esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Common-Mode Noise Suppression Filter Design
### Geometric Parameter Design of Filter  
We used ANSYS HFSS software to identify the MDCRP structure filter's related geometric parameters for simulation and analysis. This allowed us to observe the CM insertion loss (S\({}_{\text{cc21}}\)) under parameter performance changes and the CM rejection effect's impact.  
The effect of the length of slot 2 (SL2) is shown in Figure 4a. The SL2 in the second ground plane is modified to be 2 mm, 3 mm, 4 mm, and 5 mm, respectively. The CM noise suppression performance widens when the SL2 is longer. The suitable choice is 5 mm for SL2, which achieves a better rejection effect.  
The effect of the width of Slot 2 (SW2) is shown in Figure 4b. The SW2 in the second ground plane is modified to be 4 mm, 6 mm, 8 mm, and 9.6 mm, respectively. The CM noise suppression performance improves when SW2 is narrower. The suitable choice is 6 mm for SW2, which results in a wider stopband performance.  
The effect of the via quantity (VQ) is shown in Figure 4c. The VQ is modified to be 2 pcs, 4 pcs, 6 pcs, and 8 pcs, respectively. The VQ is increased to improve the CM noise suppression performance, and the stopband performance moves toward a high-frequency location. The most suitable choice is 8 pcs of vias, which achieves better stopband performance.  
The effect of the mushroom length (ML) is shown in Figure 4d. The ML in the first ground plane is modified to be 2 mm, 3 mm, 4 mm, and 4.8 mm, respectively. The CM noise stopband performance moves toward a low-frequency location when the ML is longer. The most suitable choice is 3 mm for ML, which achieves a wider stopband effect.  
The effect of the mushroom width (MW) is shown in Figure 4e. The MW in the first ground plane is modified to be 1.8 mm, 2.4 mm, 3 mm, and 3.6 mm, respectively. The CM noise stopband performance of the high-frequency range improves when the MW is narrower. The most suitable choice is 1.8 mm of MW, which results in a wider stopband performance.  
The CM insertion loss (S\({}_{\text{cc21}}\)) parameter results prove that changes in the geometric parameters of the MDCRP structure filter generate more resonance points to achieve a wider suppression bandwidth, which proves that the geometric parameter change results are consistent with the theory.  
Figure 3: The CM current return path of the differential pair transmission signal.
## 3 Data and Methodology
### UAV Photogrammetry Techniques  
The advent and progression of modern technologies such as the Global Positioning System (GPS) and UAV photogrammetry have led to a rapid evolution of remote sensing imagery surveying methods [17]. Utilizing remote sensing interpretation techniques, valuable information regarding landslide deformation characteristics and surface cracks in the study area can be extracted from postprocessed image data [18]. Remote sensing and geographic information system (GIS) technologies enable the acquisition of high-resolution imagery data, providing precise insights into the deformation characteristics of landslide surfaces. These technologies also facilitate the analysis of spatial and temporal changes by incorporating multiperiod imagery data [19; 20; 21].  
In this study, UAV remote sensing images were obtained in July 2022, October 2022, and March 2023. These datasets from the three time points served as the foundation for analyzing the progression of deformation characteristics in the pit slope of the Wolong Coal Mine. For the survey, aerial photographs of the study area were captured using a Feima E2000 UAV and a DJI AIR25 UAV. The Feima E2000 UAV and DJI AIR 25 UAV were respectively purchased from Feima Robotics in Shenzhen, Guangdong, and DJI Technology Co., Ltd. The DJI AIR25 UAV is manually controlled for close-range flights along the slope to capture image data of landslides in small areas, enabling the generation of high-precision  
Figure 5: Flow chart of multiple-integrated techniques for landslide investigations.  
three-dimensional images within localized areas. When utilizing the Feima E2000 UAV for mapping, specific parameters were established to enhance data quality during aerial photography, including an 80% heading overlap rate, a 70% side overlap rate, and a flight speed of 13.5 m/s. The image resolution (GSD, in cm) was calculated using Formula H = 36 \(\times\) GSD, where H (in m) represents the flight altitude. Considering the undulating terrain in the survey area, a flight altitude of 220 m was selected. Hence, the resolution of the remote sensing image is 6.11 cm, providing accuracy at the centimeter level. Following image data collection, postprocessing was performed using Context Capture (CC) software (10.16.0.75). The modeling process was refined iteratively by incorporating control points to enhance accuracy. Ultimately, this process yields high-resolution orthophotos (DOM) and 3D reality scene models (Figure 6), meeting the specified accuracy criteria. Then, based on the UAV images and 3D models, the fissures and fractured rock mass at the trailing edge of the pit slope can be extracted, which facilitates a more in-depth analysis of the geological structure, stratigraphic relationships, and landslide deformation and damage mechanisms in the study area.
