqURBQYHYYcSE4HWAs9WIHQYhhBASJJnPvfFK8DjAm0+JHQYhUUPHEUIIiRxjrMM7DQCRRxxs27YNkydPRmFhISQSCT766KMW63/wwQe44oorkJeXB51OhxEjRmDDhg0dEywhhJC44nQ68euvv8LlcokdSlQxJoAzlgOMiR0KIYQQEiRZz73xivFecEZaDJkkFzqOEEJIZHydBsc7vNMAELnjwG63o6ioCC+//HJY9bdt24YrrrgC69evx549ezB27FhMnjwZe/fujXGkhBBC4o3D4cDBgweT7ksHb60G49xih0EIIYQ0kazn3nh0djFkr9ihEBJVdBwhhJDw+fKBjh9p4CfqVEUlJSUoKSkJu/7y5cuDfn/yySfx8ccf49NPP0VxcXGUoyOEEBLPcnJycM0114gdRlQJbivNYUwIISRuJeO5N17x5lO01hFJSnQcIYSQ8AQ6DVziLSaf0GscCIIAq9WK7OzsZuu43W643Wfv3LRYfI3NGAOjaSAAnG0Lao/mURu1jNqnddRGLWtL+yRbezKBg9dY3uxrSqbXSgghhJDm8fY68I4GscMghBBCiEjiodMASPCOg+eeew52ux2///3vm62zdOlSLF68uEm52WymizBnMMZgs9kAABKJRORo4hO1UcuofVpHbdSytrSPyWTCd999h0GDBiEtLS2W4XUIzloN5m5+iiI6ZxFCCBGb2WzGli1bMHToUGRkZIgdTlIS3DZwlkqxwyAkZug4QgghLYuXTgMggTsO1qxZg0WLFuHjjz9Gfn5+s/UWLFiA+fPnB363WCzo1q0b9Ho9dDpdR4Qa9/wXo/R6PV3QbAa1UcuofVpHbdSytrSPIAjQ6/VIT09P+I4D3mEEx+yAsvmlh1Kh42Dbtm1YtmwZ9uzZg6qqKnz44YctDmX/4IMPsHLlSpSWlsLtdmPgwIFYtGgRJk6c2HFBE0JICpFKpUhPT4dUKupSeUmLcR5wxhNACpzzSeqi4wghhDTPv8ZRPHQaAAnacbB27VpMnz4d7733HsaPH99iXZVKBZVK1aRcIpHQxbtG/O1BbdI8aqOWUfu0jtqoZZG2j06nw8UXXxzjqGKPcR4I1kr6XACw2+0oKirCHXfcgSlTprRaf9u2bbjiiivw5JNPIjMzE2+//TYmT56MXbt20dpHhBASAxkZGUlx7o1HTODhbTgGJvBih0JITNFxhBBCQjvbaWAWO5SAhOs4WLNmDe68806sWbMGV111ldjhEEIIEYkgCHC73VAoFAl9xxJnPkkXCc4oKSlBSUlJ2PWXL18e9PuTTz6Jjz/+GJ9++il1HBBCSAwky7k3HnGmcjCu+SkLCUkWdBwhhJCm4rHTAABEPUrbbDaUlpaitLQUAHD8+HGUlpaivLwcgG+aoWnTpgXqr1mzBtOmTcNzzz2H4cOHo7q6GtXV1TCb46tRCSGExF5DQwM+/fTThD4H8LZaCG6b2GEkDUEQYLVakZ2dLXYohBCSlJLh3BuPOEtV3ExJQEis0XGEEEKCxWunASDyiIPdu3dj7Nixgd/9axHcdtttWLVqFaqqqgKdCADw2muvgeM4zJkzB3PmzAmU++sTQghJHRkZGRg5cmTCrm8geB3grNVih5FUnnvuOdjtdvz+979vto7b7Ya70SLUFovvQg1jLCXWkQiHvy2oPZpHbdQyap/WJWobpaenY8SIEdBqtTGNPVHbpy14hxGctSbi56VC25DklOg5PCGERFM8LYQciqgdB2PGjGkx4Tm3M2DLli2xDYgQQkjCUKlUKCwsFDuMNmFMAGcsp8UPo2jNmjVYtGgRPv74Y+Tn5zdbb+nSpVi8eHGTcrPZTBdhzmCMwWbzjYShtTdCozZqGbVP6xK1jXieh06ng8fjgcfjiem+nE5nQrVNWzDOBd50CgyRn3/onEUSVSLn8IQQEk3x3mkAJOAaB4QQQgjgu6Bw7NgxdOnSBSqVSuxwIsJbKmke4yhau3Ytpk+fjvfeew/jx49vse6CBQsCIxwB34iDbt26Qa/XQ6fTxTrUhOC/GKXX65P+ol1bURu1jNqndYnaRjabDbW1tTE/9/pHG6SlpSVU+0SC8V5468rBlBIAkb9G6jggiSqRc3hCCImWROg0AKjjgBBCSIKy2+3Yu3cvsrKyEupLh+Ayg7fXix1G0lizZg3uvPNOrFmzBldddVWr9VUqVcjPi0QiSdqLU23hbw9qk+ZRG7WM2qd1idhGdrsdpaWlyM7Ohlqtjum+ErF9wsWYAN5UBghcUr4+QlqSqDk8IYRES6J0GgAiL45MCCGEtFVubi6mTJmCrKwssUMJG+O94EwnxQ4jbtlsNpSWlqK0tBQAcPz4cZSWlgbWO1qwYAGmTZsWqL9mzRpMmzYNzz33HIYPH47q6mpUV1fTYnuEEBIjiXjujUec6SQEj0PsMOLetm3bMHnyZBQWFkIikeCjjz5qsf4HH3yAK664Anl5edDpdBgxYgQ2bNjQMcGSsNFxhBCSyhgTwDUcT4hOA4A6DgghhJAOw5nKwQRe7DDi1u7du1FcXIzi4mIAwPz581FcXIxHH30UAFBVVRXoRACA1157DRzHYc6cOejcuXPg59577xUlfkIIIaQ1nLUagtMkdhgJwW63o6ioCC+//HJY9bdt24YrrrgC69evx549ezB27FhMnjwZe/fujXGkhBBCSOt8nQYnILitYocSNpqqiBBCSEIym834+uuvYTAYkJGRIXY4reJtpyG4bWKHEdfGjBnT4pzNq1atCvp9y5YtsQ2IEEJIkEQ798YbwWkGb60RO4yEUVJSgpKSkrDrL1++POj3J598Eh9//DE+/fTTwE0JRHx0HCGEpKJE7DQAqOOAEEJIgpJIJJDL5QkxN7DgdYCzVokdBiGEENIuiXTujTeC1wHOVN56RRI1giDAarUiOzu72Tputxtutzvwu8XimzrCv0A3OdsW0WoP/3HEv+1kEO02SjbUPq2jNmpZordPYHqiGN5IGKu2oY4DQgghCck/d228Y0wAZywHEjTJIYQQQvwS5dwbbxjvBddwAowJYoeSUp577jnY7Xb8/ve/b7bO0qVLsXjx4iblZrM5YS9QRRtjDDab72JXNDoNGWMYNGgQAAS2mwycTid1qraA2qd11EYtS9z2YeDMFWBeZ2z3Qh0HhBBCyFmCIIDjOMhksrhOIHhzBRjnbr0iIYQQEucS5dwbT/x3GTLeK3YoKWXNmjVYtGgRPv74Y+Tn5zdbb8GCBZg/f37gd4vFgm7dukGv10On03VEqHHPfzFKr9dH5e/e4/Ek3XHEfyd0Wlpa0rymaKL2aR21UcsStX38OYBC4gaUsV1mmDoOCCGEkEYaGhrwySefYNy4ccjKyhI7nJAEpxm8o0HsMAghhJCoSIRzb7zhTCchxPguQxJs7dq1mD59Ot577z2MHz++xboqlQoqlapJuUQiSaiLU7Hmb49otInRaEzK40g02ygZUfu0jtqoZYnWPowJ4I0nwDz2hIk5lNh2dxBCCCExkpGRgWHDhiEtLU3sUEJinAec+aTYYRBCCCFRE+/n3njDWaogOE1ih5FS1qxZg9tvvx3/+te/cNVVV4kdDgmBjiOEkGTHBD7maxp0FBpxQAghJCGpVCp0795d7DBCYoyBM5WDCbzYoRBCCCFRE8/n3njDO4zgbbVih5HQbDYbjhw5Evj9+PHjKC0tRXZ2Nrp3744FCxagoqIC77zzDgBfp8G0adPwwgsvYPjw4aiurgYAaDQa6PV6UV4DaYqOI4SQZBboNPDYxQ4lKmjEASGEkITkdrtRVlYGj8cjdihN8LaapEkUCCGEEL94PvfGE8Fjp1GHUbB7924UFxejuLgYADB//nwUFxfj0UcfBQBUVVWhvLw8UP+1114Dx3GYM2cOOnfuHPi59957RYmfhEbHEUJIsvJ1GhxLqmsBbRpxwBhDfX09JBIJcnJyoh0TIYQQ0iqr1Yrvv/8e48aNg1KpFDucAMFtS7k7DCkvIISQ1BCv5954wjgPuIbjQIwWKUwE0coLxowZ0+Jij6tWrQr6fcuWLW3eF+k4dBwhhCQjJnBnRho4xA4lqiIacVBdXY1p06YhKysLBQUFyM/PR1ZWFu68807U1NTEKkZCCCGkiZycHFx77bXIzMwUO5QAJnDgTOUpc7GA8gJCCEkt8XjujSdM4OBtOJayUxVSXkDCQccRQkiyYQIHrv5Y0nUaABGMOLBYLBg5ciRsNhvuuOMO9O/fH4wxHDx4EGvWrMHXX3+NH374Aenp6bGMlxBCCAEASCQSyGQyscMIwplOgvFescPoEJQXEEJI6onHc2+8YIyBM5aBcW6xQxEF5QUkXHQcIYQkk0CngdcpdigxEXbHwQsvvACZTIYDBw4gLy8v6LH/+7//w6hRo/Diiy/ikUceiXqQhBBCyLksFgu++eYbXHjhhXHxJZS310FwWcQOo8NQXkAIIakn3s698YQ3n4LgtokdhmgoLyDhouMIISRZMN7rG2nodYkdSsyEPVXRZ599hkceeaRJEgAA+fn5WLBgAT799NOoBkcIIYS0RBAEsUMAAAheB3hLldhhdCjKCwghJDXFy7k3nvC20+AdDWKHISrKC0gk6DhCCEl0jPfCW380qTsNgAg6Dn799VeMHDmy2cdHjhyJX375JSpBEUIIIa3R6XS45JJLRL9TiQk8OGM5GEutL0CUFxBCSOqJl3NvPBGcZnCWSrHDEB3lBSRcdBwhhCS6QKdBCkxPGHbHgcViaXHxmszMTFgsqTNFAyGEEAIAvLkiJRKGc1FeQAghJNUJXgc4U7nYYcQFygsIIYSkglTqNAAi6DhgjEEqbb66RCIBYywqQRFCCCGtqaurw7p162A0GkWLgXfUg3eKt38xUV5ACCGpJx7OvfGCcR5wDcdTbsRhcygvIOGi4wghJFGlWqcBEMHiyIwx9OvXDxKJpNnHCSGEkI6Snp6OIUOGQKvVirJ/5nWBN6fu1ASUFxBCSOoR+9wbL5jAw2s8DsZzYocSNygvIOGi4wghJBExzuPrNOA9YofSocLuOHj77bdjGQchhBASEbVajV69eomyb8YEeI0nUvouQ8oLCCEk9Yh57o0XjDFwxhNJvxhipCgvIOGi4wghJNGkaqcBEEHHwW233RbLOAghhJCIuN1uVFRUIC8vD0qlskP3narrGjRGeQEhhKQeMc+98UDwOsGbTkLwOsUOJe5QXkDClerHEUJIYmGc+0yngVfsUEQR9hoHRqMRL730UsgFjcxmc7OPEUIIIbFgtVqxc+dO2O32Dt0v7zCCdzR06D7jEeUFhBCSesQ698YCYwxM4MB4LxjnhuB1QvA6IHjO+fE6fB0GtlpwdUeo06AZlBeQcCXTcYQQktyY15XSnQZABCMOXn75Zfz444+YO3duk8f0ej22b98Oi8WChQsXRjVAQgghJJTs7GxcffXVkMvDPpW1m29dg1Mdtr94RnkBIYSkHjHOve3FmADmtkLw2MHcNjDBCwhCSk83GAuUF5BwJeJxhBCSepjXBW/D0ZRfzyjsEQfvv/8+Zs2a1ezjM2fOxLp166ISFCGEENIaqVQKpVIJqTTsU1m7MCbAayqjCw1nUF5ACCGpp6PPvdHAW2vgbTgB3nYagtcJxnN0Lo8BygtIuBLxOEIISS2C10mdBmeEfaQ+evQo+vbt2+zjffv2xdGjR6MSFCGEENIaq9WK7777rsOGOfPmCloIsRHKCwghJPV09Lm3vRjvheCoEzuMlEB5AQlXoh1HCCGpRfA6wNVTp4Ff2B0HMpkMlZWVzT5eWVlJPcaEEEI6jCAIcDgcEITY3zXIOxpoXYNzUF5ACCGppyPPvdHA20+DJUisiY7yAhKuRDuOEEJSh+BxgKs/BibwYocSN8I+cxcXF+Ojjz5q9vEPP/wQxcXFEe1827ZtmDx5MgoLCyGRSFrcvt/WrVsxZMgQqNVqnHfeeXj11Vcj2ichhJDkoNfrMWbMGGRkZMR0P4LXAd5cEdN9JKJY5AWEEELiW0ede6OB8V4I9nqxw0gZlBeQcCXScYQQkjoEjx1cw1HqNDhH2B0H99xzD5577jm8/PLL4PmzjcjzPF566SX87W9/w5w5cyLaud1uR1FREV5++eWw6h8/fhxXXnklRo8ejb179+KRRx7BvHnz8P7770e0X0IIISQcjHODazhOcyGHEIu8gBBCCIkW3lZD5+8ORHkBIYSQRCW4beAajtEoxRDCXsZ+ypQpePDBBzFv3jwsXLgQ5513HiQSCY4ePQqbzYYHHngA119/fUQ7LykpQUlJSdj1X331VXTv3h3Lly8HAAwYMAC7d+/Gs88+iylTpkS0b0IIIYmtvr4e//3vfzF27FhkZmZGffuM98Jbf4zmNmxGLPICQggh8S3W595oYZyHphjsYJQXkHAlynGEEJIaBLcVXMMJutmgGWF3HADAE088gd/97ndYvXo1jhw5AsYYLr30UkydOhXDhg2LVYwBO3fuxIQJE4LKJk6ciLfeegterxcKhaLJc9xuN9xud+B3i8UCAGCMgTEW24AThL8tqD2aR23UMmqf1lEbtawt7aPRaDBo0CCoVKqotyvjPPA2HAPj3K1X7iDx+NkROy8ghBDSsbRaLQYNGgS1Wi12KC3irdVAHJ43kx3lBSQciXIcIYQkP8FlAWcso06DFkTUcQAAw4YNE+2kX11djYKCgqCygoICcByHuro6dO7cuclzli5disWLFzcpN5vNcXkRRgyMMdhsNgCARCIROZr4RG3UMmqf1lEbtawt7cPzPDp16gSO4wLPjUosbltcTm8Qr+esaOYF27Ztw7Jly7Bnzx5UVVXhww8/xDXXXNPic7Zu3Yr58+fjwIEDKCwsxIMPPohZs2ZFJR5CCCHBNBoN+vbtK3YYLRK8TvBOo9hhpCwxrxeQxJAIxxFCSPIT3DbqNAhDxB0HYjv3gpL/QkpzF5oWLFiA+fPnB363WCzo1q0b9Ho9dDpd7AJNIP421Ov1dEGzGdRGLaP2aR21Ucva0j4OhwPV1dXIyckJOeIs8hgE8JZK8O56QAFEsAxQh4jXjoNo8q99dMcdd4Q1BaF/7aO77roL7777Lnbs2IHZs2cjLy+PpjAkhJAY8Hg8UT33xgJvrRY7BEJICxLhOEIISW6C1wHOSGsZhiOhOg46deqE6urgRLC2thZyuRw5OTkhn6NSqaBSqZqUSyQSunjXiL89qE2aR23UMmqf1lEbtSzS9rFardixYwfGjRuHrKysdu2bcW7wxjIIXie9PyKitY8IISS+WSwWfP3111E598aC4HFAcFnEDoMQ0oJ4P44QQpIb49zg6mkh5HAlVMfBiBEj8OmnnwaVbdy4EUOHDqWeakIISTFZWVm48sor2z0/Ku8wgrecosQhAdHaR7FBa7K0jtqoZdQ+rUvUNsrMzERJSQnUanVMY29r+3CWyoRr07ZKlddJkk+0cnhCCGkLzloNJvBih5EwRO04sNlsOHLkSOD348ePo7S0FNnZ2ejevTsWLFiAiooKvPPOOwCAWbNm4eWXX8b8+fNx1113YefOnXjrrbewZs0asV4CIYQQkchkMmi12jY/nzEBvLkCvKMhilGRjkRrH8UGrcnSOmqjllH7tC5R24jneQiCAIfDEfN9OZ2RjQJkXgc4a+qMNqBzFklU7c3hCSGkrQSvE4LTJHYYCSXijoPLL78cH3zwATIzM4PKLRYLrrnmGmzevDnsbe3evRtjx44N/O5fi+C2227DqlWrUFVVhfLy8sDjvXr1wvr16/GnP/0Jr7zyCgoLC/Hiiy/SdASEEJKCbDYb9uzZgwEDBkT85YN5XfAaT4Bx7tYrkxZFMy9oC1r7KPpoTZbWURu1jNqndYnaRiaTCb/++iv69+8f0wt//tEGaWlpYbeP93QlBGV8rU8US/HacSB2XkDiX3tyeEIIaQ/eWiN2CAkn4o6DLVu2wOPxNCl3uVzYvn17RNsaM2ZMiwnPqlWrmpRddtll+OGHHyLaDyGEkOTDcRxMJhM4jovoebyjAby5ghZCipJo5gWRorWPYofWZGkdtVHLqH1al4htxPM8TCYTeJ6PedyRtA/vNIFxroRqy2QlZl5AEkNbc3hCCGkPweuE4DKLHUbCCbvj4Mcffwz8/+DBg0Ff1Hmex+eff44uXbpENzpCCCGkGZmZmRg3blzY9X1TE50C7zDGMKrUEQ95Aa19RAghHSvSc29HYIyBt1a3XpHEVCzygm3btmHZsmXYs2cPqqqq8OGHH+Kaa65p8Tlbt27F/PnzceDAARQWFuLBBx/ErFmzItovia14PI4QQpIfjTZom7A7DgwGQ+COj8svv7zJ4xqNBi+99FJUgyOEEEKiQfA6wRnLaGqiKIpFXkBrHxFCCImU4Gyg83sciEVeYLfbUVRUhDvuuCOs6YmPHz+OK6+8EnfddRfeffdd7NixA7Nnz0ZeXh5Nb0wIISlM8DpotEEbhd1xcPz4cTDGcN555+G7775DXl5e4DGlUon8/HzIZLKYBEkIIYScq6GhAevXr8ell17aZB7dxnhHPXhzJU1NFGWxyAto7SNCCIlv4Z57OwpjAt1BGCdikReUlJSgpKQk7PqvvvoqunfvjuXLlwMABgwYgN27d+PZZ5+l3CCOxNtxhBCS/ChXaLuwOw569OgBABAEuvBCCCFEfGq1Gv369YNarQ75OGMMvPkkTU0UI7HIC2jtI0IIiW+tnXs7mmCvA+O9YodBEB/XC3bu3IkJEyYElU2cOBFvvfUWvF5vyGkM3W433O6zI1YsFguAswt0k7NtEa32UKlU6Nu3L1QqVdK0cbTbKNlQ+7SO2qhl7WkfweMA70z+0Qax+uxEvDgyAPz666/YsmULamtrmyQGjz76aFQCI4QQQlqi1WrRv3//kI8xgQNnLIPgtnVwVKmJ8gJCCEkNLZ17OxoTOPC2WrHDICGIlRdUV1ejoKAgqKygoAAcx6Gurg6dO3du8pylS5di8eLFTcrNZjNdwDuDMQabzZdTR2MBcp7n0bVrV3AcF9huMnA6nbRAewuofVpHbdSytrYPZ64A8yb/TfBx03Hwxhtv4O6770Zubi46deoU9KZJJBK6QEAIIaRDeL1e1NXVITMzE3L52dMZ473w1h+l+Y47COUFhBCSOpo794qBt9WCCbyoMZCmxM4Lzr2o5L+Q0tzFpgULFgSmRgR8Iw66desGvV4PnU4Xu0ATiL8N9Xp9VC5qOp3OuDmORIv/Tui0tDS68BsCtU/rqI1a1tb2ETx2eCUuQCmNYXTxIW46DpYsWYInnngCDz30UCziIYQQQsJiNpuxZcsWjBs3DllZWYFy3lpNnQYdiPICQghJHc2dezsa470Q7PWi7Z80T8y8oFOnTqiurg4qq62thVwuR05OTsjnqFQqqFSqJuX+hZ6Jj789otEmFosFW7duFf04Em3RbKNkRO3TOmqjlrWlfQRbDbVnO0XccWA0GnHDDTfEIhZCCCEkbJmZmZg4cSK0Wm2gjHld4J20pkFHoryAEEJSR6hzrxh4axUYS/5pBxKRmHnBiBEj8OmnnwaVbdy4EUOHDg25vgERR7wcRwghyU1w22jq4iiIeKzGDTfcgI0bN8YiFkIIISRscrkcGRkZkMlkgTLOWg3QfLQdivICQghJHaHOvR1N8DrBO+gmgXgVzbzAZrOhtLQUpaWlAIDjx4+jtLQU5eXlAHzTDE2bNi1Qf9asWSgrK8P8+fNx6NAh/P3vf8dbb72F+++/PyrxkOiIh+MIIST58dbq1iuRVkU84qBPnz74y1/+gm+//RaDBw9u0nM/b968qAVHCCGENMdut2Pfvn3o27cvtFotBI8Dgsssdlgph/ICQghJHeeee8VAFwLiWzTzgt27d2Ps2LGB3/1rEdx2221YtWoVqqqqAp0IANCrVy+sX78ef/rTn/DKK6+gsLAQL774IqZMmdLOV0WiKR6OI4SQ5Ca4LBA8drHDSAoSFuHqCb169Wp+YxIJjh071u6gYslisUCv18NsNtNiR2cwxmA2m6O22FEyojZqGbVP66iNWtaW9jl9+jS+/PJLDB8+HDqdDt76o0k9FJExhtxu50OZ2UXsUIJQXpB86HjVOmqjllH7tC5R2+jcc2+sMMZgs9mQnp4e1D6C2wZv/dGY7TeRMMaQ130AFPrOYocShPKC5BPt41VHHUc6UnPHLOJD7dM6aqOWRdo+3tO/QvA6OyCy+MEYQ16PgVDoCqK63YhHHBw/fjyqARBCCCFtkZWVhQkTJgA4c0dBEncaxDPKCwghJHU0PveKgbdWibZvEh7KC0hrxD6OEEKSm+A0p1ynQSxFvMaBn8fjwS+//AKO46IZDyGEEBIxji4kiI7yAkIIIbEkOM0QPA6xwyBhoryAEEKIGOjaQHRF3HHgcDgwffp0aLVaDBw4MDCn4Lx58/DUU09FPUBCCCEkFKPRiPXr18NYUw7mdYkdTsqivIAQQlKH/9xrNnfsmkKMMboQkCAoLyCtEes4QghJfryjAYxzix1GUom442DBggXYt28ftmzZArVaHSgfP3481q5dG9XgCCGEkOaoVCp0794NMrdR7FBSGuUFhBCSOnzn3u5QKpUdul/BUU8XAhIE5QWkNWIdRwghyY0xBt5aI3YYSSfiNQ4++ugjrF27FsOHDw9akOKCCy7A0aO0UBUhhJCOodVqMaBnATgL3YEoJsoLCCEkdWi1WgwaNKhD98kEHryNLgQkCsoLSGvEOI4QQpKf4KgH4z1ih5F0Ih5xcPr0aeTn5zcpt9vttPI3IYSQDuN1u1BfXQ6OF8QOJaVRXkAIIamD4zgYjcYOnbeet9WC8TRPfqKgvIC0RozjCCEkuTEm0E0GMRJxx8HFF1+Mzz77LPC7/+T/xhtvYMSIEdGLjBBCCGlBXeUxbCs9AZuT7ioQE+UFhBCSOkwmE7788ktYrdYO2R/jvRDsdR2yLxIdlBeQ1nT0cYQQkvwEex3dZBAjEU9VtHTpUkyaNAkHDx4Ex3F44YUXcODAAezcuRNbt26NRYyEEEJIEIFzI13qwugLuyFdQ/OjionyAkIISR2ZmZkYN24cMjIyOmR/vLUKjNHIwkRCeQFpTUcfRwghyc03pWGt2GEkrYhHHIwcORI7duyAw+FA7969sXHjRhQUFGDnzp0YMmRILGIkhBBCgnjNlZBJgcx0NeSyiE9lJIooLyCEkNQhl8uRlZUFuTzi+88ixjgXeIcx5vsh0UV5AWlNRx5HCCHJj7fVggm82GEkrTYdqQcPHox//OMf0Y6FEEIIaZXgcYCzN8Dp8uLn8jr07JQJtZK+eIiJ8gJCCEkNDocD+/fvR+/evaHRaGK6L95eB9CU+AmJ8gLSko48jhBCkhtNaRh7YV1psVgs0Ol0gf+3xF+PEEIIiQWP6RQAwM1xOHXaisKcDOo46GCUFxBCSGpyu90oLy9Ht27dYnrBT3CawbxOQEmjChMB5QUkEh11HCGEJD/eVkNTGsZYWFdasrKyUFVVhfz8fGRmZgYWOGqMMQaJRAKep+EhhBBCYoN3msG7fAupZaZrMH5IL5EjSk2UFxBCSGrKysrClVdeGdN9MMbAWatiug8SXZQXkEh0xHGEEJL8GOcG72gQO4ykF1bHwebNm5GdnQ0A+Oqrr2IaECGEEBIKYyww2oCIi/ICQghJbIwx7DthxE/lJhyusuBErR1evvU79hhjEIQY39kn8GCCFwIDpBKA5itqDsO/7hY7Bh/KCwghhHQ03loDMCZ2GEkvrI6Dyy67LOT/CSGEkI7C2+sheF2B3802F7aXlmHo+Z2QoVWJGFnqobyAEEISl9vLY8m6n/DlT9VihxImuigQ7ygvIJEwGo348ssvMXz4cJq6ihDSJoLXAd5pFDuMlBDxpNBvv/020tPTccMNNwSVv/fee3A4HLjtttuiFhwhhBACAEzg4TFXBpUp5FLkZ2ohl9H8x2JKlryA0d0qhJAU0GBz4+F392J/uSmoXC6TQK2Qtfp8xhh4nodUKg05HU17MSbQ3YMJLlnyAhI7SqUSBQUFkMtpjTJCSNvwFprSsKNEfKR+6qmn8OqrrzYpz8/Pxx//+EdKBAghhESd11oDxnuDyrRqJQb2yhMpIuKXLHkBZ6sD9HqxwyCEkJg5Wm3FQ//8AZVGJwBAo5Rh9qTzYeiZhR55aWF1xHMc1+rit20leJ3w1h0GGANjDA6PAK0yNh0UySBeO7yTJS8gsZOWloaioiKxwyCEJCjBbYXgtokdRsqIuOOgrKwMvXo1XYyyR48eKC8vj0pQhBBCiB/jveAsNU3KeV6AzemBRiWHTEqjDsSSLHmB11IF1qk7JDKF2KEQQkjUbSytxNIP98Pt9a1PkKtTYdm0ITi/MLJpQjiOg9VqhVarhUzW+giFSPCWShptkASSJS8gsRPL4wghJPnRaIOOFfGVlvz8fPz4449Nyvft24ecnJyIA1ixYgV69eoFtVqNIUOGYPv27S3WX716NYqKiqDVatG5c2fccccdqK+vj3i/hBBCEoPHXOmbuuAcZrsLX+0tg9XhESEq4hftvEAsTODhMZ4UOwxCCIkqDyfg+U8PYtF/fgx0GvQr1OHNu0dE3GkAACaTCRs2bIj6qAPBaaa7B5NEsuQFJHZidRwhhCQ/3mmC4HWKHUZKibjj4KabbsK8efPw1Vdfged58DyPzZs3495778VNN90U0bbWrl2L++67DwsXLsTevXsxevRolJSUNHsnwtdff41p06Zh+vTpOHDgAN577z18//33mDFjRqQvgxBCSAIQvE7w9tCdwxlaFUYO6op0jbKDoyKNRTMvEBvnMIJ3msUOgxBCoqLstA1/fPVbrNt59rvVVUO64LWZv0G+Xt2mber1eowZMwYZGRnRChOMCeAsla1XJAkhmfICEhuxOI4QQlIDb60WO4SUE/FURUuWLEFZWRnGjRsXWMxGEARMmzYNTz75ZETbev755zF9+vTAhf/ly5djw4YNWLlyJZYuXdqk/rfffouePXti3rx5AIBevXph5syZeOaZZyJ9GYQQQhKAx3Sq2Tl8FXIZcnSaDo6InCuaeUE88BhPQq3KgISmvyKEJCjGGD7dfQrL//szXF4eAKCQSfCnyRfgdxd3bdeaAQqFArm5udEKFQAg2E6D8TR6MFkkW15Aoi8WxxFCSPITXGYwzk1rH3WwiDsOlEol1q5di8cffxz79u2DRqPB4MGD0aNHj4i24/F4sGfPHjz88MNB5RMmTMA333wT8jkjR47EwoULsX79epSUlKC2thbr1q3DVVdd1ex+3G433G534Hf/cDh2ZtEtcrYtqD2aR23UMmqf1lEbtSxU+/AuKzhH83d/O1weHD5Vj655OqiVEZ/OEk68fnailRf4rVixAsuWLUNVVRUGDhyI5cuXY/To0c3WX716NZ555hkcPnwYer0ekyZNwrPPPtvm6RAEzg2vpQrKzC5tej4hhIip1uzCUx/ux7e/1gXKeuSlYfGNRejXhqmJzuVwOPDzzz+jZ8+eUKvbNmqhMcZ7wdtq270dEj+inReQ5BPt4wghJPkxxiA4jG24ik3aq81N3q9fP/Tr16/NO66rqwPP8ygoKAgqLygoQHV16KEnI0eOxOrVq3HjjTfC5XKB4zhcffXVeOmll5rdz9KlS7F48eIm5WazOW4vwnQ0xhhsNt+cotRzFxq1UcuofVpHbdSyUO3jrj8Owett9jkmqxtHK03QpWkgRD7zXsKJ93NWe/MC4OwUhitWrMCoUaPw2muvoaSkBAcPHkT37t2b1PdPYfi3v/0NkydPRkVFBWbNmoUZM2bgww8/bHMcnLUG8rRsSBU0ooUQkhgEgeHTPafw8vpfYHdzgfLfXdwV867qD02UOthdLhd+/fVXdOrUKSoX/DhL6HWMSOKLRl5AklO0jyOEkOQnOOrBBC/aMOM+aaewMsj58+fj8ccfR1paGubPn99i3eeffz6iAM69gMYYa/ai2sGDBzFv3jw8+uijmDhxIqqqqvDAAw9g1qxZeOutt0I+Z8GCBUExWywWdOvWDXq9Hjpd+++6SQb+i1F6vZ4uaDaD2qhl1D6tozZq2bntw9nrIZfzgFzR7HO0ynRMvPi8jgpRdPHUcRCrvCBepjBkjPmmLMqnCx6EkPh36JQZz31yEAdPnR2ll6tT4aFrBmJU//yo7is7OxtXX311VLYleOwQnKaobIuIK5bXC0jyieZxhBCS/BgTwNtqxA4jZYXVcbB37154z9z1+cMPPzR70SuSi2G5ubmQyWRNRhfU1tY2GYXgt3TpUowaNQoPPPAAAODCCy9EWloaRo8ejSVLlqBz585NnqNSqaBSqULGShfvzvK3B7VJ86iNWkbt0zpqo5YF2oYxeM2VrbYTtaV4YpEXxNsUhpzTAq+tDvK0tk15lMhoarXWURu1jNqnddFoI5Pdg9c3HcYnu0+h8WZKigsx76r+0GkUUX8PovW+MsbgNZ1sdlv0GWpdPLVNLPICQgghBAAEex0Yz7VekcREWB0HL7zwQuDu/C1btkRlx0qlEkOGDMGmTZtw7bXXBso3bdqE3/3udyGf43A4Agss+clkMgDxlTgRQghpO85aA8Y3P0WRn8Xuwo595Sju2wkZWmUHREb8YpEXiD2Foc3FQSIP/tzZKo9DlSOB5EyukSpoarXWURu1jNqnde1pI15g+HxfLd7ZfhJW19kv0t1zNJg1vicMPfRgHgfMMVhv2Gg0YteuXRg8eDDS09PbvB3BZQJvdzT7OGMMLq9vCiP6DIUWT99/Y5EXkORlMpnw1Vdf4eKLL6ZZIAghLWICT2shiSysjoPi4mJUVVUhPz8f5513Hr7//vs2LzrY2Pz58/GHP/wBQ4cOxYgRI/D666+jvLwcs2bNAuCbZqiiogLvvPMOAGDy5Mm46667sHLlysBURffddx+GDRuGwsLCdsdDCCFEXIz3wmsNbxiiTCqFPl0FmYwuKHS0WOUFgHhTGKar5cjQNJ0aSwEblPqm6yskM5parXXURi2j9mldW9qIMYbth2rx+qYjOF5rC5RrVTJMv7wPrh/RHXJZbOf+ZYwhJycHOp0OWq22bdsQOHhtJjBl87H620erlNJnqBnx1HEQy7xgxYoVWLZsGaqqqjBw4EAsX74co0ePbrb+6tWr8cwzz+Dw4cPQ6/WYNGkSnn322ajFQ9pPLpcjMzOzyU2hhBByLt5WCybwYoeR0sI6UmdmZuL48ePIz8/HiRMnIAjRWcDqxhtvRH19PR577DFUVVVh0KBBWL9+PXr06AEAqKqqQnl5eaD+7bffDqvVipdffhl//vOfkZmZicsvvxxPP/10VOIhhBAiLo+5MuzEIE2jRFHv0FPbkdiKRV4Qr1MYcvY6yNNzIVOlteVlJSyaWq111EYto/ZpXbhtxBjD90fq8dqmwzjUaB0DAJhoKMScSf2Qq+uYBUYzMjIwZMiQdm2Dt1YDTAh7SkL6DMW/WF0vWLt2Le677z6sWLECo0aNwmuvvYaSkhIcPHgQ3bs37dT/+uuvMW3aNPztb3/D5MmTUVFRgVmzZmHGjBn48MMPoxITab/09PR2H0cIIcmP8V4Ijjqxw0h5YXUcTJkyBZdddhk6d+4MiUSCoUOHBqYIOtexY8ciCmD27NmYPXt2yMdWrVrVpGzu3LmYO3duRPsghBAS/wSvC5yjLuwLBLwgwOn2QqWQQyqliwodKRZ5QTxPYegxlkNd0J8uXhFCOtxPZUa8tvEwfjjeEFQ+sJsecyadD0Ov7A6Nh+d5OBwOqNVqSKWRj24QvA7wjobWK5KEEqvrBc8//zymT5+OGTNmAACWL1+ODRs2YOXKlVi6dGmT+t9++y169uyJefPmAQB69eqFmTNn4plnnmnDqyKx0t7jCCEkNfD202BR6ogmbRdWx8Hrr7+O6667DkeOHMG8efNw1113ISMjI9axEUIISSFeSw2UEYxYNttc+GLPCYy+sBsy0zvmTkviE6u8IF6nMBQ8DnDWWih0NMKFEBJ7jDF8d6Qe7249hj3Hgi+y9+6UjplX9MOo/nmidGYajUasX78e48aNQ1ZWVsTP580VMYiKiC0WeYHH48GePXvw8MMPB5VPmDAB33zzTcjnjBw5EgsXLsT69etRUlKC2tparFu3DldddVW7YiHR1d7jCCEk+THeC8FeL3YYBGF2HPz444+YMGECJk2ahD179uDee++ljgNCCCFRwzkaIHgdgLzpHPPNydCq8JsBhUhTh/8cEh2xygvieQpDr6UKMm0WpHJaiJsQEhscL2DzT9VYvf04DldZgx7rlqPFjPF9MW5wJ1FH2el0OlxyySVtWhiZd9RD8DS/IDJJXLHIC+rq6sDzfJPpCgsKCppMa+g3cuRIrF69GjfeeCNcLhc4jsPVV1+Nl156qdn9uN1uuN3uwO8WiwWArwMvntaREJO/LaLVHhkZGRg1ahTS0tKSpo2j3UbJhtqnddRGwThLFYRGUxhT+7QuVm0T8eLIW7duhcfjiUkwhBBCUg8TBHhNkd+BqJDLkJ+VWvPOx4tY5gXxOoUhE3h4TSehyu0d830RQlKL3cXhsx9OYe2OMlQZnUGPdc3R4g+XnYeS4sKYL3wcDqVSiU6dOkX8PCZw4C1VMYiIxINY5gXnjqxhjDU72ubgwYOYN28eHn300cBIxAceeACzZs3CW2+9FfI5S5cuxeLFi5uUm81mukB1BmMMNptvQfZojHTieR7p6elNOm0SndPppGktW0Dt0zpqozN4L7ymegBnj8GMMbi8vmmLqI1CE7XjIFaLHRFCCCFeazUELvIvmC6PF8eqTOiSkw5VJHMckXZL1byAc5ggd5oh0+jFDoUQkgSOn3Zg05YKbCithNPDBz02oKset17aC5deUABZHK3j43Q6cfjwYXTr1g1qdfjTBPLWajCBb70iSUixyAtyc3Mhk8majC6ora1tMgrBb+nSpRg1ahQeeOABAMCFF16ItLQ0jB49GkuWLEHnzp2bPGfBggWYP39+4HeLxYJu3bpBr9dDp9O1+3UkA//FKL1eH5ULdlarFdXV1ejatWtEx5F45r8TOi0tjS5qhkDt0zpqo7O8xjIolBIAZ9vBfxzSKqUp3z7NEbXjIJaLIxNCCEldAucGZ6lp03Odbi9+LqtDjk5DHQcdLJXzAo+xHGrVQEhoMT9CSBt4OQFbDtTgg13l2HfC2OTxEf1ycculvVDcKzsuvxg7HA7s378feXl5YV/wE7wO8DRPcVKLRV6gVCoxZMgQbNq0Cddee22gfNOmTfjd734X8jkOhwNyeXBO6I+juQsqKpUKKpWqSblEIonLv0Gx+NsjGm3idDoDxxGNRhOF6OJDNNsoGVH7tI7ayJczMJc5ZBtQ+4iDFkcmhBAiGq/pFBhr211pWRlaXDm8T5QjIuFItrxgwI1L8O8fvfjjqNbrCpwHXksVlJldYh8YISRpHK6y4LM9FdhYWgmTwxv0mFYpw8TiQlz3m+7o3Sm+j6U5OTlBF3HDQQsiJ79Y5QXz58/HH/7wBwwdOhQjRozA66+/jvLycsyaNQuAb7RARUUF3nnnHQDA5MmTcdddd2HlypWBqYruu+8+DBs2DIWFhe2Oh0RHW44jhJDUwFtCr2FDxBP2LZqTJk0CAFocmRBCSFTwLis4h0nsMEgbJVNekHneEPx8OvwOLM5aA3laNqSK5LlLjhASfUabBxv3VWL9DxVNFjsGgO45Glw/sidKirsgTZ2cI+d4ex0tiJwiYpEX3Hjjjaivr8djjz2GqqoqDBo0COvXr0ePHj0AAFVVVSgvLw/Uv/3222G1WvHyyy/jz3/+MzIzM3H55Zfj6aefbncshBBCYktw2yC4m+ZLRFwRZ6hvv/02AODIkSM4evQoLr30Umg0mhYXKSKEEEIaY4zBYzrZrm1Y7C7s2n8KF/bOR7pGGaXISKSSJS+osoQ/JyRjDB7jSajz+8UwIkJIIrK7OGw/VIsvfqzCrsN14IXgY4tSLsXoAfm4Zlg3nJctRWZmZkIdK81mM7Zu3YohQ4a0emGYCRx4K905mGqinRfMnj0bs2fPDvnYqlWrmpTNnTsXc+fOjXg/pONEchwhhKQO3loldggkhIg7DhoaGnDDDTfgq6++gkQiweHDh3HeeedhxowZyMzMxHPPPReLOAkhhCQRznYagsfZrm1IpRKolXJIE+iCSzJKlrygzsHg8jKoFeF9nniXFZy9HvK0nBhHRgiJdy4Pj29+OY0vfqzCN7+chodrOoJpYDc9rryoC8Zd2Bk6jQKMMZjNZhGibR+pVAqtVgtpGOu88JZKWhA5BSVLXkBiJ5LjCCEkNQhOM41QjFMRH6nvu+8+KBQKlJeXQ6vVBspvvPFGfP7551ENjhBCSPJhvBdec2W7t5OuUeGifp2gVSuiEBVpq2TKCyotkV3g8pgqwHguRtEQQuKZ1enFhtJKPLJ6L656cjP+b00pthyoCeo0yNOpcOulvfCv+y7BG3ePwLW/6Q6dJrHPWRkZGRg2bBjS0tJarCe4beAdTRd/JskvmfICEhvhHkcIIamBMQaORhvErYhHHGzcuBEbNmxA165dg8r79u2LsrKyqAVGCCEkOXlMFVG5A1EQGDxeHnK5lEYdiCiZ8oIKM4/zcsJPjRjvhcdcAVV2jxhGRQiJF6fNLmw7VIttB2vww7GGJtMQAUBWmhKXD+6E8Rd2xuDumZBKk+v8JAgCPB4P5HJ5s3cLMyaAM5/q4MhIvEimvIDERjjHEUJI6hCcDWCcW+wwSDMi7jiw2+1Bdw741dXVQaVSRSUoQgghyYl328HZ66OyLZPNiY3fH8PoC7shM10dlW2SyCVDXnAnXsO/cStOmSK/842z1UGelgOZKj0GkRFCxMTxAn4sM2Lnr3XY9WsdjlSHXrBPr1Xg0gsKcMWFnWHolQW5LHkvhDU0NOCTTz7BuHHjkJWVFbIOb62hCwApLBnyAhJb4RxHCCGpgTEBvLVG7DBICyLuOLj00kvxzjvv4PHHHwcASCQSCIKAZcuWYezYsVEPkBBCSPLwGMujtq10jRJDz+9MUxWJLBnygmGSXZAzDidMf2rT8z3GcqgLBiTUAqeEkNBqTM5AR8H3R+vgcIceIdc5S4NLL8jHpRcUYHD3zKTuLGgsIyMDI0aMaHaKEcHrBG8/3cFRkXiSDHkBia3WjiOEkNQh2OvBeK/YYZAWRNxxsGzZMowZMwa7d++Gx+PBgw8+iAMHDqChoQE7duyIRYyEEEKSgNd2OqoLHikVcnTOobu8xZYsecFFkj2wVG0EcEPEzxU8TnDWWih0BdEPjBASU1anF6UnjPjhWD2+O1yP47W2kPUkEqB/Fz1G9MvFZQML0KdTRkp2FqpUKnTp0iXkY4wx8OZTAGs6hRNJHcmSF5DYaek4QghJHUzgwNtotEG8i7jj4IILLsCPP/6IlStXQiaTwW6347rrrsOcOXPQuXPnWMRICCEkwTGeg9fU/gWRG3N7OJTVmNEpOx0qhSyq2ybhS6a84BLbv+CuGwJV7nkRP9drqYJMmwWpXBmDyAgh0WJ3c9h3pqPgh2MN+LXSghBLFQAAMrUKDOuXixH98jCsTy6y0unv2+Vy4fjx4ygsLGwy7Yxgj+4NAiQxJVNeQGKjpeMIISR18LbTUVn7kMRWxB0HANCpUycsXrw42rEQQghJUh5zBZjARXWbdpcHPx6thT5NRR0HIkv0vOB/ZgNK9KWQg0PVpqfR/frlkCo0EW2DCTy8ppNQ5faOUZSEkLawODz4qdyEH8uM+OFYA36usIRc1BjwjSq4oKseI/rlYfj5eehfqEu6xY3by2azYc+ePcjMzAy64Me8LpqjmAQkel5AYqu54wghJHUw3gvBXid2GCQMbeo4MJlMeOutt3Do0CFIJBJccMEFuPPOO6HX66MdHyGEkATHu23gbNFPCrJ1Wkwe2Tfq2yWRS/S84K2jPXFBsRE9JGXgLZU4vX0F8sfOj3gaEs5hgsxpglyTGZtACSEtYozhZL0DP5YZ8VOZCT+VGXHitL3F5/TplIGLzsvGRedlw9ArGzoNrZvTktzcXFx//fVBZYwxcOaTYEwQKarm8Y4GcMYTUOT3h1TRdMFeEhuJnheQ2Ap1HCGEpBbeWhWXeQNpKuKOg927d2PixInQaDQYNmwYGGN4/vnn8cQTT2Djxo246KKLYhEnIYSQBMQYg8d4UuwwSAwlQ15gq6/Gm5iFR9hiaCQu2A5vgaZLEXTnj494W17jSchUOkikqbFQKiFisrm8+LnCgkOnzPip3IT9ZUaYHC0vsNcrPz3QUVDcKxuZaTT9UHsJttq4nKLIU7kPlh0vgnEuQKaCqvswaHqPhTy3X0quT9FRkiEvIIQQEjuC1wneYRQ7DBKmiDsO/vSnP+Hqq6/GG2+8Abnc93SO4zBjxgzcd9992LZtW9SDjAVGi3YRQkjMcVFeELkxq8ON7w9WYFCvPKRpkvvCD2+vh7tiD9DtfLFDaSIZ8gKnsQKnUYDVmIYZeB0AUPf1q1Dnnw9lVreItiVwHngtlVBmdo1FqISkLLeXx+EqKw6dMuNQhRkHT5pRXtfyaAKZVILzC3UY3CMTg7tnwdAzC9kZNC1Ge1gsFnz99dcwGAxIT0+H4HWCi8OFDZ2Hv4Btzz8A/92MvBvu49vhPr4dirzzob3wBijzB4gbZBsJHgc44wlwDceB7vH3GpIhLyCxde5xhBCSWnhLldghJDTGeyC4bWBuq+9fjw2C2wrBbQV6DIz6/to04qBxEgAAcrkcDz74IIYOHRrV4GLJYzwJ0FBJQgiJGcZ74TVHd0HkxiSAb+7pJLxrkDEG3nQS7oo98JzaDc54wvfA5feIGlcoyZAXuBoqAAC7MRxjMn5FH+sWMM6Nmi+eQZdrn4VUHtmFRs5aC3laTsTrJBBCfOxuDkerrThcacGvVVb8WmnBkWprs2sT+GVoFBjcPRMXnukoGNBVD7WS1sCJNumZEVX+cxXi6IYsJgiwl/4Lzl/+FyiT5/QGb6kC8/puZPCe/gXmL5dAUTAQaRfeAEVu/E57KHjs4BpOgDMeD/zLW6vPVpjwZ/GCa0Yy5AUk9qQ0MpOQlBS4wE0AAIzzBNqE+f/1+DoEfGU2CJ4z/57pKADvbn6DVy6MeowRdxzodDqUl5ejf//+QeUnT55ERkZG1AKLNc5eD6+5Cgp9Z7FDIYSQpOQxngQT+JhtP12rwsX9C2O2/Y7GBB7e07/AU7EH7lN7INhPix1SWJIhL3CbqiGVAAIDPpXfggezjsFrLIen4QTqv3kTeZfOiWh7jDF4GsqhLoi/ESKExBPGGOosbvxaZcHhKisOn/n3VH3rI9UUMgn6dNZhQBc9BnTV4YKumeiRl0aLGceYTqfDyJEjAQCcpQqC1ylyRGcxzgXLN6/AU/FDoEzT/yqkGW4CeA7uk7vgOPgJeIvvpgZvzQGYNh2AsnMRtIOvhyLnPLFCBwAIbltgJAFnPA5vwwkIcTiaozXJkBeQ2Gp8HCGEpJZkHm3AeG+jkQDWMxf8gzsFgh5vrRMgTkTccXDjjTdi+vTpePbZZzFy5EhIJBJ8/fXXeOCBB3DzzTfHIsaY8ZgrIZErIU/LETsUQghJKrzLAi7G8xYyxsALAqQSScLOVcy8Lniqf4L71G54KkvBPLaQ9eRZPaHsEp9zAidDXsAEHvnpElRbGcosMhRc+yAqPpoPxnlgOfQ5NF2KkN77koi2ybtt4Oz1lGMQcobdzeFErQ3Ha204XmPDkTMjClpbkwDwDSzrmZeOAV31gZ8+nTKglNMdqx2NMQae5wHeBT6OOrh5hxGWbc+eHaEnkSJ96B3Q9Lnc97tcCXWv0VD1GAV32Q7Y938YuCjvqdoHT9U+KLsMQdrg6yHP6h7zeAW3NTCCwNtwHFzD8fBuGJAqIM/qDnlWL8izesY8zrZIhryAxJb/OCKVShM2hyeERI53GOPqhoOWMIEDc9sheM698H/u/22BMsa5YheQRAqJMh1SVQakqnRI/P8qMwJlUKbFZNcRdxw8++yzkEgkmDZtGjiOAwAoFArcfffdeOqpp6IeYKx5GsogkSkgU+vEDoUQQpICEwR4Gspjvh+j1YmNu45i9IXdkJmujvn+okVwmuGu/AGeU7vhqT4ACCEumklkUOT3h6rrUCi7XARZWm7crs2TLHlBYYav48DFAVZVV+SO/CNOb3sZAHB620tQ5fWBQtcpom16jKcgU+shkUWcbhGSsBxuDsdrrDhQdho11mqcqLXhWK0NNabwvkypFFL06ZSBvp116NvZ92/vTunQKOnvKB7U19fjk08+waXFvaHXxEfHDWcsg3nbsxAcDQAAiUID3SX3QdlpUJO6Eqn0TAfCCLiOfw3H/g8gOOoBAJ6KPfBU7IGq22+gHXwd5ProrFXj6yQ420HAGY9DsNe1/kSZAvLMHpBn94Ii29dRINN3gUTq+1ugvIAkKv9xZNy4ccjKyhI7HEJIB2BMCJ5qT7Q4GARHPXhLJThLJQRHfcgRAf7pDWNCIj1z4d/3I1Gln/2/snHHQAakSt//JQpNqx2tscoLIs7AlUolXnjhBSxduhRHjx4FYwx9+vSBVquNRXwxxxiDu+4Y1Pn9IFUm5msghJB44rVWQ+BiP+QuTaNEcd8CaFWKmO+rvThLJTyn9sBdsQdc3REATU/qErkays5FUHYdAmWhAdIY3TEQbcmSFxTqJPjhzJIcp8w8ivpPgLPyR9iObIPgcfjWO/jd05DIwv+8MYGDx1wBVXaPGEVNiDgEgeG0xYWT9Q6crLOjvM6O8tP2iDoIACArTYl+hWc7CPp2zkC33DTIaLqhuJWRkYEhg/pAIxfEDgUA4K4shXXHS4G7/KRpudBf9kCrF/0lUjk0vcdA3XMUXMe2wHHgYwhO30hJ98ldcJ/8DqoeI30dCBnhdxoLLnOjDgLftEP+jokWyVSQZ3U/00HQC/LsnpDpukAiTbw1OpIlLyCxk5GRgYsvvhhpaYmR6xJC2o+31YLxHlFjEJxmWHa8AO/pX6K4VUngwv/ZzoDgkQDBnQQZkCi0CTXaKuKOA7PZDJ7nkZ2djcGDBwfKGxoaIJfLodMl3p37TODhOn0E6oL+kMqVYodDCCEJS/C6wFk6Zj5elUKOrnnxec5hTABXfxTuU77FjXlr6LkcpZpMKLsMgarLECgKLojoonS8SJa8oHOGFIBvTY5TZh6GLkrkjZ4DV+1hcJYquE8fRv137yB3xPSItsvZ6iBPy4FMlR6DqAmJLbPDg/I6O07WOc786/v/yXo73N7wLxynqeToVZCOXvnpOK/Rv7m6xBktRnzkzI1CnQSA+Be0nb9uhO2HdwKLM8tzekM/+s+QavRhb0MiU0DT9wqoe10G55Ev4Tj4CZjbAoDBXbYD7vKdUPccDe2gayBLzw96ruA0+ToJjGc6ChqOBzofWiRXQZ7VE4ozHQTy7F6QZRRCkiSLxSZLXkBiR6VSoUcPuqmCkFTBeC8Em7jTG/LWGpi2PN3q2kESZVpwJ4Ay45yOgeBOAokiLWnO382JuOPgpptuwuTJkzF79uyg8v/85z/45JNPsH79+qgFF2v/3uvAnlMezBmVjp7ZgPv0Yajzz6cpBQghpI08xpNgrGPuQnR7OVSctiIvUwulQvwLGIz3wFN9wLe4ccUPYC5zyHoyXRffFERdh0Ce3QsSSWInGsmSF/guhPls+NmFi7ooUajXotP4B3HqowcAgYP5x4+gKbwQaT0ujmjbHmM51AUDEurOEpIaBIGh3uZGldGJygYnKhscONXgCHQQWJytrz/QmFYlQ698X8dAZ50cA3rk4ryCDOTpVPT5TwKM52CtPir6uZcJAux734Xz1w2BMlW33yBj+CxI2ngTmESuhLZ/CTR9xsL56yY4Dv3Xt+4QE+A6vhWuE19DpusMqUoHyOTgTSfD6iSQyNWQZ/k6B/z/yjI6J/VFhmTJC0jsuN1ulJeXo1OnTlAq6cZNQpIdZ6nssGsEoXgbTsC89ZnA93OpNhuq7iMg0xVCllHQaJqg9IQc6RdrEV8h37VrF55//vkm5WPGjMHChQujElRHMDkZ/r7LDgZg5Tc2PP3bTAheF9z1x6DK7ZPUyRwhhMQCZ68H77J02P7sTg9+OFyN0Rd2E+3iheC2wVO51zeyoPpHINQUTRIJFLn9fCMLug6BLIIpDxJBsuQFPTOlkEkBXgB+ruVw138acOuQNNxY3Bs5w+9A/TdvAABqt/wN3aa8CHl6btjbFjxOcNaaiNdIICQarE4vKo1OVDU4UHmmg6DK6Pt/ldEJDxfZFzmZVIIu2Rp0y01Dt9w0dM/RBv7v7yBgjMFsNkOv11OHQRLxmE7CZreLeu5lXhcs37wMT+XeQJnmgquRduENUemIl8jV0F4wGeq+4+D8ZQOcP6/3zXPMePDmU2fGpTXzXIUmRCdBp4S/QSBSyZIXkNixWq347rvvMG7cOOo4ICTJCR47BKdJtP17ag7Asu35wJSGMl0X6Mc+BJk2R7SYEk3EHQdutzuwyFFjXq8XTmfkq2OvWLECy5YtQ1VVFQYOHIjly5dj9OjRLe7/sccew7vvvovq6mp07doVCxcuxJ133hnRfusdLDDDdGmFF2anAL1GCt5lhafhBFS550X8WgghJFUx3guPqaJD95mZoUHJb3p3+FzYvO003BW+KYi8p38BQt09IVNC2WkwVF2HQFlYDKk6eYflRzsvEItOLcGSEj3+ttWKWpsADw/8/Ts7JBLgRsNkOCt/hOPELgguK2q+fBaFk5+I6I4Ur7kKMm0WpHJVDF8FSTWCwNBgc6PG7EKNyYVasws1ZidqTC7fKAKjE9YIRw34FejVZzoEtOiem4auOb5/O2dpIJel1oVQ4rs5gLM3iHbuBQDeUQ/L1ufAmcp8BRIZ0i++E5reY6K+L6lCi7RB10LTbwKcP6+Hq+wb3wgD3vf3JFFogzoI5Nm9IEvPT7lOglCSJS8gsZOdnY1rrrkGMhnd2UtIMmOMgTd37DWCxtzlu2DZuQIQfOckeW5f6C+9H1KaQjYiEXccXHzxxXj99dfx0ksvBZW/+uqrGDJkSETbWrt2Le677z6sWLECo0aNwmuvvYaSkhIcPHgQ3bt3D/mc3//+96ipqcFbb72FPn36oLa2NmRi0hqL++zClAIDdhx348oLNAAAzmGExHgKyqyWF9UihBDi4zGeBOPbdnGqraQSSYdcvGKMgTOe8E1BdGoPeFN5yHoSVQZUhRdB2fUiKDsNhiRFLhBHMy8Q29BuSrx5Yzbe2W3HBz86ITBg9R47xvVVIf+ye3Gqbh44Wx1c1Qdg3LMG2RffGva2GRPgMZ6EOq9PDF8BSSaMMVhdHGpMTl+HgOlMp4DZhVqTCzVmF05bXOD4pouth0OlkKJzpgaF2VoUZmnQOVuDwiwtCrM16JaTBrWSLugQH4Fzw2M8BaDjzr3n8jacgGXbs4HpgSQKLXSX3Atlp0Ex3a9UmYa0C29A2oU3gDEGcG4wzgWJmkbTNCdZ8gLeZQVoPYaYkEqlkMtpemhCkp3gqIfgFafD2Hn4C9h2rwLO3DKuLCyGbtTclPmOHk0RH62feOIJjB8/Hvv27cO4ceMAAF9++SW+//57bNy4MaJtPf/885g+fTpmzJgBAFi+fDk2bNiAlStXYunSpU3qf/7559i6dSuOHTuG7OxsAEDPnj0jfQkAALMr+EvW1qNnOw4AwGutgUSuhCIj/9ynEkIIaYRzGME5wlgMMMpsDjf2/FKFAT1ykab2LSrsqfoRrrJvwDwOMM4FJnCQSBW+RYdlvn8lMqXvd6nv/2hU5i+HRALeWg3edBLe+iMQHPUhY5CmF/hGFXQZAkVuv5Sc5i6aeUE80CgkmDkiHV6e4eP9Lrg44M1v7VgwXof8cQ+g8pMFABNg/OE/UBcOhrZLUdjb5p1mcE4T5JrM2L0AEvf8HQL1FjfqrC7UWd2os7hR3+jf01YX6q3uiBYgPpdUAuTr1Wc7Bs50ChSe6SDITlfShU8SFk9DGdiZu/VCnXtjzX1qDyw7XwlMByhNy4P+sgcg13fpkP37SSQSQKGGREGLerckWfICX27bsZ+xVGGxWLBz504MHjwY6el05y8hyYjxXvDWqo7fL2Nw7P8Ajv0fBMpUvS5FxrAZtH5BG0XccTBq1Cjs3LkTy5Ytw3/+8x9oNBpceOGFeOutt9C3b9+wt+PxeLBnzx48/PDDQeUTJkzAN998E/I5n3zyCYYOHYpnnnkG//znP5GWloarr74ajz/+ODQaTcjnNOfcjoPSSi9MTgGZmrMXfTzGk5DIFJBrsyLaNiGEpArGc/AYT4qzbwZwvADGmC9BOPARHD+ti/l+5Tm9z65XoOuS8hfeopUX+Ik1heG5pg1Nw1dH3LC4GDYfcWPyQC8Gdb4A2Rffiobv3gHAULv5OXS9/sWIOgK8xpOQqXQp2cmU7DhegNHugdHmgdHuQYPVjdNWd6CDwN8xUGd1R7yuQCgZGgUK9GoUZKqRr1ef+b8m8P98vZqmFCLt5rVU++68PqPxuTfWGGNw/PwZHKX/hv+OQXluX+hHz0/qKQATXbTzArHwThOYIND5OgYYY+A4rkOOI4QQcfCWKjChYxdEZoIA255VcB35MlCmGTAZaUU3pvx39vZo0/gwg8GA1atXt2vHdXV14HkeBQUFQeUFBQWorq4O+Zxjx47h66+/hlqtxocffoi6ujrMnj0bDQ0N+Pvf/x7yOW63G2732cUqLRbfwp0N9uDpjQQGbP7FhqsGqKFUnr17xnTyEJR5fZCmz4H0TNLg9Xrh9Xohk8mgUp0d5uJwOAAAarW61bpOpxOMMahUqsDcfhzHwePxQCqVQq1Wt6muy+WCIAhQKpWB4X88z8PtdjdbV6FQBC68+etKJJKgzhi32w2e56FQKKBQKIK2G05dQRDgcvkWI9FqtYG6Ho8HHMdBLpcHFkaKpC5jLDBXpkajCRwM2lv33PfTv9Cf/7W1VLct7300Pif+97O9n5Nz389w6vo/P40/U6299+39nDR+P6P5OWn8fkb6OWnpvWeMweFwQC6XQ6PRJNQxoqX33m0sh8B54PZ4IPAC5Ao5FIG6AjweDyCRQKM++9pC1+XhcLogYxy02rOfP4/HG/ibUyjkgc+J2+2BTCrgNwMKwXgvrN++CveJrxETUjlkuf2h6HIRNN0vhkzj60x2utyAyw2VUtHo/eTA8TxkMimUirPnEqfLdx5qXJfjeHg5DlKpFKpG5x2X2wPGGJQKBWSys3U93o6dCioS0cgLAHGnMDyXTi3F7Ren4cXtNgDAih02vHRdJjINU+Cs/BHOU6XgHUbUbn4ena9cFPa81gLngddSCWUmTYcY7wSBweL0wmjzwGT3nOkUcPs6BWwe1JrssLkFmOxeGO2eNq8lEEqGRoHcDBVydapmOwW0KprmgcSW4HHAa64MKstIU2H4BbG/C5vxHDw//B182bZAmar7CGQM/6NvtCCJa9HKC8TEBB68y0w3EcaAXq9v8aYQQkhiE9w28M6OnZGA8R5YvlkBz6nvA2VpxbdC27+kQ+NIRqJ3n5/b68MYa7YnSBAESCQSrF69GsOGDcOVV16J559/HqtWrWp2oaWlS5dCr9cHfrp16wYAeG/DziZ1X/j4Rzy+/C1Ynd7Az9gpd2HIb0bh0MGfYDabYTab8eabb8JgMOD+++8PlJnNZowZMwYGgwGlpaWBstWrV8NgMOCee+4Jqjtp0iQYDAbs2rUrULZu3ToYDAbMmDEjqO4111wDg8GArVu3Bso+++wzGAwG3HrrrUF1b7zxRhgMBmzYsCFQ9uWXX8JgMGDKlClBdW+//XYYDAZ8/PHHsNlsMJvN2LFjBwwGA377298G1Z01axYMBgPWrl0bKNuzZw8MBgPGjx8fVPe+++6DwWDAqlWrAmUHDhyAwWDAJZdcElT3oYcegsFgwOuvvx4oO3r0KAwGA4YMGRJUd9GiRTAYDHjhhRcCZRUVFTAYDDAYDGhoaAiUP/XUUzAYDHjqqacCZQ0NDYG6FRUVgfIXXngBBoMBixYtCtrfkCFDYDAYcPToUZjNZthsNrz++uswGAx46KGHgupecsklMBgMOHDgQKBs1apVMBgMuO+++4Lqjh8/HgaDAXv27AmUrV27FgaDAbNmzQqq+9vf/hYGgwE7duwIlH388ccwGAy4/fbbg+pOmTIFBoMBX375ZaBsw4YNMBgMuPHGG4Pq3nrrrTAYDPjss88CZVu3boXBYMA111wTVHfGjBkwGAxYt25doGzXrl0wGAyYNGlSoMxms+Gee+4JfFHwl5eWlsJgMGDMmDFB273//vthMBjw5ptvBsp++eUXGAwGDB8+PKjuwoULYTAY8MorrwTKysrKAu9n47pLliyBwWDAc889FyirqakJ1K2pqQmUP/fcczAYDFiyZEnQNvx1y8rKAmWvvPIKDAYDFi5cGFR3+PDhMBgM+OWXX1o9Rlx99dUoLi5OyGOEv6zxMaKhphzGuhpYnV7c9+jzGDn5Dnz4v22B4+e+n49h5OQ7cPW0PwUdVx9+4mWMnHwH/vXRpkDZL8crMOn3f8TEm+8JqvvXZ1/HyMl34O3//DdQdqLyNEZOvgNjrpsJu9UM41dPBXUa7BcGQ3Pli9Bc/QaEK17BLR9ocftHWiiueA7qCc9APe4JfO66FP+3WY3NrhFQDr8PymFzICuegVd3K/H3vUoI/a6DctgcqMcvxYfea3DNy8ex8qvTcMv0cHgEODwCrvrDfZg0dR5O1ZoCZWs++QKTps7D06+8GyhzeARMmfEQJk2dh6MnawNl7/9vKyZNnYfH/vb3oLpTZ/8fJk2dh4NHTwbKPtv8LUpuubetp9qE0XgKwwEDBmD58uXo1q0bVq5cGbK+fwrD9evXY/z48ejZsyeGDRuGkSNHRrxvl8sddOeb18thbE+gV5YvVTpcx+Hj/U64XB7oRsyB7MwoA+epvWj4YR2cThfcHk/QNp1OF5xOXwdcYLscB0ttOZxW4zl1nXA4HOB5PlDGcRwcDkegQ7UtdV0uFxwOR1BnCs/zEdc9N89yu91wOBzwNurQiqSuIAhwOByBztdz63oatWUkdf2dtA6HI+j9tDtcOFljxC+nGrD3eAO2HqzBJ9+fxNtf/IIX/nsASz/4CY+s3os5b+zCLcu346onvsSlf9mIK5/YjFte+Bpz3vwO/7emFM99egh/33wUH313Et/82oAfy0wor7OH3WmQoZGjZ14aLu6Tg5LiQvzhsl6YM7E3Hp0yAK/MuBjr7r8UXy2+Av99+FK88ceL8MwtF2LhlMGYMb4vJg/tigu7piE/XQqV/Gyu3Np7397PybnvZ3s/J629n239nDR+7yOp2/hz4vF44HA4gm46AtCmukF/915vyLr+v+VI6nbUMcJus8FccSjoNbvcHjidLnCNtsvzApwuN1znxOv2eOF0uc/Zbnh1BbcV5i1PBXUaaAddh4yRc+AVJHC63PB6z25XEHzb9XfS+3m83iZ1GWOBuuce8yOte26Hvr9u8PsZfl2O89V1e4Lruty+ujzftC6JLd7RIHYIhBCSUBgTwJlPdeg+BY8D5i3PnO00kMiQMWI2dRpEiWgdB7m5uZDJZE1GF9TW1jYZheDXuXNndOnSBXq9PlA2YMAAMMZw6lToD+aCBQuCLoSdPOmbUkOqygjU0at9X74U+X3B5BpkaBSBH1luX8gzu0LlroMuXQu9Xh+4G1upVAZ1Svg7PDIyMgJl/rpyuTyorv+O0/T09ECZ/67pc+v67yBOS0sLu65Wqw2UpaWlAQBkMllQXf8dxFqtNhCHv65UKm22rr/MPx+hRCIJquu/e1yj0QTKMjIyQtb137mtVqvDrqtSqQJlukYLVul0ukC5/87tSOq29n6mp6cH7saO5L1XKBQh64bz3vs/J5G896HqtvTeR/I5Ofe9b/w5SU9Pb9N739bPSeP3s3Hdtrz3jes2Pr40rtuW9z7WxwidTof0DB2kMjkACTRaLdIzfGVqjQaABDK5IlDWuK5aowmUabRaABJIZfKgujK54kxdbZO6EokUCncDtCo5tCo5ZFIpAAmUClmgTKOUn6krCZRpVfIzn1UJlPKmdYHgunK5b7sKmSyoHJCgUzqPhg1/gVD3CwCAY1I8s0ONY5ILoE7PglqthkqlgIuTwuqRQqPLgSazMzQ53WGRZONQnRx1kgKkdR+CtJ7DkXbeJdh0TInPDiuh6jcJaT2HQ5PdFTKpL16ZVAKV/OyPP97GZXKpr0wmkwaVS0LVlfnKpOds13fnugRKubRJ3WTmn8JwwoQJQeXhTmHYpUsX9OvXD/fff3+zNxMAvouIFosl6AcAxv/+bjSYLIERVKvWfoJLrr4D6RVfBZ771i47xt3+F1xyw32QGqbD/54Yd7+L2277AxYtey3wfMYYSm6ZixG/vR3Hyk4Fyj75fAtGXHUb7p03N7huSQmKioqwf//+QNlnn32GoqIizJw5M6juddddh6KiInz//feBss2bN6OoqAi33XZbUN2bb74ZRUVF2LZtW6Bs586dKCoqwg033BBUd/r06SgqKsLGjRsDZXv37kVRUREmT54cVHfOnDkoKirCJ598Eij7+eefUVRUhPHjxwfVvf/++1FUVIR///vfgbKysjIUFRXhkksuCar7l7/8BUVFRfjHP/4RKKupqUFRURGGDBuBWpMTx2qs+KnMiPlLVmDYb+/An5/7F/659ShWbvgFT64rxSUzl+PSuW/g9pd24NpntmDcok24YskW3PjCLtyx4jvMeeM7LHh3L5768ADe2Hwca785iU93V2DLgRrsPW7E8Vo7jHYvhDCnUBC8TqiZA4O6Z2L0gHxcPbQruKNfwbr3Pdw7vhCv/nEY1t1/Ke46/zSOvXM3co79B8vvGIr/u34wZk3ohxWP3I65N42H0lmNzlkaKOVSfPLJJygqKsKcOXOC2mfy5MkoKirC3r17A2UbN25EUVERpk+fHlT3hhtuQFFREXbu3Bko27ZtG4qKinDzzTcH1b3ttttQVFSEzZs3B8q+//57FBUV4brrrguqO3PmTBQVFeGzzz4LlO3fvx9FRUUoKSkJqnvfffehqKgI77//fqDsyJEjKCoqwpgxY4LqLliwAEVFRXj33XcDZRUVFSgqKsLw4cOD6i5evBhFRUV48803A2UNDQ0oKipCUVFRUN1ly5ahqKgIL7300tmp7RyOQF1/hwBjDC+99BKKioqwbNmyoG346zY0NATK3nzzTRQVFWHx4sVBdYcPH46ioiJUVFQEyt59910UFRVhwYIFQXWvvvpqGAwGHDlyJFD2/vvvo6ioCPfee6+oxwhDcTFuvfuh4L/7h5fikt9Nx/J3/geT1Tfa8cAvRzHx5rm440+PB/8tP70SE2+ei41bdwXKjpadwsSb52Lq7L8E1V2y/C1MvHkuPtm4DV5zBYwbHwV3+mffuYEHMkbMgXbQdQCAZ1e+i4k3z8V7//0i8Py6BhMm3jwXV94a3GYv//0/mHjzXPxz3fpAmdXuwMSb52LizXPBcXyg/I3VH2LizXPxxuoPA2UcxwfqWu1nPyf/XLceE2+ei5f//p+g/V15672YePNc1DWYAmXv/fcLTLx5Lp5d+W5Q3SnTH8TEm+eioqo2UPbJxm2YePNcLFn+VlDdqbP/gok3z8XRRueSjVt3YdLUeWEdo0jb8U4LGN/+UYwkWF1dHd5//30YjR2/RhohJLZ4Wy0Y13Ed27zTCNOXj8Nbe8hXIFdBf9n9UPcc1WExJDvRxjgrlUoMGTIEmzZtwrXXXhso37RpE373u9+FfM6oUaPw3nvvwWazBS5c/vrrr5BKpejaNfSQf5VKFTT9h9+AgYNwzOj7yj+xvxr/KXVCIpHi4iuuD1zc237MDe3IWUiXAJw8He66o1Dnn49bb70VN954I2QyWdDoiC1btgA4O7UNAEyZMgVXX311k7r/+9//wJhvahF/+VVXXYUrrrgCUqk0qO4HH3zQpO7ll1+Offv2Nam7Zs2awNQi/vIRI0aErPvWW28FppWx2+2QSCQoLi7Gvn37IJFIguq+8sorgWll/OX9+/cPWffZZ5/F008/HVS3R48e2LdvH4DgUSaPP/44Fi1aBLlcHigvKCgIWfeRRx7BQw89FFRXp9MF6jbe3/z58zFv3rygugqFIlC38RQ0d999N+66664m79GePXuC3k+JRILbbrsNt956a5O6X3/9dZP3/qabbsKUKVOa1P3iiy+avJ9XX301SkpKmtT99NNPm9SdMGFCyPfzvffeC0xV5C+/9NJLQ9b9xz/+0eRzcvHFF4d8P1977bUmdQcNGtSkrkQiwfLlywOfKX95nz59Qr6fS5cuxZIlS4LqdunSJWTdv/71r/i///u/oPczOzs7ZN0HHngA8+fPD6qr1WpDvvdz587F3Xff3aTdQ9WdMWMGbr/99iZ1v/322ybvfXPHiE8++QQ6nS5ou+EeI6qMTnD5Rbh64b/wa5UVEx77Ei4vD15gQPE85BUD939qBj49u+hc3g0v4hSAS//SaCG6AX9E3gDg0S+9wJfBdRvOrdvjVuT1uBVP7gCe3BFc1wlg0ms1jereibwewIoyYMUb/oWE05F3w4sAgJI3Gi0uXHAL8m64BW/XAG8HypWh6+pvQN4NN2CNGVjTqHzEDfdgFl6ChvPdCWZmOqzAvSgb2QvHAKxf679bTx7Y7tR1AoAzd+spr0beDVdjM4DNa8/e2eevO/1TAPCXT0TeDRPxDYBvGtXNvm45AGDOpsZ1xyLvhrH4AcCNjepqrnoaGgDztzWuOwp5N4zCoXPqSsY9hjwAC3cB2OUvH4q8G4YimXXUFIZLly7F4sWLQz5md3mhOHPnuH9RWq33NK7sr8T6nz3w8IC6+GY4vngWzoy+0A26Do7970MChvnD3fjE7g6689x/3dnu4gLlLq/vTl2v14uGqjLItJkAELjr1D8CEDg7FRrHcYEyAIG7iO12e9h1HQ5HoNxutwcea1zXf7evw+GAzWYLqisIQrN1/eX+5zDGAmWMMbi9AiSqDNRYvPjh10q4vDzKK05D2dUAaboeq774GXY3B7ubx8/oB93IGfisIgfbXtgOu4eHzelF7pTlkEhluOaZrY3esQHQDR+APVZgz4bDgVJ1t4sAAIerbWgLKQR4HSboNQr0790NmVo59FoF/vnWq+CcFix65AF075wLBTzYsvEzrHj5RQy/6iosumlRYBtrH98Il82GAbn3onumFIAHXrevQ8vr9Qa1JTvzQQnnvfd/TiJ570PVbem9j+Rzcu577/+cMMZgs9kCowecTmegrtVqDbzuxtv1jwiIpK7L5QqU+zsBAQTV9d+573a7A+WNOxctFksgzlB1G7NYLIEbNfx37ns8npDvp9VqbbK/xnX9F4Cbq9vce98RxwiLsfbMPlnQMY0XfPEW5maASWRweAS4OBZ4PQ7P2Tvi+TPHPw93ttx15rjapH/zdlIAAQAASURBVO6Z/6a5T8G0aQ3g9cVqdErwwh4dnrnhN4H63JkNe/mz23B6z3byNd4uJ4So2+hxh0eAXJAE6pxbl2u0FonTI0CmEILqckLw6wjU9Z4tD9Tlg+uyEHU9Z9qSF4Jfh/9z4vIKTeqmAjHXPmJMAO80QZ6e256XQM6RlpaG4uLioOlmCSGJj3FuCLbTHbY/3loN01dPQbD79ilRpkM/5kEocnp3WAypQML8mYgI1q5diz/84Q949dVXMWLECLz++ut44403cODAAfTo0QMLFixARUUF3nnnHQC+RHnAgAEYPnw4Fi9ejLq6OsyYMQOXXXYZ3njjjbD2abFYoNfrcfWSz3HazpCpluDJq/SY/b4JADCsuxJPXOm743jRBjN2HPd9Mbn30nT89gINZBodVLl9kmphDf+XscZ3RJNg1EYto/ZpXaRtZLJ78MOxBuw5Wo/dR+txst7R6nNSxcX4FtPwdygkvotXFawLXsG9aEDyf6nb8uBgKDNjP7e0GCorK9GlSxd88803GDFiRKD8iSeewD//+U/8/PPPTZ4zYcIEbN++HdXV1YHRQh988AGuv/562O32oPVa/EKtfdStWzcc/f5z5OVmB/4+vV4OHMdBJpOBSeWY/b4JJ02+C2y/H6zAncMzIAFD1X8XwlV9EACg7j4MnScuDGzD6fRdWFSplGfXweA4cF4OUpkUarUWms4DIZHJRV3XhOMFuLw8LDYnnB4OApPCZLVBodLC6eFgc7jg4RiYRAaXl4fDw8PmcMPu5uDhGFwcg8PN+X5cXji9AlweXz2Xl4dYmaZMKoFOo4BOq0CGWo4MtQz6NCWy0tWBMo2cQadRIDczHZlpSug0CkjBh7X2kdlshkajobWPQtT1n/PUajWtfdTM+8kYQ3V1deCGgnhZ+0jwumCvPAi3ywmJVAq16uxrc7k94DgvPB4P5Ge2y/MCPF4vJBJJUF23x+t77+VyyOWt13Uf3QzPj6sB5rswLtN3A188G1p9HrSas/F6vF7wvAC5TBa89tGZ6X0ar6kUqi5jDC637/udWqUMPubzfER1w1nPKNZrH/U4vxgKfWckK//1gsZrH7355pstrn30u9/9DjU1NViyZEnQ2kfhTmPov15QuX8LdBnpkKkzoM7vF82XlXCi/V2P47igzt5k4O8wT09Pp+/DIVD7tC4Z2shbfxSCu2037rTGf9OBVum7KdbbcBzmLc+AuX3HEqk2B/qxD0OuK4zJ/hMBYwx5PQZCoQs9i09bibqq2o033oj6+no89thjqKqqwqBBg7B+/Xr06NEDAFBVVYXy8vJA/fT0dGzatAlz587F0KFDkZOTg9///vdYsmRJxPu2uHzfYvUaKfrkypGtlaLBIWBfpQcejkEqAfaeOnuHzUmj72IB77TAYyyHKrtHe146IYQEsbs57DthxO6j9dhztB6Hq6zN1tVpFMhKV0KjlEEhk6Ij8womCBC8Tpy9Vy56eJ5BJmvhxTCGofZPMcK+LlB0XD4Q6zNmI1+mRX7UI4ozSX5zYSymMOzbt2+T5zQ3ElGjOXuRDwCUSgWUjS7cPDwuA/M+NIEXgP/85EWZ2Ybbh6Wh57j7cXLdvRDcVrjKv4P14HroB/0WAKDV+i6g8oJvug2BAbwgAy+RgecBm9UDcCcg03UBLwgQBMBrdoDjBXg4ARzP4OUFeHgBHGfy/cszeDkBXv7MDyfA20KZh+Ph5gS4vTzcXl8Hgf//bq/vMf9dxPFGJpUgXS1HmlqODLUCaWo50tVypKsVZ/5t+v80tW90gF6rhFYla+MXL3nIz4h/Sj/g7JpcjUf5NVfXT6lUBl1g9gt1x2UkdRtfaG8sVMdZJHXlcnmgY6AtdSUSSdBoPD+ZTBayfRpfzG6tbqi/Y4lEEpO6QOj3M5K6Lb2faWlpQW0Ujfe+rZ8TJgjwNJyATOo7fjWpq1bB6QKq6izI0WmgkMsgP/NzrsYdA36h6jKBB7f/3/D8uiFQpuxyEdKH3w0XUwUuEPipQrSNTCaDVtM0hlB1JRJJUEdEYJ9KBZRQtLkugHbXVSjkgU6Lxhp3hjSuG6rdk03jtY8AYPny5diwYQNWrlyJpUuXNqnvX/vo2LFjyM7OBgD07NmzXTHwLisEzgOpnBbljha3243Kykrk5uaGPN4RQhIP7zDGrNPgXJ7q/bBs/xsY57u5RKbvCv2YhyDTZnfI/lNNxB0H1157bcgvSP4vB3369MHUqVNx/vnnh7W92bNnY/bs2SEfW7VqVZOy/v37Y9OmTRHFfC6FUgX3mTW9MjW+ZHRYdyU+/9kFNwfsq/JCJQMcjYa9lpvOzm3I2eoglSmT+u4OQkhsNVjd+KnchJ/KjfixzIRDp8zNXryTSSUY2E2P3/TNxYjz89Cvsw5SacffhcAEAa6aQxC8Tb/AtnvbzDcdQoZGEfIcw3gOp7evgLX27PFfcd5Y1HKX4L4iNTLTk/9Lh4gDBFsUrbygo6YwbKt+eQpMG6rF29/57ibeVe7BrnIPctOk6MfuxDS8AACo3vEWHtzZCeWsBwTm6yxoWQOAo1GNVUwy6Zl1SpQyaJSyM2uYyKA5Uxb4/cz/tUoZ0tQKZGh8HQTpKgXSNb6OALWirRf+CSFt4TWdguBpeYSjzenB9z9XYfSF3ZCZ3r4L14LHAes3L8NTtS9Qpul/FdKKbgIkEiDENEAk/kUrL/CvffTwww8HlYe79tE///lPpKWl4eqrr8bjjz8estMTCD0SEQieToyzN0T9Ds5E0njNjWiwWCz45ptvcPnllyMrKysq2xRbtNso2VD7tC6R24gJHDhLRUxj97eNq+xb2HatBATfRV15bj/oLv0zpMq0hGy7aIrV64+440Cv1+Ojjz5CZmYmhgwZAsZ8C+eZTCZMmDABa9euxdNPP40vv/wSo0bF52IU70/PwpuowWkUIFPju7vw4m6+jgMA+L7cA9U5LeMfceDnMVdCIldCnpbTITETQhKXIDAcq7Hiu19qcOR0OX4qM6Giofkv5hIJ0LezDkN7Z2PIeTko6pl1ZkFgcXlMJyF4XR2+X95tR80XT8F5qjRQlv2b25B2we9whdUKhSz577iLZ9HMC+bPn48//OEPGDp0aGAKw/LycsyaNQsAmkxhOHXqVDz++OO44447AlMYPvDAA7jzzjubvUDQHjcXa5GtleKd3Q6ctvkuaNXZBdShCF1wBcZJNkEODrfxr+JJPAoO0Y+hPWRSCVQKKVRyGdRKGVRyKVQKGdQKGZQK3/9VcinUChnAOOjSNGceP/uYqtEFf41SDq0q+F+lXNp6IISQuOO1VMMbxrzEmekaTLi4V7vPvbytFuZtz4E3n/IVSGRIv/hOaHqPARC/neWkddHKC8Re+8jm4iCR+2YgkHJVULGmo0RShX8KFQBR6dCXy+UYO3YsZDJZYLvJwOl00g0PLaD2aV2ithFvq4Hg8sR0H4wxOH/dCBz4F/xD8WWdi6EYNgcuKOlmA8RRx0GnTp0wdepUvPzyy4Eh/YIg4N5770VGRgb+/e9/Y9asWXjooYcCC8bGmx5ZEtzPnsIL+DP06j4AgIu6KiCV+O4M/L7cA7Ui+I+1xibA6WXQNCr3NJRBIlNAptZ1aPyEkPjFGEOl0YlfKiz4ucKMXyotOHTKDJuLa/F53XPTMLR3Dob0zsZF52VDr42vO+g5hxGcra7D9+u11qL688fgaSgDAEhkCuSP+RPS+4wGz/NQhRjSTzpWNPMCMacwDIdEIsGk/hpc3keNzw458dlBF+weBpkE+FbyewxwHEahcAIFkhrMVK7GhoyZkMkkkEklkEoAmQSQSQGpRAKZ1Pd/meTMY1JArtRAqUmHQi6FUi6FXCaFUiaFXC6FQiYJ/F8p8z2mkEmgkEuhkEnP/ntOmVJ25qK/wveccNC6NYSkFs5WB4+pIqy6Uqmk3ede7+lfYN7+NzC3b1pGiTIdukvuhbLggnZtl8SHaF8vOPc85J8qLhRBECCRSLB69erANIbPP/88rr/+erzyyishbypYsGAB5s+fH/jdv/ZRulqODI1/mikeGq0SUkV83RDQUfwXo6K5xkHj6SGTgf9u6HOnnyM+1D6tS9Q2Ejx2eAUboIzt37TjwMfAgfcCv6t6XYb0i++EREo3EfrFquMg4sWR8/LysGPHDvTrF7xA0K+//oqRI0eirq4OP/30E0aPHg2TyRTNWKPCYrHgxNP9AQA2loZjAx7BtZddCACY/7EJP1V5m33uyusz0Sf3nLkvpTKo8/tBqmw6l2iioAsEraM2almqtk+oToKfKyywOps/jgCAUi7FgC56DOqRicHdMzG4exay4niqHYHzwFV9CExoufOjPUJNVeQ+fQRVnz8O3tEAAJCqMtBp4kJoOg8EAJhtDpT+cgrnd8uBVt10/uBkwxhDbrfz425x5GTIC/R6PaoObENGevvO5V5zFU6+fy+Y17foat5l90LXf3xE25DKlZBpsiDT6CFVibM4Wqoe0yNBbdQyap/WxUsbcY4GuOuOh12/vede1/HtsH73JnAmp5BldIbusvshz+gUVO/cRRBJU4wx5HUfEHfT50YrL/B4PNBqtXjvvfeCpjC89957UVpaiq1btzZ5zm233YYdO3bgyJEjgbJDhw7hggsuwK+//hpy7aNznbs4sp9C1ynucrCOEu3jldFoxHfffYcLLrgg5LowiSgZFraNJWqf1iViGzHG4D39Cxjnbr1yO9j3fwjHT2fXOdRe8DtoL7whYdqpo8TN4sgcx+Hnn39ukgj8/PPP4HnfdD6hFkKLJz+bNOif6US6xI6Bh5fA2fdRaAoHYVh3ZZOOgzSlBHaPr2+l3Mg36ThgAg/X6SNQF5wPqTz6834TQuKDlxNQdtqOozVWHK22ht1JAAA5GSoM6q5H3zw1Lj6/M87vok+o6Tw8DSdi2mkQir3sO9R8sSyw4JFc1xmdr1wEpb4wUEcQGOwuLwSazkBUyZAXAIAqtxck7tp23amh0HdG3qX3oPbLZQCAuh2vQl1wPpRZ3cLehsB5IFhr4LXWQCKVQ6bOgEyjh0yth0RGI2wIIdHDO83w1J+I6DltPfcyJsDx4zo4Dn4cKFMUDITuknshVSbHhUPiE628IN7WPuIdDUCKdhxEmyAIsNlsEASaWoSQRMbbamLaacAYg2P/B3Ds/yBQpr3w90gbGPocQGIj4m+gf/jDHzB9+nQ88sgjuPjiiyGRSPDdd9/hySefxLRp0wAAW7duxcCBA6MebLQs3FeEpZc2oJ/kV8h4J6rW/xUFExbg4m4GvLXLHlT3qgFq/Gef787B8nPWOfBjvBfu00egzj+fvtQTkuAYY6g2OXG02hboJDhabUN5nb3ZxYsby8lQ4fxCHfp30eH8Lnr076JDnk4dN3cWRsprrgLvsnboPs37P0PdN68DzPdlQl0wAJ0mLoRMow+qp0tT45LB4V+QJbGRDHkBAMjUOijTtRHdeRtKRp9L4az4EdafN4BxbtR88TS6XPtcm24uYALnmybMYYREIoFUlQ6ZWg+ZRpey0yUQQqKDd1nhrjsWcWdpW869jHPB8u2r8Jz8PlCm7nM50ofcBomUvjslm2jmBfG09pHAecC7bZCp0luvTFqk1+sxduxYscMghLQD87rA22pjt33G4PhpHRwHPgqUKQbdBO0Fv43ZPkloEWdqf/vb31BQUIBnnnkGNTU1AHwLFP3pT3/CQw89BACYMGECJk2aFN1Io8irysFLuB1/ZCswWPIjGO9B9YYlyL/8z8jRXoB6h+9iVZpSggnnN+o4MDV/x63gdcFddxSqvL6QJNl8fYQkI8YYas0ulJ2248RpG07U2n2dBDVWONyhOwnP1VwnQbLg3TZ4LVUdtj8m8Kj75h+w7P8kUJZ23ijkj50PqTx+p3JKdcmQF/jJtdlg2Tw8DeWtV25B7sgZcNUcgtdYDk9DGeq/eQN5l97Trm0yxsC7rL6OPBMglasg02SKOqURISQx8W473HVHwVjs7/blHQ2wbH8eXMOZTlmJBGnFt0LTbyIdt5JUNPOCeFv7iLPXU8cBIYQA4MyngBiN/GeMwb5vLZyHPg2UpRXfCtZrQkz2R1oW8RoHjVksFgCATpc4iwNbLBYMufVx5A0aBxk4rOy+CsLJb3wPSqT4oWAGXq8aDgAYfZ4SC8frMPnNOngFoGeWDG/cmN3i9uXaLKhyz4v1y4iqRL0TuiNRG7UsntvHwwk4VW/3dRDU2lFWZ0P5ad/vTk94HQRymQQ98tLQuyAD53XKQJ9OGejbOSOiToJ4bqNQGM/BVXMIAufpkP3xHicqNy2D59TZuxEzi6Yg+zfTIJGE7oytM1nx5fdHMHJQV+jTkn+quHhd46CxRM0L9Ho9zGZzIG6vuQoec2W7tusxluPUB38CO/M3VDD+QaT3Ht3ueEORSGWQqXVRm9Io0Y5XYqA2ahm1T+vEaiPB44Cr9nCbpyCM5NzrrTsMy9fLIThNAACJXI2MUXOhKjS0uh9a46B18brGQWOJnBecu8YB4FuoWd3pAkgVyXOjUDiifbyqqanBZ599hjFjxiAzM7P9AcaBRJyfviNR+7QukdqIt9eBM1fEZNuMMdhL18D582eBsvQh06DuO4HyglbEzRoHjSVSAtCYQuub7oKHHNlj7of7u5Ww/rIJYAIuqn4dl8OKzbgCo3qqIJNK0EUvwwkjjwozD15gkEmb/5ByDiMkxlNQZrVvDkVCSPg4XkCN2YVT9Y4zP3acqneg7LQdlQ0OhDHDUEBBphq9C3ydA+d1Skfvggx0z02DIoHWJIgGj7GswzoNOHsDqj5/DJ66o74CiRR5o2dDN2Bii8/TKBXo1y0baoWsA6Ik4UjUvOBcCn1nMIGH11rT5m0os7ojd9QsnN76IgDg9LaXocrtE5MLPEzgg6c0Uqb5OhE0eprSiBASIHid/8/efYfHUZ1tA7+nbZdW1bLc5AoGjA3YdBx6Lw7kTSAETM0XXkII8BIgjZIQII0QQkxCaAlJCIEQSkIAB0wzJWBsmh33btnqK622zsz5/pjd0a7aquxqV6v7d126tJo9O3N0tJo5O885z0GkccOw1i0a6LU3sukNdLz/kL0Isuytgv9z10MtY4rBsWS09gt+eIYf8eaNQMm8tO1CCMTb6+GsnJanmhUHj8eDfffdFy7X2ArAEBUDYcRhdOQmK4EQAp0r/4Tw2n/Z23wLLoZ71onDWoeOhmdIgYOnnnoKf/3rX7Ft2zbEYuk3lj788MOsVCyXVE8ZAECRgRK3itKjr4KsuRFIpMf4kvQ4PjfFxGEzzwcATC63AgdxE6hvNzCprP9mi3fsgaQ6oJWMy+nvQTSWxHUT9W3htMDAzkSgYFdreEDrDyTJElBb7sHUcV7UVXtRV+1DXbUX02t88Lm0zDsocvFgI/RQ24gcK9q8BbtfvA16sAkAIGkejD/pJngmHZjxtS6nhhkTynNdRRqA0d4v6I2jfJK1xkBn85D3UbL3CQjv/AjBDa/DjIWw55WfYOKin0BScneeEULAiAZhRINA286UlEalkJ0lHKFDNEaZetSaaWDEh7WfTNdeYRrWSMGUD/3auH1QeuTVkF2j8yYyDd5o7xecNdeD8NqXgKnzejynd7ZAK6mB7PDkoWbFwe1291g8m4hGBz2wAyIHC5sLIdD54R8QXveyvc138GVwzzwu68eiwRl04ODee+/Fd7/7XVx00UV49tlncckll2Djxo14//338fWvfz0Xdcw6LRE48LtkyJIEQELlEZdDdnjQ+uFfAADjtz2B1vdiqDzsEkwpUwFYHZ5tbZkDBwAQa90OSdGgenhTi2ggTFOguSOK3W1h1LeGUd8Wxu7WMHa1hrGzOYTdbeFBzRwAAJemJAID6QGCSZUeODlKvVdmLIR4644ROVZo+4fYvfQuiLi1jozsrcKEU2+Bs3LqgF4f1w00toVQ5nNCU/n3zJdi6Bf0xVFRB2HqMMKBIb1ekiRUL7wS0YZ1iLfXI9q4Ac3v/R5VR1ye5Zr2zdSjMDv2WIMaspzSiIhGB1OPIdqwbthBA6D/a68Z60T78nsR3/2pvc018wT45l/IRZDHkGLpF0S3vQM93AbVXdbjuVhgF1zVM0e+UkUiFothz549qKiogKZx0BbRaGGEWmFG2rO+XyFMBFf8HpH1/05skeA75HK4ZxyT9WPR4A26B7dkyRI88MAD+PKXv4zf//73uOGGGzB9+nTcfPPNaGlpyUUdsy6ZqqjM3TXqTpIkVBz8FcgOD5rffRgAEPj47xDxECbXXGKX295qAFMHdpxY8xZIisYFlIgAGKZAU3sEu9siVmCgNYzdbWE7ULCnLYy4MfjpZ26HgkmVHkys8GBSZeqXF1UlTsj9pBajdMI0EG3aNCKLJQZW/wtNb/0GSBzLWT0TvqNvgqNi4DO1OkJRvLt6JxbOnYwyHwMH+VIM/YK+SJIEZ+V0RJs2WIsSD4Hs8KDmhBuw45lvAaaOwCfPwj1xLrx1h2S5tpn1mdLIVcqRk0RFKhk0yFb6wb6uvXrbNrS/eQ+MYCLFm6TAN38x3LNOyMpxafQomn6BqaNjzUsoP+jcHk8Z4QCMaJCf84eovb0db775Jo4//niUl3OgJdFoIEwdRsfw1oDrdb/CRPCDRxDZ8Gpii4SSQ/8fXNM/l/Vj0dAMOnCwbds2HHHEEQCsKWYdHdYH6QsvvBCHHXYY7rvvvuzWMAdk1Ypql7l65iwvm3c2ZM2NxjeXABBoX/MS6jpCkHEhTKjY1jbwnKBCmIg2boSrZi/mGKaiJoRAMKKjOdKBxvYoGgIRNAQi2BOIYHdi9sCetsig0gml8jpVTK7qHhywZg5U+BxMvZElsdZtMPVoTo8hhInmdx9F4OO/29u8Uw9H9XHXoTM+uHUk/D4Xjj9oKpwOBg3yqRj6Bf2RZBnOqhmINKyDGQsNaR/O6pmoOvxSNC1/AADQsOweTP6fe6H6qrJZ1UFJS2mEnfZAB9nhhaQ6YWZhZDIR5Zcw4og2rs/qtb37tVcIgcim1xFc8SiQOG9IzhKUHvVNOMbtk7Xj0uhRDP2C5LqGgc9eQNm8L/Q6Qy/atAmu6pkMvA9BeXk5Tj31VK5xQDSKGIFdEMbQ10jqjRAmgv95CJFNr1kbJAklh14B17SjsnocGp5BBw7Gjx+P5uZm1NXVoa6uDu+++y7mzZuHzZs3j7rFKsrcvd+kKt33FEgONxqW/QIwDUg73sTX0IHf4X+xvW1wTSZMHdHGDXDVzM5pTmOiXDFNgbZQDI2BCBrao4nvEet7IILG9ggaAlFE4saQj+FxKqgtd6O2zI3xZW6ML3dbP5dbP/s9GoMDOaYHm6B35nYUmBmPoGHZ3ejc/I69zT/386g89GJAkoH44G5UKrIMD9ekyLti6hf0RZIVuKpnIrJn7ZBvwJXudwbCOz9G55Z3YUY7sOeVn2LCmXdAkgsj8CWMOPRQKxBqhRAC0XAcodAOKA43JM0NWXVBdrghqS7IqiPf1SWiDIShI9K4HmY8ktX9pl57hR5Bx/uPILrlLft5tawOpQuvheKrzupxafQohn7B6+ujOG5vF4xQCzq3vAPfjIU9yggjjkjDOjirZkBxleShlqOXoijwer35rgYRDZAZDsAIt2Z1n8I00fGf3yG6+Q1rgySh5LAr4Zp6RFaPQ8M36MDBcccdh+effx4HHXQQLrvsMlx77bV46qmn8MEHH+Ccc87JRR1zxt9H4AAASmYeDVl1Y8+/74Iw4pgnrcJV4h78vuVqCFE2qJuYph5DpHEDXOP2KpgbBERCCARCcTR3RLu+gl2PGxNBgsb2yJBSCKUqcakYnwgCpAYEkgGCEpfKwEAembEQYq3bc3oMPdSK3S/+ENHG9dYGSUbVUVfAv++pADCkD5Kd4Rg+2dSAGRPL4XEygJAvxdQv6I+kaHCOm4XInrVDyhMuSRKqj74a0aaN0IONiOxejdYVj6Pi4AtyUNvsEKYBI9oJRDvTtkuyAllzQ9JcDCgQFSAraLAOZiyc9X0nr73TSkKI/efXMNq70ha4Zh4P30EXQFJ4LhjLiqFf8PgHnThub2s0fODT53sNHACJNJ+NG+ConMq1DQchGAxi5cqV2HvvveHxcMYGUSETRhx6ILv3CoRpouO933YNPJBklBzxdbimHJbV41B2DDpw8MADD8BMrKB9xRVXoKKiAm+99RbOPPNMXHHFFVmvYC6lrnHQG+/UQzD+1Fuw+8XbIfQIZktr8NX4z7Cn5TaMr/QP6lhmLIRo8yY4q2byBinlVFw30wIA6YGBmP24JRiFPsyAAGCtMTDO70K5R8WESi/G+d0YV+pEtd+FcX4Xxpe5UeLmTd1CJYw4Io0bcrquQaxlK+r/9QPowQYAgKS5Mf6EG+GZMn9Y+9UNEy0dEUzNwvuYhq6Y+gWZyKoTrnGzENmzDsIc/FRdxVWCmuO/hZ3P3QQIE60f/hWu2v3hmTQvB7XNHSugEASiwbTtkqxC1lxWQEFzQ05854xLopFjzzTIQdAAsK69xvblCO95DjCsdRMk1QXfwZdxlCABKI5+wfvbDcj+STADOxDZvQbRpo1wVs3otawQJmLNmwHTyGsKwtFE13U0NzdD17Ob9oSIsk9v2wZhDj27RHfCNNDx7m8Q3fq2tUFSUHrkVXBOHvn132hgJDGIYZ66ruNHP/oRLr30UkyePDmX9cqZ9vZ2nHKX9Qa99mgfTtsn89oDkT3/xZbnb4VmWCPuAs4pmPul24c0qkD1VcFZUTfo1+WSEAKBQAB+v59BjT7ks40MU6A9FEdrZxStwRhaO2NoDcbQ1tn1OPlcW2cMHZHsdcBK3BrG+Z0YV+qyAgGlVjAg+bja74TXacUf+R7qXyH+nwkhEG1Yl8hxnhuhHauwZ+ldMGPW+VPxVqH21JvhrJzWoy4d4ThK3ANPS2UYBjo7OzMXLBJCCFRN3huOson5roqtWPoFfr8fgUAApaWlA3qNEe1EtHH9kDvRrSufRMt//gAAUDzlmPSFXxbUSMWh/D/2pxgDCoV4Ti8kbJ/MctFGXUGDoa3HkokZj6Dxrd8guO4Ve5tSNgWlR34DaumErB5LCIFQzITHIfM91AchBKqn7APNX5vvqtiKpV9w3Pf+gR/N/RS1mx8FAJTsfQLGHfPNjK91+CcU1N8jW7J9vtJ1He3t7VmoWeEQQiAYDMLn8/Gc1Qu2T2aF2EZGZxP0wM6s7U+YOjreWYLotvesDbKC0iOvhnPSgsyvZb8gIyEEquv2g1Zak9X9DmrGgaqq+OlPf4qLLrooq5XIl94WR+6Nq2Y2Kk+7Azuf/z5K0A5/dBu2/P0m1J31Q2gl4wZ1TD3YBElSoPknQJIHtxAoFYeYbqI9FEMgFEdbp/W9tTOGQGdKMMAOCMTQHophiGsK90qSgHKvA5UlTlSWOFHhc6KqxImKEgeqSlyo8FnPVZU64XYM7BQxWvKVUrpYy9acBg3a//uytdB84uaqo2o6ak+5Gaq3MmfHpJFVbP2CgVKcXjirpiPauHFIs3XKDvgCwrs+QXjHShihVjQsuxu1p9zS6+KLxUCY+oBnKEiKw/piH4loUHIdNIi1bsfupXch3rrN3uaacSx8By2GxDRllFAs/QKHrwLPdByGrzuehBnrRHDD66g89GIo7v6zDsQCuyBMHY7y0Rk0ISJKMqMdMNrrs7Y/Yepof/s+xLa/b22QVZQe9U04Jx6UtWNQbgz6E+oJJ5yA1157DRdffHEOqjOy+locuTfVE6bjvXm3oWbVD1EhtUAK7sKu525C7Rm3w+Ef3AibeMce6KEWaP5aqN4qRstGqbhuoj0cRyAUQ3sojvZwPO17IBTrsa09HEc4lr1pXql8LhXlXgfKugcFSp2o8DlQldhW5nVAVXhDZiwTho5oy2YY4dyM9BHCRMt/HkPbqqfsbZ66Q1Bz/PWQtcyzvAaqrSOMZSs24bB9J6LU68zafmlwiqlfMBiKqxSOyqmINQ9+sUdJkjHu2Gux42/fhBFqRXjHKux4+hpUL7wKrvGzc1TjwtNXQAGw1pRIfsmKw3qsJoIKisbgAlGKXAcNOtYtQ+ObSyB0a6FlQ3LAeeBFKNn7mJwcj0a3YukXfNToAPY+Dlj3PIQRR/t/X0b5gV/M+Lp4RwOEEYejcho/5/ehpaUFL774Io466iiUlZXluzpE1I0ZCUBv3Za1dMbC0NH+9q8Q2/GBtUHWULrwGjgnHJCV/VNuDTpwcOqpp+Lb3/42Pv30U8yfPx9erzft+bPOOitrlcu1wQQOAODkg2fgW5u+hy+1/xg10h7owUbsevZG1J7+Azgrp0EIgc6YgNchZewkCCOOWMs2xAP1UJxeyI7kl4cLKI8Q0xTojOoIRnR0RnQEI3EEIzpaglE0d8TQGowiFNMRihpWuVAUUQPojOpoD+UuAJDkUGVU+Bwo91k3+8u9DpT7un33dj3vUHkDhTIzYyFEmzbC1GO52b8eRcOyX6Bz03J7m3/OWag8/NKsn9ucDhVTx5fBofGcmU/F1C8YLNVTDpgGoi1bh/Taccf9H+pfuBUwdcRatmLnszfAN/Nz8Ew+CK7aOYOe1VhMhBG3F6Hu62orySokVesKJCgaZNWZEnRwsE9FRc/Uo4g2boAZj+Rk303LH0DHf1+2t6llkxGYvhhl0/bK+vGoOBRTv+BN6XgciX8AEGhf/QLK5p0zoOuKHmqFMA04K6cV7WzC4XC5XJgxYwacTg78ISo0ZjiAeNtWIEtZJUQ8gsBbv0R898fWBlmD/3PXwVE7Nyv7p9wb1BoHACD3M7pLkiQYRm5vpg5X6hoHz15aCY9jcDdb398Ww50vbMM38XNMknYAAGSnD66jb8bPP6nFR7viGOeTMW+ChoMmOXD0DCc0ZXAjDWTNZQURnF4oDi8kzZ3T0QqjLQ+tbpgIRXV0Rg2EojrCMet76uP0gICOYFRHZySOjnDyuThC0ZF7r6qKBL/HgVK3hlK3hhKPhnKvA36PhrK0711BAbdDGRV/D2D0vYfyoRDaKB5sRLx1R84WQtbDbdj94u2INqy1Nkgyqo64HP45Z2Z8Ldc4yKwQ1zgAiqNfMNg1DrqLt+9GrG1o+T+jjRvQ8MZ9iDVt7PGcWjIO7to5cNXuD/eEOVBLakbk/JHtNQ7ySZKVriCC6kifwWAHHAZ/U6cQzumFjO2TWTbayIyFEGncYAfZsinWug17/v1TxFq22NtK9j4B5Yd/FeFo7hc0ZS7jzApxjQOgOPoFJ93+OmRVQ6lLwr3jliCSSK1Rc+K34Zs+8EXAJVmFVjYBmq86V9UdEVzjILNCzE9fSNg+mRVCGxmhVuiB7VkLGpiRdgRe/yn0lk3WBkWD/3PXwzF+zqD3xX5BZgWxxgEAmGZubjiNNE0G3Nrg32wHT3FgztRq3L3lBlwl7sF0aRPMaBAtL9+MsLgawGw0BE0sXRfF0nVR/GN1GLee7B/w7AYhBDqCIXgcYUidzQCslAayw5MIJiS+q4UfnTdNgahuIBIzEYkbiMYNhGNG2uP0G/+J78mfE6P9w7GU56I64kb+8um7NAVel2oHAEo9iS+3Br8nuc3R47nRFASg4iNMA7HWbdA7W3J2jFjrdtT/6zboHXsAAJLqQs0J34K37pCcHTOuG2jpCKPU42T6rTwqln7BcGil4yFMHfH2PYN+rbN6Jiad/XMEPn0eLR/8GSIetp/TOxrQ0fEqOta9CgBQfVVw1c6xggkT9odWWstrSwbCNKxFrPsZjS1JEiCrViBBViEpKiRZs4MO6PYzZzFQIdCDTYi17RjyIu19EUKgY81LaHrndxCJ2YmS6kDVUVeidO/jEYnG0NLRwWsv9akY+gUta99C1X7Hoj0isK3yZIxLBA4Cnz4/qMCBMHXEWrZBDzZCK62F4i7jdRtAPB5Hc3Mz/H4/VJUzMogKgRFqht62I3v7CzYi8NqPYXRY6yRImgeln/s/OMaNnbSsxWLMnqX9rszphPpyw7EluOPfwC+3/x/+V/wKs6X/woUIrsIv8Eft61hlzkUyi81nu3Vc/fdW3H6qH1PKe29u3RD4b4OOd7ZE8dbmGHa1G6j2yVi8wIsT93JCkU00tASwq70FNT4F1T4ZsuqApLkRFm7EJCcqy8ugaZq9T8MU0A0TQgAC1o126zEQjRn2zXnrpn0czW0dcLvDkCQJumkilBilH0n8IkIAumnaQYBIIgCQGgSIxg1E4qb9XEwvvE6jU5Phc2nwuVT4XCq8LhU+p2Z9T3yV+6y1AMq9DvhcKjxOFW6HjFi4ExXl7OzR6GLGw4g2bcpJCoOk0I5V2LP0Lpgxa/S/4qlA7ak3w1k1I2fHBICOUBTLP9mBhXMno8znyumxiDJxlE2CMA3owaZBv1aSFZTN/TxK9z0V0T1rEa7/FOFdnyDasDZtJLEebEJw/WsIrn8NgPW/ZgURrGCCVjaJ16ghEEIAKamRMpEkGUKSEY2aiMS8kBXNCiwoKiRZ7Qo0JH5moIGyyYh2It62HUY0+zPujGgQjW/cl5Zu0FFRh5rjvwVHRR0AXntpbNiz6l+o2u9YAMDTe2bhG2UTEW/biUj9p4g2b4azctqg9mfGrP64rDqg+sZB9VWN6WtDIBDAsmXLcPzxx6O8vDzf1SEa84zOJuiBoc2e7o3eug2B138MM9wGAJDd5fAfcwPUsilZOwaNnEEFDkzTxKOPPoqnn34aW7ZsgSRJmDZtGv7nf/4HF1544aj6sOofRj/X55Txw1NL8YcPVPz6w2vwVbEEc6WP4ZDiuNT4FSqPuQ7rXYfhZ691oCVkor7dxJV/a0VNiQKfQ0KpS4bPKcHnkLEjoOPT+jgi3Wb8NgZN/Py1DvxlZQiGKbC7o+smvFuTUOmR0RIyEYpbQQEJgN8tw6nK6IiaCMUK76b9UDlUGR6nCo9Tgdepwu1QrJ8d1jZ34rtVpuuxz5kMDnQFCoY6MkoIgUB09Ly/iQBA72xGrCV7ixp1J4RA4OO/o/m93wOJYzgqp6H2lJuh+qpycsxUpV4XjjlgCjxOLXNhyoli6hdkg6N8CmDq0ENtQ3q9rDrhnjgX7olWzk9TjyHasA7h+k8Rqf8UkT1r7BHAAGCEWhDc+AaCG98AACjuMrhq94O7dn+4JsyBo3wyJIkjgrNNCBPCNGDG4zDCBswM73NJkgE7ZVJy9oLaLdBgBRgkWbVmP4yx/x3KTBhxxAK7hhScHIjI7jXY88pPoQcb7W2l+56GysMvTZvpzGsv9adY+gXt2z5BbYmE+g6BVbsMYP5pwIe/AwA0vf0gJpxx+5B+F1OPIda2A/H2eqjeSqi+asja2AvAlZWV4cQTT4TP58t3VYjGPKNjD/SO3VnbX6xhDdrfuBsiHgIAKCW18B97IxTv6E7ZNpYNOHAghMBZZ52FF154AfPmzcP+++8PIQTWrFmDiy++GE8//TSeeeaZHFY1e4x4BKVOz7D2ocgSLjnEi31rVLz83+swPfQ7+BqWA8JA87KfYe/PfR2/OucEfP9fAWxqNhDVgW2tmacSyxIwuUzB1kTZnYGerwnHBXZ02y4AtIVNAPkJGGiKBJemwOVQ7O9OLfFYk63Hyec0Bc7EttQb/d5eAgFuh8Jp0ESDJEwzkZqoOWfHMOMRNL7xKwQ3vGFv80w5GDXHXw/ZMbzz60CpiowST+GnbStWxdQvyBZJkuComAZhboQRGX7uXll1wD1hDtwTrDygwogj2rghMSPhU0R2r4bQu2YTGeE2dG5abo8Wll0lcI/vmpHgqJzKQEIeCGEChjmoPPSSrACSbKdGkmTFCigkggtWgEHpmtGQ+piKihACerAR8cCurKclAqxFXNtWPonAZ/+0BwHITh+qj74avmmH9yjPay/1pdj6BSfMUPDYKmt032PNh+EC37PQgw2I7PoYHeteRenexw9538I0EO9oQLyjAYrbD62kBoqrJFtVL3iqqsLv9+e7GkRjnt5eDyPYkLX9RXd8gPbl9wGm1edVK2fAf/S3IDvHzvmtGA04cPDoo4/ijTfewCuvvIJjjz027blXX30Vn//85/GHP/wBixcvznols+0/Pzsbz27bAAntwx6Je2idE4fWOSHMb6HxTS86/vsyAIHGN+5D5eFh/GLRWXjgnU68vz2GjqhAON4zP3+FR8YBEzTMn+zAYXUOlLpkfLwrhgff68SaPTocCrD3OA1TKxQ0Bk1sadHRFjZR6bXSFrk1Ca0hEy0hE3FDwOe0ZjQ4FAkSAEjWjATAuqnhcqjwODW4XQ54XU64nRogDHg9bkgSIEsSvC4VXqcKl6ZAkqzXKbIEd0pgwA4QqDJv7hMViJFITRRt2Yo9S3+MeNt2e1v5QeeifMH5I3pTMhSJYfWWRkyrLbPOYzSiiqlfkE2SLMNZNR3RxvVZTyUiKRpc4/eBa/w+KD/wixCGjmjTxq4ZCbtXw4yF7PJmpAOdW95B55Z3AACyw5uYkTAH7gn7w1E5jTeaC5R1g9gY9KK39noN9uyFrkCDJCvW7AZJ7prZkFpulIwEHkuMSAdirdtyck03okG0rXoagU+fg9Cj9nbX+P1Qc/z/Qe1jMVdee6kvxdYvOHaGir+tNhCKCby2RcIBe12C2et/DABofucheKcsgOIe/s1vIxyAEQ5AdniglYyD4qko+vNxZ2cnPv74Y8ycORMez8gMOCpWQgjsbI1ge3MEewJRNLRHCzJltACgx+NQNQ3F/e4eupFuIzPWmTYAabimtb+GAxt/DymRKn23e3+8W3oVjI+cAAbXn+2VAHRTQJVN8E3UBwF8ry77u5WEGNhy2SeddBKOO+443HTTTb0+f8cdd+D111/HSy+9lNUKZlt7ezv8fj8CgQB8LhXRpo0wU6b9D4cQAs3vPozAx8/Y28rnn4/y+efZF/+4IRCMCXRETHREBfwuCRP9vX9YE0KgIyrg1iRoSu7+M4QQCEYMlPo8kDVXYiq9Bll1Wo9VByTFMaZvLgghEAgE4Pf7i74jNxRsn8xGoo1GIjVRx9p/o2n5b7oWTNTcGHfstb2OShz0vsNxlLi1AbdPSyCIN1ZuwsGza8fE6EchBKom7w1H2cR8VwVAcfYLSktLs7ZfYeiINKzNaRCvxzFNA7HmzSkzEj6DGQ32WV52eOAav6+94LKzagYkRR3S/+NYU4xtZKdUSs5wkBRIspz4npgBkZzdYAcflK7XSYodgGC/ILP+2sjUY4i37YAeas36cc14GIFPnkfbR0/baxMBgKQ6UX7gF1F2wP/02+cfqWuvEAKhmAmPQ+Z7qA9CCFRP2QeavzbfVQFQfP2CXZ++hs+aNdzyYjuSN0x+UfsQ3LutWX2+Wceg5rj/y/rxJUWz0hh5ykdsFm8m2T6nNzY2YunSpTj88MOLZuaBEALBYBA+ny+n56xkoOCT7R34eFs7PtnegbZQFm7KEg2ZwGl4HmdJz9hb3hWH4w+4BObYXVY3b16/6UBopTVZ3eeAAwfjx4/Hiy++iAMOOKDX51euXIlTTz0Vu3dnLzdWLnS/QSBMA7GWLUPOR9ydEAKtH/4FrR/82d7m338RKg+/rGA7vQP98CvJSiKIoEFWugUVFC0RXCjOmQf8ANw/tk9muWwjYRqJ1EQtWd1vKjMeRuOb9yO4fpm9zVE5DTUn3JiVG9lDuQlnGAY6O7O/OGShKrTAQbH2C7LJWqNgbdYGKAyWECZiLVsR3vUJIvWfIlz/Gcx+UihJqguOsklQS2shPOPgrZwIzT8BWmktFE85z+8pijFwkC3JxaODYR0lPjdkRbWDDlZwQba+J2Y/2AEJSU4PQkhy0fYrgd77BcI0oXfsQbx9d9YHAZh6DO1rXkTbyidhJBYrBADIKvz7noqyA78I1ZN5kdKRuvYycJBZoQUOiq1fsOmDl1BdVYEnVoXx0HvWe75KCeAH8vcg69bP/v0XwTP3XEiKBqfTATlxzorrOvS4DlmR4XQ47H2Hw9ZgggGVjUQhKQ54/NVweMsgO30wTIFYLAZZluFyuVL2G4YQAk6nE4piBf50Xe+1bCQSgWmacDgcUFXrpp5hGIhGo32W1TQNnZ2d8Pv9ME0T0WgUkiTB7XbbZaPRKAzDgKZp0DQtbb/dy3Z2dqKtrQ2aptl1SO4XQFrZWCwGwzCgqqq938GUFUIgErHa3eVydQ3ojMeh6/qgyiqKAkfa3zOc+Hs6IUkSgsEgnE4nDMPot2zyb6/rOuLxOGRZhtPZFYiNRCIQQsDhsN4n9W1RrNrSho+3BfDZrk60dnZbIJMoTySYOBd/wjFS1z2CpeJkPI0vQqB4+3CFLBeBgwGHf1paWlBT0/fBa2pq0Nqa/VExuSbJCpxVMyB3NCDetgMDjKP0vT9JQsX8L0PWPGh+50EAQOCTZ2HGw6heeOWoHrUvTAMiFgYQRl8ZVrtmKyQDCg5Iqtb1WOEHbKJsMmMhKzVRSpqBbIs2b8Gef/8Y8bYd9rbSfU5B5RGXpy2YSGNLsfYLsklWHXBWz0SkYf2gU85kgyTJcFZOg7NyGrD/WVYgoXU7Irs+Rbj+E0TqP0u7gSj0CKJNGxBt2gAACKXuS3VCK62FVloLtXQ8NH8ttNIJ0Py1UL2Vo7p/Q9llLx5txGHGBMQw+n2SJKXMfJDtmRCQpLRAhBV0SK4FIacEHqzvduCigN+nergN8dbtWQ80CtNAx7pX0Lri8fSFlSUZJXsdj/L550ErGZfVY9LYVGz9ghO+9L947e8P4twDSrC5WcerG6JoMvz4o/5FLJYfBWB9zv/g9X/gZ2878Pvf3YeJ463/pSeefRk/W/IHnHr8kbjzO9+w93naV76B1kAHnnrop5g5dTIA4LmXXscP7/4djjliAe754fV22XMu/T/U72nCH3/9I8yZPQOSJOGF1/6Db//g5zjisEPx6O//YAdXzznnHGzYsAF//OMfceihhwIAli1bhiuvvBIHHXQQnnjiCXu/559/Pj755BM88MADdkqpd955B5dccglmz56N559/3i572WWX4T//+Q9++ctf4ogjjgAArFq1Cueddx7q6urw73//2y779a9/Ha+//jruuusufOELXwAArF27FosWLUJNTQ3eeustu+yNN96Il156Cddffz2++MUvAgB27NiBL37xi/D5fHjllVfssnfddRf++c9/4qqrrsKFF14IAGhqasKZZ54JRVHw9ttv22Xvuece/O1vf8Pll1+Or371qwCAYDCIE044AQCwfPlyO1Bx//33409/+hO+8pWv4OqrrwZgBTqOOeYYAMC///1vlJRYedkfffRRPPjgg/jCF76AG264wT7e8ccfD8Mw8Pzzz6O62krv9sQTT+C+++7D6aefjptvvtkue8YZZyAYDOLJJ5/ElClTAAB///vf8bOf/QzHHXcc7rzzTggh0B7W8ZXLvokO04Vjzr4YW9qA5mA/fUgjimjDBpx65FwcsHcdxpU68OmqFbj3V7/CPvvMxne/+z276He/8x1s3bYNN954I/bff38AVkDv5z//OaZPn4Yf/OCHdtkf/OA2rFu3Htdeew3mz18AAFizZjV+9KM7MHHiBPz4xz9J+xt9+umnuPJ//xdHHHkkAGDTpo24+eZbUF1dhV/84h5ACITCIfz2t7/FihUf4rLLLsWxxx4HANi5YwduvOkmlJSU4P7777f3u2TJr/H22+/gggu+glNOORUA0NjYgGuvvQ5OpwMPPfSwXfahhx7EsmWv4Ytf/B8sWvR5AFYQ8MorrwQA/PGPf7TLPvbYY3jppZew6Kyz8MUvfQkAEI1GcNlll9v7cjqtANqTf/0rnn3uOZx88sn2+w8ALrjggkQdl9gDj5599hk8+eRTOPbYY+x9AcBll12KaDSGX/ziblRXW+eIF1/8F/74xz/hiCMOx5VXft0qKAT+93//Fx3BIH58112YOGkSAGDZslfx0EMPY/78g3DttdfZ+7322mvQ2NiEH/zgNkyfPgMA8Pby5Vhy//2YM2dO2uyvG2+8ATt37sKNV1+O2dOsYPOHn/wXv3r4ScycNgnfvfqSrr/9Lx7E5m31uOar52HevrMAAJ+u3YSf/+ZPmDJhHG771tesgkYMm//+Hczxda2REN37PFSLuWj+1U8wrroCP/7O1+3nfvG7P+Pj1Rtx2ZfPwlGHzAMAbN25B7f+7AGU+0tw963XdP3tH30S73/0X1zwhVNw/FEHAwB2NzTj23cugcflxK/v7Po/fPDPz2L5+x/jS2cej1OPs85TrW3tuO62X0JRJDz4s67/gcee+hdeXf4BFp38OXz+lKMBAJ3hCK76zk8BAL/72XegJoKvTzz3b7y47B2ccuzhOPcs6xyiGwa+ev0dAID77vgWvG7rffLMi6/j2ZfewHFHLsCF/3OqfbzLr78dhiFw9y3fRHmZ9T7516tv46/Pv4IjD56Ly89fZJf9+rd/glAkiju/cyXGV1cCAF5563388W8v4uB5s3HlxV+0y1536z1oDXTg1uv/H+omWtfdt/7zER56/Dngpq7zfbYMOHCQjNz2RVEU6PrgI59LlizBT3/6U9TX12O//fbDPffcg4ULF2Z83fLly3H00Udjzpw5WLVq1aCP251WMg6yw4tY86asdNjL5i6C7HCj8fX7AAh0/PdliHgY4469FpJSvPlAhRGHMOJpOZZTJXPv2mmQFM2a6q4k8++qkJTkon8qgwxE/Yi370E8sHPYAc++CGGdu5qWPwBhdKUmqv7cVSiZ+bmcHHMwAsEwXl+5BYeMkVRFhSZX/YJiI2tuuKpnItKwLicLmw6GJMlwVtTBWVEH/5zTIYRAvG1H1xoJe/5r3VjsZaSz0KOItWxBrGVLzx3LKrTSGjuwoPlroSYfl4wr6n4P5ZYQAhB6b2/JIeua1SCnBRmSwQh75kPqd9kKVvSYHZFabojrRJixEOLtu2CEh7+geiohTAQ3voXWD/6EeGBX2nO+GQtRvuB8OMomDXq/vPZSX4q1XyBJEq47pgRrNtej3qjA29JCaCKOL0l/gQIDsyoM/OzEMIw9HwPjT8hZPYQQMOPWQCEjFkJ450eQnV4ozpJer9uFLBazPlckR/f3x4QE59RD8X57DVY/sQa7WiMIRXVULvoxAAnn/mqFXTYiHYXKRYfgnwEn/p3YLoRIlAXOX/IRkqfpaGwBKhfNxasxB5bbZWGXveyRtfY5PRqdg8pFP8ZyyZF2vLIz7gQg8PXHt0KWt1v9qvhMVC76MVZoWlpZ1wk3wykE/u/veyDLjYl2mIzKRT/GZ6qKc3+1ArohENVNYP5lKAGwYkfPgIFkxnHg9CrMnVyC/aeU4ttXXYzGHdtx/OIHMG+/KgDAnnUmjMBOOGI1mD6uK92VEmmCEdiJ8T7Y23d5BYzATqiR0rSyWrQFRmAnxnmEvb11uwQjsBNKhSOtrDPeCiOwE1Vuw94eaVJgBHZC8piYPs6TSOVkwqUHYAR2otKpd+0jqMII7ASksrT9eswOGIGdKNdi9naX7oAR2AnT5Uor6xVBGIGdKFOjXfXVotZ+gbSyJVInjMBOlCphe3s4LNllp1a57ZkspUoYRmAnSqTOtH0ky9ZVOlFebm0vU63jeUUwrazZXg8jEsHkcgcmJLaXazEYgZ3wmB12WSEERHA3jEAAE8tUe/vHTh1GYCdc+oy0/UqdDTAC9ZhQqtjb17kNGIGdcMYnpv/tw40wAjtR4whiarkVcNzutOqrRVz2NgBQE2XHOaP29kaXVV+lXMLUchlmLIT2N++2gwYCMkoP+3+onrYQrWs2wAjshOyJp+3XGbPeU5Va2N4eb7N+NyhlaWVdifdUuRKytysh63cTcXdaWY/RZv3t5U57u9cUif3KaWW9pvX+K5U67O0djq6/59QyCapqbS8R7dbfXrTbZXXdtMtO8QMlXmt7qWS9V73mrLTjGYFdMAwTk0oFqhPby2Tr/ecxpqSVFR31MEJhTPSZmJzYXq6ErL99vCatLIK7YQTaMMGr29vXaGG7btk24FRFsizj1FNPTZtClSoajeLFF1+EYQz8Q/ETTzyBCy+8EEuWLMGRRx6J3/72t3jwwQexevVqOwrbm0AggIMOOggzZ87Enj17BhU4yJSSQBg6oi1bYIQDA95nf4Ib3sSeZT8HEjcLPFMWoObEmwpqlG4hT7dPDSRISnIhP7Xbdi2Rk1fL2ZR2puLpH9sns2y2kTDiifNUdm8ypDJjITS+uQTBDa/b2xyV01Fz4o1w+Cdk/XhDOQ91dIaxelM9po4vg9tZ/PkTCy1VUS76BfmQy1RFqYxIB6KNG3K2Bkm2CCOOWPsetDdthxZphN6+G/H2esQD9Yh37AHMQd70kWSovmo7oJAWWCipgay5Mu+jABVy36kQjNX2kRLBhfRgQjL4kB5oMGJhBNpa4XNld2FqIQRCW99Hy/uP9Qj0eaYsQMXBF8JZNX3I+x+pay9TFWVWaKmKiq1fkExVZN88jsXx11Uh/HFlDKYApmAzvibdj0p0zeRx7vsFOOedD10ARlyHpMhwal3B80jEuvHvcGqQpcSNM8PovWw0BggBzaFBSaa2MU3osTgkWYbT0a0sJLhK/FBdJVA0N0zZCd0wIckyXKlpcKJRCNOE5tCgKolURaaBWDTWZ1lVUxEKhVFaUgJTmIhFY4AkwZ2S1igai8E0DKiaCk3V0vbbvWxTcxM+++wzzJgxA16vD4CVqi2aCCi4XC4IIfD2+lb87T/1aOkcuzn8naqMfSf6sO9EH/apdWFWjQ9eT3o6qWRao9Q0VfF4HJIk9Ug9NdiyqemkDMNALBYbUtnkGhCapvUo21fqqWg0aqfKGkpKq9TUU8NNadVX6qnBprTqL02VEAKNjY3w+XxwuVyDSmnV399T6FF07FoDU4/BoWlQlMR+DQPxuA5ZkuB0dtU3Go3BFAKaptqj7w3DRCwehyxJ0MwQAq/9BHrbVusFigO+I74B96SD0spKEtLOJ9FY3Pp7qkrK+2QwZQ20BaNwO2R43F3vv1g8DsMwoSoKNC3lfRKzzhtul7PfskKIxDkUcDkdKX9PHbphDKqsoshwpJzHw4lzvtOhdaWnG0RZXdcR1w3rb592zo9CCKT/PXUdsbiOKXsflL81Di655JLMhQA88sgjAz74oYceioMOOihtOtI+++yDz3/+87jzzjv7fN15552HWbNmQVEUPPPMM1kNHCRlcyRv59b3sWfpXfaIXVftfqg66n/hrMjBctdDUEwf7uwPaYmZDFZwQbNGh6XOarAX91OsgEQGvDHeP7ZPZtlqI72zGbHWHRCDvXk3CNHmzdiz9MeIp0SsS/c9DZWHXwZZdfTzyqHjGgeZFVrgIBf9AmDkZyKOVOAAsFKRxJo25WyWULb09f8oTAN6Z5MVREgEE/T2esTbdyPevsteNH0wFG8ltNLxVtqj0hoo7rKuL4/1vZAGWyQVU98pF9g+mWW7jYQQCO/6GC3/eQzRhrVpz7lq56DykMVwjd9n2MfhGgeFo9ACB7nqF4y0ZL9gz4YP4dV6Bvs/3hXDXa92oDFowoNOLMbDOEBaaT+/TuyFh/A1BJB5zRAaPL9bRYm7MAcMmaZp3/AbClmSUOnTUFPmxIQyF2ZP8GHWeC80ZfTnih+pxaNHs1y0kRkPQW/ZDGFk596B0bEbbcvugtlpzZqRHD74j/4WtKqZWdl/f9gvyEwIgeq6/fIXOMi2WCwGj8eDJ598Emeffba9/Zvf/CZWrVqF119/vdfXPfLII1iyZAneeecd3H777RkDB9Fo1I5IAlZHYPLkyWhra8t4g8CIBhFt2pSVvMThXZ9g90u3Q8TD9jb3xHko2fsEOKpmQCutzVveVX64S8xskOXEjIZkjlzVzpcrZAUdwbB101dJllG78u6OcQwcZDbcNjJjIcQCu7I2G6o3Vmqil9D89u/s856Vmugb8M04KmfHtY89yPNQNBZHY3MbvK6uSHsxs24QzC6YwEEuFOJMxGzTO5sRbd6S8+MMx1D+H4UQMEItVhAhUI94+y5rtkIiyGDGhn6jUdLcUFMCCdZXuf2zmhJokDV35h1mAftO/WP7ZJatNhJCILzzI7SueByR3avTnnNWz0TFIYvhnnhA1v4OI3Xt5Q2CzAotcFAskv2C1sZ6aKHe0z6E4wKPfxjCUx+FEDcFjsfLOAdPQZGs2RTtogSP4P9hDfYbyaoXrcNmluOsg6y0Oz5XYQYNeGO8f2yfzLLdRma0A3rrFggzO7Od4y2bEXjtJxBRK+uB7KmE/9iboJZmPxtBb9gvyCxXgYO8nXWbmppgGEaPBZRqamqwe/fuXl+zfv163HTTTXjzzTf7zZ+Y6s4778Rtt93WY3sgEBjQiD/hnoRY+06Y0WGOrCmfDf8JtyCw7E6IiHXjL7zzI4R3fmQ9L2tQyyZBKauDWjYFavkUKGV1kN3lOf+nEEIgFLUikGP3H7D/4FCyjWKBnusupE9BTyzKJyuQkPLYXrxPSZuuLiVz5Y5yyYscMJbfQ/0bShsJ07Qu+KE2mPHe1w3JFjMWQvC93yC6Zbm9Ta2YjtKF10GU1qIjnNspwkM5D7UGOvHWx9tx8D4Tx0Se5UIfpZ4Nd999Ny677DJcfrm1oNg999yDl156Cffff3+/MxG/9rWv4fzzz7dnIhYy1VsJYeiIpSw2XgwkSYLqrYTqrYS7Nv1GiRACZrSja6ZCMvVRez309t1pCzT3RsTDiMfDiLfXZ66H6kybsaD2EmhIfskOD69ZNGoJIRDesRKtK/6CyJ41ac9p5VNQcfAF8E49LOvv8fbOCF7/aBsWzp2MMt/oTDVGNBCywwMVZdBDbT2ec2sSLj3Ui5Nnu/D0xyG0hs/EvyN746iG++A1WlAqdeBq3I21pSfiM/+ZiKijYPaB1HNheTvFGiTE43E7rctwRSIR1NfXY9y4cf3us9yr4dR54zC12tNnGSLqyQi1Qg9stxbuyILY7k/R/uYvIHQrnZPinwT/MTdC8VRkZf9U2PIeru3emRVC9NrBNQwD559/Pm677TbstddeA97/t7/9bVx3XdfK48kZB36/f+AjCysq7A+4w7pxM3lflJ33W3SsexWBT5+D3p4SIDHj0Fs2Q2/ZjGjKS2RnCRwVdSlfU+EonwLZkb2LZ/J34qiwvg2sjQSAblPAzMRXBukL9Sn2InxWwKEr8ND1Xe5KtWQv2Kd0zZYYYcn24YyDvg2mjcx4GHqwGXpnM4Spw6kCUHO3uGi0aRMaXvlJ2uKJpfudjsrDLh2xRU2Hch5yKB4ctf8k+NwOqGNkxkExi8ViWLFiBW666aa07SeddBLefvvtPl/3yCOPYOPGjfjjH/+I22+/PeNxepuJCCQWJBuhNlZLxsE04oi39z5QIt+SbZHN9pCdJXCOK4FzXM8+nLU4bD30YCOMcJv1FWrrehwOwAi3DWjWgtCj0Dv2QO/Yk7GspGjpgQRXKRSnD7LTC9nhg+IqgexI/Oz0QXGWQHZ6IclqTtqomLB9MhtqG1kBgw/RuuIvPVISaWWTUH7QufBOP8ruD2b7b1DiceCo/SfB69Jy+vfleyizsdI2I53CMJXmnwgj3PeAw4l+Bd9YWJL46WAY4XuxZ9ndCG//EBIEZre/jH06l6Fk7xPhn3MGHOWTh1SP/BKArCAYc8Lv90NRnZA0J2TVBUlxDCjtb3fhcBg7d1ajtLR0wANCiWhg9PZ6GMGGrO0vsmU5Ot77rb1uq1q9N/yf+z/IDm/WjkGFLW9n6aqqKiiK0mN2QUNDQ49ZCADQ0dGBDz74ACtXrsRVV10FwMohJ4SAqqp4+eWXcdxxx/V4ndPp7HWBJkmSBnWD01E2AYqrBNHmzcNKXaQ4vSjb/0z49zsN4Z0fIdKwFrGWrYi1bEE8UA90WzTRjHYgUv8pIvWfpm1XS8ZZQYREMMFZUQfNP3FIF26gqz1407dvuW0jAZg6BAAYVghiOHoEIiQ5JfCQCERIkj1bwg5GSCnf7YBE6uiTvm/O8j2UWX9tJISAEW6FHmyCEelIe02uCGGi/bN/ovndR+zzmuzwoProq+GbfmTOjtuXwb6HNFVFecnIpCWh3BstMxGzRvIhBm/G0fb5MPIzETXAOwXwToEEq3Pa219TGDGYkQDMcABmpA1muA1mJAARaevaFgnADLdBxIIZjyqMOPRgI/Rg46BqK6kuSA4fhOZFwOWD5PBBdnghOVMeO3yQnd2e0zxFMctwIDibNbPBtpEQArFdHyL00ZPQm9enPaf4J8Gz/xfhrDscQlYQjA5w5MoQmIa1EGPMAGJG7hZ7F0IgErf2z/dQ78ZC4OCJJ57ANddck5bC8NRTTx1QCsPFixfj+OOPx549mQPJfZE1FxRvJfRgU+bCABS3H7Wn3oK2VX9D64q/QBgxCCOO9tUvoH31C3BWz0TJXsfBN+NzUNz+IddrpAkjDjMWhx6Mw+g++15WIakOSIoGWXVaaw0qWiKokPje7TOkpmmoqOBIZaJsEqYBvW07zEh2UhsLYSL08VMIrX7W3uaYeBBKj/gGpByte0iFKW+BA4fDgfnz52Pp0qVpaxwsXboUixYt6lG+tLQUn3zySdq2JUuW4NVXX8VTTz2FadOm5bzOiqsE7vH7INq8BUakfVj7kmQFnskHwTP5IHubqUcRb92OaCKQYAUUtsIItfR4vd7RAL2jAaGt/+naKKtwlE2yAgqVXQEFxVvFDvcYI4QJGOawAxC9SU/N1DV9NRqOIRr3dE1t7RacSA9EJF+bErxIC1oU/+jxJDMegd7ZBL2zJSvrqQxUvH03Gl77ZVpQ0lk1EzUn3DBqcuWGo3Gs3daMuvF+uBwcrVQsRsVMxGzx+xFt2gQ91Dqyx82gcGciaoDPCyBzLlVh6vZMhfTZC11femK7GWnHYEL2Qo8kpmo3IT7Iz2ayw9ttRoM1myF1RoPs9KU/7/BYaZXk0XOeK9z3UOEYaBsJIRDa9gHaPnwc0cYNac9p5VNQftB58E4/wupDjYBgKILte1pRV5Pba2+yfZjLuG9jIXBQCCkMtdJaGJ0t1uerAZAkGeUHfhEle5+AwMfPIPDZC3Z6j2jjBkQbN6DpnYfgmTwfJXsdB8+UgyGP4ptwwtQhYlYQ1OijjCSr1jqBigOS6kAwFMVn6zZj2tQ6uD0+6/qmqCN2HiMqNkaoBUZHfdYWQTZjIXS8+xvEdq6wt7lmHAvfgkvGzCAY6pLXTyDXXXcdLrzwQixYsACHH344HnjgAWzbtg1XXHEFAOvD/c6dO/GHP/wBsixjzpw5aa8fN24cXC5Xj+25JCkaXONmIRbYZc0QyCJZdcJZPRPO6vQVyY1Iuz0rIdqcCCq0bktbaBkAYOqJgMMWIOVzhezwpqc6SjxWnL6s1p/GBiFMQJhpk2Os/NVx6HIsax/u0oMLUo/gQlpQordAhD2LIj1Ykb4t80yKXLBmF7QlZhcMLwg5qOOaBsL1n6Jz41voWP+a/SEGAPxzzkTlYZeMWGqibIjGdGxraEdtpY+BgyIw2mYiZouzajrQtDGnC58PxWifRSYpGmRfFTRfVcaywjRgRNphRjtgRIMwI0GYsaD1OBrs2h4Nwox2woh2wIxaz8Mc3Ac0M9aZSLk0+CnkkuKwgwiy5oHk8EBxWN9lzQPZ4U4874Wsue1ysjP956HOTh10fUf5e2gkZJqJGNr6HyslUVN6wMBRMRXl88+Dd9rhI36jLRY3sL2hAxMqS+B25vZvy/fQ2FYoKQwlRYPiq0K8fXAzFxR3GSoOvRj+eecguH4ZOtYtQ6x5k/WkaSC09T8Ibf0PZIcX3ulHoWSvY+Gs2acg3+/DTR0mjDhgxAFY9y+CgSC2bNuBcR4TirerT2bNkletG5OyAknWAPuxmnhOTflZKZhgA9Or9Y/tk9lQ2siMh2AEdsKMZWc9RDPWici6lxBe9xJEMkWoJMF7wPlw7XUKIEl5+xvyPZRZrtomr3dazj33XDQ3N+MHP/gB6uvrMWfOHLzwwguoq6sDANTX12Pbtm35rGKfHP4JUJzDT100EIqrFO4J+8M9YX97mxAm9I4GO6AQa9mKaMsWxNt29kx3FOtEZPdqRHavTtuu+qqg+qohu8pgOvzQSyugeioSCwqWQ3GXQfWUj6obiVQ8kgEKYPipmwYiPTiR/UCFqcehh1oRM9thhNtGbHaBMA1Edq9GcOOb6Nz8To+0KKpvHKqPuRqeifNGpD7ZVFbixokLcj/bjEbGaJyJmA2SJMFVPRNGOIB4ez2MaOY8/pRdkqxA9ZQDnoEvXimEQHsoBp9DQERTggyxIIxE4MG0tycDD51pQYju/bWMxzRiMMKxYae3GlQAQnVB0lxW+gnNlfKzC7LmgqQ6OfIsy4QQCG15Fy0f/gWxpk1pzzkqp1sBg6mH5u1mGa+9NFIKKYWhMD2IRkwI0deY+v64Ic88Df6Zp0Fv3YbI5tcR3fwGzERWATPWiY7/voSO/74E2VcD1/Sj4Zx6FJTSCQUTRMh2+jlV0XDE/tY9n1As9VpooseagRlYn9tUQJbtNf8gqympeq0Agz0DPoez98LhcMH8zQoR2yezAbeRqcMINSdmzQ6fiHYgvuEl6BuXAnrKAGXNA+chV0LUzEU4LjAyd2b6qCNTGGZUlIEDALjyyitx5ZVX9vrco48+2u9rb731Vtx6663Zr9QAdaUu2pyWk3wkSJIMrXQ8tNLx8E491N4ujDhibTsQa97SNUuhZSuMzp55GfVgU1q+xkiPEhbZ4bUCCZ5yqO7yRGChrOtnTxkUdzkUt58fHmnUEkIAwgBg5ORyKIRAPBxHPJ77tA1CmIjsXoPgxrfQuXk5jF7SoEiqAyV7n4jKQxZndbF1ouEYjTMRs0Vx+6G4/V03mGOdMGMhmHo084spLyRJgqw6IGkuqAOY2ZBKCAERD6fMZEgNMnTaQQjrewhmPAQzFrYexzphxsODDjzYx85SACJJUjRIqguy5rS+qy5ImhOm5EDI5UkLNtgLaiaDEckARFpQImX7GOpXCmGic/O7aP3wL4g1b057zlE1HRXzvwxP3aH8sExjTqGkMIwrUcQCu4bwG6RwzwAmzIA4/CKEd32C4Ppl6Nz8jj0L2AzuQejjvyL08V+h+qrhnngAXLVz4Bw3C5p/Qt4ChtlOP2cYBiQzm7+LkfiKW/c1M8R3JFkGZC2RSlexZuKlzmaQkqmTFOv7AIINyZHQXq+X5+lesH0yG0gbCSFgdjbBCO2GME3AMbz/IzMSQPi/LyC84d9A6mcOSYaz7kh45pwNxTduWMfIFqYwzKxoAwejnaRocFbPQjywC/H23kc+jHh9KqfBWZk+EsiIBtNmJ1jpjnbAjGYOeCSn1cfbdmQ6euLGR5kdTFA95SkzGMqhesqguMsgu0oKZlohUTEQwkRkz3/RufEtBDct73VtFElxwDNlAXwzjrLyqWquPNQ0ewLBCN76aCvm7zUeJZ6eqWdo9BnNMxGzRXH60lIJCtNI3CxO3DyOdjKYUAQkSbJG+Ds8QMngP5AJISD0aFogwX6f2IEG60vEu28PdwWmhhGAsOtixK2FM3vpU8aGtWcAspqY2WAFJmTV3XfwQXVBUh2JbQ5IqhOyYn2XVGdiey/P5Tk4YQcMVvzFSjeawlk1E+XzvwxP3cEF8yGZ114aKYWWwlArHQ+9sykrs4YlRYV38oHwTj4QZjyMzs3voGP9MoR3fITkiF492IiOtUvRsXap9RrNDWf1TLiqZ8FZPQvOcbOg+saN2Lkhm6nD2jujeOujbfk7jwgBGDEkJ5AM5FabPZMhLVWS2jWLQZIBIw6hJ8skZkAUyLm7EDD9XGb9tZEZ7YAe2AmR+BwwnHY0Qq0I//cfCG94FTBSemuyAte0z8Gz71kFEzBIxfdQfkhijCWIam9vh9/vRyAQyPoiiEY4gGjzFohB5rvNJ2FY6VPaW5vgNDtghtugh1oTiwm2wgi32osH9lhTYThkxQooJNIhKa5SezFA2emF4vQlFgT0JhYMtB5LmjsvJwkhBDrCcS7y1we2T2a5aCMhBKINaxFMBgt6mVkkKRo8k+fDO+MoeOsOgay5s3LsbBtK+7R3hvHp+p2YMbEcbmfxp1QTQqBq8t5wlE3Md1WKSi77BbmS62ACz+mZFUsbdQUgugIJdjAiFoLQIzDjEatMPAwzHrW26RGIeDTxPQJTj6aVHW4wYkTJalqwQVK6Bx+6Ag7p5ZzWjBPVkVjwMxmMSN1P12O7nCTB1GOItWxFYNdaRNe9iHjr1rQqOatnWQGDKQsK7v01UtdeIQRCMZMjC/shhED1lH2g+WvzXZWcOfTQQzF//nwsWbLE3rbvvvti0aJFPRZHNk0Tq1enp+btnsLQ6/VmPGZ//QK9sxnR5i1D/4Uy0DubEdzwBkLbP0Rk92cZgxSyqzQtkOCsnmWl3suybF/zirEP3+c5y05tqyRSJymJNLdKSvrbRBolKZluKaU85PRUS6OUEALBYBA+n4/n9D701UZCj0Fv3wUzMvz10IzOJoTWPI/IxtcBM+X8IqtwzTgGnn3OhOId3CzakcJ+QWZCCFTX7QettGdwfTg44yCLFLcfrvH7INa82VowbxSQFA2qrxqaUgZvho6AGQ/DCLVBD7fCCLXBCLfCCHUFFrp+bs28WKBpwOhshtHZPLjRaJJsLfLn8EFJBBXkRFBBdiQCDs6UgIMj9WcvZJUjo2h0E0Ig2rjeWrNg03LowcaehWQVnsnz4UsGC4o0FZHX5cCc6YU3EoIo1yRZgeIqgeIqsbf1CCbEQjDjfSUhJLJIkmSN3NdcgLcyK/sUQsDUY+gIBuFVjW7Bh0ji59QARCLwEO8tINEzMJH1oISpw4zpQGxk1heRFA3CNHr9PZzj9kb5/PPgmTy/YD8U89pLI6nQUhiq3koY0WBaut9sUr2VKJt3NsrmnQ1TjyKy+7+INq5DtGE9Io3rewwSMiPtCG1fgdD2FV378FXBWTULzuoZ0MomQfNPhOavLajPwWPqPCJEYm0MAzCGnyHeCiokZzOkBBUSgQZ7e2KNh/7LFeZ1hixCmDCCDTCDjdb6j8NgBBsQWv0cIpvfAMyUXF6KBvfM4+GefQaUHAQdqTgwcJBlsuqAc9xeiAd2It6+J9/VySpZc0P2uzOOahFCwIx1JmYsdJvBEGq1Ag/htkTwoW1wHwCFCTPSATPSMchlkyySonUFEZKzGxzpwQZZc1tfDjekxGNJc8MwVJhKqbWdaZZoBAkhEGvamJhZ8Cb0joaehWQVnkkHwDdjITx1h0JxZh5RNdoZhonOcAwupwpF5v8kjW0MJlChSK79IDtLoGZ5RoYQwrrRbwcgugISph6B0GMQRszapkchEl+mbm3rei7W9ZyRLBdLvCaWSAOQm0nZvY0gdtbMRsX8L8M96cCCv5HDay+NpEJMYegom5xYoya3wUZZdcIzaR48k+bZ2/RQK6IN6xFtXIdI43pEG9fD7LbWYnIdw84t76RtV31V0EonQCubCM0/IfE1EVpJjZXjfwTxPDJ0wjSRXER62FcpezZEYtaD/VgCINmPpW4/A8nXJbYBie+p2yV7GyQpsfaEbl0Dk8EPMOVMX8xwAHr7LghjeEkf9Y7dCH32LKJb3kq/76Y64Z55AjyzT4fs9g+ztlTsmKooh0ZL6qJ8TrcXpgEj2gEz0g4z2gkjllgQMNoJMxa0FgiMpSwUGe2EkfJzvqbCS6oLsqMrqJAabOja5hlAGbc1hX2UXzCLJWVDLg22jYQQiDVvSgQL3oLe2xoqsgLPxAOsNERTD0vLiz7aDOU91NjagZffW4eFcyejzDe612sYCKYqyo3RmKpoOAYSTOA5PTO2Uf9Ge/vYAYq04ENXUME0ugIPQo+l/GwFLkw9BmFEIfR44nuyTCIoIStwVNRBlEyBf9K+cNXMHjXtNFLXXqYkyGwspCrKh4H0C0w9hsie/2ZlvYPhEEJADzYkggnrE8GEDYNLMSzJ0Epq0gIKqq8aqrcKqrcSsstqg2ye04uxD89zVv/6TeWUDC5IEoCuwIOUGnhIBC6swARSghKyvR8A1r5SfkaPn9H7893Kde0H3cqhqz697SvtML3VqbfXWcxYCB2BFnjkmL1g+lDpgR1WwGDbO9aaHslDqi649z4Z7r1Phews6WcPhYf/Y5kxVdEoNBpTF400SVagussAd9mgXyuESIz2CNpBBTPWmf5zNGgFINJ+7rRz9w6V0CMw9AgMtA55HzZJTg8u2Iv3Oft47Oh1e7+vG+FRJDQ0QgjEWrYguPEtdG56C/HArp6FJBnuifPgm7HQCha4RtcFP5tKPE4cvt9E+NyOfFeFaNTofWaCCTMetkdwm3oUstEBWZUBUx/29Gii0UaSJEDRoCgakKOgfDK44hplwRVee4msLAOucbMQa90OI9JzYfiRIkmSddO/pAa+GUcBsNKbxNt2ItayBfHALsQCuxBv24l4+64esxOsF5iIt9cj3l7f+zEUDYqnApK7AqESK5iQDCqo3ioo3gqonopBfd7keYRsQkBA9DrJb0yNcEYyc4cJcxg3xvXWbQh99gyi2/+D1BaUNA/ce58C996nQHYUf2YCyi7eTcwxO3VR207EO4ordVG+SZIEyeEZcv72rlGXVkDBnu0QD0PEwzBj4cRCgNZCgbFICIqZuKkSD6WVGdbMB2FasydinTAylx4aWek9sKD0HoToGZBwQFI0a1G/5Pdu28yYBFPxQFYdgKyOqg/B+RZt2YrOjW8iuOktxNt29iwgyXBPmGutWTD1MCicTggA0FQFVf7iXL+BaCRJsmylN0ukOBNCwKkG4Pb7IUkShBGHMOLWiGkjnkj5Ek88TmxjcIFoTOC1l8gia264xu0FvbMZsbadeZ99kCRJMhzlk+Eon9zjOSPSjnhgV9pXLLAT8bZdfY5wFkYcesceoGMP4r1kS00cFYqnDKqnEoqv0vruKYfi9kNxlUJx+aG4re+y08fzCFGWxVs2I/Tp3xHbuSJtu+TwwTP7NLj2OhGyxv85GhoGDkaAJElwlE+C7PQi1rKt4FMXjRW9jbrsS39T7oUQ1rT0eBgiNdhg/xxKCUAkghLdy+hdQQihR3Ow8J8VJMEwZlkMRLP9SIKkJoMKqcGG1OCDBjn1+bRARPeyDqus2lfwott2pfBH78Vat9trFsRbt/csIMlw1c6xggXTDrdm5lCaSDSODTtbMbm6BE4HL2dEuWKfs9H3B47+gwtRCIMzF4iKAa+9ROlUbyUUdxnigXrowQYUciZo6yZ+KVw1s9O2CyFghFqtdRoDu6B3Nie+mmB0tkDvbIYZ7W9mhbDXM0TThv4rIcmQHF7oihcOjxVYkF0lkJ2lkJwlkJ0lkBzW+oOS5rG+OzyQFM5OIOou3rQeoc+eQWzXqrTtkssPz+zT4Z55PCStONKBUf6wtzeCVE85ZIcnkboot4sp0ciRJMkemT+UlEu9EUZveXXTH2d6vmfZnosF5m4CoEgcb3iL+QxHejCh++N+nlMdkGStR5CjK4ChAXbQo/trHJBkNRHQcECSFas1hAk91Ip44060Nn+Gzk3LEWvZ2lut4ardLxEsOAKqp3xkG22UCcd0bNjZguoyD29eEOVZdoIL8YK+4UJEvPYS9UaSFTjKJ0H1Vljpi0ZZmmJJkqB6K6B6K+CesH+P54UQaO8Iwm22wwi1pAQVmqEHm6GHrECDEWrNPABOmBDRDijogBHaPfAZ97LWFURIDSho3l62dws6aB77cxlRMYg1rEHo02cQ3/Np2nbZXQ73PmfAPeM4SCqDbZQd7O2NMFl1wlUzG2YsBL2zBUa4FWYeb65SYZIUFYqi2mkjcsGaKREfQMAh0i0lReKGjxGDqccTP8cQj0WhCN1+Lv17snwMGKEZN8nj5pUkQ1I0CNPo5/eW4Bq/b1ewwFsxolUczcpL3DjlkBn5rgYRDdCAggumAWHqEIZura9g6vY2JJ9Le96wzrFENCJ6u/Zai1UmF7AE7MUtU59Dt5mgwkzktRbWjUZhQJiclUSjm+zwwFWzd8GlL8oGSXVCc0+Ao2xin2WEacAIt0EPNsEIB2BEEl/hAIxIO8xIu/U4HIAeCQB6dOAVMOMwI21ApG2I9XelBBQ8icfelMcp2x1eyFrysQeS6i742exU/IQQiO/5DKHPnkG8YU3ac7KnEp59z4Jr+uc4O4eyjoGDPJEdHjgcHqB8EoRpWgsSdr9pG48UVWeDCos1U8IBqA4Aw1tgt79UTj3Lmn0GIkQiEGH2sq17EKL7djNtWyIgYsbTj2XGIfQ4RmypJWEmZnb05KrZB94ZR8E3/Uio3sqRqQ8RUYGTZMUaFag6B/waIUTvQQZDt877ieBtelDCYOokKkqSJAGSbH0lf0bqTf0EIQCIrlk+wgQgQVJUa/ZkclZl4jFkBZKswjCBeLATkK1jSInjZEvy/9c0dKjBdqguDZJppPTpYoARY5CBCprqrYTi8kMPNkLvbBozAwUlWUksmtz/ZxvDMNDZ2Qmhx2BGO2BG2yGiHTAj7TCjHRDxEEQsBDPeCRELQcQ6rTUGY53WVx/rMfRH6JHE65oHv66gJEHSrCBCakBB1rxdwQXNA112I+b2WWswKsm1Ax1W0ELhOoA0NEIIxOs/Rudnf4fetD7tOdk3zgoYTF04qAXKiQaD76wCIMlyn4v8CtOEMBJBBD0GU48wqECjmiTJkFSndVNo4PeFssa6wWT0DEQYXUEGs6/nUoIPvc2o6PqupwQ1rJkGqq8KprsK3sop8E07DKqveuR/+SLT3hnBOx9vxwGzauBzc2QF0VglSRKQuMk5GMI0rZuUIjFrwTQghAGYZlcAQliP7dHQibKmoUOSONOB8kOSVajeCshOX9oNfutGfm5vSrU1N2PZG2/h4IMPRknJ8Aae9EaSVUBWrZSQDhOKx9fr7yRMPZEWM5KYMRuBiEeswAJRAZAUFZq/Fpq/FkakHXqoFUY4wM/w6NaH91ZCGeQgKmEaEPFwj4CCmQg4WI8TQYd4CGYy4JB4jMH+DYSAiAUhYkFkCln2ewaS5MQ6fS5AdUBS0gMLkuqEpFifk63tzkQZp/289ZwzrXwyQAHFkfVgLuWPEAKxnR8i9Nkz0Fs2pT2nlNTCs98iOOuOYBouyjkGDgqcJMuQZDdkzd3jOTuooEch4snZConAwhgZ1UA0WNYNJnXEI/KDmZVBA6PIMko8DshsTyIaAquPNbSgoxAC8UAAntJSSMJMBBwSsxgGEYCwtplMt0QDorhKEguxlkOS83NzSFEUlJaWQs7T8ZMkWYXkUIFuA6/sG4rxcNdNRAYTKM+SixIDgBkL2al7RttaCNky3D68JCuQnD7A6cNQbpkKIwYRCyeCC1aAwUwEFkSsM+Vx79szruPQ54HNlJkPOWIHFBLfE4/RLcjQ9diZeI2r22s0a4aErACJgK4kKWnb7OdGIGg9lghhIrb9A3R+9gyMtm1pzymlE+GZczackw/NWz+Axh4GDkaxtKBCt7hC70GFaCIlEjvPRDT6ed0OzJtZk+9qENEYJkmSdQMzC13qtCCCMFOCEGb6z6ZhjX5MDVYky6Q8D2FwsekiIClaYtHSKsiaK9/Vgc/nw4IFC/JdjT71dkNRGHHoLZthxsN5rRsRYKUslh0eaP5aCENPrAPQDiPcbgWax4B89+ElxQHJ7YDs9g/6tUIIQI8mggidMGNdQYVouBOaGbKCl0bMmg1lWPdioMfsx9Z263lkO7CZnPWe3b1mJqt2SjtIijVIT0r8LCuArEBARUxRrKBEL2V6BiS6l+klaJF6zNSfZcXallx/R5LRlbZP6krrl7ItuVaPhNR1e/raLieW7rG2DTdwIkwDemAn9D3r0LZpKYzAjrTnlbIp8M45G45JCzirhEYcAwdFqt+gghCJC1UskU9ehyoHoHmciRFwqQsPMgcwERUm0xSIxHQ4VAWyzFEuRDS6WVPNlUGnXOqPnZ4vdfHZlBkSVgAiNUAhuoIU9nfRSyAjZRvlhOIuheqtguIuK6iRnKZpIhwOw+l05n3WwUBJiga1cjrizRsh4jkc6Us0SJKi2msCCCFgxjphhNtgRDqsm89FGvwdzX14SZIAzQVFcwHoSrFk/f1MeByDG30vhGnd7NfTAw12YKFboKEr+BCznksrH0nc40kEKvSI9Xgk3kembq0nBWt9v76OWNS9hl4CClbAIRmkQGINomTwIvkYiRRaPYNIasV0ePb7PBwTDyqovkC2JdMuQtasYJGsdgWEJBmQZQgBqOEINI8HElIGyCTXNhMGYK9vpnM9pCxi4GAMshbFddoLDwohoJlOOPz+PnKImhBmPDHKrSuoYC8+mPZz4jE/SBJRjrUFw1j6wWYsnDsZZb78j8IkIio0dnq+HB6jKxjRNfPBNE1EAwG4SrwARGK76BaQSN/WtR099ieEGBPBCll1QPFWWrML1MJcu6elpQX//Oc/cfzxx6O8vDzf1RkwSVahVSSCB3o039Uh6kGSJChOHxSnD4A1AtmMhayveBhmPGyt41EE50D24btIkgyoLmvdgxxI3lhNCyzoUaBbgCJ1JgSS200DEDpgGBCi695Pj+/JG7bCsI5l9vI98fqiJgQAIxE16fpd+wqi9BfOUatmwbvf2dBq5xZNwECSFSsllupM+Z5IlTWA31EIASluzdoaaJuIxNqWXTN+UtZFYlBhwBg4oIys2QuDW8XWXnAwcaGwon7JCGDXFxLlOK2eiAbL53bikNkT4HVlb3QuERENjpVjV04LTkhCQHHqUNy9D0oZLuvDnugjCNHLdgjA7GUWRY+ghkjZb7fnrAOn/5wFkiRB9ZQlZhcMPm3GSCstLcWRRx4Jn8+X76oMmqRo0CpnIN60geseUMGTZAWKqwSKK30RcjMesYMIZjxsrXEYj4yqz9Dsw48cawBBYjS3w5u3eggh0Bk14NEEpMQ6T13Bha4AhDASgQhT7z1IkRqs6CtIYd9fMmBd07u+BFKv9YkvpAxQgEjrC1jXfKuMPXMjtW8hzF5e171PkXq8ZN9DdK2VIUxIqhNKWR2Efyo84/eBVjVz1AYMJEVNBAa6BQmyOKN2wHWRFUiyG+i2ZqwQAkZHPYxg44jXaTRi4IByomvBwcGPlkpdNNDO19t90cHkBUZ0ywfMhQaJ8kKSJGtKoWJNL5Rkzcr7rSRyTUpy1/dEPsnUzpAQAtFAO1ylPuvmU/dUGalBRyMOocfgdAjUVOSvA0xERPnRtSCgktPZFP1JD14kbwB0u2HQLTiRFsRQVMRCcbgrqiErQ1neMz8cDgdqa2vzXY0hs4MHzeutG1REo4ysuXqsdyKEsNYyjIdhxiN2KhtTj0IY8TzVtG8OTWEffgyy1oVSIEla3q7dhUwIgVDMhDbIdFf5JCkaJM0DWXNDcngSi2wXfkBQkiSopRMgO7zQ27bz3mEGDBxQwbFHrmXhhNMVSDB6BCQgRMoChL3l+U1faFCSRWIhmtEzmoOKh31jPtGJSMtbncvjyiok1QFJ0SArDkBRre+JhackWenKSTgMQggojjgUp2/AHaWOjg5s3/MpJo6vhkOREnk8Y1ZOWKYhICKiHBpu8EIIATkeSNnP6BAOh7FhwwZMnjwZTufgZiQXCkl1QK2YDr15I28WUFGQJAmSlljfsBthmlb/2Ihb6xsmggpdOfPjIz5bIRLTsbm+DROqfHBqvCVFNFpIqhOy0wdJ80J2eCEVaFrFgZJdfmhVbuitW2DGw/muTsHiWZqKmjU1afijuIQQiAUC8CTWgbCDEHZwIWWxQWFaKZhSAg9WUMLoCkb0WGwwZVvq6DUa02TNBcVVCtlVAsVZ0ud7Wdg5JVPWIUlNDWbEu74n1yNJISka5ESOQVmzphImf87G/0+uhEIhfPzJZ6isOhbukvQ8y0aoBUZgZ1HkgSUiIioUoVAIH3/8MSorK0dt4AAAZM0NtWIa9OZN7CtQUbMyAbgAre8c+mYykGDE7QCDSC4yaugw9RgkKXszdMKRGFZvaUJFiYuBA6ICYGUFAADJXrAZkOwZBZIjESgYBbMJBktSHVCrZkJv2w4z3Jbv6hQknqWJhqC3fL7Z1pVbt3ugofv2gebv7Ws/uc3dSwMnyWoil6kVLJDVgX0glxQV0iBO5/aC58KEpDhH3WjHpMrKSpxzzjm9Pqd4KiBrHsRbt3D2ARERUZb0d+0dbWSHF2p5HeKtW7pyVxONQbLqAPoZOSyEQDwQgMfnBYRuBRVSgwumYaVEsgcxJfLM9/F/VV7qwemHz8zVr0M0ZlkLEDshKU5AUSHJVtpgSIkBtVJXCmFIEiDJiawaY5skydDK62Bobujt9fmuTsFh4ICoQEmSBEgK8pW/114gKJGzN33xwfQFg0zTQLSjA06fFxLQR+AC6YsBpc2sSF2cKDU3cGpQI7UuxfHhTpJkyE5fV7DA4RmZ4w5hwfPRSNJc0Kr3gt66DWYkkO/qEBERUYGRXaXQyuoQb9vK4AFRBpKiQpI0YICDjkW3BW2TQQU9FoUitSUWsbVm8nelC06uV8iBbEQDIckKZHc5ZE9Fr+nKaOAU3zhIqgt621aeg1IwcEBEvbICFxIAKwLdX/BCFgJKXIbq9Y/YQj5dixJ2CzbYCxP2slBhSoBCpAYrugc4euxv+AEOazEoFYrTB8XlSwQKfKN2tH8hCAQCePPNN3HggQeipKSk1zKSJEOrmAqjYw/0jt0jXEMiIqLiMpBr72gju/1QxSTobdvzXRWiotJX2uD25ma8veoD6zxS1vd5xF6bMDWoYKf4TQYYkumAu9Y07LHd/vxGVDxkpw+yuwKy289ZA1kku0qhVc5i5oIUDBwQ0ajU/YZ7PmZlDIQdwJBkxAMBuPwjF1wpdrIsw+l0Qh5A8EUpqYGkuaC3bePoASIioiEazLV3NFE8FYAwoQd25rsqREVvoOcRK+igAIo27M96IhGAsNclNPsINkB0BSp6K28PLiMaeZKiQnZXQPFUQBpgWmMaPElzQauaBb11K8xoR76rk3cMHBAR5VAy5VSxpFcqJCUlJTj00EMHXF52+Tl6gIiIaBgGe+0dTRRvlRU8YH5jopzKx3lEkmRAyTyTfiCsgWHJ2Q+J4IKhQ+0MQXU7IUkps9pTUvimrSdoz4Y302axA8m1CTlLghIkCbLDB8VbCclZykGII0SSFWiV06G318MINuS7OnnFwAEREY1KpmkiHo9DVdUBd6Cs0QMzrXUPOHqAiIhoUIZy7R1NFN84CGHC6NiT76oQFa3Rfh6xBoapgNwVhBBCQNJlKB5f1n4n0S3gkLZeYPdABMy+gxUQve/Lnj2Rkn4XDFgUCklxQPFUQPZUQFIGuLAIZZ1aWgtJdcEI7OhKTz3GMHBARESjUktLC5577jkcf/zxKC8vH/DrJFnl6AEiIqIhGOq1dzRRS8YDQrCPQJQjY+E8kg3JmetA1zoRIxVmSVvfLzWYkLqmIJKP0UtZkXiYDFyYUEJhqG5X4nfoLVDRxz5SgyXJcr3WBegz6JG2vY/HPV4rehSxfsxwrCGSJBmSqkHx+KyAgbM41hEqBoqnHJLqhN66BcKI57s6I46BAyIiGpVKSkpw2GGHwev1Dun1amktJM0No237mB09QERENBjDvfaOFmppLQABI9iY76oQFZ2xch4ZzaxZE1K/kYrBBDGEEJDNIBRv9mZkFCKRGgRJfyLDdgCyilgwCNVX3G00WskOT2Ldgy0wY6F8V2dE5X1VqyVLlmDatGlwuVyYP38+3nzzzT7LPv300zjxxBNRXV2N0tJSHH744XjppZdGsLZERFQonE4nJk2aBIfDMeR9KO4yqFUzIClD3wcREdFYkY1r72ihlk6w1j0goqwaS+cRGlskSUp8yelfspL4UtO/FM3+osInKRrUyhlQ3GNrplReAwdPPPEErrnmGnz3u9/FypUrsXDhQpx66qnYtm1br+XfeOMNnHjiiXjhhRewYsUKHHvssTjzzDOxcuXKEa45ERHlWyQSwZYtWxCNDm+hY1nzQKueBdnpy1LNaDg4oICIqHBl69o7Wqj+iQweEGXZWDuPEFHxkCQZavmUxMzEsSGvgYO7774bl112GS6//HLss88+uOeeezB58mTcf//9vZa/5557cMMNN+Dggw/GrFmzcMcdd2DWrFl4/vnnR7jmRESUb8FgEB988AFCoeFPFZRkFWrFdN4cyDMOKCAiKmzZvPaOFgwe5BcHFBSfsXgeIaLiovjGQauYBknOeyKfnMvbGgexWAwrVqzATTfdlLb9pJNOwttvvz2gfZimiY6ODlRUVPRZJhqNpkWy29vbAVi5xwRXiwfQ1RZsj76xjfrH9smMbdS/obRPRUUFzj77bEiSlLV2VUonAKoLRmBnwa17MBbeO6kDCgBrwMBLL72E+++/H3feeWeP8vfcc0/az3fccQeeffZZPP/88zjwwANHospERGNKZWUlzjnnnDGXf1n1TwQkmQsmj7DkgIIlS5bgyCOPxG9/+1uceuqpWL16NaZMmdKjfHJAwR133IGysjI88sgjOPPMM/Hee++xX1BAxup5hIiKi+wqhVY5C/GWzRBGLN/VyZm8BQ6amppgGAZqamrSttfU1GD37t0D2sfPf/5zdHZ24ktf+lKfZe68807cdtttPbYHAoExcRNmIIQQCAaDAMCLdx/YRv1j+2TGNurfUNrHMIwcjVRyQLhrYbTXQ5h6DvY/NMV+zeKAgsLBQGdmbKP+sX0yG81tlLxO57Luhdg+Ssl4QHFAD+xIX9AyTwqpbXKFAwqKkyRJkMfAKF0iKn6S5oJWPQt661aY0WC+q5MTeQscJHW/QSSEGNBNo8cffxy33nornn32WYwbN67Pct/+9rdx3XXX2T+3t7dj8uTJ8Pv9KC0tHXrFi0iy0+n3+3lDsw9so/6xfTJjG/VvKO3T0tKCjz/+GHPnzoXPl+31CXwQpWXQ27bDjLRned9DU+w3CDigoHAw0JkZ26h/bJ/MRmsbBQIBfPjhh5g9ezY8Hk9OjxUOhwuwbRwQrloY7bvyPjOx2K9ZHFBQOLIdyAsEAnj77bdz1IfPj0IMdhYStk9mbKP+FXT7SAqU8mlAYAeMUEveqpGrtslb4KCqqgqKovS4GdDQ0NDjpkF3TzzxBC677DI8+eSTOOGEE/ot63Q64XQ6e2xPrnZOlq7V39kmfWEb9Y/tkxnbqH+DbZ9kuVy1qaRocFROhxFshN5RXxCjC8cCDijIPwY6M2Mb9Y/tk9lobSPDMKCqKjweT05v+CVvDni93gJsn8TggtatMGOdeatFQd48ySIOKCgc2Q50BoNB6LpedGscFGaws3CwfTJjG/Wv4NtHLYfpkGAEmwCM/PWj6AIHDocD8+fPx9KlS3H22Wfb25cuXYpFixb1+brHH38cl156KR5//HGcfvrpI1FVIiIqQKWlpTjyyCNzfhzFVw3J6YXeug1Cj2Z+AQ0JBxQUFgY6M2Mb9Y/tk9lobCO/3z8i116gsNtHUh3QqmbC6KiHEWzMd3WKGgcU5F+2A51er7doZhokFXawM//YPpmxjfo3atrH54PpK4XethXCHNmZiUUXOACA6667DhdeeCEWLFiAww8/HA888AC2bduGK664AoB1Ed+5cyf+8Ic/ALAu/osXL8Yvf/lLHHbYYfbNBbfbDb/fn7ffg4iIRp4QAqZpjshNBVnzQKveC0b7LhidzTk91ljFAQVERIVvJK+9hU6SJKilEyBrXuiBbSN+g6DYcUBBYcl2IC8ZACqmNi7kYGchYPtkxjbq32hpH8Xth6zuhXjrlqIYeJjXFWnOPfdc3HPPPfjBD36AAw44AG+88QZeeOEF1NXVAQDq6+uxbds2u/xvf/tb6LqOr3/966itrbW/vvnNb+brVyAiojxpbm7G008/jba2thE5niTJUP2ToFVMhSQrI3LMsea6667Dgw8+iIcffhhr1qzBtdde22NAweLFi+3yyQEFP//5z+0BBbt370YgEMjXr0BEVNRG+to7GshuP7TKWZDUnjefaehSBxSkWrp0KY444og+X/f444/j4osvxp///GcOKChQPI8QUbGTNBe0qpmQnaN/dlXeF0e+8sorceWVV/b63KOPPpr282uvvZb7ChER0ajg8/mwYMGCnC/O2J3s8kOr9kBv2wYzGhzRYxe7c889F83NzfjBD36A+vp6zJkzZ8ADCr7+9a/b2y+66KIefQgiIhq+fF17C52kuaBV7wW9bTvMcFu+q1M0mKGgOPE8QkRjgSSrUCumw8jzosnDlffAARER0VC4XC5MnTo1L8eWFA1a5QwYwQboHbu5cHIWcUABEVHhyue1t9BJkgytvA6G5oHeUc++QRZwQEFx4nmEiMYKSZKglk2GpLqgt+/Kd3WGhIEDIiIalaLRKHbs2IFx48bB4XDkpQ6Kbxwkhw9661YII5aXOhAREY2UQrj2FjrFVw3J4YHeugXC0PNdnVGPAwqKD88jRDTWKL5qSKozL4smD1de1zggIiIaqo6ODrz77rvo7OzMaz1kh7Vwsuwuy2s9iIiIcq1Qrr2FTnZ4oVXtBdnhzXdViAoOzyNENBbJrlKolTMhKaMrYMrAARERjUoVFRVYtGgRysrK8l0VSLICrbwOqn8iJImXViIiKk6FdO0tdJKiQa2cAcVbme+qEBUUnkeIaKySNbe1aPIoGljAuxtERDQqybIMTdMgSVK+q2JTvFVQq2ZCUp35rgoREVHWFeK1t5BJkgTVP8nKb8yBBUQAeB4horHNGlgwHYq7PN9VGRD2XoiIaFTq6OjAe++9V3DTnK1RBLOYuoiIiIpOoV57C53iqYBaOQOSouW7KkR5x/MIEY11kiRDLZ8CtWR8vquSEQMHREQ0KpmmiWg0CrMAFxdKTV0EjqYiIqIiUcjX3kInOzzWwIJRlJ6AKBd4HiEisiglNVDL6wp6VqKa7woQERENhd/vx+c+97l8V6NfircKkuaB3roVwojluzpERETDMhquvYUsue6B0b4TRmdzvqtDlBc8jxARdVHcZZAUB/TWzRCGnu/q9FC4IQ0iIqIiIDs80KpnQXaW5LsqRERElGdd6x5M4qxEIiIisu4ZVM6CpLnyXZUeGDggIqJRqbm5GU8//TRaW1vzXZWMJFmFVjkdSklNvqtCREQ0ZKPp2lvoFE8ltMoZkBQmAaCxhecRIqKeJNUBrXJmwQ04ZOCAiIhGJY/Hg7lz58Lj8eS7KgOmloyHVjENkqzkuypERESDNhqvvYVMdnihVe0FWXPnuypEI4bnESKi3kmyArViGhRvVb6rYmPggIiIRiW3242ZM2fC6XTmuyqDIrtKoVUV5jREIiKi/ozWa28hkxQNatVMyO6yfFeFaETwPEJE1DcrpeFEqP6JBZHSkIEDIiIalWKxGOrr6xGPx/NdlUGTVCe0qllQ3OX5rgoREdGAjeZrbyGTJBlaeR3UkvH5rgpRzvE8QkSUmeKtglY+FZKc31v3DBwQEdGo1N7ejuXLlyMYDOa7KkMiSTLU8ilQSycUxEgCIiKiTEb7tbfQKSU10Cryf5OAKJd4HiEiGhjZVQq1ciYkRctfHfJ2ZCIiomGoqKjA6aefDr/fn++qDIviq+a6B0RENCoUy7W3kMkuf+ImgSPfVSHKCZ5HiIgGTtbc0Kpm5W09JAYOiIhoVJJlGW63G3IRjMqTnSXQqvbiugdERFTQiunaW8hkzQ2tehZkhzffVSHKOp5HiIgGx14PyTXyAVeeqYmIaFQKBoP44IMP0NnZme+qZIWkOqBV5qczQERENBDFdu0tZJKsQq2cAcXD9ZCouPA8QkQ0eJIkQ6uYCsVXPaLHZeCAiIhGJcMw0N7eDtM0812VrJFkxeoMlNTkuypEREQ9FOO1t5BJkgS1bArU0tp8V4Uoa3geISIaOrV0AlT/xBFbJ1EdkaMQERFlmd/vx3HHHZfvauSEWjIesuqG3rYNQvBDFRERFYZivvYWMsU3DpLqhN7KfgGNfjyPEBENj+KtgqQ4oLdthchxEJYzDoiIiAqQ7PZDrZoBSdHyXRUiIiLKM9nFfgERERFZZFcp1MqZOe8XMHBARESjUnNzM5599lm0tbXluyo5I2seaFWzIDs8+a4KERHRmLj2FjK7X6C5810VoiHjeYSIKDtkzZ3zfgEDB0RENCp5PB7Mnj0bLpcr31XJKUnRoFbOgOwuy3dViIhojBsr195CJika1KqZkF3+fFeFaEh4HiEiyp6ufkFpTvbPNQ6IiGhUcrvd2HvvvfNdjREhSTK08jqoXs48ICKi/BlL195CJkkytIqpUDzefFeFaNB4HiEiyi5JkqGWT4Xi9mV935xxQEREo1I8HkdDQwPi8Xi+qzJimJqAiIjyaSxeewuZrHHENo0+PI8QEWWfJEk56RcwcEBERKNSIBDAG2+8gWAwmO+qEBERjQm89hLRcPE8QkQ0ejBVERERjUplZWU45ZRT4HZzFD4REdFI4LWXiIaL5xEiotGDgQMiIhqVVFWFz5f9HH5ERETUO157iWi4eB4hIho9mKqIiIhGpWAwiFWrViEUCuW7KkRERGMCr71ENFw8jxARjR55DxwsWbIE06ZNg8vlwvz58/Hmm2/2W/7111/H/Pnz4XK5MH36dPzmN78ZoZoSEVEhicfjaGxshK7r+a4KZRH7BUREhYvXXhpp7BcUH55HiIhGj7wGDp544glcc801+O53v4uVK1di4cKFOPXUU7Ft27Zey2/evBmnnXYaFi5ciJUrV+I73/kOrr76avztb38b4ZoTEVG+lZeX48QTT0RpaWm+q0JZwn4BEVFh47WXRhL7BcWJ5xEiotFDEkKIfB380EMPxUEHHYT777/f3rbPPvvg85//PO68884e5W+88UY899xzWLNmjb3tiiuuwEcffYR33nlnQMdsb2+H3+9HIBDghSpBCIFAIAC/3w9JkvJdnYLENuof2ycztlH/htI+uq6jvb09xzUrHEIIVFZW5rsaOcV+QWHg+SoztlH/2D6ZjdY2GqlrrxACwWAQPp9vVLXPSGK/gP2CkZLt81Ux9uF5zuof2ycztlH/2D6Z5apfkLfFkWOxGFasWIGbbropbftJJ52Et99+u9fXvPPOOzjppJPStp188sl46KGHEI/HoWlaj9dEo1FEo1H750AgYH/PY8ykoCQ7AgD4D9gHtlH/2D6ZsY36N5T2aWxsxKuvvopDDz0Ufr8/l9UrCEIIaJqGkpKSonwPsV9QOHi+yoxt1D+2T2ajtY1G6tqbvEFgmuaoap+RxH5BT+wX5Ea2z1fF2IfnOat/bJ/M2Eb9Y/tklqt+Qd4CB01NTTAMAzU1NWnba2pqsHv37l5fs3v37l7L67qOpqYm1NbW9njNnXfeidtuu63H9ilTpgyj9kRERPnR0NCA6urqfFcj69gvICIiGjz2C7qwX0BERGNdtvsFeQscJHWPgggh+o2M9Fa+t+1J3/72t3HdddfZP5umiZaWFlRWVjJKldDe3o7Jkydj+/btnI7ZB7ZR/9g+mbGN+sf2ySzZRg6HI99VySn2C/KP/4+ZsY36x/bJjG3UP7ZPZuwXDLx8b9uT2C/IjP+PmbGN+sf2yYxt1D+2T2a56hfkLXBQVVUFRVF6jBZoaGjoMUogafz48b2WV1W1zzxOTqcTTqczbVtZWdnQK17ESktL+Q+YAduof2yfzNhG/WP7ZFasH2LZLyg8/H/MjG3UP7ZPZmyj/rF9MmO/oAv7BbnF/8fM2Eb9Y/tkxjbqH9sns2z3C+Ss7m0QHA4H5s+fj6VLl6ZtX7p0KY444oheX3P44Yf3KP/yyy9jwYIFveYrJCIiotGB/QIiIiJKYr+AiIgo//IWOACA6667Dg8++CAefvhhrFmzBtdeey22bduGK664AoA1bXDx4sV2+SuuuAJbt27FddddhzVr1uDhhx/GQw89hOuvvz5fvwIRERFlCfsFRERElMR+ARERUX7ldY2Dc889F83NzfjBD36A+vp6zJkzBy+88ALq6uoAAPX19di2bZtdftq0aXjhhRdw7bXX4te//jUmTJiAe++9F1/4whfy9SsUBafTiVtuuaXHFE3qwjbqH9snM7ZR/9g+mY2FNmK/oDCMhffacLGN+sf2yYxt1D+2T2ZjoY3YLygMY+G9Nlxso/6xfTJjG/WP7ZNZrtpIEsnVgoiIiIiIiIiIiIiIaMzLa6oiIiIiIiIiIiIiIiIqLAwcEBERERERERERERGRjYEDIiIiIiIiIiIiIiKyMXBAREREREREREREREQ2Bg7GiCVLlmDatGlwuVyYP38+3nzzzT7LPv300zjxxBNRXV2N0tJSHH744XjppZdGsLb5MZg2SrV8+XKoqooDDjggtxXMs8G2TzQaxXe/+13U1dXB6XRixowZePjhh0eotvkx2Db605/+hHnz5sHj8aC2thaXXHIJmpubR6i2I+uNN97AmWeeiQkTJkCSJDzzzDMZX/P6669j/vz5cLlcmD59On7zm9/kvqJ5NNg2GqvnasoO9gsyY7+gf+wXZMZ+Qd/YL+gf+wQ00tgvyIz9gv6xX5AZ+wV9Y7+gf/nsFzBwMAY88cQTuOaaa/Dd734XK1euxMKFC3Hqqadi27ZtvZZ/4403cOKJJ+KFF17AihUrcOyxx+LMM8/EypUrR7jmI2ewbZQUCASwePFiHH/88SNU0/wYSvt86UtfwiuvvIKHHnoIa9euxeOPP47Zs2ePYK1H1mDb6K233sLixYtx2WWX4bPPPsOTTz6J999/H5dffvkI13xkdHZ2Yt68ebjvvvsGVH7z5s047bTTsHDhQqxcuRLf+c53cPXVV+Nvf/tbjmuaP4Nto7F4rqbsYL8gM/YL+sd+QWbsF/SP/YL+sU9AI4n9gszYL+gf+wWZsV/QP/YL+pfXfoGgonfIIYeIK664Im3b7NmzxU033TTgfey7777itttuy3bVCsZQ2+jcc88V3/ve98Qtt9wi5s2bl8Ma5tdg2+df//qX8Pv9orm5eSSqVxAG20Y//elPxfTp09O23XvvvWLSpEk5q2OhACD+/ve/91vmhhtuELNnz07b9rWvfU0cdthhOaxZ4RhIG/Wm2M/VlB3sF2TGfkH/2C/IjP2CgWO/oH/sE1CusV+QGfsF/WO/IDP2CwaO/YL+jXS/gDMOilwsFsOKFStw0kknpW0/6aST8Pbbbw9oH6ZpoqOjAxUVFbmoYt4NtY0eeeQRbNy4Ebfcckuuq5hXQ2mf5557DgsWLMBPfvITTJw4EXvttReuv/56hMPhkajyiBtKGx1xxBHYsWMHXnjhBQghsGfPHjz11FM4/fTTR6LKBe+dd97p0Z4nn3wyPvjgA8Tj8TzVqrAV+7masoP9gszYL+gf+wWZsV+QfewXDE6xn6cpe9gvyIz9gv6xX5AZ+wXZx37B4AznPK3moD5UQJqammAYBmpqatK219TUYPfu3QPax89//nN0dnbiS1/6Ui6qmHdDaaP169fjpptuwptvvglVLe5/o6G0z6ZNm/DWW2/B5XLh73//O5qamnDllVeipaWlKPMWDqWNjjjiCPzpT3/Cueeei0gkAl3XcdZZZ+FXv/rVSFS54O3evbvX9tR1HU1NTaitrc1TzQpXsZ+rKTvYL8iM/YL+sV+QGfsF2cd+weAU+3masof9gszYL+gf+wWZsV+QfewXDM5wztOccTBGSJKU9rMQose23jz++OO49dZb8cQTT2DcuHG5ql5BGGgbGYaB888/H7fddhv22muvkape3g3mPWSaJiRJwp/+9CcccsghOO2003D33Xfj0UcfLdpRBMDg2mj16tW4+uqrcfPNN2PFihV48cUXsXnzZlxxxRUjUdVRobf27G07ja1zNWUH+wWZsV/QP/YLMmO/ILvYLxiYsXSepuxhvyAz9gv6x35BZuwXZBf7BQMz3PN0cYc+CVVVVVAUpUcUs6GhoUd0rrsnnngCl112GZ588kmccMIJuaxmXg22jTo6OvDBBx9g5cqVuOqqqwBYFz4hBFRVxcsvv4zjjjtuROo+EobyHqqtrcXEiRPh9/vtbfvssw+EENixYwdmzZqV0zqPtKG00Z133okjjzwS3/rWtwAAc+fOhdfrxcKFC3H77beP+Qj5+PHje21PVVVRWVmZp1oVprFyrqbsYL8gM/YL+sd+QWbsF2Qf+wUDM1bO05Q97Bdkxn5B/9gvyIz9guxjv2BgsnGe5oyDIudwODB//nwsXbo0bfvSpUtxxBFH9Pm6xx9/HBdffDH+/Oc/F30OtcG2UWlpKT755BOsWrXK/rriiiuw9957Y9WqVTj00ENHquojYijvoSOPPBK7du1CMBi0t61btw6yLGPSpEk5rW8+DKWNQqEQZDn9FKwoCoCuSPlYdvjhh/doz5dffhkLFiyApml5qlXhGUvnasoO9gsyY7+gf+wXZMZ+QfaxX5DZWDpPU/awX5AZ+wX9Y78gM/YLso/9gsyydp4e9HLKNOr85S9/EZqmiYceekisXr1aXHPNNcLr9YotW7YIIYS46aabxIUXXmiX//Of/yxUVRW//vWvRX19vf3V1taWr18h5wbbRt3dcsstYt68eSNU25E32Pbp6OgQkyZNEv/zP/8jPvvsM/H666+LWbNmicsvvzxfv0LODbaNHnnkEaGqqliyZInYuHGjeOutt8SCBQvEIYcckq9fIac6OjrEypUrxcqVKwUAcffdd4uVK1eKrVu3CiF6ts+mTZuEx+MR1157rVi9erV46KGHhKZp4qmnnsrXr5Bzg22jsXiupuxgvyAz9gv6x35BZuwX9I/9gv6xT0Ajif2CzNgv6B/7BZmxX9A/9gv6l89+AQMHY8Svf/1rUVdXJxwOhzjooIPE66+/bj930UUXiaOPPtr++eijjxYAenxddNFFI1/xETSYNuqu2DsCQgy+fdasWSNOOOEE4Xa7xaRJk8R1110nQqHQCNd6ZA22je69916x7777CrfbLWpra8VXvvIVsWPHjhGu9chYtmxZv+eV3trntddeEwceeKBwOBxi6tSp4v777x/5io+gwbbRWD1XU3awX5AZ+wX9Y78gM/YL+sZ+Qf/YJ6CRxn5BZuwX9I/9gszYL+gb+wX9y2e/QBKCc1yIiIiIiIiIiIiIiMjCNQ6IiIiIiIiIiIiIiMjGwAEREREREREREREREdkYOCAiIiIiIiIiIiIiIhsDB0REREREREREREREZGPggIiIiIiIiIiIiIiIbAwcEBERERERERERERGRjYEDIiIiIiIiIiIiIiKyMXBAREREREREREREREQ2Bg6ICLfeeisOOOCAfFdjwB599FGUlZXluxpERERFif0CIiIiSmK/gGjsYuCAqABdfPHFkCQJV1xxRY/nrrzySkiShIsvvnjkK0ZEREQjjv0CIiIiSmK/gIhGCgMHRAVq8uTJ+Mtf/oJwOGxvi0QiePzxxzFlypQ81qx4xePxfFeBiIioV+wXjDz2C4iIqFCxXzDy2C+gsYiBA6ICddBBB2HKlCl4+umn7W1PP/00Jk+ejAMPPDCt7IsvvoijjjoKZWVlqKysxBlnnIGNGzemldmxYwfOO+88VFRUwOv1YsGCBXjvvffSyjz22GOYOnUq/H4/zjvvPHR0dPRZv+T0v5deegn77LMPfD4fTjnlFNTX19tljjnmGFxzzTVpr/v85z+fNvph6tSpuP3227F48WL4fD7U1dXh2WefRWNjIxYtWgSfz4f9998fH3zwQY86PPPMM9hrr73gcrlw4oknYvv27WnPP//885g/fz5cLhemT5+O2267Dbqu289LkoTf/OY3WLRoEbxeL26//fY+f18iIqJ8Yr+A/QIiIqIk9gvYLyAaCQwcEBWwSy65BI888oj988MPP4xLL720R7nOzk5cd911eP/99/HKK69AlmWcffbZME0TABAMBnH00Udj165deO655/DRRx/hhhtusJ8HgI0bN+KZZ57BP/7xD/zjH//A66+/jrvuuqvf+oVCIfzsZz/DY489hjfeeAPbtm3D9ddfP+jf8xe/+AWOPPJIrFy5EqeffjouvPBCLF68GBdccAE+/PBDzJw5E4sXL4YQIu3YP/rRj/D73/8ey5cvR3t7O8477zz7+ZdeegkXXHABrr76aqxevRq//e1v8eijj+JHP/pR2rFvueUWLFq0CJ988kmvbUtERFQo2C9gv4CIiCiJ/QL2C4hyThBRwbnooovEokWLRGNjo3A6nWLz5s1iy5YtwuVyicbGRrFo0SJx0UUX9fn6hoYGAUB88sknQgghfvvb34qSkhLR3Nzca/lbbrlFeDwe0d7ebm/71re+JQ499NA+j/HII48IAGLDhg32tl//+teipqbG/vnoo48W3/zmN9Ne173udXV14oILLrB/rq+vFwDE97//fXvbO++8IwCI+vr6tGO/++67dpk1a9YIAOK9994TQgixcOFCcccdd6Qd+7HHHhO1tbX2zwDENddc0+fvSEREVAjYL2C/gIiIKIn9AvYLiEaKOqJRCiIalKqqKpx++un4/e9/DyEETj/9dFRVVfUot3HjRnz/+9/Hu+++i6amJntkwLZt2zBnzhysWrUKBx54ICoqKvo81tSpU1FSUmL/XFtbi4aGhn7r5/F4MGPGjEG9pjdz5861H9fU1AAA9t9//x7bGhoaMH78eACAqqpYsGCBXWb27NkoKyvDmjVrcMghh2DFihV4//3300YMGIaBSCSCUCgEj8cDAGn7ICIiKmTsF6RvY7+AiIjGMvYL0rexX0CUfQwcEBW4Sy+9FFdddRUA4Ne//nWvZc4880xMnjwZv/vd7zBhwgSYpok5c+YgFosBANxud8bjaJqW9rMkSWlTEwf6GpEyPVCW5bSfgd4XFErdjyRJfW7rXp/k9t62maaJ2267Deecc06PMi6Xy37s9Xp7PE9ERFSo2C9gv4CIiCiJ/QL2C4hyiWscEBW4U045BbFYDLFYDCeffHKP55ubm7FmzRp873vfw/HHH4999tkHra2taWXmzp2LVatWoaWlZaSqDQCorq5OW/zIMAx8+umnWdm3rutpCyCtXbsWbW1tmD17NgBrsai1a9di5syZPb5kmac+IiIandgv6B37BURENBaxX9A79guIsoP/DUQFTlEUrFmzBmvWrIGiKD2eLy8vR2VlJR544AFs2LABr776Kq677rq0Ml/+8pcxfvx4fP7zn8fy5cuxadMm/O1vf8M777yT07ofd9xx+Oc//4l//vOf+O9//4srr7wSbW1tWdm3pmn4xje+gffeew8ffvghLrnkEhx22GE45JBDAAA333wz/vCHP+DWW2/FZ599hjVr1uCJJ57A9773vawcn4iIKB/YL+gd+wVERDQWsV/QO/YLiLKDgQOiUaC0tBSlpaW9PifLMv7yl79gxYoVmDNnDq699lr89Kc/TSvjcDjw8ssvY9y4cTjttNOw//7746677uq1Y5FNl156KS666CIsXrwYRx99NKZNm4Zjjz02K/v2eDy48cYbcf755+Pwww+H2+3GX/7yF/v5k08+Gf/4xz+wdOlSHHzwwTjssMNw9913o66uLivHJyIiyhf2C3piv4CIiMYq9gt6Yr+AKDsk0T2hGBERERERERERERERjVmccUBERERERERERERERDYGDoiIiIiIiIiIiIiIyMbAARERERERERERERER2Rg4ICIiIiIiIiIiIiIiGwMHRERERERERERERERkY+CAiIiIiIiIiIiIiIhsDBwQEREREREREREREZGNgQMiIiIiIiIiIiIiIrIxcEBERERERERERERERDYGDoiIiIiIiIiIiIiIyMbAARERERERERERERER2Rg4ICIiIiIiIiIiIiIiGwMHRERERERERERERERkY+CAiIiIiIiIiIiIiIhsDBwQEREREREREREREZGNgQMiIiIiIiIiIiIiIrIxcEBERERERERERERERDYGDoiIiIiIiIiIiIiIyMbAARERERERERERERER2Rg4ICIiIiIiIiIiIiIiGwMHRERERERERERERERkY+CAiIiIiIiIiIiIiIhsDBwQEREREREREREREZGNgQMiIiIiIiIiIiIiIrIxcEBERERERERERERERDYGDoiIiIiIiIiIiIiIyMbAARERERERERERERER2Rg4ICIiIiIiIiIiIiIiGwMHRERERERERERERERkY+CAaJA++OADXHjhhZg6dSqcTieqq6tx6KGH4q677kIgEMh39ajA3XrrrZAkCZIkYcuWLfmuDhERDdHFF19sn897+8r2OZ7Xj+KT/HtefPHF+a4KEREN0UcffYTLLrsMM2bMgNvtht/vx957743zzjsPjz/+eL6rNyipfRvKv2OOOQaSJGHq1Kn5rgqNYWq+K0A0mvz0pz/FTTfdBNM07W1NTU1oamrCf/7zHxx22GE45phj8ldBIiIiIiIiIsq5e++9F9dddx0Mw7C3RSIRtLe3Y926dXjqqafw5S9/OY81JCIaHs44IBqg5557DjfccANM04TH48Hvfvc7tLa2orOzE6+99hrOOeecoonMRyKRfFeh4BiGgXg8nu9qEBFRAVq2bBmEEGlfmUaHhcPhkakcZR3/dkRE9M9//hPXXHMNDMOAy+XCL3/5S+zevRuxWAybNm3C7373Oxx88MH5ruagPProo3Y/hoaO/QQqJgwcEA3QLbfcYj/++c9/jssvvxxlZWXweDw4+uij8be//Q1HHXUUAOD999/HWWedhalTp8Ln88HpdGL69On4xje+gdbWVgBANBpFRUUFJEnCWWedlXasH/3oR/YUwU8//dQu/8Mf/hD77rsvXC4XSktLccwxx+Af//hH2mv7ml44depUSJKUNiMiderbK6+8gvnz58PpdOI3v/lNVtos9ZjPPPMM9t9/f7hcLuy333549tln08qapon77rsPBx54IDweD7xeLw455BA8+uijdpn6+nr7d7v11lvt7TU1NZAkCTfddJO9bcqUKZAkCWeccYa9bffu3bjyyisxdepUOBwOVFdX48tf/jI2bNiQVpfU1AH33nsvpk+fDofDgc8++wwAsH79eixevBgTJkyAw+HAxIkT8f/+3//Dnj170vazZcsWnHbaafB4PJg0aRLuuuuu4TYpERGNMo8++qh9XXnqqadwwQUXoKysDEcccYRd5sknn8TChQtRWloKl8uFefPm4f777+/zg/vu3bvxxS9+ESUlJaiqqsK1116LWCxmPz+YvsCKFStwxhlnYPz48XA6nRg/fjyOPfZYPPjggzlpg2eeeQaXXHIJysvL4ff7cfHFF/dI9bh27Vqcf/75GD9+vH2dvfTSS7Ft2za7zNe+9jVIkgSXy4VoNAoAWLJkCSRJgqqqCAaDAICHH37YPvYHH3xgv34gbZ6aHmrZsmU444wz4PP57NGjQgj85je/wcEHHwyv1wuPx4PDDjsMf/3rX3u0wa9+9SvU1dXB7Xbj+OOPx9q1a7PWvkRENPK+//3v29eMn/3sZ7j66qtRU1MDTdMwbdo0XH755XjnnXfSXtPe3o4bb7wRe+21F5xOJ8rLy3HmmWfiww8/TCvX/XP03Llz4fF4cMABB+DVV19NKzvQa9GGDRtw3nnnYeLEiXbK5SOOOAJ33nmnXaa3/kNDQwO++tWvoq6uDi6XC5WVlZg/fz6uvfbarLQjYH1uTh73lltuwY9+9CNMnjwZbrcbxx57LFavXp1WPhAI4Prrr8eMGTPgdDpRUVGB0047DcuXL7fLPP744/Y+X3vtNQDA6tWr7W0vvvgiAGDTpk32tp/97Gf261esWIFzzjkH1dXVcDgcmDZtGq6//nq7fwEAr732mv3aJUuW4KqrrkJ1dTUmTJhgl3nllVdw8skno7y8HE6nE3vvvTduv/32HoMSly1bhgMOOAAulwtz5szpcZ+HKG8EEWW0e/duAUAAECUlJSIWi/Vb/pFHHrHLd/868sgj7XJf+9rXBADhdDpFW1ubvX3u3LkCgDjggAOEEELE43Fx7LHH9rnP++67z37tRRddZG9PVVdXJwCIo48+2t529NFHCwDC4/EIp9Npv+4Xv/jFMFqr5zHLy8uFLMtpdVYURfz73/+2y15wwQV9/n7f+ta37HKzZs0SAMRJJ50khBBi/fr1drmjjjpKCCHE9u3b7W0//vGPhRBC7Ny5U0ycOLHX/ZeXl4u1a9fax0hur6ioSCu3cuVK8fHHH4vS0tJe91NXVycaGhqEEELEYjGx11579ShTW1trP968eXNW2pmIiEZe6vV22bJlfZZL7ROkXlfmzZsnhBDi1ltv7fP6d+WVV9r7ueWWW3q9liS/Lrrool7rlqp7XyAYDIrKyspej3366adnq6nS2qC34x177LHCNE0hhBCrVq0SPp+v1zpVV1fb184//elP9va3335bCCHEV77yFXtbso9x2WWX2f03XdeH3Oapf7tFixYJIYS4+OKL+9zPT37yE3s/v//97/vtD6T+7YiIqPCl3h/w+XwiHo9nfE1HR4fYf//9e71mOJ1O8eabb9plk9fr0tJSIUlSWlmfzyeamprssgO9Fs2ePbvXMvvtt59dprf+wymnnNLr67xe73Cb0bZ58+Z++wnjxo0Te/bsEUIIEQgExH777ddrnRRFEc8++6wQwvr8n9x+xx13CCGE+N3vfmdv+973vieEEOKxxx6zt7333ntCCCFefvll4XA4ej3G/PnzRTgcFkIIsWzZsl77CX6/Xwhh9X+6//1S+1nJvs/atWvT7scAEKqqiqqqKgFY9xmI8oUzDogGYOvWrfbj6dOnQ9O0fssfeuihWLZsGfbs2YN4PI6mpiZcfvnlAIDly5dj1apVAIALL7wQgDWb4LnnngMArFu3Dh9//HHa83/+85+xbNkyAMDZZ5+NpqYmrFixAjU1NQCAG2+8EW1tbUP+/UKhEE444QRs374dzc3N+MIXvtBn2aEsmNTa2oof//jHCAQCeOyxxwBYqX++//3vAwDeeOMN/PGPfwQAHH744dixYwfWr1+P2bNnA7BGcCRH5n3uc58DALz33nswTdMeVZAcSRiLxdJGGiTL33zzzdi5cyf8fj9ef/11RKNRrFixAhUVFWhtbcV3v/vdHvVuaWnBzTffjNbWVmzevBnTp0/Htddei/b2dtTV1WHlypWIRqN45ZVXoKoqtm7dip/85CcAgMceewzr1q0DAJx77rloaWnBG2+8gfb29gG3GxERjQ7HHnts2sLIBxxwQK/l5P/P3n2HOVWlfwD/pteZZHqhgzQFIlVBUYqCoIBSVRRBdOWHgsrqKqLsWnF3FVFRbCgWZBFFFEUpNkBRqVJEUNoAM8P09HbvPb8/MrkzYVoyk0zKvJ/nycPMzcnNmzMh580595wjleLbb7+F3W7HqlWrcPLkSTz55JMAgBkzZqC4uBgWiwV33303AN8V9P6Zh9V16tQJ+fn5+OOPP9C5c2cAwLvvvhvyVex//PEHSktLAQCffPIJPB4Pzpw5g88++wxjxoyp97HVrw6sPguwIRkZGTh69CjOnDmDgQMHAvBdZbd582YAwP333y9ezff+++/DbDaLVwAWFxeL7fWVV14pnvOnn34C4Mux/PmJPxfw/zto0CDIZLJG13lKSgp27doFu92Of//739i+fbs4K3LBggUwm80oKSnBDTfcAMCXd5SWloIxhoULFwIANBoNvv32W5SVlWHYsGFB1xkhhJDYcn7/gFzu2z60sLAwIB+QSCTibP4lS5bgwIEDkMlk+PTTT+FyuXD06FFccMEFcLvdtV7Bb7FYsHDhQlRUVODRRx8FANhsNvFq+WDbotLSUvzxxx8AgMWLF8PtdqOwsBCbNm3CtGnT6n2t27dvB+Brn51OJ4qLi7F169agZhzUNtOxIQ6HA5s3b0ZFRYXYNhcVFWHJkiUAfPXoXwng7rvvRnl5Ob755htoNBrwPI+7774bgiAgNzcXnTp1AhCYJwC+voPz8wS9Xo8+ffoAAGbPng2Px4M+ffrgzz//hMvlEvsxdu/ejeXLl9eI2+l04uOPP4bNZsP27dths9lw3333gTGGUaNG4cyZM3A4HHjmmWcA+Ja62rBhAwDgqaeeEmdPLl68GGazGYsXL0ZJSUnQ9UZIxER75IKQePDzzz/XuEKwPuXl5eyee+5hHTt2rHWk+n//+59YtlOnTgwAu+666xhjjD355JPiaHlBQQFjjLEbb7xRfOzBgwfFxy5YsEA8/vnnnzPGGjfjQCKRsPz8/KDqoq7z18b/nK1btxZH0xljrF+/fgwAk0qlzG63s4cfflg85xdffCGWq35FwEsvvcQYY+y9994Tjx04cIDdeeed4tV/ANiOHTvY3LlzGeCbSeGfHVLb1ZnVb6mpqeLz+o9169Yt4PXY7XYmk8nqPU+fPn0YY4zdfvvt4rHff/9dPMett94qHqcZB4QQEr+qt4fn36rnCtWvtl+0aFHAOV5//fV625Tq7V/1q983b94snmPp0qXi8ddee61GbNWdnwsUFBSI7dqwYcPYv//9b/bFF18EXMlYl+pXB/7zn/+st2z1OnjzzTfF41988YV4/OGHHw5oZ/v16yeWEwSBtW7dukZ73bFjRwaATZgwgeXn54v5lEwmYyNHjmSlpaXilX5PP/10k+p81apVAa9p/vz5DZ7n888/Z3l5eeLvkydPFh9/7Ngx8TjNOCCEkPhSV/9AQUFBjbZg2bJljDHGBg4c2GC7YTabGWNV7XVWVhbjeZ4xxtjvv/8ulvNfQR9sW8RxnDhrvl+/fuzJJ59ka9eurdEHUFv+4L+6v2vXrmzhwoVs9erV7NixY0HVU219ELWpnlNMnTpVPG6xWMR2/NJLL2WMMXbppZeKfQkWi0UsW33W4f79+xljVd/J09PTGWO+1Quys7PZJZdcwnQ6HfN6veJqD1dffTVjzHf1f0N1On78eMZY4IyDu+66K+A1bdy4scHzzJs3jzFWlc9kZWUF9Ju0adOGATTjgEQXzTggJAjVNzg8fvw4OI6rt/ytt96KpUuX4vjx4wFrDvtV3yznlltuAQBs2rQJZrMZa9asAQBcffXVyM7OBoCAkeY2bdrU+nNxcXG9MfE8X+d9GRkZyMnJqffxTdG6deuAGQqtW7cG4NvXoLy8PKTXV/0Kwx07duCnn35CWloabr/9dgC+KwaqX13onx3SUP2UlZXVONarV6+A38vLy+utx+rnyc/PF4+1atWq1p8JIYQkhvM3R/bPLDyfyWQK+L2htgmovX2q3j7621QADV6Zdn4blp2djZdeegkGgwHffvstHnroIXG/g8cff7zB2Bqjvtirt7PVy0kkErFsWVmZWMafE+zYsUNs+0eMGIFevXphx44d2L59u7j+tL9sY+u8sX87ygcIISTx1NU/kJ2dDcYY3nnnnRqPCabd8O+H6NepUydIpb5uO7VaLR73X50ebFskk8nwzjvvICsrC7t27cJjjz2G8ePHo1WrVrjzzjvrffzrr7+ODh064MiRI3jiiScwZcoUdOrUCdddd12NdfrDoXr7n5SUBIPBAKAqx/H/azAYkJSUVOvjzu87KCkpwU8//YQ///wTgwYNwqBBg2C327F161ZxlmG08gSgqu8gNzc3oN+EcgUSC2jggJAgZGVlicsOWK1WvP3227WW4zgOTqcTX331FQCgR48eyMvLA2MML7/8cq2P8S9H5PF48N///rfGMkUAkJ6eLv585swZ8efTp0/XKKNSqcRjLpcLgG+g4vyNe6vTaDR13ne+FStWiB0jwaoec/XfpVIpUlJSQnp9bdu2Rbt27QAAGzZswO+//46BAweKm0xu3rwZv/32G4CqZYoA3+AIAHTt2jWgc8d/EwShRtzn10tqaipkMhkAYOTIkbWe5/jx4wAQsCHS2bNna/2ZEEJIy3J+u+JvmwDfJn61tSv+ZW6qq94+Vm83G5MLzJ49G8XFxfj111+xcuVKjBo1ChzH4fHHH6/RflfXvn17McZQliqqL/aUlBSxna1+H2NM/L16GX87n5+fjw8//BAAxA4Bi8WC119/HYCv3vv37w+g8XVe399ux44dteYVt912G+UDhBCSgM7vH3j33XcbfIy/3dDr9fB4PLW2G/7vuX7Vl0iubangYNsiABg/fjzy8/Oxb98+fPTRR5g6dSoYY3jrrbcClvo932WXXYbjx4/j999/x9q1a3HvvfcC8C21c/4GzOc7efIkGGPi5sTBqJ4nWK1WmM1mAFU5jv9fs9kcsFFxbX0H1S869C976M8TAOD5558X+wH8OUX1Or3rrrtqzRPO36AaqD9PWLRoUa3n8fcr+XOFgoKCgH4WyhVILKCBA0KCVP3Ku7///e94++23YTab4XA4sHXrVowfPx4//vgjOI4Tr4RTKBTQ6XQ4cuQIXnnllVrP26lTJ3GN33//+98AfCPr119/vVjmmmuuEX/2r1O4b98+cW09rVaLwYMHAwgcaf/qq6/AGMPTTz8dkasBgnXmzBksXrwYVqsVH3zwAXbt2gXAtxeEVqsNeH3PPPMMzp49i+PHj2Px4sUAfEnSiBEjxDL+BODzzz8HYwyDBg1Ceno6OnfujE2bNolXfFQfOBg9ejQA4MiRI3jsscdQWloKp9OJ3bt3Y86cOZgzZ06Dr0Oj0YjrM27atAmvvvoqLBYL7HY7tm/fjmnTpmHRokUAfAmW3+OPP47y8nJs27YNa9euDbn+CCGEJKYRI0aIneCPPfYYfv31V3g8HhQWFuL9999Hv379AtZR9nvyySdRUFCAo0eP4sUXXxSP+9uoYHOBc+fO4eGHH8aePXvQsWNHTJgwAZdffjkAX2d9JNbWXbx4MY4dO4azZ8/i6aefFo8PHTo0IJ/ZuXMnPvzwQ1itVixZskQcOKieM1TvEPjss8+g1WphMpnEDgH/hRyXXHIJlEolgMbX+fn8eQUA3HfffTh06BA8Hg/y8vLw6quvivs0tW7dWuwIWr9+Pb7//ntUVFTUOjhBCCEkflTvH7jvvvvw9ttvo6KiAk6nE3l5eTXK+9sNm82G//u//0NBQQHcbjcOHTqEBQsWYOLEiSHHEGxbBABz5szBtm3bkJOTg3HjxgW0p/VdHb9gwQJs3LgRBoMB1113HcaPHx/U4xrr008/xXfffQez2Yz58+eLHelDhw4FUJUHCIKARx55BGazGd999x0+/fRTAL52t0ePHgCAdu3aoW3btgB8eQLgGzjwf1f35wlqtRoDBgwAAHTp0gUXXHABAN/+UR9//DHsdjvMZjM2bdqE66+/XtyfsT6DBg0SZ0s8//zz+Oabb+ByuVBSUoJPP/0Uw4YNw9atWwFU9R0UFhZiyZIlsFqtWLp0acBgCCFRE5YFjwhpIf7973+La+zVdvvuu+8YY4wNHTq0xn3+vQwAsHfeeSfgvMuWLQsoO3369ID7vV4vu+KKKxpci5cx39qHUqlUvE+v1zOZTCbutVDbHgeRWjPPv6ZhRkYGk8vlATFLpVK2ZcsWsexNN93U4Np/fsuXLw+4//vvv2eMBa7JqFKpmNPpFB9z9uxZcY3A2m7V1xeu7ZjfgQMHmMFgqPM8/nWePR4P69KlS43709LSxJ9pjwNCCIlf9e1xAIB9+umnjLHA9f39eUJ1/r2N6rr524rq6+3Xtm9P9TYr2Fyg+prC59/atGnD3G53WOqqeh3UFvvQoUPFNX337NnDdDpdrTGlp6ez48ePB5y7ets+ZMiQWl/XwoULm1zntbXZM2fOrPc8fitWrKg3H6A9DgghJD411D8AVO1xYLVamclkqrNc9e/pte0NUNfeQsG2RXXdbzAYxL0Va9vjwB/L+TeFQsF+++23sNRj9ddWW56QmZnJzp07xxhjrKKignXr1q3WmGQymZh/+VXfY1ClUom5TfXXdcUVVwQ8ZsuWLbXuVem/+ftzqu9xcH4fD2OMvfvuu0H1Hx05coSpVKqA+6RSKUtJSWEA7XFAootmHBASgn/84x/45ZdfcMstt6BNmzZQKpVIS0vDgAED8Mwzz6B3794AgA8++ADXX389kpKSkJmZiYcffhjz58+v87xTpkwRr4QDApcpAgC5XI6NGzfin//8J7p16walUgm9Xo/Bgwdj3bp1AVfLd+/eHStXrkTXrl2hUqnQrVs3fP311xHdw6AhF154IT777DP07NkTSqUS3bt3xyeffILhw4eLZT744AMsWbIEJpMJarUaGo0G/fr1w/Lly/H8888HnK/6FYZyuVxcfqD6Vf79+/cPWAcyNzcXu3btwpw5c9CxY0colUqkpqbi4osvxt///nfMmzcvqNfSo0cP7NmzBzNmzEDr1q2hUCiQkZGB/v3747HHHsO0adMA+GabbNy4Eddccw3UajVycnLw+OOPY/bs2aFXICGEkIT16KOPYu3atRg6dCgMBgOUSiXatWuHa6+9Fm+++WbAUjd+a9euxcSJE6HX65Gamop7770Xb7zxhnh/sLlAWloa5s6di969eyMlJQUKhQKtWrXCzTffjC1btgTkJuHy+uuv44477oDRaERSUhKmTZuGtWvXiksw9O7dGzt37sSUKVOQmZkJuVyOnJwc3Hbbbdi5cyc6dOgQcL7qOYF/pkH79u0D6q36DESgcXVemzfffBNvvfUWLr30Uuj1emg0GnTq1AmTJk0Sl04CgNtuuw0vvvgi2rRpA5VKhSuvvDKkZRsIIYTEpn/84x/4+eefcfPNN6N169bid8yePXvilltuwWeffYYZM2YA8C1RtH37dixYsADdunWDSqVCcnIyLrzwQsyaNStgFl4ogm2LHnroIVxyySVIT0+HQqFAdnY2xo4diy1btoh7K9Zmzpw5uPLKK5GVlSV+9x0+fDi+/PLLGnsChsPf/vY3/Oc//xHbzCFDhuDbb79FZmYmAN/eBj/99BPuv/9+dOjQAQqFAkajESNHjsS3334bsHIDEJgn9O3bV8xtqvcdnJ8nDB8+HL/88gsmTZqErKwsyOVyZGdn4/LLL8eiRYswatSooF7LtGnT8O233+Laa69FamoqFAoFWrdujeHDh+PFF19Enz59APhmOWzYsAEmk0nsL1mzZk1E6peQUEkYC2GhckIICUH79u1x6tQp+oJMCCGEtGArVqwQO06+++47cUklQgghhJCTJ0+KFwb885//DGnvJEJIZNGMA0IIIYQQQgghhBBCCCGEiGjggBBCCCGEEEIIIYQQQgghIlqqiBBCCCGEEEIIIYQQQgghIppxQAghhBBCCCGEEEIIIYQQEQ0cEEIIIYQQQgghhBBCCCFERAMHhBBCCCGEEEIIIYQQQggR0cABIYQQQgghhBBCCCGEEEJENHBACCGEEEIIIYQQQgghhBARDRwQQgghhBBCCCGEEEIIIUREAweEEEIIIYQQQgghhBBCCBHRwAEhhBBCCCGEEEIIIYQQQkQ0cEAIIYQQQgghhBBCCCGEEFGLGzhgjMFisYAxFu1QCCGEEBJllBcQQgghxI/yAkIIIaSKPNoBNDer1QqDwQCz2Yzk5ORohxMTGGMwm80wGAyQSCTRDicmUR3Vj+qnYYlWR1arFfv27UPHjh2h0WiafD7GGGw2G/R6fULUTyQwxpCWlhbtMBIO5QU1JdrnVSRQHdWP6qdhsVhH4W7bm4LygoZRXhAZlBfUFIufV7GG6qh+VD8Na+46iqU2PxiUFzQsUnlBi5txQAghpOlUKhW6dOkCtVod7VAIIYQQEgbUtpNYs3XrVowZMwa5ubmQSCRYt25dveXXrl2Lq6++GhkZGUhOTsbAgQOxcePG5gmWEELiCLX5JFg0cEAIISRkUqkUCoWCRvsJIYSQBEFtO4k1drsdJpMJS5cuDar81q1bcfXVV2PDhg3YvXs3hg4dijFjxmDv3r0RjpQQQuILtfkkWC1uqSJCCCFNZ7Va8csvv6BHjx7Q6XTRDocQQgghTURtO4k1o0aNwqhRo4Iuv2TJkoDfn3nmGXz22WdYv349evfuHeboCCEkflGbT4JFAweEEEJCJggC3G43BEGIdiiEEEIICQNq20miEQQBVqsVqampdZZxu91wu93i7xaLBYBvrWjaINnHXxdUH3WjOqof1U/DmruOeJ6Hy+UCz/Nx8Xeh91DDIlU3NHBACCEkZAaDAVdccUW0wyCEEEJImFDbThLN888/D7vdjsmTJ9dZZtGiRXj88cdrHDebzdRBVcm/KSkAWtakDlRH9aP6aVhz15FEIkGfPn0AQHzeWOd0Oun904D09PSwn5MGDgghhBBCCCGEEJIwVq1ahX/961/47LPPkJmZWWe5+fPnY968eeLvFosFbdq0gcFgQHJycnOEGvP8AygGg4E67epAdVQ/qp+GNXcdcRwX8ecIJ/9sA51OR++hOtCMA0IIITGjtLQU69evx9ChQ5GSkhLtcAghhBDSRNS2k0SxevVqzJw5E2vWrMFVV11Vb1mVSgWVSlXjuEQioc6pavz1QXVSN6qj+lH9NKw566isrCzu2nx6D0WHNNoBEEIIiT9arRa9evWCVquNdiiEEEIICQNq20kiWLVqFaZPn44PP/wQ1157bbTDIYSQmERtPgkWDRwQAKBpmEGgOiKkikajwQUXXFDrFVqENNbWrVsxZswY5ObmQiKRYN26dQ0+5ocffkDfvn2hVqvRsWNHvPbaa5EPtAWgNq9hVEck0VDbTmKNzWbDvn37sG/fPgDAiRMnsG/fPuTl5QHwLTM0bdo0sfyqVaswbdo0PP/887j00ktRWFiIwsJCmM3maISfUKjNaxjVEYkn1OaTYEV14IA6CAghJD55PB4UFBTA6/VGOxSSQOx2O0wmE5YuXRpU+RMnTmD06NEYPHgw9u7di0ceeQRz587FJ598EuFICSEk8VDbTmLNrl270Lt3b/Tu3RsAMG/ePPTu3RsLFy4EABQUFIiDCADw+uuvg+M43H333cjJyRFv9957b1TiJ4SQWEVtPglWVPc48HcQzJgxAxMmTGiwvL+D4M4778QHH3yAH3/8EbNnz0ZGRkZQjyeEEBIeFosFP/74I4YPHx43ayKS2Ddq1CiMGjUq6PKvvfYa2rZtiyVLlgAAunfvjl27duG5556jvIAQQkJEbTuJNUOGDKl3s8cVK1YE/P79999HNiBCCEkQ1OaTYEV1xsGoUaPw1FNPYfz48UGVr95B0L17d9xxxx24/fbb8dxzz0U40vAoLi7GsGHDkJycjEmTJkU7nJgmkUjEKamx4Pvvv0dqamq0wyAkZqSmpuLaa6+FwWCIdiikBduxYwdGjBgRcGzkyJHYtWtXnVfPuN1uWCyWgBsAMMaa/VZUVBSQF0Qjhtpu1UU7Fv9NIpFg7969UY/Df6ueF0Q7FrrRLVy3lJQUjB49GsnJyVGPhW7B3Uhiof6C4FF/ASFNQ9/nSbCiOuMgVHV1ECxfvhxerxcKhaLGY9xuN9xut/j7+R0EoRg6dCh27NgBhUIBpVKJnj174rnnnkO/fv2Cevzrr78OmUyG8vJySKXSmEz2YimmYP9GM2bMwLvvvos33ngDd9xxh3h8zZo1mDJlCsaNG4dPP/00rHE1Rn5+Pu6880788MMPSEtLw6OPPoo777yzzvKbN2/Gww8/jD///BNt2rTB888/j2uuuaZGuYMHD6Jv374YPXp0WF9nKOgLTMMSrY4kEgnUajWA8HxuJFr9RALVTU2FhYXIysoKOJaVlQWO41BSUoKcnJwaj1m0aBEef/zxGsfNZnNQdVx9/dphw4bVyAv++9//Bp0XvPHGG5BKpSgrK4u5vCCWYvELJS9477338Prrr9fIC2688UaMHTu2ye2lP46m1JM/L9i6dSvS0tKwYMGCBvOC+fPni3nBc889F5AXBHs+fy4cSYwx2Gw2AL72gtQUi3XE8zx4nofD4Yh2KAAAp9MZM3UTq9LT06MdAqlmyJAhNfKC559/PqS8QCaToaKiAlIpbUcZLtOnT8e7776LN998s0ZeMHnyZIwbNy6oZbMjLT8/H3fccYfYX/DYY4/Vmxds374dDzzwAA4fPgy9Xo+pU6fi2WefFd87Dd1PWjapVAqNRhPtMEgciKuBg2h3EADAs88+i3vvvRcejwePPfYYJkyYgJMnTwYV/4kTJ3DhhRdCIpE06osmx3GQyyPzJ4vnDgLGGLp27YoVK1Zg5syZ4vF33nkH3bp1C0tnZDg6CG6++WZ07NgRhYWFOHjwIEaNGoXOnTvjyiuvrFH2+PHjGD9+PFauXIlrr70WGzZswMSJE/Hbb7+hY8eOYjlBEPC3v/0NAwcOrPN1UgdBbEi0OrJYLDhw4AA6deoUtoSDOggaRh0ENZ3/nvF/Dtb1Xpo/fz7mzZsn/m6xWNCmTRsYDIZGbWr37LPP4r777oPH48Gjjz6KiRMn4tSpU0E99sSJE7jooosgk8lCfl4gsnkBEHufVRKJJKiYJBIJunbtinfffTfgC/e7776Lbt26BX2ehp6j+r+NMXXqVHTs2BHnzp3DwYMHcc0116Br16515gUTJkzAhx9+KOYFkyZNwv79+8W8INjzNceVZf7/hwaDIebeR7EiFuuooqICR44cQbdu3aDT6aIaiz+v1el0MVM/sSYWv78R4N///ndAXjB+/PiAfRjq488LGtuxG+m8IJ517doV77zzTsDAgb+/IFbcdNNN6NSpE4qKinDw4EGMHDkSXbp0qTUv4Hke48aNwwMPPIAff/wRp0+fxtChQ9GxY0fMmjWrwfsJsdls2LVrF7p37x71Np/EtrgbamxMB4HZbBZvp0+fBuBL0oO5+b9cVj+/RCKBSqXCbbfdhtOnT6OkpES8/+WXX8aFF16I1NRUDBs2DH/88QckEgmmTJmC9957D8uWLUNycjLefvttSCQSfPPNN7j00kuRmpqKnj17Yv369eLz3X777bjjjjtw4403wmg04rXXXgPHcfjnP/+Jzp07IyMjA9dffz0KCgrEx8hkMrz++uvo1asXjEYjrr/+elgsFvH+v/76C9dffz2ysrKQnp6OiRMnivcdP34c48aNQ1ZWFjp06ICnn34ajLGAOvDf3n33XfTp0wcLFixARkYG2rdvj2XLlgWUWb16NS6++GKkpqbikksuwY4dO8T7PvzwQ/Tq1QsGgwHt27cXN7iqXtf+n//880907twZr7zySq2xSCQSXH311cjLy8PRo0chkUhQUFCAXbt2YezYsQHlHnroIXTo0AEGgwE9evTAxx9/HHD/nj17cNVVVyE9PR1ZWVmYO3duQEzLly9Hu3btkJGRgYceeqjOeM6/HT9+HNu3b8eiRYug1+tx6aWX4uabb8aKFStqLb9x40b06dMHY8eOhUwmw5gxYzBgwAC8//77AeWWLl2Krl27YsiQIXU+d7Dv9abe9Hp9sz1XvN4SqY60Wi2cTifUajX0en2TbzqdDhqNBjqdLiznS8QbJXQ1ZWdno7CwMOBYUVER5HI50tLSan2MSqVCcnJywA1A0J/n5+cF/p9VKhWmT59ea17QvXt3pKSkYOjQoWJeMHnyZDEvSEpKCsgLLrnkEqSkpKBHjx4BecGMGTNwxx13YMqUKTAYDAF5wQUXXID09HSMGzcuIC+QSqV4/fXX0bNnTxgMBowbN65GXjBu3DhkZmYiLS0tYG+IY8eOYezYscjMzET79u0bzAt69+6NBQsWID09He3atas1LzCZTEhJScGAAQNq5AU9e/ZEcnIy2rVr12BecMEFF9SZFwDAiBEjauQFO3fuxLhx4wLO9dBDD6F9+/ZITk7GRRddVGteMHz4cKSlpSEzM1PMC/zeeusttG3bFunp6Y3KC5599lkxL5g6dSreeeedRuUFoZ6PbnSr7SYIAiwWS53/z+kWezcSu5RKpdhfUFxcDMDXd/HSSy+hW7duMBqNGDJkCA4fPgwAmDRpEt599128+uqr0Ov1WL58OQBgy5YtGDBgAIxGIy666CJ8/vnn4nNMnz4dM2fOxOTJk5GcnIxly5bB6/Vi4cKF6NSpE9LS0jB27Fjk5+eLj5FIJHjttdfQo0cPJCcnY+zYsTCbzeL9f/75J8aOHYuMjAykpqYGLCt97NgxjBkzBhkZGWjXrh2eeuopCIJQ6+tfsWIFLr74YjzyyCNIS0tD27Zt8eqrrwaU+d///if2W/Tv3x8//fSTeN/KlSvRo0cPJCUloW3btnjsscfqHCw7evQoOnXqhKVLl9b59/DnBUeOHAHgu7q/el7g949//APt2rVDUlISLrzwQqxZsybg/t27d2PYsGFITU1FRkYG5syZE3D/W2+9hTZt2iAtLQ3/+Mc/6oznfMeOHRP7C3Q6HS655BJMnToVb7/9dq3lzWYzysrKcNttt0Emk6F9+/a46qqrcPDgwaDuJ4TneVgsljr/DxPiF1cDB7HUQeByufD2228jPT0dqampYgP89ttvY/369SgpKcH48eMxduxYeL1erFmzBlOnTsXs2bNhs9lwxx134MCBA5g8eTKeffZZlJWV4fXXX8e0adPEL7mArzGdOXMmKioqcMcdd+DRRx/FTz/9hO3bt6OgoABdunTBTTfdFBDjRx99hG+++QZ5eXk4c+YMlixZAolEAofDgauvvho9evTAyZMnUVhYKDZ0TqcTV111FYYNG4azZ89i27ZtWL16dZ0d2xKJBAcPHoRUKkVBQQFWr16N+fPnY9u2bZBIJPjqq6/w4IMPYsWKFSgrK8P8+fMxduxYlJWVQSKRIC0tDWvXroXFYsHnn3+ON998E6tWrapR1zt37sTw4cOxaNEizJkzp86/i0wmw6233irG+95772Hy5MnildD+shdffDF27tyJiooKLFy4ENOmTcPJkychkUiQn5+P4cOHY+LEicjPz8epU6cwZcoU8TmsVisOHDiAP//8E9u3b8err76KH374QTx3SkoKfvzxx1pjPHDgAHJycpCdnS0e6927N/bv319ref9VVtWPCYKAAwcOiL+fPn0aL774Ip577rka70+60S3SN6PRKK7BGu1YWtKNBBo4cCA2b94ccGzTpk3o169frcsXRpLT6cTy5cuRnp4ubjC2bNkyLF++PCAvGDNmDDweT428YObMmdi/fz8mTZoUkBfceuut4pdcAFi1apWYF8ycORMLFizAjz/+GJAX3HjjjQGxrV69OiAveOGFFwAAdrsdV111VdB5wf/+9z+88847ddbBwYMHIZFIxLzg4YcfxtatWwEAGzZswAMPPBCQF4wZMwalpaUAfOusVs8L3njjDXz44Yc1nuPXX3/FsGHDsGjRItxzzz11xiKVSnHrrbeK8b777ruYPHmyuMSan8lkCsgLbr31Vpw4cQIAcPbsWQwbNiwgL5g8ebL42PPzgldeeSVgY06j0Yjt27fXGt/+/fuRk5MTMJP24osvxv79+2stLwhCjQ4TQRDE8qGej5DaGAwGDBs2DElJSdEOhZC4R3kB5QWRzAtSU1Nx++23i8t2Hzt2DFu2bMGoUaOCup8QavNJsOJq4CAWOgjmz58Po9EInU6HVatW4dNPPxWnA77yyit44okn0LlzZ8jlcsydOxdOpxO//PJLred6/fXXMX36dAwbNgxSqRSXX345rrvuOnz00UdimREjRmDkyJHi+mOvvvoqFi9ejJycHCiVSjz11FPi1DO/hx56CFlZWTAajZgwYQJ2794NAPjiiy+gUCjw9NNPQ6fTQalUYujQoeJ9KSkpuP/++6FUKtG2bVvce++9tTbOfjqdDv/617+gVCoxcOBATJ06Fe+9955YFw8++CD69OkDqVSK8ePHo1u3btiwYQMA38bYXbp0gUTi68y/6aabAhpVAPj6669x/fXXi4MADZkxYwbef/99cByHFStWYMaMGTXKTJ06FZmZmZDJZLjxxhvRrVs38cqGDz74AH379sXs2bOhVquh1WoxePBg8bGMMSxatAhqtRrdu3fHoEGDxLoFfNO7L7/88lpjs9lsMBqNAceMRiOsVmut5UeMGIFdu3Zh3bp14DgO69atw48//hiw7NCsWbPwr3/9i5YuIYQkDJvNhn379omb3Z04cQL79u0Tp/jPnz8f06ZNE8vPmjULp06dwrx583D48GG8/fbbWL58OR544IFmi5nygiqUF0QvLwj1fIQQQiKD8oIqlBdELi8AfLNU3njjDWg0GlxwwQW47rrrcO211wZ9PyGEBCOqAwfx2EGwaNEiVFRU4PTp08jNzcVvv/0m3nfy5EnccsstMBqN4q28vBxnzpyp9VwnT57Ea6+9FlD+s88+C5hK2LZtW/HnkpIS2O12XHHFFWL57OxsKJXKgEQgOztb/Fmn04mNzalTp9CpU6dar1o9deoUDh48GBDL3//+9xozPKrLzc0NGLBp164dzp49K762Rx55JOB8+/btE+/fuHEjBg0ahPT0dHG5hZKSkoDzL1myBEOHDsWwYcPqjKG6zp07o0OHDli4cCGUSmWtm1C98MILuOiii2AwGGA0GnHw4EHxeU+dOoXOnTvXef7k5GRotVrx9+p12xC9Xh8wBRTwTR+sa3S3S5cuWLNmDZ544glkZmZi+fLluPHGG8WZNR9++CFcLhduu+22oJ6fkHArLS3FZ599hoqKimiHQhLIrl270Lt3b/Tu3RsAMG/ePPTu3VtctqagoCBgneAOHTpgw4YN+P7773HxxRfjySefxEsvvRSw3E6kUV5QhfKC6OUFoZ6PkNpQ205I01FeUIXygsjlBUeOHMH111+PF154AS6XC/n5+Th8+DDmz58f1P2EUJtPghXVgYN47CDwa9WqFd5880089NBDYsPdpk0brFmzBhUVFeLN4XDgpptuqvUcbdq0wb333htQ3mazYdmyZWKZ6hsjpaWlQavV4pdffgl4jNPpxKBBgxqMuV27djh27FitawO2bt0affv2DTivxWLBoUOH6jxffn4+vF6v+HteXh5atWolvrbnn38+4Hx2ux0PP/wwPB4Pxo8fj7vuugtnz56F2WzGrFmzasT14Ycf4vDhw7jnnnuC3vxrxowZePbZZ2u9emD79u3417/+hffeew/l5eWoqKhAjx49xHO3a9cOf/31V1DPE6pevXohPz8fRUVF4rF9+/ahZ8+edT7muuuuw549e1BWVob169fjzz//FDdG2rRpE3bv3o3s7GxkZ2fjueeew9dff43WrVtHJH5CzqfVatGtW7ca03sJaYohQ4aIS7VVv61YsQKAb73c8682u/LKK7Fnzx643W6cOHEiahu+UV5AeUEowp0XNOZ8hJyP2nZCwofyAsoLQhFqO37gwAG0bt0aEydOhFwuR05ODm677TasX78+qPsJoTafBCuqAwfx3EEAAH369MGQIUPwzDPPAADuvvtuLFy4UFxz0GKx4LPPPqtzlPmuu+7CO++8g++++w48z8PtdmPHjh3iBknnk0qlmDVrFv7+97+LVwyUlpZi9erVQcV77bXXwu12Y+HChbDb7fB4PPjuu+8A+L6Mnjt3Dq+++ipcLhd4nseRI0dq1H91drsdTz75JDweD3755ResXLkSU6dOBQDcc889+O9//4vdu3eDMQaHw4EtW7bgzJkzcLvdcLlcSEtLg0qlwi+//FLrFMfU1FR88803+PnnnzF79uygkoGbb74ZmzZtwp133lnjPovFArlcjoyMDAiCgLfffjtgc6CpU6fi119/xWuvvQa32w2Hw4Ft27Y1+JzB6NSpEy677DI88sgjcDgc+PXXX7Fy5UrMnDmzzsfs2rULHMfBarXiiSeeEDc3AoAXX3wRR44cEWfszJo1C0OHDg2YCklIJGk0GnTt2pUSDUKqobyA8oJghTsvaMz5CDkfte2EhBflBZQXBCvUdrxv377Iz8/HunXrIAgCiouL8f7774sX5TZ0PyHU5pNgxdUeB7FowYIFeOutt3D69Gncc889mD59OsaPH4/k5GR079693jX/evfujVWrVuHRRx9FRkYGWrVqhcceewxut7vOxyxatAgDBw4UNzHp27cvNm3aFFSser0eW7Zswe7du9G2bVvk5OTglVdeEe/bvHkzvvnmG7Rv3x5paWm4+eab65162KNHD3Ach5ycHEycOBFPP/20uAbiddddh2effRZ33nknUlJS0KFDB7z44osQBAFJSUl45ZVX8Le//Q3Jycl4+umnMWXKlFqfIyUlBVu2bMGePXvwt7/9rcFkQKPR4Kqrrqp1St8111yDCRMmoGfPnsjNzcWhQ4dw2WWXife3bt0aW7ZswYcffoisrCy0b98eH3/8cYP16qfX6+tNHFatWoWzZ88iIyMDEyZMwH/+8x/xSkEAuOiii7By5Urx9/nz5yM1NRWtW7fG/v378d1330Gn0wHwbWTjn22QnZ0NvV4PlUoVsJkSIZHk9XpRVFQUcBURIYTyAsoLqjRnXhDM+QhpCLXthIQf5QWUF/iFMy/o0KED/ve//+GJJ55ASkoKevTogczMTHGj64buJ4TafBIsCQt2TleCsFgsMBgMMJvNSE5OjnY4McE/00MikdS6nmFtVqxYgSVLloj7UyS6xtRRS8IYg9lshsFgoPqpQ6LVUWFhIT7//HMMHz4cKSkpTT4fYww2mw16vT4h6icSGGPieuYkfCgvqInygoZRXlC/RGvzIiEW6yjcbXtTUF7QMMoLIoPygpooL2gY5QX1i8U2L9Y0dx3FUpsfDMoLGhapvEAe9jMSQghJeEajEddccw00Gk20QyGEEEJIGFDbTgghhLQM1OaTYNHAASGEkJDJ5XLo9fpoh0EIIYSQMKG2nRBCCGkZqM0nwaI9DkijTJ8+vcVMOySE1GSz2bBv3z44HI5oh0IIiQGUFxAS/6htJ4SEC+UFhMQ2avNJsGjggBBCSMi8Xi+Ki4vBcVy0QyGEEEJIGFDbTgghhLQM1OaTYNHAAQHg2wSK1I/qiJAqKSkpuPrqq2nTOEISFLV5DaM6IomG2nZCSF2ozWsY1RGJJ9Tmk2DRwAEhhBBCCCGEEEIIIYQQQkQ0cEAIISRkZWVl+PLLL2E2m6MdCiGEEELCgNp2QgghpGWgNp8EiwYOCCGEhEytVqN9+/ZQKpXRDoUQQgghYUBtOyGEENIyUJtPgiWPdgCEEELij1arxUUXXRTtMAghhBASJtS2E0IIIS0DtfkkWDTjgBBCSMi8Xi/KysrAcVy0QyGEEEJIGFDbTgghhLQM1OaTYNHAASGEkJCZzWZ8++23sFqt0Q6FEEIIIWFAbTshhBDSMlCbT4JFSxURQggJmdFoxFVXXYWkpKRoh0IIIYSQMKC2nRBCCGkZqM0nwaKBA0IIISGTy+UwGo3RDoMQQgghYUJtOyGEENIyUJtPgkVLFRFCCAmZ3W7HgQMH4HQ6ox0KIYQQQsKA2nZCCCGkZaA2nwSLBg4IIYSEzOPx4MyZM/B4PNEOhRBCCCFhQG07IYQQ0jJQm0+CRUsVEUIICVlKSgpGjRoV7TAIIYQQEibUthNCCCEtA7X5JFg044AQQgghhBBCCCGEEEIIISIaOCCEEBKy8vJyfP311zCbzdEOhRBCCCFhQG07IYQQ0jJQm0+CRQMHhBBCQqZUKpGbmwuFQhHtUAghhBASBtS2E0IIIS0DtfkkWLTHASGEkJDpdDr06tUr2mEQQgghJEyobSeEEEJaBmrzSbBoxgEhhJCQcRwHs9kMnuejHQohhBBCwoDadkIIIaRloDafBIsGDgghhISsoqICmzdvhsViiXYohBBCCAkDatsJIYSQloHafBIsGjgghBASMoPBgKFDhyIpKSnaoRBCCCEkDKhtJ4QQQloGavNJsGiPA0IIISFTKBRIS0uLdhiEEEIICRNq2wkhhJCWgdp8EiyacUAIISRkDocDhw8fhtPpjHYohBBCCAkDatsJIYSQloHafBIsGjgghBASMpfLhWPHjsHtdkc7FEIIIYSEAbXthBBCSMtAbT4JFg0cEEIICVlqaiquu+46GI3GaIfSYjDOA8HrinYYhBBCEhS17fGFMQaB80Q7jIjaunUrxowZg9zcXEgkEqxbt67Bx/zwww/o27cv1Go1OnbsiNdeey3ygRJCSJyhNj/xRCovoIEDQgghJMYwxiB4HOBtxfCWnYTn3CF4ig6Ds5dGOzRCCCGERBETeF9+UHQYfILnBXa7HSaTCUuXLg2q/IkTJzB69GgMHjwYe/fuxSOPPIK5c+fik08+iXCkhBBCSPQITjO8xUfAO8rDfu4WuzmywNF0HEIIaayKigp8++23GDBgAJKTk6MdTtxjjIF5HWBuGwSPDczjAGNCtMMihBDSglDbHtsY7wVvL4HgKAUTeDDGoh1SxI0aNQqjRo0Kuvxrr72Gtm3bYsmSJQCA7t27Y9euXXjuuecwYcKECEVJCCHxh9r8xCC4reAtBRC8zojlBS124ICzlQCpGdEOgxBC4pJcLkdaWhrk8hbbjDQZ87oguK0Q3FYwrx1MoIECQggh0UNte2xinAe8vQiCo5wuKmjAjh07MGLEiIBjI0eOxPLly+H1eqFQKGo8xu12B6zxbbFYAFRe1NECBmeC4a8Lqo+6UR3Vj+qnYc1dRzKZDKmpqZDJZHHxd6H3UCDB6/ANGLht4jEaOAgzzl4GJgiQSGm1JkIICZVer0fv3r2jHUZcYQLvm1HgtvgGC3hvtEMihBBCRNS2xxbB64RgKwbvqgCooyQohYWFyMrKCjiWlZUFjuNQUlKCnJycGo9ZtGgRHn/88RrHzWYzdVBVYozBZvN1TkkkkihHE5uojupH9dOw5q4jQRDQuXNnCIIgPm+sczqd9P7hOfCOEghua427aOAgzJjAgXeWQ65Li3YohBASd3ieh91uh1qthkwmi3Y4MYtxHt9AgcsMwWOnL/6EEEJiFrXtscG3x9E5CC5LtEOJS+d3Kvk7UurqbJo/fz7mzZsn/m6xWNCmTRsYDAZavqOSvw4NBgN12tWB6qh+VD8Na+46crvdcdXm+2cb6HS6FvkeYgIH3noOvKMUCsYAZc2L4GngIAK81iIaOCCEkEYoLy/HV199heHDhyMlJSXa4cQUweuA4DRDcFvAvK5oh0MIIYQEhdr26BI8dvDWc7VeRUiCk52djcLCwoBjRUVF4jJctVGpVFCpVDWOSySSFtk5VRd/fVCd1I3qqH5UPw1rzjqqqKjA119/HVdtfkt8DzEmQLCXgLed862aAwDN/Ppb9MCB4HGAd9shU+miHQohhMSV5ORkDB48GHq9PtqhxARxsMBlBuPcDT+AEEIIiTHUtkeH4Lb5Zhi442OpiFg2cOBArF+/PuDYpk2b0K9fv1r3NyCEkJaK2vzYxzvKwVsLor7EcYseOAAAzlZMAweEEBIipVJZYw3ZWMEEDoLLCjAeEHhAJodEpoREpgRkyrBdoSB4nRCcFTRYQAghJCHEctueiGjAoGE2mw1//fWX+PuJEyewb98+pKamom3btpg/fz7Onj2L9957DwAwa9YsLF26FPPmzcOdd96JHTt2YPny5Vi1alW0XgIhhMQkavNjl+C2gbfkQ/A6ox0KABo4AO8oBzO2hkTW4quCEEKC5nQ6cfToUbRt2xZqtTra4QTgzGchOCtqv1Mi8Q0iyNWQKjSQKNSQKLSQyIK7Co1xHgjOcvCuClqGiBBCSEKJ5bY9kfiWJCqkAYMg7Nq1C0OHDhV/9+9FcNttt2HFihUoKChAXl6eeH+HDh2wYcMG3H///XjllVeQm5uLl156CRMmTGj22AkhJJZRmx97GOcGZymA4DJHO5QALb63nDEBnL0EiuTsaIdCCCFxw+Fw4Pfff0dmZmZMJRqC21r3oAEAMAbGucE4d0CDLJEpIFFoIVVqIVHqfIMJEolvgyHeC8Fjg+Aspy/5hBBCElastu2JQvA4wFsLKJcIwZAhQ+rd7HHFihU1jl155ZXYs2dPBKMihJD4R21+7GACD952Dry9BIjQBsdN0eIHDgDfckU0cEAIIcFLS0vD9ddfH+0wAjDGwJnPNu6xvBeMN4uDCRKJFJDKwQRvTDbehBBCSLjFYtueCASv0zfDwGWJdiiEEEIIAGrzYwFjDIKjFLy1EEzgox1OnWjgAIDAecA5KyDXGKMdCiGEkEYSbEVh22uAMQHgPWE5FyGEEEJaHsa5wVkL658JSQghhJAWR3DbwFnOxsXyx9JoBxArOFtxtEMghJC4YTab8d1338FqtUY7FAC+vQd4W1G0wyCEEELiVqy17fGK8V5wFafhKT5CgwaEEEJiErX50cE4D7xlJ+EtPRYXgwYAzTgQ8U4LBK8LUgWt7UUIIQ2RSqXQ6/WQSmNj/Jkzn/HNEiCEEEJIo8Ra2x5vmMCBtxVBsJdSTkIIISSmUZvfvBgTwFvPQbCXxF2OQAMH1XC2EihTWkc7DEIIiXlJSUno379/tMMAAAhOMwQ3XSlBCCGENEUste3xhDEBgr0EvO0cmBBfnQGEEEJaJmrzmw/vrABvyQfjvdEOpVFoaKkazl5KyR4hhARBEAS43W4IUf7MZAIPztK4DZFJbHr11VfRoUMHqNVq9O3bF9u2bau3/MqVK2EymaDVapGTk4MZM2agtLS0maIlhJDEESttezzhHaXwFv0BzlJA3yMJIYTEDWrzI0/wOuEtPQau/FTcDhoAMTBwEEsdBEzgwDvKwnIuQghJZGVlZVi/fj3MZnNU4+Bt5+K6ESaBVq9ejfvuuw8LFizA3r17MXjwYIwaNQp5eXm1lt++fTumTZuGmTNn4tChQ1izZg127tyJO+64o5kjJ4SQ+BcrbXs8EFwWeIqPgKs4Q3kIIYSQuENtfuT4Lm7Mh7fkTwhuW7TDabKoDhzEYgeBlzbXJISQBiUlJWHQoEHQ6XRRi0HwOsHbS6L2/CT8Fi9ejJkzZ+KOO+5A9+7dsWTJErRp0wbLli2rtfzPP/+M9u3bY+7cuejQoQMuv/xy3HXXXdi1a1czR04IIfEvFtr2WMYEDry9BN7io/CWnYibTQ0JIYSQ81GbHxm8oxze4j/A24oBxqIdTlhEdeAgFjsIBI8TfAKMCBFCSCSpVCrk5uZCqVRGLQbefCZhGmMCeDwe7N69GyNGjAg4PmLECPz000+1PmbQoEE4c+YMNmzYAMYYzp07h48//hjXXnttc4RMCCEJJRba9ljAeC8Ejx28oxScpQDespO+wYJzh8GZz0LwOqMdIiGEENIk1OaHl7gsUUUeGM9FO5ywitrmyP4OgocffjjgeEMdBAsWLMCGDRswatQoFBUVNdhB4Ha74Xa7xd8tFgsAgDEGVkeHk9daBKmy5Yy6+euirvogVEcNofppWKLVkdPpxLFjx9CqVSuoVKomny/U+uHtJeDd9iY/bzxJlPdOXUpKSsDzPLKysgKOZ2VlobCwsNbHDBo0CCtXrsSUKVPgcrnAcRzGjh2Ll19+uc7naUxe0NIk2udVJFAd1Y/qp2GxWEfhbtubojnqhzEBjHOBeZ1gXlfVzwIfsecMp1h67xBCCIkvTqcTx48fj4k2P54JHjsEeyl4V0XCXtQYtYGD5uogWLRoER5//PEax20uDhJ57etRSlxFUEmSIZFFrXqaFWMMNptvloVEIolyNLGJ6qh+VD8NS7Q6Kikpwb59+6BWq5GcnByWczqdzuDqRuDAlZ9pcV+YW8rrPf89wBir833x+++/Y+7cuVi4cCFGjhyJgoICPPjgg5g1axaWL19e62PqygvMZnOLqeOGJNrnVSRQHdWP6qdhsVhHkWjbmyLovCBYjIfgdoBxTt8gAecBEL+f+9RmEUIIaSy73Y69e/ciJSWFBg7qwZgA8F6wyhsEDkzgAIGD4LGDce6GTxLnot4zHukOgvnz52PevHni7xaLBW3atIFeLUeSRlFnXEqZBwpDWiNeUfzxJ50GgyFmvrjEGqqj+lH9NCzR6kin02H8+PFhO5//qkKdTtdg/XjLT0GhkACI/3oMRaJ3EKSnp0Mmk9W4eKCoqKjGRQZ+ixYtwmWXXYYHH3wQANCrVy/odDoMHjwYTz31FHJycmo8pq68wGAwxERHWSxItM+rSKA6qh/VT8NisY7C3bY3RSh5QUMErwO8pQCCx151NaAUgDK+c4lEzwsIIYRETnp6OiZMmBDtMGJC1QxENxjvBjgPGO8G4z0Jt+xQY0Rt4KC5OghUKlWto2cSiaTeJJSzl0BhyImZRD7S/PXRUl5vY1Ad1Y/qp2GJVEeReB3B1I/gsoC5zAlRhySQUqlE3759sXnzZtxwww3i8c2bN2PcuHG1PsbhcEAuD0xlZDIZgLo7VBqbF7Q0ifR5FSlUR/Wj+mlYrNVRLMUChKd+GO8FX34SjOd8QwQx9PoIIYQQEh3M6wLvLIfgMreIWQNNEbXNkat3EFS3efNmDBo0qNbHOBwOSKWBITfUQdBYjPeCd1aE9ZyEEJIozGYztm3bBqvV2mzPyZgAzny22Z6PNL958+bhrbfewttvv43Dhw/j/vvvR15eHmbNmgXAN1tg2rRpYvkxY8Zg7dq1WLZsGY4fP44ff/wRc+fOxYABA5CbmxvScwseR1hfCyGExJtotO2RxBgDVzloQAghhJAqidbmh4K3FcNTfAS8rYgGDYIQ1aWK5s2bh1tvvRX9+vXDwIED8cYbb9ToIDh79izee+89AL4OgjvvvBPLli0Tlyq67777GtVBEAzOVgy5NiXs5yWEkHgnkUggl8ub9cpE3loIxnua7flI85syZQpKS0vxxBNPoKCgAD169MCGDRvQrl07AEBBQQHy8vLE8tOnT4fVasXSpUvx97//HUajEcOGDcO///3vkJ/baysC0rPD9loIISTeRKNtjyTefIYGhQkhhJBaJFqbHywmcOBt56IdRlyJ6sBBNDsIgsG7rBC8TkgVmoicnxBC4lVycjIGDhzYbM8neB3g7SXN9nwkembPno3Zs2fXet+KFStqHJszZw7mzJnT5OflHRUQOA+kcmWTz0UIIfGoudv2SOLtJeAdZdEOgxBCCIlJidTmh4K3FYMJfLTDiCtR3xw5Wh0EweKsxVCmtm225yOEkHggCAI4joNMJov4VQqMMfAVZ6o2NCQkAhhj4KxFUKa0jnYohBASFc3ZtkeS4LaBs+RHOwxCCCEkZiVKmx8Kxnsh0MWIIYvaHgfxgnOU0WgUIYScp6ysDOvWrUNFRUXEn0uwl0DwOiP+PIRw9hJq8wkhLVZztu2RwjgPuPKTdLEBIYQQUo9EaPNDxdvOgTEh2mHEHRo4aAATeHA0zZUQQgIkJSVhwIAB0Ol0EX0exnnAWwsj+hyE+DGBB2crjnYYhBASFc3VtkcKY4JvM2QaACaEEELqFe9tfqgY56YlDBuJBg6CwFmLoh0CIYTEFJVKhbZt20KpjOx68Jz5NF0VQJqV11oERleqEkJaoOZq2yOFqzhNMxQJIYSQIMR7mx8qzlpIsxEbiQYOgiB4XeBd1miHQQghMcPtduPUqVPweDwRew7eUQbBbYvY+QmpDeO9dDUKIaRFao62PVJ46zkIzopoh0EIIYTEhXhu80MleByUIzRBowYOGGMoKSlBaWlpuOOJWZyNZh0QQoif1WrFzp07YbfbI3J+xnvB08aGcSPR8gKv9Vy0QyCEkGYX6bY9UgSX2XclIYkZiZAXCFzid6YRQlqueG3zG4O3FkQ7hLgW0sBBYWEhpk2bhpSUFGRlZSEzMxMpKSm4/fbbce5cYn/J5p1mSh4IIaRSWloabrjhBhiNxoicnzOfpTWK40Ci5gWCxwneaY52GIQQ0qwi3bZHguB1gqvIi3YYpFIi5QWcrSTaIRBCSMTEY5vfGILLQqsYNJE82IIWiwWDBg2CzWbDjBkz0K1bNzDG8Pvvv2PVqlXYvn079uzZA71eH8l4o4YxBs5WDKWxVbRDIYSQqJNIJJDJZBE5t+A0Q3BRp22sS/S8wGs9B5nGEO0wCCGk2USybY8ExnvBlZ0EE2gvpFiQaHkBZy8FEwRIpLS6MyEk8cRbm99YPM1IbLKgBw5efPFFyGQyHDp0CBkZGQH3Pfroo7jsssvw0ksv4ZFHHgl7kLGCs5dCYciFRCKJdiiEEBJVFosFP/30E3r16hXWL4BM4MBZzoTtfCRyEj0v4F1WCB4HpEpttEMhhJBmEam2PRIYY+DKT4HxNCM8ViRaXsAEDryzHHJdWrRDIYSQsIunNr+xeEc5BK8z2mHEvaCHz7/88ks88sgjNZIAAMjMzMT8+fOxfv36sAYXaxjvBe8sj3YYhBASE4QIXOHHWwrAeC7s5yXh1xLyAtrrgBDS0kSibY8E3nwGgifx12WOJ4mYF3ittM8hISRxxUub3xiMCTTbIEyCHjg4evQoBg0aVOf9gwYNwpEjR8ISVCzjrMXRDoEQQqIuOTkZl19+eXhnG3id4B1lYTsfiayWkBfwjnIInDvaYRBCSLOIRNseCbytiPKFGJSIeYHgcYCntbEJIQkoXtr8xhLspTQrMUyCHjiwWCz1bpphNBphsVjCEVNM4902CB5HtMMghJCEwpgA3kZXd8eTlpAXMMboggFCCIkhgssMzlIQ7TBILRI1L+Bo9iEhhMQVJnDUtxBGQQ8cMMYgrWdjIIlEAsZYWIKKdV4bdSIQQlq2kpISfPzxxygvD8/ybbz1HBjvDcu5SPNoKXkBZy8BE/hoh0EIIREX7rY93ASvA1x5XrTDIHVI1LyAd5ohcHTVKiEkscR6m98UvK2Ivr+FUdCbIzPG0KVLlzo3Bo6nJGBYFxUEZwWQ1LgpOby9DMzYGhJp4u9ATgghtdHr9ejbty+02qZvHCt4neDtNCAbbxIpL6gPE3hwtmIokrOjHQohhERUONv2cGO8F1zZSTCWuOsxx7tEzQsYY+BsRVAaW0c7FEIICZtYbvObgnEeCPbSaIeRUIIeOHjnnXciGUezemFiKhy/fw5j5uxGPZ4xAZy9FIqkzDBHRggh8UGtVqNDhw5hORdvPgPE6ZfJliyR8oKGeK1FkCdl1dkZQgghiSCcbXs4MSaAKztBMxNjXCLnBZytFIrkXEjqmVFBCCHxJFbb/KbirYV0kUGYBT1wcNttt0Uyjmbn/GsLuP43Qq5LbdTjOWsRDRwQQlost9uNs2fPIiMjA0qlstHn4W3FtG9MnEq0vKA+jPeCd5RBrkuLdiiEEBIx4Wrbw40rPwXB64x2GKQBiZwXMIHz5QH69GiHQgghYRGrbX5TCF4HeGfiLb0UbUEPmZeXl+Pll1+udUMjs9lc530xi/eiYt8njX64wLnBu+Lo9RJCSBhZrVbs2LEDdru90edgnAe8tTCMUZHmlHB5QQO8tDkiISTBhaNtDzfOWgiBvnPFhUTPC7y2omiHQAghYROLbX5T8ZaCaIeQkIIeOFi6dCm2bt2K5OTkGvcZDAZs27YNL7/8cliDixSnxzdtxXL4a3D2skafx2ul5IEQ0jKlpqZi7NixMBgMjT4HZz5D0wjjWCLlBcEQPE7wTnO0wyCEkIgJR9seVpwHPHXWxo1EzwsEjxO8yxrtMAghJCxirs1vIsFlhuC2RTuMhBT0wMEnn3yCWbNm1Xn/XXfdhY8//jgsQUXa53kZAADGe1Cxr/Ex804zBM4drrAIISRuSKVSKJVKSBu51ivvKIfgpi9f8SyR8oJg0awDQkgia2rbHm6cvYj2QIojLSEv4GggixCSIGKtzW8Kxhg4mm0QMUG/Q44dO4bOnTvXeX/nzp1x7NixsAQVabs6PQavxLeGl2/WQeN33OZsxeEKixBC4obVasWvv/7aqKmNTODAW85GICrSnBIpLwgW77LSnhyEkITVlLY93HhnBRjtaxBXWkJeQBcOEkISRSy1+U0lOErB6LM5YoIeOJDJZMjPz6/z/vz8/LgZqbIiGT9KhgHwbXjYlFkHnK0UTKClNgghLYsgCHA4HBAa8fnHm/PBBD4CUZHmlEh5QSho1gEhJFE1pW0PJybw4C11ty8kNrWEvIAxBs5KFw4SQuJfrLT5TcUEjvZNjLCgW+7evXtj3bp1dd7/6aefonfv3uGIqVl8wV8DyFQAAMvhjY2edcAEjnbtJoS0OAaDAUOGDEFSUlJIjxNcFvrMTBCJlhcEi3eU09WGhJCE1Ni2Pdx4WxEY741qDCR0kcoLXn31VXTo0AFqtRp9+/bFtm3b6i2/cuVKmEwmaLVa5OTkYMaMGSgtbfwKA+fj7HThICEk/sVKm99UvK2ILkqMsKAHDu655x48//zzWLp0KXi+6o/C8zxefvllvPDCC7j77rsjEmQk2JAMS+trAPhmHZTvbcqsA7rqgBBCGsKYAM5MSxQlikTLC4JFVxsSQkjkMM4DwV4S7TBII0QiL1i9ejXuu+8+LFiwAHv37sXgwYMxatQo5OXl1Vp++/btmDZtGmbOnIlDhw5hzZo12LlzJ+64444mvbbqmMCBc4RvIIIQQkjjMM4DnnKGiAt64GDChAn4xz/+gblz5yI1NRW9e/dGnz59kJqaivvuuw/z5s3DxIkTIxlr2O3WjIZErgbQtL0OeLed1jwmhLQopaWl+PTTT1FRURH0Y3hLARjviVxQpFklYl4QLM5eAsZz0Q6DEELCqjFte7hx1gIwRldzx6NI5AWLFy/GzJkzcccdd6B79+5YsmQJ2rRpg2XLltVa/ueff0b79u0xd+5cdOjQAZdffjnuuusu7Nq1KxwvUcRZaZNkQkh8i4U2v6k4Sz7AWLTDSHghLTL49NNP4+eff8b06dORm5uL7OxszJgxAzt27MCzzz4bqRgj5kCZBoYe1/p+ETiU713T6HN5KXkghLQgWq0WPXr0gFqtDqq84HHQ1QAJKNHygmAxgQdH72dCSIIJtW0PN8Fjh+CsiMpzk/AIZ17g8Xiwe/dujBgxIuD4iBEj8NNPP9X6mEGDBuHMmTPYsGEDGGM4d+4cPv74Y1x77bWNfk21Ebwu8C5LWM9JCCHNKdptflMJbhsElznaYbQI8lAfMGDAAAwYMCASsTQbZ9lZaFJb4c8SDroRN8B88EswzgXL4Y1IuXgi5Pr0kM/JO8rBjK0hkYVcpYQQEnc0Gg06d+4cVFnGGDjz6QhHRKIlEfKCxvBaiyBPyoJEIol2KIQQEhahtO2RQBsiJ4Zw5QUlJSXgeR5ZWVkBx7OyslBYWPtGmIMGDcLKlSsxZcoUuFwucByHsWPH4uWXX67zedxuN9zuqr2LLBbfgABjDKyeK1k9lnNQq+J7bfBg+euivvpo6aiO6kf107DmriO1Wo0LLrhAfO5Yd379cOazcRF3c4pUfbTIXm5b/hFoUlvBywOn7Fqk97gOFfs+FmcdZAz+v5DPyZgAzl4CRXJ2BCImhJDY4vF4UFhYiLS0NCgUinrLCrYiMK+rmSIjpHkw3gveUQa5Li3aoRBCSFiE0raHG+8op6VfSa3OH6BnjNU5aP/7779j7ty5WLhwIUaOHImCggI8+OCDmDVrFpYvX17rYxYtWoTHH3+8xnGbi4NEXs8m3c4SqKTJkMpVwb+YOMUYg81mA1Dz70F8qI7qR/XTsOauI5fLhbNnz8JoNEIuj4+uYafTCYlEAsFlBm+3RzucmEMDB2Fkyz+CjB7DAAB/FHG4rtf1MB/6EszrhOWPTUjpPRFyfUbI5+VsxTRwQAhpESwWC7Zv347hw4cjJSWlznKMc4O30VJuJDF5LYU0cEAISRjBtu3hxpgA3lrQbM9H4kN6ejpkMlmN2QVFRUU1ZiH4LVq0CJdddhkefPBBAECvXr2g0+kwePBgPPXUU8jJyanxmPnz52PevHni7xaLBW3atIFeLUeSpv4BNIXUA6UhM9SXFnf8nVEGg4E6fetAdVQ/qp+GNXcdOZ1O7N69G8OGDYNer4/48zWVf7aBVqMGZz8Fpgxp5f0WgQYOwsiW/4f48x9FXozrYYDhoutQsW9NtVkHs0M+r8B5wDvNkGkM4QyXEEJiTkpKCkaPHt3gmohcxWna5JAkLMHronafEJIwgm3bw423FYHx9VzZTVokpVKJvn37YvPmzbjhhhvE45s3b8a4ceNqfYzD4ahx5axMJgNQd4eKSqWCSlVz1oBEImmw8453lALGXEiksnrLJQJ/fVCnb92ojupH9dOw5qyj1NRUsc2Pl7+JRCKBYC8GBD5uYk4ELXKIxl50AvLKV/7HOQ4AYDRdD4lCAwCw/LEZnK24Uef20pW1hJAWQCaTQavVQiqtuxnh7SUQPDSFkCQ2r/VctEMghJCwCKZtDzfGeyE08nsXSXzz5s3DW2+9hbfffhuHDx/G/fffj7y8PMyaNQuAb7bAtGnTxPJjxozB2rVrsWzZMhw/fhw//vgj5s6diwEDBiA3Nzfs8TGBB+coC/t5CSEk0qLR5jcZz0Gwl0Q7ihYn5HfIsGHDUFFRUeO4xWLBsGHDwhFTxDHeiw4pvtGpM2YeVrcAmToZhh7X+QpUzjpoDN5pgUBreRNCEpzNZsPu3bvhcNS+HjHjvbTsQAuRCHlBQ4psPIpsfK338S4rrctNCEkIDbXtkcBbCmhmYoIJZ14wZcoULFmyBE888QQuvvhibN26FRs2bEC7du0AAAUFBcjLyxPLT58+HYsXL8bSpUvRo0cPTJo0CV27dsXatWub9Jrqw1npwkFCSPyJRpvfVJy9mHKGKAh5qaLvv/8eHo+nxnGXy4Vt27aFJajm0Dldij9LfZ0AfxRx6N9GCWOv62E++EXlXgebYbx4IhRJoa9ZyNlKoExpHe6QCSEkZnAch4qKCnAcV/v95jNgAjXqLUEi5AXdsuRgQu3v5f35Hjz8pRkA8PhIA/q3VdYo47UUQpXeMaIxEkJIpDXUtoeb4HGAd5Y3y3OR5hPuvGD27NmYPbv2ZYRXrFhR49icOXMwZ86ckJ+nsWjZQkJIPGruNr+pBLcNzGMDaG+DZhf0wMH+/fvFn3///feATYp4nsfXX3+NVq1ahTe6COqSJsUGVA4cnPOifxtl5ayDMajY+xEgcKjYuwYZV9wd8rk5eykUhlxI4mnKDyGEhMBoNGL48OG13sc7yiG4LM0cEWluiZQXrJ6ZAddf38LQd3zAcY5neGmbDd7KyQaLvrHg1QkpyE4OXMuYd1ZA4NyQymuukUwIIfGivrY9EnhLfrM9F4m8RMoLQuW1FdHAASEkrjR3m98UjDFwlrPRDqPFCnrg4OKLLxY36ahtiqFGo8HLL78c1uAiqXN6Vaf+/vyqzbh8sw7W+2YdHNkCY+9JIc86YAIH3lEGuT49bPESQkg8YLwXPDXqLUKi5QX2A59A6HkNpEqteOyzg06cKq9aosjqZnhikwVLrjdCKa/akIsxBs5aTLMNCSEkSLyzgvZBSjCJlheEwr9csVTRvBuLE0JISyA4SsFoSfioCXrg4MSJE2CMoWPHjvj111+RkZEh3qdUKpGZmQmZTFbPGWJLll6C3GQp8i0CDhR6YXML0KukkKmTYOw5FuV7VlfOOvgIGVfcE/L5vbZiGjgghCSssrIybNiwAVdccQWMRqN4nLPkgwm1rwVPEkui5QXMbUHFb2uR2v8WAECZQ8B7u3xrfkoApOmkKLEL+LOEw8vbbfj7kKSAx3P2EiiSsyGRhbwKJCGExIS62vZwY0wAb6F9kBJNouUFoeJsxVCmtIl2GIQQEpTmavObigkceGthwwVJxAT97da/AZGQIGtWSyQSXNpOhbUHnOAFYOdpD4Ze4LtCwNBzHMwH10PwOCpnHUwOedaB4HGAd9shU+kiET4hhESVWq1Gly5doFZXXVkluMwQnBXRC4o0q0TKC7w8g0ImQcX+dUi+cDTkulS89bMNDi8DAIzqrsa4HhrM/bQcbg74+g8XruqsgqlV1X4HTODFwQNCCIlHtbXtkSDYisH4mmvgk/iWSHlBY1QtV5y4gyOEkMTRXG1+U/HWc3RhYpQ16rK4o0eP4vvvv0dRUVGNxGDhwoVhCaw5XNpeibUHnACAn09WDRzI1Ekw9BhTOeuAR/mej5B5ZeizDjhbEWSqDmGNmRBCmgvH1/3FT6lSo3OXrgAAXmBgAgdv+RkwgTXquRhj4AXfTSJpuHxLxFjj6rY5xHte8NEeB6b214FxbpTv/hDuXrOw+agbAJCkkuD2AToYNFLMHqTHC1ttAIA1vzkDBg4AwGstgjwpCxJ6ExNC4pBWq0W3bt0i+hyM94K3F0X0OUj0xXte0Bi+CwhKQ77gkBBCoqE52vymErxO8I7SaIfR4oU8cPDmm2/i//7v/5Ceno7s7OyAL8cSiSSuEoGe2QrolBLYPQw7T3vACwwyqe/1GCr3OhA8DliPbkFKn0lQJGWFdH7eUQFm9EIiU0QifEIIaRJBYCi3e3CuwokzZQ6cLXXgdKkDZypvFfZoXA1IVxPU5/t/RDuCmhIhL3jzRxtuGZTu29/oj80oThkFIBkAMOYiDQwa375II7qq8cEeB4ptAn7J8+BUOYd2KVWpFOO9vj2OdGnReBmEENIkXq8XJSUlMBqNkMsjs+waby0Aa6FXpLcUiZAXNBZnLaKBA0JIXGiONr+peEs+EMMXz7UUIb87nnrqKTz99NN46KGHIhFPs5LLJOjfRonvj7lhdTMcLPTClOu7elCm0sPQYyzK9/yv2qyDOSGdnzEBnK0ECkNOJMInhJCguL08ThbZcPxc5a3IhpNFNhSZXeAbOUOAEL9EyAvKHQK0F46F/bfVABOgOfIBgNkAgHSdVCwnl0kwvqcGr+/wbei5dr8T918ZuNeB11JIAweEkLhkNpvx/fffY/jw4UhJSQn7+QWvA7yjPOznJbElEfKCW/rrIHhsAPQhPU7g3OCdZsg0hsgERgghYRLpNr+pBKcZgtsW7TAIGjFwUF5ejkmTJkUilqi4tJ1v4AAAfj7lEQcOAMDQy7/XgR3Wo98gpfekkNcu5uwlkCdn07IFhJCI43gBeSX2qgGCc1acOGfDmTJHyAP1GckqZBk1kNXx2SUIAjxeD6RSKcA5m3wlAINvBoRUKgF9WtYuVod4EiUv0HQdBddfW8DbS6Ev3YULcBR/oQv0qsB35Kjuary/2wGHh2HzURemD9AhRVM1uCB4XdRpQAiJS0ajESNHjoRWq43I+XlzfkTOS2JLIuQFD16dDOfhL2G8fGbIj/VaiygHIITEvEi3+U3BmADOQjlDrAh54GDSpEnYtGkTZs2aFYl4ml3/tkpIJYDAfPsc3DWw6j6ZSg9Dz7Eo373KN+tg70fIvHJuSOcXOA94lxlyjTG8gRNCWixeYCgod+DYORuOF/oGCI6fs+F0qR0cH1z3sl4tR06KBpkGNTKS1chN1aBNmg6t07RolaqFWln/xm4cx8FisYAznwFv55r8mhhjcHgEaJVSGmitQ6zucZAoeYFErkJqv6ko/uElAMB4fIT/YAH0SmlAOZ1SitHd1fj4Nye8PLD+oBPT+usCynit56jTgBASd+RyOZKSkhou2Ai8swKCxx6Rc5PYkih5gePIV+D6Tgj5ezzvskDwOiFVaCITGCGEhEEk2/ym4q3nwPhoLJtMahPywMEFF1yAxx57DD///DN69uwJhSJw/f65c0PrWI+2ZLUUF2UrcKDAizNmHqcrOLQxVlWLoedYmA98Xjnr4Fuk9J4c+qwDazENHBBCQsYYwzmzCyeqzSA4fs6GE0U2eLjg1gdWKaTokKlHx6wkdMzSo2OWHp2ykpCerGpSB73dbse+PTvRPkUCjYr2cWnJEikvSOoyDBX718FbnoeOkuPow3ZDp7qqRrkbemrw6QEneAH4/JATky7WQqOo+v/Eu6wQPA5IlbF3BQ8hhNTFbrfjt99+Q+fOncN6BSJjAnhLQdjOR2JbwuQFnBsV+z5B+sDQZx1w1mIoU9tGIChCCAmPSLX5TcU4NwR7SbTDINWEPHDwxhtvQK/X44cffsAPP/wQcJ9EIomfRKCage2UOFDgBQBsPebG1L5V1eKbdTAO5bs/rNrrYEhor9F31YELUoU6rHETQhIDYwxFZhdOFttxssg3MHD8nA0nztlgdwd3Nb9cJkG7DB06ZiahY7ZvoKBDph65KRpIpeG/gt/jduFcYSFaJ4e2aTxJPImUF0ikMqRdOgOFXz0OALgeH0MnHwogsNMjUy/DkE4qfPOnG2YXw5p9jpqzDiyFUKV3bK7QCSGkyTweD86dO4cOHTqE9by8rYiuHGxBEiEvcHkZ1AoJLIc2wNjr+pD3LuLspVAYW0EirX8GLyGEREuk2vym4iz5YCy4iyRJ8wh54ODEiRORiCOqLuugwps/28EAfLLfiet7aKBTVS1NYOg5BuYDnwXudRDihsecrRjKlDZhjpwQEk84XsCpYhtOFTtwstiGU5UDBXnFdjg8fFDnkEqA1mlacQaBfzZBm3Qt5DJpwycIE53EjiEX05VUJPHyAm2bvihQdUeO+zAyJUVQ5m0G0sbUKHdLXy2+P+YGLwAf/ebA6O5qpOurOgh4ZwUEzg2pXNWc4RNCSKOlpKRgxIgRYT0n4zwQbMVhPSeJbYmQF6zebcdtl+rBeA/K93yEjMH/F9LjGRPA2UqgoAtsCCExKhJtflMJLgsElyXaYZDzhDxw4OfxeHDixAl06tQJcnmjTxMTcg0yDOvsu3LQ6mZYs9+J6dWuHJSp9DD0GofyXR8CTPDtdTDk3pCeg7OXQmFoBYm0+Tr2CCHRYXdxOF1qx8kie+VAgW+A4EypA5wQ/Dr12UZ11QBBlm+AoH2GDipFdK9e4l1WcDaaPkgCJUpeIJFIsF1/Iya5/wkAcO1fDeGi4TWWHWptlGPsRb4li9wc8M5OOx4cmizezxgDZy2iiwYIIS0aZy2gKwdbqHjOC9752Y7pl6eDcS5Y/tgE48XjoUgKbRCAsxXRwAEhhASJMQGc+Wy0wyC1CLkFdzgcmDNnDt59910AwNGjR9GxY0fMnTsXubm5ePjhh8MeZHO4rb9OvHLwk98cGNdDgxRNtVkHPcbCvP+zansdTILCkBv0+ZnAg3OUQqHPiET4hJBmxBhDud2Ds2UOnCl14GypA2fLnDhTasfZMicq7MFPx5dIgJwUDdpn6NEuQ4f2mXq0z/QtOaRTx96XLCbw8JSdRIXNiR/2nMCAbrlI1tEV1S1ZIuYFJ1h77GQD0F/yKwSXGRW/rUVq/1tqlLulrxZbjrpgdTNsPuLGDT29uCC9alkjzl4KRXIOJLLY+79MCCHnKy8vx8aNG3HZZZfBYGj6Bu+C2wbBWdH0wEhcSYS8oNwhQNN1FByHPgUEDuW7/xfyhYMC5wHnrKC9DgkhMSncbX5T0bKGsSvky9/nz5+P3377Dd9//z3U6qo1+6+66iqsXr065ABeffVVdOjQAWq1Gn379sW2bdvqLe92u7FgwQK0a9cOKpUKnTp1wttvvx3y87pcbjBWdeVvuoZhZGffl30XB3y42wEAcDpdcDpdkCg0MPS63leYCSjeuQpuT+Cb2l9WEKquqvFyHJxOF9weD7hq03SdTiccDgd4vmp5Eo7j4HA44HK5zjtv8GVdLhccDgc4rmpddJ7nQy7rdDoDyrrdbjgcDni93kaVFQQBDocDDoej1rKeanUZSlnGmFi2+t/T4/GEXNbtdgc8n79s9b9nKGW9Xm+tZWupIltRAAEAAElEQVT7e4ZStqG/fVPfJ+f/PZv6Pmno79nY90n1v2coZYP929vsdhSUO7D7WCk+23kaL3/5Ox56bxemvbQdVz+xBdc98x3ueu0XPLnmAN7+9hg27svHodPmOgcNFDIJ2qapcUX3dMwY2hGPTzHh3TmDsHHBELw3uz+evrEH5ozuhjH9WqNn2xRImTcmPyNcpXmwWy1gPI/WGUlQVs5+cHu8cLrc551XgNPlhuu8+q2trCAIcLnccLoCy3q8vrJeb2BZZ5BlGWNi2ep/e6+XC7msp9p7FYBYNvD/ffBlOc5X1u0JLOty+8ryfM2ysShR8oLqrG6GzzAeHHzv74r9n4Kzl9Yol6yWYmof30wEBmDZj/aA9w4T+IC2nxBCYplKpULbtm2hVCqbfC7GGDgLXTnYEoU7L4gWTfdrIVX6ViGwHv0WnorQ38+ctSjcYRFCSFiEs81vKsa5aVnDGBbywMG6deuwdOlSXH755ZBIqjbcvPDCC3Hs2LGQzrV69Wrcd999WLBgAfbu3YvBgwdj1KhRyMvLq/MxkydPxjfffIPly5fjyJEjWLVqFbp16xbqy8BVk/8P5War+PuKj9bjvf8+CCnzdSJ9+bsTBRYeQyfehYHXTUdBUQkMPcZAqtIDAGx/fo/Fi18IOOfoqXMw8LrpOJ5XlVR8vvEHDLxuOh568iUIHid4tw0AMGrUKJhMJhw6dEgs++WXX8JkMuGuu+4KOO/48eNhMpmwa9cu8dh3330Hk8mE2267LaDszTffDJPJFNDRsmPHDphMJkyaNCmg7MyZM2EymbB582bx2L59+2AymTBmTOB6znfffTdMJhM+//xz8diRI0dgMplw9dVXB5R94IEHYDKZAhLDvLw8mEwmXH755QFlH3vsMZhMJvGKFAAoKiqCyWRC3759A8ouWrQIJpMJr732mnjMarXCZDLBZDIFdEIuXrwYJpMJixcvFo9xHCeWtVqr/vavvfYaTCYTFi1aFPB8ffv2hclkQlFRVcL33nvvwWQy4bHHHgsoe/nll8NkMgW8d1evXg2TyYQHHnggoOzVV18Nk8mEI0eOiMc+//xzmEwm3H333QFlx4wZA5PJhH379onHNm/eDJPJhJkzZwaUnTRpEkwmE3bs2CEe27ZtG0wmE26++eaAsrfddhtMJhO+++478diuXbtgMpkwfvz4gLJ33XUXTCYTvvzyS/HYoUOHYDKZMGrUqICy9913H0wmE9auXSseO3bsGEwmE4YMGRJQdv78+TCZTPjggw/EY/n5+TCZTLj00ksDyj7xxBMwmUx46623xGNlZWXi37O6//73vzCZTFi6dKl4zOl0imWrD3a89PJS9LnsKjz47BtY89MpvLD+MB54dzeG/OMjXP3Et5jw362Ys3wn/v3pIaz6MQ/b/ijBX4U2ONx170eQnqxCttoN54kdaOs9iv/c2gcf/X0wvvnX1Tj2vwfxyRM3YUh7Ca425aBzTjK+XP8ZTCYT7rvvvoDzxOJnBO+yYPfOHRh43XTMuHchurVNh1rpu5L6sf8sw8ib5mDz1l/Fxx/PO4uRN83BzbMD/788vWQ5Rt40B59vqoohv7AYN0y/DxPveCig7HPLPsDIm+bg4y++EY+Vlpsx8qY5GH1L4JVfS99eg5E3zcH7n2wQj9kcToy8aQ5G3jQnYGDlzZXrMPKmOXhz5TrxGM/zYlmbo+p98v4nGzDypjlY+vaagOcbfcu9GHnTHJSWm8VjH3/xDUbeNAfPLfsgoOyEmf/AyJvm4GxhVUL0+aZtGHnTHDy9ZHlA2ZtnP4aRN80JaEs2b/0V19wcm5sJJkpeUJ3dzVCCTOxRDgfgW6O7bNeqWsuO7aFBbrJvgGF/gRdf/xE4AOe1FQcMJhBCSKzSarXo0aMHNBpNk88lOErBvK6GC5KEE868IJqkSh2Mpht8vzAB5bs/DPkcvMsKwetsuCAhhDSzcLb5TcWZz9KyhjEs5LnzxcXFyMzMrHHcbrcHJAbBWLx4MWbOnIk77rgDALBkyRJs3LgRy5Ytq9GJCwBff/01fvjhBxw/fhypqakAgPbt24f6EurE3Fbkeo/hjLIrvAKw+HsrgKrXJFPpYOx1Pcp2fgCZFLhYczzk5+CsRZBVDj4QQiLP4eZw7JwNylYmyHTpeOHLozhn8eJMqR2FFd2ROmohdrmBXV8cFh8jT86u83wSMOSm6tAqTYNWqVr87+1X4aoowJsv/Qd9uneAWinDO++8g2fe/xC5OWNweXff52W8dxwygYe79GTV7wAqbC7oNcpm3ZSZxJ5Eywt4gcHh9f1/3Zc8DpdatkPwOGA9shnGXmOhTAncFFwhk+Cey/V4ZINvAOn1HXb0b6MUN0pmvBe8owxyXVqT4iKEkEjjOA7l5eVISkpq0pr0jPeCtxaGMTIST8KZF0SboccYVBz4HILLAttf22C8eBJUae1DOofXWgRVarvIBEgIIY0Urja/qQSnGYLb2nBBEjUSFmJv1pVXXomJEydizpw5SEpKwv79+9GhQwfcc889+Ouvv/D1118HdR6PxwOtVos1a9bghhtuEI/fe++92LdvH3744Ycaj5k9ezaOHj2Kfv364f3334dOp8PYsWPx5JNP1jlK5na7A5YhsVgsaNOmDY7t/BoZ6ali8uL1cuA4Di5eirvX2VBi94123d5PiXEXKqFSKSGVSiF4HMj78A4IHhsgkaLN5FfFvQ6cTt9VNf6ygG+pIs7LQSqTQqVUQiKRQJPbEy4PB8YYVCoVZDJf5wLHcfB4PJBKpQHTOp1OZ9BlXS7fUklKpVL8z8/zPNxud51lFQoF7HY7DAYDBEGA2+32xVmtTt1uN3ieh0KhgEKhCDhvMGV9y5D46kerrdpg0uPxgOM4yOVycYpUKGUZY+IV5BqNRvx7NrUsAHEJHLVaDYlEArPZDI1GA57n6y0r/u29Xni9XshkMqhUVWvA1/b3DKVsQ3/7pr5Pzv97BlOWMQaz2Qy1Wi2+pxr62zf1fVL97ylTqJBX4tuM+GSRBWdLHSiocCO/3IkyW+jr5CnlUuSmqNE6TYfWaVq0StUiM1mBbIMSuak6aDVVdRns354xhsLCQiQnJ0Oj0TTqfRKtzwilUgnechacrQQ8L8Dj8aDEbMO23/IwuGdrGPRquD1e399eLodcLqs8rwCP1wuJRAK1qur/S+1leVTY3NAopQH16/F6wfMC5DIZFApfvIIgiMv7aNSqessyxuBy+94DapUy8DOf50MqK5NJoVRUrV/vXzpIpVRU+3sGX5bjeHg5DlKpFCplVVmX2wPGGJQKBWSyqrIerxdtu5igNLZCLEmUvODsge+QnKSHxSVg4rtlAID+bRR4MHczyn59DwCgbTsA2dc8Wut5//udFZuP+s57aTslHh+ZJL6HpAo1NDkXBVUPscD/mW4wGOKuk6e5UB3Vj+qnYbFYR4WFhVi/fj2GDRuGlJSURp+Hq8gD7yhvUiyMMTg8ArRKaczUT6xhjCGz3YVQGHKiHUqAcOUF0WKxWGAwGJB/8HskJ+lRsX8dSnf4Zofq2l+K7JELQjqfRCKFJrdnXO93FIufV7GG6qh+VD8Na+46KiwsxOeff47hw4c3qc1vCsYEeIv+AOO9QZSlvKAhjDFktLsIiuSssJ435NZr0aJFuOaaa/D777+D4zi8+OKLOHToEHbs2FHrl/q6lJSUgOd5ZGUFvqCsrCwUFtZ+hcrx48exfft2qNVqfPrppygpKcHs2bNRVlZW53rGixYtwuOPP17jOAcZbC7uvKMyyGXAPYM0+NdmOwDg/T0e9MxRom0KD4AHoID6wjFw7FsFMAFFO/+H5MvmiI8HAM7tL1t1XvCAx+n7z+AoOAlFku8qDK+39v8g56+5HsmyHo8HNptvCaXq/wE9npodrnWdN5SyZrO5xjGv11tjvfxQy9b2fOEqyxgLqKNQz3v+2vHhKAtE7n0S6t++tvqpTV1/z2DKcryAEqsV58xunLO4cbbMhdOlTuSVOFFodkEI8YJ+vUqGbKMauSlq5BhVyElRI8fou6XqFZDW0Rh5PW6YPcHVZfW/J2MMPM/D6/UGLKtVW9mGzgs072eEy1YGT3lBtSMy6DQ69O/eClK5HA6PAEAGSGXwCIDHU22aoVQBBlSWqXr8+WUZAyBTgEmltZb1MsB73nlRx3nrKuv0MvjmSgCAFJBKQyrLMYCrpayvKREaUVYCSBXgz38dEjkgAXxNSVVZJq0aXIgliZIX2FwcJHIvzlmr2m+VDJB2ugbSg19AcJTBkfcrSk/ugzKr5iDA1N5K7MzzoMLF8PMpD7763YHBHSsHzJxeOGVn42bG4fmf6aQmqqP6Uf00LBbrSCqVYuDAgZBKpWJsoWKcC1xFzT1hQj4PY3B5fW1grNRPrInV2azhygtiRfKFo1Dx26fgHWWwn/wZruI/oc7oHPTjGRPA2UugqGdGMyGENDej0Yjhw4cjKSkpajHw1nNBDRqQ6Ap54GDQoEH48ccf8dxzz6FTp07YtGkT+vTpgx07dqBnz54hB3B+IsgYqzM5FAQBEokEK1euFHf9Xrx4MSZOnIhXXnml1qsL58+fj3nz5om/+68s1KvlSNLU3gkzqKMCE3rx+GS/C14BePknJ14YZ4RG4YtLd/E45B3+AoLbCveJrVD1mxLaFaCCBSpFGuTa1OAfE0H+pJNGf+tGdVS/cNSPIDCUWH2zBArKHSgocyK/3InCCicKyp0otrjBhzg6kJakQqtU35JC/pkDrVI1aJ2mRbK2eTcBitf3EOM5OG2noDrv85JXSiGXhG89RH/90BUEdYvVDoJEywsKbFXPZdTKkJykh6T/LSj+4SUAgGvvB0i9/r81YkrSAHMHA09s9k21feMXF7pmq9ApzZdqyWCH2hBbs0XqEq+fV82J6qh+VD8Ni8U68s/ubApvcT4EZdOXMKS8oGEtJS+INqlchZQ+k1Gy3bfPXtnOD5A7uuYFCPXhbMWQJ2XRe5kQEjPkcnnUZhoAAPO6wNtpQ+R40KjMsGfPngGb2TZGeno6ZDJZjasIi4qKalxt6JeTk4NWrVqJnQMA0L17dzDGcObMGXTuXHPkX6VSBSz/4SeRSOptuG8foMfu016cLOdxvJTHA+vNeHqUASlaabW9Dt4HmICKvR8ha9i8Os9VG29ZHqQyJWTq6I3uVeevD0pm6kZ1VL+G6ocXGMpsbhSZXSgo9w0KFJQ5fIMDlQMEXj70L0BqhQztM3XokKlH+0w92qb7lhfKTdVAo4ytKcHx+B7yVJwGBK5GzC43hyOnS9E+2yhukNxU8Vg/xCeR8gK7p+pzKEnl67BK6jIM5gOfwVN2Cu7io3Cc+An6TpfXOMfgTmoMO+HBt3+54fAyPPaVBS/fYES6XgbBbQPzOiFVams8LhbR/8eGUR3Vj+qnYbFWR06nE4cOHUKnTp0atVkiby8B41xhez2xVj8keOHIC2JJcrcRqNi3FpytCM7Te+AsOBTSEoQC5wHvrIBcG71OOkIIqc7hcODgwYONbvObijOfqVx2gMS6oHp7LBYLkpOTxZ/r4y/XEKVSib59+2Lz5s0Baxlv3rwZ48aNq/Uxl112GdasWQObzQa93jfd/+jRo5BKpWjdunVQzxsspVyCR65KxrzPKmDzMPxZzOHedeV4ZrQBrY1yGHpch4r96yC4rbD99QNS+kyG0hh8DIwJcJcchzqzc9x0IhBSF75ytsBpcwWKzC4UWVwoMrtQbHajyOJCsdmFEmvoMwb8DFoFclI0yDZqkJPiu+WmatEhU48sgxpSKX2hjATOUQaujjWK3RyHM8VW5KYlhW3ggMSPRM4Lqg8c6FW+zxaJVIbUS6aj8CvfFYalv74HXftLIJHVnLk4b0gS8i08/ijiUGIXsOArM14YZ4RWKYXXUghVesdGxUUIIZHmdruRl5eHNm3ahNyJ4NsQuaDhgiQhRSIviCUSmQIpfW9C8Q8vAgDKdr6P3DGLQhrU4mzFNHBACIkZTWnzm4p3lELw2Jv1OUnjBdXbk5KSgoKCAmRmZsJoNNbaQPqXEuB5vpYz1G7evHm49dZb0a9fPwwcOBBvvPEG8vLyMGvWLAC+5QTOnj2L997zbUp4880348knn8SMGTPw+OOPo6SkBA8++CBuv/32iLzRO6TJ8cL1RjzypRnFdgEFFgFzP63A/VcmYXBHLYymG3wbJjIB5XtWI2vY30M6PxM4uM79AZk2BYqkLBpAIDGJMQar04tzZhfOVbgq/3XiXIVvcKDQ7ERJI5YRqk6rlCEnVYOcFC1yU6oNDqRokZOigU5NHdPNTeA88JSdrvN+o16Dq/p2aMaISCxJ5LzA6q7ab0KvrHpd2jZ9oc7tBVf+fnCWAlgOb4Shx3U1Hq+SS/DEKAPmri1HoVXA8VIeD39pxuPXGJAqqYDAuSGV15zxQAgh0ZaSkoLRo0c36rG8pQBMEBouSBJSpPKCWJLUZSgq9q2B15wPV8EhOM/+Bm3ri4N+PO+yQvA46Ds/ISQmNKXNbwomcOAtdKFBPAmqN+7bb79FaqpvPf7vvvsubE8+ZcoUlJaW4oknnkBBQQF69OiBDRs2oF27dgCAgoIC5OXlieX1ej02b96MOXPmoF+/fkhLS8PkyZPx1FNPhS2m87VPlePFG4x4ZIMZJ8t4WN0MT2yyYFQ3Ne4aMBrS/Z9CcFlh+2srUvpMCWnWAeBLoDh7GTh7GWTqZCiSMiHTGBp+ICFh4B8UKLG6UWr1LSMkDg6YqwYHnJ6mJfgpOiUyDWpkGNTITFYjy6j2DRCk+gYKDFoFTUOPMZ7yU2BCzU2cCQESOy+wu6sGQXWqqnW6JRIJ0i6djrNrfUsTlu1ehaQuw2rtAEjRSPH0aAPuW1cBq5vh8DkOc9eW46nRBlygL4IypU2j4yOEkFgjuG3gnbXPUCQtQ6TyglgikcqQ0u9mFH3zHADfXgeaVqaQvsN4bcVQpbaLVIiEEBLzeEs+mBCfA8gtlYTF6q5KEWKxWGAwGFBw6Ack6XVBP87mFrD4Byu2HfeIx3KTpfhHq81Q/7ESAKC/YAiyhoc266A2UqUGiqQsyLSpzdKZyhiD2WyOqc3ZYk281ZEgMFQ4PCi1ulFi8Q0K+AcH/D+XWFwos3ng4Zp2dZhBq0CWQY1UnRyt0pOQaVAjq3KQIMugRnqyGkp50zfJi3fx9B7y2orhKcurt0yZ2Y5t+46jX9dsJGmbfvU0YwwOj0CbINaDMYb0Nl2hNMbHBrvxwp8X5B/8HslJerzzqx0f7nEAAP59nQF9WgdupH7um//C9tdWAICx92SkDbi1znP/VeLFoxssKHX4Pmd1Sgn+MSwZV13WHxJZ7M6kiqfPq2ihOqof1U/DYrGOiouL8c033+DSSy8NejkZxhi8xUfAOHdYY6G8oGGMMWS07Q6FISfaoSSU8/OC6hgTcObjufCUnQIAZF/zGHTtBgR9bolECk1uz5jOAWoTi59XsYbqqH5UPw1r7jpqTJvfVILbBm/psUY9lvKChjHGkNHuIiiSa98fsLFCbrHeeecd6PV6TJo0KeD4mjVr4HA4cNttt4UtuEiS6zMAOIIur1dJ8djVydh4xIVXttvg4oB8i4CHLJfhP/LPoOJtsB3b6tvroIlXEgoeJ9ylJyGpOAtFUibk+gxIpLImnZMkBv+AQPXBgBKLq/LfwAGBpiwd5KdSSJFl0CDL6BsEyDSokW30/e4bINBArZRRIpBABK8L3vIzDZZTyKXINGohl9GgUEuXKHmBn63aUkU6Zc3Ps9T+t8J2/CdA4GA+sA6Gi0ZDrkur9VwXpCvw8ngjHvvKgmOlHOwehn9+bcZPZ3Zj3g19kKSpuUcCIYREi1KpRFZWFuTy4L8iCraisA8akPiWaHlBdRKJFKn9pqJw0zMAfLMOtG37QSIJLh9mTABnL4EiOTuSYRJCSIMa0+Y3BWPMtyEyiTshv0OeffZZvPbaazWOZ2Zm4m9/+1vcJAKK5BxIHachcJ6GC1eSSCS4ppsGPbIVeP57Kw4WcnBDjS+5azBe8jHABJTsWo3cqx8IS4yM98JTcRZeSyHkunTIkzIhlSsbfiCJOy4Pj1KbG2XVZgWU2jziz2U2/7/hGRAAAKNWgbRkFdKSVEhP8v2bkaxGllFTOUCgRrKGlhBqSRhj8JSdBGMNz0LRqpW4qENGM0RFYl2i5AV+gZsj1+wIUCRnw3DRaJgPfA7GeVC260NkXjmnzvNl6GV44Xoj/vOtBdtP+HKOjQfLsTdvO+67rjuuvCiLPmcJITFBp9PBZDIFXZ5xbvC2oghGROJRouUF59O2vxSq9AvgLvkLntITsB//CfpOlwf9eK+1CPIkavsJIdEVapvfVLztHF1oEKdCHjg4deoUOnSouSFmu3btAtYdjnUSqRTKlLZwFf8V8mNbG+V4fpwRX/7uwls/2/GDdxiuZl8jSWKD/fhWbPhlLK7q1xlKWXiSASbw8FrPgbMV0UbKcaT6ckH+Tv/SagMD/sGAUqsHdnd41pKXSoAUvW8QwD8g4B8USEuu9rNeBQUtH0TOw1kKwbvtQZXleQE2pwcalRwyKb2XWrJEyQv8rNX2ONDXMuMAAFL6TIH1yDcQPHZYj2yBsedYKOtZs1ijkGDhiGRsOuLGqz/Z4PAwFFnceOTDfeh/QRruv6472mfq63w8IYQ0B47jYLVaodVqIZM1PNuZM58J6mID0rIkWl5wPolEgtT+t6Dgq38BAMp2rYSuw8CgVwhgvBe8sxxybWoEoySEkPqF2uY3heB10oUGcSzkgYPMzEzs378f7du3Dzj+22+/IS2t9qn6sUqmMUCuTQHnCH0zL6lEgjEXaXBpOyXe3anClqOjcAPWQAoG896PMP3P/8MtfbW4uosainANIJy/kXJyFmTq5lmLrCVzeXiU2dw4fc4GT74bFqcXdjcHu4uHw81V/lz5b+XPFXYPyu3hmx0glQCplQMCqZWDARn+gYBktW+AIFmFFJ2Slo4hjcK77fBaCoIub7a78N3eUxjcqw2MenUEIyOxLpHyAiBwqSK9qvb2W6ZOhvHiCSj79T2ACSj95V3kjFpY73klEglGdlOjdysFXthqxa7TXgDAzr9KcetLP+L2YZ1w65Ud6TOcENJkgsBgdnhRZvNfqOKBy8OBAWAMYPD94P9dqNzyzmq14uDBg+jSuTM02vovUmJeJ3hHRURfh4djUMpb1HZ8IZveNtoR1JRoeUFtNG36QJ3dHa7Cw/BWnIHtrx+Q1GVY0I/nrMU0cEAIiaqKigps3LgRw4cPR0pKSkSfizef8SUcJC6FPHBw4403Yu7cuUhKSsIVV1wBAPjhhx9w77334sYbbwx7gJGmTGkD3mUFExp3xXeGXoYHhibhVI/xsK37GmrBin74FV/ZrsMLP7TCO7/aMbq7Btd2VyMzKXyjeLzLAt5lafaNlOMZxwuwuap19Ff+bHNxMDs8KLf5OvvLbe7Kf32/Oz2R2/Fdp5IjLUkpDgpUHxhI1SvFYwatEjIp/X1JZDBBqFyiKPjGPEmrwqAeraHX0PJpLV2i5QX+pYo0Ckm9n7uGnmNhPrQBvL0EjrydcOYfgCa3Z4Pnz0yS4ZnRBvx40oPXf3GjsMINXmB4c8tf2Ha4CAsm9ESn7KSwvR5CSOLwcAKKzE4UlDtxzuzb06p63lph96Cs8t/GX7ySgl9/KQlr3CRypl8d7QhqSrS8oDa+WQe3In/9IwCAst2roO90RdCbHvNuGwSPg1YRIIREjcFgwJAhQ5CUFNnvHbytGIIn+P1lSewJeeDgqaeewqlTpzB8+HBxEw1BEDBt2jQ888wzYQ8w0iQyBRTGXHjKmjZtsl2GHuX9J6Lsl3cglTCMZuuxHLNQ4WT4cI8Dq/Y40CNHgSs7qTC4owqp2vBcURi4kXIW5Pr0hN5I2e3lUWbzwO7iYHN5xY7/qgEBr+93Jwez0wOLwwuLwwuzwxu25YAaopRLkaxVVHb+Vw0IpOmVVYMClQMDGmXzbERDSH085XkQvK6QHqOQy5CWrIlQRCSeJFpeYKtcqqiuZYr8pHIVUvtPRfH3LwIASn9+B61ueD6oQXyJRILLO6hwaedUrDooxcqtJ8ALDH+ctWD60p8weVA7zBjWCXo1bZ5MSKJye3mcM7tw2sxQXtnhX1a5tGWZzV2Z33rhcFfluW4vLQtEYl+i5QV10eT2hKbVxXCe3QfOUgjrkS1IvvCaoB/vtRZBldY+cgESQkg9FAoF0tPTI/ocjHODtxZG9DlI5IXca6lUKrF69Wo8+eST+O2336DRaNCzZ0+0a1f32r6xTqHPAG8vA++2Nek8hotGo+K3tRBcZvST7ERRqwnYkJ8BXvBNCT5Q4MWBAi9e2W5Dz2qDCClhGETwbaR8Bl5LQUxupMwLDC4PD4eHg9PDw+nm4PDw4s/OgPt8PzvcPCxOr3glVYXNA0cEr/4/n0GrQIpOiRS9Eik6JbQKICtVD6NOBb1aDp1KDt15/2pVcihp7wASRzh7KTh7aciPc7q9+PNMGdpkJkNNA2AtWqLlBTaPr2NOV8cyRdUldR4K8/7P4Ck7CXfxn7Af3w59p8FBP5ecs+NvQ7rjiguz8NSa/ThZbAcvMKzafhJf783HjGGdMPLiXCRpaACBkFjm9vLwcAIExsDxDBV2D0qsbpRZ3Si1VdvfyuoRf7e5wn9Bi1wmQYrON2M1RV85e1WvQopeCZ1KDonEN3ApAQAJIIEEUknVz06nE6dPn0ZGRgYUyto/dxjnBm+JfCcAA6tcqkiCyojJeXyLTcWeRMsL6pPafyrOnt0HACjbsxr6LsOC/g7OO8rBjK0gkVEbTwhpfg6HA3/88Qfat28PtToySw9zFadpL6QE0Ojeni5duqBLly7hjCWqlKlt4So8HNJSHeeTKtRIuXg8Sn9+BxIwTFF/gZun/h1fHXbh2z/dOGP2dXozAPsLvNhf4MUrP9rQK0eBKzqpcElbJTL00iYtORS4kXIqFEmZIU+B5HjB15Hv79A/r5Pf9zMHp5uvtcPfWe1+f9loXSEllQBJGgUMWgWStUokqX2d/Hq1Quzo16llMGr9AwS+L1cGrSJgnWnGGMxmMwwGAy0JRRKG4HXBU366UY91ezgcz69AVooupgYOPOcOwVvyF9TtBkGmz4h2OC1KIuQFHp7BPzktSdXwILBEKkPqJbeh8KvHAQClv74HXftLQ+oE8FoKcWHrjnjnnkF4/4fj+GDrCXg4AeV2DxavP4xXvj6CoT2yccOANujR1khtECER5nBzKLW6UeHw+HLZ8/LhCrsHpVY3SqxVAwKRGAQAfHmseIGKWg69SoH0ZBWyUzTINmqQplcipXJgIEWnhF4tb9JnRFFREcr+OIF+fdvAaDTWuJ8xBm/JUbDUyM9uZozB4RGgVTbtu1Eia8r31uaQCHlBQ9RZ3aBt2x+OvJ3g7SWwHP4axp5jg3osYwI4WwkUhpwIR0kIITW5XC4cPXoU2dnZERk48C1RZA/7eUnzC6q3Z968eXjyySeh0+kwb968essuXrw4LIE1N6lCA3lSFrxNvIIm+cLRqPjtU/DOCtiObUebPlNwa792uKWvFifKePxwzI0fjrlxtnIQQWDAvnwv9uX7NklM1UrRJUOObplydM1UoEuGHGq5BHzlxmUCAwQB8PIMFjeDxSXA6a2ZNDIAXj4fTu9ZuJkSbqkOLkEWcDW/w9+5X7nEj8srwOHxXTEVa5I1CvFLkVGnRJJGUfkFyj8QUDkYUHnlf5JGjmStEnqVHFLaG4CQGpggwF16Akxo3CweY5IGIwd0DHNUjcfbimHb+wE8Z3YBAByHPoX2ouuh7XYtXckVAYmaF/j3NwAAXQNLFflp2/SFJrcXnPn7wVkKYTn8NQw9xgT9nLyzAgLnhkqhwh1Xdca1fVvhla+O4tuDvnzE7RXw9d58fL03H91bGzB5UDsMuSgLKkXiLktISLh4OKFq5qrdA7dXAMcL8PACym2+WQElFpc4AFBidcPhjuzsVq1SJi5ZmayWIitFX7m0pW/Pq9TKwQCDVgGNUtasneapqakYO7buTk/BVgQW4tKGpGVI1LwgGKn9b4EjbycAoGLvGiR3GwGpIrhOOK+tGPLkbBocI4Q0u4ba/KZgXhctUZRAgho42Lt3L7xeX8f2nj176mzY4r3BUyTngHeUQ+DcjT6HVKGG0TQepT+/DYChbPf/kH31Q5BIJOiYJkfHNDmm99fieGnVIEK+peoLSplDwM+nPPj5lCcMr6i60JciaQqVQgqNUg6tUgaNSgaNUg6NUgatUl75uwxaVeX91Y9VltOo5NAoZEjWKmpc/U8IaRrGGNylxxNikyLGeeD44ws4fv8c4L1Vd/BeOPavgfvEdiRdehcU6Z2jF2QCStS8wO6uGjjXB7FUEeB7jWmXzsCZtfcDAMp2/w/6zsMgU+mCejxjDN6Ks1Cl+wbiclK0eOrmi/FngQXrd53Bxr35sFZezXz4jBmPf7Qfi+RS9GxrRP/O6ejRxoCuuQbo1LEz84eQcHF7eZTbfXtWcQKDIDC4vHxAR3+pxfez01s5s5cxONwcym0e8f9OpKgVMqRV7l+lUkohk/g2VTdULheUVn2vq8qBAa1KLsYZT7NZGecGbyuKdhgkRiVqXhAMVXpH6DpeBvvxH8E7K2A+9AVSLp4Y1GMZ7wXvLIdcmxrhKAkhpHkwxsCZaYmiRBLUt8wXX3wRycnJAIDvv/8+kvFElUQq9S1ZVPRnk86TfOEoVPy2FryzAvbjP8Jddgqq1Ko1HSUSCTqly9EpXY4ZA7Q4VsrhxxMeHD7nxZEiDjZP8047Vcql0Cil0KkU0KiqOve1Sjm0quqd/dV+93fuK2WVgwNVAwNqpQwyusqfkJjlKc8D7zQ36RwWuws//paH3p2zkaRt/v1UGGPwnNkF296VEOzF4nGJ2gBlTi+4T/4IMAG8tQAVW56AtudEaLuPgURKg5DhkKh5gdVd1f7qlcG/V1QZF0B/wRWw/bUVgsuCit8+QdqAaUE/nnOUA+flCp1zkjFvzIW4+5qu2LK/AB/9dAp/FlgB+K6i3n28DLuPlwEAJBKgbboO3VoZ0L11Mi5qY0S3VgZqi0lM4HgBHk6A2yvA5vL61vq3eVBuq9oI2P9vud0Dj1eAt/Ixzmbc20qrlCE9uaqjP0Wn8uW9lRe6aCtz3WStonKwQA2tqnlnBERaRUUFvvvuO/Tv31/8jPfjzGeoE4DUKVHzgmCl9psK+4kdABNQse8TJHcfFfQFBJy1mAYOCCHNrr42vykEW1FCXKBIqgQ1cNC7d28UFBQgMzMTHTt2xM6dO5GWlhbp2KJCpk6GXJcKzl7W6HNIFWoYL56A0h3LATCU716F7KsfrrWsRCLBBekKXJDuW0qDMYazZh5Hijj8UcThZDkHxnydAlIJIJMAUokEcimQpJbCoJZAo5Cgtu8sCpnvPq1CArUi8Gdt5e86nRYKlR42L2BMy4ZMoWr06yaExD6vuQCcraTJ55FJpTDoVZDJmr/DhLPkw7b7PXgLD1QdlEih6TIS2p7jIVVowXUdDeuut8GV/AkwAY79H8F77hCSLv0/yLQpzR5zoknUvKD6wH0wmyNXl9r/VtiO/wQIHMwHPoPhomsh1wVfJ5ytBBJIoExtG3BcpZDh2r6tMbpPK+w7UY6v9+Vj518lKKyoWi6EMeBUsR2niu3YuC8fAGDQKnBplwxc1i0Dl3ROpw2WSaO4PLxvk1+bGxzv6zQWBKC8cp3/CrsHvMDA4NsU2OzwwuzwoNzmQbnNBYuTh8vbPJ3/EgnELXTVShlS9Sqk6KqW/zHqlFArZZDLJFDIpDBWzgpIrxwo8M8EaMnkcjmMRiPk8sC64B1lENy2KEVF4kGi5gXBUqa0gf6CK2H78zsIbhvMBz5Har+bgnos77ZB8DhC3peQEEKaoq42vykErxOc7VzYzkdiQ1DvEKPRiBMnTiAzMxMnT56EICT21SZKY2vwTguY0Pjpzcndr0HFvk+qZh2UnoQqrX2Dj5NIJGhtlKO1UY7hzbGXFOeC1+uEx+mF010MmUIFqSoJMpUeUpUOUoWmGYIghDQHzlYCjzk/LOfSaZQwdcoKy7mCJXgdcBz8FM4jGwFW1RGlyLoI+r7TIDe0Fo/JU9rCOPwxOA59CsehdQBj8J47hPKv5yPp0llQ5V7crLEnmkTNC6ovVZQU5B4HforkbBguuhbmA5+BcR6U7VqJzCvnhnQOr60YTPBCnpQFmUofcJ9EIkHvjqno3TEVjDGcKXVg38ly/HHGjMNnzfir0AqOrxr4MDu82LgvHxv35UMmlaBnWyMubGOATqVAkkaOQV0zkJtKnRSJyFu5ubaH48HxDF5egNnhRYXdA6vTCy8vgOMZ3F6+sqPfC4vDI/7scHPiFf/Nve9VkkYBrVIGuUwKpVyKZK0CKTolkrUKKGVSSKUSKORScQmg9CQV0ipnCeio47/J9Ho9+vbtG3CM8V7wlvDkDiRxJWpeEIrUvjfBdmwrIPAwH1gHQ49rIVMHdxWv13oOqrQOEY6QEEKq1NbmNwVjDFxFnu+KJpJQgsqwJ0yYgCuvvBI5OTmQSCTo168fZLLaN+U7fvx4WAOMBolMAaWxFdxlpxp9jsBZB0D5nv/VOesglgicBwJXCs7u2xNBIlNAptJBqtRDpk6CRKFJqCnZhLQUnLMCnvK8sJ2PFwQ43V6oFJHfgJwxAe4T22H77X9grqollqTaNOj73AJl6/61fi5JpDLoek6EIusiWH96BYKzHMxtheWH/0LTdRR0pim0cXIjJWpeEDjjIPRlrVL6TIb1yBYIHjusR76Boee4gOWHgsE5KsA5KiBVqCHXZ0CuTYVEFpiuSSQStEnXoU26DmP6+QbMPJyAY4VWHD5jxq5jpfj1zxI4Kpd54QWGfSfLse9kuXiOlzf8gUW39MGgrhkhv04SfhwviFfxmx1eCAIDg2+N/zKbB2VWNxwe3wUtjAF2Nyeu8+/v3GeMiZ3/0SaVAMkaBYx6JZI1CqgUMijlUuhUcqToq2YBVP/XqFNCIafl5KKJ53k4HA6o1WpIK5f24yz5YELzLRlF4lMk84JXX30V//3vf1FQUICLLroIS5YsweDBg+ss73a78cQTT+CDDz5AYWEhWrdujQULFuD2228P6XldLjeS9Doxx/R6OXAcB5lMBqWyKn90On0zAFVJWUjuehUshzdC8DhQsusjGAdMg0qprFlWpRT/j3k5Ds7SAjBNBtRafbWyTjDGoFKpxLrkOA4ejwdSqRRqtbpRZV0uFwRBgFKpFK805nkebre7zrIKRdXr9ZeVSCTQaKouMnS73eB5HgqFQiwfSllBEOBy+epHq9XWKCuXy6GsrMtQyjLG4HQ6AQAaTVVfhsfjAcdxIZWVyWRQqapWaHA4fMuxqNXq/2fvvMPcqM79/z3T1KWtXrf1umIbGxZswDa2aaaZ0JIQCITiBBIIhFB+ubmQ3OQGUpwbUkhCaIFACISQEEIgOAHTTXcDN9zWZV12vV1dU8/vj5FGq22SdqWVVns+z7OPpDNnZo6OZue8c77nfd+UurquD1jX+u1VFaqq9qrb1++ZTd10v/1Qr5Oev2c2dWVZtq6pTH/7wV4n3X/PoV4n/f2e2V4nmfz2oijC4XAM6jrJ5h4hyzLa29vhcDhS+icWi4FSClEUU35PRVFACOl1nSTqItJqJkXWDSiqCkIAe7f2yopq/vYC3+24mdc1f3sZxODgdCTboKgqdN2AwPMQxWRdWTHtYYfdNmBdSilisplj1m6TUu/5up5VXZ7nIHW7X0ZjZv5cmyR2+z0zr6tpGlRNB8dxsHUbd2KyDEoBSRTB88m6ipqf3F4ZCQcPP/wwPve5z2H37t345je/ia9+9avweDx5aVCxILiroEU6oMeCgz6Glesg0hn3Otg74lYSUF2FFukCIl0AzIk4TnLFPRLc4CQXixnOYBQ5uhyC0rYXNIfqvz8Uw6vr92HpsbUoc9vT7zBI1I69CK3/oxlyKAEvwjn7Ajhnnw8ipA+vJo2ZjfLlKxH88GEohzYAAKI7/g215VN4Tv4GBO+4fDW/ZClFu4ATbAjJyQnXTJMjd4e3e1F2/CXo+PCPADXQ8eHjGLf8fwfVHkONQek8ALXrEHhnOQR39YDxkiWBw+yJPsye6MPnFk6Cqhn4ZF8n3t3Rgne3t+Jge2qsUVWn+M5TG/HTK4/HwqOYeNATWdWxrzWMQ+0R2EQOXocIj0O0hFJF1dF4pAsxI4RQtwS8UUVHWzxZr9xHiB6DAv6IgragjM6QAiN+Xy6GxVmEAB67CKfNnOQXBQ5uu2iF8rGJpr1HQKw4/xVuCQLPgQDgOWJ5CLhsAoLBwIhJ/Msw6ezsxKpVq7Bs2TKUl5fDiPlhRLsK3SzGCCBfdsEzzzyDW2+9Fffffz8WL16Mhx56CMuXL8e2bdswadKkPve59NJLceTIETz66KOYPn06WlpaoGnZT6aceenX8eY/HkFFmek18PhfX8Tv/vAMPnveGfjf//c1q97pl1yPWEzGS0/9BjXzLkNgx2uAoaH9k3/ioTVd+P6d37Lqnvelm9HpD+LZR+/B9Mm1AIAXXn4LP/zl73HGqUvw0COPWXWXL1+OQ4cO4e9//zuOPfZYAMBLL72Eb33rWzj55JPxxz/+0aqb6Psnn3wSCxYsAAC88cYbuPHGGzFv3jw888wzVt0rrrgCmzdvxsMPP4zTTz8dAPD+++/jy1/+MmbNmoUXX3zRqnvttdfio48+wq9//WucfPLJAICPP/4YX/ziF1FXV4dXX33VqnvTTTfhrbfewk9/+lN8/vOfBwDs2LEDF110EWpqavDOO+9Ydb/1rW/hP//5D/73f/8XV155JQCgsbERZ511FjweDzZs2GDV/d73vod//OMf+Pa3v42vfvWrAICWlhYsXboUgiDg008/tequXLkSTz31FG6++WZ885um12cwGLRWVW/bts2agP7lL3+JRx99FNdeey3uuMNc4KlpGurr6wEA69evt+K+P/jgg/jtb3+LL33pS/jBD35gnW/+/PnQNA1r1qxBTY3pif3EE0/gZz/7GT772c/iZz/7mVV3yZIlCAaDWL16NSZPngzAvL7vuusunHvuufjtb39r1T3rrLNw5MgR/POf/8TRRx9tXicvvIA77rgDp556Kh555BGr7gUXXID9+/fjL3/5i/U9V69ejW9+85s46aST8NRTT1l1v/CFL2D79u147LHHsGTJEgDAmjVr8LWvfQ3HHHMMnnvuOavuNddcgw0bNuD+++/HWWedBQBYt24drrzySkyfPh3//ve/rbrXX3893nvvPfz85z/HRRddBADYunUrPv/5z2PChAkpeU9uvfVWvPbaa/jRj36Eyy67DADQ0NCA8847D+Xl5fjoo4+sunfeeSdefPFFfOc738GXv/xlAMDhw4dx+umnw+FwYNOmTVbdu+++G3/9619x22234cYbbwQAdHR0YOHChQCAXbuSz5L33HMP/vjHP+LrX/86br/9dgDmxHrit//kk0+sifT77rsPDzzwAK655hr8z//8j3WMRN0PPvjACsv2yCOP4Fe/+hUuvfRS/PjHP7bqLly4ENFoFG+88QYmTjQX/Dz55JP4yU9+ggsuuAC//OUvrboXXnghurq6sGrVKsyYMQMA8Nxzz+F//ud/cOaZZ+KBBx6w6g71HvHSSy/hu9/9Lo4++mg89ljy3nPDDTfg008/xS9+8QvrOlm3bh2++c1vYsaMGXjyySeturfddhs2bNiAH939vzjlWPO7bdu5Bzd952eYMG4Mnr7/R1bd7/3sAXywfgvuvHkFlp9h3k/2NB7Ctbf/EFUVZXju0eT/y4/vfRRvvr8Bt371cnzuPPM+dbi5FV/6xvfhdjqw6qlfW3V//sCT+M8b7+PrV38el3/2HABAe6cfn7/uv8HzHN549kGr7n1/+Bue/8+bWHHZ+fjKFy8EAIQiUXzmylsBAK//7X5LqPj9U8/jL/98BV+86GzcuMJMeK/rOs65/Gaz/568Fx6XeZ386e+r8Pgz/8LF556G26+/wjrfeVfeAl038PdH/g/VlWa45Gf/9RoeeOLvOPf0RfjON79s1f38td9GKBLFU7/7IWrHm/eTF15Zg3t//zROWzQPd3/7BqvuFTd+D20dXXj0l9/DjCnmWLL67Y+w8rePY9v65L02V2QkHGzatAlnn302zj33XKxfvx633HLLiJ8gyASpvBax5u2DTgTGCTaU1X8e7e+bN/bO9X/B2LPvzGUThx1q6NBjAeixAABz1SMnucywRjY3eMnda2Ukg8EoHIYSgdzakPOEhh6nDQtmj4fLnp8V+0YsgPCmvyLW8CaA5IyaNPEEuI+/Erw7u4lOzuaBd+ntiO1ajdDGPwOGCq1zHzpf/i48J3wZ9in9r1xj9KYU7QKpYhJCcvKhwp1lqKIEvrkXwL/lJejhNkQa1yF6eDMc448ZdLsoNaCFTU9ATnJCcFeZXghc3ys5E4gChxOmV+KE6ZW45TOzcagjghZ/DOGYhlUbDuHNrUegaAbueHIjfnT5cVg8q7ooJ3gppdCN3rPqBgX84fgEfFiB0UcdRTOsVfndJ/etY4MiHNPQFq+jxlfu6wZFayCGPg5ZdPAcgUNKXgsehzmhX+m2wS7x4DkCkTcn9H3OZMifRCggn1O0tnkcYs4SaudSqGYMH16vF0uWLIHb7QY1dGj+Q4VuEmOEkC+74Je//CWuvfZaXHfddQCAe++9Fy+//DIeeOABrFy5slf9//znP3jrrbewZ88eVFSYCYcTk7TDgeCuhu/o5fBveRE2Aah37M14X6rJoJQW5VjMYDBKj4Q40p93WDYY4TaAThjycRjFCaEZWPY8z5dMsqNAwFz95Pf7M8ocrvgPQ/U3Dfp8hiaj8emvQo+Y4QEmXvKbovM6oJQiGFXhcYg5MVQ4yQFecoOzu8FJbnCClH6nIodSCr/fz1bO9QPrn/QUoo8MTUbsyA5QPfdhK3RdRzgcztnxKKWIKAYcAoXc8BrCm54FVZMrpHnveLjnXQ1p3OAnYBNonfsReO++lJjNtsmL4T7hy0Wd14VSiqramZDKCm+Ulapd8Lt/f4oXPzHH6wcuKcf0qsEJ4YEdr6H1zXsBALbq6Zjw2V+AkNx55xGOB+8sh+iuHlQyRU038P2/fII3tyaTlx090YfLl0zG5DFuABTBYAgejxvJdLO9icjJSfd0sfA1g6IzJKMtYIbiSWd+RhTdmvTvSzgYyXAEKI+H6BHj7sUkXlblMUP28BwBASAIHCrcEqo8NrjsAkj893BIPCo9Nni7eUEUE8wuSE8x9pGmaQgEzMVBmv8g9Hjo0kKQsAucEjfo/jGiXYg1fgAj0gFp7DEQx87J6b240FBKUT1pNkRf4T0n82EXKIoCp9OJv/3tb/jsZz9rld9yyy34+OOP8dZbb/Xa58Ybb8TOnTtxwgkn4E9/+hNcLhcuvPBC/PCHP0wJl9IdWZYhy7L1ORAIoLa2Fg1r/4PqqorMQxXFww9pkU4cePqroLoCcAImffEhCPEFL/2FKtJUDRzPwTvuKAiuynjd4gpVFA6H4fP5zBAgwxiqqK+QQtnUzWWoou51gd6hivx+PxwOR68wOD3rjsZQRYkxz2635yVUUbrfM5fXSfffM9vrZKDfnlKK5uZmeL3eYQlVFIvF0N7ePmD4oUxCFan+g+CUAIT4cZPhhwjstmQ/JMMPCRCEwdTV0RWS4ZBGU6giHaqm9RGqSAGltEeoIh2KqqJu1nyI3tzmomTJkdMgesZCj3TCUGOD2p8TbCg77hK0v/d7AEDn+qcx9uzv5LKJRYehRGEoUSDUCgDgBMn0RrB5WMJlBmOYoLoGuXV3XkQDAIgpKvY0dWFCpRs2KTdeRnrrdnRt+hN0/wGrjAh2OI/5PBxHnQ3C5eY8Qnkdys/5IULr/4TYnjcBAPK+d6G27Yb35G9ArJyak/OUMqVqF0SMpGE5WI8DAPDMOA3+Tc9D6dgHuXU3wg3vwj09d14t1NChhdqghdrA21wQXFXgnRUZhw4UeA53f7Ee33v6E7y1zRQPth3043t/+SRnbRxp2EUe9m4r92t8dkypcaOu2gVdpwhEVYRiqhVSiCMELolifJUXPqeExLymTTAn9M1kvTzQx4SnyybkbGU/g5FLotEodu3ahQk1FeALKBoMBarFIB9cj9i+d6A2b7bigEW3vwTOXQPHtNNhn75sUKIro3/yYRe0tbVB13UrBEyCmpoaNDc397nPnj178M4778But+Mf//gH2tracOONN6KjowN/+MMf+txn5cqVuOuuu3qVa+D78FbjoemAHFVTygBAk3UAOkDcsM86D9GtzwOGhpa1f4Fn4Q191+1+DB3oOHIQtspUe1dV+7blu4sd+a6rKApCoRAApAh5iqJkfNxs6vr9/l5lqqpaE7uDrdvX+XJVl1Ka0kfZHjcxGZ7LukD+rpNsf/u++qcv+vs9s6lbzNdJX2WJ35NSCl3Xoapqn+HVcv3bh8Nh7NmzB2PHju3zfLred36jxO8IAFQJQ4sGARAoerf7PieCAogo3ccCHuB4KAagKNnXpRQAL4JyXJ91VQqoPY6LHsellIN+eC3kjgbw4+aDq5pp3tPidaMqRTLaAQdwXL/H7auuRgGtj7rmUGIMoq7ZNr1n/xABIIA5lCTrUi4/0SBYcuQ0EI6DVD4JsZadgz6Gd/Y56Pr479AjHQjvfR9y2x7YqkbPxJSZcLkDWrgDQGrCZU5yghMdLLwRg5FDqKFDbts9aMEzE6Kyiu3721DpdQxZODBifoQ2/hnyvtR4fLYpS+Gu/yI4R9mQjt8XRLDDs+CrEMfORWjto6BqFEboCLpe/QFcx14Gx6zlJbUqMdeUql3QfYJgMDkOEhCOR+WCFWj69w8AAO1rn4BrysK8JOPW5TB0OQzSdRCCqxKCqzKjCTGB5/CjK47D6k+a8PQ7e7GrafA5nfIBIUC5S0Klxwanre97jNdhxt8vd/edVJfnCCrd5iS+1yn26TvhsAnxSf7s7mPFuFqcwRgqkUgEW7ZsQRlXB69j6KELhgtq6FCaN0Pe9x7kQ+sArfdkCQAYoSMIf/IXRLa9AMdRZ8Exczk428gOs1cs5NMu6HmPHSicj2EYIITgqaeegs/nA2CGO7rkkkvwu9/9rk+vgzvvvNOKcw4kPQ7cdgEex+DGbef8S9C482VQNYrY7tdRPf8SiBnl1NJgtwsD5jQqBAkvQTbm9Q/ro4Fh/ZOe4e6jWCxmLhaYMAFutzv9Dj2ghgY1vB+SNDzPzIn+GawnItVkhNY9BiU+56Dtfhmcuwb2aafBPq00FhTkK1QoS46cAbzdA8FVCW2QK284wYby4y9B27sPA4jnOjintL0OBqJnwmXAFBM40WH+SeYrEews8TKDkSWUUshte6DLuQsj1BflHifOWzh9SMeg1ECs4U2EP346JSyRUDEF7vnXQKyaMdRmpsVetwhi5TQE3rsPWnsDYOgIf/xnKEe2wLvg+ryIFqVAqdoFwfgKQgLAOQSPAwBw1M6DY0I9ooc+gRZohn/bv1F2zIU5aGXfUEOHGmyBGmwBb3NDcFeBd5QPOI7yHMG5x4/HOceNw9rd7Xjn0xbImgGAQlHUeCiG/vvBLvKo9JrhdbrH2e8LAqDMJaHKa0d5PBTPQAg8gcAzG4DBGE4qKytxwenzrRCrxQylFFrHHsj73kFs/wegcqBXHc5ZCfvkJeB9ExDb+zbU5i3mvmoEka3/RGT7f+CYfgYcsz4D3lk+3F+hpMiHXVBVVQWe53t5F7S0tPTyQkgwbtw4TJgwwRINAGD27NmglOLgwYNWstHu2Gy2lPAfCQghg568Exw+lB17MTrXPw1QHZ0bnkHN6bdltK8eboVgz34SL98k+oNN+vYP66OBYf2TnuHso6qqqpQwcNmi+Q8Bhj6sv+dg+0cPNsP/zq+hdzWmlBuhI4h88gyi216EY/Zn4DjqHBYhpQ8yXl517rnnAkDJJEHMFqlsIvRYYNBhPzyzzkHnxmdNr4N97yNy8BM4J9bnuJUjF6qr0HXVSrqcgBNs4CQHSEJUEB3gRHs/R2EwGEr7nl7/R8WI1rkfwbV/gNa+O1koOuGqvwyO6WcM62p/3j0GZWd+H+FNzyL66YsAALVpE9r/dTscM86Cc9Z54Oy+NEcZfZSiXRCKmWO82y5AtLuHJMARQlC5YAUOPmdOFHRu+As8Ry0bllWEuhyCLodAuLgXgrtqwLGTEIKTZlThpBlVANhqegZjtKJHuopeNNCCzZD3vQt537vQQ0d6bSeiE7ZJC2CbvBhi9UzLnrBPXgwt0ITo9n8htncNYOiALiO649+I7loN+9TT4Jx9Pvh4LHpG9uTaLpAkCfPnz8fq1atTJrdWr16Niy66qM99Fi9ejL/97W8IhULWCtqdO3eC4zhMnDgxq/OL7moAvcONZIrvmIvg3/IiDDmE0K43UX7cJZDKa9Pup0c6YfgmlESeQAaDUZrokQ4Ysd7hn4oR+eB6BD94MLlQUbDBOfsCqC2fQj2yFUB8QcGmvyG6/d9wzj4fjhlngbB5R4uMkiP3xe7du9HQ0IBTTjnFSnYyEh4us02O3B0t3A65fd+gz+3f8i+0vfsQAIDwEmrOugOuuhMHfbxckevkyPmGEA5EtKcICUR05NW4YpMoA8P6Jz3D0Udyx35ooba8HLsnnYEwPtyyH8dOGwO3I/P/ParJCG/+O6I7/g3QZJw+2+Ql4I7+IlzesoJeQ0rTZgQ/eBBGrCtZyEuwTVoI+6SFZmLFHOVayJZiSo7cF6VgF1z+23XoDCsYV+7A3249CbHm7aB0aHGaj7z2c4R2mwkcy477AioXXJ2LZmcNb/ckvRDS/C7snp4e1kcDw/onPcXWR4YaRdOuDfhg056sx/Z80D05MpUDkBs/QGzfu6Z3YE84EdKE42CfvATSuPq0YeH0cDui2/+FaMMbQPdFYYSHbfJiOI++EEJGYWUKSzElR+6LXNgFzzzzDK666io8+OCDWLRoER5++GH8/ve/x9atW1FXV4c777wThw4dwhNPPAHAjL09e/ZsLFy4EHfddRfa2tpw3XXX4dRTT8Xvf//7jM6ZsAu6OjshRQ7A0HrHcs+Uzo+fRceHfwQAuKYuxtiz7shoP8FdBVtF3aDPm2uK7X5VjLA+GhjWP+kZ7j5qb2/HW2+9hfnz52cl9FJNgdq2A3SYc9x1twsy6R9q6AhvfhbRbS9YZbx3PLxLboHgM4VkPdiMyNYXENu3JmVugti8cQHhTBCht0dasUIpRXXdnMIkR+5OR0cHvvCFL+CNN94AIQS7du3C1KlTcd1116GsrAy/+MUvctrAYsIMV9Qx6NW83tnnInJgPSKN60B1Bc2v/BhjTv0m3DNOZzfPLKDUAFUiMJRISjnhBDPMkWAHkZIeCoQbOTFaGYzBonQdHDbRAAA4jsAuCeCyuHcpR7Yi+NEjMEItVhnvGQf3iV+GOOboHkmOCoM07hiUL/8Jwlv+gVjDG4ChAboCee/bkPe+DSK5Yas9Cba6hRCrZ7Nwaigdu4BSmuJxwIkOiL7xULoODum4FSdeidCedwFDg3/zP+Gbcx4Ed1UumpwVeiwIPRYE4Q9CcFVAcFeDG0GGMIPByB+GJiPWsgsERtZje76glEI7tBb+xrfjSY572ggE4pjZsE1eDFvtieCkzL25eFcl3POvgfPoixHZsQqxXa+CajGA6vHxfg1skxbAefRFEMon5faLjQJyaRdcdtllaG9vx913342mpibMnTsXq1atQl2dOane1NSExsZk6Am3243Vq1fj5ptvxgknnIDKykpceuml+NGPfpT19yAcB6miDrGWXVnvm8A353z4N/0TerQL4T3vZpzrUAu1geOlohWFGAzGyIfjODidTnBZPM9SSqF1NQ67aJAtRsyPwHu/szwKAECqPQmeBV9LCUXEe8bCs/BrcMy5EJEt/4C8/12AUlA5gPDHf0Zk+0twzr4AjunLQEaxF1jWHgdXX301Wlpa8Mgjj2D27Nn45JNPMHXqVLzyyiu47bbbsHXr1vQHKSBD8TgAAEONIdb86aBXIFJdRcsbv0KoYY1VJnrHwTNzGTxHnQGhAO6xI83jIFs4QUrxUCCCDZxgyypJJVPIB4b1T3ry2UeqvwmK/3BOj5kOXdcRDmcWxsVQwgh//GfEGt5MFnIinHMugnP2+SC8mPUKguFAj3Qg+um/ENvzljmh0APOXgap9iTY6xZCqJqR9/BKxepxUCp2wZG2Dlz8iw8BAPOmVOC+r54EAIi17IQeG1ri4Lb3H4V/0/MAAM/MszDmtG8O6Xi5grd7414IqZ4+7J6eHtZHA8P6Jz3F0kdUVxE7sgOGJmc1tucTI9qFwEePQD28sdc2vmwS7JOXwFa3ELyzMjfnk0OI7nwZ0R3/Scm5BADShHlwzrkYYuW0nJwrlxSrx0Gp2AWJ+QK5fd+gcx0CQNfmF9D+nunt4Kw7CePO/V7G+0oVk+IhkwpLsdyvihnWRwPD+ic9w91HmqYhEMhuUbQePAIt2Jy+Yh7IdL5AbduFwDu/hhGNh14kHFzHXQ7HzOVp+1ULHEZky3OQ938AIDlVzjnK4Dz6ItinnQbCF6+AUDQeB6+88gpefvnlXjECZ8yYgf379+esYcUKJ9ohescOepKO8CLGnPH/wEkuBD79DwBADTShY+2T6Fj7FBwTj4N35llwTl7A4hrmCENTAE2BHk29KRLCgQgSiCCB423me14EEeym2JCFsMBgFAo11DrsogEAGAaFouoQBG7AlYnywXUIrXsMRrTLKhOqZ8Jz0nUQvOOHoaWDh3dWwD3/arjqvwil6RPIje9DPrQR0E2XdSPWhdiuVxDb9Qo4ZwVskxbCNmkhhIqpo8ogLhW7IBzTrfceR/L+L1XUmQsGDL2v3TKi/PgvILh9NQwljODO1+A75kLYKicPpbk5QY8FoMcCILwIwVEG3lUB3lZ8CRkZDEZ+oLqGWMtOGJoMIPOxPZ/IB9Yi+NEjoErIKuOclbBNXgz75MVWeIFcwtnccB3zeThmLUds12uIbF9lJVtWDm2AcmgDxJq5cBx1FqSxx47qVYeZUCp2QYKh5jr0zj4XXZ/8A3q4DZH9HyF2ZAfsNTMz2lfpaAQMHYK7mnnRMxiMnGIYBhRFgSAIGXkdGEoEWh+5hYoFSiliu1YjtPFJM48RzIV+nsU3QxozK6NjCN7x8J78DWhzLkZk83OQD5iLyoxoF0Lr/4jIpy+aAsLU00D4woQvLgRZf9NwOAyn09mrvK2tDTbb6HB5F7xjoUU6YKi9V6BmAuF4VC29EY4J9Qh8+h9ED30S30IRPbgR0YMbwUkuuKefCs+sM2Grmj6qJqGGC0oNUDUGqDH0NR1ECBcXEmwAL0KLatAEzcypwEtM2GEUHC3cbj5QFICuUBSvrN2DpcfWoszdO3GQEfUjtOGPkBs/tMqIYIfruMthH+bkx0OFCBJstSfCVnsiqBqDfHgj5MYPoBz+BDDMh0gj0oHo9lWIbl8Fzj3GyonAl00q+ft3qdgFEUWz3jttyYdzTrBBKpsIuWPwkx283Yuy47+Ajg8fB6iBjo/+iHHL/3cozc0pVFehhlqhhlpBOAFEckBTOOg2DrzNzUJyMRglCDV0xFp3pTzPpBvb892e8Cd/QXT7qmShzQvPiV+GbeIJw2I3cKITzqMvgOOosxFteAPR7S/BiHQAANQjW6Ae2QLwNkjjj4U05mgIVTMglNUWLPdRsVIqdkECwguQyidCbts7qP05QULFvMvQuuZ3AICOdU9i/Gd+mPH+StchqIFmCK5KM8wgS9jJYDByQEdHB1544QUsW7YM5eXlA9al1IDW1QgMLkVu3qFaDMGPHoW8/z2rTKyeCc/im8E7Bv5ufSH4JsK75JvQOhsR3vJ3KAfXATCf+UPrHkNk2wtwzrkY9ilLR8WC46ytnFNOOQVPPPEEfvhDc7AjhMAwDNxzzz04/fTTc97AYoQQYq5APLJjSMdwT1sC97QlUIMtCO58DcEdr0ELmgqeoYQR2LYKgW2rIFXUwTPzTLhnnAbBUZajb8FIB6UGqCYDmgxKKdSoClnvsiYBuwsLpteCFPdgsDFhgZF3tGgXlCFMZA4Vt0PCCTPHwWlPHSgppZD3vYPQhj+BKslwB9L44+A+4cvgXcMf2z2XENEOe90i2OsWwVAiUA6tN0WEps0ANSVII9SC6LYXEN32AnjveNMToW5R0XtYDJZSsQtUPWkIS0LqBJXgroIW7ezluZYNvrnnI7D1X9BCbYg0rkP00CY4Jhw76OPlC2poMKIBqFEVMb0LHMeBiA7wNjc4mwuc5GbjG4MxwqGGDrl1d698Yf2N7fnGiAUQeO++1FjEE08EX38NbJ6yYRfgiWCDc+a5cExfhtjeNYh8+mIyP5MuQzmwFsqBteZnXoRQPgVi1XSIldMhVE4H56wo+UUDA1EqdkF3BGcFNEcH9Kh/UPt7Zp6Jzk/+Di3QjOjBjxE9vBmO8cdkvD81dKjBFqjBFvAOL0T3GPAO36DawmAwGADg8XiwaNEiuFzpcwTp/oPm3FgRogWaEHjnXuj+ZF46x6zz4Kq/bMjCvlA+Cb6lt0Ht2IvIluegHNoAADAi7QitfRThjU9BGlcPacI8iFXTwbnHjKgFkpmSdY6Dbdu24bTTTsP8+fPx+uuv48ILL8TWrVvR0dGBd999F9OmFV/8x+4MNcdBd+SO/TlNRkqpgVjTVgS2r0Z477ugmpJagePhnHQCvDPPhLP2hJy5xpR6joNcMJg+IoRYoY9ShAU+HhKJl0pmFSeLWZieXPaRHvVDbtsz6FwruaCvOMh6uBXBj/4AtXmTVUYkN9zzr4at7uQBv3cx5jjIBkMOQj64DvL+96G2bOtzNQZfNgn2uIjAu8dkfY5izXFQKnbB2u2H8M0/mtfuJQsn4fYLj06pZ2hKPGSR1tdhMiK483W0vPErAICtejomfPYXRWlcphvzCC/GhQS36ZEgOkbk/+1QYOPewLD+SU+h+ogaBuS23X3mbilEjgO1Yy8Ca34FIxKPIU94uOddCdv0MxFVaVHYBdTQoTRvhnJwLeSD60HlgfPecI5yCJXTIVZNh1A5DWLFFBAh96vEizXHQanYBT3nC0w7YNugQxd2twHsY4/G+At/OqRrmxNsEDxjILgq8x7GiN3T08P6aGBY/6SnWHMc6JFO09ugwPQ1XyAfWIvgBw9a+QiJYIdnwddgm7QgL21Q2xsQ2fx3KE2f9F2Bt0EomwihbBKEskngy2ohlE0CJ6UXZ3JBvnIcZC0cAEBzczMeeOABrF+/HoZhYN68ebjpppswblxxGS19kUvhgOoaos3bBh3vcCAMJYJQwxoEd7yG2JFPe23nHWVwzzgN3plnQqqoG9K5mHCQnnz1EeEEEEEE4cS4mCBauRY4XgI4wSwr8t+FGQLpyVUfmeGJ9mMQt+6cEonKaDjYgrEVbkgCMeMJfvIM0G0lgq1uEdzzrgZnT3+vHenCQXeMqB/ywY9MEaG1b880oWKqlROBd2WW3LFYhQOgNOyC97cewO1PbgEAfHHJZHzzvN6xMLVwO+T2fYM+FzV0HPz7rVA6zGOMWfZf8Ew/ZdDHyxfZjnmEcHFvBJcpKEiuko/7yca9gWH9k55C9JEpGjRAj/U9UdB9bLeJ+Y+nHtvzFoJrH7PC/nH2MniXfBNi9cyitQuooUPr3AetfTfUtt1Q2xtgpIv5TDhzEqFyOsTKaRCqpoP3jB2ycFyswgEw8u2C07/zAiZMGN/72qPGoIUDQg3cHPsuxtAmAMDjtv+H3XzmXgcDH5yLL0jL3/+KYVBwXPH8LxYjrI8GhvVPeoazjyil0DQNHMdjoGGWGmr3PMEFhcK8y3FUx9na33GK/h9r2xEyHk+JN6GNy/84U2s0YIH2BmYZn8CJ9AsuOlGJZm4imslENHO1aCYT0U5qYJDc21r/uOX44hAORjK5FA4AQIt0DDreYaYoXQcR3PEagjtfhx6Ps9kdW/UMM5TR9FMGldSQCQfpKWQfEUKSAkJcUOguMBBOiHsxFG6Shk0QpCcXfaQGmqF0HcpxywZHa2cQr3y4E0um8MCWJ6C17bK2cY5yuE/8CmwT5mV8vGKdIBgqeqQdcuNHkBvfh9be0Gcdoeoo2OsWwVZ7ErgBwtEVs3AwkknYBW9vasR/P22Gybjq1Cn4+jl9Jy6U2/ZAi3QO+nyRAxvQtMrMbyB4ajDpsgeKLjZmLsY8TrRbHgmc5Cq5mMxs3BsY1j/pGe4+opSaosEAoVYSY3u+cxxQXUNo458Q2/WqVSZUzYB38S3gneVWe0eKXWDEAlDbd8fFhAZo7but1Y/9QSSX6Y1QmQhxNA1cls9xxSwcjGQCgQDO/el76SsOgnlYi6+RBwAA++hk/BTfQz4n+xkMBqPU8KET1+EhzCA7rbK19CQ8iRWQMbzPGxx0TMcuHIXtmICDmIgDqCatGe2rUBFNGI+DqMUhTMQh1OIgJiIMz5Da9NYduRcOBjXT2NXVhUcffRSffvopCCE4+uij8ZWvfAU+3+iLs2fGO2wfUtzjdEhlE1G54BpUnHglIgc3IrjjVYT3fQjEwyXIrbsgt+5C+/uPwDV5ETwzl8ExoT7vLouM4YFSCuiq5dnS3xoXK+eC9ZcQGLq958SSCY80mtAinVADzb1iEReScreEM7xbEHn7eeteBAD26cvgqv8iOKl3UrzRCO+shHPWcjhnLYceaoHc+CHkxg+gde6z6mhtOxFq24nQhicgjjna9ESoPRGcbWhGw3BSCnaBZiTXUYh8//dJqXwSdDk0aG9Dx8Tj4ZhwHKKHPoYWPAL/tn+j7JgLB3WsYsZQYzDUmBXSkfAiOMlphTjiRCcbjxiMYYJSCqV9T9r47BVeJy44eUZe26JHOxF45zfQ2pIP/PbpZ8I976oR66nE2b2wTZhnLZighgE9eBhq227LM8GMvZwcZ6gShtq0CWpTMrwj7xnXLcTR9Hji5ZH5PDfS7QI50Na3xwEAgILqgwtbeICeiKbYvzCOHsBksg9LpY/xqTB/aI3tE2KOsTkMh8hWi6eH9dHAsP5Jz3D3kWEMEPqY6qADbS8AU/RPcbn6ENww51918FglXIr3+TPhIgTDExCoOzzaMAttSHqqSzSKGnoI44yDGEsPYKxxEGPpQdgRTdlTIirqsB91SM1bGYAPzVwtmkht3EuhFm1kLHRSOBspa4+DdevW4ZxzzoHD4cBJJ50ESinWrVuHaDSKV155BfPmZb7CtBDk2uMAAAxNRqxp27DGG9djAYR2v4XAjlehtO3ptV1wV8Fz1DJ4jlqWdhUK8zhITyn1kemhEPdg4HgQTgQ4Pi42COb2uIcDOCGj78tWFqYn2z6ihgE90mEKBkWWiCjWugstb/4GajzcCgDwnrFwn3QdpDGzB3XMkbSyMBdogSbIjR9A3v8+9EAfXiSEhzh2DuyTFkKaeAI4yVW0HgelYhe8sn4v/vfZ7QCAr501AytO7z8GsxbtgtzatwdJJsitu3HwudsAAJzdg0lffHhQHoP5YjjGPEIIOMlpJlu2mSGOis3zYiDYuDcwrH/SM1x9ZIoGezPylMp3jgO1dScC7/4aRrTLLOBEeE78MuxTT+1Vt9TsAkONQmvfYwoJcTGBymkWnvE2iBWT42LCDAiV0y2PDKB4PQ5KxS4YaL5gKF7A4X0fofllM3G0VFGHiZf8Jm/5jgjHQ3BWQPBUgxMdgz4Ou6enh/XRwLD+SU8x5TjQI+3Qug72ua0QUGogsvWfiGz+OxIiPOesgPfkmyFWH1XYxmUApRRGpA1aZyO0rkboXQfM11Bzn7kRe8Hx4L0TIMRzJiT+iD31WimaHAdLly7F9OnT8fvf/x6CYCoemqbhuuuuw549e/D222/ntIG5Jh/CAVDYECJy+14Et7+K4O43YPSR6Mw+bo4Zymjq4j4NhlKaFM8Xo7mPTHEhISTwKYJDQmgAJ8AfCqOsrBIcPzJXRuWbTA0BaujQQq1Qgy15yZ8yFAw1ho51f4Z/8z+BhFBKODhmfQauuZ8DEaRBH7vUJgiyQes6ALnxfcj7P4DeV5xkToA07ljYJi3EhCVfLjrhoFTsgpc+2oMf/cPMSXHTuTPxpVOmDLif3L4PWrh90Oc98tovENr9JgCg7LhLULngmkEfK9cUaszjBAlEdIAT7OBEO4hoByfYilJQYA/AA8P6Jz3D1Udy+15o4d6hTvuiKxjB2q37MXdKNVyOwY/pPaGUIrb7VYQ2/AmIx4fnnJXwLrkVYuXUfvcpZbuAUgoj3Br3SmiA2r7L9EZMEz+fc1bEcyWYXgnj519UdMJBqdgFA80XUEoRO7J9UN7AlFIcev5bkFtMr5vhynfE270QPNUQBgiL2R/snp4e1kcDw/onPcPdRx0dHXjnnXdw3HHHwe1OLmAylAi09oZhXRg9EIYcROD9+1M89MSxx8B78k0jykO/L6gmQ/Mf6iYm7IfWdQBUCWW0P7F5u4kJteB9tRh3/PmFD1W0bt26FCMAAARBwLe//W2ccMIJOW3cSELw1ECLdMBQoukr5xhb5RTYFn8VlQtXILz/IwR3vIrIgQ3WxF6saStiTVvR9u7DcE9dDM+M02EfN2fEur4yhhdq6GYSsAFWvVNKIUdVREKNZnIdXojnZUh4MJhiA7p7NHC8WYcTWLgKAFRXoQaPQAu1DTrpWj6JHtqElrfvgxZosspkx3j4Fn4N7rH5DWtQ6piDfS2cx3wBWuc+yPvfh9z4AYxIfFLa0KAc2gDl0AZgyZcL29g+KBW7QNOT6ygEPr2xLpXXwpCDMDRlUOerOOlKhPa8Axga/JtfgHvGabBV1A3qWKWCoSmApkBHajgVwgngRBuIEBcSEoKCYGfjB4MxAKbAmZloAMQTDnIEA2ZJzBKqKwiufQzy3uRksTjmaHgX3wzOnrtFXCMNQgh49xjw7jHA5JMBmH2lde6H2t4Arc30TDDCqbGSjUgHlMhHUA58BAAYP/+iYW97OkrFLhgIQghsFXWIHdmObFNGEkJQceJVaHrpewCAznV/hnvq4rw/m+uxAPRYAKogQXCPgeCqHLHhwRgMRm7getjRVFehde4rGtFAbduFwLu/TT4Xg8A593Nwzrm4JJ4BiGCDWDk1ZREFpRRGtDPFM0HraoQeaAJo6jwRlQNQj2yFemSrVTbu+PNz3s6sRwqv14vGxkbMmjUrpfzAgQPweEa22jMUCCGQyk3joWBt4EW4py6Ge+piaOEOBHe9geCOV6HGXYyoGkVwx6sI7ngVnN0L1+QFcIw/FlLVNFCpumDtZpQWlBqgmgIg88k0Qri4N0NPUYG3hIZUbwch4zBKxY6hxqAGj0APdxTNAN0dXQ6h/YPHEdz+slVGeBG+4y+DMO0s87dg5ARCCMSKKRArpsB13OXQ2ndD3v8B5AMfJMM6FCGlYhdoevL/b6AcBwkIx0OqmIxYy860dftC9NTAN/d8+Dc9D6orOPzPb6PmrDvgnHj8oI5XylBDgy5rgNw7hIrppWAHJ9hTBAVuCB5QDEYpMBivKLfThhNnjc9ZG/RQKwLv3JuS18cx6zy46r/IFjD1AeEliFUzIFbNAGaaZUbUb4Y2at9tigkdDQMu5ikGSsUuSAcnOSF4aqAGmrPe1zGhHvZxcxFr2gLVfwjBnW/AO+vMPLSyN4amQOk6CNV/GIKrcshhjBgMxsjE6/Xi5JNPtj5TSqF17i+KqAeUUkR3vozwxj9bk+XE5oV04g1wTjy2JOaB+oMQAt5ZAd5ZAYw/ziqnugo9cAhaNzFB6zoAGhs4f1UuyHrG57LLLsO1116Ln//85zj55JNBCME777yD//qv/8Lll1+ejzaOGHibC6K7Gmoosyza+URwVaD8uM+jrP5zkFt2ILD9VYQa3gZVTY8IIxZAcPtqBLevBgAQwY5Q1VTYqqbDVj0NturpEH0TmFHPGBYoNQDdyHqQMhNCC908F/ikyBD3eLDeczxA+KLxcNDlMLRgM7RIV6Gb0i/hfR+idc390CPJ1Yr2sUej+tSbwbnHIhgKgaO0pAfuQkEIsSYPXMd/CWrbDsj73y90s/qkVOyCcDQ5ESMKHBRFgaZp4HkeNpvN2haJmGEJ7HY7eLsHomcMIp2HoakaOJ6DTUpOWEejMQCAzSZZK3pUTbPqls/7IqKHN0Npa4ChRNC06gcoP2kFyo4+B5zkhKbrUBUVhONgt3U7bkwGKIUkSeDjIkd/dWOyAmoYECURQjyUnK4bUBSl37qCmDQPE3VBCBz2ZD/IigJDN+uK8VWl2dQ1DAOybArMDofdqqsoKnRdhyAIEMUM6kZjEISIVZdSilhMBggHp8cHPh7ySKMcDAgQ7S7Y7A6rbjQajR/XYd3LMvntu9fVdX3AutZvr6pQVbVX3Wg0CkopbDYb+PhvlE1dTdOgKAo4joPdnuyfWCwGwzCyqitJkrVKWNd1yLIMQggcDseg6sqyDMMwIIoiRFG0fs9YzPzfcDqdKXV1Xc+qriAIkOL/c91/z2zqZvPbZ3udZPLbi6IIh8MxqOukv9+z6/AOaMG2rO8RoBS6YYAaFKqmgSMEtm51ZVmBQSlEUUi9n6gqCAHs3dobPrARkQ8fANS44Mfb4DrxWpBxJ0BWNdhtyWcMWVHN60TgrWvK/O1lEIODs/v/vapC1w0IPJ96j1BMG7L7vaevupRSxOL3E7tNsn5PVdWg6XpWdXmegyQmQ6lFY+Y4YpPEbr9n5nU1TYOq6eA4DjbJrMs5fKDVc8FXzYVjrgiOAHrgIOSWnVBad6EYKRW7IBNE7zjo0S4Yaiyr/Uyvgytx+IU7AACd65+GZ8apwxqaj1IDaqgVaqgVvN0DwV0NoVsODQaDUdpQSqHr5phDCIHuPwhDyV+eo0wx1AiCHz4M5cBaq0yongnPopsgC2WFa1iBIbwIoXwyhPLJKeVG1A/NHxcTOvf3vfMQyXr27Oc//zk+97nP4eqrr8bkyZNRV1eHFStW4JJLLsH//d//5aONIwqxbEJRxeIlhMBeMwtjTv0GJl/1BMYs+y+4pi4GEWwp9agWQ6x5G/xbXkDLG7/Cgb/ehL2PXYZDz38bbe8+hMCO1yB37C/KECqM0QulBgxNgaFEoMcC0CKdpgEcaIbSdRBy+z7IrQ2IHdmBaNM2RA9vRuTgRkQObEDk4CeIHt6MaPM2xI7sQKx1N+T2vVA6GuOrcJqgBlughduhRTqhx4IwlAgMNQpDk0F1FVTXQA0jYxdlQ5OhRToQa9mJ2JHtRSsaaJFONK/+PzS//CNLNCCiA1VLbsD4C1dCKpuIzmAUqz5ogD9c3KveSgHCcZDGzIb7hOILUwSUjl3wf/fcY70XeIJHHnkE9fX1uPvuu1PqLVy4EPX19Th8+DAAQPRNwF9ffB2Lzl+BH/z8oZS6533pZiw6fwX2NCZzIL3w8ltYdP4K/PcPfwPe5sKEC38KZ90CcyM10PnhH7DviSvR/PJP8N4Lj+L0i6/BLf9zT8pxv3Tjd7Ho/BXYsPlTq+zt9zdg0fkrcP1//Sil7ldu+wEWnb8C7639xCr7aOMWLDp/Ba6++XspdW+6cyUWnb8Cb7yTNNQ3fboLi85fgUu/9t8pdf/fD36FReevwKpX37HKdu1txKLzV+DCq29Nqfvdlb/DovNX4O//es0qO3D4CBadvwJnXXZjSt0f/ur3WHT+Cjz13L+tstb2Liw6fwWWXnRtSt2fP/AnLDp/BR758z+ssmA4gkXnr8Ciz1wNJRo0xwV/E35xz//h+BMX4Wc//A4ihzYh1rIT4dY9qK+vR319Pbrami3x+sEHH0R9fT1WrlyZcr758+ejvr4eLS0tVtkTTzyB+vp6fO97qX25ZMkS1NfXo7Gx0Sp75plnUF9fj29961spdc866yzU19djx44dVtkLL7yA+vp63HTTTSl1L7jgAtTX1+Pjjz+2ylavXo36+npce21q/3zhC19AfX093n8/KTquWbMG9fX1uOKKK1LqXnPNNaivr8cbb7xhla1btw719fX43Oc+l1L3+uuvR319PV566SWrbOvWraivr8fy5ctT6t56662or6/Hc889Z5U1NDSgvr4ep512WkrdO++8E/X19XjyySetssOHD6O+vh4LFy5MqXv33Xejvr4ejzzyiFXW0dFh/Z7dueeee1BfX4/77rvPKotGo1bdhIAAAPfddx/q6+txzz2p/3OJuh0dSSE903sEADz55JOor6/HnXfemVL3wgsvxHHHHYeGhmSy9eeeew719fW49dZbU+ouX74c9fX12Lo16Y7+0ksvob6+Htdff71VJnfsx2VX3zCoe0RibF+zdjPOufxmfP2On6bU/a8f/gbnXH4z1nyw0SrbtnMPzrn8Znz5NjPZK6UGwlufR3jNzy3RgHfXoPzsH+AQJuGcy2/GFTem/r/8+N5Hcc7lN+OFV9ZYZYebW/HZFbfikutS7z0/f+BJnHP5zXi22/2kvdOPcy6/GeddeUtK3fv+8Decc/nN+NPfV1lloUgU51x+M865/GboevK55vdPPY9zLr8Zv3/qeatM13WrbiiSvE7+9PdVOOfym3HfH/6Wcr7zrrwF51x+M9o7k6v/nv3Xazjn8pvx8weeTKn7+Wu/jXMuvxmHmpMLzl54ZQ3Oufxm/PjeR1PqXnHj93DO5TdjT+MhEI6DUDYJbx904qKfb0QxUip2QSYQjoM0yDCDjnFz4JhoJorWQi0IxBf0FQI9FoTctgfRw5uhBppBda1gbWEwGMNDe3s7/vGPf6Crqwt6uC1lsWCh0Dr3o+s//5MiGjhmfQZlZ3zHXIHP6AXn8EEaOxfOWefBs/CGvJwja48DSZLw61//GitXrkRDQwMopZg+fXrKyp7RDOF4SOW1kNv2FLopveBEOzzTT4Fn+ikw1BhiTVsht+1GrNX8M8JtKfWpJiN25FPEjiQfOoggQaqYAlv1dMs7QSqrZfERGSMKSilANeQ6MhAhXDwusBkfmHR7b2gK5HAMUhEn2KaUIrTrDbS99wgMOZlo3Vk7H1VLb4ToGWOVuRwSjp9RA6eteIRSRmEoFbuge9itTEIVJffjILgGb8hyoh1jz74Tv//+VVhWa/7fUV1FeN/7mADgDxcCe2M7EWp4B85JJ4AT7QMfkJEWqqvQdRWalpwYibXuQiR6GIRwUIOmMKDHAlC6DoEIEjiehT5iFD9yx35oobb0FfshMbYf6CZ4ZYOhhBH84EEohzZYaRK6pMmYds53wEkuoPPAoNvGGDmUil2QKbzNPeioAxUnXYlDBzcAADo3PAPPzGXgeizwG07MMEaHoPoPg3f4zHAZdl9O854wGIziwOPx4MQTT4RDMKAFDqffIc9EG95EaP3jQHwhDxGd8Cy8HraJZm6cbPPJMHIHoVn2vt/vh67rqKhIfUju6OiAIAjweos7yVUgEIDP54Pf789rW2Otu6FH8x9rKhdQShGMqnCSKJS2BshtDZBbd0Nu2w0t2JJ2f8JLkConw1Y1zRIUpPJJJSUmJPrIU8STvoWE9U96ir2P1EAzWtc8gGj84QUAOLsHVSd/De7pp/Zqs67rCIdz58pIKUVEMeCUuKLsn2KAUoqq2pmQyiYUuikplIpdcP+/Psaf3jWTf6/80vFYNKM8qzAk4bZ9MMJtWYUqSqkbk6G0bIe8/z1E9r4HvY+8FkSwwTnpREiTFsI+/jjYnJ68hSqKKgY8DhGGQYs7VFGPulaoIgB2uy01tIimZVWX53lIUrfQIvHf0+5wgBMkBGUDTocDOjhINidsDhcIL4HwEqLx8DqjNVQRpRR+vx92u52FKurn96SUorm5GV6vNyehikjkiCUaDDacGQEQDoet8EPZhCoyAgchf/g76KEj8doE0tEXw3n0hRBFKaVuz7BGfYUq0nUdXSEZDml0hyoyfyMZlAKSKCZ/T02DomqYdNRxEH3jUEyUil2QzXwBNXREm7YOKjZ488s/RnjfBwCAykXXouzYi7M+Rj4hhICIToRUgvLKGvB2Dwtn3AeJcc/n87FnmT5g/ZOe4e4jTdPgb2+B2r67oJFFqCYjuO5xyHvftsqE8snwLrkFvDu5cJHNF6SHUorqujkQvTU5PW7WwsHy5ctxwQUX4MYbU13LH3zwQbzwwgtYtWpVP3sWB8MlHBiagljzthER2megCU09FoDctgdy2+64mNAALZMEUJwAW+XkuFfCdNiqpkGqqCuqME7ZUOyTvoWG9U96irWPqKGja9M/0bn+qXhSaxP3tFNQufirEBxlfe4XicnYf6gN1WVOSOLQHx6YIZCeYhUOSsUueGDVFjyxxlwNe8/V87B41pg0e6ZCKUXsyKcwlGj6yumOZeiINW9DqGENQnveg9FH0isiOuCqOxHuaUvhmDgvp8mAi/V+VUyk6yPCCSCCBMKL4HjJek8Em/nKSyXdt2yCID256iNqGFA69kGLdA65TYMd22P73kPwo0cA3ZwMJ5Ib3pNvgjTu2EG3hdkF6aGUonrS7KITDkrFLsh2vkCLdkFubUhfsQdy+z4cfPabACg4uw91V/y+6JIV9xzzOMkJ3uYGZ3ODt7lH7DN+LmHj3sCw/knPcPdRKNCF7RvfQ7VXysnz/GDQAk0IvPNr6P6kR6J9+jK4510J0sPbl9kF6cmXcJD1kvAPP/wQv/zlL3uVn3baafjud7+bk0aVApwgQfSOg9J1sNBNGRK83QvnxOPgnHicVabLobhXQgOUNlNMUP09XJsMzRQaWncDiUhHnACpoi6ZgLlqGkTfePA297B9HwaDkSTWshOtb/8OSnsytBrvqkL1kq/DNfmkAfcNRxVs2NWMpcfWFszQYBQHpWIXaHpyHUU2oYoSEEJgq5iM2JHtQ3alJRwPx/hj4Bh/DKoWX4/o4S0INaxBeN97MGLxcEZqFKHdbyO0+21TRJi8AO6pS+GsPZ49wBcB1NBAFTMUUn9LSAgvgksICpwYFxREgBdSPrOHI0Z/UF2F3NYAXc6NB2C2Yzs1NIQ3Po3ozv9YZeYqwVvBu6tz0ibGyKNU7IJsERxl0F0V0MLZxQm3VU6Ge9pShBrehhHzw7/5RZTPuzRPrcwNhhKBoUSAeHQCTrCBs3ssMaGQ4ZYYDEZ6qGGg49AObNhxsGDP87HGDxD68PegWjy5PG+D56RrYZ+8eNjbwhiYrIUDWZZTYsImUFU1JbkYAxA8Y6BFOsxBtYTgbW44J9TDOSGZfE6Xw1Da91ghjuS2BqhdhwF0mzwxNChtDVDaGhDcnizm7D5IvvEQfeMg+iZA9I2P/40rutUWDEYpoMeC6NzwF/i3/AvJRA8Evrnno+LEK8FJ6WPQlnkcWL5gGniOTWiNdkrFLtCMoQkHAMBJTnPRQE8xfQgQjodzYj2cE+tBl9yA6OFNCDW8g/C+92HIIQBxEWHXmwjtehOc5IJr8gK4pi2Fc0I9ExGKmESuhXQQTgDhhXgYpO6ighAXHYSk6MAYNWiRTqhdB2F08xYcKtmM7Xq0E4F3fwutNZnU2z7lVLhPWAGSQw8oxsijVOyCwSCVTYQeDYAa2SUXLj/hCoT2vANQA12fPAfvnPNG1OI6Q5NhhGQrXBrhRfB2j+WRwJ7pGYziQunYC68NBXmep7qG8MdPIbrzFauM946Hd8ktEHwTh7UtjMzIWjg48cQT8fDDD+O3v/1tSvmDDz6I+fPn56xhpYC5+rAuJ6sPix3e5rJWRyYwlEgyzFHcQ0HtOogUMQGAEfMjFvOnJGG2juus6CYkjI8LDOMheMflNCwDgzEaUDr2w7/lRQR3vZESlkiqmIzqU2+GfcxRGR+LIwTCICdXGaVFqdgFqp7Mli4IgzegBe9Y6DF/zlYAd4fwApy18+CsnQeqfx2RQ58g3LAG4X0fwlDM8xlKGMGdryO483VwNjdckxfBPW0JHOOPLancQ6MJamjmJJQaG7AeIQSwRIS40ND9c3cvBhafekRCDQNapB1a4AgMTc758TMd29XWHQi88xsYsa74jgLc86+BfdrpzEOGUTJ2wWAgvAipfCLk9n1Z7SeVTYDnqDMQ3PEqDCUM/6bnUXHilflp5DBAddX0vIh7XxBOAG93g5PcZo4E0cHuFQxGgVA6D0CLdBXkeV4Pt5qLDtqTYd1sdYvhOfErIKJ9gD0ZhSTrJ8gf//jHOPPMM/HJJ59g2bJlAIDXXnsNa9euxSuvvJJm79EHJzkhuMdADR5JX7nE4CQnHOPnwjF+rlVmqNG4mNAApWM/VP9hqP7D0CN9u3TqkQ7okQ7Emrb02EIguKviHgrj4sJC3FvBU8MmRxiMOJQaiDSug3/zi4ge+jhlG+EllJ9wBcqOuSjr/5lQRMb6HU2YXVcFl52tch3NlIpdMNRQRQkIIZAqJiPW/CkoNdLvMNjz8CJck06Aa9IJoLqKyMGNcU+ED0BVc0WnIYcQ3LEawR2rwdk9cE0+OS4iHMMmjksQSimgqxkl5ySEszwWLLGB402hgRMA6z1vbucEEI6JxYWC6irUYAu0UFvWK5mzId3YTilFdOfLCG/8M0DNIFycswLeJbdCrJyWt3YxRhalYhcMFsFVCS3SAT0ayGq/8vlfRHDXm4ChoWvzC/DMOguiJ7dxqgsFNTRokS4g0gXA9KbkJFcytJHkYmMMgzEMqIEjUOMhxob7eV4+/DGC7z8Aqpge0+aig6thn3YGExKLnKxnVxcvXoz3338f99xzD/7617/C4XDg2GOPxaOPPooZM2bko40jHtE3DlqkI6MHuVKHEx1wjJsDx7g5KeWGGoXqb7KEhMSf4j/cZ1JIgEILtUILtfaaDAXhIHhqLO+E7n+Cu5pNljBGBYYSQXDna/Bv+VevHCREdMA78yz4jr1w0A8klAKabpS8NxUjPaViF+RKOAAATrRDLJsApfNA+so5gPAiXHUnwVV3EgxNQfTgBlNE2P9RUkSIBRHc/jKC218GZ/fBPfVkuKcugX3cHDYujkIoNUzPsyzC3BDCxQUF3hQcCGcJDYTj47kZTMEBhIehK6CGzhZzDAFDiUANtkCPdAzLeDvQ2E61GIIfPQp5/3tWmVgzB96TvwHOnnkCWUbpUyp2wVCQyichJn8KavSX5aY3oqcG3llnIbDt36BqFIee/zbGnXcXbJWT89fQAkENHXosAD1miiuEcOAkZzK0kc3NbBMGI8do4faUHKzD9TxPdRXhTX9FdPsqq4xzVcO75BaIFVPyem5GbiB0lM36BAIB+Hw++P1+eL3DZ+Rq0S7IrQ3pKxYASimCURUeR3Em4NPlUDcxISEuHILqP2yFZsgYwoF3lEFwVUJwVYJ3VpivrkoIrvh7ZyU4yZnSF8XeR4WG9U96hquP1ECzGY5ox6u98qsI3nHwzT0f3plnZpTHYCB0XUc4nLtQLJRSRBQDTolj11A/UEpRVTsTUtmEQjclr9x///2455570NTUhDlz5uDee+/F0qVL0+737rvv4tRTT8XcuXPx8ccfZ3y+hF3wvafW4dUtrQCAp29bgrrqoccWjrXshB5PZlwIDE1GpHE9wnviIkIfoU14RxmcdQvgqjsBjgn14EQHu6dnAOujgenePxzHAaSb4MAlBYek+MAnRYlu5eD4ku1fSin8fj98Pl+v76hH/aZgEMtuxfJQ6W9s14PN8K+5F7o/KYY6Zp8P17GX5m1yj9kF6aGUonrSbIi+cYVuSkmRq/kCNdQKpaMxq330qB+H/vlta9EPJ7lQc/adKbkFC8Fwj3mEEBDRYeVHIKLdfC1iMWGgezqD9U8m5LOP9FgQcuuuFJEg18/zfaF1HUDg/fuhdyXvhdKE+fAsvB6c5MrqWMwuSA+lFNV1cyB6c+utVvAlQMM9QVAoBEcZdGeZ6aLHyAre5gY/5qhe8dcppTBiAcszoae3gpWdPWUnwwp/JLfu6vecRLAlBQVnBXhnBTSpDKSsGqKrKl5ezhIRMooCSilihzeja8sLiOz7CD3ziDgm1MM390I4J80vaoObwXjmmWdw66234v7778fixYvx0EMPYfny5di2bRsmTZrU735+vx9XX301li1bhiNHBhcaMBfJkXsiVdSZIYuyWHGYSzjBFvcsOBmGGkOkcR1Ce95BpHGtledEj3ZZngjgBNgqp0CqnArqnQRx3AzYKqeAYzFHGUOAUgrQeJ6GQcTlJxxvCgm8AEK6CQzW+/6Eh3iYpRHycEkN00ZVg0dgpMlnMZzIhzaYoQVUczECEezwLLwettqTCtwyBqO4Ed3V0MMd0OVQxvvwDh8mXPQzNP37Lsitu2AoYTT9638gVUyGe8bp8Ew/BYK7Ko+tLg4opaBKpNciKMKL4EQ7OMEOIjnACXZwop09kzMYA2DmHm0Y1kgBlBqI7ngZ4U+eAYx45BVOgKv+MjhmLh8xthnDpKDCQSEnCAqBWFYLPRYs2ARCqUEIAe/wgXf4YB87O2UbpRR6pLObd4LpqaCFWqFF2qFHutBzcjVlf022BIju9NRjObsv7r1giguCs9wSFazPjnLmps/IC4YmI7T7Lfg3vwilY1/KNsJLcM84Db65F+TFxbkjEMHqD3dhybG1KHOzSUVGbvjlL3+Ja6+9Ftdddx0A4N5778XLL7+MBx54ACtXrux3v+uvvx5XXHEFeJ7H888/P6hza92SI+dKOOAEG6SyiZA79ufkeENqi2iHe9oSuKctgaFGEd6/FuGGdxA5sC4ZStHQILfusoT1EAAQDqJvPGxVUyFVToOtagpslVPBO3wF+y6M0YVpN+uDDvmZEmKJE5IeD8QUJECIWYdw5jYQgBBTfIhv7/keSO4z1LjcVFehhtvM/AUFDmvafWz3OSVEtvwdka3PW9t573h4l94GwTu+cI1kMEYQUsUkxJq3Z5XziHf4MP6CH6P5lZ8ienADAEDp2IeODx9Dx4ePwz5+LjzTT4Vr6mLwtqF7R44kqK5C11XoSPXmJBxvCggJISEuLrCFD4zRjqHJiLXu7nMOMl/P83qkA8EPHoJ6JJmnlPdNhHfRTRDK+5/nZRQvBZ3NLOQEQSHgBMmMeZylyyIjewgh8dBDFSnJmRNQXYMe7YQW7oAWboceaU++D7dDi5jvE7Gh+8OI+aHE/FDa9wxYj7N7454L5ZYHg+AyxQXeWQ7eUQbe7usVIonB6Ast3A7/1lUIfPofGD3CGPDOCvjmfAbe2efkdWLPaZcwd+oYOGxMFGPkBkVRsH79etxxxx0p5WeffTbee++9fvYCHnvsMTQ0NODJJ5/Ej370o0GfX+2W40DIkXAAAIK7Clq0C3q0r3w9hYETHfBMPwWe6afAUGOIHt6ESOM6RA9tguo/lFqZGlC7DkLtOgjsftsq5l1VsFVOMQWFqmmwVU6B4KlhYxij6KDUAHQjr5PypJugYAkRXFxsAOkmUCTeczBUGTG/H4KfFM3/TWJstyEG/1u/htq8ydpmq10A94KvghMdBWwhgzGy4EQHRO9YKD0Wo2Wy37hzv4fA9lcQ3Pk65JYd8S2ml3Hs8Ga0vvMgXJNOhHvGaXBOOgGcIOX+C4wQqKFDl8OAnLrMjxAORLSZHgqiPemtINhZMmZGyUN1FXLLrn7tn3w8z8uNHyK49lHQbiHFHTOXw1V/KQg/eu9RI52CzfgM1wSBLMuQ5aRbdCBgTrJRSguS1FNwVUELtZkDW5GQ6ItRle6C48G7qsyJjwGqGUrEFBFC7Qh1tUBU/dAjCbEh8doJ0IG9SIxYAEosAPRYFd67XYLpRWH3WmJCwquCS5Q5fFY5EexF8bA5Kq+hLBlqH1FqIHroEwS3v4Lw3g96XXO2MTPhm3sBXFNPNldUxs+ZL2wij7oab87Ow66h9JR637S1tUHXddTUpMZkrKmpQXNzc5/77Nq1C3fccQfWrFkDQcjMpOnPLujucSDwue1vqXwSorFtZqiWIoMINjgnnQjnpBMBxN2Z2/ch0LwLJLAfSvteKB37gR5t18NtiITbEGlca5VxkgtS5VRIcUHBVjUVYtlE655USrB71sCMtv4xv2fmK4oT+xi6CkqLJ8SGTeQxQWxH4PXfwAibOV9AODjrv2iFFhiu33S0XUODgfXNyEDwjoUW7YShDLwgrSeEF+Cbcx58c86D6j+M4O63ENr1VlLgNzSE972P8L73wUkuuKaeDM/002AfPzcuWjIoNUCVaK++J4SA8FJSTBAdSW8FFtaVUQJQQ4fc1gBjgBCRdknA5LG5WWhoqBGE1j0Bed8aq4xzlMOz8AZIY3sv5GWMLAr2JDdcEwQrV67EXXfd1avc7/cXzNiiYjWUiNIrZl+hoJQiIpsTAsUwCV1ciICtBlQaA8M1A7xNgEBIithAqQEa80OPdsKIdsKIxF97/XX1mnjphaFBj3s9ZAQvgbN7wdl94GxeELsv+dme+Gxu4+w+EGEgmWTwsGsoPYPtIz3citjuNxBreD35EJ+A8LDVLYJj1mcgVh8FCiAkUwD5D3Ugyyqa2oIoc9shCkM3sCmliKnmpAu7hvpmtEwQ9Pz9KaV9XhO6ruOKK67AXXfdhaOOOqrX9v7ozy6Iycn/m0goBF3O7UO3LpZD6cpuxWFhEEF900Htk+GwCXAQYoYGCByC1rHX/OvcB61jrxXzPIGhhBFr2oxY0+ZkISdCKJsEoWIKhIrJEMqnQCivAxnhq5bZuDcwrH/SU2x9RA0dwU3/QGzLMyCJsCo2D2wn3QRUH42oSjFQmM+ct4fZBWkZLXbBSIcQAlvFZMSObB/0byb6xqNi/uUon/dFyK27ENr1FkINb0OPdgEwx9/g9tUIbl8N3lUJ97RT4JlxGqTKKez/pw8opaCaDGhyL49QwovgBMkUFrq9cnz8PQtBzChyqGFAbmtIu1hZUTU0d4RQ6XUM6XleadmO4AcPpsxV2GoXwH3iV8CNsnBqpUrWd73PfvazfQ4+hBDY7XZMnz4dV1xxBWbOnJnR8fI9QXDnnXfi9ttvtz4HAgHU1tbC5/PB6/VmfJycU1EZT+jbVLg2xEkYMB6HyAyLfkjbR84xAMakPYYhB01PhUhnPElzp/k+6ocR80OP+qHHAqYBk8aLAQCgKzDCbTDCbRl9DyLYLQ+GhNcCZ/eCk1zgbW5wNjc4yQXO5jY/Sy5wNlfa1aLsGkpPNn1EdRXh/R8huP0VRA9+jJ4P6pzdB++ss+Gdcx4EV2WeWjwwsVgMmxuOYOkxE+GUhr5aMtE/Tolj11A/FOsEQa7sgqqqKvA832vxQEtLS69FBgAQDAaxbt06bNy4Ed/4xjcAAIZhgFIKQRDwyiuv4Iwzzui1X392ASVJg7myogw8l+Pr0OeDzGvQIp25PW4e6H2/EgH3DGD8jJQ6WvAIlPY9kNv2mK/te3sL34YKraMBWkdDt0IC0TceUuXUeO4E00OBd5Tl/bvlCjbuDQzrn/QUUx8pHfvQ9u7vITdtRqIlQuU0eBZ/E7yzMHYGswvSU+p2QSnBSU4I7jFQg0PLz0gIgX3MUbCPOQqVi75ieiPvfgvhve9bIXb1cDv8m/4B/6Z/QCyvhWf6aXDPOBWip7ctxehNIo9C7+yGJoRwKYKCKTTYAE6AocmghgHCM68FRmEwRYPd0GPBtHVDUQVrtzdh6bG1KHNnf81SXUNky3OIfPoCEB+PiGCH+4QVsE1ewsbuEiJr4cDn8+H5559HWVkZ5s+fD0opNm7ciK6uLpx99tl45pln8H//93947bXXsHjx4n6PM1wTBDabDTZb71XWhBQ+nqitbAJEZzm0SIc5cazGCtaWRH8Uuk+KmaH2ESEEnMMHweGDrXLKgHUpNWDIYegJMSHqT30f7YoLDF0wYgHosQCQQdItqsWgBWPQsjRaiejoJi64wElu8DZXXGgwyxRiB+/2gU+IDnHhgQg2dl3FSXcNKZ2NCGxfjeDO13vlLgDh4Jx4PDyzzoar7kQQvrChDco9Tpx94hSIPJ+z35fdh0YmubILJEnC/PnzsXr1anz2s5+1ylevXo2LLrqoV32v14vNmzenlN1///14/fXX8eyzz2LKlL7vs/3ZBZphGrwcyW2Og5RzV9TBUMIFT4CaCen+HwkhkHzjIPnGwT01+btq0S4obXsgt++B0rYXcnsD1K7DSBVAKVT/Iaj+QwjvSbo0844yCN6xED1jIHhqIHpqIHjGmK/u6oLf93rC7lkDw/onPYXsI6qbYU78W19CrGlr91bBMft8uI65pOAra9k1NDLJlV1Qaoi+8aaHuqbk5HiE4+GsnQdn7TwYS76OyP61CO5+E5ED64F4IlS18wA61v4JHWv/BPvYo+GecRrcUxeDtxdwAeUIh1IDVI0BPeZuKKWQoyoi4QNxj4Vungu8BCKIILzNKmcwco0ZnmhPRqIBAJS5HdbzfLZo/oMIvv8AtM59VplYPROehV8H767O+niM4iZra3Ds2LG44oorcN9994GLJ5QxDAO33HILPB4P/vKXv+CGG27Af//3f+Odd97p9zjDNUFQ7HCSE5LkBMomwtBk6JEu6DE/DDlUtKtIGPmHEA683QPe7gHKJqatTw0dhhzqIS4kxIakyKBHA9BjXTAyHEys46tR6GoUehrPhj6PygkpggNniwsQ3d/36engjieLLu0YnYYaRajhHQS2vwL5yPZe2wX3GHhmnQXvzGUQimgQ5jgCm8hcdRm5swsA4Pbbb8dVV12FE044AYsWLcLDDz+MxsZG3HDDDQBMb4FDhw7hiSeeAMdxmDs3NWbmmDFjYLfbe5VnQiLHQb5EA8CMV2yrqEOsdXfezlFoBEcZhPhERgJDjUJp3w+5vcHyTlA69vcSUMxxqqvPeyFAwLsqu4kKqeKC4K5mcYkZjAzQwh0IbH8ZgW3/gR7pSNnGuyrhXvh1SGNmF6h1jFIgl3ZBKUE4DlLFZMRadub82Jxoh3v6UrinL4Ue9SO0512Edr+JWPOnVp1Y8zbEmreh7d2H4aydB8/00+CsOxGcaM95e0Y7VFdBdbXf0NRmjgUxxWuBCHFRgYVEYgwCPRaA0rE/K2FyMM/zlBqI7ngZ4U+eAYy4HU94uI69BI5Z57Ok4yVK1nejRx99FO+++65lBAAAx3G4+eabcfLJJ+MnP/kJvvGNb2Dp0qVpj1XICYJihBNs4Lw1EL01oLpmTvrGAtCjgaJMqMgoHgjHWyGIUJ6+PjV0KySSIQdN0UEOw1BCyfdyKP45DL3be6pnuUrG0KzJoEF8M3CS0xQSJCc4yfR84MRu7yWnKTCITus9JznjdeLbeKmoVqtRSiG37ERg+ysINayxXIstOAHuKYvgmXU2HBOOLUrxJBSV8fGuZsysrYTTXlyrgBnDSy7tgssuuwzt7e24++670dTUhLlz52LVqlWoq6sDADQ1NaGxsTEv30PTTbFezKNwAAC8wwfBXQUtlFmIuVKAEx2wj50F+9hZVhnVNShdB61QR3L7HqhdB6H3G8qJQg+3mQJ287bemwkHwVUFwVsD0R0XFbw1ENxjIHprwDsrmLDAGLVQShFr3obA1lUI7X3XWo2cQCybCN+cz8CYsAib9rRjpldlYztj0OTSLig1eLsn7zYA7/AlkyoHjyC06y0Ed78JtfOAWcHQENn/ESL7PwIRHXBPWQT3jNPgGH8sGyeHCTPHggJoCtBP7lpCOBBesEQEwksgnGAKDZwQFx7EovPGZAwvCXtayzRHZjeyfZ7Xw60IfvAQ1JakIMl7x8Oz6EaIFSNzITcjM7IWDjRNw/bt23vlGdi+fTt03TRC7XZ7RpN0hZwgKHYIL0BwVUJwVcZj44esyddcuTcyRi+E4yE4yyE4M1AZemBoSlxUCMdFBvMvGvJDpFEYSsTcLoegx+tY9bNOCE7j+w2c2CctnGAKDWJChOjxXnKBEx0goh2cYDNDMwl2ENEWf02U283Pgwi/RKkBPdQC/+51CO54FUrH/l51pIo6eGadDc+M04rehdgwKMIxFQbzjBr15NIuAIAbb7wRN954Y5/bHn/88QH3/cEPfoAf/OAHGZ2nJ6olHORfZJTKJsKIBUb1eE54AbbKybBVTobnqGSoSUOToQVboAVboIZaoAWOQA0egRY8AjXYAiPm7/uA1IAWaoEWakGfgR85AYK7Ou6pEA9/5KmB4KoC76qA4CgDJznz8l0ZjEJhqDGEdr8F/9aXoLTvTd1IOLjqFsA75zPxRQoEnYEwG9sZQybXdkGpIfkmQI/6hyVsoeipQfm8S1F2/BegtO9FcNebCO1+y/I2omoUwZ2vI7jzdfDOcrinLYV7xmmwVU0ftb9PsUCpkRQXBoAQAvQQElL+uG7v2W9aUmiRTiidBwZ9L8n0eZ5SCnnv2witfwJUS1rZjpnL4Tr2UhAWeqvkyVo4uOqqq3DttdfiO9/5Dk488UQQQvDRRx/hJz/5Ca6++moAwFtvvYU5c+ZkdLxCTRCMJAghybA15bUw1KgV0ihdpnQGI9dwggROqABcFVYZpRSIqmkT/FFDjwsOYehx7wYj7t2gK93edxMmEoKDoUSy93ZIYGgwYsGsQzT1DwE43lztwfGmsdbPZ3A8qCZD9R/us/1EdMA97RR4Z58NW/WMEWPQeV12LDmmttDNYBQBubYLCoVmxEMVCfn38CEcn7dwBSMdTrBBKq+FVN73/cVQY5aIYL4e6fa5BYbcz33e0KAFmqAFmvo9NxHs4OOiOu8sB+/o9t4qrzCF3SL0BGMwEqj+w/BvXYXgjld7Lb7g7F54Z58D7+xzIXrGpGxjYzsjF5SKXZAvCC9AKq+F3LZn+M5JCGxVU2GrmorKBdcg2rQFoV1vIbz3XWthlx7phH/zC/BvfgGCpwauugVwTV4AW81MEJ5NDBYrlFIgHhopHaaAIPQWFHoKDSzcTFFDdRVKZyO0SNeQjpPJmG/E/Ah+9CiUQ+utMs5ZCc/C6yHVjM57+Ggka+HgV7/6FWpqavCzn/0MR46YCVZrampw22234b//+78BAGeffTbOPffc3LaUYcGJDnA+B0TfOFBdhR71Q4uacetpBglyGYxCQTgevN0L3u7FYJwqqa7CUKOWkGD9qZFuAkPUfK/2816JAEMO/UUBQwM1NAx2TZ69ZjY8s86Ge9picKJjiO1hMApHqdgFwxWqKAFv90D0jIEabBmW85UKnGiHVFEHqaKuz+26HDY9DxJiQuAI1NCRuBfDkQE936gWSysuAAAIB97uBbGXI+Quh+CsAO8oB+8sM993Ex/Y/Z0xXFBDR+TABgS2vmQmR+2BbcxM+OacB9fUJSwxJyOvlIpdkE8EZzl0Z9mQJ/4GA+F4OCfUwzmhHsaSGxBpXGuKCI1rrWckLXgE/i0vwL/lBYBwkMongSufCmPsUbBXT4dUORmcYBv2tjOGBrUEhuiA9QjHx0UEwcy1wAnxBXLm50SoJHMhHRMahhMt3A6l8+CwhDKXD65D8KNHQeWAVWabcgrc865iXrqjDEKHkIE3EDAvIK+3uENqdCcQCMDn88Hv94+odmcCNQwYchBatCsr90dKKYIZrBYfzbA+GpiR1j9UV6ErYdCe4oMaA9Xk+GsMhiqDatH4awyGGoOhme+prpoDtqGbAoKh9/ke1AAIB9E7DsQzDs7qqfDMOK3f1bQjhbauIF5buxsnz50In2voDw6UUkQUA06JGxHXUCGglKKqdiaksgmFbkq/jGS74KwfvIKQrGNSlRN/uf2UYTk3NQzEjnwKQ+0zuE7BGGn39Eyh1Ax/pwWaLY8FLdIBPdIBLdIFPdIBPdI59PB43SCiA4KjrLfXQkJocJSBiwvqpZSgslSvoVySqz7SYwEEd7wK/7Z/Qws0p2wjvGh6Nc79DOzVM9IeK9dj+1BgdkF6KKWonjQbom9coZvSLyPZLsj3fAHVVUSbthVNLkNdDiG8512EGtYg2rSlVy6UFOJigq16BmzV02Crmg6pcsqoFiVH87iXyMeA7sICJ1oCBOIe+YFQBGVlFeAElpehLyil8Pv98Pl8va4hQ5OhdDZCjwb62Tt7+hvz9UgHwhv/DLnxfauM2LzwnHQtbBNPyNn5s4XZBemhlKK6bg5Eb01OjzukVO0jyQAYDRCOSybIBWAoEWiRTugxPwxlYFWZwRhNEF6E4CgDHGV5P1fSC4iUlDHpkEQcVVsBu8iSqDGSjGS7QDXMdRTCMHkcAOa4LVVMhtyyA0NYx8HIEEIIeJsbfPV02Kqn91vP0GQzr1Sk07SjIp2p4kK0M/6+E6ADTKzAjB+tqlGo6bwYABBBMgUEuxe83Wd56PEOnyUuJP44uxe8zWM+qDNGHVTXEDmwAcGdryG8/6NenpSCewy8c86Dd+aZ1nNBJrCxnZFrRrJdkG8IL0IqmwC5j7xnhYC3ueNhzM6BLocQObAe0QMbIbc1QOlsNBdDJaAGlI59UDr2IbhjtVnG8aaYUDXdFBOqZ0CqmDyqxYTRgpWPAf2HFaaUQo6qiIQPgBDSw3tB6BXql3A8CEmEAObMcsKPSu8GNdQKteuQuTgxh/Qc86mhIbrjZUS2PJeSy0CaMA+ek64DZ8/cnmCUFoN62nj22Wfx17/+FY2NjVCU1JvDhg0bctIwxtDhJCckyQlggvkQHAuYyZVjQTZBwWAMEyQeB7vU/ufsNhHTxmefXJtRmpSCXTDcoYoS8DYXRN8EKF0Hh/W8jP7hBBs4Tw1ET/+rdSilCERkuDjZFBainT2Ehk5o0c64F0NXRl4MVFOghdqAUFvmbZWc4GxmHizOlhAWPODsHvA2r/lq95hig818JYKtJATs0YjcvhfBna8jtOtN6NGuXtsdE4+Hb875cE6ab+ZcyhI2tjNyRSnYBcOB4K6KL/TL3SriXMDb3PBMPxWe6acCAHQ1hq7Du8AH9kFpa4DcthtK54FUMcHQobTvNZMw9ykmmH9MTGAAiHvpZ+9tQwiXFBY43nxPEoID14fwwCcFiREmPBhqDErHfuhyKC/H7z7mK0e2IrTuj9ADh6ztRHLDffwVsE05hdmNo5yshYPf/OY3+O53v4trrrkG//znP/HlL38ZDQ0NWLt2LW666aZ8tJGRAzjBBs5dDdFdbYY0UkLQYwEYSgS6EgUwuEzsDAZjdKJqOlq7Iihz2yAKbGXiaKY07AICPe5xIA5DcuSeiN4acDYXlPZ9MDR52M/PGBwknutAcPgATB6wrqHG4l4MXWZ4pLjQoMcCyb+oH0b8PTLMWZUIt6cFj2Tebl60RAQuLjTwtrjYYPeCs7nBiU5TlEj8xT8z0WH40aN+BHe/heDO16D0kUyVd5TBPeM0eGefO+RQdmxsZ+SCXNsF999/P+655x40NTVhzpw5uPfee7F06dK0+7377rs49dRTMXfuXHz88ceD+CbDg1QxCbGmbUWdq5ATbBCrZ8Izaa41BhhqDEr7PshtuyG37op7JmQqJtRZIY6YmMDIBkoNQDcyDsvdE0t4sMIr9RAYEu8JZwoRhAcIZ4oUPV/zBKXUzNXlb8rrfUHVdLQ2HYDY8DzUAx9020Jgn34GXMd+AZzNk7fzM0YOWQsH999/Px5++GFcfvnl+OMf/4hvf/vbmDp1Kr7//e+jo6MjH21k5BjCcZarO2DemJSODthdEqDJMOKx3KkaA9WVklspzWAwhk4wIuODbYew9NhalLnZ5MJophTsgu7hXkS+MJOivM0N+9ijoQabzQnkAZL4MkYenGgHJ46F6B2bti6lhrmwIxaAEU0IC37zc1xg0OVg/HMQuhyAEQsByMxeo7oa94QYxP8n4XqJCamfXeAkB4johAIbOLcHnOQEL7lAJCc40QFOcplJF5kA0S9mKKL1CO54LSVhqQUnwDV5ATxHLYOzdl7OJjDY2M7IBbm0C5555hnceuutuP/++7F48WI89NBDWL58ObZt24ZJkyb1u5/f78fVV1+NZcuWWQmaixVOsEH0jR9xnoecaId97CzYx86yypJiwi7IrbsHEBP2QGnfgyCYmMAYXizhAQCGuFinuwiRKjZwpncD4YCUsoTwQMz3iTLCAYRAVyJQ/a2IhQ+A6v2HfcoFuhxC6/tPwdjxb6hIhkASKqbCfcKXIVZOzev5GSOLrIWDxsZGnHzyyQAAh8OBYDAIALjqqquwcOFC3HfffbltIWNYIDwP3uYGsacqitQwzKSwWgxU7SEqFPGqCAaDkV98bjuWzZsMm8QmFkY7pWAXdBcOhjPHQa92cBwk33jANx5U16DLAeixIIxYAIaW3wcIRvFACGfmYrC5Ad/4jPah1IAhh1PEBD0WhGEJDOZnXQ6a2xOeDdmGCaAGDDkEI0O3+WB/GzgBnGSKCKbg0Mf7vsSJuPhARIf5WmIeEHL7XgR3vIrgrrdgxPy9ttuqZ8Azcxnc004Bb8/9KkA2tjNyQS7tgl/+8pe49tprcd111wEA7r33Xrz88st44IEHsHLlyn73u/7663HFFVeA53k8//zzg/8yw4TgGWN6osnpw9oVM/2LCXvjnglZiglltRC94yB4ayB6x0L0jIXgGwfRXW0m3WUwCkiKCJGT41FoURVGHnMiGpqCwNZ/oXPj32DIISSeeojkhqv+MtinnWaFWmYwEmQtHIwdOxbt7e2oq6tDXV0dPvjgA9TX12Pv3r1sZXoJQjjOXCEmOXttMzQZVDWFBFNYiIsKg4hVx2AwRhY8x8FpZwY7ozTsAi7F46A4jGXCCxCcFRCcFQDioW7koOmNIIdyniCNMbIxwyZ5sppMppSCajFLnLK8GOQwDDVihUEylEjKZ6pEoCsRUDUy6FABMDQYMVPEGBKEAyfa40KCPUVU6P3ZARIvsz5LDnCC3fSA4MV4kkbzPThhWEQJPepHqOFtBHe8BqW9j1BEznJ4ZpwOz1FnQKqoy2tb2NjOyAW5sgsURcH69etxxx13pJSfffbZeO+99/rd77HHHkNDQwOefPJJ/OhHPxr09xhOCCGQyichdmT7iLGdMsUUE2bDPna2VdZdTIi1moKC2nWwt5gQT8DcC8JBcFVC8I5NCgqJ996x4GyekhKVGYyhQqmB0K430bH2SWih1uQGToRj5jlwHn0hOMlVuAYyipqshYMzzjgDL774IubNm4drr70Wt912G5599lmsW7cOn/vc5/LRRkaRwgk2QLCBd6RmV6e6liIkGFrUfM9WSzIYJUM4qmDznhZMm1AOp41NMoxmSsEuIFzxCQc9MSdB7WauIkphKMnV5YYSLrmJBkb+IYRYk+rwjBnUMaiu9iku6EoY0XAIIo2BqtE+63R/n7Xng9UAM6wTlAjyIqVZQoIAwsVfeTHlPVLKe2/vqy7VFaj+JsQ6DqK1dQdA9V7ndU1eCM/MZXBOPD6vsZS7w8Z2Ri7IlV3Q1tYGXddRU5OaqL6mpgbNzc197rNr1y7ccccdWLNmDQQhs6kOWZYhy8mQJYGAmaiYUjqsYysRHeDdY6AG+v5uhSTRF7nqDyLYYKuZBVvNLHjjZX15Jqhdh3rfHwGAGtBCrdBCrYgd3tz7+KITorcGgicpJgjesRA9NRA8Y1LsvlyR6z4qNVj/pCdffRQ5uBEdHz4OpX1vt1IC29RT0Og5FVOnTgERxaL/bdg1lJ589U3Wd8yHH34YhmEqwTfccAMqKirwzjvv4IILLsANN9yQ8wYyRh6EF8DzbsDmTim3wh6pUdMzQZNNrwVdHfyKNQaDURA03UBHMIbJOhu4RzulYBd0d3cXCpTjIBsIId1C2QDU0FNC0BhqrNBNZIwSCC+Cd/h6LyKhFIiq8GTgbk8pNQUINenRYAoKYdNm7MvzIWFLqhFzkYoaX6SiRjNOLJ0RhgZqaKDDZKbaxhwFz1HL4J62NC+hiNLBxnZGLsi1XdDzHkIp7fO+ous6rrjiCtx111046qijMj7+ypUrcdddd/Uq9/v9wz5BReGCopKiW3BHKUVENgXe/K3k5wHfdHC+6XBMAxww7Rsj0g49eAR6yPwzgs3m++ARUKXvsHlUjVhJmXtBOHDOKvCeGvDuGvCeGnDxV95dM+hksMPTRyMX1j/pyXUfqR17EF7/J6jNm1LKxfHHwz3vSoSFGrRub8SYmA5Kij9EIaUUMdUcW9g11Df5GrMIzeLImqbhxz/+Mb7yla+gtrY2Lw3KN4FAAD6fD36/H16vN/0OowBKKfx+P3w+X8H+AalhmImYdcV8eEwIClq8TFMKmlOBUopghg/AoxHWP+kptT7SdR3hcO7isFJKEVEMOCWuJPonH1BKUVU7E1LZhEI3xaJU7IKaybNx/A2PAgDOPX48vv+FYwvcqqFhaHK3OPbBrEMIltr9Kh+wPhqYQvWPKUIoppCgRM3cXErEEhVMkSH1M1WjoIYaX8ii9fseRrxMV+PlWk5ECt5VBc/0U+GZeQak8v6TvQ4HuR7bhwKzC9JDKUX1pNkQfeMK3RSLXNoFiqLA6XTib3/7Gz772c9a5bfccgs+/vhjvPXWWyn1u7q6UF5eDp5PToAZhgFKKXiexyuvvIIzzjij13n68jiora1FV1dXQeYLdDmE2JEdw37egSjWMU+XQ9CCR6AGmqEFmqEGj0ALNEENHIEWahnUPZoIdvDOcgjOcvDOim7vzc+J8p7hkIq1j4oF1j/pyVUfqcEj6Fz7JEK7U++RUtU0VC5YAceEegDFNeZnArML0kMpxZjJcyF6a9JXzoKsPA4EQcA999yDa665JqeNYDAIx4FwdkC091vHfFhTYCSEBK230MBgMBiM4aNU7AJShDkOhgIn2MC5bRDcVQBgho6JJZLlhgoqxDMY+YQQAiLYzHCajrK8n48aelxI0CwP2sR7DCBEEMJD8NYgJlbBV1bOHoAZJUMu7QJJkjB//nysXr06RThYvXo1Lrrool71vV4vNm9ODVtz//334/XXX8ezzz6LKVOm9Hkem80Gm83Wq5wQUpD/TcHugVQ2Hqq/adjPPRCJ/iim+5Vg90Cwe2Cvnt5rGzV0aKHWbqJCM9RAs/XZUPqeMKVaDFqgCVogTf9zAnhHWYrAoIs+wFcJwRIcKsA7ylLszNFMMV5DxcZQ+kiPBdC58W/wb/lXSghIwVODipOugnva0pTExyPxt2DXUGHI+g525pln4s0338SKFSvy0BwGo3/M+LACOPRO1AwkV5mleCnoKgy9m/cCSybJYOSErmAUb6zfg4VHT4DX1fthizF6KAW7YKSFKsoWTnKCk5wQvWNBDQOGEkoKCUqk0M1jMEYshOMHnX+AUgolWlyLXtjYzsgFubQLbr/9dlx11VU44YQTsGjRIjz88MNobGy0Qh7deeedOHToEJ544glwHIe5c+em7D9mzBjY7fZe5cWO5BsPTnJCad+ftdcgw4RwvJXfoC90OWQKCoGmuKBgeitokXbokc709pGhQQ+3QQ+3pRT33ouAs3str4XuQoMZ6q8MgqMMvKMMnN2TMrHLYGSCocnwb3kRXRufTRHEOLsH5fO+CN/Ry1OedRKwMZ+RKVkLB8uXL8edd96JLVu2YP78+XC5UjNvX3jhhTlrHIORDYlVZhD6v+n1XBmG7p8NrdtqMM2Ma8sSrzAYfWKTBEweWwZJLP54iIz8Ugp2AVdiHgcDQTgOvN0L3m6GX6C6auZHkMPQ5ZAZtoWNfQzGqISN7YxckEu74LLLLkN7ezvuvvtuNDU1Ye7cuVi1ahXq6uoAAE1NTWhsbMxp+4sFwVEGbqwTatdB6LEAWwCXY3ibG3z1dNj68FYAzGTNerQLWqQDeqQTeqQDWrgTerSzW1kn9KgfwEB2E4UR80OJ+YGOfQM3inCWmGD+Jd6XQ3CWgbf7wDvLwNu94Oxe07uOMWqhho7grjfQsfapFAGL8BJ8x1yEsuM+D97m6nd/NuYzMiWrHAcAwHH9P1ATQqDrxT2gsRwHvSmGHAfFSkJoMHQVfn8XvE57PFGebgkMKSLEKA3/wGIWpqfU+ojlOBh+ijHHAVAadsHEOSdj7lU/BwB8aekU3LR8ZoFbVTgopTDUKLo62+GxC6YHnxpjIQF7UGr39FzD+ic9xdhHxRTvmNkF6SnGHAdAadgFxTZfQCmFIYegx/zQYwEYSnTYz19s96tigRq6KTCEOxDsaoWkBaBHOy1hQUuIDpHOlPAxuYAItvhiEB+4+KIQ3u4F7/Cmfo4LDbzNU7CwSewaSk+mfUQpReTAenR8+Eco3cUowsFz1DJUnHCFFa50IIppzM8EZhekh1KK6ro5hc1xAJgJhhiM0ULCBZ0INvA2HYJ7YHEl6dFgCgvQe3gyGFo3LwedrRxhjFhUTUdHMAqv0wahxFdoMwamFOyCUg9VlA2EEHCiA7zNA9GbOuZRXTMFBC1mJpXV4oKCKo9a4ZzBKCXY2M7IBaVgFxQbhBDwdg94uwcAYGgKjFggLiQE2TNlASEcD8FVCd5ZAcVd1++kryX+xEUEPdpl/kW6TKEh6oeWKIv6MxIZqCZDC7VCC7Vm3F5OciWFBIcvVVjoLjTEt3GSk4VPKiJirbvQ/sHjiB3elFLunHQCKhasgK2iLuNjsTGfkSksSwuDkUOyjXVLKTWFhLgXAxIhkww9Xq5bYZRS641e7wZGcRCMyHh380EsPbYWZe7+k5ozGCOB0RSqaCgQXgDPC0Afbs+mgBBLeTXUKPNSYDBGEGxsZzBGBpwggXNXQXBXmRPSShh6NOGNwHIXFSPdxR8pzeSu9Zt2FxjigoIe7YrnqTL/jFgQeiwAZDg3YChhMw5+uuTPVsO5uLDgMUUFmwec5AJnc4OzuUwhwuYGJ/X4bHOBCHa2MjxHqIFmdHz0J4Qa3k4pt1XPQOXCL8Mx/pisj8nGfEamZCUcGIaBxx9/HM899xz27dsHQgimTJmCSy65BFdddRW7KTAYWUIIAeJJn7OFGkZcUNAsgaG72NBdaEitx1akjCQIIeBsbvCOMhBeTCb91qIwlMJNynlddpx23CQ4bb0TLTFGD6ViF3S/BwsCEw4GAxfPMdRTOqeGEfdMSHgpyNYrG48YjOKCje2MoVIqdsFIghBixuu3uQFMMHMXxQJxISHIkiuPQFJ+0/LatPUpNWAoEehRf9wTpbuw0OOzJTBlGKKGGpZwkfVTJ+HA2Vzg46KCwTsRcXrASW7wcZGhtwARL5Nc4AQp2zOWHHrUj84Nz8C/7d8pXiiCdxwqT7oKrqlLBn1fZWM+I1Mynq2klOLCCy/EqlWrUF9fj2OOOQaUUnz66adYsWIFnnvuOTz//PN5bCqDwegO4TgQTgKQ/YBKdQ2UdvNcMPT4Z8MUFxLvacILwgConhQeqMEmfPII4XhzRYrDTII1kLBkaAoMNQJDDsdfI8PygCDwHDxOlpBrNFNKdgFJ8Thgkxq5hHAciOQEJzl7bTM0xRQStFjcSyEWF0c1NtHBYBQANrYzhkIp2QUjGcKLEFyVEFyVzBthlEAIlyIeZQLVNehysG+hIdq38EC1WHYNowaMWBBGLGgVZSM+EF4yRQXRASI6wIl2cKIDnOgEsd7bQUQnOMkBTrCDkxwggsP8bG0332cTGaLQGGoM/s0voOuTv6f833J2LyrmfxHe2eemhFodDGzMZ2RKxsLB448/jrfffhuvvfYaTj/99JRtr7/+Oi6++GI88cQTuPrqq3PeSAaDkVsIL4DkIFIZ7ebhIHf5Yfc4TTfJhLDQQ4RIiA6J7eZnaooS2eVpLzkIL4J3+CA4ysDZPCADJJbrDidI5moMR5lVZqgx0w1VDkNXwqBqNOf9G4kp2LavFVPGlcHBVimMSkrJLuC6Gd4sVNHwwQkSIEjg4em1jRqGmZjZ0MxXTTVzBek9/piIzWDkDDa2M4ZCKdkFpQLzRmD0B+EFCM5ywFme8T6GJsOQQynPmYYcMhewKSHo3bYZcsjcnvishDMOp5SA6gr0iAIdndl+vT4hvNRDgIgLEv2IDqRbvcRnItjA8RKIYAMRJPOYOfSkooaOwPY30Lnuz9AjHcm2CxJ8x1yM8uM+3+dinMHAxnxGpmQ8c/j000/jO9/5Ti8jAADOOOMM3HHHHXjqqaeYIcBgjCISOR0oFcFLCni7d9ADp+nVYKR6QiQECGokPSPiYkOyXvyz0cd+RQ4n2s2kVI6yuEGfu+Nyoh1wVQIwDRBDjhtzcYNuqEKCqulo7gijdow3F01mjEBKyS4gXLdQRUw4KApMr7r08VapoXcTEuLeCon3hmaFd2N5gRiM9LCxnTEUSskuKFWYNwJjKHCCzQxNGX/GzAbD0BEIBOHilbiYYD6T6nK4hxiRFCKsVyUKQ41mLTz0xLQTFRgx/5CO0xPSTUhIigo9P0sgvA2c0POz+d6Qg4i17EK0+VMYkfZuB+fgmXkWKk64HMIg+n0g2JjPyJSMhYNNmzbhZz/7Wb/bly9fjt/85jc5aRSDwRh9mCvsuZx4QiToHmLJEh+6vZoChJEMy9S9LKUu7SZeGACoOfFuTb4n3tN+J+QJIeB4EbzdC8HhBe/wgRMdOfuuA0E4Pi5Q+Kx+sYwxTY6HCpGzypfgcztwxrzJeWoxYyRQSnYBC1U0ckkI2BAHFhlMgSEhLCg9vBa0uGdDPB/QKPeAY4xe2NjOGAqlZBeMBvr1RoiHqmHeCIxcQggHTnJCcPhAyJis96eUmjabaooIhho1w1ymfI7GPe+joPF8gEa8rPt+ic9UV3Ly3RKCBGQgl0sXnXULULngakjlk3J41CRszGdkSsYzdB0dHaipqel3e01NDTo7c+NCxGAwGLnAEiOGGP8vW/oUFTgeqt8Pu89X8MRwhOPA273g7amrC8zQIHERQZWt2OOGEmUPD4xelJJdwEIVlT6mwOAAMhgOEiH4kPBmMDTLswHxbYaugdNCIDwPkhCWGQwGYxRTSnbBaKS7NwIAGGo07q0chqHGQGKBAreQMZohhIAkQlzGF8MNFapr8TxbccFBjaaKDonnYDUS3x4D1eW4N6sSX4Bn5uuimgxDV+LbzPJBwUtwjD0a5fO/CMe4OTn5ngzGUMlYONB1HYLQf3We56FpbGKJwWAwCCFAD3FgJKxgNUODmDEc0cMZgupqfAVHBIYaRWfLEby5YR9OmjWOJVUapZSSXdDd44CFKmJYXgwDQCmFbPPDGReDqWGAGklhoS+xwUr6nHjPxAZGkeEPRfHWRja2MwZHKdkFDFhx3eGuNsc8ux9Ot9NaWEQTE6tqLCuvZQajWCC8AJ53AzkMGZzA9JBQLGHBFBXkPj7HRQZegFQ5FbJ9HLwu+7AsNGRjPiNTMhYOKKVYsWIFbLa+LyhZlnPWKAaDwWAUF4QXwfMieLuZxNQjlKO2LgpH9QQIvGHmTVAj7MFhFFFKdkFKqCKBCQeM7DGF1+weukyxIS4kJHL2JHL5xF/Nsvh2I/E+HoZvBAjSjPxCCMnpdSAKPMZXuiHwAwtnDEZflJJdwOgbwovgBAk8PCnl1NC7hYOJwdDiYWQGu+qawRjhmB4SNkCwAT3+X/qDUgolOnzP0mzMZ2RKxsLBNddck7YOS3TEYDAYowOXy4VjjjnW+syjGgBANQWGEgKN51DIVexIRvFRSnYBx3IcMAqAKTZIAKRB7Z8iJlAjGV6pT+Ghx3YmPIxICMeDt3vAO8rA270gvGjl7TB0JR7vOQaaWAVsZBdt2WmXMLuuKk+tZ5Q6pWQXMLKDcDx4mwuwuVLKqWGY96PEPUmNxUPDxNgYxGAUGDbmMzIlY+Hgsccey2c7GAwGgzGC0DQNXV1d8Hg84LutUiCCBF6oAJwVABJCQhBGLAiqhLKexGAUL6VkFxAuGfiehSpijBSSIZWGKDzQxKuRfKVGN5GBmgJEt+0pdZkHRF7hBMkSCjibJ56/KQnhBRBeAAdnrzCDhiabCSMTCSKVCAyt/1Xfmm4gEJbhsovg2b2QkSWlZBcwcgPhOBDJCU5yppRTSpMhj4YoeDIYjMHBxnxGpmQsHDAYDAaDkaCrqwuvvvoqli1bhvLy8n7rmUJCJXhnpfmQoEZgxAIwYn7QASYvGIzhJNXjgBnOjNFBJrkcsoFSaooNce8HucsPu8cFwBQhQGk3YUIHjLhAQZOvqeJF8nijDd7mBm/3gnf4ek24ZQMn2MAJNsBRZpVRQzcFBCUCQwmniAmBcAxvfdKIpcfWosxtH+rXYDAYjD4hhICIdnCivQ/BU0mKCGoMhi6D6iqoppqecwwGIyewMZ+RKUw4YDAYDEbW+Hw+nHHGGfB4MovZCMQfEiQXOMkFeMeZDwMxP4yYH7oSyWNrGYyBISxUEYMxZAghAC+AAKBUBC8p4O2enCT4M70aDMvzIVWE6CY+pNSjSc+J7sKEVUYBSgHQpFBRIFJDEPlS7kl5O5c9OX5TXYOhhFHp8OO0RS54nA7wPfO9dOurpLCjJcWeeC4OBoPBGAqcIAGCBN7u7bXNDLunmkKCrphigq6C6rIVto3qKvOAYzAywOuyYekxtXA7Bue5yhg9MOGAwWAwGFkjiiIqKiqGdAwi2sGLdvCeGvBqDHLHERBEAJYXgTHMEJ6FKmIwihkzPA8HkudHl1QxwXylcaEiWYZeZTSxLeEdkVLWrR414u8BEA6SpMJeXgHe5s6JwDJYCC+Ad/jgEF2o4t2DPk5CTKCGCuiaNcGHxCRf/BVsUo/BYAwCMzdQIuFs/5j3GtXM/aKroJpilVn3IiZ0MkY5As+jzMM8DRjpYcIBg8FgMLImEolg69atmDp1KhwOR/od0kAEGzhnBST3JFA1CiPaASPaxYx6xrDAQhUxGAwg7jXRYwI/X9P5lFLw8BdcNOjOUMd20+tENMVYse86lFJAV0A1Of4XtRKmMkGBwWDkAhK/D3HoP9QbNfRugmZSXDB0xfRKi4sLLDwSo1SJyip2NLajbqwPdolNDTP6h10dDAaDwciaWCyGffv2YeLEiTkRDrrDxZOoUe94GFG/KSLIoZyeg8HoTkqoIqE4JvAYDAZjuMnn2J6AEAIINpAeK4bNZKlRUCUCqkagx4IAWC4kBoORH8w8Pw5AHPhel/Sk0sxwSIbWS1gwvas0cErE9JAbhbl5GCMPWdHQ2BLAuEo3Ew4YA8KuDgaDwWBkTUVFBT7zmc/k9RyEcOCd5eCd5aCaDD3SDiPSwbwQGDmHcElziIUqYjAYo5XhGNv7w0yW6gREc4UwTymUrg4IggYj0m6GPGIwGIxhJhNPKsAUGGS7H06fzyzoITZQQ4uHcNNNoaGXAMGebxiDhxAC3uEDJ7nNBVEcDwKSGkoxJfeTjipHOc49xRfPI6Vb9cxwjDpgxN8zRj1MOGAwGAxG0UMEGwTveFDPWNMLIdIGgyVUZuQIrluOAxaqiMFgMIoEQQLvrgDvHgMj2gE91AqqMS8EBoNR3GQqNnQnxbMhLiiAGkkvB2okBQjDsOqZk8Bscne0QjgBgqsSgqcaXJrcHz3hNA0CH0hbj/bI/ZQqMMSFCEMHqJ4UxAzNvJ4TeY7YNTqiYcIBg8FgMLKms7MTr7/+OhYsWACv1zts5+3uhWCoERjhNhhRPzNGGEMiJVQRz0IVMRiM0UmhxvZ0EELAOyvBOSpgRLugh1tA1Vihm8VgMBg5I0VsyJLEivEUMSHlfXwil3Z7b5UzT4eRCCfaIXjGQHBWmuGxBkGmY76Zh4n0SvqUzROTeS1qVk4R870SFxYUUF02r2NGUcKEAwaDwWBkjSiKqK6uhiAUbhjhRCe4skmgHjUexqgdVGcJzBjZ0104YKGKGAzGaKUYxvaBMAWE+OKBqB966AgMNVroZjEYDEZBMSeOucGJDpQmV4j38mowQ9qYIWt0K8RNyueEaMGS2w8LvMML0T0GvMM35GMN55hPON4MnzSAV4QpKiigmhz/i4GqMVBdyXv7GANTnFYhg8FgMIoat9uN4447rtDNAAAQXoTgGQvqHgMj2slCGTCyhoUqYjAYjOIa29PBOXzgHD4YsYApILDwhQwGg5E1pqeDkLKIZjAkvR70uAChdxMbzJBLAheE6LLH4+3rKfsk9jND4DARojuE4yE4K8xwRGmSeWdDsY35JOFxI7lSyqmhg2oxGEoEXNAPwmuArsRDJzGGAyYcMBgMBiNrNE1DKBSCw+EAz/OFbg6ARBgjM5SB1rEHhhwqdJMYI4RUjwMWqojBYIxOinFsTwdn94Kze5mAwGAwGAUkndcDpRQidUDy+eKhb/rH8oJIxNDvFk8/KTDQpEjRZ51EHP5uSX5HmDDBCRIE9xgI7ipzxX6OGSljPuF4EMkFIjrBUzskt9v8HdUoqBKGoYRB1TALdZRHmHDAYDAYjKzp6urCf/7zHyxbtgzl5eWFbk4KhBAIZZOgtu0y4yYyGGkgnGkOCTxJ+zDDYDAYpUoxj+3pYAICg8FglAaWF0Sejj+gMGGV0fj7HomAKXrX75kw2DDLKE2WW5/TfXdOiIcjqgbvKMvrc8lIHvMJx4PY3IDNDR7mb0q1KKgcgiGHQJUwy4GYQ5hwwGAwGIys8fl8OOWUU+B2uwvdlD4hvAihfDLU9t3MjZGRFi7uccDCFDEYjNFMsY/tmZAiIASbWQ4EBoPBYKSQb2FiIMzwTAkhIS5MJIQHToQWCsOegVdGLiiFMT8BIQREdAKiE7x7jCkkKGEYchCGHABVY4Vu4oiGCQcMBoPByBpRFDFmzJhCN2NAOMkJwTsemv9QoZvCKHISbtUsMTKDwRjNjISxPVMsASHqhxZqZpMGDAaDwSg4iZBOAHoJF8MdQqmUxvyeEEJAbG5wNjeAcaCaAkMOwIj5YShhtrAwS9gTMoPBYDCyJhqNYseOHYjFivtBnHdVgXeMLNdLxvBDLI8DFqaIwWCMXkbK2J4NnMMHseqo+OQBg8FgMBgMoDTH/P4gggTeVQWxchqkmqMhlE0EIWw6PFNYTzEYDAYjayKRCLZv3z4iDA3eNwFEsBW6GYwihoUqYjAYjJE1tmcDIQRC+WRwoqPQTWEwGAwGoygo1TE/HYQTwDsrIZRPAlhuu4xgT8gMBoPByJrKykpcdNFFKCsrK3RT0kI4HkJ5HVtVwOgXFqqIwWAwRtbYni2E4yFUTAHhpUI3hcFgMBiMglPKY34mcHYfBN+EQjdjRMCekBkMBoNR8nCiA7xvfKGbwShSEsIBC1XEYDAYpQvhRYgVU0A4vtBNYTAYDAaDUWB4ZyV4T02hm1H0FFw4uP/++zFlyhTY7XbMnz8fa9as6bfuc889h7POOgvV1dXwer1YtGgRXn755WFsLYPBYDAAwO/34/XXX0cwGCx0UzKGd1aCd7J8B8VOIewCK1SRUHCziMFgMArGSBzbs4WIdgjldSw8AYPBYDBGNaNhzM8EwTOW5URMQ0GfkJ955hnceuut+O53v4uNGzdi6dKlWL58ORobG/us//bbb+Oss87CqlWrsH79epx++um44IILsHHjxmFuOYPBYIxueJ6H1+sFx42siVbeNxFEtBe6GYx+KJRdkEiOzEIVMRiM0cxIHduzhbN5WHgCBoPBYIxqRsuYnwl82URwkqvQzShaCKWUFurkCxYswLx58/DAAw9YZbNnz8bFF1+MlStXZnSMOXPm4LLLLsP3v//9jOoHAgH4fD74/X54vd5BtbvUoJTC7/fD5/OBsNU3fcL6aGBY/6Sn1PpI0zQEAoGcHY9SilAoBLfbnff+oZoMtW0nqGHk9Ty5hlKKqtqZkMpKd7KjUHbBuT99DwBwbF0ZHrx+YfYNLzFK7X6VD1gfDQzrn/QUYx/lemwfCsNhF2iBJuihlrwceziglKJ60myIvnGFbkpJweYLelOM96tig/XRwLD+Sc9w91ExjfmZkG+7gBoa1NZdoLqS82MPF5RSVNfNgejNbfglIadHywJFUbB+/XrccccdKeVnn3023nvvvYyOYRgGgsEgKioq+q0jyzJkWbY+J/4xKKUooGZSVCT6gvVH/7A+GhjWP+kptT7SdR2RSAQ2my0nqxSGtX94Cby3FmrnvvyfK4eUyrXTH4W2CwDT46DU+zkTSu1+lQ9YHw0M65/0FGMf5XpsHwrD0T+8ZywMNQYj5s/bOfJJMV07DAaDwRhZGIaBaDRaFGN+MUA4AWLFFKjtu0ENvdDNKSoKJhy0tbVB13XU1KQqITU1NWhubs7oGL/4xS8QDodx6aWX9ltn5cqVuOuuu3qV+/1+ZmzFSSh3AJj62w+sjwaG9U96Sq2P2tra8Prrr2PRokU5W40VjUaHsW946LwPRrRzmM43dEp9zCqkXbDozn8DAAjV4fePzAmkXFJq96t8wPpoYFj/pKcY+ygfY/tQGBa7QKqEFomBatH8nicPlLpdwGAwGIz80dHRgZdeegnLli1DeTmL8Q8k8iBNhtqxB2BjrEXBhIMEPY1BSmlGBuLTTz+NH/zgB/jnP/+JMWPG9FvvzjvvxO233259DgQCqK2thc/nKwqDuBhIGJ3Mbax/WB8NDOuf9JRaH4miiJNPPhlVVVUQRXHIx0usKnS5XMPWP9Tlgtahw5BDw3K+oTJaJggKYRdcev8WAIDdJsHn8w2y5aVDqd2v8gHro4Fh/ZOeYuyjXI/tQ2E47QLqnA61bTeoJqevXESMFruAwWAwGLnH6/Vi8eLFcLvdhW5KUcHZ3BB8tdC6+s6xNxopmHBQVVUFnud7rSJsaWnptdqwJ8888wyuvfZa/O1vf8OZZ545YF2bzQabzdarnBBSNEZ6MZDoD9Yn/cP6aGBY/6SnlPrIZrNh/PjxOT3mcPcPIQRixeQRH8uwVCi0XQAAIs+VxP9nLiil+1W+YH00MKx/0lNsfZSPsX0oDFf/EF6EVDkVatsuFp6AwWAwGKMCSZIwbhzLkdMXvLMcVJehB48UuilFQcECWUmShPnz52P16tUp5atXr8bJJ5/c735PP/00VqxYgT//+c/4zGc+k+9mMhgMBqMPotEodu/enRIrfiRCOAFCRR0IYXEdC00x2AUiz64DBoMxeimVsX0wEMEGoWIqswcYDAaDMSoYzWN+JgieseCd/efNG00U1DK6/fbb8cgjj+APf/gDPv30U9x2221obGzEDTfcAMAMJ3D11Vdb9Z9++mlcffXV+MUvfoGFCxeiubkZzc3NLB4xg8FgDDORSASbNm1CJBIpdFOGDCc6wfsmFroZDBTeLhCF4lj1y2AwGIWglMb2wcBJTghlkwrdDEYP7r//fkyZMgV2ux3z58/HmjVr+q373HPP4ayzzkJ1dTW8Xi8WLVqEl19+eRhby2AwGCOD0T7mZwLvmwjO5il0MwpOQYWDyy67DPfeey/uvvtuHHfccXj77bexatUq1NXVAQCamprQ2JiMK/XQQw9B0zTcdNNNGDdunPV3yy23FOorMBgMxqiksrISn/vc50omkRLvLAfvqip0M0Y9hbYLBOZxwGAwRjGlNrYPBs7hg+CbUOhmMOI888wzuPXWW/Hd734XGzduxNKlS7F8+fIUW6A7b7/9Ns466yysWrUK69evx+mnn44LLrgAGzduHOaWMxgMRnHDxvz0EEIglNeBEx2FbkpBIXSUZVUKBALw+Xzw+/0sOXIcSin8fn9RJWcrNlgfDQzrn/SUWh9pmoZAIJCz41FKEQqF4Ha7C9Y/lFJoHXuKNlkypRRVtTMhlbEJjVwSCARw7k/fAwB8fuEk/L8Ljy5wiwpPqd2v8gHro4Fh/ZOeYuyjXI/tQ6HQdoEWaIIeahn282YDpRTVk2ZD9JVujOoFCxZg3rx5eOCBB6yy2bNn4+KLL8bKlSszOsacOXNw2WWX4fvf/35G9dl8QW+K8X5VbLA+GhjWP+kZ7j4qpjE/EwppF1Bdhdq2u+jzIlJKUV03B6J34PyA2VKw5MgMBoPBGLn4/X6sWbMGxx9/PDye0nDfS6woUNt2g2os1uNoRODZgwyDwRi9lOLYPlgE7zjAUKFHOgvdlFGLoihYv3497rjjjpTys88+G++9915GxzAMA8FgEBUV/ceplmU5JcZ3YiKNUopRtsayXxJ9wfqjf1gfDQzrn/QMdx91dXWNqDG/oNcQJ0ComAK1fTeorg3/+TMkX33DhAMGg8FgZA3HcbDZbOC40grtQjgBYvlkqO27QA2j0M1hDDMsOTKDwRjNlOrYPlh4Xy2orsGQg4Vuyqikra0Nuq6jpiZ15WRNTQ2am5szOsYvfvELhMNhXHrppf3WWblyJe666/+zd9/hUVXpH8C/d0p6TyCEklCkCVIEEYJIUxAQsOO60sTCsiuLUVRAURTFtoqFYqGoC8iiAhZ+ChaqFGkqRVAEAimEtJlMytTz+yPMJUMmmSTMzJ3y/TxPHjJ3zr3z3sNkzjv33HPOnGrbdTodL3JeYL/TFwDvFq8B66h2rB/XvF1HZWVlUKvVKC8v95v/E6VjFaHJsBafhYBvtg3sOCAiIp8RHR2Na6+9VukwPELShkETlwpz4SmlQyEvY8cBEQWzQG7bG0KSJGgSWsJScAI2ExePVMqlF4mEEHW6cLRq1So8++yzWL9+PRo3blxjuRkzZiAjI0N+rNfr0aJFC8TGxnKqogvsF6M4zUzNWEe1Y/245u06ioyMRGRkpMdfx13sow0iIyMVfA9FwRYWAnPRKcAHO5bZcUBERD7DZrPBbDZDo9EEZPKnCqtcHNGiy1I6FPIiLo5MRMEs0Nv2hpAkVeX0BJzG0OuSkpKgVqurjS7Iy8urNgrhUqtXr8akSZOwZs0a3HDDDbWWDQ0NRWhoaLXtkiTx76AKe32wTmrGOqod68c1b9aREAIWi8Wv2nxfeA+pw2MBkQpLcaZiMXgbvyETEVG9FRYWYv369SguLlY6FI9RRyZBHVXzHWoUeLQa/0iaiYg8IRja9oaQVBpoE9tAUocoHUpQCQkJQY8ePbBp0yaH7Zs2bUJ6enqN+61atQoTJkzAypUrMWLECE+HSUTkl9jmN5w6Ih6amKZKh+E1HHFARET1Fh0djd69e/vV8MaG4OKIwYVTFRFRMAuWtr0hJLUW2sQ2FxZGNCsdTtDIyMjA2LFj0bNnT/Tp0wfvvfceMjMzMXnyZACV0wxlZWXho48+AlDZaTBu3Di8+eab6N27tzxaITw8HLGxsYqdBxGRr2Gbf3nUUY0ghBXWknNKh+Jx7DggIqJ6Cw0NRfPmzZUOwyvUsS0gbFbYKvRKh0IexqmKiCiYBVPb3hCSJqRK54FF6XCCwpgxY1BQUIDnnnsOOTk56Ny5MzZs2IC0tDQAQE5ODjIzL04X8e6778JiseCf//wn/vnPf8rbx48fj+XLl3s7fCIin8U2//JpopsAQsBqyFM6FI9ixwEREdVbRUUFTp06hZSUFKfzwgYSSZKgiU+DpfAkbEaD0uGQB2nVnKqIiIJXMLXtDSVpQqFNaANz4Ql2HnjJlClTMGXKFKfPXdoZsHnzZs8HREQUANjmu4cmJgUAArrzgLfWERFRvRkMBuzduxdlZWVKh+IV9sURVSEcyhnIOFUREQWzYGvbG0rShkGb0AaSmvfgERGRf2Kb7z6amJSAXhuR2Q4REdVbYmIibrvtNkhS8NyhXdl50BKWgr9gM5crHQ55AKcqIqJgFoxte0PZOw848oCIiPwR23z30sSkQJJUsJTkKh2K2/EbMhER1ZskSVCpVEGXaEgqDTSJrSFpw5QOhTyAUxURUTAL1ra9oSRtGLSJV0BSa5UOhYiIqF7Y5rufOjoZmpimSofhduw4ICKietPr9dixYwcMhuCb819SaaBNaA1Jw7kgA41Ww7SIiIJXMLftDSVpQqFNbANJHaJ0KERERHXGNt8z1FGNoIltpnQYbsVvyERERPUkqbWVFwrYeRBQOFURERHVFzsPiIiIyE4dmQRNXAsgQEZz8BsyERHVW0xMDPr27YuoqCilQ1GMpNZWjjzghYKAwamKiCiYsW1vOEkTAm3SFbyhgIiI/ALbfM9SRyRAE5caEJ0H7DggIqJ6E0LAZrNBCKF0KIqSNCEX7jLk/MaBQMsRB0QUxNi2Xx6ORiQiIn/BNt/z1OFx0Ma3hCT593dM/46eiIgUUVBQgM8//xzFxcVKh6I4dh4EDk5VRETBjG375ZM7D7RhSodCRERUI7b53qEKi4EmoRUklf9+z/TfyImISDFRUVHo2bMnIiIilA7FJ1yc31ijdCh0GTjigIiCGdt297B3HqhCWI9EROSb2OZ7jyo0CpqE1pBUaqVDaRB+QyYionoLCwtDy5YtERrK4fh2kiYU2gR2HvgzDdc4IKIgxrbdfSSVBpqE1lCFRCodChERUTVs871LFRIJjZ/eaMiOAyIiqjej0YizZ8/CZDIpHYpPkbRh7DzwY1oN0yIiCl5s291LUqmhSWwNVSgXniQiIt/CNt/7VNrwC9cK/GuKY35DJiKieispKcGuXbtQWlqqdCg+h50H/otTFRFRMGPb7n6SpIImoRVUYTFKh0JERCRjm68MSRsGbeIVkDT+M9KD35CJiKjeEhISMHr0aMTFxSkdik9i54F/4lRFRBTM2LZ7hiSpoIlvCVV4nNKhEBERAWCbryRJE1K5PqI2TOlQ6oQdB0REVG8qlQparRaSxAutNZHvJvCzoYjBjCMOiCiYsW33HEmSoI1PgzoiQelQiIiI2OYrTFJroU1s4xdrIfEbMhER1VtJSQl2797NoY0uSJrQyrsJ2HngF0K4xgERBTG27Z6niWsBdWSS0mEQEVGQY5uvPEml8Yu1kPgNmYiI6s1ms8FoNMJmsykdis+r7Dy4ApI6ROlQyAUNRxwQURBj2+4dmthmUEcnKx0GEREFMbb5vqFyLaTWPj2dISdfJiKieouNjcX111+vdBh+wz6PobnwLwiLUelwyAmVBKhVHKpLRMGLbbv3aKKbQJLUsOizlQ6FiIiCENt832GfztAiqWAtK1Q6nGp4ax0REZEXyIsgaUKVDoWc4GgDIiLyJnVUI2jimgOcX5qIiCjoaeJaQB3VWOkwquG3ZCIiqreCggJ8/vnnKCoqUjoUv2JfBImdB75Ho+aFGyIKbmzbvU8dkQhtXBo7D4iIyKvY5vsmTUwKNDFNlQ7DATsOiIio3iIiItClSxdEREQoHYrfkdRaaJOugEobrnQoVIWW0xQRUZBj264MVXgstAmtIEn8ak5ERN7BNt93VY5IbOEzNxUwOyEionoLDw/HFVdcgdBQ3jnfEJJKA01iG6hCIpUOhS7gVEVEFOzYtitHFRoNTWIbSCq10qEQEVEQYJvv29QRCdDGp/nETQXKR0BERH7HZDIhJycHZrNZ6VD8lqRSQ5PYGqrQKKVDIXCqIiIitu3KUoVEQJt4BSS1VulQiIgowLHN932qsFhoEltDUil76Z4dB0REVG96vR47duyAwWBQOhS/JkkqaBJaQRUWo3QoQY8dB0QU7Ni2K0/ShlV2HnAtJCIi8iC2+f5BFRIJTeIVkNQa5WJQ7JWJiMhvJSQkYMSIEYiNjVU6FL8nSSpo4ltCHR6vdChBTcupiogoyLFt9w2SJgTaxDZcC4mIiDyGbb7/UGnDoU1sq9hNBfyWTERE9aZSqRAeHg6VwsPmAoUkSdDEp0Idmah0KEFLw8WRiSjIsW33HZJaW7kWEqczJCIiD2Cb71+UvKmA7xAiIqo3g8GAvXv3orS0VOlQAoomtjnUUY2VDiMocaoiIgp2bNt9i6RSQ5PQGqrwOKVDISKiAMM23/9Iai00SVdAFRrt1ddlxwEREdWb1WqFXq+HzWZTOpSAo4lJgSamqdJhBB1OVUREwY5tu++RJAna+DSOSCQiIrdim++f7GskenOaY+VWVyAiIr8VGxuLQYMGKR1GwFJHNQJUalh0ZwEhlA4nKHDEAREFO7btvksT2xySSgtLSa7SoRARUQBgm++/7NMcQ62F1ZDn8ddjxwEREZEPUkckQFKpYSnKhBC8E8ST9r19L1Yd/13pMIiIiGqkjk4G1BpYdFm8qYCIiCjIaWJSIKk0sOizPfo6HJdPRET1VlBQgPXr16O4uFjpUAKaKiwWmsTWkFRqpUMJaCZDAeIjQ5QOg4hIUWzbfZ86IhHa+DRIEr/GExFRw7HNDwzqqEbQeDgvYMZBRET1FhERgQ4dOiAsLEzpUAKeKiQSmsQ2kNRapUMhIqIAxrbdP1TeVNCGNxUQEVGDsc0PHOrwOGgSWnksL+BURUREVG/h4eFo37690mEEDZU2HNrEK6DS8q54IiLyDLbt/kMVEgFtUltIGuYFRERUf2zzA4sqNKrypgIP5AUccUBERPVmNpuRl5cHs9msdChBQ9KEQBOZqHQYREQUoNi2+xdJE8q8gIiIGoRtfuBRacOhiYh3/3HdfkQiIgp4Op0OW7duhcFgUDoUIiIicgO27URERMGBbT7VFacqIiKieouLi8NNN92E8PBwpUMhIiIiN2DbTkREFBzY5lNdseOAiIjqTaPRICoqSukwiIiIyE3YthMREQUHtvlUV5yqiIiI6s1gMODgwYMoKytTOhQiIiJyA7btREREwYFtPtWV4h0HCxcuRKtWrRAWFoYePXpg27ZttZbfsmULevTogbCwMLRu3RqLFy/2UqRERGRnNptx/vx5WCwWpUOhAMO8gIhIGWzbyRcxLyAicj+2+VRXinYcrF69GtOmTcOsWbNw4MAB9OvXD8OGDUNmZqbT8idPnsTw4cPRr18/HDhwADNnzsTUqVPx2WefeTlyIqLgFh8fjxtvvBExMTFKh0IBhHkBEZFy2LaTr2FeQETkGWzzqa4kIYRQ6sWvvfZaXH311Vi0aJG8rWPHjrjlllswb968auWfeOIJfPHFFzh69Ki8bfLkyfjll1+wc+fOOr2mXq9HbGwsdDod/0AuEEJAp9MhNjYWkiQpHY5PYh3VjvXjWqDVkcVigV6vd9vxhBAwGAyIiooKiPrxBCEEEhMTlQ7Do5gX+IZA+7zyBNZR7Vg/rvliHbm7bb8czAtcY17AvMBbfPHzytewjmrH+nHN23XkS21+XTAvcM1TeYFiiyObTCbs27cPTz75pMP2IUOG4KeffnK6z86dOzFkyBCHbUOHDsWSJUtgNpuh1Wqr7WM0GmE0GuXHOp1O/lfBPhOfYv+AAsA/wBqwjmrH+nEt0Oro/Pnz+OGHH3DttdciNjb2so9nTwRsNltA1I8nCCGg1WoRHR0dkHXEvMB3BNrnlSewjmrH+nHNF+vI3W375WBe4BrzguqYF3iGL35e+RrWUe1YP655u458qc2vC+YFrnkqL1Cs4yA/Px9WqxXJyckO25OTk5Gbm+t0n9zcXKflLRYL8vPzkZKSUm2fefPmYc6cOdW2p6amXkb0REREysjLy0OjRo2UDsPtmBcQERHVH/OCi5gXEBFRsHN3XqBYx4Hdpb0gQohae0aclXe23W7GjBnIyMiQH9tsNhQWFiIxMZG9VBfo9Xq0aNECZ86c4XDMGrCOasf6cY11VDvWj2v2OgoJCVE6FI9iXqA8/j26xjqqHevHNdZR7Vg/rjEvqHt5Z9vtmBe4xr9H11hHtWP9uMY6qh3rxzVP5QWKdRwkJSVBrVZXu1sgLy+v2l0Cdk2aNHFaXqPR1DiPU2hoKEJDQx22xcXFNTzwABYTE8M/QBdYR7Vj/bjGOqod68e1QP0Sy7zA9/Dv0TXWUe1YP66xjmrH+nGNecFFzAs8i3+PrrGOasf6cY11VDvWj2vuzgtUbj1aPYSEhKBHjx7YtGmTw/ZNmzYhPT3d6T59+vSpVn7jxo3o2bOn0/kKiYiIyD8wLyAiIiI75gVERETKU6zjAAAyMjLwwQcfYOnSpTh69CgeeeQRZGZmYvLkyQAqhw2OGzdOLj958mScPn0aGRkZOHr0KJYuXYolS5bgscceU+oUiIiIyE2YFxAREZEd8wIiIiJlKbrGwZgxY1BQUIDnnnsOOTk56Ny5MzZs2IC0tDQAQE5ODjIzM+XyrVq1woYNG/DII49gwYIFaNq0Kd566y3cfvvtSp1CQAgNDcUzzzxTbYgmXcQ6qh3rxzXWUe1YP64FQx0xL/ANwfBeu1yso9qxflxjHdWO9eNaMNQR8wLfEAzvtcvFOqod68c11lHtWD+ueaqOJGFfLYiIiIiIiIiIiIiIiIKeolMVERERERERERERERGRb2HHARERERERERERERERydhxQEREREREREREREREMnYcEBERERERERERERGRjB0HQWLhwoVo1aoVwsLC0KNHD2zbtq3Gsp9//jluvPFGNGrUCDExMejTpw++/fZbL0arjPrUUVU7duyARqNBt27dPBugwupbP0ajEbNmzUJaWhpCQ0PRpk0bLF261EvRKqO+dbRixQp07doVERERSElJwcSJE1FQUOClaL1r69atGDlyJJo2bQpJkrBu3TqX+2zZsgU9evRAWFgYWrdujcWLF3s+UAXVt46C9bOa3IN5gWvMC2rHvMA15gU1Y15QO+YE5G3MC1xjXlA75gWuMS+oGfOC2imZF7DjIAisXr0a06ZNw6xZs3DgwAH069cPw4YNQ2ZmptPyW7duxY033ogNGzZg3759GDhwIEaOHIkDBw54OXLvqW8d2el0OowbNw6DBw/2UqTKaEj93HXXXfj++++xZMkSHDt2DKtWrUKHDh28GLV31beOtm/fjnHjxmHSpEk4fPgw1qxZg59//hn333+/lyP3jtLSUnTt2hXvvPNOncqfPHkSw4cPR79+/XDgwAHMnDkTU6dOxWeffebhSJVT3zoKxs9qcg/mBa4xL6gd8wLXmBfUjnlB7ZgTkDcxL3CNeUHtmBe4xrygdswLaqdoXiAo4PXq1UtMnjzZYVuHDh3Ek08+WedjXHnllWLOnDnuDs1nNLSOxowZI5566inxzDPPiK5du3owQmXVt37+7//+T8TGxoqCggJvhOcT6ltHr776qmjdurXDtrfeeks0b97cYzH6CgBi7dq1tZZ5/PHHRYcOHRy2PfTQQ6J3794ejMx31KWOnAn0z2pyD+YFrjEvqB3zAteYF9Qd84LaMScgT2Ne4BrzgtoxL3CNeUHdMS+onbfzAo44CHAmkwn79u3DkCFDHLYPGTIEP/30U52OYbPZUFJSgoSEBE+EqLiG1tGyZctw4sQJPPPMM54OUVENqZ8vvvgCPXv2xCuvvIJmzZqhXbt2eOyxx1BeXu6NkL2uIXWUnp6Os2fPYsOGDRBC4Ny5c/j0008xYsQIb4Ts83bu3FmtPocOHYq9e/fCbDYrFJVvC/TPanIP5gWuMS+oHfMC15gXuB/zgvoJ9M9pch/mBa4xL6gd8wLXmBe4H/OC+rmcz2mNB+IhH5Kfnw+r1Yrk5GSH7cnJycjNza3TMf7zn/+gtLQUd911lydCVFxD6uiPP/7Ak08+iW3btkGjCew/o4bUz19//YXt27cjLCwMa9euRX5+PqZMmYLCwsKAnLewIXWUnp6OFStWYMyYMaioqIDFYsGoUaPw9ttveyNkn5ebm+u0Pi0WC/Lz85GSkqJQZL4r0D+ryT2YF7jGvKB2zAtcY17gfswL6ifQP6fJfZgXuMa8oHbMC1xjXuB+zAvq53I+pzniIEhIkuTwWAhRbZszq1atwrPPPovVq1ejcePGngrPJ9S1jqxWK+655x7MmTMH7dq181Z4iqvPe8hms0GSJKxYsQK9evXC8OHD8frrr2P58uUBexcBUL86OnLkCKZOnYrZs2dj3759+Oabb3Dy5ElMnjzZG6H6BWf16Ww7BddnNbkH8wLXmBfUjnmBa8wL3It5Qd0E0+c0uQ/zAteYF9SOeYFrzAvci3lB3Vzu53Rgd30SkpKSoFarq/Vi5uXlVeudu9Tq1asxadIkrFmzBjfccIMnw1RUfeuopKQEe/fuxYEDB/Cvf/0LQGXDJ4SARqPBxo0bMWjQIK/E7g0NeQ+lpKSgWbNmiI2Nlbd17NgRQgicPXsWbdu29WjM3taQOpo3bx769u2L6dOnAwC6dOmCyMhI9OvXD3Pnzg36HvImTZo4rU+NRoPExESFovJNwfJZTe7BvMA15gW1Y17gGvMC92NeUDfB8jlN7sO8wDXmBbVjXuAa8wL3Y15QN+74nOaIgwAXEhKCHj16YNOmTQ7bN23ahPT09Br3W7VqFSZMmICVK1cG/Bxq9a2jmJgY/Pbbbzh48KD8M3nyZLRv3x4HDx7Etdde663QvaIh76G+ffsiOzsbBoNB3nb8+HGoVCo0b97co/EqoSF1VFZWBpXK8SNYrVYDuNhTHsz69OlTrT43btyInj17QqvVKhSV7wmmz2pyD+YFrjEvqB3zAteYF7gf8wLXgulzmtyHeYFrzAtqx7zANeYF7se8wDW3fU7Xezll8juffPKJ0Gq1YsmSJeLIkSNi2rRpIjIyUpw6dUoIIcSTTz4pxo4dK5dfuXKl0Gg0YsGCBSInJ0f+KS4uVuoUPK6+dXSpZ555RnTt2tVL0XpffeunpKRENG/eXNxxxx3i8OHDYsuWLaJt27bi/vvvV+oUPK6+dbRs2TKh0WjEwoULxYkTJ8T27dtFz549Ra9evZQ6BY8qKSkRBw4cEAcOHBAAxOuvvy4OHDggTp8+LYSoXj9//fWXiIiIEI888og4cuSIWLJkidBqteLTTz9V6hQ8rr51FIyf1eQezAtcY15QO+YFrjEvqB3zgtoxJyBvYl7gGvOC2jEvcI15Qe2YF9ROybyAHQdBYsGCBSItLU2EhISIq6++WmzZskV+bvz48aJ///7y4/79+wsA1X7Gjx/v/cC9qD51dKlATwSEqH/9HD16VNxwww0iPDxcNG/eXGRkZIiysjIvR+1d9a2jt956S1x55ZUiPDxcpKSkiL///e/i7NmzXo7aO3788cdaP1ec1c/mzZtF9+7dRUhIiGjZsqVYtGiR9wP3ovrWUbB+VpN7MC9wjXlB7ZgXuMa8oGbMC2rHnIC8jXmBa8wLase8wDXmBTVjXlA7JfMCSQiOcSEiIiIiIiIiIiIiokpc44CIiIiIiIiIiIiIiGTsOCAiIiIiIiIiIiIiIhk7DoiIiIiIiIiIiIiISMaOAyIiIiIiIiIiIiIikrHjgIiIiIiIiIiIiIiIZOw4ICIiIiIiIiIiIiIiGTsOiIiIiIiIiIiIiIhIxo4DIiIiIiIiIiIiIiKSseOAiPDss8+iW7duSodRZ8uXL0dcXJzSYRAREQUk5gVERERkx7yAKHix44DIB02YMAGSJGHy5MnVnpsyZQokScKECRO8HxgRERF5HfMCIiIismNeQETewo4DIh/VokULfPLJJygvL5e3VVRUYNWqVUhNTVUwssBlNpuVDoGIiMgp5gXex7yAiIh8FfMC72NeQMGIHQdEPurqq69GamoqPv/8c3nb559/jhYtWqB79+4OZb/55htcd911iIuLQ2JiIm6++WacOHHCoczZs2dx9913IyEhAZGRkejZsyd2797tUObjjz9Gy5YtERsbi7vvvhslJSU1xmcf/vftt9+iY8eOiIqKwk033YScnBy5zIABAzBt2jSH/W655RaHux9atmyJuXPnYty4cYiKikJaWhrWr1+P8+fPY/To0YiKisJVV12FvXv3Voth3bp1aNeuHcLCwnDjjTfizJkzDs9/+eWX6NGjB8LCwtC6dWvMmTMHFotFfl6SJCxevBijR49GZGQk5s6dW+P5EhERKYl5AfMCIiIiO+YFzAuIvIEdB0Q+bOLEiVi2bJn8eOnSpbjvvvuqlSstLUVGRgZ+/vlnfP/991CpVLj11lths9kAAAaDAf3790d2dja++OIL/PLLL3j88cfl5wHgxIkTWLduHb766it89dVX2LJlC1566aVa4ysrK8Nrr72Gjz/+GFu3bkVmZiYee+yxep/nG2+8gb59++LAgQMYMWIExo4di3HjxuHee+/F/v37ccUVV2DcuHEQQji89gsvvIAPP/wQO3bsgF6vx9133y0//+233+Lee+/F1KlTceTIEbz77rtYvnw5XnjhBYfXfuaZZzB69Gj89ttvTuuWiIjIVzAvYF5ARERkx7yAeQGRxwki8jnjx48Xo0ePFufPnxehoaHi5MmT4tSpUyIsLEycP39ejB49WowfP77G/fPy8gQA8dtvvwkhhHj33XdFdHS0KCgocFr+mWeeEREREUKv18vbpk+fLq699toaX2PZsmUCgPjzzz/lbQsWLBDJycny4/79+4t///vfDvtdGntaWpq499575cc5OTkCgHj66aflbTt37hQARE5OjsNr79q1Sy5z9OhRAUDs3r1bCCFEv379xIsvvujw2h9//LFISUmRHwMQ06ZNq/EciYiIfAHzAuYFREREdswLmBcQeYvGq70URFQvSUlJGDFiBD788EMIITBixAgkJSVVK3fixAk8/fTT2LVrF/Lz8+U7AzIzM9G5c2ccPHgQ3bt3R0JCQo2v1bJlS0RHR8uPU1JSkJeXV2t8ERERaNOmTb32caZLly7y78nJyQCAq666qtq2vLw8NGnSBACg0WjQs2dPuUyHDh0QFxeHo0ePolevXti3bx9+/vlnhzsGrFYrKioqUFZWhoiICABwOAYREZEvY17guI15ARERBTPmBY7bmBcQuR87Doh83H333Yd//etfAIAFCxY4LTNy5Ei0aNEC77//Ppo2bQqbzYbOnTvDZDIBAMLDw12+jlardXgsSZLD0MS67iOqDA9UqVQOjwHnCwpVPY4kSTVuuzQe+3Zn22w2G+bMmYPbbrutWpmwsDD598jIyGrPExER+SrmBcwLiIiI7JgXMC8g8iSucUDk42666SaYTCaYTCYMHTq02vMFBQU4evQonnrqKQwePBgdO3ZEUVGRQ5kuXbrg4MGDKCws9FbYAIBGjRo5LH5ktVpx6NAhtxzbYrE4LIB07NgxFBcXo0OHDgAqF4s6duwYrrjiimo/KhU/+oiIyD8xL3COeQEREQUj5gXOMS8gcg/+NRD5OLVajaNHj+Lo0aNQq9XVno+Pj0diYiLee+89/Pnnn/jhhx+QkZHhUOZvf/sbmjRpgltuuQU7duzAX3/9hc8++ww7d+70aOyDBg3C119/ja+//hq///47pkyZguLiYrccW6vV4uGHH8bu3buxf/9+TJw4Eb1790avXr0AALNnz8ZHH32EZ599FocPH8bRo0exevVqPPXUU255fSIiIiUwL3COeQEREQUj5gXOMS8gcg92HBD5gZiYGMTExDh9TqVS4ZNPPsG+ffvQuXNnPPLII3j11VcdyoSEhGDjxo1o3Lgxhg8fjquuugovvfSS08TCne677z6MHz8e48aNQ//+/dGqVSsMHDjQLceOiIjAE088gXvuuQd9+vRBeHg4PvnkE/n5oUOH4quvvsKmTZtwzTXXoHfv3nj99deRlpbmltcnIiJSCvOC6pgXEBFRsGJeUB3zAiL3kMSlE4oREREREREREREREVHQ4ogDIiIiIiIiIiIiIiKSseOAiIiIiIiIiIiIiIhk7DggIiIiIiIiIiIiIiIZOw6IiIiIiIiIiIiIiEjGjgMiIiIiIiIiIiIiIpKx44CIiIiIiIiIiIiIiGTsOCAiIiIiIiIiIiIiIhk7DoiIiIiIiIiIiIiISMaOAyIiIiIiIiIiIiIikrHjgIiIiIiIiIiIiIiIZOw4ICIiIiIiIiIiIiIiGTsOiIiIiIiIiIiIiIhIxo4DIiIiIiIiIiIiIiKSseOAiIiIiIiIiIiIiIhk7DggIiIiIiIiIiIiIiIZOw6IiIiIiIiIiIiIiEjGjgMiIiIiIiIiIiIiIpKx44CIiIiIiIiIiIiIiGTsOCAiIiIiIiIiIiIiIhk7DoiIiIiIiIiIiIiISMaOAyIiIiIiIiIiIiIikrHjgIiIiIiIiIiIiIiIZOw4ICIiIiIiIiIiIiIiGTsOiIiIiIiIiIiIiIhIxo4DIiIiIiIiIiIiIiKSseOAiIiIiIiIiIiIiIhk7DggIiIiIiIiIiIiIiIZOw6IiIiIiIiIiIiIiEjGjgMiIiIiIiIiIiIiIpKx44CIiIiIiIiIiIiIiGTsOCByYsKECZAkCZIkYfPmzQ7PPfvss/Jzy5cvv6xjU3Bbvnx5je8zIiLybbXlCr6kalvz7LPPytsHDBggbz916hQA4NSpU/K2AQMGKBJvMGnZsiXrmogoCNQ1Z7CXmTBhgtdiI+9i20/+hh0HREREREREREREREQk0ygdABGRP6qoqEBYWJjSYRAREZGXlZeXIzw8XOkwiIgowAghlA6BasC2n4IVRxwQucmMGTPQs2dPNGrUCFqtFnFxcRgwYAC+/PLLOu3/119/YdKkSUhNTUVISAgaNWqEIUOG4MSJE1iyZIk8bHHTpk3yPlarFY0bN4YkSRg2bJi8/dixY7jnnnvQpEkThISEoFmzZrjvvvuQmZnp8JpVh0K+9957aNu2LSIjI5Geno6DBw+6pV7s7FMitGzZEtu3b8e1116LsLAwtG7dGu+//3618qtWrUJ6ejqioqIQHh6OLl264PXXX4fVagUAWCwWREVFVRvK2atXL0iShLvvvlvedv3110OSJHTu3Fneptfr8cQTT6Bdu3YIDQ1FfHw8Ro4cif379zvEUXUo4erVq3HllVdCq9Xim2++AQDk5uZiypQpaNmypfz/9re//Q1//vmnw3EKCgpwzz33IDo6Go0aNcL06dNhMpkuu16JiMg3VJ3mp+qUQM6mpdu8ebO8bfHixXj00UeRnJyM+Ph43HXXXSgoKHA4dnZ2Nh588EG0aNECISEhaNy4Me68804cPnzYi2fYcFXP991330VGRgYaN26MyMhI3HLLLcjKynIoX5fznTdvXrWpljZs2CBv+/333wEAP/zwg7zt008/lff//vvvMXToUMTHxyM0NBTt27fH3LlzYTab5TJV/+8+/fRT3HvvvYiLi0N6erpcZs2aNejXrx9iYmIQFhaGrl27YtGiRdUu/vzvf/9D+/btERYWhmuvvRa7du1yW/0SEVFgcDZVUdXvo+vWrUOXLl0QERGBbt264YcffnDYXwiBpUuXok+fPoiOjkZ4eDg6dOiAl19+2aHM4sWLcc011yAyMhIRERHo3bs3/ve//7n1XNj2s+2nACGIqJrx48cLAAKA+PHHHx2ee+aZZ+Tnli1bJm9PS0uTt1f9kSRJbNy40emx7X777TcRGxvrdP8ff/xRlJWVifj4eAFAjBkzRt7v+++/l8utWrVKCCHEwYMHRVRUlNNjNWrUSJw8eVLe377d2WunpaUJk8nktjrt37+/ACAiIiJEeHh4tderWpdPPfWU0/gBiDvvvFMud+ONNwoAol27dkIIIcrKyoRWqxUARPPmzYUQQphMJvn1/vGPfwghhCgpKRFXXXWV0+OHhoaKbdu2ya9h/3+Ni4sTkiTJ5dauXSuysrJEs2bNnB4nPj5eHDt2TD7OwIEDq5VJSUmp8X1GRES+7dJc4eTJk/LjZ555Ri63bNmyap/1P/74Y61t8N/+9jd5/zNnzji0F1V/IiIixJ49e2qNs+rrV43L3i4DkHODqufQv39/N9WU4/kmJiZWO48OHTqI8vLyep3vjh075O0rV64UQggxa9YsedsHH3wghBDi+eefl7edO3dOrpOqbXrVnxEjRgibzVat7hISEuTfu3btKoQQ4tlnn60xX5kyZYrD+V/6elFRUXK+5s66JiIi31Pb9YWq7GXGjx8vb7N/H42JiXHaluTn58tlJ02a5LRNqtrOTJgwoca265VXXnHbObPtZ9tPgYEjDohcGDhwoNzjLEkS5syZ47Tc66+/juPHj6OkpARGoxF79uxBREQEhBBYsGBBra8xbdo06HQ6AMAjjzyCc+fO4fz58/joo4/QqFEjhIeHY+LEiQCAdevWobCwEADk3vO4uDjccsst8v4GgwEA8PHHH0On0+G1114DAJw/fx6zZs2q9vo6nQ7vv/8+iouLMX78eADA6dOnsWfPnlrjrrpQtL3H35WysjI8+OCDKC4uxrfffovQ0FAAwFNPPQWbzYaTJ09i3rx5AIC2bdvi6NGjyM7ORr9+/QBU9u7bR11cf/31AIDjx48jPz8fe/bsgdlshiRJOHv2LDIzM7F//36Ul5c7lJ8/fz5+++03qNVqrF27FhUVFTh+/DiuuOIKGI1GPPLII9XiLi4uxqRJk5CXl4ecnBz06dMHs2fPRlZWFmJjY7FlyxYYjUbs27cPCQkJKCoqkuv6hx9+wI8//ijHkJubi19//ZULZBMREVQqFbZv347c3Fx5ZNxnn30Gm80GAJg9ezZycnIAVN5pp9Pp8MknnwCobFOnTZumSNxAwxZx1Gg02Lt3L/Lz8+Xc5ffff8dHH30EoO7ne8011yAiIgIA8NNPPwEAduzYIbetO3bscPi3ffv2aNy4MQwGA6ZNmwYhBIYNG4azZ8+irKwML774IgDg66+/xoYNG6rFrVKp8MMPP6C0tBSrVq3CqVOn8PzzzwMAJk6ciPPnz0Ov1+Of//wnAGDhwoU4dOiQfE7iwl2I//vf/6DT6TBlyhQ5XyMiInJFr9dj9uzZKC4uxlNPPQUAMBgM8kj4HTt2YMmSJQCA1NRUbNmyBQaDAb/88gvGjBkDANi+fTuWL18OAJg1axZ0Oh3y8/Nx6623Aqhsry4d9Xgptv1s+ynIKNptQeSjqt4RUNtP1bvkv/jiC9G/f/9qd6bjQm+6s2MLUXmXvFqtFgBE+/bt5Z7uS/3xxx/ycd98801htVpFkyZNBADx0EMPCSGEKC0tlY/Vs2dPeV+bzSaaN28u95rb2eO45ppr5G0bNmyo1otfk6qjL6qOZHDGfmejVqsVpaWl8vY77rhDPsbvv/8uFi9eLD9+55135HKbNm2St2dkZAghhNi6dau87csvvxQvvPCCACBGjx4tgMpRGK+//rpcJisrSwghRJ8+fVz+3+p0OiGE44gD+x0RdjXdFWH/sdf1c889J2/bsGGDvP/TTz9dpztPiIjI97hrxMH06dPlso8//ri8PTs7Wwgh5La+cePGwmq1ymX79u0rgMqRjcXFxTXG6ckRB/ayVe+MdKbq+c6aNUvefujQIXn73XffXe/zHTRokAAgevToIcxms4iIiBC9e/cWTZo0kXOquLg4AUA88MADQgghvv32W5c5gD3PqFp38+bNczind9991+Vx3nrrLWGxWOTRkL169ZL3LysrEyEhIbzrkIgoCLhjxEFycrLcLh45ckQu++KLLwohhJgxY4a8bcWKFU6PX7VMTT9ffPFFrefCtp9tPwUXjjggcuHHH3+EEEL+eeaZZ6qV2b17N2677TZs2bIFxcXF1ea2s9/x7kxhYaE8b3/79u1rvAv9iiuuwNChQwEAS5Yske9OBCCPEigqKpKP1aJFC3lfSZLQvHnzaq9n17ZtW/n3qgv+Go3GGuNuqMTERPkuAQByXACQn5+P/Px8+XHVc6j6+/nz5wFUrmdgj3fnzp346aefoFKp8OijjwKovNPAfrfBFVdcgaZNmzrsX5uioiKHx+3atau2GLKr49hHhmRnZ8vbmjVr5vR3IiIKTJe2uZdy1Qbb28VmzZpBpbqYutvbRSGEy7sDfUnV9vzSHKDqv3U53/79+wMAfvnlF/z0008oKytDeno6+vTpg2PHjsl5WdWydckB7O13VV27dnV4XNfj5Ofny3MnV233w8PDkZCQ4PIYREREANCmTRu5XXSWL1Rtlzp27Oj0GA1tAy8X2/5KbPvJH7HjgMgN1q9fD4vFAgBYsGABKioqIIRAYmKiy30TEhKgVqsBVC5qfGmnQ1X2IXC//vqrPA1O+/bt0adPHwBAfHy8fKyzZ8/K+wkh5MdVy9hptVr59/pMn/Pss8/KHSotW7as0z4FBQUOHSlV40xKSkJSUpLT586cOeNQDgBCQ0Nx7bXXAqjsJNi5cyeuvPJK9O3bFzExMdixY4c8hNE+TREANGrUCAAQFRUFk8nk0DEkhIDNZkNaWppD3OHh4dXOxX6c9u3bVzuG/TgA5A4LAA6LQF26IBQREfkv+9R7AFBRUSH/fvLkyVr3c9UG29u87OxshxzB3i5KklSnfMMT7O2dfdqDuqjanl+aA1T9ty7na2/bLRYL5s+fDwBIT0+XFzB89dVX5f3tZe1tN1A5HYKz9nvp0qXV4r40D6h6nFWrVjk9zuzZs5GUlCT/H1dt9ysqKjxycYaIiAKTq3yhartkXyS4tjI7d+50+v3VflNiTdj2V2LbT8GCHQdEbmAymeTfo6OjYbFY8Morr9TpDsDw8HAMHDgQQGXHweOPP47z58+joKAAK1euxOHDh+Wyw4cPly/Qb9++HQAcGvaIiAh5LYCff/4ZK1euRElJCebPny830DfddNPlnexlMpvNmDVrFvR6PTZt2oQvv/wSQGVPfNu2bXHjjTfKdxm89dZbOH78OHJzczF37lz5GFXPwX4XwbZt21BYWIj09HSoVCpce+21OHjwoDxXYtWOg+HDhwOonBPyH//4B3JycmA0GnH48GHMmjULd9xxR53OxX6cY8eO4emnn5Y7Rfbt24eHH34YDz/8MACgb9++8j4vvfQSzp07h99++02eg5KIiPxfcnKy/CXxhx9+gMlkwqlTp7Bs2bLLOq69zTt37hxeffVVlJSUYM2aNXLHeO/evREbG3t5wXvRkiVLcODAARQUFODpp5+Wt9tzofqcb+/eveUOm/Xr1wNwvHjwf//3fwCAli1bynctpqeny/v/5z//wffff4+Kigrk5+dj7dq1GDRoELZu3eryPIYMGSLfiPH0009jz549MJlMyM3Nxccff4yePXvi9OnTUKvV6N27N4DK3GzNmjXQ6/V45plnHPJHIiIKDnv27ME333zj8JOXl3fZxx0xYoT8+8yZM7F9+3aUlZXh8OHDWLRoEYCL31+BynUWDx8+DJPJhMzMTCxcuBAdOnS47DicYdvPtp/8mHtnPiIKDLXNQVh1Xn/7GgebN2+uNrddYmKiPLdeWlqa02Pb/fbbbyI2NtbpHHmXvv7LL78sP6dSqcSZM2ccnt+/f7+IjIx0eqykpCTx119/yWXt26vOT1h1LsKqazhcLvtcylFRUSIqKqpabFVfq7a5F2+77TaH437//fcOzy9fvlwI4fj/BMDhvEtKSkTXrl1rfI2q8w3a55R0NgdhVlaWaNGiRY3HqVqvAwcOdPoeqen/mYiIfNvYsWPlz/AtW7YIIYS4++675W0RERFCpVKJiIiIap/1NbW1ztYOOn36tEhOTnbazoSHh4tdu3bVGqcn1zioq6rn62x9oA4dOsjrCNX3fPv16yc/37JlSyGEEBUVFSI0NFTePm7cOId9Pvzww2rrUTnLvZytT1HV888/X+Mxqtbrjz/+WO31wsLC5PcG5zkmIgpsrtZQXLt2rRCi9jUOqrYVNa2rNGnSJJffb2sqY/9xF7b9bPspMHDEAZEb9O/fH0uXLkWbNm0QFhaG3r17Y+PGjXW+A7Bz587Yv38/Jk6ciObNm0Or1SIxMRE33nijw3yAADBp0iS5h33w4MEOcwQCQPfu3fHzzz9jzJgxaNy4MTQaDVJSUjB+/Hj8/PPPaNWqlXtOuoESExPx3XffyXcKtGzZEu+++y4mTJggl3nxxRfx0Ucf4dprr0VERARCQ0PRqVMnvPLKK1i9erXD8fr06YOQkBD5sf1Og6p3+Tdv3tzhvKOiorB9+3bMmjULHTp0QGhoKGJiYnDllVdi8uTJeOGFF+p0Lk2bNsXevXvx8MMPo3Xr1ggJCUFCQgK6deuGRx99FBkZGXLZNWvW4O6770ZkZCQSExMxdepUh1EURETkX/744w/59/j4eADAO++8gzvvvBNxcXGIiIjAP/7xD7z88suX9TqpqanYu3cvJk2ahGbNmkGj0SApKQm33347du3aJU/Z5y/mzJmDxx9/HI0bN0Z4eDhGjRqFTZs2yfM11/d87SMPgYs5QGhoKHr06CFvrzrqEADGjRuHH374ASNGjEBCQgK0Wi2aN2+OwYMH480338TVV19dp3N56qmn8Pnnn2PgwIGIjY1FSEgI0tLSMGLECLz//vvyVIUDBgzAqlWr0LZtW4SEhKBHjx749ttvHaY8ICIiulzvv/8+PvjgA/Tu3RtRUVEICwtD+/btMWzYsBrLhIeHo02bNrjzzjuxcuVKj8TFtp9tP/kvSYhaJlQnIp/z559/omPHjrBYLFi9ejXuuusupUOqkwEDBmDLli1IS0vDqVOnlA6HiIioQX744Qd8/fXXeP311wFUdhrk5eVBo9EoHJnv2rx5szwdwbJlyxxuFiAiIqLAw7afKDDwGw6Rn9izZw/Gjh2LzMxMWCwWtG/fHrfffrvSYREREQWV++67D6dPn5YfP/PMM+w0ICIiIiKigMOpioj8RFlZGY4fPw6gctjd+vXr5YV5iIiIyDskSUJ0dDSuu+46fPLJJ/j3v/+tdEhERERERERux6mKiIiIiIiIiIiIiIhIxhEHREREREREREREREQkY8cBERERERERERERERHJ2HFAREREREREREREREQydhwQEREREREREREREZGMHQdERERERERERERERCRjxwEREREREREREREREcnYcUBERERERERERERERDJ2HBARERERERERERERkSzoOg6EENDr9RBCKB0KERERKYx5AREREdkxLyAiIrpIo3QA3lZSUoLY2FjodDrExMQoHY5PEEJAp9MhNjYWkiQpHY5PYh3VjvXjWn3qqKysDH/++ScaN26MkJAQL0WoLCEEDAYDoqKi+B6qgRACiYmJSocRcJgXVMfPdNdYR7Xz9/rxRjvMdq92rB/XmBd4BvOC6vz9M90bWEe1Y/24xjqq3aX1E4zXTFzxVF4QdCMOiIh8nV6vx65du1BaWqp0KEREREGH7TARERGR72Ku5j1BN+KAiMjXJSQkYPTo0dBo+BFNRETkbWyHiYiIiHwXczXvYQ0TEfkYlUoFrVardBhERERBie0wERERke9iruY9nKqIiMjHlJSUYPfu3Rx2R0REpAC2w0RERES+i7ma97DjgIjIx9hsNhiNRthsNqVDISIiCjpsh4mIiIh8F3M17+FURUREPiY2NhbXX3+90mEQEREFJbbDRERERL6LuZr3cMQBERERERERERERERHJ2HFARORjCgoK8Pnnn6OoqEjpUIiIiIIO22EiIiIi38VczXvYcUAAgJiYGKVD8HmsI/KWiIgIdOnSBREREUqHQkRBim2ea6yjwMV2mIjIEds811hHRN7DXM17uMYBEZGPCQ8PxxVXXKF0GEREREGJ7TARERGR72Ku5j2KjjjYunUrRo4ciaZNm0KSJKxbt87lPlu2bEGPHj0QFhaG1q1bY/HixZ4PlIjIi0wmE3JycmA2m5UOhYiIKOiwHSYiIiLyXczVvEfRjoPS0lJ07doV77zzTp3Knzx5EsOHD0e/fv1w4MABzJw5E1OnTsVnn33m4Ujd4/z58xg0aBBiYmJw5513Kh2OT5MkCQcPHlQ6DNnmzZuRkJCgdBgUJPR6PXbs2AGDwaB0KETkQcwL6o55AXkT22EiUgLzgrpjXkAU3JireY+iHQfDhg3D3Llzcdttt9Wp/OLFi5Gamor58+ejY8eOuP/++3Hffffhtdde83CklQYMGIDQ0FBERUUhISEB/fv3x969e+u8/3vvvQe1Wo3i4mKsWbPGg5EGlwkTJkCSJHzwwQcO29esWQNJknDLLbcoE9glsrOzMXz4cERGRiI1NRXvv/9+reW3b9+O3r17IzY2Fs2aNcPjjz8Om80GADAajRgwYAAaN26MmJgYdOjQAe+99543ToO8ICEhASNGjEBsbKzSoRBRLZgX+KZAzAu2bduGqKgohx+VSoWpU6cCYF7gbmyHiaghmBf4Jn/JCx588EG0b98eKpUK8+fPd1neVR5R3+MR+RPmat7jV4sj79y5E0OGDHHYNnToUOzdu9drw1NefvllGAwG5Obm4tprr61zpwdQOWKiU6dOUKkaVu0Wi6VB+wWD9u3bY9myZQ7bli1bhg4dOigUUXV/+9vf0KRJE+Tl5WHNmjWYPn06tmzZ4rSs1WrF6NGjMXr0aBQWFmLHjh1Ys2aNfBFAo9Hg7bffRnZ2NvR6PT7//HM8/fTT2LZtmzdPiTxEpVIhPDy8wZ8VROQ9zAt8U6DlBf369YPBYJB/Tpw4AbVajbvvvhsA8wJ3YztMRA3FvMA3+UNe0LVrVyxcuBC9evWqU3lXeUR9j0fkT5ireY9f1XBubi6Sk5MdtiUnJ8NisSA/P9/pPkajEXq93uEHAIQQ9f6pup9Wq8W4ceNw5swZ5OXlQQgBm82GN998Ex06dEBcXBwGDBiAI0eOQAiBO++8Ex9++CEWLlyIqKgofPDBBxBCYNOmTejVqxfi4uLQqVMnrF+/Xn6NCRMm4L777sNdd92FmJgYLFy4ECaTCU8//TTatGmDxMREjBo1CllZWfI+kiRh0aJF6Ny5M2JiYjBq1CgUFxfLzx8/fhyjRo1Co0aNkJCQ4JDInDhxAiNHjkSjRo2QlpaG559/Hlar1WldLFu2DN26dcOMGTOQmJiI1NRULFiwwKHMqlWr0KVLF8TFxeGaa67Bjh075Of++9//onPnzoiOjkZqaiqeeuop2Gw2p3V97NgxtGnTBm+//XaN/y833ngjMjMz8fvvv0MIgaysLPz8888YNWqUw7GmT5+OtLQ0REdH48orr8T//vc/h2Pt3bsXgwYNQkJCAho1aoR//etf8msAwAcffIAWLVogMTER06dPr/N7588//8T27dvx4osvIiIiAr169cI999yDpUuXOi1fXFyMwsJCjBs3DiqVCmlpaRg8eDB+++03CCGgUqnQuXNnqNVqOT5JkvDHH3806L3NH9/6KSkpwd69e2EwGBSPhT++9UO+KyQkBOPHj8eZM2dw/vx5AJVtz1tvveWQFxw9ehQAquUFS5YsAQB89913DnnBF198Ib/GhAkTMGnSJDkvWLRoEcxmM2bPnu2QF2RnZ8v7SJKExYsXO+QFOp1Ofv6PP/6oc14wd+5ceeTbpZYvX45u3bph5syZcl6wcOFChzKffPKJQ17w008/yc+tWLHCIS94+umna3zPHz9+HG3atKl1qsshQ4YgMzMTx44dA1B5V97PP/+M0aNHO5R7/PHHHfKCS+/w3Ldvn0Ne8PDDDzs8XzUvePzxx2uM51InTpzA9u3bMW/ePERGRuLaa6/F3//+dyxdurRO+3/44Ydo27Yt0tPTAQBqtRpXXXUVNBoNgMr/d0mS8Oeff9Y5JrrIYDBg7969KC0tVToUIvJTzAuYF9QnLwCAf/7znxg8eDDCwsJclq1LHlGf4xH5G+Zq3qNROoD6kiTJ4bG98bh0u928efMwZ86catt1Ol2dLsLExMRU2yaEQHl5OZYsWYKkpCTExcVBCIFFixZh6dKlWL9+PVq1aoVFixZh1KhROHToEFavXo2JEyciLi4Ob7zxBgDgl19+wV133YU1a9ZgwIAB+OmnnzBy5Ejs2rUL7du3hxACn3zyCT777DOsXLkSFRUVmDVrFvbv34+tW7ciMTERs2bNwt/+9jds3rxZju9///sfNm3ahNDQUNxwww1444038Mwzz6C0tBQ33ngj7rnnHqxYsQJarVa+mF9WVoYbbrgBU6dOxZo1a5Cbm4ubb74ZTZo0waRJk5zWwaFDhzB8+HBkZWVh3759GDZsGDp16oTrr78eGzZswPTp07Fu3Tp069YN69atw6hRo/D7778jMTER8fHx+PTTT9G2bVv88ssvGDZsGNq3b4+///3vDq+xe/du3HHHHXjttddw1113Of0/E6LyQvq9996LZcuWYd68efjwww9x5513IiwszOGCW5cuXfDoo48iMTERa9aswbhx49CjRw+0atUKWVlZGDx4MF588UV89dVXsNls2Ldvn7x/SUkJfvvtNxw7dgwnT55Er169MGzYMAwYMABA5VCpL774Atddd121GH/55RekpKSgcePGcixdu3bF4sWLnZ5TfHw8Jk6ciCVLluCJJ55AZmYmvv/+e7z11lsO5UeOHInvv/8eRqMRXbp0wS233FLtePbOMk8SQshzy9X0txjs6lNHOp0ORUVFcsdBsCgvL+f7x4WkpCSlQ6AaVM0L4uPjAQCLFi3CkiVL8OWXX6JVq1ZYuHAhRo4ciSNHjmDNmjWYMGEC4uLi5OHjv/76K+6880589tlncl4wYsQI7NmzB+3btwcArFq1CmvXrsUnn3wi5wX79u3D9u3bkZiYiJkzZ+Luu+/G1q1b5dhWr16N77//HqGhoRg0aBDeeOMNPPvssygtLcUNN9yAv//971i1apWcF9jP54YbbsC///1vfPbZZ8jNzcXw4cORkpLiNC8AgEOHDmHEiBHIycnBvn37MHToUHTu3FnOCx577DF88cUXcl4wcuRIHD9+HImJiUhISMDnn38u5wVDhw5Fhw4dHPICANizZw9uu+02vP7667jrrrtq/P9QqVQYO3Ysli1bhpdeegkffvgh7rrrrmpfnrt27YrHHntMzgvGjh2Lnj17ynnBoEGDMG/ePGzYsEHOC+zsecEff/yBkydPomfPnhg+fLicF8TFxeGrr75ymhf8+uuvSElJcbghplu3btUuqtRk6dKleOCBB6ptv/nmm/Hdd9/JecGtt95ap+ORI6vVCr1eX+MFMSIiV5gXMC+oT15QX5ebRxD5O+Zq3uNXHQdNmjRBbm6uw7a8vDxoNBokJiY63WfGjBnIyMiQH+v1erRo0QKxsbFOOwVcmTlzJubMmQO9Xo/k5GR8/vnn0Gq1AICFCxfihRdeQLt27QAAU6dOxSuvvII9e/agX79+8gUx+7/vvfcexo8fj8GDBwOoHIZ+8803Y82aNXj66achSRKGDBmCm266CQAQERGBRYsWYfv27WjatCkAYO7cuYiKisLZs2fRokULAJW95E2aNAEA3Hbbbdi9ezckScLXX38NrVaLF154QY5h0KBBAICvv/4a8fHxeOSRRwAAaWlpmDp1Kj755BPcf//91epBkiRERkbi2WefhVarRXp6Ou655x58/PHH6N+/PxYtWoTHHnsMPXr0AADcfvvteOONN/B///d/GDt2LIYPHy4fq3v37rj77ruxZcsW3HvvvfL2b7/9Fm+//TY+/vhjOU5n7OcyceJEDBw4EHPnzsWHH36I//73v/jqq6/ku+4AOBz/b3/7G1555RXs3LkTrVu3xooVK9CjRw9MmTJFLnP99dfLryGEwLx58xAeHo4rr7wS6enp2L9/PwYOHAgAKCoqqjHG0tJSxMXFOVwUjY+PR0lJSY0XSu+88048+OCDeO6552C1WvHPf/4TN998s0OZr776ClarFdu3b8eWLVsQERFR7XjemPPNfnE7NjaWF35rUJ86ioyMxA033OCNsHyGvYMuMjKS76EaBFMnkj+ZMWMGnn32WTkvWLt2rXzX94IFC/Diiy+ibdu2ACrzgpdffhm7d+9Gv379qh3r3XffxYQJE+Q277rrrsPNN9+M//3vf3j66acBVN4xN3ToUABAeHg4Fi5ciB07diAlJQVAZV4QGRmJM2fOyHnBb/SQpQABAABJREFUE088IX+xvP3227Fr1y4AlW3IpXnBwIEDIYTAV1995ZAXpKam4t///jdWrlxZ4wWCqnlBnz598Pe//x0fffQRrr/+eixYsADTp0/H1VdfDaAyP/nPf/6DDRs2YOzYsRg2bJh8nG7dusk3RVS9QPDNN9/grbfewn//+99a8wK7iRMnYsCAAZg7dy6WL1+OFStW4KuvvnIoU/X4d999N1566SX89NNPaNWqFf773/9Wywuq/r/Z84KwsDB07NgR6enp2Ldvn3yBoLi4uMbYDAYD4uLiHLbFxcWhpKTE5Xlt27YNf/31F8aNG1ftuUvzgvDwcJfHo+piY2Pr9B4jIroU84KLmBfUPS+or8vJI4gCAXM17/GrqYr69OmDTZs2OWzbuHEjevbsKV+8v1RoaChiYmIcfoCLQ7jr8wNUjmAoLi7GmTNn0LRpU/z666/y86dOncLYsWMRHx8v/xQVFSErK8uh08D+c/r0abz77rsO5devX4+cnBy5fGpqqly+oKAApaWl6N+/v1w+JSUFISEhOHv2rLxPSkqKvE9UVJR8cTozMxNt2rSBSqWqdl6nT5/GoUOHHGJ57LHHkJubW2N9NG3aFCEhIfLjli1bIjs7W66LWbNmORzv4MGD8vMbN25E37590ahRI8TFxeHdd99FQUGBQ0xvvvkmBg4ciMGDB7v8f5EkCe3atUOrVq3wzDPPICQkBNdcc021ep8/fz46d+6MuLg4xMfH49ChQ/LrZmZmom3btjW+RkxMjHxh3t5xYjAY6vTeiY6Ohk6nc9im1+sRHR3ttPzx48dx66234o033kBFRQWys7Px+++/Y+bMmdXKajQaDBgwAHl5eXjttdca9N7mD3/44x8/5HsuzQt++eUX+blTp07h3nvvRVxcnPxTVFSEs2fPOj3WqVOnsHjxYofy69evd5hiIDU1Vf49Pz8fpaWluP766+XyTZo0QUhICM6cOSOXs99MAFR+ibd/qTx9+jTatGnj9L1lzwuqxvLoo49Wu4GjqqZNmzrkY2lpacjKypLPbebMmQ7HO3jwoPz8t99+i/T0dCQlJSE2NhaLFy+uNg3l/PnzMXDgwDp/SWjbti1atWqF2bNnIyQkBD179qxW5o033kCnTp0QGxuLuLg4HDp0SH7d06dPyxd3nLHnBXZV69aVqKgoh6khgMrRZtHR0S73XbJkiTyNhDNqtRr9+/fHuXPn8Oqrr9YpHiIicg/mBRcxL6h7XlBfl5NHEBHVh6IdBwaDAQcPHsTBgwcBVC4GdPDgQWRmZgKo7K2vejfV5MmTcfr0aWRkZODo0aNYunQplixZgscee8zrsTdr1gzvv/8+nnjiCbnhbtGiBdasWYPi4mL5p6ysDH/729+cHqNFixb497//7VDeYDBg0aJFcpmqC30kJiYiIiICu3fvdtinvLxcnuO2NmlpaThx4oTTu1abN2+OHj16OBxXr9fj8OHDNR4vOzvbYVHqzMxMNGvWTD63//znPw7HKy0txZNPPgmTyYTbbrsNDz30ELKysqDT6TB58uRqca1cuRJHjx6tts5AbSZOnIiXXnoJEydOrPbc9u3b8eyzz+Kjjz5CUVERiouL0blzZ/nYaWlpHpsLuEuXLsjOzkZeXp687eDBg7jqqquclv/tt9/QvHlz3HHHHdBoNEhJScH48ePx5Zdf1vgaZrMZf/zxh9tjJ+8rKCjA+vXr3XpXChF5FvMC5gX1Ud+8wE6v12PNmjVOR4NeinlBw7EdJqLLxbyAeYEnNTSPIAoUzNW8R9GOg71796J79+7o3r07ACAjIwPdu3fH7NmzAQA5OTlyJwIAtGrVChs2bMDmzZvRrVs3PP/883jrrbdw++23KxL/1VdfjQEDBuDFF18EULn4zOzZs+UFd/R6PdavX19jL/NDDz2EZcuW4ccff4TVaoXRaMTOnTvlBZIupVKpMHnyZDz66KPyHQMFBQVYvXp1neIdMWIEjEYjZs+ejdLSUphMJvz4448AKufEPXfuHBYuXIiKigpYrVYcO3bMYe2ES5WWluL555+HyWTC7t27sWLFCnlo37/+9S+8+uqr8hoBZWVl+O6773D27FkYjUZUVFQgMTERoaGh2L17N1auXFnt+AkJCfj++++xa9cuTJkypU7JwD333IONGzc6nfdXr9dDo9GgUaNGsNlsWLp0KQ4dOiQ///e//x179uzB4sWLYTQaUVZWhm3btrl8zbpo06YN+vbti5kzZ6KsrAx79uzBihUrahzW2aNHD2RnZ2PdunWw2Ww4f/48Pv74Y/lv5eDBg9i0aRPKy8thsVjw9ddfY8WKFfIwVfJvERER6NChAxeyIvIzzAuYF9RVffMCu1WrViExMRFDhgxx2M68wL3YDhOROzAvYF5QHyaTCRUVFbDZbLBYLKioqIDFYnFati55RH2OR+RvmKt5j6IdBwMGDJDnta76s3z5cgDA8uXLqzVE/fv3x/79+2E0GnHy5ElMnjzZ+4FXMWvWLHzwwQc4c+YM/vWvf2HChAm47bbbEBMTg44dOzpt4Oy6d++OVatW4amnnkKjRo3QrFkzPP300zAajTXuM2/ePPTp0weDBg1CdHQ0evTogY0bN9Yp1qioKHz33XfYt28fUlNTkZKSggULFsjPbdq0Cd9//z1atmyJxMRE3HPPPbUOPezcuTMsFgtSUlJwxx134IUXXoB9vv+bb74ZL730Eh544AHEx8ejVatWePPNN2Gz2RAdHY0FCxbgwQcfRExMDF544QWMGTPG6WvEx8fju+++w/79+/Hggw+6TAbCw8Nxww03OB2id9NNN+H222/HVVddhaZNm+Lw4cPo27ev/Hzz5s3x3XffYeXKlUhOTkbLli3x6aefuqxXu6ioqFoTh1WrViErKwuNGjXC7bffjldeeQX9+/eXn+/UqRNWrFgBoLKT7JNPPsFzzz2H+Ph4dO7cGY0bN5YX1rZYLJg5cyaSk5PlRa9ef/113HPPPXWOl3xXeHg42rdvz0aQyA8xL2BeYOfOvMBuyZIlmDhxosMdpgDzAndjO0xE7sK8gHmBnau8YMiQIQgPD8e2bdswffp0hIeHY+7cufLzl+YFrvIIV8cj8mfM1bxHEkG22qJer0dsbCx0Ol2DFkcORPYOm/rMob18+XLMnz9fnmYq0DWkjoKJEAI6nY6LI9eiPnVUXl6O06dPIz4+vsb1WwKNEAIGgwFRUVF8D9VACIHExESlwwg4zAuqY17gGvOC2vl7XuCNdpjtXu1YP64xL/AM5gXVMS9wjXlB7fw9L/AG1lHtLq2fYLxm4oqn8gK/WhyZiCgY6HQ6bN26FQaDQelQiLxq69atGDlyJJo2bQpJkrBu3bpay3/++ee48cYb0ahRI8TExKBPnz749ttvvRMsEQUstsNEREREvou5mvew44CIyMfExcXhpptu4l1OFHRKS0vRtWtXvPPOO3Uqv3XrVtx4443YsGED9u3bh4EDB2LkyJE4cOCAhyMlokDGdpiIiIjIdzFX8x5OVUQcVlcHrKPacVida/WpI4vFAr1e76XIfAOnJHAt2KYkkCQJa9euxS233FKv/Tp16oQxY8Zg9uzZdSrPvKA6tnmusY5q5+95gTfaYbZ7tWP9uBZseYG3MC+ojm2ea6yj2vl7XuANrKPaXVo/wXjNxBVP5QUatx+RiIgui8FgwMGDB9GuXTtEREQoHQ6R37DZbCgpKUFCQkKNZYxGo8OigvaE0/6FjxyxTlxjHVVn/3vy17opKSnBgQMHPNoO+3sdeRrrxzXWDRERBSteM/EedhwQEfkYs9mM8+fPo3Xr1kqHQuRX/vOf/6C0tBR33XVXjWXmzZuHOXPmVNuu0+nqdBEmWO4+5AUp1/y1jrxxd5b9bnEAfnnXXHFxMc6dO4cmTZrAZrN57HXKy8v9sn68hfXjWlJSktIhEBEReR2vmXgPOw4IwMUhmVQz1hF5S3x8PG688UalwyDyK6tWrcKzzz6L9evXo3HjxjWWmzFjBjIyMuTHer0eLVq0QGxsbNB0CrgihJDbPF60c86f68gbuYy9U8Uf6wcAIiMjMXToUI++hv1u+sjISL+sI09j/bjmr52X5J/4Xdg11hGR9/Caifew44CIiIj82urVqzFp0iSsWbMGN9xwQ61lQ0NDERoaWm0756R1ZK8P1knNWEe18+f68Vbc/lxH3sD6ISIiIlKWSukAiIjIUWFhIb7++mvodDqlQyHyeatWrcKECROwcuVKjBgxQulwiCgAsB0mIiIi8l3M1byHIw6IiHxMWFgYWrZsiZCQEKVDIfIqg8GAP//8U3588uRJHDx4EAkJCUhNTcWMGTOQlZWFjz76CEBlp8G4cePw5ptvonfv3sjNzQUAhIeHc6g4ETUY22EiIiIi38VczXs44oCIyMdERESgU6dOCA8PVzoUIq/au3cvunfvju7duwMAMjIy0L17d8yePRsAkJOTg8zMTLn8u+++C4vFgn/+859ISUmRf/79738rEj8RBQa2w0RERES+i7ma93DEARGRjzGbzSgsLERMTAw0Gn5MU/AYMGBArYs9Ll++3OHx5s2bPRsQEQUltsNEREREvou5mvdwxAERkY/R6XT44YcfUFJSonQoREREQYftMBEREZHvYq7mPeyWISLyMXFxcbjhhhsQHR2tdChERERBh+0wERERke9iruY97DggIvIxGo0GcXFxSodBREQUlNgOExEREfku5mrew6mKiIh8TGlpKX777TeUl5crHQoREVHQYTtMRERE5LuYq3kPOw6IiHyMyWTC2bNnYTKZlA6FiIgo6LAdJiIiIvJdzNW8h1MVERH5mPj4eAwbNkzpMIiIiIIS22EiIiIi38VczXs44oCIiIiIiIiIiIiIiGTsOCAi8jFFRUX45ptvoNPplA6FiIgo6LAdJiIiIvJdzNW8hx0HREQ+JiQkBE2bNoVWq1U6FCIioqDDdpiIiIjIdzFX8x52HBAR+ZjIyEh06dIFERERSodCREQUdNgOE/mGrVu3YuTIkWjatCkkScK6detc7rNlyxb06NEDYWFhaN26NRYvXuz5QImIyKuYq3kPOw6IiHyMxWKBTqeD1WpVOhQiIqKgw3aYyDeUlpaia9eueOedd+pU/uTJkxg+fDj69euHAwcOYObMmZg6dSo+++wzD0dKRETexFzNe9hxQETkY4qLi7Fp0ybo9XqlQyEiIgo6bIeJfMOwYcMwd+5c3HbbbXUqv3jxYqSmpmL+/Pno2LEj7r//ftx333147bXXPBwpERF5E3M179EoHQARETmKjY3FwIEDER0drXQoREREQYftMJF/2rlzJ4YMGeKwbejQoViyZAnMZrPTubCNRiOMRqP82H4RSggBIYRnA/YT9rpgfdSMdVQ71o9rrKPaXVo/MTExGDBgAKKiolhnF3iqHthxQETkY7RaLRITE5UOg4iIKCixHSbyT7m5uUhOTnbYlpycDIvFgvz8fKSkpFTbZ968eZgzZ0617TqdjhejLhBCwGAwAAAkSVI4Gt/EOqod68c11lHtLq0fq9WK0NBQVFRUKByZb0lKSnL7MdlxQETkY8rKynD06FG0bNkS4eHhSodDREQUVNgOE/mvSy+42S/+13QhbsaMGcjIyJAf6/V6tGjRArGxsYiJifFcoH7EXoexsbG8oFkD1lHtWD+usY5qZyg3Y8GXfyKr2AgJgNVqQ0VFBUJCQiCpWF8AAAGszEhz+2HZcUBE5GMqKipw4sQJpKSk8IIFERGRl7EdJvJPTZo0QW5ursO2vLw8aDSaGkcRhYaGIjQ0tNp2SZJ48a4Ke32wTmrGOqod68c11lHNFn57HFt/L3DyDEcceBo7DoiIfExCQgJuvvlmpcMgHyKEgLU0H+ZQG7RRjZQOh4gooLEdJn8gbFZYyouhCY9TOhSf0adPH3z55ZcO2zZu3IiePXs6Xd+AiIh83y+nirD+57MAAEkCQjSqyic4m5wDT1UHOw6IiIh8lBACtrICWA15sFlMEDFRSodEREREPsBSfAYipCkQwB0HBoMBf/75p/z45MmTOHjwIBISEpCamooZM2YgKysLH330EQBg8uTJeOedd5CRkYEHHngAO3fuxJIlS7Bq1SqlToGIiC6DyWLDy2sPyY//PaID7kpvCYvFIi9mT5U8tS6PyiNHJSKiBisuLsZ3333HhjCICSFgLSuAOe93WHRZEFaz0iEREQUNtsPk6ywlubBV6JQOw+P27t2L7t27o3v37gCAjIwMdO/eHbNnzwYA5OTkIDMzUy7fqlUrbNiwAZs3b0a3bt3w/PPP46233sLtt9+uSPxERHR5/rvlL5w6XwoAaJcSiduuTQXAXM2bOOKAiMjH2Odh1Wj4ER2MrGVFsBrOQViMSodCRBSU2A6TL7OV62AtOad0GF4xYMCAWu+gXL58ebVt/fv3x/79+z0YFREReUNWYRk+3HwCAKBWSZg6tDXUFxZCZq7mPaxhIiIfExUVJd9ZRcHDVq6DpSSHHQZERApjO0y+ymYug6U403VBIiIiP/fhjydgtlZ2Ho/pm4bWjSPl55ireQ+nKiIi8jFWqxWlpaWwWq1Kh0JeYDOWwJz/B8xFp9hpQETkA9gOky8SVjMshachhE3pUIiIiDwqq7AM/3cgGwAQHabBuP6tHZ5nruY97DggIvIxRUVF+L//+z/O1xfgbOYymAtOwFzwF2ymMqXDISKiC9gOk68RQsBSdBrCalI6FCIiIo/7aPNfsNoqRxvc1bclosO1Ds8zV/MeTlVERORjYmJi0K9fP0RFRSkdCnmAsBhh0ecExaKGRET+iO0w+Rqr7ixsplKlwyAiIvK4rMIybNifBQCICtPgrvS0amWYq3kPOw6IiHxMSEgIkpOTlQ6D3ExYzbAazsFaVgjUstAfEREpi+0w+RKr4Xxl7kBERBQEqo42GHNhtIG45PszczXv4VRFREQ+pry8HMePH0dFRYXSoZAbCJsVlpJcmPN+h7W0gJ0GREQ+ju0w+QpbhR6WkhylwyAiIvKKswWuRxsAzNW8iR0HREQ+pqysDEeOHGEj6OeEELCW5sN8/ndYS85xMUMiIj/Bdph8gTBXwFJ8mjccEBFR0Hh/0x8X1zZIT6u2toEdczXv4VRFREQ+JjExEbfccovSYdBlsFXoYNHnQFiMSodCRET1xHaYlCZsFpiLTkHYeNMBEREFh2PZemz6tXKUXVyEFn+7rlWNZZmreQ87DoiIiNzEZi6DVZ8Dm9GgdChERETkh4QQsBSd5s0HREQUVBZ/e1z+ffzANogM4yVrX8CpioiIfIxOp8OPP/6IkpISpUOhOhJWMyzFmTCf/4OdBkREfo7tMCnJqjvLXIKIiILKvhMF2P1HPgCgSVwYbr02tdbyzNW8h903REQ+RqVSISoqCioV+3Z9nRA22AznYS3N43QCREQBgu0wKcVamg9rWaHSYRAREXmN1SawsMpogwduaIsQTe05GHM172HHARGRj4mOjsY111yjdBjkgrW8GFZ9DoTVpHQoRETkRmyHSQm2Cj0s+mylwyAiIvKqT3eextGzOgBA6+QoDOnW1OU+zNW8hx0HREQ+xmazwWg0QqvVsgfdB3EdAyKiwMZ2mLxNmCtgKT4NCKF0KERERF5zJr8UizdeHG3w6KgroVZJLvdjruY9itfuwoUL0apVK4SFhaFHjx7Ytm1breVXrFiBrl27IiIiAikpKZg4cSIKCgq8FC0RkecVFhbiyy+/hE6nUzoUqkLYLLDoznIdAyKiAMd2mLxJ2CwwF57klIdERBRUrDaBFz77DUZzZft3Z580dG+VUKd9mat5j6IdB6tXr8a0adMwa9YsHDhwAP369cOwYcOQmZnptPz27dsxbtw4TJo0CYcPH8aaNWvw888/4/777/dy5EREnhMdHY309HRERkYqHQoBEELAWpoPc97vsJayo9qTtm7dipEjR6Jp06aQJAnr1q1zuc+WLVvQo0cPhIWFoXXr1li8eLHnAyWigMZ2mLxFCAFL4SlOe0hEREHn052n8evpYgBA04RwTB7ats77MldzTljNbj+moh0Hr7/+OiZNmoT7778fHTt2xPz589GiRQssWrTIafldu3ahZcuWmDp1Klq1aoXrrrsODz30EPbu3evlyImIPCc0NBRNmzZFSEiI0qEEPZupFOb847DosiBsVqXDCXilpaXo2rUr3nnnnTqVP3nyJIYPH45+/frhwIEDmDlzJqZOnYrPPvvMw5ESUSBjO0zeYtWdgc1UqnQYREREXnW2wHGKolm3XYXwkLrPps9czZHNXA5z/p+wlBa6/diKdRyYTCbs27cPQ4YMcdg+ZMgQ/PTTT073SU9Px9mzZ7FhwwYIIXDu3Dl8+umnGDFihDdCJiLyivLycvz1118wGo1KhxK0hM0CS/EZmPP/hDBXKB1O0Bg2bBjmzp2L2267rU7lFy9ejNTUVMyfPx8dO3bE/fffj/vuuw+vvfaahyMlokDGdpi8wWrIg7WsSOkwiIiIvMpmE3jhs0PyFEV39E5F99Z1m6LIjrlaJSFssOhzYM7/w2M3Iii2OHJ+fj6sViuSk5MdticnJyM3N9fpPunp6VixYgXGjBmDiooKWCwWjBo1Cm+//XaNr2M0Gh3eSHq9HkDlsFDBxacAXKwL1kfNWEe1Y/24Vp86MhgMOHDgAOLi4oKmB92X3kPWsiJYS7IhrBalQ3HgC3Xja3bu3FntBoShQ4diyZIlMJvN0Gq11fZhXuCaL/09+irWUe38vX680Q77ex15WqDXj61cB7Mu+7KOEah1Q0REge2zXZn45VRlx3nT+HBMHtqu3scoLS3FgQMHEB8fj9DQUHeH6Bds5TpY9FkemZ6oKsU6DuwkyXG1bCFEtW12R44cwdSpUzF79mwMHToUOTk5mD59OiZPnowlS5Y43WfevHmYM2dOte06nY7J1gVCCBgMlQt91lT3wY51VDvWj2v1qaOQkBD5Yqh9n2BQXl6u7PvHZoGl5ByEuUy5GGrBNqu63NxcpzcgWCwW5OfnIyUlpdo+zAtc42e6a6yj2vl7/XirHVa83fNxgVo/wlIBa/FZCFxem8M2i4iI/M3ZgjIs+vbiFEUzb++MiND6X5pOSkrC7bff7s7Q/IawmGDRnYXNWOKV11Os4yApKQlqtbra6IK8vLxqFwHs5s2bh759+2L69OkAgC5duiAyMhL9+vXD3LlznV4gmDFjBjIyMuTHer0eLVq0QGxsLGJiYtx4Rv7LnnTGxsYGZHLuDqyj2rF+XKtPHVksvnWnuzfY7yqMjIxU5D1kLc2HtSwHWskGhCi6/E+NeIHAOWc3IDjbbse8wDV+prvGOqqdv9ePN9phpds9Xxeo9SOsZpjzT0OESAAu77yYFxARkT+x2QRe/Ow3VJgr1w68vXcqrm6dqHBU/kMIAVvpeVhLzkEIm9deV7GOg5CQEPTo0QObNm3CrbfeKm/ftGkTRo8e7XSfsrIyaDSOIavVagA1J06hoaFOh61IkhRQSejlstcH66RmrKPasX5cq2sd6fV6bN++Hd26dUN0dLSXolOeEu8hYTHCUnxxYUK+f/1LkyZNnN6AoNFokJjoPAllXlA3/Ex3jXVUO3+uH2+1w/5cR94QaPUjbFZYik4CNmvAnBMREVFdfb47EwcvTFGUEh+OfzRgiiI7nU4XVNdMbKZSWIrPQFi8v6aDolMVZWRkYOzYsejZsyf69OmD9957D5mZmZg8eTKAyrsCs7Ky8NFHHwEARo4ciQceeACLFi2SpyqaNm0aevXqhaZNmyp5KkREbiNJEjQaDb9UepBSvfXkXn369MGXX37psG3jxo3o2bOn0/UNiIjqgu0wuZsQApai0xDmCqVDISIi8rqswjIs/ObiFEUzbmvYFEV2wZKrCZsFVn0OrGWFisWgaMfBmDFjUFBQgOeeew45OTno3LkzNmzYgLS0NABATk4OMjMz5fITJkxASUkJ3nnnHTz66KOIi4vDoEGD8PLLLyt1CkREbhcTE4M+ffooHUbAspnLYS0+A5u5XOlQ6BIGgwF//vmn/PjkyZM4ePAgEhISkJqaWu2GgsmTJ+Odd95BRkYGHnjgAezcuRNLlizBqlWrlDoFIgoAbIfJ3az6bK/NRUxERORLbDaBeZ8dkqcouvXaFujZ5vKmKAqGXM1aVgRrSTaEVdmprBVfHHnKlCmYMmWK0+eWL19ebdvDDz+Mhx9+2MNREREpx2azwWKxQK1WB3wPujcJIWA1nIPVkAdwXmCftHfvXgwcOFB+bF+LYPz48Vi+fHm1GwpatWqFDRs24JFHHsGCBQvQtGlTvPXWW0G7UBYRuQfbYXIna2k+rKX5SodBRESkiLV7zmD/yco75pvEhWHKTe0v+5iBnKsJi/HC4scGpUMB4AMdB0RE5KiwsBBffPEFBg8ejPj4eKXDCQg2c1nlnICcIsCnDRgwoNbFHp3dUNC/f3/s37/fg1ERUbBhO0zuYqvQw6LPVjoMIiIiRWQXlmHhN8fkxzNvuwqRlzFFkV0g5mpCCNgMebAa8nxqOmV2HBAR+Zjo6Gj06tULkZGRSofi9zjKgIiI6ovtMLmDzVwOS/Fp5h9ERBSUbDaBFz8/hHJT5RRFt/RqgZ5XXN4URXaBlqspufixK+w4ICLyMaGhoUhNTVU6DL8nLCZYik/DZipTOhQiIvIjbIfpcgmrGZbCkxA237ljkIiIyJvW7TmD/X9VTlGUHBeGfw67/CmK7AIlVxM2K6wlObCWFigdSo1UDdlJCIH8/HwUFPjuiRER+Suj0YjTp0/DZDIpHYrfslXoYM4/zk4DL2FeQESBhO0wXQ5hs1Z2GljNSoeiGOYFRETBLaeoDAscpijq7JYpiuwCIVezletgPn/MpzsNgHp2HOTm5mLcuHGIj49HcnIyGjdujPj4eNx33304d+6cp2IkIgoqJSUl+Pnnn1FaWqp0KH7JWl4Mc+EpCJtV6VACHvMCIgpEbIepoYQQsBSdhs1crnQoimBeQEREQgi8+NnFKYpGX9Mc11yR5NbX8OdcTVjNMBeegrnolF/cZFDn7h69Xo/09HQYDAZMnDgRHTp0gBACR44cwapVq7B9+3bs378fUVFRnoyXiCjgJSYm4tZbb4VK1aBBYUFN2Kyw6rOUDiMoMC8gokDFdpgayqrPgs1YonQYimBeQEREALB+zxnss09RFBuGfw3r4PbX8NdczVqaD2tJjl9NZVjnjoM333wTarUahw8fRqNGjRyee+qpp9C3b1+89dZbmDlzptuDJCIKJpIkQa1WKx2GX7KW5EBYLUqHERSYFxBRoGI7TA1hNZz3+ekGPIl5ARER5RSV453/uzhF0ZO3dUZkmPuX1/W3XE1YjLDozsJmNCgdSr3VuWvm66+/xsyZM6slAQDQuHFjzJgxA19++aVbgyMiCkZ6vR4//fQTDAb/a1SUZDOVBfUXdm9jXkBEgYrtMNWXrVwHiz5b6TAUxbyAiCi4CSHw0tpDKLswRdHIns1xbVv3TlFk5y+5mhAC1pJzMJ8/7pedBkA9Og6OHz+O9PT0Gp9PT0/HsWPHanyeiIjqzuZHQ9d8gRACFt0ZpcMIKswLiCiQsR2murKZSmEpzlQ6DMUxLyAiCm5f7D2Ln/+svJGvcWwYHh7e3qOv5+u5ms1cBkv+H7CU5EII3461NvVa4yAuLq7G5+Pi4qDX690RExFRUIuJicF1112ndBh+xVZ6HsJcoXQYQYV5AREFKrbDVFfCYoSl6JRfXxBwF+YFRETBK7e4HG9v+F1+/OStnRAVpvXY6/lyriaEDdaSc7CWngeEUDqcy1bnjgMhRK2LTkiSBBEAFUJERP5FWEywlpxTOoygw7yAiIiCmbBZYC48ybWVLmBeQEQUnIQQmPf5IZQZL05R1Ltd9WnrgkHlKMQzEBaj0qG4Tb06Dtq1awdJkmp8noiILl9+fj6++OILDB48GPHx8UqH4/MsujO8008BzAuIKFCxHSZXhLDBUngqoC4MXC7mBUREwelLL09RBPheriZsVlhLcgJyzcU6dxwsW7bMk3EQEdEFUVFR6NGjByIiIpQOxedZy4r8dpEhf8e8gIgCFdth5azbm4szheUY27cZ1EoHUwtLUSZsplKlw/ApzAuIiIJPbnE53qoyRdETt3h2iiI7X8rVbBV6WHRZEFaT0qF4RJ07DsaPH+/JOIiI6IKwsDC0atVK6TB8nrBZYC3JVjqMoMW8gIgCFdthZfyZW4oPNlcuMpwQqcWoLrEKR1SdsJphKc7kTQtOMC8gIgouQgi8vPawPEXRiB7N0Ke9d6Yo8oVcTVjNsOiyYKvQKRqHp9U8CeElioqK8Pbbbztd0Ein09X4HBER1Y/RaERWVhZMpsDssXYXqz6b8woriHkBEQUqtsPKOHS2RP79TEG5gpE4ZzMaYM7/g50GNWBeQEQUXL7el4Xdf+QDAJJiQjF1eAevvbaSuZoQNlgNeTCf/z3gOw2AenQcvPPOO9i6dStiYmKqPRcbG4tt27bh7bffdmtwRETBqKSkBDt37kRpKYfA18RmLIG1rEjpMIIa8wIiClRsh5VxLOfiBfn8EmU7bYTNUplrGM7DUpwJ8/njMBf+BWE1KxqXL/NUXrBw4UK0atUKYWFh6NGjB7Zt21Zr+RUrVqBr166IiIhASkoKJk6ciIKCwJtzmohISXm6Crz59cUpip68tTOiwz0/RZGdUrmatawI5rxjsOhzIGzBsc5inTsOPvvsM0yePLnG5x966CF8+umnbgmKiCiYJSQkYNSoUYiN9b0h+r5ACBssuiylwwh6zAuIKFCxHVbG79nKdBwImxU2o6Hy7sHCUzCdOwJT7mGYC/6CRZ9duZ6SuRzg4r618kResHr1akybNg2zZs3CgQMH0K9fPwwbNgyZmZlOy2/fvh3jxo3DpEmTcPjwYaxZswY///wz7r///nq9LhER1UwIgZfWHkKpsXL0//CrmyHdS1MU2Xk7V7OZymDO/wOW4syAXcugJnXuODhx4gTatm1b4/Nt27bFiRMn3BIUEVEwU6lUCAkJgUpV54/ooGItOQdhMSodRtBjXkBEgYrtsPcVGkw4X6WzoLDUDKvNMxfqhcUEa1kRLLqzMJ0/BlPuIZgLTsCiz4GtQsdRBQ3kibzg9ddfx6RJk3D//fejY8eOmD9/Plq0aIFFixY5Lb9r1y60bNkSU6dORatWrXDdddfhoYcewt69e+v1ukREVLOv92dh1/EqUxSN8N4URXbeytWExQRLUWblVIWmMo++lq+q8+LIarUa2dnZSE1Ndfp8dnY2k2siIjcoKSnBnj170KlTJ0RGRiodjk+xmctgLT2vdBgE5gVEFLjYDnvfsRzHqQZsAigusyC2+qw3DSLMFbBWFMNWXsybDzzE3XmByWTCvn378OSTTzpsHzJkCH766Sen+6Snp2PWrFnYsGEDhg0bhry8PHz66acYMWJEja9jNBphNF58T9jXYRBCQHCUCYCLdcH6qBnrqHasH9f8pY7ydBV4q8oURU+M7oToMI3H4760fvR6PXbv3u2xXE0IG6wl52ArzYcQ/jElkaf+D+rccdC9e3esW7cOvXv3dvr82rVr0b17d7cFRkQUrGw2G8rKymALkjnz6koIAWvxWU4V4COYFxBRoGI77H1V1zewyzeYkeaGY1tLzsFSkuuGI1Ft3J0X5Ofnw2q1Ijk52WF7cnIycnOd/3+mp6djxYoVGDNmDCoqKmCxWDBq1Kha11aYN28e5syZU227Tqfz+Qt43iKEgMFQ+TcqSZLC0fgm1lHtWD+u+UMdCSHw4mfHYKionKJocKckdEoJgU7n+QWCL60fvV4Pg8EAg8Hg9s9qW0UJbGX5EDaLW4/raYp3HPzrX//C3XffjebNm+Mf//gH1Go1AMBqtWLhwoV44403sHLlSo8ESUQUTGJjYzFgwAClw/A5ttLzlXMMk09gXkBEgYrtsPdVXd/ArsBw+VMG2YwGdhp4iafygksvoAkharyoduTIEUydOhWzZ8/G0KFDkZOTg+nTp2Py5MlYsmSJ031mzJiBjIwM+bFer0eLFi0QGxvrdKHnYGS/GBUbG+uzFzSVxjqqHevHNX+oow37s/DzX8UAgMToUDx2axfEeGlB5EvrJzIyEtHR0W59DZu5DFZdNmym0gtXy/1r9LziHQe33347Hn/8cUydOhWzZs1C69atIUkSTpw4AYPBgOnTp+OOO+7wSJBERBTchMUIa8k5pcOgKpgXEBGRO1htAn/kllbbXmC4vDv9hNUMS/HpyzoG1Z2784KkpCSo1epqowvy8vKqjUKwmzdvHvr27Yvp06cDALp06YLIyEj069cPc+fORUpKSrV9QkNDERoaWm27JEk+e/FOCfb6YJ3UjHVUO9aPa75cR+d1FXiz6hRFt3RCbESIV2OoWj/urCdhNcNakgtrWaH8OnRRvbpPXnjhBezatQsTJkxA06ZN0aRJE0ycOBE7d+7ESy+95KkYiYiCSkFBAdauXYvi4mKlQ/EZFt1Zv5lbMJgwLyCiQMR22LtO55fDaKls4xtFX7wIUVB6eSMOLMWZEFb/mmbA37kzLwgJCUGPHj2wadMmh+2bNm1Cenq6033KysqqraNgH/nAaYeIiBpGCIGX1x2Wpyga2q0pruvYWNGY3JWrWUvzYT7/u9xpQNXVecSBXa9evdCrVy9PxOJVTByIyFdFRESgc+fOCAsLUzoUn2AtzYfNWH0KA/INgZIXEBHZsR32rqrrG6S3i8f6fZUjDC9nqiJLSS5zB4W4My/IyMjA2LFj0bNnT/Tp0wfvvfceMjMzMXnyZACV0wxlZWXho48+AgCMHDkSDzzwABYtWiRPVTRt2jT06tULTZs2dUtMRETB5psD2fjp2HkAlVMUPXJzB4UjuvxcTVhMsOjOMFeog3p3HAQKa2khEBurdBhERNWEh4ejbdu2SofhEyqHDeYoHQYREQURtsPedazK+gZ92ibgy/3nYBMNn6rIZizh9IYBYsyYMSgoKMBzzz2HnJwcdO7cGRs2bEBaWuWy2Tk5OcjMzJTLT5gwASUlJXjnnXfw6KOPIi4uDoMGDcLLL7+s1CkQEfm18/oKzP/qqPz48Vs6IcbLUxQ5czm5ms1ogKXoJISNMxrURdB2HJj0ORBN0iCp/GuxCyIKfCaTCbm5uUhMTIRW653FhnyVpfgMG3QiIvIqtsPe9fuFEQcalYR2TSKREBWC/BJTg6YqqlzXINN1QfIbU6ZMwZQpU5w+t3z58mrbHn74YTz88MMejoqIKPAJIfDy2sMouTBF0ZBuKein8BRFdg3N1YSw8RpDPQXtVXNhNcNiyFM6DCKiavR6PbZv3w6DIbiHzVnLimAzligdBhERBRm2w95jqLDgbGEFAKB14wiEaFRIurDOgb7cCrOl7l/shRBc14CIiMhNPtlxSp6iKCEqBI/c3FHhiC5qaK5mLTkHYTV5KKrAFLQdBwBg1p9jYklEPic+Ph7Dhw9HbBBPpyasZlj1WUqHQUREQYjtsPcczy2Vf2+fEgXAcYHkfEPdv9xbua4BERGRWxw+U4yF3xyXH8+6/SrE+sAURXYNydVs5jJYS897MKrAVO+Og0GDBjldtVqv12PQoEHuiMlrhM0Cc0mu0mEQETlQq9WIiIiAKoinUrPosiBsVqXDoDoIpLyAiAhgO+xNVdc3aN80EgDkEQcAcF5ft44DW4UOVo4m9wnMC4iI/Ju+3IynVx2E1SYAAPde3wp92jdSOCpH9c3VhBCwFp8FhPBwZIGn3tnw5s2bYTJVT+AqKiqwbds2twTlTZaS87BZOEyFiHyHwWDAvn37UFZWpnQoirCV62Cr0CkdBtVRoOUFRETB3g57k319AwDocGHEQdWOg/wS19/ThMUES/EZ9wdHDcK8gIjIfx08WYh/vLsbucWV0wh2SYvDgzc2bBFiT6pvrmYrzYfNXO7hqAJTnRdH/vXXX+Xfjxw5gtzci3fqW61WfPPNN2jWrJl7o/MCIWww63MQmpCmdChERAAAi8WC4uJiWCzBN5WasFlg0Z9VOgyqg0DNC4iIgrkd9iYhBI5f6DiIDdcgOTYUgONURedddBwIYYOl6BRHKfoA5gVERP6ruNSEBd8cw9f7Lk4XHBOuxZwxXaFR+94IzPrkasJigpWzzTRYnTsOunXrBkmSIEmS0yGG4eHhePvtt90anLdYSwtgi06GShumdChERIiLi8PgwYOVDkMRVn0O157xE4GcFxBRcAvmdtibsouNKKmovODfLiUKkiQBqN+IA6s+m3cQ+gjmBURE/sdmE/h6fxYW/N8x6MvN8vYOzWIw6/arkBwXrmB0NatPrmbRnYEQNg9HFLjq3HFw8uRJCCHQunVr7NmzB40aXZzfKiQkBI0bN4ZarfZIkJ4mhIBZl43QpNZKh0JEFLwsJljLiyApHQfVSSDnBURE5HlV1zfo0DRK/r1RTKj8e20dB9ayQlhLCzwTHNUb8wIiIv9yIrcEr64/jF9PF8vbIkM1mDykLW65NhVqlf9/M7eWFcJmNLguSDWqc8dBWlrlVD42W2D20ljKiqA1lUEVEqF0KEQU5AoLC7FhwwZcf/31iIuLUzocr7GUnodWEoDk/wlKMAj0vICIglewtsPedqzK+gbtUyLl32MjNNCoJVisosaOA5u5HFZdltPnSBnMC4iI/EO5yYKlP5zAJ9tPyQsgA8ANXZpg6vAOSIrx/dlY6pKrCasZVn22dwMLQHXuOKjq+PHj2Lx5M/Ly8qolBrNnz3ZLYEowFWchrLHvLfpBRMElLCwM7dq1Q1iY7zfY7mIzlkCYy4AQ35s/kVxzZ16wcOFCvPrqq8jJyUGnTp0wf/589OvXr8byK1aswCuvvII//vgDsbGxuOmmm/Daa68hMTGxQedCRBSM7bAS7AsjSwDaNbk44kAlSUiKCkGuzuh0jQNhs8BSdJrTDviwQL1eQETk77YdzcPrXx7BuQuLHwNA88QIPDbqSvRqm6RgZPVTl1zNos/mGkhuUO+Og/fffx//+Mc/kJSUhCZNmshzUQKAJEl+nQhYK/SwVuihDotROhQiCmIRERHo0KGD0mF4lVWfo3QI1EDuzAtWr16NadOmYeHChejbty/effddDBs2DEeOHEFqamq18tu3b8e4cePwxhtvYOTIkcjKysLkyZNx//33Y+3atW45PyIKPsHYDntbhdmKk3llAIDUpHBEhDpOYZMUXdlxUGq0otxkRXjIxectRZkQFqNX46W6C+TrBURE/iqnqBxvfHUU24/mydu0agnjBrTBvde3QqjWv6aSc5Wr2cp1sJUXey+gAFbvjoO5c+fihRdewBNPPOGJeBRnKs5CeBN2HBCRcsxmM/Lz8xEXFweNpkEDw/yKtayICxv6MXfmBa+//jomTZqE+++/HwAwf/58fPvtt1i0aBHmzZtXrfyuXbvQsmVLTJ06FQDQqlUrPPTQQ3jllVcuOxYiCl7B1g4r4cS5MthnR2ifElXt+UsXSG6RWLk4o6UkFzZjiVdipIYJ9OsFRET+xGK1YfWO01jy/Z+oMF+8+/6aKxLx2Kgr0SIpspa9fVdtuZqwWWDRn1UossBT7zkhioqKcOedd3oiFp9gM5XBUlakdBhEFMR0Oh02b96MkpLA/2IshIC1JFfpMOgyuCsvMJlM2LdvH4YMGeKwfciQIfjpp5+c7pOeno6zZ89iw4YNEELg3Llz+PTTTzFixIjLjoeIglcwtcNKqWl9A7tGVToO7NMV2Sr0sJac83xwdFkC/XoBEZG/+PV0ESa88xMWfHNM7jRIjA7FnDFdMX9iT7/tNABqz9Ws+hwIq0WBqAJTvW+hufPOO7Fx40ZMnjzZE/H4BHNxFtThcQ7DKomIvCUuLg5Dhw5FRETgL9ZuKyuAsDpf+JD8g7vygvz8fFitViQnJztsT05ORm6u886l9PR0rFixAmPGjEFFRQUsFgtGjRqFt99+u8bXMRqNMBovTnGh1+sBVHZiCSFq2i2o2OuC9VEz1lHt/L1+YmNjMWTIEERERHjsHPy9ji7X79mOHQeX1kNitFb+PU9nhM1cAXPR6aCtL2d8tS6C4XoBEZEv05WZsPCb4/hy78W77iUJuP3aVDw4pC2iwrS17O0farpmYjOWwFpWqFBUganeHQdXXHEFnn76aezatQtXXXUVtFrHN5x9ugB/ZrMYYS0tgCbKfxYGIaLAodFoEB0drXQYHidsVlgNvHPQ37k7L7i0014IUWNH/pEjRzB16lTMnj0bQ4cORU5ODqZPn47JkydjyZIlTveZN28e5syZU227Tqfz2Ysw3iaEgMFwYdFS3kThFOuodv5eP1arFZIkobzcs9PolZeX+2X9uMPv2ZV3CIZrVYgPscrvF7sQXJxOQWcohS7nHITV7NUYfZ2vtlnBcL2AiMgXnS0ow+bDuVix9SR0ZRfbzPZNY/D4LZ3QsXmsgtG5l7NrJsJmhaWYUxS5W707Dt577z1ERUVhy5Yt2LJli8NzkiQFTCJg0mVDHZEASVXv2ZyIiC5LaWkpfvnlF7Rt2zagRx1YS89zCGEAcFdekJSUBLVaXW10QV5eXrVRCHbz5s1D3759MX36dABAly5dEBkZiX79+mHu3LlISUmpts+MGTOQkZEhP9br9WjRogViY2MRE8M1joCLF6NiY2OD9qKmK6yj2vl7/eh0Opw4ccKj7bB9tEFkZKRf1tHlyC8xobC0sv1vmxKJmJjqN0tERFwcjSiZyxCutgJqfi+rylc7DgLleoGv1i8RkZ0QAn+dM2Dz4XPYeuQc/shxnLYnIlSNh25sh9t6p0KtCqxcw9k1E2tJDmcz8IB6dxycPHnSE3H4HGE1w2LIgzamidKhEFGQMZlMOHfuHFq1aqV0KB4jrGbYSs8rHQa5gbvygpCQEPTo0QObNm3CrbfeKm/ftGkTRo8e7XSfsrKyaothqdVqADV/4Q8NDUVoaGi17ZIkBd3Fu9rY64N1UjPWUe38uX7MZjPy8vLQunVrj8bvz3V0OY7nlsq/d0iJcnr+mioXOKzmCkgSF6n2F4FyvcBSkgfEBs7duUQUGGw2gSNnddhy+By2HDmHswVlTssNvqoJpo7ogEYxYV6O0DsuvWZiMxpgLS1QOKrA1OAMzGQy4eTJk2jTpk21L+2Bwqw/B01kEiR1YJ4fEfmm+Pj4agvEBhqrIQ/CZlM6DHIjd+QFGRkZGDt2LHr27Ik+ffrgvffeQ2ZmpjxP8owZM5CVlYWPPvoIADBy5Eg88MADWLRokTxV0bRp09CrVy80bdrUbedGRMElGNphJR2rur5B0yinZareGWlluuCX/P16gbnkHIS1JSS1/88FTkT+zWK14eDJImw5UtlZkK83Oi13ZfNYXN8pGQM6JSPVjxc+rouquVrlFEVnFI4ocNW7BS8rK8PDDz+MDz/8EABw/PhxtG7dGlOnTkXTpk3x5JNPuj1IpQibBeaSXITENVc6FCKigCEsJljLeDdAoHBnXjBmzBgUFBTgueeeQ05ODjp37owNGzYgLS0NAJCTk4PMzEy5/IQJE1BSUoJ33nkHjz76KOLi4jBo0CC8/PLL7j1JIiJym99zqnQcNKlDxwFnjPErgXK9QNisMBVnITSxpdKhEFEQMpqt2PNnAbYcPoftR/OgL6++zo9KArq1TED/Tsm4/srGSI4LVyBS5Vn12ZyiyIPqPVHkjBkz8Msvv2Dz5s0IC7s45OWGG27A6tWr3RqcL7CUnIfNwjcgEXlPUVERNmzYAJ1Op3QoHmEtyQU4b2zAcHdeMGXKFJw6dQpGoxH79u3D9ddfLz+3fPlybN682aH8ww8/jMOHD6OsrAzZ2dn473//i2bNmjX4fIiIAr0dVpLFasOJc5XTKiTHhiIu0vnd3JLl4tQLHHHgXwLpeoGltABWY6nrgkREblBmtOC7X3Pw1MqDGP7CD3ji4/3YsD/LodNAq5aQ3r4RZt7WGV/OGIR3HuiFO9PTgq7TwJ6rFeVlwVpWqHQ4Aa3eIw7WrVuH1atXo3fv3g7zUV555ZU4ceJEvQNYuHAhXn31VeTk5KBTp06YP38++vXrV2N5o9GI5557Dv/973+Rm5uL5s2bY9asWbjvvvvq/dp1IYQNZn0OQhPSPHJ8IqJLhYaGIjU1FSEhIUqH4nY2cxms5UVKh0Fu5O68gIhIaYHcDivtVH45jJbKnoAOKc6nURDCBpRfHJnIEQf+JdDyAnPxGaiTOygdBhEFqCKDCdt/z8PmQ7n4+UQBLE4avfAQNdLbN0L/Tsno064RIsP8b/o3dwsNDUVqi+ZQV+RfxiT8VBf1rt7z58+jcePG1baXlpbWe2Gv1atXY9q0aVi4cCH69u2Ld999F8OGDcORI0eQmprqdJ+77roL586dw5IlS3DFFVcgLy8PFoulvqdRL9bSAtiik6HSBuaiIkTkWyIiItC5c2elw/AIqz5X6RDIzdyZFxAR+YJAboeVVpf1Dawl56AWF++u5IgD/xJoeYHVWApLaQE0kYlKh0JEASK3uBxbj5zDlsPn8MupIticdJDHhGvR78rGGNApGT3bJCJUq/Z+oD4sIiICHZrH8aZEL6h3x8E111yDr7/+Gg8//DAAyI3/+++/jz59+tTrWK+//jomTZqE+++/HwAwf/58fPvtt1i0aBHmzZtXrfw333yDLVu24K+//kJCQgIAoGXLlvU9hXoTQsCsy0ZoUmuPvxYRkcViQVFREaKjo/1yMbma2IwG2IwlSodBbubOvEBJNovzRcaIKPgEajvsCxzWN0ip3nEgLCbYSvNRZYkDWDm9oV8JlLygKlNxFtThcZBUvHBHRA1zKs+ArUfOYfPhc/g9S++0TGJUCAZ0boIBnZLRtWU8NOp6zy4fNIz6fBScz0FUeAjrycPqnQnPmzcPN910E44cOQKLxYI333wThw8fxs6dO7Fly5Y6H8dkMmHfvn3VFkcaMmQIfvrpJ6f7fPHFF+jZsydeeeUVfPzxx4iMjMSoUaPw/PPPIzzcs/N5WcqKoDWVQRUS4dHXISIqLi7G999/j8GDByM+Pl7pcNzGWpKjdAjkAe7KC5RmMRQACY2UDoOIfECgtsO+4NiFjgOtWkLrxtW/V1n02RDChqrXADjiwL8ESl5QlbCaYdbnICSuudKhEJGfEELgeLYemw9Xjiw4dd75eiktEiPQv1My+l/ZGCnRQHxcnF+OzvImYTUjP+sPbPv1DPp1aYG4KM4O40n17jhIT0/Hjh078Nprr6FNmzbYuHEjrr76auzcuRNXXXVVnY+Tn58Pq9WK5ORkh+3JycnIzXU+lcVff/2F7du3IywsDGvXrkV+fj6mTJmCwsJCLF261Ok+RqMRRuPFuwj1+sqePSEERD3vXjEWnUVY47b12scf2OuivvURTFhHtWP9uFafOoqNjcWgQYMQFRUVMHVqK9fVurgc30Ou+WrduCsvUJqlrBBCCCbqRIS4uDgMHjwY0dHRSocSUPTlZmQXVX4va904AtpL7hC0GUtgq6hckFpT5SlnUziQ7wqUvOBSlpI8aCKTOH0xEdXIahP47XQRtlyYhii3uMJpubYp0RjQKRn9OyWjVeMoSJIEIQR0Op2XI/ZPxsLTiA7Xol+XFogK53pUntagsbdXXXUVPvzwQ7cEcOkX9Nq+tNtsNkiShBUrViA2NhZA5XRHd9xxBxYsWOB01MG8efMwZ86catsNFRZIGnO17bUqL0AIIqEOdb6Ql78SQsBgqLz7hxdMnGMd1Y7141p96shqtUKr1aKiwnmi4Y8sRWcgarllUAiBCnPl83wPOeerHQeAe/MCpQirGdbyYmgieHcxUbDTaDQcaeABx3Mu3kDQ4ZL1DYQQsOiy5MeqKrmAhSMO/E4g5AWXEkLAVHwWYY2uUDoUIvIh54rLcfBUEfb/VYjtR/NQVGqqVkaSgKtS4y6MLEhG0wTOZNJQltICWMt10KhVHGngJXXqONDr9YiJiZF/r429nCtJSUlQq9XVRhfk5eVVG4Vgl5KSgmbNmsmdBgDQsWNHCCFw9uxZtG1bfTTAjBkzkJGR4XAuLVq0QFSYBtHh2jrFWpVa6BEW27Te+/ky+8Wo2NhYXrCrAeuodqwf1+pTR3q9HqdPn0br1q09Pg2bN1jLCmFRW4Fa5h60109EiIrvoRr4UseBJ/ICX2Ax5LPjgIhQVlaGQ4cOoU2bNgHRDvuKY7Wsb2ArK4CostaMukoqwBEHvi9Q84JLWct1sJbroA6PdV2YiHyGEAJGsw0VZivKTVZUmKwoN1thvPBvhcmKcpMFFWZbld+tqDDZUGG2yPtcun+50Qp9ufMbktUqCT3aJGDAlcnod2UyEqNDvXzWgUdYLTCVnAUAlFeY8XtmPlo2iUNYCNej8qQ61W58fDxycnLQuHFjxNUw35Z9pIDVaq3TC4eEhKBHjx7YtGkTbr31Vnn7pk2bMHr0aKf79O3bF2vWrIHBYEBUVGWyefz4cahUKjRv7ny+wdDQUISGVv8DlSSpQRenbKaygLwj0V4fvGBXM9ZR7Vg/rtW1jkwmEzIzM9GiRQtERPj33QhC2GAz5NXpfcH3kP/wRF7gC6wVetjMFZyGgCjIGY1GuR1mx4H7VF0YuUOVjgNhNVdbB4lrHPiXQM0LnDEVn0VYWAzzVSI3EkLAZLHBeOHCfPmFi/SXXqyv/cL/xX0u3b/CbIU37sEK1arQu20jXN+pMfp2aIyYBtysTDUzG/KghQWSJMFoseDs+RI0TYxmx4GH1al2f/jhByQkJAAAfvzxR7e9eEZGBsaOHYuePXuiT58+eO+995CZmYnJkycDqBwtkJWVhY8++ggAcM899+D555/HxIkTMWfOHOTn52P69Om477776pXUt0nSQIiGZ6Dm4iyow7lgCRF5Rnx8PIYPH650GG5hKy2AsFYfrkn+zVN5gS+wlOZz8UOiIBdI7bCvsAkhT1UUF6FFo5iLcxJbS3IgbI7fzaqOOLBwxIHPC7S8oOPdL+C5743QaCwAKqcZAQD721Kl1UGlCQEkSd52aRn7BsnhOcmhbNWitT1Xbd8q2yQJUEmV03tJkgS1SoJKkiq3qaTKnwvPq6o8p1ZVllepJKilyuM437fyOWNFOSIjSqFWqS7sixr2laBSXfJ6VWKQ95V/l6C2l3fY5+IxLn0NSeLUpkowW2wXL8xfuIBvrHohv9qFe8uFO/Yr7+AvKTXCCkkuV7m/rfLufpPVb0aXSRIQrlUjLKTyp0lsOLq2jEe3VvHonBqHcF7E9gir0QBruQ640BkTFxWOG3q0Ujiq4FCnd3T//v2d/n65xowZg4KCAjz33HPIyclB586dsWHDBqSlpQEAcnJykJmZKZePiorCpk2b8PDDD6Nnz55ITEzEXXfdhblz59brddfcn4SKEz8itrvzkQ2u2CxGWEsLoIlKatD+RETBQNgssBrOKR0GeYCn8gJfYDEUQBvbjF9IiYjcKKuwAqXGyjvNOzSNlD9jbaYyWMuKqpWvOuLAxhEHPi/Q8oK4Vlfjl1wbgJrefPVcK5HcTu7cuLSzompHRZVOh4u/X+hccVb+ko4KlapKR4yzThLVxU4PSQIsZjPCw0Id96sSW9X9Lu1MufgagHThtap2ojjGfrHzx+lrVOkAMlltFy/kV71j38WF/6rl7M9Z/eXKPoDwEDVCtWqEh6gRVuXfsJALv1d9rL342P7cpfvL+2nVCNFwal1vE0LAVJTpuiB5RL27wpYtW4aoqCjceeedDtvXrFmDsrIyjB8/vl7HmzJlCqZMmeL0ueXLl1fb1qFDB2zatKler3EptUpC6YGVsLS7DprIxAYdw6TLhjoiAZKq5jm7iYgaoqioCN9//z169+7t3/PAGs5D2Px7ODq55u68QGnCZoG1rLDB+QER+b9AaYd9SU3rG1h1Z52Wd5iqyH+uVRECLy8g32QTgM0qAPADwh+FalX1unAfHqJBmFZ14TnNhfIqhIVoqu0fquWF/UBjKcmDzVTusE1nqMC2g6fRs30TREdw/QhPqnfHwUsvvYTFixdX2964cWM8+OCDfpMICHMZ8ne8iyZDZjZsf6sZFkMetDFN3BwZEQW7kJAQJCcnQ6Px32GOwmqGrTRf6TDICwIlL6jKUlrAjgOiIBYI7bCv+d1Jx4G1NB82c7nT8lWnKvKnu1wpMPKC3f+5Dcd3b0B0VKR8XbrqJWr7XOlhjdtBFRIOIS5uE1V3sO934Un5nVzLc6JKIeF4qAuPhUM5m6gsZxMCNhtgFQJCCNhs4sLvlX9DNtuF3y88b7WJyovvF8raLn0sKh/b9y0tK0NoaBgELmy78PzFfas8FpU/1guvWS0WOQbH8jYbqvx+MZ7K4zienz1++feq8Qsn51Pt/C45Nj9mHIRoVO69Y1+rgqmiFI0T4xEeooFKxQv7VDc2iwlmfU617VqNCo3jIqBR82ZuT6t3Nnz69Gm0alV9Hqm0tDSHaYV8WUGpFYmRapSe3AnDyZ2IatWnQccx689BE5kESc0vFUTkPpGRkejatavSYVwWa0nuZa0lQ/4jEPKCS1krSmAzl0Ol/X/2zjw+rrJe+N+zzJ496b4DLVshQMtSyo6CeAsKIggKVPAKohXEDa6v7xXUy3vBi6hYBEUR0SuCiigIVPadlraUtpSW7k2TZp99Ocvz/nEmk0z2SWaSSfJ8P5/JzDzznOc888zJOb/zW2VRVIlkIjIersPFxgf7nfoGqgLzpwbSBZEb+uzfVackIw7GFuNBLrBTcfwuhYC7f4WUlmzAW3HoCM1q9BBCEAwGKS8vH7ee3KIXw0eHoaK7cSRjOLE7jSOWbRMMhfEHAl0MNHQxavRt7Og6XneDTZYBJjNGt32Lbvuw0/sQAreuZin0O5X/XTz4XZ0e/D63jsel5l0Z6xxDBn6PPm6PIUlhMNr39ZrFwO91c+S8SaMwo4lHzhrvyZMns2HDBubOnZvV/u6771JdPTa88+5YFeK/P1kJQPOrv8A3/Sg0T8kAW/VE2CZGuEEWUZRIJHnFNE3C4TB+vx9N00Z7OjkjjARWvGe+Ysn4ZDzIBb1hRppxV84a7WlIJJJRYKxfh4uNRMpid3MMgNk1PnxuDbO9rt90hnrXVEXSD2FMMV7lgt6wkhEZpThOUBSnvoA2RE94IQRBnz2ujSsSyUhjJcKYvdRBArAsm0g8hc+jo8kU8gUl59X9zGc+w1e/+lVeeOEFLMvCsiyef/55brjhBj7zmc8UYo555+nNCdzTjwXAirXS+tZvhzyWGW7CNlP5mppEIpHQ3t7OM888QygUGu2pDAkztL9rnLVknDMe5ILeMKOtCFmRUyKZkIz163Cxse1ANJMG5LBpJdipaK8FkbuSFXEgT8VjivEqF/RFqr1OygsSiUSSZwYqiByMJnhh3W7CMamPLTQ5Rxz84Ac/YPfu3Zx99tmZvJ+2bXPllVfyX//1X3mfYKEoOf5q2p76FsKIE3r/aUoOOR3f9IU5jyOEjRGqx1M1pwCzlEgkE5Hy8nLOOOMMSktLR3sqOWMnI9jJ8GhPQzKCjBe5oDvCNrHibdKLUCKZgIzl63Ax8sH+rvUNAph9FETuiqIoqIpTAFWmKhpbjFe5oC+EZWCE6nFXzBjtqUgkEsm4wQwfwDYSfX5e6vdw8sKZlPjcIziriUnOhgO3280jjzzC97//fd599118Ph9HHXUUc+aMLcW5Fqih+oQraX7tPgCaXr6HmRf/FFXP/aCzoi3YpVNQXd58T1MikUxAXC4XNTU1oz2NIWGFexYukoxvxotc0BtmpFkaDiSSCchYvg4XI1vqo5nX8ysMRD+KgK5oacOBLFo6thjPckFfmOFG9JIaVN0z2lORSCSSMY9tJjGC/esVXLpGdZmsRzcSDLmq74IFC1iwYEE+5zLilB1xHuEPXyJ5YAtGsI62tY9QfcIVOY8jhMAI7sdTc1ABZimRSCYasViMLVu2MHfuXLzesWOQtOLt2KnYaE9DMkqMB7mgO1Yygp2Kobr9oz0ViUQygozV63AxIoTgg3on4iDg0ZiqtQx6W00FwwZTZoEZk4xHuaAvhLAx2vfhqTl4tKcikUgkY55U216E6P/iH08abNvXyqzJZXjdQ1ZtSwbBoFb3pptu4vvf/z6BQICbbrqp37533XVXXiY2EiiqxuTTvsLeP98Itkn7u3+m5OBT8FTPy3ksM9aGSyoXJBJJHkgkEmzdupWpU6eOGYWFEDZWSEYbTBTGq1zQG2akGXfV7NGehkQiGUHG4nW4WGkKp2iLGgAcUq2iMPjwgY46BzJVUfEzkeSCvjBj7eiJEJq3bLSnIpFIJGMWM96OFQ8O2C+ZMtmxv50plQFpOCgwg1rddevWYRiOwLd27do+q8SPxerx7qo5VB5zMW1r/wi2RdPL9zDjE3egqFrOY6Xa6/BOnl+AWUokkolEVVUVF1xwwWhPIyfsSBPCkoWJJgrjWS7ojhlrxVUxE0VVR3sqEolkhBiL1+FipWt9g/lVNjD4c6mW7mrLXEVFTyHlgpUrV3LnnXdSX1/PkUceyd13382pp57aZ/9kMsltt93Gww8/TENDAzNnzuQ73/kOV199dc77zpVU2168U48YF/KPRCKRjDTCtjHa9g6qb0Wpj3NPkFlfRoJBGQ5+8pOfUFbmWM5ffPHFQs5nVKg87hIiO17FaN9HsnErwU1PUnFU7jcLViKEJb0MJBLJBENYBlakcbSnIRlBxrtc0BVhW1ixVvQSme9cIpFIcmVLfafhYEF1bspULd1dpioqfgolFzzyyCPceOONrFy5kqVLl3Lfffdx3nnnsXnzZmbP7j0a8JJLLuHAgQM88MADHHLIITQ2NmKaZt7m1B+2kcCMNOEqnTwi+5NIJJLxhBHcj21KZ8RiY1AuH8ceeyzNzc0AHHTQQbS0DD435VhA0VxMOn1F5n3r27/DCB8Y0lhGcH++piWRSCYo7e3tPPfcc4RCodGeyqCwQvUD5iCUjC/Gu1zQHSPSNNpTkEgkI8hYuw4XMx90LYxcnVvkVkeqIhlwUPwUSi646667uOaaa/jCF77A4Ycfzt13382sWbO49957e+3/9NNP89JLL/HUU0/xkY98hLlz53LCCSdw8skn52U+g8EI1iOskTFUSCQSyXjBSkZz0sOGoglefncP4Zg0NBSaQUlvFRUV7Ny5E4Bdu3Zh2+NPQeSbegRlR3wcAGEmaHp5JULkLqVayShmrC3f05NIJBMIXdepqKhA14s/V5+dimHF5TlvojER5IKu2KmYLPwtkUwgxtJ1uJgxLJvtBxzDwdQShTJvbhEHevpOVdY4KH4KIRekUineeecdzjnnnKz2c845h9dff73XbZ544gkWL17MHXfcwYwZM1iwYAHf+MY3iMfjOe374Jqh/+8L25TOhBKJRJIDQghSrbty2kZTVcpLPGiaTA1XaAZ1RfzUpz7F6aefzrRp01AUhcWLF6NpvdcA2LFjR14nOJJUn3gV0d1vYUVbiO9bS+TDlyidf0bO4xjB/Wi+CpnbUCKRDImSkhIWLVo02tMYFFZw32hPQTIKTBS5oCtGpAlP1ZzRnoZEIhkBxtJ1uJjZ2RjDSGv9F9Tkfl+UKY48vm3T44JCyAXNzc1YlsWUKVOy2qdMmUJDQ0OfY7/66qt4vV7++te/0tzczPXXX09rayu//vWve90mmUySTCYz70OhEH/54iSS+9YiDuu7lkJ/pMKNaIFqVLd/SNsXG0KIzEPSO3KN+keuz8BM5DVKBfdjpfo38HZfH7/XxdEHTc58JincOgzKcHD//fdz0UUX8eGHH/LVr36Vf//3f6e0tLQgExpNVLefSad8iYZnfgBA8+v34595LJqvPKdxbCOBFW2R+ZAlEsmQsCyLWCyG1+tFLeKCrFa0GdvIzYNLMj6YKHJBV6xYG6JiJorauyJEIpGMH8bKdbjYeX93Z8qBBTW5r6MmDQdjhkLKBd2d8YQQfTro2baNoij8/ve/p7zcuYe/6667uPjii/n5z3+Oz+frsc3tt9/OrbfemtX27n9MI/TW/bimHI7qzU0X0EG07sNx43AghCASceqVSOfI3pFr1D9yfQZmoq6RbSRIte4dUOkthCCWdNLAKYqCaZgEIyncuoaqTpz16o9RNRxs2LCBc845h4997GO888473HDDDeNWQRCYeyKBg5YS3fEadiJM8xu/YspZX895nFRwP5q/CkXebEgkkhxpa2vjqaee4uyzz6aysnK0p9MrwjKwwvWjPQ3JKDGR5IIOhG1hxlpxlUwa7alIJJICMxauw8WOsAy27GvPvJ+fY2FkAE2mKhozFEIuqKmpQdO0HtEFjY2NPaIQOpg2bRozZszIGA0ADj/8cIQQ7Nu3j/nz5/fY5pZbbuGmm27KvA+FQrTeewIkg8RX38+Uj94yRCWegcdlofurhrBtcdGhjCovL59QCs1ckGvUP3J9BmYirpEQgsSB/bi9A6umO9an1OdCURSaEglef28Ppx41k5ISb6GnOiYYVcPBscceS319PZMnT+all14ilRrfxSdqll5LfN967FSUyLYXKZ1/Jv5Zx+U0hrAMzEgjrrKpBZqlRCIZr5SVlXHKKadQUlIy2lPpEytUjxjnee0lfTPR5IIOzEiTNBxIJBOAsXAdLnbMYB3bmi0A3BrMqZCpisYzhZAL3G43ixYtYtWqVVx44YWZ9lWrVvGJT3yi122WLl3Ko48+SiQSyfz/bt26FVVVmTlzZq/beDwePB5PVtuHMYsqv0Zs15tEtj5H2WEfHdJ3MIP70X2V48KZUFGUzEPSO3KN+keuz8BMtDUywwcQRnzQ37fr+pQFvJx4+HQCPveEWa/RQhZH7gXdX0n1kmsy75te/vmQ0nEYoQMIy8zn1CQSyQTA7XYzdepUXC7XaE+lV+xkRBZEnuBMNLmgAzsVx0pGR3saEomkwBT7dbjYseNB2trbaXAyLnBQlYJrCMULu0YcyPzFxU2h5IKbbrqJX/3qV/z617/m/fff52tf+xp79uzhuuuuA5xogSuvvDLT//LLL6e6uprPf/7zbN68mZdffplvfvObXH311b2mKeqL254KZl43v/5LjFADhmESjydIpYysvvF4gng8kfWdDdPpG49FMMKdEROxWIxYLJbd1zCIxWJZdRaccePEYjEsy8q0maZJLBYjkUgMuW8ikSAWi2GanXqKjvRsufTtXnA6mUwSi8UwDGNIfW3bzqxPb327GqNy6SuEyPTteh5JpVI59+3+G/X2e+bSN5ffPh/HScfvOdzjpPvvOdzjZKDfc6jHSdffM5e+ufz2Qz1OBvo9J8I5wjbiJFrrnHNlIvu7JVMp4vEERpdxbdsmnkgSjztzcOkakysDCOG0G0YvfbuNmzKMHn2FEJm+XX9PwzBz7psyul0f0n2zf8/B9zVNp2+y23UnkXT6WlbPvoVgUIaDjmJH8+bNyxQ7Ouigg3p9jBdKD/0IvulHA2BGGmld/fucxxC2mSUoSCQSyWCIx+Ns27atx4W5GBBCYMqCyBOe8SYX/M9FFRgt2wfV14w0FXg2EolktCnm63CxI2wLM7SPbS2dN7MLqofmbd3V1mBLu0FRUyi54NJLL+Xuu+/mtttu45hjjuHll1/mqaeeYs4cp3ZAfX09e/bsyfQvKSlh1apVtLe3s3jxYj772c9y/vnn89Of/jSn/b6wNcm/djg1jYQRp/GFu3jwkb+xZNlybv/Zb7L6nnnxtSxZtpz6xuZM2yN/e5Yly5bzvR/dhxk6gG06CsozzjiD2tpatm/vlDn+8pe/UFtby4033pg17nnnnUdtbS2bNm3KtD355JPU1tZy7bXXZvW96KKLqK2tZc2aNZ3f4YUXqK2t5aqrrsrqe/nll1NbW8srr7ySaXvjjTeora3l05/+dFbfa665htraWlatWpVpW79+PbW1tZx//vlZfb/85S9TW1vLE088kWn74IMPqK2t5aMfzY7Y+MY3vkFtbS2PPPJIpm3Pnj3U1tZyyimnZPX97ne/S21tLb/97W8zbY2NjdTW1vYoYn/77bdTW1vLL37xi0xbOBymtraW2traLOXmXXfdRW1tLXfddVemzTTNTN9wOJxp/8UvfkFtbS2333571v4WLVpEbW0tjY2NmbaHHnqI2tpavvvd72b1PeWUU6itrc06Xh955BFqa2v5xje+kdX3ox/9KLW1tXzwwQeZtieeeILa2lq+/OUvZ/U9//zzqa2tZf369Zm2VatWUVtbyzXXXJPV99Of/jS1tbW88cYbmbZXXnmF2tpaLr/88qy+V111FbW1tbzwwguZtjVr1lBbW8tFF12U1ffaa6+ltraWJ598MtO2adMmamtrOe+887L63njjjdTW1vKXv/wl07Z9+3Zqa2s544wzsvrecsst1NbW8vDDD2fa9u/fT21tLSeddFJW39tuu43a2lp+9atfZdpaW1szv2dX7rzzTmpra7nnnnsybfF4PNO3q8L7nnvuoba2ljvvvDNrjI6+ra2tmbZf/epX1NbWctttt2X1Pemkk6itrWX//v2Ztocffpja2lpuueWWrL4XXHABxxxzzIQ4R6Ra97B1xy6WLFvOBVdmf7fv3P5zlixbzp//8Vymbe/+A5z76X/nnM84/wOJlMGO+nb+++e/49zLVvBYl74tbUHOvWwFH//cDVnj3vPrRzn3shX87s9PZdoisTjnXraCcy9bkWVY+eXvH+fcy1bwy98/nmmzLCvTNxLrPE5+9+enOPeyFdzz60ez9vfxz93AuZetoKWt0xj92D+e49zLVvCjex/O6vupa77FuZetoK6h817ziWdf4dzLVvDDux/I6nv59d/l3MtWsGNPXaZt1ctv87HLv0ohkMWR+0BRFCad9mX2ProCYaUIbvw7JYechnfygpzGMcNN6CWTUXV3gWYqkUjGG7FYjI0bNzJp0iS83uLK12dFDiDMwliyJWOH8SYXfOQwH7GNf6V6bu2AfTNFkrVBiVASiWQMUszX4WLHCtcjLJOtzZ2a/vk1Q0shoKkK4IxjCZCl6YuXQsoF119/Pddff32vnz344IM92g477LAsJdZQeWCti3OOqsaONpJoeJ85bv+QxhHCxmjfh6dmbDhTSCQSSaExQgewkpFhjRFPGmzZ3Yw1QSLfRxNF5Bj3+fnPf56f/vSnY1ZBEAqFKC8vZ//GFykrHThvadv6x2h9y7Fuu6vmMvOiH+esLHCVTMJdNXtI8x0JhBAEg8EJVYQlV+Qa9Y9cn4HJZY1M0yQUCo3QzAaPMJMYTVsRIv8XZyEEsZSN363KY6gPhBDUzDoUd8WM0Z5KFuNBLtj134cBMPPin+KpnjfgNu7KWbhKJxd6aqOGPKcPjFyj/hnr6zMS12EhRCYP+1hco96wk5FM9Nb3nkvx3gHnNvO+T7ipCeT2HYUQ/OdzBpsanTEe/rQbn2t8rFO+EEIwafbhuMqnjfZUshgPckF5eTk71jxDqdnA/r//BwgbFI2aj/8Xvsnzcbs705h1pM3weNyo6VoGhmliGiaqpuJxOw6E3skLSNqO+cvr9Xb2NQwMw0DTtKxaC/F4HCEEHo8HTXO2M02TVCqFqqpZRs1c+iYSTlolt9uNrjt6DcuySCaTffZ1uVxEo1HKy8uxbZtkMomiKFnpn5LJJJZl4XK5MmneOsYdTF/btjNRXn6/v0dfXddxp9cyl75CiIwHuc/ny5xvU6kUpmnm1Lf7b9SRAsfr9aIoCsFgEJ/Ph2VZ/fYdym+fj+Ok4/cc7nHS/fccTN8OucDr9WaOqcH+9kM9Trr+nsM9Tvr6PXM9Tvr77YUQNDQ0UFZWhs/nG1PniMEcJx3rrmFhtXyIEDaWZTspoxQFn7fzuyVTKWzLRnfpuLqM2xyMUerV8fud/7NoNErKMLAsG13TcLn0zHHSkd6n67i99RVCkEg6UWFeT2e9BMMwMS0rp76apuLukuayI3WQx+3q8nsOvq9pmhimhaqqeLpcdxLJJEKA2+VC0zr7pgyT2Yceh6tsCvkkZ3e53/zGCc378MMP2b59O6eddlrmRDBehN6uVBz1SSIfvkKqZQep1l20v/sXKo+7JKcxzGgzeulkVJf0WJJIJGMXM7ivIEYDydhmPMkFbWv/xNSPfnvAfmakaVwbDiQSiSRXhLAzqQwtW/Bhi6Pwr/JB9dActbNSFVkyVdGYYbzIBV6vB1/pkVQc8yna1z0KwiL42j2UfurHWf18vp73+C69U9nVQap9H/6ph/fs20Upmj1uz5oMuq5nlHND7dtbFJWmaVmK1e59u/qa9tW3e4HpXPuqqlqQvoqi9NrX7XZnlMZD6QvZyuiONXK7ey/U2tu4ufz2+ThOevvt83mcDKavx+PpsT4j/dsP9ziB3n/PXPr293v6/f6sNRoL54jB9O1Y98SBDzI6BU1Tez2HenpZR1VV8Xk9+HzZa+F2ucDVe9/u9NZX6Wa06MDl0jMGg6H0BYbdt8/fqJdjWNf1jFEo3+SccLK1tZWzzz6bBQsW8PGPf5z6+noAvvCFL/D1r3897xMcbRRNZ/LpK0Bxlqpt7R9JtdcNsFU2QgiM4P6BO0okEgkQDAZ58cUXs3JrjjZWrA17mOGEkvHJeJALmiNOPsvojtdIte0dsL9tJIYdXiuRSIqXYrwOFztWuCGTyrAuJIinU4nPrxl6JKHW5U7Vkn4LY4bxIBd0pWrRZbhrDgbAaN9L61sPDmkcOxXDkHWSJBLJBMYIN+btHioUTfD6xn1E4qmBO0uGRc6GgxtvvBGXy8WePXuyrEiXXnopTz/9dF4nVyx4Jh1C+VGfAEBYBk0v35Oz160Za8NOxQbuKJFIJjwdnhQdIWqjjbBNrHDxGz+FECTr1hFZ+zDR9/5M/MPnSe5fjxVtzvKUkuSX8SAXPPRWNP1K0Lb+sUFtI4skSyTjl2K7Dhc7diqGFe0sDLu1pfOau6B66B7mqiyOPCYZD3JBVxTNxZSzvo6iOR6wwY3/ILZ37ZDGMtr3Iyxz4I4SiUQyzrCNOEaOTtj9oaoKXreOOoYi2cYqOUvDzz77LP/93//NzJkzs9rnz5/P7t278zaxYqNq8eXoZVMBSNRvJLwl94JLuUYqSCSSiUlpaSknnHACgUBgtKcCgBUq/pscK9JE6OUfEXr5R8Q/+CexjX8hsvoBQi/dSesTN9Dy53+nbdX3CK9+gPjWZ0k1vo+dig48sGRA8i0XrFy5knnz5uH1elm0aBGvvPJKv/2TySTf+c53mDNnDh6Ph4MPPphf//rXOe3z0XUxFLdT9yiy7UWMUMOA21ix9qL/v5BIJEOj2K7DxYwQArN9D3Qx0G9r7nSwml8zdONL14gDU0YcjBnGo77AXTmL6pOWZ943vvgTrETuEUnCNjFC9XmcmUQikRQ/QgiSLbvymva4xOfhuAVT8Xt7pnGS5JecaxxEo9Fe81U1Nzf3mitsvKC6vEw69Xrqn/y/ALS8+Rv8sxejB6oHPYaVCGElQmjeskJNUyKRjANs2ykSpOv6qHs72skIVqxtVOfQH8I2iW/5J9GNfwGr7zBFYcQxm7dhNm/Lald9lWjls9ArnIdWPgu9fHrGq0wyMPmUCx555BFuvPFGVq5cydKlS7nvvvs477zz2Lx5M7Nnz+51m0suuYQDBw7wwAMPcMghh9DY2Ihp5qbQj6UEvkPPI/beoyBs2tY9xuTTv9LvNkLYmNGWvBefkkgko08xXYeLna4pijroiDhQFTi4auiegLLGwdhkvOoLyo78N6K7VxPftw4r1krTKz9nyke+nXMqLjPShF5Sg+rqmXNcIpFIxiNGcH/eM7DYtiBlWOi6KqMOCkzOkvBpp53GQw89lHmvKAq2bXPnnXdy5pln5nVyxYZ/5rGULjgLADsVpfm1+3IeQ9Y6kEgkA9Ha2soTTzxBMBgc1Xl0LXRYjBhNH9D29HeIvvvHjNFA9VVSetJ1lJ32dUqOvwbf4efjnn4Mqr93I68db8No2EB8y5OE3/wF7c98h+ZHr6H1yW8SevWnRDf+heTe1Y5iRBaG7pV8ygV33XUX11xzDV/4whc4/PDDufvuu5k1axb33ntvr/2ffvppXnrpJZ566ik+8pGPMHfuXE444QROPvnknL+H79BzUd2OoiO89blBpSKS6YokkvFJsVyHix3biGFFs8+DcUOwt93R8s+pUPDqeUpVJHMVjRnGq75AUVQmn3EDqseJUIzueI3IthdzHkcIQaqteOVriUQiySdWMjKoaO5caY/EeWb1DkLR5MCdJcMi54iDO++8kzPOOIM1a9aQSqX41re+xaZNm2htbeW1114rxByLiuol1xDd8w52Ikh05xtEdr5Bybwlg97eSkYxY23o/soCzlIikYxlSktLWbJkyainSLDCB3p4ERYDdjJMdP3/ktjxUmejouCbfy7+oz+F6urp5QbpHMzBvZjtezGDe7HandfC6Ob9IGys0H6s0H7Y+1Znu+ZBL5+BXjkXvXIOetU89PJZKPrEjk7Il1yQSqV45513uPnmm7PazznnHF5//fVet3niiSdYvHgxd9xxB7/73e8IBAJccMEFfP/738fn692TL5lMkkx2HtehUAgAxeWn7MhltK/7E9gmbev/Qs3SL/Y7Z8tIYMaD4y6SUAiReUh6R65R/4z19SkpKeGkk07C7/cX7DuM9TUSQmC07UHY2Ub1bc02Hd9oQbUy5O8nhMiKODDtsbtWhaJY12M86wv0QDWTTv0yB/713wA0v/YLvNOOxFU6OadxrERI6gQkEsm4R1gmqZadBRm7xOdm8aHTZKqiESBnw8ERRxzBhg0buPfee9E0jWg0ykUXXcSXv/xlpk2bVog5FhWat4yapV+k8bk7AWh+9Rf4ph+FlvY8GAyp1t0oqjbuFA0SiSQ/eDweZsyYMapzsI14Dy/C0UYIQXLny0TW/QGRimTa9aqDKDn+alxV8/rdXnX7UScdimvSoVlj2vFWrPZ9mGmjgtW+FzNUB3a3dDdWErN1B2brjs42RUUrm542JsxFr5qLXjEn47k+EciXXNDc3IxlWUyZkp36Z8qUKTQ09O6lsmPHDl599VW8Xi9//etfaW5u5vrrr6e1tbXPOge33347t956a4/2SMIkcMh5sOFvYCUJbXkW1+GfRPX1f1MfO7APd8Xo/r/mGyEEkYjzP5ZrCoaJglyj/hnr62NZFuXl5aRSKVKpvtPgDZd4PD4m1wfAirVgx3qmHdjU2GlImF0BsdTQovUcpXinYjyaFEMea7xSrIaD8a4vKDn4FKK73yay7QXsVIzGF37M9GU/QFG1nMYx2vehectRZDo0iUQyTkm17cY2CyNHuV0606oHr4eVDJ2cDQcAU6dO7fWme6JQcvCpRLa9QGzPGqxYK61v/ZZJp3150NsL2yLZ9CHuqjk51UiQSCQTg0Qiwc6dO5k+ffqo5YK1gvuyCh2ONmZwH5HVv8Fo2pJpU1w+AkdfgveQjwz5pktRFDR/NZq/Gvf02ky7sC2scENnZEJwH2b7XuzIgewBhI0V3IcV3Edy16uZZrVkCnrlHFxdDAqqt3xIcxwL5FMu6K5EE0L0qVizbRtFUfj9739PebmzvnfddRcXX3wxP//5z3uNOrjlllu46aabMu9DoRCzZs2ixKtTVlqBeeR5BDc8DlYKc+uTVJ/0+QHmm8BX4kfRxo+3S4cyqry8fMwqNQuNXKP+GevrE4lEaGpqKuh1uCPaIBAIjLk1so0YRjgI7p7X3l3tVub1wikafvfQvpsQApdm02E8cOkK/l72N5EpVsMBjH99Qc3Sa0nUb8SMNJGo30j7hr9RecxFOY1hmynM8AFc5WPfmCKRSCTdMcKNmLH2go2fTJnsPhBkalUJHlduhltJbgzJcNDe3s4DDzzA+++/j6IoHHHEEVx99dWZm/bxjqIo1JzyJfY++hWEESf0/tOUHHI6vukLBz1GR1VxOxl1iiNNIO9UiUTSP5FIhHfeeYeKiopRMRxY0ea8Fy8aKsJMEt30OPH3nwTRqYzwzD6JwLGfQytQiLeiak5aovIZMPukTLttxLHa92C07sRs243ZtgsrWJc1NwA7coBU5ACpvW9n2lRfZXZkQuUcVH/NmFMY9UY+5IKamho0TesRXdDY2NgjCqGDadOmMWPGjKz9HH744Qgh2LdvH/Pnz++xjcfj6fX/SlEUFEWh4ugLCW16EmEZhDb/k8pjLx4wQtCKtoy7G/+O9RgPx2ehkGvUP2N5faLRKGvXrqWyshKv11uw/YzFNRJCYAX3oQD0Yujdli6MXOKG6WXD+256FzuBLcbWOk10xru+QPMEmHzm19j/9+8AgtbVv8M/61g81f1Hv3bHCB9AC1SjTvC0lxKJZHxhp2IY7YWt5RJNpNiwvZHygEcaDgpMzm4ba9as4eCDD+bHP/4xra2tNDc3c9ddd3HwwQezdu3aQsyxKHGVTqb6hCsy75tevmdIIThGpIl4w/vE6zdhBOuxizCfuEQiGVlqamq4+OKLqawc+bynwkxhhetHfL+9kdy/ntanvk188xMZxbxaMpnyM75N2dIVBTMa9Ifq8uGadCj+Qz9G2UnXUnXe7dR8+ldUnPN9So6/Bu8hH0GvPhi0njeAdryN1P51xDb9ldArP6b1iRtp+ct1tD//X0TW/YHErtcxQ/t75IsudvIlF7jdbhYtWsSqVauy2letWtVnseOlS5eyf//+TEoUgK1bt6KqKjNnzhzS99EDVZQedg4AwkwQfO+JAbcxo81D2pdEIilORvM6XOxYkQMII9HrZ41RCKY/ml+toA5T0d+1OLI1ti6NE5qJoi/wTT+KitoLnTe2SePz/5OzPkDYFkawrgCzk0gkktFB2BbJ5u0Fj4qrKvNz/snzqSgpnIOHxCHniIOvfe1rXHDBBfzyl79E153NTdPkC1/4AjfeeCMvv/xy3idZrJQd8XHC214i2fgBRrCOtrWPZBkTcsE2EqSC+yG4H80TQPNXofsrx1XqA4lEUvyYwX2jrri2Yq1E1v4uy1sfVcN/+Pn4j/hE0RUjVjQ3ruqDcFUflGlzUh3VY7buwmzreOzuUYhZpCIYBzZhHNjU2ah70Ctmo1fOc9IdVc1FK5sJOebOHSnyKRfcdNNNXHHFFSxevJglS5Zw//33s2fPHq677jrASTNUV1fHQw89BMDll1/O97//fT7/+c9z66230tzczDe/+U2uvvrqPosjD4aK2osIvf802BbBjf+g/OgL0Tx9Fyu3zRRWPIjmGx+elBKJRNIbthHDijT2+fnW5k75YX718NMKaV2GsIo3K4+kGxNJX1B1/OeI7V1LqnUXqdbdtK7+HTVLrslpDDPail4yKaeaiRKJRFKspFp3FayugWR0yNlwsGbNmiwhAEDXdb71rW+xePHivE6ukCjqkLI0dRtDY/LpK9j75xvBNml/98+UHHxKziGK3bGSUaxkFKN9H6qnFN1fieavzLngkkQiGZuEQiFeffVVjjnmGEpKRu4mwoq1YifDI7a/7ghhE9+6itiGRxFmpzeja/LhlBx/NXrZ9FGbW644qY5mopfPhHmnAOlCzNEmzHSaI6PNeRaJYPbGZhKzeRtm87bONlVHK59JzRceHcFvMTjyKRdceumltLS0cNttt1FfX8/ChQt56qmnmDNnDgD19fXs2bMn07+kpIRVq1axYsUKFi9eTHV1NZdccgk/+MEPhvWdXKWTKV1wFuEtq7BTUUKb/kHlcZf2u40ZbZaGA4lknDBa1+FiRgiB2b633/pHW5s7P1tQM3zDwXAiDoQQpPavJ77lSaxQPXr1wXhmLsI94zhUT+mw5zZa2KkYZvserPbdGG27mTT7rtGeUg/Gi75gMCiai8lnfZ19f/ka2CbBDY/jn70Y/4zagTfuQqptL76phxdolhKJRDIyFLquQVfCsSSrN9excN4kAr7iciwcb+SsPS8rK2PPnj0cdthhWe179+6ltHTsCGGeqtmQ7NtjZrC4q+ZQeczFtK39I9gWTS/fw4xP3JEXJb8QAisRwkqEUNr2ovnK0PyVaN6KIRcClUgkYwN1hP/HhWVghfaP6D67YkWbSb7yE+z2nZk2xVNGybGX45l7yrjIa6woClrJZLSSyXhmn5hpt+JtPSIT7GhT9sa2idW2a2QnPEjyLRdcf/31XH/99b1+9uCDD/ZoO+yww3qkN8oHlcd8mvAHz4GwaX/vb5QfdQGqq+8oBisexDYSqC4ZLiuRjAdG+jpc7Fjhhj5TFHWwraVTu39I9fCv27qq0FEc2RpkygNhWyT3vEls89+xgnsz7am6d0jVvQOKhnvGcfgOPhPX1KOK9p4q42zQvsepqZR+7iEfFCHjRV8wWDzVc6k+8Spa3ngAgKYX72bmxT/LKYLATsUwI83oJTWFmqZEIpEUlJGoa9AVBVBVpUe9JUn+ydlwcOmll3LNNdfwox/9iJNPPhlFUXj11Vf55je/yWWXXVaIORYEzVeO26s76YGGSeVxlxDZ8SpG+z6SjVsJbnqSiqMuyMMsOxHCxoy1Y8baUVQNzVeB7q9E9ZaNC4WaRDJeSJk2TaEETcEEjaEETcEkrZEk4bhBaziOjcpA/7G2EJhmNc/Uj1zOU2HEEZY5YvvrypTUNs5p/xl+O5Rpe993Om+VfprkrhLYNTrzGjlKgIXpB1AKnkCEamMPNeZuagznUWE19DfIqDFe5ILuuMqnUXLIaUS2vYidCBPa/HRnLuNeEEKQbN6Od8phMkJQIhnjlJWV9VlbZSJip2JYAyisDUuws81R7k8vUyj1DP/+JJeIA2GmSOx4kdiWp3oq1zUXWEa6o0Vq32pS+1ajBmrwHnQG3oNOQ/NXD3u+Q0WYKczgvoxxwGrfjdm+t0d6w7HCeJUL+qP8qAuI7V5NfP8GzEgzza/+gilnfyOnMVLB/TLLgEQiGZMIyxyRugZdKfF7OP6wsZORYCyTs+HgRz/6EYqicOWVV2KajjLH5XLxpS99if/3//5f3idYSFzl07CN2LBDaRTNxaTTvsL+J24GoPXt3xGYeyKu0il5mGVPhG1hRlswoy0omgvdV4EWqJJ5ESWSAhNNmjQFEzSFEjQGEzSFkp0GglCSxmCC9qjM55cLJ/I6/8aDuBTnetIoJvMgX2BH7BCIAUzUaoh+4LD0w8FDgmdGbT59M57kgu5UHvtpItteAgTtG/5K2ZEfR9U9ffa3jQTJlh14J80fuUlKJJK8I4TAsixUVZ3wDjpC2Jjte/pNUQSws01gpi/ZC/IQbQCgdTUc9LF7OxUlvu1fxD94GpEMZX2mVx+C/4gLcE8/BrNlO8l9q0nueh070e5sG20m9t5jxDb+Gfe0Y/AefCbu6ccUVHFrx4OY7buzogiscD2IQcg7HTWQKuagV85GK59dsHkOh/EsF/SFoqhMPvNG9j66AjsVJfLhS/jnnEDpIacNegxhGRjBetyVMws4U4lEIsk/yVGoayCEwLJtVEXJSVZL1W/AaNqK4vajestRfZXoZdNRvOUTXubri5wNB263m5/85CfcfvvtbN/uWJQOOeQQ/H5/IeZXcNxVc7GNLdgDhN4OhG/akZQdcR6hzf9EmAmaXl7JtI9/r+AHnrAMjEgTRqQJVXej+SvR/VWo7rH5e0gko4EQglDcoDHYm0HAiRpoDCaIJse75/tIIvg4f+cC5fFMyxZxOPfzJWJII2hvJCnOFDjjTS7oirtyNoGDTia64zWsWBvhLf+ifOG/9buNFQ+Ratsnb/wlkjFMS0sLTzzxBGeffTaVlZWjPZ1RxQrVI8zkgP2yCiPnob4BdCuO3E2vbsXbiG/5J4kPn8uqiwTgmnY0/iMuwDXpsMy9mGvSAlyTFhCovZTU/vUktr9Aav+7gAAhSO1fR2r/OlRfJd6DTsN70BloJZOHPHdhW1jh+i4Ggj2Y7b3UNeoD1V/tGAkq52Se1ZLJKErnooykZ2cujGe5oD/0kknUnHIdjc//DwDNr6zEN/WInNIPmZFG9JLqflMjSiQSSTFhhBqw4oO7tuWTtnCcZ9/azqlHz6KiZOD7ZCvWRuSdB0ntW9Pr54rLj1Y+A710Olr5dLSy6ehl01EDk4s2reFIkbPhIBgMYlkWVVVVHHXUUZn21tZWdF2nrKwsrxMsNIqq4ak5mMSBDxD28JSCVSdcRXT321jRFuL71hL58CVK55+Rn4kOAttMYYcOYIQOoLq86P4qtEBVv96REsl4J2XaBKMpxwCQjgroNAgkaAwmaQolSJnD82zXVIWaMg+Ty7xMLvc6r8u9TC7zUl3qodSnI4w4k6oGrlESi0bZvn07U6ZMwe0uXKEfIQRm607sEQ6FF7aFse5BrF0vZNq0eWdx6OGfZaXXLS39fVCsCoLxJhd0p/LYS4jueA2A9ncfo+zwc1A0V7/bGOEDCNvAXTELRctZ1JJIJKNMaWkpxx9/PIFAYLSnMqrYyTBWtHlQfbe2dCmMnKeIg66piuz08Fa4gdj7T5LY+TJ0vXdTFDyzTsJ/xDL0yrl9jqmoOp6Zi/HMXIwVbSax4yUSO17EjrU6+4m3Edv0N2KbnsA1dSG+g8/EPWNRv+fyzoLFXeoRBPd2pkfq90tq6GUz0So7Iwn0ijmoYziSfLzLBf1ROv8MYrtXE9n+MnYqSuOLdzPt327LMvj0hxCCVPs+GbkokUjGBFYijJGH9O9DIeBzc+z8Kfg9/d+XCWGT2P4i0fV/QBjxvvsZMczmbZjN27I/UHW00qnoZTPQyqZnHnrZNJQJomvN+W72M5/5DOeff36PwoV/+tOfeOKJJ3jqqafyNrmRQnV58VTPJdH04bDG0TwBJp3yJRqe+QEAza//Ev/MY9F85fmYZk7YRsKp3xDcj+YJoPmr0P2VAyo7JJJixrIFoZhBMJaiPZoiGDNoj6UIRlO0x4zs52iK9liKWNIa9n49LpXJZV4mlXmZVO5lcrljIKhJGwYml3upDLid4jx9IIQgGLQp8w+sGPe7SnDPnzfseQ+EGW7AUuLgHjlFvTBThN78GVbd2kxb4JjL8R56HnFD4HfnFmo4kShSu8G4lAu64qk5CP/s44ntWY0ZaSa89XnKDj93wO3MaCtWIoy7cia6v2oEZiqRSPKFx+Nhzpw5oz2NUUXYFmb73oE7ptmWjjhwazCnIv+pivTwLkKvPUly79vZF0TVhfeg0/Af9m9oOaaJ1QI1BI76FP4jLyTVsMGJQqhbm04bJDAa3sNoeA/FU4Z33qnoFbNQdC8gMNv35lywWHGXZEUQ6BVz0MqmjzsD83iXCwai5tQvEW/Y5DgT1r1LcOM/cqp/aMVDmPF2dF9F4SYpkUgkw0RYBsmWnaPm3OZx6cyc1L8h2gztJ/L2AxhNWzJtiqeMwNGfRtG92IkgdrQZM1SHFdqPHWvpOYhtYgX3YQV7Fn5WAzWOEaF0Oqq/ynn4KtB8lai+ynFjWMhZSnnrrbe46667erSfccYZfOc738nLpEYDzVeOu3z6sIslB+aeSOCgpUR3vIadCNH8xgNMOeumPM1yaFjJKFYyitG+D9VTiu6vRPOVSyOCZFQRQhBNmo6CP9rNGJD13Pl5KG7kXXla6tWpSSv/J5d7mVTmcYwDZZ3vS32uEVVmJ5NJ9uzZw9SpUwsWcWBFGrHCBwoydl/YqSihl/8Ho+kDp0HVKD3xWrxzl6YFjiLVjEv6ZbzKBV2pPO5SYntWA9C2/jFKD/3IoHJgC8sg2bwT09eKu3I2ql64CCKJRJI/RuI6XOxYwTrEYDzmgba4oDHqvD6kWkHrx5EiFzRFsID3OZenOGTjJromTFJ0L975H8V36LlovuGlk1JUFc/0Y/BMPwYr3kZix8sktr+QMQiIZIj4lidzGdHxTqycjVYxJ2MsUH2VE8I5YiLIBf2heUqYfMbXqH/y/wDQ+taD+GfU4q4avDEy1bwTqmajB0avaLdEIpH0hRCCZPOOQcsJhSBpmNQ1hZlU4cftyr4vE5ZJbMs/iG18HOzOOXrnnU7g2Mv7jOoTRgIzXI8V2o8VqsMM7ccK7seKNIDd0yHVjjZjR5sx6jf0Op7i8qP6KlH9jiFB9VVmjAqZdm85ilrcDgQ5zy6ZTGaKHHXFMAzi8b7DPsYC+SqWXLP0WuL71juFkba9QOn8M/DPOi4/kxwGQgisRAgr4RQO0zwBVE8JisuH3ctvKpHkQsKwiLfHe1H8d0QCpAhG0xEC6c8suzCKYkWBMp+Lcr+bioDz3DV1kBM54GFSmRe/p/hO0uFwmLfffpuzzz477woLIQRWqA4r2os1vYDY8XbaX/xvrPY9gHPDX3bq13BPXTii85Dkn/EsF3TgnXIovpnHEN+3HjPUQOTDlyldcOagt7fiQRLJzbgrZuaU61gikYwOhbwOjwWseDtWvG3Q/be1dKlvUJ2fPMBWpJEjtt3LCUp2ygDFU4b/sPPwHnI2qjv/qaQ0XyWBIz+B/4jzMQ5sJrH9eZL71vSqMAB6FCx2XndEJkxMJoJcMBD+mbWUH3UBwfeeQFgGB56/i5kX/mjQjntC2CRbdmGn4rgqZkwIg5NEIhk7GO11WMnIqM4hGk+xdlsDpx49K8twYDR9QPjtB7BCdZk2tWQypcdfM6DuQXF5cVXNw1WVnf1B2JbjeBmqwwrVZyIUrND+AdMfWUYsay697BXFW9bToNDN0KB4Sgad9i7f5KwxO/7447n//vv52c9+ltX+i1/8gkWLFuVtYqNFPool6/5KqpdcTdNLzho1vfxzZl1yT9EVOeqIRBBCkIwbxOL1aB4/qsuP6vahuv1FN2fJyGCYNsEuCv72rFRA2QaAjrakMbwaAf0R8OiUB1xU+N2UB9xU+F1UBDpfO89uytPtpT5X3rzdRoOqqio++clPomkDezTngrAtJ6Q+Gc7ruANhRRppf+F27EgjAIqnlPIzvt3jgiwZm4x3uaCDymMvJb5vPQBt6/5EyfzTcxLehG2RbN2NGUtHH7gmrlJJIil2CnUdHgsIM4kVHHyKIoCtzV3qG9QMX/5K1q0j/MZKSrrUYEq4J1Fz9DK8805DGYHoLUVRcU9diHvqQuxEiFTDe4hUFGEmEcJCL53Wa8FiycSRCwai6oSriO1bh9G2l1TLDlrX/IHqE6/KaQwjfAArEUTzV6L7K+W9uUQiGXXMWBvGCGcu6I2KUh/nnXhwRu9jp6JE1/+RxPbnOzspKr5DzyNw1KeGlTZIUTX0smnoZdOy2oUQ2Il2rHADdrwdO9aKHW9zXsdbsdLP/dc8EohEEDMRhLZdfXdTtU7Dgq8CzVeVea36qlD9lSjewqTJz9lw8MMf/pCPfOQjvPvuu5x99tkAPPfcc6xevZpnn3027xMcafJVLLn00I8S3vYSif0bMCONtK7+PTUnfyGPM80/wjaxEmGsRKdSUVFUx4DQ8XD5UFw+6fUwhrBsQThu9KwJ0FtdgLRRIJosXASKW1epTCv9y/0dxgDnuSLgPDoMAOV+J1rApU+sGzJVVdH1/EZCCDOF0bYTMQyj6FAw2/YQfPG/sRPtAKj+asrPvBm9bPqIzkNSOMa7XNCBb/pCvFOPJNGwCaN9H9Gdb1By0NKcx7ESYRIN7+Mqn45eOlleTyWSIqQQ1+GxgBACs203ws7NGaRrxMGCYUQcCNsmtvEvxDb9NdPWKCbzBJ/kqCNOYtn80ckVrHrL8M7N/Xw/UZkocsFAqLqbKWd9g31//TrYJu3r/4x/9mJ8047MaRzbSGAH6zGC9aguL5qvwjEiuP0FmrlEIpH0jm3ESbXuHu1pAKAqCrqmIoQgset1Imt/h0iGMp/rVQdResI16JVzCzYHRVHQ0lEBfSGEQBgxx6AQa+tiUGjr9mhP11jqA9vKpEbqjynfXjfEb9M3OUvES5cu5Y033uDOO+/kT3/6Ez6fj6OPPpoHHniA+fPn532Co0E+iiUrisKk077MvkdXIKwUwY1/p+SQ0/BOXpDHmRYeIWysZCQrDMkxJvjSkQnOQ9G9KOrEUu6OFEIIDEuQMCySKYuEkX6kLEJxo0ctgJGoC9CBpioZBX+JW6W63JeJCuhQ/ld0iwrwuiee916uhEIh3njjDY466ihKSnrPv5cLdiqG2bYTYY1sSjKj6QOCL/0IkfYY1MpmUH7mt9H8Ml/reGIiyAUdVB53CfVP/ScAbWsfITDv5CEp/oWwSbXvw4q3OdEH8uZfIikq8n0dHitY4XrsfkLue93GFnzY4giaNX6o8g/NGGonw4Re/zlGw3uZtlDlIv6r9WoS+DgCKT+OFSaSXDAQnpqDqDr+s7S+9VtA0Pj8Xcz69M+GfN23jQS20YARapBGBIlEMqII23LqGvSVum+EicSSvLthI7Oa/o7duDHTruheArWX4j3kI0Who1QUBcUdcNIrls/ss5+wbUQyhNXdoBBznjvaxQhnj4AhGA4AjjnmGH7/+9/ney5FRT6KJbvLp1O5+DJHUBA2TS/9jJkX/RhFG9seTI4xwUlz1IGiKCguL6o7gOrqSHPkL4p/1EJiWjZJw85S5ic7Xht2t/cWiZSd/d6wSKY6t08aVud4XbYtUCmALHqrC9Dd+9957ogMcBHw6CiKghCCYDBIeXm59J7NA0IITNNMFwweHla8Hat9L6I/63UBSNatI/TaT8FKAaBXH0z56d9E9ZSO6DwkI8NEkAsAfDOPxTNpPsmmbaRadhLbvZrA3BOGPJ6VjJI4sAW9dDKu0imDzn0skUgKSz6vw2MFK9KEFWnKebu9QUEi7Zcw1PoGRssOQq/ejR1L119SFPxHX8r73nNJvOn8BtbIijGSYTIe5AJ32TQQw1fQVBx9IbHda0g0bMKMNNL82v1MPvPGYY+bZUTQPWj+CnR/lTQiSCSSgpBq3TWslO75RFgmsU2PM23zo9ii0znSPXMxJYuuHJOOioqqovgqUH0VQN8pnYVlYCfaexgUOowMhWDUNdgrV67kzjvvpL6+niOPPJK7776bU089dcDtXnvtNU4//XQWLlzI+vXrCzK3fBRLrjjqk0Q+fIVUyw5Srbto3/BXKo/9dP4mWSQIIRCpOHaq00tJURQU3ZMxImSiE9TCewzZtiBl9qbQ76Kk7+q9b9hZ75NpJX9XBX8sYWBYIstQYFrFe0Pp92hpb//utQB61gQo97sp84/tugDjifLy8kGdBwfCCh/ADDfkYUa5kdj5KuG37suE2rmmHkX5KTeiyJzukjGOoihUHncpDc/8AIC2dY/gn3P8sAymQgiM0AHMcKOTw7hkMpon/wU/JRLJ4MnXdXisYEWbMUNDc5ba2jL0+gZCCBLbXyDyzm8hnSJW8ZRRtvQruCYfgb7bBKThQDI6uMqnopsezEj/aSEGQlE1Jp/1NfY+ugJhxAlvfQ7/nBMoOejkPM0UbDOJHTqAETqAqrud1Bn+SilPSCSSvGAE64elF80niQNbaH7l56Rad9PhrqD6qyhZdBWemYtHdW4jgaK50AKT0AKTenxWKIeXUTUcPPLII9x4442sXLmSpUuXct9993HeeeexefNmZs+e3ed2wWCQK6+8krPPPpsDBwpblGO4xZIVTWfy6Suc3IbCpu2d/yUw72TcFTPyPNPRxRYC0wLDFlg2GFb62TYxrQimDaYtMCywFR1L9WCpbix0LDQsdEwbDMvGtET6uetrQdJMe+N3V/D34tlfyEK9hURVwOvW8Lqch8elpd+r3d5reFwqpd5ejAHpaAH3BKsLIOlECBsrnQZlpIl98DTRtb/LvPfMPonSk7405iOtJJIO/HOOx101l1TrLpKNW4nXrcc/89hhjyuEwIy2YkZbUTQXur/jpn/ipEmRSCQjjxVtxgzWDXn7bc1d6hvU5FAw3kwReedBEjteyrTpNfMpW/pVNH8VQgi6Bi4Xsa+OZBzjrpyNsFJY8dDAnfvBVTqFmqXX0vTi3QA0vXIP3imHoQeq8jDLbGwzhR0+gBHuakSokPKERCIZEmasdViZWPKFbSYJv/UAia3P0OFUgKLgW3Au/qMulsXjC8ioanLuuusurrnmGr7wBado8N13380zzzzDvffey+23397ndtdeey2XX345mqbx+OOP57zfsjnHsG5XO35/KtPW1TDTVS4VQiCsSpJteyFdKEx06dGbQSd7e4BZlMz5N/y7/o6wDLY/+1PaFn8PFDW7b4/tuo3bxxy7fmAJMkp600o/pxX5mXabrM9MSxA3bEDBEt36ZhkDnDymhp29rWkzIql0RhuPrnZR2ncq9D2uTkW/1+0o9LPfdyr+O993fJ69va4pMtWPhObmZv7+979z1llnUVnZd6Gd3hCWgdm2CzsVK9Ds+tivEMTee4zYpsczbd75H6HkuKvGfcoyydjHFRj8/5miqFQedwkH/nUH4NQ6yIfhoCvCMjDCjRjhRseI4KtA81egekrlNUIiGQGGcx0eSwzXaACdEQeaAvMqB3d+siKNhF79CWbbrkybb8E5BI75bJajgdZlOBlxIBkNFEXBU30Qicatw5atSxecRWz320R3vo6dCNP40k+Ydt73Cnpd72pEkE4JEokkV6xkhFTL6BdDTgX3c+DZ/8oqzKxWzuVdzzkct+BEVJnZoKCMmuEglUrxzjvvcPPNN2e1n3POObz++ut9bveb3/yG7du38/DDD/ODH/xgSPs+8vLbueWPm4e07VBx83G+yxtMUppxt22m/tnbeYjPE0NetIeLS1McZb5bw6M7yviMcr8PBb2nixd/5/uOh5rl2e91abg0hXA4JHP4S0aEQCDAsccei9+fW45S24hjtu5CWKmBO+cRYduO1+CHz2Xa/AsvxL/wU/L/RTImOO5Lv+HB9TZXLLKp9A9s6ArMOxlXxUyM9n0k6jcR378R3/SFBZmbsAyMSBNGpAlF1Z0cxr4KVG+Z/P+SSArEUK/DY4l8GA2iKcG+oGM4mFup4NEHPicl968n/MZKRCpdK03zUHrCNXjnLu3Rt2sKTRlxIBktFFXDO+kQ4g3vIyxj6OMoCpNOvZ7EgfexYm3E964ltPkpyo/8tzzOtm96d0qoRPPK+mMSiaQndirmFEMe4VqJ3YnseJ2ml36SMd4quoeq4z+HfvA5HFTXgs8jMxsUmlFb4ebmZizLYsqUKVntU6ZMoaGh95zc27Zt4+abb+aVV15B1wc39WQySTKZzLwPhYYXZjhUUnj4PVfxVXEXqiI4RlnHbLGbh/g8H3A4guL0yFUAXQOXqqCp4NIcId6lgq4q6CroWvo567XTR1MVXFrXvr1t171vuk/XvqqCRweP7tyUePXO9x03FYrmQtU9KLrbqa2ge1A0t/Necw87B3XHQ9ITuT4Dk8saeb1e5s2bl9luMNjxIGb7nhG/sAszSfjNe0ntW5NpCxx3Jb4F5zifD3L+8hgaGLk2hUN1efj7piSrPkhywUIfn1sUwOfq+5qhqBqVx36axhd+DEDbuj8VzHDQFWGbmJFmzEgziqqh+crRfBVo3nIZ2SOR5BGfz8dBBx002tMoGPkwGgB82KW+wfzq/uVsYdvENv2F2MbH6Yid1kqnUnbKjegVs3rdRpURB5IiQdFceCcdQqJxK8K2hjyO5itn8uk3UP/P7wHQ8uav8c2oxV0xM08zHRxZTgkyslEikXTDjLaQah153UJXhGXS8vZvCW54PNOmlc9k2jm34KmajWVZzJ1aPmrzm0jkbDi48MILe72YKIqC1+vlkEMO4fLLL+fQQw8d1HjdxxJC9Dq+ZVlcfvnl3HrrrSxYsGDQ87399tu59dZbs9pmnXYVK1Z8BbfHQ9c9Zb3u5XqpKApWIoQwk93aex+j5+fH8X7wJhbsuQ+XFaFKaeVG/oeUXkZb2TG0lR1De+lCLL1nFELHGFnj97JfRemieFc7X2tdFftKpwJfU0FXBCnTotSr49IcRXzHtsVXLFeQKZJmQSxLbjOA3kNIFUVBUXXQXCiajqLqKKrzGrVLWx+CkhCCSCSSGUuSjVyfgclljeLxOPv27aOyshKXyzXg2FasBTvWmpd55oJIhki+/mPstu1Og6LhXvzviFknE0vlJmQIIUika5PIY6h3itVwkG+5YDSwjSSqy0PChD+tj/P+AZMffry8X+NBySGn07rmD5jhA8T3rSPRuBXv5MHLJ8NF2FZnTQRFRfOVOUYEXwWKqo3YPCSS8UgymWT//v3U1NTgdrtHezp5xYo0DbkQcne2DrK+gZ0ME3r95xgN72Xa3DMXU3ritajuvqM6slIVFek1UNKT8SAX9Ibq9uOpOZhk07ZhyWT+2YsoO/LfCG16EmGmaHz+f5jxiTtHrR5Yn5GNnlLplCCRTEBS7XUYod6duUcKM9rKgX/dQaJhU6YtcPBp+I7/Iu6yMgBShklDa4TqMh8uXd77FJKcr07l5eU8/vjjVFRUsGjRIoQQrFu3jvb2ds455xweeeQR/vu//5vnnnuOpUt7hpx2UFNTg6ZpPaILGhsbe0QhAITDYdasWcO6dev4yle+AoBt2wgh0HWdZ599lrPOOqvHdrfccgs33XRT5n0oFGLWrFl88YmfUJY+4HJB2BaJA0MvlgynY0aOpPGFu0jUbwTAbYaY0voyU1pfBkXFO+Uw/LMX45u1CHfV3IIr0YQQhOMGpT7XBFDYGc7DxnmY2Z8qmqvHQ9XcoOrYHp2yEj+qPr5uIPNBh/AsUzn1TS5rFI/HWbduHWeddRYlJX2nMxPCxmzfi222g3tkBXszWEfolf/BjjQCoOheSpfegHvaUUMar2N9/G5VHkN9UKyGg3zJBaPJ2ns/z/d/+wJPrW/AsATv1Rv859NBvn9eeZ+pNzqiDppevgdwah1M+9h3R3LaGYSwMWPtmLF2FEVF9ZY6eYy95bIwuUQyBMLhMK+//jpnn332uDIc5NNoALCtS8RBX4YDo3kbodd+2ungoCgEjr4U3+HLBrzea12LI8uIgzHDeJAL+kLzluKumkOyZdewxqk+cTnxuvUY7XUkmz6kbe0fqTr+c/mZ5DDIimxUFFRPCao7gKJ7sA0TYZeiaFJBJ5GMR4RtkWzZiRUPjuo8YvvW0/j8/2DF250GVadmyTWUHvFxIolOBV4knmL1lnpOPXoWFSXyvFRIcr6bnDp1Kpdffjn33HMPatoCbds2N9xwA6Wlpfzxj3/kuuuu49vf/javvvpqn+O43W4WLVrEqlWruPDCCzPtq1at4hOf+ESP/mVlZbz33ntZbStXruT555/nsccey6T16I7H48Hj8fRoV5ShFaFVNN0JUzzwAcI2B96gF1ylk5i+7AeEt71AdOebxOvWd0YxCJtEw2YSDZvh7YfQAtX4Zy3CP3sx/hm1/XrlDIeO9ZjwCjvbRNgmwohnNQshSMUN4vE6VFXtZlxw92JwcE84j095DA3MYNeourqa888/H5erb2OeMFNYbbsQRnzE1zy5fz3h1+/J/J+ovkrKT/8meuWcYY0rj6GxSb7kgtHEiLbxpY/O44IT5/LVB94imrRZV2dw6zNBvvexctxa78dk6YKzaH3nj1jRZmK73ybZvANPzeimNxHCxooHseLB9E1/acZ7UNEGjmCSSCRQVVWVuQ6PF6xII2aoPm/jCSHY2uJo80s9MLWk5+eJbauIrHsY0qldFG85ZSd/BfeUIwa1j66nXrs4beeSXhgPckF/6IFqbDOJERz6/5Pq8jL5rK9T9/g3wbZoW/co/lmL8U49LI8zHR5CCKxEGCsRRghBMm4Qi+1Dc3lRXF5UlxfV5UN1+VB0r4xOkEjGMLaRINm8fRgO0sNH2Bata/5A+7pHyaQ0DNQw9aPfxjvlsB5OdBUlPs45fh4uacwsOIrI0YVx0qRJvPbaaz3SBW3dupWTTz6Z5uZm3nvvPU499VTa29v7HeuRRx7hiiuu4Be/+AVLlizh/vvv55e//CWbNm1izpw53HLLLdTV1fHQQw/1uv33vvc9Hn/8cdavXz/o+YdCToHbYDA4pIiDDqx4kETTh0PevivCMojXbyS2Zw2xve9gtPeRc1TV8U09Av9sx5DgqpiVFwXbxIo4GBpDWSNF1Zz0R3oXw0JHWiTNnUmPRD8pksYKQgiCwaCMOOiHXNbINM1+67HYyQhm+26ENTTj5VARQhB//x9E332EzMW8Yjblp30DLVA97LFjKVtGHPSDEIKaWYfirpgx2lPJIp9ywWjQXS7YuKedGx54i7jhHOOnH+zhPz5SitrHcRnc+HeaX7sfgMDck5hyzn8U7TGseUsz6Yz6i56T5/SBkWvUP2N9fQa6DueDjhSGJSUlBV8jM9yAFT6Q1zHrw4Kv/D0FwHHTVb5zRqeRRRgJwm//iuSeNzJtrkmHUrp0BZqvclDjCyH4sNni5lWO0eGMeSorlowfQ04+EEIwafbhuMqnjfZUshhvckFfJFt2YUZbhrWvtrWP0Lr6YQD0smnMuvgnqC7fsMYsBIO5F1Z1t2NQ0H2obseYoLq8E8aZbqxf9wqNXJ+BGa01suJBki27huwYnQ/MSDMHnrvTcaBO45t1HFPOvAnN59Qx6H4esiyLaDQ6WlMuSoQQTJpzJK6ynll8hkPOEQemabJly5YegsCWLVuwLEew83q9gzrQL730UlpaWrjtttuor69n4cKFPPXUU8yZ43it1tfXs2fPnlynOCJovnLc5dNJBYcf7qtoLvwzj8U/81jg3zGC9cT2vkNszxri+99DWI5Qjm0S37+B+P4NtLz5G/SSyRkjgm/60agu77DnIskfwrac4lndamL0hqLqaYOCK21wcIGqpQ0O6XoM6ffjwdAg6Z9wOMzq1as54ogjCAQCmXYhBFa4ASudHmgkEWbKUQLsfi3T5p51PGUnXocizz0TmnzKBcXAwtkV3PnZo/jGwxtImPDS9iQ1AZXrTu49bVjpYefQtvZPWPF2orvepOXNX1N90tVF+X07PAdp24vq9qN5SjJpCGQaPomkk76uw2MRM1RfELkhq75Bl8LIZrCO0Kt3Y3VJieQ77N8I1F7iOMzkQNdURTLiYOww3uSCvnBXzUFYBlZi6EbGimMuJrpnDckDWzBD9TS//gCTT/9KHmc5cthmCswUFtnroWguVLfPMShkohV8E8agIJEUM0awPi86zeEQ3f02jS/cjZ0MOw2KStUJV1JReyGK0nckUySeZP22Bg6dVY3fKx0LCknOhoMrrriCa665hv/4j//g+OOPR1EU3n77bf7rv/6LK6+8EoCXXnqJI488clDjXX/99Vx//fW9fvbggw/2u+33vvc9vve97+Uy/bziKp+GbcQwY+15H7e8fBnlC5dhm0ni+98jtucdYntWY3bxFjIjjYQ2/5PQ5n+iaC680xY6KY1mL8ZdPj2vc5IUFpFOkcQgQ8Oc4s6dhgXUjoLPXQ0Q6XZNRpKMNWzbJhKJYNudN+V2MoIVbsBOjbxV3Yq1Enrlx5itOzJt/oUX4V/Y/8VcMjHIt1xQDBy3YDr/9/xW/s/j+7AF/HlDnOqAyqdre6YLVHUP1Sf/O43P3QlAcMPjAEVrPOjATsWwUzEIp+uUqLpzY59OOWClDIRdImskSCYkvV2HxyJmaD9WpKkgY29r6VkYObH7DcJv/zLjNKPoXkpPug7PrOOHtI+s4shj+6eYUIxHuaA3FEXBU3MQicYPsFPxgTfobQxVY8qZN7H3sa8izAThLc8QmHM8gbkn5nm2o4ewDKy40btBweXLpDxyohW8Uu6QSEYAYVukWnflXZeZ0xwsg5a3H8rcOwHoJZOYcva3BpW2zbYF0YSBXaR1AMcTOZ+Vf/zjHzNlyhTuuOMODhxwlNhTpkzha1/7Gt/+9rcBOOecc/jYxz6W35kWKe6qudjGcIol94+qewjMXkxg9mKE+CJGsC6T0ii+fyOkw4mEZRDft474vnW0vP5LXGXTnGiEWYvxTl+Iqves8yAZu2QMDYOIZoAuaZM03YlkSEcwoGooipZ+rTrGBkVD6fZaMrKUl5dz5plnOoJ2pBEr1tpZB2WEMZo/JPTqj7E7ihNpHsqWXIdn1gmjMh9J8TFe5YLTjzuMG9vC3PWiUyDs/jeiBFwKHz+iZwqB0kNOQ5gJml76GeAYD6xoK1UnXoWrdPKIznuoCNvMymWcihvE4vvRXB6UdA7jroaFYjaKSCTDpeM6PJYxg/uwhplGpT+2Njs36gpwcKVF5J3fEd/6bOZzrXwWZafcgF429DQ6XUVQU+oFxgzjVS7oDUXV8NQcQuLAFoRlDGkMV/k0ak7+Ak0v3wNA40s/Y9aUQ9F9FXmcafEhLAOrl4gNx6CQNiK4faiaO5361yPvSyWSPFAM9QyMUAMH/nUHyaZtmbbA3JOYdPpX0bylgxqjLODllKNmFWqKki7kXOOgKx25P4dTK2CkyVeNg67YRmJYxZKHvt848boNaUPCGsxIc6/9FN2Nb/rRTjTCrEW4yqZmfS5rHAzMRF4jRVE6DQjpSAcU1TFGKI7xAUUlFE1QXl6GqqaNE0pnfxmKOvichcK2SIZbCDbuxk5GRnCGPUnsfIXw2w+A7dwIqYEayk+9adhFkHtD1jgYmGKtcdCV8SYXmJFmfvXsZh5aE8u03XBaCct6MR4AhLY8mzEegHPzW3bkMiqP/fSgheBiYKBrnqIomVQDmcKILt+ESnckc/X2z1hfn7Fc40AIgRXcixVry9uY3UmagiseTWEJOLykja9778Vs6az95pl7KqXHfx5lGI5LQggaQhZfedJJbXP8DJWbT5epCLpSrDUOujLe5IK+sFMxEo1bnTS1Q0AIQcMzPyS2+y0A/HNOYOq53ymayN5iuRdWNJdTS0FLP1wex7CguVB0z6jec471616hkeszMCOxRk49g51DPlflg8iO12h66WedmRRUnZolV1N25LL+9SSyxsGAFE2Ng66MJQGgkKguL57quXkrljz4/foIzD2RwNwTEUJgtO0huucdYnvXOEVF0icDYaYc48KeNQC4KmZmjAjeyQtQirAAk6R4EEKAMBE2QKrPPkbcIGm09HmyzxgQ0tEPGeND5nU6IqLjdcZA0WGIGL8ChrBtrEQ7VqwNKx6iqS3Ic6s/5OSFMykPjHy0kLBtou/+kfiWJzNtrkmHUXbKDaheed6X9M14kwv0khquOnkyceMAj77rpCH4ycsRhIDzj+x57Sw77BwUzU3za/dhJyMIyyC44a+EtzxLxbEXU77w/HERASiEQKTiPVIzKKremXLA3cWoII3HkjFGS0sLTz75JGeccQYVFRWjPZ1BI4TAbN/TGSVYIHa2CcdowCaujd+PGU3nJVZ1ShZdhffgM/Mit3V1LrZkxMGYJB9ywcqVK7nzzjupr6/nyCOP5O677+bUU08dcLvXXnuN008/nYULF7J+/fphz6M/VLcfT/U8ks3bGYpfpqIoTDr9K+x79AOseDux3W+z/+//weTTbyhqw9BI0xGlAL0rCxVVR9HdaeOCx4lU0N3pqIXRNSxIJKNNKrgfI1g/avu3zRQtbzxAaPNTmTa9bBpTP/ItPJMOyXm8tnCM51ZvHzWdyURiSIaDxx57jD/96U/s2bOHVCpbkbh27dq8TGyskc9iyUNBURTcVXNwV82h8piLsJJR4nXr00WW38GKtWb6Gu37CLbvy+QS0wI1qGUzSFbPxlM5G1flLNyVs9CkglCSRzLFoodIxpCg6ZlIh65RDxljQ4eBIp2CyWkrvoLSjrEgmDYWBBGiM3mvz+1iwawqvK6RF27tVJTQ6/dg1G/ItHkPOZuS466UOUclfTKe5QJP1Wy+uCSKosCf1juK8p++EiGUsLn8OH+Pc0vp/DPwz15M+7rHCG58AmEZ2KkorW/9luDGf1C1+LOULjhrXN68CtvESkawukVLqbq7M0JBTxdG1D0omvQelhQnfr+fI444Aq/XO9pTGTRC2JituzqLCxaQrU0mH+cJlvE31HTVYjVQQ9nSG3BVH5S3/WTXOJCWg7FEvuSCRx55hBtvvJGVK1eydOlS7rvvPs477zw2b97M7Nmz+9wuGAxy5ZVXcvbZZ2fSJRUazVeOu3I2ydbdQ9pe91Uw6fSv0vD09wFBon4Tex9bQeWxnyYw9yRclbOL7n6m2BC2iUiZTg2nXnAMCy4UzZ2WQ9KGhY7X8l5HMg4RlkmydSdWvLCRlP2RatvDged+RKplZ6at5OBTmXTaV1DdPWvIDYbR1JlMNHI+M/70pz/lO9/5DldddRV/+9vf+PznP8/27dtZvXo1X/7ylwsxxzFDoYolDwXNE6DkoKWUHLTUyVXcsjOT0ihx4APooqS0os1Y0WaM+nezx/BVOEaEilm4K2fjqpyJu3I2mq9CCi2SEccxOlhDzh+qKGo6gsF5RlEzbShK5+fd2zrSLilKj76gpFM5KYDS+RmdbR25OIVlYhsJzJiFnQg5xoI+DClej4uDp1cO6XsOBzNUT+jl/8EKpz0RFI2SRVfim/+REZ+LZOww3uUC1e3HVTqZL5woUBX44zrHePDg6hgtUZsvn1KCpmZfEzVPCdUnLad84b/RuuZ/CW99DoSNFW2h6aWf0r7hr1SfsBz/nOMnxPXUNlNgpnrcsCiKmvYG9PS8gZeegZJRxOfzsWDBgtGexqARtoXZtmtEUhzayTCzPljJEqXTwcA9rZbSJdejekryui9NRhyMSfIpF9x1111cc801fOELXwDg7rvv5plnnuHee+/l9ttv73O7a6+9lssvvxxN03j88ceH83VyQi+pwTaTGKGGIW0fmHM808//IY0v3o0ZbkSYSVpXP0zr6ofRAtX4Zx6Lb+ax+GfUovnK8zz78U+HYQHi9HYXpqiak/YobURQNJdTGzDzOv0sHR8kYwQ7FXPqGZi9Z44oNMIyaVv/GG1rH8nUZ1U0NzVLv0jpYecM6z5otHQmE5GcDQcrV67k/vvv57LLLuO3v/0t3/rWtzjooIP4v//3/9La2jrwAOOcQhdLHgqKouCpOQhPzUFUHncJViJMfN964vvfJdW2l1Tbnl5vNKx4O1a8ncT+97LaVU8J7spZuCqcyAR35WwnQiFQMyEUIJKxiRA2CLurzWzEUBQF27ZJxg3csYFzgxqmRVN7jIoSDy59ZBRnqfoNhF77GcJwPHQUdwllp9yAe8oRI7J/ydhlIsgFrvLpmLE2rj4BStwqv3rLCZH/++YETVGbb55ZSpm3Zx5ivWQSk8/4KuVHf4LWtx8itvttAIy2vTQ88328U4+g+sTleKcePqLfp1gQwkYYCTASfdzA610MCd0NDB4pc0gKRiqV4sCBA1RVVeFyFbeCSFgGZutObCM+cOdhYrTsIPTaT5iZcOqq2SgEFn6KwMJPFCQXu5oVcZD34SUFIl9yQSqV4p133uHmm2/Oaj/nnHN4/fXX+9zuN7/5Ddu3b+fhhx/mBz/4wYD7SSaTJJPJzPuOugxCiCGlHXKVT8c2k5jRoclA3mkLmfmpn9Ly1oOE3386025FWwh/8C/CH/wLUHDXHJwxJHinHFpQZXbHWgyjPOaYQFgmWI5hoT8URXEi2rW0UUF1gaphxFIYmpGWVfSM0UEycY6h4ZDvNTKjLaRa92RlNhhJkk0f0vTST0m17sq0uSpmMuUj38JdNRcgp+/afX1ShkljW5SKEi8uvTjqwYw2hfr/yvkstmfPHk4++WTA8cYJh51w2CuuuIKTTjqJe+65J78zHGMoqoan5mASB7aMasGR/tC8pZQcciolhzi5IW3bJtTWjDtRj9G+L21M2IvRtgerlxypdjJCouF9Eg3vZ7UrLh/uipmOUSFtTHBXzEIvnSy9BiUTmlxP4OFYkjc313Hq0bOoKCns/44QgvgH/yS6/g+QnqdWPpPy076OVjK5oPuWjA8mglygqBruihkkW3Zx6bF+qgIq//NiGMuGN3en+OKf2vjWWaUcN7P34sCeqjlM+9h3iddvouWtB0ke2AJAomEzdX/7Fv7Zx1N2+Ln4Zx0nvdi6MGDKAc3VGZ3QNVJBc0+oQs2S/BMKhXjllVc4++yzqawsXm8224hjtu4ccjTmYBFCkPjwOSJrf5fxGAyLEp6ruI5/P+rYgu1Xk4aDMUm+5ILm5mYsy2LKlOwij1OmTKGhoXeP/m3btnHzzTfzyiuvoOuDU3Xcfvvt3HrrrT3ag8HgkJUwQqskZYX7vH4NjAvv4n9Hn/9xUnVrSdW/i3FgE1gdXsOCVPOHpJo/pH39o6B7cU85Evf0Y3BNPwatdFrei67HkmlvYWm0T5Ptwd2xRka4MWuNnOh0zTEgdEmv6zzraQOEjqLoKNr41ZkIIYhEHGdVeQz1Tr7WSFgmRuTAqKUmElaK6Lt/Ir75b52ZThQV3xEXEDj6EpK6h2Q8d7ml+3moLRjjrff3c/zhMyj1yxoHUESGg6lTp9LS0sKcOXOYM2cOb775JrW1tezcuXNMWQ9jsRilpaWZf8hUKoVpmmiahsfjyeoH4PV6UdMpRwzDwDCMfvt6queRaPoQwzQxDRNVU/G4O29i44kkCIHb7UZLx+GaloWRMlBUFa9naH0TyRTCtnG5XejpC49l2aRSqT776i4d1VeBr2oSnqlHOXkoFQWf14OVCJNq30usaSdG216sUB1mcB9mpLnHmgojTrJpG8mmbVntiubGVTEDrWwGevlM/JPmOcaFsmkYlsCyLHRdx+VyDkfbtkkmnQuxz9eZWzaVMnr0FUKQSCTT697pfWgYJqZp5tRX0zTc7k6FTTzuRI14PO6svpZl9ds3c5zk8Nvn4zjp+D2He5x0/PZD6ZtMpRC2QHfpuPT+f89kKoVt2YPqO9BvP9zjpK/fM9fjZDC/vY6F1+vpt295iZelR05HVwWWZWf/noaJqih4sn7PJEKA2+UasG8ymcIWApdLR7WTRFb/muTuNzKfu2cupvSk61Bdvqy+Wb+9YaAo4PV0/e0NbNvGpWuZG7Vc+tq2TSKRRLFV/F1/T8PAsmx0Tcs+R6QcYaPr8ddbXyEEifQx5e32v2xaVk59NU3F3cXzNJ4+pjxuV5frw+D7mqaJYVqoqoqnyzHV6+9pmqQMk2JkvMgFA6EHqjGjLViJMB9d4KXKp/LDf4UIJwUtMZtv/yPIhUf5WH68H7+7d68X37QjmfGJO4juepPWt3+L0V4HQGzPamJ7VqN6SwnMPRn/rGPxTT8azVs6kl9xzJEpkthL1KSiqI4nYLqWgqK7u6QZcHd6AqrSQ0nSk8rKSs4777yirXEghMCOtWCF6xF2YTXqwkwQXv1rkrtey7RtFwfzS77EWdMnFXTfiqKgKmALmapoLJFvuaC7Ak0I0atSzbIsLr/8cm699dacUo3dcsst3HTTTZn3oVCIWbNmUV5ePqzizqK0hETjB8PLROCbC1PnAhdhmykSDZuJ71tHvG59Vq5wzASpundI1b0DgF4yGd/MY/DNPA7fjKPRhplGrON3K/UNHDk9URl4jaz0IwU2zqMbWZEMXQwMaHr2+67GhzES0dCxPuXl5fIY6oN8rJEZbSEV3ocLE3wj74yUaNhM00s/wwjWZdrcVXOZdPpXh1QAuSvd/8c8eoCzjp2Dx633SBs7USnUvXfOd0tnnXUWf//73wG45ppr+NrXvsZHP/pRLr30Ui688MK8T7BQLF26NCtU8le/+hW1tbXcdtttWf1OOukkamtr2b+/s+jxww8/TG1tLbfccktW3zPOOIPa2lq2b9+eKZb8xDMvsWTZcr79/Z9m9b3o6q+zZNly3t/WecF/5oU3WLJsOTf8nzuz+n72+u+wZNly1r7X6eH/8htrWbJsOdd+Mzv88uqvfY8ly5bz+urOegVvr9vIkmXLuXLFd7P6fvmW21mybDkvvLo607bh/W0sWbacS774bcCJTvBNPYL//N+NfOzWF1mjn82cz/6GeZ9/BHPx17nnbTfP7i7BP/t49LKpQM9/WGGlSLXsJL7zVcLr/8iBVbez90/Xs+OBT7Hlgct46ceXsvZ3X6PplXtpW/sn9q/9B9defQWf+dxyrGQ0c/D/6N7fsWTZcn71h79mxg5HYyxZtpwly5ZjWp0RHj/79R9Zsmw5P/v1HzNtpmVl+oajnd4fv/rDX1mybDk/uvd3WfM+9RPXsGTZcppa2jNtf/jLP1mybDnf//Evs/p+9NLrWbJsOXv3dxbf+vM/nmPJsuV85/afZ/W94MobWbJsOdt27sm0PfWvV1mybDlf/96Ps/pe8sVvs2TZcja832mMef7V1SxZtpwv35Kd1/PKFd9lybLlvL1uY6bt9dXvsmTZcq7+2vey+l77zR+wZNlyXn6jszjZ2vfeZ8my5Xz2+u9k9b3h/9zJkmXLeeaFTuXy+9t2smTZci66+utZfW/+wc9Ysmw5TzzzUqZtx546lixbzsc/uyKr7/d+dB9Lli3nkb89m2mrb2xmybLlnHnxtVl9b//Zb1iybDkP/unvmba2YDjze3bl7l/+gSXLlvOLhx7LtCUSyUzfDgMCwC8eeowly5Zz9y//kDVGR9+2YGeBwQf/9HeWLFvO7T/7TVbfMy++liXLllPf2GlMe+Rvz7Jk2XK+96P7svpecs1NnHz+59mxp/NC2ts5QlNVrvvmD/j4Z29g647O4+T5V9dw7mUruKXbMfXFb/4X5162Ius4eWPNBs69bAVf63ZMrfg/dzp931hF2z//I8to8K+6aspOuQHV5QPgm9//KedetoJX3lyX6bN56w7OvWwFn//a97PG/e4d93LuZStY9fLbmbYde+o497IVXH599rnnh3c/wLmXreCJZ1/JtO1vaOLC5Tdy8Re+ndX3R/c+zLmXreCxfzyXaWtpC3LuZSv4+OduyOp7z68f5dzLVvC7Pz+VaYvE4px72QrOvWwFVpdzxC9//zjnXraCX/7+8UybZVmZvpFYZ4jy7/78FOdetoJ7fv1o1v4+/rkbOPeyFbS0BTNtj/3jOc69bAU/uvfhrL6fuuZbnHvZCuoamjJtTzz7CudetoIf3v1AVt/Lr/8u5162Ius4WfXy23zs8q9SjIwXuWAwuCtnZQT5RbPc3H9JJcfN6BTK//penKv/2MZL25N9Cm6KolAybwmzPv1zJp32FTR/VeYzOxEmvOUZDqz6f+x66HPs++vXaXn7d8T3v1dwj+LxhhA2tpnESoQwoy0YwXpSrXtINu8gcWAL8f0bie1bR2zfeuL7N5Jo3EqyeYcTcRmsx4w0Y8WD2KkYtpkaV0YwycBomkYgEEArQu9PK96O0fQBZrCu4EYDM1RP27P/mWU0WOv5CHfxbdqpYumcwhveOqIOZG3ksUO+5IKamho0TesRXdDY2NgjCgEgHA6zZs0avvKVr6DrOrquc9ttt/Huu++i6zrPP/98r/vxeDyUlZVlPcC5Xg/noeouvJPno+ruYY+lKAqay0Ng1rHULLmaWRf/lDmf+y2Tz/waJfPPQPNVZH0nM9JIeMuzNP7r/7H7oc9R9/g3aVvzByfiUdh5mY985P8BgG0ijDh2MowVb8eMNmOGGpzMEK27STZvJ9n4gWNE2r+B2N61xOs2kKjfRLLxA5LNH5Jq3Y3RXocZasCMNmPF27CTYYQRR1gpSBvf5GPsP8DJCGK015Go30iqdTcIa8TnIcwEza/dz/4nbuk0Gqg6lYs/y8yL7sI7eX7e96lrGgGfG11TR/13KKZHIcjZPHn//fdjp4XU6667jqqqKl599VXOP/98rrvuurxPcCzjKp+GNsQK4cWO6vYjyufywi4XG8MVfOm//i8AtpHgjjvuYN+HG7j07KOYP8lFqn0PRrCeHsnlhU1ATTK/GjB2Edq8K/PRf57hPO968DMouhc9UM0ZriRTT0gw23iH9vc86IFqDDVAtc+mPSEtjJLxQzSeImXmP9WZnYpyTFU7F5ycYObe32OnXV1s1cP/vALNrhouK0COYsn4ZiLJBarLh146GSPkGIlrAhq3LyvnLxvi/ObtKCkLWmI2P1gVona6i6tPDHDElN69fRRVo+zwcyldcDaxuvVEtr5AdNebzg0dgLBJNm4l2biV9nV/QtG9+KYvxDfjGPwzj8FVObtgwuFEQtiWk1rSTA7Yt8OzT9Hc6VzGBoYSd5RCXYomourytxnjRCIR1q1bx6GHHorfXxyyvJ2MYIX2j0gtA4Dk3rcJv3kfwkx7S+sejIVf4P51iwCYW6Ewt3IEDAcqGDaYMlXRmCFfcoHb7WbRokWsWrUqy+CwatUqPvGJT/ToX1ZWxnvvZdfmW7lyJc8//zyPPfYY8+bNG+I3Gjqq7sE76RASBz7Ie55xPVBF6YKzKF1wFkLYpFp3O0rkfeuI12/KpBVz5IkPSDZ+QNvaP6K6/fim1+KbdSz+mcfiKpua13lJRh5hmwjbHJQs04GiqE7qpC7RDPRIo6SiKBooqvN55lmTEZujiLBtrGQIK9aOFQ86v/0oEtu3jqaX7sGMNGbaPJMPZfLpK3BXzSnYfqPxFO/taOTgGZX4PTLVayFRRA4uVKZp8sMf/pCrr76aWbNmFXJeBSMUClFeXk59fT1TpkzJ3NjlO1VRR99UMkG4bhOKnSrqVEXxlE2pz4Vtiz5T0HRPK9NfupoefY0k0eY9mO37INpAqm2PYzmPNmPHg8Bw3YgUNF85WqAaPVCN6qtE8VbiKqnGUzYZLVCN5q8iZTs380NJVRSOG3h1RaYq6qWvEIJw3MCtCZmqqI/fUwhBY1uEUq8+YKqi1mCUF9Zs5ZiDJ1NVHhhWqiKSIWjaQHLvaowDm0FkGyT06kMInHQ9lrui37RGhU5VZFkW7ZEkPrdMVdRfqqJZ84/GXTGDYmE8yQXBYHBQKQmEbROv39gjAqA+ZHHPqxHe3pOd83bJXDefPyHAvKqBfTVsI0GifhOxuvXE963PKibWHc1fhX/mMfhmHotvRi26P3952DvO6TIlQd8MtEaOIUHLPPe4Ge8t7cA4WmshBMFgcMymJGhubub555/nhBNOGFaqkv7oyGVcUlLS7xrZRhwrVI+dDPfZJ6/zsk2i6/9I/IN/Ztq0sumUnXIjf9w5hb9uduSI5cdpnH9Y4VJkCCGIpWy+9IRJ1IDpZQo/WyZrl3RFCMGk2YfjKp822lPJkG+54JFHHuGKK67gF7/4BUuWLOH+++/nl7/8JZs2bWLOnDnccsst1NXV8dBDD/W6/fe+9z0ef/xx1q9fP+h95ioXDAYrHiTZvH3Eotcy8sS+dcT2rcVo29tnX1fZNHwzj82kSVR7cXyUcsHATLQ1UhSl04DQ3aCgaNmGBlUDFEKRmCMXqHrPfhNgzQaiP9lJWCZWIogVb8eKh0at4HFXrGSEljceSBdsd1B0N1XHX0H5wvPzXue0+/9YazDKaxt2ctz8qZT6pXwAablgzpG4ynpG5Q2HnAwHACUlJWzcuJG5c+fmdSIjRSEEgYGwjYTjZTDKlsC+GO2LnLDMdBhei5M/OtaSft2KFW3BjDmvRR48rBTdjeavRg9UofurUL3laL4yNF8Fmrfrczmqp8SxxDP6a1TsyPUZmFzWyLIsotHokPdlRZpI7ltDct9qzKat9GaYU7zl+OZ/BP8RFzhKq1GmQ0Hgd0vBsS+EENTMOrSoDAcwMeUCM9ZGsnlHj3YhBK/vSvHLN6PUBTuNdApw9gIPVy4OMK1s8EK0GW0lXrfeSadTtx4r1tZnX3fVXHwzj8E/81i8U49AdQ09N7s8pw9MIdbIMSJo6aiFrh5/nXmMe7Tl+aYsX4x1w4FpmoRChS0qOJDhQJgprHADVrzv//t8Y4UPEHpjJWbLh5k2z5wllB7/BWzNw3V/S9Ead9IH/fJCN+Xewv22HXLBV/5hEkrC1BL4+QWy+GFXitFwAPmXC1auXMkdd9xBfX09Cxcu5Mc//jGnnXYaAMuXL2fXrl28+OKLvW5bLIYDACPSRKp1z8AdC4AZaXZkiX3riNWtw070YYhUNbyTD8M3fSHeqUfgnXKYk2lAygUDIteofwZ0uOiIgFAUUNLGiPQzipJ53aNN1dJtSrfP1V7HKuZIie6yk20mseKOscBORooqbWZ05xs0vfoLrFhn+nfv9KOYfNpXcJVPL8g+ux9Dw9WZjEeKxnDwyU9+kk9+8pMsX748rxMZKUbDcACOl0Gi6cOBO44CY+UiZ6dinQaFjHGhJW1caE0bHdp6pkQaKoqaMSao3jKEuxRPoNKJbPCVo3nLs147hobiXb9CMlaOodGk0IYDM7iP5N7VpPatwWzb1WsfNVCDZ+bxeGYdj149v6gEJ2k4GJhiNRzkWy5YuXIld955J/X19Rx55JHcfffdnHrqqQNu99prr3H66aezcOHCEVEQJJq2YcV7VyyaluCZDxL8bk2MlljnNUlX4WOHebn8OD+TSnJT+AohSLXtcW76960jUb8RYaZ67atoLrxTj3AMCTOOwV1zUMYQPth9yXN6/xTLGimKkhXNQPcUA5n0A10MDUrH68JdA6ThYGD6MhwI28SKNGJHW0bMo1AIQXLny0TeeagzNZGqUXLs5/DO/yiKorC+3ub7LziRVotnqNxyemHTAnTIBV990qQ9ATV+uO+T0nDQlWI1HEh9Qd+k2vdl0h2OFkLYJJu3E9+blicOvA92XylSFbRAtZPOyD8ZX+U0XOXTcJVOxVU2FdVbNibP8YWgWOSCYqWY1qen8aEjJZMCKOmynR354pUu7QpK1nuy28EZF5wxM33JbJMxcnQZp2M9bMsg2NZGiU/HToaxU511OYsFM95O86v3Ed3xaqZNcfmoOelqSg8/J6f7jVyRhoOBKZThIGc30/POO49bbrmFjRs3smjRIgKBQNbnF1xwQd4mN57oKJacCu4fuLOkV1S3H7fbj7uy77BXYVuZ6AUrbUzIGBeirZixFqxoK3ZqECcYYadDwdozTf1mDVQ1x9DQzaCQMSx0a1PdgVG/aEqKk/ZwnBfe2cFJR8ygLND7TbIQArN1J8l9q0ntXY0Vru+1n1Y2A8+s43HPPB69co485iR5J59ywSOPPMKNN97IypUrWbp0Kffddx/nnXcemzdvZvbs2X1uFwwGufLKKzn77LM5cGBkbsjdFbNIJN7vVbGnawr/doSPjyzw8sTGOP+7LkY4KTBt+MfmBM98kODjh3u54EgfsysHJ4opioKnag6eqjlUHP1JhGWQaHg/HY2wjmTTdjqii4RlEK97l3jdu7TyW1RvGf4ZtfhmHoNvxjG4Sifncykko4gQAixjSMWzs40OXYwLSsfrrsaILq8VJyfyeKa1tZWnn36aU045hYqKihHZpxA2dqQJK9pY8KLHmX3aFsl9q4lveQqzZXumXS2ZTNmSL+OqOSTT9uKOTsXimQeNnOOBnt6VLI48dpD6gr5xV8xEmCnMfiIIC42iqHgnzcc7aT6Vx12CnYoRr99IbO864vvWdRY2BUBgRZuxos0AJLqP5fLhKnOMCHrp1LRRYYrzvmTyuL9WSMYmQtgZZ9NiurQIIUjFDQxj9I0r3RFCEPnwJZpfvz8rYsk/ezGTTr0evWTSiM9pMDoTSX7IOeJA7cc7qcPqU8yMVsRBB8nm7Zix9hHfb38Uk/V3pLCNeDrsK/1IpB/d2ux021BuyAdE1dOGhjJUbymauwTVU4LqCaB5Ol6XdGkvcdrdgaITwibiMZQruaxRJJZgy84GZk8pw+vu/K2FbWM0f0Bqr5OGyI619Lq9XnUQnpmLcc86Hr2sMKGC+UZGHAxMsUYc5FMuOPHEEznuuOO49957M22HH344n/zkJ7n99tv73O4zn/kM8+fPR9O0EU1JkAruxwj2brTrSjRp8+iGOH/ZECduZItdR07V+dhhPk4/2IPPNfRj30qEiNe9m05FsD6rQFl3XOUz8E47Es+kQ/DUHIy7cnZWaiN5Th8YuUZ0K5SYnQZAoBCKJikvDXTmP85KF6B020brO83AKBAKhXjvvfeYO3cuPp+vIPvoiDgIBAKIRBtW+EBh5M1esI04iR0vEf/gaexoU9Zn3nmnE1h0Baqr83vvarO55VmDlAUlbvjVhW5cWmF/mw654BtPmzRGodwLv75IKga6UqwRB1Jf0D/Ctkk2bcNKRvI+dj4wI03EG94n0bCZZNM2jFB936mN+kNR0UtqcJVNSxsVpmYiFfSyqWiekvxPfhSRckH/yPUZmGJdIzPSRNMrK4ntWZNpU72l1Jz8RUoOOX3E5tp9ffrSmUxkiibiwB4hD5jxirtqLraxBdvobq+XjCSqy4ea9pAYCCEEdipGqL0Fr4hiJ0LZRoZE1+eQE6EwmHoWtokVa83KCzdYFJcPzRNAdXcxKHh6eT0GjA6Snvg8LhbMqgKcGiDGgU0k960mue8dRLK31AkKrkmH4p51PJ6Zi9ACI2/xl0xc8iUXpFIp3nnnHW6++eas9nPOOYfXX3+9z+1+85vfsH37dh5++GF+8IMfDLifZDJJMtkZP9aRjkQIkXPuUL1kCmakGbuPlEEd+N0KVy3288kjvTy2Ic7jG+Mk05eJTQ0mmxrCrHwtwhkHuzn3UC+HT8m9UK7qKSVw0CkEDjrFiUgK1WdqI8TrNiCMznBnI1iHEawjvOXZTJvmr8JVPh1X+XT0smmYvim4a2bhKp+GqsuCY93pOF6KKd/sSCOsvmUdIQRm3CAlIsO6oczOT9ztNV3D/+nynqz3So/Pe3+vdHmvmSaHTC+DVCtGqsv8s9IR9DKHrLGVXrbp3K9tJDDCQVKR1ODkxjxgxVpJbHuWxIfPZ50TALSK2fgXfgrPzEUAmWO7KSr44YuO0QDgzHkqukrBj/2O/y8trYO27MLvc6xRrOsh9QX9o6gqnpqDSTR+UJQ6Ab1kEqWHTKL0kNMybVYyQrCpDleyGTN8ADNUjxFuwAgdcBwVekt1JGzMcCNmuBF4t8fHqqekM1KhrNOg4Cqbhh6oLtoaPhLJRME2UwQ3/JW2dY8izM57p8BBp1BzyrXovorRmxzZOhNJYZEaxBFGUTW8kw/FCO7HiDQNvIFk1FEUBdXtRyt14R2E9VcIgTDimYr3VqI9bVgIdWvrNEDkesMojDimEQeac/8+3YwOqieAqvtQXV4UlzdtVPGiuHyourfzdZdnVXdeK1pxWcPHC8loO81bXsfVshGjfn2Pm3sAVA3XlCOdmgUzFqH6ykd+ohJJHmlubsayLKZMyfaQmDJlCg0NDb1us23bNm6++WZeeeUVdH1wIs3tt9/Orbfe2qM9GAwOSQlj6VWkwnsH1VcFLjnazccW6Ly8w+C5D1PsaXcULHFD8M8tSf65JcnUUpVT57k4Za6LGeVD9Lp2T0I96KMEDvooftvCbN5Gqn4Dqfp3MZu39qgH1GHITtRvzLQ5/oUKaqAarXQaWtk057njdckUp5DvBEQIQSxt/ZHXwZ6M9fVJpQya20IEfG50rTBpeYQQJAwb4Sp8ZIUd3IOx7Z9Ye98Eka3gUycfhWvBeaiTjsRSFGKpznNDNCX4wYsWrXHn/UGVcMFh2X0KRcf6dKyMaTMi+x1LFKvhQDIwiqbjmXQIiQMfjFik0XBQ3QH0qnmU+Bb0OF8J28KMNGGEGjBDDWmDQkPmfV8pgu1khGTThyR7qwOp6rhKJqGXTcNVNiUdtTAZ3V+FFqhC91dNWPlDIik0qeB+ojvfJLT5KcxwZwpYzV/JpFO+RGDeklGcXSeGadEajlPm9xRMVpM45GQ4sG2bBx98kL/85S/s2rULRVGYN28eF198MVdcccWYvDEYDRRNx101G72khlSwDjsRGbHiZ5LCoygKituP6vYPqqK8EAJhJrFTUaxkBDv9cF6H06+jTnsq0qPPSBodeqBqGSMCmoeQx9fF4OBLGyK6vNZ7aUv3V9zpZ90z4c4lQghSLTuJ7VlDbO8aEge2gBD08GHW3Lin1To1C6Yfg+oO9DacRDJiFEIu6HFDKkSv41iWxeWXX86tt97KggULBj3+Lbfcwk033ZR5HwqFmDVrFuXl5UNMSVBOQnHS3w2WUh9ctsjDZ44TbGk0eXpLghe3pzJpjBrCNo9uSPLohiQzylWWzPFw/CwXR0x14dGHcn50QeAomHMU8FlsI06qZadzw96yw3FmCO7HTvQW0SSwo83Y0WaMhve6faJi+2ow/dOw/NOwAuln/zQs3yRQR94/RQHcOnh1BY+uZJ5dWhdn8D5QFXBpCi51YGV3h8Ku2MLJi4Wxvj5NiQTvfLCfU4+aid/nHXiDIdCxRoVK0SeEwGh4j/iWpzAObMz+UNXwzFmK79Dz0Cuya4elLMGuNsEbe2xe22NnjAZTS+A7Z7go945cSgIAl+YYOmzhrJWkk2I0HEh9weBRdQ+emoNJNm4d07oARdUyEQPdEUJgJyMYoXrM0IG0UaHeiVQI1WNGmuk1y7xtpvvVE+9jv6qnFD1QheZ3DAmav6rzfaa9UhoYJJIBEEKQat5OdNebRHa+gdG2J7uDolJ+5MepXPzZokoxFo4lee29fZx69CwqSgojq0kcBn1HJ4Tgggsu4KmnnqK2tpajjjoKIQTvv/8+y5cv5y9/+QuPP/54Aac6/lDdfryT5mdS4XQqhqNjwvNAkh8URcko0/VAdU7b9m90iPRibIhmDBJDMTr0wLYcL5K0J0l+MpYq3SIfvL1HRPQXJeH2dYuY8Do5k4sIOxUjtm89sb1riO15p8+UVYrLj3vGsXhmHo972tEouszvKykO8i0X1NTUoGlaj+iCxsbGHlEIAOFwmDVr1rBu3Tq+8pWvAI7CQgiBrus8++yznHXWWT2283g8eDw9/48URRmyQsNTNZtE/eacb/wVReGIqW6OmOrmS0sFr+xI8tzWBOvqjMxtdF3Q5rENcR7bEMelwhFTXSyd5+bMQ7xU+Ho/r1m2IJoShBI2LVGbhrDNgbBFW9wmkhSEkjbh5HQiyamEE0uJmwLLBh8xJnOgl0cjAaWnx6CCjRZvRIs3Qkt2GgJLaDRTQyNTaGRy+tl5tFKNoLjOyd1xqWkjgga66jy7NAVNAVV1jAyIdJsKqqKgqs52XpdjsNBVpVdjhUsjY9ToMGy4dcUZsx9Upct2Lue1J72fgfBo4MnMa2SiADr+p8aiorC8xMfptbPRdJ3WOKSstAIs/Y9pC0fVJXp7Tr/O6tvlczv9bFqCWEqgawJLOP+D/Y3bOZ7zwhIQN5xHwuxUvCnCYFbkTQ5tf4ZyY1/W90qqAbaXnsmHZR8hQQXiA4gbJtGUIJKClpgglKQHZR747pluKnwj+1sqioKmOP9slj02o1cmElJfkDuaJ4C7Zh7Jpu0Ddx6DKIqC5i1F85bC5J5OHsIyMCJNmMH6TKSCGeqMWBBm36mc7GSYVDIMrbv7nYPqLevHsJB+7auU6XwlEwphWyQaNhPd+QbRXW9i9pENxTejluqT/x1P1ZwRnuHAlAW8nHHMbPweaRwsNIM+Oz744IO8/PLLPPfcc5x55plZnz3//PN88pOf5KGHHuLKK6/M+yTHO4qioHkCaJ4A4ChHbCORrQwuwvyHktEnL0YHI44w4s4xZ8QRRgLb7PI6/ZnT3nubc7zGHeFu2B4z6VRPRjxPhggHRfd0iXzIjojoESWhe5w0TKoOmgtF01FUPdPmPDvtZLXpTj7O9LOi6qBqjtdMtAWjtZH29g+J7X2HRMOm3vOBAq6KmehTj8Y97Rhckw6VgqykKMm3XOB2u1m0aBGrVq3iwgsvzLSvWrWKT3ziEz36l5WV8d572R7wK1eu5Pnnn+exxx5j3rx5Q/hWQ0PVPbjKppIK7h/yGD6XwjmHejnnUC/NUYuXtyd5bWeKjQ1GRmFo2PDufoN39xvc90aU42a4qA5ouDUwLKgLWtQFLVpiQzsPx/Gzm3nspufaBUQkbURoyBgTOgwLPqWnjKIpFlM4wBQO9PjMFBpBKmijknYqM8/ZjwqsUcyoadhg2AIM6NUbcgyjKk5EhlujSz2A3tHVrpEb6UgOlzKg2UcApmWjaepAu8j0HyymBdGUTSQlSBjC2VZ0jiFE9nhCZPfpUMR37Zvpn1HUC0wbzBGpPZAfT2M/EU7lJc7kX1Qo2RFQTWISz3EOr1unkGr3QPvg9qspcMw0lc8dozG1dHSU9pkaB6LvCDRJcSD1BUND91UgKmeRahtc2sPxhKK5cJdPx91LlL4QAisRxAw6UQpmtBkr1ooZa8OKtmLGWjCjrQM6wtmJEKlECFp39dtP9Zajp9MgdaRD0nzlqJ5SNE8pqrcUzVuG6ilFdfvluUgy5rDNJPF964nuepPo7rf6LHrumXIYgbknEZi3pNf/zWJB11RK/dKpciQY9B3Z//7v//If//EfPYQAgLPOOoubb76Z3//+91IQyBNqN2WwsEysVAQ74XiQ26lYUYanSsYOXY0OUDmssToq3Jd4dbCNPo0LnQaK9Guz97ZOo4XzOh9FA4WZxDKT9BnvOooouhvf9KPxz16Mf9YiElo5m7bvZ15lBW5pNJAUKYWQC2666SauuOIKFi9ezJIlS7j//vvZs2cP1113HeCkGaqrq+Ohhx5CVVUWLlyYtf3kyZPxer092kcCvWyqU8smGRn2WDUBjYuO9nPR0X5CCZvVe1Ks32+wvi5FQ9hR9lk2rN5rkNZs54yqQIlHocStEnAreF2OYlhNe/fatqDcp1HqUfG6QFX8KMpkFI5CAQwF6oD9CNxmEG+iAW+yAW+iPv3svNfsnu7LumJRTQvVtPQ7x5RejuGuJOWqIuWu6vnsrsLS/FnbCCBpCpJmx7OjXE4NaIl2PJoNGwxLYFiO4aDjtWkJUjbYtsBKe3/b9tg0KdgCYoYgNqhDZyx+w4lFDY2cxSqW8goeJTvJ4XZxMP/iXNZz3IBRPqoClT6o8SvUBBSOnqpy4kyVUs/oKse0Lru3RfZ7SXEh9QVDx1U6GWGmMMI9je0TFUVR0H0V6L4KvFMP67VPRyokM9aKFW1xjAqxVsxoK1aspYuRYTAGhiCpRJBUy85BTE51DArpaArV4zwsPYAZKE+3l3UzOJSgyshxyQhjJSPE9qwhuvMNYnvX9h7Fo2r4ph9NYN4SAnNORA+MjYLDsUSKzbuamDetAp+MOigog9ZIbdiwgTvuuKPPz8877zx++tOf5mVSkp4omu5ULU9XLhe2jZ1y8t5bKScFjejDe1kiGSkURXFS6egetDwW6xWWgW0m00aGbtERg4iSyLzuaDfjCLNHBYERRS+bSmD2YvyzFuOdvjBLkAwHIzS0Rpk1eSj51iWSkaEQcsGll15KS0sLt912G/X19SxcuJCnnnqKOXOc8Nj6+nr27NkzwCijg6IoeCYvwGjbi9FHuO9QKPOqnL3Ay9kLnNydu1pN/rU1wXPbkjRHe3oMV3gVppRqlHlVyrwKFT6VqaUaU0pVqgMqpR6VMo+Cz62kjQQ96TAGDz4/fQkwo9dxrFhrpoZC5hFqwIy2YCd793TqwG0GcZtBAuzqs4/i8jkegoEatEA1ur8avaQaPVCdadN85XlPV9exRgGvjhAKtnA8og1LkEgbK6xeHLoFnX06DBwJQ5C0BAP5g1h2pzEkaQri6f3YA2wnhJO3PmkKEiaZfafMgY0Cpp2enykwiiwFt8+l4HM5x6dCZw0LJf1HoTOgIvOZ0tGmZF53PcQ72izbJpFIManURZlXTRvVnD5dn7uO0fmsDNxHcRTgthCZ9FG6qvQzpvPcdVxf5EOq9/8TX/MalC4GHoGCPWUR5rzzmF65gCuBq7r9G3d969UVAm7w6sWZCkjt8q9rCdBGbyqSAZD6guHhrpyJsJKYsfbRnsqYISsVUj+pVBwDQzhtUGjtfI61ZgwLHdEMg3JYEzZ2IoidCPZw3+jPR03R3aiesoyxQfN2iWbIPHd8XpIxOCiqPPNJBo8RbnSMBbveIL7/vV6zHCi6F//sRQTmnoR/9uKiql0wWAzTkjqTEUIRg3Rbd7vd7N69m2nTpvX6+f79+5k3bx7JZC+JMYuIUChEeXk5wWBwiEUQixc7FcPqMCYkwoOuk5C7gmDiIdeof8bi+gjbyqRq6oyG6DAsJBG2ibBMhG0gLANhmU40hWWkPzPSnzuv6WizTWds23LabAsUBd1fhe2pxF81A/+s43CVT+9zrSzLIhrtmU98PCOEIJayC1YkcjwghKBm1qG4K3oqaEcDKRf0jRlpJtW+r6AGfcsWNEZsRwFsCRRgWplGiWf4CvKROqfbZtK5YY82Y0adlANW5rXzsGKtw0+Bp2povsrsG/R0ugGt22u1w0PQE+jX2DAWr3vDxbI7DB6DK8oaSRiUeHtfnwFXrJ8OmgIBt4I2iNoOQ2UkrsNDue4J2yZV9w6xLU9iNm/L/lDz4D3oNPyHfgyttGeR0rFGx/r86FWLDQec4+3hT7szxiKJs0aTZh+Oq7z36/BII+WC4SNsm2TTtrxEL+aTiXLNE8LGToQzBgUnkjSMnY4otRNhrGQIKxF2agYmwgij8OHsqjvQaVxIp0pS3QFUtx/NE8h639tzMRgeJsoxNByGukZWPEh8/3vE694lXvcuRqi+136qt4zAnBMJzD0J38zaMRcB0319JqLOZCCEEEyacySusp71AYfDoCMOLMtC1/vurmkapjkSeUAlfeFcGPxQMgkA20xhJ8NOQdxUBDtVhDlaJJJRQlE1lI7/mRFACkuS8YaUC/pGL6lB81diRpoxI43YBYhw0lSFaWWjfyM4HFTdg1o+rV+ll7AtrHjQyW2cNi50GBqsLgaG/gooYltY0WasaPPgJ6eoqJ4Amqcsc7OebVgoJaX60csqnHQEHXmPdXcOKzC20FSFgNvxTh8IIQQuRaXUJ43B+UCYSRI7Xya25Z/YkexUJoq3HN+Cc/EdcjbqGPQYHIiuEQcDRddIRhcpFwwfRVXx1BxM4sAWbLO4DSzjEUVR0XzlTuR89eBqZQnLwEpGsOIhwqE2vMQzRoXMc9rg0OHgaSVCOaXitVNR7FQUk4ahfS/d04tBoavxoaR/44PL59Twk9fzosA24sTrN6UNBRtItezos69eMonA3CUE5p2Ed+oRRWFEkow9Bm04EEKwfPlyPJ7erVLF7jkwEVF1N6peDR11Emwrndoomr74xAcdlSCRSEaOYCTOS+t2ccJh02TBH0nRIuWC/lFUDVfZFFxlUzDj7ZjhJudGUZITiqqlUxH1nW9VCOFEXXaJXOg0NKQjF+JtWInw4G/U016HdiIMwb67df9FFd2TTjHQLRVB16gGd/oGvauXoMsnb+aKiGK5DtvxduLbniW+7TlEKtsDWSufif+wj+OZczKKNn5z+3ataWAWWbosSTZSLsgPiqbjnXq4k9Yv3IgYbtSdpKAomgvdX4nmq8Dtm0ZgEE5iQgiEmejFuNDxPjuqofM5wlBqD3XU+rNirUP8loCiOgYI3ePUSdQ9KOmH2u05q83lddo0N0lbR/P7nXqeurfHNoomHex6Q1gGicatmYiCROMHvaYfAkDV8U4+FN+MWgJzT8BdfdC4XdNikdUmAoM2HFx11VUD9pGFjoobRdU6LehphGViGQmS7S24fW6ElUKYKSdVi5WSBZglklHApWtMry5B16QSSVK8SLlg8HQU97NTMYxIE1a0VSoC8oiiKGieAJongHuAHMfCiKfTDoS7pCDouCEPpT0BO1/byTB2KjbouQgziRlJQiSH6IaO7+HydXr/9eX15wn0k47An/daDhOV0b4Om8F9xLc8RWLXaz2MXa4pC/Ef9nFc044et8qArmgy4mDMIOWC/KGoGu6KGeglk5zIxWQUOxWTssM4QVEU55rv8kHp5EFvJ4SNnYqlnUBjmeMi876X506n0ZhzDA01tZKwHRnKiPdfyGEA+nWh6Wqc0N3djBJep01zOwYGzYWipp81vbMt3U7X95m+3fpljdHxfvTvv4WwSbXsIl73LrG6d0nUb+4nslbBXTMP3/Ra/DNq8U47EtXlHdH5jhajLatNJAZtOPjNb35TyHlIRglF09HUAJrXxFVWnnUDIoTIMiTYHcYEM4ltpmS0gkRSIPxeN4fPqRntaUgk/SLlgtxR3X48VXMQ5TOcdDuRpoKkMZL0jqIonSnqSgef+1NYZhcvQOfZjIeIR9rRrZhjXOgl73EuaQiAzA25FaVHscXB0mF8GEzO4470A6rL56yLy5f2DPROCIV0f4zGdVgIgXFgE/EtT5Gqfzf7Q0XDM2cJ/sPOQ6+cO6LzGm00RaHDw7a3guOS4kHKBflH1d24K2YCndF1HWlr7GREyhATDEVR0TwlwypkK2zLqes3oPEhkqmhKYwktpmuA9ihF0o/8kpX48RooaiOgaGLQQFVQ1E0UNX0s+akPe6lDUVDUdVuz53tna+79FU1hGlgxlpJRlpoadmOnQz3OUVX+XR8M2qdx/Sj0Lzjq3brYJE6k5Fj0IYDycRDURQU3QO6Byjt8bmwbYTVceFwDAy2meg0NhSwKKREMp4xLZtQNEnA60LTpPeoRDLeUDQdV9lU9NIpWPH/z959h0dV5f8Df9/pMymTSggQQpcmAYJUkdBFRAVULEtbdpWvbZF1VcQCNlzXgrpiBbEA8kMBUVgF6U2UZkFERTpBSEIyqVPP74/JXGZSZhKYzJ1k3q/nmYfMnXPvPXNmuPcz93PPOfnuBIK1iL38wpSk1kBjigdM8fIyIQQkP/PW+Ovd4LIVw+n3LsHii/4xfiH5UPseDxdI7mEItEaodOWJBe2FxIL7+YXX3A8TJK2h/E5ADaDSwuEAbFYTVBpd5Tv8wrxnRCjPw8LlgPX4LpT+sgaO/GM+r0laEwxtBsHYbhjUpsQ6rUe48m5+Bw+RFMG8e9d5CKfdfUe5tUi+CMxeCeSPpFJfcvLBQ77R1F7mk0yo6m+XvQxlpaXQSnavm1HLy3mtX9W6COV3Wrjc17IQPkk5tSkBxqZdyhMFXaCtRS+VhozXTEKHiQO6aO6MaXkXuyoIp6O8l4K1PKngSTCUQTjtvEBCVA1LcRk2f38c/bukIS46MroaEkUiSZKgMcVDY4ovv6vdAmepxX3x2O5nsl8Kexfbu8FDOB1w2UuqTC44PReIKiUdfO8UFBd9J6q4kICo+ShNVTpf3QsqtZxkuHBXXxUJhmqHFPCU1QCS5E5ESCoAUvlzNSBJvq95/obkvkMQqvIy5RM4SxeeW4rLsP+X0+jcqhGiTQbfdaUK68nb8bymggTv5+4y3vsQtmLYzx+DLecIyo5vh6vUd9xpVVQSjO2uhqF1VrVxdqTwnuPA5RIAIrs3DJE3Sa2FxhgHGOMAeCWtbcXld5IznqC6432jaaDBYoQQUPm54cLfenA5LiQUnPbKD5cdwlH+b1Wv+SxzyMsRsKwdwuWAcLkA4XTfGCuc7ue17FVaGypdFIxNLpd7FWjjmkV8T9Cq8JpJ6DBxQHVGUmugVmsARFV6Tc5Olx+MIR/AHe6H01F+4HYALgeTDBRRYqP06H95GqKNOqWrQkQh4r6rPQEak3sSYPnCsbUYTlsRXNYS9/mSIoI7hoq9pO7nwumoZrzjC8+FvdQ9ZEH5Q9gqPLeXwWUvDf7dfi4nhKsUIoxHvewAwHnW79zYQadJaAVjh5HQN7siLMZZDgfeNxE6+XOAyC+fpHV0MoAL54ILyQTGE1R/SJIEqLVQq7VAEHpJBJMQrvJ4xunuqeByQghnpWVy0sHl9FlHLitc7mGoTAkoVcUg1hwPlYp30AfCayahw8QBKcJ3GKTAvBMJwmV3H2iddnfCweu5+3UGQlS/adRqxMUwa04UybwvHGvLl7nsZZUuBDOxTtWR1BqojWaojeZL2o4QonyYgVKvREOZV6KhRF524S49G2xWKzSSs8JdfI4LN45UepTPnxVxcZwEXdPuMLa/Btrky3hXYQXePQ44xwFR7VV1LnCPce/pwVYMYS9jPEFUS5KkAtQq9zwIQSCEgLXUzjighnjNJHSYOKB6QVJr3N3Ra3BM9nRnk3swOL0SC67yHgwup9yzQc78EoWJUqsdh47nIr2xGQYdD9NE5OYeX94ARLnHOhcul/vHf/kPf5etmBMlUtBJkmfOAwOA+IDlAXcsVngRQxJ41nUPS2CThxJwlScXPMMKuLySDEIId48IIQAh3HfwCQFAuOO78teEXMZV4W8BwPc1m82OnDwLzFG68jvehfw6ysuLCs999+FZ5iknyrdRXh+1FurYZnBGpyEqpTU0UZzcrzrscUAUfJ75aTRyPOEsnyOhyN0zwVbiPsYSEYUpXjMJHbYuNTie7my1yfzKyQavpAJcTvedccL9rxqFUBu1kISrfLmLwyhRnbDaHDh+1oLUxGieBImoWpJK5WeixAs9E4TLqWAtiWrHZ1gCheTmF+HQ3sPodVkTREfVrHdsbQkhUGJzQa3jcAT+sMcBUd2TVGqoDTFQG2Lk+/SE036hh5nDCjUsUBs0F8aF5413RKQgXjMJHbYuEQInG4QQ0KkKYDCbK90555NoKE88XPjb89yTkPD8XT7BDpMOVIW4GCOG9mipdDWIqB6qOFEicGFIAvd8CcUQ9lKef4j84Hk4fHgP88zEAVHoSOUJXLUh1v1bWPL9LewZes7TA0w4bBeGnfP8zeQCEdURxmqhw8QB0SWSVOqLnsBOTiB4JxO8J9KRJ81xuXs+uBzy3+7kBBMQRETkn2dIAt8hjkrKJ74tg8tRVv5D3855gogorGi8exww3CUKG56hhFUwVVum4rw2vkkGmzykMBERhS8mDogUJKlUAC59Qp2qkg5yckG43JNIywkIT1nP2Lsuecxdn2WkmIKiMmz7/hgyL0uF2RwHSRcNlT4aUGnK5+9wQDit7onMHGUQDqvSVSaiesQ9xFE0oI+u9JpwOeFyWGE9nwedSVc+JIHnh761/A5CXr2jhk0+D7drjBhT3QxVRDWj4hwHRPWWPE+hH+65CC8kFOTkgvyvg8MDE1EljNVCh4kDogYgWAkIb1UmFir8C5cLAgLC6YBGXQRtlAESUD7/g7PyRIRVLSOZJKmg0plgjItGo0YlMKa0hzY6JuB6wuV0JxDspfLD5Sgrn4yRiKjmJJUaKq0Ran00tNGVh+cD4J601mnzGY7A5UkqOGzstUD1nkajQlKsERo15x9QmtrrGOR0Ma4hamjcvfeNgNbot5xwOiBc9vJEQ3kPSWf50MDlPReEi4kGokjBWC10mDggoirJyYgalBVCQCsKoKtiDohA613o7VDeW0JOKjgvJCd8ynglHcSFRIRn/frUc0JSa6HWR0Oli4JKHw2VzgRJkqBxONAtKrnm21GpIemiAJ3XBKlCQDhKIWwl7kSCvcTdMyEIQbQklZ+c5Y9aAiSp/F/PaxW+B0KU79vrcySiekml0QEaXbWvC5fL3SuqYlLBa7gCDk1A4SzKoEPnVo2UrgYBUHOOAyJCzXoveBOepEL5A84KCQa5F7f9ws1tRFRvMFYLHSYOiEgxkiQBkhqAOqi9JbxdmAfCu6eDACAuPPf6G4BXWe/XXYCAvJ68jte/8nYBn7KAgJBUUMMObWws1Pood7JAU3WXOofDgaKiIhiNRqjVFzd/hiRJkLQmQHth3FEhXOWJhBK4bCWA0+a+0C+pIKk07gnCVRpApXHP26HSuJMEKvWFcrVIDPlzYRJxO+B0wOUog6qoECqNO+HBxAJR/SWpVAHvHhQul3xnoOc4IP+I91rOOwdJCU6nC8WlNhj0GqhVvJNNSWqvsIMdDoiopmqdaHA55TjEHXc4vX6vOCrPOehyeM1JyKQDUagxVgsdJg6IqEGrTc+JuiSEgLWgZr0y8vPz8eWXX2Lw4MGIj48PWh0kSQWpfFzzi0tHBI87MaGGBPddy5KIhRomaKOj3ckaaxFcVgtc1kL3nUBE1KC4kwt6oJoEakVVJRTkuwS9nss/5IkuQUFxGTbsO4b+XdIQF21QujoRzbvHgYPX5oiojriHTFLXOC7xJoR76F5bfh6MMeW/ZVyO8hjFa+5Bl1Oel9CddHB43eTGzChRbTBWCx0mDoiIwozZbMZVV12F6OjKE5dGAkmlhmQ0Q2U0AwBc9hK4yixwlRVA2MsUrh0RKUG+c7AGndOEy/OD3TeZ4H2XYFU/3Dk3A3nEmPTo06kpoo3VD8mlJEmjh9oYD0lrlHsEAgIoHwpQiAt3x8LnjlnHhe/8Rd4hK0kq93CEkgoXhiqsoHxoQk/vzUu5G9c7ccAeB0QUjiRJAtQaqDR6eejZ2hIVEgsX5gZ0Vf3c06Pe61gvhGAPiAim0ugBlQrCYY+ImDbcY7WGhIkDIqIwo9Vq0agRx+vzUGlNUGlNQExjCIe1PImQ7x5uiYioAndvBh2A2v+Q8EkmVLhj0PvOQeG0Q+UsgUqngeTzI55XNhsCrUaNJLMpcMEQktQaqAxxUBnjodJdet0qTiwKpw1CuNx33EqqCsMWqgFV+bCGF7s/4bqQxBCehF2FOrgc7rlQXA55yELvoYo4xwERNVRyj4cg8EkgCKfX/H9eCQg54XBhbkHfOQN95x+8MNwvhQP3XIlR7t/J5f96D83lmW/M5bBC2Msu/GsvazBJhXCM1RoqJg6IiMJMaWkpDh06hPT0dBgM7HbnTdLooY5Ohjo6GcJhg6ssH87S8+yJQERBceGHu/+kgxACem0BjBWGn5OHHPAefkD+8e3yGqpAeP2A9/xwd3n9oOcdg0oqs9rx+6nzSEuOgV6n3M8lSVJBZTBDZYyDpI8J2jxDAC7MaRS0LQbYn6Rydx9QawPuUwgBYS+DzZIPjbEQQD4AwMFrVkREAXl6QNTF8f1CEsLlm1AQFZeXzwlYXt7lckIjFUMbZQAgvMp6/e0p75k7UJ5PMLKTFpJKA5XO5H54kgUa/3GqZ74xldYIVJhyTDjtcNnL4HKUQdhK4XKUwWUrrXcJhXCJ1SIBW5eIKMyUlJTgl19+QUpKChMHfkgaHdTRjaCObgRhL4Oz9Dxcpec5JwIRKUaeV0ddgzGVasB76AII4TNWsu9zr7sJhavC3YVV/MAnv0ptDvx+Kg/JcSZFfoyq9NHungUGc9DuQK1PJEkCtAZI+mjoTBp4EgfQxULSOnmzABGRQi52/kAhBLRSzeb787cNT6LBnVxweSUXypMNEBcSF55khGfYPHit7/M63PERhGdHF9ZzL7iwf7kMvP6+UE54ryNENa9Vsw9JDZVGgtoQDbUuCiqdESpdFFTa4F4PkNRaqNVaqBHjs9zlsEHYS+GylcBV/q/7d/WFurs/O9/PT6kbXZSO1SIJW5eIKMwkJibi+uuvV7oa9YqkNUCjTQViU+GyFsKRf4IJBCKq94I5dIGH792CFxIQFxIPokLvhwpJCXmIg4p3GzacOwPjY4y4umfrkO5TpTW6kwXGuKAlnhoC7zkOhC4GuuSUCzcLlBVAOKzKVY6IiEJGkjzz6rhPDKHqMRcqQgjYCwpguITkyqVQaXSARgd1+TyDtVVlbxTP3Eo+8WP5kFjlf8tzkDntF+YkqwElYrVIxcQBERE1KCp9DLSJbWDP+4MXFKjGSkpKEBNzYSgQm80Gh8MBtVoNvV7vUw4ADAYDVCr3Dxe73Q673X5JZUtLS93D3+j1UKvdF4odDgdsNhtUKpVP76PalC0rK4PL5YJOp4NG4w77nE4nrFZrtWW12gsXLT1lJUmC0Xihr7PVaoXT6YRWq5XL16asy+VCWZn7rmGTyVSprEajgU6nq3VZIQRKS0sBAEajsdLnWZuy/j5P77JOpzPon30wvieez7PGZYULOl1Upe9Jxc/T33fKU1YIAfv581Ab9HA5ndBo1NBqNYAQcDkdKCsrgxACJqN7yAIhXLCWlcHpdECr0UKrUUMIF1xOJ8qsZRDCBZPBICcnbFYrHJ7PXquFKL8brrSsDBACJqMRnjsBbTYbHE4ntGp1+ffPU9YKIQSMBn355ync7e5wQlIJqPQXfrR7tmvQ6+W5gO02OxxO9/dEqy3/SVW+XQDQ67RQlRd2ONzbVakk6HXu/wOSpIHVaoNGE42ouBSo9Sb5M7KXlkKlUvl89p420+l0Pp+n3W6HJEmVPs/altVqtT6fp81mu6SyVqtVPp5cTFmXy4XS0lI4HRcuIDhdovz/nIDGmARdbCpcthI4inNRajkH4XTAaLjQZja7HU6nCxqvz0gIgTKrDQBg0Ovk/8t2u8P9napFWbVaBZ3X8dLns5f/39e8rMPhKP+eqOTvCQCUWa0QAtBptVCrL5S12evXsBJERNRwXWxvlIrcc3R45j9yQLjsEE4HXHYrVK58qLQSf+OHmCpwkbo1b948tGzZEgaDAZmZmdi6dWu1ZZcvX46hQ4ciOTkZsbGx6NOnD7766qsQ1paIqO4VFBRgw4YNKCwsVLoq9Zak0UGb2BqSRh+4MBGAfv36IS8vT37+7rvvIiMjA08++aRPud69eyMjIwOnT5+Wl3300UfIyMjAjBkzfMpmZWUhIyMDhw8flpctX74cGRkZmDZtmk/ZESNGICMjAwcOHJCXrV69GhkZGbjzzjt9yo4ZMwYZGRnYvXu3vGzjxo3IyMjAxIkTfcredtttyMjI8Imvdu7ciYyMDNx0000+ZadMmYKMjAysW7dOXrZ//35kZGRg1KhRPmXvvvtuZGRkYNWqVfKyQ4cOISMjA0OHDvUp+8ADDyAjIwNLly6Vlx0/fhwZGRm48sorfco+9thjyMjIwPvvvy8vO3v2LDIyMpCZmelTds6cOcjIyMCbb74pLyssLERGRgYyMjLg8Lrg+NJLLyEjIwMvvfSSvMzhcMhlvY+3b775JjIyMjBnzhyf/WVmZiIjIwNnz56Vl33wwQfIyMjAY4895lP2yiuvREZGBo4fPy4vW7p0KTIyMvDAAw/4lB06dCgyMjJw6NAhedmqVauQkZGBu+++26fsqFGjkJGRgf3798vL1q1bh4yMDEyZMsWn7E033YSMjAzs3LlTXrZ161ZkZGTgtttu8yk7ceJEZGRkYOPGjfKy3bt3IyMjA2PGjPEpe+eddyIjIwOrV6+Wlx04cAAZGRkYMWKEvExSqXD/9H+ia/dMrFz1BVQaPVRaA46cyEa3K/pg8PCRUBtioDbEQmOMw2NPPY/MPgPx8Yo10EQnQRvTCOeKnOhx5TBcNWw0dHFNoYtvBl18Gua89h6uyLoWH3z6FXQJzaFPSEcxYtAzaxR6DrwO+sQW0Ce2hD6xJV6Zvww9B16Pt5eshj6pFfRJreGKaoKeg25Ar8GjIaKbwpDcBobktnjn4y/Ra/AYvPjO/8PW74+hTNcI2sTWGDrmrxg6dgqKNUnQJrWFNqktln61C0Nv/DteWfgZdMmXuR+N2uO68fdh2E13IM8VB11KB+hSOuCzLQcw7Oap+M87y6FL6QRdSidok9th4tR/Yuh1t+DYqTNyu33xxRfIysrCo48+6tPut9xyC7Kysny+J19//TWysrLwr3/9y6fspEmTkJWV5fM92b59O7KysnDvvff6lJ06dSqysrLwzTff+Hz2WVlZ+Nvf/uZT9v7770dWVhY2b94sL/vpp5+QlZWF22+/3afsww8/jKysLHz55ZfyssOHDyMrKws33nijT9lZs2YhKysLK1askJedPHkSo0aNwvP/fk5e5nQJPPfcc8jKysLHH38MAFDpTMi36zHs5v/DNX+Z5u6xIbl/4v53wTIMv/VefPjpGnkbRSWlGH7rvRh+671wOp3y8ncWrcTwW+/FO4tWXtif0ymXLSoplZd/+OkaDL/1Xvx3wTKf93HNX/6B4bfei9zzBfKyT75Yj+G33osX3vjIp+zYKQ9i+K334tSZc/KyVWu3Yvit9+KZufN9yt5212MYfuu9+OP4KXnZui3f4urb7gMREVFDIkkSJLUWKq0RakMMNKYEaGMaQRffDPr4NBhTO8HUrCvK9I2x7cAZFDu15UkLqiuKtu7SpUsxbdo0zJw5E/v27UP//v0xYsQInx9Z3rZs2YKhQ4dizZo12LNnDwYOHIhRo0Zh3759Ia45EVHdUavViI2Nle9Ao4sjqbXQJrWBFORxIYmIqGGTJInn4XDhNXay0xV4CCxtfDq0KR2gMTeFpI68+SGIiIgaOkmlhs4UC3N8IvQJ6dCmdIY2uS00sU0idn6ouiQJBQch7dWrF7p374433nhDXtahQwfccMMNle7yqk6nTp0wbtw4PP744zUqb7FYYDabUVBQgNjY2Iuqd0MjhEBBQQHMCo2lVh+wjfxj+wRWmzZyOBywWCwhqll4EEKgqKgI0dHRQf8OCafdPWxRPZ9MUQiBpLTLoItrqnRVGhRPXJCdnY2UlBQOVVQ+XEhxcTHMZjNcLheHKqri85QkCQUFBTAajQ1nqKIAww/VpqznnGcwGOTvVE0/+4v9nnh/npf6PfG8T+929/7sPZ+R53vi2a53Wb1e7zsEjd3uM/yQEALnzp1DdHS0z/ekqrKe+kTSUEVOpxO5ubn4NUfguTXHAAC392uKsZnJ8ufp/T2xWq3y5+lhs9lgLyuCylYAlc0it3tDGqqoebuu0JpTQcHD6wWV8bdeYGwj/9g+gbGN/KvYPoGumbjsJRDWIrishRC2EsUmcQ4lIQSS0ztBG5sS1O0qNseBzWbDnj178PDDD/ssHzZsGHbs2FGjbbhcLhQWFiIhIaHaMlarVQ4kAchfLCEqzGoewTxtwfaoHtvIP7ZPYLVpI6fTiZKSEp+LDg1dnX6HVBpo4lvCnnu4Xo+HyP9fdctkMvkE6TqdzudioHe5irwvdF5sWe+LXR4ajUa+iHaxZb0vznmo1eoq6+Yp6/1dq66s9wXNiymrUqnqpKwkSVWWrerzrE1ZwPfz9LSRTqer8sfdpX72wfieVPXZB/N7UpOyer2+UvuE+rO/mO+JzWZDaWmpfB6uq8/IaDT6JC78lb3Uz7O2n31V76M2Zas7RtS0rKfdjcYLwwm5XKLK/5vVfUbuzzMBQAKcJXlw5J9wJ7cMlfen1WouzFVRrjZlAVxy2Wo/oyraR6PRyEkhIiKiSOOZC6m6ayYqrQnQmqCObgQhXHISwVVmgXDaFKhx/aVY4iAnJwdOpxMpKb6ZkJSUFJw5c6aatXy9+OKLKC4uxs0331xtmTlz5mD27NmVlhcUFPAiTDnPnb4AmNmsBtvIP7ZPYLVpo5ycHGzYsAF9+vSJqDudSktL6/b7Y2gMR8FJCKe97vZRh3jOIiIKjby8PKxevRqDBw9GfHy80tWJaGrVhbigJkMVVbsdk/tGM0f+iUuuExERESmrNrGaJKkgGWKhMsQC5qZw2UvhyD9e70ckCBXFEgceFS8SCSFqdOFoyZIlmDVrFj777DM0atSo2nIzZszA9OnT5ecWiwVpaWkwm80RdUHOH8/FKHaJqh7byD+2T2C1aSOtVou+ffsiKSmpyrsZGyJPb4OoqKg6/Q6JqKh62/OAiQMiotCIjY1Fv379EB0drXRVIp534sBxCYkDwJM8kODIr3o+PSIiIqofLiVWU2mN0Ca2geP8UbisRXVQu4ZFscRBUlIS1Gp1pd4FZ8+erdQLoaKlS5diypQpWLZsGYYMGeK3rF6vr7aLMi9wXuBpD7ZJ9dhG/rF9AqtpG+n1ejRp0iREtQofofgOSRoddElt6m3ygIiI6p5Op0NqKseNDweaIPU48FCb4gEI9jyoR+bNm4f//Oc/yM7ORqdOnTB37lz079+/yrLLly/HG2+8gf3798NqtaJTp06YNWsWhg8fHuJaExFRXbrUWE1SqaFJaAVnwUk4S/KCWLOGR7HBs3U6HTIzM7Fu3Tqf5evWrUPfvn2rXW/JkiWYNGkSFi9ejJEjR9Z1NYmIQq60tBS///67z/wsFDySWgttYmtI2spjNRMREfE8HD68hy12BWleQ7UpAZq4ZsHZGNWppUuXYtq0aZg5cyb27duH/v37Y8SIETh+vOpeI1u2bMHQoUOxZs0a7NmzBwMHDsSoUaOwb9++ENeciIjqUjBiNUmSoIlLgzq6+lFsSMHEAQBMnz4d7777LhYsWICDBw/i/vvvx/HjxzF16lQA7mGGJkyYIJdfsmQJJkyYgBdffBG9e/fGmTNncObMGRQUFCj1FoiIgq6kpAQ//PADSkpKlK5KgyWptdAmtGLygIiIKuF5OHyopeANVeSzXVMiNOamQdse1Y2XXnoJU6ZMwd/+9jd06NABc+fORVpaGt54440qy8+dOxcPPvggrrjiCrRt2xbPPvss2rZti88//zzENScioroUzFhNE5vKmMAPRec4GDduHHJzc/Hkk08iOzsbnTt3xpo1a5Ceng4AyM7O9rmb4K233oLD4cDdd9+Nu+++W14+ceJELFy4MNTVJyKqE4mJiRgzZozS1WjwPD0P7LmHOTESERHJeB4OHxp1cIcq8qaOSgIAOApOBXW7FBw2mw179uzBww8/7LN82LBh2LFjR4224XK5UFhYiISEhGrLWK1WnztWLRYLgAvzb9GFtmB7VI9t5B/bJzC2kX8V2ychIQGjR4+WX7tUKlMi1FDBUXACqKefQV19dxSfHPmuu+7CXXfdVeVrFZMBmzZtqvsKERFRxJBUGmgTW8OR+wdc9lKlq0NEREReVF49DlxBThwA5ckDIeCwnA76tunS5OTkwOl0Vpr/MCUlpdI8idV58cUXUVxcjJtvvrnaMnPmzMHs2bMrLS8oKOAFvHJCCBQVuScQ5Xx2VWMb+cf2CYxt5F/F9nE6nfLz4NFCGBrDaTldL4//DTZxQEREvgoKCrB161Z069YNMTExSlenwZNUGmgSWzF5ECY4CSIRKY3n4fChVtXNUEU++4hOBiDgsGTXyfbp0lS8gCaEqNFFtSVLlmDWrFn47LPP0KhR9eNXz5gxA9OnT5efWywWpKWlwWw2IzY29uIr3oB4LkaZzWZe0KwG28g/tk9gbCP/KrZPbm4u9u7dWwexWjRcUdFw5P0B4XIGcbt1j4kDIqIIoVKpoNfroVIpOg1NRJGTB3lH4LJxTGuleCZBnDdvHvr164e33noLI0aMwM8//4zmzZtXKu+ZBPHZZ59FXFwc3nvvPYwaNQq7du1Ct27dFHgHRNQQ8DwcPupyqCJvnokRmTwIH0lJSVCr1ZV6F5w9e7ZSL4SKli5diilTpmDZsmUYMmSI37J6vR56vb7SckmSePHOi6c92CbVYxv5x/YJjG3kn3f7qNVqGAwGqNXqoLeXWh8FVXI72HP/gHDagrrt+ojRMBFRmImJiUGvXr0QFRWldFUiiqTSQJPQCiod210pnASRiMIBz8Phw3ty5LpMHADu5IEmNrVO90E1p9PpkJmZiXXr1vksX7duHfr27VvtekuWLMGkSZOwePFijBw5sq6rSURECqjrWE3S6KFNagNJa6iT7dcn7HFARBRmXC4X7HY7NBoN7zYIMUmlhiahZXnPg2KlqxNROAli+ODkbIGxjfyr7+3jdDphs9nq9Dxc39uornnaxrv5na66by9VVDLUAvVizoNI+O5Mnz4d48ePR48ePdCnTx+8/fbbOH78OKZOnQrAPczQqVOn8MEHHwBwJw0mTJiAV155Bb1795Z7KxiNRpjNZsXeBxERBVcorplIai20iW3gOH8ULmuw51OoP5g4ICIKM3l5eVi1ahUGDx6M+Ph4pasTcSSV+sKwRREcIIQaJ0EMH5ycLTC2kX/1vX1ycnKwYcMG9OnTp07HOC8tLa2X7RMqpaWlcKlc8nOr3V4HEyFWxQiXLhHOonMh2NfFi4Rz1rhx45Cbm4snn3wS2dnZ6Ny5M9asWYP09HQAQHZ2No4fPy6Xf+utt+BwOHD33Xfj7rvvlpdPnDgRCxcuDHX1iYiojoTqmon7xsJWcOQfh6s0v872E86YOCAiCjMxMTHo3bs3h0hQkCSpLvQ8YPIgpDgJovI4OVtgbCP/6nv7aDQa9OrVC0lJSdDpdHWyD88d9VFRUfWyjeqap31UWqO8TJLUiI6ODk0FoqPhNBrgKDgVmv1dhEhIHADAXXfdhbvuuqvK1yomAzZt2lT3FSIiIsWF8pqJJEnQxqfDodLAWZxT5/sLN0wcEBGFGb1ej2bNmildjYh3IXlwFC5rodLVafA4CWJ44eRsgbGN/KvP7WMwGJCWllbn+6nPbRQK7skPfec4CGVbaaKTIalUcOSfDNk+iYiIKDAlrplozE0hqbVwWLJDul+lcXJkIqIwU1ZWhqNHj/qMw07K8CQPVAbeiV7XOAkiEYULnofDh0bllThQ4AZ7tSkRmrjmAJM7REREYUOpWE0d3Sji4gImDoiIwkxRURF2796NkpISpatCcN/xqIlvAZWBk+rVtenTp+Pdd9/FggULcPDgQdx///2VJkGcMGGCXN4zCeKLL74oT4J45swZFBQUKPUWiKgB4Hk4fKhUvj0OlKA2xUfcRQIiIqJwpmSspjbFQ5vQCpIqMi6pc6giIqIwk5iYiDFjxnDogjDiTh6kw5l/As7S80pXp8HiJIhEFA54Hg4fKkmCSgJcAnAq0eWgnNoYB0mlgeP8UQiXU7F6EBERkfKxmkofDU1iGzjyjkA47YrUIVSYOCAiCjOSJEEVIdnr+sSdPGgOSICzhMmDusJJEIlIaTwPhxe1SoLLKeBUeDJglT4a2qS2sOcdgXBwGCsiIiKlhEOsptIaoU1qC0feEbjspYrWpS4xIiYiCjMWiwXbt29HUVGR0lWhKmjimkMdlah0NYiIqI7wPBxe1OXDFSk1VJE3SaOHNqkNVPoYpatCREQUscIlVpPUWmgSWzfouICJAyIiolrSmJtBHZ2sdDWIiIgavHBKHACApNJAm9gK6pgUpatCRERECpNUamgSWjbYmws5VBERUZiJjY1Fv379lK4GBaCJbQJAgrPorNJVISKiIOJ5OLyEW+LAQxPTGCqtCY7845z3gIiIKITCLVaTJAkaczNIaj0cltNKVyeo2OOAiCjMCCHgcrkgFB7LlwLTxKZCE5uqdDWIiCiIeB4OL+GaOAAAlSEW2qR2UGmNSleFiIgoYoRrrKaOToY2oQUkqeFcbm8474SIqIHIzc3F8uXLkZ+fr3RVqAbU0Y2gMTdVuhpERBQkPA+HF0/iwBGGiQMAkDQ6aJLaQG1KULoqREREESGcYzWVwQxNUmtIaq3SVQkKJg6IiMJMdHQ0evToAZPJpHRVqIbUUUnQxDVTuhpERBQEPA+HF7U7bwBXmCYOAECSVNDEpUET16xB3WVIREQUjsI9VlNpTdAmtW0QPRIZ1RARhRmDwYAWLVpAr9crXRWqBbUpEZr4dECSlK4KERFdAp6Hw4taHb5DFVWkNiVCk9hw7jIkIiIKR/UhVpPUWmiS2kBliFW6KpeEiQMiojBjtVpx8uRJ2Gw2patCtaQ2xkEbn867DYmI6jGeh8OLWgrvoYoqUunK7zLURytdFSIiogapvsRqkqSCNqEl1NHJSlflovHKBhFRmCksLMQ333yD4uJipatCF0FlMEOT0BKSiqdYIqL6iOfh8OKZ48DlUrgitSCptdAktKrXFwqIiIjCVX2L1TSxTdzzItbD0Qk0SleAiIh8JSQk4Prrr4dGw0N0faXSR0OT0BqOvD8gXE6lq0NERLXA83B4qU9DFXmTJAma2CaQNEY4C05CiHqU+SAiIgpj9TFWU0clQdLo4Th/FKIe3Q3B2yGJiMKMSqWCVquFVA+z0XSBSmcqH+e4/gQzRETE83C48R6qSIj6lTwAALUpHpqk1pDUOqWrQkRE1CDU11hNpY+BNrFtvYoJmDggIgozhYWF2LVrV73pdkfVU2mN0Ca2qVeBARFRpON5OLx4hioCgHrW6UCm0pqgTea8B0RERMFQn2M1SWtwxwS6KKWrUiNMHBARhRmXywWr1QpXPeq+RtWTNHpok9pA0uiVrgoREdUAz8PhxSdxUF8zBwAklcY970FUktJVISIiqtfqe6wmqTTQJLaG2hSvdFUC4vgJRERhxmw246qrrlK6GhREkloLbVIbOPKOwGUrUbo6RETkB8/D4cU7ceBwCWgVrMulkiQJGnNTSFojnAWnOO8BERHRRWgIsZokSdDENYekMcBhyVa6OtVijwMiIqIQ8NxpyGEKiIiIas47cVDfJkiujtqUwHmQiIiICOroRtDGt4Akhecl+vCsFRFRBMvNzcXy5ctx/vx5patCQSap1O7kgcGsdFWIiKgaPA+Hl4aYOAAAlc4EbVI7qHQmpatCRERUrzS0WE1lNEOT1BqSOvz6VTJxQEQUZkwmE7p06QKTiT8kGyJJkqBNaAG1KUHpqhARURV4Hg4vDTVxALiHMtQktobaGP5jHBMREYWLhhirqbQmaJPaht0NBUwcEBGFGaPRiDZt2kCv52S6DZkmLg3q6EZKV4OIiCrgeTi8qL1+sTa0xAEASJIKmvjm0MSmKl0VIiKieqGhxmrheEMBEwdERGHGZrMhOzsbdrtd6apQHdPEpkIT20TpahARkReeh8OLxqfHgYIVqWPq6EbQJrSEpOJPdCIiIn8acqwm31AQ01jpqgBg4oCIKOxYLBZs374dRUVFSleFQkAdnQxNXHNAkgIXJiKiOsfzcHhR+SQOGnDmAIDKEAttYltIap3SVSEiIgpbkRCrqWNSwmLSZCYOiIjCTEJCAkaOHAmzmRPoRgq1KT4sggIiIuJ5ONyoI6THgYekNUCb3BYqfbTSVSEiIgpLkRKruSdNbqPoDQW8QkFEFGZUKhWMRiNU7KoeUVSGWGgSW0NSqZWuChFRRON5OLz4DFUkGt4cB1WRVBpoElpBbUpQuipERERhJ5JiNZXWCG1SG6h0UcrsX5G9EhFRtYqKirB7924UFxcrXRUKMZXOBG2isncUEBFFOp6Hw4tPjwNnZCQOAECSJGji0jgXEhERUQWRFqvJkyYrcEMBEwdERGHG6XTCYrHA1cDH8aWqSVoDtEltIGkNSleFiCgi8TwcXlQR2OPAmzo6GdqEFpw0mYiIqFwkxmo+NxSEcH5ERh9ERGHGbDZj0KBBiImJUboqpBBJrYU2sQ3HNyYiUgDPw+HFZ6giV+QlDgBAZTBDwx6JREREACI7VnPfUNAyZEMcM3FAREQUhiSVGpqEllAZ45SuChERkWK8hypyRNBQRRVdGOPYpHRViIiISEEqfQy0SW0hafR1v6863wMREdVKbm4uPvvsM+Tn5ytdFVKYJKmgjU+HOipJ6aoQEUUMnofDi3fiwBWBQxV584xxzJsKiIgokjFWAySNHtqktlDp67bXhaZOt05ERLVmMpnQvn17GAwc457cNOamgEqrdDWIiCICz8PhRc2hinx4bipgXEBERJGKsZqbpFJDm9gKDks2HIV/1sk+mDggIgozRqMRl112mdLVoDCjjk6GNsasdDWIiBo8nofDi3fiwB7BQxVVpIlNhSaacQEREUUexmq+NLGpgFpfJ/MecKgiIqIwY7fbcfbsWdjtdqWrQmFGUjPfT0RU13geDi+NYi9MCPzTiUIFaxJ+GBcQEVEkYqxWmdoUD0108Ic4ZuKAiCjMFBQUYMuWLSgqKlK6KkRERBGH5+HwckWrOGjKex1s/zUv4uc5ICIiinSM1UKHiQMiojATFxeHq6++GrGxsUpXhYiIKOLwPBxeog0adGvh/izOFdpwKJsXCYiIiCIZY7XQYeKAiDq33i4AAQAASURBVCjMaDQaREdHQ60O/vh0RERE5B/Pw+HnyssS5b+3HcpTsCZERESkNMZqocPEARFRmCkqKsL+/ftRUlKidFWIiIgiDs/D4adX6zho1O7hirYd4nBFREREkYyxWugonjiYN28eWrZsCYPBgMzMTGzdutVv+c2bNyMzMxMGgwGtWrXCm2++GaKaEhGFht1ux7lz5+BwOJSuClHIMS4gIqXxPBx+og0adG9hBgDkFtnxy2kOVxQpGBcQEVFFjNVCR9HEwdKlSzFt2jTMnDkT+/btQ//+/TFixAgcP368yvJHjhzBNddcg/79+2Pfvn145JFHcN999+HTTz8Ncc2JiOpOfHw8hg4dyvH6KOIwLiCicMDzcHjqf1mC/PfWMB2uSAgBlxBwugQcThfsThfsDhdsDhesdhfK7E6U2ZwotTlRYnOi2OpAUZn7UVjqgKXUjoIS9yO/2I7zxTbkFdmQW2RDTqH7cc5ixdnyR0PHuICIiKrCWC10JCGU6+fZq1cvdO/eHW+88Ya8rEOHDrjhhhswZ86cSuUfeughrFq1CgcPHpSXTZ06Fd9//z127txZo31aLBaYzWYUFBTwC1ZOCIGCggKYzWZIkqR0dcIS28g/tk9gtWkjh8MBi8USopqFByEEioqKEB0dze9QNYQQSExMDFywHmNcEB54TA+MbeRffW+fUJyHed7zr6r2KbE6cfu8vbA7BXQaCY1i9RACEBDw/KJ1P3evLy/zPC9/4v268NqfKF9f3k7FZfBaR0AeLsl7O6G249mrFdpzaDAuCA/1/ZgeCmwj/9g+gbGN/KvYPpF4zSSQurpeoAn6FmvIZrNhz549ePjhh32WDxs2DDt27KhynZ07d2LYsGE+y4YPH4758+fDbrdDq9VWWsdqtcJqvXA3RkFBgfyvgjmTsOL5DwiAB6hqsI38Y/sEVps2OnfuHDZs2IBevXrBbDaHonqK81wgcLlc/A5VQwgBrVaLmJiYBtlGjAvCB4/pgbGN/Kvv7ROK8zDPe/5V1z5dmkVhz7Ei2BwCJ/PKFKxheLBYLIwLvDAuqBv1/ZgeCmwj/9g+gbGN/KvYPpF4zSSQurpeoFjiICcnB06nEykpKT7LU1JScObMmSrXOXPmTJXlHQ4HcnJykJqaWmmdOXPmYPbs2ZWWN2/e/BJqT0REpIyzZ88iOTlZ6WoEHeMCIiKi2jHPYVzgjXEBERFFumDHBYolDjwqZkGEEH4zI1WVr2q5x4wZMzB9+nT5ucvlQl5eHhITE5nFK2exWJCWloYTJ06wO2Y12Eb+sX0CYxv5x/YJzNNGOp1O6arUKcYFyuP/x8DYRv6xfQJjG/nH9gmMcUHNy1e13INxQWD8/xgY28g/tk9gbCP/2D6B1VVcoFjiICkpCWq1utLdAmfPnq10l4BH48aNqyyv0WiqHcdJr9dDr9f7LIuLi7v4ijdgsbGx/A8YANvIP7ZPYGwj/9g+gTXUH7GMC8IP/z8Gxjbyj+0TGNvIP7ZPYIwLLmBcULf4/zEwtpF/bJ/A2Eb+sX0CC3ZcoArq1mpBp9MhMzMT69at81m+bt069O3bt8p1+vTpU6n82rVr0aNHjyrHKyQiIqL6gXEBEREReTAuICIiUp5iiQMAmD59Ot59910sWLAABw8exP3334/jx49j6tSpANzdBidMmCCXnzp1Ko4dO4bp06fj4MGDWLBgAebPn48HHnhAqbdAREREQcK4gIiIiDwYFxARESlL0TkOxo0bh9zcXDz55JPIzs5G586dsWbNGqSnpwMAsrOzcfz4cbl8y5YtsWbNGtx///14/fXX0aRJE7z66qsYO3asUm+hQdDr9XjiiScqddGkC9hG/rF9AmMb+cf2CSwS2ohxQXiIhO/apWIb+cf2CYxt5B/bJ7BIaCPGBeEhEr5rl4pt5B/bJzC2kX9sn8Dqqo0k4ZktiIiIiIiIiIiIiIiIIp6iQxUREREREREREREREVF4YeKAiIiIiIiIiIiIiIhkTBwQEREREREREREREZGMiQMiIiIiIiIiIiIiIpIxcRAh5s2bh5YtW8JgMCAzMxNbt26ttuzy5csxdOhQJCcnIzY2Fn369MFXX30VwtoqozZt5G379u3QaDTo2rVr3VZQYbVtH6vVipkzZyI9PR16vR6tW7fGggULQlRbZdS2jRYtWoSMjAyYTCakpqZi8uTJyM3NDVFtQ2vLli0YNWoUmjRpAkmSsHLlyoDrbN68GZmZmTAYDGjVqhXefPPNuq+ogmrbRpF6rKbgYFwQGOMC/xgXBMa4oHqMC/xjTEChxrggMMYF/jEuCIxxQfUYF/inZFzAxEEEWLp0KaZNm4aZM2di37596N+/P0aMGIHjx49XWX7Lli0YOnQo1qxZgz179mDgwIEYNWoU9u3bF+Kah05t28ijoKAAEyZMwODBg0NUU2VcTPvcfPPNWL9+PebPn49Dhw5hyZIlaN++fQhrHVq1baNt27ZhwoQJmDJlCg4cOIBly5bhu+++w9/+9rcQ1zw0iouLkZGRgf/+9781Kn/kyBFcc8016N+/P/bt24dHHnkE9913Hz799NM6rqlyattGkXispuBgXBAY4wL/GBcExrjAP8YF/jEmoFBiXBAY4wL/GBcExrjAP8YF/ikaFwhq8Hr27CmmTp3qs6x9+/bi4YcfrvE2OnbsKGbPnh3sqoWNi22jcePGiUcffVQ88cQTIiMjow5rqKzats///vc/YTabRW5ubiiqFxZq20b/+c9/RKtWrXyWvfrqq6JZs2Z1VsdwAUCsWLHCb5kHH3xQtG/f3mfZnXfeKXr37l2HNQsfNWmjqjT0YzUFB+OCwBgX+Me4IDDGBTXHuMA/xgRU1xgXBMa4wD/GBYExLqg5xgX+hTouYI+DBs5ms2HPnj0YNmyYz/Jhw4Zhx44dNdqGy+VCYWEhEhIS6qKKirvYNnrvvfdw+PBhPPHEE3VdRUVdTPusWrUKPXr0wPPPP4+mTZuiXbt2eOCBB1BaWhqKKofcxbRR3759cfLkSaxZswZCCPz555/45JNPMHLkyFBUOezt3LmzUnsOHz4cu3fvht1uV6hW4a2hH6spOBgXBMa4wD/GBYExLgg+xgW109CP0xQ8jAsCY1zgH+OCwBgXBB/jgtq5lOO0pg7qQ2EkJycHTqcTKSkpPstTUlJw5syZGm3jxRdfRHFxMW6++ea6qKLiLqaNfvvtNzz88MPYunUrNJqG/d/oYtrnjz/+wLZt22AwGLBixQrk5OTgrrvuQl5eXoMct/Bi2qhv375YtGgRxo0bh7KyMjgcDlx33XV47bXXQlHlsHfmzJkq29PhcCAnJwepqakK1Sx8NfRjNQUH44LAGBf4x7ggMMYFwce4oHYa+nGagodxQWCMC/xjXBAY44LgY1xQO5dynGaPgwghSZLPcyFEpWVVWbJkCWbNmoWlS5eiUaNGdVW9sFDTNnI6nbjtttswe/ZstGvXLlTVU1xtvkMulwuSJGHRokXo2bMnrrnmGrz00ktYuHBhg72LAKhdG/3888+477778Pjjj2PPnj348ssvceTIEUydOjUUVa0XqmrPqpZTZB2rKTgYFwTGuMA/xgWBMS4ILsYFNRNJx2kKHsYFgTEu8I9xQWCMC4KLcUHNXOpxumGnPglJSUlQq9WVsphnz56tlJ2raOnSpZgyZQqWLVuGIUOG1GU1FVXbNiosLMTu3buxb98+3HPPPQDcJz4hBDQaDdauXYtBgwaFpO6hcDHfodTUVDRt2hRms1le1qFDBwghcPLkSbRt27ZO6xxqF9NGc+bMQb9+/fCvf/0LANClSxdERUWhf//+ePrppyM+Q964ceMq21Oj0SAxMVGhWoWnSDlWU3AwLgiMcYF/jAsCY1wQfIwLaiZSjtMUPIwLAmNc4B/jgsAYFwQf44KaCcZxmj0OGjidTofMzEysW7fOZ/m6devQt2/fatdbsmQJJk2ahMWLFzf4MdRq20axsbH48ccfsX//fvkxdepUXHbZZdi/fz969eoVqqqHxMV8h/r164fTp0+jqKhIXvbrr79CpVKhWbNmdVpfJVxMG5WUlECl8j0Eq9VqABcy5ZGsT58+ldpz7dq16NGjB7RarUK1Cj+RdKym4GBcEBjjAv8YFwTGuCD4GBcEFknHaQoexgWBMS7wj3FBYIwLgo9xQWBBO07Xejplqnc+/vhjodVqxfz588XPP/8spk2bJqKiosTRo0eFEEI8/PDDYvz48XL5xYsXC41GI15//XWRnZ0tP/Lz85V6C3Wutm1U0RNPPCEyMjJCVNvQq237FBYWimbNmokbb7xRHDhwQGzevFm0bdtW/O1vf1PqLdS52rbRe++9JzQajZg3b544fPiw2LZtm+jRo4fo2bOnUm+hThUWFop9+/aJffv2CQDipZdeEvv27RPHjh0TQlRunz/++EOYTCZx//33i59//lnMnz9faLVa8cknnyj1FupcbdsoEo/VFByMCwJjXOAf44LAGBf4x7jAP8YEFEqMCwJjXOAf44LAGBf4x7jAPyXjAiYOIsTrr78u0tPThU6nE927dxebN2+WX5s4caIYMGCA/HzAgAECQKXHxIkTQ1/xEKpNG1XU0AMBIWrfPgcPHhRDhgwRRqNRNGvWTEyfPl2UlJSEuNahVds2evXVV0XHjh2F0WgUqamp4vbbbxcnT54Mca1DY+PGjX6PK1W1z6ZNm0S3bt2ETqcTLVq0EG+88UboKx5CtW2jSD1WU3AwLgiMcYF/jAsCY1xQPcYF/jEmoFBjXBAY4wL/GBcExrigeowL/FMyLpCEYB8XIiIiIiIiIiIiIiJy4xwHREREREREREREREQkY+KAiIiIiIiIiIiIiIhkTBwQEREREREREREREZGMiQMiIiIiIiIiIiIiIpIxcUBERERERERERERERDImDoiIiIiIiIiIiIiISMbEARERERERERERERERyZg4ICLMmjULXbt2VboaNbZw4ULExcUpXQ0iIqIGiXEBEREReTAuIIpcTBwQhaFJkyZBkiRMnTq10mt33XUXJEnCpEmTQl8xIiIiCjnGBUREROTBuICIQoWJA6IwlZaWho8//hilpaXysrKyMixZsgTNmzdXsGYNl91uV7oKREREVWJcEHqMC4iIKFwxLgg9xgUUiZg4IApT3bt3R/PmzbF8+XJ52fLly5GWloZu3br5lP3yyy9x5ZVXIi4uDomJibj22mtx+PBhnzInT57ELbfcgoSEBERFRaFHjx7YtWuXT5kPP/wQLVq0gNlsxi233ILCwsJq6+fp/vfVV1+hQ4cOiI6OxtVXX43s7Gy5TFZWFqZNm+az3g033OBz90OLFi3w9NNPY8KECYiOjkZ6ejo+++wznDt3Dtdffz2io6Nx+eWXY/fu3ZXqsHLlSrRr1w4GgwFDhw7FiRMnfF7//PPPkZmZCYPBgFatWmH27NlwOBzy65Ik4c0338T111+PqKgoPP3009W+XyIiIiUxLmBcQERE5MG4gHEBUSgwcUAUxiZPnoz33ntPfr5gwQL89a9/rVSuuLgY06dPx3fffYf169dDpVJh9OjRcLlcAICioiIMGDAAp0+fxqpVq/D999/jwQcflF8HgMOHD2PlypX44osv8MUXX2Dz5s147rnn/NavpKQEL7zwAj788ENs2bIFx48fxwMPPFDr9/nyyy+jX79+2LdvH0aOHInx48djwoQJ+Mtf/oK9e/eiTZs2mDBhAoQQPvt+5pln8P7772P79u2wWCy45ZZb5Ne/+uor/OUvf8F9992Hn3/+GW+99RYWLlyIZ555xmffTzzxBK6//nr8+OOPVbYtERFRuGBcwLiAiIjIg3EB4wKiOieIKOxMnDhRXH/99eLcuXNCr9eLI0eOiKNHjwqDwSDOnTsnrr/+ejFx4sRq1z979qwAIH788UchhBBvvfWWiImJEbm5uVWWf+KJJ4TJZBIWi0Ve9q9//Uv06tWr2n289957AoD4/fff5WWvv/66SElJkZ8PGDBA/OMf//BZr2Ld09PTxV/+8hf5eXZ2tgAgHnvsMXnZzp07BQCRnZ3ts+9vvvlGLnPw4EEBQOzatUsIIUT//v3Fs88+67PvDz/8UKSmpsrPAYhp06ZV+x6JiIjCAeMCxgVEREQejAsYFxCFiiakWQoiqpWkpCSMHDkS77//PoQQGDlyJJKSkiqVO3z4MB577DF88803yMnJke8MOH78ODp37oz9+/ejW7duSEhIqHZfLVq0QExMjPw8NTUVZ8+e9Vs/k8mE1q1b12qdqnTp0kX+OyUlBQBw+eWXV1p29uxZNG7cGACg0WjQo0cPuUz79u0RFxeHgwcPomfPntizZw++++47nzsGnE4nysrKUFJSApPJBAA+2yAiIgpnjAt8lzEuICKiSMa4wHcZ4wKi4GPigCjM/fWvf8U999wDAHj99derLDNq1CikpaXhnXfeQZMmTeByudC5c2fYbDYAgNFoDLgfrVbr81ySJJ+uiTVdR3h1D1SpVD7PgaonFPLejiRJ1S6rWB/P8qqWuVwuzJ49G2PGjKlUxmAwyH9HRUVVep2IiChcMS5gXEBEROTBuIBxAVFd4hwHRGHu6quvhs1mg81mw/Dhwyu9npubi4MHD+LRRx/F4MGD0aFDB5w/f96nTJcuXbB//37k5eWFqtoAgOTkZJ/Jj5xOJ3766aegbNvhcPhMgHTo0CHk5+ejffv2ANyTRR06dAht2rSp9FCpeOgjIqL6iXFB1RgXEBFRJGJcUDXGBUTBwf8NRGFOrVbj4MGDOHjwINRqdaXX4+PjkZiYiLfffhu///47NmzYgOnTp/uUufXWW9G4cWPccMMN2L59O/744w98+umn2LlzZ53WfdCgQVi9ejVWr16NX375BXfddRfy8/ODsm2tVot7770Xu3btwt69ezF58mT07t0bPXv2BAA8/vjj+OCDDzBr1iwcOHAABw8exNKlS/Hoo48GZf9ERERKYFxQNcYFREQUiRgXVI1xAVFwMHFAVA/ExsYiNja2ytdUKhU+/vhj7NmzB507d8b999+P//znPz5ldDod1q5di0aNGuGaa67B5Zdfjueee67KwCKY/vrXv2LixImYMGECBgwYgJYtW2LgwIFB2bbJZMJDDz2E2267DX369IHRaMTHH38svz58+HB88cUXWLduHa644gr07t0bL730EtLT04OyfyIiIqUwLqiMcQEREUUqxgWVMS4gCg5JVBxQjIiIiIiIiIiIiIiIIhZ7HBARERERERERERERkYyJAyIiIiIiIiIiIiIikjFxQEREREREREREREREMiYOiIiIiIiIiIiIiIhIxsQBERERERERERERERHJmDggIiIiIiIiIiIiIiIZEwdERERERERERERERCRj4oCIiIiIiIiIiIiIiGRMHBARERERERERERERkYyJAyIiIiIiIiIiIiIikjFxQEREREREREREREREMiYOiIiIiIiIiIiIiIhIxsQBERERERERERERERHJmDggIiIiIiIiIiIiIiIZEwdERERERERERERERCRj4oCIiIiIiIiIiIiIiGRMHBARERERERERERERkYyJAyIiIiIiIiIiIiIikjFxQEREREREREREREREMiYOiIiIiIiIiIiIiIhIxsQBERERERERERERERHJmDggIiIiIiIiIiIiIiIZEwdERERERERERERERCRj4oCIiIiIiIiIiIiIiGRMHBARERERERERERERkYyJAyIiIiIiIiIiIiIikjFxQEREREREREREREREMiYOiIiIiIiIiIiIiIhIxsQBERERERERERERERHJmDggIiIiIiIiIiIiIiIZEwcUcpMmTYIkST4PvV6P9u3bY9asWSgrK1O6ipdk1qxZmDVrFlauXFnpNc/7nTRpUsjrdSl++OEH3HPPPcjIyEBSUhKioqLQqVMnzJw5E4WFhZXK//LLLxgzZgwSEhJgNBrRvXt3fPDBBz5lcnNz8dhjj+HKK69EamoqDAYDWrVqhfHjx+OPP/7wKbt161ZMmTIFHTt2RHx8PGJiYpCRkYEXX3wRVqu1Tt87AZs2bZK/uwsXLlS6OkREdW7AgAHyca/iOQkAPvvsM/n1e++9t8bbXbhwobzepk2bal2vTZs2yXFGfn5+rdcPpL7GKQ2Zd9xMRERERBRKGqUrQAQANpsNhw4dwuzZs7Fz50589dVXSlfpos2ePRsAMHHiRNxwww3KViZI1qxZg9dff91n2c8//4yff/4Za9aswbfffgutVgvAnTTo06ePzwWNffv2YeLEicjOzsZDDz0EAPjtt9/w9NNP+2zzyJEjOHLkCD7//HPs3r0bbdq0AQAsWrQICxYs8Cn7ww8/4IEHHsCGDRuwevXqYL9lIiKKYLfccgu2bNkCAFi2bJl87vL45JNPfMqGyqZNm+Q4Y9KkSYiLiwvZvomIiIiIKLKwxwEpauPGjXC5XNi7dy8aNWoEAFi7di02btzod73S0tJQVI/KSZKEG264AZs2bUJJSQl2796N5s2bAwD279/v07ti+vTpyM/Ph0ajwZo1a3D69GlkZmYCAB5//HGcPHlSLtu1a1d8/PHHyM/Px4kTJzB06FAAQEFBAV5++WW5nFqtxqRJk/Ddd9+hpKQEGzZsQGxsLAB3UuO7776r6yaot+p7Dx4iIiXcdNNN0Gjc99csW7bM5zWr1YpVq1YBANLS0tC3b9+Q14/Cn81mg8vlUroaREREREQXjYkDUpwkSejWrZvPHXu7d+8G4Ns9e//+/RgwYACMRiNmzJgBwP3j/amnnkLHjh1hMBgQGxuLrKwsfPHFFz778N7Ovn370Lt3bxiNRvTo0QPffvstcnNzceuttyImJgYtW7bE3LlzK9Vz/fr1GD58OOLj46HX63HZZZfh6aefht1uB3Bh+AGP999/3+/wLh999BE6dOiAqKgo9O3bF/v37/d53WazYc6cOejcuTOMRiNiYmIwaNAgrF+/3qdcVlYWJElCixYtKrVrxeEGvv76awwcOBDJycnQ6/Vo2rQprr766iqHVfJ21113YcWKFXL7Z2Zm4r777pNf//333wEAOTk5cm+RwYMHY8SIEUhNTcU///lP+T15LsB06dIFe/bswbhx42A2m9GsWTPMmTOn0jYB4LnnnsN7772HHj16wGg0YuDAgZgwYUKVZS+V93flwIEDGDx4MEwmE5o2bYqnn34aQgif8l999RWGDBmCuLg4+Xvx2GOP+SS3LrvsMkiShKysLHnZzTffDEmS0Lt3b3nZhAkTIEkSzGYznE4ngIv7Hqxfvx6ZmZnQ6/V48803AQAWiwUPPfQQ2rVrB71ej/j4eIwaNQp79+712U5paSnuuusuJCQkwGw2Y8qUKbBYLEFpWyKi+iIpKQmDBg0CAOzZs8dnuKK1a9fKx8Vx48ZBkiR89NFHGDhwIJo0aQK9Xg+TyYSMjAy89NJL8vHcn5oco1u0aCH3NgCAli1b+pz/Z82aJZ+/jh49KperLk547bXXkJ6eDqPRiMGDB+PQoUPV1m/JkiXo27cvoqOjYTQa0aVLlxq/t9rwjl3efvtttGnTBgaDAVdccQW2b9/uU7YmMeDOnTsrxWJFRUXQaDSQJEk+R5aVlUGv10OSJNxzzz3y+r/99hsmTJiAJk2aQKfToWnTprjjjjvw559/ymWOHj0q7+Oxxx7D448/jqZNm8JgMMjfkz179mDMmDFITk6GTqdDy5Yt8cADD6CoqMjnPX3//fe48sorYTQa0aZNm0q9LYmIiIiIQkoQhdjEiRMFAAFAbNy4UV5+7733ysuff/75SmUTEhLkv//xj38Iu90uBg4cKC+r+Pjvf/9b5T4TExN9yjVu3Fj07Nmz0vpfffWVvP57770nJEmqcj8jR44ULpdLvPfee9XW5b333hNCCPl5o0aNKpVJT08XNptNCCGEw+EQQ4YMqXJbkiSJjz/+WK7bgAED5PW9ecpPnDhRCCHE0aNHhcFgqHKbd999d60/x6efflpe/4MPPhBCCLFu3Tqfz8hj79698vK//OUv1W5z27Ztcrm//vWvfvf/t7/9TS67ZcuWWte/Ov6+KwDEE088IZd95513qv1e9OvXT1itViGEEH//+98FAGEymYTdbhdCCNG0aVMBQGi1WlFSUiKEEKJ169YCgBgxYoQQ4uK+ByaTSej1erncyy+/LAoLC8Xll19e5Xb0er3YunWrvJ3JkydXKpOamlrpu0xE1NAtWLBAPvY999xz8vLx48fLy3fv3i2E8D13VHzMnDlTXtc7VvDEQDU9Rqenp1dZxnP+f+KJJ+RlR44ckfdZVZzw/vvv+z3We2IHIYR49NFHq31vN910U1Db3LPdpKSkSvsymUziwIEDQghR4xjQZrMJk8kkAIg77rhDCOEbq3hikq1bt8rLli5dKoQQ4ocffhCxsbHVtvnZs2eFEEIcOXJEXu4dqwIQ58+fF2vXrhU6na7K7WRmZorS0lIhhBB5eXlVxh2NGzeW/yYiIiIiCiX2OKCwsH//fnz88cfyc8/QNt5at26NQ4cOwWKx4N5778XixYvlIY1Gjx6NnJwc7NmzBykpKQCAhx56qMqJA6+55hqcP38e06dPBwCcOXMGv/zyC3bv3o09e/bIvQY8d8YXFRVh2rRpEEJgxIgROHnyJEpKSvDss88CAFavXo01a9Zg0qRJPnejT5w4EUIICCEqTTJ49uxZvPPOO8jPz8fEiRMBAMeOHcO3334LwH1n39dffw0AmDdvHoqLi3Hy5En06dMHQghMmzat1nf57d69Wx625rvvvoPVasWxY8ewePFiXHnllbXa1tmzZ+U5DxITE3H99dcDAM6dOyeX8QwlVPHvs2fPVrlNl8uFJ598Un4+efLkavd/6NAhLF68GID7bv5+/fr5re/FTkY5cOBA5Obm4ttvv0VSUhIA4Pnnn0d+fj4KCwvxz3/+E0IIJCcnY9euXcjNzcVNN90EANi+fTvef/99AMBVV10FACgpKcH333+PY8eO4dSpU5AkCXa7Hd999x3Onj2Lw4cP+5S/mO9BSUkJhgwZghMnTiA3Nxdjx47F3Llz8eOPP0KtVmPFihUoKyvDr7/+ijZt2sBqteL+++8H4L6z0lPnDh064PDhwzhy5AgSExNr3GZERA3FmDFjoNfrAVyICWw2mzxMUZs2beR4ZdKkSdi7dy/y8vJgt9tx9OhRdO/eHQDw+uuvV+qt5q2mx+ijR4/iiSeekNc7cuQIhBA+vQtqQgiBxx9/HABgNBqxYcMG5OXlyT0svB05ckTuDdi2bVscPHgQp0+fRv/+/eV2Wbdund/9VdfjwZ/c3FwsWbIEBQUF8v5LSkrkuZFqGgNqtVr06dMHALBjxw4AkHsuSJIk/+3dm8FzDr7//vthsViQnp6Offv2wWq1Yv369dBoNDh27Bief/75SvU+f/483nzzTVgsFhw8eBBRUVG46667YLPZ0L17d/z2228oKyvDhx9+CMDdE2H+/PkAgJdffhm5ubkALgz7uHz5cp/eDUREREREIaVUxoIil7+78gCIgQMHCpfLVanszp07fbZzyy23yK/99NNP8vKZM2fKy1etWlVpO7/99psQQog1a9bIy8aPHy+v77njbtiwYUIIIb766iu/9QUgpk+fLq/vWeZ9t17F16644gp5mXc9Fi9eLIQQ4tZbbw24zx9++EEIUfMeB9999528bPTo0eKll14SX331lbBYLDX+7IQQ4ty5c6Jr164CcN8t790zY/HixfI+HnvsMXn5b7/9Ji8fPnx4pW06nU6fz8j7rv6KDh8+LN91GRsbK77//vuAda7qDs/qVPVdEUKIBx54QF7+5Zdfii+//FJ+/sADD1T5XseMGSOEEOL48ePystdee00sWrRIABDXX3+9ACDmzJkjli9fLpfZvn27EOLivgeSJInTp0/7vKc+ffoE3E5BQYH44IMP5Ofz5s2T158/f768nD0OiCiSXHfddfLx7/Dhw+Lzzz+Xnz/66KNyuQMHDoixY8eKxo0bC7VaXekYe+bMGSFE1eejmh6jhai+V4G/1yrGCd7npJtvvlkud/jw4Uqxw5tvvikv8+7J6X3XvncMVJXq4pSqeLbZr18/eZnT6ZR7ajZu3FgIUbsY8MknnxQAhEqlEgUFBWLo0KFCrVaLkSNHCgAiOztb/pzbtm0rhBCiuLi4ys/R+9G9e3chhG+Pg4oxzqFDhwJ+tp5YYdCgQXI9i4qK5G30799fLktEREREFErscUBhQavVom3btnjkkUfwxRdf+MwV4JGRkeHzPCcnR/47LS2tyr+974D38NzxZjQa5WWeiX4BQKfTAXCPnVvdNirKy8sLWMZb27Zt5b8NBoP8d7D2WVVvhB49emDmzJkwGo1YsWIFpk+fjuHDhyMlJQXvvvtujer9559/IisrC/v374dWq8XHH3+MYcOGya8nJyfLfxcUFMh/FxYWVlkGABwOB26//Xb5Tvd//vOfmDVrVpX7P3ToEK666iocO3YMsbGxWLNmDbp06VKjul8M7+9Ss2bN5L9zcnJq9f1LS0tDy5YtAbjHW/bc9fjAAw/Idzx67nY0mUy44oorfNb3p+L3IDk5GampqT7LarKd8+fP4/Tp0/Lzpk2bVvk3EVEk8Z5/admyZT4TJXtes1gsGDZsGD799FOcOXOmynOw97w3FdX0GH2xKtanNsf6i421LpX39lUqFZo0aeJTn9rUa8CAAQDcPRt37NiBXbt2ISMjQ45ftm3bJp+XPWXPnz8fsGdnVXFYxVi1Nudxz+diNpsRFRUlv85zMBEREREphYkDUtTGjRshhIDNZsOvv/6KZ555BiaTqcqy3hf6AchDxwDAyZMn5b9PnDhRZRkPjUZTo2Ue3he658yZIw8/5P2o7eR1Wq1W/ruqJIn3Pk+fPl1pfy6XS/5x6xlGwTMMEeAeWqAqTz/9NHJycrB9+3YsXLgQvXv3RmlpKe677z44HA6/dT516hQGDBiAAwcOwGAwYMWKFRgzZoxPma5du0Klch9WvCdZ/OWXX+S/PUM3AIDdbse4cePkYaoeffRRvPDCC1Xu/8CBAxgwYABOnTqFhIQErF+/PuAQRR6eYaSEED4TFAfi/V3y/o4lJSXV+vvnGfpgx44d2L59O2JjY9G3b1907NgRO3bswLZt2wAAvXv3lr8ftfkeeFT8f+K9nejoaNhstiq3k56eLl+YAdyfd1V/ExFFkuuuu06OSxYvXiwPU9S5c2d06tQJAPDNN9/Ix8nx48cjPz8fQgiMHTu2Rvuo6TEaqDpm8PDEA4BvTFBxKKPaHOsvNtbytmnTploPqeS9fZfLJV9U9+yrNvXq1auX3DZvv/02LBYL+vbti759+wIA3nvvPTkR4TlXJyQkQK1WAwCGDx9eZeznPWG2R8VzsPd5/M4776xyOxs2bABw4XMpKChAcXGxvB7PwURERESkFCYOqN66+uqr5b8ff/xx5ObmYv/+/fJYsSaTSR6D91L07dsXZrMZAPDiiy9i/fr1KCsrQ05ODlasWIFBgwZhy5Ytcvn4+HgAwO+//+73DkN/rrnmGvnvO+64A0eOHIHNZsPvv/+Of//73z7vy3N33Z9//om9e/fC6XT6zBXg8eOPP+Kpp57Cr7/+ik6dOuHGG29Et27dALjvhPTuFVDRsWPHMGDAABw6dAhRUVFYvXo1Ro4cWalcUlIShg8fDgDYsGEDvvzyS2RnZ+PFF18E4O7N4ZkDwGq1YvTo0Vi+fDkA4N///jeeeuqpKve/f/9+ZGVl4c8//0RKSgo2bdqEHj16VN+AQfLoo48iLy8Pu3fvxsKFCwG4Lwr07t0bffv2RUxMDADg/fffx+7du3H+/Hk8+uij8vre31HPBf6jR4/ihx9+QO/evaFSqdC3b1/k5eXJ81t4LloAtfse+OPZTlFREf7v//4P2dnZsFqtOHDgAGbOnIkbb7wRANCnTx858fPaa6/hjz/+wLFjx/Dyyy/Xqt2IiBqKqKgoXHvttQCAH374QZ47ybsngs1mk/82Go3Q6XRYt24d1qxZU6N91PQYDVyIMQDgp59+8tmO9932//vf/wAA77zzjs+FdcDdg86TiPj888+xadMm5Ofny/MeeBs6dKh8Xnj11Vfx66+/4syZM/JcA4DvuS5YduzYgf/3//4fCgsL8fzzz8vzIw0cOLDSPgPFgHq9Hr169QIAfPbZZwDcsV3Xrl1hMpnktgIunIONRqN8o8HatWsxb948WCwWFBcXY9u2bZgwYYI894I/7dq1Q5s2bQC4Y4VPPvkExcXFKCgowNq1a3HDDTfgo48+AgD5ZgiXy4XHH38cFosFK1askG8sICIiIiIKubodCYmoMu8x5Gsz3nxFdrtdXHXVVdWOGfvqq6/63c7GjRurHFPfM37+gAED5GXvv/++kCSp2n15v49hw4ZVet0zVr7nuff8B9718Iwf73A4qtyO5+E9TrD3WPsqlUqYTCZhMpkq7ct7PxUfvXr18vs5eI+bXNXD+/0cPHhQxMXFVVnuueeeq/J9B3qPgebF8DcnQm1578sz30V1+3rrrbeqrVPv3r2F1WqVy3qPHQ1AzJo1SwjhO941ALFhwwZ5ndp8D/yNIV1YWCgyMjKq3Y73d33SpEmVXk9MTKz0HSUiihTec9B4Hr///rv8em5urs9xEnDPN9OqVSv5uWfOgarmOKjNMXrHjh2VXr/99tuFEELk5OSI6Ohoebnnb6PRWOn8sHDhQr/Heu/z+owZM6qtm2d8/mDxbLdx48aV9mU0GsWBAweEELWLAYUQ4rHHHvN5/ejRo0KIC+dOAKJ58+Y+6/z444/CbDYHjD285zioKh75+uuvhU6nq3Y7nvNqbm6uSEhI8Pu5EBERERGFEnscUL2l0Wjw1Vdf4YknnkD79u2h0+kQHR2N/v37Y+XKlbj33nuDtq8JEyZgw4YNGDlyJBISEqDVatGsWTMMHjwYr7zyis/wO6+++iqysrLku9EvhlqtxhdffIH//Oc/6Nq1K4xGI6KiotCuXTtMmDABb7zxhlx2+PDhmDt3Llq2bAmdToeePXv69IDwaNOmDe644w506tQJZrMZer0eLVq0wB133IGVK1dedF0rat++PXbs2IHRo0cjPj4eBoMB3bp1w8KFC/HQQw8FbT+hsGnTJgwdOhRGoxGpqal46qmn8MQTT8iv33HHHVi9ejUGDhyI2NhYn7k61q9fL8+XAQCtWrXymSfBM0SC93BLOp0OvXv3lp/X5nvgT3R0NLZt24aZM2eiffv20Ov1iI2NRceOHTF16lQ888wzctl58+bh//7v/xAXF4fY2FiMHz8e77zzTu0bj4iogbjmmmvknoeAe86g1q1by88TEhLwxRdfoGfPnjAYDGjbti0+/PDDGvcKq80xuk+fPnj22WeRlpYm9wTwSExMxGeffYaMjAwYDAY0b94cixcvRs+ePSvtc+LEiXjllVeQlpYGvV6PAQMGYNOmTVXW79lnn8UHH3yAXr16wWQyQa/Xo1OnTnj++eexdOnSGr3H2ho+fDgWLlyINm3aQKfTITMzE2vXrkXHjh0B1D4G9B7Wr0mTJnKPC+9zsHePP8A9HNXevXsxefJkNGvWDFqtFsnJybjiiivw2GOPYcKECTV6L4MHD8auXbtw0003ISUlBRqNBo0bN8aVV16JOXPmYMSIEQDgMwyjXq9Hy5Yt8cYbb8g9XoiIiIiIQk0SQgilK0FEFC4mTZokT9TMwyMREVHoeOZwmDhxojxEIBERERERKYM9DoiIiIiIiIiIiIiISMbEARERERERERERERERyThUERERERERERERERERydjjgIiIiIiIiIiIiIiIZEwcEBERERERERERERGRjIkDIiIiIiIiIiIiIiKSMXFAREREREREREREREQyJg6IiIiIiIiIiIiIiEjGxAEREREREREREREREcmYOCAiIiIiIiIiIiIiIhkTB0REREREREREREREJGPigIiIiIiIiIiIiIiIZBGXOBBCwGKxQAihdFWIiIhIYYwLiIiIiIiIiCrTKF2BUCssLITZbEZBQQFiY2OVrk5YEEKgoKAAZrMZkiQpXZ2wxDbyj+0TWKS0UWFhIfbv349WrVrBaDTWeD0hBIqKihAdHd2g2+dSCCGQmJiodDUaHMYFlUXK8ao6NTmO8ZjlH9snMLaRf2yfwBgXEBERUV2LuMQBERHVHb1ej3bt2kGj4emFiOonHseIiIiIiIiYOCAioiBSqVTQarVKV4OI6KLxOEZERERERBSBcxwQEVHdKSwsxK5du1BcXKx0VYiILgqPY0REREREREwcEBFRELlcLlitVrhcLqWrQkR0UXgcIyIiIiIi4lBFREQURGazGVdddZXS1SAiumg8jhEREREREbHHAREREREREREREREReWHigIiIgiY3NxfLly/H+fPnla4KhZk5c+bgiiuuQExMDBo1aoQbbrgBhw4dCrje5s2bkZmZCYPBgFatWuHNN9+sVObTTz9Fx44dodfr0bFjR6xYsaIu3gJFCB7HiIiIiIiImDggIqIgMplM6NKlC0wmk9JVoTCzefNm3H333fjmm2+wbt06OBwODBs2zO8EtEeOHME111yD/v37Y9++fXjkkUdw33334dNPP5XL7Ny5E+PGjcP48ePx/fffY/z48bj55puxa9euULwtaoB4HCMiIiIiIgIkIYRQuhKhZLFYYDabUVBQgNjYWKWrExaEEBBCQJIkSJKkdHXCEtvIPyEECgoKYDab2T7ViJQ2cjgcsFgstV5PCIGioiJER0c36Pa5FEIIJCYmKl2NoDl37hwaNWqEzZs3Vzue/EMPPYRVq1bh4MGD8rKpU6fi+++/x86dOwEA48aNg8Viwf/+9z+5zNVXX434+HgsWbIkYD0YF1QW6ec8z/sPxOVyQaWqf/fg5Ofn1/k+eEwPjG3kH9snsIYWFxAREVH44eTIREQUNDabDdnZ2UhKSoJWq1W6OhTGCgoKAAAJCQnVltm5cyeGDRvms2z48OGYP38+7HY7tFotdu7cifvvv79Smblz51a5TavVCqvVKj/3JLpqerE40kRimwgh4HQ6oVKpqr1g6WkXT4KlPgnFZ+r5/xSJ35+aYhv5x/YJjG1DREREdY2JAyIiChqLxYLt27dj8ODBiI+PV7o6FKaEEJg+fTquvPJKdO7cudpyZ86cQUpKis+ylJQUOBwO5OTkIDU1tdoyZ86cqXKbc+bMwezZsystLygoqNFFmEjplRDJF6RcLhdsNht0Op3fpIB38qA+KSoqCsl+SktL611SJdTYRv6xfQJLSkpSugpERETUgDFxEELnzp3DuHHjsHv3bgwfPhzLli1TukphS5Ik7Nu3D127dlW6KgCATZs2YfTo0ZwokSiAhIQEjBw5Enq9XumqUBi755578MMPP2Dbtm0By1a8aOS5SOu9vKoy1V1smjFjBqZPny4/t1gsSEtLg9lsDnlS4Ny5c7jlllvkuOD//b//F9L9BxJOF+xUKhX27t0bkrhArVbDYDAAqL4NNm7ciLFjxyI3Nzes2qkmoqOj63wfnjvFo6Ki6l37hArbyD+2T2D1LWlJRERE9U/9G5hVQVlZWdDr9YiOjkZCQgIGDBiA3bt313j9t99+G2q1Gvn5+UwaBNGkSZMgSRLeffddn+XLli2DJEm44YYblKlYBadPn8Y111yDqKgoNG/eHO+8847f8uvWrUP37t0RExODjh074ssvv/R5XQiBOXPmoEWLFoiKikK7du04GSgpTqVSwWg01stxvyk07r33XqxatQobN25Es2bN/JZt3LhxpZ4DZ8+ehUajkcd1rq5MxV4IHnq9HrGxsT4PAPJ4/rV5DBw4EAaDATExMUhMTERWVhb27NlT4/Xfeecdn7jgYupQFw9vStfFu041LTt58mSoVCrMnz/fZ/knn3wClUqF0aNH13hf1fEud7FOnz6NkSNHIiYmBi1atPAbF1itVgwaNAiNGzdGXFwcOnbsiLffftunzLZt29C3b1/Ex8cjLS0NDz30EFwuV5V154MPPhrGg4iIiKgu8cpOLf373/9GUVERzpw5g169emHMmDE1XvfIkSPo1KnTRV9QczgcF7VeJLjsssvw3nvv+Sx777330L59e4VqVNmtt96Kxo0b4+zZs1i2bBn+9a9/YfPmzVWW/eOPPzB69GjMnj0bBQUFeP755zF27Fj88ccfcpmZM2di9erV+Prrr1FUVIR169ahefPmoXo7RFUqKirC7t27UVxcrHRVKMwIIXDPPfdg+fLl2LBhA1q2bBlwnT59+mDdunU+y9auXYsePXrIc2hUV6Zv377Bq7wfjAvC06XEBUII2Gy2Or+b9/bbb5cTX0uXLsVDDz1UbVyg0Wjwyiuv4OTJk8jPz8cnn3yCJ554Alu3bgUAOJ1OjB49Gtdddx1ycnKwdetWfPLJJ5WSC0RERERERDXFxMFF0ul0mDhxIk6cOIFz584BcP/QfPXVV9G+fXvExcUhKysLBw8eBADcdNNNeP/99zFv3jxER0dj/vz5AICvv/4aPXv2RFxcHDp16oRVq1bJ+5g0aRKmTJmCm2++GbGxsXjjjTdgt9vx+OOPo3Xr1khMTMR1112H06dPy+tIkoQ333wTnTt3RmxsLK677jp5AkoA+O2333DdddchOTkZCQkJPhc4Dh8+jFGjRiE5ORnp6el4+umnq7xTDQAWLlyIrl274pFHHkFiYiKaN2+OefPm+ZT5+OOP0aVLF8TFxeGKK67Ajh075NcWLVqEzp07IyYmBs2bN8djjz1W7Q/0X3/9Fa1bt8Z///vfaj+PYcOG4fjx4zh06BAA91183333Ha6//nqfcg8++CDS09Plu/gr9vzYs2cPBg0ahISEBCQnJ+Pee+/1ef3dd99FWloaEhMT8eCDD1Zbn4oOHz6Mbdu2Yc6cOYiKikKvXr1w++23Y8GCBVWW//LLL9G9e3eMGjUKKpUK1157LXr27IkPPvgAAJCXl4eXXnoJCxYsQJs2bSBJEtLT05GamlrjOhHVBafTCYvFUu2xgyLX3XffjY8++giLFy9GTEwMzpw5gzNnzqC0tFQuM2PGDEyYMEF+PnXqVBw7dgzTp0/HwYMHsWDBAsyfPx8PPPCAXOYf//gH1q5di3//+9/45Zdf8O9//xtff/01pk2bFsq3x7iggcUFnmFS9uzZgyFDhiApKQkpKSm47777fNZ/9913kZ6ejuTkZDz00EPV1qciT1zw7LPPynHBbbfdVinZ4aFWq3H55ZdDo3GPMuq52/j3338H4J6jIy8vDxMmTIBarUaLFi0wePBgHDhwoMZ1IiIiIiIi8sbEwUUqLS3F/PnzkZSUJE8A+sYbb2D+/Pn4/PPPkZOTgzFjxmDUqFGw2WxYtmwZbr/9dtx1110oKirClClT8MMPP+Cmm27Cc889h7y8PLz11lsYP368/CMXAJYsWYIpU6YgPz8fU6ZMwcyZM7F9+3Zs27YN2dnZaNeuHW655Rafui1duhTr16/H8ePHcfLkSbz88ssAgOLiYgwZMgSdO3fG0aNHcebMGfnCeGlpKYYMGYJBgwbh1KlT2Lp1Kz7++ONqf8ACwE8//QRJkpCdnY2lS5fi4YcfxpYtWwAAa9aswQMPPICFCxciLy8PM2bMwKhRo5CbmwvAPQ768uXLYbFYsGrVKrz99ttYvHhxpX18++23GDRoEObMmYN77rmn2rqoVCqMHz9eru/777+Pm2++WR6j2CMjIwPfffcd8vPz8fjjj2P8+PE4cuQIAODUqVMYNGgQbrzxRpw+fRrHjh3DzTffLK9bWFiIH3/8Eb/99hu2bduG119/HZs2bZJfj4uLq3a87h9++AGpqak+Q2d07doVP/zwQ5XlXS5XpQsmLpdLLv/NN99Ar9dj9erVaNq0KVq2bImHH34Ydru92jYiCgWz2YxBgwYhJiZG6apQmHnjjTdQUFCArKwspKamyo+lS5fKZbKzs3H8+HH5ecuWLbFmzRps2rQJXbt2xVNPPYVXX30VY8eOlcv07dtXPl916dIFCxcuxNKlS9GrV6+Qvj/GBQ0nLjh69Cj0ej2ys7MxZMgQjB07FidPnsSRI0dw0003yesWFhbip59+wqFDh7BlyxbMmzfPJy5ISEioVVyQkZGBH3/8sdr3BACjRo2CyWRC586dkZKSgtGjR8v7mjx5MhYsWAC73Y7Dhw9j/fr1uPrqq/1uj4iIiIiIqDpMHNTSjBkzEBcXh6ioKCxZsgQrVqyQ7/56/fXX8eSTT6Jt27bQaDS47777UFpaWu2482+99RYmTZqEQYMGQaVS4corr8S1117rMznisGHDMHz4cHnc8Hnz5uGll15CamoqdDodnn76aWzfvh0nTpyQ13nooYeQkpKCuLg4jB07Fnv27AEAfPHFF9BqtXjmmWcQFRUFnU6HgQMHyq/Fx8fj/vvvh06nQ/PmzfGPf/yjyh/tHlFRUZg1axZ0Oh369OmD22+/Xb4j/vXXX8e//vUvdO/eHSqVCmPGjEH79u2xZs0aAMCIESPQrl07SJKErl274tZbb/X5sQ2477q/4YYb8MEHH/hcwK/O5MmT8eGHH8LhcGDhwoWYPHlypTK33347GjVqBLVajVtuuQXt27eX73j86KOPkJmZibvuugsGgwEmkwn9+/eX1/XMKWAwGNChQwf07dtXblsAyM/Px5VXXlll3YqKihAXF+ezLC4uDoWFhVWWHzZsGHbv3o2VK1fC4XBg5cqV2L59OywWCwB3jwOLxYI9e/bg0KFD2Lx5M9asWYPnn38+YDsRESnBcwd3xcekSZPkMgsXLqx0LhgwYAD27t0Lq9WKI0eOYOrUqZW2feONN+KXX36BzWbDwYMHazVc0KViXHBBQ4sLFi1ahMzMTPzf//1ftXHBs88+K8cFffr0wd69e+XX8/LyghYXeHz++ecoLCzEhg0bMGbMGBiNRvm1G2+8Ee+8844879HIkSMxcuTIgO1ERERERERUFSYOamnOnDnIz8/HiRMn0KRJE3z//ffya0ePHsVf/vIXxMXFyY/z58/j5MmTVW7r6NGjePPNN33Kf/bZZz5DDHiPWZ+Tk4Pi4mJcddVVcvnGjRtDp9P5XCBo3Lix/HdUVJT8I/TYsWNo3bp1lRNpHTt2DD/99JNPXf75z39WmnDSW5MmTeQxpgEgPT0dp06dkt/bI4884rO9/fv3y69/9dVX6Nu3L5KSkmA2m/Hmm28iJyfHZ/tz587FwIEDMWjQoGrr4K1t27Zo2bIlHn/8ceh0OvTo0aNSmZdffhmdOnWC2WxGXFwcfvrpJ3m/x44dQ9u2bavdfmxsLEwmk/zcu20DiY6O9hkaAnAPK1DdXdnt2rXDsmXL8OSTT6JRo0aYP38+brnlFnky0OjoaADA7NmzER0dLV/Q+eyzz2pUH6K6kpubi88++wz5+flKV4UoJBgXXNBQ4oJz586hrKwMR48eRZs2bardfijjAm9qtRoDBgzAn3/+iRdeeAEAcOjQIYwZMwYvvvgiSkpKcPLkSfzyyy945JFHalQfIiIiIiKiipg4uEhNmzbFO++8g4ceekj+QZ+WloZly5YhPz9ffpSUlODWW2+tchtpaWn4xz/+4VO+qKgIb7zxhlzGe8LExMREmEwm7Nq1y2ed0tLSGk0CmZ6ejsOHD1c5ZnCzZs2QmZnps12LxeJ3bNzTp0/7DI1z/PhxNG3aVH5vL774os/2iouL8fDDD8Nms2HMmDG48847cerUKRQUFGDq1KmV6rV48WIcPHgQ99xzT40nKJw8eTKee+65Ku8q3LZtG2bNmoUPPvgA58+fR35+Pjp37ixvOz09XR4rONi6dOmC06dP4+zZs/Ky/fv34/LLL692nWuvvRZ79+5FXl4ePv/8c/z2228YMGAAAPdwBgCqvNhDpCSTyYT27dtXGg6EqKFjXNBw4gLAPRmxp33qQlVxwffffy/vvybsdjt+++03AMCPP/6IZs2a4cYbb4RGo0FqairGjx+PL774Iuh1JyIiIiKiyMDEwSXo3r07srKy8OyzzwJwT/z4+OOPy2MRWywWfPbZZ9XefXbnnXfivffew8aNG+F0OmG1WrFz50554sSKVCoVpk6din/+85/ynYS5ubk+40P7M3LkSFitVjz++OMoLi6GzWbDxo0bAbgvUv/555+YN28eysrK4HQ6cejQoUrDBHgrLi7GU089BZvNhl27dmHRokW4/fbbAQD33HMP/vOf/2DPnj0QQqCkpARff/01Tp48CavVirKyMiQmJkKv12PXrl1VDn2QkJCA9evX45tvvsFdd91Vo4sEt912G9auXYu///3vlV6zWCzQaDRITk6Gy+XCggUL8NNPP8mv33777fj222/x5ptvwmq1oqSkBFu3bg24z5po3bo1+vXrh0ceeQQlJSX49ttvsWjRIkyZMqXadXbv3g2Hw4HCwkI8+eSTyMvLw8SJEwG4x/0eMmQIZs+ejZKSEpw+fRqvvfZapUkfiULNaDTisssuY+KAIhLjgoYTF2g0mpDEBTNnzpTjgsWLF+Ovf/1rleX379+PdevWobS0FA6HA6tXr8bixYsxbNgwAEBmZiZOnz6NlStXwuVy4dy5c/joo4/QtWvXoNSXiIiIiIgiDxMHl2jmzJl49913ceLECdxzzz2YNGkSxowZg9jYWHTo0MHvWMDdunXDkiVL8OijjyI5ORlNmzbFY489BqvVWu06c+bMQZ8+feTJRzMzM7F27doa1TU6Ohpff/019uzZg+bNmyM1NRWvv/66/Nq6deuwfv16tGjRAomJibjtttv8DknQuXNnOBwOpKam4sYbb8Qzzzwjj4187bXX4rnnnsPf//53xMfHo2XLlnjllVfgcrkQExOD119/HXfccQdiY2PxzDPPYNy4cVXuIz4+Hl9//TX27t