esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 5 Experiment Results and Analysis
This demonstrates that the ITGA can converge more rapidly, as illustrated in Figure 11.  
Figure 11 shows the comparison of algorithm convergence for one of the ten experiments conducted in experimental scenario 2. Due to space limitations, the performance of the remaining experiments is not shown, but they exhibit similar trends to Figure 11. As shown in Figure 11, the TGA can continuously escape local optimal solutions in the early stages of the algorithm and iteratively evolves towards the global approximate optimal solution. The task-set-partitioning- and conflict-resolution-optimized TGA (ITGA) further enhances local search capabilities while improving task completion rates by pruning the data transmission time of conflicting tasks, ultimately increasing the fitness value of the solution. While the DFSGA can also escape local optimal solutions, it requires a certain number of iterations to do so. In all ten experiments, the ITGA outperforms both the TGA and the DFSGA in terms of fitness and iteration, demonstrating the effectiveness of the ITbuGA approach. Additionally, as illustrated in Figure 12, the ITGA algorithm was able to identify the ground station hardware resource scheduling scheme with the maximum total fitness value in all ten experiments of Case 2. It also performed better than the DFSGA and the TGA in terms of the ANCT metric. Both the FCFSA and the PA exhibited unsatisfactory stability performance. In summary, the ITGA's stability performance is superior to that of other algorithms. Figure 11: Comparison of convergence speeds of various algorithms in a specific experiment. \begin{table}
\begin{tabular}{c c c} \hline
**Case** & **Algorithm** & **ACG** \\ \hline  & DFSGA & 70.9 \\
2 & TabuGA & 52.6 \\  & ITabuGA & 23.9 \\ \hline \end{tabular}
\end{table} 
Table 5: Case 2 comparison of ACG of each algorithm.
# Effect of Hydrogen Ion Presence in Adsorbent and Solution to Enhance Phosphate Adsorption
## 2 Material and Methods
### Physical Properties  
An image of the surface and the chemical composition was obtained using scanning electron microscopy (SEM) (Model: VEGA3 SBH, CZH) coupled with energy-dispersive X-ray spectrometry (EDX) (X-act, High Wycombe, UK). The surface area, pore size distribution, and pore volume for the GAC and GACA were calculated by the Brunauer-Emmett-Teller (BET) method (ASAP 2020, Micromerities, Norcross, GA, USA). The zeta potentials (\(\xi\)) of the GAC and GACA were measured with a Zetasizer (NanoZS, Malvern, UK).
# Quadcopters in Smart Agriculture: Applications and Modelling
## 4 Modelling of Quadcopters
### Motion Equations
#### 4.5.1 Kinematics  
The 6 DOF quadcopter is presented by the kinematics equations defined in (2) and (3) [120; 126; 127; 128; 129; 130].  
\[L_{V}=\begin{bmatrix}\dot{x}\\ \dot{y}\\ \dot{z}\end{bmatrix}=R\begin{bmatrix}u\\ v\\ w\end{bmatrix} \tag{2}\]  
\[A_{V}=\begin{bmatrix}\dot{\phi}\\ \dot{\theta}\\ \dot{\psi}\end{bmatrix}=T\begin{bmatrix}p\\ q\\ r\end{bmatrix} \tag{3}\]where \(L_{V}\) is the linear velocity vector in (m/s) in E-frame and \(A_{V}\) is the angular velocity vector in (rad/s) in E-frame. \(T\) and \(R\) are the transfer and rotation matrices, respectively, between both frames and are given in (4) and (5).  
\[T=\begin{bmatrix}1&\sin(\phi)\tan(\theta)&\cos(\phi)\tan(\theta)\\ 0&\cos(\phi)&-\sin(\phi)\\ 0&\frac{\sin(\phi)}{\cos(\theta)}&\frac{\cos(\phi)}{\cos(\theta)}\end{bmatrix} \tag{4}\]  
\[R=\begin{bmatrix}\cos(\phi)\cos(\theta)&-\sin(\phi)\cos(\phi)+\cos(\phi)\sin( \phi)\sin(\phi)&\sin(\psi)\sin(\phi)+\cos(\psi)\sin(\theta)\cos(\phi)\\ \sin(\phi)\cos(\phi)\cos(\phi)+\sin(\psi)\sin(\theta)\sin(\phi)&-\cos(\phi) \sin(\phi)+\sin(\phi)\sin(\theta)\cos(\phi)\\ -\sin(\theta)&\cos(\theta)\sin(\phi)&\cos(\theta)\cos(\phi)\end{bmatrix} \tag{5}\]  
These kinematics equations are a combination of translational and rotational movements. The translational terms are related to the quadcopter's position, while the rotational terms are related to its orientation.  
