esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Deblurring Based on Convex Non-Convex Sparse Regularization and Plug-and-Play Algorithm
## 2 Preliminaries  
In this section, we offer a concise introduction to some fundamental concepts that underpin the development of subsequent content.  
To begin with, we define the proximal operator and Moreau envelope.  
**Definition 1**.: _Given a proper, closed, convex function \(\phi\), and letting \(\alpha\) be a positive scalar parameter, the following definitions are provided:_  
1. _The proximal operator of_ \(\phi\) _is defined as_ \[\mathrm{prox}_{\alpha\phi}(x)=\arg\min_{v}\biggl{\{}\phi(v)+\frac{1}{2\alpha} \|x-v\|_{2}^{2}\biggr{\}}.\] (4)
2. _The Moreau envelope of_ \(\phi\) _is defined as_ \[M^{\alpha}_{\phi}(x)=\min_{v}\biggl{\{}\phi(v)+\frac{1}{2\alpha}\|x-v\|_{2}^{ 2}\biggr{\}}.\] (5)  
It is noteworthy that \(M^{\alpha}_{\phi}(x)\) is differentiable, and its gradient can be computed as  
\[\
abla M^{\alpha}_{\phi}(x)=\frac{1}{\alpha}\Bigl{(}x-\mathrm{prox}_{\alpha \phi}(x)\Bigr{)}, \tag{6}\]  
where \(\mathrm{prox}_{\alpha\phi}(x)\) is defined as (4).  
Subsequently, we present the algorithm utilized throughout this paper. In order to enable flexible application of the algorithm in various situations, we propose the algorithm for a general sparse regularization problem, rather than limiting it exclusively to the problem (3).  
Consider the sparse regularization problem  
\[\min_{x}\quad f(x)+\lambda\phi(x). \tag{7}\]  
where the data-fidelity term \(f(x)\) is differentiable, the regularization term \(\phi(x)\) is continuous but nonsmooth, and the regularization parameter \(\lambda\) measures the weight of \(f(x)\) and \(\phi(x)\)The basic idea of the FBS algorithm is to perform gradient descent on the smooth part \(f(x)\) and apply the proximal operator to the nonsmooth part \(\lambda\phi(x)\). More precisely, the iterative steps of the FBS algorithm are given by  
\[\begin{cases}v^{k+1}=x^{k}-\alpha\
abla f(x^{k}),\\ x^{k+1}=\text{prox}_{\alpha\lambda\phi}(v^{k+1}).\end{cases} \tag{8}\]  
The key observation of PnP is that the proximal operator is equivalent to a denoising operation [35]. By replacing the proximal operator \(\text{prox}_{\alpha\lambda\phi}\) with the denoiser \(D_{\sigma}\) in (8), we can obtain the PnP-FBS algorithm.  
\[\begin{cases}v^{k+1}=x^{k}-\alpha\
abla f(x^{k}),\\ x^{k+1}=D_{\sigma}(v^{k+1}).\end{cases} \tag{9}\]  
The denoiser \(D_{\sigma}\) with noise level parameter \(\sigma\) in (9) can be any image denoiser, such as total variation (TV) [41], block-matching and 3D filter (BM3D) [42], and deep neural network (DNN) denoisers [7; 36].
# Seismic and Geodetic Crustal Moment-Rates Comparison: New Insights on the Seismic Hazard of Egypt
## 6 Delimitation of Seismic Sources  
Based on the geological and tectonic settings (Figure 1), the spatial distribution of historical and instrumental seismicity (Figures 3 and 4), focal mechanism solutions and their related style of faulting (Figure 5), and also considering the distribution of GNSS data over the region, the studied region was subdivided into ten crustal seismic sources (h < 35 km), labeled from EG-01 to EG-10 (Figures 3-5). These sources are mainly covering the potential shallow active regions along the Gulf of Aqaba-Dead Sea Transform Fault, the Gulf of Suez-Red Sea Rift, and the Nile River and its delta. Some details about each seismic source are given in the following paragraphs.  
Figure 4: Spatial distribution of declustered instrumental seismicity (1900–2020).
