Miracle-dz/UniFAR-FTUs huggingface dataset:
The Joint UAV-Borne Magnetic Detection System and Cart-Mounted Time Domain Electromagnetic System for UXO Detection

For unexploded O=ordnance (UXO) detection, individual technology cannot achieve the best detection performance. The new detection mode of joint magnetic and electromagnetic method has attracted more and more attention. In this paper, a newly developed joint detection system is introduced, a multi-rotor UAV-based magnetic system (UAVMAG) and a cart-based time-domain electromagnetic detection system (TDEM-Cart) are combined, and the cooperative processing of magnetic field and electromagnetic data is proposed. The result of the joint inversion fuses the feature vector retrieved from the magnetic field data and the feature vector inverted from the electromagnetic data, providing more accurate positioning results and richer information, which is favorable to locate and distinguish the UXO. Two field experiments are conducted, and the results show that when the joint system works in the full-coverage survey mode, both ferromagnetic and non-ferromagnetic metal targets can be detected, avoiding missed detections. In addition, when the joint system works in the cued survey mode, the detection efficiency is improved, the positioning accuracy of joint interpretation is less than 10 cm, and it shows satisfactory performance in the recognition of targets.
Mapping Mangrove Zonation Changes in Senegal with Landsat Imagery Using an OBIA Approach Combined with Linear Spectral Unmixing

The mangrove areas in Senegal have fluctuated considerably over the last few decades, and it is therefore important to monitor the evolution of forest cover in order to orient and optimise forestry policies. This study presents a method for mapping plant formations to monitor and study changes in zonation within the mangroves of Senegal. Using Landsat ETM+ and Landsat 8 OLI images merged to a 15-m resolution with a pansharpening method, a processing chain that combines an OBIA approach and linear spectral unmixing was developed to detect changes in mangrove zonation through a diachronic analysis. The accuracy of the discriminations was evaluated with kappa indices, which were 0.8 for the Saloum delta and 0.83 for the Casamance estuary. Over the last 20 years, the mangroves of Senegal have increased in surface area. However, the dynamics of zonation differ between the two main mangrove hydrosystems of Senegal. In Casamance, a colonisation process is underway. In the Saloum, Rhizophora mangle is undergoing a process of densification in mangroves and appears to reproduce well in both regions. Furthermore, this study confirms, on a regional scale, observations in the literature noting the lack of Avicennia germinans reproduction on a local scale. In the long term, these regeneration gaps may prevent the mangrove from colonising the upper tidal zones in the Saloum. Therefore, it would be appropriate to redirect conservation policies towards reforestation efforts in the Saloum rather than in Casamance and to focus these actions on the perpetuation of Avicennia germinans rather than Rhizophora mangle, which has no difficulty in reproducing. From this perspective, it is necessary to gain a more in-depth understanding of the specific factors that promote the success of Avicennia germinans seeding.
Deep Learning-Based Radar Composite Reflectivity Factor Estimations from Fengyun-4A Geostationary Satellite Observations

Ground-based weather radar data plays an essential role in monitoring severe convective weather. The detection of such weather systems in time is critical for saving people’s lives and property. However, the limited spatial coverage of radars over the ocean and mountainous regions greatly limits their effective application. In this study, we propose a novel framework of a deep learning-based model to retrieve the radar composite reflectivity factor (RCRF) maps from the Fengyun-4A new-generation geostationary satellite data. The suggested framework consists of three main processes, i.e., satellite and radar data preprocessing, the deep learning-based regression model for retrieving the RCRF maps, as well as the testing and validation of the model. In addition, three typical cases are also analyzed and studied, including a cluster of rapidly developing convective cells, a Northeast China cold vortex, and the Super Typhoon Haishen. Compared with the high-quality precipitation rate products from the integrated Multi-satellite Retrievals for Global Precipitation Measurement, it is found that the retrieved RCRF maps are in good agreement with the precipitation pattern. The statistical results show that retrieved RCRF maps have an R-square of 0.88-0.96, a mean absolute error of 0.3-0.6 dBZ, and a root-mean-square error of 1.2-2.4 dBZ.
Adsorption-desorption processes of azo dye on natural bentonite: batch experiments and modelling

