A Distributed Pressure Measurement System Comprising Multiplexed In-Fibre Bragg Gratings Within a Flexible Superstructure
Bibliographic record
Abstract
In this work, a new distributed pressure measurement system is presented that is based on multiplexed in-fibre Bragg gratings housed within a flexible superstructure. The sensor superstructure comprises hypodermic tubing and several spacers and pressure diaphragms that together define the sensing locations and provide mechanical support to the Bragg gratings. Linear elasticity and strain-optic models are used to predict sensor performance in terms of sensitivity to hydrostatic pressure. Model predictions are validated through experimental calibration and indicate pressure sensitivities as high as 2.94 nm/MPa (MegaPascal) for a prototype with 1 mm outside diameter. The maximum measurement error from all calibration experiments is 3.4% of full-scale applied pressure. To the authors' knowledge, this is the smallest reported multiplexed pressure sensor that is based on Bragg gratings. Due to its small size, this sensor could potentially be applied in medical applications that existing sensors are too large for, specifically in angiography procedures for coronary arteries.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".