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Record W1855429889 · doi:10.1109/mwsym.1993.276726

A new look at the 3D condensed node TLM scattering

2002· article· en· W1855429889 on OpenAlexaff
P.P.M. So, W.J.R. Hoefer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsUniversity of VictoriaNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsNumberingScatteringImpulse (physics)Topology (electrical circuits)Transmission lineNode (physics)Boundary (topology)Computer scienceBoundary value problemMatrix (chemical analysis)AlgorithmElectronic engineeringMathematical analysisMathematicsPhysicsOpticsAcousticsEngineeringTelecommunicationsCombinatoricsMaterials scienceClassical mechanics

Abstract

fetched live from OpenAlex

A systematic impulse numbering scheme and impulse splitting operation are developed. This numbering scheme reveals the physics of the scattering procedure. The impulse splitting operation allows new scattering matrixes of special nodes to be derived in a straightforward manner. These special nodes can be used to model the sizes and positions of metallic boundaries with good accuracy without using an unnecessarily refined mesh. The impulse splitting operation can be generalized to model some boundary properties, such as sharp edges and corners, finite conductivity, skin effect, and implementation of discrete devices into a TLM (transmission line matrix) network, which cannot be modeled easily and accurately by placing the boundary half-way between nodes.>

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.020
GPT teacher head0.243
Teacher spread0.223 · 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

Citations7
Published2002
Admission routes1
Has abstractyes

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