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Record W2589159379 · doi:10.1002/mop.30407

Broadband phase shifters using comprehensive compensation method

2017· article· en· W2589159379 on OpenAlexaff
Hao Peng, Xinlin Xia, Serioja Ovidiu Tatu, Kai‐Da Xu, Jun Dong, Tao Yang

Bibliographic record

VenueMicrowave and Optical Technology Letters · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsInstitut National de la Recherche Scientifique
FundersChina Scholarship CouncilNatural Science Foundation of Guangdong ProvinceNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsMicrostripPhase compensationPhase shift moduleMicrowaveBroadbandReturn lossInsertion lossCompensation (psychology)PermittivityMaterials sciencePhase (matter)Transmission lineOptoelectronicsDielectric permittivityElectronic engineeringSubstrate (aquarium)DielectricEngineeringElectrical engineeringTelecommunicationsPhysicsPhase noise

Abstract

fetched live from OpenAlex

In this letter, broadband phase shifters using comprehensive compensation method have been presented. The phase shift is dependent on the differences of substrate integrated waveguide (SIW) width, SIW length, microstrip-line length, and permittivity. The overall phase shift can be compensated mutually due to different physical parameters of constitutive transmission lines, as explained in details in this letter. The fillers with different dielectric permittivity are filled in the SIW's interior with a metalized PCB closely covered on the surfaces. Measurement results show that the fractional bandwidths for 45.1° ± 3° and 90° ± 5.1° versions can be up to 70.4% and 69%, respectively. For the operating frequency band, the return losses are all better than 13.5 dB, and the insertion losses are all less than 1.7 dB. © 2017 Wiley Periodicals, Inc. Microwave Opt Technol Lett 59:766–770, 2017

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.285
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2017
Admission routes1
Has abstractyes

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