esa-sceva/satcom-chunk-collection huggingface dataset:
# A Machine-Learning-Based Software for the Simulation of Regional Characteristic Ground Motion
## 2 Methods for Ground-Motion Simulation  
Ref. [38] was the first to carry out the work on ground-motion simulation. Subsequently, there has been long-term development of ground-motion simulation methods and theories. Deterministic methods are mainly applied to simulate the low-frequency components of ground motion, and stochastic methods are proposed to simulate the high-frequency components of ground motion [39]. Hybrid simulation methods combine the advantages of both deterministic and stochastic simulations, and can simulate a wide range of earthquakes [40]. However, the deterministic approach requires a hypocenter model [41]. The stochastic and hybrid simulation methods also require parameters specific to the target region, such as stress drop, local site effects, etc. [9]. Therefore, simulating ground motion remains a huge challenge. With the increasing number of modern seismic stations and the development of seismic monitoring and ground motion databases--which are now becoming increasingly abundant--a large amount of data is bound to provide a wealth of information for research. Owing to the large amount of data, it is important to determine how to use these data. A regional ground motion field is obtained by applying machine learning methods with user-requested scenario parameters (e.g., magnitude, epicenter distance, latitude, and longitude).  
The program applies a ground-motion simulation method based on the database of actual time-history records by applying machine learning PCA for extracting characteristic ground motions and combining them with a genetic algorithm to determine the optimal solution. First, ground motion records in the database are extracted by PCA to obtain the characteristic ground motions that represent the characteristics in the target area. The extracted characteristic ground motions are independent of each other, and they are linearly combined to simulate ground motions. The linear combination coefficients are unknown. The synthesized ground motions require the simultaneous matching of the response spectrum with both the target response spectrum and the target duration characteristics. A multi-objective genetic algorithm is applied for using these two matching relationships as constraints to solve for the combination coefficients that meet the evaluation criteria. The simulation process is illustrated in Figure 1.  
Figure 1: Framework of the proposed ground-motion simulation method.
# Coarsely Quantized Decoding and Construction of Polar Codes Using the Information Bottleneck Method
## 3 Polar Code Construction Using the Information Bottleneck Method
### Information Bottleneck Construction
Further, the compressed joint distributions \(p(u_{i},t_{i})=p(u_{i}|t_{i})p(t_{i})\) also facilitate computing the capacities of the bit channels. For the construction of a polar code, this treatment is applied to all the building blocks in the structure of the code, starting at level \(j=1\) and progressing step by step to higher levels. In our running example of Figure 2, the aforementioned procedure produces quantized bit channels at level \(j=1\). Specifically, the quantized bit channels of the intermediate variables nodes labeled \(v_{0,1}\) and \(v_{2,1}\) are similar to the one obtained for \(u_{0}\) of Figure 1, i.e., \(p(v_{0,1},t_{0,1})=p(v_{2,1},t_{2,1})=p(u_{0},t_{0})\). Moreover, the quantized bit channels of the intermediate variables nodes labeled \(v_{1,1}\) and \(v_{3,1}\) are similar to the one obtained for \(u_{1}\) of Figure 1, i.e., \(p(v_{1,1},t_{1,1})=p(v_{3,1},t_{3,1})=p(u_{1},t_{1})\). At level \(j=2\), the bit channel of \(v_{0,2}=u_{0}\) now has \(t_{0,1}\) and \(t_{2,1}\) as outputs, with an output alphabet of size \(|\mathcal{T}_{0,1}|\cdot|\mathcal{T}_{2,1}|=|\mathcal{T}|^{2}\). This bit channel is quantized using the selected information bottleneck algorithm, which provides the distributions \(p(u_{0}|t_{0,2})\), \(p(t_{0,2})\) and \(p(t_{0,2}|t_{0,1},t_{2,1})\) from the input joint distribution \(p(u_{0},t_{0,1},t_{2,1})\), compressing the output alphabet to \(|\mathcal{T}|\) elements (recall that in Figure 2, \(v_{i,2}=u_{i}\)). The input joint distribution \(p(u_{0},t_{0,1},t_{2,1})\) for the algorithm is computed from Equations (16) and (17) with appropriate replacements, e.g., by replacing \(p(y_{0}|x_{0})=p(y_{1}|x_{1})\), \(x_{0}\) and \(x_{1}\) with \(p(t_{0,1}|v_{0,1})\), \(v_{0,1}\) and \(v_{2,1}\), respectively. The joint distribution \(p(u_{1},t_{0,1},t_{2,1},u_{0})\) of the bit channel of \(u_{1}\) in Figure 2 can be obtained similarly from Equations (17) and (18) with appropriate replacements, and quantized using the information bottleneck algorithm. Figure 6 shows the resulting information bottleneck graph for the polar code of Figure 2, obtained after quantizing all the building blocks, where the output alphabet of all the bit channels is restricted to \(|\mathcal{T}|\). Consequently, the compressed joint distribution \(p(u_{i},t_{i,2})\) for each bit channel of the code (level \(j=n\)) can be used to compute its capacity using Equation (12), and thus to construct information set \(\mathcal{A}\)[25].
