esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Measurements of the Electric Power Supplied by the Battery Pack  
The electric power supplied by the overall battery pack, with nominal voltage of 51 V and nominal stored energy of 15.2 kWh, during the three different operating phases was measured using the power analyzer. Figures 13 and 14 show the contemporaneous voltage and current profiles supplied by the battery pack to the electric motor, respectively, during the first task reported in Table 1: the dumpster lift operation. The corresponding electric power for the dumpster lift operation is reported in Figure 14. The waveform sample rate of the power analyzer was set at 100 kS/s.  
Figure 13: Output voltage profile of the battery pack during the dumpster lift operation.  
From Figure 13, it is possible to see that the voltage of the battery pack during this test was around the value of 56 V, that is, higher than the nominal value of 51 V and close to the maximum permitted voltage value for this battery module.  
From Figure 14, it is evident that, during this test, the current supplied by the battery module was close to 50 A. Considering the architecture of the battery pack reported in Section 2.1.3, it is possible to conclude that each battery cell supplied 0.8 A, which corresponds to about 1/3 C-rate of discharge.  
The absorbed power by the electric motor, Figure 15, during the dumpster lift operation is very close to the corresponding mechanical power value reported in Table 1; the deviation between the two power values can depend on the actual load inside the dumpster.  
For the sake of brevity and to avoid showing the power absorptions of all of the work phases presented in Table 1, which is not the final purpose of this study, only the electric power measured in the first operating condition is shown. The electric power absorbed in the other operating conditions is coherent with the absorbed mechanical power values  
Figure 14: Current profile of the supplied current from the battery pack during the dumpster lift operation.  
Figure 15: The electric power supplied by the battery pack during the dumpster lift operation.  
shown in Table 1. The operation absorbing the most power is the garbage pushing action; during this activity the current supplied by the battery module was slightly over 100 A, that is close to 3/4 C-rate discharge for one battery cell.
## 7 Numerical Results
### Doubly-Curved Panel of Translation
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & & & & & & & & \\ \hline \multirow{2}{*}{\(f\)} & \multicolumn{2}{c}{\(p=0\)} & \multirow{2}{*}{\(p=0.2\)} & \multirow{2}{*}{\(p=0.5\)} & \multirow{2}{*}{\(p=1\)} & \multirow{2}{*}{\(p=2\)} & \multirow{2}{*}{\(p=6\)} & \multirow{2}{*}{\(p=128\)} & \multirow{2}{*}{\(p=\infty\)} \\  & 3D-FEM & & & & & & & & & 3D-FEM \\ \hline  
\hline \multicolumn{10}{c}{**ED3**} \\ \hline \multirow{2}{*}{\(f\)} & \multicolumn{2}{c}{\(p=0\)} & \multirow{2}{*}{\(p=0\)} & \multirow{2}{*}{\(p=0.2\)} & \multirow{2}{*}{\(p=0.5\)} & \multirow{2}{*}{\(p=1\)} & \multirow{2}{*}{\(p=2\)} & \multirow{2}{*}{\(p=6\)} & \multirow{2}{*}{\(p=128\)} & \multirow{2}{*}{\(p=\infty\)} & \multicolumn{2}{c}{\(p=\infty\)} \\  & 3D-FEM & & & & & & & 3D-FEM \\ \hline
1 & 195.111 & 194.806 & 175.253 & 159.979 & 147.714 & 137.676 & 127.331 & 119.605 & 119.156 & 118.910 \\
2 & 198.883 & 198.906 & 179.134 & 163.286 & 150.104 & 1 On the other hand, the present results are obtained by setting \(I_{N}=I_{M}=31\) and \(I_{PN}=I_{PM}=16\). From the values in Tables 6 and 7, it is clear that the same observations illustrated for the previous case do not lose their validity. The variation of the first three natural frequencies for the doubly-curved panel of translation with variable thickness in hand is depicted in Figure 11. Finally, Figure 12 shows the first three mode shapes for \(p=1\) as exponent of the volume fraction distribution of the FGM layer. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & & & & & & & & & & & \\ \hline \multirow{2}{*}{\(f\)} & \(p=0\) & \multirow{2}{*}{\(p=0.2\)} & \multirow{2}{*}{\(p=0.5\)} & \multirow{2}{*}{\(p=1\)} & \multirow{2}{*}{\(p=2\)} & \multirow{2}{*}{\(p=6\)} & \multirow{2}{*}{\(p=128\)} & \multirow{2}{*}{\(p=\infty\)} & \multirow{2}{*}{\(p=\infty\)} \\  & 3D-FEM & & & & & & & & & 3D-FEM \\ \hline
1 & 195.111 & 194.417 & 174.592 & 159.130 & 146.764 & 136.715 & 126.998 & 119.208 & 118.983 & 118.910 \\
