esa-sceva/satcom-chunk-collection huggingface dataset:
# Nonlinear Vibration and Post-Buckling Behaviors of Metal and FGM Pipes Transporting Heavy Crude Oil
## 6 Numerical Results
### Results of Nonlinear Vibration and Post-Buckling for FGM Pipes Transporting Heavy Crude Oil
As previously explained, based on Equations (1a) and (1b), increasing \(N\) causes the material properties of Type I pipes to approach those of metal, whereas Type II pipes exhibit behavior closer to ceramics. These results demonstrate that the resistance of FGM pipes, defined by their critical flow velocity, can be effectively tuned by selecting an appropriate FGM type and adjusting the gradient index \(N\). Figure 7 illustrates how the CFV in the pipe made of FGM varies with the length \(L\) for two different material types. As observed, increasing the length results in a decrease in the CFV for both types. Additionally, Figure 8 shows the influence of the outer radius (\(r_{o}\)) and inner radius (\(r_{i}\)) on the critical flow velocity. The results indicate that an increase in \(r_{o}\) leads to a higher critical flow velocity, while an increase in \(r_{i}\) causes it to decrease. \begin{table}
\begin{tabular}{c c c} \hline \hline
**T(K)** & **Type I** & **Type II** \\ \hline
250 & 140.69 & 137.21 \\
300 & 140.13 & 136.69 \\
350 & 139.51 & 136.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Impact of \(T\) on the NF of FGM pipes transporting heavy crude oil. The nonlinear vibration and post-buckling responses explored in this study demonstrate that FGM pipes possess not only enhanced mechanical strength and thermal resistance but also customizable dynamic behavior. Through control of geometrical features (length, radii) and gradation parameters (such as the power-law index nnn), engineers can modulate the degree of nonlinearity in the structural response. These nonlinear characteristics, including stiffness variation with amplitude and frequency shifts, are especially critical in applications involving fluid-structure interaction and thermal environments, such as in oil and gas pipelines. In real-world systems, such nonlinear dynamics can either be exploited to improve vibration isolation or mitigated to avoid resonance and fatigue failure. Therefore, the ability  
Figure 8: Impact of radius on the CFV of FGM pipes transporting heavy crude oil: (**I**) impact of various \(r_{i}\) (**II**) impact of various \(r_{o}\). Figure 6: CFV changes with respect to the \(N\) for the FGM pipe, as predicted by both Euler–Bernoulli and Timoshenko beam models. Figure 7: Impact of \(L\) on the CFV of FGM pipes transporting heavy crude oil. to predict and tailor nonlinear behaviors becomes a powerful tool in the optimal design of advanced FGM-based pipeline systems.
## 4 Results
### Comparison with Other Methods  
The risk estimation problem was transformed into a text classification task using LSTM-based deep learning methods, as discussed in Section 3.3.1. Therefore, it is plausible to explore the utilization of other natural language-processing techniques for conducting risk estimation. The present study investigates the utilization of convolutional neural networks (CNN) [38] and bidirectional long short-term memory (bi-LSTM) [39] networks, in comparison with an LSTM-based risk estimation method, for estimating the risk associated with the new hazard events completely different from the existing ones. The experimental findings are presented in Table 5.  
The comparison results indicate that in terms of median accuracy, the CNN-based approach outperforms both LSTM-based and bi-LSTM-based approaches. Regarding the median acceptable rate, the CNN-based approach surpasses the bi-LSTM-based and LSTM-based approaches. Furthermore, for ensuring stable risk estimation, the CNN-based approach demonstrates superiority over both LSTM-based and bi-LSTM-based approaches. The percentages of small deviation exhibited similar values across the three methods.  
Although the CNN-based method achieved a relative higher accuracy and acceptable rate, \(RI_{dis}\) = 0.51, it tends to underestimate the risk of the hazard event when deviations occur, which is undesirable in risk estimation. Among the three methods, the LSTM-based risk estimation method demonstrates no significant bias when deviations occur, effectively balancing accuracy and stability in risk estimation.
# Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement
## 3 Methodology  
### Overview  
The network proposed in this paper is based on the U-Net architecture with denoising multiscale back-projection feature fusion (DeMBFF) blocks. The network is designed to remove complex degradation, such as blur and color bias, from underwater images, and the specific network structure is shown in Figure 1. The underwater image is input to the encoder. The features are extracted by the encoder using DeMBFF and passed to the next layer of convolution blocks. The last encoder passes all the extracted features into the first decoder. Each decoder receives both the multiscale features from prior layers and the same scale feature from the corresponding encoder via skip connections. All of them are fused by a decoder with DeMBFF, which gradually recovers the clear image. Multiple encoders and decoders with DeMBFF are cascaded to fuse feature maps of different scales from prior layers. The DeMBFF block provides error feedback for feature fusion so that the next layer can better separate the noise information and retain useful information.
# Plant Electrical Signal Classification Based on Waveform Similarity
## 2 Related Works
### Time Series Waveform Recognition and Classification  
#### 2.2.1 Artifacts Reduction  
Plant stimuli artifacts occur in stimulation process whose frequency is often much higher than that of plant AP. In consequence, they can be attenuated by signal processing methods.  
The common artifacts reduction methods in Electrocardiograph (ECG) and Electroencephalograph (EEG) includes wavelet transform [49; 50], digital filter [51; 52; 53], independent component analysis (ICA) [54], artificial neural networks [55; 56], empirical mode decomposition (EMD) [57], morphological filter [58; 59], and Hilbert transform [51; 60].  
Wavelet transform is often used to remove ECG motion artifacts [49]. It aims at separating the signals and noise, and then eliminating noise by reasonable adjusting wavelet coefficient sequences. Digital filter is also always applied to reduce the influence of noise or artifacts in ECG [51; 52; 53]. Independent component analysis is a blind source separation technique to separate signals and noises in EEG signals [54] or plant electrical signals [43]. As we know, QRS complex is a name for the combination of Q, R, and S waves in a typical electrocardiogram. Its duration is normally 0.06-0.1 S. It corresponds to the right and left ventricles of the human heart. The Q, R, and S waves occur in succession, do not all appear in all cases. In practice, Artificial neural network (ANN) is used to enhance the QRS complexes and suppress the noises in ECG [55; 56]. EMD can be used for decomposing nonlinear and non-stationary signals like ECG into intrinsic mode functions (IMFs) which are employed to remove noise existing in the to yield a clear ECG signals [57]. Morphological filtering algorithms enable baseline correction and suppression of impulse and Gaussian noises with non-flat structuring elements in [58; 59]. Hilbert transform is employed to avoid experimental threshold setting by rectification of the differentiated ECG signal while preserving the QRS peaks [51; 60].
# A Comprehensive Review on Network Protocol Design for Autonomic Internet of Things
## 3 Review of Protocol Designs
### Protocol Designs for MAC Layer
This mechanism opportunistically transmits the data to the earliest beacon sender in the previous period that is in the list of potential forwarder, hoping that this node can forward the data to the destination. The OD-MAC protocol has been validated through a simulation in a simple grid network topology. As a result, it is proved that each node can make the adjustment to duty cycle so that the protocol can support the continuous operation of the node that relies on energy harvesting activity. The OD-MAC protocol weakness is the hidden terminal problem, which is a node within the range of one of the communicating nodes, but not detected by the other one. This hidden terminal problem makes the communication susceptible to collisions when sending data simultaneously. The OD-MAC protocol also does not have a mechanism for data retransmission if there is an error in the data transmission. 2. EH-MAC  
[PERSON] and [PERSON] in [10] proposed the Energy Harvesting MAC (EH-MAC) protocol for multi-hop energy harvesting WSNs. This protocol is designed for the equality and fairness between nodes in the data transmission activity. EH-MAC applies the concept of probabilistic polling. A node can send a small polling packet that will get a response from other node in the network that want to transmit data. The nodes that are allowed to conduct the poll or make the response are those that already have sufficient energy as the result of the energy harvesting activities. Other nodes that do not have sufficient energy will be in charging state and will not participate in the poll until it has sufficient energy. Before conducting a poll, a node will detect whether the network condition is idle. The polling is conducted in an idle condition by assigning a probability number and sending it to all nodes in the network, which also have comparative probability numbers that are randomly generated. If the poll number is higher, the node will not respond. If the poll number is lower, the node will send a response and begin to send data. It is expected that only one node will respond at a time. If there is no response to the poll number, a new lower poll number will be issued. Otherwise, if there is more than one response, it will cause data collisions and in the next round the poll number should be increased. The EH-MAC protocol has been validated by simulation on random topologies. As the result, this protocol design can provide high throughput, equality, and fairness between nodes in the data transmission activity, as well as flexibility and scalability to support the implementation of a large number of nodes in a network. The EH-MAC still has weakness, i.e., it is possible that a high delay happens in the data transmission.
