Miracle-dz/UniFAR-FTUs huggingface dataset:
Finding the Optimal Multimodel Averaging Method for Global Hydrological Simulations

Global gridded precipitations have been extensively considered as the input of hydrological models for runoff simulations around the world. However, the limitations of hydrologic models and the inaccuracies of the precipitation datasets could result in large uncertainty in hydrological forecasts and water resource estimations. Therefore, it is of great importance to investigate the hydrological value of a weighted combination of hydrological models driven by different precipitation datasets. In addition, due to the diversities of combination members and climate conditions, hydrological simulation for watersheds under different climate conditions may show various sensitivities to the weighted combinations. This study undertakes a comprehensive analysis of various multimodel averaging methods and schemes (i.e., the combination of the members in averaging) to identify the most skillful and reliable multimodel averaging application. To achieve this, four hydrological models driven by six precipitation datasets were used as averaging members. The behaviors of 9 averaging methods and 11 averaging schemes in hydrological simulations were tested over 2277 watersheds distributed in different climate regions in the world. The results show the following: (1) The multi-input averaging schemes (i.e., members consist of one model driven by multiple precipitation datasets) generally perform better than the multimodel averaging schemes (i.e., members consist of multiple models driven by the same precipitation dataset) for each averaging method; (2) The use of multiple members can improve the averaging performances. Six averaging members are found to be necessary and advisable, since using more than six members only imrpoves the estimation results slightly, as compared with using all 24 members; (3) The advantage of using averaging methods for hydrological modeling is region dependent. The averaging methods, in general, produced the best results in the warm temperate region, followed by the snow and equatorial regions, while a large difference among various averaging methods is found in arid and arctic regions. This is mainly due to the different averaging methods being affected to a different extent by the poorly performed members in the arid and arctic regions; (4) the multimodel superensemble method (MMSE) is recommended for its robust and outstanding performance among various climatic regions.
Remote Sensing Image Scene Classification via Label Augmentation and Intra-Class Constraint

In recent years, many convolutional neural network (CNN)-based methods have been proposed to address the scene classification tasks of remote sensing images. Since the number of training samples in RS datasets is generally small, data augmentation is often used to expand the training set. It is, however, not appropriate when original data augmentation methods keep the label and change the content of the image at the same time. In this study, label augmentation (LA) is presented to fully utilize the training set by assigning a joint label to each generated image, which considers the label and data augmentation at the same time. Moreover, the output of images obtained by different data augmentation is aggregated in the test process. However, the augmented samples increase the intra-class diversity of the training set, which is a challenge to complete the following classification process. To address the above issue and further improve classification accuracy, Kullback–Leibler divergence (KL) is used to constrain the output distribution of two training samples with the same scene category to generate a consistent output distribution. Extensive experiments were conducted on widely-used UCM, AID and NWPU datasets. The proposed method can surpass the other state-of-the-art methods in terms of classification accuracy. For example, on the challenging NWPU dataset, competitive overall accuracy (i.e., 91.05%) is obtained with a 10% training ratio.
Comparison of Modelling Strategies to Estimate Phenotypic Values from an Unmanned Aerial Vehicle with Spectral and Temporal Vegetation Indexes

Aboveground dry weight (AGDW) and leaf area index (LAI) are indicators of crop growth status and grain yield as affected by interactions of genotype, environment, and management. Unmanned aerial vehicle (UAV) based remote sensing provides cost-effective and non-destructive methods for the high-throughput phenotyping of crop traits (e.g., AGDW and LAI) through the integration of UAV-derived vegetation indexes (VIs) with statistical models. However, the effects of different modelling strategies that use different dataset compositions of explanatory variables (i.e., combinations of sources and temporal combinations of the VI datasets) on estimates of AGDW and LAI have rarely been evaluated. In this study, we evaluated the effects of three sources of VIs (visible, spectral, and combined) and three types of temporal combinations of the VI datasets (mono-, multi-, and full-temporal) on estimates of AGDW and LAI. The VIs were derived from visible (RGB) and multi-spectral imageries, which were acquired by a UAV-based platform over a wheat trial at five sampling dates before flowering. Partial least squares regression models were built with different modelling strategies to estimate AGDW and LAI at each prediction date. The results showed that models built with the three sources of mono-temporal VIs obtained similar performances for estimating AGDW (RRMSE = 11.86% to 15.80% for visible, 10.25% to 16.70% for spectral, and 10.25% to 16.70% for combined VIs) and LAI (RRMSE = 13.30% to 22.56% for visible, 12.04% to 22.85% for spectral, and 13.45% to 22.85% for combined VIs) across prediction dates. Mono-temporal models built with visible VIs outperformed the other two sources of VIs in general. Models built with mono-temporal VIs generally obtained better estimates than models with multi- and full-temporal VIs. The results suggested that the use of UAV-derived visible VIs can be an alternative to multi-spectral VIs for high-throughput and in-season estimates of AGDW and LAI. The combination of modelling strategies that used mono-temporal datasets and a self-calibration method demonstrated the potential for in-season estimates of AGDW and LAI (RRMSE normally less than 15%) in breeding or agronomy trials.
A 3D Cascaded Spectral-Spatial Element Attention Network for Hyperspectral Image Classification

