A REVIEW OF RECONFIGURABLE INTELLIGENT SURFACES AND THEIRAPPLICATION TO MACHINE LEARNING-ASSISTED UNDERWATERCOMMUNICATIONS
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
Underwater communication systems face unique challenges that require advanced research and technologies. Environmental factors such as surface scattering, harsh sea conditions, water currents, and marine life can disrupt the propagation of acoustic signals. Integrating Reconfigurable Intelligent Surfaces (RISs) into underwater communication systems is a promising solution to address these challenges. RISs enhance signal propagation by creating optimal environments through passive beamforming and phase tuning, reducing scattering and absorption. This research proposes integrating RIS technology with machine learning (ML) techniques in underwater communication systems, leveraging recent advancements in both fields. This paper is the first to combine RIS technology with ML techniques in underwater communications while offering a comprehensive bibliometric analysis of RISs.
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.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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