esa-sceva/satcom-chunk-collection huggingface dataset:
# Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN
## 2 Materials and Methods
### Methods
#### 2.3.5 Genetic Algorithm-Back Propagation Neural Network  
The GA-BPNN model is developed by combining a back propagation neural network (BPNN) with the genetic algorithm optimization (GA). The BPNN has been widely used to find solutions for nonlinear relationships. The training samples are used to train the multi-layer BPNN using the error back propagation (BP) algorithm. During the process of modifying the weights, however, the standard BP algorithm ignores previous gradient direction, which often leads to the oscillation and slow convergence of the learning process [44]. The GA mimics biological evolution processes and has the capacity of finding global optimum solutions of the problems and thus can be utilized to optimize the thresholds and weights of the BPNN [45]. Therefore, the combination of BPNN and GA resultsin an integrated model that provides the potential of improving the efficiency and accuracy of the predictions. The flow chart of GA-BPNN with its structure [46] is shown in Figure 2.
# Study on Local Vibration Control of the 100 m X-BOW Polar Exploration Cruise Ship
## 2 Method  
### Optimal Parameter Design of TMD Based on Fixed Point Theory  
The mechanical model of TMD is shown in Figure 1, which is mainly composed of a mass element, elastic elements, and damping elements. According to the literature [18], its vibration suppression mechanism is as follows: by adjusting the natural frequency of the tuned mass damper to make it consistent with the excitation main frequency, the vibration energy of the main structure is transferred to the mass block, and the TMD gradually absorbs the vibration energy, while the vibration of the main structure is gradually reduced, which can be reduced to zero from the theoretical point of view.  
Figure 1: The mechanical model of TMD, where \(M,m\) represents the mass of the object; \(K,k\) represents the spring constant; and \(c\) represents the damping factor.  
If the displacement of the main systems is \(x_{1}\), and the mass displacement of the dynamic vibration absorber is \(x_{2}\), then the equation of motion can be expressed as  
\[\left\{\begin{array}{l}M\ddot{x}+c\dot{x}+(K+k)x_{1}-c\dot{x}_{2}-kx_{2}=f=F \sin\omega t\\ m\ddot{x}_{2}-c\dot{x}_{1}-kx_{1}+c\dot{x}_{2}+kx_{2}=0\end{array}\right. \tag{1}\]  
Assume that \(f=Fe^{i\omega t}\), \(x_{1}=\overline{X_{1}}e^{i\omega t}\), and \(x_{2}=\overline{X_{2}}e^{i\omega t}\). Here, \(\overline{X_{1}}\) and \(\overline{X_{2}}\) are the complex amplitude of \(x_{1}\) and \(x_{2}\), respectively. The solution of Equation (1) can be expressed as  
\[\frac{X_{1}}{X_{st}}(\omega) =\sqrt{\frac{\left(\gamma^{2}-\lambda^{2}\right)^{2}+\left(2\zeta \lambda\right)^{2}}{\left[(1-\lambda^{2})(\gamma^{2}-\lambda^{2})-\mu\gamma^{2 }\lambda^{2}\right]^{2}+\left[1-\left(1+\mu\right)\lambda^{2}\right]^{2}(2 \zeta\lambda)^{2}}} \tag{2}\] \[\frac{X_{2}}{X_{st}}(\omega) =\sqrt{\frac{\left(\gamma^{2}\right)^{2}+\left(2\zeta\lambda \right)^{2}}{\left[(1-\lambda^{2})(\gamma^{2}-\lambda^{2})-\mu\gamma^{2} \lambda^{2}\right]^{2}+\left[1-\left(1+\mu\right)\lambda^{2}\right]^{2}(2\zeta \lambda)^{2}}} \tag{3}\]  
where \(\Omega_{n}=\sqrt{\frac{K}{M}}\) represents the natural angular frequency of the main vibration system, \(\omega_{n}=\sqrt{\frac{k}{m}}\) represents the natural angular frequency of TMD, \(X_{st}=\frac{F}{K}\) represents the static deformation of the main vibration system, \(\mu=\frac{m}{M}\) represents the mass ratio, \(\zeta=\frac{c_{m}}{2m\Omega_{n}}\) represents the damping ratio, \(\lambda=\frac{\omega^{2}}{\Omega_{n}}\) represents the ratio of the excitation frequency to the natural frequency of the main structure, and \(\gamma=\frac{\omega_{n}}{\Omega_{n}}\) represents the ratio of the TMD natural frequency to the natural frequency of the main structure.  
