Abstract
The consolidation of smart grids is inevitably related with the development and actual implementation of different functionalities envisioned for future electric grids. This paper presents the major implementations of smart grid projects in Portugal, which resulted from a close collaboration between academia and industry. An overview of the entire development process is presented culminating with the real implementation of the developed concepts. The architectures and functional models are presented as the initial step in defining the management and control functionalities for future smart distribution networks. The intermediate step consists in validating the advances introduced by smart grids. Simulation tools are emphasized considering both electrical and communications aspects. Finally, a laboratory infrastructure implemented to be used as a real test bed and a pilot deployed in a large city are presented in the end. The associated learning has provided relevant information for future developments.

Abstract
The sensors that acquire 3D data play an important role in many applications. In addition, they have been used in the robotic field for several purposes, for instance, enhancing the navigation of mobile robots, object detection, scene reconstruction, 3D inspection of parts and others. Moreover, a significant amount of devices with distinct cost, accuracy and features have been released in the recent years which increases the difficulty of comparing each sensor in a proper manner or choosing the most suitable device for a specific task and operation field. This paper compares the Kinect v1, Kinect v2, Structure Sensor and Mesa Imaging SR4000. The noise of each sensor is characterized for different distances and considering objects with different colors. Therefore, this paper proposes a simple but quantitative benchmark for evaluating 3D devices that characterizes the most relevant features for the robotic field and in accordance with different type of operations.

Abstract
This paper introduces the concept of tomography, a mechanism to lower management overhead for component-based IoT applications. Previous research has shown the advantages of component-based software engineering, wherein applications are built and reconfigured at runtime through the composition of components. While this approach promotes code-reuse and dynamic reconfiguration, the introspection and reconfiguration of distributed applications is cumbersome and inefficient. Tomography addresses this problem by reimagining the visitor design pattern for distributed component based compositions. We evaluate the performance of this approach in a case-study of discovering/introspecting and reconfiguring a real-world IoT application. We show that in comparison to classic management operations, tomography reduces both the number of explicit queries and the volume of network messages. This significantly reduces management effort and energy consumption.

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The prospect of distributed generation investment planning (DGIP) is especially relevant in insular networks because of a number of reasons such as energy security, emissions and renewable integration targets. In this context, this paper presents a DGIP model that considers various DG types, including renewables. The planning process involves an economic analysis considering the costs of emissions, reliability and other relevant cost components. In addition, a comprehensive sensitivity analysis is carried out in order to investigate the effect of variability and uncertainty of model parameters on DG investment decisions. The ultimate goal is to identify the parameters that significantly influence the decision-making process and to quantify their degree of influence. The results show that uncertainty has a meaningful impact on DG investment decisions. In fact, the degree of influence varies from one parameter to another. However, in general, ignoring or inadequately considering uncertainty and variability in model parameters has a quantifiable cost. The analyses made in this paper can be very useful to identify the most relevant model parameters that need special attention in planning practices.

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