Mode-division and spatial-division optical fiber sensors
Bibliographic record
Abstract
The aim of this paper is to provide a comprehensive review of mode-division and spatial-division optical fiber sensors, mainly encompassing interferometers and advanced fiber gratings. Compared with their single-mode counterparts, which have a very mature field with many highly successful commercial applications, multimodal configurations have developed more recently with advances in fiber device fabrication and novel mode control devices. Multimodal fiber sensors considerably widen the range of possible sensing modalities and provide opportunities for increased accuracy and performance in conventional fiber sensing applications. Recent progress in these areas is attested by sharp increases in the number of publications and a rise in technology readiness level. In this paper, we first review the fundamental operating principles of such multimodal optical fiber sensors. We then report on the theoretical formalism and simulation procedures that allow for the prediction of the spectral changes and sensing response of these sensors. Finally, we discuss some recent cutting-edge applications, mainly in the physical and (bio)chemical fields. This paper provides both a step-by-step guide relevant for non-specialists entering in the field and a comprehensive review of advanced techniques for more skilled practitioners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".