Dynamic calibration of the fiber-optic pressure sensor based on side-hole fiber
Bibliographic record
Abstract
This paper presents a dynamic pressure calibration of the fiber-optic interferometric sensor based on highly birefringent fibers. Earlier we tested other types of fiber optic sensors based on the same principle for measurements of static and quasi-static pressure. In order to be able to apply the sensor for measurements of fast pressure changes, the dynamic analysis is crucial due to occurrence of resonance phenomena and due to possibility of false pressure readings. We applied a static calibration procedure to initially determine the pressure sensitivity and temperature stability of the sensor. Next we compared the characteristics of the fiber-optic sensor to the responses of a calibrated piezoelectric dynamic pressure sensor at an operating range of 110 bar with a sampling rate equal to 200 kHz. The dynamic characteristics of the fiber-optic sensor are in good agreement with those of the reference piezoelectric sensor for the sine pulses of a full width at half of maximum higher than 150 ms. For the sine pulses faster of about one order high oscillations occur in the piezoelectric sensor while only slight oscillations in the fiber-optic sensor. It clearly shows that the highly birefringent fiber-optic sensors can be successfully applied for measurements of rapid pressure changes.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".