esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Result and Discussion  
### Recognition of Truck Stability  
We identified the stability patterns of trucks using FCM clustering based on four indicators: the coefficient of variation of operating mileage, coefficient of variation of operating hours, coefficient of variation of the number of cities visited, and coefficient of variation of the number of days between consecutive active states. Based on the results shown in Figure 1, the optimal number of clusters is determined to be 4. Therefore, trucks are categorized into four classes, labeled C0, C1, C2, and C3, each representing a distinct stability pattern. Table 2 details the class cluster centroids, revealing the stability relationships from highest to lowest as follows: C1 > C0 > C2 > C3. As shown in Figure 2, the four truck types exhibit diverse stability patterns. Figure 2c indicates that C1 truckers demonstrate the highest stability in daily operating intensity and activity range, especially in the transport range, with more than 53% visiting the same number of cities daily (coefficient of variation = 0). C2 and C0, which are more than 50%, are both concentrated around 0.3. Figure 2a,b illustrate that C0, C1, and C2 exhibit similar stability in terms of daily operational intensity; more than 50% of truck drivers' mileage coefficients of variation are clustered in the [0.4,0.6] range and more than 75% of hours of operation coefficients of variation are clustered in the [0.3,0.5] range. However, as shown in Figure 2d, the continuous operation of C2 is notably unstable, with a CV of approximately 3.2 for consecutive operating days. C0 truckers exhibit minimal stability in their daily operating intensity and activity range, possibly by transporting specific valuable cargo without temporal or spatial constraints. Interestingly, the continuous running stability of such truckers is relatively good. The cluster mass values indicate that the stability of continuous truck operation is significantly lower than the stability of the daily operating intensity and activity range. This implies that the daily mileage and driving hours of truck drivers fluctuate less than the days between continuous activities.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Cluster Centroid** & **C0** & **C1** & **C2** & **C3** \\ \hline Coefficient of variation of operating mileage & 0.51 & 0.48 & 0.53 & 0.71 \\ Coefficient of variation of the number of cities visited & 0.39 & 0.07 & 0.36 & 0.66 \\ Coefficient of variation of operating hours & 0.44 & 0.42 & 0.45 & 0.57 \\ Coefficient of variation of interval operating days & 1.53 & 1.72 & 3.18 & 1.63 \\ \hline \end{tabular}
\end{table}
Table 2: Description of stability clustering centroids.
## 4 Results
### Assessment of the Reconstructed Time Series High Spatial Data  
Two time series of 30 m NDVI data sets were reconstructed. There were 131 NDVI data sets for Luntai, including four Landsat NDVI, 20 GF-1 NDVI, 53 HJ NDVI, and 54 synthetic NDVI. There are 131 NDVI and 131 NDVI data sets for Luntai, including four Landsat NDVI, 20 GF-1 NDVI, 53 HJ NDVI, and 54 synthetic NDVI.  
were 117 NDVI data sets for Bole, including six Landsat NDVI, 42 HJ NDVI, and 69 synthetic NDVI. The Landsat NDVI and GF-1 NDVI acquired for the same day as HJ NDVI were not used.  
To assess the quality of reconstructed time series of 30 m NDVI data, the PGQ and the model performance index were calculated. Since only images acquired in clear sky conditions were used, the PGQs for all pixels in Bole and Luntai are the same. To show the ability of each sensor to reconstruct time series high spatial data, the PGQs were calculated for varying sensor types. Table 6 shows that the PGQs of HJ and GF-1 satellites are far higher than those of Landsat, because the return cycles of HJ and GF-1 satellites are much higher that of Landsat.  
Quantitative evaluation of the model performance showed that the HPLM approach can simulate time series NDVI very well with \(R^{2}\) ranged from 0.635 to 0.952 in Lumtai and 0.719 to 0.991 in Bole, respectively (Figure 4). The _RMSE_ in Lumtai and Bole is ranged from 0 to 0.096 and 0 to 0.111, respectively. Most of pixels have an \(E\) value higher than 0.36 (Figure 5). It showed that most of the simulated values were reliable. The _CRM_ in Lumtai and Bole ranged from \(-0.054\) to 0.187 and \(-0.117\) to 0.002, respectively.  
