esa-sceva/satcom-chunk-collection huggingface dataset:
# Fault Prediction of Mechanical Equipment Based on Hilbert-Full-Vector Spectrum and TCDAN
## 4 Experiment Verification  
### Introduction of Experimental Data  
To verify the effectiveness of the proposed method, XJTU-SY bearing datasets that contain complete run-to-failure data of rolling element bearings were used, and the experimental platform is shown in Figure 8[35]. The rotating shaft of the test bed is driven by an AC motor, and the supporting bearing model is the LDK UER204 rolling bearing, and the relevant specific parameters are shown in Table 1. To obtain the life-cycle vibration signal of the bearing, two PCB 352C33 unidirectional acceleration sensors are fixed on the horizontal and vertical directions of the test bearing by magnetic seats, respectively. In the test, the sampling frequency was set as 25.6 kHz, the sampling interval was 1 min, and the duration of each sampling was 1.28 s. In this paper, Bearing 3_1 with the final outer-ring fault and Bearing 2_1 with the final inner-ring fault are taken as the experimental objects. For Bearing 3_1, its full life cycle is 42 h 18 min, the total number of samples is 2538, the operating speed is 2400 r/min, and the applied radial force is 10 kN. For Bearing 2_1, its full life cycle is 8 h 11 min, the total number of samples is 491, the operating speed is 2250 r/min, and the applied radial force is 11 kN.  
Figure 8: Bearing testbed.  
The different characteristic frequencies of the bearing are calculated as follows:  
Bearing rotation frequency \(f_{r}\):  
\[f_{r}=\frac{n}{60} \tag{17}\]  
The fault characteristic frequency of the bearing outer ring \(f_{o}\):  
\[f_{o}=0.5 zf_{r}\bigg{(}1-\frac{d}{D}\cos\alpha\bigg{)} \tag{18}\]  
Fault characteristic frequency of bearing inner ring \(f_{i}\):  
\[f_{i}=0.5 zf_{r}\bigg{(}1+\frac{d}{D}\cos\alpha\bigg{)} \tag{19}\]  
The fault characteristic frequency of the bearing rolling element \(f_{R}\):  
\[f_{R}=\frac{D}{d}f_{r}\bigg{[}1-\bigg{(}\frac{d}{D}\bigg{)}^{2}\cos^{2}\alpha \bigg{]} \tag{20}\]  
Fault characteristic frequency of bearing cage \(f_{c}\):  
\[f_{c}=0.5f_{r}\bigg{(}1-\frac{d}{D}\cos\alpha\bigg{)} \tag{21}\]  
where \(n\) is the rotation speed of the bearing, \(d\) represents the diameter of the bearing rolling element, \(D\) represents the pitch diameter of the bearing, \(\alpha\) represents the contact angle of the bearing, and z represents the number of bearing balls.  
Combining Table 1 and Equations (17)-(21), it can be obtained that the rotation frequency \(f_{r}\) of Bearing 3_1 is 40 Hz, the fault characteristic frequency of the outer ring \(f_{o}\) is 123.32 Hz, the fault characteristic frequency of the inner ring \(f_{i}\) is 196.68 Hz, and the fault characteristic frequency of the rolling element \(f_{R}\) is 165.33 Hz. The fault characteristic frequency of the cage \(f_{c}\) is 15.42 Hz.
## 5 Performance Evaluation  
In this section, we evaluate the performance of our partitioning model and task offloading algorithm through simulation experiments. We consider the following influencing factors: data size, number of Edge Node CPU Cores, and request arrival rate. Moreover, in our simulation experiments, we compare our model with three baseline algorithms and use the method of controlling variables to evaluate the impact of influencing factors on our model and algorithm.
## 3 Static/Dynamic Response Characteristics of IEPCM
### Transient Response Analysis of IEPCM  
Transient response analysis is also called the time-history dynamic response analysis of a structure bearing, at any time, a varying load. The transient response analysis of a structure can effectively  
Figure 4: The fourth-order vibration mode of IEPCM.  
