esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Artificial Neural Network as a Failure Pressure Prediction Tool  
The utilization of an artificial neural network (ANN) in the field of failure pressure assessment has improved over the years, resulting in more practical applications. In early applications of ANN, researchers took into account the physical, mechanical, operational, and environmental factors that influenced the residual strength of a pipeline [19].  
[PERSON] et al. [20] developed a multilayer feedforward neural network with backpropagation learning algorithms for predicting the failure pressure of X80 pipe with corrosion defects caused by stray current. By comparing the ANN model results to experimental  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Defect Type** & **Orientation** \\ \hline Single defect & \\ \hline \multirow{3}{*}{Interacting defects} & Longitudinally aligned \\ \cline{2-3}  & Circumferentially aligned \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of defects [12].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Corrosion Assessment Code** & **Interaction Rule** \\ \hline DNV-RP-F101 & \((s_{\mathrm{c}})_{Lim}=\pi\sqrt{Dt}\) \\ \hline API 579 & \((s_{\mathrm{c}})_{Lim}=(w_{1}+w_{2})/2\) \\ \hline BS 7910 & \((s_{\mathrm{I}})_{Lim}=3.0\sqrt{Dt}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Interaction rules applied for circumferentially aligned interacting defects in common corrosion assessment methods [18].  
burst test results and earlier failure pressure estimate model findings, it was established that the ANN model results are both accurate and efficient with a \(\mathbb{R}^{2}\) value of 0.9992. [PERSON] et al. [19] also used this approach in their research to determine the useful life of pipelines using the Levenberg-Marquart backpropagation algorithm. Their ANN model was able to predict the useful life of a pipeline with an error percentage of less than 5%.  
The architecture of an ANN is determined by the data type and desired output. The feedforward neural network algorithm is primarily used to predict the failure pressure of corroded pipelines. This form of ANN architecture (Figure 1) is designed to learn from paired datasets, in which the model is trained using one or more inputs and the matching output of the training dataset. A feedforward neural network is simple to implement and is well-suited for delivering a single output. Generally, a feedforward neural network is used with the Levenberg-Marquardt backpropagation approach to train the model, as it performs well and requires less time and epochs for convergence [21].  
For ANN applications, a small dataset is not recommended as it will not generalise the data well. [PERSON] et al. [22] and [PERSON] et al. [23] emphasised in their paper that an ANN model with reasonable accuracy and robustness cannot be achieved without sufficient real-life data. In such models, a comprehensive input is needed to ensure that the model is accurate. Hence, large training datasets need to be gathered [23]. However, obtaining a large training dataset for such cases is challenging.
# Efficient User-Serving Scheme in the User-Centric Cell-Free Massive MIMO System
###### Abstract  
A cell-free massive multiple input multiple output (MIMO) system is an attractive network model that is in the spotlight in 5G and future communication systems. Despite numerous advantages, the cell-free massive MIMO system has a problem in that it is difficult to operate in reality due to its vast amount of calculation. The user-centric cell-free massive MIMO model has a more feasible and scalable benefit than the cell-free massive MIMO model. However, this model has the disadvantage that as the number of users in the area increases, there are users who do not receive the service. In this paper, the proposed scheme creates connections for unserved users under a user-centric scheme without additional access point (AP) installation and disconnection for existing users. A downlink user-centric cell-free massive MIMO system model in which the APs are connected to the central processing unit (CPU) and the APs and users are geographically distributed is considered. First, the downlink spectral efficiency formula is derived and applied to the user-centric cell-free massive MIMO system. Then, the proposed scheme and power control algorithm are applied to the derived formula. The simulation results show that the unserved users within the area disappear by using the proposed scheme, while the bit error rate (BER) performance and sum rate improve compared to the existing scheme. In addition, it is shown that the proposed scheme works well even with a very large number of users in the area, and a significant service performance improvement for the worst 10% of users and the overall improvement of per-user throughput for the bottom 70% of users are ensured.  
