Miracle-dz/UniFAR-FTUs huggingface dataset:
On the number dependence of viscosity in three dimensional fluids

We compute the shear viscosity of three dimensional simple fluids using nonequilibrium molecular dynamics. We show that for systems of 256 particles or more, the shear viscosity is, within ½ per cent, independent of the number of particles used in the simulation. We estimate that the zero shear rate viscosity of the triple point Lennard-Jones fluid is 3·41 ± 0·07. We observe small, but statistically significant departures from the square root dependence of viscosity with respect to shear rate. We show that by truncating the Lennard-Jones potential at 2·5σ, changes the viscosity by no more than ∼½ per cent compared to the untruncated fluid.
M-CNN: A New Hybrid Deep Learning Model for Web Security

Http requests refer to the way web clients can communicate with web servers. Web attacks represent suspicious changes to normal web requests. It is important to perform detection quickly and accurately for the efficient operation of highly solicited web servers. In this paper, we propose a Memory Convolutional Neural Network “M-CNN” for effectively modeling the information contained in the request data. We also provide a method for automatically extracting robust features from raw data. Experimental results demonstrate that our M-CNN model can extract more complex features by combining a convolutional neural network (CNN) and long short-term memory (LSTM). The CNN layer is used to clean the request from useless information, the LSTM layer is suitable for modeling time information. Our proposed M-CNN model can easily detect the sequence of requests sent by a web-attacker, which is difficult to identify by the state-of-the-art techniques. Finally, the proposed M-CNN model outperforms other state-of-the-art machine learning techniques on the CSIC dataset, achieving an overall accuracy of 99.258%.
Sufficient Lyapunov conditions for pth moment ISS of discrete-time Markovian Switching Systems

In this work, we study the pth moment input-to-state stability (ISS) property for discrete-time nonlinear Markovian switching systems. This property has been studied for discrete-time nonlinear switching systems in the current literature. In this paper, we present an ISS analysis for the Markovian switching case. Particularly, we introduce the definitions of pth moment ISS and exponential pth moment ISS for this class of systems, and we provide sufficient Lyapunov conditions, guaranteeing this property. We also show with an example that our conditions are essential for the analysis of Wireless Control Networks (WCNs), where the communication channel, used to convey information between network components, may suffer from packet-losses.
Delay Margin Computation of Networked Hybrid Microgrid System

The paper presents delay margin computation for a networked hybrid Microgrid. The Microgrid is formed by Photovoltaic array and wind turbine, where they are connected to the loads through two-parallel inverter system. To coordinate the control tasks, the two inverters controllers communicate through a communication network. The master-slave control is designed in dq reference frame with Space Vector Pulse Width Modulation (SVPWM). The stability of the system is analyzed with consideration of the communication delay and Lyapunov-Krasovskii functional is applied to derive stability criterion to compute the delay margin. The conventional tuning rules of the gains of the PI controller are used in the master-slave controller loops to guarantee a stable operation of the system at specific operation point of the wind turbine and the PV array. The performance is validated through MATLAB/SIMULINK simulation. The results show that sufficient current and power-sharing is achieved.
Subspace-Based Estimation of Rapidly Varying Mobile Channels for OFDM Systems

It is well-known that time-varying channels can provide time diversity and improve error rate performance compared to time-invariant fading channels. However, exploiting time diversity requires very accurate channel estimates at the receiver. In order to reduce the number of unknown channel coefficients while estimating the time-varying channel, basis expansion models can be used along with long transmission frames that contain multiple orthogonal frequency division multiplexing (OFDM) symbols that experience the channel variation. The design of these OFDM frames need to judiciously incorporate training and data insertions in the transmitted signal while maintaining orthogonality. In this work, we propose an inter channel interference (ICI)-free training model depending on pilot symbols only and provide a corresponding time-varying channel estimation method. This scheme relies on an algorithm to determine the number of OFDM symbols per frame and the number of basis functions per path with minimal information about the Doppler bandwidth. As a performance benchmark, Bayesian Cramér Rao lower bound (CRLB) and the corresponding MSE bound are derived analytically for the proposed training model. Theoretical MSE expressions of the proposed estimation scheme are also derived as well as the MSE expressions in the presence of Doppler frequency mismatch. Simulations exhibit substantial MSE improvement and the corresponding Symbol Error Rate (SER) performances of the low complexity estimation scheme. They also corroborate that, unlike the common results in the literature, an OFDM system can perform better as the Doppler frequency increases with judicious design of training and channel estimation schemes.
Techno-Economic 5G New Radio Planning Using 26 GHz Frequency at Pulogadung Industrial Area

