Miracle-dz/UniFAR-FTUs huggingface dataset:
Evapotranspiration Estimation with the S-SEBI Method from Landsat 8 Data against Lysimeter Measurements at the Barrax Site, Spain

Evapotranspiration (ET) is a variable of the climatic system and hydrological cycle that plays an important role in biosphere–atmosphere–hydrosphere interactions. In this paper, remote sensing-based ET estimates with the simplified surface energy balance index (S-SEBI) model using Landsat 8 data were compared with in situ lysimeter measurements for different land covers (Grass, Wheat, Barley, and Vineyard) at the Barrax site, Spain, for the period 2014–2018. Daily estimates produced superior performance than hourly estimates in all the land covers, with an average difference of 12% and 15% for daily and hourly ET estimates, respectively. Grass and Vineyard showed the best performance, with an RMSE of 0.10 mm/h and 0.09 mm/h and 1.11 mm/day and 0.63 mm/day, respectively. Thus, the S-SEBI model is able to retrieve ET from Landsat 8 data with an average RMSE for daily ET of 0.86 mm/day. Some model uncertainties were also analyzed, and we concluded that the overpass of the Landsat missions represents neither the maximum daily ET nor the average daily ET, which contributes to an increase in errors in the estimated ET. However, the S-SEBI model can be used to operationally retrieve ET from agriculture sites with good accuracy and sufficient variation between pixels, thus being a suitable option to be adopted into operational ET remote sensing programs for irrigation scheduling or other purposes.
Research on an Improved MB-LBP 3D Face Recognition Method

In order to improve the accuracy and speed of 3D face recognition, this paper proposes an improved MB-LBP 3D face recognition method. First, the MB-LBP algorithm is used to extract the features of 3D face depth image, then the average information entropy algorithm is used to extract the effective feature information of the image, and finallythe Support Vector Machine algorithm is used to identify the extracted effective information. The recognition rate on the Texas 3DFRD database is 96.88%, and the recognition time is 0.025s. The recognition rate in the self-made depth library is 96.36%, and the recognition time is 0.02s. It can be seen from the experimental results that the algorithm in this paper has better performance in terms of accuracy and speed.
A combinatorial auction‐based approach for ridesharing in a student transportation system

Here, a mixed‐integer linear programming model is developed to represent a transportation system of students traveling from/to a university campus. The concept of ridesharing is used and the mechanism of combinatorial auctions is incorporated within a routing‐based model. The mathematical model is based on the vehicle routing problem along with appropriate constraints accommodating features that express the auction clearing phase. A hybrid heuristic‐based optimization framework, that takes advantage of meta‐heuristic algorithms to improve an initial solution, is also developed to solve large‐sized instances of the problem. Three meta‐heuristics, namely particle swarm optimization, dragonfly algorithm, and imperialist competitive algorithm, are implemented in the proposed framework, whose performances are assessed and compared. Moreover, two improvement heuristic procedures that attempt to improve the outcomes of the foregoing meta‐heuristics are proposed and compared as well.
Reduced-Dynamic Precise Orbit Determination of Haiyang-2B Altimetry Satellite Using a Refined Empirical Acceleration Model

The Haiyang 2B (HY-2B) satellite requires precise orbit determination (POD) products for geodetic remote sensing techniques. An improved set of reduced-dynamic (RD) orbit solutions was generated from the onboard Global Positioning System (GPS) measurements over a 14-month period using refined strategies and processing techniques. The key POD strategies include a refined empirical acceleration model, in-flight calibration of the GPS antenna, and the resolution of single-receiver carrier-phase ambiguities. In this study, the potential periodicity of empirical acceleration in the HY-2B POD was identified by spectral analysis. In the along-track direction, a noticeable signal with four cycles per revolution (CPR) was significant. A mixed spectrum was observed for the cross-track direction. To better understand the real in-flight environment, a refined empirical acceleration model was used to cope with the time variability of empirical accelerations in HY-2B POD. Three POD strategies were used for the reprocessing for superior orbit quality. Validation using over one year of satellite laser ranging (SLR) measurements demonstrated a 5.2% improvement in the orbit solution of the refined model. Reliable correction for the GPS antenna phase center was obtained from an over-420-day dataset, and a trend in radial offset change was observed. After application of the in-flight calibration of the GPS antenna, a 26% reduction in the RMS SLR residuals was achieved for the RD orbit solution, and the carrier phase residuals were clearly reduced. The integer ambiguity resolution of HY-2B led to strong geometric constraints for the estimated parameters, and a 15% improvement in the SLR residuals could be inferred compared with the float solution.
Point-Line Visual Stereo SLAM Using EDlines and PL-BoW