## 3 Results and Discussion
### Effect of HP Treatment on Texture of Meat Products  
In general, HP treatment did not significantly modify the hardness of all meat products samples compared to the untreated ones. For Bratuwrt sausages, the nitrite-made ones were the less cohesive (value equal to 0.5) and the ones with the higher hardness measured  
Figure 6: Redness (a) values of Bratuwrt sausages’ surface during storage at 5 and 10 \({}^{\circ}\)C vs. time for; (a): control samples, (\(\square\)): nitrite-made samples, (\(\Delta\)): HP-treated samples.  
(1650 g at time t = 0), compared to the nitrite-free (cohesiveness value equal to 0.75 and hardness value estimated at 850 g at time t = 0) and the HP-treated ones (corresponding values equal to 0.82 for cohesiveness and 1100 g for the hardness). Values of hardness and cohesiveness were not altered for any of the three different Bratwurst samples throughout the studied storage period. Nitrite-made samples exhibited higher gumminess and chewiness, during the whole storage period, at all storage temperatures. At time t = 0, gumminess and chewiness of nitrite-made samples was 276 g and 737 g-s, respectively, while the respective values of control samples was 179 g and 507 g-s. HP treatment did not modify gumminess and chewiness of Bratwurst sausages. Similar results about the effect of high pressure treatment on the texture of sausages have been reported by other authors. [PERSON] et al. [33] and [PERSON] [35], observed higher cohesiveness; lower gumminess, chewiness, and hardness; and unaffected adhesiveness and springiness in pressured sausages. HP-treated ham cubes exhibited higher gumminess and chewiness during the whole storage period, at all storage temperatures compared to untreated samples. At time t = 0, gumminess and chewiness of HP-treated ham cubes were 276 g and 737 g-s, respectively, while the respective values of control samples were 179 g and 507 g-s. HP treatment did not modify the hardness of the sliced cooked ham and turkeys (i.e., hardness of sliced cooked ham equal to 820 g for the control samples and 805 g for the HP-treated ones at time t = 0). Other measured parameters such as gumminess and chewiness of sliced cooked meat products were not affected at all by either the HP treatment or the storage time and temperature. Overall, storage temperature did not alter texture characteristics of any of the tested meat products.
# Probing Collisional Plasmas with MCRS: Opportunities and Challenges
## Appendix A  
In this appendix, the equation used to correct the measured resonance frequency for thermal expansion of the cavity is derived.  
For cylindrical cavities, the resonance frequency \(f_{mnp}\) for TM modes [51] is given by:  
\[f_{mnp}=\frac{c}{2\pi\sqrt{\mu_{\mathrm{r}}\varepsilon_{\mathrm{r}}}}\sqrt{ \left(\frac{x_{mn}}{R}\right)^{2}+\left(\frac{p\pi}{L}\right)^{2}}, \tag{1}\]  
and for TE modes [51] by:  
\[f_{mnp}=\frac{c}{2\pi\sqrt{\mu_{\mathrm{r}}\varepsilon_{\mathrm{r}}}}\sqrt{ \left(\frac{x_{mn}^{\prime}}{R}\right)^{2}+\left(\frac{p\pi}{L}\right)^{2}}. \tag{2}\]  
Here, \(c\) is the speed of light, \(\mu_{\mathrm{r}}\) the relative permeability, \(\varepsilon_{\mathrm{r}}\) the relative permittivity, \(R\) the radius of the cavity, \(L\) the height of cavity, \(x_{mn}\) the \(n\)th root of the Bessel function \(\int_{m}^{\prime}(x)\), and \(x_{mn}^{\prime}\) the \(n\)th root \(x\) of the derivative of the Bessel function \(\int_{m}^{\prime}(x)\). Furthermore, the mode indices \(m\), \(n\) and \(p\) relate to the number of nodes in the \(\phi\)-, \(\rho\)- and \(z\)-direction, respectively.  
