esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Capacitive Deionization (CDI)  
### Basic Principle and Operational Features  
CDI is an electrochemical ion separation process using not only the ion ad/desorption into the EDL formed on the capacitive electrodes [1] but also faradaic reactions of pseudo-capacitive and battery electrodes [4]. Commonly, CDI is comprised of a pair of porous electrodes such as activated carbons, current collectors, and a channel for treating water [2], which is a simple cell structure to stack multiple cells for industrial applications.  
Conventional CDI is operated in two consecutive steps, the charging and discharging processes (Figure 1). During the charging step (purification of water by inducing electrical energy), ions in aqueous media are removed by the adsorption within the EDL or by faradaic reactions such as intercalation, pseudo-capacitive, and redox reactions [8]. During the discharging step, (regeneration of electrodes by the electrical short-circuit or the reversed electrical energy), the immobilized ions are released, leading to the production of concentrated effluent [18]. Consumed energy during the charging step could be partially recovered during the discharging step, as encountered in energy storage devices. CDI is a considerably more energy-efficient process than other desalination technologies [36; 37].
## 2 Vision and Key Features for Future 6G Networks
### Energy-Efficient Communication  
6G will meet and fulfil many expectations, including the delivery of a high-energy performance, most especially from the perspective of pervasive utilization of the Internet-of-Things (IoTs) and with an eco-system of many minute sensors. Furthermore, extending the battery-recharge capacity of smartphones must be addressed, in line with the notion that their capabilities and abilities to deal with sophisticated multimedia signal processing leap in quantum rises as their power consumption increases [40]. Thus, low energy utilization and elongated battery charge life duration are two research topics in 6G to overcome the daily re-charging difficulties for most communication equipment and enhance communication needs. Accordingly, 6G must evoke a comprehensive energy-efficient wireless communication strategy. A fundamental goal of 6G communication is to perform whenever and wherever possible with battery-free communications, aiming at 1 pico-Joules per bit communication efficiency [16]. 6G communication has the advantages of high-power THz-waves, apart from directional beam communication with MIMO antenna arrays, thereby enabling devices to send power beams in a certain direction. This technique can potentially supply sufficient energy to devices under the network coverage. The 6G vision and directions, as published in [41], indicate that research attention should prioritize battery lifetime and service classes in 6G rather than data rate and latency. To reduce energy utilization, the computing functions of user nodes must be transferred to smart base stations equipped with a dependable power supply or universal smart radio space [42]. Cooperative relay communications and network densification can also have utmost importance in a bid to reduce the transmitting power of mobile nodes by reducing the per-hop signal propagation gap [43; 44]. Achieving long battery longevity in 6G requires an accumulation of divergent energy-harvesting strategies which not only harvest energy from ambient RF signals but also extract energy from micro-vibrations and sunlight [45; 46]. Long-range wireless power charging would also be a promising candidate to prolong battery longevity [47; 48]. In addition, distributed laser charging is a technology that may be capable of safely providing 2-W power and reach a distance of 10 m for mobile devices [16]. Table 1 presents a comparison of the major wireless charging techniques (i.e., inductive coupling, magnetic resonance coupling, microwave radiation, and distributed laser charging).
###### Abstract  
Image matching between spaceborne synthetic aperture radar (SAR) images are frequently interfered with by speckle noise, resulting in low matching accuracy, and the vast coverage of SAR images renders the direct matching approach inefficient. To address this issue, the study puts forward a multi-scale adaptive improved SAR image block matching method (called STSU-SAR-SIFT). To improve accuracy, this method addresses the issue of the number of feature points under different thresholds by using the SAR-Shi-Tomasi response function in a multi-scale space. Then, the SUSAN function is used to constrain the effect of coherent noise on the initial feature points, and the multi-scale and multi-directional GLOH descriptor construction approach is used to boost the robustness of descriptors. To improve efficiency, the method adopts the main and additional image overlapping area matching method to reduce the search range and uses multi-core CPU+GPU collaborative parallel computing to boost the efficiency of the SAR-SIFT algorithm by block processing the overlapping area. The experimental results demonstrate that the STSU-SAR-SIFT approach presented in this paper has better accuracy and distribution. After the algorithm acceleration, the efficiency is obviously improved.  
