esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Simulation Results
### Feature Tracking with the Archived Data in the SCS  
The CTD data from the Northern SCS coastal Oceanographic Process Experiment Pilot-Project (SCOPEPP) cruise from 30 June to 13 July 2008 are used in the simulation. The sample data characterizes the temperature and salinity profile along the transactions during the cruise. We only focus on the isothermal line tracking at 35 m below surface. Since resolution of the sample data is low, a longitude is identified as a unit length, and three vehicles are employed to track the temperature isothermal line of 25\({}^{\circ}\). The multiple vehicles' formation is an equilateral triangle (\(\Delta\mathrm{P}_{1}\mathrm{P}_{2}\mathrm{P}_{3}\)), and \(\mathrm{P}_{\mathrm{C}}\) is the virtual leader vehicle in the formation center. The distance between each vehicle and the equilateral triangle center is 0.2 unit length. The trajectories of each vehicle (\(\mathrm{P}_{1}\), \(\mathrm{P}_{2}\), \(\mathrm{P}_{3}\), and \(\mathrm{P}_{\mathrm{C}}\)) are shown in Figure 7(A). The connecting line of vehicle 1(P1) and the virtual vehicle center (P5) coincides with the isothermal line, and the connecting line of vehicle 1 (P1) and vehicle 3 (P3) is orthogonal to the isothermal line. The rotational angular velocity of the formation depends on the curvature of the ocean feature, and the trajectory path length of the vehicle is different from each other. Figure 7(A) shows that, the path length of vehicle 2 is bigger than that of vehicle 3, as the trajectory of vehicle 2 is at the outside of the isothermal line most of the time. The path length of vehicle 1 is almost the same as that of the center vehicle, for its trajectory coincides with that of the center vehicle. As a result, the endurance and velocity of the vehicle should be seriously considered and selected. A Kalman Filter is utilized to estimate the temperature at the virtual vehicle center (P5) with the temperature information from P1, P2, and P3. Figure 7(C) gives the distance variations between the multiple vehicles. The convergence of the multiple vehicles' formation is better in the field where the temperature changes are moderate. The isothermal line has great bending in the scalar field where the temperature changes rapidly, so when vehicles pass through these areas, the path length of each vehicle has a large difference; and formation is adjusted automatically according to the sample information from P1, P2 and P3. As a result, vehicle formation in these areas has small oscillations. Trajectory of the virtual vehicle center is shown in Figure 7(B). Temperature estimation error between the true value in P5 and the estimation result from P1, P2, and P3 is shown in Figure 7(D). From Figure 7(B,D), it can be seen that, the multiple vehicles' formation center is around the 25\({}^{\circ}\) isothermal line with a small estimation error.  
Figure 7: Temperature isothermal line tracking with marine vehicles.
# A Novel Dynamic Generalized Opposition-Based Grey Wolf Optimization Algorithm
## 4 Experiments and Discussion
### Simulation Experiments
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Function** & **Algorithm** & **Best** & **Worst** & **Mean** & **Std.** \\ \hline \multirow{8}{*}{F20} & BA & \(-3.32\) & \(-3.20\) & \(-3.27\) & \(5.92\times 10^{-2}\) \\  & ABC & \(-3.32\) & \(-3.32\) & \(-3.32\) & \(8.59\times 10^{-7}\) \\  & PSO & \(-3.18\) & \(-3.19\) & \(-2.99\) & \(0.14\) \\  & MFO \(-3.32\) & \(-3.20\) & \(-3.27\) & \(5.40\times 10^{-2}\) \\  & ALO & \(-3.32\) & \(-3.14\) & \(-3.23\) & \(6.03\times 10^{-2}\) \\  & GWO & \(-3.32\) & \(-3.09\) & \(-3.24\) & \(8.09\times 10^{-2}\) \\  & DOGWO & \(\mathbf{-3.32}\) & \(\mathbf{-3.21}\) & \(\mathbf{-3.31}\) & \(4.58\times 10^{-2}\) \\ \hline \multirow{8}{*}{F21} & BA & \(-10.1532\) & \(-10.1532\) & \(-10.1532\) & \(3.006\) \\  & ABC & \(-10.1532\) & \(-2.6305\) & \(-5.1363\) & \(4.52\times 