esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Response of Shield Tunnel with Double-Layer Lining
###### Abstract  
A shield tunnel is an assembly structure composed of connecting bolts and segments, generally considered to have good seismic performance. However, there is still a possibility of damage occurring in shield tunnels under strong seismic action. Therefore, a secondary lining can be applied on the inner side of the segment lining to improve the overall seismic performance of the shield tunnel. Using the Shiziyang Shield Tunnel as a case study, this paper employs numerical analysis to examine the seismic response characteristics of the shield tunnel with overlapped double-layer lining. Subsequently, it investigates the influence of segmental lining stiffness degradation and tunnel burial depth on the internal forces of the tunnel under seismic loads. The results indicate that under seismic loading, the stress in the segmental lining exceeds that in the secondary lining, with the maximum stress being three times higher. As the segmental lining stiffness decreases, the bending moment of the segmental lining decreases accordingly, while the secondary lining bending moment remains relatively constant. The bending moment of the segmental lining consistently surpasses that of the secondary lining. Furthermore, the variation in the axial force of the segmental lining is not significant, whereas the axial force in the secondary lining notably decreases. With increasing burial depth, the bending moment of the tunnel structure initially increases and then decreases. As the burial depth of the tunnel increases from 0.5D to 2D and 5.0D, the ratio of the maximum positive bending moment between the segmental lining and secondary lining first decreases and then increases, which are 7.56, 4.78, and 7.70, respectively. Similar patterns are also observed in axial forces. A burial depth of 2D is the critical depth between shallow and deep burial. When the tunnel is shallowly buried, the overlying strata have a significant impact on the seismic internal forces of the tunnel, which continue to increase with increasing burial depth. When the tunnel is deeply buried, it is subjected to the confining action of the strata, making it relatively safe, and the internal forces of the tunnel continue to decrease with increasing burial depth. Overall, under seismic loading, the segmental lining remains the primary load-bearing structure in a tunnel structure with double-layer lining.  
double-layer lining tunnel; seismic; segmental lining stiffness; burial depth Article  
Seismic Response of Shield Tunnel with Double-Layer Lining  
[PERSON] of Civil Engineering, Huaqiao University, Xiamen 361021, China; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]
## 2 Materials and Methods
### From SpiNNaker to Servomotor  
After injecting spikes into SpiNNaker, the next hardware communication is to receive spikes back from SpiNNaker i.e., the output layer of the SNN that it runs, and control the servo on the bases of the received spikes. The coding method and communication protocol is the same as for injecting spikes into the SNN, and the IDs of spiking neurons in output population are also sent in 40-bit packets. In our case, the single-axis robotic arm of the neuromorphic system is set to have eight different movable positions. Thus, the output layer of the SNN has eight neurons that correspond to eight positions. What the microcontroller receive is the spiking neuron ID. It is possible to set any other number of the goalkeeper positions using this digital motor.  
The rotation range of the servomotor is 120 deg, from \(-60\)deg to \(60\)deg. It is divided into eight equal steps, with the step angle of \(15\)deg, and each postion corresponds to a neuron ID. The speed of the servomotor is 120 deg/150 ms = 0.8 deg/ms (for the weight of our 'goalkeeper'). Thus, servo commands are executed at most every 150 ms. The position of the robotic arm is precisely controlled by using pulse width modulation (PWM) [28]. The range of pulse widths is from 1 ms to 2 ms, which is also divided into eight equal parts. The method of controlling the servo is described in the algorithm (Figure 4) below. The position of the robotic arm is changed after minimum 20 neuron IDs. The microcontroller keeps storing the received neuron IDs into a buffer until the number of IDs is 20. Next, the number of the most frequent ID in the buffer is found. If the number is equal to or greater than 10, a servo command corresponding to the ID will be generated. Finally, the command will be executed if the time difference from the latest execution is equal to or greater than 150 ms.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline
**Symbol.** & **0** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **A** & **B** & **C** & **D** & **E** & **F** & **EOP** \\ \hline L [0] & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 0 \\ L [1] & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 1 & 0 & 0 \\ L [2] & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 &
## 3 Results and Discussion
### Access Time  
Focusing on the United Arab Emirates as the ground station location, Figure 13 shows the difference in access time between SSO and the proposed lower inclination orbit for a 24-h period chosen randomly. A usable pass is one considered when the elevation angle is greater than 30\({}^{\circ}\) for an IoT-LoRa communication with a good link budget.  
