esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Stability Evaluation
### Grid-Connected Mode  
The Lyapunov function is considered to be:  
\[V_{gc}\Big{(}S,\ \widetilde{\mathrm{Y}}\Big{)}=\frac{1}{2}\ S^{2}+\frac{1}{2}\ \xi_{gc}\ \widetilde{\mathrm{Y}}^{2} \tag{21}\]  
where the subscript '\(gc^{\prime}\) denotes the grid-connected operation mode and we have: \(\ \widetilde{\mathrm{Y}}=\ \mathrm{Y}-\ \widetilde{\mathrm{Y}}\). The first order derivative of Lyapunov function results in:  
\[\frac{d}{dt}V_{gc}\Big{(}S(t),\ \widetilde{\mathrm{Y}}(t)\Big{)}=S(t)\frac{d}{dt} (S(t))+\ \xi_{gc}\ \widetilde{\mathrm{Y}}(t)\frac{d}{dt}\ \widetilde{\mathrm{Y}}(t) \tag{22}\]  
and according to (9) to (12), we have:  
\[\begin{split}&\frac{d}{dt}V_{gc}\Big{(}S(t),\ \widetilde{\mathrm{Y}}(t)\Big{)}=S(t)\big{(}g_{p}u_{sw}+n(t)\big{)}+\ \xi_{gc}\big{(}\mathrm{Y}(t)-\ \hat{\mathrm{Y}}(t)\big{)}\frac{d}{dt}\ \widetilde{\mathrm{Y}}(t)\\ &\qquad=S(t)\big{(}-\ \hat{\mathrm{Y}}(t)sign(S(t))+n(t)\big{)}-\xi_{ gc}\big{(}\mathrm{Y}(t)-\ \hat{\mathrm{Y}}(t)\big{)}\frac{|S(t)|}{\xi_{gc}}\\ &\qquad=S(t)n(t)-\mathrm{Y}(t)|S(t)|\leq|S(t)|(|n(t)|-\mathrm{Y}( t))\leq 0\end{split} \tag{23}\]  
Therefore, the control approach is stable in grid connected operation mode.
# Combining Unsupervised Approaches for Near Real-Time Network Traffic Anomaly Detection
###### Abstract  
The 0-day attack is a cyber-attack based on vulnerabilities that have not yet been published. The detection of anomalous traffic generated by such attacks is vital, as it can represent a critical problem, both in a technical and economic sense, for a smart enterprise as for any system largely dependent on technology. To predict this kind of attack, one solution can be to use unsupervised machine learning approaches, as they guarantee the detection of anomalies regardless of their prior knowledge. It is also essential to identify the anomalous and unknown behaviors that occur within a network in near real-time. Three different approaches have been proposed and benchmarked in exactly the same condition: Deep Autoencoding with GMM and Isolation Forest, Deep Autoencoder with Isolation Forest, and Memory Augmented Deep Autoencoder with Isolation Forest. These approaches are thus the result of combining different unsupervised algorithms. The results show that the addition of the Isolation Forest improves the accuracy values and increases the inference time, although this increase does not represent a relevant problematic factor. This paper also explains the features that the various models consider most important for classifying an event as an attack using the explainable artificial intelligence methodology called Shapley Additive Explanations (SHAP). Experiments were conducted on KDD99, NSL-KDD, and CIC-IDS2017 datasets.  
unsupervised machine learning; anomaly detection; near real-time; network traffic; explainable artificial intelligence; SHAP +
Footnote †: journal: Applied sciences
# Collaborative Trajectories Optimization for Tram and Connected Vehicles at Signalized Intersection
## 4 Methodology
### Chromosome Encoding  
The chromosomes are encoded by real numbers, and the individual gene is composed of the ratios of the maximum traction and the maximum braking force (\(P_{1}\) and \(P_{2}\)) and the traction distance and the braking distance of the tram (\(S_{1}\) and \(S_{4}\)).  
From initial speed and \(P_{1}\), tractive track can be obtained, and, from \(S_{1}\), tractive time and cruising speed can be determined. Taking the speed limit at the intersection of the tram as the last speed of the running section, braking time and terminal coasting speed can be deduced by using \(P_{2}\) and \(S_{4}\), and coasting track can be obtained. From coasting track and cruising speed, coasting time and distance can be obtained, and then cruising distance and time can be obtained. From this, the traveling distance and duration of each working condition can be obtained, and then the guiding scheme for trans can be obtained. Enough random chromosomes are generated the first time, and the running-state constraint is used as the screening condition. The size of the population is set to 50.
