esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Testbed and Simulation Results
### Energy Consumption  
We measured the energy consumption for the WSN testbed and also for simulation networks. Before rainfall we used the standard AOMDV protocol and after the rainfall we used our modified AOMDV protocol to compare the energy consumption of our proposed protocol with the standard AOMDV. Before rainfall all the nodes sent the sampled data every 3 h and use multiple routes to send data to the gateway node. When there is no rainfall, HELLO messages are being sent but after rainfall is detected at any node, it sends STATUS messages to other nodes and stops sending HELLO messages, as a result no data is now sent to the affected node and its priority to send data also increases. By rerouting, the data sent to the affected node is greatly reduced hence the receiving energy is also reduced significantly. The affected node now sends data after every 1 s, this operation is continued as  
Figure 8: The throughput differences between light rain, heavy and very heavy rain, on node 1.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Type/Rain-Level** & **Light Rain-0.05 Mbps** & **Heavy Rain-0.1 Mbps** & **Very Heavy Flood-0.5 Mbps** \\
**(Time)** & **(0–900) s** & **(900–2000) s** & **(2000–2500) s** \\ \hline Total Sent Size from cbr0 & 5000.1 kbit & 6875.4 kbit & 18,746 kbit \\ Total Sent Size from cbr1 & 4375 kbit & 13,748.7 kbit & 18,750.2 kbit \\ End-to-End Delay (cbr0) & 18.78 ms & 18.79 ms & 4465.59 s \\ End-to-End Delay (cbr1) & 16.62 ms & 16.50 s & 16.42 ms \\ Throughput (cbr0) & 5.56 kbps & 6.25 kbps & 3.75 kbps \\ Throughput (cbr1) & 4.86 kbps & 12.50 kbps & 37.50 kbps \\ \hline \end{tabular}
\end{table}
Table 5: Performance measurements before and during the time when node 1 is affected by rain.  
long it is raining. Hence when it is raining the affected nodes are receiving very less data and have just their own data to send, the overall energy for the affected nodes also decreases significantly.  
We measure the energy consumption in three modes for each sensor node, idle mode, receiving mode and transmitting mode. The energy consumed in idle mode is called idle energy (Idle Energy). The energy consumed by a node in transmission of data is called transmission energy (Trans Energy) and the energy consumed by a node to receive data is called receiving energy (Rec Energy).  
When a node is affected by rain it will receive a rain event, this event triggers a special broadcast rain packet called STATUS message. Every node that is adjacent to this node will receive this packet. The affected nodes will still send HELLO messages but stop responding to RREQ messages, this will prevent the neighboring nodes from seeing it and sending their data through the affected node.  
Hence, the nodes affected by rainfall will not receive any packets from other nodes and hence they will require very less receiving energy, on the other hand they will just have their own data to transmit, hence the transmission energy also decreases.
# A Review of Deep Learning Algorithms and Their Applications in Healthcare
## 3 Artificial Neural Networks
### Convolutional Neural Networks (CNNs)  
The CNN is one of the deep learning algorithms that can take an input image, assign importance to various objects in the image and then distinguish and differentiate between the image and others [57]. It is used to improve the classification accuracy of facial electromyography (FEMG) and speech signals. CNN is mostly used in video and image recognition, natural language processing, and image classification and analysis. It can categorize images according to the objects included in them. They can even detect human emotions in an image by classifying the effect of what that person seems to be feeling [57; 58].  
A CNN is a supervised algorithm; it is just a neural network. It introduces a new method for conducting supervised feature learning, and it provides discriminative features which generalize well. It is a type of feedforward neural network architecture in machine learning [57; 58; 59]. Figure 14 shows the architectural diagram of a CNN.  
Figure 13: A representation of a deep belief network [56].  
The role of a CNN is to reduce images into an easier form for processing. Some information will be lost from the image when using the feature detector, but the critical features that are required to get a good prediction will not be lost. The application of relevant filters allows the CNN to capture the spatial and temporal dependencies in an image. The reduction in the number of parameters involved and reusability of weights performs a better fitting to the image dataset [57; 58].  
