esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Development of Propulsion Power Prediction Model
### Data Preprocessing  
Before applying SVR, several preprocessing tasks were performed. In this step, data records that may lower the prediction performance were omitted from the dataset. Firstly, the observations with zero speed were omitted because it means that the vessel does not use propulsion power at all. Moreover, the observations whose vessel speed is less than 6 knots were also omitted because this indicates that the vessel is near a harbor and is likely to undergo frequent course changes. Such a situation may not be adequate for the propulsion power analysis.  
Also, some outlier data that has abnormal shaft power value (output feature) is omitted from the dataset. To systematically determine the outlier, [PERSON]'s criterion, is utilized [29]. Given \(N\) samples of a dataset, an observation is considered as an outlier if \(NP(>\mid\mid z\mid)<0.5\) where \(P(>\mid\mid\mid)\) is the cumulative probability of the observation is being more than \(z\) standard deviation of the mean. An example of outlier detection is shown in Figure 3. As shown in this figure, the outlier that shows a large deviation from the normal shaft power value is omitted from the dataset.  
Finally, feature scaling is applied to the dataset. Feature scaling is important for SVR, since it tries to maximize the distance between the separating plane and the support vectors. If one feature has larger magnitudes than others, it will dominate the other features when calculating the distance. In this study, we normalize each feature between the \(-1\) to \(+1\) intervals. All the preprocessing procedure was conducted using Pandas module with a python 3.6 environment.
# Static Analysis of Composite Plates with Periodic Curvatures in Material Using Navier Method
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{\(\sigma_{11}\)/\(\sigma_{33}\)\(\left(10^{-2}\) MPa\(\right)\)} \\ \hline \(\varepsilon\) & \(l_{1}\)/\(l_{3}\) & **0.1** & **0.5** & **1.0** & **2.0** \\ \hline \multirow{3}{*}{0.000} & **80.7320** & **72.4250** & **37.9440** & **7.5451** \\ \cline{2-5}  & 22.4900 & 30.1200 & 37.9310 & 18.1080 \\ \hline \multirow{3}{*}{0.001} & **80.8110** & **72.4810** & **37.9510** & **7.5438** \\ \cline{2-5}  & 22.5120 & 30.1520 & 37.9500 & 18.1090 \\ \hline \multirow{3}{*}{0.002} & **81.0530** & **72.6100** & **37.9720** & **7.5399** \\ \cline{2-5}  & 22.5800 & 30.2500 & 38.0040 & 18.1130 \\ \hline \multirow{3}{*}{0.003} & **81.4640** & **72.9400** & **38.0080** & **7.53330** \\ \cline{2-5}  & 22.6950 & 30.4160 & 38.0970 & 18.1200 \\ \hline \multirow{3}{*}{0.004} & **82.0570** & **73.3550** & **38.0610** & **7.5242** \\ \cline{2-5}  & 22.8610 & 30.6550 & 38.2290 & 18.1290 \\ \hline \multirow{3}{*}{0.005} & **82.8510** & **73.9090** & **38.1300** & **7.51240** \\ \cline{2-5}  & 23.0820 & 30.9720 & 38.4020 & 18.1420 \\ \hline \multirow{3}{*}{0.006} & **83.8720** & **74.6170** & **38.2200** & **7.4980** \\ \cline{2-5}  & 23.3670 & 31.3770 & 38.6200 & 18.1580 \\ \hline \multirow{3}{*}{0.007} & **85.1570** & **75.5020** & **38.3310** & **7.48100** \\ \cline{2-5}  & 23.7240 & 31.8820 & 38.8860 & 18.1760 \\ \hline \multirow{3}{*}{0.008} & **86.7530** & **76.5900** & **38.4670** & **7.46140** \\ \cline{2-5}  & 24.1680 & 32.5020 & 39.2060 & 18.1990 \\ \hline \multirow{3}{*}{0.009} & **88.7250** & **77.9200** & **38.6320** & **7.43910** \\ \cline{2-5}  & 24.7150 & 33.2570 & 39.5840 & 18.2240 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: \(\sigma_{11}\) and \(\sigma_{33}\) stress values for a laminated composite plate with periodic curvature. (\(E_{2}/E_{1}=10\); \(\varepsilon\), \(l_{1}/l_{3}\): parametric).
