{"id":"W4386356233","doi":"10.36227/techrxiv.24058422","title":"Physics-Based Trajectory Design for Cellular-Connected UAV in Rainy Environments Based on Deep Reinforcement Learning","year":2023,"lang":"en","type":"preprint","venue":"","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Trajectory; Reinforcement learning; Trajectory optimization; Computer science; Process (computing); Base station; Markov decision process; Real-time computing; Interference (communication); Simulation; Markov process; Artificial intelligence; Physics; Telecommunications; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000357913,0.0006022321,0.0005278772,0.0002533215,0.0002873597,0.0005156125,0.0005812999,0.0007712157,0.001514981],"category_scores_gemma":[0.001113019,0.0003514056,0.0004514503,0.0001915509,0.0005023312,0.0004542269,0.0007266721,0.0006843109,0.0001987906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007103112,"about_ca_system_score_gemma":0.0009437289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007292066,"about_ca_topic_score_gemma":0.005032099,"domain_scores_codex":[0.9998858,0.00002626031,0.000005144621,0.00002778945,0.00003037066,0.0000246374],"domain_scores_gemma":[0.9995773,0.00020277,0.00007282773,0.0000215689,0.00008647699,0.00003901542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001302221,0.000008764428,0.0002476783,0.00001296556,0.000006553225,0.00002826053,0.00001456908,0.9925884,0.0006853165,0.001346998,0.0001475046,0.004899866],"study_design_scores_gemma":[0.000001861784,0.000006305575,0.00002263158,0.000001100605,0.000001110207,0.000002061172,0.000001771649,0.9994889,0.0000735071,0.0003222463,0.00007774945,7.210301e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03562832,0.000201483,0.9598712,0.0001943732,0.00003725312,0.00003482013,0.00004435366,0.0002446058,0.003743585],"genre_scores_gemma":[0.943243,0.0001635136,0.0534968,0.00008962883,0.00001697807,0.00009252741,0.0001003963,0.0000492947,0.002747762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007292066,"threshold_uncertainty_score":0.01449925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0280680387730565,"score_gpt":0.2193886276909174,"score_spread":0.1913205889178609,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}