{"id":"W4280626932","doi":"10.36227/techrxiv.19745152.v1","title":"BER Reduction Using Partial-Elements Selection in IRS-UAV Communications with Imperfect Phase Compensation","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Satellite Communication Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Reduction (mathematics); Overhead (engineering); Phase (matter); Compensation (psychology); Selection (genetic algorithm); Computer science; Imperfect; Orthogonal frequency-division multiplexing; Process (computing); Telecommunications; Control theory (sociology); Computer network; Mathematics; Artificial intelligence; Physics","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":[0.0003343477,0.0005572134,0.0003877037,0.0001668994,0.0002609357,0.0003913803,0.0002786692,0.0002885567,0.0008915437],"category_scores_gemma":[0.001135841,0.0001493474,0.0001694788,0.000245955,0.0003551189,0.00038259,0.0003914797,0.0002345823,0.0002846593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002565027,"about_ca_system_score_gemma":0.0003447896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007764335,"about_ca_topic_score_gemma":0.001514875,"domain_scores_codex":[0.9995992,0.0001200994,0.00001179785,0.0000635984,0.0001412325,0.00006413878],"domain_scores_gemma":[0.9996009,0.0001808913,0.00007915656,0.00006115934,0.00006272301,0.00001524598],"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":[0.0005130812,0.00005776911,0.002192687,0.0001054372,0.00005060672,0.0001939526,0.0001212176,0.768016,0.1130371,0.007406077,0.0005538203,0.1077521],"study_design_scores_gemma":[0.00001364871,0.0002234048,0.00177317,0.000009806283,0.00002941078,0.0001565294,0.00004300431,0.9542255,0.04017972,0.002277558,0.001056979,0.00001126515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2817221,0.0006149753,0.710906,0.0001251987,0.00003865371,0.00002449281,0.00004282201,0.0002991365,0.006226613],"genre_scores_gemma":[0.9511851,0.0002056647,0.04657984,0.00001923513,0.00001919233,0.000009747631,0.00002885188,0.00002003631,0.001932393],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008915437,"threshold_uncertainty_score":0.002982497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08997593393711584,"score_gpt":0.3484920519198713,"score_spread":0.2585161179827555,"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."}}