Most traditional hyperspectral image (HSI) classification methods relied on hand-crafted or shallow-based descriptors, which limits their applicability and performance. Recently, deep learning has gradually become the mainstream method of HSI classification, because it can automatically extract deep abstract features for classification. However, it remains a challenge to learn more meaningful features for HSI classification from a small training sample set. In this paper, a 3D cascaded spectral–spatial element attention network (3D-CSSEAN) is proposed to solve this issue. The 3D-CSSEAN integrates the spectral–spatial feature extraction and attention area extraction for HSI classification. Two element attention modules in the 3D-CSSEAN enable the deep network to focus on primary spectral features and meaningful spatial features. All attention modules are implemented though several simple activation operations and elementwise multiplication operations. In this way, the training parameters of the network are not added too much, which also makes the network structure suitable for small sample learning. The adopted module cascading pattern not only reduces the computational burden in the deep network but can also be easily operated via plug–expand–play. Experimental results on three public data sets show that the proposed 3D-CSSEAN achieved comparable performance with the state-of-the-art methods.
Change Detection in Urban Point Clouds: An Experimental Comparison with Simulated 3D Datasets

In the context of rapid urbanization, monitoring the evolution of cities is crucial. To do so, 3D change detection and characterization is of capital importance since, unlike 2D images, 3D data contain vertical information of utmost importance to monitoring city evolution (that occurs along both horizontal and vertical axes). Urban 3D change detection has thus received growing attention, and various methods have been published on the topic. Nevertheless, no quantitative comparison on a public dataset has been reported yet. This study presents an experimental comparison of six methods: three traditional (difference of DSMs, C2C and M3C2), one machine learning with hand-crafted features (a random forest model with a stability feature) and two deep learning (feed-forward and Siamese architectures). In order to compare these methods, we prepared five sub-datasets containing simulated pairs of 3D annotated point clouds with different characteristics: from high to low resolution, with various levels of noise. The methods have been tested on each sub-dataset for binary and multi-class segmentation. For supervised methods, we also assessed the transfer learning capacity and the influence of the training set size. The methods we used provide various kinds of results (2D pixels, 2D patches or 3D points), and each of them is impacted by the resolution of the PCs. However, while the performances of deep learning methods highly depend on the size of the training set, they seem to be less impacted by training on datasets with different characteristics. Oppositely, conventional machine learning methods exhibit stable results, even with smaller training sets, but embed low transfer learning capacities. While the main changes in our datasets were usually identified, there were still numerous instances of false detection, especially in dense urban areas, thereby calling for further development in this field. To assist such developments, we provide a public dataset composed of pairs of point clouds with different qualities together with their change-related annotations. This dataset was built with an original simulation tool which allows one to generate bi-temporal urban point clouds under various conditions.
MAMOTH: An Earth Observational Data-Driven Model for Mosquitoes Abundance Prediction

Mosquito-Borne Diseases (MBDs) are known to be more prevalent in the tropics, and yet, in the last two decades, they are spreading to many other countries, especially in Europe. The set (volume) of environmental, meteorological and other spatio-temporally variable parameters affecting mosquito abundance makes the modeling and prediction tasks quite challenging. Up to now, mosquito abundance prediction problems were addressed with ad-hoc area-specific and genus-tailored approaches. We propose and develop MAMOTH, a generic and accurate Machine Learning model that predicts mosquito abundances for the upcoming period (the Mean Absolute Error of the predictions do not deviate more than 14%). The designed model relies on satellite Earth Observation and other in-situ geo-spatial data to tackle the problem. MAMOTH is not sitenor mosquito genus-dependent; thus, it can be easily replicated and applied to multiple cases without any special parametrization. The model was applied to different mosquito genus and species Culex spp. as potential vectors for West Nile Virus, Anopheles spp. for Malaria and Aedes albopictus for Zika/Chikungunya/Dengue) and in different areas of interest (Italy, Serbia, France, Germany). The results show that the model performs accurately and consistently for all case studies. Additionally, the evaluation of different cases, with the model using the same principles, provides an opportunity for multi-case and multi-scope comparative studies.
Estimating Tree Diameters from an Autonomous Below-Canopy UAV with Mounted LiDAR

