esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Discussion
### Robustness Test
#### 4.3.2 Train on the DIP Data Only  
Eighty percent of essential and non-essential protein data was randomly selected from the DIP dataset as training data, and the remaining 20% as the test data of the model. With the increase in training times, the recognition accuracy and over-fitting phenomenon are shown in Figure 6. The essential proteins of GAVIN data and DIP data are predicted, and the false positive, false negative, and correct rates are shown in Figure 7.  
It can be seen from Figure 6 that the data accuracy of the test set and training set increases gradually with the number of training iterations. They only intersected at the beginning, and the model training fails to fit. The number of training sessions has reached 2,085,000, yet the accuracy has been a smooth process. It shows that it is difficult to  
Figure 5: Only training GAVIN data results test.  
Figure 6: Only training DIP data generation model.  
achieve a good fit by increasing the number of training times. Therefore, the fitting point (x = 1,346,160, y = 87.3237%) is the closest point between the test set and the training set with good accuracy. The lowest accuracy of the test set is 74.7908% and the highest accuracy of the test set is 94.0759%. The lowest accuracy of the training set is 77.2088% and the highest accuracy of the training set is 90.1467%.  
It can be seen from Figure 7 that the overall accuracy of GAVIN is 68.2309% and that of DIP is 86.9434%. The overall accuracy of GAVIN is lower than that of DIP. The false negative and false positive aspect of the GAVIN dataset is much higher than the DIP dataset. The false negative of the GAVIN dataset is 14.6491% and the false negative of the DIP dataset is 1.2851%. The false positive of the GAVIN dataset is 59.1036% and the false negative of the DIP dataset is 44.7578%. There is no significant difference between the two datasets in terms of the number of correctly identified essential proteins. The GAVIN is 15.7497% and the DIP is 14.9589%. The number ratio of correctly identified non-essential proteins in the GAVIN dataset is much lower than that in the DIP dataset (52.4811% GAVIN and 71.9844% DIP).
# Machine Learning Powered Microalgae Classification by Use of Polarized Light Scattering Data
## 2 Materials and Methods
### Machine Learning Algorithms
#### 2.3.2 Support Vector Machine  
Support vector machine (SVM) is a machine learning algorithm that maximizes the margin between different categories of data and classifies different categories with a hyperplane [25]. SVM is designed for two-class classification tasks; one-versus-one (OVO) and one-versus-rest (OVR) are two methods applied in the classification of the multi-class task. Regarding the classification of different categories, SVM with the linear kernel can separate the data of different categories by a linear hyperplane, while the non-linear SVM model applies the Gaussian kernel to map the original features into a higher-dimensional space and realize the classification by a non-linear hyperplane.
# Enhanced Joint and Separable Reversible Data Hiding in Encrypted Images with High Payload
## 3 The Proposed Joint Data Hiding System
### Procedures of Proposed Method
#### 3.1.1 Image Encryption  
Since the content owner does not want the data hider to know any information about the original image, he encrypts the original image with an encryption key using a standard stream cipher. Among various papers for image encryption, we adopt the image encryption algorithm identical to [PERSON]'s [15] and others [16; 17; 18; 19; 20], in order to compare them conveniently and impartially.  
