esa-sceva/satcom-chunk-collection huggingface dataset:
# Comprehensive Effects of Atmosphere and Soil Drying on
## 2 Materials and Methods
### Methods
#### 2.2.3 Separating the Relative Contribution of SWC and VPD to WUE and CUE  
[PERSON] et al. [1] used SIF and reanalysis climate data for a global analysis. They estimated the difference between the SIF of the highest VPD bin and the lowest VPD bin in each SWC bin to derive \(\Delta\)SIF (VPD \(\mathrm{\mid SWC}\)), which means VPD limitation of SIF without SWC-VPD coupling. Similarly, the difference between the SIF of the highest SWC bin and the lowest SWC bin in each VPD bin is called \(\Delta\)SIF (SWC \(\mathrm{\mid VPD}\)). This method can quantify the SWC and VPD limitation of SIF without SWC-VPD coupling. In this paper, we refer to this method and apply it to WUE and CUE. We calculate the \(\Delta\)WUE (VPD \(\mathrm{\mid SWC}\)), \(\Delta\)WUE (SWC \(\mathrm{\mid VPD}\)), \(\Delta\)CUE (VPD \(\mathrm{\mid SWC}\)), and \(\Delta\)CUE (SWC \(\mathrm{\mid VPD}\)) of each site. The specific calculation formula is as follows:  
\[\Delta Var(VPD\mathrm{\mid SWC})=\frac{1}{n}\sum_{i=1}^{n}Var_{i,max}-Var_{i,min} \tag{5}\]  
where \(Var\) is WUE or CUE, \(n\) is the number of \(\mathit{SWC}\) bins, and \(i\) is the bin number of a specific SWC bin. \(i\), \(max\) and \(i\), \(min\) are the maximum and minimum VPD bin numbers in \(\mathit{SWC}\) bin \(i\), respectively. This formula can calculate the limit of VPD rise in WUE and CUE without SWC-VPD coupling (termed \(\Delta\)_Var_ (\(\mathit{VPD}\)) \(\mathrm{\mid SWC}\)). Similarly, the limit of SWC reduction in WUE and CUE without SWC-VPD coupling (called \(\Delta\)_Var_ (\(\mathit{SWC}\)_VPD_)) is calculated as follows:  
\[\Delta Var(\mathit{SWC}\mathrm{\mid VPD})=\frac{1}{m}\sum_{j}^{m}Var_{j,min}- Var_{j,max} \tag{6}\]  
where \(M\) is the number of \(\mathit{SWC}\) bins and \(j\) is the bin number of a specific VPD bin. \(j\),\(min\) and \(j\),\(max\) are the minimum and maximum \(\mathit{SWC}\) bin numbers in \(\mathit{VPD}\) bin \(j\), respectively. The response of plant photosynthesis to \(\mathit{SWC}\) and \(\mathit{VPD}\) can be nonlinear [39], which can be overcome by using this method.  
Many previous studies have shown that the correlation between VPD and SWC decreases with a decreasing time scale [14; 32], and the correlation coefficient between the two is very low at the hourly scale, indicating negligible mutual influence. Therefore, in order to decouple the effects of VPD and SWC as much as possible, hourly data were used in the analysis presented in this paper. SWC is divided into six percentile ranges: 0-15%, 15-30%, 30-50%, 50-70%, 70-90%, and 90-100%. The site meteorological and flux data are classified accordingly into these six ranges. The data were further classified using VPD, with a VPD range of 0.2 kPa selected to ensure sufficient data in each VPD bin. To reduce the impact of outliers, only the mean values of data with more than 10 samples in each bin were selected to represent the quantity of different indicators in each VPD and SWC range.
