esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Interference Mitigation Schemes
### MAC Approach
#### 3.2.11. Clique-Based WBASN Scheduling  
In [35], node-level interference is explored with respect to the type of sensor nodes in one or more WBASNs. Because sensors can be divided into different types that send data periodically or nonperiodically, all sensors in a WBASN may not be active at the same time. Because of the mobility of WBASNs, sensors in different WBASNs can be active simultaneously, causing interference. Clique-based WBASN scheduling (CBWS) places the sensors in different groups to avoid interference then schedules the sensors by applying the coloring method.  
In the system model, a clique can be formed by one or more WBASNs using a distributed method. The sensor nodes in the clique are grouped into virtual groups based on their group IDs. The sensors in a group will be allocated into time slots by the random coloring algorithm. The WBASN network topology is modeled as a graph in which the vertices represent the set of groups and the edges represent the links between groups. The color set represents the resources such as time slots or frequency bands. The color mapping algorithm assigns the vertices to the color set so that the sensors can work in different time slots. After that, each group can work during the assigned time slot.  
Figure 7: Polling with a backoff mechanism in 2L-MAC.
# Incorporating Spatial Autocorrelation into GPP Estimation Using Eigenvector Spatial Filtering
## 3 Methodology
### Accuracy Assessment
#### 3.4.3 Assessment of the Spatial Effects  
When spatial data without accounting for spatial effects are employed in a data-driven model, residual spatial autocorrelation may persist [51; 52; 53]. In an ideal scenario, the goal is to minimize or completely eliminate spatial autocorrelation in residuals. This ensures that the model's performance remains consistent across geographic regions, minimizing gross overestimates or underestimates. The EV extracted from the spatial weights matrix is pivotal in connecting geographic entities in space. Subsequently, the EV is incorporated into the SA-LGBM as a control variable, effectively identifying and separating spatial dependencies from GPP. When making predictions, we perform spatial interpolation on these sets of eigenvectors based on the extent of the raster data and use them as inputs to the model. To measure distributions and spatial autocorrelation in the residuals, [PERSON]'s I metric was used to measure spatial autocorrelation.
# AV-Teller: Browser Fingerprinting for Client-Side Security Software Identification
## 2 Background and Related Work
### Antivirus Detection Mechanisms
#### 2.1.1 Signature-Based Detection  
Signature-based detection compares file contents against a repository of predefined malware signatures, utilizing hash-based comparisons or string-matching techniques to identify threats. While effective for known malware, its reliance on static patterns renders it inadequate against novel or polymorphic malware, which alter their structure to evade detection [18].
## 5 Simulation Result and Discussion
### Simulation Based on Congestion Condition
moderate congestion, the total cost is 118.4000, and finally it is 236.8000 in high-congestion conditions. Load Balancing can obtain total costs between 0.1020 and 0.2582 under the no-congestion condition. Next, in low-congestion conditions, it can obtain a total cost of between 10.3284 and 12.9104. In moderate congestion, the total cost is 129.1050, and it is 258.21 in high-congestion conditions. Finally, Random Offloading has a total cost between 0.1171 and 0.2989 for the no-congestion condition. Next, in low-congestion conditions, it can obtain total cost between 11.8376 and 14.7970. In moderate congestion, the total cost is 147.0000, and finally it is 295.94 in high-congestion conditions. If we compare the performance of the total cost on average in terms of all congestion conditions, the total cost of our proposed approach is 228.90% better than that od Greedy Latency, 258.63% better than that of Load Balancing, and 310.19% better than that of Random Offloading. Overall, the results show that the proposed Lagrange approach outperforms the conventional approaches very well in reducing energy, latency, and computation costs, especially in high-congestion conditions.
## 3 Methods and Materials
### Methodology
#### 3.2.3 Transfer Learning for VGG16  
Move Learning is frequently utilized to classify skin injuries. Initially, a CNN is prepared on ImageNet; then, at that point, its weighting boundaries are changed to meet the characterization task. [PERSON] et al. [41] made a significant contribution to the field. Transfer learning was selected for two main reasons: the first was to avoid issues with data scarcity, and the second was to make a comparison with previous work in the same task. In our study, we applied two main actions that fall under the transfer learning method.  
1. Deleting the Top Layer In this step, we effectively removed the dense (classification) layers that were originally trained on ImageNet, allowing for the customizing of the top layers of the model according to our classification requirements.