Thus, a higher PGQ index and model performance index showed a good quality of reconstructed time series of 30 m NDVI data, both for Bole and for Luntai, indicating these data can be used in phenological extraction and crop mapping.
# Analysis of Turbulence Parameters of Typhoon Morakot along the Southeast Coast of China
###### Abstract  
The southeast coastal region of China is frequently affected by typhoons. The observation station was chosen to be located on the roof of Wenzhou University's architectural engineering building to collect real-time wind speed data during the landfalling of Typhoon Morakot to investigate the properties of the near-ground wind field of typhoons. The turbulence characteristics of the near-ground wind and its variation with time intervals are analyzed on the basis of real-time measured data. The results show that the turbulence intensity only changes with the mean wind speed under relatively low wind speeds. The gust factors exhibit a scattered distribution under low wind speeds and tend to cluster together when the wind speed exceeds 8 m/s. With increasing time intervals, the turbulence intensity and the gust factor gradually decrease. The relationship between turbulence intensity and gust factor is obtained by the measured data and then compared with the empirical formulas. The peak factor remains constant while the mean wind speed changes, but diminish as the time intervals rise. The turbulence integral scale of typhoons slightly increases with the increasing mean wind speed, and its value falls between 70 and 150.  
Typhoon Morakot; wind characteristics; field measurements; turbulence intensity; turbulence integral scale +
Footnote †: journal: _sciences_
# Review Rating Prediction Based on User Context and Product Context
## 4 Experiments and Evaluations
### The Impact of the Number of Reviews Per User and Reviews for Each Product on Our Methods  
In order to further research the impact of the number of reviews per user and reviews for each product on our methods. We hope to answer two questions through experiments. First, increasing the number of reviews per user can benefit each user's personalized RRP. Second, increasing the number of reviews per product can benefit the personalized RRP for each product.  
To study the impact of number of reviews per user on our proposed approach, in the experiment, we selected P% reviews for each user to train RRP + UC. The value of P varies from 10 to 100 at intervals of 10. The experimental results on the yelp2013 datasets are shown in Figure 1. The results of the RRP + LR + individual + user and RRP + LR + global methods are also shown in Figure 1 as a baseline method for comparison.  
From Figure 1, we can see that, as the number of reviews per user increases, the MAE of our proposed method and baseline methods are decreasing. This result is because as the number of reviews per user increases, more information is used in the training process of RRP, thus helping to learn a more accurate RRP method. In addition, the effects of our method proposed in this paper can be better than the LR + individual + user and LR + global methods in different training text sizes. This is because the LR + global approach does not capture the personalized sentiment expression of each user, while the LR + individual + user approach faces the challenge of data sparsity. In addition, the performance of our proposed method becomes more apparent than the LR + individual method when the number of training texts per user is reduced.  
However, compared with the LR + global method, our method fluctuates more with the change of the number of reviews per user. Our method is more sensitive to the training data than, and not as stable as, the LR + global method.  
Figure 1: Impacts of Number of Reviews Per User on MAE of Our Methods in Yelp2013 Datasets.  
To study the impact of reviews for each product on our proposed approach, in the experiment, we selected P% reviews for each product to train RRP + PC. The value of P varies from 10 to 100 at intervals of 10. The experimental results on the yelp2013 datasets are shown in Figure 2. The results of the LR + individual + product and LR + global methods are also shown in Figure 2 as a baseline method for comparison. From Figure 2, we can get a conclusion similar to Figure 1.
## 2 Materials and Methods
### Research Methods
#### 2.2.1 Fractal Dimension  
The commonly used methods for calculating fractal dimensions include various approaches such as the area-perimeter relation, box-counting method, and area-radius relation [20; 27; 28]. In this study, we have chosen the box-counting method to compute the fractal dimension of our samples. The box-counting method is particularly well-suited for digital image processing, offering simplicity in calculating fractal dimensions for binarized images. Compared to the Hausdorff dimension, it has the advantage of lower computational complexity. Additionally, the box-counting method demonstrates strong noise resistance, ensuring reliable results. The specific calculation involves covering the sample with squares of side length r and counting the number of non-empty boxes \(N(r)\). The value of \(N(r)\) changes as \(r\) decreases. The fractal dimension \(D\) is determined by the slope in a double logarithmic plot [29; 30], as shown in Equation (1).  
