esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Architecture
### Network and Protocol Layer  
The network and protocol layer supports most of the communication protocols used by IIoT networks such as Bluetooth Low-Energy (BLE), Ethernet, Long-Term Evolution (LTE), LoRA (Long Range), Near Field Communication (NFC), WiFi, Bluetooth, Zigbee, DNP3 ModBus, and GSM/UMTS. LoRa is an efficient energy radio communication system effective in IoT configurations. The security threats and attacks toward the network and protocol layer are DDoS, privacy tracking, MitM and blueugging. Therefore, Rivest-Shamir-Adleman (RSA), triple data encryption standard (3 DES), digital signature algorithm (DSA) and elliptic curve Diffie-Hellman (ECDH) are the most important security solutions that could be implemented to disable the security threats related to the network and protocol layer.
# Agricultural Waste-Based Biochar for Agronomic Applications
## 3 Biochar as a Soil Amendment  
### Impacts on Soil Structure and Composition  
Biochar has been described as an attractive material that can be applied in agriculture to improve agricultural soil properties and crop productivity. The main impacts of biochar on soil characteristics and agricultural products yield are listed in Table 2 and Figure 2. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Biochar** & **Application Rate** & **Plant** & \begin{tabular}{c} **Effects on Soil** \\ **Properties** \\ \end{tabular} & **Effects on Crops** & **Ref.** \\ \hline Orchard prune & - & Tomato & Increase in soil pH and & Increase in fresh biomass & \\ residues & - & Tomato & \begin{tabular}{c} Increase in soil pH and \\ pore water \\ \end{tabular} & by 43\% and dry biomass & [68] \\ \hline Straw & \begin{tabular}{c} 0, 20, 40, and 60 g \\ biochar/ \\ kg soil \\ \end{tabular} & - & \begin{tabular}{c} Enhancement in \\ aggregate stability \\ Increase in the available \\ water contents of soils \\ \end{tabular} & - & [69] \\ \hline Maize stalk & - & Zucchini & Increase in soil organic & Increase in fruit yield by 27–195\% & [70] \\ \hline Peanut shell & - & Lettuce & \begin{tabular}{c} Increase in soil pH and \\ soil organic matter \\ \end{tabular} & \begin{tabular}{c} No significant effect on \\ vegetable yield \\ \end{tabular} & [71] \\ \hline Apple branch & \begin{tabular}{c} 0, 0.5\%, 1\%, 2\% and 4\% \\ \end{tabular} & Crabapple & \begin{tabular}{c} Decrease in soil nitrate \\ leaching \\ \end{tabular} & \begin{tabular}{c} Increase in biomass and N \\ use efficiency \\ \end{tabular} & [72] \\ \hline \end{tabular} \begin{tabular}{c} - & \
\begin{tabular}{c} 5 kg/tree \\ \end{tabular} & Apple & - &
\begin{tabular}{c} Crop yield and fruit \\ quality did not show any \\ improvement \\ Trunk girth significantly \\ improved as compared to \\ the control \\ \end{tabular} & [73] \\ \hline \end{tabular}
\end{table} 
Table 2: Influence of biochar application on soil properties and agricultural product yield. Figure 1: Overview of biochar properties and different forms of its agricultural application.
## 3 Application of Proposed Method on Nanchang Subway Line 3
### Calculated Deformation of Ground Surface and Pipeline  
The ground surface deformation and pipeline deformation are calculated by our proposed method. The input parameters for the calculation are shown in Table 1. To ensure the stability of the tunnel face, the support pressure is greater than the earth pressure. A differential value of 10 kPa is selected to obtain the deformation induced by support pressure at the tunnel face. The earth pressure also represents the normal force between the shield shell and the soil. The coefficient of the frictional force is 0.3 for medium sand. The ground deformation is calculated by using the Gaussian integration method to obtain the numerical results. The pipeline deformation is obtained by the finite difference method.  
