esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Ledger Technologies and Their Applications: A Review
## 3 Applications of DLT
### DLT in Energy Trading  
Energy trading is one of many fields in which smart contracts have been used [93]. As a result of the high demand for energy, different home-based methods for generating electricity have emerged. With this power generation, consumers and service providers can establish a two-way energy exchange network. Therefore, smart grids have been developed to trade energy via central systems in traditional solutions [94]. These systems have the drawback of lacking the trust of customers and privacy [95]. Furthermore, single-point failures can cause damage to central systems. The Blockchain, with its distributed platform and smart contracts, provides the opportunity to trade energy between consumers in a secure way.  
This article discusses energy trading using Blockchain. In this method, energy is traded among peers, and only information is exchanged via the Blockchain network, which is a low-cost way to make private and secure transactions. Blockchains enable transactions to be carried out securely and safely, which eliminates the need to exchange energy and costs with third parties. This makes energy management and energy trading easier and more secure. By using smart contracts and transparency feature of them, a Blockchain network can also improve security and ease the execution of transactions compared to non-Blockchain systems. When a seller of energy sends a smart contract to the Blockchain network and requests a price, the smart contract provides transparency to the buyer.  
[PERSON] et al. [96] developed a private Blockchain and smart contracts for energy trading markets in response to the need to manage renewable energy sales. As part of this Blockchain, smart contracts are used due to the need to manage the distributed sales market for renewable energies [91]. Furthermore, [PERSON] et al. provided a survey with useful details on smart grids and smart contracts [97]. [PERSON] et al. implemented a Consortium Blockchain-based technique to solve issue of privacy and data leakages in smart grids, which can lead to leakage of the location of each customer [98]. [PERSON] et al. also used Blockchain for authentication problems in smart grids to develop a solution for attacks such as DDoS [99]. [PERSON] et al. introduced the DBACP-IoTSG method and a new consensus algorithm for Blockchain-based access control for smart grids. The communication in their method is based on peer-to-peer communication with leaders in networks [100]. Table 7 shows a comparison between DLT-based approaches and traditional methods for running a network of smart grids.
# Deep Anomaly Detection Based on Variational Deviation Network
## 5 Results
### Comparison Algorithm
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data Sets** & \multicolumn{6}{c}{**AUC-PR Performance**} \\ \hline  & V-DevNet & DevNet & REPEN & DSVDD & FSNet & iForest \\ donors & **1.000 \(\pm\) 0.000** & 1.000 \(\pm\) 0.000 & 0.508 \(\pm\) 0.048 & 0.846 \(\pm\) 0.114 & 0.994 \(\pm\) 0.002 & 0.221 \(\pm\) 0.025 \\ census & **0.368 \(\pm\) 0.015** & 0.321 \(\pm\) 0.004 & 0.164 \(\pm\) 0.003 & 0.291 \(\pm\) 0.008 & 0.193 \(\pm\) 0.019 & 0.076 \(\pm\) 0.004 \\ fraud & **0.713 \(\pm\) 0.004** & 0.690 \(\pm\) 0.002 & 0.674 \(\pm\) 0.004 & 0.688 \(\pm\) 0.004 & 0.043 \(\pm\) 0.021 & 0.254 \(\pm\) 0.043 \\ celeba & **0.326 \(\pm\) 0.006** & 0.279 \(\pm\) 0.009 & 0.161 \(\pm\) 0.006 & 0.261 \(\pm\) 0.008 & 0.085 \(\pm\) 0.012 & 0.065 \(\pm\) 0.006 \\ backdoor & **0.886 \(\pm\) 0.002** & 0.883 \(\pm\) 0.008 & 0.116 \(\pm\) 0.003 & 0.856 \(\pm\) 0.016 & 0.573 \(\pm\) 0.167 & 0.051 \(\pm\) 0.005 \\ URL & **0.695 \(\pm\) 0.015** & 0.681 \(\pm\) 0.022 & 0.103 \(\pm\) 0.003 & 0.475 \(\pm\) 0.040 & 0.149 \(\pm\) 0.076 & 0.066 \(\pm\) 0.012 \\ campaign & **0.426 \(\pm\) 0.009** & 0.381 \(\pm\) 0.008 & 0.330 \(\pm\) 0.009 & 0.349 \(\pm\) 0.023 & 0.193 \(\pm\) 0.012 & 0.328 \(\pm\) 0.022 \\ news20 & 0.650 \(\pm\) 0.003 & **0.653 \(\pm\) 0.009** & 0.222 \(\pm\) 0.004 & 0.253 \(\pm\) 0.001 & 0.082 \(\pm\) 0.010 & 0.035 \(\pm\) 0.002 \\ thyroid & **0.386 \(\pm\) 0.003** & 0.274 \(\pm\) 0.011 & 0.093 \(\pm\) 0.005 & 0.241 \(\pm\) 0.009 & 0.116 \(\pm\) 0.014 & 0.166 \(\pm\) 0.017 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: AUC-PR performance of V-DevNet and five competitive methods. The bolded data in the table indicates that the accuracy of the algorithm is higher compared to other algorithms.
