esa-sceva/satcom-chunk-collection huggingface dataset:
# Managing Access to Confidential Documents: A Case Study of an Email Security Tool
## 3 Related Work
### Adoption of Encryption Tools  
Despite existing efforts toward raising user awareness about the security and privacy of their information and disseminating knowledge of how to utilize security and privacy tools, the adoption of encryption tools remains low. Taking this issue into account, [PERSON] et al. and [PERSON] et al. [26; 31] investigated the social processes influencing people's decisions to adopt a new security tool or practice. They found that social processes played a significant role in adopting new security tools and were effective at boosting security sensitivity. Two studies evaluated the differences in motivations for (not) following computer security practices [32] and smartphone security measures [33] based on the rational decision model. They found there were differences in users' perceptions regarding the benefits, risks, and costs associated with their decisions.  
In other studies [6; 34], researchers explored the reasons why secure email tools are not widely used by users. Several barriers have been identified within the workplace that prevent the adoption of encrypted email [6]. As a result of technical issues, usability issues, and social considerations, the participants did not consider using them frequently. In another study [35], the researchers found that average users were more likely to adopt secure email tools (e.g., Virtru) when integrated with webmail, such as Gmail. [PERSON] et al. [13] evaluated three secure email systems, with Virtru being one of them. As a result of users' interactions with Virtru, fewer mistakes were made, and its perceived usability score was higher. We add to the existing literature by understanding the use perceptions of end-to-end encrypted email and how people use Virtru's security features to manage access control to their medical, tax, and employee information if sent via email.
## 4 Experimentation and Results Analysis
### Baseline Methodology  
The validity of the C-MBR model's acquisition is verified by launching a comparative analysis between C-MBR and five other candidates including EHCF (efficient heterogeneous collaborative filtering), NMTR (neural multi-task recommendation), NGCF-M (neural graph collaborative filtering-multi-behavior), NGCF-CL (neural graph collaborative filtering-contrast learning tasks), and S-MBRec (self-supervised graph neural networks for multi-behavior recommendation).  
For EHCF, we use the following parameter values. Embedding Matrix Size: The embedding matrices are set to have 100 \(\times\) 100 dimensions, indicating 100 latent features for both users and sites. The Learning Rate is set to 0.001. The model consists of 2 hidden layers, each containing 50 neurons, providing a balance between model complexity and the capacity to capture intricate patterns in the data. A consideration threshold of 0.5 is established, indicating that recommendations with scores above 0.5 are deemed suitable. The Adam optimization algorithm is employed to optimize the model parameters.  
The NMTR candidate encompasses specific parameter values as follows. The model can handle multiple recommendation tasks simultaneously, with common choices being the user-site rating prediction, site-site similarity calculation, and user clustering. In this comparison, we configure to address three tasks. Two hidden layers in the NN architecture are set to have sizes like 100 neurons each, providing flexibility in capturing complex patterns in the data across different tasks. The learning rate for training the model could be set to 0.001. To prevent overfitting, a dropout rate of 0.5 is applied during training, meaning that each neuron has a 50% probability of being dropped out during each training iteration. Training the model with a batch size of 32 samples per batch can balance computational efficiency and gradient accuracy during optimization.  
For the NGCF-M, NGCF-CL, and S-MBRec, we configure similar parameter values as follows. The sizes of the embedding vectors for users and sites are set to 64 dimensions, allowing the model to learn rich representations of users and sites in the embedding space. The model architecture includes 2 convolutional layers to capture different levels of neighborhood information in the graph structure. In the NN component, we set 128 neurons each, providing the capacity for learning complex interactions between users and sites. The dropout rate of 0.2 is deployed. The Learning Rate is set to 0.001. The attention mechanism is employed with 4 attention heads to allow the model to focus on different aspects of the neighborhood relationships.
