esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Diagnosis of Railway Point Machines Using the Locally Connected Autoencoder
## 5 Conclusions  
In this paper, a novel scheme using the locally connected autoencoder is proposed for intelligent fault diagnosis of railway point machines. To avoid the labor-intensive hand-engineering feature extraction, the proposed scheme is designed to automatically capture the informative features from raw electrical signals. Inspired by the word2 vec method, in the locally connected autoencoder the input signals are segmented and blended to enhance the temporal characteristic. To further reduce the impact of the noninformative information in the electrical signals, a weighting strategy is proposed to increase the weights of useful and robust features, whereby the meaningful hidden representations of the raw signals can be obtained. Note that it is difficult to obtain labels for the training steps; in this paper, an unsupervised anomaly detection method (the LOF algorithm) is employed. We evaluate the prediction performance of the proposed scheme by comparing its prediction skills with respect to existing methods, such as the feature-based LOF algorithm, the stacked denoising autoencoder, the sparse denoising autoencoder, and the gated recurrent unit-based sequence to sequence autoencoder. Using the self-constructed fault dataset (Cu-3300), we demonstrate that the proposed scheme achieves superior performance with AUC = 0.981, compared to these methods. In the future work, we would investigate the optimization techniques to adaptively adjust the parameters of the locally connected autoencoder. Meanwhile, more studies are also planned to evaluate if the proposed scheme is applicable to other research fields.  
Conceptualization, T.T. and Z.L.; formal analysis, T.T. and Z.L.; methodology, Z.L. and Z.Y.; writing-original draft, Z.L.; validation, Z.L. and Z.Y.; resources, C.G.; supervision, C.G. This research was funded by Beijing Postdoctoral Research Foundation (No. ZZ2019-117). The authors declare no conflicts of interest.
## 3 Results
### Changes in Splean Cytokine Content  
In the tumor transplantation control group, a significant decrease (\(p<0.01\)) in the splenic IL-1\(\beta\), TNF-\(\alpha\), and IL-10 levels was confirmed in comparison with the intact control group. However, a significant increase (\(p<0.01\)) in the spleen cytokine content was confirmed in all the sasam-Kyeongokgo-alone and combined administration groups compared with the tumor transplantation control group. In particular, a significant increase (\(p<0.01\)) in the spleen TNF-\(\alpha\), IL-1\(\beta\), and IL-10 levels was confirmed in the group administered sasam-Kyeongokgo and gefitinib in comparison with the group administered gefitinib alone. In contrast, no significant change in the splenic cytokine content was confirmed in the gefitinib-alone group in comparison with the tumor transplantation control group (Table 5).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Groups** & \multicolumn{2}{c}{**Tumor Necrosis**} & \\
**Factor-\(\alpha\)** & **Interleukin-1\(\beta\)** & **Interleukin-10** \\ \hline Controls & & & \\ Intact & 142.83 \(\pm\) 49.08 & 48.33 \(\pm\) 19.26 & 122.81 \(\pm\) 34.71 \\ TB & 26.29 \(\pm\) 10.73 a  & 8.64 \(\pm\) 3.05 a  & 26.52 \(\pm\) 12.47 a  \\ Single-formula-treated & & & \\ Gefitinib & 26.23 \(\pm\) 10.81 a  & 8.36 \(\pm\) 1.96 a  & 25.58 \(\pm\) 11.94 a  \\ SKOG & 70.03 \(\pm\) 38.12 ac  & 30.26 \(\pm\) 12.78 b  & 73.46 \(\pm\) 19.50 ac  \\ Gefitinib and SKOG & 400 mg/kg & 98.80 \(\pm\) 26.02 c  & 44.35 \(\pm\) 10.11 c  & 97.56 \(\pm\) 26.24 c  \\
200 mg/kg & 81.62\(\pm\)24.83 ac  & 37.41 \(\pm\) 12.06 c  & 82.83 \(\pm\) 22.96 b  \\
100 mg/kg & 71.75 \(\pm\) 25.06 ac  & 72.39 \(\pm\) 12.62 ac  \\ \hline \hline \end{tabular}
\end{table}
Table 5: Changes in the splenic cytokine contents in NCI-H520 tumor cell xenograft mice.  
