esa-sceva/satcom-chunk-collection huggingface dataset:
# The Integration of AI and Metaverse in Education: A Systematic Literature Review
## 4 Results and Discussion
### Rq3: What AI Techniques Are Utilized Within the Metaverse for Education? This study determined that recent technological advancements, particularly the utilization of AI within the metaverse, have had a notable impact within the educational field. An analysis of N = 60 technical contributions from the total number of eligible papers identified AI techniques as possessing significant potential for enhancing educational practices through the application of metaverse techniques. These included (1) deep learning; (2) reinforcement learning; (3) supervised learning; (4) unsupervised learning; and (5) natural language processing. Prior to analysing these techniques, we provide some definitions in Table 3 according to [15]:  
An analysis of Figure 5 reveals some key insights regarding the usage of AI techniques in educational contexts within the metaverse. First, deep learning (51%) emerges as the AI technique most frequently employed, underscoring its crucial role in handling complex educational data and facilitating advanced features such as immersive simulations and personalized learning experiences. Additionally, the use of deep learning demonstrates its importance in evaluating student engagement through visual inputs and improving augmented reality experiences. References to natural language processing (23%) are also notable, probably reflecting the need for communication and interaction in educational environments, which has contributed to the development of applications like chatbots, virtual assistants, and automated feedback systems. Reinforcement learning (17%) focuses on tailoring learning pathways by adapting content based on users' interactions in the metaverse. Supervised learning (7%) and unsupervised learning (2%) are less frequently discussed, indicating that while they play roles in predictive analytics and data clustering, they are not so central to the immersive and interactive features of the metaverse. In summary, the prevalence of key techniques is as follows: deep learning (31 mentions) ranks first, followed by natural language processing (14 mentions), reinforcement learning (10 mentions), supervised learning (4 mentions), and finally, unsupervised learning
# Region Adjacency Graph Approach for Acral Melanocytic Lesion Segmentation
## 2 Methods
### Overview  
As illustrated in Figure 2, the implemented algorithm is divided into four stages: preprocessing, simple linear iterative clustering algorithm (SLIC)--an oversegmentation method, region adjacency graph construction, and hierarchical merging. In general, using traditional segmentation methods, we obtain an intentionally oversegmented image. The input image is composed of superpixels that describe each region, and each superpixel is connected to its neighbors that succeed the topological operation. The weight of each such a connection (an edge) is defined in terms of a metric based on differences in values of attributes of the connected nodes. Those connections together with their weights form an undirected weighted graph. The graph is created in a structure called Region Adjacency Graph (RAG). The way the nodes are merged is the main characteristic of every re-segmentation approach and is responsible for the success of the method [28]. A similar workflow has already been successfully used to design transfer functions for volume rendering of medical data [29; 30] brain tumor segmentation [31; 32; 33], as well as segmentation of muscle fibers [34].
## 4 Antenna Prototype Fabrication and Test Results  
In this section, we present measurement results from a prototype of the OCROP antenna. We start by presenting the feeding network, which was formed with a 180\({}^{\circ}\) hybrid coupler. Next, we present the reflection coefficient and radiation pattern measurements for the prototype antenna to verify their performance with respect to the simulations.  
In practice, we require a differential feed when using the antenna and measuring radiation patterns. Therefore, two types of measurements are required.  
First, we needed to establish the differential S-parameters to compare the simulated and fabricated designs. The two-port VNA in our lab only supports single-ended S-parameter measurements. Since two of the ports are fed simultaneously, we cannot simply use the standard S-parameter measurement approach (which assumes a matched load at all other ports except for the measured port). Additionally, we cannot measure the differential reflection coefficients in our lab using the two-port VNA. Therefore, to characterize the differential feed, we measured the single-ended S-parameters at all ports (with all other ports match terminated) and calculated the differential reflection coefficients using an established approach [27].  
Secondly, to use the antenna in practice, we need to provide a single input and a feed network. This is required in our facility to measure the radiation pattern, as the receiver has a single port for measurement. Therefore, we measured the input reflection coefficient, as seen at the input to the combination of the feeding network and antenna together. These results are presented in this section.
