esa-sceva/satcom-chunk-collection huggingface dataset:
# An Intelligent Wireless System for Field Ecology Monitoring and Forest Fire Warning
## 2 Design of the Monitoring System
### Design of Online System Hardware
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Group** & **Sensor Type** & **Range** \\ \hline soil parameters & soil temperature (2–6 layers) & \(-\)55\(\rightarrow\)80\({}^{\circ}\)C \\ soil moisture (2–6 layers) & 0–100\% \\ \hline  & air temperature & \(-\)55\(\rightarrow\)80\({}^{\circ}\)C \\  & air moisture & 0–100\% \\  & carbon dioxide concentration & 400-5000 parts per million \\ air parameters & wind direction & 0\({}^{\circ}\)–360\({}^{\circ}\) \\  & wind speed & 0.3–60 m/s \\  & precipitation & 0–4 mm/min \\  & negative oxygen ion & 0–50,000 Ions/cm\({}^{3}\) \\  & particulate matter 2.5 & 0–500 \(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\) \\ \hline  & global radiation & 0–2000 W/m\({}^{2}\) \\ light parameters & light intensity & 0–200,000 Lux \\  & sunshine hours & threshold 120 W/m\({}^{2}\) \\  & photosynthetically active radiation & 0–4000 \(\mathrm{\SIUnitSymbolMicro m}\)/m\({}^{2}\)/s \\ \hline plant parameters & needle-type stem moisture & 0–100\% \\  & non-destructive stem moisture & 0–100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sensor configuration and performance of the forest microclimate monitoring station. components inside it, such as data acquisition unit and a solar energy controller. The soil parameter sensor group includes multiple soil moisture and temperature sensors, which may be buried in the soil at different depths in different positions for measuring the temperatures and moistures of the soil in forests. In terms of plant parameters, a non-destructive packaged stumpage stem moisture sensor and a needle-type stumpage stem moisture sensor are used to measure the volumetric moisture of stumpage stems with a diameter at breast height smaller than 10 cm and a diameter at breast height greater than 10 cm, respectively. Figure 3a-d presents the three ecological monitoring stations using Beidou satellite communication located on Yan'er Ridge (at an altitude of 440 m), Wangjing Tower (at an altitude of 750 m), and Luobadi Peak (at an altitude of 1000 m) in Beijing Jiufeng National Forest Park. No stable GPRS communication can be achieved for cell phones, as this region is far from the mobile base station and affected by the air route. Figure 4e-f present the two ecological monitoring stations using GPRS communication located on and Hulun Buir and Zhalantun in Inner Mongolia. No stable Beidou satellite short message communication can be achieved, as this region is far from the geosynchronous orbit of the Beidou satellite.
## 3 Sensor Structure  
The developed microcontroller-controlled closed-loop fluxgate sensor primarily consists of a fluxgate sensor core, an open-loop sensing circuit, a feedback current drive circuit, and a microcontroller unit, as illustrated in Figure 3.  
The magnetic core for fluxgate sensors is made of ferrite, a material with high permeability, which is essential for concentrating the magnetic field. An excitation winding \(N_{ex}\) is evenly wound around the core and is connected to the open-loop sensing circuit. Additionally, a feedback winding around the core is necessary for magnetization compensation in closed-loop fluxgate current sensors. The open-loop sensing circuit, also known as the zero-flux detector, operates on the self-oscillating fluxgate technology to detect the primary current \(I_{p}\), and its output voltage is \(V_{open}\). The ADC module inside the MCU acquires the voltage \(V_{open}\) and converts it into digital signals to perform the closed-loop control algorithms. These algorithms include a single-neuron-based self-pre-optimization algorithm and a conventional positional PID algorithm, which will be described in Section 4. The operating results of the PID-based closed-loop algorithms are converted to an analog voltage signal \(V_{DAC}\) through the DAC module inside the MCU. The voltage \(V_{DAC}\) is used to control the feedback current drive circuit to generate the appropriate feedback current to compensate for the magnetic field generated by the primary current. In the feedback current drive circuit, \(V_{DAC}\) is proportional to the controlled feedback current. Therefore, according to the zero-flux principle, \(V_{DAC}\) is also proportional to the primary current. The \(V_{DAC}\) can be output as an analog voltage and transmitted as a digital signal via the UART communication module on the MCU.