\[D=-\lim_{r\to 0}\frac{\lg N(r)}{\lg r} \tag{1}\]  
The \(D\) value calculated in this process represents the degree of spatial filling and complexity. In this study, we utilized the fraclab toolbox in MATLAB (Version 2020a) to substitute for the aforementioned calculation process. The underlying principles remain consistent with Equation (1); however, this approach allows for faster computation and more accurate results while enabling finer variations of \(r\). Scholars have indicated that fractal dimension calculations can encompass all data rather than being limited to a specific scale-invariant region; therefore, we selected all data from the double-logarithmic plot as the source for calculating the \(D\) value [31].
## 3 Proposed Methodology
### Preprocessing Steps  
To provide clean, consistent data for multi-label DDoS attack detection, preprocessing techniques were done after dataset preparation [31]. Novel preprocessing approaches improved network traffic representation while maintaining dataset structure. AFN, CTA, and MTB were used. Adaptive Feature Normalization (AFN) was used to scale features while dynamically accounting for feature-specific variability. Unlike standard normalization, AFN modifies the scaling range depending on feature distribution across the attack and normal traffic categories. The transformation for each feature \(y_{j}\) is presented in Equation (1):  
\[\bar{y}_{j}=\frac{y_{j}-\
u_{y,\text{gFP}}}{\eta_{y_{j},\text{gFP}}+\epsilon} \tag{1}\]  
where \(\
u_{y,\text{gFP}}\) and \(\eta_{y,\text{gFP}}\) represent the mean and standard deviation of feature \(y_{j}\) within a particular group (e.g., attack or regular), whereas \(\epsilon\) prevents division by zero. This technique normalises features to their group-specific behaviour, boosting the model's sensitivity to nuanced attack patterns.  
The use of Contextual Traffic Augmentation (CTA) increased training data variety. Combining weighted averages of neighbouring traffic samples with temporal and contextual relationships creates additional data points. For two traffic cases, \(z_{p}\) and \(z_{q}\), the enhanced sample \(z_{\text{aug}}\) is calculated using Equation (2):  
\[z_{\text{aug}}=\beta z_{p}+(1-\beta)z_{q},\quad\beta=\frac{e^{-\Delta_{s}}}{1 +e^{-\Delta_{s}}} \tag{2}\]  
As \(\Delta_{s}\) indicates the temporal distance between \(z_{p}\) and \(z_{q}\), samples closer in time are more likely to impact each other. This augmentation adds realistic variations while keeping data context.  
Finally, Multi-scale Temporal Balancing (MTB) addressed class imbalance, notably attack type distribution. This approach uses sample frequency over various temporal frames to determine a balancing weight, \(\omega_{c}\), for each class, \(c\). Weight assignment is stated in Equation (3):  
\[\omega_{c}=\frac{\sum_{m=1}^{M}\phi_{c,m}}{M\cdot\max_{c}(\phi_{c,m})} \tag{3}\]  
where\(\phi_{c,m}\) indicates the frequency of class \(c\) in temporal window \(m\), whereas \(M\) represents the total number of temporal windows. MTB avoids oversampling by dynamically redistributing samples between windows to balance all classes.  
Methods for preprocessing datasets make them more amenable to federated multi-label DDoS detection by improving dataset quality. It was shown that all of the transformations handled class imbalance, feature scaling, and data variety while preserving the semantic integrity of the data.
## 2 Materials and Methods  
### Time-Domain DAS  
In the tested isotropic plate structure, there are \(M\) linearly arranged transducer units forming a one-dimensional ultrasonic transducer array. The spacing between the array elements is \(d\), as shown in Figure 1.  
Figure 1: One-dimensional linear array.  
