esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on Mechanical Behavior of Shear Connectors of Square Concrete Filled Steel Tube
## Specimen Design and Test Scheme
### Materials Properties
#### 2.2.2 Steel  
The grades of the steel tube and the steel plate used in the test are both Q345. The width-to-thickness (\(B/t\)) ratio of the steel tubes are 50.0, 33.3, 25 and 14.3. The limit of the \(B/t\) in the JB 138 [19] can be calculated as follows:  
\[\frac{B}{t}\leq 60\sqrt{\frac{235}{f_{y}}} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Specimen** & \(t\) **mm** & **B/\(t\)** & \(c\) **mm** & \(s\) **mm** & \(l\) **mm** & **Concrete Grade** & **Shear Connector Type** \\ \hline PS-8-0-120 & 8 & 25 & 8 & single layer & 120 & C30 & steel plate \\ PS-4-150-120 & 4 & 50 & 8 & 150 & 120 & C30 & steel plate \\ PS-6-150-120 & 6 & 33.3 & 8 & 150 & 120 & C30 & steel plate \\ PS-8-1  
According to Equation (2), the design limit of \(B/t\) for Q345 steel tube is 50. All the test specimens are within the limit of the deign code. In different codes, there are different requirements for the limit of the width to thickness ratio. And the range for the limits of the \(B/t\) is smaller in other codes, such as Eurocode 4 [20], the limit of \(B/t\) ratio in Eurocode 4 is stricter.  
The grade of the reinforcing bars used in the test is HRB335. A series of tests have been conducted on coupons taken from the steel tube and steel plate (steel bar) to measure the material properties, where the tensile coupons were extracted from flat surfaces. The measured mechanical properties are shown in Table 4, where \(f_{y}\) is the yield strength, \(f_{u}\) is the ultimate strength, and \(\delta\) is the elongation.
## 3 SPT-AD: Self-Supervised Pyramidal Transformer Network-Based Anomaly Detection
### SPT-AD Algorithm  
As shown in Algorithm 1, the given input data \(X=[x_{1},x_{2},\cdots,x_{N}]\in\mathbb{R}^{N\times D_{\text{input}}}\) consist of time series data, where \(N\) data points are arranged along the time axis, and each data point is represented as a \(D_{\text{input}}\)-dimensional vector. These input data are projected into a high-dimensional latent space through a linear transformation defined as  
\[X_{\text{embed}}=XW_{\text{embed}}+b_{\text{embed}} \tag{10}\]  
where \(W_{\text{embed}}\) is the weight matrix used to project the input data into the latent space, and \(b_{\text{embed}}\) is the bias vector added to the projected vector. This allows the input data to gain richer representation in the high-dimensional space. Then, the initial latent representation is set as  
\[X^{(1)}=X^{\prime}_{\text{embed}} \tag{11}\]  
where \(X^{\prime}_{\text{embed}}\) denotes the normalized or further transformed \(X_{\text{embed}}\). For scales \(s=1,\cdots,S\), multi-scale convolution is performed iteratively. In this process, the convolution operation is applied using  
\[X^{(s)}=\text{Conv}_{\text{C}}(X^{(s-1)}) \tag{12}\]  
where kernel size and stride are utilized. The features extracted at different scales are then combined as  
\[X_{\text{CSCM}}=[X^{(1)},X^{(2)},\cdots,X^{(S)}] \tag{13}\]  
The dimensionality of this combined scale feature is defined as  
\[X_{\text{CSCM}}\in\mathbb{R}^{L\times D_{\text{embed}}},\quad\text{where }L=N+\sum_{s=2}^{S}\frac{N}{C^{s-1}} \tag{14}\]  
where \(L\) represents the extended length of the time series data through multi-scale convolution. For each attention layer \(l=1,\cdots,L_{\text{attn}}\), inter-scale attention and intra-scale attention are computed. The inter-scale attention is defined as  
\[H^{(s)}_{\text{inter}}=\text{Softmax}\Bigg{(}\frac{Q^{(s)}{K^{(s+1)}}^{T}}{ \sqrt{d}}\Bigg{)}V^{(s+1)} \tag{15}\]  
and the intra-scale attention is defined as  
\[H^{(s)}_{\text{intra}}=\text{Softmax}\Bigg{(}\frac{Q^{(s)}{K^{(s)}}^{T}}{ \sqrt{d}}\Bigg{)}V^{(s)} \tag{16}\]  
All the attention results extracted from different scales are combined to form the multi-resolution feature representation  
\[H_{\text{PAM}}=[H^{(1)},H^{(2)},\cdots,H^{(S)}] \tag{17}\]  
Subsequently, for each time series point \(i=1,\cdots,N\), a linear transformation is computed as  
\[z_{i}=W_{\text{pred}}h_{i}+b_{\text{pred}} \tag{18}\]This value is transformed through sigmoid normalization as  
\[a_{i}=\sigma(z_{i}),\quad\sigma(z_{i})=\frac{1}{1+e^{-z_{i}}} \tag{19}\]  
Finally, the anomaly scores are returned as  
\[X^{(1)}=X^{\prime}_{\text{embed}} \tag{11}\]0  
where each \(a_{i}\) represents the anomaly score for the corresponding time step.
