esa-sceva/satcom-chunk-collection huggingface dataset:
# Analyzing Markov Boundary Discovery Algorithms in Ideal Conditions Using the d-Separation Criterion
## 4 Algorithms and Implementation
### Iamb  
The Incremental Association Markov Blanket (IAMB) [9] algorithm discovers the Markov boundary of a target variable by running two phases. In the first phase, called the forward phase, the algorithm constructs a set of candidate variables based on their correlation with the target variable. In the second phase, the algorithm eliminates the candidates that are rendered conditionally independent from the target given the other candidates [9].  
IAMB relies on conditional mutual information as a heuristic for correlation, used to determine which variables are good candidates for the Markov boundary. The CI test employed by IAMB is thresholded conditional mutual information.  
The authors of PCMB [7] have compared their algorithm to IAMB and, interestingly, they mention that IAMB uses the G-test for conditional independence. Moreover, they mention that it uses the p-value of the test as a heuristic for correlation. However, IAMB relies on conditional mutual information, as stated earlier. On the other hand, the G-test is indeed used by HITON [5] and MMMB [6], also discussed in the PCMB article [7]. This minor inconsistency is addressed here because it is present in the references of the current article.  
Regardless, IAMB can indeed be adapted to use the G-test due to the relationship of the G-test with the mutual information upon which IAMB originally relied. This replacement was made for the experiment for consistency with IPC-MB and in order to avoid having to choose a manual threshold for conditional mutual information.  
Moreover, IAMB can also use d-separation as a CI test with ease, but it still requires a heuristic for correlation. To address its need for a heuristic, a synthetic heuristic was implemented which only relies on the Bayesian network. It returns the following values:  
* The length of the shortest path between the corresponding nodes in the Bayesian network, if the two variables of interest are d-connected, given the conditioning set;
* The value 0, if the two variables of interest are d-separated, given the conditioning set.  
This replacement was possible because IAMB only uses the heuristic to compare variables among themselves. The data-processing inequality [18] makes the length of the path an acceptable replacement for this specific case.
# A Novel Resource Allocation Scheme in NOMA-Based Cellular Network with D2D Communications
### Joint User Scheduling, Power Allocation and Link Selection Algorithm  
Because the ESA, as shown in Figure 5(a), has a high complexity cost in optimizing the user scheduling and power allocation problem, it is necessary to update the power of each user after changing the assignment of SCs in each iteration process. We propose the joint user scheduling, power allocation and link selection algorithm (JUPLA), as shown in Figure 5(b), to be an alternative low-complexity approach. The proposed algorithm first solves the SC assignment problem, then allocates power to each CU and finally selects the links for DUs and outputs the network data rate. Figure 6 shows the flow chart of the proposed JUPLA.  
Figure 6: Flow chart of the exhaustive search algorithm and the joint user scheduling, tree-based search power allocation and distance-aware link selection algorithm. (**a**) Flow chart of the exhaustive search algorithm (ESA). (**b**) Flow chart of the joint user scheduling, power allocation and link selection algorithm (JUPLA).
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
## 3 The Composition of the INS
### INS Components
#### 3.1.8 Electronic Chart Display and Information System (ECDIS)  
The essential function of the ECDIS is to support safe navigation. It should allow route planning, route monitoring, and positioning as an alternative to the paper chart. Voyage records should be also stored. Independent ECDIS back-up may be arranged to ensure safe navigation in case of a potential ECDIS failure. The performance standards of the ECDIS vary with the installation date. The latest performance standards of the ECDIS are described in Resolution MSC.232(82); these are applicable to the ECDIS systems that were installed on or after 1 January 2009 [57].  
The ECDIS is an essential component for digital navigation in contemporary maritime, as its use reduces the workload of an OOW [57]. Navigation safety is also enhanced, as the ECDIS provides the OOW with real-time navigation information, such as position of own vessel and all chart information. Moreover RADAR layout, RADAR tracked target information, and AIS data may be displayed on the ECDIS screen [57]. Two ECDISs are deployed in the bridge of modern vessels, as primary and back-up. If the back-up ECDIS does not exist, paper charts should be in place. An updated electronic chart of the navigation zone should be imported to the ECDIS.
## 3 Numerical Simulations
### Parameter Identification with Different Integration Methods
#### 3.2.1 Stiffness Identification  
Identification results of stiffness and damping of the four-DOF system were used to compare the effect of the three integration methods. During such an analysis, the integration time step is adopted with a small value of 0.02 s. To make things easier, the influence of noise is not considered here. The estimation results of the stiffness by the three integration methods are shown in Figure 3. Obviously, the identification results by the three integration methods are in good agreement with the actual value, and the estimation precisions are all higher than 98%.  