## 3 LE Identification Method Considering the Fluctuation of Wind Power Output
### The Conventional Calculation Method of APEV
#### 3.2.1 LERPF  
The LE caused by the branch removal belongs to the static security analysis research scope [25,26]. In order to calculate the impact of a branch removal on the other branches in the system, various power-flow calculation methods can be used, such as Newton iterative algorithms. However, these methods are computationally intensive, time-consuming, and have convergence problems, making it difficult to meet the requirements of online real-time control. In order to reflect the influence degree of the removal branch visually and accurately on the active power of the remaining branches, this section introduces the concept of the LERPF and derives its mathematical expression.  
1. The definition of LERPF  
After a faulty branch is removed from the power grid, the original active power on that branch will be transferred to the remaining branches according to a certain proportion, defined as the LERPF and denoted by \(d\). Considering the directionality of the transmitted active power, the LERPF can be positive or negative, and the larger its absolute value is, the greater the influence on the active power transmitted by the branch. After the faulty line \(l\) is removed, the APCV of branch \(k\) within the CLS is shown in Equation (6).  
\[\Delta\mathrm{P}_{k}^{l}=d_{k-l}\mathrm{P}_{l}^{0} \tag{6}\]  
where \(\Delta\mathrm{P}_{k}^{l}\) is the APCV of line \(k\) after line \(l\) is removed; \(\mathrm{P}_{l}^{0}\) is the active power transmitted by branch \(l\) before line \(l\) is removed; and \(d_{k-l}\) is the LERPF of line \(k\) for line \(l\). The LERPF is derived below.  
2. The mathematical expression for the LERPF  
The LERPF is derived from the branch outage simulation of static security analysis. The detailed derivation process can be found in Appendix A. Let the two end nodes of branch \(k\) be \(m\) and \(n\), respectively. According to Appendix A, we can obtain the mathematical expression of the LERPF as shown in Equation (7):  
\[d_{k-l}=\frac{x_{l}(X_{ma}-X_{mb}-X_{ma}+X_{nb})}{x_{k}(x_{l}-X_{aa}+2X_{ab}-X_{ bb})} \tag{7}\]  
where \(x_{l}\) and \(x_{k}\) are the reactance of branch \(l\) and branch \(k\), respectively; \(X_{ma}\), \(X_{mb}\), \(X_{na}\), \(X_{nb}\), \(X_{at}\), \(X_{ab}\), and \(X_{bb}\) are elements in matrix \(\mathbf{X}\); \(\mathbf{X}\) is the reactance matrix. From Equation (7), it can be seen that the LERPF is only related to the power grid topology and line parameters and are independent of the active power transmitted by the power grid.
###### Abstract  
We developed a facile and greener approach for the preparation of silica-aerogel-based ionogels using choline dihydrogen phosphate ionic liquid by the sol-gel approach. A series of silica-based aerogels as ionogels were prepared by varying the ionic liquid concentrations: 0.1, 0.5, 1, 3, 5, and 10 wt %. The as-prepared ionogels were characterized using several analytical techniques, namely, attenuated total reflectance (ATR)/FT-IR, TGA, XRD, and particle size analyses. The role of ionic liquid in the viscoelastic properties of the sol-gel transition was monitored using time-dependent rheological measurements. The addition of ionic liquid to the sol-gel system favored the formation of a more interconnected silica network structure. The formation of a silica network structure during sol-gel hydrolysis and condensation was confirmed from \({}^{29}\)Si solid-state CP/MAS NMR spectra. The effect of the ionic liquid on the morphological properties was investigated using SEM and TEM studies. The cell viabilities of the prepared gel samples were clearly evident from the cytotoxicity assay studies using Swiss and HaCaT cells. The main advantages of using biocompatible ionic liquids for the preparation of these aerogels as ionogels are that they may be used for encapsulating biological molecules and retain their conformational stability for a longer duration.  
