esa-sceva/satcom-chunk-collection huggingface dataset:
# Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping
## 1 Introduction
### Forest Disturbance Monitoring Combining SAR and Optical Data  
Optical and SAR data have been combined in remote sensing applications for many years [48; 49]. Combination methods on a data-level that preserve spectral as well as spatial characteristics of both sensors are difficult to design. The combination of SAR and optical data on a result-level avoids this problem by classifying each source individually. The results are then combined while using various methods, such as probabilistic theory, evidence theory, fuzzy theory, neural networks, or ensemble learning classifiers. This way, the characteristics of both sensors can successfully be preserved [49; 50].  
There are a number of recent studies that argue in support of a combined use of SAR and optical data for tropical forest monitoring [3; 50; 51; 52; 53]. Recent results indicate that a combined use can improve tropical forest monitoring for burnt area detection [14], for forest/non-forest mapping [54; 55; 52], for biomass assessment [56; 57; 58], and for deforestation and degradation monitoring [51; 59; 45]. A combination of ALOS PALSAR data and Landsat data was also used to enhance the discrimination of mature forest, secondary forest, and non-forest areas [60]. A recently presented workflow for near real-time deforestation detection integrates medium resolution optical data and SAR data in a Bayesian approach [45; 50]. By integrating optical Landsat 7/8 data with ALOS PALSAR-2 L-band and Sentinel-1 C-band SAR data, deforestation areas in Bolivia were detected with a mean time lag of 31 days, and detections show a user accuracy of 88% and a producer accuracy of 89. The time lag increased by a considerable six weeks when only using Landsat data and by one week when only using Sentinel-1 data [3]. However, the study only focuses on deforestation areas and does not address small forest disturbances that are difficult to detect from medium resolution data, such as Landsat, but require high resolution data instead.  
What is still missing in current research developments is an in-depth analysis of forest disturbance detection capabilities that can be achieved by combining data from the Sentinel-2 and Sentinel-1 missions. These two satellite missions currently have the highest temporal coverage of freely available data and provide imagery at high spatial resolution. One reason why these data sets have not been joined more extensively yet could be that many research studies using time series of data rely on processing services, such as Google Earth Engine ([[https://eartheengine.google.com/](https://eartheengine.google.com/)]([https://eartheengine.google.com/](https://eartheengine.google.com/))), where Sentinel-2 surface reflectance data has only been included recently.
## 3 Materials and Methods
### System Overview  
The VPS (visual prompt selection) framework is designed to enhance the processing of scenes captured by AR devices' vision sensors. This system integrates advanced object detection and segmentation techniques, with a specific focus on enabling user-guided refinement through interactive prompts. Through incorporating both object detection and segmentation in a streamlined pipeline, the VPS (visual prompt selection) provides a robust framework for accurately distinguishing objects from backgrounds and dynamically refining segmentation results based on user input.  
The VPS framework processes scenes from AR devices' vision sensors through a two-module pipeline, as illustrated in Figure 4. These two modules are as follows:  
* Object detection module: This module processes input images to generate bounding boxes and labels for detected objects. The bounding boxes are represented as lists of four coordinates (defining the top-left and bottom-right corners). Labels are assigned either as category IDs or predefined names. The output varies depending on the characteristics of the input image and the performance of the detector. This step servesas the foundation for further segmentation by providing accurate object localization within the scene.
* Segmentation module: In this module, the bounding boxes from the detection stage are used as input to generate individual masks for each object (as shown in Figure 4). A prompt encoder processes the bounding boxes, and a mask decoder generates the final mask for each detected object. This module also supports user interaction, allowing for real-time refinement of segmentation results through clicks, taps, or other gestures. The mask results contain pixel-level information, enabling precise object-background separation even in challenging scenarios.  
The VPS (visual prompt selection) framework offers significant advantages in AR environments by incorporating the Segment Anything Model (SAM), which provides rapid and accurate segmentation capabilities. SAM's lightweight prompt encoder and mask decoder enable real-time user interaction and refinement, making the system highly suitable for a wide range of AR applications.
