esa-sceva/satcom-chunk-collection huggingface dataset:
# Semantic Segmentation with High-Resolution Sentinel-1 SAR Data
## 4 Experiments and Models
### Training  
Models used in this study were implemented using MMSegmentation [39] and trained on a single GTX 1080 TI with 11 gigabytes of VRAM. MMSegmentation is a framework based on the PyTorch library, and its toolbox is open-source and appropriate for segmentation. This framework represents various semantic segmentation methods. One can construct customized datasets and framework by uniting diverse modules. The training speed is outstanding. For training, 80% of our noise-free newly-created dataset images were used, and the rest of the images were used for testing. Data augmentation was performed and input data was normalized on the training set. Flipping training images horizontally and vertically is used for data augmentation. A drop in our performance was noticed when pre-trained models, which MMSegmentation incorporates, were used in our models. The ADAM optimizer was used, with a learning rate 0.003 and an exponential learning rate decay of 0.90 applied after each iteration. Each model was trained for 80 k iterations and, during this training phase, the best model's checkpoint was preserved and then used to evaluate the test set.  
We used mIoU, _MA_, and _PA_ for the evaluation, as shown in Equations (2) and (3) [27]. IoU is a robust metric for the assessment of semantic segmentation; it shows performance of the overlapping ratio between intersection and all surface data (union). IoU is calculated as shown in Equation (1). FP and TP stand for false positive and true positive, and FN means false negative.  
\[\text{IoU}=\frac{\text{TP}}{\text{TP}+\text{FP}+\text{FN}} \tag{1}\]  
\[PA=\frac{\sum_{i}n_{ij}}{\sum_{i}\sum_{j}n_{i,j}},MA=\frac{1}{C}\sum_{i}\frac{ n_{i,i}}{\sum_{i}j} \tag{2}\]  
\[\text{mIoU}=\frac{1}{C}\sum_{i}\frac{n_{i,i}}{\sum_{j}n_{i,j}+\sum_{j}n_{i,j }+n_{i,j}} \tag{3}\]  
where \(n_{i,i}\): number of pixels, \(i\): target class, \(j\): predicted class, C: number of classes, IoU: intersection over union, _PA_: pixel accuracy, _MA_: mean accuracy, and mIoU: mean intersection over union.
# Bearing Fault Diagnosis Method Based on Improved VMD and Parallel Hybrid Neural Network
## 2 Model Construction Principle
### Reweighted Kurtosis Method  
Kurtosis is a statistic that describes the steepness or smoothness of the data distribution. It measures the relative size of the peak and tail of the data distribution and is an important indicator to describe the data distribution. With the gradual wear of rotating machinery and equipment, the impact pulse in the signal will increase, and the kurtosis will also increase [19]. Kurtosis is defined as the normalized fourth-order center distance, and the formula is as follows (3). Since the signal to be processed in this paper is a fault signal and its fault characteristics are mainly abnormal frequency and signal pulse, it is reasonable to take kurtosis as the evaluation index of the bearing fault signal, but at the same time, there is a problem with this: the calculation of the kurtosis value is very sensitive to outliers, which may be affected by the strong impact of some parts of the signal, resulting in a decline in accuracy. Therefore, this paper uses a reweighted kurtosis value instead of the kurtosis value as the evaluation index of the VMD reconstructed signal. Its advantage is that it can more effectively eliminate part of the interference while retaining the impact part of the fault signal [20; 21]. The specific implementation steps are as follows:  
First, the reconstructed signal is divided into n equal parts.  
Secondly, the kurtosis value of each segment of the signal is calculated, respectively.  
\[K=\frac{1}{N}{\sum_{k=1}^{N}}{\left(\frac{x_{i}-\overline{x}}{\sigma}\right)^{4}} \tag{3}\]  
where \(Ku\), \(\overline{x}\), \(\sigma\), respectively, represent the kurtosis value, average value, and standard deviation of the signal.  
Third, the calculated kurtosis values are arranged in ascending order and converted into row vectors.  
