esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on a Modified Model for the Hold Time of a Clean Extinguishing Agent
## 3 Experimental Study  
All tests conducted in this study were performed under the same environmental conditions in a self-manufactured test enclosure. A schematic diagram of the test enclosure can be seen in Figure 4.  
The dimensions of the test enclosure were 2 m \(\times\) 2 m \(\times\) 2 m. At the top and bottom of the test enclosure, there were 16 circular leakage holes, each with a diameter of 0.01 m. The leakage holes were 0.05 m away from the ceiling and floor boundaries. The size of the outflow volumetric flow was controlled by blocking or eliminating leakage holes using vacuum plugs. The differential pressure inside and outside the test enclosure was measured using a differential pressure gauge.  
The test enclosure was installed in a place that is about 30 times larger than the enclosure's volume that minimizes the change in pressure outside the enclosure due to the agent-air mixture discharged from the enclosure. It also minimizes the change in pressure inside the enclosure due to external wind [11; 12; 14]. There is no HVAC system installed in the test enclosure. Therefore, when calculating the hold time, the influence of bias pressure was not considered.  
In order to apply the same assumptions of the existing theoretical model, thermal effects were not considered. The thermal effects are discussed elsewhere [10; 12; 16; 22; 29]. After the CEA was discharged into the test enclosure, all leakage areas were kept closed for at least 10 min to stabilize the flow of the agent-air mixture inside the enclosure and bring the temperature difference inside and outside the enclosure to within 1 K. To establish the same initial conditions for replicates, the test enclosure was always purged with compressed air after one experiment was completed [12]. The CEA concentrations were calculated based on the oxygen concentration of the agent-air mixture. Concentrations were simultaneously measured at 7 points in 0.2 m intervals from 0.6 m to 1.8 m on the floor boundary. The concentration-measuring devices' linearity was secured through calibration.  
The leakage exponent \(n\) and leakage characteristics \(k\) were measured using the fan pressurization method, and an evaluation was performed for a total of 4 conditions, as shown in Table 1. The upper and lower leakage holes were evaluated in equal numbers to set the lower leakage fraction F to 0.5. In this study, the change in lower leakage fraction F was not evaluated. The agents used in the experiment were _IG-01_ and _HFC-125_, and a total  
Figure 4: Schematic diagram of test enclosure used in the experiment.  
of 5 experiments were repeated for each agent. The initial conditions of the test enclosure are shown in Table 2.
# Multi-Frame Blind Super-Resolution Based on Joint Motion Estimation and Blur Kernel Estimation
###### Abstract  
Multi-frame super-resolution makes up for the deficiency of sensor hardware and significantly improves image resolution by using the information of inter-frame and intra-frame images. Inaccurate blur kernel estimation will enlarge the distortion of the estimated high-resolution image. Therefore, multi-frame blind super resolution with unknown blur kernel is more challenging. For the purpose of reducing the impact of inaccurate motion estimation and blur kernel estimation on the super-resolved image, we propose a novel method combining motion estimation, blur kernel estimation and super resolution. The confidence weight of low-resolution images and the parameter value of the motion model obtained in image reconstruction are added to the modified motion estimation and blur kernel estimation. At the same time, Jacobian matrix, which can better describe the motion change, is introduced to further correct the error of motion estimation. Based on the results acquired from the experiments on synthetic data and real data, the superiority of the proposed method over others is obvious. The reconstructed high-resolution image retains the details of the image effectively, and the artifacts are greatly reduced.  
