Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of DGA-Driven Botnets using Artificial Neural networks

A botnet is a network of devices infected with malware and controlled remotely by a person with malicious intent. Botnets can launch attacks to steal data, perform phishing, spamming and set up DDoS. Some botnets use an algorithm known as DGA- a Domain Generation Algorithm is to evade detection with some well-known blacklist detection methods. Periodically generating a list of candidate control and command server domains. The bot connects with this server with the help of querying DNS servers for the available list of domains one after another till it connects to an existing and relevant server. Creating a DGA based botnets extremely difficult to detect with their high survivability. This paper aims to analyze the Domain Generation Algorithm and implement methodologies to detect DGA-based botnets using Deep Learning and Machine Learning approaches and then compare and contrast these novel techniques with traditional Machine Learning Classifiers.
A Review of SOTIF Research for Human-machine Driving Mode Switch of Intelligent Vehicles

With the development of intelligent vehicles, human-machine driving mode switch has become a research hotspot of level 3 automated driving. The challenging problem of safety of the intended functionality (SOTIF) for human-machine cooperation driving needs to be solved. Firstly, the SOTIF analysis methods for intelligent vehicle were undertaken. Then, the detailed SOTIF problems in the driving mode switching process of shared driving intelligent vehicles were analyzed. Next, different solutions and their advantage and drawbacks were expounded in combination with specific driving factors. Finally, the switching safety strategies were summarized for guaranteeing the reduction of human-machine co-driving conflicts, and the enhancement of the safety in the development of human-machine shared driving intelligent vehicles.
Choosing the Cross-section of Cable Core for Wind Power Electrical Collector Network taking into account the economic factor

Wind power plants with nominal power of 0.5-5.5 MW, as a rule, are designed at voltages of 10–35 kV. In addition to technical conditions and restrictions, a number of economic criteria must be taken into account when designing wind power electrical collector networks. For green energy facilities, due to the high green tariff, the energy losses in the electrical collector networks can significantly affect the payback period. Therefore, during the design it is necessary to choose such cross-sections of cable core that will ensure the minimum cost for the entire period of building and operation of the wind power plant. An analytical dependence is obtained, which allows to calculate the economically justified current density for the cores cross-section of such cables for a nominal voltages of 10, 20 and 35 kV for different periods of maximum generation of wind power plants. It is established that changes in the payback period in the range of ±1 year from the estimated cross-section of cable core will not change, because the scale of cores cross-sections is discrete. The values of economic current densities for different periods of maximum generation are given in the article. They allow to choose rational cross-section of cable core during the design of wind power electrical collector networks.
Twisted and Coiled Carbon Nanotube Yarn Muscle Embedding Ferritin

Electrochemical coiled carbon nanotube yarn muscles are expected to be utilized in biosensors, drug delivery, or microsurgery. However, their low tensile actuation due to the limited applying voltage in an aqueous environment restricts further investigations in applications. Here, by embedding an iron-containing protein, called ferritin into a coiled carbon nanotube yarn muscle, the muscle stroke can be increased 10.8 times. The increased tensile contraction originates from the large electrochemical applied potential without cancellation of the stroke resulting from the potential of zero charge shift which is driven by attaching charged ferritins onto CNTs. Performance enhancement by applying biocompatible molecules encourages the potential of the coiled CNT muscle's use in the bio-environment.
A Multiyear Global Surface Wind Velocity Dataset Using SSM/I Wind Observations

The Special Sensor Microwave Imagers (SSM/I) aboard three DMSP satellites have improved a large dataset of surface wind speeds over the global oceans from July 1987 to the present. These data are characterized by high resolution, coverage, and accuracy, but their application has been limited by the lack of directional information. In an effort to extend the applicability of these data , methodology has been developed to assign directions to the SSM/I wind speeds and to produce analyses using these data. Following extensive testing, this methodology has been used to generate a seven and one-half year dataset (from July 1987 through December 1994) of global SSM/I wind vectors. These data are currently being used in a variety of atmospheric and oceanic applications and are available to interested investigators. Recent results presented in this paper show the accuracy of the SSM/I wind velocities, the ability of these data to improve surface wind analyses, and the propagation of a synoptic-scale convergent cortex in the Tropics that can be tracked from year to year in annual mean SSM/I wind fields. 11 refs., 5 figs., 2 tabs.
Technological Intervention for Supporting Individuals for Social (Pragmatic) Communication Disorders