# Experimental Study of See-Saw Mode Nano-Vibration on Orifice-Type Restrictors
###### Abstract  
Slide stability is key to the aerostatic guide in ultra-precise machines; thus, it has garnered plenty of attention. Macro-scale studies are commonplace, but micro- and nano-vibration issues require more attention. Microscope vibration is mainly caused by tiny changes in the fluid parameters of lubricating gas film, which is complex and has no verdict. In this case, slide-gas interaction should be considered. In this study, the widely used orifice-type restrictor was investigated for its nano-vibration performance. A Comsol finite-element-method fluid-structure interaction model was used to simulate and analyze an orifice-type restrictor, and orifice-restrictor vibration characteristics at the nanometer scale were inspected using a high-performance laser vibrometer. The results demonstrate that see-saw mode vibrations occur in the restrictions, growing stronger with increased air-supply pressure. The see-saw vibration's axis is speculatively determined based on orifice and restrictor structures, and the vibration type is related to the number of orifices. The results also show that the vibration is random with natural frequencies at the kilohertz level. The newly provided research results are beneficial for better understanding the nano-vibrations of orifice-type restrictors.  
nano-vibration; natural frequencies; orifice-type restrictor; see-saw mode +
Footnote †: journal: Applied Sciences
# Analysis of Hydrogen Filling of 175 Liter Tank for Large-Sized Hydrogen Vehicle
## 2 Numerical Implementation
### Calculation Conditions  
In the present study, a 175 L Type IV hydrogen tank was examined using a two-dimensional (2D) axisymmetric model. To verify the adaptability of the numerical models, a numerical validation study was conducted for the experimental results of Zheng et al. [6] with a Type III hydrogen tank. The specifications of the hydrogen tank examined are listed in Table 1. Mesh grid independence study was performed by successively decreasing the element size with a structured grid: a coarse grid with 100,000 meshes and a medium grid with 150,000 and a fine grid with 200,000 meshes. The mesh grid was selected for approximately 150,000 in the present study, as shown in Figure 1, until the solution becomes independent of the mesh density, as the number of mesh elements was increased gradually. The hydrogen gas flow was treated to be compressible and unsteady. Material properties considered for the present calculation are shown in Table 2. For filling condition, the condition of inlet of the cylinder was set to pressure inlet condition (initial SOC pressure conditions) with different temperature conditions shown in Table 3. An inflated layer of hexahedral elements was generated for the mesh treatment, and a structured and non-adaptive grid (hexahedral grid) was generated with nodes. The governing equations with the boundary conditions were solved using the finite difference method with the commercial computational fluid dynamics package (ANSYS Fluent, version 18) [16].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type/Capacity (L)** & **Components** & **Length (mm)** \\ \hline  & Total length & 1030 \\  & Inner diameter & 354 \\ Type III & Outer diameter & 427 \\  ’74.3 & Thickness (liner and laminate) & 36 \\  & Inlet diameter & 5 \\ \hline  & Total length & 1850 \\  & Inner diameter & 384 \\  & Outer diameter & 450 \\  & Thickness (liner and laminate) & 33 \\  & Inlet diameter & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of hydrogen tank.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type/Capacity (L)** & **Material** & **Density (kg/m3)** & **Specific Heat (J \(\cdot\) Kg \(\cdot\) K)** & **Thermal Conductivity (W/m \(\cdot\) K)** \\ \hline Type III & Liner (aluminum) & 2700 & 902 & 238 \\
74.3 & Laminate (CFRP) & 1570 & 840 & 0.612 \\ \hline Type IV & Liner (HDPE) & 947 & 1880 & 0.36 \\
175 & Laminate (CFRP) & 1600 & 1400 & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties.  
Figure 1: The mesh grid for the numerical analysis **(a)** Type III **(b)** Type IV.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & & 313 \\ Temperature [K] & 293 \\  & 273 \\  & 243 \\ \hline  & 35 \\ SOC pressure [MPa] & 20 \\  & 10 \\  & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Calculation conditions.
