esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Elastoplastic Damage Coupling of Reservoir Mudstone Considering Permeability Change
###### Abstract  
Mudstone, a common complex medium in oil and gas reservoirs and with widely distributed micro-pore and micro-fissures, is liable to produce significant damage evolution and plastic deformation under high buried depth stress environments. Based on the analysis of the physical characteristics, the elastoplastic damage coupling mechanical characteristics of mudstone in a high buried depth reservoir for oil and gas engineering are discussed. Firstly, conventional triaxial compression tests under different confining pressures were performed to calculate the damage variable and obtain the damage evolution. The damage evolution included the elastic damage stage, the plastic-dominated elastoplastic damage coupling stage and the damage-dominated elastoplastic damage coupling stage. Secondly, a coupled elastoplastic damage mechanical model for mudstone was proposed, which was based on the degradation of the damage stiffness and plastic flow caused by the plastic and damage internal variables and considered the elastic damage coupling and elastoplastic damage coupling during the loading process. Thirdly, the elastoplastic damage coupling mechanical characteristics of mudstone were simulated. The simulation results are in good agreement with the experimental results, which reflects well the mechanical characteristics of mudstone, including the transition from volume compression to expansion, plastic hardening, damage softening and residual strength, etc. Finally, based on the relevant research results, a permeability evolution model of mudstone based on the damage was proposed, and the secondary development was carried out based on ABAQUS. UMAT and USFLD subroutines were compiled, and seepage-stress coupling simulation verification was carried out. The relevant results provide a reliable basis for engineering theory research and stability analysis of deep mudstone reservoirs.  
rock mechanics; reservoir mudstone; damage evolution; elastoplastic characteristics; permeability change 2022  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON]  
1
## 3 Results and Discussion
### Influence of Changes in the Structure and Morphology of Aluminum on Hydrothermal Oxidation  
Structural and morphological changes can significantly affect the efficiency of the hydrothermal oxidation process, and the presence of impurities on the aluminum surface further complicates process.  
When aluminum is alloyed with elements such as magnesium (as seen in the Al-Mg-Ga-In-Sn alloys), the formation of intermetallic compounds on the surface of aluminum is observed. These compounds are attributed to the segregation of elements such as indium and tin. Interestingly, these intermetallic compounds seem to play a role in enhancing the rate of hydrothermal oxidation by altering the surface properties of aluminum. However, the mere dissolution of magnesium into aluminum grains was observed to have no significant impact on the reaction. This suggests that the surface-bound compounds and segregations substantially influence the reaction kinetics.  
Conversely, in the case of copper alloyed with aluminum (Al-Cu-Ga-In-Sn), the formation of intermetallic compounds such as Al\({}_{2}\)Cu at the grain boundaries adversely affects the reaction. The formation of these compounds interrupts the direct contact between the aluminum matrix and the other alloyed phases, thereby inhibiting the reaction. This shows that not all alloying elements and their resultant compounds are beneficial; some can indeed have detrimental effects on the hydrothermal oxidation process.  
Furthermore, the presence of impurities, such as oxides or other unwanted compounds on the surface of aluminum, poses challenges in achieving efficient hydrothermal oxidation. These impurities can act as barriers, hindering the contact between aluminum and water. Consequently, the reaction rates can be reduced. For efficient hydrogen production through hydrothermal oxidation, it is critical to either minimize these surface impurities or employ strategies such as mechanochemical activation, as seen in the case of aluminum waste can powder (AWCP), to overcome these barriers.  
In summary, changing the structure and morphology of aluminum through alloying and other means can have both positive and negative impacts on hydrothermal oxidation. The presence of impurities or certain intermetallic compounds on the surface can hinder the oxidation process. Understanding and controlling the microstructure of aluminum and its alloys is crucial for optimizing its hydrothermal oxidation for hydrogen production. This calls for a meticulous selection of alloying elements and processing conditions to favorably tailor the surface properties.
# Deep Learning-Based Step Size Determination for Hill Climbing Metaheuristics
## 3 Methodology
### Creating the Machine Learning Model  
The whole idea is based on the domain-specific knowledge learned by the FNN. Therefore, it makes no sense to create a general ML model to serve all possible problems. On the contrary, it is necessary to train the neural network on a given search space to make it capable of accurate \(\epsilon\) value predictions.  
