Miracle-dz/UniFAR-FTUs huggingface dataset:
Preliminary Applicability Analysis of Soil Dielectric Constant Model of the Different Soil Texture Condition

The soil dielectric models used to describe the relationship between soil permittivity and soil water content, mainly include Mironov model, Dobson model, Wang and Schmugge model and Hallikainen model. As a key input parameter of four dielectric constant models, soil texture directly affects the simulation ability. In order to analyze the adaptability of the dielectric constant model under different soil texture conditions, two evaluation indexes of the root mean square error (RSME) and linear correlation coefficient (R) were used to evaluate the accuracy. The results show that the four models have great differences in the simulation accuracy of loamy sand, sandy loam, clay loam, sandy clay, silty clay and clay soil. The Mironov model has the highest simulation accuracy, the Haillikainen model is second, the Dobson and the Wang model are the worst. It is further shown that the Mironov dielectric constant model has the best overall applicability for soil moisture inversion for L-band microwave remote sensing, but, with the increase of the content of silt in the soil, for example the soil with abnormally high silt content in silt loam, silt clay loam and silt clay, the Dobson model was the best choice.
A Method for Solving Current Unbalance Problem of Paralleled Single-Phase Grid-Connected Unipolar-PWM Inverters With Common Dc Bus

Unipolar-pulsewidth modulation (UP-PWM) inverter takes advantages of high power efficiency and small output chokes which is widely used in the industry. However, UP-PWM inverters cannot be used in parallel operation due to a current unbalance problem. This paper studies the current unbalance problem of paralleled UP-PWM inverters with common dc bus and ac bus. First, the problem of unbalanced inductor current is defined and analyzed. Second, a technique to eliminate unbalanced currents is proposed. The proposed current balancing technique only requires one more current sensor in an inverter module rather than changing converter topology or modulation method, which makes it possible to apply UP-PWM inverters in parallel-operation while keeping the advantages of unipolar switching. The proposed current balancing method is verified by both simulation and hardware experiment. Experimental verification is performed on two 1 kW, 400 V input, and 120 V/60 Hz output prototypes, which shows a good agreement to the analytical study.
Effects of Debaryomyces hansenii treatment on intestinal mucosa microecology in mice with antibiotic-associated diarrhea

Aim To confirm the effects of Debaryomyces hansenii on intestinal microecology in mice with antibiotic-associated diarrhea (AAD). Methods This study took the mucosal microecology as the entry point and an antibiotic mixture was used to induce diarrhea in mice. D. hansenii suspension was used to treat the mice and the bacterial communities of mucosa was analyzed using high-throughput sequencing. Results The Shannon-Wiener index indicated that the sequencing depth is reasonable and reflected the majority of microbial information. The principal coordinate analysis results showed that mice in the treatment group and the normal group had a similar microbial community structure, while differences in microbial community structure were observed between the model group and the treatment group. The inter-group bacterial structures were analyzed at the phylum level and genus level. The results revealed that antibiotic treatment increased the proportion of Proteobacteria and decreased the proportion of Bacteroides, while D. hansenii treatment inhibited the increase in Proteobacteria. Linear discriminant analysis coupled with effect size measurements (LEfSe) suggested d that the beneficial bacteria Candidatus Arthromitus were the only common bacteria in the normal group (P<0.05). Conclusion The treatment with D.hansenii could contribute to the maintenance of the structure of the mucosal microbiota in comparison with the normal group and inhibit the proliferation of opportunistic bacteria. However, high-dose antibiotic treatment causes mucosal dysbiosis and the proliferation of opportunistic bacteria during the self-recovery period, such as Pseudoalteromonas, Alteromonas, Vibrio.
Reflective Writing as a Window on Medical Students’ Professional Identity Development in a Longitudinal Integrated Clerkship

Abstract Phenomenon: Integral to the formation of a professional identity is the concept of professional identity construction, an essential part of becoming a competent physician. In this framework, identity is dynamic and shaped by experiences and relationships. Minimal work has addressed the impact that clinical care settings and relationships with patients have in the construction of professional identity for medical students. This study explores how patient interactions and intentional curriculum design support professional identity construction in students participating in a longitudinal integrated clerkship focused on care for the underserved. Approach: This qualitative analysis investigated students’ professional identity construction as detailed in 45 reflective essays from 15 students while enrolled in an 11-month longitudinal integrated clerkship (LIC) in a safety-net hospital system. Researchers used an inductive analytic approach. Findings: Students provided rich and complex descriptions of their experiences. Six themes emerged: care for the underserved; therapeutic alliance; humility and gratitude; altruism; resilience; and aspirations. Insights: Professional identity construction was observable through students’ reflective essays. Student interactions with patients provided rich material for professional identity construction, and role models in patient care enhanced this process. This study suggests that clinical learning in a safety-net hospital system, coupled with an LIC curriculum that prioritizes continuity with vulnerable patients, faculty role models, and ongoing reflection, supported the professional identity construction of students as patient-centered caregivers providing equitable care and advocacy for the underserved, described here as an Equity Identity.
An Effective Multilevel Approach To Detect Changes In Buildings From Remote Sensing Images