## 4 Results and Discussion
### Flow Regime Identification
#### 4.1.2 Fast Fourier Transformation  
Fourier transformation converts the pressure signal from the time domain to the frequency domain. It is very useful in determining the dominant frequency of the signals. Figure 9 shows pressure signals in the frequency spectrum for different flow patterns. It is obvious that each flow pattern possesses its own dominant frequency range and has at least one identifiable dominant frequency where its amplitude is maximum (i.e., 1\(\times\)). In the case of stratified flow, the dominant frequency is 0.33 Hz with its corresponding amplitude of 70-97 Pa. Figure 9b shows that dominant frequency for slug flow is 1.0 Hz with corresponding amplitudes ranging from 100 to 170 Pa. The smaller peaks seen are harmonics of the dominant frequency, which is the main reason they are located at multiples of the dominant frequency. Annular flow shows a dominant frequency range of 13-17 Hz, with a corresponding amplitude of 2.0-3.5 Pa. In Figure 10, the magnitude of FFT is  
Figure 7: Void fraction of different flow regimes. (**a**) Stratified flow (\(U_{SL}\) = 0.08 m/s, \(U_{SG}\) = 0.2 m/s). (**b**) Slug flow (\(U_{SL}\) = 0.9 m/s, \(U_{SG}\) = 2.44 m/s). (**c**) Annular flow (\(U_{SL}\) = 0.5 m/s, \(U_{SG}\) = 20 m/s). (**d**) Cross section of pipe.  
normalized for all the recorded data for all flow regimes such that the frequencies of all flow regimes can be plotted on the same map. It is apparently evident that each flow regime forms a cluster of their own individual dominant frequency and occupies different frequency ranges. This frequency range depends on the characteristics of each flow regime and the involved distribution of phases. Table 4 summarizes the dominant frequency of different flow regimes, which can be used for flow regime identification.  
Figure 8: Pressure fluctuating signals of different flow regimes: (**a**) stratified, (**b**) slug, and (**c**) annular.  
Figure 9: Fast Fourier Transform of the dynamic pressure signals of different flow regimes: (**a**) stratified, (**b**) slug, and (**c**) annular.
## 1 Introduction
### Motivation
Moreover, the algorithm can identify classes at one-year intervals on the satellite images using repeated time series. Third, the workflow has a high level of automation and data processing speed due to its scripting approach, which supports machine-based data processing.
# Landslide Susceptibility Modeling Using a Deep Random Neural Network
## 3 Study Area and Data Used  
### Study Area  
The Lushui area belongs to Yunnan Province in Southwest China. It lies between 98\({}^{\circ}\)34\({}^{\prime}\) and 99\({}^{\circ}\)09\({}^{\prime}\) east longitude and 25\({}^{\circ}\)33\({}^{\prime}\) to 26\({}^{\circ}\)32\({}^{\prime}\) north latitude (Figure 2). The area has an elevation ranging from 740 m to 4160 m. The whole region is crossed by the Nujiang River Grand Canyon. Due to the special terrain, nearly half of the area has a slope higherthan 30\({}^{\circ}\). The rainy season generally starts in May and ends in October. The rainfall during this stage occupies approximately 70% of the total annual rainfall.  
The geology units of Lushui county vary from Precambrian to Quaternary. The Lushui area is widely occupied by metamorphic, igneous, and sedimentary rocks and unconsolidated sediments (Figure 3). The oldest geology units majorly consist of the Proterozoic Gaoligongshan (Ptgl) and Chongshan (Ptch) groups, and the youngest geological unit is Quaternary. According to previous studies [27], the geological unit of the area is classified into seven engineering rock groups (ERGs).  
Figure 3: Geological map of the study area.  
Figure 2: Position of the study area.  
Under the combined influence of geological, topographical, and hydrologic factors, the stress acting on the slope easily exceeds the limitation. In the study area, the slope-forming materials mainly consist of alluvial and pluvial deposits, which are widely distributed along the Nujiang River. Complex environments make the Lushui area prone to landslides. It is an urgent task to perform accurate and reliable LSM for the study area.
# A New Methodology for Estimating Surface Albedo in Heterogeneous Areas from Satellite Imagery
## 1 Introduction
In summary, this paper is structured as follows: after this Introduction section, Section 2 explores the fundamental principles of the albedo concept, and Section 3 describes the use of Landsat 8, ABOVE, and SURFRAD data. Section 4 outlines the methodology proposed, while Section 5 presents the experimental results obtained after its application. Finally, Section 6 summarizes the key findings and future directions.
## 4 Discussion
### Limitations  
Despite the promising results obtained in this study, several limitations need to be acknowledged. First, a relatively limited number of measurement points were used to compare PEM and BBM measurements, which may affect the generalizability of the results to other urban areas or more complex environments. Future research should consider increasing the number of measurement points and conducting studies in diverse locations to validate the applicability of the proposed correction factors.  
