Off-Axis Filament Based Fiber Bragg Gratings for Azimuthally Resolved Displacement Sensing
Bibliographic record
Abstract
Fiber Bragg gratings (FBGs) serve in widely varying applications across research and industry as optical sensors, signal multiplexers, and fiber laser mirrors. While interference-based phase mask inscription of FBGs offers advantages in precision and uniformity, point-by-point writing with femtosecond lasers provides a strong localized material response for exceptional flexibility in apodizing, chirping, and spatially varying the grating structure. Moreover, spatial beam shaping opens possibilities for controlling the 3D geometry of the grating elements, for example, by stretching into long and uniform filaments to influence the cladding or radiation mode coupling [1] . To this end, our group has harnessed surface aberration from glass plates to fabricate an all-fiber radiative spectrometer with low-contrast gratings [2] or to drive filament nano-explosions and open nano-capillary FBG sensing holes for accessing the external cladding environment [3] . The present work introduces crossed-filament gratings with off-centered positioning that enables tailoring of the strain-optic response for azimuthally resolved displacement sensing. The off-centering accommodates an otherwise zero photoelastic sensitivity in traditional FBGs. The laser processing is a straight-forward modification of fiber relative to the complex interferometric or microstructured assemblies used in bend sensing [4] . The filament grating promises lower crosstalk and loss relative to FBGs that use tilted gratings and cladding-core recoupling to detect bending. Since the gratings were embedded in common SMF-28 fiber, alignment and splicing is facile relative to bend sensors based on asymmetry such as multicore and eccentric core fiber [4] .
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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".