Typical convolutional neural networks consist of many layers of hierarchy, with some layers representing features while others act as conventional neural networks for classification. Convolutional and subsampling layers are the two types of altering layers. Convolutional layers perform convolution operations with multiple filter maps of equal size, while subsampling layers reduce the sizes of the following layers by averaging pixels within a small neighborhood [58; 60].  
CNN is used to solve the problems related to images, which means it can solve the issues that can be given in the form of image. As an example of a CNN's solutions, an input image of a cat or dog needs to be classified to either the cat or dog class.
## 6 Case Study
### Case of a Malicious Node
#### 6.2.1 First Case  
In this case, the Node_1 will broadcast to other nodes that it has only Node_2 as a neighbor, so it broadcasts to them [2], but it sends to the Node_6 [2,6]. Consequently, the other nodes will receive the message that Node_1 has only one neighbor, which is Node_2. Also, they will receive from Node_6 that it has Node_1 as a neighbor. Thus, all the Nodes will build the Trusted Topology except for Node_6 and Node_1.  
For example, in this case, all Nodes will build the following topology:  
\[T=\{B_{1}=\{2,?\},B_{2},B_{3},B_{4},B_{5},B_{6}=\{?\},B_{7}\} \tag{15}\]  
In this first case, other nodes will easily detect that Node_1 has sent to Node_6 the neighbors list [2,6]. Thus, they will qualify Node_1 as a malicious node and exclude it from the network.
## 4 Craniomy Trajectories by the Spherical Parallel Mechanism
### Results and Discussions  
The MATLAB program was executed for the trajectories that were recorded during the experiments on the cadavers presented in Section 2. The mechanism used for these simulations has the linkage dimensions arbitrarily set to (\(\alpha\)\(\beta\)\(\gamma\)) = (45 40 30). Based on the measurements of Section 3.2, the magnitude of the reaction force is set to 13 N. The program will calculate the torques required to generate this force for a spinning angle from \(-\pi\) to \(\pi\)  
Figure 11: (**a**) Input joint coordinates required for the minimization of the torque average. (**b**) Resulting torque average using the mechanism reconfigurability.  
For all trajectories, the resulting average torque with and without the reconfigurability are used to determine the average torque reduction ratio. These are all displayed in Figure 12.  
The detailed results of all simulations are given in Table 1. The results show that the torque average is significantly reduced on all trajectories by 21.4 to 99.7%. What does not appear in Table 1, however, is the individual torques calculated on the trajectories with and without the reconfigurability feature. The present simulations revealed that when optimized, the maximum values of individual torque vary from 1.86 to 4.34 Nm compared to 4.39 to 16.75 Nm when the reconfigurability is not used. This means that considering the trajectories recorded on the experiments on cadavers, the present method allows reducing the torque requirement from 16.75 to 4.34 Nm (74.1%) for the motorization of the robotic manipulator.  
It can be seen that there is no discontinuity on the evolution of the collected spinning angle displayed in Figure 13. This shows that a robotic manipulator based on a 3-RRR SPM can be smoothly controlled to generate craniotomy trajectories that minimize the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Trajectories**} & \multicolumn{2}{c}{**Original Torque Averages (Nm)**} & \multicolumn{2}{c}{**Optimized Torque Averages (Nm)**} & \multicolumn{2}{c}{**Reduction (\%)**} \\ \cline{2-7}  & **Min** & **Max** & **Min** & **Max** & **Min** & **Max** \\ \hline
1 & 3.32 & 3.51 & 1.05 & 1.18 & 66.4 & 68.7 \\
2 & 3.65 & 6.02 & 0.11 & 0.72 & 80.4 & 98.2 \\
3 & 2.82 & 2.85 & 1.76 & 2.22 & 21.4 & 38.6 \\
4 & 2.75 & 2.87 & 0.08 & 1.03 & 63.9 & 97.3 \\
5 & 3.41 & 3.65 & 0.04 & 0.87 & 74.9 & 98.9 \\
6 & 3.37 & 3.70 & 1.07 & 1.55 & 56.6 & 68.3 \\
7 & 2.80 & 2.87 & 1.65 & 1.77 & 37.2 & 41.9 \\
8 & 6.05 & 14.47 & 0.04 & 0.71 & 88.8 & 99.7 \\ Total & 2.75 & 14.47 & 0.04 & 2.22 & 21.4 & 99.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average torque reduction given by the mechanism’s reconfigurability.  