Figure 5: Strain distributions of IEPCM vs. frequency: (**a**) \(x\)-direction; (**b**) \(y\)-direction.  
alleviate and even avoid the negative impact aroused from structural resonance and fatigue. An AC voltage of 10 Vrms with a period of 0.4 s was applied to the electrodes. The voltage step was set as 0.2 Vrms. The transient dynamic analysis of the IEPCM was implemented. Similarly, the strains in the \(x\)- and \(y\)-direction on one point near the left edge on the top surface of the IEPCM were extracted, the corresponding features changing with time are shown in Figure 6, and its hysteretic characteristics were also analyzed.  
As demonstrated in Figure 6a,b, the strain fluctuates along with time; some hysteresis characteristics exist for the IEPCM, which may bring forth some negative effect on the fatigue. In fact, other than the influence from the IEPCM material itself and the production process, the load region of the driving voltage has a great influence on the curve shape. The amplitude of each strain response is different from the others. Therefore, during the preparation of the IEPCM, it is essential to ensure the positive and negative electrodes of the IEPCM are symmetric as far as possible, including the main branch corresponding to the positive and negative electrode branches [28].
## 4 Ground Roll Attenuation via NA-MEMD
### Synthetic Data  
In this section, we first test the proposed method on synthetic seismic data. The dataset contains a sweep signal with a 2-20 Hz frequency band to represent the ground roll and seismic wave simulated by finite difference for a three-layer model as shown in Table 1[3; 18]. According to the polarization property of ground roll, the phase of the horizontal component has a 90 degree lag compared to the vertical component. A 50 Hz Ricker wavelet was used for the body wave with a time sample interval of 1 ms. The number of samples for each trace was 1001, and the number of traces for one shoot was 200. The model parameters are presented in Table 1. Figure 4 shows the ground roll attenuation results of the synthetic data obtained using the NA-MEMD method.  
Figure 4a,d represents the horizontal and vertical components of the synthetic data, respectively. Two noise-assisted signals are embedded into the synthetic data to help overcome mode mixing when using the MEMD method. We decomposed the model data into eight IMFs. According to the NA-MEMD principle, the first two IMFs are related to noises; therefore, they are discarded. Owing to the low-frequency characteristic of ground roll, the seventh and eighth IMFs dominated by the ground roll are directly subtracted from the decomposition. We reconstructed the low-index IMFs and obtained the final data with ground roll attenuation. Figure 4b,e displays the data after removing the ground roll of the horizontal component in Figure 4a and the vertical component in Figure 4d. It can be seen that the ground roll is well suppressed. From the ground roll separated in Figure 4c,f, we can see that the residual energy of the reflection wave is weak. During the removal of ground roll using the NA-MEMD method, the number of removed IMFs is a key parameter for the suppression effect. Figure 5 shows the sensitivity for the seismic wave separation with different numbers of subtracted IMFs. We find that the more high-index IMFs are removed, the better ground roll suppression is achieved. Meanwhile, more damage occurs to the reflected wave. The parameter choice of removed IMFs is a trade-off between less damage to the reflection energy and more suppression for the ground roll.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer** & **Thickness (m)** & **Poisson’s Ratio** & \(\mathbf{V_{s}}\) (m/s) & \(\mathbf{V_{p}}\) (m/s) & **Density (kg/m\({}^{3}\))** \\ \hline
1 & 3000 & 0.253 & 1150 & 2000 & 2010 \\
2 & 1000 & 0.251 & 1730 & 3000 & 2200 \\
3 & 4000 & 0.250 & 2310 & 4000 & 2350 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model parameters.
# Slicing on the Road: Enabling the Automotive Vertical through 5G Network Softwarization
## 2 Background, Motivation, and Contributions
### Contributions  
The surveyed literature clearly shows that, overall, the topic of network slicing for the automotive vertical is still in its infancy. On the one hand, its potential has not been adequately disclosed and a clear specification of the required V2X customization in the 5G slicing architecture are still missing;on the other hand, practical design has been marginally targeted in the literature. To fill these gaps, this paper aims to advance the state of the art with the following main contributions:  
* A 5G-V2X network slicing framework is designed that enables the interplay of the MEC, SDN and NFV paradigms for the automotive vertical context. The high-level structure is inspired by a widely accepted three-layer architecture, but we make the effort to specify and customize to the V2X case the involved entities, their roles, and the mechanisms required to orchestrate the lifecycle and the behavior of V2X slices with a focus on the cooperative driving service.
