esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
This section presents the optimization results obtained from the coupling of the CST and PARSEC parameterization methods with the genetic algorithm (GA) optimization process for the well-known existing NREL S809 airfoil for the design of the NREL Phase II, Phase III, and Phase VI HAWT blades of the wind turbine. The optimization results of the S809 airfoil are further compared with the experimental data from The Ohio State University (OSU) [26]. Furthermore, upon comparing the optimization results between CST and PARSEC, the best option with a higher lift-to-drag ratio was selected for the CFD simulation for validation purposes. The main objective behind the aerodynamic shape optimization and CFD simulation of an airfoil was to maximize the coefficient of lift (\(C_{I}\)) and the lift-to-drag (\(L\)/\(D\)) ratio. The whole optimization process was carried out through coding in MATLAB, and XFOIL was used to calculate the \(C_{I}\), \(C_{d}\), and pressure data. XFOIL uses the panel technique method for the calculation of pressure data and the coefficient of pressure (\(Cp\)). For the CFD simulation of the airfoil, ANSYS ICEM was used as meshing software, while ANSYS CFX was considered for discretizing the governing equation and running the simulation. A framework and blocking were created around the airfoil for the meshing to capture the boundary wall of the airfoil and solve the governing equation on each and every node at a given set of conditions, shown in Table 3. A total of eight design variables were used for the CST method to design an S809 airfoil with a polynomial order of three degrees, while the PARSEC parameterization method used twelve basic parameters to generate a new airfoil design. The main role of this design parameter was to calculate the proper upper and lower bounds of the airfoil within the specified range of 20%. The design parameters are depicted below in Table 5.
## 4 Conclusions  
An energy-efficient approach has been developed for assessing the technical condition of pipes and 17T1C steel during the transportation of hydrogen-containing mixtures.  
1. It was established that the specific energy of destruction for 17G1C steel under the action of hydrogen decreases by 70 \(\div\) 90%.
2. The concentration of hydrogen absorbed by the metal depends on the degree of elastic-plastic deformation and increases for 17G1C steel by 5 \(\div\) 6 times compared to the initial state.
3. It was established that the value of the specific energy of destruction depends on the degree of previous elastic-plastic deformation.
4. A theoretical-experimental approach is proposed for assessing the degree of damage caused to the pipe wall from the combined action of mechanical load and a water-containing medium.
5. The developed methodology involves monitoring the degree of wear on the inner wall of the shaft based on the value of the local deformation, which was established with the help of an optical-digital correlator on the outer wall and the calculated energy of the elastic-plastic deformation.  
Conceptualization, Y.I. and Y.B.; methodology, O.H., J.S. ([PERSON]) and J.S. ([PERSON]); validation, Y.I., Y.B. and J.S. ([PERSON]); formal analysis, Z.B., J.S. ([PERSON]) and J.S. ([PERSON]); investigation, Y.I., Y.B., O.H., J.S. ([PERSON]), J.S. ([PERSON]) and Z.B.; writing--original draft preparation, Y.I., Y.B., O.H., J.S. ([PERSON]), J.S. ([PERSON]) and Z.B.; writing--review and editing, Y.I., Y.B., O.H. and J.S. ([PERSON]); visualization, O.H., Y.B., Z.B. and Y.I.; supervision, Y.I., Y.B., J.S. ([PERSON]) and [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Pressure,** & **D,** & **D1,** & **d2,** & **t,** & **We,** & **W,** \\
**MPa** & **mm** & **mm** & **mm** & **mm** & **\%** & **MJ/m3** \\ \hline \multirow{3}{*}{10.0} & 40 & 30 & 30 & 5 & 5 & 380 \\  & 40 & 30 & 32 & 4 & 4 & 450 \\  & 40 & 30 & 34 & 3 & 3 & 630 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variation in strain energy with wall thickness.
## 3 Experimental Implementation  
As shown in Figure 8, experiments were conducted to verify the feasibility of the proposed parameter identification method and the system's multi-frequency operating capability. The input voltage source for the power amplifier and the voltage reference signal for the signal processing board were provided by the function generator DGS22. The TMS32028335 was chosen as the DSP to implement the gradient descent algorithm. Different resonant frequencies on the secondary side were achieved by adjusting the compensation capacitor \(C_{s}\), while load conditions were emulated by changing the number of resistors connected to the circuit. To ensure that the system achieve a resonant state on both the primary and secondary sides, the 650 V MOEFET IMBG65R072M1H was selected to act as the power switch in SCC.  
