esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Importance Measure Index and Calculation Method
### Importance Measure Index  
In order to reasonably measure the influence of the uncertainty of the failure rate \(\lambda\) of each component on the power supply reliability of the sink nodes \(s\) given a system time \(T\), the importance measure index \(\eta_{s,i}^{2}\) in Equation (3) can be defined according to the variance decomposition theory [44; 45].  
\[\eta_{s,i}^{2}=\frac{Var_{s,\lambda_{i}}[E_{s,\lambda\sim i}(R_{s}|\lambda_{i })]}{Var_{s}[R_{s}]} \tag{3}\]  
Figure 3: System reliability function curve changing with system time.  
In Equation (3), the subscript \(s\) represents the \(s\)-th sink node of AEPS, \(s=1,2,\,\ldots,\,Num\); the subscript \(i\) represents the \(i\)-th component in the system, \(i=1,\,2,\,\ldots,\,n\); \(\
eg i\) represents the rest components of the system except for component \(i\); \(\Lambda_{\
eg i}\) represents the vector of the failure rates of the remaining components of the system except for \(\lambda_{i}\), for example, for a system containing four components, if \(i=2\), then \(\lambda_{\
eg i}=\{\lambda_{1},\lambda_{3},\lambda_{4}\}\); \(\eta_{s,i}^{2}\) is the proposed importance measure index to measuring the uncertainty contribution of the \(i\)-th component failure rate on the uncertainty of the supply reliability of the sink node \(s\). \(Var_{s}[R_{s}]\) is the unconditional variance of power supply reliability of the sink node \(s\); \(E_{s_{\lambda_{\
eg i}}}(R_{s}|\lambda_{i})\) is the conditional expected value of power supply reliability of the sink node \(s\), and to be specific, it is on the condition that the failure rate of the \(i\)-th component is given; accordingly, \(Var_{s_{\lambda_{i}}}[E_{s_{\lambda_{\
eg i}}}(R_{s}|\lambda_{i})]\) is the variance of \(E_{s_{\lambda_{\
eg i}}}(R_{s}|\lambda_{i})\).
# Reconstructed Prototype Network Combined with CDC-TAGCN for Few-Shot Action Recognition
## 4 Experiments  
### Dataset  
**NTU RGB + D 120**[9]: This is an action dataset established by the Rose Laboratory, containing 3D joint coordinates for human action recognition tasks. It comprises 114,480 samples, each with 25 body joints and 120 action classes. In the experiments conducted in this paper, the 120 action categories are divided into 80 for training, 20 for validation, and 20 for testing. For each class, 30 and 60 samples are randomly selected, denoted as two subsets, \"NTU-S\" and \"NTU-T,\" respectively.  
**Kinetics**[10]: The Kinetics dataset originates from YouTube videos. This dataset comprises 260,232 videos across 400 different classes. For each video, the skeleton data is extracted using the OpenPose algorithm, retaining the coordinates of 18 body joints as the initial joint features. Each joint contains a 2D joint coordinate and a confidence score. This experiment utilizes only the first 120 action categories, with 100 samples per class.  
The selection method for these two datasets is consistent with DASTM to ensure experimental rigor, and these two datasets are the most commonly used standard datasets in skeleton recognition.
