Bibliographic record
Abstract
Optical fiber based sensors offer several important advantages over electronic sensors, including low manufacturing cost, miniature and flexible structures, immunities to electromagnetic fields, and the capability of distributive and multi-parameter sensing on a single fiber. Extreme harsh environments such as temperature >800°C or as low as a few Kelvin, present unique challenges and opportunities to fiber optic sensors. For example, hydrogen gas leak detection in cryogenic environment is critically important in the production and use of liquid hydrogen fuels. But the sensitivity of conventional Palladium (Pd) coated hydrogen sensors degrade rapidly when temperature decreases. Another example is the quick diminishing of conventional type-I gratings with temperature range beyond 500°C, which prevent the FBG implementation in numerous high temperature applications. The objective of this thesis is to explore new fiber sensing technologies that have significant performance enhancements, or were previously not possible in extreme environment applications. Optically heated fiber sensors were developed for cryogenic Hydrogen gas and liquid level sensing in environments as well as room temperature gas flow sensing. Regenerated gratings were developed for high temperature pressure sensing. Novel in-fiber sensing techniques such as Rayleigh and Raman scattering were also exploited for fully distributed sensing operations. These technologies and devices offer reliable and flexible sensing solutions extreme environments in energy, transportation and telecom industry.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".