5G; cell-free massive MIMO; user-centric cell-free massive MIMO; unserved users +
Footnote †: journal: _Sensors_
# Finite Element Analysis of Geogrid-Incorporated Flexible Pavement with Soft Subgrade
## 3 Results
### Strain and Stress at the Subgrade  
#### 3.3.1 Vertical Stain  
The vertical compressive strain experienced by the subgrade is considered pivotal as it directly impacts the permanent deformation of the pavement. Figure 8 illustrates the vertical strain atop the subgrade for pavements with and without geogrid reinforcement, subjected to 10,000 and 20,000 load repetitions (at CBR = 1%). These strain values are measured beneath the tyre imprint and observed as they propagate towards the pavement's edge, with the normalized distance from the tyre imprint depicted in the figure. The incorporation of a geogrid layer leads to a significant reduction in vertical strain at the subgrade, albeit diminishing gradually towards the pavement's edge. Acting as a protective layer for the subgrade, the geogrid distributes loads more uniformly, thereby mitigating vertical strains. Notably, a reduction of approximately 36-38% in maximum vertical strain is observed in the reinforced section for both 10,000 and 20,000 load repetitions.  
Figure 9 displays profiles of vertical strain for reinforced pavements at various locations within the subgrade. These strain profiles indicate that both the presence of geogrid and the subgrade's strength have substantial effects on vertical strain. As subgrade stiffness/strength increases, vertical strain decreases significantly. However, the influence zone of vertical strain extends up to a depth of 300 mm within the subgrade, beyond which there is no significant variation in strains for different CBR values of the subgrade soil. At the subgrade's surface, vertical strain decreases by approximately 17%, 39%, and 49% as the CBR value increases from 1% to 3%, 5%, and 8%, respectively.
## 5 Applications in Food Products and Nutraceuticals
### Non-Food Items
#### 5.2.2 Bio-Fuel Production  
Manufacturing of biodiesel based on _Citrus_ seed oil can be seen as an alternative for second-generation biodiesel production [103]. [PERSON] et al. conducted a proof of concept for the valorization of lemon seed waste oils in biodiesel with the aim of finding combinations that guarantee high biodiesel yields. The conversion of the lemon seed oil reached a value of 98.2% after 2 h at 15 V, 0.3% by weight of NaCl, and 20 \({}^{\circ}\)C [104]. [PERSON] et al. explored the potential of citron as a novel non-edible seed oil for biodiesel production using environmentally friendly copper oxide nanoparticles synthesized with aqueous extract of purslane (_Portulaca olerca_ L.) leaves. A 93% yield of methyl ester was obtained with optimal reaction conditions: 8:1 methanol to oil ratio in 120 min reaction time and 85 \({}^{\circ}\)C temperature [105].  
Biodiesel production from _Citrus_ seed residues could be efficiently employed for renewable energy generation, which would further support circular bioeconomy approaches [106].
# General Network Framework for Mixture Raman Spectrum Identification Based on Deep Learning
## 4 Experiment and Results
### Comparative Experiment  
In this section, we compare our model's classification performance with models proposed by other scholars to verify its detection performance. The comparison includes bacterial classification algorithms based on Raman spectrum and U-Net [36], a lung cancer  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Whether** & **Qualitative** & **Quantitative** \\  & **Preprocessing** & **Detection Accuracy** & **Detection Accuracy** \\ \hline \multirow{2}{*}{AlexNet} & Yes & 97.28\% & 92.47\% \\  & No & 87.39\% & 80.09\% \\ \hline \multirow{2}{*}{VGG16} & Yes & 97.62\% & 92.75\% \\  & No & 87.6\% & 80.15\% \\ \hline \multirow{2}{*}{GoogleNet V1} & Yes & 97.95\% & 93.46\% \\  & No & 81.74\% & 80.37\% \\ \hline \multirow{2}{*}{ResNet18} & Yes & 98.45\% & 94.62\% \\  & No & 88.19\% & 80.21\% \\ \hline \multirow{2}{*}{DenseNet} & Yes & 99.13\% & 95.29\% \\  & No & 90.25\% & 82.43\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Preprocessing comparison experiment results.  
Figure 4: Fusion strategy.  
screening algorithm using an improved ResNext model combined with serum Raman spectra [37]; DeepRaman, a universal component identification algorithm based on Raman spectroscopy [33]; and RamanNet, a universal network for Raman spectroscopy analysis [38]. The comparative experimental results are shown in Table 3 and the corresponding boxplots are shown in Figure 5.  
