Miracle-dz/UniFAR-FTUs huggingface dataset:
The quasi-adiabatic ion distribution in the central plasma sheet and its boundary layer

This paper derives for the first time, the consequences of quasi-adiabatic ion motion in the magnetospheric tail, which is in fact a chaotic scattering of the ions due to their essentially nonadiabatic behavior. This is caused by the nonlinearity of their equations of motion in the strongly curved tail magnetic field and results from separatrix traversals in the phase space. In this paper it is shown how the continuous violation of adiabaticity of the ion motion in the Earth's magnetotail leads to a redistribution of ions between the central plasma sheet (CPS) and the plasma sheet boundary layer (PSBL). It is also shown that, due to the mechanism described in this paper, ions are accelerated to the PSBL even without any assumption of the existence of an electric field caused by reconnection with an χ line. For the CPS ion distribution this acceleration also results in the depletion of some domains in velocity space that can lead to substantial non-Maxwellian features.
Current Calculation and Characteristics Analysis of Fault Current in Hybrid Grids

As a crucial component for the New Power Systems, the Modular-Multilevel-Converter-based High Voltage Direct Current (MMC-HVDC) girds face challenges of short-circuit faults caused by the lightning strike. Calculating fault current and analyzing characteristics are of vital significance to the dc protection. However, the accurate calculation requires multiple iterating simulations in Electromagnetic Transient (EMT) type software. Therefore, a method of calculating dc fault currents in the rising stage in the MMC- HVDC grids is proposed. It contains equivalent modeling, basic-cutset-matrix equations formation in complex frequency domain, and inverse Laplace transformation. By employing the proposed calculation method, the computing amount is hugely decreased and can be extended to the MMC-HVDC grids with arbitrary topology. Finally, the proposed method is verified in comparison with the simulation results based on EMT software, and the influence of system parameters on fault current characteristics are further analyzed based on the proposed method.
Underwater Fish Identification in Real-Time using Convolutional Neural Network

Artificial Intelligence (AD is the wide application that learns the problem and features by given data and processes the data like the human brain. When a computer program imitates a characteristic of the human brain that is considered “innovator. ” Among the methods are statistical methods, methods for artificial intelligence, and traditional order to verify the validity. The expansion of AI is also related to virtually infinite storage and an abundance of data, including exchanges, geospatial information, video files, photos, text messages, and audio files. Machine learning is divided into deep learning and deep learning is primarily divided into numerous layers of neural networks. This pattern gives it the ability to learn a lot of information and attempt to replicate the brain function. Increasing the efficiency by attaching more covert layers can be beneficial. It is used to gather the data and transfer the data. Aquaculture production has grown into a barrier to the growth of fish culture and the counting operation represents one of the problems experienced during the spawning process. Previous studies have primarily relied on the application of manual and automated counting techniques, which has prevented it from producing accurate results. The proposed method offers a promising method for enhancing image detection by combining the IoT techniques. The image data were divided into three categories: low frequency, intermediate density, as well as high frequency. The proposed method has used 8200 images to train and 2500 images for verification. Only the data relevant data sources were used during the train and verification phase in order to find the proper parameters and create a better VGG19 parameter calibration strategy. Consequently, the improved VGG19 model can achieve an accuracy of 98%.
Architectural Design of Malware Infected File Detection Using Deep Learning

I. Abstract Currently, with the ongoing development of 5G and the IoT Cyberspace has grown to be a significant part of social and economic development thanks to the technology of social and economic development, as well as a fundamental domain of national security. Therefore, the detection of Malware and its variations are extremely important in cyberspace. Malware is a harmful software that is designed to damage and destroy computers and computer systems. Malware is constantly generating, updating, and manipulating which makes the detection more challenging. Viruses, Worms, Trojans, Ransomware, Adware, etc. are the common types of malware attacks. Signature based, Behavioral based and Heuristic ones are the three main methods used for malware detection. This paper discusses the architectural design for malware prediction in executable files of various applications and visualization techniques used to achieve better results by creating RGB images of these executable files and try to convert the malware prediction method into an image classification problem. InceptionV3 and the SVM model are used for feature extraction and prediction. Leopard mobile dataset and Malimg dataset are used here.
Corrosion Factors and Protective Measures of Transmission Line Equipment in High Humidity Areas

On account of the on-site corrosion investigation of power transmission and transformation equipment in Sichuan's high humidity area, the reasons for corrosion of equipment in high humidity area of Sichuan were analyzed from various aspects, and an analysis model of multi-factor influence of corrosion of transmission equipment was proposed. Through the dynamic grasp of the equipment rust area map, the equipment anti-corrosion measures are proposed from three perspectives: metal anti-corrosion degree, transmission line identification and management, and line maintenance. The results show that the high sulfur and high acid wet settlement environment is the factor that accelerates the corrosion rate of equipment.
The quantitative evaluation framework for non-structural hazards to transmission tower caused by bird nest using large images obtained by unmanned aerial vehicles

