Enhancing Resiliency of Integrated Space-Air Ground-Sea Networks with Renewable Energies: A Use Case After the 2023 Türkiye Earthquake
Bibliographic record
Abstract
Natural disasters can have catastrophic consequences; a poignant example is the series of 7.7- and 7.6-magnitude earthquakes that devastated Türkiye on February 6, 2023. To limit damage, it is essential to maintain the communications infrastructure to ensure individuals impacted by the disaster can receive critical information. The disastrous earthquakes in Türkiye have revealed the importance of considering communications and energy solutions together to build resilient and sustainable infrastructure. Thus, this article proposes an integrated space-air-ground-sea network architecture that utilizes various communications and energy-enabling technologies. This study aims to contribute to the development of robust and sustainable disaster-response frame-works. In light of the Türkiye earthquakes, two methods for network management are proposed. The first aims to ensure sustainability in the pre-disaster phase, and the second aims to maintain communications during the in-disaster phase. In these frameworks, communications technologies such as high altitude platform station(s) (HAPS), which are among the key enablers to unlock the potential of 6G networks and energy technologies, such as Renewable Energy Sources (RES), Battery Energy Storage Systems (BESSs), and Electric Vehicles (EVs), have been used as the prominent technologies. By simulating a case study, we demonstrate the performance of a proposed framework for providing network resiliency. The article concludes with potential challenges and future directions to achieve a disaster-resilient network architecture solution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".