esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### Maximum Absorption  
Results of measurements of the maximum absorption in the Westinghouse method for benzene and selected sorbents have been presented in Table 2. By dividing the mass of sorbed benzene by the mass of used sorbent, the maximal mass sorptivity was obtained S\({}_{\mathrm{w}}\) [kg\({}^{-1}\)kg\({}^{-1}\)]. Taking into account the density of benzene and bulk density of sorbents, a calculation was made of the value of maximum volume of liquid that can be absorbed by a unit volume of sorbent (maximal volume sorptivity S\({}_{\mathrm{v}}\) [dm\({}^{-3}\)/dm\({}^{-3}\)]). It can be seen clearly that the nanocrystalline MgO-TiO\({}_{2}\) specimen (FA) does not exhibit higher maximal mass sorptivity S\({}_{\mathrm{w}}\) or maximal volume sorptivity S\({}_{\mathrm{v}}\) in relation to sorbents used so far during rescue operations. Much better S\({}_{\mathrm{w}}\) results were obtained for expanded perlite (EP) and water-activated polyacrylamide copolymer (PC). In comparison to MgO-TiO2, the S\({}_{\mathrm{v}}\) parameter was better not only for water activated PC but also for calcinated diatomite earth sorbent (CDE). Considering also the significantly higher prices of nanooxides (min. 50 Euro/kg), their use as sorbents of hydrocarbon substances seems to be highly unprofitable for the time being. The majority of sorbents currently used by fire services have a price well below 5 Euro/kg.  
Figure 4: Diagram of stand used to test thermal effects of the neutralization process (**1**) Stopper, (**2**) electronic thermometer, (**3**) stand, (**4**) inlet, (**5**) Deware dish.
## 5 Comprehensive Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Applicable Scenarios** & **Optimization Methods** \\ \hline
**STFT** & Preliminary analysis and signal feature extraction & Multi-window techniques, linear frequency modulation, Gaussian windows \\ \hline
**WT** & Local signal feature analysis & Optimization of wavelet basis function selection \\ \hline
**WVD** & Single-component linear frequency-modulated signals & Cross-term suppression methods (e.g., DMD-WVD) \\ \hline
**EMD** & Complex signal analysis & Noise-assisted methods (e.g., EEMD), parameter optimization \\ \hline
**EMD** & Complex signal analysis & Parameter optimization (e.g., adaptive noise bandwidth), hardware acceleration \\ \hline
**MEEMD** & Multi-physical field signal fusion & Parameter optimization, multi-signal synchronous processing \\ \hline
**VMD** & Complex signal analysis & Parameter optimization (e.g., particle swarm optimization), multi-variable signal processing \\ \hline
**SVMD** & Complex signal analysis & Parameter self-adaptive update, minimum bandwidth constraint \\ \hline
**AVMD** & Complex signal analysis & Parameter self-adaptive update, optimization algorithms (e.g., differential evolution) \\ \hline
**EWT** & Complex signal analysis & Optimization of filter design, multi-variable signal processing \\ \hline
**EEWT** & Complex signal analysis & Parameter optimization, multi-signal synchronous processing \\ \hline
**EFD** & Complex signal analysis & Optimization of spectral segmentation, noise suppression \\ \hline
**MED** & Random pulse signals & Noise suppression methods (e.g., wavelet threshold), parameter optimization \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Applicable scenarios and optimization methods for various signal decomposition techniques.
# A Knowledge-Guided Competitive Co-Evolutionary Algorithm for Feature Selection
## 2 Background
### Related Work
#### 2.1.2 Non-Dominated Sorting Genetic Algorithm II  
The Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is a refined version of the original NSGA [29] and is prominently used within the field of Pareto-Based Evolutionary Multi-Objective Optimization (EMO) algorithms [30]. Specifically crafted to address multi-objective optimization challenges, the NSGA-II excels in efficiently identifying a broad spectrum of Pareto optimal solutions. The core mechanism of the NSGA-II hinges on a swift non-dominated sorting procedure which organizes solutions into several tiers or 'fronts'. These fronts are formed based on how solutions dominate or are dominated by one another.  
