Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrolyte flow in an external magnetic field: a dimensionless analysis

In this paper, some recent work on the flow induced by an external magnetic fields acting on electrochemical cell is reviewed. Although the influence of the magnetic field on the hydrodynamics has been studied for over 5 decades, the magnetohydrodynamics (MHD) remains relatively unfamiliar to all but a few research groups. There are nearly a countless number of dimensionless parameters in electrolytic flow (bubble induced flow) and MHD, but they have been introduced for convenience by different authors. The similitude parameter proposed by Solheim, Johansen, Rolseth, and Thonstad (1989) and Perron, Kiss, and Poncsak (2006) have been modified to provide a full set of parameters for electrolytic cell operating under external magnetic field. The bubble sliding characteristics underneath an inclined plane are studied using copper sulphate solution (as an electrolyte) in lab-based-scale and discussed.
Evaluation of sEMG Pattern Recognition: A Preliminary Study for Prostheses

Prostheses based on surface electromyography (sEMG) have been widely used to help amputees in daily life. To guide the manufacture of prostheses based on sEMG, in this paper we used pattern recognition composed of five features and SVM to classify 9 hand movements of 10 healthy subjects after sEMG data preprocessing. Through classification accuracy comparison, we determine the classification of finger movements is the difficulty of prostheses, and 4 or 5 sensor channels are suitable for different prostheses’ requirements. Finally, we propose that it is necessary to measure the sEMG signal of amputee for some time to determine the optimal sensor positions when customizing prosthesis.
Adversarial Attacks Against Reinforcement Learning-Based Portfolio Management Strategy

Many researchers have incorporated deep neural networks (DNNs) with reinforcement learning (RL) in automatic trading systems. However, such methods result in complicated algorithmic trading models with several defects, especially when a DNN model is vulnerable to malicious adversarial samples. Researches have rarely focused on planning for long-term attacks against RL-based trading systems. To neutralize these attacks, researchers must consider generating imperceptible perturbations while simultaneously reducing the number of modified steps. In this research, an adversary is used to attack an RL-based trading agent. First, we propose an extension of the ensemble of the identical independent evaluators (EIIE) method, called enhanced EIIE, in which information on the best bids and asks is incorporated. Enhanced EIIE was demonstrated to produce an authoritative trading agent that yields better portfolio performance relative to that of an EIIE agent. Enhanced EIIE was then applied to the adversarial agent for the agent to learn when and how much to attack (in the form of introducing perturbations). In our experiments, our proposed adversarial attack mechanisms were > 30% more effective at reducing accumulated portfolio value relative to the conventional attack mechanisms of the fast gradient sign method (FSGM) and iterative FSGM, which are currently more commonly researched and adapted to compare and improve.
An Estimation and Correction Combined Method for HVDC Model Parameters Identification

Identifying correct model parameters is important for actual power system operation and control. Though existing gradient decent method shows good timeliness, it would converge to wrong results because of inevitable linearization process when applied for strongly nonlinear models. To make up this shortcoming, an estimation and correction combined method is proposed in this paper, by which the gradient method is expected to have better initial values for avoiding the local optimum trap. In the estimation process, pattern matching is utilized based on the constructed post-disturbance trajectory based typical parameters matching database. To construct the typical parameters matching database, correlation coefficient based forward and backward cluster method is applied, with which the typical parameters matching database can be updated conveniently and quickly. In the correction process, a novel comprehensive evaluation index is put forward for gradient decent method to evaluate parameter identification effects reasonably. Finally, the proposed combined parameter identification method is verified with standard high voltage direct current (HVDC) models together with parameter sensitivity analysis, and results show effectiveness.
Geographic and Cooperative Opportunistic Routing Protocol for Underwater Sensor Networks

Underwater Sensor Networks (UWSNs) are capable to explore the many unrevealed natural resources beneath the water, such as lakes, ponds, rivers, seas, and oceans. But there are so many challenges to be faced during the design of UWSN system due to the random nature of water waves. But, perhaps the most significant challenge in UWSNs is how to relay packets efficiently with minimum energy cost to the surface sink. This can be achieved by employing geographic and opportunistic routing schemes to transfer the packets at the surface sink reliably in cooperation with relay nodes. Considering these issues, we design a new routing protocol, called the Geographic and Cooperative Opportunistic Routing Protocol (GCORP), where data packets are routed from source node to the surface sink with the aid of relay nodes. In GCORP protocol, initially, a relay forwarding set is being determined by the source node on basis of depth data, then we use a weighting scheme to choose the best relay node from the relay forwarding set. The weight calculation is performed on normalized energy, packet delivery probability, and normalized distance of the forwarding node to the known surface sink. The main purpose of developing the GCORP protocol is to provide an effective solution for UWSN routing by improving the network metrics. We perform extensive simulations in NS-3 to validate the outcomes of GCORP protocol and compared them with other routing schemes with respect to packet delivery ratio, end-2-end delay, network lifetime, and energy consumption. The simulation results reveal that the GCORP protocol outperforms the existing approaches.
P4SFC: Service Function Chain Offloading with Programmable Switches

