Miracle-dz/UniFAR-FTUs huggingface dataset:
Survey and Evaluation of Sensors for Overhead Cable Detection using UAVs

Inspecting overhead cables using autonomous Unmanned Aerial Vehicles (UAVs) is an affordable and promising future solution for providing a clear picture of the energy infrastructure. However, most of today's UAVs for powerline inspection are designed to use the Global Navigation Satellite System (GNSS) to follow the power pylons which compromises the detection accuracy. In this paper, we provide an overview of the current state-of-the-art in sensors that can be used to remotely detect power lines using UAVs. The work compares 20+ low-cost, low-power, and light-weight sensors. The performance of 6 different kinds of sensors is evaluated thoroughly in a real outdoor powerline test setup using a custom UAV. The sensor data is obtained and analyzed using Robot Operating System (ROS) and is provided openly.
A novel distributed control for hybrid AC/DC microgrid with consideration of power limit

Hybrid DC/AC microgrid is considered to be an effective structure to integrate different kinds of distributed sources and the control of each subgrid and interlinked converters (ILCs) is crucial for stable operation. The distributed control with consideration of power limit is proposed in this paper to improve power sharing accuracy and prevent power violation. Simulation results demonstrate the efficiency of the proposed method in an islanded hybrid microgrid under different scenarios.
New Real Time (M-Bug) Algorithm for Path Planning and Obstacle Avoidance In 2D Unknown Environment

This paper proposes a new sensor-based path planning and collision avoidance algorithm for nonholonomic robot travelling in unknown 2D environment named as (M-Bug) algorithm in order to reduce the overall of traveled path. It uses the difference between slope lines to find and choose the smallest angle located between slope of straight line from mobile robot to goal point and slope from mobile robot and two endpoints of edges for obstacle, by choosing the smallest one which refers to nearest leave point to reach goal point and repeat this action with other obstacles until mobile robot reach to goal point. this algorithm based on using on-board sensors to generate information about the environment and obstacle located in front of mobile robot. This algorithm creates better performance when compared to the other traditional algorithms. It provides real time obstacle avoidance, compatible with LIDAR sensor and fast enough to be implemented on embedded microcontrollers. The used algorithm is based on a modification for existing path planning and obstacle voidance algorithms (Bug1, Bug2, Tangent-Bug, K-Bug, Dist-Bug .etc.), and The Vector Field Histogram (VFH) Algorithm in unknown environment. The main advantage of this algorithm is that it is not limited to the origin-destiny line and changes the main line every leave point. MATLAB 2018a is used in simulation to validate the effectiveness of the proposed algorithm, and it is implemented on multiple scenarios to show the ability of the mobile robot to follow a path, detect obstacles, and navigate around them to avoid collision.
Interaction between 2,5-Dimethylfuran and Nitric Oxide: Experimental and Modeling Study

In the present work, the interaction between 2,5-dimethylfuran (2,5-DMF) and NO has been investigated. The study includes experimental and modeling data on the evaluation of the influence of the temperature, stoichiometry, and 2,5-DMF concentration on the NO conversion as well as on the 2,5-DMF conversion in the presence of NO. The experiments were performed in an isothermal quartz flow reactor at atmospheric pressure in the temperature range of 800–1400 K. Some of the results of the present work were compared to the experimental and modeling data from an earlier study on 2,5-DMF conversion under similar conditions but in the absence of NO. The results reveal that the temperature, stoichiometry, and 2,5-DMF concentration play an important role on the conversion of NO. Likewise, these variables also influence the 2,5-DMF conversion regime in the presence of NO.
Blind Spectrum Sensing Algorithms in CRNs: A Brief Overview