It is assumed that \(I\) is a gray-level image of uncompressed format sized \(X\times Y\), in which every pixel with a gray value is represented by eight bits. Let \(I_{i,j}\) be a pixel at position (\(i\),\(j\)), where \(I_{i,j}\) belongs to [0, 255] and \(1\leq i\leq X\), \(1\leq j\leq Y\). \(I_{i,j,k}\) denotes eight-bit binary digits of each pixel with a gray value, where \(k\) = [1, 8]. The relation between bits in a pixel and pixels with a gray value is denoted by  
\[I_{i,j,k}=\left\lfloor\frac{I_{i,j}}{2^{k-1}}\right\rfloor\text{mod}\,2 \tag{5}\]  
To encrypt the original image, random sequence \(R_{i,j,k}\) is generated according to the encryption key using a standard stream cipher. Then, bits of pixel \(I_{i,j,k}\) can be encrypted by a bitwise exclusive-or (XOR) of \(I\) and \(R\):  
\[I^{\prime}_{i,j,k}=I_{i,j,k}\oplus R_{i,j,k} \tag{6}\]  
From the bits of the encrypted pixel in (6), a pixel in the encrypted image can be written as follows:  
\[I^{\prime}_{i,j}=\sum_{k=1}^{8}I^{\prime}_{i,j,k}.2^{k-1} \tag{7}\]
# An End-to-End Classifier Based on CNN for In-Air Handwritten-Chinese-Character Recognition
## 3 Proposed Method
### Preprocessing
#### 3.1.2 Normalize Coordinates  
Since in-air handwriting has fewer space constraints and an unstable writing position, the positions of the written Chinese characters vary: some are higher, some are left and some are right. The end-to-end CNN directly takes a coordinate sequence as input, so the variation in coordinate-points distribution will greatly reduce the recognition rate. In order to decrease the variations in the spatial sizes and positions of characters, we employed coordinates normalization, following the method presented in [24]. Let \(I_{t}=\sqrt{(x_{t}-x_{t-1})^{2}+(y_{t}-y_{t-1})^{2}}\) be the distance between two consecutive points (\(p_{t}=(x_{t},y_{t})\) and \(p_{t-1}=(x_{t-1},y_{t-1})\)). In order to normalize the coordinates to a standard interval, it is first necessary to estimate the mean of the coordinates projected on the coordinate axis. We can calculate the mean \(\mu_{x}\) and \(\mu_{y}\) of XY coordinates, respectively, by  
\[\mu_{x}=\frac{1}{2}\frac{\sum_{t=2}^{T}I_{t}(x_{t}+x_{t-1})}{\sum_{t=2}^{T}I_{t}} \tag{3}\]  
\[\mu_{y}=\frac{1}{2}\frac{\sum_{t=2}^{T}I_{t}(y_{t}+y_{t-1})}{\sum_{t=2}^{T}I_{t}} \tag{4}\]  
The standard deviation \(\delta x\) on the \(x\) axis can be estimated as  
\[\delta_{x}=\sqrt{\frac{\sum_{t=2}^{T}I(x_{t}-\mu_{x})^{2}+(x_{t}-\mu_{x})(x_{t -1}-\mu_{x})+(x_{t-1}-\mu_{x})^{2}]}{3\times\sum_{t=2}^{T}I_{t}}} \tag{5}\]  
The normalized trajectory needs to keep the original character shape and stroke writing direction, the characters are only scaled by the standard deviation \(\delta x\). For the \(t\)th point of P, we can obtain the normalized point \((\bar{x}_{t},\bar{y}_{t})\) by  
\[\bar{x}_{t}=\frac{x_{t}-\mu_{x}}{\delta_{x}},\quad\bar{y}_{t}=\frac{y_{t}-\mu _{y}}{\delta_{x}} \tag{6}\]  
Some examples of processing through the above steps are shown in Figure 3. From Figure 3, we can see that the processed coordinates are evenly distributed on both sides of \((0,0)\) and the number of points has been reduced from 723 to 355.
## 3 FANET Routing Algorithms
### Heuristic-Based Routing Algorithms
These researchers' studies also demonstrate the effectiveness of particle swarm algorithms for optimizing FANET routing algorithms. While the exact algorithm can provide a globally optimal solution, its high time complexity makes it unsuitable for real-time applications, especially with larger problems. In contrast, heuristic algorithms are faster and can deliver better solutions in a shorter time frame, although they may not guarantee a globally optimal result. In order to address these issues, the researchers reviewed below have explored combining both algorithms for routing optimization. [PERSON] [87] is the first study to combine heuristic and exact algorithms for solving a multi-UAV routing problem under distance constraints. The authors establish constraint mechanisms to design ILP models and use SA and local search algorithms to assist in the search for routing solutions. Finally, the researchers demonstrate the effectiveness of this study by comparing it with a GA-only routing strategy. In contrast to [PERSON] et al., [PERSON] et al. [88] focus their optimization objective on minimizing the number of UAVs in the FANET, retaining only the minimum number of UAVs that can support the service within the self-assembled network. [PERSON] et al. propose a formula for minimizing the number of UAVs, which is solved with the aid of a GA. This algorithm uses the idea of greed to continually compress the number of drones so that when the number of drones does not satisfy the services in the network, the number of drones before this compression is the minimum number of drones. [PERSON] et al. [89] similarly combine the exact algorithm with a heuristic algorithm to minimize the total flight communication cost by formulating cost constraints on the cost of charging, acceleration, and hovering of the drones. In this, two heuristic search algorithms were used to help the model solve for the communication cost minimization in an acceptable time.
