esa-sceva/satcom-chunk-collection huggingface dataset:
# Day-Ahead Dispatch Model of Electro-Thermal Integrated Energy System with Power to Gas Function
## 3 Electro-Thermal Integrated Energy System with \"P2G Function\"  
### Structure of the Integrated System  
Taking HCNG as an example, a new model for combining heat and power system with P2G function is presented. The coupling system involves three kinds of energy network including power grid, district heating network and natural gas network. The output units include wind turbines (MTs), electrolyzers, combined heat and power units (CHPs) and gas-fired boilers (GBs), as shown in Figure 2.  
In the power system, the upstream grid based on the thermal units (TUs), the WTs and the CHPs supply the consumers' electricity demand. In the district heating network, the gas-fired boilers (the fuel contains the natural gas and the hydrogen from the abandoned wind power) and the CHPs supply the consumers' heat demand. In addition, the power of TUs is produced from coal, while the power of other production equipment (such as cogeneration units, gas boilers) is produced by natural gas or HCNG. In order to unify the power of the grid, these different energy consumptions will be converted into standard coal consumption to express the overall energy.
## 2 Methods
### Data Extraction and Quality Assessment  
Using a customized data extraction table based on the Cochrane \"Data Collection Form for Intervention Reviews: RCTs and non-RCTs,\" two authors ([PERSON] and [PERSON]) independently extracted the following information from each included study: author, year, country, cancer type, sample size, age, sex, intervention characteristics, duration of intervention, fatigue assessment tool, and main results. Disagreements were resolved by a third reviewer ([PERSON]). Duplicate records were removed using Mendeley Reference Manager (version 2.100).  
Risk of bias was assessed using the Cochrane risk of bias tool (RoB 2.0) [49], which evaluates five domains: (1) bias arising from the randomization process, (2) bias due to deviations from intended interventions (including blinding of participants and personnel), (3) bias due to missing outcome data, (4) bias in outcome measurements (including blinding of outcome assessors), and (5) bias in the selection of the reported results. As with study selection and data extraction, risk of bias assessments were performed independently by two reviewers ([PERSON] and [PERSON]), with any disagreements resolved by a third reviewer ([PERSON]).  
The methodological quality of the included trials was assessed using the PEDro scale [50], which includes 11 items covering external validity (item 1), internal validity (items 2-9), and statistical reporting (items 10-11). Items are scored as \"yes\" or \"no,\" and only clearly satisfied criteria receive a \"yes.\" The total score is based on items 2 through 11, with a maximum possible score of 10. Studies scoring below 4 points were rated as \"poor,\" scores of 4-5 were \"fair,\" 6-8 as \"good,\" and 9-10 were \"excellent.\"
## 1 Introduction
In landslide control, if the buried pile is arranged in multiple rows, it can not only provide greater anti-sliding resistance but also solve the problem of waste of pile length in the load section, and the construction safety is greatly improved. The following part will discuss the research results of double-row piles. In the study of double-row buried piles, [PERSON] [21] used the finite element method to study the pile-soil interaction of double-row buried anti-slide piles with connected beams and obtained the variation trend in internal forces of front and back piles with row distance. [PERSON] [22] theoretically analyzed the structure and force characteristics of double-row buried anti-slide piles with connected beams and, combined with engineering examples, studied the distribution of landslide thrust of buried double-row anti-slide piles. [PERSON] [23] theoretically derived the landslide thrust distribution model of the front and back rows of double-row buried anti-slide piles, analyzed the force and deformation characteristics of the front and back rows of piles with different row spacing by using the finite element method and put forward design suggestions for row spacing, pile spacing and layout. [PERSON] [24] used the variational method under the framework of limit equilibrium theory to calculate the buried depth of the rear pile and proposed a method for calculating the thrust of the rear pile based on the strain softening effect of the sliding strip. To sum up, the research on double-row buried piles is in its infancy; the design mainly relies on numerical simulation and engineering experience, and the design calculation model is still unclear. The main problems are unclear understanding of the internal force transfer mechanism of the soil in the top of the pile under the failure mode of overtopping and unclear theoretical design and calculation method of optimal pile length of double-row buried piles because most of the failure forms of anti-slide piles in engineering cases are overtop failure and overtop failure occurs easily because of the short pile length in the load section of the buried piles. Therefore, in this paper, the pile length of the load section of anti-slide piles under the failure mode of overtopping is studied, the theoretical design calculation is carried out for the first time and the reliability of the design method is verified by practical cases. This paper first explains the anti-sliding mechanism of double-row buried piles under overtopping failure and at the same time deduces the design and calculation of the optimal pile length. By using the limit equilibrium theory and the transfer coefficient method, the calculation method of the optimal pile length of the load section of double-row buried piles is obtained. It is helpful to popularize the application of double-row buried piles in engineering.
