esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result
### Computational Overhead  
Including continuous-time dynamics in G-CTRNN adds considerable computational load during training. Solving differential equations with Euler integration, combined with gradient-based optimization of the time constant under parameter constraints, lengthens  
\begin{table}
\begin{tabular}{c c} \hline \hline
**G-CTRNN Model** & **Accuracy \%** \\ \hline Static time constant equals one & 68.2 \(\pm\) 0.6 \\ Random-initialization time constant & 76.1 \(\pm\) 2.1 \\ Training the time constant & 81.9 \(\pm\) 1.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different G-CTRNN configuration runs and results of ten independent runs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**G-CTRNN Model (Recurrent Cells)** & **Network Parameters Size** & **Accuracy \%** \\ \hline G-CTRNN (16) & 572 & 81.9 \(\pm\) 1.4 \\ RNN (32) & 1548 & 81.4 \(\pm\) 1.2 \\ G-CTRNN (4) & 156 & 71.8 \(\pm\) 1.1 \\ RNN (16) & 556 & 73.1 \(\pm\) 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of G-CTRNN and RNN parameter sizes and accuracies of ten independent runs.  
the per-epoch training time. In our experiments, training G-CTRNN for 50 epochs on a machine equipped with an Intel Xeon CPU and 16 GB RAM took about 6.5 h, while a comparable RNN was trained in under 1 h (approximately 56 min) on the same setup. This reflects a \(7\times\) increase in training time due to the overhead of resolving continuous dynamics and enforcing parameter constraints. However, during inference, the target physical implementations--such as a MEMS-based or analog hardware system--will naturally solve the differential equations in real time through their intrinsic dynamics. Consequently, the computational burden at deployment shifts from software-based simulation to native physical processes, allowing for energy-efficient, real-time operation on edge devices.
# Practical Estimation of Machine Tool Spindle Dynamics for Maintenance Decision Making
## 4 Discussion
### Application to Spindle Maintenance Decision Making
The spindle was removed from the CNC machine, as shown in Figure 34, and the high torque warnings were attributed to a failed support bearing at the rear of the spindle, as shown in Figure 35. The early knowledge of an impending spindle problem allowed the maintenance staff to order and procure a replacement spindle ahead of the failure. This preemptive maintenance decision, which was motivated by the FRF data, greatly reduced the maintenance downtime when the spindle ultimately failed. Downtime savings were achieved since the replacement spindle was already at the facility and ready for installation at the time of failure, and the maintenance staff were prepared for this service. This maintenance resource planning avoided significant costs to the production facility, the most notable savings were related to lost productivity. This case study demonstrates two key aspects of FRF-based maintenance decision making. Firstly, modal characteristics were used to identify spindles with changing dynamics, and this knowledge provided an early warning for impending failure events. Secondly, cost savings were realized by taking a proactive approach to maintenance planning, which was motivated by the FRF data collected on this fleet of spindles. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Spindle Condition**} & \multicolumn{4}{c}{**Mode 1**} & \multicolumn{4}{c}{**Mode 2**} \\ \cline{2-9}  & \(\mathbf{\omega_{1}}\) & \(\mathbf{m_{1}}\) & \(\mathbf{k_{1}}\) & \(\mathbf{\zeta_{1}}\) & \(\mathbf{\omega_{2}}\) & \(\mathbf{m_{2}}\) & \(\mathbf{k_{2}}\) & \(\mathbf{\zeta_{2}}\) \\  & **Hz** & **kg** & **Ns\({}^{2}\)/m** & **\%** & **Hz** & **kg** & **Ns\({}^{2}\)/m** & **\%** \\ \hline Before spindle failure & 569 & 3.2 \(\times\) 10\({}^{-7}\) & 0.1035 & 4.22 & 1380 & 3.6 \(\times\) 10\({}^{-8}\) & 0.0689 & 3.33 \\ After spindle repair & 808 & 1.3 \(\times\) 10\({}^{-7}\) & 0.0843 & 5.57 & 1338 & 2.4\(\times\) 10\({}^{-8}\) & 0.0425 & 8.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the modal characteristics for the two dominant modes from the case study spindle five months before failure and immediately after the repair. Figure 34: The problematic spindle unit was removed from the CNC machine tool after high torque warnings were received from the controller. Figure 33: Components of the FRF from the same spindle five months before a failure occurred (red) and immediately after a repair (green): (**a**) real component; (**b**) imaginary component.