Abstract The purpose of this study was to determine whether a bentonite from the Gafsa deposit (western Tunisia) could be used to remove the Foron Blue 291 (FB) azo dye from wastewater. Batch adsorption and desorption experiments were conducted using untreated and purified bentonite and the influence of contact time, pH, adsorbent mass and temperature of the dye solution on the adsorption of FB was evaluated. Kinetic and isotherm data were fitted using two non-linear kinetic and two non-linear isotherm equations. In addition, the fits were evaluated using the coefficient of determination (R2) and the RMSE. The percentage of dye removal increased with increasing amount of adsorbent until total discolouration was achieved. The adsorption isotherms followed the Langmuir model, with the purified bentonite having a higher adsorption capacity than the raw material due to its higher specific surface area. In addition, the FB molecules were removed from the liquid medium by physical adsorption. Batch desorption experiments were conducted to study the desorption kinetics and the characteristics of the desorption isotherms as well as to quantify the portion of the FB (by mass) that was irreversibly fixed on the solid. Overall, the desorption kinetics were similar to the adsorption kinetics, which indicated that the adsorption-desorption process of azo dye is non-singular; irreversibility occurred that was underlined by an observed thermodynamic index of irreversibility (TII) of 0.69-0.94. The desorption isotherms of the FB depended on the mass of the FB that was initially adsorbed on the bentonite sample, suggesting hysteresis. The amount of irreversibly retained FB was between 46 and 68% of the initial adsorbed mass.
CscGAN: Conditional Scale-Consistent Generation Network for Multi-Level Remote Sensing Image to Map Translation

Automatic remote sensing (RS) image to map translation is a crucial technology for intelligent tile map generation. Although existing methods based on a generative network (GAN) generated unannotated maps at a single level, they have limited capacity in handling multi-resolution map generation at different levels. To address the problem, we proposed a novel conditional scale-consistent generation network (CscGAN) to simultaneously generate multi-level tile maps from multi-scale RS images, using only a single and unified model. Specifically, the CscGAN first uses the level labels and map annotations as prior conditions to guide hierarchical feature learning with different scales. Then, a multi-scale discriminator and two multi-scale generators are introduced to describe both high-resolution and low-resolution representations, aiming to improve the similarity of generated maps and thus produce high-quality multi-level tile maps. Meanwhile, a level classifier is designed for further exploring the characteristics of tile maps at different levels. Moreover, the CscGAN is optimized by jointly multi-scale adversarial loss, level classification loss, and scale-consistent loss in an end-to-end manner. Extensive experiments on multiple datasets and study areas demonstrate that the CscGAN outperforms the state-of-the-art methods in multi-level map translation, with great robustness and efficiency.
Sea Ice Concentration Products over Polar Regions with Chinese FY3C/MWRI Data

