esa-sceva/satcom-chunk-collection huggingface dataset:
# An Analysis of Cloud Security Frameworks, Problems and Proposed Solutions
## 3 Cloud Security Frameworks
### Cloud Security Alliance (STAR)  
The Cloud Security Alliance (CSA) created the CSA STAR (Security, Trust, and Assurance Registry) architecture to address the requirement for transparency and assurance in cloud service provider (CSP) security. It offers a collection of best guidelines for identifying and managing the security risks associated with cloud services [33].  
**Cloud Security**: The framework focuses on safeguarding cloud-based systems and data from unauthorized access, data breaches, and other security risks.  
**Trust**: It is critical to establish trust between cloud service providers and cloud clients. The framework's goal is to increase openness, accountability, and trust in the cloud environment.  
**Assurance**: Providing proof and validation of security controls and practices is what assurance is all about. The framework encourages third-party audits and certifications to improve cloud security assurance.
# Fault Location and Restoration of Microgrids via Particle Swarm Optimization
## 5 Numerical Results and Discussions
### Islanding Operation  
A single-line diagram of the sample system under islanding operation mode is shown in Figure 8. In islanding operation mode, the available power output of the DGs plays a key role in ensuring the stable operation of the system because no utility power grid acts as a swing bus. System frequency and voltage magnitude are kept constant in accordance with the power balance principle. Consequently, if the power generation of DGs is greater than the load demand, then the optimal dispatch method automatically adjusts the power output to meet the power balance requirement. Otherwise, the load shedding strategy should be used to maintain power balance. The loads are classified into critical and common loads. The symbol of critical load is expressed as \"O,\" and the symbol of non-critical load is represented as \"\(\gg\)\" (Figure 8). The first priority of load shedding is non-critical load and then critical load. The simulation results of the sample system and the assumed scenarios are listed in Table 4. In Scenario 1, the fault is detected in line segment 702-703 according to the minimal value of voltage deviation in the **VEM**. The tie switch S39 is closed to form  
Figure 7: Network topologies, voltage errors, and convergence characteristics of the sample system after service restoration.  
a network topology (Figure 8). The service restoration percentage is 83.61% because the power output of the DGs is less than the load demand. Load shedding is required to keep the power balance. In addition, the power loss is 22.27 kW, and all the line currents and bus voltages satisfy their limits. In Scenario 2, these two fault points are detected by two minimal values in the **VEM**. Then, two tie switches, namely, S38 and S39, are closed to form the network topology (Figure 8). The service restoration percentage is 83.61%, the power loss is 22.41 kW, all the line currents are less than 300 A, and the bus voltages are within 0.
## 2 Materials and Methods
### Statistical Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline
**Postural and Muscular** & **Before Intervention** & **After Intervention** & **Z Score** & **\(p\)** & **ES** \\
**Asymmetries** & **Median [IQR]** & **Median [IQR]** & **Z Score** & **\(p\)** & **ES** \\ \hline Standing height (cm) & 166.30 [9.57] & 168.16 [9.14] & \(-7.153\) & 0.001 & \(-0.80\) \\ \hline Thoracic kyphosis (\({}^{\circ}\)) & 43.25 [8.87] & 42.68 [6.73] & \(-0.808\) & 0.419 & \(-0.09\) \\ \hline Lumbar lordosis (\({}^{\circ}\)) & 31.39 [7.17] & 29.76 [7.16] & \(-1.927\) & 0.054 & \(-0.22\) \\ \hline Spinal rotation (A) (\({}^{\circ}\)) & 2.33 [2.29] & 1.25 [1.87] & \(-4.987\) & 0.001 & \(-0.56\) \\ \hline Spinal rotation (B) (\({}^{\circ}\)) & 0.19 [0.73] & 0.05 [0.35] & \(-1.444\) & 0.149 & \(-0.16\) \\ \hline Pelvic tilt (\({}^{\circ}\)) & 19.84 [3.71] & 19.88 [2.97] & 0.000 & 1 & 0 \\ \hline Right hamstring (90-90 test) (\({}^{\circ}\)) & 30.69 [12.60] & 23.75 [11.62] & \(-4.957\) & 0.001 & \(-0.55\) \\ \hline Left hamstring (90-90 test) (\({}^{\circ}\)) & 33.44 [12.94] & 27.19 [13.36] & \(-4.306\) & 0.001 & \(-0.48\) \\ \hline Right hip abduction (\({}^{\circ}\)) & 