# Three-Dimensional (3D) Model-Based Lower Limb Stump Automatic Orientation
## 1 Introduction
Thus, it is essential to create an algorithm that provides at least preliminary automated orientation of the 3D-model based on objective measurement data. Due to high variability in the stump characteristics and the individual shape of each stump, especially in each congenital malformation case, in certain scenarios only preliminary conclusions can be made automatically, and expert attention will, nevertheless, be required. However, even in this scenario, an automatic decision support system (DSS) could save an expert's time and provide at least preliminary positioning. In this work, we propose an automated orientation algorithm based on the adjustment of the 3D-model virtual anatomical axis of the tibia along with the vertical axis of the rectangular coordinates in three-dimensional space. The suggested algorithm is implemented, tested for performance and experimentally validated by explicit comparisons against an expert assessment. We believe that the proposed solution could be useful as a DSS for the prosthetic expert support. The automated system development is based on the algorithm of orientation of the digital geometric model of the lower limb stump in an automatic mode, and consists of the following steps:  
1. Design of the 3D-model of the lower limb stump obtained by 3D scanning of the patient. 2. Development of a decision support system for the automatic 3D-model orientation. 3. Comparative analysis of the model-based predictions with expert evaluation results. 4. Estimation of the algorithm performance including the duration of automatic orientation for a given resolution setting associated with the number of 3D-model polygons.
# Large Eddy Simulation of Film Cooling with Bulk Flow Pulsation: Comparative Study of LES and RANS
## 5 Conclusions  
Numerical simulations of film cooling from a cylindrical hole on a flat plate were performed using LES under bulk flow pulsations of 32 Hz. The LES results were compared to the experimental data and the results obtained using RANS to determine whether the LES approach better predicted film cooling performance under the unsteady flow condition than the RANS method. When the pulsations were applied, the centerline and spanwise-averaged adiabatic film cooling effectiveness decreased in both the LES and RANS results, which was consistent with the experimental data. However, the RANS turbulence models overpredicted the centerline effectiveness by more than 100%, whereas the LES results deviated only marginally from the experimental data. Even in the absence of pulsation, RANS overpredicted the central temperature of the injectant and did not properly predict the mixing of the main flow and the injectant near the wall, but LES properly predicted these phenomena. In the case of pulsation of the main flow, RANS did not properly predict the injectant behavior by phase, but LES reproduced it well. The local adiabatic film cooling effectiveness predicted by LES showed a wider spread of the coolant in the spanwise direction comparing to the RANS results. The jet shear layer vortices were shown in the LES contours at \(z/D=0\), whereas they were not shown in the RANS contours. The Q-criterion contours obtained by LES and RANS were compared, and it was found that the LES approach predicted more diverse vortex structures as compared to the RANS method. The dimensionless streamwise velocity profiles at \(x/D=5\) predicted by LES at 0 and 32 Hz were considerably closer to the experimental data than the profiles predicted by RANS. Moreover,  
Figure 12: Phase-averaged dimensionless U\({}_{\rm rms}\) profiles at \(x/D=5\) and \(z/D=0\) for 32 Hz: (a) LES (Smagorinsky–Lilly) and (b) URANS (realizable k-\(\varepsilon\)).  
the LES approach was superior to the RANS method in predicting the dimensionless U\({}_{\rm rms}\) profiles. The experimental U\({}_{\rm rms}\) values under 32 Hz pulsation between \(y/d=0\) and 0.5 were higher than those between \(y/D=0.5\) and 1, and the U\({}_{\rm rms}\) profile obtained with LES was closer to the experimental data comparing to that obtained using URANS. The phase-averaged dimensionless U\({}_{\rm rms}\) profiles obtained using LES showed that, in each phase, the shape of the U\({}_{\rm rms}\) profile was attributable to the outline of the large coolant vortices.  