Second, the body shielding effect on the PEM remains a challenging factor to fully model. While differentiated corrections for LOS and NLOS conditions were applied, other environmental factors, such as the presence of architectural structures or unidentified interference sources, may also affect the measurements.  
Limitations concerning the geographical interpolation of the values themselves must also be considered. The kriging technique is characterized by obtaining an interpolation surface that does not contain the measured values; rather, it searches for trends in them. For this reason, the correction of the sets of points does not guarantee identical results in the realization of the maps.  
Some of the weaknesses in the set of measurements used should also be highlighted; these may lead to problems in the generation of interpolation maps. Considering the location of the measurement points, the southern and eastern areas of the municipality do not have measurement points on the perimeter, which can lead to errors at the extremes of the interpolation surface; this will consequently lead to incorrect fitness adjustment values. Similarly, in the base stations located further east of the municipality, a greater number of measurements should have been made.  
In addition to the above issues, we must also consider a possible difference between the measurements made with both devices. In the proportional correction of both measurement subsets, it is implicitly assumed that the sensitivity and linearity of both instruments over their entire measurement range are identical. Although this assumption can be taken as true for properly calibrated equipment, non-linear solutions that optimize the kriging results could be tested.  
Finally, although the use of the genetic algorithm improved the accuracy of the exposure maps, this technique is computationally intensive and may not be easily applicable in all cases, especially in studies requiring real-time results. The presented methodology could benefit from the incorporation of faster optimization techniques or the integration of predictive models to reduce the need for extensive corrections.
# Advancing the Solar Radiation Pressure Model for BeiDou-3 IGSO Satellites
## 3 Results
### Performance of SRP Models for BeiDou-3 IGSO Satellites
#### 3.2.2 Orbit Assessment Based on Orbit Boundary Discontinuities
Similar to the clock estimates presented in the previous section, the degraded orbits during eclipse seasons can be improved by using the ECOM1D + BW model. Figure 12 plots the BeiDou-3 IGSO orbit 3D DBDs for the ECOM1 + BW and ECOM1D + BW solutions as a function of \(\beta\) -angle. It can be clearly seen that the orbit 3D DBDs of eclipsing C38 and C39, and C40 inside its third eclipse season (27 December 2020-17 January 2021) increase significantly with the decrease of \(|\beta|\). Once the ECOM1D + BW model is employed, the elevated orbit errors are basically removed. The mean 3D orbit DBDs of eclipsing C38, C39 and C40 for the ECOM1D + BW solution are given in Table 6. Compared with the ECOM1 + BW solution, the 3D orbit DBDs of satellite C40 inside its third eclipse season, and eclipsing C38 and C39 decreased by 54.7 cm (79.8%) on average, while those of C40 inside its first and second eclipse seasons are reduced by 3.6 cm (21.6%). The orbit consistency inside eclipse seasons can be comparable to that outside eclipse seasons. Figure 11: Orbit DBD time series (\(|\Delta R|,|\Delta A|,|\Delta C|\)) s for the ECOM1, ECOM2 and ECOM1 + BW solutions of BeiDou-3 IGSO C39 in along-track (A), cross-track (C) and radial directions.
## 3 SDMob Architecture
### Basic SDMob Architecture
#### 3.1.3 Downward Routing Process  
Standard RPL favors upstream traffic as it is the predominant traffic pattern in the IoT domain. Most enhancements made to RPL also have weaker behavior when it comes to point-to-point or downward traffic. These shortcomings stem from design choices in RPL. If there is a topological change deep in the network, the root node will only be notified after a timer for sending the DAO packet expires. SDMob relies on the downward routes provided by RPL to relay the controller's commands. It can also reinforce the downward routes by the RSSI measurements that it collects.
# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 6 Network Solutions at Data-Center Back-Ends to Meet Performance Requirements of 5G
### Using GPUs in Cloud Systems  
There is an increasing need for using GPUs in the cloud as, e.g., deep learning and AI applications can gain performance using them. An initial and simple solution is to utilize PCI-passthrough capabilities of the host computer as it is shown in [69]; however, this solution does not let the GPU  
Figure 6: Kernel networking vs. Kernel bypassing.  
resources to be shared among several virtualized environments, on the other hand, the authors only showed the implementation and performance results coupled with the Xen hypervisor.  
