esa-sceva/satcom-chunk-collection huggingface dataset:
# A 1D-2D Coupled Lattice Boltzmann Model for Shallow Water Flows in Large Scale River-Lake Systems
## 3 Numerical Experiments
### The Dam Break Flow
The slopes of the least-squares fits were 1.92, 1.916, and 1.901 for the above three cases, respectively, indicating the second-order accuracy of the 1D-2D coupled model for the dam break flow. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{\(x_{1}\)} & \multicolumn{3}{c}{\(x_{2}\)} \\ \cline{2-7}  & _h (m)_ & **AE (m)** & **RE (\%)** & _h (m)_ & **AE (m)** & **RE (\%)** \\ \hline Analytic & 7.203 & - & - & 4.414 & - & - \\
1D model & 7.180 & 0.023 & 0.32 & 4.411 & 0.003 & 0.07 \\
2D model & 7.170 & 0.033 & 0.46 & 4.410 & 0.004 & 0.09 \\
1D–2D & 7.169 & 0.034 & 0.47 & 4.414 & 0 & 0 \\ model & \multicolumn{1}{c}{} & & & & & \\ \hline
**Methods** & _u (m/s)_ & **AE (m/s)** & **RE (\%)** & _u (m/s)_ & **AE (m/s)** & **RE (\%)** \\ \hline Analytic & 0.906 & - & - & 4.557 & - & - \\
1D model & 0.938 & 0.032 & 3.53 & 4.551 & 0.006 & 0.13 \\
2D model & 0.952 & 0.046 & 5.08 & 4.550 & 0.007 & 0.15 \\
1D–2D & 0.951 & 0.045 & 4.97 & 4.544 & 0.013 & 0.28 \\ model & \multicolumn{1}{c}{} & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: At coupling interfaces of the three models at \(t=4\) s (absolute error (AE), relative error (RE)). Figure 5: Convergence rate of the 1D-2D coupled model for the dam break flow.
## 4 Estimation Strategy of RUL Calculation Using Database Model
### Adaptation and Application of the Described RUL above on Our System  
In this research, we will use the above-described strategy to calculate the RUL in the case of cracks in one magnet; the system we are examining is a piece of a magnet in the PMM. The first detection of the fault will be when a crack of 1 mm depth is detected. RUL calculation is mainly the time needed for the faulty system to deteriorate. However, in our study, the final set of data will be collected in the case of 4 mm crack depth which is a near representation of the total fracture of the magnet.  
First we will construct a synthetic offline database 'SODM'. SODM will have the form of a matrix, as shown below:  
\[SODM=\begin{bmatrix}DS01&DS02&\dots&DS0t\\ DS11&DS12&\dots&DS1t\\ DS21&DS22&\dots&DS2t\\ \vdots&\vdots&\ddots&\vdots\\ DSm1&DSm2&\dots&DSmt\end{bmatrix}\]  
SODM represents sensor data in the healthy case and the case of cracks in a magnet inside the PMM. Its size is (m x t), where:'m' is the number of states representing the systematic propagation of the studied fault, and 't' is the number of sampling points.  
The first row of matrix SODM is the data set for the case of 1 mm crack in the magnet along t sampling time. The second row is the data set for the case of 2 mm crack in the magnet along t sampling time. The mth row is the data set for the case of em mm crack in the magnet along t sampling time.  
The difference between ODM and SODM is that the sets of data in ODM are extracted at a specific time during the life cycle of the machine; however, in SODM, the sets of data are extracted at an arbitrary time during a specific state of the machine.  
Accordingly, each row of the SODM matrix is dedicated to sensor data at different depths of the magnet's cracks. We started with a 1 mm crack depth and increase this depth, each time, by 1 mm until we reach em mm, which is the radial width of the magnet. At this depth the magnet is fractured.  
We mention that the sampling times for all data sets are similar.  
After constructing SODM, the first part of the suggested RUL calculation strategy is accomplished. The second part will be to find an appropriate equation that models the incremental growth of this fault, the cracks, and the function of time. This will be presented in the next section.  
A block diagram illustrating the steps of the suggested strategy is illustrated in Figure 11.