Bird nest, as a major foreign body on high-voltage transmission lines, has potential hazards to the safety of transmission lines. Its branches may burn in hot and dry weather, and fall between insulators as conductors in rainy weather, resulting in insulator flashover. Therefore, it is of great significance to detect the bird nest's positions on the transmission line in time and eliminate the hidden danger. This paper proposes a bird nest detection framework based on the panoramic image of the transmission tower. Firstly, the transmission tower is located and Simple based on Swin Transformer is used Baseline detected its 3D attitude to obtain its key points, then clipped its key points and used DenseNet to judge whether the key points have nests, so as to realize the detection of tiny nests in the large-resolution power line vista image and judge the position relationship between them and the transmission tower, so as to realize the automatic inspection of the nests of transmission lines. Experimental results show that the proposed framework can achieve 86.15%,76.71%,81.16% accuracy, recall rate and F1 score in self-made bird nest and pylons data sets.
An Adaptive Weight Ensemble Learning Method for Probabilistic Wind Power Prediction

Due to the volatility and strong randomness of wind power generation, wind power prediction is essential in order to eventually optimize grid dispatching, relieve the pressure of frequency and peak regulation of the power system, and obtain significant economic and social benefits. This paper proposes a new adaptive weight ensemble (AWE) learning method without a regression process to data mine historical data power series and ensemble learning for probabilistic wind power prediction. First, the covariance is used to calculate the correlation coefficients of different historical data to the predicted data and assign different weights to them, and then the KNN algorithm is used to find the data set in the training set that is most similar to the test set. In order to obtain more accurate prediction results, the most suitable historical data set is selected in the data set for weighted ensemble learning. Finally, the classical probability type is approximated to the practical probability distribution to determine the confidence interval. Compared with other methods, it is determined that the adaptive weighted ensemble learning method proposed in this paper is superior in terms of accuracy and computational efficiency.
Refueling Port Identification and Positioning of Refueling Robot Based on RGB-D Camera

In order to realize the automatic operation of the refueling robot, the identification and positioning system of the refueling robot is designed for the rotary piston refueling port of a special vehicle. RealSense D435i RGB-D camera and ring light source were used to build a visual system, and Hoff gradient method and Shi-Tomasi method were used to extract the features of the refueling port. Then, 3D positioning of the refueling port was realized through the registration of color image and depth image, and visual guidance was realized through hand-eye calibration. The experimental verification is carried out on the built experimental platform. The experimental results show that the positioning error of the refueling port is within ±1mm and the angle error of the slot is within 1° through the RGB-D camera method of identification and positioning of the refueling port proposed in this paper.
NTSM Based Finite-Time Consensus Control for a Networked of Quadrotor Vehicles With Disturbances

This paper presents nonsingular terminal sliding mode-based robust finite-time consensus tracking protocols for the network of leader-follower quadrotor aerial vehicles with disturbances. The consensus protocols for attitude subsystem dynamics are designed by combining velocity consensus errors and nonsingular terminal sliding mode state vectors of the local and neighbouring vehicles. The protocols for position subsystem dynamics are constructed by using velocity consensus errors and terminal sliding mode states of the local and neighbouring vehicles. Robust adaptive learning algorithms are also integrated with the protocols for both position and attitude dynamics to learn and adapt to the modelling error and other external disturbances. Learning algorithms also introduce the protocols to learn and adapt the bound of the input of the quadrotor leader vehicle. The protocols design and analysis are assumed that the vehicles are connected by local area communication networks. Lyapunov and terminal sliding mode control method uses to show that the protocols can reach an agreement with follower vehicles and converge to the states of the leader vehicle in finite time. The design and analysis are assumed that the attitude dynamics of the vehicle are faster than the position dynamics. The consensus protocols can ensure finite-time tracking as opposed to the existing asymptotic tracking system. The protocols design is simple and easy to implement as they do not need exact bound of the disturbances that appear from external disturbances and the modelling errors. Moreover, in contrast with the convergence of other distributed designs, the design ensures faster and more robust consensus as opposed to existing asymptotic tracking design against disturbances.
Performance Analysis of LEGO Mindstorm EV3 Robot with PID Controller for Trajectory Tracking Application