2LO+64I+BFAqPRiCFDhlTZ1f/qq6/G2LFjcfnll6NJkyY4cOAA+vXrJ7/erFkzfP3111i8eDFSUlLQokULfPLJJwHb1SM6OtrvBYUlS5bg1KlTSE5OxtixY/H888/LPQgAoFOnTli0aJH8fMaMGUhISECzZs3www8/YOPGjYiKipJfX7RoEQoKCpCSkoIrrrgCw4cPx4MPPljj+hLVBbvdjrNnz3KibopYjAsaRlzgdDrRtGlTrF27Fh9//DFSU1PRqlUrfPrppzVqW8A9lJG/uGDRokU4deoUUlJS5EmxveOCyy+/XI4LHA4HHn30UaSmpiI5ORmPPvooXnjhBdx2220A3DcULF68GE8//TQSExPRpUsXNGrUCC+99FKN60tERERERORNEjXt610HtmzZIt99lp2djRUrVuCGG27wu87mzZsxffp0HDhwAE2aNMGDDz5Y5USJ1bFYLDCbzSgoKEBsbOwlvoOGwTNBpSRJNR76ZuHChZg7dy72799ft5ULExfTRpFECIGCggKYzWa2TzUipY3OnDmDVatWYfDgwYiPj6/xekIIFBUVITo6ukG3z6UQQsjznFDwMC6oLNLjAqfTibKyMuj1ep+hoSpyuVx+Xw9XoZiDhsf0wNhG/rF9AmNcQERERHVN0V87xcXFyMjIwH//+98alT9y5AiuueYa9O/fH/v27cMjjzyC++67r1Z3fxERUd2Ji4vD1VdfzQuwRFRvqVQq6PV6XqwkIiIiIqKIplFy5yNGjMCIESNqXP7NN99E8+bNMXfuXABAhw4dsHv3brzwwgsYO3ZsHdWSiIhqSqPRIDo6WulqEBFdkvrYk4CIiIiIiCiYFE0c1NbOnTvlSeA8hg8fjvnz58Nut0Or1VZax2q1+owNbLFYAFzohk++atomEydOxMSJEyOyDSPxPQfi+f/EtqlepLRRYWEh9u3bh3bt2sFkMtV4vUhpn0vBtqFwNmnSJEyaNEnpagSFEAJ2ux0ajYa9DoiIiIiIKGLVq8TBmTNnkJKS4rMsJSUFDocDOTk5SE1NrbTOnDlzMHv27ErLCwoKanQRJlKG2+AFqcDqaxt5kmV1yTMOLQBeZKlGpLRRfn4+/vzzTzRu3Bgul6tW65aWljbotgmGpKQkpatA1OAJIeByueR5HoiIiIiIiCJRvUocAJUvuHku5lb3w27GjBmYPn26/NxisSAtLQ1mszlikgKBCCHkySH5A7lq9bmNzGZzne/D8/+wPrZPqERKG0VFRWH48OG1Xs/T2yAqKqpBt8+lqK/JS6qfPOe8SCSEQGlpKUpLS/2W4cStRERERETUkNWrxEHjxo1x5swZn2Vnz56FRqNBYmJilevo9Xro9fpKyyVJ4g89L572YJtUj23kH9snsEhoo0t5f5HQPkRERERERERE9UG9mvmtT58+WLdunc+ytWvXokePHlXOb0BERKGVl5eH1atXo6CgQOmqEBFdFB7HiIiIiIiIFE4cFBUVYf/+/di/fz8A4MiRI9i/fz+OHz8OwD3M0IQJE+TyU6dOxbFjxzB9+nQcPHgQCxYswPz58/HAAw8oUX0iIqrAYDCgRYsW0Ol0SleFiOii8DhGRERERESk8FBFu3fvxsCBA+XnnrkIJk6ciIULFyI7O1tOIgBAy5YtsWbNGtx///14/fXX0aRJE7z66qsYO3ZsyOtORESVmUwmdOrUSelqEBFdNB7HiIiIiIiIFE4cZGVl+Z3sceHChZWWDRgwAHv37q3DWhER0cWy2+3Iy8tDbGwsNJp6NY0OEREAHseIiIiIiIiAejbHARERhbeCggJs2LABhYWFSleFiOii8DhGRERERESkcI8DIiJqWOLi4jBkyBDExMQoXRUioovC4xgRERERERETB0REFEQajQZxcXFKV4OI6KLxOEZERERERMShioiIKIiKi4vx448/orS0VOmqEBFdFB7HiIiIiIiImDggIqIgstlsOHnyJGw2m9JVISK6KDyOERERERERcagiIiIKovj4eIwYMULpahARXTQex4iIiIiIiNjjgIiIiIiIiIiIiIiIvDBxQEREQXP+/Hl8+eWXKCgoULoqREQXhccxIiIiIiIiJg6IiCiIdDodmjRpAq1Wq3RViIguCo9jRERERERETBwQEVEQRUVFoUuXLjCZTEpXhcLMli1bMGrUKDRp0gSSJGHlypV+y0+aNAmSJFV6dOrUSS6zcOHCKsuUlZXV8buhhozHMSIiIiIiIiYOiIgoiBwOBwoKCuB0OpWuCoWZ4uJiZGRk4L///W+Nyr/yyivIzs6WHydOnEBCQgJuuukmn3KxsbE+5bKzs2EwGOriLVCE4HGMiIiIiIgI0ChdASIiajjy8/Oxbt06DB48GPHx8UpXh8LIiBEjMGLEiBqXN5vNMJvN8vOVK1fi/PnzmDx5sk85SZLQuHHjoNWTiMcxIiIiIiIiJg6IiCiIzGYzBg4ciJiYGKWrQg3M/PnzMWTIEKSnp/ssLyoqQnp6OpxOJ7p27YqnnnoK3bp1q3Y7VqsVVqtVfm6xWAAAQggIIeqm8vWMpy0itT1iY2ORlZWF6Ojoatsg0tsoELZPYGwj/9g+gbFtiIiIqK4xcUBEREGj1WqRmJiodDWogcnOzsb//vc/LF682Gd5+/btsXDhQlx++eWwWCx45ZVX0K9fP3z//fdo27ZtlduaM2cOZs+eXWl5QUEBL8KUE0KgqKgIgLtHR6RxOp3Q6/UB58ooLS2NyPapKbZPYGwj/9g+gSUlJSldBSIiImrAmDggIqKgKSkpwcGDB9GiRQsYjUalq0MNxMKFCxEXF4cbbrjBZ3nv3r3Ru3dv+Xm/fv3QvXt3vPbaa3j11Ver3NaMGTMwffp0+bnFYkFaWhrMZjNiY2PrpP71jSeBYjabI/KincViwYkTJ/wexzx3QkdFRUVkGwXC9gmMbeRffWmfEqsTZwqs+LPACqvDhSZxejRNMCBKX/c/s5nsJiIiorrGxAEREQVNWVkZDh8+jNTUVCYOKCiEEFiwYAHGjx8PnU7nt6xKpcIVV1yB3377rdoyer0eer2+0nJJksL64lSoedojEtvEarXijz/+QJMmTWAymaotF8ltVBNsn8DYRv4p3T5CCOSXOHDWYsU5i83n37Pl/xZbq55EvbFZj/7tEzCoYxLSEhkPERERUf3ExAEREQVNQkICrr32WqWr0eAIhxUuWymcVj3U+milqxNSmzdvxu+//44pU6YELCuEwP79+3H55ZeHoGbUUPE4RtRwuYRAmd2FEqsTpTYnSmxOFFudOF9kw1mLDecKPYkBd5LA7ry4u/rPFFixbFc2lu3KRusUEwZ1TMJV7RMQH+U/AU5EREQUTpg4ICIiChNCCAhHGYS9BMJeCmEvc/8rXBBCwBldfxMHRUVF+P333+XnR44cwf79+5GQkIDmzZtjxowZOHXqFD744AOf9ebPn49evXqhc+fOlbY5e/Zs9O7dG23btoXFYsGrr76K/fv34/XXX6/z90NEROHNJQTW/XgOO347j9wiG/KK7CgsdSAYA/yoVRKSYnRoFKtDY7MeKWY9dBoVTp0vw/GcUhzKLoKrfEeH/yzB4T+PY/6m4+iWbsbATono3ToeBp06CDUhIiIiqjtMHBARUdDk5+djw4YN6NmzJ8eLrwHhtEPYSuCyl7iTBbYSCOFSulp1Yvfu3Rg4cKD83DPPwMSJE7Fw4UJkZ2fj+PHjPusUFBTg008/xSuvvFLlNvPz83HHHXfgzJkzMJvN6NatG7Zs2YKePXvW3RuhBo/HMaL6L7fIhrlf/oF9Ry0Xtb5Rq0JyrB6NYnXyv428nsdHaaFWVT+E0vliO7b8kouNP+fg9z9LAAAuAew5WoA9Rwtg1KrQt10CBndKQue0GKg4XBURERGFISYOiIgoaDQaDRITE6HR8PRSFeGwwWUrgrAVw2UrhnBYla5SyGRlZfmdyHHhwoWVlpnNZpSUlFS7zssvv4yXX345GNUjkvE4RlS/7fgtD/9dexSWUoe8TKOSkBCthdmkRZReDZPO/TCW/x1r1MiJgUaxekQb1Jc0t0J8lBbXZzbG9ZmNcSK3FBt/zsHGn3NxrtAGACi1u7D+QA7WH8hBo1gdBnZMwuBOSWgSb7jk909EREQULPxFREREQRMdHY1u3bopXY2wIZx2d4LAWgiXtQjCaVO6SkQUAI9jRPVTqc2JdzYex9ofz8nLEqK0+MfVLdG9hVmxSZbTEo2Y0D8Nf7myGX4+WYiNB3Ox7VCePLHyWYsNS785jaXfnEaHJtEY2DEJ/drFw2zSKlJfIiIiIg8mDoiIKGicTieKi4thMBigVkfe2L1CCHdvAmuhO1lgL1W6SkRUS5F+HCOqjw5lF+GF1YeRnX+hJ1/ftvG4Z1gLxBrD4wK8SpLQOS0WndNiccfAdOw6fB7rD+Rg39ECeT6Eg6eLcPB0Ed7acAzdW5iR1SERvVrHcT4EIiIiUgQTB0REFDTnz5/H//73PwwePBjx8fFKVyckhMsBV5lFThYIl1PpKhHRJYjE4xhRfeV0Cfy/b05jyc5T8sV3o1aFOwalY0jnJMV6GQSi16pwVftEXNU+EblFNmz6ORfrD+TgeK77hgOnS+C7P/Lx3R/5MGhV6N0mHgM6JKJbeiw0apXCtSciIqJIwcQBEREFTWxsLPr374/o6Gilq1KnhMMGV1kBXFYLXLZiwM/Y/URUv0TKcYyovsvOL8OLa/7AL6eL5GWXpUbhgZGtkRpXf+YKSIzWYWzPVIy5ojGOnCvBpp9zsfmXXOQW2QEAZXYXNh3MxaaDuYg1atD/sgQM6JCIy1KjFK45ERERNXRMHBARUdDodDqkpKQoXY06IRxWuErz3QkDDkFE1GA15OMYUUMghMD6Azl4a/0xlNpdAACVBNzSpynG9W4CtSo8exkEIkkSWjWKQqtGUZg0IA0HThZiU4X5ECylDqzefxar959Fo1gdVj48SOFaExERUUPGxAEREQVNaWkpfv31VzRv3hwGQ/25288fZ0keXMU5TBYQRYiGeBwjaigspXa8vu4otv96Xl7W2KzHP0e2QocmMQrWLLhUkoTL02JxeVospg5Kx96jBdh0MBe7Dp+HzeHu5XjWYlO4lkRERNTQMXFARERBU1JSgp9//hmNGjWq9xfcXGUWOAqzIexlSleFiEKoIR3HiBqS7w7n49W1R3C+2C4vG9o5CX8flA5TA548WKtRoVebePRqE48SmxPf/HYemw/mYt+xAqWrRkRERA0cEwdERBQ0iYmJuOGGG5SuxiURQsBpOQ1ncY7SVSEiBTSE4xhRQ1Jic+Ldjcex9sdz8rIYgxr3DmuJvu0SFKxZ6Jl0agzqlIRBnZKQV8QeB0RERFS3mDggIiIqJ1xOOM4fg8taqHRViIiIIt6PJyx45csj+NNrWJ7MlmbcN7wlEqN1CtZMefFRWqWrQERERA0cEwdERBQ0BQUF2LRpE3r06IGYmPo11rBw2GDP+wPCYVW6KkSkoPp8HCNqKGwOFz7a+SfW/JAHUb7MqFVhysDmGH55MiSpfk6ATERERFSfMHFARERBo1KpEB0dDZVKpXRVasVlK4Yj7wiEy6l0VYhIYfX1OEbUUBw5V4IXVh/GsZxSeVmnZjG4/+qWaBzHeUeIiIiIQoWJAyIiCpqYmBhcccUVSlejVpwleXAUnASECFyYiBq8+ngcI2oIXELgsz1n8P7Wk3A43edkjVrChCub4frMxlCr2MuAiIiIKJSYOCAioqBxuVywWq3QarX14m5dhyUbzqKzSleDiMJIfTuOETUEOYU2vPS/P/DDcYu8LC1BjwevbYOWjaIUrBkRERFR5OKvISIiCpq8vDx8/vnnKCgoULoqfgnhgj3vKJMGRFRJfTmOETUU2w7l4Z73f/RJGtyQmYKnR7dAi2STgjUjIiIiimzscUBEREETExODvn37IioqfO8OFE47HHlH4LKXBi5MRBGnPhzHiBqCEqsTb64/ig0/58rLEqO1uH9EK2Q0j0VRUZGCtSMiIiIiJg6IiCho9Ho9mjRponQ1quWyl8CRdxTCaVe6KkQUpsL9OEbUEBw4WYiX1hzGnxabvKz/ZQm4a0gLxBg1EJx3iIiIiEhxTBwQEVHQlJaW4o8//kDTpk2h1+uVro4PV1kBHOePQwiX0lUhojAWzscxovrO7nRhyY5T+OTbbLjKcwMmnRr/NyQdWR0SIUmcAJmIiIgoXDBxQEREQVNcXIx9+/YhPj4+rC64OYvOwWE5rXQ1iKgeCNfjGFF9dzKvFC+sPozf/yyRl3VqFoPpI1ohxcz/a0REREThhokDIiIKmqSkJIwdO1bpasiEEHBaTsFZnBu4MBERwu84RlTfCSGwZv9ZLNh8AlaHu9efWiXhL/2aYswVqVCr2MuAiIiIKBwxcUBERA2ScDnhOH8ULisnVyQiIlLC+WIb5n55BHuOFMjLmiUY8MA1rdGmMScgJyIiIgpnKqUrQEREDUdBQQG2bt2KwsJCReshHDbYc35j0oCIai1cjmNE9d2O3/Jw98KffJIGI7s2wtzxnZg0ICIiIqoH2OOAiIiCRpIkaDQaRSc3dFmL4Dh/FMLlVKwORFR/hcNxjKg+K7E58faGY/j6pxx5WXyUFtOubonMlnHKVYyIiIiIaoWJAyIiCprY2Fj06dNHsf07i87BUZgNCKFYHYioflP6OEZUn/18qhAvrvkDfxZY5WV928bj7qEtYDZpFawZEREREdWW4kMVzZs3Dy1btoTBYEBmZia2bt3qt/yiRYuQkZEBk8mE1NRUTJ48Gbm5nPSSiCgcuFwuOBwOiBBfuBfCBcf543BYTjNpQESXRKnjGFF9Zne68OG2k3j444Ny0sCoVWHa1S0x47o2TBoQERER1UOKJg6WLl2KadOmYebMmdi3bx/69++PESNG4Pjx41WW37ZtGyZMmIApU6bgwIEDWLZsGb777jv87W9/C3HNiYioKnl5eVi5ciXy8/NDtk/hsMKe8xucpedDtk8iariUOI4R1WfHc0rxwOKfsfSb03CV59s6No3GaxM7Y0jnZA77RURERFRPKZo4eOmllzBlyhT87W9/Q4cOHTB37lykpaXhjTfeqLL8N998gxYtWuC+++5Dy5YtceWVV+LOO+/E7t27Q1xzIiKqSkxMDHr27ImoqNBMeugqs8Ce8xuEvSwk+6OLt2XLFowaNQpNmjSBJElYuXKl3/KbNm2CJEmVHr/88otPuU8//RQdO3aEXq9Hx44dsWLFijp8FxQJQn0cI6qvXEJg5e4z+MeHP+HwnyUAALVKwoQrm2HOuA5oHGdQuIZEREREdCkUm+PAZrNhz549ePjhh32WDxs2DDt27Khynb59+2LmzJlYs2YNRowYgbNnz+KTTz7ByJEjq92P1WqF1XphjE2LxQIAEEKwC3o5T1uwParHNvKP7RNYpLSRTqdDWloaANTqvV5M+zgKz8BZ+Get61hf1ffvTnFxMTIyMjB58mSMHTu2xusdOnQIsbGx8vPk5GT57507d2LcuHF46qmnMHr0aKxYsQI333wztm3bhl69egW1/hQ59Ho9mjdvrnQ1iMLa2QIrXv7yD/x4olBelpZgwD+vaY02jZl0IyIiImoIFEsc5OTkwOl0IiUlxWd5SkoKzpw5U+U6ffv2xaJFizBu3DiUlZXB4XDguuuuw2uvvVbtfubMmYPZs2dXWl5QUFDvL8IEixACRUVFAMCuxNVgG/nH9gksUtqotLQUR48eRVJSEnQ6Xa3XrVHbCCccljMQ9pKLrGX9VN/PWSNGjMCIESNqvV6jRo0QFxdX5Wtz587F0KFDMWPGDADAjBkzsHnzZsydOxdLliy5lOpSBLNarTh27BhSU1NrfRwjigQbf87BG18fQ4nNKS+7PjMFE65Mg16r+BR6RERERBQkF5U4EEIgNzcXkiQhMTHxkipQ8SKREKLaC0c///wz7rvvPjz++OMYPnw4srOz8a9//QtTp07F/Pnzq1xnxowZmD59uvzcYrEgLS0NZrPZ5w7GSOa5GGU2mxv0Bc1LwTbyj+0TWKS0UWlpKX788UcMGjQI0dHRNV7P09sgKioqYPvYz/0KrVQG6CLr4oRSiYNgnvMvRrdu3VBWVoaOHTvi0UcfxcCBA+XXdu7cifvvv9+n/PDhwzF37txqt8eeiIFFSg+p6lgsFnz33XcYNGgQ4uPjqywT6W0UCNsnsPraRhsO5ODlL4/Iz5NjdJh2dUt0ae7+XRWs91Nf2yeU2DZERERU12qVODhz5gwefPBBrFq1CoWF7m6psbGxGD16NObMmVOp94A/SUlJUKvVlXoXnD17ttrtzJkzB/369cO//vUvAECXLl0QFRWF/v374+mnn0ZqamqldfR6PfR6faXlnrGSyc17/GiqGtvIP7ZPYJHQRklJSRg9ejRUKlWt32dN2kc47RCOsgbdhuEimOf8i5Gamoq3334bmZmZsFqt+PDDDzF48GBs2rQJV111lVzH2vRcBNgTsSYipYdUdbRaLYYOHQpJkuR2qEqNe0lFKLZPYPWtjX7JLsGra4/Jz/u3NWNSvxSY9Cq//1cuVn1rHyUkJSUpXQUiIiJqwGqcOLBYLOjbty+KioowefJktG/fHkII/Pzzz1iyZAm2bduGvXv31vgOU51Oh8zMTKxbtw6jR4+Wl69btw7XX399leuUlJRAo/GtslqtBsA7LoiIwoEkSfJxuS64rMG/MEGVBfucfzEuu+wyXHbZZfLzPn364MSJE3jhhRfkxAFQu56LAHsi1kSk9JCqjsPhCPi+a9NLKhKxfQKrb210+nwZXlp7Ck6X+/mIjGT83+D0Oqt7fWsfJfD3LxEREdW1GicOXnnlFajVahw4cMBnYkIAePTRR9GvXz+8+uqreOSRR2q88+nTp2P8+PHo0aMH+vTpg7fffhvHjx/H1KlTAbh/3J86dQoffPABAGDUqFH4+9//jjfeeEMeqmjatGno2bMnmjRpUuP9EhFR3bBYLNixYwe6dOlSJxeVha0wcCG6ZHVxzg+G3r1746OPPpKfN27cuFY9FwH2RKypSOghVZ3CwkLs3Lkz4HEsktuoJtg+gdWXNioqc+Cplb+hsMwBAOjWIhZ3DkqHSlW3QwbWl/YhIiIiaqhqHO2tXr0ajzzySKULCIB74sIZM2bg888/r9XOx40bh7lz5+LJJ59E165dsWXLFqxZswbp6ekAgOzsbBw/flwuP2nSJLz00kv473//i86dO+Omm27CZZddhuXLl9dqv0REVHdcrv/P3n3Ht1Wd/wP/3KEtWd52hjNIQhZBJGEkYRkCCeHL3tAyWmihtCmQtpQU6JdRCP1CIbQUCi0lpbSQUmZLfpAwEgiEEUjCCITs4RFvbd15fn9cSZY8JVuybOt5v156STr36Oj4WL7Xus89z9Gz1zbNOBgQ2TjmZ8KmTZuS0hLOnTsXa9asSaqzevVqzJs3b6C7RoaZbO7HCBlKVE3Hva/uwIGWCABgTIkNt5w5EaKQX+sMEUIIIYTko5RnHHz77bc9fhGfN28efv7zn6fdgeuvvx7XX399l9tWrFjRqWzx4sVYvHhx2u9DCCEk+woKCnDcccdlpW2mSmCakpW2SbJsHPMDgQB27NgRf757925s3rwZxcXFGDNmTKdZhsuXL8e4ceMwffp0yLKMZ555Bi+88AJeeOGFeBs33HADTjjhBPz2t7/F2WefjVdeeQVvvvkm1q9fn+ZPTEi7bO7HCBlKGGN47K29+HyfsYi82ybi1+ceCoclrWXyCCGEEELIEJXWGgeFhYXdbi8sLITP58tEnwghhJBOdInSFA2UbBzzN27ciJNOOin+PLbOwJVXXokVK1Z0mmUoyzJ+/vOfo6amBjabDdOnT8drr72G008/PV5n3rx5eO6553Dbbbfh9ttvx4QJE7By5Uocc8wxafWNEEJIMsYYnv+4Dm983ggAMAkcbjtnEioLO6d6I4QQQgghw1PKgQPGWI95LDmOowWaCCEkzzU1NeHVV1/F/PnzUVRUlNG2KU3RwMnGMb+6urrH13ScZXjzzTfj5ptv7rXdCy64ABdccEFafSGkJ9ncjxEyFCiqjsfe2ovVXzTGy244bTymjnLlsFeEEEIIIWSgpRU4OPTQQ7tdnIqCBoQQQpxOJ2bPng273Z7xtplMgYOBQsd8ks+yuR8jZLBr9Em47z87sK0uGC+74rjRqJ5amsNeEUIIIYSQXEg5cPDUU09lsx+EEEKGAavVivHjx2e8XV0JgelaxtslXaNjPsln2dqPETKYNfllvPBxHV7/vAGKZgSHzSKHxQvG46RpFDQghBBCCMlHKQcOrrzyymz2gxBCyDAgSRJqampQVlYGs9mcsXaZFOy9EskYOuaTfJat/Rghg9WqzQ348zt74wEDAChzmXHrOZMwscKRw54RQgghhJBc6j6BcQetra34wx/+0OViiF6vt9tthBBC8off78eGDRsQDGb2RL8uU+BgINExn+SzbO3HCBmMXtpYh0ff3BMPGlhEHucfNQIPXzGdggaEEEIIIXku5cDBI488gnfffRcFBQWdtrndbrz33nv4wx/+kNHOEUIIGVqKi4tx1llnwe12Z7ZhXclse6RHdMwn+Sxr+zFCBpl/fVSLJ9fujz9f5CnHX3/owfdOrEKBzZTDnhFCCCGEkMEg5cDBCy+8gOuuu67b7ddeey3+/e9/Z6RThBBChiae52E2m8HzKR9eUsI0NaPtkZ7RMZ/ks2ztxwgZLBhj+OcHNXj6vQPxsu8cOwo/PnUc3HYKGBBCCCGEEEPK34h27tyJSZMmdbt90qRJ2LlzZ0Y6RQghZGjy+/34+OOPM5/iQ6fAwUCiYz7JZ1nbjxEyCDDG8Mz7NfjnBzXxsiuPH41L547KYa8IIYQQQshglHLgQBAE1NbWdru9traWrswihJA8p+s6QqEQdF3PWJtMV8FY5tojvaNjPsln2diPETIYMMbw1Lr9WPlh+/796uoqXHjMyBz2ihBCCCGEDFYpf+ufOXMmXn755W63v/TSS5g5c2Ym+kQIIWSIcrvdqK6uhsvlylyjlKZowNExn+SzrOzHCMkxxhieeGcfXtxYHy+79uSxOPfIETnsFSGEEEIIGczEVCv+5Cc/wSWXXILRo0fjRz/6EQRBAABomoZHH30UDz30EP75z39mraOEEELyE6M0RQOOjvmEEDJ86IzhT2/uxaotDfGyn5w6Dqd5ynPYK0IIIYQQMtilPOPg/PPPx80334yf/vSnKC4uxsyZMzFr1iwUFxfjxhtvxJIlS3DBBRdks6+EEEIGuebmZrz00ktoa2vLWJtMUzLWFkkNHfNJPsvGfoyQXNF0hkdW74kHDTgANywcT0EDQgghhBDSq5RnHADAPffcg7PPPhv/+Mc/sGPHDjDGcMIJJ+Cyyy7D0Ucfna0+EkIIGSLsdjsOO+wwWK3WzDVKMw5ygo75JF9lZT9GSA5oOsPy13fhna3NAACeA25adAhOmlaa454RQgghhJChIK3AAQAcffTRdMKAEEJIl2w2GyZNmpTRNplOMw5yhY75JB9lYz9GyEDTdIbfrdqJd79pAWAEDX7xPxNw/JSSHPeMEEIIIYQMFSmnKiKEEEJ6I8sy6uvroSgZPNlPiyMTQgZQVvZjhAwgVdPx2//siAcNRJ7DLWdNpKABIYQQQghJCwUOCCGEZIzP58P69esRCAQy1ibNOCCEDKRs7McIGSiKqmPZqzvwwfZWAIAocFh69kTMm1Sc454RQgghhJChJu1URYQQQkh3ioqKcPrpp2c0NzgtjkwIGUjZ2I8RMhBkVcc9r2zHp7u9AACzyOHWsydh9vjC3HaMEEIIIYQMSRQ4IIQQkjGCIMBut2e2UVocmRAygLKyHyMkyyKKhnte3o5Ne30AAIvI4/ZzJ+GIse4c94wQQgghhAxVaacqOvnkk9HW1tap3Ofz4eSTT85EnwghhAxRgUAAn376KUKhUEbaY0wH07WMtEXSR8d8ko8yvR8jJNvCsoY7X/w2HjSwmnjccf6hFDQghBBCCCH9kvaMg7Vr10KW5U7lkUgE7733XkY6RQghZGhSVRVtbW1Q1QzNEqCFkXOKjvkkH2V8P0ZIFoUkDXe8uA1ba4w1OWxmHneePxnTRrly3DNCCCGEEDLUpRw4+Pzzz+OPt27divr6+vhzTdPw+uuvY9SoUZntHSGEkCGlsLAQ8+fPz1h7jNIU5QQd80k+y/R+jJBsCURU/O8L27CtLggAcFgE3HXBZEwe4cxxzwghhBBCyHCQcuDgiCOOAMdx4Diuy/QENpsNf/jDHzLaOUIIIXmOFkbOCTrmE0LI4OYPq7j9399gx0EjpZbLKuDuC6dgYoUjxz0jhBBCCCHDRcqBg927d4MxhkMOOQQff/wxysrK4tvMZjPKy8shCEJWOkkIIWRoaGlpwapVq3DCCSegsLCw3+0xnQIHuUDHfJLPMr0fIyTTvCEFtz2/DbsbjaBBgU3Eby6cgkPKaVFvQgghhBCSOSkHDsaOHQsA0HU9a50hhBAytFmtVhx66KGwWq0ZaY9SFeUGHfNJPsv0foyQTGoNKrjt+W+wtykMACi0m3DPRZMxtpSCBoQQQgghJLPSXhwZAL799lusXbsWDQ0NnU4q/PrXv85IxwghhAw9drsdU6ZMyVyDlKoo5+iYT/JNxvdjhGRIS0DGr/71DQ60RAAAxU4T7r1oCkYX23LcM0IIIYQQMhylHTj485//jB/96EcoLS1FZWUlOI6Lb+M4jk4iEEJIHlMUBU1NTSgsLIQo9ik2nYRmHORWJo/57777Lu6//358+umnqKurw0svvYRzzjmn2/ovvvgiHnvsMWzevBmSJGH69Om44447sHDhwnidFStW4Hvf+16n14bDYbpanPRZpvdjhGRCk1/Gr/71NWpbJQBAmcuMey6agpFFtK8jhBBCCCHZkfa3od/85je455578Mtf/jIb/SGEEDKEeb1erF27FvPnz0dRUVH/G6TAQU5l8pgfDAbh8Xjwve99D+eff36v9d99912ceuqpuPfee1FYWIinnnoKZ555Jj766CPMnDkzXq+goADbtm1Lei0FDUh/ZHw/Rkg/HfRK+NW/vsFBrxE0qCgw456LpqKy0JLjnhFCCCGEkOEs7cBBa2srLrzwwmz0hRBCyBBXWFiIhQsXwm7PTK5lRqmKciqTx/xFixZh0aJFKddfvnx50vN7770Xr7zyCv7zn/8kBQ44jkNlZWVG+kgIkPn9GCH9Ud8WwdKV36DRLwMARhRacM9FU1BeQEEDQgghhBCSXWkHDi688EKsXr0a1113XTb6QwghZAgTRREulytj7VGqotwaTMd8Xdfh9/tRXFycVB4IBDB27FhomoYjjjgCd999d1JgoSNJkiBJUvy5z+cDADDGwBjLTueHmNhY5Ot4CIIAp9MJAN2OQb6PUW9ofHqXyhjVtERw6/PfoDlgBNFHFVlxz4WTUeIyD/uxpc9Q72hsCCGEEJJtaQcOJk6ciNtvvx0ffvghZsyYAZPJlLT9pz/9acY6RwghZGgJBoPYsmULJk2a1O+rdZmuAvSlOKcG0zH/d7/7HYLBIC666KJ42ZQpU7BixQrMmDEDPp8PDz/8MI499tj4Z7Ary5Ytw5133tmp3Ov10kmYKMYYAoEAACSta5Ev/H4/tm7dinHjxvWY9iocDufl+KSKxqd3PY1RTauE3/x3L9pCGgBgVJEZt51RBQsnIxCQB7KbOUOfod6VlpbmuguEEEIIGcY4lua35PHjx3ffGMdh165d/e5UNvl8Prjdbni9XhQUFOS6O4MCYwxerxdut5v+Oe8GjVHPaHx6ly9j1NjYiLfeegtz5sxJax8bO1HpdDrj46MrYSiN32arq0MOYwylVZNhLhw1YO+ZrWM+x3G9Lo6c6Nlnn8U111yDV155Baecckq39XRdx6xZs3DCCSfg97//fZd1uppxUFVVhba2Nvq/ICpf9lfdaWpqwptvvtnjfqyrfRZpR+PTu57GaE9jCLc9vw3esDHrblyZDb+5YDLcdlNXTQ1L9BnqHWOMAgeEEEIIyaq0Zxzs3r07G/0ghBAyDBQVFWHBggWZaYzSFOXcYDjmr1y5EldffTWef/75HoMGAMDzPI466ihs37692zoWiwUWS+fc4BzH0cmpBLHxyMcxKSoqwsKFC3utl89jlAoan951NUY7DwZx+7+3wRcNGkyosOPuCyajwJY/QYMY+gwRQgghhOQW39cXyrKMbdu2QVXpxA4hhJDMo4WRB49cHfOfffZZXHXVVfjnP/+J//mf/+m1PmMMmzdvxogRIwagd4QQklnf1gXwq399Ew8aHFrpwD0XTsnLoAEhhBBCCMm9tAMHoVAIV199Nex2O6ZPn459+/YBMPIc33fffRnvICGEkKGjtbUVq1atgtfr7X9jNOMg5zJ5zA8EAti8eTM2b94MwJjNsHnz5nibS5cuxRVXXBGv/+yzz+KKK67A7373O8yZMwf19fWor69P+mzdeeedeOONN7Br1y5s3rwZV199NTZv3jwoFnMmQ1dG92OEpOjrWj9ue34bgpKxpsHUkU7cfeFkOK1pTxAnhBBCCCEkI9IOHCxduhRbtmzB2rVrkxaMO+WUU7By5cqMdo4QQsjQYrFYMGbMGJjN5n63xShwkHOZPOZv3LgRM2fOxMyZMwEAS5YswcyZM/HrX/8aAFBXVxcPIgDA448/DlVV8eMf/xgjRoyI32644YZ4nba2Nvzwhz/E1KlTsWDBAtTU1ODdd9/F0Ucf3Z8fm+S5TO7HCEnFVwf8+PXz2xCSjaDBYaNduPOCyXBYKGhACCGEEEJyJ+3/Rl9++WWsXLkSc+bMSco3OW3aNOzcuTOjnSOEEDK02O12HHbYYRlpi1IV5V4mj/nV1dVgjHW7fcWKFUnP165d22ubDz30EB566KG0+kFIbzK5HyO5t/NgEG9+2YSPd7YCAApsJhTYRRTYjJvbZoo/Nm7Gc5dNhMBnP7f+5/t8uOul7ZBUHQDgGVOA28+ZBKtZyPp7E0IIIYQQ0pO0AweNjY0oLy/vVB4MBofUwlVa2AsUFOS6G4QQMqyoqorW1la4XC6IYj+vlKQZBzk3XI75hKQjo/sxkhO1rRGs/7YF733Tgt2NoaRtB31yyu04rUJSMMGd8LjAJiYEIExw20TYLQL4NPaNW/YH8ODqA5BVI6g6a5wbt549CRZTn5ehI4QQQgghJGPS/jZ01FFH4bXXXsPixYsBIH7i4M9//jPmzp2b2d5lkdS6H6xsJDieruYhhJBMaWtrw1tvvYX58+ejqKioX23RjIPcGy7HfELSkcn9GBk4sWDB+9tasLMh1Gm7SeBgMwvwh1V0P/cpWSCiIRDRUNsqpVSf55AUaIjParAnPz/olfDWV03YVheMv/aoQwqx9KyJMIsUNCCEEEIIIYND2oGDZcuW4bTTTsPWrVuhqioefvhhfPXVV9iwYQPWrVuXjT5mBdMUyG0HYCkem+uuEELIsFFYWIj58+fD5XL1vzGdAge5NlyO+YSkI6P7MZJVNa0RvN9DsAAAJo9w4JTDynD85GI4rSI0nSEoqfCFO9xCSvyxN6zCF25/HluwuDc6A9pCKtpC6c2YmzupCDefMQEmgYIGhBBCCCFk8Eg7cDBv3jy8//77eOCBBzBhwgSsXr0as2bNwoYNGzBjxoxs9DFr1EATRHsRBCulLCKEkEwQRTEjV+gypoPpegZ6RPpjOB3zCUlVpvZjJDv2N4fx/rctWP9tC/Y0hrusM6nCgWMnF+O4Q4tRWWhJ2ibwXHRGgCnl91Q1Hf6ICm+oY8BB6fK5N6TG1yzoydgSCxYeXoHTjyiHSEEDQgghhBAyyPQpceuMGTPwt7/9LdN9yQmpeQ9sI6ZTyiJCCMmAUCiEL7/8EhMmTIDNZut7Q5SmaNAYTsd8QlKRsf0YyYiwrOGL/T58tseLTXt8qGmNdFmvp2BBf4kCjyKHGUUOc8qviSga/B1nNUQDCzzH4ZgJhSiz63A6nbRmDCGEEEIIGZRSChz4fD4URBcS9vl8PdYtGGILDlPKIkIIyRxJkrBv3z5UVVX164Qbo4WRc2Y4H/MJSUWm9mOkb3TGsOtgCJ/t9eKz3V58UxuAqne9KsHkEQ4ce2gxjj20GBXuzAYL+stqEmA1CSgr6LpfjDEEAoEB7hUhhBBCCCGpSylwUFRUhLq6OpSXl6OwsLDLq2IYY+A4DpqWWg7QmEcffRT3338/6urqMH36dCxfvhzHH398t/UlScJdd92FZ555BvX19Rg9ejRuvfVWfP/730/rfRNRyiJCCMmMoqIinH766f1uhxZGzp1sHvMJGQoytR8jqWvyy9i814tNe7zYtNcHX7jr4DHPAVNGOjFvUjHmHVqE8m5OyhNCCCGEEEL6L6XAwdtvv43i4mIAwDvvvJOxN1+5ciVuvPFGPProozj22GPx+OOPY9GiRdi6dSvGjBnT5WsuuugiHDx4EE8++SQmTpyIhoYGqGr/r0yVW/bCWjmNUhYRQshgQDMOciZbx3xCCImRFB1fHvBh0x4fNu31Ym9T12sVAMCIQgtmjnVj1ng3Dq8qgN1C/6sTQgghhBAyEDjGWNdzfwfAMcccg1mzZuGxxx6Ll02dOhXnnHMOli1b1qn+66+/jksuuQS7du2Kn9RIl8/ng9vtRu2Xa1HgciZtMznLYC7uOmAxnDHG4PV64Xa7KcdqN2iMekbj07t8GaPGxka89dZbmDNnTlppbGIpG2K5nlVfHbRAQxZ7OvQwxlBaNRnmwlG57sqwEvu/wOv1UuqlqHzZX3Unlf1Yx30WSdZxfDSdYW9TCJv3+rBpjxdfHvBD0br+CmIz8/CMcWPWuALMHOfGiELrAPd+YNBnqGc0Pr1jjKGkpCTX3SCEEELIMJb24shPPfUUnE4nLrzwwqTy559/HqFQCFdeeWVK7ciyjE8//RS33HJLUvmCBQvwwQcfdPmaV199FUceeST+7//+D3//+9/hcDhw1lln4e677+42B60kSZAkKf48lq+ZMYaOMRPZ3wDe5s67lEWxschhDGnQozHqGY1P7/JljEwmE8rLyyEIQlo/a8fxYZo87McqXbkYj0wd8wkZSsxmMyoqKiCKaf+bTKIkRcfXtSHsavHh65oAvq4NICR3ndqMAzCp0oFZ49yYOc6NySMcEAV+YDtMCCGEEEII6STtb0T33Xcf/vSnP3UqLy8vxw9/+MOUTyI0NTVB0zRUVFQklVdUVKC+vr7L1+zatQvr16+H1WrFSy+9hKamJlx//fVoaWnBX//61y5fs2zZMtx5552dygMRFZzYOYd28MB2mEvG51XKosTF2eiKnq7RGPWMxqd3+TJGmqZhwoQJ0HU97UUfw+FwfGzUYAhM0bPRxSErF4GDTB3zCRlKHA4HPB5PrrsxZDDG0OCT8U1dANtqA9hWF8DOg6FuFzQGgFKXGbPGuTFrnBueMQVw2ShIQwghhBBCyGCT9n/pe/fuxfjx4zuVjx07Fvv27Uu7Ax1PoMUWXOyKruvgOA7/+Mc/4Ha7AQAPPvggLrjgAvzxj3/sctbB0qVLsWTJkvhzn8+HqqoqOK0iXDZTl+9jYgGY3fmTsih2MipfUxKkgsaoZzQ+vcuXMYpEIvD7/bDb7RCE1AOwsdkGDocDHMdBDnNgHF1xmigXgYNMH/MJGQpUVe3TfiwfMMbQ6JOxqzGEXQ0h7DwYxLa6ANpCPa9LU2g3YdooJ6aPdmHWODdGF1uH9bGQEEIIIYSQ4SDtwEF5eTk+//xzjBs3Lql8y5YtaeVYLC0thSAInWYXNDQ0dJqFEDNixAiMGjUqHjQAjDURGGM4cOAAJk2a1Ok1FosFFoulUznHcd1+YVGDTRAdRXmVsig2HvQlrns0Rj2j8eldPoyR1+vF6tWrMX/+fBQVFaX12sTx4XQVGMbjNFRk6phPyFDS1taGN954o0/7seFEVnXsaw5jd0MIuxuN266GEIJS1ymHEo0qsuLQCgsOH1uM6aNdGFFoGdbHPkIIIYQQQoajtAMHl1xyCX7605/C5XLhhBNOAACsW7cON9xwAy655JKU2zGbzZg9ezbWrFmDc889N16+Zs0anH322V2+5thjj8Xzzz8fXygLAL799lvwPI/Ro0en+6P0SG7ZC2vltLxKWUQIIf3ldrtRXV0Nl8vV5zYYY2B67yemSPZl6phPyFCSif3YUCKrOmpbIzjQEkFNawR7m0LY3RBCTWsEPWQbinNYBEwZ4cTkkU5MHuHAoZVOOK0CLWxLCCGEEELIEJd24OA3v/kN9u7di/nz58cXjdN1HVdccQXuvffetNpasmQJLr/8chx55JGYO3cunnjiCezbtw/XXXcdACPNUE1NDZ5++mkAwGWXXYa7774b3/ve93DnnXeiqakJv/jFL/D973+/28WR+0pXZShtNTAX50/KIkII6S+TyYTS0tL+NaJ1Xn+G5EYmj/mEDBUZ2Y8NMowxtIUU7G82ggMHWsJGoKAlgoNeCakmQitxmnBIuR2HlDswvsyO8eV2jCi0gO8i9SghhBBCCCFkaEs7cGA2m7Fy5Urcfffd2LJlC2w2G2bMmIGxY8em/eYXX3wxmpubcdddd6Gurg6HHXYYVq1aFW+rrq4uKYey0+nEmjVrsHjxYhx55JEoKSnBRRddhN/85jdpv3cqlEAjBHsRBGt+XHFGCCH9FQqF8M0332DcuHGwWq19aoPpPefKJgMnk8d8QoaKTOzHBpqmM7QGFTT6JTT7jfsmv4wmv4wGn4yalghCcuozuUSBw5gSG8aV2jC+3G4ECcrscNu7Xh+MEEIIIYQQMvykHTiIOfTQQ3HooYf2uwPXX389rr/++i63rVixolPZlClTsGbNmn6/b6rklj2UsogQQlIUiUTw7bfforKysu8n3HSacTDYZOqYP9wwxigNyzCUkf1YBsWCArFAQJNfTgoQNAcUtATklNIKdWQz8RhVbMXoYptxX2RFVYkNo4utEAVaoJ4QQgghhJB8llLgYMmSJbj77rvhcDiwZMmSHus++OCDGenYYEEpiwghJHXFxcU466yz+tUGo1RFOZXPx/yu6DrDe9804Mu9bdjTGMDexiB8IQURRYOs6rCYeBQ6zCi0m+F2mIx7u9koc5iij03R50Y9nqdgw2CWif1YKhhjiCg6vCEVbaHkwECTX0ZTwLjva1AghgNQXmDGqGhwoKrYilHFRnCg2GGi4BchhBBCCCGkSykFDjZt2gRFMU7kfPbZZ91+wRiuXzwoZREhhAwcSlWUW/l+zE/00fYmPPr6Nmyv83dbR1J0HGyL4GBbJKU2BZ5DsdOM0gIrSlwWlLksKHFZUBy9L3FZUOI0o9hpgUmkK76HEk1nCEoq/BENgYgKX0iFN6ygLaTCF1LQFlLgDavwhhR4Qyp8YRWSqvf7fQvtIkpd5vab04zSAjNKXRaUOs0ocZlgotkDhBBCCCGEkDSlFDh4+OGHUVBQAABYu3ZtNvszaBkpi6aD4+mLFyGEdKetrQ3vvPMOjjrqqPhxI2004yCn6JgPHGgO4oFXt+Lj7c2dttnMAkpcFtjMAswij7CsoS0owxtSoKVwWbimMzT6JDT6pF7ruu0mFDuNYILbbkKB3YQCW+K92bi3meCyiXBYRVhNQl4EdbKpsbkFb7z1LiZNnQEmWuGPqAiEjYCAP6IiEFHhD6toC0oIKQyBaHlQSn0NgVQV2kWUuMwoc5lR4ozeR5+XRssowEQIIYQQQgjJhpQCBzNnzkRdXR3Ky8txyCGH4JNPPkFJSUm2+zao6KoMxVsDc1FVrrtCCCGDliiKKCwshCj2eQkdmnGQY/l4zOdFMyRFQ0hS8cKH+/DkWzsgJ1wJPnlkAa6sPgRTRrtRXmDtMtUQYwyBiApvSEZrUIY3qKAtFL0PymgLGSlnmv0Smv0SWgISWC9xBuPKdAW7GwKp/ywcYLeIsFtEOCwi7BYh4bFx77Aa5Y4O9WLbbGYBiqzBoekQeD5nqZUYY9B0BlVjUDQdqsagajqU6K39ubFd0xhkTUdE1hCWVeNe0aLP229GuRov71jPCAA5gS93Z+Xn4jmgwCaiwGZCoV00gkK25FkDZS4zip1mmCkoQAghhBBCCMmRlM7sFBYWYvfu3SgvL8eePXug6/2fVj0UKf4GCLZCSllECCHdcDqdmD17dv8aoRkHOZWPx/xjfvEKzv7dR53KKwqt+NHCQ3HKjBG9njznOA4umwkumwmjSxy9vqeq6WgNymj0SWjxS2gOGPdNfgktfjnpuZxGOhudAYHoVfGZwnNGiiWB5yEKHMwiD4tJgEXkYY7ei0L7+IgCD6tJgMXEgzG0n/jXdagqi5/413QGRTVO/KuaDjX6PBYcUHXWa3Al13gOcFpFOK0CXFYRTqsIV+xmE+G2GYEBt11EYTRA4LSKEGidC0IIIYQQQsggl1Lg4Pzzz8eJJ56IESNGgOM4HHnkkRAEocu6u3btymgHBxtKWUQIId3TNA2hUAhWqxV8H/eTNOMgt+iYb5wMvnDeWPzglEmwW/o+e6YnosCjrMCKsgJrj/UYM/Lm+0IKfGGl8330sT+sICSpCEoqQpKKkKQhKKkIy/1Pn6MzQNcYFE0DhllcTxQ42EwCrGYBNrMx28IicjDxOtx2M1y29kCAM/5YgNMigNMklBUXQKD/CQkhhBBCCCHDUErfhp944gmcd9552LFjB37605/iBz/4AVyu/LzqnlIWEUJI91pbW7Fq1SrMnz8fRUVFfWqDAge5lY/HfO+ezTjhhBMgiCKKHGZcevx4TBvtznW3ABgzGZxWE5xWE0b24fWazhCWVQQj7cGExABDUFIRjGgIySpCERXBaF1/SALHC9B0lnDToagMkqpBVnRIqgZJ0VNa26EjnjOCJyaBh0ngIArGrAWTwCc9Ngk8hPjjnuuJ0e02swCrSTDuzca9zSy2Pza1l4tdLBpcX1+PV199FfPndr8fY4whEFDB03oShBBCCCGEkGEqpcDB559/jgULFuC0007Dp59+ihtuuGHYn0ToCaUsIoSQrhUUFOC4446D0+ns0+uZpmDQ5yYZ5vLxmL/12aXY8Cdv3xf0HsQEvj3wkCrGGLxeL9xud0oLLauajljsgDFjTYKIoiGiaOA5DiaBg0nkIfLtJ/cHc6qe/u7HCCGEEEIIIWQ4SGlu9cyZM9HU1AQAWLduHWRZzmqnhgK5ZS9YHuR9JoSQdJjNZlRWVsJkSv0kZSKmD7M8KENQto757777Ls4880yMHDkSHMfh5Zdf7vU169atw+zZs2G1WnHIIYfgT3/6U6c6L7zwAqZNmwaLxYJp06bhpZdeykh/SepEgYdZ5ONrHzisIkpcFowqtmNEkQ2lBVa47WY4rCIsJmFQBw2A/u/HCCGEEEIIIWQ4SClwEFsoEcCwWCjx3rMKoUuBfrWhqxIUb02GekQIIcNDOBzG9u3bEYlE+taARmmKci1bx/xgMAiPx4NHHnkkpfq7d+/G6aefjuOPPx6bNm3Cr371K/z0pz/FCy+8EK+zYcMGXHzxxbj88suxZcsWXH755bjooovw0UedFzomJFX93o8RQgghhBBCyDCQl4sj/89hNvg+eATuM+4Ex3f9c6SCUhYRQkiyUCiEL7/8EmVlZbBae170tSu9zTjQJT+0YDOYHIAu+aP3ATApAF32g0kBMFUCZ7aDMzvBmx3gLE7wZic4s8N4bnaCtzjBmZ3gTLaUUrHkk2wd8xctWoRFixalXP9Pf/oTxowZg+XLlwMApk6dio0bN+KBBx7A+eefDwBYvnw5Tj31VCxduhQAsHTpUqxbtw7Lly/Hs88+m/J7EZKov/sxQgghhBBCCBkO8nZxZKVuC1o++QdKjrmiX+3ILXthrZwGjk9p8gYhhAxrJSUlOPfcc/v8eiYFu90W2bUO/o//nNk1EDjBCChYnOAsLvCxoILFBbFwLEzlUyHY+7bI81A1WI75GzZswIIFC5LKFi5ciCeffBKKosBkMmHDhg246aabOtWJBRu6IkkSJEmKP/f5fACM3PyM1tcA0D4W+ToexcXFOOeccwCg2zHI9zHqDY1P72iMekbj0zsaG0IIIYRkW0qBAwA47bTTAGBYLJSo6gwiz6Ft8/OwlE2A85Bj+9xWLGWRuagqgz0khJD8pEveLsuZKiOwZWXmF05mGpjkgyb5uq0iuEbAPPpIWMcfB9E9OrPvP0gNhmN+fX09KioqksoqKiqgqiqampowYsSIbuvU19d32+6yZctw5513dir3er10EiaKMYZAwEjpmI8zcjRNi//8PQmHw3k5Pqmi8ekdjVHPaHx6V1pamusuEEIIIWQYSzlwEPPUU08BAHbs2IGdO3fihBNOgM1mA2NsyPxj97u3fPjlqW4AQMM7y2EuHA1z8dg+t6f4GyDYiyBYnJnqIiGEDElerze+oG26J5uZHATT9S6PJZHd74JFjKCCWDQOphGHt88OSJglwJud4EQLdCUEJgeNNEZywHgsB9tTGyWlODJuUKVO7wsAmr8O4a//g/DX/4FYNA6WccfBOnYueFth2uMz1OT6mN/xPWIn9hPLu6rTU9+WLl2KJUuWxJ/7fD5UVVXB7XajoKAgE90e8mLj7Ha7h8z/dpnU3NyMTz/9FLNmzep2Pxa7EtrhcOTlGPWGxqd3NEY9o/HpHQW7CSGEEJJtaQcOWlpacOGFF+Kdd94Bx3HYvn07DjnkEFxzzTUoLCzE7373u2z0M6P++UkIv/7B/0Dasx5MjaD+jXsw6rwH+3XiX27eQymLCCF5j+d52O128H3YF+qyv8typqsIff3f+HPn0dfAVDy+x7YE0QLY0ksxxDQZuhQEk/3QQ61QmrZBPvg11OYdADMWCFZb90Bt3YPg5n/CXDkDlnHHwTJ6NjjRktZ7DRW5POZXVlZ2mjnQ0NAAURRRUlLSY52OsxASWSwWWCydf18cx9HJqQSx8cjHMREEAXa7HYIg9Pjz5/MYpYLGp3c0Rj2j8SGEEEIIya20z+zceOONMJlM2LdvH+x2e7z84osvxuuvv57RzmWT6+hrYC49BACg+OrQ8NYDYLrW5/aMlEW1meoeIYQMSS6XC0cffTQcDkdar2OMdbu+gbR3A/RgIwDANOLwXoMGfcUJZgj2IoiFY2Ae6YHj8ItQdOr/ouScR+CYdTnE4kMSOqxDrtsC/4Y/ovml6+H78HHI9V+BRQMMw0Uuj/lz587FmjVrkspWr16NI488EiaTqcc68+bNy2rfyPDW1/0YIYQQQgghhAwnac84WL16Nd544w2MHp2c53nSpEnYu3dvxjqWbZxoQeWCW3HgxRuhR/wI7f8UrZ8+i+KjvtvnNhX/QQj2QkpZRAjJW7quQ5ZliKKY1qwDJvmjJ92TX8OYjtDW/8Sf26edlamupoy3umGffBrsk0+D6quFtHs9InvWQw81G31UI5B2vwtp97vg7cWwjD122KyHkMljfiAQwI4dO+LPd+/ejc2bN6O4uBhjxozB0qVLUVNTg6effhoAcN111+GRRx7BkiVL8IMf/AAbNmzAk08+iWeffTbexg033IATTjgBv/3tb3H22WfjlVdewZtvvon169f346cm+a6v+zFCCCGEEEIIGU7S/jYUDAaTrjqMaWpq6nLq/2BmcpWj4pRfApwxDK2frURg94Z+tSk37wHTh9cVp4QQkqqWlha8+uqr8Hq7XuS4O3qk6/pyzSZovhoAgFh6KExlU/rdx/4QC0bC4bkIxWcth3v+bbAeUg3OZItv10MtCH/9H7Su+iVaX78VoW/+H/RwW+463E+ZPOZv3LgRM2fOxMyZMwEAS5YswcyZM/HrX/8aAFBXV4d9+/bF648fPx6rVq3C2rVrccQRR+Duu+/G73//e5x//vnxOvPmzcNzzz2Hp556CocffjhWrFiBlStX4phjjunLj0sIgL7vxwghZKD1Z8Y8IYQQQkhv0p5xcMIJJ+Dpp5/G3XffDcDIPanrOu6//36cdNJJGe9gttlHeVAy53to3vAkAKDhnYdgLhwFc9GYPrUXS1lkLhr6V5oSQki6XC4X5s6dm1aKD8ZY94GD2s3xx/apZwyaPMccx8NcPhXm8qlwzr4SUu1nkHavh1z3OcCML/GJ6yGYKg+DddxxsIw+ckith5DJY351dXWPCzmuWLGiU9mJJ56Izz77rMd2L7jgAlxwwQVp9YWQnvRlP0YIIQNJl4NQfXVQxSqY3CNy3R1CCCGEDFNpBw7uv/9+VFdXY+PGjZBlGTfffDO++uortLS04P33389GH7POPeNsSI07ENixDkwJG4sln/sgBEvfvjBSyiJCSL6yWCwYNWpUWq9hEV+3V8xp/rr4Y1N5bmcbdIcTzbCOmQPrmDnQIz5I+zYgsns91JZdRgWmQ6n7HErd5wiIVpirjoZ13HEwVUwFxw3uNCjD8ZhPSG/6sh8jhJCBoMtBaP566FKgx2A8IYQQQkgmpH3GYtq0afj8889x9NFH49RTT0UwGMR5552HTZs2YcKECdnoY9ZxHIeyE34Cc4mx4KbirUXD27/r1yKXlLKIEJKPIpEIdu/eDUmSUqqvK2Go3n3dbo8FDjhLAXjz4L/6l7cWwHboQhQtvBtF/3M/7NPOBm8viW+PrYfgfedetLx6AwKbn4PqPZDDHvdsOB7zCelNuvsxQgjJNl0OQmneCaVpB3QpkOvuEEIIISRPpD3jAAAqKytx5513ZrovOcWbrKhc8CsceHEJdMmP0L5P0LrxWRQf9Z0+tUcpiwgh+SgQCODTTz9FYWFhrznwmaZAbdndbZBVV0Lx9QGEgqE3DV8sGAnRcxHsh18ApXEbpN3rIe3/CEwJA2hfDyH89X8gFo2DZdxxsI6dC95WmNuOdzAcj/mE9CSd/RghhGRT4gwDQgghhJCB1qfAQVtbG5588kl8/fXX4DgO06ZNw/e//3243e5M929AmQoqUXHKzahb9b8A09H62XOwlE2AY9ycPrVHKYsIIfmmtLQ0pXzzTNegtOwC05Ru62i++vhj0VWZkf7lwlBfD2G4HvMJ6U6q+zFCCMkWXQpACxykgAEhhBBCcirtVEUbN27EhAkT8NBDD6GlpQVNTU148MEHMWHChF4XMBwK7KOPQMkxV8afH3z7Qcit+/vcntyyl1IWEUJIAsZ0qK17wJRIj/US1zcQXENvxkFXYushuE/8OUrOeQTO2VdALD6kvUJ0PQT/hkfR/NL18H34OOT6r/qVOq8/hvsxnxBCCBlMdClgpCRq3klBA0IIIYTkXNqBg5tuuglnnXUW9uzZgxdffBEvvfQSdu/ejTPOOAM33nhjFro48NyHnwvnhOMBwFgsefU90KRgn9rSlQgUb20mu0cIIYOWz+fD+vXrEQh0/2VXbduf0pfhpMDBEExV1JtO6yFMPxu8ozS+veN6CMEtzw14H/PhmE9IR6nsxwghJJMoYEAIIYSQwahPMw5++ctfQhTbsxyJooibb74ZGzduzGjncoXjOJSd+FOYi8cBAJS2GjS881Cfr/hU/Af7HHgghJChhue7P7So3pr4ugW90XyJgYOR/e3WoCYWjITj8ItQfOZDcM+/DdYJ1eBM9vh2Yz2E/w54v/LhmE9IV3rajxFCSKZQwIAQQgghg1na34oKCgqwb9++TuX79++Hy+XKSKcGA95kReXCW8FH1ycI7f0IrZ+t7HN7csseSllECBn2CgoKMG/ePDidndd2Uf310IJNKbcVDxxwPARHeaa6OKjF1kNwHf0DlJz7RxQc+1OYR84EOCEn/cmXYz4hiXrajxFCSCbokh9K0w4KGBBCCCFkUEs7cHDxxRfj6quvxsqVK7F//34cOHAAzz33HK655hpceuml2ehjzpgKKlEx/xcAZwxT68Z/Irjn4z61pSsRKD5KWUQIGd4YY9A0DYyxpHIt0AjNfzCNdnSofmNxZMFRBk4Qe3nF8MMJZljGHBNfD8Ex64oB70M+HfMJieluP0YIIf3VHjDYBV2mGemEEEIIGdzSPhPzwAMPgOM4XHHFFVBVFQBgMpnwox/9CPfdd1/GO5hr9qpZKD76crR89DcAQMM7v8Ooc38Hc+HotNtS/Q0QbEUQLI5Md5MQQgaF5uZmvPrqq5g/fz6KiooAAFqoGWqagVM93ApoEoDhub5Buoz1EBYM+Pvm2zGfEKDr/RghhPSHHvFBCzRQsIAQQgghQ0ragQOz2YyHH34Yy5Ytw86dO8EYw8SJE2G323t/8RBV6DkfUuMOBHe9D10Oof6NezD63N+BN6f3MzPGILfsgbViKjjKnUsIGYZcLheOOuooOBxGgFQLtUBtO5B2O0nrG7gocJAr+XjMJ6TjfowQQvrKCBgchC6Hct0VQgghhJC0pR048Hq90DQNxcXFmDFjRry8paUFoiiioKAgox0cDDiOQ3n1DahpOwC5ZS+UtgNoeOchVCxYCo5LLwCgKxHIbfthLhoDjuOy1GNCCMkNi8WCsWPHAojONOhD0AAANH/+LIw8mOXjMZ+QxP0YIYT0BQUMCCGEEDIcpH3Z+yWXXILnnnuuU/m//vUvXHLJJRnp1GDEm2yoXPAr8Gbj6rPgng/Rtun5PrWlBpogNWyDrkQy2UVCCMk5SZKwb98+hNvq+xw0ADrOOKjMRNdIH+TrMZ/kt9h+TJblXHeFEDLE6BEvlKbtUFp2U9CAEEIIIUNe2jMOPvroIzz44IOdyqurq3HrrbdmpFODlck9EhXzf4G6/3cnAIaWT/4Bc8khcIw9Ku22NCmISP3XMLlHQLAVgjdZM99hQggZYH6/Hx9//DGOP7wKhc6+79fUpBkH3acq0hnDy1s1vLlTg6YDFpGDWQAsImARALPAwRx/bGy3CIiW9bQNMMeeR28Cn3+zxPLpmM90PdddIINEbD82f/58mM3mXHeHEDIE6BEvNP9B6Eo4110hhBBCCMmYtAMHkiTFF0hMpCgKwuHh/4+SfcxsY7Hkj58GwNDw9u8w6rwHYXann0qDMR1yWw3QVgNetECwuSFYC8BbXLQGAiFkSCowa1h0zIR+n2SPzTjgRCt4a2GXdRSN4ZEPVazfm3jCl3Wo1fF534m8EVCwiNFgQjSwYDznkrcJXPyxJRakSNieHOBI3ibyGDSp7PLpmK/4DwKFhbnuBhkEiouLcc4550AQhFx3hRAyyFHAgBBCCCHDWdqBg6OOOgpPPPEE/vCHPySV/+lPf8Ls2bMz1rHBrPCIC4zFknd/AF0Oov6N32D0OQ+kvVhyIl2VoPsboPgbwHE8eKurPZAgWjLYe0IIyQ7FdxCqtwai0L/AJ9Nk6MEmAMb6Bl2dRA/IDP/3roKvGozAAAfAbQVkzbipWbh4XNWNW1CJ9zSx1xl7H55DcpAhIQjx2Pcy9jYpyadjvupvgK6Oo2MuAc/zEMW0/0UmhOQRPeyFGqgHo9SzhBBCCBnG0v5WdM899+CUU07Bli1bMH/+fADAW2+9hU8++QSrV6/OeAcHo9hiyQfa9kNpNW4Na5ej4tSlGblKlDEdWtgLLewFAPAmKwSrG4ItOhthkFyJSgghMXJbDRRfPQIhCZ9uq8PUsaVwWE19akvzH0TsRLzg6pymKCgz3PW2gp0tRh2zACw5VsRRo9uvDtZ0BlkDJBWQNEBSo881QFYBWWPxbcmPAVll8XqJr421J2ssWm6UZZrOgIhq3AyZC0qkK5+O+YzpUNpqYCk9JNddITnm8/mwYcMGzJgxA06nM9fdIYQMIhQwIIQQQkg+STtwcOyxx2LDhg24//778a9//Qs2mw2HH344nnzySUyaNCkbfRyUeLMdlQtvQ82LS6DLQQR3b0DbpudRNOuijL+XrkSgKxEo/oM0G4EQMujILfugBBoBAIwBqqaDsb6f7NaS1jdIXhhZUhmWrWsPGhRYgF+daMKk0uRZDgLPwcYDtnjsIjsBV8Z6Dip0DFhIGksISBj1e3utrBlBBD0H8YN8O+aroVaIET8EqyvXXSE5xBiDqqr92o8RQoYXChgQQgghJB/1aR72EUccgX/84x+Z7suAmXPLa7jgH2FwXHIuytiF/LHTS/HTTB3L489NmMauwTX4PXgwNH3yDJZ9Wopt/OGdXtfxtem/Z+ILG5PKzSKPMrcNZYV2lBVYUVZgRbnbirICC0oLrCh1WWASac0EQkhmMV2H3LIHaqg1XuZyWDBn2qh+tRtb3wBInnGgaAz/956CrxvbgwZ3nWJClTt3+zeOM9IIWUTAZQGyFaAAAEXLzeK9Q/2Yn6pIRILL6YDcth/WiqlQFAWqqkIQBFgs7UH6UCgEALBareCj6xEpigJFUfpVNxwOgzEGi8USz62vqipkWQbP87BarX2qG4lEoOs6zGZzPP2OpmmQJKnbuiZT+2yhWF2O42Cz2eLlkiRB0zSYTKZ4/XTq6rqOSMQ4AWe32zvVFUUxvjBxOnUZY/H1N2w2W/z/J1mWoapqSnVtNhuOPPLITmscxOpaLJZ4XUVRoGkaBEFIWkg5sW7sd6+qKhRFAc/zSb/7SCQCxhjMZnPS7zPduhzHdfp9plvXZDIlfU5kWe5XXUmSOtXVdR2SJMXHOrFu7PPXW11ZluO/+9hnijEW/5ykU9dqtSb9PmN/9139PtOpm8rvPhwOQxCEpH3EcPrdd/x9plNX1/X4+CT+3Xf1++zr56Sr32fHuqG2g9ACDTDzekJdFaqmQRB4mBP2l+GI0QeL2ZSwz0+9rqqqUFTN+N2b2+tGJAmMAWaTCYLQXldWOq9BRAghhBCSSXl5NpnjeDAYV28m3jTduMXyWCuxm2bcEq8qlaJpJD7TPPgPOwcAwIPhu9rjcCj1CCkMIdm4BWWGQPTml5Jvvohx80ZvbbFb2Li1Rm8tIT3p1hy9NQU11HoVbNnnw5uf1+PZ9Xvw+1Xf4LZnN+Paxz/C+fevQ/X/rsYZ976N7z3yAW5++lPc/8pXWPHOTrz26QF8vL0JexoCCEn0jychJHVMUyE1bk8KGmSKmjDjQCwwAgeazrD8AxWb64yggd0E3H5SboMGA03s54LTpGenXPQjtHr90OUw1GAT/vKXv8Dj8eCuu+5Kqjdnzhx4PB7U1tbGy5555hl4PB4sXbo0qW51dTU8Hg927twZL3vxxRfh8Xhw4403JtVdtGgRPB4Pvvrqq3jZa6+9Bo/Hg2uvvTap7nnnnQePx4ONGzfGy9555x14PB5ceeWVSXUvu+wyeDwevPfee/GyDRs2wOPx4MILL0yqe/XVV8Pj8WDNmjXxss2bN8Pj8eDMM89MqvvjH/8YHo8Hr776arxs27Zt8Hg8OPXUU5Pq/vznP4fH48HKlSvjZfv27YPH48Fxxx2XVPf222+Hx+PB3/72t3hZQ0MDPB5Pp3U1li1bBo/Hgz/96U/xMr/fD4/HA4/Hk7Sw94MPPgiPx4MHH3wwXqaqaryu3++Plz/xxBOorq7G8uXLk95v/vz5qK6uRlNTU7xs5cqVqK6uxn333ZdU94wzzkB1dTUOHDgQL3vppZdQXV2NO+64I6nuBRdcgOrq6qTPyeuvv47q6mrccsstSXW/853voLq6Gl9++WW8bN26daiursZNN92UVPeaa65BdXV10ufkww8/RHV1Na677rqkuosXL0Z1dTXef//9eNnmzZtRXV2Nq666KqnuL37xC1RXV+PNN9+Ml23btg3V1dW45JJLkurefvvtqK6uxn//+9942Z49e1BdXY1zzjknqe4999yD6upqPP/88/Gy+vp6VFdX47TTTkuq+8ADD6C6uhrPPPNMvKytrQ3V1dWorq5OqvvII4+guroaf/nLX+JlkUgkXjd2EhkA/vKXv6C6uhqPPPJIUhuxum1tbfGyZ555BtXV1XjggQeS6p522mmorq5GfX19vOz5559HdXU17rnnnqS63/3ud3HSSSdhz5498bL//ve/qK6uxm233ZZU95JLLkF1dTW2bdsWL3vzzTdRXV2NX/ziF0l1r7rqKlRXV2Pz5s3xsvfffx/V1dVYvHhxUt3rrrsO1dXV+PDDD+NlGzduRHV1Na655pqkujfddBOqq6uxbt26eNmXX36J6upqfOc730mqe8stt6C6uhqvv/56vGznzp2orq7GBRdckFT3jjvuQHV1NV566aV42YEDB3DmmWd22vfcd999qK6uxnPPPRcva2pqQnV1dTylXszy5ctRXV2NFStWxMsCgUD896lp7fkGH3vsMVRXV+Oxxx4DAOgRHyIHv8HJp52NUy/4AQKh9gvO/v7CKiy8dDEe+Wv7ZxUATv/uDVh46WI0t3rjZf/+71tYeOliPPDYM0l1z7/6Ziy8dDFq6hvjZa+ufg8LL12Me5Y/mVT3sutvx8JLF2PXvpp42Zp3P8Zpl/0UhBBCCCHZlJcrv/lrv8HMGVMh8EI8c3R8MjpLfs7iz5Onq7OEel+zszAluA+TlU/h4EL4Kf8IVrh+DYWzInGWO+um7VhJ5/JuXtehr2HFCEp0hzGgJSCjJSBjW2231WAzC0mzFYzZCxaUua3xx0VOCwQ6eUVIXtNVCVLjDuhdTNdv8YWw5qPtOO7wKhQ6rV28unfJMw4qoTOGRz9S8eF+44p7s2CkJzqkOH+CBmRgKW21AMvNDA+Se7GZIrErmAkh+YNpMpSmHdDlIHSVLqwihBBCSH7jWJ4lcPX5fHC73Ti441M4TJn70XU5hAMv/RxK234AgOOQY1Fxyi8HbCHjiMLQFNTQHNTRFNTRGNSjjzU0RctaQnq/c2QLPIdSlwWl8cBCh0CD27i3mITeGxtCGGPwer1wu920OHUXaHx6N1zGSJMCkJp2gWlKl9uDYQk79zdgRIkDFlPqsWnGGEKyDpuJQ8tL14HJQfD2YhSf9Xs8+amK//etcRJX5IGlJ5pwxIj8CxowxlBaNRnmwv6lgiLJYv8X7Nr4BspKi+N/n8xaBN5ZmbepioLBINxudzwFSD6lKmppacHWrVtRVVUFl6t9vYuOqYoCgQAsFgulKuqiLmMMgUAAJpOJUhV18/tkjKGxsRFOp5NSFXVTt7m5GU6nc0BSFUmBVkhtNeA1Cabo/y+MMUQk2ahrMQ/KVEVjDj0CJnd7WkdCCCGEkEzK28BBW2srLHIDtIi/9xelSG47gJqXfgZdNk4UFB9zJYqOuKCXVw0cTWdoDRsBheYwj2bJhOYw0BTQUNcWQWtYR5NPQljWem+sFwU2UzyIkDhzoTz23G1Bgc00ZE6gDpeTvtlC49O74TBGaqgFcvNesB6uxNY0DcFgMO22Y4EDKwui5aUfAQBMFdOxquRmvPCVsU/iOeBnx4mYUzW8ApOposBBdsT+L6j9ci0KXM54OcdxsFZOBW+y9fDq4Wk47K/6Q1VV+Hy+HuvETow7nc68HKPe0Pj0jsaoZwM1ProUgBY4CF0KZO09soUxhrIxUylwQAghhJCsyXmqokcffRT3338/6urqMH36dCxfvhzHH398r697//33ceKJJ+Kwww5Lyt+ZKo7nYSmdCKlpR8aCB+bC0Sg/6Weof+NuAEDLR0/DUnII7FWzMtJ+fwk8h1KHgFJH7KSbcfKPMcAfFlBgt4MTixFmZjSFeTSHODSFgOaghka/gkZfBI0+CQ3eCNqCco/v5Qsr8IUV7Kzv/p9ws8i3BxUSZiskPi51WSAK+XdlMSGDjeKtg+ztIddZlKyoqG8JoKTABpOY/gn+xDRFe9XKeNAAAH58TP4GDcjAY4xBbj0Aa/mkXHeFDDBJklBbW4vS0tKkK8kJIcOHLgeh+euHZMCAEEIIIWSgpB04OPfcc7u86iM25XTixIm47LLLMHny5F7bWrlyJW688UY8+uijOPbYY/H4449j0aJF2Lp1K8aMGdPt67xeL6644grMnz8fBw8eTPdHaO9zFoIHjnFHo+jIy9C68Z8AGA6++X8Yfd5DQ+JKEMZ0QJVghYTRZmC0GUChsY3jBfAmBzixGJxogcqZ0Rrm0RRiaPTL8aBCozcSf9zki0DRup/QIqs6alpCqGkJdVuH44BipwVlBRaUFkRnLLgtHQIOVjgsOY+BETIsMcYgt+yFGmxOqX4gLOOTb+pw/OFVKHT2IXCQsDDye03l8cfXHCmi+hAKGgy0TB7zY9K5YOCqq65KWiQ3Ztq0afFFhFesWIHvfe97neqEw+GkVBh9oUV8UEOtEO1F/WqHDC1+vx8ffPAB5s+fT4EDQoYZXQ5B89dRwIAQQgghJAVpn211u914+eWXUVhYiNmzZ4Mxhk2bNqGtrQ0LFizAypUr8dvf/hZvvfUWjj322B7bevDBB3H11VfjmmuuAQAsX74cb7zxBh577DEsW7as29dde+21uOyyyyAIAl5++eV0fwQARt5hl8sVDx4EardCCnqjeUoT8k+GjRyYFou5PVelqkJVVPACD0tSTlOjrvuICyE17kRo70fQ5SBqX/8Nys9YBpujoL1uRALiuUejuSo1DYqsgON5WC3mPtWNSDKYrsNkNkGM5jTVNN3IJ9pNXTEhD3msLjgONmtiXtUw9GAQokmEKZp71KXpsMgyRjvMcFS6wYk28GIRZB1gnAjR4kRY5dDgi6ChLYy6Zj+a/DJaw5oRXPBKaPRFEIh0v/AYY0CzX0KzXwJquk8bYDcLRiqk6HoLxQ4TihwmVBbaMKLEibICKwrtJkhSe+7bjnmPe8pNnVg3lss4k3msM5HzOpabur85rzvmpk6nbmKO2lTzWPc153Vibur+5rzu7veZ7uckld+9yWSCzWYbEvnOw8EAwk27IOiRTvuTjvsISZahazqcVhMWHDUeJkEw6ioKOA6wWhLrKsbnRBSS8lhHIhKE1v3xegdhBFwvmg6cMr49CKnrOiTZWGMhsQ+yokDTdIiCkHJ+4nTqDoZcxgMtk8d8IP0LBh5++GHcd9998eeqqsLj8eDCCy9MqldQUIBt27YllfU3aBCjtB2AYHWD42n2W74oLi7GmWeeGT82EUKGPqZEoPrroEd6TkNGCCGEEELapf0tuLKyEpdddhl27dqFF154AS+++CJ27tyJ7373u5gwYQK+/vprXHnllfjlL3/ZYzuyLOPTTz/FggULksoXLFiADz74oNvXPfXUU9i5cyf+93//N92uJzn22GPR0tICwJh58PdX1mHuGVdh2R+eSqp30gXXYu4ZV6GuoSletvKV1Zh7xlW444HHk+qe/p3FmHvGVdi9vw4VJy+BKZqHWm3bh3VP3IjE5STO+/7PMPeMq/D19t3xsjfe2YC5Z1yFG267P6nd71x/K+aecRU+++LreNm7Gz7D3DOuwrW/+E1S3e/fdAfmnnEVPvhkS7zs401fYu4ZV+GKxbcn1f3x0mWYe8ZVeGf9J/Gyz7/ejrlnXIWLfpj8+/vZHQ9h7hlXYdWb6+Nl23fvw9wzrsKZ3/kxtIgfaqAJctsB/PxnP8PMI+fgn39+EFbfNow1HUSlsgu3fn8R/nLbd7D07EOx/HtH4R83HocjAqvR+OLPcN7oOvzh6qNw+4Uz8N15IxDevg5y7eeYOtqN0gIL+F5Sm4ZkDXsbg/hkRzNWfVaLZ97biz+8vgO3PvcFvv/HDThz2Tuo/t81OGnpyzjxxhX45dMbsfy/X+Of7+3G0of+gSPnn4fb730QktKeFmX27NnweDxoaGiIlz399NPweDy4/fbksTzuuOPg8Xiwb9++eNnKlSvh8Xjw85//PKnuqaeeCo/Hk3SS69VXX4XH48GPf/zjpLpnnnkmPB5PUjquNWvWwOPx4Oqrr06qe+GFF8Lj8WDDhg3xsvfeew8ejweXXXZZUt0rr7wSHo8H77zzTrxs48aN8Hg8OO+885LqXnvttfB4PHjttdfiZV999RU8Hg8WLVqUVPfGG2+Ex+PBiy++GC/buXMnPB4Pqqurk+ouXboUHo8HzzzzTLystrYWHo8Hc+bMSap71113wePx4C9/+Uu8rKWlBR6PBx6PJ6nu/fffD4/Hg0ceeSReFg6H43VjAQQAeOSRR+DxeHD//cl/c7G6sX0EAPzlL3+Bx+PBXXfdlVR3zpw58Hg8qK1tT+PzzDPPwOPxYOnSpUl1zzrrLBxxxBHYuXNnvOzFF1+Ex+PBjTfemFR30aJF8Hg88auqAeC1116Dx+PBtddem1T3vPPOg8fjwcaNG+Nl77zzDjweD6688sqkupdddhk8Hg/ee++9eNmGDRs6nYzVlTC+f9V3ccwp5+PtNPYRr6/9ABaTCJ7nsGtfDRZeuhiXXZ/893LP8iex8NLFeHV1ex9q6xtx7lU34sOP29/rICpQEfoKf7xjMf7937fi5c2tXiy8dDFO/+4NSe0+8tfnsfDSxfj7C6viZYFQGAsvXYyFly6GprX/ff/5Hy9j4aWL8ed/vBwv0zQtXjcQav+c/P2FVVh46WI88tfnk97v9O/egIWXLkZzqzde9u//voWFly7GA489k1T3/KtvxsJLF6OmvjFe9urq97Dw0sW4Z/mTSXUvu/52LLx0MXbtq4mXrXn3Y5x22U8x0DJ1zI9JvGBg6tSpWL58OaqqqvDYY491Wd/tdqOysjJ+27hxI1pbWzvNMOA4LqleZWVlv3/2GF2Vofr7PruRDD2xRWl5ChYRMuQxxqD6aiE3bqOgASGEEEJImtKecfDkk0/i/fffT/oyxfM8Fi9ejHnz5uHee+/FT37yk17XKWhqaoKmaaioqEgqr6ioQH19fZev2b59O2655Ra899578atUeyNJEiRJij/vuNhd/GR+9MpSjhfR5XrRLKFu/B5JdeOPGANnsqFiwa3Y+/yNEJiMSfZGtG15EYWe5JOyYCyhjfb75D509X4J75tYN765q3a7rsuidRlj7T9bp7pdlHdXN6FMUyRAkSAHjMAL03WEar4Ax/HgTBZoER+gyXCozZhRroEbYUdjiQsPbf43RFHEX576BQBA1XTcdtd9ePWNd3DGeZfiqOPno8knoabJjzfWfgDeVghncSUiSg+LtuoMgqMYgqMY67c1A4ilXhmBwpNuxHoFOOl/18BtN6GswALn3B9ADbVi5Yd1GDdShl3U0CKJ4Ex2sB5+5lg562Z8Erd310Zf2u2qjVTeqz/tJrbRVVl/2+3tcW9lPfU33b519fpUy1PpQ1/b7a5ufz5/WsQHqWk3mK7HCnv/u48+liQVm7bX49DRxd33IeFRYn9M5ZMxotBIVaQwEUdOKEPjR2vwpVEzhd9b531lct2u9qGJ7SaPQ/vP3Lm/iZLGvbv9bUInOvYh3f3JQMrUMR9ov2DglltuSSrv7YKBjv055ZRTMHbs2KTyQCCAsWPHQtM0HHHEEbj77rsxc+bMlNpMheI/CMFRAl6ktDX5wO/345NPPsG0adPgcDhy3R1CSB8xVYbathe63H1aVEIIIYQQ0j2OpXk2oqioCH/7299w1llnJZW/+uqruPLKK9Ha2ort27fj6KOPRmtra7ft1NbWYtSoUfjggw8wd+7cePk999yDv//97/jmm2+S6muahjlz5uDqq6/GddddBwC444478PLLL/e4OPIdd9yBO++8s1P5tm3bUFZWlpCKQoGqqsaV7cEG6IrxD2bP6SW45FRFXdQN7dmA4HsPGBU4Hu6Tb4N5pAeRiASGDqkoNA2KooLnOFgSUwqlUVeSZOiMwWQSk1OLKEq3dUVRgKwy2C0idJ0ZqUUAWDumIdFZUmqReBqSFOqmk1oksa7d7gAniAAvQFEBnQEmixVmqw3gjHJJVsFxHCwWC4Kyjma/jINtITT6ImgNaWgLaWjyy2gOyGjySfBF2q867iuzyKHUaUGJy4QSpxluG48SpwkVhXaUFVhQ4jSjwMpD19T4lYsxkUgEjMVST7WnlVEUJa26sRzj/alrMpmS0tXIstynuowxBAIBmEymTnV1XY8H77pLa9Rb3ViKKFEU46kjGGPxtEbp1E1MPRX7uzdSlCWmHQunXTfx6tSufp+MMTQ2NsLpdCalNRpsv3tRD0P1HwRjrMf9SXd/9+Gwgi931WLKuDJYzSIUWQE4LilNmhxNVSSKIsToAsrfNKh4cL2KB9iPIHIaWk2jMOKMe6GqKnRNhyAK8TRpuq5Dju4jrB32Jx3rxn4O43eUkH5IVaGpWlp1+Q7phyLRfb45MVVRGnVVVTOOOzyflCIvIskAYzCZTRAS6iqKgsmHzYQ5OpttIGTqmA+0H/fff/99zJs3L15+77334m9/+1unVEMd1dXVoaqqCv/85z9x0UUXxcs//PBD7NixAzNmzIDP58PDDz+MVatWYcuWLZg0qfPCxl1dUFBVVYWaL95BgcvZ7fuLjmJYSsb32MfhgjEGr9cLt9vd5RoXw11zczPWrVuH2bNnw+VydVkndtxzOp15OUa9ofHpHY1Rz/o7PnrEC7VtP5je///7ByvGGMrHThsSa+kRQgghZGhKe8bB5Zdfjquvvhq/+tWvcNRRR4HjOHz88ce49957ccUVVwAA1q1bh+nTp/fYTmlpKQRB6DS7oKGhodMsBMC4+mvjxo3YtGkTfvKTnwAwTh4xxiCKIlavXo2TTz650+uWLl2KJUuWxJ/HThBUVFSgoKCgU30AYHoZpMYd0KQAXLau8tt2nfO2q7quqSegJXgAbZ89BzAd/vUPYdS5v4OrqKs0Cqm3m15dALB0KonVZYzBH1bgspmi/5h3XzeddjtxdK6LlOpqxi1+3lEGEqYaWwBwggmcZIJNEFFWbMbUMhs4oQCcYAIvmAFeNOpwHGRVR1NsMefEe2/74ya/BLXHhZ0ZatsiqG2LdFuH54Ci6MLO8cWco2swlBdYMdJiQ7nLCqGHPExut7vbbYOpbiz+OFxOMmVjLNMdo4H+fTLGoLTuhxJoBqzRdUz68HevFWgoSlwU2dr5MGM3J79+V4uO5R8CLtYMkTO+4JdXjITTIgCWrhZE5lNqN8ZhsXUu7GbB03Tq2s39rcujq3253dxFbn4zD8YGfhH4TB3zE3X8/DPGUvqbWLFiBQoLC3HOOecklc+ZMycpvdmxxx6LWbNm4Q9/+AN+//vfd2pn2bJlXV5QEIio4ESl+w6ED8KsWyGY7d3XGSZiJ+yAzr+vfMBxHI488kgAiI9DV8LhcF6OT6pofHpHY9Szvo6PFmyCHu45mD0c5Go2IiGEEELyR9pnIR566CFUVFTg//7v/3DwoJHzt6KiAjfddFM8x/GCBQtw2mmn9diO2WzG7NmzsWbNGpx77rnx8jVr1uDss8/uVL+goABffPFFUtmjjz6Kt99+G//+978xfnzXVwFaLJakq3hjOI7r9h9RThBhLZ8UDx70V/GRl0Ju2onQvk+gS34cXH0vRvzPXRDtRf1uO1Ni4zFkv7zoKpiugvVwzgeIBhgEESW8iFKXiGluERAs4AQnOF4Ax4vgeBE6BHglhiafhEafhAZvBPsbvfBLDI0+CU3RsqDU/WKlOmtf2PmbbhZ2NgkcRhbbUVVix6gSO6pKHBhdYkdVqR3lbluPQYXBZsh/hgbAYB0jpqmQm3dDi/j63bd0f74DXh2/WasipACHoD2PvKlg5KAbp3yUqWM+kP4FA4kYY/jrX/+Kyy+/PGnGT1d4nsdRRx2F7du3d7m9uwsKnFaxh2CZQdC9sObBlZ3DLRicLlXtfSHyWCoxh8ORl2PUGxqf3tEY9awv4xNPTaSFosH54Y0CB4QQQgjJtrQDB4Ig4NZbb8Wtt94aXy+g45X7Y8aMSamtJUuW4PLLL8eRRx6JuXPn4oknnsC+ffviqYiWLl2KmpoaPP300+B5HocddljS68vLy2G1WjuVZwLHC7CUTcxI8IDjeJSf/DPUvPQzKN4ayC17sO+5a1E8+1K4DzsDnNDziQqSOUxTwLReogtRVgBVvIAxbgEoEuGrYHC7XOCFYiPIIJgQUoHmoI6mgIpGv4qmgBKftdDgjaDJJ6E5IKG7/+sVjWFvYxB7G4OdtsWCCqNLYjcHqqKPKwqHVlCBDF5axA+peXfKfxe9afWH8NYnOzHvsNFwdzXLKMG+Nh13v6PAF80aM91RD0TTEAuuzC1uS/ouk8f8dC8YSLRu3Trs2LGj06LwXWGMYfPmzZgxY0aX27u6oODSI+2ArvV6ckqXQ9BCLRAdJb32Y6gbrIHOgdDS0oLXXnsN1dXVKCws7LZePo9RKmh8ekdj1LN0xkcPe6F6jdRENJ6EEEIIIZnRr7wH3aX6SdXFF1+M5uZm3HXXXairq8Nhhx2GVatWxRc9rKurw759+/r1Hv2RyeCBYHGgcuGvUPPqr6BHvGBKGM0f/hVtn78M28jDYR0xDbbKaTAVVYHjhv8VMkMF0zXjxiTokgKVl5O+jAgAygGU2wDYjCccL4ITLOB4Y30GlQloDTM0BRmaQhqaAxoO+hTUtEo40BJGTUsYstp5UeeeggqiwGFkkTEzgYIKpC+YrkHxH4TirctouzazCYdWFcNq6irFULutDTruW6cgGI1XjCvkUO0+CER3+ULB8L+qe6jp7zEfSO+CgURPPvkkjjnmmC4vFLjzzjsxZ84cTJo0CT6fD7///e+xefNm/PGPf0y5X7cscCP05QtwH9d7YEJuq4FgKwTH9/wZJ0OX3W7HtGnTktaHIYQMTozp0Hx10IJNue4KIYQQQsiw06fAwb///W/861//wr59+yDLctK2zz77LK22rr/+elx//fVdbluxYkWPr73jjjtwxx13pPV+6cpk8MBcNAZjLvojWj75O3xfrwbAoIVaENixFoEdawEAvMUFa+VUWCunwVY5HZayCTQjYYhh0bRJiYoAFDmASQ4AZbFSDoAdOrOhOcSh1s9Q49NR69VR06agxquipk2GrHaerqBqDPuagtjXlFpQYXSJkQ6Jggr5jTEGLeKFFmyBFvaCsc4Bq/6yWkyYMLLnNGwf7tew/H0VSvTtJxRz+NWJIrT19Yj1SCgYmfG+kb7J5DG/LxcMeL1evPDCC3j44Ye7bLOtrQ0//OEPUV9fD7fbjZkzZ+Ldd9/F0UcfnVbfQltfQWTiPFgrp/ZYj2kKFF8dzIWj02qfDB02mw2HHnporrtBCOkFUyWorXuhK+Fcd4UQQgghZFhKO3Dw+9//HrfeeiuuvPJKvPLKK/je976HnTt34pNPPsGPf/zjbPQx5zI688DmRtkJP0HB1NPQ8skzCNd9Aaa2n4jRJT9Cez9GaO/HxnsLZljKJ8FaOR22ymmwVEyBYHH0qw9kcOE5DmUOoMzBwVMpwJjHYASLdMbQHNRR49VQ49VQG72v8RmPZa1ze70HFWzGDIVSO0YX2zG61EFBhWFOkwLQgi1QQ62dglqZpqgaGttCKHRaYBKTr8hWdYZ/btHwytftH9wjRnD4+XEmWEWgOWDkvucsBeDNtJ8bDLJxzE/3ggG3241QKNRtew899BAeeuihPvUlCWM4+M6DqLrg9+BNXSxmnUD1N0B0lII30RXpw5Esyzh48CCKi4thMtHFG4QMRlqoFZrvAJie+YsgCCGEEEKIIe3AwaOPPoonnngCl156Kf72t7/h5ptvxiGHHIJf//rXaGlpyUYfB4X24MF2aFLnE7LpspRNxIjT7wDTVEhNOxGp34pI/VaE67dCj7QvpMs0GZG6rxCp+wptRkdgLh4XnZEwDdYR0/Ii13K+4jkOZU4BZU4BR4xK3pYYVEgMKMSedx9UCGFfUwjYlrxN5IERbhNGFVkwusiCUcUWVBXbMLrYjvIiK0yCCPCCkZ6DE6KLSVOqjsFKV8JQgy3QQi3QVbn3F2SIPyThw601OP7wKhQ62z8fzSGGB99X8E1j+wyaE8fxuH6OCJHnoMkhsEgrAFrfYDDJl2P+pv0yZlaZofrq0fTBX1B+4uIe6zPGILfth7Vs0gD1kAwkn8+H9957D/Pnz0dRUc8zqAghA4sxHZq3Blpo+ByDCCGEEEIGq7QDB/v27cO8efMAGFO5/X4/AODyyy/HnDlz8Mgjj2S2h4OIETyYlLHgAQBwgghrxWRYKyYDnnPBGIPSdiAeRIjUb4Xqq29/AdMhN++C3LwLvq/+CwAQXRUJgYTpMBWOpkXB8kAqQYWuAgq1Pg1SFxedqzqwv1XB/lYFQPLMGpEHKl0CRrkFjHS33492C6goMIHneUgRFZGI3VjjgecBXgTHCQDPg4sGGsDzAJfwnOOT70m/ME2BGmyBGmqBLnd/hXY2uZ1WzJ81DhZz++9zU62O329oXwRZ5IHLjxDwP5OF+L5K87evtSDS+gaDRr4c82/7TxtW/XQMmBqB/5vVcIw9Go5xx/T4Gi3sgxb2QrC5B6iXZKAUFRVh0aJFtMYBIYMMpSYihBBCCBlYaQcOKisr0dzcjLFjx2Ls2LH48MMP4fF4sHv3bjDWORf7cJON4EFS+xwHc1EVzEVVKJi6EACgBpvbZyTUbYXcvBtA+1ir/oMI+A8isP0dAABvdcFaMQ22EdNgrZwGSymtk5BvEoMKni6CCi2x9Ee+5BRIPQUVDng1HPB2nsYQCyqUOzmMLfJjZKGIUdGgQrmTTyv9UTyIwPEJsxsSnicEIoz7hHKONwIWiUGJPAigMV2HFm41ggURf873wwLPw2419jeazvCvLzS88JUW32OV2oGfHWfCoaXJi8BHtr/Z3oa7aqC6S3qRL8f8A20aHLOvQOCjJwAADev+gKryQyHae77aXG47AKu1IC/2NflEEAQ4HJQujZDBRAu3QfPup9REhBBCCCEDKO3Awcknn4z//Oc/mDVrFq6++mrcdNNN+Pe//42NGzfivPPOy0YfB51sBw86Eh0lcE44Hs4JxwMANCkI6eA38RkJUsO3YFrCOgkRP0J7P0Jo70dGf0UzLOWTjRkJldNgrZgC3mzPer/J4MRzHEqdAkq7CCqwxDUV0g4qAJ/VJFeIBRUSZymMcgsYVSCgwtU5qMB0DYCGTJ2ObA8s8O0BB44HOC45KMFx7dv49rLY7Ih4ECLWFsfnNDDBGIMe8UENtUALtWVlkeO+CoZlfLGrASUlhXh8I/Dlwfbf5uyRPBbPFeGyJI+b0rIb0u73AACcyQ7r+OMGtM+ke/l0zLceUg29fgtCez+CHvGi8d1HULnwth7/znUlAtXfAFNBxQD2lGRbIBDApk2bMHnyZNjt9P8SIbnEmA7NVwct2JzrrhBCCCGE5J20AwdPPPEE9OiVHtdddx2Ki4uxfv16nHnmmbjuuusy3sHBaqCDB4kEiwP2MbNhHzMbgJGeRGrcGQ0kfIVI/VboCYs4M1VGpPYLRGq/iHUe5pJxsFVOg6mwCqKzDKqpCLowghZeznNcKkGFLgIKNd70ZyoIiemPCgSMKjQCCqPcXQcV+oIxHdD0jAUiOuKiwYX2WRF8h8BC+z0DByUYhoKQMTMCXDQ4wYGLPTYajW9rLzfumSpBC3uhRfxZX+S4r1RNx5Z6DW9tYvHURDwHfMcj4KypAvgOJ2EZYwhu+gdis6js088Bb3ENcK9Jd/LpmM9xHMpO/AkOPL8NWrgNob0fw//N6vjsv+4ovjqIjmKa2TeMqKqK5uZmqOrg3M8SkjdUGUpTDaBKue4JIYQQQkhe4lgauQZUVcU999yD73//+6iqGpqpJHw+H9xuN7xeLwoKCvrdHtO1nAQPesKYDqV1f3xGQqRuK9RAQ0qv5S1OiM5yiK4ymJzlEF3lEJ1lxs1VDsHqzsuUDIwx+MMKXDZTXv78vdF1HftaZHglPh5IqE0IMETSPPcSCyqMjAYSEm+ZCioMtHz4DOmM4R8bg3jmszD06JGl2AYsOdaEqeV8l6+RDmyE772HAACcowLFp/8WvEgnYLvCGENp1WSYC0f1XjkDhsMxPxWx/wtqv1yLApcTwT0fo/6NuwEAnGhF1QUPw+Qe2WMborMUluKxA9HdAcEYg9frhdudn8d8VVXh8/l6rMMYQyAQgNPpzMsx6g2NT+9ojHqmBhrhbzwAm5mj8ekGYwxlY6bC5Ka1oQghhBCSHWkFDgDA6XTiyy+/xLhx47LUpezKdOAAGJzBg47UQCPC9V8jUmfMSJBb9gJ9uA6bE83RQEI5RGc5TK6y9ueucoiOkmG5yG0+nPTtj57GhzGG5pDe5SyFTAYVRroFVA7ioMJw/wy1hXX89m0fNu5X4mWeSg43zDPBbe388zKmQz7wKQKfPQ091AIAMM+5AQXjjhqW45MJAx04AIb+MT8VHQMHANCw7hH4v3kDAGCpmIJRZ93X67HNVjl12KQBpMABBQ76i8andzRGXWOaAtV7AFrYi5Csw27OjzWr+oICB4QQQgjJtrRTFZ1yyilYu3Ytrrrqqix0Z2jKZdqiVInOMrgmlsE18QQAgCYFIDXugOo/CMXfgLD3ILhwE1R/I9RgE9BNznSmylDaaqC01XT9RhwP0VESDSyUdTlzgTdZs/VjkkGI4ziUOgSUOgR4Oly0GwsqdAwo9BRU0HTE63yyP3mbwAMVzo4BBR6j3OKgDioMdV/WKbjnTR+agsZ+gwPDOVM4XDbT1Dk1ka5C2rsBoa2vQvPVxstN5VMhjJg1oP0mvcvXY37pvKsRrv0cqq8O0sFv0Lb53yiadXGPr5Fb98NaMXmAekiyqaWlBa+//jqOO+44FBYW5ro7hOQNPeKF6j0AplGaMEIIIYSQwSDtwMGiRYuwdOlSfPnll5g9ezYcjuSc+GeddVbGOjeUDIXgQSLB4oR99BEAOl8JzXQNWqgFir8BaqARavReCTQYgYVAA1h3uUaZbrwm0Njte/PWgmgwIXmmgmB1Q7AVQLC6wVucw3LmAkmWGFQ4vIugQktspkJCQKG3oEKtz6ifTlChwslDFCiokC6dMfx7SxhPfhSMpyYqtHK4YFIYJ012JgUNmCojsvtdhL7+L/Rg8v5BLB4P5zHXQqIrCgedfD3m8yYbKk5agppXfwkwHS2fPgtb1SxYyyZ1+xpNCkBu2QdTURVdHTvEWa1WTJgwARaLJdddISQvGAsg19ICyIQQQgghg0zaqYp4vus81YBxElDTOi+COphkI1VRoqGQtqijdFOoMMagR3zRYEIssGAEFZSAEWTQIz1P8e8dB97qNIIJ1oJ4UIGPPbYWQLAZ93z0MS9m7wv+cE8z018DPT59CSr0hOdiCzUbgQQjoGCkQ6p0CRkJKgy3z1BrSMeD6/z4cK8cL/OMNOGXJzlgYeF4mRZoRHjHm4jsXAsmB5LaMJVNhn3a2TCNOBwAKCVBL3KRqmioH/NT0VWqopiWT55B62crAQCmwtEYfd5Dvc6aE6wFsJSMByekfW3GoEGpiihVUX/R+PSOxsigyyGobfs6XZTEGKP/C3pBqYoIIYQQkm1pf6vV9a5T2BADxwuwlE6E1LRjSAUP0sFxnHHS3uaGpWxil3V0JRKfrdA+U8GYraAGGqAGW7pNh2Rg0CN+6BE/lB5qJfVLtPQYYBCsBeBt7YEI3uIAx3V/UowMXhzHocQhoKSHmQqdAwo6arxql0EFnSXOVEj+xBlBBT6+jkIsoDDKnbmgwlDCGMPqbRIe3xCAXzLizhyAy2bZcfmRdiiKgrrGIOz+HZB3vgm5ZhM6rqdiGnE47NPOhrl8SlK7ZPDJ92N+0axLENr3KaSmHVDaDqD5o7+h7Lhre3yNFvEh0rAN5qIqCNbMX6BAsk9RFDQ3N8PtdkMUh24AiJDBjDEGzV8PLdgI0P8AhBBCCCGDEn0bygJOEId98KA3vMkKc1EVzEVVXW5nmgo11BxPfaSGWqCFfdAjXmgRn3ELG4+ZEu6yjU5tqlI8MJESjm+fsWBxgbc4wJsdEKL3vMUJ3uyMltuhMAuUgkIIFid4s52CDoNUYlBhRocLsBhjaA0z1HhV1EQDCUZAQeslqKCj1qcD3QQVpleaMHecBUdWmWEzDb9AgqIxbGtQsaVWxod7ZXzT0D5QbiuHX55cgKPGmKFJATRtXoXgl69BV1uSG+FFWKqOgW3KIpiKxw/wT0BI33CCiPKTl+DAizeCqTJ8X/0XjrFHwV7V83ocuhJBpGE7eLMNJlcFBHsxXTE7hHi9XrzzzjuYP38+ioqKct0dQoYdpkSgtu2DnuL/+IQQQgghJDfSChzouo4VK1bgxRdfxJ49e8BxHMaPH48LLrgAl19+OX0pThALHkQat0OXQ7nuzqDDCSJMrgqYXBW91tVVGbrkjwYSvPGggt4hwKBFvEbwQfL3MpshiunQwm3Qwm0pz2poa/8JwJvt7QEGiwOCORZwiAUdHNEgQ3sgIhaU4Ew2+nvJAY7jUGznUGw3pxVUqPVpCCudr4ZrDypIWPOtBBMPeEaZMHesBXPGmVHuHJrrdKgaw7eNKjbXythSq2BrvdJlUGX+JAuunWuH1bsNje++A//2dWBqBLaEOry9GNaJp8A2oRq81T1gPwPpPzrmG8xFVSg55vtoev9PAICGtQ+j6sI/pDSbQJfDkJr3gGurgclZBtFZNqRTGOWLwsJCnHrqqXA6nb1XJoSkRQs0QvPXg6XyvzohhBBCCMmplNc4YIzhzDPPxKpVq+DxeDBlyhQwxvD111/jiy++wFlnnYWXX345y93tv2yvcdAR09RBHzwYbrnXGdOhS4GkoILeVYAh9jjiBVPl3hvOJI6PBxmE+KwGR8Ksh8TZDtFyk80IVpjs4E22QXXyabh9hjqKBRVqvRoORNdRiAUU9repkLpZU2FCiYg548yYO9aMiaUCghF1UI6RphuBgi21CrbUyviyrutAQUyVm8f1hzVgfPBDBHa+CzXQ1KmOqWI6bJNOhXnUrJQWOqdcxr0byDUOhssxPxU9rXEQwxhD3ao7ED7wGQDAccixqDjll2l/VjmOh+gogegq73WthFyiNQ5ojYP+ovHpXb6NEVNlY5aBnNpsbPq/oHe0xgEhhBBCsi3lM48rVqzAu+++i7feegsnnXRS0ra3334b55xzDp5++mlcccUVGe/kUMYJIqxlkwZ98GA44aIpiARrAVA4OqXXME2BJgehSwHoUhC6HIQuBaHJASMIIQUhhfwQtLCxTW6vp0kBQE9zNV6mQ5f80CU/0nxlHCea24MIZnt7UMFs63Af2xatlxiAMNvBiRb6QtaL9pkKPA4bYUraJmsMW2oVbNgj4cO9MhoD7VfQ7WxWsbNZxT8+DaHYzmP2KBHHT2CYNdoMi5i7Mdd0hh1NRqBgc42CL+uVLmdUxJQ5eHhGmXBkYSMmhjeA278eygc1CTNwDJzJBuv4E1Bjm4kxE6bAYjF11RwZAuiYn4zjOJRX/xT7n18MXfIjuOt9BLavhevQk3p/cQLGdCiBRiiBRgg2t5HGyOrKUq9JXwWDQXz++eeYOHEi7HZ7rrtDyJCnBZug+evA8nzdHEIIIYSQoSblGQcLFizAySefjFtuuaXL7ffeey/WrVuHN954I6MdzLSBnnEQM5hnHgz3q8UzoacxYoyBabIRdIgHHBKDEEbgwQg4tJdrCQGIlFIrZQvHx4MJXNKsBis4kxW8aDGCDqIVvMkSvbcm3FvAiRaENBEup9Noy2RN6Srz4YYxhp3NGj7cK2HDHhnfNnYdFrKIwKzRxkyEY8ZaUGzP7noZms6wszk6o6BGwRf1CkJy97v+YjuPI0aa4BllwuFuLxwN7yOw813ITbs6V+YF2EfPhHPCCXCMOwZtYR3vbtqFo6aMgMtuSbmPdGVh7wZyxsFwOeanIpUZBzGBXe/j4Jr7AAC82Y7RF/wBJld5v96fN9thcpVDsBUOmv1mvs84aGxsxJo1azB37ly43V2nWMu3q8XTRePTu3wYI6bKUL37oUuB9F9L/xf0imYcEEIIISTbUg4cVFZW4vXXX8cRRxzR5fZNmzZh0aJFqK+vz2T/Mi5XgQNg8AYPKHDQu2yOEWMMTAlHZy+0z3YwAg7R4IMSgi4bN6aEo8/DRll0W06DD13hRfCmhCCDaAVnskTvrR3uuyq3GPcmm/E4MVAxSE6u9aYpqOHjvTI27JXx2QEZstZ1vSnlIiaUihB5QOA4CDwg8ADPcejTx40BEZWhLayjNaTj20YVgR4CBUU2Dp5RZiNYMNKESrMfwV0fILBzHSL1X3fxCg7WEdPhnHgCnOPnQbC1n1jTNA3BYPqLwtMJgt4NZOBguBzzU5FO4AAADr7zEALfvg0AsI44DCPPvAcc1//gH8fx4K0uiPYiCNYCcELuZuzke+CAUhX1H41P74b7GPV3lgH9X9A7ChwQQgghJNtSTlXU0tKCioruF7KtqKhAa2trRjo1XFHaItIVjuPiqYZEZ1mf2ojPepBD0JUwWDTIoCvJwQVdjgYdkuoklMthMDWSmR9MV6OzK9K/yqw3nGCOz3QwggrGcz5ebo6WJ9RJYRsvtrfLCSbj1o8TgqUOAadPs+H0aTaEZR0f7A5jS52GD/fKaA23n8j/pkHFNw19TVqVvkIbB89IMzzRQEFVoQBdDiK4+30E3n8Xe2u2dBmIspRNMoIFE46H6CgZsP6SgUfH/O6VzvshIrVfQA00IlL3Jbyfv4xCz3n9bpcx3ViLJ+wFAPBmGwRLAQSrC7zFBY7P7swkQgjJBKZEoHoPpLyWASGEEEIIGbxSDhxomgZR7L66IAhQ1YE78TVUcYIIa8UUaKEWKL566EqGTtKSvMZxXPwkOFDUr7aYrhmBBSUCXY0Y97HHqhTdJrVvU43tciQEgclgqpT8WlUCi9bJ1KwIpslgmgxIfnRzEX/m8GI0iGAGJ5jACyYjUMGbwInt5e11OjwXo495Ew7TBRw1woprRplQGxDwTRPwdaOGGh8HHTx08NDAQ4cADTwYeGgQotuEhO3tdRK3MXR9YrHAykWDBMasgjFFAjiOgxpoQujAJhz8+GME923scq0OU1EVXBNPhHPC8TC5R/Y6XN5AGOs27cHRaaYqIoMLHfO7J1gcKD/pJtT+51YADM0f/x220TNhKRmf0fcxZpWFofgPguM48BYnBIsLgs0N3kx597OptbUVq1evxrx587pNVUQIScaYDs1/EHqwCWywzYIlhBBCCCF9knLggDGGq666ChZL1yeCJEnKWKeGO47jIDpKIDpKoEV8yaloVDnX3SN5juMFCBYnYOk9ZUdMKqmcjFkRSjyIEA9CqFL0eaTDvVEeL1MjnQIWTDUCFboqpb9Adap0FUxXwZQwAPQ7UBGbf2ECMCN6Q4Zm4DNwACcAHA9wPDheAMcL4HkeaBDANQrAFwL28zyYpkL1H+yyHdFVDueEE+GceALMxWPTShFgEgWMLHFCFIZGOinStXw85qdzMt42cgYKPeeibcuLgK6i4e0HMfq8B7OWXogxBi3ihxbxA95acLwIweqCYC0Ab3GAN9my8r75ymw2Y/To0TCbzbnuCiFDgh7xQfXVgqnD79hACCGEEJLPUg4cXHnllb3WueKKK/rVmXwkWAsgWNvXWmC61inFDFMiSHEpCkIGLWNWhBkQzUmf+UxhumbMRFDl6CwHKR5UMMol6NFAQ7yOllivwzZdMQIdmgymKsZzVW4v05RBt64EBwYwFYjtLjTjYSrBDsFeBOchx8E58QRYyif3OZ+w3WrG1LGlfXotGTzy8ZhvLT0EfLg25QB+8VHfRWj/Z5Bb9kBu2YPmj/+O0rnfz3IvDUxXoYZaoYaMdFEcL4K3RBe2N9vji9STvnE4HJgxY0auu0HIoMc0Baq3BnrEm+uuEEIIIYSQLEg5cPDUU09lsx8kiuOF6FWErngZ03XjiusOeetpGjAh7Yyr622AyYaButY9MVjBdDUaWIgFFxToCY+ZKiEcjsAi6O3BB10FdB2MaV3cawn3enK5rgHMeM50vb0u041tSc/1LtrSADCYCqtgr5oF++iZsJQfmpFFp1VNhy8owWE1QRAoJ/tQlY/HfE4wwVI2EZGD24y/oxTql5/8Mxx48SZAV+H9/GU4xh4F28iBP+HMdBVa2Act3L6gL8cLRgAhFkgw28GJVlorIQWqqqKtrQ0ulwsCzZ4ipBPGGPRgE7RAfZ8XPyaEEEIIIYNfyoEDkjscz8cXz03UPishHM2FHDJORBJCBkRisKI3jDGwXtI5DQe+YATrtuzD8YdXodBJVzyToYU32WApnQCpcXtKM/0sJeNQcvQVaP7wrwAY6t+4B6XH/wiuiSdmv7O9YLoGTQpAS1ignuM4Y80VkxW8aE1YYD56G8b7pnS0tbXhzTffxPz581FU1L91gwgZbnQpANV7gNISEUIIIYTkAQocDGFGKoLkE5a6KrevlxC9MU3JUQ8JIfmmwGHB8TOq4LRRbnAyNAlWF8zFYyE170mpvvvwsxHatxHh2s+hy0E0vPUAQns+Qsm8H0C0D66TzowxMFUGVBkafEnbEoMKHG+GGlGhmhkEk9WYqZBHQQW3242TTz4ZLper98qE5AmmKdB8ddDCrbnuCiGEEEIIGSAUOBhmeNEMXjQDtsJ4GdPUaDAhbNwrYVo3gRCSFaIgoNBFMw3I0CY6SqCrEhRvXa91OY5HxYJfoWn9owjseBcAENj5HgK73oe9ajZck+fDMfborC2cnCmJQQXGGJSwAkltjQcMeNEMTrS2z1AwRWcrCJZhl/7IZDKhuLg4190gZFCgtESEEEIIIflreH3TI13iBBGCtQCmggpYSsbDVjkNttEzYRsxDdayibAUj4XJWQaTswyivRCCxWFcdZhHVxcSQjIjLCnYtq8ZEZnSppGuPfrooxg/fjysVitmz56N9957r9u6a9euNa6E73D75ptvkuq98MILmDZtGiwWC6ZNm4aXXnqp3/00u0dCdKa20LdgcaBi/i9QPv8X4M0Oo5DpCO37BAfX3Ic9f78Cje89hkjDt0M2aK+rMrSID0qgEXLbAUiNOxGu24rQgU0I1XyOSMO3kFr2QvHVQw21QJMCxkLzQ/DnDYVC+OqrrxAOh3PdFUJySpf8UBq3QfXVUtCAEEIIISQP0YyDPMVxHDiTkZudZwyiZoLZ7e4ULIgv7KopCYu/KmC6Aqap0YVgVVqomRACAJBkFfsafBhR4oTVTIcYkmzlypW48cYb8eijj+LYY4/F448/jkWLFmHr1q0YM2ZMt6/btm0bCgoK4s/Lysrijzds2ICLL74Yd999N84991y89NJLuOiii7B+/Xocc8wx/eqvuWgMWPSEeSpcE0+AbcR0eL98Df7t70ALNgEwcoL7tq6Cb+sqmApHwzV5PlyTToLoKOlX/wYLpinQNAWAv8vtnGCK33jBbDwWze3lvAmcMHj2F5FIBHv27MHo0aNhs/W+hg0hww1TJai+OugRb667QgghhBBCcohjQ/FSsH7w+Xxwu93wer1JJyHyGWMMXq8X7i4CBym3oanRYEIvtyEaYGCMwZ8HC9v2FY1P7/JljDRNQzAYTPt1jDGEZB12Mz+sx6c/GGMorZoMc+GoXHelz4455hjMmjULjz32WLxs6tSpOOecc7Bs2bJO9deuXYuTTjoJra2tKCws7LLNiy++GD6fD//v//2/eNlpp52GoqIiPPvss732qbf/C5iuIdKwDbqc3tXnTNcQrv0c/m1vI7jnAyMNUCKOh22Ux0hlNG4OeNGSVvvZlIv9Fcfx0UCCCHA8wPHgeMG45wSAT7znwfEiwAvtgYcMpktSVRU+X8/BIsYYAoEAnE4n7bO6QOPTu8E4RkxToAUOQgu1ADn+ipjr/wsY04HYTdeTn7NUnrPOzxErY9HxZdHXddgGY3t322JtM6ZjzMk/gck9YsDHhxBCCCH5YfBc3kWGNE4QjS/7pp6vzGO61j5LQZON57oKRO+ZrnV+PESDDYQQQtrJsoxPP/0Ut9xyS1L5ggUL8MEHH/T42pkzZyISiWDatGm47bbbcNJJJ8W3bdiwATfddFNS/YULF2L58uVdtiVJEiRJij+PnSBmjHWdVofjYS6ZgMjBb8A0pcd+dnydbdQRsI06Arp8HQK71iPw7duI1G81tjMd4QObED6wCZxogXXEDNirZsI20gNT0WhwXO6yScbGYiCvLWHMOOYjjSFOxPECOF5Mmt3Q8Tl4IaUgQyo/ey7GaCih8endYBkjxhiY5IceaYMe8Q6alET9HR9dDkFp+ApK3RdQvQfANBnQVeN7R3QmNXSt2xP/Q8bJP8l1DwghhBAyjFHggAwo44u9DUhjjUim6wBrDya0Bxo6Bh1U44ogCjoQkjPeQATrt+zF7EMr4bIPniuoSe41NTVB0zRUVFQklVdUVKC+vr7L14wYMQJPPPEEZs+eDUmS8Pe//x3z58/H2rVrccIJJwAA6uvr02pz2bJluPPOOzuVe73eHk9Q6ZYKyC17+3hcMYEbexJcY0+C3V+PyK61iOxcBz3YAMBICxLevxHh/RsBAJzJDrFsMkxlk2EqOxRi6aHgewnMZxJjDCHJWKdksFwJ3bvUIw5cbDZD9MbxIjhOMC6C4AS0+gL4+LMt8Bx2GJwFLoATumwnHA4PofEZeDQ+vcvlGDElDF3yg8kB4//mQYYxhohi7G9TGSPGdOitu6Af/BLawS+gt+4cWgEAQgghhJBBiAIHZNAzrgw0Uhiki0UDCUbgISHQwPR48AFMj95rCUGKDtsIISkRRR6lBTaIQu6uliaDW6e1dBjr9qTQ5MmTMXny5PjzuXPnYv/+/XjggQfigYN021y6dCmWLFkSf+7z+VBVVQW3291LCkM3NIcVUtPO/l0hbKsCyi8HO+Y7iNRtRWD72wjt+xRauLW9/0oISu0mKLWbjAKOh7l4LKwVU2CpmAJrxRSIrsqsnXCM/XzDPbUaoEVvMsAARNd0d+hhlDpFWOV6mPzNAIxgA3ge4ARwHA8GDoqswSyGwMeCEAlplWL1EH0dxwnRx0JOZ5MMlNiV4g6HY5h/hvouF2OkyyFjZkG4rX0GlQgAg+8zGdsP9ZSqSA+3Qa77HHL951DqvwCTe0iVyPFAbD0VXgTHm9r/HhNuyc+55DK+qzqpPueMG7j2tsG1bwMXfz9wHLgO9eJ9SXhME3oIIYQQkm0UOCDDGsfz4Hhzv9vRNRVKWxtsLns0L2ksIKFTEIKQBA6rGYcdUp6VtjnBBN5WBI4XoQXqB006BZKa0tJSCILQaSZAQ0NDpxkDPZkzZw6eeeaZ+PPKysq02rRYLLBYOs+G4Tiu15N3or0QKB4LqWVvyv3tDscJsI+aAfuoGWCMQW7Zg/CBzYjUf43Iwa+hhdvaKzMdcvNuyM27ga3GWg6CrRCWismwFI+DuXgszEVjYHKPytgiw7HxyMeTvg6bBTM67ceYkdYEGhiiJzVVHSwiQU93jLjYyULBeBwNOoATjIslokGGpPIOgYt4oILvejbEYJDPn6FUDcQY6UoIetgbDRa0r7UyFH4vHceH6SqUpu2Qa7dAqfscalv3+2KhYCTMlYfDNGIGzGVTwJmsA9XtAZPrNFeEEEIIGf4ocEBICjjeSGHAm2z9/qKVGFBICjwk5liNBxuYkfc5HpDQkx7H26IvDmSQ0DQdwbAMq0WEkMHFSkX3KPD2kvjfH28rhOo9AD3S8wKmZPAwm82YPXs21qxZg3PPPTdevmbNGpx99tkpt7Np0yaMGNG+EOTcuXOxZs2apHUOVq9ejXnz5mWm4x2IzlLoqgTF13UqpL7gOA6WkvGwlIwHPOeCMQbVfzAeRIgc3Aa5ZU9S2g0t3IbQno8Q2vNRe0O8CJN7ZDSIMBLmwlEwFYyEyT0CvLVgSJwoHAyytR+Lix3bYVxM0N8jOMfzHQIMPc9+6LkefUaGA10JQw9H1yxQpd5fMIhpgUYoB7+AXPc5lINfgSldL1TPmewwVR4G8wgPzJWHQXCUDnBPCSGEEEKGHwocEDLA+pN6qTuMsYTgQzTgEA9MaMbaD10FKJIes+TgRHRmRfwxISnwBiN4e9NeHH94FQqdmbm6jxPEpKCBUWaCqXg89LAXqu8AmKZm5L1Idi1ZsgSXX345jjzySMydOxdPPPEE9u3bh+uuuw6AkUaopqYGTz/9NABg+fLlGDduHKZPnw5ZlvHMM8/ghRdewAsvvBBv84YbbsAJJ5yA3/72tzj77LPxyiuv4M0338T69euz9nOYC0eBaTLUYEtW2uc4DqaCSpgKKuE61FgIWpdDiDRsR+Tg15AOfoPIwW+gd0zLoatQWvdBad3XqU3e4oTJPdIIJBSOhMlVAdFZBtFRCtFZmtFj0lCXjf1YNhmzr3QAar+DEEmzIbpKsRRLvdLVLAmeBwdjOwOiFzboRhsk65gSgRZLQzSEgwVMjUBu+AZy3RZItV8gFKjrtq5YPB7mEYfDPMIDsWTioJ6BQwghhBAyFFHggJBhgOM4QBCRresEk4MIsYADiz/WdQ2S1weL0w6AdVsvMUjRuZwCFcOBy27B3Omj4LT1P0VYDG8t7PYqWN7mhsnigOargxbKzklckjkXX3wxmpubcdddd6Gurg6HHXYYVq1ahbFjxwIA6urqsG9f+0lvWZbx85//HDU1NbDZbJg+fTpee+01nH766fE68+bNw3PPPYfbbrsNt99+OyZMmICVK1fimGOOyerPYi4eB6Yp0CL+rL5PDG+2wz7aA/toDwBjIVDFVw+lZS/k1n2QW/YZ920HAL1zIE2XApAavoXU8G2X7Qu2QojOUoiOMgjOUmiWYnCFFTC5yiA6yyDYCvPmpFw29mNDRuJsCK3vsyEYY1BkHbI/mp8+nrM9mrc9+rg993t7Wcc87p1ywSflfY/lv08o57h4AKNjXvnhMqPCuGBEBVMj0CU/9IhvyAYLGGPQ2vZDrv/cmFXQuK3LfRgAcGYnzCNmRGcVHA7e5h7g3hJCCCGE5BeO5dkZOp/PB7fbDa/X28siiPmDMQav1wu32z1svlBlGo1Rz7IxPkYapoQABJKDDYmBie7KOz9maA9ssORtxpsmP88gxhj8YWXYLzaqaRqCwR4WJ+wGYwwhWe9yEURT2STwJnuvbehSAKr3wJA9edIbxhhKqybDXDgq110ZVvrzfwHTVEQatkFXIlnqXfqYpkLxH4TirYXirYne10Hx1kANNKHPp4J5AaK92JilEA0wiM7ShOelwyYdUir7sZ72WWRwj0/yQrUJwYbYjAogHshIqtsx4NEhEJJy3WgZYwyBQAAOhx1cbO0sXYumq1Sis0Q1I0Cgqe1pLnV1yK+ZpUt+yPVfQq7bAqX+C+iJa7ok4niIJROjswoOh1g0PjpzlwDG31nZmKkwuUf0XpkQQgghpA9oxgEhZFCKp3TK0fuzaEAiFrRonymhR7e3P2doD0KAMeO5nvw6xnSIXAgmhxUcx0VfEzuBx2JvGn1NQnn8HB+DrkTANGWghqBPIpKCHTWtqCpzwWLu/yGGEy0pBQ2AaCqWskOh+Q9CCzT0+70J6Q0niLCUTUTk4LZB87fJCSLMhaOiAaajkrbpqgTVVw/ZWws10Jhwa4IabIIWak1aRyH5xVq8frfvLZrbAwrRFEiCvRiCrRCCvdCY1WArBJeB9YKyKdP7MTK4GMflLsoHvB/RWRm+wRdcyTSma1Cbd0CuM2YVqC270d2I845SY1HjyhlQi6bA4XQN+/EhhBBCCBms6NsQIYR0gUu8GhHodwCDMQYT54W5n7MyNCkALdQGNdQyaE5UJgrLKnbUtKCs0J6RE26CrSit+hzHQywYAU40Q2070O/3J6Q3vGiBNRY8yMJspUziRQvMxWNhLh7b5XamqVBDLVADjfC31MMkt0INNkUDC0bQQO8hNRNT5egsh5oe+8EJZgg2txFQsLkh2IqMe2sBOJMVvGgxgoaiNRo8NO6NMgs4kxWcYM7aycRM78cIyUdaoBFy/RfGrIIeFjWGYIa5YhpMlTNgHnE4BNcI4wILxqDJg3ufSgghhBAy3NG3IUIIGUIEixOCxQlT4ShoES/UQJOR23iQZJ0rctlw2tETMtYen2bgIEawlwDgoXr3J8zsICQ7eLMd5tLxkJt2DZq/xb7gBBEmVzlEZxkU96QuU6vpSgRqsNkIJgQaowGFpoTnTd2fIIximtzr7IUUegve4gBvcUGwuiBYXOCtLgiWAuPe6uqwrQCC1QVOtPYacMj0foyQ4Y4xBj3QALnhaygN30Bp/Bp6sKnb+oK7Kp5+yFR2KDghD9cTIYQQQggZAihwQAghQxDHcRCjaT+YpsRP5OnDKL8/b3GCE/t+MkGwFwEcB7VtHwUPSNaJtkLwFVMgtx0YsAWTc4E3WRNSIXXGGIMuBxPSH7VBC7dCC7dBC3uT7yO+7lMj9YpBlwLG2ia+ujR+ABGCtQC8xRkNKiQEGswO8GYHIFoh6zw4kx28yW6kVjLbjaBDniwQTUhPGGPQ/PVQGr6O3/Rwa7f1ObMT5soZMI+YAVPl4cbxmRBCCCGEDHoUOCCEkCGOE0wwFVTCVFAJLeKHGmyGFmrNSdoUXzCCDZ/vxxGTKuC09e8KQt5W2O/+CLZCcOCgtO2l4AHJOt5sh7X8UKjhNqj+RuiSf0jPQOgLjuPiM6MsJeN6rMt0DZrkjwYXvNAlP3Q1AqZK0BUJTJXA1IixvosqtW9TJTAlDF0KQpP80KVA6h3UVWihFmihFvQp2RtviqdNYrwZkslInwTBEi/nRDM4wWIEGkQzuI7bBUu8nBONtEuIldPCr2QQYoxB89UYswmiswr0SFv3LxBMMJVMhKliOi1qTAghhBAyhFHggBBChhEheuUsK6qK5ipvgi6HBu79eR4uuxl8f3OPcxx4qzsjfeJtbpgwloIHZMDEZwPpGrSIz7jCPuIblOuS5BLHC/Gx6g+ma9ClgBFEiPihRfzQJJ9RFvEZZZJRrkd88XpMk9N/M10BkxUw2QhWaNFbxiQEJjjBHH/cHngwJwQfLF1sT35drB4FJkg6mKZCbdsLpfFbKE3boDRsA5N83b9AsMBUOgmm8ikwlU+FqWQCOME0cB0mhBBCCCFZkfPAwaOPPor7778fdXV1mD59OpYvX47jjz++y7ovvvgiHnvsMWzevBmSJGH69Om44447sHDhwgHuNSGEDG4cL8DkLIPJWQZdDhmpjIItYLqa1fd12MzwTKzodzu82QmOz9whioIHJBc4XoBoL4IYTcuhScF4ip6BDOgNdxwvRBdZTi/YqKtSQlDBB10KQldC0CIBRIJeMCUEpoTBlBB0OQymhsFUCVCN2RC6JgOqBLAMhg46BCYyLh6YMHcOKlBgIm8wxgBNgi6HjM+5HIQe8UFp3gm16VsoLbuAHgKdnGiFWHYozOVTYSqfaswoEHL+tZIQQgghhGRYTv/DW7lyJW688UY8+uijOPbYY/H4449j0aJF2Lp1K8aMGdOp/rvvvotTTz0V9957LwoLC/HUU0/hzDPPxEcffYSZM2fm4CcghJDBjzfbYTbbYXKPghZuhRpsMXKLZ4GuM0RkFWZRAM/3fdZBJtIUdW6TggcktwSLA4LFAWAUdFWGFjFy/euRQE5Si+U7XrSAd1ogOkuTyhVFheL19bgfY4whJOuwm3lA18C0WGolCUh4zDS5/bEqxetBlcG0SLRc7rAt+XXZCUxkrskkscCEYAYTLJBM0UADLwK8aJxc5oSE54Jxz0fLBbH9efxe6PS8+22isQ5FpzbEYRXUME78y9ET/0EwOQRdDrYHAZQQWHSbLsceJ29L53PFmewwlU02ZhOUT4FYNI7W+yCEEEIIyQMcy2Hy3WOOOQazZs3CY489Fi+bOnUqzjnnHCxbtiylNqZPn46LL74Yv/71r1Oq7/P54Ha74fV6UVBQ0Kd+DzeMMXi9XrjdbnD9TS8yTNEY9YzGp3eDbYx0VYIWaoOuhI2TCEokI7nYG1v9WP3Rtzj+8CoUOq0pvy7xJBzH8zBXTMvojINEetg7JIMHjDGUVk3udlFa0jeD4f8CpuvQlZBxxbschK4YV7bn6l80xhj8YQUum2lQ7K8GWir7saR9VpbHiOlqUiACakLAITFIEQ0+IB6IiLQHJQYqMDGoccmBhY5BDF4EYoELLqFOUnCiu6BEx3Khm0BHQhknIKLxsIoA1LBx4j968t842R8LBISgR4MD8W1KENCz93vjneUwlUyCqWwSTKWTILjHDHjgZSD/xoYqxhjKxkyFyT0i110hhBBCyDCVsxkHsizj008/xS233JJUvmDBAnzwwQcptaHrOvx+P4qLi7utI0kSJEmKP/f5jKtsGWN5t2Bhd2JjQePRPRqjntH49G6wjREnmCG6yuPPGWPxk0i6EmlfhFQOp5XeyGEz46gpI2C3iGn9rInjw5scACdkbaw4awHEwjFQWodW8GCwfHZI5nE8H19QOIbpenRh4DB0ORS9T+/vkfSN02bB0VNGwmEdHDnaOV4EZxYBsyMr7bcHJuRo4CHSaRZEcuBC6rwtacaFHF/EGpqU1RPc6WHGjAtdiT0bFCID9UYcB85kB2eygzc7wJkd0cdGGWd2QHSPhql0UlZm/RFCCCGEkKEnZ4GDpqYmaJqGiorkXNgVFRWor69PqY3f/e53CAaDuOiii7qts2zZMtx5552dyr1eL52EiWKMIRAwcunSFT1dozHqGY1P74bWGFkBwQoIACzGIol6LKiQeAKpi9Qqusbgctig6IAip556hTGGyP9n777Dori6OAD/Zmm7wNKriqAigr2gAsZeiBqDJsbeYknsLTGJsaEmsSS2GI0lColRNAkYO2LXWLGgRhAUQaLih2BBLLQ93x+ECctWFFjU8z4Pj+7MnZkzd2fuzt4zeye3oLyxmQzZWWU0vrfICGTmhLzMu6g43Ufa8WfWm0WQSCCYFnTqwcJenK7IywHlPvv310LPoMj99+7zCtM5++ozNTGCs13ZdNJXRGWRmFAayokUBcM5Ud6/wzrlAYq8giSY+G9+sdfFp+XrtQzl5wNUZH6+tnWq20ZFfoC5AMFEBsHUAhITcwim5v/+3+Lf/xdJCvw7X2mesRSC8PoM18QYY4wxxsqewZ9iVbwDjYj06lQLCwtDcHAwtm3bBicnJ43lpk6dismTJ4uvMzMz4ebmBmtrax6q6F+FnVEVZQiViojrSDuuH91exzpS5D4vGF4l93lBR2bOMzzOeoy0+5lwtZfDzET/8Y8L68fCzBimdk7lNHayJRTmslfmlwecOGAAIDE2BYxNVR4ETPm5RRJ7OVDk5xS5azyHj58SeJ6Th6TUh6jkYAkzE4NfKr/yCobtMYIAU0OHohMR/ZvoyNOStMjXnLzI//df+rdMvrokRcG/pMhFXl4ejCWSf38BYK4mKVCYELCAYMId/4wxxhhjrHwZ7NuQg4MDjIyMVH5dkJaWpvIrhOK2bNmCYcOG4ffff0eHDh20ljUzM4OZmZnKdEEQXpvOu9JQWB9cJ5pxHWnH9aPb61ZHRqYyGJnKlKZlpqYi9mYsHCtVh4WFCSjvecHzE/J1P4lTEARIZNaQGJXfR5ORzAaCIEHeg5v8cFr2ShOMTGBkZAIUGe6oKEVeTkECIT9X+U9R+P88UH5Fvtu6/Dx7noPY5HTYyaWcOHjDCIIACIWJDtXvD6WJx/BnjDHGGGMVncG+DZmamqJJkybYt28fevToIU7ft28fgoKCNC4XFhaGoUOHIiwsDF27di2PUBljjOnJwdER7733nsp0IsV/SYS8bJWEgmBsBiMzG5jYupR3yJBIrWBs687JA/ZaK/ylgi4Fv1zIQfbDBzCzkP5713RhkqHo/1/foZFsrczR1d/T0GEwxhhjjDHGmEEZ9DaqyZMnY+DAgfD19YW/vz/WrFmDlJQUjBw5EkDBMEO3b9/GL7/8AqAgaTBo0CAsW7YMfn5+4q8VZDIZrK2tNW6HMcaYYQmCBIKJOWBirjS9IKGQDcFYipwyf66BZhKpFYztPJB3P5mTB+yNJhiZQCIxhpFZLowtNA+tRgrFv79WyCv4JUORpELhEC0kDuuSz+cVY4wxxhhjjL1iDJo46N27NzIyMjBnzhykpqaibt262L17N9zd3QEAqampSElJEcuvXr0aeXl5GDNmDMaMGSNOHzx4MEJDQ8s7fMYYY8U8evQIx44dQ6NGjSCXy3WWL0goyCrE+OsSMzknDxjTkyCRQJCYAcZmAHQ/VLcg0fDfGPHKY70XGTeeFAXPHCFFsf//O/Y8UZmfn4+fZOP0lVuoV90JlrKKPy4/Y4wxxhhjjJUFgw/cOnr0aIwePVrtvOLJgMOHD5d9QIwxxl6YRCKBmZkZJJJX8wGOBcmDasi7n8TJA8ZKUUGiwRQopQfkkkIBUP6/SYj8f/9f5BcOijxAoQCR8v8L5v27rIaEpSABTE2MIOFx5xljjDHGGGNvMIMnDhhjjL0+5HI5mjdvbugwXorEzJKTB4xVcIJEAkACwcjkhdfxX/IhTykRYWedg+Zy5/+GWBL/Cn/tQCBFPgRFLiQmEgBU8MsIKIr8OsLwv6JijDHGGGOMsZfBiQPGGGOlRqFQIDc3F8bGxhrHRn8VcPKAsdefpuSDIicHiufQ2o4REYyzsmBiaam2TMGvGf4bXqn40EvKiYii0/9LThSWo2LLKK2LkxWMMcYYY4yxMsKJA8YYY6Xm/v372L59O9q3bw9bW1tDh/NSJGaWMLZ1R+6DZO6QY+wNUhrtWEEyQSgY96hwWinFpw1pSjBoTFYUKVeiZAWVy/MmGGOMMcYYY4bDiQPGGGOlRi6Xw8/PDxYWuh+W+iqQSK1gYuOO3Ic3OXnA2BviVW7HBEFi4GRFQZJBQQoYP3kCE3NzCAL+bT+pyL/4L1mBIr+WKFKOlMqrWV7jfBQkOtStV20MrxZB8u97LBgVDK+Vn2fokBhjjDHG2GuKEweMMcZKjZmZGapUqWLoMEqVRGYNY3JD3sMUQ4fCGCsHr2M7VpaKJysAQEIEwTgfElPzV2LYuv9+TaElifFvskFpCCp1SYxiCQzlJAj+WycRJEa5MJKZQpAYQRCMAEEABKN/kwNGgEQCARJAYgQIEggSI9XYFXmgvGxQ7jNQzlMocp+C8rLLtsIYY4wxxtgbgRMHjDHGSs3z58+RnJwMV1dXmJmZGTqcUmNkbgtQPvIe3TZ0KIyxMva6tmNMs4Lkh5b5ZbBNIoKRURaMNTwnQ1+CxBiCqTFgagH8+yMZUuT9l0TIeQrKfQpS5JdS5Iwxxhhj7E0h0V2EMcYY009WVhbOnj2Lp0+fGjqUUmdk4QBjK1dDh8EYK2OvczvG3gyCxBgSqRWM5S4wsa8OU5e6MHXyhrFNVRhZOEBial7w6wbGGGOMMca04MQBY4yxUmNvb4/33nsPNjY2hg6lTBhZOsHI0snQYbzSVq5ciWrVqkEqlaJJkyY4duyYxrIRERHo2LEjHB0dYWVlBX9/f+zdu1epTGhoKARBUPl7/vx5We8Ke0297u0YezMJxmYwMreFsXVlmDjUhKlLXZg4eMLYqhIkMhsIRiaGDpExxhhjjFUwPFQRY4yxUiMIAiSS1zsnbWzlCpAC+U/SDR3KK2fLli2YOHEiVq5ciRYtWmD16tXo3LkzYmNjUbVqVZXyR48eRceOHfHNN9/AxsYGISEh6NatG06fPo1GjRqJ5aysrBAfH6+0rFQqLfP9Ya+nN6EdY0wQJBBMLQBTCxQ+OYEUeaD8HCA/F/TvHxRF/p+f+0o+UJoxxhhjjL0YThwwxhgrNZmZmTh+/DgaNGgAS0tLQ4dTZoytKxckD57eN3Qor5TFixdj2LBhGD58OABg6dKl2Lt3L3788UfMmzdPpfzSpUuVXn/zzTfYtm0bduzYoZQ4EAQBLi4uZRo7e3O8Ke0YY8UJEmMIEmNAy48PCpILuUB+zr8JhRxQXs6/r3P4WQqMMcYYY68RThwwxhhjL8DYxg0gQv6zB4YO5ZWQk5ODc+fO4YsvvlCa3qlTJ5w4cUKvdSgUCjx+/Bh2dnZK07OysuDu7o78/Hw0bNgQc+fOVUosMMYYKx3/JRdkaueTIv/fXy2oSyxkgxT8iwXGGGOMsVcFJw4YY4yVGisrK7Ro0cLQYZQbIxs3ECmgeP7I0KFUeOnp6cjPz4ezs7PSdGdnZ9y9e1evdSxatAhPnjxBr169xGne3t4IDQ1FvXr1kJmZiWXLlqFFixa4ePEiatasqbKO7OxsZGdni68zMzMBAEQEInqRXXvtFNbFm1ofcrkcAQEBAKCxDt70OtKF60e317aOBAkEYylgLIW6xy+TIg+UlwPKy/4vmZD371+RXyu8tvVTirhuGGOMMVbWOHHAGGOs1BARFAqF+IDa150gCDC2dUfeg2QonmcaOpxXQvHjgoj0OlbCwsIQHByMbdu2wcnpvwdU+/n5wc/PT3zdokULNG7cGMuXL8f333+vsp558+Zh9uzZKtMfPXrEnTD/IiJkZWUBUH2/3gR5eXl4/Pixznbs2bNnb2T96IvrR7c3u45MAMEEMLb47xsp5Rf8SiEvG4q8XGTjCYR8AhS5Bo20ouLPLMYYY4yVNU4cMMYYKzUZGRnYvn072rdvD1tbW0OHUy4KkgceyLufBEX2Y0OHU2E5ODjAyMhI5dcFaWlpKr9CKG7Lli0YNmwYfv/9d3To0EFrWYlEgqZNm+LatWtq50+dOhWTJ08WX2dmZsLNzQ3W1tawsrLSc29eb4WdUdbW1m9kp+bdu3exb98+tGvXTmM7VngntIWFxRtZR7pw/ejGdaQdEcEoK+vf54zQf79KyHsOyn1e8G9+DvAGd55z4oAxxhhjZY0TB4wxxkqNpaUlfH19YW5ubuhQypUgCDC2K0weZBk6nArJ1NQUTZo0wb59+9CjRw9x+r59+xAUFKRxubCwMAwdOhRhYWHo2rWrzu0QEWJiYlCvXj21883MzGBmZqYy/U35lYy+CuvjTawTuVwOX19fnR26b3Id6YPrRzeuI+3+qx8JYGpe8FcEkeLfRMKzf//+/T/xcxQYY4wxxkoDJw4YY4yVGqlUCg8PD0OHYRCCIIGxXTVOHmgxefJkDBw4EL6+vvD398eaNWuQkpKCkSNHAij4NcDt27fxyy+/AChIGgwaNAjLli2Dn5+f+GsFmUwGa2trAMDs2bPh5+eHmjVrIjMzE99//z1iYmKwYsUKw+wke+W9ye0YY68SQZBAMDEHTP5LKBARkJcNRe7Tf5MJTwsSCpxMYIwxxhgrMU4cMMYYKzXZ2dm4desWnJycYGpqauhwyh0nD7Tr3bs3MjIyMGfOHKSmpqJu3brYvXs33N3dAQCpqalISUkRy69evRp5eXkYM2YMxowZI04fPHgwQkNDAQAPHz7ERx99hLt378La2hqNGjXC0aNH0axZs3LdN/b6eNPbMcZeZYIgACZSGJlIxWlEBMWzh1A8uQdF7jMDRscYY4wx9moR6A0bHDEzMxPW1tZ49OgRj2X8LyLCo0eP3tixjPXBdaQd149ub0od3b1794WecVD4MFZLS8vXon6IFKWePCAiOLjVgqlN5VJbJ+PrAnXelPZKE33asdetzSptXD+6cR1pV1b1o8h+jPystNciuU9EcKzqAxNrV0OHwhhjjLHXFP/igDHGWKmxs7NDUFAQjI3f7I8X/uUBY68ubscYe31JzOSQmMmhyH2K/MdpUDx/ZOiQGGOMMcYqLImhA2CMMfb6kEgkMDEx4bsn8V/yQGJmaehQGGMlwO0YY68/iYk5TOw8YOpYC0bmtgCf74wxxhhjKjhxwBhjrNQ8fvwYp0+fxpMnTwwdSoXAyQPGXj3cjjH25hBMpDC2qQpTR28YWThAEPjrMWOMMcZYIb4yYowxVmoUCgWys7OhUCgMHUqFwckDxl4t3I4x9uYRjE1hbF0ZJk7eMLJ0giDhr8mMMcYYYzx4K2OMsVJjbW2NVq1aGTqMCue/Zx4kQ5H92NDhMMa04HaMsTeXYGQCYytXkKUTFE/Skf80HZSfZ+iwGGOMMcYMgm+lYIwxxspBQfLAAxKplaFDYYwxxpgWgsQIRnJnmDj5wNi6MgQjE0OHxBhjjDFW7jhxwBhjrNRkZGQgIiICDx48MHQoFZIgSGBs6wGJ1NrQoTDGNOB2jDFWSBAkMLJwKEgg2FSBYGxm6JAYY4wxxsoNJw4YY4yVGnNzc9SvXx/m5uaGDqXCEgQBxrbuMJLZGjoUxpga3I4xxooTBAFG5vYwdfKGsa07BBOpoUNijDHGGCtz/IwDxhhjpUYmk8HT09PQYVR4BcmDqoAgIP/pfUOHwxgrgtsxxpg2RjIbGMlsoHj+CPlZaVDkPDV0SIwxxhhjZYJ/ccAYY6zU5OTkIDU1Fbm5uYYO5ZVgbOMGIwsHQ4fBGCuC2zHGmD4kUmuYONSEiX11SMwsDR0OY4wxxlip48QBY4yxUpOZmYnjx48jKyvL0KG8MoytK8PI0snQYTDG/sXtGGOsJCRmcpjY14CJgyckZnJDh8MYY4wxVmp4qCLGGGOlxs7ODl27doWZGT88sCSMrVwhSIyQl5lq6FAYe+NxO8YYexESUwtI7KtDkfsU+Vn3oHj20NAhMcYYY4y9FE4cMMYYKzUSiQQymczQYbySjCydAEGCvEe3DR0KY280bscYYy9DYmIOia07yNIZ+U/SkP/sIUBk6LAYY4wxxkqMhypijDFWarKysnD27Fk8efLE0KG8kowsHGBsU/DQZMaYYXA7xhgrDYKJFMY2VWHq6A0jC3sIAn/1Zowxxtirha9eGGOMlZr8/HxkZmZCoVAYOpRXlpG5LUxs3bmDgTED4XaMMVaaBGNTGFtXgYmTN4wsHfnznTHGGGOvDB6qiDHGWKmxtrZGu3btDB3GK08itYaxfXXk3b8B4s5LxsoVt2OMsbIgGJnA2KoSyNKp4BkIT9P5M54xxhhjFRrf7sAYY4xVQBJTCxjbe0Iw4hw/Y4wx9roQJMYwtnKFiVNtGMtdIEiMDB0SY4wxxphanDhgjDFWajIyMrBt2zY8fPjQ0KG8FiQmMpjY14RgbGboUBh7Y3A7xhgrD4LECEZyZ5g414axVSUIRiaGDokxxhhjTAknDhhjjJUac3NzeHt7QyqVGjqU14ZgbAoT+xqQmMgMHQpjbwRuxxhj5UkQJDCydISJkzeMrStDMDI1dEiMMcYYYwD4GQeMMcZKkUwmQ61atQwdxmtHMDKBsX0NGJnxLw8YK2vcjjHGDEEQJDCycIDE3B6KZw+Qn5UGyss2dFiMMcYYe4PxLw4YY4yVmtzcXKSlpSE3N9fQobx2BIkRjGTWhg6Dsdcet2OMMUMSBAFG5nYwdfKGsa07BBP+9RNjjDHGDIMTB4wxxkrNo0ePcPToUWRlZRk6FMYYeyHcjjHGKgojmQ1MHWvBxM4DElNzQ4fDGGOMsTcMD1XEGGOs1NjY2ODtt9+GTMbj8TPGXk3cjjHGKhqJ1BoSqTUU2Y+Rn5UGRTYnNhljjDFW9jhxwBhjrNQYGxvD0tLS0GEwxtgL43aMMVZRSczkkJjJoch5grzMu4YOhzHGGGOvOR6qiDHGWKnJyspCTEwMnj59auhQGGPshXA7xhir6CSmFjCxrw5jubOhQ2GMMcbYa8zgiYOVK1eiWrVqkEqlaNKkCY4dO6a1/JEjR9CkSRNIpVJUr14dq1atKqdIGWOM6ZKbm4t79+4hLy/P0KGwCqosPvfDw8NRu3ZtmJmZoXbt2ti6dWtZhc/eANyOMcZeFYLE4F/nGWOMMfYaM+iVxpYtWzBx4kRMmzYNFy5cQMuWLdG5c2ekpKSoLZ+UlIQuXbqgZcuWuHDhAr788kuMHz8e4eHh5Rw5Y4wxdWxtbdGxY0dYWVkZOhRWAZXF5/7JkyfRu3dvDBw4EBcvXsTAgQPRq1cvnD59urx2i71muB1jjDHGGGOMMUAgIjLUxps3b47GjRvjxx9/FKf5+Pige/fumDdvnkr5zz//HNu3b0dcXJw4beTIkbh48SJOnjyp1zYzMzNhbW2NR48e8RfCfxERHj16BGtrawiCYOhwKiSuI+24fnR7U+ooLy8PmZmZJV6OiJCVlQVLS8vXun5eBhHB3t7e0GG8lLL43O/duzcyMzOxZ88esczbb78NW1tbhIWF6YyJrwtUvSntlSb6tGPcZmnH9aMb15F2XD+6vQ7XBYwxxhir2Az2cOScnBycO3cOX3zxhdL0Tp064cSJE2qXOXnyJDp16qQ0LTAwEOvWrUNubi5MTExUlsnOzkZ2drb4+tGjR+K/BsyZVCiFHQQA+MJcA64j7bh+dHtT6ujevXs4ePAgmjdvDmtra72XK+wgUCgUr3X9vAwigomJCeRy+StZR2X1uX/y5ElMmjRJpczSpUvVrpOvC3R7U9orTfRpx7jN0o7rRzeuI+24fnR71a8LGGOMMVbxGSxxkJ6ejvz8fDg7Kz/QydnZGXfv3lW7zN27d9WWz8vLQ3p6OlxdXVWWmTdvHmbPnq0yvWrVqi8RPWOMMWYYaWlpcHR0NHQYJVZWn/uaymhaJ18XMMYYe528qtcFjDHGGKv4DJY4KFT87ggi0nrHhLry6qYXmjp1KiZPniy+VigUuH//Puzt7fnOjH9lZmbCzc0N//zzDw/ToAHXkXZcP7pxHWnH9aNbYR2ZmpoaOpSXUhaf+yVZJ18X6Mbno25cR9px/ejGdaQd149ur8t1AWOMMcYqLoMlDhwcHGBkZKRyR2BaWprKnYOFXFxc1JY3NjbWOL4buwmbAAB3FUlEQVSjmZkZzMzMlKbZ2Ni8eOCvMSsrK74w14HrSDuuH924jrTj+tHtVe3cLqvPfU1lNK2Trwv0x+ejblxH2nH96MZ1pB3Xj26v6nUBY4wxxio+iaE2bGpqiiZNmmDfvn1K0/ft24eAgAC1y/j7+6uUj4qKgq+vr9rnGzDGGGOsYiirz31NZTStkzHGGGOMMcYYY7oZLHEAAJMnT8ZPP/2E9evXIy4uDpMmTUJKSgpGjhwJoGA4gUGDBonlR44ciZs3b2Ly5MmIi4vD+vXrsW7dOnz66aeG2gXGGGOM6aksPvcnTJiAqKgoLFiwAFevXsWCBQuwf/9+TJw4sbx3jzHGGGOMMcYYe20Y9BkHvXv3RkZGBubMmYPU1FTUrVsXu3fvhru7OwAgNTUVKSkpYvlq1aph9+7dmDRpElasWIFKlSrh+++/x/vvv2+oXXgtmJmZYdasWSpDN7D/cB1px/WjG9eRdlw/ur0OdVQWn/sBAQHYvHkzpk+fjhkzZqBGjRrYsmULmjdvXu7797p4HY61ssZ1pB3Xj25cR9px/ejGdcQYY4yxsiZQ4VMGGWOMMcYYY4wxxhhjjDH2xjPoUEWMMcYYY4wxxhhjjDHGGKtYOHHAGGOMMcYYY4wxxhhjjDERJw4YY4wxxhhjjDHGGGOMMSbixAFjjDHGGGOMMcYYY4wxxkScOHhDrFy5EtWqVYNUKkWTJk1w7NgxjWUjIiLQsWNHODo6wsrKCv7+/ti7d285RmsYJamjoo4fPw5jY2M0bNiwbAM0sJLWT3Z2NqZNmwZ3d3eYmZmhRo0aWL9+fTlFaxglraONGzeiQYMGMDc3h6urKz788ENkZGSUU7Tl6+jRo+jWrRsqVaoEQRDw559/6lzmyJEjaNKkCaRSKapXr45Vq1aVfaAGVNI6elPbalY6+LpAN74u0I6vC3Tj6wLN+LpAO74mYIwxxlhFwImDN8CWLVswceJETJs2DRcuXEDLli3RuXNnpKSkqC1/9OhRdOzYEbt378a5c+fQtm1bdOvWDRcuXCjnyMtPSeuo0KNHjzBo0CC0b9++nCI1jBepn169euHAgQNYt24d4uPjERYWBm9v73KMunyVtI7++usvDBo0CMOGDcOVK1fw+++/Izo6GsOHDy/nyMvHkydP0KBBA/zwww96lU9KSkKXLl3QsmVLXLhwAV9++SXGjx+P8PDwMo7UcEpaR29iW81KB18X6MbXBdrxdYFufF2gHV8XaMfXBIwxxhirEIi99po1a0YjR45Umubt7U1ffPGF3uuoXbs2zZ49u7RDqzBetI569+5N06dPp1mzZlGDBg3KMELDKmn97Nmzh6ytrSkjI6M8wqsQSlpH3377LVWvXl1p2vfff09VqlQpsxgrCgC0detWrWU+++wz8vb2Vpr28ccfk5+fXxlGVnHoU0fqvO5tNSsdfF2gG18XaMfXBbrxdYH++LpAO74mYIwxxpih8C8OXnM5OTk4d+4cOnXqpDS9U6dOOHHihF7rUCgUePz4Mezs7MoiRIN70ToKCQlBYmIiZs2aVdYhGtSL1M/27dvh6+uLhQsXonLlyvDy8sKnn36KZ8+elUfI5e5F6iggIAC3bt3C7t27QUT43//+hz/++ANdu3Ytj5ArvJMnT6rUZ2BgIM6ePYvc3FwDRVWxve5tNSsdfF2gG18XaMfXBbrxdUHp4+uCknnd22nGGGOMlQ9jQwfAylZ6ejry8/Ph7OysNN3Z2Rl3797Vax2LFi3CkydP0KtXr7II0eBepI6uXbuGL774AseOHYOx8et9Gr1I/dy4cQN//fUXpFIptm7divT0dIwePRr3799/LcczfpE6CggIwMaNG9G7d288f/4ceXl5ePfdd7F8+fLyCLnCu3v3rtr6zMvLQ3p6OlxdXQ0UWcX1urfVrHTwdYFufF2gHV8X6MbXBaWPrwtK5nVvpxljjDFWPvgXB28IQRCUXhORyjR1wsLCEBwcjC1btsDJyamswqsQ9K2j/Px89OvXD7Nnz4aXl1d5hWdwJTmGFAoFBEHAxo0b0axZM3Tp0gWLFy9GaGjoa3t3IVCyOoqNjcX48eMxc+ZMnDt3DpGRkUhKSsLIkSPLI9RXgrr6VDedvVltNSsdfF2gG18XaMfXBbrxdUHp4usC/bxJ7TRjjDHGytbrfUsUg4ODA4yMjFTubkpLS1O5a6e4LVu2YNiwYfj999/RoUOHsgzToEpaR48fP8bZs2dx4cIFjB07FkDBF2IigrGxMaKiotCuXbtyib08vMgx5OrqisqVK8Pa2lqc5uPjAyLCrVu3ULNmzTKNuby9SB3NmzcPLVq0wJQpUwAA9evXh4WFBVq2bImvvvrqjb9zzsXFRW19Ghsbw97e3kBRVUxvSlvNSgdfF+jG1wXa8XWBbnxdUPr4ukA/b0o7zRhjjLHywb84eM2ZmpqiSZMm2Ldvn9L0ffv2ISAgQONyYWFhGDJkCDZt2vTaj61a0jqysrLC5cuXERMTI/6NHDkStWrVQkxMDJo3b15eoZeLFzmGWrRogTt37iArK0uclpCQAIlEgipVqpRpvIbwInX09OlTSCTKTbCRkRGA/+6ge5P5+/ur1GdUVBR8fX1hYmJioKgqnjeprWalg68LdOPrAu34ukA3vi4ofXxdoNub1E4zxhhjrJyU99OYWfnbvHkzmZiY0Lp16yg2NpYmTpxIFhYWlJycTEREX3zxBQ0cOFAsv2nTJjI2NqYVK1ZQamqq+Pfw4UND7UKZK2kdFTdr1ixq0KBBOUVb/kpaP48fP6YqVapQz5496cqVK3TkyBGqWbMmDR8+3FC7UOZKWkchISFkbGxMK1eupMTERPrrr7/I19eXmjVrZqhdKFOPHz+mCxcu0IULFwgALV68mC5cuEA3b94kItX6uXHjBpmbm9OkSZMoNjaW1q1bRyYmJvTHH38YahfKXEnr6E1sq1np4OsC3fi6QDu+LtCNrwu04+sC7fiagDHGGGMVAScO3hArVqwgd3d3MjU1pcaNG9ORI0fEeYMHD6bWrVuLr1u3bk0AVP4GDx5c/oGXo5LUUXGvewcBUcnrJy4ujjp06EAymYyqVKlCkydPpqdPn5Zz1OWrpHX0/fffU+3atUkmk5Grqyv179+fbt26Vc5Rl49Dhw5pbVfU1c/hw4epUaNGZGpqSh4eHvTjjz+Wf+DlqKR19Ka21ax08HWBbnxdoB1fF+jG1wWa8XWBdnxNwBhjjLGKQCDi374yxhhjjDHGGGOMMcYYY6wAP+OAMcYYY4wxxhhjjDHGGGMiThwwxhhjjDHGGGOMMcYYY0zEiQPGGGOMMcYYY4wxxhhjjIk4ccAYY4wxxhhjjDHGGGOMMREnDhhjjDHGGGOMMcYYY4wxJuLEAWOMMcYYY4wxxhhjjDHGRJw4YIwxxhhjjDHGGGOMMcaYiBMHjDHGGGOMMcYYY4wxxhgTceKAMYbg4GA0bNjQ0GHoLTQ0FDY2NoYOgzHGGHst8XUBY4wxxhhjjBMHjFVAQ4YMgSAIGDlypMq80aNHQxAEDBkypPwDY4wxxli54+sCxhhjjDHGWHnjxAFjFZSbmxs2b96MZ8+eidOeP3+OsLAwVK1a1YCRvb5yc3MNHQJjjDGmFl8XlD++LmCMMcYYY28yThwwVkE1btwYVatWRUREhDgtIiICbm5uaNSokVLZyMhIvPXWW7CxsYG9vT3eeecdJCYmKpW5desW+vTpAzs7O1hYWMDX1xenT59WKrNhwwZ4eHjA2toaffr0wePHjzXGVzgswN69e+Hj4wNLS0u8/fbbSE1NFcu0adMGEydOVFque/fuSndFenh44KuvvsKgQYNgaWkJd3d3bNu2Dffu3UNQUBAsLS1Rr149nD17ViWGP//8E15eXpBKpejYsSP++ecfpfk7duxAkyZNIJVKUb16dcyePRt5eXnifEEQsGrVKgQFBcHCwgJfffWVxv1ljDHGDImvC/i6gDHGGGOMsfLEiQPGKrAPP/wQISEh4uv169dj6NChKuWePHmCyZMnIzo6GgcOHIBEIkGPHj2gUCgAAFlZWWjdujXu3LmD7du34+LFi/jss8/E+QCQmJiIP//8Ezt37sTOnTtx5MgRzJ8/X2t8T58+xXfffYcNGzbg6NGjSElJwaefflri/VyyZAlatGiBCxcuoGvXrhg4cCAGDRqEAQMG4Pz58/D09MSgQYNARErb/vrrr/Hzzz/j+PHjyMzMRJ8+fcT5e/fuxYABAzB+/HjExsZi9erVCA0Nxddff6207VmzZiEoKAiXL19WW7eMMcZYRcHXBXxdwBhjjDHGWLkhxliFM3jwYAoKCqJ79+6RmZkZJSUlUXJyMkmlUrp37x4FBQXR4MGDNS6flpZGAOjy5ctERLR69WqSy+WUkZGhtvysWbPI3NycMjMzxWlTpkyh5s2ba9xGSEgIAaDr16+L01asWEHOzs7i69atW9OECROUliseu7u7Ow0YMEB8nZqaSgBoxowZ4rSTJ08SAEpNTVXa9qlTp8QycXFxBIBOnz5NREQtW7akb775RmnbGzZsIFdXV/E1AJo4caLGfWSMMcYqAr4u4OsCxhhjjDHGypuxIZIVjDH9ODg4oGvXrvj5559BROjatSscHBxUyiUmJmLGjBk4deoU0tPTxTsGU1JSULduXcTExKBRo0aws7PTuC0PDw/I5XLxtaurK9LS0rTGZ25ujho1apRoGXXq168v/t/Z2RkAUK9ePZVpaWlpcHFxAQAYGxvD19dXLOPt7Q0bGxvExcWhWbNmOHfuHKKjo5XuJMzPz8fz58/x9OlTmJubA4DSOhhjjLGKjK8LlKfxdQFjjDHGGGNlhxMHjFVwQ4cOxdixYwEAK1asUFumW7ducHNzw9q1a1GpUiUoFArUrVsXOTk5AACZTKZzOyYmJkqvBUFQGrJA32WoyLABEolE6TWg/kGDRdcjCILGacXjKZyubppCocDs2bPx3nvvqZSRSqXi/y0sLFTmM8YYYxUVXxfwdQFjjDHGGGPlgZ9xwFgF9/bbbyMnJwc5OTkIDAxUmZ+RkYG4uDhMnz4d7du3h4+PDx48eKBUpn79+oiJicH9+/fLK2wAgKOjo9JDEfPz8/H333+Xyrrz8vKUHowYHx+Phw8fwtvbG0DBQyTj4+Ph6emp8ieRcNPHGGPs1cTXBerxdQFjjDHGGGOli6+SGavgjIyMEBcXh7i4OBgZGanMt7W1hb29PdasWYPr16/j4MGDmDx5slKZvn37wsXFBd27d8fx48dx48YNhIeH4+TJk2Uae7t27bBr1y7s2rULV69exejRo/Hw4cNSWbeJiQnGjRuH06dP4/z58/jwww/h5+eHZs2aAQBmzpyJX375BcHBwbhy5Qri4uKwZcsWTJ8+vVS2zxhjjBkCXxeox9cFjDHGGGOMlS5OHDD2CrCysoKVlZXaeRKJBJs3b8a5c+dQt25dTJo0Cd9++61SGVNTU0RFRcHJyQldunRBvXr1MH/+fLUdDqVp6NChGDx4MAYNGoTWrVujWrVqaNu2bams29zcHJ9//jn69esHf39/yGQybN68WZwfGBiInTt3Yt++fWjatCn8/PywePFiuLu7l8r2GWOMMUPh6wJVfF3AGGOMMcZY6RKo+ECjjDHGGGOMMcYYY4wxxhh7Y/EvDhhjjDHGGGOMMcYYY4wxJuLEAWOMMcYYY4wxxhhjjDHGRJw4YIwxxhhjjDHGGGOMMcaYiBMHjDHGGGOMMcYYY4wxxhgTceKAMcYYY4wxxhhjjDHGGGMiThwwxhhjjDHGGGOMMcYYY0zEiQPGGGOMMcYYY4wxxhhjjIk4ccAYY4wxxhhjjDHGGGOMMREnDhhjjDHGGGOMMcYYY4wxJuLEAWOMMcYYY4wxxhhjjDHGRJw4YIwxxhhjjDHGGGOMMcaYiBMHjDHGGGOMMcYYY4wxxhgTceKAMcYYY4wxxhhjjDHGGGMiThwwxhhjjDHGGGOMMcYYY0zEiQPGGGOMMcYYY4wxxhhjjIk4ccAYY6zMHD58GIIg4OHDh3ovExwcjIYNG5ZZTC9qyJAh6N69u6HDYK+I0NBQ2NjYGGTbycnJEAQBMTExBtn+q6JNmzaYOHGi3uVfh3ot6T5XBC/yOVKRvYrvAWOF/vzzT3h6esLIyAgTJ07U67Ouol7XFefh4YGlS5caOoyXwteqjDHGShsnDhhjjL2UEydOwMjICG+//bahQ6nwKloHWHh4OGrXrg0zMzPUrl0bW7duVZqflZWFPn36wNXVFX369MGTJ09KtH4PDw8IggBBECCTyeDh4YFevXrh4MGDass/e/YMtra2sLOzw7NnzwAUdMAXrkPT3+HDh0FEWLNmDZo3bw5LS0vY2NjA19cXS5cuxdOnT8Vt3L9/HxMnToSHhwdMTU3h6uqKDz/8ECkpKUqxDBkyBIIgYOTIkSpxjh49GoIgYMiQISWqj9dJREQEOnbsCEdHR1hZWcHf3x979+5VKafrGNu4cSPc3NxgZ2eHKVOmKM1LTk6Gl5cXMjMzy2wf5s6dq3d5Nzc3pKamom7dumUSD1MvICAAqampsLa2NnQor43IyEgIgoC7d+8qTXdxcYGbm5vStFu3bkEQBERFRZVniK+l1yH5+PHHH6Nnz574559/MHfuXPTu3RsJCQmGDqtUREdH46OPPtK7fEW7pmOMMcbKAicOGGOMvZT169dj3Lhx+Ouvv1Q6X1nFdfLkSfTu3RsDBw7ExYsXMXDgQPTq1QunT58WyyxduhSWlpaIioqCubn5C92JN2fOHKSmpiI+Ph6//PILbGxs0KFDB3z99dcqZcPDw1G3bl3Url0bERERAIDevXsjNTVV/PP398eIESOUpgUEBGDgwIGYOHEigoKCcOjQIcTExGDGjBnYtm2b2OF1//59+Pn5Yf/+/Vi5ciWuX7+OLVu2IDExEU2bNsWNGzeU4nFzc8PmzZvFJAYAPH/+HGFhYahatWqJ6+JVcvjwYXh4eGicf/ToUXTs2BG7d+/GuXPn0LZtW3Tr1g0XLlwQy+g6xtLT0zF8+HB899132Lt3L37++Wfs2rVLXH7UqFGYP38+rKysymQf7ezsIJfL9S5vZGQEFxcXGBsbl0k8rwIiQl5eXpmsOz8/HwqFQmW6qakpXFxcIAhCmWz3TfTWW2/B2NgYhw8fFqfFxcXh+fPnyMzMxPXr18Xphw4dgomJCVq0aFFu8eXk5KhMe9FjryyP2TdNVlYW0tLSEBgYiEqVKkEul0Mmk8HJycnQoZUKR0dHmJubl/t2+RhljDFWkXHigDHG2At78uQJfvvtN4waNQrvvPMOQkNDtZYv/En7n3/+CS8vL0ilUnTs2BH//POPStkNGzbAw8MD1tbW6NOnDx4/fizOi4yMxFtvvQUbGxvY29vjnXfeQWJiYmnvnlqzZ8+Gk5MTrKys8PHHHyt1cBARFi5ciOrVq0Mmk6FBgwb4448/ABTcadi2bVsAgK2trdId64bYn6VLl6Jjx46YOnUqvL29MXXqVLRv314pOfDw4UN4eXmhXr168Pb2xqNHj0q8HblcDhcXF1StWhWtWrXCmjVrMGPGDMycORPx8fFKZdetW4cBAwZgwIABWLduHQBAJpPBxcVF/DM1NYW5ubnStD///BMbN25EWFgYvvzySzRt2hQeHh4ICgrCwYMHxXqfNm0a7ty5g/3796NLly5iTHv37oWJiQnGjBmjFE/jxo1RtWpVMYkBFNyl7ubmhkaNGum1/9qO9cTERAQFBcHZ2RmWlpZo2rQp9u/fr7S8h4cHvvnmGwwdOhRyuRxVq1bFmjVrlMqcOXMGjRo1glQqha+vr1LnfVlZunQpPvvsMzRt2hQ1a9bEN998g5o1a2LHjh1KZbQdYzdu3IC1tTV69+6Npk2bom3btoiNjQUAbNq0Caampnjvvfd0xlJ41+fevXvRqFEjyGQytGvXDmlpadizZw98fHxgZWWFvn37Kv36pPiQMbrquvjdwi+6XXXDYTRs2BDBwcHia0EQsHr1arzzzjswNzeHj48PTp48ievXr6NNmzawsLCAv7//S7cTv/76K3x9fcXztF+/fkhLS1Nbt76+vjAzM8OxY8fw+PFj9O/fHxYWFnB1dcWSJUtU6jMnJwefffYZKleuDAsLCzRv3lypo7rw82Dnzp3ir1Ju3rypEmPxu3oLl9u7dy98fHxgaWmJt99+G6mpqVr3VVv7XHQ7Bw4cgK+vL8zNzREQECC2U/Hx8RAEAVevXlVa7+LFi+Hh4QEiAgDExsaiS5cusLS0hLOzMwYOHIj09HSNcT148ACDBg2Cra0tzM3N0blzZ1y7dk2lnnR9bu7YsQNNmjSBVCpF9erVMXv2bI2dkYXtTdH34/Dhw3jrrbfw1ltvqUxv1qwZLCws1A45s3TpUq1JRn3qpE2bNhg7diwmT54MBwcHdOzYUeOxl52djfHjx8PJyQlSqRRvvfUWoqOjleJVt9zFixfRtm1byOVyWFlZoUmTJjh79qzWuIvS57hTKBSYM2cOqlSpAjMzMzRs2BCRkZHi/GrVqgEAGjVqBEEQ0KZNG7Xbys/Px7Bhw1CtWjXIZDLUqlULy5Yt0xnjlStX0LVrV1hZWUEul6Nly5ZiG6ErtsL2LSIiAm3btoW5uTkaNGiAkydPivVamGht166d+Gs/dUMVzZ8/H87OzpDL5Rg2bBieP3+uEmtISAh8fHwglUrh7e2NlStX6h1LoePHj6N169YwNzeHra0tAgMD8eDBAwC6z3d1irfNgiDgp59+Qo8ePWBubo6aNWti+/btYoyarun0bWuKHqPr1q3T2b686HHBGGOMvRRijDHGXtC6devI19eXiIh27NhBHh4epFAoxPmHDh0iAPTgwQMiIgoJCSETExPy9fWlEydO0NmzZ6lZs2YUEBAgLjNr1iyytLSk9957jy5fvkxHjx4lFxcX+vLLL8Uyf/zxB4WHh1NCQgJduHCBunXrRvXq1aP8/HyNsX799ddkYWGh9e/o0aMalx88eDBZWlpS79696e+//6adO3eSo6OjUlxffvkleXt7U2RkJCUmJlJISAiZmZnR4cOHKS8vj8LDwwkAxcfHU2pqKj18+NBg++Pm5kaLFy9WmrZ48WKqWrWq+DopKYmqV69OxsbG5O3tTbdu3dK4PnXc3d1pyZIlKtMzMjJIEARasGCBOO369etkZmZG9+/fp4yMDDIzM6PExESVZVu3bk0TJkxQmvbuu+9SrVq1tMaSn59PNjY29NFHH6md//XXX5MgCJSRkUFEBe93UFAQLV68mNq3by+Wa9++PS1ZsoSCgoJo8ODBGrenz7EeExNDq1atokuXLlFCQgJNmzaNpFIp3bx5Uyzj7u5OdnZ2tGLFCrp27RrNmzePJBIJxcXFERFRVlYWOTo6isfljh07qHr16gSALly4oLSeWbNmaa2jog4dOkTu7u56l8/Pzyc3Nzdavny5OE3XMXb//n2Sy+V0/vx5ysjIoGrVqlFkZCRlZGRQjRo1KCUlRe9YAZCfnx/99ddfdP78efL09KTWrVtTp06d6Pz583T06FGyt7en+fPni8sVP5Z01XVSUpJSvb7odtWdFw0aNFB6fwBQ5cqVacuWLRQfH0/du3cnDw8PateuHUVGRlJsbCz5+fnR22+/rVcdadrndevW0e7duykxMZFOnjxJfn5+1LlzZ5W6rV+/PkVFRdH169cpPT2dhg8fTu7u7rR//366fPky9ejRg+RyudK6+/XrRwEBAXT06FG6fv06ffvtt2RmZkYJCQlE9N85EhAQQMePH6erV69SVlaWSsyaPkc6dOhA0dHRdO7cOfLx8aF+/fpp3Xdt7XPR7TRv3pwOHz5MV65coZYtWyqds02aNKHp06crrbdJkyY0depUIiK6c+cOOTg40NSpUykuLo7Onz9PHTt2pLZt22p8D959913y8fGho0ePUkxMDAUGBpKnpyfl5OQo7a+2tiQyMpKsrKwoNDSUEhMTKSoqijw8PCg4OFhrfXh5eYmvP/jgA/r2229pwYIFSnVZrVo1cZ9nzZpFDRo0UFrPkiVLtLYV+taJpaUlTZkyha5evUpxcXEaj73x48dTpUqVaPfu3XTlyhUaPHgw2draim23puXq1KlDAwYMoLi4OEpISKDffvuNYmJixBgAUEhIiMb90Oe4W7x4MVlZWVFYWBhdvXqVPvvsMzIxMRGP+TNnzhAA2r9/P6WmpooxF5eTk0MzZ86kM2fO0I0bN+jXX38lc3Nz2rJli8b4bt26RXZ2dvTee+9RdHQ0xcfH0/r16+nq1at6xVbYvnl7e9POnTspPj6eevbsSe7u7pSbm0vZ2dkUHx9PACg8PJxSU1MpOzubQkJCyNraWoxjy5YtZGpqSmvXrqWrV6/StGnTSC6XKx03a9asIVdXVwoPD6cbN25QeHg42dnZUWhoqF6xEBFduHCBzMzMaNSoURQTE0N///03LV++nO7du0dEus93dYq3zQCoSpUqtGnTJrp27RqNHz+eLC0tKSMjQ+s1nb5tTfFjVFf7os9xUXjtwhhjjJUWThwwxhh7YQEBAbR06VIiIsrNzSUHBwfat2+fOF9dhw8AOnXqlFgmLi6OANDp06eJqKBjwtzcnDIzM8UyU6ZMoebNm2uMIy0tjQDQ5cuXNZbJyMiga9euaf17+vSpxuUHDx5MdnZ29OTJE3Hajz/+SJaWlpSfn09ZWVkklUrpxIkTSssNGzaM+vbtq7Y+DLk/JiYmtHHjRqVpGzduJFNTU6Vp+fn5lJqaqpQQ0pemxAERkbOzM40aNUp8/eWXX1L37t3F10FBQTRt2jSV5dQlDnx8fOjdd9/VGsvdu3cJgMZ4IiIilI7Dwi/f9+7dIzMzM0pKSqLk5GSSSqV07949vRIHuo51dWrXrq3U+e7u7k4DBgwQXysUCnJycqIff/yRiIhWr16t9rgsnjho166d0nrVKZp0kkqlJAiC0jRtndQLFy4kOzs7+t///idO0+cYi4iIoLp161KNGjXEjvMPP/yQli5dSkeOHKGGDRtSnTp16Pfff9e47cLzav/+/eK0efPmEQCl5NPHH39MgYGB4mt1iQNtda0pcVDS7eqbOCjagXTy5EkCQOvWrROnhYWFkVQq1Vgv6qg7f4oq7Nh8/Pix0j7++eefYpnMzEwyMTFRek8ePnxI5ubm4rqvX79OgiDQ7du3ldbfvn17sROs8Bwp2nmrjqbPkevXr4tlVqxYQc7OzhrXUZL2uej7uWvXLgJAz549I6KCztfq1auL8ws7Uq9cuUJERDNmzKBOnTopbeOff/4ROxeJlN+DhIQEAkDHjx8Xy6enp5NMJqPffvtNaX+1tSUtW7akb775Rmm7GzZsIFdXV411EhUVRQDozp07RETk5OREZ86coVOnTlGlSpWIiCglJYUA0IEDB4joxRIH+tZJw4YNlcqoO/aysrJU2pWcnByqVKkSLVy4UONyRERyuVzsmFanVq1aFBERoXG+PsddpUqV6Ouvv1ZarmnTpjR69GgiUm1DSmL06NH0/vvva5w/depUqlatmphwKk7f2H766Sdx/pUrVwiAmDx98OABAaBDhw6JZYonDvz9/WnkyJFK22nevLnScePm5kabNm1SKjN37lzy9/fXO5a+fftSixYt1O6rPue7OuoSB0Xb4aysLBIEgfbs2UNE6q/pStLWFD9GdbUv6hQ/LjhxwBhjrLS9uYO0MsYYeynx8fE4c+aMOIyLsbExevfujfXr16NDhw4alzM2Noavr6/42tvbGzY2NoiLi0OzZs0AFPxcvOjY466urkrDZyQmJmLGjBk4deoU0tPTxXGxU1JSND641M7ODnZ2di++wwAaNGigNP6tv78/srKy8M8//yAtLQ3Pnz9Hx44dlZbJycnROayNofan+JjhRKQyTSKRwMXF5aW2o07RbeXn5+Pnn39W+sn9gAEDMGnSJMyePRtGRkZ6r+tl4gFU68TBwQFdu3bFzz//DCJC165d4eDgoNc6dR3rT548wezZs7Fz507cuXMHeXl5ePbsmcqzQurXry/+XxAEuLi4iOdDXFyc2uOyuAMHDuiMt+gDO0+fPo3PP/9cacgSmUymdrmwsDAEBwdj27ZtKmNd6zrGevTogR49eoivDx8+jMuXL+OHH36Ap6cnwsLC4OLigmbNmqFVq1Zax9IuWk/Ozs4wNzdH9erVlaadOXNG4/LF11G8rstyu/qsFwDq1aunNK1wTPoXfQ7EhQsXEBwcjJiYGNy/f1+p7aldu7ZYruhxfOPGDeTm5ortNQBYW1ujVq1a4uvz58+DiODl5aW0vezsbNjb24uvTU1NlfZTX+bm5qhRo4b4uuhnxLFjx9C5c2dx3urVq+Hl5aV3+1w0HldXVwBAWloaqlatij59+mDKlCk4deoU/Pz8sHHjRjRs2FCsq3PnzuHQoUOwtLRUiTkxMVGlPuLi4mBsbIzmzZuL0+zt7VGrVi3ExcWJ03S1JefOnUN0dLTSs2Py8/Px/PlzPH36VO247S1atICpqSkOHz6MBg0a4NmzZ2jcuDGICJmZmbh27RpOnjwJMzMzBAQEqCyvL33rpOj+FVV0emJiInJzc5Wet2BiYoJmzZop1Ze69U2ePBnDhw/Hhg0b0KFDB3zwwQdKx1DxIWLU0XbcZWZm4s6dOyrPgmjRogUuXryoc93FrVq1Cj/99BNu3ryJZ8+eIScnR2WYqKJiYmLQsmVLmJiYqMwrSWyajn9vb2+94o6Li8PIkSOVpvn7++PQoUMAgHv37uGff/7BsGHDMGLECLFMXl6eygPQtcUSExODDz74QG0MsbGxL3w9VlzRGCwsLCCXy7V+JpRk28WPUV3tC1Dy44Ixxhh7WZw4YIwx9kLWrVuHvLw8VK5cWZxGRDAxMcGDBw9ga2urcVl1nbxFpxX/4isIgtJDM7t16wY3NzesXbsWlSpVgkKhQN26ddU+ULHQN998g2+++UbrPu3ZswctW7bUWkadovHt2rVLqU4AwMzMTOvyhtgfFxcX3L17V2laWlqa2EFZljIyMnDv3j1xvOe9e/fi9u3b6N27t1K5/Px8REVFKXUCquPl5aXSaVSco6MjbGxsxDH0i7t69SoEQVDqFCo0dOhQjB07FgCwYsUKrdspTtuxPmXKFOzduxffffcdPD09IZPJ0LNnT5X3Xdv5UJjwKA2enp7i/2/dugVjY2Olaeps2bIFw4YNw++//66SMCzpMZadnY3Ro0fj119/xfXr15GXl4fWrVsDKHiPT58+jW7dummMpWg9CYKgsx3RtY4XWUaf7UokEpX3LTc3V+d6NU3TFZ8mT548QadOndCpUyf8+uuvcHR0REpKCgIDA1WOQQsLC/H/mpJsRfdJoVDAyMgI586dU0n8Fe1AlslkL5T0U1fHhdv39fVVSoI5OzuL570+7bO2OnZ1dUXbtm2xadMm+Pn5ISwsDB9//LFYXqFQoFu3bliwYIFKzIUdn0VpOn/VJUO1tSUKhQKzZ89W+0wQqVSqdhvm5uZo1qwZDh06hPv37+Ott94S36uAgAAcOnQIJ0+ehL+/v7gOfY/dovStk6LHWFH6HnvFpxVfX3BwMPr164ddu3Zhz549mDVrFjZv3qyUuNRF23FXdJqu2HT57bffMGnSJCxatAj+/v6Qy+X49ttvxQfLq6MpsVvS2EqzjVGncF1r165VSpgBUGkrtMWibX9f5nqsuJJ+JpRk28WPUV3ty4scF4wxxtjL4sQBY4yxEsvLy8Mvv/yCRYsWoVOnTkrz3n//fWzcuFHsaFW37NmzZ8W7VePj4/Hw4UO972bLyMhAXFwcVq9eLXaK//XXXzqXGzlyJHr16qW1TPEvecVdvHgRz549E7+wnjp1CpaWlqhSpQpsbW1hZmaGlJQUsbOzOFNTUwAFHeKG3h9/f3/s27cPkyZNEqdFRUW91J2l+lq2bBkkEgm6d+8OoCAJ1adPH0ybNk2p3Pz587Fu3TqdiYN+/fqhT58+2LZtG4KCgpTmFd49a21tjV69emHjxo2YM2eO0q8onj17hpUrVyIwMFDtrzjefvttsSM1MDBQ7/3UdawfO3YMQ4YMETuusrKykJycrPf6AaB27drYsGGDynFZHsLCwjB06FCEhYWha9euKvNLeozNnTsXnTt3RuPGjXHhwgWlB7vm5uYqnTevMkdHR6UHqmZmZiIpKanc47h69SrS09Mxf/58uLm5AYBeD4utUaMGTExMcObMGXG5wjvUC9u+Ro0aIT8/H2lpaS+UjH0ZMplMJeFV+PBlbe2zvvr374/PP/8cffv2RWJiIvr06SPOa9y4McLDw+Hh4QFjY91fs2rXro28vDycPn1aPC8yMjKQkJAAHx8fsZyutqRx48aIj4/Xmegrrm3btti8eTMePHig9KDe1q1b4/Dhwzh58iQ+/PBDcbqjoyPu3r2r1OFcNEmjTknrRBtPT0+Ymprir7/+Qr9+/QAUtA1nz55VejC3Jl5eXvDy8sKkSZPQt29fhISElChxoI2VlRUqVaqEv/76C61atRKnnzhxQnzf1F0DqHPs2DEEBARg9OjR4jRdD0KvX78+fv75Z+Tm5qp0dusTW2nx8fHBqVOnMGjQIHFa0c8kZ2dnVK5cGTdu3ED//v1feDv169fHgQMHMHv2bJV5pXm+a6Pu/XzZbWtrX17kuGCMMcZeFicOGGOMldjOnTvx4MEDDBs2TOWn5T179sS6des0Jg5MTEwwbtw4fP/99zAxMcHYsWPh5+en95dXW1tb2NvbY82aNXB1dUVKSgq++OILncuVxtA+OTk5GDZsGKZPn46bN29i1qxZGDt2LCQSCeRyOT799FNMmjQJCoUCb731FjIzM3HixAlYWlpi8ODBcHd3hyAI2LlzJ7p06QKZTGaw/ZkwYQJatWqFBQsWICgoCNu2bcP+/fv1SloAwA8//ICtW7fqHALn8ePHuHv3LnJzc5GUlIRff/0VP/30E+bNmwdPT0/cu3cPO3bswPbt21WGZRo8eDC6du2Ke/fuwdHRUeM2evXqha1bt6Jv376YMWMGOnbsCEdHR1y+fBlLlizBuHHj0L17d3z99dc4cOAAOnbsiIULF6Ju3bpISkrC9OnTkZubq/HXBEZGRuIvGnQNm1SUrmPd09MTERER6NatGwRBwIwZM0p8Z2e/fv0wbdo08bhMTk7Gd999p1Kuffv26NGjh8bzEoDSrwO8vb1x6tQppWmmpqbiMRcWFoZBgwZh2bJl8PPzE8vJZDKxTSjJMXblyhVs2bJF7IT09vaGRCLBunXr4OLigqtXr6Jp06YlqpuKql27dggNDUW3bt1ga2uLGTNmlOi4Ki1Vq1aFqakpli9fjpEjR+Lvv//G3LlzdS4nl8sxePBgTJkyBXZ2dnBycsKsWbMgkUjEzmQvLy/0798fgwYNwqJFi9CoUSOkp6fj4MGDqFevHrp06VLWu6cSs672WV/vvfceRo0ahVGjRqFt27ZKCdoxY8Zg7dq16Nu3L6ZMmQIHBwdcv34dmzdvxtq1a1Xe55o1ayIoKAgjRozA6tWrIZfL8cUXX6By5cpKSVBdbcnMmTPxzjvvwM3NDR988AEkEgkuXbqEy5cv46uvvtK4L23btsXcuXORmpqKTz/9VJzeunVrzJ8/H48fP0bbtm3F6W3atMG9e/ewcOFC9OzZE5GRkdizZ4/WobJKWifaWFhYYNSoUeKxV7VqVSxcuBBPnz7FsGHDNC737NkzTJkyBT179kS1atVw69YtREdH4/3339d72/qYMmUKZs2ahRo1aqBhw4YICQlBTEwMNm7cCABwcnKCTCZDZGQkqlSpAqlUqnINBRR8Nvzyyy/Yu3cvqlWrhg0bNiA6Olr8lZ46Y8eOxfLly9GnTx9MnToV1tbWOHXqFJo1a4ZatWrpjK20TJgwAYMHD4avry/eeustbNy4EVeuXFEavi04OBjjx4+HlZUVOnfujOzsbJw9exYPHjzA5MmT9drO1KlTUa9ePYwePRojR46EqakpDh06hA8++AAODg6ldr5ro+6a7mXbGm3ty4scF/peqzHGGGOaSAwdAGOMsVfPunXr0KFDB7VfeN9//33ExMTg/Pnzapc1NzfH559/jn79+sHf3x8ymQybN2/We9sSiQSbN2/GuXPnULduXUyaNAnffvvtC+9LSbRv3x41a9ZEq1at0KtXL3Tr1g3BwcHi/Llz52LmzJmYN28efHx8EBgYiB07dohf6ipXrozZs2fjiy++gLOzs5h0MMT+BAQEYPPmzQgJCUH9+vURGhqKLVu2qAwdoEl6erped7rNnDkTrq6u8PT0xMCBA/Ho0SMcOHAAn3/+OQDgl19+gYWFBdq3b6+ybNu2bSGXy7Fhwwat2xAEAZs2bcLixYuxdetWtG7dGvXr10dwcDCCgoLEXwk4ODjg1KlTaNu2LT7++GNUr14dvXr1QvXq1REdHa3UsVGclZVViceR13WsL1myBLa2tggICEC3bt0QGBiIxo0bl2gblpaW2LFjB2JjY9GoUSNMmzZN7ZAgiYmJSE9P17ouV1dXrX9Fh0FZvXo18vLyMGbMGKUyEyZMEMvoe4wRET766CMsWbJEHLpBJpMhNDQUc+bMwbBhw/DDDz/o/EXQq2Lq1Klo1aoV3nnnHXTp0gXdu3dXO0RWSSUnJ0MQBKXnUmjj6OiI0NBQ/P7776hduzbmz5+vNumkzuLFi+Hv74933nkHHTp0QIsWLeDj46M0LE5ISAgGDRqETz75BLVq1cK7776L06dPi79SKG+62md9WVlZoVu3brh48aLKHdOVKlXC8ePHkZ+fj8DAQNStWxcTJkyAtbU1JBL1X7tCQkLQpEkTvPPOO/D39wcRYffu3Up3jetqSwIDA7Fz507s27cPTZs2hZ+fHxYvXgx3d3et++Lv7y8On9KkSRNxetOmTZGfnw+ZTKZ0vvr4+GDlypVYsWIFGjRogDNnziglHNR5kTrRZv78+Xj//fcxcOBANG7cGNevX8fevXu1Do9oZGSEjIwMDBo0CF5eXujVqxc6d+6sdLe6IAgIDQ0tcTxFjR8/Hp988gk++eQT1KtXD5GRkdi+fTtq1qwJoOBZFd9//z1Wr16NSpUqqfxCrtDIkSPx3nvvoXfv3mjevDkyMjKU7jJXx97eHgcPHkRWVhZat26NJk2aYO3ateJxpCu20tK7d2/MnDkTn3/+OZo0aYKbN29i1KhRSmWGDx+On376CaGhoahXrx5at26N0NDQEp2LXl5eiIqKwsWLF9GsWTP4+/tj27Zt4q9aSut810bdNd3Lbltb+/Iix4W+12qMMcaYJgKV5uC4jDHGmBahoaGYOHEiHj58aOhQGGPstXL48GH06NEDN27c0NqJWhaePHmCypUrY9GiRVrv/GYlx5+bZS85ORk1a9ZEbGxsqXekM8YYY4y9ynioIsYYY4wxxl5xkZGR+PLLL8slaXDhwgVcvXoVzZo1w6NHjzBnzhwA0HgHNWMVWWRkJD766CNOGjDGGGOMFcOJA8YYY4wxxl5x8+fPL9ftfffdd4iPj4epqSmaNGmCY8eOwcHBoVxjYKw0jBw50tAhMMYYY4xVSDxUEWOMMcYYY4wxxhhjjDHGRPxwZMYYY4wxxhhjjDHGGGOMiThxwBhjjDGDEQQBf/75p6HDMLjDhw9DEATxAaihoaGwsbF56fVy/apHRPjoo49gZ2cHQRAQExODNm3aYOLEieUWQ2m9xy+j+HFXESQnJ4vvSUmsWbMGbm5ukEgkWLp0aZnEVlLBwcFo2LChocMoMxXhGC5P5d1GGFLxffXw8FA6r8r7s6UitlWMMcbYm4ATB4wxxpgGQ4YMgSAIKmOH//nnnxAEwUBRvXnu3r2LcePGoXr16jAzM4Obmxu6deuGAwcOiGU8PDwgCAIEQYBMJoOHhwd69eqFgwcPGjDyF9e7d28kJCQYNIZatWrB1NQUt2/fNmgcZSEyMhKhoaHYuXMnUlNTUbduXURERGDu3LlimeIdZYBhOkoPHTqELl26wN7eHubm5qhduzY++eQT8X0p7FATBAESiQTW1tZo1KgRPvvsM6Smpmpdd0BAAFJTU2FtbV0eu6IXNzc38T3RV2ZmJsaOHYvPP/8ct2/fxkcffVSGEbJCFaGdKlT8M8Db2xvffvstKtKovEOGDEH37t1VphfGLQgC5HI5fH19ERERUf4BahEdHV1u55W6BE1FbKsYY4yxNwEnDhhjjDEtpFIpFixYgAcPHhg6lAorNze3zNadnJyMJk2a4ODBg1i4cCEuX76MyMhItG3bFmPGjFEqO2fOHKSmpiI+Ph6//PILbGxs0KFDB3z99ddlFl9ZkclkcHJyMtj2//rrLzx//hwffPABQkNDDRZHWUlMTISrqysCAgLg4uICY2Nj2NnZQS6XGzo0JatXr0aHDh3g4uKC8PBwxMbGYtWqVXj06BEWLVqkVDY+Ph537txBdHQ0Pv/8c+zfvx9169bF5cuXNa7f1NQULi4uFSoRamRkJL4n+kpJSUFubi66du0KV1dXmJubv9C21bVlZdm+veoM3U4VV/gZEBcXh08//RRffvkl1qxZY+iw9BISEoLU1FRER0ejQYMG+OCDD3Dy5ElDhyVydHR84fOq0MucSxWxrWKMMcbeBJw4YIwxxrQo7LSbN2+e1nLh4eGoU6cOzMzM4OHhodKpV1zhEBbr169H1apVYWlpiVGjRiE/Px8LFy6Ei4sLnJycVDq9Hz16hI8++ghOTk6wsrJCu3btcPHiRXF+YmIigoKC4OzsDEtLSzRt2hT79+9XWsfKlStRs2ZNSKVSODs7o2fPnuI8dXdZN2zYEMHBweJrQRCwatUqBAUFwcLCAl999RUAYMeOHWjSpAmkUimqV6+O2bNnIy8vT1zu2rVraNWqFaRSKWrXro19+/ZprSMAGD16NARBwJkzZ9CzZ094eXmhTp06mDx5Mk6dOqVUVi6Xw8XFBVWrVkWrVq2wZs0azJgxAzNnzkR8fLzGbZRGffz444/o3LkzZDIZqlWrht9//12cXzj0yubNmxEQEACpVIo6derg8OHDGmNSd2d7WdSvJuvWrUO/fv0wcOBArF+/XuWuXQ8PD8ydOxf9+vWDpaUlKlWqhOXLlyuVSUlJQVBQECwtLWFlZYVevXrhf//7n1KZr776Ck5OTpDL5Rg+fDi++OILlaFdQkJC4OPjA6lUCm9vb6xcuVJp/u3bt9G7d2/Y2trC3t4eQUFBSE5O1rhvQ4YMwbhx45CSkgJBEODh4QFA+S7XNm3a4ObNm5g0aZJ4J/Dhw4fx4Ycf4tGjR+K0wuMgJycHn332GSpXrgwLCws0b95c5f0NDQ1F1apVYW5ujh49eiAjI0PzGwDg1q1bGD9+PMaPH4/169ejTZs28PDwQKtWrfDTTz9h5syZSuWdnJzg4uICLy8v9OnTB8ePH4ejoyNGjRqlcRvFh/+4efMmunXrBltbW1hYWKBOnTrYvXu3xuV//fVX+Pr6iudev379kJaWprL+AwcOwNfXF+bm5ggICNB6PhYfqkjXOkJDQ1GvXj0AQPXq1SEIgvj+6zpn1LVlRdvmwl85EZHOthcA5s+fD2dnZ8jlcgwbNgzPnz/XuJ+aeHh44JtvvsHQoUMhl8tRtWpVlc7vy5cvo127dpDJZLC3t8dHH32ErKwscX7hne3fffcdXF1dYW9vjzFjxih13KobasbGxkZMFBa+DxEREWjbti3Mzc3RoEEDpc5sde1U8Toofk6ru5u8e/fuGDJkiPhan/NJncLj0MPDA8OHD0f9+vURFRUlzn/w4AEGDRoEW1tbmJubo3Pnzrh27ZrSOo4fP47WrVvD3Nwctra2CAwM1HjjQGRkJKytrfHLL78A0N4WBQcH4+eff8a2bduU2pRCNjY2cHFxgbe3N1atWgWpVIrt27fj6NGjMDExwd27d5W2/cknn6BVq1Ya6+Lhw4f46KOP4OzsDKlUirp162Lnzp0AgIyMDPTt2xdVqlSBubk56tWrh7CwMK11q+6zMDU1Vefn3m+//YY2bdpAKpXi119/1bntIUOG4MiRI1i2bJlYT8nJyWqHKtJ13aXPucQYY4wxHYgxxhhjag0ePJiCgoIoIiKCpFIp/fPPP0REtHXrVir6EXr27FmSSCQ0Z84cio+Pp5CQEJLJZBQSEqJx3bNmzSJLS0vq2bMnXblyhbZv306mpqYUGBhI48aNo6tXr9L69esJAJ08eZKIiBQKBbVo0YK6detG0dHRlJCQQJ988gnZ29tTRkYGERHFxMTQqlWr6NKlS5SQkEDTpk0jqVRKN2/eJCKi6OhoMjIyok2bNlFycjKdP3+eli1bJsbl7u5OS5YsUYq1QYMGNGvWLPE1AHJycqJ169ZRYmIiJScnU2RkJFlZWVFoaCglJiZSVFQUeXh4UHBwMBER5efnU926dalNmzZ04cIFOnLkCDVq1IgA0NatW9XWUUZGBgmCQN98843O90pd3EXXsWDBArXLlVZ92Nvb09q1ayk+Pp6mT59ORkZGFBsbS0RESUlJBICqVKlCf/zxB8XGxtLw4cNJLpdTeno6EREdOnSIANCDBw+IiCgkJISsra3FbZRF/WqSmZlJFhYW9Pfff1NeXh45OzvTwYMHlcq4u7uTXC6nefPmUXx8PH3//fdkZGREUVFRRFRwrDZq1IjeeustOnv2LJ06dYoaN25MrVu3Ftfx66+/klQqpfXr11N8fDzNnj2brKysqEGDBmKZNWvWkKurK4WHh9ONGzcoPDyc7OzsKDQ0lIiInjx5QjVr1qShQ4fSpUuXKDY2lvr160e1atWi7Oxstfv38OFDmjNnDlWpUoVSU1MpLS2NiIhat25NEyZMIKKC46ZKlSo0Z84cSk1NpdTUVMrOzqalS5eSlZWVOO3x48dERNSvXz8KCAigo0eP0vXr1+nbb78lMzMzSkhIICKiU6dOkSAIYn0tW7aMbGxslN7j4hYvXkwA6M6dO1rfr+LHTlFLliwhAPS///1Pr2W7du1KHTt2pEuXLlFiYiLt2LGDjhw5onHb69ato927d1NiYiKdPHmS/Pz8qHPnzirrb968OR0+fJiuXLlCLVu2pICAAI3rLDxfLly4oNc6nj59Svv37ycAdObMGUpNTaW8vDyd5wyR+rZs1qxZZGFhQYGBgXT+/Hm6ePGiXm3vli1byNTUlNauXUtXr16ladOmkVwuVzqe9eHu7k52dna0YsUKunbtGs2bN48kEgnFxcURUcExX6lSJXrvvffo8uXLdODAAapWrRoNHjxYXMfgwYPJysqKRo4cSXFxcbRjxw4yNzenNWvWKO178bbB2tpa/NwqfB+8vb1p586dFB8fTz179iR3d3fKzc0lItV2Sp86KHqeFQoKClKKX9f5pKneCttqhUJBhw4dIplMRr179xbLvPvuu+Tj40NHjx6lmJgYCgwMJE9PT8rJySEiogsXLpCZmRmNGjWKYmJi6O+//6bly5fTvXv3VGIPCwsjuVxOf/75p/i+aGuLHj9+TL169aK3335bqU3R9F5YWVnRJ598QkREXl5etHDhQnFebm4uOTk50fr169XWRX5+Pvn5+VGdOnUoKipKPJd3795NRES3bt2ib7/9li5cuECJiYli+33q1CmN71Pxz0J9P/c8PDzE9vv27ds6t/3w4UPy9/enESNGiPWUl5en0lbpc92l61xijDHGmG6cOGCMMcY0KEwcEBH5+fnR0KFDiUg1cdCvXz/q2LGj0rJTpkyh2rVra1z3rFmzyNzcnDIzM8VpgYGB5OHhQfn5+eK0WrVq0bx584iI6MCBA2RlZUXPnz9XWleNGjVo9erVGrdVu3ZtWr58ORERhYeHk5WVldJ2i9K3o3zixIlKZVq2bKnSwb9hwwZydXUlIqK9e/eSkZGRmHwhItqzZ4/Wju3Tp08TAIqIiNC4b9riLuTs7EyjRo1SO6+06mPkyJFKZZo3by5us7ADZf78+eL83NxcqlKlipjQ0JU4KIv61WTNmjXUsGFD8fWECROof//+SmXc3d3p7bffVprWu3dvsdM4KiqKjIyMKCUlRZx/5coVsXOXqKCOxowZo7SOFi1aKHUyurm50aZNm5TKzJ07l/z9/YmooOO6Vq1apFAoxPnZ2dkkk8lo7969GvdxyZIl5O7urjRNV0cZker7QkR0/fp1EgSBbt++rTS9ffv2NHXqVCIi6tu3r9r60pY4GDVqFFlZWWmcX0hb4qDwGDh9+rRey9arV0+pY72kzpw5QwDEhErh+vfv3y+W2bVrFwGgZ8+eqV2HpsSBtnVcuHCBAFBSUpJYRtc5Q6S+LZs1axaZmJiICSUi/dpef39/te3AiyQOBgwYIL5WKBTk5OREP/74IxEVnJ+2traUlZUlltm1axdJJBK6e/cuERV8drm7u1NeXp5Y5oMPPlDqRNc3cfDTTz+J8wvP4cKO1+Lngz51oCtxoM/5pI67uzuZmpqShYUFmZiYEACSSqV0/PhxIiJKSEggAOJrIqL09HSSyWT022+/EVHBedqiRQuN2yiMfcWKFWRtba2UUNWnLSp6TVFU0ffi+fPnNHfuXAIgdvQvWLCAfHx8xPJ//vknWVpaKh0DRe3du5ckEgnFx8dr3JfiunTpIiYqiu5rIXWJA30+95YuXfrS2yZSbav0ue7SdS4xxhhjTDceqogxxhjTw4IFC/Dzzz8jNjZWZV5cXBxatGihNK1Fixa4du0a8vPzNa7Tw8NDaUx1Z2dn1K5dGxKJRGla4dAf586dQ1ZWFuzt7WFpaSn+JSUlITExEQDw5MkTfPbZZ6hduzZsbGxgaWmJq1evIiUlBQDQsWNHuLu7o3r16hg4cCA2btyIp0+flrg+fH19lV6fO3cOc+bMUYprxIgRSE1NxdOnTxEXF4eqVauiSpUq4jL+/v5at0H/Do/zsmMaE5HGdZRWfRTfF39/f8TFxWksY2xsDF9fX5UympRF/Wqybt06DBgwQHw9YMAAREREKA0RoW79Rfc5Li4Obm5ucHNzE+cXHpOFZeLj49GsWTOldRR9fe/ePfzzzz8YNmyY0n5/9dVX4vF+7tw5XL9+HXK5XJxvZ2eH58+fIzExEceOHVNaduPGjS9UJ9qcP38eRAQvLy+lbR05ckSMMy4uTm19aaPtuNVXSc+h8ePH46uvvkKLFi0wa9YsXLp0SWv5CxcuICgoCO7u7pDL5WjTpg0AiO1Nofr164v/d3V1BQClIY30UdJ16DpnChVvywDA3d0djo6OSuvS1fa+yHusz74KggAXFxdxX+Pi4tCgQQNYWFiIZVq0aAGFQqE0BFSdOnVgZGQkvnZ1dS1xnRePRVe9l0Yd6HM+aTJlyhTExMTgyJEjaNu2LaZNm4aAgAAxNmNjYzRv3lwsb29vj1q1aoltUkxMDNq3b691G+Hh4Zg4cSKioqLQtm1bcbqutkiXvn37wtLSEubm5li8eDG+++47dO7cGUDB8D3Xr18Xh+dbv349evXqpXQMFBUTE4MqVarAy8tL7fz8/Hx8/fXXqF+/vnhMR0VFqZy3uujzuVf8/Cqtbet73aXtXGKMMcaYbvo/dYwxxhh7g7Vq1QqBgYH48ssvlcZiBtR38BV22GljYmKi9FoQBLXTFAoFAEChUMDV1VXtWM+F40xPmTIFe/fuxXfffQdPT0/IZDL07NkTOTk5AArGgD5//jwOHz6MqKgozJw5E8HBwYiOjoaNjQ0kEolK7OoeaFi8w0KhUGD27Nl47733VMpKpVK19aGrM7NmzZoQBAFxcXHo3r271rKaZGRk4N69e6hWrZra+aVVH+ro01mrb4duWdSvOrGxsTh9+rT4gN1C+fn5CAsL0zpeftFtaur0Lj5d23lTeNyvXbtWqbMPgNghqlAo0KRJE7UJAUdHR5iamopj5QMFibjSplAoYGRkhHPnzil11AKApaUlAP3ag+K8vLzw6NEjpKamih22JVXYiVf4HAddhg8fjsDAQOzatQtRUVGYN28eFi1ahHHjxqmUffLkCTp16oROnTrh119/haOjI1JSUhAYGCi2N4WKtmuF73nh+6uvkq5D1zlTSF3nq7r2TVfbW5q0fQ5oSygVna5tHYWv9WnbSuO9K0pXm6rP+aSJg4MDPD094enpifDwcHh6esLPzw8dOnTQeA4WrU+ZTKYz/oYNG+L8+fMICQlB06ZNlepEW1uky5IlS9ChQwdYWVmpPHDayckJ3bp1Q0hICKpXr47du3drfeaDrv1YtGgRlixZgqVLl6JevXqwsLDAxIkTVc7bF1H82Cx+LpXWtvW97tJ1HjDGGGNMO/7FAWOMMaan+fPnY8eOHThx4oTS9Nq1a+Ovv/5SmnbixAl4eXmpdHy8jMaNG+Pu3bswNjYWO0cK/xwcHAAAx44dw5AhQ9CjRw/Uq1cPLi4uKg+KNTY2RocOHbBw4UJcunQJycnJOHjwIICCDo7U1FSxbGZmJpKSkvSKLT4+XiUuT09PSCQS1K5dGykpKbhz5464TNGHbKpjZ2eHwMBArFixAk+ePFGZX/wOeHWWLVsGiUSiNfFQGvVR/EHNp06dgre3t8YyeXl5OHfunEoZTcqiftVZt24dWrVqhYsXLyImJkb8++yzz7Bu3TqN+1P4unB/CuP5559/xPmxsbF49OgRfHx8AAC1atXCmTNnlNZx9uxZ8f/Ozs6oXLkybty4obLPhYmgxo0b49q1a3ByclIpY21tDZlMpjSt6C98dDE1NVX5xZC6aY0aNUJ+fj7S0tJUYnBxcRHrQ119adOzZ0+Ymppi4cKFaufrOv6fPXuGNWvWoFWrVnp1XBZyc3PDyJEjERERgU8++QRr165VW+7q1atIT0/H/Pnz0bJlS3h7e1eoO3l1nTMlXZeuttfHx6fE7/GLqF27NmJiYpTaxOPHj0MikWi8w1yd4m3btWvXXujXVkXpUwfFt5ufn4+///5bfK3P+aQPW1tbjBs3Dp9++imICLVr10ZeXh5Onz4tlsnIyEBCQoLYJtWvXx8HDhzQut4aNWrg0KFD2LZtm1JCTVdbBKhvPwq5uLjA09NTJWlQaPjw4di8eTNWr16NGjVqqNxtX1T9+vVx69YtJCQkqJ1/7NgxBAUFYcCAAWjQoAGqV6+u8pBofejzufci29ZWT4XK67qLMcYYe9Nx4oAxxhjTU7169dC/f38sX75cafonn3yCAwcOYO7cuUhISMDPP/+MH374AZ9++mmpbr9Dhw7w9/dH9+7dsXfvXiQnJ+PEiROYPn262OHq6emJiIgIxMTE4OLFi+jXr5/S3XU7d+7E999/j5iYGNy8eRO//PILFAoFatWqBQBo164dNmzYgGPHjuHvv//G4MGD9foSPnPmTPzyyy8IDg7GlStXEBcXhy1btmD69Oli7LVq1cKgQYNw8eJFHDt2DNOmTdO53pUrVyI/Px/NmjVDeHg4rl27hri4OHz//fcqwyQ8fvwYd+/exT///IOjR4/io48+wldffYWvv/4anp6eatdfWvXx+++/Y/369UhISMCsWbNw5swZjB07VqnMihUrsHXrVly9ehVjxozBgwcPMHToUJ11UJr12759e/zwww9qt5Gbm4sNGzagb9++qFu3rtLf8OHDce7cOVy8eFEsf/z4cSxcuBAJCQlYsWIFfv/9d0yYMEGMp379+ujfvz/Onz+PM2fOYNCgQWjdurU4dMW4ceOwbt06/Pzzz7h27Rq++uorXLp0Seku0uDgYMybNw/Lli1DQkICLl++jJCQECxevBgA0L9/fzg4OCAoKAjHjh1DUlISjhw5ggkTJuDWrVt61a0mHh4eOHr0KG7fvo309HRxWlZWFg4cOID09HQ8ffoUXl5e6N+/PwYNGoSIiAgkJSUhOjoaCxYswO7duwEUDAEUGRkp1tcPP/yAyMhIrdt3c3PDkiVLsGzZMgwbNgxHjhzBzZs3cfz4cXz88ceYO3euUvm0tDTcvXsX165dw+bNm9GiRQukp6fjxx9/1HufJ06ciL179yIpKQnnz5/HwYMHxU7V4qpWrQpTU1MsX74cN27cwPbt21ViMiRd50xJ6NP2TpgwAevXr1dqB65cuVLau4X+/ftDKpVi8ODB+Pvvv3Ho0CGMGzcOAwcOLNEvatq1a4cffvgB58+fx9mzZzFy5EiVu7NLSp86aNeuHXbt2oVdu3bh6tWrGD16tFISTJ/zSV9jxoxBfHw8wsPDUbNmTQQFBWHEiBH466+/cPHiRQwYMACVK1dGUFAQAGDq1KmIjo7G6NGjcenSJVy9ehU//vijeP4XjfHQoUPisEWAfm2Rh4cHLl26hPj4eKSnp+v96zUACAwMhLW1Nb766it8+OGHWsu2bt0arVq1wvvvv499+/YhKSkJe/bsEdscT09P7Nu3DydOnEBcXBw+/vhj3L17V+9YCunzuVecPtv28PDA6dOnkZycjPT0dLW/ECit6y5tn4mMMcYYA/jhyIwxxpgG6h5kmJycTGZmZlT8I/SPP/6g2rVrk4mJCVWtWpW+/fZbreueNWuWykMz1W2v+EMCMzMzady4cVSpUiUyMTEhNzc36t+/v/gQ2qSkJGrbti3JZDJyc3OjH374QWkdx44do9atW5OtrS3JZDKqX78+bdmyRVz/o0ePqFevXmRlZUVubm4UGhqq9mHA6h64GxkZSQEBASSTycjKyoqaNWtGa9asEefHx8fTW2+9RaampuTl5UWRkZF6Pbz3zp07NGbMGPHhl5UrV6Z3332XDh06JJZxd3cnAASATE1NqWrVqtSrVy+lh1eqU1r1sWLFCurYsSOZmZmRu7s7hYWFifMLHxK5adMmat68OZmampKPjw8dOHBALKPr4cilVb/u7u5KsRf1xx9/KD1gtbh69erRuHHjxPXMnj2bevXqRebm5uTs7KzyEMybN2/Su+++SxYWFiSXy+mDDz5QWfecOXPIwcGBLC0taejQoTR+/Hjy8/NTKrNx40Zq2LAhmZqakq2tLbVq1Urpgdmpqak0aNAgcnBwIDMzM6pevTqNGDGCHj16pHY/iPR7OPLJkyepfv36Kuf7yJEjyd7engCIdZmTk0MzZ84kDw8PMjExIRcXF+rRowddunRJXG7dunVUpUoVkslk1K1bN/ruu++0Phy50L59+ygwMJBsbW1JKpWSt7c3ffrpp3Tnzh0i+u/YAUCCIJBcLqcGDRrQlClTKDU1Veu6ix93Y8eOpRo1apCZmRk5OjrSwIEDKT09XePymzZtIg8PDzIzMyN/f3/avn272gcbF31ws7oHGRel6eHI2tahaZ26zhl17Y+6tplId9tLRPT111+Lx/PgwYPps88+U1pX4b5o2nci/R7IfunSJWrbti1JpVKys7OjESNGiA+kJlL/WTJhwgRq3bq1+Pr27dvUqVMnsrCwoJo1a9Lu3bvVPhy58H0gInrw4AEBENtede2UrjrIycmhUaNGkZ2dHTk5OdG8efOUHo5cWEbX+aRPvRERjRgxgurUqUP5+fl0//59GjhwIFlbW5NMJqPAwEBKSEhQKn/48GEKCAggMzMzsrGxocDAQPHYK95GxMbGkpOTE02ePJmIdLdFaWlp1LFjR7K0tFSqR30+B4mIZsyYQUZGRuK5r01GRgZ9+OGHZG9vT1KplOrWrUs7d+4U5wUFBZGlpSU5OTnR9OnTadCgQUrHjD4PR9bnc6/o8aPvtuPj48nPz49kMpl4vqhrB3Rdd+lzLmn7TGSMMcYYkUD0AoOuMsYYY4wxAAVjJm/dulXjcEjJycmoVq0aLly4gIYNG5ZrbGXFw8MDEydOFO+2LS0dO3aEi4sLNmzYUKrrZawiCA0Nxddff43Y2NiXvrv/VREcHIw///xT6Vkj7MWMGDEC//vf/7B9+3ZDh8IYY4yxNwQ/HJkxxhhjjJW7p0+fYtWqVQgMDISRkRHCwsKwf/9+7Nu3z9ChMVYmIiMj8c0337wxSQNWOh49eoTo6Ghs3LgR27ZtM3Q4jDHGGHuDcOKAMcYYY4yVO0EQsHv3bnz11VfIzs5GrVq1EB4ejg4dOhg6NMbKxObNmw0dAnsFBQUF4cyZM/j444/RsWNHQ4fDGGOMsTcID1XEGGOMMcYYY4wxxhhjjDGRxNABMMYYY4wxxhhjjDHGGGOs4uDEAWOMMfaKSU5OhiAIr+XDJocMGaLxIcOvo8OHD0MQBDx8+NDQobw0Dw8PLF26tNTX+yLHhCAI+PPPP0s9lhf1Op+zFYk+x0rx4/Tu3bvo2LEjLCwsYGNj81LbDw0Nfel1vOqK168hz8Xy2HZZtXuvgtLed33O3zZt2mDixImltk3GGGOMaceJA8YYYwZx9+5djBs3DtWrV4eZmRnc3NzQrVs3HDhwwNChlZsX7TR2c3NDamoq6tatWzaBveK4Y4GVJ0EQtP4NGTLE0CGK5s2bh6ZNm0Iul8PJyQndu3dHfHy8UpnvvvsOzs7OcHZ2xpIlS5TmnT59Gk2aNEF+fr7ObRER1qxZg+bNm8PS0hI2Njbw9fXF0qVL8fTpUwBAcHCwWE9GRkZwc3PD8OHDce/evdLb6WKio6Px0Ucfia+XLFmC1NRUxMTEICEh4bVK5r2o0mxDU1NT0blz51JZlybBwcFo2LBhmW5DXxUtaVmWip9LL2vZsmUIDQ0ttfUZ2qFDh9ClSxfY29vD3NwctWvXxieffILbt28D+O8aUBAESCQSWFtbo1GjRvjss8+Qmppq4OgZY4yxApw4YIwxVu6Sk5PRpEkTHDx4EAsXLsTly5cRGRmJtm3bYsyYMS+8XiJCXl5eKUZaMeMwMjKCi4sLjI2Ny2wb7NWWm5tr6BDeGKmpqeLf0qVLYWVlpTRt2bJl5RZLcHCw1kTFkSNHMGbMGJw6dQr79u1DXl4eOnXqhCdPngAALl++jJkzZyIsLAybNm3Cl19+ib///htAwTE1cuRIrFq1CkZGRjpjGThwICZOnIigoCAcOnQIMTExmDFjBrZt24aoqCixXJ06dZCamoqUlBT8+OOP2LFjBwYNGvRyFaGFo6MjzM3NxdeJiYlo0qQJatasCScnpzLb7pvKxcUFZmZmGudzW6VeedXLy2yn+Ln0sqytrV+bX+ysXr0aHTp0gIuLC8LDwxEbG4tVq1bh0aNHWLRokVLZ+Ph43LlzB9HR0fj888+xf/9+1K1bF5cvXzZQ9Iwxxth/OHHAGGOs3I0ePRqCIODMmTPo2bMnvLy8UKdOHUyePBmnTp0CoH5oj4cPH0IQBBw+fBjAf3dr7d27F76+vjAzM8O6desgCAKuXr2qtM3FixfDw8MDRAQAiI2NRZcuXWBpaQlnZ2cMHDgQ6enpYvk//vgD9erVg0wmg729PTp06CB2rhWnLo5jx46BiLBw4UJUr14dMpkMDRo0wB9//CHuX9u2bQEAtra2SncmR0ZG4q233oKNjQ3s7e3xzjvvIDExUdxe8bop3P6BAwfg6+sLc3NzBAQEqNxJXFROTg7Gjh0LV1dXSKVSeHh4YN68eUr1Va9ePVhYWMDNzQ2jR49GVlYWAODRo0eQyWSIjIxUWmdERAQsLCzEcrdv30bv3r1ha2sLe3t7BAUFITk5WSyfn5+PyZMni/v52Wefie+PJhkZGejbty+qVKkCc3Nz1KtXD2FhYeL8IUOG4MiRI1i2bJl4J19ycrLSe9SoUSPIZDK0a9cOaWlp2LNnD3x8fGBlZYW+ffuKd0MDQHZ2NsaPHw8nJydIpVK89dZbiI6OFuc/ePAA/fv3h6OjI2QyGWrWrImQkBCl92nz5s0ICAiAVCpFnTp1xOO3qHPnzml973bs2IEmTZpAKpWievXqmD17tlJyShAErFq1CkFBQbCwsMBXX32l13LFHT58GM2aNROHbWnRogVu3rwpzt++fTt8fX0hlUrh4OCA9957T2n5p0+fYujQoZDL5ahatSrWrFmjNL80jgl1w2M0bNgQwcHBGvdL13Z17bc2Li4u4p+1tTUEQVCZVujGjRto27YtzM3N0aBBA5w8eVJpXSdOnECrVq0gk8ng5uaG8ePHa2x3XkRkZCSGDBmCOnXqoEGDBggJCUFKSgrOnTsHAIiLi0P9+vXRrl07tG/fHvXr10dcXBwA4Ntvv0WrVq3QtGlTndv57bffsHHjRoSFheHLL79E06ZN4eHhgaCgIBw8eFBs+wDA2NgYLi4uqFy5Mt555x2MHz8eUVFRePbsmdp169M2f/fdd3B1dYW9vT3GjBmj1Dla9Pjx8PBAeHg4fvnlF7EN1tQuqxMaGoqqVavC3NwcPXr0QEZGhkqZH3/8ETVq1ICpqSlq1aqFDRs2KM1/9OgRPvroIzg5OcHKygrt2rXDxYsXxfkXL15E27ZtIZfLYWVlhSZNmuDs2bMaYxIEAT/99BN69OgBc3Nz1KxZE9u3b1cqc+TIETRr1gxmZmZwdXXFF198IbYLmtpQddLS0tCtWzfIZDJUq1YNGzduVBtP4R34hW3ib7/9hjZt2kAqleLXX38FAISEhMDHxwdSqRTe3t5YuXKl0npu3bqFPn36wM7ODhYWFvD19cXp06cRGhqK2bNn4+LFi2K86u5ab9euHcaOHas0LSMjA2ZmZjh48KDa/UtMTERQUBCcnZ1haWmJpk2bYv/+/WrLAgXHEwD06NEDgiCIr4EXb8OLKnqHuqZfNb3odnQdp5r2t/iwVLqOvStXrqBr166wsrKCXC5Hy5Ytxeub4kMVPXnyBIMGDYKlpSVcXV1VOtyBgmuZzz77DJUrV4aFhQWaN2+u9BlbOHzY3r174ePjA0tLS7z99tsqd/SvX78ederUEc+JoseKrnO0uFu3bmH8+PEYP3481q9fjzZt2sDDwwOtWrXCTz/9hJkzZyqVd3JygouLC7y8vNCnTx8cP34cjo6OGDVqlMZtMMYYY+WGGGOMsXKUkZFBgiDQN998o7VcUlISAaALFy6I0x48eEAA6NChQ0REdOjQIQJA9evXp6ioKLp+/Tqlp6dTkyZNaPr06Urra9KkCU2dOpWIiO7cuUMODg40depUiouLo/Pnz1PHjh2pbdu24nxjY2NavHgxJSUl0aVLl2jFihX0+PFjtbFqiuPLL78kb29vioyMpMTERAoJCSEzMzM6fPgw5eXlUXh4OAGg+Ph4Sk1NpYcPHxIR0R9//EHh4eGUkJBAFy5coG7dulG9evUoPz9fbd0Ubr958+Z0+PBhunLlCrVs2ZICAgI01u+3335Lbm5udPToUUpOTqZjx47Rpk2bxPlLliyhgwcP0o0bN+jAgQNUq1YtGjVqlDj//fffpwEDBiit8/3336e+ffsSEdGTJ0+oZs2aNHToULp06RLFxsZSv379qFatWpSdnU1ERAsWLCBra2v6448/KDY2loYNG0ZyuZyCgoI0xn3r1i369ttv6cKFC5SYmEjff/89GRkZ0alTp4iI6OHDh+Tv708jRoyg1NRUSk1Npby8PLGO/Pz86K+//qLz58+Tp6cntW7dmjp16kTnz5+no0ePkr29Pc2fP1/c3vjx46lSpUq0e/duunLlCg0ePJhsbW0pIyODiIjGjBlDDRs2pOjoaEpKSqJ9+/bR9u3bld6nKlWqiPs4fPhwksvllJ6ervd7FxkZSVZWVhQaGkqJiYkUFRVFHh4eFBwcLJYBQE5OTrRu3TpKTEyk5ORkvZYrKjc3l6ytrenTTz+l69evU2xsLIWGhtLNmzeJiGjnzp1kZGREM2fOpNjYWIqJiaGvv/5aXN7d3Z3s7OxoxYoVdO3aNZo3bx5JJBKKi4sr1WPC3d2dlixZohR7gwYNaNasWUr1sXXrVr22q2u/SyIkJISsra1VphceC97e3rRz506Kj4+nnj17kru7O+Xm5hIR0aVLl8jS0pKWLFlCCQkJdPz4cWrUqBENGTJE7+3PmjWLBg8erHf5a9euEQC6fPkyERHFxsaSra0t3bx5k5KTk8nGxoZiY2Pp2rVrVLNmTcrMzNRrve+++y7VqlVLr3gbNGigNG3RokUEQO22dLXNgwcPJisrKxo5ciTFxcXRjh07yNzcnNasWSOuo+jxk5aWRm+//Tb16tVLbIM1tcvFnTp1igRBoHnz5lF8fDwtW7aMbGxslN7/iIgIMjExoRUrVlB8fDwtWrSIjIyM6ODBg0REpFAoqEWLFtStWzeKjo6mhIQE+uSTT8je3l5sY+rUqUMDBgyguLg4SkhIoN9++41iYmI01mlhm7Np0ya6du0ajR8/niwtLcX13bp1i8zNzWn06NEUFxdHW7duJQcHB/H80dSGqtO5c2eqW7cunThxgs6ePUsBAQEkk8mUzs+i52LheeDh4UHh4eF048YNun37Nq1Zs4ZcXV3FaeHh4WRnZ0ehoaFERPT48WOqXr06tWzZko4dO0bXrl2jLVu20IkTJ+jp06f0ySefUJ06dcR4nz59qrLtjRs3kq2tLT1//lyMbdmyZeTh4UEKhULt/sXExNCqVavo0qVLlJCQQNOmTSOpVKrUNhQ/ngBQSEgIpaamUlpaGhG9eBteXHZ2triPqampdPDgQZJKpbRu3bqX2o6u41ST4m2xPseenZ0dvffeexQdHU3x8fG0fv16unr1KhEVnL9F2/pRo0ZRlSpVKCoqii5dukTvvPMOWVpa0oQJE8Qy/fr1o4CAADp69Chdv36dvv32WzIzM6OEhAQiKmiTTUxMqEOHDhQdHU3nzp0jHx8f6tevn7iOlStXklQqpaVLl1J8fDydOXNG3C99ztHiFi9eTADozp07Wuuv8PP/wYMHKvOWLFlCAOh///uf1nUwxhhjZY0TB4wxxsrV6dOnCQBFRERoLVeSxMGff/6ptOzixYupevXq4uv4+HgCQFeuXCEiohkzZlCnTp2Ulvnnn3/EzqJz584RALVf3NVRF0dWVhZJpVI6ceKEUtlhw4aJnevavjQWVdgZUdi5pylxsH//fnGZXbt2EQB69uyZ2nWOGzeO2rVrp7HDpLjffvuN7O3txdcRERFkaWlJT548ISKiR48ekVQqpV27dhER0bp166hWrVpK68/OziaZTEZ79+4lIiJXV1elTvrc3FyqUqWK1sSBOl26dKFPPvlEfN26dWuljgUi9XU0b948AkCJiYnitI8//pgCAwOJqOA9NDExoY0bN4rzc3JyqFKlSrRw4UIiIurWrRt9+OGHauMqfJ/U7eOCBQs0xlX8vWvZsqVKom3Dhg3k6uoqvgZAEydOVCqjz3JFZWRkEAA6fPiw2vn+/v7Uv39/tfOICjqRiiaTFAoFOTk50Y8//khEpXdMlDRxoGu7uva7JHQlDn766Sdx2pUrVwiAmFgZOHAgffTRR0rLHTt2jCQSicbz+OjRo2RhYSH+mZiYkLGxsdK0osmdohQKBXXr1o3eeustpek//vgjeXl5kZeXl/jetW/fnrZu3Uq///471alThxo2bEhHjhzRWA8+Pj707rvvapxfqHjiIC4ujjw9PalZs2Zqy+tqmwcPHkzu7u5KHd0ffPAB9e7dW3xd/PgJCgpSSrbo2y737duX3n77baVpvXv3Vnr/AwICaMSIEUplPvjgA+rSpQsRER04cICsrKyUOrOJiGrUqEGrV68mIiK5XC52oOsDgFLiPCsriwRBoD179hAR0ZdffqlyPqxYsYIsLS3F5LS6NrS4ws/VwqQtUcH7B0Bn4mDp0qVK63Jzc1NKXBMRzZ07l/z9/YmIaPXq1SSXyzV21KpLQBXf9vPnz8nOzo62bNkizm/YsKHGRKomtWvXpuXLl4uv1XWeF26z0Iu24dqkp6dTjRo1aPTo0S+9HV3HqSbq9l3bsTd16lSqVq0a5eTkqF1f0cTB48ePydTUlDZv3izOz8jIIJlMJh6b169fJ0EQ6Pbt20rrad++vXijSEhICAGg69evi/NXrFhBzs7O4utKlSrRtGnT1Makzzla3KhRo8jKykrtvKK0tTV79uwhAHT69Gmd62GMMcbKEg+OzBhjrFzRv8OOCIJQauv09fVVet2nTx9MmTIFp06dgp+fHzZu3IiGDRuidu3aAAqGhTl06BAsLS1V1pWYmIhOnTqhffv2qFevHgIDA9GpUyf07NkTtra2escRGxuL58+fo2PHjkplcnJy0KhRI63rSUxMxIwZM3Dq1Cmkp6dDoVAAAFJSUrQ+ELl+/fri/11dXQEUDCVRtWpVlbJDhgxBx44dUatWLbz99tt455130KlTJ3H+oUOH8M033yA2NhaZmZnIy8vD8+fP8eTJE1hYWKBr164wNjbG9u3b0adPH4SHh0Mul4vrOHfuHK5fvw65XK603efPnyMxMRGPHj1Camoq/P39xXnGxsbw9fXVOlxRfn4+5s+fjy1btuD27dvIzs5GdnY2LCwstFWp2jpydnaGubk5qlevrjTtzJkzAAreh9zcXLRo0UKcb2JigmbNmonDt4waNQrvv/8+zp8/j06dOqF79+4ICAhQ2qa6fSxcXl1cxd+7c+fOITo6Gl9//bVSPTx//hxPnz4Vx5gufh7ou1whOzs7DBkyBIGBgejYsSM6dOiAXr16ifHExMRgxIgR6itWzX4UDtmTlpYmxlMWx4QuurbbqVMnrftdmjS9z97e3mKcRYd7ISIoFAokJSXBx8dHZX2+vr5Kw7l9//33uH37NhYsWCBOs7OzUxvL2LFjcenSJfz1119K00eOHImRI0eKr0NDQyGXy+Hv749atWohOjpaHDYmKSlJ7fj1RKR3G3/58mVYWloiPz8f2dnZaNOmjcoQV4UaNGigs22uU6eO0jMYXF1dy2Ss8Li4OPTo0UNpmr+/v9IQbnFxcSoPj23RooX43Itz584hKysL9vb2SmWePXsmDt8yefJkDB8+HBs2bECHDh3wwQcfoEaNGlpjK3qcWVhYQC6Xi+dhXFwc/P39ld6fFi1aICsrC7du3VL7eaFp/wvPz0Le3t56jVFfdJl79+7hn3/+wbBhw5Tal7y8PHGYr5iYGDRq1EjjsawPMzMzDBgwAOvXr0evXr0QExODixcvan2Q8ZMnTzB79mzs3LkTd+7cQV5eHp49e4aUlJQSbftF23BNcnNz8f7776Nq1apKz1B50e3oOk43btyIjz/+WJy3Z88etGzZUm1s2o69mJgYtGzZEiYmJjr3MTExETk5OUqfB3Z2dqhVq5b4+vz58yAieHl5KS2bnZ2tdE6Zm5srnTOurq5iTGlpabhz5w7at2+vNg59ztHiStL+aVIW18qMMcbYi+DEAWOMsXJVs2ZNCIKAuLg4pbFsi5NICh7DU7TDUNND/Ip3Gru6uqJt27bYtGkT/Pz8EBYWpvSlV6FQoFu3bkqda0WXNTIywr59+3DixAlERUVh+fLlmDZtGk6fPo1q1appjLloHIWd/bt27ULlypWVyml7UCQAdOvWDW5ubli7di0qVaoEhUKBunXrIicnR+tyRb+MF37ZLIyjuMaNGyMpKQl79uzB/v370atXL3To0AF//PEHbt68iS5dumDkyJGYO3cu7Ozs8Ndff2HYsGHie2BqaoqePXti06ZN6NOnDzZt2oTevXuLD2xWKBRo0qSJ2jGvHR0dte6HNosWLcKSJUuwdOlS8RkMEydO1Fk3hYrXUfEODEEQxDrT9MW9aKdA586dcfPmTezatQv79+9H+/btMWbMGHz33Xda4yi+Tm3vnUKhwOzZs1WeJwAAUqlU/H/x80Df5YoKCQnB+PHjERkZiS1btmD69OnYt28f/Pz8IJPJtO5T8f0o3Jei+1Eax4REIlFJJGh7wKc+29W236VJ1/v88ccfY/z48SrLaerMlclk8PT0FF/b2dkhMzNTaZo648aNw/bt23H06FFUqVJFY7n09HTMmTMHR48exenTp+Hl5YWaNWuiZs2ayM3NRUJCAurVq6eynJeXl0pyTJNatWph+/btMDIyQqVKlbS2j/q0zdqOwdKkbzJLW/uhUCjg6uqq9rknhR3wwcHB6NevH3bt2oU9e/Zg1qxZ2Lx5s0rSoihd7Zq6mNTFqs3LdGyq+6xcu3YtmjdvrlSuMAGkT9ujj+HDh6Nhw4a4desW1q9fj/bt28Pd3V1j+SlTpmDv3r347rvv4OnpCZlMhp49e+r9eVPoRdtwTUaNGoWUlBRER0eLn7kvux1tx+m7776r9N4Uv6YpStuxV5L3UZ/zS6FQwMjICOfOnVN5YHvRG0PUxVS4fl0x6XOOFufl5SUmwl80AV3YfhZ9TgZjjDFmCPxwZMYYY+XKzs4OgYGBWLFihdqHfj58+BDAfx16RR9gV/TOWl369++PLVu24OTJk0hMTESfPn3EeY0bN8aVK1fg4eEBT09Ppb/CL9SCIKBFixaYPXs2Lly4AFNTU2zdulXv7deuXRtmZmZISUlR2YabmxuAgs53oOCOwEIZGRmIi4vD9OnT0b59e/j4+ODBgwd6b7ckrKys0Lt3b6xduxZbtmxBeHg47t+/j7NnzyIvLw+LFi2Cn58fvLy8cOfOHZXl+/fvj8jISFy5cgWHDh1C//79xXmNGzfGtWvX4OTkpLL/1tbWsLa2hqurq/gwbKDgDtPCh7RqcuzYMQQFBWHAgAFo0KABqlevjmvXrimVMTU1VarTF+Xp6QlTU1OlO7Jzc3Nx9uxZpbu/HR0dMWTIEPz6669YunSpyt3S6vbR29tb7zgaN26M+Ph4lXr09PQUE2yluVyjRo0wdepUnDhxAnXr1sWmTZsAFNxJeuDAAb3jVhdPaRwTjo6OSu1CZmYmkpKSXni7uva7vBS2S+rer8K24mUREcaOHYuIiAgcPHhQayIUACZOnIhJkyahSpUqyM/PV0rQ5OXlaTzP+vXrh4SEBGzbtk1tDI8ePRJfm5qawtPTE9WqVdOZVAVevm3WRV27rE7t2rWVjlUAKq99fHxUftFx4sQJsf1o3Lgx7t69C2NjY5X33MHBQVzGy8sLkyZNQlRUFN577z3xAewvonbt2jhx4oRSx+yJEycgl8vFDmF92lAfHx/k5eUpPag5Pj5e/AzXl7OzMypXrowbN26o1EHh8Vm/fn3ExMTg/v37atehb5tfr149+Pr6Yu3atdi0aROGDh2qtfyxY8cwZMgQ9OjRA/Xq1YOLi4vGB0UXMjExUYnlRdtidRYvXowtW7Zg+/btKnfBv+h2dB2ncrlcaV0vmsipX78+jh07pjXRW8jT0xMmJiZK59SDBw+QkJAgvm7UqBHy8/ORlpamsr8uLi56xSSXy+Hh4aHxs03fc7Sonj17wtTUFAsXLlQ7X9c58uzZM6xZswatWrV6qRstGGOMsdLAiQPGGGPlbuXKlcjPz0ezZs0QHh6Oa9euIS4uDt9//734s3SZTAY/Pz/Mnz8fsbGxOHr0KKZPn673Nt577z1kZmZi1KhRaNu2rdIdcmPGjMH9+/fRt29fnDlzBjdu3EBUVBSGDh2K/Px8nD59Gt988w3Onj2LlJQURERE4N69e2qHCtFELpfj008/xaRJk/Dzzz8jMTERFy5cwIoVK/Dzzz8DANzd3SEIAnbu3Il79+4hKysLtra2sLe3x5o1a3D9+nUcPHgQkydP1nu7+lqyZAk2b96Mq1evIiEhAb///jtcXFxgY2ODGjVqIC8vD8uXL8eNGzewYcMGrFq1SmUdrVu3hrOzM/r37w8PDw+lu7P79+8PBwcHBAUF4dixY0hKSsKRI0cwYcIE3Lp1CwAwYcIEzJ8/H1u3bsXVq1cxevRonV+oPT09xTuO4+Li8PHHH+Pu3btKZTw8PHD69GkkJycrDfVUUhYWFhg1ahSmTJmCyMhIxMbGYsSIEXj69CmGDRsGAJg5cya2bduG69ev48qVK9i5c6fKcbJixQpxH8eMGYMHDx7o7LAqaubMmfjll18QHByMK1euIC4uTrwrvjSXS0pKwtSpU3Hy5EncvHkTUVFRSEhIEPdn1qxZCAsLw6xZsxAXF4fLly9r7BhRp7SOiXbt2mHDhg04duwY/v77bwwePFjlbtOSbFfXfp85cwbe3t64ffu23vv6Ij7//HOcPHkSY8aMQUxMDK5du4bt27dj3LhxGpfJycnB3bt3xb+RI0di/vz5StOysrLE8mPGjMGvv/6KTZs2QS6Xi2WePXumsu59+/bh2rVrGDNmDACgWbNmuHr1Kvbs2YM1a9bAyMhIadiQonr16oXevXujb9++mDdvHs6ePYubN29i586d6NChAw4dOvRCdVQabbMu6tpldQp/obJw4UIkJCTghx9+UBqmCCi4Yz00NBSrVq3CtWvXsHjxYkRERODTTz8FAHTo0AH+/v7o3r079u7di+TkZJw4cQLTp0/H2bNn8ezZM4wdOxaHDx/GzZs3cfz4cURHR7/U/o4ePRr//PMPxo0bh6tXr2Lbtm2YNWsWJk+eLHYu69OGFg5zN2LECJw+fRrnzp3D8OHDX6hTOTg4GPPmzcOyZcuQkJCAy5cvIyQkBIsXLwYA9O3bFy4uLujevTuOHz+OGzduIDw8HCdPnhTjTUpKQkxMDNLT05Gdna1xW8OHD8f8+fORn5+v9VcbQMHnTUREhDisUb9+/XR+nhR2Qt+9e1dM+r9oG17c/v378dlnn+G7776Dg4ODeP4WJuJedDu6jtPSMnbsWGRmZqJPnz44e/Ysrl27hg0bNiA+Pl6lrKWlJYYNG4YpU6bgwIED+PvvvzFkyBClBIiXlxf69++PQYMGISIiAklJSYiOjsaCBQuwe/duveMKDg7GokWL8P333+PatWs4f/48li9fDkD3OaqOm5sblixZgmXLlmHYsGE4cuSIeP5+/PHHmDt3rlL5tLQ03L17F9euXcPmzZvRokULpKen48cffxTLbN26tUQ3HDDGGGOlphyfp8AYY4yJ7ty5Q2PGjCF3d3cyNTWlypUr07vvvis++JiIKDY2lvz8/Egmk1HDhg0pKipK7cORNT3E8oMPPiAAtH79epV5CQkJ1KNHD7KxsSGZTEbe3t40ceJEUigUFBsbS4GBgeTo6EhmZmbk5eWl9DDE4jTFoVAoaNmyZVSrVi0yMTEhR0dHCgwMVHqo6Jw5c8jFxYUEQRAf0Llv3z7y8fEhMzMzql+/Ph0+fFjtAyaLPxy56PYvXLhAACgpKUltzGvWrKGGDRuShYUFWVlZUfv27en8+fPi/MX/b+9uQqJa4ziOH8E5ek560pB0pHFUYshNSWlFkeHCXIj4BiIk7tw5CKLUIqZFu4hACI0iioHalSAuBBHctDEicufCFxScNi1MsFTkdxcXnzvjHGeke7ldut8PzGLm4cw553ljmD8zv8ePFQwG5TiOmpubFY/Hfe9xZGRElmUpFoulnSORSKivr08lJSXKy8tTdXW1+vv7tbm5KenP4NvBwUF5nqeioiINDQ2pr68vYzjy169f1dbWpoKCAp0+fVr37t1LO2ZxcdHMm4M+8OsjvyDbwyGb379/VzQaNfdw/fp1zc/Pm/YHDx6opqZGjuPo1KlTamtr0/LysqS/xunNmze6cuWKbNtWTU2NZmdnzfHHHbvp6Wldu3ZNjuPI8zxdvnxZz549M+3J8yNZtuOSffnyRe3t7QoGg7JtW+FwWLFYzASmStLbt29VW1sr27ZVUlKizs5O03ac0OJ/Yk5sbm6qu7tbnucpFArp1atXGcORs503230fjNFRaylZtnDkTGHvkjQ/P6+mpiYVFBToxIkTOn/+/JHhxsnXlulxuF/8Hi9fvkx53+3tbUUikZTrlaTnz5+rtLRUFRUVmpqaytgX+/v7Gh8fV319vVzXled5unTpkkZHR7W9vS3p6FDbo2Tbm5PDVQ8MDg7q5s2b5nm2cGTJf1/28+LFC505c0aO46i1tVWPHj1KG/+xsTFVV1crEAgoEokoHo+ntH/79k3RaFTl5eUKBAIKhUK6ffu21tbWtLOzo56eHoVCIdm2rfLycg0MDBwZli357wUnT55MGeO5uTnV19fLtm2VlZXpzp072tvbM+1+e6ifRCKhlpYW5eXlqaKiQvF4PGNYsN86OPD69WuztxQXF6uhoUHv3r0z7aurq+rq6pLneXJdV3V1dSY49sePH+rq6lJRUVHKfPbri62tLbmumxIqfJSVlRU1NjbKcRyFQiE9efIkLTj68P1OTk7q7Nmzys3NVTgcNq//7B6e7P79+77rN3mO/ux5ss1TP8cJhj489z5//qxbt27JdV0VFhbqxo0bWlpakpS+fre2ttTb2yvXdVVaWqqHDx+m9f/u7q5isZgqKysVCARUVlamjo4OLSwsSPLfkycmJnT4a5CnT5+az2rBYFDRaNS0ZVqjmczMzKi5uVnFxcXKz8/XuXPnNDw8rI2NDUmp+3dOTo4KCwt14cIFjYyMKJFIpLzXQcgzAAD/thzpb6TNAQAAwNfq6qpVVVVlffr0yaqtrf3VlwMA/3vr6+tWZWWl9eHDB+vixYu/+nIAAAD+0whHBgAAAAD8tvb29qxEImHdvXvXunr1KkUDAACAYyDjAAAAAADw23r//r0VDoetjx8/+mb2AAAAIB1/VQQAAAAAAAAAAAx+cQAAAAAAAAAAAAwKBwAAAAAAAAAAwKBwAAAAAAAAAAAADAoHAAAAAAAAAADAoHAAAAAAAAAAAAAMCgcAAAAAAAAAAMCgcAAAAAAAAAAAAAwKBwAAAAAAAAAAwKBwAAAAAAAAAAAAjD8AjE3Ubwdf2/IAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1560x1780 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1560x1780 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from plot_cd_comparison import cd_comparison_data, plot_cd_comparison, save_cd_comparison\n",
+    "\n",
+    "cd_curves = cd_comparison_data(DATA)\n",
+    "cd_curves.to_csv(DATA / \"cd_uncertainty_comparison.csv\", index=False)\n",
+    "for phase in (\"powered\", \"coast\"):\n",
+    "    fig = plot_cd_comparison(cd_curves, phase=phase)\n",
+    "    save_cd_comparison(fig, f\"cd_{phase}_comparison\", DATA)\n",
+    "    plt.show()\n",
+    "    plt.close(fig)\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "feab3ba7",
diff --git a/docs/user/rocket/datcom.rst b/docs/user/rocket/datcom.rst
index 9852cb8f0..99869d654 100644
--- a/docs/user/rocket/datcom.rst
+++ b/docs/user/rocket/datcom.rst
@@ -70,6 +70,111 @@ assembled objects, including surfaces and motor added after the DATCOM fins.
 Adding a :class:`rocketpy.DatcomFins` instance with ``add_surfaces`` also selects
 DATCOM aerodynamics.
 
