esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation on Interference Test for Wells Connected by a Large Fracture
## Appendix A Pressure Transient of WsCLF with Crossflow of the Reservoir  
When the crossflow of a reservoir to the large fracture cannot be ignored, the flow regime is shown in Figure 11. Zone 1 is the large fracture, and Zone 2 is the reservoir.  
The dimensionless quantities are defined as follows  
\[p_{D}=\frac{2 Kh}{q\mu B}(p_{i}-p),\ x_{D}=\frac{x}{b},\ t_{D}=\frac{\eta t}{b^ {2}},\ C_{f}=\frac{\eta}{\eta_{m}},\ F_{D}=\frac{K_{m}}{K},\]  
where \(\eta_{m}\) is the pressure conductivity coefficient of reservoir matrix (m\({}^{2}\)/s), and \(K_{m}\) is the permeability of reservoir matrix (m\({}^{2}\)). It is easy to derive the dimensionless governing equations, initial conditions and boundary conditions. In Zone 1, the governing equation is [42]  
\[\frac{\partial^{2}p_{1D}}{\partial x_{D}^{2}}+2F_{D}\left.\frac{\partial p_{2D }}{\partial y_{D}}\right|_{y_{D}=0}=\frac{\partial p_{1D}}{\partial t_{D}} \tag{12}\]  
The initial conditions and boundary conditions are  
\[p_{1D}(0,x_{D},0)=0 \tag{13}\]\[p_{1D}(t_{D},\infty,0)=0 \tag{10}\] \[\frac{\partial p_{1D}}{\partial x_{D}}\bigg{|}_{x_{D}=0}=-1 \tag{11}\]  
In Zone 2, the governing equation is [42]  
\[\frac{\partial^{2}p_{2D}}{\partial y_{D}^{2}}=C_{f}\frac{\partial p_{2D}}{ \partial t_{D}} \tag{12}\]  
The initial conditions and boundary conditions are  
\[p_{2D}(t_{D},x_{D},0)=p_{1D}(t_{D},x_{D},0) \tag{13}\]  
\[p_{2D}(0,x_{D},y_{D})=0 \tag{14}\]  
\[p_{2D}(t_{D},x_{D},\infty)=0 \tag{15}\]  
The followings can be obtained from Equations (11)-(15) through Laplace transformation  
\[\frac{\partial^{2}\overline{p}_{1D}}{\partial x_{D}^{2}}+2F_{D}\left.\frac{ \partial\overline{p}_{2D}}{\partial y_{D}}\right|_{y_{D}=0}=u\overline{p}_{1D} \tag{16}\]  
\[\overline{p}_{1D}(u,\infty,y_{D})=0 \tag{17}\]  
\[\left.\frac{\partial\overline{p}_{1D}}{\partial x_{D}}\right|_{x_{D}=0}=- \frac{1}{u} \tag{18}\]  
\[\frac{\partial^{2}\overline{p}_{2D}}{\partial y_{D}^{2}}=C_{f}u\overline{p}_ {2D} \tag{19}\]  
\[p_{2D}(u,x_{D},0)=p_{1D}(u,x_{D},0) \tag{20}\]  
\[p_{2D}(u,x_{D},\infty)=0 \tag{21}\]  
The following can be derived using Equations (19) and (21)  
\[p_{2D}=c_{1}e^{-\sqrt{C_{f}u}y_{D}} \tag{22}\]  
where \(u\) is the Laplace transform variable, and \(c_{1}\) is constant with respect to variable \(y_{D}\). By combining Equations (20) and (22), it yields  
\[\overline{p}_{1D}(u,x_{D},0)=c_{1}(u,x_{D}) \tag{23}\]  
Substituting Equations (23) and (22) into Equation (16) results in  
\[c^{\prime\prime}{}_{1}=\Big{(}u+2F_{D}\sqrt{C_{f}u}\Big{)}c_{1} \tag{24}\]  
Using Equations (24), (17) and (18), the pressure transient solution of Zone 1 in the Laplace domain can be obtained as follows  
\[\overline{p}_{1D}(u,x_{D},0)=\frac{1}{u\sqrt{u+2F_{D}\sqrt{C_{f}u}}}e^{-\sqrt {u+2F_{D}\sqrt{C_{f}u}}x_{D}} \tag{25}\]  
The pressure transient solution in the real domain can be obtained using the solution (25) in the Laplace domain using the Stehfest numerical inversion method.