Robotics is an emerging field of interest among students to explore attractive tasks with their ideas. For engineering design, analytical observations are mandatory for the identification of further improvements in the design. LEGO Mindstorm robotics is the one which gives a platform for both design and analytical inference from its operation. In this work, a Proportional Integral Derivative (PID) controller-based two-wheeled line follower is developed, and its performance is analyzed from the tools of the LEGO platform. Colour and ultrasonic sensors are used in three scenarios to check the ruggedness of the controller inline tracking and obstacle detection. The robotic performance is informative and impressive with 36% more efficient in the path tracking with different scenarios using LEGO modules.
Ego Lane Estimation Using Visual Information and High Definition Map

This paper presents a new ego lane estimation method which makes full use of visual information and high-definition map. Compared with existing matching-based method, our method realizes the fusion of multiple road elements using all observed visual information and high-precision map and builds filters to apply map constraints, which achieves higher lane matching accuracy. Lane change information is also used to implement lane tracking for the robustness of our method. The experiment results show that an integrated navigation system based on this method can achieve a lane matching accuracy of more than 94%.
A novel device for measuring velocity of projectile

A novel device used to measure the velocity of a projectile for the military has been developed. This set of equipment based on the principle of statistics is composed of five groups of sensors. These five groups of sensors are symmetrically arranged to ensure that the groups of sensors measure the same point velocity, and the average value of the groups of sensors is regarded as the final test result. Compared with the traditional test method for projectile velocity, this method reduces the uncertainty of measurement by increasing the measurement samples. In this paper the F-test, T-test and variance are used to prove that the measurement value of this device is an unbiased, consistent and a valid estimation of the true value. Experiments are carried out over a wide operational range from 40 m/s to 2000 m/s velocity and from 4-mm to 12.7-mm calibre. The results show that the measurement uncertainty of this device is less than 1‰ (k = 2). This device can be suggested as a prior calibration in order to estimate other devices for measuring projectile velocity.
Uncertainty Management in Electrical Power Systems

This research work intends to propose a novel methodology for the determination of the uncertainty in the measurement of electrical losses for distribution transformers class H dry type, submerged in refrigerant liquid series 15 kV. The proposed methodology performs conformity assessment within the measurement system that may exist in routine testing laboratories to record electrical losses with test compliance certificates and associated measurement uncertainty. This research work will address the national and international technical, regulatory and normative aspects that must be implemented, analyzed and evaluated in accordance with the current dynamics that occur in the sector.
A numerical study of the propagation of organic gellant-based fuel-rich premixed spray flames

The behavior of laminar rich, organic gellant-based premixed spray flames is investigated for the first time. The pulsating evaporation of such spray droplets, that has been observed experimentally, is accounted for. The numerical investigation discloses two main important findings. First, the phenomenon of the creation of flame hot-spots in organic gellant-based spray diffusion flames, associated with the pulsating evaporation, is absent in the premixed flame context. Second, the influence of the temperature of onset of the pulsations and their frequency on the propagation velocity of premixed gel spray flames was examined. These parameters were found to impact on whether the flame front moves steadily through the initial pre-mixture or whether it exhibits oscillations in its movement.
A Sub-meter Accurate Positioning using 5G Double-difference Carrier Phase Measurements

Apart from communications, the Fifth Generation (5G) technology is also motivated by positioning requirements down to the sub-meter level across industry verticals. In this work, we utilize Universal Software Radio Peripheral to transmit and receive the 5G New Radio signals. The obtained samples are then processed by the proposed 5G code and carrier phase software defined receiver. In this process, the dedicated 5G Positioning Reference Signal (PRS) is adopted to obtain time of arrival estimates using more accurate carrier phase measure-ments instead of code phase measurements. The continuously transmitted 5G PRSs over time enable accurate carrier phase tracking and avoid ambiguity problems during the tracking stage in line-of-sight environments. Furthermore, a roadside unit is set up to observe the same signals and cancel out the measurement error introduced by the clock offset between two transmitters. Finally, some field experiments are carried out, and the experimental results indicate that using 5G double-difference carrier phase measurements from only four transmitters, a position root mean square error of 0.790m is achievable.
A Brief Overview of Deep Learning based Techniques for the Detection of Wheat Leaf Disease: A Recent Study

