esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data
### Cosmic-Ray Neutron Probe (CRNP) Stations  
Eight cosmic-ray neutron probe (CRNP) stations are spatially distributed on agricultural areas within the Rur catchment to monitor soil moisture. Measuring hourly neutron intensity, the stations are using either CRS-1000 or CRS-2000/B cosmic-ray neutron probe (Hydroinnova LLC, Albuquerque, NM, USA), providing soil moisture information within a circular area of 150 m to 200 m diameter around the CRNP within a soil depth between 15 cm to 70 cm. The CRS-1000 probes are filled with \({}^{3}\)He gas, while the CRS-2000/B probes are filled with \({}^{10}\)BF\({}_{3}\) enriched gas [60]. Since the neutrons are partially absorbed  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Soil Depth [m]**} & **Clay [\%]** & **Sand [\%]** & **SOC [g/kg]** & **Bulk Density [kg/m\({}^{3}\)]** \\ \cline{3-6} \multirow{4}{*}{\({}^{\circ}\)**Model**} & \multirow{2}{*}{RUL\_C\_005} & 0 & 25.0 & 22.9 & 36 & 1222.7 \\ \cline{3-6}  & & 0.10 & 25.0 & 22.8 & 38 & 1236.2 \\ \cline{3-6}  & & 0.30 & 26.8 & 23.3 & 10 & 1365.6 \\ \cline{3-6}  & & 0.60 & 29.4 & 23.0 & 7 & 1414.9 \\ \cline{3-6}  & & 0 & 18.5 & 36.9 & 43 & 1093.0 \\ \cline{3-6}  & & 0.10 & 18.4 & 36.6 & 42 & 1123.9 \\ \cline{3-6}  & & 0.30 & 19.1 & 37.1 & 20 & 1227.4 \\ \cline{3-6}  & & 0.60 & 20.8 & 39.1 & 9 & 1346.4 \\ \cline{3-6}  & & 0 & 19.2 & 35.2 & 43 & 1139.6 \\ \cline{3-6}  & & 0.10 & 19.4 & 35.2 & 40 & 1153.5 \\ \cline{3-6}  & & 0.30 & 19.7 & 35.6 & 24 & 1271.7 \\ \cline{3-6}  & & 0.60 & 21.2 & 36.7 & 10 & 1393.9 \\ \cline{3-6}  & & 0 & 22.6 & 33.6 & 59 & 1054.1 \\ \cline{3-6}  & & 0.10 & 22.7 & 33.3 & 60 & 1095.9 \\ \cline{3-6}  & & 0.30 & 24.3 & 34.5 & 27 & 1179.1 \\ \cline{3-6}  & & 0.60 & 25.3 & 35.8 & 16 & 1357.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Soil Depth [m]** & **Clay [\%]** & **Sand [\%]** & **SOC [g/kg]** & **Bulk Density [kg/m\({}^{3}\)]** \\ \cline{3-6}  & & 0 & 17.8 & 41.2 & 87 & 958.5 \\ \cline{3-6}  & & 0.10 & 17.9 & 41.2 & 82 & 1038.9 \\ \cline{3-6}  & & 0.30 & 18.7 & 41.4 & 24 & 1117.4 \\ \cline{3-6}  & & 0.60 & 19.8 & 43.8 & 13 & 1307.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of Apulian Tavoliere site.  
by the gas as they pass through the probes, electrons are produced that are attracted to an anode. The resulting electric currents are then amplified, detected, and counted by a pulse module [61]. For soil moisture estimation, the CRNP probes were calibrated using the N\({}_{0}\) method [62], using a one-time representative in-situ soil moisture measurement within the CRNP footprint. Even though the method does not account for biomass correction, it gives reasonable results with an RMSE \(\leq\) 0.033 cm\({}^{3}\)/cm\({}^{3}\)[63]. In this study, the daily average was used for comparison.
