esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
These well-electrically conductive environments can concentrate a more significant part of the induced electrical currents, where their current paths can close, create a high-current density, and thus the heart itself can be largely shielded from the effect of the induced electrical currents. Another hypothesis is the idea of the \"resilience\" of the sinoatrial node and the entire electrical conduction system of the heart against the effects of external electromagnetic fields. It should be stressed, however, that these are the effects of an explicitly low-frequency, not the high-frequency impact of electromagnetic fields, known in physical medicine from the use of diathermy. At the same time, however, certain changes can be observed based on complex variability. The results support the safety aspect of the high-induction magnetic stimulation but do not in any way imply that high-induction magnetic stimulation could be applied risk-free to the heart area where it is usually contraindicated, especially in cardiac patients or in risk patients. The presence of a pacemaker or other electronic surrogate in the body is a primary contraindication not only to the HIMS application but also to distance electrotherapy and any other application of induced electric currents and electromagnetic fields that could adversely affect the pacemaker or any other electronic surrogate with a fatal effect. It is evident that at first look (and based on the basic descriptive statistics, i.e., meanRR and meanHR parameters), changes in cardiac activity are not apparent. Still, certain significant transitions show up in the parameters describing variability or complex variability (complexity). Therefore, we can say that although it seems that HIMS has no effect on the electrical conduction system of the heart based on the results of the fundamental analysis, the results indicate changes in variability, which decreases during or after the HIMS application. Therefore, it can be assumed that the HIMS application affects cardiac activity. Hence, there are changes in variability, but for more accurate results, it would be necessary to include a larger number of subjects in the study and possibly to extend the spectrum of applied pulses (with respect to their frequency).
# Assessment of Active Wheelset Steering System Using Computer Simulations and Roller Rig Tests
## 5 Results Summary  
Computer simulations show that active wheelset steering is a very promising, practically implementable, method that could bring reasonable decrease of guiding forces on modern railway vehicles. For the practical implementation of active wheelset steering system is essential to provide the controller by the actual track curvature. The track curvature calculation method based on the measurement of yaw angle of bogies towards the car body has been proposed. The advantage of such method is independence on the track map and system for the positioning of the vehicle on the track. However, the bogie rotation varies due to the varying position of the wheelsets within the gauge clearance and causes error in the track curvature estimation. Therefore, the sets of simulations for varying track radius, uncompensated lateral acceleration, and friction condition were performed. The actual track radius and track radius calculated using bogie rotation were compared. This comparison was then used for linear approximation formula that decreases track curvature estimation error. The method shows the maximal error of track curvature calculation in small and very small track radiuses about 14% in dry track conditions and 25% in low friction track conditions. The method of detection of the track curvature was successfully tested by simulating the vehicle running on the real more than 20 km long track section with irregularities. By this simulation, the error in the track curvature estimation was below 10% in constant curvature sections, with very short peaks of large errors in transition track sections. These results can be considered satisfactory for the use of estimated track curvature as an input to the wheelset steering controller.  
The experimental bogie has been equipped with the actuated mechanism that its capable to steer wheelsets positions in the yaw direction and measure forces transmitted between axle-boxes and bogie frame. The roller rig was modified to fully simulate vehicle run in a curved track, including effects of centrifugal acceleration, and equipped with measurement of guiding forces. The performance of the experimental device was successfully tested during initial tests. Additionally, the strong dependence of the magnitude of the guiding forces on the yaw angle of the wheelsets towards the bogie frame and the possibility to significantly reduce the guiding forces by active wheelset steering were experimentally confirmed. The scaled roller rig and the experimental two-axle bogie are ready for conducting the experiments in varying speed, track radius, uncompensated lateral acceleration, and various control algorithms of the wheelset steering.
