OTDR and OFDR for distributed multi-parameter sensing
Bibliographic record
Abstract
The drive for high spatial resolution (millimeters) distributed fiber sensors has renewed the interest in optical frequency domain reflectrometry (OFDR) systems. Because millimeters equivalent spatial resolution in optical time domain reflectrometry (OTDR) systems would require a data acquisition card with a bandwidth of 10 GHz and a sampling rate of tens of G Samples/s, such a digitizer or data acquisition card plus the pulse generator and detection system will make a distributed sensors very expensive, while a tunable laser with a wide tuning range can provide millimeters resolution with short sensing range (<100m). We developed a high precision temperature (0.1°C) and strain (1μ strain) resolution and 2.5mm spatial resolution over 180m range by auto and cross-correlation of OFDR in PMF. The dual modes of PMF allow the discrimination of the temperature and strain with distinct dependency. The application of this sensor for internal crack detection of concrete beam has been demonstrated. For distributed dynamic measurement, the upper frequency is limited by the repetition rate of the laser pulse in sensing fiber; in addition the weak Rayleigh scattering signal demands many averaging to improve SNR. The continuous wavelet transform approach has been introduced in phase OTDR sensor system to suppress random noise, and multiple vibration disturbances have been measured simultaneously for power generator monitoring. For the high frequency vibration detection, the coherent detection combined with polarization diversity scheme is implemented.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".