esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Performance Evaluation of the Proposed Algorithm
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Instances**} & \multicolumn{2}{c}{**GA**} & \multicolumn{2}{c}{**PSO**} & \multicolumn{2}{c}{**TS**} & \multicolumn{2}{c}{**PSO-TS**} \\ \cline{2-9}  & **AVG** & **BST** & **AVG** & **BST** & **AVG** & **BST** & **AVG** & **BST** \\ \hline A1 & 50.1 & 49 & 57.3 & 50 & 54.6 & 51 & 49.9 & **48** \\ A2 & 89 & 85 & 101.9 & 94 & 104.2 & 97 & 88 & **84** \\ A3 & 119.2 & 116 & 131.8 & 123 & 144.4 & 137 & 115.4 & **112** \\ A4 & 115.2 & 108 & 134.3 & 128 & 125.6 & 111 & 112.2 & **107** \\ A5 & 293.4 & 275 & 339.5 & 313 & 307.8 & 289 & 281.1 & **258** \\ A6 & 378.4 & 360 & 451.8 & 445 & 377.7 & 353 & 372.5 & **345** \\ A7 & 352.4 & 341 & 400.6 & 367 & 352.2 & 338 & 343.3 & **332** \\ A8 & 310.4 & 305 & 341.5 & 323 & 358.3 & 346 & 298.4 & **292** \\ A9 & 457.1 & 436 & 495.1 & 476 & 496 & 475 & 451.9 & **422** \\ A10 & 515.7 & 501 & 549.3 & 529 & 556.9 & 539 & 483.1 & **449** \\ B1 & 186.9 & 176 & 221.1 & 202 & 222.5 & 213 & 177.5 & **169** \\ B2 & 358.3 & 343 & 398.8 & 379 & 402 & 383 & 348 & **330** \\ B3 & 801.8 & 779 & 889.7 & 864 & 824.8 & 800 & 786.1 & **740** \\ B4 & 370.3 & 357 & 416.1 & 396 & 403 & 380 & 356.7 & **340** \\ B5 & 566.4 & 551 & 625.9 & 607 & 630.7 & 606 & 545.2 & **522** \\ B6 & 701.2 & 675 & 744.9 & 720 & 763 & 743 & 666 & **630** \\ B7 & 400 & 384 & 448 & 436 & 439.8 & 412 & 376.9 & **362** \\ B8 & 743.5 & 706 & 823.3 & 795 & 834.3 & 796 & 705.8 & **679** \\ B9 & 1397 & 1352 & 1441.8 & 1367 & 1430.9 & 1355 & 1384.7 & **1325** \\ B10 & 944.9 & 912 & 996.9 & 932 & 1016.1 & 922 & 944.3 & **886** \\ X1 & 183.6 & 172 & 215.1 & 206 & 215.1 & 202 & 178 & **170** \\ X2 & 299.9 & 293 & 336.5 & 318 & 347.3 & 331 & 291.9 & **279** \\ X3 & 496.1 & 473 & 529.4 & 502 & 540.8 & 523 & 469.8 & **450** \\ X4 & 215.2 & 209 & 240 & 228 & 252.9 & 237 & 205 & **196** \\ X5 & 594.1 & 568 & 659.5 & 616 & 647.2 & 625 & 574.1 & **543** \\ X6 & 842.5 & 811 & 917.2 & 893 & 905.7 & 883 & 798.1 & **765** \\ X7 & 378.2 & 361 & 375.8 & 352 & 413.3 & 377 & 367.9 & **350** \\ X8 & 1058.2 & 1021 & 1095.2 & 1032 & 1071.2 & 1021 & 1056.4 & **996** \\ X9 & 2025.3 & 1950 & 2028.8 & 1891 & 1982.2 & 1942 & 1952 & **1878** \\ X10 & 1342 & 1289 & 1390.7 & 1295 & 1357.5 & 1284 & 1326.7 & **1275** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparisons of Particle swarm optimization-Tabu search (PSO–TS), Genetic algorithm (GA), Particle swarm optimizaition (PSO), and Tabu search (TS) over ten runs.
## 3 Remote Terminal Unit (RTU)  
A RTU is based on the 16-bit high performance microcontroller (MCU) PIC18F8723 which has among other features up to 16 channels of bit resolution analog to digital converter (ADC), two enhanced addressable USART modules and two master synchronous serial port (MSSP) modules supporting SPI and I2C master and slave modes.  
