esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Hierarchical Cooperative MIMO Clustering
### Clustering
#### 4.2.2 Clusters Formation  
Clusters formation consists of following steps:  
1. Each node compares its energy with that of its neighbors. If a node \(v\) has greatest amount of energy in its neighborhood, it declares itself a clusterhead. If there is a tie, it is resolved on the basis of node ID. Once a node declares itself a clusterhead, it propagates this message (\"Cluster Formation\") to its next hop neighbors. Nodes which listen to this message become part of that cluster by sending a \"Confirmation Message\". Once a node becomes a clusterhead or a part of a cluster, recurrent messages for clusterhead election are discarded by that node.
2. If \(v\) is not the best node, it sends a \"Clusterhead Message\" to the node with the highest residual energy to become a clusterhead in case the best node has not already declared itself a clusterhead.
3. After sending the \"Clusterhead Message\" to the best node, node \(v\) waits for a fixed duration of time (\(\delta\)) for the best node to reply.  
If that best node and not any other node in the neighborhood sends a \"Cluster Formation\" message, node \(v\) declares it self to be a clusterhead after a fixed time interval (\(\delta\)).
# Prediction of Ground Subsidence Risk in Urban Centers Using Underground Characteristics Information
## 2 Research Methods and Data
### Data  
The data on the attributes of underground facilities, permeability coefficients, stratigraphic thickness, height, and historical ground subsidence were collected to develop a ground subsidence risk prediction model for urban centers in South Korea. The collected data, accumulated manually by the managing entities over time, contained numerous missing and erroneous values. The data were preprocessed by deleting inaccurate data to address the inaccuracies. Furthermore, all data were in the form of digit strings. Table 1 outlines the conditions of the datasets. In Table 1, the risk levels were established by adjusting the risk assessment intervals based on the frequency of ground subsidence occurrences. Three distinct risk cases were presented, with risk level 2 defined by categorizing the number of incidents as either 1, 1-2, or 1-3 occurrences.  
Figure 1: Study flowchart.
# CDL-GAN: Contrastive Distance Learning Generative Adversarial Network for Image Generation
## 4 Experiments
### Settings and Evaluation Metrics  
We evaluate our models against the above existing models for three public datasets: MNIST [44], CIFAR-10 [45], and CelebA [46]. For preprocessing of data sets, we follow the detailed settings in [47]. MNIST contains 70 K 28 \(\times\) 28 handwritten digits with 10 labels; 60 K for training and 10 K for testing. We use all unlabeled training images in our experiments, resized to 32 \(\times\) 32. CIFAR-10 consists of 60 K 32 \(\times\) 32 natural images in 10 classes, out of which 50 K for training and 10 K for testing. We use all the training images without labels. For CelebA, we use the aligned face version in two resolutions: about 200 K images at a resolution of 64 \(\times\) 64 and 30K images reshaped to size 128 \(\times\) 128. In this paper, we regard the higher resolution images of CelebA as CelebA-HD.  
To assess the quality of generated images against corresponding real images, we adopted the Frechet Inception Distance (FID) [28] as a standard metric, which is used to measure the distance between generated and real image features and proved to correlate well with human evaluation. In our experiments, we calculated FID scores on different datasets with different image numbers: we used 10 K generated images vs. 10 K real images on CIFAR-10, 50 K vs. 50 K face images on CelebA in the size of 64 \(\times\) 64, and 3 K vs. 3 K on the high-resolution CelebA. Lower FID scores indicate better quality of the synthetic images.  
For all experiments, we use a single Tesla V100 GPU with our implementation in PyTorch. Meanwhile, we chose the Adam optimizer [42] with the learning rate of 0.0002 and set the batch size of images as 64. For iterations, the number of discriminator steps was 5 per generator step. For hyperparameters, we initialized \(\gamma=0.05\), \(\lambda_{dis}=10\), and \(\lambda_{gen}=10\). For objective function, we used hinge loss as a basic criterion except for CFD-GAN, because CFD-GAN utilizes a characteristic function distance as its objective function.  
To integrate CDL into GAN architecture perfectly, we updated the DCGAN and SNGAN backbone with Siamese modules, allowing the generator to transform the global structure of an input image by ChCD regularization. We further enhanced the training stability and applyied ChCD regularization and switched the depth-wise concatenation to adaptive instance normalization in the discriminator. Our generator consists of four down-sampling blocks, four intermediate blocks, and four upsampling blocks, all of which inherit preactivation residual units. Our discriminator is a projection discriminator [27], which contains multiple linear output branches. The discriminator contains six pre-activation residual blocks with leaky ReLU.