* Solutions on a lower-numbered front have no dominators from other fronts;
* Each subsequent front is progressively dominated by those in prior fronts. In terms of operational dynamics, the NSGA-II follows a structured process: 1. Population Generation: The NSGA-II begins with the generation of a random initial population;
2. Evaluation and Sorting: Each solution is evaluated and ranked based on objective function performance and then sorted into fronts;
3. Selection: Selection favors solutions on better-ranked fronts. Within these fronts, a further selection criterion based on crowding distance determines choice, emphasizing less crowded areas to enhance diversity among the chosen solutions;
4. Genetic Operations: Following selection, genetic mechanisms like crossover and mutation are employed to generate new solutions, aiding in the exploration and expansion of the solution space;
5. Elitism and Generational Transition: To ensure robustness across generations, the NSGA-II integrates elitism, preserving top-performing solutions and carrying them forward. The blended population of parents and offspring undergoes another round of sorting and selection, emphasizing the superior solutions for the next generation.  
Through this structured approach and emphasis on maintaining a diverse set of high-quality solutions, the NSGA-II not only adeptly explores the Pareto front but also ensures rapid convergence to optimal solutions, effectively addressing the complexities associated with multiple objectives.
###### Abstract  
Vegetation indices, especially the normalized difference vegetation index (NDVI), are widely used in urban vegetation assessments. However, estimating the vegetation abundance in urban scenes using the NDVI has constraints due to the complex spectral signature related to the urban structure, materials and other factors compared to natural ground surfaces. This paper employs the 3D discrete anisotropic radiative transfer (DART) model to simulate the spectro-directional reflectance of synthetic urban scenes with various urban geometries and building materials using a flux-tracking method under shaded and sunlit conditions. The NDVI is calculated using the spectral radiance in the red (0.6545 \(\upmu\)m) and near-infrared bands (0.865 \(\upmu\)m). The effects of the urban material heterogeneity and 3D structure on the NDVI, and the performance of three NDVI-based fractional vegetation cover (FVC) inversion algorithms, are evaluated. The results show that the effects of the building material heterogeneity on the NDVI are negligible under sunlit conditions but not negligible under shaded conditions. The NDVI value of building components within synthetic scenes is approximately zero. The shaded road exhibits a higher NDVI value in comparison to the illuminated road because of scattering from adjacent pixels. In order to correct the effects of scattering caused by building geometry, the reflectance of the Landsat 8/OLI image is corrected using the sky view factor (SVF) and then used to calculate the FVC. Jilin-1 satellite images with high spatial resolution (0.5 m) are used to extract the vegetation cover and then aggregated to 30 m spatial resolution to calculate the FVC for validation. The results show that the RMSE is up to 0.050 after correction, while the RMSE is 0.169 before correction. This study makes a contribution to the understanding of the effects of the urban 3D structure and material reflectance on the NDVI and provides insights into the retrieval of the FVC in different urban scenes.  
NDVI; FVC; urban geometry; building materials; DART 202517143
## 2 Stress Analysis Method of the Arch Ring Section
### The Arch Ring Section Stress Optimization Method
#### 2.2.2 Matlab-Based Solution of Buckle and Prestressing Influence Matrix  
First, taking two buckle ropes of any section of the arch ring as a group, and tension the group of buckle ropes with the unit rope force value (for the convenience of analysis, the buckle force of each rope of the group is 10 kN as the unit rope force value) as the applied amount. Second, using the Ansys model for operational analysis, calculating the effective value of each parameter (the value does not include the parameter effect value under the action of constant load alone) and integrating the difference to obtain the effect array of the rope on each parameter. Similarly, we can obtain the effect array of each other buckle ropes on the parameters. Last, combining all the buckle ropes to obtain the effect matrix of the buckle force on each parameter. Similarly, we can also obtain the effect matrix of the temporary to prestressing on each parameter.  
