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Three-dimensional analysis of dielectric-loaded waveguide discontinuity by edge FEM combined with SOC technique

2000· article· en· W1982005895 on OpenAlexaff
Runnan Chen, D. X. Wang, La Chang, Ke Wu

Bibliographic record

VenueMicrowave and Optical Technology Letters · 2000
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsDiscontinuity (linguistics)Finite element methodClassification of discontinuitiesWaveguideMatrix (chemical analysis)Electronic engineeringEngineeringAlgorithmMathematical analysisComputer sciencePhysicsMathematicsMaterials scienceOpticsStructural engineering

Abstract

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In this paper, the application of the edge-based vector finite-element method combined with the short-open calibration (SOC) technique to three-dimensional waveguide discontinuity was presented. This SOC technique is directly accommodated in the FEM algorithm, and is used to truncate the computational domain. The developed FEM algorithm is applied to the modeling of dielectric-filled waveguide discontinuities that can be segmented into two distinct sections: the static model of feed lines, and the dynamic model of circuit discontinuity. The FEM is formulated in such a way that the port voltages and currents are explicitly represented through relevant network matrices. The SOC technique is used to remove or separate unwanted parasitics brought by the approximation of the impressed voltage source, as well as the problem of the resulting consistency between different simulations. Truncation of the computational domain by the SOC technique makes the iterative solvers for large sparse linear matrix equations from the FEM converge much faster than by perfectly matched layers (PML). Results for full-height/partially dielectric-filled waveguide discontinuities are very well compared with available publications. © 2000 John Wiley & Sons, Inc. Microwave Opt Technol Lett 27: 438–444, 2000.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.004
GPT teacher head0.184
Teacher spread0.180 · 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 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

Citations7
Published2000
Admission routes1
Has abstractyes

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