esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Appendix
### Data Processing  
To maintain the concordance between MODIS and OCO-2 data, we selected a 16-day temporal resolution at a spatial resolution of 1 km. FAPAR, LAI, and GPP data were resampled to 1 km and compiled to 16 days using the maximum value composition (MVC) technique [44]. As mentioned above, the re-visit cycle of OCO-2 is 16 days, and point data were obtained during its clear-sky overpass. SIF data were also compiled in 16 days and grouped according to MODIS products. To avoid spatial autocorrelation, 200 SIF points were randomly selected every day for 16 days to calculate their relationship with vegetation. Squares with 1 km length that cover the study region were built, and then the mean value of SIF and the vegetation index values of NDVI, EVI, LAI, FAPAR, and GPP within each square were extracted.
# Extended Geometry and Probability Model for GNSS+ Constellation Performance Evaluation
## 4 Experimental Results and Analyses
### Bds-3+Leo+Heo Constellation  
The augmentation of GNSS with LEO satellites is one of the most interesting trends in the future. We consider the addition of 288 LEO satellites in polar orbits which is mentioned in [PERSON] et al. (2019) to form the BDS-3+LEO+HEO constellation. The primary constellation parameters are listed in Table 8. These LEO orbits are distributed in 12 planes and orbit altitudes of 1000 km as shown in Figure 10 in the 3D graphic view. Using the EGAPM, the performance of the BDS-3+LEO+HEO constellation is analyzed. The results are plotted in Figure 11.  
As can be seen in Figure 11, after introducing the LEO satellites, the DOPs have decreased significantly, which means that the geometric distribution has been improved. Since all LEO satellites are in polar orbits, a lot more satellites can be observed in high latitude regions than in the middle and low latitude areas (Figure 11a). Although hundreds of LEO satellites in polar orbits are introduced, the correlation between station height and receiver clock difference is still higher (Figure 11h). As for the positioning precision ratio between the longitude and latitude, illustrated in Figure 11g, Figure 9, and Figure 8g. We can find that LEO satellites mainly contribute to longitude component performance. Table 9 reveals the average, minimal, and maximal improving rates of visible satellites and DOPs on a global scale. More satellites, with improving rate of 198% and 184%, respectively, for BDS-3 and GPS, can be observed. Besides, DOP values are about 40% lower than BDS-3 and GPS, which may improve the standard point positioning (SPP) accuracy and accelerate the precise point positioning (PPP) convergence speed.  
Figure 11: Estimated DOPs, positioning precision in longitude versus in latitude, and the correlation between station height and receiver clock difference of the BDS-3+LEO+HEO constellation with 5\({}^{\circ}\) CEA. (**a**) Number of visible satellites; (**b**) GDOP; (**c**) HDOP; (**d**) PDOP; (**e**) TDOP; (**f**) VDOP; (**g**) The ratio of positioning precision between the longitude component and the latitude component; (**h**) The correlation level between station height and receiver clock difference.
# A Multidimensional Analysis of Released COVID-19 Location-Based Mobile Applications
## 4 The Framework of Mapping and Evaluating COVID-19 LBMAs
### Technology Aspects of COVID-19 LBMAs  
#### 4.2.1 Location Tracking Technologies  
The importance of location has been highlighted by previous researchers that refer to the location as \"actionable information\" that can be used to improve human health [16]. They are utilized from LBMAs to estimate the exact location of the users. Cellular Network is a commonly utilized signal to track the exact location of mobile users. Network carriers can track the location of the phone user even if it is not a smartphone. Cellular data cannot be shared with anyone outside of the carrier company or the mobile application provider unless it is a case of an emergency where the authorities are asking for additional information. It utilizes the carrier signal strength in order to estimate the spatial location of the smart device user [26]. The main method for location estimation through Cellular Network is triangulation, where the different distances between the smartphone and the cellular towers consist of a triangle where the user is identified in terms of location. The accuracy of this method is from hundreds of methods up to kilometers. This accuracy is achieved by calculating the distance of the antennas of each mobile phone company with the user's mobile phone.  
The GPS of smartphones offers location services on the majority of the LBMAs by utilizing the satellite signal to estimate the position of the user on the earth [16]. The GPS antenna needs four satellites to provide geospatial information to the user with an approximate accuracy of 4.9 m [27].  
