Miracle-dz/UniFAR-FTUs huggingface dataset:
A Secure and Efficient Anonymous User Authentication and Key Agreement Scheme for Global Mobility Networks Based on Bilinear Pairing

With the prompt advancement of wireless network technique, handheld mobile devices like mobile phones are becoming increasingly popular. Secure roaming of mobile devices has become one of the key services for mobile networks. The secure roaming mechanism allows handheld mobile devices to access the network anywhere without being limited by the network coverage of a single telecom operator. Therefore, efficient and secure authentication by the remote server of the legitimacy of mobile devices has become a research focus in wireless networks. This paper devises a secure and efficient anonymous user authentication and key agreement protocol for global mobility networks users employing bilinear pairing, which has the features of lower computational cost and communication overhead while providing strong user anonymity, and the mutual authentication and key agreement phase requires two handshakes. The details of each phase of our protocol are given. Security analysis demonstrates that our protocol is robust to resist various threats. A comparison shows that our protocol outperforms other related works in light of computational cost and communication overhead, and achieves more security features.
Resource Allocation Based on User Pairing and Subcarrier Matching for Downlink Non-Orthogonal Multiple Access Networks

The traditional orthogonal multiple access (OMA) is unable to satisfy the needs of large number of smart devices. To increase the transmission rate in the limited spectrum resource, implementation of both non-orthogonal multiple access (NOMA) and successive interference cancelation (SIC) is essential. In this paper, an optimal resource allocation algorithm in NOMA is proposed to maximize the total system rate in a multi-sector multi-subcarrier relay-assisted communication network. Since the original problem is a non-convex problem with mixed integer programming which is non-deterministic polynomial-time (NP)-hard, a three-step solution is proposed to solve the primal problem. Firstly, we determine the optimal power allocation of the outer users by using the approach of monotonic discrimination, and then the optimal user pairing is determined. Secondly, the successive convex approximation (SCA) method is introduced to transform the non-convex problem involving central users into convex one, and the Lagrangian dual method is used to determine the optimal solution. Finally, the standard Hungarian algorithm is utilized to determine the optimal subcarrier matching. The simulation results show that resource allocation algorithm is able to meet the user performance requirements with NOMA, and the total system rate is improved compared to the existing algorithms.
Sorption properties of the iminodiacetate ion exchange resin, amberlite IRC‐718, toward divalent metal ions

The sorption properties of the commercially available cationic exchange resin, Amberlite IRC-718, that has the iminodiacetic acid functionality, toward the divalent metal-ions, Fe 21 ,C u 21 ,Z n 21 , and Ni 21 were investigated by a batch equilibration technique at 258C as a function of con- tact time, metal ion concentration, mass of resin used, and pH. Results of the study revealed that the resin exhibited higher capacities and a more pronounced adsorption toward Fe 21 and that the metal-ion uptake follows the order: Fe 21 > Cu 21 > Zn 21 >Ni 21 . The adsorption and binding capacity of the resin toward the various metal ions investigated are dis- cussed. 2007 Wiley Periodicals, Inc. J Appl Polym Sci 107: 1316- 1319, 2008
Collection of Carbon Particles Using the Attractive Force Created by Surface Charging

A novel type of an electrostatic precipitator, in which dielectric surface works as a collection electrode, is developing as a technique to collect and decompose soot in exhaust gas from incineration devices including diesel engines. Because the charging of the dielectric surface is one of the keys in this technique, what surface potential distributions are built is investigated using glass plates as the dielectric. Voltages applied to discharge electrodes placed on the glass is AC high voltage that is given a positive bias. The voltage is applied continuously and intermittently. The influence of the thickness of the glass plates, carbon black particles that are applied to the surface to simulate real adhering soot, a grounded electrode over the glass plates were revealed. Moreover, it was found intermittent applied voltage can also build the surface potential as continuous operation does.
A Greener Meta-Heuristics Scheduling Algorithm With Energized Optimization Dynamics by Deeper Intelligence Fusion

