{"id":"W4416650094","doi":"10.1109/jsac.2025.3637022","title":"UAV-Assisted Physical Layer Security for Space–Air–Ground Integrated Networks (SAGIN) With Multiple Eavesdroppers","year":2025,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Excellence Research Chairs, Government of Canada","keywords":"Physical layer; Artificial noise; Resource allocation; Benchmark (surveying); Secrecy; Optimization problem; Genetic algorithm; Resource management (computing); Power control","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":[0.0004929486,0.0006923441,0.0004156108,0.0001945874,0.000372789,0.0006244072,0.0005279668,0.0005155019,0.0005410949],"category_scores_gemma":[0.0007931261,0.0001496593,0.000282048,0.0002302769,0.0006970758,0.0008447019,0.0009346771,0.0005893369,0.0001003455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005101949,"about_ca_system_score_gemma":0.0005717384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001899807,"about_ca_topic_score_gemma":0.003037906,"domain_scores_codex":[0.9996755,0.0001274528,0.00000880517,0.00004970357,0.00007243713,0.00006604048],"domain_scores_gemma":[0.9996427,0.0001667569,0.00006875837,0.0000416157,0.00005300845,0.00002705365],"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":[0.00009771311,0.00004100141,0.001070809,0.00007315666,0.00004534787,0.0002550946,0.00008047002,0.9411365,0.01075946,0.0184447,0.000511929,0.02748387],"study_design_scores_gemma":[0.00000443518,0.00006342475,0.0001215078,0.000004374317,0.000008496268,0.00005511604,0.00002057318,0.9963491,0.001499823,0.001491764,0.0003770976,0.000004159336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0962735,0.0007823874,0.8979627,0.0001669523,0.00005211342,0.00004386218,0.00002754551,0.0001326091,0.004558332],"genre_scores_gemma":[0.9699388,0.0002310135,0.02879379,0.00003738883,0.00001263393,0.00001638536,0.00001511393,0.000006252443,0.0009485953],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001899807,"threshold_uncertainty_score":0.003777444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01414948362516395,"score_gpt":0.2650183339333964,"score_spread":0.2508688503082325,"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."}}