esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Privacy-Preserving Smart Metering with Authentication Capability
### Primitive Functions  
#### 4.1.1 Symmetric Homomorphic Encryption  
To tackle the authentication issue introduced by previous aggregation schemes that use [PERSON]'s asymmetric homomorphic encryption [9] in which signature generation and verification coud not be integrated into an aggregation scheme without revealing individual (_i.e._, not aggregated) metering data to the concentrator, we exploit symmetric additively homomorphic encryption [37]. This encryption algorithm is a slightly modified version of a stream cipher, replacing the exclusive-OR operation with modular addition. It is provably secure and allows the efficient aggregation of encrypted data. It is composed of the following two algorithms: \(Enc\) and \(Dec\).  
1. **Encryption.** Given integer message \(m\) and randomly generated key stream \(k\), for \(m,k\in[0,M-1]\) where \(M\) is a large integer, the algorithm computes \(c=Enc_{k}(m)=m+k\) (mod \(M\)).
2. **Decryption.** Given ciphertext \(c\) and key \(k\), the algorithm computes \(Dec_{k}(c)=c-k\) (mod \(M\)).  
Additive homomorphism can be achieved simply. Let \(c_{1}=Enc_{k_{1}}(m_{1})\) and \(c_{2}=Enc_{k_{2}}(m_{2})\). Then, \(c_{1}+c_{2}=Enc_{k_{1}+k_{2}}(m_{1}+m_{2})\) (mod \(M\)). In practice, if \(n\) different ciphertexts are added, \(M\) should be larger than \(\sum_{i=1}^{n}c_{i}\) for correct decryption. It is important to note that each ciphertext that needs to be added can be encrypted under different keys, as opposed to Paillier's encryption.  
**Theorem 1**.: _The above encryption scheme is perfectly secure (A cryptosystem is perfect secure if the a posteriori probability that the plaintext is x, given that the ciphertext y is observed, is identical to the a priori probability that the plaintext is x.)._  
Proof.: The homomorphic encryption described above is a simple generalization of the stream cipher, where bits are replaced by integers modulo \(M\). In terms of security, it has exactly the same properties as a stream cipher, that is, perfect secrecy if and only if the keystream is random, of the same length as the plaintext, and used only once. Thus, its security can be proven using a similar proof. The security relieson two features: the key changes from one message to another, and all of the operations are performed modulo integer \(M\).  
Intuitively, if we have \(\text{Pr}[Enc_{k}(m_{1})=c]=\text{Pr}[Enc_{k}(m_{2})=c]\) for every \(m_{1},m_{2}\) in the plaintext space, the encryption is perfectly secure because every plaintext has the same probability of being encrypted and results in a given ciphertext. The formal security proof is given in [37].
# 2D-Unitary ESPRIT Based Multi-Target Joint Range and Velocity Estimation Algorithm for FMCW Radar
## 5 Conclusions  
This paper proposes a 2D-Unitary ESPRIT-based multi-target joint range-velocity estimation algorithm for the FMCW radar. Firstly, the FMCW radar signal model and range-velocity estimation principle are analyzed. Then, the 2D-spatial smoothing technique is applied to preprocess the signal matrix to construct a 2D virtual array matrix. Finally, the 2D-Unitary ESPRIT algorithm is utilized in this virtual array matrix to obtain the paired range-velocity estimates of multiple targets. The simulation results show that the proposed algorithm has high accuracy in range and velocity estimation in the single-target scenario. In the multi-target scenario, the proposed algorithm has better pairing robustness than the existing algorithms. Moreover, the proposed algorithm has significantly better resolution than other algorithms when there are multiple targets with similar ranges or velocities, which fully reflects the superior performance of the proposed algorithm. The proposed algorithm enhances the range and velocity estimation performance of millimeter-wave FMCW radar, as well as improving its target discrimination capabilities, and thus can be applied to the fields of autonomous driving, target recognition, and related domains.  
Conceptualization, D.W. and H.Y.; methodology, D.W. and H.Y.; software, D.W.; validation, H.X. and W.Z.; formal analysis, D.W.; investigation, W.Z.; resources, H.X.; data curation, D.W. and H.X.; writing--original draft preparation, D.W.; writing--review and editing, D.W. and H.Y.; visualization, D.W.; supervision, H.Y.; project administration, W.Z.; funding acquisition, D.W. and H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was partly funded by the Special Projects for Key R&D Tasks in the Autonomous Region of Xinjiang Grant No. 2022B01009, and was partly funded by the Youth Innovation Promotion Association CAS from the Chinese Academy of Sciences.  
Not applicable.  
