Miracle-dz/UniFAR-FTUs huggingface dataset:
Characteristics of energetic solar flare electron spectra

A 55 event survey of energy spectra of 0.1-100 MeV interplanetary electrons originating from solar flares as measured by two spectrometers onboard the ISEE 3 (ICE) spacecraft for the years 1978-1982 has been completed. Spectra generated using the maximum flux of a given event in each energy channel were restricted to events with a well-defined flux rise time. Two broad groups of electron spectra are considered. In one group, the spectra are well represented by a single power law in rigidity with spectral index in the range 3-4.5. The spectra in the other group deviate from a power law in rigidity systematically in that they harden with increasing rigidity. Events with near power-law spectra are found to be correlated with long-duration soft X-ray events, whereas those with hardening spectra are correlated with short-duration events. The possible variation of acceleration and propagation processes with the properties of the flare site is discussed, using the duration of the soft X-ray flare emission as an indicator of the physical parameters of the flare site (flare volume, density, coronal height, and magnetic field geometry). 29 refs.
Fast CU Partition Method Based on Extra Trees for VVC Intra Coding

In this paper, we propose a method to skip unnecessary CU encoding modes for VVC Intra coding based on the extra trees model. Two extra tree models with calculated features are used to simplify the encoding process, where the first model determines whether to early terminate the partition and the best partition direction and the second model selects the better partition mode between the binary and ternary partition modes. Experimental results show that our proposed method can save encoding time from 34.68% to 46.70% with only from 0.81% to 1.65% increase of BDBR compared to VVC reference software (VTM 10.0). Besides, the method gets a great tradeoff when applied on VVenC 1.0, an efficient encoder ofVVC, at both preset slower and preset medium.
Re-Evaluation Strategies for 5G NR V2X Communications

5G NR V2X introduces novel MAC features to support advanced V2X services for connected and automated driving. One of these features is the re-evaluation mechanism designed to detect and avoid packet collisions. The re-evaluation mechanism checks before every transmission if the selected resources are still available. If another vehicle has selected the same resources, the re-evaluation mechanism triggers a reselection of resources. The 3GPP standard defines different strategies for vehicles to decide when to perform a re-evaluation check, and how many times a re-evaluation check should be executed before the transmission of a packet. This study evaluates for the first time the impact of two standard strategies on the operation and performance of NR V2X mode 2. The evaluation demonstrates that the two strategies offer a balance between transmission latency and computational cost. We then propose an alternative re-evaluation check strategy that can simultaneously reduce the transmission latency and computational cost without actually degrading the reliability of NR V2X mode 2 transmissions.
The physics of dissipational galaxy formation.

A number of arguments lead one to consider the possibility that the first massive galaxies were formed after redshift 10. The formation of galaxies at small redshifts can only have occurred if the formation process was dissipative. This paper is concerned with one theory of dissipative galaxy formation: the theory which arises from the supposition that a protogalactic cloud would not have fragmented very extensively during the first phase of its infall and thus that the collapse of the cloud would be halted by the formation of shock fronts around a caustic surface. Detailed calculations of the formation and evolution of these shock fronts yield values for the physical conditions of the protogalactic gas and the rate of radiation of galactic binding energy by such shocks. The adiabaticity or isothermality of these shock fronts is found to be dependent on the characteristic infall velocity and the surface density associated with the shock front but not on the shape of the initial density profile perpendicular to the incipient shock surfaces. Protogalaxies which recollapsed at high z are more likely to have generated isothermal shocks than ones which collapsed at low z. It is found that this binding energy release could makemore » collapsing protogalactic clouds very high surface brightness objects. It appears that there is a scale radius approx.30 kpc associated with the collapse of the most massive galaxies and that it may be possible to explain the original inability of the material of galactic disks to fragment in terms of this scale radius. A similar theory of the origin of Magellanic irregulars, low surface brightness spheroidals, and dwarf ellipticals is suggested. « less
A partially high conductive coating adjusting surface charge distribution for mitigating surface flashover

High voltage direct current (HVDC) energy transmission has solved the problems of deliverying electric power of large capacity over long distances. During the operation of DC excitation, large amounts of charges inevitabley accumulate on insualtion surface, posing a hazardous effect on surface insulation performance. How to effectively control surface charge accumulation still remains as a chanllenge that we must face. In this study, a partially high conductive coating method for regulating surface charge distribution on spacers is proposed. The surface charge behavior after DC excitation is obtained on four kinds of partial coating, namely HV coating, GND coating, MID coating and $\mathbf{HV}+\mathbf{GND}$ coating. The DC flashover performance on this four kinds of coated spacers is also measured. The corresponding charge transport and flashover mechanism are revealed. It is hoped this research could provide valueable referances for designing insulation structure of DC power equipment.
Modeling Aspects of Controllable Load Resources in Bitcoin Mining Applications

