Distributed time delay sensing in a random fiber grating array based on chirped pulse φ-OTDR
Bibliographic record
Abstract
A high-precision distributed time delay measurement in a chirped pulse phase optical time domain reflectometry (CP <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>φ</mml:mi> </mml:math> -OTDR) system based on a random fiber grating array is proposed and demonstrated, in which a temperature-induced refractive index and fiber dimension change associated time delay could be measured for distributed temperature sensing. The random fiber grating array includes many inscribed refractive index change locations at periods of sub-micron. When laser pulses are launched into the fiber grating, the backscattered light possesses many unique localized speckle patterns at different locations. These patterns change with temperature, and hence the backscattering spectral response will change accordingly. By measuring the localized speckle pattern change due to the change of the temperature over the chirped pulse spectrum and performing cross-correlation calculation, we can realize distributed temperature measurements in real time using a megahertz bandwidth distributed feedback laser. Unlike a conventional <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>φ</mml:mi> </mml:math> -OTDR sensing system which measures a distributed phase change along the fiber using an ultra-narrow linewidth laser, the distributed time delay presented in this Letter is directly measured in real time. It is shown that the time-resolved localized pattern trace is stable with very small fluctuation, thanks to the enhanced inhomogeneity and reflectivity. The minimum detectable temperature variation is about 0.028°C at meter order of magnitude spatial resolution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 teacher head, 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".