2. Adding Custom Top Layer In this step, additional layers (Conv2D, Flatten, Dropout, and Dense) were defined to create a new top layer structure tailored to our specific classification task where the number of classes = 3 (bcc, mel, and nv).  
Transfer learning was applied to a pre-trained VGG16 model. For further clarification, the diagram in Figure 3 illustrates the structure of the model before customization.  
The pre-prepared VGG16 was prepared on a subset of the ImageNet dataset, and an assortment of more than 14 million pictures having a place with 22,000 classifications [42] was selected to create a custom skin anomaly diagnosis model for the following reasons: VGG has more convolution and pooling layers compared to previous architectures. In addition, VGG16 achieved good results with only 7.0% of the top 5 test errors [42]. Our last reason for this selection was to compare it with a previous example that used the same backbone for the same goal.  
The main functions of each layer of VGG16 are as follows:  
* Convolutional Layers Convolutional layers extract features from the image, from basic edges to complex patterns.
* Max-Pooling Layers These layers reduce the spatial dimensions to control overfitting and reduce computational complexity.
* Fully Connected Layers These layers combine and refine the highlights extracted by the convolutional layers into a last feature vector. Between the layers that we used in our model, two activation functions were applied:
* ReLU: ReLU acquaints non-linearity with the organization, permitting it to learn complex examples in the information.
* Softmax: This function converts the classification scores into probabilities, providing the final output for classification.  
The transfer learning process applied in our current study fundamentally altered the information specific to the first layer of the model according to this study's needs.
# Markov-Based Malware Propagation Modeling and Analysis in Multi-Layer Networks
## 4 Epidemics Spreading in Multi-Layer Complex Networks via Markov Random Fields
### Analysis of Random Markov Fields on Spreading Malware  
We considered a Markov Random Field (MRF)-based framework for modeling the macroscopic behavior of a multi-layer CCN under random attack, where malicious threats propagate through direct interactions and follow the Susceptible-Infected-Susceptible infection paradigm. We studied the behavior of two-layer systems for various topological and malware-related parameters with respect to the general random attacks considered. We demonstrate the effectiveness of the MRF framework in capturing the evolution of SIS malware propagation and use it to assess the robustness of synthetic CCNs with respect to the involved parameters.  
We focus on the cumulative number of infected nodes and not on the state of each node in particular. The MRF framework abstracts connectivity and local interactions through the neighborhood system, focusing on the macroscopic evolution of the system through point-to-point interactions between adjacent nodes. Direct interactions between infected and sensitive nodes are recorded, covering cases where malware infections spread locally similarly to [2].  
For malware propagation, \(L=0,1\) is the phase space, where '1' corresponds to an infected state and '0' corresponds to the non-infected (susceptible) state. We focus on the interactions between neighboring S-I pairs of nodes. Such pairs drive the evolution of malware propagation. The dynamics of malware depend mainly on interactions between attacking nodes and sensitive users [25, 43, 44, 64]. Thus, in relation to defining an appropriate possible operation for an MRF malware, a pairwise potential is employed. Singleton terms could have also been included, representing the contribution of the nodes themselves to the spread of malware. In such cases, where a node suddenly changes its status from vulnerable to infected and starts spreading malware, various types of malware could be represented, e.g., trojan horse, trapdoors, etc. [65].  
With respect to attacks, we start from a single attacker, which is constantly infected. The attacker and infected nodes only have point-to-point contacts with susceptible nodes, which are homogeneously infected, i.e., they become infected with the same infection probability when in contact with infected/attack nodes. Here, we focus on these types of attacks in order to demonstrate how the MRF can be used for network robustness analysis and reveal salient features of the framework.
## 3 Results
### Model Accuracy and Evaluation Analysis
#### 3.2.2 The Evaluation of Classified Model Performance  
After completing the model training, we validated the classification performance of the ResNet18 model using the ROC curve (receiver operating characteristic curve). The ROC curve is an important tool for measuring the performance of a classification model, and its shape and area can intuitively reflect the model's classification stability under different thresholds and the accuracy of the model under different thresholds. Figure 6 shows the ROC curve of the ResNet18 model, which shows a smooth and continuous upward trend, indicating that the model exhibits high classification stability in distinguishing the risky and non-risky areas of collapse disasters.  
