esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Conceptual Design
### Finite Element Analyses and Optimization Results
#### 3.5.2 C2 FEA and Results  
The C2 FEM model was used to perform static analyses under the high-lift flapped condition (Figure 21) shows the overlapping among the baseline (blue curve), the actuated morphed down (red curve), and the target morphed shapes (green curve).  
Static analyzed results showed local stress spikes, as shown in Figure 22, due to local gradient effects. This aspect suggested the implementation of more refined models to assess the most critical regions.  
Furthermore, an actuation sensitivity analysis was executed to collect trends in terms of actuator locking force, actuator transversal force and downward tip vertical displacement as functions of the actuator stroke, as reported in Figure 23. The most important outcome  
Figure 21: C2 Baseline, actual and target shapes.  
Figure 22: C2 FEA results: Von-Mises stress contour map [N/m\({}^{2}\)].  
was that the order of magnitude of the actuation locking force is \(10^{3}\) N, sensibly lower with respect to the C1 corresponding value.
## 3 First-Passage Reliability Problem
### Parameter Calibration Using Observational Data
#### 3.2.3 Fitting the Variance of \(\dot{Y}(x)\)  
Based on the monitored deformation data \(y_{1},y_{2},\ldots y_{n}\) at depths \(d_{1},d_{2},\ldots d_{n}\), the samples of \(\dot{Y}\) are first approximated by \(\left\{\frac{y_{i+1}-y_{i}}{d_{i+1}-d_{i}}\right\}\) for \(i=0,1,2,\ldots n-1\), where \(y_{0}=2y_{1}-y_{2}\) and \(d_{0}=2d_{1}-d_{2}\). With this, \(\mathbb{E}(\dot{Y}\left(x\right))\) is fitted using a polynomial expression similar to Equation (14). Subsequently, by referring to Equation (15), the variance of \(\dot{Y}(x)\) is determined by  
\[\mathbb{V}(\dot{Y})=\mathbb{E}\{[\dot{Y}-\mathbb{E}(\dot{Y})]^{2}\}=\frac{1}{ n}\sum_{i=1}^{n}\left(\frac{y_{i}-y_{i-1}}{d_{i}-d_{i-1}}-\widetilde{Y}(d_{i}) \right)^{2}, \tag{16}\]  
where \(\widetilde{Y}(d_{i})\) is the fitted function \(\dot{Y}(x)\) evaluated at depth \(d_{i}\).
## 3 Results
### Relationship between Various Driving Factors and NPP
#### 3.2.3 Relationship between Human Activities and NPP Change  
The average Pearson correlation coefficient between NPP and population in the Yangtze River Delta was \(-0.0096\), the positively correlated area accounts for 35.22%, (e.g., Xuancheng city, Huzhou city, Hangzhou city, and Jinhua city, etc.), and the negatively correlated area accounts for 64.78%, mainly in the northwestern and southern parts of the research region (Figure 10).  
Figure 9: Pearson’s correlation coefficient between climate driving factors and NPP: Note: (**a**) ‘TEM’ means average annual temperature; (**b**) ‘PRE’ means total annual precipitation; (**c**) ‘TSH’ means total annual sunshine hours.  
Different land cover types correspond to different land ecosystem structures, their NPP are also different, and the variability of land cover types and their spatial distribution directly determines the spatial distribution characteristics of NPP. As illustrated in Figure 11, considering the NPP corresponding to each land type during 2001-2019, forest land NPP (655.51 g C m\({}^{-2}\) a\({}^{-1}\)) was the highest, followed by grassland NPP (600.12 g C m\({}^{-2}\) a\({}^{-1}\)) and cropland NPP (487.51 g C m\({}^{-2}\) a\({}^{-1}\)). Despite the large differences in NPP among different land use types, the NPP trends of forest land, grassland, and cultivated land were similar, with all showing an oscillating upward trend in which cultivated land had the fastest rate of increase, followed by forest land and grassland.
# Resolving Gas Bubbles Ascending in Liquid Metal from Low-SNR Neutron Radiography Images
## 3 Image Processing
### Pre-Processing  
Image pre-processing is performed in _Imagej_ as shown in Algorithm 2.  