Polar sea ice affects atmospheric and ocean circulation and plays an important role in global climate change. Long time series sea ice concentrations (SIC) are an important parameter for climate research. This study presents an SIC retrieval algorithm based on brightness temperature (Tb) data from the FY3C Microwave Radiation Imager (MWRI) over the polar region. With the Tb data of Special Sensor Microwave Imager/Sounder (SSMIS) as a reference, monthly calibration models were established based on time–space matching and linear regression. After calibration, the correlation between the Tb of F17/SSMIS and FY3C/MWRI at different channels was improved. Then, SIC products over the Arctic and Antarctic in 2016–2019 were retrieved with the NASA team (NT) method. Atmospheric effects were reduced using two weather filters and a sea ice mask. A minimum ice concentration array used in the procedure reduced the land-to-ocean spillover effect. Compared with the SIC product of National Snow and Ice Data Center (NSIDC), the average relative difference of sea ice extent of the Arctic and Antarctic was found to be acceptable, with values of −0.27 ± 1.85 and 0.53 ± 1.50, respectively. To decrease the SIC error with fixed tie points (FTPs), the SIC was retrieved by the NT method with dynamic tie points (DTPs) based on the original Tb of FY3C/MWRI. The different SIC products were evaluated with ship observation data, synthetic aperture radar (SAR) sea ice cover products, and the Round Robin Data Package (RRDP). In comparison with the ship observation data, the SIC bias of FY3C with DTP is 4% and is much better than that of FY3C with FTP (9%). Evaluation results with SAR SIC data and closed ice data from RRDP show a similar trend between FY3C SIC with FTPs and FY3C SIC with DTPs. Using DTPs to present the Tb seasonal change of different types of sea ice improved the SIC accuracy, especially for the sea ice melting season. This study lays a foundation for the release of long time series operational SIC products with Chinese FY3 series satellites.
Sentinel-2 Remote Sensed Image Classification with Patchwise Trained ConvNets for Grassland Habitat Discrimination

The present study focuses on the use of Convolutional Neural Networks (CNN or ConvNet) to classify a multi-seasonal dataset of Sentinel-2 images to discriminate four grassland habitats in the “Murgia Alta” protected site. To this end, we compared two approaches differing only by the first layer machinery, which, in one case, is instantiated as a fully-connected layer and, in the other case, results in a ConvNet equipped with kernels covering the whole input (wide-kernel ConvNet). A patchwise approach, tessellating training reference data in square patches, was adopted. Besides assessing the effectiveness of ConvNets with patched multispectral data, we analyzed how the information needed for classification spreads to patterns over convex sets of pixels. Our results show that: (a) with an F1-score of around 97% (5 × 5 patch size), ConvNets provides an excellent tool for patch-based pattern recognition with multispectral input data without requiring special feature extraction; (b) the information spreads over the limit of a single pixel: the performance of the network increases until 5 × 5 patch sizes are used and then ConvNet performance starts decreasing.
Hybrid Beamforming and Passive Reflection Design for RIS-Assisted mmWave MIMO Systems

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) communication systems with hybrid analog and digital beamforming architectures have emerged as a key enabling technique for providing very high date rate. Recently, reconfigurable intelligent surface (RIS) has been regarded as a revolutionary technology for future wireless communication systems owing to its capability of tailoring propagation environments. Thus, RIS technique is very promising and suitable for mmWave MIMO communications to tackle the inherent but fatal blockage problem. In this paper, we consider RIS-assisted mmWave MIMO systems, in which both the hybrid beamforming schemes at the transceivers and the RIS employ low-resolution phase shifters (PSs) for practical implementation. Our goal is to design active hybrid beamforming and passive RIS reflection to maximize the spectral efficiency. To this end, we propose to decouple the original problem and separately consider the passive RIS reflection and hybrid beamformer design. A successive refinement optimization method is first developed to design the reflection pattern of RIS. Then with the designed reflection matrix and the resulting effective channel, an efficient algorithm is introduced to solve the joint hybrid precoder and combiner design problem. Simulation results illustrate the significant performance improvement of our proposed hybrid beamforming and RIS reflection design algorithm compared with existing works.
Hyperspectral Image Super-Resolution under the Guidance of Deep Gradient Information

Hyperspectral image (HSI) super-resolution has gained great attention in remote sensing, due to its effectiveness in enhancing the spatial information of the HSI while preserving the high spectral discriminative ability, without modifying the imagery hardware. In this paper, we proposed a novel HSI super-resolution method via a gradient-guided residual dense network (G-RDN), in which the spatial gradient is exploited to guide the super-resolution process. Specifically, there are three modules in the super-resolving process. Firstly, the spatial mapping between the low-resolution HSI and the desired high-resolution HSI is learned via a residual dense network. The residual dense network is used to fully exploit the hierarchical features learned from all the convolutional layers. Meanwhile, the gradient detail is extracted via a residual network (ResNet), which is further utilized to guide the super-resolution process. Finally, an empirical weight is set between the fully obtained global hierarchical features and the gradient details. Experimental results and the data analysis on three benchmark datasets with different scaling factors demonstrated that our proposed G-RDN achieved favorable performance.
Design and Implementation of a Multiport Converter Using Z-Source Converter

