esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Analysis of the Sealing Ring Compression Process  
By analyzing the deformation and stress of the seal ring under different compression rates, the stress concentration position of the seal ring during operation could be obtained, and the influence of the compression rate on the shape of the seal ring could be judged. After completing the testing of the sealing contact surface in the first step, the second step of the piston rod compression seal ring action was carried out. The stress nephogram of the seal ring corresponding to the completion of the second step of the GS seal ring under different compression rates is shown in Figure 10.  
According to the stress nephogram in Figure 10, we can observe the following maximum stresses for different compression ratios: compression ratios of 10, 15, 20, 25 and 30% result in maximum stresses of 16, 26, 41, 72, and 126 MPa. From the graph, it is evident that there is a stress concentration at the lip of the sealing ring, which can potentially lead to excessive wear of the lip and result in the loss of sealing performance. At a compression ratio of 30%, the O-ring is completely compressed into the sealing groove and experiences squeezing against the step seal ring, as indicated in Figure 10e. In practical applications, this can lead to shear damage of the O-ring, failing the sealing ring, especially when subjected to shock loads from hydro-pneumatic springs. Excessive stress on the step seal ring can also accelerate its wear and reduce the sealing ring's service life. Therefore, in the subsequent sections, we will not further report the sealing conditions at a compression ratio of 30%.  
The path of the contact surface between the step ring and the piston rod was selected, and the contact pressure on the same path under different compression rates was measured. As shown in Figure 11, the contact pressure of sealing surface I under different compression amounts increases with the increase in the compression amount, and the width of the contact surface also increased with the increase in the contact pressure. When comparing the contact pressure of sealing surfaces III and II under different compression amounts, as shown in Table 2, we found that the peak contact pressure of the two sealing surfaces was approximately equal, and the contact width was roughly symmetrical, so the following only analyze the sealing conditions of sealing surfaces I and III.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compression Ratio** & **10\%** & **15\%** & **20\%** & **25\%** & **30\%** \\ \hline Sealing Surface I & 26.96 & 42.93 & 64.28 & 120.78 & 213.47 \\ Sealing Surface II & 3.03 & 4.49 & 6.88 & 14.96 & 34.46 \\ Sealing Surface III & 3.03 & 4.49 & 6.83 & 14.79 & 33.07 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Peak contact pressures on different sealing surfaces at various compression ratios (unit: MPa).  
Figure 10: Seal stress nephogram at different compression rates (unit: MPa): (**a**) compression ratio of 10%, (**b**) compression ratio of 15%, (**c**) compression ratio of 20%, (**d**) compression ratio of 25%, (**e**) compression ratio of 30%.
# A Theoretical Study on the Resilience Evaluation Method of Operational Road Tunnel Systems
## 4 Theoretical Analysis Model of Road Tunnel Resilience
### Basic Properties of Road Tunnel Resilience  
Combined with the theoretical analysis model of road tunnel resilience, the resilience level can be described using three aspects:  
1. [label=(0)]
2. The influence degree of the road tunnel system subjected to external disturbances.
3. The ability of the road tunnel system to respond to external disturbances.
4. Road tunnel system function recovery level.  
Based on the 4R attributes of resilience, five resilience attributes of road tunnels were proposed to describe the resilience level, as shown in Table 2.  
Resistibility and responsiveness are used to describe the ability of road tunnel systems to respond to external disturbances. A higher resilience level indicates greater resistance to external disturbances and shorter response times. Adaptability is used to describe the influence degree of the road tunnel system subjected to external disturbances. A higher resilience level indicates a lower impact when subjected to external disturbances. Recuperability and robustness are used to describe the system function recovery level. A higher resilience level indicates shorter recovery times and higher post-disaster functional levels.  
Figure 6: Ideal resilience curve of the road tunnel system, where \(\Delta\)t is the disaster loss lag time.
## 2 Materials and Methods
### DB Implementation and Designations of the Specimens  
DB (Figure 2) was implemented on an Index Traub CNC lathe (Germany). The main governing factors in this process are the radius r of the spherical-ended diamond insert, the burnishing force Fb, the feed rate f, and the burnishing velocity. In this study, the smoothing DB process [1; 6] was implemented. The governing factor values were obtained from [6] and are listed in Table 1. A special burnishing device mounted on the tool post of the lathe was used. The required burnishing force was set by deforming a linear axial spring located in the device. The necessary burnishing force magnitude was defined in advance via the screw-nut mechanism based on the spring's characteristic. The deforming diamond insert was then brought into contact with the specimen at its centerline and normal to the surface being treated. The device was fed a further 0.05 mm into the specimen to allow the diamond insert to become disengaged from the stop in the device; thus, an elastic contact between the diamond insert and the surface being burnished was achieved.  