multi-frame blind super-resolution; motion estimation; blur kernel estimation; the confidence weight; Jacobian matrix +
Footnote †: journal: Applied Sciences  
1
## 6 Seismic Data Integration in the Static Reservoir Model
Water-bearing Lithology classes, according to the well and seismic data, are characterized by P-impedance values above 3100, but a clear distinction between water-bearing sand and clay inside reservoir interval based on seismic data only was not possible. Based on described probability analysis, a probability cube has been built for each single Lithology class representing the main input for the propagation of each Lithology class in the modeled reservoir volume. However, having five lithology classes with their probabilities increased the uncertainty in the lithology model in cases of equal probability for two or more  
Figure 14: Cross-plot showing a correlation between P-impedance log, Zp, and Lithology classes. Figure 15: Results of lithology probability analysis in dependence on P-impedance at wells (**a**) and for the range of Zp values in the entire reservoir interval (**b**). classes. Away from well control facies, probability cubes allow great facies variations. For that reason, the lithology model has been constrained by applying two-step facies modeling, i.e., by separately modeling the gas-bearing (pay) and water-bearing (non-pay) parts of the volume in the first step and then distributing lithology classes (three in pay and two in non-pay parts (Figure 16)) constrained by tentatively named pay/non-pay model. Reservoir properties modeling, as the last step, was constrained by the lithology model and driven by trends from seismic inversion and DFNN porosity volume (Figure 17). Based on available logs and advanced petrophysical analysis, as well as analog field data, it is expected that permeability and water saturation differ more for different lithology classes than porosity. The parameter that, according to the well log analysis, showed significant variations and impact on reservoir volumetrics and behavior is water saturation. It was concluded that all the parameters could be satisfactorily controlled by lithology classes, corresponding reservoir properties ranges, and trends from seismic inversion. Figure 16: Probability properties of gas-bearing (pay) and water-saturated (non-pay) Lithology classes (**a**) and resulting lithology distribution (**b**). Figure 17: Series of sections through the reservoir model showing porosity distribution.
# Prospects for the Development of High Energy Density Dielectric Capacitors
## 2 A Review of the Literature on Energy Storage Using Dielectric Materials
### Percolation Effect  
In all the analyses in Section 3, it will be assumed that the particles are uniformly distributed in the host polymer and the charge transfer to the polymer is unimpeded by any surface layer between the particle and the polymer. In other words, the analysis assumes an ideal arrangement that is not likely to occur in practice. Deviations from the ideal are well known from testing of polymer composites and are termed percolation effects [23; 24; 25; 26; 27].  
Figure 1: Dielectric constant data for polymer mixes involving PVDF [16]. (**a**) Dielectric constant of ArPTU, PVDF-TrFE-CFE, and PVDFTFFE-CFE/ArPTU composite films; (**b**) Dielectric loss of ArPTU, PVDFTFFE-CFE, and PVDF-TrFE-CFE/ArPTU composite films.  
Most analyses indicate that the increase in the dielectric constant is linear with the volume loading factor f\({}_{\rm v}\). In those cases involving percolation, change in the dielectric constant is very non-linear of the form.  
\[\varepsilon/\varepsilon_{\rm host}=((\rm f_{vc}-f_{c})/f_{c})^{-p}\]  
where f\({}_{\rm vc}\) is the percolation threshold loading and p is the exponent in the percolation effect relationship. The f\({}_{\rm vc}\) and p values are determined experimentally by varying f\({}_{\rm v}\) and measuring the effect on \(\varepsilon\). Typical experimental data for a polymer composite using carbon nanotubes (MWCNT) are shown in Figure 2. For this test, the threshold value f\({}_{\rm cv}\) is about 14% and the maximum \(\varepsilon\) is very high being about 8000. The conductivity of the composite increases over many orders of magnitude as f\({}_{\rm v}\) is changed, indicating the arrangement of the particles is undergoing large changes. The threshold value can range from a few % to 40-50%, depending on the materials in the device being tested and its thickness. There seems to be a linear zone before the rapid increase of \(\varepsilon\) in the percolation zone. Incorporating a better understanding of the percolation effects and how to optimize them in developing materials for high energy density dielectric devices will be very important.
## 4 Applications of AI in Modern Communication Networks
### Network Optimization
#### 4.1.2 Latency Reduction  
AI can help reduce latency by predicting and managing network traffic [100]. Deep learning models can analyze traffic patterns to detect potential bottlenecks and proactively reroute traffic, ensuring that latency-sensitive applications like VoIP or video streaming experience minimal delay [101].
## 7 Experiments  
### Test Environment  
The proposed VTL algorithm was tested in an indoor asynchronous PL navigation system in the SNUGL. Navigation was based on pseudorange measurements. However, the available space in the SNUGL indoor PL navigation system, a 7 \(\times\) 7 m plane and 3 m height, is not large enough for code-based navigation. We therefore applied a moving average filter to the navigation results just to see how well the VTL works and to verify VTL performance. The filtered results are not applied to the tracking loop algorithm; they are only for monitoring the navigation results. Figure 13 shows the SNUGL indoor PL navigation system and test setup.  
