esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods and Materials
### Physical Simulation (or Modeling)  
A physical model was built according to the geological and mining conditions. The dimensions of the frame of the model were \(1800\times 160\times 1300\) mm. The modeled strata were about 150 m thick. The geometric scaling constant was 150:1, constant of density similarity (the ratio of prototype to the model) was 1.6:1, constant of strength similarity was 240:1, and constant of time similarity was 12.2:1.  
Figure 1: Generalized stratigraphy column.  
Figure 2: The panel layout.  
The total number of strata was 17. The floor strata were 9 m thick, the coal seam was 9 m thick, and the overburden strata were 136.5 m thick. The properties of the strata are shown in Table 1. The model materials consisted of aggregate (sand), cement materials (lime and plaster), and water. The detailed information about each stratum in the model and usages of materials used for constructing the physical model are given in Table 2. The constructed model is shown in Figure 3. Nine measuring lines were embedded in the model to monitor the movement of strata. The average vertical distance between two adjacent measuring lines was 10 cm. There were 17 measuring points on each line. The average horizontal interval between points was 10 cm, too. In total, 153 measuring points were distributed within the model.  
The digital image correlation method (DICM) was utilized to measure the strata movement. It is an optical method that is widely applied in many areas of science and engineering, which uses tracking and image registration techniques for accurate 2D and 3D measurements of changes in images. It is widely adapted to measure displacement, deformation, strain, etc. [28; 29]. The images before any excavation were set as datum images; deformed images were then compared with the datum images to get deformation, displacement, or strain data. In order to obtain data that are of high accuracy, a high definition camera and DICM analysis software were combined.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Lithology** & **Density/kg\(\cdot\)m\({}^{-3}\)** & **Uniaxial Compressive Strength UCS/MPa** \\ \hline Silstone & 2350 & 44.5 \\ \hline Fine-grained sandstone (FGS) & 2350 & 42.5 \\ \hline Mudstone & 2200 & 32.8 \\ \hline Limestone & 2700 & 62.5 \\ \hline Coal & 1400 & 13.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of the strata.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Lithology** & **Thickness (cm)** & **Number of** **Sublayers** & **Sublayer** & **Weight of** **Thickness (cm)** & **Weight of** **Each Layer** & **Sand Weight** **of Each Layer** & **Time Weight of** **Each Layer** & **Plaster Weight of** **Water Weight** \\ \hline Silstone & 9 & 3 & 3 & 14.86 & 13.21 & 0.83 & 0.83 & \(1.04\) \\ \hline Mudstone & 12 & 4 & 3 & 14.86 & 13.21 & 0.
## 7 Control Strategy and State Space Equations
### Inductor and Capacitor Loss Calculation  
\[\begin{array}{l}P_{L}=iLrms^{2}rl\\ P_{L1}=\frac{4P_{0}(D+1)^{2}}{RL(1-D)^{4}}rl1\\ P_{L2}=\frac{4P_{0}(D+1)^{2}}{RL(1-D)^{4}}rl2\\ P_{L3}=\frac{4P_{0}(1+D)^{2}}{RL(1-D)^{2}}rl3\\ P_{L4}=\frac{4P_{0}(1+D)^{2}}{RL(1-D)^{2}}rl4\end{array} \tag{74}\]\[\begin{array}{l}P_{\rm C}=Icrms^{2}rc1\\ P_{\rm C1}=\frac{4P_{\rm F0}(D+1)^{2}}{RL(1-D)^{4}}rc1\\ P_{\rm C2}=\frac{P_{\rm F0}(1+D)^{2}(3D+1)}{RL(1-D)^{4}}rc2\\ P_{\rm C3}=\frac{P_{\rm F0}(1+D)^{2}(3D+1)}{RL(1-D)^{4}}rc3\\ P_{\rm C4}=\frac{4P_{\rm F0}(1+D)^{2}}{RL(1-D)^{2}}rc4\\ P_{\rm C5}=\frac{P_{\rm F0}(D-D^{2}+4(D+1)^{2})}{RL(1-D)}rc5\\ \end{array} \tag{75}\]  
Formulas (74) and (75) facilitate the computation of power losses in the inductors (PL) and capacitors in the proposed converter, respectively. The converter incurs losses across MOSFETs, diodes, inductors, and capacitors. The aggregate power loss (TPLPC) of the converter, as outlined in Equation (76), amalgamates power losses in power MOSFETs (MTL), total diode losses (DTL), and losses in all inductors (ITL) and capacitors (CTL). Efficiency, a pivotal gauge of a converter's effectiveness, is determined by Equation (77). It is worth noting that opting for SiC MOSFETs with minimal on-state resistance proves advantageous in curtailing conduction losses. Furthermore, employing low-value inductors with exceptionally low internal resistance contributes to augmenting the overall efficiency and performance of the proposed converter.
