Abstract
Three-dimensional scanning is a task of great importance for our modern society and has brought significant advances in the precision of material inventory. These sensors map the material surface continuously, allowing real-time inventory monitoring. Most technologies are expensive because this process is complex, and even inexpensive ones are considerate smart investments for the average user. Therefore, this work presents the simulation of a low-cost time-of-flight based 3D scanning system that performs the volume estimation of an object-filled indoor space after a voxelization process. The system consists of a 2D LIDAR scanner that performs an azimuthal scan of 180. through its rotating platform and a motor that rotates the platform in angle inclination.

Abstract
In recent years, with the rise of a growing economic and technological interest in lithium mineral resources, there has been a parallel concern, on the part of some local populations and even national environmental groups, for a hypothetical contamination problem that this type of exploitations may cause on the quality of groundwater. Thus, the present study was based on an evaluation of an open-pit quarry, in the so-called Alijó quarry, located in the North of Portugal, in the parish of Canedo, Ribeira de Pena municipality and Vila Real district. This exploitation, under concession by the company José Aldeia Lagoa & Filhos, SA, has been going on for at least 11 years and mainly supplies the ceramic and glass industry. It is in this context that this work is carried out. The general objectives are to assess signs of impacts that extractive activities, carried out in the open-pit exploitation area of Alijó, may have caused on the surrounding subsoil. In this sense, it was necessary to consider the aforementioned hypothesis of the existence of possible sources of water drainage with ionic anomalies for the surrounding environment. If this hypothesis were confirmed, then the level of underground conductivity would have to be proportionally high and obtainable through equivalent low values of electrical resistivity (high electrical conductivity). The current study was limited to geophysical tools along with a few chemical analysis of water samples collected in the open-pit exploration for control purposes. The signs we sought for could possibly be manifested in the form of anomalous concentrations of some of the elements of the mineralization of this lithiniferous pegmatite and whose effects could, hypothetically, be measured in the form of anomalous low values of underground electrical resistivity, as well as anomalous pH valuespresent in the drainage water. To this end, a study mainly supported by the electrical resistivity method was carried out. This method is based on the measurement of electrical resistivity variations of different subsoil geological materials, since rocks and soils, depending on their mineralogical composition, texture, porosity, fracturing and the content/chemical composition of the water contained in them, could exhibit anomalous, low electrical resistivity. The results revealed that no low resistivity values were found, typical of areas that normally exhibit natural or anthropogenic geochemical anomalies, or even, in more extreme cases, contaminations with acid drainages whose acidity and resistivity would be even lower and more anomalous.

Abstract
Large solar power stations are usually located in remote areas and connect to the main grid via a long transmission line. The energy storage unit is deployed locally with the solar plant to smooth its output. Capacities of the grid-connection transmission line and the energy storage unit have a significant impact on the utilization rate of solar energy, as well as the investment cost. This article characterizes the feasible set of capacity parameters under a given solar spillage rate and a fixed investment budget. A linear programming-based projection algorithm is proposed to obtain such a feasible set, offering valuable references for system planning and policy making.

Abstract
The pericardium is a thin membrane sac that covers the heart. As such, the segmentation of the pericardium in computed tomography (CT) can have several clinical applications, namely as a preprocessing step for extraction of different clinical parameters. However, manual segmentation of the pericardium can be challenging, time-consuming and subject to observer variability, which has motivated the development of automatic pericardial segmentation methods. In this study, a method to automatically segment the pericardium in CT using a U-Net framework is proposed. Two datasets were used in this study: the publicly available Cardiac Fat dataset and a private dataset acquired at the hospital centre of Vila Nova de Gaia e Espinho (CHVNGE). The Cardiac Fat database was used for training with two different input sizes - 512 512 and 256 256. A superior performance was obtained with the 256 256 image size, with a mean Dice similarity score (DCS) of 0.871 ± 0.01 and 0.807 ± 0.06 on the Cardiac Fat test set and the CHVNGE dataset, respectively. Results show that reasonable performance can be achieved with a small number of patients for training and an off-the-shelf framework, with only a small decrease in performance in an external dataset. Nevertheless, additional data will increase the robustness of this approach for difficult cases and future approaches must focus on the integration of 3D information for a more accurate segmentation of the lower pericardium.

Abstract
First-mile logistics tackles the movement of products from retailers to a warehouse or distri-bution centre. This first step towards the end customer has been pushed by large e-commerce platforms forming extensive networks of partners and is critical for fast deliveries. First-mile pickup requires efficient methods different from those developed for last-mile delivery, among other reasons due to the complexity of cargo features and volume - increasing the relevance of advanced packing methods. More importantly, the problem is essentially dynamic and the pickup process, in which the vehicle is initially empty, is much more flexible to react to disruptions arising when the vehicles are en route. We model the static first-mile pickup problem as a vehicle routing problem for a hetero-geneous fleet, with time windows and three-dimensional packing constraints. Moreover, we propose an approach to tackle the dynamic problem, in which the routes can be modified to accommodate disruptions - new customers' demands and modified requests of known customers that are arriving while the initially established routes are being covered. We propose three reactive strategies for addressing the disruptions depending on the number of vehicles available, and study their results on a newly generated benchmark for dynamic problems. The results allow quantifying the impact of disruptions depending on the strategy used and can help the logistics companies to define their own strategy, considering the characteristics of their customers and products and the available fleet.

Abstract
Decision trees are often preferred when implementing Machine Learning in embedded systems for their simplicity and scalability. Hoeffding Trees are a type of Decision Trees that take advantage of the Hoeffding Bound to allow them to learn patterns in data without having to continuously store the data samples for future reprocessing. This makes them especially suitable for deployment on embedded devices. In this work we highlight the features of a HLS implementation of the Hoeffding Tree. The implementation parameters include the feature size of the samples (D), the number of output classes (K), and the maximum number of nodes to which the tree is allowed to grow (Nd). We target a Xilinx MPSoC ZCU102, and evaluate: the design's resource requirements and clock frequency for different numbers of classes and feature size, the execution time on several synthetic datasets of varying sizes (N) and the execution time and accuracy for two datasets from UCI. For a problem size of D=3, K=5, and N=40000, a single decision tree operating at 103MHz is capable of 8.3x faster inference than the 1.2 GHz ARM Cortex-A53 core. Compared to a reference implementation of the Hoeffding tree, we achieve comparable classification accuracy for the UCI datasets.