## 5 Conclusions
### Future Works  
Concerning future work, two main paths could be taken. At first, the expansion of the satellite scheduling model can be more applicable to various situations in reality,such as multi-satellite scheduling with different payload types, large-scale constellations scheduling and multi-satellite scheduling for moving target search and tracking, etc.  
In addition, we will continue to study algorithms for multi-satellite scheduling problems. The internal mechanism of the search algorithm will be further explored, and a more effective method for multi-satellite scheduling will be put forward, even considering the integration of artificial intelligence theory to achieve an end-to-end intelligent satellite scheduling process.  
Conceptualization, Y.L.; methodology, Y.L., X.F., D.Y. and P.L.; software, X.F. and Y.L.; validation, X.F., D.Y., P.L. and G.W.; formal analysis, X.F.; investigation, X.F. and D.Y.; resources, D.Y., P.L., G.W. and C.Z.; data curation, G.W. and C.Z.; writing--original draft preparation, Y.L., X.F., G.W., D.Y. and P.L.; writing--review and editing, Y.L., X.F., G.W., D.Y., P.L. and C.Z.; supervision, D.Y., P.L., G.W. and C.Z.; project administration, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the CETC Aerospace Information Application Technology Key Laboratory Open Fund Project, grant number XX17629X009, the Science Research Project, grant number JSZL2020203B004 and the National Natural Science Foundation of China, grant number 52075117.  
The authors declare no conflict of interest.
## 4 Recommendation on Improving the Efficiency of Intensity Measures
### EDP-IM Relationship  
To examine correlations between the various parameters of interest, we first extract the inter-story drift ratio (ISD) computed for all considered frames. Using the Pearson correlation coefficient, we then quantify the correlation between ISD and IMs by applying linear regression to fit the measured ISDs with a given IM (Equation (1)).  
Figure 5 shows the variation of ISD for six of ten different IMs, including the correlations coefficient (\(\rho\)), efficiency (\(\sigma\)), practicality (\(b\)) and proficiency (\(\zeta\)) measures. The mean predictions for the EDP along with the standard deviations (solid and dashed lines) are plotted for each IM. Both peak measures, \(PGA\) and \(PGV\), show similar correlation (\(\rho\)) with EDP; however, the variability is lower for \(PGV\). \(VSI\) and \(I_{A}\) are good indicators for spectral and energy measures of IMs, respectively. Among all IMs, the correlation of _Tsig_ with inter-story drift is smallest. _ASI_, \(PGV\) and \(VSI\) show similar proficiency; however, proficiency is higher for \(I_{A}\) and _Tsig_.  
For comparison, Table 5 shows the correlation properties of 15 different IMs, including the IMs proposed by [PERSON] et al. [23], [PERSON] [17], [PERSON] et al. [25], [PERSON] et al. [27] and [PERSON] et al. [28]. Larger correlations for spectral measures compared to other IMs are consistent with the results of [PERSON] et al. [52], who used building response of existing structures exposed to earthquake motions. The current overall correlation properties do not distinguish buildings with different natural periods; hence, we divide frames by their natural periods and examine the effect on the correlations.