In this article, a multiport dc–dc converter is proposed based on the Z-source converter. The proposed structure is composed of paths to flow power from input sources to load and battery. The proposed converter includes a bidirectional port for charging and discharging the battery. Single stage of power transferring from input sources and battery to load causes high-efficiency performance of the converter. The proposed converter contains four ports that can be extended to five ports due to the structure of the Z-source converter and used method for the additional source. Switching topology of the converter makes definition of three duty cycles possible that are used to regulate output voltage and input power of the two sources. The proposed converter presents innovative structures for the additional controllable source and the bidirectional port. As a battery integrated system, the performance of the converter is set based on three operation modes. A high-efficiency method is utilized to increase voltage gain of the converter. The converter can operate properly with variable sources, which is validated by its dynamic and transient responses. In addition, voltage stress on most of the semiconductors is too low. Finally, a prototype for the proposed converter is prepared to validate feasibility and effectiveness of the proposed converter by experimental results.
Physical Retrieval of Rain Rate from Ground-Based Microwave Radiometry

Because of its clear physical meaning, physical methods are more often used for space-borne microwave radiometers to retrieve the rain rate, but they are rarely used for ground-based microwave radiometers that are very sensitive to rainfall. In this article, an opacity physical retrieval method is implemented to retrieve the rain rate (denoted as Opa-RR) using ground-based microwave radiometer data (21.4 and 31.5 GHz) of the tropospheric water radiometer (TROWARA) at Bern, Switzerland from 2005 to 2019. The Opa-RR firstly establishes a direct connection between the rain rate and the enhanced atmospheric opacity during rain, then iteratively adjusts the rain effective temperature to determine the rain opacity, based on the radiative transfer equation, and finally estimates the rain rate. These estimations are compared with the available simultaneous rain rate derived from rain gauge data and reanalysis data (ERA5). The results and the intercomparison demonstrate that during moderate rains and at the 31 GHz channel, the Opa-RR method was close to the actual situation and capable of the rain rate estimation. In addition, the Opa-RR method can well derive the changes in cumulative rain over time (day, month, and year), and the monthly rain rate estimation is superior, with the rain gauge validated R2 and the root-mean-square error value of 0.77 and 22.46 mm/month, respectively. Compared with ERA5, Opa-RR at 31GHz achieves a competitive performance.
Effects of Canopy and Multi-Epoch Observations on Single-Point Positioning Errors of a GNSS in Coniferous and Broadleaved Forests

Global navigation satellite systems (GNSS) can quickly, efficiently, and accurately provide precise coordinates of points, lines, and surface elements, plus complete surveys and determine various boundary lines in forest investigations and management. The system has become a powerful tool for dynamic forest resource investigations and monitoring. GNSS technology plays a unique and important role in estimating timber volume, calculating timber cutting area, and determining the location of virgin forest roads and individual trees in forests. In this study, we quantitatively analyzed the influence of crown size and observation time on the single-point positioning accuracy of GNSS receivers for different forest types. The GNSS located single points for different forest types and crown sizes, enabling the collection of data. The locating time for each tree was more than 10 min. Statistical methods were used to analyze the positioning accuracy of multi-epoch data, and a model was developed to estimate the maximum positioning errors under different forest conditions in a certain positioning time. The results showed that for a continuous positioning time of approximately 10 min, the maximum positioning accuracies in coniferous and broadleaf forests were obtained, which were 12.13 and 15.11 m, respectively. The size of a single canopy had no obvious influence on the single-point positioning error of the GNSS, and canopy density was proven to be closely related to the positioning accuracy of a GNSS. The determination coefficients (R2) in the regression analysis of the general model, coniferous forest model, and broadleaved forest model that were developed in this study were 0.579, 0.701, and 0.544, respectively. These results indicated that the model could effectively predict the maximum positioning error in a certain period of time under different forest types and crown conditions at middle altitudes, which has important guiding significance for forest resource inventories and precise forest management.
In Situ Localized Surface Plasmon Resonance (LSPR) Spectroscopy to Investigate Kinetics of Chemical Bath Deposition of CdS Thin Films