Figure 8 shows the different components of the ground surface deformation induced by the shield tunnelling. \(Y\)-axis is taken as the shield tunnel construction direction, and the tunnel face is located at y = 0. The maximum ground surface deformation induced by the ground volume loss, support pressure, frictional force, and torsional force are 8.29, 0.79, 0.89, and 1.77 mm, respectively. The ground volume loss causes most of the deformation behind the shield tail. A tiny protrusion before the tunnel face is induced by the support pressure at the tunnel face and the frictional force of the shield shell. The torsional force of the cutter head produces an antisymmetric deformation of which one side is concave, and the other side is convex.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(z_{g}\) (m) & \(z_{p}\) (m) & \(H\) (m) & \(R\) (m) & \(V_{1}\) (\%) & \(S\) (m) & \(\mu\) & \(E_{s}\) (MPa) & \(EI\) (GPam\({}^{3}\)) & \(\Delta P\) (kPa) & \(T\) (kNm) \\ \hline
0.0 & 4.0 & 15.6 & 3.0 & 1.5 & 9.0 & 0.3 & 20.0 & 200.0 & 10.0 & 2500 \\ \hline \hline \end{tabular} Note: \(z_{g}\) and \(z_{p}\) are the depths of the ground surface and the pipeline, respectively; \(S\) is the length of the shield shell; \(\Delta P\) is the differential value of support pressure; \(T\) is the torsional force of the cutter head.  
\end{table}
Table 1: Input parameters.  
Figure 5: A plan view of Nanchang Subway.  
Figure 6: A partial plan view of Jing Jiashan-Shi Zijie section of Nanchang Subway Line 3.  
[PERSON] et al. [34] have indicated that the deformation induced by the torsional force was so small that it needs no more attention. However, the conclusion neglects the spatiotemporal characteristic of the tunnel construction. Due to the superposition of the deformation during the shield tunnelling, more attention should be paid to the deformation induced by different force patterns. Moreover, the pipeline and ground surface have similar longitudinal deformation profiles induced by the shield tunnelling. The magnitudes of the pipeline deformation are larger than those of the ground surface. The relevant description of the pipeline deformation is omitted.
## 3 Online Root Fault Identification Method
### Feature Vector Extraction
#### 3.1.5 Feature Vector Extraction  
The same fault of the wind turbine can happen multiple times. However, the alarm lists generated during the same fault are not always the same, since the physical processes are not deterministic, and the environmental conditions may differ when a fault occurs. This section aims to extract the feature vector of alarm vectors belonging to the same fault. The feature vector is used as a unique signature representing the occurrence of a fault.  
There are \(l_{k}\) alarm lists generated when fault \(k\) occurs. The alarm vector belonging to fault \(k\) is expressed as:  
\[V_{k}^{j}=\left[v_{k1}^{j},v_{k2}^{j},\ldots,v_{ki}^{j},\ldots,v_{KN}^{j} \right]^{\mathsf{T}}, \tag{7}\]  
where \(v_{ki}^{j}\) is the binary value of alarm \(i\) in the \(j\)-th alarm vector of fault \(k\); \(j=1,2,\ldots,l_{k}\). The feature vector of fault \(k\) is expressed as:  
\[C_{k}=\left[c_{k1},c_{k2},\ldots,c_{ki},\ldots,c_{KN}\right]^{\mathsf{T}}, \tag{8}\]  
where \(c_{ki}\) is the binary value of alarm \(i\) in fault \(k\); \(k=1,2,\ldots,P\); and \(P\) is the number of fault types. The feature vector \(C_{k}\) is built using the \(l_{k}\) alarm vectors belonging to fault \(k\). The binary value of alarm \(i\) in the alarm vector of fault \(k\) is calculated as follows:  
\[c_{ki}=\left\{\begin{array}{l}1,\;\text{if}_{i=1}^{\frac{k}{l_{k}}}v_{ki}^{ j}\\ \frac{k}{l_{k}}\geq f\text{ }\\ 0,\;\text{if}_{i=1}^{\frac{k}{l_{k}}}<f\text{ }\end{array}\right., \tag{9}\]  
where \(v_{ki}^{j}\) is the binary value of alarm \(i\) in the \(j\)-th alarm vector of fault \(k\); and \(ffeature vector is set to one; otherwise, it is set to zero. The value of _fr_ is set to 0.5 in this paper. It is determined according to the final performance.  
