Co-located angularly offset fiber Bragg grating pair for temperature-compensated unambiguous 3D shape sensing
Bibliographic record
Abstract
A 10 mm-long three-dimensional shape sensor in a single-mode fiber is described and demonstrated experimentally. The sensor is based on a pair of fiber Bragg gratings inscribed at the same location along the fiber axis but offset along different radial directions away from the fiber center. Each offset grating generates cladding mode resonances over a ${\sim}{20}\;{\rm{nm}}$-wide spectral bandwidth, and the two gratings are also offset in period so that their transmission spectra are separated by 40 nm, and thus non-overlapping and fully distinguishable. Directional bending sensitivity results from the differential amplitude response of the cladding mode resonances from the two gratings, depending on the relative orientation of the bend with the azimuthal direction of the grating offsets. It is further demonstrated that both axial deformation and temperature have no influence on the shape measurement as they both only cause a global wavelength shift of the spectra without amplitude change. The experimental results demonstrate that the shape orientation of an object can be unambiguously determined for bend directions covering the full 360° range around the fiber axis with sensitivities of the order of ${{1}}\;{\rm{dB/}}{{\rm{m}}^{- 1}}$ and small curvatures between 0 and ${{1}}\;{{\rm{m}}^{- 1}}$.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".