For translational movements, the quadcopter's linear velocities are defined in the B-frame, while its linear positions are defined in the E-frame, as given in Table 4. Therefore, the rotation matrix \(R\) is used for transforming the linear terms between these two reference frames, as provided in (2).  
For rotational movements, the quadcopter's angular velocities are defined in the B-frame, while its angular positions are defined in the E-frame, as given in Table 4. Therefore, the transfer matrix \(T\) is used for determining the relationship between the rate of change in the Euler angles and the angular velocities in the B-frame, as provided in (3).
# Establishment and Application of Crowd-Sensing-Based System for Bridge Structural Crack Detection
## 3 Development of the Crowd Sensing Crack Collection System
### Mobile App Development
#### 3.3.2 Page Design  
The mobile application can be installed directly through the generated APK file. Once installed, the Crack Detection Application can be accessed as shown in Figure 11a. After logging in with a username and password, users can access the main interface as shown in Figure 11b. The main interface of the application features a prominently displayed green button used to activate the camera for crack photographing. At the same time, the GPS sensor is also activated to record relevant location information.  
Figure 11: Crack collection application. (**a**) Mobile Appicon. (**b**) Main interface of the application.  
Figure 10: Crack information collection process.  
After clicking on the photographing button and activating the camera, users are led to the image capturing interface shown in Figure 12. Figure 12a displays the real-time camera feed, and after capturing an image, users can view the results as shown in Figure 12b. The captured image displays a thumbnail of the crack, the GPS coordinates recorded, the phone's orientation recorded by the gyroscope, and a landmark nearest to the capture location based on the GPS coordinates on the map. If the captured image clearly displays the features of the crack, users can proceed to add a text description. Otherwise, users may choose to recapture the crack image.  
Users can input the text description directly using their device's input method. After completing the description, users can click on the upload button to upload the image, GPS coordinates, phone orientation data, and text description to the cloud management platform simultaneously.  
There are two ways for participants to view the crack information they have captured. Firstly, they can click on \"records\" on the main interface. Secondly, they can directly view the crack photos stored in the phone folder. In the photo viewing mode, users can browse the crack images directly in the folder stored in the phone. In the record viewing mode, users can view all the information related to the crack and see its location on the map. Keeping records can show participants their past work and encourage them to take more initiative. It can also serve as one of the supporting documents for potential reward mechanisms.
## 5 Conclusions  
This paper proposes an intelligent smart waste disposal system using GA-FIS. The system not only calculates whether the smart bin is full but also notifies the concerned people about the clearance of garbage from the bins. A Mamdani FIS is used for predicting the probability of waste to determine whether the smart bin is nearly full. The accuracy of the proposed model is 95.44%, as the integration of GA with FIS results in better fitness evolution. The derived metrics precision and recall are 96.68 and 93.96, respectively. The GA-FIS component is used to select the best rule for optimal decisionmaking and is also used for image classification of different types of waste. This system is mainly designed for outdoor use with cost-effective sensors. Categorizations of recyclable and non-recyclable items are performed to minimize resource waste. Performance analysis on other smart bin loads is conducted to compare traditional GA with the proposed GA-FIS, and it is evident that better fitness evolution results in reduced costs. The proposed algorithm performs better for a variety of reasons, ranging from waste collection, segregation, and reuse to waste recycling, as it truly improves the nation's circular economy and also helps to improve the nation's environmental and, as a result, sanitary conditions. A comparative analysis is performed with existing machine learning and deep hybrid models to show that the proposed model achieves superior accuracy, precision, and recall.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Techniques** & **Accuracy** & **Precision** & **Recall** \\ \hline CNN+MLP [39] & 91.6 & 97.1 & 92.3 \\ CNN [39] & 87.7 & 88.6 & 96.8 \\ Naive Bayes [53] & 86.5 & 88.2 & 88.2 \\ MLP [53] & 86.8 & 88.3 & 87.2 \\ KNN [53] & 88.1 & 89 & 89 \\ Proposed GA-FIS Model & 95.44 & 96.68 & 93.96 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison of the proposed model with existing smart garbage systems.  
Figure 25: Classification test accuracy of the recyclable items.  
**Author Contributions:** Conceptualization, S.T.I.; Methodology, S.R.; Validation, V.M.; Writing--review and editing, A.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is funded by School of Information Technology and Engineering, Vellore Institute of Technology, Vellore-632014, Tamil Nadu, India.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors state that there is no conflict of interest.