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 2 System Design
### Hardware Components  
According to the World Health Organization (WHO) [13] and the US Environmental Protection Agency (EPA) [14], about 60% of drinking water contamination events originate from physicochemical anomalies, so we chose pH, TDSs (total dissolved solids), turbidity, and temperature as the main water quality monitoring parameters to comprehensively reflect the physicochemical characteristics of the water body. In the process of sensor selection in Table 1, we comprehensively considered core indicators such as accuracy, sensitivity, price, power consumption, calibration cycle, etc., to ensure the accuracy of data, system stability, and feasibility of long-term operation. In the end, we chose DS18B20 (Manufacturer: Maxim Integrated, San Jose, VA, USA) DFROBOT Gravimetric TDS (Manufacturer: DFROBOT, Beijing, China), DFROBOT SEN0189 (Manufacturer: DFROBOT, Beijing, China), and pH-4502C (Manufacturer: DFROBOT, Beijing, China) as the main monitoring sensors. DS18B20 sensors are low-cost, highly resistant to interference, and have a calibration cycle of up to 6 months, making them an ideal choice for long-term deployment of an IoT monitoring system. The DFROBOT Gravimetric TDS sensor's measuring accuracy is lower than high-end models, but its price advantage and stability make it suitable for applications with limited budgets. Further, dFROBOT SEN0189 is a low-cost turbidity sensor that provides sufficient accuracy for non-precision measurement tasks, and pH-4502C sensors offer high accuracy and stability for long-term water quality monitoring needs and low power consumption for online monitoring systems.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Sensor Type** & **Model** & **Accuracy** & **Sensitivity** & **Price** & **Power Consumption** & **Calibration Cycle** \\ \hline
**pH** & Atlas Scientific EZO-pH & \(\pm\)0.002 & High & High & Low & 4–6 weeks \\
**pH** & DFROBOT SEN0161-V2 & \(\pm\)0.1 & Medium & Low & Low & 2–4 weeks \\
**pH** & pH-4502C & \(\pm\)0.02 & Medium & Medium & Medium & 2–3 weeks \\
**TDS** & Atlas Scientific EZO-EC & \(\pm\)2\% & High & High & Low & 4–6 weeks \\
**TDS** & DFROBOT Gravity TDS & \(\pm\)10\% & Medium & Low & Low & 2–4 weeks \\
**Turbidity** & In Situ Aqua TROLL 200 & \(\pm\)0.1 NTU & High & High & Low & 8 weeks \\
**Turbidity** & DFROBOT SEN0189 & \(\pm\)3\% & Medium & Low & Low & 4 weeks \\
**Temperature** & DS18B20 (Digital) & \(\pm\)0.5 °C & High & Low & Low & 6 months \\
**Temperature** & PT100 (Analog) & \(\pm\)0.1 °C & High & High & High & 6 months \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sensor selection summary.  
Figure 3: Simulation diagram for system testing and validation.