Moreover, the effect of time step on the identification results of the stiffness by the three integration methods was also discussed, in which the \(2^{\text{nd}}\) story was selected as the analyzing target. From Figure 4, it can be found the relative errors of stiffness estimates are very small, and even the simple rectangular integral can achieve good results. Nevertheless, the error slightly increases with the increased time step, especially when the time step is larger than 0.005 s.
## 4 Methods
### Dataset  
In the experimental design, each stage will be documented sequentially using Jupyter Notebooks, to ensure reproducibility of the experiments and to have a detailed record of each step executed. The phases considered in the experimental process are described below. The first corresponds to data analysis and cleaning, where the datasets selected for the experiments will be examined, applying purification techniques to leave them in optimal conditions for training Machine-Learning (ML) and/or Deep-Learning (DL) models. The datasets used in the experiments are those most commonly employed in recent studies focused on DDoS attack detection in IoT environments, as previously described in Section 3.1. Specifically, the selected datasets are as follows:  
* CIC-DDoS2019: Dataset containing network traffic from DDoS attacks and normal traffic. It includes over 12 million labeled records representing both benign and malicious flows, captured in a realistic testbed environment. The attacks simulated cover various DDoS types, such as UDP Flood, TCP SYN, ICMP Flood, HTTP Flood, LOIC, Slowloris, and Hulk. Each record was extracted from PCAP files using CICFlowMeter and included more than 80 flow-based features, making it suitable for training and evaluating machine-learning models for DDoS detection.
* N-BaIoT: Dataset containing network traffic from IoT devices compromised by botnets, as well as normal traffic. It includes over 5 million labeled instances collected from real IoT devices such as cameras, doorbells, and smart thermostats operating under benign conditions and during infections by Mirai and Bashlite botnets. The traffic was captured and processed using Argus and Bro/Zeek tools, generating flow-based features including duration, byte counts, packet rates, and connection flags. This dataset supports the development and evaluation of anomaly detection models in IoT environments focused on identifying botnet activity.  
Table 3 compares the CIC-DDoS2019 and N-BaIoT datasets, highlighting their differences in approach, labeling, devices, attack types, data volume, and feature extraction tools. This comparison allows us to identify their applicability in detecting DDoS attacks on traditional and IoT networks.
## 3 Methods
### Network and User Processing  
In this chapter, the methods to process the non-rank-deficient observation, as given by Equation (2), are presented. This entails the estimation of the parameters, the modeling of their temporal behavior, the stochastic modeling of the observations, and the strategy to compute the ambiguity-fixed solution.  
The observations are processed sequentially with a Kalman filter. Only the forward-filtered results are considered here to analyze the implemented methods for real-time applications. Note that only the final satellite orbits used here are not eligible for real-time processing. All other information could be made available in real-time. An overview of the processing scheme is given in Figure 3. This processing scheme is valid for the network and the user. Note that at the network level, the coordinates are not estimated, but are constrained by their reference values. At the user level, receiver coordinates are estimated and, in this study, compared to their reference values afterward.  
Figure 2: Connectivity in GNSS Networks: (**a**) For the third satellite from the left, observations on two frequencies (“freq”) do not exist (or one code and phase observation). As a result, the ionospheric slant delay cannot be separated from the satellite clock (when no temporal constraints are imposed), and observations from this satellite are rank-deficient with the CC-model. (**b**) A connected graph and its ST. If this is the graph of a specific phase signal, e.g., GPS L5, then the ambiguities belonging to the ST could be used as an S-basis restriction, and the remaining ambiguities would be integer estimable.
# Joint Geoeffectiveness and Arrival Time Prediction of CMEs by a Unified Deep Learning Framework
## 2 Methods
### Geoeffectiveness Prediction of CMEs
#### 2.1.2 Feature Map Fusion Module  
The feature map fusion module is proposed to solve the situation that the number of images collected in each CME event is not fixed. The illustration of the feature map fusion module is shown in Figure 3. When the deep residual network embedded with the attention mechanism extracts the feature map for each input image in the CME event, the feature map fusion module fuses the feature map of each image into a fused feature map based on the attention mechanism. It can be divided into three steps:  
1. Calculate the importance of each point in the feature map based on the attention mechanism. The module attention mechanism can focus limited attention on important regions. Through the above-mentioned channel attention module and spatial attention module, the feature map attention weights of the different channels axes and spatial axes are obtained, then the two attention weights are combined by adding to obtain the each point importance of the feature map.
2. Adjust the point importance of each feature map in combination with the mutual influence between the time series feature maps. If there are \(N\) feature maps, for each point coordinate \((x,y,z)\) of the \(i\)th feature map, it will adjust its weight through the weight of the same position in other feature maps as follows: \[Wi_{x,y,z}=\frac{e^{Wi_{x,y,z}}}{\sum_{j=1}^{N}e^{Wi_{x,y,z}}},\] (4) where \(Wi_{x,y,z}\) represents the importance of the point whose coordinates of the \(j\) feature map is \((x,y,z)\). Here, assuming that the shape of the feature map is \(\mathbb{R}^{H\times W\times C}\), then \(x\in[0,\ H)\), \(y\in[0,\ W)\), \(z\in[0,\ C)\).