A pair of a fault and its feature vector is expressed as:  
\[(f_{k},C_{k}), \tag{10}\]  
where \(f_{k}\) is fault \(k\); \(C_{k}\) is the feature vector of fault \(k\); and \(k=1,2,\ldots,P\). The fault-template database is composed of faults and their feature vectors. It is expressed as:  
\[T=\left\{(f_{1},C_{1}),(f_{2},C_{2}),\ldots,(f_{k},C_{k}),\ldots,(f_{p},C_{p}) \right\}, \tag{11}\]  
where \(f_{k}\) is fault \(k\); \(C_{k}\) is the feature vector of fault \(k\); and \(k=1,2,\ldots,P\).
## 4 Results and Discussion
The point locations are shown in Figure 6c. Our validation supports the above observations. Only 32.9% of samples in case of VD 1.0 were confirmed, whereas 98.7% of samples were positively evaluated for VD 2.0, which:  
1. corroborates the finding that static image selection is volatile in areas where inter-annual or spatial variability in phenology is high, and
2. confirms the robustness of a data-driven approach using phenological metrics. However, it also needs to be noted that the expert could not arrive at a decision in 4.4% of cases (for both VD 1.0 and VD 2.0), primarily because disturbances were present, in which case the expert could not reliably judge the results of a monotonic trend fitted through the full time series.
# Error-Bounded Learned Scientific Data Compression with Preservation of Derived Quantities
## 1 Introduction
* _Autoencoder-based compression:_ AEs are trained on the transformed tensor coefficient space. Since tensor basis functions can take care of spatial correlation, AEs encode and decode tensor basis coefficients. We use AEs to obtain the encoded coefficients using standard loss measures and employ a quantization scheme to store the encoded coefficients. * _Error guarantees on PD:_ Error guarantees in the form of bounded reconstruction error are achieved by selectively applying error-bounded compressors on the residuals. (The residual is the difference between the reconstructed data of the AE and the PD). The residuals are processed with error-bounded lossy compressors to guarantee bounds on the overall PD error. * _QoI satisfaction:_ Preservation of QoI is addressed using constraint satisfaction on the reconstructed PD. After residual post-processing, we perform constrained optimization while minimizing the changes to the PD reconstruction. The parameters produced as a result of the QoI satisfaction step are quantized and stored with the encoded coefficients and residuals. Our experimental results show that our framework can achieve high compression ratios when the sizes of the latent space and the quantization dictionary are controlled. Additionally, we show that selective residual post-processing prevents significant distortion of data even at high compression ratios. Furthermore, the constraint satisfaction step can directly manage QoI preservation with tolerable errors (up to two to three orders of magnitude in compression ratios). We compare our framework to MGARD, which is currently the leading error-bounded lossy compressor with QoI preservation, and show that our approach can achieve a significantly better fidelity of PD and QoI. The rest of the paper is organized as follows. In Section 2, we discuss the X-point included Gyrokinetics Code (XGC) dataset in detail. In Section 3, our framework is presented. The experimental results are discussed in Section 4. Section 5 concludes the paper.