+Engineering Sensitivity Runs
+----------------------------
+
+With DATCOM fins, ``Flight`` runs the nominal simulation and two additional
+isolated simulations through apogee by default. Disable the extra simulations
+with ``Flight(..., datcom_uncertainty=False)``. Ordinary rockets retain their
+existing aerodynamic equations, simulation and printed output.
+
+The current engineering rule is:
+
+* Static wind-axis drag CD varies by **at least ±20%**, at every Mach and AoA,
+  for both powered and coast aerodynamics.
+* Within Mach 0.8–3.02 and alpha -6 to +6 degrees, use a larger native-relative
+  axial-force discrepancy from Wu et al. where available. The transferred CA
+  discrepancy is a **CD-error proxy**, not a measured universal CD uncertainty.
+  Outside that envelope, use the 20% excursion without extrapolating the
+  canard study's larger errors.
+* CP's distance from the nose varies by **±2%**. Static pitching/yawing moments
+  are transferred consistently about the original case CG reference.
+* Perpendicular aerodynamic force and native DAMP derivatives are retained.
+  The earlier independent CN and CM-alpha error scaling is no longer used.
+
+The design-accuracy reference is J. T. White (1995), *An assessment of missile
+DATCOM prediction accuracy relative to generic Body+Wing+Tail missile pitch
+aerodynamics*, 13th Applied Aerodynamics Conference, AIAA 95-1893,
+`doi:10.2514/6.1995-1893 <https://doi.org/10.2514/6.1995-1893>`_. The filename
+of the supplied PDF says 2012, but the paper is from 1995. White tests revision
+6/93 on 15 configurations, more than 900 cases, Mach 1.5–5.0, AoA 0–24° and
+roll orientations 0°/45°, without control deflections. His cited preliminary
+accuracy criteria concern **normal force** and CP relative to **body length**.
+They do not establish universal ±20% CD or ±2% nose-to-CP confidence bounds.
+Using the requested percentages throughout flight is an explicit engineering
+assumption, including below the paper's supersonic coverage. The ambiguous
+entries in his CP summary table are not used as standard deviations.
+
+The larger drag discrepancies adapt Table 2 of Hanzhou Wu, Min Gao, Weidong
+Song, Zhao Jie and Yi Wang (2019), *Accuracy Analysis of Aerodynamic Calculation
+of Two-dimensional Ballistic correction Projectile based on Missile Datcom*,
+IOP Conference Series: Materials Science and Engineering **612**, 032096,
+`doi:10.1088/1757-899X/612/3/032096
+<https://doi.org/10.1088/1757-899X/612/3/032096>`_, under
+`CC BY 3.0 <https://creativecommons.org/licenses/by/3.0/>`_. It studies one
+canard projectile, including deflected canards. The signed published error
+uses the measurement denominator:
+``e = (DATCOM - measured) / measured``. With ``e`` as a fraction, convert to
+``u_Wu = abs(e / (1 + e))`` and linearly interpolate these magnitudes with
+Mach/alpha inside the paper's envelope. The applied CD excursion is
+``u_CD = max(0.20, u_Wu)`` there, and ``0.20`` elsewhere. The original raw
+CN/CM-alpha tables remain in the source for provenance, not as active bounds.
+
+At zero alpha and beta this directly multiplies the static CD curve by
+``1 + u_CD`` or ``1 - u_CD``. At finite incidence, the added force lies along
+the relative airflow, preserving the perpendicular force. CA is not blindly
+substituted for CD. Body-axis components are recalculated for Flight; the
+static drag component follows the same multiplier. Native damping-load
+increments remain unchanged. Geometry, calibration inputs, native tables and
+ordinary RocketPy algebraic equations are not overwritten.
+
+CP uses a distance measured aft from the nose, in meters:
+``x_CP_new = x_CP_native * (1 ± 0.02)``. Consequently the change in stability
+margin is ``delta_margin = ±0.02 * x_CP_native / diameter``, in calibers,
+not ±2% of the nominal stability margin. The native small-angle CP is computed
+at each Mach and case reference. Added drag acts there, the new static force
+is transferred to the shifted CP, and the native residual moment couple is
+retained. Both rocket axial coordinate orientations and wet/dry case CG
+references are respected. Undefined native CP remains unavailable.
+
+``upper`` means more drag and CP farther aft from the nose; ``lower`` means
+less drag and CP farther forward. These are two correlated scenarios. They
+do not guarantee the greatest/smallest apogee or stability margin among all
+independent combinations of drag and CP errors. No bias correction is applied
+to the nominal run. The report still gives the fraction of ascent time outside
+Wu's Mach/total-incidence coverage, identifying the scope of that source.
+
+Inspect the calculated results without running simulations during printing::
+
+    flight.prints.apogee_conditions()
+    flight.prints.stability_margin()
+    uncertainty_m = flight.datcom_apogee_uncertainty
+    report = flight.datcom_uncertainty
+    print(report.policy)
+    print(report.cases)  # nominal / upper / lower; SI units, stability in calibers
+    print(report.uncertainty("max_speed"))  # m/s, ascent only
+    upper = report.flights["upper"]
+    lower = report.flights["lower"]
+
+Printed ``apogee ± datcom_uncertainty`` is the **largest absolute departure
+from nominal**, in meters, for ASL and AGL. It summarizes potentially asymmetric
+scenario results; it is not a standard deviation, confidence interval or
+percentile. The charts use the actual asymmetric range across all three
+apogees. Peak ascent speed is inertial speed; apogee freestream speed is
+relative to the wind. Each scenario uses its own apogee and rail-exit time.
+Incomplete or failed ascents are unavailable, not zero-width uncertainty.
+
+Replay rockets and environments are copied before nominal integration. They
+reuse native tables and atmosphere schedules, stop at apogee and do not launch
+new DATCOM runs for unchanged decks. They do not refit the atmosphere schedule
+or adaptive grid: check each replay's envelope. Callbacks/controllers execute
+again; their external side effects remain the caller's responsibility. Python
+and NumPy global random states are restored after replay. This covers only the
+stated aerodynamic sensitivity, not wind, mass, thrust, recovery or measurement
+uncertainty. ``datcom_uncertainty="upper"`` or ``"lower"`` runs an individual
+case. Replay Flight loads/stability use its local scenario model; the shared
+Rocket's reporting curves remain nominal.
+
 Body Profile Selection
 ----------------------
 