To solve this issue, the authors of [70] introduce a solution, rCUDA, in which case the video hardware can be shared among several virtualized environments by using a client-server based solution, making available utilizing the resources of remote GPUs supporting the use of hardware, located in compute nodes that are running virtualized environments leaving the GPU resources idle. On the other hand, more GPU resources can be assigned to an application than in the case of using only local GPUs. Such a system is also capable of rescheduling the video hardware resources, in case of a blocking CPU phase, to other remote applications leading to higher GPU utilization. The rCUDA architecture can be seen in Figure 7. According to the results, this can lead the reduction of energy consumption up to 40%. Furthermore, another great advantage of this solution is that no source code modification is required.  
Taking the advantages of rCUDA, the authors of [71] discuss the implementation of an extension module to OpenStack, which enables configuring virtual machines initialized by OpenStack using remote GPUs. In [72], a GPU Scheduler as a Service (GSaaS) extension is implemented for OpenStack, based on rCUDA. GSaaS lets the user to define requests related to the needed GPU resources and scales the related resources according to the given requests and enables four working modes, including exclusive or shared using of local and remote GPUs. The components of GSaaS can be seen in Figure 8.  
Figure 8: Components of GSaaS [72].  
Figure 7: Local GPUs vs rCUDA architecture.  
Each of these solutions can benefit from GPUDirect RDMA, which provides a direct access to the network device to the GPU's memory therefore saving memory copying cycles.
## 2 Comparative Evaluation of UCS and TCP Estimation Methods  
To evaluate the performance and robustness of the algorithm proposed in Section 3 for joint estimation of the User Coordinate System (UCS) and Tool Center Point (TCP), a comparative verification study was conducted. This study involved four distinct methods  
Figure 1: Industrial robot coordinate systems.  
commonly used in industrial and academic practice for UCS and/or TCP determination. The objective was to assess the accuracy, consistency, and reliability of each method under equivalent measurement conditions.
## 6 Conclusions and Future Directions  
Wireless sensor networks provide additional advantages over previous telemetry methods in collecting spatio-temporal data, which make them suitable for various wildlife monitoring applications. Two types of movement modeling are possible using these networks (_i.e._, Lagrangian, and Eulerian). Collecting spatio-temporal data with wireless sensor networks especially for the Eulerian approach has various unexplored domains, though research in this domain are few and relatively sparse. To provide suitable outcome for ecological modeling, further improvements are still needed in terms of software (for extracting spatio-temporal features) as well as hardware (for sensing). Different schemes in the domain of human sensing have not yet been applied to wildlife monitoring. For instance, gait biometric, a well-explored biometric in human-sensing, has not been used in wildlife monitoring projects. Gait pattern has the potential to be detected with different sensors (radar, seismic,visual, and acoustic) for extracting species type, identity or maybe physiological state information that it conveys. Furthermore, in some domains such as chemical sensing the technology still has to improve to be able to provide spatio-temporal data. For instance, although there is evidence that different physiological states may be measured through chemical change in individuals, the technology that is usable in conjunction with wireless sensor networks remains undeveloped.
## 3 Results
### Spatial Patterns of IMERG Precipitation Data and Gauge Observations
Gauge observations, on the other hand, revealed a belt-like area characterized by high-frequency precipitation in this region, possibly due to its climatic characteristics and topographical conditions. Conversely, IMERG underestimated the precipitation frequency in the Sichuan Basin, possibly due to the limitations of this technology in accurately simulating complex terrain and precipitation systems. In Europe, IMERG provided good estimates of precipitation frequency, largely replicating the spatial patterns of gauge observations, with RB values below 20%. This was likely because the  
Figure 3: Average annual precipitation frequency from 2001 to 2020 obtained from gauge observations (**a1–d1**) and IMERG data (**a2–d2**), and the bias of IMERG data relative to gauge observations (**a3–d3**), for North America, Europe, North Asia, and South Asia, respectively. climate in Europe is relatively temperate, and the atmospheric circulation and precipitation systems can be reversed more accurately, resulting in a higher consistency between IMERG data and gauge observations. Figure 4 illustrates noticeable discrepancies in the estimates of precipitation intensity derived from IMERG compared to gauge observations across different regions. In both China and America, IMERG tended to underestimate high-intensity precipitation events. In southern China and the Beijing-Tianjin-Hebei region, IMERG could identify the areas characterized by high-intensity precipitation, but the maximum detected frequency reached only 2 mm/h, which was approximately 0.5 mm/h lower than that measured at meteorological stations. This led to IMERG data having an overall negative RB of up to \(-50\%\), particularly in the Beijing-Tianjin-Hebei region. A similar situation was observed in the southeastern United States, where the IMERG data for precipitation intensity were spatially consistent with gauge observations, but the former technology underestimated the magnitude. Conversely, in the Sichuan Basin, China, IMERG overestimated the hourly precipitation intensity but underestimated the precipitation frequency. For areas where precipitation intensity exceeded 1.5 mm/h, the RBs in this parameter reached 50%. In Europe, the IMERG estimates of precipitation intensity were notably higher than gauge observations. This overestimation could be attributed to the higher estimates of average annual precipitation totals in Europe and the good performance of IMERG in estimating annual precipitation frequencies.
