esa-sceva/satcom-chunk-collection huggingface dataset:
# Current Practices of Railway Ballastless Track Design Methods in China
## 3 Case and Calculation Model
### Finite Element Model of Track Structure on Bridge  
Under the action of a train load and temperature load, the prefabricated track slab is prone to cracking and arching. With the foundation of a bridge, the bridge deflection will also cause the deformation of the track, which is more unfavorable to the stress of the track structure. Therefore, a static finite element model of the track on the bridge foundation is established, as shown in Figure 7. To eliminate the influence of boundary conditions, the length of the model is 97.1 m, a three-span bridge. Six track slabs are laid in each span of a simply supported beam. Considering the most unfavorable situation, the N5520 track slab in the middle of the second span is mainly analyzed.  
The CHN60 rail is simulated by solid elements. The fastener is simulated using a 3-D spring element, which is endowed with vertical and lateral stiffness and longitudinal resistance. The track slab, SCC, base plate and bridge beam are all simulated using solid elements according to the actual size. The main structure dimensions and material parameters of the model are summarized, as shown in Table 1.  
Figure 6: Structure diagram of the prefabricated track.  
Figure 7: Finite element analysis model of track structure on bridge.  
ment with the load are basically the same between the test and simulation. What is more, it can be seen from (b) that the test and simulation values at different measurement points are basically corresponding and distributed around the 45\({}^{\circ}\) contour. The simulation results fit well with the test results, and the error is small, proving the reliability of the model.
## 7 Evaluation and Discussion
### Experimental Results
#### 7.2.3 Performance of _VmGuards_  
We tested the applications shown in Table 1 with commercial software Code Virtualizer1.3.1.0 (short for CV) and VMProttect1.7.0 (short for VMP) compared with our _VMGuards_. We set CV to the lowest protection strength and VMP to the fastest execution time. Figure 9 shows the change of average file size and execution time.  
As we can see from Figure 9, comparing with commercial software, our _VMGuards_ does not increase the space overhead. However, as for runtime overhead, our _VMGuards_ shows some unstable results.  
There are three reasons: (a) whether the x86 instructions are different types or different addressing modes, the sequence of the Handler used to interpret the VI is different from each other when the aforementioned _VMGuards_ interprets the execution (the x86 instructions); (b) there are still some x86 instructions that cannot be interpreted by existing Handlers. The program needs to retreat from _VMGuards_, executing the x86 instruction by the host machine then back to _VMGuards_ and continue to interpret execution to the left VI. Thus, the runtime overhead can be huge; (c) owing to the design of multiple Dispatchers, execution time to different instances can be short or long. In addition, we need to do further study on it.  
Figure 8: Memory cost of the running test cases before and after they are protected.  
Figure 9: File size and execution time differences compared with different commercial PVMs with our _VMGuards_.
# Development and Evaluation of Nano-Silica Sustainable Concrete
## 3 Results
### Modulus of Rupture  
The modulus of rupture test was performed after 28 days of curing. The maximum applied load and the calculated modulus of rupture are shown in Table 4. The addition of 1% and 2% dosages of nano-silica increased the flexural strength of 70% GGBS concrete mixtures (M2 and M3) by 3.4% and 35.9%, respectively, compared to the control mixture (M1). Therefore, the flexural strength of the 70% GGBS concrete mixtures increased as the nano-silica's dosage increased.  
\begin{table}
\begin{tabular}{c c c} \hline
**Mix.** & **Maximum Applied Load (KN)** & **Modulus of Rupture (MPa)** \\ \hline M1 & 15.6 & 11.7 \\ M2 & 16.1 & 12.1 \\ M3 & 21.2 & 15.9 \\ M4 & 19.2 & 14.4 \\ M5 & 20.2 & 15.0 \\ M6 & 17.0 & 12.7 \\ \hline \end{tabular}
\end{table}
Table 4: The modulus of rupture of all the mixtures.  
Figure 5: The modulus of elasticity results in all the mixtures.  