## 3 Results and Discussion
### Composites Characterization  
#### 3.2.1 Morphological Characterization  
The dispersion state of BC particles and the BC-PP interfacial interaction are two important factors that influence the properties of the composites. The SEM images of BC/PP composite systems prepared with different types of biochar are shown in Figure 4. It can be seen that the majority of BC particles are distributed uniformly in the PP matrix, however, the composites also show an agglomeration. It should be note that the BC/PP composites interface properties is highly dependent on the type of BC. The composites samples made with BC prepared at relatively higher temperatures, especially BC500, BC600, and BC700 show relatively better interfacial adhesion between the filler and matrix as compared to other composites prepared with BC obtained at lower temperatures. This improved interface quality can be attributed to the large surface area and porous structure of BC prepared at higher temperatures. The molten PP matrix, during processing, penetrates into BC pores, which results in better filler-matrix interlocking [29]. Nevertheless, filler pull-out effects, as indicated by the debonding of BC particles, are also observed. It is important to note that good mechanical strength can only be achieved if there is an excellent interfacial bond between the filler and matrix, and ultimately it must lead to a uniform and efficient transfer of applied stress. The fillers can pull out from the matrix in the lack of interfacial bond and results in an annulment of the reinforcing effect [30].  
Figure 4: _Cont._  
Figure 3: SEM images of biochar samples prepared at different temperatures. (**A,B**) for BC300, (**C,D**) for BC400, (**E,F**) for BC500, (**G,H**) for BC600 and (**IJ**) for BC700.  
Figure 4: SEM images of BC/PP composites. (**A**,**B**) for BC300/PP, (**C**,**D**) for BC400/PP, (**E**,**F**) for BC500/PP, (**G**,**H**) for BC600/PP and (**I**,**J**) for BC700/PP.
## 4 Results and Discussions
### Regression Models
#### 4.3.2 Vibration Model Based on Cutting Parameters  
Using the regression coefficients in Table 3, the vibration level induced in the machining can be related to the cutting parameters in terms of the following form.  
\[\begin{array}{ll}V_{b}=&-5.18\times 10^{-3}+\ 4.29\times 10^{-6}\times n\ +2.60\times 10^{-3} \times a_{p}+3.21\times 10^{-6}\times a_{p}\times n\\ &-6.3\ \times 10^{-6}\times a_{p}\times f\ -4.2\times 10^{-10}\times n\ \times f\ -2.6\times 10^{-10} \times a_{p}\times n\ \times f\end{array} \tag{12}\]  
As listed in Table 3, the \(p\)-value of spindle speed and its interaction with cutting depth are far less than 0.05, which demonstrates the significant influences on the vibration level of the machining system in the modeling of machining vibration. Figure 8 shows that the predicted values are in good agreement with the measured values. According to Table 2, there is a significant correlation between the predicted and measured values, with R-square and adjusted R-square of ~0.80. The average percentage of prediction accuracy for all testing data is about 86.1% with an RMSE of 4.7%. This indicates that machining vibration can be appropriately estimated from the cutting parameters by using this model.
## 3 Model Calibration  
The medium-specific parameter \(n\) required for the MC diffusion approach to determine the effective diffusion coefficient for every species (Equation (5)) was calibrated using an experiment with U(VI) and Opalinus Clay of the sandy facies ([PERSON], 2013). We set up a model equal to the one presented in [PERSON] et al. (2011) with the additional application of the MC diffusion approach. For the diffusion experiment, the precise composition of the minerals was not determined. Therefore, the mineral composition used here is based on the results given in [PERSON] et al. (2011) (23 wt.% kaolinite, 9 wt.% illite, 4 wt.% illite/smectite mixed layers and 9 wt.% chlorite) for a clay mineral content of 45 wt.% corresponding to the sandy facies ([PERSON] et al., 2011). The parameter \(n\) was varied until the best coincidence between the MC diffusion simulations and the experimental data was achieved. For the simulations considering sorption on all clay minerals (MC\({}_{4 Clay}\), illite, montmorillonite, chlorite and kaolinite), an exponent n of 2.6 was determined, whereas for the model concept with only two clay minerals (MC\({}_{2 Clay}\), illite and montmorillonite) the best match between experimental and modeled data was observed for an n of 2.1. Both results and the experimental data are shown in Figure 2.  
