esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Peak Matching  
The clusters are modified to address minor differences in the line shapes, frequency bins, and the peak magnitudes of the clustered components to generate the ultimate metabolites' basis set. The linear least square was applied constrained to full width half maximum (FWHM) of each metabolite to adjust the clustered components to make the model more closely represent the observed MR spectra. Mathematically, this approach finds a \(\theta\) by solving the following problem:  
\[\min_{\theta}\sum_{n=-FWHM}^{FWHM}\left(y[n]-\hat{y}(\theta)[n]\right)^{2} \tag{10}\]  
\[\hat{y}(\theta)[n]=w(\theta)x(\theta)[n], \tag{11}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Group 1** & \multicolumn{2}{c}{**Group 2**} & \multicolumn{2}{c}{**Group 3**} \\ \hline
**Metabolite** & **Cluster Centroid** & **Metabolite** & **Cluster Centroid** & **Metabolite** & **Cluster Centroid** \\ \hline NAA 1 st & 2.01 & Choline & 3.18 & Aspartate & 3.86 \\ Lactate & 1.10 & Creatine 1 st & 3.02 & Creatine 2 nd & 3.90 \\ Alanine & 1.46 & Myo-inositol 1 st & 3.52 & Myo-inositol 2 nd & 4.05 \\ Valine & 0.97 & Gaba 1 st & 2.36 & NAA 2 nd & 4.38 \\ Gaba 2 nd & 1.85 & Glutamate & 2.51 & Glucose & 4.63 \\  & & Glutamine & 2.40 & ATP & 5.00 \\  & & Scylylo-inositol & 3.34 & & \\  & & Glutathione & 3.75 & & \\  & & Taurine & 3.28 & & \\  & & Glycerol & 3.60 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cluster centroids used for initializing K-means.  
Figure 3: Spectrum of the in vivo data FID signal components clustered into basis sets of the metabolites.  
where, in Equation (10), \(y[n]\) and \(\tilde{y}(\theta)[n]\) are the MR spectra and the estimate of constructed metabolite spectra, respectively. The difference between each metabolite model and the observed MR spectra is only considered over the FWHM for the peak of that metabolite. The FWHM constraint was applied to omit the data points of the MR spectra with little or no information content for the chosen metabolite. In Equation (9), \(x(\theta)[n]\) is the basis set of metabolites, which is generated by the clustered components, and \(w(\theta)\) is the weight vector that corresponds to each specific metabolite. The basis set is not orthogonal, because metabolite peaks overlap and, thus, the solution is iterative. The process is iterative until all of the metabolites' spectra match with the pre-processed MR spectra.
# Secure and Time-Aware Communication of Wireless Sensors Monitoring Overhead Transmission Lines
## 1 Introduction
* We present a case study, in which we develop a smart grid model in Quality of Protection Modeling Language (QoP-ML), in order to better understand the relation between transmission delays, routing algorithms and different transmission grid architectures and test our ideas in practice. Through a set of simulations, we try to identify the optimal positions of direct connections, in order to minimize transmission delay, energy consumption and financial cost of sensors' arrangement. * We introduce a secure and time-aware protocol to ensure communication security. Additionally, we try to answer questions about the confidentiality, integrity, and availability of the data traversing smart grid environments. The remainder of this paper is organized as follows: in Section 2, we present the related work. Moving on to Section 3, we discuss our environment, describing considered architectures and scenarios. Section 4 is focused on practical representation of our approach. Next, in Section 5, we analyze the results and investigate the relationship between time delays and different architectures. Finally, we conclude with Section 6, in which we summarize our work.
# A Novel Implicit Neural Representation for Volume Data
## 4 Results and Discussion
### Using SIREN with Our Architecture  
Based on our architecture, we use the LANCZOS resampling technique to downsample our high-resolution volumetric medical images (512 \(\times\) 512 \(\times\) 463) to (128 \(\times\) 128 \(\times\) 115). This technique decreases the number of voxels from 121,372,672 to just 1,884,160. For simplicity, we show the results on a selected 2D slice, but it should be considered that it can be expanded to 3D data. Figure 3 shows a selected slice that is downsampled from 512 \(\times\) 512 \(\times\) 1 to 128 \(\times\) 128 \(\times\) 1 using LANCZOS, which decreases the number of voxels from 262,144 to just 16,384.  
