Miracle-dz/UniFAR-FTUs huggingface dataset:
PMD test method in the fiber link with optical amplifier

To learn the surrounding conditions in the fiber link and its effect on PMD, and to provide the first-hand design basis, we have carried out the data observation of PMD in a fiber link for a long time. We have tested the first-order and second-order PMD. The fiber tested is the G652 fiber produced by Corning Co. of USA, and the testing distance is 1000km; n segments of same fibers are linked into one, and n equals to 40, that is to say, the length of every segment is 25km; for the requirement of dispersion compensation in the high-speed and long distance fiber optical communication system, one fiber grating dispersion compensator is added in the place of every 200km, and there are five compensators; one EDFA is added in the place of every 100km, and there are eleven EDFA. The result suggests that, with the increase of length of fiber link, the distribution of PMD intends to be stable, that is, with the number n increasing, the relative error of PMD becomes less. The testing methods are the Jones matrix eigenanalysis technique and interference technique. HP8509B fiber polarization analyzer of Agilent in USA is used for measuring instrument of the Jones matrix eigenanalysis technique; FPMD-5600 Femtosecond PMD Analyzer of EXFO in Canada is used for measuring instrument of interference technique. The difference between these two testing methods is analyzed. With the Jones matrix eigenanalysis technique, fibers of 1000km are inspected through 48 hours, and the result suggests that, at nine o'clock in the morning, PMD reaches the maximum, at nine o'clock in the evening, it reaches the minimum, during other time, its change is very little. So it can be concluded that, PMD in the long distance fiber link is affected by temperature of the lab. Stress testing is carried in the ultra-short fiber (less than one meter). PMD has no obvious change in the range of stress which can be endured by the fiber.
Rock Thin Section Image Identification Based on Convolutional Neural Networks of Adaptive and Second-Order Pooling Methods

In order to enhance the ability to represent rock feature information and finally improve the rock identification performance of convolution neural networks (CNN), a new pooling mode was proposed in this paper. According to whether the pooling object was the last convolution layer, it divided pooling layers into the sampling pooling layer and the classification pooling layer. The adaptive pooling method was used in the sampling pooling layer. The pooling kernels adaptively adjusted were designed for each feature map. The second-order pooling method was used by the classification pooling layer. The second-order feature information based on outer products was extracted from the feature pair. The changing process of the two methods in forward and back propagation was deduced. Then, they were embedded into CNN to build a rock thin section image identification model (ASOPCNN). The experiment was conducted on the image set containing 5998 rock thin section images of six rock types. The CNN models using max pooling, average pooling and stochastic pooling were set for comparison. In the results, the ASOPCNN has the highest identification accuracy of 89.08% on the test set. Its indexes are superior to the other three models in precision, recall, F1 score and AUC values. The results reveal that the adaptive and second-order pooling methods are more suitable for CNN model, and CNN based on them could be a reliable model for rock identification.
Effect of Palm Kernel Cake Supplementation on Voluntary Feed Intake, In Situ Rumen Degradability and Performance in Buffaloes in the Eastern Amazon

Simple Summary The destination of agro-industrial residues and deforestation to form new pasture areas are urgent concerns in the Amazon region. Diverse studies have evaluated the inclusion of these residues in ruminant diets and have enabled the intensification of animal production systems. Palm kernel cake (PKC), for example, is generated after oil extraction for the food and cosmetics industry, but it still has crude protein and metabolizable energy levels of around 17% and 7.6 MJ/kg in the matter dry (DM), respectively. This research aims to evaluate the inclusion of a PKC-based supplementation for grazing animals on feed intake, degradability, and productive performance of buffaloes in the eastern Amazon. Although some changes in nutrient intake and degradability were observed, the inclusion of the co-product in the diet did not promote changes in productive performance and reduced forage consumption. Thus, the use of PKC up to 1% of body weight in supplements is recommended as an alternative source of nutrients for grazing animals. Abstract The objective was to evaluate the effects of palm kernel cake (PKC) supplementation on voluntary feed intake, in situ rumen degradability and performance in the wettest (WS—January to June) and less rainy seasons (LR—July to December) in the eastern Amazon. A total of 52 crossbred buffaloes that were neither lactating nor gestating were used, with 24 for the LR, aged 34 ± 04 months and an initial average weight of 503 ± 48 kg, and 24 for the WS aged 40 ± 04 months with an average weight of 605 ± 56 kg. The four treatments (levels of PKC in relation to body weight) were distributed in a completely randomized design, with 0% (PKC0), 0.25% (PKC0.2), 0.5% (PKC0.5) and 1% (PKC1) with six repetitions. The animals were housed in Marandu grass paddocks, intermittently, with access to water and mineral mixture ad libitum. Degradability was evaluated by the in situ bag technique in four other crossbred buffaloes with rumen cannulae, in a 4 × 4 Latin square (four periods and four treatments). The inclusion of PKC increased supplement consumption and production of ether extracts and reduced the intake of forage and non-fibrous carbohydrates. The dry matter degradability of Marandu grass was not affected; however, the fermentation kinetics in neutral detergent fiber (NDF) differed between the treatments. The co-product dry matter colonization time was greater in PKC1 and the highest effective degradability rates were from PKC0, but the productive performance of the animals was not influenced. Supplementation of buffaloes with PKC is recommended for up to 1% of body weight.
A Deep Learning Model for Predicting Damaged Points via Random Vibration Signal Analysis