As shown, for this day, the SSO provides four usable passes a day, while the low-incline concept provides eight in the same 24-h window. With longer access times, the revisit time is minimized, enabling near real-time data transmission and reception for IoT applications and other communication missions.  
Figure 11: Twenty\(-\)four\(-\)degree coverage time as a function of latitude.  
Figure 12: Accumulated coverage for SSO and lower inclination orbits for 7 days.  
Figure 13: Access times to UAE for SSO and lower inclination orbit.  
Additionally, the number of accesses as a function of latitude was plotted in Figure 14. It is clear that the satellite in a lower inclination orbit will have much more contacts than the satellite in SSO. Since the satellite in SSO provides global coverage, the number of accesses to the near-equatorial region, the region of interest, will be small and constant for all latitude values (approximately 3.7). However, the number of accesses of the satellite with a 24-degree inclination angle will be approximately 8.5, which is more than twice as high as that with SSO, and then it will start increasing till it reaches the peak at 22\({}^{\circ}\) N point, where the GS is located. This means that we will get more data from the satellite with the lower inclination orbit. Thus, the number of satellites will be smaller in case we need to have a constellation.
## 3 Results and Discussion
### Discussion
We chose the O- and Zr- modelsfor the highest and lowest mechanical strengths, respectively. The cohesive shear strength \(\tau^{{}^{\prime}}_{max}\) and work of shear separation \(\phi_{t}\) in the exponential CZM can be identified by the ideal shear strength \(\tau_{max}\) and unstable stacking energy \(\gamma_{us}\), respectively. Hence, it is necessary to determine the mechanical properties of the interface, such as the theoretical tensile strength, work of separation, theoretical shear strength, and unstable stacking energy. In this study, shear simulations of c-ZrO\({}_{2}\)(001)/\(\alpha\)-Al\({}_{2}\)O\({}_{3}\)(1\(\overline{1}\)02) interface models along the \(<\)1\(\overline{1}\)01\(>\) and \(<\)1\(\overline{1}\)0\(>\) directions of the \(\alpha\)-Al\({}_{2}\)O\({}_{3}\) lattice were performed using first-principles calculations. In practice, c-ZrO\({}_{2}\)(001)/\(\alpha\)-Al\({}_{2}\)O\({}_{3}\)(1\(\overline{1}\)02) interfaces may slip along other directions and undergo different structural transformations. Therefore, further studies on the shear behavior of c-ZrO\({}_{2}\)(001)/\(\alpha\)-Al\({}_{2}\)O\({}_{3}\)(1\(\overline{1}\)02) interfaces along other directions can provide more information for optimizing the TBC structure to meet the harsh requirements. In addition, the shear simulation method implemented in this study is the constrained shear method; which produces normal stress in the simulation process. [PERSON] (2018) observed that the mechanical properties of silicon are affected by the internal displacement of atoms and normal stresses inside the cell. Based on this, we can investigate the effect of normal stresses on the ideal shear strength of the c-ZrO\({}_{2}\)(001)/\(\alpha\)-Al\({}_{2}\)O\({}_{3}\)(1\(\overline{1}\)02) interface using the unconstrained shear method. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Models**} & \(\sigma_{max}\) & \(W_{sep}\) & \multirow{2}{*}{**Shear Direction**} & \(\tau_{max}\) & \(\gamma_{us}\) \\  & **(GPa)** & **(m/m\({}^{2}\))** & & **(GPa)** & **(m/m\({}^{2}\))** \\ \hline \multirow{3}{*}{O-} & \multirow{3}{*}{9.012} & \multirow{3}{*}{1208} & \(<\)1\(\overline{1}\)01\(>\) & 5.04 & 399 \\  & & & \(<\)1\(\overline{1}\)02\(>\) & 5.69 & 612 \\ \cline{1-1} \cline{3-5}  & & & \(<\)1\(\overline{1}\)01\(>\) & 3.66 & 349 \\ \cline{1-1} \cline{3-5}  & & & \(<\)1\(\overline{1}\)02\(>\) & 4.52 & 494 \\ \cline{1-1} \cline{3-5} Zr- & \multirow{3}{*}{6.893} & \multirow{3}{*}{715} & \(<\)1\(\overline{1}\)01\(>\) & 2.94 & 245 \\ \cline{1-1} \cline{3-5}  & & & \(<\)1\(\overline{1}\)02\(>\) & 3.95 & 437 \\ \hline \end{tabular}
\end{table} 
Table 1: The basic mechanical properties of the c-ZrO\({}_{2}\)(001)/\(\alpha\)-Al\({}_{2}\)O\({}_{3}\)(1\(\overline{1}\)02) interfaces, including the tensile strength (\(\sigma_{max}\)), the work of separation (\(W_{sep}\)), the shear strength (\(\tau_{max}\)), and the unstable stacking energy (\(\gamma_{us}\)). Figure 5: The shear stress–displacement curves of the c-ZrO\({}_{2}\)(001)/\(\alpha\)-Al\({}_{2}\)O\({}_{3}\)(1\(\overline{1}\)02) interface models fitted by the Fourier series.
