esa-sceva/satcom-chunk-collection huggingface dataset:
# Multidisciplinary Design Optimization of Cooling Turbine Blade: An Integrated Approach with R/ICSM
## 4 Optimization Results and Discussion
### Comparison of Optimization Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Tip Section** & **Value** & **Middle Section** & **Value** & **Root Section** & **Value** \\ \hline \(\frac{R_{1,\text{cap}}-R_{\text{down}}}{R_{\text{down}}}\) & \(-1.09\%\) & \(\frac{R_{1,\text{cap}}-R_{\text{down}}}{R_{\text{down}}}\) & \(+23.05\%\) & \(\frac{R_{1,\text{cap}}-R_{\text{down}}}{R_{\text{down}}}\) & \(-3.30\%\) \\ \hline \(\frac{\beta_{1,\text{cap}}-\beta_{1,\text{lbna}}}{R_{\text{down}}}\) & \(-4.50\%\) & \(\frac{\beta_{1,\text{cap}}-\beta_{1,\text{lbna}}}{P_{\text{clabna}}}\) & \(-4.66\%\) & \(\frac{\beta_{1,\text{cap}}-\beta_{1,\text{lbna}}}{P_{\text{clabna}}}\) & \(-7.42\%\) \\ \hline \(\frac{\beta_{1,\text{cap}}-\beta_{1,\text{lbna}}}{P_{\text{lbna}}}\) & \(+6.27\%\) & \(\frac{\beta_{1,\text{cap}}-\beta_{1,\text{lbna}}}{P_{\text{lbna}}}\) & \(-8.72\%\) & \(\frac{\beta_{1,\text{cap}}-\beta_{1,\text{lbna}}}{P_{\text{lbna}}}\) & \(-7.13\%\) \\ \hline \(\frac{W_{\text{clap}}-W_{\text{clabna}}}{W_{\text{clabna}}}\) & \(+7.32\%\) & \(\frac{W_{\text{clap}}-W_{\text{clabna}}}{W_{\text{clabna}}}\) & \(-27.88\%\) & \(\frac{W_{\text{clap}}-W_{\text{clabna}}}{W_{\text{clabna}}}\) & \(-2.88\%\) \\ \hline \(\frac{\beta_{1,\text{cap}}-\beta_{1,\text{lbna}}}{\omega_{\text{lbna}}}\) & \(-3.96\%\) & \(\frac{\beta_{1,\text{cap}}-\beta_{1,\text{lbna}}}{\omega_{\text{lbna}}}\) & \(+20.03\%\) & \(\frac{\beta_{1,\text{cap}}-\beta_{1,\text{lbna}}}{\omega_{\text{lbna}}}\) & \(+10.19\%\) \\ \hline \(\frac{\gamma_{1,\text{cap}}-\gamma_{1,\text{lbna}}}{\gamma_{1,\text{lbna}}}\) & \(+16.63\%\) & \(\frac{\gamma_{2,\text{cap}}-\gamma_{2,\text{lbna}}}{\gamma_{2,\text{lbna}}}\) & \(+8.61\%\) & \(\frac{\gamma_{2,\text{cap}}-\gamma_{2,\text{lbna}}}{\gamma_{3,\text{lbna}}}\) & \(-1.19\%\) \\ \hline \(\frac{S_{1,\text{cap}}-S_{1,\text{lbna}}}{S_{1,\text{lbna}}}\) & \(+13.25\%\) & \(\frac{S_{2,\text{cap}}-S_{2,\text{lbna}}}{S_{2,\text{lbna}}}\) & \(+12.23\%\) & \(---\) & \(---\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of 20 blade profile design variables of the first-stage rotor blade in the initial and improved scheme. Figure 12: Comparison of the tip, middle, and root profile of the first stage rotor blade before and after optimization. (**a**) Root section; (**b**) middle section; (**c**) tip section. Figure 13 contrasts the three-dimensional models of the first-stage rotor blade between the initial and enhanced schemes. After optimization, there is an amplified discrepancy in installation angles between the blade tip, middle, and root sections, accompanied by significant blade body twisting. Moreover, except for the root section, the axial length of the blade is notably extended.
## 3 Method  
### K-Means Clustering Method  
The technical workflow of the data mining method proposed in this study is illustrated in Figure 5. All algorithms are implemented in MATLAB R2019b. The association rules algorithm is not equipped to handle numerical variables, which constitute the majority of landslide monitoring data types. Therefore, it is crucial to convert these numerical variables into the classified variables that the association rules algorithm can process prior to conducting the correlation analysis. K-means clustering is a widely used unsupervised learning algorithm capable of managing large-scale unlabeled data, including both numerical and discrete variables. This algorithm automatically groups data objects with high similarity into several clusters and has been widely applied in data mining research [36]. The core of K-means clustering methods is an optimization expectation maximization algorithm (see Figure 6), with the specific processes outlined as follows:  
Figure 5: Workflow of data mining method.  
