{"id":"W4400038006","doi":"10.1016/j.comnet.2024.110615","title":"Collaborative computation offloading and wireless charging scheduling in multi-UAV-assisted MEC networks: A TD3-based approach","year":2024,"lang":"en","type":"article","venue":"Computer Networks","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Scheduling (production processes); Wireless; Computation; Computer network; Wireless network; Computation offloading; Distributed computing; Real-time computing; Mathematical optimization; Embedded system; Telecommunications; Internet of Things; Algorithm","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":[0.0002026197,0.0002163588,0.0002319351,0.000205615,0.0001013741,0.000294067,0.0000938201,0.0001470089,0.000002003582],"category_scores_gemma":[0.000001758336,0.0002344518,0.00003426001,0.001087545,0.00003243007,0.0001880769,0.00003890136,0.000304323,0.000002187451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001106788,"about_ca_system_score_gemma":0.00002235933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006459272,"about_ca_topic_score_gemma":0.00001099035,"domain_scores_codex":[0.9989211,0.00004987179,0.0003081682,0.0003525736,0.00008813897,0.0002801624],"domain_scores_gemma":[0.9995797,0.0001300273,0.00003600251,0.0001255415,0.00005762487,0.00007107438],"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":[0.000004052396,0.00002470371,0.0002036573,0.00008258359,0.00002958652,0.000004660582,0.0003516788,0.9503624,0.00002519142,0.0003117882,0.0001357573,0.04846397],"study_design_scores_gemma":[0.0004330694,0.00001086752,0.001254933,0.0002975517,0.00001687625,0.000004375354,0.00005125331,0.9975423,0.000008283472,0.00001145235,0.0001245995,0.0002444252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02722955,0.002487135,0.9688323,0.00003041086,0.0003996163,0.0003953395,0.000002546286,0.0005232216,0.00009983714],"genre_scores_gemma":[0.8041824,0.0001115783,0.1951845,0.000041625,0.0002309156,0.00007684332,0.0001203173,0.00004763679,0.000004192219],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7769528,"threshold_uncertainty_score":0.9560665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01144191154079868,"score_gpt":0.2281350249855426,"score_spread":0.2166931134447439,"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."}}