Miracle-dz/UniFAR-FTUs huggingface dataset:
Global Solution to Enskog Equation with External Force in Infinite Vacuum

The author first gives hypotheses of the bicharacteristic equations corresponding to the Enskog equation with an external force.Since the collision operator of the Enskog equation is more complicated than that of the Boltzmann equation,these hypotheses are more complicated than those given by Duan et al.for the Boltzmann equation.The hypotheses are very related to collision of particles of moderately or highly dense gases along the bicharacteristic curves and they can be used to make the estimation of the so-called gain and loss integrals of the Enskog integral equation.Then,by controlling these integrals,it is shown that there exists a unique global mild solution to the Enskog equation in an infinite vacuum for moderately or highly dense gases.Finally,the author makes some remarks on the locally Lipschitz assumption of the collision factors in the Enskog equation.
Modeling errors due to Timoshenko approximation in damage identification

The use of accurate computational models for damage identification problems may lead to prohibitive costs. Damage identification problems are often characterized as inverse ill‐posed problems. Thus, the use of approximate models such as simplified physical and/or reduced‐order models typically yields misleading results. In this paper, we carry out a preliminary study on a particular simplified physical model, the Timoshenko beam model in the context of damage identification. The actual beam is a two‐dimensional relatively high aspect ratio (thickness/length) beam with a distributed damage that is modeled as a spatially varying Young modulus. We state the problem in the Bayesian framework for inverse problems and carry out approximative marginalization over the related modeling errors. The numerical experiments suggest that the proposed approach yields more stable results than using the Timoshenko beam model as an accurate model. Due to the severity of the Timoshenko approximation, however, the posterior error estimates of the proposed approach are not always feasible in the probabilistic sense.
Enhanced PCB Based Slicing

Program slicing can be used to improve software reliability. It enables identification and checking of critical points by removing program parts that do not influence them. The standard slicing method computes the dependencies of an entire program as a prerequisite to the slicing. Demand-driven techniques such as our work Predicate Control Block (PCB)-based slicing computes only the dependencies affecting critical points. This improves the performance for single slices. This paper extends PCB-based slicing to efficiently compute several slices from the same code. This is done by storing the computed data dependencies in a form of graph to reuse them between individual slices. We also show how PCB-based slicing can be done interprocedurally in a demanddriven fashion. Moreover, we describe a filtering technique that reduces the exploration of irrelevant paths. These two improvements enhance the algorithm performance, which we show using synthetic benchmarks.
Anomalies Detection and Proactive Defence of Routers Based on Multiple Information Learning †

Routers are of great importance in the network that forward the data among the communication devices. If an attack attempts to intercept the information or make the network paralyzed, it can launch an attack towards the router and realize the suspicious goal. Therefore, protecting router security has great importance. However, router systems are notoriously difficult to understand or diagnose for their inaccessibility and heterogeneity. A common way of gaining access to the router system and detecting the anomaly behaviors is to inspect the router syslogs or monitor the packets of information flowing to the routers. These approaches just diagnose the routers from one aspect but do not correlate multiple logs. In this paper, we propose an approach to detect the anomalies and faults of the routers with multiple information learning. First, we do the offline learning to transform the benign or corrupted user actions into the syslogs. Then, we construct the log correlation among different events. During the detection phase, we calculate the distance between the event and the cluster to decide if it is an anomalous event and we use the attack chain to predict the potential threat. We applied our approach in a university network which contains Huawei, Cisco and Dlink routers for three months. We aligned our experiment with former work as a baseline for comparison. Our approach obtained 89.6% accuracy in detecting the attacks, which is 5.1% higher than the former work. The results show that our approach performs in limited time as well as memory usages and has high detection and low false positives.
λ-Scale Embedded Active Region Photonic Crystal (LEAP) Lasers for Optical Interconnects

The distances optical interconnects must cover are decreasing as Internet traffic continues to increase. Since short-reach interconnect applications require many transmitters, cost and power consumption are significant issues. Directly modulated lasers with a wavelength-scale active volume will be used as optical interconnects on boards and chips in the future because a small active volume is expected to reduce power consumption. We developed electrically driven photonic crystal (PhC) lasers with a wavelength-scale cavity in which the active region is embedded in a line-defect waveguide of an InP-based PhC slab. We call this a λ-scale embedded active region PhC laser, or a LEAP laser. The device, whose active region has six quantum wells with 2.5 × 0.3 × 0.15 μm3 active volume, exhibits a threshold current of 28 μA and provides 10 fJ/bit of operating energy to 25 Gbit/s NRZ (non-return-to-zero) signals. The fiber-coupled output power is 6.9 μW. We also demonstrate heterogeneous integration of LEAP lasers on a SiO2/Si substrate for low-cost photonic integrated circuits (PICs). The threshold current is 40.5 μA and the output power is 4.4 μW with a bias current of 200 μA. These results indicate the feasibility of using PhC lasers in very-short-distance optical communications.
A novel modular ANN architecture for efficient monitoring of gases/odours in real-time

