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Node2vec 22. 6.6
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Graphs_generation 21. 6.4. full_coll_with_node_to_vec_fin 21. 6.5
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Конфигурационный файл JSON 20. 6.3
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Данные для обучения моделей. 19. 6.2
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Работа ПО 19. 6.1
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GCN и GAT энкодеры 15. 6
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Архитектура TransformerConv 14. 5.3
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Нейронная сеть с механизмом Self-attention 13. 5.2
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Описание используемых моделей 12. 5.1
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Реализация и внедрение результатов проекта 10. 5
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Порядок контроля и приемки 10. 4
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Требования к программной документации 9. 3.3
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Требования к транспортированию и хранению 9. 3.2
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Требования к составу и параметрам технических средств 8. 3.1.7
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Условия эксплуатации 8. 3.1.6
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Требования к надежности 8. 3.1.5
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Результаты 8. 3.1.4
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Исходные данные (модели комбинационных схем) 8. 3.1.3
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2026-02-24T10:27:03.419000Z
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Выполняемые функции 8. 3.1.2
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Требования к функциональным характеристикам 8. 3.1.1
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2026-02-24T10:26:59.034000Z
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Lec.
Требования к ПО 8. 3.1
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Lec.
Задачи проекта 7. 3
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Lec.
Назначение разработки 7. 2.3.1
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11,483
2026-02-24T10:26:53.837000Z
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Lec.
Основания для разработки 7. 2.3
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true
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2026-02-24T10:26:51.555000Z
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Lec.
Актуальность проекта и характеристика области применения 5. 2.2
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false
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2026-02-24T10:26:49.600000Z
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Lec.
Краткая характеристика области применения 5. 2.1.3
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true
false
11,480
2026-02-24T10:26:47.648000Z
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Lec.
Наименование проекта 5. 2.1.2
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true
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11,479
2026-02-24T10:26:45.902000Z
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Lec.
Введение 5. 2.1.1
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true
false
11,478
2026-02-24T10:26:44.380000Z
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Lec.
Техническое задание 5. 2.1
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true
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11,477
2026-02-24T10:26:42.515000Z
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Lec.
Аннотация 4. 2
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11,476
2026-02-24T10:26:40.647000Z
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Lec.
Москва, 2025. 1
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true
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11,475
2026-02-24T10:26:38.670000Z
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Lec.
Подгорный Леонид Евгеньевич БИВ215
true
false
false
11,474
2026-02-24T10:26:36.840000Z
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Lec.
О Т Ч Е Т. по проектной работе. 1799: “Разработка системы предсказания параметров цифровых схем с использованием методов машинного обучения”
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2026-02-24T10:26:35.048000Z
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Lec.