# Blockchain Technology for Secure Communication and Formation Control in Smart Drone Swarms
###### Abstract
In order to shape the future of these technologies and their impact on society, as well as address some of the most pressing problems facing us today, research into the uses of blockchain technology in swarms of drones is, therefore, of utmost importance. The purpose of this work is to provide a system that is efficient and resistant to outside attacks, where drones use blockchain technology to exchange data and coordinate their movements in a predetermined formation specified by the swarm's leader (mission-based). In order to mimic the behavior of the swarm, open source tools, including Ganache, Gazebo, PX4 models, and the MAVSDK library, were utilized. In addition, a smart contract was created that automates the process of choosing a specific position in the formation of the swarm members. A decentralized, secure communication network can be created thanks to this technology, which has the potential to improve network performance and member confidence. This study contributes to advancements in knowledge in the following areas. Firstly, a comprehensive discussion has been undertaken to elucidate the significance of integrating blockchain technology within UAV clusters, thereby facilitating accelerated progress across multiple domains. Secondly, a novel and secure scheme has been introduced, featuring a leader-election mechanism that leverages drones as blockchain nodes. This innovation enhances the operational robustness of the system. Additionally, an innovative smart contract has been developed to enable decentralized decision making pertaining to formation in UAV swarms, contingent upon mission requirements. This approach deviates from the existing literature, offering unique insights. Lastly, the design of the proposed smart contract focuses on efficiency, resulting in a lightweight and easily deployable solution. This optimization streamlines the practical implementation of the smart contract within the UAV clusters. The results are highly encouraging and it is certain that they could considerably increase the security of such systems. However, there are still problems that need to be resolved. Among them are the short battery life, price, scalability, storage, and ethical issues. Researchers are urged to work on these in an effort to develop a system that could be widely adopted by many industries in the future. This paper's structure is as follows. In Section 2, works related to this topic are discussed. Section 3 presents a thorough examination of some of the key theoretical ideas underlying blockchain technologies and their applications in UAV swarms. In Section 4, an in-depth review of the proposed smart contract, along with the tools that are used, is conducted. The results of the proposed scheme regarding the simulation and the functionality of this model are presented in Section 5. Finally, in Section 6, a discussion of the proposed model is provided, along with the limitations and the future work that researchers can undertake in order to improve the proposed scheme.
## 3 Numerical Simulations  
### Numerical Model and Boundary Conditions  
COMSOL Multiphysics analysis was conducted to investigate the tunneling-induced effect on the settlement of the overlying embankment. To simplify the calculation, each stratum was considered homogeneous and isotropic in this paper. It was assumed that the damage of moderately weathered argillaceous siltstone follows the Hoek-Brown criterion [28] and the other strata follow the Mohr-Coulomb criterion [29]. The friction angle was greater than 22 degrees, and the Mohr-Coulomb yield criterion was well adapted and accurate, so it was used to model the mechanical behavior of the soil. Meanwhile, the seepage field was assumed to satisfy [PERSON]'s law [30; 31] because the pressure gradient of groundwater flow was not large, and the coupling condition was assumed to satisfy the Biot-Willis law [32; 33]. A numerical model dimension of 80 m (length) \(\times\) 200 m (width) \(\times\) 30 m (height) was used to simulate the shield tunneling process, and the radius of the two tunnels was 3.1 m. The mesh and the whole 3D model used in the simulations are depicted in Figure 3. The mesh parameters of this model were maximum size 10 m, minimum size 0.6 m, maximum growth rate of adjacent cells 1.5, curvature factor 0.6 and thin domain resolution 0.02.  
