Abstract
Cutting raw-material into smaller parts is a fundamental phase of many production processes. These operations originate raw-material waste that can be minimised. These problems have a strong economic and ecological impact and their proper solving is essential to many sectors of the economy, such as the textile, footwear, automotive and shipbuilding industries, to mention only a few. Two-dimensional (2D) nesting problems, in particular, deal with the cutting of irregularly shaped pieces from a set of larger containers, so that either the waste is minimised or the value of the pieces actually cut from the containers is maximised. Despite the real-world practical relevance of these problems, very few approaches have been proposed capable of dealing with concrete characteristics that arise in practice. In this paper, we propose a new general heuristic (H4NP) for all 2D nesting problems with limited-size containers: the Placement problem, the Knapsack problem, the Cutting Stock problem, and the Bin Packing problem. Extensive computational experiments were run on a total of 1100 instances. H4NP obtained equal or better solutions for 73% of the instances for which there were previous results against which to compare, and new benchmarks are proposed.

Abstract
Despite recent advances, always-on broadband Internet connectivity is still not available in Temporary Crowded Events (TCEs). To solve this problem, this paper envisions a novel concept named Traffic-Aware Multi-Tier Flying Network (TMFN). A TMFN consists of a mobile and physically reconfigurable network of Flying Mesh Access Points (FMAPs) and Gateways, which is able to dynamically reconfigure its topology according to the users' traffic demands - characterized by the users' positions and offered traffic. To implement this concept, a novel traffic-aware Network Planning (NetPlan) algorithm is proposed, which dynamically determines the FMAPs' coordinates and Wi-Fi cell ranges according to the users' traffic demands, in order to improve the TMFN's aggregate throughput, without compromising the overall coverage. Simulation results obtained in scenarios typically observed in TCEs demonstrate improved Quality of Service metrics, specifically the mean throughput, thus validating the proposed NetPlan algorithm.

Abstract
Most distributed photovoltaic systems (DPVSs) are normally located behind the meter and are thus invisible to utilities and retailers. The accurate information of the DPVS capacity is very helpful in many aspects. Unfortunately, the capacity information obtained by the existing methods is usually inaccurate due to various reasons, e.g., the existence of unauthorized installations. A two-stage DPVS capacity estimation approach based on support vector machine with customer net load curve features is proposed in this paper. First, several features describing the discrepancy of net load curves between customers with DPVSs and those without are extracted based on the weather status driven characteristic of DPVS output power. A one-class support vector classification (SVC) based DPVS detection (DPVSD) model with the input features extracted above is then established to determine whether a customer has a DPVS or not. Second, a bootstrap-support vector regression (SVR) based DPVS capacity estimation (DPVSCE) model with the input features describing the difference of daily total PV power generation between DPVSs with different capacities is proposed to further estimate the specific capacity of the detected DPVS. A case study using a realistic dataset consisting of 183 residential customers in Austin (TX, U.S.A.) verifies the effectiveness of the proposed approach.

Abstract
The large scale integration of Distributed Energy Resources (DER) at the Low Voltage (LV) distribution network offers new opportunities for the improvement of power quality and network reliability. Currently, the occurrence of large disturbances at the transmission network causing severe voltage sags at the distribution level could lead to the disconnection of a large share of DER units connected to the LV network, causing a more severe disturbance. In this paper, Low-Voltage-Ride Through (LVRT) requirements and current support strategies are proposed to mitigate the impact of severe voltage sag at the distribution level for DER units connected to LV network. The impact of adopting the proposed LVRT strategies will be analyzed through simulation and experimentally. A developed in house ESS prototype incorporating the developed LVRT strategies is also presented, and its capacity to comply with the proposed LVRT requirements is demonstrated using an experimental Power Hardware-in-the-Loop (PHIL) setup.

Abstract
Heart failure (HF) affects at least 26 million people worldwide and is considered a global pandemic. Almost 1 out of 4 hospitalised patients are re-hospitalised for HF within the 30-day post-discharge period. This can be avoided if changes to the hemodynamics of the HF patient's body are early detected. Bioimpedance Spectroscopy (BIS) is a method that allows the measurement and analysis of multi-frequency body complex impedance and can be used to detect changes to the HF patient's hemodynamics. This paper presents the calibration and validation process of a low-cost portable BIS system, as well as, the study of the best Cole parameter estimation methods. The BIS system was calibrated using a three reference circuit method and a Resistance-Capacitance-Inductance (RCL) meter as calibration system. After calibration, BIS impedance measurements of validation circuits presented an average phase error of 0.76 degrees and an average magnitude relative error of 0.6 % when compared with standard values. Regarding Cole parameters estimation, using the Impedance model and the Non-Linear Least Squares method for curve fitting, the relative errors were below 4 % when compared with the expected values.

Abstract
Matching two regions represented in bathymetric data that have some form of geographical overlapping is an important and challenging aspect in underwater mapping. It is important because of the possible error in estimating the geographical location of each point underwater. It is challenging due to the size of the acquired bathymetric data points. The matching could also play a vital role in the registration of underwater images and/or maps fusion, if both bathymetric and intensity scans are considered. Compared to the exhaustive search that requires polynomial time, O(n(2)), an efficient bathymetric matching algorithm is proposed in this work that finds several match points in linear time, requiring thus O(n) computations. The paper thus presents a new algorithm that allows to compile the bathymetric data of the common areas of two submarine areas that have been sampled in underwater missions.