Figure 1: General overview of the proposed system, on the left side the ISM network, on the center and right side the KU Band Satellite link and on the right side the monitoring center.
## 2 Materials and Methods
### Data Analysis
#### 2.3.1 Image Decomposition  
For the structural similarity analysis, and the detection of aponeuroses and muscle fascicles (Figure 2), the ultrasound images were decomposed using a steerable, oriented pyramid employing complex and oriented wavelets [32]. The oriented wavelets were created by successive application of low and high pass filters, and a mask selecting only a specific orientation range of the resulting bandpass filter. Additionally, complex instead of real wavelets were employed, since most structure information is provided by the relative phase patterns of the wavelet coefficients [33].  
In this study, the frequency spectra of the ultrasound images were separated using maximally six decomposition levels. The detail images resulting from this decomposition contained image structures of an increasing size range with increasing decomposition level (Table 1). Orientation zero of the wavelets was defined as 0 rad, which was parallel to the ultrasound beam, and was incrementally increased with \(\frac{\pi}{n}\) rad, with \(n\) the number of chosen orientations.  
\begin{table}
\begin{tabular}{c c} \hline
**Decomposition Level** & **Bandpass Filter Size Range (Pixels)** \\ \hline
1 & 1.5–2.6 \\ \hline
2 & 3.0–5.3 \\ \hline
3 & 5.8–10.4 \\ \hline
4 & 11.4–20.2 \\ \hline
5 & 21.6–38.9 \\ \hline
6 & 41.7–73.0 \\ \end{tabular}
\end{table}
Table 1: Full width at half maximum ranges of the bandpass filters in the spatial domain. The pixel sizes of the acquisitions ranged between 0.07 and 0.15 mm.  
Figure 1: Experimental set-up of the endurance runners. (**Left**) The transducer is fixated onto the upper leg to image the belly of the vastus lateralis muscle in the longitudinal direction. The transducer is connected to a mobile phone, which is attached to the upper arm of the volunteer. (**Right**) A representative ultrasound image obtained at the 5 th km of all included volunteers.
## 6 Existing Multi-UAV Communication Architectures  
The communication architecture of a multi-UAV system is a key factor in the structure of the network infrastructure and the interoperability between UAVs. It has undergone various improvements, moving from centralized to decentralized communications [1; 19] (see Figure 8). Centralized architectures are essentially based on the multi-directional communication model between UAVs and GCS [6], while the decentralized architectures are based on various models, allowing us to guarantee the scalability of UAVs, the interoperability between UAVs, and the coordination of several tasks on the UAVs to achieve a common goal [45].  
The multi-UAV communication architectures are classified into four types, centralized, single-group, multi-group, and multi-level architectures [1], as shown in Figure 8. In this section, we will discuss the different research works conducted toward the decentralization of multi-UAV communication architectures.
# Impact Analysis of Satellite Geometry Variation on ARAIM Integrity Risk over Exposure Interval
## 5 Conclusions  
We conducted a preliminary assessment of NES-based ARAIM integrity performance by presenting a methodology to calculate the actual integrity risk based on a geometry-sensitive risk-evaluation model. According to the simulation analysis, compared with the reference risk value, the results evaluated by traditional NES-based ARAIM methods are usually proved to be optimistic. In contrast, the proposed method can realize rigorous bounding for the reference risk value by considering all epoch information within the exposure interval during the evaluation of integrity risk. Using the proposed method and baseline conditions, we rigorously analyzed the impact of satellite geometry variation on the ARAIM integrity performance over the exposure interval. Several key conclusions are summarized as follows.  
First, in the nominal LPV-200 with 150 s exposure interval time and the nominal RNP 0.1 with 3600 s, the traditional NES-based ARAIM does not produce overly optimistic integrity risk-evaluation results. According to the global simulation results, the maximum average availability loss of traditional method is only 0.82% after considering the impact of satellite geometry variation.  
Second, in the situation of a single-satellite outage during LPV-200 approach, the traditional method is overly optimistic in evaluating the integrity risk based on the geometry constant assumption. According to the global simulation results, the average availability loss has significantly increased to 6.94%, and the coverage meeting 95% availability has reduced by 53.70%. This implies that ensuring the rigor of integrity risk evaluation in the case of aircraft banking and ionospheric scintillation is challenging for the NES-based method.  
Finally, a potential real-time integrity risk evaluation approach is briefly introduced, which involves utilizing multiple-epoch information through batch processing or filtering methods. In future research, we will focus on applying these methods to enhance the current ARAIM algorithm.  