Considering the complexity of calculating the sample, the LinProg toolbox in Matlab is used to solve the influence matrix. The influence coefficients of buckle force and temporary prestressing effect are determined, which takes the stress at the top and bottom flanges of the main arch ring in each construction stage state as the planning target.  
The influence coefficients of buckle force and temporary prestressing effect are determined in the maximum cantilever condition of the arch ring as an example. The mathematical expression is: \([x,fval]=linprog(c,A_{1},b_{1},A_{2},b_{2},l,u)\), where solving the minimized linear programming of the objective function \(c^{T}x\), \(A_{1}x\leq b_{1}\) and \(A_{2}x=b_{2}\) are constraints, the variables are \(x\), and the upper and lower bounds \((l,u)\) of \(x\) are defined.  
Given a large amount of data, the surfaces of the influence of the buckle force on the stress and the bending moment of the arch ring are shown in Figures 5 and 6.  
Figure 5 shows the surface diagram of the influence of the buckle force on the stress of the control section of the arch ring, and the control section is each end section of the arch ring. The closer to the buckle change, the more obvious the change of the stress of the arch ring section, and the change of the buckle force near \(1/4\) of the arch ring has a greater influence on the stress of the section. Figure 6 shows the surface diagram of the influence of the buckle force on the bending moment of the control section of the arch ring, and the control section is selected as the end section of each section. The change of the buckle force has the most obvious influence on the bending moment of the section of the front section, and the amount of influence of the buckle force is smaller as the cantilever section increases, and the fitting results are consistent with the actual situation.
# How Resilient Are Kolmogorov-Arnold Networks in Classification Tasks? A Robustness Investigation
## 4 Methodology  
In this study, we systematically evaluate the robustness of several neural network architectures against adversarial attacks across different training conditions and datasets. The focus is on understanding how Kolmogorov-Arnold Networks (KAN) and Multilayer Perceptron (MLP) architectures respond to adversarial perturbations, specifically FGSM, BIM, PGD, and CW attacks, using specific parameter configurations such as epsilon values, step sizes, and iteration counts. The experiments were conducted on system equipped with an Intel(R) Core(TM) i9-14900K 3.20 GHz processor, 64 GB of RAM, and two NVIDIA GeForce RTX 3090 GPUs. We explored three distinct training conditions: standard, adversarial, and randomized smoothing training, and evaluated various architectures, including KAN, MLP, KAN-Mixer, and MLP-Mixer, as well as KAN-convolution and convolution layer models, across three traffic sign classification datasets. The performance and resilience of these models are assessed using key evaluation metrics, including accuracy, SAR, and DoC, to analyze their vulnerabilities and robustness in clean and adversarial conditions. Additionally, t-SNE visualizations were employed to qualitatively assess the latent space representation of clean and adversarial examples. This provided deeper insights into the robustness characteristics of each model. Detailed methodologies and analyses of the different models are discussed in the corresponding subsections.
# Comprehensive Review of RF MEMS Switches in Satellite Communications
## 3 Strategies for Optimizing RF MEMS Switch Performance
### Excellent RF Performance
#### 3.2.3 Using Serial-Parallel Configurations  
Employing serial-parallel configurations, a common method to enhance RF performance in traditional RF switches like FETs, is also applicable to RF MEMS technology, including capacitive and metal contact switches. This structure offers improved RF performance [47; 154; 155] but at the cost of increased design and manufacturing complexity. For instance, to ensure the optimal performance of an SP4T switch before its application in a phase shifter array, its isolation can be enhanced using a series-type shunt topology at each output port [156].  
