esa-sceva/satcom-chunk-collection huggingface dataset:
# Reviewing the Potential of Sentinel-2 in Assessing the Drought
## 1 Introduction
Data fusion methods have overcome the Sentinel-2 thermal band limitation, especially the absence of thermal band limits and its direct applicability on drought to an extent. It was observed that Sentinel-2 data fusion occurred mainly in estimating the drought indicators using the surface energy balance model. In drought-related research, Sentinel-2 has been mainly fused with Landsat-8 [41; 42; 43; 44] and Sentinel series (1&3) [45; 46; 47; 48]. In a comparative approach with Landsat-8, Sentinel-2 was applied to evaluate the vegetation's response to varying soil moistures in extreme drought conditions. The Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 (10 m) and Landsat-8 (30 m) correlated with soil moisture from five different locations at varying depths. Sentinel-2 outperformed Landsat-8 and showed a significant correlation with soil moisture (<30 cm depth). However, a poor correlation was observed over less vegetated areas. The researchers suggest that a minimum of 30-40% of vegetation cover is required for a better correlation [49]. Remotely sensed bio-geophysical variables provide an ample amount of information in assessing the drought. Absorbed Photosynthetically Active Radiation (FAPAR) is one of such variables and a key quantity in carbon cycle estimation [50; 51]. Precipitation, evapotranspiration, and land surface temperature are the key components of drought [52]. To an extent, their variation and influence on physical components of the environment can be measured through various indices such as the normalized difference drought index, the normalized multi-band drought index [53], and the normalized difference water index (NDWI) [54]. Sentinel-2 data was used to delineate the impacts of hydrological drought that happened over Germany in 2018 at a national level. A convolutional neural network-based automated processing chain was applied for extracting the water bodies. Despite varying land and atmospheric conditions, the adopted method produced an overall high accuracy (\(\geq\)0.95) and Kappa Coefficient (\(\geq\)0.89). The variations in water extent mapped and a time series map is produced from the changes delineated between 2017 and 2018 data [55]. This paper focuses on reviewing the potential of Sentinel-2 in assessing drought through vegetation characteristics, soil moisture, evapotranspiration, surface water in cluding wetland, and land use and land cover changes. Therefore, the present paper is categorized into four sections: introduction; properties of Sentinel-2; progress in remote sensing of drought from a climatological and ecological perspective (remote sensing of vegetation in drought, remote sensing of land use and land cover changes, remote sensing of evapotranspiration, remote sensing of soil moisture, remote sensing of surface water, and wetland analysis); summary and conclusion. To the best of our knowledge, this review paper will be the first of its kind in assessing the potential of Sentinel-2 in drought studies. The following Figure 1. Illustrates the methodological concept used in this review paper.
# Virtual Reality-Based Interface for Advanced Assisted Mobile Robot Teleoperation
## 1 Introduction  
### Motivation  
The main area of this work is mobile robots, which play an increasingly primary role in our society. Due to rapid development of AI, powerful lithium batteries, and low-power microchips, mobile robots are becoming cheaper, available to more people, and introduced in various areas of life, taking more significant roles in society and removing labor-intensive jobs in such areas as rescue operations [1; 2; 3], space exploration [4; 5; 6], military application [7; 8] industrial use [9; 10; 11], underwater exploration [12; 13; 14], and healthcare applications [15; 16; 17], among others.  
While many approaches can be found in the literature regarding the automatic control and navigation of this kind of robot, most of the mentioned applications imply the interaction between humans and robots. This collaboration is usually done in such a way that the human guides the robot remotely while the robot navigates in a hostile and/or dangerous environment for the human. However, many approaches do not develop a natural and intuitive interaction for the human [18; 19; 20; 21] and, hence, the resulting human-robot cooperation may be dismissed.  
This paper develops a new virtual reality-based interface for mobile robot navigation in unknown scenarios, providing an intuitive and natural interaction for the user.
## 5 Implementation
### Hardware Setup
#### 5.1.1 Connecting the Sensors  
In designing the hardware, we connected each pair of LDRs and lasers to the Arduino Mega. Each sensor connected to a specific pin on Arduino to detect interruptions in the laser lights caused by passing vehicles; we had to ensure a secure connection to produce accurate sensor readings.  
