esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods  
### Research Methodology  
The research methodology, as illustrated in Figure 1, consisted of four main stages, each of which was meticulously planned to guarantee a complete analysis and inference. The initial stage of the research process involved the gathering of data, which began with the formulation of a comprehensive data collection plan. The plan delineated the specific data to be collected, the sources from which it would be obtained, and the methodology to be employed in its collection. Subsequently, the data collected were subjected to a preliminary analysis with the objective of verifying the quality. At this stage, the completeness of the data was verified, and any anomalies or measurement errors were identified and assessed.  
The second stage consisted of statistical analyses that were conducted with the objective of examining the relationships between variables. A Chi-square test was used to assess the presence of dependencies between categorical variables, which proved particularly effective in the context of data grouped by categories. Furthermore, the Kruskal-Wallis test was employed as a nonparametric alternative to ANOVA, allowing for the comparison of medians across more than two groups. This test proved invaluable when the data did not follow a normal distribution, as it prevented the potential for errors that could arise from using an inappropriate analysis method.  
The third stage was dedicated to the utilisation of machine learning algorithms for the development of predictive models. Neural networks (NNs), SVM (Support Vector Machine), and Bagged Trees (BT) were employed to construct a variety of models that facilitated effective data analysis and prediction.  
The fourth and final stage comprised the evaluation of the results and discussion thereof. The efficacy of the developed models was evaluated using a range of quality indicators that assessed the precision, sensitivity, and accuracy of the predictive models. For the assessment of classification models, the Receiver Operating Characteristic (ROC) curve was employed to illustrate the model's capacity to differentiate between classes, along with the Area Under the Curve (AUC), which quantified the quality of the classifier. A higher AUC value indicated a better performance of the model.
# Energy Efficient GNSS Signal Acquisition Using Singular Value Decomposition (SVD)
## 4 Simulation and Performance
### Performance Metrics  
To evaluate the performance of the proposed compression scheme, several objective tests were made. Factors such as the signal to noise ratio (SNR), computational complexity, probability of detection, probability of false alarm and graphical comparison of the execution time of each operation were computed.
# Review of the Main Mechanical Testing Methods for Interlayer Characterization in Laminated Glass
## 3 Evaluation of Interlayer Properties According to EN 16613:2019
### F.E.M. Modeling  
The finite element (FE) software ABAQUS was employed to conduct the numerical analyses. There are three main aspects to be considered to simulate the creep bending response of LGs. These are:  
1. Constitutive assumptions of the materials (Section 3.2.1), in particular the viscoelastic behavior of interlayers;
2. The numerical model (finite element type, mesh, boundary conditions, and the load application (Section 3.2.2);
3. Validation of the finite element model established with the experimental results (Section 3.2.3).
## 3 Results and Discussions  
### Model Evaluation Index  
To evaluate the deflection prediction model under different machine learning models (MLs) from multiple perspectives, four evaluation indexes were selected to quantify the prediction performance of the model, namely, R\({}^{2}\), RMSE, MAE, and MAPE [43]. The more R\({}^{2}\) is equal to 1, the closer the predicted value is to the measured value, and the more accurate the model's prediction result is. RMSE is used to assess the deviation between the predicted value and the measured value. The more the RMSE value approaches 0, the better the prediction performance of the model. Both MAE and MAPE represent the mean deviation between predicted and measured values. The closer the MAE and MAPE values are to 0, the more accurate the model's predictions are. For a perfect prediction model, the values of these metrics should equal their ideal optimal values. The calculation methods of various evaluation indexes are shown below:  
\[R^{2}=1-\frac{\sum_{i=0}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=0}^{n}(y_{i}- \overline{y})^{2}} \tag{9}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{n}} \tag{10}\]  
\[MAE=\frac{\sum_{i=1}^{n}(\hat{y}_{i}-y_{i})|}{n} \tag{11}\]  
\[MAPE=\frac{\sum_{i=1}^{n}\left|\frac{y_{i}-\hat{y}_{i}}{y_{i}}\right|\times 1 00\%}{n} \tag{12}\]  
where \(y_{M}\) is the average of the actual values, \(y_{i}\) is the actual value of the sample tensile stress, \(\hat{y}_{i}\) is the predicted value, and \(n\) is the number of tensile stress samples.
# Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks
## 4 Statistical Analysis of Channel Propagation Characteristics
### Power-Related Characteristics
#### 4.2.2 Pathloss  
The Pathloss (PL) is calculated based on the downlink reference signal. This downlink reference signal is determined by the SS/PBCH block index and the estimated PL based on the SS/PBCH block index is given as:  
\[PL=Reference\;Signal\;Power\;-\;RSRP \tag{8}\]  
where the reference signal power is the average energy per resource element and is determined by the parameter SS-PBCH-Block Power. In our measurements, the reference signal power for Op1 and Op2 is 14 dBm and 11 dBm, respectively. The boxplots and average value of the PL for Op1 and Op2 at all detected locations are shown in Figure 13. Path loss is influenced by many factors such as the distance between the transmitter and the receiver, antenna height, environment, reflections, scattering, absorption, etc. From the boxplots, we can observe the relationship between the PL and the distance. In general, PL increases with the distance, and this can bee seen from the the OP1 case, where the locations are roughly ordered in increasing distance from the BS. It shows that as the distance between the BS and the measurement location increases, the path loss also increases. With an increase in distance, the signal undergoes more attenuation that increases the PL. We can also see a similar trend in the case of Op2, where locations 1, 4, and 6 are closer to BS than the other locations; therefore, the signal experience less attenuation at these locations; this leads to a decrease in PL.  
Figure 12: Measured PDP and fitted (red dashed) line showing the linear dependence between the power versus delay.  
The measured PL at different locations of DIET department are also compared with predictions from the model 3 GPP (UMi). The Friis Free Space Path Loss (FSPL) is also shown as a reference. The FSPL and PL using 3 GPP (UMI) model is calculated by taking the estimated distance between the base station and the measurement locations. For Op1, the measured PL is on average about 20 dB higher than the PL predicted by the 3 GPP model. The reason for this difference is an effect of being an indoor measurement, directivity of SSBs, distance errors, etc. In case of Op2, the PL estimation by 3 GPP model is more accurate. A possible explanation for this difference is that the 3 GPP model does not account for SSB-based transmission, leading to a PL estimate for single wide-beam-based transmission (for Op2) more accurate than for the SSB-based transmission (for Op1).
# The 6G Ecosystem as Support for IoE and Private Networks: Vision, Requirements, and Challenges
## 2 From 1G to 5G
### The First Generation (1G Analogue Technology)  
During the first generation of mobile technology (1980-1990), data rates ranged from 1 Kbps to 2.8 Kbps, employing a circuit switch for communication. This era utilized analog phone service as its output technology, with a bandwidth of 40 MHz and a frequency range between 800 and 900 MHz, supporting only voice calls. The communication technique used during this time was FDMA [21].  
Although it marked the initial steps in mobile communication, the first generation faced several limitations. Call quality was relatively low, and energy consumption was high. Users experienced poor voice connections, limited data capacity, a deficiency in security measures, and an unreliable transfer process [21; 25]. Despite these drawbacks, the first generation laid the foundation for future advancements, paving the way for more sophisticated and capable mobile networks in subsequent generations.