Fourthly, the weight corresponding to the kurtosis value is calculated.  
\[W_{i}=\frac{K_{i}}{\sum_{k=1}^{N}K_{i}} \tag{4}\]  
Fifthly, the calculated n weights are arranged in descending order and converted into row vectors.  
\[RK=K\cdot(W)^{T}. \tag{5}\]
# Transmission Tower Re-Identification Algorithm Based on Machine Vision
## 2 The Proposed Method
### Transmission Tower matching Network
#### 2.3.1 Matching Network Architecture  
The matching network framework in this paper is based on the architecture of the FaceNet [35]. When two images are inputted, the matching network maps them into feature vectors, as illustrated in Figure 5. Each image, upon processing through the backbone, yields a feature map. The feature map undergoes average pooling (AVG) and L2 normalization (L2) to obtain a k-dimensional feature vector. 'k' represents the number of channels in the feature map obtained after the image passes through backbone. This k-dimensional feature vector is mapped to a d-dimensional feature vector (features1) through the fully connected layer FC1, and then mapped to an m-dimensional feature vector (features2) via the fully connected layer FC2. In this paper, we implemented three distinct networks as alternatives for the backbone of the matching network. These networks are MobileNet [41], MobileViT [42], and Inception-ResNet-v1 [43]. MobileNet (k = 1024) is a convolutional neural network devoid of residual structures. MobileViT (k = 320) is founded on the Vision Transformer architecture, and Inception-ResNet-v1 (k = 1792) is a convolutional neural network equipped with residual structures.  
During the matching process, the network directly maps the input image to features1, facilitating subsequent identity matching for the transmission tower. Our approach to train the matching network involved the utilization of triplet loss, in conjunction with cross-entropy loss. Furthermore, we introduced a method for optimizing the generationsequence of triplets to enhance the training efficiency of the network. Detailed explanations of these training techniques are comprehensively outlined in Section 2.3.5.
# Analysis and Demonstration of First Cross-Support Interferometry Tracking in China Mars Mission
## 2 Theorem and Methods
### Algorithm Denolping and Analysis
Secondly, after obtaining the delay model and residual phase of each station receiving signal, difference the two stations' delay model and residual phase. Finally, multi-tones are used to compute the group delay. With a priority delay model used to resolve the constant value difference in the two stations' delay model, a narrow spanned bandwidth is then induced for integer cycle ambiguity resolution, and ultimate delay observable with high measurement accuracy is obtained with the widest spanned bandwidth.
## 1 Introduction
In the method, the optimal frequency scale factor inferred from the anthropometric parameters was applied to the known HRTFs to obtain the approximation of the individualized HRTFs. Generally, the current mainstream method is to use correlation analysis and multiple linear regression analysis to find anthropometric parameter sets that are closely related to HRTFs [18; 19; 20; 21]. Data analysis methods have also been employed in the individualization of HRTFs. [PERSON] et al. synthesized HRTFs using a sparse representation with different pre-processes and post-processes trained from anthropometric parameters [22]. Furthermore, the radial basis neural network has been utilized in HRTF estimation based on anthropometric parameters and achieved promising performance [23]. [PERSON] et al. proposed a sparsity-constrained weight mapping to individualize HRTFs, which obtained optimal weights to combine HRTFs based on the relationships of anthropometric parameters between the trained subjects and the target subject [24]. [PERSON] et al. used aversive feature elimination support vector machine (RFE-SVM) to select anthropometric parameters [25]. Although the abovementioned studies have conducted beneficial research on HRTF customization, more sufficient methods are needed to customize HRTFs according to the essence of HRTF characteristics. In this work, a hybrid algorithm based on anthropometric parameters is proposed. In the method, decomposition of HRTFs is first performed using independent component analysis (ICA) to explore the independent characteristics of HRTFs. Then, the selection of key anthropometric parameters is implemented using a factor analysis to detect the inner structure among the anthropometric parameters. Finally, the establishment of a prediction formula is accomplished using multiple linear regression. In this study, median-plane HRTF prediction in the CIPIC database (Center for Image Processing and Integrated Computing, University of California) is used as an illustrative example. The rest of the paper is organized as follows. Section 2 discusses in detail the principle and effect of the proposed hybrid algorithm based on anthropometric parameters; Section 3 validates the effectiveness of the proposed algorithm using objective spectral distortion criteria and a subjective listening experiment. Section 4 summarizes the whole work.
