A Simulation of Reflected ADS-B Signals over the North Atlantic for a Spaceborne Receiver
Bibliographic record
Abstract
Automatic Dependent Surveillance-Broadcast (ADS-B) is an air traffic surveillance technology in which aircraft broadcast position, identification and status an average of 6.2 times per second on 1090 MHz. The Royal Military College of Canada has developed an ADS-B receiver that is scheduled to fly as a technology demonstrator on the Canadian Advanced Nanospace eXperiment-7 (CanX-7) nanosatellite. A signal propagation model was developed to determine the power level and number of signals that will be detected by CanX-7. Since the ADS-B messages are alternately transmitted from upper and lower antennas, both the direct and reflected signals were considered. A simulation using the model was run over the North Atlantic with aircraft data supplied by air traffic services and a satellite altitude of 800 km. Power at the receiver for reflected ADS-B signals ranged from -109.5 to -118 dBm depending on aircraft-satellite geometry and was approximately 18 dBm less than the direct path signal strength. With a sensitivity of -103 dBm, the CanX-7 ADS-B receiver should detect virtually all of the direct path signals while reflected signals are below the detection threshold. Although the reflected signals should not be a factor for the CanX-7 mission, they could be a consideration for a large operational satellite with a more sensitive receiver. The reception of both direct and reflected ADS-B signals from multiple aircraft could lead to signal collisions and subsequent loss of aircraft tracking information, particularly in coastal regions where there are additional sources of the 1090 MHz signal.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".