esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Challenges and Future Perspectives  
Based on the literature review, the authors have identified several major challenges in current research on shale microfractures and provided future research prospects. These include:  
(1) The difficulty in quantifying experimental data such as strain and energy in in situ observation experiments of shale microfractures. To address this issue, future studies can utilize quantitative research methods such as high-precision acoustic emission and microscale digital image correlation during in situ observation experiments to obtain information on energy and strain during rock fracture processes. In addition, developing thin shale sections for transmission electron microscopy observations can reveal the atomic-scale deformation and fracture mechanisms of shale, thus advancing in situ observation studies.  
(2) Limited research on the formation and evolution of non-planar fractures at multiple scales (macro, meso, and micro). More investigations are needed to understand the experimental phenomena, common patterns, and mechanisms involved in fractures at different scales.  
(3) In-depth exploration of the definition of microscale heterogeneity and its influence on the initiation and propagation of microfractures in shale. Questions regarding the definition of microscale heterogeneity in shale and the identification of the fundamental elements of heterogeneity at different magnifications have yet to be answered. The advancement of research in constitutive rock relationships and fracture theory will lead to significant changes alongside deeper and more detailed studies. This requires the improvement of experimental techniques and interdisciplinary research collaboration among rock mechanics, materials science, and physics.  
(4) Difficulties in obtaining microscale mechanical parameters of shale, leading to a significant lag in research compared to fields such as metals and materials science. The problem of acquiring macro-scale interface mechanical parameters of rocks remains unsolved, and obtaining microscale interface mechanical parameters is even more challenging. If the microscale interface mechanical properties of rocks can be obtained, it would facilitate research on macroscale rock interface issues and further advance rock mechanics theory.  
(5) The main challenges in numerical simulation studies of shale microfractures lie in inaccurate microscale mechanical parameters, difficulties in determining boundary conditions, and unclear microfracture criteria. Consequently, numerical simulation studies often rely on assumptions, model simplifications, and other limitations, making it difficult to reach widely accepted conclusions. Despite the short-term difficulty in overcoming these challenges, numerical simulations still play a crucial role in exploring rock fracture theories, qualitative analysis, comparative studies, and visualization presentations.
## 4 Discussion
The stability of the cell membrane is one of the main parameters for cellular responses under drought conditions and the abiotic stress tolerance factor in plants [39]. In the current research, PC, APX, and CAT activities in rice calli treated with different concentrations of PEG were increased in all genotypes. Choman and Shawre had the greatest amount of APX and CAT activities. These increases minimize the production of free radicals and protect the cell membrane from degradation. In our study, in all analyzed genotypes, a significant increase in proline content was recorded in PEG-treated plants. Shawre had the largest PC amount, whereas the minimum mean values of PC were recorded by Kalar and Gwll Swr. In a comparative study of rice varieties cultivated in hydroponic culture, increased proline accumulation was correlated with a higher degree of tolerance against PEG 6000-induced drought stress [40]. In the correlation analysis, CI was also positively and significantly associated with CAT in all stressed conditions. Based on this finding, CI and CAT were considered to be reliable predictors for the ability of these six local rice genotypes to produce callus in PEG-supplemented medium. White Bazyan and Gwll Swr genotypes reported the maximum increase in Na, Mg, K, and Ca contents, while the largest decreases in Na, K, Ca, Mg, S, and P contents were found in the Choman genotype. As can be seen from the results, PEG-tolerant genotypes (Choman and Shawre) contained a smaller amount of most mineral elements than PEG-sensitive genotypes (Kalar and Gwll Swr), suggesting that biochemical traits (enzymatic and non-enzymatic stress markers) are more important for enhancing tolerance in rice genotypes. Based on the findings of callus growth and biochemical parameters under stress treatments,a notable genotype differentiation based on the tolerance reaction was recognized as follows: Choman > Shawre > White Bazyan > Red Bazyan > Gwll Swr > Kalar. According to this classification, the tolerance response in the Choman genotype is higher than that of the other genotypes, even though Choman showed the highest values of CI, CFW, CL, CW, CWT, APX, and CAT, and modest values of CDW and PC.