As is known to all, the heterogeneous green scheduling objects have the intelligent feedback related to the efficiencies corresponding to the schemes, which has been largely ignored in most existing studies. That is why the existing optimization dynamics in green meta-heuristics scheduling algorithms, generally appear underpowered and vulnerable in the face of the rapid extension from homogeneity to heterogeneity of scheduling objects. Then, with respecting and ingeniously leveraging hardware (i.e., heterogeneous scheduling objects) intelligence, an efficient meta-heuristics algorithm with re-energized majorization dynamics for heterogeneous greener scheduling (i.e., CAr_FI(HS)), is proposed. The experimental results show that compared with the other meta-heuristics scheduling algorithms, CAr_FI(HS) has obvious advantages in the overall performance and the solution quality, for both data intensive and computing intensive instances.
Proposal to Solve the Time−Stress Discrepancy of Tube Models

Recently, Liu et al. (Macromolecules 2006, 39, 3093) showed a systematic discrepancy of tube model predictions for describing the apparent plateau modulus of short (weakly entangled) linear chains while predicting very accurately their terminal relaxation times. In the present article, we investigate the origin of this problem, which We call "time-stress discrepancy", by confronting our time-marching algorithm (TMA) with experimental viscoelastic data of nearly monodisperse linear polymers. We show that the contour length fluctuations of the outer molecular segments are overestimated, not taking into account the fact, that I chain needs a short but essential time to be considered in equilibrium ill its tube, Indeed, tube models consider that stress relaxation by reptation or contour length fluctuations starts at time t = 0 after an imposed small deformation, whereas in reality. there is a proceeding fast Rouse relaxation, which is especially important for shorter chains. Therefore, We propose to use a new segment coordinate system for describing the contour length fluctuations process, which restores the consistency with the tube definition and ensures that the necessary time for reaching an equilibrated system is equal to the relaxation time of an entanglement segment, tau(e). Results obtained with the so-corrected TMA model show I very good agreement with experimental data. In particular, the molecular-weight dependence of the apparent plateau modulus, the zero-shear viscosity, and the terminal relaxation time are now correctly predicted.
Extended Noise Resistant Correlation Method for Period Estimation of Pseudoperiodic Signals

Pseudoperiodic signals are commonly observed in many real applications. Identifying the period is crucial before analyzing the signals for such applications as monitoring, detection, and diagnosis. However, attention paid to period estimation of such signals is limited due to two contradictory challenges, arising from high-level period fluctuations and strong background noises, although numerous methods have been developed for periodic signals during the past decades. A novel method, which extends the noise-resistant correlation (NRC) method to pseudoperiodic signals and is called the extended NRC (ExNRC) method, has been developed in this article to solve the two challenges simultaneously. The ExNRC method can achieve a balance between feature enhancement and noise suppression by tuning a free parameter. Therefore, this method can be applied to pseudoperiodic signals contaminated with high-level period fluctuations or strong background noises. Properties of the ExNRC method are analyzed, and its performance is compared with conventional methods via simulation studies and real applications.
A Triple-Band Printed Monopole Antenna for WLAN/WiMAX/5G Applications

A compact and simple printed monopole antenna which can be used for WLAN/WiMAX and 5G applications is presented in this paper. Compared with many other similar works presented before, a simpler structure is used in this antenna which consists of a rectangular loop, a vertical strip and aT-shaped strip inside the loop, microstrip feed line and ground plane. The lower band which covers from 2.14GHz to 2.49GHz is mainly controlled by the size of rectangular loop. The middle band of 3.15GHz-4.06GHz is controlled by the dimension of T -shaped strip. The upper band of 5.12GHz-5.93GHz is decided by the vertical strip. What's more, steady omni-directional radiation patterns in H-plane and bidirectional radiation patterns in E-plane are obtained in these three operating frequency bands respectively.
Text Classification Based on Title Semantic Information

with the rapid development of big data technology, text classification plays an important role in practical application, its applications span a wide range of activities such as sentiment analysis, spam detection, etc. Traditionally, we model the relationship between document and label. However, in many scenarios, document have specific relationship with corresponding title. Inspired by this, a text classification model based on title Semantic Information is proposed in this study. In our model, long short-term memory(LSTM)is used to learn title embedding, document embedding is obtained by using promoted LSTM(TS-LSTM) which take into account the title information. The experimental results on the standard text classification datasets show that its performance is better than the existing state-of-the-art text classification algorithms.
Evaluation of the growth and quality of the “Ife Plum” tomato as affected by boron and calcium fertilization

