Bibliographic record
Abstract
This paper reports on a cross-domain communication system for marine robots based on code-division multiple access (CDMA). The underwater communications channel is a difficult transmission medium due to its link quality variability which depends on location and the application. It also depends on environmental parameters which can vary temporally from hours to days to months to season. Understanding the channel characteristics for data transmission is essential to the development and evaluation of existing or new MAC and Routing Level protocols which better utilize the limited resources within this unpredictable channel. CDMA is the most promising physical layer and multiple access technique for underwater acoustic sensor networks because it: i) is robust to frequency selective fading, ii) compensates for multi-path effects at the receiver, and iii) allows receivers to distinguish among signals simultaneously transmitted by multiple devices. For these reasons, CDMA increases channel re-use and reduces packet retransmissions, which results in decreased energy consumption and increased network throughput. Development of a framework for autonomous co-ordination and networking of marine robot teams from extended ranges to transmit images/information from underwater to above water is proposed using CDMA which is a complete stack of communications protocol from the physical to the application layer for marine robots that spans multiple domains, i.e. under, on and above water. Simulation of the proposed network was performed using network simulator-3 (NS-3). The proposed protocol edge CDMA properties provide multiple access to the limited underwater bandwidth whereas previous reported work only considered CDMA for the physical layer. Simulations show the proposed protocol outperforms existing underwater acoustic network protocols.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".