Implementing a Small Satellite Information Enterprise Using a Modular Open Architecture Approach Based on International Standards
Bibliographic record
Abstract
Today each Small Satellite system has a “stove pipe” approach to exposing their requests for tasking and sensor observations / products to the end user. This approach is cost effective from the satellite operator perspective but from an end user perspective the costs can get very expensive when integrating multiple satellite systems, airborne and in-situ sensors. The Group on Earth Observations (GEO) disaster management mission space is an example where multiple sensor systems are needed and integrating the data is critical piece for mission success. GEO and the Small Satellite community can utilize an existing Open Geospatial Consortium (OGC®) Sensor Web Enablement (SWE) international standard, started in 1999, to address and reduce integration costs with new and legacy end-user systems. The OGC® mission is “To serve as a global forum for the collaboration of developers and users of spatial data products and services, and to advance the development of international standards for geospatial interoperability.” This paper makes recommendations for moving forward and provides details on how to reduce costs of implementing the OGC® SWE though government and commercial open software efforts. For additional interoperability improvements we also seek to advance the OGC® SWE standards to meet mission requirements. Encourage the use of OGC® SWE standards over proprietary solutions throughout the Small Satellite Community to expose their sensor observations, request collection, provide feasibility analysis, and collection request tracking to new and legacy systems, thus enabling a federated Small Satellite information enterprise.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".