{"id":"W3189761152","doi":"10.1109/icc42927.2021.9500621","title":"Secrecy Analysis for Energy Harvesting-Enabled Cognitive Radio Networks in Cascaded Fading Channels","year":2021,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Jamming; Secrecy; Fading; Cognitive radio; Computer science; Energy harvesting; Underlay; Computer network; Physical layer; Reliability (semiconductor); Maximum power transfer theorem; Channel (broadcasting); Wireless; Transmission (telecommunications); Electronic engineering; Energy (signal processing); Telecommunications; Signal-to-noise ratio (imaging); Power (physics); Computer security; Engineering; Mathematics; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.0002441912,0.0001624586,0.0003620193,0.0003215355,0.00006048848,0.00008545244,0.000191545,0.0001476389,0.00009669537],"category_scores_gemma":[0.0001277313,0.0001897102,0.0001405426,0.001419522,0.00001951313,0.000186687,0.00007359449,0.0001844696,0.000001093536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009566989,"about_ca_system_score_gemma":0.00002048319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002531692,"about_ca_topic_score_gemma":0.001583254,"domain_scores_codex":[0.9989701,0.0000832057,0.0003300476,0.000221433,0.00008930198,0.0003059166],"domain_scores_gemma":[0.998928,0.000493945,0.00004047542,0.0003166096,0.0001519612,0.00006899264],"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":[0.00002429169,0.0001142748,0.005935597,0.0001038487,0.00160809,0.00004425698,0.001915541,0.9416085,0.00226176,0.02978634,0.0008522293,0.01574525],"study_design_scores_gemma":[0.000383291,0.000009607626,0.0007541165,0.00007166781,0.0001116832,0.000004704199,0.0002521002,0.9624568,0.03372959,0.0008124206,0.001141179,0.0002728602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03765646,0.0009530065,0.9549788,0.00006606582,0.00006577441,0.0001468678,0.00000781128,0.0006542194,0.005470955],"genre_scores_gemma":[0.9919235,0.0003225358,0.006452698,0.0001091181,0.00006014494,0.0002249969,0.0001931713,0.00003951886,0.0006743416],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.954267,"threshold_uncertainty_score":0.7736156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02250837781059751,"score_gpt":0.252766594393617,"score_spread":0.2302582165830195,"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."}}