# A Blockchain-Assisted Security Protocol for Group Handover of MTC Devices in 5G Wireless Networks
## 7 Performance Evaluation
### Discussion of the Simulation Results  
We have Table 4 to discuss the security features of the BSPGH, NASS, PPHAP, and 5G-AKA schemes in detail. The 5G-AKA scheme faces security problems in handover authentication including lack of forward secrecy for keys, vulnerability to DoS and DDoS attacks, session key leakage, susceptibility to linkability attacks, and the absence of mutual authentication between an MTC device and its target gNB. The NASS scheme is vulnerable to DoS and DdoS attacks. The PPHAP scheme struggles to resist DdoS attacks. On the other hand, the BSPGH scheme can overcome all of the abovementioned security vulnerabilities, ensuring the highest level of security during the handover authentication process.  
We have conducted experiments for performance comparison among the four handover authentication schemes in terms of handover authentication time for a group of 30 MTC devices. Our proposed protocol, the BSPGH scheme, is faster than the NASS scheme. This time improvement comes from the fact that the NASS scheme performs a full RSA signature verification and modular exponentiation calculations, which result in significant computational overhead, while the BSPGH approach works based on certifiateless aggregate signatures and ECDH for session key generation, which need only point multiplication, point addition, and hashing operations so that relatively lower computational costs are incurred. The reduction in computational functions leads to a shorter authentication time, making the BSPGH scheme faster than the NASS scheme for handover authentication.  
However, the BSPGH scheme takes a longer time for handover authentication compared to the 5G-AKA and the PPHAP schemes. This is because the BSPGH scheme employs asymmetric ECDH to replace the less secure symmetric key-based key derivation function (KDF) used in the 5G-AKA and the PPHAP scheme. The computational overhead of the ECDH is much higher than that of the KDF, but the KDF is weak to resistant exhaustive attacks, side-channel attacks, and replay attacks. By incorporating the ECDH, the BSPGH scheme cannot only address the security issues associated with key derivation functions but also ensure perfect forward secrecy/backward secrecy and the prevention of linkability attacks for session keys.  
Considering the exponential growth in the number of 5G mobile devices in the future, the risks associated with DoS and DDoS attacks in large-scale MTCD scenarios will become more pronounced. Our solution effectively leverages the decentralized nature of blockchain, ensuring the authenticity and security of critical information, and more efficiently defending against DoS and DDoS attacks during the multi-user authentication process. Moreover, our protocol exhibits outstanding performance in the environments susceptible to these security attacks. Therefore, we believe that these overheads are tolerable to protect future 5G wireless networks.
