Securing Underwater Wireless Communication with Frequency-Hopping Spread Spectrum
Bibliographic record
Abstract
The significance of subsea communication has increased substantially, with growing interest in replacing wired communication with wireless alternatives. However, subsea communication faces several challenges due to the harsh environment, including high noise, significant multipath propagation, signal attenuation, salinity, and temperature variations. Additionally, it is vulnerable to cyber-attacks. Ensuring secure and reliable communication for authorized users remains a significant challenge, requiring an integrated approach that balances efficiency, stability, and security considerations. Current efforts primarily focus on designing multimodal wireless communication systems, but integrating robust security measures into subsea wireless infrastructure is challenging due to its inherently open nature and susceptibility to external interference. Technical advancements are necessary to strengthen the security of subsea wireless communication while managing power loss effectively. To address these challenges, we propose a mechanism that implements a pre-determined frequency hopping schedule, enhancing security and improving reliability through periodic changes in transmission frequency. The hopping sequence is shared in advance among authorized parties. While the current system does not adapt to real-time threats, future work could explore dynamic adjustments in response to detected threats or unauthorized access attempts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".