Figure 2: Geological cross-section of the tunnels.  
Figure 3: The whole 3D model and detailed mesh shape in the simulation (unit: m). (**a**) The whole 3D model. (**b**) The mesh of the 3D model. (**c**) The mesh of the twin tunnels.  
The model was treated with roll support all around, so the normal direction on both sides was fixed and could only move in the vertical direction. The bottom of the model was fixed and constrained and could not be moved. The top surface of the model was free. The gravity field was set and applied to the numerical model, considering a lateral pressure coefficient of 1.5. According to the perennial water level of Liuyang River, the water level on the bank side and the riverbed side was set to 29 m. In addition to providing the osmotic pressure on the riverbed surface, the gravity of the water body flowing in the river channel also acts as pressure on the riverbed surface, and the water body in the river channel was applied as an applied load on the riverbed surface. It was assumed that water has no flow exchange on any other surface or outside the model except for the ends of the tunnel and the bed surface, and the remaining surfaces were set as no flow. Borehole samples were taken at the construction site, and a professional organization was commissioned to experimentally determine the parameters of each stratum. Thus, Table 1 lists the main parameters of the surrounding strata used in the numerical model.
## 2 Background
PR-AUC is often recommended, because ROC-AUC may provide a misleadingly positive view of the performance of the classification model, especially in highly skewed domains [39]. To exemplify this fact, Figure 1 presents a ROC curve (with an AUC of 95.34) and a PR curve (with an AUC of 82.01) for an SVM classifier applied to an imbalanced training dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Formula * & **Description** \\ \hline \multirow{3}{*}{Sensitivity or recall} & \multirow{3}{*}{\(R=\frac{TP}{IT+FN}\)} & It is the fraction of positive patterns that are correctly classified. Under the presence of imbalanced classes, recall typically measures the coverage of the minority class. \\  & & It evaluates the proportion of correctly classified instances among the ones classified as positive. For imbalanced classification, it often calculates the accuracy of the minority class. \\  & & It is used to measure the fraction of negative patterns that are correctly classified. It is only appropriate when false negatives are highly costly. \\ \multirow{3}{*}{Precision} & \multirow{3}{*}{\(P=\frac{TP}{IT+TP}\)} & It is the harmonic mean of precision and recall. \\  & & The \(F1\)-measure is the most often used threshold metric for learning from imbalanced data. \\ \hline \hline \end{tabular}  
* \(TP\): True Positives; \(FP\): False Positives; \(TF\): False Negatives; and \(TN\): True Negatives.  
\end{table} 
Table 1: Threshold metrics to evaluate the imbalanced classification task  
Figure 1: Examples of the Receiver Operating Characteristic (ROC) and Precision–Recall (PR) curves for an Support Vector Machine (SVM) applied to the same imbalanced dataset; (**a**) The ROC curve (AUC = 95.34) (**b**) The PR curve (AUC = 82.01).
## 1 Introduction
Validations are carried out by simulations using comprehensive electromagnetic simulation software, FEKO (EMSS, Stellenbosch, South-Africa), which uses the MoM\(/\)FEM (method of moment/finite element method) and SEMCAD-X (SPEAG, Switzerland), which uses the FDTD (finite difference time domain) method. The path loss occurring from the in-to-out body is critical for link budget calculations, and the PL model can help to find the maximum distance that can be covered between the Tx and the Rx. As it is difficult for the manufacturers to test their system on actual humans, the proposed model can be used by them to evaluate the performance of in-to-out body WBAN systems.