In this setup, the LDR is connected to the Arduino pin A0 through a 10K resistor, forming a voltage divider. The laser is placed on the side of the road, directly facing the LDR. When the laser beam hits the LDR, the Arduino reads a small voltage from A0, indicating  
Figure 1: Schematic diagram of hardware.  
that the beam has not been interrupted. If a car interrupts the beam, the resistance of the LDR increases, creating a voltage at A0. The Arduino can detect this change and activate various functions, such as changing traffic lights or counting pedestrians. One pair of LDR and laser beam components is illustrated in Figure 2.
## 2 Materials and Methodology
### Effect of Relative Humidity (40%, 50%, and 60%)
#### 3.3.1 Mixed Recycled Concrete Aggregates (MRAs)  
The results for MRA-RP1 and MRA-RP2 (Figure 11) show a reduction in captured CO\({}_{2}\) with decreasing RH. For MRA-RP1, the maximum CO\({}_{2}\) captured was 11.9%, 4.0%, and 2.7%, at 60%, 50%, and 40%, respectively. For MRA-RP2, the maximum was 22.0%, 7.7%, and 2.7% at 60%, 50%, and 40%, respectively. Lastly, for MRA-RP3, the maximum values of CO\({}_{2}\) captured were 12.3%, 24.6%, and 16.4% at 60%, 50%, and 40%, respectively. In the last RA studied (MRA-RP3), the value of CO\({}_{2}\) uptake achieved at 50% RH can be considered as an outlier, considering the other values obtained for MRA-RP3 in Sections 3.1 and 3.2. It is possible that sample contamination occurred during sampling processing. Based on the results of the carbonation of MRAs, reductions in RH hinder the amount of CO\({}_{2}\) captured. As previously mentioned, the carbonation reaction requires the presence of water. When RH content is reduced, less water will be available for the reaction to occur and less calcium will be dissolved from the samples to react with the provided CO\({}_{2}\), which, in turn, only reacts if dissolved in the liquid phase [53]. In addition, the reduction in water content can also cause a reduction in the rate at which CO\({}_{2}\) is captured and converted into calcium carbonate. This trend was found in the samples MRA-RP1 and MRA-RP2. In MRA-RP1, at 60% RH, the maximum CO\({}_{2}\) uptake occurred after 5 h of forced carbonation, while when reducing the RH to 50% and 40%, the time increased to 18 h, indicating that the carbonation rate decreases at lower RH values.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**CO\({}_{2}\) Maximum Uptake (\%)/ Carbonation Period (h)**} \\ \cline{2-9}  & **MRA-RP1** & **MRA-RP2** & **MRA-RP3** & **RCA-RP1** & **RCA-RP2** & **RCA-IW1** & **CA-L** \\ \hline \multirow{3}{*}{RH} & 40\% & 4.7\%/18 h & 2.7\%/12 h & 16.4\%/12 h & 7.6\%/12 h & 4.3\%/3 h & 11.9\%/3 h & - \\ \cline{2-9}  & 50\% & 4.0\%/18 h & 7.7\%/12 h & 24.6\%/12 h & 10.0\%/5 h & 6.9\%/3 h & 18.7\%/8 h & 10.1\%/5 h \\ \cline{1-1} \cline{2-9}  & 60\% & 11.9\%/5 h & 22.0\%/5 h & 12.3\%/5 h & 12.9\%/12 h & 5.2\%/12 h & 34.8\%/12 h & 16.9\%/5 h \\ \hline \hline \end{tabular}
\end{table}
Table 7: Maximum CO\({}_{2}\) capture by weight of cement paste in RA and carbonation time at varying relative humidities.  
Figure 10: Maximum CO\({}_{2}\) uptake content captured by each RA at 60%, 50%, and 40% RH in relation to respective cement paste contents.
# Electronically Excited States of Free Radicals
## 1 Introduction  
Organic compounds are used in chemistry and exist in everyday life. They consist of organic molecules. The vast majority of these molecules exist in a singlet state S\({}_{0}\). Upon the absorption of light, a molecule reaches an excited S\({}_{1}\) state. S\({}_{1}\) can undergo intersystem crossing (ISC) and populate the lowest triplet state T\({}_{1}\). These processes are presented in the Jablonsky diagram [1; 2] (Figure 1). Singlet states are \"close shells\"; they have two electrons with opposite spins on each occupied molecular orbital (MO). A triplet state has two single-occupied MO (SOMO) with parallel spins. That way, the spin angular momentum of the singlet states is \(s=0\); for the T state, \(s=1\) (The spin multiplicity is \(2s+1\). The spin multiplicity is presented as a superscript in a state notation.).  