The data presented in this study are available on request from the authors.  
The authors declare no conflict of interest.
# Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification
## 3 Proposed Method
### Weight Adjustment
#### 3.2.2 Task Weight for Loss Calculation  
In a typical DL, loss is a metric that represents the difference between the model's predictions and the actual target. Minimizing this difference allows the model to learn the desired outcome more effectively. Loss is often calculated through an objective function (loss function), most commonly the cross-entropy, mean squared error, etc. In multi-task classification, the loss is different for each task, so it is necessary to calculate the loss for each task step by step and combine them effectively to obtain the final loss. Equations (2) and (3) indicate the method for accumulating losses in multi-task classification. In Equation (2), \(y_{i}\) is the model's predicted value, \(y_{i}\) is the actual value, and \(f_{i}\) is the objective function for task \(i\). After calculating the loss for each task, they are combined to obtain the final loss. In Equation (3), _Total Loss_ is the final loss, which is the aggregate of the losses from each task, \(N\) is the number of tasks, and \(\alpha_{i}\) is a weight that represents the relative importance of each task.  
\[L_{i}=W_{kj}\;\times f_{i}(y_{i}^{\prime},\;y_{i}) \tag{2}\]  
Figure 4: Structure of the proposed MTL.  
\[Total\ Loss=\sum_{i=1}^{N}\alpha_{i}\times L_{i} \tag{3}\]  
In MTL, performance and learning time can vary due to differences in the difficulty of each task. Typically, easier tasks converge quickly to achieve high accuracy, while more difficult tasks face complications in convergence and require more extensive training. Allocating equal weights to all tasks in MTL may not be appropriate, as it could lead to higher weights for easier tasks, diminishing the model's learning capacity for difficult tasks. Therefore, in MTL, it is essential to consider the difficulty of each task and assign appropriate weights. Equation (4) illustrates a method for determining the weights for each task in light of their respective difficulties.  
\[\alpha_{i}=\frac{E_{i}}{\sum_{i=1}^{N}E_{i}} \tag{4}\]  
In Equation (4), \(E_{i}\) represents the minimum number of epochs required to converge to performance \(\beta\). \(\beta\) is measured by accuracy and can be dynamically adjusted. However, continuous weight adjustments may decrease the model's stability and increase the risk of overfitting to specific tasks. Therefore, we set \(\beta\) to 90% through various experiments. For example, assuming that there are four tasks and it takes 5 epochs in task #1, 10 epochs in task #2, 15 epochs in task #3, and 20 epochs in task #4 to achieve 90% accuracy each, the weights are set to 0.1 (5/50), 0.2 (10/50), 0.3 (15/50), and 0.4 (20/50), respectively.
## 5 Numerical Simulation and Verification
### Simulation Investigation and Verification
#### 5.3.3 Investigation for Case 2  
The geometrical and physical parameters of the steel pipe used in case 2 are shown in Table 3. Similarly, the 3D finite element model was constructed in ABAQUS/Explicit and steps (1) to (3) were performed first. After the circumferential scanning simulations, 30 sets of data were obtained in total, then the original scanned image, the enhanced 2D scanned image, smoothed 2D scanned image and enhanced 2D scanned binary image shown in Figure 14a,b were constructed with the simulated signals. The circumferential and axial positions of the reflector were displayed in the images clearly. In order to determine the type of the reflector, steps (4) to (10) were carried out. The displacement loads of the Gabor pulse excitation signal \(S_{i}(n)\) with parameters (\(\sigma\), \(\mu\), \(\theta_{in}\), \(f_{c}\)) = (3e - 5s, 1.5e - 5s, \(\pi\)/2 rad, 64 kHz) were uniformly applied in the tangential direction of the excitation nodes to generate the T(0,1), \(S_{r}(n)\) were obtained through the receiving nodes.  
Through steps (6) and (7), \(\Psi_{1}\) and \(\Psi_{2}\) were calculated in MATLAB and shown in Figure 14c. The minimum values were \(D_{1}\) = min\(\{D(S_{i},S_{ri})\}\) = 31.65 and \(D_{2}\) = min\(\{D(\overline{\overline{S}_{i}},S_{ri})\}\) = 32.97, respectively. \(D_{1}\)\(<\)\(D_{2}\), therefore the front-edge of the reflector was a step down, indicating the reflector to be a defect feature. The values of the \(RICR\) was equal to 3.75 (\(RICR\)\(>\) 1.05), thus the judgement was reliable. Finally, the result was updated to the final 2D image, as shown in Figure 14d.  
