esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Validation  
The control algorithms of the target wind turbine, including the feed-forward control, available power estimation, LQRF control algorithms, and wind speed estimation algorithm, were designed using Matlab/Simulink (R2024b) and finalized in DLL format. The DLL format external controller was integrated with the Bladed dynamic simulation program for control performance verification. Bladed simulation, while less reliable than experimental validation, is a performance verification method that can be considered before applying control algorithms to actual wind turbines. Although it may differ slightly from real-world conditions, it is commonly used in research for verifying control algorithms. Figure 5 provides a brief overview of the procedural steps involved in the simulation utilized in this study, the process of which can be divided into control algorithm design, DLL file creation, and simulation using the Bladed program. Specifically, the Bladed simulation involved several detailed stages, including wind turbine modeling, wind condition setting, controller application, and analysis.  
Figure 6 presents the comparison between the wind speed estimation results and the rotor mean wind speed under average wind speed conditions of 18 m/s and normal turbulence model (NTM) Class A. The rotor mean wind speed represents the equivalent wind applied across the rotor plane of the wind turbine, assumed as the reference value for comparison in this study. The black line denotes the rotor mean wind speed, the blue line shows the wind speed estimation results based on the 3D look-up table method, and the red line indicates the wind speed estimation results based on the Kalman filter method. Both wind speed estimation methods exhibit similar trends to the estimated wind speeds, and the validation of wind speed prediction was conducted by comparing mean wind speeds and standard deviation values. Table 2 quantitatively presents the rotor mean wind speed and wind speed estimation results under the condition of an average wind speed of 18 m/s. For the 3D look-up Table method, the errors in mean wind speed and standard deviation are approximately 2.58% and 2.75%, respectively; meanwhile, for the Kalman filter method, they are approximately 5.67% and 3.61%, respectively.  
To compare the wind speed estimation algorithms, one algorithm each from SISO-based classical control, algorithms applicable to wind farm control, and MIMO-based modern control was selected and simulated. The following section presents the simulation conditions and results, and compares the performance results of the two wind speed estimation methods.  
Figure 5: Setup and procedures for Bladed simulation.
# A Comparative Analysis for Optimizing Machine Learning Model Deployment in IoT Devices
## 3 Methodology
### Convolutional Neural Network  
Convolutional Neural Networks (CNNs) are deep learning models commonly used for computer vision. They consist of layers that can detect features using a process known as convolution, which uses activation layers to bring out more relevance to these features and pooling to reduce the dimensions for faster inference.  
We have used a basic Convolutional Neural Network for both Pytorch and Libtorch to keep our experimental setup uniform. The architecture is as follows:  
\[3\times 3\ \text{ Conv.}\rightarrow\text{Activation}\to 3\times 3 \text{ Conv.}\rightarrow\text{Activation}\rightarrow\text{MaxPool} \tag{1}\]  
In our study, we used basic Convolutional Neural Network (CNN) architecture for both PyTorch and Libtorch to ensure uniformity in our experimental setup. The architecture consists of two \(3\times 3\) convolutional layers, each followed by an activation function, and concludes with a MaxPool layer. This specific design was chosen for its balance between computational efficiency and the ability to capture essential spatial features from input images. Using \(3\times 3\) convolutions is a common practice that minimizes computational overhead while effectively extracting fine details.  
The simplicity of this architecture facilitates the ease of implementation and reduces the error rate, which is crucial when comparing the performance of different machine learning libraries. By keeping the architecture consistent, we ensure that any observed differences in performance, power consumption, or carbon footprint can be attributed to the libraries themselves rather than variations in the network design.  
The chosen architecture also provides a reliable baseline for benchmarking the performance of different machine learning libraries under controlled conditions. For the FashionMNIST dataset, which includes various patterns and textures in clothing items, this CNN architecture is particularly suitable. It captures intricate patterns with its double layer of \(3\times 3\) convolutions and effectively reduces spatial dimensions with the MaxPool layer, improving classification performance while maintaining computational efficiency. Additionally, the simplicity of the network helps prevent overfitting, a common issue with more complex architectures, especially when dealing with datasets of moderate complexity like FashionMNIST. This approach ensures that our comparative analysis focuses on the libraries' performance and environmental impact, providing clear insights into their capabilities and ecological footprints.