Similarly, the addition of a 1% dosage of nano-silica increased the flexural strength of the 30% GGBS concrete mixture (M5) by 4.2% compared to the control mixture (M4). However, the 2% dosage of nano-silica decreased the modulus of rupture of M6 by 11.8% compared to the control mixture (M4). This possibly due to the agglomeration effect, the mixing process, or the released calcium hydroxide from the Portland cement during the hydration process was not consumed efficiently by the 2% dosage of nano-silica.
# Monocular Vision-Based Depth Estimation of Forward-Looking Scenes for Mobile Platforms
## 4 Conclusions  
To address the demands of depth estimation in various environments for IMPs, we propose a novel method for monocular depth estimation based on self-supervised learning. Our approach incorporates an intermediate module between the encoder and decoder of the depth estimation network, facilitating cross-level feature fusion. Additionally, we integrate ECANet into the depth estimation network to enhance accuracy by increasing the weights of significant channels, allowing for the extraction of more prominent features and improving the retention of semantic information in the results. Experimental results demonstrate that our method is well-suited for depth estimation across diverse environmental conditions. Furthermore, it shows that the proposed approach outperforms baseline methods in both real-world and simulated scenarios.  
Nevertheless, some challenges persist regarding our method. Firstly, the model exhibits limited generalization ability due to the absence of large-scale training and testing datasets. Currently, the available datasets for depth estimation are primarily sourced from ground platforms and lack pixel-level dense ground truth, which poses a challenge for achieving quantitative evaluation. Secondly, there remains a significant gap when compared to active sensors such as LIDAR. Therefore, it is beneficial to explore the joint  
Figure 9: The results of qualitative comparison based on the _AirSim_ datasets. (**a**) Results from _AirSim_Mountain_. (**b**) Results from _AirSim_CityRoad_.  
application of vision sensors with other technologies, such as inertial navigation and radar, to facilitate mutual learning. Moreover, integrating the depth map generated by the depth estimation algorithm with semantic or instance segmentation algorithms can complete distance measurements to specific targets within an image. This is one of the key areas we are currently exploring.  
Conceptualization, L.W. and M.D.; methodology, L.W.; software, S.L.; validation, L.W. and S.L.; data curation, S.L.; writing--original draft preparation, L.W.; writing--review and editing, L.W. and S.L.; visualization, L.W.; supervision, M.D.; project administration, M.D.; funding acquisition, M.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. U2433207 and U2033201), the Aeronautical Science Foundation of China (No. 20220058052001), and the School-level Research Fund Project of Nanhang Jincheng College (No. XJ202307).  
Not applicable.  
The proposed algorithm and datasets in this paper can be obtained by contacting the corresponding author of this paper ([EMAIL_ADDRESS]).  
All individuals included in this section have consented to the acknowledgement.  
The authors declare no conflict of interest.
## 1 Introduction
* We proposed the SwFB model, introducing the feature enhancement module Co-G-Ma. On the COFIE dataset, we conducted a series of experiments, and our proposed feature-enhancement module achieved the best performance. * We conducted comparative experiments with other models on publicly available datasets CORD [16] and SROIE [17]. On the CORD dataset, our model performed the best, and on the SROIE dataset, our model also achieved performance comparable to other end-to-end models. Figure 1: Results of text detection, recognition, and extraction. In conclusion, traditional visual text extraction methods typically consist of three distinct stages: text detection, text recognition, and text extraction, each supported by separately trained models. This separation may lead to cumulative errors throughout the process. In contrast, our proposed end-to-end neural network model utilizes feature enhancement techniques to directly convert raw images into structured textual information, significantly enhancing the accuracy of text extraction. Furthermore, considering the lack of research on text extraction from cross-border waybill images, our study has a specific focus with significant advantages and potentials for practical applications compared with other techniques for text extraction. The rest of the paper is organized as follows: This section introduces the problem caused by courier order damage in cross-border logistics, and discusses the challenges in addressing this problem. In Section 2, we review related work about visual text extraction. Section 3 provides a detailed introduction to the principles and model structure of the proposed method. In Section 4, we describe the dataset, evaluation metrics, and experimental process used for the experiment. We conduct multiple ablation experiments and explore the impact of hyperparameters on model training. Additionally, we perform comparative experiments between our model and others across multiple datasets to illustrate the performance superiority of our approach. Lastly, we analyze the experimental results. In Section 5, we conclude our work.