Figure 6: NK cell activity changes in NCI-H520 tumor cell xenograft mice. AR = Adenophorae Radix; KOG = _Kyeongokgo_; SKOG = _sasam-Kyeong
# Detecting Malicious Devices in IPSEC Traffic with IPv4 Steganography
## 6 Conclusions  
In an era characterized by the ever-evolving intricacies of communication protocols, the significance of network steganography is gaining prominence. As communication techniques advance, the need for more sophisticated methods becomes imperative. This paper introduces a network steganography approach, demonstrating that steganography can function as a crucial tool for fortifying the security aspects of data transmission systems. The primary objective of our proposed steganographic technique is to enhance the security of data transmission by enabling the concealment and transmission of specific information, specifically the MAC address. The ultimate goal is to effectively isolate malicious devices and bolster the overall network security infrastructure. With IPSec, we employ a novel steganographic method that leverages the IPv4 protocol fields, precisely the identification and time-to-live parameters. Incorporating these elements into our steganographic technique enables the covert transmission of information within the network. This approach facilitates the targeted exclusion of adversarial devices based on their MAC addresses, strengthening the network's ability to identify and neutralize potential security threats.  
In summary, our paper introduces a network steganography method that allows the retrieval of the MAC addresses of compromised devices. By utilizing this technique, we demonstrate the potential for enhancing security measures in data transmission systems by covertly transmitting crucial information and the targeted isolation of malicious devices. This research contributes to the ongoing efforts to fortify network security in an environment marked by the relentless evolution of communication protocols.  
[PERSON] contributed to IPSec and method description. [PERSON] contributed to the architecture. [PERSON] contributed to the Section 5. [PERSON], contributed to the Sections 1 and 2 and revision. All authors have read and agreed to the published version of the manuscript.  
Figure 18: Network overhead test dashboard.  
**Funding:** This research work was supported and funded by the European Defence Agency, grant no. B 1520 IAP4 GP covering the Ad Hoc Research and technology Project entitled \"Software Defined Tactical and Theatre Network\".  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Thales. All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Multi-Class Confidence Detection Using Deep Learning Approach
###### Abstract  
The advancement of both the fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has enabled the development of effective automatic systems for analyzing human behavior. It is possible to recognize gestures, which are frequently used by people to communicate information non-verbally, by studying hand movements. So, the main contribution of this research is the collected dataset, which is taken from open-source videos of the relevant subjects that contain actions that depict confidence levels. The dataset contains high-quality frames with minimal bias and less noise. Secondly, we have chosen the domain of confidence determination during social issues such as interviews, discussions, or criminal investigations. Thirdly, the proposed model is a combination of two high-performing models, i.e., CNN (GoogLeNet) and LSTM. GoogLeNet is the state-of-the-art architecture for hand detection and gesture recognition. LSTM prevents the loss of information by keeping temporal data. So the combination of these two outperformed during the training and testing process. This study presents a method to recognize different categories of Self-Efficacy by performing multi-class classification based on the current situation of hand movements using visual data processing and feature extraction. The proposed architecture pre-processes the sequence of images collected from different scenarios, including humans, and their quality frames are extracted. These frames are then processed to extract and analyze the features regarding their body joints and hand position and classify them into four different classes related to efficacy, i.e., confidence, cooperation, confusion, and uncomfortable. The features are extracted using a combination framework of customized Convolutional Neural Network (CNN) layers with Long Short-Term Memory (LSTM) for feature extraction and classification. Remarkable results have been achieved from this study representing 90.48% accuracy with effective recognition of human body gestures through deep learning approaches.  
human gestures; convolutional neural network; confidence detection Article
## 4 Simulated and Measured Results
### MIMO Performance Parameters  
In order to investigate the robustness of the proposed antenna's MIMO operation, three bands are explored: 26.6-28.5 GHz, 37.5-38.5 GHz, and 0.128-0.131 THz. Several MIMO performance parameters can be investigated, such as the envelope correlation coefficient (\(ECC\)) [42; 43; 44], mean effective gain (\(MEG\)) and power balance condition [44; 45], diversity gain (DG) [46], and channel capacity loss (CCL) [47]. A robust MIMO antenna system should satisfy two important conditions, which are linked to both \(ECC\) and the branch power ratio condition in terms of \(MEG\). These two conditions are described as follows:  
\[ECC\leq 0.5 \tag{4}\]  
\[k=\left|\frac{MEG_{i}}{MEG_{j}}\right|\cong 0 dB \tag{5}\]  
where \(k\) is the branch power ratio, \(MEG_{i}\) is the mean effective gain of \(i\)th antenna element, and \(MEG_{j}\) is the mean effective gain of the \(j\)th antenna element. The calculation of MEG can be performed using (6) [46].  