Figure 14 presents a more specific description of the test environment. The user receiver antenna was installed on a toy train moving along a track oriented towards the PL PRN 4. The reference receiver antenna was fixed on the floor. Because implementation of real-time VTL was not possible at the time of the experiment, the interfrequency (IF) sampled signal received by the user was stored for the postprocessing software receiver on which VTL and ITL were implemented. A commercial-grade GPS receiver based on the GP2000 chipset was used as a reference receiver and the measurements were also logged to be processed in a software receiver.  
Figure 13: SNUGL indoor PL navigation system.  
The goal of this test was to study and compare the tracking performance of VTL and ITL in the presence of the near-far problem as the user moves between the near and far zones of PL PRN 4. The tracking results of VTL and ITL, implemented on a software receiver processing stored IF sample data with logged reference receiver measurements, were therefore compared [21].  
Three tests and one experimental test were performed. First, the user moved from the near to the far zone of PL PRN 4. This was the situation in which one of the PL signals was attenuated by other strong near signals. Second, the user moved to the near zone from the far area of PRN 4 and so the other far signals were to be attenuated by one strong PL signal, PRN 4. Third, the test environment was same to the first one except that the number of visible satellites is limited to three. As an experimental test, signal tracking performance of VTL with three visible satellites, the marginal number of satellites for 2-dimensional positioning, was studied.
## 1 Introduction
In [34], a recognized detail-preserving anisotropic diffusion is presented for the SAR image that mainly undergoes low presentation at the edges and severe blocking artifacts in homogeneous regions. The study of [35] proposed a new despeckling method in SAR images using the arrangement of different alternative analysis processes on the same speckled images. The proposed method is reliable as pixel-wise feature extractions of the image. It works precisely in the diversity by combining useful performance metrics for each image region. It becomes enhanced as a simple despeckling technique by subsequently considering and highlighting the complementary characteristics of only two state-of-the-art tools. In [36], the homomorphic method has been proposed with the efficient implementation of the anisotropic diffusion and method-noise threshold with the addition of db2-based 2D-DWT. To preserve the fine details, the median and wiener filters are implemented on the LL block. After obtaining the despeckled images, they become further despeckled by the intelligent use of the newly proposed method, known as method-noise thresholding, which produces the final filter images. The experimental results showed that the results of [37] were tested on simulated and real radar images and were compared and validated using the performance metrics. Combining DWT with non-linear diffusion and method noise thresholding enhances speckle noise mitigation in Synthetic Aperture Radar (SAR) images. DWT decomposes the image into various frequency bands, facilitating targeted noise reduction. Non-linear diffusion smooths the image while preserving edges, addressing high-variance noise. Method noise thresholding applies a dynamic threshold to distinguish between noise and significant features. Integrating these techniques with fusion-based thresholding improves the overall effectiveness by leveraging the strengths of each method. The proposed method merges these two approaches fusion-based thresholding for noise separation and non-linear diffusion for detail preservation, offering a superior noise reduction solution over existing methods. The contributions of this work are as follows:  
1. The proposed model effectively suppresses granular noise in synthetic aperture radar (SAR) images while maintaining intricate details. This method shows a superior ability to preserve fine structures, such as edges and textures, in both homogeneous and non-homogeneous land regions. 2. It exhibits a heightened potential to retain crucial image features, including edges and texture, which are essential for accurate image analysis and classification. This capability ensures that the integrity of the original image features is maintained, facilitating more reliable interpretations. 3. The experimental findings have been rigorously validated using two pieces of simulated SAR imaging data. This validation confirms the robustness and effectiveness of the proposed method in practical applications, demonstrating its utility in real-world scenarios.