## 2 Materials and Methods
### FTIR Measurements  
The Fourier Transform Infrared spectra (FTIR) of the samples were recorded on a Cary 630 FTIR Spectrometer (Agilent Technologies Pty Ltd, Mulgrave, Australia) fitted with a diamond ATR accessory. Spectra, in the absorbance mode, were recorded from 4000 to 650 cm\({}^{-1}\), by accumulation of 64 scans at a resolution of 4 cm\({}^{-1}\). The baseline correction, ATR correction, and the spectra average were performed using the Resolution Pro FTIR Software program (version 5.2.0, Agilent Technologies Pty Ltd., Mulgrave, Australia).
## 1 Introduction
This technique offers an improved SNR and facilitates more straightforward spectral analysis, allowing the direct extraction of local acoustic velocity without requiring precise knowledge of the source-to-detector distance [31; 32]. [PERSON] et al. [33] demonstrated the effectiveness of grating laser ultrasonics in imaging surface and subsurface defects using SAW velocities, confirming its potential for defect evaluation in AM components. Nevertheless, existing research on grating laser ultrasonics in the context of AM remains limited, particularly regarding the evaluation of porosity or void-type defects. In this work, a grating laser ultrasonic method is proposed for the non-contact evaluation of porosity in metal AM components, with a focus on establishing the relationship between SAW velocity and porosity level. Benefiting from its remote sensing capability and high spatial resolution, the proposed method is well suited for online testing during the AM process. Numerical simulations and laser ultrasonic experiments are conducted to validate the theoretical model and assess the feasibility and effectiveness of the proposed method for defect detection. The remainder of this paper is organized as follows: Section 2 presents the theoretical framework and measurement methodology; Section 3 describes the simulation and experimental setups along with the analysis of their results; and Section 4 summarizes the conclusions and discusses future perspectives.
## 3 Results
### Grasping with a Simulator  
The results of the simulated grasp are shown in Figures 8 and 9. In Figure 8, we show the maximum grasp quality out of 10 planning grasps of each constrained condition and disk diameter. The maximum evaluation of grasping under the free condition is significantly higher than that under the IM-L, MRL-L, and IMRL-L conditions except when the disc diameter is 120 mm. The particular case at 120 mm can be explained by the simulated annealing algorithm combining much randomness, leading to a probability event. In addition, it should be noted that except for the three grasping postures with an asterisk, the planned grasping postures all belong to the _precision disk_ defined by [PERSON]. For the hand whose ab/d DOF is restricted, the simulator had difficulty finding a way to grasp the disk with this posture as the diameter increased and was then forced to switch to the _extension_ defined by [PERSON] [30]. Such cases imply that ab/d DOFs could enable a superior grasp gesture to achieve a larger grasp workspace.  
Each point in Figure 9 represents the average grasp quality of 10 superior grasping postures under four different conditions. The error bar is the standard deviation. We can see that the grasp quality under the free condition is significantly higher than that under the other three conditions, and the grasp quality under the IMRL-L condition is the lowest. Setting the average grasp quality under IMRL-L as a benchmark, the grasp quality under the previous three conditions increased by 76.7%, 50.1%, and 25.1%, on average. It is worth noting that when the disk diameter exceeds 130 mm, the grasp qualities under all four conditions exhibit sharp decreasing trends, indicating an increasing grasp difficulty as the object size increases.  
Figure 8: Maximum grasp quality with the simulator. The grasp gesture at each point is the optimal gesture out of 10 gestures. The asterisk represents an exceptive gesture that is not a _precision disk_ grasp.
## 4 Results and Discussion
### Data Fitting and the Index  
The energy values of the five tests were averaged, and their mean value and standard deviation were obtained, as shown in Figure 10a. As the tension increases, the energy value gradually increases. Before the tension reaches 10 tons, the energy value increases continuously. Although the tension value fluctuates after 10 tons, the curve still exhibits a consistent upward trend. The mean energy value is fitted, as shown in Figure 10b. The fitted curve demonstrates that the magnitude of the energy value is directly proportional to the square root of the tension. As expected, it is in good agreement with Equation (9), and the R-squared value of the fit is 0.9157. However, it is evident from the fitted curves that the test results for 2-9 tons are lower, while the test results for 11-17 tons are higher. This discrepancy may be affected by the accuracy of the testing equipment and environmental interference. The aim of this paper is to propose a practical and universal testing method. The details will be further refined and optimized to enhance testing accuracy in the future.  
