esa-sceva/satcom-chunk-collection huggingface dataset:
# EEG-Based Measurement for Detecting Distraction in Coal Mine Workers
## 4 Results
### EEG Features Extraction
#### 4.2.3 Frequency Domain: Band Power Ratios  
Figure 9 presents topographical maps illustrating the scalp distribution of various EEG frequency band power ratio indices for both groups. The focused group exhibit significantly higher values in the occipital region for three power ratio indices: \(\theta/\beta\) (Index A), \(\alpha/\beta\) (Index C), and \((\alpha+\theta)/(\alpha+\beta)\) (Index F) compared to the distracted group (\(p<0.05\)). Conversely, for the \((\alpha+\beta)/\theta\) (Index E) ratio, the distracted group shows significantly higher values in the occipital area compared to the focused group (\(p<0.05\)). Although spatial variations in \(\theta/\alpha\) (Index B) and \((\alpha+\theta)/\beta\) (Index D) ratios between the groups are noticeable in the topographic maps, these differences are not statistically significant.  
These comparative results suggest significant differences at most occipital electrodes (PO3, PO5, POZ) and some parietal electrodes (P1, P3, PZ) across different attentional states. Therefore, the band power ratio indices from these significantly different electrodes are also incorporated as candidate indices for input into the classifier.  
Through the feature extraction and significance testing of the EEG data described above, we successfully constructed a comprehensive feature set consisting of 143 features (Table 2).  
Figure 8: Average power of 4 frequency bands and \(p\)-values across channels for two groups.  
Figure 9: Frequency band power ratio indices and \(p\)-values across channels for two groups.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Indicators** &
\begin{tabular}{c} **Number of** \\ **Significant** \\ **Features** \\ \end{tabular} \\ \hline Time domain & P300 amplitude & 10 \\  & \(\delta\)-band & 1 \\  & Spectral amplitude & \(\theta\)-band & 4 \\  & \(\alpha\)-band & 19 \\ Frequency domain & \(\beta\)-band & 19 \\  & A & 10 \\  & Band power ratios & C & 33 \\  & E & 26 \\  & F & 21 \\  & Total & 143 \\ \hline \end{tabular}
\end{table}
Table 2: EEG feature indicators and the number of significant features.
# Location Optimization of Emergency Bell Based on Coverage Analysis for Crime Prevention
## 4 Results and Discussion
### Optimal Location of Emergency Bells
#### 4.3.1 EBLSCP  
The result of the EBLSCP implementation to cover a cumulative total of 17,437 crimes as shown in Figure 6 is as follows. To achieve complete coverage of all unmonitored crime coordinates, the following numbers of new emergency bells are needed in each district: 1743 in Gwangsan District, 1741 in the North District, 1255 in the West District, 614 in the East District, and 875 in the South District. This totals 6228 newly installed emergency bells.  
Meanwhile, the price of the HB-112, which is the most commonly used model in security emergency bells, is approximately 1,019,000 Korean won [32], equivalent to $771.67. If 6228 emergency bells were to be installed, it would require a budget of $4,805,960.76.  
However, because of budget constraints, it is necessary to determine the most optimal locations using a methodology that allows for more efficient installation within the limited budget (Figure 6a).