Wheat is an important cereal crop and is the second most consumed cereal after rice globally. It is a staple food for more than one-third of the world’s population. The production of wheat depends on various factors, such as climate, temperature, soil, pests, bacteria, and other biotic and abiotic factors. However, diseases can have a significant impact on wheat production. Various wheat diseases can affect crop yields,including leaf rust, leaf spot, spike infection, virus, bacterial back chaff, bacterial spike blight, and aphids. Leaf rust, in particular, is known to cause severe damage to wheat leaves. To combat these diseases, researchers have been investigating the use of advanced technologies such as deep learning and image-processing approaches for plant disease recognition. The process of disease detection involves several steps, including image preprocessing, segmentation, feature extraction, and classification. The accuracy of these steps directly affects the reliability and accuracy of the classification algorithms used to identify plant diseases. Recent studies have reviewed the state-of-the-art techniques used in this context and evaluated their effectiveness in real life applications. Overall, the use of advanced technologies such as deep learning and image processing for disease detection in wheat crops holds great promise in improving crop yields and reducing losses due to diseases. By identifying diseases at an early stage, farmers can take appropriate measures to control the spread of the disease and prevent significant crop damage. This research work presents a comparative analysis of the state-of-the-art work that has been carried out in this context and the effectiveness of the techniques in real-time.
Optimal Design of Flexible-Link Mechanisms With Desired Load-Displacement Profiles

Robot mechanisms that exploit compliance can perform complex tasks under uncertainty using simple control strategies, but it remains difficult to design mechanisms with a desired embodied intelligence. In this article, we propose an automated design technique that optimizes the desired load-displacement behavior of planar flexible-link mechanisms. To do so, we replace a subset of rigid with flexible links in an existing mechanism, and optimize their rest shape. We demonstrate the efficacy of our approach on a set of examples, including two fabricated prototypes, illustrating applications for grasping and locomotion tasks.
Performance Evaluation of Deep Learning Algorithm for Forest Fire Detection

The forest fire caused further destruction to the woodland region. It must be monitored and detected as early as possible. In the study, the pre-trained deep learning performance of Inception, ResNet, and ViT was compared for the categorization of photos of forest fires. These models' performances are evaluated with one another. The findings from the study indicate that the ViT model has the best accuracy at 88.03%. The ResNet-26, ResNet-50, Inception v3, and Inception v4 models have an accuracy of 86.66%, 85.64%, 83.25%, and 80.86%, respectively.
5G Traffic Sort-Term Prediction Based on OVMD Noise Reduction Decomposition and Hybrid CNN- BIGRU Optimized by Attention Mechanism

With the popularization and development of 5G networks, the performance requirements of mobile networks are getting higher and higher. Accurate 5G traffic prediction is of great help to network operation and maintenance management and daily network detection. Aiming at the nonlinear and non-stationary characteristics of 5G traffic data, this paper proposes a traffic prediction model based on OVMD noise reduction decomposition and hybrid depth CNN-BIGRU-ATTENTION model. First, the optimized VMD model decomposes the historical traffic data into stationary series to reduce data noise. Then, CNN-BIGRU-ATTENTION is constructed to predict the decomposed components one by one. CNN is used for feature extraction, BIGRU is used for time series fitting, and ATTENTION is used to focus on the influence of information. Finally, the final result is obtained by integrating components. To verify the validity of the model, this paper selects the Italian laboratory data as an example for analysis. The experimental results show that the model is superior to other comparison models and has the best flow prediction performance.
Selecting More Informative Training Sets with Fewer Observations

A standard text-as-data workflow in the social sciences involves identifying a set of documents to be labeled, selecting a random sample of them to label using research assistants, training a supervised learner to label the remaining documents, and validating that model’s performance using standard accuracy metrics. The most resource-intensive component of this is the hand-labeling: carefully reading documents, training research assistants, and paying human coders to label documents in duplicate or more. We show that hand-coding an algorithmically selected rather than a simple-random sample can improve model performance above baseline by as much as 50%, or reduce hand-coding costs by up to two-thirds, in applications predicting (1) U.S. executive-order significance and (2) financial sentiment on social media. We accompany this manuscript with open-source software to implement these tools, which we hope can make supervised learning cheaper and more accessible to researchers.
Machine learning-based classification of competitors performance: evidence from Chinese logistics companies

Digital technology is drastically altering business patterns. Enterprises strive to use these technologies to discover the worth of their consumers while understanding their rivals is required to get a decisive advantage in competitive marketplaces. In this situation, existing competitive intelligence practices require more fundamental theories to underpin them and the awareness to apply these strategies. This study will propose theory-based indicators as criteria to evaluate competitors intelligently through machine learning techniques and depict the competitive landscape using the experts' suggestions. The results reveal that the XGBoost model provides the best performance in evaluating competitors and will be used for a competitive landscape depiction with experts' suggestions. The competitive landscape shows that companies with medium R&D and high R&D are mainly located in Eastern China. In all provinces, Beijing is the most competitive. Given the number and proportion of high-R&D logistics companies, the Beijing market is nearly saturated and presents a high market entry barrier for entrants.