## 5 Conclusions  
In this study, the BERT model was applied to semantic role labeling for Korean, a low-resource language, and state-of-the-art performance was obtained. In addition, we proposed two feature information aspects that can be easily obtained from the corpus. Experimental results demonstrate that both the feature information aspects considerably improved the Korean semantic role labeling performance. In addition, by applying the same method to the CoNLL-2009 English benchmark, it was confirmed that there was an improvement in performance in both Korean and English semantic role labeling. Conclusively, we were able to get all the answers to the questions raised at the beginning of the paper as follows: (1) It was confirmed that BERT worked well in Korean, a low  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **Dev F1** & **Test F1** \\ \hline LSTM-GCNs [14] & 84.2 & - \\ \hline Bi-Affine [15] & 85.6 & - \\ \hline BERT-BiLSTM-RNN-MLP [22] & & \\ (Bert-large) & 88.7 & 89.8 \\ (Bert-base) & 89.3 & 90.3 \\ \hline BERT-BiLSTM-RNN-CRF (our model) & & \\ (Bert-base) & 87.05 & 88.29 \\  + Semantic features & 87.66 & 88.90 \\  + Text features & 88.84 & 89.87 \\  + Semantic features + Text features & 89.08 & 90.16 \\ (Bert-large) & 89.02 & 90.13 \\  + Semantic features + Text features & 89.34 & 90.39 \\ \hline \hline \end{tabular}
\end{table}
Table 2: F1 on the CoNLL-2009 English benchmark with gold predicates.  
resource language. (2) Simple feature information was easily obtained and used easily. (3) There was an improvement in performance in Korean and English datasets through simple feature information.  
Further study is required to address the various follow-up questions, such as \"will the use of syntactic feature information improve performance further?\" and \"will the performance improve when we apply our proposed features to other NLP tasks?\" This will be dealt with in the follow-up study.  
Conceptualization, J.B.; Formal analysis, C.L.; Methodology, J.B.; Writing--original draft, J.B.; Writing--review & editing, C.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Institute for Information & Communications Technology Promotion (IITP) grants funded by the Korean government (MSIT) (No. 2013-0-00131, Development of Knowledge Evolutionary WiseQA Platform Technology for Human Knowledge Augmented Services).  
The authors declare no conflict of interest.
# Design, Challenges and Developments for 5G Massive MIMO Antenna Systems at Sub 6-GHz Band: A Review
## 2 5G Massive MIMO at Sub-6 GHz
### 5G Smartphone mMIMO Array Antenna Techniques
#### 2.2.3 10-Element 5G Massive MIMO  
Wide impedance bandwidth (IBW) (5G), multi-antenna, 10-element, sub-6 GHz, and mMIMO terminals are advised. The hybrid loop antenna element's inverted-F stub is fed by a grounded coplanar waveguide. To generate wider IBWs, multiple resonant modes are merged by tuning. As illustrated in Figure 12, we selected an antenna element location around the edge of a standard smartphone backplane (120 mm \(\times\) 70 mm). Two elements were positioned along the horizontal edge and orthogonal to the other elements; however, they were positioned along the vertical edge to enhance the variety performance. An experimental antenna with a large impedance bandwidth (\(-\)6 dB) centered at 63% at 4.68 GHz was constructed and measured. The isolation between measurements was at least 18 dB. The simulated envelope's most significant correlation coefficient was 0.21, and its minimum antenna effectiveness was 78.4%. Each component was printed on a Rogers 4003 substrate that was 1.52 mm thick and measured 17.2 mm \(\times\) 3.8 mm in size. The suggested multi-antenna terminal is suited for LTE bands (42, 43, and 46) and 5G sub-6 GHz (3.2-6.1 GHz) communications [60].  
A future 5G smartphone will feature a multi-band, 10-antenna array to support mMIMO applications in the sub-6 GHz spectrum (LTE bands 42-43-46), as shown in Figure 13. To implement the (10 \(\times\) 10) MIMO in all three bands of the LTE applications, 10 T-shaped, coupled fed-slot antenna elements with the capacity to excite dual resonant  
Figure 11: Fabricated prototype of 12-port, (**a**) Front view. (**b**) Back view [59].  