Location tracking technologies are centralized technologies that collect users' information for governmental and research purposes. Specifically, when the first pandemic wave arrived, the U.S. Government inspected the Cellular Network data to watch the places of high human traffic that consisted of potential spreading regions of COVID-19, and further measures were taken such as restrictions and shutdowns [28].
## 4 Case Study for Comparison  
In the previous section, the details of the proposed method were explained. This section presents the validation of the method in terms of computational cost and prediction accuracy. A crack growth problem, based on the Paris model [6], was used to generate degradation data, and an ANN [13] was employed as a typical data-driven prognostic method for comparison.  
The simulations presented in this study were performed on a desktop equipped with an AMD Ryzen 9 5900X Processor (3.7 GHz, 12 cores) and 32 GB of RAM. The operating system was Windows 10 x64, and the simulations were executed using MATLAB R2018a.
## A Wireless Sensor Network-Based Portable Vehicle Detector Evaluation System  
**[PERSON]**  
School of Computer and Communication Engineering, Daegu University, Jilyang-up, Gyeongsan, Gyeongbuk 712-714, Korea; E-Mail: [EMAIL_ADDRESS]; Tel.: +82-53-850-6644;  
Fax: +82-53-850-6629  
_Received: 16 August 2012; in revised form: 1 December 2012 / Accepted: 31 December 2012 / Published: 17 January 2013_  
**Abstract:** In an upcoming smart transportation environment, performance evaluations of existing Vehicle Detection Systems are crucial to maintain their accuracy. The existing evaluation method for Vehicle Detection Systems is based on a wired Vehicle Detection System reference and a video recorder, which must be operated and analyzed by capable traffic experts. However, this conventional evaluation system has many disadvantages. It is inconvenient to deploy, the evaluation takes a long time, and it lacks scalability and objectivity. To improve the evaluation procedure, this paper proposes a Portable Vehicle Detector Evaluation System based on wireless sensor networks. We describe both the architecture and design of a Vehicle Detector Evaluation System and the implementation results, focusing on the wireless sensor networks and methods for traffic information measurement. With the help of wireless sensor networks and automated analysis, our Vehicle Detector Evaluation System can evaluate a Vehicle Detection System conveniently and objectively. The extensive evaluations of our Vehicle Detector Evaluation System show that it can measure the traffic information such as volume counts and speed with over 98% accuracy.  
**Keywords:** traffic monitoring; reference instrument; vehicle detection system; wireless sensor networks; VDS; WSN
# Real-Time Moving Ship Detection from Low-Resolution Large-Scale Remote Sensing Image Sequence
## 4 Conclusions  
The deep convolutional network can effectively complete object detection, semantic segmentation, and completion generation of remote sensing images, but it is also faced with the problems that large-width data cannot be processed in real-time, and low-resolution data is difficult to identify accurately. Especially in the scene with many broken background clouds, the low-resolution data can only rely on the outline and target gray level distribution to distinguish the target. The traditional CNN algorithm often gets many false alarms suspected of being ship targets. To solve these problems, this paper proposes a ship-moving target detection method for low-resolution and large-width remote sensing image sequences, which improves the detection accuracy and real-time computation by combining the multi-level cascade preprocessing method traditional feature detection, sequence association, and CNN features discrimination. In the four-level identification cascade method, false alarms with large target segmentation areas are proposed in the first level, false alarms with large differences between training targets and scene features are eliminated in the second level, non-moving false alarm targets are eliminated by multi-frame cascade search in the third level, and false alarms are further eliminated by fine feature discrimination among the remaining suspected targets in the fourth level. Compared with the traditional Yolo Tiny V2/V3 series network, the proposed method has faster calculation speed, and better detection performance, and the F1 number of detection results increased by 3%. The computation time was reduced by 66%.  
Figure 10: The ROC curves of proposed using different SNRs.  
**Author Contributions:** Validation, W.L.; Resources, X.S.; Writing--original draft, J.Y.; Visualization, X.W.; Project administration, D.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** This study aims to improve and optimize the ship targets detection with low-resolution remote sensing images and does not involve human or animal studies. Therefore, ethical review and approval was waived for this study.  
