esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Data
### Remotely Sensed LAI Maps  
Remotely sensed images with four bands (blue, green, red, and near-infrared) at 30-m spatial resolution were acquired from the Small Satellite Constellation for Environment and Disaster Monitoring and Forecasting and based on data collected by the A and B satellites (HJ-1A/B satellites) from March to June of 2009. A series of pre-processing steps was performed using ENVI software [46] and included radiometric calibrations, atmospheric corrections, and geometric corrections to convert HJ CCD radiance to reflectance with the correct geographic information.  
To extract regional winter wheat LAI maps, a two-layer canopy reflectance model (ACRM) [47] was employed to establish the lookup table (LUT) [48]. To avoid an ill-posed inversion, the estimated LUT was regularized according to the statistical information of the image and a priori knowledge obtained from winter wheat observations. To eliminate inversion distortion from clouds, moisture, aerosols, and mixed pixels, a Savitzky-Golay filter [49, 50] was used to smooth the time series of the LAI maps. Finally, acceptable LAI maps of winter wheat were extracted with an \(R^{2}\) of 0.82, an RE of 10.75%, and an RMSE of 0.46.
## 3 Results
### Urban Expansion Prediction  
Figure 5 shows the probability map of urban expansion in 2031, based on the land-cover maps of 2019 and XGBoost model developed in the study. The output raster cells  
Figure 4: Factor importance (SHAP value).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted**} & \multicolumn{1}{c}{**Total**} \\ \cline{3-5}  & \multicolumn{1}{c}{**Non-Urbanized**} & \multicolumn{1}{c}{**Urbanized**} & \multicolumn{1}{c}{} & \\ \hline \multirow{2}{*}{**Actual**} & **Non-urbanized** & 60,524 & 10,241 & 70,765 \\ \cline{2-5}  & **Urbanized** & 8786 & 29,402 & 38,188 \\ \hline \hline \multicolumn{2}{c}{**Total**} & 69,310 & 39,643 & 108,953 \\ \hline \hline \multicolumn{2}{c}{**Accuracy (\%)**} & 85.53\% & 76.99\% & 82.54\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy of XGBoost model.  
were calculated as 10 m \(\times\) 10 m units. As a result, the predicted areas of urban expansion at 80%, 90%, and 95% level of probability were 2245.36 km\({}^{2}\), 501.74 km\({}^{2}\), and 131.31 km\({}^{2}\), respectively, which account for 0.57%, 0.12%, and 0.03% of the whole country.  
One of the most noticeable points in this map is that raster grids with a relatively high probability of urban expansion were found to be concentrated in the northwestern part of the country. This seems mainly due to the existence of the Seoul Metropolitan Area (SMA)  
Figure 5: Probability map of urban expansion in South Korea (2031).  
within this region, which comprises the city of Seoul, Incheon, and Gyeonggi-do provinces. Those areas account for almost half of the national population and GDP [49; 50]. For a similar reason, the raster grids that are adjacent to other metropolitan areas in South Korea, including Busan, Daegu, and Daejeon, showed high probability of being urbanized area.  
In addition, the spatial boundaries of expanding urban areas seem to be highly dependent on the geographical and topographical characteristics of the country. The vast majority of raster grids with less than 10% probability of urban expansion are currently covered by the country's mountainous areas with high elevations and slopes. Instead, flat and wide agricultural areas such as paddy fields showed relatively high probability of being urbanized.
# Effect of Cohesive Properties on Low-Velocity Impact Simulations of Woven Composite Shells
## 2 Finite Element Models
### Boundary Conditions and Contact Definitions  
Identifically to the previous numerical study presented by the authors [16], the generated FE models replicate the experimental testing conditions used in [1,2]. In this low-velocity impact test, the composite shell specimens were stroked by an impactor with a 10 mm diameter hemispherical head and a mass of 2.826 kg. The energy of 5J was used to promote visible damage without full perforation of the specimens (further details about the experimental testing setup can be found in [2]). The amplitude of the impactor's velocity throughout the step time was also defined according to the experimental evidence. All the rotations of the impactor were constrained, and only the displacements along the \(y\)-direction were allowed. The fixed rigid body supports represented in Figure 1a simply support the bottom straight faces of the composite shell. Notice that, as presented in detail in [16], only one-fourth of the semicylindrical composite shell was generated to reduce the computational cost of the simulations. Despite this, due to the significant scale of the 9-layer FE model, i.e., consisting of 403,753 elements and 814,462 nodes, the computational complexity necessitated the utilisation of a computer cluster for the numerical simulations. To expedite the computations, 20 CPUs and 40 GB of RAM were allocated. Despite these computational resources, the solution times ranged from 7 days to 20 days.  