This paper discusses the modeling aspects of Controllable Load Resources (CLRs) in cryptocurrency mining applications, especially in the context of bulk power systems. It is still a relatively nascent topic for all stakeholders, including transmission system owners, operators and balancing authorities. The paper highlights the findings from literature review germane to this topic, proposes an approach to model the frequency and voltage response characteristics of the CLRs, and finally presents the results from an example case study to reinforce and validate the models.
Twisted crossed products of C*-algebras

Group algebras and crossed products have always played an important role in the theory of C*-algebras, and there has also been considerable interest in various twisted analogues, where the multiplication is twisted by a two-cocycle. Here we shall discuss a very general family of twisted actions of locally compact groups on C*-algebras, and the corresponding twisted crossed product C*-algebras. We shall then establish some of the basic properties of these algebras, motivated by the requirements of some applications we have in mind [2, 9, 10]. Some of our results will be known to others, at least in principle, but we feel that a coherent account might be useful.
Comparison of Axial and Radial Flux Permanent Magnet Machines

Permanent-magnet (PM) machines are being used in many applications due to their high efficiency. There are two main topologies of PM machines based on the direction of the flux: the radial and axial flux PM machines. The goal of this research is to investigate the differences between them. Exploring the differences can help select the proper layout for the desired application, such as lower heat loss and vibrations of the machine. There are many criteria when it comes to design of the machine, including material usage, sizing, power output. The focus is given to the comparison of the following parameters: structure of the machines, electromagnetic losses, noise and vibration, and cooling methodologies for both topologies as these are some of the essential parameters to consider picking the suitable machine.
IoT Device Control with Offline Automatic Speech Recognition on Edge Device

Automatic speech recognition (ASR) on edge device still barely used in industry. Most of ASR such as speech-to-text commonly depend on the network presence. This is the disadvantages of using the server-based architecture that cannot always be reliable and available. In some conditions, the speech recognition need to be used in offline condition and edge-processing. Raspberry Pi, one of many edge devices will use offline ASR platform with only runs on python language. These platforms will encourage to do speech recognition in offline condition and edge device processing. Vosk, Picovoice/Cheetah, Sopare runs with python language and some of it required Word-Error-Rate (WER) on the result to validate result value. The 5 simple commands trained on each platform. Overall result, these 3 platforms are working well in offline environment with good accuracy (>80%). Vosk has around 84% accuracy, Picovoice/Cheetah has around 72% and Sopare has around 95% accuracy. Picovoice/Cheetah need an internet connection at first application startup.
Detectability of periodic gravitational waves by initial interferometers

I review three recent theoretical developments in neutron star physics predicting that rotating neutron stars could be very strong emitters of periodic gravitational waves. These imply a small but nonzero chance that ground-based interferometers could detect their first periodic signal in the next few years rather than after advanced upgrades. They also imply that upper limits will become astrophysically interesting before advanced upgrades. I discuss the implications for near-future searches and for the astrophysical payoffs of proposed small upgrades to initial interferometers.
The poly-alphabetic substitution ciphers - a viable solution for IoT applications?

Trying to answer to a simple question, with a rather complicated answer, as “are poly-alphabetic substitution ciphers a viable solution for IoT? ”, this work approaches the domain of symmetric cryptography through two poly-alphabetic encryption algorithms - Vigenere's and Baptista's. The former, from classical cryptography, the latter from chaos-based enciphering domain. Text and image have been subject to encryption using both methods in MATLAB simulation and their performances were compared. They proved to be similar and weaker when opposed to a third algorithm from the literature, when applied to the same data from two frequently used databases. A basic analysis of the 802.14. 5 technical standard used in the Internet of Things (IoT) private-key cryptography-based security is investigated. Furthermore, the same statistical attack utilizing the coincidence index and the Chi-Squared test proven efficient on Vigenère's cipher is advanced as a possible valid cryptanalysis for all poly-alphabetic substitutions, including Baptista's cryptographic system and IoT's Advanced Encryption Standard (AES).
Design and Implementation of Reconfigurable RS Processor