# High Performance Computing in Satellite SAR Interferometry: A Critical Perspective
## 4 High Performance Computing: Fundamentals Concepts and Models
### Parallel Computing Architectures
Methods for designing efficient parallel applications are beyond the scope of this paper. They are not further discussed here for the sake of conciseness; the interested reader can refer to the existing literature [154; 155; 156; 158; 159; 160].
# Inverse Multiscale Discrete Radon Transform by Filtered Backprojection
## 1 Introduction  
The Radon transform is of great importance in the field of medical imaging and in general in problems where a magnitude cannot be directly sensed, and instead what is available is the magnitude integrated along a line, in which a beam of energy interacted with the physical medium [1, 2].  
Direct Radon transform performs integrals along lines while the inverse Radon transform takes the values in the transformed domain and must find out what values of the measure domain gave place to these already integrated values. This is a much harder problem to solve, as there may be many or no solutions (in the presence of noise), which makes the reverse path notably more complicated than the direct path.  
Discretization is necessary at some point when using computers for solving the Radon transform pair. The projection-slice theorem or Fourier-slice theorem links the Radon transform with the Fourier transform, and has made it possible to solve the inverse Radon transform by means of the so-called filtered backprojection method. This method consists of placing Radon angular projections as one-dimensional slices of the Fourier bidimensional transform domain, then performing the inverse Fourier transform to finally obtain the bidimensional Radon inversion after high pass filtering has been applied [3].  
Although this is the most common approach, an alternative method for computing the direct Radon transform was developed in the 90s [4, 5, 6]. It is known as the discrete multiscale approach of the Radon transform (DRT) and it avoids the use of Fourier transform, so it can operate only with integers. This transform, however, lacked an inverse algorithmuntil recently. In 2006 it was demonstrated by [PERSON] that this discrete version of the Radon transform has a fast and exact inverse, although iterative, using a multigrid method [7].  
The purpose of this work is to demonstrate that it is possible to anti-transform the discrete multiscale version of the Radon transform using a filtered backprojection. With this goal in mind we pursue to obtain an exact algorithm that is faster than the already existing methods: those based on the Fourier-slice theorem and the [PERSON]'s discrete iterative inversion. And since the filtering is the computationally most expensive part of the method we will additionally characterize imprecise solutions that can be used to get approximated solutions, even faster.