This training needs a training database which is easy to produce. It is possible to run several HC algorithms from random starting points of the search space. The BF method always determines the best \(\epsilon\) value for a given location. The objective of the ML model is to build a function capable of predicting the best \(\epsilon\) values based on the already known values. These are as follows:  
* The iteration number (\(i\)).
* The current \(p_{i}\) location.
* The \(f(p_{i})\) fitness value of the current location.
* The current \(\epsilon_{i}\) value (size of the previous step).  
* Historically, the last \(k\) of these values, where \(i\geq k\)  
To ensure the generalization of the model, the method presented in this paper does not use the \(p\) values, it only uses the current (and last \(k\)) fitness and \(\epsilon\) values.
# Review of Photogrammetric and Lidar Applications of UAV
## 3 Classification of UAV
### Based on Wings and Rotors
#### 3.4.4 Fixed-Wing Hybrid VTOL UAVs  
Hybrid types of VTOL UAVs combine the advantages of fixed-wing and rotor designs. This type of UAV uses small rotors that are attached to fixed wings. It allows the UAV to hover, take off, and land vertically. After takeoff, VTOL UAVs continue to glide liketypical aircraft using aerodynamics, making them very efficient. There are few of this new category of hybrids on the market, but as technology advances, this option may become much more popular. The advantage of this technology is that it has a built-in autopilot function that maintains the UAV's stability, while the human pilot's task is only to control the UAV during flight. Hybrid VTOL UAVs are perfect for either hovering or forward flight. There are only a few hybrid VTOL UAVs on the market, and the technology is still developing. It is expected that this type of UAV will be able to serve as the delivery vehicle for shipments [23; 24].
# Prospects and Technical Challenges in Hydrogen Production through Dry Reforming of Methane
## 2 Recent Advances on Catalysts for Syngas Production
### Transition Metals-Based Catalysts
#### 2.2.1 Nickel Catalysts  
Nickel-based catalysts have been extensively reported as highly active for DRM, with a catalytic activity comparable to iridium (a noble metal with higher DRM than Pt and Pd) [63]. Comprehensive studies have shown that the general bi-functional mechanism of DRM goes through a CH\({}_{4}\) dehydrogenation over non-valent metal sites, while basic sites on supports and promoters are responsible for CO\({}_{2}\) dissociation [64]. It has been reported that Ni active sites may present a favorable electron structure for favoring the activation and cleavage of the C-H bonds, which is an important mechanism step for CH\({}_{4}\) activation and H\({}_{2}\) production [65; 66]. [PERSON] et al. [67] investigated hydrogen production over 10 wt% Ni/Al\({}_{2}\)O\({}_{3}\) with CeO\({}_{2}\) modification to better understand the role of feed composition and CeO\({}_{2}\)-Al\({}_{2}\)O\({}_{3}\) interaction on the activity and stability of Ni-based catalysts. The authors reported around 75% conversion of CH\({}_{4}\) and CO\({}_{2}\) at 800 \({}^{\circ}\)C, over 10 wt% Ni/Al\({}_{2}\)O\({}_{3}\) catalysts with a H\({}_{2}\)/CO ratio of 0.90. Adding up to 5 wt% CeO\({}_{2}\) to the alumina support led to an increase in both CH\({}_{4}\) and CO\({}_{2}\) conversion to 85% and 90%, respectively. Moreover, H\({}_{2}\) selectivity also increased, leading to a syngas with a H\({}_{2}\)/CO of 0.93. Increasing CeO\({}_{2}\) loading from 5 to 15 wt% led to the reduction in the surface area from 104.5 to 90.5 m\({}^{2}\) g\({}^{-1}\), increased the probability of Ni particles' agglomeration and decreased the catalyst activity. Regarding feed composition,
## 4 Conclusions  
Boro-tellurate glasses were fabricated using four various modifier oxides and investigated the influence of these oxides on both the mechanical and radiation shielding properties of the prepared samples. Monte Carlo simulation and the Makishima-Mackenzie model have been used in this work. The linear and mass attenuation coefficients for the glass with PbO are higher than the other samples. Thus, the SBT-Pb glass sample has a better attenuation competence than the SBT-Ti, SBT-Zn, and SBT-Ba samples. The thinner HVL achieved at the lowest gamma-ray energy among the studied energy range (i.e., 0.015 MeV) takes values 0.006, 0.005, 0.004, and 0.002 for samples SBT-Ti, SBT-Zn, SBT-Ba, and SBT-Pb, respectively. At moderate energy, the HVL varies the range of 0.191-3.588, 0.171-3.397, 0.114-3.117, 0.054-2.717 cm with average HVL of 1.844, 1.739, 1.546, and 1.234 cm for glass samples SBT-Ti, SBT-Zn, SBT-Ba, SBT-Pb, respectively. While the HVL in the high energy region varied between 5.768-5.543, 5.340-5.078, 4.579-4.223, 3.746-3.358 cm for samples SBT-Ti, SBT-Zn, SBT-Ba, SBT-Pb. At 1 MeV, the RPE increases between 11.23-69.63%, 11.82-71.60%, 12.86-74.76%, and 14.93-80.17% for the fabricated glass samples SBT-Ti, SBT-Zn, SBT-Ba, and SBT-Pb, respectively. The TF and RPE results reaffirm that utilization of the PbO in the prepared glasses has a considerable role in the attenuation performance for the prepared samples.  
