esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Characteristics of the Signal Energy Ratios  
To verify the consistency of weakening effect of the pipe on two kinds of signals, which are the impulse response signals and leakage-induced signals measured by maintaining a distance of 14 m between the two sensors while varying the distance between sensor 1 and the simulated leak orifice. Since the maximum amount of leakage-induced signal energy occurs in frequencies near 124 kHz, the signal-to-noise ratio at this frequency band is higher than at other bands, the impulse response signal in frequencies near 124 kHz was also extracted with wavelet packet transform, and the signal energy ratios between the two sensors were calculated. Figure 12 shows the fitting curves of the signal energy ratios as a function of the propagation distance. The horizontal axis denotes the distance between sensor 1 and the simulated leak orifice. The vertical axis denotes the energy ratios of the leakage-induced signal and the impulse signal measured by the two sensors. The difference between the two fitting curves is small, which implies that the weakening effect of the pipe on the AE signals is proportional to the propagation distance, irrespective of any leakage-induced signals or impulse response signals. Therefore, the signal energy ratio can also be used for locating the vibration excitation source.  
Figure 11: Comparison of the power spectral densities of the leakage-induced signal in the different distances (L\({}_{1}\) = 1 m, 2 m) away from the leak source.  
Figure 12: Fitting curves of the signal energy ratios of the AE signals measured by the two sensors.
## 3 Results and Discussion
### Effects of Scaling and Moisture Content on the Shear Strength Parameters of CGS  
To further investigate the impact of scaling and moisture content on the CGS shear strength, this study analyzed peak failure strengths and shear stress relationships using the Mohr-Coulomb (M-C) criterion. Table 4 compiles the cohesion (\(c\)) and internal friction angle (\(\phi\)) for each specimen group. The data indicate that reducing \(d_{\mathrm{max}}\) lowers both \(c\) and \(\phi\), with the extent of this effect varying by moisture content and scaling method. At \(w=0.91\%\), scaling causes only a slight drop in cohesion, while \(\phi\) remains largely stable. In air-dried conditions, where moisture is minimal, CGS shear strength stems primarily from interparticle friction and interlocking--driven by particle strength, shape, roughness, and contact distribution--factors minimally affected by size. Thus, scaling preserves these mechanisms, keeping \(\phi\) steady, with the small cohesion decrease likely due to reduced contact areas, an effect diminished by the lack of moisture-induced bonding.  
At natural (\(w=2.45\%\)) and saturated (\(w=7.66\%\)) moisture levels, reducing \(d_{\mathrm{max}}\) markedly lowers both \(c\) and \(\phi\). Relative to the original gradation, ESM specimens show average reductions of 20 kPa in \(c\) and \(5^{\circ}\) in \(\phi\), while SGM specimens exhibit drops of 35 kPa  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specimen Gradation** & \begin{tabular}{c} **Maximum Particle** \\ **Size (mm)** \\ \end{tabular} & \begin{tabular}{c} **Moisture Content** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Cohesion (kPa)** \\ \end{tabular} &
\begin{tabular}{c} **Internal Friction** \\ **Angle (\({}^{\circ}\))** \\ \end{tabular} \\ \hline \multirow{2}{*}{Initial Gradation} & \multirow{2}{*}{60} & 0.08\% & 109.22 & 52.43 \\  & & 2.45\% & 125.71 & 46.86 \\  & & 7.66\% & 107.02 & 44.71 \\ \hline \multirow{2}{*}{ESM Gradation} & \multirow{2}{*}{30} & 0.08\% & 96.09 & 51.14 \\  & & 2.45\% & 96.18 & 40.93 \\  & & 7.66\% & 81.00 & 39.45 \\ \hline \multirow{2}{*}{SGM Gradation} & \multirow{2}{*}{30} & 0.08\% & 70.09 & 51.58 \\  & & 2.45\% & 80.66 & 38.94 \\ \cline{1-1}  & & 7.66\% & 71.53 & 36.39 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Shear strength parameters.  