Figure 6: Schematic diagram of K-means clustering.  
1. The number of clusters (\(K\)) is determined using methods such as the Elbow method and Silhouette analysis. \(K\) initial cluster centers (\(C_{1}\), \(C_{2}\), \(\ldots\), \(C_{K}\)) are randomly selected from the \(N\) sample datasets.
2. The Euclidean distance (as defined in Equation (1)) between each cluster center \(C_{i}\) and the remaining data objects \(X\) is calculated to identify the target objects closest to the cluster center \(C_{i}\), which are classified as the cluster members of cluster \(i\), where \(m\) is the dimension of the data objects, \(C_{ij}\) is the value of the \(j\)th dimension of the cluster center \(C_{i}\), and \(X_{j}\) is the value of the \(j\)th dimension of the data object \(X\). \[D(C_{i},X)=\sqrt{\sum_{i=1}^{m}\bigl{(}C_{ij}-X_{j}\bigr{)}^{2}}\] (1)
3. For each cluster, \(i\) obtains \(n_{i}\) samples, \(\sum n_{i}=N\), where \(i=1\), \(2\), \(\ldots\), \(K\). The mean vector \(M_{i}\) (Equation (2)) of cluster \(i\) is defined as the centroid of the cluster. This is updated to the new cluster center \(C_{i}\), where \(X_{ji}\) is the \(j\)th sample belonging to cluster \(i\): \[M_{i}=\left(\frac{1}{n_{i}}\right)\sum_{j=1}^{n_{i}}X_{ji}\] (2)
4. The sum of squared errors (SSE, as defined in Equation (3)) for all data objects is an important indicator for measuring clustering performance when aiming to improve the tightness of the cluster by minimizing SSE. SSE is calculated as follows: \[SSE=\sum_{i=1}^{K}\sum_{X\in C_{i}}\left|D(C_{i},X)\right|^{2}\] (3)
5. It is necessary to repeat steps (2)-(4). The calculations are updated iteratively, depending on the Euclidean distances between cluster members and centers, until a stable position is reached where the cluster centers no longer change. Alternatively, the maximum number of iterations is reached, or the SSE is locally minimized.
## 3 Challenges in Terahertz-Based FSO Communication
### Atmospheric Absorption
#### 3.1.2 Challenges in Implementation  
High cost: The fabrication of low-loss PCFs and metamaterials is cost-intensive.  
Bandwidth trade-offs: selecting specific frequencies reduces the available bandwidth for communication.  
Complex integration: Adaptive systems require sophisticated algorithms and hardware for real-time operation.
## 3 HMC-MAC Protocol
### Neighbor discovery  
Neighbor discovery enables us to know which nodes are prone to interfere and which nodes are authorized to transmit simultaneously. In order to avoid overloading the network with long control messages to exchange neighborhood information, we use bitmaps to represent neighbors. Using the local propagation order (which is a list of all the node addresses of the network), every node is able to build and manage a bitmap that represents all the nodes in the network. Each index of the bitmap corresponds to the node address with the same index in the propagation order. This way, any node is able to represent its neighbors using one bit for each neighbor. Thus, when the network is dense, neighborhood information will not overload the nodes with control traffic and neighbor list tables using addresses or identifications.  
In order to avoid interference when acknowledgments are used, 3-hop neighborhood needs to be discovered. In [20], we discussed why that the reuse of channels should be considered up to 3-hop neighborhood to avoid collisions and interference when immediate MAC layer acknowledgments are used. Figure 2 shows an example of how the use of the same frequency channel by a 3-hop neighbor might cause a collision. Nodes \(A\) and \(D\) are 3-hop neighbors using the same channel. A collision can occur on \(C\) when \(B\) is sending data to \(A\) and \(D\) is sending an acknowledgment to \(C\). Thus, the same channel cannot be used in a 3-hop neighborhood.  
One hop neighbor list is built using the source address of received beacons. When a node receives a beacon from another node, it considers that this node is a neighbor. This does not guarantee that the link is bi-directional, but we assume that it is an interfering node. In order to build 2-hop neighbor lists, each node includes the bitmap of its 1-hop neighbors in the beacon. Hence, when a node receives all the beacons from its neighbors, it is then able to build the list of its 2-hop neighbors. When a node figures in the bitmap of a neighbor, it can consider that a bi-directional link exists with that neighbor. In order to build 3-hop neighbor lists, each node should include the bitmap of its 1-hop neighbors and the bitmap of its 2-hop neighbors in the beacon it sends.  