## 5 Heuristic Scheduling Method  
### Framework of the Heuristic Scheduling Method  
The flowchart of the heuristic scheduling method is summarized in Figure 1. Based on the predicted processing time outlined in Section 4, a robust initial scheduling plan is generated using DMOGWO considering three optimization objectives. Then, during the implementation of the schedule, the actual processing time of each operation is only determined when the operation is about to begin, bringing about a deviation in the estimated processing time. Consequently, a scheduling decision should be made at that time to make adjustments as the reaction to the change in processing time. In this study, a hybrid heuristic scheduling method that combines three adjustment strategies is proposed according to different scenarios with the aim of ensuring both the scheduling efficiency and robustness in uncertain production environments. The relevant description is as follows:Strategy 1: If the processing time of the current operation is reduced or remains unchanged, no rescheduling is triggered. Instead, a left-shift adjustment is applied within the current factory to locally optimize the schedule.  
Strategy 2: If the processing time of the current operation increases, the first, the binary tree [32] for the subsequent operations corresponding to the current operation is constructed. In this binary tree, the left node stores the subsequent processes belonging to the same workpiece, while the right node stores the subsequent processes allocated in the same machine. A local adjustment strategy based on insertion is applied to all operations in the tree. The new makespan in this factory is then calculated. The critical factory with the maximum makespan in the current schedule plays a decisive role in improving the overall scheduling efficiency. If the new makespan is smaller than that of the critical factory, no rescheduling is required.  
Strategy 3: If the new makespan exceeds that of the critical factory, a DMOGWO-based global rescheduling method is triggered to schedule a new DFISP. At each rescheduling point, the operations that are completed and being processed on the machine in the original schedule remain unchanged, while the remaining unprocessed operations are reassigned to new machines for sequencing to achieve global optimization.
## 4 Platform Design
### ToU Ranges Using GRU Model Predictions and K-Mean Clustering
#### 4.1.2 Definition of ToU Ranges Using On-Chain Clustering
```
1:\(sumX1\gets 0\)
2:\(sumX2\gets 0\)
3:\(sumY1\gets 0\)
4:\(sumY2\gets 0\)
5:\(TemArray\gets 0\)
6:\(ClusterSize\gets 0\)
7:\(Cluster2 Size\gets 0\)
8: Centroid NewC2
9:for\(i\gets 0\) to 24 do
10:if clusters[i] = 1 then
11:\(sumX1\leftarrow\)X[i]
12:\(sumY1\leftarrow\)Y[i]
13:\(Cluster1 Size\gets Cluster1 Size+1\)
14:elseif clusters[i] = 1 then
15:\(sumX2\leftarrow\)X[i]
16:\(sumY2\leftarrow\)Y[i]
17:\(Cluster1 Size\gets Cluster1 Size+1\)
18:endif
19:endfor
20:\(NewC1\leftarrow\left\{\frac{sumX1}{ClusterSize},\frac{sumY1}{ClusterSize}\right\}\)
21:\(NewC2\leftarrow\left\{\frac{sumX2}{Cluster2 Size},\frac{sumY2}{Cluster2 Size}\right\}\)
22:tempArray.push(NewC1)
23:tempArray.push(NewC2)
24:return TempArray
```  
**Algorithm 3** SetNewCentroids  
```
1:\(TemArray1\gets SetCluster(InitC1,InitC2)\)
2:\(TemArray2\leftarrow[]\)
3:\(NewC1\gets GetNewCentroids()[0]\)
4:\(NewC1\gets GetNewCentroids()[1]\)
5:\(Exit\gets False\)
6:while!Exitdo
7:\(TemArray2\gets SetCluster(InitC1,InitC2)\)
8:if\(TemArray2!=TemArray1\)then
9:\(Exit\gets true\)
10:else
11:\(TemArray1\gets TempArray2\)
12:\(NewC1\gets GetNewCentroids()[0]\)
13:\(NewC2\gets GetNewCentroids()[1]\)
14:\(TempArray2\gets SetCluster(NewC1,NewC2)\)
15:endif
16:endwhile
```  
**Algorithm 4** Main  
As can be observed from Table 1, there is a slightly higher value in deploying K-mean smart contract, and a much bigger gas consumption in calling K-mean Smart Contract main function compared to calling purchase function [65]. Calling K-mean Smart Contract is 160 times costlier. However, K-mean clustering is only called once in each trading round, which makes its gas cost reasonable and its implementation feasible and practical, due to its low invoking frequency.
