esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method for Defect Shape Estimation
### Dimensionality Reduction  
The defect shape attributes, such as width and depth, of the polarizers are crucial. We expect to obtain these attribute data from the saturated image. The dimensions of the image itself and the feature vector are very high. However, our sample size is extremely limited because of the difficulty of acquiring samples. In the original high-dimensional space, much redundant information and noise are included. The machine learning model is prone to over-fitting, leading to low generalization ability. An effective approach to alleviate this dimensionality problem is to use low-dimensional embedding in high-dimensional space. The variables in the low-dimensional space are likely to be the information desired by the study, such as the refractive index of the polarizers, defect width, and defect depth.  
Suppose that the feature space of the defect area is represented by a set of image features, \(\mathcal{X}=\left\{x_{i}:x_{i}\in\mathcal{R}^{D}\right\}_{i=1}^{n}\) where \(D\) is the feature dimension and \(n\) is the number of samples. The corresponding set of labels for the true defect shape attribute is denoted by \(\mathcal{L}=\left\{l_{i}:l_{i}\in\mathcal{R}\right\}_{i=1}^{n}\). The goal of dimensionality reduction is to learn a one-to-one projection in the embedded subspace by projecting \(\mathcal{X}\) onto a low-dimensional manifold subspace, say \(\mathcal{Y}=\left\{y_{i}:y_{i}\in\mathcal{R}^{d}\right\}_{i=1}^{n}\), where \(d\) is the new dimension after dimensionality reduction, and \(d\ll D\). This projection model can be formulated as \(\mathcal{Y}=P(\mathcal{X},\mathcal{L})\), where \(P(\cdot)\) represents a linear or nonlinear projection function. The projection function can be found through unsupervised or supervised learning methods [18].  
Because of the small sample size, manifold embedding techniques including principal component analysis (PCA), isometric mapping (Isomap), local linear embedding (LLE), and Laplacian eigenmaps (LE) are feasible dimensionality reduction approaches. PCA [19] is a linear dimensionality reduction method that maps data along the direction of the variance maximization in the target space. Although this is a linear dimensionality reduction method, it is also compared with nonlinear ones because it is frequently used. Isomap [19] is a nonlinear extension of multidimensional scaling that maintains the distance between samples in low-dimensional space as close as possible to the distance in geodetic distance space. LLE [20] is also a nonlinear method that computes low-dimensional, neighborhood-preserving embedding of high-dimensional inputs, which can help the reduced data more effectively maintain its original structure. Similar to LLE, the basic concept of LE [19] is to use spectral techniques to obtain a relationship such that the adjacent points are as close as possible in the lower-dimensional space.  
According to many researchers, estimation of out-of-samples can also be employed for projection onto a low-dimensional space; please refer to [21] for the details.
# Using Graph Neural Networks for Social Recommendations
## 4 Experimental Evaluation
### Ablation Study  
We perform an ablation study to explore whether the item-item graph \(G_{T}\) that we create, such that similar items are connected, enriches our embeddings. To evaluate this hypothesis, we employ an item-item graph \(G_{T}^{*}\) that is created by connecting items randomly. We achieve that by replacing every edge \(e=(v,u)\in G_{T}\) (positive edge) with a negative edge \(e^{*}\) that connects \(v\) with a node \(u^{*}\) that is not in the neighborhood of \(v\). We then reduce the degree of each node by 10 but cap the minimum degree at 1. The ablation results, shown in Table 5, are consistent with our hypothesis that the item-item graph \(G_{T}\) enriches our model, as using an item-item graph with negative edges and fewer connections decreases the performance of the model in terms of RMSE, HR@5/10/15 and NDCG@5/10/15. It is important to note that the greatest difference between our experimental results and the ablation results is in HR@5 and NDCG@5, while HR@10/15 and NDCG@10/15 are closer. This is expected for the specific dataset since after preprocessing, there exist many items that have fewer than 10 ratings. When the number of training samples is less than the number of predicted ratings, the hit rate and NDCG values have less relevance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Layers**} & \multirow{2}{*}{**Metric**} & **RelationalNet with \(G_{T}\)** & **RelationalNet with \(G_{T}^{*}\)** \\  & & **(Tables 3 and 4)** & **(Ablation)** \\ \hline k = 3 & RMSE & **0.1674** & 0.1855 \\ \hline k = 2 & HR@5 & **0.2946** & 0.2901 \\ \hline k = 2 & HR@10 & **0.3288** & 0.3243 \\ \hline k = 2 & HR@15 & **0.3739** & 0.3714 \\ \hline k = 2 & NDCG@5 & **0.5058** & 0.5036 \\ \hline k = 2 & NDCG@10 & **0.5347** & 0.5324 \\ \hline k = 2 & NDCG@15 & **0.5416** & 0.5404 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study.  