## 4 Heat Dissipation Performance Simulation and Analysis
### Simulation Parameter Settings
#### 4.2.2 Physical Model  
The governing equation used in the simulation of heat dissipation components in this study was primarily based on thermal conductivity Equations (1)-(5). Generally, heat transfer was divided into heat conduction, heat convection, and heat radiation, although the simulations and analysis of results in this study did not consider heat radiation conditions. During experimentation, one side of the lower cold plate was attached to a heat generator (heat source) while the other end of the lower cold plate was attached to thermal insulation cotton to insulate against heat; therefore, the boundary condition was set as the heat insulation boundary while the remaining surface area was in direct contact with environmental air, which was set as the natural convection boundary. Generally, the heat transfer coefficient of air medium under natural convection was 5-25 \(W/m^{2}\cdot K\)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Element** & **Spec. Heat \(C_{p}\)** & **Density \(\rho\)** & **Thermal Conductivity \(k\)** \\ \cline{3-4}  & & _J/(kg\(\times\)K)_ & _kg/m\({}^{3}\)_ & _W/(m\(\times\)K)_ \\ \hline a & Upper Heat Transfer Panel & 386 & 8940 & 391.1 \\  & (Copper C1100) & 386 & 8940 & 247 \\ b & Middle Heat Transfer Panel & 872 & 2700 & 247 \\  & (Aluminum 6061-T4) & 386 & 8940 & 391.1 \\ c & Lower Heat Transfer Panel & 386 & 8940 & 391.1 \\  & (Copper C1100) & 385 & 8940 & 20,000 \\ d & (Oxygen-free copper & 385 & 8940 & 20,000 \\  & C1020) & & & \\ e & (Oxygen-free copper & 385 & 8940 & 16,000 \\  & C1020) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material and Physical Parameters.  
The natural convection coefficient set in this study was 20 \(W/m^{2}\cdot K\), and environmental temperature was set as \(T_{0}=25\)\({}^{\circ}\)C.  
\[k\equiv-\hat{q}/\frac{\partial T}{\partial X} \tag{1}\]  
\[Q_{conduction}=kA\frac{\Delta T}{L} \tag{2}\]  
\[R_{th}=\Delta T/Q \tag{3}\]  
\[\hat{q}=Q/A=k\Delta T/L \tag{4}\]  
\[\rho C_{P}\frac{\partial T}{\partial t}+\
abla\cdot(-k\
abla T)=Q \tag{5}\]
## 3 SLD-GEDIR Protocol
### Explanation of Phases of SLD-GEDIR  
As soon as a vehicle receives a packet for forwarding towards a destination vehicle, it uses steps 1-10 of the SLD-GEDIR algorithm. In the 1 st step, initialization of variables is performed. In the 2 nd step, the SONV set is assigned information about neighbors of the current vehicle. In the 3 rd step, CFV checks whether ODV is in the SONV set. If ODV is found in the SONV set, CFV transmits a packet to the destination vehicle using the available direct link. In the case ODV where it is not in the SONV set, the algorithm executes step 4, in which a set of segment vehicles (SSV) with smallest possible sector angle is determined. The quality of one hop links associated with each vehicle of SSV is computed in the 5 th step. In the 6 th step, degree of connectivity of each vehicle in the SSV is calculated. In the 7 th and 8 th steps, an NHV is determined to forward the packet further. In the 9 th step, the packet is delivered to the NHV and the NHV becomes the CFV for the next hop forwarding. Steps 1 to 10 are used at each hop until the packet is delivered to the ultimate destination. To facilitate the understanding of logical flow of steps in the algorithm, a flowchart is presented in Figure 7.  
Figure 7: Flowchart of the SLD-GEDIR algorithm.