## 3 Results
### Experiment 1 and Experiment 2 Comparative Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**MSE**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**MAE**} & \multirow{2}{*}{**R**} & \multirow{2}{*}{**R2**} & \multirow{2}{*}{**STD**} & \multirow{2}{*}{**MaxAE**} \\ \cline{1-1} \cline{6-10}  & & & & & & **Train** & **Test** & **Train** & **Test** & **Test** \\ \hline Model 4201 & 1.2546 & 1.1201 & 0.7214 & 0.9946 & 0.9892 & 1.1204 & 11.0010 \\ \hline Model 4208 & 1.1643 & 1.0790 & 0.6938 & 0.9951 & 0.9900 & 1.0790 & 9.8748 \\ \hline Model 4301 & 1.2046 & 1.0976 & 0.7101 & 0.9948 & 0.9896 & 1.0979 & 10.0270 \\ \hline Model 4303 & 1.1720 & 1.0826 & 0.6885 & 0.9950 & 0.9899 & 1.0827 & 11.1998 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparing the results of the best model from each experiment and the best models from the training datasets. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Activation**} & \multirow{2}{*}{**Lambda**} & \multirow{2}{*}{**L1**} & \multirow{2}{*}{**L2**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**R2**} & \multicolumn{2}{c}{**MSE**} & \multicolumn{2}{c}{**MAE**} \\ \cline{5-10}  & & & & & **Train** & **Test** & **Train** & **Test** & **Test** \\ \hline Model 4204 & ‘sigmoid’ & 0.000214222 & 125 & 148 & 1.0388 & 1.1125 & 0.9913 & 0.9893 & 1.2376 & 0.7407 \\ \hline Model 4208 & ‘sigmoid’ & 6.93 \(\times\) 10\({}^{-5}\) & 85 & 141 & 1.0426 & 1.0790 & 0.9912 & 0.9900 & 1.1643 & 0.6938 \\ \hline Model 4210 & ‘tanh’ & 0.001612633 & 82 & 154 & 1.0451 & 1.1023 & 0.9912 & 0.9895 & 1.2150 & 0.6988 \\ \hline Model 4211 & ‘tanh’ & 0.00199953 & 74 & 175 & 1.0460 & 1.1083 & 0.9911 & 0.9894 & 1.2284 & 0.7068 \\ \hline Model 4212 & ‘sigmoid’ & 7.22 \(\times\) 10\({}^{-5}\) & 73 & 112 & 1.0463 & 1.0963 & 0.9911 & 0.9896 & 1.2019 & 0.7184 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The five best models from Experiment 1 (10-fold CV). In addition, Model 4208 has a high correlation coefficient (R) of 0.9950, indicating a strong linear relationship between the predicted and actual values. The coefficient of determination (R\({}^{2}\)) is 0.98996, which means that the model captures a substantial portion of the variance in the data. The standard deviation (STD) is 1.07898, which indicates a relatively low dispersion of the residuals. The table displays the performance metrics of different models in predicting hardness. Each model is assessed based on various metrics, including mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R), coefficient of determination (R\({}^{2}\)), standard deviation (STD), and maximum absolute error (MaxAE). To summarize, Model 4208 is a promising candidate for hardness prediction. It shows a balanced performance on various metrics, with a particularly notable strength in minimizing the squared error and a high correlation with the actual data. Figure 5: Comparison of training losses for the two best models from the training and test datasets.