## 3 Experimental Results
### Results  
From the tests (the results of which are presented in Table 7), it can be noted that the problem of crack detection benefited from the more sophisticated architecture of ConvNets as the 101 layer ResNet slightly outperformed the 34 layer ResNet. Further inspection of the obtained results revealed that many of the misclassifications were due to our labeling methodology. A manually masked defect  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & \begin{tabular}{c} **Base** \\ **Learning** \\ **Rate** \\ \end{tabular} & \begin{tabular}{c} **Maximum** \\ **Learning** \\ **Rate for** \\ **Layer \(L\)** \\ \end{tabular} & \begin{tabular}{c} **Momentum** \\ **Lower** \\ **Bound** \\ \end{tabular} & \begin{tabular}{c} **Momentum** \\ **Upper** \\ **Bound** \\ \end{tabular} & \
\begin{tabular}{c} **Weight** \\ **Decay** \\ \end{tabular} &
\begin{tabular}{c} **Batch** \\ **Size** \\ \end{tabular} \\ \hline
**VGG16** & \(4.0\times 10^{-7}\) & \(1.0\times 10^{-5}\) & 0.85 & 0.95 & 0.01 & 32 \\ \hline
**ResNet34** & \(3.2\times 10^{-6}\) & \(8.0\times 10^{-5}\) & 0.85 & 0.95 & 0.01 & 32 \\ \hline
**ResNet101** & \(4.0\times 10^{-5}\) & \(1.0\times 10^{-4}\) & 0.85 & 0.95 & 0.01 & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Hyper-parameters for training the models used in this work.  
Figure 9: Fine-tuned training of ResNet101. Training loss is displayed for each processed batch and validation loss for each epoch. The cyclical learning rate throughout the training process can be seen on the right.  
at the corner of the image may not have passed the 5% threshold, therefore confusing the classifier (refer to Figure 10d). A few of the false positives were due to miscellaneous shapes on the road, such as tire marks, spills, etc. (refer to Figure 10b). Most of the other misclassifications included segments that were ambiguous due to low image quality and lack of context.  
Any trained network can be employed to find and localize the defects from the whole orthoframe. Figure 11 depicts an orthoframe that can be considered highly problematic due to a number of sharp contoured shadows. Yet, the network was able to localize a definite crack on the left side of the image. It also suggested another damaged area on the right where the pavement was apparently problem free, however.
# Stereo Digital Image Correlation in MATLAB
## 6 Conclusions  
The theory of a subset-based, Stereo-DIC framework, that is predominantly consistent with current state-of-the-art techniques, and its implementation as a modular 202 line MATLAB code, was presented. This framework is an extension of the previously presented ADIC2D framework and as such is also comprised of square or circular subset shape selection, assigning of Gaussian filtering parameters, altering of the interpolation method, the zero, first and second-order SFs, reference image strategy selection, the Phase Correlation Method to determine SFP initial estimates for temporal matching and calibration through using MATLAB's image calibration toolbox. ADIC3D, being capable of performing Stereo-DIC to determine both in-plane and out-of-plane displacements in the world CS, is additionally comprised of stereo matching, SIFT feature matching to determine SFP initial estimates for stereo matching and displacement transformation using the polynomial and linear triangulation methods. Furthermore, ADIC3D allows for assignment of SF order, subset shape, and subset size on a per image and per subset basis enabling straightforward integration with adaptive strategies. The framework is modular, enabling the reader to gain a deeper understanding of DIC as well as encouraging readers to adapt ADIC3D for new applications. Validation of the full framework coupled with its modularity makes it appealing not only as an educational resource, but also as a starting point to develop the capabilities of DIC. Conceptualization, T.H.B. and D.A. ; Methodology, D.A. and T.H.B. ; Software, D.A. ; Validation, D.A. ; Investigation, D.A. ; Resources, T.H.B. ; Writing--original draft preparation, D.A. ; Writing--review and editing, T.H.B. ; Visualization, D.A. ; Supervision, T.H.B. ; Funding acquisition, T.H.B. Both authors have read and agreed to the published version of the manuscript. This research was funded by the Mechanical and Mechatronic Engineering department of the University of Stellenbosch. Not applicable. **Data Availability Statement:** Data available in a publicly accessible repository that does not issue DOIs. Publicly available datasets were analyzed in this study. This data can be found here: [[[https://sem.org/3](https://sem.org/3) ddic]([https://sem.org/3](https://sem.org/3) ddic)] (accessed on 10 May 2021). **Acknowledgments:** The authors would like to thank [PERSON] for information and advice received about using the Stereo-DIC Challenge. **Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. **Glossary of Symbols**
