esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 A Prototype System for Pedestrian Positioning
### Probability Map  
The digital map is divided into three types of area. The areas have their associated weights:  
* forbidden areas--buildings, ponds, walls, fences _etc._, \(w_{\mathrm{map}}(x,y)=0.0\),
* probable areas--lawns, fields _etc._, \(w_{\mathrm{map}}(x,y)\in(0;1)\),
* preferred areas--pavements, streets, squares, alleys _etc._, \(w_{\mathrm{map}}(x,y)=1.0\).  
\(w_{\mathrm{map}}(x,y)\) denotes the type of an area at \((x,y)\) coordinates. Each weight corresponds to the probability of the user being in a given area. Since the system is destined for outdoor navigation, it is rather improbable that the user will traverse buildings, walls and fences--_c.f._, Figure 8. The user is likely to travel along pavements and alleys, however, he or she can also walk across lawns, although less likely. The probability map mitigates the problem of gyroscope drifts and stepmeter inaccuracy. The particle filtering approach to the discussed problem will be explained in Section 3.9. Here the term particles denotes a collection of hypothetical locations of the user and the weights associated with each particle is understood as the probability of the user being at this location. When particles enter a forbidden area, they will be gradually eliminated. Particles that move along a preferred area will be preserved. Therefore, the direction will align itself and the direction drift will be reduced. The weight assigned for probable areas should not be too low, because otherwise when a user crosses a wide street, the particles' weights will be successively reduced and consequently die out. Consequently, the user's location will not be correctly calculated. The project was developed with a view of pedestrians, especially blind ones.  
Figure 8: An example of a probability map of the University Campus. Forbidden, probable and preferred areas are denoted by black, grey and white respectively. The streets at the campus are paved and have the same texture as pavements. A blind user would rather refrain from walking through lawns.
## 2 Materials and Methods
### Tissue Segmentation  
Ground truth annotations were created for seven slides using QuPath software (version 0.1.2, Queen's University Belfast, Northern Ireland, UK) to differentiate between three histological categories: bone tissue, residual bone graft material, and non-mineralized tissue [49]. The three categories were separated based on their distinct morphological features. To address the imbalanced nature of the segmentation problem, class weights were calculated based on the ground truth of the training set.  
These maps were split into tiles of 512 \(\times\) 512 pixels in size with a 64-pixel overlap in each direction and then divided into a five-fold cross-validation setup, meaning that, within each fold, the model was trained on 80% of the data and validated on 20%. U-Net architecture [45] was trained for the semantic segmentation task, modified from the original to include batch normalization in the 3 \(\times\) 3 2D convolution blocks and dropout after the concatenations, to combat overfitting and improve the generalizability with limited training data, which was confirmed by the resulting validation accuracies being 0.9548, 0.9565, 0.9304, 0.9608, 0.9607 for the five folds, respectively. The exact architecture is  
Figure 2: Visualization of the matched LoFTR keypoints between the two imaging modalities: an in silico microCT slice and the histology; this specific match corresponds to the peak of the curve presented in Figure 1. Notice that there are only parallel lines connecting the keypoints; crossing lines would indicate false matches.  
depicted in Figure 3. The model was compiled using the Adam optimizer, categorical cross-entropy loss function, a batch size of 16, and a 5% dropout rate and trained for 200 epochs with a learning rate schedule that started from 1 \(\times\) 10\({}^{-3}\) and decreased by a factor of 10 down to 1 \(\times\) 10\({}^{-5}\) if the validation loss did not improve for 30 epochs on a single NVIDIA V100 32 GB GPU. The segmentation maps were then obtained by ensembling the five models trained on the five folds, producing a three-component vector for each pixel on the input image, where each component corresponded to a probability for one of the classes. The results were reconstructed by averaging the overlaps and were then subjected to bilateral filtering with a filter size of 32 pixels and standard deviation in both color and a pixel space of 64 to remove neural network artifacts. It is advantageous to use bilateral filtering instead of Gaussian filtering for noise elimination, since the latter blurs the sharp edges, while the former does not due to it also applying a filter in the \"color space\" as well as in the \"real space\". The segmentation maps were thresholded so that each pixel belonged to a single, most probable class. An example from the segmentation process is presented in Figure 4.  
