Miracle-dz/UniFAR-FTUs huggingface dataset:
Relational Sequence Encoding Graph Attention Network for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims to classify all the aspects’ sentiment polarity in a sentence. However, most of the related research didn’t take full advantage of the dependency relations between words, the interaction between the node and its corresponding dependency relations is not taken into consideration, which causes the aspect words mistakenly focus on other context word. To solve these problems, a graph attention network model with relational sequence encoding (RSE-GAT) is proposed in this paper. The model constructs an aspect-rooted syntactic dependency tree, which constructs the representation of virtual connections and relation between aspect words and other words by dependency sequence encoding. At the same time, the composition operator is applied to enhance the interaction between word embedding and relation embedding. Finally, GAT is utilized for feature extraction. The experimental results on three public datasets demonstrate that the proposed RSE-GAT model achieves better results. Compared with the classical GCN-based model ASGCN[5], the accuracy on RES14, LAP14[9] and Twitter[10] is increased by 3.05%, 2.05% and 3.68% respectively.
A parameter selector of OFDM waveform based on GAMP algorithm

Orthogonal frequency division multiplexing (OFDM) signal has become one of the most promising technologies in satellite communication modulation due to its effective anti-frequency selective fading and high spectrum resource utilization. However, in power limitation of satellite communication, OFDM signal produces nonlinear distortion with clipping because of the high peak-to-average power ratio issue. It was recently shown that generalized approximate message passing (GAMP) algorithm has good effect in dealing with the problem of nonlinear distortion of the clipped OFDM signal, but it will also lead to the reduction of signal spectrum utilization. This puts forward higher requirements for the design of OFDM waveform parameters. In this paper, an improved waveform parameter selector based on GAMP algorithm is proposed, which for design the signal system of OFDM and optimize the parameters of OFDM signal with different clipping ratios requirements. The simulation results show that the spectral utilization of OFDM is significantly improved.
Third-order nonlinearities in silicon at telecom wavelengths

The two-photon absorption coefficient and Kerr coefficient of bulk crystalline silicon are determined near the telecommunication wavelengths of 1.3 and 1.55 μm using femtosecond pulses and a balanced Z-scan technique. A phase shift sensitivity of the order of 1 mrad is achieved, enabling the accurate measurement of third-order nonlinear coefficients at fluences smaller than 100 μJ/cm2. From the two-photon absorption coefficient (β∼0.8 cm/GW) and the Kerr coefficient (n2∼4×10−14 cm2/W) at a wavelength λ=1.54 μm, a value F∼0.35 for the nonlinear figure of merit for all-optical switching is determined.
Spatiotemporal characteristics of ultra-low frequency dispersive scale shear Alfvén waves in the Earth’s magnetosphere

The high-latitude nightside auroral zone is threaded by geomagnetic field lines supporting localized ultra-low frequency (ULF) shear Alfven waves (SAWs). A particular class of dispersive scale ULF waves can be attributed to resonant mode conversion of global scale near-monochromatic compressional waves that are excited by high-speed solar wind flow past the magnetosphere. In the more distant magnetotail, warm plasma dispersive effects preclude the excitation of latitudinally narrow waveforms. Closer to Earth, flux tubes are loaded with colder plasma that favors inertial scale dispersive SAWs and nonlinear wave processes. The spatiotemporal characteristics of ULF shear Alfven field line resonances near midnight are analyzed, and it is demonstrated that in warm plasma, small-scale nonlinear structuring occurs on field lines where two competing wave dispersion mechanisms cancel. Over realistic time scales, this situation occurs naturally as a result of SAW ponderomotive forces that initiate up-flowing ion mo.
Visual Analysis of Thane Intelligent Transport System

In fast growing countries like India, the focus on improvement of urban public transport services is essential. We must focus on enhancement of service features like route optimization, trip reliability, optimizes infrastructure including fleet size, and solution towards traffic problems. Importantly, the city transport system needs a continuous upgradation for augment services. Under Smart City mission, 83 out of 100 shortlisted smart cities have implemented an Integrated Command and Control Centre (ICCC) to gather, analyze, evaluate, and respond to big data inputs from various smart city projects. The database of transport systems consists various amorphous structure which requires proper preprocessing before analysis. Due to various fields and large spectrum of details, it is difficult to analyze without proper tools of visualization supported with infographic details. This becomes essential feeder for various stakeholder including citizen, administrators and policy makers. Historical transport data of April 2022 pertaining to Thane Smart city have been analyzed. The preprocessing and visualization have been carried out by looking future large-scale integration. The obtained raw data has three components i.e. GPS tracking data for buses, trip entry data handled by conductor/operator and route wise ticket collection details managed by conductors. Some typical errors have been identified and corresponding data have been cleaned. The cleaned data is preprocessed with various requisite details for visual analysis. Various analysis reports have been prepared for dashboard visualization. All reports contain interactive details with infographics. We have reported various critical features for visualization and analytics of Intelligent Public Transport system. The actual number of trips, total date wise and slot wise trips, route wise and time slot wise ticket collection have been correctly identified. Apart from the same, Common data tables designed for various analysis have also been suggested. It is also recommended to have essential attributes in all smart cities ITS systems for visualization dashboards.
Edge fluctuations for the one dimensional supercritical contact process