# An Efficient and Reliable Algorithm for Wireless Sensor Network
## 7 Result Comparison and Performance Evaluation
### Protocol Evaluation  
The following metrics, i.e., throughput, PDF and overhead are used in this experiment as shown in Table 2. Throughput is the amount of data packet transmission from source to destination at a specific interval and PDF is the amount of data packet received divided by the transmitted data. Finally, overhead is the amount of total packet send divided by the total packet received.
## 4 Results and Discussion  
The aerodynamic shape optimization and simulation process is carried out to have an objective to decrease the horizontal aerodynamic force (minimize drag) subject to different airfoil and their structural constraints. The whole optimization process was accomplished with in-house MATLAB code. CFD and ICEM CFD were used to validate the airfoil pressure distributions and meshing, respectively, while ANSYS CFX was employed for computing the results. Blocking and framework have been created to mesh original and optimized airfoil and solve it under a given set of conditions. The design conditions, constraints, and optimization objectives are shown in Table 3. These optimization objectives and constraints are depicted in both the subsonic and transonic airfoil.  
In total, eight design variables were used for the CST parameterization method to generate a new airfoil (S-821 and RAE-2822) shown in Table 4. In the optimization process by the CST method for a 3\({}^{\circ}\) polynomial order, four variables for each lower and upper surface are needed to design an airfoil. The role of these design parameters is to find suitably optimized airfoil within the range of upper and lower bounds of shape coefficients. These eight design variables are important for the generation of the optimized airfoil by GA code.  
Figure 8: Meshing design topology and its naming.  
\begin{table}
\begin{tabular}{l l} \hline \hline Angle of Attack (AOA) & 3\({}^{\circ}\) \\  & Thickness \(\approx\)22\% of chord length for NREL S-821. \\ Geometric constraints & Thickness \(\approx\)12.5\% of chord length for RAE-2822 \\  & Minimum thickness \(\geq\)1\% of chord length. \\  & \(T_{TE}\) and \(T_{off}=0\) \\ Aerodynamic constraint & Coefficient of drag (C\({}_{d}\)) \textless{} original airfoil S-821 \& RAE-2822. \\  & Lift to drag ratio \textgreater{} original airfoil S-821 \& RAE-2822 \\ Objective & Minimize the coefficient of drag (C\({}_{d}\)) \\ Termination Condition & GA stopped based on bit string affinity value \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimization objective and constraints.
# Improved Image Denoising Algorithm Based on Superpixel Clustering and Sparse Representation
## 3 Proposed SC-SR Algorithm
### Superpixels Clustering
Next, Equation (6) is equal to the following problem:  
\[min||\overline{\mathbf{y}_{i}}-\mathbf{Y_{i}}\mathbf{c}_{i}||_{2}+\mu||\mathbf{c}_{i}||_{1}+ \frac{\gamma}{2}\sum\
olimits_{ij}||\mathbf{c}_{i}-\mathbf{c}_{i}||^{2}W_{ij} \tag{15}\] \[= min||\overline{\mathbf{y}_{i}}-\mathbf{Y_{i}}\mathbf{c}_{i}||_{2}+\mu||\mathbf{c }_{i}||_{1}+~{}\gamma tr\Big{(}\mathbf{CLC^{T}}\Big{)}~{}\mathrm{subject~{}toc}_{i} \mathbf{I}=1,\]  
where \(\mathbf{L}\) is the Laplacian matrix defined as \(\mathbf{L}=\mathbf{D}_{\mathbf{a}}-\mathbf{W}\), and \(\mathbf{D}_{\mathbf{a}}\) is the diagonal matrix with row sums, \(\mathbf{D}_{\mathbf{ii}}=\sum\
olimits_{j}\mathbf{W}_{ij}\) The parameter \(\gamma\) is the weighted parameter that balances the effect of the Laplacian regularization term (\(\gamma\) was empirically set to 0.2 in our experiments) [15]. By solving Equation (15), a sparse coefficients matrix \(\mathbf{C}=[\mathbf{\zeta}_{1},\mathbf{\zeta}_{2},\ldots,\mathbf{\zeta}_{M}]\) was obtained, which was not symmetric. Therefore, we updated it by \(\mathrm{C}_{ij}=\mathrm{C}_{ji}=|\mathrm{C}_{ij}+\mathrm{C}_{ji}|\) to make it symmetric. A directed graph \(\mathrm{G}=(V,~{}E)\) was built by utilizing the sparse coefficients matrix \(\mathbf{C}\). The vertices of the graph \(V\) were the data set \(\{\mathbf{u}_{i}\}_{i=1}^{M}\), and the new adjacent matrix was \(\mathbf{A}=\mathbf{H}-\mathbf{C}\), where \(\mathbf{H}_{ii}=\sum\
