esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In particular, several performance markers, such as the \"support prediction\", and \"contradict prediction\" values, were be used to aid the assessment of each of the ML model's abilities in deciding which criteria should be considered in the selection of potential communication technology for the electrical distribution substation. Based on the performance output, the average score for each technical criteria was calculated and ranked in terms of importance for the selection of communication technology for the electrical distribution substation. The contributions of this paper are:  
* A list of the potential communication technologies to be applied at the electrical distribution substation, based on extensive literature review. * The creation of an ML dataset of the potential communication technologies based on the specifications in the literature. * A thorough investigation on the ML models with the highest performance in selecting the most important criteria for electrical distribution substation communication technology. * A ranking of the criteria from the most important to the least important, and an evaluation of the criteria that most strongly influence the selection of the best communication technology for the electrical distribution substation. The remainder of the paper is structured as follows. Section 2 describes the communication technologies in detail, and Section 3 discusses the criteria and dataset specification for the ML model. Section 4 discusses the ML process and the preliminary results. The acronym used in the rest of this paper is tabulated in Table 1.
# Seismic Ground Response Prediction Based on Multilayer Perceptron
###### Abstract  
**Abstract** Earthquake disasters can cause enormous social and economic damage, and therefore the sustainability of infrastructure requires the mitigation of earthquake consequences. In seismic design of infrastructures, it is essential to estimate the response of the site during earthquake. Geotechnical engineers have developed quantitative methods for analyzing the seismic ground response. This study proposes a multilayer perceptron (MLP) model to evaluate the seismic response of the surface based on the seismic motion at the bedrock (or 100 m level), and compares its performance with that of a conventional model. A total of 6 sites, with 100 earthquake events at each site, were selected from the Kiban Kyoshin Network (KiK-net) and used as datasets. The acceleration response spectra were calculated from the predicted and measured (baseline) acceleration histories and compared. The proposed MLP model predicted the magnitudes of response and the natural periods where the response amplifies closely with the measured ground motions (baseline). The MLP model outperformed the conventional model for seismic ground response analysis. However, the proposed model did not perform as well for earthquakes whose response spectra exceed 2 g due to a deficiency in large earthquake measurements in the training datasets.  
earthquake; seismic ground response analysis; multilayer perceptron; SHAKE2000; acceleration response spectrum +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion  
### Surface Structure and Morphology  
The surface morphology and composition of TA3 before and after the implantation of Nb ions are shown in Figure 1. Compared to the substrate, the surface morphology of TA3 implanted with Nb ions appears smoother, while some small pits appear on the metal surface. Studies have suggested that some pits are formed on the surface due to the bombardment and cascade collisions caused by high-energy ion implantation [18; 19]. Additionally, the etching effect produced during the continuous bombardment of TA3 with Nb ions reduces the relative height of the surface bulge, which explains the flattening of surface morphology [20]. In general, the smoother the surface, the better its corrosion resistance due to the smaller potential difference between the concave and convex [21; 22]. Therefore, the effect of ion implantation on a metal's surface can improve the corrosion performance appropriately. Moreover, as shown in Figure 1, the content of Nb ions is approximately \(2.24\) at%, indicating that the Nb ions were implanted into the sample successfully. However, the content of Nb is lower, and this may be due to the far greater effective penetration depth of EDS than the implanted depth of Nb.  
Figure 2 shows the XRD pattern of samples before and after Nb ions were implanted. Compared to the substrate, no new diffraction peaks were observed after Nb ions were implanted, indicating that  
Figure 1: Backscattered SEM images and EDS analysis: (**a**,**b**) substrate, (**c**,**d**) Nb-implanted sample.  
no new phase was formed and Nb ions mainly existed in the solid solution element, which would improve the mechanical properties of the metal surface owing to the solid solution intensification. Figure 2 shows that the diffraction peaks of Ti (101) and Ti (002) shifted towards the higher angle ride after implantation. It is well known that the crystal imperfections resulting from ion implantation may lead to lattice distortion, leading to the shift of diffraction peaks [23]. As the ionic radius of Nb is greater than that of Ti, the implanted Nb ions occupy the position of Ti and may lead to localized compressive strain, explaining the shift direction of the diffraction peak. Different from tensile stress, the presence of residual compressive stress does not induce stress corrosion properties.