## 4 Results and Discussion
### Random Forest Regression Model for Reliability Prediction
Figure 19: RQMSE contour plot as a function of the coefficients of the fit of the constant and the linear coefficient. Figure 18: Comparison between actual remaining useful life and fitted model. The results of the training and validation partitions are presented in Figures 20 and 21, respectively. In the adjustment process, the adjustment index of the constant is 1.5, and the adjustment index of the linear coefficient is 2.05. In this scenario, the value of R\({}^{2}\) was 0.996, and the square root of the mean squared error was 109.96 h. The observation of the model's limitation in predicting the results of the remaining useful life for operating times greater than 5600 h led to the consideration of only data prior to this interval. The final model considering the operation of the ceramic plates is given by  
\[RUL_{RFR1}=4822\times 1.5+13,178\times 2.05\times ln[R(t)_{estimated}]\]  
\[RUL_{RFR1}=7233+27,014.9\times ln[R(t)_{estimated}]\]  
This is therefore a model capable of predicting, based on only process variables with instantaneous real-time sensor readings, the remaining useful life of ceramic plates operating for up to 5600 h. The final model is independent of operation hours, so if ceramic plates indicate similar operating conditions until the end of life soon after installation, it will be possible to identify this failure and analyze corrections or any early planned  
Figure 14:0 Comparison between actual remaining useful life and fitted validation model. Figure 14:1 Comparison between actual remaining useful life and fitted training model. replacements. On the other hand, a great gain is that a fixed time for replacing plates is not established, and in the scenario where plates operate beyond their average useful life with good health indicators, users will be able to make the decision to continue operation and monitor the plates' condition, reducing replacement costs. The random forest regression model presented together with the developed analytical expression can then be launched in a production environment for performance monitoring with acceptable performance.
## 6 Performance Evaluation
### Experiment 3: Packet Loss with Increasing Bitrate and Payload Size  
In this experiment, we observed packet loss in the network across different bitrates and payload sizes. The results showed that increasing the bitrate and payload caused an increase in packet loss, significantly impacting the network's performance. The results for Topologies 1 and 2 can be seen in Figures 16 and 17, while the results of two scenarios of Topology 3 are reflected in Tables 7 and 8. This is because of the same reason, when the bitrate is increased, more datagrams are transmitted, which leads to congestion and, eventually a high packet loss. This experiment highlights the importance of carefully selecting the bitrate and payload size keeping in view the use case scenario, to avoid excessive packet loss in the network. It has been further discussed in Section 7 in detail.  
Table 7 shows that due to high congestion, there is an increase in packet loss which can even cause a complete 100% packet loss. The results in this table can also be matched with that of Table 5, where we observed a \"0 ms\" jitter due to no communication. For the child-to-child communication scenario, it can be seen in Table 8 that till 30 kbps bitrate, we can achieve communication with zero packet loss, but as the bitrate and payload is increased, we observed high packet losses.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Bitrate (kbps)**} & \multicolumn{8}{c}{**Packet Loss Ratio (\%)**} \\ \cline{2-10}  & **500 Bytes** & **1000 Bytes** & **1208 Bytes** & **1500 Bytes** & **2000 Bytes** & **3000 Bytes** & **4000 Bytes** & **5000 Bytes** \\ \hline
10 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline
40 & 56 & 54 & 56 & 78 & 88 & 90 & 92 & 100 \\ \hline
50 & 65 & 63 & 60 & 86 & 94 & 94 &
## 4 Investigation of the Influence of Major Parameters on the Pipeline Damping Performance
### Effect of Particle Size on Damping Performance of the Pipeline System  
To investigate the effect of particle size on damping performance, steel balls with particle sizes of 1, 2, and 3 mm were selected for the damping test as shown in Figure 12. A single variable is controlled and the filling rates were set to 20% and 80%. The frequency domain comparison diagram of different particle sizes filled in the particle impact damper is obtained as follows.  
Figure 12(a) shows that the first-order resonant frequencies of the bare pipe in the particle impact damper filled with 20% steel balls of three particle sizes are shifted compared to Figure 8. The corresponding amplitudes are significantly reduced. Among them, the resonant frequency offset of the pipe filled with 1 mm steel balls was about 60 Hz, the corresponding amplitude was reduced by 68.8%. While the resonance frequency offset of the pipe filled with 2 and 3 mm steel balls was about 52 Hz, the corresponding amplitudes were reduced by 56.3% and 64.9%, respectively. According to the comparison of the first-order resonance amplitudes of the three particle sizes, the particle impact damper filled with 1 mm steel balls has the best damping effect at a 20% filling rate, followed by that filled with 3 mm balls, and by that filled with 2 mm balls. Similarly, the particle impact damper filled with 2 mm steel balls has an excellent damping effect, followed by that filled with 3 mm balls, and by that filled with 1 mm balls at an 80% filling rate in Figure 12(b).  