## 2 Full Form of (2+1)-Dimensional Fifth-Order KdV Equation and (2+1)-Dimensional Gardner Equation
### (2+1)-Dimensional Gardner Equation (KdV-mKdV Equation)  
Now, assume \(\beta\approx\gamma\approx\alpha^{2}\), as in Section 6 of [10]. Then, the Boussinesq equations obtained by retaining terms up to the second order take the following form  
\[\eta_{t}+w_{x}+\frac{\gamma}{\beta}\int w_{yy}\,dx+\alpha(\eta w) _{x}+\frac{\alpha\gamma}{\beta}\bigg{(}\eta_{y}\int w_{y}\,dx+\eta\int w_{yy} \,dx\bigg{)}-\frac{1}{6}\beta w_{3x}=0, \tag{18}\] \[w_{t}+\eta_{x}+\alpha ww_{x}+\frac{\alpha\gamma}{\beta}\,w_{y} \int w_{y}dx-\frac{1}{2}\beta(w_{xxt}+2\tau\eta_{xxx})=0. \tag{19}\]Proceeding as in Section 6 of [10], only supplementing \(w\) with a correction term of the order of \(\gamma^{2}/\beta^{2}\)  
\[w=u -\frac{\alpha}{4}u^{2}+\beta\bigg{(}\frac{2-3\tau}{6}\bigg{)}u_{xx} -\frac{1}{2}\frac{\gamma}{\beta}\int\bigg{(}\int u_{yy}\,dx\bigg{)}dx+\frac{ \alpha^{2}}{8}u^{3}\] \[+\frac{\alpha\gamma}{\beta}\bigg{(}\frac{5}{8}\int(\int(u_{y}^{2 }+uu_{yy})\,dx)\,dx-\frac{3}{8}\int(u\int u_{yy}\,dx)\,dx\bigg{)} \tag{20}\] \[+\frac{\gamma^{2}}{\beta^{2}}\frac{3}{8}\int(\int(\int(\int u_{4y }\,dx)\,dx)\,dx\]  
we obtain from [PERSON]'s Equations (18) and (19) the same nonlinear wave equation  
\[\eta_{t}+\eta_{x} +\frac{3}{2}\alpha\eta\eta_{x}+\frac{\gamma}{2\beta}\int\eta_{yy }dx-\frac{3}{8}\alpha^{2}\eta^{2}\eta_{x}+\frac{1}{6}\beta(1-3\tau)\eta_{3x}\] \[+\frac{\alpha\gamma}{\beta}\bigg{(}\frac{1}{8}\int(\eta_{y}^{2} +\eta\eta_{yy})\,dx+\frac{1}{8}\eta\int\eta_{yy}\,dx+\eta_{y}\int\eta_{y}\, dx-\frac{1}{2}\eta_{x}\int(\int\eta_{yy}\,dx)\,dx\bigg{)}\] \[-\frac{1}{8}\frac{\gamma^{2}}{\beta^{2}}\int(\int(\int\eta_{4y} \,dx)\,dx)\,dx=0. \tag{21}\]  
Equation (21), containing the new (last) term, constitutes the full (2+1)-dimensional Gardner equation, (sometimes referred to as the KdV-mKdV equation). The form published in [10], without this term, was obtained under the condition \(\eta_{4y}=0\). As in the case of the (2+1)-dimensional fifth-order KdV equation, the presence of a new term ensures the existence of solitary wave solutions to th (2+1) dimensional Gardner Equation (21), as shown in Section 4.
## 2 System Model
### Shadowing Correlation  
In the presence of correlation between log-normal shadowing of two channels \(\Omega_{1}\) and \(\Omega_{2}\), the joint probability density function (PDF) of \(\Omega_{1}\) and \(\Omega_{2}\) is given by  
\[\begin{split}& f_{\Omega_{1},\Omega_{2}}(x,y)=\frac{\xi^{2}}{2 \pi\sigma^{2}xy\sqrt{1-\rho^{2}}}\\ &\times\exp\left(\frac{1}{\rho^{2}-1}\Bigg{[}\frac{(10\log_{10}x -\mu_{1})^{2}}{2\sigma^{2}}+\frac{(10\log_{10}y-\mu_{2})^{2}}{2\sigma^{2}}- \frac{\rho}{\sigma^{2}}(10\log_{10}x-\mu_{1})(10\log_{10}y-\mu_{1})\Bigg{]} \right),\end{split} \tag{8}\]  
where \(x\geq 0\) and \(y\geq 0\). Also, \(|\rho|\leq 1\) is correlation coefficient between \(\Omega_{1}\) and \(\Omega_{2}\). If \(\rho=0\), the two channel \(h_{1}(d_{1})\) and \(h_{2}(d_{2})\) are uncorrelated, which corresponds to the joint probability density function as below:  
\[f_{\Omega_{1},\Omega_{2}}(x,y)=\frac{\xi^{2}}{2\pi\sigma^{2}xy}\exp\Bigg{(}- \Bigg{[}\frac{(10\log_{10}x-\mu_{1})^{2}}{2\sigma^{2}}+\frac{(10\log_{10}y- \mu_{2})^{2}}{2\sigma^{2}}\Bigg{]}\Bigg{)}. \tag{9}\]  
In contrast, if \(\rho=1\), then \(\Omega_{1}=\Omega_{2}\) following the marginal probability density function in (5). When \(\rho>0\), the diversity gain diminishes due to the correlated large-scale fading compared to the uncorrelated case (i.e., \(\rho=0\)).  