aerogels; ionogels; silica; ionic liquid; choline dihydrogen phosphate
# Stochastic Model Predictive Control for Urban Traffic Networks
## 6 Conclusions  
In this paper, we have proposed a stochastic MPC model for urban traffic signal coordination and control. Under the framework of the stochastic MPC model, we use chance constraint programming to model the uncertain traffic demands and uncertain disturbances of the road network. The detailed mathematical description of the model has been provided. Since the proposed model is a stochastic MPC model with chance constraints, by embedding the stochastic simulation and neural networks (NNs) into a genetic algorithms (GA), we propose a hybrid intelligent approach to solve the stochastic model. In addition, aiming to demonstrate the performance of the proposed stochastic MPC model, which was solved by HIA (abbreviated as MPC-HIA), the performance are compared with a deterministic MPC model (M1), which was solved by Sequential Quadratic Programming (SQP) and a deterministic MPC model (M2), which was solved by a Genetic Algorithm (GA). Numerical simulations illustrate that, compared with the deterministic MPC model, the proposed stochastic MPC model has a better ability to deal with uncertainties and avoid queue spill-back in urban road networks.  
In future research, additional testing in different types (or sizes) of networks and with different arrival types (or probability distribution model) should be investigated. In order to reduce the computational cost, we can design hierarchical or distributed control strategies and algorithms to reduce the computational complexity of the stochastic MPC model. The mismatch between the proposed MPC model and real traffic systems will be considered. Furthermore, other performance indexes (e.g., the one which can reflect the fuel consumption or vehicle emissions) can also be embedded in the objective function of the proposed stochastic MPC model.  
Figure 8: Comparison of the queue length at each control step.  
**Acknowledgments:** The authors would like to acknowledge the financial support of the National Natural Science Foundation of China under Grants No. 61603154 and No. 61374066, and by the Zhejiang Provincial Natural Science Foundation of China under Grant No. LY15F030021.  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] conceived, designed and performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Optical Interference Suppression Based on LCD-Filtering
## 3 Interference Reduction
### Modulated Interference  
In a second scenario, we assume a point light source inducing interference at the receiver from a distinct direction. If such a light source is of unmodulated nature or only contains power-line frequency components [12], electrical domain filtering is possible. As already mentioned in Section 3.1,  
Figure 4: Reduction of solar-induced interference using the LCD filter.  
Figure 5: Suppression of unmodulated and diffuse interfering light sources using the method described in Section 3.1.  
the problem of receiver saturation remains. Typically, the power of such sources is in the same scale as the useful signal; thus, noise enhancement due to the interfering signal is not of relevance in general.  
For modulated signals, DC-blocking is not a valid strategy. One possibility is an algorithmic approach, applying techniques like SIC, but this is limited to specific scenarios and increases the computational complexity at the receiver. Another example for modulated interference is multipath where delayed signal components deteriorate the receiver performance if not taken into account in the signal design or by algorithmic mitigation. Since this kind of interference is caused by reflections on the propagation path, we can assume the incoming directions to differ from the line-of-sight (LOS) path signal. Thus, direction-based filtering using an LCD is a very promising method.  
In the following, we expect the interfering signals to be spatially separated from the desired signals by the minimum required angular difference for perfect interference suppression, derived in (5). In other words, we expect the interference signals to be attenuated by \(\eta_{1}\left(\theta\right)\) while the useful signals pass the LCD with \(\eta_{0}\left(\theta\right)\). This assumption leads to the results shown in Figure 6 with an interference reduction in the range of 18 dB to 35 dB for the blue-colored 454 nm LED.
