Bibliographic record
Abstract
Simulation of multiple UAV platforms with both benign flights and malicious flights where UAV experiences GPS spoofing attack. Simulation environment follows standard Gazebo/PX4 setup (https://dev.px4.io/v1.9.0/en/simulation/gazebo.html). When the attack starts, normal GPS messages are stopped and injected messages are created and sent from the Gazebo environment to the autopilots GPS sensor for 30 seconds. An autonomous survey flight is conducted over the University (~20min flight time). Full flight plan is located in the dataset package as "OTU-Survey.plan". PX4 Autopilot (v1.10.1 stable) (https://px4.io) running on Pixhawk 4 flight controller. QGroundControl (v4.0.9) used for GCS (http://qgroundcontrol.com).Telemetry data is contained in TLOG files (https://ardupilot.org/copter/docs/common-mission-planner-telemetry-logs.html)Full flight data is contained in ULOG files (https://dev.px4.io/v1.9.0/en/log/ulog_file_format.html)CSVs can be extracted from logs.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.026 |
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".