Figure 14: _Cont._
# Bearing Fault Diagnosis Based on Improved Convolutional Deep Belief Network
## 2 Theoretical Background and Proposed Method
### Fault Diagnosis Model Based on Improved CDBN  
Based on the CDBN model in 2.2.2 2.2, we further improved the model according to two aspects: changes to the preprocessing stage and the introduction of an optimizer. First, in the preprocessing stage, we introduced a band-pass filter to process the original samples after fast Fourier transform (FFT) and folding. The center frequency of the band-pass filter is set to 50% of the bandwidth, and the passband width is set to 40% of the bandwidth. The spectrogram of the band-pass filter is shown in Figure 4. Second, during the model training phase, the Adam optimizer [20] is introduced, enabling the model to adjust to different learning rates for each parameter. For frequently changing parameters, smaller steps are applied, whereas larger steps are better for sparse parameters. This algorithm is used to further improve the model's convergence speed and to reduce the number of model errors.  
Figure 5 is the flowchart of the improved CDBN-based bearing fault diagnosis model proposed in this study. The input vibration signal is at a length of 1024. After FFT, it is normalized to between 0 and signals of different fault types and degrees of failure. Moreover, band-pass filtering is conducted to enhance texture features and reduce the noise effect. The CDBN model is a combination of two CRBM layers. For the first CRBM layer, the convolution kernel size is \(7\times 7\), the number of link weight matrices is 9, each size matches the size of the convolution kernel, the pooling kernel size is \(2\times 2\), and the maximum pooling method is used. The output of the first layer with a size of \(13\times 13\) is a feature map, and it is used as the input of the second CRBM layer. The second hidden layer adopts a size of \(9\times 9\) with 16 link weight matrices. The size of the second convolution kernel is \(5\times 5\), and the size of the second pooling kernel is \(2\times 2\). The proposed model also uses the maximum pooling method. At the end of the model, softmax is used for classification. The extracted features are mapped to 13 different types, divided according to fault types or degrees. Unlike the standard CDBN, before classification, the outputs of the first layer and second layer use feature fusion to further increase the diagnostic accuracy. Table 1 displays the specific parameters of the network.  
Figure 4: The spectrogram of a band-pass filter.
## 3 Methodology
### Block Area Calculation  
The in situ block area distributions of the non-persistent fractures in the DFNs were calculated as followed:  
First, the coordinates of each intersection between two fracture traces (lines) are determined by the MATLAB function _lineSegmentInterest_ (Ref. [49], Figure 2).  
The \(X\) and \(Y\) coordinates of the intersection, \(X_{I}\) and \(Y_{I}\), are given by:  
\[X_{I}=X_{A,begin}+u_{A}\cdot\left(X_{A,end}-X_{A,begin}\right) \tag{8}\]  
\[Y_{I}=Y_{A,begin}+u_{A}\cdot\left(Y_{A,end}-Y_{A,begin}\right) \tag{9}\]  
where \(X_{A,begin}\), \(X_{A,end}\), \(Y_{A,begin}\), and \(Y_{A,end}\) are the \(X\) and \(Y\) coordinates of the beginning and termination of fracture \(A\), and the slope, \(u_{A}\), is given by:  
\[u_{A}=\frac{\left(X_{B,end}-X_{B,begin}\right)\cdot\left(Y_{A,begin}-Y_{B,begin }\right)-\left(Y_{B,end}-Y_{B,begin}\right)\cdot\left(X_{A,begin}-X_{B,begin }\right)\right. \tag{10}\]  
In the next step, the lines are checked if they share an adjacency matrix (e.g., see [50]) by using the built-in MATLAB function _adjacency_. Points or line segments that are not part of any adjacency matrix are removed for further calculation steps.  
For each of the adjacency matrices, closed polygons are determined, which result from crossing the fracture traces (i.e., lines). Closed polygons, which are formed from the combination of more than one other closed polygons, are excluded, i.e., only the smallest possible polygons are counted.  
Next, the surface area, \(A\), of these polygons are then calculated by the built-in MATLAB function _polyarea_ according to:  
\[A=\left[(X_{1}+X_{2})\cdot(Y_{1}-Y_{2})+(X_{2}+X_{3})\cdot(Y_{2}-Y_{3})+ \cdots+(X_{n}+X_{1})\cdot(Y_{n}-Y_{1})\right]/2 \tag{11}\]  
where \(X\) and \(Y\) are the coordinates of the vertices and \(n\) is the number of vertices.  
When the surface areas of all polygons of an adjacency matrix are calculated, this procedure is repeated for the next adjacency matrix until no more exist.  