# Spectroscopy Imaging Techniques as In Vivo Analytical Tools to Detect Plant Traits
## 3 Hyper- and Multispectral Imaging  
It is nearly impossible to monitor a crop's status through the naked eye. The human eye is limited in its ability to measure or quantify due to fatigue. Consequently, bias results may occur. Moreover, manual laboratory analyses require highly skilled technicians and are time-consuming. Thus, the utilization of nondestructive technologies, such as HSI and MSI, is a promising tool. It offers numerous beneficial aspects, such as being nondestructive and noninvasive with rapid inspection [15]. Due to its advantages, the use of HSI and MSI has become a necessity. HSI and MSI offer both spatial and spectral information [40; 52; 58; 59; 60]. For a review of the variability in illumination and camera type, readers can refer to [49]. To ease readers into the topic, we provide an illustration of HSI/MSI during data collection in Figure 3.  
In terms of application, HSI is used for fundamental research and MSI is more applicable in field works [15]. In addition, HSI provides high resolution but is time-consuming compared to MSI. To apply MSI, HSI should be performed first to obtain the optimum wavebands [15]. According to [PERSON] et al. [15], there are three scanning methods to acquire 3D hyperspectral cubes, namely (i) point scan, (ii) line scan, and (iii) area scan, as presented in Figure 4. Point scan obtains single-point spatial information by collecting each pixel. This method is time-consuming since it captures two spatial dimensions (x, y). Furthermore, point scan does not cover a wide area. Secondly, line scan is an extension of the former scanning method and captures slit spatial information. This method is suitable for moving samples, i.e., sortation. Additionally, the area scan method instantly captures 2D  
Figure 3: Installation of hyper- and multispectral imaging during image and spectral acquisition.  
Figure 2: Plant–light interaction: (**a**) reflection; (**b**) scattering; (**c**) absorption (e\({}_{\text{i}}\)\({}^{-}\) represents the initial position of an electron and e\({}_{\text{f}}\)\({}^{-}\) represents an exiled electron after accepting whole energy of light); and (**d**) transmission. Red dashed arrows illustrate the movement of light after interacting with leaf.  
single-band grayscale images with full spatial information. Moreover, if the target object has undesirable movement, the HSI method is acceptable. In contrast, line and area scans are more suitable for MSI than point scan [15]. Line scan only works for the selected tracks during scanning time. Finally, area scan is fast and collects images at various wavelengths at once.
## 3 High-Dimensional Mapping Entropy
### High-Dimensional Mapping Theory
#### 3.1.1 Kernel Function Selection
It is obtained after constructing the kernel matrix for the kernel function as a necessary condition [27]: \[\sum_{i,j=1}^{m}G\big{(}X_{i},X_{j}\big{)}=\left\|\sum_{i=1}^{m}\phi(X_{i})^{2 }\right\|\geq 0\] (10)  
In practice, the existing and commonly used positive definite kernel functions are used--mainly the Gaussian radial basis kernel function. The application of the Gaussian radial basis kernel function [27] is:  
\[K(X,Y)=exp\{-\gamma\|X-Y\|^{2}/\sigma^{2}\} \tag{11}\]  
where \(\gamma\) and \(\sigma\) are kernel function parameters. According to Equation (11), the kernel parameter is too large or too small, which will affect the spatial distribution of the mapping points and the distance between the points (degree of proximity). For this reason, the experimental Gaussian kernel function parameter \(\gamma\) is taken as 1, and \(\sigma\in[0.01,\ 0.05]\). The use of the Gaussian kernel function is founded upon the Gaussian distribution concept and projects the input vector into a feature space of high-dimensions. Notably, the Gaussian kernel function exhibits outstanding non-linear processing capacities and can adeptly adapt to diverse data distributions. In bearing signal fault mode recognition, the kernel function enables the identification and discrimination of various fault modes. The Gaussian kernel function can properly capture the non-linear features in the vibration signal by mapping the vibration signal to the high-dimensional feature space. This enables the pattern recognition algorithm to differentiate between normal working states and various fault modes, thus achieving precise fault diagnosis and monitoring.