# Efficient Model-Based Object Pose Estimation Based on Multi-Template Tracking and PnP Algorithms
## 4 Model-Based 3D Pose Estimation
### PnP Solver  
Once the initial pose of the OOI has been obtained, the optimal pose solution can be further estimated based on the correspondences between the 2D feature points and the 3D control points. Let \(\pi(\mathbf{X},\mathbf{R},\mathbf{T})\) denote a projection function of a 3D point associated with a rotation matrix \(\mathbf{R}\) and a translation vector \(\mathbf{T}\) as follows:  
\[\pi(\mathbf{X},\mathbf{R},\mathbf{T})=\mathbf{K}(\mathbf{R}\mathbf{X}+\mathbf{ T}), \tag{16}\]  
where \(\mathbf{K}\) is the camera intrinsic matrix. Then, the re-projection error \(E(\mathbf{R},\mathbf{T})\) between the 2D-3D correspondences \(\{\mathbf{p}_{n}^{(j)},\mathbf{C}_{n}^{(j)}\}_{n=1}^{N_{c}}\) can be computed by:  
\[E(\mathbf{R},\mathbf{T})|_{\{\mathbf{p}_{n}^{(j)},\mathbf{C}_{n}^{(j)}\}_{n=1 }^{N_{c}}}=\sum_{n=1}^{N_{c}}\|\mathbf{p}_{n}^{(j)}-\rho(\pi(\mathbf{C}_{n}^{(j )},\mathbf{R},\mathbf{T}))\|^{2}, \tag{17}\]where \(\rho(\textbf{X})\) is the mapping function of a 3D point **X** defined previously. Hence, the proposed PnP solver aims to refine the initial pose (\(\textbf{R}_{0}\),\(\textbf{T}_{0}\)) of the OOI by minimizing the re-projection error of Equation (17) such that:  
\[(\textbf{R}_{opt},\textbf{T}_{opt})=\arg\min_{\textbf{R},\textbf{T}}E(\textbf{ R},\textbf{T})\big{|}_{\{\textbf{P}^{(j)}_{\textbf{R}},\textbf{C}^{(j)}_{ \textbf{I}}\}_{n=1}^{N_{\textbf{C}}}}, \tag{18}\]  
where \(\textbf{R}_{opt}\) and \(\textbf{T}_{opt}\) are the optimal rotation matrix and the optimal translation vector of the OOI w.r.t. the camera system, respectively. The performance of the proposed model-based object pose estimation algorithm is evaluated in the next section.
# Unrepeated 240-km 64-QAM Transmission Using Distributed Raman Amplification over SMF Fiber +
## 4 Simulations  
To investigate the possible performance improvement using the current system set-up (Figure 1) assuming an ideal error-free launch signal, we simulated the transmission of the 28-Gbaud Nyquist-shaped optical 64-QAM signal with pump powers as in the optimal experimental results obtained at each distance. In each Raman configuration, signal power excursion was simulated using the experimentally verified model with appropriate boundary conditions that were fully described in References [7; 17; 18]. In simulations, we used room temperature with the assumption that Raman pumps are fully depolarized. The noise was calculated in a bandwidth of 0.1 nm. The Raman gain and attenuation coefficients at the laser wavelength were obtained from measured gain and attenuation curves for standard SMF silica fiber [18]. The values of the Rayleigh backscattering coefficients for the pump wavelength at 1366 nm, the lasing at 1455 nm, and the frequency of the signal were assumed to be \(1.0\times 10^{-4}\), \(6.5\times 10^{-5}\), and \(4.5\times 10^{-5}\) km\({}^{-1}\), respectively. In Figure 6, we show numerically calculated optimal signal power profiles for the best-performing results for each distance (see Figures 3 and 4).  
To simulate transmission results, a random binary sequence of length \(2^{15}\) was firstly mapped into the complex plane using 64-QAM, oversampled by a factor of four, and then passed through a Nyquist filter to generate a Nyquist-shaped signal. The filter length was 80 and the baud rate was 28 Gbaud. The propagation of the signal in the fiber was simulated using a split-step Fourier method, with a step size of 0.1 km considering the simulated Raman gain and noise profiles shown in Figure 6.  
Figure 7 shows, for illustration, simulation results of the impact of nonlinear distortion only without ASE and chromatic dispersion for the three transmission distances considered.  
The results of the simulated error-free launch signal are shown in Figure 8, where we present simulated constellation diagrams for all distances with and without applying DBP. Finally, a comparison of the optimal experimental (Figures 3-5) and simulation (Figure 8) results is shown as a function of  
Figure 6: Signal power distribution in fiber with distributed Raman amplification.  
Figure 7: Constellation diagrams of simulated nonlinear distortion.  
the received OSNR in Figure 9. The simulation confirms that, with an ideal error-free 64-QAM launch signal, a 240-km transmission is possible using the proposed Raman configuration.
# Biomaterials for Ophthalmic Applications
## 1 Introduction  
The eye is an organ of great complexity and, compared to our other organs, is easier to observe and easier to access for surgery. It is also the first organ in which a foreign material was implanted to fulfil the function of what is now called a biomaterial. With the introduction of synthetic hydrogels, i.e., polymers capable of addressing and retaining water without dissolving in an aqueous medium, the range of ophthalmic biomaterials has grown considerably [1]. The use of biomaterials in general and ophthalmic biomaterials in particular is growing and integrates knowledge and ideas from multiple disciplines, such as medicine, biology, chemistry, physics, materials science and engineering [2].  