# A Kernel Least Mean Square Algorithm Based on Randomized Feature Networks
## 3 The Mean Square Convergence Analysis
### Steady State Mean Square Error Analysis  
The steady state mean square error (MSE) is an important indicator of the algorithm's performance, and it can be represented by  
\[MSE=\lim_{n\rightarrow+\infty}E[|e(n)|^{2}]=\sigma_{v}^{2}+\lim_{n\to +\infty}E[|e_{a}(n)|^{2}] \tag{31}\]  
because the noise \(v(n)\) is dependent from the vector \(\mathbf{x}(n)\). The steady state analysis of the algorithm can be equivalent to obtaining the excess mean square error (EMSE)  
\[\lim_{n\rightarrow+\infty}E[|e_{a}(n)|^{2}]. \tag{32}\]  
Regarding the steady state, the following assumption can be made:  
\[\lim_{n\rightarrow+\infty}E\left[||e_{w}(n+1)||^{2}\right]=\lim_{n\to +\infty}E\left[||e_{w}(n)||^{2}\right] \tag{33}\]  
Thus, Equation (27) is simplified as  
\[\begin{split}\mu^{2}\lim_{n\rightarrow+\infty}& E\left[||\varphi(\mathbf{x}(n))||^{2}e_{a}(n)^{2}\right]+\sigma_{v}^{2} \lim_{n\rightarrow\infty}||\varphi(\mathbf{x}(n))||^{2}\\ &-2\mu\lim_{n\rightarrow+\infty}E\left[|e_{a}(n)|^{2}\right]=0 \end{split} \tag{34}\]  
After derivation, the following is obtained:  
\[\begin{split}\lim_{n\rightarrow+\infty}E\left[|e_{a}(n)|^{2} \right]&=\frac{\mu}{2}\lim_{n\rightarrow+\infty}E\left[|| \varphi(\mathbf{x}(n))||^{2}e_{a}(n)^{2}\right]\\ &+\frac{\mu\sigma_{v}^{2}}{2}\lim_{n\rightarrow+\infty}|| \varphi(\mathbf{x}(n))||^{2}\end{split} \tag{35}\]  
The above equation is similar to the steady state MSE results of the linear LMS obtained in [26], confirming the concept that the KLMS is merely the linear LMS in a high-dimensional feature space [4]. To evaluate tighter expressions of the steady state performance, the following two conditions are considered:  
1. When the step size \(\mu\) is sufficiently small, the term \(\frac{\mu}{2}\lim_{n\rightarrow+\infty}E\left[||\varphi(\mathbf{x}(n))||^{2}e_{a }(n)^{2}\right]\) can be assumed to be far less than \(\frac{\mu\sigma_{v}^{2}}{2}\lim_{n\rightarrow+\infty}||\varphi(\mathbf{x}(n))||^{2}\). Therefore, for a small \(\mu\), the following is obtained: \[\lim_{n\rightarrow+\infty}E\left[|e_{a}(n)|^{2}\right]=\frac{\mu\sigma_{v}^{2} }{2}\lim_{n\rightarrow+\infty}||\varphi(\mathbf{x}(n))||^{2}\] (36)When the step size \(\mu\) is large (the value is not infinitesimally small to guarantee filter stability), the following independence assumption is required to find the expression for EMSE. At steady-state, the input signal \(\mathbf{x}(n)\) is statistically independent of \(e_{a}(n)\). Thus, we obtain \[\lim_{n\rightarrow+\infty}E\left[|e_{a}(n)|^{2}\right]=\frac{\mu\sigma_{v}^{2 }\lim_{n\rightarrow+\infty}||\varphi(\mathbf{x}(n))||^{2}}{2-\mu\lim_{n\rightarrow+ \infty}||\varphi(\mathbf{x}(n))||^{2}}\] (37)
## 4 Simulation Results
The reason is that the PDCT algorithm tries its best to allocate resources to users so that the number of users meeting QoS can reach the maximum. In the uplink, the PDCT algorithm can achieve performance close to the eICIC algorithm. This is because although PDCT sacrifices some uplink resources, it makes up for them through the LP-ABS strategy. In summary, the PDCT algorithm proposed in this article can effectively improve the average throughput of uplink and downlink services and effectively reduce the outage probability of soft QoS services. Figure 8: The comparison on the average uplink throughput. Figure 9: The comparison on the average downlink outage probability.