We consider the one dimensional supercritical contact process with initial configurations having infinitely many particles to the left of the origin and only finitely many to its right. Starting from any such configuration, we first prove that in the limit as time goes to infinity the law of the process, as seen from the edge, converges to the invariant distribution constructed by Durrett [12]. We then prove a functional central limit theorem for the fluctuations of the edge around its average, showing that the corresponding diffusion coefficient is strictly positive. We finally characterize the space time structure of the system. In particular we prove that its distribution shifted in space by at (t denotes the time and a the drift of the edge) converges when t goes to infinity to a 2-2 mixture of the two extremal invariant measures for the contact process. 1. Introduction. In this article we study the one dimensional supercritical contact process, a stochastic process with infinitely many interacting particles which move on Z, the set of all the integers. We consider initial states with infinitely many particles to the left of the origin and finitely many to its right. We first prove that at large times the state of the system approaches some "definite structure" which, as time increases, "rigidly shifts to the right": A steady state propagates from the left to the right with constant speed through the formerly empty region. The system is a (very schematic and rough) microscopic model for the formation and propagation of one dimensional shock waves, as we shall see in some more detail in the sequel. At large times we can distinguish three different space regions: The first two are semiinfinite intervals which extend to - oo and + oo, respectively, while the third one is the interval which connects the first two. In the first region the state looks like the nonzero density equilibrium state for the supercritical contact process, while, in the second it is the empty state. The third is the interesting region. It describes the "wave front. " We find that the state there is (close to) a superposition of the two extremal equilibrium states. The weight of the decomposition ranges from 1 (all the weight to the nonzero density state) to 0, as one moves from left to right. As the time t increases, the previous picture shifts to the right and, at the same time, the size of the front wave increases (like tl/2). The picture is very similar indeed to that found by Wick [25] in a zero range model. Our result is a consequence of a very detailed analysis of the motion of the first particle in the system, the edge, and of a precise knowledge of the structure of the state as seen from the edge. Such
An Efficient Dual Control Scheme with Output Voltage Regulation and INC Based MPPT for a PV System with Three Level Boost Converter

An efficient dual control strategy for a photovoltaic application with a DC-DC Three-Level Boost Converter (TLBC) has been put forth in this work. Due to the great efficiency on voltage stress reduction, higher bosting capability and minimal inductor size, TLBC has better performance over Conventional Boost Converter (CBC) and thus being used as a PV fed converter. In order to achieve the maximum power, Incremental Conductance based tracking performance for variable load and variable irradiance, has been implemented here. A Proportional Integral (PI) based output voltage control scheme is also integrated with this system for load voltage regulation. To balance out the MPPT and Output Voltage Control throughout Variable Load, a Power Hysteresis Control (PHC) has been incorporated as well. Additionally, the performance of the (INC) technique for MPPT control is assessed and analyzed with that of the conventional Perturbation & Observe (P&O) technique. This method provides a convenient workaround for fluctuating load conditions. The system under investigation is modelled in MATLAB/Simulink, and the various conditional responses are investigated under all conceivable operational circumstances.
A Study Real-Time WLAN Sensing System Using Channel State Information

Recently, the Wi-Fi human activity recognition (HAR) system has attracted attention, using channel state information (CSI). CSI is a characteristic that indicates the radio propagation environment from transmitter to receiver, indoor environment. Therefore, sensing technology that predicts the indoor environment by calculating backward from the CSI is being considered. We first analyzed CSI in the time direction using singular value decomposition (SVD). In addition, a long short-term memory (LSTM) is applied to the obtained features to classify Absence/Presence. Furthermore, we implemented this system, where software-defined radio (SDR) receiver and common commercial wireless access point.
Degradation of linear filter performance due to modeling error