s +
Footnote †: journal: applied sciences  
article
# The Application of Numerical Ductile Fracture Simulation in the LBB Evaluation of Nuclear Pipes
## 3 Numerical Ductile Fracture Simulation
### Simulation Results  
The crack growth behavior of a full-scale pipe with a circumferential through-wall crack under a four-point-bending load is simulated based on the parameters calibrated in Section 3.2. A quarter of the FE model for full-scale pipe is constructed because of the symmetry, as shown in Figure 9. The element sizes of L\({}_{\rm e}\) = 0.2 mm (\(\omega_{\rm c}\) = 0.69), 0.4 mm (\(\omega_{\rm c}\) = 0.41), and 0.8 mm (\(\omega_{\rm c}\) = 0.22) are applied to the cracked surface of full-scale pipes for simulation [32], but a simulation with L\({}_{\rm e}\) = 0.2 mm fails due to numerical problems. Figure 10 presents the numerical simulation results of the crack propagation diagram for a crack with \(20=60^{\circ}\). It can be seen that significant plastic deformation characteristics are exhibited by the crack-tip region, and the crack propagation demonstrates obvious non-uniformity--with the propagation rate in the central region being significantly higher than that at the sides. The underlying mechanism of this phenomenon is primarily related to the stress state distribution along the crack front: in the central region of the crack tip, due to the strong constraint of plane strain, a higher stress triaxiality characteristic is exhibited by this area. Based on the SMCS, it can be inferred that the critical fracture strain value in this region is relatively small. In contrast, near the free surfaces on both sides, the plane stress effect dominates, resulting in relatively lower stress triaxiality. According to the SMCS theory, the critical fracture strain value in these regions is comparatively larger, thereby leading to a slower crack propagation rate.  
Figure 8: FE simulation for determining element-size-dependent parameter \(\omega_{\rm c}\): (**a**) FE model for CT specimen; (**b**) effect of \(\omega_{\rm c}\) on J-R curves of CT specimen when L\({}_{\rm e}\) = 0.4 mm; (**c**) value of \(\omega_{\rm c}\) according to L\({}_{\rm e}\).  
A comparison between the numerical simulation results and the experimental crack morphology from previous tests shows good agreement, confirming the accuracy and reliability of the SCMC model (Figure 11). The load-CMOD curves obtained from the simulation are compared to those from the full-scale pipe tests, as shown in Figure 12a. The crack length at each time step is measured, and the J-R curves are calculated, as shown in Figure 12b. It can be seen the simulation results agree quite well with the test results, and the element sizes (I\({}_{\text{ee}}\) = 0.4 mm and 0.8 mm) show little effect on the simulation results.  
Figure 11: Comparison of full-scale pipe fracture morphology between experiments and numerical simulations [29].  
Figure 9: Numerical simulation model for full-scale pipe tests.
# A Novel Carrier Tracking Approach for GPS Signals Based on Gauss-Hermite Kalman Filter
## 5 Simulation Setup and Experiment Results
### Dynamic Weak Signal Environment Scenario  
In order to verify the performance of the proposed algorithm for a weak signal under many dynamic conditions, a scenario of a moving receiver was designed with attenuation of the received signal strength. In this case, the signal started at 50 dB-Hz and descended by 0.5 dB/s. The minimum power reached 30 dB-Hz at 50 s. Regarding the dynamic scene simulation, three dynamic periods were simulated with different Doppler rates of 50 Hz/s, 100 Hz/s, and 150 Hz/s. Figure 5 shows the \(C/N_{0}\) estimates with the three tracking algorithms. As can be seen in the figure, the accuracy of the \(C/N_{0}\) estimation, in all algorithms, decreased upon reducing the signal power. It is shown that the EKF algorithm could follow the actual signal strength only when the signal power was high. When the \(C/N_{0}\) reached 32 dB-Hz, the \(C/N_{0}\) estimation showed a large error, and the receiver lost the lock at about \(t=45\) s. In addition, the \(C/N_{0}\) estimates in the UKF-based tracking algorithm showed a large deviation from the actual value after 45 s of tracking. In contrast, GHKF algorithm still locked the signal, and the \(C/N_{0}\) parameter was correctly estimated with an accuracy of approximately \(\pm 2\) dB-Hz.  
Figures 6 and 7 show estimates of \(\hat{A}\) and \(\hat{f}_{d}\). From these figures, we can see that all algorithms had the same estimate of these RNPs until 45 s of tracking, where \(C/N_{0}\ =\ 34\) dB-Hz. After that, the EKF algorithm incorrectly estimated the parameters until the end of experiment, where \(C/N_{0}=30\) dB-Hz. For example, it can be noted that, from 46 s onward, at which \(C/N_{0}\approx 33\) dB-Hz, the Doppler frequency obtained in the EKF-based tracking system was determined incorrectly (Figure 7b). Moreover, the amplitude measurement accuracy in the UKF-based tracking algorithm decreased after 45 s of tracking. In contrast, the GHKF algorithm provided the highest tracking stability compared to the other two methods.
