Miracle-dz/UniFAR-FTUs huggingface dataset:
An Adaptive State of Charge Estimation Method of Lithium-ion Battery Based on Residual Constraint Fading Factor Unscented Kalman Filter

It is crucial to conduct highly accurate estimation of the state of charge (SOC) of lithium-ion batteries during the real-time monitoring and safety control. Based on residual constraint fading factor unscented Kalman filter, the paper proposes an SOC estimation method to improve the accuracy of online estimating SOC. A priori values of terminal voltage were fitted using cubic Hermite interpolation. In combination with the Thevenin equivalent circuit model, the method of adaptive forgetting factor recursive least squares is used to identify the model parameters. To address the problem of the UKF method strongly influenced by system noise and observation noise, the paper designs an improved method of residual constrained fading factor. Finally, the effectiveness of this method was verified by the test of Hybrid Pulse Power Characteristic and Beijing Bus Dynamic Stress Test. Results show that under HPPC conditions, compared with other methods, the algorithm in the paper estimates that the SOC error of the battery remains between -0.38% and 0.948%, reducing the absolute maximum error by 51.5% at least and the average error by 62.7% at least. Moreover, under the condition of Beijing Bus Dynamic Stress Test the algorithm estimates the SOC error of the battery stays between -0.811% and 0.526%, the SOC estimation errors are all within 0.2% after operation of ten seconds. Compared with other methods, the absolute maximum error can be reduced by 42.7% at least, the average error is reduced by 95% at least. Finally, the test proves that the method is of higher accuracy, better convergence and stronger robustness.
Detecting Corporate’s Earnings Manipulation in Thailand

This study detects earnings manipulation among Thai listed firms from 2014 to 2019. We apply six accrual models, including Jones Model (1991), Modified Jones Model (1995), DD Model (2002), Modified DD Model (2002), Performance-matched Jones Model (2005), and Performance-matched Modified Jones Model (2005), to detect earnings manipulation or often referred to as earnings management, and explore the explanatory power of each model. This study utilizes 1,854 firm-year observations, and the findings indicate that the Modified DD Model (2002) is the most suitable model among six accruals models, due to this model incorporating two essential components of the company’s earnings (cash flows and accruals), as other models only consider either cash flows or accruals.
Onion: Dependency-Aware Reliable Communication Protocol for Magnetic MIMO WPT System

Magnetic wireless power transfer (WPT) has received widespread attention from both academia and industry, and magnetic resonance coupling (MRC) based WPT systems have a longer charging distance to support the scenarios with multiple transmitters (TXs) and multiple receivers (RXs). In such systems, an in-band reliable TX-RX communication protocol is essential to guarantee to charge performance. In this paper, we devise Onion, a dependency-aware in-band communication protocol for MIMO MRC-WPT systems. Technically, we extend the well-known EPCglobal C1G2 protocol in Radio Frequency Identification (RFID) fielded and make it suitable for mutual inductance based communication links in MRC-WPT systems. Furthermore, we craft an innovative onion-style layer-dependency based communication mechanism to utilize the positive impact of the relay phenomenon. We design and implement the Onion prototype and conduct extensive experiments to evaluate it. The experiment results demonstrate the effectiveness of the proposed protocol, which increases the communication success ratio by an average of 40% as compared to the dependency-unaware scheme.
Passive Moving Target Imaging via Generalized Wirtinger Flow

In this paper, we present a method for passive radar detection of ground moving targets using interferometric measurements. We assume the scene is illuminated by a source of opportunity and measure the backscattered signal using a single receiving antenna. We correlate the fast-time measurements from one slow-time pulse to another, then form a linear forward model that is akin to the direction-of-arrival (DOA) problem formulation. We model the range variations induced by moving scatterers as directions of arrival, and use Discrete Fourier Transform (DFT) methods to obtain an estimate of the unknown range variation over each slow-time. We propose an alternating optimization based approach, using the Generalized Wirtinger Flow algorithm to estimate the scene reflectivities. We demonstrate the performance of our method with numerical simulations.
Calculation of Solar Noise in Selected Underwater Depths

This paper is devoted to the calculation of the relative optical intensity of passing solar radiation to a selected depth below the water surface. The spectral character of solar noise at a given depth depends on the vertical chlorophyll concentration profile. Four depth profiles of chlorophyll concentration are modeled in the paper. Two of the profiles represent a very pure water environment and the other two profiles represent rather pure water with respect to chlorophyll pollution. The work shows the spectral attenuations of the optical signal for selected depths and defined chlorophyll concentration profiles. The work shows that under certain circumstances we can consider layers of water with a higher concentration of chlorophyll as a filter of solar noise. The results of the work can be used to assess the solar noise level in the selected depths, where the solar noise can negatively affect the qualitative and quantitative parameters of the useful optical signal.
Driving a capacitive load without dissipating fCV/sup 2/

