Columbus Ground Systems: What Current Operator Interfaces Can Teach Us About Efficiency, Effectivity and Worker Satisfaction for Future Astronautical Exploration Missions
Bibliographic record
Abstract
The Columbus module of the International Space Station (ISS) is one of Europe's most significant contributions to astronautical space exploration today. Columbus was launched on February 7th, 2008, docked to the ISS a few days later on February 11th and has been a part of the ISS since then. On behalf of the European Space Agency (ESA), Columbus is currently operated from the Columbus Control-Center (Col-CC), which is part of the German Space Operations Center (GSOC), at the German Aerospace Center (DLR e.V.) near Munich, Germany. All software used at Col-CC for operations supports the flight controllers to perform their tasks on console effectively, efficiently and to their own satisfaction. In doing so, the used software should consider relevant human factors in this context, specifically situational awareness, workload, human error and multitasking. The existing software covers those aspects to some extent, but no formal analysis of the human factors has been conducted with the current version of the ground software before. Therefore, the goal of this work is to identify which parts of the ground software lack in these aspects and how to optimize the ergonomics of the software, while also enabling the flight controllers to maintain a high level of situational awareness, do multi-tasking, and handle the corresponding workload on console. For that purpose, this paper is logically divided into two parts. The first part highlights how the Columbus module of the ISS is operated nominally and which ground tools are currently used. The second part of this paper highlights an empirical study that was conducted at Col-CC, identifying areas of improvements in terms of effectivity, efficiency and worker satisfaction, when flight controllers are performing nominal scheduled on-board activities. The study was conducted as semi-structured interviews with 13 flight controllers at Col-CC, which were subsequently analysed, using qualitative content analysis. This study is first and foremost supposed to help improve current operator interfaces in use at Col-CC. Furthermore, since the Lunar Gateway is also going to be operated from GSOC, the findings will provide useful insights for the design of future operator interfaces, as well as for further developments in the context of astronautical space exploration missions. Finally, with the recent emergence of Artificial Intelligence (AI) and Machine Learning (ML), the way operations are performed today, is about to be revolutionized. Intelligent assistant systems, like the Mars Exploration Telemetry-driven Information System (METIS) are bound to not only improve what the operators can do, but also how they do it. This work will give first hints for improvements of operator interfaces, in order to facilitate these new AI/ML capabilities, while also allowing operators to perform at the same or at a better level than before.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".