esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a 3-DOF Cylindrical Ultrasonic Motor Based on Non-Standard Modes
## 4 Simulation and Analysis
### Structural Parameters  
The detailed parameters of the stator structure are given in Table 2.  
In this design, higher-order bending vibrations are used due to the use of low odd-order longitudinal vibrations with much higher inherent frequencies compared to bending vibrations of the same order.  
As depicted in Figure 4, the stator support body and the matching block are supplied with adjustment slots to reduce bending stiffness and increase vibration amplitude. By adjusting the adjustment slots, the resonant frequencies of the longitudinal and bending vibrations are close to each other, and the two modes are coupled. The adjustment slots are identical in size. Due to the short length of the matching block, there is little room for variation in the position and size of the adjustment slots.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Unit Size Parameters** & **Triangular Mesh Values** & **Tetrahedral Mesh Values** \\ \hline Max element size (m) & 0.00166 & 0.1 \\ Minimum element size (m) & 1.2 \(\times\) 10\({}^{-4}\) & 0.005/\(K\) \\ Maximum element growth rate & 1.4 & 1.5 \\ Curvature factor & 0.4 & 0.1 \\ Resolution of narrow regions & 0.7 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mesh parameters for the designed stator.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Triangular Mesh Values** \\ \hline Diameter of the stator (mm) & 20 \\ Height of the stator & 29 \\ Height of the matching block (mm) & 4.5 \\ Slot depth (mm) & 1.5 \\ Height of flexible chains (mm) & 0.4 \\ Thickness of the PZT (mm) & 0.5 \\ Radial width of the PZT (mm) & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Structural parameters of the stator.  
As illustrated in Figure 5, the stator's first-order longitudinal and third-order bending vibration mode frequencies are calculated at 0.3 mm intervals for slot depths ranging from 0.6 mm to 3 mm. As the slot depth increases, the eigenfrequencies fall, and the bending vibration frequency decreases more rapidly than the longitudinal frequency. With slot depths ranging from 1.5 to 1.8 mm, the eigenfrequencies of both modes are similar. Therefore, a slot depth of 1.5 mm is chosen.  
Figure 6 shows a front view of the clamping body. Four flexible chains are provided inside the clamping body to reduce vibration transmission, avoid energy loss, and further increase the amplitude of stator surface vibration. Figure 7 illustrates the relationship between the height of the flexible chains and the amplitude, with the amplitude increasing as the height increases, with the maximum amplitude at a height of approximately 0.4 and then decreasing. The height of the flexible chain is therefore taken to be 0.4 mm.  
Figure 4: Adjustment slots of support body (**left**) and matching block (**right**).  
Figure 5: Relationship between slot depth and eigenfrequency.  
Figure 6: Front view of the clamping body.
## 3 Ransomware Dynamic Analysis Studies Utilizing both Machine Learning and Deep Learning
### Ransomware Counteraction
#### 6.2.1 Prediction  
Prediction is aimed at stopping the ransomware attack before it takes place. It acts as a first step to stop occurrence of unfavorable outcomes. Information is gathered for analysis to make prediction about the possible attack [25].  
An android based ransomware prediction and detection system was proposed in [119]. This permission-based system scanned all the apps in an uninfected mobile to extract all the permissions. The existing ransomware detection solutions were also reviewed to make a comprehensive set of app permissions. Data mining techniques were applied on extracted dataset to predict the occurrence of the attack. Machine learning classifiers were used to implement this system.  
A ransomware prediction and detection system was presented in [98]. This system utilized honeypot concept to collect the data from trap files. A large set of 30,000 attributes from an online repository of machine learning, i.e., RISS of ICL were used to predict the occurrence of ransomware. For the feature selection five attributes were considered from large set of attributes. Six different machine learning algorithms were used to implement the detection system.
# Software Engineering Techniques for Building Sustainable Cities with Electric Vehicles
## 5 Case Studies
### Case Study: EV Charging Infrastructure Projects in San Diego and Amsterdam  
The cities of San Diego and Amsterdam carried out projects to incorporate electric vehicle (EV) charging infrastructure into their smart grids [82; 83]. Despite geographical differences, the projects shared common objectives, faced similar challenges, and yielded noteworthy results, emphasizing the essential role of software engineering in shaping smart cities.  
Software engineers in both cities designed systems that enabled bi-directional communication between the charging stations and the smart grid. These systems enabled real-time energy usage monitoring and integrated with other city systems such as traffic management and public transportation. As a result, the charging infrastructure increased in efficiency, reducing charging times and expanding the capacity for simultaneous charging. Let us consider some specific performance improvement data from both projects:  
* In San Diego, the integration of the EV charging infrastructure with the smart grid led to a 20% reduction in peak charging times, from an average of 4 h to 3.2 h. The system's ability to charge multiple vehicles simultaneously increased by 15%, accommodating an additional 30 vehicles per charging station per day [82].
