esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Reliability Analysis of the PSC-I Girder
### Performance Degradation Analysis Considering a Corrosive Environment
The time until performance degradation of the PSC-I girder was observed to be sensitive to the crack width. Cracks wider than 0.3 mm in the girder could cause performance degradation even within the first year after the completion of construction, depending on the environmental conditions. Figure 3 presents the performance degradation ratio of the PSC-I girder against the service life for different corrosive environments and crack widths. The results showed that the performance degradation was more rapid with wider initial cracks and in areas with higher surface chloride content. In a mild environment, the performance degradation was 6.67% in girders without cracks, but 14.12% in the case of girders with a 0.3 mm wide crack. In a normal environment, the performance degradation was 10.19% in girders without cracks, but 14.78% in girders with a 0.3 mm wide crack. In a severe environment, the performance degradation was 13.39% in girders without cracks, but 15.17% in girders with a 0.3 mm wide crack. The performance degradation was greatest in the severely corrosive environment. In the severe environment, there were small differences in the performance degradation with and without cracks. The rate of increase in the performance degradation with cracks was significantly smaller because the performance degradation occurred even before cracking. These results indicate that a corrosive environment has a significant effect on the performance degradation of PSC-I girders.
## 6 Case Study
### Realization of Optimization
#### 6.2.1 Determination of Weight Coefficients in Penalty Function  
As mentioned in Equation (3), the weight coefficients \(\kappa\), \(\lambda\), and \(\mu\) correspond to the weight penalty, occupied space penalty, and interference penalty, respectively. The weight coefficient determines the relative importance of each indicator in the optimization process. Since the calculation methods of the three penalties are different, their values may vary significantly.  
Considering that the difficulty of the assembly sequence is determined mainly by component connection interference, the optimization rule of interference penalty should be given priority. This means we aim to minimize the interference penalty as much as possible. Therefore, the basic principle for determining the weight coefficient is to optimize the weight penalty and the occupied space penalty on the premise of having minimized the interference penalty. For this reason, the initial value of \(\mu\) is set slightly higher and then gradually reduced until the value of the component interference penalty reaches its critical state at the minimum. This approach ensures that all three optimization rules play their respective roles effectively.
# Interference Alignment in Multi-Hop Cognitive Radio Networks under Interference Leakage
## 6 Conclusions  
In this work, we investigate the IA performance of the cognitive multi-hop network in the presence of multiple primary users. For the proposed scheme, we derived closed-form outage probability and ergodic capacity expressions for Rayleigh fading channel. The results, which were validated with the simulations, show that the system performance degraded as the number of interferers and/or leakage level increased.  
This work can be extended to various practical scenarios where relays are affected both by primary-secondary interference and nodes mobility. Moreover, different clustering and/or relay selection approaches can be adopted, and the system performance of the overall multi-hop CR network can be presented.  
The authors [PERSON] and [PERSON] contributed to the methodology, validation, and writing; [PERSON], M.N., and A.B. contributed to the writing, investigation, and software; [PERSON]-A. contributed to the conceptualization, review, and editing.  
This research received no external funding.  
The content of this study has partially been submitted to the IEEE 41 st International Conference on Telecommunications and Signal Processing (TSP 2018) [38].  
The authors declare no conflict of interest.  
Figure 6: Ergodic capacity performance of the secondary network versus \(P/N_{0}\) for various interference leakage levels.
## 3 Background
### Hotspot
#### 3.4.1. Tethering  
A private hotspot can be created, configured, and managed using tethering. Tethering uses a phone as a modem (PAM) that shares the phone's (device's) Internet connection with authorized (by password or pin number) nearby devices such as computers, smartphones, tablets, and notebooks. The connection in tethering is established via a physical cable connection (e.g., a USB cable), Bluetooth, or WLAN-based Wi-Fi technologies. Nowadays,  
Figure 1: Generic structure of Wi-Fi hotspots.  
tethering is done via WLAN, which is called a mobile hotspot. Such mobile hotspots can serve as both dynamic and portable routers for Internet access. Most operating systems for mobile devices (Windows 6.5 or higher, Android 2.2 or higher, iOS 3.0 or higher) support this feature. These smart devices are equipped with the necessary software and hardware to enable wireless Internet access [85; 86]. Tethering over Wi-Fi is also referred to as a personal hotspot. Tethering can also be done via Network Address Translation (NAT) that is based on the existing Internet connection of the mobile device. Such NAT is used for IPv4 networks where the mobile device has a single IPv4 address, but multiple devices can be identified with such a network address.