## 4 Simulation Results
### Applications of the SL Algorithms
To make it easier to see if the model is overfitting or underfitting, and to obtain a better understanding of this, Figure 4 shows the learning curves of each model, where each curve represents how each model learned by comparing the training data with the test data. In Figure 4, we observed that the model that learns best is the SVM model; however, since the validation metrics values are low, we need to obtain the best results. When analyzing the graph corresponding to KNN, the validation curve is not close to the learning curve, so we inferred that the model is not correctly adjusted. In the case of the MLPC model, both curves approach each other at the point where we reach 120 input data. Furthermore, the cross-validation metric for this model is 90.16%, and the accuracy obtained in the testing phase was 90.9%. The best result of fitting the model to the dataset is when there is a difference of less than 1%. After applying PCA to the original dataset, the model that obtained the best results was the MLPC. Figure 5 compares the MLPC of this work and the RF of the previous paper. However, these results are optional, so we searched for the best hyperparameters. Figure 3: Confusion matrices of each model (DT, RF, SVM, KNN, and MLPC). Figure 4: Learning curves of each model. Figure 5: Comparison of the results obtained in both papers.
# Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance
###### Abstract  
Supercapacitors have garnered significant interest in recent years due to their high power density, rapid charge/discharge rates, and long cycle life. MXenes, a family of two-dimensional (2D) transition metal carbides/nitrides, have emerged as promising electrode materials for supercapacitors. However, one major challenge associated with incorporating MXenes in supercapacitor structures is the occurrence of sticking, wherein individual MXene flakes agglomerate, leading to reduced electrode performance. This review paper discusses various causes of sticking and approaches to preventing it, offering insights into the design and development of high-performance MXene-based supercapacitors. The morphology and size of MXene flakes, flake surface chemistry, thickness, surface area/volume ratio, electrode processing techniques (including solvent selection, additives incorporation, and deposition technology), and environmental factors were shown to be the basic factors resulting in sticking of MXene sheets. Among the strategies to mitigate this challenge, surface functionalization and passivation, integration with polymer matrices or carbon nanomaterials, and electrode processing optimization were considered. Possible paths for optimization and future directions of study, such as novel MXene compositions, understanding of interfaces and electrode-electrolyte interactions, development of advanced electrode architectures, and integration of energy storage systems, were assumed.  
MXene; nanosheets sticking; supercapacitor; storage devices +
Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON]; [PERSON] [PERSON] Effect of MXene  
Nanosheet Sticking on Supercapacitor  
Device Performance. _Appl. Sci._**2024**, 14, 2452. [[https://doi.org/10.3390/app14062452](https://doi.org/10.3390/app14062452)]([https://doi.org/10.3390/app14062452](https://doi.org/10.3390/app14062452))
# Transcriptional Analysis of Masson Pine
## 3 Results  
### Transcriptome Sequencing and De Novo Assembly  
The cDNA libraries of the four treatments (0 h (CK), 6 h, 12, and 24 h) were sequenced and generated a total number of 49,314,299, 47,459,322, 45,980,036, and 60,876,932 raw reads and 48,795,571, 46,976,134,45,496,760, and 60,205,674 clean reads, respectively (Table 1). The raw data and sequences can be found online at the NCBI ([[https://www.ncbi.nlm.nih.gov/](https://www.ncbi.nlm.nih.gov/)]([https://www.ncbi.nlm.nih.gov/](https://www.ncbi.nlm.nih.gov/))) Sequence Read Archive (SRA) database (accession number PRJNA561037). Compared with the reference sequences obtained from Trinity assembly, there were generated a total number of 16,879,027, 16,459,249, 15,859,340 and 21,108,414 mapped reads of 0 h, 6 h, 12 h, and 24 h treatments, respectively. An average mapped ratio of 70% was obtained. The Q30, a key parameter that represents the quality of sequenced bases, was 94.04%, 94.22%, 93.86% and 93.4% for the 0 h, 6 h, 12 h, and 24 h treatments, respectively (Table 1).  