In cognitive radio, the spectrum is being used in those modern applications that can monitor spectrum, i.e., alternative users (SUs), could use the spectrum that the main user (PU) does not actually control. Any stakeholders have found out that the approved spectrum is not in operation all the time/locations. To maximize spectrum usage, SUs are enabled to get access to frequency bands already allocated to PUs while they are not using it, under the condition that the SUs do not interact with the PU. To avoid interference to the PU, the SU CR faces a difficult task, i.e., detecting the existence of the PU signal in low-SNR (negative dB) area. This is due to signal attenuation due to multipath and shadowing that can occur before the signal hits the SU. For sensing, the energy detectors are commonly used, but they suffer from the noise uncertainty issue in such noise conditions. The SNR wall is the operating threshold at which image identification is no longer possible. This ambiguity is due to how imprecise the calculations of noise variance are over time, and due to the changing noise variance. In this article, a short overview is given for researchers in the field of cognitive radio technology (CRNs).
Scalable Arabic text Classification Using Machine Learning Model

Recently, with the existence of internet we have witnessed great development in computer systems. Artificial intelligence means to developing computer systems that able to perform intelligent tasks. [1] Machine learning is one method of make such systems. In this paper, using SVM classifier, I build a Multi-label text classification model for Arabic text. This model is basically used to classify articles on their topics. The results show that using SVM classifier on the dataset generated the best results with 82.2% accuracy. The model was build using Python.
Voltage Balancing Considerations for Series-Connected IGBTs in MV Solid-State DC Breakers

Solid-state DC breakers employing insulated gate bipolar transistors seem to be a promising solution for protecting medium-voltage DC power grids, due to their anticipated short fault clearance times. However, at elevated grid voltages, multiple high-power IGBTs connected in series must be considered and hence, design challenges arise, such as transient and steady-state voltage mismatches between the IGBTs. This study presents a novel hybrid method for achieving even voltage sharing in a solid-state breaker comprising two series-connected devices, by utilizing snubber circuits and a mutually coupled gate core technique. The proposed method minimizes the required snubber capacitances and hence, the size and cost of the entire breaker are reduced. Simulation results reveal that the required snubber capacitance is reduced by 60%. Additionally, a down-scaled laboratory prototype has been designed and tested in a 1100V DC grid. Experimental results validated the proposed concept by interrupting a 34A current, and by sharing the voltage between two series-connected IGBTs evenly when a propagation gate signal delay of 1μs was introduced.
Dual-Band CMOS RF Front-End Employing an Electrical-Balance Duplexer and N-Path LNA for IBFD and FDD Radios

This article proposes a dual-band complementary metal–oxide–semiconductor (CMOS) radio frequency (RF) front end employing a hybrid transformer-based electrical-balance duplexer (EBD) and $N$ -path filter embedded low-noise amplifier (LNA) for both in-band full duplexing (IBFD) and frequency-division duplexing (FDD) radios. To securely obtain sufficient self-interference cancellation (SIC) or transmitter (TX) leakage rejection in IBFD and FDD modes of the EBD at both low-band (LB) (0.7–1 GHz) and mid-band (MB) (1.8–2.2 GHz) frequency ranges, a band-switchable hybrid transformer is designed with a finely adjustable balance network to control the antenna impedance variations. In addition, the four-phase $N$ -path LNA is utilized to further offer TX leakage rejection in the FDD operation. This RF front-end is fabricated using a 65-nm CMOS technology and is mainly characterized by long-term evolution frequency bands. The demonstrated design achieves SIC and TX leakage rejection greater than 55 dB for IBFD and FDD modes of all measured frequency bands, which has a maximum power handling capability of +25 dBm. Furthermore, the design attains voltage gains greater than 8.7 dB for LB and 3.7 dB for MB, noise figures of 10.4 dB for LB and 12.1 dB for MB, out-of-band (OB) P1dBs greater than +21.4 dBm for LB and +21.5 dBm for MB, and OB IIP3s greater than +39.7 dBm for LB and +43.1 dBm for MB. It draws a bias current of 23.8 mA from the LNA and output buffer with a nominal supply of 1.2 V and has an active area of approximately 2 mm2.
A Clock Synchronization Ramp Sloping Active Clamped PWM Circuit