## 2 Materials and Methods
### Statistical Analysis  
For the quantitative assessment of the denoised images, the mean scores of PSNR and SSIM of deeper U-net and RED-CNN were calculated. All the qualitative image quality scores were compared between deeper U-net and RED-CNN using the Wilcoxon signed-rank test. For each abnormal lesion, a 5-point score \(\geq\) 3 was regarded as true positive (TP), and a 5-point score \(<\) 3 as false negative (FN). He detectability of abnormal lesions was calculated based on the following equation: \(\frac{TP}{TP+FN}\) (sensitivity). The detectability of abnormal lesions was compared between deeper U-net and RED-CNN using the McNemar test. Statistical analyses were performed using JMP(r) (version 14.2, SAS Institute Inc., Cary, NC, USA). All tests were two sided with a significance level of 0.05.
# V2X Wireless Technology Identification Using Time-Frequency Analysis and Random Forest Classifier
## 1 Introduction
As a result, Machine Learning (ML) techniques, such as Support Vector Machine (SVM) and Random Forest, have been widely used in related studies [18; 19; 20]. Combined with simulation based data generation, they have shown promising results with small datasets. The aim of this paper is thus to exploit the power of ML techniques to identify ITS-G5, LTE-V2X and NR-V2X signals in an Additive White Gaussian Noise (AWGN) channel. Wireless technology identification is a substantial field of knowledge but the vehicular context has not been considered in existing studies. The proposed approach addresses three main issues: the confusion between two close technologies such as LTE and NR; the sensitivity of accuracy to noise level; and the high computational complexity. The first step is the extraction of the feature vector by performing a time-frequency (TF) analysis on the received signal. It consists of decomposing it into Intrinsic Mode Functions (IMF) then computing their Instantaneous Frequency (IF). This combination has the power to bring out the local and unique characteristics of signals. In order to achieve the best estimation of the raw features using fewer dimensions, we also implement the Singular Value Decomposition (SVD) technique. The obtained feature vector can then be fed into any classifier for the decision making step. In this study, we used the random forest classifier because of its simplicity. To demonstrate the superiority of its performance, we compared different classification metrics of the proposed technique with those of the SVM classifier used with Spectral Correlation Function (SCF) features [20]. The accuracy of a cyclostationarity-based technique proposed in [12] was also evaluated to show the limitations of the statistical approach. The rest of the paper is organized as follows: Section 2 reviews some signal pre-processing techniques that are relevant to our study. An overview of the considered V2X signals is presented in Section 3 along with their physical layer parameters. Section 4 presents the proposed identification algorithm based on instantaneous frequency features and the random forest classifier. After a description of the data generation process, the obtained results are evaluated in Section 5, where the confusion matrix and other classification metrics are compared with those of the cyclostationarity-based approach. Section 6 concludes this work and proposes some future research directions.