For example, [PERSON] et al. [157] proposed an electrostatically actuated serial-parallel RF microelectromechanical system switch, as shown in Figure 11a. This switch has ON, OFF, and Deep-OFF states and is normally in the OFF state, driven to the ON or DEEP-OFF states using two electrostatic actuators. This switch shows an insertion loss of 0.6 dB and an isolation of 65 dB at 20 GHz, suitable for low-power and high-performance RF tuning. The switch shown in Figure 11b [158] achieves high isolation while maintaining a compact switch size by combining two serial and parallel configurations of thin films on a single substrate. Both capacitive and ohmic beam membranes can be electrostatically  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & \(\epsilon_{r}\) & **Insertion Loss (dB)** & **Isolation (dB)** & \(C_{r}\) & **Ref** \\ \hline Si\({}_{3}\)N\({}_{4}\) & 7.5 & \(-0.90\) dB @ 40 GHz & \(-23.7\) dB @ 40 GHz & 22.6 & [147] \\ HfO\({}_{2}\) & 25 & \(-0.20\) dB @ 20 GHz & \(-14\) dB @ 20 GHz & 19.66 & [149] \\ AlN & 9.8 & \(-0.68\) dB @ 40 GHz & \(-35.8\) dB @ 40 GHz & 32.4 & [147] \\ Ta\({}_{2}\)O\({}_{5}\) & 32 & \(<\)0.80 dB @ 30 GHz & \(-40\) dB @ 30 GHz & 19 & [150] \\ STO & 120 & 0.08 dB @ 10 GHz & 42 dB @ 5 GHz & 600 & [151] \\ \hline \hline \end{tabular}
\end{table}
Table 2: RF performance comparisons of using different high dielectric constant materials.  
Figure 10: **(a)** The dual-warped-beam switch [152]. Reprinted/adapted with permission from [152], 2010, IEEE. **(b)** The composite metal-dielectric warped membranes [153]. Reprinted/adapted with permission from [153], 2009, IEEE.  
driven at the same voltage level. The voltage provided to the parallel beam membrane closes the switch, while the voltage provided to the serial beam membrane opens the switch. However, there are two issues with these serial shunt switches. First, the serial or parallel switch structures need individual biasing, which is difficult to design. Secondly, as they are vertically driven, they require higher driving voltages and thus are unsuitable for many low-voltage applications. The single thermally actuated lateral contact switch shown in Figure 11c is a good alternative [42], as this design simplifies the driving mechanism and reduces the required voltage level.
## 2 Operating Principle
### Polar Coordinate Transformation and Feature Extraction
than more local zones corresponding to the second amplitude of the 16 QAM signal, the feature \(Z_{2}\) is still able to ensure the identification performance based on the symmetry properties. \(Z_{3}\) is the sum of symbol number in the three local zones, \(T_{4}\) (30:31, 15:24), \(T_{5}\) (30:32, 36:44), and \(T_{6}\) (30:31, 55:64). There are six clusters of the 32 QAM signal in these zones. \(Z_{4}\) represents the sum of symbol number in the four local zones, \(T_{7}\) (22:25, 9:14), \(T_{8}\) (22:25, 27:34), \(T_{9}\) (22:25, 47:54), and \(T_{10}\) (22:25, 66:72), which correspond to four clusters in the ninth amplitude of the 64 QAM signal. Meanwhile, \(Z_{5}\) indicates the sum of symbol number in the four local zones, \(T_{11}\) (42:43, 15:18), \(T_{12}\) (42:43, 22:27), \(T_{13}\) (42:43, 34:38), and \(T_{14}\) (42:43, 44:47), which correspond to four clusters in the second amplitude of the 64 QAM signal. As a high order of a modulation format requires more complex features to be extracted, \(Z_{4}\) and \(Z_{5}\) are mainly applied to identify the features for 64 QAM signals. Finally, \(Z_{6}\) denotes the number of symbols in the local zones, \(T_{15}\) (27:28, 4:6), \(T_{16}\) (27:28, 23:26), \(T_{17}\) (27:28, 43:46), \(T_{18}\) (27:28, 54:58), \(T_{19}\) (27:28, 63:66), and \(T_{20}\) (27:28, 74:77). Extracting the number of symbols in more zones is conducive to the accurate identification of the 128 QAM signal since the cluster distribution of 128 QAM is more dispersed than any other modulation format. Based on the approach mentioned above, the extracted six-dimensional features \(Z_{1}\), \(Z_{2}\), \(Z_{3}\), \(Z_{4}\), \(Z_{5}\), and \(Z_{6}\) are identified by the Gaussian weighted KNN algorithm. \[\theta=\arctan\frac{Q}{I}, \tag{2}\]4 The vertical view of three-dimensional polar coordinate graphs for different modulation formats at different OSNR values, specifically: **(a)** QPSK at 26 dB, **(b)** 16 QAM at 33 dB, **(c)** 32 QAM at 37 dB, **(d)** 64 QAM at 38 dB, and **(e)** 128 QAM at 42 dB.
# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 5 Discussion
### Study and ML limitations  
The findings of this study should be considered in light of some limitations:  
* The data collected in this study mostly covered the essential sources for predicting NO\({}_{2}\) emissions.
* More data volume for all data sources would have enhanced performance. The data collected was just for the year 2019, and it would have been beneficial to increase the sample size for all datasets.
* Sensor calibration and validation are integral for reliable remote sensing and proper quality of the derived variables/data. This study has ensured extraction of validated and filtered data to retrieve higher quality images of remotely sensed data. These processes are essential for better predictive modeling and, if lacking, can provide incorrect results.
* Passive remote sensing techniques (such as TROPOMI) record solar radiation reflected and emitted from Earth which can be sensitive to weather conditions, lowering their accuracy.
* The ML models and their predictive ability are the second main area of limitation.
* For our first experimental scenario, the LSTM model captured extreme values well. However, it lacked low error rate results and had weak predictive ability, similar to our early testing models.
* The inclusion of a different LSTM architecture was a response to the unsatisfactory results of the original LSTM model tested, and the stacked architecture improved the results for the most part, but not significantly.
* The nonlinear nature of the data, along with the time series element, has traditionally been a complex problem to solve due primarily to random outcomes. The study's main goal was to take a sample of results from multiple machine learning algorithms to draw conclusions and show that estimating NO\({}_{2}\) emissions for power plants using remote sensing data was feasible.
* The study's experiments were rigorously conducted and validated, but they were ultimately constrained by the volume of data and the predictive performance of our models.
* Additional data sources correlated with NO\({}_{2}\) emissions and better remote sensing resolution could considerably improve the results in the future.
## 4 Results
### RQ5: Network Security Attack in IoT and WSN Layers
#### 4.5.2 Replay Attacks  
When an attacker replays a flood of messages between the sender and the receiver, updating the line table (DT) to steal information, it is called a replay attack [33]. It is a network layer attack in which a third party intercepts the data during transmission. The attacker retransmits this data by either modifying or delaying it, spoofing the sender's IP address to the attacker's IP address, and impersonating the legitimate sender.