# Evolutionary Features for Dynamic Link Prediction in Social Networks
## 4 Results
### Feature Importance
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature Name** & **Information Gain** & **Chi-Square Attribute Evaluation** & **Support Vector Machine Evaluator** & **Random Forest Evaluator** & **Total** \\ \hline \multicolumn{5}{c}{\(G_{MIT}\)} \\ \hline \(sim_{1}(a,b)\) & 5 & 5 & 1 & 4 & 15 \\ \(sim_{2}(a,b)\) & 2 & 2 & 5 & 1 & 10 \\ \hline \(sim_{3}(a,b)\) & 3 & 3 & 3 & 5 & 14 \\ \(sim_{4}(a,b)\) & 4 & 4 & 4 & 3 & 15 \\ \hline \(sim_{3}(a,b)\) & 1 & 1 & 2 & 2 & 6 \\ \hline \multicolumn{5}{c}{\(G_{Small}\)} \\ \hline \(sim_{1}(a,b)\) & 5 & 5 & 3 & 3 & 16 \\ \(sim_{2}(a,b)\) & 2 & 2 & 1 & 2 & 7 \\ \hline \(sim_{3}(a,b)\) & 3 & 3 & 5 & 1 & 12 \\ \(sim_{4}(a,b)\) & 4 & 4 & 2 & 4 & 14 \\ \hline \(sim_{3}(a,b)\) & 1 & 1 & 4 & 5 & 11 \\ \hline \multicolumn{5}{c}{\(G_{UICI}\)} \\ \hline \(sim_{1}(a,b)\) & 2 & 2 & 1 & 2 & 7 \\ \hline \(sim_{2}(a,b)\) & 1 & 1 & 2 & 1 & 5 \\ \hline \(sim_{3}(a,b)\) & 5 & 5 & 3 & 4 & 17 \\ \hline \(sim_{4}(a,b)\) & 4 & 4 & 5 & 5 & 18 \\ \hline \(sim_{3}(a,b)\) & 3 & 3 & 4 & 3 & 13 \\ \hline \multicolumn{5}{c}{\(G_{FF}\)} \\ \hline \(sim_{1}(a,b)\) & 3 & 3 & 4 & 3 & 13 \\ \(sim_{2}(a,b)\) & 1 & 1 & 3 & 1 & 6 \\ \hline \(sim_{3}(a,b)\) & 5 & 5 & 5 & 5 & 20 \\ \hline \(sim_{4}(a,b)\) & 2 & 2 & 1 & 2 & 7 \\ \hline \(sim_{3}(a,b)\) & 4 & 4 & 2 & 4 & 14 \\ \hline \multicolumn{5}{c}{\(G_{Ratt}\)} \\ \hline \(sim_{1}(a,b)\) & 2 & 2 & 1 & 2 & 7 \\ \hline \(sim_{2}(a,b)\) & 1 & 1 & 2 & 1 & 5 \\ \hline \(sim_{3}(a,b)\) & 5 & 5 & 5 & 3 & 18 \\ \hline \(sim_{4}(a,b)\) & 4 & 4 & 4 & 5 & 17 \\ \hline \(sim_{5}(a,b)\) & 3 & 3 & 3 & 4 & 13 \\ \hline \multicolumn{5}{c}{\(G_{\rm fil}\)} \\ \hline \(sim_{1}(a,b)\) & 2 & 2 & 3 & 1 & 8 \\ \hline \(sim_{2}(a,b)\) & 1 & 1 & 5 & 3 & 10 \\ \hline \(sim_{3}(a,b)\) & 5 & 5 & 1 & 5 & 16 \\ \hline \(sim_{4}(a,b)\) & 3 & 3 & 2 & 2 & 13 \\ \hline \(sim_{5}(a,b)\) & 4 & 4 & 4 & 4 & 16 \\ \hline \multicolumn{5}{c}{\(G_{\rm fil}\)} \\ \hline \(sim_{1}(a,b)\) & 5 & 5 & 2 & 4 & 16 \\ \hline \(sim_{2}(a,b)\) & 3 & 3 & 1 & 1 & 8 \\ \hline \(sim_{3}(a,b)\) & 2 & 2 & 3 & 2 & 9 \\ \hline \(sim_{4}(a,b)\) & 1 & 1 & 5 & 5 & 12 \\ \hline \(sim_{3}(a,b)\) & 4 & 4 & 4 & 3 & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The rank of different dynamic features constructed in this study using different algorithms for directed, undirected and co-authorship networks. Ranks are in increasing order with number one denoting the highest ranking. The total column represents the aggregation of all ranking scores to generate the final ranking.
## 3 Performance Assessment
### Experiment
#### 3.2.2 Experimental Results  
Figure 8 shows the comparison of each estimated composite channel from the transmit filter to the matched filter for the TR process between the proposed method and the TR acoustic communication using SC modulation. The channel estimation was carried out based on the training symbols when the input signal-to-noise ratio (SNR) was 23 dB. The adaptive algorithm for channel estimation was RLS algorithm and the forgetting factor was 0.999. Figure 8 shows that the ISI span of the composite channel decreases from about 60 symbols to around eight symbols using the proposed method compared with the TR acoustic communication using SC modulation.  
