esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Simulation of the Influence of Vehicle Load on Civil Air Defense Construction Vibration
### Vehicle Load  
The load calculation example of a certain type of car is selected. The vehicle weight \(P_{0}\) is 20 kN, the vehicle length \(L\) is 4 m, the distance between left and right tires is 1.5 m, the distance between front and rear tires is 2.0 m, and the vehicle speed \(v\) is 60 km/h. The vehicle load is simplified as a concentrated load. The vehicle load is simulated by using steady sine wave vibration. Considering the vehicle weight and speed, the vehicle load calculation expression is as follows [36]:  
\[P=P_{0}+P_{d} \tag{11}\]  
where \(P_{0}\) is the static load of the vehicle; \(P_{d}\) is the dynamic load of the vehicle.  
\[P_{d}=M_{0}\alpha\omega^{2}\text{sin}(\omega t) \tag{12}\]  
where \(M_{0}\) is the unsprung mass of the vehicle, and the value is 120 N\(\cdot\)s\({}^{2}\)/m; \(\alpha\) is the sagittal height, taken as 2 mm; \(\omega=2\pi v/L\).  
The numerical value is substituted into the vehicle load expression as follows:  
\[P=20,000+164.49\text{sin}(26.18t) \tag{13}\]  
The load expression of a single tire is:  
\[P^{\prime}=5000+41.12\text{sin}(26.18t) \tag{14}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Order**} & \multirow{2}{*}{**Elastic Modulus**} & \multirow{2}{*}{**Thickness of Overburden**} & \multirow{2}{*}{**Density of Soil Layer (Mgg m\({}^{-3}\))**} & \multicolumn{3}{c}{**Elastic Modulus of Soil Layer (MPa)**} & \multirow{2}{*}{**Vehicle Speed**} & \multirow{2}{*}{**Vehicle**} \\ \cline{3-3} \cline{5-10}  & & & & & & & **1 st** & **2 nd** & **3 rd** \\ \cline{5-10}  & & & & & & & **Layer** & **Layer** & **Layer** \\ \hline
1 & 32.5/34.5/36/37 & 3 & 1800 & 1850 & 1870 & 3 & 5 & 7 & 60 & 20 \\
2 & 32.5 & 3/4/5 & 6 & 1800 & 1850 & 1870 & 3 & 5 & 7 & 60 & 20 \\
3 & 32.5 & 3 & 1750/1800/1850/1900 & 3 & 5 & 7 & 60 & 20 \\
4 & 32.5 & 3 & 1800 & 1850 & 1870 & 3 & 5 & 7 & 15/30/45/60 & 20 \\
5 & 32.5 & 3 & 1800 & 1850 & 1870 & 3 & 5 & 7 & 60 & 20/40/60/80 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Numerical simulation conditions.  
Figure 10: Numerical calculation model.  
The vehicle load is applied at the mid \(-\)span of the civil air defense construction. The vehicle load is a concentrated load moving along the axial node and its value is sinusoidal, and it is evenly distributed at the four tires. The numerical calculation loading diagram is shown in Figure 11.
# Iterative Positioning Algorithm for Indoor Node Based on Distance Correction in WSNs
## 5 Experimental Results  
### The Method  
To accurately and quantitatively verify the performance of the iterative positioning algorithm proposed, the experimental positioning area is set as a square of 40 m \(\times\) 40 m with nine anchor nodes located at the vertices, center, and in the middle of the edges of the square. Now, 50 target nodes are generated in the square area by random and their positioning results calculated in turn.  
In this experiment, for different noise impact factors \(F_{N}\), positioning error is discussed by three methods, namely, the centroid positioning algorithm, the weighted centroid positioning algorithm, and the iterative positioning algorithm based on distance correction. The value of \(F_{N}\) is taken into account in two situations: that involving a constant value and that involving a random value.