In the literature, values for the medium-specific parameter n of 2.4 ([PERSON], 2013) and 2.5 ([PERSON], 2013; [PERSON], 2013) are given for Opalinus Clay, which is in line with the value n = 2.6 of our MC\({}_{4 Clay}\) concept. For bentonite mainly consisting of montmorillonite, a value of n = 1.9 can be found ([PERSON], 2013), which is similar to the MC\({}_{2 Clay}\) concept with only illite and montmorillonite as clay minerals and an exponent n = 2.1. However, the experimental data can be reproduced as shown in Figure 2. The relative Root Mean Square Errors (rRMSE) are calculated in relation to the experimental data. The values for the rRMSE are 28% for an n = 2.6 (MC\({}_{4 Clay}\)) and 27% for an n = 2.1 (MC\({}_{2 Clay}\)). Due to the scattering of the data, we consider our model concepts for the exponents n = 2.6 (MC\({}_{4 Clay}\)) and n = 2.1 (MC\({}_{2 Clay}\)) as best fit and calibrated for further application.
## 3 Results
### Curve of Breakthrough Modeling
#### 3.6.3 The BDST Model Application  
The \(t\)-\(Z\) lines at \(C_{\mathit{eff}}/C_{0}\) of 0.10, 0.60, and 0.88 values are presented in Figure 11. The affined BDST constants that occur in accordance to the slopes and lines intercept, such as \(K\) and, were determined using Equations (7)-(11); their results are shown in Table 5. The \(K\) value describes the extent to which the fluid stage turned into the solid one. In the case that \(K\) is large, even a small bed would avert breakthrough; though, as \(K\) declines, an increasingly deeper bed is essential to avert breakthrough [49]. The capacity of BDST adsorption (\(N_{0}^{\prime}\)) is a crucial constant for evaluating the efficiency of adsorbent; a powerful adsorbent is typically characterized by higher \(N_{0}^{\prime}\) values [49]. The lowest height of column (\(Z_{0}\)) essential for effluent concentration (\(C_{b}\)) of 0.1 mg\(\cdot\)L\({}^{-1}\) production was determined by Equation (8). As shown in Table 5, as the \(C_{\mathit{eff}}/C_{0}\) value improved, the constant (\(K\)) and the bed's capacity of adsorption/ a unit bed volume (\(N_{0}^{\prime}\)) improved, while the minimum column height (\(Z_{0}\)) decreased. The \(R^{2}\) values were observed between 0.9299 and 0.9975, which specified the BDST model soundness for this method. BDST model constants could  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(C_{0}\) & \(Q\) & \(Z\) & \(k_{AB}\times 10^{4}\) & \(N_{0}\times 10^{-4}\) & \(R^{2}\) \\ **(mg\(\cdot\)L\({}^{-1}\))** & **(mL\(\cdot\)min\({}^{-1}\))** & **(cm)** & **(L\(\cdot\)mg\({}^{-1}\)min\({}^{-1}\))** & **(mg\(\cdot\)L\({}^{-1}\))** & **(\(R^{2}\))** \\ \hline
1000 & 5.0 & 8.50 & 1.14 & 3.808 & 0.975 \\
1000 & 7.5 & 8.50 & 0.59 & 4.887 & 0.981 \\
1000 & 10.0 & 8.50 & 1.31 & 4.039 & 0.982 \\
400 & 10.0 & 8.50 & 1.68 & 5.421 & 0.979 \\
2000 & 10.0 & 8.50 & 1.52 & 5.776 & 0.997 \\
1000 & 10.0 & 12.75 & 0.59 & 5.796 & 0.994 \\
1000 & 10.0 & 15.30 & 1.07 & 5.096 & 0.999 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The Adams–Bohart model’s parameters under various circumstances.  
Figure 10: The experimental vs. predicted curves of breakthrough gained at dissimilar (**a**) pheno’s inlet concentrations, (**b**) phenol rates of flow, and (**c**) bed depths according to Adams–Bohart model.  
be useful to increase the progression in any other rates of flow as well as concentrations with no more experimental runs.
