esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
## 4 Agricultural Applications
### Disease Monitoring  
Ensuring healthy crop growth and maintaining yields are fundamental objectives of agricultural processes. Diseases can cause significant reductions in crop yields, resulting in substantial damage to agriculture [105]. In modern society, the continuous increase in agricultural yields and efficiency is largely attributed to the application of deep learning and computer vision in crop disease monitoring [106]. Therefore, integrating computer vision and deep learning for disease detection, classification, and diagnosis is a critical component of crop growth management and a major trend in development. Crop diseases can be categorized into plant diseases and insect pests (Figure 7). Plant diseases are caused by pathogens (such as fungi, bacteria, or viruses) or non-biological factors (such as nutrient deficiencies or pesticide damage), leading to abnormal plant physiological functions and resulting in growth inhibition, yield reduction, or quality deterioration. Insect pests, on the other hand, cause crop damage through direct feeding by harmful insects or indirect transmission of pathogens.
## 3 Synthesis of Crown Ethers and Their Derivatives
### Antifungal Potential  
The crown ethers, their complexes of sodium moieties along with their alkali metal complexes, and other crown ether derivatives were investigated against fungal strain _Candida albicans_, revealed moderate to significant antifungal activity when compared with positive controls [54; 62]. The values are mentioned in Table 1.  
Four novel 4,4'-di(2,2'-benzimidazoloyl)dibenzo18-crown-6 **(16a-d)** were investigated for their antifungal efficacy against _Aspergillus_ sp. using DMF and bavistin as control and standard. To study the antifungal activity, agar well diffusion method was chosen. The compound **16d** given in Figure 6. was found to be most active against fungal strains with maximum inhibitory activity (Table 2) [53].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Samples** & _E. coli_ & _S. typhi_ H & _Br. abortus_ & _L. monocytogenes_ & _C. albicans_ \\ \hline
**12** & 16 & 13 & 23 & 12 & 24 \\ \hline
**13** & 17 & 14 & 24 & 12 & 23 \\ \hline
**14** & - & 14 & 15 & 12 & 22 \\ \hline K30 & 25 & 20 & - & 15 & - \\ \hline
**AMP10** & 10 & 11 & - & 16 & - \\ \hline
**SXT25** & 18 & 17 & - & 11 & - \\ \hline
**SCF** & - & - & 12 & - & - \\ \hline
**NYS100** & - & - & - & - & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of antibacterial and antifungal activity of crown ethers and standard drugs (zone of inhibition in mm).  
Figure 5: Structure of chromene crown ether complexed with the sodium ion **15**.
## 2 Results
### Change of the Main Vanilla Flavors  
#### 2.3.1 Change of Vanillin  
The change of vanillin content at the curing stages was shown in Figure 3A. Vanillin was the most abundant odor compound in cured vanilla beans (1-3%) as previously reported in vanilla beans from different countries [5; 35; 38]. In the current study, vanillin content increased significantly after blanching, and the highest content (2.97%) was observed in cured beans.  
The vanillin contents in green, blanched, sweating and drying beans were 0.03%, 0.05%, 2.30% and 2.69%, respectively. This result was in accordance with previous studies [25]. Most of the vanillin was produced in the period of sweating. [PERSON] et al. reported that oxidative and hydrolytic reactions might occur during this period [39]. Therefore, we established that vanillin was formed by the hydrolysis of glucovanillin or oxidation of vanillyl alcohol. According to [PERSON] et al. [35] and [PERSON] et al. [39], drying and curing may lead to loss in vanillin content. The yield of vanillin was more than what was detected in drying and curing beans.  
Figure 2: Principal component analysis (PCA) scores of vanilla beans during the curing process and the quality control sample. FVB: Fresh vanilla bean; BVB: Blanched vanilla bean; SVB: Sweating vanilla bean; DVB: Drying vanilla bean; CVB: Cured vanilla bean; mix: Mixture of extract of samples.
