esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Evaluation, Benefits, and Limitations of GA-Based Routing
### Trade-Offs between Multiple Objectives  
In multi-objective message routing for EnFVs, optimizing multiple objectives often leads to trade-offs among different performance metrics. These trade-offs arise due to the inherent conflicts and dependencies between objectives, making it challenging to find a single optimal solution that simultaneously satisfies all objectives. For instance, in electric vehicle routing, two primary objectives are minimizing travel time and minimizing energy consumption [17; 80]. These objectives are inherently contradictory, as reducing the travel time often requires higher vehicle speeds and increased energy consumption. On the other hand, minimizing energy consumption might lead to longer travel times. Therefore, finding an optimal routing solution necessitates striking a balance between these conflicting objectives. Similarly, in the case of FV routing, objectives such as minimizing flight time, avoiding restricted airspace, and optimizing passenger comfort can come into play [9; 55]. These objectives may compete with each other, as minimizing the flight time might involve flying through congested or restricted areas, potentially compromising passenger comfort or violating airspace regulations.  
To handle these trade-offs, GAs offer a valuable approach. GAs utilize the concept of Pareto dominance and non-dominated sorting to identify a set of Pareto-optimal solutions, also known as the Pareto front [14]. These solutions represent different trade-offs between the multiple objectives, where improving one objective comes at the expense of another. By analyzing the Pareto front, decision-makers can choose the most appropriate solution based on their preferences and priorities. Several metrics can be employed to evaluate the trade-offs between objectives, such as the hypervolume indicator, epsilon dominance, and weighted sum of objectives. These metrics provide insights into the quality and diversity of solutions along the Pareto front, allowing decision makers to understand the trade-offs and make informed decisions [81]. Previous studies in the field demonstrated the effectiveness of GAs in identifying trade-offs and enabling decision-making in multi-objective message routing for EnFVs.
## 3 Results
This agrees with earlier studies where it was suggested that hydrophobic interactions, hydrogen bonds and van der Waals interactions are the driving forces of adsorption process of polyphenols with \(\beta\)-glucan [16,17,22,23]. Hydrogen bonds between quercetin derivatives and \(\beta\)-glucan could occur through their -OH groups [17] and after the formation of hydrogen bonds, van der Waals interactions could be formed because the distance between the polyphenol molecule and \(\beta\)-glucan rings becomes short [17]. The same type of bonding could have happened between quercetin derivatives and  
Figure 5: Langmuir, Dubinin-Radushkevich and Hill isotherms of quercetin derivatives adsorption onto \(\beta\)-glucan obtained with linear regression. \(\beta\)-glucan. Furthermore, the mean free adsorption energy (\(E\)) was the highest for quercetin-3-galactosidase (4327 J/mol), followed by glucoside (3623 J/mol) and rhamn (2854 J/mol). This suggests that the mean free energy of adsorption was higher for polyphenols which showed lower adsorption onto \(\beta\)-glucan (lower \(q_{\rm e}\)). Adsorption could be a cooperative process (\(n_{\rm H}>1\)). When a molecule of quercetin derivative adsorbs to the surface of \(\beta\)-glucan, the affinity for other molecules changes. Overall, the quercetin derivatives showed some differences in the adsorption onto \(\beta\)-glucan: the adsorption intensity and capacity were somewhat stronger for quercetin-3-rhannoside and -glucoside which can be related to lower heat of adsorption. Indeed, due to the lower heat of adsorption, molecules of quercetin-3-rhannoside and -glucose may adsorb more easily on the surface of \(\beta\)-glucan. The different behavior of quercetin derivatives can be connected to their chemical structure which can influence adsorption capacity as shown in earlier studies [20; 35]. Namely, all three glycoside molecules attached to main aglycon contain -OH groups, but their spatial arrangement is different (Figure 6). For this reason, the spatial arrangement of -OH groups in the sugar molecule might have an effect on the adsorption capacity and behavior in the adsorption process. The arrangement in glucose and rhamn molecule might favor the adsorption onto \(\beta\)-glucan. The results of earlier studies have also shown that adsorption capacity depends on the chemical structure of polyphenols [20; 35]. In particular, the effect of different glycosylation can have various effects on the properties of polyphenols, and also on the adsorption capacities. However, earlier study could not find definite correlation between glycosylation and adsorption [20].
