{ "cells": [ { "cell_type": "markdown", "id": "fe6987ee-7907-4b32-b8b4-30927d4e7970", "metadata": {}, "source": [ "# Re-entry trajectory using custom aerodynamic guidance\n", "\n", "## Objectives\n", "\n", "This examples focuses on the application of aerodynamic guidance in the context of a re-entry trajectory of the Space Transportation System (STS).\n", "\n", "The aerodynamic guidance updates the angle of attack and the bank angle of the vehicle based on its flight conditions.\n", "The angle of attack is set to 40deg for a Mach number above 12, to 10deg for a Mach number below 6, and varies linearly between them. The bank angle is computed such that the derivative of the flight path angle over time equals 0, and the flight path angle is then constant.\n", "To do so, this example also showcases how to extract and use the flight condition\n", "and body properties during the simulation.\n", "\n", "The initial state of the STS is most notably its initial altitude of 120km, velocity of 7.5km/s, and its flight path angle of -0.6deg.\n", "\n", "A high number of dependent variable are also propagated in this example. All of them are then plotted at the end of this script." ] }, { "cell_type": "markdown", "id": "5203a79a", "metadata": {}, "source": [ "## Import statements\n", "\n", "The required import statements are made here, at the very beginning.\n", "\n", "Some standard modules are first loaded. These are `numpy` and `matplotlib.pyplot`.\n", "\n", "Then, the different modules of `tudatpy` that will be used are imported." ] }, { "cell_type": "code", "execution_count": 1, "id": "ff69831a", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:28:11.394983Z", "iopub.status.busy": "2025-09-16T07:28:11.394875Z", "iopub.status.idle": "2025-09-16T07:28:11.637302Z", "shell.execute_reply": "2025-09-16T07:28:11.636970Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# Load standard modules\n", "import math\n", "import numpy as np\n", "from matplotlib import pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "id": "c46fdfff", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:28:11.638957Z", "iopub.status.busy": "2025-09-16T07:28:11.638811Z", "iopub.status.idle": "2025-09-16T07:28:11.765591Z", "shell.execute_reply": "2025-09-16T07:28:11.765132Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# Load tudatpy modules\n", "from tudatpy.interface import spice\n", "from tudatpy import dynamics\n", "from tudatpy.dynamics import environment_setup, environment, propagation_setup, propagation, simulator\n", "from tudatpy.astro import element_conversion\n", "from tudatpy import constants\n", "from tudatpy.util import result2array\n", "from tudatpy.astro.time_representation import DateTime" ] }, { "cell_type": "markdown", "id": "eb4e8f6c", "metadata": {}, "source": [ "## Aerodynamic guidance class\n", "\n", "First of all, let's create a class that contains the aerodynamic guidance. This class needs to be inherited from `propagation.AerodynamicGuidance`.\n", "\n", "During the initialisation of this class, the current system of simulated bodies will have to be input.\n", "\n", "Then, the class must contain an `updateGuidance()` function that will be called at each simulation time step, taking the time as an input.\n", "Most importantly, this function updates both the angle of attack (`self.angle_of_attack`) and bank angle (`self.bank_angle`) of the vehicle.\n", "\n", "The angle of attack $\\alpha$ should be updated as a function of the Mach number $M$ as follows:\n", "\n", "- $\\alpha = 40$ deg if $M > 12$.\n", "- $\\alpha = 10$ deg if $M < 6$.\n", "- $\\alpha$ varies linearly between the two boundaries for other $M$.\n", "\n", "In practice, the following Logistic function is used so that the transition in $\\alpha$ between $M=12$ and $M=6$ is smoother:\n", "$$\n", "\\alpha = \\frac{30}{1 + e^{-2 (M-9) }} + 10\n", "$$\n", "\n", "The bank angle $\\sigma$ is computed so that, ideally, the flight path angle $\\gamma$ should stay constant over time ($\\dot{\\gamma} = 0$).\n", "The change in flight path angle over time $\\dot{\\gamma}$ is related to the bank angle $\\sigma$ trough the equation below. We thus compute $\\sigma$ so that this equation equals 0.\n", "$$\n", "V \\dot{\\gamma} = \\frac{L}{m} \\cos \\sigma - \\left( g_d - \\frac{V^2}{r} \\right) \\cos \\gamma + 2 \\omega_E V \\cos \\delta \\sin \\chi + \\omega^2_E r \\cos \\delta (\\cos \\delta \\cos \\gamma + \\sin \\gamma \\sin \\delta \\cos \\chi)\n", "$$\n", "In this equation, $\\chi$ and $\\delta$ are the heading and latitude angles. $L$, $m$, $V$, and $\\omega_E$ are the lift force, vehicle mass, airspeed, and Earth rotation rate. $g_d$ is the downwards (towards the centre of Earth) component of the gravitational acceleration. All of these values are either directly obtained or computed from the flight conditions, the aerodynamic coefficient interface, or the aerodynamic coefficient interface." ] }, { "cell_type": "code", "execution_count": 3, "id": "c18b2ffe", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:28:11.767294Z", "iopub.status.busy": "2025-09-16T07:28:11.767172Z", "iopub.status.idle": "2025-09-16T07:28:11.773138Z", "shell.execute_reply": "2025-09-16T07:28:11.772738Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# Create a class for the aerodynamic guidance of the STS, inheriting from 'propagation.AerodynamicGuidance'\n", "class STSAerodynamicGuidance:\n", "\n", " def __init__(self, bodies: environment.SystemOfBodies):\n", "\n", " # Extract the STS and Earth bodies\n", " self.vehicle = bodies.get_body(\"STS\")\n", " self.earth = bodies.get_body(\"Earth\")\n", "\n", " # Extract the STS flight conditions, angle calculator, and aerodynamic coefficient interface\n", " environment_setup.add_flight_conditions( bodies, 'STS', 'Earth' )\n", " self.vehicle_flight_conditions = bodies.get_body(\"STS\").flight_conditions\n", " self.aerodynamic_angle_calculator = self.vehicle_flight_conditions.aerodynamic_angle_calculator\n", " self.aerodynamic_coefficient_interface = self.vehicle_flight_conditions.aerodynamic_coefficient_interface\n", "\n", " self.current_time = float(\"NaN\")\n", "\n", " def getAerodynamicAngles(self, current_time: float):\n", " self.updateGuidance( current_time )\n", " return np.array([self.angle_of_attack, 0.0, self.bank_angle])\n", "\n", " # Function that is called at each simulation time step to update the ideal bank angle of the vehicle\n", " def updateGuidance(self, current_time: float):\n", "\n", " if( math.isnan( current_time ) ):\n", " self.current_time = float(\"NaN\")\n", " elif( current_time != self.current_time ):\n", " # Get the (constant) angular velocity of the Earth body\n", " earth_angular_velocity = np.linalg.norm(self.earth.body_fixed_angular_velocity)\n", " # Get the distance between the vehicle and the Earth bodies\n", " earth_distance = np.linalg.norm(self.vehicle.position)\n", " # Get the (constant) mass of the vehicle body\n", " body_mass = self.vehicle.mass\n", "\n", " # Extract the current Mach number, airspeed, and air density from the flight conditions\n", " mach_number = self.vehicle_flight_conditions.mach_number\n", " airspeed = self.vehicle_flight_conditions.airspeed\n", " density = self.vehicle_flight_conditions.density\n", "\n", " # Set the current Angle of Attack (AoA). The following line enforces the followings:\n", " # * the AoA is constant at 40deg when the Mach number is above 12\n", " # * the AoA is constant at 10deg when the Mach number is below 6\n", " # * the AoA varies close to linearly when the Mach number is between 12 and 6\n", " # * a Logistic relation is used so that the transition in AoA between M=12 and M=6 is smoother\n", " self.angle_of_attack = np.deg2rad(30 / (1 + np.exp(-2*(mach_number-9))) + 10)\n", "\n", " # Update the variables on which the aerodynamic coefficients are based (AoA and Mach)\n", " current_aerodynamics_independent_variables = [self.angle_of_attack, mach_number]\n", " \n", " # Update the aerodynamic coefficients\n", " self.aerodynamic_coefficient_interface.update_coefficients(\n", " current_aerodynamics_independent_variables, current_time)\n", "\n", " # Extract the current force coefficients (in order: C_D, C_S, C_L)\n", " current_force_coefficients = self.aerodynamic_coefficient_interface.current_force_coefficients\n", " # Extract the (constant) reference area of the vehicle\n", " aerodynamic_reference_area = self.aerodynamic_coefficient_interface.reference_area\n", "\n", " # Get the heading, flight path, and latitude angles from the aerodynamic angle calculator\n", " heading = self.aerodynamic_angle_calculator.get_angle(environment_setup.aerodynamic_coefficients.AerodynamicsReferenceFrameAngles.heading_angle)\n", " flight_path_angle = self.aerodynamic_angle_calculator.get_angle(environment_setup.aerodynamic_coefficients.AerodynamicsReferenceFrameAngles.flight_path_angle)\n", " latitude = self.aerodynamic_angle_calculator.get_angle(environment_setup.aerodynamic_coefficients.AerodynamicsReferenceFrameAngles.latitude_angle)\n", "\n", " # Compute the acceleration caused by Lift\n", " lift_acceleration = 0.5 * density * airspeed ** 2 * aerodynamic_reference_area * current_force_coefficients[2] / body_mass\n", " # Compute the gravitational acceleration\n", " downward_gravitational_acceleration = self.earth.gravitational_parameter / (earth_distance ** 2)\n", " # Compute the centrifugal acceleration\n", " spacecraft_centrifugal_acceleration = airspeed ** 2 / earth_distance\n", " # Compute the Coriolis acceleration\n", " coriolis_acceleration = 2 * earth_angular_velocity * airspeed * np.cos(latitude) * np.sin(heading)\n", " # Compute the centrifugal acceleration from the Earth\n", " earth_centrifugal_acceleration = earth_angular_velocity ** 2 * earth_distance * np.cos(latitude) * (np.cos(latitude) * np.cos(flight_path_angle) + np.sin(flight_path_angle) * np.sin(latitude) * np.cos(heading))\n", "\n", " # Compute the cosine of the ideal bank angle\n", " cosine_of_bank_angle = ((downward_gravitational_acceleration - spacecraft_centrifugal_acceleration) * np.cos(flight_path_angle) - coriolis_acceleration - earth_centrifugal_acceleration) / lift_acceleration\n", " # If the cosine lead to a value out of the [-1, 1] range, set to bank angle to 0deg or 180deg\n", " if (cosine_of_bank_angle < -1):\n", " self.bank_angle = np.pi\n", " elif (cosine_of_bank_angle > 1):\n", " self.bank_angle = 0.0\n", " else:\n", " # If the cos is in the correct range, return the computed bank angle\n", " self.bank_angle = np.arccos(cosine_of_bank_angle)\n", " self.current_time = current_time" ] }, { "cell_type": "markdown", "id": "dd9232e0", "metadata": {}, "source": [ "## Configuration\n", "\n", "NAIF's `SPICE` kernels are first loaded, so that the position of various bodies such as the Earth can be make known to `tudatpy`.\n", "\n", "Then, the start and end simulation epochs are setups. In this case, the start epoch is set to `0`, corresponding to the 1st of January 2000. The times should be specified in seconds since J2000.\n", "Please refer to the [API documentation](https://py.api.tudat.space/en/latest/time_representation.html) of the `time_representation` module for more information on this." ] }, { "cell_type": "code", "execution_count": 4, "id": "6ea394a1", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:28:11.774295Z", "iopub.status.busy": "2025-09-16T07:28:11.774211Z", "iopub.status.idle": "2025-09-16T07:28:11.802128Z", "shell.execute_reply": "2025-09-16T07:28:11.801864Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# Load spice kernels\n", "spice.load_standard_kernels()\n", "\n", "# Set simulation start epoch (January the 1st, 2000 plus 6000s)\n", "simulation_start_epoch = DateTime(2000, 1, 1, 1, 40).epoch()\n", "\n", "# Set the maximum simulation time (avoid very long skipping re-entry)\n", "max_simulation_time = 3*constants.JULIAN_DAY\n", "\n", "\n", "# ## Environment setup\n", "# \n", "# Let’s create the environment for our simulation. This setup covers the creation of (celestial) bodies, vehicle(s), and environment interfaces.\n", "# " ] }, { "cell_type": "markdown", "id": "c14e6f50", "metadata": {}, "source": [ "### Create the bodies\n", "\n", "Bodies can be created by making a list of strings with the bodies that is to be included in the simulation.\n", "\n", "The default body settings (such as atmosphere, body shape, rotation model) are taken from `SPICE`.