Data pre-processing is tremendously used for enhanced classification of gases. However, it suppresses the concentration variances of different gas samples. A classical solution of using single artificial neural network (ANN) architecture is also inefficient and renders degraded quantification. In this paper, a novel modular ANN design has been proposed to provide an efficient and scalable solution in real–time. Here, two separate ANN blocks viz. classifier block and quantifier block have been used to provide efficient and scalable gas monitoring in real—time. The classifier ANN consists of two stages. In the first stage, the Net1-NDSRT has been trained to transform raw sensor responses into corresponding virtual multi-sensor responses using normalized difference sensor response transformation (NDSRT). These responses have been fed to the second stage (i.e., Net2-classifier). The Net2-classifier has been trained to classify various gas samples to their respective class. Further, the quantifier block has parallel ANN modules, multiplexed to quantify each gas. Therefore, the classifier ANN decides class and quantifier ANN decides the exact quantity of the gas/odor present in the respective sample of that class.
Performance Improvement via Iterative Connection of Passive Systems

This paper addresses <italic>model-set-based</italic> quantitative analysis of feedback systems. In particular, we find a model set describing the subsystems such that the performance improvement of the feedback system is achieved. To this end, we introduce the parameter-integrated passivity to accurately describe each passive subsystem and their feedback system. A model set describing passive systems is characterized by the two matrix parameters. The matrix parameters enable to evaluate the <inline-formula><tex-math notation="LaTeX">$L_2$</tex-math></inline-formula>-gain “of the model set”, which is defined as the <inline-formula><tex-math notation="LaTeX">$L_2$</tex-math></inline-formula>-gain of the worst-case system in the model set. With the parameter-integrated passivity, the quantitative analysis of a feedback system composed of two passive subsystems is provided as the parameter transition. Then, we find conditions on the matrix parameters to achieve the performance improvement such that the <inline-formula><tex-math notation="LaTeX">$L_2$</tex-math></inline-formula>-gain of the model set describing the feedback system is strictly less than that describing the subsystems. Subsequently, the performance improvement of the feedback system is extended to that of an iterative feedback system, which is a network system constructed by the feedback connection of multiple subsystems in a step-by-step manner. Then, we find conditions on the passivity parameters describing the baseline subsystem to achieve a gradual performance improvement with the subsystem connection.
MAC protocol for indoor optical wireless networks

Optical wireless communication has emerged as a promising candidate for future high data rate indoor applications such as virtual reality. Even though physical layer of optical wireless networks has rapidly developed during last decade, upper layer architecture that harness the physical layer capabilities has not yet been developed in the same pace. To this end, the authors develop a novel contention-based medium access control (MAC) protocol that accompanies a service differentiation mechanism and a dynamic contention window tuning algorithm. The proposed service differentiation mechanism can identify the diverse traffic types and facilitate their throughput and delay requirements. To add more robustness to the contention-based MAC protocol which depends on contention windows to avoid collisions, the authors also propose an algorithm that dynamically changes the contention window sizes to suit the congestion level. They analyse the performance of the proposed MAC protocol under diverse network configurations and they show that it is far more effective to use end-user network metrics such as throughput in dynamic adaptation algorithms in addition to collision rate due to the wide range of traffic types present in the network. The proposed results demonstrate that the proposed MAC protocol can handle next-generation traffic types and their stringent latency requirements in an effective manner.
On some inferential issues for the destructive COM-Poisson-generalized gamma regression cure rate model

Abstract In this paper, we assume the initial number of malignant cells to undergo a destructive process and hence what we record is only from the undamaged portion of the original number of malignant cells. This gives a realistic and practical interpretation of the biological mechanism of the occurrence of a cancerous tumor. We propose to model the initial number of malignant cells by the flexible Conway-Maxwell (COM) Poisson distribution, which includes some of the commonly used discrete distributions as its particular cases. Furthermore, we propose to model the time taken by each active malignant cell after an initial treatment by the wider class of generalized gamma distribution, which includes some of the commonly used lifetime distributions as its particular cases. The main contribution is in developing the likelihood inference based on the expectation maximization algorithm for such a flexible and general destructive cure rate model. An extensive simulation study is carried out to demonstrate the performance of the proposed estimation method. The flexibilities of the COM-Poisson distribution and the generalized gamma distribution are also utilized to carry out a two-way model discrimination using some likelihood-based methods. Finally, a melanoma dataset is analyzed for illustrative purposes.
Uncertainty Management at the Airport Transit View

