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Record W4388536225 · doi:10.1049/tje2.12302

Electromagnetic analysis of moving structures in a moving reference frame

2023· article· en· W4388536225 on OpenAlexaff
Mohammad Marvasti, Halim Boutayeb

Bibliographic record

VenueThe Journal of Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsUniversité du Québec en OutaouaisCégep de l'Outaouais
Fundersnot available
KeywordsFinite-difference time-domain methodObserver (physics)Doppler effectPhysicsElectromagnetic radiationRotating reference frameReference frameMoving frameFrame of referenceMicrowaveDiscretizationPlane waveFrame (networking)Plane (geometry)AcousticsClassical mechanicsOpticsComputer scienceMathematical analysisGeometryMathematicsTelecommunications

Abstract

fetched live from OpenAlex

Abstract In this work, the finite‐difference time‐domain (FDTD) method is employed to study electromagnetic problems with moving bodies in a moving system. The proposed approach consists in modeling objects with time‐varying positions and using the direct discretization of Maxwell's equations in space and time domains. Doppler effects are investigated for problems with moving observer, source, or reflector, in a moving frame. A distinction is also made between a high‐impedance or low‐impedance plane wave source in motion. The full‐wave electromagnetic simulations are compared with closed‐form equations that agree with wave theory. The proposed analysis shows that, for Doppler radars used every day, the motion of the Earth relative to the Cosmic Microwave Background has a negligible effect and only relative motions in the Earth frame are relevant.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.264
Teacher spread0.250 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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