Abstract
This paper presents a three-phase Distribution System State Estimator (DSSE) based on a Bayesian inference approach to manage different sampling rates of typical sources of information present in distribution networks. Such information comes from smart meters, supervisory control and data acquisition (SCADA) measurements, phasor measurement units and typical load profiles from pseudo measurements. The temporal aspect of the measurement set is incorporated in the estimation process by using a sampling layer concept, dealing separately with each group of measurements according to the respective updating rate. A Bayesian information fusion procedure provides the final estimation. The proposed DSSE consists in a multiple stage estimator that combines a prior model for the state variables, updated by new observations from measured values in each sampling layer, through Maximum a Posteriori estimation. This work also introduces an orthogonal method for the information fusion numerical solution, to tackle the severe ill-conditioning associated with practical distribution systems. Simulations with IEEE distribution test feeders and a Brazilian real distribution feeder illustrate the features of the proposed DSSE and its applicability. By exploring the concept of credibility intervals, the method is able to detect events on the grid, such as subtle load variation and contingencies, while maintaining accuracy.

Abstract
The daily stress we, as population, are subjected has negative impacts on physical and mental health, making people age faster. With the increase in life expectancy, the population started to search for ways to ensure they reach the older ages with a healthier and better lifestyle, resulting in an increase of interest and demand for thermalism and wellness tourism. Although thermalism is recognized to have several benefits with regard to the combat and prevention of certain pathologies, this has not yet been fully proven. So, in this paper, we presented a sensing system, capable of not only recording thermalist's biometric data, but also getting the data ready and prepared to be processed by any analytic tool with the aim of showing the impact that this sector has on the health of its practitioners.

Abstract
Recent commitments and consequent advances towards an effective energy transition are resulting in promising solutions but also bringing out significant new challenges. Models for energy management at the building and microgrid level are benefiting from new findings in distinct areas such as the internet of things or machine learning. However, the interaction and complementarity between such physical and virtual environments need to be validated and enhanced through dedicated platforms. This chapter presents the Multi-Agent based Real-Time Infrastructure for Energy (MARTINE), which provides a platform that enables a combined assessment of multiple components, including physical components of buildings and microgrids, emulation capabilities, multi-agent and real-time simulation, and intelligent decision support models and services based on machine learning approaches. Besides enabling the study and management of energy resources considering both the physical and virtual layers, MARTINE also provides the means for a continuous improvement of the synergies between the Internet of Things and machine learning solutions.

Abstract
Currently, there are several security-related standards and recommendations concerning Domain Name System (DNS) and Hypertext Transfer Protocol (HTTP) services, that are highly valuable for governments and their services, and other public or private organizations. This is also the case of Higher Education Institutions (HEIs). However, since these institutions have administrative autonomy, they present different statuses and paces in the adoption of these web-related security services. This paper presents an overview regarding the implementation of security standards and recommendations by the Portuguese HEIs. In order to collect these results, a set of scripts were developed and executed. Data were collected concerning the security of the DNS and HTTP protocols, namely, the support of Domain Name System Security Extensions (DNSSEC), HTTP main configurations and redirection, digital certificates, key size, algorithms and Secure Socket Layer (SSL)/Transport Layer Security (TLS) versions used. The results obtained allow to conclude that there are different progresses between HEIs. In particular, only 11.7% of HEIs support DNSSEC, 14.4% do not use any SSL certificates, 74.8% use a 2048 bits encryption key, and 81.1% use the Rivest-Shamir-Adleman (RSA) algorithm. Also, 6.3% of HEIs still negotiate with the vulnerable SSLv3 version. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
The harvesting operation is a recurring task in the production of any crop, thus making it an excellent candidate for automation. In protected horticulture, one of the crops with high added value is tomatoes. However, its robotic harvesting is still far from maturity. That said, the development of an accurate fruit detection system is a crucial step towards achieving fully automated robotic harvesting. Deep Learning (DL) and detection frameworks like Single Shot MultiBox Detector (SSD) or You Only Look Once (YOLO) are more robust and accurate alternatives with better response to highly complex scenarios. The use of DL can be easily used to detect tomatoes, but when their classification is intended, the task becomes harsh, demanding a huge amount of data. Therefore, this paper proposes the use of DL models (SSD MobileNet v2 and YOLOv4) to efficiently detect the tomatoes and compare those systems with a proposed histogram-based HSV colour space model to classify each tomato and determine its ripening stage, through two image datasets acquired. Regarding detection, both models obtained promising results, with the YOLOv4 model standing out with an F1-Score of 85.81%. For classification task the YOLOv4 was again the best model with an Macro F1-Score of 74.16%. The HSV colour space model outperformed the SSD MobileNet v2 model, obtaining results similar to the YOLOv4 model, with a Balanced Accuracy of 68.10%.

Abstract
The microgrid operation is addressed in this article based on a multicarrier energy hub. Natural gas, electricity, heating, cooling, hydrogen, carbon dioxide, and renewable energies are considered as the energy carriers. The designed microgrid optimizes and utilizes a wide range of resources at the same time including renewables, electrical storage, hybrid storage, heating-cooling storage, electric vehicles (EVs) charging station, power to gas unit, combined cooling-heating-power, and carbon capture-storage. The purpose is to reduce the environmental pollutions and operating costs. The resilience and flexibility of the energy hub is also improved. Vehicle to grid and fully-partial charge models are incorporated for EVs to improve the system resilience and supplying the critical loads following events. Different events are modeled to evaluate the system resilience. The model is expressed as a stochastic mixed integer linear programming problem. Both active and reactive powers are modeled. The microgrid is simulated under four different cases. The results show that the multitype energy storages reduce the annual cost of energy while the integrated charging station can decrease the load shedding.