The Pulogadung industrial area is a widely known developing industrial area that is perfectly ideal for the implementation of 5G technology to help run the Indonesian economy. Nonetheless, it is noteworthy that knowing the level of economic feasibility of an operator is highly crucial before making an investment in a network performance. This study focuses on an analysis of 5G network design in terms of coverage using the Urban Micro propagation model in the Uplink (UL) and Downlink (DL) Outdoor to Outdoor (O2O) Line of Sight (LOS) scenarios. In addition, it also aims to cover the discussion on the economic level of project feasibility using an optimistic scenario assuming 80% users, which is based on the projected increase in population growth of 5G users using the bass growth model method since its implementation in 2021–2030. The economic analysis used the parameters of Capital Expenditure (CAPEX), Operational Expenditure (OPEX), Net Present Value (NPV), Internal Rate of Return (IRR) to determine the feasibility of planning a 5G New Radio network in the Pulogadung Industrial Area. The calculation of Cost Benefit in the optimistic techno-economic scenario shows that each UL O2O LOS NPV scenario resulted in Rp. 28.369. 498.095. 53 with an IRR of 31.18%, while DL O2O LOS NPV resulted in Rp. 24.862. 173.071. 28 with an IRR of 26.68%. This result indicates that the performance of the 5G NR network in the Pulogadung Industrial Estate assuming the projections for the next 10 years is feasible.
A convex approach to robust LQR

In this paper, we propose some new convex strategies for robust optimal control. In particular, we treat the problem of designing finite-horizon linear quadratic regulator (LQR) for uncertain discrete-time systems focusing on minimax strategies. A time-invariant linear control law is obtained just solving sequentially two convex optimization problems, hence obtaining a feedback law that takes into account all the available systems samples. In the case of stabilizable systems, we also generalize our approach by including additional constraints on the closed-loop stability in the optimization scheme. Extensions to time-variant control rules are also discussed, leading to novel and intriguing connections between optimal control and multitask learning.
Ultra‐high performance multi‐turn TOF‐SIMS system with a femto‐second laser for post‐ionization: investigation of the performance in linear mode

The performance of a newly developed TOF‐SIMS System with a femtosecond laser postionization and a multiturn mass spectrometer was investigated in linear mode. This system would be very effective for analyzing valuable material such as space samples. By using postionization the secondary ion signals of Ag were increased (up to 100 times), compared with the conventional TOF‐SIMS experiments. The laser power dependence on the signal intensities was also investigated and it was confirmed that the signal intensities reached specific values above the specific laser power. Lateral resolution of the elemental map was calculated to be about 40 nm. Copyright © 2010 John Wiley & Sons, Ltd.
Safety and Security Study for Shore Power System: State-of-the-Art

Over the years, the shore power technology of ships has become one of the most important measures for energy saving and emission reduction in ports, and it has been paid more and more attention by governments around the world. Shore power technology is to stop the use of auxiliary machinery while the ship is at the dock, and switch to shore power supply to obtain the power required by its pump set, ventilation, lighting, communication and other facilities. The shore power supply system mainly includes the transformation and frequency conversion of the power supply with the connection between the shore power supply system and the ship. While shore power technology is becoming more and more widely used, we must also begin to pay attention to the safety requirements of shore power applications, to ensure the reliability and stability of shore power facilities and equipment, and to ensure the safety of operators and users. This article summarizes the safety and security application methods of shore power system from three aspects: insulation monitoring, shore power equipment safety improvement technology, and fault diagnosis.
A Nash equilibrium-based moving target defense against stealthy sensor attacks

This paper investigates a moving target defense strategy based on detector threshold switching against stealthy sensor attacks. We model the interactions between the attacker and the defender as a game. While the attacker wants to remain stealthy and maximize its impact, the defender wants to minimize both the cost for investigating false alarms and the attack impact. We define the moving target defense as a mixed strategy Nash equilibrium and are able to formulate an equivalent finite matrix game of the original game. We provide a necessary and sufficient condition for the existence of a moving target defense strategy. A globally optimal moving target defense strategy is obtained via a linear optimization problem by exploiting the structure of the matrix game. Simulations with a four tank system verify that by applying an optimal moving target defense strategy, the defender reduces its cost compared to the optimally chosen fixed detector threshold.
Optimal Stabilization Control of Connected Vehicle Systems