## 5 Conclusions  
In this study, approaches to measure the failure response characteristics of LRBs to extreme loads such as a BDBE for small facilities or devices were proposed. A small LRB was developed and tested to improve seismic performance in the case in which an earthquake exceeds the DBE of safety-related structures in NPPs. Static and dynamic experiments and verification analyses were performed. The amplified floor response was applied to analyze and test the buckling behavior of the extreme response. To understand the actual LRB damage characteristics, shaking table tests were conducted, and the fracture surface was observed. In addition, stress and slip analyses were simulated with video and cut view reviews to verify the results. The following conclusions were drawn from this study:  
1. The results of the seismic analysis of the LRB for BDBE input can simulate the failure initiation and process in the shaking table test. It matches well with the plastic deformation of the lead core, laminated rubber, and steel plate.
2. The results of the static test and fracture surface observation of a small LRB show that buckling or partial damage starts at the part where the asymmetry and inverse slope of the hysteresis curve appears. The overall damage to the rubber laminate can proceed rapidly from partial buckling without signs of gradual propagation.
3. It is useful to evaluate the internal soundness of the LRB and estimate the partial damage by comparing the natural frequency and orthogonal response of the seismic isolator before and after the occurrence of a BDBE.
4. Even when the seismic isolation bearings supporting the facility are completely broken, additional isolation by slip behavior can maintain the superstructure within the interface limit without impacting the surrounding facilities.
5. The small-sized LRB studied in the paper can be a good option among seismic isolation devices for the seismic enhancement of the selective safety-related equipment of operating nuclear plants.  
Conceptualization, [PERSON], [PERSON] and [PERSON]; methodology, [PERSON] and [PERSON]; validation, [PERSON] and [PERSON]; formal analysis, [PERSON] and [PERSON]; investigation, [PERSON], [PERSON], and [PERSON]; writing--original draft preparation, [PERSON] and [PERSON]; writing--review and editing, [PERSON] and [PERSON]; funding acquisition, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the Ministry of Trade, Industry, and Energy through KETEP 363 (Korea Institute of Energy Technology Evaluation Planning) (No. 20181510102380) and the Korea National University of Transportation in 2020.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
This was supported by the Ministry of Trade, Industry, and Energy through KETEP and the Korea National University of Transportation in 2020.  
The authors declare no conflict of interest.
## 3 Methodology
### Data Analysis and Results
#### 3.3.2 Fuzzy Set Qualitative Comparative Analysis

Table 4: Goodness of model fit. The four configurations are compared, revealing high values of information quality and platform-interactivity appearing in all configurations and leading to high community participation intention. Thus, these two antecedent conditions are necessary for high community participation intention. This finding is consistent with the results of SEM. The fsQCA also provides some unique insights compared with SEM. The high degree of marketers-interactivity appears in combination with other antecedent conditions in two of the configurations. However, the absence and low marketers-interactivity are observed in the other two configurations, implying that the marketers-interactivity is not a necessary condition related to the high degree of community participation intention. Participation intention can also be high, even when the marketers-interactivity is low or absent. Similarly, high customers-interactivity appears in combination with other antecedent conditions in two of the configurations. The absence or low customers-interactivity in combination with other antecedent conditions can also lead to high engagement community participation intention which does not necessarily relate to a high participation intention. High brand-interactivity appears in combination with other antecedent conditions in three of the configurations. While it is absent in one configuration. So it is a sufficient condition rather than a necessary condition for high community participation intention. High perceived flow appears in combination with other antecedent conditions in two of the configurations. While it is absent in other two configurations. This study differs from past studies which have indicated that high flow must result in high participation intention. The fsQCA result provides a unique insight that the perceived flow does not always positively affect participation intention. In some situations, it has no effect on participation intention.
# A Study of Cavitation Erosion in Artificial Submerged Water Jets
## 2 Numerical Simulation
### Experimental Procedure  
The schematic diagram and physical diagram of the experimental setup are presented in Figure 2. The experimental apparatus consists of various components, including nozzles, a high-pressure plunger pump, a centrifugal pump, pressure regulating valves, pressure gauges, control valves, flow meters, a water storage tank, and an experimental water tank. In the experiment, a high-pressure plunger pump was utilized to generate upstream pressure, reaching a maximum of 30 MPa, and providing a flow rate of 15 L/min. The outlet pressure was monitored using a pressure gauge. Ordinary tap water was employed, with an inlet filter installed to eliminate impurities and ensure proper pump operation. Neglecting pressure losses in the pipeline between the high-pressure pump and the nozzle, the flow meter was used to measure the water flow rate. The artificial submerged cavitation water jet nozzle comprises two concentric parts: an inner section for the high-pressure water jet and an outer section for the coaxial low-pressure water jet. The nozzle was securely mounted on a bracket to prevent any displacement during the experiment. The analysis of the erosion mechanism of the artificially submerged cavitation jet was carried out by using a Phantom camera to obtain the macroscopic surface morphology of the workpiece surface before and after the erosion experiments. The cavitation intensity of the artificial submerged cavitation jet was evaluated by measuring the mass loss in Aluminum 1060 (Chinese Industrial Standard). The chemical composition and physical properties of the workpiece are shown in Tables 2 and 3 [36].  