# An Aquatic Mobile Sensing USV Swarm with a Link Quality-Based Delay Tolerant Network
## 6 Performance Evaluation
### Simulator Description  
As mentioned in Section 5, a realistic aquatic simulation environment based on ROS and Gazebo was developed. In Gazebo, a world describes a collection of robots and objects (such as buildings, tables, and lights), and global parameters including the sky, ambient light, and physics properties. After launching all services, nodes, and topics, the simulation starts in Gazebo and Rviz as shown in Figure 11, where the lake world is shown on the left in Gazebo.  
For the navigation module, a grid map type is selected. The simulated USV has two thrusters, each with a unique frame, which in turn enables the lookup of the transformation matrix of each thruster and the USV's body through the use of tf (a package that lets the user keep track of multiple coordinate frames over time). We use a function from [PERSON] et al. [31] to automatically generate the thruster allocation matrix and translate controls into each thruster's commands.  
To generate the waypoints in the navigation module, a grid map is being updated by the sensors, given the path to a destination point. The position of each robot in the grid map can be updated by the ground truth value given by Gazebo, or the simulated GPS value. The latter can have an associated noise with it.  
In Gazebo, a lower real time factor (RTF) does not invalidate the simulation, but a RTF of 1 is desirable, so simulated results can be compared with the results from a real environment with the same modules; however, depending on the machine resources, the RTF can be lower. Step size and update rate are the parameters that can change the RTF. For a computer with an Intel Core i7-7500U, a NVIDIA GEFORCE 940 Mx Graphic Card and 8 GB of RAM, Table 4 shows how these parameters affect the RTF.  
Another factor that influences the RTF is the graphic display, which can be disabled, allowing a higher number of USVs in the simulation. For the computer described above, this allows simulations with up to 11 USVs. We can also use ROS bag tools for simulating more USVs.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Number of USVs** & **Step Size** & **Update Rate** & **RTF** \\ \hline
4 & 0.001 & 1000 & 0.7 \\
4 & 0.01 & 100 & 8 \\
7 & 0.01 & 100 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Real time factor depending on number of USVs, step size and update rate.  
Figure 11: Gazebo on the left and Rviz Editor with an Rviz configuration file to the namespace odin1 on the right.
# Insights into Bending Stiffness Modeling of Elementary Flexure Hinges
## 6 Comparison of Analytical and Numerical Solutions
### Case III  
In this last case study, the effects of \(\alpha_{Q}\) and \(\alpha_{M}\) on the rotation \(\theta_{F}\) are analyzed when the other parameters are fixed. In the analyses, the following parameter values are chosen: \(\alpha_{F}=0.05\), \(\rho=15\) and \(\beta_{y}=0.1\).  
Results are shown in Tables 3 and 4 and in the sensitivity maps of Figure 15. From the tables and the figure, it can be noted that the error decreases as \(\alpha_{Q}\), in absolute value, decreases and \(\alpha_{M}\) increases. Furthermore, it can be observed that the angular displacements increase when \(\alpha_{M}\) decreases and when \(\alpha_{Q}\) approaches 1 in absolute value.  
In Figure 16, the values of the maximum tension \(\sigma\) in the flexure hinge are shown for different geometries (varying \(\rho\) and \(\beta_{y}\)) and different load conditions (varying \(\alpha_{Q}\) and \(\alpha_{M}\)). In the first case, the value of tension is quite uniform for the different geometries. It increases greatly when \(\beta_{y}\) increases and \(\rho\) decreases. In the second case, it can be observed, as expected, that the value of stress is not uniform at the different load conditions. In particular, the stress increases for small values of \(\alpha_{M}\) and when \(\alpha_{Q}\), in absolute value, is close to 1.  
To summarize, tables and figures in all the proposed case studies provide information about the effect of geometric parameters in the kinematic behavior of a flexure joint, showing the importance of a proper stiffness model that will be required to move it accurately.  
Nonlinear models can be typically avoided when small axial forces are applied to the free end of the flexure hinge, whereas the differences between the FE model and the linear analytical model become relevant when more important axial forces are involved.  