## 3 Path-Planning Design and Dynamic-Obstacle Avoidance Strategy
### Dynamic-Obstacle-Avoidance Strategy Based on Artificial-Potential-Field Method
#### 3.3.2 Escape Method for Local Optimal Solutions of Artificial-Potential-Field Method
```
Input: Two nodes that require reachability testing \(\{point1,point2\}\); Sampling step\(\{steplength\}\) Outputs a Boolean value of whether two points are directly accessible to each other \(\{hasLOS\}\)
1 initial \(hasLOS=true\);
2 initial \(X_{max}\),\(Y_{max}\),\(Z_{max}\) are the maximum value of the map x-axis, y-axis and z-axis; if point1 or point2 is outside the map rangethen
3\(hasLOS=false\);
4 else
5 ifThe line segment formed by point1 and point2 is parallel to the xy plane or yz plane or xz planethen
6\(hasLOS=2 Dbresenhamlineofsight(point1,point2)\)
7
8 end if
9 ifThe line segment formed by point1 and point2 and the plane formed by the coordinate axis are not parallelthen
10\(dis_{z}=abs(point1.x-point2.x)\); \(dis_{y}=abs(point1.y-point2.y)\);
11\(dis_{z}=abs(point1.z-point2.z)\);
12 if\(MAX(dis_{x},dis_{y},dis_{z})=dis_{z}\)then
13 if\(point1.x>point2.x\)then
14 for\(i=point2.x\) to point1.x steplengthdo
15\(x=i\);
16\(y=(point2.y-point1.y)(i-point1.x)/(point2.x-point1.x)+point1.y\);
17\(z=(point2.z-point1.z)*(i-point1.x)/(point2.x-point1.x)+point1.z\);
18 ifThe coordinates \((x,y,z)\) correspond to an obstaclethen
19\(hasLOS=false\);
20
21 end if
22
23 end if
24
25 Exchange the position of \(point1\) and \(point2\) in the spatial linear equation
26 end if
27
28 end if
29 if\(MAX(dis_{x},dis_{y},dis_{z})=dis_{y}\)then
30 Similarly, the y-axis is sampled according to the \(steplength\) and the coordinates \((x,y,z)\) of the sampled points are calculated. 31 ifThe coordinates \((x,y,z)\) correspond to an obstaclethen
32\(hasLOS=false\);
33
34 end if
35
36 end if
47 return\(hasLOS\);
```  
**Algorithm 3**3D Bresenham Line
## 3 Materials and Methods
### Training  
The previously described architectures underwent a training process aimed at identifying the optimal configuration of hyperparameters. This optimization was carried out using random search and grid search techniques, enabling a systematic and thorough exploration of a wide range of possible configurations. Among the hyperparameters analyzed, particular attention was given to defining the structure of the neural networks, including parameters such as the number of layers and the number of neurons per layer. The selection of these parameters was guided by theoretical considerations that took into account the specific characteristics of the dataset, such as the number of features and the complexity of their relationships, as well as practical experiments aimed at balancing the model's ability to learn complex representations without overfitting.  
Regarding the network's depth, various configurations were tested, from simpler architectures to deeper networks, to identify a compromise between model complexity and generalization ability. Similarly, the number of neurons per layer was optimized through an iterative process: initial configurations with an excessive number of neurons showed a significant increase in training time without proportional improvements in performance.  
Regularization techniques were also considered to improve the model's performance and generalization, including dropout and L2 regularization, both of which proved effective in reducing the risk of overfitting.  
Another hyperparameter analyzed was the choice of optimizer. After a comparative analysis, the Adam optimizer (Adaptive Moment Estimation) [53] was selected, known for its ability to combine the advantages of stochastic gradient-based optimization with adaptive learning rate adjustments, ensuring more stable and faster convergence.  
Training was conducted over a maximum of 100 epochs to ensure adequate learning, but the early stopping mechanism was implemented to avoid overfitting. This approach allowed the training process to stop early if improvements in Validation-Set performance were no longer significant for a predefined number of consecutive epochs. This criterion optimized computational efficiency, reducing training time, and ensured that the final model maintained good generalization capabilities.  
Overall, the optimization process and training design were crafted to ensure the model achieved optimal performance, effectively balancing Accuracy on the Training-Set with the ability to generalize to test data. Full details on the optimal hyperparameter values and the configurations selected are provided in the following sections.
