Development of Plug-N-Play (Flight) Control Systems for Responsive Spacecraft
Bibliographic record
Abstract
[Abstract] The Operationally Responsive Spa ce (ORS) program envisions building a 6 day to support fast changing tactical missions. A key requirement would be the ability to rapidly compose the spacecraft that would perform both the needed mission - and spacecraft -oriented functionality using (PnP) enabled spacecraft components. To address this need, a service -oriented spacecraft architectural model is under development as part of the Air Force Research Laboratory (AFRL) Responsive Space Testbed effort to provide a reusable , reference infrastructure for Responsive Space. The Lockheed Martin ATC is pursuing the development of a Java -based distributed architecture environment that supports this service -oriented, reference spacecraft architectural model. A key component of th is approach involves a simulation architecture that is based on spacecraft services, much like the service -oriented models now widely used in the consumer marketplace, but is an evolutionary step to extend simulation to operations. To span the entire range from simulation to operations seamlessly, a single vertically integrated software architecture is needed. The Java -based distributed architecture provides such an environment with its evolution from desktop, to enterprise, to mobile devices, and now to real time systems. The Java environment addresses the complexity needed for operational simulations and ultimate deployment for integrated spacecraft flight and payload control systems. The Real Time Specification for Java (RTSJ) supports hard real time, s oft real time and non -real time processes all interoperating within the same virtual machine. Initial prototyping is being done using IBM's Real Time Java (RTJ) implementation of the RTSJ. The Java platform support several libraries, and one in particular, the JINI protocol, which supports the operation of dynamically changing networks of distributed services (and devices). Using JINI, running as a non -real time process within IBM's RTJ environment, provides the rich set of Plug -N-Play capabilities needed to demonstrate both automatic configuration, as would be needed for Assembly, Integration and Test (AI&T), as well as for operational fault tolerance and reconfigurability needed for on -orbit operations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".