Analysis and quantification of laser-dazzling effects on IR focal plane arrays
Bibliographic record
Abstract
Today Optronic Countermeasure (OCM) concerns imply an IR Focal-Plane Array (FPA) facing an in-band laser irradiation. In order to evaluate the efficiency of new countermeasure concepts or the robustness of FPAs, it is necessary to quantify the whole interaction effects. Even though some studies in the open literature show the vulnerability of imaging systems to laser dazzling, the diversity of analysis criteria employed does not allow the results of these studies to be correlated. Therefore, we focus our effort on the definition of common sensor figures of merit adapted to laser OCM studies. In this paper, two investigation levels are presented: the first one for analyzing the local nonlinear photocell response and the second one for quantifying the whole dazzling impact on image. The first study gives interesting results on InSb photocell behaviors when irradiated by a picosecond MWIR laser. With an increasing irradiance, four different successive responses appear: from linear, logarithmic, decreasing ones to permanent linear offset response. In the second study, our quantifying tools are described and their successful implementation through the picosecond laser-dazzling characterization of an InSb FPA is assessed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".