Figure 9 summarizes experiment results for each subcarrier of the proposed method. The first row of Figure 9 shows the performance of the TR process and adaptive equalization for each subcarrier where BER and the output MSE are used as performance indicators. It is noticed that the BER of the TR process for subcarrier one is the highest and that of subcarrier seven is the lowest. To demonstrate performance visually, the scatterplots for subcarriers one and seven are shown in the second row and the third row of Figure 9. The dots in each scatterplot denote the output symbols of the TR process or  
Figure 8: Comparison of each estimated composite channel from the transmit filter to the matched filter for the TR process between two communication methods in the experiment: (**a**) the proposed method; and (**b**) the TR acoustic communication using SC modulation.  
adaptive equalization, which should locate at \(-1\) and \(1\) for BPSK without noise and ISI. Therefore, the scatterplots in Figure 9 further confirm that the performance of subcarrier seven is better than that of subcarrier one.  
Figure 10 shows the overall performance of the proposed method and the TR acoustic communication using SC modulation. In Figure 10, the BER and output MSE of the proposed method  
Figure 9: Experiment results for each subcarrier of the proposed method: (**a**) BER of the TR process and adaptive equalization; (**b**) output MSE of the TR process and adaptive equalization; (**c**,**d**) scatterplots of the TR process and adaptive equalization for subcarrier one; and (**e**,**f**) scatterplots of the TR process and adaptive equalization for subcarrier seven.  
are both the average over all subcarriers, and the dots in scatterplots for the proposed method correspond to the output symbols for all subcarriers. It is noticed that the performance of the TR process is improved using the proposed method compared with the TR acoustic communication using SC modulation. Moreover, when the total number of taps \(N_{i}\) is fixed and the performance of adaptive equalization for post-process in two communication methods is relatively close, the computational complexity of adaptive equalization is cut by 7/8 using the proposed method compared with the TR acoustic communication using SC modulation. The experiment results are quite consistent with the simulation results.
# Post-Earthquake Damage Inspection of Wood-Frame Buildings by a Polarimetric GB-SAR System
## 2 Materials and Methods
### Signal Processing  
The signal processing procedure mainly consists of pulse compression, antenna phase center calibration, SAR processing, and polarimetric imaging.
# Towards the Implementation Process of Accessible Virtual
## 5 Process for the Implementation of an Accessible Virtual Campus
### Proposal of a Method to Implement an Accessible Virtual Campus
#### 5.2.5 Phase 5--Tracing  
This phase is responsible for verifying compliance with the deliverables of each phase through the defined objectives, the strategic plan, and the activities carried out by those involved in each previous phase. The tracing phase can assign new changes so that the goal is achieved, and that the checklists are complied with. By the tracing phase, it is possible to discover new issues or new elements to consider in the next iteration plan. According to the PDCA cycle, the Act stage allows the continual improvement by consolidating the external and internal stakeholders' feedback into the development of strategic plans [25]. Table 1 describes the PDCA process alongside indicators to consider in each dimension for each phase of the methodology of [PERSON] et al. [63], for the implementation of an accessible virtual campus. \begin{table}
\begin{tabular}{l l} \hline \hline
**PDCA** & **Methodology from [PERSON] et al.** \\ \hline \multirow{7}{*}{_Plan_} & **Diagnosis phase** \\  & **Activity: Formation of the work team** \\  & Organizational dimension \\ \cline{1-1}  & \(\bullet\) & Is a professional with knowledge of governmental and institutional policies on educational inclusion included? \\ \cline{1-1}  & Pedagogical dimension \\ \cline{1-1}  & \(\bullet\) & Is a pedagogical professional trained in accessibility issues or experience that favors the training of students with disabilities included? \\ \cline{1-1}  & Academic dimension \\ \cline{1-1}  & \(\bullet\) & Is a member of the administrative staff knowledgeable about the processes involved in the HEI included? \\ \cline{1-1}  & Technological dimension \\ \cline{1-1}  & \(\bullet\) & Is a member of the ICT management staff knowledgeable about standards and accessible technology included? \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of criteria considered by each phase to verify the implementation of an accessible virtual campus.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis was performed using the SPSS v. 25 software package. The statistical methodology applied included the following procedures: Kolmogorov-Smirnov tests were used to test the normality of the distributions of continuous variables. Kolmogorov-Smirnov tests assessed for normality in a small sample of 10 participants. Alternative methods like the use of histograms and Q-Q plots were also used. The level of significance was strictly set at \(\alpha=0.05\). Two-way mixed-methods ANOVA (2 groups \(\times\) 3 time points) was performed to examine differences over time (midpoint vs. endpoint) and between groups (control vs. intervention). This analysis was conducted for measurements of spasticity using the Modified Ashworth Scale, results from the adolescent QOL questionnaire, subscales of the QOL questionnaire for children, and scores on the ABILHAND Scale. When the criteria for the Mauchly's Test of Sphericity were not met, the Greenhouse-Geisser correction was applied.
