esa-sceva/satcom-chunk-collection huggingface dataset:
# A Wideband Reflectarray Antenna for Satellite Application with Low Cross-Polarization
## 3 Reflectarray Antenna Implementation and Results
Figure 10: Simulated and measured realized gain and aperture efficiency. Figure 9: Comparison of the simulated and measured co-pol patterns, and measured x-pol patterns at 11.725 GHz. (**a**) Azimuthal plane. (**b**) Elevation plane. Table 1 compares the performance of the proposed RA with the previous literature. It should be kept in mind that RA of a high F/D ratio possesses high aperture efficiency. The reason for this is that as the distance between the feed and aperture increases, the amplitude distribution is more uniform. Another factor is that the phase error at the edges also reduces, which makes the GBW increase. Despite the larger aperture area and smaller F/D ratio, the proposed design has considerable GBW. The last two columns of the table that compare the x-pol level and SLL are the distinct features of the design that make it superior to the published work. The minimum x-pol level is attributed to the new structure of the unit cell that cancels the currents in the x-pol direction. The low SLLs authenticate the proper design of RA with the negligible effect of phase error on the collimation of reflected waves.
# Numerical Simulation of Hot Jet Detonation with Different Ignition Positions
## 2 Numerical Model and Methods
### Numerical Method  
In this study, the numerical simulations are performed using ANSYS Fluent software. The calculation is solved on the basis of two-dimensional Navier-Stokes equations for a viscous compressible gas coupled with chemical kinetics and the equation of state of ideal gas. The vertical jet tube causes obvious shear flows in the detonation chamber, so SST k-\(\omega\) turbulence model is employed which defines the transport of the turbulence shear stress in the turbulent viscosity to resolve the unsteady turbulent flow equations [34; 35]. The combustion model uses the eddy-dissipation concept model (EDC) [19; 36]. The pressure correction equation is solved by a PISO algorithm coupling with second-order upwind, which has an advantage in shock capture and accurately simulate detonation [19; 37; 38]. The 26-species 34-steps skeletal reaction mechanism of propane/air is selected [39]. This mechanism is believed to better reflect flow field characteristics, detonation wave structure and chemical kinetics of detonation.  
Figure 1: Schematic of geometry, (**a**) computational domain and (**b**) ignition position.
# A Circularly Polarized Millimeter Wave Radar for Wind Turbine Sensing
## 3 Antenna Design and Experimental Test  
### Antenna Design and Structure  
Due to the trend of radar miniaturization and high integration, microstrip array antenna is the preferred choice for mmWave radar antenna [20]. This paper designs a pair of tangential circularly polarized antenna. Figure 6 demonstrate the stack structure and linear array of the proposed antenna. The antennas are simulated with CST Studio Suite 2019, a three-dimension electromagnetic simulation software. The linear antenna array includes 14 radiating elements fabricated on a substrate made of 3003 Roger material, which is 0.127 mm thick and has a permittivity and a loss tangent of 3 and 0.005, respectively. By adjusting the feed position and cutting angle, the amplitude ratio of the orthogonal electric field is controlled until two orthogonal modes with equal amplitude are excited. Among them, the length and width of the circularly polarized patch unit meet p = W\(\cdot\)L, q = W/L, and the relevant parameters of the final circularly polarized antenna unit are L = 1.07 mm, m = 0.04 mm, and W = 1.06 mm. The length of the circularly polarized patch unit is about half of the waveguide wavelength, and to ensure the same radiation of each unit, the spacing between the circularly polarized patch units is generally about one wavelength. Finally, after optimization simulation, the specific parameters are determined as follows: d = 2.62 mm, L = 1.12 mm, h=0.127 mm, L\({}_{\text{match}}\) = 0.9 mm, m = 0.03 mm, W = 1.06 mm, and W\({}_{\text{match}}\) = 0.39 mm.  
Figure 5: Schematic diagram of the normalized direction of the receiving array.  
Figure 6: Antenna line array configuration.  