# Induction Motor Dynamics Regimes: A Comprehensive Study of Mathematical Models and Validation
## 1 Introduction
Therefore, it is essential to identify the primary influencing factors and avoid making the task overly complicated [49; 50; 51; 52]. For induction motor transient processes research and analysis, mathematical modeling methods are widely applied [53; 54; 55; 56]. The growing demand for precision in modeling induction motor dynamics has driven advancements in computational tools. MATLAB/Simulink, with its user-friendly interface and extensive library, is widely used for real-time modeling of dynamic regimes in induction motors [57; 58; 59; 60; 61]. However, Simulink has notable disadvantages, particularly in specific applications. For instance, predefined macromodules often lack transparency regarding their internal structure, which limits their adaptability for non-standard scenarios or fault conditions [62; 63]. Additionally, while Simulink blocks offer ease of use through drag-and-drop functionality, their predefined functionality and options can be restrictive. Users may struggle to find blocks that suit specific needs, or they may face challenges in adjusting the behavior or appearance of existing blocks. Compatibil  
Figure 1: Global electricity consumption by electric motors. ity issues between different versions or plat-forms of Simulink can further complicate its application [62; 64]. Another significant limitation of Simulink, particularly in the context of power systems and power electronics, is its computational speed. For large-scale systems, the state-space solver used by Simulink can become considerably slower than traditional solvers, such as the nodal admittance methods employed in tools like EMP-RV and PSCAD. This drawback is especially critical in real-time applications, where low and consistent simulation time steps are essential [65]. In contrast, custom modeling approaches, such as those developed in Fortran, overcome these limitations by offering greater precision, reliability, and flexibility, particularly in scenarios requiring detailed customization [66]. Fortran's strengths enable the efficient simulation of various operating modes, including power asymmetry and transient processes, making it a robust platform for advancing the study of induction motor dynamics beyond the constraints of predefined tools. Moreover, its adaptability allows it to serve as a verification tool for validating the functionality of predefined tools like Simulink. This study aims to address these challenges by developing mathematical models tailored for analyzing the dynamic regimes of induction motors (IMs), which are crucial in numerous industrial applications. By leveraging four distinct modeling approaches, it provides a comprehensive framework for simulating key parameters such as rotational frequency, electromagnetic torque, and current profiles under diverse operating conditions. This approach enhances the understanding of transient processes in IMs and lays the foundation for optimizing their performance, particularly in energy-intensive industries.
## 3 Results and Discussion
### Permanent Deformation  
In Figure 7, the mean Wheel tracking slopes (\(WTS_{air}\)) as a function of bitumen content for the various mixes are shown. The data was obtained from the tests described in Section 2.3.3. The horizontal lines in the graph represent the maximum permitted value (according to Spanish norms) of the \(WTS_{air}\) parameter for both high-volume and low-volume heavy vehicle traffic.  
As may be observed, the AC-PA mixes, made with the control aggregate, exhibited a low Wheel tracking slope for bitumen contents of less than 4.5%, in compliance with the norms; however, with bitumen content increasing to 5-5.5%, the Wheel tracking slope increased significantly. This likely occurs because, for a bitumen content greater than 4%, the mix is in the ascending part of the VMA curve, which corresponds to a region associated with excessive flow and deformation, since the bitumen occupies spaces where the mineral framework separates.  
The AC-WC and AC-MT mixes were in compliance with Spanish norms (less than 0.07-0.1 mm/10\({}^{3}\) load cycles) at all levels of bitumen content studied. This indicates good resistance to permanent deformation. These results coincide with the Marshall deformation results given in Figure 4, in which the mixes with mine wastes exhibited less deformation than the control mix.
## 3 Current Distribution with Port Excitation  
From the above CMA and port excitation, it is shown that the structure generates dual resonance at 25.5 and 38 GHz. Figure 7 indicates the current oscillation at the respective frequencies when the port is excited. At 25.5 GHz, the current oscillation is along the \(\pm x\)-axis (i.e., vertical oscillation) because the patch length at the center is close to half-wavelength at 25.5 GHz, which is \(0.4\lambda_{0}\) in this case. The current is at its maximum in the feed and the center of the patch along the \(x\)-axis. This gives rise to the TM\({}_{10}\) mode at this frequency. However, at 38 GHz, the current oscillation in Figure 7b can be seen along the \(\pm y\)-axis because the patch width is close to half-wavelength at 38 GHz, which is \(0.6\lambda_{0}\) in this case. Two current maximums along the patch width give rise to the TM\({}_{02}\) mode at this frequency.
## 3 Mathematical Model Description
### Radiation Model  
In this work, the instantaneous efficiency was examined for a type LS2 PTC under the conditions of typical sunny and winter days in Budapest. First, the geographical location (47\({}^{\circ}\)48\({}^{\prime}\) N/19\({}^{\circ}\)03\({}^{\prime}\) E) was used to estimate the main angle that defined the radiation intensity. In this study, transmitted beam radiation was obtained using the Hottel model under an altitude of 0.1 km for various types of weather conditions for this location, as expressed in Equation (1). Next, the beam radiation was defined, as expressed in Equation (2) [27]; the ambient temperatures were obtained from the weather station of the Budapest University of Technology and Economics.  
