esa-sceva/satcom-chunk-collection huggingface dataset:
# Evapotranspiration in Dynamic of Highland Barley Using PML ET Product in Tibet
###### Abstract  
Highland barley is the unique germplasm resource and dominant crop in Tibet with low-level precipitation and a severe shortage of available water resources. Understanding the characteristics and dynamics of evapotranspiration (ET) components (vegetation transpiration (Ec), soil evaporation (Es), and canopy interception evaporation (Ei)) of highland barley can help better optimize water management practices. The seasonal and interannual variations in ET components of highland barley were investigated using the PML-V2 ET product during 2001-2020. The results suggested that Es was the most important ET component and accounted for 77% of total ET for highland barley in Tibet. ET components varied obviously over the altitude, Es, and Es/ET ratio; a decreasing trend was observed with the increase in altitude from 3500 m to 3800 m and then this changed to an increasing trend until reaching the altitude of 4100 m, while Ec, Ei, and their ratios presented an opposite changing pattern to that of Es. Seasonal variation in daily ET components of highland barley displayed a parabolic pattern, peaked in August, while the temporal distributions differed considerably among different ET component ratios. The seasonal variations in ET components were correlated significantly with air temperature, relative humidity, and precipitation, while ET components ratios were more influenced by the environment, irrigation practice, and management rather than meteorological variables. Es and its ratio in highland barley decreased significantly during 2001-2020, while the Ec/ET ratio generally showed an opposite trend to the Es/ET ratio, and Ei and its ratio presented an insignificantly decreasing trend. The interannual variations in ET components were not correlated significantly with meteorological variables, while Ei was more influenced by meteorological variables, especially the precipitation characteristics.  
evapotranspiration components; temporal variation; meteorological variables; PML V2 2021134884. [[https://doi.org/10.3390/rs13234884](https://doi.org/10.3390/rs13234884)]([https://doi.org/10.3390/rs13234884](https://doi.org/10.3390/rs13234884))
## 3 Scientific Background
### Black, Elemental and Organic Carbon (BC, EC and OC)  
EC specifically refers to materials constituted by carbon atoms only. Amorphous and graphitic EC were observed in particles emitted by combustion processes [80]. However, nearly all available EC data result from thermal and thermal-optical measurements (applied to PM samples collected on filters). Such measurements rely on EC's refractory property, i.e., EC does not volatilise or combust below given temperature thresholds. Various purely thermal and thermal-optical methods were used in recent decades. These methods generally lead to significantly different EC values. In all thermal and thermal-optical methods, EC is operationally defined. A European standard [97; 98] for determining EC in PM\({}_{2,5}\) deposited on filters was recently developed (EN 16909), which is not applied worldwide.  
BC has been used for years as a catch-all term to describe a variety of types of carbonaceous particles [99]. Nowadays, the definition of BC as the carbonaceous component of atmospheric particulate matter that absorbs all wavelengths of solar radiation present in the troposphere (280-2500 nm) is generally accepted [100]. BC mass concentrations cannot be directly measured. In contrast, a vast range of methods is available for determining the aerosol light absorption cross section, representing the aerosol \"blackness\". Absorption coefficient data (M m\({}^{-1}\)) can be converted to equivalent black carbon (eBC) mass concentration data (\(\mu\)g m\({}^{-3}\)) by applying an a priori chosen mass absorption cross section (MAC, m\({}^{2}\) g\({}^{-1}\)). Converting aerosol light absorption data to eBC means neglecting that not all light-absorbing aerosols are BC, and MAC values are highly variable with time and space [101]. In addition to \"light absorption\" methods, combined measurements of single-particle laser-induced incandescence can lead to refractory black carbon (rBC) mass concentrations by simultaneously determining the refractory mass of the incandescent particle and its chemical composition [102].  