## 2 Numerical Models
### Numerical Algorithm  
The discrete form of the average Reynolds equation (Equation (1)) can be obtained by finite difference method and the obtained linear equations at each unknown pressure nodes are given by:  
\[A_{j-1,k}p_{h(j-1,k)}+A_{j+1,k}p_{h(j+1,k)}+A_{j,k-1}p_{h(j,k-1)}+A_{j,k+1}p_{h(j,k+1)}-A_{j,k}p_{h(j,k)}=B_{j,k} \tag{24}\]  
The successive overrelaxation (SOR) method was used for solving Equation (24), which is described by:  
\[p_{h}^{\rm(new)}=p_{h}^{\rm(old)}+\lambda\Big{(}\widetilde{p}_{h}^{\rm(new)} -p_{h}^{\rm(old)}\Big{)} \tag{25}\]  
where \(\widetilde{p}_{h}^{\rm(new)}=\Big{(}B_{j,k}^{\rm(old)}-A_{j-1,k}p_{h(j-1,k)} ^{\rm(old)}-A_{j+1,k}p_{h(j+1,k)}^{\rm(old)}-A_{j,k-1}p_{h(j-1)}^{\rm(old)}-A_{ j,k+1}p_{h(j+1)}^{\rm(old)}\Big{)}\)\(/\,A_{j,k}\), and \(\lambda\) is the relaxation factor--its value is assumed to be 1.5 in the current simulation.  
The flow chart used to simulate the fluid-solid-heat coupled performance of the JWR system is illustrated in Figure 6, which includes a procedure for the calculation of fluid pressure, contact pressure, thermal-elastic deformation, temperature distribution, and shaft deflection. In the present study, a calculation procedure based on Fortran is developed to determine the FSH coupling performance of the water-lubricated rubber bearing considering propeller shaft deflection. After numerical testing at a computer with a 1.61 GHz CPU and 16 GB RAM, it was observed that the evaluation time for the bearing properties is around 30 min. Furthermore, it is worth mentioning that for the statistical hydrodynamic and contact models, only the average Reynolds equation was calculated for the hydrodynamic lubrication regime; however, both the average Reynolds equation and asperity contact model were calculated for the mixed friction regime.  
The pressure, including the water film pressure and contact pressure, is controlled by the error given in Equation (26). The temperature and attitude angle are controlled by the error given in Equations (28) and (29), respectively.  
\[\frac{\sum\limits_{j=1}^{n}\sum\limits_{k=1}^{m}\Big{|}p_{j,k}^{\rm(new)}-p_{ j,k}^{\rm(old)}\Big{|}}{\sum\limits_{j=1}^{n}\sum\limits_{k=1}^{m}\Big{|}p_{j,k}^{ \rm(old)}\Big{|}}\leq 1.0\times 10^{-6} \tag{26}\]  
\[\frac{\Big{|}T_{\rm max}^{\rm(old)}-T_{\rm max}^{\rm(new)}\Big{|}}{T_{\rm max} ^{\rm(old)}}\leq 1.0\times 10^{-6} \tag{27}\]  
where \(T_{\rm max}\) = (\(T_{\rm max}\)+\(T_{W\rm max}\)+\(T_{R\rm max}\))/3, with \(T_{\rm max}\), \(T_{W\rm max}\), and \(T_{R\rm max}\) being the maximum temperature of the journal, water film, and rubber bushing, respectively.  