The dimensions of the cavity are subject to changes in the temperature \(T_{\mathrm{cav}}\) of the metal structure. The temperature dependent radius and height are expressed by the following two equations,  
\[R(T_{\mathrm{cav}}) =R_{0}(1+\alpha(T_{\mathrm{cav}}-T_{0})), \tag{3}\] \[L(T_{\mathrm{cav}}) =L_{0}(1+\alpha(T_{\mathrm{cav}}-T_{0})), \tag{4}\]  
where \(\alpha\) is the thermal expansion coefficient of the metal structure and \(T_{0}\) a fixed temperature for the whole measurement set at which the cavity has a radius \(R_{0}\) and a height \(L_{0}\).  
By filling Equations (A3) and (A4) in (A1) and (A2), the following equation for the apparent resonance frequency \(f_{\text{ap}}\) is found,  
\[f_{\text{ap}}(T_{\text{cav}})=\frac{f_{mnp}}{1+\alpha(T_{\text{cav}}-T_{0})}.\] (A5)
## 5 Conclusions
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**MIT Group** & **PRE** & \(\pm\)**SE** & **POST** & \(\pm\)**SE** & _p-Value_ \\ \hline
**CSA type I** & 7158.88 & 686.07 & 5728.19 & 736.73 & No interaction * \\
**CSA type II** & 4935.29 & 563.18 & 2997.95 & 607.57 & No interaction * \\ \(\bullet\) & **CSA type IIa** & 5258.13 & 550.38 & 3346.37 & 582.53 & \\ \(\bullet\) & **CSA type IIax** & 5021.66 & 751.79 & 2791.90 & 751.79 & \\ \(\bullet\) & **CSA type IIx** & 4651.15 & 774.15 & 2594.31 & 718.46 & \\ \hline
**HIT Group** & **PRE** & \(\pm\)**SE** & **POST** & \(\pm\)**SE** & _p-Value_ \\ \hline
**CSA type I** & 6342.86 & 556.08 & 7066.21 & 814.24 & No interaction * \\
**CSA type II** & 3984.34 & 451.67 & 4148.86 & 679.05 & No interaction * \\ \(\bullet\) & **CSA type IIa** & 4125.27 & 459.84 & 4097.39 & 618.50 & \\ \(\bullet\) & **CSA type IIax** & 3808.58 & 642.59 & 4646.30 & 946.16 & \\ \(\bullet\) & **CSA type IIx** & 3755.41 & 574.67 & 4289.79 & 845.52 & \\ \hline \hline \end{tabular} Values are displayed as mean (in \(\mu\)m\({}^{2}\)) and standard error (SE). PRE: before the start of the 12-week intervention; POST: after the end of the 12-week intervention; CSA: cross-sectional area. * no significant interaction was found, no post-hoc test.  
\end{table} 
Table 4: Results of the mixed model analysis of variances for the cross-sectional area of the multifidus. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**MIT Group** & **PRE** & \(\pm\)**SE** & **POST** & \(\pm\)**SE** & _p-Value_ \\ \hline
**RCSA type I** & 78.68 & 3.05 & 73.50 & 2.85 & No interaction * \\
**RCSA type II** & 21.32 & 3.04 & 26.50 & 2.84 & No interaction * \\ \(\bullet\) & **RCSA type IIa** & 13.03 & 2.35 & 14.53 & 2.01 & \\ \(\bullet\) & **RCSA type IIax** & 6.34 & 1.50 & 5.62 & 1.18 & \\ \(\bullet\) & **RCSA type IIx** & 7.04 & 2.02 & 9.57 & 2.02 & \\ \hline
**HIT Group** & **PRE** & \(\pm\)**SE** & **POST** & \(\pm\)**SE** & _p-Value_ \\ \hline
**RCSA type I** & 76.31 & 2.63 & 77.32 & 3.14 & No interaction * \\
**RCSA type II** & 23.59 & 2.63 & 22.59 & 3.13 & No interaction * \\ \(\bullet\) & **RCSA type IIa** & 14.60 & 1.72 & 14.16 & 2.72 & \\ \(\bullet\) & **RCSA type IIax** & 4.30 & 1.80 & 5.54 & 1.65 & \\ \(\bullet\) & **RCSA type IIx** & 7.03 & 1.57 & 3.14 & 2.56 & \\ \hline \hline \end{tabular} Values are displayed as mean (in \(\%\)) and standard error (SE). PRE: before the start of the 12-week intervention; POST: after the end of the 12-week intervention; RCSA: relative cross-sectional area. * no significant interaction was found, no post-hoc test.  