\[\tau_{b}\ =\ a_{0}+a_{1}\exp(\frac{-k}{\cos\theta_{z}}) \tag{1}\]  
\[G_{b}\ =\ G_{on}*\tau_{b} \tag{2}\]  
Thus, factors \(a_{0}\), \(a_{1}\), and \(k\) were calculated and implemented in MATLAB code depending on the altitude of the location. The corresponding values for these factors are 0.133, 0.763, and 0.386, and 0.125, 0.748, and 0.344, for winter and summer seasons, respectively. \(G_{b}\) is the beam radiation and \(G_{on}\) is the extraterrestrial radiation (W/m\({}^{2}\)). In this research, the PTC was assumed to be continuously tracked along the E-W direction. The incident angle specified for this system was based on declination angle \(\delta\) and hour angle \(\omega\) for typical sunny and winter days, as in Equation (3) [48].  
\[\cos(\theta)\ =\ \left(1-\cos^{2}\delta\sin^{2}\omega\right)^{1/2} \tag{3}\]  
The incident angle modifier of the LS2 model PTC that was used to describe the radiation intensity effect on the glass cover and absorber was defined as a fourth-order polynomial and is represented by Equation (4) [49].  
\[k_{\theta}\ =\ \cos(\theta)+0.000884\ \theta-0.00005369\ \theta^{2} \tag{4}\]  
Accordingly, the optical efficiency can be calculated for each angle by multiplying the incident angle modifier (\(k_{\theta}\)) in the maximum optical efficiency (\(\eta_{opt,max}\)), which is acquired for a zero-incident angle, as shown in Equation (5).  
\[\eta_{opt}\ =\ k_{\theta}*\eta_{opt,max} \tag{5}\]
# Path Planning of Obstacle-Crossing Robot Based on Golden Sine Grey Wolf Optimizer
## 5 Simulation Experiment and Analysis
### Path-Planning Simulation Experiment
#### 5.3.2 Path-Planning Experiment  
In order to explore the feasibility of the GSGWO in the field of obstacle-crossing robot path planning, three different scale grid maps of 20 \(\times\) 20, 30 \(\times\) 30 and 40 \(\times\) 40 were selected for simulation and comparison experiments. The starting point is set in the lower left corner of the map, and the end point is set in the upper right corner. The experimental results and convergence curves are shown in Figure 6.  
By comparing the performance of the three algorithms, it can be found that the GWO has slow convergence speed, poor search ability and multiple local optimizations in the path planning of the obstacle-surmounting robot, which leads to search stagnation. The TGWO improves the convergence speed of the GWO and the probability of falling into the local optimum to a certain extent by improving the initial population through the chaotic sequence, but there is still a local optimum. The IGWO has a fast convergence speed and can plan the path at the fastest speed in simpler maps, but there will be premature  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Numerical Value** \\ \hline \(t_{max}\) & 100 \\ \(num_{p}\) & 50 \\ \(func(x)\) & Equation (15) \\ \(I_{max}\) & Map rows \(n\) \\ \(I_{min}\) & 1 \\ \(dim\) & \(n-2\) \\ \(\lambda\) & 1.5 \\ \(\mu\) & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental parameters of path planning.  
Figure 6: Experimental results of path planning: (**a**) path of 20 \(\times\) 20; (**b**) path of 30 \(\times\) 30; (**c**) path of 40 \(\times\) 40; (**d**) convergence curve of 20 \(\times\) 20; (**e**) convergence curve of 30 \(\times\) 30; (**f**) convergence curve of 20 \(\times\) 20.  
convergence in complex maps. Compared with the former three, the GSGWO performs better. It can jump out of the local optimum faster while having a higher convergence speed. It has the shortest path length, fewer inflection points and the smallest corners and has better trafficability. By comparing the performance of the algorithm in different complex working environments, it can be found that with the increase in the complexity of the working environment, the overlap part of the paths planned by the four algorithms gradually decreases, and the advantages of the paths planned by the GSGWO are greater.