# A Soft-Voting Ensemble Classifier for Detecting Patients Affected by COVID-19
###### Abstract  
COVID-19 is an ongoing global pandemic of coronavirus disease 2019, which may cause severe acute respiratory syndrome. This disease highlighted the limitations of health systems worldwide regarding managing the pandemic. In particular, the lack of diagnostic tests that can quickly and reliably detect infected patients has contributed to the spread of the virus. Reverse Transcriptase--Polymerase Chain Reaction (RT-PCR) and antigen tests, which are the main diagnostic tests for COVID-19, showed their limitations during the pandemic. In fact, RT-PCR requires several hours to provide a diagnosis and is not properly accurate, thus generating a high number of false negatives. Unlike RT-PCR, antigen tests provide rapid diagnosis but are less accurate in detecting COVID-19 positive patients. Medical imaging is an alternative diagnostic test for COVID-19. In particular, chest computed tomography allows detecting lung infections related to the disease with high accuracy. However, visual analysis of a chest scan generated by computed tomography is a demanding activity for radiologists, making widespread use of this test unfeasible. Therefore, it is essential to lighten their work with automated tools able to provide accurate diagnosis in a short time. To deal with this challenge, in this work, an approach based on 3D Inception CNNs is proposed. Specifically, 3D Inception-V1 and Inception-V3 models have been built and compared. Then, soft-voting ensemble classifier models have been separately built on these models to boost the performance. As for the individual models, results showed that Inception-V1 outperformed Inception-V3 according to different measures. As for the ensemble classifier models, the outcome of experiments pointed out that the adopted voting strategy boosted the performance of individual models. The best results have been achieved enforcing soft voting on Inception-V1 models.  
deep learning; CNN; ensemble voting; chest CT +
Footnote †: journal: Applied Sciences
# Low Tensor Rank Constrained Image Inpainting Using a Novel Arrangement Scheme
## 3 Methods  
### Quarter Arrangement  
To explore the more efficient low-rank structure of an image, we propose a novel rearrangement scheme, named the quarter arrangement (QA) scheme, to turn a color image into other forms of data. The QA scheme can maintain the internal similarity of the original image in the rearranged data.  
The basic QA scheme: For example, as shown in Figure 1a, M is a 2D matrix (\(8\times 8\)). First, the entries of M are extracted for every other row and column to obtain four smaller matrices. Each smaller matrix has a size of \(4\times 4\). Then, these four smaller matrices are placed along the third dimension in a designed order. Lastly, a 3D tensor of size \(4\times 4\times 4\) is obtained from the \(8\times 8\) matrix M without changing the total number of entries. The entries in the four smaller matrices are labeled as the MATLAB notation \((:,:,1)\), \((:,:,2)\), \((:,:,3)\)and \((:,:,4)\). If M is smooth (most images satisfy), the four smaller matrices are similar in structure due to the adjacent entries.  
Applying the basic QA scheme on the single Lena image, the Lena image can be divided into 4 smaller Lena images. As shown in Figure 1b, the four smaller Lena images are similar to each other. In Figure 1c, the pixel values curves of the four smaller images have overlapped into one curve. It can be said that the similarity of the local image structure is mostly maintained by the basic QA scheme.  
Under this basic QA scheme, three flexible QA schemes are proposed for permuting the image into three flexible forms of data. The three flexible QA schemes can enhance the low rankness for an image by matrix SVD, tensor train decomposition, and tensor-SVD. Then, by exploiting the flexible QA schemes, three low rank-constrained methods that use the TT rank, tubal rank, and matrix rank as constrained priors are exploited for image inpainting.  
The three flexible QA schemes and methods are described in detail in the following three sections. By using the flexible QA scheme, tensor decomposition models based on low rank can be used not only for a color image or video completion but also for grayscale image operations.
