Overview of the commercial OPAL LiDAR optimized for rotorcraft platforms operating in degraded visual environments
Bibliographic record
Abstract
Neptec has developed a family of obscurant-penetrating 3D laser scanners called OPAL 2.0 that are being adapted for rotorcraft platforms. Neptec and Boeing have been working on an integrated system utilizing the OPAL LiDAR to support operations in degraded visual environments. OPAL scanners incorporate Neptec’s patented obscurantpenetrating LiDAR technology which was extensively tested in controlled dust environments and helicopters for brownout mitigation. The OPAL uses a scanning mechanism based on the Risley prism pair. Data acquisition rates can go as high as 200kHz for ranges within 200m and 25kHz for ranges exceeding 200m. The scan patterns are created by the rotation of two prisms under independent motor control. The geometry and material properties of the prisms will define the conical field-of-view of the sensor, which can be set up to 120 degrees. Through detailed simulations and analysis of mission profiles, the system can be tailored for applications to rotorcrafts. Examples of scan patterns and control schemes based on these simulations will be provided along with data density predictions versus acquisition time for applicable DVE scenarios. Preliminary 3D data acquired in clear and obscurant conditions will be presented.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.011 | 0.004 |
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".