# Light-Induced Advanced Oxidation Processes as PFAS Remediation Methods: A Review
## 2 PFAS Applications and Related Concerns
### Analytical Techniques for PFAS Detection in Water
#### 2.4.1 Chromatographic Techniques  
Several chromatography analysis techniques have been reported since the early 2000s, including gas chromatography (GC), high-performance liquid chromatography (HPLC), ultra-high-performance liquid chromatography (UHPLC), and capillary liquid chromatography (CLC). HPLC is in regular use for ionic PFASs such as perfluorinated carboxylic acids (PFCAs) and perfluorinated sulfonic acids (PFSAs), while GC is mostly employed for more volatile compounds such as fluorotelomer alcohols and perfluorinated sulfonamide ethanols [34]. Capillary electrophoresis has been investigated as an alternative for ionic and polar PFAS determination, but sensitivities and detection limits are lower than those of HPLC [31]. Specifically, the required detection limits for aqueous matrices can be in the range of parts-per-billion or even parts-per-trillion due to the health risks associated. For example, an analysis of groundwater contamination by recently identified PFAS compounds was conducted with a large-volume injection high-performance liquid chromatography (LVI-HPLC) procedure with a detection range of 0.71-67 ng/L (ppt) [35].  
PFAS precursors varying in chain length (short or ultra-short chain) and moieties (anionic, cationic, or zwitterionic), have been detected in both tap and bottled water [36]. For ultra-short-chain PFAS, methods coupling LC to a quadrupole time-of-flight mass spectrometer (MS) as well as ultra-performance (UP) convergence chromatography coupled to MS were also reported [37; 38]. The detection of 11 PFACs and 4 PFSAs and short-chain PFCAs (C2-C8) at levels ranging from 70 to 200 ng/L in surface and ground waters was performed using HPLC-MS/MS [39; 40], and an analysis of 19 PFAS in potable water was performed using UPLC-MS/MS [41].  
HPLC coupled to ultra-high resolution orbitrap mass spectrometry (HPLC-uHRMS) for PFAS (C5-C17) monitoring showed to be advantageous in longer chain monitoring, with detection limits estimated at 0.005-0.2 ng/mL [42].
# Evaluation of the Mid-Latitude Ionospheric trough Using GRACE Data
## 3 Results and Discussion
### Trough Occurrence Dependence on Space Weather Indices  
We analyzed the occurrence of the trough relative to space weather indices. The results are presented in Figure 20. The Hp30 index shows that the trough occurred more often with increasing geomagnetic activity in both hemispheres. The trough occurrence increases with decreasing SYM-H index (which indicates storm occurrence). Solar wind plasma speed has a slight influence on trough occurrence, increasing with increased wind speed. Flow pressure also correlates with MIT formation. With increased flow pressure,  
Figure 19: Hp30 index dependence of the trough minimum position, trough width, and trough depth for the northern and southern hemispheres.  
Figure 18: SYM-H index dependence of the trough minimum position, trough width, and depth for the northern and southern hemispheres.  
the trough occurrence increases. The influence of IMF on the MIT was investigated by [PERSON] et al. (1992) [9] and by [PERSON] et al. (2006) [16]. The Bz component has the most significant effect on magnetosphere-ionosphere coupling; consequently, it should have the most significant impact on the trough [16]. As shown in Figure 20, the Bz component has the greatest influence on the occurrence percentage value of the MIT.