Let the ultrasonic guided wave \(u_{m}^{a}(t)=A(t)\) be excited by the actuation unit located at \(\overset{\rightarrow}{s}_{m}\). The guided wave field signal scattered by the damage (secondary acoustic wave source) located at \(\overset{\rightarrow}{r}\) propagates to a one-dimensional linear array as shown in the figure. The signal received by the array element located at \(\overset{\rightarrow}{s}_{n}\) is \(u_{n}^{s}(t)\). Ignoring the dispersion effects of the guided wave, the time difference of the received guided wave signal relative to the exciting signal can be expressed as:  
\[\Delta_{t}\Big{(}\overset{\rightarrow}{r}\Big{)}=\frac{\overset{\rightarrow} {k}_{m}\cdot\Big{(}\overset{\rightarrow}{r}-\overset{\rightarrow}{s}_{m} \Big{)}-\overset{\rightarrow}{k}_{n}\cdot\Big{(}\overset{\rightarrow}{s}_{n }-\overset{\rightarrow}{r}\Big{)}}{\omega_{0}}=c_{p}\Big{|}\overset{ \rightarrow}{r}-\overset{\rightarrow}{s}_{m}\Big{|}+c_{p}\Big{|}\overset{ \rightarrow}{r}-\overset{\rightarrow}{s}_{n}\Big{|},\ m,n=1,2,\ldots,M \tag{1}\]  
In this context, \(c_{p}\) represents the phase velocity of the guided wave corresponding to the central frequency, while \(\overset{\rightarrow}{k}_{m}\) and \(\overset{\rightarrow}{k}_{n}\) are the wave number vectors. In isotropic materials, the magnitudes of the wave numbers are the same. Under the condition that the potential location \(\overset{\rightarrow}{r}\) of the acoustic source (damage) in the structure is unknown, the structure is probed point by point at \(\overset{\rightarrow}{r}_{s}\). A time-domain delay \(\Delta_{t}\Big{(}\overset{\rightarrow}{r}_{s}\Big{)}\) is applied to the received signals \(u_{n}^{s}\Big{(}t,\overset{\rightarrow}{r}\Big{)}\) from each array element, and all the signals from the array elements are superimposed:  
\[u_{DAS}\Big{(}t,\overset{\rightarrow}{r},\overset{\rightarrow}{r}_{s}\Big{)} =\sum_{m=0}^{M-1}\sum_{n=0}^{M-1}\frac{A(t)}{\sqrt{|\mathbf{r}-\mathbf{s}_{m}||\mathbf{r}- \mathbf{s}_{n}|}}e^{[\omega(t+\Delta_{t}(\overset{\rightarrow}{r}_{s}))- \overset{\rightarrow}{k}\cdot(\overset{\rightarrow}{r}-\overset{ \rightarrow}{s}_{m})-\overset{\rightarrow}{k}_{m}\cdot(\overset{ \rightarrow}{r}-\overset{\rightarrow}{s}_{n})]} \tag{2}\]  
When the condition \(\overset{\rightarrow}{r}_{s}=\overset{\rightarrow}{r}\) is satisfied, the signal phases of each array element are synchronized, and the damage imaging index is taken as the zero moment of the sound source emission:  
\[I_{1}\Big{(}\overset{\rightarrow}{r}_{s}\Big{)}=\left.u_{DAS}\Big{(}t, \overset{\rightarrow}{r},\overset{\rightarrow}{r}_{s}\Big{)}\right|_{t=0} \tag{3}\]  
The maximum value will be obtained at the actual location of the damage. The flowchart of the time-domain DAS method is shown in Figure 2.
# Analysis of the Impact of Multipath on Galileo System Measurements
## 6 Conclusions  
This work presents a comprehensive analysis of the impact of the multipath effect for Galileo system and proves its increased robustness against this phenomenon compared to GPS. However, the continuous modernization and development of GNSS systems offer many opportunities for future analyses.  
Galileo still does not provide the same satellite availability as GPS. Similarly, the L5 observation is broadcast by a small number of GPS satellites. Therefore, an analysis of the impact of multipath on the accuracy of Galileo and GPS positioning should be performed again when both the Galileo observations and the GPS L5 signal are transmitted by the target number of satellites.  
A new civilian signal on the L1 frequency of the GPS system, modulated using TimeBOC, is expected to have similar multipath robustness characteristics as the Galileo E1C signal. However, this signal is still not recorded by receivers. Therefore, an analysis of the multipath effect on the accuracy of these two observations might be another point for future work.  
