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Record W2142660660 · doi:10.1109/tadvp.2007.909449

Passive Closed-Form Transmission Line Macromodel Using Method of Characteristics

2008· article· en· W2142660660 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

VenueIEEE Transactions on Advanced Packaging · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsWestern University
Fundersnot available
KeywordsPassivityDiscretizationTransmission lineElectric power transmissionComputer scienceLossy compressionKey (lock)Line (geometry)Transmission (telecommunications)Control theory (sociology)Electronic engineeringAlgorithmMathematical optimizationMathematicsMathematical analysisEngineeringElectrical engineeringGeometryTelecommunications

Abstract

fetched live from OpenAlex

This paper presents an efficient passive time-domain macromodeling algorithm for distributed lossy transmission lines based on the generalized method of characteristics (MoC). A new theorem is described that specifies sufficient conditions to guarantee the passivity of the MoC by construction. A key feature of the proposed methodology is that the curve fitting to realize the MoC depends only on the per-unit-length (p.u.l.) parameters and not on the discretization of the macromodel. Thus, with the knowledge of the rational functions derived from the p.u.l. parameters, the MoC can be formulated in a closed-form manner for any line length while ensuring passivity.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.661
Threshold uncertainty score0.870

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 head0.017
GPT teacher head0.275
Teacher spread0.257 · 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