## 5 Results and Discussions
### Amplitude-Based and Energy-Based DIs in Frequency Domain  
The normalized magnitude-based and energy-based DIs in the time domain can provide convenient conditions for crack propagation quantification, but their effectiveness in depicting crack size and damage level are still controversial. This is because small-scale cracks can cause a feeble variety for the crack-scattered wave in the time domain, that is, the magnitude of fatigue crack is often altered at an imperceptible level within the temporal information. Therefore, the signal features associated with the frequency domain are extracted to improve the sensitivity and stability of amplitude-based and energy-based DIs, in accordance with a pitch-catch sensor network configuration [25]. Fast Fourier transform (FFT) is used to process time-domain signals to obtain a frequency spectrum [31], as shown in Figure 9. Compared with the magnitude variations in the time domain from Figure 7, the amplitude variations in the frequency domain are illustrated in a more obvious and reliable manner.  
The signals shown in Figure 8 are subsequently preformed with fast Fourier transform (FFT) to obtain the corresponding frequency spectrogram, as shown in Figure 10. The overall trend of DIs obtained in the frequency domain is basically consistent with that of the time domain. However, when the crack of the A\({}_{1}\)\(-\)S\({}_{4}\) actuator-sensor path spreads from 0.0 cm to 6.0 cm, the A\({}_{2}\)\(-\)S\({}_{5}\) actuator-sensor path spreads from 0.0 cm to 11.0 cm, or the A\({}_{3}\)\(-\)S\({}_{6}\) actuator-sensor path spreads from 0.0 cm to 15.0 cm, there are no outstanding peaks for the amplitude-based and energy-based DIs. It is clearly observed that the variations of DIs are gradually and steadily obverted to the stable stage. In addition, the variations of DIs can be regarded as insignificant in the stable stage, which are consistent with the observations in Figure 8.  
Figure 10: The amplitude and energy DIs of different actuator–sensor paths in the frequency domain (**a**) Normalized magnitude-based DIs of A\({}_{1}\)\(-\)S\({}_{4}\), A\({}_{2}\)\(-\)S\({}_{5}\), and A\({}_{3}\)\(-\)S\({}_{6}\) paths; (**b**) Normalized energy-based DIs of A\({}_{1}\)\(-\)S\({}_{4}\), A\({}_{2}\)\(-\)S\({}_{5}\), and A\({}_{3}\)\(-\)S\({}_{6}\) paths; (**c**) Normalized magnitude-based DIs of A\({}_{2}\)\(-\)S\({}_{4}\), A\({}_{2}\)\(-\)S\({}_{5}\), and A\({}_{2}\)\(-\)S\({}_{6}\) paths; and (**d**) Normalized energy-based DIs of A\({}_{2}\)\(-\)S\({}_{4}\), A\({}_{2}\)\(-\)S\({}_{5}\), and A\({}_{2}\)\(-\)S\({}_{6}\) paths.
# Elimination of the Solid Graininess Issue with Different Micro-Pattern Structures at Flexo Printing
## 4 Conclusions  
As a result of all the examinations, significant differences were detected when the prints made without micro-pattern and with micro-patterns were examined. It has been observed that the ink cannot be transferred completely in areas without micro-patterns, while in areas with micro-patterns, the pattern ratio per unit area seriously affects the ink transfer.  
In areas without micro-patterns, ink laydown is not fully successful because the ink accumulates on the plate but is not transferred well to the substrate. With the developed micro-patterns, trail edge voids in the solid areas and the ink accumulation edge of the screen dots are eliminated. Ink is distributed more homogeneously without graininess. It has been determined that this results in a more stable measurement of SID and eliminates the measurement of grainy solids and low SID.  
In these micro-pattern studies, the best results in homogeneous laydown of ink transfer on the substrate were obtained with a micro-pattern that is filled with at least 50% of the unit area. If the micro-pattern ratio per unit area increases, it resembles a non-pattern  
Figure 28: Trail edge voids—no pattern applied. The mounting tape used was medium-hard on the left and medium-soft on the right.  