Due to current control characteristic may be lost when slope compensation is too large in a current PWM controller. A clock synchronization ramp sloping compensation method is presented in this paper. Firstly, a voltage-current circuit is used to convert the clock signal into a synchronous current ramp signal, and then the wave of PWM is produced by comparing it with the loop error. Moreover, the RS flip-flop stability is ensured from the aspects of circuit timing and circuit structure. External resistivity parameters and external voltage set the maximum duty ratio so that the circuit can avoid sub-harmonic oscillation. Using the delay circuit to delay the main power MOSFET (NMOS) signal rise edge and active clamp MOSFET (PMOS) signal fall edge, realize interactive drive time adjustment of active clamped circuit. A high voltage BCD process fabricates the designed circuit to satisfy the voltage requirement from 8V to 36V and prevents the generation of latch-up. Simulation and verification results show that the clock synchronous ramp sloping active clamp PWM circuit has a good load adjustment rate and high efficiency. The maximum duty cycle can reach 80%, and the efficiency reaches 90%.
Circuit Type Multiple Beamforming Networks for Antenna Arrays in 5G and 6G Terrestrial and Non-Terrestrial Networks

To support the ever-increasing demand on connectivity and datarates, multiple beam antennas are identified as a critical technology for the fifth generation (5G), the sixth generation (6G) and more generally beyond 5G (B5G) wireless communication links in both terrestrial networks (TNs) and non-terrestrial networks (NTNs). To reduce the cost and power consumption, there is a marked industrial interest in adopting analogue multiple beam antenna array technology. A key sub-system in many of such antenna arrays is the circuit type multiple beamforming network (BFN). This has led to a significantly renewed interest in and new technological developments of Butler matrices, Blass matrices, and Nolen matrices as well as hybrid structures, mostly at millimeter-wave frequencies. To the best of the authors’ knowledge, no comprehensive analysis and comparison of circuit type multiple BFNs have been properly reported with focus on 5 G and 6 G applications to date. In this paper, the principle of operation, design, and implementation of different circuit type multiple BFNs are discussed and compared. The suitability of these sub-systems for 5 G and B5G antenna arrays is reviewed. Major technology and research challenges are highlighted. It is expected that this review paper will facilitate further innovation and developments in this important field.
Vulnerability Assessment and Attack Simulation of Power IoT Based on the Attractiveness of Equipment Assets

With the rapid development of the electric power Internet-of-Things (power IoT) technology and the widespread use of general-purpose software, hardware and network facilities, the power IoT has become more and more open, which makes the traditional power system face new cyber security threats. In order to find the vulnerable device nodes and attack links in the power IoT system, this paper studies a set of attack path calculation methods and vulnerability node discovery algorithms, which can construct a power IoT attack simulation program based on the value of equipment assets and information attributes. What’s more, this paper has carried on the example analysis and verification on the improved IEEE RBTS Bus 2 system. Based on the above research plan, this paper finally developed a set of power IoT attack simulation tool based on distribution electronic stations, which can well find the vulnerable devices in the system.
Research on transmission line trip prediction based on logistic regression algorithm under icing condition

The number of transmission line trip accidents caused by icing is increasing year by year. In order to ensure the safety of power grid and reduce the equipment damage caused by icing, this paper studies the prediction of transmission line trip probability under icing condition, and proposes a prediction model of icing trip based on logistic regression algorithm, which mainly includes feature extraction, model construction and prediction test. Firstly, the data mining method is used to preprocess the data and obtain the effective data as the input features of the model. Then, the logistic algorithm is used to build the prediction model to realize the transmission line icing trip prediction, which provides reference information for the actual line safety operation and maintenance.
Intelligent, sensor-based condition monitoring of transformer stations in the distribution network