## 3 Results and Discussion
### FTIR Spectral Analysis  
Purified PHBV samples were analyzed by FTIR spectroscopy to gain insight into its chemical structure; results are given in Table 3. The C-C stretching band occurs at 977 cm\({}^{-1}\)[38; 39]. The C-O stretching bands are observed between 1054 and 1129 cm\({}^{-1}\) and from 1226 to 1275 cm\({}^{-1}\)[38; 39; 40; 41]. The band at 1179 cm\({}^{-1}\) was assigned to the C-O-C stretching of the amorphous PHBV [42]. The band at 1720 cm\({}^{-1}\) (highest intensity) was assigned to a C=O stretching of an ester structure in PHBV [6; 38; 40]. The C-H stretching bands of methyl (-CH\({}_{3}\)) and methylene (-CH\({}_{2}\)) groups appeared at 2975 cm\({}^{-1}\) and 2933 cm\({}^{-1}\), respectively [39; 40; 43]. Analysis of the FTIR spectra can be used to gain insight into the relative amounts of amorphous versus crystalline material in PHBV by determining the carbonyl index (\(I_{C=O}\)). Curve fitting of the crystalline band at 1720 cm\({}^{-1}\) to amorphous band at 1740 cm\({}^{-1}\) was used to determine band areas and \(I_{C=O}\) (Figure 1) [25; 26; 31]. The \(I_{C=O}\) for purified PHBV ranged from 2.01 to 2.59 (Table 4). A poor correlation (\(R^{2}\) = 0.53) between \(I_{C=O}\) and \(X_{c}\) by DSC (17-29%) was observed. These results are lower than reported values for PHB (\(I_{C=O}\) 6.94 and \(X_{c}\) 57.6%) [38], and higher than that reported for PHBV with a 25% 3 HV content (\(I_{C=O}\) 1.6 and \(X_{c}\) 53.7%) [44]. It has been shown that as the crystallinity index decreased from 0.99 to 0.81 as HV content increased from 0% to 47% [45]. \begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Sample**} & \multicolumn{3}{c}{**Molar Mass a,b [6]**} \\ \cline{2-4}  & \(M_{w}\) & \(M_{n}\) & \(PDI\) \\  & **(\(\times\)105 g\(\cdot\)mol\({}^{-1}\))** & **(\(\times\)105 g\(\cdot\)mol\({}^{-1}\))** & \\ \hline PHBV-15 & 28.0 \(\pm\) 0.43 & 19.6 \(\pm\) 0.5 & 1.43 \(\pm\) 0.04 \\ PHBV-32 & 8.67 \(\pm\) 0.31 & 7.98 \(\pm\) 0.33 & 1.09 \(\pm\) 0.06 \\ PHBV-39 & 6.96 \(\pm\) 0.38 & 6.45 \(\pm\) 0.32 & 1.08 \(\pm\) 0.08 \\ PHBV-46 & 8.54 \(\pm\) 0.18 & 7.52 \(\pm\) 0.09 & 1.14 \(\pm\) 0.03 \\ PHBV-61 & 6.49 \(\pm\) 0.18 & 5.99 \(\pm\) 0.21 & 1.08 \(\pm\) 0.05 \\ PHBV-71 & 6.86 \(\pm\) 0.49 & 6.40 \(\pm\) 0.39 & 1.07 \(\pm\) 0.1 \\ PHBV-78 & 8.45 \(\pm\) 0.16 & 7.96 \(\pm\) 0.15 & 1.06 \(\pm\) 0.03 \\ PHBV-84 & 11.3 \(\pm\) 0.25 & 10.1 \(\pm\) 0.21 & 1.12 \(\pm\) 0.03 \\ \hline \end{tabular}  
* Symbols are defined as: \(M_{w}\) is the weight-average molecular weight; \(M_{n}\) is the number-average molecular weight; \(PDI\) is the polydispersity index (\(PDI\) = \(M_{w}\)/\(M_{n}\)). b Values are mean \(\pm\) standard deviation. The standard deviation has been rounded up to the mean’s reported precision wherever necessary.  
\end{table} 
Table 2: Molar Mass and Polydispersity index of purified PHBV obtained from CHCl\({}_{3}\) extraction of biomass a,b. Figure 1: (**a**) FTIR spectrum for purified PHBV-39 sample; (**b**) expanded spectrum showing the carbonyl region before fitting; (**c**) expanded spectrum showing the carbonyl region after fitting; (**d**) fitted carbonyl bands.
## 2 Major Factors Contributing to Energy Consumption in 5G Network Technologies  
One of the primary drivers of energy consumption in last-generation mobile telecommunication is the denser deployment of base stations and small cells compared to previous generations [14,15], as shown in Figure 1.  
Figure 1: Fifth-generation infrastructures, showing the dense deployment of base stations and interconnected equipment and devices. Adopted from [15].  
The need for more infrastructure to support higher frequencies and increased data traffic can result in significantly higher energy usage. The following aspects of 5G deployment are the primary factors that are expected to contribute to higher energy consumption in the future:  
* Advanced antenna technologies can enhance network performance but will also lead to greater energy consumption [16]. Denser base station antenna infrastructures, such as the transition from 4-8 antennas to 64-128 antennas, will induce higher energy demands.
* The increasing demand for faster data transfer in 5G networks will require more advanced components and devices, thus inducing higher energy consumption [17, 18]. As an example, one can mention the transition from homogeneous networks (comprising 1 to 3 base stations (BSs) per km\({}^{2}\)) to heterogeneous networks (comprising 10 to 100 nodes per km\({}^{2}\)). Furthermore, the growing need for larger storage capacities adds to energy requirements.
* The shift to higher frequency bands [19, 20] (from 100 MHz up to 5 GHz) in 5G networks will require an increase in the number of base station antennas; this, in turn, will increase energy demands.