\[MEG_{i}=0.5\left[1-\left(\sum_{j=1}^{4}\lvert S_{ij}\rvert\right)\right] \tag{6}\]  
Ideal MIMO antenna systems should have equal power levels among different branches (\(k=0\) dB), but practically, MIMO antennas can have excellent MIMO and diversity performance if the branch ratio is less than 3 dB (\(k<3\) dB) [45].  
For the purpose of conciseness, this paper solely focuses on analyzing the \(ECC\) and MEG aspects, since they provide a distinct assessment of the strength and stability of the suggested MIMO antenna system.  
Figure 19: Simulated normalized XY plane patterns of the proposed four-element QMSIW mode antenna at 0.13 THz. [CST \(E_{\theta}\) in solid black, HFSS \(E_{\theta}\) in dashed-dotted black, CST \(E\varphi\) in dashed red, and the HFSS \(E\varphi\) in dotted red].  
The simulated and measured results of \(ECC\) for the proposed four-element QMSIW antenna were calculated based on far-field patterns at 28 GHz, 38 GHz, and 0.13 THz. Figure 20a shows both the measured and simulated results at the 28 GHz and 38 GHz frequency bands, while co-simulation results are presented in Figure 20b for the sub-THz frequency band. The results for each figure are in good agreement, and all are well below the critical limit of \(ECC\), as described in (4) [42; 43; 44]. Thus, the \(ECC\) results assured excellent MIMO and diversity performance.  
Figure 21 shows both the MEG and the branch power results over the operational frequency bands. The results in Figure 21a,b validate each other and show that the values of MEG for each antenna element are very close, ensuring a very low branch ratio over the frequency band of interest. This is clearly evident in Figure 21c,d, as the branch power ratio values are below 3 dB. Thus, as both diversity conditions are satisfied, the proposed antenna represents a very efficient MIMO antenna solution over the three frequency bands (26.5-28.5 GHz, 37.5-38.5 GHz, and 0.128-0.131 THz).  
Figure 20: Simulated and measured results of ECC: (**a**) Simulated and measured results at the 28 GHz and 38 GHz bands. (**b**) Co-simulation results at the 0.13 THz band.  
Figure 21: _Cont._
## 3 Results  
### Q\({}_{c}\) Estimates  
The collected seismograms were visually checked to prevent erroneous \(Q_{c}\) estimates. The visual inspection of the envelopes was carried out on recordings filtered with a Butterworth two-pole filter with a central frequency \(f_{c}\) = 8 Hz (bandwidth ranging between 5.7 Hz and 11.3 Hz). We removed all the recordings that present abrupt variations in the amplitude of the envelope, i.e., bumps of energy due to concomitant seismic events or other environmental or artificial sources. We also removed the recordings with sudden changes in the envelope slope in the portion of the seismogram immediately following the S-wave arrival [42]. The suitable recordings were processed with the addition of two time-markers (Figure 3):  
1. An initial marker, named \(T3\), placed after the S-wave arrival, when the envelope shows a decreasing trend;
2. A terminal marker, named \(T4\), placed before a bump, or an abrupt change of the slope, or when the oscillations cannot be distinguished from the seismic noise.  
The selected envelopes were linearly fitted inside the time interval \([T3,T4]\) in a logarithmic diagram for different \(t_{L}\) = 10, 15, 20, 25, 30, 35, 40 s and \(f_{c}\) = 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 16 Hz, reaching a maximum of 70 measures of \(Q_{c}^{comp}\) for each seismogram component. We accepted only the components for which the condition \(T3<t_{L}<T4\) was satisfied. \(Q_{c}^{comp}\) was computed from the slope \(m\) of the linear regression in Equation (1): \(Q_{c}^{comp}=-\frac{\pi f_{c}}{m}\). Any coda amplitude decay with a positive \(m\) was rejected. In Figure 4, the number of \(Q_{c}^{comp}\) estimates was plotted. It can be inferred that, for \(t_{L}\) = 20, 25, and 30 s, the maximum number of \(Q_{c}^{comp}\) estimates is reached at all frequencies, while for \(t_{L}\) = 10 s the minimum number of \(Q_{c}^{comp}\) estimates is available; all series show stability in the number of \(Q_{c}^{comp}\) estimates starting at \(f_{c}\) = 3 Hz, indicating that for \(f_{c}\) = 2 Hz, the results are less robust.  