diff --git a/rocketpy/prints/flight_prints.py b/rocketpy/prints/flight_prints.py
index cfa7164c8..2bff15763 100644
--- a/rocketpy/prints/flight_prints.py
+++ b/rocketpy/prints/flight_prints.py
@@ -236,11 +236,13 @@ def apogee_conditions(self):
         """
         print("\nApogee State\n")
         print(f"Apogee Time: {self.flight.apogee_time:.3f} s")
+        uncertainty = self._datcom_uncertainty_suffix("apogee")
         print(
-            f"Apogee Altitude: {self.flight.apogee:.3f} m (ASL) | "
-            f"{self.flight.altitude(self.flight.apogee_time):.3f} m (AGL)"
+            f"Apogee Altitude: {self.flight.apogee:.3f}{uncertainty} m (ASL) | "
+            f"{self.flight.altitude(self.flight.apogee_time):.3f}{uncertainty} m (AGL)"
         )
         print(f"Apogee Freestream Speed: {self.flight.apogee_freestream_speed:.3f} m/s")
+        self._print_datcom_uncertainty("apogee")
         print(f"Apogee X position: {self.flight.x(self.flight.apogee_time):.3f} m")
         print(f"Apogee Y position: {self.flight.y(self.flight.apogee_time):.3f} m")
         print(f"Apogee latitude: {self.flight.latitude(self.flight.apogee_time):.7f}°")
@@ -324,6 +326,7 @@ def maximum_values(self):
             f"Maximum Speed: {self.flight.max_speed:.3f} m/s "
             f"at {self.flight.max_speed_time:.2f} s"
         )
+        self._print_datcom_uncertainty("velocity")
         print(
             f"Maximum Mach Number: {self.flight.max_mach_number:.3f} Mach "
             f"at {self.flight.max_mach_number_time:.2f} s"
@@ -427,12 +430,14 @@ def stability_margin(self):
         """
         print("\nStability Margin\n")
         print(
-            f"Initial Stability Margin: {self.flight.initial_stability_margin:.3f} c "
+            f"Initial Stability Margin: {self.flight.initial_stability_margin:.3f}"
+            f"{self._datcom_uncertainty_suffix('initial_stability_margin')} c "
             f"at {self.flight.time[0]:.2f} s"
         )
         print(
             "Out of Rail Stability Margin: "
-            f"{self.flight.out_of_rail_stability_margin:.3f} c "
+            f"{self.flight.out_of_rail_stability_margin:.3f}"
+            f"{self._datcom_uncertainty_suffix('out_of_rail_stability_margin')} c "
             f"at {self.flight.out_of_rail_time:.2f} s"
         )
         print(
@@ -443,6 +448,90 @@ def stability_margin(self):
             f"Minimum Stability Margin: {self.flight.min_stability_margin:.3f} c "
             f"at {self.flight.min_stability_margin_time:.2f} s"
         )
+        self._print_datcom_uncertainty("stability")
+
+    def _datcom_uncertainty_suffix(self, metric):
+        """Format an already calculated scenario excursion; never run flights."""
+        report = getattr(self.flight, "datcom_uncertainty", None)
+        delta = None if report is None else report.uncertainty(metric)
+        return "" if delta is None else f" ± {delta:.3f}"
+
+    def _print_datcom_uncertainty(self, section):
+        """Report nominal/upper/lower ascent results and their interpretation."""
+        report = getattr(self.flight, "datcom_uncertainty", None)
+        if report is None:
+            status = getattr(self.flight, "datcom_uncertainty_status", None)
+            if status and section == "apogee":
+                print(status)
+            return
+
+        def value(case, metric):
+            number = report.cases.get(case, {}).get(metric)
+            return "unavailable" if number is None else f"{number:.3f}"
+
+        if section == "velocity":
+            print(
+                "DATCOM nominal peak ascent speed: "
+                f"{value('nominal', 'max_speed')}"
+                f"{self._datcom_uncertainty_suffix('max_speed')} m/s"
+            )
+            for case in ("upper", "lower"):
+                print(
+                    f"DATCOM coefficient {case} peak ascent speed: "
+                    f"{value(case, 'max_speed')} m/s"
+                )
+        elif section == "apogee":
+            print(
+                "DATCOM nominal: peak ascent speed "
+                f"{value('nominal', 'max_speed')}"
+                f"{self._datcom_uncertainty_suffix('max_speed')} m/s"
+            )
+            for case in ("upper", "lower"):
+                print(
+                    f"DATCOM coefficient {case}: apogee "
+                    f"{value(case, 'apogee_agl')} m (AGL) | peak ascent speed "
+                    f"{value(case, 'max_speed')} m/s | apogee freestream speed "
+                    f"{value(case, 'apogee_speed')} m/s"
+                )
+            print(
+                "DATCOM ±: largest departure from nominal in two engineering "
+                "sensitivity runs; not a confidence interval. "
+                "Upper/lower mean more/less drag and farther-aft/forward CP, "
+                "not guaranteed altitude bounds."
+            )
+            print("DATCOM sensitivity: CD at least ±20%; nose-to-CP distance ±2%.")
+            for case in ("nominal", "upper", "lower"):
+                fraction = report.cases.get(case, {}).get("outside_paper_fraction")
+                if fraction is not None:
+                    print(
+                        f"DATCOM {case}: {100 * fraction:.1f}% of ascent time "
+                        "outside Wu Mach 0.8–3.02 / incidence ±6° coverage; "
+                        "20% CD floor used outside its alpha/Mach envelope."
+                    )
+            for case, error in report.errors.items():
+                print(f"DATCOM {case} uncertainty unavailable: {error}")
+        else:
+            print(
+                "DATCOM nominal stability at apogee: "
+                f"{value('nominal', 'apogee_stability_margin')}"
+                f"{self._datcom_uncertainty_suffix('apogee_stability_margin')} c"
+            )
+            print(
+                "DATCOM nominal ascent stability min/max: "
+                f"{value('nominal', 'min_stability_margin')}"
+                f"{self._datcom_uncertainty_suffix('min_stability_margin')} / "
+                f"{value('nominal', 'max_stability_margin')}"
+                f"{self._datcom_uncertainty_suffix('max_stability_margin')} c"
+            )
+            for case in ("upper", "lower"):
+                print(
+                    f"DATCOM coefficient {case} stability: initial "
+                    f"{value(case, 'initial_stability_margin')} c | rail exit "
+                    f"{value(case, 'out_of_rail_stability_margin')} c | apogee "
+                    f"{value(case, 'apogee_stability_margin')} c | ascent min/max "
+                    f"{value(case, 'min_stability_margin')} / "
+                    f"{value(case, 'max_stability_margin')} c"
+                )
 