Images are cropped as indicated in Figure 1a,b. Dark current compensation is performed by subtracting the mean projection of 5 K-10 K recorded dark current noise images from all images in the raw image sequence (typically 3 K-9 K images). Then the dark current-compensated flat field is computed from the mean projection of 5 K-10 K neutron beam flux distribution images. Afterwards the dark current-compensated FOV images are normalized with respect to the flat field compensated for the dark current. These corrections for the cropped raw FOV images can be expressed as \[I=\frac{I_{0}-\langle I_{dark}\rangle}{\langle I_{beam}\rangle-\langle I_{dark}\rangle} \tag{1}\]  
where \(I\) are the luminance maps of the corrected images, \(I_{0}\) are the cropped raw images, and \(I_{dark}\) and \(I_{beam}\) are the dark current and neutron beam flux images. This correction results in the spatial dependence of the SNR as a consequence of less neutrons detected behind the sample compared to the open beam.  
```
Input: Raw image sequence: 16-bit 1-channel TIFFs, \(1024\times 1024\) pixels
1 Crop the FOV to the liquid metal domain (Figure 1)
2 Compensate the images for camera dark current (mean)
3 Compensate the flat field images for dark current (mean)
4 Perform flat field correction (32-bit precision)
5 Convert to 16-bit
6 Remove outlying bright luminance values (median thresholding)
7 Normalize the resulting images (pixel luminance re-scaled to \([0;1]\))
8 Output: Dark current and flat-field corrected normalized images
```  
**Algorithm 2**Pre-processing for raw images  
Afterwards, bright outliers are removed using median thresholding with a 1- to 2-pixel radius. The threshold is chosen such that outlier removal modifies only the pixels where luminance by far exceeds the local median (within the designated radius). Pixels with luminance above the threshold are then assigned the local median values. Finally, the images are normalized and saved, then passed to _Wolfram Mathematica_ for further processing.
# User Experience of 5G Video Services in Indonesia: Predictions Based on a Structural Equation Model
## 2 Underlying Studies and Theories
### Previous Studies  
Much research related to measurement and efforts to obtain a good QoE has been previously carried out. In [25], a survey related to QoE on good internet Wi-Fi was conducted to measure the level of customer loyalty using SEM analysis. However, this study has not demonstrated user predictions and is limited to specific subjectivity and preferences. In [26; 27; 28; 29], various methods were carried out to measure QoE in video streaming services, starting with measuring data bitrates and creating special measuring tools for traffic, theoretical approaches, and data retrieval through testbed applications in Europe related to video streaming performance parameters. These approaches indeed show several results to assess QoE, which still leads to improvements for existing networks and does not refer to predictions on 5G technology. In [30], modelling with a neural network was used as a predictive model for QoE video streaming. In [31; 32], various ways to obtain predictions of good QoE results were presented through various observations, including data management, network settings, and applications related to video streaming services. However, both studies used computer network media that were already available and not on cellular networks and did not include user preferences. Besides [25], there are [33; 34; 35] studies that also use survey methods for data collection and SEM in their analysis. These three studies also attempted to approach predictions and find the value of user satisfaction. The scope of these studies was loyalty from the emotional attachment of mobile gaming users and strategies for mobile banking users.  
Table 1 summarises the four previous studies that utilize the SEM method, and presents their scores which distinguish them from our research.  
There are fundamental differences between this study and the other studies. The first difference is the research focus on predictions about 5G video streaming in Indonesia. Second, this research focuses specifically on UX as an element of QoE. Third, while there is a correlation approach as in [23; 24; 25], this study has a simpler construction for the novelty. Finally, in this study, novelty is used as a reference in the implementation of 5G technology in Indonesia, especially to identify the priorities that need to be considered from the UX side regarding the transition of video streaming users from 4G to 5G technology and its factors.
## 5 Performance Evaluation and Analysis
### Network-Related Performance Measure  
In this paper, we apply the ACO algorithm to network routing. Therefore, it is important how the ACO algorithm affects network performance. One of the main concerns of this paper is the path recovery time in the end-to-end delay and link failure and the link overhead caused by the routing itself. In this section, we analyze how these two network performance indicators can be related to the ACO parameters and how there is a relation between the two values.
# A GIS Partial Discharge Defect Identification Method Based on YOLOv5
## 2 Acquisition of Partial Discharge Datasets  
### Partial Discharge Defect Model  
The internal defects of GIS mainly include free metal particles inside the GIS, conductor tips, floating electrodes formed by poor conductor contact, surface contamination of solid insulators and internal air gaps. If these defects are not eliminated in advance, they will cause GIS metal particle discharge, corona discharge, floating electrode discharge, and insulation discharge respectively in the process of GIS operation, which will pose a great threat to the safe operation of GIS equipment. Therefore, based on the four common defects that may occur in manufacturing transportation and use of GIS in the process, a GIS experimental model was established to obtain partial discharge defects as shown in Figure 1.  
During the partial discharge experiment, the influence of the external environment on the experimental equipment should be eliminated by increasing the sensor threshold. Before the start of the experiment, the ultra-high frequency (UHF) signal of GIS equipment under normal operation was measured, and the UHF sensor threshold range was adjusted to minimize the UHF acquisition value under normal operation of GIS, which can avoid to a certain extent the influence of environmental noise on the acquisition of GIS partial discharge signal, and then clear and accurate PRPD maps were obtained. This paper simulates four types of typical GIS partial discharge types. We placed four different discharge types of defects in the simulated gas chamber. To simulate corona discharge defect type, we used a copper pin pasted perpendicular to the high-voltage conductor on the GIS bus, the length of the copper pin is 2 mm, as shown in Figure 1a. To simulate the floating electrode discharge defect type, we use insulating tape to suspend the epoxy between the high-voltage electrode and the metal housing, as shown in Figure 1b. The types of insulation discharge defects are simulated by setting bubbles of different sizes inside the insulator, as shown in Figure 1c. The metal particle discharge defect type is simulated by placing metal particles not exceeding 1 mm on the inner wall of the GIS enclosure, as shown in Figure 1d.
## 4 Quantizer Design
### Asymptotic Performance Analysis  
According to [18] (p. 206), the asymptotic statistical distribution of the modified test statistic \(2\ln T(\mathbf{y})\) asymptotically follows:  
\[2\ln T(\mathbf{y})\overset{a}{\sim}\left\{\begin{aligned} \chi_{1}^{2},&\text{under }H_{0},\\ \chi_{1}^{\prime 2}(\lambda),&\text{under }H_{1}, \end{aligned}\right. \tag{10}\]  
where \"\(a\)\" denotes an asymptotic distribution; \(\chi_{r}^{2}\) denotes a chi-squared distribution with \(r\) degrees of freedom (DOFs); \(\chi_{r}^{\prime 2}(\lambda)\) designates a non-central chi-squared distribution with \(r\) DOFs and the non-centrality parameter \(\lambda\) given by:  
\[\lambda=(\sigma_{1}-\sigma_{0})^{2}\text{FI}(\sigma)|_{\sigma=\sigma_{0}} \tag{11}\]  
where \(\text{FI}(\sigma)\) denotes the Fisher information (FI) associated with the PDF under \(H_{1}\), which is derived by taking the negative of the expectation of the second derivative with respect to \(\sigma\) of the logarithm of \(P_{\mathbf{Y},1}(\mathbf{y};\sigma)\). Specifically, it can be shown:  
\[\text{FI}(\sigma;\mathbf{\tau})=-E\left\{\frac{\partial^{2}\ln P_{\mathbf{Y},1}( \mathbf{y};\sigma)}{\partial\sigma^{2}}\right\}=\sum_{n=1}^{N}\Psi_{n}(\sigma ;\tau_{n}) \tag{12}\]  
where \(\mathbf{\tau}=[\tau_{1},\tau_{2},\ldots,\tau_{N}]\) and \(\Psi_{n}(\sigma;\tau_{n})\) is:  
\[\begin{split}\Psi_{n}(\sigma;\tau_{n})&=\frac{ \left(2-4\tilde{p}\right)^{2}\left(\frac{\tau_{n}^{2}}{\sigma^{4}}\right)p_{w} ^{2}\left(\frac{\tau_{n}}{\sigma}\right)}{\left[\left(2-3\tilde{p}\right)- \left(2-4\tilde{p}\right)F_{w}\left(\frac{\tau_{n}}{\sigma}\right)\right]}\\ &\qquad\times\frac{1}{\left[\left(-1+3\tilde{p}\right)+\left(2-4 \tilde{p}\right)F_{w}\left(\frac{\tau_{n}}{\sigma}\right)\right]}\end{split} \tag{13}\]  
where \(p_{w}(\cdot)\) denotes the probability density function (PDF) of \(w_{n}\). In light of the asymptotic statistical property of the GLRT detector presented in (10), the probability of the false alarm of the GLRT detector is given by:  
\[P_{FA}=Q_{\chi_{1}^{2}}\left(2\ln\eta\right) \tag{14}\]  
where \(Q_{\chi_{1}^{2}}(\cdot)\) represents the right-tail probability of the chi-squared distribution with one DOF. The detection probability of the GLRT is calculated as:  
\[P_{D}=Q_{\chi_{1}^{2}(\lambda)}(2\ln\eta) \tag{15}\]where \(Q_{\chi_{1}^{2}(\lambda)}(\cdot)\) denotes the right-tail probability of the noncentral chi-squared distribution with one DOF and noncentrality parameter \(\lambda\).
