esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Feature-Based Methods  
Feature-based matching methods can, to a certain extent, overcome the problems of matching due to noise and other factors. Owing to the efficient performance and invariance to scale and rotation, scale-invariant feature transform (SIFT) has become the most representative method [20]. Subsequently, varieties of improved SIFT methods have been reported. An improved version of SIFT is proposed to obtain better matching features [7]. It uses spatially consistent matching strategy to obtain more reliable feature point pairs for optical and SAR image matching. In order to match the high-resolution optical and SAR images, OS-SIFT was introduced [8]. This uses a multi-scale ratio of exponentially weighted averages (ROEWA) operator and a multi-scale Sobel operator, respectively to compute the consistent gradient magnitudes in optical and SAR images. Additionally, some papers focus on integrating the advantages of area-based methods and feature-based methods. [PERSON] et al. [9] introduced a two-stage method for image registration, which acquired coarse results by SIFT and then achieved precise registration based on mutual information. An iterative multi-level strategy for adjusting parameters to re-extract and match features has been proposed [21] to improve the matching performance of optical and SAR images. However, conventional matching methods, as reviewed above, require expertise to design, and may disregard useful patterns hidden in the data [22].
## 3 Results
### Comparison of Accuracy between Different Models  
Many studies [18; 19; 20] have shown that graph neural networks are more effective than traditional deep learning methods in the study of the human skeleton. Thus, we chose to compare the ST-GCN with several typical ST-GCN-based multi-stream graph convolutional networks, such as RA-GCNv1 [26], RA-GCNv2 [27], and 2s-AGCN on Kinect25 and Azure32. The results of the comparative analysis are shown in Tables 4 and 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Top-1** & **FLOPs** & **\#Params** \\  & **(\%)** & **(\(\times\)109)** & **(\(\times\)106)** \\ \hline ST-GCN & 83.95 \(\pm\) 4.05 & 5.44 & 3.07 \\ RA-GCNv1(3s) & 91.21 \(\pm\) 5.97 & 8.29 & 6.05 \\ RA-GCNv2(3s) & 91.23 \(\pm\) 3.78 & 8.29 & 6.05 \\
2s-AGCN & 91.06 \(\pm\) 3.93 & 9.46 & 6.94 \\
**STJA-GCN** & **93.17 \(\pm\) 2.76** & **2.44** & **2.18** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of results on Kinect25.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Joints** & **Top-1** & **FLOPs** \\  & **(\%)** & **(\(\times\)109)** \\ \hline
25 & 83.2 \(\pm\) 7.88 & 5.44 \\
**19** & **83.95 \(\pm\) 4.05** & **4.13** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of different numbers of joints.
As shown in Tables 4 and 5, the STJA-GCN model outperforms the models used for comparison in terms of accuracy, FLOPs, and number of parameters on both datasets. The STJA-GCN is 9.22 and 20.65% more accurate than the ST-GCN on the Kinect25 and Azure32 datasets, respectively. Owing to the early fusion between branches and the reduction of spatial-temporal graph convolutional layers, the STJA-GCN achieves better FLOPs and number of parameters than RA-GCNv1, RA-GCNv2, and 2s-AGCN, as evidenced by its lower complexity and substantially fewer parameters. Moreover, the final accuracy of the model is improved across the board. However, RA-GCN v2 and other models with excessive numbers of convolutional layers fail to achieve desirable results on Azure32 owing to the small sample size of the dataset. In contrast, the STJA-GCN model achieves good recognition accuracy on the dataset.  
Furthermore, the performance of the model was evaluated with a confusion matrix to reflect the difference between the actual and predicted values. Figure 6 shows the confusion matrices for maximum and minimum accuracies on the two datasets.  
As shown in Figure 6, the results of each gait type achieved good results, but the classification accuracy of the Trendelenburg gait in these four experiments was not as good as that of the other gaits; in the (a2) 360 Trendelenburg gait samples, 89 cases were misclassified as normal gait, and 93 cases were misclassified as steppage gait. Misclassification of the Trendelenburg gait as the steppage gait was also present in the other three experiments.
## 4 Experimental, Numerical and Empirical Comparison
### Steady-State Numerical and Experimental Comparison  
Table 6 shows the steady-state thermal results for the numerical and experimental methods. In addition, the empirical results calculated using Equations (6)-(8) are shown.  