According to the literature [19], the optimal homology conditional of TMD is  
\[\gamma=\frac{\omega_{n}}{\Omega_{n}}=\frac{1}{1+\mu} \tag{4}\]  
The optimal damping ratio of TMD is  
\[\zeta_{opt}=\sqrt{\frac{3\mu}{8(1+\mu)^{3}}} \tag{5}\]  
According to Equations (4) and (5), the mass ratio is crucial for solving the optimal parameters.
## 3 Results and Discussion
### Antioxidant Activity Determination
#### 3.2.3 FRAP Assay  
This method has been developed by [PERSON] and strain (1996) [30]. It is based on the reduction of ferric ions (Fe\({}^{3+}\)) to ferrous ions (Fe\({}^{2+}\)) by an antioxidant under acidic solution to maintain iron solubility [24; 30]. The ferric salt solution is a pale-yellow color, but when reduced to the ferrous form, this changes to blue, and absorbance at 593 nm increases. However, this method is not convenient to the compounds that act as radical quenching or hydrogen transfer, particularly thiols and protein, and it does not react with glucose and fructose [20; 24].  
Table 2 shows that the ferric reducing potential of _Laminum album_ extracts varied significantly (\(p\) = 0.05) from 19.48 to 44.74 umol TE/g of extract, which represents a variation of two-folds. The extract at 50 \({}^{\circ}\)C showed very strong ferric ion reducing activities 44.74 umol TE/g of extract, followed by 60 \({}^{\circ}\)C which showed 28.32 umol TE/g of extract and 40 \({}^{\circ}\)C showed the lowest value of 19.48 umol TE/g of extract. Furthermore, the reducing power of plant extracts by FRAP assay have been reported in various studies [37; 38; 39].  
In similar studies, [PERSON] et al. (2009) [38] investigated antioxidant activities and the total phenolic content of 30 commonly used plants using DPPH, ABTS, FRAP, ORAC, and SOD assays. Their findings show that the ferric ion reducing activities of the 30 plant extracts tested did not differ significantly from their DPPH and ABTS scavenging activities [38]. Additionally, [PERSON] et al. (2004) [39] examined the total phenolic content and total antioxidant potential in 70 medicinal plant infusions. Their results revealed significant differences in total antioxidant capacity (FRAP) between the medicinal plant infusions, with FRAP values ranging from 59 to 25,234 umol FeII/L of infusion. Additionally, the significant linear correlation between total phenolics and FRAP of medicinal plant extracts was confirmed.
# Analyzing the Characteristics of Soil Moisture Using GLDAS Data: A Case Study in Eastern China
## 3 Results and Discussion
### Characteristics of Climate Change in East China
#### 3.1.3 The Long-term Changes in Soil Moisture  
Figure 8 shows a time curve of soil moisture in four different layers (0-10 cm, 10-40 cm, 40-100 cm, and 100-200 cm). All layer soil moisture variation characteristics have similar declining trends;  
Figure 7: The evolution of each layer of soil moisture and precipitation in eastern China: (**a**) 0–10 cm and precipitation; (**b**) 10–40 cm and precipitation; (**c**) 40–100 cm and precipitation; (**d**) 100–200 cm and precipitation.  
the changes in soil moisture are also very significant in different years in eastern China from 1961 to 2011. Before 1964, soil moisture in eastern China appeared to be increasing. In 1964, the soil moisture value reached a peak. After 1964, soil moisture declined sharply, but gradually shifted from wet towards drought conditions.  
In addition to the curve of change over time, we calculated different seasons' climate tendency rate of soil moisture in different layers (Table 2). Table 2 shows that the climate tendency rate is positive in the different layers of soil moisture in the spring and summer, but is negative in autumn and winter. The 0-10 cm layer has a small difference between spring and summer, and the characteristics of the other layers significantly differ each season. The climate tendency rates of the four layers are negative for every year considered, which indicates that the soil became dryer. It was also confirmed that in most cases the soil tends to become dryer the deeper down you go from the surface. These findings reached 99% on the significance test, which is the same as the result given by [18] using observation data from 1981 to 1991. Although there were some differences between GLDAS data and observation data, GLDAS data reflected the inter-annual variability of the observation data and peak events leading to the catch of the soil moisture.
