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The employment of technology and digitization is crucial for cultural organizations to establish and sustain digital repositories for their cultural heritage artifacts. This exploitation is also essential in facilitating the presentation of cultural works and exhibits to a broader audience. Consequently, in this work, we propose a custom-developed digital repository that functions as software-as-a-service (SaaS), primarily promoting the safe storage, display, and sharing of cultural materials; enhancing accessibility; and fostering a deeper understanding and appreciation of cultural heritage. The proposed digital repository service is designed as a multitenant architecture, which enables organizations to expand their reach, enhance accessibility, foster collaboration, and ensure the preservation of their content. Moreover, our technology stack incorporates robust and reliable backend technologies, such as Django, to ensure data security and efficient management. Meanwhile, the frontend is powered by Angular, which guarantees a user-friendly and engaging interface for exploring and interacting with cultural materials. Specifically, this project aims to assist each cultural institution in organizing its digital cultural assets into collections and feeding other digital platforms, including educational, museum, pedagogical, and games, through appropriate interfaces. The creation of this digital repository offers a cutting-edge and effective open-access laboratory solution. It allows organizations to have a significant influence on their audiences by fostering cultural understanding and appreciation. Additionally, it facilitates the connection between different digital repositories and national/European aggregators, promoting collaboration and information sharing. By embracing this innovative solution, cultural institutions can benefit from shared resources and features, such as system updates, backup and recovery services, and data analytics tools, attributes that are currently provided by the platform. [ABSTRACT FROM AUTHOR]

The cooperative aerial and device-to-device (D2D) networks employing non-orthogonal multiple access (NOMA) are expected to play an essential role in next-generation wireless networks. Moreover, machine learning (ML) techniques, such as artificial neural networks (ANN), can significantly enhance network performance and efficiency in fifth-generation (5G) wireless networks and beyond. This paper studies an ANN-based unmanned aerial vehicle (UAV) placement scheme to enhance an integrated UAV-D2D NOMA cooperative network.The proposed placement scheme selection (PSS) method for integrating the UAV into the cooperative network combines supervised and unsupervised ML techniques. Specifically, a supervised classification approach is employed utilizing a two-hidden layered ANN with 63 neurons evenly distributed among the layers. The output class of the ANN is utilized to determine the appropriate unsupervised learning method—either k-means or k-medoids—to be employed. This specific ANN layout has been observed to exhibit an accuracy of 94.12%, the highest accuracy among the ANN models evaluated, making it highly recommended for accurate PSS predictions in urban locations. Furthermore, the proposed cooperative scheme allows pairs of users to be simultaneously served through NOMA from the UAV, which acts as an aerial base station. At the same time, the D2D cooperative transmission for each NOMA pair is activated to improve the overall communication quality. Comparisons with conventional orthogonal multiple access (OMA) and alternative unsupervised machine-learning based-UAV-D2D NOMA cooperative networks show that significant sum rate and spectral efficiency gains can be harvested through the proposed method under varying D2D bandwidth allocations. [ABSTRACT FROM AUTHOR]