# Improving Lossless Image Compression with Contextual Memory
## 3 PAQ8 PX Algorithm for Lossless Image Compression in Detail
### Image Compression
#### 3.4.3 Least Squares Modeling  
An ordinary least squares modeling is used to predict the value of the next pixel (not bit prediction) based on a given set of context values and acts as a maximum likelihood estimator. The prediction is a linear combination of the regressors, which are the explanatory variables. The update phase tries to minimize the sum of squared differences of the true pixel value and the predicted value. Finding the values of the weight vectors is done online by the method of normal equations that uses a Cholesky decomposition that factors the design matrix into an \(n\) by \(n\) lower triangular matrix, where \(n\) is the number of regressors. The matrix is then used to analytically find the weight values. The bias vector and the covariance matrix are updated using parametrized momentum.  
The value of the prediction is not used directly, but is used in combination with the known bits of the current byte and the bit position in the byte as a key into a stationary context map.
# Ordered Subspace Clustering for Complex Non-Rigid Motion by 3D Reconstruction
## 1 Introduction
It is proved by ordered subspace clustering (OSC) [16; 17] methods which is recently proposed, that clustering using sequential or ordered properties will improve clustering accuracy significantly. On the other hand, there is no constraint for the structure of the affinity matrix in current NRSfM methods. From the success of the subspace clustering methods [17; 18; 19], which adopted structure of the affinity matrix such as block-diagonal property, it is necessary to make constraints for the structure of the affinity matrix in NRSfM clustering methods. [PERSON] et al. [21] proposed a joint framework that both segmentation and reconstruction benefit each other. In the trajectory space and shape space there are multiple subspaces with better reconstruction results. While [PERSON] et al. [22] demonstrated a different view about dense NRSfM problem by considering dense NRSfM on a Grassmann manifold. In this paper, based on ordered subspace clustering for complex and various non-rigid motion by 3D reconstruction, a novel method is proposed, where the sequential property is properly formulated in the procedure of learning the affinity matrix for clustering with simultaneously recovering the 3D non-rigid motion from 2D point tracks. Experiments results on a few sequential datasets show the benefits of the proposed model about the complex non-rigid motion analysis and its results outperform state-of-the-art methods of motion segmentation problem. The contributions are mainly listed in detail:  
* A novel framework is proposed for segmentation of complex and various non-rigid motion from 3D reconstruction using ordered subspace clustering. * Instead of nuclear normal, a quadratic constraint is used in the self-representation model to improve the clustering performance. * An efficient algorithm is implemented solving the complicated optimization involved in proposed framework. This paper is organized as follows. First, related works are presented in Section 2. Section 3 gives the proposed model in detail. The solution to the optimization model above is given in Section 4. In Section 5, the proposed method along with state-of-the-art methods are evaluated on several public datasets. Finally, we give the conclusions of this paper in Section 6.