By using the beacon and the propagation order to build 1-hop, 2-hop and 3-hop interference neighborhood, we avoid using additional control traffic such as HELLO messages that are traditionally used by the routing protocols. By using bitmap codification and sending beacons in a TDMA manner, neighboring information are exchanged efficiently between nodes without collision and with light  
Figure 2: 3-hop neighborhood interference in channel allocation with acknowledgments. A and D are 3-hop away nodes, if they are allowed to receive data during the same time slots, the ACK sent from D to C will collide with the data sent from B to A.  
overhead. It should be noted that only new arriving nodes are included in the propagation order to avoid sending the complete list in the beacon and overload the frame payload.
# Secure Image Transmission Using Multilevel Chaotic Encryption and Video Steganography
## 3 Methods and Materials
### Steganography  
This research employs steganography to conceal encrypted information within video frames. It leverages the LSB technique to achieve minimal modification while providing robust security. The proposed method improves privacy and resilience against steganalysis by combining chaotic encryption with LSB-based steganography [19].  
Video Steganography Using LSB  
This research breaks down the video into separate frames before implementing steganography and encryption to bolster security [21]. This frame segmentation allows for targeted encryption and embedding, preserving video quality while improving processing speed. Since the segmentation technique adheres to a fixed frame interval, it aligns effectively with the proposed encryption strategy [14].
## 3 Mathematical Model
### Gauss-Markov Model  
Motivated by the findings in the previous section, we propose a \(p\)-order Gauss-Markov autoregression model [25] that predicts the future position of a mobile node from its previous mobility trace. We use the Yule Walker equations [26] to determine the degree of correlation between a node's future position and its past positions.  
The \(p\)-order Gauss-Markov process is a random process that models the randomness of a practical system as a function of time. The order \(p\) reflects the contribution of the history of the practical system measurements to the current measurement under investigation. For vehicle positioning, we can assume that vehicle movement, and hence its successive positions, follow a similar process, where a vehicle \(v\)'s future position \(X_{n}\) is a time function of \(v\)'s previous positions. This assumption is reasonable because a vehicle's movement is constrained by the road map and its movement dynamics. A \(p\) -order Gauss-Markov process is defined as:  
\[X_{n}=A_{1}X_{n-1}+A_{2}X_{n-2}+:::+A_{p}X_{n-p}+\emptyset_{n} \tag{3}\]  
Figure 4: Autocorrelation of latitudinal location measurements for vehicle \(v_{2}\).  
Figure 3: Autocorrelation of longitudinal location measurements for vehicle \(v_{2}\).  
where \(n=1,2,...,N\) is the discrete time interval of measurements, \(A_{i},i\leq p\), are model parameters, \(p\) is the number of parameters in the model, and \(\phi_{n}\) represents the independent white Gaussian. The goal is to predict \(X_{n}\) based on the linear combination of \(X_{n-1},X_{n-2},...X_{n-p}\) by estimating the parameters \(A_{i}\), and the variance of \(\phi_{n}\), \(\sigma_{\phi}^{2}\), hence, fully characterize the model. The best linear prediction of \(\overline{X_{n}}\) with minimum mean square error is the one that meets two criterion; \(\overline{X_{n}}\) can be defined as a linear combination of \(X_{n-1},X_{n-2},...X_{n-p}\) and the prediction error, \(\phi_{n}=X_{n}-\overline{X_{n}}\) is uncorrelated with the linear combination of \(X_{n-1},X_{n-2},...X_{n-p}\).