After the downsampling stage, our architecture uses SIREN with 2, 3, and 4 layers and 128 neurons for each layer. Figures 4-6 show the results, respectively. The Adam was selected as the optimizer with a learning rate of 0.0015 and batch size of 2\({}^{16}\). MSE was used as the loss function of the MLP. In order to reconstruct the HR slice, we used SRDenseNet and applied it to reconstructed LR images. We also trained it on this kind of CT data in advance so that the trained SRDenseNe is suitable for the targeted class. The final results of using SIREN with our architecture are shown in Figures 7-9, respectively. Table 4 compares the results of the SIREN application with 2, 3 and 4 layers, using our architecture.  
Figure 3: Left column shows the high-resolution slices with a size of 512 \(\times\) 512 and the right is the low-resolution slices with a size of 128 \(\times\) 128. Low-resolution slices were obtained by applying Lanczos resampling on the high-resolution slices.  
Figure 4: From left, the first image shows the original down-sampled image. The second image illustrates the reconstructed counterpart of the down-sampled image using SIREN. The next image shows the plot of the PSNR while training the network (best PSNR: 45.38) and the last image shows the plot of the loss values during the SIREN training (loss: \(1.4805471\times 10^{-5}\)).  
Figure 5: From the left, the first image shows the original down-sampled image. The second image illustrates the reconstructed counterpart of the down-sampled image using SIREN. The next image shows the plot of the PSNR while training the network (best PSNR: 48.29) and the last image shows the plot of the loss values during the SIREN training (loss: \(1.0805471\times 10^{-5}\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Number of** & **Best PSNR** & **Training Time(s)/50,000 Iters** & **Compression Rate** & **GPU Memory (KB)** \\ \hline Two Layer & 34.670 & 55.76 & 3.65 & 1038 \\ Three Layer & 34.865 & 74.80 & 1.96 & 1296 \\ Four Layer & 35.140 & 99.61 & 1.28 & 1554 \\ \hline \end{tabular}
\end{table}
Table 4: Shows comparison results of the implementation of SIREN with our architecture in terms of quality, speed, GPU memory allocation, and compression rate with 2, 3, and 4 layers of SIREN.
# Functional Outlier Detection by Means of h-Mode Depth and Dynamic Time Warping
## 4 Analytical Test Cases
### Comparison with State-of-the-Art Methodologies
#### 4.2.5 Results
The main advantage of this methodology is its capability of finding outliers in multivariate functional data sets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**N = 100, _p = 1\%_** & **Model 1** & **Model 2** & **Model 3** & **Model 4** \\ \hline Algorithm & **100.00** & **96.94** & **100.00** & **100.00** \\ \hline DO & 59.26 & 39.51 & 100.00 & 0.00 \\ \hline FB & 2.33 & 0.00 & 100.00 & 0.00 \\ \hline HDR & 89.47 & 69.64 & 100.00 & 0.00 \\ \hline
**N = 100, _p = 5\%_** & & & \\ \hline Algorithm & **91.14** & **96.79** & 99.17 & **97.50** \\ \hline DO & 58.23 & 54.40 & **100.00** & 0.00 \\ \hline FB & 2.53 & 4.18 & 11.95 & 0.00 \\ \hline HDR & 48.35 & 44.8 & 49.48 & 0.00 \\ \hline
**N = 100, _p = 10\%_** & & & \\ \hline Algorithm & **81.50** & **75.49** & 86.67 & **92.37** \\ \hline DO & 47.25 & 45.97 & **99.63** & 0.00 \\ \hline FB & 0.75 & 1.71 & 7.41 & 0.00 \\ \hline HDR & 22.25 & 23.41 & 14.07 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of the different algorithms on the test models. The results are expressed as a percentage (detection rates). DO: Directional Detector; FB: Functional Boxplots; HDR: High-Density Regions. The values given in bold correspond to the best results for each model.
