For countering nuclear proliferation, satellite imagery is being used to monitor suspicious nuclear activities in inaccessible countries or regions. Monitoring such activities involves detecting changes over time in nuclear facilities and their surroundings, and interpreting them based on prior knowledge in terms of nuclear proliferation or weaponisation. Therefore, analysts need to acquire and analyze satellite images periodically and have an understanding of nuclear fuel cycle as well as expertise in remote sensing. Meanwhile, as accessibility of satellite information has been increasing and accordingly a large amount of high-resolution satellite images is available, a lack of experts with expertise in both fields to perform satellite imagery analysis is being concerned. In this regard, the Institute of Korea Nonproliferation and Control (KINAC) has developed a prototype of semi-automatic satellite imagery analysis system that can support monitoring of potential nuclear activities to overcome the limitations of professionals and increase analysis efficiency. The system provides a satellite imagery database that can manage acquired images, and the users can load images from the database and analyze them in stages. The system includes a preprocessing module capable of resizing, correcting and matching images, a change detection module equipped with a pixel-object-based change detection algorithm for multi-temporal images, and a module that automatically generates reports with relevant information. In particular, this system continuously updates open-source information database related to potential nuclear activities and provides users with an integrated analytics platform that can support their interpretation by linking related images and textual information together. As such, the system could save time and cost in processing and interpreting satellite images by providing semi-automated analytic workflows for monitoring potential nuclear activities.

The acoustic emission (AE) is proposed as a feasible method for the real-time monitoring of the structural damage evolution in concrete materials that are typically used in the storage of nuclear wastes. However, the characteristics of AE signals emitted from concrete structures subjected to various environmental conditions are poorly identified. Therefore, this study examines the AE characteristics of the concrete structures during uniaxial compression, where the storage temperature and immersion conditions of the concrete specimens varied from 15℃ to 75℃ and from completely dry to water-immersion, respectively. Compared with the dry specimens, the water-immersed specimens exhibited significantly reduced uniaxial compressive strengths by approximately 26%, total AE energy by approximately 90%, and max RA value by approximately 70%. As the treatment temperature increased, the strength and AE parameters, such as AE count, AE energy, and RA value, of the dry specimens increased; however, the temperature effect was only minimal for the immersed specimens. This study suggests that the AE technique can capture the mechanical damage evolution of concrete materials, but their AE characteristics can vary with respect to the storage conditions.