## 5 Conclusions  
Previous studies have shown that an OFDM signal with ICI on the receiver side can be regarded as an MC-CDMA system, in which the spreading code matrix is the ICI matrix. This study extended the system model to TV-CFO estimation. Meanwhile, intelligent optimization algorithms are frequently used for channel estimation, and the DE algorithm is a prevalent example. The DE algorithm has a unique feature in that its crossover operation is very suitable for solving problems in which there are dependencies between adjacent variables. Considering the change in the relative velocity of the sending and receiving terminals, which produces the TVCFO in the received signal, the velocity changes in different moments are linked by the acceleration. Therefore, the DE algorithm was employed to capture the dependencies between the CFO values on adjacent OFDM blocks. Simulation results showed that the performance of the DE algorithm was better than those of other intelligent optimization algorithms (the SAMGA and GA) in terms of the RMSE of the estimated CFO values and the BER of the demodulation. Furthermore, the proposed scheme not only achieved a demodulation performance with actual TVCFO values, but also reduced the demodulation deterioration caused by noise in both the PSK and QAM modulations. Future research could focus on the effect of noise on the DQPSK modulation with the DE estimator. The DE estimator could also be further utilized to achieve a better balance between the computational complexity and accuracy of TVCFO estimation.  
Conceptualization, H.W.; methodology, W.J. and Q.H.; software, H.W. and W.J.; validation, Q.H.; formal analysis, W.J.; investigation, W.J.; data curation, J.Z. and Y.J.; writing--original draft preparation, H.W.; writing--review and editing, J.Z. and Y.J.; visualization, Y.J.; supervision, Q.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number No. U22A2012; the APC was funded by grant number No. U22A2012.  
Not applicable.  
The authors declare no conflict of interest.
## 3 The Basic of GNSS
### Digital Map  
An important part of the GNSS implementation process in railway applications is the digital map, which is a central and key element of safety-related positioning. Some navigation functions such as determining the distance travelled or actual speed can be performed without the use of a digital map. Others such as positioning cannot be performed without such a map, as it provides a complete description of the topological information of railway tracks and can be used as a reference to calibrate position estimation error and make the position result identified within a one-dimensional location coordinate defined on the track [28; 29; 30].  
A database containing track maps enables train positioning, which, supported by various integration solutions at different levels of coupling, is considered a relatively low-cost and effective choice. The input data resulting from the information source, in the form of a digital map, supplements the satellite-based train positioning system with relevant information resulting from the map matching process, and should also be taken into account to evaluate and improve the integrity of positioning, which is important to ensure safety in satellite location-based systems in railway applications. As part of the studies included, in [31], high-integrity map matching algorithms for transportation applications were investigated and factors affecting the integrity of the map matching approach were analyzed, while [32] presented a solution based on multiple hypotheses to increase the correctness of identifying an occupied track section and achieve effective map matching. With the above in mind, it is important to be aware that the digital map is one of the elements generating possible positioning errors, as typical errors can arise at the stage of its creation. Even verification or validation may result in an insufficient level of security due to the risk of the above errors [33; 34; 35].
## 4 FE-Based Method
### Application of Loads and Restraints to the 3D Model  
In the 2D model of the floor, the perimeter beams are represented through one-dimensional frame elements, and the intersection between two perimeter beams is identified by a single point, called the vertex node.  
Conversely, in the solid elements model (3D model), the floor cell is represented with its 3D geometry and, consequently, the intersection between two adjacent beams is a parallelepiped solid region whose edges are highlighted by the green color in Figure 11. At intersection regions, loads are applied to the two vertical cross-sections of the parallelepiped aligned with the longitudinal axes of the two converging beams; see the red hatched planes in Figure 11.  
With it being taken into account that, in the model, perimeter beams are discretized in smaller finite solid elements through a mesh, it follows that loads at intersection regions are applied to the mesh nodes belonging to the parallelepiped vertical cross-sections mentioned above, as shown in Figure 12 for one of the two parallelepiped cross-sections.  
On the basis of what has been discussed, it can be gathered that the loads acting at vertex nodes along X or Y direction are applied to the mesh nodes belonging to the parallelepiped vertical cross-section orthogonal to the load direction.  
Roller supports, preventing translations along one of the two principal horizontal directions of the floor cell and vertical displacements, are also applied to the mesh nodes belonging to the parallelepiped vertical cross-section parallel to the horizontal direction in which translations are allowed. Regarding the pin supports present in the static scheme  
Figure 11: Parallelepiped constituting the intersection between two adjacent beams in the floor cell model made by solid finite elements.  
of MODE 1 (Figure 10), they are applied to the mesh nodes belonging to the vertical segment of the parallelogram constituting the intersection between the vertical cross-sections mentioned above. This segment is highlighted in blue in Figure 11.  