Figure 1: Jablonsky diagram for a molecule. Abbreviations of the electronic states of a molecule are presented below the horizontal lines reflecting the position of a state. An analog of the Jablonsky diagram for a radical is presented in the same diagram, with notions of the states placed above the horizontal lines.  
The Jablonsky diagram is described in detail in textbooks on photochemistry [1, 2]. Such books and review articles present all of the processes occurring in excited states, as well as the known rare exceptions to the diagram [1, 2]. Figure 1 presents only the absorption of a light quantum and the intersystem crossing (ISC) to the T\({}_{1}\) state initiated by spin-orbit coupling (SOC) [3]. The probabilities of the occurrences of the processes in the excited states of molecules obviously vary from 0 to 1.
## 3 Results
### GC-IMS Analysis
#### 3.2.2 Olfactory Results  
According to the combination of olfactory time and gas chromatography-retention time analysis, 31 compounds were screened out, including five alcohols (16.13%), three esters (9.68%), eight aldehydes (25.81%), two ketones (6.45%), six pyrazines heterocyclic compounds (19.35%), one furan heterocyclic compounds (3.23%), two hydrocarbons (6.45%), and four others (12.90%) (Table S3). Among them, 23 aroma compounds exhibited an OAV greater than 1 (Table 2). These compounds, which possess distinct aromas, are considered as significantly contributing to the overall aroma profile of yellow horn seed oil. Strong oily, fatty, and nutty odors are provided by the aroma compounds, such as 2-octenal, nonanal, and 2,4-decaional in yellow horn seed oil. The grassy, green, and fruity aromas are mainly provided by aroma compounds, such as hexanol, 1-octen-3-ol, hexanal, 2-pentylfuran, and ethyl 2-methylbutyrate. Studies have reported that 2,4-decaional provided a pleasant oily fragrance in fragrant rapeseed oil [57]. Similarly, nonanal is an oxidation product of oleic acid and contributes an oily aroma to food, as reported in [32; 58]. (E)-2-octenal, which is a product of the oxidation of linoleic acid, was detected in nut oil [59]. (E)-2-octenal presented elevated content and low odor threshold, causing the nut oil to have its distinctive fatty aroma. 1-octen-3-ol and 2-pentylfuran were also detected in walnut oil and provided a green aroma in relation to walnut oil [60]. In this study, the synergistic effect of these aroma compounds contributed to the development of a robust aroma profile in hot-pressed yellow horn seed oil.
## 3 Analytical Results  
### Project Introduction  
Located in western Sichuan province, China, a shallow-buried tunnel through soft-hard stratum junction is considered in this paper. The rock mass along the tunnel is organized into two classes with grade IV and VI (the rock is worse with growing grade numbers) according to the Chinese Code for Design of Road Tunnel (JGJ 70-2004). In order to reduce the influence of the sharp change of ground properties on the tunnel responses, the transition zone and flexible connections were suggested to be adopted for the project. Note that the positions of Joint 1, Joint 2 and Joint 3 are located between Region 1 and Region 2, between the hard layer and soft layer, and between Region 2 and Region 3, correspondingly. In light of on-site geologic investigation of the tunnel, the shear velocities of the hard layer (Rock 1) and the soft layer (Rock 2) are 687 m/s and 430 m/s, respectively.  
The spring constants of foundation are \(K_{1}\) = 0.63 GPa in Rock 1 and \(K_{2}\) = 0.36 GPa in Rock 2, which are obtained using following equation [15]:  
\[K=\frac{16\pi\rho_{soil}V_{s}^{2}(1-\
u)}{3-4\
u}\frac{d}{\lambda} \tag{22}\]  
where \(\rho\) and \(\
u\) are density and Poisson's ratio of soil, respectively, vs. is shear velocity, \(d\) is the diameter of a tunnel, and \(\lambda\) is wavelength.  