The surface-to-surface contact interactions between the supports, impactor, and the composite shell were carefully defined using the penalty enforcement contact method available in ABAQUS/Explicit [17]. To accurately capture the behaviour of completely delaminated interfaces, this contact formulation was implemented across all interfaces of the composite shell, ensuring frictional effects were properly accounted for.  
Specific friction coefficient values were assigned to the tangential behaviour between different surfaces. For the contact between the metal hemispherical head of the impactor and the top surface of the composite shell, a friction coefficient of \(\mu=0.3\) was specified [31,32]. Additionally, a value of \(\mu=0.7\) was assigned to the contact between the composite laminate surfaces and the metal surfaces of the supports. For all other interfaces within the composite shell, a friction coefficient of \(\mu=0.5\) was considered.
# Research on TBM Cutterhead Crack Damage and Fatigue Reliability
## 3 Fatigue Reliability Analysis of the Cutterhead Crack
### The Fatigue Reliability Calculation Results and Analysis  
During a cutterhead's lifetime, it is common that it will crack under the action of multidirectional random impact loads, which reduces the boring performance and reliability of the cutterhead [41]. Therefore, it is necessary to evaluate the detection of cracks; generally, nondestructive testing technology is used to detect 0.1-1 mm cracks [42]. However, due to the influence of complex factors, the initial crack size is random. The influence of the initial crack depth on the reliability of the cutterhead is discussed by changing the depth of the initial crack. Figure 9 shows the relationship between the reliability and initial crack depth under five kinds of rocks.  
Figure 8: Calculation flowchart of the Joint Committee on Structure Safety method (known as the JC method).  
It can be seen from Figure 9 that the reliability of the cutterhead decreased with the increase in the initial crack depth and the influence degree was more significant. Therefore, in engineering practice, the detection of the initial crack size should be strengthened to ensure the safety of the project and the construction efficiency.  
In the fatigue failure analysis, the critical crack depth was related to the stress, which is generally determined according to the instability criterion or engineering experience. In this study, the influence of the critical crack depth on the reliability of a cutterhead was studied by changing the critical crack depth. The calculation results are shown in Figure 10.  
It can be seen from Figure 10 that the reliability increased with increasing critical crack depth, but the change in the reliability with the crack depth was very small. Therefore, in actual engineering analysis, the critical crack depth value can be selected within a reasonable range according to experience.  
Regarding the detection of a cutterhead crack, there will be various shapes. The crack shape will affect the crack propagation path; therefore, it is necessary to analyze the reliability of cutterheads with different shape ratios. In this study, the range of shape ratios was 0.4-1 and the analysis results are shown in Figure 11.  
Figure 10: Relationship between the reliability of the cutterhead and the critical crack depth.  
Figure 9: Relationship between the reliability of the cutterhead and the initial crack depth.  
It can be observed from Figure 11 that the smaller the shape ratio, the lower the reliability, and thus, the more likely it is to fail. When the crack shape ratio was less than 0.5, the reliability of the cutterhead decreased significantly. This was consistent with the previous calculation and analysis of the SIF, where the smaller the crack shape ratio, the more prominent the damage. Therefore, in practice, cracks with smaller shape ratios may be detected, and thus they require more attention. If necessary, relevant repair methods should be used to interfere with their propagation to avoid engineering accidents.
# Quantum-Key-Distribution (QKD) Networks Enabled by Software-Defined Networks (SDN)
## 6 Conclusions  
SDN is a technology that separates the control and management of the networks from the data transmission and forwarding functionalities. It is also a promising idea to be applied in QKD networks for convenient optimization of the interaction of devices, resources and operations. This paper systematically introduces the QKD networks enabled by SDN, which logically controls QKD resources and abstract them to the application layer through northbound and southbound interfaces and related protocols, to finally realize a flexible and intelligent QKD networks. Moreover, three-use cases, including multi-resources allocation, secret-key management, and survivability guarantee, are provided as paradigms worthy of study. We described the process of the three-use cases, and numerous simulation results show the effectiveness of them. However, the compatibility of QKD networks and traditional optical networks is a current major challenge, and high costs will arise due to the redeployment of QKD networks. Also, more interesting related issues will be researched in the future.  
