esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Framework
### Proposed CNN and MBGRU Architecture
\[\mathbf{f}_{\mathsf{T}}=\odot(\dot{\omega}_{\mathsf{T}}\cdot[\hat{\mu}_{ \mathsf{T-1}},\;\omega_{\mathsf{T}}]\;+\;\theta_{j}) \tag{1}\]  
\[\mathbf{\dot{\iota}}_{\mathsf{T}}=\odot(\dot{\omega}_{i}\cdot[\hat{\mu}_{ \mathsf{T-1}},\;\omega_{\mathsf{T}}]\;+\;\theta_{i}) \tag{2}\]  
\[\mathbf{\dot{\epsilon}}_{\mathsf{T}}=\tan\;\hbar\;(\dot{\omega}_{e}\cdot[\hat {\mu}_{\mathsf{T-1}},\;\omega_{\mathsf{T}}]\;+\;\theta_{e}) \tag{3}\]  
\[\mathbf{\mathcal{C}}_{\mathsf{T}}=\mathbf{f}_{\mathsf{T}}\;\times\;\mathbf{ \mathcal{C}}_{\mathsf{T-1}}+\;\mathbf{\dot{\iota}}_{\mathsf{T}}\;\times\; \mathbf{\dot{\epsilon}}_{\mathsf{T}} \tag{4}\]  
\[\mathbf{\mathcal{O}}_{\mathsf{T}}=\odot(\dot{\omega}_{\mathbf{\mathcal{O}}} \cdot[\hat{\mu}_{\mathsf{T-1}},\;\omega_{\mathsf{T}}]\;+\;\theta_{\mathbf{ \mathcal{O}}}) \tag{5}\]  
\[\mathbf{\dot{\pi}}_{\mathsf{T}}=\;\mathbf{\mathcal{O}}_{\mathsf{T}}\times\tan \hbar(\odot(\mathbf{\mathcal{C}}_{\mathsf{T}}). \tag{6}\]  
The problem of losing long sequence information is addressed by LSTM using the three-gate mechanism input, output, and forget. The mathematical representation of each gate is shown in Equations (1)-(6). In these equations the output of each gates is represented through \"\(\mathbf{\dot{\iota}}\)\", \"\(\mathbf{\dot{\jmath}}\)\" and \"\(\mathbf{\mathcal{O}}\)\" where \"\(\mathbf{\mathcal{O}}\)\" represents the activation function. The weights of the gates are represented through \"\(\dot{\omega}\)\", whereas\" \(\mathbf{\dot{\mathcal{H}}}_{\mathsf{T-1}}\)\" refers to the output of previous LSTM block and \"\(\mathbf{\mathcal{O}}\)\" represents the bias of the gates. The LSTM structure is complex and computationally expensive due to these gates' units and memory cells. To overcome the concern of LSTM, another lightweight architecture is developed called GRU [54] which comprises the reset and update gates. The mathematical representation of GRU gates are shown in Equations (7)-(10) where the update gate examines the earlier cell memory to remain active,  
\[\mathbf{\dot{\mathit{x}}}_{\mathsf{T}}=\;\odot(\;\dot{\omega}_{x}\cdot[\hat{ \mu}_{\mathsf{T-1}},\;\omega_{\mathsf{T}}]+\;\theta_{x}) \tag{7}\]  
\[\mathbf{\dot{\mathit{r}}}_{\mathsf{T}}=\;\odot(\;\dot{\omega}_{r}\cdot[\hat{ \mu}_{\mathsf{T-1}},\;\omega_{\mathsf{T}}]+\;\theta_{r}) \tag{8}\]  
\[\mathbf{\dot{\mathit{k}}}_{t}=\of 3.1, respectively and used ReLU as an activation function in these layers. After convolutional layers, two bidirectional GRU layers are incorporated to learn the temporal information of the electricity historical data. Finally, the fully connected layers are integrated for the final output prediction.