The thermal transmittance under steady-state conditions shows good agreement between the results obtained using the numerical and empirical methods. With respect to the experimental hot-box results with a 70% of relative humidity, a 6% difference was found between the methods.
## 4 The Proposed NSGA-II-MOEA/D-CIS Algorithm
### Design of the Lower-Lever MOEA/D Algorithm
#### 4.3.3 Objective Decomposition  
One of the key features of the MOEA/D algorithm is that it aggregates multiple objectives from the original multi-objective problem into several single-objective optimization problems using linear or nonlinear methods. Considering the characteristics of the model, this paper adopts the Chebyshev method for objective aggregation [36]. This method evaluates the overall performance of a candidate solution by measuring the maximum weighted deviation from the ideal point (typically the best value of each objective function in the current population). The mathematical formulation of this process is given in Equations (46) and (47).  
\[g(x|\lambda,Z^{*})=\underset{1\leq i\leq m}{max}\{\lambda_{i}\cdot\left|f_{i} (x)-Z^{*}_{i}\right|\} \tag{46}\]  
\[Z^{*}=min\{f_{i}(x)|x\in\Omega\} \tag{47}\]
# A Novel Cloud Approach for Connected Vehicles
###### Abstract  
Cooperative intelligent transport systems (C-ITSs) are being deployed all around the world. Shortly, in addition to vehicles, bicycles, pedestrians, buses, and all moving equipment will be compatible with C-ITS. These systems are connected through wireless local area networks based on WIFI IEEE 802.11p. The large number of C-ITSs and services will lead to a glut in the bandwidth of wireless networks. To overcome this limitation, we propose in this paper a new approach using the information-centric networking (ICN) paradigm which allows vehicles to communicate with the cloud environment. This scheme is denoted by vehicular central data networking (GeoVCDN). Our approach aims to reduce bandwidth consumption and improve data freshness by taking benefit from the existing application beacons and the geographical routing used by C-ITS actors. We have compared the performances (in terms of the network overhead and data freshness) of our solution to two other well-known ICN-based solutions. Each of them represents one of ICN categories, in particular, rendez-vous network (RENE) and named data networking (NDN). To do so, we have proposed a probabilistic model that allows us to evaluate the freshness and the load of the network. Furthermore, we have implemented these methods in a simulator. Our proposal outperforms the other methods in terms of network overhead and data freshness.  
C-ITS; connected vehicles; vehicle cloud; ICN +
Footnote †: journal: Applied  
10 March 2023  
19 April 2023  
1
## 5 Real-Time PPP Performance of Smartphone
### Static PPP Performance
Moreover, compared to GPS/BDS combination, the positioning accuracy of the GPS/BDS/Galileo combination was improved to 0.08, 0.13 and 0.20 m, with an improvement of 40%, 8.90% and 3.81% in three components. Compared to the single-frequency GPS-only solution, the positioning accuracy of 0.07, 0.13 and 1.13 m can be achieved for dual-frequency GPS L1/L5 combination in the E, N and U directions, respectively, with an improvement of 90.98%, 79.47% and 15.01%. For the Huawei P40, the positioning accuracy of single-frequency GPS-only is 0.23, 0.68 and 1.39 m, compared to single-frequency GPS-only; the GPS/BDS combination can be improved by about 58.41% and 78.41% in the N and U components, while the improvement is 43.12%, 53.16 and 55.64% in the E, N and U components compared to BDS-only solution (0.49, 0.60, 0.67m), respectively. Compared to the GPS/BDS combination solution, the positioning accuracy of GPS/BDS/Galileo is 0.19, 0.61 and 1.03 m, with an improvement of 67.87%, 3.90% and 59.20% in the E, N and U components, respectively. For dual-frequency real-time static PPP, compared to the single-frequency GPS-only solution, the GPS L1/L5 combination can be improved by 19.83%, 10.18% and 25.63% in the E, N and U components, respectively. For the Novatel geodetic receiver, i.e., DH04, the decimeter-level positioning accuracy can be achieved for five combinations in east, north and up directions, respectively.
# Image Deblurring Based on Convex Non-Convex Sparse Regularization and Plug-and-Play Algorithm
## 4 Proposed Algorithms
### Computational Complexity and Convergence Analysis  
In this subsection, we briefly discuss the computational complexity and convergence of the proposed PnP-FBS-CNC algorithm.  