## 4 Primitiveness Test  
As mentioned in Section 2.2, the \(m\)-sequences generated by an LFSR in \(GF(2^{n})\) can be decomposed into \(n\)\(m\)-sequences generated by \(n\) LFSRs in \(GF(2)\), so that when the feedback polynomial of the LFSR in the extended field is primitive, all LFSRs in \(GF(2)\) have the same feedback polynomial, and it is also primitive. This relationship is what allows us to propose an algorithm to check if a polynomial is primitive over \(GF(2^{n})\).  
In general terms, to check if a polynomial \(f(x)\) over \(GF(2^{n})\) is primitive, we propose to build an LFSR whose feedback polynomial is \(f(x)\) and generate \(2\cdot m\cdot n+1\) elements, at least. The sequence generated by concatenating all generated elements is decomposed into \(n\) binary sequences by decimating by \(n\), as it is stated in Equation (7). Next, the \(n\) sequences are processed to obtain the minimal polynomials of the binary LFSRs that generate them (This can be achieved using the Berlekamp-Massey algorithm). If all the \(n\) sequences are generated by the same polynomial and it is also primitive, then \(f(x)\) is primitive over \(GF(2^{n})\). Algorithm 1 can be extended including one more step (step 5) to perform the check, resulting in Algorithm 2.  
As an example, let us consider the degree 6 polynomial \(f(x)=0x1+0x2\cdot x+0x4\cdot x^{2}+0x8\cdot x^{3}+0x5\cdot x^{4}+0x9\cdot x^{5 }+x^{6}\in GF[2^{4},x]\), where the primitive polynomial \(h(x)=x^{4}+x+1\) has been used to construct \(GF(2^{4})\). In order to check if \(f(x)\) is primitive, we consider \(f(x)\) as the feedback polynomial of an LFSR in the \(GF(2^{4})\) of 6 cells. For a random seed \((0x9,0x4,0x5,0x9,0x5,0x1)\), we generate \(2 mn+1=49\) elements, giving rise to the following decimated sequences:  
\[\begin{array}{l}D^{0}=0010011011100000011010100000010101100111100\\ D^{1}=01011001110011100000101011101111001011010001\\ D^{2}=000000001100011101010010001001000010001000101\\ D^{3}=1111011111000110011010110110101001000010100001\\ \end{array} \tag{18}\]The same minimal polynomial \(p(x)\) is obtained for each \(D^{i}\) sequence by means of the Massey-Berlekamp algorithm [18]. The polynomial is the following:  
\[p(x)=x^{24}+x^{23}+x^{22}+x^{21}+x^{20}+x^{19}+x^{15}+x^{14}+x^{9}+x^{8}+x^{7}+x ^{6}+1 \tag{19}\]  
Since \(p(x)\) is primitive over \(GF(2)\), we can conclude that \(f(x)\) is primitive over \(GF(2^{4})\).