# Functional Connectome Fingerprinting Through Tucker Tensor Decomposition
## 3 Results
### Within-Condition Fingerprinting  
The concept of within-condition fingerprinting reflects the ability to correctly match two scans of the same participant when all evaluated scans belong to the same fMRI condition. The proposed fingerprinting framework yields a substantial matching rate increase across all considered parcellation granularities relative to vectorized original FCs, which were adopted as baseline. For original FCs, identifiability matrices were obtained by computing pairwise Pearson's correlation between test and retest vectorized upper triangular original FCs.  
In Figure 4A, it is shown, for each condition, the highest matching rate obtained for all possible combinations of brain parcellation rank and participant rank. The highest matching rate increase with respect to the original FCs was observed for parcellation granularity of 214, for which the fingerprinting framework generated matching rate improvements ranging from 11% (Language) to 36% (Emotion). Resting-state matching rates were the highest in all parcellations, achieving 100% for all parcellation granularities.  
Given the mismatch in scan duration between resting-state and tasks, a matching rate comparison between fMRI tasks and resting-state when resting-state time series are cropped to match the duration of tasks is shown in Figure 4B. To do so, and in order to avoid possible biases when using the first time-points, resting-state FCs are estimated by using the \(L\) central time points, where \(L\) is the duration of the corresponding task. For conditions with short scanning length (Emotion, Relational, and Gambling), resting-state displays a lower fingerprint than the fMRI tasks in all parcellation granularities. The previous is also true for Social and Language tasks and parcellation granularity 214.  
The fingerprinting framework was carried out in two ways. First, by inputting a tensor consisting of test FCs while aiming to estimate the participants' factor matrix of the retest session. Second, by inputting a tensor consisting of retest FCs while aiming to estimate the participants factor matrix of the test session. The results of both procedures were averaged for all combinations of ranks considered. The reported values correspond to the highest of such averages.  
Figure 4: **(A).** Within-condition fingerprinting comparisons between highest matching rates obtained with ST-HOSVD and FCs directly for all fMRI conditions and parcellation granularities 214, 414, and 814. **(B).** Within-condition comparisons between highest matching rates obtained when resting-state FCs are estimated with time series of length equal to those of fMRI tasks. Specifically, the middle time points from resting-state time series are chosen to compute FCs. For example, when resting-state is shortened to match the scanning length of Emotion, resting-state FCs were computed with time points \(\left(\frac{100}{2}-\frac{166}{2}\right):\left(\frac{1100}{2}+\frac{166}{2}\right)\).
# Deep Representation of EEG Signals Using Spatio-Spectral Feature Images
## 4 Results
### Inter-Subject Dependency Analysis  
The results of the ISD analysis are shown in Figures 6 and 7. Figure 6 shows a matrix representing the performance of the CNN models trained on each subject and tested on all 25 subjects. It is interesting to note that this matrix is not symmetric. Symmetry would have suggested that the performance of a model trained on subject A and tested on subject B is similar to the performance of a model trained on subject B and tested on subject A. It can be observed that compared to other models, the CNN model trained on subject S1 performs well on the other subjects. In contrast, the CNN model trained on subject S19 and S21 performs poorly on the other subjects. The results from Figure 6 can be summarized in two other plots, namely the average performance of all the trained models when tested on data from one single subject, as shown in Figure 7a, and the average performance of each model when tested on data from other subjects, as shown in Figure 7b. Figure 7b highlights that the model trained on S1 performs well on data from the other subjects, with a very low variability in performance. In contrast, Figure 7a indicates that the average performance of the models trained on every subject but S1 is low, with high variability when tested on data from S1. The performance of all the models tested on S19 and S21 is consistently higher than 0.5.  
Figure 6: Inter-subject dependency analysis matrix that represents the performance of each CNN model on every subject.
