esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Analysis of Underwater Torpedo during Straight-Line Navigation
## 4 Numerical Model  
### The Establishment of Simulation Model  
The torpedo in this paper is modeled by the commercial software, OrcaFlex. The simulated scene is in a lentic environment, ignoring the effects of the wind, waves, and current, and the depth of water is 100 m. The torpedo model has been simulated by an OrcaFlex 6D buoy with a total length of 13.2 m. The model was divided into four sections, with the length from the head to the tail being 0.5 m, 0.7 m, 10.5 m, and 1.5 m, respectively. The outer diameters are, respectively, 1.0 m, 1.5 m, 2.0 m, and 1.0 m. An elastoplastic solid fairing with an axial and normal stiffness of 100 KN/m\({}^{3}\) was arranged on the head of a torpedo. The overall centroid coordinates of the torpedo are (0 m, 0 m, and 0 m). In the tail of a torpedo, there are four tail wings with a wingspan of 1.5 m and a chord length of 1.5 m. The local coordinate origin of the tail fin in the torpedo body coordinate system is, respectively, (\(-\)6 m, 0 m,1.6 m), (\(-\)6 m, 1.6 m, 0 m), (\(-\)6 m, \(-\)1.6 m, 0 m), (\(-\)6 m, \(-\)1.6 m, 0 m), and (\(-\)6 m, \(-\)1.6 m, 0 m). The lift and damping coefficients of the tail wings have a low impact on the dynamic response of the torpedo. In order to save calculation time, these two coefficients are set to 0. The coordinate of the initial position of the torpedoin the global coordinate system is (0 m, 0 m, \(-\)5 m). The fixed thrust T = 50 KN in the \(x\)-direction is applied at the origin of the mine coordinate system, the normal and axial drag force coefficients of the mine body are 1.0 and 0.1, respectively, and the additional mass coefficients are both 1.0. The total simulation time is 100 s. The torpedo model is shown in Figure 1.
# A Performance Evaluation for Software Defined Networks with P4
## 1 Introduction
### Motivations  
Control and data plane programmable functions motivate us to investigate how this emerging approach can improve the performance of a network by processing packets faster. It is, we feel, crucial to investigate the extent to which a solution with programmability in both the control plane and forwarding plane will provide an improved core network that can accommodate both stringent performance and flexibility as the amount of network traffic continues to increase.  
After reviewing related work (see Section 2), this study is motivated to addressing the research gaps that while there are performance bottlenecks in SDN+Open vSwitch, the performance evaluation of combining _SDN+P4_ is underexplored, especially across different topologies representing the Internet at different scales, and with a varied traffic types, combinations, and protocols. The above issues are important because of the following.  
* Programmability at both control plane and data plane provides the potential to meet dynamic and stringent network performance;
* Evaluating data plane and control plane programmability against control plane programmability will help in guiding research to the adaptation of programmable networks;
* Resilience, low processing overhead, and high throughput are crucial for future networks. The potential benefits provided via the programmability at both the control and data plane, can pave the way for increased performance in the core network.  
In this paper, we hypothesise that a combination of data plane and control plane programmability (_SDN+P4_) will elevate the performance of the network, to accommodate stringent performance requirements for future applications in comparison to 'Open vSwitch (OvS) [15] coupled with control plane programmability' (_SDN+OvS_). The performance of _SDN+P4_ and _SDN+OvS_ will be considered for different topologies, involving a range of different types of traffic.
