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Record W3203113362 · doi:10.24018/ejece.2021.5.5.360

Signal Propagation in Transmission Lines with Losses Using Fibonacci Wave Functions

2021· article· en· W3203113362 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

VenueEuropean Journal of Electrical Engineering and Computer Science · 2021
Typearticle
Languageen
FieldComputer Science
TopicCellular Automata and Applications
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsResistorHeaviside step functionElectric power transmissionLossless compressionFibonacci numberTransmission lineCapacitorInductorTopology (electrical circuits)Electrical impedanceTransfer functionComputer sciencePhysicsElectronic engineeringElectrical engineeringVoltageTelecommunicationsMathematicsEngineeringMathematical analysisAlgorithm

Abstract

fetched live from OpenAlex

In this paper, the general model for an infinite LC ladder network using Fibonacci wave functions that were applied to lossless transmission lines will be extended to transmission lines including losses. The general model that was derived from a first order system transfer function representing a simple RC or RL circuit will be used to model and analyze transmission lines presenting losses. The LC ladder network model can be applied to any order for each inductor current with its parasitic rc resistor and for each capacitor voltage with its parasitic rL resistor. The extension of the proposed general model to transmission lines with losses is subject to Heaviside condition for both resistors rc and rL .

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: Methods · Consensus signal: none
Teacher disagreement score0.769
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.196
Teacher spread0.184 · 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