This paper studies the optimal stabilization control of the connected vehicle systems (CVSs). The CVS is expected to maintain a desired formation: each vehicle runs with a desired speed, and the distances between a vehicle and its neighbors are kept at desired values. The CVS dynamic is described by the discrete time difference equations. Each vehicle is able to measure its own speed, its nearest predecessor’s speed and the distance between the vehicle and its nearest predecessor. Applying vehicle-to-vehicle (V2V) communication, the vehicle transmits its measured data to its nearest follower. Due to the uncertainties of the communication, it is stochastic that the data is effectively received. Under the above-mentioned setup, an optimal stabilization control framework is proposed for the CVSs under the local stochastic communication. Based on the system stability analysis and the optimization theory, the optimal stabilization control scheme is effectively designed. Finally, the simulations illustrate that the CVS is well maintained a desired formation under the proposed control scheme.
Competitive Improvement of the Time Complexity to Encode Fractal Image: By Applying Symmetric Central Pixel of the Block

By combining the basics of self-similarity, scaling correlation, and statistical components, Benoit Mandelbrot formulated the idea of a natural fractal entity, an entity described by those fundamentals. As a result of these principles, fractal image codings are being used in many substantial applications already, such as image compression, image signature, image watermarking, image attribute extraction, and even image texture segmentation. Thus, while fractal image coding is relatively new in the field of image encoding, it has gained broad acceptance at a rapid pace. In light of its beneficial qualities, such as quick decomposition, high compression ratio, and the independence of resolution at any size make these applications conceivable. However, compared to its advantages, fractal image coding is extremely time-complex and so remarkably expensive, which hinders its prevalence. A wide hunting domain blocks for the relevant range blocks caused this difficulty. We proposed several improvements to the Jacquin design in this paper. We first used max-pooling as an alternative for the medium bonding of spatial contractions to validate the value of the edge textures of the block. Secondly, we construct the odd-size pixel block alternative to an even-size pixel block for validation of the symmetric central pixel (CP). Finally, before the search started, we proposed a shortening of block space, using the central pixel of the block to convert each eight-bit pixel to a two-bit pixel. As a consequence, the symmetrical CP of odd pixels block, reduction of block space, and edge pixel selection accomplished faster coding and competitive image quality than existing known exhaustive search algorithms.
Systematic Design Methodology of Broadband Doherty Amplifier Using Unified Matching/Combining Networks With an Application to GaN MMIC Design

This paper presents a new design methodology for broadband Doherty architecture using the three-port input and output networks technique. The proposed topology was developed to overcome the Doherty power amplifier (DPA) bandwidth limitations. The output three-port network performs the impedance matching from any load impedance to the optimum loads for both main and peaking transistors and also combines the power delivered from the two devices at any power ratio. On the other hand, the input-splitting network is proposed for matching the input impedances of the two transistors to the source impedance. The freedom in choosing the power division ratio of the input network enables us to achieve a tradeoff between efficiency and linearity. Also, it provides a way to accomplish the phase compensation using an arbitrary phase difference between the two branches of the Doherty power amplifier and thus, helps obviate the need of the highly bandwidth limiting offset lines found in the Doherty design. A two-stage broadband Doherty power amplifier is implemented using 0.25-um GaN HEMT MMIC process to validate the proposed topology. The fabricated DPA was measured under both continuous wave (CW) and modulated signal at different operating frequencies. Across 3.3–3.7 GHz, the implemented DPA delivers a maximum output power exceeding 42 dBm, power added efficiency (PAE) over 52 % at the peak power and over 38 % in the back-off state over the operating 400 MHz bandwidth. The fully integrated circuit has a chip-size of 4.4 mm $\times\,\,3.5$ mm.
A Novel Machine Learning Approach for Link Adaptation in 5G Wireless Networks

This study addresses a Machine Learning (ML) based Link Adaptation (LA) scheme for 5G New Radio (NR) wireless networks, which aims to improve the system throughput by selecting the best possible choice of the Modulation Coding Scheme (MCS). This work proposes a Deep Neural Network(DNN) based regression model to maximize the Spectral Efficiency (SE) of the system under the 10% Block Error Rate (BLER) and thus finding the best MCS. We consider a 5G NR Frequency Range-1(FR-1), i.e., the Sub-6GHz operating band for the study. Our simulation results show the mapping of Signal to Interference and Noise Ratio (SINR) to the Channel Quality Indicator (CQI) and thus the best possible selection of modulation and coding scheme in case of perfect channel estimation based system which is found to improve the system throughput.
Phylogenetic Manifold Regularization: A semi-supervised approach to predict transcription factor binding sites

The computational prediction of transcription factor binding sites remains a challenging problems in bioinformatics, despite significant m ethodological d evelopments f rom t he field of machine learning. Such computational models are essential to help interpret the non-coding portion of human genomes, and to learn more about the regulatory mechanisms controlling gene expression. In parallel, massive genome sequencing efforts have produced assembled genomes for hundred of vertebrate species, but this data is underused. We present PhyloReg, a new semi-supervised learning approach that can be used for a wide variety of sequence-to-function prediction problems, and that takes advantage of hundreds of millions of years of evolution to regularize predictors and improve accuracy. We demonstrate that PhyloReg can be used to better train a previously proposed deep learning model of transcription factor binding. Simulation studies further help delineate the benefits o f t he a pproach. G ains in prediction accuracy are obtained over a broad set of transcription factors and cell types.
Towards Higher Sense of Presence: A 3D Virtual Environment Adaptable to Confusion and Engagement