In addition, the density and Poisson's ratio of the Rock 1 are 2200 kg/m\({}^{3}\) and 0.3, respectively. The density and Poisson's ratio of the Rock 2 are 1800 kg/m\({}^{3}\) and 0.35, correspondingly. The tunnel is subjected to a sinusoidal shear motion with wavelength \(\lambda_{1}\) = 344 m in Rock 1 and \(\lambda_{2}\) = 215 m in Rock 2. Note that the seismic input motion is assumed as an incident sinusoidal wave. The East-West(EW) component of seismic waves in Mao County is imposed at the bottom of the surrounding rock, and the peak ground acceleration is 0.4 g (g is the gravitational acceleration), as shown in Figure 3. The maximum free transverse displacement in Rock 1 is \(u_{\text{max1}}\) = 0.017 m, and the maximum displacement in Rock 2 is \(u_{\text{max2}}\) = 0.029 m, which were analyzed from DEEPSOIL. The cross sections of the tunnel segments are nearly circular. The outer diameter of segment 1 is 10.8 m and the outer diameter of the rest of the segments is 11.4 m, which are determined by equivalent area. The inner diameter of all the segments is 9.6 m. The moment of inertia of segment 1 is 242 m\({}^{4}\), and the moment of inertia of segment 2, segment 3 and segment 4 is the same, with 398 m\({}^{4}\). The Young's modulus and the shear correction coefficient of tunnel are \(E\) = 30 GPa and \(\kappa\) = 0.52, respectively.
# Study on Compressive Strength and Water Permeability of Steel Slag-Fly Ash Mixed Permeable Brick
## 1 Introduction  
In recent years, the rapid urbanization has led to a large area of urban surface covered by impervious materials such as concrete, resulting in a significant increase in surface rainwater runoff, which not only aggravates the pollutant load of the receiving water body, but also makes urban flood control and urban stormwater management face more severe challenges [1; 2]. On the other hand, China, as an industrial power, has a large output of steel slag and fly ash. Although it has been applied in the fields such as building materials, its second utilization rate is still at a low level [3; 4]. Existing studies found that steel slag and fly ash have a significant removal effect on phosphorus in wastewater [5]. Therefore, this kind of industrial waste residue can be mixed into permeable brick by certain technical means and used for urban pavement or riverbank paving, which can not only reduce rainwater runoff and reduce phosphorus pollution, but also recycle industrial waste [6; 7; 8].  
Based on the above background, the experiment used fly ash and steel slag as the base material, basalt crushed stone and cement as the aggregate and adhesives supplemented by a plant-type foaming agent. The constituents were stirred and mixed in the container, then injected into the formwork for forming and curing. The optimum material mix ratio was determined by investigating its influence on the permeability and strength of the permeable brick. On this basis, the intensive research on the compressive strength and water permeability coefficient of the permeable brick after forming was carried out with reference to the relevant ASTM standards and regulations.
## 3 Materials and Methods
### The Framework of DG-STFN  
Figure 1 shows the framework of our proposed DG-STFN, which consists of a dynamic graph generation module (DG) and a spatio-temporal feature network (STFN). The DG is used to explore the global dependencies and local dynamics of the correlations between the historical information of the nodes in the graph and generate the graph adjacency matrix and graph convolution kernel. The whole process is completely data-driven. The STFN mainly consists of two spatio-temporal feature blocks (STF Block) for extracting temporal and spatial features from the data. The detailed description of each component is shown in the following.
# Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images
## 2 Materials and Methods
### Methodology
#### 2.3.3 Accuracy Assessment  
For the results of winter wheat extraction, this study used independent validation samples to calculate the confusion matrix for evaluation. The Overall Accuracy (OA), User's Accuracy (UA), Producer's Accuracy (PA), F1-score (Equation (1)) and Kappa coefficient can be obtained by calculating the confusion matrix, which can be used to evaluate the results in multiple perspectives.  
\[\text{F1-score}=2\times\frac{\text{PA}\times\text{UA}}{\text{PA}+\text{UA}} \tag{1}\]
## 2 Kinematic Modeling  
### Coordinate Frame  
A prototype docking motion simulation system was built at the Harbin Institute of Technology to simulate on-orbit docking motion of the spacecrafts and test the comprehensive performance of the docking mechanisms. The parallel robot consists of a fixed base, a mobile platform, six identical length changeable hydraulic cylinders, and twelve Hooke's joints. The parallel robot has six degrees of freedom.  