* In Amsterdam, the development of a software application for locating and reserving charging stations resulted in a 25% increase in the utilization of available charging stations, equating to an additional 50 vehicles charged per day. The app also reduced the average searching time for a charging station from 15 min to less than 5 min [83].  
Furthermore, software engineers were instrumental in addressing security and privacy concerns. They designed systems with robust security measures and privacy safeguards, enabling the cities to gather and utilize data optimally while maintaining citizens' privacy. This required expertise in security best practices and the ability to incorporate these effectively into the developed software systems.
## 3 Results
### Establishment of \(R_{\rm n}\) Estimation Model  
According to the aforementioned research, considering only the effect of individual factors on \(R_{\rm n}\), a non-interaction regression model was developed in RStudio based on the main controlling factors as follows:  
\[R_{\rm n}=1.3316-0.6002X_{1}-0.4583X_{2}-0.0323X_{3}+0.0009X_{4}, \tag{6}\]  
where \(X_{1}\) is the \(\eta\) value, \(X_{2}\) is the \(A_{\rm slope}\) value, \(X_{3}\) is the \(v\) value, and \(X_{4}\) is the \(\theta\) value.  
In the non-interaction model analysis, falling rock shape, slope surface material, and initial velocity were found to have a much greater influence on \(R_{\rm n}\) than slope angle. However, this contrasts with the sensitivity analysis results. The \(R_{\rm n}\) prediction relying only on individual factors was inaccurate, and significant interactions among main controlling factors may exist. The non-interaction regression model, which neglects the effects of interaction on \(R_{\rm n}\), amplifies the effect of individual factors, failing to accurately reflect the actual influence of the main controlling factors. Therefore, it is essential to study the interactions among the main controlling factors and establish a comprehensive quantitative model.  
Combined with the theoretical basis of interaction analysis, a polynomial regression model was established. The model analysis results are shown in Table 4.  
From Table 4, the \(p\)-values of the one-factor explanatory variables (falling rock shape, slope surface material, initial velocity, and slope angle) are lower than 0.01, indicating that the one-factor explanatory variables significantly affect the response variables. For the interaction parameters, the two-factor explanatory variables (falling rock shape-slope  
Figure 11: The effect of slope angle on \(R_{\rm n}\).  
Figure 10: The effect of initial velocity on \(R_{\rm n}\).  
surface material, falling rock shape-initial velocity, falling rock shape-slope angle, and slope surface material-initial velocity) are as significant as the three-factor explanatory variable (falling rock shape-slope surface material-initial velocity). Substituting Equation (4) to Equation (3), the transformation model that considers the interaction is derived as follows:  
\[\begin{array}{l}R_{\rm n}{=0.7286-0.4630x_{1}-0.3962x_{2}-0.3723x_{3}{+0.0783x_ {4}+},\\ 0.4945x_{1}x_{2}{+0.3987x_{1}x_{3}{+0.2705x_{2}x_{3}-0.1824x_{1}x_{4}-0.4841x_{1 }x_{2}x_{3}}}\end{array} \tag{7}\]  
where \(x_{1}\) is the \(\eta\) value after transformation, \(x_{2}\) is the \(A_{\rm slope}\) value after transformation, \(x_{3}\) is the \(v\) value after transformation, and \(x_{4}\) is the \(\theta\) value after transformation.
## 2 Materials and Methods: Ten-Koh Mission Description  
### Ten-Koh Project Objectives  
The Ten-Koh satellite mission described in this paper has the following primary objectives:  
1. To characterize the plasma environment around a spinning spacecraft.
2. To detect MeV-range electrons in LEO and investigate the space environment in the presence of an extreme low solar activity.
3. To investigate the change of physical properties of LATS (Lightweight Ablator series for Transfer vehicle) and CFRP (Carbon Fiber Reinforced Polymer) material samples exposed to the space environment.  
Involving students in satellite development, manufacturing, testing and operations is an important part of their curriculum and enhances their education. Providing this involvement has been set as a secondary mission objective of Ten-Koh.  
Ten-Koh also provided a flight opportunity for two technology demonstration payloads, which constituted another secondary objective of the mission. These payloads are the Thermal Switch developed at Sigma Space Systems, which flight-proves a novel design of a switchable passive thermal switch [14], and the Ultracapacitor Experiment that aims to quantify the performance of an ultracapacitor as a satellite energy storage device. These two experiments are not discussed in detail in this paper because they are not directly related to particle physics and they do not address the primary mission objectives. However, their location within the Ten-Koh architecture is presented for the sake of completeness.  