\[\Big{|}\theta^{\rm(new)}-\theta^{\rm(old)}\Big{|}/\theta^{\rm(old)}\leq 1 \times 10^{-6} \tag{28}\]  
The attitude angle can be adjusted by the force component in \(x\) and \(y\) directions, which is given by:  
\[\theta^{\rm(new)}=\theta^{\rm(old)}+\arctan\biggl{(}\frac{F_{x}}{F_{y}}\biggr{)} \tag{29}\]
# Thermal Stability and Entropy Generation Analysis for
## 1 Introduction  
A good number of researchers have paid renewed interest and attention to the flow of reactive fluids because of their impact in technological advancements. In particular, energy transfer and the variable viscosity property of a fluid have been of vast interest among researchers [1; 2; 3; 4; 5; 6; 7; 8]. Industrial and technological applications are found in areas such as atmospheric flows, thermal regulation, the cooling of electronic devices, nuclear reactors, thermal hydraulics, and many other devices have proliferated the engineering community. Meanwhile, entropy generation minimization in thermo-fluid flows is critically important in improving the efficiency of machines and processes, as explained by [PERSON] [9]. Therefore, efficient energy conversion and management is key in any thermal processes. In the industry, issues like productivity, sustainability, and competitiveness require engineering solutions, and such solutions are heavily reliant on mathematical models. Modern economics are being urged to curtail their energy dependence on fossil fuels in order to mitigate the continuous depletion of the ozone layer that is causing the acceleration of global warming. The deleterious consequences of global warming, like runaway fires, rising sea levels, and the melting of glaciers, are occurring at unsustainable rates in the present day. As more clean renewable energy forms are being sought to replace fossil fuels, it has become more and more urgent for economic systems to conserve more and more energy for continued sustainability. Thus, the need for continued research on entropy generation minimization cannot be over-emphasized. The assumption of the no-slip boundary condition has been the norm in most studies involving impervious and non-polishedsurfaces. Many researchers are of the opinion that the no-slip boundary condition is a hypothesis. In fact, evidences of the slip of a fluid on a solid surface have been reported by several authors, including [PERSON] and [PERSON] [10] and [PERSON] and [PERSON] [11]. [PERSON] [12] and [PERSON] and [PERSON] [13] reported this effect on MHD flow in porous media and on the MHD peristaltic flow of a Jeffrey fluid, respectively. [PERSON] et al. [14] reported a magnetized flow with fluid slippage. The aim of this present study is to formulate a theoretical model, where we investigate the combined effects of slip, variable viscosity, porous-medium permeability, and channel porosity on entropy generation in the steady flow of a reactive third-grade fluid through an inclined channel filled with a porous saturated medium. A survey of the literature revealed that the scope of our study herein has not been fully accounted for in previous studies. Therefore, the main objective of this work is to present the thermal stability and entropy generation in the flow of combustible third-grade fluids with shear-induced slippage down a permeable channel filled with porous materials by using rapidly convergent spectral methods.
## 4 Experimental Results  
To assess the vibration mitigation performance of the SPPTMD under various loading conditions, the displacement peak vibration-reduction rate \(\eta_{d}\) and the displacement root mean square (RMS) vibration-reduction rate \(\eta_{RMS}\) are defined as follows:  
\[\eta_{d}=\frac{(D_{0}-D_{1})}{D_{0}}\times 100\%, \tag{14}\]  
\[\eta_{RMS}=\frac{(A_{0}-A_{1})}{A_{0}}\times 100\%, \tag{15}\]  
where \(D_{0}\) and \(D_{1}\) are the displacements of the uncontrolled and controlled models, respectively, and \(A_{0}\) and \(A_{1}\) are the root-mean-square (RMS) displacements of the uncontrolled and controlled models, respectively.
# An Improved Hilbert-Huang Transform for Vibration-Based Damage Detection of Utility Timber Poles
## 4 Experimental Program  
### Equipment  
To validate the proposed method, vibration testing method using impact hammer was implemented on three serviceable/healthy and three unserviceable/unhealthy in-situ timber poles. Since timber is an orthotropic material, its material properties vary in three different directions: longitudinal, radial, and circumferential. [PERSON] et al. [41] proposed an appropriate sensor set up for the non-destructive assessment of cylindrical type structures which has been utilised in this study in order to capture the three dimensional behaviour of timber pole. For field testing, an impact hammer, 12 accelerometers and a data acquisition system were employed. Modal hammer Endevco 2303 was used in this study (1 mV/lbf, 5000 lb range) to generate impact, while Instron 46A16 sensors were used for capturing accelerations with sensitivity of 100 mv/g (range \(\pm\)50 g). The NI cDAQ-9133 (1.33 GHz, 16 GB) from National Instrument was used as the data acquisition system with NI 9232 (3 channel, +/30 V, 102.4 kS/s/channel, 24-Bit, IEEE AI C Series) input modules. A total of 12 accelerometers were used and was connected to the pole by mechanically means (as shown in Figure 6a). Figure 6b depicts the portable device used for the testing.
