Designing an Integrated Database for Human Spaceflight Data Management
Bibliographic record
Abstract
The universe has fascinated mankind for thousands of years. At the beginning of the last century, manned space flight opened a new chapter in space exploration. The German Aerospace Center (DLR) and SVA System Vertrieb Alexander GmbH (SVA) are collaborating on the Mars Exploration Telemetry-driven Information System (METIS) research project to develop an artificial intelligence (AI)-based assistance system. Before METIS is used as part of a Mars mission, it will first be used as part of the Columbus operation. METIS can help Columbus flight controllers make faster decisions or get a better overview of ambiguous situations. In this way, METIS can make an important contribution to the safety of human spaceflight and facilitate the work of flight controllers. In my bachelor thesis I will design a NoSQL exchange database that combines several data sources of the Columbus module. The research question is: How can data from multiple sources be consolidated into a single database solution to enable effective data management for the operation of a Machine Learning (ML)-based human spaceflight system? Several NoSQL database technologies are evaluated and a database management system is selected. Based on the real data, a database design was created, which serves as \nthe basis for a first database prototype.
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.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.003 | 0.001 |
| 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 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".