The DB process was implemented under the following four lubrication and cooling configurations: (1) conventional flood lubrication with Vasco 6000 lubricant; (2) cold air cooling without lubrication using a special device (Emuge-Franken, Germany) with a cold air nozzle based on the principle of vortex tubes; (3) nitrogen-assisted cooling without lubrication using the same device but with nitrogen from a pressure bottle instead of air; and (4) dry conditions without cooling. In the second and third cooling configurations, the following temperatures were measured on the surface of the nozzle using a thermocouple located as close as possible to the contact zone between the diamond insert and the treated specimen: \(-19\ ^{\circ}\)C and \(-31\ ^{\circ}\)C, respectively. The thermocouple was K-type with a range of \(-40\ ^{\circ}\)C to \(+100\ ^{\circ}\)C. The signal from it entered a portable device for measuring temperature through thermocouples.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DB Process Type** & **r, mm** & **Fb, N** & **f, mm/rev** & **v, m/min** \\ \hline Smoothing & 3 & 300 & 0.07 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Magnitudes of the governing factors used in this study.  
Figure 1: Specim geometry: (**a**) tensile test; (**b**) rotating bending fatigue test.  
In all the performed tests, the diamond-burnished specimens were denoted as follows: F (flood lubrication), A (air cooling), N (nitrogen cooling), and D (dry without cooling). The same notations are used throughout to indicate the corresponding DB processes.
# Deep Learning-Based Low-Frequency Passive Acoustic Source Localization
## 3 Test Case Setups
### Case 2: Two-Dimensional ASL on the Horizon
#### 3.2.1 Case 2 (i): Monopole (S1)  
To generate the training set, a monopole with unit source strength was placed at a real, random value of \(\theta\) (in the range of \(\theta\)) from the center of the four-microphone array and at a fixed radial distance \(r\) (=10 m). The cross-spectral matrix was calculated with the same procedure as in case 1 and its real part was used as an input to a CNN. The value of \(\theta\) falls into a certain class and the index of this class was the ground truth against which the network was trained.
# Dead-Beat Control Cooperating with State Observer for Single-Phase Electric Springs
## 3 Dead-Beat Control for Single-Phase ESs
### Dead-Beat Control Cooperating with State Observer for Single-Phase ES  
The execution of the dead-beat control could be quite demanding, to avoid the shortcoming of significantly increasing the maximum width of the voltage pulses at the VSI output, it is convenient to, calculate \(v_{i}(k)\) s one control period ahead. This implies that \(v_{i}(k)\) must be calculated in the (\(k-1\)) control period by resorting to an estimation of the associated variables. A good tool to estimate the variables of a system is a state observer [18], as shown in Figure 4a,b. Here, a full order observer is used, expressed as  
\[\left\{\begin{array}{rl}\hat{x}(k)&=(A-HC)x(k)+Bu(k)+Hy(k)\\ \hat{y}(k)&=C\hat{x}(k)\end{array}\right. \tag{15}\]  
Figure 4: The proposed dead-beat control cooperating with state observer. (**a**) Full control diagram; (**b**) Diagram of the state observer adopted in the control.  
where, \(H\) is the observer gain matrix. The error vector of the state observer is  
\[e(k+1)=x(k+1)-x(k+1)=(A-HC)(x(k)-\hat{x}(k)) \tag{16}\]  
The dynamic properties of the error vector depend on the eigenvalues of the matrix (\(A-HC\)). For a stable (\(A-HC\)), the error vector tends to zero for any initial error vector \(e(0)\). In other words, \(\hat{x}(k)\) will converge to \(x(k)\) regardless of the values of \(x(0)\) and \(\hat{x}(0)\).  
If the control system is completely observable, it can be proven that \(H\) can be chosen in order that (\(A-HC\)) is asymptotically stable and has the desired dynamics o that the error vector moves toward zero (origin) at a speed fast enough. This outcome is achieved by a suitable placement of the eigenvalues of (A-HC). In practice, the poles of the observer are placed within three to five times far away from the imaginary axis than the poles of the control system.