# Biomechanical Analysis of Gait Compensation Strategies as a Result of Muscle Restriction
## 2 Materials and Methods
### Apparatus  
Every gait experiment was conducted on a treadmill with a 1.4 \(\times\) 0.5 m\({}^{2}\) walking surface (OMEGA3, Johnson Health Tech Co., Ltd., Taichung City, Taiwan). A motion capture system (MAC 3D system, Motion Analysis Corporation, Rohnert Park, CA, USA) was used to record the gait motion at a frequency of 100 Hz. The joint patterns and subjects' postures were calculated by fitting the positions of reflective markers attached to the subject to a motion module of Software for Interactive Musculoskeletal Modeling (SIMM, Musculographics Inc., Santa Rosa, CA, USA). Furthermore, four flexible force-sensing resistors (FSR-400, Interlink Electronics, Lake Forest, CA, USA) were embedded at each shoe tip and shoe sole of comfortable sport shoes, to detect heel contact (HC) and toe off (TO) events.  
The MARTT, which is a wearable device, that basically incorporates belts and force sensors, for restricting lower-limb muscles by tightening the limb [18], was used in this experiment. With MARTT, the muscle contraction was disturbed by limiting the expansion of the cross section area of the muscle. The MARTT mainly restricted the GAS and TA muscles, when it was used on the calf, and the HAM, VAS, and RF muscles, when it was used on the thigh. The same restriction force was applied to each leg, which was  
Figure 1: Location and abbreviations of muscles referred in this study. ILPS: iliopsoas muscle, RF: rectus femoris muscle, VAS: vastii muscle group, HAM: hamstring muscle group, GAS: gastrocnemius muscle, SO: soleus muscle, TA: tibialis anterior.  
monitored with the incorporated force sensors in MARTT. The applied force corresponded to a pressure of approximately 180 mmHg, that is in the range of safe blood flow restriction (160-230 mmHg [19]) and below 200 mmHg that could cause muscle fatigue [20].
## 4 Experimental Results
### Fault Diagnosis Accuracy for Single Bearing Faults
Based on these results, we can know that our proposed bearing fault diagnosis method provides much higher prediction accuracy compared to the existing method for single bearing faults. Moreover, our bearing fault diagnosis method provides fast convergence ability (only 3 to 4 epochs for training), as shown in Figure 7.
## 3 Results and Discussion
### Evolution of Phase Composition and Microstructure
The spectra are normalized to the intensity of the main Zr peak and slightly shifted along y-axis for clarity. Blue dot: larger grain; red dot: smaller grain; green dot: near-pore area. the presence of unreacted tetragonal 5 YSZ (T), second praseodymium-enriched fluorite (F2) and pyrochlore (P) phases. Increasing T\({}_{\text{f}}\) promotes the dissolution of the T and P phases and the merging of the F and F2 phases into a single fluorite-type phase with an intermediate lattice parameter for P05-1550-4. A similar picture is observed for the \(x\) = 0.10 samples (Figure 2B). In the case of the samples with the largest (\(x\) = 0.15) praseodymia additions, the firing time was insufficient for the full homogenization of the samples. For all T\({}_{\text{f}}\), the samples contain the pyrochlore-type intermediate, while the broadening of the peaks seems to imply the transient distribution of the lattice parameters of the fluorite phase (Figure 2C). The area ratio of the main peaks of the pyrochlore and fluorite phases, A\({}_{(222)\text{P}}\)/A\({}_{(III)\text{F}}\), was obtained by the integration of the corresponding reflections (Figure 2D) and used as a criterion for the relative contents of a pyrochlore to fluorite phases and its dependence on the overall composition and firing conditions. The deconvolution of the main and secondary fluorite phase peaks was performed by assuming two Gaussian distributions (Figure 2E). The peaks of the main contribution at slightly higher 2\(\Theta\) were used to evaluate the lattice parameters by the Nelson-Riley method [44] based on extrapolation of:  
\[\frac{\left(h^{2}+k^{2}+l^{2}\right)^{1/2}\lambda}{2 sin\theta}\ =\ a_{o}+b\left(\frac{ cos^{2}\theta}{sin\theta}+\frac{ cos^{2}\theta}{\theta}\right). \tag{5}\]  
The results are summarized in Table 3.
## 3 Uncertainty Modeling Techniques
### Possibilistic Methods
#### 3.2.4 Z-Numbers  
Z-numbers are an extension of fuzzy numbers that include a measure of certainty about the provided information. This allows them to be more effective in environments where the reliability of data is questionable, enhancing decision-making processes under uncertainty.  