\n", "\n", "These settings can be adjusted. Please refer to the [Available Environment Models](https://docs.tudat.space/en/latest/_src_user_guide/state_propagation/environment_setup/environment_models.html#available-model-types) in the user guide for more details." ] }, { "cell_type": "code", "execution_count": 5, "id": "06dd4f2c", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:28:11.803207Z", "iopub.status.busy": "2025-09-16T07:28:11.803105Z", "iopub.status.idle": "2025-09-16T07:28:11.822592Z", "shell.execute_reply": "2025-09-16T07:28:11.822322Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# Create default body settings for \"Earth\"\n", "bodies_to_create = [\"Earth\"]\n", "\n", "# Create default body settings for bodies_to_create, with \"Earth\"/\"J2000\" as the global frame origin and orientation\n", "global_frame_origin = \"Earth\"\n", "global_frame_orientation = \"J2000\"\n", "body_settings = environment_setup.get_default_body_settings(\n", " bodies_to_create, global_frame_origin, global_frame_orientation)" ] }, { "cell_type": "markdown", "id": "8000875d", "metadata": {}, "source": [ "### Create the vehicle\n", "\n", "Let's now create the 5000kg vehicle for which Earth re-entry trajectory will be simulated." ] }, { "cell_type": "code", "execution_count": 6, "id": "e75afb5e", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:28:11.823791Z", "iopub.status.busy": "2025-09-16T07:28:11.823670Z", "iopub.status.idle": "2025-09-16T07:28:11.825499Z", "shell.execute_reply": "2025-09-16T07:28:11.825281Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# Create empty body settings for the satellite\n", "body_settings.add_empty_settings(\"STS\")\n", "\n", "body_settings.get(\"STS\").constant_mass = 5000\n" ] }, { "cell_type": "markdown", "id": "88b6d10d", "metadata": {}, "source": [ "### Add an aerodynamic coefficient interface\n", "\n", "An aerodynamic coefficient interface is now added to the STS vehicle. These coefficients are interpolated from files that tabulate them as a function of angle of attack and Mach number." ] }, { "cell_type": "code", "execution_count": 7, "id": "b1611884", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:28:11.826288Z", "iopub.status.busy": "2025-09-16T07:28:11.826202Z", "iopub.status.idle": "2025-09-16T07:28:11.828424Z", "shell.execute_reply": "2025-09-16T07:28:11.828141Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# Define the aerodynamic coefficient files (leave C_S empty)\n", "aero_coefficients_files = {0: \"input/STS_CD.dat\", 2:\"input/STS_CL.dat\"}\n", "\n", "# Setup the aerodynamic coefficients settings tabulated from the files\n", "#coefficient_settings = environment_setup.aerodynamic_coefficients.tabulated_force_only_from_files(\n", "# force_coefficient_files=aero_coefficients_files,\n", "# reference_area=2690.0*0.3048*0.3048,\n", "# independent_variable_names=[environment.angle_of_attack_dependent, environment.mach_number_dependent],\n", "# are_coefficients_in_aerodynamic_frame=True,\n", "# are_coefficients_in_negative_axis_direction=True\n", "#)\n", "\n", "# Setup the aerodynamic coefficients settings tabulated from the files\n", "coefficient_settings = environment_setup.aerodynamic_coefficients.tabulated_force_only_from_files(\n", " force_coefficient_files=aero_coefficients_files,\n", " reference_area=2690.0*0.3048*0.3048,\n", " independent_variable_names=[environment_setup.aerodynamic_coefficients.AerodynamicCoefficientsIndependentVariables.angle_of_attack_dependent, environment_setup.aerodynamic_coefficients.AerodynamicCoefficientsIndependentVariables.mach_number_dependent],\n", ")\n", "\n", "# Add predefined aerodynamic coefficients database to the body\n", "body_settings.get(\"STS\").aerodynamic_coefficient_settings = coefficient_settings\n" ] }, { "cell_type": "markdown", "id": "dd644e21", "metadata": {}, "source": [ "The system of bodies is created using the settings. This system of bodies is stored into the variable `bodies`." ] }, { "cell_type": "code", "execution_count": 8, "id": "3780e845", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:28:11.829838Z", "iopub.status.busy": "2025-09-16T07:28:11.829714Z", "iopub.status.idle": "2025-09-16T07:28:11.837948Z", "shell.execute_reply": "2025-09-16T07:28:11.837684Z" } }, "outputs": [], "source": [ "# Create system of bodies\n", "bodies = environment_setup.create_system_of_bodies(body_settings)" ] }, { "cell_type": "markdown", "id": "2802069f", "metadata": {}, "source": [ "### Add rotation model based on aerodynamic guidance" ] }, { "cell_type": "code", "execution_count": 9, "id": "c7a6dfb7", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:28:11.838973Z", "iopub.status.busy": "2025-09-16T07:28:11.838894Z", "iopub.status.idle": "2025-09-16T07:28:11.840691Z", "shell.execute_reply": "2025-09-16T07:28:11.840434Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# ### Add rotation model based on aerodynamic guidance\n", "# Create the aerodynamic guidance object\n", "aerodynamic_guidance_object = STSAerodynamicGuidance(bodies)\n", "rotation_model_settings = environment_setup.rotation_model.aerodynamic_angle_based(\n", " 'Earth', '', 'STS_Fixed', aerodynamic_guidance_object.getAerodynamicAngles )\n", "environment_setup.add_rotation_model( bodies, 'STS', rotation_model_settings )" ] }, { "cell_type": "markdown", "id": "ea092482", "metadata": {}, "source": [ "## Propagation setup\n", "\n", "Now that the environment is created, the propagation setup is defined.\n", "\n", "First, the bodies to be propagated and the central bodies will be defined.\n", "Central bodies are the bodies with respect to which the state of the respective propagated bodies is defined." ] }, { "cell_type": "code", "execution_count": 10, "id": "55c21079", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:28:11.841615Z", "iopub.status.busy": "2025-09-16T07:28:11.841514Z", "iopub.status.idle": "2025-09-16T07:28:11.843056Z", "shell.execute_reply": "2025-09-16T07:28:11.842806Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# Define bodies that are propagated\n", "bodies_to_propagate = [\"STS\"]\n", "\n", "# Define central bodies of propagation\n", "central_bodies = [\"Earth\"]" ] }, { "cell_type": "markdown", "id": "cfd2b48e", "metadata": {}, "source": [ "### Create the acceleration model\n", "\n", "The acceleration settings that act on the `STS` vehicle are now defined.\n", "In this case, these simply consist in the Earth gravitational effect modelled as a point mass and of the aerodynamic acceleration of the Earth atmosphere.\n", "\n", "The acceleration settings defined are then applied to `STS` vehicle in a dictionary.\n", "\n", "This dictionary is finally input to the propagation setup to create the acceleration models." ] }, { "cell_type": "code", "execution_count": 11, "id": "5b5139ec", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:28:11.844083Z", "iopub.status.busy": "2025-09-16T07:28:11.843995Z", "iopub.status.idle": "2025-09-16T07:28:11.845794Z", "shell.execute_reply": "2025-09-16T07:28:11.845578Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# Define the accelerations acting on the STS (Earth as a Point Mass, and Earth's atmosphere)\n", "accelerations_settings_STS = dict(\n", " Earth=[\n", " propagation_setup.acceleration.point_mass_gravity(),\n", " propagation_setup.acceleration.aerodynamic(),\n", " ]\n", ")\n", "\n", "acceleration_settings = {\"STS\": accelerations_settings_STS}\n", "\n", "# Create the acceleration models\n", "acceleration_models = propagation_setup.create_acceleration_models(\n", " bodies, acceleration_settings, bodies_to_propagate, central_bodies\n", ")" ] }, { "cell_type": "markdown", "id": "ae1bc6a2", "metadata": {}, "source": [ "### Define the initial state\n", "\n", "The initial state of the vehicle that will be propagated is now defined. Most importantly, the `STS` vehicle starts 120km above Earth, at a velocity og 7500m/s, and a flight path angle of -0.6 deg (from the horizon).\n", "\n", "This initial state always has to be provided as a cartesian state, in the form of a list with the first three elements representing the initial position, and the three remaining elements representing the initial velocity.\n", "\n", "In this case, let's make use of the `spherical_to_cartesian_elementwise()` function that is included in the `element_conversion` module, so that the initial state can be input as Spherical elements, and then converted in Cartesian elements.\n", "\n", "Finally, the initial state has to be converted from the Earth-fixed frame in which it is defined with the Spherical elements to the inertial frame." ] }, { "cell_type": "code", "execution_count": 12, "id": "bb67e486", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:28:11.846765Z", "iopub.status.busy": "2025-09-16T07:28:11.846675Z", "iopub.status.idle": "2025-09-16T07:28:11.849024Z", "shell.execute_reply": "2025-09-16T07:28:11.848567Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# Set the initial state of the STS as spherical elements, and convert them to a cartesian state\n", "initial_radial_distance = bodies.get_body(\"Earth\").shape_model.average_radius + 120e3\n", "\n", "# Convert the initial state\n", "initial_earth_fixed_state = element_conversion.spherical_to_cartesian_elementwise(\n", " radial_distance=initial_radial_distance,\n", " latitude=np.deg2rad(20),\n", " longitude=np.deg2rad(140),\n", " speed=7.5e3,\n", " flight_path_angle=np.deg2rad(-0.6),\n", " heading_angle=np.deg2rad(15),\n", ")\n", "\n", "# Convert the state from the Earth-fixed frame to the inertial frame\n", "earth_rotation_model = bodies.get_body(\"Earth\").rotation_model\n", "initial_state = environment.transform_to_inertial_orientation(\n", " initial_earth_fixed_state, simulation_start_epoch, earth_rotation_model\n", ")" ] }, { "cell_type": "markdown", "id": "b6dba9f1", "metadata": {}, "source": [ "### Define the dependent variables to save\n", "\n", "In this example, we are interested in saving not only the propagated state of the vehicle over time, but also a set of so-called dependent variables, that are to be computed (or extracted and saved) at each integration step.\n", "\n", "[This page](https://py.api.tudat.space/en/latest/dependent_variable.html) of the tudatpy API website provides a detailed explanation of all the dependent variables that are available." ] }, { "cell_type": "code", "execution_count": 13, "id": "9c4bf3ba", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:28:11.850077Z", "iopub.status.busy": "2025-09-16T07:28:11.849989Z", "iopub.status.idle": "2025-09-16T07:28:11.852114Z", "shell.execute_reply": "2025-09-16T07:28:11.851899Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# Define the list of dependent variables to save during the propagation\n", "dependent_variables_to_save = [\n", " propagation_setup.dependent_variable.flight_path_angle(\"STS\", \"Earth\"),\n", " propagation_setup.dependent_variable.altitude(\"STS\", \"Earth\"),\n", " propagation_setup.dependent_variable.bank_angle(\"STS\", \"Earth\"),\n", " propagation_setup.dependent_variable.angle_of_attack(\"STS\", \"Earth\"),\n", " propagation_setup.dependent_variable.aerodynamic_force_coefficients(\"STS\"),\n", " propagation_setup.dependent_variable.airspeed(\"STS\", \"Earth\"),\n", " propagation_setup.dependent_variable.total_acceleration_norm(\"STS\"),\n", " propagation_setup.dependent_variable.mach_number(\"STS\", \"Earth\")\n", "]" ] }, { "cell_type": "markdown", "id": "60721bd6", "metadata": {}, "source": [ "### Create the propagator settings\n", "\n", "The propagator is finally setup.\n", "\n", "First, a termination condition is defined so that the propagation as soon as one of these conditions is fulfilled:\n", "\n", "- The altitude gets below 25km.\n", "- The simulation time gets above 3 days.\n", "\n", "Combined termination settings are then needed, which can be done using the `propagation_setup.propagator.hybrid_termination()` function.\n", "\n", "Subsequently, the integrator settings are defined using a RK4 integrator with the fixed step size of 0.5 seconds." ] }, { "cell_type": "code", "execution_count": 14, "id": "9d21dfa3", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:28:11.853072Z", "iopub.status.busy": "2025-09-16T07:28:11.852963Z", "iopub.status.idle": "2025-09-16T07:28:11.855566Z", "shell.execute_reply": "2025-09-16T07:28:11.855343Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# Define a termination conditions to stop once altitude goes below 25 km\n", "altitude_limit = 25.0e3\n", "termination_altitude_settings = (\n", " propagation_setup.propagator.dependent_variable_termination(\n", " dependent_variable_settings=propagation_setup.dependent_variable.altitude(\n", " \"STS\", \"Earth\"\n", " ),\n", " limit_value=altitude_limit,\n", " use_as_lower_limit=True,\n", " )\n", ")\n", "# Define a termination condition to stop after a given time (to avoid an endless skipping re-entry)\n", "termination_time_settings = propagation_setup.propagator.time_termination(simulation_start_epoch + max_simulation_time)\n", "# Combine the termination settings to stop when one of them is fulfilled\n", "termination_conditions = [termination_altitude_settings, termination_time_settings]\n", "# Add string representations of the termination conditions\n", "termination_conditions_repr = [\n", " f\"Altitude Termination at {altitude_limit/1e3}km\",\n", " \"Time Termination\",\n", "]\n", "\n", "combined_termination_settings = propagation_setup.propagator.hybrid_termination(\n", " termination_conditions, fulfill_single_condition=True\n", ")\n", "\n", "# Create numerical integrator settings\n", "fixed_step_size = 0.5\n", "integrator_settings = propagation_setup.integrator.runge_kutta_fixed_step(\n", " fixed_step_size, coefficient_set=propagation_setup.integrator.CoefficientSets.rk_4\n", ")\n", "\n", "# Create the propagation settings\n", "propagator_settings = propagation_setup.propagator.translational(\n", " central_bodies,\n", " acceleration_models,\n", " bodies_to_propagate,\n", " initial_state,\n", " simulation_start_epoch,\n", " integrator_settings,\n", " combined_termination_settings,\n", " output_variables=dependent_variables_to_save\n", ")" ] }, { "cell_type": "markdown", "id": "b09089d3", "metadata": {}, "source": [ "## Propagate the trajectory\n", "\n", "The re-entry trajectory is now ready to be propagated.\n", "\n", "This is done by calling the `create_dynamics_simulator()` function of the `dynamics.simulator module`.