Virtual Reality scenarios where emitters convey information to receptors can be used as a tool for distance learning and to enable virtual visits to company physical headquarters. However, immersive Virtual Reality setups usually require visualization interfaces such as Head-mounted Displays, Powerwalls or CAVE systems, supported by interaction devices (Microsoft Kinect, Wii Motion, among others), that foster natural interaction but are often inaccessible to users. We propose a virtual presentation scenario, supported by a framework, that provides emotion-driven interaction through ubiquitous devices. An experiment with 3 conditions was designed involving: a control condition; a less confusing text script based on its lexical, syntactical, and bigram features; and a third condition where an adaptive lighting system dynamically acted based on the user’s engagement. Results show that users exposed to the less confusing script reported higher sense of presence, albeit without statistical significance. Users from the last condition reported lower sense of presence, which rejects our hypothesis without statistical significance. We theorize that, as the presentation was given orally and the adaptive lighting system impacts the visual channel, this conflict may have overloaded the users’ cognitive capacity and thus reduced available resources to address the presentation content.
Human Arm Stabilization and Rehabilitation using Intelligent Control Techniques

This work presents a study of the planar model of movement performed by a human arm with hemiplegia, being actuated through an electro stimulus, with the aim to reduce the angular error of the same movement using techniques of adaptive control and genetic algorithm-based control and comparing with an Integral Proportional Controller. The verification and validation of the proposed methodology are carried out by using a simulation of a eletrostimulation device compared with a given angle reference, aiming regulation.
Using bioinformatics methods to explore the connections between expression and subcellular localization of proteins and gastric cancer progression

Since the expression and localization of protein plays the important role for GC, this study is to explore the connections between these proteomic information and GC prognosis. After we use bioinformatics method to analyze the dataset from Human Protein Atlas, we found that the proportions of high expression intensity in GC are less than those in normal stomach tissue (NST) samples, whereas the proportion of low expression intensity in GC are greater than those in NST; the proteins located in the nucleus of normal cells are more stable than others; MEN1 is such a tumor suppressor gene that has favorable prognosis.
Distributed Composite Adaptive Synchronization of Multiple Uncertain Euler-Lagrange Systems using Cooperative Initial Excitation

proposes a distributed composite adaptive synchronization algorithm for multiple uncertain EulerLagrange (EL) systems, where parameter convergence is achieved under a relaxed mathematical condition as compared to the state-of-the-art. Classical adaptive controllers require an analytical condition, called persistence of excitation (PE), to ensure parameter convergence, which results in better transient performance and robustness to disturbance. The PE condition is extended to Cooperative-PE (C-PE) condition for distributed adaptive controllers with cooperative estimation strategies. The PE and C-PE conditions are restrictive in nature since these conditions are not satisfied in most practical applications. Recent literature in adaptive control has relaxed the PE condition to Initial Excitation (IE), which is shown to be sufficient for parameter convergence. The IE condition is argued to be significantly milder than PE and can be satisfied in many practical setting. The proposed result further extends the IE condition to Cooperative-IE (C-IE) condition in distributed adaptive control architecture in the context of synchronizing multiple EL systems. It is established that the C-IE condition is milder than PE, IE, and C-PE conditions. Two-tier filter based estimation algorithm with strategic switching ensures parameter convergence under the C-IE condition and thereby provides exponential convergence of tracking and parameter estimation error to zero. Simulation results validate the efficacy of the proposed algorithm as compared to conventional distributed adaptive controllers in terms of superior tracking and estimation performance.
Calibration of the Vector Magnetometer Based on Solution of the System of Linear Equations

The paper contains the description of measurement errors of a vector magnetometer caused by its non-ideal quality. The mathematical modeling of the signal measured by a magnetometer was made for the magnetometer calibration process investigation. The method of the calibration parameters estimation based on the iterative solution of the system of linear equations has been proposed. The capabilities of this method have been studied.
Automated Data Acquisition and Processing for Factory Layout Planning

Factory planning is an important tool for manufacturing companies to raise their efficiency and to maintain their competitiveness by changing market or customer requirements. A special challenge is the acquisition of layout data and the processing of this data in suitable planning tools. Current approaches still measure manually or have to transfer acquired data from laser scanners by hand into planning tools, which leads to a high effort and error proneness.This paper presents a holistic concept for automated and systematic data acquisition and processing for factory planning processes.