Change detection in satellite images taken over the same place at two different times is of primary importance in areas like disaster assessment, population growth estimation, land use monitoring, medical diagnosis and treatment, etc. The exact location, accurate identification of objects and the extraction of appropriate image features form the basis for change detection in the images of interest. Locating the damaged buildings aids in effectively assessing the rate of damage assessment. In this work, various features such as geometric features, spectral band features and similarity metrics are exploited for efficient damage calculation. This work is mainly composed of five stages: preprocessing, FCM based clustering, Edge based segmentation and Feature extraction for both the pre-event image and the post-event image. The geometric features extracted from each segment of both the pre-event and post-event images are formed into a feature vector for further processing to detect the changes. The damage level index calculated using the similarity metrics was found to be 0.9737 % and accordingly, the post-event image was categorized under Very Slight Damage (VSD).
Joint Detection of Airplane Targets Based on Sar Images and Optical Images

In airplane target detection, there was the drawback of weak recognition ability for dark targets and high false alarm rate for detected targets. In order to address the problem, we proposed a detection method based on SAR and optical image feature fusion. It extracted texture, moment and backscattering characteristics from SAR images and combined with optical features. Moreover, the novel airplane edge templates incorporating SAR and optical images were created to acquire saliency map. During the process of detection, first, the saliency map and the One-Class-SVM (OCSVM) classifier were used to initially recognize the suspected airplane targets. Then, the combination features were adopted to further identify the misidentified airplane target. The experimental results showed that the Precision of the proposed method was 61.82% and the False Alarm Rate was 20%, which was better than the HIS-based detection method.
Dynamic Community Detection Based on a Label-Based Swarm Intelligence

The dynamic network tails after the development of the real-world that is essential for particle applications such as traffic flow analyses and social network analyses. The requirement of maximizing the quality of the community structure at current time step and minimizing the difference of the community structure between two successive time steps synchronously brings serious challenges to the dynamic community detection. Some existing approaches (i.e., the multi-objective particle swarm optimization, named as DYNMOPSO) utilize the swarm intelligence pattern to solve such a community detection problem in dynamic networks. Nevertheless, the DYNMOPSO has the deficiency of the undesirable prematurity constringency and monotonicity of particles because of the high choice stress. Thus, a label-based swarm intelligence on the basis of the evolutionary clustering framework is presented for overcoming those disadvantages. The label propagation approach initializes the labels of particles and is used for escaping the prematurity constringency. The crossover and mutation methods are introduced to improve the variety of particles and retain preferable the community structure synchronously. Experiments in synthetical and real networks prove that our algorithm is valid and exceeds state-of-the-art approaches.
Tool for Automatic Formulation of State Equations in the MATLAB Environment

The paper deals with an educational tool for automatic formulation of state equations for linear nondegenerate circuits in the MATLAB environment. The procedure of formulating the state equations for circuit is not based on the topology concept but on knowledge, that if the values of state variables are known at a certain time instant and the values of input variables thereafter, then the value of any state variable can be determined for any subsequent time. The tool formulates state equations automatically in a symbolic form without numerical values of elements for given circuit. After substitution of numerical values of elements into the state equations, their analytical solution and graphical representation of complete responses are given.
Axis Airborne Sar System: Flight-Test Results

In this work, we present some results of the flight-test campaign carried out with the AXIS system, which is an Italian single-pass interferometric airborne X-Band Synthetic Aperture Radar (SAR) based on the frequency modulated continuous wave technology. The system has recently been developed in the frame of a cooperation between a public research institute (IREA-CNR) and a private company (Elettra Microwave). The campaign was performed in 2018 over the Salerno area, South of Italy, and some corner reflectors (CRs) were deployed over the illuminated area with the aim of verifying the system performances in terms of geometric resolution and target location.
Exact Feedback Linearization of the Collocated Constrained Dynamics of the Three-Link With Adjustable Torso and its Application in the Underactuated Planar Walking*

The collocated virtual holonomic constraints for the planar mechanical three-link system are designed to impose the fully exact feedback linearizable constrained dynamics. The linear methods are then applicable both to generate and exponentially track the target walking-like trajectory. More specifically, for every double support phase at the beginning of the step, the mass placed on the torso can be adjusted and the specific constraint designed to ensure the above mentioned full exact feedback linearizability. Furthermore, the design of the impact invariant families of trajectories inside that constrained dynamics is provided as well. To illustrate these results the hybrid cyclic walking-like trajectory is designed and simulated.
Woodland Detection Using Most-Sure Strategy to Fuse Segmentation Results of Deep Learning