Figure 12: Prototype of the (10 \(\times\) 10) MIMO antennas with the IFS fed by end launch 50 \(\Omega\) connectors at the top layer and slots at the bottom layer [60].  
modes were incorporated on the system board. These parts were equipped with spatial and polarization diversity approaches to enhance isolation and reduce the coupling effects. The performance of the suggested antenna array prototype was assessed in the lab. In the high and low bands, the antenna efficiency was evaluated as desirable above 62% and 42%, respectively. For a LTE band (42/43/46) and a 10 \(\times\) 10 MIMO system, the maximum ergodic channel capacities were found to be 48 bps/Hz and 51.4 bps/Hz, respectively [61].
## 4 Discussion
### Method Performance
#### 4.1.2 Operational Performance  
Acquisition of the surveys during the four weeks dedicated to surveying activities was limited by the availability of vehicles over 10 days in total, during which the four resistivity surveys were acquired. Soundings were acquired one day by a single operator, and the rest of the days by a crew of three or four operators. A single experienced operator could acquire four soundings in 8 h, working at a fast pace. A crew of three or four trained operators could acquire four soundings in 6 h, working at a moderate pace: Such values are commensurate with the speed of conventional VES acquisitions, which require at least three operators to achieve around four soundings per day. Whilst operational efficiency of the resistivity method implemented is significantly inferior to that of 2-D surface ERT, it is still perfectly acceptable, especially in developing-world contexts.
## 5 Results and Discussion
### Fault Diagnosis Phase  
#### 5.2.1 Expression of Results  
After PCA-SOM cluster analysis, high-dimensional data should be transformed into two-dimensional data visual output, and the output mode is analyzed below.  
S1 in Figure 19 shows the scatter diagram of cluster analysis, which can display all data in the form of points on the coordinate system to show the degree of mutual influence between variables. The position of points is determined by the value of variables. From the distribution of data points, correlations between variables can be inferred. If there is a correlation between the variables, most of the points will show a trend. If the variables are not related to each other, then they will appear as randomly distributed discrete points. S2 in Figure 19 shows the pie chart of cluster analysis. A pie chart is a basic graph in data visualization, often used to show the proportion of each category in a categorical variable. According to the angle size of each sector in the pie chart, the proportions of various data can be compared.  
S3 in Figure 20 shows the UMAP weight graph, which is a representation of a weighted graph, and edge weights represent the possibility of connecting two points. To determine connectivity, UMAP extends a radius outward from each point, and when these radii overlap, it connects the points. UMAP accomplishes this process by selecting a radius based on the distance from each point to the NTH adjacent point. Then, as the radius increases, the possibility of connection decreases, making the UMAP map \"fuzzy\". Finally, by specifying that each point must be connected to at least its nearest point, UMAP ensures a balance between local and global structures. The color depth of the map represents the similarity relationship between data points. S4 in Figure 20 is the confusion matrix diagram, also known as the possibility matrix or error matrix. Confusion matrices are visual tools that can be used for unsupervised learning and are the most intuitive and computationally simple way to represent the accuracy of a classification model.  
Figure 19: (**S1**) Scatter diagram of cluster analysis. (**S2**) Cluster analysis pie chart.
# Analysis of Acoustic Flow Field Characteristics of Key Components of High-Speed Train Bogies
## 1 Introduction  
### Background and Significance of the Study  
Mechanical failure has a major impact on rail car safety, with bogie failure accounting for 30-40% of all failures [1]. As a key component of high-speed trains, the bogie supports the entire body of the vehicle, transmits various loads and forces, and regulates how the train's body interacts with the track [2]. It is essential to quickly locate and address any mechanical bogie issues in order to ensure safe train running.  