[PERSON] performed the CFD simulations, analyzed the data, and wrote the paper. [PERSON] supervised the research, analyzed the data, and wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
The experimental data is available at https://github
## Data processing Flow Chart
### Post Processing  
Following the correction for the USV's attitude and magnetization, short-wavelength noise was still observed (Figure 7a) due to the unique technical characteristic of the fluxgate sensor exhibiting low sensitivities with short-wavelength wiggling noise. Also, VM from the ship's attitude changes could be affected during data acquisition [9]. In ferromagnetic ships, VM are typically significant, resulting in magnetic field variations that are either very long-wavelength or appear constant during directional changes [3]. However, in the case of the Unmanned Surface Vehicle (USV), short-wavelength noise is dominantly observed during abrupt yaw variation (see Figure 3). This is likely due to the USV being constructed from a paramagnetic material, where instantaneous VM occurs during changes in vessel orientation, manifesting as short-wavelength noise.  
These noise components can be removed through data filtering techniques. We first eliminated spiky noise exceeding \(\pm\)100 nT from the main trend of the magnetic field. Subsequently, we applied a Butterworth low-pass filter with a 0.003 Hz cutoff frequency was applied to remove higher frequency noise artifacts. Then, by removing the vector components of the IGRF from the filtered magnetic field, we obtained the geomagnetic anomalies for each vector component.  
Figure 7: Magnetic field correction results for data acquired along repeated tracklines. (**a**) Results of correction steps along the entire survey track: attitude-corrected anomalies (blue), anomalies after applying magnetic correction (Equation (3); black), and the final anomalies after applying a low-pass filter (red solid line) for X, Y, Z components, along with Yaw change. Shaded areas indicate specific segments. (**b**) Corrected magnetic field components (X, Y, Z) and scalar intensity plotted against latitude for all repeated tracklines shown in Figure 1c, demonstrating the consistency achieved after applying the magnetic correction (Equation (3)). Each color corresponds to a specific trackline shown in Figure 1c.
## 4 Results
### Validation of the Proposal
For this purpose, the highest accuracy value and the highest OP value achieved in the experiments have been identified, and the percentage of difference (loss) with respect to these maximum values has been calculated for all the algorithm-sampling strategy pairs. Finally, the differences for the two metrics have been averaged. The pairs with the lowest average loss values will be the most appropriate, as they are the ones that provide the lowest global loss between accuracy and OP. The same procedure has been carried out for accuracy and G-mean. Table 8 shows the accuracy values for all the algorithm-sampling method pairs, as well as the loss of accuracy of all those pairs with respect to the best value. The best accuracy is 0.92, which is provided by the algorithm bagging-random forest and the clustering-RUS sampling method. The values of OP and G-mean and their corresponding loss with respect to the best value (OP = 0.70 and G-Mean = 0.75) are presented in Tables 9 and 10. The last columns of these tables contain the average loss of accuracy and OP/G-Mean, respectively. This information is also represented in Figures 11 and 12, with the aim of better visualizing the lowest values and the differences between the use of RUS and clustering-RUS. Figure 10: Critical difference diagram to compare the classifiers obtained using RUS vs. clustering-RUS (C-RUS) regarding the G-mean metric. Figure 9: Critical difference diagram to compare the classifiers obtained using RUS vs. clustering-RUS (C-RUS) regarding the OP metric. In Table 9, we can see that the highest OP value, namely, 0.70, is obtained with the random tree algorithm and the RUS sampling method. However, this pair cannot be considered the best, since it also provides the lowest accuracy value, as shown in Figure 2 and Table 8. The best pair will be the one that provides a good balance between accuracy and OP. In our case, bagging-random tree, used together with clustering-RUS, would be the best, providing the lowest average loss of accuracy and OP with respect to the best values of those metrics. Table 9 shows that this average loss only amounts to 2.20%, which is far from the worst average loss of 14.79%. The same behavior can be observed when analyzing G-mean values versus accuracy (Table 10). Once more, the best value of G-mean, 0.75, is yielded by random tree paired with RUS, but the lowest average loss of accuracy and G-mean is given by the pair bagging-random tree and clustering-RUS, with a value of 1.61%, followed very closely by random committee-random forest and clustering-RUS, with 1.65%. In all cases, our clustering-RUS proposal is involved in the best pairs. Figures 11 and 12 also show that our sampling strategy provides better results than the simple RUS strategy for almost all algorithms. They also show the bad behavior of the J48 algorithm, both when used as a simple classifier and as part of ensemble classifiers.