To quantify the classification performance of the model, this study further calculated the area under the receiver operating characteristic (ROC) curve (AUC), which ranges from 0 to 1, with values closer to 1 indicating superior classification performance. In this study, The ResNet18 model achieved an AUC value of 0.7879, demonstrating strong discriminatory ability. Although this value does not reach an exceptionally high level (e.g., above 0.9) and is lower than the peak accuracy of 90% observed on the test set, this discrepancy does not indicate an anomaly but rather reflects the inherent differences  
Figure 4: Loss curve of the ResNet18 model during training for collapse hazard risk assessment using multi-source remote sensing data.  
Figure 5: Accuracy curve of ResNet18 model for road collapse hazard risk assessment based on multi-source remote sensing data during training.  
between AUC and accuracy as evaluation metrics: accuracy is based on a single threshold and may be inflated by imbalanced data distributions (e.g., a higher proportion of non-risk samples), whereas AUC integrates performance across all thresholds. In the task of road collapse risk assessment using multi-source remote sensing data--including slope gradient, lithology, vegetation coverage, and annual precipitation--the AUC is constrained by data complexity and noise, making values above 0.9 challenging to attain. Combined with the training performance in terms of loss rate and accuracy, an AUC of 0.7879 still confirms the reliability and applicability of The ResNet18 model for road collapse risk assessment [41].  
This figure visualizes the classification performance of the ResNet18 model in the avalanche hazard risk evaluation task. As can be seen from the curve, the model maintained a good balance between high sensitivity (true positive rate) and specificity (1 false positive rate) under different thresholds. This performance further verified the robustness and stability of the model in dealing with multi-factor input data, indicating that its prediction results possess high accuracy and reliability and provide theoretical support for the risk zoning and management of collapse disasters in Guangxi.
# Pilot-Based Adaptive Channel Estimation for Underwater Spatial Modulation Technologies
###### Abstract  
Spatial Modulation Technologies (SMTs) are schemes that reduce inter-carrier interference (ICI), inter-channel interference, inter-antenna synchronization (IAS), and system complexity for multiple-input multiple-output (MIMO) communication systems. Moreover, high spectral and energy efficiency have rendered SMTs attractive to underwater acoustic (UWA) MIMO communication systems. Consequently, this paper focuses on SMTs such as spatial modulation (SM), generalized spatial modulation (GSM), and fully generalized spatial modulation (FGSM) in which one constant number and one multiple number of antennas are active to transmit data symbols in any time interval for underwater acoustic communication (UWAC). In SMTs, the receiver requires perfect channel state information (P-CSI) for accurate data detection. However, it is impractical that the perfect channel knowledge is available at the receiver. Therefore, channel estimation is of critical importance to obtain the CSI. This paper proposes the pilot-based recursive least-square (RLS) adaptive channel estimation method over the underwater time-varying MIMO channel. Furthermore, maximum likelihood (ML) decoder is used to detect the transmitted data and antennas indices from the received signal and the estimated UWA-MIMO channel. The numerical computation of mean square error (MSE) and bit error rate (BER) performance are computed for different SMTs like SM, GSM and FSGM using Monte Carlo iterations. Simulation results demonstrate that the RLS channel estimation method achieves the nearly same BER performance as P-CSI.  
Channel Estimation; MSE; BER; MIMO; ML; Spatial Modulation Technologies +
Footnote †: journal: Applied Soft Computing
## 3 The Proposed Method
### PolSAR Data Pre-Processing  
Since the obtained PolSAR data contain the complex coherent matrix, they cannot be directly fed into the Dual-CNN model and a pre-processing is required. The pre-processing of the PolSAR data contains three steps: creating a 6 Ch to allow the input and representation of polarimetric data; generating a Pauli RGB image to obtain the spatial feature; patching the images with fixed size to adapt to the CNN.
## 3 Results
### Dry Density  
The relationship between dry density and interfacial shear adhesion at the dry side is presented in Figure 9. It is observed that there is a linear relationship between dry density and shear adhesion, wherein an increase in dry density leads to an increase in shear adhesion at each moisture content value. This finding highlights the importance of compaction in influencing interfacial adhesion behaviour.  