Differential equations for determining the dynamic and steady-state effects of a particular class of disturbances on the error in the estimate of the state vector of a stochastic linear dynamic system are obtained. For the problem of evaluating near optimal filter performance, the technique permits the performance degradation due to the deletion of certain state vector components in the design of a Kalman filter to be obtained.
Graphical approach for defining natural internal coordinates

A simple, customizable connectivity scheme is rigorously defined in which pairs of atoms are classified into three categories. The tools of graph theory are used to analyze the molecular graph and to efficiently find rings and ring assemblies through a combination of pruning and homeomorphic reduction. The definition of natural internal coordinates is extended in a nonredundant fashion for the various cases of weakly interacting components and for fused ring systems. The ring system coordinates were tested and found to be superior to Z‐matrix coordinates. © 1998 John Wiley & Sons, Inc. J Comput Chem 19: 504–511, 1998
Effect of Drive Train Model Simplification on the Stability of Grid-connected DFIG

In this article, the effect of shaft modelling simplifi-cation on the stability of a grid-integrated doubly fed induction generator (DFIG) based wind energy conversion systems (WECS) is studied. The consequence of drive train parameters on stability of grid-connected DFIG is also studied. The full order model of DFIG is considered including the GSC controllers. The stability study of a grid-interfaced DFIG-based WECS is performed by modal analysis and time-domain simulations. The results obtained by using lumped-mass model of drive train shows that the results are over optimistic in stability view-point.
A Novel Framework of Network Packet Loss Detection Using Random Forest Algorithm over Support Vector Machine Learning Algorithms to Improve Accuracy

The objective of this paper is to employ a novel framework of network packet loss detection with increased accuracy rate using Novel Random Forest classifier compared to a Support Vector Machine (SVM) Classifier. Materials & Procedures: The data set used in this study utilizes the publicly available Kaggle network traffic data set and UCI machine learning repositories. The sample size of network packet loss detection with improved accuracy rate sample size was 50 (Groups 1 and 2 each had 25 participants), and the computation was performed using a G-power of 0.8, with alpha and beta values of 0.05 and 0.2 and a 95% confidence interval. A network packet loss detection with improved accuracy rate is performed by Random Forest (RF) whereas multiple samples $(\mathrm{N}=10\mathrm{A})$ SVM were a number of samples $(\mathrm{N}=10)$ Results: The Novel Random Forest classifier has 93.56% higher accuracy rates in comparison to the accuracy rate of SVM is 89.23%. There exists a statistical significance difference between two groups $(\mathrm{p}=0.0231;\mathrm{p} < 0.05)$ with confidence interval 95%. Conclusion: Novel Random Forest when compared, a classifier offers more accurate results. to SVM for network packet loss detection with improved accuracy rate.
Optimization of energy consumption with hybrid cooperative NOMA for secure MEC

The widespread use of non-orthogonal multiple access (NOMA) has changed the traditional physical layer security limits on per-user transmission rates, with the attendant increase in complexity and system energy consumption. In view of this, a secure transmission method for mobile edge computing based on NOMA hybrid collaborative interference is proposed. Firstly, a multi-slot hybrid collaboration scheme is designed for each user data processing process for the task offloading problem. The scheme eliminates auxiliary interference signals transferred the offload task to local computing in the specified time slot. Then the scheme optimizes the total energy consumption of the system while ensuring fairness among users. Secondly, a closed expression for the system secrecy outage probability is derived. And the optimization problem is decomposed into three sub-problems. Finally, the block coordinate descent algorithm is used to iterate cyclically. With the objective of minimizing system energy consumption, the best offloading decision scheme for the user is obtained and compared with orthogonal multiple access and full offloading schemes. With reasonable time slots, the simulation results demonstrate the proposed solutions can well weigh the relationship between security and energy consumption. And it can effectively reduce system energy consumption while ensuring information security.
Android Ransomware Detection Toolkit

Ransomware is a type of malware that illegally encrypts the data of victims' devices to attain financial gains. The impact of ransomware is not limited to corporate desktops but also extends to the very lucrative mobile devices market, whose major chunk is backed by Android OS. These ransomware attacks lead to huge financial, reputational, and data losses to the masses of people. The implemented research uses the static and dynamic artifacts generated by the ransomware to designate an android application as a malign or benign sample. The static artifacts used are the permissions and the APIs invoked by the ransomware. These static artifacts are passed through a branched artificial neural network to attain the best metrics. The dynamically generated network traffic was passed through a fine-tuned LGBMClassifier to detect ransomware on the network. Both of these models amalgamate together to defeat the state-of-the-art solutions present in the contemporary world.
Subpicosecond photoconducting dipole antennas