     def all(self):
         """Prints out all data available about the Flight. This method invokes
diff --git a/rocketpy/rocket/datcom_uncertainty.py b/rocketpy/rocket/datcom_uncertainty.py
new file mode 100644
index 000000000..94d7c923f
--- /dev/null
+++ b/rocketpy/rocket/datcom_uncertainty.py
@@ -0,0 +1,187 @@
+"""DATCOM drag/CP engineering sensitivity with a Wu et al. drag-error floor.
+
+Hanzhou Wu, Min Gao, Weidong Song, Zhao Jie and Yi Wang (2019), "Accuracy
+Analysis of Aerodynamic Calculation of Two-dimensional Ballistic correction
+Projectile based on Missile Datcom", IOP Conf. Ser.: Mater. Sci. Eng. 612,
+032096. DOI: 10.1088/1757-899X/612/3/032096. Tables adapted under CC BY 3.0
+(https://creativecommons.org/licenses/by/3.0/). The source validates one
+canard projectile. Transferring its errors to other vehicles, symmetrizing
+them, and holding boundary errors outside its envelope are assumptions,
+not a statistical uncertainty model or a validation of RocketPy rockets.
+
+The 20% drag floor and 2% nose-to-CP variation are engineering assumptions
+motivated by J. T. White (1995), AIAA 95-1893, DOI 10.2514/6.1995-1893.
+White studies supersonic CN/CP accuracy, not general CD uncertainty.
+"""
+
+from copy import copy
+
+import numpy as np
+from scipy.interpolate import RegularGridInterpolator
+
+from rocketpy.rocket.datcom import DatcomSurface, zero_alpha_index
+
+
+class Wu2019Errors:
+    """Published signed errors, relative to wind-tunnel measurements.
+
+    Mach and alpha (degrees) are linearly interpolated and clamped to the
+    published envelope. Table 4 supplies Mach-only pitching-slope errors.
+    CN's zero-alpha magnitude is the mean of the six nonzero-alpha magnitudes.
+    No uncertainty is assigned to damping or lateral coefficients.
+    """
+
+    mach = np.array([0.8, 1.05, 1.2, 1.52, 2.01, 2.51, 3.02])
+    alpha = np.array([-6, -4, -2, 0, 2, 4, 6])
+    ca_error_percent = np.array(
+        [
+            [17.727, 16.380, 16.971, 14.127, 17.537, 21.429, 27.765],
+            [-33.449, -34.727, -36.417, -35.593, -29.490, -23.369, -17.202],
+            [-44.090, -47.563, -48.274, -47.981, -46.602, -43.404, -38.227],
+            [-37.588, -39.327, -39.747, -40.246, -39.582, -37.759, -34.335],
+            [-36.308, -38.934, -39.883, -40.076, -39.704, -37.219, -32.938],
+            [-27.944, -31.351, -32.320, -32.401, -33.054, -31.042, -26.883],
+            [-19.125, -23.563, -26.203, -28.598, -26.508, -23.681, -17.933],
+        ]
+    )
+    cn_error_percent = np.array(
+        [
+            [20.943, 9.173, -30.941, 645.672, 95.265, 62.621, 54.258],
+            [35.300, 20.209, -28.900, 554.288, 124.391, 91.398, 78.942],
+            [30.573, 14.207, -25.887, 590.827, 89.558, 65.719, 59.669],
+            [32.915, 27.855, 9.193, 69.492, 39.432, 36.843, 37.602],
+            [13.922, 9.772, -4.739, 121.543, 26.513, 24.852, 26.066],
+            [10.010, 10.531, 12.080, -23.744, -0.833, 3.548, 6.527],
+            [4.373, 6.713, 9.702, -44.785, -13.970, -6.577, -2.769],
+        ]
+    )
+    moment_mach = np.array([0.8, 0.95, 1.05, 1.15, 1.2, 1.52, 2.01, 2.51, 3.02])
+    cma_error_percent = np.array(
+        [-6.56, 81.47, 15.15, -26.15, -41.49, 19.47, -0.36, -15.23, -26.82]
+    )
+
+    def __init__(self):
+        # e = (prediction - measurement) / measurement. A symmetric excursion
+        # about the prediction must instead use |e / (1 + e)|. Applying the
+        # printed 646% directly to DATCOM would use the wrong denominator.
+        def relative(percent):
+            return np.abs(percent / (100 + percent))
+
+        normal = relative(self.cn_error_percent)
+        # The source has canard-induced CN at alpha=0 and near-zero tunnel
+        # measurements. Use the mean absolute discrepancy at nonzero AoAs
+        # instead of transferring that ill-conditioned zero-incidence error.
+        normal[:, self.alpha == 0] = normal[:, self.alpha != 0].mean(axis=1)[:, None]
+        self._forces = RegularGridInterpolator(
+            (self.mach, self.alpha),
+            np.stack(
+                [relative(self.ca_error_percent), normal],
+                axis=-1,
+            ),
+        )
+        self._moment = relative(self.cma_error_percent)
+
+    def fractions(self, mach, alpha):
+        """Return nonnegative native-relative CA, CN, CM-alpha excursions.
+
+        ``alpha`` is in degrees. Boundary values are held outside the paper's
+        envelope; they are not measured error bounds at those conditions.
+        """
+        m = float(np.clip(mach, self.mach[0], self.mach[-1]))
+        a = float(np.clip(alpha, self.alpha[0], self.alpha[-1]))
+        ca, cn = self._forces([m, a])[0]
+        cma = np.interp(m, self.moment_mach, self._moment)
+        return ca, cn, cma
+
+
+class DatcomUncertaintySurface(DatcomSurface):
+    """Flight-local CD/CP scenarios with unmodified native damping derivatives.
+
+    Static wind-axis drag is varied by at least 20%, preserving perpendicular
+    force. Inside Wu's Mach/alpha envelope, larger native-relative CA errors
+    are used as a CD-error proxy. CP's distance from the nose varies by 2%.
+    Upper means more drag and CP farther aft; lower reverses both. These two
+    correlated scenarios are not guaranteed extrema of the trajectory.
+    """
+
+    @classmethod
+    def from_model(cls, model, sign, *, nose_position):
+        """Share native tables; provide the nose tip in the rocket's axial frame."""
+        if sign not in (-1, 1) or not np.isfinite(nose_position):
+            raise ValueError(
+                "DATCOM sensitivity needs sign ±1 and a finite nose position"
+            )
+        result = cls.__new__(cls)
+        result.__dict__ = copy(model.__dict__)
+        result.sign = sign
+        result.nose_position = float(nose_position)
+        result.errors = Wu2019Errors()
+        result.outside_alpha_envelope = False
+        result.outside_mach_envelope = False
+        result.outside_beta_envelope = False
+        return result
+
+    def drag_fraction(self, mach, alpha):
+        """Native-relative CD excursion; alpha in degrees, 20% outside Wu data."""
+        if (
+            self.errors.mach[0] <= mach <= self.errors.mach[-1]
+            and self.errors.alpha[0] <= alpha <= self.errors.alpha[-1]
+        ):
+            return max(0.2, float(self.errors.fractions(mach, alpha)[0]))
+        return 0.2
+
+    def _native_cp(self, powered, mach):
+        """Native small-angle CP at the selected case's reference, in meters."""
+        table = self.tables[powered]
+        zero = zero_alpha_index(table[5])
+        cna = np.interp(mach, table[4], table.derivatives["CNA"][:, zero])
+        cma = np.interp(mach, table[4], table.derivatives["CMA"][:, zero])
+        if abs(cna) < 1e-12:
+            return float("nan")
+        return self.references[powered] + self.csys * self.reference_length * cma / cna
+
+    def coefficients(self, powered, mach, alpha, beta=0):
+        """Vary static drag along the airflow and transfer its torque consistently.
+
+        Alpha/beta are radians. The native static moment's residual couple is
+        retained; added drag acts at the native small-angle CP. The CP shift
+        transfers the new static force. Roll and all DAMP terms are unchanged.
+        """
+        values = super().coefficients(powered, mach, alpha, beta).copy()
+        # Include the native linear sideslip contribution when there is no beta
+        # grid. Leave that contribution for DatcomSurface to add exactly once.
+        cy = values[3]
+        if len(self._interpolators[powered].grid) == 2:
+            cy += values[6] * np.degrees(beta)
+        forces = np.array([cy, -values[0], -values[2]])
+        direction = np.array([np.tan(beta), np.tan(alpha), 1.0])
+        direction /= np.linalg.norm(direction)
+        drag = -float(forces @ direction)
+        delta = (
+            -self.sign * self.drag_fraction(mach, np.degrees(alpha)) * drag * direction
+        )
+        changed = forces + delta
+        values[0] -= delta[1]
+        values[2] -= delta[2]
+        values[3] += delta[0]
+        values[-1] = -float(changed @ direction)
+
+        native_cp = self._native_cp(powered, mach)
+        if np.isfinite(native_cp):
+            arm = (
+                (native_cp - self.references[powered])
+                * self.csys
+                / self.reference_length
+            )
+            distance_from_nose = (self.nose_position - native_cp) * self.csys
+            shift = -self.sign * 0.02 * distance_from_nose / self.reference_length
+            # z cross (Fx, Fy, Fz) = (-Fy, Fx, 0). Static moments are
+            # nondimensionalized by diameter, with no change of reference CG.
+            values[1] -= arm * delta[1] + shift * changed[1]
+            values[4] += arm * delta[0] + shift * changed[0]
+        return values
+
+    def small_angle_cp(self, mach):
+        """Coast CP with ±2% nose-to-CP distance, in the user's axial frame."""
+        native_cp = self._native_cp(False, mach)
+        return native_cp - self.sign * 0.02 * (self.nose_position - native_cp)
diff --git a/rocketpy/simulation/datcom.py b/rocketpy/simulation/datcom.py
index 9b6f0e0f6..9b35cc9eb 100644
--- a/rocketpy/simulation/datcom.py
+++ b/rocketpy/simulation/datcom.py
@@ -5,6 +5,8 @@
 
