esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussion
### Airflow Simulations
#### 6.2.1 Effect of Scrubber  
The effect of the flooded-bed scrubber on the airflow pattern for the test conditions with face air quantity at 23.23 m\({}^{3}\)/s and scrubber capacities at 0%, 50%, and 100% are presented in Figures 11 and 12. Figure 11 shows the average velocity gradients on a plane parallel to the mine floor, whereas Figure 12 presents the velocity gradients on the planes perpendicular to the airflow direction in the longwall panel. A relatively high air velocity is observed near the scrubber inlet with high scrubber capacity. Additionally, a similar trend (high-turbulence region at high scrubber capacity) is seen at the flooded-bed scrubber outlet. The scrubber capacity also influences the airflow in the walkway. The airflow is relatively less turbulent and uniform with the scrubber at low capacity. As a portion of air is passing through the scrubber system, the airflow quantity in the walkway decreases downwind of the scrubber inlet with the increase in the scrubber capacity. In addition, due  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Cross-Section No.**} & \multicolumn{3}{c}{**Quantity (m3/s)**} \\ \cline{2-4}  & **0\% Fan Max RPM** & **50\% Fan Max RPM** & **100\% Fan Max RPM** \\ \hline
1 & \(-9.88\) & \(-5.16\) & \(-5.11\) \\
2 & \(3.07\) & \(0.33\) & \(1.78\) \\
3 & \(-6.79\) & \(-5.77\) & \(-3.70\) \\
4 & \(-9.53\) & \(-4.52\) & \(-7.22\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of experimental and CFD simulation results for the quantity of air passing through different cross-sections at different scrubber fan capacities.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Cross-Section No.**} & \multicolumn{6}{c}{**Velocity (m/s)**} \\ \cline{2-10}  & \multicolumn{3}{c}{**0\%**} & \multicolumn{3}{c}{**50\%**} & \multicolumn{3}{c}{**100\%**} \\  & \multicolumn{3}{c}{**Scrubber Capacity**} & \multicolumn{3}{c}{**Scrubber Capacity**} & \multicolumn{3}{c}{**Scrubber Capacity**} \\ \hline
1 & \(-0.56\) & \(2.01\) & \(-2.14\) & \(5.45\) & \(2.69\) & \(1.58\) & \(7.07\) & \(2.52\) & \(1.46\) \\  & \(-10.99\) & \(-6.86\) & \(-6.46\) & \(-10.93\) & \(-8.71\) & \(-3.12\) & \(-  
to the same reason, the airflow in the space between the simulated coal face and the shearer body is low and recirculating as compared to a case with low scrubber capacity. Moreover, a low-velocity recirculation zone was observed between the scrubber and fan modules in all the cases.  
Figure 11: Average velocity gradient at a plane 1.65 m above the floor with face air quantity at 23.23 m\({}^{3}\)/s and scrubber capacities at 0%, 50%, and 100%.
