{"id":"W4405632122","doi":"10.1109/tap.2024.3518060","title":"On the Synthesis of Null-Scanning Leaky-Wave Antennas (NSLWAs) for Millimeter-Wave Direction-Finding Applications","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Antennas and Propagation","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Startup Research Fund of Zhengzhou University; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Extremely high frequency; Directional antenna; Null (SQL); Physics; Fresnel zone antenna; Optics; Reflective array antenna; Slot antenna; Acoustics; Antenna (radio); Computer science; Telecommunications","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":[0.0004033975,0.0002595641,0.0002388244,0.0003772175,0.0003878197,0.000123834,0.00007397927,0.0001182377,0.00005654432],"category_scores_gemma":[0.00002196763,0.000204387,0.0001819837,0.0004064788,0.00007367585,0.000182029,0.000001532578,0.000280221,0.00001576649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007684776,"about_ca_system_score_gemma":0.00002681341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001249117,"about_ca_topic_score_gemma":0.0000153411,"domain_scores_codex":[0.998691,0.00005332079,0.0004375838,0.0003551586,0.0002090482,0.0002538723],"domain_scores_gemma":[0.9988539,0.0006449793,0.00006430324,0.0002449612,0.0001176891,0.00007412847],"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":[0.0001607839,0.000163578,0.000002796078,0.001163888,0.0005135047,0.000004319762,0.001629631,0.03442622,0.4905462,0.002639153,0.0003702244,0.4683797],"study_design_scores_gemma":[0.0001567489,0.0001179225,0.00001326127,0.000458171,0.0001274885,0.00001984858,0.0002581325,0.6230405,0.374082,0.0007150988,0.0007720268,0.0002388763],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03108913,0.0004514712,0.9654437,0.0004008996,0.0005248245,0.0009956502,0.0001161675,0.0003121045,0.0006660996],"genre_scores_gemma":[0.9963646,0.0006012628,0.001683914,0.00007859786,0.00008801334,0.0008230034,0.0000111504,0.00006991019,0.0002795319],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9652755,"threshold_uncertainty_score":0.8334662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03671644150872663,"score_gpt":0.2446149299953972,"score_spread":0.2078984884866706,"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."}}