Maritime air defence firing tactics
Bibliographic record
Abstract
A typical firing doctrine is the Shoot-Look-Shoot tactic. In this tactic, the defence launches a salvo of interceptors against the targets (Shoot), assesses the outcomes of the engagements (Shoot-Look), and launches another salvo (Shoot-Look-Shoot) if time and the inventory of interceptors permit. In the open literature, it is often assumed that the targets are identical. This is not always true as targets come in with different ranges, speeds, sizes, cross sections etc. In this paper, we consider two types of targets. Each type of target has a different number engagement opportunities due to their ranges and speeds. Through the use of dynamic programming, a genetic algorithm, and a recursive generating function, we determine the probability of raid annihilation (the probability of neutralizing all of the targets) for two different Shoot-Look-Shoot (SLS) tactics. The first SLS tactic is based on variable size salvos and maximizes the probability of raid annihilation (PRA) for heterogeneous targets. The second SLS tactic is based on fixed-size salvos and is robust as it is independent of the number and types of targets. Theoretical results are validated through some computer simulations.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".