3. Fuse all feature map weights and point importances into one feature map. We multiply the point importance of each feature map with the original feature map weights to obtain the contribution of each feature map in the final fused feature map, and add the contribution of each feature map to get a fused feature map.
# Recognition Algorithm Based on Improved FCM and Rough Sets for Meibomian Gland Morphology
## 1 Introduction
### Fcm
#### 2.2.2 Improved FCM Algorithm  
The FCM algorithm is very simple and does not need to conduct large-scale operations because the establishment of the sample category's fuzzy description can well reflect the objective world. However, when dealing with practical applications, there are still some problems [22].  
First, the FCM algorithm must give the initial cluster center before clustering. Like most nonlinear optimization problems, the FCM clustering effect is directly affected by the initial value.  
Second, the FCM algorithm has a good effect on data with strong regularity distribution. However, when the samples contain noise, the clustering center is shifted to the noise point, and even the noise will be selected as the cluster center, which seriously affects the clustering effect.  
To solve these problems, we proposed a method for selecting the initial clustering center based on distance. Our clustering results were globally optimal. According to the [PERSON]'s criterion [23; 24], the noise point in the data is defined as follows: the deviation between the point and the mean is more than twice the standard deviation of the samples. In this paper, we located the noise points according to the sample's distance, and then dealt with them to make the algorithm insensitive to noise points.  
Before introducing the improved algorithm, several related concepts are introduced as follows:  
1. Distances between samples (Euclidean distance): \[d(x_{i},x_{j})=\sqrt{|x_{i1}-y_{j1}|^{2}+  
1. Calculate the mean distance \(m_{i}\) from sample \(x_{i}\) to other samples, generate the sample distance vector, and take the sample point with the smallest mean distance as the first cluster center;
2. Calculate the mean of the distances of all samples \(d\), mark the sample point \(x_{i}\), which satisfies \(m_{i}>2d\) as the noise point, and put the noise point into a separate set;
3. Use the distance vector to determine the non-isolated samples whose distance from the first clustering center is larger than \(d\), and choose the second cluster center with the smallest mean distance from the first center from among these samples.
4. Repeat step 3 until \(c\) cluster centers are found.
5. According to the distance, classify the noise points to the corresponding classification.  
The operation flow of the improved FCM algorithm is shown in Figure 2.
# A Robust Hybrid Iterative Linear Detector for Massive MIMO Uplink Systems
## 6 Conclusions  
This paper proposes a robust hybrid iterative linear detector for mMIMO systems when BUAR is relatively small. Proposed detector utilizes the advantages of iterative methods such as the JA and the GS methods. In addition, a stair matrix structure is also exploited to initialize the proposed detector. Numerical results show that the proposed detector obtained the target performance with the lowest complexity. The JA and the NS methods are not suiting the detector design when the BUAR is small.  
Utilization of hybrid iterative methods could be extended to propose efficient hybrid detectors such as the JA-SOR, JA-RI, GS-RI, and RI-SOR. In addition, early mentioned hybrid detectors could be initialized based on the stair matrix structure. The performance-complexity profile of such detectors has to be tested in realistic radio channels, i.e., the QUAsi Deterministic RadIo channel GenerAtor (QuaDRiGa) package.  
Conceptualization, M.A.A.; methodology, M.A.A.; software, M.A.A.; validation, M.H.A.; formal analysis, M.A.A.; project administration, M.A.A. and S.K.; funding acquisition, S.K. All authors have read and agreed to the published version of the manuscript.  
The research leading to these results has received funding from the Research Council (TRC) of the Sultanate of Oman under the Block Funding Program. TRC Block Funding Agreement No. BFP/RGP/ICT/18/079). This work was supported by the Research Program through the National Research Foundation of Korea (NRF-2019 RIA2C1005920).  
The authors declare no conflict of interest.
## 3 Home Area Network
### Smart Home HAN Requirements
#### 3.2.2 Network Operation  
Interoperability  
The interoperability and completeness of the infrastructure strongly affects the effectiveness and efficiency of the overall performance of the system. Smart home elements involve a number of common and interoperable standards for communications purposes. To integrate the various technologies and communications protocols, this kind of capacity is desirable with the aim of mutual recognition and offering continuous data transference. Inefficient and deficient integration and interaction among elements could delay the response time and also damage the global system's throughput and operation.  
