esa-sceva/satcom-chunk-collection huggingface dataset:
# Virtual and Physical Prototyping of Reconfigurable Parallel Mechanisms with Single Actuation
## 3 Mobility Analysis
### Analysis for a Single Kinematic Chain
For example, when \(\tilde{s}_{i}^{x}=0\) (the leg projects directly on the swinging arm), the rank equals five, and the chain imposes one constraint on the platform. Such configurations are known as leg singularities [34] and should be avoided during motion planning. Let us consider the situation when the driving wheel is fixed. As we will see in the following subsection, this case is important for analyzing the mobility of the output link concerning several kinematic chains. In this case, the carriage does not move, and we can perform a similar analysis as above, leaving twist \(\tilde{\xi}_{i1}\) out from matrix \(\mathbf{T}_{i}\). Corresponding upper triangle matrix \(\mathbf{U}_{i}\) will have a form as below:  
\[\mathbf{U}_{i}=\left[ \begin{array}{cccccccc}1&0&0&1&0&0\\ 0&1&0&0&1&0\\ 0&0&1&0&0&1\\ 0&0&0&L_{i}\tilde{s}_{i}^{z}&0&-L_{i}\tilde{s}_{i}^{x}\\ 0&0&0&0&-L_{i}\tilde{s}_{i}^{z}&L_{i}\tilde{s}_{i}^{y}\\ 0&0&0&0&0&0\\ \end{array} \right]. \tag{3}\]  
The matrix above has a rank equal to five, and there exists one constraint on the platform by the chain. This constraint has wrench \(\tilde{\chi}_{i}\) reciprocal to twists \(\tilde{\xi}_{i2}-\tilde{\xi}_{i7}\) that can be found using Equation (A3) from Appendix A. Using this equation, we find:  
\[\zeta_{i}=\left[ \begin{array}{cc}\tilde{s}_{i}^{\text{T}}&\mathbf{0}^{\text{ T}}\end{array} \right]^{\text{T}}. \tag{4}\]  
The obtained wrench corresponds to a force directed along the leg and preventing the platform translation along this direction.
## 3 Control Effect of Ground Improvement on Tunnel Displacement
### Simulation Conditions of Ground Improvement
#### 3.4.2 Effect of Ground Improvement Depth  
Figure 11 shows the evolution curves of \(\eta_{\rm h}\) with \(h\). It is observed that under the four conditions with different \(w\) values, \(\eta_{\rm h}\) increases nonlinearly with \(h\), and the larger \(q_{\rm u}\) is, the greater the increase in \(\eta_{\rm h}\) that is obtained. The characteristics of the curve show that \(\eta_{\rm h}\) increases obviously before \(h=2.0H_{\rm t}\) and slows down obviously after \(h=2.0H_{\rm t}\), which may be related to the relative position of the improved soil in the displacement field outside the pit.  
As shown in Figure 12, the excavation-induced unloading makes the soil outside the pit move towards the pit, forming a potential \"sliding surface\". The soil displacement above the sliding surface is greater than that below the sliding surface, and the toe of the sliding surface is near the pit bottom elevation. If the ground improvement depth \(h>1.0H_{\rm t}\), the improved soil below the sliding surface plays an embedded role, which is similar to an anti-slide pile and can block the displacement of the soil outside the pit. The deeper the improved soil embedded below the sliding surface is, the better the barrier effect it plays, and a better control effect on tunnel displacement is obtained. However, when the embedded depth is greater than \(1.0H_{\rm t}\), that is, the ground improvement depth \(h>2H_{\rm t}\), the contribution of the embedded depth to the barrier effect decreases, and the increasing trend of \(\eta_{\rm h}\) slows down with \(h\).  
It is concluded from the above analysis, that an optimal value exists for the ground improvement depth outside the pit. If it exceeds this value, the contribution of the ground  
Figure 12: Relative positions between ground improvement and potential sliding surface.  
improvement depth to the tunnel displacement control effect will be weakened. Considering the control effect and engineering economy, it is suggested that the ground improvement depth \(h=2H_{\text{e}}\). For this example, because \(H_{\text{t}}=H_{\text{e}}\), it is suggested that the ground improvement depth \(h=2H_{\text{t}}\).