Z-numbers, which integrate both the reliability and uncertainty of information, have been effectively applied in power systems for decision-making in complex and uncertain environments. In [43], a Z-number-based framework is used for the site selection of electric vehicle (EV) battery swapping stations. By combining K-Means clustering with a multi-criteria decision-making method under the Z-number environment, the framework accounts for uncertainty in evaluating alternative sites. In [44], Z-numbers are used in supplier selection to address uncertainty in the context of green, resilient, and inclusive development. The enhanced best-worst method and TOPSIS, integrated with scenario-varying Z-numbers, assess suppliers under dependent uncertain events. By assigning Z-numbers to different criteria, the model improves the reliability of rankings in uncertain scenarios.  
A Z-number is defined as an ordered pair \(Z=(A,B)\), where \(A\) is a fuzzy set that imposes a restriction on the possible values of an uncertain variable \(X\), denoted by \(X\) as \(A\). This restriction is characterized by a fuzzy membership function \(\mu_{A}(u)\), which quantifies the degree of compatibility of each value \(u\) with the fuzzy set \(A\). \(B\) quantifies the reliability or certainty of the restriction provided by \(A\), usually expressed in terms of a linguistic variable, such as \"very sure\" or \"likely\".  
The possibilistic restriction on the variable \(X\), imposed by \(A\), can be expressed as [45]  
\[R(X):X\;is\;A\;\implies\;Poss(X=u)=\mu_{A}(u) \tag{51}\]  
where \(\mu_{A}(u)\) is the membership function of the fuzzy set \(A\), and \(u\) represents a generic value of \(X\).  
When \(X\) is considered as a random variable, its probability distribution acts as a probabilistic restriction on \(X\). This probabilistic restriction can be mathematically expressed as \(R(X):X\;is\;p\), where \(p\) represents the probability density function of \(X\). Consequently, for a small interval around \(u\), the probability that \(X\) takes a value within \([u,u+du]\) is given by  
\[R(X):\;X\;is\;p\implies\;Prob(u\;\leq\;X\;\leq\;u+du)=p_{X}(u)du \tag{52}\]  
The relationship between \(X\) and \(A\) can be extended to include a restriction on the probability measure of A as \(R(X):X\;is\;A\) and \(Prob(X\;is\;A)\) is \(B\), where \(B\) evaluates the confidence level in the fuzzy restriction provided by \(A\). Figure 6 shows a simple Z-number with the membership function \(\mu_{A}\).  
Figure 6: Z-number with the membership function \(\mu_{A}\) within the probability density function \(p_{X}\).
# Two-Phase Deep Learning-Based EDoS Detection System
## 3 Background Knowledge  
### EDoS Attack Analysis  
As mention in [19], an EDoS is a type of low-rate DDoS attack. Unlike a high-rate DDoS attack, however, an EDoS attack is sophisticated and arduous to detect because of its low-rate traffic and stealthy behavior. EDoS and high-rate DDoS attacks are also different in terms of their purpose. In high-rate DDoS attacks, the attacker's goal is to disrupt the services offered by a cloud service provider. Therefore, high-rate DDoS attackers irrationally launch attacks over a short amount of time with maximum resources. Conversely, EDoS attacks target the financial component of the service provider. As depicted in Figure 1, EDoS attacks exploit the auto-scaling feature of cloud computing and cause an unwanted installation of new virtual machines. The costs associated with this unpaid malicious usage burden the cloud service provider. EDoS attackers gradually push illegitimate traffic over a longer period of time. Because EDoS attack traffic looks similar to benign traffic, its detection is a challenging task.  
Like DDoS attacks, EDoS attacks can be categorized into two types: bandwidth depletion and resource depletion attacks [20]. In bandwidth depletion attacks, attackers flood a victim with unwanted traffic, which exhausts the network bandwidth of the victim. The source IP address of an attacker generates one or two flows with a high-level packet volume in each flow. ICMP flooding, Smurf, and Fraggle attacks are representatives of this type. With respect to resource depletion attacks, the attacker aims to generate a large number of flows to a victim address. Each generated flow contains a small number of packets. Because these packets are spoofed IP packets, there will be no reply packets from the victim server. Thus, these flows remain alive during the attack. This consumes a significant number of resources of the victim, including the CPU and memory, and the victim server has to require more resources from the cloud service provider. A TCP SYN flooding attack is a representative of this type. In [13; 21], an attack that targets a specific application (such as a database API request attack) and a Yo-Yo attack are introduced. However, when we study the characteristics of these attacks, we see they can be classified into the above two types. In conclusion, the common types of EDoS attacks can be summarized in Table 1.  