All the analyzed envelopes, cut in the time window between the markers \(T3\) and \(T4\), are available in for the reproducibility of our results.
## 4 Experimental Verification
### Pressure Relief Hole Identification Test  
#### 4.3.1 Related Parameter Setting  
First of all, the improved SinGAN model was used to generate 50 images for each relief hole image, and a total of 11,271 image samples were obtained. Subsequently, the sizes of all images were adjusted to 300 by scaling or clipping, so as to improve the training speed of the model, emerge the relief hole contour and shape features, and then optimize the network structure and model parameters of the relief hole target detection model. Then, labellmg software was used to add pressure relief hole labels. After labeling, each image was converted into one XML file, and the file save name corresponded to the image name. Finally, 11,271 image samples in the dataset were divided into ten parts, and the 10-fold cross validation was also used to test the performance of the network.  
The relevant parameters were as follows: the initial learning rate was set to 0.00001, the momentum factor was set to 0.9, and the weight attenuation factor was \(5\times 10^{-4}\). The newly added convolution layers were all initialized by Gaussian distribution, with a mean value of 0 and a standard deviation of 0.01. The hyperparameters during training were set as Table 2.
## 2 Finite Element Model
### Chip Separation Criterion  
In the machining process, where the material is cut using a suitable cutting tool, defining a failure criterion is of crucial importance to simulate the machining precisely. Failure criteria are usually associated with the intrinsic mechanical and thermal properties of the material. As part of this study, the shear failure criterion is specifically chosen, and its modeling is assigned to the finite element analysis software Abaqus. The process of assessing damage starts with gathering the initial mechanical characteristics, which are detailed in Table 1. Subsequently, the motion of the milling cutter induces a force exerted on the workpiece, where the determination of dynamic normal and shear stresses is carried out through various interfaces. When the rupture coefficient, denoted \(\omega\), exceeds a critical threshold of 1 (Equation (6)), it indicates deterioration of the material and the formation of chips.  
\[\omega=\sum\frac{\Delta\varepsilon}{\varepsilon^{\prime}} \tag{6}\]  
where \(\omega\) represents the shaping limit value or destruction coefficient, \(\Delta\varepsilon\) represents the incremental equivalent plastic strain, and \(\varepsilon^{\prime}\) indicates the total strain at which the material experiences damage.
# High-Precision Tea Plantation Mapping with Multi-Source Remote Sensing and Deep Learning
## 2 Study Area and Data
### Data
#### 2.2.3 DEM Data  
Tea plants are commonly cultivated in hilly areas, and different altitudes have significant effects on tea growth. Therefore, this study chooses to utilize the Shuttle Radar Topography Mission (SRTM) DEM data as an auxiliary data source for tea plantation mapping. The SRTM dataset, produced by NASA and NGA, was acquired during the Shuttle Radar Topography Mission from 11-22 February 2000 and provides 30 m resolution digital elevation models covering approximately 80% of Earth's landmass between 60\({}^{\circ}\) North and 56\({}^{\circ}\) South latitudes. Based on the original DEM data, slope and aspect were further calculated as auxiliary data for tea plantation mapping and analysis.
