A Synchronous Transmission Method for Array Signals of Sensor Network under Resonance Technology
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
The traditional transmission methods for array signals face problems like signal loss and inaccurate output, due to the inadequacy of signal processing. To solve the problems, this paper presents a synchronous transmission method for array signals of sensor network under resonance technology. For better transmission efficiency, the array signals were collected through three-node collaboration in the sensor network, and denoised through wavelet transform. After that, the abnormal nodes in the sensor network were detected to improve transmission accuracy. On this basis, vibration frequency of the array signals was adjusted by the degree of harmonic vibration. Finally, the synchronous and accurate transmission of array signals was realized through normalization and adaptive solution of echo signals. Experimental results show that the proposed method achieved greater information throughput and higher transmission accuracy than traditional methods within the same time. Therefore, this research provides a highly applicable synchronous transmission method for array signals.
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 | 0.000 | 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) | 0.000 | 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