## 4 Finite Element Modeling
### Process of Mesh Convergence  
The accuracy and reliability of the results are assessed through a mesh convergence procedure, adjusting the solution number iteratively until the maximum stress changes between two consecutive values in a manner that closely matches the shorter of the finite element reliability criteria referenced by [PERSON] and [PERSON] [31]. Table 5 illustrates the convergence rate of the mesh based on the elements and nodes utilized. Utilizing 21 elements and 44 nodes yields a viable solution (with a deviation of 0.72% at lower processing).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Output** & **Analytical Method (Hz)** & **FEA (Hz)** \\ \hline Natural frequency,\(f_{1}\) & 3.4894 & 3.4895 \\ \hline Natural frequency,\(f_{2}\) & 21.551 & 21.556 \\ \hline Natural frequency,\(f_{3}\) & 58.990 & 59.032 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results comparison between analytical method and FEA.  
Figure 4: Mesh pattern. (**a**) Mast structure, (**b**) local reinforcement.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Movement** & **Value (m/s2)** \\ \hline Sway & 3.77 \\ \hline Heave & 4.86 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Acceleration values.
## 5 Experimental Validation
### Discussion  
In order to verify whether this method is correct and whether the detection instruments are designed reasonably, only short span tests are carried out in this paper. When measuring the two surfaces with large span, as long as the detection mechanisms are in a balanced state, there will be no sliding. Therefore, this method can still be used for measurement of large span surfaces.  
Because the contact lines \(A\), \(B\), and detection line \(C\) have shape errors, and the combined projection waveform is equal to the measurements minus the initial value, therefore the influence of the shape errors of the contact lines \(A\), \(B\), and detection line \(C\) on the measurement results can be eliminated.  
During the detection progress, the measurements will be affected by motion error, surface roughness, spring damping and other factors. However, the influence of these factors accounts for a small proportion of the measurements. Since the position relation curve is macro, the trend curve reflects the overall shape of the measurements. The extracted trend curve has a certain inclusiveness to the peak and valley of the waveform. When the trend curve of the waveform is extracted, the error components in the measurements will be largely separated. Therefore, the error has little effect on the shape of trend curve and the error of the final positional relation curve mainly comes from the method of extracting the trend curve.  
The article focuses on verifying the correctness of the detection method and whether the detection mechanism can be applied to actual measurement. The next work will focus on the analysis of the uncertainty of detection methods and the detection process.
# Application of a New Enhanced Deconvolution Method in Gearbox Fault Diagnosis
## 5 Experimental Verification
### Decomposition Results of the Method Proposed in this Paper  
The signals were analyzed using the method proposed in this paper. First, the collected vibration signal is filtered once by the ARMA filter, and the filtered signal is as shown in Figure 10. Figure 10a is the time-domain signal of the experimental signal, and Figure 10b is the time-domain diagram of the experimental signal after ARMA processing. It can be seen that the amplitude of the noise amplitude of the signal after ARMA filtering has an intuitive drop, and the effective residual signal is retained. This makes the effect of noise on the MOMEDA filter weakened, making the extraction of weak faults more accurate.  
Figure 8: Time domain and envelope spectrum of fault signal. (**a**) The time-domain diagram; (**b**) spectral envelope diagram.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Original Signal** & **MOMEDA** \\ \hline Permutation Entropy & 4.7653 & 4.3218 \\ Fault Energy Ratio & 0.03658 & 0.04136 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of experimental results of MOMEDA.  
Figure 9: Time domain and envelope spectra of experimental signals. (**a**) The time-domain diagram; (**b**) spectral envelope diagram.  
Then, according to the work of [35], the length of filter L = 200 is selected in this paper, and the calculated failure frequency is 162.4 Hz, and the corresponding sampling points are 295.5, so the selected filter interval is [220, 24,000]. The MOMEDA algorithm is used to extract the fault features of the ARMA noise-reduced signal. The extraction result is shown in Figure 11. Figure 11a is the time-domain graph of the analysis result, and Figure 11b is the envelope spectrum graph of the analysis result. It can be seen that the amplitude of the time-domain diagram is more obvious after the processing of the method proposed in this paper and five peaks can be clearly highlighted in the envelope spectrum. The extracted fault frequency is 162.4 Hz.  
Compared with the MOMEDA method, the proposed method can extract fault information with higher precision and has better performance in weak fault feature extraction. It can be seen from Table 4 that the value of the permutation entropy of the proposed method is smaller than the that with the MOMEDA method, and the fault energy ratio is larger than the MOMEDA method. It can be seen that, in practical applications, the proposed method is superior to the traditional MOMEDA method.
