Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical Validation of the Squire-Young Formula for Profile Drag Prediction

The Squire–Young formula is widely used to calculate profile drag in two-dimensional airfoil analyses. Although experience shows that the formula provides reasonable and accurate predictions, full Navier–Stokes computational fluid dynamics allows for a numerical validation of the formula. A method is presented for evaluating the Squire–Young formula from a computational fluid dynamics solution, and demonstrated for several airfoils. The agreement between profile-drag coefficients determined using the Squire–Young formula and those calculated directly from the computational fluid dynamics solution using surface integration is typically within 2–3% in the low-drag range, with error increasing at higher lift coefficients. The applicability of the underlying assumptions is discussed, and the validity of the assumed variation of shape factor and edge velocity in the wake is investigated for a symmetric airfoil at zero lift. The formula is also demonstrated as being insensitive to spurious drag that may occur d.
An optimization algorithm for checking feasibility of robust H ∞-control problem for linear time-varying uncertain systems

An algorithm for checking feasibility of the robust H ∞-control problem for systems with time-varying norm bounded uncertainty is suggested. This algorithm is an iterative procedure on each step of which an optimization problem for a linear function under convex constraints determined by LMIs is solved. The effectiveness of the proposed algorithm is demonstrated on the numerical example of a parametrically disturbed pendulum.
Foot mounted inertial system for pedestrian navigation

This paper discusses algorithmic concepts, design and testing of a system based on a low-cost MEMS-based inertial measurement unit (IMU) and high-sensitivity global positioning system (HSGPS) receivers for seamless personal navigation in a GPS signal degraded environment. The system developed here is mounted on a pedestrian shoe/foot and uses measurements based on the dynamics experienced by the inertial sensors on the user's foot. The IMU measurements are processed through a conventional inertial navigation system (INS) algorithm and are then integrated with HSGPS receiver measurements and dynamics derived constraint measurements using a tightly coupled integration strategy. The ability of INS to bridge the navigation solution is evaluated through field tests conducted indoors and in severely signal degraded forest environments. The specific focus is on evaluating system performance under challenging GPS conditions.
Ultracompact polarization diversity components for future large-scale photonic integrated circuits on silicon

The giant birefringence of SOI nanowires makes ultra-compact polarization diversity components available for many applications, like polarization-transparent silicon photonics, coherent optical communications, and on-chip quantum photonics. Particularly, the ultrasmall footprint makes these components very attractive for realizing on-chip polarization handling used in the future large-scale photonic integrated circuits, e.g., photonic network on chip. In this paper, a review is given for our ultra-compact polarization diversity components including polarization-beam splitters and polarization rotators.
Variational estimates of the self-consistent type for the effective behaviour of some model nonlinear polycrystals

A new class of self‐consistent estimates for nonlinear polycrystals is obtained by making use of the classical self‐consistent estimates for the ‘linear comparison polycrystal’ in the nonlinear variational procedure of deBotton and Ponte Castañeda (1995). A distinctive feature of the new self‐consistent estimates is that they satisfy the recent bound of Kohn and Little (1997) for two‐dimensional ideally plastic polycrystals, which is known to be significantly more restrictive than the Taylor bound at large grain anisotropy. In addition, the new self‐consistent estimates are found to satisfy the Taylor and Hashin–Shtrikman bounds, which are sharper than the Kohn–Little bound at low grain anisotropy. This result suggests that the new self‐consistent estimates may also be more accurate than other self‐consistent estimates, like those arising from the incremental model of Hill (1965), for three‐dimensional nonlinear polycrystals.
The Effects of Underextraction in Factor and Component Analyses

The consequences of underextracting factors and components within and between the methods of maximum likelihood factor analysis (MLFA) and principal components analysis (PCA) were examined. Computer-simulated data sets represented a range of pattern structures. Manipulated conditions included component (factor) structure coefficients (aij = .8, .6, and .4), sample size (N = 75, 150, 225, and 450), and variable-to-component (factor) ratio (p:m = 6:1 and 4:1). The principal components score and the Anderson and Rubin factor score estimate were calculated for both the correct patterns and the incorrect (underextracted) patterns. In Study 1, underextraction led to substantial degradation of scores within both methods, but the component score degraded less rapidly. Score degradation was related to the number of original components (factors). In Study 2, between-method comparisons indicated very high similarity for baseline score patterns, but dissimilarity occurred with underextraction.
Optical metamaterial for polarization control