# A Usable Encryption Solution for File-Based Geospatial Data within a Database File System
## 4 Automated and Transparent Encryption and Decryption
### Automated Encryption and Decryption Model  
The security solution allows spatial end-users to use the encryption facility with minimal changes to how they used to work previously with their GIS and applications. This consists of an automated model that allows encryption-decryption processing to be performed in the background and hidden from the spatial user. _Session-Based Triggers:_ To perform decryption and encryption procedures automatically when a spatial user accesses or exits the DBFS, a _session-based_ trigger based on the ECMSDK user sessions' ODMZ_SESSION table is used, that calls the single-user and multi-user file decryption and encryption procedures upon user login and logoff, respectively. This way, files are decrypted and encrypted automatically, without any change to how users interact with file shares using GIS applications. _Concurrent User Sessions:_ An encryption solution must also keep track of the active user sessions to prevent _double-encryption_ and _double-decryption_, which would make the files unusable for other privileged users still using them (have active sessions). If certain multi-user files are already decrypted for a privileged user (_user A_), another privileged user's session creation would call the decryption again, overwriting the temporary LOB with the original encrypted file content _again_, and by doing so, would overwrite the modifications made by _user A_. Similarly, upon another privileged user's session termination, encryption should be avoided, as this would make the LOB point to the one in the permanent tablespace containing the encrypted file content, making the file unusable to other privileged users who still have active sessions, while their file changes would be lost. To allow other privileged users to keep using the _multi-user_ spatial files, when one privileged user creates or terminates a DBFS user session, the session-based trigger calls the check_in_use() procedure that ensures there are no active sessions from all intended users who should have access to these files only after which decryption or encryption procedures are executed. The entire proposed encryption-decryption solution for single-user and multi-user spatial files is implemented in PL/SQL and contained within the _DBFS File Crypto package_. The package, along with the session-based trigger, is available as part of SupplementaryMaterials. The automated model and the encryption-decryption processes are directly connected, without any intermediary processes involved. As a result, the only performance concern when implementing the entire encryption system is the processing time of encryption and decryption, as shown in Table 1.  
The entire decryption process executed upon login onto the DBFS for user's single- and multi-user files is encapsulated in Algorithm 1 next. The encryption process, included as part of Supplementary Materials, has similar steps.
## 6 Results and Analysis
### Impact of Network Parameters on Average Residual Energy
Moreover, high-degree centrality nodes have more adjacent nodes, and more shortest paths pass through high-betweenness centrality nodes than ordinary nodes. These nodes play a more crucial role in the network, and the attack targets of maximum-degree attack and  
Figure 7: Effects of changing \(k\) on average residual energy for varied attacks. maximum-betweenness attack are these necessary nodes. However, the failure of essential nodes will induce frequent network route reconstruction, and the average residual energy of the network decreases faster. In addition, the rapid decline of the average residual energy may generate energy holes, so the average residual energy of maximum-degree attack and maximum-betweenness attack drops promptly at a larger number of communication rounds.