# Enhancing Malware Detection via RGB Assembly Visualization and Hybrid Deep Learning Models
## 1 Introduction
### Current Challenges in Malware Detection
#### 1.1.3 Feature Extraction and Representation Challenges  
Extracting meaningful features from Portable Executable (PE) files presents substantial difficulties, particularly when dealing with obfuscated samples. Traditional feature extraction methods often struggle with high-dimensional feature spaces, leading to the curse of dimensionality and reduced classification performance. The heterogeneous nature of malware families further complicates feature selection, as different malware types exhibit varying behavioral patterns and structural characteristics. Additionally, the rapid evolution of malware techniques continuously introduces new features that existing models may not adequately capture [6].
## 3 Motivation and Research Contribution
#### Research Contribution  
This article makes the following contributions:  
1. A novel hybrid hierarchical architecture within the SDN framework for VANETs is introduced that integrates cloud computing capabilities with VANET infrastructure, addressing the dynamic nature and heterogeneity of vehicular networks.
2. The formulation of the RC-LAHR protocol is a primary contribution. This protocol leverages cloud computing for data handling and location-based information to optimize routing.
3. RC-LAHR significantly improves the reliability and scalability of message dissemination in VANETs. By efficiently handling frequent topology changes and varied hardware, it ensures more stable and consistent communication in vehicular networks.
4. A time complexity analysis is performed to test the efficacy of the proposed routing protocol. The proposed routing protocol works in \(O(n^{3})\) time complexity.
5. Our comprehensive comparative analysis of RC-LAHR with existing protocols like GPSR and OPBR, focusing on PDR, EED, and network reliability, provides empirical evidence of the superiority of RC-LAHR.
## 4 Robustness Analysis of the ADN against the CCF
### Robustness Analysis
#### 4.1.3 Independence and Robustness of the ADN  
The relationship between the final expected proportions \(R_{PF}(\infty)\) and \(R_{IF}(\infty)\) of remaining normal operational nodes and the interdependence between two sub-networks is given by the following Theorem 1.  
**Theorem 1.**_Given network topology parameters and the unintentional random initial failure rate \(\Theta_{P}\) of a node in the PN, the robustness of a ADN is better when the value of average control fan in \(<\)K\({}_{2}\)\(>\) of a node in the PN and the value of average power fan in \(<\)O\({}_{1}\)\(>\) of a node in the CN are greater._  
**Proof.** In the nonlinear Equation (5) (\(1-\lambda\times f_{P}(\lambda)\)) \(<1\), when the value of average power fan in \(<\)O\({}_{1}\)\(>\) of a node in the CN is greater, the value of \(\gamma\) is greater. Correspondingly, given the unintentional random initial failure rate \(\Theta_{P}\), in the nonlinear Equation (5) (\(1-\gamma\times f_{\rm I}(\gamma)\)) \(<1\), when the value of average control fan in \(<\)K\({}_{2}\)\(>\) of a node in the PN is greater, the value of \(\lambda\) is greater. In addition, according to the conclusion in literature [23], given network topology, \(f_{\rm I}(\gamma)\) and \(f_{P}(\lambda)\) are nondecreasing functions. When the value of average control fan in \(<\)K\({}_{2}\)\(>\) of a node in the PN and the value of average power fan in \(<\)O\({}_{1}\)\(>\) of a node in the CN is greater, the final expected proportions \(R_{PF}(\infty)\) and \(R_{IF}(\infty)\) of remaining normal operational nodes in the PN and CN are greater, so the robustness of the ADN is better.  
Therefore, the third conclusion of our analysis is that the robustness of the ADN is improved when the value of average control fan in \(<\)K\({}_{2}\)\(>\) of a node in the PN and the value of average power fan in \(<\)O\({}_{1}\)\(>\) of a node in the CN are greater.
# Detection and Quantification of Damage in Metallic Structures by Laser-Generated Ultrasonics
## 2 Background
### Feature Extraction  
As indicated earlier, damage to a system is expected to alter the geometric portrait of the attractor in the state space. The difference between the undamaged and damaged attractors then becomes a good candidate feature for damage detection. Here, we introduce a certain metric called NNPE as the feature to identify the difference between the attractors.  