## 4 Instrument Design
### Detailed Designs
#### 4.2.1 Design of Collimator System
The energy loss of 200 keV electrons in the light-blocking layer is about 20 keV. Hence, the residual energy is 180 keV, which meets the requirements of the measurable range of electronics. * High-energy proton collimation design  
When the collimation system in the high-energy proton probe was designed, its external structure was similar to that of the high-energy electron probe. The opening diameter of the upper and lower ends was 31 mm and 10 mm, respectively, and the length was 32 mm, forming a 40\({}^{\circ}\) measurement field of view. In contrast with the high-energy electron probe, which used a sawtooth-shaped structure to prevent interference, the high-energy proton probe used a permanent magnet in the collimator to deflect the electrons in order to eliminate the interference and irradiation effects of high-energy electrons on proton detection. For high-energy protons and electrons with the same energy, their energy losses in the silicon semiconductor sensor were the same, and they cannot be identified on the circuit. Therefore, a deflection magnet was used inside the instrument to deflect the high-energy electrons so that they cannot be incident on the silicon sensor, thereby achieving the purpose of excluding interference. Figure 8 shows the external structure of the proton probe. The basic shape of the deflection magnet in this scheme was the structure of a permanent annular magnet. The central magnetic field strength can reach 4200 Gs, which can effectively remove the influence of electrons < 1 MeV. Additionally, in order to shield the magnetic field of the permanent magnet and reduce the magnetic distance of the instrument, pure iron with high magnetic permeability was used for the shielding around the magnet [30; 31]. According to the description of the light-blocking layer in the previous section, a 15 \(\upmu\)m aluminum film was also used in the collimator of the high-energy proton probe. The 15 \(\upmu\)m aluminum light-blocking layer can block protons below 1 MeV. The proton measurement index starts from 3 MeV. Thus, it fully meets the requirements for the measurable range of electronics. Figure 8: External structure of the proton probe.
## 3 Performance Analysis for Single SWIPT Relaying System
### Approximation SWIPT Relay to AF Relay  
As mentioned earlier, the BER expression in (19) offers new insight into the performance characteristics of SWIPT relaying systems. In this subsection, we demonstrate that the BER expression for the SWIPT relay can also be derived from that of a conventional non-SWIPT AF relay by replacing the SR and RD link SNRs with \(\bar{\gamma}_{\rm sr}\) and \(\bar{\gamma}^{\prime}_{\rm rd}\), respectively. Accordingly, the indirect link SNR can be approximated as  
\[\frac{\gamma_{\rm sr}\gamma^{\prime}_{\rm rd}}{\gamma_{\rm sr}+\gamma^{\prime} _{\rm rd}}\simeq\min\{\gamma_{\rm sr},\gamma^{\prime}_{\rm rd}\}=\gamma \tag{23}\]  
where the PDF of \(\gamma\) is given by \(f_{\gamma}(x)=\frac{1}{\tau}\exp\Bigl{(}-\frac{x}{\bar{\gamma}}\Bigr{)}u(x)\) with \(\bar{\gamma}=\bigl{(}1/\bar{\gamma}_{\rm sr}+1/\bar{\gamma}^{\prime}_{\rm rd }\bigr{)}^{-1}\)[16; 23]. The average BERs for the indirect and combined links can be expressed, respectively, as  
\[\begin{split} P_{B,\rm id}^{\rm AF-App}&=\frac{1}{ \pi}\int_{0}^{\pi/2}\frac{1}{\bar{\gamma}_{\rm s}+1}d\theta\\ P_{B,\rm cb}^{\rm AF-App}&=\frac{1}{\pi}\int_{0}^{ \pi/2}M_{\gamma\rm sd}(\mathsf{s})\frac{1}{\bar{\gamma}\mathsf{s}+1}d\theta. \end{split} \tag{24}\]  
It is worth noting that by the same argument as Appendix A, \(P_{B,\rm id}^{\rm AF-App}\) of (24) can be asymptotically approximated as the form of (19).