On the contrary, it is evident from the proposed results that the linearization due to small rotations and the neglection of the term due to the axial deformation are both noninfluential contributions, at least for the typical rotation range allowed to flexure hinges. Their removal from the model is then allowed with the aim of obtaining a solvable formulation in analytical form. A linear model with an analytical solution is, therefore, the best choice in case axial forces have limited values.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \(\rho\) & \(\beta_{y}\) & \(\theta_{f,A}^{+}[^{\circ}]\) & \(\theta_{f,\mu}^{+}[^{\circ}]\) & \(\theta_{f,\mu-\varepsilon}^{+}[^{\circ}]\) & \(\theta_{f,\mu-T}^{+}[^{\circ}]\) & \(\theta_{f,\mu-a(\bar{\chi})}^{+}[^{\circ}]\) & \(\theta_{f,FEM}^{+}[^{\circ}]\) \\ \hline  
\hline \end{tabular}
\end{table}
Table 2: CASE II—Comparison between analytical and numerical models with \(\alpha_{F}\), \(\alpha_{Q}\) and \(\alpha_{M}\) fixed while \(\rho\) and \(\beta_{y}\) vary.
## 7 Performance Evaluation
### Simulations Settings  
Implemented applications have been run in Tossim. We have used traces (Intel Berkeley Research Lab on [[http://db.csail.mit.edu/labdata/labdata.html](http://db.csail.mit.edu/labdata/labdata.html)]([http://db.csail.mit.edu/labdata/labdata.html](http://db.csail.mit.edu/labdata/labdata.html))) containing temperature, humidity and light readings gathered by multisensors in a building. Thus, the data gathered for our simulation comes from a scenario close to reality. It contains readings of 54 sensor nodes deployed in laboratories at intervals of 31 seconds. These readings were held during the day, between 28 February and 5 April 2004.  
We embed all four application versions within the sensor nodes in the Tossim. Then, the performance of prediction accuracy of the different applications was measured. Also, the energy consumption of data communication in an original application version was tracked. The energy consumption of the original version with three enhanced versions was compared, with two using simple linear regression and one using multiple linear regression (our proposed solution).  
The two parameters used to reveal the overperformance or underperformance of prediction accuracy of our solution compared to current works are the Residual Sum of Squares (SSerr) and coefficient of determination (R\({}^{2}\)). SSerr [Equation (8)] is the sum of power of prediction errors for each dependent variable using simple or multiple linear regression. R\({}^{2}\) [Equation (9)] represents the improvement of the sum of the power of prediction errors. More details about these parameters can be found in [PERSON] _et al._[9]:  
\[SSerr=\sum_{t=1}^{n}(Y_{t}-Y_{q_{i}})^{2} \tag{8}\]  
Figure 11: Coefficients packet length (version 4).  
Figure 12: Correlation packet length (version 4).  
Figure 13: Readings packet length (version 4).  
Let:  
\[SSreg=\sum\
olimits_{i=1}^{n}(Y_{q_{i}}-\bar{Y})^{2}\ ;\ \ SStot=\sum\
olimits_{i=1}^{n}(Y_{i}-\bar{Y})^{2}\]  
\[R^{2}=\frac{SSreg}{SStot} \tag{9}\]  
where \(Y_{q_{i}}\) represents an one-dimensional vector, which contain the values of the predictions made by one dependent variable \(q\). \(Y_{q_{i}}=y_{q_{1}},...,y_{q_{i}}\), where \(i=1,...,n\) and \(n\) is the number of samples. \(Y\) is an one-dimensional vector, which represents samples window of the independent variables, with \(Y=y,...,y_{i}\), where \(i=1,...,n\) and \(n\) is the number of samples. \(\bar{Y}\) represents the average of samples of the vector. SSreg is the regression sum of squares and SStot is the total sum of squares.  
The performance evaluation of our solution was also measured by ranging the sample amount. This shows how much our solution is affected by the trade-off between prediction accuracy and energy consumption. We repeated the scenario that had the best results among the scenarios simulated to check the behavior of our solution.
# Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine
## 2 The Preliminaries
### Multi-Instance Multi-Label Learning
Let \(S=\{(\mathbf{x}_{1},Y_{1}),(\mathbf{x}_{2},Y_{2}),\)\(\ldots,(\mathbf{x}_{m},Y_{m})\}\) be the dataset, where \(\mathbf{x}_{i}{\in}X\) is an instance and \(Y_{i}{\subseteq}Y\) a set of labels \(\{y_{1}^{(i)},y_{2}^{(i)},\ldots,y_{l_{i}}^{(i)}\}\), \(y_{k}^{(i)}{\in}Y(k=1,2,\ldots,l_{i})\). We can first obtain a vector \(\mathbf{v}_{l}\) for each class label \(l{\in}Y\) by averaging all of the training instances of label \(l\), _i.e._, \(\mathbf{v}_{l}=\frac{1}{|S_{l}|}\sum\limits_{\mathbf{x}_{i}\in S_{l}}\mathbf{ x}_{i}\), where \(S_{l}\) is the set of all of the training instances \(\mathbf{x}_{i}\) of label \(l\). Then, each instance can be transformed into a bag, \(B_{i}\), of \(|Y|\) instances by computing \(B_{i}=\{\mathbf{x}_{i}{-}\mathbf{v}_{l}|l{\in}Y\}\). As such, the single-instance multi-label dataset \(S\) is transformed into an MIML dataset \(S^{\prime}=\{(B_{1},Y_{1}),(B_{2},Y_{2}),\)\(\ldots,(B_{m},Y_{m})\}\). The benefits of such a transformation are intuitive. First, for an object associated with multiple class labels, if it is described by only a single instance, the information corresponding to these labels is mixed and thus difficult to learn. However, by breaking the single-instance into a number of instances, each corresponding to one label, the structure information collapsed in the single-instance representation may become easier to exploit. Second, for each label, the number of training instances can be significantly increased. Moreover, when representing the multi-label object using a set of instances, the relation between the input patterns and the semantic meanings may become more easily discoverable. In some cases, understanding why a particular object has a certain class label is even more important than simply making an accurate prediction while MIML offers a possibility for this purpose. For example, using MIML, we may discover that one object has label \(l_{1}\) because it contains \(instance_{n}\); it has label \(l_{k}\) because it contains \(instance_{i}\); while the occurrence of both \(instance_{1}\) and \(instance_{i}\) triggers label \(l_{j}\). Formally, the task of MIML is to learn a function \(f_{MIML}\): \(2^{X}\to 2^{Y}\) from a given dataset \(\{(X_{1},Y_{1}),(X_{2},Y_{2}),\ldots,(X_{m},Y_{m})\}\), where \(X_{i}{\subseteq}X\) is a set of instances \(\{x_{1}^{(i)},x_{2}^{(i)},\ldots,x_{n_{i}}^{(i)}\}\), \(x_{j}^{(i)}{\in}X(j=1,2,\ldots,n_{i})\) and \(Y_{i}{\subseteq}Y\) is a set of labels \(\{y_{1}^{(i)},y_{2}^{(i)},\ldots,y_{l_{i}}^{(i)}\}\), \(y_{k}^{(i)
## 4 Validation and Evaluation  
We show the experimental findings acquired using the SM-TCNNET model on two distinct datasets: the self-collected dataset and the StanWiFi dataset, in this section. In addition to confirming the performance results of our proposed SM-TCNNET model on the self-collected dataset, we conducted a comparison research analysis of the SM-TCNNET model on the StanWiFi dataset with other state-of-the-art models.
## 2 Materials and Methods
### Samples
#### 2.1.1 NIR Measurements  
All the available samples were analyzed in diffuse reflectance by a Fourier transform-Near infrared (FT-NIR) instrument (Nicolet 6700, Thermo Scientific Inc., Madison, WI, USA) equipped with an integrating sphere which allowed the direct analysis of the samples without any further pretreatment.  