Output pad drivers often contribute a large part of the total power dissipation of a chip, due to the large capacitive load they drive. Here, we describe a driver capable of significant reduction of this dissipation, for given values of voltage swing, load capacitance, and switching frequency, without performance loss. We accomplish this by stepwise charging, an inductor-free form of adiabatic charging.
A comparative Analysis of 1D Convolutional Neural Networks for Bearing Fault Diagnosis

In many industrial applications, bearing monitoring is ranked as a crucial duty for condition-based maintenance. It enables the avoidance of unplanned maintenance while lowering the cost. For this objective, a variety of strategies have been devised to assure accurate and effective monitoring. This study offers a One-Dimensional Convolution Neural Network (1D-CNN) that is used to diagnose and identify early bearing faults based on Variational Mode Decomposition (VMD). Methods such as VMD-based feature extraction for defect detection are followed by multi-scale feature extraction for classification and diagnosis using CNN and pooling layers. Bearing fault detection and diagnosis are evaluated using the Case Western Reserve University experimental dataset to determine its robustness and performance. Demodulation techniques and machine learning algorithms are also contrasted for their ability to identify and diagnose the fault. A VMD filter and 1D-CNN approach for monitoring bearing fault are clearly promising, according to the results obtained from the experiments.
An Update on the Fully Digital Phased Array Development for Next Generation Weather Radar

This paper is an update on our development of a Digital Polarimetric Phased Array Radar (DPPAR), an ongoing effort sponsored by the National Oceanic and Atmospheric Administration (NOAA). DPPAR leverages commercial-off-the-shelf microwave processing technology to meet NOAA's future meteorological radar needs for rapid, volumetric scanning and superior data quality. The DPPAR prototype is a digital-at-every-element phased array aperture, comprised of two identical panels, each containing 64 dual-polarized elements operating at S-band. The current state of development, preliminary test results and future efforts will be discussed.
Using RapidEye satellite images for the sustainable management of the extension of El Jadida city (Morocco)

The ever-increasing urban extension and the many changes it provokes are the source of many problems both inside and outside the city. To address these issues and manage the city intelligently, one must first assess and map land use changes resulting from urban sprawl. Thus, the main objective of this study was to quantify the urbanisation of El Jadida between 2010 and 2020 to highlight its impact on the environment and make it a smart city. Maps of the spatial distribution of the built-up area were produced based on two high-resolution satellite images (RapidEye). These images were subjected to a series of digital image processing techniques to individualise the built-up areas. The comparison between the two images generated (2010 and 2020), has allowed to deduce that the city of El Jadida has recorded during this period an urban growth of approximately 331 ha, an increase of 17% in 10 years. This comparison also shows that the urban expansion in El Jadida is random and generally to the detriment of agricultural land. Thus, to manage this urban expansion intelligently, it must be directed towards non-agricultural land along the Atlantic coast.
Performance Bounds for Cyberattack Detectors Using Multiple Observations

In this paper, we consider False Data Injection (FDI) attacks with small injected error, and derive risk upper bounds for cyberattack detectors using multiple observations. FDI attacks with small injected error are "slow" attacks which are harder to detect, but such slow attacks can cause major failures if continuously applied over a long time period. A natural question to ask is to what degree the cyberattack detection problem becomes easier if multiple observations acquired over a long time period are used for threat assessment, and the risk level reduction achieved for each new observation. For a cyberattack detector, the false alarm rate is the probability of triggering an alarm when there is no cyberattack, and the probability of miss is the probability of not detecting a cyberattack. The risk level of a cyberattack detector is defined as the sum of the probability of false alarm and the probability of miss. By using the notion of Hellinger distance, we derive bounds on the minimum possible (achievable) risk level under multiple observations, and study asymptotic properties of such bounds. It is proved that that the minimum possible risk level converges to zero exponentially as the number of observations goes to infinity.
A method for endpoint aware inspection in a Network Security Solution