Structural health monitoring is an area of growing interest and is worthy of new and innovative approaches. Since the automatic diagnosis of structures is very complex and challenging, recent research to apply deep learning techniques has been actively conducted. In this study, we assumed that a PLA beam copied by 3D printing is the smallest unit constituting a complex structure and applied GRU to detect defects. To set the defect point of the beam, a total of four holes were drilled at regular intervals, and then a mass was attached. Signals at different locations were collected through a vibrator and trained through GRU, and the results were compared in terms of RMSE value. As a result of this experiment, we checked the defect by inputting test data into the trained model and were able to measure the defect degree of the PLA beam with a weighted average F1 score of 84%.
Formation of Porous Structures and Crystalline Phases in Poly(vinylidene fluoride) Membranes Prepared with Nonsolvent-Induced Phase Separation—Roles of Solvent Polarity

PVDF membranes were prepared with nonsolvent-induced phase separation, using solvents with various dipole moments, including HMPA, NMP, DMAc and TEP. Both the fraction of the polar crystalline phase and the water permeability of the prepared membrane increased monotonously with an increasing solvent dipole moment. FTIR/ATR analyses were conducted at the surfaces of the cast films during membrane formation to provide information on if the solvents were present as the PVDF crystallized. The results reveal that, with HMPA, NMP or DMAc being used to dissolve PVDF, a solvent with a higher dipole moment resulted in a lower solvent removal rate from the cast film, because the viscosity of the casting solution was higher. The lower solvent removal rate allowed a higher solvent concentration on the surface of the cast film, leading to a more porous surface and longer solvent-governed crystallization. Because of its low polarity, TEP induced non-polar crystals and had a low affinity for water, accounting for the low water permeability and the low fraction of polar crystals with TEP as the solvent. The results provide insight into how the membrane structure on a molecular scale (related to the crystalline phase) and nanoscale (related to water permeability) was related to and influenced by solvent polarity and its removal rate during membrane formation.
Design and development of customized elbow orthosis using additive manufacturing

The orthosis is an assistive device to help people with disabilities. Orthosis, further known as braces, is used to support and modify the characteristics of human neuromuscular and musculoskeletal systems. In this study, elbow hyperextension is considered a significant cause of hand deformity. The elbow hyperextension occurs when the elbow joint in hand is being bend beyond its normal range of motion. Most commonly, orthosis was employed to treat the elbow hyperextension externally. In recent times, elbow orthosis design is customized ergonomically to fit a specific patient. The design is based on the customized profile of the human hand from the three-dimensional (3D) scanner. Sense 3D body scanner is used for scanning the human hand and 3D modeling of orthosis is done in SolidWorks. In order to reduce material consumption, topological optimization was performed. A developed novel mechanism was validated for failure analysis. The developed elbow orthosis is produced by additive manufacturing processes namely fused deposition modeling (FDM) and PolyJet technology. In FDM, acrylonitrile butadiene styrene material has been used whereas in the case of PolyJet printing, vero white material is used. The developed ergonomically designed orthosis can be worn for a long period of time without causing pain and therefore curing time is faster. A significant advantage of this proposed elbow orthosis is a self-operated novel mechanism.
Rendering of Surfaces from Volume Data

Abstract : The application of volume rendering techniques to the display of surfaces from sampled scalar functions of three spatial dimensions is explored. Fitting of geometric primitives to the sampled data is not required. Images are formed by directly shading each sample and projecting it onto the picture plane. Surface shading calculations are performed at every voxel with local gradient vectors serving as surface normals. In a separate step, surface classification operators are applied to obtain a partial opacity for every voxel. Operators that detect isovalue contour surfaces and region boundary surfaces are presented. Independence of shading and classification calculations insures and undistorted visualization of 3-D shape. Non-binary classification operators insure that small or poorly defined features are not lost. The resulting colors and opacities are composited from back to front along viewing rays to form an image. The technique is simple and fast, yet displays surfaces exhibiting smooth silhouettes and few other aliasing artifacts. The use of selective blurring and super-sampling to further improve image quality is also described. Examples from two applications are given: molecular graphics and medical imaging.
Optimal Network Models for Reconstructing 3D Point Cloud from a Single 2D Image