Figure 10: Relationship of shear stress and shear displacement under \(w=7.66\%\).  
in \(c\) and \(7^{\circ}\) in \(\phi\). This is tied to coarse particle content (see Figure 6), highest in the original gradation, followed by ESM and SGM. Higher moisture enhances particle sliding, rolling, and displacement, particularly in specimens with fewer coarse particles, which are more susceptible to movement. During shearing, the shear box's constraints hinder particle realignment in high-coarse-content specimens, boosting shear strength.
## 2 Experiments
### Experimental Results
#### 2.5.3. Fractography  
After the experiments, scanning electric microscope (SEM) observations were performed to unravel the FCG mechanism from the fracture surfaces of the specimens. The SEM images of the fracture surfaces of the RP1, RP2, and RP3 specimens, at various growth times, are shown in Figures 13-15. [PERSON] et al. [17] performed fatigue tests under mode II loading, with high growth rates and short crack lengths, and observed severe rub marks and many wear particles and oxides on the fracture surfaces. In RP1 and RP2, however, there was no evidence of contact between the crack surfaces at all growth periods, and no oxide debris generated from the surface appeared. At high magnifications, microcleavage and rippling could be observed, implying that the attenuation due to surface friction was very limited. As in previous non-proportional mixed mode I/II loading experiments [18; 19], clear striation patterns were seldom seen. Because there was no contact between the crack faces, the striation patterns were not worn out by the mating face.  
Figure 11: Branch crack growth rates against \(\Delta K_{\text{eq}}\) for RP.  
Figure 12: Branch crack growth rate against \(\Delta K_{\text{eq}}\) for RF.  
Figure 14: Fracture surface of RP2 observed via SEM (arrows indicate the direction of the crack growth). (**a**) 2 mm from the notch tip, (**b**) an enlarged view indicated by the frame in (**a**), (**c**) 4 mm from the notch tip, (**d**) frame in (**c**), (**e**) 6.8 mm from the notch tip, (**f**) frame in (**e**), (**g**) 8 mm from the notch tip, and (**h**) frame in (**g**).
## 4 Result and Analysis
### Results Analysis
Accordingly, low _MSE_ and _MAPE_ values indicate a high-performing model [80]. Apart from this, the results of the regression analysis for the training and testing of the fifth and second models are shown in Figure 6. The \(R^{2}\) value of the testing for the fifth model was 0.92 compared to the second model, which achieved a predictive accuracy of 0.80. This shows that the fifth model has a good correlation with the actual value and is therefore favorable and reliable. Therefore, the fifth model was considered the neural network model (PSO-BP) for further analysis. Figure 6: The regression plot and the fitted line between the actual and predicted values of the PSO-BP neural network model. **(a)** the smallest accuracy; **(b)** highest accuracy.
## 2 Materials and Methods  
### Samples Preparation  
Methylparaben (MP), propylparaben (PP), paracetamol (PCT), sulfamethoxazole (SMX), and carbamazepine (CBZ) were dissolved in ultrapure water (1 mg/L of each compound) to create solutions with individual PPCPs and mixtures of two to five PPCPs (Table 1). These solutions were then treated by ozonation as described in detail by [PERSON] et al. [18]. Briefly, ozonation of each effluent was performed in a 2 L glass reactor at a temperature of 25 \(\pm\) 1 \({}^{\circ}\)C. The duration of ozonation was set by the time required for the total degradation of the initial compounds (reaction time; Table 1). Ozone was produced by an ozone generator (802N, BMT, Berlin, Germany) from pure oxygen (99.9%). The ozone concentrations at the entrance and exit of the reactor were quantified by gas analyzers (Ozone analyzer 963 and 964, BMT, Berlin, Germany, respectively) and used to determine the transferred ozone dose (TOD; Table 1). All the materials used in the preparation process were sterilized beforehand. The degradation of the PPCPs in the solutions following ozonation was followed by high-performance liquid chromatography (HPLC) as also described in detail by [PERSON] et al. [18].  