Due to the surge in remote work after the outbreak of COVID-19, network security has gained an enormous focus. The issue of erroneous inspection decisions in network security solutions has long been criticised, but the importance of the decision accuracy has never been as important as today. In this paper we provide a solution for improving the inspection decision accuracy by specifying a method for endpoint aware inspection in a network security solution capable of performing deep packet inspection. The method utilises a subset of the protected network to gather hash fingerprints from the endpoint application network traffic patterns. The information gathered from this subset is then utilised for gaining endpoint awareness for the rest of the protected network. We use methods that work on the application layer of the protocol stack. This makes the method applicable not only for local implementations, such as NGFWs and IPSs, but also for SaaS and SASE solutions. The method is, however, easily utilised with lower layer information, such as network and transport layer information, for operating system awareness as well. We also present a proof-of-concept case study where we observe that, of the applicable network connections, 100% could be identified when the operating system and endpoint application were present in the source group. To our knowledge, this is the first method to enhance the inspection process accuracy by leveraging a subset of the protected network to gain endpoint awareness.
Adaptive synchronization of fractional-order complex-valued neural networks with time-varying delays

In this paper, the adaptive synchronization of fractional-order complex-valued neural networks with time-varying delays(FOCVNNTDs) is investigated. First, two novel fractional-order differential inequalities with time delays are established, which can be seen as an extension of Halanay inequality. Besides, complete synchronization and quasi-projective synchronization of FOCVNNTDs are investigated based on the two novel inequalities using a novel adaptive controller. In addition, instead of separating the complex-valued neural networks into two real-valued networks, a non-decomposition method is adopted to study the adaptive synchronization of FOCVNNTDs, which avoids the difficulty and complexity of theoretical analysis. Finally, some numerical simulation examples are provided to demonstrate the validity of our theory.
FitPath: QoS-Based Path Selection with Fittingness Measure in Integrated Edge Computing and Software-Defined Networks

Integrating Software-Defined Networking (SDN) and Edge Computing (EC) technologies becomes a promising approach to harness enormous data traffic in the Internet. Recent studies examined the Quality of Service (QoS) based routing paradigms for different data applications such as IoT, heterogeneous networks, and 5G network slicing in such integrated networks. In this paper, our study formulates the QoS-specific routing optimization as a minimum-cost resource allocation problem, while multi-dimensional factors of bandwidth cost, latency, and packet los are jointly considered. Since we use the fittingness factor to help the problem solving, we propose a path selection scheme based on the measure of fittingness factor, briefly named as FitPath. The FitPath scheme can appropriately adjust network resources to maintain delivery time against various service requirements of user demands. Simulation results show that the FitPath scheme can perform better than the Open Shortest Path First (OSPF) and Greedy heuristic schemes in terms of the total of link costs, delivery latency, and packet loss rate under both of normal and heavy data traffic experiments. The effects by the FitPath scheme can be further enhanced with the top-B% path selection policy in integrated network environments.
Tim Berners-Lee: Inventor of the World Wide Web

As USA TODAY, the Nation's No. 1 Newspaper, puts it, "[Tim Berners-Lee] has been compared to Johannes Gutenberg, who invented the printing press, which in turn changed religion, government and science. It's bizarre to even think that there is a living, breathing inventor of the Web. He's funny and likable and about as intimidating as that Pikachu runt from my kids' Pokemon collection. " Few people can name the inventor of the World Wide Web - and that's how he prefers it. Tim Berners-Lee - born in 1955 to parents who were computer programmers - was in the right place at the right time. He grew up believing computers could do more than just store data, and in 1989 he came up with a way for computers around the world to share information. His efforts developed into what most of us use on a daily basis - the World Wide Web. Tim Berners-Lee now directs the World Wide Web Consortium, a group that continues to ensure the Web remains free and accessible to everyone, everywhere. He is also working to bring the Web to its full potential. "The Web," he says, "is far from `done. ' "
Frequency-modulation input-shaping strategy for double-pendulum overhead cranes undergoing simultaneous hoist and travel maneuvers

Large payloads hoisted by overhead cranes exhibit the same behavior as that of a double pendulum. Changes in the hoisting cable length during payload transfer maneuvers gives rise to time-dependent system frequencies rendering input-shaped commands ineffective. In this paper, a frequency-modulation input-shaping strategy that enables simultaneous hoist and travel maneuvers using single-mode input-shaping is presented. This strategy utilizes model-based feedback and partial feedback linearization techniques. Simulations are carried out using arbitrary travel and hoist commands combined with the common single-mode zero-vibration (ZV) and zero-vibration-derivative (ZVD) input-shapers. Sensitivity analysis reveals robust performance in the presence of high system uncertainties.
Comparative Analysis of Deep Learning Models for Aspect Level Amharic News Sentiment Analysis

