Miracle-dz/UniFAR-FTUs huggingface dataset:
The research on continuous carrier phase time transfer method based on parameter transfer

The method of continuous GNSS carrier phase time transfer is studied in detail, and the cause of jump in carrier phase time transfer results is analyzed. The relationship between multi-day data jump and daily jump is deduced in theory, and the correctness of the deduced result is verified by the calculation result of actual measured data. Solution is given to daily jump in carrier phase time transfer. Since the time transfer reference signal is a continuously running high-precision atomic clock signal, a continuous PPP time transfer method based on clock difference parameter transfer is proposed. The method proposed in this paper is validated and analyzed with the actual measured data, and compared with the traditional GNSS carrier phase time transfer solution. The results show that the method given in this paper can solve the problem of daily jump jump to a certain extent, effectively improve the continuity, availability and reliability of time transfer results, and also improve the frequency stability of time transfer results.
Fisheye Image Correction Based on Three-Dimensional Control Field

The fisheye image has severe distortions, which is not in line with human visual habits and brings inconvenience to its application. This paper classifies them into spherical structural distortion and optical distortion, and proposes a fisheye image correction method based on 3D control field. First, the spherical transformation radius and the optical center of the fisheye image are accurately solved, and the fisheye image is corrected by combining the spherical perspective projection; then, the distortion model of the fisheye camera is introduced into the DLT model to calibrate the optical distortion, and the results are used to recorrect the image. This method has been experimentally proven to be fast and effective.
Functional product map design in lifting and transportation machine building digitalization conditions

In the Industry 4.0. mechanical engineering is undergoing major changes. Combining industry and digital technologies for the flexible and efficient production creating is a main idea of the Fourth Industrial Revolution. The article proposes a high-tech product functional map designing approach based on the author’s functions classification, taking into account the specifics of products designed within the Industry 4.0 framework. Based on the warehouse floor transport market trends study, as well as the forklifts consumers survey results, a functional map of a product designed at the Russian lifting and transport engineering enterprise was proposed.
A 12-bit 30 MS/s SAR ADC in 180 nm CMOS for PMT signal readout

Photomultiplier Tubes (PMTs) are widely used as the photon detector in high energy physics experiments for their fast response and high sensitivity. High precision charge measurement is usually required for PMT readout, and especially in cosmic ray experiments a large dynamic range is also demanded. This paper describes an Analog-to-Digital Converter (ADC) Application Specific Integrated Circuit (ASIC), which aims to work with a front-end analog ASIC to perform charge measurement based on the digital peak detection method. This ADC design is based on the power-efficient Successive Approximation Register (SAR) architecture, to achieve a 12-bit resolution with a sampling rate of up to 30 MS/s. Test results indicate that the Effective Number of Bits (ENOB) of this ADC is better than 9.2 bits with a sampling rate of 30 MS/s.
Model reproduction of non-equilibrium thermodynamics principles as a means to provide software self-development

Further evolution of technologies in material sciences, hardware and software demands reproduction of self-organization as a manifestation of non-equilibrium thermodynamics principles in new artificial environment. This is especially actual for software engineering, where reproduction of non-equilibrium thermodynamics principles will allow enhancing speed and quality of development process. In this article we reveal, list and analyze principles to be reproduced in artificial systems to provide capabilities for self-development, self-healing and self-improvement. It is shown that self-development process is impossible without reproducing the metaphor of electric charge with its abilities for attraction and repulsion. But this metaphor is usually avoided in computer models. We propose and consider a method to reproduce the metaphor of electric charge in computer models by means of UNIX/Linux operating system tools. The obtained results provide the basis to establish an artificial physics, a new research area dedicated to adequate reproduction of principles of physics in the artificial environment of computer models.
Hyperspectral Image Classification Method Based on Multi-scale Densenet and Bi-RNN Joint Network

With the depth of the Convolutional neural network(CNN) increases, CNN may lead to the problem of gradient disappearance. Simultaneously, single scale convolutional kernel may not reflect the complex spatial structural information in hyperspectral image(HSI). In addition, the CNN based approach regards the spectral band data on a single pixel of the HSI as a disordered high dimensional vector for processing, which does not meet the characteristics of the spectral band data. To tackle these aforementioned issues, a novel classification approach based on multi-scale densely connected convolutional network(Densenet) and bi-direction recurrent neural network(Bi-RNN) with attention framework is introduced in this study. Specifically, multi-scale Densenet is exploited to fully extract the multiple scales complex spatial structural information and utilize the strong complementary yet correlated spatial feature information between convolution layers, and Bi-RNN with attention is designed to obtain inner spectral correlations within a continuous spectrum. For comparison and verifying the effectiveness of our proposed method, we test the proposed method with nine other recently proposed methods on Salinas dataset, and the experimental results demonstrate that the proposed method can sufficiently exploit spectral and spatial information and outperforms other competitive methods.
Concealment of sensor network node interaction

