High Performance, Low Cost Loran-C Cycle Identification and ECD Estimation
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
A Loran-C system is a hyperbolic navigation system which works based on time difference of arrival (TDOA). Cycle identification is the task of finding time of arrival of the incoming signal. Finding time of arrival of the signal needs choosing a reference point which is defined to be the third zero crossing of the signal. ECD is the varied Loran-C signal’s envelope from the original pulse. Cycle identification and ECD estimation accuracies have considerable effects on the Loran-C system receivers’ localization accuracy and the error in cycle identification or ECD estimation has direct effect on each other so it is important to estimate the reference point and ECD as precise as possible. In this paper, algorithms for cycle identification and ECD estimation are proposed.Furthermore, this paper addresses the problem of the reference points existence between two samples and proposes two algorithms to estimate the reference points time of arrival between two samples which leads to reach high accuracy using low sampling frequency. The simulation results show that the proposed methods for cycle identification and ECD estimation are robust in noisy conditions and intersample cycle identification algorithms give accurate estimate of the reference points between two samples.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 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 it