esa-sceva/satcom-chunk-collection huggingface dataset:
# Phytochemical Analysis of _Symphytum officinal_ Root Culture Extract
## 2 Materials and Methods
### Determination of Antioxidant Activity by Spectrophotometric Method (DPPH-Method)  
The method is based on the ability of the reactive radical 2,2-diphenyl-1-picrylhydrazyl (DPPH) molecules to bind with antioxidants in the samples under study. Each extract sample was mixed with 2.85 mL of a freshly prepared 0.1 mM solution of 2,2-diphenyl-1-picrylhydrazyl (DPPH) in ethanol. The sample was incubated for 30 min at room temperature in the dark. The decrease in optical density at 515 nm (UV-3600, Shimadzu, Japan) was measured spectrophotometrically [16].  
Solutions of precisely known concentrations of ascorbic acid, trolox (a water-soluble analog of vitamin E), and quercetin were used as standards for determining antioxidant activity by the methods described above.  
Sample preparation included preparation of a saturated solution, filtration, and mass measurement. The study of antioxidant activity (AA) was carried out by spectrophotometric method based on inhibition of the stable chromogen radical 2,2-diphenyl-1-picrylhydrazyl (DPPH). In the visible region of the spectrum, DPPH in organic solvents has a wide absorption maximum at wavelengths of 515 \(\div\) 520 nm, which disappears when the radical interacts with substances--donors of hydrogen atoms or free radicals of a different structure.  
The reaction of DPPH with antiradical antioxidants occurs in a serial-parallel mechanism. At the first stage (the limiting step of the reaction), the antioxidant molecule gives the radical the most mobile hydrogen atom:  
\[\text{DPPH-}+\text{AH}\rightarrow\text{DPPH-H}+\text{A}. \tag{1}\]  
At the second stage, the antioxidant radical formed in reaction (1) attacks a new DPPH molecule in the para-position of the phenyl substituent:  
\[\text{A-}+\text{DPPH-}\rightarrow\text{A-DPPH}. \tag{2}\]  
This also results in an uncolored reaction product. Reaction (1) can proceed by two independent mechanisms. The first one, based on the direct abstraction of a hydrogen atom from an AA molecule, proceeds at the highest rate in non-polar solvents. The second, based on the transfer of an electron by an ionized phenolic AA molecule to a DPPH molecule, prevails in solvents with a high affinity for the proton.
# Optimization of CART Models Using Metaheuristics for Predicting Peach Firmness
## 2 Literature Review
The study in [23] introduces a differential evolution-based neural architecture search approach for brain vessel segmentation, while the work in [24] uses the improved particle swarm optimization algorithm to optimize the initial weights of the BP neural network and increase its efficiency. In [25], a black hole optimization algorithm is used to fine-tune the hyperparameters of the convolutional neural network model with the goal of increasing its accuracy. The modified African buffalo algorithm described in [26] is used to create efficient and optimal decision trees. The results show that the African buffalo algorithm successfully optimizes the decision tree model, increasing its accuracy and reducing its size. The obtained optimized trees are shown to be more stable and efficient than conventional decision trees. Further decision tree optimization research is described in [27], in which trees are optimized using the artificial bee colony optimization algorithm. This paper presents machine learning algorithms that use five predictor variables as inputs to predict peach firmness. In contrast to the other peach firmness prediction research [2], which only employs impedance data as input to the created ANN model, there are more predictor variables in this study. Another study that employed impedance to predict fruit maturity is [11]. It uses the measured impedance value to predict the maturity of banana fruit using a simple linear regression. Regression tree models have an advantage over other peach firmness prediction methods discussed in that they produce easily interpretable rules. Since regression trees are nonlinear and nonparametric, they do not presuppose a particular connection between the predictors and the outcome. This is an advantage when modeling datasets with many features or many complex, non-linear relationships among features and outcomes [28]. Other fruit maturity prediction methods utilize image data and machine learning models such as discriminant analysis [4] and convolutional neural networks [12]. Compared to the previous research [1], new metaheuristic algorithms are introduced to test which algorithm gives the largest prediction accuracy improvement when applied to the regression tree model optimization problem.
