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Record W2804014206 · doi:10.1109/tmtt.2018.2833112

Modeling Effective Anisotropy of Substrate Integrated Dielectric Waveguides for Polarization Diversity

2018· article· en· W2804014206 on OpenAlexaff
Ahmed A. Sakr, Walid Dyab, Ke Wu

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsDielectricAnisotropyPolarization (electrochemistry)Materials scienceWaveguideFinite element methodElectric fieldOpticsCoupling (piping)Computational physicsOptoelectronicsPhysicsComposite materialChemistry

Abstract

fetched live from OpenAlex

A field-based analytical procedure is presented and developed for accurate modeling of the effective parameters of substrate integrated nonradiative dielectric (SINRD) waveguide. This modeling strategy is based on the eigenmode analysis of a dielectric substrate material perforated with air holes and enclosed between two horizontal metallic plates. From this model, it is found that the resulting effective dielectric constant of the periodic geometry of the SINRD waveguide is dependent on the direction of an electric field vector with respect to the material periodicity. In other words, the guiding parts of the SINRD waveguides act as anisotropic materials. A full demonstration for the proposed analysis is supported with simulation results using a full-wave finite-element method. The proposed model provides a scheme for the isotropization of anisotropic substrates. This is in addition to clarifying the exact regimes of operation inside the SINRD waveguide based on the guiding mechanism. Validation examples are studied based on polarization-selective coupling and effective polarization-independent coupling. A prototype is realized and measured where good agreement is achieved between simulation and measurement results.

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: none
Teacher disagreement score0.642
Threshold uncertainty score0.817

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 head0.008
GPT teacher head0.217
Teacher spread0.209 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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