As shown in Figure 9a, primary-side current \(i_{p}\) is converted to voltage signal by a current transformer that set current decay at 1/50 and by a high precision resistor. A second order Butterworth LPF is implemented to filter out interference signals above 800 kHz. It should be noted that this filter will contribute about 0.3 us delay to the signal. Then, the filtered signal will be transformed to an RMS value using LTC1968 and to a zero-cross-detection (ZCD) signal by a high-speed comparator TLV3501. The ZCD signal of current will be fed into the phase detection circuit and the ePWMSYNCIN pin of DSP to generate a control signal of SCC. Phase detection in Figure 9b is realized by D flip-flop and Exclusive OR (XOR) gates, the former of which indicates whether the current is leading or lagging, with the ZCD of voltage being a reference. The XOR gate will produce a signal that contains information on the absolute phase difference.
## 4 Experiment and Results
### Q-Network Algorithm
#### 4.2.3 Algorithm Description
Each element was a list of four values that we used to set (_old state_, _action_, _reward\({}_{2}\)_, _new state\({}_{2}\)_), then we ran forward our Q-network using as input the _old state_ and stored the result in _old qval_, where we selected the greatest value of _old qval_  
Figure 14: [PERSON] trying to stand up and failing. and saved the index of it in \(maxQ\). Later, we defined a vector \(X\) with the same values of _old state_, and a vector \(Y\) with the same values of _old qcal_. At this point, we needed to check whether the _reward\({}_{2}\)_ belonged to a terminal state, i.e., if the variable _update_ was equal to the value of the _reward\({}_{2}\)_; if not, _update_ = (_reward\({}_{2}\)_ + (_gamma\(\times\) maxQ\()\)_). This variable _update_ was the rule used to compute the update of the neural network. The value of the variable _update_ needed to replace the value in the \(Y\) vector in the position of _index_. For instance, suppose we have a \(Y\) = [1.2, \(-\)0.36, 2.2, 0.0], _index_ = 3, and _update_ = 10, such that the result after replacing is \(Y\) = [1.2, \(-\)0.36, 10, 0.0]. This will be the target for the Q-network, which should be done for all the elements in the minibatch. Once we finished the loop, we used the entire \(Y\) and \(X\) vector to train the Q-network using backpropagation. The Q-network algorithm is shown in Figure 15.  
Figure 15: Q-network Algorithm.
# Spatial Assessment and Prediction of Urbanization in Maseru Using Earth Observation Data
###### Abstract
One of the fundamentals of sustainable urban planning is the availability of LULC data to support the monitoring and forecasting of urban growth, which is crucial for decisions and policymaking [23]. Monitoring and forecasting city growth requires the use of historical and current remote sensing data for future city management [24]. A shortage of data often poses challenges in such regions. The lack of research studies in Maseru on the impact of urbanization on LULC changes and future urban growth brings a gap in the city's literature. In response, our objective in this research was: (i) to characterize LULC classes from 1988 to 2019 with Landsat data, (ii) to quantify LULC changes over the study period, and (iii) to simulate and forecast future urban growth using ANN-CA in the city of Maseru, Lesotho, for 2050.
## 2 Basic Theory
### Energy Entropy  
Energy entropy has a strong negative correlation with the regularity of time series and is generally used to measure the regularity of time-series signals [25]. The energy-distribution changes of mine microseismic signals are closely related to signal types with different frequency characteristics. Energy entropy can effectively express the energy-distribution characteristics of vibration signals in the time-frequency domain, so the LMD energy entropy can be used as a feature vector.  
The LMD energy entropy describes the time-frequency energy change of the signal by halving the time envelope characteristics of the product-function (_PF_) component, and the envelope energy \(E(p)\) is calculated by Equation (3).  
\[E(p)=\int_{T_{p}}a_{p}(t)^{2}dt=\int_{0}^{\infty}a_{p}(t)^{2}dt=\sum\limits_{i =1}^{m}a_{p}(i)^{2} \tag{3}\]  
where \(a_{p}(t)\) is the envelope of the PF component of the p-th product function, \(a_{p}(t)\) is the \(i\)-th discrete data of \(a_{p}(t)\), m is the number of discrete data of \(a_{p}(t)\). \(i\) = 1, 2, \(\cdots\), \(m\), and \(Tp\) is the time range of the \(p\)-th PF component. The envelope signal energy of the PF component of the product function is normalized, and the proportion \(e(p)\) of the p-th characteristic PF component in the total energy is calculated, as shown in Equation (4).  
\[e(p)=\frac{E(p)}{\sum\limits_{p=1}^{n}E(p)} \tag{4}\]  
Finally, the energy entropy \(H(p)\) of the PF component is determined based on the definition of energy entropy, as shown in Equation (5), so as to characterize the complexity of different types of microseismic signals on a time scale. In the following equation, \(H(p)\) is the energy entropy of the \(p\)-th PF component.  
\[H(p)=-e(p)\lg e(p) \tag{5}\]
# Post-Quantum Authentication in the MQTT Protocol
## 7 Conclusions and Future Work  
In this paper, we have outlined the importance of using authentication with MQTT for IoT devices, especially in a postquantum world.  