## 5 Fault Location in Direct Current (DC) Grid Having Cable and Overhead Line Segments
### Natural Frequency of a Traveling Wave  
Typically, traveling waves originate from dc fault point and inter-reflect between fault point and VSCs. Frequency spectrum of traveling waves of the fault is created by the series of fundamental (dominant) and harmonic components of the frequency. The lowest harmonic order frequency is characterized by its high amplitude and is designated as dominant frequency while amplitude decreases as the harmonic order of frequency increases. Surge reflection routes in faulted line and spectrum of traveling wave at converter stations are shown in Figure 6. Total reflection would be positive when traveling wave reaches at end of TL and total reflection would be negative when TW strikes the fault point. This process continues till the time period \(T_{1}=4d/v\), where \(v\) is the velocity of the propagation of traveling wave. When the system impedance is zero, the total reflection will be positive on the first reach of traveling wave at VSC station and total reflection will be negative on the reach of traveling wave at fault point. The terminal voltage changes with time period \(T_{2}=2d/v\).  
This phenomenon of natural frequency is employed for finding the FL in MT-HVdc systems. For this, a relationship is derived between dominant component of natural frequency and distance of fault from the converter station.
## 3 Methodology
### Irradiance Distribution Characteristic of PV Array  
The size of airship is much larger than that of single PV module, with each module being considered a panel. The PV array of airship adopts a \(6\times 6\) TCT configuration, totaling  
Figure 3: Single-diode model of solar cell.  
36 modules. During stable altitude cruising, the helium inside the airship's envelope experiences superheating and overpressure due to radiant heating, resulting in regular fluctuations in pitch angle. Simultaneously, the yaw angle frequently changes in response to variations in the flight trajectory and wind conditions. The simultaneous influence of pitch angle, yaw angle and solar incidence angle results in irregular variations in the irradiance distribution of PV array. To investigate this distribution characteristics, the airspace over 23\({}^{\circ}\) N, 115\({}^{\circ}\) E is selected as a representative flying area, with the typical flight conditions specified in Table 1.  
Figure 4 illustrates the irradiance distribution characteristics of the PV array corresponding for 4 different attitude angles. At a yaw angle of 270\({}^{\circ}\) and an angle of 10\({}^{\circ}\) (Figure 4a), the PV array's irradiance intensity ranges from 7 to 1015 W/m\({}^{2}\). The sun is located in the right rear position of the airship. Consequently, the irradiance intensity decreases diagonally from right to left and from rear to front. A significant change in irradiance distribution occurs when the yaw angle shifts to 90\({}^{\circ}\) (Figure 4d). In this scenario, the top-left corner exhibits the highest irradiance intensity, peaking at 1100 W/m\({}^{2}\), while the bottom-right corner displays the lowest intensity at 76 W/m\({}^{2}\). Similar patterns are observed in Figure 4b,c. In Figure 4b, module 61 in the bottom-left corner demonstrates the highest irradiance intensity, whereas module 16 in the top-right corner exhibits the lowest intensity. Conversely, Figure 4c reveals an opposite pattern compared to Figure 4b.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Condition** & **Date** & **Time** & **Pitch Angle** & **Yaw Angle** \\ \hline
1 & 12.25 & 10:00 & \(-\)8 & 270 \\
2 & 12.25 & 10:00 & 10 & 10 \\
3 & 12.25 & 10:00 & 8 & 180 \\
4 & 12.25 & 10:00 & \(-\)6 & 120 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Flight conditions and attitude angles.  
Figure 4: Irradiance distribution characteristics: (**a**) condition 1; (**b**) condition 2; (**c**) condition 3; (**d**) condition 4.  
Despite the significant and frequent variations in irradiance intensity among the airship's PV modules, there is always a certain pattern: the irradiance intensity gradually decreases radially and circumferentially from a particular component with the highest irradiance intensity. This is a unique characteristic observed in PV arrays of airships, distinguishing them from ground-based PV systems.
