Self-organized synchronization based on a chirp-sequence waveform for an HF ocean radar network
Bibliographic record
Abstract
HFocean radars are usually installed along the coast and deliver remote sensing information by transmitting a radio signal with an operating frequency between 3 and 30 M1z. The frequency band allows for a large coverage of ocean surface that could extend more than 200 kilometers offshore depending on the transmit frequency and other operating conditions. To provide a dense coverage of the sea surface, the installation of ocean radars in a radar network may require that the transmitting and receiving units of a radar network demand time synchronization between the units to perform correct space-time measurements of ocean parameters. In this paper, a selforganized synchronization for 1W ocean radars is considered without the use of Global Positioning System (GPS) devices. In the case of multiple transmitters, the power peaks are observed in spectra simultaneously for each of the transmitters; hence the time shift can be estimated separately for each of them. The selforganized approach is very useful for the case of multiple transmitters and receivers in a radar network as well as in multiple-input-multiple-output (MIMO) radar configurations, which utilize the feasibility of occupying less space for a 1W radar receive antenna while maintaining the high spatial resolution of the radar data.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".