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 3 Carbon Satellite Remote Sensing Inversion Techniques
### Optimal Estimation Algorithm
#### 3.2.3 Statistical Methods for Principal Component Analysis (PCA)  
Solving the radiative transfer equation for atmospheric CO\({}_{2}\) is essentially a pathological problem due to the unavailability of a sufficient number of parameters. Due to the pathological nature of the inversion problem, the use of a priori information to constrain the range of inversion values has become the basic idea of the current CO\({}_{2}\) inversion method, at the same time determining the value of the a priori information is one of the most important parts of the optimal estimation of the inversion algorithm. The PCA method [87; 88] is actually to find the transformational relationship between the CO\({}_{2}\) profiles and the simulated irradiance, which provides the initial value for the algorithm. The transformed relationship between CO\({}_{2}\) contours and simulated irradiance can be obtained from the expressions of the covariance matrix \(C_{cov}\) for the CO\({}_{2}\) sample set and the covariance matrix \(R_{cov}\) for the simulated irradiance sample set. The expressions of \(C_{cov}\) and \(R_{cov}\) are:  
\[C_{cov} =\Delta C(\Delta C)^{\mathrm{T}}/N \tag{8}\] \[R_{cov} =\Delta R(\Delta R)^{\mathrm{T}}/N \tag{9}\]  
where \(\Delta C\) is the relative deviation matrix between the CO\({}_{2}\) profile and its sample set, \(N\) is the number of spectral channels, \(\Delta R\) is the deviation matrix between the simulated irradiance spectra and its mean value, and the transformation relationship between \(\Delta C\) and \(\Delta R\) obtained by using the PCA method is  
\[\Delta C=S\Delta R \tag{10}\]  
where S is the transformation matrix between \(\Delta R\) and \(\Delta C\). The vertical profile of CO\({}_{2}\) obtained by inversion using PCA method \(C_{re}\) is  
\[C_{re}=\overline{C}+S\Delta R_{obs} \tag{11}\]  
where \(\Delta R_{obs}\) is the difference matrix between the measured and simulated spectra, and \(\overline{C}\) is the mean matrix of the CO\({}_{2}\) sample set. The PCA method can be used to quickly obtain the relatively real CO\({}_{2}\) profiles under different conditions, which provides a better a priori value for the PPDF and improves the inversion accuracy of CO\({}_{2}\) from the surface.
## 3 Results and Discussion
### Factors Controlling the Cloud Droplet Concentration  
To investigate factors controlling the \(N_{\text{CD}}\), \(N_{\text{CD, cal}}\) was calculated by using the aerosol number size distribution, the hygroscopicity parameter (\(\kappa\)), and the \(SS_{\text{eff}}\). Where \(\kappa\) and the ambient \(SS\) were known, the activation diameter (\(d_{\text{a, cal}}\)) can be calculated based on the Kohler theory (Equations (2) or (4)). Then, the \(N_{\text{CD}}\) can be calculated by inputting the calculated \(d_{\text{a, cal}}\) values and the size distribution into Equation (1). Here, each \(N_{\text{CD, cal}}\) was calculated for the following three patterns: (1) mean size distribution shape, (2) mean \(SS_{\text{eff}}\), and (3) mean hygroscopicity parameter \(\kappa\) (at 0.21% \(SS\) of the CCN measurements) for the cloud-shrouded periods. Then the calculated value was compared to the measured value (\(N_{\text{CD}}\)). As a result, relative errors were 278.7%, 139.4% and 43.4%, respectively, when size distributions, \(SS_{\text{eff}}\) and \(\kappa\) were fixed to their mean values. Therefore, the \(N_{\text{CD, cal}}\) can be estimated with \(\pm\)50% error by using the mean value of \(\kappa\) during cloud-shrouded periods (Figure 10c). However, the \(N_{\text{CD, cal}}\) cannot be reproduced well if the mean number size distribution or the mean \(SS_{\text{eff}}\) are used in the calculations (Figure 10a,b). These results indicate that the size distribution is the most important factor controlling the cloud droplet number concentration at the MFRS, but that the concentration can be reproduced reasonably well when using a representative aerosol chemical composition. In addition, the evaluation of the \(SS_{\text{eff}}\) seems to be of greater importance than the chemistry for accurately predicting cloud droplet number concentrations at the MFRS during summer, where well-mixed, free tropospheric aerosol is observed.  
Figure 9: Normalized number size distribution of the cloud droplets at the MFRS classified according to air mass origins.