Visual Simultaneous Localization and Mapping (SLAM) technologies based on point features achieve high positioning accuracy and complete map construction. However, despite their time efficiency and accuracy, such SLAM systems are prone to instability and even failure in poor texture environments. In this paper, line features are integrated with point features to enhance the robustness and reliability of stereo SLAM systems in poor texture environments. Firstly, method Edge Drawing lines (EDlines) is applied to reduce the line feature detection time. Meanwhile, the proposed method improves the reliability of features by eliminating outliers of line features based on the entropy scale and geometric constraints. Furthermore, this paper proposes a novel Bags of Word (BoW) model combining the point and line features to improve the accuracy and robustness of loop detection used in SLAM. The proposed PL-BoW technique achieves this by taking into account the co-occurrence information and spatial proximity of visual words. Experiments using the KITTI and EuRoC datasets demonstrate that the proposed stereo Point and EDlines SLAM (PEL-SLAM) achieves high accuracy consistently, including in challenging environments difficult to sense accurately. The processing time of the proposed method is reduced by 9.9% and 4.5% when compared to the Point and Line SLAM (PL-SLAM) and Point and stereo Point and Line based Visual Odometry (sPLVO) methods, respectively.
An Improved Method of Soil Moisture Retrieval Using Multi-Frequency SNR Data

Soil moisture monitoring using Global Navigation Satellite System (GNSS) multipath signals has gained continuous interests in recent years. However, traditional GNSS-interferometric reflectometry (GNSS-IR) soil moisture retrieval methods generally utilize a single frequency or single satellite, which fail to take full advantage of different and complementary of satellite signals with different frequencies. An improved algorithm for soil moisture retrieval based on principal component analysis (PCA) and entropy method using multi-frequency amplitude and phase offset fusion data was proposed in this research. The performance of the proposed soil moisture retrieval method was evaluated using data recorded by Plate Boundary Observatory (PBO) H2O networks and a self-built site in Henan, China. The results from GPS and BeiDou both showed that the retrieved soil moisture has a stronger correlation with in situ soil moisture, which can better reflect the fluctuation of ground truth measurements. Compared with the traditional method, the retrieval accuracy of the proposed method in terms of root-mean-square error (RMSE) was improved by 50.93%, and the average correlation coefficient were increased by 11.71%. This research proved that the proposed method could effectively improve retrieval accuracy due to the increasing number of frequencies and tracks clustering. Moreover, this study has illustrated the feasibility of BeiDou signals to precisely estimate surface soil moisture.
ERTFM: An Effective Model to Fuse Chinese GF-1 and MODIS Reflectance Data for Terrestrial Latent Heat Flux Estimation