Finally, the displacement along X or Y direction at intersection regions is obtained from the average of the displacement values along X or Y, respectively, of the mesh nodes belonging to the vertical cross-sections of the parallelepiped constituting the considered intersection region.
# Path Planning for Localization of Radiation Sources Based on Principal Component Analysis
## 2 Proposed Method
### Reconstruction of Radiation Source Distribution via Simple Back-Projection
```
Result: Voxels with measurement counts \(f(V)\) // Initialization \(f(V)\longleftarrow\) 0; foreach\(\{E_{si},E_{ai},P_{si},P_{ai},V_{i}\}\in G\)do if\(|E_{s}+E_{a}-E_{rad}|<\Delta E\)then // Back projection on both side ifonComptonCone(\(E_{si}\), \(E_{ai}\), \(P_{si}\), \(P_{ai}\), \(V_{i}\))then \(f(V_{i})\longleftarrow f(V_{i})+1\);  end if ifonComptonCone(\(E_{ai}\), \(E_{si}\), \(P_{ai}\), \(P_{si}\), \(V_{i}\))then \(f(V_{i})\longleftarrow f(V_{i})+1\);  end if  end if // Function to check if a voxel is on Compton cone FunctiononComptonCone(\(E_{s}\), \(E_{a}\), \(P_{s}\), \(P_{a}\), \(V_{i}\)): \(\epsilon\longleftarrow\) Error threshold; \(k=P_{s}-P_{a}\); \(m=V_{i}-P_{s}\); \(\theta\longleftarrow\) Calculate the angle between \(k\) and \(m\); \(\omega\longleftarrow\) Calculate the scattering angle using \(E_{s}\) and \(E_{a}\); if\(|\omega-\theta|>\epsilon\)then return True; else return False;  end if  end foreach
```  
**Algorithm 1**Simple Back Projection  
Figure 5: Simple back-projection method: let \(k\) be the vector connecting the interacting elements on the absorber and scatterer layers, and \(m\) the vector connecting the interacting element on the scatterer and the voxels in projection space. The pixel value of the voxels, depicted in orange, can be calculated using the delta function in Equation (2). After sensing the gamma rays at each measurement point, the measurement data are projected into the 3D voxel space via the simple back-projection method. The voxels in 3D space exhibit the number of times they overlap with the Compton cone, which occurs for each gamma-ray event. To compress the data, voxels with high values are extracted. However, the information with respect to the \(Z\) axis is not required for determining the movement direction of the robot, because the mobile robot can move in two-dimensional (2D) space. Therefore, the proposed method projects the voxel data onto a 2D plane. Finally, the radiation source distribution is obtained, as illustrated in Figure 6. The proposed method generates a measurement path based on the 2D information.
# ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru
## 2 Materials and Methods
### Study Area and Susceptibility to Flooding
Historically, 120 floods have been documented and association with ENSO has been reported [58], particularly in the years 1578, 1650, 1728, 1750, 1790, 1828, 1876, 1891, 1925, 1982, 1997, 2002, 2004, 2006, 2009, 2015, and 2017. With regard to the last hundred years, the chronological distribution of the flood events highlighted that there have been three periods of higher intensity in Peru, i.e., 1982-1983, 1997-1998 and 2017-2018 (Table 1). The direct relationship with the activation of ENSO events is so strongly believed that the whole period is referred to as \"El Nino Extraordinario (i.e., the extraordinary El Nino event)\" by scholars [8; 9; 11; 21; 26; 59]. As already mentioned in Section 1, these events caused significant damage, hundreds of fatalities, and more than 2 million people were affected [29]. These recent events were selected as the focus of this investigation, since they are representative of the destructiveness that these hydro-meteorological events can bring. The satellite-based observations of the impact across the urban  
Figure 2: Sentinel-2 images collected on (a) 28/12/2015 showing the typical cloud cover in Lima and on (b) 22/03/2017 during the catastrophic flooding event analyzed in this paper. Contains Copernicus Sentinel-2 data 2015, 2017.