# Numerical Simulation of Red Blood Cell-Induced Platelet Transport in Saccular Aneurysms
## Mathematical Model
### Constitutive Relations
#### 2.2.4 The Diffusive Flux of the Platelets  
In this section, we discuss the modeling of the platelets' migration. Usually, the diffusivity of a species transported in the flow can be modeled using a Brownian diffusion approach. However, due to the presence of the RBCs, the platelets tend to move to the walls of the vessel and the concentration of the platelets near the walls can be several times higher than the concentration near the vessel centerline [14; 19; 35]. In order to model the RBCs-induced platelets transport, based on the ideas proposed by [PERSON] et al. [36], [PERSON] et al. [37] and [PERSON] [27], we assume that the platelets diffusion flux, \(\mathbf{Q}\), can be given by,  
\[\mathbf{Q}=D_{C}\
abla((1+q(\phi))C)=\big{(}D_{B}+\xi f_{s}(\phi)\dot{\gamma}_{m} \big{)}(C\
abla(1+q(\phi))+(1+q(\phi))\
abla C) \tag{11}\]  
where \(\dot{\gamma}_{m}=\big{(}2~{}trD_{m}{}^{2}\big{)}^{1/2}\) is the mixture (whole blood) shear rate, \(q(\phi)\) is a function of \(\phi\), based on the experimental data, \(D_{B}=1.58\times 10^{-13}\) m\({}^{2}\)s\({}^{-1}\) is the Brownian diffusion constant, and \(C\) represents the concentration of the platelets in flow. The term \(\xi f_{s}(\phi)\dot{\gamma}_{m}\) represents the dependence of the platelets transport flux intensity on the local shear rate which is proportional to the cells collision frequency; this function disappears as \(\phi=0\). For the sake of simplicity, we assume \(f_{s}(\phi)=\phi\), and \(\xi=6.0\times 10^{-14}\) m\({}^{-2}\) is an empirical constant from experiments [10]. The term \(C\
abla(1+q(\phi))\) is the diffusive flux of the platelets responsible for the movement away from regions of high RBC content, such as the center of the blood vessel, and \((1+q(\phi))\
abla C\) represents the RBCs-enhanced diffusion of the platelets in the direction of the gradient of the platelet concentration. We assume that \(q(\phi)\) has the following properties: (1) \(q(\phi)\) must be a monotonic increasing function of \(\phi\), so that the platelets are always  
Figure 1: Viscosity of the red blood cells (RBCs) as a function of the shear rate. Experimental data is by [PERSON] [29]. The figure is reused with permission from [22], Copyright Elsevier, 2015.  
excluded by the RBCs and diffuse to a region with lower volume fraction of the RBCs; (2) When \(\phi=0\), \(q(\phi)=0\), which means that when there is no RBC, the RBCs-induced diffusion effect disappears. In the current work, we simply represent \(q(\phi)\) as a polynomial function, where,  
\[q(\phi)=\sum_{i}^{N}A_{i}\phi^{i};i\geq 1,\ A_{i}\geq 0. \tag{12}\]  
Through numerical simulations and by quantitatively comparing this with experimental measurements for three benchmark problems, [PERSON] et al. (2017) [PERSON] et al. (2017) suggested a polynomial function for \(q(\phi)\),  
\[q(\phi)=0.26\phi+10.6\phi^{2}+537\phi^{3} \tag{13}\]  
In the following section, we will apply the above function to study the blood flow and platelets migration in several representative problems.
## 5 Results and Analysis  
### Efficiency Analysis  
Figure 5 provides an in-depth view of the efficiency for the 32 IMs and seven DMs considered in this study. Remarkably, the efficiency of each DM remains relatively consistent across different IM parameters. The \(\beta_{\rm D}\) corresponding to \(F_{\rm NC}\) is the lowest, followed by \(F_{\rm SC}\), \(M_{\rm C}\), and \(R_{\rm E}\), and finally \(D_{\rm e}\), \(\theta_{\rm c}\), and \(\theta_{\rm max}\). Notably, energy-type DMs outperform those based on displacement. In the context of IMs, acceleration-type IMs display relatively lower \(\beta_{\rm D}\) values, signifying superior efficiency. Figure 6 showcases the \(\beta_{\rm D}\) values for each IM when \(F_{\rm NC}\) serves as the DM, elucidating the inferior efficiency of displacement-type IMs relative to their acceleration-type counterparts. A detailed comparative analysis of \(\beta_{\rm D}\) for various IM types under each DM is depicted in Figure 7, which reveals that SMA, \(A_{\rm rs}\), Arias intensity, ASI, and PGA are the most efficient IMs. Furthermore, acceleration-type IMs outperform those based on displacement in efficiency, which is true for each DM considered herein.
