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Record W1986648002 · doi:10.1109/lmwc.2014.2306891

Analytical <formula formulatype="inline"><tex Notation="TeX">$S$</tex> </formula>-Parameter Sensitivity Formula for the Shape Parameters of Dielectric Objects

2014· article· en· W1986648002 on OpenAlexaff
M. Sadegh Dadash, Natalia K. Nikolova

Bibliographic record

VenueIEEE Microwave and Wireless Components Letters · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSensitivity (control systems)ComputationDielectricSolverElectromagnetic fieldIsotropyFinite element methodField (mathematics)MathematicsExpression (computer science)MicrowaveMathematical analysisAlgorithmNotationGeometryComputer scienceOpticsElectronic engineeringPhysicsEngineeringMathematical optimizationPure mathematicsQuantum mechanicsArithmetic

Abstract

fetched live from OpenAlex

An exact sensitivity expression is proposed for the computation of S-parameter derivatives with respect to the shape parameters of isotropic dielectric objects. While an exact expression has already been developed for the shape parameters of volumetric metallic objects, to our knowledge, the case of dielectric objects has only been treated with approximations. The proposed expression integrates the field solution on the surfaces of the objects of interest. This solution is already available from the simulation computing the S-parameters. Therefore, it features negligible computational overhead compared with the full-wave simulation. At the same time, it provides the best accuracy for the given numerical field solution. It is validated through the sensitivity analysis of microwave structures simulated by a commercial electromagnetic solver based on the finite-element method.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.194
Threshold uncertainty score0.648

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1940.099

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 head0.019
GPT teacher head0.229
Teacher spread0.211 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations6
Published2014
Admission routes1
Has abstractyes

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