## 3 Results
### Variable Importance  
The DEM was the most important variable in the final model when considering the overall PVIM values, followed by the water-related indexes nNDMI and NDWI and by the S1-VH SAR band (Figure 3). On the other hand, although still providing important contributions to the performance of the final RF classifier, the SAR indexes RVI and VH/VV, the soil index GSI, and the blue band (B) were the predictors causing the smallest decreases in mean Overall Accuracy when excluded from training data. At the classes level, the predictors displayed different levels of importance among the habitat types (Table S4). Nevertheless, the DEM was also among the two most important predictors for all habitat types except for rocks, for which the water-related predictors and the radar's S1-VH were the most relevant. Similarly, S1-VH was among the four most important across most habitat types, except for macroalgae. Finally, surface water content played an important role in separating some habitat types, with nNMDI and NDWI displaying overall high importance scores across most habitat types (Table S4).  
Figure 3: Permutated variable importance measure (PVIM) obtained with the final random forest model including the best predictors set (14 predictors). Values presented were rescaled in relation to the maximum value, to range between 0 and 1 (see methods). Predictor acronyms are described in Table 2.
## 3 Algorithm Design
### Combination of BP Neural Network Optimized by BBO Algorithm and EKF
#### 3.3.2 Implementation Process of BBOBP-EKF Algorithm  
The BBO algorithm is coupled with a BP neural network and applied to the correction of the estimated value of the EKF algorithm. Figure 7 shows the concrete implementation process diagram of the BBOBP-EKF algorithm. The specific method of SoC estimation is as follows: firstly, the EKF algorithm is used to estimate the SoC and calculate the input of the BP neural network, that is, the Kalman gain matrix \(\mathbf{P}_{k}\) and the SoC state value difference \(\Delta\hat{\mathbf{x}}_{k+1}(=\hat{\mathbf{x}}_{k+1}-\hat{\mathbf{x}}_{k})\) estimated by the EKF algorithm at time \(k\) and time \(k+1\) are used as two inputs of the network. As the temperature has a certain impact on the battery, the battery temperature \(T\) is also one of the inputs. Then, based on the neural network structure, the BBO algorithm is used to optimize the initial weight and threshold of the BP neural network, and the optimized weight and threshold are assigned to the neural network. Then, the network is trained and tested, and the SoC estimation error is output after meeting the conditions, which is the compensation value of the EKF algorithm's estimation error. Finally, the SoC value estimated using the EKF method is added to the compensation value output by the BP neural network to obtain the final SoC estimation value.  
Figure 7: BBOBP-EKF algorithm estimation of battery SoC flowchart.
# An EV SRM Drive and Its Interconnected Operations Integrated into Grid, Microgrid, and Vehicle
## 3 EV SRM Drive
### Battery-Powered Varied DC-link Voltage SRM Drive
#### 3.2.2 Battery-Powered EV SRM Drive  
In the developed EV drive, the DC-link voltage command is set depending on the motor speed. The DC-link voltages set for comparison are listed below:  
Fixed DC-link voltage: \(v_{dc}=550\) V, \(0\leq\omega_{r}\leq 2000\) rpm.  
Varied DC-link voltage: \(v_{dc}=170\) V, \(0\leq\omega_{r}\leq 500\)  rpm. \(v_{dc}=170\) V \(<v_{dc}\leq 550\) V, \(500\)  rpm \(\leq\omega_{r}\leq 2000\)  rpm. The measured results of the EV SRM drive under fixed and varied DC-link voltages with trapezoidal speed command at \(R_{g}=210\)\(\Omega\) are shown in Figure 12. Note that \(P_{b}\) represents the battery output power, and \(E_{b}=\int P_{b}dt\) represents the battery consumed energy. The battery energy is \(E_{b}=7.84\) kJ under varied voltage compared with \(E_{b}=9.31\) kJ under fixed voltage. Figure 13 shows the measured results of the SRM drive under the speed pattern defined by ECE15 at \(R_{g}=75\)\(\Omega\) with fixed and varied DC-link voltages. The reduction of battery energy from \(E_{b}=90.4\) kJ to \(75.1\) kJ can also be observed.