2 & 198.883 & 198.769 & 178.995 & 163.111 & 149.881 & 138.513 & 127.198 & 120.846 & 120.655 & 121.123 \\
3 & 238.283 & 238.123 & 214.142 & 194.820 & 178.703 & 164.866 & 150.941 & 141.676 & 141.411 & 141.494 \\
4 & 271.427 & 269.211 & 241.714 & 220.222 & 202.998 & 188.972 & 175.675 & 166.849 & 166.586 & 168.298 \\
5 & 298.882 & 299.905 & 268.805 & 244.095 & 223.786 & 206.673 & 189.934 & 179.201 & 178.900 & 177.985 \\
6 & 334.441 & 335.016 & 299.602 & 271.162 & 247.482 & 227.221 & 207.134 & 194.371 & 194.021 & 193.330 \\
7 & 385.418 & 385.910 & 346.136 & 314.937 & 289.804 & 269.227 & 249.750 & 237.176 & 236.810 & 236.270 \\
8 & 419.503 & 418.787 & 374.723 & 340.174 & 312.403 & 289.771 & 268.552 & 255.042 & 254.651 & 255.202 \\
9 & 429.229 & 426.953 & 383.297 & 349.130 & 321.786 & 299.631 & 278.844 & 265.174 & 264.766 & 266.854 \\
10 & 524.643 & 522.524 & 468.056 & 425.524 & 391.573 & 364  
Figure 11: Variation of the first three natural frequencies for a CFCF doubly-curved panel of translation with variable thickness as a function of the exponent \(p\).
# Using Mechanical Metamaterials in Guitar Top Plates: A Numerical Study
## 3 Results
### Eigenfrequency Studies
#### 3.1.1 Unbraced Soundboards  
At first, we computed the first 12 eigenfrequencies (and relative eigenmodes) of the plates detached from the rest of the body and without any bracing. These simulations were performed in simply supported boundary conditions, which were implemented by imposing a fixed constraint on the lower outer edge of the plates, thus preventing displacement at the boundary while still allowing for free bending rotation. The use of simply supported boundary conditions was meant to obtain a situation resembling that of the plate mounted to the rest of the body [33].  
The computed eigenfrequencies for the plates with longitudinal (\(p=1\)) and radial (\(p=1\)) holes, as well as the solid plate are reported in Figure 2a, which also shows the percentage variations for the two metamaterial plates with respect to the solid case. Clearly, most of the eigenfrequencies were lowered, if slightly, when introducing the metamaterial. There seems to be a clear separation between the case of the longitudinal holes and that of the radial holes; however, the eigenfrequencies were indeed lowered far more in the latter case, with those for modes 5 and 12 showing a decrease of \(\sim\)8%, whereas the variation with the longitudinal holes did not exceed \(\sim\)5%.
## 4 Simulation Results and Analysis
### Validation of the AWGN Module  
In the AWGN module, the spectrum of the signal after the addition of Gaussian white noise is shown in Figure 15. Additive Gaussian white noise is uniformly distributed in the frequency spectrum. Therefore, adding additive Gaussian white noise to a signal will increase the power of the signal's spectrum compared to its original state.  
As shown in Figure 15, after noise addition, the power at the bottom of the signal's spectrum increases slightly, which is caused by the added Gaussian white noise. Therefore, the noise addition functionality in the designed AWGN module is correct.
## 4 Experimental Preparation
### Parameter Setting and Experimental Environment  
In our experiment, we utilized low-resolution images obtained by downscaling and blurring the original remote sensing images as inputs for reconstructing high-resolution images at a 4\(\times\) magnification factor. We compared the reconstruction performance and inference speed of our proposed model against several established super-resolution recon  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset Name** & **Extracted Patches** & **Size** & **Spatial Resolution (m)** \\ \hline ISPRS Potsdam & 1368 & 1000 \(\times\) 1000 & 0.05 \\ UC Merced Land-Use & 99 & 256 \(\times\) 256 & 0.3048 \\ NWPU-RESIC45 & 101 & 256 \(\times\) 256 & 0.8 \\ Draper Satellite & 11 & 1000 \(\times\) 1000 & 0.2 \\ Image Chronology & & & \\ Ship Images from & 180 & \(\sim\)421 \(\times\) 388.5 & 0.8 \\ Google & Total: 1759 & Avg: 856 \(\times\) 853 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of image selection for RSSOD dataset.  
struction methods, including bicubic, EDSR, RCAN, SwinIR, ECBSR, and HAT. Bicubic represents a classical interpolation method, while EDSR and RCAN are CNN-based models, and SwinIR and HAT represent transformer models. ECBSR stands as a typical lightweight re-parameterized model, encompassing a spectrum of traditional and contemporary techniques for remote sensing image reconstruction.  