Ссылка_на_видео_моделирования. import pandas as pd. import drjit as dr. import mitsuba as mi. import numpy as np. import xml.etree.ElementTree as ET. import subprocess. import math. import traci. import time. import webbrowser. import os. import sionna.rt. from sionna.rt import load_scene, PlanarArray, Transmitter, Receiver, Camera, PathSolver, ITURadioMaterial, SceneObject. def timer(func):. def wrapper(*args, **kwargs):. start = time.perf_counter(). result = func(*args, **kwargs). end = time.perf_counter(). print(f"[TIMER] {func.__name__} выполнена за {end - start:.6f} сек"). return result. return wrapper. def projection(scene, veh_arr, veh_arr_pred):. new_veh_arr =[]. for i in range (len(veh_arr)):. x = veh_arr[i].get('x_coor'). y = veh_arr[i].get('y_coor'). ray = mi.Ray3f(. o=mi.Point3f([x, y, 1000]),. d=mi.Vector3f([0, 0, -1]). ). z = scene.ray_intersect(ray).p[2]. pred_veh = next((veh for veh in veh_arr_pred if veh.get('vehId')==veh_arr[i].get('vehId')),None). if pred_veh:. z_pred = pred_veh['z_coor']. if abs(z_pred - z) >5:. ray = mi.Ray3f(. o=mi.Point3f([x, y, -1000]),. d=mi.Vector3f([0, 0, 1]). ). z = scene.ray_intersect(ray).p[2]. if z!= 0 :. new_veh_arr.append({'vehId':veh_arr[i].get('vehId'),. 'x_coor':veh_arr[i].get('x_coor'),. 'y_coor':veh_arr[i].get('y_coor') ,. 'z_coor':z[0],. 'angle':veh_arr[i].get('angle'),. 'velocity': veh_arr[i].get('velocity'). }). return new_veh_arr. def run_sumo_server(scenario:str = 'test_scenario',. port:int = 8813):. process = subprocess.Popen(. f'sumo -c scenarios/{scenario}/sumo_dir/osm.sumocfg --remote-port {port}'.split(' '),. stdout=subprocess.PIPE,. stderr=subprocess.PIPE,. text=True. ). print (f'***Starting SUMO server on port {port} ***', process.pid). def get_config_coordinates ():. url = 'https://prochitecture.com/blender-osm/extent/?blender_version=4.2&addon=blosm&addon_version=2.7.14'. webbrowser.open(url=url). with open('config.txt', 'w') as f:. coords = input("Enter coordinates: "). f.write(coords). def import_scenario():. subprocess.run('blender --background --python blender_auto.py'.split(' ')). print('Scenario installed'). @timer. def frame_handler(scene,. veh_arr,. car_material,. distance = 100,. render: bool = False,. scenario:str = 'scenario',. camera_default:bool = True,. resolution = [650,500],. step:int = 0):. # Only process frames that contain vehicles. if len(veh_arr)!=0:. frame_rssi = {v['vehId']: {} for v in veh_arr}. frame_loss = {v['vehId']: {} for v in veh_arr}. # Create 3D car models for visualization. cars = [SceneObject(. fname= sionna.rt.scene.low_poly_car,. name=f'{veh_arr[i]["vehId"]}',. radio_material=car_material. ) for i in range(len(veh_arr))]. scene.edit(add=cars). for i in range(len(veh_arr)):. cars[i].position = mi.Point3f(. veh_arr[i]['x_coor'],. veh_arr[i]['y_coor'],. veh_arr[i]['z_coor']+1. ). cars[i].orientation = mi.Point3f(float(veh_arr[i]['angle']), 0, 0). cars[i].scaling = mi.Float(1.5). # Add all vehicles as both transmitters and receivers. scene.add(Transmitter(. f'tx-{veh_arr[i]["vehId"]}',. position=[veh_arr[i]['x_coor'], veh_arr[i]['y_coor'], veh_arr[i]['z_coor']+3],. display_radius=2. )). scene.add(Receiver(. f'rx-{veh_arr[i]["vehId"]}',. position=[veh_arr[i]['x_coor'], veh_arr[i]['y_coor'], veh_arr[i]['z_coor']+3],. display_radius=2. )). # Calculate signal paths between all pairs of vehicles. if len(veh_arr) > 1:. p_solver = PathSolver(). paths = p_solver(. scene=scene,. max_depth=4,. los=True,. specular_reflection=True,. diffuse_reflection=True,. refraction=True,. synthetic_array=False,. seed=42. ). # Extract channel impulse response and calculate power for each pair. for i in range(len(veh_arr)):. for j in range(len(veh_arr)):. if i != j:. # Calculate distance between vehicles. dist = math.sqrt(. (veh_arr[i]['x_coor'] - veh_arr[j]['x_coor'])**2 +. (veh_arr[i]['y_coor'] - veh_arr[j]['y_coor'])**2 +. (veh_arr[i]['z_coor'] - veh_arr[j]['z_coor'])**2. ). if dist < distance:. # Get CIR between this pair. a, _ = paths.cir(normalize_delays=False,. out_type='numpy'). path_powers = np.abs(a[i][0][j][0])**2. total_power = np.sum(path_powers). total_power_log = 10*np.log10(total_power) if total_power > 0 else -200. frame_rssi[veh_arr[i]['vehId']][veh_arr[j]['vehId']] = total_power_log. freq = scene.frequency. fspl = 20*np.log10(dist) + 20*np.log10(freq) + 20*np.log10(4 * np.pi / 3e8) # free-space path loss. tx_power = 20 # dBm, typical V2X transmission power. rssi = np.abs(tx_power - total_power_log - fspl.item()) #Power of Tx - free space loss - power of Rx. frame_loss[veh_arr[i]['vehId']][veh_arr[j]['vehId']] = rssi. else: # Out of range. frame_rssi[veh_arr[i]['vehId']][veh_arr[j]['vehId']] = 