Air traffic networks, where airports are the nodes that interconnect the entire system, have a time-varying and stochastic nature. An incident in the airport environment may easily propagate through the network and generate system-level effects. This paper analyses the aircraft flow through the Airport Transit View framework, focusing on the airspace/airside integrated operations. In this analysis, we use a dynamic spatial boundary associated with the Extended Terminal Manoeuvring Area concept. Aircraft operations are characterised by different temporal milestones, which arise from the combination of a Business Process Model for the aircraft flow and the Airport Collaborative Decision-Making methodology. Relationships between factors influencing aircraft processes are evaluated to create a probabilistic graphical model, using a Bayesian network approach. This model manages uncertainty and increases predictability, hence improving the system’s robustness. The methodology is validated through a case study at the Adolfo Suarez Madrid-Barajas Airport, through the collection of nearly 34,000 turnaround operations. We present several lessons learned regarding delay propagation, time saturation, uncertainty precursors and system recovery. The contribution of the paper is two-fold: it presents a novel methodological approach for tackling uncertainty when linking inbound and outbound flights and it also provides insight on the interdependencies among factors driving performance.
Hybrid MU-MIMO Precoding Based on K-Means User Clustering

Multi-User (MU) Multiple-Input-Multiple-Output (MIMO) systems have been extensively investigated over the last few years from both theoretical and practical perspectives. The low complexity Linear Precoding (LP) schemes for MU-MIMO are already deployed in Long-Term Evolution (LTE) networks; however, they do not work well for users with strongly-correlated channels. Alternatives to those schemes, like Non-Linear Precoding (NLP), and hybrid precoding schemes were proposed in the standardization phase for the Third-Generation Partnership Project (3GPP) 5G New Radio (NR). NLP schemes have better performance, but their complexity is prohibitively high. Hybrid schemes, which combine LP schemes to serve users with separable channels and NLP schemes for users with strongly-correlated channels, can help reduce the computational burden, while limiting the performance degradation. Finding the optimum set of users that can be co-scheduled through LP schemes could require an exhaustive search and, thus, may not be affordable for practical systems. The purpose of this paper is to present a new semi-orthogonal user selection algorithm based on the statistical K-means clustering and to assess its performance in MU-MIMO systems employing hybrid precoding schemes.
Analysis of Field-Aligned Currents in the High-Altitude Nightside Auroral Region:
 Cluster Observation

In this paper we present analysis of current density when the Cluster spacecraft pass the nightside auroral region at about 4-5 RE from the center of Earth. The analysis is made when the inter-spacecraft separation is within 200 km, which allows all four spacecraft to be situated inside the same current sheet. On 22 February 2002, two field-aligned current (FAC) events were observed in both the southern and the northern hemispheres. The FACs were calculated with magnetic field data obtained by the four spacecraft using the Curlometer method. The scales of the FACs along the spacecraft trajectory and the magnitudes were hundreds of kilometers and tens of nA/m2, respectively, and both events were mapped to the auroral region in the ionosphere. We also examined reliability of the results with some parameters, and found that our results are adequately comparable with other studies. Nevertheless, some limitations that decrease the accuracy of current estimation exist.
Spurious PIV Vector Correction Using Linear Stochastic Estimation

Techniques for the experimental determination of velocity fields such as particle image velocimetry (PIV) can often be hampered by spurious vectors or sparse regions of measurement which may occur due to a number of reasons. Commonly used methods for detecting and replacing erroneous values are often based on statistical measures of the surrounding vectors and may be influenced by further poor data quality in the region. A new method is presented in this paper using Linear Stochastic Estimation for vector replacement (LSEVR) which allows for increased flexibility in situations with regions of spurious vectors. LSEVR is applied to PIV dataset to demonstrate and assess its performance relative to commonly used bilinear and bicubic interpolation methods. For replacement of a single vector, all methods performed well, with LSEVR having an average error of 11% in comparison to 14% and 18% for bilinear and bicubic interpolation respectively. A more significant difference was found in replacement of clusters of vectors which showed average vector angle errors of 10°, 9° and 6° for bilinear, bicubic and LSEVR respectively. Error in magnitude was 3% for both interpolation techniques and 1% for LSEVR showing a clear benefit to using LSEVR for conditions that require multiple clustered vectors to be replaced.
Adaptive disturbance observer‐based control for stochastic systems with multiple heterogeneous disturbances