The three-dimensional structure of the bone tissue and bone graft material was recovered by applying the same transformations calculated in the 3D reconstruction step described in Section 2.4.
# Quantitative Analysis of Drag Reduction Methods for Blunt Shaped Automobiles
## 2 Literature Review
### Vehicle Shapes
#### 2.2.1 Hatchback  
Hatchback is a simple box shaped vehicle often referred as bluff or blunt bodied (see Figure 2), where the total height of the wake region is equal (or even larger [22], see Figure 2a) to the height of the vehicle, which was visualized by [PERSON] et al. [23] for a pickup truck.  
Heavy trucks usually have a bluff shape with large wake regions; in Figure 2, the wake regions are indicated with blue. These wake region sizes vary due to external effects (e.g. crosswind, vortex shedding); flow fluctuation can be asymmetrical or periodic. Yet at a certain velocity, the time averaged size could be considered constant. When aerodynamic enhancement is made, the aim can be to reduce the size of the blue zone.  
Bluffness also causes instability problems in crosswind scenarios, as [PERSON] et al. [24] stated. [PERSON] et al. [25] have made experimental and numerical investigations into the relationship of the cross wind affecting the container wagons, and their drag related problems.  
Long vehicles, such as lorries in crosswind, could have instability problems due to drag; even at higher yaw angles, von Karman vortex could also be observed [24]. Later, it was supported by [PERSON] et al. [16], where they investigated heavy trucks with large eddy simulations (LES). They observed two notable wake regions: one at the corner vortices (see Figure 2b), which originate from either side of the vehicle, and one at the back. The size of the corner vortices was considerably deformed when the yaw changed 2.5\({}^{\circ}\)-10\({}^{\circ}\). The instability with CFD or with PIV can be highlighted by examining the symmetry or the wake region; if the asymmetry of the wake regions increasing, instability could occur. A symmetric scenario can be seen in Figure 2b, and the asymmetric scenario is detailed by [PERSON] et al. [16]. In addition, [PERSON] et al. [23] showed that flow separation occurs over the hood of these vehicles.  
Further wind tunnel measurements were made for the well-defined and simplified geometries, such as the Windsor body [26], Asmo body [27; 28; 29], or Ahmed body without rear slant [9].  
The Windsor model is a well-specified car geometry [30; 31], where 2% accuracy validated numerical models were examined. [PERSON] et al. [31] concluded that with slight modifications, it could reduce the drag by 4 Counts, which could be expressed by 0.5 CO\({}_{2}\) g\(\cdot\)km\({}^{-1}\) or 2 km additional distance for an electric vehicle.  
Figure 2: Side (**a**) and top (**b**) view of a heavy truck (based on [16; 23]).