Below-canopy UAVs hold promise for automated forest surveys because their sensors can provide detailed information on below-canopy forest structures, especially in dense forests, which may be inaccessible to above-canopy UAVs, aircraft, and satellites. We present an end-to-end autonomous system for estimating tree diameters using a below-canopy UAV in parklands. We used simultaneous localization and mapping (SLAM) and LiDAR data produced at flight time as inputs to diameter-estimation algorithms in post-processing. The SLAM path was used for initial compilation of horizontal LiDAR scans into a 2D cross-sectional map, and then optimization algorithms aligned the scans for each tree within the 2D map to achieve a precision suitable for diameter measurement. The algorithms successfully identified 12 objects, 11 of which were trees and one a lamppost. For these, the estimated diameters from the autonomous survey were highly correlated with manual ground-truthed diameters (R2=0.92, root mean squared error = 30.6%, bias = 18.4%). Autonomous measurement was most effective for larger trees (>300 mm diameter) within 10 m of the UAV flight path, for medium trees (200–300 mm diameter) within 5 m, and for trees with regular cross sections. We conclude that fully automated below-canopy forest surveys are a promising, but still nascent, technology and suggest directions for future research.
Spatially Modeling the Synergistic Impacts of Global Warming and Sea-Level Rise on Coral Reefs in the South China Sea

Global warming and sea-level rise (SLR) induced by rising atmospheric CO2 concentrations can cause coral bleaching, death, and submergence of the world’s coral reefs. Adopting the GIS and RS methods, we modeled how these two stressors combine to influence the future growth of the atolls and table reefs of three archipelagoes in the South China Sea (SCS), based on geomorphic and ecological zones. A large-scale survey of the coral communities in Xisha Islands in 2014, Dongsha Islands in 2014–2016 and Nansha Islands in 2007 provided zone-specific process datasets on the range of reef accretion rates. Sea surface temperature and extreme (minimum and maximum) SLR data above 1985–2005 levels by 2100 in the SCS were derived from the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) models forced with the Representative Concentration Pathways (RCPs). Our model projected that: (1) the Xisha Islands and Dongsha Islands may have a better growth status, because the reef flat biotic sparse zone may be recolonized with hard coral and become a biotic dense zone; (2) the southern Nansha Islands reefs have a risk of stopping growing due to their earlier annual bleaching years. The increasing of water depths of these reefs is stronger in the RCP with more emissions. Our approach offers insights into the best-case and worst-case impacts of two global environmental pressures on potential future reef growth under a changing climate.
Design and synthesis of cauliflower-shaped hydroxyl functionalized core-shell polymer

Conventional crosslinked polymers and homopolymers both have their own limitations. As a result, core–shell polymer was synthesized to obtain cauliflower-shaped and highly hydroxyl functionalized polymer. For the core, acrylate-based copolymers were synthesized by varying crosslinkers and porogens at different crosslink density. Owing to high surface area (554 m2/g), poly(MMA-co-DVB) was used as a core and low-molecular weight (24,600 g/mol) poly(GMA) was used as a shell in core–shell approach. Average particle sizes of the core polymers were in the range of 15–75 μm. In order to evaluate reactivity efficiency of core–shell polymer, hydroxyl content was evaluated with a value of 3.97 mmol/g. Importantly, hydroxyl content demonstrated the successful increase in reactive sites of the core–shell polymer over conventional crosslinked hydroxyl polymer. Notably, synthesized core–shell polymer has more surface area and pore volume which substantially attributes for better polymer efficiency during application. Scanning electron microscopy images revealed the spherical, uniform, and slightly conglomerated properties of core–shell polymer. Due to higher reactivity, insolubility, and more surface area of hydroxyl functionalized core–shell polymer, its use become inevitably essential.
Analytical possibilities for the relative estimation of the antioxidative capacity of honey varieties harvested in different regions of Serbia

Two different approaches, spectroscopic and electrochemical, were applied for the rough determination of the antioxidative capacity of honey samples. Honey samples of diverse botanical origin were collected in different geographical regions of Serbia. The total phenolic content (TPC) was deter- mined by the Folin-Ciocalteu method. Cyclic voltammograms on a glassy car- bon electrode in KCl supporting electrolyte were used to check the electrode sensitivity to the presence of honey. In order to calculate the Trolox equivalent antioxidant capacity (TEAC) of the studied honey samples, cyclic voltammo- grams were recorded for the Trolox standard. The results were expressed as µmol of Trolox equivalents per kg of sample (µmol TE kg -1 ). Good correl- ations were observed between the cyclic voltammetry data and the TPC deter- mined by the Folin-Ciocalteu method and the radical scavenging activity (RSA) determined using the DPPH·(1,1-diphenyl-2-picrylhydrazyl) radical test. Cyclic voltammetry appears to be a highly attractive alternative method for a rapid estimation of the antioxidative capacity of honeys. It was found that polyfloral honey samples had the highest, whereas acacia honey showed the lowest values of TPC.
Deep Learning for Automated Detection and Identification of Migrating American Eel Anguilla rostrata from Imaging Sonar Data