## 6 Lamb Waves and Shear Horizontal Waves
### S0 Lamb Wave Mode  
The six eigensolutions at a frequency of \(3.188\times 10^{6}\) rad/s and a wavenumber of \(591.57\) rad/m are shown in Table 7, where the shear vertical solutions have real-valued \(k_{z}\) and real-valued eigenvectors. This frequency-wavenumber pair corresponds with the red-diamond symbol plotted in the dispersion-curve plot shown in Figure 2. These solutions correspond with the bulk waves reflecting within the plate waveguide (see Figure 7).  
The displacement solution is constructed in a manner analogous to the A0 mode, except now there are bulk waves present:  
\[\begin{split} u_{i}(z)=B^{(+L)}u_{i}^{(+L)}e^{-ik_{z}^{(+L)}(z-H) }+B^{(-L)}u_{i}^{(-L)}e^{-ik_{z}^{(-L)}z}+B^{(-SV)}u_{i}^{(-SV)}e^{-ik_{z}^{(- SV)}(z-H)}+\\ B^{(+SV)}u_{i}^{(+SV)}e^{-ik_{z}^{(+SV)}z}.\end{split} \tag{43}\]  
\(B^{(+L)}\), \(B^{(-SV)}\) and \(B^{(+SV)}\) are determined by using three of the four BCs (Equations (39)-(42)) (assuming that \(B^{(-L)}=1\)), and are found to be \(B^{(+L)}=-0.998\), \(B^{(-SV)}=-0.2139+0.4780i\) and \(B^{(+SV)}=0.2139-0.4779i\). As before, Equation (43) can now be used to plot the wave structure of the S0 mode at this frequency. Occasionally, the plotted wave structure will be at a phase that is inconsistent with the conventional wave structure figures. This can be corrected by multiplying all of the displacements by \(e^{-i\phi}\), where \(\phi=\tan^{-1}\left(\frac{imag(u_{x})}{real(u_{x})}\right)\). This multiplication is equivalent to letting the wave propagate over some non-zero distance and/or time.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Partial Waves** & \(k_{x}\)**, rad/m** & \(u_{x}\) & \(u_{y}\) & \(u_{z}\) \\ \hline \((+L)\) & \(312.93i\) & \(-0.884i\) & 0 & 0.468 \\ \((-L)\) & \(-312.93i\) & \(0.884i\) & 0 & 0.468 \\ \((+SV)\) & \(841.13\) & \(-0.818\) & 0 & 0.575 \\ \((-SV)\) & \(-841.13\) & \(0.818\) & 0 & 0.575 \\ \((+SH)\) & \(841.13\) & \(0\) & \(1\) & 0 \\ \((-SH)\) & \(-841.13\) & \(0\) & \(-1\) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Eigenvalues and normalized eigenvectors from the Christoffel equation for aluminum at \(\omega=3.188\times 10^{6}\) rad/s and \(k_{x}=591.57\) rad/m.  
Figure 7: Conceptual depiction of the partial waves, which are necessary for the S0 Lamb wave problem, overlaid on a plate in vacuum. The angle for the propagation direction of the shear vertical partial waves was calculated using \(\tan^{-1}(k_{x}/k_{z})\).
## 4 Experimental Results and Analysis
### Request-Response Latency and Average Latency Modeling  
Request-Response Latency is defined as the total time from the transmission of a request frame to the completion of response reception, and its components are shown in Equation (17):  
\[\text{Request - Response Latency}=RTT+t_{\text{processing}}+T_{\text{out}}\times N \tag{17}\]  
where RTT (Round-Trip Time) and \(t_{processing}\) represent the round-trip channel latency and application-layer processing time during the final successful retransmission, respectively. \(T_{out}\) denotes the timeout period: if the host fails to receive a response within \(T_{out}\), the retransmission mechanism is triggered, with \(N\) being the number of retransmission failures.  