# A Sector-Matching Probability Hypothesis Density Filter for Radar Multiple Target Tracking
## 3 Sector-Matching PHD Filter  
A sector-matching PHD filter is proposed in this section. We first propose the sector-matching and sector division issues, and rederive the prediction and update steps based on the actual measurement time. Then, a state correction step is given.
Utilizing these techniques effectively mitigates undesired correlations during the learning process of the Meta-model by eliminating redundant and irrelevant features. In the present work, a novel ensemble framework is proposed, namely a Parallel-PCA-Cascaded (Pa-PCA-Ca) ensemble structure for crop type mapping, with the entire methodology developed and implemented in GEE. In the proposed framework, the outputs of a Pa structure at input data level, after being fed to PCA for redundant information elimination, are used in a Ca structure by a Meta-model. Various ML models, such as RF, SVM, Gradient Boosting Tree (GBT), and Classification And Regression Tree (CART), were employed in the proposed methodology. Additionally, the methodology incorporates different sources of satellite imagery to map different crop types in Mahabad city, Iran. The main contributions of this work are summarized below:  
1. A novel ensemble ML framework is proposed based on a Pa-Ca structure combined with PCA transformation, which integrates the outputs of MLs and multi-source satellite data for improved crop type classification. 2. Both MS and SAR RS satellite imageries (S1/2 and L8/9) were employed, and the proposed method was evaluated using the Ground Truth (GT) data of different crop types collected using extensive field surveys in Mahabad, Iran. 3. The study involved conducting a comparative analysis of multiple ML models within the proposed methodology, alongside a comparison between the proposed methodology and two conventional methods used for classifying crop types.
# A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning
## 2 Materials and Methods
### Models for Carbon Flux Prediction  
The CNN model [34] was used in this study as a robust deep learning approach to predict carbon fluxes. For comparison, two conventional machine learning models were also applied, including ANN [35] and RF [36]. The main components of CNN include convolution layer and pooling layer. The convolution layer projects the features of the input data to the next layer through convolution operation, while the pooling layer reduces the number of parameters and computation by down sampling the input data. After the above processing, the data are processed and outflow through the fully connected layers. The basic structure of ANN is a perceptron, which is a kind of artificial neuron. It first accepts input and calculates weighted sum, and then produces output using activation functions. A neural network consists of several layers of perceptron, including input layer, fully connected layer, and output layer. In regression problems, the number of neurons in the last output layer of ANN and CNN is the number of output dimensions. In our study, three neurons were considered in the output layer to predict the values of GPP, RECO, and NEE. RF is an ensemble of multiple decision trees. It uses the bagging method to randomly select a feature subset from all available features to train different decision trees at each time (iteration), and it finally takes the average of the predicted results of all the decision trees as the final predicted value.
###### Abstract  
This paper studies an intelligent hybrid compensation scheme for the uncertain parameter and disturbance of hypersonic flight vehicles (HFV). For the longitudinal model of HFV with modeling errors, a nominal nonlinear dynamic inverse (NDI) controller ensures that the system output can accurately track the reference command. In the presence of rotational inertia uncertainty, a multi-learning law adaptive NDI controller is proposed to directly compensate for its impact on tracking performance, making the system robust to the uncertainty and reducing high maneuvering attitude angles and velocities vibration. Then, an improved adaptive NDI controller with a sliding mode disturbance observer is designed to actively compensate for the elastic mode disturbance, and continuously ensure the system's anti-disturbance flight quality. Ultimately, this active-passive hybrid control scheme compensates for both high maneuvering inertia uncertainty and global disturbance. The Lyapunov functions prove the system's stability, and the semi-physical simulation platform verifies the effectiveness of the method.  