In order to ensure that most of the transmitted electromagnetic waves are received back to the radar after scattering by the target, the beam width of the receiving antenna array is generally wider than that of the transmitting antenna array. This is reflected in the fact that the number of cells of the transmitting antenna is more than that of the receiving antenna [20]. By cascading the designed circularly polarized line array and the transmitting and receiving power splitter together, fine-tuning and optimization can obtain the transmitting \(8\times 14\) and receiving \(3\times 14\) circularly polarized surface array, as shown in Figure 6(a),b.
## 4 Results and Discussion
### Buffer Size  
In order to manifest how protocols react to the changes in different buffer size, we change the buffer space in the network from 10 M to 90 M as shown in Table 3, and we ran it 15 times with each simulation setting for each protocol.  
Figure 5 shows the performance of the three protocols with different buffer sizes. Figure 5a,c depicts that the performance of the three protocols are improved obviously when the size of the node buffer increases. In addition, Scheduling-PROPHET also performs better in this simulation. In Figure 5b, the delivery delay of the protocols except Scheduling-PROPHET increases when the size of the node buffer increases. We observe in Figure 5e that Scheduling-PROPHET has a much lower packet  
Figure 4: Performance comparison in different nodes distribution with four metrics: (**a**) Delivery Rate; (**b**) Delivery Delay; (**c**) Overhead; (**d**) Hop Counts; (**e**) Dropped Packets.  
drop rate than others. The number of dropped packets in Scheduling-PROPHET decreases quickly with increasing buffer space. This is probably due to the fact that too many messages are blocked in buffer, while we use scheduling to make transmission more efficient in Scheduling-PROPHET. The evaluation on hop count is given in Figure 5d. With the increasing of the buffer size, the hop count of Scheduling-PROPHET, Epidemic and PROPHET almost converge towards the same level.  
Figure 5: Comparison in different buffer size with four metrics: (**a**) Delivery Rate; (**b**) Delivery Delay; (**c**) Overhead; (**d**) Hop Counts; (**e**) Dropped Packets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Parameter** & \multicolumn{3}{c}{**Value**} \\ \hline Messages & \multicolumn{2}{c}{Generating Interval} & \multicolumn{2}{c}{5 s} \\ \hline  & \multicolumn{2}{c}{Grouping} & Pedestrians & Automobiles & Trans \\ \cline{3-5} Nodes & \multicolumn{2}{c}{Setting1} & 2 M & 2 M & 10 M \\  & \multicolumn{2}{c}{Setting2} & 5 M & 5 M & 25 M \\  & \multicolumn{2}{c}{Setting3} & 8 M & 8 M & 40 M \\  & \multicolumn{2}{c}{Setting4} & 10 M & 10 M & 50 M \\  & \multicolumn{2}{c}{Setting5} & 12 M & 12 M & 60 M \\  & \multicolumn{2}{c}{Setting6} & 15 M & 15 M & 75 M \\  & \multicolumn{2}{c}{Setting7} & 18 M & 18 M & 90 M \\ \cline{2-5}  & \multicolumn{2}{c}{Nodes Distribution} & 80 & 40 & 24 \\ \hline Bluetooth & \multicolumn{2}{c}{Transmission Range} & 10 m & 10 m & 10 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters for buffer space.
