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Analysis of unbounded and bounded circuits considering finite substrate extent and inhomogeneous dielectric layer

2000· article· en· W2053244477 on OpenAlexaff
Xiaohong Jiang, Ke Wu

Bibliographic record

VenueInternational Journal of Numerical Modelling Electronic Networks Devices and Fields · 2000
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsClassification of discontinuitiesFinite thicknessDielectricMicrostripLossy compressionResonatorBounded functionBoundary value problemMaterials scienceLeakage (economics)RadiationPhysicsMathematical analysisOpticsMathematicsOptoelectronicsMechanics

Abstract

fetched live from OpenAlex

A generalized method of lines algorithm is presented for characterizing unbounded and bounded circuits. Finite substrate extent and inhomogeneous dielectric layers are rigorously considered in this field-based model. Radiating properties of unbounded regular and irregular microstrip patch resonators and arrays are studied with emphasis on effects of mutual coupling and finite dielectric extent on complex resonant frequencies. In addition, unbounded loss effects for microstrip open-end and 90° angular bend deposited on finite substrate as well as chip-to-chip discontinuities are also investigated. Our developed algorithm incorporates an absorbing boundary condition using the Padé approximation to simulate any potential radiation and leakage losses for resonator structures while an improved lossy absorbing boundary condition (LABC) that can handle both propagating and evanescent waves is used to determine the unbounded effects for waveguiding structures. Results indicate interesting properties of the finite extent of dielectric substrate on resonance and radiation characteristics, and also on unbounded radiation and leakage losses. Copyright © 2000 John Wiley & Sons, Ltd.

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)0.001
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.219
Teacher spread0.208 · 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

Citations0
Published2000
Admission routes1
Has abstractyes

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