Figure 10: (a) The average energy ratio and standard deviation of the five tests, and (b) the fitted curve of the average.  
Finally, an index H\({}_{5\text{-}\text{Shannon}}\) for prestress monitoring was introduced by calculating the Shannon entropy using Equations (15) and (16). The results of five tests are shown in Figure 11. Figure 11a-c display the results of the Shannon entropy for the entire frequency range (0-1000 kHz), the low-frequency range (0-600 kHz), and the high-frequency range (600-1000 kHz) calculated for the five trials, respectively. It can be observed that the entropy results in the low-frequency range (Figure 11b) exhibit more random fluctuations, while the entropy results in the high-frequency range (Figure 11c) demonstrate good linearity. This is due to the shorter wavelength of the high-frequency component of the signal, which enables it to better avoid external interference and provides strong anti-interference capability. Therefore, we utilized the entropy results calculated in the high-frequency range as an index, and the results obtained after processing its mean value are depicted in Figure 11d.  
As shown in Figure 11d, H\({}_{5\text{-}\text{Shannon}}\) was high at low tension, implying high prestressing force, and low at high tension, indicating low prestressing force. There is a good linear relationship between the index and the prestressing force. The conclusion shows that as the tension increases, the index gradually decreases, indicating that the prestress system is more stable. The tension force in this paper reached 18 tons, which far exceeded the 16,256 N tested by [PERSON] et al. [46]. However, it should be noted that the inevitable interference of surrounding environmental factors affected the resulting curve for each tension level, causing some degree of fluctuation. Nevertheless, the Shannon entropy and tension force followed a linear trend, with resolution even at high force levels, making it suitable for monitoring early prestressing force.
## 2 Mathematical Formulation
### Formulation of the Objective Function  
To solve the optimization problem, an objective function, i.e., the annualized costs function of the energy losses (\(f_{1}\)) and the annualized investment costs function of the D-STATCOMs (\(f_{2}\)), are determined via Equations (1) and (2), respectively.  
\[f_{1}=C_{KWh}T\sum_{h\in\mathcal{H}}\sum_{k\in\mathcal{N}}\sum_{m\in\mathcal{N }}Y_{km}V_{kh}V_{mht}\cos(\delta_{kh}-\delta_{mht}-\theta_{km})\Delta h, \tag{1}\]\[f_{2}=T\bigg{(}\frac{k_{1}}{k_{2}}\bigg{)}\sum_{k\in\mathcal{N}}\bigg{(}\alpha \big{(}Q_{k}^{\text{DS}}\big{)}^{2}+\beta Q_{k}^{\text{DS}}+\gamma\bigg{)}Q_{k}^ {\text{DS}}, \tag{2}\]  
where \(C_{kWh}\) is the average cost per kWh; \(T\) is a constant related to the study period (i.e., 365 days); \(Y_{km}\) is the admittance magnitude of the line linking nodes \(m\) and \(k\) with an angle \(\theta_{km}\); \(V_{kh}\) and \(V_{mhl}\) are the voltages related to nodes \(m\) and \(k\) in period \(h\) with angles \(\delta_{kh}\) and \(\delta_{mh}\), respectively; \(\delta_{h}\) corresponds to the period in which the electrical variables are assumed to be constant (i.e., 1 h); \(k_{1}\) refers to the annualized investment costs constant; and \(k_{2}\) is the D-STATCOM lifetime constant. The parameters \(\alpha\), \(\beta\), and \(\gamma\) are continuous and positive, and they represent the variable installation costs of a D-STATCOM of nominal power \(Q_{k}^{\text{DS}}\). Finally, \(\mathcal{H}\) and \(\mathcal{N}\) are the sets containing all the network periods and nodes, respectively. To obtain the general objective function of problem (3), the algebraic sum of Equations (1) and (2) is performed.  
