Speed and Maneuverability Benefits of Sikorsky's X2 Technology versus Air-Defense Artillery
Bibliographic record
Abstract
An aircraft's survivability in a hostile environment is a mixture of factors that stem from both susceptibility and vulnerability. Conducting analyses that incorporate these factors into a blended solution is vital. One such analysis was conducted using the government-provided Air-Defense Artillery (ADA) simulation tool Radar Directed Gun System (RADGUNS). RADGUNS provides a three-dimensional engagement space to conduct one versus one encounters against Radio Frequency (RF) guided threats. Complex user-generated flight paths can be simulated with varying relative starting locations of the aircraft relative to the threat being considered. The simulations conducted incorporated various aircraft parameters. Aircraft velocity, acceleration rates, deceleration rates, vulnerable area, and radar cross section (RCS) were the primary parameters whose effects were investigated. For each encounter, the Probability of Hit (P H) and Probability of Kill given a Hit (P K|H) were calculated, accounting for the susceptibility and vulnerability segments of the kill chain, respectively. A holistic metric of the kill chain, Probability of Kill (PK), and Engagement Time were the primary results of each engagement. Varying prominent aircraft input parameters can provide key insights in the prediction of an aircraft's survivability. This paper will focus on the benefits of speed and maneuverability, obtainable with Sikorsky's X2 Technology™ in the realm of survivability versus radar and human guided threats.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".