###### Abstract
However, the above papers only consider the transportation after the production process, and does not consider the transportation before the production process, that is, the process of suppliers providing materials to the factory. In addition, complex products in real life are often manufactured from multiple materials, and different materials may be provided by different suppliers, even different suppliers supply different materials with different costs. Base on above, this paper takes into consideration the fact that complex products are made from multiple materials on the basis of the three-echelon supply chain, taking into account factors such as maximum material supply quantity, vehicle capacity, material costs, processing cost and distribution costs, and proposes a model of the vehicle routing problem in complex supply chain (VRPCSC) with an aim to minimize the purchasing, production, and transportation costs. The VRPCSC satisfies the transport constraints, which is an NP-hard problem. More and more scholars have been applying meta heuristic algorithms to deal with this kind of problem [18; 19]. Based on our previous studies [20; 21], the Discrete Bat Algorithm (DBA) can solve routing problems effectively. Because there are two-part routings in the VRPCSC (one is between supplier and manufacturers, the other is between manufacturers and the warehouse), this paper proposes a hybrid discrete bat algorithm optimized by the local search. In addition, this paper adopts a discrete chaotic initialization strategy (DCIS) [22] to strengthen the probleme convergence rate and overall convergence effect of the algorithm. The contributions of this paper are as follows: (1) Based on a three-echelon supply chain included suppliers, manufacturers, and warehouses, considering the fact that complex products with multiple materials are process by-manufacturers, a model of VRPCSC is proposed; (2) For the complex supply chain model, a hybrid chaotic discrete bat algorithm with a variable neighborhood search (denoted as HCDBAVNS) is presented to solve it. The remainder of this paper is organized as follows: Section 2 presents the model of VRPCSC to describe the vehicle routing among suppliers, manufacturers, and warehouses. Section 3 proposes the HCDBAVNS algorithm and describes the implementation in detail. 20 test instances are chosen as the study case to demonstrate the proposed algorithm and its optimization strategies in Section 4. Some conclusions and future work are finally addressed in Section 5.
## 6 Experimental Verification
### Control Logics Used for Experimental Comparisons
#### 6.1.1 Linear Quadratic Tracking Control  
The default tracking control of the MLS system used in Matlab is Linear Quadratic (LQ) control (Figure 5). The control and tracking policy is built for an equilibrium point. For each value of a ball position, the current values are recalculated: velocity, coil current and the control signal. All needed information about the mathematical apparatus of LQ controller can be found in [29].
## 4 Discussion
### Assessment of Temporal Accuracy  
The daily precipitation estimates by all products had lower performance compared with those at the monthly and annual time scales when assessed against the measurements. This is because remote sensing products are based on instantaneous observations, for example, 3 h intervals (discrete observations) which are then scaled over a given pixel to daily precipitation values, while local measurements provide accumulated/averaged over time (continues observations). Although the products by GLDAS, MERRA, and GPCP do not directly use data from these sensors, they can present these errors, as they indirectly use products based on these sensors in their models. Thus, the longer the precipitation accumulation period, the greater the probability these products will estimate values close to those measured at the surface [30]. Moreover, the rapid development of convective precipitation processes in the region is common. These products are unable to estimate daily precipitation with the same precision as the weather stations [31]. The monthly and  
Figure 10: Taylor diagrams showing the comparisons of (**A**) monthly (mm month\({}^{-1}\)) and (**B**) annual (mm year\({}^{-1}\)) of the GPCP product at each of the weather stations in the state of Mato Grosso, Brazil. In panels (**A**,**B**), the Pearson correlation coefficient (r) was represented by the azimuth angle (black outlines), the RMSE was represented by the red dashed outlines, and the standard deviation (SD) was indicated by the axes x and y, respectively. For (**A**), the monthly values for the entire study period were averaged, while for (**B**), the annual values for all years during the study period were averaged.  
annual precipitation estimated by the TRMM and GPM products showed higher correlation and Willmott coefficients and smaller errors. This occurred because the remote sensing precipitation estimates assimilate a higher frequency of high intensity events [32].