\[\min\ A_{cost}=f_{1}+f_{2}, \tag{3}\]  
with \(A_{cost}\) being the predicted annual costs of the distribution network, which depend on the installation costs and the power losses of the D-STATCOMs. The objective function denoted in (3) and its two components \(f_{1}\) and \(f_{2}\) are defined in (1) and (2), respectively, both of which are nonlinear and non-convex functions of the main decision variables. \(f_{1}\), due to the presence of products between tensions and trigonometric functions, is nonlinear and non-convex [39].Similarly, \(f_{2}\) is nonlinear, and because it has a cubic function in its formulation, it is non-convex [40].
## 3 Results and Discussion
### Compaction Mechanism of OGFC-5 UIPFC with Different Thickness
#### 3.2.2 Micromechanical Response  
Figure 8 presents the direction results of the normal contact force of the particles after loading for 1 h. The contact normal forces of the different models were distributed in all directions, and there were no distribution concentrations in any specific direction. Remarkably, the contact normal force of the 20 mm model exhibited a sudden increase in several directions. This phenomenon was also found in the 40 mm model, while the increment of the contact normal force was relatively small in comparison to that of the 20 mm model. As for the 63.5 mm model, the force values were relatively uniform in each interval. The maximum contact normal force values of the 20 mm and 40 mm models were 8 times and 2.67 times that of the 63.5 mm model, respectively. The smaller the molding height, the greater the force value observed.  
The force chain and contact force were further studied to analyze the load transmission in the model. Figures 9 and 10 show the simulation results of the contact force and force chain, respectively. The colors of the particles in Figure 9 indicate the magnitude of the contact force. A phenomenon occurred where the contact forces of some particles were much stronger than that of other particles. The maximum force value increased with a decrease in model height, and the particle size showed a random trend in the force concentration region, indicating that the concentrated force region was independent of the particle size.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline UPFC Height/mm & 20 & 40 & 63.5 \\ Displacement of loading wall/mm & 1.42 & 8.61 & 17.46 \\ \(\mathrm{D_{H}}\) & 0.07 & 0.22 & 0.27 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The DH values.  
Figure 8: Direction distribution of contact normal force of different thickness UPFC model: (**a**) general drawing; (**b**) 20 mm; (**c**) 40 mm; (**d**) 60 mm.  
Figure 9: Contact force distribution of different thickness UPFC models: (**a**) 20 mm; (**b**) 40 mm; (**c**) 60 mm.  
A schematic of the force chain clearly reflects the load transmission in the model. The thickness of the force chain in Figure 10 indicates the magnitude of the contact force. It can be found that the 63.5 mm model had more uniform thickness in the force chain, while the 20 mm model had less force in other areas owing to the existence of a strong chain area. With a decrease in height, the force chain became more significant, resulting in a load that is more difficult to fully diffuse and transfer inside the model [39]. Particles in the weak force chain region could not move and rotate sufficiently, making the model more difficult to compact. In conclusion, the target height of the UPFC specimen designed by the MCM method was 63.5 mm, which varied from the actual UPFC paving thickness, making it difficult to achieve the specified on-site compaction degree. Therefore, it is possible to consider reducing the target height of the UPFC to make the MCM design more in line with construction.
# Authentication and Billing for Dynamic Wireless EV Charging in an Internet of Electric Vehicles
## 5 Proposed Authentication and Billing Scheme
### EV Privacy  
One of the most important considerations in vehicular communications is the privacy of vehicular information. In the proposed model, the identities of the EVs requesting charge are kept confidential during the communication. This is achieved by utilizing pseudonyms that are assigned randomly each time an EV decides to charge, thereby eliminating the need to reveal the real identity of the EV. Furthermore, EV-related information such as current SoC and distance until the charging lane are not explicitly shared by the EV in any of the messages exchanged with the CSC or with the PO. During communication with the CSC, only the EV energy demand is shared as a single value, \(reqE\), in the encrypted message, \(m_{3}\), together with the EV identity. Upon receiving its pseudonym from the CSC, the EV shares its charging parameters, \(param\), in \(m_{7}\) using its pseudonym without revealing its real identity to the PO. Hence, the privacy of EV information is preserved in the proposed scheme.
