Frequency Response Enhancement by Periodical Nonuniform Sampling in Distributed Sensing
Bibliographic record
Abstract
The sensing fiber length and the use of averaging methods limit the maximum detectable frequency response range of vibration in phase-sensitive optical time domain reflectometry (φ-OTDR), especially for long-haul monitoring. We propose a method to significantly extend the frequency response range of φ-OTDR systems by impressing vibration signals on a set of backscattering signals that are carried by different wavelengths within one measurement interval. The sampled traces are recombined into a trace group using a time multiplexing method, leading to higher effective sampling rate at each sensing point on the sensing fiber, which allows for a larger frequency response range. In experiments, two pulses carried by different wavelengths with a time delay of 0.75 μs are launched into a 3024-m long sensing fiber, and frequency response range of 30 kHz is realized. The system can overcome the tradeoff between the frequency response range and the sensing fiber length.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".