Abstract A local variety of tomato (Lycopersicon esculentum, Ife plum cv. 51691) was grown in soil culture for 5 months and treated with B at concentrations of 0, 1, 2, 4, 8, and 16 ppm as H3BO3, and Ca at 0, 40, 80, and 160 ppm as Ca(OH)2. A significantly positive correlation was established between organic matter and water ‐ soluble B (r = 0.970), while the relationship between pH and B was negative (r = ‐0.490). Application of B at 2 ppm improved all growth parameters studied. Boron application higher than 2 ppm, induced leaf chlorosis and later necrosis of nodes and roots. Fruit yield correlated positively with soil ‐ B, stem diameter and floral number (r = 0.597, r = 0.650 and r = 0.812, respectively). Soil‐ and plant‐B were positively correlated (r = 0.790). Calcium when applied singly at higher levels increased total chlorophyll content of the leaf. Tomato fruit yield was optimum at B:Ca treatment concentration of 2 ppm B (4.48 kg/ha B) and 160 ppm Ca (358.4 kg/ha Ca), corresponding to a B:Ca ferti.
Attention Consistent Network for Remote Sensing Scene Classification

Remote sensing (RS) image scene classification is an important research topic in the RS community, which aims to assign the semantics to the land covers. Recently, due to the strong behavior of convolutional neural network (CNN) in feature representation, the growing number of CNN-based classification methods has been proposed for RS images. Although they achieve cracking performance, there is still some room for improvement. First, apart from the global information, the local features are crucial to distinguish the RS images. The existing networks are good at capturing the global features since the CNNs’ hierarchical structure and the nonlinear fitting capacity. However, the local features are not always emphasized. Second, to obtain satisfactory classification results, the distances of RS images from the same/different classes should be minimized/maximized. Nevertheless, these key points in pattern classification do not get the attention they deserve. To overcome the limitation mentioned above, we propose a new CNN named attention consistent network (ACNet) based on the Siamese network in this article. First, due to the dual-branch structure of ACNet, the input data are the image pairs that are obtained by the spatial rotation. This helps our model to fully explore the global features from RS images. Second, we introduce different attention techniques to mine the objects’ information from RS images comprehensively. Third, considering the influence of the spatial rotation and the similarities between RS images, we develop an attention consistent model to unify the salient regions and impact/separate the RS images from the same/different semantic categories. Finally, the classification results can be obtained using the learned features. Three popular RS scene datasets are selected to validate our ACNet. Compared with some existing networks, the proposed method can achieve better performance. The encouraging results illustrate that ACNet is effective for the RS image scene classification. The source codes of this method can be found in https://github.com/TangXu-Group/Remote-Sensing-Images-Classification/tree/main/GLCnet.
Additive Manufacturing of Coplanar Transmission Lines on Alumina Substrate up to 24 GHz using Laser Assisted Selective Metallization

This work proposes a novel approach for fabrication of high precision radio frequency circuits on low loss ceramic substrates by an additive manufacturing laser assisted selective metalization process. Coplanar waveguides are realized and measured with a GSG probe setup revealing an attenuation coefficient of approximately 110dB/m at 24 GHz, hence suggesting feasibility of the proposed approach. The simplicity of the process makes it an auspicious candidate for development of future communication and sensor applications due to the combination of high performance substrates and simple fabrication technique, while the required precision for mmWave range and above may be achieved by improving the optical laser system and metal plating.
Diagnosing Coronavirus Disease 2019 (COVID-19): Efficient Harris Hawks-Inspired Fuzzy K-Nearest Neighbor Prediction Methods