OC represents the bunch of C atoms belonging to organic molecules. In contrast with EC, organics can be volatilised in inert atmospheres at temperatures ranging from ambient to a few hundreds of Kelvin. Most OC compounds absorb IR and UV radiation strongly but are relatively transparent to visible (VIS, 400-700 nm) and near-IR (700-2500 nm) wavelengths. However, certain types of OC absorb radiation efficiently in the near-UV (300-400 nm) and VIS ranges [72].
# Diffusion and Consolidation of Slag-Based Geopolymer for Concrete Payment Rehabilitation
## 3 Results and Discussion
### Microscopic Test
#### 3.5.2 Microscopic Analysis of Consolidation Characteristics  
In order to further study the consolidation characteristics of the geopolymer slurry of the aggregate with different particle sizes, samples were taken from the core area of the slurry bulbs of specimens with three single-graded aggregates of 9.5-4.75 mm, 4.75-2.36 mm, and 2.36-1.18 mm under grouting pressures of 0.4 MPa, 0.8 MPa, and 1.0 MPa, respectively. The microstructure of the consolidated slurry was observed by SEM.  
As shown in Figure 18, under the condition of a 1.0 MPa grouting pressure for the aggregate with a 2.36-1.18 mm particle size, the aggregate was wrapped by the geopolymer slurry. Because the porosity of the 2.36-1.18 mm aggregate was small, the diffusion of the slurry between the aggregate was affected. The consolidated slurry-wrapped aggregate was slightly loose, and there were many granular gels on the surface of the consolidated slurry. As shown in Figure 19, under a grouting pressure of 0.8 MPa, the pores of the aggregate with a 4.75-2.36 mm particle size were filled with the geopolymer slurry. The increase in the porosity of the aggregate facilitated the diffusion of the geopolymer slurry, resulting in a denser structure after consolidation. Cracks appeared locally at the bonding interface between the aggregate and geopolymer slurry, but the width of the cracks was  
Figure 16: SEM image and EDS spectra of geopolymer-consolidated 4.75–2.36 mm aggregate: (**a**) SEM image; and (**b**) EDS spectra analysis.  
Figure 17: SEM image and EDS spectra of geopolymer-consolidated 9.5–4.75 mm aggregate: (**a**) SEM image; and (**b**) EDS spectra analysis.  
Figure 15: SEM image and EDS spectra of geopolymer-consolidated 2.36–1.18 mm aggregate: (**a**) SEM image; and (**b**) EDS spectra analysis.  
still very small. As shown in Figure 20, under a grouting pressure of 0.4 MPa, with a further increase in aggregate pores, the diffusion of the slurry in the pores was better and the density of the consolidated slurry was the highest for the aggregate with particle sizes of 9.5-4.75 mm. Due to the relatively large particle size of the aggregate, its bonding interface with the geopolymer slurry was also larger, and the shrinkage of the slag-geopolymer bonding interface was also greater. Compared with the fine aggregate, the interface cracks were relatively wider.
# Designing Parallel Adaptive Laplacian Smoothing for Improving Tetrahedral Mesh Quality on the GPU
## 5 Discussion
### Efficiency of the Parallel Adaptive Laplacian Mesh Smoothing
#### 5.1.3 Efficiency Analysis of Multiple Block Iteration  
In this paper, we analyzed and compared the performance of the kernel with a single block and multiple blocks based on a GPU with the CPU version. As seen in Figure 11, the GPU version runs faster than the CPU version, up to approximately 20 times faster, and the multiple block version runs faster than the single block version overall. In addition, we compared the single block version and the multiple block version individually. The results are shown in Figure 12. The performance advantage of the multiple block version becomes more apparent as the size of the experimental data increases. When the input mesh has 109k nodes, the speed can reach approximately 2.0 times. Thus, it can be seen that a kernel with multiple block can significantly improve iteration efficiency and make full use of the efficient parallel performance of the GPU.  