## 3 Approach
### Examination of the Available Databases
#### 3.1.3 Aerial Imagery  
The city of Kaiserslautern possesses aerial imagery captured at various times of the year and at different resolutions. Initially, we utilized images from the spring aerial survey of 2018, which are openly accessible at a resolution of 40 cm through the geoportal of the federal state of Rhineland-Palatinate [17]. Higher-resolution aerial images with a ground resolution of 6 cm are not freely available for data protection reasons but are provided by the municipality for research purposes.  
One objective was to assess whether and under what circumstances higher resolution leads to improved detection. If the benefits of a higher resolution are not significant, the use of freely available, non-protected data becomes feasible. This facilitates the transferability of results to other municipalities and the application of models to more recent aerial images.  
Furthermore, a comparison will also be made with flights from other seasons, each with different foliage conditions. It is expected that the preference for different seasons will vary depending on the application. For example, when determining the location (tree disc, tree row, or green area), it is likely that aerial imagery from seasons without foliage will give better results.
# Machine-Learning-Enabled Intrusion Detection System for Cellular Connected UAV Networks
## 3 System Model
### UAV Threats and Vulnerability
#### 3.1.4 Denial of Service (DoS)  
In a DoS attack, the attackers flood the controller with numerous requests, causing a network overload that depletes the bandwidth and resources to the UAV. The adversaries might use Telnet software to send several requests to the controller. Thus, the communication between the UAV and its controller is disrupted, and as a result, the UAVs may behave abnormally and might crash. Some other effects of DoS attacks on UAVs can be battery exhaustion, poor performance, latency, and system seize.
# R-PreNet: Deraining Network Based on Image Background Prior
## 2 Related Works
### Model-Driven Methods
In particular, the rain images obtained from real scenes are often complex and variable, so the performance of directly establishing models for removing rain is always unsatisfactory. Therefore, data-driven deep learning deraining algorithms have become the latest trend in deraining tasks.
## 3 Analysis of Monitoring Results
### Displacement Analysis of Retaining Piles at Different Temperatures
#### 3.1.2 Horizontal Displacement Analysis of Pile Crown  
The horizontal displacement of the pile crown is an important index of characterizing the safety of the supporting structure. The variation in the horizontal displacement of the pile top with the time measured on site is delineated in Figure 9. It is not difficult to find that the pile crown was displaced into the pit at the initial stage of the excavation, and pile 3 was located in the middle of the foundation pit, which led to the maximum displacement, thereby showing an obvious time-space effect. In the subsequent heating stage, the pile crown was displaced to the outside of the pit, and the horizontal displacement of the pile crown was highly consistent with the trend of change in the temperature. By the end of monitoring, the horizontal displacement of the crown of pile 1, 2, and 3 increased by 1.15, 2.81, and 3.42 mm, respectively. It can also be seen that the pile in the middle of the foundation pit was most affected by the temperature. It was noted that before mid-March, the horizontal displacement of the pile crown enlarged slowly with the  
In Figure 10, the horizontal axis represents the temperature difference (\(\Delta\)T), and the vertical axis denotes the ratio of the increment in the horizontal displacement caused by the temperature variation to the excavation depth (\(\Delta\)D/H). [PERSON] et al. (2020) analyzed the stress on, and the deformation of, the diaphragm wall of a foundation pit by a numerical simulation. Their results showed that the slope of the curve of \(\Delta\)D/H versus \(\Delta\)T was 0.1335. The slope was large, which might be due to the large excavation section of the foundation pit and the wider temperature influence range. [PERSON] et al. (2021) and [PERSON] et al. (2022) derived the calculation formula correlating the temperature variation with the displacement of the support structure, and demonstrated that the slope of the curve of \(\Delta\)D/H versus \(\Delta\)T ranged from 0.0043 to 0.0079 and from 0.0020 to 0.0078, respectively. The growth rates were too small because the method simplified some important factors in practical engineering. In this paper, the slope of the curve of \(\Delta\)D/H versus \(\Delta\)T was in the range of 0.1313 to 0.322, which is obviously larger than the one reported by other scholars. The reason is that the foundation pit in this work used steel H-section beams and rigid panels as the supporting structure, and the temperature variation had a more significant impact on the whole supporting system. Therefore, close attention should be paid to the safety of the steel supporting structures in areas with a high temperature difference.  
Figure 10: The relationship between \(\Delta\)D/H and \(\Delta\)T.  
Figure 9: The horizontal displacement of the crown of the retaining piles.