# Experimental Analysis of Driver Visual Characteristics in Urban Tunnels
## 4 Results
### Clustering Analysis of Visual Characteristics of Urban Tunnel Drivers
#### 4.3.2 Analysis of the Influence of Urban Tunnel Length on Saccade Time  
In a safe and comfortable environment, the driver's eye movement is mainly in the form of fixation, accompanied by a little scanning behavior, and the fixation point moves forward synchronically with the vehicle. When the driving environment is poor, drivers will constantly adjust their sight to adapt to the surrounding environment, which manifests as an increase in drivers' saccade behavior and a decrease in corresponding fixation behavior. At the same time, the driver's visual recognition is reduced, with the operation reaction time prolonged. The average percentage of glancing time of the tested drivers in each tunnel is shown in Figure 8, and the percentage of glancing time of the drivers shows a certain changing trend with the change of tunnel length. The relationship between the two is further fitted, as shown in Figure 9, and the fitting polynomial is shown in Equation (3):  
\[y=2.49+2.3x-0.4x^{2},\text{ and }R^{2}=0.93. \tag{3}\]  
As can be seen from Figure 9, when the tunnel length is small, drivers can still maintain a high fixation behavior and a relatively low percentage of saccade time due to the suitable visibility of the tunnel, which has little impact on drivers' visual distance. With the increase in tunnel length, the percentage of drivers' scanning time increases rapidly, which indicates that the dark environment inside the tunnel has a significant impact on driving behavior. Drivers constantly adjust their sight to adapt to the dark environment inside the tunnel. In the tunnel length of more than 2.5 km of Xuanwuhu Tunnel and Jiuhuashan Tunnel, the driver scanning time percentage was the largest, but the growth rate was lower than other several \"short\"  
Figure 8: Experimental data of tunnel length and percentage of scanning time.  
Figure 9: The fitted curve of tunnel length and percentage of scanning time.  
tunnels. This is because the influence of a tunnel on driving behavior is mainly strongest at the entrance and exit sections. After light and shade adaptation, the illumination value in the middle segment is relatively stable, and the influence on driving behavior fluctuates little, so the contribution rate to the percentage of saccade time is not large.  
By comparing the fitting curves in Figures 7 and 9, it can be seen that when the tunnel length changes from 0.6 to 2 km, the maximum pupil diameter of drivers increases by 73%, while the percentage of scanning time increases by 47%. Therefore, when the length of an urban tunnel is less than 2 km, the pupil diameter value is relatively more sensitive to the change of tunnel length. When the length of the urban tunnel exceeds 2 km, the growth rate of maximum pupil diameter and percentage of saccades time decrease simultaneously, and the growth rate of the former decreases more obviously.
## 3 Satellites and the Space Law Framework
### The ITU and Satellite Orbits
Footnote 8: United Nations General Assembly (2021) outlines that the session heard a presentation from United States representatives on the topic of ‘Perspectives from the United States on coexistence (and sustainability) of large satellite constellations and (terrestrial) astronomy’ (para 18(h)(i)), as well as suggestions that the topic of ‘General exchange of views regarding satellite system effects upon terrestrial-based astronomy’ should be considered at the 2022 session of the Scientific and Technical Subcommittee (para 297). Provision for practical and functionality considerations appear to come to the fore with regards to the law pertaining to satellites as investigated in this section. The ITU and much of the UN Space Law framework regulate from the perspective of facilitating the acceleration of more satellite activity into orbit. However, as Section 4 will discuss, the increase in satellites in orbit is obstructing the practice of astronomy. While this could be interpreted as an inconvenience that should be tolerated in favour of 'progress', as was noted earlier, the foundational Outer Space Treaty emphasised the use of Outer Space for the benefit of all mankind--including the peoples to whom astronomy is central to their culture and ways of life.