However, it is important to note that although multiple block iteration is more efficient, this kernel design method does not always guarantee that there will be no data conflicts  
Figure 10: Running time and speedup when using different iteration forms.  
between the \(q\) iteration and the \(q+1\) iteration; thus, in practical applications, how to choose the iteration method will depend on the user's requirements. For simple mesh models with few nodes, the kernel with a single block has better performance, and for complex and multinode meshes, the kernel with multiple blocks more easily ensures computational efficiency.
# Person Search via Deep Integrated Networks
## 3 Overview of the Deep Integrated Networks for Person Search
### Pedestrian Detection Network  
The first module aims to perform the pedestrian detection. Several objection detection algorithms based on deep learning networks have been proposed in the recent years. These algorithms include Fast R-CNN [62], Faster R-CNN [35], SSD [36], You Only Look Once (YOLO) [63] and Mobile Net [64], which can be roughly categorised into classification-based [35; 62] and regression-based methods [36; 63; 64]. Note that the classification-based methods apply strategies to search the candidates (i.e., proposals) in which the object might appear. Classification is then performed on these candidates for further refinement. Meanwhile, regression-based methods discard the process of searching candidates and directly obtain the object locations. The regression-based methods are generally faster than those based on classification, but the classification-based methods provide more precise detection results (i.e., most parts of objects are enclosed within a compact bounding box) [35]. Considering that the ultimate ambition is pedestrian identification, a precise detection result is practically given a higher priority. Thus, the first module was based on the Faster R-CNN [35], which can provide relatively fast detection results among the classification-based methods.  
For the Faster R-CNN network configuration [35], the first module was composed of three parts: sharing convolutional layers, RPN was used to predict the proposals (i.e., candidate locations, where the pedestrians might appear in the image), and ROI pooling and classification layers were used to classify where a pedestrian exists in those proposals. Figure 2 shows the network configuration of the first module in detail. Four steps were applied to obtain the final detection result when one image was input to the first module. Firstly, the low-level features of the image were extracted by the sharing convolutional layers. The feature map of the final convolutional layer was then input to the following parts of the networks. Secondly, the RPN was used to generate proposals by performing a sliding window on the feature map. Thirdly, the feature map from the sharing convolutional layer and the generated proposals were input to the ROI pooling layer; hence, the corresponding features for each proposal could be extracted. The final classification layer output the classification result, and bounding box regression was applied to obtain a more precise object location. The network configuration and operations performed in each step are introduced in the subsections that follow.  
Figure 1: Overview of the proposed deep integrated person detection and identification system (IPnetwork).
## 1 Introduction
Design guidelines for obtaining PI + Notch controller coefficients based on the maximum tolerable grid-side current THD and transient DC link voltage deviation were established in [51]. Nevertheless, neither of the above-mentioned notch-filter-based solutions took into account the fact that a universal grid-connected converter must be capable of supporting both 50 Hz and 60 Hz grid frequencies. Moreover, utilizing transient DC link voltage deviation may yield non-optimal performance in terms of phase margin \"overkill\". Recently, a disturbance observer-based approach [50] was extended to employ dual-notch-based action in [52], yet the methodology may only be applied to existing systems with slow PI or type-II controllers and cannot be employed in from-scratch designs. In addition, the control structures of disturbance observer-based methods are quite complex and not easy to implement. Consequently, the main contributions of this work are as follows:  
1. It is suggested to combine a PI term with two notch filters, tuned to 50 Hz and 60 Hz, respectively, in order to support universal grid interfacing without the need for controller redesign. 2. The minimum allowed phase margin is used as the second performance merit (along with the maximum tolerable grid-side current THD) rather than the maximum tolerable transient DC link voltage deviation. Such an approach allows the optimization of the latter rather than setting a desired value a priori (which may lead to a non-existing solution, requiring further iterations). An explicit process of deriving coefficients of the proposed PI + N\({}^{2}\) controller employed for regulating the DC-link voltage of single-phase converters interfacing either 50 Hz or 60 Hz mains is proposed in this work. The proposed methodology allows for the optimization of the DC-link voltage transient caused by step-like load changes while attaining prescribed values of grid-side current THD and DC-link voltage loop phase margin under the prescribed mains frequency uncertainty range. The rest of the paper is organized as follows. The essentials of single-phase grid-connected power conversion system operation are brought forward in Section 2. The steady-state performance of the system in terms of total harmonic distortion and its dynamic response in terms of DC-link voltage deviation upon step-like load variation are revealed analytically in Sections 3 and 4, respectively. Guidelines for the proposed controller coefficient derivation are established in Section 5. The simulations and experiments of the proposed methodology applied to a 500 W grid-connected converter are demonstrated in Section 6. The paper is then concluded in Section 7.