# Controlling Remotely Operated Vehicles with Deterministic Artificial Intelligence
## Appendix A MATLAB Code  
_dai_analysis.m_  
clc clear close all  
log = '20220128002145_10x_psi_step_30'; load(['scripts/_log_'/'log'_ADAPTIVE_CTRL_STAT.mat']);t = UNIX_timestamp - UNIX_timestamp(1); t_start = 0; t_end = 26; to_use = t > t_start & t < t_end; t = t(to_use); psi = polar_correct(PSI(to_use), -180, 180); psi_dot = PSI_DOT(to_use); psi_trajectory = polar_correct(PSI_TRA)(to_use), -180, 180); setpoint = polar_correct(UC(to_use), -180, 180); first_start = 1.25; first_end = 6; first_step = t > first_start & t < first_end; first_step_info = stepinfo(psi(first_step), t(first_step) - first_start, 'RiseTimeLimits', [0, 0.999], 'SettlingTimeThreshold', 0.001) first_traj = psi_trajectory(first_step); first_traj(1) = 0; first_psi = psi(first_step); first_error = first_traj - first_psi; first_rms_error = sqrt(mean(first_error.^2)) first_max_error = max(abs(first_error)) last_start = 21.8; last_end = 26; last_step = t > last_start & t < last_end; last_step_info = stepinfo(psi(last_step), t(last_step) - last_start, 'RiseTimeLimits', [0, 0.999], 'SettlingTimeThreshold', 0.001) last_traj = psi_trajectory(last_step); last_psi = psi(last_step); last_error = last_traj - last_psi; last_rms_error = sqrt(mean(last_error.^2)) last_max_error = max(abs(last_error)) label_fontsize = 24; axis_fontsize = 18; linewidth = 1.25; ax = [t_start & t < t_end 0 31];  
figure  
subplot(2, 1, 1) plot(t, psi, 'blue', 'linewidth', 2) title 'Yaw Step Response' ylabel('$\psi$ (deg)', 'Interpreter', 'Late', 'FontSize', axis_fontsize) xlabel('Time (s)') hold on plot(t, setpoint,'red', 'linewidth', 2) plot(t, psi_trajectory, '--c', 'linewidth', 2) legend(['$\psi$', 'Setpoint', 'Trajectory'], 'Interpreter', 'late', 'FontSize', 22); set(gca, 'FontSize', axis_fontsize) axis(ax);subplot(2,1,2) plot(t,psi_trajectory-psi,'blue','linewidth',2); title'YawError' ylabel('$psi$Error(deg)','Interpreter','Late'); xlabel'Time(s)' set(gca,'FontSize',axis_fontsize) axis([t_startt_end-0.10.15]) figure plot(psi,psi_dot) axisequal
# Additive Manufacturing of Variable Density Lenses for Radio Frequency Communications in X-Band
## 3 Horn Antenna and Gradient Index Lens Design
### Electromagnetic Design
#### 3.1.2 Gradient Index Lenses
The optimizer achieved three different results: a flat heterogeneous lens design, referred to as lens A (Figure 9a); an airy disk pattern, lens B (Figure 9b); and a ring pattern, lens C (Figure 9c). The values of the relative dielectric constant for the different sectors of the three realizations are shown in Table 4. Both lens B and C present patterns associated with Fraunhoffer diffraction through circular apertures. Figure 9: X-Band GRIN Lenses: (**a**) Lens A, material Composite X resin, technology VPP; (**b**) Lens B, material Dental resin, technology VPP; (**c**) Lens C, material Rigid 10K resin, technology VPP.
# An Improved Q-Learning-Based Sensor-Scheduling Algorithm for Multi-Target Tracking
## 5 Simulation Results
### Simulation Setup  
A total of 200 sensors are randomly deployed in a two-dimensional field (\(100\times 100\)\(m^{2}\)) and the sink is located at (50, 50). The sensing range and communication range of each sensor are identically fixed to 20m and 40m. The size of the control packet and data packet is 8 bytes and 256 bytes. The simulation lasted for 1000 time steps. The parameters that appeared in the simulations are listed in Table 1.  
