Sailing the Cosmic Seas: Improving Dependability in IoT-Based Deep Space Exploration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As humanity takes its next giant leap in the pursuit of becoming a multi-planetary species, we must develop new foundational communication technologies to facilitate space exploration. To truly scale space exploration missions, the dollar cost must drastically decrease. To that end, planetary telemetry data can be gathered using large-scale, low-cost, and resourceconstrained direct-to-satellite IoT (DtS-IoT) deployments. The IoT devices continuously sense and transmit data to orbiting small satellites and nanosatellites. Utilizing the delay and disruption tolerant networking (DTN) paradigm, these satellites can route data to Earth through inter-satellite links and the interplanetary network. Analysis of this data will enable the development of colonization feasibility models for different planets. We propose creating software-based optimizations to improve the dependability of IoT-based space exploration missions. By targeting lifetime, resource constraints, and reliability, these optimizations aim to reduce the annual cost of operation, enabling massively scaled and economically feasible exploration. This work contributes to the future Solar System Internet, interconnecting humans across planets, natural satellites, and spacecraft.
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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 | 0.000 | 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 it