This study proposes a series of models for reconstructing 3D point clouds from a single 2D image to obtain the best network model. Four and six improved models are proposed for the encoder and decoder of 3D-LMNet and 3D-PSRNet, respectively, which combine various modules of such encoders and decoders and analyze the relationship of each parameter to the network layer. Optimal allocation parameters are proposed, and four training types are presented for the encoder and decoder to obtain the best model. The model adds a fifth convolution layer to the 3D-PSRNet coding layer. This layer has 512 layers. The convolution size is set to 5 × 5 and the stride is 2. The proposed model does not require professional hardware equipment and cumbersome manual procedures.
Fusarium Photoreceptors

Light is an important modulating signal in fungi. Fusarium species stand out as research models for their phytopathogenic activity and their complex secondary metabolism. This includes the synthesis of carotenoids, whose induction by light is their best known photoregulated process. In these fungi, light also affects other metabolic pathways and developmental stages, such as the formation of conidia. Photoreceptor proteins are essential elements in signal transduction from light. Fusarium genomes contain genes for at least ten photoreceptors: four flavoproteins, one photolyase, two cryptochromes, two rhodopsins, and one phytochrome. Mutations in five of these genes provide information about their functions in light regulation, in which the flavoprotein WcoA, belonging to the White Collar (WC) family, plays a predominant role. Global transcriptomic techniques have opened new perspectives for the study of photoreceptor functions and have recently been used in Fusarium fujikuroi on a WC protein and a cryptochrome from the DASH family. The data showed that the WC protein participates in the transcriptional control of most of the photoregulated genes, as well as of many genes not regulated by light, while the DASH cryptochrome potentially plays a supporting role in the photoinduction of many genes.
A Hybrid Active Learning and Progressive Sampling Algorithm

 Abstract —Sampling techniques for data mining applications can be broadly categorized into Random Sampling (RS), Active Learning (AL) and Progressive Sampling (PS). Progressive Sampling techniques grow an initial sample up to the point beyond which model accuracy no longer significantly improves. These methods have been shown to be computationally efficient. The sampling schedule to be used with progressive sampling techniques is still an ongoing issue of research due to the fact that available sampling schemes may either overshoot, resulting in a final sample which is larger than necessary, or they may grow the sample too slowly thus requiring many iterations of the algorithm before convergence is reached. We demonstrate how using Batch Mode Uncertainty Sampling from the domain of active learning, to progressively grow the sample, can significantly improve the performance of progressive sampling. Through a series of trials on both simulated and real data, we show that our proposed Progressive Batch Mode Uncertainty Sampling (PBMUS) algorithm converges with a comparable or smaller number of data points at higher accuracy and in some cases, less computational time.
Infrared Radiance Modeling by Optimal Spectral Sampling

This paper describes a rapid and accurate technique for the numerical modeling of band transmittances and radiances in media with nonhomogeneous thermodynamic properties (i.e., temperature and pressure), containing a mixture of absorbing gases with variable concentrations. The optimal spectral sampling (OSS) method has been designed specifically for the modeling of radiances measured by sounding radiometers in the infrared and has been extended to the microwave; it is applicable also through the visible and ultraviolet spectrum. OSS is particularly well suited for remote sensing applications and for the assimilation of satellite observations in numerical weather prediction models. The novel OSS approach is an extension of the exponential sum fitting of transmittances technique in that channel-average radiative transfer is obtained from a weighted sum of monochromatic calculations. The fact that OSS is fundamentally a monochromatic method provides the ability to accurately treat surface reflectance and spectral variations of the Planck function and surface emissivity within the channel passband, given that the proper training is applied. In addition, the method is readily coupled to multiple scattering calculations, an important factor for treating cloudy radiances. The OSS method is directly applicable to nonpositive instrument line shapes such as unapodized or weakly apodized interferometric measurements. Among the advantages of the OSS method is that its numerical accuracy, with respect to a reference line-by-line model, is selectable, allowing the model to provide whatever balance of accuracy and computational speed is optimal for a particular application. Generally only a few monochromatic points are required to model channel radiances with a brightness temperature accuracy of 0.05 K, and computation of Jacobians in a monochromatic radiative transfer scheme is straightforward. These efficiencies yield execution speeds that compare favorably to those achieved with other existing, less accurate parameterizations.
Hybrid UWB-Inertial TDoA-Based Target Tracking With Concentrated Anchors

