Experimental Observation and Analysis of Ionosphere Echoes in the Mid-Latitude Region of China Using High-Frequency Surface Wave Radar and Ionosonde
Bibliographic record
Abstract
Ionospheric clutter is a major factor affecting the performance of high-frequency surface wave radar (HFSWR). Previous studies have been mainly focused on the development of ionospheric clutter suppression methods involving delicate signal processing techniques or additional antennas. However, ionospheric clutter originates from the interaction between HF waves and the ionosphere, thus it contains the characteristics of the latter. Therefore, ionospheric clutter can be analyzed to obtain the parameters of the reflecting ionosphere, expanding the value of HFSWR. This article presents the preliminary coordinated observation and analysis results of the characteristics of the ionosphere at mid-latitudes of China using both HFSWR and ionosonde. The results demonstrate the existence of an oblique skywave propagation path (0.5 jump, 1 jump, etc.) in addition to the vertical reflection path and ionosphere-ocean mixed path. HFSWR beams were also found split into an O-trace and an X-trace after entering the ionosphere. Furthermore, range-folded ionospheric echoes are related to the short-term thickening of the F2-layer, showing strong fluctuation in the range-Doppler spectrum of HFSWR. These observed characteristics of ionospheric echoes are useful for the development of an efficient ionospheric clutter suppression algorithm for HFSWR and further investigation of the ionospheric mechanisms at mid-latitudes.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".