In the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders, a new diagnostic category called social (pragmatic) communication disorder (SPCD) has been added (DSM-5). The goal of the review is to summarize and synthesize important information on social pragmatic language impairment and other previously used words associated with SPCD. This review also focuses on the technological interventions that are previously existing and supporting the special individuals to improve their style of communication and livelihood.
An MRAS-based Sensorless Control Algorithm for Permanent Magnet Brushless AC Machines

This paper presents a sensorless control scheme for Surface-Mounted Permanent Magnet Brushless AC Machines (PMBACMs), which estimates both electric rotor speed and position through a Model Reference Adaptive Systems (MRAS) approach. In particular, starting from the PMBACM sampled-data model, a priori and a posteriori quantities are introduced, based on which the adaptive law is set in accordance with the Popov’s Hyperstability Criterion. Additionally, the issues arising from the duality of the solution computed by the MRAS-based algorithm are overcome by the support of Hall sensors. The effectiveness of the proposed MRAS-based Hall-assisted sensorless algorithm is verified through numeric simulations, which also regard the employment of a double MRAS-based algorithm to enhance rotor speed and position estimation further.
Data-Driven Feed-Forward Control of Hydraulic Cylinders using Gaussian Process Regression for Excavator Assistance Functions

In this work, we introduce a data-driven feed-forward control approach for hydraulic cylinders using Gaussian process regression. Gaussian process (GP) models provide advantageous features for data-based models like the incorporation of prior knowledge and the consideration of uncertainty. We employ these properties to combine physical expert knowledge and training data in hybrid modelling and for online model adaptation. In order to match the real-time requirements for our application, we introduce a novel sparse GP methodology to reduce the computational effort for data reduction and inference. The overall control concept is implemented and validated on a test vehicle.
Implementation of Smart Transportation Application Using YOLO Deep-Learning Neural Network

Parking and running red lights are the major problems in traffic violations. This study presents two application systems for finding parking spaces and controlling traffic lights. An intelligent traffic light system adaptively controls the time of traffic lights so that the period of green lights is adjusted according to the real-time traffic flow. The traffic jam is thus effectively reduced. An intelligent parking system is also implemented to help drivers find parking spaces efficiently. Initially, a web camera is utilized to capture images. Hence, vehicles are identified to compute the remaining number of parking spaces. A website shows the remaining parking spaces. This system can help a user to find suitable parking spaces quickly. Therefore, parking violations are reduced. The YOLO (You Only Look Once) neural network is employed to identify vehicles, where the networks are trained through transfer learning. Only a small quantity of training images is required. The experimental results show that the vehicle identification accuracy rate can reach 93%, the recall rate reaches 83%, and the overall F measure reaches 87.7%. Therefore, the vehicle identification system can be applied to intelligent traffic light control and roadside parking spaces.
Excitation mechanism of forbidden Fe II 1.644 micron emission in Seyfert and starburst galaxies

The excitation mechanism of the forbidden Fe II emission in AGN and starbust nuclei (SBN) is studied by comparing the intensity of the forbidden Fe II 1.644 micron emission with those of low-ionization optical lines. A linear correlation is found between the line ratio of forbidden Fe II/Br-gamma and forbidden O I /H-alpha, while the correlation between forbidden Fe II/Br-gamma and forbidden N II/H-alpha is poor. The forbidden O I lines is radiated from hydrogen transition regions while the forbidden N II line comes from fully ionized regions. It is concluded that the forbidden Fe II emission is excited by electron collisions in extended transition regions. In SBN, the forbidden Fe II emission is due to shock heating caused by starburst winds rather than photoionization. The forbidden Fe II emission is stronger in Seyfert and composite galaxies than in pure starburst galaxies. 66 refs.
Robust design of dynamic characteristics consistency of Busbar-Mounted Electromagnetic Contactor based on a fast calculation surrogate model

The dynamic characteristics of Busbar-Mounted Electromagnetic Contactor (BMEC) directly impact the reliability of system. In design process of BMEC, single product can be designed based on digital prototype methods cored with finite element model (FEM), but for batches of products, the dispersion of some parameters makes the dynamic characteristics out of design tolerance. The consistency of batch products is hard to control in some cases. This paper presents a Taguchi method based robust design method to improve the consistency of batch products using a type of fast calculation surrogate model. The dynamic characteristics of batch products is calculated by establishing a fast calculation surrogate model based on digital prototype data. Then, parameter design and tolerance design are accomplished for the parameters that have a great impact on the static and dynamic characteristics of products through sensitivity analysis and Signal-to-Noise ratio analysis. The effective control of the dynamic characteristics of batch products is implemented and the manufacturing robustness of BMEC is enhanced.
Partial Sample Transmission and Deep Neural Decoding for URLLC V2X System

