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Record W3155586162 · doi:10.1109/ecie52353.2021.00012

Bandpass Filtering Power Divider With High Selectivity And Wide Stopband Based On Integrated Substrate Gap Waveguide

2021· article· en· W3155586162 on OpenAlexaff
Weida He, Dongya Shen, Xiupu Zhang, Zhidong Ruan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsConcordia University
FundersNational Natural Science Foundation of China
KeywordsPassbandMaterials scienceStopbandPower dividers and directional couplersInsertion lossMicrostripOptoelectronicsBand-pass filterOpticsResonatorPhysics

Abstract

fetched live from OpenAlex

A bandpass filtering power divider (FPD) using integrated substrate gap waveguide (ISGW) technology is proposed, which has high selectivity and wide stopband. The filtering power divider includes three-layer substrates. The top layer substrate realizes a perfect magnetic conductor (PMC) using periodic plated vias and metallic patches. It is applied for packaging filter power divider composed of substrate integrated waveguide (SIW) and microstrip to avoid interference and losses induced by external radiation emission and internal radiation leakage. The middle layer substrate generates a gap layer. and the bottom layer substrate etch the slots on the metal plane and place a metal through hole in the middle of the metal layer to generate resonance and transmission zeros, and connect the microstrip to form a filter power divider circuit. The results show that FPD has multiple transmission zeros, the passband is 13.2 to 15.5 GHz, and an insertion loss of -4 dB. The return loss in the passband is less than -15 dB, and the isolation below is -16.5 dB.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.008
GPT teacher head0.182
Teacher spread0.174 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2021
Admission routes1
Has abstractyes

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