## 3 Histogram-Based Reversible Data Hiding with Quadtree Decomposition
### The Three-Round Embedding Process
Suppose that an image with the size of 8 \(\times\) 16 is partitioned by quadtree decomposition in Figure 5. The three-level decomposition of the block map **B** can be represented as follows:  
* For the first level with the block size of 8 \(\times\) 8, the block map **B** to be coded is '01';
* For the second level with the block size of 4 \(\times\) 4, the block map **B** to be coded is '0100';
* For the third level with the block size of 2 \(\times\) 2, the block map **B** to be coded is '0000'. With the formation of **B**, quadtree decomposition can be included into the histogram-based reversible data hiding. Finally, at Round 3, after embedding the user-defined information \(S\) at Round 2, histogram-based embedding should be performed on **X'**, and the max point in **X'**, \(a^{\prime}\), should be at least 8,200 in Figure 4(c). If the max point in **X'** is incapable of embedding 8,200 bits, we will search for luminance values \(c^{\prime}\) \(<\) \(a^{\prime}\), such that the occurrences for both \(a^{\prime}\) and \(c^{\prime}\) are greater than 8,200. After the embedding of the block map **B** is performed, the two-byte side information, containing \(a^{\prime}\) and \(c^{\prime}\), is transmitted to the decoder. That is, \(K\) = (\(a^{\prime}\), \(c^{\prime}\)). Therefore, with our algorithm, very little overhead is needed for decoding. This amount of side information is comparable to those shown in literature.
# VEPO-S2S: A VESsel Portrait Oriented Trajectory Prediction Model Based on S2S Framework
## 5 Experiments
### Model Performance Comparison
#### 5.2.1 Comparison Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name** & **10->1** & **10->2** & **10->3** & **10->4** & **10->5** \\ \hline Kalman & \(2.42\times 10^{-4}\) & \(3.00\times 10^{-4}\) & \(4.22\times 10^{-4}\) & \(5.51\times 10^{-4}\) & \(6.71\times 10^{-4}\) \\ VAR & \(0.98\times 10^{-4}\) & \(2.80\times 10^{-4}\) & \(50.43\times 10^{-4}\) & - & - \\ ARIMA & \(3.42\times 10^{-4}\) & \(5.30\times 10^{-4}\) & \(7.21\times 10^{-4}\) & \(9.15\times 10^{-4}\) & \(11.12\times 10^{-4}\) \\ LSTM & \(0.88\times 10^{-4}\) & \(1.16\times 10^{-4}\) & \(2.48\times 10^{-4}\) & \(3.46\times 10^{-4}\) & \(4.60\times 10^{-4}\) \\ BiLSTM & \(2.64\times 10^{-4}\) & \(2.65\times 10^{-4}\) & \(3.51\times 10^{-4}\) & \(4.41\times 10^{-4}\) & \(5.45\times 10^{-4}\) \\ GRU & \(0.86\times 10^{-4}\) & \(1.58\times 10^{-4}\) & \(2.41\times 10^{-4}\) & \(3.32\times 10^{-4}\) & \(4.30\times 10^{-4}\) \\ BiGRU & \(3.16\times 10^{-4}\) & \(3.24\times 10^{-4}\) & \(3.96\times 10^{-4}\) & \(4.71\times 10^{-4}\) & \(5.21\times 10^{-4}\) \\ LSTM-LSTM & \(1.22\times 10^{-4}\) & \(1.94\times 10^{-4}\) & \(2.76\times 10^{-4}\) & \(3.64\times 10^{-4}\) & \(4.58\times 10^{-4}\) \\ BiLSTM-LSTM & \(1.44\times 10^{-4}\) & \(2.21\times 10^{-4}\) & \(3.07\times 10^{-4}\) & \(3.99\times 10^{-4}\) & \(4.96\times 10^{-4}\) \\ GRU-GRU & \(0.98\times 10^{-4}\) & \(1.69\times 10^{-4}\) & \(2.49\times 10^{-4}\) & \(3.36\times 10^{-4}\) & \(4.29\times 10^{-4}\) \\ BiGRU-GRU & \(1.34\times 10^{-4}\) & \(2.21\times 10^{-4}\) & \(3.14\times 10^{-4}\) & \(4.11\times 10^{-4}\) & \(5.12\times 10^{-4}\) \\ Transformer & \(1.31\times 10^{-4}\) & \(1.74\times 10^{-4}\) & \(2.35\times 10^{-4}\) & \(3.07\times 10^{-4}\) & \(3.86\times 10^{-4}\) \\ METO-S2S & \(2.02\times 10^{-4}\) & \(2.03\times 10^{-4}\) & \(2.28\times 10^{-4}\) & \(2.73\times 10^{-4}\) & \(3.35\times 10^{-4}\) \\ Ours & \(\mathbf{0.74\times 10^{-4}}\) & \(\mathbf{1.11\times 10^{-4}}\) & \(\mathbf{1.71\times 10^{-4}}\) & \(\mathbf{2.40\times 10^{-4}}\) & \(\mathbf{3.17\times 10^{-4}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison results of VEPO-S2S with various baselines under RMSE evaluation metric. Here, 10->5 represents the RMSE value of the last 5 trajectories predicted by 10 historical trajectories. