esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Changes of Soil Firmness before and after Subsoiling  
The change of soil firmness is one of the important indicators of the subsoiling effect. In this study, in order to verify the effect of vibration subsoiling, shovel tip E was selected, the change of soil firmness before and after subsoiling was tested at 3 km/h and 30 cm depth. The soil firmness showed an increasing trend and then decreased with the increase in depth before subsoiling, the maximum value appeared between 20-30 cm (Figure 16), and this was the area of the plough pan. The soil firmness values decreased sharply after the subsoiling, and the decrease in soil firmness was most obvious in the bottom of the subsoiling trenches. In this experiment, the interval between two subsoiling was 60 cm, the area with the minimum subsoiling disturbance was 30 cm laterally from the subsoiling ditch bottom. The soil firmness in this area decreased sharply, indicating that both the vibrating subsoiling and non-vibrating subsoiling had achieved good operating results. Compared with the change of soil firmness of non-vibrating subsoiling (Figure 16a) and that of vibrating subsoiling (Figure 16b), the firmness at the transverse 30 cm from the subsoiling ditch bottom was slightly greater than that of non-vibrating subsoiling operation. The reason was that it was not easy for the tip of the vibrating subsoiler to form big soil nuclear and avoided greater soil disturbance. This vibration subsoiling method not only achieved the effect of comprehensive subsoiling but also helped to create a soil environment with the combination of loose and compact, which helped to grow crops.  
Figure 16: Variation of soil firmness with depth before and after subsoiling: (**a**) The changes of soil firmness without vibration subsoiling; (**b**) the changes of soil firmness with vibration subsoiling.
# Accurate Determination of Geographical Origin of Tea Based on Terahertz Spectroscopy
###### Abstract  
This paper proposes a structured model for the identification of green tea, as well as tracing its geographical origins. Considering that the features of different types of green tea are similar under THz time-domain spectroscopy, we designed a program to perform principal component analysis (PCA) of the spectroscopic data of various green tea samples and to determine the data sequences of principal components. We then established a training set for the principal components to train a support vector machine (SVM) model via a genetic algorithm (GA). We used this model to optimize the parameters and develop a GA-based SVM model with an identification rate of 96.25% for the tested samples. Taken together, our results confirm that THz time-domain spectroscopy combined with GA-SVM can be effectively applied to rapidly identify types of green tea with different geographical origins.  
Keywords:THz; SVM; principal component analysis; genetic algorithm; green tea +
Footnote †: journal: Applied sciences
# Research on Bandwidth Improvement of Fine Tracking Control System in Space Laser Communication
## 1 Introduction
### Research Status  
Currently, the research on PZT FSM is primarily divided into two aspects. One of the aspects involves modeling the hysteresis characteristics of PZT FSM and intelligently identifying the model parameters. The other aspect focuses on studying the control methods. The typical control methods include feedforward control, feedback control, and compound control combining feedforward and feedback.
## 4 Soil Parameter Updating
### Response-Surface Model  
A response-surface model was produced utilizing the Minitab software tool [42] for three input variables, and seven (S1-S7) responses were considered to be targets. Soil density (\(\gamma\)), the frictional angle of the soil (\(\phi^{\prime}\)), and the stiffness modulus number (K) were considered to be factors or input variables. Ranges of the factors for the cubic, axial, and center points are presented in Table 1. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multirow{2}{*}{**Range**} & \multicolumn{2}{c}{**Cubic**} & \multicolumn{2}{c}{**Axial**} & \multirow{2}{*}{**Central**} \\ \cline{3-3} \cline{5-6}  & & **Min.** & & **Max.** & & **Min.** & **Max.