{"id":"W4417051969","doi":"10.1109/ton.2025.3636013","title":"Energy Harvesting in Solar-Powered UAV Communication With Rate Splitting Multiple Access","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Networking","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Nanyang Technological University; Agency for Science, Technology and Research; National Science Foundation","keywords":"Resource allocation; Energy harvesting; Lyapunov optimization; Wireless; Markov decision process; Communications system; Optimization problem; Throughput; Resource management (computing); Adaptability","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":[0.0006142269,0.0004847123,0.0004495191,0.000619351,0.0008881039,0.000563369,0.0005864783,0.0003061091,0.00004356042],"category_scores_gemma":[0.000007588725,0.0005674949,0.0001153598,0.002716134,0.00009345738,0.0007596912,0.00001059826,0.0008781032,0.000006653977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000446693,"about_ca_system_score_gemma":0.0001210268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001289379,"about_ca_topic_score_gemma":0.005235395,"domain_scores_codex":[0.9973176,0.00025411,0.0009499796,0.0006148446,0.0001948029,0.0006686398],"domain_scores_gemma":[0.9977694,0.000848713,0.0002328943,0.0009261958,0.0001374738,0.00008533515],"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":[0.00008499313,0.0001456564,0.0005653975,0.00007789861,0.00009942395,0.000002175894,0.0002409849,0.7491809,0.000183934,0.0001986982,0.00001909212,0.2492009],"study_design_scores_gemma":[0.001465113,0.00004125339,0.0005038535,0.002345821,0.0001272852,0.000003150362,0.0001326309,0.9866993,0.003276164,0.0001633579,0.00471603,0.000526058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007631443,0.001369539,0.9847479,0.0002115819,0.0008013663,0.0005667374,0.00001162466,0.0003092077,0.004350524],"genre_scores_gemma":[0.9816022,0.003905254,0.01301521,0.000169432,0.0001107425,0.0003598296,0.0000266297,0.0001039499,0.0007067862],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9739707,"threshold_uncertainty_score":0.9996777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01536674993878548,"score_gpt":0.2390692082809612,"score_spread":0.2237024583421758,"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."}}