Today’s maintenance and renewal planning in transformer stations of energy distribution networks is mainly based on expert knowledge, experience gained from historical data as well as the knowledge gathered from regular on-site inspections. This approach is already reaching its limits due to insufficient databases and almost no information about the stations’ condition being gathered between inspection intervals. A condition-based strategy that requires more maintenance for equipment with a high probability of failure is needed. Great potential is promised by intelligent sensor-based diagnostics, where objective comparability can be achieved by condition monitoring of the station fleet. Cost-effective micro-electromechanical (MEMS)-bases sensor systems promise to provide a suitable solution for network operators and enable a widespread use. In our paper, we present a MEMS-based sensor system, that can be used to gain information about network transparency, station safety as well as maintenance and renewal planning. Moreover, we propose an intelligent measurement scheme which adaptively selects relevant data and avoids unneeded redundancy (Smart Data instead of Big Data).
LSTM and RBFNN Based Univariate and Multivariate Forecasting of Day-ahead Solar Irradiance for Atlantic Region in Canada and Mediterranean Region in Libya

For optimal functioning, grid-connected photovoltaic (GCPV) systems need day-ahead power forecasting, as this ensures overall enhanced management in areas such as reliability, scheduling, and efficiency in energy trading. Solar irradiation forecasts are especially important for obtaining photovoltaic (PV) power production predictions, given that that PV output represents a function of solar irradiation. Recently, the Long Short-Term Memory (LSTM) model is being increasingly applied in solar irradiance forecasting, but the performance of LSTM is still relatively unknown. The present paper explores how meteorological and geographical (i.e., exogenous) and past records of solar irradiance (i.e., endogenous) variables may be incorporated as input features in day-ahead solar irradiance forecasting models that use deep learning models. In this study, the results for the LSTM model are compared to those for the Radial Basis Function neural network (RBFNN) in relation to both multivariate time series forecasting (MTSF) and univariate time series forecasting (UTSF). The results of the comparisons show that the UTSF_LSTM model performs better than other models with regard to minimum forecasting errors. Our results have also been validated with data from a region that features different climatic conditions from those originally tested. Overall, the outcome of these investigations clearly indicate the superiority of the proposed UTSF_LSTM method when compared to the UTSF_RBFNN, MTSF_RBFNN, or MTSF_LSTM developed models with regard to the coefficient of determination (R2) and the Root Mean Square Error (RMSE).
On a New SNR Estimation Approach With Polar Codes

A maximum likelihood polar code-aided (ML-PCA) SNR estimation algorithm is proposed in this paper. In the ML-PCA algorithm, the posterior soft information of polar BP decoding is adopted to help the SNR estimation process. Benefiting from the excellent performance of polar code, the ML-PCA algorithm is able to acquire more reliable decoding information and further improve the SNR estimation quality. By detailed derivation, the SNR estimator and Cramér-Rao Lower Bound (CRLB) of the proposed algorithm are achieved. Numerical simulations demonstrate that the ML-PCA algorithm considerably outperforms the conventional M2M4 and ML-NDA algorithms.
Uncertainty and Disturbance Estimator-Based Robust Control of Quadrotor without Velocity Measurements

In this paper, an uncertainty and disturbance estimator (UDE)-based position controller for quadrotors in the absence of velocity measurements is presented. Firstly, the nonlinear dynamics of a quadrotor is derived and simplified into a decoupled control design model using small angle approximation. Secondly, an UDE is constructed to compensate the system uncertainties and external disturbances. By applying passivity techniques to produce damping in the system, the proposed UDE-based controller can achieve trajectory tracking without velocity measurements and has a good robust performance. Moreover, the stability analysis of the closed-loop system is also introduced. Finally, the proposed method and the control method based on classical uncertainty and disturbance estimator are implemented and compared in the Gazebo simulation and flight experiment with a real quadrotor platform, are implemented and compared in the Gazebo simulation and flight experiment with a real quadrotor platform. Both simulation and experiment results demonstrate the effectiveness and advantages of the proposed control method in signal tracking and disturbance rejection.
Enhanced Prediction for Investment Portfolio Management using Multiple Kernel-based Long Short-Term Memory-Network for Nickel Price Prediction

