NTL-Unet: A Satellite-Based Approach for Non-Technical Loss Detection in Electricity Distribution Using Sentinel-2 Imagery and Machine Learning.

This study introduces an orbital monitoring system designed to quantify non-technical losses (NTLs) within electricity distribution networks. Leveraging Sentinel-2 satellite imagery alongside advanced techniques in computer vision and machine learning, this system focuses on accurately segmenting urban areas, facilitating the removal of clouds, and utilizing OpenStreetMap masks for pre-annotation. Through testing on two datasets, the method attained a Jaccard index (IoU) of 0.

On the Analysis of Cryogels and Xerogels Using Cellulose Nanofibers and Graphene Oxide.

Aerogels are highly porous and ultralight three-dimensional materials with great potential for various applications. To obtain highly porous and structurally stable aerogels, a carefully designed synthesis process is required. These materials offer flexibility in manipulating their properties, allowing the incorporation of modifying agents according to specific needs.