An aliquot of each sample was introduced into a glass vial collocated on the top of the window of the integrating sphere. For each sample, two analytical replicates were analyzed in the spectral range between 4000 cm\({}^{-1}\) and 10,000 cm\({}^{-1}\), at a nominal resolution of 4 cm\({}^{-1}\). Spectra were collected in reflectance mode, visualized by the OMNIC software (Thermo Scientific Inc., Madison, WI, USA) and exported as.CSV files to be further processed by means of in-house written functions running in MATLAB (R2015b; The Mathworks, Natick, MA, USA). Prior to the chemometric analysis, signals (originally acquired as reflectance, R) were converted to pseudo-absorbance (log(1/R)).
# Adaptive Backstepping Control of Quadrotor UAVs with Output Constraints and Input Saturation
## 5 Conclusions  
In this paper, we study the quadrotor UAV position-attitude trajectory tracking problem with external disturbances, internal uncertainties and input-output constraints. An ANNBC is proposed by combining BLF, DSC, an adaptive neural network and an anti-saturation auxiliary system. A tandem control scheme is adopted to design position and attitude controllers for the UAV. An adaptive neural network is used to approximate the uncertainty term of the UAV model. A robust term is designed to balance the total system disturbance. The BLF is used to cope with attitude constraints, while a new auxiliary system is introduced to solve the input saturation problem. Using the ANNBC, the UAV achieves high accuracy in tracking performance and input-output constraints. The stability of the system is guaranteed and all signals are bounded.  
Due to limited conditions, the designed controllers in this paper are not simulated in a sufficient number of specific environments. In future research, we will consider validating the performance in more complex environments. In addition, we will further consider the UAV trajectory tracking problem with asymmetric time-varying output constraints, which is not considered in this paper.  
Methodology, [PERSON] ([PERSON]) and L.W.; software, [PERSON] ([PERSON]); validation, [PERSON] ([PERSON]), [PERSON] ([PERSON]) and K.H.; writing--original draft preparation, [PERSON] ([PERSON]) ; writing--review and editing, [PERSON] ([PERSON]) and [PERSON]; supervision, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Hubei Province grant number 2022 CFB449.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 9: Curves of control inputs without anti-saturation strategy and with the anti-saturation strategy.
## 3 Methods
### Resilience Evaluation and Network Repair
#### 3.3.2 Repair Strategies  
Under heavy rains, it is assumed that when all the top-10 high-risk nodes for both landslide and flood scenario fail, the repairment of transfer stations requires 2 days and other stations require 1 day [26]. Here, we consider four recovery strategies: random repair, priority recovery based on the topological importance index, service efficiency index, and optimal recovery using a genetic algorithm. Each strategy offers a distinct approach to restoring system performance. Random repair involves selecting nodes for recovery in a random sequence, providing a baseline for comparison. Priority recovery based on the topological importance index targets nodes with the highest topological importance in the network, aiming to quickly restore connectivity and robustness. Similarly, the service efficiency index prioritizes nodes that play a critical role in maintaining service performance. The final strategy identifies the optimal recovery sequence by simulating and evaluating multiple strategies, ensuring the most efficient and effective restoration process.  
To optimize the recovery strategy, the Genetic Algorithm (GA) was employed. The genetic algorithm is an optimization technique based on natural selection and genetic  
Figure 5: Schematic diagram of the metro network performance under disruptions.  
mechanisms, simulating the biological evolution process to find the global optimal solution through multiple iterations. In this algorithm, the population consists of several individuals, each representing a possible solution, with the population size being 50. The solution is continuously optimized through selections, crossovers, and mutations. This study's genetic algorithm represents the repair sequence using a permutation encoding scheme, with each individual corresponding to a permutation of nodes. The crossover operator employs Partially Mapped Crossover (PMX), where two crossover points are randomly selected. Genes within the interval are exchanged between parents, and external conflicting genes are adjusted without explicit parameters. The mutation operator utilizes random swap mutation, randomly swapping the positions of two genes with a mutation probability of 0.15, enhancing the exploration capability of the solution space. The maximum number of generations is 50. As the number of iterations increases, the genetic algorithm progressively optimizes the priority of node recovery to enhance the overall network recovery efficiency, ultimately achieving the optimal resilience value.