The main cost of the PnP-FBS-CNC algorithm arises from updating \(v^{k+1}\), \(u^{k+1}\), and \(x^{k+1}\). The update of \(v^{k+1}\) involves matrix-vector production but can be efficiently implemented using fast Fourier transform (FFT). The computational complexity of this step is \(\mathcal{O}(n\log n)\), where \(n\) is the size of the image. The update of \(u^{k+1}\) and \(x^{k+1}\) is implemented by denoisers \(D_{\sigma_{1}}\) and \(D_{\sigma_{2}}\). Therefore, the computational complexity depends on the type of denoisers employed. According to the the computational complexity analysis in [44], if both \(D_{\sigma_{1}}\) and \(D_{\sigma_{2}}\) are TV denoisers, then the computational complexity of these two steps is \(\mathcal{O}(n)\). On the other hand, if \(D_{\sigma_{1}}\) and \(D_{\sigma_{2}}\) are BM3D denoisers, then the computational complexity becomes \(\mathcal{O}(n\log n)\). If \(D_{\sigma_{1}}\) and \(D_{\sigma_{2}}\) are DNN denoisers with identical architecture but different parameters, as indicated by the computational complexity analysis in [45; 46], the computational complexity of these two steps is \(\mathcal{O}(nn_{l}n_{k}n_{f})\), where \(n_{l}\), \(n_{k}\), \(n_{f}\) represent the number of layers, kernel pixels, and features, respectively.  
Note that the essence of PnP lies in the integration of the noise reducer within the iterative optimization algorithm, thereby enabling the convergence of the algorithm to be proven within the framework of iterative optimization. The strong convexity of the data-fidelity term in the image deblurring model (11), combined with the convergence conclusions from reference [35; 40], allows us to guarantee that the proposed PnP-FBS-CNC algorithm converges when the residuals of the denoisers \(D_{\sigma_{1}}\) and \(D_{\sigma_{2}}\) exhibit contractive behavior.
# A Seamless Image-Stitching Method Based on Human Visual Discrimination and Attention
## 3 Methods
### Visual Perception Quantification
#### 3.1.3 Visual Masking  
VM refers to the phenomenon that a visual stimulus in an image will be masked by its surroundings, which is difficult to be detected by HVS, especially when the characteristics of the stimulus are similar to those of the environment [29]. In the final stitched image, discontinuous edges due to misalignment usually seriously damage the image quality, but when it appears in areas with very complicated edge information, it has limited impact. At the same time, in the smoothing regions without edges, even if a slight mismatch of the image occurs in the area, it will not significantly affect the quality of the stitched image.  
Based on this principle, the visual masking characteristics are quantified. The image is divided into texture regions, smoothing regions and other regions. The texture regions are mainly used to mask discontinuous edges in the mosaic image. Therefore, the more chaotic the edge information of texture regions is, the more helpful it is to improve the quality of stitched images. The degree of chaos in these regions is measured using local entropy, and the formula is shown in Equation (4):  
\[H=-\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}p_{ij}\cdot\log p_{ij} \tag{4}\]  
where \(H\) is the local entropy. \(m\), \(n\) are the length and width of the window around the pixel under calculating. Besides \(p_{ij}\) is defined as:  
\[p_{ij}=\frac{n_{ij}}{N}=\frac{n_{ij}}{m\cdot n}, \tag{5}\]  
which is the probability that a gray level pixel \((i,j)\) appears in the \(m\cdot n\) neighboring window.  
For pixels in smoothing regions, the selection of seamless also has priority. The smaller the difference, the smoother the regions, and thus the better for the final result. Therefore, if we seam the images in very similar regions, the stitching line will be invisible. The degree of similarity is represented by a local range defined as:  
\[R=x_{\text{max}}-x_{\text{min}}, \tag{6}\]  
where \(R\) is the local range. \(x_{\text{max}}\) and \(x_{\text{min}}\) are the maximum and minimum of gray value in the neighborhood window, respectively. The value of \(R\) is zero when a pixel is same with its adjacent pixels.  