We simulate a scenario where multiple targets move based on a Gauss-Markov mobility model [25] in the surveillance area. Initially, a random speed and direction are assigned to each target. At each time step \(t\), the movement parameters of targets are updated by  
\[v_{t}=\eta v_{t-1}+(1-\eta^{2})\overline{v}+\sqrt{1-\eta^{2}}v_{t-1}^{G}, \tag{18}\]  
\[\theta_{t}=\eta\theta_{t-1}+(1-\eta)\overline{\theta}+\sqrt{1-\eta^{2}}\theta _{t-1}^{G}, \tag{19}\]  
where \(v_{t}\) and \(\theta_{t}\) are the current speed and direction of the target at time \(t\); \(\overline{v}\) and \(\overline{\theta}\) are constants denoting the mean value of speed and direction; \(v_{t-1}^{G}\) and \(\theta_{t-1}^{G}\) are random variables from a Gaussian distribution; and \(\eta\in(0,1)\) is a parameter that is used to vary the randomness of target motion. Therefore, the position of the target is calculated as follows:  
\[\left\{\begin{array}{l}x_{t}=x_{t-1}+v_{t-1}\cos(\theta_{t-1})\\ y_{t}=y_{t-1}+v_{t-1}\sin(\theta_{t-1})\end{array}\right.. \tag{20}\]  
The localization method we use to calculate the target position is the weighted centroid localization (WCL) [26]. At each time step \(t\), the estimated target position \((x_{t}^{est},y_{t}^{est})\) is calculated based on the distance-related weights of \(N\) tracking nodes around it:  
\[\left\{\begin{array}{l}x_{t}^{est}=\sum\limits_{u=1}^{N}d_{u}^{-\xi}*x_{u}/ \sum\limits_{u=1}^{N}d_{u}^{-\xi}\\ y_{t}^{est}=\sum\limits_{u=1}^{N}d_{u}^{-\xi}*y_{u}/\sum\limits_{u=1}^{N}d_{u} ^{-\xi}\end{array}\right. \tag{21}\]  
where \(d_{u}\) is the distance between the target and sensor \(u\), and exponent \(\xi\) determines the weight of the contribution of each sensor. According to Equation (21), the closer the sensor is to the target, the more it contributes to the calculation of the target position.
# TSKT-ORAM: A Two-Server \(k\)-ary Tree Oblivious RAM without Homomorphic Encryption +
## 2 Related Work
### Multi-Server ORAMs  
There are several multi-server ORAM schemes in the literature. [PERSON] and [PERSON] proposed one of the earliest multiple-server ORAM constructions [33], in which there is no server-to-server communication, but the client-server communication incurs at the cost of \(O(\log N\cdot B)\) per query. Despite the asymptotic result, as pointed out in [27], this construction actually incurs very high client-server communication overhead in practice.  
[PERSON] and [PERSON] propose another multi-server ORAM construction [27], which follows the basic design of partition-ORAM [25]. In this scheme, the client-server communication cost is reduced to a constant number, but the server-server communication cost is \(O(\log N\cdot B)\). In addition, it requires the client to store \(O(\sqrt{N})\) data blocks in the local storage.  
Recently, another multi-server ORAM called CNE-ORAM was proposed, which incurs \(O(B)\) client-server communication cost using at least four non-colluding servers. In CNE-ORAM, each data block is split into two parts using secret sharing techniques. Each part of one data block is further copied into two copies, and each copy is stored onto two out of the four servers. The remaining part is also copied and stored onto the other two servers. At the server side, the storage is organized as a binary tree with of a height \(H=O(\log N)\), and each tree node can store \(\theta\) data blocks. For each data query, the target data block is retrieved using the mechanism of XOR-based private information retrieval (PIR). The client then writes \(\phi\) data blocks to the root node of each server. After \(\chi\) queries, the data eviction process is executed to prevent the root node from overflowing. During data eviction, the client guides the servers to merge nodes on the evicting path. In the post eviction process, the client retrieves a block for the leaf node of the evicting path and replaces it with an empty block if it is a noise block. The computation cost is mainly contributed by data XOR operations, where for each data query, more than \(0.5\theta\cdot L\) blocks are XORed, and the communication cost is mainly contributed by uploading \(\phi\) data blocks to the root per query, where \(L=O(\log N)\) is the height of the tree.
## 2 Methods
### Fitness Evaluation  
Before the fitness tests, all participants performed a standard 20-min warm-up routine (slow jogging, followed by static and dynamic stretching) supervised by the researchers. The players were allowed 10-min of passive rest between tests, as well as water breaks and extra rest time. Each participant was verbally instructed and encouraged to give his/her maximum effort. Three trials were completed for each test. The first was a practice trial for the familiarization with the test and the second and third trials were retained for analysis. All players completed seven fitness tests, from which nine variables were collected for analysis. The established order of physical tests allowed for avoiding performing two consecutive tests for the upper or lower body. In each team, due to competition constraints, the players were divided into groups of four elements and went through the established order. The researchers collected all data.  
Speed test. The 20 m speed test was performed and consisted of a 20 m linear sprint effort [31]. The time of the speed test was recorded in seconds and hundredths of a second, using photoelectric cells (Wireless Sprint system, Brower Timing Systems, Salt Lake City, UT, USA), and the best time of two attempts was registered.  