This algorithm produces a list of block areas of all polygons, whereby the total sum of all block areas is equal to the area of the study region, herein assumed as 50 m \(\times\) 50 m (i.e., 2500 m\({}^{2}\)). Only the smallest possible polygons are counted in this way.
## 4 Materials and Methods
### Soybean TPS Cloning and Sequences Analysis  
Specific primers were designed to clone the soybean _GmOCS_, and the PCR result was cloned into the _pESI-T_ vector provided in Hieff Clone(r) Zero TOPO-TA Cloning Kit (Yeasen Biotech Co., Ltd., Shanghai, China) for sequencing confirmation. Soybean and _Arabidopsis_ TPSs were subjected to the online Clustal Omega service ([[http://www.ebi.ac.uk/Tools/msa/clustalo/](http://www.ebi.ac.uk/Tools/msa/clustalo/)]([http://www.ebi.ac.uk/Tools/msa/clustalo/](http://www.ebi.ac.uk/Tools/msa/clustalo/))) (accessed on 6 January 2023) with defaulted parameters for sequence alignment. The alignment was subsequently processed by MEGA X [71] to construct a neighbor-joining tree with 1000 bootstraps. Sequences with the following GenBank accession numbers were used in either phylogeny or alignment analysis: AtTPS10 (ACF41947), AtTPS14 (NP001185286), AtTPS21 (NP001190374), AtGA1 (Q38802.1), AtGA2 (Q9 SAK2.1), AtTPS3 (NP_173143.4), PtTPS2 (AEI52902.1), MdAFS (AAO22848.2), CIGES (CAD29734.2), PIOS (ABY65110), LjE9 OS (AAT86042), and MtTPS4 (QSUB07). _GmTPSs_ exon/intron structures were analyzed based on the genome annotation file downloaded from Phytozyme v13 ([[https://phytozome-next.jgi.doe.gov/](https://phytozome-next.jgi.doe.gov/)]([https://phytozome-next.jgi.doe.gov/](https://phytozome-next.jgi.doe.gov/))) (accessed on 8 January 2023). The conserved motifs were predicted by the MEME Suite 5.5.1 ([[https://meme-suite.org/meme/tools/meme](https://meme-suite.org/meme/tools/meme)]([https://meme-suite.org/meme/tools/meme](https://meme-suite.org/meme/tools/meme))) (accessed on 6 February 2023) with default parameters, except for a maximum of 10 motifs selected. Both gene structures and conserved motifs were visualized with Gene Structure View (Advanced) plugin in TBtools [43]. The chromosomal locations of the _GmTPS_ genes were presented with Gene Location Visualize from GTF/GFF plugin integrated with TBtools [43]. The cis-acting regulatory element in promoter sequences was analyzed by PlantCARE ([[https://bioinformatics.psb.ugent.be/webtools/plantcare/html/](https://bioinformatics.psb.ugent.be/webtools/plantcare/html/)]([https://bioinformatics.psb.ugent.be/webtools/plantcare/html/](https://bioinformatics.psb.ugent.be/webtools/plantcare/html/))) (accessed on 16 February 2023) and visualized by Simple BioSequence Viewer in TBtools [43]. GmOCS sequence properties were analyzed with the ExPASy Protparam tool ([[https://web.expasy.org/protparam/](https://web.expasy.org/protparam/)]([https://web.expasy.org/protparam/](https://web.expasy.org/protparam/))) (accessed on 18 February 2023) and Plant-mPLocserver ([[http://www.csbio.sjtu.edu.cn/bioinf/plant-multi/](http://www.csbio.sjtu.edu.cn/bioinf/plant-multi/)]([http://www.csbio.sjtu.edu.cn/bioinf/plant-multi/](http://www.csbio.sjtu.edu.cn/bioinf/plant-multi/))) (accessed on 26 February 2023) with default parameters.