## 1 Introduction
[1, 2, 5, 6, 12, 13, 14, 15, 16, 17, 18, 19] heavily relied on dynamic system models and inaccuracies in complex or incomplete models, leading to estimation errors and unstable and slow convergence outcomes. Recently, to address the limitations of dynamic system-based methods, machine learning-based approaches for vehicle mass estimation have been proposed. Ref. [20] proposed a new method combining learning-based models and model-based methods to estimate HDV mass. Ref. [21] proposed a hybrid approach using transfer neural networks to provide preliminary mass estimates, which were refined by an adaptive extended Kalman filter. Ref. [22] proposed a trained feedforward neural network (FFNN), training the network with signals typically available in HDVs to estimate mass. Ref. [23] proposed an integrated estimation strategy using data-driven techniques and an artificial neural network (ANN) classifier to estimate road friction coefficient and vehicle mass. Most previous studies required additional sensors to estimate vehicle mass or involved constructing dynamic models followed by filtering and learning. Dynamic models include various physical parameters and nonlinear elements, making them complex and demanding higher computational power for real-time operation, which makes it challenging to apply the proposed methods to ECUs. Unlike [20, 21, 22], this study focuses on designing a more practical supervisory ANN model-based approach to estimate vehicle mass by using only minimum inertial information from existing sensors. This study uses a pure ANN model to estimate the vehicle mass without any other means, such as a hybrid model or the assistance of model-based approaches. In addition, compared to other ANN-based methods [20, 21, 22], the perturbed engine torque and vehicle longitudinal acceleration (instead of original engine torque and vehicle acceleration) are selected as the input of the ANN model to guarantee more accurate and faster estimation performance, which is intuitive selection tracking the complicate pattern change in the ratio between acceleration and traction force (or engine torque). Finally, the proposed estimation method shows a faster estimation response with less transient responses (overshoot) and more accurate results compared to previous model-based approaches (RLS, KF). The effectiveness of the ANN classifier has been validated using both TruckSim and actual test data. The rest of this paper is organized as follows: Section 2 introduces the ANN-based classifier and explains modifications for better results. Section 3 presents the validation of the proposed work with real test data. Finally, conclusions are drawn.
## 4 Dynamic BWP Allocation: Dynamic Multiplexing vs. Orthogonal Slicing
### Simulation Results
It was found that \(D_{threshold}\) increases with smaller eMBB MCS indices as DM becomes more tolerable against puncturing because of the increased error-correcting capability. From the results shown in Figures 4 and 5, it is clear that \(D_{threshold}\) is governed by a relative MCS level between eMBB and URLLC. Figure 6 shows the capacity regions of DM and OS when the number of CBGs, \(N_{CBG}\), for DM is varied. The size of the CBGs decreases with increasing \(N_{CBG}\) for a given TB size. For a smaller CBG size, the amount of puncturing URLLC traffic over the CBG is reduced; however, the error-correcting capability of CBG \(l\) is also reduced. As the number of CBGs increases, it is possible that one of them is successfully transmitted even if the error-correcting capability of each CBG is reduced. As indicated by the red circles in Figure 6, the performance is degraded when \(N_{CBG}\) is large, i.e., \(l\) is reduced under a low URLLC load. The opposite performance is observed under a high URLLC load, as a large \(N_{CBG}\) results in a diversity gain. Furthermore, \(D_{threshold}\) increases with an increase in \(N_{CBG}\). This implies that a greater number of CBGs leads to a higher DM gain under a high URLLC load. Figure 4: Capacity region with varying the URLLC MCS index: DM vs. OS.
## 3 Results
### Energy Harvesting Performance  
An ALLPOWER 200W solar panel was located on the roof of the passenger van to supply power to the USB dongle and the user's laptop. This solar panel is capable of delivering up to 2 A and 5 V under a luminous flux of 80,000 lx of sunlight. As the USB dongle's current consumption is 1.12 A, the USB dongle was completely self-powered in the previous conditions of luminosity. Moreover, a 26,800 mAh power bank battery was connected to the solar panel to store the generated power. In this set-up, the outputs of the power bank battery were connected to the laptop and the USB dongle. It took 13.4 h to charge the battery with the power generated by the solar panel, and the battery life was 22.3 h. These values allowed us to verify that the USB dongle and the laptop were self-powered by the solar panel. Therefore, the user could watch UHD videos without worrying about the power supply of the laptop or the USB dongle. It is important to clarify that without the energy harvesting capabilities, the USB dongle and the user's laptop had an autonomy of 22.3 h.
# Multi-Scale Indoor Scene Geometry Modeling Algorithm Based on Segmentation Results
## 3 Methods
### Recognition and Modeling of Regular Geometric Shapes
#### 3.2.2 Recognition and Modeling of Planar Geometric Objects  
The most common indoor scenes are dominated by flat structures such as desktops, floors, and boxes. Using flat surfaces as the main matching surfaces allows for quick recognition of geometric objects that contain flat structures. For objects with only flat surfaces, the largest face is first identified by finding the largest visible face in the current perspective. This face is considered as the main matching surface. The relationship between the face normal vector and the adjacent face normal vectors is then determined. If the normal vectors are perpendicular to each other, the object is considered as a cuboid, corresponding to packaging boxes and other similar structures in the scene. If the normal vectors are parallel to each other, further fusion of the faces is needed, using a larger flat surface as a whole, corresponding to desktops, walls, or floors in the scene.  