10^{-13}\) \\  & PSO & \(-10.1532\) & \(-2.3215\) & \(-4.7358\) & \(1.6195\) \\  & MFO \(-10.1532\) & \(-2.6305\) & \(-7.9587\) & \(2.7942\) \\  & ALO & \(-10.1532\) & \(-2.6305\) & \(-6.5294\) & \(2.9342\) \\  & GWO & \(-10.1531\) & \(-2.6828\) & \(-9.3972\) & \(1.9975\) \\  & DOGWO & \(\mathbf{-10.1532}\) & \(\mathbf{-10.1532}\) & \(\mathbf{-10.1532}\) & \(5.81\times 10^{-7}\) \\ \hline \multirow{8}{*}{F22} & BA & \(-10.4029\) & \(-1.8376\) & \(-5.7487\) & \(3.21\) \\  & ABC & \(-10.4029\) & \(-10.4029\) & \(-10.4029\) & \(6.35\times 10^{-14}\) \\  & PSO & \(-9.0894\) & \(-2.1988\) & \(-5.0820\) & \(1.57\) \\  & MFO \(-10.4029\) & \(-2.7519\) & \(-7.3236\) & \(3.42\) \\  & ALO & \(-10.4029\) & \(-3.7243\) & \(-8.6734\) & \(2.71\) \\  & GWO & \(-10.4029\) & \(-5.0877\) & \(-10.0482\) & \(1.35\) \\  & DOGWO & \(\mathbf{-10.4029}\) & \(\mathbf{-10.4029}\) & \(\mathbf{-10.4029}\) & \(1.74\times 10^{-6}\) \\ \hline \multirow{8}{*}{F23} & BA & \(-10.5364\) & \(-1.6766\) & \(-5.8869\) & \(3.66\) \\  & ABC & \(-10.5364\) & \(-10.5364\) & \(-10.5364\) & \(2.41\times 10^{-13}\) \\  & PSO & \(-9.5666\) & \(-2.3740\) & \(-5.4016\) & \(1.89\) \\  & MFO \(-10.5364\) & \(-2.4273\) & \(-9.3062\) & \(2.81\) \\  & ALO & \(-10.5364\) & \(-2.4273\) & \(-8.2891\) & \(3.29\) \\  & GWO & \(-10.5364\) & \(-10.5356\) & \(-10.5361\) & \(1.93\times 10^{-4}\) \\  & DOGWO & \(\mathbf{-10.5364}\) & \(\mathbf{-10.5364}\) & \(\mathbf{-10.5364}\) & \(8.12\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
Figure 19: ANOVA test of global minimum for F1. Figure 33: _Cont._  
Figure 32: ANOVA test of global minimum for F21. Figure 33: _Cont._
# Advancements in Semi-Active Automotive Suspension Systems with Magnetroheological Dampers: A Review
## 4 Semi-Active Control of Suspension Systems
### Classical Control Strategies  
The classical control strategies of semi-active suspensions primarily comprise skyhook control, groundhook control, and hybrid control strategies.
# SCCDNet: A Pixel-Level Crack Segmentation Network
## 3 Results and Experimental Evaluations
### Evaluation Matrix  
In order to quantitatively evaluate the accuracy of crack segmentation, we need to select appropriate evaluation indicators. First, in crack detection sphere, we use the following evaluation indicators:  
\[Precision=\frac{True\;Positives}{True\;Positives+False\;Positives}, \tag{5}\]  
\[Recall=\frac{True\;Positives}{True\;Positives+False\;Negatives}, \tag{6}\]  
\[F-score=\frac{2\times Precision\times Recall}{Precision+Recall}. \tag{7}\]  
Precision represents the proportion of pixels that are correctly detected as cracks, which reflects the impact of false detection on the results; Recall represents the proportion of real crack pixels that are correctly detected, which reflects the impact of missed detection on the results; F-score represents the harmonic average of the two, which reflects the comprehensive ability of the model to resist false detection and missed detection.  
In addition, we also introduce two common evaluation criteria in the sphere of semantic segmentation, Dice and Intersection over union (IoU) [16]. Let \(X(i,j)\) be the segmented image output by the model, and \(Y(i,j)\) be the corresponding truth image in the data set, where \(i\in[0,\;H]\), \(j\in[0,\;W]\), \(H\) and \(W\) are the length and width of the image respectively, the formula is:  
\[Dice=\frac{2\left|\sum_{i}\sum_{j}X(i,j)\cdot Y(i,j)\right|}{\left|\sum_{i} \sum_{j}X(i,j)\right|+\left|\sum_{i}\sum_{j}Y(i,j)\right|}, \tag{8}\]  
\[IoU=\frac{\left|\sum_{i}\sum_{j}X(i,j)\cdot Y(i,j)\right|}{\left|\sum_{i}\sum _{j}[(X(i,j)+Y(i,j))-X(i,j)\cdot Y(i,j)]\right|}. \tag{9}\]  
Figure 4: Representative samples in the dataset. (**a**) Is a single crack in a complex environment, (**b**) is a compound crack in a simple environment, (**c**) is a crack taken at an oblique angle, (**d**) is a net-like crack, (**e**) is a coarse crack taken at close range, and (**f**) is an image without cracks.  