We present the design, characterization, and modeling of a specific optical metamaterial, and employ it to manipulate the light polarizations at optical frequencies. Experimental results reveal that the maximum polarization conversion efficiency, i.e., the energy portion converted from $s$ to $p$ polarization after reflection, can be as high as 96% at the wavelength of $\ensuremath{\sim}685\text{ }\text{nm}$. Simulations and analytical results, which are in reasonable agreements with the experimental results, reveal that the underlying physics are governed by the particular electric and magnetic resonances in the optical metamaterial.
Aerodynamic Redesign Using Discrete Adjoint Approach on Overset Mesh System

An adjoint-based design approach for the delicate treatment of complex geometry is presented by using an overset mesh technique. Overset blocks, such as collar and tipcap grids, which are commonly used in accurate drag prediction, are employed to evaluate the applicability of the proposed design approach to practical problems. Various pre- and postprocessing techniques for overset flow and sensitivity analyses are implemented to develop a robust gradient-based optimization method on an overset mesh topology. In preprocessing, overlap optimization, which can provide an accurate overset solution and enhance convergence characteristics, is adopted to automatically construct the block connectivity. A new postprocessing method, the spline-boundary intersecting grid scheme, is introduced by reflecting the ratio of the surface cell area for accurate prediction of aerodynamic coefficients and a convenient evaluation of sensitivities under a parallel computing environment. For the sensitivity analysis, the adjoint formulation for the overset boundary condition is implemented in the fully hand-differentiated sensitivity analysis code. A three-dimensional discrete adjoint solver on the overset mesh system is developed by exploiting the overset flow analysis techniques. Good convergence characteristics of the adjoint solver can be achieved by using the automatic construction process of block connectivity. The derivatives of aerodynamic coefficients can be obtained by an efficient and accurate postprocessing technique. The present overset adjoint formulation and flow analysis techniques are validated by comparing the flow and sensitivity analyses, as well as the design results, with those of a single-block case for a transonic wing. Finally, careful designs are carried out by minimizing the drag of a three-dimensional wing-body configuration. The design results successfully demonstrate the capability of the present design approach.
Two efficient algorithms for iterative linearized inversion of seismic waveform data

We present two equivalent algorithms for iterative linearized waveform inversion for 3-D Earth structure with respect to an arbitrary 3-D starting model; one is a matrix formulation, and the second is a wavefield formulation. Both algorithms require the computation of accurate synthetic seismograms, but neither requires that any particular method be used to compute the synthetics. The matrix formulation is equivalent to our previously published algorithm (Hara, Tsuboi & Geller 1991), but requires less than 10 per cent of the CPU time of the previous algorithm. The wavefield algorithm is equivalent to that of Tarantola (1986) and Mora (1987), but appears to be substantially more efficient.
Randomized Recompression of H -Matrices for BEM

The technique of H -matrices was introduced to deal with (large-scale) dense matrices in an elegant way. It provides a data-sparse format and allows an approximate matrix algebra of nearly optimal complexity. The primary step in the construction of an H - matrix is how to approximate the subblocks of a given dense matrix by matrices that have low numerical rank. Randomized algorithms have recently been introduced as a highly efﬁcient tool for computing approximate factorization of low-rank matrices. In this paper, we consider various randomized algorithms to construct an H -matrix and perform the corresponding H -matrix algebra. In particular, by taking advantages of randomization, we present a simple but fast algorithm of complexity O ( 4 k 2 n + k 3 ) for truncating an n × n low-rank matrix of (ﬁxed) rank k . The corresponding efﬁcient deterministic algorithm has the complexity O ( 12 k 2 n + 23 k 3 ) . We provide numerical examples applying to a BEM model associated with Laplace equation in one and two dimensions to show the efﬁciency of the proposed randomized methods versus the previously known efﬁcient deterministic methods. The gain of efﬁciency about 10– 25% is achievable when a moderate oversampling parameter p = 5 is used in the computations. In this case, the experimental results show that the proposed algorithm not only has a lower cost but also is more accurate than the conventional methods.
AFFINE KAC-MOODY ALGEBRAS AT THE CRITICAL LEVEL AND GELFAND-DIKII ALGEBRAS

We prove Drinfeld's conjecture that the center of a certain completion of the universal enveloping algebra of an affine Kac-Moody algebra at the critical level is isomorphic to the Gelfand-Dikii algebra, associated to the Langlands dual algebra. The center is identified with a limit of the W-algebra, defined by means of the quantum Drinfeld-Sokolov reduction.
On Tangent Cones of Analytic Sets and Łojasiewicz Exponents