Considering the requirements of the toxicity tests applied in this study (see below), namely the need to ensure that cells are supplied with the necessary nutrients to grow and the effects noticed relate exclusively to the treatments, samples (2 mL) of ozone-treated and untreated solutions were freeze-dried (ScanVac, CoolSafe Basic & Pro, LaboSGene ApS, Lillerod, Denmark) for 36 h at \(-\)100 \({}^{\circ}\)C in sterile microtubes, which were then stored at 4 \({}^{\circ}\)C in the dark until shortly before the onset of each corresponding toxicity test. As necessary to set up the test, lyophilized samples were reconstituted by adding 2 mL of cell growth medium (see below). This allowed establishing the full-strength test treatment (100%), which was then diluted with cell growth medium to obtain the remaining test treatments (12.5, 25 and 50% of sample).  
\begin{table}
\begin{tabular}{c c c c} \hline
**ID** & **Solution Composition** & **Reaction Time (min)** & **TOD (mg O\({}_{3}\)/L)** \\ \hline MP & MP & 10 & 7.56 \\ PP & PP & 8 & 10.85 \\ PCT & PCT & 20 & 16.70 \\ SMX & SMX & 6 & 6.53 \\ CBZ & CBZ & 1.5 & 2.10 \\ Mix 2 & MP + PP & 12 & 12.39 \\ Mix 3 & MP + PP + PCT & 30 & 14.17 \\ Mix 4 & MP + PP + PCT + SMX & 40 & 20.85 \\ Mix 5 & MP + PP + PCT + SMX + CBZ & 60 & 25.09 \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition of each solution under study (1 mg/L of each compound), and ozonation treatment parameters allowing the full degradation of the compounds as confirmed by [PERSON] et al. [18]. MP: methylparaben; PP: propylparaben; PCT: paracetamol; SMX: sulfamethoxazole; CBZ: carbamazepine; TOD: transferred ozone dose.
# Modalflow: Cross-Origin Flow Data Visualization for Urban Mobility
## 3 Design Requirements
### Domain Problem  
In the target domain of application, there is not a standard methodology that can embrace current pervasive data sources, as these are mostly secondary sources, i.e., data that were not originally captured for the purpose of transport analysis. We consider pervasive data to be mode-unspecific and stemming from networks, such as social networks, Wi-Fi networks, and mobile phone networks. Primary sources, on the other hand, are datasets whose raw form and original purpose are inherently related to mobility (and not only as the consequence of an additional processing, as in XDR-inferred trips). There are other data sources that also enable mode-specific analysis, such as automatic passenger counting mechanisms in public transport [5], which, although highly granular, lose, to a certain extent, the origin and destination aspect of flows.  
In the urban planning framework, there are three methodological approaches for generating primary data: qualitative, quantitative, and tracking. Qualitative methodology relies on focus groups and discourse analysis, whereas quantitative methodology relies on representative sampling, self report, and statistics. Tracking, on the other hand, relies on GPS devices that unobtrusively record an individual's path with a higher level of detail than any of the aforementioned and at a (relatively) lower cost.  
Although pervasive data may seem to be more related to GPS data and tracking methodologies its coarser accuracy makes it more useful for quantitative analysis, such as reproducing travel surveys [36] or evaluating urban interventions of any kind [37]. The difficulties of working with network data are not just technical, as it is a massive data source, but also theoretical: there is an original methodological gap that separates mobile phone network data from survey data, which may, at least partially, explain its resistance to enter the traditional workflow. Recent developments show that public institutions are starting to make use of mobile phone data to understand mobility. In Spain, it is being used as a pre-census study [38] and, in Chile, it is being used to complement a travel survey in a small city in a pilot study [39]. This implies a rising need on tools that are aimed at experts to assess mobile phone data.
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 4 Discussion
### Satellite-Derived GWS Validated with In Situ Data Improves Robustness  
Our cross-validation with national well observations demonstrates a close match (R > 0.7, RMSE < 5 mm/year) between GRACE-based GWS estimates and field data. Similar validation exercises were effectively conducted by [49] (R \(\approx\) 0.72-0.73), which confirms GRACE's reliability in data-scarce regions. However, our integration of field data and satellite in an Andean-Amazon transect remains novel and enhances confidence in similar approaches.