A schematic overview of feature NNPE extraction from the reconstructed state-space attractors is provided in Figure 3. First, \(P\) random points \(x(i)\) (\(i\) = 1,2,...,\(P\)) are selected from the baseline attractor reconstructed from the undamaged system. The selection of the value of \(P\) influences the feature extraction result. \(P\) is set to \(N/100\) in this study based on [PERSON]'s work [30], where \(N\) is the number of recorded data. Next, \(Q\) points \(y(j)\) (\(j\) = 1,2,...,\(Q\)) closest to each point \(x(i)\) are selected from the current-line attractor reconstructed from the compared system, which may suffer from certain damage. When selecting the closest \(Q\) points in current-line attractor, the same \(P\) points \(x(i)\) (\(i\) = 1,2,...,\(P\)) will be used in the baseline attractor. For example, if point (1, 1) was selected as the random point in baseline attractor, then the point (1,1) will still be selected as the point in the current-line attractor. Even though in most cases, this point may not be contained on the trajectory in the current-line attractor. When the point \(x(i)\) is selected in the current-line, the closest \(Q\) points in the current-line attractor will be selected by the Euclidean distance of the specific point from the point \(x(i)\) in the state space. The \(Q\) points that have the shortest Euclidean distance from the point \(x(i)\) in current-line attractor will be selected as the closest \(Q\) points. Also, the choice of the value of \(Q\) should guarantee that the local dynamic properties of the attractor can be described clearly and that the feature extraction results will not be hidden by the noise. A proper choice is \(Q\) = \(N/1000\)[31]. Meanwhile, to avoid the time correlation between \(x(i)\) and \(y(j)\), a Theiler Window with a step \(2T\) is proposed when selecting the neighboring points. In this way, the minimum separation between \(x(i)\) and its closest neighbors in time will be at least \(2T\). After that, \(x(i)\) and \(y(j)\) will be evolved with \(L\) steps (\(L\) = 2 in this study) into the future. The prediction value of \(x(i)\) after \(L\) steps in time, \(\hat{x}(i+L)\), can be computed by  
\[\hat{x}(i+L)
## 1 Introduction  
The expansion of wind power and photovoltaics is progressing inexorably. Due to that, renewable heat supply has often received less attention in the past. At the same time, in many countries, the private households contribute to the high energy consumption, especially the heat consumption. Here, DHC (district heating and cooling) networks can offer several benefits compared to decentralized heating systems such as higher heating plant efficiencies, cogeneration, and tapping waste heat potentials. Unfortunately, first to fourth generation DHC networks cause many heat losses due to the distribution process, the high temperature levels (above 70-200 \({}^{\circ}\)C) and often weak insulation (firstto third generation) [1]. Therefore, they also require a high demand density to operate economically, making them little or not applicable in rural areas. Future construction of more \"climate neutral\" buildings and the ongoing refurbishment of existing buildings will reduce the demand for conventional district heating grids even more, resulting in a reduced heating demand density also in more dense areas [2]. Furthermore, in densely populated areas, district heating is often more expensive compared to conventional, mainly fossil fuel-based decentralized heat supply systems [3]. Reducing the grid temperature in fifth generation DHC networks can reduce both grid losses and construction costs for insulation [4], and thus lead to competitive specific heating prices in less dense areas. Low temperature heating and cooling sources such as vertical heat exchangers, low-temperature waste heat, or horizontal ground collectors can supply these fifth generation low-temperature DHC networks [5]. With temperature levels of the distribution network around the soil temperature, heat losses can be equalized [6]. In some cases, the fluid temperature is even below the soil temperature, which generates a heat yield along the length of the piping [6]. De facto, the distribution network serves as a horizontal heat collector. Nowadays, especially in Germany, a lot of research is carried out in the field of low-temperature DHC, as displayed in Table 1.  
Within those projects, the municipality of Wustennot, which is addressed in this work, was one of the first cities trying horizontal heat exchangers together with a low-temperature DHC network for research purposes in 2011. Since then, the development of the systems is progressing. In the summer of 2019, the local energy distributor of Bad Nauheim (Germany) built the largest known horizontal geothermal collector with 22,000 m\({}^{2}\) of collector surface distributed over two layers [7, 8]. Together with the approximately 6 km long low-temperature district heating and cooling network, the system supplies heat and cooling for around 1000 people in more than 400 residential units.