## 3 Additional Degree Reduction with the Multi-Homogeneous Theorem
### 2-Homogeneous Formulation for Watt II Six-Bar Slider-Crank Mechanism  
For the multi-homogeneous formulation of the synthesis equations for the Watt II slider-crank mechanism, the 12 unknowns are arranged into two groups \(\lambda_{1}\) and \(\lambda_{2}\) that constitute the loop \(OABC\) and loop \(OCDE\) shown in Figure 1 as follows.  
\[\lambda_{1}:\big{(}r_{2x},r_{2y},r_{3x},r_{3y},M_{1},M_{2}\big{)},\,\lambda_{2 }:\big{(}r_{4x},r_{4y},r_{5x},r_{5y},M_{3},M_{4}\big{)}. \tag{39}\]  
In order to determine the multi-homogenous Bezout number for a polynomial system whose unknowns are arranged into \(m\) homogeneous groups \(\lambda_{1}\) to \(\lambda_{m}\), let \(l\) be the number of polynomial equations to solve, \(d_{i,j}\) be the degree of the \(i\)th equation with respect to the unknowns in group \(\lambda_{j}\), and \(k_{j}\) be the number of unknowns in group \(\lambda_{j}\). Then, the multi-homogenous Bezout number of the system is defined as the coefficient of the term \(\prod_{j=1}^{m}\lambda_{j}^{k_{j}}\) of the following equation [35].  
\[\prod\limits_{i=1}^{l}\left(\sum\limits_{j=1}^{m}d_{i,j}\lambda_{j}\right)=( d_{1,1}\lambda_{1}+\cdots+d_{1,m}\lambda_{m})(d_{2,1}\lambda_{1}+\cdots+d_{2,m} \lambda_{m})\cdots(d_{i,1}\lambda_{1}+\cdots+d_{i,m}\lambda_{m}), \tag{40}\]  
Note that \(\lambda_{j}\) in Equation (40) is used as a null value for calculating the multi-homogenous Bezout number.  
Since the system of 12 synthesis equations for the Watt II slider-crank function generator, Equations (19) and (37) with Equation (39), is a 2-homogeneous system, substituting \(l=12\), \(m=2\), \(k_{1}=k_{2}=6\), and \(d_{i,j}\) shown in Table 1 into Equation (40) gives  
\[\prod\limits_{i=1}^{12}\left(\sum\limits_{j=1}^{2}d_{i,j}\lambda_{j}\right)= (2\lambda_{1}+2\lambda_{2})^{8}(2\lambda_{1})^{2}(2\lambda_{2})^{2}. \tag{41}\]Hence, the multi-homogenous Bezout number of the system, the coefficient of the term \(\lambda_{1}^{6}\lambda_{2}^{6}\) in Equation (41), can be obtained by using the binomial theorem as \((2^{8}!/4!4!)2^{2}2^{2}=286,720\), which agrees with the results reported by [PERSON] et al. [20] for the synthesis of the Watt II six-link function generator with revolute joints only for 9 precision positions.  
If other unknowns than \(r_{1x}\) and \(r_{1y}\) are selected as the free choices, the number of auxiliary equations to convert the system into a homogenous structure increases and 2-homogenous Bezout number becomes higher. For example, if \(r_{2x}\) and \(r_{2y}\) are assumed as the free choices, it is necessary to define 6 auxiliary equations and the 2-homogeneous Bezout number is \((2^{8}!/4!4!)2^{4}2^{2}=1,146,880\). Therefore, it is important to select the unknown that minimize the number of auxiliary equations as the free choices. In this case, it is efficient to assume \(r_{1x}\) and \(r_{1y}\) as the free choices to reduce the 2-homogeneous Bezout number in consideration of the calculation time.
## 4 Performance Metrics Study
Hardware capacity (bottlenecks): when some component of the network cannot assume a certain amount of traffic, thus encouraging data flow. Congestion in the network: when a device is at 100% capacity, generating a queue waiting to be discarded after a particular time if it is a long time, the difference with bottlenecks is that it can be a general problem and not just a device. Bugs in the software of network devices: When the software of the devices has errors. Other: interference or \"noise\" in the wireless network, proximity to other wireless devices, distance, physical elements (walls), etc. Then, the packet loss leads to the following problems: Information out of date (delay in the information received), slow loading, interruption of loads, closure of connections, incomplete information.