## 3 Results and Discussion
### Prototype Development System
#### 3.1.2 Test for Virtual Measurement System Repeatability and Accuracy
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**The Measurement System Repeatability Study**} \\ \hline \multicolumn{6}{c}{**(Unit: mm)**} & \multicolumn{2}{c}{**Tooth Thickness**} & \multicolumn{2}{c}{**Repeatability Error**} \\ \hline \multicolumn{1}{c}{**No. of Tooth**} & \multicolumn{1}{c}{**Test 1**} & \multicolumn{1}{c}{**Test 2**} & \multicolumn{1}{c}{**Test 3**} & \multicolumn{1}{c}{**2 nd–1 st**} & \multicolumn{1}{c}{**3 rd–1 st**} \\ \hline T1 & 10.14783 & 10.13657 & 10.16716 & \(-\)0.011 & 0.019 \\ T2 & 9.895499 & 9.904919 & 9.946733 & 0.009 & 0.051 \\ T3 & 32.5769 & 32.52688 & 32.581 & -0.050 & 0.004 \\ T4 & 10.05784 & 10.05223 & 10.05939 & \(-\)0.006 & 0.002 \\ T5 & 10.20149 & 10.19728 & 10.20142 & \(-\)0.004 & 0.000 \\ T6 & 9.865836 & 9.884316 & 9.871897 & 0.018 & 0.006 \\ T7 & 9.779203 & 9.751502 & 9.73667 & \(-\)0.028 & \(-\)0.043 \\ T8 & 32.575737 & 32.76112 & 32.77379 & 0.004 & 0.016 \\ T9 & 10.26547 & 10.26095 & 10.24714 & \(-\)0.005 & \(-\)0.018 \\ T10 & 10.40329 & 10.41216 & 10.43104 & 0.009 & 0.028 \\ T11 & 10.43783 & 10.45585 & 10.40663 & 0.018 & \(-\)0.031 \\ T12 & 10.13186 & 10.13569 & 10.13275 & 0.004 & 0.001 \\ T13 & 32.73424 & 32.60537 & 32.66642 & \(-\)0.129 & \(-\)0.068 \\ T14 & 10.34993 & 10.2971 & 10.29994 & \(-\)0.053 & \(-\)0.050 \\ T15 & 10.262 & 10.2998 & 10.29871 & 0.038 & 0.037 \\ T16 & 10.36323 & 10.33171 & 10.35833 & \(-\)0.032 & \(-\)0.005 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Measurement system repeatability test on tooth thickness (measurements were repeated thrice). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Error Zone Distribution Percentage**} \\ \hline \multicolumn{1}{c}{**Error Max (mm)**} & \multicolumn{1}{c}{**Count**} & \multicolumn{1}{c}{**Cumulative Number**} & \multicolumn{1}{c}{**Cumulative Number \%**} \\ \hline
0–0.01 & 13 & 13 & 40.63\% \\
0.01–0.02 & 6 & 19 & 59.38\% \\
0.02–0.03 & 2 & 21 & 65.63\% \\
0.03–0.04 & 4 & 25 & 78.13\% \\
0.04–0.05 & 3 & 28 & 87.50\% \\
0.05–0.06 & 2 & 30 & 93.75\% \\
0.06–0.07 & 1 & 31 & 96.88\% \\
0.07–0.08 & 0 & 31 & 96.88\% \\
0.08–0.09 & 0 & 31 & 96.88\% \\
0.09–0.10 & 0 & 31 & 96.88\% \\
0.10–0.11 & 0 & 31 & 96.88\% \\
0.11–0.12 & 0 & 31 & 96.88\% \\
0.12–0.13 & 1 & 32 & 100.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Repeatability error zone distribution. With respect to the influence of the tooth thickness error, the tooth edge positions could not be determined, because of the sampling interval error of the tooth thickness. Without considering the 0.129 mm maximum repeatability error volume (only one), the maximum tooth thickness error was 0.068 mm. \[\text{Sampling interval}=\pi\text{D}\times\frac{\theta}{360} \tag{12}\]
# Cognitive Routing in Software-Defined Underwater Acoustic Networks
## 4 Performance Evaluation
(**a**) overhead ratio when \(M=1\); (**b**) overhead ratio when \(M=3\); and (**c**) overhead ratio when \(M=5\). CA-SDUN: cognitive acoustic software-defined underwater network; Cog-AA-RP: cognitive AUV-aided routing method integrated path planning; Cog-DVRP: cognitive diagonal and vertical routing protocol; AUV: autonomous underwater vehicle. Figure 6 shows the data rate as a function of different frequencies with the bandwidth as a parameter. The data rate decreases dramatically with increasing frequencies because the path loss increases with an increase in frequency. However, increasing the bandwidth improves the data rate, but the operating region for majority of acoustic systems has limited bandwidth in underwater networks due to strong attenuation. Therefore, we achieve a low data rate for low bandwidth. Figure 7 shows the relationship between bit error rate and various SNR values. Bit error rate is used to determine the quality of transmitted signal in a channel. The lower the bit error rate, the better the quality of the received signal. It can be seen from the curve that the bit error rate improves with the increase in SNR and transmission power. Figure 6: Data rate as a function of different frequencies. CA-SDUN: cognitive acoustic software-defined underwater network. Figure 7: Bit error rate as a function of signal-to-noise-ratio (SNR). CA-SDUN: cognitive acoustic software-defined underwater network.