## 5 Discussion and Results  
### Electrical Power and Syngas Production  
During Test 1, the air flow changed from 8 to 16 Nm\({}^{3}\)/h. The gasifier was turned on and, after 50 min, the engine (ICM-generator block) was also turned on. There was no observed increase in water in the filter, and the pressure drop was steady. Therefore, amendments implemented in the gasification facility, described in Section 3.2, provided positive results, with pressure drop increments in the range of pressures established by the literature, between 8 and 12 mbar (Figure 6 left).  
Thus, the volume of syngas produced, which depends on the inlet air flow, also increases. As a result, the DGPP reached a maximum electrical power of 5 kW in steady-state conditions, as shown in Figure 6 (right) from minute 92 to minute 99, when the engine was switched on. Similarly, the gasifier was fueled with 42.05 kg of sieved pellets mixed with 2.40 kg of coal char pellets during Test 2. At the end of the test, the total biomass consumption was 11.8 kg, while the air flow varied from 8 to 15 Nm\({}^{3}\)/h. The water level in the filter was stable, as well as the pressure drop, and the electrical power was stable at approximately 4.8-5 kW. Test 2 (Figure 7) proved that the operation of the experimental plan was feasible, similar to Test 1, again validating the ability of the gasifier design to meet an electric power of 5 kW in correspondence to the maximum syngas production. Indeed, the pressure drop shows minimal oscillations, being bounded within appropriate values for stable operation of the gasification facility.  
Figure 5: Process diagram to produce syngas from biomass in a downdraft gasifier. Source: own elaboration based on [38; 39].  
Figure 6: Results obtained from Test 1.
## 4 Controller Design for Unmanned Helicopter Hovering Position
### Height Control Law Design  
The dynamic equation in the \(Z\)-axis direction is  
\[m\ddot{P}_{z}=-T_{mr}+mg+n_{pz} \tag{36}\]  
The expected height of \(P_{zr}\) is assumed, and thus the error of the height is \(e_{z}=P_{zr}-P_{z}\). The slide mode surface is designed of \(s_{z}=\beta_{z}\cdot e_{z}+\dot{e}_{z}\), satisfying \(\beta_{z}>0\), and \(s_{z}\) takes the time derivative as  
\[\dot{s}_{z}=\beta_{z}\cdot\dot{e}_{z}+\ddot{e}_{z}=\beta_{z}\cdot\dot{e}_{z}+ \frac{T_{mr}}{m}-g-\frac{n_{pz}}{m} \tag{37}\]The progression law of \(-(k_{z}+\overline{k}_{z})\text{sgn}(s_{z})-q_{z}s_{z}\) is considered, where \(k_{z}\) and \(\overline{k}_{z}\) are positive constants, satisfying \(\overline{k}_{z}\geq|n_{Pz}/m|\). Similarly, \(\overline{k}_{z}\) is designed for suppressing chattering and improving robustness. The control law of height can be designed as  
\[T_{mr}=mg-m(\beta_{z}\cdot\dot{e}_{z}+(k_{z}+\overline{k}_{z})\text{sgn}(s_{z} )+q_{z}s_{z}) \tag{38}\]  
Proof.: The Lyapunov function of \(V_{z}=s_{z}^{2}/2\) is considered. \(V_{z}\) is positive definite, and it takes the time derivative as  
\[\begin{array}{ll}\dot{V}_{z}&=s_{z}\cdot\dot{s}_{z}\\ &=s_{z}(\beta_{z}\cdot\dot{e}_{z}+\frac{T_{mr}}{m}-g-\frac{n_{Pz}}{m})\\ &=s_{z}(-(k_{z}+\overline{k}_{z})\text{sgn}(s_{z})-q_{z}s_{z}-\frac{n_{Pz}}{m} )\\ &\leq-(k_{z}+\overline{k}_{z})|s_{z}|-q_{z}s_{z}^{2}+|\frac{n_{Pz}}{m}|\cdot| s_{z}|\\ &\leq-k_{z}|s_{z}|-q_{z}s_{z}^{2}\\ &\leq 0\end{array} \tag{39}\]  
If and only if \(s_{z}=0\), the equality of the above formula holds true. Thus, \(\dot{V}_{z}\) is negative definite, and the stability of the system is certificated. \(\square\)  
According to the height control law, the servo's rotation angle can be calculated as follows:  
\[\delta_{col}=-(\theta_{col}+0.0553)/0.4095 \tag{40}\]  
where taking the parameters of Table 1 into the Equation (11), the collective pitch angle of the main rotor blades can be calculated as  
\[\theta_{col}\approx(-T_{mr}/0.867+v_{i,mr}-w_{a}-au_{a}+bv_{a})/27 \tag{41}\]
## 2 Theory of TRM-SBDC UWA Communication
### Principles of Copy-Correlation  
Actually, copy-correlation can be viewed as a matched filter that can achieve a maximum SNR gain under ideal conditions. In the paper, we use copy-correlation technique to estimate the UWA acoustic channel and calculate the time delay shift value of each symbol.  
