Advanced Integrated-Optic and MNT Technologies for the Infrared Spectral Monitoring of Spacecraft Vital Life-Support Systems and Remote Astronaut Health Diagnostics
Bibliographic record
Abstract
Manned space systems have many requirements for the monitoring of vital life support systems including quality of cabin air and the recycled water supply, as well as direct monitoring of vital indicators of astronaut health. Infrared (IR) spectroscopy is an attractive monitoring technique because it requires minimal consumables while providing relatively high chemical specificity for the detection of a wide variety of biochemicals using the characteristic vibrational modes of chemical bonds. For space-based systems, the important drivers are reliability, power consumption, mass and simplicity of operation. MPB has advanced its IOSPEC™ technology for miniature integrated IR spectrometers to provide performance comparable to large bench-top IR systems but in a compact and ruggedized footprint weighing under 2.5 kg. Relatively high spectral resolution (4 to 8 nm) over broad spectral ranges exceeding 4000 nm is provided with low background signal scattering (<0.05%) using precision master gratings and a proprietary IR integrated waveguide structure. This paper considers the use of advanced smart thin-film technologies and MEMS integrated-optics for active binary-coded optical processing to extend the performance attainable for miniature IR spectrometer systems. These new technologies facilitate relatively independent optimization of the spectrometer system spectral resolution and optical throughput. By coupling the IR measurement system to additional micro-nano technologies for microfluidics, a compact, robust system can be realized for online fluid analysis. Dedicated fiber-optic sensors can be used to supplement the IR measurements to provide additional data, such as pH level and temperature measurements of the liquid samples.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".