## 5 Conclusions  
In the scenario of HSRs, the effective coverage distance of gNodeB has little relation with the height of base station, but it is strongly correlated with the horizontal distance between gNodeB and rail. This is fundamentally different from the non-HSRs. The power attenuation values of wireless signals are unlike under the diverse path loss models and various region types, so it is necessary to distinguish and apply them reasonably in the construction of 5G-R. When both large-scale fading and shadow fading are taken into account, the design of overlapping coverage areas between gNodeBs along the rail is relatively complex.  
The process of the handover in 5G-R can be optimized through the planning of handover zone, the estimate of the speed of HST and the location recognition of UE, and the weight calculation of the handover. It can effectively reduce the delay of the handover and improve the success rate of the handover. With the increase of speed of a train in the range of 150 km to 450 km, the delay of the fast handover algorithm is  
Figure 16: Comparison of the success rates of the handover between the fast handover algorithm and the A3 handover algorithm.  
significantly reduced. The speed of HST is inversely proportional to the success rate of handover, and the proposed algorithm can improve the success rate of handover. In the future, with continuously increasing train running speeds, fast handover algorithms will become more important. As 6G technology is being developed, the fast algorithm can be further applied for HSRs with distinct characteristics such as a definite trajectory of movement, a knowable range of running speed, and an identifiable handover chain.  
[PERSON] wrote the manuscript, conducted most of the analysis, and interpreted results. [PERSON] and [PERSON] provided critical advice on analysis methods and interpretation of results, and afforded lab resources, advice on method. [PERSON] and [PERSON] helped with preparing the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by National Natural Science Foundation of China, grant number 61661025 and 61661026, and in part by the Science and Technology Plan of Gansu Province (20 JR10 RA273).  
Not applicable.  
Data Availability StatementData available on request from the authors.  
We are very grateful to China Mobile Communications Group Gansu Co., Ltd. for the large amount of raw measured data.  
The authors declare no conflict of interest.
## 2 System Model
### MUSIC Algorithm
By performing eige-decomposition on \(\mathbf{R}\), we obtain  
\[\mathbf{R}=\mathbf{Q}_{s}\mathbf{D}_{s}\mathbf{Q}_{s}^{H}+\mathbf{Q}_{n} \mathbf{D}_{n}\mathbf{Q}_{n}^{H} \tag{24}\]  
where \(\mathbf{D}_{s}\) is a \(K\times K\) diagonal matrix whose diagonal elements represent the largest \(K\) eigenvalues in magnitude, and \(\mathbf{D}_{n}\) is a diagonal matrix containing the smallest \(M\times N-K\) eigenvalues. \(\mathbf{Q}_{s}\) is the matrix formed by the eigenvectors corresponding to the largest \(K\) eigenvalues of \(\mathbf{R}\), while \(\mathbf{Q}_{n}\) consists of the eigenvectors associated with the remaining eigenvalues. It is important to observe that \(\mathbf{Q}_{s}\) and \(\mathbf{Q}_{n}\) correspond to signal and noise subspaces, respectively, and the direction vectors of the signal and noise subspaces are orthogonal to each other. Therefore, the MUSIC algorithm scans all possible angles and generates the spatial spectrum:  
\[\mathrm{P}_{\mathrm{MUSIC}}(\theta,\varphi)=\frac{1}{(\mathbf{A}^{H}\mathbf{Q }_{n}\mathbf{Q}_{n}^{H}\mathbf{A})} \tag{25}\]  
The values of \(\theta\) and \(\varphi\) that maximize the spatial spectral function correspond to the elevation and azimuth angles of the signal source.