Figure 7: Training loss. (**a**) Training loss of the Diffnet++ model [13]. (**b**) Training loss of the RelationalNet model.
## 3 Results
### Flow Cytometry: Aquaporin Expression and Granularity of MG-63 Cells after AC Electrical Stimulation  
Due to electrical stimulation, ROS species are generated in the medium around and inside the cells. The AQP channels are permeable for H\({}_{2}\)O\({}_{2}\); therefore, it is worth knowing if AC can influence their expression. The AQPs 1, 3, 5, 7, 8, and 9 were investigated via flow cytometry after 10 min of electrical stimulation with 1 V or 5 V at 20 Hz. AQP 10 was not considered, because it was not expressed and is not involved in the transport of hydrogen peroxide [16]. No consistent trend can be observed concerning a change in the AQP expression (Figure 2). At 5 V/20 Hz, the channel of AQP 8 was significantly increased.  
To evaluate whether changes in cell granularity are an outcome of ES with low frequencies (20 Hz), we analyzed the flow cytometric dot plots concerning the side scatter (SSC) of MG-63 cells. The mean channel value of the unstimulated control was set to 1, and all other values were normalized to the control. While the cell size (detected with forward scatter, FSC) shows no change, the cell granularity (SSC) increased significantly  
Figure 1: Expression and localization of classic aquaporins (AQPs 1, 5, 8) and aquaglycerolporins (AQPs 3, 7, 9, 10) in human osteoblast-like MG-63 cells. AQPs 1, 5, 7, and 8 (green) are clearly visible. AQPs 3 and 10 were nearly not expressed, and therefore, cells were framed. (LSM780, Carl Zeiss, AQPs in green, nucleus in blue, scale bars 10 \(\upmu\)m).  
Figure 2: Expression of AQPs in MG-63 cells after short-time AC electrical stimulation of 10 min. The respective unstimulated control was normalized to 1. (FACScalibur; n = 3 independent experiments, mean \(\pm\) s.e.m., RM one-way ANOVA post hoc Fisher ’s LSD; \({}^{*}\)\(p\)\(\leq\) 0.05).  
after electrical stimulation with 5 V/20 Hz. The relative SCC values for 5 V/20 Hz and 1 V/20 Hz are 1.080 \(\pm\) 0.101 and 0.991 \(\pm\) 0.028, respectively (Figure 3).
## 4 Methods
### Node Feature Matching Module (NFMM)  
To address the issue of non-matching point pairs that may arise after network training, we design a Node Feature Matching Module (NFMM). This module aims to identify and exclude mismatched point pairs. The specific steps are detailed as follows:  
1. Threshold Setting: The module begins by computing the cosine similarity matrix from the enhanced node features of the corresponding points in the two images. A threshold filter is then applied, which considers pairs with a cosine similarity below 0.8 as non-matching and excludes them. The pairs with the highest cosine values are selected to form a preliminary coarse correspondence matrix \(H_{x}\).
2. Prediction Matrix Generation: We map each edge from the edge matrix \(A_{x}\) of one of the images to be registered, based on the point correspondence matrix \(H_{x}\), by scanning \(A_{x}\) row by row. This process results in the predicted point correspondence matrix \(C_{x}\) for the target image.
3. Accumulation Matrix Construction: An accumulation matrix \(S_{x}\) is constructed to match the generated edge correspondences. The first column of \(S_{x}\) represents the positive accumulation for each point, while the second column represents the negative accumulation.