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name** & **10->1** & **10->2** & **10->3** & **10->4** & **10->5** \\ \hline Kalman & \(1.73\times 10^{-4}\) & \(2.34\times 10^{-4}\) & \(3.27\times 10^{-4}\) & \(4.24\times 10^{-4}\) & \(5.16\times 10^{-4}\) \\ VAR & \(0.87\times 10^{-4}\) & \(2.12\times 10^{-4
## 3 Results and Analysis
### Ablation Experiments for Improved Processes  
To ascertain the efficacy of the improvement methods, ablation experiments were conducted. These experiments assessed the contributions of three distinct improvement strategies and their combinations to model performance enhancement. The results are presented in Table 1. Experiment (1) evaluated the original YOLOv8n model. In Experiment (2), VanillaC2f was utilized to streamline YOLOv8n's backbone network, forming the Vanilla-Backbone lightweight network. This, along with deep training and an extended activation function, resulted in a 15% reduction in model size, a 14.6% decrease in parameters, and a 17.1% reduction in GFLOPS, while maintaining mAP values comparable to the baseline YOLOv8n model. Experiment (3) involved the use of a one-time aggregated cross-stage localized network module (VoVGSCSP) and the ghost shuffling module (GSConv) to optimize the neck network, creating the Efficient-Neck lightweight Neck network. This led to a 13.3% reduction in both model size and parameters, and, coupled with channel blending, slightly enhanced mAP compared to YOLOv8n. Experiment (4) improved model detection accuracy by substituting the original CIoU bounding-box loss function with the WIoU bounding-box loss function, resulting in [EMAIL_ADDRESS] values consistent with the baseline model and a slightly higher [EMAIL_ADDRESS]:0.95. Experiment (5) combined the strategies from Experiments (2) and (3), integrating the Vanilla-Backbone and Efficient-Neck networks. This approach reduced the model size by 23.3%, parameters by 24.3%, and GFLOPS by 28.1%, while increasing the [EMAIL_ADDRESS] value by 1.3% compared to the baseline model. Experiment (6) combined the improvements of Experiments (3) and (4), combining the Vanilla-Backbone and Efficient-Neck networks with the WIoU bounding box loss function. This yielded a 2.7% increase in [EMAIL_ADDRESS] and a 1.2% increase in [EMAIL_ADDRESS]:0.95 compared to the baseline model, without additional increases in parameters, model size, or GFLOPS.  
Figure 14: Comparison of P-R curve: (**a**) VEW-YOLOv8n P-R line graph; (**b**) YOLOv8n P-R line graph.  
Figure 13: Comparison of mAP and loss value curves: (**a**) [EMAIL_ADDRESS] line graph; (**b**) [EMAIL_ADDRESS]:0.95 line graph; (**c**) loss line graph.
# Relational Action Bank with Semantic-Visual Attention for Few-Shot Action Recognition
## 3 Method
### Relational Action Bank
Similar to [82; 83], we define the GNN operations for our RAB module as follows:  
\[\mathbf{z}_{i,j,m}^{(l)}=\sigma(w_{m}^{(l-1)}\mathbf{z}_{i,j,m}^{(l-1)}+\sum_{ k\in\mathcal{N}(m)}w_{k}^{(l-1)}\mathbf{z}_{i,j,k}^{(l-1)}), \tag{1}\]  
where \(l\) denotes the \(l\)-th layer, \(w_{m}^{(l-1)}\) and \(w_{k}^{(l-1)}\) are learnable parameters, \(\sigma\) is a non-linear function, \(\mathcal{N}(m)\) denotes the neighbor nodes of node \(m\) (neighbor is defined as having connected relations with node \(m\) on the graph). \(f_{1}\) and \(f_{2}\) are aggregate operations and \(g\) is the expansion function which is the reverse process of aggregation in \(f_{1}\). We can write the output of our RAB as follow (omit the non-linear function for simplicity):  
\[\widetilde{\mathbf{Z}}=f_{2}\circ g\circ f_{1}(\mathbf{Z}). \tag{2}\]  
In practice, we use three \(1\times 1\times 1\) 3D CNNs to realize the aggregation function (\(f_{1}\) and \(f_{2}\)) and expansion function (\(g\)). As Equation (1) shows, we add BN and ReLU as non-linear function between \(f_{1}\) and \(g\) to enhance the non-linear modeling abilities for clip relation modeling, and we add BN and ReLU following GNN operation \(f_{2}\). As shown in Figure 3, we reshape the input feature maps \(\mathbf{Z}\) and consider the \(s\times d\) as the channel dimension of input into \(f_{1}\) (\(1\times 1\times 1\) 3D CNNs) to aggregate the \(s\times d\) into \(1\). To expand to the original dimension, we still use \(g\) (\(1\times 1\times 1\) 3D CNNs) to resume the channel dimension to \(s\times d\) and obtain the clips relation enhanced features. Later, we use the aggregate operation \(f_{2}\) (\(1\times 1\times 1\) 3D CNNs) to aggregate all clips in each category into one feature vector. During the