0. frame_loss[veh_arr[i]['vehId']][veh_arr[j]['vehId']] = -200. else: # Same vehicle. frame_rssi[veh_arr[i]['vehId']][veh_arr[j]['vehId']] = 0. frame_loss[veh_arr[i]['vehId']][veh_arr[j]['vehId']] = 0. if render:. if camera_default:. avg_x = sum(v['x_coor'] for v in veh_arr) / len(veh_arr). avg_y = sum(v['y_coor'] for v in veh_arr) / len(veh_arr). avg_z = sum(v['z_coor'] for v in veh_arr) / len(veh_arr). cam = Camera(. position=[avg_x, avg_y - 200, avg_z + 200],. look_at=[avg_x, avg_y, avg_z]. ). else:. cam = Camera(. # position=[826.43,-485.76, 222.7],. # look_at=[334.50,86.23,13.11]. position=[334.50,86.23,1000],. look_at=[334.50,86.23,13.11]. ). try:. scene.render_to_file(. camera=cam,. filename=f'scenarios/{scenario}/render_frames/{int(step)}.png',. resolution=resolution,. paths=paths if len(veh_arr) > 1 else None. ). except Exception as e:. print(f"Rendering error: {e}"). scene.render_to_file(. camera=cam,. filename=f'scenarios/{scenario}/render_frames/{int(step)}.png',. resolution=resolution. ). for i in range(len(veh_arr)):. scene.remove(f'tx-{veh_arr[i]["vehId"]}'). scene.remove(f'rx-{veh_arr[i]["vehId"]}'). scene.edit(remove=cars). return frame_rssi,frame_loss. def signal_propogation(scenario: str = 'scenario',. begin_frame:int = 0,. stop_frame:int =100,. distance: int = 500,. render:bool =False,. camera_default: bool = True,. resolution: list = [650, 500],. output_video_name: str = 'render',. port:int = 8813):. flag = input('Continue simulation y/n: '). if flag == 'n':. return. # Load the scene and configure antenna arrays. scene = load_scene(f'scenarios/{scenario}/scenario.xml'). # Configure transmitter array properties. scene.tx_array = PlanarArray(num_rows=1,. num_cols=1,. vertical_spacing=0.5,. horizontal_spacing=0.5,. pattern="iso",. polarization="V"). # Configure receiver array properties. scene.rx_array = PlanarArray(num_rows=1,. num_cols=1,. vertical_spacing=0.5,. horizontal_spacing=0.5,. pattern="iso",. polarization="cross"). # Create radio material for vehicles. car_material = ITURadioMaterial("car-material",. "metal",. thickness=0.01,. color=(0.8, 0.1, 0.1)). os.makedirs(f'scenarios/{scenario}/render_frames', exist_ok=True). os.makedirs(f'scenarios/{scenario}/output_data', exist_ok=True). try:. # Connect to the SUMO server. traci.init(port). print(f"Connected to SUMO server on port {port}"). step = 0. x_max =float(traci.simulation.getNetBoundary()[1][0]). y_max =float(traci.simulation.getNetBoundary()[1][1]). columns = ['Frame','Cars_Data', 'PathLoss', 'RSSI']. all_rssi_df = pd.DataFrame(columns=columns). veh_arr_pred =[]. terrain = mi.load_file(f'scenarios/{scenario}/terrain.xml'). while step < stop_frame: # Run for 1000 simulation steps. traci.simulationStep() # Advance the simulation by one step. step += 1. if step<begin_frame:. continue. vehicle_ids = traci.vehicle.getIDList(). offset = [0,0]. veh_arr = []. for vehID in vehicle_ids:. veh_arr.append({'vehId':vehID,. 'x_coor':float(traci.vehicle.getPosition(vehID=vehID)[0]-x_max/2) + offset[0],. 'y_coor':float(traci.vehicle.getPosition(vehID=vehID)[1]-y_max/2) + offset[1],. 'angle':-np.radians(float(traci.vehicle.getAngle(vehID=vehID) + 90)),. 'velocity':float(traci.vehicle.getSpeed(vehID=vehID))}). veh_arr = projection(terrain,veh_arr=veh_arr,veh_arr_pred=veh_arr_pred). veh_arr_pred = veh_arr. print(f'Frame: {step}, Number of vehicles: {len(veh_arr)}'). result, loss = frame_handler(scene=scene,. veh_arr=veh_arr,. car_material=car_material,. distance=distance,. render=render,. scenario=scenario,. camera_default=camera_default,. resolution=resolution,. step=step. ). new_row = pd.DataFrame([{. 'Frame': step,. 'Cars_Data': veh_arr,. 'PathLoss': result,. 'RSSI': loss. }]). all_rssi_df = pd.concat([all_rssi_df,new_row],ignore_index=True). all_rssi_df.to_csv(f'scenarios/{scenario}/output_data/output{begin_frame}-{stop_frame}.csv',sep = ' ', index = False). print('Simulation Finished for all vehicles'). except KeyboardInterrupt:. print('Interrrupted'). finally:. # Close the connection to SUMO. traci.close(). print("Disconnected from SUMO server."). return. if __name__ == '__main__':. # Example usage with custom parameters. scenario = 'scenario_tunnel'. run_sumo_server(scenario=scenario). signal_propogation(. scenario=scenario,. begin_frame = 1,. stop_frame = 500,. distance=1000,. render=False,. camera_default=False,. resolution=[650,500]. ). # get_config_coordinates(). #sumo -c scenarios/test_scenario/2025-04-21-16-39-58/osm.sumocfg --remote-port 8813
false
true
false
11,472
2026-02-24T10:26:30.115000Z
2026-02-24T10:26:30.115000Z
Lec.