\n", "This function requires the `bodies` and `propagator_settings` that have all been defined earlier.\n", "\n", "After this, the dependent variable history is extracted.\n", "The column indexes corresponding to a given dependent variable in the `dep_vars` variable are printed when the simulation is run, when `create_dynamics_simulator()` is called.\n", "Do mind that converting to an ndarray using the `result2array()` utility will shift these indexes, since the first column (index 0) will then be the times.\n", "\n", "In this example, we are not interested in analysing the state history. This can however be accessed in the `dynamics_simulator.propagation_results.state_history` variable." ] }, { "cell_type": "code", "execution_count": 15, "id": "40cd08f9", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:28:11.856446Z", "iopub.status.busy": "2025-09-16T07:28:11.856346Z", "iopub.status.idle": "2025-09-16T07:28:12.089715Z", "shell.execute_reply": "2025-09-16T07:28:12.089023Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# Create the simulation objects and propagate the dynamics\n", "dynamics_simulator = simulator.create_dynamics_simulator(\n", " bodies, propagator_settings\n", ")\n", "\n", "# Extract the resulting simulation dependent variables\n", "dependent_variables = dynamics_simulator.propagation_results.dependent_variable_history\n", "# Convert the dependent variables from a dictionary to a numpy array\n", "dependent_variables_array = result2array(dependent_variables)" ] }, { "cell_type": "markdown", "id": "f3cf93f9", "metadata": {}, "source": [ "## Post-process the propagation results\n", "\n", "The results of the propagation are then processed to a more user-friendly form." ] }, { "cell_type": "markdown", "id": "67a46739", "metadata": {}, "source": [ "### Termination Condition\n", "\n", "First, let's assess which of the two termination conditions that were defined in the `hybrid_termination()` condition triggered the termination of the propagation.\n", "This can be retrieved from the `termination_details` attribute of the `propagation_results` object, which will be an instance of `PropagationTerminationDetailsFromHybridCondition`.\n", "\n", "The `PropagationTerminationDetailsFromHybridCondition.was_condition_met_when_stopping` attribute is a list of boolean flags, which signs if each of the defined termination conditions in the `hybrid_termination()` condition was fulfilled." ] }, { "cell_type": "code", "execution_count": 16, "id": "f52f194d", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:28:12.091103Z", "iopub.status.busy": "2025-09-16T07:28:12.090985Z", "iopub.status.idle": "2025-09-16T07:28:12.093306Z", "shell.execute_reply": "2025-09-16T07:28:12.093030Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Termination Conditions fulfilled:\n", "Altitude Termination at 25.0km : True\n", "Time Termination : False\n" ] } ], "source": [ "termination_details = dynamics_simulator.propagation_results.termination_details\n", "condition_met_flags = termination_details.was_condition_met_when_stopping\n", "\n", "condition_fulfilled = [\n", " f\"{condition:<35}: {met}\"\n", " for condition, met in zip(termination_conditions_repr, condition_met_flags)\n", "]\n", "\n", "print(\"Termination Conditions fulfilled:\")\n", "print(\"\\n\".join(condition_fulfilled))" ] }, { "cell_type": "markdown", "id": "3c2a9717", "metadata": {}, "source": [ "### Altitude over time\n", "\n", "We can confirm that the propagation was terminated due to the altitude termination condition, by plotting it over the relative propagation time:" ] }, { "cell_type": "code", "execution_count": 17, "id": "d8dc6404", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:28:12.094216Z", "iopub.status.busy": "2025-09-16T07:28:12.094125Z", "iopub.status.idle": "2025-09-16T07:28:12.183799Z", "shell.execute_reply": "2025-09-16T07:28:12.183441Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Extract the time from the dependent variables array (and convert from seconds to minutes)\n", "time_min = (dependent_variables_array[:, 0] - dependent_variables_array[0, 0]) / 60\n", "\n", "# Define a matplotlib.pyplot figure\n", "plt.figure(figsize=(9, 5))\n", "# Plot the altitude over time\n", "plt.plot(time_min, dependent_variables_array[:, 2] / 1e3, label=\"Spacecraft Altitude\")\n", "plt.axhline(altitude_limit / 1e3, color=\"r\", label=\"Altitude Termination Limit\")\n", "plt.legend()\n", "# Add label to the axis\n", "plt.xlabel(\"Relative Time [min]\"), plt.ylabel(\"Altitude [km]\")\n", "# Add a grid\n", "plt.grid()\n", "# Use a tight layout to save space\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "d48ce681", "metadata": {}, "source": [ "### Airspeed vs altitude\n", "\n", "Let's now plot the altitude of the vehicle as a function of its airspeed. This gives insights into how the vehicle decelerates." ] }, { "cell_type": "code", "execution_count": 18, "id": "00436c5f", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:28:12.184835Z", "iopub.status.busy": "2025-09-16T07:28:12.184736Z", "iopub.status.idle": "2025-09-16T07:28:12.249566Z", "shell.execute_reply": "2025-09-16T07:28:12.249260Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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7upxmQcgDAAAA0GYtzMqXJA3uGKPIYLuHq2kehDwAAAAAbdYXG/ZLks7t4RuXakqEPAAAAABtVEFZjZZur590ZVy/FA9X03wIeQAAAADapM/W58plpAFp0eoQF+rpcpoNIQ8AAABAmzQvM0eSdGn/ZA9X0rwIeQAAAADanG35ZVqxu0h+Nmlcf9+5VFMi5AEAAABog976do8kaXSvRCVFBXu4muZFyAMAAADQplTW1um9lfskSRPP6ujhapofIQ8AAABAm/L+qmyV1dSpY1yoftLVNxZAPxIhDwAAAECbUed06ZVF2yVJ14/oJD8/m4cran6EPAAAAABtxrw1OdpXVKW4sEBdfUYHT5fTIgh5AAAAANqEOqdLLy7YJkm66SfpCgn093BFLYOQBwAAAKBN+NeKfdpeUKHoULuu88EJVw4j5AEAAADweeU1dXouI0uSdNfobooItnu4opZDyAMAAADg815euE0HymuVHh+ma4f57iieRMgDAAAA4OO27i/Tq4t3SJJ+P7anAgN8Owb59qsDAAAA0KY5XUb3v7dWDqfR6J4JGtM70dMltThCHgAAAACf9Y9lu7RqT7HCgwL0pyv6ymbzvXXxfoyQBwAAAMAnZeWV6YnPNkuS7r+oh1KiQzxcUesg5AEAAADwOVW1Tt0+d5Vq6lw6p3s7n59s5UiEPAAAAAA+Z+q8DdqaX66EiCA9d9UA+fn5/mWahxHyAAAAAPiUvy/bpXdX7JXNJs345UDFhwd5uqRWRcgDAAAA4DMWbynQtP9ulCTdd2FPjega7+GKWh8hDwAAAIBPyMor021zV8npMvr54FTdMqqzp0vyCEIeAAAAAK+360CFJs5arrLqOp3RKUbTx7eN5RKOhpAHAAAAwKvlllTp2teXq6CsRj2TIvT6pDMUFODv6bI8hpAHAAAAwGtlF1dpwmvLlV1cpfT4MP3jpmGKCrV7uiyPCvB0AQAAAABwKrYXlOu615crp6Ra7aND9NavhqldRNuaSfNoCHkAAAAAvM6GnBJNmvWdCitq1aVdmN761TAlR4V4uixLIOQBAAAA8Cpfbtyvu95ZrYpap/qkROrvN56puDa2Ft7xEPIAAAAAeAVjjF7/Zqemf7ZJxkjDO8fpleuGKCqkbd+D92OEPAAAAACWV+1w6o8frde/VuyTJF1zZgc9enkf2f2ZS/LHCHkAAAAALG17Qblue3uVNueVyc8mPXhJb904slObXQfvRAh5AAAAACzro8xs/eH9daqodSouLFAzrh6on3Rr5+myLI2QBwAAAMByiitr9cePNmjemhxJ0rD0WP31mkFKjAz2cGXWR8gDAAAAYClfbdqv37+/TgVlNfL3s+m287rqzp92VQD33zUJIQ8AAACAJRRV1Gr6p5v075X1k6t0aRemZ68aqIFp0Z4tzMsQ8gAAAAB4lMtl9J+V+/TEZ5tUVOmQzSbdNDJd917YQ8F2f0+X53UIeQAAAAA8ZlNuqR7+cL1W7C6SJPVIjNBjP+urMzrFergy70XIAwAAANDqDpTX6K9fbdXby/fI6TIKDfTX787vrutHdmLtu9NEyAMAAADQaqpqnXrjfzv18sLtKq+pkyRd1CdJf7y0t1KiQzxcnW8g5AEAAABocXVOlz5Yna1n529RXmm1JKlf+yg9cHFPjegS7+HqfAshDwAAAECLqXO6NG9NjmZ+vU07D1RIktpHh+i+i3ro0v4p8vOzebhC30PIAwAAANDs6pwufZiZoxe+3qpdhZWSpJhQu357bhdNGt6JWTNbECEPAAAAQLNxuKR3V+zTa0t2afcR4e4353TRdcM7KjyICNLSmvQOr1279qRP3Lt3bwUE0EAAAACgLThYUavZ/9uhN1b6q7xuoyQpNixQvzmns647q6PCCHetpknv9MCBA2Wz2WSMadJJ/fz8tGXLFnXu3Pm0igMAAABgbTsPVGjWkh36z8p9qna4JNmUEhWsG89O1zVndiDceUCT3/Hly5erXbt2JzzOGKO+ffueVlEAAAAArMvpMvp6c77e+na3Fm8t0OGxoL4pkRoSVqTfX3u2QoKDPFtkG9akkDdq1Ch17dpV0dHRTTrpOeeco5AQ1rgAAAAAfEl+WbXe/W6v/vndHuWU1C+DYLNJo3sm6NfndNbg1Ah99tlnCmAxc49qUshbsGDBSZ30008/PaViAAAAAFiL02W0dPsBvfPdXn2xIU91rvphu9iwQF05NFXXntlRHeJCJUkOh8OTpeIQLpAFAAAA0Mj2gnK9t3Kf3l+V7V68XJKGdIzRxLM6aGzfZJZBsKiTDnnGGP3nP//RggULlJ+fL5fL1WD/+++/32zFAQAAAGg9JVUOfbw2R/9ZuU+r9xS7t0eF2HXZgBRNGNZBvZIjPVcgmuSkQ95dd92lV199Veedd54SExNls7FCPQAAAOCtqmqd+mrzfn28JldfZ+Wrtq5+EMffz6Zzu7fTz4ekanSvBAUFMGrnLU465L311lt6//33dfHFF7dEPQAAAABaWE2dU4uyCvTftbn6atN+VdY63ft6JEboF0NSdfmgFCVEBHuwSpyqkw55UVFRrbb+XV1dnaZOnaq3335beXl5Sk5O1vXXX6+HHnpIfn71M/YYYzRt2jS9+uqrKioq0rBhw/Tiiy+qT58+rVIjAAAA4A2qHU4t3X5An6zN0/wNeSqrqXPvS4sN0bj+KRrXP1m9kyO5Ws/LnXTImzp1qqZNm6Y33nijxZdJeOqpp/TKK69ozpw56tOnj1asWKEbbrhBUVFRuuuuuyRJTz/9tJ577jnNnj1b3bt312OPPaYLLrhAWVlZioiIaNH6AAAAACsrqXJoYVa+5m/Yr4VZ+ao4YsQuKTJY4/ona9yAFA1IjSLY+ZCTDnlXXnml/vnPfyohIUGdOnWS3W5vsH/VqlXNVtyyZct0+eWX65JLLpEkderUSf/85z+1YsUKSfWjeDNmzNCDDz6o8ePHS5LmzJmjxMREzZ07VzfffHOz1QIAAAB4g7ySamVszNP8jfu1bHuhe8kDqT7YjemTqHH9UzS0Y4z8/Ah2vuikQ97111+vlStXauLEiS0+8crZZ5+tV155RVu2bFH37t21Zs0aLVmyRDNmzJAk7dy5U3l5eRozZoz7OUFBQRo1apSWLl1KyAMAAIDPc7qM1uwr1sLN+Vq4pUBr95U02N8tIVxj+iRqTO8k9WsfRbBrA0465H3yySf64osvdPbZZ7dEPQ3cf//9KikpUc+ePeXv7y+n06nHH39c11xzjSQpLy9PkpSYmNjgeYmJidq9e/cxz1tTU6Oamhr349LSUkn1izee6gKOh5/HApDWQU+sib5YDz2xHnpiPfTEmtpyXworarVk6wEt2npAS7YVqqjyh/fAZpMGpUXr/F7tdH7PBKXHh7n3OZ11cjqPdsbm0ZZ70hqa+r6edMhLS0tTZGTrrI3x7rvv6q233tLcuXPVp08fZWZmasqUKUpJSdHkyZPdx/14NNEYc9wRxieeeELTpk1rtH3+/PkKDQ09rZozMjJO6/lofvTEmuiL9dAT66En1kNPrKkt9MVppD3l0uZimzYV+2lPuWT0w++7If5GPaKNekcb9Yo2igw8IJUe0KbvNmmTB+ptCz3xhMrKyiYdZzPGmBMf9oNPPvlEM2fO1CuvvKJOnTqdSm1NlpaWpt///ve67bbb3Nsee+wxvfXWW9q8ebN27NihLl26aNWqVRo0aJD7mMsvv1zR0dGaM2fOUc97tJG8tLQ0HThw4JQDrMPhUEZGhi644IJG9ynCM+iJNdEX66En1kNPrIeeWJMv98UYo235FfrfjkIt235Qy3cdVEVNwyG4nkkROrd7vM7pFq9BaVEK8PfzULU/8OWeWEFpaani4+NVUlJy3Nxy0iN5EydOVGVlpbp06aLQ0NBGzTt48ODJV3sMlZWV7qUSDvP395fLVb9AY3p6upKSkpSRkeEOebW1tVq0aJGeeuqpY543KChIQUFBjbbb7fbT/mFsjnOgedETa6Iv1kNPrIeeWA89sSZf6Ut2cZX+t+2Alm47oP9tL1RBWU2D/dGhdo3oEqdR3dtpVPcEJUVZdw07X+mJ1TT1PT3pkPf888+32vSql156qR5//HF16NBBffr00erVq/Xcc8/pxhtvlFR/meaUKVM0ffp0devWTd26ddP06dMVGhqqCRMmtEqNAAAAwKnIKa7SdzsPavnOg/p2R6F2HqhosD/Y7qczOsXq7K7xGtk1Xr2TI5k0BU1ySrNrHktVVdXp1NLIzJkz9fDDD+vWW29Vfn6+UlJSdPPNN+uPf/yj+5j77rtPVVVVuvXWW92Loc+fP5818gAAAGAZxhjtKqzUdzsLtXznQX2386D2FTX83dnfz6b+qVE6u2u8RnSJ1+CO0QoK8PdQxfBmJx3ybrvtNr344ouNtldUVOiSSy7RwoULm6MuSVJERIRmzJjhXjLhaGw2m6ZOnaqpU6c22/cFAAAATofLZZS1v0zf7zroDnU/vvzS38+mvu2jNCw9Vmd2itWZnWMVGcwljjh9Jx3y5s+fr4ceekiPPfaYe1tFRYUuuuiiZi0MAAAA8BYllQ6t3lukVXuKtXpPkTL3FKuspq7BMYEBfhqYFl0f6tJjNbhDjMKCTvrXceCETinknX322YqLi9Pvfvc7lZWV6cILL1RAQIA+++yzlqgRAAAAsAyXy2hbQblW7S7Sqj31wW5bfnmj40ID/TWkY8yhUBen/qlRCrZz+SVa3kmHvPT0dH3xxRc699xz5efnp3feeUdBQUH65JNPFBYWduITAAAAAF7kQHmN1u4r1pq9JVq1p0iZe4tVVl3X6LhOcaEa3CFGgzrGaHCHaPVIjLDEsgZoe05pfLhv3776+OOPdf7552vYsGH6+OOPFRIS0ty1AQAAAK2qpNKhtdnFWruvRGv3FWvdvhLllFQ3Oi7E7q8BaVEa3CGmPth1iFZceOMlugBPaFLIGzRo0FGXTQgKClJOTo5Gjhzp3rZq1armqw4AAABoIeU1ddqQXVIf6LLrQ93uwspGx9lsUtd24eqXGqVBadEa1CFGPZMYpYN1NSnkXXHFFS1cBgAAANByiitrtTGnVBtzS7Uhp1Trs0u0raBcxjQ+tmNcqPqnRqt/+yj1T41Sn/ZRCmeCFHiRJv20PvLIIy1dBwAAAHDajDHKLq7Sxpz6MLcxt1Qbc0qVXXz09ZxTooLVLzWqPtSlRqlf+yhFhwa2ctVA8+K/JAAAAOCVHE6XciqkDzNztHl/hXukrqTKcdTjO8SGqndypPqkRKp3SqT6p0arXQT30cH3NCnkxcbGasuWLYqPj2/SSTt06KBvvvlGHTt2PK3iAAAAAGOMckuqlZVXps15Zdqyv/7ztvwyOZwB0tr1DY63+9vULSFCvVMOBbrkSPVKiWShcbQZTQp5xcXF+uyzzxQVFdWkkxYWFsrpdJ5WYQAAAGh7Siod2pxX6g5yWXllytpfdtQlCyQp2N+oX1qs+qREuUfouiVEKDCASVHQdjX5cs3Jkye3ZB0AAABoQ6odTm3LL3eHuKxDgS6vtPFyBZIU4GdT53Zh6pEUqZ5JEeqRGKHO8cFas3Shxl1yhux2RumAw5oU8lwuV0vXAQAAAB9UWu3QtvzyRh97iyqPOrOlJLWPDlHPpAh1T4qoD3RJEeocH95odM7hcGhd41W+gDaPiVcAAABwWowxKqyo1bb8cm3NL9f2/HJtzS/Ttvxy7S+tOebzokPt6pEY0SDQdU+MUAT3zgGnhZAHAACAJnG6jLKLqrTjQLm2F1Ro26EgtzW/XMWVR5/RUpISI4PUNSFc3RIi1CUhXF3bhatbYrjiwgJlszEUBzQ3Qh4AAADcjDEqqnRoR0G5dhyo0I6CCu08UK4dBRXaXVipWufRb+Ox2aTUmBB1S4hQ14TwBh/Magm0LkIeAABAG1TtcGrngQrtPFDhDnQ7D4W6Y60zJ0mBAX7qFBeqzvH1o3GHg1zn+HCFBPq34isAcCyEPAAAAB9VW+fSvqJK7T5YqV2HQtzhIJdTUnXMiU+k+slPOrcLU3p8/UfnduHqHB+mlOgQ+ftxiSVgZacU8rZv364333xT27dv11/+8hclJCTo888/V1pamvr06dPcNQIAAOAYqh1O7TkU4vYcrNSuwvrLKncVVii7qEqu4wS5yOCA+vDWLkydDwW59PgwdYoLY1QO8GInHfIWLVqksWPHauTIkVq8eLEef/xxJSQkaO3atXr99df1n//8pyXqBAAAaLPKa+q0+4jwtqfwhzCXW3L0deUOC7H7q2NcqDrGhSo9PvxQmKsfnYtl4hPAJ510yPv973+vxx57THfffbciIiLc28877zz95S9/adbiAAAA2gJjjArKa7T3YJX2HqzUnoOV2l1Yqd2FFdpVWKkD5cdehkCSIoID1CkuTB3jQtUpLkwdDn3uFBeqdhFBBDmgjTnpkLdu3TrNnTu30fZ27dqpsLCwWYoCAADwNeU1de4At/dgpfYVVbm/3ltUqWrH0WetPCwuLNAd3joe8bljXJhiQu0EOQBuJx3yoqOjlZubq/T09AbbV69erfbt2zdbYQAAAN7E4XQpp7hKew8eCm9F9YFu38FK7S2q0sGK2uM+388mJUeFKC02RGkxoeoU33BkjmUIADTVSYe8CRMm6P7779e///1v2Ww2uVwu/e9//9O9996rSZMmtUSNAAAAHucyUn5ZjfLKyutH344IcnsPVim35PiTnEhSTKhdHWJDlRobqrSYUHWIDVVabIg6xIYqOSpEgQF+rfNiAPi0kw55jz/+uK6//nq1b99exhj17t1bTqdTEyZM0EMPPdQSNQIAALS4OqdLuSXVyi6uUnZRlbKLq7SvqLL+88Eq7Svyl/PbRcc9R1CAn9JiD4W3mBClxYbWf8TUh7kIRuMAtIKTDnl2u11vv/22Hn30Ua1evVoul0uDBg1St27dWqI+AACAZlHtcCqnuMod4vYVVTUIdCceibO5L6lMjQk5NAr3w2hcWmyo2oUzyQkAzzvlxdC7dOmiLl26NGctAAAAp6y8pu5QeKv8IcgdEehONEOlJAX6+yklOljtY0KUGh2q9jEhah8dosQIu7as/lZXX36RQoODWuHVAMCpa1LIu/vuu5t8wueee+6UiwEAADgah9OlvJJq5ZZUK6e4SjklVcotPvx1/eeSKscJzxMa6K/UQ8GtPsDVB7nUmBClRocoPjxIfn6NR+IcDocKN0l2f+6ZA2B9TQp5q1evbvB45cqVcjqd6tGjhyRpy5Yt8vf315AhQ5q/QgAA4NNcLqMDFTXKLa5WbkmVsourlVtc5b4/LrekSvllNTInmNREkqJD7fUB7lCIS40JVfvoEHewi2apAQBtQJNC3oIFC9xfP/fcc4qIiNCcOXMUExMjSSoqKtINN9ygn/zkJy1TJQAA8Fql1Q7lFB8aeSup+tHX1corqVat8/hrxEn1l1ImRwcrJSrE/Tkl+oev28eEKDzolO9EAQCfcdJ/Ez777LOaP3++O+BJUkxMjB577DGNGTNG99xzT7MWCAAArKuytk55JfVBLaekfgTucHjLOTQaV15Td8Lz2GxSYkRwfWCLDlFKVLCSD4W4lOj6r+PCAo96KSUAoKGTDnmlpaXav3+/+vTp02B7fn6+ysrKmq0wAADgOcYYlVbXHboPrsp9P9z+0vrPh7eXVp84wEn1l1HWj7w1DG8p0SFKjgpWYmQw97sBQDM56ZD3s5/9TDfccIOeffZZnXXWWZKkb7/9Vv/3f/+n8ePHN3uBAACgeblcRgcra93BLa+kfsQtr7TaPSqXW1KtKoezSecLC/RXknvkrf5z+0OXUR7eFhrIZZQA0FpO+m/cV155Rffee68mTpwoh6N+FquAgADddNNNeuaZZ5q9QAAA0HR1TpcKymuOGG07cvStyv3Y4WzCLCaqH4FLigxWclSwkqLqR92SIoMPhbr6zyzwDQDWctIhLzQ0VC+99JKeeeYZbd++XcYYde3aVWFhYS1RHwAAOKSq1qm80vqQduRlk3kl1cotrQ9xBWU1J1jQu57NJsWHB7lDW3JUsBIPB7fIEHeAC7b7t/wLAwA0q1O+diIsLEz9+/dvzloAAGiTDo++7S+tqQ9sxRX63x4/LXx/vQrKarW/tP5SyrIm3v8W4GdT4qHRth+Puh2+/y0hIliBAdwDBwC+6KRD3nnnnXfc9WW+/vrr0yoIAABfYYxRcaVD+8vqR9vyS2sajMTtP/T4QPnR1oDzk7JzGp0zxF5//1tCRFDDSyiPGJGLCw+SP7NQAkCbddIhb+DAgQ0eOxwOZWZmav369Zo8eXJz1QUAgKVV1TrdYS2v9IcAV/919aEwV6PauhOv/yZJ/n42JUQEKSEyWAnhgao+mKcz+3VXckyYkiKDlRgZpMSoYEUEBbCYNwDguE465D3//PNH3T516lSVl5efdkEAAHhSndOlA+W1R4S3HwKbO9SVVDd56QBJigm1KzGy/jLJIwNbYkT9CFxCZJDiwn4YfXM4HPr000918ajOstuZ1AQAcHKabT7jiRMn6swzz9Sf//zn5jolAADNxhijkiqH+xLJ/aXV2l9SfehSyhrlH7qk8kB50yYukRpeOpl06F63xEMhLunQ1+0igpi8BADQqpot5C1btkzBwcHNdToAAJqs2uF0j7DtL6upD28/uoxyf2m1ak7h0smkyKAjwlswl04CACzvpEPejxc8N8YoNzdXK1as0MMPP9xshQEAUOd0qbCiftHuH09WcuTjkipHk8955KWT7hG341w6CQCAtznpkBcZGdngfy39/PzUo0cPPfrooxozZkyzFgcA8E3GGJVW1bnD2rHufWvqmm8Sl04CAHDYSYe82bNnt0AZAABfcfjSycMjbvk/voyyrD7AVTu4dBIAgJZw0iGvc+fO+v777xUXF9dge3FxsQYPHqwdO3Y0W3EAAOtwuowOlNec8N63k7l0MjrUrqTI4OMGONZ8AwDg5Jx0yNu1a5ecTmej7TU1NcrOzm6WogAArcsY1S/aXV6p7OIqZRdVuT/nllQp7yQvnQy2+x0R3g6Nth0Ob4fuf0uI5NJJAABaQpND3rx589xff/HFF4qKinI/djqd+uqrr9SpU6dmLQ4A0DyMMSqqdGh3YYX2HRHgsourtO9gpXYf8FfNtwtOeB5/P5vahQc1Cm0/vg8uMphLJwEA8JQmh7wrrrhCkmSz2TR58uQG++x2uzp16qRnn322WYsDAJyc8po67TpQoR0HKrTrQIV2HvH18S+jrA9k8eGBah8dovYxIfWfo0OUEh2ipKj6ETkunQQAwPqaHPJcrvob5NPT0/X9998rPj6+xYoCABxfbZ1L2wvKtTmvVJtyy7Qpt1RZeWXKL6s57vOSIoOVGlMf4lJjQtQ+OlRJEXZtW/udrr70QkWGsd4pAADe7qTvydu5c2dL1AEAOAaXy2hrfrlW7ynSqj1FWpddqm35ZXI4j36DXFxYoNLjw9QpPkzp8WHqfOjrTnFhCglsfA+cw+FQ2VYddR8AAPA+TQp5f/3rX/Wb3/xGwcHB+utf/3rcY++8885mKQwA2qo6p0tr9pVoydYDWrH7oDL3FKuspq7RcRFBAeqZHKFeyZHqmRSpnskR6hIfrqhQuweqBgAAVtGkkPf888/r2muvVXBwsJ5//vljHmez2Qh5AHAK9h6s1MKsfH2z9YCW7ShUWXXDUBca6K/+qVEa3CFGA9Ki1Ts5UqkxIUxuAgAAGmlSyDvyEk0u1wSA5rGjoFyfrc/TZ+tztT67tMG+qBC7RnaN0/Au8RrcIVo9EiMU4O/noUoBAIA3Oel78h599FHde++9Cg0NbbC9qqpKzzzzjP74xz82W3EA4GtKKh36aE223v1+rzbk/BDs/GzS0I6xOqd7vH7SrZ36to9iFksAAHBKTjrkTZs2TbfcckujkFdZWalp06YR8gDgR4wx+nbHQf3zuz36fEOeauvqZysO8LNpRNd4je2bpAt6Jyo+PMjDlQIAAF9w0iHPGHPUe0DWrFmj2NjYZikKAHxBTZ1T/12TqzeW7NTG3B9G7XomReiXZ6TpioHtFRMW6MEKAQCAL2pyyIuJiZHNZpPNZlP37t0bBD2n06ny8nLdcsstLVIkAHiTaodTf1+2S699s1MFh9atC7b76WeDUnXNmWnq1z6KCVMAAECLaXLImzFjhowxuvHGGzVt2jRFRUW59wUGBqpTp04aPnx4ixQJAN7A5TJ6b9U+PZexRbkl1ZLqFx+fNKKjJpzZQdGhjNoBAICW1+SQN3nyZElSenq6RowYIbuddZgA4LA9hZW6/721WrajUJKUEhWsKRd0188GtZedWTEBAEAralLIKy394V6SQYMGqaqqSlVVVUc9NjIysnkqAwAv8em6XN377zWqrHUq2O6n353fXZNHdFKw3d/TpQEAgDaoSSEvOjr6hPePHJ6Qxel0NkthAGB1xhg9l7FFM7/eJkk6Mz1Wz/yivzrGhXm4MgAA0JY1KeQtWLCgpesAAK/idBk99OE6/fO7vZKkX/8kXfdf1JMFywEAgMc1KeSNGjWqSSfLzMw8nVoAwCu4XEb3/nuNPlidLT+b9OT4/rrqjDRPlwUAACBJOu3/ci4pKdFLL72kwYMHa8iQIc1REwBYljFG0/67QR+szlaAn01/vWYQAQ8AAFjKKYe8r7/+WhMnTlRycrJmzpypiy++WCtWrGjO2gDAcl5auF1zlu2WJD33y4Ea1z/FwxUBAAA01OQlFCRp3759mj17tt544w1VVFToqquuksPh0HvvvafevXu3VI0AYAmfr8/TM19kSZIeubS3LhtAwAMAANbT5JG8iy++WL1799bGjRs1c+ZM5eTkaObMmS1ZGwBYxrb8Mt3zr0xJ0o0j03XDyHTPFgQAAHAMTR7Jmz9/vu6880799re/Vbdu3VqyJgCwlLJqh37zj5WqqHXqrM6x+sPFPT1dEgAAwDE1eSTvm2++UVlZmYYOHaphw4bphRdeUEFBQUvWBgCW8MhHG7SjoELJUcF6YcJglkkAAACW1uTfVIYPH67XXntNubm5uvnmm/XOO++offv2crlcysjIUFlZWUvWCQAe8VFmtt4/tFTCzGsGKT48yNMlAQAAHNdJ/3d0aGiobrzxRi1ZskTr1q3TPffcoyeffFIJCQm67LLLWqJGAPCIvQcr9dAH6yVJd/y0m4Z2ivVwRQAAACd2Wtcc9ejRQ08//bT27dunf/7zn81VEwB4nNNl9Lt3M1VWU6chHWN0x0+7erokAACAJmmWG0v8/f11xRVXaN68ec1xugays7M1ceJExcXFKTQ0VAMHDtTKlSvd+40xmjp1qlJSUhQSEqJzzz1XGzZsaPY6ALQtbyzZqRW7ixQeFKAZvxzIfXgAAMBrWPq3lqKiIo0cOVJ2u12fffaZNm7cqGeffVbR0dHuY55++mk999xzeuGFF/T9998rKSlJF1xwAfcIAjhl2wvK9ef59evhPXRJL6XFhnq4IgAAgKY7qcXQW9tTTz2ltLQ0vfnmm+5tnTp1cn9tjNGMGTP04IMPavz48ZKkOXPmKDExUXPnztXNN9/c2iUD8HJOl9H//XuNaupcOqd7O/3yjDRPlwQAAHBSLB3y5s2bpwsvvFBXXnmlFi1apPbt2+vWW2/Vr3/9a0nSzp07lZeXpzFjxrifExQUpFGjRmnp0qXHDHk1NTWqqalxPy4tLZUkORwOORyOU6r18PNO9flofvTEmqzel9eX7NKqPcUKDwrQY5f1Ul1dnadLanFW70lbRE+sh55YE32xHnrSspr6vtqMMaaFazllwcHBkqS7775bV155pb777jtNmTJFf/vb3zRp0iQtXbpUI0eOVHZ2tlJSUtzP+81vfqPdu3friy++OOp5p06dqmnTpjXaPnfuXIWGclkW0Fbtr5KeXuOvOmPT1Z2dGp5o2b8eAQBAG1RZWakJEyaopKREkZGRxzzO0iN5LpdLQ4cO1fTp0yVJgwYN0oYNG/Tyyy9r0qRJ7uNsNluD5xljGm070gMPPKC7777b/bi0tFRpaWkaM2bMcd+s43E4HMrIyNAFF1wgu91+SudA86In1mTVvjhdRle//p3qTInO6RanR68bfNy/R3yJVXvSltET66En1kRfrIeetKzDVyCeiKVDXnJysnr37t1gW69evfTee+9JkpKSkiRJeXl5Sk5Odh+Tn5+vxMTEY543KChIQUGNFzS22+2n/cPYHOdA86In1mS1vryxaLsy95YoIihAT/1igAIDAz1dUquzWk9AT6yInlgTfbEeetIymvqeWnp2zZEjRyorK6vBti1btqhjx46SpPT0dCUlJSkjI8O9v7a2VosWLdKIESNatVYA3mvL/jI9O3+LJOnhcb2VHBXi4YoAAABOnaVH8n73u99pxIgRmj59uq666ip99913evXVV/Xqq69Kqr9Mc8qUKZo+fbq6deumbt26afr06QoNDdWECRM8XD0Ab+BwunT3vzJV63Tppz0TdOXQVE+XBAAAcFosHfLOOOMMffDBB3rggQf06KOPKj09XTNmzNC1117rPua+++5TVVWVbr31VhUVFWnYsGGaP3++IiIiPFg5AG8x8+ttWp9dquhQu54c36/N3IcHAAB8l6VDniSNGzdO48aNO+Z+m82mqVOnaurUqa1XFACfsGZvsV5csE2S9KfL+yohMtjDFQEAAJw+S9+TBwAtpdrh1N3/ypTTZTSuf7IuHZBy4icBAAB4AUIegDbp6c+ztL2gQu0igvSny/t6uhwAAIBmQ8gD0OYs3X5Ab/xvpyTp6Z/3V0xY21suAQAA+C5CHoA25UB5jaa8kylJuubMNJ3XM8GzBQEAADQzQh6ANsPlMrr7X2uUX1ajbgnhenhcb0+XBAAA0OwIeQDajL8t3qHFWwoUbPfTi9cOVmig5ScYBgAAOGmEPABtwopdB/Xn+VmSpGmX9VH3RNbSBAAAvomQB8Dn7S+t1q1vr5LTZXT5wBRdNTTN0yUBAAC0GEIeAJ9W7XDqN/9YqfyyGnVPDNfjP+snm83m6bIAAABaDCEPgM8yxujBD9Zrzd5iRYfa9fqkMxQexH14AADAtxHyAPis177ZofdW7ZO/n00vThisDnGhni4JAACgxRHyAPikjzKzNf3TzZKkBy/upZFd4z1cEQAAQOsg5AHwOf/bdkD3/nuNJOn6EZ10w8hOni0IAACgFRHyAPiUDTkluvkfK+VwGl3SL1l/HNebiVYAAECbQsgD4DO25Zdr8hvfq7ymTsPSY/XsVQPk50fAAwAAbQshD4BP2FFQrgmvfasD5TXqlRypVycNVbDd39NlAQAAtDpCHgCvt7uwQhNeW678shr1TIrQ278apqgQu6fLAgAA8AhCHgCvtruwQte8+q3ySqvVLSFcb/1qmGLDAj1dFgAAgMewKjAAr7U5r1TXzfpOBWU16tIuTHN/fZbiw4M8XRYAAIBHEfIAeKWVuw/qhje/V2l1nXomRejvN52pdhEEPAAAAEIeAK+zaEuBbvnHSlU5nBrSMUZvTD5DUaHcgwcAACAR8gB4mX+v2Ks/fLBODqfRqO7t9PLEwQoN5K8yAACAw/jNCIBXcLmM/jw/Sy8t3C5JunRAip69coACA5g/CgAA4EiEPACWV1Xr1D3/ztSn6/IkSXf8tKt+d353FjoHAAA4CkIeAEvLLanSLf9YqTX7ShTo76cnf95P4wenerosAAAAyyLkAbCspdsP6I65q1VYUauYULv+dt1QnZke6+myAAAALI2QB8ByjDH62+IdevrzzXIZqVdypP42cYg6xIV6ujQAAADLI+QBsJSyaof+799r9fmG+vvvfj44VY//rK+C7f4ergwAAMA7EPIAWMbK3UWa8u5q7T1YpUB/Pz1yWW9NOLODbDYmWAEAAGgqQh4Aj6tzuvTigu3669db5XQZpcaE6IUJgzUwLdrTpQEAAHgdQh4Aj9p7sFK/ezdTK3YXSZKuGJiiR6/oq8hgu4crAwAA8E6EPAAeYYzRO9/t0eOfbFJZTZ3CgwL02BV9dcWg9p4uDQAAwKsR8gC0usJq6frZK7V0x0FJ0uAO0frL1YOUFsvsmQAAAKeLkAeg1bhcRn//do+eWuOvWtdBBQX46d4xPXTj2eny92NyFQAAgOZAyAPQKrLyyvTQh+v0/a4iSTad0SlGT/9igNLjwzxdGgAAgE8h5AFoUWXVDv3ly616c+kuOV1GYYH+Gtu+Vo9fP1RBQYGeLg8AAMDnEPIAtAhjjOatydHjn2xSflmNJOnCPon6w0Xdlbl0gfy4PBMAAKBFEPIANLvNeaWaOm+Dvj00sUrHuFBNvayPzuuRIIfDoUzPlgcAAODTCHkAms3+0mo9N3+L/r1yr1xGCgrw0+3nddWvz+msYLu/p8sDAABoEwh5AE5bZW2dXl28Q39btENVDqck6eJ+SXpgbC+WRQAAAGhlhDwAp6zO6dL7q7L1bEaW9pfW33c3qEO0Hrqkl4Z0jPVwdQAAAG0TIQ/ASXO5jP67NkczvtyqnQcqJElpsSG6/6KeuqRfsmw2JlUBAADwFEIegCYzxuiLDfv1fMYWZe0vkyTFhNp167ldNWlERwUFcN8dAACApxHyAJyQMUYLswr0bEaW1meXSpIiggP0m5901g1npys8iL9KAAAArILfzAAck8tlNH9jnl5csF3rskskSWGB/rrx7HT96uzOigq1e7hCAAAA/BghD0AjDqdL8zJz9PKi7dqWXy5JCrH7a+JZHXTLqC6KCw/ycIUAAAA4FkIeALdqh1P/XrFXryzaoeziKkn1l2VeP6KTrh/RiXAHAADgBQh5AFRQVqO3vt2tt5fv1oHyWklSfHigbjq7syae1UERwVyWCQAA4C0IeUAbtjmvVLO+2amPMnNU63RJktpHh+jmUZ111dA0BduZLRMAAMDbEPKANsblMlq4JV+zluzU/7YVurcPTIvWTWena2zfJAX4+3mwQgAAAJwOQh7QRhRV1Oo/K/fp7eW7tauwUpLkZ5PG9k3WjWena0jHGA9XCAAAgOZAyAN8mDFGq/cW661vd+vjtbmqrau/JDMiKEBXn5mmySM6KTUm1MNVAgAAoDkR8gAfVFlbp48yc/TWt7u1IafUvb13cqSuG95Rlw1IURgLmAMAAPgkfssDfMThUbv/rNyn/2bmqKymTpIUGOCncf2TNfGsjhqUFi2bzebhSgEAANCSCHmAl8svq9YHq7L175X73AuXS1KnuFBdO6yjfjEkVTFhgR6sEAAAAK2JkAd4odo6l77evF//XrFPC7cUyOkykqRgu5/G9k3WlUNSdVbnOPn5MWoHAADQ1hDyAC/hchkt33lQ89Zk69N1eSqpcrj3De4QrSuHpumS/smKZOFyAACANo2QB1iYMUbrs0v1UWa2Pl6bq7zSave+hIggjR+cql8MSVXXhHAPVgkAAAArIeQBFrS9oFz/XZOjeZk52nGgwr09MjhAF/dL1mUDUjSsc5z8uRwTAAAAP0LIAyzAGKPNeWX6bH2ePl+fqy37f5hAJdjup9G9EnX5gBSN6tFOQQH+HqwUAAAAVkfIAzzEGKN12SX6bH2ePluXq12Fle59dn+bRnaN1+UDU3RB7ySFs6YdAAAAmojfHIFW5HQZrd5TdGjELk/ZxVXufYEBfhrVvZ3G9k3S6F6JigphAhUAAACcPEIe0MIqaur0zdYD+mrTfn29OV+FFbXufSF2f/20Z4Iu6puk83omMGIHAACA08ZvlEALyCmu0leb8/Xlxv1atqNQtXUu976IoACN7pWgi/oma1T3dgoJ5B47AAAANB9CHtAMXC6j9Tkl+nJTfbDbmFvaYH+H2FCN7pWgC3ol6oz0WNn9/TxUKQAAAHwdIQ84RcWVtfpm6wEt2lKgxVsKlF9W495ns0mDO8S4g13XhHDZbCx3AAAAgJZHyAOayOkyWrOvWIuyCrRoS4HW7iuWy/ywPzTQX+d0a6fRvRJ0Xs8ExYcHea5YAAAAtFmEPOA49pdWa9GW+lC3ZOsBlVQ5GuzvnhiuUd3b6Zzu7XRGp1gF27m/DgAAAJ5FyAOOUFbt0PIdB/W/7Qe0dFuhsvaXNdgfERygn3SL16ju7fSTbu2UEh3ioUoBAACAoyPkoU2rdji1ak+Rlm4r1P+2H9DafSVyHnENps0m9W8fpVHd22lUj3YakBqtACZNAQAAgIV5Vch74okn9Ic//EF33XWXZsyYIUkyxmjatGl69dVXVVRUpGHDhunFF19Unz59PFssLMnpMlqfXeIeqft+10HVHLG8gSR1igvV8C7xGtk1TiO6xCs2LNBD1QIAAAAnz2tC3vfff69XX31V/fv3b7D96aef1nPPPafZs2ere/fueuyxx3TBBRcoKytLERERHqoWVmGMtHV/ub7fU6L/bTugb3cUqrS6rsEx7SKCNLJLnEZ0jdeILnFKjQn1ULUAAADA6fOKkFdeXq5rr71Wr732mh577DH3dmOMZsyYoQcffFDjx4+XJM2ZM0eJiYmaO3eubr75Zk+VDA8xxmh3YaWW7SjUkq0FWrzZX2XfLm1wTERwgM7qHKeRXeI0sms8yxsAAADAp3hFyLvtttt0ySWX6Pzzz28Q8nbu3Km8vDyNGTPGvS0oKEijRo3S0qVLCXltRE5xlZZtL9TS7YVatv2Ackqqj9hrU7DdT2d0iq0Pdl3j1TclkvvqAAAA4LMsH/LeeecdrVq1St9//32jfXl5eZKkxMTEBtsTExO1e/fuY56zpqZGNTU/LFxdWloqSXI4HHI4HMd62nEdft6pPh9NV1heo293FmnZjoP6dsdB7T5Y2WC/3d+mgWnROqNDlPwLt+mmy0crLOSHNeuMyymHy9naZeMQ/qxYDz2xHnpiPfTEmuiL9dCTltXU99XSIW/v3r266667NH/+fAUHBx/zuB9fameMOe7ld0888YSmTZvWaPv8+fMVGnp692NlZGSc1vPRWGWdtK3Upq0l9R+5VQ17a5NRh3CpW6RRtyijzhFGgf4FkqNAipQWLfjKQ5XjePizYj30xHroifXQE2uiL9ZDT1pGZWXliQ+SZDPGmBMf5hkffvihfvazn8nf/4cFpp1Op2w2m/z8/JSVlaWuXbtq1apVGjRokPuYyy+/XNHR0ZozZ85Rz3u0kby0tDQdOHBAkZGRp1Srw+FQRkaGLrjgAtnt9lM6B+pV1NRpxe4ifbuzSN/uOKgNuaX68U9pz6QIDe8cq7M6x+qMjtGKCG78ntMTa6Iv1kNPrIeeWA89sSb6Yj30pGWVlpYqPj5eJSUlx80tlh7JGz16tNatW9dg2w033KCePXvq/vvvV+fOnZWUlKSMjAx3yKutrdWiRYv01FNPHfO8QUFBCgoKarTdbref9g9jc5yjrTm8Vt3h++rW7C1WnathquvSLkwjusRreJc4ndU57qSWNaAn1kRfrIeeWA89sR56Yk30xXroScto6ntq6ZAXERGhvn37NtgWFhamuLg49/YpU6Zo+vTp6tatm7p166bp06crNDRUEyZM8ETJaAKH06W1+4q1dFt9qFu5p0i1P1qrLi02RMM7x7mDXWLksS/XBQAAAPADS4e8prjvvvtUVVWlW2+91b0Y+vz581kjz0KcLqONOaVauv2Alm6vX4C8srbhxCcJEUEa0eWHUJcWy1p1AAAAwKnwupC3cOHCBo9tNpumTp2qqVOneqQeHF1BWY0WbSnQwqx8fbP1gEqqGs4EFBNq1/AucRrepX4B8s7xYaxVBwAAADQDrwt5sKY6p0uZe4u1MKtAC7fka312aYP9EUEBGta5fq26EV3i1TMpQn5+hDoAAACguRHycMryy6q1KKtAC7cU6JstBSqtrmuwv2/7SJ3bPUHn9mingWnRLEAOAAAAtAJCHprMGKOs/WWav2G/Mjbu17rskgb7o0Ls+km3eJ3bI0HndI9XQgSTpQAAAACtjZCH46pzurRid5EyNu7X/I152nuwqsH+/qlROrd7O43qkaABqVGM1gEAAAAeRshDIw6nS4uyCvT5hjx9tWm/iip/mDQlKMBPP+kWrzG9k3RezwS1i2i83iAAAAAAzyHkwW1bfrn+tWKv3l+1TwfKa93bo0Pt+mnPBI3pnaRzuscrNJAfGwAAAMCq+G0d2pBToplfbdPnG/Lc2+LDg3TpgGSN6Z2kMzrFcBkmAAAA4CUIeW1YabVDT3++WW8v3yNjJJtNGt0zQb88o4PO7dFOdoIdAAAA4HUIeW3U2n3FuuUfK5VTUi1JunRAiu78aVd1S4zwcGUAAAAATgchrw36fH2u7nwnU7V1LnWKC9UT4/treJc4T5cFAAAAoBkQ8tqY+RvydPvc1apzGY3umaDnrx6oyGC7p8sCAAAA0EwIeW3I+uwS3f7P+oB3xcAUPXvVQPn72TxdFgAAAIBmxMwabURptUO3vLVStXUundejnf585QACHgAAAOCDCHltxJOfbda+oiqlxYZoxi8HsSQCAAAA4KP4Tb8NWLHroOYu3yNJeuYXAxQVyj14AAAAgK8i5Pk4Y4ymf7pJknTV0FSd1ZlZNAEAAABfRsjzcV9tyteqPcUKtvvp3jE9PF0OAAAAgBZGyPNhxhjN+GqLJGnyiE5KiAz2cEUAAAAAWhohz4ct21Go9dmlCrb76eZzuni6HAAAAACtgJDnw17/Zqck6RdDUhUbFujhagAAAAC0BkKej9qWX6avN+fLZpNuOruzp8sBAAAA0EoIeT5q1pJdkqTzeyUqPT7Ms8UAAAAAaDWEPB90sKJW76/aJ0n61dnpHq4GAAAAQGsi5Pmguct3q6bOpb7tI3VmeqynywEAAADQigh5Pqa2zqW/L9stSbrp7HTZbDYPVwQAAACgNRHyfMzHa3OUX1ajhIggXdIvxdPlAAAAAGhlhDwfYozRrCX1yyZMHtFJgQG0FwAAAGhrSAE+ZPnOg9qQU7/4+bXDOni6HAAAAAAeQMjzIYdH8X4+OFXRoSx+DgAAALRFhDwfsfNAhb7ctF+SdCPLJgAAAABtFiHPR7zw9TYZI/20Z4K6tAv3dDkAAAAAPISQ5wN2HqjQB6vrFz+/c3Q3D1cDAAAAwJMIeT7gr19tlctIo3smaGBatKfLAQAAAOBBhDwvty2/XB9lZkuSppzf3cPVAAAAAPA0Qp6Xe/rzzXIZ6fxeieqXGuXpcgAAAAB4GCHPi327o1DzN+6Xv59N91/Uw9PlAAAAALAAQp6XcrmMHv9kkyTp6jPS1C0xwsMVAQAAALACQp6X+mhNttZllyg8KEC/u4B78QAAAADUI+R5oWqHU09/niVJuvW8LooPD/JwRQAAAACsgpDnhV5bvEO5JdVqHx2iG0eme7ocAAAAABZCyPMy+aXVennRdknSfRf1ULDd38MVAQAAALASQp6X+fP8LFXWOjWoQ7QuG5Di6XIAAAAAWAwhz4tsyCnRv1fukyQ9dElv2Ww2D1cEAAAAwGoIeV7CGKPHPt4kY6RLB6RoSMcYT5cEAAAAwIIIeV7iy035WrajUIEBfrrvQhY+BwAAAHB0hDwvUFvn0vRP6xc+v+nsdKXFhnq4IgAAAABWRcjzAnOX79bOAxWKDw/Ured28XQ5AAAAACyMkGdxNXVOvbJohyTprvO7KyLY7uGKAAAAAFgZIc/iPliVrbzSaiVGBumqoameLgcAAACAxRHyLMzpMnrl0MLnv/5JZwUFsPA5AAAAgOMj5FnYl5v2a1dhpaJD7brmzA6eLgcAAACAFyDkWdjc5XskSVef0UFhQQEergYAAACANyDkWdSewkot3logSZrAKB4AAACAJiLkWdQ73++RMdI53dupQxzr4gEAAABoGkKeBTldRu+t2idJuuaMNA9XAwAAAMCbEPIsaNn2Qu0vrVFUiF0/7ZXg6XIAAAAAeBFm87CgszrH6s0bzlBBaQ3LJgAAAAA4KYQ8Cwrw99N5PRjBAwAAAHDyuFwTAAAAAHwIIQ8AAAAAfAghDwAAAAB8CCEPAAAAAHwIIQ8AAAAAfAghDwAAAAB8CCEPAAAAAHwIIQ8AAAAAfAghDwAAAAB8CCEPAAAAAHwIIQ8AAAAAfAghDwAAAAB8CCEPAAAAAHwIIQ8AAAAAfAghDwAAAAB8CCEPAAAAAHxIgKcLsAJjjCSptLT0lM/hcDhUWVmp0tJS2e325ioNp4GeWBN9sR56Yj30xHroiTXRF+uhJy3rcF45nF+OhZAnqaysTJKUlpbm4UoAAAAA4PjKysoUFRV1zP02c6IY2Aa4XC7l5OQoIiJCNpvtlM5RWlqqtLQ07d27V5GRkc1cIU4FPbEm+mI99MR66In10BNroi/WQ09aljFGZWVlSklJkZ/fse+8YyRPkp+fn1JTU5vlXJGRkfxAWww9sSb6Yj30xHroifXQE2uiL9ZDT1rO8UbwDmPiFQAAAADwIYQ8AAAAAPAhhLxmEhQUpEceeURBQUGeLgWH0BNroi/WQ0+sh55YDz2xJvpiPfTEGph4BQAAAAB8CCN5AAAAAOBDCHkAAAAA4EMIeQAAAADgQwh5zeSll15Senq6goODNWTIEH3zzTeeLsknLF68WJdeeqlSUlJks9n04YcfNthvjNHUqVOVkpKikJAQnXvuudqwYUODY2pqanTHHXcoPj5eYWFhuuyyy7Rv374GxxQVFem6665TVFSUoqKidN1116m4uLiFX513euKJJ3TGGWcoIiJCCQkJuuKKK5SVldXgGPrSul5++WX179/fvSbR8OHD9dlnn7n30w/Pe+KJJ2Sz2TRlyhT3NvrS+qZOnSqbzdbgIykpyb2fnnhGdna2Jk6cqLi4OIWGhmrgwIFauXKlez99aV2dOnVq9OfEZrPptttuk0Q/vIbBaXvnnXeM3W43r732mtm4caO56667TFhYmNm9e7enS/N6n376qXnwwQfNe++9ZySZDz74oMH+J5980kRERJj33nvPrFu3zvzyl780ycnJprS01H3MLbfcYtq3b28yMjLMqlWrzHnnnWcGDBhg6urq3MdcdNFFpm/fvmbp0qVm6dKlpm/fvmbcuHGt9TK9yoUXXmjefPNNs379epOZmWkuueQS06FDB1NeXu4+hr60rnnz5plPPvnEZGVlmaysLPOHP/zB2O12s379emMM/fC07777znTq1Mn079/f3HXXXe7t9KX1PfLII6ZPnz4mNzfX/ZGfn+/eT09a38GDB03Hjh3N9ddfb5YvX2527txpvvzyS7Nt2zb3MfSldeXn5zf4M5KRkWEkmQULFhhj6Ie3IOQ1gzPPPNPccsstDbb17NnT/P73v/dQRb7pxyHP5XKZpKQk8+STT7q3VVdXm6ioKPPKK68YY4wpLi42drvdvPPOO+5jsrOzjZ+fn/n888+NMcZs3LjRSDLffvut+5hly5YZSWbz5s0t/Kq8X35+vpFkFi1aZIyhL1YRExNjXn/9dfrhYWVlZaZbt24mIyPDjBo1yh3y6ItnPPLII2bAgAFH3UdPPOP+++83Z5999jH30xfPu+uuu0yXLl2My+WiH16EyzVPU21trVauXKkxY8Y02D5mzBgtXbrUQ1W1DTt37lReXl6D9z4oKEijRo1yv/crV66Uw+FocExKSor69u3rPmbZsmWKiorSsGHD3MecddZZioqKoodNUFJSIkmKjY2VRF88zel06p133lFFRYWGDx9OPzzstttu0yWXXKLzzz+/