This paper considers calculation methods for the electric field intensity and magnetic flux density in the vicinity of the overhead transmission lines, as well as the calculation of alternating current (AC) corona onset electric field intensity. Calculations within this paper are made using the 2D algorithms of Charge Simulation Method (CSM) and Biot-Savart (BS) law based method. In order to obtain more accurate results, calculations are made by representing each overhead transmission line conductor with a large number of electric and magnetic field sources. By applying this approach, bundle conductors can be represented in a more realistic way and also singularity problems can be avoided when calculating electric field intensity. The presented methods are applied to a real overhead transmission line configuration, and obtained results are compared with field measurement results over the lateral profile. For considered overhead transmission line, AC corona onset electric field intensity is calculated and compared with calculated electric field intensity on the conductor’s surface. A comparison of calculated and measured results shows that considered calculation methods give satisfactory results.

In the fields of computer vision and digital image processing, image segmentation denotes a process whereby an image is segmented into multiple regions. Image segmentation based on multilevel thresholding has received significant attention in recent literature. In this paper, a multilevel thresholding approach based on three different Rao algorithms and Kapur’s entropy is investigated. The performance of the considered thresholding methods is evaluated on a dataset of 10 standard benchmark images using the mean of objective function values, the standard deviation of objective function values, and the best objective function value obtained over a fixed number of independent runs. The experimental results demonstrate the effectiveness of the multilevel thresholding approach based on Rao algorithms and Kapur’s entropy.

Social media is an important source of real-world data for sentiment analysis. Hate speech detection models can be trained on data from Twitter and then utilized for content filtering and removal of posts which contain hate speech. This work proposes a new algorithm for calculating user hate speech index based on user post history. Three available datasets were merged for the purpose of acquiring Twitter posts which contained hate speech. Text preprocessing and tokenization was performed, as well as outlier removal and class balancing. The proposed algorithm was used for determining hate speech index of users who posted tweets from the dataset. The preprocessed dataset was used for training and testing multiple machine learning models: k-means clustering without and with principal component analysis, naïve Bayes, decision tree and random forest. Four different feature subsets of the dataset were used for model training and testing. Anomaly detection, data transformation and parameter tuning were used in an attempt to improve classification accuracy. The highest F1 measure was achieved by training the model using a combination of user hate speech index and other user features. The results show that the usage of user hate speech index, with or without other user features, improves the accuracy of hate speech detection.

The combination of reinforcement learning and deep learning has shown some remarkable results in many scientific fields. Deep reinforcement learning algorithms are particularly good at understanding and modeling adaptive decision-making in dynamic environments. In recent years, this concept has been successfully applied to smart grids. In this paper, we provide a brief introduction to the concepts of reinforcement and deep reinforcement learning to the power system engineers and present research progress and prospects in the field. Additionally, we identify smart grid engineering domains that need extensive pattern-based modeling as being particularly suitable for deep reinforcement learning.

The detonation properties of nonideal explosives are highly dependent on charge diameter and existence and properties of confinement. In this study, the effect of different confinements on the detonation velocity of ANFO explosives was experimentally determined along with the results of the plate dent test. ANFO explosive was selected as one of the most commonly used nonideal explosives. Following the measurement results, we found that the detonation velocity increased with increasing wall thickness, and the velocity increase was different for different confinement materials. A strong correlation existed between the ratio of the mass of confiner and explosive (M/C) and the detonation velocity (R = 0.995), and between (M/C) and the depth of the dent (δ) (R = 0.975). The data presented in this paper represent preliminary findings in developing a confinement model required for reliable numerical modeling of nonideal explosives.

Ghrelin, a stomach-produced hormone, is well-recognized for its role in promoting feeding, controlling energy homeostasis, and glucoregulation. Ghrelin’s function to ensure survival extends beyond that: its release parallels that of corticosterone, and ghrelin administration and fasting have an anxiolytic and antidepressant effect. This clearly suggests a role in stress and anxiety. However, most studies of ghrelin’s effects on anxiety have been conducted exclusively on male rodents. Here, we hypothesize that female rats are wired for higher ghrelin sensitivity compared to males. To test this, we systematically compared components of the ghrelin axis between male and female Sprague Dawley rats. Next, we evaluated whether anxiety-like behavior and feeding response to endogenous or exogenous ghrelin are sex divergent. In line with our hypothesis, we show that female rats have higher serum levels of ghrelin and lower levels of the endogenous antagonist LEAP-2, compared to males. Furthermore, circulating ghrelin levels were partly dependent on estradiol; ovariectomy drastically reduced circulating ghrelin levels, which were partly restored by estradiol replacement. In contrast, orchiectomy did not affect circulating plasma ghrelin. Additionally, females expressed higher levels of the endogenous ghrelin receptor GHSR1A in brain areas involved in feeding and anxiety: the lateral hypothalamus, hippocampus, and amygdala. Moreover, overnight fasting increased GHSR1A expression in the amygdala of females, but not males. To evaluate the behavioral consequences of these molecular differences, male and female rats were tested in the elevated plus maze (EPM), open field (OF), and acoustic startle response (ASR) after three complementary ghrelin manipulations: increased endogenous ghrelin levels through overnight fasting, systemic administration of ghrelin, or blockade of fasting-induced ghrelin signaling with a GHSR1A antagonist. Here, females exhibited a stronger anxiolytic response to fasting and ghrelin in the ASR, in line with our findings of sex differences in the ghrelin axis. Most importantly, after GHSR1A antagonist treatment, females but not males displayed an anxiogenic response in the ASR, and a more pronounced anxiogenesis in the EPM and OF compared to males. Collectively, female rats are wired for higher sensitivity to fasting-induced anxiolytic ghrelin signaling. Further, the sex differences in the ghrelin axis are modulated, at least partly, by gonadal steroids, specifically estradiol. Overall, ghrelin plays a more prominent role in the regulation of anxiety-like behavior of female rats.