Figure 12: Average torque reduction ratio provided by the mechanism of reconfigurability on craniotomy trajectories.  
required torques. The main limitation of the present study is that the effect of gravity on the calculated torques is not taken into consideration. Indeed, the general orientation of the robotic manipulator on the patient's skull remains uncertain at the moment.
## 3 Results and Discussions
### Summary and Comparison of Power Reduction  
Table 4 summarizes the rotor power reductions using active twist control over the baseline rotor. The active twist control enhances the rotor performance by reconciling the distribution of loads. When compared to the single-harmonic control, the multi-harmonic increases the power reduction by promoting the control schedule along the circumferential direction. With the increase in segment number, the benefit that is achieved by the segment control configuration increases. However, the increase in rotor power reduction begins to plateau when the segment number exceeds 3.  
Figure 29: Optimal control schedules of the 2-seg control with saturation limits for condition B.  
Figure 27: Rotor power reductions with saturation limits of active twist rate for condition B.  
Figure 28: Optimal control schedules of the uniform twist control with saturation limits for condition B.
## 3 Experimental Data and Results
### Usefulness of Camera-Based Optical Communications in the Detection Process
#### 3.1.2 Real-Time Analysis and Processing through Optical Camera Communication Applications  
When it comes to real-time OCC applications, smartphone devices should have a fast way of processing information based on received images. The aspect in which the frame rate must be established initially and not include variations must not be ignored, and it is essential to conduct the processing during the whole duration of the frame. We assume that Rtf is the frame rate of the camera, then 1/Rtf is required to be the time limit for each frame made in real time. The literature states that for an OCC system that is based on a smartphone device with an Android operating system, an image processing application must be designed and developed to extract the data (see Figure 5). Whether we use OpenCV or another library dedicated to capturing images and extracting data, we need to outline an architecture. Therefore, a dedicated library with programmed functions that deal with the way of computerized extraction in real time can be outlined using Android Studio.  
The most important aspect is the way and the permissions we build for the camera and other applications that are based on access to it. Therefore, the API must set priority levels for all elements that access the hardware component, from defining the size of windows to managing information. When setting the device orientation, we need to adjust the functions in DeviceManager. The image obtained from an LED can be processed to obtain information about the conversion and the required format. A key function is to identify the width of the LED strips. The need for an integrated library within the platform is intended to provide pixel reading functionality, being a threshold for object detection. The features of the new generation of Android systems describe the creation of execution threads that  
Figure 4: Diagram showing the technique of extracting information from an LED using optical communications based on the smartphone’s camera.  
Figure 5: Dedicated OCC processing software architecture through the OpenCV library.  
are extremely difficult to implement in the way requests are processed. Thus, it works to capture single frames and create buffer data sets. Creating a CameraManager class is required by priming multiple requests when they form a queue, which keeps the full frame rate constant. Data from images are encapsulated as objects and accessed using a class called ReaderImageCls using a TYUV_233_521 format initialized according to the operating system. Camera shutter speed management is controlled so one can focus directly on the area of the white LEDs and identify the region (RoI). The extraction of the bit stream that is transmitted is predetermined in the antenna, an aspect that is characterized as the main initial task.