We have seen, throughout our work, that adding postquantum signature-based authentication to MQTT is likely to have acceptable overhead in the order of kilobytes, except memory usage in cases where the device is constrained. Postquantum KEM-based authentication for MQTT similarly has acceptable overhead, with the advantage of a much faster connection speed, making it particularly useful in contexts where swift and secure communication is essential.  
Next steps would be to implement a full version of the KEM-based authentication in MQTT with one of the existing MQTT brokers, e.g., Mosquitto or wolfMQTT. The protocol could then be run on IoT devices to determine the overhead of authentication in practice. This could be carried out with the KEM algorithm CRYSTALS-Kyber and compared with postquantum digital signature algorithms such as CRYSTALS-Dilithium and Falcon. It is important that this implementation be assessed as to the security it provides against side-channel attacks such as timing, fault and power attacks.  
Based on our findings, we believe that postquantum authentication is an important part of MQTT in IoT. We also believe that KEMs are a valuable alternative to digital signatures to provide authentication, because they can provide a more efficient interaction between clients and brokers.  
Conceptualization, C.G.; methodology, C.G. and J.S.; software, J.S.; validation, C.G. and J.S.; formal analysis, J.S.; investigation, J.S.; resources, C.G.; data curation, J.S.; writing--original draft preparation, J.S.; writing--review and editing, C.G. and J.S.; supervision, C.G.; project administration, C.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Code used and data generated can be found in the GitHub repositories mentioned in the Implementation sections.  
We would like to thank [PERSON] for his support in writing the Python skeleton code for the KEM-based authentication.
## 3 Results and Discussion
### Sigmoid activation function:  
\[S(x)=\frac{1}{1+e^{-x}} \tag{14}\]  
ReLU activation function:  
\[ReLU(x)=\max(0,x) \tag{15}\]  
As can be seen from Figure 8, the ReLU activation function outputs 0 at \(x<0\) and \(x\) at \(x>0\). The sudden change of output at \(x=0\) is the reason for the unsmooth time-series output. The sigmoid activation function is smooth in the whole range, so its time-series output is also smooth. ReLU activation function has widely replaced sigmoid activation function in the field of deep learning for three reasons:  
1. The sigmoid activation function is computationally intensive while the ReLU activation function is much less so when the backpropagation algorithm is solving for the gradient.
2. For deep neural networks with sigmoid activation function, the vanishing gradient problem can easily occur when the backpropagation algorithm is solving for the gradient.  
Figure 7: Two-hidden-layer M-ANN with sigmoid activation function. Comparison of predictive values and true values of current density change evolution for the PEM fuel cell with the DEA mode. (**a**–**l**) represent the 12 transient processes. (**a**–**h**), (**i**,**j**) and (**k**,**l**) are the train set, validation set, and test set, respectively.  
3. The ReLU activation function can make the output of some neurons zero, which will cause the sparsity of the neural network, reduces the interdependence of parameters and alleviates the overfitting problem.  
It can be seen from the results in this paper that in the case of a small number of neurons, the ReLU activation function will make the time-series results unsmooth. Obviously, a smooth prediction curve is more representative of the reality. In this respect, the sigmoid activation function has an advantage over the ReLU activation function. Although from the perspective of mean relative error, there is almost no difference in performance between the two in this study.
## 2 Numerical Methods  
### Vehicle Dynamics  
The vehicle dynamics were simulated utilizing an improved version of the single-track model that included the roll component of motion [3]. Figure 1 shows visualizations of the single-track model, including the roll degree of freedom and an overview of the vehicle motion in the crosswind.  
Figure 1: The graphic representations of (**a**) the vehicle dynamics model and (**b**) the simulated vehicle motion in the crosswind.  