# Evaluation of Low-Temperature Performance of Recycled
## 3 Results and Discussion
### Scanning Electron Microscope (SEM)  
#### 3.2.1 RAP and Binder Interface  
In Figure 7, the dark area represents asphalt, while the bright area represents indoor-simulated RAP aggregate. It can be observed that the surface morphology of RAP-coated asphalt varies in asphalt mixtures with different thermal histories. Compared to UVRAP-coated asphalt, THRAP-coated asphalt exhibits a rougher surface with a more pronounced flocculent structure and an increased presence of angular fragments resulting from higher stiffness due to aging [27]. Microscopic analysis reveals that the THRAP surface asphalt in the mixture shows more advanced aging compared to the UVRAP surface asphalt. Additionally, no distinct'sandwich' structure is observed in the RAP surface asphalt, indicating a lack of strict boundaries between the old and new asphalt. Studies suggest that this phenomenon is linked to the inclusion of a rejuvenator in the mixture [28]. However, some demarcation between asphalt and RAP is still noticeable, albeit with a relatively smooth transition at the asphalt-aggregate interface. It can be seen that the combination of asphalt and RAP is relatively effective under the action of the rejuvenator, which helps enhance the crack resistance of the asphalt mixture.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \begin{tabular}{c} **Thermal** \\ **History** \\ \end{tabular} & **Total Mass** & **Mass Loss** & \begin{tabular}{c} **Asphalt** \\ **Content** \\ \end{tabular} & \
\begin{tabular}{c} **Correction** \\ **Coefficient** \\ \end{tabular} &
\begin{tabular}{c} **Asphalt** \\ **Content** \\ \end{tabular} \\ \hline THRAP & 2000 g & 109.2 g & 5.46\% & 0.92\% & 4.54\% \\ UVRAP & 2000 g & 114.8 g & 5.74\% & 0.92\% & 4.82\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Asphal content of RAP.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \begin{tabular}{c} **Thermal History** \\ **Item** \\ \end{tabular} & \begin{tabular}{c} **Original** \\ **Asphalt** \\ \end{tabular} & \begin{tabular}{c} **Aging** \\ **Asphalt** \\ \end{tabular} & \begin{tabular}{c} **TRRAP** \\ **Rejuvenator Content** \\ **4\%** \\ \end{tabular} & \begin{tabular}{c} **Aging** \\ **12\%** \\ \end{tabular} & \begin{tabular}{c} **UVRAP** \\ **Asphalt** \\ \end{tabular} & \begin{tabular}{c} **Content** \\ **Rejuvenator Content** \\ **8\%** \\ \end{tabular} & \begin{tabular}{c} **Coefficient** \\ **12\%** \\ \end{tabular} & \
\begin{tabular}{c} **Coefficient** \\ **16\%** \\ \end{tabular} \\ \hline
\begin{tabular}{c} Penetration, \\ 15 \({}^{\circ}\)C, 100 g, 5 s \\ Penetration, \\ 25 \({}^{\circ}\)C, 100 g, 5 s \\ Penetration, \\ 25 \({}^{\circ}\)C, 100 g, 5 s \\ Penetration, \\ 30 \({}^{\circ}\)C, 100 g, 5 s \\ Softening point \\ (TR&\(\!\!
# Fractal Metasurfaces and Antennas: An Overview for Advanced Applications in Wireless Communications
## 4 Fractal Metasurfaces
### Fractal Metamaterial Absorbers  
One important application of metasurfaces is in electromagnetic wave absorption. Metamaterial absorbers (MAs), as proposed in [60], can be designed to efficiently absorb a specific frequency. These can be used across the frequency spectrum from RF to ultraviolet (UV) for various applications [61; 62; 63; 64], including the reduction in electromagnetic interference, the mitigation of multipath problems in indoor wireless applications, 5G communications, the improvement of the electromagnetic compatibility of electronic devices, and the design of photonic devices.  
MAs typically consist of a periodic frequency-selective surface printed on a thin, grounded dielectric substrate (see Figure 14a). Some examples of unit cell geometries are reported in Figure 14b. To achieve the absorption of a specific frequency, \(f_{0}\), it is necessary to synthesize the MA unit cell to match the impedance of free space, \(\xi_{0}\). In this way, the unit cell reflection coefficient, \(\Gamma=\frac{Z_{cell}-\xi_{0}}{Z_{cell}+\xi_{0}}\), approaches zero, obtaining a near-unity absorption (A=1 - 1 \(\Gamma\)1 2 \(\rightarrow\)1). Several MA unit cells have been proposed in the literature [62] in the last few decades. More recently, the fractal concept has been used to create compact and multiband metamaterial absorbers. Although the results obtained from the application of fractals are very interesting, this approach is still considered to be in the development phase. Therefore, ongoing research in these areas continues to explore new designs.  
Figure 14: General structure of a metamaterial absorber: (**a**) side view; (**b**) examples of unit cells.