# Analyzing Zone-Based Registration under 2-Step Paging in Mobile Communication Network
## 3 Analysis of ZBR under 2-Step Paging
### Analysis of Time Spent in a Zone in 1 ZR
 \tag{7}\]  
The average number of cells visited in the zone should be obtained by only considering when the location registration is performed for the first time in the current cell; therefore, each probability of becoming state 1 (\(p_{1}\)) and state 2 (\(p_{2}\)) is as follows:  
\[p_{1}=\frac{P[A_{1}]}{P[A_{1}]+P[A_{2}]}=\frac{\pi_{1}\times(q+r)+\pi_{5} \times 2r}{\pi_{1}\times(q+r)+\pi_{5}\times 2r+\pi_{2}\times q+\pi_{3}\times r +\pi_{6}\times r} \tag{8}\]  
\[p_{2}=\frac{P[A_{2}]}{P[A_{1}]+P[A_{2}]}=\frac{\pi_{2}\times q+\pi_{3}\times r +\pi_{6}\times r}{\pi_{1}\times(q+r)+\pi_{5}\times 2r+\pi_{2}\times q+\pi_{3} \times r+\pi_{6}\times r} \tag{9}\]  
Therefore, the average number of cells visited in the zone from state 1 and state 2 until moving to another zone can be calculated using the follow equation:  
\[E(N)=E(N_{1})\times p_{1}\ +\ E(N_{2})\times p_{2} \tag{10}\]  
where \(E(N_{1})\) represents the average number of cells visited from state 1 until absorbing to state 0 and \(E(N_{2})\) represents the average number of cells visited from state 2 until absorbing to state 0. Finally, when the time spent in a cell follows an exponential distribution with a mean \(1/\ \lambda_{m}\)[9; 16], the average time spent in a zone, \(E[T_{Z}]\), can be obtained as follows:  
\[E[T_{Z}]=E(N)\times\frac{1}{\lambda_{m}} \tag{11}\]  
Figure 10: States in a zone when \(n=9\).
# Comparison of NeQuick G and Klobuchar Model Performances at Single-Frequency User Level +
## 2 Data and Methods
### TEC Accuracy  
To test the accuracy of the broadcast ionospheric models for both NeQuick G and Klobuchar, the observations derived from the selected networks of stations described above, are compared. For the investigation of the accuracy of the modelling we present results in a vertical total electron content (VTEC) format. This is directly comparable to common ionospheric models and avoids having the elevation angle as a dimension in the plots. We note this because for other parts of this paper STEC is used in the underlying calculations, as the NeQuick G requirement is defined using STEC. The VTEC accuracy, performed for the Galileo and the GPS constellations, is obtained following the three steps below:  
1. The computed VTEC values are acquired using the navigation models. The models are different depending on the constellation, NeQuick G for Galileo and Klobuchar for GPS. The computation follows the algorithm as described in [3] and in [2] for NeQuick G and Klobuchar, respectively. The different institutions involved in the study, used different approaches, in particular, for NMA/TUW, the VTEC for NeQuick G and Klobuchar is calculated every 5 min using the received ionosphere parameters, for the set of satellites observed by the GNSS reference stations (Figure 1), using the actual observation geometry (receiver and satellite positions).
2. For the same set of points, the reference VTEC is computed. A description of the methodologies is provided, detailing the computation of the reference VTEC. For NMA, the observed VTEC values are calculated every 5 min based on dual-frequency measurements from the GNSS reference stations (Figure 1). For the high-latitude stations, differential code biases (DCBs) are retrieved from the NMA's ionosphere monitoring system as the receivers used here are part of the much larger network processed by that system. For the low-latitude stations, DCBs are retrieved from the International GNSS Service (IGS) archives. For TUW, the observed VTEC values are calculated every minute based on dual-frequency measurements from the GNSS reference stations (Figure 1). For the mid-latitude stations, DCBs are obtained by a  
Figure 1: Locations of GNSS stations used in this study. The orange lines show the magnetic latitudes.  
least squares adjustment based on P1 and P2 code observations, as well as satellite DCBs downloaded from the Astronomical Institute of the University of Bern (AIUB) CODE database. The standard Single Layer Model (SLM) is used to convert slant TEC to vertical TEC (ionosphere altitude 350 km).
3. The VTEC error is finally calculated as the difference between the model-derived VTEC values and the reference VTEC values obtained through the observations.