# Driving-Style Assessment from a Motion Sickness Perspective Based on Machine Learning Techniques
## 5 Experimental Results of the Applied Methodology
### Model Training  
The first batch of results studied the models created by the three clustering algorithms for the two groups of variables selected in the previous subsection. The process has handled a variety of parameters, such as the number of clusters, initializations, and different oversampling algorithms.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(\mathbf{PCA}_{1}\) & \(\mathbf{PCA}_{2}\) & \(\mathbf{PCA}_{3}\) & \(\mathbf{PCA}_{4}\) & \(\mathbf{PCA}_{5}\) \\ \hline
**BP** & 0.429 & 0.403 & 0.357 & 0.198 & \(-\)0.69 \\ \hline
**ERPM** & \(-\)0.343 & 0.570 & \(-\)0.387 & \(-\)0.546 & \(-\)0.244 \\ \hline
**GP** & \(-\)0.184 & \(-\)0.03 & \(-\)0.421 & 0.164 & \(-\)0.256 \\ \hline
**PGP** & \(-\)0.126 & \(-\)0.078 & \(-\)0.510 & 0.653 & \(-\)0.211 \\ \hline
**SWA** & 0.561 & 0.039 & \(-\)0.336 & \(-\)0.164 & 0.225 \\ \hline
**SWRS** & 0.571 & 0.085 & \(-\)0.402 & \(-\)0.053 & 0.107 \\ \hline
**VS** & \(-\)0.089 & 0.705 & 0.104 & 0.423 & 0.536 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coefficients of the PCA model trained with driver-controlled variables.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \begin{tabular}{c} _MSDVxy_ \\ (Normalized) \\ \end{tabular} & \begin{tabular}{c} _MSDVxy_ \\ (Original Values) \\ \end{tabular} & \
\begin{tabular}{c} _Category_ \\ \end{tabular} &
\begin{tabular}{c} _Sample Number_ \\ 371 \\ \end{tabular} \\ \hline
0.2–0.4 & 3.99–5.71 & Low & 335 \\ \hline
0.4–0.6 & 5.71–8.30 & Normal & 154 \\ \hline
0.6–0.8 & 8.30–11.19 & High & 43 \\ \hline
0.8–1 & 11.19–28.83 & Very High & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Categorization divisions and the original values of the utilized criteria.  
The obtained silhouette coefficients for the first group of variables (ACCy, YR, SWRS, ACCx, ACCx_neg) are presented in Table 4. Moreover, the results of the silhouette coefficients regarding the models created with the PCA variables (PCA\({}_{1}\), PCA\({}_{2}\), PCA\({}_{3}\), PCA\({}_{4}\), PCA\({}_{5}\)) using the same combination of algorithms and parameters are shown in Table 5.  
Concerning the clustering result evaluation, as the representation spaces used to evaluate the silhouette coefficients are not the same, coefficient results for both approaches cannot be directly compared. However, we can argue that K-means obtained the best results in both classes. The difference is especially high in Table 5 where the worst K-means model to appear in the Table is almost better than any model of the other algorithms. As a general rule, fewer clusters obtain better scores but since the final objective is to utilize the created clusters to identify driving patterns and styles, more clusters may be beneficial in pattern identification.  
Due to the nature of the data, a good amount of overlapping is expected, which explained the low silhouette scores, but the results allow us to objectively identify the best models.
## 3 Image Feature Extraction and Feature Matching Based on SIFT
### Description of Key Points  
The key point description is the expression of the key point in mathematical language, which is a important step in realizing the matching of image feature points. It describes the key point and the surrounding pixels that contribute to it. The pixel area to be solved is first divided into blocks, and the gradient histogram of the corresponding block is then calculated to generate the direction vector. Therefore, the image information is expressed in an abstract form. As is shown in Figure 3, the gradient value of each block of pixels is Gaussian weighted to obtain eight orientations through which a 32-dimensional vector can be generated to be utilized as the mathematical expression of the key point. Experiments show that, for each key point, using a 128-dimensional vector descriptor to represent the key point can achieve the best effect.
## 3 Results and Discussion
### Pareto Front Points
#### 3.1.4 Cyclone Performance
The movement of solid particles at different realizations inside the body of cyclones A and B are represented in Figure 14. The strands of the solid particles are represented as a function of particle diameter. It can be seen that the solids of different diameters mix very well. The smaller particles start escaping at a very early stage, whereas the mid to large-ranged solid particles get closer to the outer wall and follow a definite pattern.
## 3 Materials and Methods
### Architecture  
#### 3.2.1 Rain Streaks Removal  
The rain streak removal step is based on residual networks (ResNets) [43]. In order to maintain real-time performance and not adding to the computational complexity of the object-detection framework The base architecture of progressive residual networks has been adopted. So, instead of utilizing deeper and complex networks, rain streak removal is performed in multiple stages, such that ResNet is used at each stage. Additionally, in order to avoid the increase in the number of network parameters and the susceptibility of overfitting, recursive computations have been applied by sharing same network parameters with multiple stages.  