* Special attention is given to the way of handling cross-operator interaction that is crucial for V2X due to mobility of vehicles. Specifically, the concept of federated slicing is considered vital to manage the roaming procedures for the V2X slice.
* Some reproducible PoC experiments are implemented through the Mininet-WiFi [18] wireless SDN emulator, which has been overhauled to demonstrate the designed framework capability of supporting the challenging case of cooperative driving in a multi-operator environment.
## 4 Conclusions  
In this work, the ZrO2(MgO)/CaAl2O4+CaAl4O7 bilayer structure used for a sulfur sensor was successfully fabricated by the laser powder cladding method using CaAl2O4 and CaAl4O7 as the coating materials and ZrO2(MgO) as the substrate. The properties of this structure in terms of the microstructure, composition, ionic conductivity and application in sulfur determination were investigated. The conclusions drawn are as follows.  
1. The laser cladding layer was composed of Ca0.15 Zr0.85O1.85, CaAl2O4 and CaAl4O7 phases, which achieved good densification and adheres to the substrate firmly.
2. The fine bilayer structure showed a high ionic conductivity that increased linearly with the increase of temperature.
3. The sulfur sensor was assembled using the fabricated bilayer structure to measure the sulfur content in the carbon-saturated liquid iron, and the sulfur content could be calculated through the following empirical equation based on the EMF measured with the sensor: \[E = 52.53\ln[\mathrm{wt}\%\mathrm{S}]+42.04\;\;R = \;0.96\;\;T = \;1773\;\mathrm{K}\] (8) \[E = 54.74\ln[\mathrm{wt}\%\mathrm{S}]+14.71\;\;R = \;0.99\;\;T = \;1823\;\mathrm{K}\] (9)  
[PERSON] and [PERSON] designed the experiment and wrote the paper; [PERSON] performed the experiments; [PERSON] and [PERSON] prepared the calcium aluminate powders used as the coating materials; [PERSON] guided the experiments and revised the original paper. All authors contributed to the analyses and discussion of the results.  
This research was funded by the National Natural Science Foundation of China (51874083, 51404056).  
This work was financially supported by the National Natural Science Foundation of China (51874083, 51404056).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 5: Relationship between sensor electromotive force (EMF) and sulfur content.
## 2 Materials and Methods
### Numerical and Experimental Phantoms
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tissue Type and Respective** & **Permititivity at Infinite** & **Debye Dispersion \(\Delta\varepsilon\)** & **Conductivity \(\sigma_{\mathrm{s}}\) (S/m)** \\
**Color in Figure 2d** & **Frequency \(\varepsilon_{\infty}\)** & **Debye Dispersion \(\Delta\varepsilon\)** & **Conductivity \(\sigma_{\mathrm{s}}\) (S/m)** \\ \hline skin (red) & 37.65 & 11.36 & 0.62 \\ fat (orange) & 8.61 & 2.92 & 0.08 \\ bone (blue) & 8.48 & 4.38 & 0.08 \\ white matter (green) & 35.89 & 6.73 & 0.45 \\ gray matter (brown) & 40.03 & 14.47 & 0.72 \\ blood (black) & 44.67 & 18.02 & 1.32 \\ CSF (yellow) & 66.08 & 4.61 & 2.34 \\ dura (pink) & 39.89 & 6.00 & 0.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Debye parameters for the Zubal head phantom. Figure 3: Photos of the experimental head phantom (**a,b**), antenna (**c**) and system (**d,e**) used to validate the 3-D DBIM-FISTA algorithm. Details of the setup and experiment can be found in [54]. The number of required antennas for a 2-D microwave imaging problem has been defined in [56] using the approximation,  
\[M\approx 2k_{0}\alpha, \tag{35}\]  
where \(\alpha\) is the radius of the investigated region, while for 3-D problems the required number is slightly larger. This requirement may be difficult to satisfy in practice due to experimental limitations; for example, our experimental setup uses eight antenna hosted by an eight-port VNA system, and the same setup is used for each antenna ring in our CST scenarios. We note that the antennas are modelled as point sources in our algorithms, and the received signals by the antennas are normalized with respect to the source. The calibrated scattered fields are a standard total field calibration procedure [57] as,  
\[E_{cali}^{s}(i,j)=\frac{E_{b}(i,j)}{S_{b}(i,j)}\big{(}S_{t}(i,j)-S_{b}(i,j) \big{)} \tag{36}\]  
where \(S_{b}\) and \(S_{t}\) represent the S-parameters for the background case and for the unknown case to be imaged, as measured experimentally or modeled in CST. In (36), \(E_{b}\) represents the background incident signals calculated by the FDTD forward model, and \((i,j)\) refers to the transmitter-receiver pair.
