Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis of Phase-Locked Loop Systems under Conditions of a Disturbance Based on the Model of the Combined Maximum Principle and the Discrete Invariant Immersion Method

The synthesis of phase-locked loop systems is based on a two-stage optimization procedure. This allows to apply the combined maximum principle to a model of the signal phase dynamics under the conditions of structural uncertainty of regular actions. The estimation equations in the form of a two-point boundary value problem are obtained using the discrete maximum principle of L.S. Pontryagin. By application of the invariant immersion method a recursive estimation algorithm is obtained. Evaluation of the efficiency of the synthesized phase-locked loop system is carried out on the basis of mathematical modeling in comparison with the traditional loop system in terms of convergence rate and accuracy.
Anomaly Detection for Insider Threats Using Unsupervised Ensembles

Insider threat represents a major cybersecurity challenge to companies, organizations, and government agencies. Insider threat detection involves many challenges, including unbalanced data, limited ground truth, and possible user behavior changes. This research presents an unsupervised learning based anomaly detection approach for insider threat detection. We employ four unsupervised learning methods with different working principles, and explore various representations of data with temporal information. Furthermore, different computational intelligence schemes are explored to combine these models to create anomaly detection ensembles for improving the detection performance. Evaluation results show that the approach allows learning from unlabelled data under challenging conditions for insider threat detection. Insider threats are detected with high detection and low false positive rates. For example, 60% of malicious insiders are detected under 0.1% investigation budget, and all malicious insiders are detected at less than 5% investigation budget. Furthermore, we explore the ability of the proposed approach to generalize for detecting new anomalous behaviors in different datasets, i.e., robustness. Finally, results demonstrate that a voting-based ensemble of anomaly detection can be used to improve detection performance as well as the robustness. Comparisons with the state-of-the-art confirm the effectiveness of the proposed approach.
Hyperspectral Image Superresolution Using Global Gradient Sparse and Nonlocal Low-Rank Tensor Decomposition With Hyper-Laplacian Prior

This article presents a novel global gradient sparse and nonlocal low-rank tensor decomposition model with a hyper-Laplacian prior for hyperspectral image (HSI) superresolution to produce a high-resolution HSI (HR-HSI) by fusing a low-resolution HSI (LR-HSI) with an HR multispectral image (HR-MSI). Inspired by the investigated hyper-Laplacian distribution of the gradients of the difference images between the upsampled LR-HSI and latent HR-HSI, we formulate the relationship between these two datasets as a $\ell _{p}$ $(0 < p < 1)$-norm term to enforce spectral preservation. Then, the relationship between the HR-MSI and latent HR-HSI is built using a tensor-based fidelity term to recover the spatial details. To effectively capture the high spatio-spectral-nonlocal similarities of the latent HR-HSI, we design a novel nonlocal low-rank Tucker decomposition to model the 3-D regular tensors constructed from the grouped nonlocal similar HR-HSI cubes. The global spatial-spectral total variation regularization is then adopted to ensure the global spatial piecewise smoothness and spectral consistency of the reconstructed HR-HSI from nonlocal low-rank cubes. Finally, an alternating direction method of multipliers-based algorithm is designed to efficiently solve the optimization problem. Experiments on both the synthetic and real datasets collected by different sensors show the effectiveness of the proposed method, from visual and quantitative assessments.
Magneto-Refractive Effect and Mechanism Analysis of Erbium-Ytterbium Co-Doped Silica Fiber

The magneto-refractive properties of Er/Yb co-doped silica fiber (EYDF) are important in magnetic field sensing. Here, we proposed a theoretical calculation model of the EYDF using density functional theory (DFT) and demonstrated it by an interferometric digital hologram magneto-refractive measurement system. The calculation results show that the fiber material, doped with Er/Yb atoms, has a large net spin and asymmetric spin distribution, which enables the elements to play an important role in the magnetic characteristics of the material. The experimental results show that the refractive index of the fiber decreases linearly with the increase of the magnetic field. The sensitivity of EYDF is 3.8279 × 10-5 RIU/Gs and is two orders of magnitude higher than that of single-mode fiber which is confirmed by the theoretical calculation. It demonstrated that the EYDF has advantages and potential of high sensitivity, miniaturization, and easy integration for magnetic field detection in harsh environments.
Nonlocal Block-Term Decomposition for Hyperspectral Image Mixed Noise Removal