## 3 Results and Discussion
### Effect of Window Position  
The window location was studied for three positions: top (up), bottom (down), and center (mid) of the walls for the tested house and office buildings. The difference in energy use was not significant due to the window positions. For instance, the energy use ranged from 94 to 96 MJ/m\({}^{2}\)/year for the house building and from 851 to 875 MJ/m\({}^{2}\)/year for the tested office building for various widow positions. However, the most efficient window location was at the middle of the wall
## 2 Experimental  
### Raw Materials  
The modified MPC was prepared by blending dead-burned magnesia powder (MgO), ammonium dihydrogen orthophosphate (ADP, NH\({}_{4}\)H\({}_{2}\)PO\({}_{4}\)), borax (B), wollastonite powder (CaSiO\({}_{3}\)) and tap water together. The first three ingredients were industrial-grade of purity 95%-99%. The wollastonite consists of 45.6% of CaO and 48% of SiO\({}_{2}\), mainly in amorphous form and has an average specific surface area of 842.7 m\({}^{2}\)/kg. Based on the former study (unpublished) [20], three binder proportions were designed as presented in Table 1. The molar ratio of Wollastonite and MgO to ADP was kept at 9:1. MA1 is the control mix for MPC. Based on the control mix, wollastonite was added to replace 5% and 10% (in molar percent) of MgO dosage. The mass ratio of water to powder was kept at 0.15. The borax was added by 5% of MgO weight to control setting time. The organic adhesive used in this paper was a two-part epoxy resin. The epoxy resin consists of parts A and B that are blended at a weight ratio of 8:2, and stirred until a homogeneous mixture is obtained. Unidirectional carbon fiber sheets were used. The mechanical properties of the carbon fiber sheets and epoxy resin provided by the manufacture are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Mix**} & **Wollastonite** & \multirow{2}{*}{**MgO**} & \multicolumn{3}{c}{**Weight Dosage**} \\ \cline{3-5}  & **(Molar Percent)** & & **Wollastonite** & **MgO** & **ADP** \\ \hline MA1 (control) & 0 & 100 & 0 & 100 & 31.9 \\ MA2 (5\%) & 5 & 95 & 15.3 & 100 & 33.6 \\ MA3 (10\%) & 10 & 90 & 32.2 & 100 & 35.5 \\ \hline \hline \end{tabular}  
* MgO is dead-burned magnesia powder and ADP is ammonium dihydrogen orthophosphate.  
\end{table}
Table 1: Details of the designed binder proportions.  
For interfacial bond testing, concrete with dimensions of 100 mm \(\times\) 100 mm \(\times\) 100 mm was prepared in the laboratory, respectively, with the following three mixing proportions: cement (319, 370 and 425 kg/m\({}^{3}\)), water (179, 170 and 170 kg/m\({}^{3}\)), sand (755, 784 and 801 kg/m\({}^{3}\)), coarse aggregate (1042, 1043 and 1051 kg/m\({}^{3}\)) and superplasticizer (5.42, 4.25 and 8.50 kg/m\({}^{3}\)). The concrete specimens were cured for 28 days in a fog room before the bond strength test. To protect the carbon fiber sheets from fire damage during the high temperature treatment, a tunnel fire coating was used which is produced by the Beijing Mercutio Fire Source Factory (China). This tunnel fire coating has a density of 600 kg/m\({}^{3}\), a specific heat 1000 J/(kg\(\cdot\) K) and a heat conductivity coefficient 0.12 W/(m\(\cdot\) K).
# Adaptive Clustering via Symmetric Nonnegative Matrix Factorization of the Similarity Matrix
## 3 The Validity Indices  
Since the solution \(W\) of problem (6), computed by starting with different initial approximations \(W_{0}\), may be only a local minimum, tools to evaluate the validity of the clustering \(\Pi\) induced by \(W\) are needed. Denoting by \(\pi_{j}\) the \(j\)th cluster, a clustering \(\Pi=\{\pi_{1},\ldots,\pi_{k}\}\), with \(k\geq 2\), corresponds to a partitioning of the indices \(\{1,\ldots,n\}\) (actually, the number \(k_{e}\) of clusters effectively obtained by applying Partition\((W)\) can be smaller than the required \(k\)). A clustering algorithm generally aims at minimizing some function of the distances of the points of the \(j\)th cluster from its centroid defined by  
\[\epsilon_{j}=\frac{1}{n_{j}}\,\sum_{i\in\pi_{j}}\mathbf{p}_{i},\quad\text{with} \quad n_{j}=\#\,\pi_{j},\]  
where the symbol \(\#\) denotes the cardinality. Over the years, dozens of _validity_ indices have been proposed (see [20] and its references). Most of them take into account the _compactness_, which measures the _intra-cluster_ distances, and the _separability_, which measures the _inter-cluster_ distances. A good clustering algorithm should produce small intra-cluster distances and large inter-cluster distances.