# Autonomous Mutual Authentication Protocol in the Edge Networks
## 3 Methodology  
The architecture for autonomous-device connectivity in general is shown in Figure 1. The autonomous devices can connect 1:1, 1:M (many), and M:M device connections, whereas autonomous devices can operate independently or collectively. The purpose of connectivity is to receive status, position, information exchange, and control remote devices. The applications are given as drone-based delivery, robotic fire extinguishers, drone base smart farming, self-driving car/bus, car platooning, smart surveillance, etc.
# Unsupervised Image Classification Based on Fully Fuzzy Voronoi Tessellation
## 1 Introduction
It decides the label of fuzzy polygons belonging to and guides fuzzy Voronoi polygon optimization in reverse. Finally, the optimal classification results can be obtained by fuzzy comprehensive decision theory under the optimal parameter solution. The validity of the proposed algorithm is demonstrated through experimental validation using the simulated image and remote sensing images with different resolutions from different sensors, such as a panchromatic image with 0.5 m from WorldView-1, multispectral image with 1 m from IKONOS, SAR with 30 m from RADARSAT-I, etc. The qualitative and quantitative analysis of the classification results indicates that the proposed algorithm achieves a good balance between effectively fitting boundaries and resisting noise. The main contributions of this paper are summarized as follows,  
1. The hierarchical fuzzy membership model is proposed. It can fully explore the uncertainty of classification to optimize boundaries. 2. It implements an explicit expression of Voronoi tessellation, which is beneficial for obtaining analytical solutions and achieving rapid parameter optimization. 3. It can maintain high noise resistance and detail preservation while quickly classifying. This paper is organized as follows: Section 2 reviews the existing related works. Section 3 introduces the modeling and solving process of the proposed method. Section 4 shows the effectiveness of the proposed algorithm through image classification experiments using seven comparative algorithms. Section 5 thoroughly discusses the parameter impact, robustness, time complexity, and remaining challenges of the proposed algorithm. Section 6 is the conclusion.
# CT-Video Matching for Retrograde Intrarenal Surgery Based on Depth Prediction and Style Transfer
###### Abstract  
Retrograde intrarenal surgery (RIRS) is a minimally invasive endoscopic procedure for the treatment of kidney stones. Traditionally, RIRS is usually performed by reconstructing a 3D model of the kidney from preoperative CT images in order to locate the kidney stones; then, the surgeon finds and removes the stones with experience in endoscopic video. However, due to the many branches within the kidney, it can be difficult to relocate each lesion and to ensure that all branches are searched, which may result in the misdiagnosis of some kidney stones. To avoid this situation, we propose a convolutional neural network (CNN)-based method for matching preoperative CT images and intraoperative videos for the navigation of ureteroscopic procedures. First, a pair of synthetic images and depth maps reflecting preoperative information are obtained from a 3D model of the kidney. Then, a style transfer network is introduced to transfer the ureteroscopic images to the synthetic images, which can generate the associated depth maps. Finally, the fusion and matching of depth maps of preoperative images and intraoperative video images are realized based on semantic features. Compared with the traditional CT-video matching method, our method achieved a five times improvement in time performance and a 26% improvement in the top 10 accuracy.  
u +
Footnote †: journal: Medical Image Analysis  
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Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
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Footnote 2: [[https://www.mdpi.com/journals/pdf/1005/100](https://www.mdpi.com/journals/pdf/1005/100)]([https://www.mdpi.com/journals/pdf/1005/100](https://www.mdpi.com/journals/pdf/1005/100))or registration after a point cloud reconstruction based on video images. The accuracy of point cloud reconstruction is affected heavily by kidney stones, water-filled, bubbles, flocculent, and other impurities in RIRS, and is not suitable for the RIRS scene.