To validate the performance differences between the models, we conducted a one-way ANOVA analysis for both Qualitative Detection Accuracy and Quantitative Detection Accuracy. The results of the ANOVA analysis show an F-statistic of 9.89 and a \(p\)-value of 0.0017 for qualitative detection, and an F-statistic of 3.96 and a \(p\)-value of 0.0354 for quantitative detection. Since the \(p\)-values for both analyses are less than 0.05, we can conclude that the differences in performance between the models are statistically significant. This indicates that the superior performance of our proposed model compared to other models (U-Net, ResNext, DeepRaman, and RamanNet) is not due to random chance but represents a meaningful improvement in both qualitative and quantitative detection tasks.
On ASDEX-Upgrade, it has been observed that the time needed by a pellet to trigger an ELM is less than 200 \(\upmu\)s [25], while numerical simulations with JOREK found, for the JET tokamak, a triggering time around 270 \(\upmu\)s [22]. From a practical point of view, the assessment of ELM pacing efficiency is of great relevance. Understanding when a pellet has triggered an ELM or not is a key piece of information to develop optimised pacing strategies, especially for future tokamaks. As will be discussed in Section 2, such an assessment is not an easy task because of the presence of both natural and triggered ELMs. Consequently, to identify whether an ELM was triggered by a paced pellet or not, and which pellet triggered, or not, an ELM statistical approach such as unsupervised methods is required [26; 27; 28]. This work aims at presenting a new method for the assessment of triggering efficiency by combining a convolution-based approach of spectrogram cross-correlation (SPCC) [29] with the use of unsupervised machine learning. The algorithm has been tested on JET data. Section 2 is dedicated to the description of the diagnostics used, while Section 3 presents the method developed to perform the triggering assessment. Section 4 shows the application of the method on real data and discusses the capabilities of this new approach. The last section closes the paper with a discussion and conclusion of the found results.
# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 3 ITS Sensing
### Vehicle Onboard Sensing
#### 3.3.1 Traffic Near-Crash Detection  
Traffic near-crash or traffic near-miss is the conflict between road users that has the potential to develop into a collision. Near-crash detection using onboard sensors is the first step for multiple ITS applications: near-crash data serves as (1) surrogate safety data for traffic safety study, (2) corner-case data for autonomous vehicle testing, and (3) input to collision avoidance systems. There were some pioneer studies on automatic near-crash data extraction on the infrastructure side using LiDAR and camera [145; 146; 147]. In recent years, near-crash detection systems and algorithms using onboard sensors have been developed at a fast pace. [PERSON] et al. [148] and [PERSON] et al. [149] each applied conventional machine learning models (SVM and random forest) in their near-crash detection frameworks and achieved fairly good detection accuracy and efficiency on regular computers. The state-of-the-art methods tend to use deep learning for near-crash detection. The integration of CNN, LSTM, and attention mechanisms was demonstrated to be superior in recent studies [149; 150; 151]. [PERSON] et al. presented that a bi-directional LSTM with self-attention outperformed a single LSTM with a normal attention mechanism [150]. Another feature in recent studies was the combination of onboard camera sensor input and onboard telematics input, such as vehicle speed, acceleration, and location to either improve the near-crash detection performance or increase the output data diversity [9; 149; 152]. [PERSON] et al. mainly used onboard video for near-crash detection but also collected telematics and vehicle CAN data for post analysis [9].
# Inversion and Analysis of the Initial Ground Stress Field of the Deep-Buried Tunnel Area
## 3 Case Study
### Regression Results Analysis of Initial Ground Stress  
#### 3.4.1 Significance Test of Regression Analysis  
The significance test of the regression analysis results obtained by the three coefficient estimation methods is shown in Table 6.  