## 2 Materials and Methods
### In-Field Measurements Results  
#### 3.2.1 Boundary Conditions  
The boundary conditions (external and internal) of the two monitoring periods are plotted in Figure 8, while the solar radiation data are shown in Figure 9. The data acquired during the first monitoring period (Test_1) show an outdoor temperature of between 5 \({}^{\circ}\)C and 24 \({}^{\circ}\)C, with a maximum global incident solar radiation (Sol_I) of about 900 W/m\({}^{2}\). During the second monitoring period (Test_2), the outdoor conditions are characterized by lower external air temperatures (6-18 \({}^{\circ}\)C) and with some cloudy days (21 and 22 October). Regarding the internal air temperature of the test rooms surrounding environment (Tint),  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**PCM Type** & **Test** & **Type** & \(c_{p}\) & \(h_{A}\) & \(h_{A,Let}\) & \(h\) & \(h_{\text{,}lat}\) \\  & & & **[kJ/kgK]** & **[kJ/m\({}^{2}\)]** & **[kJ/m\({}^{2}\)]** & **[kJ/kg]** & **[kJ/kg]** \\ \hline \multirow{2}{*}{X} & Test 1 & Melting test & 3.6 (melted) & 530 & 218 & 103 & 42 \\  & Test 2 & Solidification test & 3.6 (solid) & 444 & 125 & 86 & 24 \\ \hline \multirow{2}{*}{Y} & Test 1 & Melting test & 2.5 (melted) & 735 & 514 & 143 & 100 \\  & Test 2 & Solidification test & 2.9 (solid) & 671 & 419 & 131 & 81 \\ \hline \multirow{2}{*}{Z} & Test 1 & Melting test & 2.9 (melted) & 596 & 341 & 119 & 71 \\  & Test 2 & Solidification test & 3.3 (solid) & 663 & 369 & 126 & 70 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Equivalent heat capacity and latent heat capacity results.  
the values for Test_1 are relatively stable around \(\sim\)21 \(\pm\) 1 \({}^{\circ}\)C, and then decrease during Test_2 (\(\sim\)19 \({}^{\circ}\)C).
## 3 Unconventional Fibers
### UOF with Non-Circular, Non-Symmetric, or Eccentric Cores  
For unconventional fibers of non-circular cores there are a set of circular layers where the refractive index varies between the inner and outer core and cladding values, respectively. In any such case, the function \(n(\varphi)^{2}\) is a sum of a steady component \(n^{2}\) and a periodic function of \(\varphi\) of period \(2\pi\) thus can be written as a Fourier series \(n(\varphi)^{2}=n^{2}+\sum_{-\infty}^{+\infty}N_{k}\exp(jk\varphi)\). Taking into consideration that the convolution of the product of an exponential function \(\exp(jk\varphi)\) with any function \(\mathrm{A}(\varphi)\) of a Fourier Transform \(A(l)\) is equal to \(A(l+k)\), i.e., the convolution generates \"harmonics\". The function \(n(\varphi)^{2}\) is in a set of cylindrical thin layers, a sum of step functions alternating between the values \(n_{1}^{2}\) and \(n_{2}^{2}\), where \(n_{1}\) and \(n_{2}\) are refractive indexes of core and cladding. Considering that in optical fibers the refractive indices of core and cladding are very close, one effectively has that \((n_{1}-n_{2})/n_{1}\ll 1\). As a result, any harmonic factors \(\mathrm{N_{k}}\) of the function \(n(\varphi)^{2}\) are negligible in comparison to its steady component \(n^{2}\) and can be omitted.  
As an example, the harmonics become maximal for equal alternation steps. In this case, the first harmonic, that has the maximum value of all harmonics, is equal to \(A_{1}=\frac{2\left(n_{1}^{2}-n_{2}^{2}\right)}{\pi}\), while the steady component \(n^{2}\) equals \((n_{1}^{2}+n_{2}^{2})/2\) and we may make an approximation as \(A_{1}/n^{2}\simeq(4/\pi)(n_{1}-n_{2})/n_{1}\ll 1\).  
Thus, for optical fibers we can always assume that \(n(l)^{2}{\simeq}n^{2}\). Then the system (18) will become equivalent to the following  
\[\left\{\begin{array}{c}\frac{l}{r}\overline{H_{2}}-j\beta\overline{H_{ \varphi}}=jn^{2}\overline{E_{r}}\\ \\ j\beta\overline{H_{r}}-\frac{\partial\overline{H_{2}}}{\partial r}=jn^{2} \overline{E_{\varphi}}\\ \\ \frac{1}{r}\frac{\partial(r\overline{H_{\varphi}})}{\partial r}-\frac{l}{r} \overline{H_{r}}=jn^{2}\overline{E_{2}}\end{array}\right. \tag{19}\]  
We can then follow the analysis that we did with the conventional fibers, where \(n^{2}\) is the average value of the \(\eta^{2}(\varphi)\) of each layer along \(\varphi\) in the \([0,2\pi]\) interval.
