Bio-regenerative Life Support Systems (BLSSs) are artificial ecosystems that consist of multiple symbiotic relationships. BLSSs are crucial for sustaining long-duration space missions by facilitating food production and managing essential material cycles for respiratory air, water, biomass, and waste. The EDEN NEXT GEN Project, part of the EDEN roadmap at the German Aerospace Center (DLR), aims to develop a fully integrated ground demonstrator of a BLSS comprising all subsystems, with the ultimate goal of realizing a flight-ready BLSS within the next decade. This initiative builds upon insights from the EDEN ISS project, which investigated controlled environment agriculture (CEA) technologies for space exploration. EDEN ISS, a near-closed-loop research greenhouse deployed in Antarctica from 2017 to 2021, focused on crop production, including lettuces, bell peppers, leafy greens, and various herbs. To ensure the safe and stable operation of BLSSs, we explore methods to mitigate risks regarding system health, particularly regarding food production and nourishment shortages for isolated crews.

Separating signals from an additive mixture may be an unnecessarily hard problem when one is only interested in specific properties of a given signal. In this work, we tackle simpler "statistical component separation" problems that focus on recovering a predefined set of statistical descriptors of a target signal from a noisy mixture. Assuming access to samples of the noise process, we investigate a method devised to match the statistics of the solution candidate corrupted by noise samples with those of the observed mixture. We first analyze the behavior of this method using simple examples with analytically tractable calculations. Then, we apply it in an image denoising context employing 1) wavelet-based descriptors, 2) ConvNet-based descriptors on astrophysics and ImageNet data. In the case of 1), we show that our method better recovers the descriptors of the target data than a standard denoising method in most situations. Additionally, despite not constructed for this purpose, it performs surprisingly well in terms of peak signal-to-noise ratio on full signal reconstruction. In comparison, representation 2) appears less suitable for image denoising. Finally, we extend this method by introducing a diffusive stepwise algorithm which gives a new perspective to the initial method and leads to promising results for image denoising under specific circumstances.