# Response of Global Terrestrial Carbon Fluxes to Drought from 1981 to 2016
## 4 Discussion  
### Uncertainties of Simulated Terrestrial Carbon Fluxes and Drought Assessment  
In this study, the BEPS model was driven using remotely sensed LAI to simulate carbon fluxes at daily time steps. Owing to drawbacks in model structure, parameterization, and inputs, there were definitely some uncertainties in the simulated carbon fluxes. For example, by not accounting for the horizontal movement of soil water, the model might overestimate/underestimate the drought-induced decrease in carbon fluxes in low-lands/uplands. NEP is affected by a number of factors including climate change, land use change, and disturbances [4; 28]. Land cover and land use changes could significantly influence terrestrial carbon fluxes [24; 73]. In this study, a static land cover map was used to drive the BEPS model. The impacts of land cover and land use changes as well as disturbances on carbon fluxes were only partially captured through remotely sensed LAI. This simplification was another source of uncertainty in the simulated carbon fluxes.  
LAI is an important input into the BEPS model and an important determinant for the temporal trends of simulated carbon fluxes [64]. The LAI during 1981-2016 used here was generated through fusing AVHRR NDVI and MODIS data inverted pixel by pixel [69]. Overall, these two datasets are consistent. However, their consistency was poor for some regions, which might cause uncertainties in the temporal trends of simulated carbon fluxes and their responses to drought.  
Moreover, we should keep in mind that the drought identified by SPEI represented a change in water balance over the year relative to the multi-year mean over the study period. The SPEI is a standardized variate over the conditions that are normal at a given site, which can be quantitatively compared across sites with very different climatology; that is, a drought of SPEI equal to \(-1\) in a tropical forest region will be very different from a drought of SPEI equal to \(-1\) in the Sahel region, but both situations are comparable because they represent the same degree of deviation from the normal conditions at each site [74].  
Figure 7: Changes in the sensitivity of terrestrial GPP (**a**), NPP (**b**), ER (**c**), and NEP (**d**) to drought with regional mean annual water balance averaged over the period from 1981 to 2016.  
Therefore, a year with low SPEI in a wet area may not necessarily cause vegetation water stress and may still be wetter than a year with high SPEI in a dry region [63]. In this case, SPEI is unable to capture ecological drought which affects vegetation productivity [75; 76].
## 5 Future Directions  
To address the evolving challenges and opportunities in steganography, future research must consider the impact of emerging technologies such as quantum computing and brain-like intelligence, particularly in relation to the proposed Octree technique. The hierarchical structure and cube-based segmentation of the Octree technique could be well-suited for adaptation to quantum computing, which thrives on efficient data organization and processing. Quantum algorithms could enhance the Octree technique by optimizing the embedding and extraction processes, significantly reducing execution time while increasing the capacity and robustness of the hidden data. However, adapting the Octree approach to quantum systems would require rethinking certain elements, such as incorporating quantum error correction and quantum-resistant cryptographic schemes. Future studies could investigate how Octree's segmentation and data compaction principles align with the computational models of quantum systems, identifying which classical processes can be retained and which need modification to fully harness quantum computing's potential [37].  
Brain-like intelligence and advanced AI technologies also present transformative opportunities for the Octree technique. The hierarchical and spatially adaptive nature ofthe Octree framework makes it compatible with AI-driven analysis, which can process data with high complexity and correlation. AI models inspired by brain-like intelligence could further enhance the detection and embedding processes by leveraging their ability to analyze multi-dimensional data. Conversely, the Octree technique itself could inspire new steganographic methods aimed at evading AI detection. For example, the technique's ability to exploit insignificant coefficients in highly correlated regions could be adapted to embed data that remains imperceptible to even the most advanced AI models. This could lead to the development of \"AI-targeted steganography\", where the Octree approach is refined to interact specifically with the ways AI systems perceive and process data [38; 39].  
By extending the Octree technique into these emerging domains, steganography can evolve to meet the challenges of a rapidly changing technological landscape. The hierarchical structure and adaptability of the Octree provide a strong foundation for integrating quantum and AI-based advancements, ensuring the technique remains relevant and effective in an increasingly AI-driven and quantum-enabled world. These future directions highlight the potential of the Octree technique not only as a robust steganographic tool but also as a platform for exploring new intersections between steganography and cutting-edge technologies.