45.75 [6.76] & 45.88 [5.67] & \(-0.151\) & 0.880 & \(-0.02\) \\ \hline Left hip abduction (\({}^{\circ}\)) & 46.41 [5.34] & 47.06 [5.44] & \(-0.923\) & 0.356 & \(-0.10\) \\ \hline Right hip external rotation (\({}^{\circ}\)) & 29.13 [4.27] & 32.31 [4.35] & \(-4.137\) & 0.001 & \(-0.46\) \\ \hline Right hip internal rotation (\({}^{\circ}\)) & 25.94 [5.16] & 28.00 [6.68] & \(-2.304\) & 0.021 & \(-0.26\) \\ \hline Left hip external rotation (\({}^{\circ}\)) & 25.63 [6.43] & 27.50 [5.63] & \(-2.325\) & 0.020 & \(-0.26\) \\ \hline Left hip internal rotation (\({}^{\circ}\)) & 28.13 [4.99] & 30.44 [4.22] & \(-3.168\) & 0.002 & \(-0.35\) \\ \hline Right leg length (cm) & 92.21 [5.76] & 92.26 [5.76] & \(-0.905\) & 0.366 & \(-0.10\) \\ \hline Left leg length (cm) & 92.34 [5.73] & 92.46 [5.79] & \(-2.460\) & 0.014 & \(-0.28\) \\ \hline Stork Test—opened eyes right leg (s) & 26.72 [6.95] & 28.33 [5.79] & \(-2.940\) & 0.003 & \(-0.33\) \\ \hline Stork Test—opened eyes left leg (s) & 26.46 [6.99] & 28.24 [5.65] & \(-2.986\) & 0.003 & \(-0.33\) \\ \hline Stork Test—closed eyes right leg (s) & 10.69 [8.74] & 14.28 [10.31] & \(-3.515\) & 0.001 & \(-0.39\) \\ \hline Stork Test—closed eyes left leg (s) & 9.69 [9.19] & 13.24 [10.30] & \(-3.833\) & 0.001 & \(-0.43\) \\ \hline \end{tabular} ES: Effect Size estimate.  
\end{table} 
Table 3: Descriptive data and Wilcoxon Test. Figure 3: Deep squat: pre- and post-intervention results, \(\hat{\gamma}\) = effect size. Figure 2: Ankle passive dorsiflexion: pre- and post-intervention results, V = effect size.
# Design of Encoding Algorithm for Underwater Wireless Optical Communication Based on Spinal Code
## 2 Spinal Code Encoding Algorithm
For BSC (Binary Symmetric Channel) and BEC (Binary Erasure Channel), \(c=1\) and \(x_{i,j}=\) b can be chosen. This means that the single bit produced by the mapper is directly transmitted as the channel input to the receiver. For the AWGN channel, the mapper can employ either truncated Gaussian mapping or uniform mapping. \[\mathrm{x}_{i,j}=\Phi^{-1}(\gamma+(1-2\gamma)\mu)^{\sqrt{P}} \tag{3}\]  
where \(u=(b+1/2)/2^{c}\), and \(\Phi^{-1}(\cdot)\) is the inverse of the CDF of a standard normal variable. Here, \(\gamma=\Phi(-\beta)\), where \(\beta\) determines the truncation range, limiting the mapped results to the interval \([-\beta\sqrt{P},\beta\sqrt{P}]\), and \(P\) is the average power of the transmitted signal. When the parameter \(\beta\) and \(c\) approach infinity, the symbols generated by the truncated Gaussian mapping function are equivalent to a Gaussian signal with power \(P\). For uniform mapping in the AWGN channel, the output symbol is  
\[\mathrm{x}_{i,j}=(u-1/2)\sqrt{12P} \tag{4}\]  
Figure 1 provides a block diagram of the rateless Spinal code encoder. The encoding process of Spinal codes can be divided into the following steps:  
Step 1: The \(n\)-bit information sequence \(m=(m_{1},m_{2},\ldots,m_{n})\) is uniformly divided into \(d\) non-overlapping blocks, each containing \(k\)-bits. Thus, the total number of blocks is \(d=n/k\), and the partitioned sequence is \(m=(m_{1},m_{2},\ldots,m_{d})\). Step 2: The sender and receiver are initialized with the same hash function value \(s_{0}\). The \(d\) information segments \(m_{1},m_{2},\ldots,m_{d}\) are sequentially fed into the hash function according to the following equation:  
\[s_{i}=h(s_{i-1},m_{i}),1\leq i\leq d \tag{5}\]  
This generates \(d\) new hash states, as follows: \(s_{1},s_{2},\ldots,s_{d}\). Step 3: Based on each node state value \(S_{i}\) where \(i\)\(\in\)\(\{1\),\(2\),..., \(d\}\), the encoder invokes the RNG to generate a series of \(c\)-bit binary sequences. Step 4: All the generated bit sequences are input into the mapper, which maps them into a series of numerical values. This sequence constitutes the channel input symbols, and the encoded symbol sequence is \(x=(x_{1},x_{2},\ldots,x_{d})\). The numerical sequence generated in the \(l\)-th pass is denoted as \((x_{1,l},x_{2,l},\ldots,x_{d,l})\), where \(x_{i,l}\) represents the \(l\)-th element of the sequence \(x_{i}\). The transmitter stops sending encoded packets upon reaching this information; i.e., no further packets are added.
