A simulation System of Systems to assess military aircraft protection
Bibliographic record
Abstract
A simulation-based System of Systems (SoS), called a Virtual Proving Ground (VPG), is being developed as a comprehensive and robust approach to support aircraft protection engineering and training. For more than 20 years, laboratories and specialized equipment were developed and operated for the evaluation of aircraft self-protection. Most of these systems were originally designed on a standalone basis to answer specific questions. On the other hand, operational requests followed an ad-hoc process that did not allow traceability and knowledge management. As these systems are complementary, their integration came as a logical path to increase the capability. This paper presents an overview of the VPG SoS, including the individual systems, the common architecture framework and the services that are key to a comprehensive approach: collaborative tools and computer-assisted processes. The paper also introduces the SoS inner capability to evolve and discusses the benefits of the approach. The VPG links hardware-in-the-loop (HWIL) systems within a common virtual environment. The simulation includes not only services like time and error management, but also a robust execution engine that allows the execution of a broad variety of digital models. It was designed to facilitate reusability by flexible composition schemes and standardized interfaces. Like building blocks, the SoS allows the substitution of digital models with their HWIL counterparts. The decision to use a digital or hardware component must be based on the requirements for a specific level of fidelity or performance. The SoS extends beyond the sole integration of software and hardware systems. It also includes a set of collaborative tools with associated processes, and implements an overarching verification and validation methodology. The collaborative tools are developed to facilitate the communication and interaction, and bring a cultural change within the operational community. More than just a database or a portal, it unifies the way requests are managed by the stakeholders by clarifying the roles and responsibilities; it centralizes data from databases that were up to now located in different places; and finally, it greatly fosters knowledge management and robust problem solving within the organization.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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".