The experiment comprised four training rounds, each spanning 30,000 epochs. The best-performing model from the previous round was employed as the pre-training model for the subsequent round. The [gt_size, rate] set for each training round was as follows: \(\left\{144^{2},\ 5\times 10^{-4}\right\}\), \(\left\{192^{2},\ 1\times 10^{-4}\right\}\), \(\left\{208^{2},\ 5\times 10^{-5}\right\}\), and \(\left\{208^{2},\ 5\times 10^{-5}\right\}\).  
The deep learning framework used in these experiments was TensorFlow 2.7. The hardware training platform was a single GPU computer (CPU: Intel Core [EMAIL_ADDRESS] GHz 16 Core, RAM: 64 GB and GPU: Nvidia Geforce RTX 3090).
## 6 Discussion
By training predictive models using datasets from real-time monitoring and CFD simulations, the DT can identify unfavourable conditions, such as excessive vacuum or high surge pressures. This capability can assist in decision-making by recommending optimal valve operation strategies and predicting potential failure risks before they occur. However, some limitations must be considered. ML models may struggle with highly complex or unforeseen hydraulic scenarios that are not well represented in the training phase. Furthermore, the computational cost of real-time learning and inference can be significant, especially for large-scale pipeline systems with continuously changing conditions. Despite these limitations, the integration of machine learning into the Digital Twin framework represents a valuable advance in optimising pipeline operations and safety thus leveraging predictive capabilities through real-time data and CFD modelling.
# A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems
## 6 Discussion and Conclusions  
Air pollution is an essential environmental issue due to the tremendous impacts on public health, global environment, and worldwide economy. Urban air pollution with non-uniform distribution trend arises the necessity for pollution monitoring with high spatio-temporal resolution, which the conventional air pollution monitoring systems cannot provide because of the limited data availability and non-scalability of the systems. By utilizing the advance sensing technologies, MicroElectroMechanical Systems (MEMS) and Wireless Sensor Network (WSN), researchers are pushing the concept of The Next Generation Air Pollution Monitoring System (TNGAPMS) to the limit and have achieved great progresses. Many of state-of-the-art air pollution monitoring systems have been implemented and tested. All of these systems evidence that an air pollution monitoring system with high spatio-temporal resolution, cost and energy efficiency, deployment and maintenance feasibility, convenient accessing ability for the public or professional users are achievable. However, from Sections 4 and 5, we can conclude that there are still some issues or challenges of these existing systems that need to be addressed. Also there are some abilities or characteristics of these existing systems that we want to carry forward or enhance when building the future systems.
# Data-Free Area Detection and Evaluation for Marine Satellite Data Products
## 2 Materials and Methods
### Methods  
The framework and technical approach of this study are described in Figure 2. The specific pre-processing process and methods for the Level 1 and Level 2 data are described in Sections 2.3.1 and 2.3.2. The improved method of the automatic acquisition of the optimal thresholds of the SAM algorithm as applied to the marine object recognition and classification in this study is presented in Section 2.3.3. The corresponding product analysis and evaluation index calculation methods and the results are described in Section 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data** & **Satellite** & **Band Number** & **Spectrum Range (\(\upmu\)m)** & **Spatial Resolution (m)** & **Temporal Resolution (Day)** \\ \hline GOCI & COMS & 8 & 0.412–0.865 & 500 & 0.05 \\ MODIS & Terra/Aqua & 36 (1.2/3–7/8–36) & 0.412–14.38 & 250/500/1000 & 1 \\ OLCI & Sentinel 3 & 21 & 0.4–1.02 & 300 & \textless{}2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the GOCI, MODIS and OLCI satellite sensors.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data** & **Level 1** & **Level 2** \\ \hline GOCI & COMS\_GOCL1B & COMS\_GOCL12A \\ MODIS & MOD02 KM & L2\_LAC\_OC \\ OLCI & OL\_1\_EFR & OL\_2\_WFR \\ \hline \hline \end{tabular}
\end{table}
Table 2: Product introduction.