**Informed Consent Statement:** This study is not applicable. This study did not involve human studies.  
**Data Availability Statement:** This research is mainly aimed to improve and optimize the ship targets detection with low-resolution remote sensing images. Experimental data and results are mainly obtained by public data like Google Earth.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 The Proposed Approach
### Proposed Approach 2  
The second approach evaluates the signals from the six accelerometers using data fusion at the indicator level. Figure 3b shows the workflow of this approach. It has five stages: (1) Data acquisition, (2) Feature extraction, (3) Data-fusion, (4) Feature selection, and (5) Classification. Each of the stages is explained below.  
1. Data acquisition: the six vibration signals, three accelerometers mounted on the left side LV, LL, LA, and three accelerometers placed on the right side RV, RL, RA, were acquired under different faults, load and velocity conditions.
2. Feature extraction: for each signal, different condition indicators were obtained according to two domains: (i) 30 CIs in time domain and (ii) 24 CIs in the frequency domain. All those condition indicators are combined, so, for each vibration signal, 54 CIs are obtained. Therefore, six data sets were used, three different sets of CIs per each accelerometer.
3. Data-Fusion: from the six data sets of CIs obtained in the feature extraction stage, data fusion is performed at the indicator level. Three new sets of indicators are obtained: (i) The sets of CIs extracted from signals of the three accelerometers on the left side (LV, LA, LL) are fused, obtaining the first set of fused CIs called CI_DFL; (ii) the CIs of the three accelerometers on the right side (RV, RA, RL) are fused, obtaining a fused data set called CI_DFR; and finally (iii) the CIs of the six accelerometers are fused, obtaining the third set of fused CIs called CI_DFRL.
4. Feature selection and classification: from the three data sets fused in stage 3, the next stages 4 (feature selection) and 5 (classification) were carried out by following the same procedures, experimental conditions, and CIs analysis of the stages Feature Selection and Classification of the approach 1 detailed in Section 4.1 and presented in Figure 3a.
# Cardiac Radiofrequency Ablation Simulation Using a 3D-Printed Bi-Atrial Thermochromic Model
## 2 Materials and Methods
### Thermochromic Paint Formulation and RFA-Sensitivity  
Thermochromic pigments display a color-changing effect, which is induced by heating or cooling [11; 12]. In terms of whether the original color can be recovered, thermochromic materials can be classified as reversible or irreversible. Reversible pigments are usually composed of a color developer, a color former, and a co-solvent. On heating, the co-solvent changes from solid to liquid and allows the former to separate from the developer, leading to the color change. On cooling, the process is reversed (Figure 2a). Irreversible pigments have a volatile dye that evaporates when heated to the phase transition temperature, resulting in a permanent colorless state (Figure 2b).  
Since we want the color change to be permanent and occur at typical temperatures required for RFA lesion formation, we selected an irreversible thermochromic pigment with a transition temperature of 60 \({}^{\circ}\)C. This pigment was black below the transition temperature and colorless above this temperature (Special FX Creative, Newhaven, UK, [[https://www.sfxc.co.uk/products/sfxc-irreversible-thermochromic-pigment-60-c-black](https://www.sfxc.co.uk/products/sfxc-irreversible-thermochromic-pigment-60-c-black)]([https://www.sfxc.co.uk/products/sfxc-irreversible-thermochromic-pigment-60-c-black](https://www.sfxc.co.uk/products/sfxc-irreversible-thermochromic-pigment-60-c-black)), accessed on 10 November 2021). The pigment was mixed with (white) unpigmented acrylic paint (UAP) (10:1 UAP:pigment mass ratio) to form a grey thermochromic paint that could be applied to the 3D-printed Layfomm-40.  
Several discs (radius 20 mm and thickness 5 mm) of Layfomm-40 were printed and painted with the thermochromic paint. These were soaked in 0.9 w/w saline solution and then tested for RFA-sensitivity. The discs were immersed in a saline bath (~38 \({}^{\circ}\)C) and radiofrequency was delivered through a Stockert 70 Cardiac Ablation RF Generator (Biosense Webster, USA) and a non-irrigated ablation catheter. The catheter tip was kept orthogonal to the disc plane. Ablations of 60 s duration were applied while varying the power (30-90 W) and temperature (50-80 \({}^{\circ}\)C) settings. Only one ablation was applied for each combination of power and temperature. Ablations were also applied to a piece of chicken breast for comparison. The diameter of the lesions was measured for each setting with a digital caliper. Once parameters for consistent size lesion creation were identified, the experiment was repeated using a 50 \(\times\) 50 \(\times\) 2 mm\({}^{3}\) sample of thermochromic-paint-coated Layfomm-40 which was divided into nine subsections. Nine ablations were applied keeping the power and temperature settings between 70-80 W and 70-80 \({}^{\circ}\)C, respectively. The lesion sizes were measured, and the average size computed for these power/temperature settings.  
