Partitioned Fitting and DC Correction for the Simulation of Electromagnetic Transients in Transmission Lines/Cables
Bibliographic record
Abstract
This letter proposes a two-stage fitting procedure for transmission line/cable functions in which low frequency samples are exclusively considered. At the first stage, fitting is performed for a reduced band by excluding frequencies close to DC. Reducing the fitting range improves the numerical conditioning of the overall system of equations and relieves fitting. The second stage consists of finding a correction term for the out-of-band samples close to DC. The procedure, when used with the recently introduced frequency-dependent cable model (FDCM) approach, allows modeling transmission lines and cables with improved fitting precision at low frequencies. Overall, the new approach is called FDM (Frequency Dependent Model) with DC correction, i.e., FDM/DC. It can be used to complement the prevailing Universal Line Model (ULM) in two ways: for reducing the integration errors due to unbalanced fitting, and for improving the precision of DC response. Two examples are provided to demonstrate the utility of the new approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".