Currently, in addition to manual detection methods, bogie fault detection technologies are also being developed using signal sensors. Commonly, there is on-board detection, where sensors are installed at key bogie components. An example of an on-board detection device is the JK00430 type locomotive walking part jointly developed by Beijing Bureau of Locomotive Service and Beijing Tangzhi Technology Development Co. (Beijing, China) [3]. The device is consists of sensors to extract signals and process them, while an online host with DSP as the core saves the information to achieve the online diagnosis of railway vehicles without disintegration. Although this type of method can obtain vibration signals in time, the installation location is restricted, prone to interference and the omission of data, and other problems [4, 5]. In the 1980s, there was continuous development in acoustic testing technology, progressing from the initial single-point test method to gradually adopting array test technology. This advancement involved incorporating sound sourcepositioning and imaging techniques to achieve the acoustic imaging of intricate structures, thereby effectively identifying various types of faults within them. For example, [PERSON] [6] studied a track side non-contact acoustic sensor array to achieve the acoustic detection of train bogie axle box bearings. [PERSON] [7] used normalized diagnostic feature maps and convolutional neural networks for bearing fault diagnosis. In practical applications, the fault diagnosis capability of acoustic signals is often combined with other indicators, such as vibration signals, statistical indicators, etc. [PERSON] [8] studied a hybrid fuzzy v-structure fuzzy fault estimator for fault diagnosis and crack size identification bearings using vibration signals.
## 2 Materials and Methods
### Activity Assay  
Substrate specificity was determined using substrates 4-nitrophenyl-N-acetyl-\(\beta\)-d-glucosamineimide (pNG), 4-nitrophenyl-\(N\),\(N^{\prime}\)-diacetyl-\(\beta\)-D-chitobioside (pNGG), and 4-nitrophenyl-\(\beta\)-d-\(N\),\(N^{\prime}\),\(N^{\prime\prime}\)-triacetylLitotrioside (pNGGG). The reaction mixture with a total volume of 80 uL containing pure enzyme at concentration 2.7 ug/mL, substrate at concentration 2 mM, and Assay buffer (60 uL, Sigma-Aldrich, St Louis, MO, USA, A4855) was incubated at 37 \({}^{\circ}\)C and the reaction was stopped by the addition of 0.4 M Na\({}_{2}\)CO\({}_{3}\) (40 uL) after 45 min. Increase of optical density at 405 nm was measured with respect to background readings for identical mixtures without enzyme.  
The effects of pH on activity, optimum temperature and kinetic parameters were measured using pNGG and standard assay conditions. The pH optimum was found using 0.1 M citric acid-potassium phosphate buffer (pH 3-7), 0.1 M potassium phosphate buffer (pH 6-8) and 0.1 M glycine buffer (pH 9-10). The temperature optimum was determined in the range of 40-90 \({}^{\circ}\)C in 0.1 M citric acid-potassium phosphate, pH 5.5.  
Kinetic parameters were calculated from initial rate parameters with concentrations of pNGG from 0.02 to 4.2 mM in 0.1 M citric acid-potassium phosphate buffer pH 5.5, and incubation time 30 min (linear increase was observed in the first 70 min of reaction). \(K_{\rm m}\) and \(k_{\rm cat}\) were calculated from an average of three measurements according to the Michaelis-Menten equation. The kinetics interpretation and fitting were performed using GraphPad Prism version 7.02 for Windows (GraphPad Software, La Jolla California USA, www.graphpad.com). Activity on colloidal chitin was checked using 0.5% (\(w/v\)) carboxymethyl chitin as a substrate according to [PERSON] and Peberdy [26].  
The influence of selected compounds on the activity and kinetic parameters were investigated. The protein sample was pre-incubated with each reagent in the reaction buffer at room temperature for 20 min. Then, it was used to measure the change in activity and Michaelis-Menten dependence as described above. The final concentration of the reagent in the reaction was 5 mM.