Figure 10: (**a**) Variation of the transmission factor (TF) and (**b**) the radiation protection efficiency (RPE) versus the SBT glasses thickness.  
**Author Contributions:** M.I.S.: Software; investigation, writing--review and editing, M.K.H.: Conceptualization; investigation, writing--review and editing, M.H.A.M.: Methodology; investigation, writing--review and editing, K.A.N.: Formal analysis, writing--original draft preparation, K.A.M.: Validation: resources; data curation, writing--review and editing, M.U.K.: Writing--original draft preparation, supervision, H.O.: Funding acquisition, project administration, B.H.E.: Supervision, funding acquisition. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors acknowledge the support of Taif University Researchers Supporting Project number (TURSP-2020/127), Taif University, Taif, Saudi Arabia.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not Applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Effects of Bogies on the Wake Flow of a High-Speed Train
## 6 Conclusions  
In this study, we investigated the wake flows of three CRH380A high-speed train models with different degrees of simplification, i.e., the real model with bogies on the bottom of the train, the semi-simplified model with only the last bogie, and the simplified model without any bogies; a numerical study using the IDDES method and the SST \(k\)-\(\omega\) turbulence model was conducted. Analyses of the aerodynamic forces, train-induced slipstream, and TKE were conducted to determine the effects of the bogies on the external flow of the high-speed train. It was found that the existence of the bogies on the bottom of the high-speed train, especially the last bogie, not only enhanced the wake flow but also introduced large perturbances into the wake flow.  
Moreover, the generation and evolution of the vortices in the wake flows of the three high-speed train models were obtained by analyzing the instantaneous flow fields and decomposing the wake flows using the DMD method. The results showed that three pairs of vortices occurred in the wake flow of the simplified high-speed train model. The lower pair of counter-rotating and alternately changing vortices was generated by the cowcatcher, whereas the upper pairs of alternately changing and rapidly damping vortices were generated by the trailing nose of the train. In the real high-speed train model, a pair of large, counter-rotating streamwise vortices occurred in the wake flow field. Comparisons of the characteristics of the trailing vortices of the real and simplified model were conducted to determine the detailed mechanism of generation and evolution of the trailing vortices in a real high-speed train. It was demonstrated that the pair of large, counter-rotating streamwise vortices was generated by the cowcatcher of the trailing car and was enhanced by the perturbances introduced by the bogies on the bottom of the train. Furthermore, it is also concluded that the geometry of bogies should not be neglected during the analysis of the wake flow of high-speed trains in experiments or numerical simulations because bogies are an important influencing factor on the generation and evolution of trailing vortices.  
Conceptualization: [PERSON] and G.Y.; analysis and writing: W.L.; validation: [PERSON] and D.C.; review: [PERSON] and G.Y.  
The project was supported by the National Key R&D Program of China (2016 YFB1200506-22) and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB22020100).  
The authors declare no conflict of interest.