## 5 The Parameter Estimation of the Original System
\right]\in\mathbb{R}^{(n_{b}+n_{p})\times n_{a}}\]  
It is easy to know that  
\[\hat{\mathbf{\vartheta}}_{1}(t)=[\mathbf{S}^{\mathrm{ T}}(t)\mathbf{S}(t)]^{-1}\mathbf{S}^{ \mathrm{ T}}(t)\mathbf{B}(t) \tag{29}\]  
From Equation (29), we can get the estimates \(\hat{a}_{i}(t)\) and \(\hat{b}_{i}(t)\) of parameters \(a_{i}\) and \(b_{i}\) from \(\hat{\mathbf{\vartheta}}_{1}(t)\). According to the definition of \(\hat{P}(t,z)\) in Equation (26), similarly, expanding the equation and comparing the coefficients on both sides of it gives the matrix equation,  
\[\mathbf{S}_{1}(t)\hat{\mathbf{\vartheta}}_{2}(t)=\mathbf{B}_{1}(t)\]  
where  
\[\hat{\mathbf{\vartheta}}_{2}(t):=[\hat{c}_{1}(t),\hat{c}_{2}(t),\cdots,\hat{c}_{ n_{c}}(t)]^{\mathrm{ T}}\in\mathbb{R}^{n_{c}},\]\[\mathbf{B}_{1}(t):= \begin{bmatrix}\hat{\mathbf{p}}_{1}(t)-\hat{\mathbf{a}}_{1}(t)\\ \hat{\mathbf{p}}_{2}(t)-\hat{\mathbf{a}}_{2}(t)\\ \vdots\\ \hat{\mathbf{p}}_{n_{a}}(t)-\hat{\mathbf{a}}_{n_{a}}(t)\\ \hat{\mathbf{p}}_{n_{a}+1}(t)\\ \hat{\mathbf{p}}_{n_{a}+2}(t)\\ \vdots\\ \hat{\mathbf{p}}_{n_{p}}(t)\end{bmatrix} \in\mathbb{R}^{n_{p}},\quad\mathbf{S}_{1}(t):= \begin{bmatrix}1&0&\cdots&\cdots&0\\ \hat{a}_{1}(t)&1&\ddots&&\vdots\\ \hat{a}_{2}(t)&\hat{a}_{1}(t)&1&\ddots&\vdots\\ \vdots&\hat{a}_{2}(t)&\hat{a}_{1}(t)&\ddots&0\\ \hat{a}_{n_{a}-1}(t)&&\ddots&\ddots&1\\ \hat{a}_{n_{a}}(t)&\hat{a}_{n_{a}-1}(t)&&\ddots&\hat{a}_{1}(t)\\ 0&\hat{a}_{n_{a}}(t)&\ddots&&\hat{a}_{2}(t)\\ \vdots&\ddots&\ddots&\hat{a}_{n_{a}-1}(t)&\vdots\\ \vdots&&\ddots&\hat{a}_{n_{a}}(t)&\hat{a}_{n_{a}-1}(t)\\ 0&\cdots&\cdots&0&\hat{a}_{n_{a}}(t)\end{bmatrix} \in\mathbb{R}^{n_{p}\times n _{c}}\]  
Then, we obtain  
\[\hat{\mathbf{\vartheta}}_{2}(t)=[\mathbf{S}_{1}^{\mathrm{r}}(t)\mathbf{S}_{1}(t)]^{-1}\mathbf{ S}_{1}^{\mathrm{r}}(t)\mathbf{B}_{1}(t) \tag{30}\]  
Based on Equation (30), we can obtain the estimates \(\hat{c}_{i}(t)\) of \(c_{i}\) from \(\hat{\mathbf{\vartheta}}_{2}(t)\). Hence, we obtain all of the parameter estimates \(\hat{a}_{i}(t)\), \(\hat{b}_{i}(t)\) and \(\hat{c}_{i}(t)\). According to the above derivation, it is clear that the model equivalence-based recursive least squares (ME-RLS) algorithm in Equations (20)-(25) and (29)-(30) increases the complexity of computation compared with the RGLS algorithm. However, as the information vector of the ME-RLS algorithm does not contain noise vectors to be estimated, the estimation errors become smaller.
## 2 O-RAN Architecture and Development
### O-RAN Architecture
#### 2.2.3 O-Cu  
The O-CU is another white box hardware that performs the O-CU function of upper L2 and L3. The O-CU hardware motherboard contains a processing unit, memory, the internal I/O interfaces, and external connection ports. The midhaul (MH) is used to carry the traffic between O-CU and O-DU\({}_{(7-2)}\), and the backhaul (BH) interface is for carrying the traffic between the O-CU and core network. Other hardware functional components, such as the storage for software, hardware and system debugging interfaces, board management controller, and more, are based on the specific needs of the implementation. The hardware of the O-CU is similar to the O-DU\({}_{(7-2)}\). However, the hardware accelerator is mandatory to offload computationally intensive functions and to optimize the performance under varying traffic and loading conditions.