With the rapid development of intelligent transportation systems (ITS), mission-critical vehicular services such as vehicle platooning, safety alarming, and remote driving play a key role in the future ITS. In order to support these emerging services, low-latency and high reliability transmission should be ensured. In the 4G Long Term Evolution (LTE) and 5G New Radio (NR) vehicle-to-everything (V2X) systems, it is very difficult to meet the latency requirement since group of orthogonal frequency division multiplexing (OFDM) symbols are processed in a form of the resource block. In this paper, we propose a novel low-latency packet transmission scheme for V2X systems, referred to as partial sample transmission (PST). In PST, we map the transmit information into subcarrier positions and then decode it using a small fraction of received samples at the receiver. To perform the efficient decoding, we propose a deep learning (DL)-based PST decoding. From the numerical evaluations on the V2X system, we demonstrate that the proposed PST technique outperforms conventional transmission schemes in terms of the block error rate (BLER) and the signaling latency.
Multi-port Smart Transformer Integration in Residential Buildings

The smart transformer (ST) will become a fundamental element in the future of smart distribution grids. STs allow energy management by facilitating the connection between high and low voltage electric grids points, renewable energies, and energy storage devices. Multiport transformers have multiple windings for inputs and outputs, allowing different loads and generators to be connected to the same device. Multiport transformers combined with solid-state transformers (SST) could be an optimal solution for smart grids, as they can configure their operation mode on each port in accordance with the desired control strategy. This work proposes the use of a Multiport ST (MPST) in a residential building, which has batteries, photovoltaic panels, and electronic drivers to power the elevators. The paper proposes a coordination strategy for the different ports and tests its operation in different simulation cases.
Electro-Mechanical Design of Sensor-Hub for Indoor Smart Irrigation: System Prototype

A smart irrigation system, involving sensors to monitor the soil and weather parameters, helps in reducing water and power wastages. These smart systems are in general, built around Arduino board, a widely used microcontroller board, which collects the digital data from the sensors. The sensors are geographically distributed, and thus require communication modules like XBee (Zigbee), for forwarding the collected data to the edge, for further processing and transmission. While the sensors need to be exposed to soil and weather to measure the parameters accurately, the hardware needs to be protected from the environment. In this paper, the electro-mechanical design of the prototype of a standalone sensor-hub with sensors, battery pack and XBee, integrated around Arduino, for the application of indoor smart irrigation, is described and characterized. This electro-mechanical design has the advantages of being compact, standalone and scalable, and to the best of our knowledge the only design discussed so far in open literature, from electro-mechanical perspective.
The Thermal Explosion Confined by a Constant Temperature Boundary:I—The Induction Period Solution

The time history of a spatially varying thermal explosion in a vessel with constant wall temperature is considered. A one-step irreversible, high activation energy reaction of the Arrhenius type is assumed to occur in a rigid, nondiffusing, combustible material. The induction period equations are solved numerically for a system confined to a slot-like region. A stiff-equation integrator is employed to delineate the nature of the thermal runaway process. A well defined hot spot is observed to form in the vicinity of the symmetry line. A precise description of the hot spot development is given in terms of an asymptotic theory valid close to the explosion time. The solution is constructed in terms of a slowly varying conduction-controlled outer region surrounding a much smaller zone in which the relatively rapid chemical kinetics determine how the hot spot therein develops. This analytical solution describes the final phase of the spatially varying induction period thermal runaway process, which cannot be ob.
A Roadmap for Energy Saving Using Dynamic Load Balancing in Cloud and Fog Architecture

In recent years there is an exponential surge in healthcare IOT devices that subsequently led to generation of massive amount of data. IOT devices send these complex and huge medical data to cloud for analysis and storage. Most of the organization do not prefer this due to latency, privacy and security issues. To overcome the limitations of cloud-based systems, a novel paradigm called as fog computing has been created. Even though fog nodes have several advantages, they require high amount of energy to function. Software Defined Networking or SDN is a cutting-edge technology which enables intelligent and centralized network management also ‘programming’ using software applications. In this paper we present a energy efficient SDN enabled fog computing architecture for healthcare data by controlling the service rate. In this model based on buffer load it will be decided whether they will upload it in batch mode with higher processing speed or they will process the data in listening interval with low processing speed. The proposed SDN based architecture perform effectively and save energy compared to existing model. This model balances the load dynamically and handle real time data traffic concurrently.
Numerical simulations of impulsively generated mass density perturbations in a solar coronal loop