A Service Function Chain (SFC) is an ordered sequence of network functions (NFs). Software-based NFs in Network Function Virtualization (NFV) could introduce significant performance overhead. In this paper, we present P4SFC, a high-performance SFC system that leverages P4-capable switches to accelerate packet processing by offloading proper NFs to the switches. First, considering the current limitations of P4, we analyze the offloadability of NFs and divide them into three categories: fully offloadable, partially offloadable, and non-offloadable. Second, when deploying new SFCs, P4SFC automatically offloads proper NFs to switches based on their position and offloadability. To deploy new SFCs at runtime, we design a dynamic P4 data plane, whose execution logic can be reconfigured at runtime without interrupting the current execution logic. Finally, to maintain state consistency between the server and the switch for partially offloaded NFs, we design a state library to automatically synchronize states between servers and switches. Experimental results show that P4SFC achieves significant performance improvement for real-world SFCs.
Transient and Steady State Characteristics of Induction Motor Drive Using DTC-SVM Technique For EV Applications

Electric vehicle motion control and smooth operation of a drive mainly relies on the type of control technique used and to what extent current and torque ripple can be minimized by this technique both at steady state and transient conditions. The present paper aims to validate less harmonic stator current and torque ripple of a drive by taking appropriate gain values for torque and flux controllers with a new direct torque control scheme using space vector modulation. In this work a constant switching frequency operation is performed to minimize the current and the torque ripple under steady state operating conditions for different values of speed and torque. Also the transient response analysis of Induction motor is performed for step change in the torque command for different values of rotor speed. Transient response to rated torque reversal of the DTC-SVM scheme is also presented in this work. The corresponding sectors for the operation are realized. Mathematical model is presented and discussed in stator fixed reference frame for building up the simulation work. The DTC-SVM control scheme is found to be robust with less settling time during both the modes of operation and also for torque reversal. The THD levels of the stator current are found to be as per the standards. The effective utilization of the dc link voltage is also obtained. Simulations are performed in MATLAB 2020.
Chromatic Dispersion Based Wide-Band, Fiber-Coupled, Tunable Light Source for Hyperspectral Imaging

Hyperspectral imaging is a powerful label-free imaging technique that provides topological and spectral information at once. In this work, we have designed and characterized a hyperspectral source based on the chromatic dispersion property of off-the-shelf lenses and converted a supercontinuum laser light source into a hyperspectral imaging light source for 490 nm to 900 nm wavelength range with a spectral resolution of 3.5 nm to 18 nm respectively. The potential of the source was demonstrated by imaging two color dots with different absorption bands. Further, we generated the hypercube of the lily ovary and dense connective tissue and measured their spectral signature as a function of wavelength. We also imaged the lower tongue of a healthy volunteer at 540 nm, 630 nm, and white light. Our simple hyperspectral light source design can easily be incorporated in a standard endoscope or microscope to perform hyperspectral imaging.
Performance Analysis of Graphene Nanoribbon Field Effect Transistor (GNRFET) based 6T and 7T SRAMs

With the advancement of technology and recent developments on Internet of Things (IoT), devices with low power and smaller size come into consideration to keep pace with the current generation’s demand. Shrinking Silicon devices introduce leakage power which is inappropriate for portable devices. These limitations of silicon-CMOS transistor technology can be mitigated by new Graphene nanoribbon Field effect transistor (GNRFET) technology. GNRFET’s larger on off current ratio, extremely low leakage current and low supply voltage applications makes it a perfect fit for digital applications. Static Random Access Memory (SRAM) is an integral part of a chip which consumes large amount of area and power. To accomplish current technological needs GNRFET can be used to design SRAMs for low power devices. In this paper we have analysed Read Static Noise margin (RSNM), power, delays of conventioal 6T SRAM with three other 7T SRAMs using GNRFET and results show significant improvement.
Parameter Optimization and Performance Analysis of State-of-the-Art Machine Learning Techniques for Intrusion Detection System (IDS)

The use of Intrusion Detection System (IDS) as one of the most trusted layers of security to an organization to defend against all sorts of cyber attacks is ubiquitous. The uniqueness as well as the severity of recent cyber threats is significant. The ability of state-of-the-art machine learning techniques to discover new types of malicious network packets has been explored in many research works in the past. In our study, we present an experimental analysis to empirically find the optimal parameter settings of 22 classification techniques from 9 machine learning families. To investigate the studied algorithms, we choose 4 widely used IDS datasets that resemble real-world network traffic for both benign and malicious activities. We conduct experiments on binary classification, i.e., given an IDS log, we predict whether the log is either benign or malicious, on such datasets. Moreover, we take a step further to analyze such algorithms’ performances on multiclass classification; for example, we aim to detect the type of the attack from each dataset. As similar work in this domain is very limited to none, our study will play a vital role to select the optimal parameter settings for a certain studied classification algorithm.
Virtual Machine Security Migration Strategy Based on the Edge Computing Environment