# Adaptive Leakage Protection for Low-Voltage Distribution Systems Based on SSA-BP Neural Network
## 6 Conclusions  
1. In this paper, the influencing factors of normal leakage current change are analyzed, and it is found that the normal leakage current of the line is affected by the environmental temperature, humidity, aging, and line voltage drop, and that the normal leakage current may change greatly or even double under multi-factor extreme conditions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Algorithms** & **Adjustment** & **Adjust in** & **Protective** \\  & & **Range** & **Advance** & **Effectiveness** \\ \hline
[17] & VMD-DFNN & Full Range & No & Medium \\
[34] & Fuzzy Logic & [\(-\)30\%, 20\%] & No & Low \\ Proposed & SSA-BP & Full Range & Yes & High \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison results of several methods.  
2. The SSA-BP neural network is used to build a prediction model of normal leakage current, and the model is trained based on the current and historical data collected. In the leakage protection experiment, the prediction accuracy is more than 95%, and the operation rate and action accuracy of the leakage protection device are more than 90%. It is verified that the predicted results can be used as a reference for the development trend of a normal leakage current.
3. The predicted leakage current is used to adjust the protection action threshold in advance, which improves the action accuracy of the leakage detector. It not only ensures the operation availability of the leakage detector, but also makes the leakage detector more sensitive in identifying the high-resistance ground leakage fault. In future work, we will explore the impact of input data quality on prediction accuracy and how to improve the accuracy of leakage prediction by improving data quality.  
Conceptualization, [PERSON] and W.J.; methodology, H.Y.; experiment, Z.L., H.Y. and W.J.; formal analysis, Z.L.; investigation, [PERSON]; resources, Z.L.; data curation, H.Y.; writing--original draft preparation, Z.L.; writing--review and editing, Z.L. and H.Y.; visualization, H.Y.; supervision, W.J.; project administration, W.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Optimizing Routing Protocol Design for Long-Range Distributed Multi-Hop Networks
## 7 Conclusions  
This paper proposes the LB-OLSR protocol as an ideal method for constructing LoRa distributed multi-hop networks. The OLSR protocol faces issues such as redundant MPR nodes, which increase control message flooding and consume limited channel resources. To address this, we propose a connectivity necessity-based MPR selection algorithm to reduce redundant nodes within the MPR set. The traditional OLSR protocol uses the minimum hop count as the routing criterion, which is inadequate for scenarios with heavy traffic or uneven network conditions. Our approach considers various communication requirements by incorporating communication delay, link reliability, node occupancy rate, and node load rate as constraints. Business nodes are prioritized accordingly, and load cost matrices are used to plan transmission paths that meet these constraints. Our simulation results indicate that the improved LB-OLSR protocol achieves a more balanced load among terminal nodes in the network compared to the traditional OLSR protocol, enhancing traffic distribution and resulting in reduced average service delay. In future research efforts, network performance can be optimized through metrics such as network overhead, jitter, and packet loss, particularly in scenarios where terminal nodes experience high loads and have limited energy. Enhancing the longevity of terminal nodes to achieve more balanced residual energy across the network becomes particularly crucial. To accomplish these research objectives, it will be necessary to develop more advanced routing optimization algorithms to address increasingly complex routing optimization challenges.  