Equations (1)-(6) provide the equations that govern the dynamics of the vehicle. The vehicle's longitudinal velocity was set at \(v_{x}=25\) m/s. Utilizing a linear tire model, both front and rear tyre forces, \(F_{y12}\) and \(F_{y34}\), were calculated. The symbols, \(M_{ax}\), \(M_{az}\), and \(F_{ay}\), respectively, stand for the aerodynamic roll moment, yaw moment and lateral force that crosswind applies to the vehicle. The qualitative descriptions of \(F_{ay}\), \(M_{az}\), and \(M_{ax}\) are given in Figure 2. The other details of the parameters in Equations (1)-(6) are given in Table 1. The effects of suspension moment as a resistance to the body roll motion were represented by the suspension roll stiffness, \(K_{\phi}\), and damping, \(C_{\phi}\), components in the vehicle model.  
\[\dot{x}=Ax+B\delta+C \tag{1}\]  
In Equation (1), \(\delta\) is the steering angle and the other parameters, e.g., \(A,~{}x,~{}B,\) and \(C\), are explained in Equations (2)-(6).  
\[A=\begin{bmatrix}-\frac{\lambda I_{xq}}{mv_{x}I_{xx}}&\frac{\tau I_{xq}}{mv_{x }I_{xx}}-v_{x}&-\frac{hc_{\phi}}{I_{xx}}&\frac{h(mgh-K_{\phi})}{I_{xx}}\\ \frac{\tau}{v_{x}I_{xx}}-\frac{\sigma}{v_{x}I_{xx}}&0&0\\ -\frac{\lambda h}{v_{x}I_{xx}}&\frac{\tau h}{v_{x}I_{xx}}&-\frac{C_{\phi}}{I_{ xx}}&\frac{mgh-K_{\phi}}{I_{xx}}\\ 0&0&1&0\end{bmatrix},~{}x=\begin{bmatrix}v_{y}\\ \dot{\phi}\\ \dot{\phi}\\ \phi\end{bmatrix}, \tag{2}\]  
\[B=\begin{bmatrix}\frac{C_{y12}I_{xq}}{mv_{x}I_{xx}}\\ \frac{C_{y12}I_{xq}}{I_{xx}}\\ 0\end{bmatrix},C=\begin{bmatrix}\frac{F_{ay}}{m}\\ \frac{M_{ax}}{I_{xx}}\\ \frac{M_{ax}}{I_{xx}}\\ 0\end{bmatrix} \tag{3}\]  
\[\lambda=C_{y12}+C_{y34} \tag{4}\]  
\[\tau=C_{y34}b-C_{y12}f \tag{5}\]  
\[I_{xq}=I_{xx}+ml^{2} \tag{6}\]  
Figure 2: A descriptive explanation of the aerodynamic forces and moments as well as the geometric features of the Ground Transportation System (GTS) [22] vehicle model. \(F_{a,x}\), \(F_{a,y}\), \(F_{a,z}\), and \(M_{a,x}\), \(M_{a,y}\), \(M_{a,z}\) are aerodynamic forces and moments in \(x\)-, \(y\)- and \(z\)-directions, respectively.
## 4 Experiments and Results  
### Establishment of Experimental Conditions  
An experimental setup is constructed using a KUKA KR300 R2500-ultra robot equipped with a hole-making end-effector and an FARO Vantage-E6 laser tracker, to measure the robot errors and verify the accuracy of the joint angle compensation. The SMR is installed at the front of the end-effector, with its sphere center defined as the TCP. Before the start of the experiment, the robot base coordinate system {B} and the tool coordinate system {T} are calibrated, following the calibration method described in Section 2.3. Subsequently, the task workspace is defined in Cartesian space, including the TCP coordinate range in [B] and the rotation between {T} and {B}, which is defined by Z-Y-X Euler angles. Specifically, the x-axis value range of the task workspace is [1600 mm, 2400 mm], the y-axis value range is [\(-\)400 mm, 800 mm], and the z-axis value range is [\(-\)400 mm, 400 mm]. Meanwhile, the Euler angle range of A, which represents the rotation around the Z-axis, is from 85\({}^{\circ}\) to 95\({}^{\circ}\), the Euler angle B is from 75\({}^{\circ}\) to 105\({}^{\circ}\), and the Euler angle C is from 60\({}^{\circ}\) to 120\({}^{\circ}\). Based on the value range of the task workspace, a set of 160 joint angle sampling points is obtained using the proposed uniform sampling method in configuration space, and the positioning errors of the robot at the sampling points are measured. Using the dataset containing 160 sampling points and positioning errors, the proposed spatial-interpolation-based unbiased estimation method with joint weights optimization is employed to establish a positioning error prediction model, and the optimal joint weights are determined based on the particle filter method. The optimal joint weights for predicting the x-axis error component are \(W_{x}\) = [0.985, 0.619, 1.157, 1.068, 1.216, 0.686], the optimal joint weights for the x-axis are \(W_{y}\) = [0.954, 1.183, 0.700, 0.667, 0.688, 1.015], and the optimal joint weights for the z-axis are \(W_{z}\) = [0.813, 0.349, 1.542, 0.476, 1.218, 0.353].  