###### Abstract  
In recent years, the in situ resource utilization of CO\({}_{2}\) in the Martian atmosphere by low-temperature plasma technology has garnered significant attention. However, numerical simulation is extremely time-consuming for modeling the complex CO\({}_{2}\) plasma, involving tens of species and hundreds of reactions, especially under Martian pressure. In this study, a deep neural network (DNN) with multiple hidden layers is introduced to investigate the CO\({}_{2}\) conversion in radio-frequency (RF) discharges at a given power density under Martian pressure in almost real time. After training on the dataset obtained from the fluid model or experimental measurements, the DNN shows the ability to accurately and efficiently predict the various discharge characteristics and plasma chemistry of RF CO\({}_{2}\) discharge even in seconds. Compared with conventional fluid models, the computational efficiency of the DNN is improved by nearly 10\({}^{6}\) times; thus, a real-time calculation of RF CO\({}_{2}\) discharge can almost be achieved. The DNN can provide an enormous amount of data to enhance the simulation results due to the very high computational efficiency. The numerical data also suggest that the CO\({}_{2}\) conversion increases with driving frequency at a fixed power density. This study shows the ability of the DNN-based approach to investigate CO\({}_{2}\) conversion in RF discharges for various applications, providing a promising tool for the modeling of complex non-thermal plasmas.  
deep neural network; CO\({}_{2}\) conversion; radio-frequency discharges; Martian pressure +
Footnote †: journal: Applied Sciences
# Long-Term Structural State Trend Forecasting Based on an FFT-Informer Model
## 1 Introduction
In many LSTF tasks, feature extraction methods [27; 28; 29] have been applied to time series data, which are able to explain sequence relationships and help forecasting models learn the nonlinear characteristics of structural state data. Moreover, extracted features, which reflect the state of the structure to a certain extent, are also widely used in the SHM [30; 31], especially the amplitude and phase of data [32; 33]. Among them, FFT has been widely recognized for its characteristics of decomposing data to make them stable, fast convergence, and reflecting the trend to a certain extent [29; 34]. Therefore, a long-term structural state trend forecasting method based on LSTF with the improved Informer model integrated with FFT is proposed, namedthe FFT-Informer model. Specially, by using FFT, the structural state trend feature is represented by extracting amplitude and phase of a certain period of a data sequence. Structural state trend, a long sequence, can be forecasted in a one-forward operation by the Informer model that can achieve high inference speed and accuracy of prediction based on the Transformer model. Furthermore, a Hampel filter that filters the abnormal deviation of a data sequence, is integrated into the Multi-head ProbSparse self-attention in the Informer model to improve forecasting accuracy by reducing the effect of abnormal data points. Experimental results on two classical data sets show that the FFT-Informer model achieves high and stable accuracy and outperforms the comparative models in forecasting accuracy. It indicates that this model can effectively forecast the long-term state trend change of a structure and is proposed to be applied to structural state trend forecasting and early damage warning. The main contributions of our work are as follows:  
1. An efficient LSTF method combined FFT with an improved Informer model is proposed, which successfully applies machine learning to long-term structural state forecasting in a one-forward operation rather than a step-by-step way. * The Multi-head ProbSparse attention in the Informer model is integrated with a Hampel filter, named the Multi-head ProbHamSparse attention, focuses on filtering abnormal deviations by setting the deviation upper limit, which reduces the impact of abnormal data items and obtains more accurate trends and dependency. * Experimental results on two classical data sets show that the FFT-Informer model achieves high and stable accuracy and outperforms the comparative models in forecasting accuracy. It indicates that the proposed method can be applied in real structural state forecasting. The rest of this article is organized as follows. In Section 2, the proposed FFT-Informer model is elaborated in detail. The experiments and results are presented and discussed in Section 3. Finally, a conclusion and potential direction for future research works is given.