## 3 Simulation and Experimental Setup  
### Data Description  
To evaluate the efficiency of the proposed algorithms, we used the simulated and experimental data for CPWC image reconstruction. All datasets are provided by the Plane-wave Imaging Challenge in Medical Ultrasound (PICMUS) [34; 35]. The simulated data was generated by Field II [36; 37] software. The experimental data contains phantom data and in vivo data. All datasets were acquired with a linear array. The element spacing of the 128-element linear array was 0.3 mm and the center frequency was 5.208 MHz. The system sampling frequency was 40 MHz and the propagation speed of the ultrasonic wave in the medium was 1540 m/s. Each dataset was acquired using 75 plane-wave transmissions. The steering angles were spaced from \(-16^{\circ}\) to \(16^{\circ}\) with an angular interval of 0.43\({}^{\circ}\).  
A Gaussian distributed noise with a signal-to-noise ratio (SNR) of 10 dB was added to the channel RF data. The dynamic range of all B-mode images in this paper is 60 dB. 25 plane-waves (\(N=25\)) with steering angles spaced uniformly between \(-16^{\circ}\) to \(16^{\circ}\) were used to reconstruct B-mode images. Parameters \(\beta=2,3\), \(M_{0}=2\) and \(Q=10\%\) are respectively set in NSVD, GCF, and SLSC for the realizations of the proposed methods.
# Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-Band OpenMote Hardware
## 4 Measurements
### Device State Current Consumption
#### 4.3.2 868 MHz CC1200 Radio  
The device state consumption values when using the CC1200 radio, i.e., 868 MHz, are shown in Table 15. The values for the TX state were measured when the transmit power of the radio was set to 0 dBm. When the transmit power is set to 14 dBm, i.e., the current default in OpenWSN, the consumption of the TX states is 91.94 mA for an active CPU and 88.25 mA for a sleeping CPU. The CC1200 radio Sleep state is the Idle state with the crystal oscillator turned off ([[http://www.ti.com/product/CC1200/datasheet/](http://www.ti.com/product/CC1200/datasheet/)]([http://www.ti.com/product/CC1200/datasheet/](http://www.ti.com/product/CC1200/datasheet/))). Consumption is expected to be lower when the Sleep state of the CC1200 chip is used or when the CC1200 is turned completely off.  
As expected, the difference in the consumption between an active or a sleeping CPU is nearly identical for all radio states: the CPU in active mode consumes on average 3.81 mA more than when being in sleep mode, with a standard deviation of only 0.15 mA.  
When both the CPU and the radio are put in the Sleep state, the consumption is still high. This is caused by the high current consumption of the CPU, which is put in the least possible sleep mode PM_NOACTION. When putting the CPU in a deeper sleep, i.e., the PM2 power mode, while the CC1200 radio is in Sleep and Idle state, the consumption dropped to 0.27 mA and 2.64 mA, respectively.
## 3 Problem Statement and Main Contributions  
The number of related publications is enormous, and this review provides only examples of solutions, which in the authors' opinion give a map of the development fields. However, the review of related works indicates that although a variety of solutions is used in the IoT healthcare-monitoring domain for indoor and outdoor environments, a methodological approach to the design process is still missing; where design is understood as \"scientific principles, technical information and imagination in the definition of a structure, machine or system to perform pre-specified functions with the maximum economy and efficiency\" [39]. Furthermore, using a multi-environmental information system for behavior recognition and classification requires improvement and development.  
To fill the gap in the methodological approach to the design of a comprehensive information system for healthcare applications, the objective of this paper is to propose a systematic design procedure, which can enhance the development of healthcare appliances. Apart from technical requirements, the procedure considers multifarious constraints, including the lifetime, energy efficiency, usage comfort and even the price. The case study of the design process is an IoT-based system for monitoring people and things multi-environmentally capable inter alia of behavior recognition and diagnosis. The system is dedicated to support and localize elderly people in their multi-room apartments along with a multi-story building, but even outdoors in the building's surroundings. The system's functionalities consist of monitoring vital signs, posture recognition, suspicious behavior detection and classification.  
The development procedure approaches the design target from the perspective of the stakeholders, the authority in charge and the potential users, as the view of the system developers. The proposed design methodology is modelled and then implemented and validated on the case study of the system for multi-environmental monitoring of elderly people living alone. The system has been implemented and validated in real scenarios.