## 3 Classification of Optical Waveguide Sensors
### Microring Structured Optical Waveguide Sensor  
MRRs are a key component in the field of integrated optics and belong to the common refractive index sensor class, with several advantages such as a high quality factor, simple preparation, and easy mass production. Therefore, MRRs are widely used in the field of sensors, in addition to other fields such as lasers and detectors [22]. There are two approaches for improving the sensor _LOD_[23, 24]. First, the quality factor \(Q\) of the resonator can be maximized to reduce the influence of noises on the measurement of the resonance wavelength. Second, the mean resonance wavelength shift can also be maximized by increasing the interaction between the light and biomolecules attached to the waveguide surface.  
Materials used for integrated optics are increasingly diverse owing to the development of new materials such as SiO\({}_{2}\), LiNbO\({}_{3}\), SOI, GaAs, InP, and organic polymers. SOI-based optical waveguide systems have several advantages such as a large refractive index difference, low transmission loss, and high integration capability.
# Graphene-Based Materials for the Separator Functionalization of Lithium-Ion/Metal/Sulfur Batteries
## 2 Preparation Methods of Graphene-Based Materials
### Others
#### 2.3.4 Arc Discharge  
The direct current arc discharge method is a viable approach for the direct preparation of heteroatom-doped graphene. [PERSON] et al. conducted direct current arc discharge by placing two graphite rods of different sizes in an atmosphere filled with varying ratios of hydrogen and helium mixtures without catalysts and produced mostly 2-4 layer graphene flakes in the inner region of the arc chamber. They explored the optimal conditions for graphene preparation, including current, voltage, and hydrogen pressure [87]. [PERSON] et al. achieved up to 3.5% nitrogen content in the graphene produced via the arc discharge method using GO with solid nitrogen source polyaniline (PANI) as the anode carbon filler, which is significantly higher than in previous related studies (1-1.5%) [88]. [PERSON] et al. systematically investigated the growth mechanism of oligomeric graphene prepared by arc discharge in different buffer gases, including an inert atmosphere of He, an oxidizing atmosphere of O\({}_{2}\)-He, and a reducing atmosphere of H\({}_{2}\)-He. The results showed that the reactivity of the corresponding buffer gas played a crucial role in the generation of few-layered graphene (FG) in the arc discharge method. The graphene growth process was demonstrated in two stages. First, the formation of carbon vapor at the center of the arc and the crystallization and growth of carbon clusters during the diffusion from the center to the side. In the second stage, the reactive gas reacted with the carbon clusters, thus terminating the carbon-centered radical and avoiding the closure of the graphene layer (Figure 4D) [89].
## 3 Results and Discussion
### Adsorption Studies
#### 3.3.2 Effects of Polymer Mass  
Batch adsorption of the MIP was conducted with polymer masses ranging from 15 to 55 mg, respectively, to find the optimum adsorption mass that will be used throughout the experiments whilst other parameters such as sample pH (7.0), target component concentration (5 mg\(\cdot\)L\({}^{-1}\)), and sample volume (10 mL) remained fixed. High recoveries were observed across all masses, with extraction efficiencies ranging from >83%. A mass of 55 mg, as shown in Figure S1, had the best results for all target compounds, as expected from its greater mass and, thus, greater surface area. The MIP (Figure S1a) exhibited superior extraction efficiencies when compared to the NIP (Figure S1b).