wXb64jlbt25VSkqK0tPTdfXVV2vHjh2S6ImnzJs3T0OHDtWVV16phIQEDRo0SK+99pp7P33xrNraWr311lu68cYbZbPZ6IcXIeSdpgMHDsjpdCoxMbHB9sTEROXl5Xmoqrbh8Pt7vPc+Ly9PgYGBiomJOe4xCQkJjc6fkJBAD0/AGKO7775bZ599tvr27SuJvnjKunXrFB4erqCgIN1yyy364IMP1Lt3b/rhQe+8845WrVqlJ554otE++uIZw4YN09///nd98cUXeu2115SXl6cRI0aosLCQnnjIjh079PLLL6tbt2764osvdMstt+jOO+/U3//+d0n8WfG0Dz/8UMXFxbr++usl0Q9vEuDpAnyFzWZr8NgY02gbWsapvPc/PuZox9PDE7v99tu1du1aLVmypNE++tK6evTooczMTBUXF+u9997T5MmTtWjRIvd++tG69u7dq7vuukvz589XcHDwMY+jL61r7Nix7q/79eun4cOHq0uXLpozZ47OOussSfSktblcLg0dOlTTp0+XJA0aNEgbNmzQyy+/rEmTJrmPoy+eMWvWLI0dO1YpKSkNttMP62Mk7zTFx8fL39+/0f865OfnN/pfDjSvwzOiHe+9T0pKUm1trYqKio57zP79+xudv6CggB4exx133KF58+ZpwYIFSk1NdW+nL54RGBiorl27aujQoXriiSc0YMAA/eUvf6EfHrJy5Url5+dryJAhCggIUEBAgBYtWqS//vWvCggIcL9n9MWzwsLC1K9fP23dupU/Kx6SnJys3r17N9jWq1cv7dmzRxL/pnjS7t279eWXX+pXv/qVexv98B6EvNMUGBioIUOGKCMjo8H2jIwMjRgxwkNVtQ3p6elKSkpq8N7X1tZq0aJF7vd+yJAhstvtDY7Jzc3V+vXr3ccMHz5cJSUl+u6779zHLF++XCUlJfTwKIwxuv322/X+++/r66+/Vnp6eoP99MUajDGqqamhHx4yevRorVu3TpmZme6PoUOH6tprr1VmZqY6d+5MXyygpqZGmzZtUnJyMn9WPGTkyJGNluHZsmWLOnbsKIl/UzzpzTffVEJCgi655BL3NvrhRVptihcfdngJhVmzZpmNGzeaKVOmmLCwMLNr1y5Pl+b1ysrKzOrVq83q1auNJPPcc8+Z1atXu5enePLJJ01UVJR5//33zbp168w111xz1Gl8U1NTzZdffmlWrVplfvrTnx51Gt/+/fubZcuWmWXLlpl+/foxje8x/Pa3vzVRUVFm4cKFDaZYrqysdB9DX1rXAw88YBYvXmx27txp1q5da/7whz8YPz8/M3/+fGMM/bCKI2fXNIa+eMI999xjFi5caHbs2GG+/fZbM27cOBMREeH+95qetL7vvvvOBAQEmMcff9xs3brVvP322yY0NNS89dZb7mPoS+tzOp2mQ4cO5v7772+0j354B0JeM3nxxRdNx44dTWBgoBk8eLB7OnmcngULFhhJjT4mT55sjKmfWvmRRx4xSUlJJigoyJxzzjlm3bp1Dc5RVVVlbr/9dhMbG2tCQkLMuHHjzJ49exocU1hYaK699loTERFhIiIizLXXXmuKiopa6VV6l6P1Q5J588033cfQl9Z14403uv/+adeunRk9erQ74BlDP6zixyGPvrS+w+t52e12k5KSYsaPH282bNjg3k9PPOO///2v6du3rwkKCjI9e/Y0r776aoP99KX1ffHFF0aSycrKarSPfngHmzHGeGQIEQAAAADQ7LgnDwAAAAB8CCEPAAAAAHwIIQ8AAAAAfAghDwAAAAB8CCEPAAAAAHwIIQ8AAAAAfAghDwAAAAB8CCEPAAAAAHwIIQ8A4JUWLlwom82m4uJiT5dySq6//npdccUVx9w/e/Zs2Ww22Ww2TZkypdXqkqSpU6e6v/eMGTNa9XsDAE4fIQ8AYFlLly6Vv7+/Lrrookb7RowYodzcXEVFRXmgstYRGRmp3Nxc/elPf2qW8y1cuFDJyckyxhz3uHvvvVe5ublKTU1tlu8LAGhdhDwAgGW98cYbuuOOO7RkyRLt2bOnwb7AwEAlJSXJZrMd9blOp1Mul6s1ymwxNptNSUlJioiIaJbzzZs3T5dddtkx37PDwsPDlZSUJH9//2b5vgCA1kXIAwBYUkVFhf71r3/pt7/9rcaNG6fZs2c32P/jyzVnz56t6Ohoffzxx+rdu7eCgoK0e/duLVy4UGeeeabCwsIUHR2tkSNHavfu3ZLqL0scOHCg/va3vyktLU2hoaG68sorG10C+uabb6pXr14KDg5Wz5499dJLLzXYn52drV/+8peKiYlRXFycLr/8cu3atcu93+l06u6771Z0dLTi4uJ03333nXA07Vg6deqkxx57TJMmTVJ4eLg6duyojz76SAUFBbr88ssVHh6ufv36acWKFY2eezjkSdJ//vMf9evXTyEhIYqLi9P555+vioqKU6oJAGAthDwAgCW9++676tGjh3r06KGJEyfqzTffPGEwqqys1BNPPKHXX39dGzZsUGxsrK644gqNGjVKa9eu1bJly/Sb3/ymwUjWtm3b9K9//Uv//e9/9fnnnyszM1O33Xabe/9rr72mBx98UI8//rg2bdqk6dOn6+GHH9acOXPc3/O8885TeHi4Fi9erCVLlig8PFwXXXSRamtrJUnPPvus3njjDc2aNUtLlizRwYMH9cEHH5zye/P8889r5MiRWr16tS655BJdd911mjRpkiZOnKhVq1apa9eumjRpUoP3a8OGDcrLy9Po0aOVm5ura665RjfeeKM2bdqkhQsXavz48accPAEAFmMAALCgESNGmBkzZhhjjHE4HCY+Pt5kZGS49y9YsMBIMkVFRcYYY958800jyWRmZrqPKSwsNJLMwoULj/o9HnnkEePv72/27t3r3vbZZ58ZPz8/k5uba4wxJi0tzcydO7fB8/70pz+Z4cOHG2OMmTVrlunRo4dxuVzu/TU1NSYkJMR88cUXxhhjkpOTzZNPPune73A4TGpqqrn88suP+frffPNNExUV1Wh7x44dzcSJE92Pc3NzjSTz8MMPu7ctW7bMSHK/BmOMefzxx8348eONMcasXLnSSDK7du065vc//L2ef/754x4DALAeRvIAAJaTlZWl7777TldffbUkKSAgQL/85S/1xhtvHPd5gYGB6t+/v/txbGysrr/+el144YW69NJL9Ze//EW5ubkNntOhQ4cGE4wMHz5cLpdLWVlZKigo0N69e3XTTTcpPDzc/fHYY49p+/btkqSVK1dq27ZtioiIcO+PjY1VdXW1tm/frpKSEuXm5mr48OHu7xEQEKChQ4ee8vtz5GtMTEyUJPXr16/Rtvz8fPe2jz76yH2p5oABAzR69Gj169dPV155pV577TUVFRWdcj0AAGsJ8HQBAAD82KxZs1RXV6f27du7txljZLfbVVRUpJiYmKM+LyQkpNGkIm+++abuvPNOff7553r33Xf10EMPKSMjQ2edddZRz3H4+TabzT1xy2uvvaZhw4Y1OO7wpCQul0tDhgzR22+/3ehc7dq1a+IrPjl2u71RvUfbdrj+vLw8rVq1Spdccom79oyMDC1dulTz58/XzJkz9eCDD2r58uVKT09vkZoBAK2HkTwAgKXU1dXp73//u5599lllZma6P9asWaOOHTseNUydyKBBg/TAAw9o6dKl6tu3r+bOnevet2fPHuXk5LgfL1u2TH5+furevbsSExPVvn177dixQ127dm3wcTgMDR48WFu3blVCQkKjY6KiohQVFaXk5GR9++23DV7jypUrT+NdOjnz5s3T8OHDFR8f795ms9k0cuRITZs2TatXr1ZgYOBp3ScIALAORvIAAJby8ccfq6ioSDfddFOjNfB+8YtfaNasWbr99tubdK6dO3fq1Vdf1WWXXaaUlBRlZWVpy5YtmjRpkvuY4OBgTZ48WX/+859VWlqqO++8U1dddZWSkpIk1c/AeeeddyoyMlJjx45VTU2NVqxYoaKiIt1999269tpr9cwzz+jyyy/Xo48+qtTUVO3Zs0fvv/++/u///k+pqam666679OSTT6pbt27q1auXnnvuuVZdxH3evHm6/PLL3Y+XL1+ur776SmPGjFFCQoKWL1+ugoIC9erVq9VqAgC0HEIeAMBSZs2apfPPP/+oi5z//Oc/1/Tp07Vq1aomnSs0NFSbN2/WnDlzVFhYqOTkZN1+++26+eab3cd07dpV48eP18UXX6yDBw/q4osvbrBEwq9+9SuFhobqmWee0X333aewsDD169dPU6ZMcX+PxYsX6/7779f48eNVVlam9u3ba/To0YqMjJQk3XPPPcrNzdX1118vPz8/3XjjjfrZz36mkpKS03inmqaiokJfffWVnn/+efe2yMhILV68WDNmzFBpaak6duyoZ599VmPHjm3xegAALc9mDPMlAwDapqlTp+rDDz9UZmamp0tpZPbs2ZoyZcppj/i9//77euihh7Rx48aTfm6nTp00ZcoUd6AFAHgH7skDAMCiSkpKFB4ervvvv/+UzxEeHq6nnnrqpJ4zffp0hYeHa8+ePaf8fQEAnsNIHgCgzbLySF5ZWZn2798vSYqOjm4waUpLO3jwoA4ePCipfobQo106CwCwLkIeAAAAAPgQLtcEAAAAAB9CyAMAAAAAH0LIAwAAAAAfQsgDAAAAAB9CyAMAAAAAH0LIAwAAAAAfQsgDAAAAAB9CyAMAAAAAH0LIAwAAAAAf8v9MPFu666mxrwAAAABJRU5ErkJggg==", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the airspeed vs altitude\n", "plt.figure(figsize=(9, 5))\n", "plt.plot(dependent_variables_array[:,8], dependent_variables_array[:,2]/1e3)\n", "plt.xlabel(\"Airspeed [m/s]\"), plt.ylabel(\"Altitude [km]\")\n", "plt.grid()\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "fce6a312", "metadata": {}, "source": [ "### g-load over time\n", "\n", "The following plot then shows the total acceleration on the vehicle in `g` (Earth's gravitational acceleration at sea level)." ] }, { "cell_type": "code", "execution_count": 19, "id": "629b9b44", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:28:12.250712Z", "iopub.status.busy": "2025-09-16T07:28:12.250593Z", "iopub.status.idle": "2025-09-16T07:28:12.315759Z", "shell.execute_reply": "2025-09-16T07:28:12.315233Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the g-load over time\n", "plt.figure(figsize=(9, 5))\n", "plt.plot(time_min, dependent_variables_array[:, 9] / 9.81)\n", "plt.xlabel(\"Relative Time [min]\"), plt.ylabel(\"Total g-load [-]\")\n", "plt.grid()\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "4ed1a97c", "metadata": {}, "source": [ "### Aerodynamic coefficient over time\n", "\n", "Plotting the aerodynamic coefficients over time can also give a good set of insights into what happens during re-entry." ] }, { "cell_type": "code", "execution_count": 20, "id": "cc405878", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:28:12.316932Z", "iopub.status.busy": "2025-09-16T07:28:12.316823Z", "iopub.status.idle": "2025-09-16T07:28:12.405725Z", "shell.execute_reply": "2025-09-16T07:28:12.405398Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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Q6Ohot747m81GQkICAwcOxGq1VmJC+TP1vbHU/8Zxt+9/PPwjD614iIYBDVk4bGG1+T2RY8vh6i+uJqcwh3eufIceEcaeK1gW+rk3jvreOOp7Y7nT/5mZmdSrV4+MjIxSdcof1ciRPKfTyQcffMCYMWPOW+ABmM1munfvzp49e87ZxsfHBx8fn1LbrVZrqY622+2YTCbMZnPJc+MqWdG0yaJji/vMZjMmk+ms32tZlPd9cvHU98ZS/xunrH2/PNk1VbN/bP8L/l6sSqHWUIY2G8qnuz7lv3v/y2XRxq766Q793BtHfW8c9b2xytL/Zf1+amS1sGzZMvbu3cvYsWMv2NbpdJKYmEhkZGQVJBMREalahY5Clh5eCsCVMVcamuVsRrUcBcBPh38iNSfV4DQiIrWDoUVednY2iYmJJCYmAnDgwAESExNJSkoCYMqUKdx6662l3vf+++/To0cP2rVrV+q1Z555hu+//579+/eTmJjI2LFjSUxMZNy4cZX6WURERIyw8dhGMgsyqeNTh071Oxkdp5S4OnF0j+iOw+ngs12fGR1HRKRWMLTIW79+PZ07dy5e+XLy5Ml07tyZJ598EnAtrlJU8BXJyMhgwYIF5xzFO336NHfffTetW7dm0KBBHD16lOXLl3PJJZdU7ocRERExwI+HfwSgb3RfLOayrxxclUa3ci3wNX/XfHJtZVs0QEREys/Qc/L69u3L+dZ9iY+PL7UtJCTkvKvKTJ06lalTp1ZEPBERkWrN6XTyY5KryOsX3c/gNOd2ZfSVxATFkJSVxBd7v+CW1rcYHUlExKPVyHPyREREBHae3ElKTgp+Xn70iupldJxzspgt3Nb2NgDmbJtDocP9y9iIiEjZqcgTERGpoZYkLQGgV2QvfL2q7rI+5TGs2TDCfMNIzklm8cHFRscREfFoKvJERERqIKfTyfcHvwdgUONBBqe5MF8v3+Jz8z7c9uF5T9cQEZGLoyJPRESkBtp5cicHMw/ia/Gt1ufj/dFNLW/Cz8uPnSd38kvKL0bHERHxWCryBHBdZP3LL78sfr5z50569uyJr68vnTp1MiyXiIic3bcHvwXg8kaX42/1NzhN2YT6hvKX5n8B4MNfPzQ4jYiI5zJ0dU2pWrfffjunT58uUcwVSUlJoU6dOsXPn3rqKQICAti1axeBgYHEx8czceJETp8+fdZ99+3bl5tuuomrr76aJk2aFG8PDAwkJiaGvn37MnHiRJo3b17RH0tEpNZxOp18f8A1VXNwk8EGp3HPmDZj+HTnp6xOWc3OkztpFdbK6EhSDe1I30H8tnjS89LBCU40vbcyOZwO0rPT+XLJl5hNGgP6s0cueYQWdVoYHcMtKvIEgIiIiBLP9+3bxzXXXENsbOwF33vy5ElWrVrFxx9/jM1mA+CHH36gbdu25ObmsnXrVqZPn07Hjh35+uuv6d+//1n3Y7PZsFqtF/9hREQ83JYTW0jOScbfy5/LG15udBy3NAxsyKDGg/j2wLd8+OuHvNLnFaMjSTVy2nGaf/z8D7499K3RUWqlA8cOGB2hWsouyDY6gttU5Angmq75xRdfMGLECEwmEwAbNmzg2Wef5YorrmDZsmXF7cA10vf0008D8M0339CxY0caNmzIwYMHAahbt25x4di0aVOGDh1K//79GTt2LPv27cNisfD000/z5ZdfMn78eJ5//nkOHjyI3W7n+++/5/nnn+fXX3/FYrHQq1cvpk+fTrNmzYrzrlq1invvvZedO3fSrl07Hn/8cUaOHMmmTZs0vVREPN53B74DoF9Mv2q/qubZ3NH2Dr498C3fH/ye8V3G0zCwodGRxGBnCs/wry3/4oPMDyjMdF1iY0iTIVze6HIsJgsmTAYn9GyF9kISNyXSqXMnvCwqD/6scUhjoyO4Td+ilJKSksKAAQO4+uqreeihh/D39+fDDz/kySefZNeuXYBrGmaRhQsXMnz48PPu02w2M2HCBEaOHMmGDRu45JJLANi7dy+fffYZCxYswGKxAJCTk8PkyZNp3749OTk5PPnkk4wcOZLExETMZjNZWVkMHTqUIUOG8Mknn3Do0CEmTpxYOZ0hIlINFV0A/erGVxucpHxa121Nz8ie/JLyCx/++iGP93zc6EhiEKfTyXcHv+PNDW+SmpMKQJfwLjxyySO0qdvG4HS1h81mw77NzlWxV2lWlYdQkVcBnE4nZwrPVOoxHA4HZwrP4GXzwmz+fa60n5df8ehaRYmIiMDLy4vAwMDi0biQkBBMJlOpaZ35+fl8//33PPnkkxfcb6tWrvMuDh48WFzkFRQUMHfuXOrXr1/c7rrrrivxvvfff5/w8HC2b99Ou3bt+PjjjzGZTLz33nv4+vrSpk0bjh49yt/+9reL+twiIjXBiTMnSM5JxoSJ7hHdjY5Tbnd3uJtfUn7hiz1fcHeHuwn3Dzc6klSxpMwknl39LGtS1wAQGRDJFc4r+Hv/v+Pt7W1wOpGaTUVeBThTeIYen/Qw5Nhrbl5j6KpqP/74I3Xr1qV9+/YXbFt0TaQ/FqWxsbElCjxwnQ/4xBNP8Msvv3DixAkcDgcASUlJtGvXjl27dtGhQwd8fX+folRUNIqIeLrdJ3cDEBscS4A1wOA05detQTc6h3dmU9om4rfF83D3h42OJFXE5rDx723/ZtbmWeTb8/G1+DK2/VhuaXELPy7+scL/eC1SG2n5HLkoZZmqWWTHjh0AJVbfDAgo/Q+UoUOHkp6eznvvvceaNWtYs8b1F76CggLAVSz++ReALqorIrXF4azDQM08R+SPTCYT/9fh/wD4z67/cDLvpMGJpCpsPb6VUf8bxfSN08m359MrshefD/uccR3H1cjzS0WqK43kVQA/Lz/W3LymUo/hcDjIysoiKCio1HTNquDt7Y3dbi+xzel08vXXXzNnzpwLvt/hcDBjxgyaNGlC586dz9kuPT2dHTt28O6773L55a4V41auXFmiTatWrfj444/Jz8/Hx8cHgPXr17v7kUREaqQj2UcAaBTYyOAkF693VG/a1m3LtvRtzNk2h4ldJxodSSqJzWHj3c3v8t7W93A4HYT6hPJw94e5tum1GrkTqQQq8iqAyWSq9CmTDoeDQq9C/K3+JYo8d2VkZJCYmFhiW1hY2AXf17hxY7Kzs1myZAkdO3bE39+f7du3k5OTQ58+fUq1T09PJzU1ldzcXH799VemTZvG2rVr+eabb4oXWDmbOnXqULduXWbPnk1kZCRJSUk8+uijJdrcfPPN/OMf/+Duu+/m0UcfJSkpiddffx1AvyhExOMdyXIVeZ6wImXRaN74n8Yzb+c87mh3ByE+IUbHkgq2P2M/U1ZMYXv6dsC1auajlzxKHd86F3iniJSXirxaZunSpaVG0m677bYLvq93796MGzeOUaNGkZ6ezlNPPYXdbueaa67By6v0j9GAAQMA8Pf3JzY2ln79+jF79mzi4uLOexyz2cynn37K+PHjadeuHS1btmTGjBn07du3uE1wcDBff/0199xzD506daJ9+/Y8+eST3HzzzSXO0xMR8URF0zVjgmMMTlIx+kb3pUWdFuw+tZuPd3zMvZ3uNTqSVBCH08G8nfOYumEq+fZ8gr2DeaLXEzV2VViRmkRFXi0SHx9PfHz8OV/7oz+P9gG88847vPPOO8XPO3TowOOPl1z2unHjxmU+P+7pp58uvtbeHw0YMIDt27eX2Pbnffbu3ZvNmzcXP//444+xWq3ExHjGP3pERM7G6XSSlJUEQHRQtMFpKobJZOLuDnfz0LKH+GjHR9za5lYCvQMv/Eap1k7lneKxlY+x8qjrlIveUb15tvezNAhoYHAykdpBRZ6US0FBAddddx2DBw825Phz5syhadOmNGzYkM2bN/PII49w44034udXNecoiogYIT0vnTOFZzCbzB4xXbPIgJgBNAlpwoGMA8zdMZd7Ot5jdCS5CJvSNvH3ZX/nWO4xfCw+PNjtQW5qeZNOqRCpQlpdU8rF29ubp556iqCgIEOOn5qayl//+ldat27NpEmTuOGGG5g9e7YhWUREqkpSpmsULzIgEm+L51xHzGK2cG9H1zTNOdvmcDrvtLGBpFycTifxv8Zzx3d3cCz3GI2DG/PxkI8Z3Wq0CjyRKqYiT2qkhx9+mIMHD5KXl8eBAweYOnUq/v7GXS9QRKQqeNpUzT8a1HgQLeu0JNuWzQfbPjA6jrgpIz+D8T+O540Nb2B32hncZDCfXvspLcNaGh1NpFZSkSciIlJDFI3keWKRZzaZGd9lPADzdswjLTfN4ERSVr+e+JUbv76RpUeW4m325omeT/DK5a8QYC19LVwRqRoq8kRERGqIossnxAR55iJTlze8nE71O5Fnz2P2Fk3Br+6cTifzds5jzLdjSM5JplFgI+YOmcuNLW/U9EwRg6nIK6eyriAp1Ye+MxGp6YqnawZ73kgeuFbaLBrNW7B7QfHlIqT6ySrI4sFlD/LimhcpdBTSP6Y/nw39jDZ12xgdTURQkec2q9UKQG5ursFJxF1F31nRdygiUpM4nc7i6ZqeOpIH0D2iO72jelPoLGTW5llGx5Gz2HZiGzd+fSMJhxLwMnnx925/Z2rfqQR5G7MYm4iUpksouMlisRAaGkpamutcAX9//yqZkuBwOCgoKCAvLw+zWbW5O5xOJ7m5uaSlpREaGorFYjE6koiI2zLyM8iyZQHQKKiRwWkq1/gu41mVvIqv933NHW3vIK5OnNGRBNfv0092fsLr61+n0FFIVEAUr13xGh3qdzA6moj8iYq8coiIiAAoLvSqgtPp5MyZM/j5+WmeezmFhoYWf3ciIjVN0VTNcP9w/Lw8+5qgbeu2ZWDsQBIOJTB903TeuvItoyPVepkFmTz585MsSVoCwJXRV/Lspc8S4hNicDIRORsVeeVgMpmIjIwkPDwcm81WJce02WwsX76cPn36aLphOVitVo3giUiNVlTkefJUzT+6v9P9/Jj0I0sPL2VtylouibzE6Ei11tbjW/n78r9zNPsoXmYvHur2EDe3ull/dBapxlTkXQSLxVJlhYPFYqGwsBBfX18VeSIitdDhTNciJJ54+YSzaRralBta3MCnuz7l1XWvMv/a+VjM+mNdVfvP7v8UL67SKLARr1/xOm3rtTU6lohcgE7uEhERqQE8+ULo53Jvp3sJ8g5i16ldfLXvK6Pj1CoF9gKeXvU0z65+tsTqmSrwRGoGFXkiIiI1wIGMAwA0CWlicJKqU8e3DuM6jANgxsYZ5NhyDE5UOxzLOcYd393Bgj0LMGFiQpcJWj1TpIZRkSciIlLNOZ3OWlnkAYxuNZrY4FjS89L519Z/GR3H4208tpFR/xvFlhNbCPYO5u0Bb3NX+7t0/p1IDaMiT0REpJpLy00jtzAXi8lSaxZeKWK1WJncdTIA/9727+JiVyqW0+lk3s55jP1+LOl56bSo04JPr/2UyxpeZnQ0ESkHFXkiIiLV3P6M/YDrfDyrpfYtvtUvuh+XNbwMm8PGC7+8gNPpNDqSR8krzOPxnx93LbDiLGRw48HMHTy3Vp3/KeJpVOSJiIhUc7V1qmYRk8nEYz0ew8fiw5rUNXxz4BujI3mMlOwUbvvuNhbuW4jZZOahbg/xSp9X8Lf6Gx1NRC6CijwREZFqrmgkr7YWeeAaxfy/Dv8HwGvrXiMjP8PgRDXf2pS1jPrfKLanbyfUJ5R3B77LbW1v0/l3Ih5ARZ6IiEg1dzDjIABNQ5oaG8Rgt7e9nSYhTTiZd5IZG2cYHafGcjqdzNk2h7sT7uZU/ilah7Vm/rXz6RnZ0+hoIlJBVOSJiIhUcxrJc7FarDzR8wkAPtv9GetS1xmcqOY5U3iGR1c8ymvrX8PutDOs2TDmDJ5DVGCU0dFEpAKpyBMREanGsgqyOH7mOKAiD6B7RHeua34dAE/8/AS5tlyDE9UcR7KOMGbRGBYdWITFZOHRSx7l+Uufx9fL1+hoIlLBVOSJiIhUY0VTNev71dfFqH/zULeHiAyI5Gj2Ud5Y/4bRcaq9AnsB/9r6L/6y8C/sOrWLMN8w3hv0Hre0vkXn34l4KBV5IiIi1ZimapYW6B3Ic5c+B7imba46usrgRNXX8iPLGfnVSKZvnM6ZwjN0Ce/C/Gvn0z2iu9HRRKQSqcgTERGpxmr75RPOpUdkD25qeRPgmrZ5Mu+kwYmql8OZh3lgyQPct+Q+krKSqOdXjxcve5H4q+OJCIgwOp6IVDIVeSIiItXYvtP7ABV5ZzOp6ySahDQh7Uwaj614DIfTYXQkw50pPMNbm95ixFcjWHpkKV4mL25veztfj/iaoc2GanqmSC2hIk9ERKQa23N6DwAt6rQwOEn142/15/UrXsfH4sPPyT/zwa8fGB3JME6nk8UHFzPsy2HM3jKbAkcBvSJ7sWD4Ah7s9iCB3oFGRxSRKqQiT0REpJrKtmVzNPsooCLvXFrUacFjPR4DYOammWw4tsHgRFVv3+l9/G3x33hw2YOk5qQSFRDFtL7TeHfgu7X+2ooitZWKPBERkWqqaKpmuH84IT4hBqepvkbGjeTaptdid9qZvHQyydnJRkeqElkFWby67lWuX3g9a1LX4GPx4Z6O9/DliC/pH9tfUzNFajFDi7zly5czdOhQoqKiMJlMfPnll+dtv3TpUkwmU6nbzp07S7RbsGABbdq0wcfHhzZt2vDFF19U4qcQERGpHEVTNZvXaW5wkurNZDLxRM8naBXWipN5J3ngxwc8+vp5DqeDr/Z+xdAvhjJ3+1wKnYVcGX0lXw7/kns73Yufl5/REUXEYIYWeTk5OXTs2JGZM2e69b5du3aRkpJSfGve/PdffqtXr2bUqFGMGTOGzZs3M2bMGG688UbWrFlT0fFFREQqlc7HKzt/qz8z+s2grm9ddp/azSPLH6HQUWh0rAq3PX07t357K4///Djpeek0Dm7MrAGzmH7ldBoFNTI6nohUE15GHnzw4MEMHjzY7feFh4cTGhp61temTZvGwIEDmTJlCgBTpkxh2bJlTJs2jXnz5l1MXBERkSq19/ReAJqHaiSvLCIDI5nWbxpjvx/L0iNLeWrVUzx36XOYTTX/7JT0M+n8M/Gf/Hf3f3HixM/Lj3EdxzGm9RisFqvR8USkmjG0yCuvzp07k5eXR5s2bXj88cfp169f8WurV69m0qRJJdpfddVVTJs27Zz7y8/PJz8/v/h5ZmYmADabDZvNVrHhy6koR3XJU5uo742jvjeW+t84NpsNp9NZPJLXNKipvocyalunLS9d+hIPr3yYhfsW4m/x5+9d/17m89Oq2899XmEen+z6hA+3fUhOYQ4Ag2MHM6HzBML9w8EBNkf1yHqxqlvf1ybqe2O50/9l/Y5qVJEXGRnJ7Nmz6dq1K/n5+cydO5f+/fuzdOlS+vTpA0BqaioNGjQo8b4GDRqQmpp6zv2+9NJLPPPMM6W2L168GH9//4r9EBcpISHB6Ai1lvreOOp7Y6n/jZHhzCDblo0ZMztX7WSvaa/RkWqUEX4jWJC7gE93f8rRQ0cZ5DvIrYVIjP65dzgdbLVtZfGZxWQ4MwCIskQxxG8IjTMas37pekPzVSaj+742U98bqyz9n5tbtvONa1SR17JlS1q2bFn8vFevXhw+fJjXX3+9uMgDSv1P3Ol0nvd/7FOmTGHy5MnFzzMzM4mOjmbQoEEEBwdX4CcoP5vNRkJCAgMHDsRq1bSMqqS+N4763ljqf+PYbDbeWvQWAE1DmjLsmmEGJ6p5hjCE5rub8/L6l1mRv4L60fWZ0m0KFrPlvO8z+ufe6XSyMnkls7bOYkfGDgAa+Dfg/o73M7jxYI+YenouRvd9baa+N5Y7/V804/BCalSRdzY9e/bko48+Kn4eERFRatQuLS2t1OjeH/n4+ODj41Nqu9VqrXY/6NUxU22hvjeO+t5Y6n9jHC10XR+vTb026v9yuqXtLXhbvXn+l+f5fO/nZNmyeOGyF8q0+mRV/9w7nU5+Tv6ZtxPfZuuJrQD4eflxV/u7GNNmTK1aMVP/zzGO+t5YZen/sn4/Nb7I27RpE5GRkcXPe/XqRUJCQonz8hYvXkzv3r2NiCciIlIuyXbXtd7a1G1jcJKa7YYWNxDqE8ojyx8h4VACh7MOM7Xv1GqzEqXdYWfpkaV8+OuHbD6+GQBfiy+jW43m9na3E+YbZnBCEamJDC3ysrOz2bv393MMDhw4QGJiImFhYcTExDBlyhSOHj3KnDlzANfKmY0bN6Zt27YUFBTw0UcfsWDBAhYsWFC8jwkTJtCnTx9eeeUVhg8fzldffcUPP/zAypUrq/zziYiIlJeKvIozMHYgdQbW4cFlD7Lz5E5G/W8Uz/R+hgGxAwzLlGvL5cu9X/LRjo84nHUYAB+LD6NajuKOdndQz6+eYdlEpOYztMhbv359iZUxi86Lu+2224iPjyclJYWkpKTi1wsKCnjooYc4evQofn5+tG3blm+++YYhQ4YUt+nduzeffvopjz/+OE888QTNmjVj/vz59OjRo+o+mIiIyEU4fuY4Wc4szCYzLeu0vPAb5IK6RXRj/rXzeXDpg2w5sYVJSydxdeOrmdJjSpWNljmdTraf3M4Xe75g0f5FZNmyAAj2DubGljdyc6ubqe9fv0qyiIhnM7TI69u3L06n85yvx8fHl3j+8MMP8/DDD19wv9dffz3XX3/9xcYTERExxI6TrgU3Ggc3xt9avVZ5rskiAiL48OoPmbV5Fh/8+gHfHfyOn4/+zNj2Y7ml9S34evlWynGTMpNYkrSE/+3/H7tP7S7eHhMUw5g2YxjWbJi+ZxGpUDX+nDwRERFPs/PkTgBa12ltcBLP423xZnyX8fSP7c9TPz/FrlO7mLZxGh/v+JibWt3EiCYjLvoYTqeTXad2sSRpCT8c+qH4ovYA3mZv+sf2Z2TcSHpE9vDo1TJFxDgq8kRERKqZopG81mEq8ipL27ptmX/tfBYdWMRbm94iJSeFtza9xTuJ79DY0pgze8/QM6onscGxF7y+nsPpIDk7mU1pm1h/bD1rUtZwNPto8esWk4XuEd0ZEDOAq5tcTYhPSGV/PBGp5VTkiYiIVDP7MvYB6Hy8SmYxWxjabChXNb6K7w9+zyc7PuHX9F/ZW7iXF9a+AECQdxDNQ5sT7h9OXb+6eJm8MJvN5BTkcCr/FMnZyezP2M+ZwjMl9u1j8eHSqEvpH9ufKxpdocJORKqUijwREZFqxO6wk5rrut5rw8CGBqepHbwt3gxtNpShzYayN30vb//wNmmBaew8tZOsgiw2pm284D68zF60rNOSSyIuoVtEN7o16Kbz7ETEMCryREREqpG03DQKHYVYsFDfTystVrXY4Fiu8L2CIYOGgAX2ntrLoaxDpOWkcTLvJHanHbvTTqA1kFCfUML9w2ka2pTooGisZl1EWkSqBxV5IiIi1ciR7CMAhJpDsZgtBqep3axmK63rtqZ1XZ0bKSI1i5Z0EhERqUaKFuwINYcaG0RERGosFXkiIiLVSFGRV8dcx+AkIiJSU6nIExERqUaOZqnIExGRi6MiT0REpBopGskLM4cZnERERGoqFXkiIiLVSNHCKxrJExGR8lKRJyIiUk0U2As4nnscUJEnIiLlpyJPRESkmkjOTsaJE1+LL/4mXUhbRETKR0WeiIhINZGcnQxAw8CGmEwmg9OIiEhNpSJPRESkmig6Hy8qIMrgJCIiUpOpyBMREakmDmUeAiA6KNrgJCIiUpN5laXRjBkz3N7xHXfcQVBQkNvvExERqa2SMpMAiAmKgWMGhxERkRqrTEXexIkTadSoERaLpUw7PXz4MNdee62KPBERETccynKN5MUExXCSkwanERGRmqpMRR7A+vXrCQ8PL1NbFXciIiLusTvsHMlynZOnIk9ERC5Gmc7Je+qppwgMDCzzTh977DHCwsLKHUpERKS2SclJweawYTVbaeDfwOg4IiJSg5VpJO+pp55ya6dTpkwpVxgREZHaKinLdT5edFA0FnPZTo8QERE5m4taXfPll1/m9OnTFRRFRESk9ipedCU4xuAkIiJS011Ukffiiy9y8qTOGRAREblYRZdPiA2KNTiJiIjUdBdV5DmdzorKISIiUqsVTdfUSJ6IiFwsXQxdRESkGtB0TRERqShlvoTC2Wzfvp2oqKiKyiIiIlIr2Ry24ssnaLqmiIhcrIsayYuOji7zBdJFRETk7JIykyh0FuLv5U9EQITRcUREpIYrU5EXFhbGiRMnyrzTmJgYDh06VO5QIiIitcme03sAiAuNw2QyGZxGRERqujJN1zx9+jTffvstISEhZdppeno6drv9ooKJiIjUFvtO7wMgrk6cwUlERMQTlPmcvNtuu60yc4iIiNRae0/tBVwjeSIiIherTEWew+Go7BwiIiK11t7TriKvWWgzg5OIiIgn0CUUREREDJRvzy++Rl7z0OYGpxEREU+gIk9ERMRABzIO4HA6CPEJoZ5fPaPjiIiIB1CRJyIiYqDiqZohzbSypoiIVAgVeSIiIgYqWnSleR1N1RQRkYqhIk9ERMRAO07uAKBFnRYGJxEREU/hdpFnsVhIS0srtT09PR2LxVIhoURERGoDp9PJtvRtALSt29bgNCIi4incLvKcTudZt+fn5+Pt7X3RgURERGqL5JxkMvIz8DJ7abqmiIhUmDJfDH3GjBkAmEwm/vWvfxEYGFj8mt1uZ/ny5bRq1ariE4qIiHio7enbAdelE7wt+kOpiIhUjDIXeVOnTgVcI3mzZs0qMTXT29ubxo0bM2vWrIpPKCIi4qG2nXBN1WxTt43BSURExJOUucg7cOAAAP369ePzzz+nTp06lRZKRESkNig+H6+ezscTEZGKU+Yir8hPP/1UGTlERERqlUJHIVuObwGgQ70OBqcRERFP4naRZ7fbiY+PZ8mSJaSlpeFwOEq8/uOPP1ZYOBEREU+159QecgtzCbQGEhcaZ3QcERHxIG4XeRMmTCA+Pp5rrrmGdu3aYTKZKiOXiIiIR9uYthGAjuEdsZh1CSIREak4bhd5n376KZ999hlDhgy56IMvX76c1157jQ0bNpCSksIXX3zBiBEjztn+888/55133iExMZH8/Hzatm3L008/zVVXXVXcJj4+njvuuKPUe8+cOYOvr+9FZxYREakIm9I2AdAlvIvBSURExNO4fZ08b29v4uIqZlpJTk4OHTt2ZObMmWVqv3z5cgYOHMiiRYvYsGED/fr1Y+jQoWzatKlEu+DgYFJSUkrcVOCJiEh1UlTkdQ7vbHASERHxNG6P5D344INMnz6dmTNnXvRUzcGDBzN48OAyt582bVqJ5y+++CJfffUVX3/9NZ07//5L0mQyERERcVHZREREKsvJvJOk5aYB0LauVtYUEZGK5XaRt3LlSn766Se+/fZb2rZti9VqLfH6559/XmHhLsThcJCVlUVYWFiJ7dnZ2cTGxmK32+nUqRPPPfdciSLwz/Lz88nPzy9+npmZCYDNZsNms1VOeDcV5agueWoT9b1x1PfGUv9Xnh3HdwDQKLARVqyl+lh9bxz1vXHU98ZR3xvLnf4v63dkcjqdTndCnO18tz/68MMP3dnd70FMpguek/dnr732Gi+//DI7duwgPDwcgF9++YW9e/fSvn17MjMzmT59OosWLWLz5s00b978rPt5+umneeaZZ0pt/+STT/D39y/X5xERETmXVXmrWJS3iDbWNtwccLPRcUREpIbIzc3l5ptvJiMjg+Dg4HO2c7vIqyzuFnnz5s3jrrvu4quvvmLAgAHnbOdwOOjSpQt9+vRhxowZZ21ztpG86OhoTpw4cd7Oq0o2m42EhAQGDhxYavRUKpf63jjqe2Op/yvP0788zcL9C/lbu79xT4d7Sr2uvjeO+t446nvjqO+N5U7/Z2ZmUq9evQsWeW5P1wQoLCxk6dKl7Nu3j5tvvpmgoCCSk5MJDg4mMDCwPLt0y/z58xk7diz/+c9/zlvgAZjNZrp3786ePXvO2cbHxwcfH59S261Wa7X7Qa+OmWoL9b1x1PfGUv9XvL0ZewFoXa/1eftWfW8c9b1x1PfGUd8bqyz9X9bvx+0i79ChQ1x99dUkJSWRn5/PwIEDCQoK4tVXXyUvL49Zs2a5u0u3zJs3jzvvvJN58+ZxzTXXXLC90+kkMTGR9u3bV2ouERGRsih0FLL3lKvIa1mnpcFpRETEE7l9CYUJEybQrVs3Tp06hZ+fX/H2kSNHsmTJErf2lZ2dTWJiIomJiQAcOHCAxMREkpKSAJgyZQq33nprcft58+Zx66238sYbb9CzZ09SU1NJTU0lIyOjuM0zzzzD999/z/79+0lMTGTs2LEkJiYybtw4dz+qiIhIhUvKTKLAUYCflx+NghoZHUdERDxQuVbX/Pnnn/H29i6xPTY2lqNHj7q1r/Xr19OvX7/i55MnTwbgtttuIz4+npSUlOKCD+Ddd9+lsLCQ++67j/vuu694e1F7gNOnT3P33XeTmppKSEgInTt3Zvny5VxyySXuflQREZEKt+vULgCa12mO2eT231pFREQuyO0iz+FwYLfbS20/cuQIQUFBbu2rb9++nG/dl6LCrcjSpUsvuM+pU6cydepUt3KIiIhUlV0nXUWepmqKiEhlcftPiAMHDixxUXKTyUR2djZPPfUUQ4YMqchsIiIiHqdoJE9FnoiIVBa3R/KmTp1Kv379aNOmDXl5edx8883s2bOHevXqMW/evMrIKCIi4jF2n9wNQMswFXkiIlI53C7yoqKiSExMZN68eWzcuBGHw8HYsWO55ZZbSizEIiIiIiUdyzlG2pk0zCYzLeq0MDqOiIh4qHJdJ8/Pz48777yTO++8s6LziIiIeKytJ7YC0Dy0Of5Wf4PTiIiIpypTkbdw4UIGDx6M1Wpl4cKF5207bNiwCgkmIiLiabac2AJA+/q6dquIiFSeMhV5I0aMIDU1lfDwcEaMGHHOdiaT6awrb4qIiAhsOe4q8jrU62BwEhER8WRlKvIcDsdZH4uIiEjZFDoK2Z6+HYAO9VXkiYhI5dFVWEVERKrArpO7OFN4hiBrEE1CmhgdR0REPJjbRd748eOZMWNGqe0zZ85k4sSJFZFJRETE4/yS8gsAXSO6Yjbpb6wiIlJ53P4ts2DBAi699NJS23v37s1///vfCgklIiLiadamrgWgR0QPg5OIiIinc7vIS09PJyQkpNT24OBgTpw4USGhREREPEmBvYCNxzYC0CNSRZ6IiFQut4u8uLg4vvvuu1Lbv/32W5o2bVohoURERDzJ5uObybPnEeYbRlxonNFxRETEw7l9MfTJkydz//33c/z4ca688koAlixZwhtvvMG0adMqOp+IiEiNt/TwUgB6R/XGZDIZmkVERDyf20XenXfeSX5+Pi+88ALPPfccAI0bN+add97h1ltvrfCAIiIiNZnT6eTHpB8B6B/T3+A0IiJSG7hd5AHcc8893HPPPRw/fhw/Pz8CAwMrOpeIiIhH2H1qN0eyj+Bj8aF3VG+j44iISC1QriKvSP369Ssqh4iIiEdKOJQAQK+oXvhb/Q1OIyIitUGZirwuXbqwZMkS6tSpQ+fOnc97PsHGjRsrLJyIiEhN5nA6WLhvIQBXN77a4DQiIlJblKnIGz58OD4+PgCMGDGiMvOIiIh4jDUpa0jJSSHIO0jn44mISJUpU5FXp04dzGbX1RbuuOMOGjVqVPxcREREzu6/u/8LwJAmQ/D18jU4jYiI1BZlqtQmT55MZmYmAE2aNNFFz0VERC4gKTOJH5J+AOCGFjcYnEZERGqTMo3kRUVFsWDBAoYMGYLT6eTIkSPk5eWdtW1MTEyFBhQREamJPtz2IQ6ngz6N+tAyrKXRcUREpBYpU5H3+OOP88ADD3D//fdjMpno3r17qTZOpxOTyYTdbq/wkCIiIjXJ/tP7+XLPlwCMbTfW2DAiIlLrlKnIu/vuuxk9ejSHDh2iQ4cO/PDDD9StW7eys4mIiNQ4TqeTV9e9SqGzkL7RfenSoIvRkUREpJYpU5E3Y8YM7r77btq1a8eHH35Ir1698PPzq+xsIiIiNc6CPQv4OflnvMxe/L3b342OIyIitZDbC6/ceeedZGVlVWooERGRmmhH+g5eWfsKAA90foCYYJ2nLiIiVU8Lr4iIiFSAgxkHueeHe8iz59E7qje3t73d6EgiIlJLaeEVERGRi7QudR2Tl07mdP5pWtZpyetXvI7ZpOvJioiIMbTwioiISDmln0ln1uZZfLrrUwDa1m3LP/v/kyDvIIOTiYhIbVamIg8gKCioeOGVSy+9FB8fn8rMJSIiUi3Z7DbWHVvH9we/Z9H+ReTZXacvXNf8Oh7u/jD+Vn+DE4qISG1X5iKvyG233cbp06eZO3cu+/bt4+9//zthYWFs3LiRBg0a0LBhw8rIKSIiNYDT6XSvPWVvX5n7Lmpvs9vILcwlx5ZDri2XbFs2ablppOSkkJKdws5TO9mZvpMCR0Hx+9rVbcf4LuPpFdXLreOJiIhUFreLvC1btjBgwABCQkI4ePAgf/vb3wgLC+OLL77g0KFDzJkzpzJy1npDvxpKRm4GUz+fWqb27v7jxh1V8Q8tt9pXdh6nk0JbIS//52VMmCp+/9Xs87qrsvPY7Xae/fTZStu/2/3jdvOa//Pw+CePu/0eTxfmG0b/mP4MbjKYbg26YTJd+P8NIiIiVcXtIm/SpEncfvvtvPrqqwQF/X7OweDBg7n55psrNJz8Lj0vnTxnHtl52UZHqb1sRgeoxRxGB5DaxIQJf6s/AV4B+Fv9qe9fnwj/CCICImga2pQO9ToQHRStwk5ERKott4u89evXM3v27FLbGzZsSGpqaoWEktLmXDWH5SuWc/nll+PlVbavrSyjThfTHnD7HzluH6Mc/4aq6M9dWFjIsmXLuOKKK4r7vrI/d3m+C/e7tvp9339ubyu08dNPP9GvXz+sXtYKyVQebn+Oyv7vohzHKA97oZ0ffviBAQMGlOn/O1XyuSv5u7Carfh5+amAExGRGs3tIs/X17f4wuh/tGvXLurXr18hoaS0uNA4dlt20zy0OVbr2f+xK5XDZrNRz1KP2OBY9X0Vs9lshJpDiQyIVN8bwGazEWgOJMw3TP0vIiJSg7h9EZ/hw4fz7LPPYrO55q6ZTCaSkpJ49NFHue666yo8oIiIiIiIiJSd20Xe66+/zvHjxwkPD+fMmTNcccUVxMXFERQUxAsvvFAZGUVERERERKSM3J6uGRwczMqVK/nxxx/ZuHEjDoeDLl26MGDAgMrIJyIiIiIiIm5wu8grcuWVV3LllVdWZBYRERERERG5SG5P1wRYtmwZQ4cOJS4ujubNmzNs2DBWrFhR0dlERERERETETW4XeR999BEDBgzA39+f8ePHc//99+Pn50f//v355JNPKiOjiIiIiIiIlJHb0zVfeOEFXn31VSZNmlS8bcKECbz55ps899xzuiC6iIiIiIiIgdweydu/fz9Dhw4ttX3YsGEcOHCgQkKJiIiIiIhI+bhd5EVHR7NkyZJS25csWUJ0dHSFhBIREREREZHycbvIe/DBBxk/fjz33HMPc+fO5aOPPmLcuHFMmDCBhx56yK19LV++nKFDhxIVFYXJZOLLL7+84HuWLVtG165d8fX1pWnTpsyaNatUmwULFtCmTRt8fHxo06YNX3zxhVu5REREREREaiq3i7x77rmHTz/9lK1btzJx4kQmTJjAr7/+yvz58/m///s/t/aVk5NDx44dmTlzZpnaHzhwgCFDhnD55ZezadMmHnvsMcaPH8+CBQuK26xevZpRo0YxZswYNm/ezJgxY7jxxhtZs2aNW9lERERERERqonJdJ2/kyJGMHDnyog8+ePBgBg8eXOb2s2bNIiYmhmnTpgHQunVr1q9fz+uvv851110HwLRp0xg4cCBTpkwBYMqUKSxbtoxp06Yxb968i84sIiIiIiJSnbk9krdu3bqzjoqtWbOG9evXV0ioc1m9ejWDBg0qse2qq65i/fr12Gy287ZZtWpVpWYTERERERGpDtweybvvvvt4+OGH6dGjR4ntR48e5ZVXXqnUaZGpqak0aNCgxLYGDRpQWFjIiRMniIyMPGeb1NTUc+43Pz+f/Pz84ueZmZkA2Gy24uLRaEU5qkue2kR9bxz1vbHU/8ZR3xtHfW8c9b1x1PfGcqf/y/oduV3kbd++nS5dupTa3rlzZ7Zv3+7u7txmMplKPHc6naW2n63Nn7f90UsvvcQzzzxTavvixYvx9/e/mLgVLiEhwegItZb63jjqe2Op/42jvjeO+t446nvjqO+NVZb+z83NLdO+3C7yfHx8OHbsGE2bNi2xPSUlBS+vcp3iV2YRERGlRuTS0tLw8vKibt26523z59G9P5oyZQqTJ08ufp6ZmUl0dDSDBg0iODi4Aj9B+dlsNhISEhg4cCBWq9XoOLWK+t446ntjqf+No743jvreOOp746jvjeVO/xfNOLwQt6uyokVNvvrqK0JCQgA4ffo0jz32GAMHDnR3d27p1asXX3/9dYltixcvplu3bsUd0qtXLxISEpg0aVKJNr179z7nfn18fPDx8Sm13Wq1Vrsf9OqYqbZQ3xtHfW8s9b9x1PfGUd8bR31vHPW9scrS/2X9ftwu8t544w369OlDbGwsnTt3BiAxMZEGDRowd+5ct/aVnZ3N3r17i58fOHCAxMREwsLCiImJYcqUKRw9epQ5c+YAMG7cOGbOnMnkyZP529/+xurVq3n//fdLrJo5YcIE+vTpwyuvvMLw4cP56quv+OGHH1i5cqW7H1VERERERKTGcbvIa9iwIVu2bOHjjz9m8+bN+Pn5cccddzB69Gi3K//169fTr1+/4udFUyZvu+024uPjSUlJISkpqfj1Jk2asGjRIiZNmsQ///lPoqKimDFjRvHlEwB69+7Np59+yuOPP84TTzxBs2bNmD9/fqmFYkRERERERDxRuU6iCwgI4O67777og/ft27d44ZSziY+PL7XtiiuuYOPGjefd7/XXX8/1119/sfFERERERERqHLevkyciIiIiIiLVl4o8ERERERERD6IiT0RERERExIOoyBMREREREfEgbhd569atY82aNaW2r1mzhvXr11dIKBERERERESkft4u8++67j8OHD5fafvToUe67774KCSUiIiIiIiLl43aRt337drp06VJqe+fOndm+fXuFhBIREREREZHycbvI8/Hx4dixY6W2p6Sk4OVVrsvuiYiIiIiISAVxu8gbOHAgU6ZMISMjo3jb6dOneeyxxxg4cGCFhhMRERERERH3uD309sYbb9CnTx9iY2Pp3LkzAImJiTRo0IC5c+dWeEAREREREREpO7eLvIYNG7JlyxY+/vhjNm/ejJ+fH3fccQejR4/GarVWRkYREREREREpo3KdRBcQEMDdd99d0VlERERERETkIpWpyFu4cCGDBw/GarWycOHC87YdNmxYhQQTERERERER95WpyBsxYgSpqamEh4czYsSIc7YzmUzY7faKyiYiIiIiIiJuKlOR53A4zvpYREREREREqhe3L6EgIiIiIiIi1Ve5Fl5Zu3YtS5cuJS0trdTI3ptvvlkhwURERERERMR9bhd5L774Io8//jgtW7akQYMGmEym4tf++FhERERERESqnttF3vTp0/nggw+4/fbbKyGOnIvlm4l0TjqAZeE3YC7rLNtyFN3lKtTLc5xyHMagz2NxOOh4+DCWbxLO3fdV0W9V9t1Un+OYHQ7aHz6I+fvlv/V9zf48F32cqvpD2m/HMTsctDm6H/OSNWX4/0417bPyHMdsBe+A326BrvuAehAcBUGR4OXjfgYREZEq5HaRZzabufTSSysji5yHadvnxNhy4aTRSWofM9AYIN3YHLWRBWgKcMLgILWUBWgOkGZwkOomuBFEdoSozhDX33WvmSwiIlKNuF3kTZo0iX/+859MmzatEuLIuTj6PsaObb/SunUrLGZLGd7hdO8ATjfb16Jj2O0Odu/eTYsWLbBYzG5HKscbyvE5PPMYdruDvXv3EBfX3NX3F73/0se4cPPaegwndoeD/fv307RpUyxlmUFQrs/hbqwq+Lm1F0BBLhRkQ0GO6z7nOGQmQ2EeZB5x3XZ9Az89D8ENocut0O1OCAx3/3giIiIVzO0i76GHHuKaa66hWbNmtGnTBqvVWuL1zz//vMLCye8cl4xj34lFtOw5BMuf+lwql8NmY3fmIuIuU99XNYfNxs7cRTTtq743gsNmY3v+Ihr3V/8DrgIzNx2O74KUREhaDXt/hMyjsPQl+HkG9HkQet2vKZ0iImIot4u8Bx54gJ9++ol+/fpRt25dLbYiIiK1g8nkOjcvoB40vhR63Qe2PNj5P1g9E5I3wZJnYftCuHEO1Ik1OrGIiNRSbhd5c+bMYcGCBVxzzTWVkUdERKTmsPpC++uh3XWw9T/w7SOuUb7ZV8CYL1zn64mIiFQxty+GHhYWRrNmzSoji4iISM1kMkGHG+H/lrsKuzOnYM5wSE40OpmIiNRCbhd5Tz/9NE899RS5ubmVkUdERKTmCo2GWxdCo0sgLwM+udG1YIuIiEgVcnu65owZM9i3bx8NGjSgcePGpRZe2bhxY4WFExERqXF8g+GvC+CDqyFtG3x2K9zxHVjc/pUrIiJSLm7/xhkxYkQlxBAREfEgvsFw00fwbl84sg5WvwWXTTI6lYiI1BJuF3lPPfVUZeQQERHxLGFN4eqX4Kt74aeXoOUQqN/S6FQiIlILuH1OnoiIiJRRp5shbiDY8+G7R41OIyIitYTbRZ7dbuf111/nkksuISIigrCwsBI3ERER+Y3JBENeA7MV9v0I+34yOpGIiNQCbhd5zzzzDG+++SY33ngjGRkZTJ48mb/85S+YzWaefvrpSogoIiJSg4U1ge5jXY8TngSHw9g8IiLi8dw+J+/jjz/mvffe45prruGZZ55h9OjRNGvWjA4dOvDLL78wfvz4yshZ6/1l1i8cP2lh5r6fMZlMZXqPibK1K/Ee999SLmX9DMXty3WMinmP0+kkI8PC7EOrz5q7Kvq5XF9LOTqgyvq5jO2cTienTln499G11fdnpjxHqoLvvyI+i9PpID3dzLzUdZhMZ/+boNs/y1XUx+4ex2wyEeBjwd/bi0AfL/y9LdTx9yYq1I+oUF9iwvypG+jjdo5iff4Omz6G1C2w+ztoNaT8+xIREbkAt4u81NRU2rdvD0BgYCAZGRkAXHvttTzxxBMVm06K7U3L5ozNROqZHKOj1FImjuRkGR2iljJxMPu00SFqMTN7Mk8ZHaJaaBjqR6eYUPq2qM+gthGE+Fkv/KYiAfXgkrtg5VT4ebqKPBERqVRuF3mNGjUiJSWFmJgY4uLiWLx4MV26dGHdunX4+FzEXznlvN4b04VffllDjx49sHhZLvwGp3v7d7O56z1uH8O9N7i7f9cx3GxfhoPY7XbWrV1H90u6Y7aUoe8vMlSV9FMl/3yUpV/Lcgx7oZ0NGzfQtUvXUj/37h+iHJkqvZ/c3X/VfgZ7YSGJiYl06tQJi9fZf12U57t2V1X0U6HdSW6BnZyCQnLyC8nJt5OeU0DK6TMknz5DSmYeR0+f4ejpM3yzJYV/fPErwzpFcV+/OJrUCyjbQXqMg9X/hMO/QNIvENPT7ZwiIiJl4XaRN3LkSJYsWUKPHj2YMGECo0eP5v333ycpKYlJk3QNoMrSo0kY6Tuc9GwaVuoC9FK5bDYb2Xuc9GleT31fxWw2G7aDTga2CVffG8Bms2E+sokhHSJrff9n5dnYeiSDtQdPsmhrCruPZfPfDUf4ctNR7r8yjvv6xWG1XOA096AI6HgTbJwDP89QkSciIpXG7SLv5ZdfLn58/fXX06hRI1atWkVcXBzDhg2r0HAiIiLVQZCvld5x9egdV4+JA1qwKekU05fsYemu40z7YQ8r9pzgX7d2o06A9/l31Hs8bJwLu76BE3uhXlzVfAAREalVLvo6eT179mTy5Mkq8EREpNboHFOHD2/vzozRnQn29WLDoVNcP2sV6dn5539jvebQfJDr8YYPKz+oiIjUSm6P5AHs3r2bpUuXkpaWhuNPS0E/+eSTFRJMRESkOjOZTAzrGEXriCBu/WAt+47ncGf8Oj69uxd+3uc5f7f7WNjzPWz6CK58HKx+VRdaRERqBbeLvPfee4977rmHevXqERERUWJZc5PJpCJPRERqleYNgpg7tgc3zFrF5iMZPPfNdl4c2f7cb4gbACExkJEE276ETqOrLKuIiNQObk/XfP7553nhhRdITU0lMTGRTZs2Fd82btxYGRlFRESqtbjwQGaM7ozJBJ+sSeKH7cfO3dhsga63uR4nflw1AUVEpFZxu8g7deoUN9xwQ2VkERERqbEub16fv13eFICnv95Gns1+7sbtrnPdJ62GvIwqSCciIrWJ20XeDTfcwOLFiysji4iISI02cUBzIkN8OXLqDO8t33/uhmFNoG5zcBTC/qVVlk9ERGoHt8/Ji4uL44knnuCXX36hffv2pa6dNH78+AoLJyIiUpP4e3vx6OBWTPg0kfdW7Of2SxsT5HuOaww2Hwjpe2DvEmgzvGqDioiIR3N7JG/27NkEBgaybNkyZs6cydSpU4tv06ZNczvA22+/TZMmTfD19aVr166sWLHinG1vv/12TCZTqVvbtm2L28THx5+1TV5entvZRERE3HVthyia1Q8gM6+QOasPnbthkytc9wdXVk0wERGpNdweyTtw4ECFHXz+/PlMnDiRt99+m0svvZR3332XwYMHs337dmJiYkq1nz59eomLsRcWFtKxY8dS5wgGBweza9euEtt8fX0rLLeIiMi5WMwm7r8yjknzN/Phzwe5u09TrJaz/E01theYzHByH2QmQ3BU1YcVERGP5PZI3tKlSyvs4G+++SZjx47lrrvuonXr1kybNo3o6Gjeeeeds7YPCQkhIiKi+LZ+/XpOnTrFHXfcUaKdyWQq0S4iIqLCMouIiFzItR2iqB/kw4nsfBLOtdKmbwhEdnQ91mieiIhUILeLvKuvvppmzZrx/PPPc/jw4XIfuKCggA0bNjBo0KAS2wcNGsSqVavKtI/333+fAQMGEBsbW2J7dnY2sbGxNGrUiGuvvZZNmzaVO6eIiIi7rBYzo7pFA/DRL+eZstn4ctf9geVVkEpERGoLt6drJicn89FHHxEfH8/TTz9N//79GTt2LCNGjMDb27vM+zlx4gR2u50GDRqU2N6gQQNSU1Mv+P6UlBS+/fZbPvnkkxLbW7VqRXx8PO3btyczM5Pp06dz6aWXsnnzZpo3b37WfeXn55Ofn1/8PDMzEwCbzYbNZivzZ6pMRTmqS57aRH1vHPW9sdT/F+f6LpH8c+leVu1L59DxTKJC/Uq1MUX3wosZOA+soPAP/ay+N4763jjqe+Oo743lTv+X9TsyOZ1OZ3kDJSYm8sEHHzBv3jwcDge33HILY8eOpWPHjhd8b3JyMg0bNmTVqlX06tWrePsLL7zA3Llz2blz53nf/9JLL/HGG2+QnJx83uLS4XDQpUsX+vTpw4wZM87a5umnn+aZZ54ptf2TTz7B39//gp9FRETkbN7aZmFvpolhMXb6Nyz969bLfobBW+7BjIPFbd/kjHc9A1KKiEhNkZuby80330xGRgbBwcHnbOf2SN4fderUiUcffZSwsDBefvllPvjgA95++2169erFrFmzSqx6+Wf16tXDYrGUGrVLS0srNbr3Z06nkw8++IAxY8ZccPTQbDbTvXt39uzZc842U6ZMYfLkycXPMzMziY6OZtCgQeftvKpks9lISEhg4MCBpS5bIZVLfW8c9b2x1P8X73S9wzz19Q72FYbyxpBeZ2904l1I3siVTX1wdhgCqO+NpL43jvreOOp7Y7nT/0UzDi+kXEWezWbjq6++4oMPPiAhIYFu3boxc+ZMRo8ezcmTJ3nkkUe44YYb2L59+zn34e3tTdeuXUlISGDkyJHF2xMSEhg+/PzXC1q2bBl79+5l7NixF8zqdDpJTEykffv252zj4+ODj49Pqe1Wq7Xa/aBXx0y1hfreOOp7Y6n/y29op0Y8+81OtiVncTSjgMb1Ako3atIHkjfidXg1dP1riZfU98ZR3xtHfW8c9b2xytL/Zf1+3F545YEHHiAyMpJx48bRokULNm3axOrVq7nrrrsICAggOjqal19++YLTLQEmT57Mv/71Lz744AN27NjBpEmTSEpKYty4cYBrhO3WW28t9b7333+fHj160K5du1KvPfPMM3z//ffs37+fxMRExo4dS2JiYvE+RUREqkpYgDc9m4YBsGRn2tkbNflt8ZWDWnxFREQqhtsjedu3b+ett97iuuuuO+dUyaioKH766acL7mvUqFGkp6fz7LPPkpKSQrt27Vi0aFHxapkpKSkkJSWVeE9GRgYLFixg+vTpZ93n6dOnufvuu0lNTSUkJITOnTuzfPlyLrnkEjc/qYiIyMW7slUDft6bzo87jzH2sialG0T3BJMFTifB6cMQGl31IUVExKO4XeQtWbLkwjv18uKKK64o0/7uvfde7r333rO+Fh8fX2pbSEgIubm559zf1KlTmTp1apmOLSIiUtmubBXOc//bzpr9J8nKsxHk+6epNj6BENUJjm6AQz9D6E2G5BQREc9RpiJv4cKFZd7hsGHDyh1GRETE0zSpF0DTegHsP5HDyj0nGNw+snSj2EtdRd7BldBRRZ6IiFycMhV5I0aMKPHcZDLxxysvmEym4sd2u71ikomIiHiIPi3qs/9EDqv3p5+9yGt8GayaAYdWVX04ERHxOGVaeMXhcBTfFi9eTKdOnfj22285ffo0GRkZLFq0iC5duvDdd99Vdl4REZEap2fTugCs3pd+9gYxPQETnNwHWalnbyMiIlJGbp+TN3HiRGbNmsVll11WvO2qq67C39+fu+++mx07dlRoQBERkZquR5MwTCbYk5bN8ax86gf96bI9viEQ0R5St7imbLY6/6WEREREzsftSyjs27ePkJCQUttDQkI4ePBgRWQSERHxKHUCvGkVEQzAmgPnGM1r/NsfTw/9XEWpRETEU7ld5HXv3p2JEyeSkpJSvC01NZUHH3xQlykQERE5h14XmrIZe6nr/qCKPBERuThuF3kffPABaWlpxMbGEhcXR1xcHDExMaSkpPD+++9XRkYREZEar+ii6Kv3n6vI6+26P7ELco5XUSoREfFEbp+TFxcXx5YtW0hISGDnzp04nU7atGnDgAEDSqyyKSIiIr+7pImryNt/PIdTOQXUCfAu2cA/DMLbQto2TId/ASxVH1JERDyC20UeuC6ZMGjQIAYNGlTReURERDxSqL83TesHsP94DomHT9OvVXjpRrG9XUXeoVXA5VWeUUREPIPb0zUBli1bxtChQ4mLi6N58+YMGzaMFStWVHQ2ERERj9I5ug4Am5JOnb1BY9d5eeYkXS9PRETKz+0i76OPPmLAgAH4+/szfvx47r//fvz8/Ojfvz+ffPJJZWQUERHxCJ1jQgHYdPj02RsULb6Sth1rYXaVZBIREc/j9nTNF154gVdffZVJkyYVb5swYQJvvvkmzz33HDfffHOFBhQREfEUnaJDAUg8fBqHw4nZ/Kdz2QPDoV4LTCd2Uzdnd9UHFBERj+D2SN7+/fsZOnRoqe3Dhg3jwIEDFRJKRETEE7WKCMLXaiYrr5D9J84xUvfbKpt1s3ZWYTIREfEkbhd50dHRLFmypNT2JUuWEB0dXSGhREREPJGXxUyHRqEAbEo6ffZGsa6LotfLVpEnIiLl4/Z0zQcffJDx48eTmJhI7969MZlMrFy5kvj4eKZPn14ZGUVERDxG5+hQ1h44yabDp7mh21n+OPrb4ishZw5RmJcJ1rpVnFBERGo6t4u8e+65h4iICN544w0+++wzAFq3bs38+fMZPnx4hQcUERHxJMWLr5xrJC84CmedJphOHcB0ZC20Hlxl2URExDO4VeQVFhbywgsvcOedd7Jy5crKyiQiIuKxOv12GYXdx7LIs9nxtZa+6LkzpreryEtapSJPRETc5tY5eV5eXrz22mvY7fbKyiMiIuLRGgT7UDfAG7vDyc7UrLO2ccT0AnAVeSIiIm5ye+GVAQMGsHTp0kqIIiIi4vlMJhNtG4YAsC0546xtnDGuFTZNKYlQkFNV0URExEO4fU7e4MGDmTJlCr/++itdu3YlICCgxOvDhg2rsHAiIiKeqG1UMMt3H+fXo5lnbxAaQ661Lv62dDi8BppdWbUBRUSkRivXwisAb775ZqnXTCaTpnKKiIhcQLso10je9nOM5AGkB7bC/9TPcGiVijwREXGL29M1HQ7HOW8q8ERERC6sbVQwADtSs7DZHWdtcyKolevBwZ+rKpaIiHgIt4u8P8rLy6uoHCIiIrVGTJg/QT5eFBQ62Hc8+6xt0gNauh4cXQ+2M1WYTkREajq3izy73c5zzz1Hw4YNCQwMZP/+/QA88cQTvP/++xUeUERExNOYzSZa/zaat+0c5+Xl+DTAGdgA7AVwZH1VxhMRkRrO7SLvhRdeID4+nldffRVvb+/i7e3bt+df//pXhYYTERHxVEVTNn8913l5JlPxKpsc0pRNEREpO7eLvDlz5jB79mxuueUWLJbfL+DaoUMHdu7cWaHhREREPFXR4ivbks+xwiaoyBMRkXJxu8g7evQocXFxpbY7HA5sNluFhBIREfF0bRu6RvK2J2ficDjP2sZRVOQdXgeFBVUVTUREaji3i7y2bduyYsWKUtv/85//0Llz5woJJSIi4uni6gfi42UmO7+QpJO5Z29UrwX414PCM5C8sWoDiohIjeX2dfKeeuopxowZw9GjR3E4HHz++efs2rWLOXPm8L///a8yMoqIiHgcL4uZVhFBbD6Swa/JGTSuF1C6kckEsb1hx0I4uBJielZ9UBERqXHcHskbOnQo8+fPZ9GiRZhMJp588kl27NjB119/zcCBAysjo4iIiEdq2/DC5+XR+DLXvc7LExGRMnJ7JA/gqquu4qqrriq1PTExkU6dOl1sJhERkVqheIXNo+dYYRMg9lLXfdIasBeCpVy/ukVEpBa5qIuhA2RkZPD222/TpUsXunbtWhGZREREaoW2v62wuT05E6fz7IuvEN4GfEPBlgMpm6sunIiI1FjlLvJ+/PFHbrnlFiIjI3nrrbcYMmQI69frYq0iIiJl1SoiCIvZRHpOAccy88/eyGx2nZcHcGhl1YUTEZEay60i78iRIzz//PM0bdqU0aNHExYWhs1mY8GCBTz//PNaXVNERMQNvlYLcfUDgTJO2Tyo8/JEROTCylzkDRkyhDZt2rB9+3beeustkpOTeeuttyozm4iIiMcrul7e+RdfKTovbzU47FWQSkREarIyF3mLFy/mrrvu4plnnuGaa67BYrFUZi4REZFaoei8vF+TzzOS16A9eAdBfiYc+7WKkomISE1V5iJvxYoVZGVl0a1bN3r06MHMmTM5fvx4ZWYTERHxeO1+W2Fz+/lG8ixev18j78CKKkglIiI1WZmLvF69evHee++RkpLC//3f//Hpp5/SsGFDHA4HCQkJZGVlVWZOERERj9T6tyLv6OkznM4tOHfDZv1c9/uWVEEqERGpydxeXdPf358777yTlStXsnXrVh588EFefvllwsPDGTZsWGVkFBER8VjBvlYa1fEDYEfKef5g2qy/6/7gz1CQWwXJRESkprqo6+S1bNmSV199lSNHjjBv3ryKyiQiIlKrtIn8bcpmynmmbNZvCcGNwJ4Ph1ZVUTIREamJLvpi6AAWi4URI0awcOHCitidiIhIrdL6tyJvx/mKPJMJ4q50PdaUTREROY8KKfJERESk/NqUZfEVgLgBrvu9P1RyIhERqclU5ImIiBisaLrm3rRsCgod527Y5AowWeDEbjidVEXpRESkplGRJyIiYrBGdfwI8vGiwO5g3/Hsczf0C4VG3VyP92rKpoiInJ3hRd7bb79NkyZN8PX1pWvXrqxYce7r/yxduhSTyVTqtnPnzhLtFixYQJs2bfDx8aFNmzZ88cUXlf0xREREys1kMpXtvDz4fcqmzssTEZFzMLTImz9/PhMnTuQf//gHmzZt4vLLL2fw4MEkJZ1/CsquXbtISUkpvjVv3rz4tdWrVzNq1CjGjBnD5s2bGTNmDDfeeCNr1qyp7I8jIiJSbkXn5V2wyCu6lML+ZWC3VXIqERGpiQwt8t58803Gjh3LXXfdRevWrZk2bRrR0dG88847531feHg4ERERxTeLxVL82rRp0xg4cCBTpkyhVatWTJkyhf79+zNt2rRK/jQiIiLl1zoyCLjAZRQAojqBXxjkZ8KRdZUfTEREahzDiryCggI2bNjAoEGDSmwfNGgQq1ad//o/nTt3JjIykv79+/PTTz+VeG316tWl9nnVVVddcJ8iIiJG+n26ZhZOp/PcDc0WiPttNG/P4ipIJiIiNY2XUQc+ceIEdrudBg0alNjeoEEDUlNTz/qeyMhIZs+eTdeuXcnPz2fu3Ln079+fpUuX0qdPHwBSU1Pd2idAfn4++fn5xc8zM11/RbXZbNhs1WMqTFGO6pKnNlHfG0d9byz1f9VqEuaLxWziZE4BR0/mAOfue1PT/nht/Q/O3YspvOIfVRnT4+nn3jjqe+Oo743lTv+X9TsyrMgrYjKZSjx3Op2lthVp2bIlLVu2LH7eq1cvDh8+zOuvv15c5Lm7T4CXXnqJZ555ptT2xYsX4+/vX6bPUVUSEhKMjlBrqe+No743lvq/6tT3sZB6xsS871bQts65+95aaGcwJkxp2/jxy4/I8w6r4qSeTz/3xlHfG0d9b6yy9H9ubm6Z9mVYkVevXj0sFkupEba0tLRSI3Hn07NnTz766KPi5xEREW7vc8qUKUyePLn4eWZmJtHR0QwaNIjg4OAyZ6lMNpuNhIQEBg4ciNVqNTpOraK+N4763ljq/6r3Q84Wvt6Sil9kHOTtOW/fO0/GYzq6jv4xdpxdhlRxUs+ln3vjqO+No743ljv9XzTj8EIMK/K8vb3p2rUrCQkJjBw5snh7QkICw4cPL/N+Nm3aRGRkZPHzXr16kZCQwKRJk4q3LV68mN69e59zHz4+Pvj4+JTabrVaq90PenXMVFuo742jvjeW+r/qtG0YytdbUtlzPJemQRfo+xZXwdF1eO3/EXrcVbVBawH93BtHfW8c9b2xytL/Zf1+DJ2uOXnyZMaMGUO3bt3o1asXs2fPJikpiXHjxgGuEbajR48yZ84cwLVyZuPGjWnbti0FBQV89NFHLFiwgAULFhTvc8KECfTp04dXXnmF4cOH89VXX/HDDz+wcuVKQz6jiIhIWbX5w+IrVwVdoHGLQfDT87B/KRTmg1fpP1aKiEjtZGiRN2rUKNLT03n22WdJSUmhXbt2LFq0iNjYWABSUlJKXDOvoKCAhx56iKNHj+Ln50fbtm355ptvGDLk92kqvXv35tNPP+Xxxx/niSeeoFmzZsyfP58ePXpU+ecTERFxR9EKmwdP5pJvv0DjiA4QGAHZqXBoFTTrV/kBRUSkRjB84ZV7772Xe++996yvxcfHl3j+8MMP8/DDD19wn9dffz3XX399RcQTERGpMvWDfKgf5MPxrHxSLnRuvckEzQfApo9g9/cq8kREpJihF0MXERGRkopG847mnntV6GItf5vJsvN/cL5r64mISK2iIk9ERKQaKTov72hOGYq8ZleC1R8yDkNKYuUGExGRGkNFnoiISDXSOtK14kqZijyrHzQf6Hq84+tKTCUiIjWJijwREZFqpGgkLzkXHI4yTMFsPcx1ryJPRER+oyJPRESkGmlSLwAfLzMFDhNJpy60+grQfBBYvOHEbji+q/IDiohItaciT0REpBrxsphp0SAQcF0v74J8g6FpX9fjHQsrL5iIiNQYKvJERESqmdYRrvPydqSWocgDaD3Udb9dRZ6IiKjIExERqXaKFl/59Whm2d7QcgiYLJC6BU7srcRkIiJSE6jIExERqWa6xIQCsPHwaexlWXwloJ7rcgoAWz+rvGAiIlIjqMgTERGpZlo2CMLX4iQn386OlDKO5nW40XW/5TNdGF1EpJZTkSciIlLNWMwmmga5CrU1B06W7U2trgFrAJw6AEc3VGI6ERGp7lTkiYiIVENNg11F3rqyFnneAa5CD2DL/EpKJSIiNYGKPBERkWqo2W8jeesOnsRZ1umXRVM2f/0c7LZKSiYiItWdijwREZFqKCYQfLzMpOcUsO94Ttne1LQf+NeD3BOw78fKDSgiItWWijwREZFqyMsMHRuFAK7RvDKxeP0+mrfh35WUTEREqjsVeSIiItVUt9g6gBvn5QF0vd11v/s7yEyu+FAiIlLtqcgTERGppro3dhV5ZV5hE6B+S4jpDU47bPqokpKJiEh1piJPRESkmuocHYLFbOLo6TMknz5T9jcWjeZtnAMOe6VkExGR6ktFnoiISDUV4ONF26hgwI3z8gDaDAffUMg4rAVYRERqIRV5IiIi1dgljcMAN6dsWn2h082ux+v+VQmpRESkOlORJyIiUo11b+Iq8txafAWg21jX/e7v4cTeCk4lIiLVmYo8ERGRaqz7byN5e9KyOZlTUPY31ouDFoMBJ6x5p3LCiYhItaQiT0REpBoLC/CmeXggAGsPpLv35l73ue43fQy5bo4EiohIjaUiT0REpJrr3awuAD/vdbPIa3wZRHSAwjOw/v1KSCYiItWRijwREZFqrndcPQB+3nfCvTeaTND7AdfjVTPhzKkKTiYiItWRijwREZFqrmfTuphNsP94DqkZee69ud11UL815J2GldMqI56IiFQzKvJERESquRA/K+0bhgDw8143R/PMFhjwtOvxmlmQcaRiw4mISLWjIk9ERKQGKPeUTYAWV0FMbyjMg59equBkIiJS3ajIExERqQEubeYq8lbtTcfpdLr3ZpMJBj7rerz5Ezi2vYLTiYhIdaIiT0REpAbo1rgO3l5mUjPz2H8ix/0dRHeH1kPB6YDvHgV3C0UREakxVOSJiIjUAL5WC11j6gCwyt3z8ooMfA4sPnBgGWz/qgLTiYhIdaIiT0REpIa4rLlryuay3eUs8sKawGUTXY+//wcUlGNEUEREqj0VeSIiIjXEFS3qA7Bq3wnyC+3l28llkyA0BjKPwPLXKzCdiIhUFyryREREaoi2UcGEB/mQW2Bn7YGT5duJ1Q+u+m2FzVUzIPXXigsoIiLVgoo8ERGRGsJkMtGvZTgAP+08Xv4dtboGWl0LjkL46l6wF1ZQQhERqQ5U5ImIiNQg/Vq5pmz+tCut/DsxmeCaN8A3FFI2u0b0RETEY6jIExERqUEujauH1WLiwIkc9qZll39HQRFw9cuux0tfhuO7KyagiIgYTkWeiIhIDRLka+Xy5q7RvP9sOHxxO+t4E8QNAHs+fHE3FBZUQEIRETGaijwREZEa5qbu0QAs2HCEgkJH+XdkMsHQGeBXB5I3wY/PVVBCERExkoo8ERGRGubKVuGEB/lwIruAJTuOXdzOQhrCsJmux6tmwN4lFx9QREQMpSJPRESkhvGymLmhWyMAPlmbdPE7bH0tdL/L9fiLcZB9EYu6iIiI4VTkiYiI1ECjusUAsHLvCQ6fzL34HQ56HsLbQE4afHYb2G0Xv08RETGEijwREZEaKKauP5fF1cPphPnrLnIBFnBdJP2Gf4N3ECStgu+mXPw+RUTEECryREREaqhberhG8z5Zm0SezX7xO6zfAq57z/V43Xuwcc7F71NERKqc4UXe22+/TZMmTfD19aVr166sWLHinG0///xzBg4cSP369QkODqZXr158//33JdrEx8djMplK3fLy8ir7o4iIiFSpgW0a0DDUj5M5BSxMTK6YnbYcDP3+4Xr8zYNwaFXF7FdERKqMoUXe/PnzmThxIv/4xz/YtGkTl19+OYMHDyYp6ewnkS9fvpyBAweyaNEiNmzYQL9+/Rg6dCibNm0q0S44OJiUlJQSN19f36r4SCIiIlXGy2Lmtt6xAHzw8wGcTmfF7Pjyh6D1MLAXwLzRcHxXxexXRESqhKFF3ptvvsnYsWO56667aN26NdOmTSM6Opp33nnnrO2nTZvGww8/TPfu3WnevDkvvvgizZs35+uvvy7RzmQyERERUeImIiLiiUZ1i8Hf28LO1CxW70+vmJ2azfCX2dCoO+Sdho+uh6yLvFSDiIhUGcOKvIKCAjZs2MCgQYNKbB80aBCrVpVtaojD4SArK4uwsLAS27Ozs4mNjaVRo0Zce+21pUb6REREPEWIv5Xrurgup/DByoMVt2OrH4z+FMKaQkYSfHw9nDldcfsXEZFK42XUgU+cOIHdbqdBgwYltjdo0IDU1NQy7eONN94gJyeHG2+8sXhbq1atiI+Pp3379mRmZjJ9+nQuvfRSNm/eTPPmzc+6n/z8fPLz84ufZ2ZmAmCz2bDZqscS0kU5qkue2kR9bxz1vbHU/8Zxt+//ekkj5v5yiB92HGP7kVM0bxBYMUG8Q+Cm+XjFD8aUugXHR9dhH/0f8AmqmP1XQ/q5N4763jjqe2O50/9l/Y5MzgqbwO+e5ORkGjZsyKpVq+jVq1fx9hdeeIG5c+eyc+fO875/3rx53HXXXXz11VcMGDDgnO0cDgddunShT58+zJgx46xtnn76aZ555plS2z/55BP8/f3L+IlERESM88EuM5tPmulaz8GtzR0Vuu/g3CQu3fsS3vYcTgS25JdmD2E3+1ToMURE5MJyc3O5+eabycjIIDg4+JztDBvJq1evHhaLpdSoXVpaWqnRvT+bP38+Y8eO5T//+c95CzwAs9lM9+7d2bNnzznbTJkyhcmTJxc/z8zMJDo6mkGDBp2386qSzWYjISGBgQMHYrVajY5Tq6jvjaO+N5b63zjl6fvYTpmMeOcXNqWbefmvl9O4bkCFZjIl98D5yV+ol72LIZkfYb9hLlg97w+h+rk3jvreOOp7Y7nT/0UzDi/EsCLP29ubrl27kpCQwMiRI4u3JyQkMHz48HO+b968edx5553MmzePa6655oLHcTqdJCYm0r59+3O28fHxwcen9F8krVZrtftBr46Zagv1vXHU98ZS/xvHnb7vFFuXK1uF8+PONN5beYhXr+9YsWFiL4FbFsDckZgPLMM8f7TrnD3f6vHH0Iqmn3vjqO+No743Vln6v6zfj2FFHsDkyZMZM2YM3bp1o1evXsyePZukpCTGjRsHuEbYjh49ypw5rouxzps3j1tvvZXp06fTs2fP4lFAPz8/QkJCAHjmmWfo2bMnzZs3JzMzkxkzZpCYmMg///lPYz6kiIhIFbmvXxw/7kzj841HeeDK5kSHVfBIW0wPGPM5fHwDHPoZ5gyHvy4A/7ALv1fEXU4nOOxFTwyN4vHsNkzOQrDbqsFVtKshk8W16nANYmiRN2rUKNLT03n22WdJSUmhXbt2LFq0iNhY1zV/UlJSSlwz791336WwsJD77ruP++67r3j7bbfdRnx8PACnT5/m7rvvJjU1lZCQEDp37szy5cu55JJLqvSziYiIVLWusXW4NK4uP+9NZ+oPu3nzxk4Vf5CYnnDb1zB3JCRvhPcHwY1zoEGbij+W1E5p2yFxLmz5DPIzjE5TK1iBYQCJxuaotu74DmJ7XbhdNWJokQdw7733cu+99571taLCrcjSpUsvuL+pU6cyderUCkgmIiJS8/z9qlb8vPdnvth0lLsua0qbqEqYThnVCe74Fj76C6TvgfeuhGvfhE43V/yxpHYoLMC07XMu2/061k3nXkdBRMrG8CJPREREKk6n6FCu6RDJN1tSeOW7nfz7zkqayRLeCv5vOXz+N9j3I3x5Dxz8GQa/Aj4VdAkH8XzZabAhHta9j1d2KnUBp9kLU6troOsdEPnbuaUmE2AyMKhnsxUWsnjxYgYNGoTVS+VBKd417/9p+hZFREQ8zMNXtWTxtlSW7T7Oz3tPcGlcvco5UEA9uOW/sOINWPoSJH7kOlfvL+9BdPfKOaZ4huRNsOZd+HUB2AsAcAaEszPoMuJufBZrWLTBAWsZm41CrwDwDQEtvOIRatYZhCIiInJBsXUDuKWH6/z25/63nUJ7xV43rwSzBa54GG5dCMGN4NQB+OAqWPoy2Asr77hS89htrqLu/UEwuy9snucq8Bp1h+vep/CBRHZHjoCgCKOTitR4KvJEREQ80Pj+zQn1t7IzNYv4VQcr/4BNLod7fob2N4DT7hrZ+2AQHNte+ceW6i3nBCx/Daa1h//eCYfXgNkKHUbBXT/CXT9A++vB4m10UhGPoSJPRETEA4UFeDNlcCsApibsJiXjTOUf1C8UrvsXXPc++ITA0Q3wbh/48QUozK/840v1kpwIX94Lb7aBH5+HrBQICIe+U2DSNvjLbGjU1eiUIh5JRZ6IiIiHuqFrNF1iQskpsDNhXiI5+VU0fbL99XDfL9DyGnDYYPmrMOsy2PdT1RxfjGO3wa+fw/tXwewrIPFjsOdDw66uczUnbYO+j0JQA6OTing0FXkiIiIeymw28fJ1HQjy8WLtwZPc9sFasvJsVXPw4Ci46WO44d+u0ZsTu2HuCJgzwjXCI54l5wQsfx2mdYD/3gGHf3FNyWx/I9y1BP72I3S4Ebw0JVOkKqjIExER8WAtGgQx964eBPt6sf7QKca8v5aM3Coq9EwmaDsC7l8LPca5/tG//yfXCM9/74T0fVWTQypHQQ5sXwgL/vbblMznICsZAurDFY/CpF/huvegUTejk4rUOiryREREPFyn6FA++VtPQv2tJB4+zQ3vriL5dBWco1fEr47r+nkPrHeN7GByrbI4sxv8dywc21Z1WeTiZKfBhn/DJ6PglSbw2RjY+plrSmZUZxg52zUls98UrZIpYiBdJ09ERKQWaNcwhHl/68ltH6xl97FsRr79M/F3XELryOCqC1GnsWtkp/cDrlGfPYvh1/+6bi0Gw+UP6vp61dGJPbDzG9i1CA6vBZy/v1ansevcy7YjXSN2Jl2wXKQ6UJEnIiJSS7SODOaL+y7l9g/WsictmxtmrWbaqE4MaFPFi2BEdoBb/gMpm2HFm7D9K9j9resW3QO6/w3aDAMvn6rNJS4OBxxd/3thd2J3ydejOrsKu1bXQHhrFXYi1ZCKPBERkVqkYagf/x3Xm7vnrmfNgZPcNWc94/s3Z2L/5pjNVfyP9ciOcOO/XSNFP0+DzZ+6rqF2eA18Vw+63Ard7oDQmKrNVRvlZ8H+ZbDne9j9PWQf+/01s9V1HcSWQ1y3kIbG5RSRMlGRJyIiUsuE+FuZO7YHL3yznX+vPsSMJXvYeuQ0U0d1ItTfgNUP6zWH4f+Efo/Dxjmw4UPXNdVWvum6xV4GHUdBm+HgG1L1+TyR0+kqrvcsdt0OrXJd7qKITzA0H+gq6poPVL+L1DAq8kRERGohby8zzwxvR/tGofzji638tOs4V09bwRs3duTSuHrGhAqOhL6PwOWTXdME1/0LDiyHQytdt28eghaDoNW10HwQ+IcZk7OmysuApF9+K+wS4PShkq/XaQItrnL1bePLdbkDkRpMRZ6IiEgtdn3XRrSKCGL8vE3sP5HDLf9aw98ub8JDV7XEx8tiTCiL1TVq12Y4nD7sWr1x83w4sQt2fO26mSwQ29s10tS0r84NAyjMh4wjkHHY1W9F96eTIH0vZKeWbG/xhsaXuYq65oOgbjNjcotIhVORJyIiUsu1axjC/8ZfxvPf7OCTNUm8t+IAP+5M48WR7enRtK6x4UKjXatuXjYZUre4CrydiyBtGxxc4boB+NdzFSxNLofonlC/FVg87J85+dl/KOCSShZyGYchK5USK1+eTWgMxA1wFXVN+oB3QJVEF5Gq5WH/9xMREZHy8Pf24sWR7enXMpwpn29l3/EcRs3+hVHdopkypJUx5+r9