## 3 Relative Permeability Validation: Concurrent Flow in a 2D Channel  
We first used the above model to simulate a pressure driven flow through a porous media. In the single-phase case, the intrinsic permeability \(K\) can be determined according to the [PERSON]'s law [4]  
\[\mathbf{u}=-\frac{K}{\mu}\mathbf{\
abla}P,\quad K=-\frac{\mu\mathbf{u}}{\mathbf{\
abla}P}, \tag{21}\]where \(\mathbf{u}\) is the Darcy velocity in the porous medium. Note that the intrinsic permeability does not depend on the nature of the flow field but only on the geometry of the porous medium. In case of two-phase flows, the relative permeability \(K_{r,i}\) can be given as [4]  
\[\mathbf{u}_{i}=\frac{KK_{r,i}}{\mu_{i}}\mathbf{\
abla}P_{i},\quad K_{r,i}=\frac{\mu_{i} \mathbf{u}_{i}}{K\mathbf{\
abla}P_{i}},\quad i=w,nw. \tag{22}\]  
Here, \(i=w\) and \(nw\) refer to the wetting and non-wetting fluids respectively, \(\mu_{i}\) is the dynamic viscosity of fluid \(i\), and \(\mathbf{\
abla}P_{i}\) is the pressure gradient of fluid \(i\).  
First, we tested whether the LBM model can accurately predict relative permeability for a simple immiscible two-phase cocurrent flow. Specifically, we calculated the relative permeabilities of the layered two-phase flow in a channel under different saturation values. In the simulation, a constant pressure difference was applied on the inlet and outlet boundaries following Zou and He [40], and a non-slip (bounce-back) boundary condition was applied on the upper and lower walls using the halfway bounce-back scheme. The wetting phase flowed along the walls (\(a<|y|\leq b\)) and non-wetting phase flowed in the central region (\(|y|<a\)). One can obtain the analytical solutions for the relative permeability, a function of wetting phase saturation \(S_{w}\) and viscosity ratio \(\lambda\)[41],  
\[K_{r,nw}=(1-S_{w})\left[\frac{3}{2}\lambda+(1-S_{w})^{2}(1-\frac{3}{2}\lambda) \right],\quad K_{r,w}=\frac{1}{2}S_{w}^{2}(3-S_{w}), \tag{23}\]  
where \(S_{w}\) is the wetting phase saturation, which is defined as \(S_{w}=1-a/b\), and \(\lambda\) is the viscosity ratio of the non-wetting to wetting fluids, i.e., \(\lambda=\mu_{nw}/\mu_{w}\).  
For two typical viscosity ratios of \(\lambda=1\) and \(\lambda=0.1\), Figure 1 shows excellent agreement between the computed relative permeabilities and the analytical solutions.
# HYPSO-1 CubeSat: First Images and In-Orbit Characterization
## 3 In-Orbit Payload Performance
### Spectral Resolution
The RMSE values are also generally higher for the single full resolution frame than for the 10 frames average, indicating that averaging frames reduces noise and makes features in the spectrum more visible. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & & **Spectral Resolution \(\pm\) RMSE** \\
**Spectral Range** & **Full Resolution,** & **Binned,** & **Full Resolution,** \\  & **1 Frame** & **1 Frame** & **10 Frames Avg** \\ \hline
430–480 nm & 5.9 \(\pm\) 0.0047 nm & 9.6 \(\pm\) 0.0064 nm & 6.0 \(\pm\) 0.0030 nm \\
480–530 nm & 5.0 \(\pm\) 0.0052 nm & 6.6 \(\pm\) 0.0035 nm & 5.2 \(\pm\) 0.0038 nm \\
530–580 nm & 6.6 \(\pm\) 0.0042 nm & 8.2 \(\pm\) 0.0032 nm & 7.2 \(\pm\) 0.0018 nm \\