## 4 Discussion
Limited tested sample size and reference database for healthy subjects. Healthy and young subjects were recruited without any disabilities. Different races, nationalities and type of disability may influence the results, as well as affect anthropometric data. Kinect sensors are not consistent in data collection for different environments, and different types of clothing can significantly change the accuracy of the detection of joints, as was noticed in our study. Future planned research is to use the Qualisys system to improve the algorithm by reducing the number of limitations. Video analysis is widely applied in the context of human movement detection, and real-time implementation using reliable algorithms based on the postural recognition of healthy persons should provide postural data that can be used to assess the effectiveness of clinically prescribed exercise regimes for patients, as well as allow for variations in exercise regime, dependent on the data collected. Such data would be useful in optimized treatment by exercise therapy. The advantages of such an approach could also be extended to veterinary applications. Very few studies address automatic video-based analysis of animals--for example, canine behavior as a means of monitoring animal health and wellbeing [28; 29; 30]--with some of these studies using a 3D Kinect camera to detect joint position. In [28], the authors present a system capable of identifying static postures for canines that does not rely on hand-labeled data at any point, although the system can only identify the \"standing,\" \"sitting\" and \"lying\" postures with approximately 70%, 69% and 94% accuracy, respectively. Paper [29] presents a depth-based tracking system for the automatic detection of animals' postures and body segments, as well as an exhaustive evaluation on the performance of several classification algorithms, based on both a supervised and a knowledge-based approach. Furthermore, [PERSON] et al. addressed a problem of automatic behavioral analysis of kenneled dogs using 3D video monitoring [30]. Dog body segment detection was done using standard Structural Support Vector Machine classifiers, and the automatic tracking of the dog was also implemented. However, this tool has a high margin for improvement. A number of studies were also found in the literature using wide-ranging applications in the biomechanics of animals, as well as in prosthetics to prevent injuries, monitoring rehabilitation after surgical operations, choosing the appropriate orthopedic devices and prostheses, training and others [34; 35; 36]. Therefore, the classification algorithm of posture can also be useful in not only human medicine, but also veterinary applications, influencing veterinary intervention using exercise regimes, as well as monitoring animals' health and behavior. Further studies using the Qualisys system and neural network, which would be trained to recognize a dog's skeleton using cost-effective video cameras, are planned; so far, such work has only been carried out for humans.
## 2 Proposed Method
### Detailed Settings of MCRTF-CM  
The model was trained using Mean Squared Error (MSE) as the loss function. To eliminate scale differences between features, input data were preprocessed using Scikit-Learn's MinMaxScaler [35] before being fed into the MCRTF-CM model. The Adam optimizer [36] with a learning rate of 0.01 was employed during training. To prevent overfitting, dropout [37] with a probability of 0.1 was applied, and the input sequence window length was set to 24. Other parameters are detailed in Table 1.
## 3 Used Datasets and Training Tricks
### Data Augmentation Methods  
The dataset is usually limited and iterating over it for multiple epochs would lead to the model overfitting the data. Because of this, data augmentation methods are usually employed to help the model better generalize. There are multiple ways in which this can be done, but for simplicity, they will be grouped into five categories:  
* Geometric transformations: scaling, left-right or upside-down flip, rotation, shear. These transformations must also be done on the ground truth annotations;
* Color space transformations: Hue-Saturation-Value transformations, random noise;
* Kernel filters: Gaussian-blur, sharpening;
* Data loss: Random erasing [55], Dropout [56], CutOut [57], etc.;
* Mixing images: CutMix [58], MixUp [59], Mosaic [60], etc.  
The first three categories are straightforward. Random erasing overwrites patches from the image with random data or a set value. CutMix works the same as CutOut, but instead of a set value, it uses another image. DropOut works at the layer level, each layer having a probability to drop some of its output. MixUp interpolates two images. In the case of both CutMix and MixUp, the labels are modified to include both the classes from the original image as well as the classes from the additional image used in the process. Mosaic concatenates 4 images, the annotations of each image being offset to match the position of the image. Examples of CutOut, CutMix, MixUp, and Mosaic can be seen in Figure 7.