# Influences of High-Speed Train Speed on Tunnel Aerodynamic Pressures
## 2 Numerical Simulation
### Grid Generation  
The surface meshes of train nose and tunnel portal are shown in Figure 6. The hexahedral mesh is adopted in grid generation. The quality of the near-wall mesh can be evaluated by the dimensionless distance y\({}^{+}\). The y\({}^{+}\) values of train surface and tunnel  
Figure 4: Boundary conditions.  
Figure 5: Schematic diagram of information exchange on common interface of sliding mesh method.  
wall in our simulation are controlled between 30 and 180 for the train speed varies from 275 km/h to 400 km/h, which meet the requirements of the standard [35]. The elements nearby the train streamline nose are refined manually as these regions have significant influences on the simulation results.  
To study the influences of mesh density on the calculation results, two kinds of mesh densities, coarse mesh (16 million) and fine mesh (20 million), are used. In our simulation, the train speed and the tunnel length are 350 km/h and 1000 m, respectively. The comparations of aerodynamic pressure time histories calculated by coarse mesh and fine mesh are shown in Figure 7. The differences between the results obtained by the two mesh densities are negligible. The difference between the maximum positive peaks (maximum negative peaks) of these two mesh densities is only 1.0% (0.2%). Thus, the coarse mesh density is fair enough, and is used in the subsequent numerical simulations.  
Figure 6: Surface meshes around train nose and tunnel portal. (**a**) Surface mesh of train nose; (**b**) Surface mesh of tunnel portal.  
Figure 7: Aerodynamic pressure time histories obtained by coarse mesh and fine mesh.
# Asymmetric Measurement-Device-Independent Quantum Key Distribution through Advantage Distillation
###### Abstract  
Measurement-device-independent quantum key distribution (MDI-QKD) completely closes the security loopholes caused by the imperfection of devices at the detection terminal. Commonly, a symmetric MDI-QKD model is widely used in simulations and experiments. This scenario is far from a real quantum network, where the losses of channels connecting each user are quite different. To adapt such a feature, an asymmetric MDI-QKD model is proposed. How to improve the performance of asymmetric MDI-QKD also becomes an important research direction. In this work, an advantage distillation (AD) method is applied to further improve the performance of asymmetric MDI-QKD without changing the original system structure. Simulation results show that the AD method can improve the secret key rate and transmission distance, especially in the highly asymmetric cases. Therefore, this scheme will greatly promote the development of future MDI-QKD networks.  
quantum key distribution; asymmetric MDI-QKD; advantage distillation technology +
Footnote †: journal:  
0023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
## 3 Results and Discussion
### Analysis of Deposited Zinc Mass and Zinc Coating Quality
#### 3.2.1 Electrolytic Zinc Coating
This final phase of the study confirmed the presence of a continuous and homogeneous zinc coating not only on the reference (untreated) substrate but also on substrates blasted with steel grit and ground surfaces. In contrast, the zinc coating on the synthetic corundum-blasted substrate exhibited a higher occurrence of microcracks, predominantly located within surface depressions formed by the impact of angular abrasive particles [6; 10]. This irregular surface microgeometry poses challenges for uniform coating deposition and may also reduce the effectiveness of chemical pre-treatment [9]. Additionally, increased surface roughness has been associated with a higher tendency for corrosive agents to adhere, as reported in [38; 39]. The observed differences in coating quality between synthetic corundum-blasted and ground substrates, despite the use of a comparable abrasive material, can be attributed to the fundamental differences in the mechanisms by which these mechanical pre-treatment methods interact with the steel surface [20]. Figure 10: Surface roughness parameters Ra and Rz before and after electrolytic zinc coating (arithmetic mean \(\pm\) standard deviation; immersion time: 4500 s).
# Energy Efficient Routing Protocol in Sensor Networks Using Genetic Algorithm
## 1 Introduction
In this paper, we propose an energy-efficient multi-path algorithm called genetic algorithm-based AOMDV (GA-AOMDV). Fitness function (FF) selects the route with nodes that maintains the least consumed energy utilizing routes selected by the AOMDV routing protocol. In other words, AOMDV returns multiple paths with the minimum number of hops. Next, FF is applied on the AOMDV routes to optimize the route going through nodes with the greatest residual energy. Our algorithm is a multi-path so, in case of nodes failure, alternative routes are available.