## 3 Results
The results showed an insignificant trial (first vs. second) main effect for the 10 m [\(F\) (1, 17) = 1.06, \(p\) = 0.317, \(\eta p^{2}\) = 0.05], 20 m [\(F\) (1, 17) = 0.19, \(p\) = 0.636, \(\eta p^{2}\) = 0.02], and 30 m [\(F\) (1, 17) = 0.05, \(p\) = 0.830, \(\eta p^{2}\) = 0.002] sprint tests. Likewise, the sequence (AB vs. BA) main effect proved to be insignificant for the 10 m [\(F\) (1, 17), \(p\) = 0.067, \(\eta p^{2}\) = 0.26], 20 m [\(F\) (1, 17) = 0.34, \(p\) = 0.569, \(\eta p^{2}\) = 0.06], and 30 m [\(F\) (1, 17) = 0.19, \(p\) = 0.672, \(\eta p^{2}\) = 0.02] sprint tests. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & \multicolumn{6}{c}{**1 st Week**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Mean \(\pm\) SD for AB**} & \multicolumn{2}{c}{**Mean \(\pm\) SD for BA**} \\ \cline{2-9}  & **Baseline** & **Post-WU** & **After 12** & **Baseline** & **Post-WU** & **After 12** & **Total** \\ \hline Ts (\({}^{\circ}\)C) & 32.08 \(\pm\) 0.80 & 31.49 \(\pm\) 0.65 & 31.72 \(\pm\) 0.69 & 32.20 \(\pm\) 0.76 & 31.59 \(\pm\) 0.72 & 32.08 \(\pm\) 0.71 & 31.86 \(\pm\) 0.75 \\
10 m (s) & & 1.82 \(\pm\) 0.05 & 1.83 \(\pm\) 0.09 & & 1.87 \(\pm\) 0.09 & 1.92 \(\pm\) 0.08 & 1.87 \(\pm\) 0.07 \\
20 m (s) & & 3.15 \(\pm\) 0.12 & 3.18 \(\pm\) 0.17 & & 3.21 \(\pm\) 0.015 & 3.29 \(\pm\) 0.15 & 3.21 \(\pm\) 0.14 \\
30 m (s) & & 4.40 \(\pm\) 0.19 & 4.46 \(\pm\) 0.25 & & 4.48 \(\pm\) 0.25 & 4.61 \(\pm\) 0.24 & 4 Figure 3: Bar plots show the treatment \(\times\) trial interaction effect on sprint performance across different distances (10 m, 20 m, and 30 m). The data were analyzed using a 2 \(\times\) 2 crossover design to explore the carry-over effect. The separate panels represent different sprint distances of 10 m (**a**), 20 m (**b**), and 30 m (**c**), whereas the x- and y-axis indicate the treatment sequence (AB and BA) and the respective outcome variable (in seconds). Error bars show the standard deviation of the mean, and asterisks denote significant differences between treatment sequences (* \(p<0.05\), ** \(p<0.01\)). Figure 2: Bar plots showing the treatment \(\times\) time-point interaction effect on skin temperature and sprint performance (2 \(\times\) 2 repeated-measure ANOVA). The separate panels represent skin temperature (**a**) or different sprint distances of 10 m (**b**), 20 m (**c**), and 30 m (**d**), whereas the x- and y-axis indicate treatment (control and experimental) and the respective outcome variable (in Celsius or s). Error bars depict the standard deviation of the mean. Asterisks denote significant differences between treatments (* \(p<0.05\), ** \(p<0.01\), *** \(p<0.001\)).
## 6 Results and Discussions
### Access Point 2  
The data traffic and energy consumption of Access Point 2 when using each of the evaluated routing protocols are as follows:  
* Shortest-path routing method: Access Point 2 receives bytes from Access Point 1. Figure 8b shows the number of received bytes in Access Point 2. While receiving these bytes, the receiving energy consumed in Access Point 2 increases, as shown in Figure 10b. Then, Access Point 2 transmits the received bytes to Access Point 4, as shown in Figure 7b. In this case, the transmission energy in Access Point 2 is high, as shown in Figure 9b. At 200 s, the number of bytes transmitted and received drops sharply since the battery of Access Point 2 diminishes, and so does the consumed energy, either for receiving (see Figure 10b) or for transmitting (see Figure 9b). This is due to the fact that, when only the shortness of the path is considered to assign the routes, Access Point 2 works during the whole time Access Point 1 and Access Point 4 are communicating.
* Proposed SDN-based energy-aware routing method: Access Point 2 receives bytes from Access Point 1 and transmits them to Access Point 4 (shortest-path routing). This holds until the SDN controller, based on the estimation of the energy level of Access Point 2, decides to shift the routing path to the energy-aware routing path to prevent Access Point 2's failure. In this way, Access Point 2 stops receiving and transmitting bytes. At 160 s, when the battery level of Access Point 2 increases, the routing path is shifted to the shortest-path routing, and Access Point 2 receives and transmits bytes again. According to Figures 7b and 8b, where the received and transmitted bytes in Access Point 2 are shown, respectively, Access Point 2 is active from 0 to 60 s and 160 to 240 s, whereas it is idle for the rest of the transmission time. The transmission and receiving energies, shown in Figures 9b and 10b, respectively, follow the same pattern the traffic data does. They increase between 0 and 60 s while Access Point 2 is transmitting and receiving, decrease when Access Point 2 is idle from 60 to 160 s, increase again between 160 and 240 s, and decrease one more time between 240 and 400 s. In this way, the battery of Access Point 2 lasts longer.