Detailed analysis revealed that the impact of dry density on shear adhesion is more pronounced at the dry side of the compaction curve compared to the wet side. When kaolin clay is compacted to a greater extent within the dry side, the soil particles come closer to each other, resulting in creating a denser surface area. At an MC of 10%, adhesion values range significantly from approximately 23.6 to 124 kPa across varied dry densities. This densification of the soil enhances the contact area between kaolin and the brass mould. Consequently, the interfacial shear adhesion is strengthened due to the increased contact  
Figure 8: Compaction curve of kaolin at different compaction levels.  
between the soil particles and the mould surface. It should be noted that the extent of the increase in dry density is dependent on the moisture content. In other words, the effect of the dry density on interfacial shear adhesion varies with the moisture content.
## 3 Results and Discussions
### Hydrogen/Air Mixture
#### 3.1.1 Time-Average Thrust  
The time-averaged thrust variation with the ER, for different oxidizer pressures and exhaust pipe lengths, is presented in Figures 3-6. For oxidizer pressures above 6 bar, no stable detonation regimes were found. This is due to the aerodynamic system configuration. If the oxidizer pressure is too high, fuel injection cannot sufficiently penetrate the vortex barrier formed using the parallel oxidizer injection and the alternating oscillating waves from the resonators. Hence, improper mixing for detonation results.  
For low oxidizer pressures and short pipes (exhaust pipe length of 200 mm and 300 mm), the higher thrust values are obtained for high spark plug frequencies, 233 Hz (Figure 5) and 350 Hz (Figure 6). Conversely, for long exhaust pipes (400 mm and 500 mm), the higher thrust values are recorded for low spark plug frequencies, 100 Hz (Figure 3) and 150 Hz (Figure 4).  
For high oxidizer pressures, where less stable detonation regimes were achieved, the spark plug influence on the performance is less clear, and the low frequencies prevail only for the exhaust pipe length of 400 mm.
## 4 Optimal Control Modeling for the UAV-UGV Formation-Maintenance Controller
### Optimal Formation-Maintenance Controller Design
#### 4.2.2 Design of Non-Zero Set-Point OPTIMAL Controllers  
To maintain the output quantity \(\mathbf{Y}=\big{[}\begin{array}{ccc}x&y&z\end{array}\big{]}^{T}\) at a non-zero set point, the state and control input of the system at the steady state must also be non-zero. The optimal control input based on Equation (35) should take the following form.  
\[\dot{\mathbf{U}}^{*}=-\mathbf{R}_{1}^{-1}\mathbf{B}_{1}^{T}\mathbf{P}\mathbf{X}_{1}+\mathbf{U}_{0}^{ \prime}=-\mathbf{K}\mathbf{X}_{1}+\mathbf{U  
To ensure that the output of the augmented system is consistent with the output of the original system, the output matrix of the augmented system is set to  
\[\mathbf{C}_{1}=\left[\begin{array}{cccccc}1&0&0&0&0&0\\ 0&1&0&0&0&0\\ 0&0&1&0&0&0\end{array}\right] \tag{40}\]  
By substituting the control quantity (37) into the augmented system (21), we obtain  
\[\dot{\mathbf{X}}_{1}=(\mathbf{A}_{1}-\mathbf{B}_{1}\mathbf{K})\mathbf{X}_{1}+\mathbf{B}_{1}\mathbf{U}_{0}^{ \prime} \tag{41}\]  
Because the closed-loop system (41) is asymptotically stable, the following condition should exist at steady state:  
\[\lim_{t\to\infty}\dot{\mathbf{X}}_{1}(t)=0 \tag{42}\]  
This leads to the following asymptotically stable form.  
\[\mathbf{0}=(\mathbf{A}_{1}-\mathbf{B}_{1}\mathbf{K})\mathbf{X}_{10}+\mathbf{B}_{1}\mathbf{U}_{0}^{\prime} \tag{43}\]  
where \(\mathbf{X}_{10}\) is the steady-state value of state \(\mathbf{X}_{1}\).  