# An All-Mach Number HLLC-Based Scheme for Multi-Phase Flow with Surface Tension
## Appendix B Eigenvalues and Vectors  
The governing equation set presented in this work may expressed in strong compact form in 2-D as  
\[\frac{\partial\mathbf{U}}{\partial t}+\frac{\partial F_{x}}{\partial x}+\frac{ \partial F_{y}}{\partial y}=\mathbf{S},\]  
where subscripts \(x\) and \(y\) denote the flux in the respective Cartesian directions. Further,  
\[\mathbf{F}_{x}(\mathbf{U})=\begin{bmatrix}\rho\;u\\ \rho u^{2}+p\\ \rho\;uv\\ (\rho E+p)u\\ \alpha u\end{bmatrix},\quad\mathbf{F}_{y}(\mathbf{U})=\begin{bmatrix}\rho\;v\\ \rho\;uv\\ \rho v^{2}+p\\ (\rho E+p)v\\ \alpha v\end{bmatrix},\quad\text{ and }\quad\mathbf{S}=\begin{bmatrix}0\\ \sigma\kappa\frac{\partial\alpha}{\partial x}\\ \sigma\kappa\frac{\partial\alpha}{\partial y}\\ \sigma\kappa\Big{(}u\frac{\partial\alpha}{\partial x}+v\frac{\partial\alpha} {\partial y}\Big{)}\\ \alpha\Big{(}\frac{\partial\alpha}{\partial x}+\frac{\partial\alpha}{\partial y }\Big{)}\end{bmatrix}.\]  
The above system of equations may be written in terms of the primitive variables \(\mathbf{W}=\big{[}\rho,u,v,p,\alpha\big{]}^{T}\) in linear quasi-conservative form as  
\[\frac{\partial\mathbf{W}}{\partial t}+\mathbf{A}(\mathbf{W})\frac{\partial\mathbf{W}}{\partial x }+\mathbf{B}(\mathbf{W})\frac{\partial\mathbf{W}}{\partial y}=\mathbf{0},\]  
where \(\mathbf{A}(\mathbf{W}),\mathbf{B}(\mathbf{W})\) are the Jacobian matrices and for the \(x-\)direction,  
\[\mathbf{A}(\mathbf{W})=\begin{bmatrix}u&\rho&0&0&0\\ 0&u&0&\frac{1}{\rho}&-\frac{\sigma\kappa}{\rho}\\ 0&0&u&0&0\\ 0&\rho c^{2}&0&u&0\\ 0&0&0&0&u\end{bmatrix}.\]  
The eigenvalues associated with the Jacobian matrix \(\mathbf{A}\) are  
\[\mathbf{\Lambda}_{\mathbf{A}}=diag(u-c,u,u,u,u+c),\]  
with _diag_ denoting a diagonal matrix. The corresponding eigenvectors are  
\[\mathbf{K}_{\mathbf{A}}=\begin{bmatrix}\rho&1&0&0&\rho\\ -c&0&1&0&c\\ 0&0&0&0&0\\ \rho c^{2}&0&0&\sigma\kappa&\rho c^{2}\\ 0&0&0&1&0\end{bmatrix}.\]  
The characteristic eigenvector corresponding to the Jacobian \(\mathbf{B}\) matrix associated with the remaining dimension can easily be derived in a similar way and is not included here. Finally, for a system of equations of arbitrary dimension \(d\), normal to the interface, there are three unique and real eigenvalues: \(\lambda_{1}=\mathbf{u}\cdot\mathbf{u}-c\), \(\lambda_{2}=\mathbf{u}\cdot\mathbf{u}\) and \(\lambda_{3}=\mathbf{u}\cdot\mathbf{n}+c\) (multiplicity of spatial dimensions + one for each transport equation), where the interface normal is \(\mathbf{n}\).