According to Equations (1) and (3), the received sync signal can be expressed as:  
\[p_{r}(t)=\sum\limits_{i=1}^{N}A_{i}p(t-\tau_{i})+n_{p}(t) \tag{5}\]  
after copy-correlation processing, the output is:  
\[\begin{array}{ll}R(t)&=\int p_{r}(\tau)p(\tau-t)d\tau\\ &=\int\left[\sum\limits_{i=1}^{N}A_{i}p(\tau-\tau_{i})\right]p(\tau-t)d\tau+ \int n_{p}(\tau)p(\tau-t)d\tau\\ &=\sum\limits_{i=1}^{N}A_{i}\!\!\int p(\tau-\tau_{i})p(\tau-t)d\tau+n^{\prime} _{p}(t)\end{array} \tag{6}\]  
where \(n^{\prime}_{p}(t)=\int n_{p}(\tau)p(\tau-t)d\tau\), denotes the noise component. We can see that copy-correlation outputs a series of correlation peaks, and the positions of the peaks corresponding to the time delay of the multiple paths. According to the correlation peaks and their positions, we can obtain the channel estimation.  
Then the TRM is performed to compresses the multipath structure, and in an ideal case, the final sync signal can be expressed as:  
\[p^{\prime}_{r}(t)=\sum\limits_{i=1}^{N}A_{i}p(t-\tau_{1})+n^{\prime\prime}_{ p}(t) \tag{7}\]  
and copy-correlation outputs only one correlation peak at \(t=\tau_{1}\), which is the time delay of the sync code. Similarly, if the received signal is subjected to copy-correlation processing using the pattern pulse signal, we can obtain the correlation peaks of the symbols and calculate the time delay shifts.
# A Fast Dynamic Dim Target Tracking Approach for UAVs Using Improved Lightweight Siamese Networks
## 3 Materials and Methods
### Feature Extraction Network  
In the SiamRPN network target tracking algorithm using AlexNet [19] as the feature extraction network, the depth of the network is restricted and cannot extract enough deep features, so MobileNetV2 was chosen as the feature extraction backbone network. The MobileNetV2 network compared to feature extraction networks such as AlexNet, VGG [20] and ResNet [21] has the advantages of having fewer parameters and a faster operation speed.  
The MobileNetV2 network is designed for target detection with a final pooling layer and a \(1\times 1\) convolutional layer for channel number up-dimensioning for classification. It is not needed in tracking tasks. Moreover, the overall step size of the network is 32, and the resolution of the final extracted feature map is too low for the similarity measure of the SiamRPN network. In order to extract features from multiple layers for fusion for subsequent similarity measures, it is also necessary to adjust the network structure so that the layers that extract features output the same feature resolution.  
For this, the following modifications have been made to the MobileNetV2 network.  
1. Remove the final pooling layer and the \(1\times 1\) convolution layer.
2. Adjust the step size of the last four layers of the network to 1 and the total step size of the network to 8.
3. The remaining layers with linear bottlenecks in the inverted residual structure are removed from the first layer, as well as all layers in layers 6 and 7, using the null convolution [23] to extend the perceptual domain.  
The structure of the modified feature extraction network is shown in Table 2.  