In summary, we can work the VM map out via Equations (4)-(7):  
\[\text{VM}=\left\{\begin{array}{ccc}k_{1}\text{-}(255-R)&,&\text{pixels in smoothing regions}\\ k_{2}\text{-}h&,&\text{pixels in texture regions}\\ 0&,&\text{pixels in other regions}\end{array}\right., \tag{7}\]  
where VM denotes visual masking value of each pixel. \(k_{1}\) and \(k_{2}\) are constants and set to 0.8 and 1 based on experimental experience.
# New Insights into the Neuromyogenic Spectrum of a Gain of Function Mutation in SPTLC1
## 4 Discussion
### Molecular Signature of p.(A20S)-SPTLC1-Mutant Lymphoblastoid Cells
Further hints of perturbed lipid homeostasis are given by decrease of diacylglycerol kinase epsilon (DGKE; 0.42-fold decrease) that regulates levels of bioactive lipids [37; 38; 39; 40; 41], and of \(N\)-myc downstream regulated family member 1 (NDRG1;0.31-fold decreased), which functions in lipid metabolism [42]. Recessive NDRG1-variants were linked to manifestation of a demyelinating neuropathy [43]. Interestingly, GO-term based analysis of our proteomic findings obtained in immortalized lymphoblastoid cells displayed retina homeostasis as an affected (up-regulated) biological process and, in 2019, retinal disease was reported in patients carrying SPTLC1 variants [44]. Further up-regulated processes are indicative for activation of the immune response, and immunoglobulin Ig kappa up-regulation was already identified by proteomic investigations on lymphoblastoid cells of HSAN1-patients [34], and could moreover be identified in cells of our patient (3.90-fold increase).
## 4 Conclusions  
The high shear treatment rate (100/s) reduced the elongation at break of HDPE compared to VPE without shear treatment. The crystalline type of shear-treated HDPE had no changes from shear deformation. However, degree of crystallinity was decreased with the shortening of long period, core-crystalline layer, and intermediate layer as compared to VPE. A positive correlation was detected between the relationship of elongation at break and the thickness of the intermediate layer. The proposed physical degradation mechanism from shear treatment is shown in Figure 12. Shear treatment at 100/s affected a decreasing of the thickness of the core-crystalline and intermediate layers, which also related to the decrease of the amount of chain entanglement and the reduction of the elongation at break [26; 27]. Furthermore, shear deformation also affected the occurrence of crystalline orientation in shear-treated HDPE. With the changes of elongation at break, lamellar size, and morphology, shear-treated HDPE showed similar molecular weight distribution and chemical composition as VPE, which can prove that there were no changes in polymer chemistry from shear deformation. This study indicated that mechanical treatment affected changes to mechanical properties and lamellar structure. These findings can be further improved for development of HDPE properties and plastic mechanical recycling technology.  
Figure 11: FT-IR spectra of VPE and shear-treated HDPE at 100/s.  
Figure 12: Proposed physical degradation mechanism of shear-treated HDPE at 100/s.  
**Author Contributions:** Conceptualization, P.P. and S.Y.; methodology, P.P. and S.Y.; validation, P.P. and S.Y.; formal analysis, H.K., P.P. and H.O.; investigation, H.K., P.P. and H.O.; resources, H.K., P.P. and H.O.; data curation, H.K., P.P. and H.O.; writing--original draft preparation, H.K. and P.P.; writing--review and editing, P.P.; visualization, H.K. and P.P.; supervision, S.Y.; project administration, S.Y.; funding acquisition, S.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the New Energy and Industrial Technology Development Organization (NEDO) grant number JPNP20012, Japan.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** No new data were created or analyzed in this study. Date sharing is not applicable to this article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 7 Land-Based Systems Implementation-Evolution: Early Rail Defect Detection Capability
### Signal Pre-Processing  
The employed excitation signal is a Hanning-windowed tone burst signal whose center frequency is equal to 35 kHz. The measurements are acquired by exciting one piezoelectric transducer, acquiring the response of both transducers of the array, and then repeating the process by exciting the other transducer. This cycle is repeated \(k\) times, and the average of the \(k\) acquired measurements is stored as a new signal in the measurement set, which consists of four time domain signals.  
Noise from passing trains has to be avoided since it would overwhelm the useful signal. For this reason, the system first senses the presence of train noise and proceeds with the acquisition only in its absence; otherwise, it delays its acquisition. In the observation interval \(m\), distinct sets of measurements are recorded.  