At the same filling rate, the smaller the particle size, which involves a greater number of particles, the more the momentum exchange of particle-particle and particle-container wall interactions is relatively adequate, which means that the energy dissipation is relatively high, and the vibration reduction effect is relatively adequate. At a different filling rate, the vibration reduction effect of particles with different diameters is different due to the high nonlinearity of particle damping, so there is no obvious pattern in the damping effect of three sizes at the two filling rates.  
Figure 12: Filling materials of three kinds of particle size. (**a**) 1 mm; (**b**) 2 mm; (**c**) 3 mm.
# Inhomogeneous Illumination Image Enhancement Under Extremely Low Visibility Condition
## 2 Materials and Methods
### Hmhe  
#### 2.2.1 Overview  
The HMHE consists of three components: the SDIF module, the visual optimization module, and the image enhancement module. The flow chart of the algorithm is presented in Figure 2. In the SDIF module, an estimator based on the Structure Similarity Index Measurement (SSIM) [28], was proposed to filter out the background light. The filtered image was then processed by the visual optimization module, which addressed the grayscale banding introduced due to applying an LPF to the image. The desired signals were obtained by subtracting the visually optimized filtered image to the input image. In the image enhancement module, both the visually optimized filtered image and the subtracted image were enhanced separately and then recombined to produce the final output.  
Based on the flowchart, assuming a compression rate of \(1/4\) and the kernel range within [a, b], the computational complexity of HMHE can be expressed as the following:  
\[\text{O}(HMHE)=\text{O}(\text{MN})+\text{O}\bigg{(}\frac{MN}{16}\text{log}(MN) \bigg{)}+\text{O}(MN)*4=\text{O}(\text{MNlog}(\text{MN})) \tag{10}\]  
This equation reflects the logarithmic complexity of the algorithm, accounting for the different stages of processing.  
Figure 1: An illustration of the image and its histogram with different processing: (**a**) The histogram of an example image \(U\). (**b**) HE-enhanced histogram of (**a**). (**c**) The histogram of the example image \(U_{homo}\). (**d**) HMHE-enhanced histogram of (**a**).
# Numerical Simulation of the Hydrodynamic Performance and Self-Propulsion of a UUV near the Seabed
## 4 Results and Discussion
### The Hydrodynamic Characteristics of Near-Seabed Bare UUIV
#### 4.1.2 Correlation between the Hydrodynamic Characteristics of Bare UUV and \(Re_{L}\)
The variation rule of pitch moment coefficient with \(Re_{L}\) is not consistent with the lift coefficient because the increment of pitch moment with velocity is smaller than the second-squares increment of velocity. When \(Hd\) is greater than 7, the relationship between lift coefficient and pitching moment coefficient and \(Re_{L}\) become weaker.
## 4 Discussion
### Impact of Landscape Pattern on Carbon Sequestration Capacity  
This study suggests that highly aggregated landscapes (such as those with high CONTAG values) can enhance the coherence and connectivity between landscape types, promote material and energy flow, and thus increase regional NPP levels [15]. In addition, concentrated land use patches can significantly enhance vegetation's carbon sequestration capacity [15; 31]. For example, under the same total area, high-density urban green spaces are cooler than fragmented green spaces, and their NPP levels are significantly higher [32]. In contrast, landscape fragmentation (such as high SPLIT values and low JII values) limits the connections between ecological patches and weakens the flow of matter and energy within the region. This obstacle not only reduces vegetation NPP but also indirectly affects carbon sequestration capacity. This study points out that the negative effects of fragmentation mainly manifest in the blockage of ecological corridors and the damage to the microenvironment, which puts greater environmental pressure on vegetation photosynthesis and growth [33; 34].  