# Pitfalls in the Detection of Hearing Loss via Otoacoustic Emissions
## 3 Results
### Audiometric Characteristics of the TEOAE Outcomes from the Accuscreen Devices  
After the initial assessment across the two tested devices, the TEOAE outcomes were classified in three groups of ears, i.e., refer-refer, pass-pass, and pass-refer (see definition of the group names in the statistical analysis section).  
For the refer-refer group, the average hearing thresholds exceeded 25 dB at all frequencies. For the pass-pass group, the average hearing thresholds did not exceed 25 dB in the frequency range 0.125-4 kHz but were above 25 dB at 8 kHz. The most inconsistent results were observed in cases of ears classified as pass-refer. Average hearing thresholds were approximately 25 dB; at 2 kHz the hearing threshold was 25.95 dB, but at 4 and 8 kHz the average hearing thresholds were >40 dB. The audiometric thresholds of these groups are shown in Figure 1A.
## 2 Methodology
### Phenomenon Spatialization  
The RP values and their respective coordinates were used to generate a continuous surface of the rainfall RP occurred between September 2015 and February 2016. The interpolation method used was the geostatistical method, which is based on a series of assumptions, such as spatially homogeneous distribution of data and variance. For variogram analysis and spatial autocorrelation model fitting, the gstat package [13], operational in R environment [14] was used. After adjusting the experimental variograms, kriging was performed in SAGA software [15].  
To meet the kriging assumptions, initially, the dependent variable RP was transformed by its natural logarithm. Subsequently, given the existence of a spatial trend, the data were adjusted using polynomial regression. The residues of this regression were used as the database for kriging, which adopted the following model:  
\[a+b\cdot\left[1-e^{-(\frac{b}{2})^{2}}\right] \tag{2}\]  
where \(a\) is the nugget effect (0.1209), \(b\) is the range (0.82879) and \(h\) is the distance (range and distance in degrees). After kriging the regression residues, this surface was added to the area resulting from the initial polynomial interpolation. It is noteworthy that the observed  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{1}{c}{**Municipality/**} & \multicolumn{1}{c}{**Institution Responsible**} & \multicolumn{1}{c}{**Code**} & \multicolumn{1}{c}{**Longitude**} & \multicolumn{1}{c}{**Latitude**} & \multicolumn{1}{c}{**Altitude (m)**} \\ \hline Maringa/PR & INMET & 83767 & \(-\)51.932 & \(-\)23.405 & 549 \\ Colorado/PR & IAT & 2251033 & \(-\)51.89 & \(-\)22.9 & 490 \\ Umurama/PR & IAPAR & 2353008 & \(-\)53.28 & \(-\)23.73 & 400 \\ Jardim Olinda/PR & IAT & 2252013 & \(-\)52.04 & \(-\)22.55 & 260 \\ Campo Mourão/PR & INMET & 83783 & \(-\)52.36 & \(-\)24.05 & 616.4 \\ Londrina/PR & INMET and IAPAR & 83766 and 2351003 & \(-\)51.13 & \(-\)23.31 & 566 \\ Ivaí/PR & INMET and IAT & 83811 and 2550006 & \(-\)50.86 & \(-\)25 & 808 \\ Arapongas/PR & IAT & 2351048 & \(-\)51.43 & \(-\)23.4 & 810 \\ Urai/PR & IAT & 2350203 & \(-\)50.8 & \(-\)23.21 & 440 \\ Paranavai/PR & IAT & 2252027 & \(-\)52.53 & \(-\)22.88 & 510 \\ Paranavai/PR & IAT & 2252017 & \(-\)52.52 & \(-\)22.65 & 345 \\ Diamante do Norte/PR & IAT & 2252015 & \(-\)52.86 & \(-\)22.65 & 340 \\ Porto Rico/PR & IAT & 2253002 & \(-\)53.27 & \(-\)22.77 & 250 \\ Navirai/MS & ANA & 2354000 & \(-\)54.19 & \(-\)23.06 & 345 \\ Cascavel/PR & IAT & 2453056 & \(-\)53.24 & \(-\)24.96 & 680 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rainfall stations used to obtain precipitation series—in situ measurements.  
nugget effect was relatively expressive, that is, there was a large local variability, which was not explained by the spatial autocorrelation structure.
