Random Fiber Grating Characterization Based on OFDR and Transfer Matrix Method
Bibliographic record
Abstract
Random fiber gratings (RFGs) have shown great potential applications in fiber sensing and random fiber lasers. However, a quantitative relationship between the degree of randomness of the RFG and its spectral response has never been analyzed. In this paper, two RFGs with different degrees of randomness are first characterized experimentally by optical frequency domain reflectometry (OFDR). Experimental results show that the high degree of randomness leads to low backscattering strength of the grating and strong strength fluctuations in the spatial domain. The local spectral response of the grating exhibits multiple peaks and a large peak wavelength variation range when its degree of randomness is high. The linewidth of its fine spectrum structures shows scaling behavior with the grating length. In order to find a quantitative relationship between the degree of randomness and spectrum property of RFG, entropy was introduced to describe the degree of randomness induced by period variation of the sub-grating. Simulation results showed that the average reflectivity of the RFG in dB scale decreased linearly with increased sub-grating entropy, when the measured wavelength range was smaller than the peak wavelength variation range of the sub-grating. The peak reflectivity of the RFG was determined by κ2LΔP (where κ is the coupling coefficient, L is the grating length, ΔP is period variation range of the sub-grating) rather than κL when ΔP is larger than 8 nm in the spatial domain. The experimental results agree well with the simulation results, which helps to optimize the RFG manufacturing processes for future applications in random fiber lasers and sensors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".