[PERSON] and [PERSON] jointly conceived and designed the research; [PERSON] carried out the filed experiment, processed raw data, and created a research methodology; [PERSON] developed the software, performed the data analysis, and validated the experiment results. [PERSON] and [PERSON] wrote the first draft of the manuscript, reviewed the text, and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Science Centre, Poland: project No. 2016/23/D/ST10/00498.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Blind Image Deblurring Based on Local Edges Selection
## 3 Results and Discussion  
### The Results of Image Edges Selection  
First, tests are carried out to verify the effectiveness of the proposed edges selection method. Figure 1 shows the comparison of different edges selection methods. It can be seen from Figure 1 that the first-order image gradients [9] and Laplace gradients cannot effectively represent the edges well in each direction. Although Canny edges selection method [34] can better extract the details of image edges, its thinning operation on image edges will bring unnecessary noise to the image. In contrast, the proposed method can get more complete and smooth image edges in each direction. Moreover, because the proposed edges selection method is based on image patch, it also has rotation invariance. Figure 1e shows the edges of gray value images obtained by the proposed method whose dark channel and bright channel are calculated by Equations (9) and (10). We can see from Figure 1e that the proposed method can select image edges effectively. Figure 1f is the edges of color images, and \(\Phi_{1}(x)\), \(\Phi_{2}(x)\) are calculated by Equations (6) and (7). The method utilizes the color information of the image, and the selected edges are more representative than the edges in Figure 1e. It can be concluded from the test that the proposed edges selection method based on local information is more effective than others. In addition, salient edges and detailed texture information can be better obtained using color information.  
The proposed edges selection method is also applicable to blurred images. Figure 2 shows the comparison of the selected edges in a patch of the image in Figure 1. In the comparison methods, image gradients are obtained by using the difference between adjacent pixels. When the image becomes blurred, image structure becomes blurred as well, the difference between adjacent pixels is not so large near image edges, so edges cannot be extracted well. In contrast, the proposed edges selection method can preserve image structure as much as possible, and can avoid bringing unnecessary noise.
# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Bayesian and Uncertainty Modeling
#### 6.3.3 Multidimensional Class Evaluation  
Accordingly, from Table 10, papers of this class explicitly cover various forms of drift, particularly sudden and gradual concept drift, as well as general concept drift and data drift scenarios. This broad coverage demonstrates how Bayesian methods and uncertainty quantification naturally accommodate different drift types by updating prior knowledge based on observed evidence. The application contexts highlighted are predominantly specialized, spanning IoT, marine science, energy, finance, anomaly detection, process mining, video surveillance, and industrial processes. Notably, marine science and energy contexts indicate an affinity for Bayesian methods in environments characterized by significant uncertainty and dynamic conditions, reflecting the strength of probabilistic frameworks in capturing real-world variability. Regarding the scope, there is a slightly stronger emphasis on global explanations compared to local explanations. This suggests Bayesian approaches predominantly provide high-level insights, possibly due to their ability to summarize uncertainty and systemic shifts comprehensively, rather than purely granular, instance-level explanations. The target audience is consistently data scientists across all papers, indicating that the interpretability provided by Bayesian frameworks generally demands a strong background in probability theory and statistical inference. No quantitative metrics for interpretability or compliance are explicitly mentioned. Thus, interpretability is implicitly assumed through posterior distributions, uncertainty intervals, or scenario analyses rather than explicitly quantified metrics. The learning mode is primarily online, reflecting the adaptability of Bayesian methods to continuous data streams. The presence of hybrid and offline learning, albeit limited, demonstrates the flexibility of Bayesian methods across varied data-processing scenarios.
# An Optimal Authentication Scheme through Dual Signature for the Internet of Medical Things
## 6 Informal Security Analysis
### Nonrepudiation  
According to nonrepudiation, a party cannot deny the information it has transmitted or received. Our mechanism offers nonrepudiation by ensuring that each participant successfully completes the verification process via a signature verification mechanism. Therefore, once information has been conveyed via communication, a party cannot deny it. Therefore, in such a way our scheme effectively provides this crucial security feature.