{"id":"W4404179523","doi":"10.1109/jsac.2024.3492699","title":"Resource Allocation for Adaptive Beam Alignment in UAV-Assisted Integrated Sensing and Communication Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Key Industry Innovation Chain of Shaanxi; National Natural Science Foundation of China","keywords":"Computer science; Resource allocation; Resource management (computing); Resource (disambiguation); Computer network; Integrated optics; Distributed computing; Telecommunications","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":[0.0008391712,0.0007090451,0.000815394,0.0003292509,0.0004285408,0.0006407968,0.0009972365,0.0005822565,0.001095255],"category_scores_gemma":[0.002188975,0.0003332956,0.0002672761,0.0005544067,0.000674488,0.001049114,0.001082565,0.0007337885,0.0001632183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007606717,"about_ca_system_score_gemma":0.001057453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004816868,"about_ca_topic_score_gemma":0.004874497,"domain_scores_codex":[0.9993219,0.0001972558,0.00002246668,0.0001579554,0.000145345,0.0001550687],"domain_scores_gemma":[0.999243,0.0004260658,0.0001284678,0.0000458465,0.0001109369,0.00004574265],"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":[0.0001111935,0.00005780191,0.0006529756,0.00005863963,0.00002917031,0.00009132347,0.0000724558,0.9478515,0.003997433,0.007603006,0.0009413809,0.03853309],"study_design_scores_gemma":[0.000003880603,0.00001729592,0.00006203987,0.000002095946,0.000003416156,0.00001292811,0.000007879403,0.9981654,0.0003280401,0.001278083,0.0001163693,0.000002492852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04168012,0.000560514,0.9545462,0.0001827097,0.00003933171,0.00003357204,0.00003691005,0.0001729943,0.002747729],"genre_scores_gemma":[0.9554126,0.0002063898,0.04283544,0.00009066183,0.00002497844,0.00007018723,0.00003602927,0.00002037349,0.001303391],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004816868,"threshold_uncertainty_score":0.009577632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0220462117012261,"score_gpt":0.2590060062042014,"score_spread":0.2369597945029753,"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."}}