# Lightweight Physical Layer Aided Key Agreement and Authentication for the Internet of Things +
## 3 Physical Layer Aided Authentication and Key Agreement (PL-AKA)
### Proposed PL-AKA Protocol
#### 3.2.2 Procedures of Proposed Protocol  
Based on the integration strategy, detailed procedures of the proposed PL-AKA [17] are illustrated in Figure 1. As shown in the chart, ten messages, which are divided in three steps--(i) initial attach (\(M_{1}\sim M_{2}\)), (ii) key generation and distribution (\(M_{3}\sim M_{4}\)), and (iii) authentication (\(M_{5}\sim M_{10}\))--are exchanged as follows:  
* \(M_{1}\): The IoT device sends international mobile subscriber identity (IMSI) from the universal subscriber identity module (USIM) card of the device for user identification.
* \(M_{2}\): The MME requests authentication data to the HSS by forwarding user identification and network information.
* \(M_{3}\): The HSS generates authentication vectors (AVs) which include a secret key for PLA and transmits them to the MME.
* \(M_{4}\): The MME forwards the secret key for the PHY-CRAM to the BS, while it retains the other authentication information used for cryptographic challenge-response authentication.
* \(M_{5}\): For authentication of the IoT device, the BS transmits a challenge signal to the IoT device.
* \(M_{6}\): The IoT device sends the BS a response signal with a secret key which is encapsulated with channel phases.
* \(M_{7}\): For authentication of the network, the IoT device transmits a challenge signal to the BS.
* \(M_{8}\): The BS sends the IoT device a response signal with a secret key, which is encapsulated with channel phases.
* \(M_{9}\): If a feature score is in \"Gray\", the MME selects an unused AV, retrieves RAND and AUTN, and sends them to the IoT device. Here, RAND and AUTN mean random challenge and authentication token, respectively, in cryptography-based authentication.
* \(M_{10}\): If a feature score is in \"Gray\", the IoT device authenticates the networks and transmits RES to the MME. Here, RES means response in cryptography-based authentication.  
The main difference between the proposed protocol and an existing AKA protocol (e.g., EPS-AKA) is that the PHY-CRAM scheme comes under the authentication step, whereas the steps of (i) the initial attach and (ii) the key generation and distribution steps are similar to those of the conventional AKA protocol. At this time, an important issue that arises is how to integrate PLA with cryptography-based authentication. To this end, as shown in Figure 1, the PHY-CRAM method is employed as preemptive authentication between the IoT device and the BS. After performing the PHY-CRAM method, it is determined whether to conduct cryptography-based authentication procedures (\(M_{9}\) and \(M_{10}\)) in the protocol in accordance with a result of the preemptive authentication.
## 4 Benchmarking
### Test on QUBO Problems
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Problem**} & \multirow{2}{*}{\(n\)} & **Best** & **VNS** & \multicolumn{3}{c}{**B-VNS**} & \multicolumn{3}{c}{**Test (\(p\)-Value) *} \\
**Number** & & **Known** & **BestDif** & **AvgDif** & **Time** ** & **BestDif** & **AvgDif** & **Time** ** & **Dif** & **Time** \\ \hline
1a & 50 & 3414 & 0 & 1.667 & 0.003 & 0 & 1 & 0.003 & 0.459 & **...** \\
2a & 60 & 6063 & 0 & 0 & 0.012 & 0 & 0.011 & - & 0.006 \\
3a & 70 & 6037 & 0 & 8.9 & 0.017 & 0 & 11.467 & 0.016 & 0.773 & **...** \\
4a & 80 & 8598 & 0 & 0 & 0.035 & 0 & 0.030 & - & 0.009 \\
5a & 50 & 5737 & 0 & 0 & 0.004 & 0 & 3.867 & 0.003 & - & 0.041 \\
6a & 30 & 3980 & 0 & 0 & *** & 0 & 0 & *** & - & *** \\
7a & 30 & 4541 & 0 & 0 & *** & 0 & 0 & *** & - & *** \\
8a & 100 & 11,109 & 0 & 1.467 & 0.128 & 0 & 0 & 0.121 & - & *** \\ \hline