###### Abstract  
This study proposes an action identification system for home upper extremity rehabilitation. In the proposed system, we apply an RGB-depth (color-depth) sensor to capture the image sequences of the patient's upper extremity actions to identify its movements. We apply a skin color detection technique to assist with extremity identification and to build up the upper extremity skeleton points. We use the dynamic time warping algorithm to determine the rehabilitation actions. The system presented herein builds up upper extremity skeleton points rapidly. Through the upper extremity of the human skeleton and human skin color information, the upper extremity skeleton points are effectively established by the proposed system, and the rehabilitation actions of patients are identified by a dynamic time warping algorithm. Thus, the proposed system can achieve a high recognition rate of 98% for the defined rehabilitation actions for the various muscles. Moreover, the computational speed of the proposed system can reach 125 frames per second--the processing time per frame is less than 8 ms on a personal computer platform. This computational efficiency allows efficient extensibility for future developments to deal with complex ambient environments and for implementation in embedded and pervasive systems. The major contributions of the study are: (1) the proposed system is not only a physical exercise game, but also a movement training program for specific muscle groups; (2) The hardware of upper extremity rehabilitation system included a personal computer with personal computer and a depth camera. These are economic equipment, so that patients who need this system can set up one set at home; (3) patients can perform rehabilitation actions in sitting position to prevent him/her from falling down during training; (4) the accuracy rate of identifying rehabilitation action is as high as 98%, which is sufficient for distinguishing between correct and wrong action when performing specific action trainings; (5) The proposed upper extremity rehabilitation system is real-time, efficient to vision-based action identification, and low-cost hardware and software, which is affordable for most families.  
upper extremity identification; color and depth sensors; skeleton points; rehabilitation actions; home rehabilitation; computer vision 2018 8 1161; doi:10.3390/app8071161
# Heart Sound Classification Using Wavelet Analysis Approaches and Ensemble of Deep Learning Models
## 3 Proposed Method
### Signal Segmentation  
To classify diseases and heart sounds using PCG signals, preprocessing was performed to adjust the original signals containing various signal lengths to the same signal length. Additionally, because PCG signals occur differently for each person depending on the location, shape, size, and health status of the heart and heart valves, features in specific areas can be extracted in more detail through signal segmentation [24]. PCG signals are composed of various signal lengths, and by adjusting the original signal length to a constant length, important features in the signal can be confirmed. Information loss can occur in the process of cropping a long signal to fit a short signal or increasing a short time to a long signal. In addition, if a long signal is used as is without dividing it, important features and patterns that appear minutely in the S3 and S4 sections of the PCG signal, which may indicate heart-related diseases, may be missed; thus, a segmentation process is performed for accurate analysis and diagnosis results. To perform signal segmentation, each dataset is read, the division time interval of the original PCG signal is set, and the division is automatically performed using MATLAB's 'for' loop so that the divided signals do not overlap. The signal divided from the original PCG signal corresponding to each class is saved as a new WAV file for that class, and the signal's label is designated as the label belonging to each class. The minimum signal of the PhysioNet/CinC 2016 Challenge Dataset was 5.31 s, cut into 5-s segments and stored, and signals shorter than 5 s were not used. When dividing the PASCAL Classifying Heart Sounds Challenge Dataset A and Dataset B into 1 s, which is the minimum time, a signal that does not include the components S1, S2, S3, and S4 cycles of the PCG signal was included; therefore, it was divided into 3 s intervals. Figure 2 shows the signal segmentation process for the Abnormal and Normal classes of the PhysioNet/CinC 2016 Challenge Dataset.