# Machine Learning in Cereal Crops Disease Detection: A Review
## 4 Methods Adopted to Carry Out the Survey
### Research Questions  
This review paper aims at identifying machine learning methods adopted for the detection of cereal crop diseases. In addition, it is especially aimed at finding the most relevant and state-of-the-art machine learning approaches utilized in the past five years. Thus, the primary research question we plan to answer is:  
PRQ: _\"What are the state-of-the-art machine learning approaches utilized for the problem of detecting cereal crop diseases in the past 5 years?\"_  
Secondary questions are also prepared to better help in narrowing down the desired answer to the primary research question. These are:  
* SRQ1: What are the most important cereal crop species?
* SRQ2: What are the most damaging and prevalent cereal crops disease?
* SRQ3: What kind of datasets are available?
* SRQ4: What are the primary evaluation metrics used in cereal crops disease detection?
* SRQ5: Which machine learning frameworks are commonly used?
## 4 Experiment and Analysis  
The experiment is divided into three parts.  
The first part verified whether the improved method has high accuracy in calculating the entropy of five special terrain types shown in Figure 2.  
In the second part, a set of grid DEMs with no numerical differences, but with varying point locations that lead to increased complexity, was generated through iteration. The performance of the Euclidean-distance-based terrain entropy method was compared to existing terrain entropy methods. The goal was to evaluate the strengths and weaknesses of the methods when handling DEMs with complexity variations caused by changes in point positioning.  
The third part focused on evaluating the differences in performance between the Euclidean-distance-based terrain entropy method and current methods under conditions where the complexity of the grid DEM increased due to changes in the numerical values. This experiment aimed to analyze how variations in numerical values influenced the calculation of terrain entropy and the reliability of different methods in capturing complexity.
# A Systematic Literature Review on Distributed Machine Learning in Edge Computing
## 3 Research Methodology
### Threats to Validity  
The conducted systematic mapping and its results may have been affected by some threats to validity. In the following, we discuss some of these limitations.  
Figure 2: Number of papers excluded step by step in the SLR.  
The completeness of this systematic mapping may have been affected by missing relevant studies. To reduce this threat, we used electronic databases that are among the most relevant available sources in computer science and engineering. However, there are still limitations:  
* Some studies may have been missed due to technical limitations of the automated search engines, an issue that is out of our control.
* The selected electronic databases do not represent an exhaustive list of publication sources, so other databases might also be included.
* We did not perform snowballing, a useful technique that consists of checking the reference lists of the read studies aiming to find additional studies that were not retrieved in the automated search procedure. Therefore, other possibly relevant studies could have been identified and considered in this systematic mapping.
# Covert Anti-Jamming Communication Based on Gaussian Coded Modulation
## 2 Signal Design
### Gaussian Coding with Coding Gain
#### 2.2.1 Concatenation Coding  
To provide an encoding method with coding gain, concatenation coding was considered. Namely, the encoding of a digital message \(\text{Enc}:\{-1,1\}^{L}\mapsto\mathbb{R}^{2N}\) becomes a serial combination of two different coding methods, as depicted in Figure 4. The message is encoded twice, first by an outer code \(\text{Enc}_{out}:\{-1,1\}^{L}\mapsto\{-1,1\}^{N_{out}}\) and then by an inner code \(\text{Enc}_{in}:\{-1,1\}^{N_{out}}\mapsto\mathbb{R}^{2N}\). We used a finite-field coding method, which yields a coding gain, as the outer code, and then used the Gaussian-LBC as the inner code so that the system could obtain the advantages of both the error-correcting coding and Gaussianity of the signal.  