# Study of Fast and Reliable Time Transfer Methods Using Low Earth Orbit Enhancement
## 2 Experimental Principles
### LEO-Augmented PPP Model
\begin{array}{l}c\overline{\delta}\Upsilon_{R}^{G}=c\cdot\delta T_{R}+ \Upsilon_{delay\_P,IF,R}^{G}\\ \overline{N}_{IF,R}^{G,S}=N_{IF,R}^{G,S}+\Upsilon_{UPD,IF,R}^{G}-\Upsilon_{UPD,IF }^{G,S}\\ \Upsilon_{UPD,IF,R}^{G}=\Upsilon_{delay\_P,IF,R}^{G}-\Upsilon_{delay,P,IF,R}^{G,S}/\lambda_{IF}^{G,S}\\ \Upsilon_{UPD,IF,R}^{G,S}=\Upsilon_{delay,\_P,IF}^{G,S}-\Upsilon_{delay,P,IF}^{ G,S}/\lambda_{IF}^{G,S}\end{array} \right. \tag{9}\]  
where \(\Upsilon_{UPD,IF,R}^{G}\) is the uncalibrated phase delay at the receiver end; \(\Upsilon_{UPD,IF}^{G,S}\) is the uncalibrated phase delay at the satellite end; and \(\overline{N}_{IF,R}^{G,S}\) is the \(IF\) combined ambiguity parameter, which contains the hardware delay deviation at the satellite and receiver ends. By combining the simulated observational data from the LEO satellites, the PPP model for LEO enhancement was constructed as follows:  
\[\left\{ \begin{array}{l}P_{IF,R}^{G,S}=\rho_{R}^{G,S}+c\cdot\overline{\delta} \Upsilon_{R}^{G}+\Upsilon_{trop}+\epsilon_{P,IF,R}^{G,S}\\ \Phi_{IF,R}^{G,S}=\rho_{R}^{G,S}+c\cdot\overline{\delta}\Upsilon_{R}^{G}+ \lambda_{IF}^{G,S}\overline{N}_{IF,R}^{G,S}+\Upsilon_{trop}+\epsilon_{\Phi,IF,R }^{G,S}\\ P_{IF,R}^{G,S}=\rho_{R}^{L,S}+c\cdot\overline{\delta}\Upsilon_{R}^{G}+ \Upsilon_{trop}+\epsilon_{P,IF,R}^{G,S}\\ \Phi_{IF,R}^{G,S}=\rho_{R}^{L,S}+c\cdot\overline{\delta}\Upsilon_{R}^{L}+ \lambda_{IF}^{L,S}\overline{N}_{IF,R}^{L,S}+\Upsilon_{trop}+\epsilon_{\Phi,IF,R }^{L,S}\end{array} \right. \tag{10}\]  
Among them, the \(L\) superscript represents the LEO satellite.
## 2 Methodology
### SVM (Support Vector Machine)  
Introduced by [PERSON] [59], Support Vector Machine (SVM) is widely applied in classification and regression tasks, significantly benefitting forward-looking forecasts in hydrology, as well as various other fields.  
Operating on principles of statistical learning theory, SVM is designed to provide a unique, optimal solution for a dataset, unlike other algorithms that might yield multiple solutions. This characteristic makes it particularly effective in mitigating overfitting scenarios by leveraging a kernel function, which is instrumental in defining decision boundaries within nonlinear contexts.  
When utilized specifically in regression tasks, SVM is referred to as Support Vector Regression (SVR) [60]. While ANN primarily aim to minimize the empirical risk, the principal objective of SVR is to facilitate the minimization of statistical learning, enhancing the predictability and robustness of the model [61]. Due to its versatility and efficacy, SVM is popularly adopted not just for regression but also for classification purposes, establishing itself as one of the most prominent and successful algorithms in the machine learning arena, particularly because of its various modifications. These adaptations of SVM have been widely researched and have demonstrated effective outcomes in numerous studies [48; 62; 63; 64; 65]. The mathematical formulation of SVM can be seen in Equation (9), which models the relationship between input and output variables:  
\[f(x)=(w,\phi(x))+b \tag{9}\]  
where \(f(x)\) is a high-dimensional feature space, \(w\) is a weight of the output variable, and \(b\) is the bias term.  
The performance of models can vary depending on the choice of kernel functions such as linear, polynomial, radial basis function (RBF), sigmoid, and Gaussian. In this study, the Gaussian kernel was chosen due to its significant impact on model outcomes. When deploying the Gaussian kernel, there are three crucial parameters that influence the performance: the scale parameter of the activation function (\(\gamma\)), the positive constant (\(C\)), and epsilon (\(\epsilon\)), as discussed by [PERSON] et al. [66]. During this research's computations, these parameters were automatically optimized within the MATLAB program to maximize their efficacy on the model results. For a more comprehensive understanding of the theory and formulation of SVM, interested readers can refer to the detailed literature provided by [PERSON] et al. [67] and [PERSON] [59].