T-Test. The T-test was used for the change of direction (COD) ability assessment [6; 31]. The time was recorded in seconds and hundredths of a second, using photoelectric cells (Wireless Sprint system, Brower Timing Systems, Salt Lake City, UT, USA), and the best time of two attempts was registered.  
Jump tests. The vertical jumping ability was tested using the countermovement jump (CMJ) and countermovement jump with arms swing (CMJ-S) tests [32]. The height (cm) and relative power (Watts/kilograms, W/kg) of vertical jumps were recorded with a Chronojump measurement technology (Bosco System, Globus, Italy). The best of two attempts was considered. In both tests, the retry interval was 10 s.  
Two-kilogram medicine ball throw. The upper-limb explosive strength was tested using the 2 kg medicine ball throw (MBT) [6]. Participants started the test from a sitting position with their back against the wall using a release from the chest. The distance (cm) attained in the best of two attempts was recorded.  
Handgrip strength. The handgrip (HG) strength was assessed with a handgrip test using a dynamometer (Takei Physical Fitness Test, TKK 5001, GRIP--A, Tokyo, Japan) [33]. Subjects performed the test twice with each hand, and the sum of the best results, achieved by the left and right hands, was considered (in kg).  
Sit and reach test. Flexibility was assessed using the sit and reach test [34]. The score of the test was recorded to the nearest centimeter as the distance reached by the tip of the fingers. The vertical line of the feet' soles was considered as a plane counted as 0 cm. Negative and positive centimeters were considered when the players reached forward, respectively, before and after this vertical plane.
# Modelling and Simulation of Traditional Craft Actions
## 2 Related Work
### Vibration Analysis  
In industry, vibration analysis plays a crucial role in understanding the dynamic behaviour of structures and materials, such as accurately modelling the location of notches [54], or increasing the efficiency of lathe-based manufacturing methods [55]. Vibration analysis is integral to some traditional crafts, providing manufacturing and evaluation benefits.  
Textile weaving relies on rhythmic beating, which generates vibrations that ensure the even distribution of threads and the tightness of the fabric. The uniform application of vibrations during weaving enhances the quality and durability of the textile [56]. Traditional weavers use tactile feedback from the loom's vibrations to guide their work, demonstrating the critical role of vibration analysis in achieving precision and consistency.  
The crafting of musical instruments, particularly string instruments, is dependent on understanding and manipulating vibrations. The resonance and sound quality of instruments like violins and guitars are directly linked to how materials respond to vibrations during crafting and playing. In traditional Japanese drum-making, the tensioning of the drumhead involves careful manipulation of vibrations to achieve the desired pitch and tonal quality [57].  
Traditional East Asia papermaking involves using vibrations to settle pulp fibres uniformly on a screen, which is crucial for producing even, high-quality paper. Specifically, controlled vibrations enable artisans to manipulate the paper's thickness and texture [58].  
In contrast to industrial tasks where electronic sensors and computers are used, in traditional crafts, vibration analysis is performed by the perception and cognitive functions of the practitioner rather than electronic sensors. In musical instruments, perception and cognition are employed; it is the recognition of sound quality, in pottery in the recognition of haptic texture, and in papermaking in the proprioceptive control of fibre shaking. Such human sensory and cognitive aspects are integral in crafts but beyond the scope of this work and are revisited in the discussion in Section 6.
# Lightning Protection Methods for Wind Turbine Blades: An Alternative Approach
## 3 Wind Turbine Blades and Its Protection Methods
### Protection Methods for Blades  
There are four main types of lightning protection methods developed as recommended and outlined in IEC 61400-24 [7]. The methods are as follow:  
1. receptors placed in the tip and an internal wire (i.e., conductor) is used to carry the current to the hub
2. metallic conductor placed around the edges to serve as termination and down conductor
3. metal mesh used on the side of the blade Regardless of the methods, the main function [12; 13; 14] of the lightning protection on the blades is:  
- Successful attachment of the lightning strike to a designated or preferred air termination or down conductor system to conduct the current safety without damaging the blades;  
- Provide passage for the lightning current through sufficient cross-section conductors, diverters, and air terminators to earth. Preventing damage to the system and minimizing the high level magnetic and electric field due to high currents;  
- Minimizing the high level of voltages induced and observed inside and outside of the turbine.  