# Multihop Latency Model for Industrial Wireless Sensor Networks Based on Interfering Nodes
## 3 DAO Multi-Hop Analytical Model
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Description** \\ \hline \(P_{dio}\) & Probability of a node transmitting a DIO message \\ \(T_{trickle}\) & DIO Control message transmission period \\ \(SF_{RPL}\) & Size of the slotframe dedicated to the RPL traffic plane, represented as the number of timeslots that are periodically repeated. \\ \(t_{pdr}^{DAO}(k)\) & Time taken to send the DAO message based on the probability of the PDR parameter \\ \(k\) & Auxiliary variable to differentiate the behaviour of the first hop from the rest \\ \(H\) & Number of hops to the root \\ \(N_{I}(H)\) & Vector of interfering nodes, \(N_{I}\), as a function of the number of hops to the root, \(H\). \\ \(n_{hx}\) & Number of interfering nodes in \(hx\) hop \\ \(PDR\) & Packet Delivery Rate Parameter \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Analytical model variables DAO-RPL. number of hops, represented by the variable \(H\), and the values of each position indicate the interfering nodes in each hop, \(n_{hx}\). The following equation shows a vector of size three, which indicates the number of interfering nodes in a three-hop link up to the root. \[N_{I}(H)=[n_{h1},n_{h2},n_{h3},\,\dots] \tag{4}\]  
To consider possible collisions with DIO messages, the \(P_{dio}\) parameter has been defined, which indicates the probability that a DIO message is being transmitted in a given slotframe. This parameter is calculated according to the Trickle Timer period, \(T_{trickle}\), with which the control messages are being transmitted and the size of the slotframe dedicated to RPL traffic, \(SF_{RPL}\). \[P_{dio}=\frac{SF_{RPL}}{T_{trickle}} \tag{5}\]  
Finally, to calculate the time it takes to send the DAO the time to transmit the DAO as a function of the PDR is considered, \(t_{pdr}^{DAO}(k)\). It also takes into account the possible interference with DIO messages in each of the hops. For this purpose, the summation goes through the vector of values of interfering nodes of Equation (2). \[T_{DAO}=\frac{t_{pdr}^{DAO}(1)}{(1-P_{dio})^{N(0)}}+\sum_{i=1}^{H-1}\frac{t_{ pdr}^{DAO}(0)}{(1-P_{dio})^{N(i)}} \tag{6}\]  
The first fraction of Equation (5) represents the sending time of the DAO message in the first hop of the whole path to the root node. Thus, \(t_{pdr}^{DAO}(1)\) takes into account that the creation of the DAO message can occur at any time during the slotframe, with an average value of \(SF_{RPL}/2\), as explained in Equation (2). In addition, the vector of inter-hop nodes uses the first element of the array, \(N_{I}(0)=n_{h1}\). In the second part of Equation (6), all other hops along the path to the root are considered, using \(t_{pdr}^{DAO}(0)\), since the forwarding of the DAO message is assigned to the next slotframe after receiving the previous forwarded message. In this case, the summation allows the vector of interfering nodes to be traversed for each hop.
## 6 Conclusions  
This paper has a new data-driven load frequency control scheme tailored for power systems vulnerable to Denial-of-Service attacks. The method employs an input-output data-driven LFC algorithm, eliminating the need for a precise power system model and thus streamlining controller design. First, the multi-area interconnected power system under DoS attacks is modeled as a switching system consisting of two subsystems. An event-triggered model-free adaptive LFC algorithm is subsequently introduced within the switching system framework to manage load frequency control. The effectiveness of this scheme is demonstrated by applying it to a three-area interconnected power system. Simulation results confirm that the switching-based, model-free adaptive LFC algorithm maintains robust performance even when facing DoS attacks. However, the methodology has certain limitations, including the inherent dependence on data quality, potential computational complexity, and possible stability issues in highly dynamic or noisy environments typically associated with model-free adaptive control approaches.  
Figure 11: Random loaddisturbances.  
Figure 12: Frequency responses of the LFC scheme.  
**Author Contributions:** Conceptualization, G.T. and F.W.; methodology, G.T. and F.W.; software, G.T. and F.W.; validation, G.T. and F.W.; formal analysis, G.T.; writing--original draft preparation, G.T., writing--review and editing, G.T. and F.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The original contributions presented in the study are included in the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Dynamic Stability of an Electric Monowheel System Using LQG-Based Adaptive Control
## 2 Problem Statements
### Selection of Motor  
A pair of 350 W, 24 V brush-DC motors was considered most suitable for this system due to its low overall construction cost and high ratio of torque to inertia. These motors require a straightforward and inexpensive controller that is operational in extreme environments, possesses a suitable maximum RPM, and that is feasible to rebuild for extended life compared to brushless DC motors. On the downside, brushes have lower life, provide poor heat dissipation due to the rotor's limitations, have high rotor inertia, and exhibit electromagnetic interference (EMI) generated by brush arcing.
# Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach
## 4 Results and Experiment
### Predictive Accuracy  
Figure 2 illustrates a comparative analysis of the predictive accuracy of the three machine learning models--Logistic Regression (LR), Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks--based on their classification results. The chart highlights that the SVM model consistently outperformed LR and LSTM across all evaluation metrics, including precision, recall, and F1 score. This visual comparison validates the robustness and reliability of the SVM model for classifying stress levels using static environmental data.  
Each model's performance was further analysed using confusion matrices for the five stress level categories: Very Low, Low, Medium, High, and Very High. These matrices provided detailed insights into the models' classification capabilities and highlighted areas for potential improvement. The following subsections discuss the key findings from this evaluation in detail, focusing on each model's strengths and limitations in addressing the classification task.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 Score (\%)** \\ \hline LR & 96.04 & 94.34 & 96.04 & 95.18 \\ SVM & 97.95 & 96.22 & 97.95 & 97.07 \\ LSTM & 70.03 & 49.00 & 70.03 & 56.65 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance Metrics of Machine Learning Models for Stress Level Classification.  
Figure 2: Comparison of Precision, Recall, and F1 Score Across Machine Learning Models: SVM Outperforms LR and LSTM in Handling Static Environmental Data.
# An Overview of Fog Computing and Edge Computing Security and Privacy Issues
## 5 Security and Privacy Issues of Edge Computing
This attack happens when an attacker constantly sends innumerous packets towards the victim's device from the compromised distributed devices as a result of which exhausts the hardware resources of the victim to handle any other packet and, therefore, fails to fulfill any genuine request in time. * Data Tampering Attack: In data tampering attack, the attacker can alter the data transmitted over the communication channel or saved in the storage. * Service Manipulation: It is an attack in which the adversary takes control over the edge data center, and as a result it can misrepresent or alter the services. * False Data Injection: False data injection is an attack in which the attacker injects a false code in the network which gathers all the stored data from the database and brings it to the attacker [39]. * Physical Attack: This attack occurs when the physical protection of the edge infrastructure is weak or careless. Physical attack will affect the services in particular geographical areas as the deployment of edge servers is distributed [39]. * Rogue Gateway: Rogue gateway is an attack launched by attackers where they inject high traffic into the entire edge computing network infrastructure and the consequences are the same as the man-in-the-middle attack. In edge computing, if the invaders have acquired sufficient control privilege over the edge data center, they can act as a genuine administrator or misrepresent the services. This \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Core Side** & **Edge Servers** & **Edge Network** & **Mobile Edge Devices** \\ \hline Challenges & Privacy Leakage & Privacy Leakage & DoS & Data Injection \\  & Data Tampering & DoS & Man-in-the-middle & Service Manipulation \\  & DoS & Privilege Escalation & Rogue Gateway & \\  & Service Manipulation & Service Manipulation & & \\  & & Rogue Data Center & & \\  & Physical Damage & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Security Attack in Edge Computing Architecture. enables the invaders to launch several attacks on the network such as man-in-the-middle, DoS, etc. there are several privacy issues in edge computing include:  
* Weak security techniques and algorithms for system protection which can increase the vulnerability of the network that will pave way for the malicious users to invade over the system or add external nodes to collect data in unauthorized manner. * Unsafe communication sessions between devices. * Difficult recovery and data backup when the system outage occurs. * No specific pattern of update reception and implementation on the system. * Lack of proper network visibility. * Lack of user's selective data collection. Now that we have some good understanding and knowledge of the security and privacy issues of both fog and edge computing the next chapter will illustrate some countermeasures to handle such attacks and mitigate the risk caused by it.