# Reliability Model Based Dynamic Multi-Level Trust Analysis
## 6 Experiment
### Execution Time  
The execution time includes the MDD generation time and MDD evaluation time. The MDD generation time is showed in Figure 10. The x axis represents different hops from start node. It can be observed that the longer the path, the longer the MDD generation time. However, the MDD evaluation complexity is linear to the size of the MDD model generated. Figure 10 shows the MDD evaluation time in comparison to subjective logic algorithm.  
From Figures 10 and 11, it is not difficult to find that the generation of MDD takes a certain amount of time, but once the MDD is generated, the time for trust evaluation will be greatly shortened, and it will not be seriously affected by the length of the paths.  
Figure 9: Accuracy of subjective logic based and MDD-based method: (**a**) root mean square error (RMSE); (**b**) Pearson correlation coefficient (PCC).
## 4 Experimental Validation
### Case 2: Laboratory Bearing Dataset of Shenzhen Technology University (SZTU)
#### 4.2.3 Results Analysis of Case 2  
For the laboratory dataset, four sets of experiments are conducted with the load adjusted from 0 N\(\cdot\)m to a maximum of 50 N\(\cdot\)m to simulate actual operating conditions as much as possible. Similar to case 1, we plotted the corresponding accuracy curves shown in Figure 11. As we can see, the accuracy of the proposed method is higher than the other methods under four operating conditions. Notably, the performance of the proposed method, ISCV-ViT, improves slightly as the load increases. The experimental results of the PU in Case 1 are also reflected accordingly. Especially from the results shown in (c) of Figure 11, the convergence speed of ISCV-ViT is almost consistent with Resnet50. Furthermore, we can clearly see that there are certain misjudments between the FBO and FBB faults on the laboratory dataset by drawing the visual confusion matrices shown in Figure 12. The reasons for this need further research. Finally, Figure 13 shows the statistical results of all trials. It is obvious that the accuracy of the ISCV-ViT is higher than that of the other methods under the four operating conditions. Notably, the performance of the proposed ISCV-ViT method does not decrease and even slightly improves as the load increases. The experimental results are consistent with those of the PU dataset in Case 1. In addition, ISCV-ViT has the highest average accuracy of 94.87% and the lowest standard deviation. Compared to the experimental results of case 1, the validation accuracy on the laboratory dataset is slightly lower than that on the public dataset PU. Therefore, we can conclude that the generalization ability and accuracy of the proposed method are excellent.  
Figure 11: Accuracy curves for different methods on the laboratory dataset SZTU: **(a)** Condition 1, **(b)** Condition 2, **(c)** Condition 3, and **(d)** Condition 4.  
Figure 12: Confusion matrices for the best results of ISCV-ViT on the laboratory dataset SZTU: **(a)** Condition 1 (92.9%); **(b)** Condition 2 (93.3%); **(c)** Condition 3 (95.4%); **(d)** Condition 4 (94.2%).