# Benchmarking MRI Reconstruction Neural Networks on Large Public Datasets
## 5 Results
### Quantitative Results  
The quantitative results in Tables 1-4 show that the PD-net [9] outperforms its competitors in terms of image quality metrics but also has the least amount of trainable parameters. It is slightly slower than the Cascade-net [11] though, which can be explained by its higher number of iterations, therefore involving more costly Fourier transform (inverse or direct) operations. These results hold true on the two data sets, fastMRI [12] and OASIS [13]. The only exception is that KIKI-net [10] is slightly better than the U-net [28] on the OASIS data set, but still far from the best performers. We can also note that the standard deviation of the image quality metrics is way higher in the fastMRI data set than in the OASIS data set. This higher standard deviation is explained by the fact that the two contrasts present in the fastMRI dataset, Proton Density with and without Fat Suppression (PD/PDFS), have widely different image metrics values. The standard deviations when we compute the metrics for each contrast separately are more in-line with the OASIS ones. The range of the image quality metrics is also much higher in the OASIS results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **PSNR-mean (std) (dB)** & **SSIM-mean (std)** & **\# params** & **Runtime (s)** \\ \hline
**Zero-filled** & 26.11 (1.45) & 0.672 (0.0307) & 0 & 0.165 \\ \hline
**U-net** & 29.8 (1.39) & 0.847 (0.0398) & 482k & **1.202** \\ \hline
**KIKI-net** & 30.08 (1.43) & 0.853 (0.0336) & 1.25M & 3.567 \\ \hline
**Cascade-net** & 32.0 (1.731) & 0.887 (0.0327) & 425k & 2.234 \\ \hline
**PD-net** & **33.22 (1.912)** & **0.910 (0.0358)** & 318k & 2.758 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative results for the **OASIS** dataset. PSNR and SSIM mean and standard deviations are computed over the 200 validation volumes. Runtimes are given for the reconstruction of a volume with 32 slices.  
Figure 6: Reconstruction results for a specific slice (16 th slice of file1000196, part of the validation set). The first row represents the reconstruction using the different methods, while the second represents the absolute error when compared to the reference.  
Figure 7: Reconstruction results for a specific slice (15 th slice of sub-OAS30367_ses-d3396_T1w.nii.gz, part of the validation set). The top row represents the reconstruction using the different methods, while the bottom row represents the absolute error when compared to the reference.
# An Improvement on Estimated Drifter Tracking through Machine Learning and Evolutionary Search
## 5 Experiments
### Results
#### 5.3.2 Deep Learning  
The neural network methods use the loss value to calculate how well a model is trained. The loss value decreases as the model accurately predicts the training data. It is better to use cross-entropy and _softmax_ in the final layer of neural network-based classifiers [43]. However, we used MSE as the loss function since we predicted continuous values, not discrete ones.  
The loss function measures the difference between the correct answer of the training data and the value predicted by the model, which may not relate to MSE and NCLS. In order to identify whether or not loss and MAE are related to each other, we examined several neural network models. Neural networks learn the values of the weights to reduce loss, and a reduction of MSE can prove worthwhile. We investigated the loss according to epoch, MAE, and MAE of the test data in the three deep learning methods, CNN, RNN, and LSTM. From Jeju data, Case 1 was used as the test data, and the rest were used as training data. Figure 5 shows the results of DNN.  
DNN calculates the error between individual data independently. As the training progresses, the loss value generally decreases. However, the MAE of the training data and test data decreases sharply only at the beginning, and the performance does not significantly improve thereafter. We looked for an additional way to solve this problem, since the MAE deviation between each epoch is large even when the training is complete. We attempted to solve the problem by bagging among the ensemble techniques. The final epoch of DNN is set to 1500. The MAE of the test data was low in the 100-200 epoch section when training was incomplete, and the MAE deviation between epochs was large even post-training in the case of DNN. After 1500 epochs, the loss value did not decrease further, so we set the final epoch of DNN to 1500.  
Figure 6 shows the MAE and loss values of RNN. The data are continuous time-series data of the movement of a drifter over time. Although the loss value is reduced above 100 epochs, the MAE of the training and test data did not decrease. We set the final epoch of RNN to 1000. LSTM was similar to RNN, but there were ups and downs on the MAE graph as learning progressed. The final epoch of the LSTM was set to 500. Figure 7 shows the MAE and loss values of LSTM.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data** & & **Case 1** & **Case 5** & **Case 6** & **Case 7** \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Computing time \\ (CPU second) \\ \end{tabular} } & DE & 2120.8 & 2705.8 & 2663.2 & 3066.2 \\  & PSO & 302.5 & 345.7 & 344.9 & 371.5 \\ \cline{1-1}  & CMA-ES & 117.5 & 152.8 & 152.9 & 173.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Computing time of evolutionary computation methods.  
Figure 5: MAE and Loss of DNN.