Adult American eels (Anguilla rostrata) are vulnerable to hydropower turbine mortality during outmigration from growth habitat in inland waters to the ocean where they spawn. Imaging sonar is a reliable and proven technology for monitoring of fish passage and migration; however, there is no efficient automated method for eel detection. We designed a deep learning model for automated detection of adult American eels from sonar data. The method employs convolution neural network (CNN) to distinguish between 14 images of eels and non-eel objects. Prior to image classification with CNN, background subtraction and wavelet denoising were applied to enhance sonar images. The CNN model was first trained and tested on data obtained from a laboratory experiment, which yielded overall accuracies of >98% for image-based classification. Then, the model was trained and tested on field data that were obtained near the Iroquois Dam located on the St. Lawrence River; the accuracy achieved was commensurate with that of human experts.
A bibliometric analysis of race-related research in LIS

The purpose of this work in progress is to quantify the amount of attention given to questions of racial inequity experienced by BIPOC in LIS research. We find that despite a recent surge in BIPOC-related research output, the publications are low in numbers and tend to receive fewer citations than other work in the same research area. BIPOC-related research is present but unevenly distributed across several areas of the field. These trends may help create and sustain momentum towards addressing the persistent lack of diversity and equity in LIS.
Integrating Aerial LiDAR and Very-High-Resolution Images for Urban Functional Zone Mapping

This study presents a new approach for Urban Functional Zone (UFZ) mapping by integrating two-dimensional (2D) Urban Structure Parameters (USPs), three-dimensional (3D) USPs, and the spatial patterns of land covers, which can be divided into two steps. Firstly, we extracted various features, i.e., spectral, textural, geometrical features, and 3D USPs from very-high-resolution (VHR) images and light detection and ranging (LiDAR) point clouds. In addition, the multi-classifiers (MLCs), i.e., Random Forest, K-Nearest Neighbor, and Linear Discriminant Analysis classifiers were used to perform the land cover mapping by using the optimized features. Secondly, based on the land cover classification results, we extracted 2D and 3D USPs for different land covers and used MLCs to classify UFZs. Results for the northern part of Brooklyn, New York, USA, show that the approach yielded an excellent accuracy of UFZ mapping with an overall accuracy of 91.9%. Moreover, we have demonstrated that 3D USPs could considerably improve the classification accuracies of UFZs and land covers by 6.4% and 3.0%, respectively.
Retrieving Doppler Frequency via Local Correlation Method of Segmented Modeling

The high accuracy radio Doppler frequency is critical for navigating a deep space probe and for planetary radio science experiments. In this paper, we propose a novel method based on the local correlation of segmented modeling to retrieve Doppler frequency by processing an open-loop radio link signal from one single ground station. Simulations are implemented, which prove the validity of this method. Mars Express (MEX) and Tianwen-1 observation experiments were carried out by Chinese Deep Space Stations (CDSS). X-band Doppler frequency observables were retrieved by the proposed method to participate in orbit determination. The results show that the accuracy of velocity residuals of orbit determination in open-loop mode is from 0.043 mm/s to 0.061 mm/s in 1 s integration; the average accuracy of Doppler frequency is about 3.3 mHz in 1 s integration and about 0.73 mHz in 60 s integration. The Doppler accuracy here is better than that of the digital baseband receiver at CDSS. The algorithm is efficient and flexible when the deep space probe is in a high dynamic mode and in low signal to noise ratio (SNR). This will benefit Chinese deep space exploration missions and planetary radio science experiments.
Microalgae Technology: A Patent Survey

Abstract For a long time now, it is known that an infinite number of compounds can be obtained from microalgae due to their promising composition. This fact is used by several companies to produce at medium-scale different high value products such as carotenoids. Furthermore, this last decade research in this topic has significantly increased given the potential solution these microorganisms present to meet World environmental agreements regarding CO2 emissions. However, the culture of large-scale microalgae in order to obtain products which are required in huge quantities (e.g., oil for manufacturing biodiesel) and at the same time removing pollutants appeared as a difficult task due to the great number of variables that must be taken into account. The photobioreactor design for cultivating the microalgae is perhaps the most important task, but there are other bottle-necks, such as the harvesting system, cleaning system, control system, the chosen microalgae, and the extraction of products from the microalgae. This review focuses on around 200 patents covering microalgae technology, from different photobioreactors designs to microalgae applications, in order to construct a perfect large-scale facility removing CO2 in the process.