## 2 Theoretical Calculation  
### System Structure  
The schematic of the spatio-tempo hybrid modulated SBFTIS with a zoomable spectral resolution is shown in Figure 1, which consists of a polarizer P, an analyzer A, and a dual-Wollaston prism group DWP. A DWP is formed by two identical Wollaston prisms WP\({}_{1}\) and WP\({}_{2}\), with an adjustable air gap of width \(s\) between them. A Wollaston prism is composed of two uniaxial wedge-shaped crystals with mutually perpendicular optical axes, characterized by the structure angle \(\beta\) and the plate thickness \(t\). Take WP\({}_{1}\) as an example, the left crystal's optical axis lies in the plane of the paper and is parallel to the positive \(z\)-axis, while the right crystal's optical axis is perpendicular to the paper and parallel to the \(y\)-axis.  
A wave separates into two waves with orthogonal polarization directions and parallel propagation directions after passing through the DWP. The wave, which is ordinary in the first crystal, becomes extraordinary in the second and vice versa, and the two waves are designated as the eoeo and eoeo waves, respectively. The two waves are then converted into two linearly polarized waves with the same polarization direction by the analyzer A. After passing through the imaging lens, they intersect on the detector, forming an interference pattern and a target image. The spectral information of the incident light can be obtained by Fourier transforming the interference pattern.  
When collecting data with a spatio-tempo hybrid modulated SBFTIS, all target elements within a certain area enter the FOV each time. Each target element enters the SBFTIS system at different incident angles simultaneously, but only one interference datum (corresponding to one incident angle) can be obtained for each target element in an interference pattern. To obtain the complete interference information of each target element, it is necessary to move the system relative to the target, so that the incident angle of each target element changes relative to the previous position and the interference pattern records the interference information of each target element at the new incident angle when the system moves to a new position. The incident angle is the angle between the incident light and the normal to the crystal surface. By repeating this process as the imaging system moves over the entire area, the incident angles of each target element traverse the process of the positive maximum incident angle, zero, and negative maximum incident angle, and a data cube is obtained, completing the process of the path difference sampling and acquisition of the target elements, as shown in Figure 2. Extracting and reorganizing all interference information belonging to the same target element from different frame images can obtain the complete interference information of the target element. This process is necessary to complete the data collection with the spatio-tempo hybrid modulated SBFTIS.
# Borophene Is a Promising 2D Allotropic Material for Biomedical Devices
## 2 Main Properties of Borophene
### Borophene in DNA Sequencing  
In a recent study [27], DNA sequencing was performed by defining the order of different bases (thymine (T), adenine (A), cytosine (C), and guanine (G)) as a function of energy absorption and described in turn as a variation of the electric conductivity within B\({}_{36}\) borophene nano-wells. Density functional theory (DFT) can explain the level of energy absorption shown by each nitrogenous base observed within the hexagonal structures of the B\({}_{36}\) while plotting the HOMO and LUMO profiles. It is well-known that each nitrogenous base, when it comes into contact with B\({}_{36}\) borophene, has a different level of energy absorption, with a different electrical conductivity reaction. These variations can be translated with a specific electrical signal. The future challenge is to produce electronic devices based on hybrid nanostructures containing B\({}_{36}\) borophene, which will be able to correctly define the DNA sequences by transducing different specific electrical signals.
# Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3 GPP Standards
## 4 4G and NSA-Based 5G V2X Services
### Network Support
#### 4.2.1 Core Network  
The core network reflects the technical reports and specifications produced by the core network and terminals (CT-TSG). The CT TSG in 3 GPP standardization provides V2X control function and VAE support to 4G & 5G V2X services. In what follows next, we discuss core network support.