## 4 Application and Results
### Cross-Year Comparison of Country Ranking
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Country**} & \multicolumn{2}{c}{**2008**} & \multicolumn{2}{c}{**2015**} \\ \cline{2-5}  & **Index Score** & **Ranking** & **Index Score** & **Ranking** \\ \hline Country Group 1 & & & & \\ AT & 0.579 & 11 & 0.705 & 6 \\ BE & 0.614 & 10 & 0.769 & 3 \\ CHI & 0.532 & 12 & 0.688 & 8 \\ DE & 0.691 & 4 & 0.800 & 2 \\ DK & 0.629 & 9 & 0.575 & 10 \\ ES & 0.710 & 3 & 0.528 & 11 \\ FI & 0.664 & 5 & 0.719 & 5 \\ FR & 0.647 & 6 & 0.528 & 12 \\ IE & 0.865 & 1 & 0.746 & 4 \\ IT & 0.159 & 13 & 0.221 & 13 \\ NL & 0.631 & 8 & 0.636 & 9 \\ SE & 0.747 & 2 & 0.870 & 1 \\ UK & 0.632 & 7 & 0.702 & 7 \\ Average & 0.623 & & 0.653 & \\ \hline Country Group 2 & & & & \\ CZ & 0.667 & 4 & 0.804 & 1 \\ EE & 0.854 & 1 & 0.66 & 2 \\ HU & 0.748 & 2 & 0.394 & 7 \\ LT & 0.631 & 5 & 0.6 & 5 \\ PL & 0.478 & 7 & 0.607 & 4 \\ PT & 0.699 & 3 & 0.631 & 3 \\ RO & 0.242 & 8 & 0.196 & 8 \\ SK & 0.558 & 6 & 0.479 & 6 \\ Average & 0.610 & & 0.546 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Index scores and corresponding rankings of countries within each group for different years. Figure 4: The change in each country’s respective ranking based on the index score between 2008 and 2015. In addition, the relationship between the developed safety performance index and the road safety final outcome indicator (i.e., the number of road fatalities per million inhabitants, see Appendix B) of these countries was also investigated. We found that in general, the countries in Group 1 had a fewer number of fatalities per inhabitants than the countries in Group 2 for both 2008 and 2015. Such a result is consistent with the results based on the developed safety performance index. However, the countries' ranking based on the derived index score is different from the traditional ranking of countries based on the fatality data, which is in line with the findings from the SUNflowerNext study and the DaCoTA study [12,15]. Moreover, if we take the percentage change of the road fatalities per million inhabitants during these two years into account, the variance is large, which implies that we may not simply or completely attribute the percentage reduction in road fatalities of one country by its improvement in safety performance. In doing so, some other relevant aspects, such as the changes in socioeconomic conditions, in motorization levels, and in road safety policy interventions of each country might also be taken into account.
# Overlapping Community Detection of Bipartite Networks Based on a Novel Community Density
## 3 Community Density
### Key Definitions
\[D_{1_{(U1,V1)}} =\frac{\sum_{k=1}^{L(U1)}\varphi(U1,v_{k})+\sum_{k=1}^{L(V1)} \varphi(u_{k},V1)}{L(U1)+L(V1)}\] \[=\frac{\varphi(U1,V1)+\varphi(U1,V2)+\varphi(U1,V3)+\varphi(U1,V 1)+\varphi(U2,V1)+\varphi(U4,V1)+\varphi(U6,V1)}{3+4}. \tag{9}\] \[=\frac{1+1+1+1+1+1+0}{7}=\frac{6}{7}\]  
In the \(t\)-th community \(G_{t}=(U_{t},V_{t},E_{t})\), by adding and averaging the node pair density \(D_{t_{(u_{i},v_{j})}}\) of node pairs \((u_{i},v_{j})\), the density \(D_{t}\) of the \(t\)-th community can be shown as Formula (10). Among them, \(P_{t}\) represents the sum of all U nodes in the \(t\)-th community, \(Q_{t}\) represents the sum of all V nodes in the \(t\)-th community, \(\sum_{i=1}^{P_{t}}\sum_{j=1}^{Q_{t}}D_{t_{(u_{i},v_{j})}}\) represents the sum of node pair density of all node pairs in the \(t\)-th community, \(\sum_{i=1}^{P_{t}}\sum_{j=1}^{Q_{t}}\varphi(u_{i},v_{j})\) represents the sum of all node pairs in the \(t\)-th community. \[D_{t}=\frac{\sum_{i=1}^{P_{t}}\sum_{j=1}^{Q_{t}}D_{t_{(u_{i},v_{j})}}}{\sum_{i =1}^{P_{t}}\sum_{j=1}^{Q_{t}}\varphi(u_{i},v_{j})}. \tag{10}\]  