## 3 Evaluation Experiments
### Evaluation Experiments for Sift-Based Method  
To confirm the effectiveness of the phase spectrum with the proposed STFT-based method, we conducted an evaluation experiment using the observed speech, enhanced speech restored only by amplitude-spectrum reconstruction, enhanced speech restored by both amplitude spectrum and phase spectrum constructed using the GLA [25; 26] (200 iterations) with the initial phase of observed speech, and enhanced speech with the proposed STFT-based method. The amplitude spectrogram of each speech is shown in Figure 11. It can be seen from Figure 11 that the result Figure 11e of the proposed STFT-based method has less noise than the result Figure 11c,d. And in particular, it can be confirmed that the noise in the band below 0-1 kHz was suppressed, and the harmonic structure became clear.  
Figure 11: Spectrogram of speech-enhancement results: (**a**–**e**) are clean speech, observed speech, enhanced speech with reconstructed amplitude and observed phase, enhanced speech with reconstructed amplitude and phase reconstructed using GLA, and enhanced speech with proposed STFT-based method], respectively.  
To evaluate the proposed phase reconstruction method, we conducted an evaluation experiment using the phase of the observed speech, the phase applied to the GLA, and the phase reconstructed with the proposed method. Figure 12 shows the cosine distance between the restored phase and clean speech phase, and the smaller the cosine distance, the higher the accuracy of phase reconstruction. The cosine distance with the proposed method is about 0.4 smaller than that of using the GLA in the frequency band of 0-4 kHz. In the 0-8 kHz band, the cosine distance with the STFT-based method was about 0.2 smaller than that of the GLA. These results indicate the effectiveness of the STFT-based method for phase reconstruction.
## 3 Materials and Methods
### HSV Colour Space Model Approach
#### 3.3.1 Gaussian Filtering Approach  
The next step was to choose the Gaussian with the highest peak, which corresponds to the RoI and ignore the rest. The curve was selected according to the Gaussian mixture weights. These weights are normalised to 1, motivated by the assumption that the model must explain all the data, then use the law of total probability. So, in that sense, they are the probabilities of the point being part of the cluster. In other words, the weights are the estimated probability of a draw (i.e., distribution curve) belonging to each respective normal distribution. Even with the background noise, the data distribution in the RoI zone is well defined. The probability that this region is a normal distribution is much higher, meaning that the weight is higher. Selecting the higher weights leads to the RoI Gaussian, thereby enabling to separate pixels of tomatoes from pixels of the background. For a more careful analysis, a boxplot was also generated for each RoI, through the function \"matplotlib.pyplot.boxplot\" (see [[https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.boxplot.html](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.boxplot.html)]([https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.boxplot.html](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.boxplot.html)), accessed on 29 July 2021). The boxplots represent the values within 3 standard deviations of the mean, corresponding to 99.7% of the Gaussian.  
Figure 5: Representation of the H-spectrum and a Gaussian mixture model probability distribution.  
Based on the results obtained, correlating the Hue histogram mean of each sample with its respective class, a statistical classifier was reached. These results will be presented and exploited later in the respective Section 4.1.
## 4 Experiment Verification
### Performance Degradation Prognosis Results  
In order to train and calibrate the prediction model, the HI time series data were further divided into a training set and a test set. Moreover, the LSTM network descended in the opposite direction of the gradient, so that the prediction value approximated the true value by iterative approximation, i.e., the loss function \(J\) was minimized. The normalized pre-processing could make the prediction model smoother in the gradient descent process, which helped converge to the optimal solution and also avoided the problem of poor fitting due to gradient explosion. Therefore, the z-score method was used to normalize the training set data and the test set data.  
After normalizing the HI of the intake filter, the first 90% of the test data were selected as the training set and the remaining 10% as the testing set. The LSTM network was trained using the training set, and the trained prediction model was used to predict the trend of the HI of the intake filter. The Adam optimization algorithm was used to create an LSTM network, and the following parameters were set for the LSTM network: The number of nodes in the input layer was 3, the number of nodes in the output layer was 1, the number of implied units was 18 * 3, the number of iterations was 250, the gradient threshold was set to 1, the initial learning rate was 0.005, and the learning rate was reduced after 125 rounds of training by multiplying by a factor of 0.2. The prediction results of the HI of the intake filter based on the LSTM network are shown in Figure 7.  
As can be seen in Figure 7, the predicted values of the HI of the intake filter based on the LSTM network could follow the measured values very well, and the error between the predicted and measured values was very small, with a maximum error of less than 0.02 kPa, which proved that the performance degradation prognosis method based on the LSTM network had good prediction accuracy.