## 4 Results and Analysis
### Analysis of NonCompliance Behaviors
#### 4.2.2 Value Conflicts and Noncompliance  
Employees develop various value conflicts towards ISP; they often violate ISP because of their preference-based conflicts. Reference [39] argued that employees' noncompliant behavior is not only because of risk perceptions but also from task completion impediments. Employees often engage in noncompliant security behaviors because of task completion deadlines; they often indulge in a value conflict (i.e., task completion is above ISP compliance). It has been shown in this study that self-justification (to justify the noncompliant act to oneself) and sunk-cost (i.e., lack of loss acceptance) are the main influential factors for engaging in noncompliant behavior towards ISP. Reference [42] emphasized the value of information; these researchers conducted a qualitative study on seven focus groups from Brunei's government. A total of 55 semi structured interviews were conducted with IT managers and professionals and a value-driven information security compliance theory was developed. This study suggests that users' perceptions about the value of information (i.e., how vital the information is) determine their compliance behavior with ISP. Furthermore, the theory of value-driven proposals based on a qualitative study, which still needs to be tested with quantitative studies, has been discussed. An empirical study presented by [40] has examined ISP noncompliance behavior with PMT and IT vision conflict. This study focused on measuring IT vision conflict mediation effects on PMT constructs and attitude towards ISP noncompliance. The authors argued that users' perceptions of IT and ISP were such that they believed that IT is much different to the rest of the organization, which in turn causes a conflict called IT vision conflict. Hence, this study concluded that IT vision conflict influences only perceived severity, not all PMT constructs; furthermore, it has been shown that PMT constructs have significant effects on noncompliance behaviors towards ISP. Reference [101] discussed the reasons for employees' less effortful behaviors towards information security tasks. In this study, the researchers elaborated on two coping mechanisms--procrastination (i.e., saving present time and pushing security tasks for the future) and psychology detachment
## 3 Component Selection and Criteria
### Frame Design and Weight Estimation  
From the literature survey [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17], it was found that the octocopter-based frame has been used for high-stability-based/high-payload-based applications; thus, it was implemented in this work. The application addressed in this work was very challenging; therefore, instead of a complicated design, a simple configuration was imposed, as follows:  
\[\text{Angle of the Connecting arm}=\frac{\text{Overall rotational degree}}{\text{Arm count of UAV frame}}=\frac{360}{8}=45^{\circ}.\]  
With the help of the cosine theorem, the length of the arm was obtained using Equation (5), which assumes that all the arms are of the same length, as follows:  
\[\text{Length of the arm}=\sqrt{\frac{\left(\text{Diameter of the Propeller}\right)^{2}}{2\times(1-\cos 0)}} \tag{5}\] \[\text{Length of the arm}=\sqrt{\frac{\left(6\right)^{2}}{2\times(1- \cos(45))}}\Rightarrow 7.84\text{ inches}=0.199136\text{ m }\approx 0.20\text{ m}.\]  
Figure 4: Historical relationship between pitch versus diameter of the octocopter’s propellers.  
In multirotor UAVs, the fineness ratio (FR) plays a significant role in the design stage. The conventional relationship of the FR ratio is shown in Equation (6), which plays a predominant role in the design of the length and thickness of the connecting arms, as follows:  
\[\text{FR}=\frac{\text{Length of the connecting arm}}{\text{Thickness of the connecting arm}} \tag{6}\]  
Generally, an FR ratio of 8-to-10 is more suitable for long arms, so the FR ratio was assumed to be 10, as follows:  
\[10=\frac{\text{Length of the arm}}{\text{Thickness of the arm}}\Rightarrow\text{ Thickness of the arm}=\frac{0.2}{10}=0.02\text{ m}.\]  
In general, to provide a better platform for other components and easy attachments, a square cross-section provides a nearly perfect fit for the connecting arms; thus, the same cross-sectional shape was used in this work. Therefore, the dimensions of a single connecting arm were 0.2 \(\times\) 0.02 \(\times\) 0.02 = 0.00008 m\({}^{3}\). A previous study [8] has shown that a GFRP composite can resist aerodynamic loads; thus, the same material was chosen as the construction material for the octocopter frame. A structural simulation was performed using a GFRP with an average density of approximately 1500 kg/m\({}^{3}\). Thus, the single-arm weight was 120 g, and the overall weight of the connecting arms was 960 g [11; 12; 13; 14; 15].
## 3 Experimental Study
### Experimental Design
#### 3.1.1 Experimental Configuration  
The integrated testing apparatus consists of three parts: a constant-pressure-controlled grouting system, visualized grouting diffusion system, and measurement system. Their relationship and the overall experimental layout are illustrated in Figure 5.  