## 3 Results and Findings
### Results of Empirical Study
#### 3.2.1 Demographic Profile of Respondents  
While conducting an empirical investigation, a thorough analysis of a survey is essential. A close view of the participants, such as essential information related to an organization, helps conclude more significant results [81]. In this study, we gathered participants' demographic and organization-related information to obtain more precise and authentic questionnaire survey results. This section comprises us with the empirical investigation carried out in the current study. The survey was dedicatedly carried out regarding **QR2**. The correct sample size for PLS-SEM is 200 or above [82]. A total of 222 samples was carried out during the present research work. Table 4 shows the respondents' demographic characteristics, including gender, education, working experience, and position.
# Exploring the Spectral Variability of Estonian Lakes Using Spaceborne Imaging Spectroscopy
## 1 Introduction
In total, nine applications performed on 12 images (six of Lake Peipsi and six of Lake Vortsjarv) are presented, including Rrs validation, classification of optical water types (OWTs), PC concentration estimation, macrophyte detection, and characterization of reflectance when lakes are covered by ice and snow. For Rrs validation, S2 and S3 multispectral data and in situ measurements (from fieldwork and the HYPSTAR spectroradiometer) were used as reference data. In addition, environmental data, i.e., lake surface water temperature, wind speed, and precipitation amount, were included to enrich the analysis. This study underscores the significant potential of high-resolution hyperspectral data in advancing the assessment of lake ecological status, providing an important foundation for interpretation of the results presented in the following sections.
## 3 Results and Analysis  
### Cascade Infiltration Process of Water Molecules into CNTs  
Taking the combination of CNT(16,16)+CNT(13,13) as an example, Figures 2 and 3 show the transient process of water molecules invading into carbon nanotubes in DNEAS and SNEAS, respectively. Driven by external pressure, water molecules first invade into CNTs with large pore size and then invade into the smaller CNTs. However, due to the viscous dissipation during water molecules transporting in CNT, the pressure increased gradually during the process of water molecules filling CNT. Although the infiltration pressure was strongly dependent on the pore size of nanoporous media, systems with CNTs featuring multiple pore sizes did not necessarily exhibit cascade energy storage characteristics. When the difference in pore size between CNTs is not significant, water molecules may begin to infiltrate into the smaller tube before the larger one is filled. In such cases, the pressure-volume characteristic curve of the large tube penetration process will merge with that of the small tube penetration process, resulting in a lack of cascade characteristics within the system.  
The critical infiltration pressure of the system is known to vary with temperature, and the response of the critical infiltration pressure to system temperature change differs from different pores size. In Figure 4, we further extract the pressure-volume characteristics of DNEAS and SNEAS at different system temperatures. As depicted in the figures, when the system temperature was elevated, there was a reduction in the critical infiltration pressure difference between the two tubes. This phenomenon has the potential to cause the loss of cascade characteristics in NEAS. Apply solid line to represent systems with cascade characteristics and dash line to represent systems without cascade characteristics in Figure 4. For DNEAS in Figure 4a, it displays cascade characteristics at system temperatures of 280 K, 300 K, and 320 K. For SNEAS in Figure 4b, it displays cascade characteristics at system temperatures of 280 K and 300 K. Obviously, for NEASs with different configuration, although the components are both CNT(17,17) and CNT(13,13), DNEAS displays cascade characteristics at 320 K, while SNEAS does not.  
Figure 3: Instantaneous process of water molecules invading carbon nanotubes in SNEAS.  
Figure 2: Instantaneous process of water molecules invading carbon nanotubes in DNEAS.  
The above analysis raises two questions: How do temperature changes affect the cascade characteristics of NEAS? What is the minimum difference between larger and smaller pores for a potential multistage NEAS to display cascade characteristics at different temperatures? We will address these questions in detail by analyzing the critical pore difference and the critical infiltration pressure in the following sections.