# Design Optimization of the Magnet-Free Synchronous Homopolar Motor of a Subway Train
## 6 SHM Optimization Parameters and Procedure
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Machine length without end winding parts \(L\), mm & 302.5 \\ \hline Length of one stator stack & 151.25 \\ \hline Stator housing radius, mm & 267 \\ \hline Axial clearance between excitation winding and rotor, \(\Delta_{\text{a}}\), mm & 29 \\ \hline Radial clearance between field winding and rotor \(\Delta_{\text{r}}\), mm & 22 \\ \hline Shaft radius \(R_{shaft}\), mm & 40 \\ \hline Stator lamination yoke \(h_{s}\) yoke, mm & 21 \\ \hline Rotor lamination yoke \(h_{r}\) yoke, mm & 17 \\ \hline Stator wedge thickness, \(\epsilon_{2}\), mm & 2 \\ \hline Stator unfilled area thickness, \(\epsilon_{1}\), mm & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Some geometric parameters fixed during the optimization. Figure 5: SHM geometric parameters. (**a**) Stator slot; (**b**) stator winding; (**c**) rotor core; (**d**) other dimensions. The sizes of the armature wire \(w_{x}\) and \(w_{y}\) (see Figure 5b) necessary to compute the DC and eddy current loses in it [23] are determined according to Equation (10):  
\[b_{p}=w_{x}+a_{x};h_{p}=2\cdot(w_{y}+\Delta w)\cdot N_{acc}+a_{y}, \tag{10}\]  
where \(a_{x}=1.51\) mm, \(a_{y}=1.8\) mm, \(\Delta w=0.31\) mm are the constants taking into account the width of the slot insulation, layer insulation, impregnation, etc. Only DC losses are assumed to be in the excitation winding. To calculate them, the copper filling factor 0.8 is adopted. Among parameters varied during the optimization, there are current angles measured from the middle of the rotor tooth in modes 1,2,3,4. To reduce the number of the optimized parameters, the angle in operating point 5 is assumed to be the same as in operating point 4, the ratio of currents in in the excitation winding section, and the armature winding layer is assumed to be the same in all modes. Additionally, the ratio of the rotor slot widths \(\alpha_{2}/\alpha_{1}\) (see Figure 5c) is assumed to be constant during the optimization. The mechanical losses are assumed to be mainly windage losses and proportional to the speed cubed. The mechanical losses at \(n_{max}\) are assumed to be 3.55 kW.
# Comparative Analysis of Image Classification Models for Norwegian Sign Language Recognition
###### Abstract  
Communication is integral to every human's life, allowing individuals to express themselves and understand each other. This process can be challenging for the hearing-impaired population, who rely on sign language for communication due to the limited number of individuals proficient in sign language. Image classification models can be used to create assistive systems to address this communication barrier. This paper conducts a comprehensive literature review and experiments to find the state of the art in sign language recognition. It identifies a lack of research in Norwegian Sign Language (NSL). To address this gap, we created a dataset from scratch containing 24,300 images of 27 NSL alphabet signs and performed a comparative analysis of various machine learning models, including the Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Convolutional Neural Network (CNN) on the dataset. The evaluation of these models was based on accuracy and computational efficiency. Based on these metrics, our findings indicate that SVM and CNN were the most effective models, achieving accuracies of 99.9% with high computational efficiency. Consequently, the research conducted in this report aims to contribute to the field of NSL recognition and serve as a foundation for future studies in this area.  
Keywords:Norwegian Sign Language recognition; machine learning; image classification; assistive technologies; fingerspelling +
Footnote †: journal: _technologies_
# A Strong Noise Reduction Network for Seismic Records
## 2 Methodology
The reason for using a convolutional kernel of 3 \(\times\) 3 is that it has better feature extraction capabilities and can extract finer-grained features [50]. The inception module introduced in this paper facilitates the encoder's ability to efficiently learn and represent the intricate features of the input data, enabling the network to capture features of different scales within the seismic signal during feature extraction. The configuration of the inception module is illustrated in Figure 1b. The CA mechanism [51] introduced in this paper is shown in Figure 1c. Seismic signals and noise may coexist across various frequency channels or within specific frequency bands. The CA mechanism directs the network to focus its attention on these pivotal frequency channels or bands, consequently amplifying the network's capacity to perceive seismic signals. With the integration of the CA mechanism, the network achieves a more refined distinction between seismic signals and noise and assigns higher weights to seismic signals, retaining the details and structure of the seismic signals more effectively throughout the denoising process and elevating the denoising performance. Figure 1: The detailed structure diagram of the proposed network. (**a**) The structure of our network. The inputs are the real and imaginary parts of the noise data after the STFT, and the outputs are the masks corresponding to the signal and noise. The different colored squares represent different modules in the network and the numbers represent the size of the feature map after the output of each layer of modules. (**b**) The inception module used in our network. (**c**) The CA module used in our network.