# A Critical Review of the Status of Current Greenhouse Technology in China and Development Prospects
## 6 Analysis of Key Regions of China's Greenhouse Market
### Northeastern Part of China  
The northeastern region of China has always been a major agricultural production center. This land is rich in natural resources, has suitable climatic conditions, and has a long agricultural tradition. In recent years, with the progress of science and technology and agricultural technology innovation, the northeast region also began to focus on the development of greenhouses. Firstly, the climatic conditions of the northeast offer unique advantages for the development of greenhouses. Northeast China is colder in winter, but the weather in spring and autumn is relatively warm, which is very beneficial for the growth of greenhouse vegetables. At the same time, the summer temperatures in northeast China are also relatively low, preserving the taste of vegetables and fruits grown in greenhouses. Secondly, the northeast is rich in land resources and fertile soil, providing a good foundation for the construction and development of greenhouses. In addition, transportation in the northeast region is also very convenient, providing convenient conditions for the transport and sale of greenhouses.  
Greenhouses in the northeast region are mainly represented in Liaoning Province, which is one of the famous birthplaces of solar greenhouses in China; here, the development of solar greenhouses dates back to the 1950s [64]. The average winter temperatures of the Liaoning region range from \(-12\) to \(-8\)\({}^{\circ}\)C, with good daytime solar radiation levels and large temperature differences between day and night. Furthermore, most of the soil layer is deep, as well as the groundwater table. Here, the solar greenhouses adopted have the specific features of large arcs, small shed spans, and large slopes on the north rear roof to increase both light penetration and heat preservation performance. Liaoning Province is the largest facility vegetable base in the northeast, with a 2020 vegetable production of 19.6 million tonnes [65]. Liaoning's vegetable area, solar greenhouse vegetable area, and production are among the highest in China, and it is the earliest province in northern China to produce fresh vegetables in winter.
## 3 Detection in Receiver  
### Threshold Decision  
The difference between the waveforms of \"0\" and \"1\" is very small, so a traditional IIR or FIR filter with a narrow bandwidth can erase the minute difference information and leave only a sine wave, such that we cannot perform detection in the receiver [12]. Therefore, a well designed SIF which can separate the tiny difference of the received waveforms must be used in solving this problem. The transfer function of the proposed SIF can be written as follows:  
\[H\big{(}\omega\big{)}=\frac{1+b_{\mathrm{i}}e^{\cdot j\omega}+b_{\mathrm{2}}e ^{-j\omega}}{1+\sum_{i=1}^{2n}a_{i}\cdot e^{-j\omega}} \tag{3}\]  
where n is the pair number of the conjugate poles.  
Therefore a simple amplitude detector can be used in separating the symbols \"0\" and \"1\" because of the existence of high impulse in coded 1 s. From reference [13] we can get the optimal threshold if the symbols only interfered with AWGN. The threshold can be obtained as follows:  
\[u_{T}=\frac{1}{2}\Bigg{(}A_{1}+A_{0}+\frac{2\sigma^{2}}{A_{0}-A_{1}}\ln\sqrt{ \frac{A_{0}}{A_{1}}}\Bigg{)} \tag{4}\]  
where the \(\sigma^{2}\) is the noise variance, \(A_{0}\) and \(A_{1}\) is the maximum amplitude of the filter output corresponding to code \"0\" and \"1\", respectively, as is shown in Figure 2.  
According to reference [11], the value of \(A_{0}\) and \(A_{1}\) can be obtained through the following equations:  
\[A_{0}=A\cdot\big{|}H\big{(}\omega_{c}\big{)}=\sqrt{2E_{b}\,/\,T}\cdot\big{|}H \big{(}\omega_{c}\big{)}\big{|} \tag{5}\]  
and:  
Figure 2: The threshold decision with \(\sigma^{2}=0\).  
\[A_{1}=A_{0}+\Delta A \tag{6}\]  
where the value of \(\Delta A\) can be obtained in Reference [13].  
Though the SIF transforms phase modulation into amplitude changes, several signal cycles that followed the change part are distorted. Obviously, if we use short bit duration, the subsequent symbol will be interfered with as is shown in Figure 3. The phase shift is influenced and so the impacted amplitude of the following symbol would be not as high as the one with long bit duration.  
Figure 4 shows the signals envelope of SIF output with N = 20 in (a) and N = 5 in (b). If we use a short bit duration N, then ISI occurs, and the fuzzy interval between symbol \"0\" and \"1\" is large.  
Also, the signal amplitude of symbol \"1\" with short bit duration is lower than that with long bit duration, but for symbol \"0\" both of them are almost identical. According to the relationship between \(A_{0}\) and \(A_{1}\) in (6), we know that if the \(\Delta A\) is low by using short bit duration, it becomes difficult to detect the amplitude by TD.  