 from rocketpy.mathutils.vector_matrix import Matrix, Vector
 
+from .datcom_uncertainty import DatcomUncertaintyMixin
+
 
 def datcom_derivative(method):
     """Wrap an RHS with a local implicit incidence-rate solve when DATCOM is active."""
@@ -26,13 +28,14 @@ def derivative(t, u, post):
     return evaluate
 
 
-class DatcomFlightMixin:
+class DatcomFlightMixin(DatcomUncertaintyMixin):
     """Time-dependent loads and deterministic unsteady aerodynamic coupling."""
 
     def _datcom_surface_loads(self, surface, time, *args):
         """Supply time and the instantaneous incidence rate to DATCOM only."""
         if surface is self.rocket._datcom_model:
-            loads = surface.compute_forces_and_moments_at_time(
+            model = getattr(self, "_datcom_load_model", surface)
+            loads = model.compute_forces_and_moments_at_time(
                 time, getattr(self, "_datcom_alpha_dot", 0.0), *args
             )
             if any(
@@ -113,7 +116,8 @@ def _datcom_rail_force(self, time, velocity, speed, mach, rho, reynolds, fallbac
         """Constrained axial load uses body-axis CA rather than stability-axis CD."""
         if self.rocket._datcom_model is None:
             return fallback
-        loads = self.rocket._datcom_model.compute_forces_and_moments_at_time(
+        model = getattr(self, "_datcom_load_model", self.rocket._datcom_model)
+        loads = model.compute_forces_and_moments_at_time(
             time,
             0.0,
             velocity,
diff --git a/rocketpy/simulation/datcom_uncertainty.py b/rocketpy/simulation/datcom_uncertainty.py
new file mode 100644
index 000000000..8121a3442
--- /dev/null
+++ b/rocketpy/simulation/datcom_uncertainty.py
@@ -0,0 +1,169 @@
+"""Isolated ascent replays for paper-based DATCOM sensitivity reporting."""
+
+import random
+import warnings
+from copy import deepcopy
+from dataclasses import dataclass, field
+
+import numpy as np
+
+from rocketpy.rocket.datcom_uncertainty import DatcomUncertaintySurface
+
+
+@dataclass
+class DatcomUncertaintyReport:
+    """Three deterministic ascent scenarios, not confidence limits.
+
+    ``cases`` maps nominal/upper/lower to SI results (stability in calibers).
+    ``flights`` holds the two additional Flights for plots. Missing apogees
+    or failed replays are explicit; an incomplete envelope has no scalar ±.
+    """
+
+    cases: dict = field(default_factory=dict)
+    flights: dict = field(default_factory=dict)
+    errors: dict = field(default_factory=dict)
+    source: str = "https://doi.org/10.1088/1757-899X/612/3/032096"
+    policy: str = "CD: at least ±20%, larger Wu CA discrepancies inside its envelope; nose-to-CP distance: ±2%. Engineering scenarios, not confidence intervals."
+    design_reference: str = "https://doi.org/10.2514/6.1995-1893"
+
+    def uncertainty(self, metric):
+        """Largest absolute excursion from nominal, or None if incomplete."""
+        values = [
+            self.cases.get(case, {}).get(metric)
+            for case in ("nominal", "upper", "lower")
+        ]
+        if any(value is None or not np.isfinite(value) for value in values):
+            return None
+        return max(abs(value - values[0]) for value in values[1:])
+
+
+def _ascent_results(flight):
+    """Sample each trajectory only from its initial state to its own apogee."""
+
+    def finite(value):
+        return float(value) if np.isfinite(value) else None
+
+    reached_apogee = len(flight.apogee_state) > 1
+    end = flight.apogee_time if reached_apogee else flight.t_final
+    times = np.asarray(flight.time)
+    times = np.unique(np.r_[times[times <= end], end])
+    speed = np.asarray(flight.speed(times))
+    margin = np.asarray(flight.stability_margin(times))
+    # Uniform time samples make the out-of-paper coverage fraction independent
+    # of ODE adaptive step density. This is coverage, not a confidence measure.
+    coverage_times = np.linspace(flight.t_initial, end, 1001)
+    mach = np.asarray(flight.mach_number(coverage_times))
+    alpha = np.asarray(flight.angle_of_attack(coverage_times))
+    outside = (mach < 0.8) | (mach > 3.02) | (np.abs(alpha) > 6)
+    return {
+        "apogee": float(flight.apogee) if reached_apogee else None,
+        "apogee_agl": float(flight.apogee - flight.env.elevation)
+        if reached_apogee
+        else None,
+        "max_speed": float(np.max(speed)),
+        "apogee_speed": float(flight.free_stream_speed(end))
+        if reached_apogee
+        else None,
+        "initial_stability_margin": finite(margin[0]),
+        "out_of_rail_stability_margin": finite(flight.out_of_rail_stability_margin),
+        "apogee_stability_margin": finite(margin[-1]) if reached_apogee else None,
+        "min_stability_margin": finite(np.min(margin)),
+        "max_stability_margin": finite(np.max(margin)),
+        "outside_paper_fraction": float(np.mean(outside)),
+    }
+
+
+class DatcomUncertaintyMixin:
+    """Keep scenario models and replay state local to each Flight."""
+
+    def _initialize_datcom_uncertainty(self, setting, parameters):
+        """Snapshot pristine inputs before controllers or parachutes mutate them."""
+        if not (setting is True or setting is False or setting in ("upper", "lower")):
+            raise ValueError(
+                "datcom_uncertainty must be True, False, 'upper' or 'lower'"
+            )
+        self.datcom_uncertainty = None
+        self._datcom_uncertainty_setting = setting
+        self.datcom_uncertainty_status = None
+        self._datcom_load_model = self.rocket._datcom_model
+        if self._datcom_load_model is None or setting is False:
+            return
+        if setting in ("upper", "lower"):
+            self._datcom_load_model = DatcomUncertaintySurface.from_model(
+                self._datcom_load_model,
+                1 if setting == "upper" else -1,
+                nose_position=next(
+                    position.z
+                    for surface, position, _ in self.rocket.aerodynamic_surfaces
+                    if surface in self.rocket.nosecones
+                ),
+            )
+            return
+        inputs = {key: value for key, value in parameters.items() if key != "self"}
+        if hasattr(inputs["initial_solution"], "solution"):
+            inputs["initial_solution"] = inputs["initial_solution"].solution[-1]
+        self._datcom_uncertainty_inputs = deepcopy(inputs)
+        self._datcom_uncertainty_rng = (np.random.get_state(), random.getstate())
+
+    def _complete_datcom_uncertainty(self):
+        """Replay upper/lower static-coefficient scenarios without recursive runs."""
+        inputs = self.__dict__.pop("_datcom_uncertainty_inputs", None)
+        if inputs is None:
+            return
+        initial_rng = self.__dict__.pop("_datcom_uncertainty_rng")
+        if len(self.apogee_state) == 1:
+            self.datcom_uncertainty_status = (
+                "Nominal flight did not reach apogee; DATCOM uncertainty unavailable."
+            )
+            return
+        report = DatcomUncertaintyReport(cases={"nominal": _ascent_results(self)})
+        current_rng = (np.random.get_state(), random.getstate())
+        try:
+            for case in ("upper", "lower"):
+                np.random.set_state(initial_rng[0])
+                random.setstate(initial_rng[1])
+                options = deepcopy(inputs)
+                options.update(
+                    datcom_uncertainty=case,
+                    terminate_on_apogee=True,
+                    verbose=False,
+                    name=f"{self.name} DATCOM {case}",
+                )
+                try:
+                    flight = type(self)(**options)
+                    report.flights[case] = flight
+                    report.cases[case] = _ascent_results(flight)
+                    if len(flight.apogee_state) == 1:
+                        report.errors[case] = "Flight did not reach apogee."
+                except (ValueError, RuntimeError, FloatingPointError) as error:
+                    report.errors[case] = str(error)
+                if case in report.errors:
+                    warnings.warn(
+                        f"DATCOM {case} sensitivity run incomplete: {report.errors[case]}",
+                        RuntimeWarning,
+                        stacklevel=2,
+                    )
+        finally:
+            np.random.set_state(current_rng[0])
+            random.setstate(current_rng[1])
+        self.datcom_uncertainty = report
+
+    @property
+    def datcom_apogee_uncertainty(self):
+        """Maximum scenario apogee excursion in meters; None if unavailable."""
+        return (
+            None
+            if self.datcom_uncertainty is None
+            else self.datcom_uncertainty.uncertainty("apogee")
+        )
+
+    def _datcom_stability_margin(self, mach, time):
+        """Scenario CP/CG distance in calibers; ordinary rocket behavior unchanged."""
+        model = getattr(self, "_datcom_load_model", self.rocket._datcom_model)
+        if model is self.rocket._datcom_model:
+            return self.rocket.stability_margin(mach, time)
+        return (
+            (self.rocket.center_of_mass(time) - model.small_angle_cp(mach))
+            * self.rocket._csys
+            / (2 * self.rocket.radius)
+        )
diff --git a/rocketpy/simulation/flight.py b/rocketpy/simulation/flight.py
index 70b8f63c8..56dbbde93 100644
--- a/rocketpy/simulation/flight.py
+++ b/rocketpy/simulation/flight.py
@@ -539,6 +539,7 @@ def __init__(  # pylint: disable=too-many-arguments,too-many-statements
         simulation_mode="6 DOF",
         post_step_callback=None,
         use_udot_rail2=False,
+        datcom_uncertainty=True,
     ):
         """Run a trajectory simulation.
 
@@ -640,6 +641,21 @@ def __init__(  # pylint: disable=too-many-arguments,too-many-statements
             directly from the 1-DOF ``udot_rail1`` phase to the generalized
             6-DOF dynamics at the upper button exit, as in previous versions.
             Default is False.
+        datcom_uncertainty : bool or str, optional
+            With DATCOM fins, True (default) runs two additional isolated ascent
+            simulations with a 20% CD excursion, increased where Wu et al.
+            (2019), DOI 10.1088/1757-899X/612/3/032096, reports larger native-
+            relative CA discrepancies inside its Mach/alpha envelope. CP's
+            distance from the nose varies by 2%. White (1995), AIAA 95-1893,
+            motivates these engineering assumptions but does not validate
+            general drag uncertainty. Results are in
+            ``datcom_uncertainty``; ``datcom_apogee_uncertainty`` is the largest
+            apogee excursion in meters. These are sensitivity scenarios, not
+            confidence bounds. Outside Mach 0.8–3.02 and alpha ±6 degrees the
+            drag excursion is 20%; coverage is reported. False skips
+            the replays; 'upper' or 'lower' runs just that coefficient scenario.
+            Replays stop at apogee and execute callbacks/controllers again on
+            copied inputs. This option has no effect on ordinary rockets.
         Returns
         -------
         None
@@ -655,6 +671,7 @@ def __init__(  # pylint: disable=too-many-arguments,too-many-statements
         self.env = environment
         self.rocket = rocket
         self.rocket.prepare_datcom()
+        self._initialize_datcom_uncertainty(datcom_uncertainty, locals())
         # Warn about an aerodynamically unstable rocket now that it is fully
         # assembled and about to be simulated. Doing it here (instead of on every
         # add_surfaces call during construction) avoids spurious warnings for
@@ -703,6 +720,7 @@ def __init__(  # pylint: disable=too-many-arguments,too-many-statements
         # Initialize prints and plots objects
         self.prints = _FlightPrints(self)
         self.plots = _FlightPlots(self)
+        self._complete_datcom_uncertainty()
 
     def __repr__(self):
         return (
@@ -4026,7 +4044,7 @@ def stability_margin(self):
             is defined as the distance between the center of pressure and the
             center of gravity, divided by the rocket diameter.
         """
-        return [(t, self.rocket.stability_margin(m, t)) for t, m in self.mach_number]
+        return [(t, self._datcom_stability_margin(m, t)) for t, m in self.mach_number]
 
     # Rail Button Forces
 
@@ -4493,6 +4511,7 @@ def to_dict(self, **kwargs):
             "time_overshoot": self.time_overshoot,
             "name": self.name,
             "equations_of_motion": self.equations_of_motion,
+            "datcom_uncertainty": self._datcom_uncertainty_setting,
             # The following outputs are essential to run all_info method
             "solution": self.solution,
             "out_of_rail_time": self.out_of_rail_time,
@@ -4578,6 +4597,7 @@ def from_dict(cls, data):
             time_overshoot=data["time_overshoot"],
             name=data["name"],
             equations_of_motion=data["equations_of_motion"],
+            datcom_uncertainty=data.get("datcom_uncertainty", True),
         )
 
     class FlightPhases:
diff --git a/tests/integration/test_datcom.py b/tests/integration/test_datcom.py
index 31c7bb29c..8f2248849 100644
--- a/tests/integration/test_datcom.py
+++ b/tests/integration/test_datcom.py
@@ -1,6 +1,7 @@
 """DATCOM text parsing, isolated executable lifecycle and flight integration."""
 
 import os
+import random
 import re
 import shutil
 import subprocess
@@ -247,6 +248,167 @@ def test_datcom_flight_uses_full_model(mode, equations, datcom_rocket):
     assert len(rocket.aerodynamic_surfaces) == 3
 
 
+@pytest.mark.parametrize(
+    "mode,equations",
+    [("6 DOF", "standard"), ("6 DOF", "solid_propulsion"), ("3 DOF", "standard")],
+)
+def test_datcom_uncertainty_flight_replays_and_prints(
+    mode, equations, datcom_rocket, capsys
+):
+    """Three independent ascents report numeric ± without changing nominal loads."""
+    # Arrange
+    rocket = datcom_rocket
+    tables = parse_datcom_output(SAMPLE.parent)
+    environment = Environment(elevation=100)
+    options = dict(
+        rail_length=5,
+        inclination=90,
+        terminate_on_apogee=True,
+        max_time=60,
+        max_time_step=0.2,
+        simulation_mode=mode,
+        equations_of_motion=equations,
+    )
+    # Act
+    with patch("rocketpy.rocket.datcom.run_datcom", return_value=tables) as run:
+        baseline = Flight(rocket, environment, datcom_uncertainty=False, **options)
+        flight = Flight(rocket, environment, **options)
+    report = flight.datcom_uncertainty
+    flight.prints.apogee_conditions()
+    flight.prints.stability_margin()
+    output = capsys.readouterr().out
+    # Assert
+    assert report is not None
+    assert not report.errors
+    assert set(report.cases) == {"nominal", "upper", "lower"}
+    assert run.call_count == 2  # replays reuse the native tables
+    assert flight.apogee == pytest.approx(baseline.apogee, abs=1e-8)
+    np.testing.assert_allclose(flight.solution, baseline.solution, atol=1e-8, rtol=0)
+    assert (
+        report.cases["upper"]["apogee"]
+        < flight.apogee
+        < report.cases["lower"]["apogee"]
+    )
+    assert flight.datcom_apogee_uncertainty > 0
+    assert report.uncertainty("initial_stability_margin") > 0
+    assert report.cases["nominal"]["apogee_agl"] == pytest.approx(flight.apogee - 100)
+    for case in ("upper", "lower"):
+        replay = report.flights[case]
+        assert replay.rocket is not rocket
+        assert replay.datcom_uncertainty is None
+        assert replay._datcom_load_model is not replay.rocket._datcom_model
+        assert replay.terminate_on_apogee
+    assert flight._datcom_load_model is rocket._datcom_model
+    assert "±" in output
+    assert "DATCOM coefficient upper: apogee" in output
+    assert "DATCOM coefficient lower stability" in output
+    assert "not a confidence interval" in output
+    assert "outside Wu" in output
+
+
+def test_datcom_uncertainty_incomplete_ascent(datcom_rocket, capsys):
+    """A cutoff before apogee cannot silently produce a zero-width uncertainty."""
+    # Arrange
+    tables = parse_datcom_output(SAMPLE.parent)
+    # Act
+    with patch("rocketpy.rocket.datcom.run_datcom", return_value=tables):
+        flight = Flight(datcom_rocket, Environment(), rail_length=5, max_time=0.02)
+    flight.prints.apogee_conditions()
+    output = capsys.readouterr().out
+    # Assert
+    assert flight.datcom_uncertainty is None
+    assert flight.datcom_apogee_uncertainty is None
+    assert "did not reach apogee" in output
+    assert "±" not in output
+
+
+def test_datcom_uncertainty_replays_restore_random_states(datcom_rocket):
+    """Replays do not consume the caller's random sequence beyond nominal flight."""
+    # Arrange
+    tables = parse_datcom_output(SAMPLE.parent)
+    options = dict(
+        rail_length=5,
+        inclination=90,
+        terminate_on_apogee=True,
+        max_time=60,
+        max_time_step=0.5,
+    )
+
+    def callback(_flight):
+        np.random.random()
+        random.random()
+
+    original_numpy_state, original_python_state = (
+        np.random.get_state(),
+        random.getstate(),
+    )
+    # Act
+    try:
+        with patch("rocketpy.rocket.datcom.run_datcom", return_value=tables):
+            np.random.seed(123)
+            random.seed(123)
+            Flight(
+                datcom_rocket,
+                Environment(),
+                post_step_callback=callback,
+                datcom_uncertainty=False,
+                **options,
+            )
+            expected = (np.random.random(), random.random())
+            np.random.seed(123)
+            random.seed(123)
+            flight = Flight(
+                datcom_rocket, Environment(), post_step_callback=callback, **options
+            )
+            actual = (np.random.random(), random.random())
+    finally:
+        np.random.set_state(original_numpy_state)
+        random.setstate(original_python_state)
+    # Assert
+    assert actual == expected
+    assert not flight.datcom_uncertainty.errors
+
+
+def test_datcom_uncertainty_ordinary_rocket_unchanged(calisto, capsys):
+    """The default introduces no replay or DATCOM output for ordinary rockets."""
+    # Arrange
+    environment = Environment()
+    options = dict(rail_length=5, max_time=0.1)
+    # Act
+    baseline = Flight(calisto, environment, datcom_uncertainty=False, **options)
+    flight = Flight(calisto, environment, **options)
+    flight.prints.apogee_conditions()
+    output = capsys.readouterr().out
+    # Assert
+    np.testing.assert_array_equal(flight.solution, baseline.solution)
+    assert flight.datcom_uncertainty is None
+    assert flight.datcom_apogee_uncertainty is None
+    assert "DATCOM" not in output
+    assert "±" not in output
+
+
+@pytest.mark.parametrize("setting", [True, False, "upper", "lower"])
+def test_datcom_uncertainty_setting_serialization(setting, datcom_rocket):
+    """Flight dictionary round trips preserve opt-out and individual scenarios."""
+    # Arrange
+    tables = parse_datcom_output(SAMPLE.parent)
+    # Act
+    with patch("rocketpy.rocket.datcom.run_datcom", return_value=tables):
+        flight = Flight(
+            datcom_rocket,
+            Environment(),
+            rail_length=5,
+            max_time=0.02,
+            datcom_uncertainty=setting,
+        )
+        data = flight.to_dict()
+        restored = Flight.from_dict(data)
+    # Assert
+    assert data["datcom_uncertainty"] == setting
+    assert restored._datcom_uncertainty_setting == setting
+    assert restored.apogee == flight.apogee
+
+
 @pytest.mark.slow
 @pytest.mark.parametrize("powered_base_drag", ["none", "nozzle"])
 @pytest.mark.parametrize(
diff --git a/tests/unit/rocket/test_datcom_uncertainty.py b/tests/unit/rocket/test_datcom_uncertainty.py
new file mode 100644
index 000000000..ff710691f
--- /dev/null
+++ b/tests/unit/rocket/test_datcom_uncertainty.py
@@ -0,0 +1,220 @@
+"""Paper-table provenance, coefficient scenarios and honest scalar envelopes."""
+
+import numpy as np
+import pytest
+
+from rocketpy.mathutils.vector_matrix import Vector
+from rocketpy.rocket.datcom import DatcomSurface
+from rocketpy.rocket.datcom_uncertainty import DatcomUncertaintySurface, Wu2019Errors
+from rocketpy.simulation.datcom_uncertainty import DatcomUncertaintyReport
+
+
+@pytest.mark.parametrize(
+    "mach,alpha,errors",
+    [
+        (0.8, -6, (17.727, 20.943, -6.56)),
+        (1.2, -2, (-48.274, -25.887, -41.49)),
+        (3.02, 6, (-17.933, -2.769, -26.82)),
+    ],
+)
+def test_wu2019_native_relative_denominator(mach, alpha, errors):
+    """Published percentages refer to measurements, not to native predictions."""
+    # Arrange
+    profile = Wu2019Errors()
+    expected = np.abs(np.asarray(errors) / (100 + np.asarray(errors)))
+    # Act
+    actual = profile.fractions(mach, alpha)
+    # Assert
+    assert actual == pytest.approx(expected)
+    assert max(actual) < 1
+
+
+def test_wu2019_interpolation_and_boundary_assumption():
+    """Interpolate fractional magnitudes; hold, rather than extrapolate, edges."""
+    # Arrange
+    profile = Wu2019Errors()
+    low = np.asarray(profile.fractions(0.8, 0))
+    high = np.asarray(profile.fractions(0.8, 2))
+    # Act
+    middle = profile.fractions(0.8, 1)
+    outside = profile.fractions(0.1, -20)
+    # Assert
+    assert middle == pytest.approx((low + high) / 2)
+    assert outside == pytest.approx(profile.fractions(0.8, -6))
+    assert profile.fractions(0.95, 0)[2] == pytest.approx(81.47 / 181.47)
+
+
+def test_wu2019_zero_alpha_uses_other_angles_mean():
+    """Stability avoids the paper's near-zero normal-force denominator artifact."""
+    # Arrange
+    profile = Wu2019Errors()
+    percentages = profile.cn_error_percent[0, profile.alpha != 0]
+    expected = np.mean(np.abs(percentages / (100 + percentages)))
+    # Act
+    _, normal, moment = profile.fractions(0.8, 0)
+    # Assert
+    assert normal == pytest.approx(expected)
+    assert normal != pytest.approx(645.672 / 745.672)
+    assert moment == pytest.approx(6.56 / 93.44)
+
+
+@pytest.mark.parametrize(
+    "mach,alpha,expected",
+    [
+        (0.6, 0, 0.2),
+        (0.8, 0, 0.2),
+        (1.2, -2, 48.274 / 51.726),
+        (3.1, 0, 0.2),
+        (1.2, 7, 0.2),
+    ],
+)
+def test_datcom_drag_floor_and_larger_published_errors(
+    mach, alpha, expected, datcom_rocket, datcom_table_factory
+):
+    """CD has a 20% floor; larger Wu discrepancies apply only inside its data."""
+    # Arrange
+    table = datcom_table_factory()
+    native = DatcomSurface(
+        datcom_rocket, {True: table, False: table}, {True: 0, False: 0}
+    )
+    model = DatcomUncertaintySurface.from_model(native, 1, nose_position=1.2)
+    # Act / Assert
+    assert model.drag_fraction(mach, alpha) == pytest.approx(expected)
+
+
+@pytest.mark.parametrize("sign", [-1, 1])
+@pytest.mark.parametrize("beta", [0, 3])
+def test_datcom_scenarios_scale_drag_preserve_lift_and_native_tables(
+    sign, beta, datcom_rocket, datcom_table_factory
+):
+    """At finite alpha/beta the airflow-parallel force changes by exactly 20%."""
+    # Arrange
+    table = datcom_table_factory(
+        {
+            "CA": 0.5,
+            "CN": 0.2,
+            "CYB": -0.02,
+            "CNA": 0.1,
+            "CMA": -0.2,
+            "CNQ": 2,
+            "CMQ": -4,
+        }
+    )
+    native = DatcomSurface(
+        datcom_rocket, {True: table, False: table}, {True: 0.2, False: 0}
+    )
+    model = DatcomUncertaintySurface.from_model(native, sign, nose_position=1.2)
+    alpha, beta_rad = np.radians([2, beta])
+    before = native.coefficients(False, 0.6, alpha, beta_rad).copy()
+    direction = np.array([np.tan(beta_rad), np.tan(alpha), 1.0])
+    direction /= np.linalg.norm(direction)
+    original_force = np.array([before[3] + before[6] * beta, -before[0], -before[2]])
+    original_drag = -original_force @ direction
+    # Act
+    values = model.coefficients(False, 0.6, alpha, beta_rad)
+    force = np.array([values[3] + values[6] * beta, -values[0], -values[2]])
+    drag = -force @ direction
+    # Assert
+    assert drag == pytest.approx(original_drag * (1 + sign * 0.2))
+    assert values[-1] == pytest.approx(drag)
+    np.testing.assert_allclose(
+        force + drag * direction, original_force + original_drag * direction, atol=1e-12
+    )
+    np.testing.assert_array_equal(values[6:20], before[6:20])
+    np.testing.assert_array_equal(
+        native.coefficients(False, 0.6, alpha, beta_rad), before
+    )
+    assert values[5] == before[5]  # roll moment unchanged
+
+
+@pytest.mark.parametrize("direction", [-1, 1])
+@pytest.mark.parametrize("sign", [-1, 1])
+@pytest.mark.parametrize("powered", [True, False])
+def test_datcom_scenario_cp_matches_varied_slopes(
+    direction, sign, powered, datcom_rocket, datcom_table_factory
+):
+    """A 2% nose-to-CP shift agrees with the force/moment slopes in both frames."""
+    # Arrange
+    table = datcom_table_factory({"CA": 0.5, "CNA": 0.1, "CMA": -0.2})
+    table.body["CN"][:] = 0.05 + 0.1 * table[5]
+    table.body["CM"][:] = 0.03 - 0.2 * table[5]
+    native = DatcomSurface(
+        datcom_rocket, {True: table, False: table}, {True: 0.2 * direction, False: 0}
+    )
+    native.csys = direction
+    nose = direction * 1.2
+    model = DatcomUncertaintySurface.from_model(native, sign, nose_position=nose)
+    step = 1e-4  # degrees
+    cp_before = model._native_cp(powered, 0.6)
+    cp_expected = cp_before - sign * 0.02 * (nose - cp_before)
+    # Act
+    upper = model.coefficients(powered, 0.6, np.radians(step))
+    lower = model.coefficients(powered, 0.6, -np.radians(step))
+    slopes = (upper - lower) / (2 * step)
+    cp_from_moments = (
+        native.references[powered]
+        + direction * model.reference_length * slopes[1] / slopes[0]
+    )
+    # Assert
+    assert cp_from_moments == pytest.approx(cp_expected, rel=1e-8)
+    if not powered:
+        assert model.small_angle_cp(0.6) == pytest.approx(cp_expected)
+    assert (nose - cp_expected) / (nose - cp_before) == pytest.approx(1 + sign * 0.02)
+
+
+def test_datcom_scenario_preserves_dynamic_load_increments(
+    datcom_rocket, datcom_table_factory
+):
+    """The static drag/CP scenario does not rescale native DAMP contributions."""
+    # Arrange
+    table = datcom_table_factory(
+        {"CA": 0.5, "CNA": 0.1, "CMA": -0.2, "CNQ": 2, "CMQ": -4, "CAQ": 1, "CYR": 3}
+    )
+    native = DatcomSurface(
+        datcom_rocket, {True: table, False: table}, {True: 0, False: 0}
+    )
+    model = DatcomUncertaintySurface.from_model(native, 1, nose_position=1.2)
+
+    def loads(surface, omega):
+        return np.array(
+            surface.compute_forces_and_moments_at_time(
+                0, 0, Vector([0, -5, -100]), 100, 0.6, 1.2, Vector.zeros(), omega, 1e6
+            )
+        )
+
+    # Act
+    rates = Vector([0.1, 0.2, 0.3])
+    increment = loads(model, rates) - loads(model, Vector.zeros())
+    expected = loads(native, rates) - loads(native, Vector.zeros())
+    # Assert
+    np.testing.assert_allclose(increment, expected, rtol=1e-10, atol=1e-10)
+
+
+def test_datcom_scenario_undefined_cp(datcom_rocket, datcom_table_factory):
+    """Unknown CP stays unavailable while the drag scenario remains finite."""
+    # Arrange
+    table = datcom_table_factory({"CA": 0.5})
+    native = DatcomSurface(
+        datcom_rocket, {True: table, False: table}, {True: 0, False: 0}
+    )
+    model = DatcomUncertaintySurface.from_model(native, 1, nose_position=1.2)
+    # Act / Assert
+    assert np.isnan(model.small_angle_cp(0.6))
+    assert model.coefficients(False, 0.6, 0)[-1] == pytest.approx(0.6)
+
+
+def test_datcom_report_asymmetric_and_incomplete_envelope():
+    """± is the largest nominal departure; an absent case is never zero error."""
+    # Arrange
+    report = DatcomUncertaintyReport(
+        cases={
+            "nominal": {"apogee": 1000},
+            "upper": {"apogee": 900},
+            "lower": {"apogee": 1200},
+        }
+    )
+    # Act / Assert
+    assert report.uncertainty("apogee") == pytest.approx(200)
+    assert report.uncertainty("unavailable") is None
+    report.cases.pop("lower")
+    assert report.uncertainty("apogee") is None