The aim of this study is to increase the security of sensor network hosts by means of node interaction concealment. This paper outlines existing methods and approaches to information security of sensor network and internet of things systems. The authors developed a new secure data communication method based on dynamic network topology. This method allows to conceal sensor network data communication from an intruder breached an outer security perimeter. The new method secures data transfer and differs from the existing switching approaches that conceal sensor network hosts.
Influence of Gd Doping on the Structural and Optical Properties of ZnO

Doping transition metal or rare-earth metal ion are one of the most popular topics in semiconductors. In this work, gadolinium (Gd) doped zinc oxide thin films was prepared using spin-coating technique with different concentrations in atomic percent (at.%). The influences of rare-earth ions doped into the zinc oxide films were studies. The effects on the physical and optical properties of the films were investigated by field emission scanning electron microscope, x-ray diffraction, atomic force microscopic and ultraviolet-visible spectrophotometer. It was found that the properties of zinc oxide can be tuned by changing the concentration physical and optical of Gd.
Distribution of Mixing Efficiency in a Split-Cylinder Gas-Lift Bioreactor with Immobilized Yarrowia Lipolytica Cells Used for Olive Oil Mill Wastewater Treatment

The distribution of mixing in a split-cylinder gas-lift bioreactor has been investigated for suspensions of immobilized Yarrowia lipolytica cells with particles diameter varying between 3 and 4.2 mm. The results indicated important variation of mixing time on the height of riser or downcomer, as well as different behavior of suspensions flows in these two regions. Therefore, for the riser, the mixing efficiency increases from its bottom to the top, allowing the biocatalyst particles with intermediate size (3.6-mm diameter) reaching the most intense circulation of suspension. The analysis of the suspension flow in the downcomer region revealed that the intermediate positions are associated with the highest mixing mainly for the largest immobilized yeast particles (4.2-mm diameter). In both cases, the influence of aeration on turbulence extent is positive only for air superficial velocity up to 1.05–1.35 × 10−3 m/s, the magnitude of this effect being correlated with the biocatalyst size and position on the riser or downcomer. By means of the experimental data, mathematical correlations for mixing time have been proposed for each circulation region, taking into consideration both the operational parameters and the distance from the bioreactor bottom. These equations offer a good concordance with the experiment, the average deviation being of 5.82% for the riser and 6.06% for the downcomer zone.
Fabrication of Green Lignin-based Flame Retardants for Enhancing the Thermal and Fire Retardancy Properties of Polypropylene/Wood Composites

Despite growing extensive applications, until now the poor thermal stability and flame retardancy properties of wood–plastic composites (WPC) remain effectively unsolved. Meanwhile, industrial lignin has emerged as a potential component for polymer composites due to many advantages including abundance, rich reactive functional groups, high carbon content and tailored capability for chemical transformations. Herein, we have fabricated one biobased flame retardant based on industrial lignin chemically grafted with phosphorus, nitrogen and copper elements, as the functional additive for the WPC. Compared with unmodified lignin (O-lignin), functionalized lignin (F-lignin) is more effective to improve the thermal stability and flame retardancy of WPC because of presence of the flame retardancy elements (P and N) and the catalytic effect of Cu2+ on the char-formation. The presence of F-lignin not only reduces the heat release rate, total heat release and slows down the combustion process but also decreases tota.
Study on Si/Ag composite materials for lithium battery anode

Si-Ag composite material as lithium battery anode was synthesized by making silver nanoparticles adhering to the surface of silicon particle.Silver nanoparticles were obtained by reducing silver nitrate with ethanol in the presence of poly(N-vinylpyrr-olidone)(PVP)as the surfactant.Different conditions,such as the size of silicon powder(30 nm and 80 nm)and the amount of Ag which was added to the reaction,were studied.The investigation exhibited that the cyclability of Si-Ag composite was improved with the increase of the amount of Ag.
Performance evaluation of indigenous earth dwelling in rural area of Nigeria

According to 2006 census the vast majority of household live in the rural area of Nigeria, inhabits indigenous earth dwelling of various typologies. These accounted for thirty five percent of total national housing stock. They are characterized with deterioration and dilapidation. Ensuring an adequate performance level of well built earth dwelling, it requires high level of maintenance. Furthermore, many of these structures are built in ignorance of rules and regulations of good building practice. The focus group of professional especially architect and builder were organized to coordinate the study process, ensuring adequate evaluations of issues that can facilitate improved performances of earth as a building materials. The attitude of lack of maintenance was also considered and observed. Their recommendations are evaluated and classified according to issues and strategies: Seven prevailing factors facilitating fast deterioration of earth dwelling were identified. The study recommended fundamental strategies that could help to: Upgrade traditional building, Improve the performance and durability of earth dwelling, Reduce the amount of maintenance cost required by ensuring adequate building precautions and to create a practical and affordable construction based on local practice. © 2015 Elixir All rights reserved ARTICLE INF O Artic le h istory: Received: 7 December 2014; Received in revised form: 20 January 2015; Accepted: 6 February 2015;
Combining a DSGE Model with Variational Bayesian Neural Networks