The eye is the organ responsible for vision situated in a bony cavity, called the orbit, is connected to the brain via optic pathways and is surrounded by a nutrient membrane, the choroid, as well as a protective membrane, the sclera. The eye is a hollow, spherical structure, surrounded by attachments with a motor paper or protective paper. The anatomy and physiology of the eye are very complex and extensively documented [3; 4; 5].  
The consequences of an increasingly ageing society on the health system have become a topic of great importance and concern. Concerning the maintenance of vision, adequate care is vitally important as it helps to maintain the independence of the elderly. All tissues and all aspects of the eye are affected by ageing to some degree. The most common vision disorders associated with increasing age are presbyopia, dry eyes, cataract or glaucoma [1].  
In relation to ophthalmic biomaterials, contact lenses are the oldest and most well-known of these, and are presently composed of hydrogel and silicone hydrogel. Intracolar lenses can replace a clouded eye lens that is removed during cataract operation. They are usually composed of derivative acrylics or silicone copolymers [6; 7]. Other biomaterialswidely marketed are artificial tears, used mainly for dry eye treatment. They reduce irritation symptoms, reduce friction, increase lubrication, stabilize the tear film and protect against dehydration. They are dispensed in the form of eye drops, gel or ointment [8]. Inlays are used to correct presbyopia and work according to different approaches [9].  
Locations for the possible ophthalmic application of biomaterials is provided by several authors [10; 11]. Briefly, in the anterior segment, soft contact lenses and artificial tears may be used. Intraocular lenses and inlays can be used in the lens. On the posterior segment, biomaterial application mainly involves vitreous substitutes.  
In pathological situations, such as retinal detachment, it is necessary to replace the biological vitreous [12]. The ideal vitreous substitute should have a variety of properties to perfectly mimic the necessary physicochemical properties [13; 14].
## 5 Experimental Results
Using the data in Table 5, two algorithms were used to packing the signal sets into frames to obtain the respective package combinations. They are shown in Tables 6 and 7. Without modification in the numbers of both ECU and transmitted signal, based on the NFD algorithm proposed in this paper and the BCBFD algorithm [15], a reconstruction of transmitted signal is executed. Then, the database is built by Network Designer, and imported into the softwares CANoe and FlexRay. As shown in Figure 10, four node ECUs are set up in the simulation network of CANoe. FlexRay that are used to transmitted frames. The proposed NFD algorithm and BCBFD algorithm were simulated and compared. Network performances of two different algorithms are compared and shown in Table 8. Data in the table shows that the busload is reduced by 16.3%, the bandwidth utilization is increased by 32.8%, and the number of frame FIDs is reduced by 53.8%. The proposed algorithm can reduce the number of time slots and effectively reduce the network payload, and the network utilization has also been improved. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Message** & **Signal** & **ECU** & **Repetition** & **Size (bit)** \\ \hline
1 & 1, 2, 3 & 1 & 1 & 42 \\
2 & 4, 5, 6, 7, 8 & & 2 & 54 \\
3 & 9, 10, 11, 12, 13 & & 4 & 54 \\
4 & 14, 15, 16, 17 & & 8 & 56 \\
5 & 18, 19, 20 & 2 & 1 & 24 \\
6 & 21, 22, 23, 24, 25 & & 2 & 56 \\
7 & 26, 27, 28, 29 & & 4 & 52 \\
8 & 30, 31, 32, 33 & 3 & 2 & 46 \\
9 & 34, 35, 36, 37 & & 4 & 51 \\
10 & 38, 39, 40, 41 & & 8 & 42 \\
11 & 42, 43, 44, 45, 46 & 4 & 2 & 54 \\
12 & 47, 48, 49, 50 & & 8 & 28 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Signal packing for Next Fit Decreasing (NFD) scheduling. Figure 9: Running time ratio.
