{"id":"W4323338614","doi":"10.1109/twc.2023.3250227","title":"Multi-Domain Resource Multiplexing Based Secure Transmission for Satellite-Assisted IoT: AO-SCA Approach","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Satellite Communication Systems","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Computer science; Multiplexing; Computer network; Transmitter power output; Transmission (telecommunications); Node (physics); Spatial multiplexing; Telecommunications link; Precoding; Wireless; Resource allocation; Eavesdropping; Secure transmission; Channel (broadcasting); Telecommunications; Transmitter; MIMO; Encryption","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007938913,0.000453254,0.0004724757,0.0006872175,0.00122207,0.0001181836,0.001749089,0.00035647,0.00001784949],"category_scores_gemma":[0.00001084373,0.0005054727,0.0003866768,0.001752335,0.0002042044,0.0001500918,0.00001179597,0.0007825291,0.0001224639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002679937,"about_ca_system_score_gemma":0.00006744622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003057357,"about_ca_topic_score_gemma":0.00009898555,"domain_scores_codex":[0.9972255,0.0004747138,0.0008699034,0.0004504508,0.0003511941,0.0006282362],"domain_scores_gemma":[0.9946051,0.001489159,0.0001248044,0.003373491,0.0001685938,0.0002388185],"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":[0.0001674454,0.001462189,0.00006391261,0.0009554349,0.000435232,0.00000230698,0.007508222,0.5240893,0.1072205,0.0005389477,0.0005610746,0.3569955],"study_design_scores_gemma":[0.001668939,0.00003557741,0.0003332879,0.0002552844,0.00004979166,0.000006932261,0.001316584,0.8615695,0.007466288,0.00002425401,0.1267247,0.0005488429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00377052,0.001326637,0.9875181,0.0008248544,0.0002174177,0.001642595,0.0003574214,0.002896365,0.001446123],"genre_scores_gemma":[0.8778653,0.001522377,0.1176085,0.00008606096,0.00002671123,0.001832352,0.0005516552,0.000216309,0.0002906971],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8740948,"threshold_uncertainty_score":0.9997397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07119528223995909,"score_gpt":0.2888615041918626,"score_spread":0.2176662219519035,"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."}}