## 2 Materials and Methods
### Well Logging and Production Data Preprocessing
#### 2.1.3 Data Splitting  
In Figure 1, to ensure that the machine learning model is able to generalize the unseen data, the collected data are divided into a training set and a testing set. In our study, we utilized scikit-learn's train_test_split function for data partitioning, which performs a random splitting approach. train_test_split randomly divides the dataset into training and testing subsets, ensuring a heterogeneous mix of data points in both sets. This is in contrast to sequential splitting, where the data are divided based on a sequence, such as time. The test_size = 0.2 parameter ensures that 20% of the data are reserved for testing, while the random_state = 42 ensures the reproducibility of the results by using the same seed for random number generation.
## 4 Experimental Results and Analysis
### Tool Formal Security Validation
#### 4.4.4 Discussion on Formal Verification  
By employing both AVISPA and ProVerif, we have provided a dual-layer security validation, ensuring that our ECC-based key exchange protocol is mathematically sound and practically resistant to adversarial threats. The integration of parameterized contractions \((\alpha,\beta)\), modular reduction, and factorization techniques significantly enhances the security posture of the protocol while maintaining computational efficiency.  
Our approach confirms that lightweight yet highly secure cryptographic solutions can be effectively deployed in IoT environments, bridging the gap between resource efficiency and cryptographic robustness.  
Conclusion  
The inclusion of AVISPA and ProVerif analysis in our security assessment demonstrates that the proposed ECC-based key exchange protocol is not only mathematically secure but also resilient to real-world cyber threats. By modeling and verifying the cryptographic interactions, we ensure that our approach can be trusted for secure IoT communication in heterogeneous device environments.
## 2 Basic Theory of the TPOD Method and the Rotor Model with Coupling Faults  
### Basic Theory of the TPOD Method  
In general, the multiple-DOFs system can be written as Equation (1) via the equivalent transformation:  
\[\ddot{Z}=-C\dot{Z}-KZ+F \tag{1}\]  
where \(C\) is the equivalent damping matrix.\(K\) is the equivalent stiffness matrix.\(F\) is the equivalent force vector.  
The construction processes are listed as follows:  
1. Provide the initial conditions, proceed numerical simulation and obtain displacement information from the transient process of various DOFs, which is denoted by \(z_{1}(t),z_{2}(t),\ldots z_{M}(t)\). \(M\) is the number of DOFs in the system, recording transient time interval displacement sequence of all DOFs \(z_{i}=(z_{i}(t_{1}),z_{i}(t_{2}),\ldots z_{i}(t_{N}))^{T},i=1,\ldots,M\). The number of time interval is \(N\) and the interval is equal to each other, the time series form the matrix \(X=[z_{1},z_{2},\ldots,z_{M}]\), the order of \(X\) is \(N\times M\). When calculating the self-correlation matrix \(T=X^{T}X\), the order of matrix is \(M\times M\). The eigenvector of the matrix can therefore be denoted by \(\varphi_{1},\varphi_{2},\ldots,\varphi_{M}\), and the corresponding eigenvalues are \(\lambda_{1}>\lambda_{2}>\ldots>\lambda_{M}\).
2. \(U\) is the matrix formed by the first \(n\) orders of \(T=X^{T}X\) and it can also indicate that the matrix \(U\) contains the first \(n\) largest eigenvalues of \(T\). The order of \(U\) is \(M\times n\), so we know that the order of \(U^{T}U\) is \(n\times n\). Taking the coordinates transformation on system coordinates \(Z\), acquiring the new coordinates \(P\), \(Z=UP\), substituting \(Z\) into Equation (1), gaining the Equation (2): \[U\ddot{P}=-CU\dot{P}-KUP+F\] (2)  
Due to the column vector of \(U\) is orthogonal and nonzero, and the matrix \(U^{T}U\) is \(n\) order diagonal and full rank, hence there exits inverse matrix of \(U^{T}U\). For the Equation (2), taking \(\left(U^{T}U\right)^{-1}U^{T}\) left multiplication on both sides, we can obtain (3):  
\[\ddot{P}=-\left(U^{T}U\right)^{-1}U^{T}CU\dot{P}-\left(U^{T}U\right)^{-1}U^{T }KUP+\left(U^{T}U\right)^{-1}U^{T}F \tag{3}\]  
Setting \(C_{R}=-\left(U^{T}U\right)^{-1}U^{T}CU,K_{R}=\left(U^{T}U\right)^{-1}U^{T}KU, F_{R}=\left(U^{T}U\right)^{-1}U^{T}F\), then we receive the Formula (4):  
\[\ddot{P}=-C_{R}\dot{P}-K_{R}P+F_{R} \tag{4}\]  
In this way, the original system is transformed into the \(n\)-DOFs reduced system by the TPOD method.