Coarse spatial resolution sensors play a major role in capturing temporal variation, as satellite images that capture fine spatial scales have a relatively long revisit cycle. The trade-off between the revisit cycle and spatial resolution hinders the access of terrestrial latent heat flux (LE) data with both fine spatial and temporal resolution. In this paper, we firstly investigated the capability of an Extremely Randomized Trees Fusion Model (ERTFM) to reconstruct high spatiotemporal resolution reflectance data from a fusion of the Chinese GaoFen-1 (GF-1) and the Moderate Resolution Imaging Spectroradiometer (MODIS) products. Then, based on the merged reflectance data, we used a Modified-Satellite Priestley–Taylor (MS–PT) algorithm to generate LE products at high spatial and temporal resolutions. Our results illustrated that the ERTFM-based reflectance estimates showed close similarity with observed GF-1 images and the predicted NDVI agreed well with observed NDVI at two corresponding dates (r = 0.76 and 0.86, respectively). In comparison with other four fusion methods, including the widely used spatial and temporal adaptive reflectance fusion model (STARFM) and the enhanced STARFM, ERTFM had the best performance in terms of predicting reflectance (SSIM = 0.91; r = 0.77). Further analysis revealed that LE estimates using ERTFM-based data presented more detailed spatiotemporal characteristics and provided close agreement with site-level LE observations, with an R2 of 0.81 and an RMSE of 19.18 W/m2. Our findings suggest that the ERTFM can be used to improve LE estimation with high frequency and high spatial resolution, meaning that it has great potential to support agricultural monitoring and irrigation management.
Feature Learning for SAR Target Recognition with Unknown Classes by Using CVAE-GAN

Even though deep learning (DL) has achieved excellent results on some public data sets for synthetic aperture radar (SAR) automatic target recognition(ATR), several problems exist at present. One is the lack of transparency and interpretability for most of the existing DL networks. Another is the neglect of unknown target classes which are often present in practice. To solve the above problems, a deep generation as well as recognition model is derived based on Conditional Variational Auto-encoder (CVAE) and Generative Adversarial Network (GAN). A feature space for SAR-ATR is built based on the proposed CVAE-GAN model. By using the feature space, clear SAR images can be generated with given class labels and observation angles. Besides, the feature of the SAR image is continuous in the feature space and can represent some attributes of the target. Furthermore, it is possible to classify the known classes and reject the unknown target classes by using the feature space. Experiments on the MSTAR data set validate the advantages of the proposed method.
The Impacts of College Students’ Civic Responsibility on Civic Engagement via Online Technology: The Mediations of Civic Learning and Civic Expression

This study focused on college students’ attitudes toward the relationship between online civic responsibility and online civic engagement and its impacts. It also investigated the mediating roles of online civic learning and online civic expression in this relationship. A survey was conducted in Taiwan, testing for indirect effects with mediated variables using a structural equation model. The study tested hypotheses about the mediations of online civic learning and online civic expression on this relationship between online civic responsibility and online civic engagement for college students. The results indicate that the mediators of online civic learning and online civic expression fully mediate the relationship between online civic responsibility and online civic engagement.
The Use of Ground Penetrating Radar to Assess the Concrete Anas

Ground Penetration Radar (GPR) is a modern and promising geophysical technique for near-subsurface exploring and observing because of its characteristic working scheme (instantaneous underground radargram displaying and subsurface features preserving during the detection tests). In this technique a very high and/ or ultra-high electromagnetic radiation frequencies were utilized to be transmitted to the targeted underground area, then the reflected ones which occur because of the sudden changes in the medium electric properties or texture would be recorded and processed to achieve the final GPR radargram. The main goal of this study is to find out the GPR radiation extension which is suitable for concrete or rebar tiling identifying and measuring in addition to discover the cracks in the concrete walls, the minor goal is studying the effect of GPR device parameters changing on produced radar imagery and identifying the most effective parameter settings for concrete buildings cornerstone locating and wall cracks detection. These parameters are (radiation phase velocity m/sec., frequency coding or sampling, time windows in nano sec., and background removal for unwanted layers removing). The study executed using 1000 MHz antenna on 12 paths and clearly showed that the most important filter/ and or parameter in concrete sites locating and mapping is the "background removal" filter, while other parameters were image improvement ones or sometimes had a negative role in detection procedure.
Evaluation of nutritive value of water hyacinth ( Eichhornia Crassipes ) and guinea grass ( Panicum maximum ) mixture as animal feed in the tropics

