Analysis of the Carrying Capacity of Kranji Traction Substation in the Operation of Soekarno-Hatta Airport Train
Bibliographic record
Abstract
This research was prepared with the aim of carrying out the calculation of the capacity of the traction substation and analyzing the carrying capacity of the Kranji Traction Substation at the operation of the Soekarno-Hatta Airport Train at rush hour in the morning. This research was conducted with data sources in the form of a train circuit arrangement, filling distance between traction substations, headway, double track type, train consumption ratio, and total train weight. The capability parameters of the substation carrying capacity are measured based on the narrowing of the 8.5 minute, 5 minute and 3 minute headway and the blackout of the Cakung substation. The plan load value is obtained based on the ratio of the maximum current load to the headway constriction load. Carrying capacity is measured based on the comparison of planned load values to the existing capacity of substations. The results of the analysis stated the rectifier load was 2626.42 kW and the transformer load was 3533 kVA on all types of parameters. With a rectifier capacity of 43% and a transformer of 51%. An evaluation of the results of the data analysis showed that the Kranji Traction Station was able to supply power for the operation of the Soekarno-Hatta Airport Train in the morning rush hour.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".