## 2 Structure and Parametric Model of Leaf Spring Torsional Vibration Damper  
Figure 1a depicts the typical composition of the current marine diesel engine leaf spring torsional vibration damper, comprising a leaf spring group (refer to Figure 1b), hub, positioning block, fastening ring, etc. The leaf spring, hub, and fastening ring are constructed from chromium alloy, while the positioning block is crafted from 45 steel. The leaf spring group serves as the primary component influencing the damper's crucial performance aspects. Torsional vibrations in the diesel engine cause reciprocal torsional displacement in the hub concerning the outer ring (comprising the leaf spring group, positioning block, fastening ring, etc.). The hub's clamping groove applies force to the leaf spring's end, inducing bending and deformation in the leaf spring group. A well-designed, manufactured, and installed leaf spring torsional vibration damper ensures that the thin edge of the leaf spring group embeds into the clamping groove, while the thick edge remains securely fixed by the fastening ring and positioning block.  
Figure 1: Structure of leaf spring torsional vibration damper. (**a**) Main body of damper. (**b**) Leaf spring group.  
In Figure 2, the parameter model of the leaf spring torsional vibration damper is depicted. In this figure, marked as 1 and 2 are the leaf springs. In this figure, components labeled 1 and 2 represent leaf springs, with a red copper gasket positioned between their middle sections, while their left ends are fixed at \(T_{1}\). \(O\) denotes the coordinate origin; \(O_{1}\) represents the center of the circular arc on both sides of the clamping groove, originating from point \(O_{\mathrm{r}}\) with a radius \(r\); \(N\) marks the contact point between the leaf spring and the arc; \(a_{1}\) and \(a_{2}\) stand for the thicknesses of the leaf spring's left and right ends; \(L\) signifies the horizontal distance of point \(N\). \(F_{\mathrm{c}}\) denotes the interaction force between the two leaf springs at the red copper gasket's end, where \(S\) represents the bending part's length of the red copper gasket, and \(b\) stands for the leaf spring's width. Additionally, the force acting on leaf spring 1 during the deformation process aligns with the \(O_{1}N\) direction. Due to the small magnitude of the horizontal component force and its negligible impact on spring bending, this component force is disregarded. Only the vertical component force is considered and denoted as force \(F\).
## 3 Validation  
### Grid Independence  
Grid independence analysis plays a crucial role in ensuring the accuracy and efficiency of numerical simulations. Figure 5a shows three different mesh models, namely coarse, medium, and fine, which were created by adjusting the mesh sizes of the train surface and the moving area. The total number of grid cells in the respective computing domains is 25.4 million, 34.3 million, and 44.7 million, respectively.  
The process of a HST passing through the curved wind barrier area of a bridge with a speed of 83.33 m/s under a crosswind of 15 m/s was simulated using three different mesh models. Figure 5b shows a comparative analysis of the lateral force coefficient obtained from the simulations using the three mesh models. It can be seen that a significant error was observed when using the coarse mesh model, while the results obtained with the medium mesh model had smaller deviations compared to the fine mesh model. Therefore, to maintain a balance between computational accuracy and efficiency, the medium mesh model was selected for subsequent numerical calculations.  
Figure 4: Aerodynamic load of HST in crosswind conditions.
# Class Retrieval of Detected Adversarial Attacks
## 2 Adversarial Attacks
### Adversarial Attack Detection  
Defenses against adversarial attacks are required to prevent security threats in real-world application of neural networks. Most defenses rely on one of the following three main approaches:  
* Modifying the training process, e.g., adding adversarial samples (adversarial training) [18] or modifying the input before testing (decision making) [22; 23].
* Modifying the network, e.g., adding extra masking layer before the last layer [24] and changing the loss function by penalizing the degree of variety per class in the output [11].
* Using external model as a detector, e.g., SafetyNet [12] and convolutional filter statistics detector [25].  
Detectors can be a reliable choice providing the highest accuracy reaching \(\sim 85\%\)[25] preventing most security breaches. There are a lot of other detectors [26; 27] separating the clean image from the adversarial one by finding some distinguishable features and properties e.g. convolution filter statistics [25] and manifolds [26]. Although detectors are considered strong defenses against adversarial attacks, an attack can cause a halt in the system hindering the achievement of any task. In a time sensitive task e.g self-driving cars, where an on-the-spot decision has to be drawn, detectors are not sufficient and a retrieving approach has to be installed recovering the original class of the input.