# Nonlinear Optical Microscopy of Interface Layers of Epitaxial Gamet Films
## 1 Introduction
As an example, magnetic domains were visualized via the SHG microscopy in [33] in the scheme of the magnetooptical Faraday effect in iron garnet films of different crystallographic orientations, which provided additional information on the domain's magnetization distribution as compared to a linear magnetooptical probe. Symmetry analysis of the magnetic nonlinear optical response at the SHG wavelength was discussed in detail in [34; 35]. It was underlined that high-rank tensors describing different components of the SHG process that are of the first and of second orders in magnetization \(\mathbf{M}\), as well as induced by gradients of \(\mathbf{M}\), can be used for a thorough characterization of the residual magnetic structure of garnets and other objects. Third-order nonlinear optical spectroscopy is also a well recognized and powerful technique [36] that is widely applied to the studies of biological objects and tissues [37; 38], resonant and local field effects in non-magnetic nanostructured objects [39] and metasurfaces [40; 41]. One of substantial benefits of this probe is an intrinsic usage of the two-color scheme. In that case, no laser-induced damage is expected if a tissue is transparent to the intense laser radiation of the infrared spectral range, while a much less intense third harmonic of visible or UV radiation and which is specific to a concrete type of a tissue, enables label-free visualization of biological structures on the scales down to a cell size. At the same time, to the best of our knowledge, magnetization-induced third-order nonlinear optical effects, including third harmonic generation (THG), have not been applied to the studies of magnetic microstructures. This can be due in part to the low values of the magnetization-induced effects in THG as compared to the case of SHG, as was demonstrated in [31] for the case of metallic magnetic nanostructures. Recently it was shown that the SHG microscopy can distinguish stripe domains in the bulk of garnet films, as well as the closure domains at the outer garnet/air interface [42]. At the same time, the domain structure of the hidden interface of the ferrimagnetic film with the substrate remained unexplored. In this paper we apply magnetization-sensitive nonlinear optical microscopy to the study of magnetic ordering at the garnet/GGG interface and show that it reveals a complicated domain structure similar to that of the garnet/air one. Another finding is the extension of the magnetization-sensitive nonlinear optical microscopy method to the case of third harmonic generation, which shows higher resolution as compared to the case of the SHG probe.
# Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism
###### Abstract  
Image quality plays a critical role in medical image analysis, significantly impacting diagnostic outcomes. Sharp and detailed images are essential for accurate diagnoses, but acquiring high-resolution medical images often demands sophisticated and costly equipment. To address this challenge, this study proposes a convolutional neural network (CNN)-based super-resolution architecture, utilizing a melanoma dataset to enhance image resolution through deep learning techniques. The proposed model incorporates a convolutional self-attention block that combines channel and spatial attention to emphasize important image features. Channel attention uses global average pooling and fully connected layers to enhance high-frequency features within channels. Meanwhile, spatial attention applies a single-channel convolution to emphasize high-frequency features in the spatial domain. By integrating various attention blocks, feature extraction is optimized and further expanded through subpixel convolution to produce high-quality super-resolution images. The model uses L1 loss to generate realistic and smooth outputs, outperforming existing deep learning methods in capturing contours and textures. Evaluations with the ISIC 2020 dataset--containing 33126 training and 10982 test images for skin lesion analysis--showed a 1-2% improvement in peak signal-to-noise ratio (PSNR) compared to very deep super-resolution (VDSR) and enhanced deep super-resolution (EDSR) architectures.  
attention mechanism; convolutional self-attention block; ISIC 2020; super-resolution 2020 IEEEexample:BSTcontrol
## 5 Simulation Results
### Rates for Different Transmitting Powers  
The power allocation is a critical design aspect in the deployment of a communication system. It can improve the SINR and so the capacity, but only with a logarithmic scaling. Thus, we test the performance of the system according to an increase on the transmitting power \(P_{TX}\). The chosen values are 47 dBm (50 W), 50 dBm (100 W), and 53 dBm (200 W). The other parameters are the same as before, but now we adopt a fixed UPA configuration of \([36\times 2]\), in both the single and dual elements polarization case. Figure 10 shows the shift of the CDF curves towards higher rates together with the power increase, in both polarization options, and the outage tends to decrease. This is not a surprising result, as the SINR will benefit from a higher \(P_{TX}\). Nevertheless, we should consider that a stronger signal could increase the interference among other cell sites in the nearby area.  
Figure 9: CDF of UT rates for different UPA configurations at the TX, \(N_{t}=72\), \(N_{r}=1\), \(P_{TX}=47\) dBm, traffic density = 2500 users/\(\mathrm{km}^{2}\).