# Quasi-All-Passive Thermal Control System Design and On-Orbit Validation of Luojia 1-01 Satellite
## 4 Thermal Analysis
### Parameters of Working Condition in Thermal Analysis  
First, the high and low temperature conditions are defined by the peaks and valleys of the solar constant in a year and the life expectancy of coatings [12]. The high temperature condition is defined like this, owing to the degeneration of the coatings at the end of the life expectancy, coupled with the peak of the solar constant at December solstice, the radiation regions absorbing the external heat flux is at the highest level. In the meantime, all the instruments are working at their maximum power consumption. The low temperature is defined like this. At the beginning of the life of the coatings, coupled with the valley of the solar constant at the June solstice, the radiation regions absorbing the external heat flux are at the lowest level. In the meantime, the satellite is in Sun pointing mode.  
In the end, taking the long-term flight attitude, external heat flux and working modes of the satellite as a whole into consideration, the parameters and definition of temperature condition is defined in subsections 3.2.1 and 3.2.2.
## 4 Conclusions  
In this paper, a fuzzy clustering algorithm is proposed based on FCS by maximizing the between-cluster matrix and minimizing the within-cluster matrix with weighted features. Two adjustment formulations are derived for adjusting the values of \(\mu_{g}\) and the \(\omega_{k}\) respectively. Through the proposed WFCS, problem for the membership of the data point lying on the crisp boundary can be solved. Experimental results show that the proposed WFCS generally outperforms the four existing clustering algorithms (FCS, WFCM, WSRFCM and ESSC).  
The proposed algorithm can handle linear datasets, whereas, the high-dimensional nonlinear data has not been considered in this paper. In the future, we will employ the kernel methodology [19; 20; 21] to analyze the high-dimensional nonlinear data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Dataset** & **WFCS** & **ESSC** & **WSRFCM** & **WFCM** & **FCS** \\ \hline \multicolumn{6}{c}{Australian} \\ \hline mean & **0.0551** & 0.1730 & 0.6312 & 0.7261 & 0.5971 \\ std & 0.0000 & 0.1313 & 0.0011 & 0.4054 & 0.2144 \\ \hline \multicolumn{6}{c}{Breast Cancer} \\ \hline mean & **0.3849** & 0.3843 & 0.5388 & 0.6035 & 0.4510 \\ std & 0.0010 & 0.0084 & 0.0000 & 0.0000 & 0.0000 \\ \hline \multicolumn{6}{c}{Balance-scale} \\ \hline mean & **2.1924** & 3.6567 & 5.8354 & 6.1232 & 5.6191 \\ std & 0.7932 & 0.1983 & 0.0911 & 0.1686 & 0.7135 \\ \hline \multicolumn{6}{c}{Heart} \\ \hline mean & **1.1191** & **1.1191** & 2.3297 & 3.3005 & 1.8087 \\ std & 0.0000 & 0.0991 & 0.0012 & 0.0000 & 0.0000 \\ \hline \multicolumn{6}{c}{Iris} \\ \hline mean & **0.0729** & 0.3300 & 0.8423 & 0.6224 & 0.5366 \\ std & 0.0079 & 0.0097 & 0.6452 & 0.0311 & 0.0000 \\ \hline \multicolumn{6}{c}{Pima} \\ \hline mean & **0.2828** & 0.6195 & 1.0043 & 0.4897 & 0.4832 \\ std & 0.0012 & 0.0003 & 0.0627 & 0.0073 & 0.0000 \\ \hline \multicolumn{6}{c}{Vehicle} \\ \hline mean & **0.1908** & 0.2850 & 0.5000 & 0.4128 & 0.6351 \\ std & 0.0010 & 0.0087 & 0.0149 & 0.0000 & 0.0000 \\ \hline \multicolumn{6}{c}{Wine} \\ \hline mean & 0.9821 & 0.9934 & 2.0174 & 1.4317 & **0.8599** \\ std & 0.0038 & 0.0350 & 0.0028 & 0.0000 & 0.0000 \\ \hline \multicolumn{6}{c}{ENGINE} \\ \hline mean & **0.4866** & 0.8500 & 1.6315 & 1.6072 & 0.8484 \\ std & 0.0079 & 0.0060 & 0.0056 & 2.3408 & 0.0000 \\ \hline \multicolumn{6}{c}{LTT} \\ \hline mean & **0.9206** & 1.6253 & 3.0179 & 1.9023 & 1.8894 \\ std & 0.0075 & 0.0281 & 0.0106 & 0.0002 & 0.0000 \\ \hline \end{tabular}
\end{table}
Table 6: Within-Between(WB) index of algorithms.