The input feature map is firstly up-dimensioned to six times that of the previous dimension. The purpose of this step is to enable the convolutional layer to convolve using information from the high-dimensional space. Subsequently, depth-separable convolution is used to extract feature information; finally, we obtain the new feature map by dimensionality reduction and use linear bottlenecks instead of nonlinear activation functions  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network Layer** & **Input** & **Channel** & **Step** & **Attention** \\ \hline layer0 & \(127\times 127\times 3\) & 44 & 2 & \\ layer1 & \(63\times 63\times 44\) & 22 & 1 & \\ layer2 & \(63\times 63\times 22\) & 33 & 2 & \\ layer3 & \(31\times 31\times 33\) & 44 & 2 & Y \\ layer4 & \(15\times 15\times 44\) &  
to reduce the number of parameters of the whole model and improve the computational speed while retaining most of the nonlinear information.
# Discrete-Time Asymptotic Tracking Control System for a Satellite with a Solar Panel
## Appendix A Derivation of the Model (1) Using the Euler-Lagrange Method  
We model the satellite with an attached panel as two rigid bodies with a flexible connection, and we define the total kinetic and potential energies \(T\) and \(V\) as follows:  
\[T=\frac{I(\dot{\alpha})^{2}}{2}+\frac{p(\dot{\beta})^{2}}{2}\,,\quad V=\frac{ k(\alpha-\beta)^{2}}{2}\,. \tag{150}\]  
By introducing the Lagrangian  
\[L(\alpha,\beta,\dot{\alpha},\dot{\beta})=T(\dot{\alpha},\dot{\beta})-V(\alpha, \beta)=\frac{I(\dot{\alpha})^{2}}{2}+\frac{p(\dot{\beta})^{2}}{2}-\frac{k( \alpha-\beta)^{2}}{2} \tag{151}\]  
and computing its partial derivatives, as well as taking into account the generalized torques \(Q_{1}\) and \(Q_{2}\)  
\[\begin{array}{rcllcl}\frac{\partial L}{\partial\dot{\alpha}}&=&-k(\alpha- \beta)\,,&&\frac{\partial L}{\partial\beta}&=&k(\alpha-\beta)\,,\\ \frac{\partial L}{\partial\dot{\alpha}}&=&I\dot{\alpha}\,,&&\frac{\partial L}{ \partial\dot{\beta}}&=&p\dot{\beta}\,,\\ \frac{\mathrm{d}}{\mathrm{d}t}\big{(}\frac{\partial L}{\partial\dot{\alpha}} \big{)}&=&I\dot{\alpha}\,,&&\frac{\mathrm{d}}{\mathrm{d}t}\big{(}\frac{ \partial L}{\partial\dot{\beta}}\big{)}&=&p\ddot{\beta}\,,\\ Q_{1}&=&\tau-b(\dot{\alpha}-\dot{\beta})\,,&&Q_{2}&=&-b(\dot{\beta}-\dot{ \alpha})\,,\end{array} \tag{152}\]  
we easily obtain the Euler-Lagrange equations  
\[\left\{\begin{array}{rcll}I\ddot{\alpha}+k(\alpha-\beta)&=&\tau-b(\dot{ \alpha}-\dot{\beta})\,,\\ p\ddot{\beta}+k(\beta-\alpha)&=&-b(\dot{\beta}-\dot{\alpha})\,,\end{array}\right. \tag{153}\]  
which represent Model (1).
# MOCA: A Network Intrusion Monitoring and Classification System
## 5 Measurement Results  
In this section, the performance of MOCA in classifying attack classes was evaluated in the CICIDS2017 and CICDDOS2019 datasets. A total of 84 features in CICIDS2017 and 88 features in CICDDOS2019, including labels, were extracted based on real-world data (PCAPs). Flow ID, timestamp, IP, source, and destination port features were removed. In addition, 8 extra features in CICIDS2017 and 13 extra features in CICDDOS2019 were removed because they were all zeros. Based on the original features, connection features were derived for each sample. As a result, the datasets were left with 68 basic features in CICIDS2017 and 67 basic features in CICDDOS2019.