From the mentioned primary objectives, the novelty of the Ten-Koh satellite derives from observations of space radiation and ionosphere environment at the end of the 24 th Solar cycle, with an increase in GCR flux in higher latitudes. This is associated with Primary Objectives 1 and 2. As for Primary Objective 3, resin composites are expected to deteriorate when exposed to the in-orbit conditions, with the deterioration rate dependent upon the current state of the LEO environment. Satellite observations of this environment combined with measurements of the degradation of composite materials make the Ten-Koh spacecraft unique, especially in the class of small satellites.  
This paper focuses on the space radiation and its direct effects on electronics, not material degradation. Thus, the focus is placed on Primary Objectives 1 and 2 while others, notably Primary Objective 3, will be discussed in dedicated publications due to their specific nature. However, the accommodation of the instruments required by Primary Objective 3 is described here for the sake of completeness.
## 4 Materials and Methods
### Proposed Architecture
#### 4.2.4 App Component
Figure 9: Scripts used in the framework. 4. Protection of user data: To guarantee the privacy of the data handled in the architecture and the mobile application, we will work with the personal data protection Law 81, enacted in March 2019 in Panama [79]. There are principles, obligations, and procedures for data processing in this law that are established to protect the right to privacy and to identify the conditions that must be met by companies or individuals who manage user databases. 5. Testing: Usability tests were performed to ensure the quality of the application on the prototype by applying Nielsen heuristic metrics [80; 81; 82] and evaluation techniques, testing, and user satisfaction test and validation [83; 84]. For this case, we used the System Usability Scale (SUS) validation technique. Table 8 shows the System Usability Scale (SUS) validation technique. The scale itself consists of 10 questions, each of which can be scored from 1 to 5, where 1 means Strongly Disagree and 5 means Strongly Agree. The median score of the scale is 68. If the score is below 68, it means that it is very likely that there are serious problems with usability that need to be identified and fixed. A score above 68 is considered positive but denotes that there is some shortcoming in usability. An interpretation of the SUS scale is proposed in a range of adjectives such as: \"worst unimaginable\", \"poor\", \"acceptable\", \"good\", \"excellent\", and \"best impossible\". Figure 10 shows the relationship of the SUS scale to these easily interpretable adjectives. To complement the quantitative analysis provided by the questionnaire, it is supplemented with two qualitative questions to learn a little more about the users' opinion. 1. What do you think is the best aspect of this interface and why? 2. What do you think needs to be improved and why? Figure 10: Range of adjectives for SUS scale. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **(+) Positive** & \multicolumn{3}{c}{**(-) Negative**} \\ \hline
**5** & **4** & **3** & **2** & **1** \\ \hline \multirow{2}{*}{Strongly Agree} & Some & Neither Agree & Somewhat & Strongly \\  & Agreement & nor Disagree & Disagree & Disagree \\ \hline \multicolumn{5}{c}{**System Usability Scale (SUS)**} \\ \hline \hline \end{tabular}  
* Q1: I think I would like to use this application frequently.
* Q2: I find this application unnecessarily complex.
* Q3: I think the application is easy to use.
* Q4: I think I would need support to make use of the application.
* Q5: I find the various functions of the application well integrated.
* Q6: I have found too much inconsistency in this application.
* Q7: I think most people would learn to make use of the application quickly.
* Q8: I have found the app quite cumbersome to use.
* Q9: I have felt very confident making use of the app.
* Q10: I would need to learn a lot of things before I could operate the app.  
\end{table} 
Table 8: Validation technique for user satisfaction (System Usability Scale (SUS)).
# Prediction of Carbonate Reservoir Porosity Based on CNN-BiLSTM-Transformer
## 2 Methods
### Evaluation Indicators  
To evaluate the stability and prediction accuracy of the CNN-BiLSTM-Transformer algorithm, this study adopts root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2) as evaluation metrics [21; 22; 23; 24]. Their calculation formulas are as follows:  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{2}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}| \tag{3}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}-\hat {y}_{i})^{2}} \tag{4}\]  
where:  
\(y_{i}\) represents the actual values;  
\(\hat{y}_{i}\) represents the mean value.  
The smaller the RMSE, the better the model's performance and the lower the prediction error. A smaller MAE indicates a lower average error, meaning the prediction results are closer to the actual values. An \(R^{2}\) value closer to 1 signifies stronger explanatory power of the model for the data and a higher degree of fit.  
Figure 3: Correlation analysis diagram.