1b & 40 & 133 & 0 & 18.033 & *** & 0 & 21 & *** & 0.512 & - \\
2b & 50 & 121 & 0 & 0.733 & *** & 0 & 2.667 & *** & 0.096 & *** \\
3b & 60 & 118 & 0 & 4.667 & 0.001 & 0 & 8.533 & 0.001 & 0.065 & 0.401 \\
4b & 70 & 129 & 0 & 13.867 & 0.004 & 0 & 18.733 & 0.003 & 0.112 & 0.005 \\
5b & 80 & 150 & 0 & 0 & 0.012 & 0 & 0 & 0.011 & - & *** \\
6b & 90 & 146 & 13 & 35.933 & 0.021 & 13 & 37.833 & 0.016 & 0.277 & *** \\
7b & 80 & 160 & 0 & 0 & 0.027 & 0 & 2.533 & 0.025 & - & *** \\
8b & 90 & 145 & 0 & 6.633 & 0.062 & 0 & 5.433 & 0.053 & 0.307 & *** \\
9b & 100 & 137 & 0 & 2 & 0.156 & 0 & 2.433 & 0.141 & 1 & *** \\
10b & 125 & 154 & 0 & 0.233 & 0.467 & 0 & 0.233 & 0.414 & 1 & *** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results for Glover [11] problems. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Problem** & \(n\) & **Best** & \multicolumn{2}{c}{**VNS**} & \multicolumn{2}{c}{**B-VNS**} & \multicolumn{2}{c}{**Test (\(p\)-Value) \({}^{*}\)**} \\
**Number** & **Known** & **BestDif** & **AvgDif** & **Time \({}^{**}\)** & **BestDif** & **AvgDif** & **Time \({}^{**}\)** & **Diff** & **Time \({}^{**}\)** & **Diff** & **Time \\ \hline  
3 & 511.853 & 0.513 & 0.04 \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**\(n\)** & **Problem** & **Best** & \multicolumn{2}{c}{**VNS**} & \multicolumn{2}{c}{**B-VNS**} & \multicolumn{2}{c}{**Test (\(p\)-Value) \({}^{*}\)**} \\
**Number** & **Known** & **BestDif** & **AvgDif** & **Time \({}^{**}\)** & **BestDif** & **AvgDif** & **Time \({}^{**}\)** & **Diff** & **Time \({}^{**}\)** & **Diff** & **Time \\ \hline
50 & 1 & 2098 & 68 & 127.3 & 0.003 & 0 & 93.867 & 0.003 & 0.062 & 0.305 \\  & 2 & 3702 & 0 & 15 & 0.003 & 0 & 22.967 & 0.003 & 0.497 & 0.006 \\  & 3 & 4626 & 0 & 11.367 & 0.003 & 0 & 19 & 0.003 & 0.2
# Blockchain-Based Federated Learning: A Survey and New Perspectives
## 3 Blockchain vs. Federated Learning
### Reasons Why Blockchain Enables Federated Learning
#### 3.3.2 Privacy Storage  
In blockchain-based federated learning, the digital records on the blockchain cannot be tampered with or forged and can form a new storage model. Therefore, taking advantage of the immutability of blockchain, FL can prevent malicious participants from tampering with model information to protect the global model from being destroyed [54]. The model parameters of federated learning are stored in the blockchain, which not only ensures the security and reliability of the model parameters, but also promotes participants to contribute data.  
In addition, the blockchain provides a node data confidentiality mechanism. [PERSON] et al. [55] used the digital signature and smart contract technology of the blockchain and combined IPFS to propose a private blockchain federated learning (PriChainFL), which reduces the storage risk and risk of federated learning.
## 2 Background  
Robust and precise localization is a critical service for adaptive self-organizing ad hoc networks. Extensive reviews on positioning using the angle-of-arrival (AoA), time-of-arrival (ToA), time-difference-of-arrival (TDoA), and received-signal-strength (RSS) are available in [10, 11, 12, 13, 14, 15]. ToA positioning methods [16] are increasingly popular and have received growing interest in the last decade. Multi-antenna systems may leverage ToA measurements to estimate the position and orientation, the precision of which is dictated by the quality of the original measurements [17, 18, 19]. For broadcast systems such as the Global Positioning System (GPS) that have extremely high-quality clocks [20, 21], it is easy to ignore the timing aspect of this problem; however, for consumer-grade solutions, tracking the relative clock offsets is absolutely critical.