## 3 Ring Shear Tests
### Shear Stress Measurements  
When the initial setup desired by the analyst is completed before the start of the experiment, all values are set to zero before preparing the next step for conducting the experiment, i.e., selection of normal stress, shearing speed, and drainage condition. For a given test condition, the normal stress and sample height remain constant during shearing. The drainage condition is selected to reproduce the drained (opened valve) or undrained condition (closed valve in the ring shear box). Finally, the desired shear speed is selected. In this study, the shear speed is limited to the range 0.1 to 100 mm/s. At a shear speed higher than 100 mm/s, the rubber edge in the ring shear box may break and result in water leakage; thus, tests were performed at a limited shear speed. Therefore, the test results may correspond to a landslide moving at a very slow or moderate rate. Figure 3 shows the test results in which the samples are subjected to a normal stress of 50 kPa and a shearing speed of 0.1 mm/s for both drained and undrained conditions. The shear stress and pore water pressure are measured for 500 s. Both typically exhibit strain-softening behavior, i.e., they have peak and residual value. The maximum value (i.e., peak stress) was observed  
Figure 2: Initial condition for shearing controls in ring shear tests.  
in the shear stress-shearing time curve. The shear stress showed an immediate reaction in approximately 10 s in the drained condition, while it reacted in approximately 40 s in the undrained condition. The difference may be due to the existence of ambient water in the shear zone in the ring shear box because strong contact between particles and particles can occur in the drained condition rather than the undrained condition. In this case, the shear stress measured in the undrained condition is slightly larger than that in the drained case. The differences at the peak and residual states are approximately 10 and 2-3 kPa, respectively. It should be noted, however, that the difference in shear stress is generally very small when the shear speed is given by a relatively low value (e.g., \(\leq\)0.1 mm/s), regardless of the drainage condition. The pore water pressure is not significant in the drained condition, but it is gradually increased and stabilized in approximately 300 s in the undrained condition. The maximum pore pressure in the undrained condition is approximately 1 kPa. In this case, it is actually not a sufficiently high value to affect the determination of shear stress. However, the value of pore water pressure generally depends on the shearing speed [26]. Most of the experimental results showed a similar trend. In this study, the shear strength depending on the shear displacement and speed according to the drainage condition is investigated.
## 2 Results and Discussion
### Alternative Geometries for TWA SPME Sampling  
As was shown above (Figure 5), an increase of the internal diameter of a protecting needle provides more space for analytes to diffuse around the coating and better reach the side of the coating. It decreases the controlling role of the fiber coating tip and should lead to more accurate and reproducible results.  
[PERSON] [20] proposed using SPME liner for TWA SPME to avoid sorption of analytes onto metallic walls of a protecting needle. The same approach can be used to avoid equilibration of analytes between the fiber tip and the surrounding space after sampling over longer time periods. At variable concentrations of analytes (as simulated in the previous section), calculated recoveries for VOCs using \(Z=67\) mm (Figure 8a) are 73-84%, which are close to the values obtained using retracted fiber at \(Z=40\) mm. No improvement was observed because of 0.75-mm I.D. SPME liner has 4.9 times greater cross-sectional area than 23 ga protecting needle, which results in 2.9 times greater theoretical flux of analytes from sampled air to the coating under the set \(Z\) (67 and 40 mm, respectively). To decrease the flux of analytes, the liner can be manufactured with a lower I.D. (e.g., 0.34 mm as for 23 ga needle) from the sampling side almost to the expected location of the fiber as shown in Figure 8b. Under these conditions, recoveries increased to 88-91% (Figure 9).  
Figure 8: Alternative geometries for TWA SPME sampling: (**a**) used by [PERSON] [18], and (**b**) proposed in this research to minimize sources of deviation from [PERSON]’s law of diffusion calibration.  
Figure 7: Sampling (\(Z=10\) mm) profiles (**a**) of four analytes from air having their varying concentrations (\(C_{0-49,000}=1.176\) μm\(\cdot\)m\({}^{-3}\), \(C_{49,000-51,000}=1.176\) μm\(\cdot\)m\({}^{-3}\), \(C_{51,000-100,000}=0.1176\) μm\(\cdot\)m\({}^{-3}\)) and recoveries of analytes (**b**) at \(t=100,000\) s and different \(Z\).  
The use of alternative geometries (Figure 10) resulted in a more uniform distribution of the analytes in the coating; for 0.75-mm I.D. SPME liner concentrations of analytes near the fiber tip were only 1.1-2.7 times greater than at another side of the coating. This should result in faster desorption of analytes, less pronounced peak tailing and greater accuracy of the method. A similar effect is achieved when using Radiello(r) passive air sampler [29], which provide a greater surface area of an adsorbent available for the diffusive air sampling.