The decoding procedure is also two-fold. Given a jamming-mitigated codeword \(\widetilde{\mathbf{c}}\), the Gaussian-LBC codeword is decoded first by \(\text{Dec}_{in}:\mathbb{R}^{2N}\mapsto\{-1,1\}^{N_{out}}\) ; then, the outer codeword is decoded by \(\text{Dec}_{out}:\{-1,1\}^{N_{out}}\mapsto\{-1,1\}^{L}\). Here, we focus on the decoding of the Gaussian-LBC \(\text{Dec}_{in}\). Because the decoding of the Gaussian-LBC is equivalent to an overdetermined linear inverse problem (LIP), ML decoding can be performed using the least-squares method with \(O(N\cdot N_{out})\) computations, where \(N_{out}\) is the length of the outer codeword. If the code rate of the outer code is fixed (i.e., \(O(N_{out})=O(L)\)), the complexity is equivalent to \(O(NL)\). Based on the output of the Gaussian-LBC decoder \(\text{Dec}_{in}\), the original message is decoded using the outer code decoder \(\text{Dec}_{out}\), i.e., the recovered message vector \(\mathbf{\hat{m}}:=\text{Dec}_{out}(\text{Dec}_{in}(\widetilde{\mathbf{c}}))\).  
Figure 4: Concatenation coding method. The message is first encoded using the outer code and then again using the Gaussian linear block code (LBC).
## 2 Materials and Methods
### Chemical Reagents  
Alkane standard solution (C\({}_{9}\)-C\({}_{21}\)) for calculating linear retention indices (RIs) was supplied by TanMo Quality Testing Technology Co., Ltd. (Beijing, China). The internal standard solution was prepared using dichloromethane prior to use [21]. Ultrapure water was produced using a Barnstead GenPure Pro system (Thermo Fisher Scientific, Waltham, MA, USA). The following reference standards were acquired: ethyl decanoate, theanine, and a series of catechins comprising catechin (C), epicatechin (EC), epigallocatechin (EGC), epicatechin gallate (ECG), gallocatechin gallate (GCG), and epigallocatechin gallate (EGCG); all were sourced from Shanghai Yuanye Biotechnology Co., Ltd. (Shanghai, China). Additionally, the caffeine standard was obtained from Beijing Weiyesi Metrology Technology Research Institute (Beijing, China).  
High-Performance Liquid Chromatography-Ultraviolet (HPLC-UV) Analysis of Caffe, Theantine, and Catechins  
Caffeine, theanine, and catechin content were quantified using high-performance liquid chromatography-ultraviolet according to the method in our previous research [22; 23] (HPLC; Waters Alliance 2695 system equipped with a 2489 UV/Vis detector, Waters Technologies, Milford, MA, USA). Analytes were identified by comparing retention times to
## 6 Results and Analysis
### Model Validation and Comparison  
Validation of the new proposed model and other benchmark landslide models was carried out on both the training and testing datasets. The comparative validation results of the different models are shown in Figure 5 and Tables 1 and 2, for the training and validation datasets. Out of these, the training dataset in the modelling was used for goodness-of-fit analysis. The performance or predictive capability of the models were evaluated using the validation dataset. The results of the models based on the training dataset revealed that all models predicted the spatial distribution of landslides very well. However, the results showed that the new proposed model, MBNBT, had the highest SEN (0.882), SPC (0.900), ACC (0.891), AUC (0.924), and also lower error values of MAE (0.168) and RMSE (0.224) metrics in comparison to the other models (Table 1). Similar results were obtained for the MBNBT model, based on the validation dataset: SEN (0.763), SPC (0.778), ACC (0.770), AUC (0.831), and also lower error values of MAE (0.236) metrics (Table 2). The results of the benchmark models based on the training dataset showed that although the NBT had the most goodness-of-fit (AUC = 0.831), the most predictive capability using the validation dataset was of the MLPNs (AUC = 0.810) model, followed by the NBT (AUC = 0.802) and SVM (AUC = 0.800) models (Table 2). It can be concluded that the new proposed model (MBNBT) outperformed and outclassed the other soft computing benchmark models (SVM, MLPN, and NBT) for the spatial prediction of landslides.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Criteria** & **MLPN** & **SVM** & **NBT** & **MBNBT** \\ \hline TP & 148 & 134 & 132 & 157 \\ \hline TN & 128 & 131 & 125 & 153 \\ \hline FP & 26 & 40 & 42 & 17 \\ \hline FN & 46 & 43 & 49 & 21 \\ \hline SEN (\%) & 0.763 & 0.757 & 0.729 & 0.882 \\ \hline SPC (\%) & 0.831 & 0.766 & 0.749 & 0.900 \\ \hline ACC (\%) & 0.793 & 0.761 & 0.739 & 0.891 \\ \hline MAE & 0.307 & 0.302 & 0.340 & 0.168 \\ \hline RMSE & 0.313 & 0.391 & 0.430 & 0.224 \\ \hline AUC & 0.818 & 0.814 & 0.831 & 0.924 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model performance using the training dataset.  