\end{table} 
Table 3: Results of the mixed model analysis of variances for the relative cross-sectional area of the erector spinae.
# Urban Seismic Networks: A Worldwide Review
## 2 USN Catalogue
### Geographical Distribution  
The worldwide distribution of USNs (Table 1) is shown in Figure 1a. The networks are not uniformly distributed around the world; North America and the Euro-Mediterranean region are the two zones where most of the USNs are located (Figure 1b,c). A total of 37 different countries are present in the catalogue. The USA, Italy, and Turkey are the most represented, with 13, 9, and 7 USNs, respectively. Several cities represented the target for more than one urban network over the time, mainly driven by their high seismic hazard. These include Istanbul (Turkey), Bursa (Turkey), Quito (Ecuador), Bishkek (Kyrgyzstan), Bucharest (Romania), Gyeongju (South Korea), Los Angeles (USA), and Bogota (Colombia), to mention only some of them. The distribution of USNs roughly follows the global seismic hazard zoning (Figure 2); however, there is not a clear correlation. In Figure 2, the colour shades represent the global earthquake hazard as proposed by [12]; in particular, they indicate the expected Peak Ground Acceleration (PGA) with 10% of excedance in 50 years. Most USNs are located along the Pacific Ocean coasts, both western and eastern, where the probability of excedance of the ground motion is high (PGA \(>\) 4 m/s\({}^{2}\)) according to [12]. Several others are in areas where the expected ground motion is generally between 2 and 5 m/s\({}^{2}\), e.g., in southern Europe and south-eastern Asia. Only few USNs are located in regions with lower hazard, e.g., northern Europe and eastern North America, where the expected shaking is  
Figure 1: (**a**) Global distribution of the USNs collected in this review. Detailed maps are provided for North America (**b**) and Europe (**c**), where USNs are the most common. characterised by PGA \(<\) 2 m/s\({}^{2}\). Finally, some USNs even fall into very low hazard zones with PGA \(<\) 0.5 m/s\({}^{2}\).
## 4 Discussion  
The aim of this paper was to gain insight into which factors drive spatial patterns in the mono-specific pioneer zone of a salt marsh, and through which vegetation properties these drivers affect reflectance. We found that in spring, in our study area flood duration has by far the largest effect on large scale spatial patterns and is the main mechanism behind these patterns. Flood duration is known to have a large effect on vegetation zonation, [2; 26; 31; 32]. Here, we showed that, in our study area, it is the driving factor behind spatial patterns in the mono-specific pioneer zone in spring. A higher flood duration was found to decrease the leaf area index. Flood duration explained 70% of the total spatial variation in LAI, over three times as much as the effect of wave forcing, which was found to be the second most important driver. Wave forcing is also known to be a shaping feature [36], and our result indicate that it indeed affected LAI. However, wave forcing only explains 19% of the total variation and the relationship appears noisy. The other two drivers behind spatial patterns, competition and Creek influence, both show the same pattern: High LAI values only occur at low distances to competitors or creeks, low values occur everywhere. These drivers explain respectively 9% and 17% of the total variation only. At this spatial scale, it is clear that flood duration is most correlated to the differences in reflectance, overshadowing the effects of other factors.  
Figure 5: Spatial distribution of Leaf Area Index (LAI) estimated from the inversion of the ProSail model applied to a Rapid Eye image of June 5 th 2015.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Spatial Driver** & **Absolute Contribution to t-Value** & **Coefficient** \\ \hline Flood Duration & 67.743 & \(-\)0.01078 \\ Wave Forcing & 9.124 & 0.00033 \\ Creek Influence & 2.321 & 0.00007 \\ Competition & 0.900 & 0.00007 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The contribution to the t value in the multiple-regression model relating spatial drivers with leaf area index.  
It is important to note that this study is a first step towards understanding how drivers behind spatial patterns affect reflectance of the vegetation of single species and create spatial patterns. We focused on the monospecific pioneer zone of a single salt marsh in spring. Therefore, our results have to be applied to other species and sites with other conditions, before conclusions can be drawn on salt marshes in a broader sense.