Conceptualization, J.L. and S.P.; methodology, J.L.; software, J.L.; validation, J.L., S.P. and H.W.; formal analysis, J.L.; investigation, J.L.; resources, J.L.; data curation, J.L., X.W. and J.F.; writing--original draft preparation, J.L.; writing--review and editing, J.L.; visualization, R.P. and Z.Y.; supervision, J.L.; project administration, J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Key Industry Innovation Chain Project of Shaanxi Province (No. 2021 ZDLGY07-10, No. 2021 ZDLN703-08), the Science and Technology Plan Project of Shaanxi Province (No. 2022 GY-045), the Key Research and Development plan of Shaanxi Province (No. 2018 ZDNM-GY-041), a Scientific Research Program Funded by Shaanxi Provincial Education Department (Program No. 21 JC030), the Science and Technology Plan Project of Xi'an (No. 22 GXFW0124, No. 2019 GXYD17.3), the National Innovation and Entrepreneurship Training Program for College Students (No. 202311664001), and the Key Research and Development Project of Shaanxi Province (Program No. 2024 GX-YBXM-025).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# Frequency-Separated Attention Network for Image Super-Resolution
## 4 Experiments
### Settings
#### 4.1.1 Datasets and Metrics  
We train and test our network following methods in prior work [16]. LR images in DIV2K are obtained by bicubic downsampling of HR images by factors of \(\times 2\), \(\times 3\), and \(\times 4\). We evaluate our model on widely used benchmark datasets: Set5 [41], Set14 [42], BSD100 [43], Urban100 [44], and Manga109 [45], which contain 5, 14, 100, 100, and 109 images, respectively, covering common scenes from daily life. The model evaluation metrics include the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) [46], with results computed on the Y channel (i.e., luminance) of the YCbCR space. Higher PSNR and SSIM values closer to 1 indicate better quality of the reconstructed SR images.
## 5 Conclusions  
This paper evaluated the performance of two FC-based TWRCR schemes through both simulation and analysis, focusing on OP, SOP, and the average number of transmissions for the successful data exchange. The results showed that the Mod-Scm scheme achieves better OP and SOP performance compared to the Cov-Scm scheme. Additionally, the Mod-Scm scheme can reduce the average number of FC packet transmissions, which in turn lowers delay and power consumption. In Cov-Scm, the order in which the source transmits data also significantly affects the performance. Finally, key parameters such as \(K\), \(N_{\text{max}}\), and \(\alpha\) should be appropriately designed to enhance performance for both the Cov-Scm and Mod-Scm schemes.  
**Author Contributions:** Conceptualization, H.T.N. and T.T.D.; methodology, N.-T.H.; software, N.V.T.; validation, V.T.T.; investigation, N.-T.H., N.V.T. and V.T.T.; writing--original draft preparation, H.T.N. and T.T.D.; writing--review and editing, N.-T.H., N.V.T. and V.T.T.; supervision, H.T.N. and T.T.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This paper is supported by Posts and Telecommunications Institute of Technology under grant number 07-2024-HV-KTDT2.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 10: The average number of transmissions for the successful data exchange as a function of \(\alpha\) when \(\Delta=8.5\) dB, \(K=5\), and \(N_{\text{max}}=7\).
## 3 Results and Discussion  
### Thermal Curing Process of PDMS  
PDMS thermal curing performance during PDMS assisted thermal reflow process is a key factor for shape evolution. The curing process of PDMS can be described by the dual-Arrhenius equation [27] shown as follows:  
\[\ln\eta(t)=\ln\eta_{\infty}+\frac{E_{a,\eta}}{RT(t)}+\int_{0}^{t}k_{\infty}e^{ \frac{-E_{a,k}}{RT(t)}}dt \tag{8}\]  
where \(T\), \(t\) and \(R\) are the temperature, the time and the ideal gas constant, respectively, \(\eta_{\infty}\) is the calculated viscosity at \(T=\infty\), \(k_{\infty}\) is the calculated apparent kinetic factor at \(T=\infty\), \(E_{a,k}\) is the activation energy for the gelation, and \(E_{a,\eta}\) is the active energy for the viscosity. Here, \(\eta_{\infty}\), \(k_{\infty}\), \(E_{a,k}\) and \(E_{a,\eta}\) are unknown parameters, which can be calculated with measured data including the viscosity varying over temperature at initial time and the viscosity varying over time at given temperatures. Then, we can obtain the fitting dual-Arrhenius equation and the predicated viscosity curves for PDMS. The isothermal viscosity curves are measured by rheometer (Discovery HR-2) and shown in Figure 2a indicated by the solid lines. The corresponding predicated viscosity curves are shown in Figure 2a, indicated by the dashed lines. It should be noted that in Figure 2a, there are discrepancies between the measured and predicted viscosities curves, which may have a certain impact on the simulation results. However, with the increase of time, the deviations become much smaller, especially for those results at a comparatively high temperature of 90 \({}^{\circ}\)C and 110 \({}^{\circ}\)C. For the thermal reflow process is conducted at a relatively high temperature of 180 \({}^{\circ}\)C, it will have a very limited influence on the overall simulation results.  