# Advancing Data Privacy in Cloud Storage: A Novel Multi-Layer Encoding Framework
## 4 The Anticipated Cloud Storage Structure
### Cloud Storage, Privacy-Preserving Components, and the Experimental Environment  
Privacy-preserving technologies aim to safeguard the personally identifiable information (PII) of users while ensuring the functionality of cloud storage systems. Researchers have proposed various privacy-preserving solutions for cloud computing, employing techniques such as encryption, access rights, access control, and _data/user anonymization_. In this section, we will discuss existing frameworks and hardware used by researchers over time.  
The algorithmic steps for ensuring privacy-preserving attributes in a multi-layer encoding framework are as follows (_Step 1_ to _Step 12_), and the corresponding source code is presented in _Appendix A_:  
_Step 1_: Receive input data containing potentially sensitive attributes, such as personally identifiable information (PII) or financial records.  
_Step 2_: Analyze the data schema to identify attributes that require protection to uphold privacy.  
_Step 3_: Initialize encryption processes by establishing cryptographic frameworks and generating encryption keys.  
_Step 4_: Employ a multi-layer encoding approach, utilizing various encryption techniques like symmetric or asymmetric encryption, to enhance data security.  
_Step 5_: Selectively apply encryption to sensitive attributes based on their level of sensitivity and privacy requirements.  
_Step 6_: Transform the data into an encoded format while ensuring integrity during the encoding process.  
_Step 7_: Manage encoding layers effectively, including tracking encryption keys and maintaining access controls to uphold data integrity.  
_Step 8_: Implement robust access control mechanisms to restrict unauthorized access to the encoded data.  
_Step 9_: Utilize secure storage and transmission methods to prevent unauthorized access and data breaches.  
_Step 10_: Decrypt the data selectively, allowing for the retrieval of original attributes when necessary.  
_Step 11_: Validate privacy preservation measures through regular assessments and audits to ensure compliance with privacy standards.  
_Step 12_: Continuously monitor and update encryption algorithms, access controls, and privacy policies to adapt to emerging threats and maintain effective data privacy protection.
## 3 Results and Discussion
### Extent of Dust Accumulation and Soil Loss
#### 3.2.3 Temporal and Spatial Variation  
The order of abundance in soil loss varied among study sites, with some sites demonstrating consistency in their patterns, while others exhibited significant differences based on the methods used (wind erosion, wind surface, and dust deposition). For the sites S1, S2, S3, S4, S5, S6, S8, S11, S15, S16, S17, S18, and S19, the highest soil loss was recorded on 20 July 2022, while the lowest was observed on 30 August 2022. In contrast, site S9 displayed a different trend: for wind erosion, the highest soil loss occurred on 10 July 2022,and the lowest on 30 August 2022. For wind surface, the highest and lowest amounts were recorded on 20 July 2022, and 30 August 2022, respectively. For dust deposition, the highest and lowest were also recorded on 10 July 2022, and 30 August 2022, respectively. This reorganization of the results allows for a clearer comparison across methods and sites.  
This variability can be explained by the interplay of local environmental conditions, such as soil texture, topography, and vegetation cover, which affect the efficiency of each method in capturing soil movement. Uniform wind speeds, land-use practices, or soil composition likely had an impact on the processes causing soil loss at sites where patterns were consistent, whether through wind erosion, surface wind impacts, or dust deposition. These factors led to predictable results. On the other hand, locations exhibiting notable disparities could have been impacted by diverse microclimatic circumstances or anthropogenic actions that shaped the amount of soil lost, moved, or deposited. These variations highlight the value of employing a variety of techniques to gain a thorough understanding of wind-induced soil loss, since a single technique might not fully capture the scope of erosion processes at various sites. These observations align with studies such as those by [PERSON] et al. [9] and [PERSON] et al. [4], which highlight the complexity of wind erosion processes and how environmental factors and land-use changes influence soil loss patterns across regions.  