In this article, hybrid radio/inertial mobile target tracking for accurate and smooth path estimation is considered. The proposed tracking approach builds upon an ultra wideband (UWB)-based positioning algorithm, based on the linear hyperbolic positioning system (LinHPS), with Time-Difference-of-Arrival (TDoA) processing and anchors concentrated on a single hotspot at the center of the environment where the target moves. First, we design an adaptive radio-based extended Kalman filter (AREKF), which does not require a priori statistical knowledge of the noise in the target movement model and estimates the measurement noise covariance, at each sampling time, according to a proper lookup table (LUT). In order to improve the performance of AREKF, we incorporate inertial data collected from the target and propose three “hybrid” radio/inertial algorithms, denoted as hybrid inertial measurement unit (IMU)-aided radio-based EKF (HIREKF), hybrid noisy control EKF (HNCEKF), and hybrid control EKF (HCEKF). Our results on experimentally acquired paths show that the proposed algorithms achieve an average instantaneous position estimation error on the order of a few centimeters. Moreover, the minimum target path length estimation error, obtained with HCEKF, is on the order of 6% and 1% for two paths with lengths equal to approximately 17 and 46 m, respectively.
Malvaceae from Serra do Lenheiro, Minas Gerais, Brazil

Abstract We present a taxonomic treatment of the Malvaceae from Serra do Lenheiro, a montane range in the municipality of São João del-Rei, Minas Gerais, Brazil, in order to contribute to the knowledge of this family and the state’s flora. Eleven genera and twenty-nine species were recorded. Sida, Pavonia and Peltaea are the richest genera. Among the recorded species, nine are considered as ruderal and ten are endemic of Brazil. Three species have São João del-Rei as the type-locality. Analytic keys, morphological descriptions, photographic plates and comments about the treated species are presented. Illustrations are presented for two species: one has no iconography in Brazilian studies for Malvaceae (Sida plumosa) and one has only poor iconographic representations (Sida viarum).
Base-State Substitution: An Idealized Modeling Technique for Approximating Environmental Variability

AbstractBase-state substitution (BSS) is a novel modeling technique for approximating environmental heterogeneity in idealized simulations. After a certain amount of model run time, base-state substitution replaces the original horizontally homogeneous background environment with a new horizontally homogeneous environment while maintaining any perturbations that have developed during the preceding simulation. This allows the user to independently modify the kinematic or thermodynamic environments, or replace the entire sounding without altering the structure of the perturbation fields. Such an approach can provide a powerful hypothesis test, for example, in a study of how an isolated convective storm would respond to a different environment within a horizontally homogeneous setting. The BSS modifications can be made gradually or instantaneously, depending on the needs of the user. In this paper both the gradual and instantaneous BSS procedures are demonstrated for simulations of deep moist convection, usi.
QueryCom: Secure Message Communication and Data Searching Protocols for Smart Transportation

Vehicular Cloud Computing (VCC) extends the scope of smart transportation applications through sharing vehicular data and resources. It is thus pivotal to transmit, save, and extract vital data with adequate security and privacy protections. There is yet no lightweight vehicular data exchange mechanism for resource-constrained environments that simultaneously offers secure and low-cost message transmission, data storage, and information searching. State-of-the-art vehicular communication protocols are also vulnerable to crucial security attacks and require more computational resources during the implementation. We, therefore, propose secure and efficient message communication, data storage, and information searching protocols (named QueryCom) for smart transportation. The QueryCom is designed using SHA-512 and Advanced Encryption Standard (AES-256), preserving security and user anonymity attributes. Further, an index-based sequential searching method is used to effectually obtain information from the Vehicular Cloud (VC) database. We evaluate QueryCom based on the security proof and attacks analysis to confirm its security robustness against crucial security attributes. The experimental testbed results (on Raspberry Pi 3B+) are discussed to analyze the computational proficiency based on different performance parameters, i.e., time complexity (to search information from the VC database), computation time, storage cost, communication overhead, and energy consumption.
Adaptive Control of Teleoperation Systems With Linearly and Nonlinearly Parameterized Dynamic Uncertainties

Existing work concerning adaptive control of uncertain teleoperation systems only deals with linearly parameterized (LP) dynamic uncertainties. Typical teleoperation system dynamics, however, also posses terms with nonlinearly parameterized (NLP) structures. An example of such terms is friction, which is ubiquitous in the joints of the master and slave robots of practical teleoperation systems. Uncertainties in the NLP dynamic terms may lead to significant position and force tracking errors if not compensated for in the control scheme. In this paper, adaptive controllers are designed for the master and slave robots with both LP and NLP dynamic uncertainties. Next, these controllers are incorporated into the 4-channel bilateral teleoperation control framework to achieve transparency. Then, transparency of the overall teleoperation is studied via a Lyapunov function analysis. Simulation studies demonstrate the effectiveness of the proposed adaptive scheme when exact knowledge of the LP and NLP dynamics is unavailable.