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In recent years, Portugal has seen wide variability in wildfire damage associated to high unpredictability of climatic events such as severe heatwaves and drier summers. Therefore, timely and accurate detection of forest and rural wildfires is of great importance for successful fire containment and suppression efforts, as wildfires exponentially increase their spread rate from the moment of ignition. In the field of early smoke detection, the CICLOPE project currently trailblazes in the employment of a network of Remote Acquisition Towers for wildfire prevention and observation, along with a rule-based automatic smoke detection system, covering over 2, 700, 000 hectares of wildland and rural area in continental Portugal. However, the inherent challenges of automatic smoke detection raise issues of high false alarm rates that affect the system’s prediction quality and overwhelm the Management and Control Centers with numerous false alarms. The research work presented in this paper evaluates the potential improvement in wildfire smoke detection accuracy and specificity using deep learning-based architectures. It proposes a solution based on a Dual-Channel CNN that can be deployed as a secondary prediction confirmation layer to further refine the CICLOPE automatic smoke detection system. The proposed solution takes advantage of the high true alarm coverage of the current detection system by taking only the predicted alarm images and respective bounding box coordinates as inputs. The Dual-Channel network combines the widely used DenseNet architecture with a novel detail selective network with spatial and channel attention modules trained separately with image data obtained from CICLOPE, fusing the extracted features from both networks in a concatenation layer. The results demonstrate that the proposed Dual-Channel CNN outperforms both single-channel networks, achieving an accuracy of 99.7% and a low false alarm rate of 0.20% when re-examining the alarms produced by the CICLOPE surveillance system.

Network management is a crucial task to maintain modern systems and applications running. Some applications have become vital for society and are expected to have zero downtime. Software-defined networks is a paradigm that collaborates with the scalability, modularity and manageability of systems by centralizing the network’s controller. However, this creates a weak point for distributed denial of service attacks if unprepared. This study proposes an anomaly detection system to detect distributed denial of service attacks in software-defined networks using generative adversarial neural networks with gated recurrent units. The proposed system uses unsupervised learning to detect unknown attacks in an interval of 1 second. A mitigation algorithm is also proposed to stop distributed denial-of-service attacks from harming the network’s operation. Two datasets were used to validate this model: the first developed by the computer networks study group Orion from the State University of Londrina. The second is a well-known dataset: CIC-DDoS2019, widely used by the anomaly detection community. Besides the gated recurrent units, other types of neurons are also tested in this work, they are: long short-term memory, convolutional and temporal convolutional. The detection module reached an F1-score of 99@ in the first dataset and 98@ in the second, while the mitigation module could drop 99@ of malicious flows in both datasets.

With the current utilization of Floating, Production, Storage, and Offloading (FPSO) units by the petroleum industry, and the trend towards optimizing the energy efficiency of electrical systems, Variable Frequency Drives (VFDs) play a crucial role in enhancing the performance and power quality (PQ) of the power system on these offshore platforms. The FPSO unit operates in various modes, depending on its regular activities, such as contingency, production and offloading. Each operating mode has multiple grid configurations with different generation and load connections, including the contingency mode that is used for planned maintenance or failure in one of the aeroderivative gas turbine generators (GTGs). During the contingency mode, the FPSO unit runs with two GTGs and implements load shedding. Therefore, this paper investigates a specific case of the contingency operating mode, considering scenarios with zero, one, two, and three VFDs connected to the largest motors. Additionally, it analyzes VFDs with both passive and active front-end rectifiers, aiming to minimize the number of loads to be shed during contingency. The main result shows that by using the active front-end VFDs, 14.3 MW of loads that would be shut down during contingency mode can continue to operate.

The grid-forming converter that supplies voltage magnitude and frequency is a key element for isolated remote communities, and then wear-out analysis is an essential figure of merit. Therefore, this paper proposes the two-phase three-wire (2$\Phi$3 W) grid forming converter applied in a case study of isolated riverside communities in the Brazilian Amazon region. The proposed xyn-converter displaces the voltage line angle in 180$^{\circ }$, differing from $\alpha \beta$n-converter and abn-converter that displace voltage line angle in 90$^{\circ }$ and 120$^{\circ }$ respectively. First, the authors have also proposed a semiconductor current stress evaluation for 2$\Phi$3 W converters comparing the RMS current. Then, the lifetime evaluation of the three evaluated grid-forming converters is conducted. The results show that the converter operating in the xyn-converter presents the highest reliability among the three converters. Finally, the thermal stress analysis is validated by means of a 2$\Phi$3 W converter prototype with an open semiconductor module. For the evaluated converters, the xyn-converter has the least thermal stress for balanced and unbalanced load conditions and therefore the longest lifespan. The experimental validation shows that the average temperature of the xyn-converter is 19.4% and 23.3% lower than both the abn-converter and the $\alpha \beta$n-converter under balanced load conditions.

