A Multichannel Spatial-Domain Fiber Cavity Ringdown Pressure Sensor
Bibliographic record
Abstract
We propose and demonstrate a multichannel spatial-domain fiber cavity ringdown (FCRD) pressure sensing scheme based on frequency-shifted interferometry (FSI). In contrast to existing multichannel FCRD techniques, multichannel FSI-FCRD measures intensity decay rates of continuous-wave (CW) light from different fiber ringdown cavities (RDCs) in the spatial domain, rather than those of pulse light in the time domain. It shares one CW light source, one slow detector and one slow data collector, which greatly reduces the system cost. We experimentally investigated a dual-channel FSI-FCRD pressure sensing system. The locations and pressures applied of the two FCRDs were obtained by measuring the corresponding ringdown distances. The measurement sensitivities were 0.030 (km-1· MPa-1) and 0.042 (km-1· MPa-1), with the minimum detectable pressure of 0.126 MPa and 0.403 MPa, respectively. By power budge analysis, the maximum sensor number was predicted to be 37 over a 50-km distance under the same experimental settings. The experimental and simulated results show that the proposed scheme has the advantages of low cost, high sensitivity, good linear response and good stability, which can enhance the multiplexing capacity and meet the requirements for multipoint measurement.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".