For an analytic set V in K n , K = R or C , which contains the origin 0 ∈ K n , the geometric tangent cone of V at 0 is the set of vectors in K n which are the limits of secant lines passing through the origin and non-zero sequences in V that converge to the origin; the algebraic tangent cone of V at 0 is the algebraic set deﬁned by the ideal generated by the initial forms of all analytic functions f whose germs at 0 are in the ring of germs of analytic functions in K n about 0 which vanish on the germ of V at 0. In this paper, we give some characterization for the geometric tangent cone and compare these two tangent cones of V at 0. In particular, we characterize the geometric tangent cone of V at 0 via the so-called Ł ojasiewicz exponent of an analytic map germ along a line and compute this number in some special cases.
An Empirical Study of the Ratio Estimator and Estimators of its Variance

Abstract This paper reports results from an empirical study of the ratio estimator for a finite population total. From each of six real populations, 1,000 simple random samples, 1,000 restricted random samples, and three nonrandom samples of size 32 are drawn. Performance of the ratio estimator and of five estimators of its variance is compared with theoretical results generated using (a) prediction (superpopulation) models and (b) probability sampling distributions. The results, presented graphically, show that theory based on prediction models can reveal relationships that are essential in making inferences, but that are concealed in probability sampling analyses.
Comparison of regional clear-sky albedos inferred from satellite-observations and model computations

Abstract We have taken an important first step in validating climate models by comparing model and satellite inferred clear sky TOA (top-of-atmosphere) albedos. Model albodos were computed on a 1° × 1° latitude-longitude grid, allowing for variations in surface vegetation type, solar zenith angle, orography, spectral absorption/scattering at surface and within the atmosphere. Observed albedos were inferred from GOES-2 minimum narrowband (0.55–0.75 μm) brightness for November 1978 over South America and most of North America and adjacent ocean regions. Comparisons of TOA albedos over ocean agree within ±1% (the unit for albedo is in percent and the differences in percent denote absolute differences), and thus lie within both theoretical uncertainties (due to water vapor and aerosol concentrations, and ocean surface spectral reflectivity), as well as observational uncertainties. The ocean comparisons also show significant latitudinal variations in both model and observations. Albedos over land mostly agree .
Logistic analysis in case-control studies under validation sampling

SUMMARY This paper proposes an analysis of case-control data under a double-sampling scheme, when covariates are missing or measured with error at the first stage of sampling and are validated at the second stage in a subsample. The method combines risk information from both samples. It is derived under the assumptions that (i) the prospective disease incidence model is of logistic form, (ii) the proxy or partial information takes on finitely many values, and (iii) the error is nondifferential. The method of Prentice & Pyke (1979) is extended to a two-sample design to derive an estimating equation for the odds-ratio parameters. It provides an alternative estimator to that given by Breslow & Cain (1988). Consistency and asymptotic normality of the estimates are derived and a variance formula is presented. Parameters can be estimated by use of standard packages for quantalresponse data. The method can easily be extended to the analysis of stratified designs with large strata.
RECOVERY LENGTH IN MULTILAYERED SPIRAL STRANDS

An insight is given into the recovery length phenomenon in axially preloaded multilayered spiral strands. Recovery length is defined as the length measured from the fractured end of the wire, in which the wire will be able to carry its appropriate share of the axial load. Numerical results on realistic spiral strand constructions suggest that the recovery length in various layers is a weak function of the mean axial load on the cable. The lay angle has been found to be the primary parameter determining the magnitude of the recovery length of various layers in multilayered spiral strands and, hence, a straightforward method for determining variations of recovery length with lay angle in any layer of any spiral strand construction aimed at practicing engineers has been proposed. Using such estimates of recovery length, a minimum length of test specimens (for axial fatigue loading) equal to about 10 lay lengths is recommended for spiral strands.
A model of supersonic flow past blunt axisymmetric bodies, with application to Chester's solution

A simple approximate model is set forth for the flow field between the nose of a blunt body of revolution and its detached shock wave. The model tends to explain the poor convergence of Chester's solution, which is based on an improvement of the Newtonian approximation. It suggests a modification of his series for the body shape which appears to improve its convergence considerably.
Particle flow and collection process in single wire HGMS studies

A television system for the observation of HGMS particle flow and buildup on a ferromagnetic wire or small array of wires is described for three geometries. Data for the collection process and a sequence of pictures of buildup are presented for Mn 2 P 2 O 7 , Mn 2 O 3 , and Cr 2 O 3 particles collected on 125 μm nickel wires. The wake, formed in some cases downstream from the particle collection, has been studied. Data on the wake is given and compared with published data on wake formation. New results on downstream and upstream collection on a wire as a function of slurry velocity is presented.
A low-light image enhancement method based on AUDD-GAN