# Automated Modal Analysis Using Stochastic Subspace Identification and Field Monitoring Data
## 2 Subspace Identification  
The numerical algorithms for subspace state space system identification (N4 SID) have gained increasing attention in the field of system identification [16; 17; 18]. The advantages of N4 SID include easy implementation, the requirement for less prior knowledge, high compatibility, versatile applications, and so on. In the following derivation, matrices and vectors are represented using bold font, while scalars are represented using italic font. Considering a generic stochastic excitation sampled as a time interval equal to Dt, the state-space formulation of a linear time-invariant (LTI) structural system with a degree-of-freedom (DOF) \(n\) is:  
\[\mathbf{x}_{k+1}=\mathbf{A}\mathbf{x}_{k}+\mathbf{w}_{k} \tag{1}\]  
\[\mathbf{y}_{k}=\mathbf{C}\mathbf{x}_{k}+\mathbf{v}_{k} \tag{2}\]  
where \(\mathbf{x}_{k}\) (\(2n\times 1\)) is the discrete state at the \(k\)-th step, \(\mathbf{y}_{k}\) (\(m\times 1\)) is the measured response, and \(m\) indicates the number of measurements. The discrete-time state is usually composed of structural displacement and structural velocity; on the contrary, the measured response is usually composed of structural acceleration in the ambient environment. \(\mathbf{w}_{k}\) and \(\mathbf{v}_{k}\) represent the system and measurement noise at the \(k\)-th step, respectively; they are assumed to be stationary and zero-mean random processes. Additionally, state-space matrices \(\mathbf{A}\) and \(\mathbf{C}\) represent the state (or system) matrix and output matrix, respectively. Unquestionably, the above equations form the background for SSI.
# Model-Based Assessment of Energy Efficiency in Industrial Pump Systems: A Case Study Approach
## 3 Energy-Efficiency Model of the Electric Drive
### Preliminary Considerations
#### 3.1.2 Thermal Limit Utilisation  
Manufacturers of industrial motors generally pursue two strategies to ensure a long service life and high efficiency at the same time. First, they limit the temperature rise of the motor to comply with thermal class 130 (B), but use better insulation systems designed for class 155 (F) to effectively extend the life of the insulation (labelled B/F) [46]. Secondly, high-efficiency motor designs use better active materials, such as low-loss electrical steel, and are designed with a larger geometry and improved cooling performance.  
It is common practise to design modern motors in such a way that the actual temperature rise under continuous full load is well below 80 K. The resulting temperature reserve (TR) can be utilised without damaging the windings, but this inevitably leads to a reduction in service life and higher losses. The trade-off therefore depends mainly on the operating time of the motor: continuous operation around the clock favours highly efficient operation, as the energy consumed accounts for most of the total cost, while intermittent operation with a short duty cycle actually favours the use of a compact motor with fully utilised TR [47].  
The temperature rise \(T_{r}\) varies from motor to motor and is determined during type testing. A motor with a fully utilised TR would therefore have a \(\sqrt{78/T_{r}}\)-times higher equivalent of torque rating, whereby 78 K is used instead of 80 K in order to take into account the losses that do not depend on the square of the current [47; 48]. In the following, both types of utilisation are expressed in the form of two different torque limits in the speed-torque characteristics.
## 3 Results and Discussion
### Analysis of the Default Design Scenario  
The first part of the results section regards the default operating scenario with the maximum cycle temperature at 800 K and the high pressure at 200 bar. The condenser temperature is equal to 25 \({}^{\circ}\)C, the solar direct beam irradiation level 800 W/m\({}^{2}\) and there is a zero-incident angle. Table 7 includes the results of the thermodynamic data of the cycle state points.  