nonlinear dynamic inverse; hypersonic flight vehicle; disturbance observer; hybrid compensation; rotational inertia uncertainty
# A Multi-Objective Approach for Optimizing Virtual Machine Placement Using ILP and Tabu Search
## 2 Problem Description and Related Work
### Objectives and Challenges  
The VMP is guided by several objectives, focusing on maximizing resource utilization across CPU, memory, and storage [6; 7; 8; 9; 10; 11]. Minimizing energy consumption is another key objective, achievable through intelligent server management that reduces the number of active servers or by migrating VMs to energy-efficient servers [12; 13; 14; 15]. Load balancing is also important to prevent resource bottlenecks and improve system reliability [16; 17; 18; 19]. Additionally, meeting QoS requirements, as outlined in SLAs, ensures that performance levels are met [20; 21; 22]. Other factors include scalability, which ensures that the system can adapt to changing demands, and minimizing migration costs when relocating VMs [23; 24; 25; 26; 27]. These objectives are interrelated, requiring a careful balance to achieve optimal performance, cost efficiency, and sustainability in cloud environments.  
Figure 1: Comparative analysis of VMP solutions: solution 1 vs. solution 2. (**a**) VMP solution 1; (**b**) VMP solution 2.
# A Comprehensive Analysis of Smart Grid Systems against Cyber-Physical Attacks
## 8 Countermeasures  
The attacks we have demonstrated target the grid to create an imbalance in the power demand and supply in a particular region. Once an attacker compromises a cyber system to control a physical process, it is challenging for power engineers to protect the system or prevent further attacks. Furthermore, the attacker performs attacks on regular intervals until the grid loses stabilization. Power engineers do not currently consider such attacks during their contingency planning, and hence, they are unprepared to deal with them. Power engineers should consider such a family of attacks during the contingency planning and provide sufficient resources to withstand them. The resources are as follows:  
1. Power storage at different levels.
2. The higher capacity of power lines.
3. Backup power lines for each high voltage line.
4. Use demand response as a spinning reserve [11].
5. Gas storage near peaker plants to recover from the attacks on a gas distribution pipeline.
6. Use trusted platform chips in each hardware such as Programmable Logic Controllers, Remote Terminal Units, smart meters, etc., so that the attacker is not able to install malwares.
7. Use the BAGS tool to maintain the security status of various cyber components of the grid.  
Patch all the vulnerable and out of date cyber components in the grid using RL-BAGS.
* Power engineers must focus on the optimal scheduling of DERs to maintain the resilience of the grid. It involves understanding the power consumption pattern and predicting power outages before they happen.  
Power engineers must know the total number of DERs in a given region and the average amount of power generated by them. They should maintain sufficient resources (such as power storage) to mitigate an attack (such as Attack II described in Section 4.3) on a certain number of DERs so that the area's frequency never diverge beyond protective thresholds.  
Power engineers should perform secure device enrollment into their system so that malicious devices are not enrolled. Many cloud solutions provide secure device enrollment and lifecycle management such as Amazon Cloud [44], IBM Watson [45], etc. Furthermore, the devices should be equipped with the trusted platform module so that even if an adversary tries to compromise those devices by installing malware, the system admin will get notified and the device will stop working.
# Influence of CeO\({}_{2}\) Addition to Ni-Cu/HZSM-5 Catalysts on Hydrodeoxygenation of Bio-Oil
## 3 Results and Discussion
### Characterization of Catalysts
#### 3.2.2 Tcm  
The catalyst image before an HDO reaction was captured by TEM (see Figure 5a-d for CeO\({}_{2}\) contents of 0, 5, 15, and 20%, respectively). The average size of the catalyst with CeO\({}_{2}\) is smaller than  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Catalyst** & **Ni–Cu/HZSM-5** & \begin{tabular}{c} **5\%CeO\({}_{2}\)–Ni–Cu/** \\ **HZSM-5** \\ \end{tabular} & \
\begin{tabular}{c} **15\% CeO\({}_{2}\)–Ni–Cu/** \\ **HZSM-5** \\ \end{tabular} &
\begin{tabular}{c} **20\% CeO\({}_{2}\)–Ni–Cu/** \\ **HZSM-5** \\ \end{tabular} \\ \hline Crystal particle (nm) & 46.5 & 32.6 & 19.2 & 19.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The crystal particle size of the catalyst.  