As a representative error correction code, Reed-Solomon code is widely used in the related fields of information transmission and storage for its excellent performance. Application Specific Integrated Circuit (ASIC) is a mainstream hardware implementation solution. Although it has a high energy efficiency ratio, it has poor flexibility and cannot be applied to other communication environments. It needs to be redesigned and tape-out according to the application requirements, and the cost is enormous. Given the above problems, this paper proposes a coarse-grained reconfigurable processor suitable for RS encoding and decoding technology, which can meet the requirements of Forward Error Correction (FEC) technology in different communication scenarios. The design can conFigure RS codec circuits with different codes, different code lengths and different degrees of parallelism according to the instructions, and can support dynamic configuration of the processor under the condition that the total code length is constant and the signal code length is adjustable. There are broad application prospects in terms of flexibility.
Multi Fish Detection and Tracking Using RGB-D Vector

Multi fish detection and tracking has got cogent significance in the area of image processing. The tracking methodologies has improved tremendously during the last decades, however, it is still a complex problem to solve. The importance of object tracking can be seen in various applications like video communication, machine inspection, robot navigation and augmented reality. This paper presents Microsoft Kinect camera sensor-based fish detection and tracking. The Kinect sensor provides depth and RGB images along with reduced noises and holes. The admiring traits of the depth and color information given by the Kinect sensor brings up various opportunities to resolve elementary issues in the field of computer vision. Fish detection and tracking in a constrained environment (aquarium) aids in the research of marine biologists to understand and study the behaviour of fishes. The use of commodity hardware devices like Microsoft Kinect for detection and tracking is economical for the researchers and also helps to automate the study of fishes round the clock without any requirement of manual intervention. In this paper, we have calibrated depth and RGB images to detect and track multiple fishes present in each frame of a recorded video. Due to the bleak body deformation of fish that results in their complex motion and multiple occlusions, robust fish tracking from video image sequences is a highly challenging task. In this paper a methodology is proposed to overcome these problems and multiple fishes can be tracked using centroid of the fish as central component and also the detection accuracy for RGB and RGBD frames are compared. The results show that the performance of RGBD method has 12.51% more accuracy on average over RGB based input.
An Anchor-Free Defect Detector for Complex Background Based on Pixelwise Adaptive Multiscale Feature Fusion

Defect detection is a task to locate and classify the possible defects in an image. However, unlike common object detection tasks, defect detection often needs to deal with images with relatively complex backgrounds, for example, in industrial product quality inspection scenario. The complex background can greatly interfere with the feature of the target objects in the multiscale feature fusion process and therefore puts great challenge on the defect detector. In this work, a channel-space adaptive enhancement feature pyramid network (CA-FPN) is proposed to eliminate this interference from the complex background. By extracting the inner relationship of different scale features, CA-FPN realizes adaptive fusion of multiscale features to enhance the semantic information of the defect while avoiding background interference as much as possible. In particular, CA-FPN is very lightweight. Moreover, considering that defects are often of varying sizes and can be extremely tiny or slender, a flexible anchor-free detector CA-AutoAssign is proposed by combining CA-FPN and an anchor-free detection strategy AutoAssign. Based on the Alibaba Cloud Tianchi Fabric dataset and NEU-DET, CA-AutoAssign is compared with the state-of-the-art (SOTA) detectors. The experimental results show that CA-AutoAssign has the best detection performance with AP50 [mean average precision (mAP) with the intersection over union (IOU) threshold of 50%] reaching 89.1 and 82.7, respectively. Despite the improvement in accuracy, the processing time has barely increased. Furthermore, CA-FPN is applied to other classical detectors, and the experimental results demonstrate the competitiveness and generalization ability of CA-FPN. The code is available at https://github.com/EasonLuht/CA-AutoAssign.git.
Meshfree modelling of one-dimensional contaminant transport in unsaturated porous media

This paper deals with the application of a meshless method called element-free Galerkin method (EFGM) for modelling the contaminant transport through the unsaturated porous media with transient flow condition. In EFGM, an approximate solution is constructed entirely in terms of a set of nodes and no elements or characterization of the interrelationship of the nodes is needed to construct the discrete equations. For analysis, the one-dimensional form of advection–dispersion equation with linear first-order degradation is considered. The unknown concentration values are approximated by using moving least square approximants. The weak form of the governing equation is formulated and the Lagrange multiplier method is used for enforcing the essential boundary conditions. The van Genuchten model is used for describing the hydraulic properties of the unsaturated soil. MATLAB code is developed to obtain the numerical solution. Numerical examples representing various phenomena that occur during the migration of contaminants are presented to illustrate the applicability of the proposed method and the results are compared with those obtained from the finite element method and the analytical solution.
Hardware characterization of Integer-Net based seizure detection models on FPGA