olimits_{j}\mathbf{C}_{ij}\) The spectral clustering algorithm was used to segment the graph \(G\) to obtain the result of clustering superpixels. Superpixel clustering learned NSS prior, which contributed to the preservation of structural information of the denoised image. When the dictionary was learned, the learned NSS prior obtainedby superpixels clustering added the structural feature and edge feature into the atoms of the dictionary. The richer the features of the dictionary, the stronger the ability to reconstruct original image.
# Integrated Robotic and Network Simulation Method
## 4 A Case-Study Illustrating the Use of the IRoNS Method
### Study Case--Experimentation Design  
The experimentation objective is to compare the performance among the referred three rendezvous algorithms in terms of how long it takes for each one to conclude the task, with and without message losses. Fifteen initial conditions were sorted and applied in the same way to each algorithm, resulting in 15 samples of convergence time.  
We used the following notation for the rendezvous algorithms: A--Average (Section 4.2.1), C--Circumcenter (Section 4.2.2) and MPC--Model Prediction Control (4.2.3). For the network parameter, we used: I--ideal network without losses and C--network with message collisions/losses.  
Initially, for the sake of simplification, we compare algorithms in pairs, defining three comparison experiments: A \(\times\) MPC, A \(\times\) C and MPC \(\times\) C. Formulating the experimentation we consider that the first algorithm of each pair assumes the '\(-\)' factor role and the second the '+' role. Thus, in the A \(\times\) MPC case, for example, we want to analyze the performance gain/loss when changing from the Average to MPC technique.
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 5 UAVs in 5G and IoT Networks
For IoT terminals, both total energy constraint and peak transmit power constraint must be kept into consideration while optimization of the communication strategy as recharging the battery equipped with IoT devices is hard. Moreover, another critical concern is the amount of sensing data which may alter dynamically. Accordingly, the communication strategy should support the data sensing status. Practically, both budget transmission duration constraint and budget power constraint can be incorporated to link data sensing condition and communication strategy optimization. A comprehensive study on UAV related challenges including sky pollution, physical collision, safety, privacy and regulations is presented in [101].
# Security Challenges for Light Emitting Systems
## 2 Security Challenges in VLC System
### Security in Physical Layer  
#### 2.2.1 Theoretical Basis at the Physical Layer Security  
The most advanced study subject is PLS in visible light communication (VLC) systems, which may incorporate secure communication zones, artificial noise creation, and modulation-based secret key generation at the physical layer [5]. The MAC layer's security is based on edge-to-edge encryption, cryptography authentication, strong password protection, and control mechanisms based on strong cryptography algorithms: symmetric for edge-to-edge encryption and asymmetric for authentication, key generation, and key transfer. [PERSON] presented an eavesdropping channel model for physical layer security (PLS) in 1975, which was later extended to various channel models, including those characterizing wireless systems [5].  