###### Abstract  
Coasts are continually changing and remote sensing from satellites has the potential to both map and monitor coastal change at multiple scales. Unlike optical technology, synthetic aperture radar (SAR) is uninfluenced by darkness, clouds, and rain, potentially offering a higher revision period to map shoreline position and change, but this can only be feasible if we have a better interpretation of what shorelines as extracted from SAR imagery represent on the ground. This study aims to assess the application of shorelines extracted from SAR from publicly available satellite imagery to map and capture intra-annual to inter-annual shoreline variability. This is assessed in three tidally controlled coastal study areas that represent sand and gravel beaches with different backbone environments: low-lying dunes and marsh; steep, rocky cliff; and urban environments. We have found that SAR shorelines consistently corresponded to positions above the high-water mark across all three sites. We further discuss the influence of the scene geometry, meteorological and oceanographic conditions, and backshore environment and provide a conceptual interpretation of SAR-derived shorelines. In a low-lying coastal setting, the annual change rate derived through SAR presents a high degree of alignment with the known reference values. The present study contributes to our understanding of the poorly known aspect of using shorelines derived from publicly available SAR satellite missions. It outlines a quantitative approach to automatically assess their quality with a new automatic detection method that is transferable to shoreline evolution assessments worldwide.  
coastal erosion; Earth observation; remote sensing; shoreline; SAR +
Footnote †: journal: Marine Science and Engineering  
_Marine Science_  
_and Engineering_
## 1 Introduction
[PERSON] [22] conducted a series of direct tensile tests on marble rocks with different water contents. The test results show that the tensile strength of the marble rock significantly decreases as the water content increases, characterizing variable fracture surfaces. [PERSON] [23] conducted triaxial tests on granite with free maximum moisture content and studied the deformation and failure characteristics and mechanisms of granite. [PERSON] [24] carried out creep disturbance tests on mudstone specimens with different water content levels under fractional loading. The results show that an increase in the moisture content leads to a significant increase in the creep failure strain of mudstone and that the accelerated creep rate is greatly accelerated. Moreover, as the moisture content increases, the type of mudstone creep disturbance failure gradually changes from accelerated creep failure to disturbance failure. However, it should be noted that the above research is more aimed at the influence of the different moisture contents on the rock strength indicators. There remains a lack of research on the brittleness and plasticity of rocks under an aqueous environment. Therefore, it is necessary to study the strength and brittleness of rocks under different moisture content conditions. Guizhou Province is a region with a wide distribution of karst landforms. Here, carbonate, sulfate, and halide saltstones are commonplace, and the nature of the rock masses is easily affected by groundwater and surface water. The area containing a tunnel under construction features a variety of lithological properties; complex combinations of weathered mudstone, argillaceous limestone, and limestone are widely distributed and jointed fractures in the rock mass are commonly found. Additionally, unfavorable geological conditions, such as folds and faults are very common, and the region is rich in atmospheric precipitation. Here, surface water directly infiltrates into the ground, and the main source of groundwater recharge is sufficient. In addition, during the tunnel excavation process, the original rock mass will inevitably become disturbed, thereby increasing the seepage path of the groundwater into the rock mass, resulting in a change in moisture content in the surrounding rock of the tunnel. Therefore, it is of great practical and theoretical significance to clarify whether the interaction between the water and rock will lead to changes in the strength and failure characteristics of a rock mass under different moisture content conditions and whether these changes will threaten the safety of tunnel engineering. Based on the previous studies, we select the widely distributed limestone in the surrounding rock of Tongzi Tunnel as the representative sample and use the uniaxial compression test method to study the mechanical properties, brittleness indexes, and plastic deformation of limestone with different moisture contents.
# Clustering-Based Energy-Efficient Self-Healing Strategy for WSNs under Jamming Attacks
## 6 Simulation Results
### Seventh Scenario  
Finally, the most damaging effects of the jammer happen when it is located in the quadrant GH12. For this scenario, the CH for each cluster is distributed in an asymmetrical form. However, the unique isolated nodes from the blue, green, and red clusters are in a perfect diagonal relative to their corresponding CH. However, only the blue cluster has 50% of isolated nodes. In Figure 15, a unique event occurs, the node H1 cannot communicate with any neighbor, using the maximum transmit power of 14 dBm. This node requires a transmit power of 18.28 dBm to reach the closest neighbor and 19.45 dBm to reach the CH.  
For the cooperation strategies, the information transmitted is 245,868 kb for CP and 328,092 kb for PAC. Then, the residual energy is 484.50 J for CP and 517.78 J for PAC. Here, the relay nodes in positions E2 and G4 have the highest number of associations and exhaust their batteries first. Finally, the information transmitted for FCPA is 914,288 kb and the residual energy is 409.66 J. As for the cooperation strategies, the FCPA strategy cannot provide cooperation to node H1 owing to the maximum transmission power being limited by the transceiver hardware.  
Figure 14: Sixth scenario: where the jammer is in the quadrant EG23, several nodes from the blue cluster are now associated with the CH of the yellow cluster. Additionally, one node from the green cluster decides to associate with the CH of the blue cluster.