## 2 Related Research
These sub-characteristics are shown remotely when the computer program is utilized as a portion of a computer framework, and then internal quality is achieved. The core merit of this model is that the characteristics stated are pertinent to each kind of computer program, giving steady wording for software item quality [40]. Conclusively, ISO/IEC 9126, since it is based on past activities and models, is more finalized than other models and reasonable to be utilized within the assessment of MLPs. ISO/IEC 9126 engages all pivotal attributes such as progressive structure, criteria for assessment, comprehensive expression and terms, basic and exact definitions, and one to numerous connections between distinct layers of the model. In expansion, ISO/IEC 9126 aids key decision-making efforts and also exempts expensive errors. Functionality is chosen based on the fact that it is dependent on the application domain (education), while reliability focuses on information presentation and content in academic products. Usability is also included because it is a vital figure, particularly for client-driven applications. Productivity is additionally added because it refers to the potential of the computer program to supply utilizable work to realize its purpose. Viability, be that as it may, is cleared out according to the framework as it may be assessed either by the designer or a third party when getting to the project's technical documentation and the source code. Even though most products sense maintainability as a critical quality attribute, only in the early stages of development were assessed. This can be in line with computer program product assessment within the scholastic space, which makes maintainability and portability less vital features. Upon reviewing the literature, certain studies were located to apply FMEA in combination with other techniques but in different contexts. For instance, one study applied FMEA combined with Pythagorean Fuzzy Sets (PFS) and Dimensional Analysis (DA) risk assessment in product design [41]. Meanwhile, another study employed the integrated Fuzzy-GRA-TOPSIS-FMEA method to investigate the personal information security risk assessment for e-waste recycling [42]. Another study examines a combined failure mode effect analysis (FMEA) with a hybrid analytic hierarchy process (AHP) and the preference ranking organization method for enrichment evaluation (PROMETHEE) in supply chain management [43]. In reviewing the literature for this study, it was observed that no study on MLPs has applied GRA-FAHP-FMEA techniques in evaluating the potential risk and quality of mobile learning platforms. This study is unique in comparison to other relevant studies that implement FMEA in collecting and evaluating data collected from various MLPs, which are then applied to FAHP for weighting risks. Studies that combine these methods in evaluating MLPs could not be located in the literature.
# Interference Mitigation for Metro Train Location Based on Karhunen-Loeve Decomposition
## 4 Experimental and Analysis of Results
### Chirp Signal Interference  
In the third scenario, we add pulse signal interference. The frequency sweep time of the interference signal is set to 1s, and the bandwidth is 2 MHz. First, we provide the cross ambiguity function (CAF) of the receiver acquisition process to illustrate the acquisition capability of the CPIS. The more obvious the peak value is, the better the acquisition performance. Figure 11 below shows the waveforms of CAF before and after the interference mitigation algorithm is executed. It can be seen from Figure 11a,b that after the interference suppression is executed, the correlation peak can be highlighted to improve the acquisition success rate.  
Figure 10: Mitigation performance of KL algorithm under pulse interference with different duty cycle.  
Then we study the influence of the number of eigenvalues L of the interference signal on the algorithm performance. As shown in Figure 12, the relationship between the number of eigenvalues and MSE is given. It can be seen from the figure that when L is too large, the eigenvalues related to the positioning signal are regarded as interference signals, and the energy of the real signal is weakened after interference suppression. When the L selected is too small, the interference signal is not completely suppressed, and the suppression effect decreases. This indicates that the choice of L value needs to be made based on performance and computational complexity.  
We conducted Monte Carlo simulations 100 times, and the JSNR increase from 30 dB to 45 dB, and calculated the average MSE of the three algorithms before and after chip signal interference suppression. It can be seen from Figure 13 that the KL algorithm still maintains excellent interference suppression performance, and with the increase of JSRN, the interference suppression performance decreases slightly. The KL algorithm has an average improvement of 2.4 dB compared with the STFT algorithm and 0.8 dB compared with the WPCT algorithm. Compared with the other two interference suppression algorithms, the KL algorithm in this paper has better suppression performance and robustness under chirp signal interference.  
Figure 11: CAF of the waveform with PRN = 19. (**a**) CAF waveform before interference mitigation is performed; (**b**) In the chirp signal interference scenario, the waveform of CAF after interference mitigation is performed. JSNR = 30 dB.