## 4 Discussion
On this premise, it can be stated that the best result is obtained using the amor wavelet with an accuracy of 99.34% at a learning rate of 0.0001. Figure 16: Average accuracy results with test data with the application of the morse wavelet. Figure 15: Accuracy results with test data using the morse wavelet. To highlight how our results compare with prior studies, [7] presents a review of bearing fault detection and diagnosis using the Case Western Reserve University dataset with deep learning approaches. Models like convolutional neural networks (CNNs) achieved up to 99.91% accuracy without requiring preprocessing, while Deep Belief Networks (DBNs) reached 100% accuracy, even in noisy environments. Additionally, Generative Adversarial Networks (GANs) have enhanced fault detection by generating synthetic data. On the other hand, strategies like Reinforcement Learning (RL) have shown improvements in detection without the need for labeled data, and transfer learning with ResNet-50 achieved an accuracy of 99.99%, outperforming models such as VGG-16 and VGG-19. Results in our study indicate that precise hyperparameter tuning enhances diagnostic accuracy. Scalograms proved particularly effective in identifying distinct vibration patterns for faults in bearings' inner and outer races. Scalograms have been employed in this study to convert one-dimensional time-domain signals into two-dimensional time-frequency representations. Scalograms provide a highly localized time-frequency representation, making them particularly useful for analyzing transient and non-stationary signals, such as vibration signals from faulty bearings. Unlike the Short-Time Fourier Transform (STFT), which uses a fixed window size and may lead to poor time-frequency resolution trade-offs, the wavelet transform adapts its resolution dynamically. High-frequency components are represented with fine time resolution, while low-frequency components maintain high frequency resolution. This adaptability is critical in bearing fault diagnosis, where faults often generate impulses that appear as transient events within the signal. Compared to the Hilbert-Huang Transform (HHT), which decomposes signals into intrinsic mode functions (IMFs) using empirical mode decomposition (EMD), wavelet-based scalograms offer a more robust representation without requiring additional signal decomposition steps. While HHT is effective for extracting mode-dependent features, it is highly sensitive to noise and mode-mixing issues, which may introduce artifacts in the time-frequency representation. Additionally, the MUSIC algorithm, which is based on eigenvalue decomposition for spectral estimation, is effective for resolving closely spaced frequency components but lacks the ability to capture time-domain variations effectively. Since bearing faults manifest as transient events rather than stationary frequency components, MUSIC is less suited for this application.
# Proactive Service Caching in a MEC System by Using Spatio-Temporal Correlation among MEC Servers
## 4 Proactive Service Caching Method
### Service Cache Decision  
Given a sequence of \(\Omega(t)s\), we construct a training set and a validation set. We group a set of consecutive images \(X(t)=\{\Omega(t),\Omega(t-1),\dots,\Omega(t-w_{a}+1)\}\) as aninput to the ConvLSTM model and use \(Y(t)=\Omega(t+1)\) as a label for \(X(t)\). Once a ConvLSTM model is trained, it predicts \(\Omega(t+1)\) when \(X(t)\) is given. We denote the predicted \(\Omega(t+1)\) as \(\hat{\Omega}(t+1)=\{\hat{v}_{1}(t+1),\dots,\hat{v}_{M}(t+1)\}^{T}\). For each MECS \(m\in\mathcal{M}\), a controller sorts the elements in \(\hat{v}_{m}(t+1)\) in a descending order and makes a sorted set \(\tilde{v}_{m}(t+1)\). We denote the sorted \(\hat{\Omega}(t+1)\) as \(\hat{\Omega}(t+1)=\{\tilde{v}_{1}(t+1),\dots,\tilde{v}_{M}(t+1)\}^{T}\), where \(\tilde{v}_{m}(t+1)=\{\tilde{v}_{m,1}(t+1),\dots,\tilde{v}_{m,K}(t+1)\}\).  
Since the cache size of a MECS \(m\) is \(\eta_{m}\), a controller determines the services for \(S_{m}(t+1)\) by selecting the most popular \(\eta_{m}\) services according to \(\tilde{v}_{m}(t+1)\). In other words, \(S_{m}(t+1)\) is composed of the services whose predicted popularity is larger than or equal to \(\tilde{v}_{m,\eta_{m}}(t+1)\). We summarize the service cache decision algorithm in Algorithm 1.  
```
1:Initial State:\(X(t)=\{\Omega(t-1),\Omega(t-2),\dots,\Omega(t-w_{a})\}\)
2:At the end of a time slot :
3: Collect \(Z_{m}(t)\)s from all \(m\in\mathcal{M}\).
4: Calculate \(f_{m,k}(t)\) in Equation (8), \(\forall m\in\mathcal{M}\) and \(\forall k\in\mathcal{K}\).
5: Normalize \(f_{m,k}(t)\)s to make \(v_{m}(t)\) in Equation (10).
6: Construct a heatmap \(\Omega(t)\) in Equation (11).
7: Build an input sequence \(X(t)=\{\Omega(t),\Omega(t-1),\dots,\Omega(t-w_{a}+1)\}\).
