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Record W2291199921 · doi:10.1109/nemo.2015.7415093

Modeling substrate integrated waveguide structures using effective material properties

2015· article· en· W2291199921 on OpenAlexafffund
Nathan Jess, B. Syrett, Langis Roy, Rony E. Amaya

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsCarleton University
FundersCMC Microsystems
KeywordsMaterials scienceWaveguidePermittivitySlabCeramicSubstrate (aquarium)Leakage (economics)OptoelectronicsHomogeneousMaterial propertiesOpticsDielectricComposite materialPhysics

Abstract

fetched live from OpenAlex

A substrate integrated waveguide is analyzed using effective material properties for the first time. The analysis is performed by replacing subwavelength periodic metallization with a frequency dependent homogeneous material. For example, the post wall of the waveguide is represented as a negative permittivity material. A substrate integrated waveguide fabricated in low temperature co-fired ceramic is analyzed. The phase constant of the guide is calculated with slab waveguide equations, using effective material properties to replace much of the metal structuring, and it is shown to be almost the same as that simulated and measured. An SIW coupler that utilizes side wall leakage for coupling is simulated using effective material properties. The simplified structure shows results matching the full structure with a reduction in simulation time of 88%. These results demonstrate a substrate integrated waveguide can be accurately and efficiently simulated by replacing subwavelength periodic structures with a frequency dependent homogeneous material.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.032
GPT teacher head0.220
Teacher spread0.187 · 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 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

Citations3
Published2015
Admission routes2
Has abstractyes

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