# A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images
## 2 Materials and Methods
### R50A3-LWBENet Holistic Network Instructure  
R50A3-LWBENet model takes a single RGB image as input, introduces the ResNet50 backbone, introduces the attention mechanism, and realizes the fusion of global and local information, and the fusion of large-scale and small-scale information, and refines the edge contours of the lake water bodies. In the model encoding part, we used the SE-ResNet50 network composed of residual modules based on the improved SE block, which pays attention to both the holistic and salient features of the feature map channel while ensuring that deepening the number of network layers does not degrade the model performance. After the encoding is finished, CBAM containing channel attention module and spatial attention module is added, in which the channel attention module is processed by the global average pooling and global maximum pooling of the feature map in the fully connected layer, which can adaptively adjust the weight of each channel to help the network pay attention to the important feature information in a specific channel and inhibit the irrelevant features, so as to improve the efficiency of the network in utilizing the features of different channels, and to capture the important information in the image in a better way. The spatial attention module takes the maximum value and the average value on each pixel's channel, after that these two results are stacked based on channel, after a convolution operation to downsize to 1 channel, and then after a sigmoid activation operation to get the weights  
Figure 4: The structure of the spatial attention mechanism module.  
Figure 5: The structure of the CBAM module.  
of each pixel of the input feature map, and finally multiply this weight by the original input feature map. Spatial attention is achieved by learning the weights of each pixel's spatial location. It can adaptively adjust the feature map according to the importance of different locations, enhance the feature expression of important regions, and suppress the feature information of secondary regions. The CBAM module is able to fully consider the importance of each channel and each spatial location in the feature map through the joint action of channel attention and spatial attention, which can help the network to better focus on the important regions in the image and extract more discriminative features. The decoding part uses bilinear interpolation for up-sampling, and the feature maps obtained from the up-sampling process are fused with the feature maps of the same size from the down-sampling process of the ResNet50 feature extraction network by shortcut connections, thus obtaining the final segmented image of the lake body, and the holistic network instructive of R50A3-LWBENet is shown in Figure 6.
## 5 Conclusions
In addition, increasing the radius of the domain of influence of the PDM approximation function effectively eliminated the oscillation that occurs when the number of TPs is increased in the PDM application process to improve the accuracy of the kinematic variable calculation. To confirm the impact of the number of TPs transmitting the displacement information to the nodes of the PDM model on the accuracy of the kinematic variable calculation, the relative error with respect to the number of TPs in the PDM model was investigated, which is essential to validate the numerical analysis. The error analysis clearly showed that the accuracy of the synthetic structural analysis improved as the number of TPs increased. Although the application of the expanded TP consumed slightly more computation time, it is more effective in increasing the resolution. Increasing the number of TPs should not represent a major problem or sacrifice the resolution because the difference in the total calculation time after including DIP is quite small. This study is a preliminary research on the development of the methodology to be applied to the behavior analysis of large civil structures in the future. The synthetic structural analysis method presented in this study could be a useful solution if an actual experiment involves a large volume of data or if limitations due to large experimental scales or complex hardware equipment exist. Moreover, by considering nonlinear effects and the validation of the DIP's accuracy, future research could improve the applicability of the proposed synthetic structural analysis. As such, the method of integrating the DIP and PDM with the structural analysis approach could be applied in the field of structural integrity evaluation and maintenance because it has relatively fewer limitations with regard to time and space. **Author Contributions:** Conceptualization, funding acquisition, project administration, Y-C.Y. ; Data curation, investigation, methodology, software, validation, J.P. and K.-H.K. ; Formal analysis, K.-H.K. ; Resources, supervision, S.-H.L. ; Visualization, writing-original draft, J.P.; Writing-review & editing, J.P., Y.-C.Y., and S.-H.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning, grant number NRF-2017R1A1A1A05001196. **Conflicts of Interest:** The authors declare no conflict of interest.
# User-Cell Association for Security and Energy Efficiency in Ultra-Dense Heterogeneous Networks
## 5 Numerical Results
### With Macrocell  
We have also considered a second case: users can associate not only with the small-cell layer but also with the macrocell that is placed in the center of the area and is always  
Figure 4: Association performance vs. number of users \(\mathcal{N}_{U}\) when \(\mathcal{N}_{C}=10\).  