The Trinity software generated 140,863 transcripts with an average length of 891 bp and an N50 of 1463 bp. In total, 92,424 unigenes were obtained in the range of 201-15,491 bp. Of these, 48,592 (52.57%) were less than 500 bp, 22,267 (24.09%) were 501-1000 bp, 12,887 (13.94%) were 1001-2000 bp and the remaining 8678 (9.39%) were >2000 bp (Table 2). TransRate and software BUSCO evaluated the assembly results, and transcript score was 0.20045 and 77.7%, respectively, and unigenes was 0.30498 and 74.2%, respectively (Table 2). According to previous reports [26; 27], in Masson pine, unigene obtained by transcriptome sequencing under other stress treatments ranged from 70,896 to 101,806. Our results were similar to them. Combined with the N50 and Q30, we believe that the sequencing results are relatively reliable and could be further analyzed.
###### Abstract
Figure 1 illustrates the systematic workflow of ultrasonic guided wave inspection for composite structures, comprising four critical stages. The process initiates with the ultrasonic inspection model, which elucidates the wave propagation and interaction mechanisms within the composite material. The second stage encompasses data acquisition, where experimental measurements are executed using an array of ultrasonic transducers to collect wave signals. The third stage concentrates on signal data analysis, where the captured waveforms are processed and scrutinized in both time and frequency domains to extract salient features. Finally, the defect localization and imaging stage employs advanced signal processing techniques to produce visual representations of damage locations and characteristics via various imaging methods such as time-of-flight mapping and wave field analysis. This integrated approach facilitates a comprehensive structural health assessment of composite materials through non-destructive evaluation. The remainder of this paper is structured as follows: Section 2 delves into the prevalent failure mechanisms in laminated composites, providing an in-depth analysis of their causes and effects. Section 3 explores the use of guided ultrasonic waves for SHM in laminated composites, emphasizing sensor technologies and recent advancements in the field. Section 4 examines various damage detection models that utilize ultrasonic guided waves for SHM in laminated composites, highlighting their methodologies and effectiveness. Section 5 discusses future directions and current challenges, and Section 6 concludes with a summary of the key findings and potential research avenues.
# Advanced Micro/Nanocapsules for Self-Healing Coatings
## 5 Types of Self-Healing Capsules
### Nanocapsules in Self-Hailing Coatings  
Nanocapsules play a crucial role in the development of self-healing coatings, offering innovative solutions for enhancing the durability and longevity of various materials. These micro/nanocapsules are designed based on different types of micro/nanocontainers and are at the forefront of cutting-edge research in material science. The synthesis of micro/nanocapsules involves effective parameters that influence their structure and performance, leading to the fabrication of self-healing coatings that exhibit remarkable capabilities [76; 77]. By encapsulating active agents within these capsules, such as healing agents or corrosion inhibitors, self-healing coatings can autonomously repair damage and protect surfaces from environmental factors, extending the lifespan of coated materials [40].  
Self-healing capsules play a crucial role in the restoration and maintenance of materials by releasing mending agents upon crack formation. These capsules are evenly dispersed within the material matrix, with the volume fraction typically below 5% of the matrix to ensure effectiveness [78]. The capsules, assumed to act independently and not cluster, rupture when a microcrack emerges, releasing reactive agents that flow to the fracture plane and form a polymer network, effectively sealing the crack [7; 79; 80]. Different structures of self-healing capsules have been explored, including micro/nanocapsule embedment, spherical capsules, and storage in polyurethane microcapsules, each designed to enhance the healing process and maintain material integrity [76; 78; 79]. Additionally, hollow fiber embedment has shown promising results in improving compressive strength and reducing permeability in cement matrices, highlighting the diverse applications of self-healing capsules in various material systems [76; 78]. Through innovative designs and strategic distribution, these capsules release healing agents efficiently, enhancing the self-healing capabilities of materials and extending their lifespan [78; 80].