4. Comparison: Matrix \(C_{x}\) is compared row by row with the edge distance matrix \(B_{x}\) of the corresponding image to be registered. If an edge from \(C_{x}\) is found in \(B_{x}\), the positive integral for the corresponding points in the integral matrix \(S_{x}\) is incremented by one; otherwise, the negative integral is incremented.
5. Updating the Correspondence Matrix: After completing the comparison of all edges, the integral matrix \(S_{x}\) is used to determine the retention of edges. An edge is retained if the absolute value of its positive integral exceeds its negative integral; otherwise, it is discarded. The final output is the feature-matched point cloud correspondence matrix \(H_{x}^{\prime}\).  
The algorithm module is described as Figure 3:
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 3 Preliminaries
### Elliptic Curve Signcryption
#### Initialization Phase  
In this preliminary phase, the global public parameters are chosen. In addition, the key pairs for the sender and the receiver are also generated. The steps carried out in the initialization phase are explained below:  
* [PERSON], the sender, and [PERSON], the receiver, agree on the elliptic curve \(E\), which is defined over finite field \(GF(q^{n})\) with \(q\geq 2^{160}\) and \(n=1\), or \(q=2\) and \(n\geq 160\).
* A big prime \(p\) with order \(q^{m-1}\) is selected.
* \(G\) of order \(p\) on elliptic curve \(E\) is randomly chosen, which becomes the base point of the elliptic curve.
* Keyed hash function \(KH\) is chosen.
* Hash function \(H\) is selected.
* Encryption algorithm \(ENC\) and decryption algorithm \(DEC\) are agreed upon.
* [PERSON] randomly selects a private number \(v_{x}\) in the range \([1\ldots p-1]\), which is her private key.
* [PERSON] generates her own public key \(P_{x}\) by computing: \[P_{x}=v_{x}G\] (6)
* [PERSON] randomly selects a private number \(v_{y}\) in the range \([1\ldots p-1]\) which is his private key.
* [PERSON] generates his own public key \(P_{y}\) by computing: \[P_{y}=v_{y}G\] (7)
## Literature Review
### Digital Twin Technology in Landscape Architecture Technology Programs
#### 2.2.2 Real-Time Monitoring of Plant-Growth Environment  
Monitoring the growth environment of garden plants is crucial for ecosystem protection, biodiversity maintenance, and promoting sustainable ecological development [51]. Traditional methods of garden ecological monitoring typically rely on manual observation and periodic sampling, resulting in a low monitoring frequency and delayed data acquisition [52]. However, the advent of digital twin technology offers new solutions for garden ecological monitoring [53]. Digital-twin-based garden environment monitoring leverages advanced technologies such as sensing technology, network transmission technology, and artificial intelligence to map the real-world scenarios of plants and their growth environments into a virtual digital world [54]. By integrating multidimensional physical entity data and utilizing data mining, data analysis, and three-dimensional simulation technologies, it can present real-time environmental parameters, equipment status information, and plant-growth status [55]. This facilitates timely adjustments to environmental conditions, the control of equipment operation, and the regulation of plant-growth processes [56]. Furthermore, the application of digital twin technology involves not only real-time data monitoring but also the effective processing and analysis of sensor data [57]. For example, [PERSON] et al. [11] demonstrated how digital twin technology could monitor temperature, humidity, and light intensity in a greenhouse environment, though their study faced errors in data processing that compromised its scientific validity. [PERSON] et al. [58] proposed an aquaculture-production control platform showcasing the application of digital twin technology in optimizing fish and plant resources. This platform monitored environmental indicators such as temperature, light, water flow, pH, and dissolved salts through sensor data, optimizing system resource management [59]. However, this technology was not applicable to complex environments. To comprehensively evaluate the practical effects of digital twin technology, future research should focus more on analyzing and experimenting with its economic feasibility, effectiveness, and adaptability in various garden environments.
## 4 Machine Learning Techniques for Forecasting
### RBF Neural Networks  
As a two-layer feedforward network, a radial basis function (RBF) neural network is composed of an input layer, a hidden layer and an output layer [27]. An RBF network is a special case of BP network, and the major difference lies in that the former uses a radial basis function as activation function instead of other functions, such as a sigmoid activation function. The sigmoid activation function forces the neurons to have a large input visible area [52]. In contrast, the activation function in an RBF network has a small input space region. Consequently, an RBF network needs more radial basis neurons. Moreover, an RBF network is mainly applied to the one-dimensional output case. Figure 6 plots the RBF neural network with the case \(D_{2}=1\).  