## 5 Characterization of Membranes
### Pore size and distribution measurement  
To observe pores of membranes, a capillary flow porometer (CFP) is operated as much as scanning electron microscopy (SEM) [21; 22; 23]. Table 2 described statistical values of average pore size of membranes. The reason that pore size distribution was measured by operating CFP is because accurate pore size distribution is not available to be measured by SEM on account of different coating thickness of membranes [24; 25]. As the graft copolymer was added into the casting solution, the mean pore size was reduced. As shown in Figure 5, the pore size distribution range of A-1 and B-1 membranes were broadly dispersed. In contrast, the pore size distribution range of A-2 and B-2 membranes was narrow. Figure 5 indicated A-2 and B-2 membranes had more uniform pore size in comparison to A-1 and B-1 membranes. In particular, B-2 containing PVDF-g-PHEA showed the highest peak of PSD (_i.e._, about 45%) at 50.9 nm, whereas B-1 without additive indicated value less than approximately 10% at 48.3 nm. According to [17], PVDF membranes containing PVDF-g-PSPMA had more uniform pore size than pristine PVDF membrane. It was confirmed that the hydrophilic copolymer contributed to more uniform pore size. In conclusion, A2 and B2 membranes included the hydrophilic property unlike A1 and B1 membranes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Membrane** & **A-1** & **A-2** & **B-1** & **B-2** \\ \hline Contact Angle & 68.6\({}^{\circ}\) & 64.3 & 68.0\({}^{\circ}\) & 61.8\({}^{\circ}\) \\ Mean pore size (nm) & 60.6 & 60.5 & 53.4 & 53.2 \\ \hline \hline \end{tabular}.  
\end{table}
Table 2: Contact angle values of membranes.
# Development of Post Processing for Wave Propagation Problem: Response Filtering Method
## 3 Response Filtering Method for Improved Transient Stress  
This section presents a new engineering approach, i.e., the RFM, to improve the transient stress calculated using the finite element approach. In static analysis, spatial-averaging approaches for the stress have been proposed, but to the best of our knowledge, an averaging scheme in the time domain has not been previously proposed.
## 4 Results and Discussion
### Prediction of Initial Chord Elastic Modulus with Ensemble Method
Moreover, although the TR mode frequency predicted the \(Ei\) with higher accuracy, it is more appropriate to use the LT mode, which appears accurately up to the fourth mode. This is because \(f\)3 of the TR mode has a large dispersion and low consistency, and some frequencies are unclear. Figure 14: Comparison of Ei predicted with machine learning (ML) and actual Ei. (**a**) Ei predicted by frequency of LT mode, (**b**) Ei predicted by frequency of TR mode. Figure 13: Relative importance according to combination of variables in TR mode. Additionally, the initial chord elastic modulus (\(Ei\)) values predicted with the ensemble, artificial neural network (ANN), multiple linear regression (MLR), and correction factor methods are compared with the actual \(Ei\) for the LT mode frequency (i.e., with a greater correlation with the \(Ei\) than the TR mode). As a result of comparing the predicted \(Ei\) and actual \(Ei\) values, the MALR of the MLR method was 5.04%, and the correction factor method had an error of 6.33%. The MLR method using two frequencies has a higher predictive performance than the normal correction. In addition, the nonlinearity of the low age mix was partially compensated for by using two frequencies. For the ANN and ensemble methods, the MAPEs were 3.31% and 3.51%, respectively, and the use of ML reduced the errors as compared to normal calibration and MLR analysis. This is because the ANN and ensemble methods can compensate for nonlinearity of data by providing weight correction for additional frequencies. Therefore, it is possible to more accurately predict the \(Ei\) through the ML method.
# Dispersion Management and Pulse Characterization of Graphene-Based Soliton Mode-Locked Fiber Lasers
## 3 Fabrication of Graphene/PMMA-SA  
First and foremost, the GPTF is detached from the polymer substrate using drops of deionized (DI) water. This wet GPTF is then placed on a filter paper before cutting it into a size of 1 mm \(\times\) 1 mm, as shown in Figure 2a, to be placed on a fiber facet. Through the adhesion effect, the GPTF remains stuck to the fiber facet. The wet filter paper is then removed from the GPTF by using tweezers, as shown in Figure 2b, and the GPTF is left to dry on the fiber facet for five minutes, as shown in Figure 2c. Finally, the fiber ferrule with the GPTF is connected to another clean fiber ferrule through a fiber adaptor to form the sandwich-structured graphene/PMMA-SA, as illustrated in Figure 2d.  
The composition of the transferred GPTF on the fiber ferrule is characterized by using EDX spectroscopy, and the result is illustrated in Figure 3. The elements \"Si\" and \"O\" indicate the bare element of silica optical fiber, with their respective weight percentage of 19.62% and 33.15%. The presence of element \"Zr\" with a weight percentage of 12.56% originates from the ceramic zirconia fiber ferrule. The element \"Ge\" is the germanium ions for silica fiber dopant. Graphene material, represented by element \"C\", remarks the highest weight percentage of 34.67%. The high purity of each material composition  
Figure 1: (**a**) Trivial Transfer GrapheneTM, (**b**) schematic diagram, and (**c**) 3D profiler image of GPTF (PMMA and graphene layer) on polymer substrate.  
has been confirmed based on the absence of other chemical elements observed from the EDX spectrum.