## 3 Experimental Results
### Slope Failure Process and Stages
In both the tests, the initial failure stage involved the appearance of small cracks on the slope surface, followed by the development of an upward rupture zone (S1) from the bedrock dislocation point and a downward rupture zone (S2) from the surface cracks on the slope. With an increase in the bedrock dislocation, the rupture zones S1 and S2 developed into the MRZ in both the tests. However, in the soil rupture test of the slope crest, S1 penetrated through the overlying layer, causing S2 to penetrate the slope body and leading to severe overall slope instability. In contrast, in the soil rupture test of the slope toe, S1 extended upward to a certain extent within the overlying layer and then terminated, while S2 penetrated the slope body and extended into the overlying layer. This explains why the soil rupture at the slope crest was more severe than that at the slope toe. Figure 6: Failure process diagram of sandy soil slope during soil body rupture at the slope crest (T1): (a–j) refers the bedrock dislocation ranging from 10 mm to 100 mm. Based on the above observations, the failure process of soil slopes across bedrock faults can be preliminarily divided into three stages: Crack damage stage (I), crack expansion and penetration stage (II), and failure and stabilization stage (III).
## 3 Results
### Clinical Outcomes Variables
#### 3.4.4 Grundemann Modification of the Stain Index (GMSI)  
As shown in Table 7, no statistically significant differences were observed at baseline, week 3 and week 6 visits between groups. The same results were found when comparing the intragroup assessment for both groups; no statistically significant differences were detected (Table 7).  
\begin{table}
\begin{tabular}{l c c c c c c} \hline  & **Test Group** & **Control Group** & **Mean** & **95\% CI Bounds** & \(p\) \\ \cline{2-6}  & **Mean (SD)** & **Mean (SD)** & **Difference** & **Lower** & **Upper** & \\ \hline \multicolumn{6}{l}{**Lingual**} \\ \hline Baseline & 0.56 (0.15) * & 0.52 (0.17) * & 0.039 & \(-\)0.05 & 0.12 & 0.395 \\ \hline
3 weeks & 0.36 (0.18) & 0.35 (0.24) & 0.013 & \(-\)0.10 & 0.13 & 0.819 \\ \hline
6 weeks & 0.32 (0.17) ** & 0.35 (0.20) ** & \(-\)0.023 & \(-\)0.12 & 0.07 & 0.649 \\ \hline Change BL-3w & 0.19 (0.17) & 0.17 (0.13) & 0.025 & \(-\)0.06 & 0.11 & 0.555 \\ \hline Change BL-6w & 0.22 (0.19) & 0.16 (0.11) & 0.062 & \(-\)0.02 & 0.14 & 0.147 \\ \hline \multicolumn{6}{l}{**Proximal**} \\ \hline Baseline & 0.53 (0.14) * & 0.50 (0.15) * & 0.035 & \(-\)0.04 & 0.11 & 0.403 \\ \hline
3 weeks & 0.33 (0.16) & 0.33 (0.21) & 0.000 & \(-\)0.10 & 0.10 & 0.996 \\ \hline
6 weeks & 0.31 (0.17) ** & 0.35 (0.15) ** & \(-\)0.042 & \(-\)0.13 & 0.04 & 0.360 \\ \hline Change BL-3w & 0.19 (0.17) & 0.16 (0.14) & 0.034 & \(-\)0.05 & 0.12 & 0.433 \\ \hline Change BL-6w & 0.22 (0.15) & 0.14 (0.12) & 0.076 & \(-\)0.00 & 0.15 & 0.051 \\ \hline \end{tabular}
\end{table}
Table 6: _Cont._  
\begin{table}
\begin{tabular}{l c c c c c} \hline  & **Test Group** & \multicolumn{3}{c}{**Control Group**} \\ \cline{2-5}  & **Mean (SD)** & **Median (IQR)** & **Mean (SD)** & **Median (IQR)** & \(p\) \\ \hline
**n** & 29 & 29 & 26 & 26 & \\ \hline
**All** & & & & & \\ \hline Baseline & 0.38 (0.27) & 0.41 (0.35) & 0.39 (0.26) & 0.40 (0.26) & 0.960 \\ \hline
3 weeks & 0.38 (0.25) & 0.42 (0.30) & 0.39 (0.26) & 0.40 (0.31) & 0.807 \\ \hline
6 weeks & 0.39 (0.25) & 0.42 (0.30) & 0.41 (0.28) & 0.40 (0.33) & 0.807 \\ \hline Change BL-3w & 0.00 (0.08) & 0.00 (0.01) & \(-\)0.00 (0.05) & 0.00 (0.01) & 0.792 \\ \hline Change BL-6w & \(-\)0.00 (0.09) & 0.00 (0.01) & \(-\)0.02 (0.06) & 0.00 (0.03) & 0.892 \\ \hline
**Marginal** & & & & & \\ \hline Baseline & 0.36 (0.27) & 0.41 (0.40) & 0.34 (0.27) & 0.33 (0.36) & 0.691 \\ \hline
3 weeks & 0.34 (0.24) & 0.33 (0.40) & 0.36 (0.30) & 0.33 (0.40) & 0.980 \\ \hline
6 weeks & 0.35 (0.24) & 0.33 (0.39) & 0.39 (0.32) & 0.36 (0.47) & 0.926 \\ \hline Change BL-3w & 0.02 (0.12) & 0.00 (0.01) & \(-\)0.01 (0.09) & 0.00 (0.01) & 0.640 \\ \hline Change BL-6w & 0.01 (0.12) & 0.00 (0.01) & \(-\)0.04 (0.12) & \(-\)0.00 (0.08) & 0.057 \\ \hline \end{tabular}
\end{table}
