esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Electronic Nose Detection of Different Oolong Tea Samples  
In Figure 2 and Table S5, the electronic nose results showed significant differences (\(p<0.05\)) between the samples of suitable and unsuitable cultivars. In the heat map (Figure 2A), all samples were divided into two major categories by cluster analysis, agreeing with the traditional suitability classification of oolong tea cultivars. LY-type sensors (LY2/AA, LY2/gCTL, LY2/gCT, LY2/G, and LY2/GH) had positive response values to the suitable tea cultivars (MZ, HD, and TGY) and negative response values to the unsuitable tea cultivars (FD, WNZ, and YS). Unlike the 5 LY-type sensors, the P-type sensors (P30/2, P10/2, P30/1, P40/1, P40/2, P10/1, and PA/2) and T-type sensors (T40/2, T30/1, and T70/2) showed intergroup differences in their response values to the six different samples. Based on the average response values of sensors, cluster analysis divided the sensors into three categories, with 15 sensors distinguishing the suitability grouping of oolong tea cultivars at \(p<0.01\) (Figure 2A, \(p\) value, Kruskal-Wallis test, with \(p<0.05\) for TA2), excluding T40/1 and LY2/LG due to \(p>0.05\).  
Moreover, the normalized data (z-score normalization) of the electronic nose sensor response values were further examined by PCA analysis, and the score map also showed the data difference in the two groups (Figure 2B). In the score chart, the sum contribution rates of principal components 1 (\(x\)-axis) and 2 (\(y\)-axis) to the overall model exceeded 90%, and the scores of the suitable tea cultivars were significantly different (\(p<0.05\)) from those of the unsuitable cultivars in principal component 1 (92.15%) on the distribution (\(x\)-axis). Meanwhile, in Table S6, the Adonis analysis results show a significant difference (R\({}^{2}=0.5707\) and \(p<0.01\)) between the samples of different suitability, further verifying the reliability of sample suitability grouping.  
Electronic nose, a sensor system simulating the human nose, can quickly identify tea aroma differences [19]. In this study, the electronic nose was able to significantly distinguish the products of suitable and unsuitable oolong tea cultivars, and all the data were statistically significant (\(p<0.05\)). Not all sensors played a significant role in this process, so we excluded T40/1 and LY2/LG in the subsequent analysis. Due to the differences in the contents of aroma substances, oolong tea products from cultivars of different suitability varied in the response values of the electronic nose sensors, and the tea products in the same suitability group showed certain similarities in their aroma characteristics. Furthermore, the electronic nose data were consistent with the sensory review data, which not only reflected the accuracy of electronic nose detection, but also the close relationship between the electronic nose data and human senses (sensory review data). Both datasets were derived from the aroma components of different samples.
## 3 Experimental Activities
### Investigation of the Fragmentation Process
#### 3.3.3 Complex Mock-Up  
In 2021, an impact test was performed on a 5 \(\times\) 5 \(\times\) 5 cm\({}^{3}\) picosatellite mock-up with a 1.6 g nylon cylinder that hit the center of one face of the satellite at a velocity of 2.72 km/s; frames from the video of the impact can be seen in Figure 10. The mock-up included plastic elements and consumable electronic boards to simulate the different materials employed in modern spacecraft; the fraction of different materials was about 25% metals, 20% plastics, and 55% of electronic components. The collision led to the complete fragmentation of the target; the fragments were collected, weighed, and divided into size classes [47].  
Figure 8: Fragment cumulative size distributions for the four CFRP thin plate tests.  
Figure 9: Fragment cumulative size distributions for the tests on sandwich panels [46].  
The results showed that, despite the different materials employed in the mock-up, the characteristic length distribution is in line with NASA SBM prediction, even for sizes smaller than 1 cm (see Figure 11). However, the analysis of fragments' area-to-mass indicated a strong influence from fragment material and a consistent deviation from the NASA SBM prediction [47,48].
## 3 Theoretical Basis for Magnetic Coupled Field Simulation
### Magnetic Coupling Method Based on Generalized Stretching  
Upon developing the average finite element model of the magnet, this study does not incorporate the electromagnetic-structural coupling node for electromagnetic stress analysis. This decision is based on the axisymmetric property of the gradient magnet, which enables the establishment of a two-dimensional rotating coordinate system O\({}_{r\text{-}z}\). The electromagnetic load is segmented into radial and circumferential components using the generalized stretching command, and these components are then applied to each node of the coil through the body load command to facilitate the calculation of electromagnetic and mechanical field coupling. Simultaneously, the program considers the magnet as a rotating body, simplifying the magnet model, and disregards the impact of coil insulation and coil outlet on the structural integrity of the magnet.
