{"id":"W4389104679","doi":"10.1109/tvt.2023.3337106","title":"Cooperative Trajectory Planning and Resource Allocation for UAV-Enabled Integrated Sensing and Communication Systems","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Engineering and Physical Sciences Research Council; Chongqing Municipal Education Commission; Natural Sciences and Engineering Research Council of Canada; Chongqing Science and Technology Commission; National Natural Science Foundation of China","keywords":"Computer science; Resource allocation; Quality of service; Flexibility (engineering); Trajectory; Resource management (computing); Convergence (economics); Telecommunications link; Transmission (telecommunications); Real-time computing; Distributed computing; Computer network; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004504141,0.000599821,0.0005511401,0.0003733581,0.0004470496,0.0006161283,0.0007193767,0.000574307,0.0009113824],"category_scores_gemma":[0.001008413,0.000325421,0.0003341002,0.0007479143,0.0004782939,0.0005563784,0.0009058555,0.0005762447,0.0001988639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007026721,"about_ca_system_score_gemma":0.001513376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00907523,"about_ca_topic_score_gemma":0.007406946,"domain_scores_codex":[0.9996489,0.0001083373,0.00001375421,0.00007677916,0.0000795137,0.00007280295],"domain_scores_gemma":[0.9997162,0.0001191938,0.00005956679,0.00003062863,0.00004984283,0.00002453777],"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":[0.00004745298,0.00002302397,0.0003229875,0.00004246346,0.00001850077,0.00007244333,0.00006332856,0.9592096,0.002368388,0.005373186,0.0005887228,0.03186993],"study_design_scores_gemma":[0.000003544144,0.00001459642,0.00007123004,0.000002281077,0.000003018522,0.0000105255,0.00001431757,0.9980097,0.0003192546,0.00127604,0.0002730188,0.000002459888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01748769,0.0003580329,0.9800178,0.000108215,0.00002005965,0.00002770685,0.00003279556,0.0001534845,0.001794107],"genre_scores_gemma":[0.8814866,0.0003620113,0.1158965,0.00005134608,0.00002550791,0.0001170189,0.0001022643,0.00002416618,0.001934522],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00907523,"threshold_uncertainty_score":0.01804483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01146702773628951,"score_gpt":0.22278920955672,"score_spread":0.2113221818204305,"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."}}