Antidisturbance control problem is discussed for stochastic systems with multiple heterogeneous disturbances, which include the white noise and the disturbance with unknown frequencies and amplitudes. An adaptive disturbance observer is designed to estimate the disturbance with unknown frequencies and amplitudes, based on which, an adaptive disturbance observer‐based control scheme is proposed by combining adaptive technique and linear matrix inequality method. It is proved that the closed‐loop system is asymptotically bounded in mean square when multiple heterogeneous disturbances exist simultaneously and that the equilibrium is globally asymptotically stable in probability as additive disturbance disappears. Finally, two simulation examples, including a wind turbine system, are given to show the effectiveness of the proposed scheme.
A semiparametric approach for modelling multivariate nonlinear time series

In this article, a semiparametric time‐varying nonlinear vector autoregressive (NVAR) model is proposed to model nonlinear vector time series data. We consider a combination of parametric and nonparametric estimation approaches to estimate the NVAR function for both independent and dependent errors. We use the multivariate Taylor series expansion of the link function up to the second order which has a parametric framework as a representation of the nonlinear vector regression function. After the unknown parameters are estimated by the maximum likelihood estimation procedure, the obtained NVAR function is adjusted by a nonparametric diagonal matrix, where the proposed adjusted matrix is estimated by the nonparametric kernel estimator. The asymptotic consistency properties of the proposed estimators are established. Simulation studies are conducted to evaluate the performance of the proposed semiparametric method. A real data example on short‐run interest rates and long‐run interest rates of United States Treasury securities is analyzed to demonstrate the application of the proposed approach. The Canadian Journal of Statistics 47: 668–687; 2019 © 2019 Statistical Society of Canada
Virus-Like Particle Production in Atmospheric Eubacteria Isolates

Culturable eubacterial isolates were collected at various altitudes in Earth’s atmosphere, including ~1.5 m above ground in Tallahassee, FL, USA; ~10.0 m above sea level over the mid-Atlantic ridge (~15° N); ~ 20 km above ground over the continental United States; ~20 km above sea level over the Pacific Ocean near southern California; and from the atmosphere of Carlsbad Cavern, Carlsbad Cavern National Park, NM, USA. Isolates were screened for the presence of inducible virus-like particles (VLP) through the use of mitomycin C and epifluorescent direct counts. We determined that 92.7% of the isolates carried inducible VLP counts in exposed versus non-exposed culture controls and that the relationship was statistically significant. Further statistical analyses revealed that the number of isolates that demonstrated VLP production did not vary among collection sites. These data demonstrate a high prevalence of VLP generation in isolates collected in the lower atmosphere and at extreme altitudes. They also show that species of eubacteria that are resistant to the rigors of atmospheric transport play a significant role in long-range atmospheric inter- and intra-continental dispersion of VLP and that long-range atmospheric transport of VLP may enhance rates of evolution at the microbial scale in receiving environments.
A benchmark testing technique to characterize the stress–strain relationship in materials based on the spalling test and a photomechanical method

The experimental testing of concrete and other brittle materials under high-strain rate tensile loading remains a major issue in many research fields and industrial applications. Among the experimental methods, the spalling technique provides a major advantage as it does not rely on a static mechanical balance of the sample. A short compressive pulse transmitted to the sample reflects from the rear free surface as a tensile pulse leading to sample tensile fracturing. However, the classic processing (i.e. Novikov’s acoustic approximation) based on the measurement of the sample rear face velocity profile, relies on strong assumptions that can lead to an over-estimation of the material tensile strength. A new processing technique was proposed by Pierron and Forquin (2012 Strain 48 388–405) to derive the stress field and apparent Young’s modulus using only data from an ultra-high speed camera. This purely inertial method is based on the time resolved measurement of axial displacement fields and the virtual fields methods (VFM). However, the measurement accuracy as well as uncertainty remains to be studied in more detail and better understood. In the present work, a series of spalling tests have been performed with a sample made of aluminium alloy behaving linearly elastic. Such test allows investigating a possible influence of several testing parameters (e.g. interframe time, lens focusing, number of pixels per period…), processing parameters (mainly the levels of spatial and temporal smoothing for regularization) as well as the influence of the camera sensor technology. The results are compared to data from strain gauges and to the expected Young’s modulus and stress–strain response of the tested aluminium alloy, so the accuracy of measurements is evaluated. Finally, the spalling set-up applied to a sample of well-known mechanical properties appears to be a good benchmark to assess the quality of stress versus strain measurements based on photomechanical methods.