esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Fidelity Drone Simulation with Depth Camera Noise and Improved Air Drag Force Models
## 2 Depth Camera Modeling and Implementation
### Experimental Setup  
To investigate the impact of distance and material of the detecting object on the depth error, indoor experiments using Intel Realsense D435 depth camera have been conducted to collect the depth values with 3 different materials. Our experimental setup is similar to the setup proposed in [30]. While we also vary the distance in the experiment, the significant difference between our setup and the setup in [30] is that we adopt different wall materials (plastic, cement, formboard) to analyze their effects on the depth noise. The experimental setup can be seen in Figure 3.  
Before conducting the experiment, we calibrated the D435 depth camera by utilizing Intel's Dynamic Calibration tool, following the connection of the D435 camera to a laptop via a USB3 cable [30]. During the calibration process, the program analyzes all depth values within the full field of view. In addition, we assessed the depth quality using the depth quality tool provided by the Intel RealSense SDK [30; 31]. To confirm depth quality, the camera needed to have more than 50% valid depth points, without the requirement for a flat or perpendicular surface. Following calibration, we employed the Realsense ROS program to publish the sensor data from the depth camera to our laptop. For each material, the distance of the depth camera to the wall was manually set from 0.5 m to 8 m at 1 cm step (751 camera positions in total) using a roller. The distance was measured with the accuracy of 1 mm. After calibrating the camera's position, we check again if the camera is  
Figure 3: Experimental setup to calculate standard deviation values in both simulation and real experiment using Intel Realsense D435 depth camera. The depth camera was positioned to perpendicularly face a wall, and the distance was set from 0.5 m to 8 m at 1 cm step. A depth image with the size of 460 \(\times\) 320 was taken at each distance.  
Figure 2: (**a**) Intel Realsense D435 depth camera, (**b**) stereo IR depth camera diagram.  
perpendicular to the wall using the depth image. Using the real-time data streaming from a ROS topic, we divided the depth image into four sections; the perpendicular condition is then guaranteed if the average depth values of all sections were the same. A depth image with the size of \(460\times 320\) pixels was obtained at each distance. For 751 camera positions, 751 corresponding depth images can be obtained for each material.
## 2 Classification of Routing Protocols
### Deterministic Energy-Efficient Clustering (DEC) Protocol
#### 2.3.2 Energy Heterogeneity  
DEC calculates a threshold residual energy to make sure that the advanced node does not become cluster heads again and again when their residual energy becomes equal to that of the normal node after some rounds. Threshold residual energy \((Th_{RE})\) is defined, [17; 18], as  
\[Th_{RE}=E_{0}(1+\frac{aE_{dis.NN}}{E_{dis.NN}-E_{dis.AN}}) \tag{4}\]  
where \(E_{0}\) is initial energy, \(E_{dis.NN}\) is energy dissipation of normal node, and \(E_{dis.AN}\) is the energy dissipation of the advance node. In DEC, the Residual Energy (RE) information of a cluster member (CMs) is known by their relevant CH's. This information is utilized for rotation of CH in the succeeding rounds. The current CHs review the stored RE information received from CMs at the end of each round. They determine whether they will stay as CHs or replace them with any node in their clusters with the highest RE to become a new CH. The probabilities for normal, advanced, and intermediate nodes to become CH in DEC are:  
\[P_{norm}=\frac{P_{opt}}{1+ma} \tag{5}\]  
\[P_{Int}=\frac{P_{opt}(1+b)}{1+ma} \tag{6}\]  
\[P_{Adv}=\frac{P_{opt}(1+a)}{1+ma} \tag{7}\]  
\[E_{Total}=nE_{0}(1+ma) \tag{8}\]  
\(P_{opt}\) is the optimal percentage of nodes that have to become cluster heads in each round in space. \(P_{norm}\) is the weighted probability for normal nodes, \(P_{adv}\) is the weighted probability for the advanced nodes, m is the proportion of advanced nodes with a times more energy than the normal nodes, and \(E_{Total}\) is the total initial energy of the network [16; 17]. As a result, three of the energy-efficientprotocols are briefly summarized in this section. The protocol which is introduced in this study is introduced in Section 3.