# Cotton Yield Estimate Using Sentinel-2 Data and an Ecosystem Model over the Southern US
## 5 Conclusions  
In this study, we evaluated the potential of Sentinel-2A and 2B data for mapping cotton lint yield in an ecosystem model. Three Sentinel-2 tiles in southern US were chosen, covering regions with low to high cotton yields. BEPS-simulated GPP values at the pixel level were averaged into the county level and compared to the cotton lint yield data collected from NASS. Our study suggests that BEPS-simulated GPP explains 85% of variation in cotton lint yield and can be used as a reliable indicator for estimation of cotton lint yield. We also found that there may be systemic bias in our GPP simulation due to limited knowledge about cotton V\({}_{\text{cmax}}\), which is related to leaf nitrogen content. Accurate GPP simulation for cotton in a double-cropping system relies on the knowledge of the start and end dates of the cotton growing season, and this posed a challenge in our simulation due to certain cloud contamination in Sentinel-2 images in southern Georgia; therefore, we suggest the fusion of multi-satellite data, such as from Landsat 7 and 8, Sentinel-2, or synthetic aperture radar data from Sentinel-1 at similar spatial resolutions in order to track the key phenology stages of cotton development for a more reliable estimation of cotton yield.  
Supplementary Materials can be found at [[http://www.mdpi.com/2072-4292/11/17/2000/s1](http://www.mdpi.com/2072-4292/11/17/2000/s1)]([http://www.mdpi.com/2072-4292/11/17/2000/s1](http://www.mdpi.com/2072-4292/11/17/2000/s1)).  
[PERSON] and [PERSON] designed the experiment. [PERSON] conducted the analysis, and wrote the draft. [PERSON] prepared the model inputs and refined the draft. They both revised the manuscript.  
This research received no external funding.  
The authors thank [PERSON] for her constructive discussion and thank [PERSON] for his insightful comments.  
The authors declare no conflicts of interest.
# Validation of Seismic Performance of Stainless Press-to-Connect Piping System under Cyclic Loadings
###### Abstract  
Earthquakes with magnitudes over 5.0 occurred near Gyeongju and Pohang in southern Korea in 2016 and 2017, respectively. These earthquakes had both low- and high-frequency components. Due to earthquakes with high-frequency motions, damage to nonstructural systems has been observed to be relatively more than that in structural systems. Consequently, the seismic design or performance evaluation of nonstructural components in critical facilities has emerged as a key research area in Korea. This study presents the results of experimental and numerical analyses using a high-fidelity finite element (FE) simulation in the ABAQUS platform for a press-to-connect piping system as a nonstructural component. Press-to-connect piping systems based on NFPA-13 with two elbows, a flexible coupling, and a T-joint were used. In addition, a cyclic loading protocol was applied using the KBC 2016 and IBC 2015. Based on the component-level experimental test, an FE model of the press-to-connect elbow was developed, and the high-fidelity large-scale piping system with an elbow was validated in this study. In both the experimental and analytical results, no leakage or plastic deformation of the piping system was observed under cyclic loading conditions. The results of the high-fidelity simulation model of the large-scale piping system were identical to those of the experimental test. More specifically, the error of the von-Mises stress at the upper and lower elbows was less than 9%, and the angle between the elbows was less than 2%, corresponding to the limit state of the drift ratio of the building system. Therefore, the high-fidelity simulation model of a large-scale piping system can have high application value. In addition, the design requirements and engineering demands of the piping system, such as the condition of ASME B and PV section III for service level D, were satisfied.  