This study is devoted to proposing a useful intelligent prediction model to distinguish the severity of COVID-19, to provide a more fair and reasonable reference for assisting clinical diagnostic decision-making. Based on patients’ necessary information, pre-existing diseases, symptoms, immune indexes, and complications, this article proposes a prediction model using the Harris hawks optimization (HHO) to optimize the Fuzzy K-nearest neighbor (FKNN), which is called HHO-FKNN. This model is utilized to distinguish the severity of COVID-19. In HHO-FKNN, the purpose of introducing HHO is to optimize the FKNN’s optimal parameters and feature subsets simultaneously. Also, based on actual COVID-19 data, we conducted a comparative experiment between HHO-FKNN and several well-known machine learning algorithms, which result shows that not only the proposed HHO-FKNN can obtain better classification performance and higher stability on the four indexes but also screen out the key features that distinguish severe COVID-19 from mild COVID-19. Therefore, we can conclude that the proposed HHO-FKNN model is expected to become a useful tool for COVID-19 prediction.
GPU-based Raycasting of Hermite Spline Tubes

Visualizing curve and trajectory data is a common task in many scientific fields including medicine and physics. Tubes are an effective visualization primitive for this sort of data, but they require highly specialized renderers to achieve high image quality at frame rates sufficient for interactive visualization. We present a rendering algorithm for Hermite spline tubes, i.e. tubes that result from Hermite splines interpolating the data, with support for varying-radii circular tube cross sections. Our approach employs raycasting and works directly on this continuous representation without the need for surface tessellation, made possible by an efficient ray-tube intersection routine suitable for execution on modern GPUs.
Graph Convolution Network with Double Filter for Point Cloud Segmentation

To solve the problem of information loss caused by point cloud segmentation using voxels. A method of transforming point cloud into graph data and using double filter graph convolution network for segmentation is proposed. The first filter is for point clouds to reduce the number of nodes in the graph. Considering the feature as a signal, the convolution is defined in the spectral domain using a Laplacian matrix, and the Chebyshev polynomial is used to reduce the computational complexity of the matrix decomposition. The second filter is a low-pass filter for the Chebyshev polynomial, which reduce the computation. Finally, the 2D data is detected using CNN to optimizes the segmented result. Experiments were performed on the ShapeNet dataset to demonstrate the efficiency of the method.
Simulation Comparison of Birdcage Coil and Metamaterial Liner for MRI at 3T and 4.7T

The application of a thin metamaterial (MTM) liner to the RF shield of the magnetic resonance imaging (MRI) system is proposed to improve whole-body imaging compared to conventional "birdcage" body volume coils. The MTM liner facilitates travelling wave (TW) MRI, where the radio-frequency (RF) magnetic field propagates within the MRI bore, which acts as circular waveguide. Full-wave electromagnetic simulations were performed comparing a conventional birdcage coil to a novel MTM liner design in terms of key MR RF performance metrics: transmit efficiency, homogeneity and specific absorption rate (SAR). The designs are compared at 3T (128 MHz) and 4.7T (200MHz), two high-field strengths where the RF field homogeneity and SAR constraints are critical. The ring-structure design of the MTM liner is described and simulations with an empty MRI bore and a homogeneous human body model are performed, including realistic losses for the lumped components used in practical construction. The results show that the MTM liner has a similar mean transmit efficiency, while having reduced 10g averaged local SAR compared to the conventional birdcage.
Adjustable Speed Induction Motor drive fed by 13-level Cascaded Inverter and 54-Pulse Converter