## 3 Results and Discussion
### Screening for Hop Oil Bioactive Components by HPTLC Hyphenations
In this separation system, the \(\beta\)-farnesene standard migrated to the zone at \(hR_{\mathrm{F}}\) 73 and inhibited both the \(\alpha\)-glucosidase enzyme and, to a weaker extent, _A. fischeri_. When screening the more polar components, six bioactive zones separated by chloroform were selected at \(hR_{\mathrm{F}}\) 16, 35, 52, 61, 70, and 75 (Figure 3e-h). The hop oil samples showed \(\alpha\)-glucosidase inhibition, but the responsible compounds could not be identified as no demarked inhibition zones were revealed and no compounds were visible in the darker areas even after derivatisation under UV light. Compounds in the six marked zones inhibited the lipase enzyme as well as the bioluminescence of Gram-negative _A. fischeri_, displaying an antibacterial effect. The \(\beta\)-farnesene, geraniol, d-limonene, and \(\beta\)-pinene standards (components of hop oils) were tested along with the samples. In HPTLC development with the use of chloroform, \(\beta\)-farnesene migrated to the front. No standard was active in the lipase assay, and \(\beta\)-farnesene was the only compound to inhibit the \(\alpha\)-glucosidase enzyme. In the HPTLC-_A. fischeri_ assay, \(\beta\)-farnesene, d-limonene, and \(\beta\)-pinene exhibited a weak antibacterial effect, while geraniol showed a strong antibacterial effect. Geraniol had the same \(hR_{\mathrm{F}}\) value as the lowest marked zone. Gram-positive _B. subtilis_ was not sensitive to the hop oil components.
# Fusing Local and Global Information for One-Step Multi-View Subspace Clustering
## 4 Optimization
### Update \(\mathbf{S^{v}}\)  
When \(\mathbf{U}\) and \(\mathbf{F}\) are fixed, the objective function about \(\mathbf{S^{v}}\) becomes:  
\[\begin{split}\min\limits_{\mathbf{S^{v}}}\left\|\mathbf{X^{v}}-\mathbf{X^{v}} \mathbf{S^{v}}\right\|_{F}^{2}+\lambda_{1}\left\|\mathbf{S^{v}}\right\|_{F}^{2}+\sum \limits_{i=1}^{n}\sum\limits_{j=1}^{n}\left\|\mathbf{x}_{i}^{v}-\mathbf{x}_{j}^{v} \right\|_{2}^{2}s_{ij}^{v}+\left\|\mathbf{S^{v}}-\mathbf{U}\right\|_{F}^{2}\\ s.t.0\leq s_{ij}^{v}\leq 1,\mathbf{1^{\text{T}}}\mathbf{s}_{i}^{v}=1,diag(\bm {S^{v}})=0\end{split} \tag{6}\]  
In this paper, we adopt a two-step approximation strategy [25] to optimize \(\mathbf{S^{v}}\).  
Firstly, we ignore the constraints in Model (6) to solve \(\mathbf{S^{v}}\) as:  
\[\min\limits_{\mathbf{S^{v}}}\left\|\mathbf{X^{v}}-\mathbf{X^{v}}\mathbf{S^{v}}\right\|_{F}^{2} +\lambda_{1}\left\|\mathbf{S^{v}}\right\|_{F}^{2}+\sum\limits_{i=1}^{n}\sum \limits_{j=1}^{n}\left\|\mathbf{x}_{i}^{v}-\mathbf{x}_{j}^{v}\right\|_{2}^{2}s_{ij}^{ v}+\left\|\mathbf{S^{v}}-\mathbf{U}\right\|_{F}^{2} \tag{7}\]  
Through making the derivative of Model (7) of \(\mathbf{S^{v}}\) as zero, we have:  
\[\mathbf{\hat{S}^{v}}\text{= }((\mathbf{X^{v}})^{\text{T}}\mathbf{X^{v}}+\mathbf{I}_{n}+\lambda_{1} \mathbf{I}_{n})^{-1}((\mathbf{X^{v}})^{\text{T}}\mathbf{X^{v}}+\mathbf{U}-\frac{1}{2}\mathbf{B^{v}}) \tag{8}\]  
where \(b_{ij}^{v}=\left\|\mathbf{x}_{i}^{v}-\mathbf{x}_{j}^{v}\right\|_{2}^{2}\) is the \(ij\)-th element of \(\mathbf{B^{v}}\in\mathbb{R}^{n\times n}\) and \(\mathbf{I}_{n}\in\mathbb{R}^{n\times n}\) denotes the identity matrix.  