In the spirit of the DSGE-VAR approach, I employed Temporal Difference Variational Auto-Encoder (TDVAE) proposed by Gregor et al. (2019) as a reduced timeseries model on which theoretical restrictions from a DSGE model are imposed. TDVAE is virtually a general-form stochastic state-space model implemented via variational Bayesian inference (VI) with neural networks. With the flexibility of neural networks, this DSGE-TDVAE approach tries to address the Consolo et al. (2009)’s criticism on DSGE-VAR that an unrestricted VAR may not represent the data correctly due to its shortage in expressiveness. The empirical results showed that forecast performance is certainly gained by combining the standard New Keynesian DSGE with TDVAE for the actual macroeconomic data of Japan. It confirmed that some support for the DSGE model is still found in the data even when evaluated against the more general statistical benchmark of TDVAE rather than VAR. At the same time, this indicated that the DSGE holds considerable degree of misspecification as the optimal intensity of DSGE restriction was small. Furthermore, DSGE-TDVAE achieved the superior forecasting performance to DSGE-VAR.
Eocene Willwood Formation of the 2 Bighorn Basin , Wyoming , USA 3

13 Interaction of allogenic and autogenic forcing in building alluvial stratigraphy remains a complex 14 subject that is critical for paleoenvironmental and paleoclimate reconstruction and subsurface rock 15 property prediction. Astronomical forcing of alluvial stratigraphy is poorly documented so far as this 16 driver strongly interacts with autogenic and other allogenic processes making it difficult to trace 17 astronomical climate changes in these laterally highly variable sediments. In the lower Eocene Willwood 18 Formation, Bighorn Basin, Wyoming, USA, a lot of evidence has been gathered to relate dominant 19 floodplain aggradation cycles to precession-scale climate change. One floodplain aggradation cycle 20 consists of two phases: (1) a longer overbank phase with relative channel stability and strong paleosol 21
New Insights into the Metastable β Form of RDX

The secondary explosive RDX (1,3,5-trinitrohexahydro-1,3,5-triazine) has five known polymorphic forms, though only the α and β phases are observable at room temperature and pressure. Many literature reports suggest that the metastable β-RDX polymorph is extremely rare due to the limited number of solvents from which it can be grown and its facile conversion to the more stable α-RDX form. Herein we show that β-RDX can be consistently obtained using drop cast crystallization methods from a broad range of solvents [acetone, tetrahydrofuran (THF), nitromethane, dimethylsufoxide (DMSO)] and that crystals grown this way remain stable over extended periods of time up to a year. The distribution of α:β crystals as a function of solvent and drop concentration on various modified and unmodified glass surfaces was determined using Raman microscopy. β-RDX was the most prevalent phase deposited from dilute drops regardless of the solvent, though more concentrated DMSO drops tended to give either β or α exclusively. In.
Pulling back information geometry

Latent space geometry has shown itself to provide a rich and rigorous framework for interacting with the latent variables of deep generative models. The existing theory, however, relies on the decoder being a Gaussian distribution as its simple reparametrization allows us to interpret the generating process as a random projection of a deterministic manifold. Consequently, this approach breaks down when applied to decoders that are not as easily reparametrized. We here propose to use the Fisher-Rao metric associated with the space of decoder distributions as a reference metric, which we pull back to the latent space. We show that we can achieve meaningful latent geometries for a wide range of decoder distributions for which the previous theory was not applicable, opening the door to `black box' latent geometries.
Graphs that are minor minimal with respect to dimension

Erdős, Harary, and Tutte defined the dimension of a graph G as the smallest natural number n such that G can be embedded in R with each edge a straight line segment of length 1. Since the proposal of this definition, little has been published on how to compute the exact dimension of graphs and almost nothing has been published on graphs that are minor minimal with respect to dimension. This paper develops both of these areas. In particular, it (1) establishes certain conditions under which computing the dimension of graph sums is easy and (2) constructs three infinitely-large classes of graphs that are minor minimal with respect to their dimension.
Dynamic Shape Modeling to Analyze Modes ofMigration During Cell Motility