Since the facility restrictions and weather conditions, hyperspectral image (HSI) is generally seriously polluted by a variety of noises. Recently, the method based on block-term decomposition with rank-<inline-formula><tex-math notation="LaTeX">$(L, L, 1)$</tex-math></inline-formula> (BTD) has attracted wide attention in HSI mixed noise removal. BTD factorizes third-order HSI data into the sum of a series of component tensors, where each of the component tensors is represented by the outer product of a rank-<inline-formula><tex-math notation="LaTeX">$L$</tex-math></inline-formula> matrix <inline-formula><tex-math notation="LaTeX">$\mathbf {A}_r\mathbf {B}_r^T$</tex-math></inline-formula> and a column vector <inline-formula><tex-math notation="LaTeX">$\mathbf {c}_r$</tex-math></inline-formula>. BTD has clear physical interpretation because its latent factors <inline-formula><tex-math notation="LaTeX">$\mathbf {A}_r\mathbf {B}_r^T$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\mathbf {c}_r$</tex-math></inline-formula> can be interpreted abundance map and spectral signature, respectively. The essential uniqueness of BTD is under the low-rank assumption of <inline-formula><tex-math notation="LaTeX">$\mathbf {A}_r\mathbf {B}_r^T$</tex-math></inline-formula>. However, the low-rank assumption is not always held in real scenarios. The BTD-based method usually sets <inline-formula><tex-math notation="LaTeX">$L$</tex-math></inline-formula> to full rank to achieve satisfactory results. In this article, we suggest a novel model based on nonlocal block-term decomposition (NLBTD) for HSI mixed noise removal. More specifically, for each grouped similar image block, BTD is introduced to capture nonlocal self-similarity and global spectral low-rankness, the unidirectional total variation is introduced to preserve local spectral smoothness. By faithfully exploring nonlocal self-similarity, global spectral low-rankness, and local spectral smoothness, the proposed method is expected to produce promising results with guarantee the essential uniqueness of BTD. To tackle the resulting model, we design an efficient algorithm based on the proximal alternating minimization with the theoretical guarantees. Extensive numerical experiments in HSI mixed noise removal demonstrate that the proposed NLBTD method achieves satisfactory performance compared with state-of-the-art methods.
A Fast CFAR Algorithm Based on Density-Censoring Operation for Ship Detection in SAR Images

In this letter, we propose a new constant false alarm rate (CFAR) detector to accelerate the existing superpixel (SP)-based CFAR detectors for ship detection in synthetic aperture radar (SAR) images. In our method, we design a new density-censoring operation to rapidly identify background clutter SPs (BCSPs) with high densities before the local CFAR detection. In this way, a large number of non-informative BCSPs are removed without time-consuming calculation of decision thresholds, and only a few candidate ship target SPs (STSPs) are retained. This reduces the computational cost of the subsequent local CFAR detection and the number of false alarms produced by it. During the local CFAR detection process for the retained candidate STSPs, we also propose an improved method to define their neighboring clutter regions (for the calculation of decision thresholds) using BCSPs identified by the density-censoring operation. Experiments on measured SAR images validate that the proposed CFAR method reduces the computational cost of commonly used SP-based CFAR methods by 75%-96% with similar or better detection performance.
Theoretically Enhance the Vertical Light Extraction Efficiency of the AlGaN-Based Nanowire Photonic Crystal Ultraviolet Light-Emitting Diodes by Selecting the Band Gap

Nanowire photonic crystal (PhC) structure is one of promising approaches to enhance vertical light extraction efficiency (LEE) of ultraviolet light emitting diodes (UV LEDs). There are lots of investigations to study the structure of nanowire UV LEDs by using the finite-difference time-domain (FDTD) method. The optimal LEE are obtained by using some optimization algorithms. However, it needs a broad window, not the best values of geometrical parameters of nanowires in practical fabrication. In this paper, the vertical LEE of UV LEDs are theoretically enhanced by selecting the band gaps of AlGaN-based nanowire PhC structure. It illustrates that all the gaps can inhibit the horizontal propagation of light. However, the inhibiting ability of Gap 1 are weaker than them of Gap 2 and Gap 3. The reason is that the radii of nanowires in Gap 1 are too small to support enough guided modes. As a comparison, both Gap 2 and Gap 3 can efficiently inhibit the radiated mode propagation, and finally enhance the vertical LEE. In addition, the variations of height, number, and refractive index of nanowires on vertical LEE and optical band gaps are discussed. This work has benefits for the practical production of nanowire PhC UV LEDs.
Computational Method for Heat Partition at the Rail-Armature Interface Based on Least Squares Regression

The sliding electrical contact interface will generate a huge total heat source including Joule heat and frictional heat when an armature slides along a pair of rails driven by Lorentz forces. The total heat is partitioned at the contact interface flowing into armature and rail respectively. The heat partition at the rail-armature interface and the resulting temperature rise have important effects on the melt wear characteristics of the contact material. A computational method was proposed for determining heat partition at the rail-armature interface based on least squares regression. Temperatures equations of rail and armature were developed in terms of an unknown heat partition function. Solution equations of heat partition function were derived by matching the average temperature of rail and armature at the contact surfaces. The heat partition function was assumed to be a polynomial form and an estimate for the heat partition function was obtained by minimizing the error term in the least squares sense. Then, effects of polynomial order on the heat partition function are analyzed. Furthermore, the heat partition solution from the least squares regression and previous analytical method are compared. Variation characteristics of heat partition coefficient were also analyzed. In the end, the variation mechanism and influencing factors of heat partition coefficient were discussed.
Memory Efficient Grasping Point Detection of Nontrivial Objects