## 4 Calibration of Electrical Inertia Simulation Accuracy
### Calibration of Basic Inertia  
Through the standard inertia test, the inertia \(J=J_{s}\) of each test system can be determined. Because the inertia simulation equipment itself has inertia \(J_{bu}\) (which is called the basic inertia of the system for convenience of expression), it is necessary to define the basic inertia \(J_{ba}\) of the equipment before the electrical inertia simulation test, so that the simulation inertia \(J_{\alpha}\) of the motor can be defined.  
\[J_{s}=J_{ba}+J_{\alpha} \tag{48}\]  
To calibrate the basic inertia, mechanical connection is adopted, as shown in Figure 20. The to-be-calibrated inertia includes the tested motor (6), reducer (5), torque meter (4), and coupling. During the test, the driving end is in torque control mode, and the calibrated end is powered off. Under this structure, the basic inertia \(J_{ba}\) of the system can be obtained by the same principle as the standard inertia.  
Figure 19: Deviation of standard inertia.  
Figure 18: Calibration test results under equivalent inertia of 10497.6 kg\(\cdot\)m\({}^{2}\).  
The test result is shown in Figure 21. According to the design and manufacture drawings, the theoretical basic inertia is 512 kg\(\cdot\)m\({}^{2}\). The average acceleration of the system is 1.79 rad/s\({}^{2}\) during the speed 150-200 r/min under the condition of average torque \(T_{t1}=1500\)Nm, and 2.02 rad/s\({}^{2}\) under the torque of \(T_{t2}=1620\) Nm. The actual inertia is 523.96 kg\(\cdot\)m\({}^{2}\) after calibration.
## 2 Methodology
### Robustness Computation Method  
Based on the principles and the evaluation index proposed above, the method to compute the watermarking algorithmic robustness is studied here. The main theory for solving this problem is the enumeration of the watermark distribution after image cropping attack, based on combinatorial mathematics. By dividing all the enumerations (denoted as \(E_{total}\)) by the number of successful situations, from which watermarks have been correctly extracted (denoted as \(E_{suc}\)), the extraction probability \(R\) is calculated directly as:  
\[R=\frac{E_{suc}}{E_{total}}. \tag{4}\]  
The question is then becomes how to calculate \(E_{total}\) and \(E_{suc}\). Obviously, the calculation of \(E_{total}\) is easy to conduct according to the permutations, which will be given in the following section. However, the difficulty of this problem is to calculate \(E_{suc}\). From the deduction above, \(E_{suc}\) can be acquired by enumerating the situations under image cropping attack with the watermark can still be extracted. But as far as we are concerned, counting the situations directly is easy to confuse some situations and complicated to give the computation formula correctly. In order to solve the problem, the auxiliary function has been adopted to calculate \(E_{suc}\).
## 4 Discussion
#### Strengths and Limitations  
The primary strengths of this research are that it deals with a very important topic, includes a large sample, has strong measurement, and focuses on an important age group (i.e., middle school students) that is often very difficult to study. It also helps bring the field of communication to bear on an important new topic and increasingly common problem that adversely affects the lives of many adolescents and young adults. A key limitation is that it was conducted as part of a larger study, making it impossible for us to measure all the possible predictors of cyberbullying perpetration and victimization that might have been included if this was the sole focus of data collection. Thus, we chose to focus on one key communication variable (i.e., verbal aggression) that has been shown to be related to other types of aggressive behavior in this [29] and other age groups [25]. For example, based on previous research, [PERSON] et al. [17] examined the relationships between numerous variables previously suggested might be related to cyberbullying perpetration when looked at individually (things such as other risky behaviors, technology use, parental behaviors, etc.). However, when analyzed together, only one of these variables (i.e., risky behaviors) remained a statistically significant predictor of cyberbullying perpetration in high school students. Simultaneously evaluating the effects of additional and multiple variables in a middle school sample remains a ripe area for future research.
## 3 Results
### Homa-Ir According to RHR  
Table 4 and Figure 2 shows HOMA-IR values according to the RHR. A logistic regression analysis was performed after adjusting for sex, age, marital status, education level, family income, alcohol drinking, smoking status, blood pressure, BMI, and HbA1c TC, TG, HDL-C, and LDL-C levels. The HOMA-IR was found to have a statistically significant effect on the RHR (\(p<0.05\)). Compared with \(\leq\)67 bpm, the odds ratio (OR) was 1.277 (95% confidence interval [CI], 1.045-1.560) for 68-71 bpm, 1.599 (95% CI, 1.336-1.913) for 72-79 bpm, and 1.919 times (95% CI, 1.567-2.350) for \(\geq\)80 bpm. In Figure 2, the dashed line represents the represents the reference value(\(\leq\)67 bpm), and the gray lines represent the 95% confidence intervals for each odds ratio.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Resting Heart Rate**} \\ \cline{2-5}  & \(\leq\)**67 bpm** & **68–71 bpm** & **72–79 bpm** & \(\geq\)**80 bpm** \\ \hline \multirow{2}{*}{HOMA-IR} & \multirow{2}{*}{1.00} & 1.277 & 1.599 & 1.919 \\  & & (1.045–1.560) & (1.336–1.913) & (1.567–2.350) \\ \hline \hline \end{tabular}  
Adjusted for sex, age, marital status, education, family income, alcohol drinking, smoking status, blood pressure, and body mass index and glycated hemoglobin, total cholesterol, triglyceride, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol levels.  
