Sub-centimeter spatial resolution dynamic strain sensing using time-expanded 𝝓OTDR
Bibliographic record
Abstract
Time-expanded phase-sensitive (TE-φ )OTDR is a distributed optical fiber sensing (DOFS) technique that takes advantage of the dual-frequency comb technology to offer distributed, dynamic, and high-spatial resolution measurements. The performance delivered by this recent approach is unmatched by any other DOFS, combining the high resolution of OFDR with the potential for long range and fast sampling of φ OTDR. In this contribution, we present an optimized TE-φ OTDR scheme with important improvements with respect to the traditional one. In particular, the new architecture uses electrooptical phase modulation instead of intensity modulation, increasing the energy-efficiency. Additionally, it employs an optical hybrid to double the spectral efficiency of the system, which in practical terms results in doubling the spatial resolution for the same interrogating comb bandwidth. The proposed architecture has been experimentally validated through a scheme providing 5 mm of spatial resolution, 80 m of range and 70 Hz sampling rate with a simple, compact and low-cost setup using field-programmable gate arrays (FPGA) and relatively low bandwidth photodetection (2 MHz).
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".