###### Abstract  
This study introduces a novel technique called Synchronized Multi-Augmentation (SMA) combined with multi-backbone (MB) ensembling to enhance model performance and generalization in deep learning (DL) tasks in real-world scenarios. SMA utilizes synchronously augmented input data for training across multiple backbones, improving the overall feature extraction process. The outputs from these backbones are fused using two distinct strategies: the averaging fusion method, which averages predictions, and the dense fusion method, which averages features through a fully connected network. These methods aim to boost accuracy and reduce computational costs, particularly in Edge Intelligence (EI) systems with limited resources. The proposed SMA technique was evaluated on the CIFAR-10 dataset, highlighting its potential to enhance classification tasks in DL workflows. This study provides a comprehensive analysis of various backbones, their ensemble methods, and the impact of different SMAs on model performance. The results demonstrate that SMAs involving color adjustments, such as contrast and equalization, significantly improve generalization under varied lighting conditions that simulated real-world low-illumination conditions, outperforming traditional spatial augmentations. This approach is particularly beneficial for EI hardware, such as microcontrollers and IoT devices, which operate under strict constraints like limited processing power and memory and real-time processing requirements. This study's findings suggest that employing SMA and MB ensembling can offer significant improvements in accuracy, generalization, and efficiency, making it a viable solution for deploying DL models on edge devices with constrained resources under real-world practical conditions.  
deep learning; classification; deep neural network; MobileNetV3; multi-backbone; multi-backbone ensembling; data augmentation; synchronized multi-augmentation; data fusion; CIFAR10; edge computing; edge intelligence +
Footnote †: journal:  
Article
# Joint Resource Allocation for Multiuser Opportunistic Beamforming Systems with OFDM-NOMA
## 4 Problem Solution
### Subcarrier Scheduling and User Pairing  
In this subsection, we discuss subcarrier scheduling and user pairing scheme, when the power allocation result is fixed. The optimization problem (24) can be rewritten as  
\[\max_{\vartheta,k_{\text{xc}}}D_{2} =\sum_{n_{c}=0}^{N_{c}-1}\left[\log_{2}\left(1+\frac{P_{1_{\text {xc}}}\left|I_{n_{c},k_{\text{xc}}}\right|^{2}}{P_{2_{\text{xc}}}\left|I_{n_{c},k_{\text{xc}}}\right|^{2}+\sigma^{2}}\right)+\log_{2}\left(1+\frac{P_{2_{ \text{xc}}}\left|I_{n_{c},k_{\text{xc}}}\right|^{2}}{\sigma^{2}}\right)\right],\] (31) s.t. \[\mathcal{C}4:(1_{n_{c}},2_{n_{c}})\in\mathbb{U}_{s}.\]  
Since the number of subcarriers \(N_{c}\), the dimensions of \(\vartheta\) and the elements of \(\mathbb{U}_{s}\) are finite, an exhaustive searching algorithm can be applied to achieve the optimal solution of (31), as shown in Algorithm 1.  
According to Algorithm 1, the optimal subcarrier scheduling \(k_{n_{c}}\) and user pairing \(\vartheta\) scheme is determined.  
```
1:Input:\(N_{c}\), \(U\), \(I_{n_{c},k_{n_{c}}}\left|{}_{2}^{2}\), \(P_{1,n_{c}}\) and \(P_{2,n_{c}}\).
2:Output: Subcarrier mapping \(\vartheta\) and user pairing \(k_{n_{c}}\).
3:Initialize: \(n_{c}=0\) and \(i=1\), where \(i\) denotes the \(i\)th element of \(\mathbb{U}_{s}\);
4:while\(n_{c}\in[0,N_{c}-1]\)do
5:while\(i\in[1,U\cdot(U-1)]\)do
6: Select the \(i\)th element of \(\mathbb{U}_{s}\);
7: Determine the \(1_{n_{c}}\)th and \(2_{n_{c}}\)th users;
8: Calculate \(E_{\text{total}}\) as (16);
9: Record \(E_{\text{total}}\), subcarrier scheduling \(\vartheta\) and user pairing \(k_{n_{c}}\);
10:\(i=i+1\);
11:endwhile
12:\(i=1\);
13:\(n_{c}=n_{c}+1\);
14:endwhile
15:Search the maximum \(E_{\text{total}}\) and corresponding subcarrier scheduling \(\vartheta\) and user pairing \(k_{n_{c}}\).