In S12, S14, and S20, the highest and lowest soil loss for wind erosion and wind surface were recorded on 20 July 2022, and 30 August 2022, while for dust deposition, different dates were recorded: 20 July 2022, and 15 July 2022. In S9, the highest soil loss due to wind erosion and dust deposition occurred on 10 July 2022, and the lowest on 30 August 2022, while for wind surface, the highest loss occurred on 20 July 2022, and the lowest on 30 August 2022.
# Explainable Internet Traffic Classification
## 4 Experimental Results
### SVM Classifier  
As far as the SVM classifier is concerned, we have used the implementation available in WEKA Toolkit ([[https://www.cs.waikato.ac.nz/ml/weka/](https://www.cs.waikato.ac.nz/ml/weka/)]([https://www.cs.waikato.ac.nz/ml/weka/](https://www.cs.waikato.ac.nz/ml/weka/)) accessed on 20 May 2021) based on the Sequential Minimal Optimization training algorithm [46]. The parameters of the algorithm have been set as  
* \(\epsilon=10^{-12}\)
* \(tolerance=10^{-13}\)  
In Table 4, we show the achieved accuracy on both datasets and for each feature extraction method. Moreover, Tables 5 and 6 show the results in terms of \(TPR\) and \(FPR\) for each class of the UniBS and UPC data sets, respectively. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Description of the Feature** & **U/M** & \multicolumn{2}{c}{**Features**} \\  & **Forward** & **Reverse** \\ \hline Flow duration & _ms_ & \(\Delta\) & \\ Number of transferred packets & - & _f\_N_ & _r\_N_ \\ Transfered volume & \(B\) & _f\_V_ & _r\_V_ \\ Minimum packet size & \(B\) & _f\_S\({}_{m}\)_ & _r\_S\({}_{m}\)_ \\ Maximum packet size & \(B\) & _f\_S\({}_{M}\)_ & _r\_S\({}_{M}\)_ \\ Average packet size & \(B\) & _f\_S\({}_{n}\)_ & _r\_S\({}_{n}\)_ \\ Standard deviation of packet size & \(B\) & _f\_S\({}_{v}\)_ & _r\_S\({}_{v}\)_ \\ Minimum inter-packet time & _ms_ & _f\_T\({}_{m}\)_ & _r\_T\({}_{m}\)_ \\ Maximum inter-packet time & _ms_ & _f\_T\({}_{M}\)_ & _r\_T\({}_{M}\)_ \\ Average inter-packet time & _ms_ & _f\_T\({}_{\mu}\)_ & _r\_T\({}_{\mu}\)_ \\ Standard deviation of inter-packet time & _ms_ & _f\_T\({}_{\sigma}\)_ & _r\_T\({}_{\sigma}\)_ \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical features. Regarding the accuracy, the best performance is achieved, for both datasets, adopting composite features and \(H=10\) (\(ACC=0.874\) over the UPC data set and \(ACC=0.896\) over the UniBS data set). In both cases, adopting statistical features, the SVM classifier achieves better performances rather than adopting composite features with \(H=3\), and even with \(H=5\) in the case of UniBS data set. Finally, for a deeper analysis, Table 7 reports the confusion matrix for the UniBS case with composite features and \(H=10\) (note that for sake of brevity we do not show the confusion matrix for all the cases, as they would not add any significant insight). Note that, for the considered case, the worst results are obtained for Skype, which is often classified as Amule. Such a result can be justified by the fact that the two applications have a similar architecture. \begin{table}
\begin{tabular}{c c c} \hline
**Data-Set** & **Features** & **Accuracy** \\ \hline \multirow{4}{*}{UniBS} & Statistical & 0.759 \\  & Composite (\(H=3\)) & 0.662 \\  & Composite (\(H=5\)) & 0.717 \\  & Composite (\(H=10\)) & **0.874** \\ \hline \multirow{4}{*}{UPC} & Statistical & 0.71 \\  & Composite (\(H=3\)) & 0.538 \\ \cline{1-1}  & Composite (\(H=5\)) & 0.776 \\ \cline{1-1}  & Composite (\(H=10\)) & **0.896** \\ \hline \end{tabular}
\end{table} 
Table 4: Accuracy achieved by the support vector machine (SVM) classifier.