This study presents a performance evaluation of a high power induction motor drive, which is operated by a 13-level cascaded H-bridge multilevel inverter (CHB-MLI) and a 54-pulse AC-DC converter. The higher number of voltage levels to adhere the power quality standard IEEE 519 at drive end and less- number of power switches are feasible due to asymmetric DC supplies, having 1:2:3 voltage ratio. A Nearest level control switching technique is presented to minimize the pole voltage transitions for CHB-MLI, thereby diminishing the switching losses of the high power converter. A 54-pulse rectification system is presented to adhere the power quality standard IEEE519 at utility end. Simulation results demonstrate the performance of a 30kW induction motor drive in steady and dynamic states. A sensitivity study is conducted in the experimental setup with 7.5kW(10hp) to evaluate the steady and dynamic state performances of this drive system. A comprehensive study shows a superior power quality performance both at grid and drive end of the system.
A Sliding Window Common Spatial Pattern for Enhancing Motor Imagery Classification in EEG-BCI

Accurate binary classification of electroencephalography (EEG) signals is a challenging task for the development of motor imagery (MI) brain–computer interface (BCI) systems. In this study, two sliding window techniques are proposed to enhance the binary classification of MI. The first one calculates the longest consecutive repetition (LCR) of the sequence of prediction of all the sliding windows and is named SW-LCR. The second calculates the mode of the sequence of prediction of all the sliding windows and is named SW-Mode. Common spatial pattern (CSP) is used for extracting features with linear discriminant analysis (LDA) used for classification of each time window. Both the SW-LCR and SW-Mode are applied on publicly available BCI Competition IV-2a data set of healthy individuals and on a stroke patients’ data set. Compared with the existing state of the art, the SW-LCR performed better in the case of healthy individuals and SW-Mode performed better on stroke patients’ data set for left- versus right-hand MI with lower standard deviation. For both the data sets, the classification accuracy (CA) was approximately 80% and kappa ( $\kappa $ ) was 0.6. The results show that the sliding window-based prediction of MI using SW-LCR and SW-Mode is robust against intertrial and intersession inconsistencies in the time of activation within a trial and thus can lead to a reliable performance in a neurorehabilitative BCI setting.
An Ultra-low Power Relaxation Oscillator with Novel Ultra-low Leakage Switch and Temperature-compensated Resistor

This paper proposes an ultra-low power relaxation oscillator with novel Ultra-Low Leakage Switch (ULLS) and temperature-compensated resistor (TCR). The ULLS decreases the leakage current over 90 dB, and the leakage variation against temperature can be also reduced about $\times 67$. The TCR designed for the oscillator can reach a temperature coefficient of 0.08 % $\mathrm{ppm}^{\circ} \mathrm{C}$. By utilizing resistive frequency locked structure with the proposed ULLS and well-designed TCR, the proposed oscillator achieves higher energy efficiency and better temperature performance than most existing oscillators in literature. It has an ultra-lower power of 87.6 nW (with an ultra-low leakage of 3 nW) simulated temperature sensitivity and supply voltage sensitivity are $23.9 \mathrm{ppm} /^{\circ} \mathrm{C}$ and $0.41 \% / \mathrm{V}$, respectively.
A High Step up DC-DC Converter Based on the Cascade Boost, Voltage Multiplier Cell and Self Lift Luo Converter

In this study, a high step-up DC-DC converter has been proposed. The topology of the proposed converter is based on the cascade boost circuit, voltage multiplier cell and the self-lift Luo converter. The detailed properties of the proposed converter have been described in continues current mode. Based on the mentioned description, the various parameters of the proposed converter have been described mathematically. Moreover, the non-ideal voltage ratio based on the non-ideal mode of the circuit components has been expressed. The simulation results were extracted by PLECS. The inductors current and the capacitors voltage have been studied. Moreover, the extracted results have been compared with theoretical relations.
Modified H-bridge Multilevel Inverter for Grid Integration with Voltage Balancing Circuit

This paper presents a single source, a five-level modified H-bridge multilevel inverter for grid-integrated renewable energy applications. The switching scheme used is in-phase pulse width modulation (IP-PWM). It is found that the proposed topology has simple switching and is suitable for any loads. The capacitor voltage balance circuit based on the resonant switched capacitor is implemented. This topology is implemented to integrate active power into the grid, and the reactive power compensation, harmonic mitigation are also achieved. The grid current harmonic level is maintained as per the IEEE-519 standard. The system is validated in MATLAB Simulink and verified.