Figure 4: The signals envelope of SIF output with SNR = \(-\)2 dB, N = 20 in (a) N = 5 in (b).  
Figure 3: The envelop of symbol “1” and its followed symbol “0” with bit duration N = 4.
# Study on Corrosion Behavior of Waterborne Polyurethane Coating with High Thermal Conductivity
## 3 Results and Discussion  
### Thermal Conductivity  
Figure 2 shows the thermal conductivity results of the waterborne polyurethane coating with different proportions of high thermal conductivity filler. Obviously, the waterborne polyurethane coating displays a very low thermal conductivity, and it is 0.1638 W/(m\(\cdot\)K). When the composites fillers are added into the waterborne polyurethane coating, the thermal conductivity markedly increases. Especially for modified coating with the 2 wt % fillers, the thermal conductivity is up to 1.222 W/(m\(\cdot\)K), which is higher than the previously reported results [17; 18]. It may be attributed to the synergistic effect of different composite in fillers. As the addition amount of fillers continues to increase, the thermal conductivity  
Figure 1: The topography of the mixture fillers (**a**) and the magnification picture of the red triangle box region (**b**).  
has a slight decreasing trend, which may be related to the titanium dioxide added during the toning process. Overall, this result indicates that the composites fillers can be used to significantly enhance the thermal conductivity of the waterborne polyurethane coating, which is beneficial for improving the heat dissipation performance of the transformer. Subsequently, to observe the corrosion protection performance of the modified coating in the corrosive atmospheric environment, the salt spray experiment is conducted for the coating with 2 wt % fillers.
## 3 Results and Discussion
### Generalized Model  
For the drying conditions used in this study, a generalized model was developed using the Page model by fixing \(n\) and therefore employing \(k\) as a single drying parameter. As \(n\) value variation was relatively small, a modification was introduced by averaging the \(n\) values (\(\sum\limits_{i=1}^{N}n/N\) ) of all drying trials after the first fitting. Once the \(n\) value was fixed at the mean value (\(n\) = 0.784), the experimental data were fitted again for each drying condition in order to re-adjust the \(k\) value. This modification was proposed by [PERSON] and [PERSON] [63] and it was concluded that the model predictability was slightly reduced whereas the complexity of the generalized model was condensed. Hence, a variation of \(k\) between 3.660 \(\times\) 10\({}^{-3}\) and 2.998 \(\times\) 10\({}^{-2}\) for \(T\) = 10-50 \({}^{\circ}\)C, 9.820 \(\times\) 10\({}^{-3}\) and 8.025 \(\times\) 10\({}^{-3}\) for \(RH\) = 20-60% and 8.904 \(\times\) 10\({}^{-3}\) and 9.940 \(\times\) 10\({}^{-3}\) for \(v\) = 0.15-1.00 ms\({}^{-1}\) was ascertained  
Figure 5: Drying rate \(dXdt^{-1}\) vs. time \(t\); (**a**) temperature \(T\) ranging from 10 to 50 \({}^{\circ}\)C at relative humidity \(RH\) = 40%, airflow velocity \(v\) = 0.15 ms\({}^{-1}\); (**b**) \(RH\) ranging from 20 to 60% at \(T\) = 30 \({}^{\circ}\)C, \(v\) = 0.15 ms\({}^{-1}\); (**c**) \(v\) ranging from 0.15 to 1.00 ms\({}^{-1}\) at \(T\) = 30 \({}^{\circ}\)C, \(RH\) = 40%. Solid lines represent Page model fitting, dashed lines show extrapolation beyond the dataset used for fitting.  
accordingly. The perceived drying constants \(k\) were modeled based on an Arrhenius-type relationship as affected by the drying air conditions \(k=f\) (\(T\), \(RH\), \(v\)).  
\[\begin{array}{c}k=2.80\times 10^{-3}\times e^{0.059~{}T}\times RH^{-0.139}\times v^{0.0 25}\\ R^{2}=0.989,RMSE=6.202\times~{}10^{-4},MAPE=7.4\%\end{array} \tag{25}\]  
The inclusion of Equation (25) in Equation (6) yielded a generalized model able to describe with high accuracy the temporal behavior of moisture ratio \(X^{*}\) (\(R^{2}=0.997\), \(RMSE=1.285\times~{}10^{-2}\), \(MAPE=6.5\%\)). The resulting course of the drying constant \(k\) as a function of temperature \(T\), relative humidity \(RH\), and airflow velocity \(v\) is displayed graphically in Figure 6.