Figures 3 and 4 show the thermodynamic cycle in usual depictions in order to explain it properly. More specifically, Figure 3 illustrates the temperature--specific entropy, while Figure 4 exhibits the pressure--enthalpy depiction. It can be said that the cycle is a transcritical one with its low pressure to be close to the CO\({}_{2}\) critical pressure. More specifically, the low cycle temperature is 64.34 bar which is close to the critical pressure of 73.8 bar. Figure 4 shows that the enthalpy increase that the PTC gives to the cycle (h\({}_{4}\)-h\({}_{3}\)) is 313.25 kJ/kg, while the recuperator gives an enthalpy increase (h\({}_{3}\)-h\({}_{2}\)) of 397.49 kJ/kg. Therefore, it is obvious that the recuperator is responsible for 56% of the heat input in the CO\({}_{2}\), while the PTC gives the rest 44%. Therefore, it can be said that the existence of the recuperator in the system is very important and it is a correct design idea. Moreover, it  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Default Value** & **Range** \\ \hline Solar direct beam irradiation & (G\({}_{\text{b}}\)) & 800 & [400–1000] \\ Condenser temperature [\({}^{\circ}\)C] & (T\({}_{\text{con}}\)) & 25 & [15–30] \\ Turbine inlet pressure [bar] & (P\({}_{\text{b}}\)) & 200 & [100–220] \\ Turbine inlet temperature [K] & (TIT) & 800 & [600–1000] \\ \hline \hline \end{tabular}
\end{table}
Table 6: The examined parameters in the parametric analysis.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**State Points** & **T (K)** & **P (bar)** & **h (kJ/kg)** & **s (kJ/kgK)** \\ \hline
1 & 298.2 & 64.34 & \(-\)231.99 & 0.510 \\
2 & 321.3 & 200.00 & \(-\)211.08 & 0.519 \\
3 & 545.8 & 200.00 & 186.41 & 1.495 \\
4 & 800.0 & 200.00 & 499.66 & 1.967 \\
5 & 676.0 & 64.34 & 366.29 & 2.002 \\
6 & 331.3 & 64.34 & \(-\)31.21 & 1.172 \\ \hline \hline \end{tabular}
\end{table}
Table 7: State point thermodynamic properties for the default scenario.  
has to be said that the collector efficiency is 73.62%, the cycle efficiency is 32.96% and the system efficiency is 24.27% for the nominal design point.
# A Filter Feature Selection Algorithm Based on Mutual Information for Intrusion Detection
## 4 Experiment and Results
### Experiment and Analysis
#### 4.3.3 Remote-to-Login Test Experiment  
This section presents the detection performance of different models for the R2L and normal samples in the test set. In the training phase, the numbers of normal and R2L samples are 1800 and 200, respectively, while in the testing phase, the numbers of normal and R2L samples are 1800 and 200, respectively.  
In Table 9, the selected features of the different feature selection methods, which are used to detect R2L, are summarized. It can be seen that the number of selected features with the RPFMI algorithm is smallest.  
Figure 8: Precision Rate on the U2R test data.  
Figure 7: False Positive Rate on the U2R test data.  
Table 10 shows the results of the confusion matrices for the normal and R2L data in different models. Among these methods, the RPFMI algorithm has the smallest FP and the largest TN. Comparisons between these methods regarding the ACC, DR, FPR and PR are carried out. The ACC of the RPFMI algorithm (i.e., 91.077%) is highest (Figure 9). Compared with other methods, the DR of the RPFMI algorithm ranks fourth in the DR (Figure 10), whereas its FPR is only larger than those of the MIFS [19], mRMR [20] and CIMI [22] algorithms (Figure 11). The largest PR obtained with the RPFMI algorithm (i.e., 99.403%) is also confirmed (Figure 12). In summary, the proposed RPFMI algorithm can improve the ACC and PR.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **FP** & **FN** & **TN** & **TP** \\ \hline All features & 89.47 & 189.89 & 1710.53 & 10.11 \\ MIFS (\(\beta=0.3\)) [19] & 35.84 & 176.1 & 1764.16 & 23.9 \\ MIFS (\(\beta=0.7\)) [19] & 41.51 & 161.535 & 1758.49 & 38.465 \\ mRMR [20] & 71.36 & 173.42 & 1728.64 & 26.58 \\ MIFS-U (\(\beta=0.3\)) [21] & 2.25 & 199.22 & 1797.75 & 0.78 \\ MIFS-U (\(\beta=0.7\)) [21] & 2.25 & 199.22 & 1797.75 & 0.78 \\ CIMI [22] & 79.04 & 170.47 & 1720.96 & 29.53 \\ MMIFS [32] & 0.13 & 181.05 & 1799.87 & 18.95 \\ FMIFS [33] & 0.13 & 180.64 & 1799.87 & 19.36 \\ RPFMI & 0.13 & 178.33 & 1799.87 & 21.67 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Confusion matrices in different models using the remote to login (R2L) test data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Number of Features** & **Selected Features** \\ \hline MIFS (\(\beta=0.3\)) [19] & 14 & 8, 9, 10, 14, 15, 16, 19, 21, 22, 20, 14, 17, 12, 18 \\ MIFS (\(\beta=0.7\)) [19] & 20 & 8, 9, 10, 14, 15, 16, 19, 21, 22, 20, 12, 18, 14, 17  
Figure 11: False Positive Rate on the R2L test data.  
Figure 10: Detection Rate on the R2L test data.