Robotic manipulation with a nontrivial object providing various types of grasping points is of an industrial interest. Here, an efficient method of simultaneous detection of the grasping points is proposed. Specifically, two different 3 degree-of-freedom end effectors are considered for simultaneous grasping. The method utilizes an RGB data-driven perception system based on a specifically designed fully convolutional neural network called attention squeeze parallel U-Net (ASP U-Net). ASP U-Net detects grasping points based on a single RGB image. This image is transformed into a schematic grayscale frame, where the positions and poses of the grasping points are coded into gradient geometric shapes. In order to approve the ASP U-Net architecture, its performance was compared with nine competitive architectures using metrics based on generalized intersection over union and mean absolute error. The results indicate its outstanding accuracy and response time. ASP U-Net is also computationally efficient enough. With a more than acceptable memory size (77 MB), the architecture can be implemented using custom single-board computers. Here, its capabilities were tested and evaluated on the NVIDIA Jetson NANO platform.
R2D2, the CEA Graded Nb3Sn Research Racetrack Dipole Demonstrator Magnet

As a part of a collaborative effort with CERN on the long road to FCC-hh, CEA is developing a 1.4 m-long single aperture short dipole demonstrator. This Nb3Sn block dipole, named F2D2, is foreseen to produce 15.5 T with a 14% margin at 1.9 K, and will use a shell-based support structure. In order to be compact, this magnet relies on the use of different cable grades in each coil. Among the milestones in this project, a single-layer graded racetrack dipole will be constructed in order to demonstrate grading in a simplified geometry while preserving key features of F2D2. This magnet, named Research Racetrack Dipole Demonstrator (R2D2), will permit the mastering of the fabrication methods foreseen for F2D2 and to confirm the behavior of Nb3Sn cables representative of FCC. It should also validate some key technological aspects: compactness, operation at high-current (up to 16 kA), intermediate magnetic field (in the range of 11-12 T), and quench protection at very high energy/unit length (up to 650 kJ/m, corresponding to 168 MJ/m3).
Wavelength Dependent Anisotropic Photo Sensing Activity of Zirconium Trisul�de Crystal

According to the performance requirements, either bulk or nanocrystalline form of the material can be used for different types of device applications. In the present study, zirconium triselenide bulk crystals were grown by direct vapour transport technique. The as-grown crystals and powder have been examined under Carl Zeiss optical microscope and scanning electron microscope for morphological studies which revealed the evolution of crystalline phases of the material by the layered kind of growth mechanism. The energy dispersive analysis of X-ray (EDAX) characterization con�rms that no impurity is found in the resulting product except the desired material. To study the effect of different wavelength sources (Blue-470 nm, Green-540 nm, Red-670 nm) on bulk zirconium trisul�de photodetectors, pulsed photo response experiment was carried out. The anisotropic behaviour is also revealed using the same sources. Various device parameters like responsivity, sensitivity, detectivity and external quantum e�ciency (EQE%) were calculated. The highest responsivity and detectivity of 81.7 µA/W and 3.56 × 10 07 Jones were achieved for blue (470 nm) light source respectively.
Contribution of Motivational (cid:31)rientations to (cid:31)tudent (cid:31)utcomes in a Discover(cid:31)(cid:31)Based Pro(cid:31)ram of (cid:31)ame Desi(cid:31)n (cid:31)earnin(cid:31)

: This paper explored relationships between middle school and high school teams of students’ motivational orientation and team outcomes in a guided discovery-based context in which students learned while designing web games. Teams of students with greater initial intrinsic motivation or a greater increase in intrinsic motivation during the activity had higher programming scores. Wiki activity contributed to outcomes. Age was negatively associated with outcomes indicating possibly the program is more conducive to middle school students. Findings contribute to scholarly debates on incompatibilities between discovery learning due to cognitive load, and self-determination theory.
Programmable Low-End Networks: Powering Internet Connectivity for the Other Three Billion

We propose the following exploratory research question: can we boost low-end networking through forwarding plane programmability? The implications of a positive answer to this question are manifold, unlocking low-end networks for innovative use cases, such as powering affordable internet access to remote communities and helping bridge the digital divide. As a first step towards answering this question, we introduce and discuss in this paper a conceptual architecture for programmable low-end networking. In summary, we take advantage of Low Power Wide Area Network (LPWAN) transceivers and use a reconfigurable pipeline of match+action stages to enable researchers and practitioners to write lightweight networking protocols that can seamlessly bring isolated communities to the Internet. We then exercise our architecture through the design and implementation of a Lightweight Tunnel Protocol, for optimized data communication over narrow-band links, with a throughput gain as high as 23%. Our results mainly provide evidence of programmable low-end networks’ potentialities, helping us close the digital divide and bring affordable Internet for the other three billion.
Singing Voice Detection in Opera Recordings: A Case Study on Robustness and Generalization

