Polarization-Dependent Strain and Twist Sensors Based on Tilted Fiber Bragg Grating
Bibliographic record
Abstract
Tilted fiber Bragg grating (TFBG) shows various advantages in sensing parameters such as, strain, temperature and twist angle.In particular, multiple data collected from cladding modes of TFBG provide a multi-functional modality.According to this feature, a temperature-independent strain sensor and a high sensitivity twist sensor based on TFBG are introduced in this thesis.Both strain and twist sensors are designed with simplex structures by employing sole TFBG inscribed inside standard single mode fiber respectively.The relative wavelength shifts of P and S polarized spectra are utilized in analyzing strain sensors based on their strong temperature-independent characteristics.Linear experimental results which relate to the wavelength separation from the Bragg mode resonance are presented.Meanwhile, sensitive amplitude variations with respect to the strain applied to TFBG are also evident.Moreover, TFBG shows high sensitivity to twist angle due to its unique structure.The sensitivity of Polarization-dependent loss (PDL) presents an obvious contrast to that of insertion loss; the comparisons are described experimentally.In particular, PDL spectrum of TFBG has much higher twist sensitivity than that of insertion loss spectrum.
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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 | 0.000 |
| 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".