8: Predict \(\Omega(t+1)\): \(\hat{\Omega}(t+1)\) = ConvLSTM (\(X(t)\)).
9:for\(m\in\mathcal{M}\)do
10: Build \(\tilde{v}_{m}(t+1)\) by sorting \(\hat{v}_{m}(t+1)\) in a descending order
11:\(S_{m}(t+1)=\emptyset\)
12:\(i=1\)
13:while\(i\leq\eta_{m}\)do
14: Get service id \(k\) corresponding to the \(i\)-th element of \(\tilde{v}_{m}(t+1)\)
15:\(S_{m}(t+1)=S_{m}(t+1)\cup\{k\}\)
16:\(i++\)
```  
**Algorithm 1** Service Cache Decision Algorithm
## 3 Results
In contrast, the QuEChERS method produced an average of 2.10 ng/mg per sample, including those without pesticide residues (\(p>0.001\)). When considering only the samples with pesticide residues, the SMPE method found an average of 6.77 ng/mg per sample, while the QuEChERS method identified only 3.99 ng/mg per sample (\(p>0.001\)). A visual representation of these numerical differences can be observed in Figure 1.  
In different locations characterized by varying pesticide application loads, the detection of chlorpyrifos exhibited discrepancies among environments and between methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Insect Families** & **N** & \
\begin{tabular}{c} **SMPE (ng of** \\ Chlorpyrifos/mg of Insect)** \\ \end{tabular} & **N QuEChERS** &
\begin{tabular}{c} **QuEChERS (ng of** \\ Chlorpyrifos/mg of Insect)** \\ \end{tabular} \\ \hline Nocutiidae & 2 & 0.14 & 3 & 0.15 \\ Plutellidae & 1 & 0.29 & - & - \\ Psyllidae & 1 & 0.61 & - & - \\ Pteromalidae & - & - & 1 & 0.21 \\ Sacophagidae & 1 & 0.65 & 1 & 0.99 \\ Staphylococcus & 2 & 0.24 & - & - \\ Tenebrionidae & 1 & 0.33 & - & - \\ Syrphidae & - & - & 1 & 0.87 \\ \hline Low & 9 & 0.04 & 14 & 0.03 \\ \hline Agromyzidae & - & - & 1 & 0.09 \\ Anthomide & 1 & 0.04 & 1 & 0.006 \\ Aphididae & - & - & 1 & 0.02 \\ Apidae & 1 & 0.003 & 1 & 0.005 \\ Calliphoridae & 1 & 0.04 & 1 & 0.05 \\ Chloromonadie & 1 & 0.09 & 1 & 0.002 \\ Gelechidae & 2 & 0.05 & 2 & 0.08 \\ Muscidae & 1 & 0.01 & 1 & 0.03 \\ Mycetophilidae & - & - & 1 & 0.02 \\ Nitidulidae & 2 & 0.06 & 3 & 0.01 \\ Staphylococcus & - & - & 1 & 0.02 \\ \hline Total & 63 & 7.56 & 57 & 5.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
Figure 1: **(A)** Percentage of samples with detected pesticides; **(B)** pesticide quantity in all samples (including non-detected samples) in ng/mg of sample; **(C)** pesticide amount in samples with pesticide detection (ng/mg of sample). In high-pesticide-load environments, the SMPE method detected 15.03 ng/mg of insects, followed by 12.71 ng/mg of insects for the QuEChERS method. In contrast, in the environment with a medium pesticide load, the values were 0.42 ng/mg insect for SMPE and 0.46 ng/mg insect for QuEChERS. In low-pesticide-load environments, the values were 0.04 ng/mg insect for SMPE and 0.03 ng/mg insect for QuEChERS. The detection values of chlorpyrifos varied among entomological families. For example, Psyllidae had 56.71 ng of chlorpyrifos per mg of insect, Hemerobiidae had 49.16 ng of chlorpyrifos per mg of insect, and Cicadellidae had 23.91 ng of chlorpyrifos per mg of insects. Values for all entomological groups are provided in Table 4.  
Usually, the SMPE method also presents different values from the QuEChERS method among entomological families but does not necessarily present higher values. Due to the low number of samples per entomological family, it was not possible to make a statistical comparison between the chlorpyrifos detection values among the groups. However, in absolute terms, some families had higher detection values, particularly in locations characterized by high pesticide application loads.