Designing a GPS Receiver for the UNSW BlueSat Microsatellite
Bibliographic record
Abstract
This paper describes a GPS-based navigation system designed for the University of New South Wales (UNSW) BLUEsat microsatellite. BLUEsat is a research-focussed low earth orbit microsatellite project designed and built entirely by students. Through the use of a modified, off-the-shelf GPS receiver, position, velocity and time data can be robustly provided in a highly dynamic environment. To allow a standard GPS receiver to function in space, many software and hardware modifications are necessary. Hardware and software interfaces have been carefully designed to allow integration within the BlueSat project. A Mitel GPS-chipset Software Development Kit (SDK) was available. The SDK consists of all source code that “drives ” a GPS receiver, permitting all operations of the GPS receiver to be examined and, if necessary, permitting new algorithms for signal acquisition, tracking and navigation solution to be developed and tested. A modified Canadian Marconi Corporation AllStar GPS boardset was used as the baseline receiver hardware. The AllStar is based on a twelve-channel GPS chipset, consisting primarily of the GP2015 RF frontend and the GP2021 correlator chip. A 40MHz ARM60 microprocessor provides overall control of the system. Three RS-232 ports are available – two for general input/output and one for debugging. This hardware had to be interfaced to the main BlueSat computer. Techniques have been developed for processing the data and providing the system with navigation criteria. Risk mitigation is also examined. Limited testing has been performed, with the high velocity receiver able to robustly track GPS signals. The system’s firmware is currently being ported to a new generation hardware platform provided by the Australian company Signav Pty Ltd (SIGNAV, 2003). Further testing will be carried out when a multi-channel GPS constellation simulator becomes available. 1.
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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 | 0.000 | 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 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".