Sailing the Cosmic Seas: Improving Dependability in IoT-Based Deep Space Exploration
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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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".