Real-time distributed simulations in an HLA framework: Application to aircraft simulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents some ongoing research carried out in the context of the PRISE Project (Research Platform for Embedded Systems Engineering). This platform has been designed to evaluate and validate new embedded system concepts and techniques through a special hardware and software environment. Since much actual embedded equipment is not available, corresponding behavior is simulated within a high-level architecture (HLA) federation implemented with a run-time infrastructure (RTI) called CERTI and developed at ONERA. HLA is currently largely used in many simulation applications, but the limited performances of the RTIs raise doubts over the feasibility of HLA federations with real-time requirements. This paper addresses the problem of achieving real-time performances with the HLA standard. Several experiments are discussed using well-known aircraft simulators such as Microsoft Flight Simulator, FlightGear, and X-plane connected with the CERTI RTI. The added value of these activities is to demonstrate that according to a set of innovative solutions, HLA architecture is well suited to achieve hard real-time constraints. Finally, a formal model guaranteeing the schedulability of concurrent processes is also proposed.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it