Figure 2: Mechanisms of action of thermochromic microcapsules during heating and cooling. (**a**) Reversible and (**b**) irreversible.
# Factors Affecting the Stability of Loess Landslides: A Review
## 2 Relevant Measured Parameters
### Shear Strength  
The determination of slip surface shear strength parameters is key to landslide stability assessment and engineering design. Previous limitations in accounting for the randomness and uncertainty associated with geotechnical parameters are overcome using inverse analysis to determine slip surface shear strength parameters [91]. The angle of inclination of the fracture surface determines the strength and deformation characteristics of a loess. A larger angle of inclination results in higher tensile stress and tensile strength [38]. Under seismic loading, super-porous pressure accumulates and plastic deformation develops rapidly in the shear zone. This leads to liquefaction of the loess and a reduction in shear strength [92]. Rapid slope failure may not result from shear expansion and consolidation caused by intermittent or prolonged rainfall. Loess landslides may suffer from shear dilatation during acceleration of slope movement, leading to a reduction in pore water pressure and landslide deceleration [73]. The accuracy of prediction and monitoring in moisture-induced landslide environments may be improved by combining groundwater mapping with modulus distributions [93]. Strain-softening behavior observed in direct shear tests of these media has been linked to sudden collapse events in loess landslides [94]. Figure 6 shows the relationship between shear strength parameters and water content for different slip zone soils. Mechanical parameters such as cohesion, angle of internal friction, and deformation have been shown to vary in a quasi-linear fashion with water content [95]. Horizontal tensile stresses within landslides predominantly occur in the upper and middle sections, while vertical tensile stresses are concentrated near the sliding surface. Intense rainfall conditions lead to the expansion of the plastic shear strain zone along the entire sliding surface, triggering landslides [40].  
Figure 5: Schematic diagram of stress and instability failure of the slope under seismic ground motion in Lanzhou New District, China. (**a**) The infiltration zone before vibration; (**b**) the tensile (compressive) stress from adjacent soil elements on the left and right sides of the soil elements in the slope as earthquake wave transmitted; (**c**) the soil microstructure is gradually damaged and leads to residual deformation under the continuous tensile and shear action of the dynamic stress with the increase in the ground motion; (**d**) the sliding displacement decreases gradually from the surface to inside. Reprinted/adapted with permission from Ref. [90]. 2020, Springer Nature.
# Instability Study of Magnetic Journal Bearing under S-CO\({}_{2}\) Condition
## 2 Magnetic Bearing Analysis for Instability Study
### Magnetic Bearing Lubrication Model Analysis
\[\frac{\partial}{\partial X}\left(\frac{\partial h^{3}}{k_{x}\mu}\frac{\partial p }{\partial X}\right)=\frac{\partial(\rho h)}{\partial t}+\left(\frac{hu}{2} \right)\frac{\partial\rho}{\partial X}+\left(\frac{u}{2}\frac{\partial h}{ \partial X}\right)\rho, \tag{13}\]  
Each term on the RHS was spatially analyzed to be compared, as shown in Figure 9. The change in \(\left(\frac{hu}{2}\right)\frac{\partial\rho}{\partial X}\) value with angular position is described in Figure 9a, and the change in \(\left(\frac{u}{2}\frac{\partial h}{\partial X}\right)\rho\) value in Figure 9b. In Figure 9a,b, it is presented that the difference between case 1 and case 2 is larger in \(\left(\frac{hu}{2}\right)\frac{\partial\rho}{\partial X}\) than \(\left(\frac{u}{2}\frac{\partial h}{\partial X}\right)\rho\). This is because \(\left(\frac{u}{2}\frac{\partial h}{\partial X}\right)\rho\) is less affected by \(\frac{\partial\rho}{\partial X}\) when \(\rho\) itself is large. In conclusion, the pressure distribution is dominated by the density's spatial change under high-density conditions. The value of \(\frac{\partial\rho(\rho h)}{\partial t}\) is shown in Figure 9c as well, and it did not show any difference between air and CO\({}_{2}\). The left-hand side (LHS) around the shaft is shown in Figure 9d. The forces are