Nickel is a key competitive material source with commodities and economic characteristics, the price volatility of which would influence shareholder's decisions. For this reason, an accurate pattern prediction of nickel prices is a crucial concern for the strategic planning of investors in the nickel trade; however, the conventional neural network models are not effective in terms of predictive performance and suitability. So, Gaussian Processes-Long Short-Term Memory (GP-LSTM) model was suggested to learn the LSTM kernels and predict the nickel prices. However, this kernel function representation was not plenty for a huge amount of data captured from various sources. Also, it cannot completely learn dynamic, composite and structural data attributes. Therefore in this article, a Multiple-Kernel LSTM (MKLSTM) network model is proposed for learning the huge amount of data and predicting the nickel prices efficiently. The core objective of this MKLSTM model is to resolve the difficulty of adding LSTM and multiple kernel functions using data from nickel industries. This model is applied to find the significant attributes for representing suitable data. Also, the proper larger kernels for learning dynamic and structural data attributes are selected by constructing a Directed Acyclic Graph (DAG). By using the selected kernels, the MKLSTM is learned to predict the nickel price and its fluctuations. At last, the experimental results exhibit the performance of MKLSTM using the nickel price database compared to the LSTM and GP-LSTM models.
Hyperspectral Image Processing Pipelines on Multiple Platforms for Coordinated Oceanographic Observation

The large size of hyperspectral images and the computational demands required for interpreting them complicates their acquisition, distribution, and utilization by remote agents. The Norwegian University of Science and Technology is building an ocean observation system, including the HYPSO-1 hyperspectral imaging satellite, to monitor ocean color in coastal waters. The images collected by HYPSO-1 will be used to inform an ensemble of robotic agents which locally monitor ocean conditions. Several agents will be unmanned aerial vehicles that likewise carry hyperspectral imagers. Hyperspectral images will be processed on-board the different agents, as well as on the ground. Here, we discuss the architecture, development, current status, and future opportunities of hyperspectral image processing pipelines on these platforms.
Transmit Beampattern Synthesis for Planar Array with One-bit DACs

In this paper, the problem of transmit beampattern synthesis for planar array, which deploys one-bit digital-to-analog converters (DACs), is investigated. By properly designing the emitted signal quantized by one-bit DACs, the synthesized transmit beampattern is able to approximate a desired beampattern closely. However, the formulated problem is highly nonconvex due to the quartic objective and the discrete binary constraint. To tackle this problem, we first reformulate it into quadratic objective with interval constraint. On this basis, the alternating direction multiplier method (ADMM) framework is used to solve the resulting quadratic program (QP) problem. Simulation results will demonstrate the effectiveness of our method.
Mathematical Model of a Three-Phase PWM Rectifier with Direct Power Control System

in the article an energy efficient electric drive system with PWM rectifier is considered. The article describes a detailed mathematical description of the mathematical model for a PWM rectifier with a direct power control (DPC) system. The paper also discusses a PWM rectifier control algorithm based on direct power control. In the article the functional and structural diagrams of the automatic control system for PWM rectifier, as well as the results of mathematical simulation are shown. The simulation results of an electric drive with a PWM rectifier show the correct work and high performance of the direct power control system. The developed mathematical model allows to significantly reduce the simulation time for complex electric drive systems in comparison with models, based on the using the SimPowerSystem library from the Simulink application.
Autonomous Collaborative Transport of a Beam-Type Payload by a Pair of Multi-rotor Helicopters

Collaborative payload carrying by multi-rotor Unmanned Aerial Vehicles (UAVs) is presented in this paper. We propose a unique control strategy for a pair of UAVs operating with a beam-type payload that is independent of precise localization techniques or unconventional sensor equipment, allowing the system to be operable outside of the laboratory environments. The designed control system comes out with the dynamics of the coupled system, which corresponds to a bicopter aerial vehicle. Such a configuration allows for the use of estimation and control methods typical for a conventional multi-rotor aerial vehicle. The proposed master-slave control system consists of a feedback controller and an MPC reference tracker on the side of the master agent. The slave agent serves as an actuator under command of the master. In addition to the control, a system for payload detection and localization is presented. We fuse the data from RGB and depth cameras to provide sufficient conditions during payload grasping. A state machine was designed to synchronize the master-slave collaborative operations, including payload grasping or response to failure.