Figure 1: EDoS effect.
# Satellite Monitoring of Italian Vineyards and Spatio-Temporal Variability Assessment
## 3 Results  
The statistical analysis was carried out on a dataset of 2304 vineyards after the sorting. The monitoring across six vintages retrieved a total of 13,093 observations (vineyards and vintages). In total, sixteen dependent variables were compared to seventeen independent ones. The results of the Italian vineyards' variability analysis are presented in the following paragraphs, and each paragraph focuses on a different aspect. In each paragraph, the PCA plots are first displayed. The PCA plots depict the relationship of the tested numerical variables among the first two principal component dimensions. The same numerical variables are listed in Table 2. The comparison between the numerical variables also included relationship type with the VIs indices: positive, negative, or null. Second, a table retrieves the ANOVA outcomes in which the most significant categorical variables are highlighted, which are joined with the vintage's effect. Those categorical variables and the related classes are listed in Table 3. Hence, ANOVA can also evaluate the time effect by decoupling the vintage from the VIs seasonal parameters against the PCA. Finally, some boxplots are presented to highlight the VIs distribution across each class of the categorical variables and spit by vintage. Not all variables (independent) were plotted, but rather only those that referred to the selected ones from the previous PCA and ANOVA analysis.
# Fine-Tuned Visual Transformer Masked Autoencoder Applied for Anomaly Detection in Satellite Images
## 2 Materials and Methods  
As mentioned above, various methods have been used for anomaly detection. Here, we consider the Vision Transformer with Masked Autoencoder as a model for anomaly detection and the Grad-CAM method that can be used to create saliency areas.
## 4 Numerical Simulation Analysis
### Parameter Calibration  
In order to ensure that the numerical model can better reflect the shear failure process of a rock-like specimen, it is necessary to carry out parameter calibration of the numerical model [33]. The numerical simulation parameters were calibrated by simulating the mechanical properties of a standard cylindrical specimen (50 mm in diameter and 100 mm high) under uniaxial compression [34].  
First, standard cylindrical specimens were prepared according to the ratio of the rock-like material in the direct shear test, and uniaxial compression tests were performed, as shown in Figure 8a. Then, a uniaxial compression cylindrical numerical model of the same size was built and embedded in the model with zero thickness cohesion elements, as shown in Figure 8b. We compare the results of the numerical model with the results of the previous uniaxial compression tests and keep correcting the parameters until the simulation results are similar to the test results.  
Figure 9 shows the numerical simulation and experimental results of the uniaxial compression test. Test 1, Test 2, and Test 3 were three uniaxial compression specimens. It can be found that the numerical simulation of uniaxial compression and the curve of the indoor test have a high degree of similarity. In terms of failure mode, both the numerical simulation results and the experimental results were dominated by vertical tensile failure. Referring to the research of [PERSON] et al. [26], when the simulation results of the uniaxial  
Figure 8: Uniaxial compression test and uniaxial compression numerical model construction: (**a**) uniaxial compression test; (**b**) uniaxial compression numerical model construction.  
Figure 7: The insertion process of the zero-thickness cohesive elements: (**a**) adjacent solid elements; (**b**) discretize solid elements; (**c**) construct a zero-thickness cohesive element; (**d**) insert zero-thickness cohesive elements into solid elements.  
compression numerical model are closer to the experimental results, the parameters can be used in the shear numerical model, as shown in Table 3.
## 4 Implementation and Results
### System Implementation  
#### 4.3.1 Sensor Manager  
The sensor manager in the proposed system processes the sensing data collected from the WSN sensor nodes installed outdoors to store in the database or to send the data to some other server system, and the implemented sensor manager parses the raw data collected from environmental and soil sensors into the required unit, as shown in Figure 14.  