## 3 Numerical Results and Discussion
### Verification of Convergence and Correctness  
Here, we validate the convergence and accuracy of the previously established model of the coupled system of the composite laminated rotationally stiffened plate and acoustic cavities. The convergence of the coupled system primarily depends on the truncation values of the composite laminated rotationally stiffened plate (\(M_{\rm p}\), \(N_{\rm p}\)) and the cavities (\(M_{\rm c}\), \(N_{\rm c}\), \(Q_{\rm c}\)). Table 3 provides the natural frequencies of the coupling system among the composite laminated rotationally stiffened plate and cylindrical-cylindrical cavities under various plate and cavity truncation conditions obtained using the present method. These results are compared with finite element simulation results [28]. The boundaries of the composite laminated rotationally stiffened plate are set to SSSS, the number of stiffeners (\(n\)) is 1, and the coupling system's geometric parameters are as follows: \(R_{1}\) = 0.4 m, \(R_{2}\) = 1.2 m, \(L_{1}\) = 0.5 m, \(L_{2}\) = 1 m, \(h_{\rm p}\) = 0.02 m, \(\theta\) = 90\({}^{\circ}\), \(R_{\rm b1}\) = 0.8 m, \(b_{1}\) = 0.06 m, and \(h_{1}\) = 0.04 m. The material of the laminated plate is graphite epoxy resin with a layer angle of \([0^{\circ}/90^{\circ}]\), the material of the laminated beams is Q235 steel, and the medium in the cavities is air. From Table 3, it is evident that when \(M_{\rm c}\)\(\times\)\(N_{\rm c}\)\(\times\)\(Q_{\rm c}\) is 3 \(\times\) 3 \(\times\) 3 and \(M_{\rm p}\)\(\times\)\(N_{\rm p}\) is 18 \(\times\) 18, the natural frequencies of various modes have essentially converged. Compared with finite element simulation results, the maximum error in the natural frequencies of all modes is less than 2.12%. The size and coordinate system used in the finite element model are the same as that of the model established in this paper, the mesh size is 0.02 of the global size, and the element shape of the sound cavity is the AC3D20:20 node-acoustic quadric hexahedron element. The shape of the composite structural element is C3D20R, the twenty-node hexahedron element. The natural frequencies of the coupling system among the composite laminated rotationally stiffened plate and the cylindrical-spherical cavities under different plate truncation values and cavity truncation conditions obtained using the present method and the finite element method are shown in Table 4. In this case, the boundaries of the composite laminated rotationally stiffened plate are scheduled to SSSS, the number of stiffeners (\(n\)) is 1, and the coupling system's geometric parameters are as follows: \(R_{1}\) = 0.4 m, \(R_{2}\) = 1.2 m, \(\phi_{1}\) = 30\({}^{\circ}\), \(\phi_{2}\) = 90\({}^{\circ}\), \(L\) = 1.5 m, \(h_{\rm p}\) = 0.02 m, \(\theta\) = 90\({}^{\circ}\), \(R_{\rm b1}\) = 0.8 m, \(b_{1}\) = 0.06 m, and \(h_{1}\) = 0.04 m. The material of the laminated plate, laminated beams, layer angles, and the medium in the cavities are the same as in Table 3.
# Tailored Nanoparticles as Vaccine Components
## 3 Types and Uses of Nanoparticles in Vaccines  
The nanoparticles currently used in vaccine development can be loosely grouped into six categories (Table 1, Figure 2) [5; 35].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Particle Type** & **Characteristics** & **Size** \\ \hline Emulsions & Oil-in-water or water-in-oil. Used as adjuvarts in & 50–600 nm \\  & vaccine delivery. & \\ \hline ISCOMS & Made of saponin, phospholipids, and cholesterol. & 40–60 nm \\  & Cage-like particles. & \\ \hline Liposomes & Biodegradable lipidic NPs. Encapsulation and controlled & 200–1000 nm \\  & release of antigens. & \\ \hline Inorganic NPs & Controllable synthesis and rigid structure. & 2–150 nm \\  & Non- biodegradable & \\ \hline Polymeric NPs & Synthetic or natural polymers. Biodegradable. Allows & Variable \\  & controlled release of antigens & \\ \hline VLPs and & Self-assembling viral capsids lacking genetic material. & Variable \\ Self-assembled protein cages & Fold into complex quaternary structures. & 10–40 nm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the six types of nanoparticles used in vaccine development, their main characteristics and sizes.
# Instantaneous Beam Prediction Scheme against Link Blockage in mmWave Communications
## 3 System Model
### Blockage Model

Table 4: List of notations for the mmWave channel.