The writing of the draft was by [PERSON], the review, editing of the draft were responsible by [PERSON], and the language improvement of the draft was by A.N.  
This research was funded by China State Grid Corp Science and Technology Project, 5210 ED180047, National Natural Science Foundation of China (NSFC), 61822105, 61571058, 61601052; BUPT Excellent Ph.D. Students Foundation, CX2019215.  
No conflict of interest.
## 3 Numerical Examples and Discussions
### TCR Matching Optimization  
Setting \(C_{H}=1.2\), \(\tau=3.25\), \(U_{T}=5\) kW/K, and \(C_{L}/C_{H}=1\), and taking \(P\) as the objective function and \(u_{L}\) as the optimization variable, the influences of HR TCR ratio (\(C_{L}/C_{H}\)), \(U_{T}\) and \(\tau\) on the characteristics of \(P_{\text{max}}\)-\(C_{wf1}/C_{H}\) were studied.  
Figures 9-12 show the influences of the \(C_{L}/C_{H}\), \(U_{T}\), \(\tau\) and \(\eta_{e}\) on the characteristics of \(P_{\text{max}}\)-\(C_{wf1}/C_{H}\). It can be seen that with the increases of \(C_{wf1}/C_{H}\), \(P_{\text{max}}\)-\(C_{wf1}/C_{H}\) shows a parabolic-like change that first increases and then decreases, that is, there is an optimal \(C_{wf1}/C_{H}(\left(C_{wf1}/C_{H}\right)_{opt})\) which makes the cycle reach double-maximum power point (\(\left(P_{\text{max}}\right)_{\text{max}}\)).  
Figure 9 shows the influence of \(C_{L}/C_{H}\) on the characteristics of \(P_{\max}\)-\(C_{wf1}/C_{H}\). As can be seen, with the increase of \(C_{L}/C_{H}\), \(\left(P_{\max}\right)_{\max}\) and \(\left(C_{wf1}/C_{H}\right)_{opt}\) increase. When \(C_{L}/C_{H}\) takes 1, 2, 3, and 5, \(\left(P_{\max}\right)_{\max}\) is 33.78, 38.52, 40.11, and 41.37 kW, and \(\left(C_{wf1}/C_{H}\right)_{opt}\) is 0.42,  
0.49, 0.52, and 0.54, respectively. \(C_{L}/C_{H}\) increases from 1 to 5, \(\left(P_{\max}\right)_{\max}\) increases by about 22.5%, \(\left(C_{wf1}/C_{H}\right)_{opt}\) increases by about 28.6%.  
Figure 10 shows the influence of \(U_{T}\) on the characteristics of \(P_{\max}\)-\(C_{wf1}/C_{H}\). When \(U_{T}\) increases, \(\left(P_{\max}\right)_{\max}\) and \(\left(C_{wf1}/C_{H}\right)_{opt}\) increase. When \(U_{T}\) takes 2.5, 5, 7.5, and 10 kW/K, \(\left(P_{\max}\right)_{\max}\) are 20.84, 33.78, 42.44, and 48.56 kW, and \(\left(C_{wf1}/C_{H}\right)_{opt}\) is 0.27, 0.42, 0.50, and 0.56, respectively. When \(U_{T}\) increases from 2.5 to 10 kW/K, \(\left(P_{\max}\right)_{\max}\) and \(\left(C_{wf1}/C_{H}\right)_{opt}\) increase by about 133.01% and 107.4%, respectively.  
Figure 11 shows the influence of \(\tau\) on the characteristics of \(P_{\max}\)-\(C_{wf1}/C_{H}\). When \(\tau\) increases, \(\left(P_{\max}\right)_{\max}\) increases and \(\left(C_{wf1}/C_{H}\right)_{opt}\) is unchanged. When \(\tau\) takes 3.25, 3.5, 3.75, and 4, \(\left(P_{\max}\right)_{\max}\) is 33.78, 40.58, 47.75, and 55.25 kW, respectively, and \(\left(C_{wf1}/C_{H}\right)_{opt}\) is 0.42. When \(\tau\) increases from 3.25 to 4, \(\left(P_{\max}\right)_{\max}\) increases by about 63.6%.  
