{"id":"W2953761555","doi":"10.1109/tvt.2019.2925736","title":"A Novel Energy Harvesting Scheme for Mixed FSO-RF Relaying Systems","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Optical Wireless Communication Technologies","field":"Engineering","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Fundamental Research Funds for the Central Universities; State Key Laboratory of Advanced Optical Communication Systems and Networks; Department of Education of Guangdong Province; Shanghai Jiao Tong University; National Natural Science Foundation of China","keywords":"Energy harvesting; Electronic engineering; Radio frequency; Energy (signal processing); Relay; Correctness; Monte Carlo method; Component (thermodynamics); Computer science; Communications system; Topology (electrical circuits); Mathematics; Algorithm; Physics; Engineering; Telecommunications; Electrical engineering; Power (physics); 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001345776,0.0002723502,0.0003684952,0.0006279735,0.0001662861,0.00004694157,0.0006306095,0.0006869072,0.00001006351],"category_scores_gemma":[0.00003372295,0.000294781,0.0001322558,0.000722682,0.0001301608,0.0001500341,0.000007586748,0.0006256939,0.00008276477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001634594,"about_ca_system_score_gemma":0.00002023558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002396571,"about_ca_topic_score_gemma":0.00001825167,"domain_scores_codex":[0.9986287,0.00001374787,0.0004063897,0.0003505412,0.0001507638,0.0004498897],"domain_scores_gemma":[0.9984755,0.0002054893,0.00006591381,0.001098243,0.0001066493,0.00004822255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001694294,0.000163378,0.00003022099,0.0002040024,0.0002563838,0.000003334718,0.00002121388,0.3159495,0.5842415,0.04284504,0.00007460842,0.05619393],"study_design_scores_gemma":[0.0008975553,0.0001650124,0.000009141863,0.0001833005,0.00003198813,0.00005574658,0.0003602034,0.5972856,0.3856822,0.0004101126,0.01444377,0.0004753871],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1300879,0.0006387242,0.8631516,0.0004229507,0.0006709652,0.000407764,0.00002243786,0.004242599,0.000355143],"genre_scores_gemma":[0.9682112,0.0001460838,0.03055244,0.00002308362,0.0000123972,0.0006617758,0.000005347705,0.00008916738,0.0002985345],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8381233,"threshold_uncertainty_score":0.9999504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01430308168476375,"score_gpt":0.2165501277349297,"score_spread":0.2022470460501659,"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."}}