# Computational Investigation of a Novel Box-Wing Aircraft Concept
## 4 Aero-Structural Characterization of BWA/SL Aircraft
### Computational Set-Up
#### 4.2.2 FSI Computational Set-Up  
The FSI computations are achieved using the same process as previously adopted for CRM cases. In contrast to the CRM aircraft, however, no structural pre-design exists for the BWA/SL concept. Therefore, a Finite Element Method (FEM) model is first built from scratch, upon some necessary but reasonable simplifications.  
First, the three wings and V-tail are assumed to be the primary load-carrying structures. The latter come as an ensemble of shells that are classically reinforced using ribs, spars, stringers, connections, etc. All of these structural elements exhibit various thicknesses, which are chosen so as to offer the best trade-off in terms of structural characteristics versus weight penalty. For instance, the wings and V-tail comprise a total of 40+ ribs overall, as well as stringers and spar caps (cf. right side of Figure 13). All are wrapped up with shells of thickness 7 mm, the stringers and spar caps being modelled using rods of equivalent radius 17.8 mm. The connections between wings and wingtips (or between the third wing and the V-tail) are assumed to be flexible bar structures (cf. right side of Figure 13). The wing-to-wing junctions are idealized and modelled as beams, using connecting rod elements of 20 mm radius. For a better realism (and in contrast to previous structural  
Figure 12: BWA/SL aircraft: Unstructured mesh used for the aerodynamic (CFD) computations.  
analyses of the PrandtlPlane [17]), the third wing root is here allotted some degrees of freedom, i.e., it is free to move in the vertical and axial directions. For a fair comparison, the material chosen is aluminum again, i.e., it fits with the one previously used for the CRM aircraft structural study. Based on the above, an unstructured FEM mesh is generated, which gathers a total of 0.46 million of elements against a total 0.3 million of nodes. The FEM elements are of variable types and of maximum cell size 15 \(\times\) 10\({}^{-2}\) m (i.e., \(b_{\text{ref}}/400\)).
## 7 Conclusions  
This article proposes a probabilistic reliability measure method that considers uncertainties of input variables and their distribution parameters simultaneously. The uncertain distribution parameters are represented through fusing the insufficient data and evidence theory data with a Gaussian interpolation method, and the epistemic uncertainty variables are probabilistically represented, taking account of uncertainties in the distribution parameters. The performance reliability index is calculated based on a linear approximation of the performance function. Through the proposed method, the reliability index of a crank-slider mechanism and a composite laminated plate are calculated considering the aleatoric uncertainty and epistemic uncertainty with uncertain distribution parameters. The comparison results with the Monte Carlo method demonstrate that the proposed method is accurate and efficient.  
Conceptualization, Y.L. and X.P.; methodology, Y.L. and Z. L.; software, Y.L. and Z.L.; validation, X.P., Z.L., J.T., and C.Q.; formal analysis, Y.L. and Z.L.; investigation, Y.L. and X.P.; resources, C.Q.; data curation, C.Q.; writing--original draft preparation, Y.L.; writing--review and editing, Z.L., X.P., J.T. and C.Q.; visualization, Y.L. and X.P.; supervision, Z.L.; project administration, J.T.; funding acquisition, Z.L. and J.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (grant number 51935009, 51875525 and 51875517) and the Zhejiang Provincial Natural Science Foundation of China [grant number LY21E050008 and LY20E050015].  
The authors declare no conflict of interest.  
Figure 5: Failure probability of composite laminated plates.
## 5 Materials and Methods
### Validation of Data Security and Compliance  
Ensuring the security of stored and processed data was a fundamental requirement, validated through multiple assessments. Security mechanisms, including user access control and integration with Active Directory, were tested, demonstrating effective user management and robust authentication procedures [83]. Additionally, the system's capacity for long-term data storage and anonymization was evaluated using backup and recovery simulations. Implemented anonymization techniques, such as pseudonymization and signal mapping, successfully protected sensitive industrial data while preserving its analytical usability [87; 88].