\end{table}
Table 4: Logistic regression for the effect of HOMA-IR on resting heart rate.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{4}{c}{**Resting Heart Rate, \(n\) (\%)**} & \multirow{2}{*}{\(p\) *}{\({}^{*}\)} \\ \cline{2-2} \cline{4-6}  & **Total** & \(\leq\)**67 bpm** & **68–71 bpm** & **72–79 bpm** & \(\geq\)**80 bpm** \\ \hline
**TG, mg/dL** & & & & & \\ \(<\)200 & 5381 (87.0) & 1802 (88.6) & 1029 (87.1) & 1488 (86.7) & 1062 (84.5) & \\ \(\geq\)200 & 807 (13.0) & 231 (11.4) & 152 (12.9) & 229 (13.3) & 195 (15.5) & 0.07 \\
**HDL-C, mg/dL** & & & & & & \\ \(<\)40 & 827 (13.4) & 283 (13.9) & 141 (11.9) & 244 (14.2) & 159 (12.6) & \\ \(>\)40 & 5361 (86.6) & 1750 (86.1) & 1040 (88.1) & 1473 (85.8) & 1098 (87.4) & 0.237 \\
**LDL-C, mg/dL** & & & & & & \\ \(<\)160 & 5639 (91.4) & 1831 (90.3) & 1078 (91.6) & 1567 (91.4) & 1163 (92.7) & 0.119 \\ \(\geq\)160 & 533 (8.6) & 196 (9.7) & 99 (8.4) & 147 (8.6) & 91 (7.3) & 0.119 \\
**HOMA-IR** & & & & & & \\ \(<\)2.5 & 4191 (67.7) & 1527 (75.1) & 833 (70.5) & 1109 (64.6) & 722 (57.4) & \\ \(\geq\)2.5 & 1997 (32.3) & 506 (24.9) & 348 (29.5) & 608 (35.4) & 535 (42.6) & \(<\)0.001 \\ \hline \hline \end{tabular}  
* Calculated by chi-square test. Abbreviations: HDL-C, high-density lipoprotein cholesterol; HOMA-IR, homeostatic model assessment of insulin resistance; LDL-C, low-density lipoprotein cholesterol; TC, total cholesterol; TG, triglyceride.  
\end{table}
Table 3: _Cont._  
Figure 2: Odds ratios for homeostatic model assessment of insulin resistance by resting heart rate categories.
## 2 Data and Methodology
### In Situ Measurements  
NO\({}_{2}\) concentrations near the surface are routinely monitored in Thessaloniki by in situ chemiluminescence analyzers that are administrated by the Network for Air Quality Monitoring of the Municipality of Thessaloniki ([[http://www.envdimosthes.gr](http://www.envdimosthes.gr)]([http://www.envdimosthes.gr](http://www.envdimosthes.gr)), accessed on 20 January 2023). The main advantage of in situ instruments compared to remote sensing systems, such as the MAX-DOAS, is their relatively lower procurement cost and their ability to measure NO\({}_{2}\) concentrations continuously (also at nighttime) and irrespective of weather conditions, while the MAX-DOAS requires sunlight and thus its operation is limited to daytime. The highest temporal resolution of the in situ data that are publicly available is hourly averaged concentrations. The MAX-DOAS-derived diurnal and weekly cycles of the surface NO\({}_{2}\) concentrations are compared with in situ data measured in a background urban environment (40.644\({}^{\circ}\) N, 22.957\({}^{\circ}\) E, 174 m above sea level) (Figure 1). As described in [39], this particular site is selected because it is less affected by local NO\({}_{\rm x}\) traffic emissions and the reported data are considered more representative of the average NO\({}_{2}\) concentrations in the local boundary layer. The in situ dataset that is used in this study spans from 1 June 2020 to 30 November 2022 with very few gaps in the time series due to maintenance of the instrumentation.