The nutritive value of water hyacinth (Eichhornia crassipes) and Guinea grass ( Panicum maximum ) mixture as animal feed resources in the tropics were evaluated. The mixture of the treatments: A (0% water hyacinth + 90% Guinea grass + 10% concentrate), B (30% water hyacinth + 60% Guinea grass + 10% concentrate), C (45% water hyacinth + 45% Guinea grass + 10% concentrate), D (60% water hyacinth + 30% Guinea grass + 10% concentrate) and E (90% water hyacinth + 0% Guinea grass + 10% concentrate) were incubated in vitro for 96 hours to monitor total gas production (ml/200 mg DM) at post incubation. Methane (mmol/200mg DM) was evaluated by introducing 10 M NaOH into the content. Metabolizable energy (MJ/Kg DM), organic matter digestibility (%) and Short chain fatty acids (mmol) were calculated. Results showed that the crude protein and neutral detergent fibre (g/100 g DM) ranged from 10.28 to10.42 and 55.62 to 56.29, respectively. Inclusion levels of water weed apparently reduced the total gas production. It was concluded that water hyacinth as forage may be a good combination with Guinea grass for livestock production, but must not be higher than 30 % inclusion for optimal performance. Key words : Guinea grass, in vitro gas fermentation, Ruminants, Secondary metabolites, Water hyacinth
Ring-Based Quantum Network with Quantum Key Distribution (QKD) and Quantum Digital Signature (QDS)

Quantum technological developments offers a great promise for implementing secure quantum internet. In this study, we propose a ring topology-based quantum network with QKD and QDS for the quantum internet. The combination of QKD and QDS allows both the detection of the intruder and the increase in security in the network. The proposed network has been demonstrated to work by being implemented in the SQUANCH network simulator [1].
Density functional theory study of structural, mechanical, electronic and optical properties of MoAlX (X = B, C, N and O) compounds

The structural, mechanical, electronic and optical properties of MoAlX (X = B, C, N and O) were studied by density functional theory calculations. The hardness of MoAlB and MoAlN is 20.45 GPa and 9.03 GPa with softer and machinable character as opposed to other borides. The analysis of the band structure and density (DOS) of states shows that MoAlX compounds have a metallic nature. The electronic density difference plots show a spherical charge distribution with ionic bonding in MoAlB and oblate charge distribution in MoAlX (X = C, N and O) indicating covalent bonding. The optical properties of MoAlX are investigated. In the energy range up to ∼15 eV, MoAlX possesses high reflectivity with the strongest absorption in the energy range of 0–35 eV. Furthermore, the plasma frequencies of MoAlX ranges from 21–24 eV and have the ability to change from metallic to dielectric response should the frequency of the incident light be greater than 24 eV.
Sowing time and seeding rate in the new wheat varieties cultivation for seeds

The most effective agricultural techniques in the production of high-quality and productive seeds of wheat varieties are the sowing time and seeding rate. The results of the study showed that the best time for new varieties of wheat in the conditions of 2019-2020. is sowing on May 25 with a seeding rate of 5.5 million viable grains per hectare. In terms of yield, the promising variety Beiskaya surpassed the zoned variety Krasnoyarskaya 12 in all variants by 0.1 - 0.3 t / ha. The response of varieties to seeding rates is more noticeable at a late sowing period: the difference in yield in thinned and thickened crops was 0.4 t / ha, while at an early date - 0.2 t / ha. Indicators of the parameters of the elements of the structure of yield (weight of 1000 grains, grain content of an ear, productive stalk) increased with a late sowing period. Analysis of variance of wheat varieties by yield revealed that this indicator significantly depended on the sowing time - 54%, seeding rates - 22%, as well as on the hereditary characteristics of the studied forms - 14%. The seed multiplication factor (the ratio of the mass of harvested seeds to the mass of sown seeds) was 20-22 at a seeding rate of 3.5 million germinating grains per hectare for both varieties and 12-14 at a seeding rate of 5.5 million germinating grains per hectare; the reduced seeding rate allows you to get an additional 4-6 tons of seeds.
An improved sine cosine algorithm for assignment problem

