Abstract
Findings of Iron Age metallurgical activities related to tin metal and mining are very rare. In the present work, we present a detailed study of the Outeiro de Baltar hillfort, dated to the Late Iron Age/Early Roman period, located in a place where 20th century tin mining work took place. Elemental and microstructural analysis by portable, micro and wavelength dispersive X-ray fluorescence spectrometry (pXRF, micro-XRF and WDXRF) and scanning electron microscopy with energy dispersion spectrometer (SEM-EDS) showed that metallurgical debris found at the archaeological site is related to tin smelting and binary and ternary bronze productions. Analysis of the artefacts of diverse typologies found at the site showed that a variety of metals and alloys were in circulation and use. Samples of tin ores (cassiterite) from the region were analyzed for comparison with an archaeological tin slag from the site. The analytical results point to the production of tin metal using local cassiterite and the production of bronze by directly adding cassiterite into a smelting process. Furthermore, data of remote sensing (airborne Light Detection and Ranging (LiDAR) and historical aerial imagery) and Geographical Information System (GIS) mapping were combined with archival mining documentation and maps to retrieve a landscape context for the site. The study showed that the place of the Outeiro de Baltar hillfort (NW Iberia) was mined periodically over time.

Abstract
The ELASTIC European project addresses the emergence of extreme-scale analytics, providing a software architecture with a new elasticity concept, intended to support smart cyber-physical systems with performance requirements from extreme-scale analytics workloads. One of the main challenges being tackled by ELASTIC is the necessity to simultaneously fulfil the non-functional properties inherited from smart systems, such as real-time, energy efficiency, communication quality or security. This paper presents how the ELASTIC architecture monitors and manages such non-functional requirements, working in close collaboration with the component responsible for the orchestration of elasticity. © 2022, Ada-Europe. All rights reserved.

Abstract
Modern organizations face numerous physical security threats, from fire hazards to more intricate concerns regarding surveillance and unauthorized personnel. Conventional standalone fire and intrusion detection solutions must be installed and maintained independently, which leads to high capital and operational costs. Nonetheless, due to recent developments in smart sensors, computer vision techniques, and wireless communication technologies, these solutions can be integrated in a modular and low-cost manner. This work introduces Integrated Physical Security System (IP2S), a multi-agent system capable of coordinating diverse Internet of Things (IoT) sensors and actuators for an efficient mitigation of multiple physical security events. The proposed system was tested in a live case study that combined fire and intrusion detection in an industrial shop floor environment with four different sectors, two surveillance cameras, and a firefighting robot. The experimental results demonstrate that the integration of several events in a single automated system can be advantageous for the security of smart buildings, reducing false alarms and delays.

Abstract
Billions of interconnected Internet of Things (IoT) sensors and devices collect tremendous amounts of data from real-world scenarios. Big data is generating increasing interest in a wide range of industries. Once data is analyzed through compute-intensive Machine Learning (ML) methods, it can derive critical business value for organizations. Powerful platforms are essential to handle and process such massive collections of information cost-effectively and conveniently. This work introduces a distributed and scalable platform architecture that can be deployed for efficient real-world big data collection and analytics. The proposed system was tested with a case study for Predictive Maintenance of Home Appliances, where current and vibration sensors with high acquisition frequency were connected to washing machines and refrigerators. The introduced platform was used to collect, store, and analyze the data. The experimental results demonstrated that the presented system could be advantageous for tackling real-world IoT scenarios in a cost-effective and local approach.

Abstract
As IoT technologies mature, they are increasingly finding their way into more sensitive domains, such as Medical and Industrial IoT, in which safety and cyber-security are paramount. While the number of deployed IoT devices continues to increase annually, they still present severe cyber-security vulnerabilities, turning them into potential targets and entry points to support further attacks. Naturally, as these nodes are compromised, attackers aim at setting up stealthy communication behaviours, to exfiltrate data or to orchestrate nodes of a botnet in a cloaked fashion. Network covert channels are increasingly being used with such malicious intents. The IEEE 802.15.4 is one of the most pervasive protocols in IoT, and a fundamental part of many communication infrastructures. Despite this fact, the possibility of setting up such covert communication techniques on this medium has received very little attention. We aim at analysing the performance and feasibility of such covert-channel implementations upon the IEEE 802.15.4 protocol. This will enable a better understanding of the involved risk and help supporting the development of further cyber-security mechanisms to mitigate this threat.

Abstract
Renewable Energy Communities (RECs) are emerging as an effective concept and model to empower the active participation of citizens in the energy transition, not only as energy consumers but also as promoters of environmentally friendly energy generation solutions, particularly through the use of photovoltaic panels. This paper aims to contribute to the management and optimization of individual and community Distributed Energy Resources (DER). The solution follows a price and source-based REC management program, in which consumers' day-ahead flexible loads (Flex Offers) are shifted according to electricity generation availability, prices, and personal preferences, to balance the grid and incentivize user participation. The heuristic approach used in the proposed algorithms allows for the optimization of energy resources in a distributed edge-and-fog approach with a low computational overhead. The simulations performed using real-world energy consumption and flexibility data of a REC with 50 dwellings show an average cost reduction, taking into consideration all the seasons of the year, of 6.5%, with a peak of 12.2% reduction in the summer, and an average increase of 32.6% in individual self-consumption. In addition, the case study demonstrates promising results regarding grid load balancing and the introduction of intra-community energy trading.