Figure 4: The change in the grouting diffusion distance with time under different roughness factors.  
The constant-pressure-controlled grouting system mainly includes an air compressor pump, slurry storage buckets, grouting pipes, control valves, and sealing rings, as shown in Figure 5. The slurry storage bucket is cylindrical. It is made of aluminum alloy, which has a certain resistance to corrosion. The storage bucket has a 300 mm height, an inner diameter of 300 mm, and a wall thickness of 5 mm, and it has a strong pressure-bearing capacity. This system can meet the test requirements of single-slurry and double-slurry grouting. An air inlet hole is opened at the top of the slurry storage bucket to connect the regulating air valve. One end of the regulating air valve is connected to the storage bucket, while the other end is connected to the air delivery pipe of the air compressor pump. The regulating valve can realize the double function of pressure adjustment and a monitoring display to ensure that the grouting pressure is maintained at a constant value during the testing process.  
In the visualized grouting diffusion system, the grouting diffusion platform is the main body, which is made of a transparent polymethyl methacrylate. Its dimension from the vertical view is 1550 mm \(\times\) 1050 mm (length \(\times\) width). The original size of the fracture openness is 5 mm, which can be finely adjusted by adding different thickness gaskets. The grouting platform is mainly composed of an upper fracture surface, a lower fracture surface, a sealing connection device, and a water inlet and outlet device, which constitutes the visualization system of grouting diffusion with an additional filming system. A grating is drawn on the outside of the upper fracture surface, which is convenient for observing and recording the grouting diffusion trace. The whole system is shown in Figure 6.  
Figure 5: Overall layout of the experiment.  
The main objects of the measurement system are the grouting pressure and the diffusion trace and area. The measurement system and the distribution of pressure sensors are shown in Figure 7.
## 4 Image Acquisition Method and Information Processing of Yinghong No. 9 Tea Leaves
### Image Preprocessing  
Image preprocessing was divided into feature extraction and data standardization. In this paper, the characteristic information of the tea leaves was extracted from the two aspects of color and texture, and the Z-score standardization method was used to standardize the data, which can make the mean value that is characteristic of the tea leaves 0 and the standard deviation 1 [17].  
In terms of the color information, the collected tea leaves images were converted to RGB, LAB, and HSV color spaces for analysis. The RGB color mode is a color standard in the industry, which obtains various colors by changing the three color channels of red, green, and blue and superimposing them on each other; HSV is a color space created by [PERSON] in 1978 based on the intuitive characteristics of colors, where H represents the hue of the color, S represents the saturation, and V represents the brightness; LAB is a color space formulated by CIE, which is insensitive to light noise and offsets the deficiencies of the RGB color space, where L is the pixel brightness, A represents the degree of red to green, and B represents a range from yellow to blue [18].  
The texture information was extracted based on the gray-level co-occurrence matrix (CCM). We used m (mean), u2 (second-order moment), u3 (third-order moment), U (consistency), and e (entropy) to describe the texture information of the tea leaves [19]. The mean value describes the roughness of the tea leaves texture; the u2 and u3 describe the variance of the tea leaves' pixels and reflect the local gray correlation of the tea leaves' surface; the U refers to the uniformity of the tea leaves' pixel distribution; and the entropy value describes the chaotic degree of the tea leaves image information and reflects the randomness of the pixels.  
After extracting and standardizing the color features of the tea leaves images with different moisture contents, as shown in Figure 6a, as the moisture content of the tea leaves decreases, the color feature parameters change significantly, and the three values of RGB show a downward trend, resulting in a decrease in the overall brightness of the tea leaves images. The V and L parameters representing the brightness decrease, and the saturation, S, decreases. In Figure 6a, H and a gradually increased, while b gradually decreased, which means that the green component in the tea leaves image was decreasing, and the blue component was increasing. After the texture features were standardized, as shown in Figure 6b, the e first increased and then decreased, and the u2 first decreased and then increased. This shows that the tea leaves had a clear texture and large differences in the early stage of the moisture content reduction. In the later stage, the tea leaves surface was relatively uniform and smooth, and an overall decline in the m and U was indicated. The u3 first decreased and then increased, the skewness of the tea leaves color increased, and the overall color depth increased.