## 3 Results and Discussion
### Evaluation of the HS-BA\(\mu\)E/GC-MS Methodology
#### 3.2.1 Optimization Assays
Consequently, the  
Figure 3: Effect of sorbent phase selectivity (**a**), equilibrium time (**b**), and back-extraction solvent (**c**) on the efficiency of the six target BVOCs obtained by the HS-BA\(\mu\)E/GC-MS methodology. The error bars represent the standard deviations of three replicates. combination of 3 h of equilibrium time at 35 \({}^{\circ}\)C appeared to promote greater recoveries with acceptable RSDs ranging from 6.4% to 26.2% and thus was selected for the remaining assays. The main parameters for back-extraction in the liquid phase were investigated, focusing on the choice of solvent and desorption time. Seven organic solvents, namely ACN, MeOH, DCM, _n_-C6, _n_-C7, isooctane, and a mixture of _n_-C6 + _n_-C7 (1:1, _v/v_), covering a wide range of polarities, were evaluated to determine the optimal back-extraction conditions. Analysis of the data presented in Figure 3c reveals that highly polar solvents like ACN and MeOH did not facilitate complete back-extraction of all six BVOCs, yielding either incomplete recovery or very low yields. Among the nonpolar solvents tested, _n_-C6 demonstrated the highest recoveries for all six BVOCs, whereas DCM showed a negligible response. Isooctane emerged as a viable alternative among the hydrocarbons tested, particularly effective for thymol and cryophyllene oxide. Furthermore, the impact of sonication time (15, 30, 45, and 60 min.) was assessed, with the results indicating that a sonication period of 60 min resulted in optimal desorption efficiencies, accompanied by acceptable RSDs ranging from 0.4% to 20.4% for the six target BVOCs. Consequently, based on these achievements, _n_-C6 under sonication for 60 min was identified as the preferred back-extraction condition for the study.
## 4 Experiment of Direction Finding
The test antenna is located only once per sector, and training data are generated based on the received power value. Initial training data are set as from the power value received in eight patterns when the test antenna is located in sector 1 to the power value received in eight patterns when the test antenna is located in sector 8. Then, after placing the test antenna in an arbitrary position, the received power values are entered into the DNN to present the probability of the number of times the antenna's position is estimated to be actually located in the sector. The experimental set-up of direction finding with a single-patch multi-beam antenna is shown in Figure 14.  
Figure 14: Experimental set-up for direction finding with the single-patch multi-beam antenna. Figure 13: Process of the proposed direction-finding system: (**a**) Training stage, (**b**) Actual direction finding stage. Figure 15 shows the estimated probabilities by sector. Figure 15a is the result before improving sector estimation ambiguity, and Figure 15b is the result of improving sector estimation ambiguity by reducing beamwidth by applying parasitic elements to the single-patch multi-beam antennas. Based on the power value measured for each sector, a set of 5000 power value data per sector is trained. In addition, when an arbitrary power value is input, it shows the number of times the sector is correctly estimated and its probability. Red indicates incorrect estimation and green indicates correct estimation. For example, in Figure 15a, when the test antenna is in any position within sector 1, the number of times that it correctly estimated sectors by using the DNN algorithm is 4791, and the number of times that it incorrectly estimated as sector 7 is sector 18. In addition, at this time, the probability correctly estimated as sector 1 is 95.8%. However, as a result of estimation using a single-patch multi-beam antenna that improves estimation ambiguity by applying a parasitic element, the number of times that it correctly estimated sectors by using the DNN algorithm is 4983, and the number of times that it incorrectly estimated as sector 7 is sector 15. In addition, at this time, the probability correctly estimated as sector 1 is 99.7%. As shown in Figure 15b, the average estimated probability increased from 94.2 % to 97.7 % after improved sector estimation ambiguity.
## 4 Training Method of COMA Policy Network Based on Differential Evolution
### Optimization of COMA Policy Network with DE  
As is well known, the ultimate goal of reinforcement learning tasks is to train an excellent policy that guides the agent to make the most reasonable actions at each time step. Therefore, policy training requires special attention. The trade-off between parameter sharing and individualization is an ongoing research topic. Sharing parameters can effectively improve sampling efficiency and learning effectiveness but may lead to a lack of policy diversity and poor convergence. On the other hand, independent parameters may increase training complexity and sample requirements. Firstly, the centralized training and decentralized execution architecture allow agents to share a set of network parameters for better learning of global information. However, a single policy network may result in poor algorithm convergence and a lack of policy diversity among agents. On the other hand, using multiple sets of policy network parameters may lead to complex global information and increase the required sample size for training, thus affecting the agents' collaborative objectives.  