```  
**Algorithm 1**Subcarrier scheduling and user pairing scheme.
# Deep Learning-Based Flap Detection System Using Thermographic Images in Plastic Surgery
## 2 Materials and Methods
### Neural Network
#### 2.4.2 Bottleneck  
Before reaching the bottleneck of the AER2U-Net model, the input feature maps undergo extensive downsampling and feature extraction through the encoding path, which includes four R2 CLs and max-pooling layers. These feature maps, which are rich in information but have a lower spatial resolution, enter the bottleneck, where they are further processed by the ASPP module. The atrous convolution, integral to the ASPP module, makes it possible to arbitrarily increase the field-of-view of the filters in each layer of a deep convolutional neural network (CNN). State-of-the-art deep CNNs typically use spatially small convolution kernels (typically 3 \(\times\) 3) to keep both the computation and the number of parameters within limits. Atrous convolution with rate \(r\) introduces \(r-1\) zeros between consecutive filter values of a \(k\)x\(k\) filter to \(k_{e}=k+(k-1)(r-1)\) without increasing the number of parameters or computational complexity. This method provides an efficient mechanism for controlling the field-of-view that strikes an optimal balance between accurate localization (small field-of-view) and context assimilation (large field-of-view). In the ASPP layer, multiple atrous convolutions with different rates are applied in parallel to the input feature maps, allowing the network to capture information at various scales. This parallel processing produces a rich multi-scale feature representation, which is then concatenated and further refined. The result is a set of feature maps that integrate both local and global context and improve the network's ability to accurately segment flaps of different sizes and in different contexts. Following the ASPP module, these enriched feature maps pass through the DAM, introduced by [PERSON] et al. [34], which adaptively integrates local semantic features using the self-attention mechanism. More specifically, it uses a position attention module and a channel attention module to capture global dependencies in the spatial and channel dimensions, respectively.  
This comprehensive processing within the bottleneck ensures that the subsequent decoding path receives highly informative and context-aware features, ultimately leading to more accurate and robust segmentation results. The enriched features, having been refined by both ASPP and the DAM, provide the decoder with a balanced and detailed representation and improve the network's ability to accurately segment flaps in complex thermographic images.
## Materials and Methods
### Preparation of nZVI@ATP and nZVI  
ATP (200-mesh sieved) and FeSO\({}_{4}\cdot\)7H\({}_{2}\)O were dissolved in a three-necked flask with 100 mL of deionized water at different mass ratios of iron to ATP (3:1, 2:1, 1:1, 1:2, and 1:3) and stirred for 2 h, followed by the addition of 100 mL of anhydrous ethanol and a 30 min stirring. Subsequently, 400 mL of a 0.5-mol/L NaBH\({}_{4}\) solution was immediately added at a rate of 1-2 drops/s, and nZVI@ATP was obtained after centrifugation. Further, the nZVI@ATP was washed thrice with ethanol and deionized water and dried under vacuum at 40 \({}^{\circ}\)C for 6 h. This procedure was conducted in a N\({}_{2}\) atmosphere, and the nZVI@ATP was stored under a vacuum seal in a refrigerator freezer layer for backup. The preparation process of the nZVI was the same as the process mentioned above, with the only difference being the absence of ATP. The reaction equation is shown in Equation (1).  