# Immobilization of Levocyetririzine on Mesoporous Silica for Antiallergenic Gel Formulation
## 3 Results and Discussions
### Immobilization of LevdC on Mesoporous Material
With XRD analysis, we monitored the changes in the immobilization process of LevdC on the Syloid carrier. Figure 3 shows a characteristic X-ray diffractogram of the samples made in both aqueous and methanolic medium. By XRD analysis, only amorphous peaks were detected for all samples, which belong to the Syloid carrier; the characteristic peaks of LevdC were not visible. Neither increasing the stirring time nor varying the applied solvent (water or methanol) resulted in detectable Levo content on the Syloid carrier by XRD. Based on the IR spectra, a low amount of active substance is probable inside the pores of the carrier for _sample SyLev2_. However, the IR spectrum of _sample SyLev1_ with a higher active substance content is the same as that of pure Syloid. Given that, like XPS, IR measurements can only analyze the substances on the surface, it can be concluded that the active substance is located inside the Syloid pores. This is also supported by the XPS measurements. In the _samples SyLev3_, SyLev4_, and SyLev5_, prepared in a methanolic medium, the active ingredient is also probably inside the pores of the Syloid, which is also supported by the IR measurement results (Figure S3). The morphology of the LevdC immobilized Syloid samples treated in water and methanol solvents was examined using SEM. According to the microscopic observations, the samples have an irregular shape and a wide range of grain sizes. Highly agglomerated small particles (1-2 \(\upmu\)m) are characteristic of the samples in addition to the largest particles (5-6 \(\upmu\)m), which belong to the MSM carrier (Figure S4). Regarding the morphology changes in MSM after the immobilization of LevdC, no notable differences were observed between the coated Syloid particles and the reference. LevdC could not be distinctly identified on the surface of Syloid particles even under high magnification, as illustrated in Figure 4. Both the BET and the XPS analysis, however, supported that LevdC is probably positioned mainly in the pores of the carriers and, to a lesser extent, on the surface as well, since the SSA decreased obviously in the LevdC-treated samples compared with the untreated, pure Syloid. The immobilization of LevdC on Syloid in aqueous and methanol media shows that the active substance occupied about 20-25% of the mesopores, which is supported by nitrogen adsorption tests and IR measurements. A maximum of 24.5% surface coverage was available in both media, which decreased over time due to the solubility of LevdC and weak binding. Figure 3: XRD spectra of samples Syloid, levocetriazine dihydrochloride (LevdC), _SyLev1_, and _SyLev4_.
## 3 Experimental Results and Discussion  
### Results of the Channel-Based Evaluation Procedure  
#### 3.1.1 Results Obtained Using All the EEG Channels  
In this section, we present the results obtained for our proposed approach using all the EEG channels and all the CWD-based time-frequency features. The average \(\pm\) standard deviations classification accuracies obtained for each one of the 24 subjects (\(S_{1}-\)\(S_{24}\)) are shown in Figure 4. In particular, the average classification accuracy values computed for the 24 subjects is between 73.8%, which was obtained for \(S_{22}\), and 93.3%, which was obtained for \(S_{4}\). Moreover, the average classification accuracy value obtained for each subject is significantly higher than the random classification rate, which is defined as the reciprocal of the number of classes, and equal to 50% (the red dashed line in Figure 4). The average \(\pm\) standard deviation classification accuracy value achieved in distinguishing between the no-pain and pain classes, which is computed over all 24 subjects, is equal to 84.6% \(\pm\) 4.3%. The average \(\pm\) standard deviations of \(F_{1}-\) measure values computed for the no-pain and pain classes per each subject are provided in Figure 5. In particular, the average \(\pm\) standard deviation \(F_{1}-\) measure values computed for the no-pain and pain classes over all subjects are \(81.7\%\pm 5.9\%\) and \(85.1\%\pm 4.2\%\), respectively.  
Figure 4: The average classification accuracy values computed for each subject over the ten train–test repetitions of the employed cross-validation procedure. The black vertical lines represent the standard deviations of the classification accuracy and the red dashed line represents the random classification rate. The last column represents the mean value of the average classification accuracies computed over the 24 subjects.  
Figure 5: The average \(F_{1}-\) measure accuracy values computed for the no-pain and pain classes per each subject over the ten train–test repetitions of the employed cross-validation procedure. The black vertical lines represent the standard deviations of the classification accuracy and the red dashed line represents the random classification rate. The last column represents the mean value of the average \(F_{1}-\) measure values of the no-pain and pain classes computed over the 24 subjects.
