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Calculation of Single Conductor Capacitance by Estimating the Electrostatic Field

2012· article· en· W2026203759 on OpenAlexaff
Yong Chen, Jian Yuan

Bibliographic record

VenueAdvanced materials research · 2012
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsCanadian Association of Emergency Physicians
Fundersnot available
KeywordsConductorDisplacement currentCapacitanceElectric fieldElectrical conductorCurrent (fluid)Direct currentDisplacement (psychology)Electrical engineeringDielectricField (mathematics)Skin effectComputationMaterials scienceMechanicsVoltagePhysicsEngineeringComputer scienceMathematicsElectrode

Abstract

fetched live from OpenAlex

The capacitance between two terminals of a single conductor working with time-dependent signals is defined by the amount of energy stored in the electric field outside the conductor. A simple approach for calculating the capacitance is presented in this paper, which only needs the computation of an electrostatic field. The approach is derived based on two assumptions, (1) the distribution of potentials on the conductor surface is almost the same, created by a time-dependent current and a direct current flowing in the conductor, (2) the distribution of the potential created by a direct current in the conductor can be modeled by an electrostatic field, in which the conductor is replaced by a dielectric with high permittivity. The approach is only suitable for low-frequency situations, where the displacement current and the inductive electric field can be disregarded.

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)0.002
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.052
GPT teacher head0.379
Teacher spread0.327 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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