Helicopter Flight Test of the Obscurant Penetrating Autosynchronous Lidar (OPAL) in Degraded Visual Environments: Part II - See-through Capability Assessment
Bibliographic record
Abstract
In support to the Directorate of Technical Airworthiness and Engineering Support (DTAES-6) of Canada's Department of National Defence (DND), DRDC Valcartier is currently evaluating the Obscurant Penetrating Autosynchronous Lidar (OPAL) developed by Neptec Technologies Corp. This sensor is targeted at supporting rotary-wing aircraft operating in degraded visual environments (DVE) during critical flight phases (i.e. take-off and landing). OPAL consists of a scanning pulsed lidar that generates 3D images of obstacles on the landing zone that are engulfed in an obscurant cloud. The sensor performance evaluation study is conducted in three phases under the MATPILA project (Multipurpose airborne 3D polarimetric imaging ladar assessment). Phase I focused on acquiring knowledge on brownout and whiteout phenomenologies and on defining proper testing conditions and performance metrics for Phase II. Phase II aimed at quantifying the sensor see-through capability as a function of optical depth and target reflectivity. Phase III consisted of in-flight validation trials. This paper presents Phase I to III with an emphasis on control environment test and on the in-flight validation trials.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".