Photoconducting antennas have been demonstrated which are capable of generating and coherently detecting subpicosecond electrical pulses. These antennas, when illuminated with femtosecond optical pulses, radiate electrical pulses which have frequency spectra that extend from 2 THz. Microscope dipoles measuring 50, 100, and 200 mu m have been fabricated and tested. Integrated photoconductors of radiation-damaged silicon-on-sapphire were used both for impulsive current excitation of the transmitting antennas as well as for gating the receiving antennas. >
Design and Modeling of an Electrical Vehicle with Wireless Charging Technology

As greenhouse gas emissions and global warming are major concerns in the 21st century, the use of electric vehicles (EVs) can substantially cut carbon dioxide emissions. Since their first manufacture, electric vehicles have reached the 21st century, outliving numerous technical concepts. In this study, the EV system is divided into two main sections: the first is a wireless grid-connected charging system for EVs, in which the battery performance of charging and discharging phases are analyzed considering different RPM and speed while discharging, and the second is the architecture of EVs, in which the motor speed is analyzed based on a practical scenario. In addition, the technology takes advantage of charging infrastructure built into the pavement itself to provide electricity to the car while it is in motion. This study focuses into both on-the-go (dynamic) and end route (quasi-dynamic) wireless charging for electric vehicles.
Unity Power Factor Operation for Non Linear Load Applications Using Interleaved Manitoba Rectifier

The electric vehicle receives high attention in recent days as it owns the benefits like eco-friendliness and battery storage capacity. The Electric vehicles act as a paradigm shift in both the transportation and power sectors, which have the potential of helping both sectors by combining them. This coupling requires the implementation of efficient power correction techniques for charging EV batteries, which decreases the power quality difficulties of the supply front-inherent end. For power factor correction an upgraded bridgeless landsman converter is used. In this proposed system bridgeless landsman converter is used. It manages the link voltage with improved efficiency by conducting current across the minimal semiconductor components, which further reduces the losses. A PI controller is utilized, which assists in prediction and classification with respect to the response time. The hysteresis controller is connected to a PWM generator, which estimates the steady-state switching frequency of the converter and produces reliable results. The obtained results indicate that the proposed approach maintains sustainable environment with enhanced efficiency and minimal harmonics.
Active-LATHE: An Active Learning Algorithm for Boosting the Error Exponent for Learning Homogeneous Ising Trees

The Chow–Liu algorithm (IEEE Trans. Inform. Theory, 1968) has been a mainstay for the learning of tree-structured graphical models from i.i. d. sampled data vectors. Its theoretical properties have been well-studied and are well-understood. In this paper, we focus on the class of trees that are arguably even more fundamental, namely <italic>homogeneous</italic> trees in which each pair of nodes that forms an edge has the same correlation <inline-formula> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula>. We ask whether we are able to further reduce the error probability of learning the structure of the homogeneous tree model when <italic>active learning</italic> is allowed. Our figure of merit is the <italic>error exponent</italic>, which quantifies the exponential rate of decay of the error probability with an increasing number of data samples. We design and analyze an algorithm <underline>Active</underline> Learning <underline>A</underline>lgorithm for <underline>T</underline>rees with <underline>H</underline>omogeneous <underline>E</underline>dges (ACTIVE-LATHE), which surprisingly boosts the error exponent by at least 40% when <inline-formula> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> is at least 0.8. For all other values of <inline-formula> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula>, we also observe commensurate, but more modest, improvements in the error exponent. Our analysis hinges on judiciously exploiting the minute but detectable statistical variation of the samples to allocate more data to parts of the graph in which we are less confident of being correct.
Solar Energy Prediction Model using machine learning and Internet of Things

There is been a huge shortage in the primary source of electricity all over the world and in upcoming years it is going to be vanished. One of the most worrying problems today is rising energy prices. Energy costs are rising as the Earth's resources are gradually depleted. Fortunately, technology has provided new resources from natural entities such as solar energy. Although the demand for energy continues to rise, there are things that every homeowner can do to reduce their costs and help the environment. One of the most abundant, economic and pollution free source of power is the solar energy which can be used as the power source if we collect and use the proper information of the solar power. Proposed prediction model uses the data available over the different areas and provide the wholesome information about the solar energy like Intensity, timings, weather condition effects. Different algorithms of Machine Learning (ML) like regression are been used for the predictions and calculations of the relevant data and for the household consumption stats the Internet of Things (IoT) modules are being used. Different areas households' consumption stats will be monitored and compared to draw the maximum power outputs from the solar energy so that multiple appliances can be used. The designed prototype is validated with several test cases and comparison of the performance is carried with state of art prediction schemes.