## 4 Experimental Results
### Cross-Validation Experiments
** \\ \hline DIMLP (25) & 96.1 (0.6) & 98.3 (0.5) & 95.8 (0.9) & 96.7 (0.6) & 11.8 (0.4) & 2.9 (0.1) \\ DIMLP (50) & 96.2 (0.6) & 98.1 (0.4) & 95.6 (0.5) & 96.7 (0.5) & 12.0 (0.4) & 2.9 (0.0) \\ DIMLP (100) & 96.2 (0.5) & 98.3 (0.7) & 95.9 (0.6) & 96.9 (0.6) & 11.8 (0.4) & 2.9 (0.0) \\ DIMLP (150) & 96.2 (0.5) & 98.1 (0.6) & 95.7 (0.7) & 96.8 (0.5) & 11.8 (0.4) & 2.8 (0.0) \\ \hline RF (25) & 96.3 (0.3) & 98.1 (0.5) & 95.2 (0.6) & 96.6 (0.4) & 23.1 (0.4) & 3.5 (0.1) \\ RF (50) & 96.0 (0.4) & 97.4 (0.8) & 94.6 (1.1) & 96.5 (0.5) & 24.0 (0.5) & 3.6 (0.0) \\ RF (100) & 96.1 (0.6) & 98.1 (0.7) & 94.7 (0.9) & 96.3 (0.6) & 24.0 (0.8) & 3.6 (0.0) \\ RF (150) & 96.3 (0.3) & 98.1 (0.6) & 94.5 (0.7) & 96.3 (0.3) & 24.1 (0.7) & 3.6 (0.0) \\ \hline RF-3 (25) & 94.8 (0.6) & 99.2 (0.5) & 94.3 (0.8) & 94.9 (0.6) & 9.8 (1.3) & 2.5 (0.1) \\ RF-3 (50) & 94.9 (0.6) & 98.9 (0.4) & 94.4 (0.6) & 95.1 (0.6) & 8.6 (0.9) & 2.4 (0.1) \\ RF-3 (100) & 95.0 (0.5) & 99.1 (0.3) & 94.8 (0.6) & 95.3 (0.6) & 8.2 (0.8) & 2.3 (0.1) \\ RF-3 (150) & 95.2 (0.3) & 99.1 (0.4) & 94.8 (0.6) & 95.4 (0.6) & 7.6 (0.6) & 2.3 (0.1) \\ \hline GB (1,25) & 95.2 (0.3) & **99.8** (0.2) & 95.0 (0.3) & 95.2 (0.3) & **3.2** (0.4) & **1.3** (0.1) \\ GB (1,50) & 95.3 (0.3) & 99.5 (0.2) & 95.0 (0.4) & 95.4 (0.4) & 6.4 (0.6) & 1.9 (0.1) \\ GB (1,100) & 95.7 (0.3) & 99.3 (0.3) & 95.5 (0.3) & 95.9 (0.3) & 9.2 (0.3) & 2.5 (0.0) \\ GB (1,150) & 95.7 (0.3) & 98.8 (0.7) & 95.8 (0.3) & 96.3 (0.5) & 10.3 (0.2) & 2.7 (0.0) \\ \hline GB (2,25) & 95.3 (0.3) & **99.8** (0.2) & 95.2 (0.4) &Table 10 presents a comparison between the best average accuracies of the rulesets produced by two groups of models. In the first group, we had ensembles of DIMLPs or DTs, while the second included Skope-Rules. A Welch \(t\)-test was performed to compare the average accuracies obtained by the two groups, with \(p\)-values in the last column. Numbers in bold represent significantly better average predictive accuracies. Overall, in seven of the eight classification problems, the average predictive accuracy obtained by the rulesets generated from the approach proposed in this work was significantly better. The average number of rules generated from the most accurate rulesets provided by ensembles of DIMLPs or DTs is presented in Table 11. Again, a Welch \(t\)-test was performed to compare our ensembles to SR. It turned out that on only one classification problem, the average number of rules was significantly lower with SR.
# A Lateral-Directional Control Method for High Aspect Ratio Full-Wing UAV and Flight Tests
## 3 Lateral-Directional Control Law Design
### Lateral-Directional Attitude Control Law Based on ADRC and NDI
#### 3.1.1. Yaw Angle Control Loop  
The first part of Equation (9) can be represented as:  
\[\psi=f_{\psi}+g_{\psi}r \tag{10}\]  
where \(f_{\psi}=q\sin\phi\sec\theta\) and \(g_{\psi}=\cos\phi\sec\theta\).  
Based on the NDI control theory, the feedback control law of the yaw angle is designed as follows:  
\[r_{c}=\left[K_{\psi}\left(\psi_{c}-\psi\right)-f_{\psi}\right]g_{\psi}^{-1} \tag{11}\]  
Because there is a singularity in the yaw angle, Equation (11) can be improved as:  
\[r_{c}=\left[K_{\psi}\left(\psi_{c}-\psi+2\pi n\right)-f_{\psi}\right]g_{\psi}^ {-1} \tag{12}\]  
where \(n\) is an integer that makes \(-\pi<\psi_{c}-\psi+2\pi n\leq\pi\). In order to prevent excessive yaw rate command, the command is limited to \(r_{cmax}\).  
\[r_{c}=\text{sat}(r_{c},-r_{cmax},r_{cmax}) \tag{13}\]  
where \(\text{sat}(\cdot)\) is the saturation function.