A large number of images taken under conditions of insufficient lighting, often with overall darkness, low contrast, noise and inconspicuous details, thus affecting people’s observation of the image content and the subsequent use of the image. This paper presents a novel method, Attention U-Net Dual Discriminator-Generative Adversarial Networks (AUDD-GAN), which combines attention mechanism, U-Net network structure and the dual discriminator. First, the method generates a normalized attention map to guide the subsequent modules to perform illumination enhancement to prevent overexposure or underexposure problems; second, the U-Net generative network extracts characteristics of diverse levels to acquire richer detail information; then, a dual discriminator is used as the discriminative network, where PatchGAN is used as the local discriminator to achieve better enhancement effects; ultimately, we combine multiple loss functions as the joint loss function. AUDD-GAN is verified to enhance the global contrast of the image while enhancing the detail features more effectively.
Sentinel-2 Research on the Detection and Classification Methods of Maritime Ship Targets from Remote Sensing Images

There are problems such as low recognition accuracy and large classification error in the existing classification methods for ship identification based on optical remote sensing images. In this paper, we will analyze the characteristics of ships and determine the indicative factors for applying remote sensing to monitor ships in combination with optical remote sensing images. Using optical remote sensing image data, combined with U-Net and AttU-Net deep neural network models, we assist in extracting new remote sensing indices with strong generality and clear physical meaning, and establishing rules for monitoring ships, so as to establish a more general and clear physical meaning of the monitoring and identification method of remote sensing satellite images. The method is applied and evaluated with port optical remote sensing image data. The data show that compared with traditional machine learning methods, the accuracy of ship monitoring using U-Net and AttU-Net deep learning models in this paper reaches 89.04%, and the recall rate and accuracy rate are better than SVM. it shows that the model can detect ships effectively.
Design of Recommendation Algorithm Based on Knowledge Graph

The commodity data in the shopping system contains a wealth of feature information, and different commodities are related by these features. Traditional collaborative filtering algorithms represent commodity data in a structured way, but they have issues such as low commodity similarity calculation accuracy, poor recommendation effect, and unfriendly recommendation results. The commodity recommendation algorithm based on the knowledge graph put forward in this paper initially automatically extracts the entities and entity relationships in the commodity data as the vertices and edges of the graph and then stores the commodity data in the Neo4j graph database. Finally, the similarity between commodities is calculated based on the graph’s path similarity, and the list of TOP-K commodities with the highest similarity is chosen for recommendation. The experimental findings indicate that the recommendation algorithm based on the knowledge graph can not only more accurately represent the similarity between commodities, but also that the graph structure can display the recommended path in a more user-friendly manner, and has better recommendation explainability as well as higher recommendation trust.
Research on Time Synchronization and Spatial Calibration Algorithms of Radar and Camera

The intelligent connected vehicle can detect 360° full coverage around the vehicle by installing multiple radars and cameras. However, the sampling frequency of each sensor is different and each sensor outputs the target motion state information based on its coordinate system. the accurate and rapid realization of time synchronization and spatial position alignment between sensors is very important for multi-sensor data fusion. This paper presents a method of radar and camera time synchronization based on least squares cubic spline curve fitting. Based on time synchronization, a fast space joint calibration method of radar and camera is proposed based on the longitudinal symmetry plane of the vehicle. The validation data of the target object show that the proposed algorithm is accurate and simple.
External Attention Mechanism-Based Modulation Classification

This paper considers the modulation classification of radio frequency (RF) signals. An external attention mechanism-based convolution neural network (EACNN) is proposed. Thanks to the external attention layers, the EACNN network can capture the potential correlations of different modulation data, which helps reduce computational consumption and memory costs efficiently during training. Moreover, to account for the variation of the signals induced by channel fading, we further propose a customized batch normalization (BN) layer in EACNN to improve the classification accuracy with less training time. Numerical experiments on RML2016.a dataset shows that the proposed method outperforms the baseline method CNN2 by 7% in terms of classification accuracy.
Anomaly detection based on lightweight deep learning algorithms

The safe and reliable operation of power transmission lines is the key to the sustainable and stable operation of power grids. Using the artificial intelligence techniques for the channel status monitoring greatly improves the inspection efficiency of transmission lines. However, the discrimination is mostly conducted on the cloud side, which does not meet the requirement of real-time processing. In this paper, we build an intelligent monitoring system for transmission lines based on edge computing. According to the specific characteristics of the real-world power filed applications, a lightweight deep learning algorithm model is designed. It is then transplanted on a self-developed AI chip and a field test is carried out. The experimental results show that the model designed in this paper balances well between the accuracy, the model size and the inference speed. It realizes status local discrimination of the transmission lines, and promising detection results are obtained.