# Partial Order Rank Features in Colour Space
## 2 Background
### Partial Orders
#### 2.5.2 Loewner Order  
The Loewner (partial) order is defined on symmetric matrices. Given two symmetric matrices \(\mathbf{A}\), \(\mathbf{B}\) we write:  
\[\begin{cases}\mathbf{A}\preceq_{\mathcal{L}}\mathbf{B}&\text{if }\,(\mathbf{B}- \mathbf{A})\in\mathcal{S}_{+}\\ \mathbf{A}\
sim_{\mathcal{L}}\mathbf{B}&\text{if neither }\mathbf{A}\preceq_{ \mathcal{L}}\mathbf{B}\text{ nor }\mathbf{B}\preceq_{\mathcal{L}}\mathbf{A}\end{cases} \tag{2}\]  
where \(\mathcal{S}_{+}\) indicates the set of positive semi-definite matrices. Applying this to a colour space requires mapping colour values to symmetric matrices. Following [5], we start from a modified colour space HCL obtained from HSL ([25], Section 4.6) by setting \(\bar{L}=2L-1\) for the (modified) luminance and replacing saturation with chroma \(C=\max\{R,G,B\}-\min\{R,G,B\}\). The resulting colour gamut fills  
Figure 1: Product order in the RGB space: A generic colour \((r_{0},g_{0},b_{0})\) dominates all the colours in the blue volume and is dominated by all the colours in the red volume.  
a bicone with axis \(\bar{L}\) and opening angle \(90^{\circ}\). We isometrically map colours to the space \(Sym(2)\) of symmetric \(2\times 2\) matrices by setting [5]:  
\[\mathbf{M}\left(h,c,\bar{l}\right)=\frac{1}{\sqrt{2}}\begin{pmatrix}\bar{l}-c&h \\ h&\bar{l}+c\end{pmatrix}. \tag{3}\]  
For two colours \(\mathbf{u}=\left(h_{u},c_{u},\bar{l}_{u}\right)\) and \(\mathbf{v}=\left(h_{v},c_{v},\bar{l}_{v}\right)\) in the HC\(\bar{L}\) space we therefore write:  
\[\begin{cases}\mathbf{u}\preceq_{L}\mathbf{v}&\text{if }\mathbf{M}\left(h_{v},c_{v}, \bar{l}_{v}\right)\preceq_{\mathcal{L}}\mathbf{M}\left(h_{u},c_{u},\bar{l}_{ u}\right)\\ \mathbf{u}\
sim_{L}\mathbf{v}&\text{if neither }\mathbf{u}\preceq_{L} \mathbf{v}\text{ nor }\mathbf{v}\preceq_{L}\mathbf{u}\end{cases} \tag{4}\]  
where \(\preceq_{\mathcal{L}}\) is defined in Equation (2). Geometrically (Figure 2), a given colour \(\mathbf{v}\) weakly dominates all colours of lower luminance that fall in a cone with its vertex in \(\mathbf{v}\) and its axis parallel to the \(\bar{L}\) axis.