above process, we choose BN and ReLU as the non-linear function to enhance the non-linear representation ability. Figure 3: The detailed design of our proposed relational action bank module. **Discussion** The basic function of relational action bank is to cluster the clip feature vectors in each category into one feature vector to represent the corresponding category. The most direct idea is to use kmeans to cluster the clips of each category into one vector (we denote it as kmeans-RABSVA for convenient narrative). The kmeans-RABSVA can not dynamically learn the representation feature vector for the corresponding class adaptively and the feature vector is specified when the clips are specified. Another naive idea is that we can use one layer GNN to aggregate the clips in each class into one feature vector (We denote this method as naive-RABSVA). The naive-RABSVA can learn the representation vector adaptively. However, it omits the relations of clips in each video and suffer the much performance loss for that there are strong relations among these clips in each video.
# Research on Wear Detection of End Milling Cutter Edge Based on Image Stitching
## 4 Results and Discussion
### Algorithm Test Analysis  
#### 4.4.1 Comparison with Other Segmentation Algorithms  
The current image segmentation algorithms are mainly threshold segmentation method, edge segmentation method, etc. [28; 29]. A total of 30 holographic images of the end mill side edge are selected randomly. The commonly used Otsu threshold adaptive segmentation algorithm and K-means clustering algorithm are used to compare with the FCN segmentation model to verify the segmentation performance of the FCN segmentation model for tool wear images. Partial segmentation results are shown in Figure 12.  
The purpose of using the segmentation algorithm is to identify the wear area of the tool effectively. It can be seen from Figure 12 that the above three algorithms can all segment the wear area of the tool, but the recognition accuracy of the Otsu threshold adaptive segmentation algorithm and the K-means clustering segmentation algorithm is relatively poor, and misrecognition occurs. Part of the non-wear area is identified as wear, especially the misidentified area of the Otsu threshold adaptive segmentation algorithm is large.  
The segmentation effect of the FCN model is good, only the wear area is extracted and segmented, and other non-wear regions do not interfere. Therefore, the FCN model can effectively identify the tool wear area. In order to further illustrate the segmentation effect of FCN model segmentation, MPA, MIOU, and FWIOU are used to compare the three segmentation algorithms, and the results are shown in Table 2.  
The results show that the FCN model segmentation evaluation results are higher than the Otsu threshold adaptive segmentation algorithm and the K-means clustering segmentation algorithm. In contrast, the segmentation effect of the Otsu threshold adaptive segmentation algorithm is relatively poor, and the MIOU is only 0.32. Although the Otsu threshold segmentation algorithm can effectively segment the tool wear area and other backgrounds, the segmentation effect is poor for the parts with similar colors to the wear area and the unworn area of the mill tool side edge. Although the K-means clustering segmentation algorithm alleviates the problem of identifying the worn and unworn areas of the tool after segmentation to a certain extent, it cannot effectively remove the unworn  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Segmentation Algorithm** & **RMPA** & **RMIOU** & **RFWIOU** \\ \hline Otsu threshold adaptive segmentation algorithm & 0.43 & 0.32 & 0.39 \\ K-means clustering algorithm & 0.75 & 0.61 & 0.67 \\ FCN model segmentation & 0.82 & 0.76 & 0.77 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test results of three segmentation algorithms.  
Figure 12: Segmentation results of three algorithms. (**a**) Stitched original image; (**b**) Otsu threshold adaptive segmentation algorithm; (**c**) K-means clustering algorithm; (**d**) FCN segmentation.  
area of the tool. The FCN model segmentation can not only effectively identify the tool wear area, but also better avoid the interference of the unworn area.
