{"id":"W4412748127","doi":"10.1109/tifs.2025.3594198","title":"Multi-User Key Rate Optimization for Near-Field Extremely Large-Scale Antenna Array Communications","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Antenna Design and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"National Key Research and Development Program of China; Engineering and Physical Sciences Research Council; Canada Excellence Research Chairs, Government of Canada; Natural Sciences and Engineering Research Council of Canada; Queen's University; National Natural Science Foundation of China; Queen's University Belfast","keywords":"Computer science; Key (lock); Antenna (radio); Scale (ratio); Telecommunications; Computer security; Physics","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":[0.0001531656,0.0001376135,0.0001615774,0.0001423033,0.0004224694,0.0001332595,0.0001081732,0.0001169774,0.00002551582],"category_scores_gemma":[0.00001044222,0.0001423168,0.0001076334,0.0003072113,0.00004713262,0.0005706731,0.000002020403,0.0001856297,0.000009453329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002917488,"about_ca_system_score_gemma":0.00002809985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001056494,"about_ca_topic_score_gemma":0.0001435196,"domain_scores_codex":[0.9993188,0.00002150253,0.0003290968,0.00009132404,0.00007177581,0.0001674332],"domain_scores_gemma":[0.9992838,0.00009363113,0.00004578067,0.0003089296,0.0002130347,0.00005481769],"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":[0.0006391468,0.0009877019,0.0003386919,0.002099616,0.00184705,0.00000179304,0.03080841,0.840495,0.01096828,0.02922069,0.01105898,0.07153459],"study_design_scores_gemma":[0.0006910036,0.00002068354,0.00002466192,0.00003576799,0.00007527284,8.806418e-7,0.0005169899,0.9826455,0.001770306,0.0003960556,0.01368082,0.0001420188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007207193,0.00007305677,0.9974098,0.0004632581,0.000248112,0.0002296006,0.0001909791,0.0001611052,0.0005034254],"genre_scores_gemma":[0.9278458,0.001124572,0.06949602,0.0009597961,0.00001097545,0.00008569499,0.0001467348,0.00001544062,0.0003149775],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9279137,"threshold_uncertainty_score":0.5803509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01159028437429649,"score_gpt":0.2302309787438307,"score_spread":0.2186406943695342,"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."}}