Space Situational Awareness – Key to Sustainability in Space and on Earth (Presentation Slides)
Bibliographic record
Abstract
Sustainable development on earth is directly related to space sustainability. It is an accepted fact today that human beings are dependent on space activities and space technology for day-to-day functioning on earth, such as communications, disaster management, earth observation and weather forecasting. Space technology also plays an important role in sustainable development in earth and in giving effect to the sustainable development goals of 2030. For example, several NewSpace actors are working towards improving communication networks to provide connectivity to populations to remote and rural areas both in developed and developing countries. However, to ensure that space technology can continue to contribute to sustainable development in earth, it is necessary to preserve the space environment. Space sustainability is dependent on our knowledge of positions of space objects in space. Ability to see what is happening in space or space situational awareness (SSA) is the first step towards developing comprehensive mechanism for space sustainability. At present, most States and private operators are dependent on the United States (USA) for SSA data. Though, India, China and Russia have some SSA capabilities, the USA still is undisputed leader in SSA capabilities and there is heavy reliance on USA for the SSA data. However, due to both technological as well as political considerations, it is not prudent for an entire industry to be dependent on one actor for vital SSA data. As we saw with the 2009 Iridium-Cosmos collision, the USA was not able to predict the collision of its operational maneuverable satellite with defunct Russian satellite, despite its stellar SSA capabilities. The fact is with the present level of technology, it is not possible for any one State to ubiquitously track all satellites persistently at all times. Further, only the satellite owner-operators have the most accurate real-time data of the location of their satellite. That the USA’s SSA data is not completely accurate is evidenced by the fact that an Intelsat study concluded that the collision warnings provided by the USA military had nearly a 50 % false positive rate (half of the warnings were issued when there was not actually a potential collision) and a 50% false negative rate (warnings were not issued for half of the actual close approaches). It is to be remembered that maneuvering satellites needs utilization of fuel, which is a limited resource and hence false positive warnings may limit the lifetime of a satellite. Further, collision warning if not issued may result in destruction of a space object creating debris, which may lead to exponential collisions making an entire orbit unusable. Therefore, in order to preserve the space environment, it is necessary for all actors involved in space activities to co-operate and enter into partnerships. Space sustainability is the key space industry as well as survival of humans on earth.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".