## 4 Discussion
It was previously reported that CMJ peak force does not directly equate to jump height, take-off velocity and net impulse [47] or may even be at odds with those metrics [48] and thus may not be the best metric to represent ballistic capacity. This could also be the reason for the absence of correlations between CMJ peak force and performance metrics, while CMJ jump height showed moderate associations with all performance tests except CoD ability. Therefore, alternative forms of the DSI calculated from time-limited jump metrics suggested by [PERSON] and [PERSON] (2022) [49] may be more suitable to reflect fast dynamic strength capabilities as they underpin jump performance to a greater extent than peak force [48]. In summary, more studies are needed to obtain insight into how alternative forms of DSI influence general physical performance and detect training adaptations and whether any of them can be used to navigate training-related decisions. Some limitations of the study must be acknowledged. First, the relatively small sample size (\(n=44\)) limits the statistical power of the analyses and increases the likelihood of Type 1 errors. While the sample was sufficient to detect moderate correlations (\(r>0.40\)) with 80% power at an alpha level of 0.05, the multiple correlations performed increased the probability of false positives. Although the low DSI group showed superior results compared to the other two groups, these results must be interpreted with caution as the low DSI group exhibited significantly higher strength ability based on the IMTP data compared to the other two groups. In addition, this is a cross-sections study, meaning that results may change during the basketball season. More surveys during the season would be needed for a more reliable assessment and relationship between DSI and the physical performance of basketball players. Finally, the sample studied was very homogeneous (i.e., highly trained male basketball players); thus, the results cannot be generalized to performance metrics and athletes from other sports. _Practical Application_  
* DSI may present a useful method to detect neuro-muscular characteristics of basketball players;
* DSI seems to be easy to apply in high-level sports, thus may also be used during the competitive season to follow neuro-muscular adaptations;
* basketball is predominantly ballistic-oriented in its nature; thus, more maximal strength training may be required to improve the physical performance of basketball players.
## 4 Extracellular Vesicles
### Size and Morphology  
The determining factors that discriminate the different classes of EVs are size, morphology, origin and content (Table 1) [66].  
It is possible to distinguish three main EV categories: apoptotic bodies, microvesicles and exosomes (Figure 1) [67].  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Exosomes** & **Microvesicles** & **Apoptotic Bodies** \\ \hline Size & 30–100 nm & 50–1000 nm & 800–5000 nm \\ \hline Morphology & Cup-shaped & Spherical structures & Heterogeneous \\ \hline Content & \begin{tabular}{c} Coding RNA, \\ noncoding RNA, \\ proteins, lipids, DNA \\ \end{tabular} & \begin{tabular}{c} RNA, proteins, \\ lipids, cytosol \\ \end{tabular} & \begin{tabular}{c} Proteins, lipids, DNA, \\ RNA, cytosol \\ \end{tabular} \\ \hline Biogenesis & \begin{tabular}{c} By exocytosis in \\ which ILVs within the \\ lumen of MVEs fuse \\ with the membrane to \\ release ILVs \\ \end{tabular} & \
\begin{tabular}{c} By outward budding \\ and fission of the \\ plasma membrane \\ and the sub-sequent \\ release of vesicles into \\ the \\ extracellular space \\ \end{tabular} &
\begin{tabular}{c} Outward blebbing of \\ the cell membrane \\ \end{tabular} \\ BiLVs = intraluminal vesicles; MVEs = multivesicular endosomes. & \\ \end{tabular}
\end{table}
Table 1: General features of exosomes, microvesicles and apoptotic bodies.  
Aopoptotic bodies have a size range of 800-5000 nm with a heterogeneous morphology. They also include smaller vesicles known as apoptotic cell-derived microparticles or apoptotic blebs released by apoptotic cells [66].  
Microvesicles were originally described as \"platelet dust\", as they were first isolated from platelets [68]. They have a diameter of 50-1000 nm, a spherical structure, and are expelled through exocytosis of the plasma membrane [69].  
The last class of vesicles are smaller than those mentioned above, have a size range of 30-100 nm, a cup-shaped morphology and are called exosomes [70]. However, some studies have shown that the cup-shaped morphology could be an experimental artefact and the overlapping range of size may lead to unclear terminology [71]. Further isolation and characterization protocols are expected to establish an appropriate classification and nomenclature [72].