# Motion Correction for Brain MRI Using Deep Learning and a Novel Hybrid Loss Function
## 4 Results
### Quantitative Improvements for Motion-Corrupted Images  
Table 1 shows the SSIM (mean \(\pm\) standard deviation (SD)) and PSNR (mean \(\pm\) SD) values of the motion-corrected images compared to those for the uncorrected images in the test set of Dataset 1. The two-stage solution had the best performance among the three algorithms implemented (L1; L1 + TV; two-stage), although commonly only by a relatively small margin.  
In comparison to the previously published UNet-like structure with optional input-to-output concatenation (\"U + O\") [2], the proposed UNet solution (\"Model U\" in Table 1) showed significantly improved performance across the various methods (paired \(t\)-tests correspond to \(p<0.005\) for all contrasts). This difference was especially pronounced for the SSIM, which improved only marginally from 0.773 (corrupted images) to 0.816 (corrected images) for the U + O method with two stages while improving to 0.919 (corrected images) for the two-state algorithm without optional concatenation (U). Therefore, going forward, we will call the UNet algorithm with the two-stage multi-loss function but without optional concatenation the \"MC-Net\" algorithm.  
Figure 3 shows the relationship between the SSIM and the motion magnitude (defined as the standard deviation across 256 time points) for corrupted images and images corrected with the MC-Net algorithm. Without correction of motion artifacts, the SSIM decreases with the motion magnitude (from \(>\)0.95 for small motions below 1.5 mm/\({}^{\circ}\)) to approximately 0.75 for severe motion (with a slope of the linear regression curve = \(-\)0.028). MC-Net consistently improved image quality but was especially effective for large movements (slope = \(-\)0.014). Hence, the effect of motion on image quality decreased 2-fold (slope of regression line).  
Figure 3: SSIM values of images corrected with MC-Net (blue dots) relative to those of uncorrupted images (red dots) are plotted against the magnitude of motion simulated (standard deviation of motion across 256 time points, in mm/\({}^{\circ}\)). The red line (Y = 0.99 \(-\) 0.028\(\mathrm{\SIUnitSymbolMicro}\)) and blue line (Y = 0.98 \(-\) 0.014\(\mathrm{\SIUnitSymbolMicro}\)) show linear regression of images without and with motion correction against the motion magnitude.
# A Review of Acoustic Emission Source Localization Techniques in Different Dimensions
## 3 Sound Source Localization Methods for Two-Dimensional (2D) Structures
### Triangulation Method  
The triangulation method uses the time difference between the sound wave and different sensors to locate the sound source. It is an extension of the one-dimensional time difference positioning method, which essentially involves solving a system of equations, but two-dimensional methods require solving more systems of equations.  
Since the 1960s, triangulation technology has been used for AE non-destructive testing of rocket motorcycles. [PERSON] [1], [PERSON] and [PERSON] [2] and others studied sound source localization technology in isotropic plates. For the triangular time difference localization method in an isotropic plate using the difference in the AE signal arrival times (TDOA) at the three sensors (Figure 3), obtain the sound emission source position S0 by calculating the difference between several matrices.  
\[||X_{S}-X_{R,j}||-\big{|}\big{|}X_{S}-X_{R,j}\big{|}\big{|}=c\ \big{(}t_{i}-t_{j} \big{)},\ \forall i,\ j\ [1,\ 2,\ \ldots,\ N]\]  
Figure 1: One-dimensional time difference methods.  
The triangulation method has the following requirements: (1) wave velocity is constant in the medium; (2) arrival of the wave signal can be caught perfectly; (3) a straight wave path; (4) sensor locations are accurately determined; and (5) it requires a lot of sensors.  
By improving the above limitations, many scholars have also proposed new methods based on triangulation. We can classify the new methods based on the limitations of the improvements.