Secondly, through adding the constraints of \(\mathbf{S^{v}}\), the solution of \(\mathbf{S^{v}}\) can be obtained by:  
\[\begin{split}\min\limits_{\mathbf{S^{v}_{i}}}\left\|\mathbf{s}_{i}^{v}- \mathbf{\hat{s}}_{i}^{v}\right\|_{2}^{2}\\ s.t.s_{ij}^{v}=0,\mathbf{S^{v}_{i}}\geq 0,\mathbf{1^{\text{T}}}\mathbf{S^{v}_{i}}=1 \end{split} \tag{9}\]Model (9) is a constrained quadratic optimization problem, which can be effectively solved by the iterative algorithm in the work [49].
## 2 Materials and Methods
### Letter Segmentation and Feature Extraction  
The recorded measurement files of the SR subtask were given as input to GT Analyzer 1.0, a custom-made application [43] that enables (1) reading of recorded (\(X\), \(Y\), \(p\)) signals;(2) \"letter\" segmentation, extraction and export of 177 standard kinematic handwriting features from (\(X,Y,p\)) for each subject; and (3) visualization of extracted features as well as raw (\(X,Y,p\)) data. An example of the visualization of raw (\(X,Y,p\)) data is shown in Figure 1.  
\"Letter\" segmentation in the SR subtask (a division of consecutive small cursive letters \"1\" to segments containing only one \"1\" letter) was performed in the following steps:  
* Calculation of velocity per \(y\)-axis \(Vy(t)=dY(t)/dt\) for the whole SR subtask;
* Finding local maximums of \(Vy(t)\) and using these maximums as letter boundaries within the SR subtask.  
After \"letter\" segmentation, features were calculated on each \"letter\" segment and used for further statistical analysis. The list of 177 implemented standard handwriting features consisted of statistical handwriting features and \"letter\" handwriting features. Eleven statistical handwriting features (median, mean, standard deviation, variance, coefficient of variation, maximum value, minimum value, 10 th, 25 th, 75 th, and 90 th percentiles) were calculated for 15 variables presented in Table 2 (\(X(t)\), \(Y(t)\), \(p(t)\) and their 1 st, 2 nd, and 3 rd derivatives as well as three total variables: total velocity, total acceleration, and total jerk). In total, 165 statistical handwriting features were extracted (11 statistical features \(\times\) 15 variables = 165 features) on each valid \"letter\" segment. Twelve \"letter\" handwriting features were calculated on valid \"letter\" segments, and the list of \"letter\" features is presented in Table 3. The feature named normalized jerk was determined using the approach presented in [44].  
Following the feature extraction, validation for each \"letter\" was performed by checking two criteria:  
* The absolute difference between LL for the \"letter\" and the median LL in the entire subtask does not exceed 20% of the median LL;
* \(Vy(t)\) during the \"letter\" drawing time contains local minima.  
If both criteria were satisfied, the \"letter\" segment was considered as the valid one, and the corresponding extracted features were exported for further analysis.  
Figure 1: GT Analyzer visualization of the position (\(X,Y\)) raw data (**left**) and pressure (\(p\)) raw data (**right**) for subject ID9 of the control group. Five minimums in \(p(t)\) with low or null values correspond to five in-air blue lines in the (\(X,Y\)) graph. AIR—in-air recordings; SUR—on-surface.
## 3 Proposed Method
### Model  
The network architecture in Figure 2 is our proposed fusion model Dual-Branch-Net, and its specific architecture is shown in Figure 3. Dual-Branch-Net uses two branches to extract features from IKP images and FV images, respectively. The network adopts a multilevel deep fusion method to make full use of the two types of modal information. We detail the structure of the branch network and the feature fusion method below.  
Dual-Branch-Net uses the Inception-ResNet-v1 [18] network as the base network for the two branches. The Inception-ResNet-v1 network includes three types of basic network modules as follows: (a) Stem--preliminary feature extraction for the original image; (b) Inception-Resnet-A/B/C--for further feature extraction, in which the input features of this module have the same number and size of dimensions as the output features; and (c) Reduction-A/B/C--further feature extraction. With this module, the height and width of the output feature will become smaller than the input feature. Meanwhile, the number of output channels will increase. Considering the recognition performance and computational efficiency, we do not directly use the original Inception-ResNet-v1 network structure but make some adjustments. The details are as follows: the number of Inception-Resnet-A was reduced from 5 to 3, the number of Inception-Resnet-B was reduced from 10 to 6, and the number of Inception-Resnet-C was reduced to 3.  