580–630 nm & 5.4 \(\pm\) 0.0028 nm & 5.8 \(\pm\) 0.0035 nm & 5.4 \(\pm\) 0.0028 nm \\
630–680 nm & 3.4 \(\pm\) 0.0058 nm & 5.8 \(\pm\) 0.0047 nm & 3.4 \(\pm\) 0.0041 nm \\
680–730 nm & 3.9 \(\pm\) 0.0035 nm & 4.1 \(\pm\) 0.0034 nm & 4.2 \(\pm\) 0.0018 nm \\
730–780 nm & 5.7 \(\pm\) 0.0053 nm & 4.0 \(\pm\) 0.0033 nm & 3.4 \(\pm\) 0.0027 nm \\ \hline
430–780 nm & 5.4 \(\pm\) 0.0045 nm & 8.2 \(\pm\) 0.0042 nm & 5.6 \(\pm\) 0.0033 nm \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Best estimated spectral resolution in 50 nm bands, and corresponding RMSE values for full resolution, binned, and frame-averaged data. Red and violet text identify the lowest and highest estimated resolutions, resp. The estimated spectral resolution is overall slightly worse than what was found during pre-launch calibration. This can be due to the pre-launch measurements using spectral lamps, looking at few spectral lines at the time and estimating the spectral resolution by calculating the FWHM. The method used here, comparing the full recorded spectrum with simulated solar spectra of different resolutions, gives an indication of what spectral resolution can be achieved, but might not be as accurate as using spectral lamps and calculating the FWHM. The results presented here shows that a spectral resolution of about 5 nm is a realistic performance of the instrument in some spectral ranges, but further investigations should be carried out to estimate the exact spectral resolution as a function of wavelength with the in-orbit data.
# Research and Application of Slag-Nanosilica Stabilizer for Silt Subgrade
## 4 Results and Discussion
### Laboratory Test Results
#### 4.2.3 Water Stability Test  
It can be seen from Table 11 that the water stability coefficient of SNS-S with the same content was higher than that of CS-S. After water bath curing, the \(R_{c}\) of SNS-S decreased significantly compared with the \(R_{w}\). With the increase in water bath curing time, the compressive strength of the stabilized soil first decreased and then increased. The \(R_{w}\) of CS-S was 2.84 MPa, and the strength loss rate of CS-S with curing age of 3 d was the highest (28.17%). The water stability coefficient was 0.72, and the \(R_{w}\) of SNS-S was 4.45 MPa. The strength loss rate of SNS-S with curing age of 3 d was the lowest (13.26%), and the water stability coefficient was 0.87. The decreasing rate of the water stability coefficient in the early period of CS-S was larger than that of SNS-S, and the decreasing rate of the water stability coefficient in the later period of SNS-S was slower. With the increase in age, the water stability coefficient in the later period of CS-S and SNS-S gradually increased, and the stabilized soil tended to be stable under the water immersion environment [33].  
In the above table, the age refers to the number of days of water bath curing after 6 d standard curing, \(R_{w}\) represents the standard UCS, \(R_{c}\) represents the UCS after water bath curing, \(D_{w}\) represents the strength loss rate, and \(K_{w}\) represents the water stability coefficient.