The principle of security in cryptography systems is: as long as computing the offensive power of the hitters is below the limit, the system is secure. For example, it is assumed that an eavesdropper does not have enough computing power to perform a brute force attack on the private key in a short period of time to decode the encrypted message [5].  
PLS is a remarkable complement to cryptography-based protection, as it introduces an additional layer of secrecy that is demonstrably unbreakable, regardless of the attackers computing power, as well as an option for standalone privacy solutions for systems that are limited in hardware and/or power, such as in internet of things (IoT) applications [5].
# Hybrid Edge-Cloud-Based Smart System for Chatter Suppression in Train Wheel Repair
## 6 Experimental Validation  
After the full integration of the chatter detection and suppression system, it was tested under normal production conditions. The process was performed using Kennametal KRR658675 SP97 CV rectangular inserts with TiAlN coating and 3/16 inch corner radius. A theoretical depth of cut of 15 mm, a feed per revolution of 1.2 mm/rev, and a cutting speed of 50 m/min were used in the validation tests (Figures 13 and 14).  
Figure 12: Resultant machining behavior extracted from data analytics for a large quantity of same type of rail wheel with the smart system activated. The red dots mark the reset points.  
The cloud-based monitoring system was used to analyze the cutting process and valuable information was obtained, such as the graph shown in Figure 13, where the wheel profile was drawn  
Figure 14: Chatter detection and suppression system performance, analyzed at the data visualization block: (**a**) wheel profile and tool trajectory; (**b**) vibration severity; (**c**) warnings and alarms; (**d**) activation of CSSV and the spindle speed (override percentage).  
Figure 13: Vibration severity values over the wheel profile with the smart system activated. The red dots mark the reset points and the black circles show where the warning (w) and the alarm (a) were activated.  
from the tool trajectory. The severity values recorded at each instant were overlapped on this graph, thus obtaining a graphic representation of the location of the main critical machining areas.  
As highlighted in Figure 12, the critical zones were concentrated in the segment between the first and the fourth reset points. In the beginning of the initial radius, chatter grew until the alarm level was reached. The spindle speed was reduced and CSSV was activated, stabilizing the process. The vibration gradually increased below the warning level until the second reset point.  
Table 4 shows in detail the performance of the chatter detection and suppression system in a chronological sequence, starting before the second reset point and continuing the fourth reset point. All data were gathered and analyzed using the cloud-based monitoring system.  
At the beginning, the machining was stable (I) and the situation was maintained over the second reset point. At a certain point, a high vibration amplitude was measured (II). The smart system prompted a warning first and subsequently an alarm. Therefore, the real-time controller sent the command to the CNC to apply the spindle speed reduction and CSSV. The cutting became stable again (III) with the help of the chatter suppression techniques. After a while, the third resetting point was reached (IV) and cutting conditions returned to the original values. Machining remained stable until light chatter started (V) and a warning was displayed. In this case, only the spindle speed reduction technique was applied, and the cutting became stable again (VI). Figure 14 shows the actuation of the smart system in a graphical way.
## 2 Materials and Methods--Implementation, Integration, and Testing of the IDEASSat Flight Model
### IDEASSat Satellite Subsystems
#### 2.1.3 Electrical Power Subsystem (EPS)  
The electrical power subsystem had 4 functions, including power generation, storage, distribution, and subsystem protection. The two deployable solar panels each had a string of 7 solar cells connected in series. The single body mounted panel had a string of 6 solar cells. The AzurSpace 3G30A Triple Junction Solar Cell Assembly was selected for the solar panels owing to its extensive flight heritage on university CubeSats such as the Miniature X-Ray Solar Spectrometer (MinXSS) from the University of Colorado, the solar panel design of which was also shared by IDEASSat [10; 11]. In order to avoid damage to a single cell rendering an entire string unusable, a bypass circuit design was utilized between adjacent cells.  