For obtaining information about ecosystem resource, GF-1 satellite was launched on April 26, 2013, which is the first satellite of the China’s High-Resolution Earth Observation System. After obtaining some of the remote sensing images from GF-1, we selected WFV(wide field vision) images and detected the woodland to separate it from other geography types in the images. First, WFV images were clipped and labeled, then two deep learning models, POI-Net and Deep-UNet were used for training. We fused the prediction matrixes of deep learning networks using proposed "Most-sure strategy". The results show that our method can effectively improve the accuracy of woodland detection and segmentation results are outstanding. In addition, the proposed framework can also detect woodland in images returned by GF-6 satellite.
Analysis of the Azimuth Ambiguity and Imaging Area Restriction for Circular SAR Based on the Back-Projection Algorithm

Circular synthetic aperture radar (CSAR) has a 360° observation capability on the central observation scenario. A typical way to process CSAR imaging is to cut data into small sub-apertures because most targets are only coherent at a very small observation angle. There are many sub-aperture imaging methods after development in recent years. The back-projection algorithm is widely used because it is simple and can be applied to an arbitrary trajectory. Because of the limitation of the Nyquist sampling frequency and influence of the antenna sidelobe, azimuth ambiguity is a phenomenon that may occur in the radar imaging process. The existing researches typically choose the back-projection (BP) imaging area according to the SAR platform flight path and the antenna beam width. The limitation of the CSAR imaging area and its azimuth ambiguity region are rarely analyzed theoretically. This paper focus on the sub-aperture imaging of CSAR, based on the BP algorithm, which derives the relationship of azimuth ambiguity with CSAR parameters such as the pause repeat frequency (PRF), slant range angle, velocity of radar platform, etc. This paper proposes an equation for the calculation of the azimuth ambiguity region and analyzes the limitations, which provides theoretical support for CSAR parameter design, imaging area selection, and azimuth ambiguity analysis.
Assessment of Polarimetric Variability by Distance Geometry for Enhanced Classification of Oil Slicks Using SAR

In this paper, we introduce a new approach for investigation of polarimetric Synthetic Aperture Radar (PolSAR) images for oil slick analysis. Our method aims at enhancing discrimination of oil types by exploring the polarimetric features that can be produced by processing PolSAR scenes without dimensionality reduction. Taking advantage of a mixture description of the interactions among classes within the dataset and a characterization of their intra- and inter-class variability, our algorithm is able to quantify the areal coverage of different elements. These estimates can be used to hence improve classification. Experimental results on a PolSAR dataset acquired by unmanned aerial vehicle (UAV) on oil slicks in open water show the capacity of our method.
Dietary patterns of 6–24‐month‐old children are associated with nutrient content and quality of the diet

Abstract We determined the associations of dietary patterns with energy/nutrient intakes and diet quality. Previously collected single 24‐hr dietary recalls for children aged 6–11 months (n = 1,585), 12–17 months (n = 1,131), and 18–24 months (n = 620) from four independent studies in low socio‐economic populations in South Africa were pooled. A maximum‐likelihood factor model, with the principal‐factor method, was used to derive dietary (food) patterns. Associations between dietary pattern scores and nutrient intakes were determined using Kendall's Rank Correlations, with Bonferroni‐adjusted significance levels. For both 6–11 months and 12–17 months, the formula milk/reverse breast milk pattern was positively associated with energy and protein intake and mean adequacy ratio (MAR). The family foods pattern (6–11 months) and rice and legume pattern (12–17 months) were positively associated with plant protein, fibre, and PU fat; both for total intake and nutrient density of the complementary diet. These two patterns were also associated with the dietary diversity score (DDS; r = 0.2636 and r = 0.2024, respectively). The rice pattern (18–24 months) showed inverse associations for nutrient intakes and nutrient densities, probably because of its inverse association with fortified maize meal. The more westernized pattern (18–24 months) was positively associated with unfavourable nutrients, for example, saturated fat and cholesterol. These results highlight that underlying dietary patterns varied in terms of energy/nutrient composition, nutrient adequacy, nutrient densities of the complementary diet, and dietary diversity.
Selective Harmonic Minimization Scheme Applied to Cascaded H-Bridge Inverter for Satisfying CIGRE WG 36-05 and EN 50160 Grid Codes