press-to-connect; elbow; high-fidelity simulation; large-scale piping system; earthquake
## 3 Results and Discussion
### Verification of Sowing
#### 3.2.3 Seeder Bench Experiment  
The seeder bench experiment was performed on the computer vision-based seeder test bench in the Intelligent Agricultural Machinery Laboratory at Guangxi University, as  
Figure 10: Seeding simulation counting.  
illustrated in Figure 11. The test bench parameters were calibrated to align with those of the simulation experiment. The conveyor belt speed was set to 5 km/h, and the seeder rotational speed alternated between 9.26 r/min and 47.62 r/min. A weight of seeds of 0.2 kg was used for testing. The data collection durations were 5 s for low-speed and 3 s for high-speed tests, with 13 datasets processed. After image screening, datasets containing broken seeds or foreign matter were excluded, and only valid data were retained for analysis, to ensure the accuracy of the bench experiment data. The bench experiment results were compared with the simulation test results, as summarized in Table 5. The replay-seeding rate from the bench experiment was 12.55%, resulting in a percentage error of 5.90% relative to the simulation experiment. The missed-seeding rate from the bench experiment was 7.53%, leading to a percentage error of 5.84% relative to the simulation experiment. Both percentage errors were below 10%. These results confirm the reliability of the Sesbania simulation parameter calibration and validate the applicability of the established discrete-element model for Sesbania seeds used in the seeder simulation experiment.
## 1 Introduction
Therefore, intentional non-uniform sampling has the potential to reduce the hardware cost in control applications [29]. Due to the complexity of arbitrary non-uniform sampling, most of the literature works have focused on periodically non-uniformly sampled-data systems [30; 31]. For periodically non-uniformly sampled-data Hammerstein systems, [PERSON] et al. derived the lifted transfer function model by means of the lifting technique and presented a least squares-based iterative algorithm for parameter estimation [32]. The lifting technique is a benchmark tool to deal with multirate and non-uniformly sampled-data systems [33; 34]. However, the corresponding lifted models are complex and involve a large number of parameters, which brings a great challenge for identification. To simplify the model structure and reduce the identification complexity, [PERSON] et al. put forward a novel input-output representation of linear systems with non-uniform sampling by introducing a time-varying backward shift operator \(\delta^{-1}\)[35]. On the basis of that work, this paper aims to propose a \(\delta^{-1}\)-based model to describe periodically non-uniformly sampled-data Hammerstein systems and presents an auxiliary model-based multi-innovation SG algorithm to estimate the model parameters. The rest of this paper is organized as follows. Section 2 formulates the identification problem of periodically non-uniformly sampled-data Hammerstein systems. Identification algorithms are proposed in Section 3, and an example is provided in Section 4 to examine their estimation performance. Finally, concluding remarks are given in Section 5.
###### Abstract  
Network traffic classification (NTC) has attracted great attention in many applications such as secure communications, intrusion detection systems. The existing NTC methods based on supervised learning rely on sufficient labeled datasets in the training phase, but for most traffic datasets, it is difficult to obtain label information in practical applications. Although unsupervised learning does not rely on labels, its classification accuracy is not high, and the number of data classes is difficult to determine. This paper proposes an unsupervised NTC method based on adversarial training and deep clustering with improved network traffic classification (NTC) and lower computational complexity in comparison with the traditional clustering algorithms. Here, the training process does not require data labels, which greatly reduce the computational complexity of the network traffic classification through pretraining. In the pretraining stage, an autoencoder (AE) is used to reduce the dimension of features and reduce the complexity of the initial high-dimensional network traffic data features. Moreover, we employ the adversarial training model and a deep clustering structure to further optimize the extracted features. The experimental results show that our proposed method has robust performance, with a multiclassification accuracy of 92.2%, which is suitable for classification with a large number of unlabeled data in actual application scenarios. This paper only focuses on breakthroughs in the algorithm stage, and future work can be focused on the deployment and adaptation in practical environments.  
network traffic classification; convolutional adversarial autoencoder; Internet of things: unsupervised learning; deep clustering +
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