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Record W3116866699 · doi:10.1002/jnm.2853

Chebyshev polynomials for the numerical modeling of non‐uniform substrate integrated waveguides

2020· article· en· W3116866699 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Numerical Modelling Electronic Networks Devices and Fields · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of CalgaryÉcole de Technologie Supérieure
Fundersnot available
KeywordsChebyshev polynomialsChebyshev filterLossy compressionMethod of moments (probability theory)Convergence (economics)Matrix (chemical analysis)Scattering parametersTransmission lineFinite element methodBasis functionAlgorithmComputer scienceTopology (electrical circuits)Mathematical analysisMathematicsElectronic engineeringMaterials sciencePhysicsEngineeringTelecommunications

Abstract

fetched live from OpenAlex

Abstract In this paper, a new method‐of‐moments‐based approach is proposed for the analysis of non‐uniform lossy substrate integrated waveguides (SIW) transmission lines. The approach incorporates Chebyshev expansion in the frequency domain to compute the scattering parameter matrix of the line. To validate the proposed approach of non‐uniform structures are analyzed where two of them have been fabricated and measured. The analytical and measured S parameters were compared to those obtained through electromagnetic finite element‐based simulations. The good match observed between the two sets of results for a relatively reasonable number of basis functions confirms the accuracy and the fast convergence of the proposed approach. This makes it the most suitable for integration into computer‐aided design tools.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score0.492

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 head0.017
GPT teacher head0.233
Teacher spread0.216 · 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