active (but operates on a different frequency band). The results are presented in Figure 5 when the mean number of small cells varies and the mean number of users is \(\mathcal{N}_{U}=30\). In this case benefits of the proposed association metric are more evident. Indeed, as we can observe it allows to find the best trade-off between different behaviors. With the Max-SINR approach users tend to select the macrocell as serving BS, thus they do not take into consideration security that instead is provided by a suitable exploitation of the intra-layer interference that characterizes the small-cell layer. Consequently, users achieve poor performance also in terms of secure-throughput. The opposite occurs with the Max-Secure Area approach. In this case, the users tend to select the small cells as serving ones, because the intra-layer interference allows a higher protection of communications. This results in very poor performance in terms of secure throughput and EE, because SINR values are quite poor. The proposed method permits to achieve a good trade-off, indeed the secure area is very close to that of the Max-Secure Area method, while it presents a significantly higher secure throughput and EE. In terms of consumed power being the macrocell always active in any case, we cannot appreciate significant differences among different methods. Indeed the macrocell power is significantly higher than the others.  
For supporting the results' behavior described before, Figure 6 presents the average percentage of users that are connected with different types of cells for the considered association policies. We can see that while Max-SINR tends to associate users with the macrocell, the Max-Secure Area does the opposite. The proposed association metric provides a trade-off between the previous two.  
Figure 5: Association performance vs. number of cells \(\bar{\mathcal{N}}_{c}\) when \(\bar{\mathcal{N}}_{U}=30\) (with an always-active macrocell).  
Finally, we want to show that the number of activated cells is usually lower than the maximum, thus significantly reducing the computational complexity of the proposed approach as stated before. Toward this goal Table 3 reports the mean number of activated cell per type in a scenario with \(\bar{\mathcal{N}}_{\mathrm{C}}=10\) and \(\bar{\mathcal{N}}_{U}=50\) considering the macrocell or not.
## 5 Applications
### Electrocatalyst  
Graphyne has recently garnered attention in electrocatalysis owing to its conjugated \(\pi\)-electron system, high surface area, and tunable electronic properties through doping or  
Figure 24: **(a,b)** In-plane diffusion pathways of Li ions in bulk graphyne. (**c,d**) Out-of-plane diffusion barriers indicating 3D ion mobility for energy storage. Reprinted with permission from Reference[161] @ 2011 ACS. (**e**) Top view and (**f**) side view of the optimized atomic configuration of LiC4, illustrating lithium atoms intercalated within the graphyne lattice. The arrangement highlights the spatial positioning of Li ions in relation to the \(\mathrm{sp/sp^{2}}\) carbon framework, with lithium residing both above and within the hexagonal carbon layers. Reprinted with permission from Reference [161] @ 2011 ACS.  
structural modifications. Its unique sp-sp\({}^{2}\) hybridized network introduces active sites suitable for redox reactions, especially when decorated with transition metals or heteroatoms. Among various graphyne forms, \(\gamma\)-graphyne has shown promising activity in oxygen evolution reactions (OER), hydrogen evolution reactions (HER), and carbon dioxide reduction due to its modifiable band structure and favorable adsorption energy for catalytic intermediates (Figure 25).  
Advanced computational investigations have demonstrated that graphyne-based materials, when functionalized with elements such as Fe, Co, or Ni, exhibit comparable or superior activity to traditional catalysts [162]. For instance, density functional theory (DFT) calculations reveal that Fe-doped \(\gamma\)-graphyne significantly reduces the Gibbs free energy of hydrogen adsorption, thus enhancing HER kinetics. Furthermore, palladium nanoparticles anchored on \(\gamma\)-graphyne frameworks have shown improved catalytic behavior in 4-nitropenol reduction, emphasizing the synergistic effect between metal centers and the extended conjugation of the graphyne sheet [163; 164].  
These findings underscore the potential of graphyne derivatives as next-generation electrocatalysts [165]. Their conductivity, coupled with abundant binding sites and chemical tunability, positions them as strong candidates for sustainable catalysis in energy conversion systems such as water-splitting cells and fuel cells. However, to transition from theoretical insights to practical implementation, challenges related to structural stability, synthesis scalability, and long-term electrochemical performance must still be addressed.