# The Ground-Based Absolute Radiometric Calibration of the Landsat 9 Operational Land Imager
## 2 Methodology
### Automated Measurements: The Radiometric Calibration Test Site (RadCaTS)
#### 2.2.4 Railroad Valley, Nevada, USA  
The RadCaTS is located at Railroad Valley, Nevada, USA, which has a usable area of 15 km \(\times\) 15 km at an altitude of 1435 m. Preliminary work by UArizona in the 1990s sought to identify suitable ground test sites in the Southwestern United States for the reflectance-based approach and also for the cross calibration of Earth-observing sensors that operate in the solar-reflective regime (400 nm to 2500 nm) [78]. Examples of the principal criteria for a suitable site included the following:  
* Surface reflectance: \(>\)0.3, with the aim to reduce uncertainties in the path radiance.
* Spatial uniformity: to reduce uncertainties due to sensor misregistration during cross calibration studies.
* Large size: to reduce uncertainties from adjacency effects.
* Arid region: to reduce surface reflectance changes due to precipitation and/or the presence of clouds.
* High altitude: to reduce uncertainties in atmospheric characterization due to aerosols.
* Accessibility: in the 1990s, UArizona deployed a mobile lab pulled by a truck during field campaigns, so the test site had to be accessible with the truck and trailer.  
A preliminary exploratory trip to Railroad Valley occurred in 1996, and regular field work commenced soon after. Other sites used by UArizona include Lunar Lake, Nevada; [79; 80], Ivanpah Playa, California; and White Sands Missile Range, New Mexico [81]. By early 2000, Railroad Valley and Ivanpah Playa were the two main sites used by the group for sensors such as Landsat 5 TM, Landsat 7 ETM+, Aqua and Terra MODIS, and ASTER. There were three main collection areas used by UArizona at Railroad Valley as follows: one for pushbroom sensors (e.g., ASTER), one for whiskbroom sensors (e.g., TM and ETM+), and one for large-footprint sensors such as MODIS. The original concept for the RadCaTS design specified that it was to be suitable for moderate-resolution sensors such as MODIS, so the large-footprint area at Railroad Valley was chosen as the region of interest (ROI) for  
Figure 3: The current locations of the seven RadCaTS GVRs (21–25, 41, and 42), the meteorological station (Met), and Cimel sun photometer(s) (Cimel) at Railroad Valley. GVR 23 has 90 cm of linear motion. The feature running north–south is a gravel road.  
the RadCaTS. The choice to use a preexisting area was also important because it meant that a library of surface BRF measurements was already being established.  
The RadCaTS ROI is 1 km \(\times\) 1 km in size and centered at 38.497\({}^{\circ}\) latitude, \(-\)115.690\({}^{\circ}\) longitude. In terms of the World Reference System-2 (WRS-2), which is a global notation system for Landsat data, it is path 40 and row 33. Figure 4 shows a Landsat 9 OLI image of Railroad Valley collected on 10 July 2022 at 18:21 UTC. The 1 km\({}^{2}\) RadCaTS ROI shown in Figure 3 is indicated by the yellow box in Figure 4.
# A New Multivariate Approach for Real Time Detection of Routing Security Attacks in VANETs
## 4 The Proposed Detection Scheme  
This section describes the proposed approach called Multivariate Statistical Detection Scheme (MVSDS) for detecting routing security attacks in VANETs by using statistical techniques based on multivariate normality (MVN) tests. Indeed, our proposed detection approach allows separating legitimate behavior from malicious behavior by following four main steps explained below and depicted in Figure 2.