Table 7: Gründemann modification of the stain index (GMSI) analyzed by means of Mann–Whitney U test and Friedman test for paired samples with Bonferroni correction.
## 2 Methods
### Beer-Lambert Law  
By measuring the intensity drop of an incident electron beam, it is possible to estimate the thickness of the sample. This method depends on proportionality between thickness and signal decay described by the Beer-Lambert attenuation law:  
\[I_{tr}=I_{0}e^{-t/\lambda_{total}} \tag{6}\]  
In this equation, \(I_{tr}\) refers to the transmitted beam intensity, \(I_{0}\) is the incoming primary electron beam intensity, and \(\lambda_{total}\) is the total mean free path, or the average distance an electron travels in the sample before a collision. Using the number of electrons as a measure of beam intensity, plotting the natural logarithm of primary over transmitted intensity versus thickness reveals a linear dependence under this condition.  
In this study, the range of linearity is determined as described in [PERSON] (2022) [22]. For the energy scale in that study, the authors note that the range of 0-180 nm was consistently linear, so in this study a short initial range approximated as linear is used in a regression to determine a slope and y-intercept for the plot. Using these parameters and extending the regression line indefinitely, when the R\({}^{2}\) value describing the relationship of the data to the fit was \(\geq\)0.95, the data were said to be linear, so the maximum thickness was defined as the largest value of \(t\) for which this condition was met. For higher energies where linearity is extended, initial linear ranges were first estimated via visual inspection and then tested by slightly increasing and decreasing the range to ensure that no notable change in maximum thickness was observed, and then the same method was used. Variations in initial linear range and different thresholds for linear behavior would change maximum linear depth measurements, but this study utilized methods established by [PERSON] for consistency.  
It is worth noting that for higher energies and lighter materials, some regions of nonlinearity were identified at the top of the sample, which is likely a result of forward scattering. This effect means that high-energy beams through thin samples display less attenuation, which limits the applicability of the Beer-Lambert law in such situations and can interfere with image interpretation, prompting the need for consideration of beam energy for different sample materials and thicknesses (i.e., lower energies for thinner samples). Forward scattering is important to understand when imaging with high-energy beams but is not the focus of this study, so to avoid producing inaccurate linear fits, these regions were omitted from the regression and therefore did not contribute to identifying the maximum linear depth. For these instances, the omitted range is reported in the Results section below.
# Labeling Expert: A New Multi-Network Anomaly Detection Architecture Based on LNN-RLSTM
## 9 Conclusions and Future Work  
The LNN-RLSTM model not only has strong anomaly data detection capability, but also constructs different kinds of anomaly detection network models from network operation and maintenance business perspective and operator bias perspective, respectively. Compared with traditional classification methods, such as quadrature method, random forest and lightGBM-genetic algorithm [43; 44; 45; 46; 47], its performance is better in terms of Accuracy, Precision, Recall, F1-Score and other indexes. Especially in the case of complex business scenarios, the LNN-RLSTM network architecture can efficiently learn the business knowledge of business operators and can quickly and automatically iterate training to learn new abnormal data types in a timely manner in the face of big data environments. The self-learning habit of this network architecture makes it possible to detect anomalies in the field of anomaly detection, not only at the numerical level but also at the operational level. Of course, in future research, we will still study the factors influencing the formation of data-compression windows to avoid false anomalies from overlearning and further investigate the impact of multi-neural network models in anomaly detection performance.  
