Improved positioning criterion and imaging scheme of deep UV planar laser-induced fluorescence technology
Bibliographic record
Abstract
In summary, we have developed a physical model to describe the evolution of the nitric oxide-based planar laser-induced fluorescence technology (NO-PLIF) signal in a high-enthalpy wind tunnel and analyzed the factors that affect the contrast of fluorescent stripes obtained from two successive ultraviolet (UV) laser exposures. Based on this, we improved the flow velocity calculation algorithm with a centroiding positioning criterion, which is more accurate for flows with velocities of above 3 km/s and has better adaptability to background noises compared to the conventional peak value positioning criterion. Meanwhile, we have identified that a shortened frame interval is an effective means to improve the measurement accuracy of a hypervelocity flow, where a dual-frame high-speed UV imaging system is developed with a frame interval that is two orders of magnitude shorter than existing UV cameras. Consequently, transient N 2 flows with velocities ranging from 3.2 to 5.2 km/s were successfully measured. Further efforts will pay on extending the proposed positioning criterion to the cross-correlation algorithm for higher velocity measurement accuracy.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".