** \\ \hline \multirow{3}{*}{\(\gamma\)} & Coded & \(-1\) & \(+1\) & \(-\alpha\) & \(+\alpha\) & 0 \\  & Actual & \(1396.9\) & \(1703.1\) & \(1300\) & \(1800\) & \(1550\) \\ \cline{1-1}  & Coded & \(-1\) & \(+1\) & \(-\alpha\) & \(+\alpha\) & 0 \\ \cline{1-1}  & Actual & \(38.428\) & \(43.572\) & \(36.6\) & \(45.2\) & \(40.9\) \\ \cline{1-1}  & Coded & \(-1\) & \(+1\) & \(-\alpha\) & \(+\alpha\) & 0 \\ \cline{1-1}  & Actual & \(274.43\) & \(825.57\) & \(100\) & \(1000\) & \(550\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Ranges of the sample values of the input variables. Figure 6: Central Composite Design: (**a**) Cubical point; (**b**) axial point. The bending strain as a response to the corresponding factors was adopted in the response-surface model. The relationship between the responses (S1-S7) and factors (\(\gamma\), \(\Phi^{\prime}\), \(K\)) was developed from Equation (5) as below:  
\[S=u_{0}+\sum_{m=1}^{3}u_{m}t_{m}+\sum_{m=1}^{3}u_{mnt}t_{m}^{2}+\sum_{m=1}^{3} \sum_{m<n}^{3}u_{mn}t_{n}t_{n}+z \tag{8}\]  
So, the second-order polynomial equation became Equation (9). \[S=u_{0}+u_{1}\gamma+u_{2}\phi^{\prime}+u_{3}K+u_{11}\gamma^{2}+u_{22}\phi^{ \prime 2}+u_{33}K^{2}+u_{12}\gamma*\phi^{\prime}+u_{13}\gamma*K+u_{23}\phi^{ \prime}*K \tag{9}\]  
This equation was integrated for the analysis of the response-surface model. The coefficient of the developed equation is listed in Table 3. For each response (S1-S7), these coefficient values were taken from the response-surface model in Minitab software. This model is trained by the actual response to obtain the best fit and the targeted response. An efficient process for determining the model fitness is analysis of variance (ANOVA) [43]. The \(p\)-value in ANOVA is the chance of incorrectly rejecting the null
## 3 RS Applications in Ocean
### Ocean Tide (OT)
#### 3.5.5 LiDAR  
LiDAR data, which provide topographic data over ocean and land, are considerably helpful for OT studies. Since elevated shorelines can be detected using multiple images over a range of tide and surge elevations, DEM data generated from LiDAR systems are useful. Therefore, DEM data have been locally generated and applied along with waterline methods to detect tidal flat changes [305; 306; 307; 308; 18; 29]. However, it should be noted that the coastal DEMs which are globally generated (e.g., WorldDEMTM, SRTM, and AW3D30) do not include tidal flats because of the water-impermeable nature of existing RS approaches [49].  
As mentioned, the most useful RS data for tidal channels detection are DEM, which can be generated from LiDAR data. Different algorithms of fluvial channels extraction using DEM were provided by [311]. For example, several studies generated a high-resolution LiDAR DEM using a multilevel knowledge-based approach with multiscale edge detection methods to map tidal channels [266; 312]. LiDAR products, such as DEM and Digital Surface Model (DSM), are also beneficial for tidal wetland classification [273; 282]. For example, [PERSON] and [PERSON] [273] investigated tidal wetland changes under increasing tidal range through time at two tidal creeks in south-eastern North Carolina, USA. Aerial photographs, LiDAR DEM and DSM data, Worldview-2 optical images, the National Agriculture Imagery Program (NAIP) orthophotos, and NOAA tide-gauge data were applied to classify tidal wetlands using an object-based classification algorithm. It was reported that LiDAR products were significantly helpful in tidal wetland mapping and change analysis.
# LADRC-Based Magnetic Field Measurement Method for a Nuclear Magnetic Resonance Rotation Sensor
## 5 Experimental Evaluation
### Transient Response Performance Comparison  
In this experiment, the effectiveness of the proposed method was examined. A step signal (5 \(nT\)) was added to the magnetic field of z-axis. The responses of the PLL-based method (parameter strategy I and II) and the LADRC-based method were recorded to be a comparative experiment. The parameters of the controller are listed in Table 1.  
The experimental performance comparison is presented in Figure 5. Figure 5a-c are the phase-loop responses of the tested method. Figure 5d-f are the frequency-loop responses of the tested method. Figure 5g-i are the observed magnetic fields of the tested method calculated by the frequency observed from the frequency loops (the results of the PLL-based methods need to be filtered by a low-pass filter).  
