Technical and operational investigations of the real-time communication for robotic missions
Bibliographic record
Abstract
Robotic missions become more and more interesting for many applications in space. Especially there where human space flight is too expensive or not applicable, one is tempted to use robotic missions to reach the target. Whereas operations of such missions are maybe not as complex as human ones (no life support environment needed), they are still very challenging, especially for teams which until now worked mainly with non-robotic satellites. When talking about robotic mission operations, one needs to discuss in general some typical scenarios. This could include debris removal, refueling or in general on orbit servicing activities. Even all of them use in such or another way robotic fixtures, operational fingerprint may be different. Whereas one type of the mission needs short but intensive activity of the operations team, another one can be stretched in even years with short periods of activities only. The paper gives an overview of such missions and specific operational aspects. The GSOC prepares its infrastructure and operations for the upcoming and potential robotic missions. These preparations include wide spectrum of technical and operational investigations, as such missions impose many new requirements. One of areas which are especially important for the robotic mission operations is the communication chain. Aiming for the real-time telepresence, including haptic feedback and stereoscopic imaging, makes the communications essential for the mission. For the operator on the ground it is very important to have a feeling of immersion to perform all tasks. Not only the technical arrangement, but maybe even more importantly the operational environment, needs to fit to the requirements. Analyzed operational impacts include mission safety, operational procedures, priority regulations and training of the personnel. The analysis which we performed shows how challenging such setup could be. The results of the analysis are presented, together with a discussion on side aspects of such solutions and their influence on satellite operations. Further analysis directions are proposed. As a technical verification, we performed intensive investigations on a packet delay in IP networks. The measurement setup and overview of the results is shown as well. Also the analysis of usage of different off-the-shelf components (basebands, edge router) has been performed and the operational impact has been assessed. The tradeoffs between different software and hardware solutions are shown as well. Finally we spend some place on a proposal for a future mission operations concept with real-time communications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".