Development of a novel high-frequency reciprocal structure fiber optical pulsed current sensor
Bibliographic record
Abstract
A novel all-fiber optic current sensor (FOCS) is designed specifically for the measurement of large transient currents based on the Faraday effect. A reciprocal symmetric structure is incorporated into the optical sensing loop, and the current dependent phase demodulation is achieved by using a passive optical fiber coupler and the homodyne detection scheme. This design offers several advantages, including structural simplicity, high voltage insulation, low noise, high linearity, and excellent frequency response, and is highly suitable for use in any system of high-voltage, high-power, and high-frequency in nature. A current source based on fast capacitor discharge is used for the bench-test of the FOCS system, and several laser sources with different wavelengths and linewidths have been used to test the FOCS performance. Experimental results show that the phase noise of the laser is independent of both wavelength and linewidth. The sensitivity of the FOCS system has been calibrated against a commercial Rogowski current sensor. This FOCS offers precise and flexible high-current pulse measurements with a measured phase noise of 1.4 × 10-3 rad, using a 1550 nm laser with a 1 kHz linewidth. The fully reciprocal sensing loop ensures that the phase noise remains unchanged as the loop length increases. These features make the FOCS a robust and adaptable tool for high-precision current sensing in challenging environments. Finally, the FOCS system has consistently demonstrated its superior and stable performance in terms of high-frequency response and low noise with minimal dependence on the laser parameters.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".