It is obvious that the PLL-based method with parameter strategy I controlled the phases of two isotopes coherently as is shown in Figure 5a. In Figure 5b, the PLL-based method with parameter strategy II performed weakly on synchronization because the lifetime of the \({}^{129}\)Xe is longer than the \({}^{131}\)Xe. The results in Figure 5d,g show that great overshot appeared in the test of the parameter strategy I, which leads to great transient errors in the magnetic field measurement. Figure 5e,h show that strategy II has a great advantage in frequency observation. The overshot was suppressed and a monotone nondecreasing step response was obtained.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**PLL-Based Method** & **PLL-Based Method** & **LADRC-Based Method** \\
**Strategy I** & **Strategy II** & \\ \hline K\({}_{\text{p1}}\) = K\({}_{\text{p2}}\) = 3 mHz/deg & K\({}_{\text{p1}}\) = K\({}_{\text{p2}}\) = 3 mHz/deg & K\(c\) = 3 mHz/deg \\ T\({}_{\text{i1}}\) = T\({}_{\text{i2}}\) = 1.4 s & T\({}_{\text{i1}}\) = 10 s, T\({}_{\text{i2}}\) = 4 s & \(\omega_{\text{o}}\) = 2.16 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the Tested Method.  
The LADRC-based method combined the advantages of both, as is shown in Figure 5c,f,i. The issue is decoupled in that the phase is controlled by LADRC, and the frequency is observed by LESO. The fast synchronous tracking phase loop and monotone nondecreasing frequency loop can be achieved at the same time, which is impossible for the PLL-based method.
## 3 Research Methodology
### Machine Learning Algorithms  
Different machine learning techniques were adopted to create a customer churn prediction system. These techniques were adopted to find the best possible way to predict customer churn through a model with high predictive accuracy. The five techniques used were the Logistic Regression model, the Random Forest classifier, the Naive Bayes classifier, The K-Nearest Neighbor classifier, and the Decision Tree Classifier. All the models and their functions are described below.  
**Logistic Regression:** Logistical models attempt to create a regression model based on data with the binary response variable. These models can be used, among other things, to estimate population groups where a statement may be true or false, such as churn or non-churn. In Logistic Regression, the logit function is used to determine the probability of a binary outcome.  
**K-Nearest Neighbor (KNN) Classifier:** KNN is a non-parametric algorithm and a lazy-learner algorithm, meaning it does not make any assumptions about the underlying data and does not immediately learn from the training set until new data is obtained. The KNN algorithm assumes that the new data is similar to the existing data and assigns the new data to the category that is most similar to the existing category based on the distance between two points.  
**Naive Bayes Classifier:** This procedure is based on Bayes' Theorem and the assumption that the predictors are unrelated. In contrast to other models, the Naive Bayes model is straightforward to create and is particularly successful when dealing with large datasets. Additionally, it is easy to use.  
**Decision Tree:** Decision Tree learning is a fundamental technique in decision theory. These trees comprise a root at the top and knots that are interconnected by branches. Nodes can be classified as either internal or terminal. At each internal node, a specific attribute is tested, and the result guides the selection of different branches, eventually leading to a terminal node. The terminal nodes, or \"leaves\", correspond to a classification [46].  
**Random Forest Classifier:** A Random Forest is a combination method that works with Decision Trees as building blocks. The algorithm generates a predefined number of trees and takes a cut of the total number of trees and uses it as its predictor [47]. In the task, the Random Forest model can be adjusted to achieve the best possible performance by adjusting the number of trees and the number of cuts used in the algorithm.
## 3 Fabrication, Measurements and Discussions  
### Fabrication  
The specific main process flow can be divided into nine steps:  
1. The whole structure of the proposed high-isolation RF MEMS switch was fabricated on a high-resistivity silicon substrate with a thickness of 400 \(\upmu\)m. At the bottom of the substrate is 0.2-\(\upmu\)m-thick Au.
2. SiO\({}_{2}\) (0.3-\(\upmu\)m-thick), which acts as an insulating layer, is grown on the substrate by means of thermal oxidation. The SiO\({}_{2}\) layer can increase the adhesion of materials used in subsequent processes.
3. The high-resistance DC bias lines are sputtered and patterned, and 0.16-\(\upmu\)m-thick Si\({}_{3}\)N\({}_{4}\) is deposited on top of the bias lines.
4. The CPW transmission line consists of a 0.2-\(\upmu\)m-thick Au center conductor and 2-\(\upmu\)m-thick Au ground planes.