The coordinate frame is established and shown in Figure 1 to analyze the kinematic performance of the parallel robot. The static coordinate frame \(\text{O}_{\text{B}}\text{-}xyz\) is set up at the hexagon center of the fixed base. The dynamic coordinate frame \(\text{O}_{\text{D}}\text{-}xyz\) is set up at the hexagon center of the mobile platform. The motion output of the parallel robot is expressed by the pose of the mobile platform, denoted by position vector \(\mathbf{t}\) (\(\mathbf{t}=[x\,y\,z]\)) and orientation vector \(\boldsymbol{\omega}\) (\(\boldsymbol{\omega}=[\alpha\,\beta\,\gamma]\)).
## 3 Results
### Shelf Life of Untreated and Treated Tomato Samples  
The dependence of shelf life on storage temperature based on microbial growth and sensory deterioration (overall acceptance) as calculated from Equations (7) and (8) is presented in Figure 7a,b, for untreated, OD-treated, PEF-OD-treated and HP-OD-treated samples. A marked difference can be observed overall between untreated tomatoes and tomatoes that have undergone osmotic dehydration. For both characteristics, the shelf life curves are shifted towards higher storage temperatures, indicating that all treated products are significantly less perishable compared to the fresh-cut produce.  
Based on the calculation of shelf life from the two quality parameters (sensory deterioration and microbial growth), it was evident for all samples that the defining characteristic for shelf life was sensory acceptance. The shelf life for each final product and each storage temperature is presented in Table 3. At typical chilled storage temperatures (5 \({}^{\circ}\)C), it was observed that all OD treated samples exhibited a significant (\(p<0.05\)) shelf life extension by up to four-fold. The increase in shelf life of nonthermally treated samples could be attributed to the solids uptake, lower water activity and pH reduction. It must also be noted that the required duration of the OD process for PEF and HP pretreated samples was half (90 min vs. 180 min) compared to the untreated samples which were not able to achieve adequate moisture loss even after 3 h of OD. Therefore, apart from offering a significant shelf life extension, nonthermal treatments can have a profound impact on the duration of dehydration, possibly leading to energy savings and increases in productivity. The increased resilience of the product's quality at higher temperatures means that food waste could be reduced along the cold chain, as the quality deterioration of the product would be greatly limited. These observations might also pave the way for incorporating fresh-like vegetables, such as cherry tomato, into long shelf life, ready-to-eat products which, without processing, would not be an option.
# GAN-Based Map Generation Technique of Aerial Image Using Residual Blocks and Canny Edge Detector
## 4 Experiments  
### Datasets and Environments  
The Maps dataset [27] used in this paper is constructed using aerial image and map tiles from Google Maps, each with a resolution of 600 \(\times\) 600 pixels. As shown in Table 2, the dataset is divided for training and testing, consisting of 4384 images set at a 1:1 ratio. The dataset includes aerial and map images in and around New York City. This dataset provides diverse scenarios applicable to image-to-image translation research, allowing the model to train and evaluate the visual style of maps during training. It is based on the publicly available dataset from the Pix2 Pix [6] project. The dataset images were resized from the original 600 \(\times\) 600 resolution to 256 \(\times\) 256 using OpenCV functionality.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Train** & **Test** & **Sum** \\ \hline Aerial Image & 1096 & 1096 & 2192 \\ Map Image & 1096 & 1096 & 2192 \\ Sum & 2192 & 2192 & 4384 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed information of datasets.  
All experiments are conducted on an Ubuntu 18.04 LTS operating system. An NVIDIA GeForce RTX 3090 graphics card with 24 GB of memory is utilized for training and inference. The programming is based on Python version 3.9 and the model implementation employs PyTorch framework version 1.10.1. Training is performed over 100 epochs, with an initial learning rate 0.0002. The learning rate is maintained for the first 50 epochs and then linearly decreases to zero over the remaining epochs until epoch 100. This approach gradually optimizes the model's performance. The Adam optimizer is selected with \(\beta_{1}=0.9\) and \(\beta_{2}=0.999\). Given the batch size of 1, the training is effectively done using Stochastic Gradient Descent. The model's input and output image sizes are set to 256 \(\times\) 256 pixels. In the loss function, the hyperparameters are set as \(\lambda_{D}=0.5\), \(\lambda_{1}=10\), and \(\lambda_{\text{Edge}}=100\). For the Canny edge detection, weak edges are set at 10% of the maximum pixel value (255), and strong edges are set at 30%.