As can be seen from Table 6, in the significance test results of the regression equation obtained by the three coefficient estimation methods, the multiple correlation coefficient is close to 1.0, and the fitting effect is good. According to the F test distribution table, \(F_{0.05}(5,60)=2.37\). Compared with the results of the significance test, under the condition that the confidence degree of significance level reaches 95%, there is a very significant linear relationship between the dependent variable \"ground stress component regression calculated value\" and the independent variable \"the calculated value of initial ground stress component corresponding to the measured point under different boundary conditions\". The fitting result of the multiple linear regression equation of the initial ground stress is reliable. It can be seen from D-W = 1.88 that there is only slight multicollinearity among the sample variables selected for regression analysis, while the statistics are close to 2.0, so it can be considered that the sample variables are independent of each other. The significance test  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Coefficient Estimation Methods** & \(\mathbf{\lambda_{\text{opt}}}\) & \(\mathbf{R}\) & \(\mathbf{F}\) &
\begin{tabular}{c} **Durbin-Watson** \\ **Statistic (D-W)** \\ \end{tabular} \\ \hline OLS & — & 0.9630 & 183.63 & \\ RR & 0.03489 & 0.9627 & 182.13 & 1.88 \\ LR & 17.57536 & 0.9629 & 183.33 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Significance test results of the regression equation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Coefficient Estimation Method** & \(\mathbf{C_{0}}\) & \(\mathbf{C_{1}}\) & \(\mathbf{C_{2}}\) & \(\mathbf{C_{3}}\) & \(\mathbf{C_{4}}\) & \(\mathbf{C_{5}}\) \\ \hline OLS & 376.510 & 0.994 & 449.840 & 139.220 & \(-\)37.940 & \(-\)17.010 \\ RR & 303.703 & 0.978 & 442.477 & 144.120 & \(-\)49.772 & \(-\)32.500 \\ LR & 394.779 & 1.003 & 433.987 & 140.273 & \(-\)37.052 & \(-\)22.863 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Regression results of three coefficient estimation methods.  
results of OLS and LR analysis are the same. Therefore, considering the multicollinearity between variables and the unbiased estimation of regression fitting values, the coefficient estimation of the initial ground stress field regression equation is conducted and extended by the lasso regression method in the follow-up case.
## 6 Conclusions  
In this paper, indentation and scratch tests were conducted with a Berkovich indenter on the (0001) surface of 4H-SiC single crystals. The following conclusions were obtained.  
1. The fracture toughness of 4H-SiC single crystals was evaluated by a series of indentation tests in which the maximum normal force ranged from 0.4 to 3.3 N. The values of fracture toughness, calculated by both [PERSON]'s equation and [PERSON]'s equation, decreased with increasing _c/a_ ratio. An appropriate value of _c/a_ ratio is significant to calculate fracture toughness of 4H-SiC single crystals.
2. According to the linear relationship between \(a\) and \(c\), a critical condition with no cracks on the edge of the indentation was obtained through the fitting method, and the experimental fitting results agreed well with [PERSON]'s results.
3. The surface morphologies of the groove were analyzed by SEM. Plastic deformation was observed and characterized by the smooth groove without cracks and ductile chips on the edge of the groove in the initial stages of the scratch. With the increase in normal force, median cracks, radial cracks, and microcracks appeared in turn. With a sustained increase in normal force, the crack system was not able to continue extending stably, and the brittle fracture dominated the material removal.
4. The sizes of the edge damage of five characteristic positions generated relatively large peeling of the scratched groove, which were measured using SEM, and the experimental data highly agreed with the predicted curve.
5. A modified calculation model considered the previously proposed elastic recovery of normal force during the scratching process. Compared with the traditional calculation model, the modified predicted curve was closer to the experimental data. These results prove that the elastic recovery of 4H-SiC single crystals cannot be ignored during micro-nano machining.  
Conceptualization, P.C. and S.L.; methodology, P.C. and Y.L.; validation, X.Y. and S.L.; investigation, P.C. and S.L.; data curation, P.C., S.L., and Y.L.; writing--original draft preparation, P.C.; writing--review and editing, S.L., X.Y., and Y.L.; visualization, P.C.; project administration, Y.L.; funding acquisition, Y.L. and S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Nature Science Foundation of China (No. 51575442) and Shaanxi Provincial Natural Science Foundation (2016 JZ011, 2019 CGXNG-022).  
