In this paper are described measurements at high-altitude of both radiation environment and effects. These measurements comprise cosmic ray neutrons and SBU/MCU on nanoscales devices. Results obtained at Pic-du-Midi, France, and in the city of Puno, Peru, are presented and analyzed. Analyses and cross comparisons based-on MUSCA SEP³ calculations show a good agreement between experimental data and modeling, thus illustrating the importance of the knowledge of the radiation field for a reliable prediction.

Aquaculture plays a vital role in meeting global food demands, necessitating technological innovations for sustainable production. This study investigates deep learning-based semantic image segmentation for enhanced monitoring of rainbow trout (Oncorhynchus mykiss) in Puno, Peru. We conducted three experiments using UNET and UNETR architectures, varying image resolution, loss functions, and optimizers on a dataset of 1200 high-resolution images. Experiment 1, with UNET and 256 × 256 pixel images, achieved an IoU of 0.942854 after 20 epochs, using MSELoss and Adam, demonstrating superior segmentation accuracy. Experiment 2, utilizing UNET with 512 × 512 pixel images, resulted in an IoU of 0.803244 after 50 epochs, with L1Loss and Adam, indicating satisfactory performance despite increased complexity. Experiment 3, employing UNETR with 256 × 256 pixel images, yielded lower IoU scores, with a best IoU of 0.253928, highlighting the challenge of training Transformer-based models with limited data. A critical aspect of this study was the use of a coin as a scale reference in all experiments, enabling precise conversion of pixel measurements to physical dimensions. This, combined with OpenCV for contour detection, allowed for accurate fish size estimations, validated by comparisons with real images. The results underscore UNET’s effectiveness for aquaculture image segmentation, while also emphasizing data requirements for UNETR. This approach provides a non-invasive, automated method for monitoring fish growth and health, contributing to sustainable aquaculture practices.