According to Table 1 in [34], there are more than 20 shadowing correlation models in the existing literature. The simplest one is the constant model, in which \(\rho\) is always fixed (e.g., \(\rho=0.5\) in [36]). Also, the most common correlation model is a decaying exponential of distance (i.e., \(\rho=e^{-d/d_{0}}\) with a constant \(d_{0}\)), as in [37; 38; 39; 40]. Extended from these models, more complex models are proposed. For example, [PERSON] et al. used the sum of two independent exponential processes [41]. In [42], a model is proposed with \(\rho=e^{(d/d_{0})^{\rho}}\) for \(v>0\). On  
\[\rho=e^{-\Delta_{t}/\Delta_{0}}, \tag{10}\]  
where \(\Delta_{0}>0\) is the de-correlation distance, which is tunable depending on channel environments. As in [40], we assume \(\Delta_{0}=1.7\) m.  
As noted in (10), the macro-diversity gain increases as \(\Delta_{t}\) due to smaller correlation \(\rho\). However, as \(\Delta_{t}\) increases, the path loss becomes more severe, because \(\mu_{i}\) in (6) is a decreasing function of \(\Delta_{t}\). Such increase in path loss by \(\Delta_{t}\) is greater with higher path-loss exponent \(n\). For this reason, there may exist an optimal inter-separation between the two transmitters \(\Delta_{t}^{*}\) that minimizes the error probability.
# Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion
## 3 Methods for Path Loss Prediction
### Decision Tree  
A decision tree usually contains a root node, some internal nodes, and some leaf nodes. A single decision tree model often has overfitting risk. Thus, new algorithms based on decision tree are proposed, such as AdaBoost and RF [23]. Here, we put the focus on the descriptions of RF.  
RF is a machine learning method that combines decision tree and bagging. It applies bootstrap aggregating to each decision tree learner for training samples selection. Furthermore, the random selection of features is introduced in tree training, which means just a set of features is randomly selected for each node split. Therefore, RF is less affected by sample disturbance and feature disturbance, and has higher generalization performance.  
For path loss prediction, the predicted value of new samples can be made by averaging the predictions from all the individual decision tree, which is expressed as  
\[y^{\prime}=\frac{1}{T}\sum_{t=1}^{T}\hat{h}_{t}\left(\mathbf{x}\right) \tag{8}\]  
where \(T\) is the number of decision tree learner, \(\hat{h}_{t}\left(\mathbf{x}\right)\) is the prediction of the \(t\)th decision tree learner.
# Stimuli-Responsive Polymeric Nanosystems for Controlled Drug Delivery
## 3 Stimuli-Responsive Polymeric Nanosystems
### Exogenous Stimuli-Responsive Polymeric Nanosystems
#### 3.2.2 Thermo-Responsive Polymeric Nanosystems  
Heat is the other extensively explored stimulus for developing activatable drug delivery nanop platforms. This kind of nanocarrier is engineered from the polymers incorporated with the functional moieties that undergo a structure change in response to thermo stimulus.  
Figure 6: (**a**) Schematic illustration of using NIR light excitation of UCNPs to trigger dissociation of BCP micelles. (**b**) NIR light-triggered photoreaction with the used BCP of PEO-b-PNBMA and UCNPs of NaYF4:TmYb. Reproduced from ref. [150] with permission from the American Chemical Society.  