## 8 Numerical Results and Discussion
### The SNR in the VLC Scenario Without IRS Position Optimization  
However, SBC mechanisms perform poorly compared to MRC, as seen in pyramidal and hemispherical ADRs with SBC, which share maximum and minimum SNR values of 60.78 dB and 38.62 dB, respectively. These configurations, while improving the single-PD setup, exhibit limited robustness in maintaining a high SNR across the coverage area. The results underscore the critical role of ADR geometry and signal processing mechanisms in optimizing the SNR distribution. MRC is proving particularly effective in mitigating spatial variability in the SNR and ensuring uniform communication performance in indoor VLC systems.
# A New Intra-Cluster Scheduling Scheme for Real-Time Flows in Wireless Sensor Networks
## 3 Performance Evaluation
### Impact of Intra-Cluster Time Slots  
In this section to analyze the performance of the intra-cluster time slots. The numbers of IntraSend and IntraRecv time slots were varied from 1 to 3. The numbers of clusters and flows are fixed to 6. As shown in Figure 7a-c the proposed scheme has a high acceptance rate than flows in the previous scheme. The reason is, for IntraRecv scheduling has allocated 1 slot to schedule flows which is insufficient. Therefore, in the proposed scheme if the clusters have only incoming flows then that clusters can utilize the IntraSend time slots.  
Figure 6: Simulation results for w.r.t Clusters.  
In Figure 8a,b the proposed scheme has also a high acceptance rate than flows in a previous scheme. The reason is, for IntraSend scheduling has allocated 1 slot to schedule flows which is insufficient. Therefore, in the proposed scheme the clusters that have only incoming flows then that clusters can utilized the IntraSend times time slots. In contrast to the proposed scheme, the previous scheme for the clusters that have outgoing flows then the IntraSend scheduling cannot utilize the IntraRecv time and vice versa.
# HMD-Based VR Tool for Traffic Psychological Examination: Conceptualization and Design Proposition
## 3 TPE HMD-Based Tool Concept and Design
### User Interface  
In order to test the proposed tool, we would use the HMD technology with original embedded eye-tracking technology HTC VIVE pro eye, which provides a satisfactory resolution of 1440 \(\times\) 1600 pixels per eye (2880 \(\times\) 1600 pixels combined), refresh rate 90 Hz and field of view 110 degrees. The device is equipped with several sensors: SteamVR Tracking, G-sensor, gyroscope, proximity, IPD sensor and eye tracking. Eye fixation on relevant objects was found to be related to driver's situation awareness [70; 71], as discussed above. ET provides binocular 120 Hz gaze data output with accuracy 0.5\({}^{\circ}\)-1.1\({}^{\circ}\) and 5-point calibration. Trackable field of view covers the whole 110\({}^{\circ}\). The ET data output provides gaze origin, gaze direction and pupil position, pupil size and blinks (eyes openness). Headphone systems allow for auditory instruction (Figure 4, left). Since a movement metaphor (i.e., UI control of the interface) was identified as problematic regarding REAL-VR equivalence for the majority of previous studies, the current VR TPE tool was complemented with the corresponding haptic interface--i.e., Thrustrmaster T300 RS--which represents basic, but fully functional cockpit interface, including all crucial control aspects (Figure 5).  
Figure 4: Hypothesis of SEM diagram capturing VR-based TPE measures.  
The testing environment will be created using UNITY software and will employ a simple driving scenario, where several driving actions are measured concurrently with the driver's behavioral and sensori-motor reactions. The instruction will be given by auditory input announced via embedded headphones. The simulation will be completed with the basic physics of the car movement and noises corresponding to the driving activity. Meaningful driving environments with an adequate number of visual stimuli can be adopted using graphical assets from the Unity Asset Store [117]. The virtual environment employed embedded interaction and logging components including eye-tracking, which is more deeply introduced in [PERSON] and colleagues [18]. The environment offers a controllable and persistent environment where the specific road characteristics are not manipulated. Regarding used technologies, the interface includes head tracking, eye tracking, haptic control and pedal-type inputs. The sensory outputs are visual, auditory and servo motors provide haptic feedback in the form of steering wheel resistance. Concurrent triangulation, automatic logging and UI sensors allow us to record robust data about human activity for further analysis. The measured variables were identified with respect to significant predictors of real driving abilities and they are presented in Table 3.