Surface wave technology for high-speed communications is a current research topic aimed to respond to increasing data rate demands on existing copper infrastructures. Also, the topic of surface waves has recently gained importance in the modeling of transmission line towers hit by lightning strikes with spectral content in the megahertz band. The single-wire transmission line structure (return conductor absent) cannot support TEM waves; it supports a TM Sommerfeld wave fully described by two propagation constants and two characteristic impedances. Nonetheless, the literature on single-wire transmission-line structures has been employing quasi-static per unit length constitutive parameters, inductance and capacitance, borrowed from ordinary two-conductor transmission line TEM analysis. This work develops, discusses, and compares various possible definitions of these constitutive parameters using different physical approaches: TEM-approach, circuit-approach, and energy-approach. Numerical results for nonmagnetic and magnetic wires, copper and steel wires, respectively, are obtained in the range 1 Hz to 1 GHz. Our analysis shows that in some circumstances the TEM and circuit approaches may lead to nonphysical results, but, remarkably, all the approaches seem to converge to a common dominant term either in the per unit length capacitance or in the per unit length inductance, whose product is frequency-invariant. Considering the different approaches under discussion, the differences among the observed results for the per unit length constitutive parameters are negligibly small, of second-order importance.

The integration of offshore wind energy power system is a solution to reduce carbon emissions in the oil and gas sector. However, the intermittent profile of wind power generation can bring challenges of voltage/frequency fluctuations to the electrical grid of the platform. This article proposes a coordinated decentralized control used to mitigate voltage fluctuation in oil and gas platform caused by connection of wind power system through short- and long-umbilical cables. The proposed control is split into optimum loss control embedded in the wind power system control, and modified dynamic volt-var functions applied to active front-end variable frequency drives. The decentralized control is evaluated by means of simulation results considering a real case test bench of a typical Brazilian offshore platform with a wind power system of 10 and 50 MW, and umbilical cable of 12 and 150 km. The results indicate an effective contribution of the dynamic volt-var function in reducing voltage fluctuations for the three implemented wind power system configurations (i.e., 10; 50, and 50 MW/150 km).

This work proposes a methodology for evaluating the antenna array diversity and resultant radiation pattern from digitally-processed-based beamforming systems. Our approach enables to interpret the advanced antenna systems (AAS) coverage and massive multiple-input multiple output (mMIMO) spatial resolution, by the inspection of the array resultant multiple beams. Particularly, the impact on the mMIMO spatial resolution is conducted as a function of the channel correlation, by means of an exclusively radiofrequency (RF) approach, with the advantage of not considering the system-level features, and using a new figure of merit ( $\Lambda $ factor). The new figure of merit enables to evaluate the antenna array diversity from two- and three-dimensional (2D and 3D) beamforming, by computing the number of lobes with gain higher than a unique element. In the 3D case, the figure of merit is based on a differential calculation, which enables performing an infinitesimal counting of lobes per antenna element beamwidth. Such parameter is initially applied to a theoretical dipole-based array, with the purpose of quantifying the created multiple beams by the overlapped radiation patterns from mMIMO systems. Furthermore, the $\Lambda $ factor is exploited for analyzing the performance of a 64-element and dual-polarized SICL-based slot antenna array and a 64-element dipole-based antenna prototypes, both previously developed by our research group. Finally, it is demonstrated that the $\Lambda $ factor increases as diversity increases and dipole-based antenna array could be used as an upper limit, attaining 11.46 lobes/rad

The sixth generation of mobile communication (6G) systems is recently rising a lot of interest, introducing new futuristic and challenging use cases that will demand much more than just communications to become a reality. Higher throughput, lower latencies, higher number of connections will push the requirement of the future mobile networks to a new level, but also sensing, positioning and imaging will play an important role in the new foreseen use cases. The integration of techniques developed for wireless communications with those conceived for optical links will be essential to provide the infrastructure for the 6G networks. In this context, this paper presents a review on wireless and optical convergent access solutions towards the 6G systems. The manuscript brings the use cases, requirements and enablers for 6G networks including a discussion about the state-of-the-art on THz and sub-THz communications, wireless and optical convergence, visible light communication, integrated and free-space optics, new antenna designs, power-over-fiber deployments and the use of machine learning in the physical layer of future networks. By reviewing the most relevant contributions available in the literature for wireless and optical communications and presenting their main contributions, this paper clearly shows that, more than a technological trend, the convergence of wireless and optical technologies is a fundamental step towards the development of the 6G network infrastructure.

Presents corrections to the article “Anomaly Detection in Hyperspectral Images via Regularization by Denoising”.