To get a comprehensive study, one should employ instruments capable of sensing various part of the irregularity spectrum simultaneously. The 22-23 June 2015 geomagnetic storm was \"an anomalously intense event with large magnetic fields\" [22]. It was one of the most intense storms of solar cycle 24 [23]. A number of significant papers have described and analyzed this event. [PERSON] et al. [23] observed an extreme enhancement of the electron density Ne and vertical total electron content VTEC in the Northern (summer) Hemisphere. They concluded that the extreme topside response was the result of a combination of the prompt penetration electric fields, disturbance dynamo, and the storm-time thermospheric circulation. [PERSON] et al. [24] studied global the ionospheric and thermospheric effects of the storm. They found that dayside neutral mass density increased by three to five times as compared to the quiet time level. [PERSON] et al. [25] studied the equatorial electrodynamics. They found an increase in the eastward electric field and in the upward E\(\times\)B drift on the dayside (which led to rapid strong enhancement of the VTEC and electron/ion density), as well as downward vertical drift with a decrease in the VTEC and in the plasma density on the nightside. [PERSON] and [PERSON] [26] studied small-scale structures in the equatorial region. They suggested that the westward penetration of the electric field into the equatorial sector during the local midnight caused an abrupt decrease in the virtual height to ~200 km and suppressed plasma bubbles in the Indian sector. Comprehensive analysis was carried out by [PERSON] et al. [27] who showed an increase in the ionospheric polar convection and an expansion of the lower boundary of the convection region toward low latitudes (from 60\({}^{\circ}\)N to 50\({}^{\circ}\)N). Geomagnetic storms and TIDs are also known as sources for GPS positioning quality deterioration. GPS scintillations lead to a range error in GNSS due to diffraction [28]. Deep signal fades that appear during small-scale irregularities effect result in navigation outages [29]. [PERSON] et al. [30] studied several strong storms during solar cycle 24, including the 17 March 2015 St. Patrick storm. They reported that the precise point positioning (PPP) error was less than 0.32 m at mid-latitudes. In this paper, we study small-scale structures at mid-latitudes during the 22 June 2015 magnetic storm. The mid-latitude irregularity formation mechanism is not quite clear. We have tried to fill the knowledge gap by recording new scintillation data in different frequency bands at mid-latitudes. The data help to reveal physical mechanism for the irregularity generation in the mid-latitude ionosphere. Another point is to reveal how the irregularities affect GPS precision during the storm.
## 3 Methodology for Human Navigation Path Simulation via Inverse Reinforcement Learning
### Global Path Planning Analysis  
This study develops an enhanced FrozenLake-v1 navigation model based on the OpenAI Gym framework [24], featuring an 8 \(\times\) 8 grid world (Figure 5, left) with the following specifications:  
1. Environmental Dynamics: A stochastic wind disturbance mechanism (optional mode) is introduced, which triggers with probability \(p_{wind}\) post-action execution, displacing the agent randomly by 1 grid unit in \(\{up,down,left,right\}\);
2. Spatial Semantics: The start point \((0,0)\) (yellow cell) and goal \((7,7)\) (yellow cell) define navigation terminals, with obstacle cells \[O=\{(2,3),(3,5),(4,3),(5,1),(5,2),(5,6),(6,1),(6,4),(6,6)(2,3)\}\] modeled as crevasses (Figure 5, right) causing task failure upon contact;
3. Visualization Interface: Real-time decision rendering via Pygame engine dynamically displays agent position (top-left avatar) and target status (bottom-right reward marker).  
Figure 3 demonstrates the path planning results of the \(A^{*}\) algorithm on an occupancy grid map. The algorithm employs Manhattan distance as the heuristic function:  
\[h(n)=|x_{\text{current}}-x_{\text{goal}}|+|y_{\text{current}}-y_{\text{goal}}| \tag{8}\]  
While ensuring obstacle avoidance and shortest-path acquisition, it restricts robot movement to four directions (up/down/left/right) as shown in the trajectory.  
However, when applied to complex terrains (e.g., outdoor grid maps with narrow passages), the \(A^{*}\) algorithm exhibits inherent risks: its generated shortest paths may force robots to traverse corridors flanked by cliffs or geohazard zones (e.g., high-risk landslide areas). Such paths not only impose stringent requirements on sensor localization accuracy and control system robustness but also significantly increase the task failure probability.  