# Speaker-Independent Spectral Enhancement for Bone-Conducted Speech
## 4 Experiment
### Experimental Setup  
The parameter configuration of the generator and discriminator in the BSEGAN-SI method proposed in this paper is listed in Tables 1 and 2. The initial learning rate of the generator network is set to \(2e^{-4}\), and the initial learning rate of the discriminator network is set to \(1e^{-4}\). In the preprocessing stage of this experiment, all BC and AC speech are downsampled to 16 kHz and use WORLD vocoder to extract fundamental frequency \(F_{0}\), \(Ap\) and \(Sp\) features from the voice frame moving 5 ms every 25 ms [24]. Then transform \(Sp\) features into \(MCEPs\) for model training. To improve the randomness of each batch, instead of using the speech spectral feature sequence directly in order, we randomly intercepted a segment with a fixed length of 128 frames from the \(MCEPs\) feature sequence. Use the Adam optimizer to regularize parameter \(\lambda_{sd}\), which is set to 10. For each dataset, the training data and testing data are divided by a ratio of 4:1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Module** & **Kernel Size** & **Stride Size** & **Channels** \\ \hline \multirow{8}{*}{Generator} & Conv2D & \(5\times 15\) & 1 & 128 \\ \cline{2-5}  & Encoder & \(5\times 5\) & 2 & 256 \\  & \(5\times 5\) & 2 & 512 \\ \cline{2-5}  & Conv2D & \(5\times 5\) & 1 & 512 \\  & Conv2D & \(3\times 3\) & 1 & 512 \\  & Conv2D & \(5\times 5\) & 1 & 1024 \\  & Conv2D & \(3\times 3\) & 1 & 1024 \\ \cline{2-5}  & Decoder & \(5\times 5\) & 1 & 512 \\ \cline{2-5}  & \(5\times 5\) & 1 & 256 \\ \cline{2-5}  & Conv2D & \(5\times 15\) & 1 & 24 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter setting of BSEGAN-SI generator.
# Analysis of Erosion Minimization for a Slurry Pump Using Discrete Phase Model Simulations
## 2 Mathematical Models
### Erosion Model  
Erosion occurs owing to various factors, such as particle impact and the characteristics of the fluid and walls. Several models exist for erosion analysis [18; 19; 20]. However, the Finnie erosion model is the most widely used. [PERSON] suggested an exponential correlation between the erosion rate and the kinetic energy of colliding particles. This model uses both Lagrangian particle tracking and Eulerian-Eulerian multiphase approaches. Additionally, it can be used in the ANSYS Fluent software. Equation (1), presented below, was applied to the Finnie model, where E denotes the erosion rate.  
\[E=kU_{p}^{n}f(\theta) \tag{1}\]  
Here, \(k\) denotes a constant that varies depending on the fluid properties, such as temperature, density, and viscosity. \(U_{p}\) denotes the impact velocity, and \(n\) denotes the velocity index that varies depending on the material of the eroded surface. \(f(\theta)\) represents a function of the impact angle, which is presented in Equations (2) and (3).  
\[f(\theta)=\frac{1}{3}\cos^{2}(\theta),\;if\tan(\theta)>\frac{1}{3} \tag{2}\]  
\[f(\theta)=\sin(2\theta)-3\sin^{3}(\theta),\;if\tan(\theta)<\frac{1}{3} \tag{3}\]  
\(\theta\) = 18.4\({}^{\circ}\) denotes the critical value used to differentiate between sliding wear and impact wear. Sliding wear is dominant in the \(\theta\) < 18.4\({}^{\circ}\) section, whereas impact wear is dominant in the \(\theta\) > 18.4\({}^{\circ}\) section [21].