If all eigenvalues of matrix \((\mathbf{A}_{1}-\mathbf{B}_{1}\mathbf{K})\) are in the left half-complex plane, then \((\mathbf{A}_{1}-\mathbf{B}_{1}\mathbf{K})\) is a non-singular matrix, and the following equation can be obtained by combining with Equation (43).  
\[\mathbf{X}_{10}=-(\mathbf{A}_{1}-\mathbf{B}_{1}\mathbf{K})^{-1}\mathbf{B}_{1}\mathbf{U}_{0}^{\prime} \tag{44}\]  
Then, based on Equation (38), the relationship between the non-zero set point \(\mathbf{Y}_{10}^{*}\) and the steady-state value \(\mathbf{X}_{10}\) of state \(\mathbf{X}_{1}\) is obtained as  
\[\mathbf{Y}_{10}^{*}=\mathbf{C}_{1}\mathbf{X}_{10} \tag{45}\]  
Finally,  
\[\dot{\mathbf{U}}^{*}=-\mathbf{R}_{1}^{-1}\mathbf{B}_{1}^{\mathrm{T}}\mathbf{P}\mathbf{X}_{1}+ \left[\mathbf{C}_{1}\Big{(}-\mathbf{B}_{1}\mathbf{R}_{1}^{-1}\mathbf{B}_{1}^{\mathrm{T}}\mathbf{P }-\mathbf{A}_{1}\Big{)}^{-1}\mathbf{B}_{1}\right]^{-1}\!\!\mathbf{Y}_{10}^{*} \tag{46}\]  
can be used to achieve optimal control of the relative motion system between the UGV and the UAV, as described in Equation (14); thus, maintaining the UAV-UGV formation in the desired state.
## 5 Performance Analyses
### Frequency Domain Equalization Comparison  
This subsection discusses a numerical comparison between a few frequency domain equalizers (8). To perform this comparison, we adopted \(P_{\rm T}\in[-10,40]\) (dBm), the PLC channel corrupted by the presence of AWGN and ACGN, \(L_{\rm CP}=14\), and \(\Delta=7\). Moreover, the UA technique is combined with the square \(16-\)QAM constellation.  
Figure 9 depicts the results attained with C-ZF, M-ZF, and ST-ZF. The performance curves show that the C-ZF scheme yields the best results, which is similar to what was reported in [27] for the OFDM scheme. For instance, while considering AWGN and \(P_{\rm T}=30\) dBm, HS-OFDM reaches values of \(\mathbb{P}_{\rm e}\) equal to \(5.06\times 10^{-4}\), \(9.57\times 10^{-4}\), and \(1.68\times 10^{-3}\) when considering C-ZF, M-ZF, and ST-ZF, respectively. Similar results were found using the other multiplexing modulation schemes. Under the same type of additive noise, the OCDM scheme obtains values of \(\mathbb{P}_{\rm e}\) equal to \(2.42\times 10^{-5}\) for the C-ZF scheme, while the other frequency domain equalizers, M-ZF and ST-ZF, yield \(2.21\times 10^{-4}\) and \(1.08\times 10^{-3}\), respectively. Moreover, for the SCCP scheme, considering the C-ZF scheme, the scheme reaches \(\mathbb{P}_{\rm e}=8.28\times 10^{-4}\), while the other frequency domain equalizers obtained higher values of \(\mathbb{P}_{\rm e}\). Moreover, the OTFDM scheme achieves a value of \(\mathbb{P}_{\rm e}\) equal to \(10^{-3}\) for the C-ZF scheme, \(1.82\times 10^{-3}\) for the M-ZF, and \(3.13\times 10^{-3}\) for the ST-ZF. Finally, similar behavior is also observed when AWGN is replaced by ACGN, but with higher values of \(\mathbb{P}_{\rm e}\). All further numerical analyses regarding the data rate and BEP in the following sections use the C-ZF scheme since it yields the best results among all of the frequency domain equalizers considered.  
Figure 8: Comparison between closed-form expressions of \(\mathbb{P}_{\rm e}\) and Monte Carlo (MC) simulations, i.e, BER, and \(\mathbb{P}_{\rm e}^{\rm LB}\) for the OCDM and OTFDM while considering \(L_{\rm CP}=14\), \(\Delta=7\), \(16-\)QAM, and AWGN.  
Figure 9: Comparison between the frequency domain equalizers C-ZF, M-ZF, and ST-ZF in terms of \(\mathbb{P}_{e}\times\ P_{\mathrm{T}}\) (dBm) for the OCDM, HS-OFDM, SCCP, and OTFDM when \(L_{\mathrm{CP}}=14\), \(\Delta=7\), square \(16-\)QAM.