For mobile edge computing, the migration time between traditional cloud servers and edge devices is long, and there are security issues such as man-in-the-middle attacks in the process. In this regard, a migration scheme centered on edge nodes is proposed. The edge node is closer to the edge device, which can shorten the migration time. The solution uses the Transport Layer Security (TLS) protocol for key exchange to establish a session-secure communication channel, and virtual machine migration between edge devices is carried out in the channel by dynamic migration. The simulation results show that compared with that of other schemes, the migration time of the virtual machines is shortened. Security analysis shows that this solution can not only meet the requirements of data confidentiality and integrity but also resist man-in-the-middle attacks.
Design of Omni-Directional radiation antenna by using two antennas with orthogonal polarizations

An omni-directional radiation antenna combined with two antennas with orthogonal polarizations is proposed in this paper. Different with the general "omni-directional" antennas, this proposed antenna has no obvious null both in azimuth plane and elevation plane. Two antennas are placed on the opposite sides of the platform and each antenna cover half of the space. Because the two antennas are with orthogonal polarizations, there is little interference for the two antennas, especially at the border of the two half spaces. The antennas with the platform are designed with the platform. The simulated results show that the gain is higher than -5dB for both azimuth and elevation planes. An antenna prototype was fabricated and measured. The measured radiation patterns are agreeable with the simulated ones.
Research on Salt and Pepper Noise Removal Method Based on Adaptive Fuzzy Median Filter

Aiming at the problem that the traditional median filtering algorithm can not well retain the image details while eliminating image noise, this paper proposes an adaptive fuzzy median filtering algorithm based on the mean of absolute average gradient difference. The whole filtering process is divided into three parts: coarse noise detection, fine noise detection and noise filtering. First, the extreme value method is used to perform rough noise detection on the image to be tested, which is divided into suspected noise points and signal points; then use this algorithm to construct the fuzzy membership function of the mean of absolute average gradient difference between the detected suspected noise points and the signal points in the window (including the filtered noise points), and use this function to perform secondary detection on the suspected noise points to determine Whether it is noise and fuzzy classification processing; finally, perform different filtering processing on the noise fuzzy classification results. Experimental results show that the algorithm proposed in this paper has significant improvements in PSNR, MSE, and SSIM compared to the traditional median filtering algorithm. Therefore, the algorithm in this paper can effectively remove the salt and pepper noise of the image while protecting the image texture details.
Evaluation of Diffusion Tensor White Matter Network of Alzheimer’s Disease

Alzheimer’s disease (AD) would produce complicated changes of diffusion tensor white matter (WM) networks and its importance for AD progress indicators is still worth discussing. In our study, a cohort of 15 cases of subjects (5 AD, 5 mild cognitive impairment (MCI), and 5 normal controls (NC)) were chosen and the network characteristics of weighted characteristic path length (Lp), weighted clustering coefficient (Cp), global efficiency (Eg), local efficiency (El) and small-word (σ) were measured to observe the WM changes of AD. The statistical analysis of one-way analysis of variance (ANOVA) was used to compare the three groups via global and local analyses. The results showed that the global network characteristics of Lp and Cp gradually increased, while Eg and El gradually decreased for NC, MCI and AD. There were significant differences for bilateral central anterior gyrus and right superior temporal gyrus for local analyses with the same trend with global analyses. The proposed WM network characteristics could be used as imaging indicators for AD prediction.
A Review on Key Management and Lightweight Cryptography for IoT

Internet of things (IoT) is a technology that allows devices that can be completely different to connect via internet for data exchange. The example of such devices can be a toy which is used by a child, a stereo system in your car or a kitchen appliance. It wasn't a distinct idea even by the end of the previous century, but in the last twenty years in has become a part and parcel of our lives. The core reason for this is super paced adaptability by the consumers. The unfortunate aspect for sharing this bulk amount of data between different IoT devices is that hackers and the unethical users gets interested in such data and these devices becomes a target for such people who dwell into vulnerabilities to acquire the data. Moreover, if this data goes in the hands of hackers or unethical users, it can easily compromise the entire organization which is the owner of that data. In this paper we initially investigate the security requirements for IoT (Internet of Things), those security requirements are classified in terms of layers of the TCP/IP model. After the in-depth analysis and critical evaluation of the security requirements, we look into the key management perspective of systems and IoT in particular. In last we discuss lightweight cryptography which is particular used for IoT. We perform in depth analysis of all the possible algorithms that are used in IoT with lightweight cryptography. Complete comparisons with strengths and weakness of all the lightweight algorithms are also carried out in our research work. The major security concerns in IoT configurations are also authentication and confidentiality. Therefore, we conclude that for client to client communication secret key cryptography should be used and where clients need to communicate with the server in IoT configuration, Elliptic Curve Cryptography based on lightweight algorithms can be used for authentication and secret key mechanism can be followed for confidentiality.
Circuit-on-Display: A Flexible, Invisible Hybrid Electromagnetic Sensor Concept