Conceptualization, R.L., L.L., and Z.N.; methodology, R.L. and L.L.; validation, Z.N., Y.D., and X.X.; formal analysis, Y.D. and X.X.; investigation, L.L. and Z.L.; resources, R.L. and L.L.; writing--original draft preparation, R.L.; writing--review and editing, R.L. and L.L.; visualization, R.L., Z.N., and Z.L.; supervision, Z.N. and Z.L.; project administration, L.L.; funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript.  
National Key Research and Development Program (No. 2021 YFB3901300), the National Natural Science Foundation of China (Nos. 62373117 and 62003109), the Fundamental Research Funds for Central Universities (Nos. 3072019 CF0401, 3072020 CFT0403).  
Figure 10: Availability map of the ARAIM under RNP 0.1 nominal situation. The subfigure (**a**,**b**) represents the availability of ARAIM based on the traditional method and the proposed method, respectively.
# Helmholtz Coils Based WPT Coupling Analysis of Temporal Interference Electrical Stimulation System
## 2 Principles and Methods Analysis  
### Magnetically Coupled Resonant Wireless Power Transfer System  
Magnetically coupled resonant wireless power transfer technology was first proposed by a Massachusetts Institute of Technology research team in 2007 [24]. This technique uses the concept of near-field coupling and the principle of resonance. The circuit parameters of the transmitter and receiver are set appropriately to have the same resonant frequency. When a current at the resonant frequency is passed, the system will reach a resonant state, thus achieving an efficient energy transfer from the transmitter to the receiver.  
The magnetic field signal at the resonant frequency is first obtained through the high-frequency signal generator. Then, it is added to the transmitting coil after the high-frequency power amplifier increases the transmission power. At that point, the transmitting coil is matched to the transmitter circuit through an impedance matching network. The transmitter transmits the energy via magnetic coupling to the receiver, which rectifies and filters the received high-frequency electricity to provide energy for the load. Due to the resonance characteristics, the transmission efficiency of the system is highly selective for frequency. When the operating frequency deviates from the coil's resonant frequency, the transmission efficiency decreases sharply.  
The key to the efficient transmission of MCR-WPT lies in the fact that the whole system works in a resonant state. The system establishes a stable energy transmission channel between the coils through magnetically coupled resonance, thus enabling an efficient wireless transmission of non-radiative energy. Compared to the traditional magnetic inductive coupling WPT system, the MCR-WPT system has the advantages of being highly efficient and having a long transmission range and greater penetration into non-magneticmedia. This is why the MCR-WPT system has a broader application prospect in the field of implantation device engineering.
# Along-Track Multistatic Synthetic Aperture Radar Formations of Miniatellites
## 2 SIMO SAR Formation  
Let us assume to have \(N\) SAR antennas, each put on an independent platform deployed along the track at given positions (known with reasonable accuracy). We also suppose, for now, that all the satellites fly on the same orbit, i.e., the formation has no real-time interferometric or tomographic capabilities. The formation is a Single Input Multiple Output system, one satellite transmits the waveform and each sensor of the formation collects the return backscattered in the common imaged area. The multiple transmitters case (Multiple Input Multiple Output, MIMO) can easily be extended from the model below, but it is beyond the purpose of the study. Initially, we suppose the sensors at a nominal and uniformly spaced distance \(dx\), multiples of 2:PRI apart. In Figure 1, we show the geometry of the formation.
