A Line-of-Sight Light Antennation Measurement for Droplet sizing (LADS)
Bibliographic record
Abstract
We present a novel Line-of-Sight Light Antennation Measurement technique and system for Droplet Sizing (LADS). This method revolves around the measurement of attenuation responses and light scattering phenomena when a particle or droplet stream intersects with an incident light beam. The system processes recorded light scattering signals, analyzing their attenuation-time characteristics, and employing wavelet-based matching filter algorithms for precise attenuation-time transient extraction.The sizes of particles or droplets are determined by comparing these signals to a calibrated library containing signals corresponding to specific particle/droplet sizes. We specifically investigated the temporal intervals between rise and fall elements in the wavelet traces of spray signals. These traces, marked by identified matching coefficients, were meticulously compared using Continuous Wavelet Transform (CWT) to establish precise patterns. Particles falling within a predetermined threshold of 75% similarity, as determined by Multiresolution Analysis (MRA), were categorized as belonging to the same size spectrum and marked as "found." This research offers three folds of contributions: 1. We successfully developed and tested an innovative technique, allowing the conversion of low-resolution field samples into high-definition size distributions. 2. We extended the application of the offline droplet size distribution technique to field measurements. Additionally, we developed an algorithm capable of accurately determining in-situ parameters using an offline library. 3. In capturing the diffraction peaks, we employed wavelet derivatives to enable simultaneous scaling. Our system underwent rigorous testing on various droplet streams and liquid sprays. Validation was achieved by comparing its results with those obtained from a commercial droplet sizer, the Malvern Spraytec. LADS represents a straightforward technique that provides rapid analysis, rendering it suitable for the examination of diverse samples, including powders and liquids.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".