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.
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 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".