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Record W2026248553 · doi:10.1109/aps.2006.1711353

Comparison of Three FDTD Modeling Techniques for Coaxial Feed

2006· article· en· W2026248553 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

Venue2006 IEEE Antennas and Propagation Society International Symposium · 2006
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsMcGill University
Fundersnot available
KeywordsFinite-difference time-domain methodDiscretizationCoaxialElectrical conductorConductorFinite difference methodAcousticsNumerical modelingElectronic engineeringTransmission lineAntenna (radio)Computational electromagneticsComputer scienceOpticsPhysicsElectromagnetic fieldMaterials scienceEngineeringElectrical engineeringMathematicsTelecommunicationsMathematical analysis

Abstract

fetched live from OpenAlex

This paper addresses the difficulties reported in the numerical modeling of coaxial feed probes. A range of finite-difference time-domain (FDTD) modeling techniques (e.g., gap, magnetic frill and transmission line modeling) was successfully applied for specific applications. The same problem was challenged for the case of electromagnetically coupled patch antenna (EMCP). This structure has intricate resonance behavior and operates based on two-mode excitation. These two modes have close values of resonant frequency and can be revealed only with precise probe modeling. The following modeling techniques for the probe feed have been implemented and compared with the measurement results: electric gap modeling, sub-cell modeling of inner conductor and very fine discretization of coaxial probe. The results are discussed in the light of advantages and downsides of each technique and the associated computational parameters to obtain a reliable solution

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.854
Threshold uncertainty score0.470

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 head0.023
GPT teacher head0.304
Teacher spread0.281 · 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