Deployment of deep neural network (DNN) infer-ence on platforms like field programmable gate array (FPGA) for acceleration can be challenging because of the limited resource availability and a large number of floating-point matrix operations involved. Therefore, in this work a hardware efficient algorithm called Integer-Net which is based on approximate floating-point operations is studied for edge DNN inference deployment. This algorithm uses integerized floating-point arithmetic with a scalar correction for the matrix operations. Electroencephalo-gram (EEG) signal based automatic high-speed epileptic seizure detection using Integer-Net convolutional neural network (CNN) was hardware-implemented on Zynq-7000 SoC to characterize performance efficiency and hardware resources utilized against the full precision model. Implementation was benefited by the accelerated outputs, leveraged the optimum on-board resources, and at the same time with the help of configurable integer bit-width, facilitated keeping the accuracy close to the original model. This is the first time, Integer-Net based designs and their novel hybrid versions were employed and investigated for the hardware acceleration of a CNN network for seizure detection on FPG A. A latency acceleration of 5.65x with the on-chip memory usage reduction factor of 5.99x was achieved by the optimized hybrid integerized CNN model.
Flight Delay Prediction Based on Gradient Boosting Ensemble Techniques

In recent years, the volume of airline transportation has increased with the rapid development of aviation. With an increased demand for flights, aviation is confronted with the issue of flight delays, which becomes a series of issues that must be addressed efficiently. Correct flight delay prediction can improve airport operations efficiency and passenger travel comfort. The current study uses Gradient boosting ensemble models to build a machine learning flight delay prediction model. The Airline dataset was subjected to three different gradient boosting techniques: CatBoost, LightGBM, XGBoost, and Decision tree. to validate the performance and efficiency of the proposed method, a comparative analysis between the top performed Boosting techniques with other Ensemble Techniques is performed. CatBoost improves prediction accuracy while maintaining stability, according to the comparison results on the given dataset.
Oxygen vacancy formation energy and its effect on spontaneous polarization in Bi 4 Ti 3 O 12 : A first-principles theoretical study

We have studied the formation energy of oxygen vacancy and its effect on spontaneous polarizations of ${\text{Bi}}_{4}{\text{Ti}}_{3}{\text{O}}_{12}$ (BT) by first-principles theoretical calculations. We have found that neutral and $+2$ charged oxygen vacancies prefer sites in the ${\text{Bi}}_{2}{\text{O}}_{2}$ layers, while $+1$ charged oxygen vacancies are not stable. The spontaneous polarizations of the perfect-crystal BT in both $a$ and $c$ directions are in agreement with experiment. We have also shown that even if neutral or charged oxygen vacancies exist in a ${\text{Bi}}_{2}{\text{O}}_{2}$ layer, the spontaneous polarizations are not altered significantly from those of the perfect crystal.
Detecting communities in large networks using the extended Walktrap algorithm

In this paper, we extended the Walk trap algorithm [16], which improve consisting of vertices connecting First, we propose an extended lazy random walk (ELRW), and then we use this walk to define the distance between vertices on a graph similar to the Walktrap algorithm. When applying this algorithm, if we choose the coefficient of a reasonably extended lazy random walk, we will get better clustering results when using the Walk trap algorithm. Finally, we also program to demonstrate the efficiency of this algorithm.
effects of driving mechanisms in geodynamo models

We compare the influence of different mechanisms for driving convection in the Earth's core on the structure of the magnetic and velocity fields using a 3D numerical dynamo model. We find dynamos with a dipolar magnetic field in cases of chemical convection or convection driven by an imposed temperature contrast. With purely internal heating we obtain only dynamos with a quadrupolar or more complex field. The relative strength of convection and magnetic field generation in the regions close to the poles depends on whether a condition of fixed composition or of fixed chemical flux is specified on the inner core boundary. If applicable to the geodynamo, our results favor the dominance of chemical convection during the past 3 Gyr.
Convolutional Neural Networks based Liver Tumor Classification

The most common and widespread disease among today's population is liver disease, which is caused by excessive alcohol consumption, polluted gas produced by various chemical factories, drugs, spoiled or tainted food, and obesity. Liver is the most important body organ as it performs the detoxification process. As a result, early disease detection plays a crucial role in the disease diagnosis and recovery process. Early prediction of liver disease has been made possible with the introduction of machine learning technology. This technology provides significant benefits to the healthcare sector by developing new ways to deploy early disease prediction system even in a remote location. SVM, KNN, K-Mean clustering, neural networks, decision trees, and other machine learning techniques are used to implement liver disease diagnosis in order to provide varying levels of accuracy, precision and sensitivity. Unsupervised learning, supervised learning, semi-supervised learning, and reinforcement learning can also be used. This research study intends to compare all the machine learning algorithms to investigate and predict the liver disease and the resultant performance is evaluated based on sensitivity, relevance, accuracy, and precision.