where high-pressure inlet \(P_{H}\) and low-pressure inlet \(P_{L}\) are set to 10 MPa and 0.02 MPa, respectively. The increase in the number of meshes mainly comes from the nozzle throat section. It is observed that with the increase in the number of meshes, there is very little change at two designated points in velocity and pressure. The velocity and pressure at point A (\(\alpha\)/d = 30, \(y\)/d = 0) and point B (\(x\)/d = 40, \(y\)/d = 0) tend to stabilize when the number of meshes exceeds 1584125. To achieve a balance between computational accuracy and time efficiency, the cavitation phenomenon of the artificially submerged cavitation water jet is investigated with 1584125 meshes.
## 3 Methods
### Classification
#### 3.3.2 Support Vector Machine  
We also employed SVMs, given that this type of classifier is very commonly used in PD detection studies. This technique implements a kernel-based transformation to a latent high-dimensional space in order to separate homogeneous data points into separate regions. Several studies report high classification accuracy employing SVM for PD detection [12; 27; 30; 36; 59]. In the experiments, we employed a linear kernel with a grid search for the hyperparameter 'C' (\(C=2^{x},x\in[-11:2:13]\)).
## 2 Experimental Setup
### Physical Model  
In avionics, the surface dimension of the standard electronic module (SEM) is 100 \(\times\) 100 mm. The dimension of PCM needs to match the size of the SEM and has a certain thickness. In order to observe the variation of solid liquid interface and temperatures during the melting process at different inclination angles, a thickness of 30 mm is selected in the experiment. Figure 1 shows the structure of the specimen container. The heating wall is made of aluminum alloy with 3 mm in thickness. A commercial apparatus Hotdisk is used to measure the thermal conductivity of aluminum alloy, which can measure the thermal conductivity ranged from 0.005 W/mK to 500 W/mK with an uncertainty of \(\pm\)3% over a wide temperature range. During the measuring process, the sensor of Hotdisk is sandwiched between two pieces of aluminum alloy with the dimension of \(\Phi\)70 \(\times\) 20 mm. After supplying a small constant current for a short time, the thermal conductivity is calculated by monitoring the temperature increase in the sensor. The measuring result is 153.39 W/mK, which is much larger than the thermal conductivity of paraffin. Other sides are made up of Plexiglass sheet with 10 mm in thickness to realize the visualization and photography of the solid liquid interface during the melting process of paraffin. In addition, the container is insulated by 30 mm thick heat insulation tiles with the thermal conductivity of 0.085 W/mK [22].  
In order to investigate the melting behavior of the paraffin in a rectangular enclosure at different angles from 0 to 180\({}^{\circ}\), the specimen is mounted on a rotatable frame together with the container and the heat insulation attachment. The physical model is shown in Figure 2. Except for heating wall, the other walls are considered adiabatic. Besides, the specimen can be rotated to any angle (\(\alpha\)) in the XY plane.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline Specific heat (J\(\cdot\)kg\({}^{-1}\cdot\)K\({}^{-1}\)) & \(2.10\times 10^{3}\) \\ Kinematic viscosity (m\({}^{2}\cdot\)s\({}^{-1}\)) & \(4.464\times 10^{-6}\) \\ Thermal expansion coefficient (K\({}^{-1}\)) & \(1.10\times 10^{-4}\) \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: The structure of the specimen container.