## 4 Discussion
### Statistic Analysis
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Variables** & \(p\)**-Value for Shapiro–Wilk Normality Test** &
\begin{tabular}{l} **H0 = Samples Have Normal Distribution** \\ **(with 95\% Confidence)** \\ \end{tabular} & **Paired Test to Use** \\ \hline Breathing frequency & 0.18171 & H0 non-rejected & \(t\)-test \\ Heart rate & 0.15011 & H0 non-rejected & \(t\)-test \\ Sleep rhythm & 0.02347 & H0 rejected & Wilcoxon T \\ Body temperature & 0.28179 & H0 non-rejected & \(t\)-test \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Normality test for male subjects and corresponding paired test to use. Figure 15: Q–Q plot of measurements captured for two conditions, without and with sensors, for male subjects. Figure 16 shows the histogram of the difference of the different measurements for the female subjects. In this figure it is difficult to observe if any of this has a normal distribution. Figure 17 shows the Q-Q plot for all measurements of the female subjects, and we observe that for all the variables, there appears to be divergences from normality in all data. However, this is not conclusive. Figure 16: Histogram of difference between two conditions, without and with sensors, for female subjects. Red line is estimated mean. Figure 17: Q–Q plot of measurements captured for two conditions, without and with sensors, for female subjects. Table 13 shows the table with the results of the \(p\)-value for each type of measurement for male subjects. From this table, we observe that three of the measurements can use a paired \(t\)-test, and one requires the use of a Wilcoxon \(t\)-test. We observe that null hypothesis for \"breathing frequency\" is in the border to be rejected. The ratio of the larger to the smaller sample variance for breathing frequency, heart rate, sleep rhythm, and body temperature sensors for males are 1.53213, 0.25152, 0.66405, and 1.15626 respectively; therefore, we consider all measurements to have equal variance. Table 14 shows the results of the paired \(t\)-test for all measurements. We observe that, statistically, the means of the conditions in both scenarios, no-sensors and sensors, are different for \"Breathing frequency\", \"Heart rate\", \"Sleep rhythm\", but not for \"Body temperature\". From the above analysis, we conclude that in general \"Breathing frequency\", \"Heart rate\" and \"Sleep rhythm\" measures for female subjects are affected when they know they are under the system.
# P-Wave Reflection Approximation of a Thin Bed and Its Application
## 2 Theory  
### P-Wave Reflected Approximation of a Thin Bed  
For a thin bed in elastic isotropic media, as shown in Figure 1, [PERSON] et al. [21] gave second-order series approximation for P-wave reflection as follows,  
\[R_{PP}\approx A_{0}+A_{2}\sin^{2}\theta, \tag{1}\]  
where \(\theta\) is incidence angle, \(A_{0}\) and \(A_{2}\) are the intercept and gradient, respectively, which are presented in Appendix A in detail.  
Figure 1: A thin-bed model with two horizontal interfaces. P is incident P-wave, PS is reflected or transmitted S-wave, PP is reflected or transmitted P-wave, other parameters are introduced in Appendix A[21].  
When the middle layer's thickness \(h\) is ultrathin, then \(\omega h/v_{\rm P2}<<1\) and \(\omega h/v_{\rm S2}<<1\), which are defined as \(\tau\) and \(q\), respectively, in Equation (1) (shown in Appendix A). That is, \(\tau<<1\) and \(q<<1\). Using first-order Taylor expansions,  
\[\sin\tau\approx\tau,\ \sin q\approx q,\ \cos\tau\approx 1,\ \cos q\approx 1, \tag{2}\]  
Equation (1) is simplified as  
\[R_{PP}\approx P+G\sin^{2}\theta, \tag{3}\]  
with  
\[P=\frac{1}{n_{0}}\bigg{(}\frac{z_{P3}}{z_{P1}}-1\bigg{)}+\frac{j\tau_{1}}{n_{0} }\bigg{(}\frac{z_{P2}}{z_{P1}}-\frac{z_{P3}}{z_{P2}}\bigg{)}, \tag{4}\]  
\[\begin{array}{ll}G=\frac{1}{2n_{0}}&\bigg{(}\frac{z_{P3}}{z_{P1}}+j\tau\frac {z_{P2}}{z_{P1}}+\frac{4j\tau_{1}}{r_{1}^{2}}\bigg{)}\bigg{(}P+\frac{2}{r_{1} }A-1\bigg{)}+\frac{l_{1}l_{2}}{n_{0}r_{3}}\big{(}1-\frac{2}{r_{3}}\big{)} \frac{z_{P3}}{z_{P1}}B\\ &+\frac{1}{n_{0}}\bigg{[}\frac{2}{r_{1}^{2}}+j\tau\bigg{(}l_{1}-\frac{2l_{1}} {r_{2}^{2}}+\frac{2}{r_{1}^{2}}\frac{z_{P1}}{z_{P2}}\bigg{)}\bigg{[}z_{P1} \bigg{]}(1+P+A)\\ &+\frac{j\tau_{1}}{n_{0}r_{1}}\big{(}1-\frac{4}{r_{1}^{2}}\big{)}A+\frac{l_{1} ^{2}l_{2}}{n_{0}}\bigg{(}\frac{1}{2}-\frac{2}{r_{3}^{2}}\bigg{)}\frac{z_{P1}} {z_{P1}}\frac{2}{n_{0}}\big{(}1+\tau^{2}\big{)}\end{array} \tag{5}\]  
Other parameters are presented in Appendix B in detail.  