Figure 29: No trail edge voids are observed. The mounting tape used is medium hard on the left and medium soft on the right.  
structure. When the micropattern per unit area is applied at less than 50%, ink transfer decreases, SID drops, and ink laydown worsens.  
This study has shown that reducing the cell volume of the anilox roller allows us to obtain a more uniform ink laydown with less ink on the corresponding micro-pattern.  
According to [PERSON], thinner lines offer the best height/width ratio, producing networks with better conductivity, clearly demonstrating the feasibility and value of flexographic processing using micropatterning of conductive structures [25].  
This study was tested in the narrow web flexo. It is especially used in small-size packaging and label printing. This research is going to provide a solution for better ink transfer with plastic substrates for narrow web applications.  
Conceptualization, N.K.; Formal analysis, T.S.; Investigation, N.K.; Writing--original draft, N.K.; Writing--review & editing, T.S.; Funding acquisition, N.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 4 Results and Discussion  
### Instructional Technology Experts' Perceptions of the Proposed Platform  
The following are the descriptive statistics obtained from the questionnaire on the perspectives of instructional technology experts regarding the suggested ETBP. Section 2: Table 1 presents the viewpoints of the instructional technology experts on the importance of the suggested ETBP for instructing the skills of designing and producing online ETBs. The mean score for questions Q1 and Q5 was 4.86, indicating that the instructional technology experts regard the proposed ETBP as a new digital teaching aid for designing and producing online ETBs. It would also help students studying instructional and information technology to create online ETBs without dealing with web programming challenges, which makes the process simple. With this proposed platform (ETBP), they believed it would assist target students in designing and creating online ETBs for multiple domains. This is evident from the mean value for Q2 of 4.93. The participants concurred that the suggested ETBP is a beneficial tool for assisting the instructors in teaching the design and production of online ETBs more effectively, as shown by the mean of Q3 of 4.71. The mean score for Q4 was 4.57, illustrating that students would more efficiently prepare a digital instructional design of online ETBs in the proposed platform. The suggested ETBP is suitable for designing and producing online ETBs for both regular and blind learners, as demonstrated by the average score of 4.29 for Q6. The participants confirmed that the ETBP would support developing online ETBs with audio duration according to microlearning principles. This is evident from the mean for Q7 of 4.79. Furthermore, 100% of the participants had an entirely positive attitude (strongly agree and agree) regarding Q1, Q2, Q3, Q5, and Q7. Approximately 93% of the participants had a positive attitude regarding Q4 and Q6. However, one participant in Q6 strongly disagrees with using the proposed ETBP in designing and producing online ETBs for both regular and blind students. The reason for this was not stated. According to the questionnaire results for this section, approximately 98% of all participants had favorable opinions regarding the importance of implementing the proposed ETBP in teaching skills for designing and producing online ETBs in higher education. The total mean, which is 4.71, shows that the participants generally approved. Section 3: Table 2 illustrates the participants' perceptions regarding the usefulness of the proposed platform tools. The mean score for question items Q1 and Q4 was 4.71, indicating that the experts agreed that the proposed ETB designer tool and ETB
## 1 Introduction  
Recently, the strategy for systematically designing the morphology and structure of diverse inorganic materials has been a cutting-edge research focus owing to the novel physical and chemical properties essentially affected by their shapes and sizes [1; 2; 3; 4; 5]. Particularly, nanobelts have been paid great attention due to their novel physical and chemical properties and wide application prospects [6; 7]. Moreover, supercapacitors (SCs) are considered as one of the most attractive pieces of new-generation apparatus in energy storage, and their performance is closely related to the characteristics of electrode materials. Metal oxides with novel structures show excellent specific capacitance and energy density because of their energy storage mechanism [8; 9; 10]. Meanwhile, there is a wide utilization in various fields, such as energy, biology, gas sensors, etc. [11; 12; 13].  
