HERON: Demonstrating a Novel Biological Platform for Small Satellite Missions
Bibliographic record
Abstract
Long-duration deep space missions pose a significant health risk for both humans and their resident microorganisms. The GeneSat, PharmaSat and O/OREOS missions have previously explored biological questions regarding the effects of spaceflight on S. cerevisiase, B. subtilis, and E. coli. However, there currently exists both a knowledge and an accessibility gap in small satellite biological experiments. These payloads require precise instrumentation and complex platforms that are usually reserved for large research organizations. This makes it difficult for smaller organizations to perform biological research in low Earth orbit (LEO). To address these challenges, the University of Toronto Aerospace Team (UTAT) Space Systems Division is currently developing the HERON CubeSat. HERON houses a payload platform which measures the effects of the LEO environment on the gene expression and drug resistance of Candida albicans, a yeast commonly found in the human gut microbiome. Previous research has suggested that C. albicans might display increased pathogenicity and drug resistance in response to microgravity, which has important implications for long-duration human spaceflight. The yeast cells are housed in custom acrylic microfluidics chips containing 32 wells with channels for media and drug delivery. A measurement printed circuit board (PCB) contains custom optics capable of measuring minute changes in cell fluorescence. The entire payload stack is then housed in a temperature- and humidity-controlled 2U pressure vessel. Space Systems as a whole is an undergraduate student-led and student-funded design team, dedicated to the development of small satellite missions with a focus on education and undergraduate learning. HERON is scheduled to launch Q1 2022 into a Sun-synchronous orbit via a SpaceX Falcon 9 rocket at an altitude of approximately 550 km. Our platform is open-source and can serve as a low-cost template for future biological CubeSat missions. This paper serves as a technical and scientific description of the platform, along with the lessons learned during the payload design, assembly, and validation processes.
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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 | 0.001 |
| 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 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".