Ad Hoc Deployment  
Since it is important for different network devices to be deployed in a non-structured mode and spread through the house, several wireless HAN applications lack the requirements to predetermined locations of individual stations. Nodes have to perform many setup and configuration steps autonomously. Those steps should incorporate the establishment of communications with near sensor nodes and find out their positions in order to start their sensing responsibilities. The information available directly affects the variability of the mode of the sensor nodes' operation.  
Self-Management  
Various sensor network applications are programmed to operate with no infrastructure support or the prospect of maintenance and repair. Consequently, in order to configure themselves, operate and collaborate with others, to adapt to failures, changes in the environment or environmental stimuli, it is a requirement of the sensor nodes to be self-managed, which means to be completely independent of human intervention.  
Maintainability  
Maintainability is a requirement that essentially reflects how durable and reliable the HAN is. The environment can change, which means depleted batteries, failing nodes and new tasks. Thus, HAN units have to monitor their own health and status in order to change operational parameters or to choose different trade-offs, such as providing lower quality when energy resource becomes scarce. In order to repair quickly and cost-effectively the various devices and communication components, the HAN must be designed for the purpose of an easy maintenance.
# ViT-Cap: A Novel Vision Transformer-Based Capsule Network Model for Finger Vein Recognition
## 3 Materials and Methods
### Motivation  
At present, CNNs are the dominant deep neural network architectures in computer vision. From image classification [26], object detection [27], and image segmentation [28] to action recognition [29], various types of CNNs have proved their effectiveness in these computer vision tasks and have also shown good performance in the field of finger vein recognition. Although CNN offers translation invariance, the translation invariance it provides through pooling technology is limited, which is also the reason why CNN needs much data rotated from different viewpoints.
# Semi-CNN Architecture for Effective Spatio-Temporal Learning in Action Recognition
## 3 Proposed Architecture
### Comparison with 2D and 3D CNN  
This section presents a comparison of the layers configuration in 2D, 3D and our semi-CNN, for models VGG, ResNet and DenseNet. The network configuration was simplified and presented in Table 1 for easy reference and comparison.  
The notation for the configuration setting for semi-CNN was set as ([ss], \(tt\), \([st]\)), where \(ss\) denotes the number of spatial layers with convolutional kernel (\(1\times x\times x\)), \(tt\) denotes the number of temporal encoding layers with kernel size (\(x\times 1\times 1\)) and \(st\) denotes the number of spatio-temporal layers with convolutional kernel size of (\(x\times x\times x\)). The value of convolutional kernel size, \(x\), was set with reference to the configurations in the 2D base model. For instance, the spatial kernel size for semi-VGG-16 was (\(1\times 3\times 3\)), while for semi-ResNet-18, the kernel size for the first spatial convolutional block was (\(1\times 7\)\(\times\) 7). The notation [...] denotes a building block configuration that consists of a stack of convolutional layers. The value shown inside the bracket denotes the number of blocks constructed in the network. Both ResNet and DenseNet models were constructed with building blocks.  
For VGG model, we compared with 2D VGG-16 [20], a 3D convolution network (C3D) [7] and our semi-VGG-16. C3D had 11 layers (eight spatial layers and three fully-connected layers), while VGG-16 had 16 layers (13 spatial layers and three fully connected layers) in their networks.  
For ResNet, we compared with 2D ResNets [14], 3D ResNets [8] and our semi-ResNets. ResNet models were constructed with two types of residual blocks [14]--basic block and bottleneck block. Semi-ResNet was constructed with reference to the building blocks used in the 2D model.  
Comparison of DenseNet is the 2D model [21], 3D model (derived from 2D network) and our semi-DenseNet model. Similarly, the network configuration is represented by a stack of building blocks that consists of densely connected layers. Dense connections between layers in each block were preserved in the semi-CNN architecture.  
The configurations described in Table 1 were not optimized, but have shown reasonably good performance when validated in the experimental section. A detailed architecture comparison could be found in Table 1 in the Appendix A.
## 6 Performance Analysis and Comparison  
In this section, we present performance analysis and a comparison of the computation cost in the authentication and key establishment phase. We investigated the key management of the smart grid based on ECC, the certificateless approach and blockchain technology to complete the comparison with our scheme.  
Table 2 shows the execution time of different cryptographic elements which are based on an Alibaba cloud sever that has a 2.9 GHz Intel Xeon E3-1240v6 processor and 2 GB random access memory. The results presented in Tables 2 and 3 show the comparative computational cost. In this article, (5) and (6) need to be calculated twice, and (6), (7) and (8) need to be calculated once to complete the whole authentication process; thus, the computational cost of our method is 12 \(T_{mul}+2\)\(T_{add}+6\)\(T_{h}\).  
The results show that although our scheme has little improvement in computational overhead compared with [5], it shows a sharp improvement compared with [11; 19], and we provides key escrow flexibility and a trusted key distribution process compared with [5].