## 4 Results
### Susceptibility Assessment
#### 4.2.2 Susceptibility Calculation
These values of the weighted density map were then normalized from zero to one (Figure 4) to remove the per area output of the model, providing a relative likeliness of between 0 and 1, where 0 means that there is very little (but not no) confidence of finding significant amounts of gas hydrates at that location and 1 means that there is certainty of finding gas hydrates at that location. Figure 3: Theoretical up-dip limit of the GHSZ in the study area (modified from Niño-Varela, 2020). In the volcanic area of Macaronesia (the Canary and Madeira islands), the up-dip limit has been eliminated because of the absence of hydrocarbon reservoirs on the flanks of the islands. Regarding the weighted map of the theoretical GHSZ (Figure 3), the up-dip limit of the GHSZ in the vicinity of the low-latitude volcanic islands in the Atlantic Ocean (i.e., the Azores, Madeira and the Canary Islands) was not taken into account because of the absence of hydrocarbon reservoirs at these geological sites. Finally, this map was weighted in relation to the mean value of the normalized density map of evidence and indicators (mean = 0.00228; Figure 4). Thus, according to its likelihood of being affected by dissociation processes, the GHSZ on the seafloor was weighted using expert criteria. The strip of up-dip of the GHSZ was weighted with 0.00228 and the rest of the GHSZ with 0.00114 (half the likelihood). Seafloor areas outside the GHSZ were given a value of zero. The susceptibility assessment was performed by map algebra, taking into account the control maps of density of hydrate evidence and indicators and the weighted map of the GHSZ on the seafloor. Specifically, the final map (Figure 5) was conceived as a segmentation in three levels by quantiles resulting from the addition of the above control maps:  
\[Sc=\delta_{ei}+GHSZ_{w} \tag{1}\]  
where \(Sc\) is the susceptibility map; \(\delta_{ei}\) is the normalized weighted density map of hydrate evidence and indicators; and \(GHSZ_{w}\) is the weighted map of the GHSZ on the seafloor. The final \(Sc\) value was masked with the positive values of the GHSZ map. Seafloor areas outside the GHSZ have a susceptibility value of zero. Figure 4: Normalized (zero to one) and weighted density map of hydrate evidence and indicators. Lattice of hydrate evidence and indicators overlapped, red dots. Density map developed with the “kernel density” algorithm of ArcGIS®. Parameters: population field, weight (taken from Table 3); cell size, 5000; method, geodesic. Susceptibility values were segmented into three levels by quantiles: low from 0.009 to 0.0129, middle from 0.0129 to 0.0325 and high from 0.0325 to 1.009. The susceptibility assessment shows seven areas with high values: Svalbard, the northern Norwegian margin--Barents Sea, the continental slope of the mid-Norwegian margin and the North Sea, the Gulf of Cadiz, the eastern Mediterranean and the Black Sea. Moderate values are located on the west Greenland continental shelf, near the northwest British Islands and on the continental slope of the western and northern Mediterranean Sea.
# A Study on the Vertical Bearing Characteristics of Screw Piles in Permafrost Regions
## 3 Numerical Simulation Analysis
### Calculation Model and Model Parameters  
The soil around the pile is 10 times the pile's diameter, meaning it spreads 12 m in the horizontal direction, and the soil depth is 1.5 times the pile's length, meaning it continues 18 m in the vertical direction, which is indicated in Figure 7a. The soil layer consists of silty clay (0-6 m) and sub-clay containing peat (6-12 m). The water content of both of the two kinds of soil is approximately 40%. Previous studies [8, 9] reveal that the mechanical properties of the two types of soil are closely related to temperature: their cohesion is greatly affected by temperature change, and the internal friction angle of the soil is close to 0 \({}^{\circ}\)C under negative temperature conditions. The change in the soil cohesion with temperature is depicted in Figure 7b [11, 12]. The temperature field was assigned to the model by way of a pre-defined field, and the temperature field is indicated in Figure 7c. The pile's body is subjected to frost heave during loading in February in winter, which was simulated by applying prestress onto the surface of the active layer on the side of the pile. The Coulomb friction model was used on the contact surface of the melting active layer, and the friction coefficient was 0.28. The bond model was used to simulate the characteristics of the frozen soil's contact surface by defining the maximum damage strength and the influence curves of temperature on the shear characteristics of the contact surface, as depicted in Figure 8 [9].