Automatically detecting the presence of singing in music audio recordings is a central task within music information retrieval. While modern machine-learning systems produce high-quality results on this task, the reported experiments are usually limited to popular music and the trained systems often overfit to confounding factors. In this paper, we aim to gain a deeper understanding of such machine-learning methods and investigate their robustness in a challenging opera scenario. To this end, we compare two state-of-the-art methods for singing voice detection based on supervised learning: A traditional approach relying on hand-crafted features with a random forest classifier, as well as a deep-learning approach relying on convolutional neural networks. To evaluate these algorithms, we make use of a cross-version dataset comprising 16 recorded performances (versions) of Richard Wagner’s four-opera cycle Der Ring des Nibelungen. This scenario allows us to systematically investigate generalization to unseen versions, musical works, or both. In particular, we study the trained systems’ robustness depending on the acoustic and musical variety, as well as the overall size of the training dataset. Our experiments show that both systems can robustly detect singing voice in opera recordings even when trained on relatively small datasets with little variety.
Two-phase flow structure at header-channel junctions : PIV experiments and modeling

The discharge of a two-phase flow from a stratified region through single or multiple branches is an important process in many industrial applications including the pumping of fluid from storage tanks, shell-and-tube heat exchangers, and the fluid flow through small breaks in cooling channels of nuclear reactors. Knowledge of the flow phenomena and flow structure involved during the onset of gas entrainments (OGE) in branches is essential for the design and/or performance prediction of such thermal systems. In the present investigation, extensive data were generated for the two-phase flow structure at the onset of gas entrainment from an air-water stratified region through small branches (d = 6.35 mm) over a wide range of Froude numbers (0 to 100). The test sections were in close dimensional resemblance with that of a CANDU header-feeder system, with branches mounted at orientation angles of 0, 45 and 90 degrees from the horizontal. Three groups of new data were generated for single discharge, dual discharge and triple discharge configurations. The Particle Image Velocimetry (PIV) was used to provide detailed measurements of the two-phase flow field. In each of these measurements, the critical height at the onset of entrainment was first achieved, and the volume of interest close to the branch-header junctions was then determined and divided by a number of horizontal image planes. Each image plane required a separate spatial and temporal calibration for PIV measurements. The vorticity profile, stream lines, flow field development and coherent structure, were presented over a wide range of operating conditions. A theoretical analysis for the onset of gas entrainment in a single downward discharge, from a stratified gas-liquid region, was developed. The discharge was modeled as a point-sink and Kelvin-Laplace's equation was used to incorporate surface tension effects. The dip geometry was experimentally investigated and a correlation was developed relating the dip radius of curvature to the discharge Froude number. The correlation was used in conjunction with the theoretical model. It was found that the predicted critical height demonstrated a good agreement with experimental data. The inclusion of surface tension improved the model's capability to predict the critical height, particularly at discharge Froude numbers below one. The single-discharge model was then extended to dual and triple discharge cases, with considering the branches as point sinks and two-dimensional slots. The results of dual and triple discharges were found to be a function of mass flow rate through the branches, and the position of the secondary branch (maintaining liquid phase flow only) with respect to the primary branch position (at which OGE occurs) and the angle between the branches. The present analysis applies to any two immiscible fluids with the term "gas entrainment" referring to the appearance of the lighter fluid through the upper branch.
Multiuser Coding Techniques for Mismatched Decoding

This paper studies multiuser coding techniques for channel coding with a given (possibly suboptimal) decoding rule. For the mismatched discrete memoryless multiple-access channel, error exponents are obtained which are tight with respect to the ensemble average, and positive within the interior of Lapidoth’s achievable rate region. In the special case of maximum-likelihood decoding, the ensemble tightness of the exponent of Liu-Hughes is proved. Alternative expressions for the error exponents and rate regions are given, including expressions obtained using Lagrange duality which extend immediately to general alphabets. In the setting of single-user mismatched decoding, similar analysis techniques are applied to two types of superposition coding. The standard version is shown to yield an achievable rate which is at least as high as that of Lapidoth’s expurgated parallel coding rate after the optimization of the parameters. A refined version of superposition coding is shown to achieve rates at least as good as the standard version for any set of random-coding parameters, and it is shown that the gap between the two can be significant when the input distribution is fixed. Index Terms Mismatched decoding, multiple-access channels, superposition coding, random coding, error exponents, ensemble tightness, duality, maximum-likelihood decoding.