Frequency sweep extension using the Kerr effect for static temperature measurement range enhancement in Chirped Pulse φ-OTDR
Bibliographic record
Abstract
In Chirped Pulse φ-OTDR systems used for sensing temperature or strain along an optical fiber, the largest disturbance between two single-shot measurements that can reliably be detected depends on the range of frequencies swept by the chirped pulse. If electrical modulation is used to generate the laser frequency sweep, the achievable sweeping range is limited by the electrical components, leading to a narrow measurement range for static measurements. In this work, we demonstrate the extension of the frequency range of a chirped laser pulse by all-optical means using evenly spaced frequency sidebands generated via the Kerr effect to improve the Chirped Pulse φ-OTDR measurement range. We report chirp extensions by factors up to 13 and apply the effect to achieve a sixfold increase in the measurement range of a Chirped Pulse φ-OTDR system measuring the temperature of a random fiber grating array. The method described in this paper can be applied to other optical systems utilizing chirped laser pulses and allow for variable extension of their chirping range.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".