Reinforcement learning methods demonstrate effective risk mitigation in global path planning. Specifically, explicit penalty mechanisms impose path penalties on annotated obstacle-adjacent regions (e.g., crevasse-surrounding grids in FrozenLake environments), guiding Q-learning algorithms to avoid high-risk areas during policy convergence. Conversely, inverse reinforcement learning frameworks (e.g., EIRL) autonomously derive safe navigation policies by decoding implicit risk-aversion patterns from expert demonstration  
Figure 5: Grid map and ice lake model V1 of an 8 \(\times\) 8 ice lake environment with ice caves,  
data. Empirical findings reveal that explicit penalty methods achieve high efficiency in low-dimensional state spaces (e.g., 8 \(\times\) 8 grids), while inverse reinforcement learning exhibits superior generalization capabilities in high-dimensional complex scenarios (e.g., environments exceeding 50 \(\times\) 50 grids).
# Vision Transformers and Transfer Learning Approaches for Arabic Sign Language Recognition
## 2 Related Works
### Overview of SL Classification Research Based on Arabic Language
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref.** & \
\begin{tabular}{c} **Background** \\ **Conditions** \\ \end{tabular} & **Hands** & **Signers** & **Signs** &
\begin{tabular}{c} **Acquisition** \\ **Mode** \\ \end{tabular} & **Lexicon Size** & **Method** & **Dataset** \\ \hline
[23] & - & - & 11 &
\begin{tabular}{c} 22 signed \\ letters \\ \end{tabular} & - & 11,008 & CNN & LIS & \\ \hline
[59] & - & \begin{tabular}{c} Double- \\ handed \\ \end{tabular} & - & \begin{tabular}{c} 24 static \\ gestures \\ \end{tabular} & \
\begin{tabular}{c} HTC Vive, \\ Vive Hand \\ Tracking SDK \\ 0.8.2. \\ \end{tabular} & 35,400 &
\begin{tabular}{c} MLP, \\ SVM \\ KNN, \\ RF \\ \end{tabular} & PSL \\ \hline
[47] & - & - & 150 &
\begin{tabular}{c} 570 words \\ and \\ 30 phrases \\ \end{tabular} & Kinect sensor & 90,000 & MLP & MSL & \\ \hline
[16] & \begin{tabular}{c} Nonuniform \\ background \\ \end{tabular} & \
\begin{tabular}{c} Single- \\ handed \\ \end{tabular} & - &
\begin{tabular}{c} 26 letters \\ and 10 digits \\ \end{tabular} & - & - & LMC & ASL & \\ \hline
[55] & \begin{tabular}{c} White \\ backgrounds \\ \end{tabular} & \begin{tabular}{c} Double- \\ handed \\ \end{tabular} & - & \
\begin{tabular}{c} 50 sets of \\ 36 Bangla \\ basic sign \\ characters \\ \end{tabular} & Camera &
\begin{tabular}{c} 1800- \\ character \\ images \\ \end{tabular} & CNN & BdSL & \\ \hline
[3] & \begin{tabular}{c} Uniform \\ background \\ + \\ nonuniform \\ background \\ \end{tabular} & \begin{tabular}{c} Single- \\ handed \\ \end{tabular} & 10 & \begin{tabular}{c} 28-letter \\ Arabic \\ alphabet \\ \end{tabular} & 9240 images & \
\begin{tabular}{c} 1800- \\ character \\ images \\ \end{tabular} &
\begin{tabular}{c} CNN \\ \end{tabular} & ArSL \\ \hline
[34] & \begin{tabular}{c} Nonuniform \\ background \\ \end{tabular} & \
\begin{tabular}{c} Single + \\ double \\ \end{tabular} & 43 & 226 signs &
\begin{tabular}{c} Microsoft \\ Kinect v2 \\ \end{tabular} & 38,336 & CNN & TSL \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the key parameters of sign language recognition studies.