## 5 Results and Discussion
### Discussion
On the other hand, the proposed method and others used to calculate SIFs, such as the COD, the decomposition integral [13], or circular J integral [36], heavily rely on the crack tip position; for a numerical approach, this is not a problem as the crack tip location is known beforehand, but for an experimental work, it is an unknown variable. However, such an evaluation is out of the scope of this paper and will be addressed elsewhere. Furthermore, using LEFM requires that its intrinsic assumptions (sharp crack tip, linear boundary conditions, homogeneous and linear elastic material, and small-scale yielding) are met. Stresses and deformations must be sufficiently linear to comply with the linear elastic assumption of the Westergaard stress function. Furthermore, when selecting the AOI, one must check the stress field and the plastic zone to discard the displacements in the near region. One must remember such limitations when selecting a method to calculate SIFs from experimental fields.
# Modified, Reliability-Based Robust Geotechnical Design Method, in accordance with Eurocode 7
## 1 Introduction
The main shortcoming of SIV, considering its more general application in geotechnics, is the assumption that all random variables are independent and normally distributed, which is often not the case in practice. [PERSON] and [PERSON] [10] suggest a robust procedure of optimisation for a typical foundation for wind turbines. The robustness of the design is measured using a standard deviation of differential settlement and calculated using the Monte Carlo method. The Monte Carlo method is generally simpler, but in a scenario with a large number of random variables and complex objective functions, it becomes computationally expensive. [PERSON] et al. [11] simplify the RGD procedure by reducing the problem of multi-objective optimisation to a sequence of single-objective optimisations, and by substituting the FORM method with integrated PEM with the Monte Carlo method. This makes the RGD procedure more intuitive and more user friendly, while maintaining the robustness measure. An example of a rock slope design is given. Since the objective functions in the example are mathematically simple, the suggested method is more efficient than the original one. The authors suggest an assessment of the method's efficiency in examples with more complex objective functions and a greater number of random variables. The summary of reviewed literature is presented in Table 1.  
In this paper the RGD method is simplified, made less computationally expensive, and its results are harmonised with the criteria prescribed in the Eurocode 7. The simplification of the method's application stems from the change of the robustness measure, where a generalised reliability index (\(\beta\)) is chosen as a substitute of \(\beta_{\beta}\). It has been used for years in structural reliability analyses to express construction reliability. Due to this fact, its value can be considered intuitive. Furthermore, the values are within the approximate 0-20 range, which means that it can also be considered \"user friendly\". In this paper we demonstrated that \(\beta\), by definition, fits into the robustness concept. Based on the chosen robustness measure, the required number of reliability analyses has been reduced. A modified PEM method (IPEM) has been suggested for the calculation of \(\beta\), which enables a use of simpler mathematical operations while maintaining a high degree of accuracy. The total number of reliability analyses is further reduced by conducting them only for the designs chosen in the process of optimisation, using a genetic algorithm. Additional safety constraints are introduced into the optimisation process, to adjust the designs to the ultimate limit state (ULS) and serviceability limit state (SLS) criteria, as defined by Eurocode 7. The suggested modifications can be applied to geotechnical problems in general; however, for illustration purposes, the methodology in this paper is elaborated using the example of shallow foundations in cohesionless soils.
# An Efficient SS-MAC Protocol for IEEE 802.15.4-Based WSNs of Cluster Tree Topology
## 4 Modeling Analysis
### \(W_{s}\) and \(\alpha\)
When \(T_{ch}\) is fixed, the closer \(\alpha\) is to 1, the smaller \(W_{s}\) is; when \(\alpha\) is fixed, the smaller \(T_{ch}\) is, the smaller \(W_{s}\) is. \(W_{s}\) is ultimately determined by \(N_{t}\), \(N_{p}\), \(\gamma\), \(R_{c}\) and the packet size.