After the discovery of VO2, it aroused great interest among scientists for its structural phase transition [14; 15], and the crystallography data of VO2 polymorphs were studied systemically (in Table 1) [16; 17; 18; 19; 20; 21]. In the past decades, B-type vanadium dioxide (defined as VO2(B)) has attracted widespread attention to the application of lithium-ion batteries due to its chemical structural characteristics [22; 23]. Nowadays, VO2(B) has arisen people's interest in the field of supercapacitors. For examples, concerning one kind of VO2(B) nanobelts, it was reported that a specific capacitance of 142 F-g\({}^{-1}\) is displayed at the voltage window limit of \(-\)0.4-0.6 V [24]. Concerning VO2(B) nanosheets, [25] reported that the particles show a high rate capability of 200 mAh g\({}^{-1}\). Furthermore, hybrid materials related to VO2(B) were also developed to apply to the material of the electrodes. For instance, VO2(B)/C were synthesized through the hydrothermal method, which exhibited the specific capacitance of 182 F-g\({}^{-1}\) when the voltage window limit is \(-\)0.6-0.8 V. The VO2(B)/rGO composites exhibited a capacity of 456 mAh g\({}^{-1}\)[26]. According to the published data, the electrochemical properties of VO\({}_{2}\)(B) as SCs remains to be optimized further. Therefore, it is necessary and significant for material researchers to improve the specific capacitance or voltage window.  
In this paper, we designed a simple and convenient route to synthesize VO\({}_{2}\)(B) nanobelts. That is, NH\({}_{4}\)VO\({}_{3}\) is reduced by H\({}_{2}\)C\({}_{2}\)O\({}_{4}\).2H\({}_{2}\)O using hydrothermal method without adding any template or surfactant. cyclic voltammetry (CV) and galvanostatic charge-discharge (GCD) tests have been applied to assess the electrochemical properties of the VO\({}_{2}\)(B) nanobelts.
## 4 Results
### Analysis of Enhancing Question Diversity
#### 4.3.2 Two-Way Analysis of Variance (ANOVA)  
To deeply analyze the impact of generation methods and cognitive levels on question quality, we conducted a two-way Analysis of Variance (ANOVA). Before performing the analysis, we verified the prerequisites for ANOVA, including normality (using the Shapiro-Wilk test) and homogeneity of variances (using [PERSON]'s test). The results showed that the data generally met these assumptions (\(p>0.05\)).  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Generation Method** & **Cognitive Level** & **Mean** & **Standard Deviation** & **N** \\ \hline \multirow{4}{*}{Multi-Examiner} & Memory & 2.13 & 1.01 & 30 \\  & Understanding & 2.27 & 1.11 & 30 \\  & Application & 2.07 & 0.95 & 30 \\  & Analysis & 1.97 & 0.95 & 30 \\  & Evaluation & 1.77 & 0.94 & 30 \\  & Creation & 2.12 & 1.08 & 30 \\ \hline \multirow{4}{*}{GPT-4} & Memory & 2.97 & 0.99 & 30 \\  & Understanding & 2.80 & 0.96 & 30 \\  & Application & 3.05 & 1.00 & 30 \\  & Analysis & 2.77 & 0.99 & 30 \\  & Evaluation & 2.20 & 0.71 & 30 \\  & Creation & 3.09 & 0.94 & 30 \\ \hline \multirow{4}{*}{Human} & Memory & 3.35 & 0.91 & 30 \\  & Understanding & 3.67 & 1.03 & 30 \\ \cline{1-1}  & Application & 3.18 & 0.81 & 30 \\ \cline{1-1}  & Analysis & 2.88 & 1.01 & 30 \\ \cline{1-1}  & Evaluation & 2.27 & 1.11 & 30 \\ \cline{1-1}  & Creation & 3.43 & 0.96 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Descriptive statistics of scores by generation method across six cognitive levels of [PERSON]’s taxonomy.  
Table 11 presents the results of the ANOVA, where the dependent variable is the question quality score, and the independent variables are the generation method and cognitive level. The analysis revealed: (1) Significant main effect of generation method: F(2, 530) = 13.76, p \(<\) 0.001, partial \(\eta^{2}\) = 0.05. According to [PERSON] (1988), this is considered a medium effect size, indicating significant differences in question quality across different generation methods. (2) Significant main effect of cognitive level: F(5, 530) = 26.37, \(p<\) 0.001, partial \(\eta^{2}\) = 0.20. This represents a large effect size, suggesting that cognitive level has a significant impact on question quality. (3) Significant interaction effect between generation method and cognitive level: F(10, 530) = 2.27, \(p=\) 0.013, partial \(\eta^{2}\) = 0.04. Although the effect size is small, it indicates that the combination of generation method and cognitive level has a noticeable impact on question quality.