# An Improved Fuzzy Vector Signature with Reusability
## Preliminaries
### External Diffie-Hellman (XDH) Assumption  
Let \(\mathbb{G}_{1}\), \(\mathbb{G}_{2}\) be groups with prime order \(q\) and \(g,h\) be generators of \(\mathbb{G}_{1}\), \(\mathbb{G}_{2}\), respectively. The XDH problem in \(\mathbb{G}_{1}\) is defined as follows: given \(D=(g,g^{a},g^{b},h)\in\mathbb{G}_{1}^{3}\times\mathbb{G}_{2}\) and \(T\in\mathbb{G}_{1}\) with a Type-3 pairing, the goal of an adversary \(\mathcal{A}\) is to distinguish whether \(T\) is either \(g^{ab}\) or random \(R\). For any PPT algorithm \(\mathcal{A}\), the advantage in solving the XDH problem in \(\mathbb{G}_{1}\) is defined as  
\[\mathsf{Adv}_{\mathcal{A}}^{\mathsf{XDH}}(\lambda)=\big{|}\Pr\big{[}\mathcal{ A}(D,g^{ab})\to 1\big{]}-\Pr\big{[}\mathcal{A}(D,T)\to 1\big{]}\big{|}.\]  
We say that an XDH assumption holds in \(\mathbb{G}_{1}\) if, for any PPT algorithm \(\mathcal{A}\), the advantage \(\mathsf{Adv}_{\mathcal{A}}^{\mathsf{XDH}}(\lambda)\) is negligible for \(\lambda\).
# Inference of Drawing Elements and Space Usage on Architectural Drawings Using Semantic Segmentation
## 3 Model Structure of Semantic Segmentation
### Encoder-Decoder  
Deep neural networks widely used for classification, such as AlexNet and VGG (Visual Geometry Group), are not suitable for semantic segmentation, since those models have a layer, which reduces the number and dimensionality of parameters, causing the loss of detailed location information. To address the challenge, semantic segmentation models usually have downsampling & upsampling [27]. This concept is shown in Figure 1.  
Downsampling reduces the dimensionality for the sake of a deep convolution with less memory. A convolution with two or more strides or pooling is often used. Information about features is lost in downsampling [28]. At the end of downsampling, a fully connected network (FCN) is usually used without inserting a fully-connected layer. Since the FCN model suggested the foregoing method, most models have used this method. Upsampling is a process of increasing the dimensionality of the result received from the downsampling to the same dimensionality as that of inputs. In upsampling, FCN, SegNet, and UNet are well-known models that have the encoder-decoder types.
# A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data
## 3 Methodology
### Geometric Analysis of Reliability  
The estimated variance \(\sigma_{\mathbf{k}}\) provides information on the precision of the transformation parameters. However, it is a poor indicator for the reliability of the point cloud correction because it ignores the geometry of the scanned scene. The transformation parameters can be precise even if they result from two point clouds that capture the road surface only. Therefore, we propose to use the point normals \(\mathbf{n}\) to evaluate the scenery. We assume that well-distributed point normals will provide data for reliable results. In other words, the quantification of the point normal distribution allows us to infer reliability.  
Our idea is to interpret the normals \(\mathbf{n}\) as points on a unit sphere and to count the occurrences at discrete bins. We choose the Fibonacci lattice (see Figure 7 left) to evenly distribute \(L\) points on the sphere. It stands out for constructing almost equal-sized surface patches at low complexity. The \(L\) lattice points \(\mathbf{l}\) lie along a spiral, with successive points at the greatest possible distance and the angle between them based on the golden ratio [43]. The vector of the grid point \(\mathbf{l}_{j}\) that spans the smallest angle with the point normal defines the bin to which it is assigned (cf. Equation (16)).  
\[j_{max}(i)=\underset{1\leq j\leq L}{\text{argmax}}\langle\mathbf{n}_{i}, \mathbf{l}_{j}\rangle \tag{16}\]  
Consequently, we can analyze the distribution of point normals by plotting the histogram of \(j_{max}\) for each profile. Figure 7 right shows the normals distributions for a profile capturing a tunnel and an overland scene with vegetation. The histogram of the latter (orange) indicates that normals dominate in the vertical direction (\(L=51\), see Figure 7 left), while the tunnel scene also provides many points on vertically oriented surfaces.  
Figure 7: The Fibonacci spiral provides an evenly spaced lattice on a unit sphere (**left**) for evaluating the point normal distribution for a scanned profile of a tunnel and an overland scene (**right**).