Figure 12 shows the influence of \(\eta_{e}\) on the characteristics of \(P_{\max}\)-\(C_{wf1}/C_{H}\). When \(\eta_{e}\) increases, \(\left(P_{\max}\right)_{\max}\) and \(\left(C_{wf1}/C_{H}\right)_{opt}\) increase. When \(\eta_{e}\) takes 0.85, 0.9, 0.95, and 1, \(\left(P_{\max}\right)_{\max}\) is 22.06, 30.36, 39.01, and 47.97 kW, \(\left(C_{wf1}/C_{H}\right)_{opt}\) is 0.38, 0.41, 0.43, and 0.46, respectively. When \(\eta_{e}\) increases from 0.85 to 1, \(\left(P_{\max}\right)_{\max}\) increases by about 117.5%, and \(\left(C_{wf1}/C_{H}\right)_{opt}\) increases by about 21.1%.
## 2 CFD Simulation Methodology
### Computational Grid  
As shown in Figure 2, the mesh has unstructured mesh forming tetrahedral elements. Mesh size usually affects the accuracy and time taken for the calculation. Firstly, the coarse mesh may lead to a singular solution, and the mesh size should be small, which is an important prerequisite to ensure the correctness of numerical simulation. Moreover, the duration of numerical simulation should be shortened as much as possible, when the accuracy is guaranteed [18]. To quantitatively compare the results with different sizes, mesh sizes of 3, 4, and 5 mm, had been used to simulate the flow field in three different pipe diameters, respectively. The differences between the solutions were not very significant, which were less than 2%. Therefore, the mesh size of 5 mm was chosen in the simulations to reduce the simulation time.
## 4 Discussion  
A number of studies have investigated tumor volume dynamics in response to radiotherapy. [PERSON] et al. (2015) employed a mathematical modelling approach including sufficient details of radiobiological theory to describe tumor volume dynamics in 18 non-small cell lung cancer (NSCLC) patients' response to stereotactic ablative radiotherapy (SABR). This work highlighted the significant variability of the estimated model parameters across different patients, suggesting that, if the model is intended to describe GTV dynamics for individual patients, then a population modelling approach may not be desired.  
Further work considered starting with a population-average model subsequently updated from an individual's tumor volume change to develop a more personalized prediction, based on 25 NSCLC patients treated with helical tomotherapy [22]. [PERSON] et al. considered a mathematical model of tumor response to radiotherapy based on CBCT data for 13 uterine cervical cancer patients, with promising results ranging from 13% to 21% for average model fitting errors on three group specific parameter sets based on tumor type and treatment and one general parameter set.  
Such mathematical approaches are dependent to some extent on the ability of radiobiological models to accurately reflect a complex tumor microenvironment and its response to radiation and such models are largely population derived. The potential advantage of a more empirical AI based approach using individual patient characteristics is in a more personalized, or at least stratified, prediction. The AI approach described within this work presents promising results with a mean difference between predicted and actual volume change of 6.5% \(\pm\) 2.5%, based on a stepwise regression model which demonstrated that age, dose per fraction and weight were the best predictors for TVRR.  
The patient sample within this study is small, although of the order of previous studies, and hence it is to be expected that improved results may be obtained with a larger patient sample. For further validation of the model the results should ideally be tested with an independent dataset. However, this is a preliminary feasibility study and further development of the model with more patients is planned.  
It is important to note that the results obtained within this study of rectal cancer patients will not be applicable to other cancer sites, although a similar methodology may be employed.  
Figure 7: The predicted TVRR is plotted against the actual TVRR with individual differences shown in yellow.  
For some cancer sites it will also be important to consider changes in the volume of nearby organs at risk (OAR) in order to develop an effective adaptive radiotherapy strategy.
# SRv6-Based Edge Service Continuity in 5G Mobile Networks +
## 6 Testbed and Performance Evaluation
### Performance Evaluation
#### 6.2.2 Analysis in Static Conditions  
We analyzed the performance in terms of the request-response time. We measured the request-response time of a TCP-based client-server application as in [24], consisting of a client that sends 100 B requests with a rate of 0.5 s, and a server sending an echo response. For the comparison, we considered the non-SRv6 approach as well as two different SRv6 flavors: the first, called _Loose SRv6_, introduces only the required segments, which is the egress edge node End SID. The second one, called _Hop-by-Hop SRv6_, includes in the SRH the segments of all the traversed devices within the core network. In Figure 18, we show the results of the request-response time over 50 repetitions when no migration is involved. As shown, the adoption of the Loose SRv6 approach does not worsen the performance with respect to the standard IPv6 routing mechanism. Hop-by-hop SRv6 flavor, instead, does not scale with the number of hops. What affects the performance, making the approach less efficient from a latency point of view, is the bigger SRH that must be processed by each node within the network. Therefore, if there are no reasons necessitating a hop-by-hop approach, such as in the case of traffic engineering, it is recommended to use the Loose SRv6 flavor.