Equation (3) is the linear analytic approximation of a thin-bed P-wave reflection coefficient. The approximate formula is written as a sum of the constant term and the first order term of the sine-squared incidence angle. Compared with Equation (1), the linear analytic approximation has a relatively compact form (shown in Appendices A and B), which is more convenient to achieve AVO/AVA analysis and inversion of a thin bed.  
Compared with linearized approximations to the Zoeppritz equations for a single interface [22; 23; 24], the intercept and gradient of the linear analytic approximation for the thin-bed P-wave reflectivity are frequency dependent; furthermore, they are functions of elastic parameters of the target thin bed and the upper and lower half spaces. Meanwhile, the linear analytic approximation is derived with no weak impedance contrast hypothesis, so it is valid for thin-bed models with strong impedance contrasts, such as coal seams, etc.
## 2 Related Works
### Data-Driven Methods for RUL Forecasting and Its Benefits  
Data-driven techniques for RUL prediction utilize machine learning models and statistical methods to extract patterns and relationships from historical data, enabling accurate estimation of the RUL of a system or component. These methods include regression models, time-series algorithm analysis, and deep learning approaches. By leveraging large volumes of sensor data, operational parameters, and historical failure records, data-driven methods offer flexibility, scalability, and predictive accuracy for proactive maintenance planning and decision-making. Leveraging historical data, context informed by data forecasts the pace of battery deterioration. The mechanisms by which data-driven models operate and disseminate remain uncertain. Weight constraints in a data-driven mathematical framework are determined by analyzing the training data, without applying physics-based methods. Recently, the adaptability and relevance of data-driven models have garnered global interest. Accurate RUL predictions enable predictive maintenance, allowing degradation patterns to be studied and failure points forecasted. This facilitates timely system updates, including software and operating system upgrades, which improve system longevity and adaptability [15]. RUL insights also contribute to optimized battery management, enabling energy storage systems to allocate resources more effectively by prioritizing high-RUL batteries for critical tasks and reserving lower-RUL batteries for secondary applications [16]. The foresight provided by monitoring the remaining useful life enhances system reliability by enabling early detection of potential failures and performance issues. Preemptive corrective actions reduce the risk of unexpected malfunctions and unprecedented failures [17]. Additionally, RUL predictions lead to significant cost savings by optimizing maintenance schedules and avoiding premature or delayed battery replacements, thereby reducing unplanned downtime [18]. Safety is another critical benefit, as RUL monitoring helps prevent incidents like thermal runaway and short circuits by enabling proactive battery replacements in sensitive environments [19]. Finally, RUL prediction promotes sustainability by extending battery lifespans, reducing waste, air and land pollution, and decreasing the demand for raw materials and carbon-intensive manufacturing processes, ultimately lowering the environmental impact of battery usage [20].
# Analyzing TCP Performance in High Bit Error Rate Using Simulation and Modeling
## 3 Results
### FTP Server: Ethernet Frames Sent  
Table 6 lists the scenarios (probabilities of packet dropping) and the corresponding total number of Ethernet frames transmitted by the FTP server. We observe that more Ethernet frames were sent because of increasing packet drop probabilities. For instance, 7946 Ethernet frames were retransmitted (highest number) by the FTP server at the packet dropping rate of 10%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenario** & **Response Time (s)** &
\begin{tabular}{c} **Response Time (s)** \\ **Difference from \(p_{drop}=0\%\)** \\ \end{tabular} \\ \hline \(p_{drop}=0\%\) & 60.21 & 0 \\ \hline \(p_{drop}=0.01\%\) & 60.42 & 0.35 \\ \hline \(p_{drop}=0.10\%\) & 61.46 & 2.08 \\ \hline \(p_{drop}=1\%\) & 76.83 & 27.60 \\ \hline \(p_{drop}=10\%\) & 1953.61 & 3144.66 \\ \hline \hline \end{tabular}
\end{table}
Table 4: FTP Download Response Time for Five Selected Scenarios.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scenario** & **Retransmission Count** \\ \hline \(p_{drop}=0\%\) & 0 \\ \hline \(p_{drop}=0.01\%\) & 1 \\ \hline \(p_{drop}=0.10\%\) & 7 \\ \hline \(p_{drop}=1\%\) & 62 \\ \hline \(p_{drop}=10\%\) & 1092 \\ \hline \hline \end{tabular}
\end{table}
Table 5: FTP Server: Retransmission Count for Five Selected Scenarios.