In community 1, \(P_{t}=4\), \(Q_{t}=4\), the calculation formula of \(D_{1}\) is shown in Formula (11). \[D_{1} =\frac{\sum_{i=1}^{4}\sum_{j=1}^{4}D_{1_{(u_{i},v_{j})}}}{\sum_{i =1}^{4}\sum_{j=1}^{4}\varphi(u_{i},v_{j})}\] Figure 4:0 Figure 4:1  
Likewise, we can obtain \(D_{2}\approx 0.78\). After obtaining the density of a certain community \(G_{t}\), the density of the whole bipartite network community \(D\) is the average density of all communities. X represents the number of communities in the bipartite network \(G=(U,V,E)\). Figure 4:2  
For the community detection shown in Figure 1, \(D=\frac{1}{2}\sum_{t=1}^{2}D_{t}=\frac{1}{2}(D_{1}+D_{2})\approx 0.85\). When we use community density \(D\) as an evaluation criterion of community detection, the higher the community density is, the closer the node pairs within the community are, and the sparser the node pairs between the communities are. This trend is very consistent with the community structure required by community detection. Therefore, community density can be used to determine whether community detection results are good or bad.
# Improvements in Rock Mass Description for Stope Design by Geophysical and Geochemical Methods
## 3 Industry Survey on Stope Design  
### Survey Methodology  
A survey was conducted among mining industry professionals to analyze stope design practices, identify the degree of implementation of new design tools, and characterize the most pressing challenges in stope design. The survey included 19 questions with multiple-choice and free-answer options. The following topics were asked of the respondents: (1) mining method employed and changes in the mining method selection, (2) stope design methods applied, (3) timing of stope design and allocated resources, (4) parameters affecting stope design, (5) stope stability monitoring, (6) the most urgent improvements for stope design, (7) utilization of rock mass characterization, and (8) willingness to improve the stope design process.  
Altogether, 36 responses were received from 20 countries across different regions, including Africa (4 responses), Asia (7), Australia (4), Europe (10), North America (2), South America (5), and other regions (4).
# The Exoskeleton Balance Assistance Control Strategy Based on Single Step Balance Assessment
## 3 Exoskeleton Balance Assistance Control Strategy  
The exoskeleton balance assistance control strategy based on single step balance assessment is shown in Figure 8. The control framework mainly consists of three parts: human motion state estimation, human single step balance assessment, and active balance assistance control. Based on the human kinematics and the sensor data from the inertial measurement unit (IMU) and foot pressure insoles, the state of CoM (\([x(t),\dot{x}(t)]\)) and the state of the swing foot (\([x_{swing}(t),\dot{x}_{swing}(t)]\)) are estimated. Grounding in the state of CoM and the swing foot, the single step balance assessment method evaluates the single step balance degree of the current posture of a human and detects incipient falls. Finally, the fuzzy inference system is used to establish the relationship between the single step balance degree \(M\) and the assistant coefficient \((\alpha_{G},\alpha_{C},\alpha_{I})\), and then the assistant torques are calculated based on the human dynamics model and the assistant coefficient.