During data frame transmission, the randomness of packet loss and retransmission mechanisms leads to probabilistic variations in actual latency across frames. To analyze the overall system performance, expectation must be introduced to model the Average Latency. The Average Latency is defined as the mean value of the total time from sending a request frame to fully receiving its response, which includes the Round-Trip Time (RTT), application processing time \(t_{processing}\), and \(T_{out}\). Let \(k\) be the number of retransmissions required before successful transmission, and let \(p\) represent the probability of transmission failure. The probability of success at the \(k+1\) attempt equals the product of the failure probabilities of the first \(k\) retransmissions and the success probability of the \((k+1)\)-th retransmission. The expected number of Retransmissions is derived as  
\[E(K)=\sum_{k=1}^{N_{\text{max}}}k\times p^{k}(1-p) \tag{18}\]  
Each retransmission introduces a timeout period, and the expectation \(E[K]\) represents the average retransmission count. Therefore, by defining \(D_{avg}\) as the Average Latency, the Average Latency can be expressed as  
\[D_{\text{avg}}=RTT+t_{\text{processing}}+T_{\text{out}}\times E(K)=RTT+t_{\text{ processing}}+T_{\text{out}}\times\sum_{k=1}^{N_{\text{max}}}k\times p^{k}(1-p) \tag{19}\]
## 4 Modeling Setup and Realistic Experimental of Simulation Scenarios
### RFI Emitter-Tracking Using High-Degree Nonlinear Filters  
This section covers the scenario of accurate RFI emitter-tracking using the high-degree nonlinear tracking filters approach based on extracted data from the Orolia Skydel Simulator. In this scenario, three LEO satellites are considered to receive RFI emitted a signal from a UAV under high speed and powerful rotation. As mentioned in the previous  
Figure 10: Example of geolocation geometry.  
section, the UAV moves circularly at the initial equal position (1000, 1000) with an altitude of 1000 m and an absolute speed of 100 m/s. The velocity has adjusted vertically to the line from the circle center to the origin position. In addition, the initial position of each LEO satellite is \(\mathbf{s}_{1}=(798.50,4502.10,5008.70)\) km, \(\mathbf{s}_{2}=(1236.50,-4547.90,4876.07)\) km, \(\mathbf{s}_{3}=(1668.80,-4572.70,4721.00)\) km, and their velocity is \(\mathbf{v}_{1}=(7007.70,-993.50,2020.60)\) m/s, \(\mathbf{v}_{2}=(6926.40,-678.30,-2399.70)\) m/s, \(\mathbf{v}_{3}=(6813.10,-360.00,-2767.90)\) m/s. The initial estimate \(\hat{\mathbf{x}}_{0|0}\) values with different uncertainty level values were introduced gradually from the Gaussian distribution \(\mathcal{N}(\hat{\mathbf{x}}_{0|0},\mathbf{P}_{0|0})\) in which the true initial value \(\mathbf{x}_{0|0}\) is \(\mathbf{x}_{0|0}=(1000\text{ m},\,100\text{ m}/\text{s},\,1000\text{ m},\,\,0\text{ m}/\text{s},-3\text{ deg}/\text{s})^{\prime}\) and \(\mathbf{P}_{0|0}\) being the initial covariance: \(\mathbf{P}_{0|0}=\text{diag}(100\text{ m}^{2},10\text{ m}^{2}/\text{s}^{2},100 \text{ m}^{2},10\text{ m}^{2}/\text{s}^{2},100\text{ m}\,\text{rad}^{2}/\text{s}^ {2})\).  
The steps of emitter-tracking using GHQF are detailed as  
1. **Input parameters:** TDOA and FDOA measurements based on data from the Orolia simulator; \(\mathbf{Q}_{k-1}\); initial position and velocity for sensors and RFI emitter; number of Monte Carlo runs (\(N\)); scan times (\(K\)); and number of quadrature points (\(m\)). 1. **First step:** 1. Initialization \(\rightarrow\hat{\mathbf{x}}_{0|0}\), and \(\mathbf{P}_{0|0}\) **for**\(i=1,\,2,\cdots,m\)**do**, 2. Calculating the quadrature points via (31), 3. Calculating first element of the respective weights, **end** loop \(i\), 2. **Start tracking:** Time update and Measurement update. \(\mathbf{for}=1,\,2,\cdots,N\)**do**, \(\mathbf{for}=1,\,2,\cdots,K\)**do**, 2. **Second step:** 1. Computing of evaluate state via (33) and (34), 2. Computing of estimate state via (35) and (36), **end** loop \(j\), 3. **Third step:** update. 2. Computing of evaluate state via (37) and (38), 2. Computing of estimate state via (39) to (44), **end** loop \(j\), **end** loop \(k\), 3. **Evaluation:** Evaluate the posterior density via (45). 4. **Output result:** The accurate estimates of the emitter position and velocity.
## 3 The Proposed IoMT Model
### Generation of Initialization Parameters  
The description of the notations of the parameters used in the proposed scheme is given in Table 2.  