In this paper, a selective harmonic minimization-PWM (SHM-PWM) scheme for three-phase five-level cascaded H-bridge (CHB) inverter has been presented with having four number of switching transitions to meet the gird code limits such as CIGRE WG 36-05 and EN 50160, for the medium-voltage level applications. Here, the DC-link voltages has been taken to vary equally because it helps to minimize more no. of higher order harmonics with a reduced no. of switching transitions for the full modulation region. The variable DC-link voltages and switching angles have been determined by applying particle swarm optimization (PSO) technique. The efficacy of the proposed SHM-PWM scheme has been studied through simulation and compared with the SHE-PWM scheme. The proposed PWM scheme has been validated by hardware results extracted from the experimentation on a three-phase five-level CHB inverter.
A NoSQL Approach for Aspect Mining of Cultural Heritage Streaming Data

Aspect mining constitutes an essential part of delivering concise and, perhaps more importantly, accurately tailored cultural content. With the advent of social media, there is a data abundance so that analytics can be reliably designed for ultimately providing valuable information towards a given product or service. Naturally representing and efficiently processing a large number of opinions can be implemented with the use of streaming technologies. Big data analytics are especially important in the case of cultural content management where reviews and opinions may be analyzed in order to extract meaningful representations. In this paper, a NoSQL database method for aspect mining of a cultural heritage scenario by taking advantage of Apache Spark streaming architecture is presented.
Bare Iron Oxide Nanoparticles: Surface Tunability for Biomedical, Sensing and Environmental Applications

Surface modification is widely assumed as a mandatory prerequisite for the real applicability of iron oxide nanoparticles. This is aimed to endow prolonged stability, electrolyte and pH tolerance as well as a desired specific surface chemistry for further functionalization to these materials. Nevertheless, coating processes have negative consequences on the sustainability of nanomaterial production contributing to high costs, heavy environmental impact and difficult scalability. In this view, bare iron oxide nanoparticles (BIONs) are arousing an increasing interest and the properties and advantages of pristine surface chemistry of iron oxide are becoming popular among the scientific community. In the authors’ knowledge, rare efforts were dedicated to the use of BIONs in biomedicine, biotechnology, food industry and environmental remediation. Furthermore, literature lacks examples highlighting the potential of BIONs as platforms for the creation of more complex nanostructured architectures, and emerging properties achievable by the direct manipulation of pristine iron oxide surfaces have been little studied. Based on authors’ background on BIONs, the present review is aimed at providing hints on the future expansion of these nanomaterials emphasizing the opportunities achievable by tuning their pristine surfaces.
Convolutional Neural Network with PCA and Batch Normalization for Hyperspectral Image Classification

A new deep learning based spectral-spatial approach for hyperspectral image classification is developed, which uses spectral reduction as preprocessing and batch normalization in every layer of the deep network. The spectral data is reduced by Principal Component Analysis and the spatial dimension is sliced into patches of 9x9. These patches hierarchically deliver discriminative features when feed to the proposed network. The training process is regularized and the overfitting (previously often encountered problem) is avoided by using combination of batch normalization and dropout. Moreover, oversampling and augmentation in training data is used to expand the training data and to create some variation in available training data. Finally the experimental results demonstrated the performance of our method in comparison to other methods especially for hyperspectral classification tasks.
Improving Spectral Resolution of Multispectral Data Using Convolutional Neural Network

Hyperspectral data, despite of possessing high spectral resolution, suffers from narrow swath, which hinders its wider applications. Till now, there are many fusion methods to improve the resolution of data. However, most of the fusion methods are focused on enhancement of the overlapping area and cannot extend the swath of the hyperspectral data. Thus, in this paper, a multispectral image spectral resolution improving method using convolutional neural network (CNN) is proposed, which is based on the hypothesis that there exists learnable nonlinear mapping between hyperspectral data and multispectral data, and when the land cover is the same, the mapping between data from the overlapped area is the same as that from the non-overlapped area. By training the network using the data of the overlapped area, the method can predict the hyperspectral data of nonoverlapping area, or in other words, extend the swath of hyperspectral data. The architecture used in this method is a relatively simple three-layered structure yet powerful to extend the swath of hyperspectral data as the experimental results shows.
Determination of Total Precipitable Water from GNSS Data in Thailand

The relationship between the total precipitable water (TPW) in the atmosphere and Global Navigation Satellite System (GNSS) Zenith Total Delay (ZTD) has been investigated. The study was performed for ZTD observations derived from GPS measurements at 5 sites across Thailand, over one-year period, July 2017 to August 2018. The ZTD was then collocated in space and time with the TPW data available from Global Data Assimilation System (GDAS) Numerical Weather Prediction (NWP) model. The result showed that the ZTD observations are highly correlated with the TPW but there are some biases between the ground base station locations largely due to the altitude differences. An empirical model was then developed to remove the altitude dependence and provide a unique relationship between the ZTD and the TPW. This model can be useful in the further TPW measurements from widely available GNSS receivers.