{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001421047,0.0001262103,0.00014765,0.000419527,0.0002861593,0.00004260223,0.00006417655,0.0002160499,0.000001062243],"category_scores_gemma":[0.000007400795,0.0001332514,0.00001780642,0.0006555109,0.00008258881,0.00007528548,0.000001476121,0.0002112923,0.000003679031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005583259,"about_ca_system_score_gemma":0.00001064631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001765925,"about_ca_topic_score_gemma":0.00002055172,"domain_scores_codex":[0.9994015,0.00003004067,0.0001813354,0.0001851973,0.00005183406,0.0001500659],"domain_scores_gemma":[0.9995306,0.00009461751,0.00002969389,0.0002324609,0.00008359669,0.0000289918],"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":[0.00001126935,0.00001128829,0.000006423742,0.00004273935,0.00005327635,8.185625e-7,0.0002977623,0.9690692,0.01621184,0.0005873925,0.0001513079,0.01355664],"study_design_scores_gemma":[0.0003620113,0.00004939596,0.00003216816,0.00007968725,0.00003648324,0.00001804926,0.001313962,0.9803982,0.01437603,0.00007706565,0.00311537,0.0001415835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1795918,0.0004617883,0.8180756,0.0002807928,0.00005189007,0.0005057564,0.0000164422,0.0009657976,0.00005017849],"genre_scores_gemma":[0.9951021,0.0002843155,0.004214312,0.00001434708,0.000007538447,0.0002116554,0.00005629506,0.00003450266,0.00007490918],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8155104,"threshold_uncertainty_score":0.5433835,"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."}}