There have been scarce interactions between microwave circuits and display technologies over the past few decades. However, the two share many similarities leaving room for integration and co-development. Hence, we present a Circuit-on-Display (CoD) concept that dually functions as a radio-frequency transceiver and a high-resolution touch screen display. The approach is realized through a flexible, invisible hybrid electromagnetic sensor (HEMS) which is situated inside a high-resolution display panel. The CoD is integrated within a real-life millimeter-wave 5G NR Android-based cellular handset prototype to demonstrate a foreside beamforming coverage and a touch operation with high-resolution and optimal image quality. The devised HEMS prototype independently features flexibility, optically invisibility (transmittance >88%), and multi-functionality (system gain of 20.72 dB, beam scanning range of ±40°, and mutual capacitance change ratio >4.42%). The devised approach provides a new class of approach to enable microwave integrated circuits for new generation wireless electronics.
Ammonia Sensing Performance of RGO-Based Chemiresistive Gas Sensor Decorated With Exfoliated MoSe2 Nanosheets

Chemiresistive ammonia gas sensor based on Reduced graphene oxide (RGO) is an excellent alternative for developing low-cost chemiresistive ammonia gas sensors. However, pure RGO based devices have several disadvantages associated with it such as the response of these devices are very low and the response and recovery time is significantly high. In this work, we have demonstrated that loading MoSe2 nanosheets on RGO sheets helps in improving its ammonia sensing capabilities significantly. The RGO/MoSe2 nanocomposite showed limit of detection down to 300 ppb, whereas, pure RGO based sensor couldn’t reliably measure concentration below 1 ppm. The responsivity of nanocomposite-based sensor was 2.6 times better compared to pure RGO sensor. The sensor device could be operated at room temperature with an operating voltage of 2 mV, which demonstrated ultra-low power consumption compared to other commercially available metal oxide Taguchi sensors or electrochemical sensors. A sensing mechanism based on charge transfer is proposed to elucidate the better performance for the device.
A Security Quantification Method for Mimic Defense Architecture

The model of security assessment of the mimic defense architecture has been developing continuously since it was proposed, but it is not perfect to analyze the complex architecture quantitatively only from a certain aspect. The security quantization model is constructed from multiple angles to analyze the different characteristics of the mimic defense architecture to obtain a relatively complete security quantization method. This method is more consistent with the characteristics of the mimic defense architecture, and more appropriate to describe the security of the mimic defense architecture.
Privacy-Preserving Techniques for Protecting Large-Scale Data of Cyber-Physical Systems

As Cyber-Physical Systems (CPSs), such as power and gas networks, generate heterogeneous and large-scale data sources from devices and networks, they need efficient privacy-preserving techniques to protect data and systems from cyber attacks. To safeguard CPSs from potential cyber threats, it is vital to identify vulnerabilities of CPSs’ components to prevent Advanced Persistent Threats (APTs) and protect their generated data using privacy-preserving techniques. This paper aims to review the current state of privacy-preserving techniques for protecting CPSs and their networks against cyber attacks. Concepts of Privacy preservation and CPSs are discussed, illustrating CPSs’ components and how they could be hacked using cyber and physical hacking scenarios. Then, types of privacy preservation, including perturbation, authentication, machine learning (ML), cryptography and blockchain, are discussed to demonstrate how they would be applied to protect the original data in CPSs and their networks. Finally, we explain existing challenges, solutions and future research directions of privacy preservation in CPSs.
Load Balancing Mechanisms of Unmanned Surface Vehicle Cluster Based on Marine Vehicular Fog Computing

The unmanned surface vehicle (USV) cluster, during marine task execution, works in a challenging communication environment. The cluster’s network topology and states of wireless channel change rapidly with time. And the computing resources of fog nodes may be shared by several task requests simultaneously. Therefore, it’s necessary to find an effective load balancing mechanism to cope with the ever-changing adverse factors. The load balancing problems of vehicular fog computing of USV cluster are investigated in this work. And furthermore, the corresponding mathematical models, including marine vehicular fog computing networks, wireless channels, and several typical scheduling mechanisms, are established. The analytical models and simulation results show that the proposed scheduling algorithm based on minimum response time performs better than other selected algorithms and can significantly reduce the response time and blocking probability of task requests.