Figure 2: FV and IKP fusion network framework.  
Unlike some works [22; 23] mentioned in the introduction, which directly concatenate the features of the two modalities only once, we fuse the features of the two modalities at a deeper level. The network layers at different depths have different receptive fields, and the output features of different network layers represent the features of the original image at different levels. Thus, we perform feature fusion operations at multiple levels of the network. Specifically, we splice the output features of both branch after the Stem modules, the last Inception-Resnet-A module, the last Inception-Resnet-B module, and the last Inception-Resnet-C module, respectively. The spliced features are used as the input of the subsequent network layer of the corresponding branch of the FV image, as shown in Figure 3. We have marked the number of channels, height, and width of the output of each module in the figure. Suppose we use the number of channels (C), height (H), and width (W) to represent the shape of a 3D feature; for two features of shape (C, H, W), we concatenate them into a full feature of shape (C, 2H, W) tensor. Considering that when only single-modality is used for recognition, the recognition effect of the method based on the FV is better than that based on the IKP; so, we fuse the feature of the IKP into the branch corresponding to the FV.
## 3 Materials and Methods
### Hierarchical Segmentation Model for Crowdsourced Trajectories
#### 3.2.2 Object-Level Segmentation  
The category-level segmentation of trajectory data is to extract the coarse information about whether the trajectory points are road intersection or non-intersection. The next step is to perform object-level segmentation on the candidate trajectory points of road-intersection, so as to determine the location and spatial coverage of each road intersection. As the DBSCAN algorithm does not need to set the number of clusters in advance, and has certain advantages of finding different shaped clusters and handling noise points, this paper uses the DBSCAN algorithm [56] to further segment road-intersection trajectory points. Suppose the road-intersection trajectory points are \(P=\{p_{1}\ldots p_{n}\}\), the object-level segmentation process based on two parameters of _Eps_ and _MinPlts_ is carried out as follows.  
**Step1:** Calculate the neighborhood point set of each point \(p_{i}\) in \(P\) as is in Formula (6), where \(Dis(p_{i},p_{j})\) is the Euclidean distance between \(p_{i}\) and \(p_{j}\), and then choose those road-intersection trajectory points with \(\left\lvert\,\right\rvert N_{Eps}(p_{i})\left\lvert\,\right\rvert\) larger than _MinPlts_ as the core seed point.  
\[N_{Eps}(p_{i})=\left\{p_{j}\big{|}Dis\big{(}p_{i},p_{j}\big{)}\leq Eps,j=1,2 \ldots n\right\} \tag{6}\]  
**Step2:** Randomly select one unprocessed core seed point and establish a new cluster for it, and meanwhile, add the neighborhood points of the current core seed point to the newly-created cluster.  
**Step3:** Sequentially traverse each unprocessed core seed point in the current cluster, and add the road-intersection trajectory points density-connected with the core seed point to the current cluster until all core seed points in the current cluster are processed.  
**Step4:** Return to Step2 to traverse the remaining unprocessed core seed points until all core seed points have been processed.  
Based on DBSCAN, each obtained cluster \(C\) of road-intersection trajectory points actually corresponds to a road intersection object. After that, the location of the corresponding road intersection is defined as the cluster center \(c\) and the spatial coverage of the corresponding road intersection is calculated as the maximum distance from the cluster points to the cluster center according to Formula (7). Finally, we select the trajectory segments in the spatial coverage of each road intersection as the input of the following step of turning-modes segmentation.  
\[d=\max_{p_{i}\in C}\{Dis(p_{i},c)\} \tag{7}\]
# Quantifying Football Shooting Precision: The Expected Shot Impact Timing (xSIT) Approach
###### Abstract  
Background: Current advanced metrics do not sufficiently isolate and quantify the quality of the shooter's technical execution under match conditions. Objective: This study aimed to develop an Expected Shot Impact Timing (xSIT) model to evaluate the shooting action by considering the spatial configuration of the shooter, the goalkeeper (GK), and all outfield players, as well as incorporating dynamic variables such as ball velocity and player reaction time. Additionally, this study sought to compare the performance and discriminative capacity of two existing post-shot expected goal metrics (xSIT and xGOT, expected goals on target) in evaluating the probability of scoring for shots on target after the moment of execution. Methods: Formal definitions were established for the following: (i) the ball shot location, (ii) the ball velocity, (iii) the GK location, and (iv) the outfield player's location. An xSIT model incorporating geometric parameters was designed to optimize performance based on ball position and players' position. The model was tested using all shots from the 2023 Women's World Cup and the 2022 Men's World Cup. A 5-fold cross-validation procedure was applied to evaluate the x SIT model's performance, and an independent Student's \(t\)-test was performed to statistically compare the performance of the xSIT and xGOT models. Results: The k-fold cross-validation yielded an AUC-ROC score of 0.92 and 84% accuracy, confirming the model's ability to differentiate successful shooter performance. Statistically and clinically significant differences were observed between the xSIT and xGOT metrics across all analyzed variables, including total shots on target, goal shots, and saved shots (\(p<0.001\) in all cases). Conclusions: The xSIT metric offers a more nuanced and context-sensitive assessment of shot execution by the shooter, representing a significant advancement over existing post-shot evaluation models. Significant differences were observed between men's and women's tournaments.  