The impact of patch size on NPP exhibits nonlinear characteristics. This study indicates that a larger proportion of maximum patches (with higher LPI values) significantly increases regional NPP levels, but patches of different land use types, even with similar areas, still exhibit significant differences in carbon sequestration capacity. For example, the NPP of forest patches is usually higher than that of grasslands or cultivated land, indicating that land cover area and landscape composition play a significant role in determining NPP across agricultural, forest, and grassland ecosystems [15]. In addition, the differences in plant species and community structure among different patch types can lead to different manifestations of NPP in patches [35]. Edge complexity (high ED value) shows positive effects in grasslands and forests, helping to enhance biodiversity and ecological activity, thereby increasing NPP levels. However, our findings show that in cultivated land areas, the improvement of connectivity and the reduction in fragmentation are more crucial for the stable growth of NPP. Contrastingly, a study highlighted that ED (edge density) exhibits a significantly stronger effect on built-up areas and unused lands [15]. This difference indicates that the interaction between edge effects and land use types is crucial for understanding the patterns of NPP changes [36].
# Enhanced Proton Acceleration from Laser Interaction with a Tailored Nanowire Target
## 2 Simulation Setup  
The two-dimensional simulations were carried out using relativistic PIC code EPOCH [31], which is able to self-consistently simulate the intense laser plasma interaction. The p-polarized laser pulse is incident from the left boundary. It has a flat-top temporal shape with a duration of \(15T_{0}\), including a rising edge of \(2T_{0}\) and a descending edge of \(2T_{0}\). Its peak intensity is \(I_{0}=8.6\times 10^{19}\) W/cm\({}^{2}\), which corresponds to a normalized vector potential, \(a_{0}=8\). The wavelength is \(\lambda_{0}=1\) um, and the period is \(T_{0}=3.3\) fs. The spatial profile of the laser pulse is Gaussian in the \(y\)-direction, expressed as \(I=I_{0}\exp(-\frac{y^{2}}{r^{2}})\), with \(r=8\) um. The size of the simulation box is \(80\) um\(\times\)\(40\) um (\(0\) um \(<x<80\) um\(,-20\) um \(<y<20\) um) with \(4000\times 4000\) grids in the \(x\), \(y\) plane. Each cell is filled with \(100\) electrons, \(10\)\(An^{50+}\) ions and/or \(100\) protons. The temporal resolution is set as \(r=0.01T_{0}\), which is sufficient to accurately calculate the laser plasma interaction. The PNT has a comb-like structure, consisting of three parts, as seen in Figure 1a. The first part is nanowires made of \(Au\) with diameters \(d_{1}=0.3\) um, vacuum spacing \(d_{2}=0.3\) um and length \(L=15\) um. To save computation resources, they are assumed to be pre-ionized \(Au^{50+}\) with ion density of \(n_{i}=n_{c}\) and a corresponding electron density of \(n_{e}=50n_{c}\). Here \(n_{c}=m_{e}\omega_{0}^{2}\epsilon_{0}/e^{2}\) is the critical density, and \(\omega_{0}\), \(\epsilon_{0}\), \(e\) are the laser frequency, vacuum permittivity and elementary charge, respectively. According to [PERSON] et al. [32], the pre-ionized \(Au^{50+}\) can be obtained in such an intense laser condition, and are adopted in many previous simulation works [33; 34]. The second part is a substrate of the same materials with a thickness of \(D=2\) um, to which the nanowires adhere. The nanowires and the substrate are placed in the region \(-15\) um\(<y<15\) um and \(10\) um\(<x<27\) um. The third part is in the rear side of the substrate, where a \(2\) um thick and \(20\) um wide hydrogen plasma layer, with \(n_{H}=n_{e}=10n_{c}\), is placed in the region \(-10\) um\(<y<10\) um and \(27\) um\(<x<29\) um. The CNT has the same shape and parameters as the PNT, except that its front surface is corrugated (labeled by a green-dashed line in Figure 1b). The profile of the corrugated surface is modeled by a sinusoidal curve, expressed as \(x=10\lambda_{0}+A\)  cos(\(2\pi Ky\)). This configuration ensures that the total number of electrons in the simulation box is basically the same in both targets. A series of simulations were carried out in this work. Firstly, the shape of the corrugated profile was chosen as \(A=3\lambda_{0}\) and \(K\lambda_{0}=0.25\). The heating of the electrons and proton acceleration via TNSA were investigated in detail. Furthermore, the parametric dependencies were also studied by changing the parameters of the corrugated profile.
