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Record W1606730456 · doi:10.1109/isplc.2015.7147610

On PLC channel emulation via transmission line theory

2015· article· en· W1606730456 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEmulationElectric power transmissionSolverChannel (broadcasting)Power-line communicationTransmission lineComputer scienceComputationElectronic engineeringConductorLine (geometry)Transmission (telecommunications)Power (physics)Topology (electrical circuits)AlgorithmEngineeringElectrical engineeringTelecommunicationsMathematicsPhysics

Abstract

fetched live from OpenAlex

The emulation of power line communication (PLC) channels is an important tool for the analysis and development of PLC systems. Especially for the study of PLC networks, the so-called bottom-up channel emulation using transmission-line theory (TLT) is the proper method. The underlying assumption of this approach is that the power line propagation characteristics are known. That is, the per-unit length (PUL) parameters of the power lines need to be computed. However, especially for multi-conductor lines this can be theoretically and computationally challenging. In this paper, we present a compact overview of the computation of PUL parameters. We briefly discuss the assumptions associated with different solution methods and develop a numerical solver applicable to general multi-conductor transmission lines. Considering a sample set of different cable types, we demonstrate the effects of simplifying assumptions on the accuracy of computed PUL parameters and channel frequency responses.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score0.210

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 head0.029
GPT teacher head0.252
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

Quick stats

Citations29
Published2015
Admission routes1
Has abstractyes

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