# A Novel Real-Time Match-Moving Method with HoloLens
## 5 Conclusions  
In this paper, we proposed a novel match-moving method for producing a stable virtual-real image synthesis for use in film production. The proposed system consists of two sub-systems. First, MS HoloLens is used to obtain the camera trajectory by obtaining position information in real time. This information was further used to create a virtual camera for match-moving. Secondly, an efficient pre-visualization system was implemented to preview the result of match-moving during recording. In this study, we exploited a system that overcomes the limitations of existing commercial software such as Boujou, which uses the feature point method. Our experimental results show that the proposed system is not affected by the external factors that were causing errors in conventional methods. The proposed method performed significantly better (98%) than the Boujou method (46%) especially when it comes to the motion-blur and occlusion in RGB camera video. However, the proposed system's limitation is that it does not have an optical zoom system. In the future, the authors of this paper are motivated to implement an optical zoom control system by installing a device such as focus gear ring that would be able to send necessary zoom information to the PC for further processing. In addition, Microsoft has released HoloLens 2 with a wider field-of-view that can be useful for spatial understanding required in the proposed system and needs further research.  
It can be predicted that, with the advancement of the film industry, the demand for match-moving will also increase and therefore a more efficient and cost-effective system would be required. Boujou is a commercial software that costs around $11,195 that is very expensive as compared to the proposed system which uses MS HoloLens. We expect that the system presented in this paper will be used for effective match-moving of the virtual-real image synthesis in low-budget production companies.  
Conceptualization, J.L. and J.H.; Data curation, J.L.; Formal analysis, S.K.; Funding acquisition, S.L.; Investigation, J.L. and J.H.; Methodology, J.L. and J.H.; Project administration, S.K.; Resources, K.K.; Software, K.K.; Supervision, S.L.; Validation, S.K.; Visualization, S.L.; Writing--original draft, J.H.; Writing--review and editing, S.K. and J.H.  
This research received no external funding.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2018-2015-0-00448) supervised by the IITP (Institute for Information & Communications & Technology Promotion).  
The authors declare no conflict of interest.  
Figure 12: Performance comparison: (**a**) Spectator View; (**b**) proposed method.
# Comparing Commit Messages and Source Code Metrics for the Prediction Refactoring Activities
## 4 Experimental Results and Analysis
### RQ1. How Effective Is Text-Based Modeling in Predicting the Type of Refactoring?
#### 4.2.4 Classification Using Naive Bayes  
Naive Bayes is one of the intuitive classification algorithms. However, it only works best with categorical data. It takes the freight class occurrence with correlation with other classes into consideration. From Table 10, it is clear that naive Bayes is not very robust for code metrics, and this model was only 35% accurate in predicting refactoring class. Figures 5 and 6 demonstrate the confusion matrix for each of the classifier.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Extract & 0.56 & 0.29 & 0.38 & 380 \\ \hline Inline & 0.79 & 0.72 & 0.76 & 417 \\ \hline Move & 0.94 & 0.69 & 0.80 & 429 \\ \hline Pull up & 0.41 & 0.13 & 0.20 & 418 \\ \hline Push down & 0.62 & 0.40 & 0.48 & 400 \\ \hline Rename & 0.32 & 0.90 & 0.47 & 443 \\ \hline Accuracy & & & 0.47 & 2481 \\ \hline Macro avg & 0.53 & 0.46 & 0.45 & 2481 \\ \hline Weighted avg & 0.53 & 0.47 & 0.45 & 2481 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results of Logistic Regression model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Extract & 0.64 & 0.24 & 0.35 & 380 \\ \hline Inline & 0.79 & 0.73 & 0.76 & 417 \\ \hline Move & 0.69 & 0.30 & 0.42 & 423 \\ \hline Pull up & 0.29 & 0.11 & 0.16 & 418 \\ \hline Push down & 0.61 & 0.29 & 0.40 & 400 \\ \hline Rename & 0.29 & 0.92 & 0.44 & 443 \\ \hline Accuracy & & & 0.44 & 2481 \\ \hline Macro avg & 0.55 & 0.43 & 0.42 & 2481 \\ \hline Weighted avg & 0.55 & 0.44 & 0.42 & 2481 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results of SVM model.  
For the study where we considered commit messages as input for the LSTM model, the \"move\" class was the most accurately predicted. After training supervised learning algorithms with code metrics as input, the random forest algorithm predicted inline class with 99% accuracy, whereas logistic regression predicted \"move\" with 94% of accuracy. The overall accuracy of the logistic regression classifier was noticeably poorer. SVM and naive Bayes predicted inline and rename refactoring classes more accurately.