## 4 Numerical Simulation Results
### Elastic Modulus
#### 4.2.2 Nonlinear Equation of Elastic Modulus Influenced by RFCA and Confining Stress  
Based on the above analysis, a nonlinear equation for the elastic modulus as a function of RFCA and confining stress is fitted as follows:  
\[E=a_{1}\cdot e^{(\frac{H}{2})}+a_{3}\cdot\sigma_{3}+a_{4}\cdot e^{(\frac{H}{2} )}\cdot\sigma_{3}+a_{5} \tag{4}\]  
where \(E\) denotes the elastic modulus, \(\sigma_{3}\) represents the confining stress, and \(a_{1}\), \(a_{2}\), \(a_{3}\), \(a_{4}\), and \(a_{5}\) are fitting coefficients. The elastic modulus increases exponentially with the increase in RFCA and rises linearly with the confining stress. The fitting coefficients are provided in Table 4, and the results of the fitting surfaces are depicted in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(a_{1}\) & \(a_{2}\) & \(a_{3}\) & \(a_{4}\) & \(a_{5}\) & \(R^{2}\) \\ \hline
8.54 & 0.33 & 24.34 & \(-1.64\) & 366.63 & 0.993 \\ \hline \end{tabular}
\end{table}
Table 4: Fitting coefficient of the nonlinear equation for the elastic modulus.  
Figure 7: Flow rule of the elastic modulus under different RFCAs and confining stresses.
## 3 Preliminaries
### Energy Model  
Wireless channel model is another important factor that should be formulated to identify energy consumption for communication between the sensor-nodes or the sensor-nodes with the mobile-sink. In our research, we adopt the first order radio model in [(48)]. The model defines a threshold value, Equation (3) which establishes two different fashions for the communication model. If the distance between transmitter and receiver is less than \(d_{0}\), then we adopt the free space model (\(d^{2}\) power loss). Otherwise the multipath fading channel is being considered (\(d^{4}\) power loss) as shown in the following equations.  
\[E_{Tx}=\left(l,d\right)=\left\{\begin{array}{ll}l\times E_{elec}^{Tx}+l\times \xi_{fs}\times d^{2}&d<d_{0}\\ l\times E_{elec}^{Tx}+l\times\xi_{fs}\times d^{4}&d\geq d_{0}\end{array}\right. \tag{1}\]  
\[E_{Rx}\left(l\right)=l\times E_{elec}^{Rx} \tag{2}\]  
where \(E_{elec}^{Tx}\) and \(E_{elec}^{Rx}\) are the energy consumption per bit in the transmitter and receiver circuits. \(l\) refers to bit transformation for the sensor-node. In addition, \(\xi_{fs}\) and \(\xi_{fmp}\) are the energy consumption factor of amplification for the free space and multipath radio models, respectively. The threshold value \(d_{0}\) is defined as follows.  
\[d_{0}=\sqrt{\frac{\xi_{fs}}{\xi_{mp}}}. \tag{3}\]
## 5 Evaluation of the Proposed Monitoring Approach
### Effects of Unbalanced Datasets  
As presented in Section 3 it is possible to observe that ALMOND uses only a small portion of samples provided by NTU [27], whose cardinality dominates the other datasets, as visible in Table 4. We explore deeper this issue with some preliminary experiments, our intent was investigating if the inclusion of a larger number of samples could create a better dataset. Performed experiments were structured comparing ALMOND, presented in Section 3, and its version with two edits, the former including 200 samples more from NTU, the latter including 400 NTU samples. Extra samples have been added only for classes where NTU provided it. In all the cases the dataset has been balanced by actions, except for the two reject classes, duplicating samples of each action to reach the samples number of group's most populated action, as indicated in Section 3.1. As experiments results, we observed that an increment of samples from NTU did not lead to better performance or a richer dataset in terms of provided information. Figure 5 shows the true positive accuracy difference for each datasets that form ALMOND, missing datasets mean there were no changes in terms of accuracy, the positive values mean the percentage of increment in action recognition, otherwise negative values mean worse performance. We can notice that an increment of NTU samples corresponds to worse generalization capacity for the model, giving only a relevant increase of accuracy on samples provided by NTU. With the inclusion of 400 NTU samples the behavior is the same as with 200, but even more pronounced. Generally, increasing the number of samples from a single dataset does not lead to a merged dataset with more useful information, rather shows a loss of heterogeneity. Facing the results of preliminary experiments, we use ALMOND as described in Section 3 without the inclusion of additional samples from NTU.