# Conformal Titanyl Phosphate Surface Passivation for Enhancing Photocatalytic Activity
## 3 Results and Discussion  
### Morphology Characterization  
The importance of crystallinity in the metal oxide photocatalysts has been widely studied. The photochemical energy conversion efficiency can be determined by their crystallinity [28; 29]. In the XRD patterns (Figure 1a), the crystalline TiO\({}_{2}\) in anatase/rutile mixed crystalline phase (JCPDS# 21-1272 for anatase, JCPDS# 21-1276 for rutile) was observed for bare TiO\({}_{2}\) nanoparticles. Considering the phase transition behavior of TiO\({}_{2}\), [30] the annealing process at 750 \({}^{\circ}\)C resulted in the anatase/rutile mixed crystalline phase. However, the rutile phase peaks were diminished in TP-TiO\({}_{2}\) while the anatase phase and strong titanyl phosphate (JCPDS# 35-802) peaks were observed, even the same annealing process was conducted at 750 \({}^{\circ}\)C for TP-TiO\({}_{2}\). The suppressed phase transformation between anatase and rutile is attributed to the phosphate anions, of which good affinity to Ti atoms hindered the phase transformation of TiO\({}_{2}\)[31]. Even the phosphate anions induced these significant changes in crystallinity, no significant changes in morphology were not characterized in SEM images (Figure 1b and c). However, a slight increase in the size of overall particles was observed in TP-TiO\({}_{2}\). The estimated particle size was _avg_. 51 nm (\(\pm\) 27) for bare TiO\({}_{2}\) and _avg_. 58 nm (\(\pm\) 18) for TP-TiO\({}_{2}\). This presumably resulted from the phosphate anions. HR-TEM images of TP-TiO\({}_{2}\) (Figure 1d and e) demonstrate that the surface of TP-TiO\({}_{2}\) was conformally passivated by the titanyl phosphate layer of which thickness was ca. 5 nm. The fringe pattern in Figure 1e and the reduced fast Fourier transform (FFT) pattern in the insert image indicate that the core TiO\({}_{2}\) is composed of the anatase phase. The anatase (101) was clearly defined by the distinct lattice space of 0.35 nm. For further determination of this TiO\({}_{2}\)-core @ titanyl phosphate-shell structure, the elemental mapping of Ti, O, and P atoms was performed using TEM-EDS (Figure 1f). As shown in the high angle annular dark field scanning (HAADF) TEM image, the mapping was focused on the surface passivation layer. This elemental mapping image shows that all elements (Ti, O and P atoms) are well dispersed in the passivation layer, which implies that the surface of TP-TiO\({}_{2}\) is conformally passivated by titanyl phosphate.
# ML-Based 5G Network Slicing Security:
## 1 Introduction
### Contributions of This Paper  
1. This paper introduces all of the basics of 5G, the current trends of 5G, network slicing, layers within network slicing, and various standardized architectural frameworks (Section 3);
2. The paper also provides a state-of-the-art comparison and map of existing related surveys, with an emphasis on their major contributions (Section 2);
3. The paper demonstrates the use of state-of-the-art applied ML algorithms in different stages of network slicing, such as resource allocation and slice admission (Section 4);A taxonomy of attack prevention from the proposed frameworks, services, and security considerations of network slicing.
* The paper also discusses machine learning-based network slicing and the ML-based techniques that can be applied during different stages of slicing to prevent attacks over the network.
* The paper discusses network slicing threats and their countermeasures throughout the complete lifecycle of a network slice.
* Finally, the paper outlines ongoing research on and future directions for 5G network slicing security to enable 5G to become more secure and robust without affecting network and end-user connectivity (Section 7).
# A landslide Warning Method Based on K-Means-ResNet Fast Classification Model
## 4 Case Study: Shangmao Landslide
### K-Means Clustering  
The GNSS-2 with more obvious deformation and rainfall data was selected for the study. From 22 April 2021 to 7 November 2022, 20,739 monitoring data were collected, including 20,152 GNSS-2 data and 587 daily rainfall data.  
As in the case of the Zhongma landslide, the displacement data were processed by one-time sample difference to attain an isochronous order. We used the slide window with the size of 48 and step size of 2 to construct the landslide displacement subseries. Finally, 14,080 data were obtained.  
Figure 13: Location of the study area (**left**) and a photo of the Shangmao landslide (**right**).  
As Figure 16 shows, we can find that the gray correlation of all factors is greater than 0.9, so these factors can be used as input features of the classification model. A total of 29 parameters were selected to construct a dataset for classification.
