Mid-wave and long-wave infrared signature model and measurement of power lines against atmospheric path radiance
Bibliographic record
Abstract
The signal to noise ratio and corresponding visibility of power cables as seen by military aircrafts is critical for crew safety. During low altitude operations, rotorcraft systems must be able to navigate these power lines during flight. Many of these military missions are flown at night which means the reflective bands including the visible, near infrared and short-wave infrared do not provide sufficient light. However, the emissive bands of the mid-wave infrared (MWIR) and long-wave infrared (LWIR) can be used to distinguish the location of these wires. LWIR sensors are typically used for pilotage applications. In both the LWIR and MWIR, the signal to noise depends on the wire emissivity and reflectivity as well as the ground and sky background path radiance. The signal to noise ratio is strongly dependent on the elevation of the viewing angle. In this paper, we model the signal to noise ratio as a function of elevation viewing angle using wire reflectivity and emissivity as well as MODTRAN calculations for path radiance. We also take MWIR and LWIR measurements to compare these two bands to the modelling results. We provide a summary of both model and measurements and make conclusions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".