Figure 4: The XRD patterns of HZSM-5 and Ni–Cu/HZSM-5 with different CeO\({}_{2}\) contents.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Relative Content** & & \multicolumn{4}{c}{**Upgrading Oil**} \\ \cline{3-6}
**(\%)** & **Bio-Oil** & \begin{tabular}{c} **Ni–Cu/** \\ **HZSM-5** \\ \end{tabular} & \begin{tabular}{c} **5\%CeO\({}_{2}\)–Ni–Cu/** \\ **HZSM-5** \\ \end{tabular} & \
\begin{tabular}{c} **15\%CeO\({}_{2}\)–Ni–Cu/** \\ **HZSM-5** \\ \end{tabular} &
\begin{tabular}{c} **20\% CeO\({}_{2}\)–Ni–Cu/** \\ **HZSM-5** \\ \end{tabular} \\ \hline Aldehydes & 7.9 & 6.0 & 5.9 & 5.4 & 5.7 \\ Hydrocarbons & 0.8 & 27.8 & 28.4 & 29.6 & 28.6 \\ Acid & 15.4 & 3.6 & 3.0 & 2.9 & 3.0 \\ Ketones & 28.0 & 17.3 & 15.0 & 14.2 & 14.2 \\ Phenols & 24.5 & 21.8 & 21.8 & 21.0 & 21.4 \\ Ethers & 6.9 & 2.5 & 2.1 & 1.4 & 2.0 \\ Esters & 6.4 & 13.9 & 14.4 & 16.8 & 15.4 \\ Alcohols & 7.4 & 5.0 & 4.6 & 4.0 & 3.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The chemical composition of bio-oil and upgrading oils.  
that without adding CeO\({}_{2}\). A suitable CeO\({}_{2}\) (e.g., 15%) content can effectively inhibit grain growth and, hence, provide a better dispersion of Ni with the smallest particle size (Figure 5). However, at a higher 20% CeO\({}_{2}\) content, the aggregation of Ni appears due to excessive CeO\({}_{2}\) accumulation on the pore/surface of the catalyst. Therefore, the catalyst with a 15% CeO\({}_{2}\) addition is more suitable as the catalyst for the HDO of bio-oil.
# An Improved PIN Diode Model Design for a Tunable Frequency Selective Absorber
## 2 Unit Design Process  
### Design Concept  
For the tunable frequency selective absorber, it is divided into four parts from top to bottom: FSS layer, substrate, isolation layer and metallic plate, as shown in Figure 1a. Figure 1b is the equivalent circuit of the corresponding structure in Figure 1a, and the metallic plate can be equivalent to a short circuit. The isolation layer is equivalent to a transmission line with characteristic impedance, \(Z_{0}\), whose length is equal to the thickness, \(d\), of the isolation layer. The FSS layer can be equivalent to a series or parallel circuit, and its impedance is set as \(Z_{FSS}\). The substrate is also equivalent to a section of transmission line, which can be ignored in the calculation of the impedance of the whole FSS structure, in general, due to its very low thickness.  
According to the transmission line theory, the reflectivity formula of the absorbing structure can be determined as follows:  
\[\Gamma=\frac{Z_{in}-Z_{0}}{Z_{in}+Z_{0}} \tag{1}\]  
The input impedance of the adjustable structure in Figure 1 is assumed to be \(Z_{in}\):  
\[Z_{in}=\frac{1}{\frac{1}{Z_{FSS}}+\frac{1}{\left|Z_{0}\tan\beta d\right|}} \tag{2}\]  
\(\beta=2\pi/\lambda\) is the propagation constant of electromagnetic wave. Thus, the reflection coefficient can be simplified as follows:  
\[\Gamma=\frac{Z_{FSS}(jZ_{0}\tan\beta d)-(Z_{FSS}+jZ_{0}\tan\beta d)Z_{0}}{Z_{ FSS}(jZ_{0}\tan\beta d)+(Z_{FSS}+jZ_{0}\tan\beta d)Z_{0}} \tag{3}\]  
In addition, the structural reflection coefficient can be adjusted by changing the impedance of the FSS.  
