{"id":"W4313315618","doi":"10.1109/nana56854.2022.00081","title":"3D Deployment of UAVs for Communications under Multiple Eavesdroppers","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Networking and Network Applications (NaNA)","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Research Foundation of Korea","keywords":"Beamforming; Base station; Jamming; Computer science; Signal strength; Software deployment; Interference (communication); SIGNAL (programming language); Signal-to-noise ratio (imaging); Real-time computing; Genetic algorithm; Signal-to-interference-plus-noise ratio; Noise (video); Computer network; Wireless sensor network; Telecommunications; Artificial intelligence; Machine learning","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":[0.0001936424,0.0007243741,0.00036578,0.0003572202,0.0003900517,0.0005264896,0.0004197923,0.0005537418,0.0005357984],"category_scores_gemma":[0.0005972579,0.0002520074,0.0004253486,0.0003987245,0.0003895086,0.0004687173,0.0007340001,0.0003835904,0.0002347711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003451874,"about_ca_system_score_gemma":0.0003473349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002916665,"about_ca_topic_score_gemma":0.003046304,"domain_scores_codex":[0.9997885,0.00007416585,0.000008424469,0.00004247969,0.00004556467,0.00004094758],"domain_scores_gemma":[0.9997888,0.00007109346,0.00005244279,0.00003697423,0.00002810759,0.00002255293],"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":[0.00008053423,0.00002714356,0.002579918,0.00006823545,0.00005095367,0.0004503381,0.00009259696,0.9142573,0.03900235,0.006280147,0.0007332705,0.0363772],"study_design_scores_gemma":[0.00001445837,0.000125113,0.001312195,0.00001115053,0.00002396888,0.0002288838,0.0001003629,0.9864876,0.007004362,0.002147074,0.002527347,0.00001758952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1436163,0.0007236761,0.8477817,0.0002967719,0.0001196426,0.00004423121,0.00009522305,0.0003623219,0.006960266],"genre_scores_gemma":[0.8894777,0.0007636836,0.108145,0.00008434135,0.00001751181,0.00005734558,0.00007776887,0.00001804218,0.001358647],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002916665,"threshold_uncertainty_score":0.005799413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03468793002741009,"score_gpt":0.2658498746067582,"score_spread":0.2311619445793482,"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."}}