5. The bottom electrode is covered with a 0.16-\(\upmu\)m-thick Si\({}_{3}\)N\({}_{4}\) layer, which is deposited using the plasma-enhanced chemical vapor deposition (PECVD) for DC isolation.
6. A total of 0.2 \(\upmu\)m Au was evaporated as the MIM floating metallic membrane.
7. After the thermal curing process, a 2-\(\upmu\)m-thick polyimide is used as the sacrificial layer.
8. The anchor and beam are formed by electroplating for 1 h.
9. A supercritical release method is used to release the polyimide sacrificial layer, so that the MEMS switch beam is be suspended.  
Figure 13 shows the high-isolation RF MEMS switch obtained after processing.
# LSH Models in Federated Recommendation
## 3 NCF Federated Recommendation Based on DP Results
### Model Weight Setting Based on Participation  
In the parameter aggregation of the federated recommendation system, the weight of the model parameters uploaded by all clients is equal. However the quality of the uploaded models varies due to the randomness of the federated learning training parameters. The quality of client-side data, the level of participation in federated learning, user hardware equipment, and other factors can impact the quality of the aggregated model. To enhance the recommendation effect, it becomes necessary to elevate the weight of the optimal model and diminish the weight of other models. In each round of federated learning, a portion of users is randomly selected as samples to participate in the training. Due to the random nature of the training process, some clients have participated in multiple rounds of training while others have only recently started. Consequently, models with low engagement are likely to underfit their own data. Therefore, during the aggregation stage of federated learning, weight parameters can be assigned to the model uploaded by the client based on their level of training participation. The core idea of the model weight setting based on participation is to adjust the weight \(w_{i}\) of each client by recording the participation number \(x_{i}\). Then, the function \(w_{i}=f(x_{i})\) is explored to determine the weight value. The specific process is as follows:  
* The recommendation system server initializes the participation number of the client side;
* Calculates the aggregate weight \(w_{i}=f(x_{i})\) of each client and saves it on the server side;
* The participation number of all clients in this round is increased once, \(x^{\prime}_{i}=x_{i}+1\).
* The recommendation system recalculates the aggregation weights \(w^{\prime}_{i}=f(x^{\prime}_{i})\) according to the participation number and updates the weights on the server side.  
The choice of function determines the convergence effect of this federated learning algorithm, and a better function is conducive to the training of the federated recommendation model; conversely, a poorer function may hinder the training process. In this paper, a simple liner function is conducive to speeding up the training of the federated recommendation model; the specific formula is as follows:  
\[w_{i}=f(x_{i})=\begin{cases}x_{i}&x_{i}<T\\ T&x_{i}\geq T\end{cases} \tag{5}\]  
where \(T\) is the threshold of the weight to prevent excessive growth of a user's weight. According to Formula (5), the clients' aggregation weights are linearly positively correlated with the number of users participating in federated training. In other words, the more times a user participates, the greater the weight. Once the weight reaches the threshold \(T\), it is set to \(T\) and remains constant.
# Lightning Protection Methods for Wind Turbine Blades: An Alternative Approach
## 3 Wind Turbine Blades and Its Protection Methods
### Lightning Protection in General  
Lightning protection systems for wind turbines are based on International Electrotechnical Commission (IEC) IEC 61400-24. According to this standard, the lightning protection levels (LPLs) have been set in accordance with the probability of minimum and maximum expected lightning currents, I to IV. The maximum protection, LPL I levels should not be exceeded with a probability of 99% for negative flashes, meanwhile, for positive flashes it is below 10% [12]. The parameters for LPL II and III-IV are the reduced values of LPL I by 75% and 50%, respectively.  
The rolling sphere method (RSM) was used to identify the locations of the air termination system on a given structure. The method assumes that there is a spherical region with a radius equal of the striking distance located around the tip of the oncoming lightning leader to a structure. Owing to that, the RSM method demonstrated on a wind turbine with 20 m radius (LPL I). This radius, \(r\), is in relation to the peak current \(I\) of the first stroke. According to the IEEE, the equation is:  
\[r=10\text{f}^{0.65} \tag{1}\]  
There are many different proposals regarding the calculations of the radius for the rolling sphere in relation with the peak current, but the suggested values for each protection level are set by the standards [7] where for each LPL and radius, there is a corresponding minimum peak current value which, against the protection level, it gives protection.