The initial parameters of a flat surface can be calculated from the center and normal vector of the face. Based on this, the RANSAC algorithm is utilized to fit the parameters of the plane, which speeds up the fitting process of optimal parameters. After determining the specific type of the flat geometric object and the parameters of each face, direct modeling of the geometric object is not feasible without knowing the object's dimensions. To address this problem, the minimum oriented bounding box is computed for the segmented object, according to the construction method of the bounding box. For a cuboid, two perpendicular plane normal vectors can be used as the first and second principal axes, and the third principal axis can be obtained by utilizing the property of mutually orthogonal coordinate axes. Then, the point cloud of the object is projected onto the three directions to obtain the maximum and minimum values of coordinates in each direction. This can determine the length, width, and height of the cuboid, and also obtain the center of the bounding box to determine the object's position in the scene.  
Modeling of segmented objects is performed by combining the geometric parameters of the internal planes of flat geometric objects with the dimensions of the oriented bounding boxes. To visually display the modeling results, point cloud data corresponding to the generated geometric objects are obtained using known parameters, representing the reconstruction results. The reconstruction results of flat surfaces and cuboids in the scene are shown in Figure 4. Figure 4a shows the original point cloud data with certain missing parts. The green-bordered box in Figure 4b represents the oriented bounding box for the object. Figure 4c shows the overlaid result of the reconstruction and the original point cloud data, from which we can observe that the reconstruction effectively fills in the missing original data on the flat objects [20].  
Figure 3: Schematic diagram of basic geometric models.
## 4 FE Analysis and Result
### Material Model
#### 4.2.2 Concrete  
The nonlinear behavior of the concrete material can be expressed by a concrete equivalent uniaxial stress-strain curve, as shown in Figure 14. In the case of compressed concrete, this curve was divided into three parts. The first part was assumed to be in the elastic range up to the proportional limit stress. The proportional limit stress was calculated as \(0.4f_{ck}\), as  
Figure 12: FE model and mesh for push-out specimen (ex: S4-45). Figure 13: Stress–strain relationship: (**a**) bi-linear stress–strain curve (H beam, rebar); (**b**) tri-linear stress–strain curve (stud). given in Eurocode-4. Here, \(f_{ck}\) is the concrete compressive strength of a cylinder test piece. \(f_{ck}\) is 0.8\(f_{cu}\), and \(f_{cu}\) is the concrete compressive strength of a cubic test piece. The strain \(\varepsilon_{c1}\) that corresponds to \(f_{ck}\) is 0.0022. The initial elastic modulus was calculated using Equation (2), an empirical equation given by Eurocode-2 [38]. The Poisson ratio of the concrete was considered to be 0.2 [3]. The second part of the curve is a nonlinear parabola that starts at the proportional limit stress 0.4\(f_{ck}\) and extends to the concrete compressive strength \(f_{ck}\). This curve can be determined by Equation (3), which is given in Eurocode-2. \[f_{c}=\bigg{(}\frac{kn-n^{2}}{1+(k-2)n}\bigg{)}f_{ck} \tag{3}\]  
Here, \(n=\varepsilon_{c}/\varepsilon_{1}\), \(\varepsilon_{c1}=0.0022\) (the strain at the maximum compressive stress \(f_{ck}\)), and \(k=1.1E_{c}\times(\varepsilon_{c1}/f_{ck})\). The third part of the stress-strain curve of the compressed concrete is the declining region from \(f_{ck}\) to \(rf_{ck}\). Here, \(r\) is the reduction coefficient given by [PERSON] et al. [39]. \(r\) is given variously in the range of 0.5-1.0 depending on the amount of compressive strength in a cubic concrete test piece (30-100 MPa). The \(r\) value of 0.85 that was used by [PERSON] and [PERSON] (2009) was employed in this study as well. During failure corresponding to \(rf_{ck}\), the ultimate strain of the concrete \(\varepsilon_{cu}\) was the same as \(\alpha\varepsilon_{c1}\). According to Eurocode-2 and BS 8110 [40], \(\varepsilon_{cu}\) is 0.0035 (\(\alpha=1.75\)). [PERSON] et al. [39] used 11 as the value of \(\alpha\) for a concrete model that was confined in a dense state due to the rebar (confined concrete). [PERSON] and [PERSON] (2009) used various \(\alpha\) values for correction between the load-slip curve derived through analysis and the experimental data, and they found an optimal match at \(\varepsilon_{cu}=0.01\) (\(\alpha=5\)). We used the same values [PERSON] and [PERSON] [4] did. The concrete stress of the tensile region was assumed to increase linearly in proportion to the strain until cracking occurred in the concrete. In addition, the tensile stress of the