###### Abstract  
Over the past decade, there have been many reports on solution-processed oxide thin-film transistors (TFTs) with high mobility (even \(>\)100 cm\({}^{2}\) V\({}^{-1}\)s\({}^{-1}\)). However, the capacitance uncertainty of the solution-processed oxide gate dielectrics leads to serious overestimation of the mobility. Here, solution-processed AlO\({}_{\text{x}}\) dielectrics are investigated systematically, and the effect of mobile ions on the frequency-dependent capacitance of the solution-processed AlO\({}_{\text{x}}\) dielectrics is also studied. It was found that the capacitance of the AlO\({}_{\text{x}}\) depends on the frequency seriously when the annealing temperature is lower than 300 \({}^{\circ}\)C, and the water treatment causes more seriously frequency-dependent capacitance. The strong frequency-dependent capacitance of the AlO\({}_{\text{x}}\) annealed at 250 or 300 \({}^{\circ}\)C is attributed to relaxation polarization of the weakly bound ions in the incompletely decomposed AlO\({}_{\text{x}}\) films. The water treatment introduces a large number of protons (H\({}^{+}\)) that would migrate to the ITO/AlO\({}_{\text{x}}\) interface under a certain electric field and form an electric double layer (EDL) that has ultrahigh capacitance at low frequency.  
Aluminum oxide; dielectric; thin-film transistor; solution-processed; oxide semiconductor +
Footnote †: journal: Applied  
_Applied Sciences_  
sweeps at a certain \(V_{GS}\) step, which means that the charging of the gate dielectric is step-by-step, meaning quasistatic capacitance is more appropriate for the mobility calculation). For this reason, it is indispensable to regulate the capacitance measurement for calculating the field-effect mobility of TFTs. In this paper, the properties of solution-processed AlO\({}_{\mathrm{x}}\) dielectrics are investigated systematically, and the effect of mobile protons on the frequency-dependent capacitance and on the performance of the oxide TFTs is also studied.
## 2 System Composition and Working Sequence
### Working Sequence of Oxidant System  
In order to analyze the influence of different starting schemes and different regulating schemes on the dynamic characteristics of the engine, three schemes were set up, namely, Scheme ASYNC, Scheme SYNC, and Scheme JOINT. Scheme ASYNC and Scheme SYNC  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **Value** & **Unit** \\ \hline Thrust of the engine & 8.4–33.6 & kN \\ Operating time & 80 & s \\ Total flow of the engine & 3–12 & kg/s \\ Oxidant flow of the engine & 2.6–10.6 & kg/s \\ Stage of operation & 3 & / \\ Adjustment ratio & 4:1 & / \\ Mixture ratio & 7.5 & / \\ Maximum supply pressure & \(\
ot<\)6.9 & MPa \\ Total power of electric pumps & 100 & kW \\ \hline \end{tabular}
\end{table}
Table 1: Basic technical parameters of the oxidant system.  
\begin{table}
\begin{tabular}{c c c} \hline
**Period** & **Time (s)** & **Oxidant Flow (kg/s)** \\ \hline
1 st & 0–40 & 2.6 \\
2 nd & 40–45 & 5.3 \\
3 rd & 45–80 & 10.6 \\ \hline \end{tabular}
\end{table}
Table 2: Working process of the oxidant system.  
adjust only the pump speed during the starting and regulating of the propulsion system, while the regulating valve opening is constant. The difference between these two schemes is whether the two pumps are started synchronously or asynchronously. Scheme JOINT adjusts the pump speed and the regulating valve opening at the same time, and the working sequence is the same as Scheme SYNC. The flow in the first stage and the second stage is small; thus, a single pump can be started during these stages. In the third stage, the two pumps work at the same time, i.e., Scheme ASYNC. Since the states of the two pumps are consistent, the two pumps can be kept in a working state at all three stages considering the symmetry, i.e., Scheme SYNC. Considering that the regulating valve can adjust the throat area, the pump speed and the opening of the regulating valve can be adjusted at the same time, i.e., Scheme JOINT. The working sequence diagrams in the asynchronous (Scheme ASYNC) and synchronous (Scheme SYNC) cases are shown in Figures 2 and 3, respectively. The working sequence diagram under the condition of joint adjustment for pump speed and valve opening (Scheme JOINT) is shown in Figure 4.