Before defect detection or monitoring, the acquired data must be pre-processed to compensate for unwanted effects. The pre-processing steps are illustrated in Figure 17 and described in the following sub-sections. Data pre-processing involves (a) _phased array processing_; (b) _dispersion compensation_; (c) _signal stretching and scaling_; (d) _signal reordering_. These steps are required to discriminate the direction of the reflections, to reduce the influence of dispersion and to compensate for some of the changes occurring in EOC, respectively. Signal reordering accounts for the use of a mass as a defect emulator: the mass, in fact, behaves in a reversed time order with respect to a crack (see Section 7.2.3).
# Viability of Cupola Slag as an Alternative Eco-Binder and Filler in Concrete and Mortars
###### Abstract  
Obtaining new materials capable of meeting society's demands motivates the search for new solutions that are capable of satisfying twofold requirements: respect for the environment and obtaining more durable and resistant materials. Cupola slag is a by-product generated in the process of obtaining ductile iron. When the slag undergoes rapid cooling, its vitrification is favored, leaving the silica in an amorphous structure and, thus, susceptible to reacting. Through reaction, the slag can develop cementing properties and cement can consequently be partially replaced with residue, providing savings in economic and environmental costs compared to traditional hydraulic binders. In this study, the physical and chemical properties of cupola slag and its recovery process are analyzed. Mortars that incorporate traditional admixtures (fly ash and limestone filler) have been manufactured and consistency and mechanical properties have been compared with mortars that incorporate cupola slag admixture. Mortars have also been manufactured with normalized sand and with Portland cement replacements (0, 10, 20, and 30% by weight) with cupola slag, and both the consistency and the mechanical properties have been compared at 7, 28, 60, and 90 days. The results obtained show the suitability of cupola slag as a binder and as an admixture, with respect to the traditional ones, and how the mechanical properties tend to converge for all of the replacement levels characterized, for ages close to 90 days of age.  
cupola slag; alternative binder; sustainability; waste recovery; mechanical properties; mortar; concrete 2 February 2021  
19 February 2021  
10.3390/app11041957
## 1 Introduction  
V2X is a technology that is now relatively mature and aims to revolutionize safe transport by enabling communication between vehicles. The idea is that road users and transport network operators cooperate by sharing information to make traffic safer and more efficient. V2X can be divided into two groups according to the communication technology applied. The first, which was called ITS-G5 in Europe, also known as DSRC in the US, is a wireless short-range communication technology based on the IEEE 802.11p set of standards specifically tailored for ITS applications. The other is the cellular-based C-V2X system, which has the significant advantage of having 4G and 5G infrastructures and their complete cellular ecosystems in many places to be relied on to speed up service deployment efforts [1]. In addition, it is essential to note that the architectures developed for V2X in Europe (ITS-G5-standardized by the European Telecommunications Standards Institute) and the US (implementing the IEEE Wireless Access in Vehicular Environments standard family) differ in several respects, such as the spectrum allocation or the architecture of the protocols used. Consequently, interoperability between the two systems is difficult [2]. In Europe, 802.11p is still the most widely used access technology, while in the US, cellular has almost completely replaced the Wi-Fi approach [3]. MCO in V2X communication refers to the use of multiple wireless channels to enable more efficient and reliable communication between vehicles and infrastructure components. In a multi-channel V2X system, different channels can be used for different types of communication, such as safety/critical messages, traffic information, or entertainment content. It is essential to model MCO because it enables us to understand and optimize the system's behavior in complex and dynamic communication environments. By modeling MCO in V2X, we can evaluate the impact of different design choices on the performance of the system, such as the allocation of communication resources among channels, the selection of protocols, and the design of the communication network topology. This enables us to optimize the system to meet performance objectives, such as maximizing throughput, minimizing latency, and ensuring the reliable transmission of safety-related messages.  
The remainder of this paper is structured as follows. Section 2 reviews the background of the V2X communication technology and MCO basics. Section 3 discusses similar works available in the literature. This is followed by the details of the simulation environment we used and our proposed dynamic MCO model in Section 4. Section 5 covers the results of our initial simulation experiments within the implemented model. Finally, Section 6 summarizes and concludes the paper.