The commonly used radial basis function in an RBF neural network is the Gaussian function. Under this circumstance, the activation function for a given input feature \(\mathbf{x}\) can be expressed as  
\[\varphi(\mathbf{x};\mathbf{u},\sigma)=\exp\biggl{(}-\frac{1}{2\sigma^{2}}\|\mathbf{x}-\mathbf{ u}\|^{2}\biggr{)} \tag{11}\]  
where \(\mathbf{u}\in\Re^{D_{1}\times 1}\) and \(\sigma\) are the center and the standard deviation of the Gaussian function, respectively. The mathematical model of the RBF network with \(L\) hidden units can be written as  
\[\mathbf{y}\approx f_{\text{RBF}}(\mathbf{x};\mathbf{\theta},\mathbf{w})=\sum_{j=1}^{L}w_{j} \varphi(\mathbf{x};\mathbf{u}_{j},\sigma_{j}) \tag{12}\]  
where \(\mathbf{w}=\left(w_{1},w_{2},\ldots,w_{L}\right)^{\text{T}}\) is the weights vector connecting the hidden layer to the output layer, \(\mathbf{\theta}=\left\{\left(\mathbf{u}_{j},\sigma_{j}\right)\right\}_{j=1}^{L}\) is a set composed by \(L\) center vectors and \(L\) standard deviations.  
Formally, the parameters of the RBF neural network can be obtained by minimizing the following errors:  
\[\min_{\mathbf{\theta},\mathbf{w}}\frac{1}{2}\sum_{i=1}^{L}(\mathbf{y}_{i}-f_{\text{RBF}}( \mathbf{x}_{i};\mathbf{\theta},\mathbf{w}))^{2} \tag{13}\]  
If \(\mathbf{\theta}\) is fixed, the optimal weights vector \(\mathbf{w}\) is calculated as  
\[\mathbf{w}\leftarrow\mathbf{\Phi}^{\dagger}\mathbf{Y} \tag{14}\]  
where \(\mathbf{Y}=\left(\mathbf{y}_{1},\mathbf{y}_{2},\ldots,\mathbf{y}_{N}\right)^{\text{T}}\), the notation \(\dagger\) is the generalized inverse of a matrix, \(\mathbf{\Phi}=\left(\varphi_{ij}\right)_{N\times L}\) is the design matrix with \(\varphi_{ij}=\varphi\left(\mathbf{x}_{i};\mathbf{u}_{j},\sigma_{j}\right)\). The parameters set can be deter  
Figure 6: Diagram of an RBF neural network.  
mined by the gradient descent or cross-validation method. In practice, \(\mathbf{\theta}\) and \(\mathbf{w}\) can be updated alternately.
# Stability Analysis of Multi-Agent Tracking Systems with Quasi-Cyclic Switching Topologies
## 5 Conclusions  
In the paper, the switching problem of the multi-agent tracking systems with quasi-cyclic communication topologies is investigated. During this tracking process, several tracking agents have to cooperate and communicate with each other to track a maneuvering target. The communication links among the multiple agents may be interrupted due to the limitation of communication range or the appearance of obstacles, leading to unconnected communication topologies, which correspond to unstable subsystems. Existing results usually require the piecewise topologies to be connected or switch circularly. In order to relax the constraints, this paper proposes a quasi-cyclic method for MASs which contain a more practical switching rule with respect to a looser dwell time criterion. Moreover, it is proved that the tracking error converges exponentially in a less conservative way. Sufficient conditions that guarantee the stability of MASs with unstable subsystems are achieved. A numerical simulation is carried out to illustrate the validity of the theoretical results. It is concluded that the convergence rate and the maximum allowable dwell time of unstable subsystems are contradictory. Besides, the proposed method may be used not only in such scenarios as collaborative target tracking and convoy of vehicles, but also be promising in surveillance of a specific area and multi-robot coordination, in the case that the communication topologies of multiple agents are quasi-cyclic switched. However, there are still some challenging problems for MASs with switching topologies to be solved. In our future work, we will try to investigate a more widely applicable approach, considering the systems with nonlinear agents, and unknown time-varying communication delays.  
