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Record W2782235529 · doi:10.23919/eumc.2017.8230906

Miniaturized folded ridged half-mode substrate integrated waveguide

2017· article· en· W2782235529 on OpenAlexaff
Thomas R. Jones, Mojgan Daneshmand

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMiniaturizationWaveguideMaterials scienceSubstrate (aquarium)OpticsRidgeTransverse modeOptoelectronicsMode (computer interface)Propagation constantPhysicsComputer scienceGeologyNanotechnology

Abstract

fetched live from OpenAlex

Significant miniaturization of substrate integrated waveguides (SIW) is presented, utilizing folded techniques on ridged half-mode substrate integrated waveguide transmission lines. By moving the ridge structure inside the main waveguide channel, the transverse field distribution is wrapped around two substrate layers, achieving a miniaturization of 25.2% compared to ridged half-mode SIW, and 56.7% compared to half-mode SIW. The complex propagation constant of the folded ridged half-mode substrate integrated waveguide is extracted, showing negligible loss in performance compared to the ridged half-mode structure, and improved performance compared to conventional half-mode SIW. To verify performance experimentally, a folded ridge half-mode SIW is fabricated, and the measured scattering parameters are compared to simulation, with good agreement. To the best of the authors' knowledge, this work presents the first folded ridge half-mode SIW structure in the literature.

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 head0.014
GPT teacher head0.237
Teacher spread0.223 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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