Design and Implementation of a Tuning-Matching Framework for a High-Sensitivity Broad Band Proton Precession Magnetometer Sensing Coil
Bibliographic record
Abstract
The proton precession magnetometer (PPM) is always employed for geomagnetic observation, magnetic anomaly detection, etc., while the tuning performance of the PPM determines the signal-to-noise ratio (SNR) of the free induction decay (FID) signal output from the sensing coil. A tuned circuit was always used for the PPM sensing coil because of its narrow-band characteristic and high sensitivity. However, the FID signals might be distorted by the narrow bandwidth, which influences the PPM's measurement accuracy for the geomagnetic field. To address this problem, we propose a tuning-matching framework consisting of an adjustable tuning capacitor network (ATCN) in parallel with a π-type LC band-pass filter (LCBF), dubbed ATCN-LCBF. The equivalent circuit of the ATCN-LCBF are investigated and the corresponding electrical parameters are theoretically calculated. Consequently, the proposed ATCN-LCBF is conducted with a bandwidth of 3500 Hz, a pass-band gain of 5 dB, and an average noise floor of 1.67 nV/Hz <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2</sup> . The field experimental results demonstrate that the ATCN-LCBF can not only enhance the strength of the FID signal but also suppress the external noises, improving the signal detection performance of the PPM in a scenario with strong interference.
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 | 0.001 | 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 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".