## 3 Results and Discussions
### Nanocomposite Characterization
According to Figure 3b, the saturation magnetism (Ms) was 28.8 and 67 emu/g for MnFe\({}_{2}\)O\({}_{4}\)/GO and manganese ferrite alone, respectively, indicating the reduced size nanocomposite after the loading of manganese ferrite over graphene oxide. Both of them exhibit superparamagnetic behavior. The nanocomposite's magnetic properties are attributed to the load of magnetic ferrite over the graphene oxide [46; 47]. Raman spectra of graphene oxide and MnFe\({}_{2}\)O\({}_{4}\)/graphene oxide were showed in Figure 3c. The structural defects are responsible for the D peak that resulted from out of the plane of sp\({}^{2}\) bonded carbon atoms, while the G peak is attributed to in-plane vibrations [48]. Figure 2: XRD (**a**) and FT-IR (**b**) of MnFe\({}_{2}\)O\({}_{4}\)/graphene oxide nanocomposite and its components. The defect of the material was represented by the (ID/IG) intensity ratio. According to Figure 3c, ID/IG ratio for MnFe\({}_{2}\)O\({}_{4}\)/graphene oxide nanocomposite is 1.13 that is higher than that of graphene oxide (which is equal to 0.94), indicating the interaction between graphene oxide's oxygen groups and manganese ferrite nanoparticles [49]. This interaction is responsible for the increased degree of the defect and causes this difference in peak ratio.
# Labeling Expert: A New Multi-Network Anomaly Detection Architecture Based on LNN-RLSTM
## 2 Literature Review  
Threats to validity: In this literature review, we include studies that (1) address algorithmic research in three areas of anomaly detection; (2) anomaly detection research for cyber threats, including intrusion detection, spam detection and malware detection; and (3) discuss performance evaluation for accuracy, recall, etc. We used various string combinations, such as \"anomaly detection\" and \"machine learning and network intrusion\" [8], \"business data anomalies\" and \"network traffic\", to retrieve peer-reviewed articles in journals, conference proceedings, book chapters and reports. We targeted five databases, namely ACM Digital Library, IEEE Xplore, ScienceDirect, SpringerLink and Web of Science. Google Scholar was also used for forward and backward searches. We focused on the latest advances in the last decade. A total of 154 papers was retrieved, and titles and abstracts were screened to identify potential articles. The full texts of 56 studies were evaluated for relevance to the inclusion criteria. We excluded articles that discussed (1) cyber threats, such as image-mapping-based intrusion detection and spam detection, (2) threats to cyber-physical systems, and (3) threats to IoT devices, smart grids, and smart cities, by searching before and after. Finally, a total of 50 studies was selected for data extraction. There are many different machine learning techniques used to improve anomaly detection performance, which can be generally classified as supervised, unsupervised, semi-supervised and statistical-based models. Supervised methods require extremely high quality of data labels, such as K-nearest neighbors, neural networks and support vector machines. However, during the real-time monitoring of the network, new types of anomalies often appear frequently, which are not present in the training set, which causes anomaly detection failure. Unsupervised learning uses numerical analysis of data attributes alone for detection, for example, by filtering outliers through clustering methods. Since this detection method is limited to anomalies from a statistical perspective only, it often does not meet the detection requirements of the business environment. Semi-supervised learning methods are more efficient than supervised learning through semi-learning and can outperform unsupervised learning in the business detection perspective. Statistical models were the first algorithms to be used for anomaly detection. They are mostly based on the comparison of some statistical properties to determine the outliers. To solve the three types of problems in Section 1, an accurate, efficient and versatile anomaly detection system needs to be developed. Traditional network anomaly detection has been concluded below. Some details of related work are summarized in Table 1.
## 3 Methodology
### Satellite Image Preprocessing
#### 3.2.3 Spatial Indexing  
Once we obtain the filtered points, we want to efficiently calculate the closest point to the current position of the plane. Comparing the current position of the UAV with each possible satellite keypoint is an \(O(n)\) algorithm. This can be inefficient for real-time applications.  
The problem of rapidly finding 2D or 3D points near a target point is known as spatial indexing [64]. This is a highly studied problem and there are some efficient solutions like the K-dimensional tree (KD-tree) [65], Geohash [66], and Quad-tree [67]. In this paper, we  
Figure 12: Distance histogram and its fitted exponential decay probability distribution.  
Figure 13: Map of filtered keypoints based on the flight plan.  
implemented Quad-tree to efficiently obtain the K-nearest neighbors to search for a good match to reset the position.  
In Figure 14, we offer a general scheme for the Quad-tree algorithm. As its name suggests, Quad-tree is a tree data structure in which each node has exactly four children. This allows us to search points and query the closest point in \(O(log(n))\). The tree separates a two-dimensional space into four parts, and each part can subsequently be separated into another four parts.  
We have validated that Quad-tree is sufficient to archive in real time for our particular case, but we reserve a comparison with other spatial indexing techniques for future research.