## 3 Results
### Microhardness Testing  
Figure 8 shows representative hardness maps measured from the cross-sections of the repaired hole (Experiment 2) and the groove (Experiment 3), respectively. The dashed lines represent the original dimensions of the defects to be repaired. The measured peak hardness is 172 HV in the repaired hole, and 168 HV in the repaired groove. In the deposited material, the hardness shows higher values inside the repaired volume than at the interface of mixing, a trend observed in both hole and groove repair. The average hardness of the feed rods is measured to be 164 HV. Although the deposited material is generally softened when compared to the original feedstock, most of the repaired volume exhibits a hardness value above 140 HV, which is 85% of the feed material value. There are even localized areas in the repaired volume showing comparable hardness to the stock AA7075-TT651.  
Figure 8: Hardness mapping of the (**top**) repaired hole and (**bottom**) repaired groove samples. The dashed lines represent the original perimeters of the hole and groove, respectively.  
Through additive friction stir deposition in this work, the grain size of AA 7075 is reduced from several hundreds of microns in the feed material to a few microns in the deposited material, which alone should lead to a slight increase in the strength and hardness. However, additive friction stir deposition of AA 7075 has been carried out in the range of 80-90% of the melting temperature of aluminum, which is close to or above the solutionizing temperature of AA 7075 [32; 33]. Although the typical exposure to the peak temperature lasts only for a few seconds in additive friction stir deposition, the extended dwell period in repair could cause a significant portion of precipitates to dissolve into the lattice. Such a substantial weakening of precipitation hardening ultimately results in the decrease of the microhardness in the as-deposited material. Compared to the repaired volume, the edges of the hole and groove--which consist of elongated grains--exhibit even lower values of hardness. This is because the AA 7075 at these locations undergoes much less deformation than the deposited material, resulting in no dynamic recrystallization. Instead, the material is mostly in the thermomechanically-affected zone or heat-affected zone. The resultant grain growth and precipitate dissolution can thus lead to a lower value of hardness than the deposited material.
## 4 Electric Propulsion System Characterization
### Energy Consumption and Efficiency  
Based on static maps of electrical quantities, the characterization of EPUs in the form of efficiency was performed, since this is a key parameter when designing the system. For the purpose of estimating the flight time, it is necessary to show the electric current depending on the required thrust force for the selected EPUs. Therefore, the overall efficiency of the EPU was represented by the ratio of thrust and electric power and is shown as a function of electric power (energy consumption). Figures 19-21 show the electric current depending on the required thrust force and overall efficiency of the EPU for three series of measurements (setups). Based on the obtained curves, the components of the propulsion and energy system can be selected for further analysis.  
Figure 19: Energy consumption of 1806 BLDC series: (**a**) electric current with respect to thrust force; (**b**) overall efficiency.  
Figure 17: Aerodynamic effects with respect to rotor angular velocity, MN 2214 BLDC motor: (**a**) thrust force; (**b**) drag torque.