This paper develops a generative statistical model for representing, modeling, and comparing the morphological evolution of biological cells undergoing motility. It uses the elastic shape analysis to separate cell kinematics (overall location, rotation, speed, etc.) from its morphology and represents morphological changes using transported square-root vector fields (TSRVFs). This TSRVF representation, followed by a PCA-based dimension reduction, provides a convenient mathematical representation of a shape sequence in the form of a Euclidean time series. Fitting a vector auto-regressive (VAR) model to this TSRVF-PCA time series leads to statistical modeling of the overall shape dynamics. We use the parameters of the fitted VAR model to characterize morphological evolution. We validate VAR models through model comparisons, synthesis, and sequence classifications. For classification, we use the VAR parameters in conjunction with different classifiers: SVM, Random Forest, and CNN, and obtain high classification rates. Extensive experiments presented here demonstrate the success of the proposed pipeline. These results are the first of the kind in classifying cell migration videos using shape dynamics.
A concise method for feature selection via normalized frequencies

Feature selection is an important part of building a machine learning model. By eliminating redundant or misleading features from data, the machine learning model can achieve better performance while reducing the demand on com-puting resources. Metaheuristic algorithms are mostly used to implement feature selection such as swarm intelligence algorithms and evolutionary algorithms. However, they suffer from the disadvantage of relative complexity and slowness. In this paper, a concise method is proposed for universal feature selection. The proposed method uses a fusion of the filter method and the wrapper method, rather than a combination of them. In the method, one-hoting encoding is used to preprocess the dataset, and random forest is utilized as the classifier. The proposed method uses normalized frequencies to assign a value to each feature, which will be used to find the optimal feature subset. Furthermore, we propose a novel approach to exploit the outputs of mutual information, which allows for a better starting point for the experiments. Two real-world dataset in the field of intrusion detection were used to evaluate the proposed method. The evaluation results show that the proposed method outperformed several state-of-the-art related works in terms of accuracy, precision, recall, F-score and AUC.
An adaptive Origin-Destination flows cluster-detecting method to identify urban mobility trends

Origin-Destination (OD) flow, as an abstract representation of the object`s movement or interaction, has been used to reveal the urban mobility and human-land interaction pattern. As an important spatial analysis approach, the clustering methods of point events have been extended to OD flows to identify the dominant trends and spatial structures of urban mobility. However, the existing methods for OD flow cluster-detecting are limited both in specific spatial scale and the uncertain result due to different parameters setting, which is difficult for complicated OD flows clustering under spatial heterogeneity. To address these limitations, in this paper, we proposed a novel OD flows cluster-detecting method based on the OPTICS algorithm which can identify OD flow clusters with various aggregation scales. The method can adaptively determine parameter value from the dataset without prior knowledge and artificial intervention. Experiments indicated that our method outperformed three state-of-the-art methods with more accurate and complete of clusters and less noise. As a case study, our method is applied to identify the potential routes for public transport service settings by detecting OD flow clusters within urban travel data.
Enantioselective Enzymatic Hydrolysis of rac-Mandelonitrile to R-Mandelamide by Nitrile Hydratase Immobilized on Poly(vinyl alcohol)/Chitosan–Glutaraldehyde Support

Nitriles, amides and carboxylic acids have varied applications in active pharmaceutical intermediate (API), drug and chemical industry, and particularly in the synthesis of pure enantiomers of chiral compounds. The chemical hydrolysis of nitrile compounds is well studied, but it necessitates harsh conditions along with the protection of sensitive functional groups, which on deprotection leads to generation of a large amount of byproducts. Immobilization of nitrile hydratase using a novel support reduces the cost and allows green processes. In this work, enantioselective hydrolysis of rac-mandelonitrile was studied using poly(vinyl alcohol) (PVA)/chitosan–glutaraldehyde cross-linked Rhodococcus rhodochrous ATCC BAA-870 nitrile hydratase (NHase). Immobilized NHase converted rac-mandelonitrile to (R)-amide by dynamic kinetic resolution with enantiomeric excess (ee) up to 81%. The temperature of 40 °C and pH 8 were found to be optimum for the enantioselective nitrile hydrolysis. In the presence of various cos.
Emergence of a Giant Rotating Cluster of Fish in Three Dimensions by Local Interactions

Schooling fish exhibit giant rotating clusters such as balls, tori, and rings, among other collective patterns. In order to account for their giantness and flexible shape change, we introduce an agent-based model that limits the number of agents that each agent can interact with (interaction capacity). Incorporating autonomous control of attractive interactions, we reproduce rotating clusters (balls, tori, and rings) that are an order of magnitude larger than the interaction range. We obtained a phase diagram of patterns including polarized schools and swarms. In our model, the scaling law between the number of agents and the projected area of the cluster is in good agreement with experimental results. The model indicates that giant rotating clusters are formed at low interaction capacity, without long-range interactions or inherent chirality of fish.