Figure 12: Cyclic searching compensation method for robot joint angles.
# Development of Automatic Berthing Support Program for Autonomous Ships
## 3 Results
### Verification of the Automatic Berthing Support Program  
#### 3.2.1 Experimental Condition  
The experiment was conducted when Mokpo National Maritime University's training ship, SEGERO, was berthing at the university after completing a coastal voyage (12 September 2024, around 13:00). During berthing, one tug was positioned at the port stern, along with the use of the bow thruster and main engine. A laptop with the program installed was connected to GARMIN's MSC 10 Marine Satellite Compass (GARMIN, St. Olathe, KS, USA) to input GPS position, heading, course, and velocity data into the program. The experimental setup is shown in Figure 6, and the specifications of the MSC 10 Marine Satellite Compass are provided in Table 5.  
The specifications of the SEGERO are listed in Table 6. The thruster power used for the bow and the tug power at the stern during the approximately 8 min berthing maneuver, until achieving a parallel position with the pier, were compared with the bow and stern thruster output values calculated by the automatic berthing support program. At the time of the experiment, the wind was blowing from 140\({}^{\circ}\) at 12 knots, and the current was coming from 130\({}^{\circ}\) at 0.1 knots. Given that the current was acting along the bow-stern axis of the ship with the pier directly ahead, a current speed of 0 knots was applied. The pier depth, adjusted for tides, was 9.43 m.  
Figure 5: Program screen.  
The specifications of the tugboat used during the berthing process are listed in Table 7, and the tug usage record, organized based on voice recordings, is provided in Table 8. Additionally, the specifications of the bow thruster are listed in Table 9.  
\begin{table}
\begin{tabular}{c c} \hline
**Category** & **Information** \\ \hline Gross tonnage (t) & 9196 \\ Length \(\times\) breadth \(\times\) depth (m) & \(133\times 19.4\times 11.05\) \\ Transverse projected area (m\({}^{2}\)) & 300 \\ Lateral projected area (m\({}^{2}\)) & 1430 \\ \hline \end{tabular}
\end{table}
Table 6: Specifications of the SEGERO ship.  
\begin{table}
\begin{tabular}{c c} \hline
**Category** & **Information** \\ \hline Gross tonnage (t) & 145 \\ Length \(\times\) breadth \(\times\) depth (m) & \(27.17\times 8.6\times 3.8\) \\ Main engine & 2 sets \(\times\) Caterpillar (1700 hp) \\ Propeller type & Aquamaster propeller \\ \hline \end{tabular}
\end{table}
Table 7: General specifications of the tugboat.  
Figure 6: Experimental equipment.
# A Ka-Band Circular Polarized Waveguide Slot Antenna with a Cross Iris
## 5 Conclusions  
In this paper, a Ka-band circularly polarized waveguide slot antenna with a cross iris is proposed. By using a hexagonal shape cavity, the proposed antenna achieves a circular polarization characteristic (LHCP) by generating two orthogonal modes. Improved impedance matching is realized by applying a rectangular matching slot, which acts as capacitance and inductance in the equivalent circuit. In addition, widening the axial ratio bandwidth is introduced by using a cross iris. Through adjusting the two different lengths of the cross iris, equal amplitude and 90\({}^{\circ}\) phase difference between two orthogonal electric field modes are realized over the wide \(-\)10 dB reflection coefficient bandwidth. From the experimental results, a 21.7% (31.51 GHz-39.21 GHz) fractional impedance bandwidth for \(-\)10 dB reflection coefficient is obtained. The measured 3 dB axial ratio bandwidth is 6810 MHz (31.72 GHz-38.53 GHz) and the fractional bandwidth is about 19.38%. Because the overlap between the 3 dB axial ratio bandwidth and \(-\)10 dB reflection coefficient bandwidth is greater than 88%, we can conclude that the proposed antenna is well suited for the design goal. Along with the wide operating frequency bandwidth, the proposed antenna has a high gain with a directional pattern showing relatively good agreement between the simulated and measured results. Furthermore, since the proposed antenna is made of all metal and is compact in size, it can be a good candidate for tough operating environments such as radar, spacecraft, and satellite communication.  
The presented work was carried out in collaboration between all the authors. [PERSON]-[PERSON] wrote the paper. [PERSON] supervised the research. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Human Resources Development program (No. 20194010201860) of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government Ministry of Trade, Industry and Energy.  
The authors declare no conflict of interest.