However, although the proposed architecture in this paper improves in time complexity and business detection capability, it does not consider the impact brought by adversarial attacks [48], and this algorithm model only considers network business data anomalies caused by non-human factors [49], which is not comprehensive for network security detection. In the future, we will focus our research on anomaly detection by adversarial attacks, which is a method where an adversary intentionally adds an adversarial perturbation to the input file to disable the detector. Such anomaly detection is considered necessary in white-box attack prevention [50], where the attacker generates adversarial examples to fool the model based on its architecture, parameters and input features. Our future work will be devoted to the problem of adversarial attacks to improve the generalizability and robustness of the model.  
Conceptualization, X.T. and H.Y.; methodology, X.T.; software, X.T. and S.X.; validation, X.T., S.X. and H.Y.; formal analysis, X.T. and S.X.; investigation, X.T. and S.X.; resources, H.Y.; data curation, S.X.; writing X.T.; writing--review and editing, X.T. and S.X.; visualization, S.X.; supervision, X.T. and H.Y.; project administration, H.Y.; funding acquisition, H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (61773012), the National natural sciences fund youth fund project (61807017) and the research start-up fund of Jiangsu University of science and technology (1052932101).  
Not applicable.  
The dataset for this study is from the 2021 Chinese Big data competition.  
The authors declare no conflict of interest.
## 3 Computation and Analysis
### Orbit Shift over a Long Time Span
Using orbital data from both GPS and Galileo systems from DOY 029-038 of 2023, a combined reference orbit was established. We calculated the average unit spherical distance from the positions of satellites G01-G32 and E01-E36 to this combined orbit for the same period. The results, shown in Figure 17c,d, reveal that while the average deviation distance  
Figure 15: Satellite tracks of all Galileo satellites observable at the MATE location over the four-year period from 2020 to 2023. The tracks are color-coded by year: 2020 in red, 2021 in blue, 2022 in orange, and 2023 in purple. for Galileo satellites from the combined orbit remains stable at 0.004 rad, the deviation for GPS satellites shows a significant reduction to 0.003 rad. Additionally, these distances do not exhibit significant long-term fluctuations. Figure 16: Eccentricity (the blue line), Right Ascension of the Ascending Node (the red line), orbital inclination (the black line), argument of perigee (the green line), and semi-major axis (the purple line) for satellites G02 (**a**), G20 (**b**) and G21 (**c**). In Figure 17, the average deviation distances for both GPS and Galileo satellite orbits generally stay within 0.02 rad. Using this threshold, satellite coordinates within this distance from the reference orbit on a unit sphere are considered consistent, indicating an orbit repeat. Orbit repeatability for both systems is then calculated daily from DOY 039 to 365 in 2023 and is presented in Figure 19, with reference to both individual constellations and the combined constellation. When using the GPS satellite orbits from DOY 239 to 248 as the reference, the GPS orbit repeatability gradually declines, dropping to 84.5% after DOY 072. Figure 17: Average unit spherical distances of the GPS and Galileo satellites from reference orbits before and after the GPS/Galileo combination. Panel **(a)** shows the distances of GPS satellites from the GPS reference orbit, panel **(b)** shows the distances of Galileo satellites from the Galileo reference orbit, panel **(c)** shows the distances of GPS satellites from the GPS/Galileo combined reference orbit, and panel **(d)** shows the distances of Galileo satellites from the GPS/Galileo combined reference orbit. Figure 18: Average spherical distance of GPS satellites from the reference orbit after using the GPS satellite orbits from DOY 239–248 as the new reference. This reduction negatively impacts the accuracy and stability of the multipath error model. However, when GPS and Galileo satellite orbits are combined, the GPS orbit repeatability remains stable at 100%. Galileo satellites consistently show 100% repeatability throughout the observation period, thanks to their inherent orbital stability. Thus, integrating GPS and Galileo systems for multipath modeling and mitigation is expected to improve correction accuracy and ensure more stable results, particularly for GPS.