Techniques that can characterize the early stages of thin film deposition from liquid phase processes can aid greatly in our understanding of mechanistic aspects of chemical bath deposition (CBD). Here we have used localized surface plasmon resonance (LSPR) spectroscopy to monitor the in situ kinetics of early stage growth of cadmium sulfide (CdS) thin films on Ag nanoparticle on quartz substrates. Real-time shift during CdS deposition showed that the LSPR wavelength red-shifted rapidly due to random deposition of CdS on the substrate but saturated at longer times. LSPR modeling showed that these features could be interpreted as an initial deposition of CdS islands followed by preferential deposition onto itself. The CdS also showed significantly enhanced Raman signals up to 170 times due to surface-enhanced Raman scattering (SERS) from the CdS/Ag NP regions. The ex situ SERS effect supported the LSPR shift, suggesting that these techniques could be used to understand nucleation and growth phenomena from .
Facile Fabrication of ZSM-5 Zeolite Catalyst with High Durability to Coke Formation during Catalytic Cracking of Paraffins

Post-synthetic HNO3 treatment of ZSM-5 zeolite synthesized in the absence of organic structure-directing agent removes framework Al selectively from the external surface, producing a unique ZSM-5 zeolite catalyst that has very few acid sites on its external surfaces. The resulting external acid sites can be readily deactivated at the early stage of the reaction, giving a potentially long-life catalyst as a result of a high durability to coke formation during the cracking of hexane or other paraffin molecules.
One-Class Classification of Natural Vegetation Using Remote Sensing: A Review

Advances in remote sensing (RS) technology in recent years have increased the interest in including RS data into one-class classifiers (OCCs). However, this integration is complex given the interdisciplinary issues involved. In this context, this review highlights the advances and current challenges in integrating RS data into OCCs to map vegetation classes. A systematic review was performed for the period 2013–2020. A total of 136 articles were analyzed based on 11 topics and 30 attributes that address the ecological issues, properties of RS data, and the tools and parameters used to classify natural vegetation. The results highlight several advances in the use of RS data in OCCs: (i) mapping of potential and actual vegetation areas, (ii) long-term monitoring of vegetation classes, (iii) generation of multiple ecological variables, (iv) availability of open-source data, (v) reduction in plotting effort, and (vi) quantification of over-detection. Recommendations related to interdisciplinary issues were also suggested: (i) increasing the visibility and use of available RS variables, (ii) following good classification practices, (iii) bridging the gap between spatial resolution and site extent, and (iv) classifying plant communities.
Understanding the tensile behaviors of ultra-thin ZnO nanowires via molecular dynamics simulations

By using molecular dynamics (MD) method, the tensile behavior of ultra-thin ZnOnanowires in orientation with three different diameters have been investigated respectively. Through the numerical simulations, the tensile properties including Young’s modulus and yielding stress are obtained as functions of strain rates, temperatures and diameter sizes. The simulation results indicate that the nanowire Young’s modulus and yielding stress would decrease with the increasing of diameter size. In addition, a significant dependence of tensile properties on temperature was also observed with the Young’s modulus and yielding stress decreasing on average by 8% and 18% respectively, while the temperature rises from 0.1 K to 400 K. However, in our simulations the Young’s modulus and yielding stress have no obvious change with different strain rates. Lastly, the structure of ultra-thin ZnOnanowires could be transformed at the strain of ∼7%-11% while the nanowires eventually fracture at the strain of nearly 15%.