Figure 1: Proposed Secure IoMT Model.  
The following processes take place during the initialization procedure:  
* The \(HC\) assigns an identification number \(HC_{id}\) to itself, \(P_{id}\) to each patient, and \(GHC_{id}\) to each guest's healthcare;
* HC, the central sensor in each patient, and each GHC are preloaded with encryption and decryption algorithms.
## 4 Results and Discussion
### Tool Flank Wear  
Figure 7 demonstrates the tool flank wear data obtained for both systems in various cutting conditions. A considerable reduction in tool wear of up to 46.77% was achieved at the higher cutting speed of 200 m/min and feed rate of 0.15 mm/rev when using the Cut-list. In general, low flank wear levels were noticed using the Cut-list of \(\sim\)30 \(\upmu\)m, except at 200 m/min and 0.1 mm/rev cutting speed and feed rate, respectively). This can be attributed to the combined cooling effect on the cutting insert in feed and against feed direction where nozzle position in feed direction helped the impinging jet to penetrate deeply into machining zone. The other position (i.e., against feed direction) afforded enough space for assisting chip evacuation and aided to cool the tool tip at end of the cyclic process. In higher cutting conditions, the Cut-list affords a larger jet velocity of 10.83 m/s, which increased the ability  
Figure 6: Workpiece temperature versus cutting conditions for the two systems.  
to accelerate heat dissipation from the cutting tip, thereby reducing the tool flank wear. On the other hand, the conventional supply system failed to disseminate heat sufficiently from the cutting tool at the same cutting conditions, which resulted in accelerated tool flank wear, adhesion, and micro-chipping, as shown in Figure 8.  
Figure 8: SEM (Scanning Electron Microscope) images of the cutting tools used with the Cut-list at feed rate of 0.15 mm/rev and cutting speed of 95 and 200 m/min, respectively (**a**,**b**) and conventional supply system at feed rate of 0.15 mm/rev and cutting speed of 95 and 200m/min, respectively (**c**,**d**).  
Figure 7: Tool flank wear results for the two systems.
## 3 Master-Slave Optimization Framework
### Slave Stage: Load Flow Problem via Successive Approximations
* **Fitness feedback to MVO**: The final value of the fitness function is returned to the master stage, allowing it to update and refine the search for more optimal BESS scheduling strategies in subsequent iterations. This framework allows for a detailed and dynamic assessment of MG performance under various BESS operating strategies. The SA method ensures efficient resolution of the power flow problem, while the MVO enhances voltage stability, system reliability, and technical and environmental performance.
## 3 Principle of the Prediction Model
### Dimension Reduction  
Since the dimension of the feature vector obtained after feature extraction is large (the feature vector has 13,331 dimensions when the image size is 40 \(\times\) 80), it is not conducive to the establishment of a machine learning model. Therefore, the dimension of the original feature vector is reduced by PCA.  
PCA is a statistical method that transforms a set of possible correlation variables into a set of linear uncorrelated variables through orthogonal transformation; the covariance matrix of the original variables is converted into a diagonal matrix. After PCA, a decrease in the feature's quantity can greatly reduce the complexity of the analysis, and more information can be obtained based on fewer features.  
There is a certain correlation between the key features of the temperature field in the SLS process. In the original key feature vector, each variable has a specific meaning. After orthogonal transformation, the original feature vector is transformed into a new vector whose components are not related to each other, and that lose the original special meaning. However, from the perspective of information, the new vector retains most of the valuable information contained in the original key feature vector. The importance of each component in the new vector is calculated. Taking the feature vector of 13,331 dimensions extracted based on an image with the size of 40 \(\times\) 80 as an example, the importance of each component after transformation is shown in Table 1.  
As shown in Table 1, the importance of the first three components reaches 80%. To retain more information, we use the feature vector of the first seven features after dimension reduction, the importance of which reaches 90% of the original vector.
## 3 Remote Sensing of Ecosystem Health
### Remote Sensing of Organization  
Ecosystem organization represents both species diversity and the interactions between species within the system [98]. The indicators of organization can be species richness, landscape diversity, structural traits including canopy height, Leaf Area Index (LAI), canopy morphology, and horizontal structure represented by the spatial arrangement of green vegetation, NPV, and bare soil. Since remote sensing of green vegetation, NPV, and bare soil has been reviewed in last section, we will focus on species richness and biodiversity and structural traits here.