Additionally, the input impedance, \(Z_{in}\), can be expressed as the sum of its real and imaginary components (R, X), allowing the reflectance to be defined in terms of these parts:  
\[\log(\Gamma)=20\log\left|\frac{(R-Z_{0})+jX}{(R+Z_{0})+jX}\right| \tag{4}\]  
Figure 1: (**a**) The overall structure of tunable frequency selective absorber; (**b**) equivalent circuit model.  
Based on the above equation, the relationship between reflectance and the real and imaginary parts of the input impedance can be visualized. Figure 2 illustrates how reflectance varies as the real and imaginary parts of the input impedance change within a specific range. In the figure, different colors represent different levels of reflectance magnitude. The analysis reveals that adjusting the impedance of the AFSS structure allows for control of its reflectance. Therefore, impedance tuning is the key to enabling the structure's switching functionality. This impedance adjustment can be achieved by modifying either the real part or the imaginary part of the impedance.
## 3 Watermarking Approaches
### Watermarking Methods
#### 3.2.4 Watermark Embedding via Decoder Parameter Manipulation  
Another key area of watermarking in diffusion models involves embedding watermarks in the decoder stage to maintain secure and traceable image generation [36]. These methods focus on modifying the decoder component to embed watermarks, providing an additional layer of traceability.  
[PERSON] et al. [28] introduce a watermarking method specifically designed for latent diffusion models. By fine-tuning the VAE decoder, Stable Signature embeds invisible watermarks into images during generation. These watermarks are resistant to removal and can verify the model used to generate the image, embedding unique user identifiers without degrading quality. [PERSON] et al. [32] present a method for embedding unique fingerprints into generative models like Stable Diffusion. It allows model distributors to create user-specific models by modulating the model's weights, enabling tracing of generated images back to individual users. This prevents malicious use of the models and ensures high attribution accuracy without compromising quality. [PERSON] et al. [41] present a watermarking method that integrates watermarking directly into the image generation process using multi-scale embedding modules. This ensures robustness against various image transformations while maintaining high perceptual quality. LaWa also functions as a post-generation watermarking method without fine-tuning, offering flexibility for embedding watermarks in generated content. Embedding watermarks at the decoder stage provides an additional layer of security, enabling traceability of generated content back to its origins, ensuring accountability, and deterring unauthorized usage, all while maintaining a low computational cost during training [36].
# Detecting the Absence of Lung Sliding in Lung Ultrasound Using Deep Learning
## 2 Materials and Methods
### Automated M-Mode Classification
#### 2.2.2 Slice Selection  
In M-mode images, we distinguish two patterns: Seashore and barcode. Seashore patterns exhibit a noise-like appearance, which comes mainly from the lung tissue artifact. In order to capture these patterns, M-mode slices must at least partially intersect the lung tissue artifact. Therefore, we propose two methods for slice selection in the automated M-mode classification:  
1. Lung slice selection: We use our U-Net to segment the lung tissue. All the slices between the minimum and maximum of the lung mask x-coordinate are passed to the classification network. This approach ensures that all the slices that contain information about lung sliding are forwarded for classification.
2. Centroid slice selection: We use binary segmentation U-Nets for three tissues, lung, pleura, and rib. We use the centroid of the lung and pleura to select two slices. To select the final slice, we use the rib mask. First, we ascertain whether we have one or two rib tissue segments. Then, if we have two segments, we use the mean of the x-coordinates of both centroids; otherwise, we replace one, either 0 or image width, based on the position of the other rib segment.  