Figure 14: Implementation of the Sensor Manager.  
\begin{table}
\begin{tabular}{c|c|c|c} \hline \multirow{2}{*}{**Module**} & \multicolumn{3}{c}{**Power consumption**} \\ \cline{2-4}  & **Voltage** & **Current** & **Power** \\ \hline GPS Module & DC 5 V & 0.2 A & 1 W \\ \hline Embedded Board Module & DC 9 V & 500 mA & 5 W \\ \hline CCTV Camera & DC 9 V & 400 mA & 4 W \\ \hline Soil Sensor & DC 5 V & 10 mA & 0.05 W \\ \hline Environmental Sensor & DC 3 V & 2.3 A & 6.9 W \\ \hline
**Total** & DC 12 V & 1.4 A & 16.95 W \\ \hline \multirow{2}{*}{**Module**} & \multicolumn{3}{c}{**Supply Power**} \\ \cline{2-4}  & **Voltage** & **Current** & **Power** \\ \hline Solar Cell & DC 26.4 V & 7.6 A & 200 W \\ \hline \multirow{2}{*}{Battery} & **Voltage** & **Capacity (20 HR)** \\ \cline{2-4}  & DC 12 V & 64 A \\ \hline \end{tabular}
\end{table}
Table 5: Power Consumption of Module and Power Supply of Solar Battery.  
The soil and weather data values obtained by parsing the raw data are stored in the database through the sensor manager. Figure 15 shows the results of that the soil and weather data values obtained by parsing the raw data that are stored in the database.  
In addition, the sensor manager sends data to other server systems. Figure 16 is a screen showing the data sent by another server system, where environmental data values processed on another server are coming in, even though it does not collect any data through WSN sensors.  
Figure 16: Screen that Receives Data Sent by Another Server System.  
Figure 15: Environmental Information Table **(a)** and Soil Information Table **(b)**.
## 3 Results and Discussion
### Comparisons and Disscussion
#### 3.2.2 Accuracy Analysis  
Besides the performance of robustness, accuracy is another important criterion in the study. As a result, three loss indicators are utilized to compare the four testing models, i.e., MAE, MSE and Huber loss, as their equations are discussed in Section 2.4.4. The outliers in the testing samples are eliminated before loss calculation using the two-tailed test, which is introduced above in Figure 6. The comparison result is shown in Table 7. According to the result, ANN performs the worst as its three loss indicators are much more than those of other models. Hence, the testing ANN is inapplicable when directly trained on samples with extreme outliers. Besides, although KNN and DTR show similar robustness, their accuracy indicators are quite different. KNN obtains the best MAE indicator, whereas the MSE value of KNN is bigger than that of the proposed RNN, due to the small number of outliers calculated by KNN. Comparing those indicators comprehensively, the proposed RNN shows the highest performance, as it achieves the best MSE and Huber loss indictors with a small MAE value as well.
## 4 Comparative Analysis
### ANOVA on the FP-Growth Mean  
The ANOVA (Analysis of Variance) test is a parametric statistical method used to determine whether there are any statistically significant differences between the means of three or more independent groups. The purpose of the ANOVA is to test the null hypothesis that all group means are equal versus the alternative hypothesis that at least one group mean is different.  
ANOVA breaks down the total variability in the data into  
* Between-group variance due to differences between group means.
* Within-group variance due to random variation within each group.  
\[F=\frac{Variance\;Between\;Groups}{Variance\;Within\;Groups}\]  
If the F-statistic is significantly large (with a small \(p\)-value), it suggests that the group means are not all equal.  
To assess whether the FP-Growth execution time varied significantly across support levels, a one-way ANOVA was conducted.  
The results showed a statistically significant difference (\(p<0.05\)), indicating that the support threshold had a measurable impact on performance.  
Table 1 and Figure 2 shows how FP-Growth means vary significantly across the three min_support levels (0.10, 0.15, and 0.20). The reduction in median values and variability confirms the statistical significance highlighted in the ANOVA.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Support** & **Mean** & **Std. Deviation** \\ \hline
0.1 & 774.79 & 302.93 \\ \hline
0.15 & 260.42 & 106.96 \\ \hline
0.2 & 127.89 & 46.14 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Anova distribution.  
Figure 2: ANOVA distribution showing FP-Growth at varying support levels.  
These results confirm that higher support levels significantly reduce the FP-Growth execution time, likely due to the smaller number of frequent itemsets generated.
## 4 Case Study
### Results and Analysis
#### 4.2.1 Optimization Results
The optimized Pareto solution set is partially shown in Table 12, and its binocular values are shown in Table 13, where different solutions represent the different selected cities. Table 14 shows the changes in the selection of classification detection stations and recycling processing stations.