## 3 Results
_Color saturation represents the strength of the correlation coefficients (darker hues indicate stronger correlations)._  
Similarly, ECCP was significantly associated with SJ (\(p\) = 0.001, R\({}^{2}\) = 0.425), CMJ (\(p\) = 0.001, R\({}^{2}\) = 0.495), COD (\(p\) = 0.001, R\({}^{2}\) = 0.443), COD0% (\(p\) = 0.001, R\({}^{2}\) = 0.446), 0-10 m sprint (\(p\) = 0.001, R\({}^{2}\) = 0.332), 0-20 m sprint (\(p\) = 0.005, R\({}^{2}\) = 0.237), 20-30 m sprint (\(p\) = 0.003, R\({}^{2}\) = 0.251), and 0-30 m sprint (\(p\) = 0.001, R\({}^{2}\) = 0.359). Its relationship with 10-20 m sprint, however, was not statistically significant (\(p\) = 0.140). Likewise, ECCPP demonstrated significant associations with SJ (\(p\) = 0.001, R\({}^{2}\) = 0.459), CMJ (\(p\) = 0.001, R\({}^{2}\) = 0.496), COD (\(p\) = 0.001, R\({}^{2}\) = 0.490), COD0% (\(p\) = 0.001, R\({}^{2}\) = 0.465), 0-10 m sprint (\(p\) = 0.001, R\({}^{2}\) = 0.353), 0-20 m (\(p\) = 0.001, R\({}^{2}\) = 0.323), 20-30 m (\(p\) = 0.001, R\({}^{2}\) = 0.253), and 0-30 m (\(p\) = 0.001, R\({}^{2}\) = 0.412). Its relationship with 10-20 m sprint did not reach statistical significance (\(p\) = 0.052). The ECCPF/BM variable showed significant associations with SJ (\(p\) = 0.039, R\({}^{2}\) = 0.108), CMJ (\(p\) = 0.035, R\({}^{2}\) = 0.111), and COD (\(p\) = 0.041, R\({}^{2}\) = 0.108), but not with COD0%, nor with any sprint intervals (\(p\) > 0.05). The PP variable demonstrated significant relationships with all performance outcomes: SJ (\(p\) = 0.001, R\({}^{2}\) = 0.581), CMJ (\(p\) = 0.001, R\({}^{2}\) = 0.599), COD (\(p\) = 0.001, R\({}^{2}\) = 0.507), COD0% (\(p\) = 0.001, R\({}^{2}\) = 0.396), 0-10 m (\(p\) = 0.001, R\({}^{2}\) = 0.253), 10-20 m (\(p\) = 0.001, R\({}^{2}\) = 0.363), 0-20 m (\(p\) = 0.001, R\({}^{2}\) = 0.344), 20-30 m (\(p\) = 0.001, R\({}^{2}\) = 0.338), and 0-30 m (\(p\) = 0.001, R\({}^{2}\) = 0.463). In contrast, the stiffness variable showed a significant association only with 0-10 m sprint performance (\(p\) = 0.023, R\({}^{2}\) = 0.123), RSI was significantly associated only with COD0% (\(p\) = 0.019, R\({}^{2}\) = 0.076), and CONPF/BM showed a significant relationship solely with 10-20 m sprint (\(p\) = 0.015, R\({}^{2}\) = 0.158). No statistically significant relationships were observed between ECCPF/BM or PSAP and any of the performance variables (\(p\) > 0.05). Table 3 presents the results of the multiple regression analysis for male athletes. No statistically significant associations were found between the eccentric utilization ratio (EUR) and any of the performance variables (\(p\) > 0.05). In contrast, the Reactive Strength Index (RSI) demonstrated significant relationships with SJ (\(p\) = 0.012, R\({}^{2}\) = 0.170), CMJ (\(p\) = 0.013, R\({}^{2}\) = 0.295), 10-20 m sprint (\(p\) = 0.021, R\({}^{2}\) = 0.145), and 0-30 m sprint (\(p\) = 0.047, R\({}^{2}\) = 0.163).
# Magnetic Field-Based Vehicle Positioning System in Long Tunnel Environment
## 3 Proposed System Explanation
### Generation of Magnetic Field DB  
The objective of the proposed system is to determine the RP with the highest correlation by comparing the user buffer with the DB, which necessitates construction of the magnetic field DB. The interval of the DB RP was set to 5 m, considering the resolution of the magnetic field. Following the application of the smoothing filter to reduce the magnetic sensor noise, the interpolation was performed using the spline technique [23] to generate RP at intervals of 5 m. There were a total of 2341 RPs in the test bed. Although the exact vehicle position could be estimated for narrow spacing between RPs, the computational load of the calculation increases. In contrast, the resolution of the magnetic field DB decreases and the position error may increase for wide spacing between RPs.  
Figure 7: Indoor magnetic field values of two different smartphones. Magnetic sensor values were corrected through frame transformation and calibration.