# Progress in Energy Storage Technologies and Methods for Renewable Energy Systems Application
## 5 Discussion
### Summary of Energy Storage  
Various forms of energy storage technologies have been developed: Physical energy storage, electromagnetic energy storage, electrochemical energy storage, and phase change energy storage (Figure 6). Physical energy storage includes pumped storage, compressed air storage, and flywheel energy storage; electromagnetic energy storage includes superconducting energy storage and supercapacitor energy storage; electrochemical energy storage includes lead-acid, lithium-ion, sodium-sulfur, and liquid flow battery energy storage; phase change energy storage includes thermal and cold storage energy storage, etc. At present, in distributed power generation systems, superconducting energy storage units are commonly used in island-type wind power generation systems and photovoltaic power generation systems. With the development of wind power generation to scale and industrialization, superconducting energy storage technology will also be applied in grid-connected wind power generation systems.  
The battery energy storage system consists of batteries, DC/AC inverters, control devices, auxiliary equipment, etc. It is currently most widely used in small-scale distributed power generation. It is worth noting that lithium-ion batteries, as a new type of high-energy secondary battery that has emerged in recent years, are valued and welcomed for their high operating voltage, small size, high energy storage density, non-pollution, long cycle life, and other characteristics. In addition, the charge/discharge conversion rate of lithium-ion batteries is up to more than 90%, which is higher than that of pumped storage power plants and is more efficient than hydrogen fuel cells for power generation. Pumped storage is mostly used for peaking in modern power grids and is more frequently used in centralized power generation. Compressed air energy storage, phase change energy storage, and other systems are currently not much used in distributed power generation systems and can be used in large-capacity distributed power generation systems in the future.
# A Feature Selection Method for Multi-Label Text Based on Feature Importance
## 4 Experiment and Results
### Experimental Settings  
In order to demonstrate the effectiveness of the proposed method, we designed two parts of experiments.  
(1) Proposed algorithm validation experiment. This part includes two groups of experiments. In the first group, in different feature space dimensions, the proposed feature selection method is compared with the baseline method which keeps all features to demonstrate the effectiveness of the proposed method. ALA is used to transform multi-label texts into single-label texts, and BRKNN and MLKNN are used as the classifiers. For MLKNN, the number of nearest neighbors and the value of smooth are set as 10 and 1 respectively [42]. For BRKNN, the number of hidden neurons is set as 20% of the number of features in the feature space, the learning rate is set as 0.05, and the number of iterations for training is set as 100 [42]. Let \(t\) denote the proportion of the dimension of the feature space to the dimension of the original feature space, this is, \(t\) denotes the proportion of the number of selected features to the number of all features. We run experiments of this group with the value of \(t\) ranging from 10% to 90%, and 10% as an interval. In the second group, the method based on CC proposed in the paper is performed on different label assignment methods to further demonstrate the effectiveness of the proposed method. BRKNN is used as the classifier and the value of \(t\) ranges from 10% to 50%, with 10% as an interval.  
(2) Performance comparison experiment. In this part, we compare the performance of the proposed feature selection method with that of the commonly used feature selection methods to demonstrate the effectiveness of the proposed method. The feature selection method based on DF and the feature selection method based on MI are selected as the comparison methods. ALA is used to transform multi-label texts into single-label texts, and BRKNN is used as the classifier. The value of \(t\) ranges from 10% to 50%, with 10% as an interval.  
All the code in this paper is implemented in MyEclipse version 2014 in a Windows 10 using 3.30 GHz Intel (R) CPU with 8 GB of RAM. The Term Frequency-Inverse Document Frequency (TF-IDF) method [43] is used to calculate the weight of the feature in each text. BRKNN and MLKNN multi-label classification algorithms are implemented based on MULAN software package [44]. The cross validation is used in the experiments. And all the experimental results shown in this paper are the average of ten-fold cross validation.