Non-symmetric oscillations in a solar coronal loop are numerically studied in the limit of a two-dimensional plasma. The obtained numerical results show that impulses, which are launched in the ambient plasma, excite oscillations in mass density profiles that reveal asymmetry in a cross-section of the loop. This asymmetry is a consequence of the fact that the loop oscillates as a whole. This feature has not been observed in the earlier numerical studies of impulsively generated waves, and it is important for the detecion of the spatial location of the source of the loop oscillations. It is also important for the observations because the density perturbations can produce corresponding perturbations of EUV or thermal X-ray emission.
Projection-Based Consensus Algorithm for Secure Localization in Ultra-Wideband Sensor Networks

Secure localization in sensor networks is a promising research in engineering field. This paper considers a scene that there exists malicious anchor in the sensor network composed by ultra-wideband (UWB) devices. A projection based consensus localization algorithm is proposed to overcome the effect of malicious anchors and achieve precise positioning for tag nodes. The algorithm firstly adopts the constrained consensus method to control the virtual agents until they stop move. Then, a selection strategy based on median is used to choose appropriate virtual agents and calculate the mean of their positions. At last, the gradient descent method is used to achieve localization with only considering the measurements from anchors corresponding to the chosen virtual agents in the previous step and setting the mean position of the chosen virtual agents as the initial solution. Furthermore, three simulations are conducted to illustrate the validity of the proposed algorithm. Compared with the traditional method, the proposed algorithm can locate the tag successfully when there exists malicious anchor in the environment.
Application of Extreme Learning Machine (ELM) Classification in Detecting Phishing Sites

Phishing site is a website created by internet criminals as closely as possible to resemble a real site to trick internet users by making it look like accessing a site from an official website. In overcoming the many phishing sites that exist in this study, the Extreme Learning Machine (ELM) classification method is used because ELM is one of the algorithms that is often used in classification and regression in machine learning. In this study, the accuracy value obtained from the test which was repeated 10 times was between 82-84% and the time between 5–11 $s$ with the best accuracy of 84.02% with a time of 7.98 $s$, the accuracy results generated from the ELM algorithm are indeed not very good. This large amount occurs because of the overfitting experienced by the formed classification model so that the false positives obtained are quite large. Referring to the dataset itself, the most influential feature or attribute in the labeling of phishing sites is the time domain expires, if the time domain expires has reached 200 days then the site has a phishing site label. In this study, ELM was compared with several other machine learning algorithms such as Support Vector Machine (SVM), Naive Bayes and Decision Tree.
SACA: System-level Analog CIM Accelerators Simulation Framework: Architecture and Cycle-accurate System-to-device Simulator

Analog Computation-In-Memory (CIM) architectures promise to bring to the edge the required compute and memory demands of TinyML applications while consuming extremely low power. However, the analog CIM paradigm is suitable for accelerating vector-matrix multiplication patterns alone, and the accuracy of the computation itself is stirred by the CIM device and its driving circuit non-idealities. Despite these practical constraints, CIM accelerators are often developed and evaluated in isolation without considering real-world system-level conditions, such as sharing system resources (host CPU, main-memory, and interconnect) for inter-layer pre/post-processing, data alignment, and data movement. These make it challenging to evaluate the energy, performance, area, and accuracy tradeoff for practical, end-to-end applications. To address this, we propose a first SoC level, gem5-based, open-source simulation framework for CIM accelerator design. It supports the modeling of hierarchical CIM accelerators for different device technologies and digital/mixed-signal driving circuit configurations, along with their non-ideal behaviour. The associated full-stack software provides APIs for (re-)configuring the CIM accelerator for offloading computations at the system level. To demonstrate some capabilities of the SACA, we carried out design space exploration on two representative TinyML tasks - Human Activity Recognition and CIFAR10 image classification. The results lead to optimal accelerator profiles and indicate a tradeoff between the energy, area, performance, and accuracy for different configurations.
ROS2 as an Interface for a Motorcycle Simulator

Simulators have been used for decades in many different areas. Military and aerospace were the first areas where simulators appeared to train people and more. More recently, we have assisted to the emergence of a multitude of simulators in many other contexts. One of them is automobiles and motorcycles.External devices, such as touch-screen displays, are often part of these simulator environments to make them more realistic and test new concepts and ideas. Usually, to expand its functionalities, simulators allow the integration of external devices, requiring those devices to be coupled to the simulators’ extension mechanisms and behaviour. This is often a difficult task as it requires to learn about the extension mechanisms, and when moving the devices to other simulators, part of the code must be rewritten.This paper shows how ROS2, a well-known and accepted middleware for robotic as well as automotive applications, can also play a role in these contexts acting as a universal interface between external devices and a simulator, thereby significantly improving portability and reusability of external devices as well as opening the simulator to the vast possibilities offered by the ROS2 ecosystem.