These polymers exhibit a phase transition in response to different solution temperatures, leading to a dramatic change in their solubility [151; 152]. In the case of lower critical solution temperature (LCST), the polymer is miscible with the solvent when the temperature is below LCST, while the polymer will be hydrophobic when the temperature is above LCST. The other case is upper critical solution temperature (UCST). In this condition, the polymers will undergo a phase transition when the temperature is cooled below UCST. Poly(\(N\)-isopropyl acrylamide) (pNIPAAm) is one of the most extensively explored thermo-sensitive polymers with an LCST of 32 \({}^{\circ}\)C below body temperature, indicating that it is not a good candidate for construction of drug delivery nanoplatforms [153; 154]. To regulate the LCST of pNIPAAm, the copolymers with hydrophobic comonomers were synthesized and a variety of polyNIPAAm-based thermo-sensitive polymeric materials with the desired LCST range have been obtained [151]. [PERSON] et al. constructed the thermo-sensitive polymeric micelles based on poly(\(N\)-isopropylacrylamide-co-\(N\),\(N\)-dimethylacrylamide)-b-poly(D,L-lactide) (P(IPAAm-DMAAm)-b-PLA) and studied their cellular uptake mechanism induced by temperature [155]. The Hydrodynamic size of prepared micelles is about 20 nm and the LCST is about 39 \({}^{\circ}\)C. When the temperature is below the LCST, their cellular uptake can be significantly inhibited. Interestingly, the micelles can be internalized into cells and localize at Golgi apparatus when the temperature is above the LCST (42 \({}^{\circ}\)C). Additionally, the other thermo-responsive polymers that can be used for drug delivery applications include poly(oligo(ethylene glycol)(methyl ether) (meth)acrylate) (POEGMA) [156] poly(ether)s [157] poly(2-alkyl-2-oxazoline)s [158] poly(\(N\),\(N\)-dimethylaminoethyl methacrylate) [159] and poly(\(N\)-vinyl amide) (mainly poly(\(N\)-vinylcaprolactone), PVCL) [160].
## 4 Test Results and Discussion
### Relationship between Shear-Compression Ratio and Pressure Features
#### 4.4.2 Ultimate Strength of FHSB T-Stub Connection  
The smaller the ratio, the closer the ultimate strength is to the initial friction load. The mean ultimate strengths of MC-60, MC-45 and MC-35 specimens were 2207.95 kN, 1570.78 kN and 788.60 kN, respectively. Table 4 provides the initial friction load, ultimate strength and the improved coefficient of each specimen.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{\(\mathbf{\lambda}\)} & \multirow{3}{*}{**Specimen**} & \multicolumn{2}{c}{**Initial Clamping**} & \multirow{3}{*}{**Initial**} & \multirow{3}{*}{**Compressive**} & **Ultimate** & **Improved** \\  & & & **Force P/kN** & & **Friction Load** & **Load** & **Strength** & **Coefficient** \\ \cline{3-8}  & & **Left Bolt** & **Right Bolt** & **F\({}_{\mathrm{I}}\)/kN** & **F\({}_{\mathrm{C}}\)/kN** & **F\({}_{\mathrm{U}}\)/kN** & **v** \\ \hline \multirow{6}{*}{0.70} & NMC-35 & 166.8 & 165.6 & - & - & 700.2 & - \\  & MC-35-01 & 165.0 & 169.1 & 2179.6 & 1047.5 & 2179.6 & 7.07 \\ \cline{1-1} \cline{2-8}  & MC-35-02 & 167.6 & 167.5 & 2223.6 & 561.0 & 2223.6 & 7.19 \\ \cline{1-1} \cline{2-8}  & MC-35-03 & 164.9 & 166.3 & 2293.1 & 1192.2 & 2293.1 & 7.50 \\ \cline{1-1} \cline{2-8}  & MC-35-04 & 163.9 & 167.3 & 2145.6 & 698.7 & 2145.6 & 7.02 \\ \cline{1-1} \cline{2-8}  & MC-35-05 & 164.4 & 165.8 & 2208.5 & 650.1 & 2208.5 & 7.25 \\ \cline{1-1} \cline{2-8}  & MC-35-06 & 163.9 & 165.3 & 2197.3 & 1047.3 & 2197.3 & 7.23 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The initial friction load, compressive load, ultimate strength and improved anti-slip coefficient of each specimen.  
Figure 10: The friction resistance curve of the specimens.