## 4 Discussion  
To the best of the knowledge of the researchers, this is the first study to use IMU sensors and machine learning to identify feelings of energy and fatigue, when measured as two separate moods, using single-task walking gait. Since previous literature using sensor technology and machine learning to identify subjective fatigue had measured fatigue as ratings of perceived exertion [11; 12; 13; 14; 15; 16], there is no comparable literature to the findings of this study. Literature examining the effect of the association between feelings of energy and fatigue, as two unique moods, on walking gait does suggest that these two moods [31; 32; 35] influence unique aspects of gait, and the findings of this study support those works. These findings add three unique aspects to the literature: (1) when using machine learning to identify feelings of energy and fatigue, signals from IMU sensors during walking gait are unique to each mood; (2) machine learning models are able to predict these moods fairly accurately; and (3) there are advantages to identifying both feelings of energy and fatigue as two distinct unipolar moods in walking gait, as they influence annotation in their own unique ways.
# Comparative Analysis of 5G Mobile Communication Network Architectures
###### Abstract  
Mobile communication technology is evolving from 4G to 5G. Compared to previous generations, 5G has the capability to implement latency-critical services, such as autonomous driving, real-time AI on handheld devices and remote drone control. Multi-access Edge Computing is one of the key technologies of 5G in guaranteeing ultra-low latency aimed to support latency critical services by distributing centralized computing resources to networks edges closer to users. However, due to its high granularity of computing resources, Multi-access Edge Computing has an architectural vulnerability in that it can lead to the overloading of regional computing resources, a phenomenon called regional traffic explosion. This paper proposes an improved communication architecture called Hybrid Cloud Computing, which combines the advantages of both Centralized Cloud Computing and Multi-access Edge Computing. The performance of the proposed network architecture is evaluated by utilizing a discrete-event simulation model. Finally, the results, advantages, and disadvantages of various network architectures are discussed.  
5G mobile communication network; multi-access edge computing; centralized cloud computing; hybrid cloud computing +
Footnote †: journal: Applied
## 1 Introduction  
Many studies have tested the effects and ability of growth factors to induce osteogenic capacity of gingiva-derived mesenchymal stem cells (GMSCs) [1; 2; 3]. A previous report showed that bone morphogenetic protein-4 promoted osteogenic differentiation through upregulation of collagen I and Sp7 expression [1]. Fibroblast growth factors were shown to be involved in osteogenic differentiation of stem cell spheroids while maintaining cell viability [4]. Optimal concentrations of growth factors for proliferation or differentiation activity have been studied previously [1; 4; 5]. Increases in osteogenic differentiation were achieved in a dose-dependent manner at up to 200 ng/mL of fibroblast growth factor-4 [4]. In another study, 10% concentrated growth factor significantly promoted proliferation of GMSCs, but higher concentrations produced gradual decrease in cellular proliferation [5].  
Connective tissue growth factor (CTGF), which is a member of the cysteine-rich 61 family, is synthesized by osteoblasts [6]. CTCF is involved in diverse biological and pathological processes as well as many cellular functions, including cell proliferation,cell migration, cell adhesion, and extracellular matrix production [7]. CTCF is reported to be involved in the proliferation and differentiation of osteoblasts [8]. In one study, CTCF gene expression was upregulated in human periodontitis tissues, where it enhanced osteoclast formation and suppressed anti-osteoclastogenic factor expression throughout the experimental period, inducing indirect osteoclastogenesis [9]. Moreover, application of CTCF led to inhibition of BCL6 mRNA expression, and its protein expression was clearly downregulated in bone marrow-derived macrophages treated with CTCF compared to those in the control group [9]. Overexpression of CTCF in vivo resulted in osteopenia, a secondary cause of decreased bone formation, possibly by antagonizing the signaling and activity of bone morphogenetic protein and Wnt [10]. Application of CTCF to GMSCs has not been performed. More recently, stem cell spheroids have been of great interest in stem cell therapy [11; 12]. Stem cell spheroids have the potential to be applied for various applications including cell therapy [13].To the authors' knowledge, there is no previous microwell study reporting the effects of CTCF on cell spheroids composed of GMSCs. Therefore, we hypothesized that addition of CTCF will have specific effects on the cell viability and osteogenic capacity cell spheroids. In light of promising findings on CTCF, the purpose of this present study was to investigate the effects of CTCF on cell viability, osteogenic capacity, and mRNA expression of three-dimensional culture.