## 1 Introduction
In addition, the employment of the PEG derivatives as matrices for environmental monitoring could be strategic for the high swelling factor in water [56; 57], allowing a fast diffusion of pollutant cations inside the matrix and facilitating the interaction with the sensitive moiety (NCs in our case). In this work, we report on the synthesis of silver nanoclusters (AgNCs) stabilized by poly(methacrylic acid) (PMAA). We studied the optical properties of the AgNCs-PMAA system in a water solution and in a hybrid hydrogel matrix based on PEGDA. AgNCs-PMAA show a fluorescence peaked at 660 nm when excited with a LED source at 340 nm. The fluorescence band is independent from the excitation wavelength (within the range 340-540 nm), indicating a high monodispersion in size [58; 59]. The average size of the synthesized NCs was 1.84 nm, measured by High-Resolution Transmission Electron Microscopy (HRTEM). We tested hybrid hydrogel samples in the presence of several metal ions. The sensitivity to Pb(II) ions found in liquid solution, was preserved in the solid-state device. The interaction kinetics between the Pb(II) cations and the matrix containing the fluorescent material was evaluated. The performance of the hybrid device in terms of limit of detection (LOD) and linear behavior (form 0 to 100 \(\upmu\)M) as a function of AgNCs-PMAA content were also studied. This result represents a first step toward the fabrication of a solid-state optical sensor for water quality monitoring based on fluorescent AgNCs-PMAA.
## 3 Experimental Evaluation
### Evaluation of Shift-Peristrophic Multiplexing  
Next, we evaluated the shift-peristrophic multiplexing; the experimental methodology is shown in Figure 8. First, 20 shift multiplexing processes were performed along the \(x\)-direction with 50 \(\mu\)m. The medium was then rotated by 10\({}^{\circ}\), 20 shift multiplexing processes were repeatedly performed up to an angle of 170\({}^{\circ}\), and the diffraction power from each hologram for each rotation angle was measured. The center of rotation of the medium was always set to the center of the shift-multiplexed holograms. Figure 9 shows the diffraction power at each angle. The horizontal axis indicates the shift amount of the medium, and the vertical axis indicates the diffraction power. Multiplexing nearly 18 times was experimentally proven, and the diffraction power of the hologram changed as the multiplex number increased.  
Figure 6: Rotational selectivity.  
Figure 7: Rotational selectivity of shift multiplexed holograms.  
A case of reproduced image obtained by this experiment is shown in Figure 10. Although the image was distorted partially its overall quality is acceptable. The signal-to-noise ratio (SNR) defined by:  
\[\mathrm{SNR}=20\log_{10}\frac{\mu_{1}-\mu_{0}}{\sigma_{1}+\sigma_{0}} \tag{4}\]  
was 3.5 dB, where \(\mu_{0}\) and \(\mu_{1}\) are the average number of black and white pixels, and \(\sigma_{0}\) and \(\sigma_{1}\) are the standard deviations of number of black and white pixels, respectively. We obtained at least 1.5 dB from the other holograms. Therefore, it is confirmed that a relatively high multiplexing number can be achieved when recording using shift multiplexing together with peristrophic multiplexing. The optimization of the method and improvement of the medium will be studied in future research to further improve storage density.  
Figure 8: Experimental procedure for the shift-peristrophic multiplexing.  
Figure 9: Evaluation results of shift-peristrophic multiplexing.
###### Abstract  
In order to optimize the algorithm on contact pressure and interference magnitude of multi-layer interference cylinders, the effects of centrifugal force and temperature gradient on the performance of multi-layer interference cylinders are considered. The mathematical matrix model of contact pressure and interference magnitude of multi-layer interference cylinder is constructed under centrifugal force and temperature gradient. Four kinds of interference cylinder models are established, and the numerical solutions using finite element methods are compared with the analytical solutions. The results show that the contact pressure of each contact surface of multi-layer interference cylinder decreases with speed increase; the slip rate of contact pressure increases with speed increase; the maximum radial displacement of an interference cylinder under centrifugal force occurs at the outermost layer. The contact pressure of a multi-layer interference cylinder decreases linearly with the increase of temperature gradient. The analytical solution is basically consistent with the numerical solution, and the maximum relative error of midpoint is \(11.62\%\) and \(-7.69\%\) under two factors. It means that the algorithm can provide a theoretical guide for the design of a multi-layer interference cylinder considering centrifugal force and temperature gradient.  
centrifugal force; temperature gradient; multi-layer interference cylinder; contact pressure Article