Conceptualization, D.F. and L.D.; methodology, D.F. and L.D.; software, D.F. and L.D.; validation, D.F.; formal analysis, H.S.; investigation, D.F.; resources, H.S.; data curation, D.F.; writing--original draft preparation, D.F.; writing--review and editing, L.D.; visualization, D.F.; supervision, H.S.; project administration, H.S.; funding acquisition, H.S. and L.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant 61903022, and funded by Beijing Advanced Innovation Center for Intelligent Robots and Systems under Grant 2019 IRS11.  
The authors declare no conflict of interest.The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Analysis of Factors Affecting the Stability of the Skid-and-Mud-Return Line
### Analysis of Factors Affecting the Stability of the Skid-and-Mud-Return Line
The drag force on a unit length of the structure can be determined using Equation (3), as shown in the following, where \(f_{c}\) is the drag force per unit length of the structural member, \(\rho\) is the density of the fluid, \(C_{D}\) is the drag force coefficient, \(D_{C}\) is the hydraulic outside diameter of the structural member, and \(v\) is the velocity of the fluid at the point perpendicular to the structural member. \[f_{c}=\frac{1}{2}\rho D_{C}C_{D}v|v| \tag{3}\]  
(2) Flow-induced inertia load  
In unsteady flow around a circular structure, the fluid exerts not only drag force but also inertial force due to the fluid's acceleration. The structure disturbs and changes the velocity of fluid particles around it. This leads to a change in the pressure distribution of the original flow field. These changes are most significant near the structure surface and gradually decrease with an increase in the distance from the structure. The decay pattern depends on the shape of the structure's cross-section and the direction of fluid flow. Therefore, the disturbance caused by the structure results in an additional inertial force on the structure from the portion of the surrounding fluid. Consequently, this change in the fluid motion state results in an additional inertial force on the structure. This force is also known as the added mass force. The true effect of the inertial force of the accelerating fluid on the structure in the direction of flow can be determined using Equation (4), as shown in the following, where \(f_{l}\) is the drag force per unit length of the structural members, and \(C_{M}\) is the inertial force coefficient. \[f_{l}=\rho C_{M}\frac{\pi D_{\rm C}^{2}}{4}\frac{du}{dt} \tag{4}\]  
The lateral load acting on the mud return pipeline is primarily caused by the seawater current's force. In the condition of pure seawater current, the Morison equation is used to calculate the water flow force on the pile column. The wave-induced force per unit length is derived from Equation (5), as shown in the following [36]:  
\[f=\frac{1}{2}\rho D_{\rm C}C_{D}\
u|\
u|+\rho C_{M}\frac{\pi D_{\rm C}^{2}}{4} \frac{du}{dt} \tag{5}\]  
For Factor 3, the relationship between the torque and the rotational speed, as well as the one between the amplitude and the rotational speed, can be obtained by carrying out experiments. These are shown in Tables 1 and 2. By using the correlated torque and amplitude data, an analysis can be conducted on the skid-and-mud-return line. ABAQUS is adopted to analyze the effect of the aforementioned factors on the stability of the skid-and-mud-return line. The mud return pipeline is made of V150 steel with an outer diameter of 168.3 mm and a wall thickness of 12.7 mm. The upper pipeline section is 700 m long, and the lower pipeline section is 1300 m long. The relevant variables for each analysis factor are shown in Table 3.
## 3 Experimental and Calculation Procedures
### Investigation Methods  
#### 3.3.1 Scanning Electron Microscopy (SEM)  
The surface morphology of the samples was studied by the field emission Scanning Electron Microscopy (SEM) employing a QuantaTM 3D FEG (FEI Company, Hillsboro, OR, USA) apparatus operating at 5 kV. A high vacuum (4 \(\times\) 10\({}^{-4}\) Pa) mode was applied for imaging the investigated samples. Prior to measurement, the samples were mounted on aluminum stubs and sputtered with gold.