###### Abstract  
Machine learning (ML) algorithms have been widely used to improve the performance of telecommunications systems, including reconfigurable intelligent surface (RIS)-assisted wireless communication systems. The RIS can be considered a key part of the backbone of sixth-generation (6G) communication mainly due to its electromagnetic properties for controlling the propagation of the signals in the wireless channel. The ML-optimized (RIS)-assisted wireless communication systems can be an effective alternative to mitigate the degradation suffered by the signal in the wireless channel, providing significant advantages in the system's performance. However, the variety of approaches, system configurations, and channel conditions make it difficult to determine the best technique or group of techniques for effectively implementing an optimal solution. This paper presents a comprehensive review of the reported frameworks in the literature that apply ML and RISs to improve the overall performance of the wireless communication system. This paper compares the ML strategies that can be used to address the RIS-assisted system design. The systems are classified according to the ML method, the databases used, the implementation complexity, and the reported performance gains. Finally, we shed light on the challenges and opportunities in designing and implementing future RIS-assisted wireless communication systems based on ML strategies.  
reconfigurable intelligent surface (RIS); machine learning; deep learning; wireless communication systems +
Footnote †: journal:
# Saliency Guided DNL-Yolo for Optical Remote Sensing Images for Off-Shore Ship Detection
## 3 Experiments
### Discussion  
With the help of scene classification and saliency detection, the proposed method can promote the performance many off-shore ship detection methods in optical satellite images. Unfortunately, the proposed method does not consider the amount of model parameters,  
Figure 10: Detection maps for the Nan-Hai dataset based on different methods. Among them, each row represents different scenes: cloudless, big waves, scattered clouds, thin clouds, and thick clouds. The first column represents the original image, columns 2–6, represent the results of our method, YOLOv4, Faster RCNN, SSD, and SPPNet, respectively. Furthermore, the green boxes represent the true ship targets; red boxes represent detected result targets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **P(\%)** & **R(\%)** & **AP(\%)** \\ \hline Our approach & **93.23** & **82.44** & **87.05** \\ \hline YOLOv4 & 90.77 & 80.30 & 85.21 \\ \hline SSD & 84.98 & 77.76 & 81.33 \\ \hline Faster-RCNN & 83.59 & 76.49 & 80.59 \\ \hline SPPNet & 88.32 & 75.22 & 79.46 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance results of different methods (P: precision, R: recall).  
resulting in a relatively long running time of the system. From the perspective of practical application, the lightweight network model will be our next research goal. In addition, the introduced saliency detection method is not good enough for images with a complex background. It still has some noise in the obtained saliency map, especially for the scenes with scattered clouds. It is very important to design a good saliency detection method for ocean backgrounds. Furthermore, it can also be considered from the perspective of multi-frame continuous images. For marine scenes dominated by moving ships, ship targets will appear in a series in multiple frames of continuous images that are different from the background.
## 4 Results
### Cohen's Kappa Evolution with Increasing Number of Features  
Based on the feature importance computation (Figure 6), the two classification algorithms are trained and tested for progressively larger feature spaces, as shown in Figure 8. The first point on the x-axis refers to a feature space comprising solely of the most important feature. For the second point onwards, features keep on populating the feature space, one by one, according to their importance ranking. It is observed that the total number of variables is large, with certain variables being strongly correlated and others being redundant by default.  
In Figure 9 the SVM kappa coefficient evolution for all three levels of the nomenclature is depicted. All curves are almost monotonically increasing up until they reach a plateau, hence demonstrating the successful estimation of feature importance. Crop family and season of cultivation classifications reach their first optimal solution for spaces of fewer than 25 features, as indicated by the onset of the curves' flat region. On the other hand, crop type K\({}_{\text{c}}\) evolution demands more than 45 to stabilize.  
Figure 8: Kappa coefficient evolution for an increasing number of features for both SVM and RF crop type classifications.  
Inspecting Figure 10, it is evident that RF performs exceptionally well for the crop family and season of cultivation classifications, with maximum \(\mathrm{K_{c}}\) values comparable to the ones of SVM. However, crop type classification provides suboptimal performance. Introducing a larger number of spectrally similar classes, such as the constituent types of the cereal family, presents a challenge to the RF classification.