The EPS contained a self-made power distribution control board, with the flight model being the fourth revision since the initiation of the IDEASSat mission. Many of the revisions were based on the consideration of reducing power consumption from individual components. Most of the solid-state relay (SSR) subsystem power switches used in earlier revisions were replaced by power metal-oxide-semiconductor field-effect transistors (MOSFETs), and gate source resistors were added to the switch design to prevent damage caused by high currents, such as the possible electrostatic effects during assembly. The blocking diodes of the solar panels were also replaced by MOSFET elements. Based on the consideration that the power source and the load share the same ground, the switches of the subsystems were changed from the low-voltage side to the high-voltage side, after it was found that an earlier revision with the switches on the low-voltage side resulted in an inability to power off connected loads.  
Table 2 shows the input voltage and power consumption of each subsystem. The operating range of the EPS is 3.3-8 V. The telecommunications subsystem had two frequency bands: an S-band transmitter and a UHF transceiver, with variable power consumption depending upon whether or not they were in transmit mode.
# Analysis of Development Coordination Levels between Skywalk Systems and Urban Spatial Environments
## 4 Results
### Cluster Analysis  
As shown in Table 4, the degree of coordination between the skywalk system and the urban spatial environment was categorized using K-means cluster analysis with SPSSAU (18.0) software. As can be seen from the following table, four types of clusters were obtained with proportions of 13.75%, 28.75%, 15.00%, and 42.50%, respectively. Overall, the distribution of the four types of clusters appears to be more uniform, suggesting a better clustering effect.  
After obtaining the clustering categories, an analysis of variance (ANOVA) was performed to explore the differential characteristics of each category. Referring to Table 5, it can be seen that the clustering category groups showed a statistical significance (\(p<0.05\)) for all the research items, and the specific differences can be assessed by comparing means and eventually combining with the naming process of the clustering categories.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**City** & **C-Value** & **T-Value** & **D-Value** & **Level** & **City** & **C-Value** & **T-Value** & **D-Value** & **Level** \\ \hline Las Vegas & 0.900 & 0.216 & 0.441 & 5 & Singapore The Tube & 0.866 & 0.271 & 0.485 & 5 \\ Lincoln & 0.795 & 0.053 & 0.205 & 3 & Singapore Golden Mile & & 0.938 & 0.192 & 0.425 & 5 \\ Toronto & 0.994 & 0.571 & 0.753 & 8 & Bandung & 0.938 & 0.352 & 0.575 & 6 \\ Hamilton & 0.972 & 0.161 & 0.396 & 4 & Bangkok & 0.912 & 0.186 & 0.411 & 5 \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._Cluster naming was determined based on the variability of the mean values across the four classes of clusters. The class with low coupling and low coordination was designated the first class, with a D-value interval of 0.25 \(\pm\) 0.09 and a C-value interval of 0.58 \(\pm\) 0.13. The second category has a D-value interval of 0.71 \(\pm\) 0.10 and a C-value interval of 0.98 \(\pm\) 0.02 and is named the high-coupling high-coordination category. The third category has a D-value interval of 0.52 \(\pm\) 0.07 and a C-value interval of 0.70 \(\pm\) 0.05 and is named the low-coupling high-coordination category. The fourth category has a D-value interval of 0.39 \(\pm\) 0.11 and a C-value interval of 0.91 \(\pm\) 0.07 and is named the high-coupling low-coordination category.
## 1 Introduction
The design of the metasurface is crucial for achieving a wide operating CP bandwidth with low loss and steady gain across the bandwidth. The antenna's performance is typically simulated using the electromagnetic simulation software Ansys-HFSS 18.0.
## 5 The Development of the Prediction Model for the Efficiency of Evacuation
### Defining the Model for Predicting Excuation Efficiency
#### 5.1.1 Risk Groups (R)  
When an emergency occurs, passengers may not be aware of the need for an emergency evacuation. However, as the transmission of information during the outbreak of an emergency increases, passengers gradually begin to seek evacuation information. Groups in this state are influenced by groups around them that have already begun to exhibit pre-evacuation behaviours and gradually develop pre-evacuation behaviours.