Nowadays, in the era of social media, customer input and feedback have significant impact on firm’s services and products. Companies face a significant challenge in extracting meaningful information from this unstructured, unorganized, massive, and fragmented data. Some research works has been done on Amharic sentiment analysis (AMSA), however none of them have looked at the aspect level by utilizing a deep learning approach. This work focuses on sentiment analysis of Amharic text utilizing aspect level with a hybrid deep learning approach. The dataset was acquired from Amhara Media Corporation's official Facebook page in Microsoft Excel format. Comment exporter software was used to create a dataset of 10,000 in excel format. Different machine learning techniques such as Convolutional neural network (CNN), Long short-term Memory (LSTM), CNN-LSTM and CNN-GRU were used to train and test the dataset. The result of the study shows that LSTM model performed better than other models with training accuracy of 99.10% having a very little difference from CNN-GRU model with 99.08% training accuracy.
Power Cycling Reliability of SiC MOSFETs in Discrete and Module Packages

This paper presents an analysis of power cycling (PC) capabilities of two industry-standard packages with silicon carbide power MOSFET dies: the discrete TO-247 package and a base-plate-less power module with silicone gel encapsulation. PC experiments show a more than an order of magnitude higher number of cycles to failure for the TO-247. The significant spread in the PC lifetime of TO-packaged SiC power MOSFETs by different manufacturers is correlated with die thickness, bond wire diameter, and number of bond wires. A failure analysis and electro-thermo-mechanical simulations point out that shorter PC lifetime in the case of power modules is due to a high stress in solder, while in the case of TO-packages it is ascribed to wire lift-off. The simulations indicate that rather than die thickness, the wire diameter is a critical parameter influencing wire lift-off.
Activation Function Modulation in Generative Triangular Recurrent Neural Networks

Autonomous generation of time series is challenging because the network must capture short-term features while tracking long-term time dependencies. This paper introduces the modulation of the activation function slopes of the upper-lower triangular neural networks (ULTRNNs) for dynamic variation of memory through a secondary recurrent network with its own independent states. A zigzag propagation algorithm for weight updates is proposed that accounts for the dynamic interaction of the states between the ULTRNN and the secondary network. A novel training method is proposed that distributes the eigenvalues of the closed-loop system around the unit circle in the complex z-plane to ensure that the network behaves as a nonlinear oscillator with an output that neither collapses nor saturates but continues to emulate the target. Examples encompassing the Lorenz series, Santa Fe laser data, Kolam patterns, electrocardiogram (ECG) signals, stock pricing data, and smart grid data are presented to demonstrate that the proposed approach is highly effective in the generative modeling of complex periodic, chaotic, and nonstationary time series. The qualitative and quantitative performance of the ULTRNN obtained with the proposed activation-function modulation technique is comparable to that of state-of-the-art techniques including feedforward networks and generative adversarial networks, but with far fewer trainable parameters and shorter computation times.
Serially Concatenated Continuous Phase Modulation with SOVA Turbo Decoding

For a Serially Concatenated Continuous Phase Modulation (SCCPM) system that concatenates a rate of 1/2 Convolutional Code (CC) and an M-ary full response continuous phase modulation (CPM) signal, we design a turbo decoding scheme using the Soft Output Viterbi algorithm (SOVA) and study the system performance. A decomposition model is used in CPM to reduce the number of states and separate the continuous phase encoder (CPE) with the modulator. As a soft-input soft-output (SISO) decoding algorithm, SOVA is used to generate and update the soft information of decoded signal symbols during the iterative process for both the CPM signal and the CC. Newly generated soft information from one component decoder will be used by the other component decoder to constitute an iterative (i.e., turbo) decoding process. Simulation results show that a decoding gain of at least 1 dB can be obtained by using the turbo decoding compared to that without turbo decoding.
Inductance Characteristic Assessment for Dynamic Wireless Power Charger

With the increased use of electric vehicles (EVs), charging batteries while driving becomes a very attractive technology to increase the driving range of EVs. To achieve this goal, the so called Dynamic Wireless Charger (DWC) uses Wireless Power Transfer (WPT) technology with the receiver coil receiving power from different transmitter coils installed on the road. This paper investigates the inductance characteristic of a DWC. The change in self-inductance and impedance between a range of frequencies is observed, along with the mutual inductance between the two coils. Misaligning, increasing the distance between the coils, and testing a range of frequencies was used to observe and plot the change in mutual inductance between the secondary and primary coils.
Simple yet Efficient Deployment of Scientific Applications in the Cloud

Scientific applications can benefit greatly from getting deployed on a cloud computing platform, but such deployments require skills and expertise that are beyond the reach of many scientists. We address this issue with a framework that simplifies the process of writing cloud-ready scientific applications, and that automates their deployment and execution on top of cloud infrastructures. This paper presents (1) our domain-specific language whose syntax is simple to learn and use, and (2) our compiler that exploits potential data parallelism opportunities and handles load balancing automatically for the users. Our framework prototype demonstrates the feasibility of our approach, and our scalability analysis looks promising.