# Time Series Classification with Shapelet and Canonical Features
## 3 Definition and Background
### Shapelet Transformtion Technology
To improve efficiency, for 25 time series typical features, we randomly select \(a\) features to perform subsequent transformations of the dataset instead of using all of them (line 2). The transformation process that follows has two key differences from the traditional Shapelet transformation method. First, the restrictedSubDist method (line 7) limits the scope of Shapelet matching, i.e., allows Shapelet to have the maximum offset of each _shift_ size on the left and right (as shown in Figure 3, Shapelet \(\mathbf{S}\) can only look for the best match with other time series between the two dotted lines). In this way, the algorithm is still able to overcome the time warping problem that is prevalent in time series to a large extent, and the computational complexity of Shapelet matching is reduced from \(O(l\cdot m)\) to \(O(shift\cdot m)\) (\(shift<<l\)). Second, when transforming time series \(\mathbf{T}_{i}\) with Shapelet \(\mathbf{S}_{j}\), the traditional method finds the subsequence \(\mathbf{S}^{\prime}\) most similar to \(\mathbf{S}_{j}\) in \(\mathbf{T}_{i}\), and only takes the normalized Euclidean distance of \(\mathbf{S}^{\prime}\) and \(\mathbf{S}_{j}\) as a feature value of the transformed instance. On this basis, the proposed method calculates \(a\) different feature values of \(\mathbf{S}^{\prime}\), and merges the value of a value into the transformed instance (lines 9-12), so that the transformed data representation contains richer information. ``` Input: Dataset \(\mathbf{D}\), Shapelet minimum length \(min\), Shapelet maximum length \(max\), number of Shapelets \(k\), number of typical time series features \(a\), Shapelet maximum offset shift Output: Randomly selected Shapelets and their locations, randomly selected feature index \(\mathbf{Atts}\), transformed dataset \(\mathbf{D^{\prime}}\)
1. Shapelets, Locations\(\leftarrow\)randSampShapelets(\(\mathbf{D}\),min,max,k);
2. \(\mathbf{Atts}\leftarrow\)randSampAtIndices({1,2,\(\cdots\),25},a);
3. \(\mathbf{D^{\prime}}\leftarrow\)[\(F_{1},F_{2},\cdots,F_{n}\)];
4. for \(\mathbf{T}_{i}\) in \(\mathbf{D}\)
5. \(\mathbf{F}_{i}\leftarrow\)null;
6. for Shapelet \(\mathbf{S}\) in Shapelets
7. dist, \(\mathbf{S}^{\prime}\leftarrow\)restrictedSubDist(\(\mathbf{S}_{j}\),\(\mathbf{T}_{i}\),shift,Locations[j]);
8. \(\mathbf{F}_{i}\).add(dist);
9. for \(c\leftarrow\)1 to \(a\)
10. \(\textit{val}\leftarrow\)computeFeature(\(\mathbf{S}^{\prime}\),\(\mathbf{Atts}[c]\));
11. \(F_{i}\).add(val);
12. end for
13. end for
14. \(\mathbf{F}_{i}\).add(\(y_{i}\));
15. end for
16. return Shapelets, Locations, \(\mathbf{Atts}\), \(\mathbf{D}^{\prime}\);  
Figure 3: Illustration of Shapelet matching under restriction.
# On the Feasibility of Interhemispheric Patch Detection Using Ground-Based GNSS Measurements
## 2 Methodology and Data Set
Although the work is aimed at the feasibility of GNSS-based patch detection, the presented experiment involved processing data obtained from \(\sim\)180 and \(\sim\)60 permanent stations located in northern and southern hemispheres, respectively (Figure 2). The extension of the analyzed area provides a better preliminary view of ionospheric conditions and, in some cases, allows the explanation of interhemispheric differences. The receivers that were used for patch detection, which had an altitude-adjusted corrected geomagnetic (AACGM) latitude higher than 75\({}^{\circ}\), were marked with red dots for clarity of presentation. The utilized stations belonged to the International GNSS Service (IGS), EUREF Permanent Network (EPN) or participate in Plate Boundary Observatory (PBO) mission. The investigations are based on multi-GNSS (GPS + GLONASS) measurements, but it should be noted that only a few polar stations are equipped with two-constellation receivers. Figure 2 highlights the inequality between the distributions of receivers in both hemispheres, which are connected through land mass configuration. However, analyzing only the polar cap areas, the coverage of the northern part seems to be only slightly better due to the shorter distance of the Greenland stations from the north geomagnetic pole and larger number of stations for this area.