In addition to measuring the segmentation accuracy of the model, the complexity of the model is also an important indicator. We select model parameters and Floating Point of Operations (FLOPs) to represent the complexity of the model. The formulas are as follows:  
\[Parameters=(D_{K}\times D_{K}\times M)\times N+N, \tag{10}\]  
\[FLOPs=[(D_{K}\times D_{K}\times M)\times N+N]\times(H\times W), \tag{11}\]  
where \(D_{K}\) represents the size of the convolution kernel, \(M\) and \(N\) represent the number of channels of the input feature map and output feature map, respectively, and \(H\) and \(W\) represent the length and width of the output feature map, respectively.
## 3 Results and Discussion
### Selection of Total Reflection Angle  
To select a suitable total reflection angle, we firstly conducted a total-reflection-angle test experiment. A tilt was used to achieve total reflection. By adjusting the distance between the transmitter and the receiver, and using the long or short sides of the water tank, we achieved a UWOC link based on a random total internal reflection angle. In this experiment, 0\({}^{\circ}\) to 40\({}^{\circ}\) were chosen for the total-reflection experiment test; the experimental results are shown in Figure 7.  
Figure 7 shows that total reflection angles of 0\({}^{\circ}\) to 40\({}^{\circ}\) has little effect on the experimental results. Therefore, any total reflection angle from 0\({}^{\circ}\) to 40\({}^{\circ}\) will have a similar effect on the performance of the UWOC system. We selected a UWOC link with a total reflection angle of 7\({}^{\circ}\) for the following frequency and amplitude experiments. In order to compare the results with the subsequent experiments under non-ideal conditions, the optical power curve and waveform under ideal 7\({}^{\circ}\) experimental conditions were tested. The experimental results are shown in Figure 8, we obtained the optical power as 80 \(\upmu\)W, the amplitude of the waveform diagram is between \(-\)0.4 V and 0.4 V.  
Figure 8: The experimental data of the UWOC link with total reflection angle of 7\({}^{\circ}\): (**a**) optical power curve, (**b**) waveform diagram.  
Figure 7: BER versus data rate at the total reflection angle of 0\({}^{\circ}\) to 40\({}^{\circ}\).
# Control of a Mobile Line-Following Robot Using Neural Networks
## 6 Discussion  
In general, all the methods used were able to efficiently control the robot along a line drawn on the ground. Consequently, both the PID controller and the LSTM and CNN networks proved to be feasible approaches for this type of control. However, due to the simple nature of the proposed line-following robot, PID control was revealed to be the most effective.  
It was observed that, while the PID controller is the most efficient method, it is not perfect as it cannot overcome some obstacles on the track, such as sharp angles, which the CNN network can handle more easily. This shows that, although PID has more advantages over the other methods in the line-following experiment, neural networks and deep learning are powerful tools that can be used in more complex scenarios.  
Since the control methods are quite different from each other, it is difficult to obtain common metrics to compare the performance between them. Table 7 shows the error performance indices of Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), the time taken for each method to cover one lap of the track and the robot oscillations along the path. The sign \"+\" indicates the level of oscillations; the higher the number of \"+\", the greater the oscillation. These data were obtained by running each method five times and obtaining the average values and standard deviations for each of the control methods. As the values obtained in each test run were quite consistent between each other, the somewhat arbitrary number of five test runs for each method was chosen. This value represents a fair equilibrium between the execution time required to execute each run and the variation in the standard deviation.  
Figure 27: Plots of the motor speeds and the output of the CNN over one lap of the track.  
As mentioned, in a central position, the error of the sensor reading is 0, while when it loses the line, the error becomes \(\pm\)6000, with any value in this range defining an intermediate position value. With this approach, the error values, especially the MAE, of the PID controller are quite good compared to the other methods. Although the LSTM method exhibits higher errors, it is still satisfactory when evaluated against the other controllers.  
