Micro-structured fibers and their applications in fiber-optic sensors and random fiber lasers
Bibliographic record
Abstract
Micro-structured fibers are important devices that have drawn intensive attentions and proved to be powerful platforms for various applications over the past decades due to their remarkable merits and advantages, such as small footprint, immunity to electromagnetic interferences, light weight, high physical flexibility, and low cost. Modifications in optical fibers can be used as light-steering elements to excite and couple back different core and cladding modes and form various in-fiber structures, including in-line fiber interferometer, fiber micro-cantilever, fiber random gratings, and so on. These micro-structures, when applied as fiber-optic sensors in the presence of external disturbances, show high sensitivity in terms of the significant changes in the guided light features. Novel micro-structured bend-insensitive fiber-based in-line fiber interferometer and micro-cantilever have been proposed to realize both static and dynamic parameter measurements, including temperature, axial strain, surrounding refractive index, and vibration. We have also developed a novel fiber random grating along with a spectral correlation algorithm for simultaneous measurement of three static measurands. To move a step forward, random fiber lasers based on fiber random grating are achieved for either improving the laser performances or sensing applications of temperature, strain, and ultrasound measurements with high sensitivity.
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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 | 0.000 | 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".