## 3 Results and Discussion
### Case Study
#### 3.2.3 Tibet Earthquake  
Tibet is located on the Qinghai-Tibet Plateau with high altitudes and complex terrain. There was an earthquake with a magnitude of 5.6 at 17:22:14 on 19 July 2019. The epicenter was located at 27.67\({}^{\circ}\) N, 92.89\({}^{\circ}\) E. The depth was 10 km. Anomalies before the earthquake are shown in Table 5. From 30 days to 20 days before the earthquake, only the IR4 channel showed abnormalities. Anomalies in the IR3 and IR4 channels appeared on the 11 th day before the earthquake. IR1 and IR2 showed abnormalities later. The occurrence time and disappearance time of the anomalies in the four channels were relatively close. The relative powers spectrum on 9 July 2019 is shown in Figure 16. Anomalies in all four channels were close in location. The distance between the anomalies and the epicenter is 11.5 km. The  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Channel**} & **Start Time** & **End Time** & \multirow{2}{*}{**Maximum**} & **Time of** & **Maximum** & **Distance** \\  & **(Days)** & **(Days)** & & **Maximum** & **(Days)** & **(Pixes)** & **Distance** \\ \hline IR1 & \(-8\) & \(-5\) & 22.1271 & \(-7\) & 2068 & 134.1929 \\ IR2 & \(-8\) & \(-5\) & 21.1125 & \(-6\) & 2202 & 134.1929 \\ IR3 & \(-10\) & \(-6\) & 19.0396 & \(-7\) & 1587 & 1.6510 \\ IR4 & \(-8\) & \(-5\) & 23.6295 & \(-6\) & 1625 & 240.8767 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The spatial-temporal characteristics of anomalies before the Taiwan earthquake.  
Figure 14: Power spectrum images on 9 December 2018.  
Figure 15: The cover area of the anomalies before and after Taiwan earthquake.  
cover area of the anomalies before and after the earthquake is shown in Figure 17. The variation trend of the cover area of anomalies in the four channels is similar during the four-channel abnormal period. For this seismic case, the anomalies of the four channels show obvious similarities in spatiotemporal characteristics and the evolution process.
# Machine Learning Reconstruction of Wyrtki Jet Seasonal Variability in the Equatorial Indian Ocean
###### Abstract  
The Wyrtki Jet (WJ), a pivotal surface circulation system in the equatorial Indian Ocean, exerts significant regulatory control over regional climate dynamics through its intense eastward transport characteristics, which modulate water mass exchange, thermohaline balance, and cross-basin energy transfer. To address the scarcity of in situ observational data, this study developed a satellite remote sensing-driven multi-parameter coupled model and reconstructed the WJ's seasonal variations using the XGBoost machine learning algorithm. The results revealed that wind stress components, sea surface temperature, and wind stress curl serve as the primary drivers of its seasonal dynamics. The XGBoost model demonstrated superior performance in reconstructing WJ's seasonal variations, achieving coefficients of determination (\(R^{2}\)) exceeding 0.97 across all seasons and maintaining root mean square errors (RMSE) below 0.2 m/s across all seasons. The reconstructed currents exhibited strong consistency with the Ocean Surface Current Analysis Real-time (OSCAR) dataset, showing errors below 0.05 m/s in spring and autumn and under 0.1 m/s in summer and winter. The proposed multi-feature integrated modeling framework delivers a high spatiotemporal resolution analytical tool for tropical Indian Ocean circulation dynamics research, while simultaneously establishing critical data infrastructure to decode monsoon current coupling mechanisms, advancing early warning systems for extreme climatic events, and optimizing regional marine resource governance.  
equatorial Indian Ocean; Wyrtki Jet; seasonal variability; machine learning; XGBoost
# Implementation of Explosion Safety Regulations in Design of a Mobile Robot for Coal Mines
## 2 State of Art  
### Legislation Overview  
When designing the robot in the underground coal mine environment, it is necessary to take into account the requirements for safety in potentially explosive atmospheres, based on the standards in force in the country of use.  
In the European Union, the legislation is based on the European Commission Directive 2014/34/EU (also called ATEX), which sets requirements for manufacturers and operators of equipment that is designed to work in potentially explosive atmospheres [5]. The requirements in this document result from national standards adopted by individual countries. In the EU, these national standards are harmonized with the IEC 60079 Series Explosive Atmosphere Standards [6].  