kcnkWqglsiNc+TicPAC7vnVNPUz6BXJPwPYvXTcALz+IaO9aCTKyA9Rt7jr3rzpO8XQ4XPmzUlyFWlYKZKaUfJ5xBM6cvPC+vPxchXFI9B/uYyCsmevz+1bhJTNExDAq8kRERKTYwDYNuKRxGC9/t5N5a5OYv/4wP+w4xsSBLRjdPRovi9noiC5hTaDXva5bYQEc3fD7yF5yIuRnwpG1rtsf+dd1FXwhDSE4CoKiXPeBDcAvFCz+WOx5roVJ3OF0QmGea7QtP9O1WmXRLS8DctNdRVruyd8en3Ld5550FXiOwrIdxyfEVbSVKuSiISQGAupp2qqIqMgTERGRkkL8rbz0l/b8pUtDHvt8K3vSsnniy1/596qDPDakFf1ahmOqToWElzfE9nLdrnjYNTJ2cj8kb4LkjZC2HU7shcwjvxVW6XD47LuyAtcCzq33uKYymr1c5wiara57k9m1CqW90HXvKHQ9LjxT9kLtrEwQUB+CIn4rPiMgKPK3+98K0dBoj17l0uFwcuwMfJmYzLaUbLYlZ5Cdbzc6Vq3gdDrJyrTwzoHV1eu/7Writes70K5hzfpvT0WeiIiInFX3xmEsmnA5n6xJYtoPu9mbls2d8evp3awuDw5qSdfYOkZHPDuzGerFuW4dbvh9e0GOawGS9L2u6ZCZyZCV7HqcfQzyM3HmZWByFGJy2l0jcuXhHQQ+QeAT+Nt9sGuaqH9d8Pvt3j/MdfMLc42+BTZwFZG1hNPp5OjpM2w5ksHmI6fZcjiDrUczyM73gsRfjY5XS5kgN8voENXSGVvN+2ODijwRERE5J6vFzG29GzOic0Pe/mkvH/58kFX70ln1ziquaFGfOy5tzCVNwvD3rgH/pPAO+P28vnMoLCjg+/99yVVX9MSKDey230bufhu1c9h/G9nz+v3e7AVevq6CzjvQVWRKCSey89ly5DSbD2ew5chpth7N4ER2Qal2VrOTDo3q0DG6Dh0ahVA3UJdxqAqFhXbWrl3LJZdcgpdRq+pWYy3Cg4yO4LYa8H9kERERMVqIn5UpQ1rz156xzPxxL//deIRlu4+zbPdxrBYTnaJD6dGkLl0b16FLTB1C/GroqJTJhN3i47pmn7WGfgaDZebZ+PVIBpuPuAq6LUcyOHr6TKl2XmYTrSKD6NAolI6NQmgTEcieDSsYes0lWNX3Vcpms5G528llcXXV9x5CRZ6IiIiUWXSYP69c34H7+sXx7vJ9LN11nKOnz7Du4CnWHTwFuNb9aBEeRJfYOnSLrUOX2DrEhvljNutcH09SaHeQdDKX/cdz2H8im50pWWw+cpp9x3NKtTWZoGm9ADo2CqVDoxA6RIfSJjIYX+vvo0Y2m439+hERqRAq8kRERMRtMXX9eWFke5xOJ0knc1m9L521B0+y4dApDqXnsutYFruOZTFvbRIAgT5etI4Mok1kMK0jg4kLD6RZ/UDqBGg6XnXmdDo5kV3AofQc9h/PYd+JbFdRdzybpJO52OxnX4W0YagfHaND6PBbUde+YQhBvhohEqkqKvJERESk3EwmE7F1A4itG8BNl8QAcDwrnw2HTrEx6RTrD57k16OZZOcXlhjtK1I3wJtm9QNpFh7guq8fSGxdf6JC/UqM8kjlsdkdJJ8+w6H0XA6dzOXwyVwOpedwKN31OKfg3ItO+FrNNKkXSNP6ATQPD6Rjo1DaNwqhXqBPFX4CEfkzFXkiIiJSoeoH+XB1uwiubhcBuIqIfcez2ZGSyfbkTHamZrH/eA5HT58hPaeA9JyTrD1Y+kLf4UE+NKrjR6M6/jSq40fDOn40DPWjQbAv9YN8qOPvjUVTQM/K6XRSYHdwpsBOboGdjDM2TuUUcDK3oLigSzqZy6H0XI6ePoPdce7rAppMEBXiR5N6ATSt7yrGm9YPoGn9QCKDfTUNV6QaUpEnIiIilcpqMdMqIphWEcGM7Pz79pz8Qg6cyGHf8Wz2pWWz77jrcdLJXHIL7KRl5ZOWlc/GpNNn3a/FbKJugDf1g3yoH+RDvUAfQv2shPhZCfG3EuzrehzsZyXEz4sgXyu+XhZ8vc14W8xVfj0wp9NJocNJQaHDdbO77vMLHeQX2ktszy2wk2dzFWi5BXbOFBS67m324sLN9byw+PkZW1Fb1+PzFW5/5uNlJibMn9i6/kSH+RMb5k9s3QBi6roKbB+tuChSo6jIExEREUME+HjRrmFIqYsMO51OTufaOHLqDEdO5Za4P3r6DCey80nPKcDucBYXgu4ymcDPasHPasHXasHXasZqMWM2QVamhQ8Or8HLYsZiMmE2uwpK829Fod3hxO5w4nSC3el67Pjtvuixze4sLuAKCu0U2F2PnWWvuyqMt8VMsJ+VsAArof7eRAT7lirmwoN8NCIn4kFU5ImIiEi1YjKZqBPgTZ0Ab9o3Cjlrm0K7g5M5BaRl5XM8O5/jWa5b5hkbGb/dMvN+f5yRayM7v5CiwS2nk+LRsLMk4HBORuV9wN9YzCa8LWa8vX67Wcz4/PbYz9uCv7cFP6vXb/eW4m3+3hb8vL3wsxY9/n27r9WCv7dX8XY/qwWrRdftE6ltVOSJiIhIjeNlMRMe7Et4sG+Z3+P8bYQtr9BOXoGdPJuDMzbXtMgzNjs2u4MCWyFr1q6jS5euYLYUj9AV3ZtMYDaZsJhNWEwmTEWPzSW3W38r2s5WwPl4WfD2Mut8QhGpNCryREREpFYwmUx4e5nw9jITfI7l/G02G9l7nPRvHa6LQotIjaXxexEREREREQ+iIk9ERERERMSDqMgTERERERHxICryREREREREPIiKPBEREREREQ+iIk9ERERERMSDqMgTERERERHxICryREREREREPIiKPBEREREREQ+iIk9ERERERMSDqMgTERERERHxICryREREREREPIiKPBEREREREQ+iIk9ERERERMSDqMgTERERERHxICryREREREREPIiX0QGqI6fTCUBmZqbBSX5ns9nIzc0lMzMTq9VqdJxaRX1vHPW9sdT/xlHfG0d9bxz1vXHU98Zyp/+L6pOieuVcVOSdRVZWFgDR0dEGJxERERERESkpKyuLkJCQc75ucl6oDKyFHA4HycnJBAUFYTKZjI4DuKr26OhoDh8+THBwsNFxahX1vXHU98ZS/xtHfW8c9b1x1PfGUd8by53+dzqdZGVlERUVhdl87jPvNJJ3FmazmUaNGhkd46yCg4P1H59B1PfGUd8bS/1vHPW9cdT3xlHfG0d9b6yy9v/5RvCKaOEVERERERERD6IiT0RERERExIOoyKshfHx8eOqpp/Dx8TE6Sq2jvjeO+t5Y6n/jqO+No743jvreOOp7Y1VG/2vhFREREREREQ+ikTwREREREREPoiJPRERERETEg6jIExERERER8SAq8mqIt99+myZNmuDr60vXrl1ZsWKF0ZE83ksvvUT37t0JCgoiPDycESNGsGvXLqNj1UovvfQSJpOJiRMnGh2lVjh69Ch//etfqVu3Lv7+/nTq1IkNGzYYHcvjFRYW8vjjj9OkSRP8/Pxo2rQpzz77LA6Hw+hoHmn58uUMHTqUqKgoTCYTX375ZYnXnU4nTz/9NFFRUfj5+dG3b1+2bdtmTFgPc76+t9lsPPLII7Rv356AgACioqK49dZbSU5ONi6wB7nQz/0f/d///R8mk4lp06ZVWT5PVpa+37FjB8OGDSMkJISgoCB69uxJUlJSuY6nIq8GmD9/PhMnTuQf//gHmzZt4vLLL2fw4MHl/tKlbJYtW8Z9993HL7/8QkJCAoWFhQwaNIicnByjo9Uq69atY/bs2XTo0MHoKLXCqVOnuPTSS7FarXz77bds376dN954g9DQUKOjebxXXnmFWbNmMXPmTHbs2MGrr77Ka6+9xltvvWV0NI+Uk5NDx44dmTlz5llff/XVV3nzzTeZOXMm69atIyIigoEDB5KVlVXFST3P+fo+NzeXjRs38sQTT7Bx40Y+//xzdu/ezbBhwwxI6nku9HNf5Msvv2TNmjVERUVVUTLPd6G+37dvH5dddhmtWrVi6dKlbN68mSeeeAJfX9/yHdAp1d4ll1ziHDduXIltrVq1cj766KMGJaqd0tLSnIBz2bJlRkepNbKyspzNmzd3JiQkOK+44grnhAkTjI7k8R555BHnZZddZnSMWumaa65x3nnnnSW2/eUvf3H+9a9/NShR7QE4v/jii+LnDofDGRER4Xz55ZeLt+Xl5TlDQkKcs2bNMiCh5/pz35/N2rVrnYDz0KFDVROqljhX3x85csTZsGFD56+//uqMjY11Tp06tcqzebqz9f2oUaMq9P/3Gsmr5goKCtiwYQODBg0qsX3QoEGsWrXKoFS1U0ZGBgBhYWEGJ6k97rvvPq655hoGDBhgdJRaY+HChXTr1o0bbriB8PBwOnfuzHvvvWd0rFrhsssuY8mSJezevRuAzZs3s3LlSoYMGWJwstrnwIEDpKamlvjd6+PjwxVXXKHfvQbIyMjAZDJpRkEVcDgcjBkzhr///e+0bdvW6Di1hsPh4JtvvqFFixZcddVVhIeH06NHj/NOp70QFXnV3IkTJ7Db7TRo0KDE9gYNGpCammpQqtrH6XQyefJkLrvsMtq1a2d0nFrh008/ZePGjbz00ktGR6lV9u/fzzvvvEPz5s35/vvvGTduHOPHj2fOnDlGR/N4jzzyCKNHj6ZVq1ZYrVY6d+7MxIkTGT16tNHRap2i36/63Wu8vLw8Hn30UW6++WaCg4ONjuPxXnnlFby8vBg/frzRUWqVtLQ0srOzefnll7n66qtZvHgxI0eO5C9/+QvLli0r1z69KjijVBKTyVTiudPpLLVNKs/999/Pli1bWLlypdFRaoXDhw8zYcIEFi9eXP656FIuDoeDbt268eKLLwLQuXNntm3bxjvvvMOtt95qcDrPNn/+fD766CM++eQT2rZtS2JiIhMnTiQqKorbbrvN6Hi1kn73Gstms3HTTTfhcDh4++23jY7j8TZs2MD06dPZuHGjfs6rWNECW8OHD2fSpEkAdOrUiVWrVjFr1iyuuOIKt/epkbxqrl69elgsllJ/OUxLSyv1F0apHA888AALFy7kp59+olGjRkbHqRU2bNhAWloaXbt2xcvLCy8vL5YtW8aMGTPw8vLCbrcbHdFjRUZG0qZNmxLbWrdurYWeqsDf//53Hn30UW666Sbat2/PmDFjmDRpkkazDRAREQGg370Gstls3HjjjRw4cICEhASN4lWBFStWkJaWRkxMTPHv3kOHDvHggw/SuHFjo+N5tHr16uHl5VWhv39V5FVz3t7edO3alYSEhBLbExIS6N27t0Gpagen08n999/P559/zo8//kiTJk2MjlRr9O/fn61bt5KYmFh869atG7fccguJiYlYLBajI3qsSy+9tNSlQnbv3k1sbKxBiWqP3NxczOaSv5YtFosuoWCAJk2aEBERUeJ3b0FBAcuWLdPv3ipQVODt2bOHH374gbp16xodqVYYM2YMW7ZsKfG7Nyoqir///e98//33RsfzaN7e3nTv3r1Cf/9qumYNMHnyZMaMGUO3bt3o1asXs2fPJikpiXHjxhkdzaPdd999fPLJJ3z11VcEBQUV/0U3JCQEPz8/g9N5tqCgoFLnPgYEBFC3bl2dE1nJJk2aRO/evXnxxRe58cYbWbt2LbNnz2b27NlGR/N4Q4cO5YUXXiAmJoa2bduyadMm3nzzTe68806jo3mk7Oxs/r+9ew+JKn3jAP49auPolLtMtY2V16wxst3NlBopL1uUUpDYxdKlIrd2i7IL3TMvWRMVWUY3N8NxIwUpMBTJIjRiiZg028ClKDWCNYzSJMrK8f39ER0ay9R0Zvqdvh8Y8Lxz3ud55lUcnjnnzLl//768XV9fj5qaGmi1Wnh7e2PdunUwGo0YPXo0Ro8eDaPRCHd3dyQkJDiwamX43NoPHz4c8+bNQ3V1NUpLS2GxWOT3X61WC5VK5aiyFaG7v/vODfWAAQOg0+mg1+vtXaridLf2mzZtQnx8PMLDwxEVFYWLFy+ipKQElZWVX5aw376nk2zq2LFjwsfHR6hUKhEcHMyv8bcDAJ985OXlObq0bxJvoWA/JSUlIigoSLi6uorAwEDx559/Orqkb0Jra6tYu3at8Pb2Fmq1Wvj7+4sdO3aI169fO7o0RaqoqPjk//glS5YIId7dRiEtLU3odDrh6uoqwsPDxZ07dxxbtEJ8bu3r6+u7fP+tqKhwdOn/97r7u++Mt1DoPz1Z+9OnT4uAgAChVqvFTz/9JIqLi784nySEEF/WHhIREREREdHXhtfkERERERERKQibPCIiIiIiIgVhk0dERERERKQgbPKIiIiIiIgUhE0eERERERGRgrDJIyIiIiIiUhA2eURERERERArCJo+IiIiIiEhB2OQREdFXq6GhAZIkoaam5quI01e+vr44fPiw3fMuXboUkiRBkiQUFxf3OVZsbGyv5vj6+sr5W1pa+pSfiIi6xyaPiIhs4sPGwsXFBd7e3li5ciWam5ttnrdzE+Ll5YXGxkYEBQXZJKfJZJJfa1ePyspKmM1mrFixwiY1dCc6OhqNjY2IiYnpU5zs7GyYTKZezTGbzTh//nyf8hIRUc+5OLoAIiJSrujoaOTl5aG9vR21tbVYtmwZWlpaUFhYaNc6nJ2dodPpbBY/Pj4e0dHR8nZcXByCgoKwa9cueUyr1UKlUtmshu64urr2yxp89913vZ4zdOhQaLXaPucmIqKe4ZE8IiKymfeNxciRIzFjxgzEx8fj0qVLVvvk5eVh7NixUKvVCAwMxPHjx7uMZ7FYkJSUBD8/P7i5uUGv1yM7O1t+Pj09Hfn5+bhw4YLVEbQPT9fs6OjAyJEjcfLkSavY1dXVkCQJdXV1AIDnz59jxYoV+OGHH+Dh4YFffvkFt2/f/mRdbm5u0Ol08kOlUsHd3f2jsc6na0qShJycHMyePRvu7u4YO3Ysrl+/jvv37yMyMhIajQYGgwEPHjywyldSUoKJEydCrVbD398fGRkZaG9v79Hv5L33a1JUVISpU6fCzc0NoaGhuHfvHsxmM0JCQjBw4EBER0fjyZMn8rzOR0ojIyORnJyMzZs3Q6vVQqfTIT09vVe1EBFR/2KTR0REdlFXV4eLFy9iwIAB8tipU6ewY8cO7NmzB//++y+MRiN27tyJ/Pz8T8Z436AVFRWhtrYWqamp2L59O4qKigAAGzduxIIFC+RTExsbGxEWFmYVw8nJCQsXLsTZs2etxgsKCmAwGODv7w8hBGbNmoXHjx+jrKwMVVVVCA4OxrRp0/Ds2bN+XZfMzEwsXrwYNTU1CAwMREJCAn7//Xds27YNN2/eBACsXr1a3r+8vBy//vorkpOTUVtbi5ycHJhMJuzZs+eL8qelpSElJQXV1dVwcXHBokWLsHnzZmRnZ+PatWt48OABUlNTPxsjPz8fGo0GN27cwP79+7Fr1y5cvnz5i+ohIqJ+IIiIiGxgyZIlwtnZWWg0GqFWqwUAAUBkZWXJ+3h5eYmCggKreZmZmcJgMAghhKivrxcAxK1bt7rMs2rVKjF37lyrvHPmzLHap3Oc6upqIUmSaGhoEEIIYbFYxIgRI8SxY8eEEEJcuXJFeHh4iLa2Nqs4o0aNEjk5Od2+9oiICLF27dqPxn18fMShQ4fkbQAiJSVF3r5+/boAIE6fPi2PFRYWCrVaLW9PnTpVGI1Gq7hnzpwRnp6eXdbzuTXJzc21ygVAXLlyRR7bu3ev0Ov1XcaKiIgQU6ZMsYodGhoqtmzZYjVWUVEhAIjm5uYu6yQiov7Ba/KIiMhmoqKicOLECbx8+RK5ubm4d+8e1qxZAwB48uQJHj16hKSkJCxfvlye097e/tnrvk6ePInc3Fw8fPgQr169wps3b/Dzzz/3qq4JEyYgMDAQhYWF2Lp1K65evYqmpiYsWLAAAFBVVYUXL15g8ODBVvNevXr10amTffXjjz/KPw8bNgwAMH78eKuxtrY2tLa2wsPDA1VVVTCbzVZH7iwWC9ra2vDy5Uu4u7v3e/6mpqYexwAAT0/PbucQEZHtsMkjIiKb0Wg0CAgIAAAcOXIEUVFRyMjIQGZmJjo6OgC8O2Vz0qRJVvOcnZ0/Ga+oqAjr16/HwYMHYTAYMGjQIBw4cAA3btzodW2JiYkoKCjA1q1bUVBQgJkzZ2LIkCEA3p0W6unpicrKyo/mff/9973O9Tkfnr4qSVKXY+/Xq6OjAxkZGYiLi/sollqttkn+97l7EqOnc4iIyHbY5BERkd2kpaUhJiYGK1euxPDhwzFixAjU1dUhMTGxR/OvXbuGsLAwrFq1Sh7rfGRNpVLBYrF0GyshIQEpKSmoqqrCuXPncOLECfm54OBgPH78GC4uLvD19e3Zi7OT4OBg3L17V26eiYiIOmOTR0REdhMZGYlx48bBaDTi6NGjSE9PR3JyMjw8PBATE4PXr1/j5s2baG5uxoYNGz6aHxAQgL/++gvl5eXw8/PDmTNnYDab4efnJ+/j6+uL8vJy3L17F4MHD+7y1E8/Pz+EhYUhKSkJ7e3tmDNnjvzc9OnTYTAYEBsbi3379kGv1+O///5DWVkZYmNjERIS0v+L00OpqamYPXs2vLy8MH/+fDg5OeGff/7BnTt3sHv3bofVRUREXw9+uyYREdnVhg0bcOrUKTx69Ai//fYbcnNzYTKZMH78eERERMBkMlk1bR/6448/EBcXh/j4eEyaNAlPnz61OqoHAMuXL4der0dISAiGDh2Kv//+u8taEhMTcfv2bcTFxcHNzU0elyQJZWVlCA8Px7JlyzBmzBgsXLgQDQ0N8nVrjjJz5kyUlpbi8uXLCA0NxeTJk5GVlQUfHx+H1kVERF8PSQghHF0EERER2c7SpUvR0tKC4uJih9VQWVmJqKgoNDc39/t1jUREZI1H8oiIiL4BpaWlGDhwIEpLS+2ee9y4cYiJibF7XiKibxWP5BERESlcU1MTWltbAby7vYFGo7Fr/ocPH+Lt27cAAH9/fzg58TNmIiJbYpNHRERERESkIPwojYiIiIiISEHY5BERERERESkImzwiIiIiIiIFYZNHRERERESkIGzyiIiIiIiIFIRNHhERERERkYKwySMiIiIiIlIQNnlEREREREQKwiaPiIiIiIhIQf4HYWXriq9gc3QAAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot C_D, C_L, and L/D over time\n", "plt.figure(figsize=(9, 5))\n", "plt.plot(time_min, dependent_variables_array[:,5], label=\"Drag\")\n", "plt.plot(time_min, dependent_variables_array[:,7], label=\"Lift\")\n", "plt.plot(time_min, dependent_variables_array[:,7]/dependent_variables_array[:,5], label=\"Lift/Drag\")\n", "plt.xlabel(\"Relative Time [min]\"), plt.ylabel(\"Aerodynamic coefficient [-]\")\n", "# Also add a legend\n", "plt.legend()\n", "plt.grid()\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "4cfa2687", "metadata": {}, "source": [ "### Angles over time\n", "\n", "Plotting the angle of attack and bank angle over time allows to check if the aerodynamic guidance behaves as expected. Moreover, plotting the flight path angle over time allows to check how efficient the guidance was at keeping it constant." ] }, { "cell_type": "code", "execution_count": 21, "id": "e289aa3e", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:28:12.407222Z", "iopub.status.busy": "2025-09-16T07:28:12.407121Z", "iopub.status.idle": "2025-09-16T07:28:12.486178Z", "shell.execute_reply": "2025-09-16T07:28:12.485771Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot various angles over time (bank angle, angle of attack, and flight-path angle)\n", "plt.figure(figsize=(9, 5))\n", "plt.plot(time_min, np.rad2deg(dependent_variables_array[:,3]), label=\"Bank angle\")\n", "plt.plot(time_min, np.rad2deg(dependent_variables_array[:,4]), label=\"Angle of attack\")\n", "plt.plot(time_min, np.rad2deg(dependent_variables_array[:,1]), label=\"Flight-path angle\")\n", "plt.xlabel(\"Relative Time [min]\"), plt.ylabel(\"Angle [deg]\")\n", "plt.legend()\n", "plt.grid()\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "6f1b8772", "metadata": {}, "source": [ "### Angle of attack vs Mach number\n", "\n", "Plotting the angle of attack as a function of the Mach number allows to check that it indeed is of 10deg below Mach 6, of 40deg above Mach 12, and that it varies more or less linearly (and smoothly) in-between." ] }, { "cell_type": "code", "execution_count": 22, "id": "2954e9fb", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:28:12.487492Z", "iopub.status.busy": "2025-09-16T07:28:12.487251Z", "iopub.status.idle": "2025-09-16T07:28:12.587545Z", "shell.execute_reply": "2025-09-16T07:28:12.587166Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the AoA over Mach number\n", "plt.figure(figsize=(9, 5))\n", "plt.plot(dependent_variables_array[:,10], np.rad2deg(dependent_variables_array[:,4]))\n", "plt.xlabel(\"Mach number [-]\"), plt.ylabel(\"Angle of attack [deg]\")\n", "# Set the x-axis ticks spacing to 1\n", "plt.xticks(np.arange(0, 28.1, 1))\n", "plt.grid()\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "bfc0dabb", "metadata": {}, "source": [ "### Derivative of flight path angle over time\n", "\n", "Plotting the derivative of the flight path angle over time finally allows to analyse how constant the flight path angle really was." ] }, { "cell_type": "code", "execution_count": 23, "id": "a0c10a1a", "metadata": { "execution": { "iopub.execute_input": "2025-09-16T07:28:12.588645Z", "iopub.status.busy": "2025-09-16T07:28:12.588544Z", "iopub.status.idle": "2025-09-16T07:28:12.836480Z", "shell.execute_reply": "2025-09-16T07:28:12.836044Z" }, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "flight_path_angle = dependent_variables_array[:, 1]\n", "# Compute the derivative of the flight path angle over time (dot(gamma) = Delta gamma / Delta t)\n", "flight_path_angle_derivative = np.fabs(( flight_path_angle[1:flight_path_angle.size] - flight_path_angle[0:-1])/fixed_step_size)\n", "# Plot the derivative of the flight path angle over time\n", "plt.figure(figsize=(9, 5))\n", "plt.plot(time_min[0:-1], np.rad2deg(flight_path_angle_derivative))\n", "plt.xlabel(\"Relative Time [min]\"), plt.ylabel(\"Absolute flight-path angle rate [deg/s]\")\n", "# Make the y-axis logarithmic\n", "plt.yscale(\"log\")\n", "# Have a tick on the y-axis every power of 10\n", "plt.yticks(10**np.arange(-12, 0.1, 1))\n", "plt.grid()\n", "plt.tight_layout()\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "tudatpy10", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.13" } }, "nbformat": 4, "nbformat_minor": 5 }