summarized in Table 1. From Figure 9b,c, the influences of density and its temporal change in S-CO\({}_{2}\) conditions and air are very close to each other, showing that these terms are not the source of difference between air and CO\({}_{2}\). However, the spatial change of density in Figure 9a shows substantial differences, which causes the phase and magnitude differences in the forces. The combined results are shown in Figure 9d. From Table 1, it can be concluded that \(F_{r}\) grows due to the density change. This can explain the tendency in Figure 7a. Furthermore, because these forces are highly sensitive to the fluid properties, additional analysis regarding the fluid properties is required to increase the accuracy of the lubrication model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Thermal Condition** & \(F_{r}\) **(N)** & \(F_{t}\) **(N)** \\ \hline Air at 8 MPa, \(-153.7\)\({}^{\circ}\)C & 35.3 & \(-1433.6\) \\ \hline CO\({}_{2}\) at 8 MPa, \(30\)\({}^{\circ}\)C & 265.2 & \(-1344.7\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Forces acting on the shaft with various conditions (\(\varepsilon=0.25\) and 30,000 rpm)
## 3 System Model
### Mobile Devices
#### 3.2.3 Transmission  
The transmission queue is also a first-in-first-out queue. Each task must wait until all earlier tasks are transmitted before it can be transmitted. In our simulation, the data rate for a connection is approximated using the Shannon-Hartley theorem (5).  
\[r=B.\log_{2}(1+P/N) \tag{5}\]  
where \(B\) is the bandwidth of the channel, \(P\) is the received power of a transmission in watts, and \(N\) is the received noise power in watts. The received power is approximated using Friis transmission equation  
\[P=P_{t}.G_{t}.G_{r}.(\omega/(4\pi.D))^{2} \tag{6}\]  
where \(P_{t}\) is the transmitted power of the transmitter in watts, \(G_{t}\) and \(G_{r}\) are the transmitter and receiver gains in dBi, \(\omega\) is the wavelength of the channel in meters, and D is the distance between the transmitter and receiver in meters. The wavelength of the channel \(\omega\) can be calculated using the frequency of the channel \(f\).  
\[\omega=c/f \tag{7}\]  
where \(c\) is the speed of light. To calculate the received noise power, we approximate the received noise with a Gaussian white noise \(N_{g}\) in dBi; then, we convert it into watts.  
\[N=10^{N_{g}/10} \tag{8}\]  
To calculate the total wait time for a task \(t_{i}\) from the moment it arrives at the mobile device \(m_{j}\) until it is executed at the edge node \(e_{k}\), provided that the task is sent from a mobile device to this edge device, we have  
\[d_{r,o}(m_{j},e_{k},t_{i})=d_{l,st}(m_{j},t_{i})+d_{r,el}(e_{k},t_{i}) \tag{17}\]
## 4 Experimental Result and Evaluation
### Evaluation
#### 4.3.3 Result Discussions  
The experimental results evaluated in the operating environment with static/moving obstacles which are randomly running in simulations. The experiments of the proposed model are done with the numbers of nodes (cells) in the range of (80-550).  
The average results derived from the experiments of the proposed model with the number of nodes in the range of 80 to 200 reaches a 90-100% coverage path rate with static obstacles. When the number of nodes increased from 200 to 550, the robot coverage path rate was about 90-98%. The rules considered in the knowledge base can be influenced with a coverage rate about 100%, however, the repetition rate can be increased. To address the problems, the weights of these rules should be considered to be matching the rules of a robot with its various situations in regular, irregular, multiple regular, and multiple irregular obstacles while running the simulations. In further testing results, a repetition rate of the proposed model in the experiment is about 2% with regular, 4% with irregular, multiple regular, and 7% with multiple irregular obstacles. Knowledge of experts has a significant role of enhancing the rule quality, which updates the knowledge base. It can reduce the repetition rate, while experts update the matched rules in the knowledge base. It is possible to optimize the cover path, repetition rate and running time, mostly based on the rules considered. We can create an objective function to adjust suitable parameters influenced by the robot cover path, repetition rate and running time.