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 1 Introduction
### The Applications of Accelerometers Used in the Industry
#### 1.5.5 Handheld Products  
Accelerometers are incorporated into many personal devices, such as phones or tablets, where they detect motion and orientation. The study [38] investigates accelerometer accuracy obtained with three smartphones. Results demonstrate that the tested smartphone accelerometers are valid and reliable for estimating accelerations.
## 4 Results of Mission Analysis
### Attitude Control Analysis  
After separation from the launch vehicle, the deployment mechanism will cause a tumbling rate applied to the spacecraft about 10 deg/s. This rate needs to be de-tumbled to less than 3 deg/s in all axis, which will lead the spacecraft to the magnetometer deployment. To decrease the angular rate for all axis in the de-tumbling mode, Y-Magnetorquer actuator, and XYZ-axis rate sensors will be activated to achieve B-dot control mode and MEMS Rate estimation mode. Simulations were conducted using EOS Software [28] to verify the de-tumbling functionality of the ADCS. The results are shown in Figure 19. As mentioned earlier, Y-Thomson Controller will reduce X-axis and Z-axis angular rate and control Y-axis angular rate to reach \(<\)1 deg/s. Figure 20 shows the simulation results of the ADCS in Y-Thomson mode. It will take the system 3 min to control the satellite to within 1 deg/s from an initial state of 3 deg/s. A further detailed and comprehensive analysis and simulation of the ADCS was conducted, however, it is beyond the scope of this paper.  
Figure 19: Results of the de-tumbling mode.
## 2 Methodology
### Taguchi Method  
The Taguchi method is a powerful tool for optimizing parameters including wind turbine (WT) geometry [36]. In this method, the signal-to-noise ratio (S/N) is used to assess the qualitative nature of each system factor [37]. In the Taguchi method, there are three types of S/N ratio: nominal is better (NB), larger is better (LB), and smaller is better (SB).  
In this paper, the aerodynamic characteristics obtained from CFD simulation were used as output parameters to calculate the signal-to-noise ratio (S/N) using the Taguchi method. The torque coefficient (C\({}_{\text{m}}\)) was used to calculate the S/N by the \"larger is better\" criterion (S/N\({}_{\text{LB}}\)), since the goal was to maximize the rotor efficiency. The drag coefficient (Cd), in turn, was used to calculate the S/N by the \"smaller is better\" criterion (S/N\({}_{\text{SB}}\)), since reducing the drag is directly related to reducing energy losses and increasing the overall efficiency of the wind turbine.  
\[S/N_{LB}=-10\log\left(\frac{1}{n}\sum_{i=1}^{n}\frac{1}{y_{i}^{2}}\right) \tag{9}\]  
\[S/N_{SB}=-10\log\left(\frac{1}{n}\sum_{i=1}^{n}y_{i}^{2}\right) \tag{10}\]  
In the initial formulation of the problem, for each rotor diameter value in the range from 1.5 m to 4.0 m with a step of 0.25 m (11 levels in total), 7 chord length options were considered, from 0.2 m to 0.8 m with a step of 0.1 m. Thus, the full factorial combination of parameters included 77 unique combinations.  
To improve the efficiency of calculations and reduce the number of CFD calculations, while maintaining the reliability of the results, the Taguchi method was used. With its help, it was possible to optimize the experimental plan, reducing the number of combinations to 20 most informative options based on an orthogonal array. This made it possible to significantly reduce the computational load while maintaining the representativeness of the entire range of parameters studied.  
To implement the Taguchi method in this work, an orthogonal array L20 was used, corresponding to a combination of two factors with different numbers of levels. Table 5  
Figure 6: Dependence of torque coefficient on number of grid elements.  
illustrates the structure of the L20 orthogonal array for two factors: rotor diameter (D) and chord length (c).  
The L20 (5 \(\times\) 4) orthogonal array ensures a uniform and balanced distribution of parameter combinations, allowing only 20 calculations to be performed instead of the 77 required for a full factorial experiment. This approach preserves the statistical significance of the results and allows the influence of each parameter to be assessed independently of the others, eliminating cross-distortions.