Centroid selection is a sub-optimal method to select M-mode slices, since the x-coordinate of the centroid is not always the optimal slice, i.e., it is not an x-coordinate giving the highest probability of correct classification. Moreover, it inherits bias from the segmentation networks. The optimal position of the slice may be shifted left or right from the centroid. We used a statistical approach to find the optimal shift value. First, we trained five different classifiers to categorise the lung slice selection. We then classified slices randomly taken from the training samples while keeping track of the position of the slices relative to the estimated centroid. We calculated the counts of the correctly and incorrectly classified slices and plotted them against the distance of the slices from the estimated tissue centroid (positive distance is to the right, negative to the left; see Figure 4). The optimal shift from a specific centroid is the value giving the highest probability of correct classification of the slice. We limited the shift to be within the range of \(\pm K\) pixels from the centroid. In our case, we set this hyperparameter to 32 pixels. We added these constant shifts to calculated positions of the centroids. The values we found were \(-10\), \(-19\) and \(+28\) pixels for the centroids calculated for the lung, pleura, and rib tissue, respectively.
# AODV-EOCW: An Energy-Optimized Combined Weighting AODV Protocol for Mobile Ad Hoc Networks
## 3 AODV-EOCW Protocol
### Subjective Weighting
Its calculation method is shown in Equation (2):  
\[CR=\frac{CI}{RI}=\frac{\lambda_{max}-n}{\lambda^{\prime}_{max}-n} \tag{2}\]  
where \(CI\) stands for the consistency index, and \(RI\) stands for the random consistency index, \(\lambda_{max}\) stands for the largest eigenvalue of \(A\), and \(\lambda^{\prime}_{max}\) stands for the largest eigenvalue of the positive reciprocal matrix of \(A\). When \(CR<0.1\), the comparison matrix is considered to have a satisfactory level of consistency; otherwise, fine-tuning is required. The fine-tuned matrix of \(A\) is \(A^{*}\); obviously, \(A^{*}\) is a comparison matrix too. If the largest eigenvalue of \(A^{*}\) is \(x\), the corresponding eigenvector is \(\mathbf{w}\). After normalizing the eigenvector \(\mathbf{w}\), the weight vector \(\mathbf{z}=[w_{1},w_{2},w_{3}]\) can be obtained, where the values \begin{table}
\begin{tabular}{c c} \hline \hline \(\mathbf{a_{ij}}\) & **Meaning** \\ \hline
1 & \(i\) and \(j\) are equally important \\
3 & \(i\) is slightly more important than \(j\) \\
5 & \(i\) is more important than \(j\) \\
7 & \(i\) is more important than \(j\) \\
9 & \(i\) is much more important than \(j\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Explanation of \(a_{ij}\) in Equation (1). represent the weights corresponding to the congestion degree, residual energy, and hop count, respectively. The comparison matrix \(A\) (or \(A^{*}\)) reflects the importance of the metrics. The rows and columns represent the congestion degree, the residual energy, and the number of hops. According to Table 1, \(a_{12}=3\) means that the congestion degree is slightly more important than the hop count. This study empirically designed the following comparison matrix to reflect the importance of the residual energy. If the residual energy level is above 80%, the comparison matrix \(A_{1}\) is chosen to calculate the weights; if the residual energy level is above 50%, the comparison matrix \(A_{2}\) is chosen to calculate the weights; and if the residual energy level is below 30%, the comparison matrix \(A_{3}\) is chosen to calculate the weights. The three matrices are shown in Equation (3). Finally, the subjective weights from those three matrices are calculated as \(\mathbf{\omega}=[0.5396,0.297,0.1634]\), \(\mathbf{\omega}=[0.637,0.2583,0.1047]\), and \(\mathbf{\omega}=[0.7514,0.1782,0.0704]\), respectively. \[A_{1}= \begin{pmatrix}1&2&3\\ 1/2&1&2\\ 1/3&1/2&1\end{pmatrix} ,A_{2}= \begin{pmatrix}1&3&5\\ 1/3&1&3\\ 1/5&1/3&1\end{pmatrix} ,A_{3}= \begin{pmatrix}1&5&9\\ 1/5&1&5\\ 1/9&1/5&1\end{pmatrix}  \tag{3}\]