###### Abstract
Herein, we modify the operation of the conventional linear discriminant analysis (LDA) by generating a multimodel linear discriminant analysis (MM-LDA) based on two discriminant functions. In addition, a one-to-many model approach is applied to the scan data to reduce the estimation error of the scan failure/delay where the scan response samples obtained in various network scenarios overlapped. The goal of our proposed method is to correctly estimate the causes of scan failure/delay in IP-connected devices. The main contributions of this study are summarized as follows. 1. Firstly, we estimate the status of the network and examine various attributes of the network that can help to determine an appropriate period that is good for performing a network scan. 2. Secondly, we identify the possible cause of network scanning failure/delay and categorize the causes as (i) port blocking, (ii) no network connection, (iii) insufficient communication quality, and (iv) network congestion. 3. We devise a method to determine whether the cause of scan failure/delay is any of the four causes identified above. 4. In addition, we analyze the differences in the distribution of multiple quality of service (QoS) features from the result of network scanning and identify the feature value that is useful for accurate failure/delay estimation. 5. We propose a multimodel LDA (MM-LDA) machine learning algorithm and adopt a one-to-many model approach to classify the cause of scan failure/delay of scan response results. 6. Finally, we evaluate the performance of our proposed estimation algorithm using the scan response data acquired through a computer simulation assuming a cellular (LTE) network as the targeted IoT wireless network and using LTE-connected devices as the targeted IoT devices. Three basic network scenarios are simulated: (i) normal, (ii) low SNR, and (iii) network congestion. Our MM-LDA estimation algorithm correctly estimates the cause of failure/delay of the network scan at an average probability of 98% in various network scenarios and outperforms various conventional machine learning classification algorithms. The rest of this paper is organized as follows. In Section 2, we review related work to network scanning. Section 3 presents the overview of the cause of network scan failure/delay. This section also describes the cause of scan failure and the network conditions that may lead to the causes. Section 4 introduces our proposed cause estimation algorithm and model for estimating the cause of network scan failure/delay. Section 5 presents the scan failure/delay estimation process. In Section 6, we describe the performance evaluation of our proposed algorithm, and Section 7 concludes the entire paper.
# Overrelaxed Sinkhorn-Knopp Algorithm for Regularized Optimal Transport
## 2 Sinkhorn Algorithm
### Discrete Optimal Transport  
We considered two discrete probability measures \(\mu_{k}\in\mathbb{R}_{+*}^{n_{k}}\). Let us define the two following linear operators:  
\[A_{1}:\begin{cases}\mathbb{R}^{n_{1}n_{2}}\to\mathbb{R}^{n_{1}}\\ (A_{1}x)_{i}=\sum_{j}x_{ij}\end{cases} A_{2}:\begin{cases}\mathbb{R}^{n_{1}n_{2}}\to\mathbb{R}^{n_{2}}\\ (A_{2}x)_{j}=\sum_{j}x_{ij},\end{cases}\]as well as the affine constraint sets:  
\[\mathcal{C}_{k}=\{\gamma\in\mathbb{R}^{n_{1}n_{2}}\mid A_{k}\gamma=\mu_{k}\}.\]  
Given a cost matrix \(c\) with nonnegative coefficients, where \(c_{i,j}\) represents the cost of moving mass \((\mu_{1})_{i}\) to \((\mu_{2})_{j}\), the optimal transport problem corresponds to the estimation of an optimal transport matrix \(\gamma\) solution of:  
\[\min_{\gamma\in\mathcal{C}_{1}\cap\mathcal{C}_{2}\cap\mathbb{R}^{n_{1}n_{2}}_ {+}}\langle c,\gamma\rangle:=\sum_{i,j}c_{i,j}\gamma_{i,j}.\]  
This is a linear programming problem whose resolution becomes intractable for large problems.