Figure 2: (**a**) Measured and predicted viscosity curves of PDMS at different isothermal temperatures. (**b**) Viscosity curves varying over time at the two different heights of the simulation model.  
In the simulation process, the viscosity equation of PDMS is expressed in the form of user-defined function. The viscosity curves varying over time at different heights can be obtained by recording viscosity value in the simulation process, as shown in Figure 2b. The red line and the blue line are the viscosity curves at the height 0.5 \(\upmu\)m and 12 \(\upmu\)m above the substrate, respectively. These two heights represent the bottom and the top layer of PDMS surrounding the microstructures, respectively. It can be seen that the viscosity is relatively low in two layers before 1.2 s. Then, the viscosity increases sharply from a relatively lower viscosity to 385 Pa-s (the viscosity of the photoresist) in less than 2 s, as shown in the inset in Figure 2b. At this stage, the viscosity gradient in height direction will become larger. Along with the process of heating, PDMS at different heights will get high viscosity and reach the gel point gradually.
# Experimental and Numerical Predictions of Cryogenic Leakages in Welded Steel Plates
## 2 Experimental Preparations
### Experimental Setup of the Cryogenic Liquid Test  
Figure 2 depicts the schematic of the experimental cryogenic liquid test. The experimental setup is composed of an LN\({}_{2}\) tank, LN\({}_{2}\) pipeline, valve, and data acquisition (DAQ) system. LN\({}_{2}\) induces the cryogenic environment in the experiment, and the LN\({}_{2}\) supply rate into the test welded plate was controlled manually. The cooling zone was defined as the welding plate's center location with dimensions 220 mm \(\times\) 460 mm. This area was isolated by a trough, fabricated from polyurethane foam. T-type thermocouples measured the temperature variations on the front side of the specimen during the cryogenic leakage test. The operating temperature of the T-type thermocouple, which consisted of copper and copper-nickel wires, was between \(-\)250 \({}^{\circ}\)C to +350 \({}^{\circ}\)C. The DAQ apparatus measured the temperature history of the plate during the experiment. Strain gauges were attached to the plate's top surface to measure the thermal strain while cooling, providing data to the DAQ system.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**C** & **Si** & **Mn** & **P** & **S** & **Ni** & **Cr** \\ \hline
0.18 & 0.10–0.50 & 1.22 & 0.035 & 0.035 & 0.040 & 0.20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical compositions of AH36 steel.  
Figure 1: (a) Geometry of the Y-groove joint and (b) schematic diagram of the FCAW process.
## 3 Experiments and Results
### Experimental Parameters  
After multiple rounds of parameter optimization and adjustment, by comparing the model's performance after each parameter adjustment, its optimal parameters were gleaned and saved for later use as the experimental parameters. These model parameters are listed in Table 6.
# Passive Earth Pressure and Soil Arch Shape: A Two-Dimensional Analysis
###### Abstract  
This paper introduces an analytical method for passive earth pressure calculation based on a rigorous stress field analysis within the sliding wedge. Unlike traditional horizontal layer methods, this approach directly solves for the stress state at any point within the wedge by analyzing the equilibrium of 2D differential soil elements under appropriate boundary conditions, eliminating the need for a priori assumptions about the soil arch shape. The method yields the passive earth pressure distribution on the retaining structure and derives the soil arch shape analytically from major principal stress trajectories. This derived arch shape differs notably from conventional circular or parabolic assumptions, especially at higher soil-wall friction angles. Parametric studies show that the passive earth pressure coefficient increases with internal friction angle and surcharge. However, a key finding is the non-monotonic dependence of the passive earth pressure coefficient on the soil-wall friction angle, contrasting with many existing theories. Comparisons show predictions by the proposed method align well with experimental data, particularly offering a better representation of pressure distributions in the lower regions of retaining walls compared to Coulomb theory and other existing methods.  
passive earth pressure; soil arching effect; two-dimensional element analysis; major principal stress trajectory; retaining structures +
Footnote †: journal: Applied sciences