# XLNet-Based Prediction Model for CVSS Metric Values
## 4 Experiments and Results
### Analysis of Results
\begin{table}
\begin{tabular}{c l c c c c c} \hline \hline
**Metric** & **Model** & **AV** & **AC** & **Au** & **C** & **I** & **A** \\ \hline \multirow{4}{*}{Accuracy} & Our Method & 0.971 & 0.917 & 0.960 & 0.907 & 0.917 & 0.895 \\  & 1-L CNN [22] & / & / & / & / & / \\  & 2-L CNN & / & / & / & / & / \\  & 1-L BiLSTM & / & / & / & / & / \\  & 2-L BiLSTM & / & / & / & / & / \\  & 1-L Attention-Based BiLSTM & / & / & / & / & / \\  & 2-L Attention-Based BiLSTM & / & / & / & / & / \\ \hline \multirow{4}{*}{Precision} & Our Method & **0.922** & **0.871** & 0.757 & **0.883** & **0.893** & **0.874** \\  & 1-L CNN & 0.887 & 0.806 & 0.836 & 0.772 & 0.778 & 0.768 \\  & 2-L CNN & 0.819 & 0.660 & 0.737 & 0.694 & 0.706 & 0.683 \\  & 1-L BiLSTM & 0.878 & 0.796 & 0.826 & 0.794 & 0.803 & 0.787 \\  & 2-L BiLSTM & 0.903 & 0.772 & **0.843** & 0.730 & 0.749 & 0.719 \\  & 1-L Attention-Based BiLSTM & 0.887 & 0.811 & 0.839 & 0.797 & 0.808 & 0.798 \\  & 2-L Attention-Based BiLSTM & 0.892 & 0.823 & 0.835 & 0.798 & 0.814 & 0.801 \\ \hline \multirow{4}{*}{Recall} & Our Method & 0.897 & 0.811 & 0.755 & **0.883** & **0.892** & **0.875** \\  & 1-L CNN & 0.892 & 0.779 & 0.847 & 0.771 & 0.775 & 0.767 \\  & 2-L CNN & 0.837 & 0.645 & 0.702 & 0.664 & 0.702 & 0.679 \\  & 1-L BiLSTM & 0.891 & 0.787 & 0.844 & 0.791 & 0.801 & 0.782 \\  & 2-L BiLSTM & **0.913** & 0.771 & **0.855** & 0.715 & 0.742 & 0.697 \\  & 1-L Attention-Based BiLSTM & 0.896 & 0.805 & 0.852 & 0.794 & 0.807 & 0.793 \\  & 2-L Attention-Based BiLSTM & 0.901 & **0.817** & 0.848 & 0.797 & 0.813 & 0.798 \\ \hline \multirow{4}{*}{F1} & Our Method & **0.909** & **0.831** & 0.756 & 0.883 & 0.892 & 0.874 \\  & 1-L CNN & 0.873 & 0.795 & 0.816 & 0.766 & 0.767 & 0.765 \\  & 2-L CNN & 0.820 & 0.634 & 0.710 & 0.655 & 0.697 & 0.678 \\ \cline{1-1}  & 1-L BiLSTM & 0.879 & 0.785 & 0.818 & 0.791 & 0.801 & 0.783 \\ \cline{1-1}  & 2-L BiLSTM & 0.905 & 0.768 & 0.825 & 0.716 & 0.744 & 0.702 \\ \cline{1-1}  & 1-L Attention-Based BiLSTM & 0.887 & 0.804 & **0.840** & 0.795 & 0.807 & 0.795 \\ \cline{1-1}  & 2-L Attention-Based BiLSTM & 0.892 & 0.814 & 0.839 & 0.797 & 0.813 & 0.799 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of related methods on the CVSS v2 dataset. Figure 6: Performance comparison of XLNet with other algorithms on the CVSS v3.1 dataset. (**a**) Evaluation metric: accuracy; (**b**) evaluation metric: precision; (**c**) evaluation metric: recall; (**d**) evaluation metric: F1-score. Figure 7: Performance comparison of XLNet with other algorithms on the CVSS v2 dataset. (**a**) Evaluation metric: accuracy; (**b**) evaluation metric: precision; (**c**) evaluation metric: recall; (**d**) evaluation metric: F1-score.