## 4 Experiments
### Music Identification Experiment
#### 4.2.2 Mixing  
In order to create a dataset with an environment similar to broadcast contents, music signals and speech signals should be mixed to appropriate SNRs. The voice activity detection (VAD) was used to find the section in which the actual speech exists to blend to the desired SNR. The equation below is for creating mixed data:  
\[\alpha=10^{\frac{\beta}{20}}\times\frac{P_{avg}(x_{s}[v_{s}])}{P_{avg}(x_{m}[v _{m}])} \tag{5}\]  
\[y=x_{s}+\alpha\times x_{m} \tag{6}\]  
where \(\alpha\) is a mixing factor with the target SNR \(\beta\) (dB), \(x_{s}\) is a speech signal, \(x_{m}\) is a music signal, and \(P_{avg}(\cdot)\) is average power. The \(v_{s}\) and \(v_{m}\) is an output vector of WebRTC [34] and represents a vector where actual speech signal and music signal are located, respectively. The \(y\), which is the mixed signal, can be obtained by linearly adding \(x_{m}\) multiplied by \(\alpha\) and \(x_{s}\).
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 4 Methods for Measuring the SUHI
### Statistical Models  
Statistical models have also been proposed to measure the SUHI by some researchers. Among these, a Gaussian surface model has been mostly utilized in previous studies [25; 97; 127; 128; 129] since it can provide not only the intensity but also the spatial extent and the central location of the SUHI in an area. The kernel convolution method has also been proposed to study urban heating effects because of its high efficiency in characterizing the temperature values over space in a continuous surface [130]. Recently, [PERSON] et al. [131] estimated SUHI intensity by the linear regression functions between LST and regionalized ISA. These statistical models could avoid the bias caused by the definitions of urban-rural areas or the choice of the representative pixels, and, thus, facilitate the SUHI comparisons among cities. However, they do not work in the cities partially covered by clouds and in arid cities, and, thus, have been only applied in a few SUHI studies to date.
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
## 6 Performance Analysis
### Computational Cost  
The execution time in seconds (s) of the operations considered is shown in Table 4. [PERSON] et al. [7] and [PERSON] et al. [6] adopted those values and performed tests with the following operational characteristics: CPU: Intel (R) Core (TM) 2 Quad Q8300, 2.50 Hz; memory: 2 GB; operational system: Windows 7 Professional.  
All four phases were analyzed and all operations executed were considered. Table 5 shows a comparison of the computational costs among our protocol and those of [PERSON] et al. [7], [PERSON] et al. [6], details of the operations performed at each phase and the total time in seconds.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Description** & **Cost (Seconds)** \\ \hline T\({}_{\text{S}}\) & Execute/Verify a Signature & 0.3317 s \\ T\({}_{\text{P}}\) & Bilinear Pairing & 0.0621 s \\ T\({}_{\text{E}}\) & Encrypt/Decrypt (Symmetric) & 0.0087 s \\ T\({}_{\text{H}}\) & One Way Hash Function & 0.0005 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Execution time of each operation considered.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Security Objectives** & **[PERSON] et al. [7]** & **[PERSON] et al. [6]** & **Our Protocol** \\ \hline Mutual Authentication & Yes & Yes & Yes \\ Forward/Backward Secrecy & Yes & Yes & Yes \\ Confidentiality & No & Yes & Yes \\ Non-Repudiation & Yes & Yes & Yes \\ Anonymity & No & Yes & Yes \\ Patient’s Non-Traceability & No & Yes & Yes \\ Session Key Security & Yes & Yes & Yes \\ Resistance to patient’s mobile device loss/stealing & No & Yes & Yes \\ Resistance to Impersonation attack & Yes & Yes & Yes \\ Resistance to replay attack & Yes & Yes & Yes \\ Resistance to Denial of Service (DoS) & Yes & No & Yes \\ Resistance to man-in-the-middle attack & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of security objectives among protocols.  
Our protocol required the lowest computational cost, therefore, it performs the operations necessary in a shorter time and offers the best computational cost, due to the exclusive use of symmetric cryptography (low communication cost) for the authentication procedures. [PERSON] et al. [7] and [PERSON] et al. [6] conducted some signature operations and bilinear pairing, which incurred higher computational costs.  
Figure 7 confirms with a graphic representation of costs that our proposed protocol has the best performance regarding computational costs when compared to [6] and [7].