## 3 Results
### Experiments and Evaluation Scenarios
#### 3.5.4 Fourth Evaluation Scenario: Multiuser Collaboration  
In this scenario, multiuser collaboration of the Holo-Cloud platform was evaluated by projecting two holographic models to all viewers: a brain model and a molecular ribbon model, both provided in USD file format. All viewers loaded the models, and then one viewer interacted with the holograms by rotating and moving them using Microsoft HoloLens 2 hand gestures, and other users could seamlessly observe the changes in the FOV of their Microsoft HoloLens 2 HMD, HoloLens 2 emulator, and Android HHD at their respective physical locations (Figure 15). In this interaction, the data related to the coordination change in the brain and molecular ribbon models in each frame must be sent to the Holo-Cloud server, and the client software needs to receive the updated coordinates  
Figure 14: The server sends the study data to clients upon request, illustrated by the spikes. The clients cache the data after receiving the data of one full CINE MRI sequence cycle then only metadata is transmitted. The blue solid line is transmitted TCP packets per second.  
Figure 12: CPU utilization spikes when the server loads and caches the study data for the first time. Holo-Cloud was able to fulfill the processing requirements. Also, minor spikes are seen when client connection and data requests are served by the server. The blue solid line is % CPU utilization and the red horizontal dashed line is the maximum CPU utilization.  
Figure 13: Memory utilization spikes occur when the first client requests study data. Then, the server caches the data. The Holo-Cloud platform provides a sufficient amount of memory to run the FI3D. The blue solid line is memory utilization and the red horizontal dashed line is the maximum memory utilization.  
from the server to project on the respective device. The results showed seamless projection of the collaborative task on all client devices.  
In all the mentioned scenarios, no network slowness was observed. The network bandwidth requirement of the FI3D application is minimal, and as the network utilization tables show, the provided internet and end-to-end bandwidth of the cloud server and the clients were considerably higher than the bandwidth utilization of the application. The animation played on client devices was smooth, with no considerable frame drops or lags.  
During the experiments, no slowness in the interaction between the clients and servers was observed. Also, the 4D CINE MRI sequence was played smoothly and had an acceptable visual framerate quality after the first cycle of the animation was cached by the client software.
## 3 Topological Classification
### Hybrid FCS and Battery System
#### 3.1.1 Direct Parallel Connection of Both FC and Battery  
The FCS, battery, and DC bus are directly connected in parallel without using a DC/DC power converter, as illustrated in Figure 7[37]. To transform the DC voltage from the FCS and battery to the AC voltage needed by the motor, a DC/AC converter is utilized [19]. This connection method offers several benefits, including a simple design, minimal space requirements, cost-effectiveness [38; 39], and the possibility for implementing sensor-less controllers [40]. Nevertheless, the drawback of this structure is that when the load varies, the output voltage from the energy sources is frequently less than what the DC bus needs [41]. This action leads to a significant voltage variation at the DC bus, lessens the reliability, and leads to a loss of control of the power system. Additional diodes play a crucial role in maintaining the stability of the power system by preventing the DC bus voltage from surpassing the FC output voltage, which can cause backward current flow and power system failure [42; 43]. However, this additional protection will lower the power system's overall efficiency.
## 4 Methodology
### Attention-Based Spatial-Temporal Fusion Graph Module (ASTFG)
#### 4.2.1 Spatial Dependency Modeling  
Figure 2 illustrates the AST-GCN block composition, where the spatial-temporal GCN and attention mechanism are concurrently designed. In our scheme, we append the temporal GCN to the end of each spatial GCN to alternately process the input data in time and space. As different nodes have different intensities of intersections, we design the attention mechanism to the adjacency matrix before inputting to the spatial GCN layer with the purpose of learning distinct nodes' effects on the prediction precision. The residual connections are leveraged to enable the model to transform features with a higher gradient and accelerate the learning rate.  