\[\mathrm{Fe}^{2+}+2\mathrm{BH}_{4}{}^{-}+6\mathrm{H}_{2}\mathrm{O}\rightarrow \mathrm{Fe}^{0}+2\mathrm{B}(\mathrm{OH})_{3}+7\mathrm{H}_{2} \tag{1}\]
# Damage Identification Method Using Additional Virtual Mass Based on Damage Sparsity
## 4 Numerical Simulation of Simply Supported Beam and Space Truss  
### Simply Supported Beam Model  
#### 4.1.1 Model and Damage Scenario  
Because of the shortcomings of three traditional structural damage identification methods based on sparsity, that is, the OMP method, Lasso regression model with \(l_{1}\) norm, and ridge regression model with \(l_{2}\) norm, the IOMP method was developed in this study to minimize the shortcomings. These four methods were combined with the additional virtual mass method described in Section 2 to assess the structural damage in multi-damage scenario. A numerical simulation was conducted using to verify and analyze the advantages and disadvantages of the above four methods.  
A simply supported steel beam was used as the experimental model. The elastic modulus and density of the steel were 210 GPa and \(7.85\times 10^{3}\) kg/m\({}^{3}\), respectively. The section width, height, and span of the beam were 500 mm, 80 mm, and 1 m, respectively. The structure was divided into 10 substructures, each of which was divided into two units, consisting of a total of 20 units. The finite element model is shown in Figure 1. There were multiple damage cases, indicating 70%, 80%, and 60% residual stiffness in substructures 3, 5, and 8, respectively.
# Enhanced Atrous Extractor and Self-Dynamic Gate Network for Superpixel Segmentation
## 3 Methods
### Overall Architecture  
To fix the problems of under-segmentation and low compactness, we design EAGNet, an enhanced atrous extractor and self-dynamic gate network. This is shown in Figure 1. First, the original input \(I\) is fed into several CNN blocks to extract the pixel feature. Then,  
Figure 1: The image is divided into 16 \(\times\) 16 grids and we compute the associate map between the pixel and surrounding nine grids.  
we concatenate them to obtain the multi-scale pixel feature. And we introduce the enhanced atrous extractor to extract the multi-scale superpixel feature. After that, we split the multi-scale superpixel feature in the channel dim to obtain the superpixel feature of different scales. Finally, we concatenate the pixel and superpixel feature to obtain the pixel-superpixel relationship information of different scales and concatenate them for the final prediction. The whole feedforward process of the overall architecture can be written as:  
\[p_{1},p_{2},p_{3},p_{4}=Backbone(I) \tag{6}\]  
\[p_{m}=concat(p_{1},p_{2},p_{3},p_{4}) \tag{7}\]  
where \(p_{1},p_{2},p_{3},p_{4}\) is the pixel feature of different scales. \(Backbone(.)\) is the CNN backbone and \(I\) is the input. \(p_{m}\) is the multi-scale pixel feature and \(concat(.)\) stands for the concatenate operation.  
\[s_{m}=EAE(p_{m}) \tag{8}\]  
\[s_{1},s_{2},s_{3},s_{4}=split(s_{m}) \tag{9}\]  
where \(EAE(.)\) is the enhanced atrous extractor, and \(p_{m}\) is the multi-scale pixel feature. The \(s_{m}\) is the multi-scale superpixel feature. \(s_{1},s_{2},s_{3},s_{4}\) is the superpixel feature of different scales. The \(split(.)\) stands for the operation of splitting in the channel dim.  
\[f_{sp}^{i}=G(p_{i},s_{i})\{i=1,2,3,4\} \tag{10}\]  
\[F_{m}=concat(f_{sp}^{1},f_{sp}^{2},f_{sp}^{3},f_{sp}^{4}) \tag{11}\]  
\[Q=Predict(F_{m}) \tag{12}\]  
where \(i\) is the number of different scales. \(f_{sp}^{i}\) means the fused feature. \(p_{i}\) and \(s_{p}\) stand for the pixel and superpixel features of different scales. \(G\) means our proposed self-dynamic gate. The \(Concat()\) means the concatenate operation. \(F_{m}\) is the multi-scale pixel-superpixel relationship information. \(Q\) is the associate map that reconstructs the property vector. The \(Predict()\) means our segmentation head. Then, we introduce the detail information of the different parts of EAGNet.