GitHub. https://github.com/vvoovv/blosm
false
false
false
11,471
2026-02-24T10:26:28.365000Z
2026-02-24T10:26:28.365000Z
Lec.
Source code is in the branch “release”. (n.d.)
false
true
false
11,470
2026-02-24T10:26:26.658000Z
2026-02-24T10:26:26.658000Z
Lec.
Global coverage
false
false
false
11,469
2026-02-24T10:26:24.879000Z
2026-02-24T10:26:24.879000Z
Lec.
A few clicks import of Google 3D cities, OpenStreetMap, terrain
false
true
false
11,468
2026-02-24T10:26:22.888000Z
2026-02-24T10:26:22.888000Z
Lec.
Blender Foundation. (n.d.). blender.org - Home of the Blender project - Free and Open 3D Creation Software. blender.org. https://www.blender.org/. vvoovv/blosm: Blosm addon for Blender
false
true
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11,467
2026-02-24T10:26:20.854000Z
2026-02-24T10:26:20.854000Z
Lec.
OpenStreetMap. https://www.openstreetmap.org
false
true
false
11,466
2026-02-24T10:26:18.978000Z
2026-02-24T10:26:18.978000Z
Lec.
OpenStreetMap. (n.d.)
false
true
false
11,465
2026-02-24T10:26:17.141000Z
2026-02-24T10:26:17.141000Z
Lec.
The 21st IEEE International Conference on Intelligent Transportation Systems, 2018-11-04 - 2018-11-07, Maui, USA. doi: 10.1109/ITSC.2018.8569938
false
true
false
11,464
2026-02-24T10:26:15.022000Z
2026-02-24T10:26:15.022000Z
Lec.
In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC), pp. 2575-2582
false
true
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11,463
2026-02-24T10:26:13.053000Z
2026-02-24T10:26:13.053000Z
Lec.
Alvarez Lopez, Pablo and Behrisch, Michael and Bieker-Walz, Laura and Erdmann, Jakob and Flötteröd, Yun-Pang and Hilbrich, Robert and Lücken, Leonhard and Rummel, Johannes and Wagner, Peter and Wießner, Evamarie (2018) Microscopic Traffic Simulation using SUMO
true
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11,462
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2026-02-24T10:26:10.251000Z
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Влияние строительных материалов и структур на распространение радиоволн на частотах выше приблизительно 100 МГц. https://www.itu.int/dms_pubrec/itu-r/rec/p/R-REC-P.2040-3-202308-I!!PDF-R.pdf
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11,461
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2026-02-24T10:26:08.546000Z
Lec.
Международный союз электросвязи. (2024)
false
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11,460
2026-02-24T10:26:06.735000Z
2026-02-24T10:26:06.735000Z
Lec.
IEEE standard test procedures for antennas. https://antennatestlab.com/wp-content/uploads/2017/02/IEEE_Std_149-1979_Test_Procedures_for_Antennas.pdf
false
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11,459
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2026-02-24T10:26:04.913000Z
Lec.
IEEE Antenna Standards Committee. (1979)
false
true
false
11,458
2026-02-24T10:26:03.150000Z
2026-02-24T10:26:03.150000Z
Lec.
Available: https://mitsuba-renderer.org4
false
false
false
11,457
2026-02-24T10:26:01.461000Z
2026-02-24T10:26:01.461000Z
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Zhang, “Mitsuba 3 Physically Based Renderer,” 2022. [Online]
false
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11,456
2026-02-24T10:25:59.538000Z
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Lec.
Leroy, and Z
false
true
false
11,455
2026-02-24T10:25:57.696000Z
2026-02-24T10:25:57.696000Z
Lec.
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false
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11,454
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Vicini, “Dr.Jit: A just-in-time compiler for differentiable rendering,” Transactions on Graphics (Proceedings of SIGGRAPH), vol. 41, no. 4, Jul. 2022. 4
false
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11,453
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2026-02-24T10:25:54.044000Z
Lec.
Roussel, and D
false
false
false
11,452
2026-02-24T10:25:52.285000Z
2026-02-24T10:25:52.285000Z
Lec.
Speierer, N
false
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11,451
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2026-02-24T10:25:50.792000Z
Lec.
Https://github.com/NVlabs/instant-rm
false
false
false
11,450
2026-02-24T10:25:49.182000Z
2026-02-24T10:25:49.182000Z
Lec.
NVlabs/instant-rm: Instant Radio Maps (Instant RM) - Fast and Differentiable Radio Maps. (n.d.)
false
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11,449
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