It should be noted that the indices for the CNN method do not accurately reflect the network's performance because it does not use the infrared sensors for control that were used to calculate the error, but rather the camera, so the infrared sensors may not align with the line. In addition, as the line in the second path is less wide, in a central position, no sensor would detect the line, which will cause an increase in error.  
Furthermore, the sample rate of each method was also measured, that is, how many samples each method can evaluate from the sensors and return new motor speeds. The sample rate was set to the highest value that the processor could handle. The PID method had a sample rate of 80 Hz, LSTM 25 Hz, and CNN 24 Hz.
## 3 Results  
### Gas Chromatography-Mass Spectrometry Analysis  
In our study, a total of 22 compounds were identified in OEO and 21 compounds in TEO. The predominant constituents of OEO, detected at the highest concentrations, include carvacrol (81.20%), \(p\)-cymene (3.69%) and \(\mathrm{y}\)-terpinene (3.52%). In TEO, the major components comprise thymol (41.84%), \(p\)-cymene (19.85%), \(\mathrm{y}\)-terpinene (12.64%), \(\mathrm{\beta}\)-caryophyllene (2.88%) and carvacrol (2.14%) (Table 1). Given the antimicrobial activity under investigation, the relative abundance of these key constituents is of paramount significance, as it directly influences the bioactivity and efficacy of the essential oils.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \begin{tabular}{c} **Retention** \\ **Time (Min)** \\ \end{tabular} & \begin{tabular}{c} **\% of Total** \\ **Ion Current** \\ \end{tabular} & \
\begin{tabular}{c} **Retention** \\ **Time (Min)** \\ \end{tabular} &
\begin{tabular}{c} **\% of Total** \\ **Ion Current** \\ \end{tabular} \\ \cline{2-5}
**No. Compound** & \multicolumn{2}{c}{**Thyme EO**} & \multicolumn{2}{c}{**Oregon EO**} \\ \hline \(\alpha\)**-Thujene** & **9.11** & **1.38** & **9.05** & **0.12** \\ \hline \(\alpha\)**-Pinene** & **9.32** & **1.32** & **9.27** & **0.43** \\ \hline \(\beta\)**-Pinene** & **9.84** & **1.19** & **9.79** & **0.16** \\ \hline \(\beta\)**-Pinene** & **10.75** & **0.76** & **10.71** & **0.08** \\ \hline \(\beta\)**-Myrcene** & **11.23** & **0.88** & **11.15** & **0.64** \\ \hline \(\alpha\)**-Phellandrene** & **-** & **-** & **11.16** & **0.11** \\ \hline \(\alpha\)**-Terpinene** & **12.06** & **1.18** & **12.02** & **0.90** \\ \hline \(\mathbf{p}\)**-Cymene** & **12.31** & **19.85** & **12.28** & **3.69** \\ \hline \(\beta\)**-Phellandrene** & **-** & **-** & **12.45** & **0.21** \\ \hline \(\mathrm{y}\)**-Terpinene** & **13.41** & **12.64** & **13.37** & **3.52** \\ \hline \(\mathrm{S}\)**-Blineno hydrate** & **13.79** & **0.57** & **13.73** & **0.16** \\ \hline \(\beta\)**-Linalool** & **14.74** & **2.03** & **14.73** & **3.76** \\ \hline \(\mathrm{Camphor}\) & **16.12** & **1.17** & **-** & **-** \\ \hline \(\mathrm{Terpinolen}\) & **-** & **-** & **14.25** & **0.10** \\ \hline \(\mathrm{Borneol}\) & **16.90** & **1.81** & **16.87** & **0.71** \\ \hline \(\alpha\)**-Terpineol** & **-** & **-** & **17.60** & **0.24** \\ \hline \(\mathrm{Terpinen}\)**-4-ol** & **17.16** & **0.84** & **-** & **-** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of thyme and oregano EOs.
# Real-Time Anomaly Detection in Cold Chain Transportation Using IoT Technology
## 4 Results
### Dashboard for Real-Time Vehicle Monitoring  
Each member of the logistics management team at the grocery distributor was given user credentials to access the monitoring dashboard. The dashboard allows its users to view and gain insights on the real-time data associated with all of the users assets in one centralized location. Once logged in, users are presented with the dashboard as shown in Figure 3. Users of the dashboard can only view the dashboard and respective sensors that they have been given access rights to, meaning the system is scalable to multiple companies. Each van being monitored is easily identified for the user on the dashboard using both the van model and vehicle registration. The interface shows the real-time readings of the environmental parameters (temperature and humidity), battery voltage and trip detection. A 1 reported by trip tells the user the vehicle is currently in motion, while a 0 tells the user the vehicle is stopped. In the corner of each element, the time of last update is visible.  