# Multi-Scale CNN-Transformer Dual Network for Hyperspectral Compressive Snapshot Reconstruction
## 3 Materials and Methods  
### Multi-Scale CNN-Transformer Complementary Reconstruction Network  
In order to better represent the spatial-spectral coupling correlations in hyperspectral images, we integrate the advantages of CNN and the Transformer and design a dual-branch CNN-Transformer complementary module (DualCT). Using DualCT as the basic module, we further construct a reconstruction network called DualCT-Net to learn the reconstruction mapping from snapshot measurements to the original hyperspectral image. Similar to the multi-scale structure in [25], DualCT-Net is configured as an encoder, a bottleneck module, and a decoder. As shown in Figure 1, the encoder in DualCT-Net takes the snapshot measurement Y as input. Through the reverse dispersion process, the snapshot measurement Y is backward-shifted to obtain the initial reconstruction \(H_{0}\in R^{H\times W\times N_{\lambda}}\), and \(H_{0}\) is mapped into features \(X_{0}\in R^{H\times W\times C}\) through a \(3\times 3\) convolutional process. \(X_{0}\) then flows into the multi-scale feature extraction stage. Each scale of the encoder contains two DualCT modules and one downsampling operation. The downsampling operation employs a convolutional layer with a stride of \(4\times 4\) stride to reduce the spatial dimensions while doubling the number of channels. The output features of the third scale in the encoder enter the bottleneck module and then flow into the decoder. The bottleneck contains two DualCT modules.  
The decoder of DualCT-Net is designed as a symmetrical structure of the encoder. Each scale of the decoder has two DualCT modules and one upsampling module, and the upsampling is realized as a deconvolution operation. The same spatial resolution scale of the encoder and decoder are connected through skip connections, which convey features from encoder to decoder. The skip connections are beneficial for reducing the information loss caused by downsampling operations. We denote the output of the highest resolution scale of the decoder as \(X_{0}^{\prime}\in R^{H\times W\times C}\). After a \(3\times 3\) convolution processing upon \(X_{0}^{\prime}\), we can obtain the reconstruction \(R_{0}\in R^{H\times W\times N_{\lambda}}\). Due to the presence of global residual connections in DualCT-Net, \(R_{0}\) represents the incremental relative to the initial reconstruction \(H_{0}\). Finally, the reconstructed hyperspectral image \(H\in R^{H\times W\times N_{\lambda}}\) can be obtained by adding \(R_{0}\) and \(H_{0}\). The specific structure of the DualCT module is introduced in detail below.  
Figure 2: Schematic diagram of the CASSI system principle.
## 1 Introduction  
Permafrost is soil or rocks wherein the temperatures have remained 0 \({}^{\circ}\)C for at least two continuous years, and it spans approximately 23-25% of the land in the northern hemisphere, primarily found in Russia, Canada, USA, and China in Figure 1[1]. In recent years, as a result of global warming, the average temperature in the permafrost regions has risen at about twice the rate of the rest of the world [2, 3]. Meanwhile, the construction of highways will change the thermal stability of the permafrost and cause its degradation, such as the Alaska Highway in the United States, the trans-Siberian Highway in Russia, the Dempster Highway in Canada, and the Qinghai-Tibet Highway in China [4, 5, 6, 7]. Therefore, it is a big challenge to build highway in a permafrost region, under the dual role of the above.  
Over an extended period, scientists have been diligently striving to address the issue of damage to highway pavements in permafrost areas, and they have suggested numerous beneficial strategies [8; 9; 10]. The two-phase closed thermosyphon (TPCT) is one of the most efficient and cost-effective measures. The TPCT can prevent permafrost degradation to some extent and has been used in permafrost regions [11; 12; 13; 14]. However, damage still occurs. As shown in Figure 2, the long cracks are still occurring with the applications of the TPCTs. Therefore, it is necessary to summarize the working principles and factors of TPCT and then design new appropriate measures.  
In view of the above, the paper is divided into two main sections: schematic analysis of the TPCT and applications. The first section includes the following: operating principles, factors affecting the operation of the TPCT. The second section lists the applications with special attention to the permafrost region, and the normal experimental and numerical approaches of applications of the TPCT.  
Figure 1: Distribution of permafrost in the northern hemisphere [7].  
Figure 2: The cracks on the roadbeds with TPCTs [13].