To be specific, given the adjacency matrix and feature matrix, the GCN competently operates to recover attributes in the Fourier domain. The graph convolution can be stated as shown below:  
\[H_{l+1}=\varpi(\tilde{D}^{-1/2}\tilde{A}\tilde{D}^{-1/2}H^{l}W^{l}) \tag{7}\]  
where \(H_{l}\) represents the output of the \(l-th\) layer. \(\tilde{A}=A+I\), \(A\) is the adjacency matrix of \(G\), and \(I\) is an identity matrix. \(\tilde{D}\) denotes the diagonal node degree matrix, \(\tilde{D}=\sum_{j}\tilde{A}_{ij}\), \(W^{L}\) is a matrix comprising the parameters of \(l-th\) and the impact of \(\tilde{D}^{-1/2}\tilde{A}\tilde{D}\) is to normalize the \(\tilde{A}\). The \(\varpi(\cdot)\) is the activation function, where the \(ReLU(\cdot)\) that has had its outstanding performance in deep learning proved is accepted in our work. For the spatial GCN, its kernel size is \(\Theta_{G}\in\mathbb{R}^{5\times 1}\) and the layer convolves \(S\) nodes at a time. In contrast, the temporal operates in the time domain \(T_{h}\), and the kernel is set as \(\Theta_{G}\in\mathbb{R}^{T\times 1}\).  
To enhance the model's robustness and speed up the model convergence rate, we exploit the batch normalization (BN) for the graph. For each batch, the input data are normalized by the following:  
\[\hat{x}^{k}=\frac{x^{k}-e[x^{k}]}{\sqrt{v[x^{k}]}} \tag{8}\]  
where \(x^{k}\) and \(\hat{x}^{k}\) are the BN input and output, while \(e[x^{k}]\) and \(v[x^{k}]\) are the mean and variance of the input data, respectively.  
As mentioned above, we introduce the attention mechanism to indicate other vehicles' heterogeneous influence on the target vehicle movements. With regard to each adjacency matrix, we design a learning representation matrix \(M\) and conduct the inner product of \(M\) and \(\tilde{A}\) denoted by \((M\odot\tilde{A})_{ij}\). The \(M\) is firstly initialized as an identity matrix, which can be continuously updated to achieve the goal of distinguishing the interaction density.  
Generally, the GCN is employed to mine the spatial characteristics of the trajectory prediction problem in depth. Note that both the vehicle-vehicle features and the vehicle-traffic rules relationships in space are all successfully captured in the AST-GCN block. The aim of stacking GCN layers is to extract higher-level spatial dependencies.
# Secure Image Transmission Using Multilevel Chaotic Encryption and Video Steganography
## 5 Results and Testing
### Image Encryption Result
#### 5.2.2 Differential Attack Analysis  
Important picture information is indicated by the differential attack factors that the UACI and NPCR detect. When the value of a pixel in the original image is modified arbitrarily, the percentage of pixels in the encrypted image that change is known as the NPCR. The UACI represents the change in a pixel value in the encrypted image following a random modification to the pixel value in the original image. When the encrypted image differs significantly from the original plaintext image, and the pixel change is small, the encryption algorithm can effectively withstand differential attacks [43]. The relation for UACI is as shown below:  
\[NPCR=\sum_{i=0}^{M}\sum_{j=0}^{N}D(ij)\times 100\%\]  
where M and N stand for the columns and rows of the image, in order. Tables 3 and 4 show the proposed algorithm's NPCR and UACI values compared with those from research studies. Using the example images, we showcase the results comparable to those in the research studies on alternative encryption methods.  
According to the results in Table 4, the suggested method works better than several reference methods in most test photos, obtaining wider pixel intensity variations that strengthen the encryption. For example, the House image showed strong encryption capacity with UACI = 37.23, outperforming several current methods. The suggested approach also achieved 34.46 for the Baboon image, guaranteeing high unpredictability in encrypted images. Despite a minor decline in the Peppers image, the values were still competitive. These results validate that the encryption is a strong solution for safe data transfer, maintaining high security and sensitivity to plaintext changes.
# A State Validation System for Augmented Reality Based Maintenance Procedures
## 4 Workflow  
The peculiarity of the proposed system consists of the sequence of image processing operations performed to validate the user's operations. Figure 4 shows the workflow of the overall system with respect to the steps of a standard maintenance procedure (green blocks): The automatic validation of the user's operation is repeated at the end of each step and only upon the user's request. The system consists of three image processing blocks (motion detection, status analysis and validation), an initialization block and a Graphical User Interface (GUI) to provide feedback to the user (blue blocks). The video input to the various blocks of the system is provided by a camera that frames the environment (in purple).  