The interface shows the temperature in all zones where sensors have been installed as well as the thresholds for the gauges (red-orange-green). The temperature thresholds were set according to the company's needs. The gauges provide a quick, easy to decipher visualisation into the transportation condition of the food. Green indicates that the food is being transported correctly, yellow indicates that the temperature is above the ideal temperature range but still does not compromise food quality and red indicates alarming status, and an automatic notification (Figure 4) is sent to personnel for timely necessary action. Further details on the notification system are discussed in the next section.  
Figure 3: Developed dashboard shows environmental parameters in real time.
# Modeling and Test Results of an Innovative Gyroscope Wave Energy Converter
## 6 The Proposed Modified P&O  
### Adaptive Perturbation Size  
From Figures 8-13, it can be concluded that the energy output under a single variable has a single peak, and there is a unique optimal output power in every sea state. Moreover, it can be concluded from Figures 14 and 15 that under the combined action of the two input conditions, the power output has the only optimal damping coefficient. However, this parameter is also variable under different sea conditions and flywheel rotating speeds. At present, the MPPT algorithm has been widely used in energy fields, and the principle is applied to electronic PWM control to optimize the generator load. The P&O control algorithm is widely used in MPPT of photovoltaic power generation, which is simple and has excellent convergence. Its working principle is to adjust the perturbation direction according to the comparison result of the power value before and after the perturbation signal.  
\[X_{new}=X_{old}+\Delta X\times\phi \tag{17}\]  
In Equation (17), X is the control variable, which can be voltage (U), current (I), or duty cycle (d), and \(\Delta X\) is the perturbation step size. Simultaneously, \(\phi\) represents the direction of perturbation, and +1 and \(-1\) correspond to the increase and decrease in X. However, the working point cannot be stabilized at the MPP, and will fluctuate around the stable power as the perturbation changes. Therefore, it is necessary to obtain an adaptive perturbation step size \(\Delta\)x to ensure the power loss when fluctuation around the MPP is minimized.  
The inverse proportional function is a common mathematical function, the expression of which is y = k/x. The conventional inverse proportional function makes the product of two consecutive perturbation steps a fixed value, which not only has strict requirements on the value of the constant but also inevitably produces energy waste caused by oscillation. A dynamic inverse proportional function method to adjust the size of the perturbation step is applied in this paper. Define that the product of two consecutive perturbation steps is equal to the absolute value of \(\Delta\)P: \(|\Delta p|=step_{n-1}\cdot step_{n}\).  
After assigning an initial step size \(a\), this formula ensures the perturbation step size will vary with the change of \(\Delta\)P. Besides, no matter how the external conditions change, \(\Delta\)P and the perturbation size will not be zero to ensure the stability of the algorithm. After the initial perturbation step length is given, when the value of \(\Delta\)P is larger, the perturbation size will increase accordingly, thereby reducing \(\Delta\)P. At this time, the new perturbation step size will decrease with the decrease in \(\Delta\)P, and the power output will reach the vicinity of MPP eventually.
# Effects of Microbiota on the Soiling Process of PV Modules in Arid Zones of the Atacama Desert
## 1 Introduction
Understanding the microbiota associated with soiling not only helps to improve the operational efficiency of PV systems but also prolongs their lifespan by mitigating the adverse effects of microbial accumulation. This is crucial to maximizing the return on investment in solar infrastructure in the long term. This study focused on characterizing the structure and microbial diversity present in the soiling of PV modules installed in the Atacama Desert, Antofagasta Region, Chile. The extreme radiation and dry conditions in this desert create a scenario for the growth of microorganisms that have adapted to hostile environments with high temperature variations between day and night, high UV radiation, high salinity, dryness, etc. [11; 13]. Studying the microbiota present in soiling allows for understanding how these organisms may be involved into the soiling process on PV modules. This knowledge not only has direct applications in solar technology but also contributes to the general understanding of environmental microbiology and the ecology of extremely arid ecosystems. Finally, this work aimed to provide new perspectives to help develop effective maintenance strategies to improve the performance and durability of PV systems in extreme environments.