With insulator-based materials blades, such as glass fiber composites, the conductors can be placed outside of the blade to divert lightning from the blade surface, also, can be placed inside, with air-terminations at specific point outside of the blade. When carbon fiber composites are used, a layer of conducting material is placed over it which can then carry the current to the blade root. Withboth cases, sliding connectors are used to carry the current from the blade to the hub towards the ground [12; 13; 14].  
For the earth termination and down conductors, it has to carry the lightning current safely to the ground where common materials are aluminum, steel, and copper. In general, air termination and for down conductor, the cross-section of at least 50 mm\({}^{2}\) is recommended [7; 12; 13; 14].
## 4 Model Validation
### Analysis of the Effect of Microseismic Energy Change Prediction  
In order to further verify the application value of the BO-LSTM model in microseismic energy prediction of rock bursts, the model is used to predict the daily maximum energy, daily frequency, and daily total energy value and compared with the actual observation value in the field. As illustrated in Figure 9a-c, the model demonstrates an accurate prediction of four microseismic energy events above \(10^{5}\) in the prediction of the maximum energy value and the total daily energy value. The overall fitting effect is deemed to be excellent. In the prediction of daily frequency, although the overall fitting effect is not as good as the other two data sets, it made six accurate predictions among the high-frequency microseismic events, so it also has a certain level of generalizability.  
The results of the evaluation are presented in Table 3. The evaluation of the prediction performance of the four evaluation indicators revealed that the MAE of the daily total energy and the maximum energy was less than 0.01, the MAPE was less than 0.3, and the MSE value was less than 0.01. In comparison to the prediction of impact risk, the results demonstrated a more accurate effect, and the variance accounted for was close to 1, indicating that the model exhibited certain generalization. Although the overall assessment of daily frequency is not as favorable as the other two monitoring data points, its mean absolute error (MAE), mean absolute percentage error (MAPE), and mean squared error (MSE) are 0.072, 0.233, and 0.008, respectively. Furthermore, the relative coefficient is greater than 70%. The predictive outcomes retain their utility as a reference point.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **MAE** & **MAPE** & **VAF** & **MSE** \\ \hline BO-LSTM & 0.026272 & 0.226405 & 87.02\% & 0.001102 \\ GRU & 0.064641 & 0.421889 & 69.73\% & 0.011055 \\ CNN & 0.079188 & 0.536704 & 60.98\% & 0.011501 \\ \hline \end{tabular}
\end{table}
Table 2: Performance evaluation of impact hazard prediction.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **MAE** & **MAPE** & **VAF** & **MSE** \\ \hline Daily Total Energy & 0.007176 & 0.26512 & 81.67\% & 0.000482 \\ Daily Maximum & 0.006125 & 0.296618 & 89.30\% & 0.000252 \\ Energy & 0.072523 & 0.233373 & 73.69\% & 0.008628 \\ \hline \end{tabular}
\end{table}
Table 3: Performance evaluation of microseismic energy prediction.  
Figure 9: BO-LSTM microseismic data prediction results: (**a**) daily maximum energy prediction results; (**b**) daily total energy prediction results; (**c**) daily frequency prediction results.
# Study of RF Stray Currents in ITER Neutral Beam Test Facilities
## 2 Spider Electrical System
### Beam Source  
SPIDER beam source is installed in a vacuum vessel, and it is used to generate and accelerate a beam of negative ions. The components considered in this study are:  
* RF drivers: eight plasma drivers (two of them shown in Figure 1) are used to produce hydrogen or deuterium plasma, from which negative ions are extracted and accelerated. The plasma source is divided in four sectors, each consisting of two RF drivers connected in series. Impedance matching is achieved with a capacitive network installed as close as possible to the drivers. In this work, only one sector is considered.
* Plasma Grid (PG): the PG produces a magnetic filter field, used to reduce the amount of co-extracted electrons in the ion beam.  
Figure 1: Representation of the overall electrical system of SPIDER. From left to right: AGPS, HVD and ISEPS—transmission line—beam source.  
* Extraction Grid (EG): the EG is used to extract negative ions from the plasma generated by the RF drivers.
* Grounded Grid (GG): the GG is the last stage of beam acceleration, and the potential difference between EG and GG represents the beam accelerating voltage.
* Electrostatic Screen (ES): an electrostatic screen referred to the PG potential surrounds the beam source to provide a uniform electric field distribution.