Figure 4: Workflow of the proposed validation algorithm.  
A description of the overall system's workflow is provided here, whereas the details of each image processing block is explained in the following subsections. When the user request a step's validation, the initialization module performs some preliminary operations to enable the other modules to properly elaborate the frames from the camera. Then, the status analysis module performs the tracking of the machinery and evaluates the features of the tracked object in order to detect status changes' occurrences: If any change is detected, then the validation module is evoked to evaluate the correctness of the operation performed by the maintainer. Meanwhile, if any motion is detected in the scene, the status analysis and validation modules are instantly informed by the motion detection module (red arrows): This implies that any operation performed by these two modules will take into account this information in order to avoid errors. Validation will not be available till the motion stops and the data structures used for the status analysis and the validation phase will be cleared from any inconsistent value. Moreover, depending on the input and output of each module, the GUI module may be evoked by any of the previous modules to provide the user a feedback (green arrows). Finally, if a status change is detected and the validation is successful, the system can provide a positive response to the user and the maintenance procedure can proceed to the following step.
# Design of Machine Learning Solutions to Post-Harvest Classification of Vegetal Species
## 3 Experimental Evaluation
### Comparison
#### 3.2.2 Region-Based Segmentation  
As with the color spaces, we analyzed the effectiveness of image segmentation in solving image classification problems. We confirmed that the effects of image segmentation vary depending on the training dataset based on the results presented in Table 3. When DS1 was used for training, the use of segmentation led to a better accuracy in classification compared to the results obtained without segmentation. Conversely, the classification without segmentation achieved a better accuracy when DS2 was used for training. The raw data for DS1 and DS2 were obtained from different products but from the same farm on different dates. From a practical perspective, different datasets obtained under different conditions must be used. Therefore, we need to use segmentation carefully as a preprocessing step according to the characteristics of the datasets.
## 6 Detailed Review of CS Based ECG IoT Framework and Analysis
### CS Realtime Implementations and IoT Framework  
Technologies such as the IoT and robotics systems will be essential in modernizing the healthcare system. Certain researches that are carried out by implementing hardware through IoT platform is discussed in this section. Low-complexity Field Programmable Gate Array (FPGA) hardware implementation was proposed for healthcare applications by [PERSON] et al. in [233] in which the design was based on pipeline optimization of the Programmable Logic (PL). Simulation of MP algorithm was carried out by Vivado tools, and MATLAB. A low-cost Xilinx board was utilized to examine the MP algorithm and the above scheme was capable of attaining reduced computational time and energy consumption. Though OMP is more complex than the MP algorithm, the result obtained was similar when compared to the former one. Authors [PERSON] et al. [234] aim to address issues such as power consumption and medical record security in an IoT-based health monitoring system based on CS technique. A unique sparse sensing matrix was constructed, with sparse Bernoulli matrix and linear shift feedback registers (LFSRs) architectures to realize an efficient encryption module. Based on an orthonormal Symlet-4 wavelet matrix representation matrix, a sparsifying matrix was constructed and OMP algorithm was considered for reconstruction of the ECG signal. This research displayed that transmission is kept secure even if the attacker can acquire 95% of the information. The reconstructed ECG signal in the high quality (HQ) had minimal inaccuracy and can be used directly for human diagnosis. The quality of the reconstructed ECG in the low quality (LQ) scenario is susceptible to degradation and the ECG features can still be detected and utilized. In another hardware implementation, [PERSON] et al. [235] identified the changes present in original and reconstructed signal of QRS complex in ECG signal. To overcome the above issues the authors presented a novel approach by concatenating the high accurate method known as Near-Precise Compressed (NPC) algorithm and CS algorithms together for the compression of ECG signal. NCP can be easily implemented using low-cost hardware. Here, hardware has been implemented using VLSI technology, Taiwan Semiconductor Manufacturing Company's (TSMC) 0.18 \(\upmu\)m Complementary Metal-Oxide-Semiconductor (CMOS) technology. NCP compresses the region of high change between the original and recovered signal and CS algorithm takes care of other regions of ECG signal compression. SNR and PRD are identified as evaluation matrices. Compared the