A basic ResNet has three parts: (i) a convolutional layer \(f_{in}\), (ii) multiple ResBlocks \(f_{res}\) to extract deep representations, and (iii) a convolutional layer \(f_{out}\), as shown in Figure 2. Inference of the PRN at stage \(t\) is shown in Equation (2).  
\[\begin{split}& x^{t-0.5}=f_{in}(x^{t-1},y),\\ & x^{t}=f_{out}(f_{res}(x^{t-0.5})),\end{split} \tag{8}\]  
Figure 2: A basic ResNet.  
Network parameters are reused across different stages, as \(f_{in}\), \(f_{res}\), and \(f_{out}\) are stage invariant. As shown in Equation (2), \(f_{in}\) takes the concatenation of the current estimation \(x^{t-1}\) and rainy image \(y\) as input, the inclusion of \(y\) further improves the deraining performance. The progressive network architecture is shown in Figure 3:  
* Filter sizes \(3\times 3\), padding \(1\times 1\);
* \(f_{in}\) is a convolutional layer with ReLU non-linearity [63], due to the concatenation of 3 channels from \(y\) and another 3 channels from \(x^{t-1}\), the convolution of \(f_{in}\) has 6 input channels and 32 output channels;
* \(f_{res}\) includes 5 ResBlocks;
* \(f_{out}\) is a single-layer convolution, it takes the output of \(f_{res}\) with 32 channels as input and outputs a 3-channel RGB image.
## 2 Materials and Methods
### Land Use Limitation Degree Mapping  
Once the land use recommendations and limitations mapping are completed, the next objective is to determine the degree of territorial limitation based on the overlap of the two layers that comprise it. For this purpose, the land use limitation degree mapping is generated for the southeastern margin of the Ria de Arosa. This process involves a Boolean analysis through data combination geoprocessing based on specific conditions of the study area [21].  
The goal of this method is to identify areas with high and very high conservation quality where natural hazards have also been detected. Both layers are reclassified to be treated as categorical layers, allowing the application of Boolean operations [21].  
The following combination matrix shows the values assigned to areas that meet the predefined criteria: 1 for areas with high or very high conservation quality, 0 for areas without high or very high conservation quality, 1 for areas with natural hazard presence, and 0 for areas without natural hazard presence (Table 10). This layer overlap results in a total of four different scenarios: 1-1, 1-0, 0-1, and 0-0.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Conservation Quality** & **Natural Hazards Presence** & **Result (Land Use Limitation Degree)** \\ \hline
1 (High and very high quality) & 1 (Hazard present) & High limitation (Red zone) \\
1 (High and very high quality) & 0 (No hazard) & Medium limitation (Yellow zone) \\
0 (No high and very high quality) & 1 (Hazard present) & Medium limitation (Yellow zone) \\
0 (No high and very high quality) & 0 (No hazard) & Low limitation (Green zone) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Combination matrix for the analysis of the degree of land use limitation.  
The first scenario represents areas where high and very high conservation quality (1) and natural hazard presence (1) are identified. Using the Raster Calculator, the intersection of both layers is performed, and areas where the resulting value is equal to 2 are extracted. These areas are reclassified as high land-use limitation zones.  
To identify areas where there is a natural hazard presence (1) but no high or very high conservation quality (0), the Raster Calculator is used to perform the intersection, assigning a \"0\" value to the conservation quality layer (\"quality_file\" == 0). Similarly, to obtain areas with high and very high conservation quality (1) but no natural hazard presence (0), the same procedure is applied by assigning a \"0\" value to the hazard layer (\"hazards_file\" == 0). These areas represent the medium land-use limitation degree.  
Finally, low land-use limitation zones are identified by extracting areas where the intersection of both layers (\"quality_file\" and \"hazards_file\") is equal to 0, indicating that no areas of high and very high conservation quality (0) or natural hazard presence (0) have been detected.