Environmental issues become an increasing global concern because of the continuous pressure on natural resources. Earth observations (EO), which include both satellite/UAV and in-situ data, can provide robust monitoring for various environmental concerns. The realization of the full information potential of EO data requires innovative tools to minimize the time and scientific knowledge needed to access, prepare and analyze a large volume of data. EO Data Cube (DC) is a new paradigm aiming to realize it. The article presents the Swiss-Armenian joint initiative on the deployment of an Armenian DC, which is anchored on the best practices of the Swiss model. The Armenian DC is a complete and up-to-date archive of EO data (e.g., Landsat 5, 7, 8, Sentinel-2) by benefiting from Switzerland’s expertise in implementing the Swiss DC. The use-case of confirm delineation of Lake Sevan using McFeeters band ratio algorithm is discussed. The validation shows that the results are sufficiently reliable. The transfer of the necessary knowledge from Switzerland to Armenia for developing and implementing the first version of an Armenian DC should be considered as a first step of a permanent collaboration for paving the way towards continuous remote environmental monitoring in Armenia.

HPC clouds may provide fast access to fully configurable and dynamically scalable virtualized HPC clusters to address the complex and challenging computation and storage-intensive requirements. The complex environmental, software, and hardware requirements and dependencies on such systems make it challenging to carry out our large-scale simulations, prediction systems, and other data and compute-intensive workloads over the cloud. The article aims to present an architecture that enables HPC workloads to be serverless over the cloud (Shoc), one of the most critical cloud capabilities for HPC workloads. On one hand, Shoc utilizes the abstraction power of container technologies like Singularity and Docker, combined with the scheduling and resource management capabilities of Kubernetes. On the other hand, Shoc allows running any CPU-intensive and data-intensive workloads in the cloud without needing to manage HPC infrastructure, complex software, and hardware environment deployments.

Unmanned aerial vehicle (UAV) swarms have gained significant attention for their potential applications in various fields. The effective coordination and control of UAV swarms require the development of robust mathematical models that can capture their complex dynamics. The paper introduces mathematical models and relevant paradigms based on the design and analysis of self-organizing swarms of UAVs. The logical and technological construction of the model relies on the theorems developed by authors for obtaining full information exchange during the swarm quasi-random walk. The suggested rotor-router model interprets the discrete-time walk accompanied by the deterministic evolution of configurations of rotors randomly placed on the vertices of the swarm graph. The recommended optimal and fault-tolerant gossip/broadcast schemes support the resilience of swarm to internal failures and external attacks, and cryptographic protocols approve the security. The proposed cloud network topology serves as the implementation framework for the model, encompassing various connectivity options to ensure the expected behavior of the UAV swarms.

Unmanned aerial vehicles (UAV) swarms offer a cost-effective, time-efficient data collection and analysis solution across various applications. The study presents a cutting-edge self-organizing UAV swarm simulation platform empowered by collective artificial intelligence designed to facilitate terrain monitoring and optimize task performance using a fleet of UAVs. The cloud-based multi-user platform provides users with interactive features for seamless user collaboration and real-time video viewing for collective exploration of dynamic terrain imagery, allowing users to generate requests seamlessly from the QT interface. The UAV map configurator facilitates the creation and modification of UAV swarm navigation maps, optimizing their behavior and performance. Additionally, the parameter gossip system fosters communication and coordination among swarm members, while the QT service layer ensures secure data transfer to cloud servers. This integrated data fuels the formation of essential swarm and target tasks, determining key parameters such as swarm participant count, initial relative coordinate positions, and statuses (imager and/or strike). The server employs advanced algorithms to achieve these functionalities, including the research road graph based on the rotor-router model and the comprehensive information exchange graph using the gossip/broadcast model. These algorithms work synergistically within the server environment, enabling efficient task planning and coordination among the UAV swarm. Furthermore, the platform allows for the seamless transmission of the formed target tasks to the memory of individual swarm participants, enhancing their decision-making capabilities and overall swarm performance.