g remarkRemarks: footnotetext: _Applied Sciences_
# Flow-Induced Vibration Analysis by Simulating a High-Speed Train Pantograph
## 3 Pantograph Structural Modal Simulation Analysis
### Pantograph Modal Simulation Analysis Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Order**} & \multicolumn{3}{c}{**Frequency [Hz]**} \\ \cline{2-4}  & **With Preload** & **Without Preload** & **Error** \\ \hline
1 & 2.29 & 2.28 & 0.24\% \\
2 & 5.10 & 5.20 & 1.92\% \\
3 & 8.79 & 8.79 & 0.03\% \\
4 & 10.48 & 10.48 & 0.02\% \\
5 & 13.41 & 13.40 & 0.05\% \\
6 & 17.22 & 17.32 & 0.55\% \\
7 & 23.13 & 22.71 & 1.82\% \\
8 & 25.51 & 25.53 & 0.07\% \\Figure 17: _Cont._  
From the modal diagrams of the entire pantograph structure, it can be observed that the first modal frequency is characterized by vertical oscillation of the bow head and upper arm, the second mode involves lateral oscillation of the bow head and upper arm, the third mode is characterized by fore-and-aft oscillation of the upper and lower arms, the fourth mode involves lateral torsional oscillation of the bow head, upper arm, and pull rod, and the fifth mode is lateral torsional oscillation of the bow head and pull rod. The first modal frequency is the vertical oscillation of the bow head and upper arm, the second is the lateral oscillation of the bow head and upper arm, the third is the lateral torsional oscillation of the bow head, upper arm, and pull rod, the fourth is the fore-and-aft oscillation of the upper and lower arms, and the fifth is the lateral torsional oscillation of the bow head and pull rod. In this chapter, finite element software was utilized to construct the modal simulation model of the high-speed pantograph, and modal characteristic analyses were performed on two types of high-speed pantographs. The analysis found that, under the same conditions, the modal vibration trends of the two types of pantographs are essentially consistent; moreover, the first 10 vibration frequencies of both pantographs are concentrated within 40 Hz, and the values of the first five frequencies are all less than 15 Hz. Therefore, the pantograph can be considered a typical low-frequency vibration structure.
## 1 Introduction
Therefore, constant-time state search and insertion are possible due to the limited size of the state area and the number of states stored in it. Because MSSA is associated with the match-action table structure, the state area can be allocated or released by following the addition and deletion of table/entries, the useless state area can be released in time, and the state storage space can be fully reused. In summary, we make the following contributions:  
1. We propose the multi-scope state area (MSSA) that stores state in a hierarchical tree structure. MSSA is associated with the switch's multilevel match-action table structure to split the scope of state sharing packets and reuse state storage space in time. Based on the scope of state sharing, we categorize the state into four types, namely global state, table state, flow state, and packet state, and stores them in different state areas. 2. In the MSSA, we also propose the state search and state programming methods. The state search method, according to the state-sharing scope, reduces the state required in packet processing to a limited range, enabling constant-time state search. State programming is accomplished by reusing the switch's existing functions (match-action table and instructions), thus reducing expansion costs and improving compatibility. 3. We built a packet forwarding pipeline that supports MSSA. Moreover, in the MSSA's state search/insertion time, we evaluated two state-comparison methods and state store-space utilization. The rest of the paper is organized as follows. In Section 2, we introduce the match-action model, which is the most widely used packet processing model in data-plane forwarding devices. Then, we introduce the MSSA's structure, as well as the state search and state programming methods that fall under this structure. In Section 4, we depict the implementation of the packet forwarding pipeline that supports MSSA, as well as the results of the state search/insertion time, two state-comparison methods, and MSSA space utilization. In Section 5, we introduce related work, and we conclude in Section 6.