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Record W4226372485 · doi:10.1109/tcsii.2022.3163568

Dual-Layer Slow-Wave Half-Mode Substrate Integrated Waveguide E-Plane Coupler

2022· article· en· W4226372485 on OpenAlexfundno aff
Shui Liu, Feng Xu, Senshen Deng, Ling Yang, Jingxia Qiang, Min Li

Bibliographic record

VenueIEEE Transactions on Circuits & Systems II Express Briefs · 2022
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsResonatorWaveguideCoupling (piping)OpticsHybrid couplerMaterials scienceRat-race couplerAperture (computer memory)OptoelectronicsPower dividers and directional couplersPhysicsAcousticsComposite material

Abstract

fetched live from OpenAlex

In this brief, an ultra-compact broad wall coupler based on slow-wave half-mode substrate integrated waveguide (SW-HMSIW) is presented. The E-plane coupling between different layers of circuits under slow-wave effect is studied for the first time. The slow-wave construction is realized by complementary split ring resonator (CSRR) loaded HMSIW. Moreover, CSRRs could serve as coupling sections at the same time. Hence, the slow-wave effect and energy coupling can support each other and operate well together simultaneously. Furthermore, the slow-wave effect could enhance the coupling level of each aperture and realize coupler size reduction in general. As a result, the presented coupler can operate in a wide band (45%) with 50% size reduction comparing to typical HMSIW fast-wave one, which would be suitable for applications in six-port circuit and Butler matrix with strict size and performance requirements. Simulated results are in good agreement with measured data.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.747
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.215
Teacher spread0.194 · 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.

Study designSimulation or modeling
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
Published2022
Admission routes1
Has abstractyes

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