In China, the GB standard--Guobiao system is applied (Chinese national standards issued by the Standardization Administration of China), together with standards GB3836, which are identical to IEC 60079 [7].  
In the United States of America, this issue is addressed by the legislation of Hazardous Locations (abbreviated to HazLoc), which aims to control the risks that are related to the explosion in certain environments [8].  
In the Russian Federation and several neighbouring countries, the document \"Technical Regulations CU TR 012/2011 on the safety of equipment in explosion hazardous environments\" [9] is in use.  
In Australia, this is solved by the NSW Coal Mine Health and Safety Regulation [10]. In Brazil, the INMETRO Regulation \"Portaria 83:2006\" states the requirements for electrical equipment for use in explosive atmospheres of vapours and gases [11].  
It should be mentioned that all of the above-mentioned documents approach the problem more or less equally--they classify environments into several levels of risk and for each level offer specific ways to achieve the required safety.
## 5 FPGA Implementation
### Resource Description  
The resources of the Xilinx 7 series FPGAs are:  
1. LUTs (lookup tables): These contain the logical elements that determine the output from one or multiple inputs. They are essentially truth tables created from the description of the VHDL program.
2. FFs (flip flops): Sequential logical elements with one bit of memory.
3. RAM blocks (random access memory): Each block has a storage capacity of 32 \(K\)-bit.
4. DSP blocks (digital signal processing): Specialized blocks for product, sum, and accumulation operations for signed numbers in two's complement format. These operations are called: multiply-accumulate (MAC).
5. Power consumption: Determines the energy consumption of the system.  
From Section B in Figure 12, it can be seen that the channel blocks share control, which synchronizes MAC operations, phase generation, and sine and cosine values, i.e., the trigonometric functions that make up the base. Combining the common control operations with sine and cosine generation reduces the number of hardware resources.  
Figures 14 and 15 show the resources used in the proposed architectures. In the case of one channel, both architectures have identical resource consumption. Architecture Mode 2 presents lower resource consumption for more than one channel.  
Figure 14: Resources, Mode 1: We observe that as the number of channels increases, resource consumption linearly increases in all FPGAs, and there is no difference in consumption.  
Figure 16 shows the results of the following relation:  
\[\frac{\text{mode 1 - mode 2}}{\text{mode 1}}=1-\frac{\text{mode 2}}{\text{mode 1}}. \tag{21}\]  
Figure 16: Using Equation (21), we calculated the percentage of resources consumed by Mode 2 compared to Mode 1. As the number of channels increases, this percentage also increases. For LUTs, the percentage is nearly identical. For FFs, both Kintex-7 and Virtex-7 have identical performance.  
Figure 15: Resources, Mode 2: We observe that resource consumption is lower for LUTs, FFs, and BRAM (block RAM) compared to Mode 1 as the number of channels increases. In particular, there is no increase in BRAM consumption as the architecture uses the same resources. The DSP consumption is identical for both architectures.  
The relation gives the percentage of resources that Mode 2 consumes compared to Mode 1. For two channels, 5.9% fewer LUT resources are obtained for Artix and Kintex. For four channels, 8.9% fewer LUT resources are obtained for all FPGAs. For 16 channels, 11% fewer LUT resources are obtained for all FPGAs.  
For two channels, 21% fewer FF resources are obtained for all FPGAs. For 16 channels, 40% to 46% fewer FF resources are obtained. For 16 channels, BRAM consumption is reduced by a factor of 15, as architecture Mode 2 uses only one DDS module. For 16 channels, power consumption is reduced by 17%, 12%, 16%, and 14% in Artix, Virtex, Kintex, and Spartan, respectively.  
Figure 17 shows the power consumption. Mode 2 has lower current consumption. For both modes, Artix-7 has the lowest consumption.