The authors are grateful to the National Natural Science Foundation of China and Shaanxi Provincial Natural Science Foundation, which enabled research on the rails.  
The authors declare no conflict of interest.
# Effects of Pulsed Pressure Curing on Beef Quality
## 3 Results and Discussion
### Volatile Compound  
These volatile substances comprised acids, alcohols, aldehydes, hydrocarbons, esters,furan, and ketones and, determined by their chemical structures, are documented in Table 3. The three most prevalent volatile flavoring compounds in beef were alcohols, aldehydes, and hydrocarbons [33]. The acid, ester, hydrocarbon, and furan contents of the beef through various curing procedures were unaffected. The levels of ketones, aldehydes, and alcohol were considerably greater in the PPC curing treatment groups compared to the SC curing treatment (\(p<0.05\)).  
The primary byproducts of fatty acid breakdown are aldehydes. They greatly affect the aroma of meat items, have a low odor threshold, and have a fatty smell [34]. Aldehydes were the most prevalent volatile flavoring substances in meat, as reported previously [21; 35]. Benzaldehyde was the sole identified aromatic aldehyde, whereas the majority of aldehydes were aliphatic [21]. Hexanal was the predominant aldehyde in beef flesh as reported in the previous literature. This was in line with [PERSON]'s findings that the PPC curing process encouraged the oxidation of fat to produce volatile chemicals [36].  
Ketones are produced by either the Strecker degradation of amino acids or the oxidation of unsaturated fatty acids. Volatile ketones have a fruity or creamy taste. Some ketones serve as significant catalysts in the production of heterocyclic aroma molecules [37]. In the current investigation, substantially fewer kinds and lower amounts of ketones than  
Figure 4: Effects of different curing methods on muscle fiber of beef. (**a**) SC; (**b**) VC; (**c**) PC; (**d**) PPC. SC = static curing, VC = vacuum curing, PC = pressurized curing, PPC = pulsed pressure curing.  
aldehydes were found in beef flesh. The similar findings were also previously documented for pork samples.  
Alcohols are produced by the oxidation of fatty acids and help to establish the taste of cooked meat [38]. The decreased synthesis of 1-octen-3-ol and 1-hexanol during curing may be advantageous for the odor of processed beef products [39] because both substances are thought to be significant contributors to off-flavors. Conclusively, PPC curing process can decrease the production of off tastes when compared to other curing techniques.
# Research on Feature Identification and Trajectory Planning of Pavement Cracks
## 3 Intelligent pavement Crack Detection and Repair Algorithm
### Image Filtering Stage
#### 3.2.1 Image Classification Policy Engine  
Due to unstable environmental conditions such as light, angle and background tone, the relative size of the main crack feature points and noise points contained in the images collected by the system camera, and the degree of noise dispersion is often uncontrollable, which in turn affects the subsequent image filtering and feature extraction. In 2021, [PERSON] et al. [13] conducted an integrated analysis of mainstream crack detection and classification techniques, and proposed the necessary conditions for a pavement crack feature identification algorithm. That is, the morphological characteristics of cracks and noise clusters in different types of scene images should be comprehensively considered, and a targeted filtering model should be established. Accordingly, we experimented with global variance calculation on multiple crack images. Figure 3 shows the comparison of the original binarization effect of the two types images, while Table 1 shows the global variance calculation results of the images. After a large number of experiments, we found that under the condition of fixed size, the image in Figure 3 has relatively narrow cracks and the noise distribution of this type of image after binarized is also relatively scattered, meanwhile, the global variance of the calculated pixel matrix is usually in the range of 0-300 as shown in Table 1. However, the image in Figure 3 has a relatively wide crack and the noise distribution of this type of image after binarization is concentrated on the edge side of the crack. The amount of noise of this type is much larger than that of the narrow crack type image, and the global variance of the calculated pixel matrix is usually greater than 800. Therefore, for a certain size of the crack scene binarized image, the threshold discrimination method is introduced as the image classification policy engine, and 500 is selected as the appropriate classification threshold. According to the difference of the global variance value of the pixel matrix, the image is divided into high-density noise wide crack type and low-density noise narrow crack type, and the corresponding processing strategies are adopted, respectively.