The assignment problem is a NP-hard combinatorial optimization problem, where assignees are being assigned to perform tasks. This paper presents an improved sine and cosine algorithm (ISCA) to solve this problem. Nonlinear decreasing inertia weight, chaotic map and greedy strategy are added to the original sine cosine algorithm (SCA) to enhance the ability of focusing on optimal and avoiding local optima. Simulation results show that the proposed algorithm can get more competitive solutions when compared with differential evolution (DE), particle swarm optimization (PSO) and original SCA.
A Different Shaped Radiating Element Wide Band Multi-Band Massive MIMO Antenna for 5G/WLAN applications with Enhanced Correlation Coefficient

In this paper, a different shaped 8-element multiband multiple input multiple output (MIMO) massive antenna is investigated for 5G and various applications. It can be operated on various wireless communication application such as WLAN (5.15GHz to 5.30GHz and 5.73GHz to 5.85GHs) and 5G band & C-band (4 to 8GHz) for satellite communication with wideband. The 8-element Massive MIMO antenna is designed for 6GHz. This antenna is working on multiband with wideband bandwidth. The defected ground structure (DGS) systems are used for enhancement of return loss, isolation, total active reflection coefficients (TARC), correlation coefficients (CC) and envelope correlation coefficients (ECC). The rectangular shaped DGS is used in the ground plane for improvement of various parameter of antenna. This antenna provides return loss (RL) and isolation is less than -10dB.
Prediction of polar vortex intensity signal based on convolution smoothing and long short-term memory

Polar vortex is an important weather system that affects the atmospheric circulation in the Northern Hemisphere and the climate change in the Arctic. The intensity variation of polar vortex is related to El Nino-Southern Oscillation (ENSO), Arctic Oscillation (AO) and many other climate phenomena. However, there are few researches on the prediction of polar vortex intensity change, our study analyzes and predicts the intensity variation of the Northern Hemisphere stratospheric polar vortex, and further uses convolution smoothing and depth learning methods to improve the accuracy of the prediction. The result shows that the long-short time memory network method’s prediction accuracy is not enough high. After the convolution smoothing of the time series of intensity signal, the prediction accuracy of neural network has been significantly improved. The average absolute error of the traditional long short-term memory network method is 18.29, while the average absolute error of the smoothed prediction intensity and the actual intensity is 13.77. In addition, the correlation between the predicted results and the real values is also as high as 0.9981.
Research on character Action Control Method Based on Multi Phase-Functioned Neural Network and State Machine

Aiming at the situation that the phase-functioned neural network can’t produce natural action or interact with the world poorly in some specific scenes, this paper proposes to divide different states according to the phase information of characters, and train several independent networks with different weights to predict the next frame state of characters. The experimental results show that, while the complexity of the model is reduced, the action generated by this method is more natural and fluent, which has good application value.
Deep learning based news text classification software design

New technologies such as artificial intelligence have developed at a rapid pace in recent years and are increasingly being used in the process of managing news in bulk. The development of deep learning has facilitated unprecedented progress in the field of computing and has opened our eyes to the possibility of using AI for news text classification. In this paper, based on the system requirements analysis, we describe the process of functional modules arising from the requirements analysis, design the internal details of functional modules, including algorithms and detailed principles, and finally obtain a prototype of news text classification software, which results in the pre-design expectations. The research in this paper makes the system development work more concrete, while providing software users, software developers, and analysts and testers with a unified and comprehensive understanding of the system’s functional implementation.
Extreme lows of wheat production in Brazil

Wheat production in Brazil is insufficient to meet domestic demand and falls drastically in response to adverse climate events. Multiple, agro-climate-specific regression models, quantifying regional production variability, were combined to estimate national production based on past climate, cropping area, trend-corrected yield, and national commodity prices. Projections with five CMIP6 climate change models suggest extremes of low wheat production historically occurring once every 20 years would become up to 90% frequent by the end of this century, depending on representative concentration pathway, magnified by wheat and in some cases by maize price fluctuations. Similar impacts can be expected for other crops and in other countries. This drastic increase in frequency in extreme low crop production with climate change will threaten Brazil’s and many other countries progress toward food security and abolishing hunger.