###### Abstract  
Hyperspectral image (HSI) denoising is a crucial step in image preprocessing as its effectiveness has a direct impact on the accuracy of subsequent tasks such as land cover classification, target recognition, and change detection. However, existing methods suffer from limitations in effectively integrating multi-scale features and adaptively modeling complex noise distributions, making it difficult to construct effective spatial-spectral joint representations. This often leads to issues like detail loss and spectral distortion, especially when dealing with complex mixed noise. To address these challenges, this paper proposes a multi-scale differentiated denoising network based on spatial-spectral cooperative attention (MDSSANet). The network first constructs a multi-scale image pyramid using three downsampling operations and independently models the features at each scale to better capture noise characteristics at different levels. Additionally, a spatial-spectral cooperative attention module (SSCA) and a differentiated multi-scale feature fusion module (DMF) are introduced. The SSCA module effectively captures cross-spectral dependencies and spatial feature interactions through parallel spectral channel and spatial attention mechanisms. The DMF module adopts a multi-branch parallel structure with differentiated processing to dynamically fuse multi-scale spatial-spectral features and incorporates a cross-scale feature compensation strategy to improve feature representation and mitigate information loss. The experimental results show that the proposed method outperforms state-of-the-art methods across several public datasets, exhibiting greater robustness and superior visual performance in tasks such as handling complex noise and recovering small targets.  
deep learning; hyperspectral image denoising; convolutional neural networks; spatial-spectral attention; multi-scale feature fusion +
Footnote †: journal: Applied sciences
## 4 Results and Discussions
### Simulation Results of the Normal Impact  
In this study, the mass of the projectile varies, depending on the shape of the noses. The penetration characteristics of the projectile were analyzed with the penetration depth per mass of the projectile. The kinetic energy of the projectile was expressed as 0.5 mV\({}^{2}\). As the mass of the projectile increases, the kinetic energy proportionally increases. However, the kinetic energy by mass has the same value.  
Also, as previously described, the penetration depth of an aluminum target (Al 6061-T6511) was analyzed. The velocity of the projectile was 1208 m/s for all cases. The penetration depth per mass is shown in Figure 10. The cylinder geometry showed the lowest value with the value of \(-\)1.21 m/kg. Blunt shape (\(-\)5.69 m/kg), conical shape (\(-\)6.23 m/kg), and truncated shape (\(-\)7.4 m/kg) are the next.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Striking Velocity** & **Angle of Obliquity (\({}^{\circ}\))** & **Pitch** & **Yaw (\({}^{\circ}\))** & **Experimental Result [17]** & **Simulation Result** \\ \cline{4-7}
**(m/s)** & **Obliquity (\({}^{\circ}\))** & **(\({}^{\circ}\))** & **(\({}^{\circ}\))** & **X (mm)** & **Y (mm)** & **X (mm)** & **Y (mm)** \\ \hline
1156 & 30 & 3.25U & 0 & 113.2 & \(-\)149.5 & 106.83 & \(-\)123.7 \\
753 & 30 & 0.75D & 0.25L & 71.4 & \(-\)55.2 & 69.89 & \(-\)65.11 \\
1209 & 15 & 0.75U & 0.5L & 70.5 & \(-\)201.2 & 70.9 & \(-\)191.5 \\
985 & 15 & 2.5U & 0.75L & 39.1 & \(-\)130.7 & 37.1 & \(-\)129 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the positions of tip ends for various conditions.  
Figure 9: Final deformed shape after impact for V = 985 m/s, 15\({}^{\circ}\) angle of obliquity: (**a**) Experimental result [17] (**b**) Simulation result.  
Among seven projectile geometries, the projectile having the ogive nose showed increased penetration depth. Even with the same category of geometries, the penetration depth per mass varies significantly depending on the CRH value. The penetration depth to mass is 7.75 m/kg with a CRH of 1, 10.51 m/kg with a CRH of 3, and 11.47 m/kg with a CRH of 5.