# Decision-Tree-Based Horizontal Fragmentation Method for Data Warehouses
## 3 Materials and Methods
### FTree Method  
FTree has four steps, as depicted in Figure 1. It takes as the input the DW to be horizontally fragmented and W, i.e., the upper limit of partitions required by the DW administrator (DWA). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Work** & **Classification** & **Validation** \\ \hline
[9] & Data mining (clustering) &
\begin{tabular}{c} TPC-DS benchmark employing Scala language applied in a cluster of similar nodes, a Hadoop-YARN platform, a Spark engine, and Hive. \\ \end{tabular} \\
[12] & Metaheuristic &
\begin{tabular}{c} APB-1 benchmark. \\ TPC-H benchmark. \\ \end{tabular} \\
[13] & Minterm predicates &
\begin{tabular}{c} TPC-H benchmark. \\ \end{tabular} \\
[14] & Cost &
\begin{tabular}{c} APB-1 benchmark. \\ \end{tabular} \\
[15] & Cost &
\begin{tabular}{c} SSB (star schema benchmark). \\ TPC-DS benchmark. \\ \end{tabular} \\
[16] & Data mining (association) &
\begin{tabular}{c} Recall and Real2 join graphs randomly extracted from real-life guests of \\ \end{tabular} \\
[17] & Graph &
\begin{tabular}{c} Redshift with different volumes and frequencies as well as the \\ TPC-DS benchmark. \\ \end{tabular} \\
[18] & Other (Map–Reduce) &
\begin{tabular}{c} TPC-DS benchmark. \\ \end{tabular} \\
[19] & Affinity &
\begin{tabular}{c} APB-1 benchmark. \\ \end{tabular} \\
[20] & Cost &
\begin{tabular}{c} APB-1 benchmark. \\ TPC-DS benchmark. \\ \end{tabular} \\
[21] & Data mining (clustering) &
\begin{tabular}{c} TPC-DS benchmark. \\ \end{tabular} \\
[22] & Cost &
\begin{tabular}{c} APB-1 benchmark. \\ JSON file which contains Neo4j vast data combined with DW metadata. \\ Experiments in a workstation to identify dependencies such as the \\ \end{tabular} \\
[24] & Metaheuristic &
\begin{tabular}{c} execution time of the method compared to the number of data \\ warehouses and the distribution quality. \\ Experiments on a computer cluster utilizing the TPC-DS benchmark. \\ Experiments with the TPC-DS benchmark through a cluster of \\ homogeneous nodes, a Spark engine, a Hadoop-YARN platform, a Hive \\ system, a Ray system, and a Redis database. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main characteristics of horizontal fragmentation methods. To explain FTree, we use the SSB (star schema benchmark) [27]; this is demonstrated in Table 2. The SSB consists of a data warehouse with the _lineorder_ fact table as well as the _customer_, _supplier_, _part_, and _date_ dimension tables. The data warehouse (DW) is made up of several dimension tables \(D=\{d_{1}\), \(d_{2}\),..., \(d_{m}\}\) and a fact table \(F\). To carry out the fragmentation, the following steps are performed:  
_1. Obtain relevant queries from DW._ For this step, it is necessary to keep only the queries performed in the DW that imply joins of a dimension table \(d_{i}\) with \(F\); every di
## 2 Problem Formulation  
### Vision Guidance  
The problem under study consists in guiding a DWMR through a given path by means of a vision guidance system. An onboard camera is assumed to be mounted on the front of the DWMR, looking down obliquely at colored stripes on the ground, as shown in Figure 1.  
Colored stripes in the robot field of view are recognized as guide paths. A position error \(e_{d}\) and an orientation error \(e_{\theta}\) of the DWMR are defined with respect to a guide path, as shown in Figure 2. Moreover, \(e_{d}\) denotes the \(x\)-coordinate of the intersection of the guide path with the \(X_{k}\)-axis, \(e_{\theta}\) referring to the angular error between the tangent of the guide path and the \(Y_{k}\)-axis. Fields of view 1, 2 and 3 in Figure 2 represent a sequence of vision samplings at three consecutive instants, as the robot moves. Since the field of view is relatively small, namely, 0.4 \(\times\) 0.3 m, a guide path can be approximated as a straight line by means of least squares. The linearization error increases with a decrease in the radius of curvature, as shown in Table 1.  
When high-curvature arcs are used for guide paths, the tangents of their different sections undergo a significant change in the field of view. In these cases, an approximation method based on binary-tree guidance window partition can be used to replace the curve with a series of piecewise lines at any given approximation accuracy [32]. Hence, only straight paths are considered as the target of tracking control in the field of view, as shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline Curvature [m] & 2 & 1.8 & 1.6 & 1.4 & 1.2 & 1.0 \\ Error [\(10^{-3}\) m] & 5.06 & 5.63 & 6.34 & 7.25 & 8.48 & 10.20 \\ Relative error [\%] & 0.253 & 0.313 & 0.396 & 0.518 & 0.707 & 1.02 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Linearization error.  