Figure 4: Factor selection using the One-R Attribute Evaluation (ORAE) method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Criteria** & **MLPN** & **SVM** & **NBT** & **MBNBT** \\ \hline TP & 54 & 59 & 57 & 58 \\ \hline TN & 53 & 52 & 50 & 56 \\ \hline FP & 20 & 15 & 17 & 16 \\ \hline FN & 21 & 22 & 24 & 18 \\ \hline SEN (\%) & 0.720 & 0.728 & 0.704 & 0.763 \\ \hline SPC (\%) & 0.726 & 0.776 & 0.746 & 0.778 \\ \hline ACC (\%) & 0.723 & 0.750 & 0.723 & 0.770 \\ \hline MAE & 0.342 & 0.314 & 0.350 & 0.236 \\ \hline RMSE & 0.464 & 0.426 & 0.426 & 0.466 \\ \hline AUC & 0.810 & 0.800 & 0.802 & 0.831 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model performance using the validation dataset.  
Figure 5: Modelling results based on the RMSE error value in the training and validation phases.
## 5 Conclusions  
This study analyzed the long-term trends of GPP and GPP\({}_{\text{max}}\) across China's terrestrial ecosystems over the past 40 years by integrating results from two sets of terrestrial ecosystem models and two sets of remotely sensed datasets. Additionally, partial correlation analysis was used to examine the factors affecting inter-annual variations in GPP for these ecosystems; the study found different proportions of upward trends in GPP and GPP\({}_{\text{max}}\) across various datasets. Among the multi-source data, the highest proportions of datasets with upward trends in China's grassland, cropland and forest were 81%, 98%, and 84%, respectively. The spatial patterns of GPP\({}_{\text{max}}\) trends were closely related to those of overall GPP data. There was notable spatial heterogeneity in the trends of GPP and GPP\({}_{\text{max}}\) derived from both models and remote sensing data. This finding underscored the insufficiency of relying on a single data source for a comprehensive analysis of GPP and GPP\({}_{\text{max}}\) trends. To gain a detailed understanding of the spatial and temporal dynamics of ecosystem GPP in China, it is essential to compare multiple GPP datasets. Therefore, the study recommended using a combination of multiple data sources to evaluate the long-term dynamics of GPP. A comparison of the effect sizes of CUP and GPP\({}_{\text{max}}\) on interannual GPP variations using partial correlation analyzes showed that GPP\({}_{\text{max}}\) controlled interannual GPP variations in most areas of China's terrestrial ecosystems. This was particularly evident in grassland, where more than 99% of the inter-annual variation in grassland GPP was controlled by GPP\({}_{\text{max}}\). In conclusion, the integration of multiple data sources and the dominant role of GPP\({}_{\text{max}}\) highlighted the complex and critical factors driving the inter-annual variations and long-term trends of GPP in China's terrestrial ecosystems.  
Conceptualization, L.L. and H.S.; methodology, H.S.; software, H.S. and Y.C. and Q.A.; writing and editing, H.S. and L.L. All authors contributed to the editing of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (Grant number: No. 52079138).  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors are grateful to Moderate Resolution Imaging Spectroradiometer (MODIS) team, The Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) group, the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) group, the Global LandSurface Satellite (GLASS) team, and the Multisource Data Synergised Quantitative (MuSyQ) team, for making the data freely available.  
The authors declare no conflicts of interest.