The broader use of devices powered by rechargeable batteries, especially constrained embedded devices, makes the efficient Battery Management System (BMS) increasingly more important. The estimation accuracy of the amount of remaining charge in the battery is critical as it affects the device’s operation and reliability. For that reason, the estimation of state-of-charge (SoC) is considered one of the main functionalities of a BMS. However, SoC estimation remains a complex task that depends on a range of internal and external factors. Most traditional SoC estimation methods are either computationally complex, require special laboratory equipment or additional configuration efforts. In addition, most methods require continuous measurement of battery parameters, which, in turn, renders these methods not applicable to the class of constrained embedded devices. This paper aims to extend the Coulomb counting method to the class of duty-cycled energy-constrained devices by designing an algorithm that combines voltage-based evaluation and pre-recorded task power profiles to estimate the SoC. In addition, a setup for identifying the battery parameters and algorithm validation setup were also developed and described in the paper.

Ambient conditions, especially temperature and humidity, have a huge impact on the performance of an air quality sensor. In this paper, four correction models were built to compensate the impact of ambient conditions. Linear regression and machine learning algorithms were used for building the models. Correction models were trained by using three types of measurement data. Raw measurement data was used in the first case. Secondly, measurement data was corrected and a significant improvement was shown. Lastly, measurements of various ambient conditions were used as well. Using corrected and extended measurement data brought a great improvement in accuracy of the models. A neural network correction model proved to be the most efficient in all cases. Compensating the impact of ambient conditions on the performance of an air quality sensor by using correction models was efficient and this method could be used in the air quality monitoring applications. This is of particular importance for usage of low-cost sensors in the air quality monitoring.

This paper treats the problem of 3D outdoor environment mapping using images acquired by Unmanned Aerial Vehicle (UAV). The main focus is on the generation of 3D model for large scale environments. In order to perform 3D model reconstruction and mapping from 2D aerial images we employed a Structure from Motion (SfM) based approach. The obtained results using this approach for different scenarios, the rubble field and village, are presented. The generated UAV 3D point cloud data are compared with the ground truth using the least square method, where the ground truth represents a reference model with high accuracy geodetic precision. The comparison of the 3D environment models with the rubble field and village scenarios and the ground truth data is also given.

The Advanced Distribution Management System (ADMS) has grown to be a highly complicated system that comprises distribution generation, batteries, power electronics, and, in case of an urban area, an electric transportation system. One of the most essential features of ADMS is maintaining node voltages and branch thermal ratings within defined limits while maintaining minimal system losses and maximizing the use of renewable energy. Voltage VAr control (VVC) is extensively used to address these challenges and is becoming increasingly significant in ADMS. A side from the necessity to manage the system status, VVC must be adaptable to accommodate future Smart City (SC) requirements such as electric-vehicle charging and energy recuperation management. The majority of existing systems control the DC electric transportation system separately from the entire AC system. This paper attempts to tackle the problem using a hybrid single model that incorporates both: AC and DC network components.

The scientific discipline of Computer Vision (CV) is a fast developing branch of Machine Learning (ML). It addresses various tasks important for robotics, medicine, autonomous driving, surveillance, security or scene understanding. The development of sensor technologies enabled wide usage of 3D sensors, and therefore, it increased the interest of the CV research community in creating methods for 3D sensor data. This paper outlines seven CV tasks with 3D point cloud data, state-of-the-art techniques, and datasets. Additionally, we identify key challenges.

The article presents a series of measurements conducted on the fully-operated Quantum Key Distribution system. These measurements primarily focus on the Quantum Bit Error Rate (QBER), which is the most important parameter of the quantum channel. This parameter was observed and measured for 16 days under the quantum channel’s operating conditions to determine any correlations between the QBER and other quantum link parameters, such as secret key rate. A thorough statistical analysis of the measured data was performed as a part of this investigation and is presented in the paper.

New generation networks are facing ever greater demands. When testing new network devices that must process packets at extremely high rates, it is essential to test their functionality and desired performance under maximum traffic load. As a result, in order to test the hardware, a traffic generator is required. This paper proposes an affordable and extensible high-speed FPGA-based Ethernet traffic generator. The proposed solution is able of fully utilizing a 40GbE link, with the possibility of manipulating traffic characteristics at the level of an individual packet. Although intended to run on the DE10-Pro system, the proposed design is portable to other FPGA boards with minimal development effort and changes.

A QKD network can be considered an add-on technology to a standard communication network that provides IT-secure cryptographic keys as a service. As a result, security challenges resulting in the suspension of functional work must be addressed. This study analyzes a Denial of Service (DoS) attack on the Key Management System (KMS), one of the critical components of the QKD network in charge of key management and key provisioning to authorized consumers. Through simulation methods performed in the QKDNetSim, we show that legitimate customers experience significantly worse service during an excessive DoS attack on KMS.