# DDS: A Delay-Based Differentiated Service Virtual Network Embedding Algorithm
## 3 Virtual Network Embedding Problem Formulation
### Measurements of Substrate Network Resources
#### 3.4.3 Resource Utilization  
In order to qualify the resource consumption of a node or a link in the substrate network, the resource utilization is defined as follows:  
\[U(R_{s})=\frac{\text{occupied}(R_{s})}{\text{total}(R_{s})}, \tag{10}\]  
where \(R_{s}\) could be the computing or storage resource of the substrate node or the bandwidth resource of substrate link, \(\text{occupied}(R_{s})\) represents the consumed resources, and \(\text{total}(R_{s})\) represents the total amount of resources.
## 3 Results and Discussion
### Compression Properties
#### 3.3.2 Regression Models and 3D Plots of Compression Distribution
According to the regression models shown in Equations (7)-(9), Figure 17 illustrates the impact of the AMC parameters on the mechanical properties of yield strength, compression strength, and fracture strain. The AMC's pressure and Al2O3% were observed to have a proportional relationship with the yield strength values, as shown in Figure 17a-e, while the slight decrease in the H/D showed an increase in the specimen's yield strength. For all percentages of the Al\({}_{2}\)O\({}_{3}\)%, the maximum yield strength was attained at 900 MPa and a 0.75 H/D. Figure 17e illustrates the highest possible yield strength, determined using the regression model (Equation (7)), which was attained at 10 Al\({}_{2}\)O\({}_{3}\)%, 900 MPa, and a 0.75 H/D, with a value of 352 MPa, which was better than the yield strength obtained experimentally with a value of 312 MPa at 10 Al\({}_{2}\)O\({}_{3}\)%, 700 MPa, and a 1.25 H/D. Figure 17: Three-dimensional response plot of mechanical properties; (**a**-**e**) \(\sigma_{y}\), (**f**-**j**) \(\sigma_{\text{uc}}\), and (**k**-**o**) \(\epsilon_{\text{f}}\) at (**a**,**f**,**k**) zero % (**b**,**g**,**l**) 2.5%, (**c**,**h**,**m**) 5%, (**d**,**l**,**n**) 7.5%, and (**e**,**j**,**o**) 10% of Al\({}_{2}\)O\({}_{3}\). The interaction charts shown in Figure 17f-j indicate that the best compression strengths of all Al\({}_{2}\)O\({}_{3}\)% were discovered at a lower H/D. On the other hand, the effect of pressure on the compression strength was somewhat different. The maximum compression strength of 900 MPa was achieved with Al\({}_{2}\)O\({}_{3}\)% at concentrations ranging from 0% to 5%. Aside from that, the greatest compression strength was achieved at 500 MPa when there was 7.5% to 10% Al\({}_{2}\)O\({}_{3}\) in the composition of the material. The best compression strength of 10% Al\({}_{2}\)O\({}_{3}\), as determined by the regression model (Equation (8)), was 421 MPa and was achieved at 500 MPa and a 0.75 H/D, as shown in Figure 17). This is superior to the compression strength that was obtained experimentally, which had a value of 384 MPa at 700 MPa, a 1.25 H/D, and 10% Al\({}_{2}\)O\({}_{3}\). Figure 17k-o illustrate the effect on the fracture strain that occurred as a result of modifying the AMC parameters, which were identified by regression models calculated by applying Equation (9). It was found that the fracture strain was inversely related to the pressure, H/D, and Al\({}_{2}\)O\({}_{3}\)%. Figure 17k depicts the maximum achievable fracture strain with a value of 13 approximately at 0% Al\({}_{2}\)O\({}_{3}\), 500 MPa, and a 0.75 H/D, which was almost the same as that observed experimentally with a value of 13 at 700 MPa, a 1.25 H/D, and 0% Al\({}_{2}\)O\({}_{3}\).