Figure 1: The field of view of an onboard camera.  
Figure 2: Top view of path tracking of a DWMR.  
In Figure 3, point C, the centre of mass of the platform, indicates the position of the DWMR, while the angle \(e_{\theta}\) defines its orientation error with respect to the path. Path tracking can be converted into a trajectory-planning problem from the current pose (point C along the \(Y\)-axis) to a target pose (point \(T_{k}\) along the path). It is noteworthy that _path_ is distinguished from _trajectory_ here. The former is the colored stripe marked on the floor, which serves to guide the robot; the latter is the curve traced by a reference point of the platform. We choose this point at the platform centre of mass.
## 3 Methodology
### Proposed LSTM-Attention-BiTCN Model
#### 3.3.4 Dropout Layers  
In our research, we integrated the dropout layer strategy, an essential technique for mitigating the risk of overfitting within machine learning models. Overfitting is particularly prevalent in scenarios where large neural networks are trained on relatively small datasets. The dropout layer addresses this issue by randomly nullifying a proportion of the input units during each training iteration. This process is controlled by a predetermined frequency, known as the dropout rate, which lies within the range of [0, 1]. Concurrently, the remaining active units are scaled up by a factor of \(1/(1-rate)\), ensuring the total input signal's intensity remains consistent.  
In the architecture of our proposed LSTM-Attention-BiTCN model, we employed a spatial dropout layer, a variant of the standard dropout layer, which is particularly advantageous in the context of convolutional networks. This spatial dropout layer was strategically positioned immediately following the embedding layer. Its primary function was to randomly deactivate the entire feature maps in the convolutional layers, as opposed to individual neurons, thereby enhancing the model's generalization capabilities.  
Additionally, we incorporated another dropout layer just before the final dense layer of the model. Both dropout layers, the spatial dropout layer and the standard dropout layer, were configured with a dropout rate of 0.1. This rate was carefully chosen to strike a balance between preventing overfitting and maintaining sufficient information flow through the network. By employing these dropout layers at these critical junctures in the model, we aim to enhance the robustness and generalizability of the LSTM-Attention-BiTCN model, thereby improving its performance in accurately detecting and analyzing patterns indicative of suicidality in social media posts.
# Cybersecurity Threats and Their Mitigation Approaches Using Machine Learning--A Review
## 4 Future Improvements and Challenges for ML-Based Cybersecurity
### Quantum Computing  
While in their infancy, quantum computers were once established as having the potential to break the security offered by asymmetric encryption techniques [186]. Asymmetric key encryption relies on public and private keys. These keys are generated by factoring two extremely large prime numbers. Factoring small prime numbers is possible but keys that are very large can take thousands of years to decrypt, making our data secure. [PERSON]'s algorithm [187] provides an alternative solution to factor these large prime numbers, but, again, it is slow. Quantum computing, with its principal of superposition, can rapidly derive the factors at a fraction of the time that a binary computing system would take. This makes algorithms such as RSA and DES, elliptic curve algorithms such as ECDSA and digital signature algorithms no longer secure. The authors in [188] mentioned that to break a 56-bit DES, [PERSON]'s algorithm [189] utilizing quantum computing would only require 185 searches for key identification. Symmetric key algorithms such as AES are still resistant to quantum computing. Researchers are exploring both quantum and mathematical techniques to circumvent these limitations. An example is the BB84 protocol [190], which is a type of quantum key distribution. Mathematical approaches such as lattice-based cryptography [191] are also being explored. While quantum computing can be detrimental to asymmetric encryption, it can also speed up machine learning if used as sub-routines [192]. This can significantly reduce the prediction time if used by algorithms such as SVM, which can require a lot of time, implementing kernel transformations to derive a hyperplane. They can also be used in deep learning if configured properly. However, there are some challenges since quantum neural networks have linear dynamics [193].