{"id":"W2593711471","doi":"","title":"New adaptive polynomial and neural network predistortion techniques for satellite transmissions","year":2002,"lang":"en","type":"article","venue":"International Symposium on Antenna Technology and Applied Electromagnetics","topic":"Advanced Power Amplifier Design","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Quadrature amplitude modulation; Predistortion; Amplifier; Constellation diagram; QAM; Electronic engineering; Communications satellite; Spectral efficiency; Adjacent-channel interference; SIGNAL (programming language); Adjacent channel; Adjacent channel power ratio; Modulation (music); Polyphase system; Computer science; Interference (communication); Amplitude modulation; Control theory (sociology); Telecommunications; Physics; Engineering; Radio frequency; Channel (broadcasting); Bit error rate; Beamforming; Bandwidth (computing); Frequency modulation; Acoustics; Satellite","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":[0.00003609928,0.0001996611,0.0001634669,0.0001547148,0.00008975794,0.00002416866,0.0001489982,0.0002193273,0.00001597436],"category_scores_gemma":[0.000004471421,0.0002086358,0.00003039247,0.0001577869,0.00009685302,0.000051852,0.00001977584,0.0002582162,0.000002457263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004655341,"about_ca_system_score_gemma":0.000003925552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.020262e-7,"about_ca_topic_score_gemma":0.000002643624,"domain_scores_codex":[0.9991425,0.000004828951,0.0002046826,0.0002660062,0.00008697707,0.0002949776],"domain_scores_gemma":[0.9996891,0.00005558664,0.00003766354,0.0001161467,0.00002690281,0.00007461996],"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":[0.0003373518,0.00006579924,0.0001462626,0.00001942057,0.0001261309,0.000005608695,0.000157459,0.000917928,0.6175263,0.08444781,0.00585948,0.2903905],"study_design_scores_gemma":[0.004723725,0.006260968,0.001165064,0.0002218057,0.0002590424,0.0004424561,0.0001244674,0.4247441,0.2368181,0.09182192,0.2310429,0.002375424],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04836352,0.005219166,0.9205402,0.006218594,0.000738672,0.001602145,0.00005339604,0.002543773,0.01472051],"genre_scores_gemma":[0.9680892,0.00188242,0.02907245,0.0001353809,0.0002096766,0.000100951,0.00001150645,0.00003932185,0.0004591174],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9197257,"threshold_uncertainty_score":0.8507921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008196009419575746,"score_gpt":0.2061725310135553,"score_spread":0.1979765215939796,"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."}}