Therefore, the balance between policy diversity and parameter sharing remains an unresolved issue. Additionally, due to the powerful representation capabilities of neural networks and their deep connection with parameter settings and adjustments, they play a crucial role in determining the algorithm's convergence ability. Finding the best output that aligns with the current state is the primary task of neural networks. This study models the problem as a combinatorial optimization problem and employs evolutionary algorithms to find the most suitable network parameters for solving the problem model.  
To address the trade-off between policy network diversity and parameter sharing in COMA, this paper optimizes the COMA policy network using the DE algorithm. The policy network is encoded as a set of vectors, representing the evolutionary individuals. Throughout the evolutionary process, survival of the fittest occurs within the population.
# Application of Compound Terrain Factor LSW in Vegetation Cover Evaluation
## 4 Results and Discussion
### Correlation Analysis of Erodibility Factor
#### 4.3.1 Qualitative Relationship Reasoning between LSW and K
Statistically, the standard deviation of the K value corresponding to categories 1 to 6 were 1.27 \(\times\) 10\({}^{-3}\), 8.84 \(\times\) 10\({}^{-4}\), 1.17 \(\times\) 10\({}^{-3}\), 1.12 \(\times\) 10\({}^{-3}\), 1.95 \(\times\) 10\({}^{-3}\), and 1.46 \(\times\) 10\({}^{-3}\) (t\(\cdot\)hm\({}^{2}\cdot\)h)/(hm\({}^{2}\cdot\)M)\(\cdot\)mm), and the Coefficients of Variation (CV) of the K value for categories 1 to 6 were 0.12, 0.09, 0.15, 0.17, 0.27, and 0.20, respectively. It is evident that the standard deviation and CV of the K value was the largest in the medium rolling hills, with the coefficient of variation above the moderate level [92]. It suggests that this type of terrain has a high degree of soil erosion differentiation. Studies have already pointed out that changes in geomorphology can lead to changes in soil physicochemical properties [92], which ultimately affects the value of K. Therefore, it could be assumed that there is a strong correlation between soil erosion variability and topographic differentiation in the middle rolling hills, so the rebound of the K value during the transition from area4 to area 5, which can therefore be interpreted as the result of topographic divergence. Moreover, the study area has experienced earthquake hazards. The surface remodeling induced by earthquake has a more intuitive impact on topographic divergence [93; 94], which confirms the correctness of the hypothesis. In terms of the above regional analysis, the LSW value and K value can be preliminarily judged to be negatively correlated. In addition, analysis of Figure 6b,c shows that the greater the level of K values, the higher the degree of soil erosion and the lower the vegetation cover tends to be. Although there are some local limitations to the interpretation of soil erosion status using the mean LSW, it can still broadly explain the vegetation development tendency for medium and large rolling mountains with relatively complex topographic differentiation.
## 4 IMOAHD Algorithm
### Individual Encoding and Initialization  
In the optimization problem described in Section 3, the proportions of internal blending bins require optimization, resulting in a total of nine optimization variables, represented as \(\text{x}_{1}\) to \(\text{x}_{9}\) and encoded using floating-point numbers. Therefore, each individual X in the IMOAHD algorithm is a 9-dimensional row vector, as shown in Equation (12). Figure 2 gives a coding example for an individual.  
\[X=[x_{1},\cdots,x_{i},\cdots,x_{9}] \tag{12}\]  
\(x_{1}\) to \(x_{9}\) are optimization variables corresponding to bins 2 to 10 (\(b_{2}\sim b_{10}\)) and numbers in the box represent the current blending ratio of each raw material.  
Figure 2: An individual coding example.  
During initialization, 90% of the individuals in the population are randomly generated and normalized to satisfy Equation (2). Meanwhile, considering that the original proportions have certain reference values and that there is a practical preference for minimizing fluctuations in operating conditions, the remaining individuals are set according to Equation (13):  
\[x_{i}=\begin{cases}x_{i}^{*}-0.1*r_{0}&u-0.5\leq 0\\ x_{i}^{*}+0.1*r_{0}&u-0.5>0\end{cases} \tag{13}\]  
where \(r_{0}\) and u are stochastic numbers between 0 and 1, and x\({}_{i}^{*}\) is the original proportions.