esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Sem  
Figure 1a shows the SEM image of MWCNTs. The average outer diameter of nanotubes ranges from 9 to 16 nm. The Mn-0 composite looks similar to MWCNTs (Figure 1b), however, an irregular increase in the diameter of individual MWCNTs is observed, apparently due to the presence of a manganese oxide layer with different thicknesses on their surface. The presence of manganese on the MWCNTs surface is also confirmed by the EDX results (Figure S1). A comparative analysis of the average outer diameter, carried out on individual nanotubes (MWCNTs and Mn-0 samples), showed that the thickness of the manganese oxide layer in the Mn-0 composite is about 10-20 nm. The Mn-580 sample looks strikingly different (Figure 1c), namely: on the CNT surface, there are spherical particles with a characteristic size of 5-20 nm, fairly evenly distributed over the entire surface of the MWCNTs. Apparently, the formation of nanoparticles is due to the processes of the thermally initiated crystallization of manganese oxide. Rhenium (Re-360) formed on the surface of MWCNTs, just like manganese oxide in the Mn-0 sample, covers individual sections of nanotubes with a layer that has a different thickness in different areas (Figure 1d). The insertion of rhenium into the Mn-580 composite (Mn-580 + Re sample) leads to a decrease in the characteristic particle size of manganese oxide below 15 nm (Figure 1e). Herewith, rhenium is distributed in the form of agglomerates with a characteristic size of 50-100 nm. The presence of manganese and rhenium in this sample is confirmed by the EDX results (Figure S2). In the Mn-700 + Re sample, an insignificant amount of manganese oxide particles is observed on the CNT surface, and rhenium particles are combined into larger agglomerates than in the Mn-580 + Re sample (Figure 1e,f). We believe that part of the manganese oxide may be present in the agglomerates separately or together with rhenium, since a number of processes occur as a result of annealing in a vacuum at a temperature of 700 \({}^{\circ}\)C: (i) on the one hand, the restoration of the graphene structure of MWCNTs results in any functional surface oxygen-containing groups being removed [41; 42], thereby increasingthe hydrophobicity of the MWCNTs surface, (ii) on the other hand, the association of separate particles of manganese oxide and their crystallization.
# Scheduling Optimization of Prefabricated Construction Projects by Genetic Algorithm
## 5 Computational Experiments
### Experimental Results
#### 5.2.2 Results
In contrast with the method of randomly generating a certain number of population in some literature, then selecting better individuals to enter the initial population, and even screening out the infeasible solution after genetic operation, the method adopted in this study reduces the generation of infeasible individuals to a large extent, reduces the search space and improves the efficiency \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & **J30 (\%)** & **J60 (\%)** & **J120 (\%)** \\ \hline GA [This work] & 0.00 & 9.32 & 30.16 \\ MA(2020) [70] & 0.00 & 10.55 & 31.12 \\ S-FB(2018) [71] & 0.00 & 10.58 & 31.16 \\ COA(2017) [72] & 0.00 & 10.58 & 31.22 \\ Heuristic(2017) [73] & 0.03 & 10.91 & 32.52 \\ GA-part(2017) [74] & 0.01 & 10.71 & 31.81 \\ ACO-CRO(2017) [75] & – & 11.40 & 26.51 \\ H-RPSO(2016) [76] & 0.01 & 10.11 & 30.25 \\ ReVNS(2016) [77] & 0.00 & 10.88 & 32.21 \\ MAOA(2015) [78] & 0.01 & 10.64 & 31.02 \\ MJPSO(2014) [79] & 0.02 & 10.85 & 32.40 \\ PSO-HH(2014) [80] & 0.01 & 10.68 & 31.23 \\ GA-MBX(2013) [81] & 0.00 & 10.65 & 31.30 \\ HGA(2013) [82] & 0.01 & 10.63 & 30.66 \\ Art.Imm.Alg(2011) [83] & 0.00 & 10.55 & 31.48 \\ JPSO(2011) [84] & 0.04 & 11.00 & 32.89 \\ ACOSS(2010) [85] & 0.01 & 10.98 & 30.56 \\ DBGA(2007) [86] & 0.02 & 10.68 & 30.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the average deviation between the proposed algorithm and the other algorithms. of operation. In addition, in the design of the selection operation, the proposed algorithm applies an elite strategy based on the roulette method, which ensures that the optimal individual is not eliminated, and enhances the global convergence ability of the algorithm.
# Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information
## 6 Dynamic Detection Algorithm  
As we mentioned earlier, each client will not always remain stationary in an indoor wireless environment. Although our static detection algorithms are effective against Sybil attack in a static position, attackers may constantly move their position to change their channel characteristics, adding extra noise to the phase values. According to the literature [45], the CSI amplitude can sense the movement or gesture in the environment. Therefore, we can analyse the attack behaviour of malicious movement according to different fluctuation characteristics provided by different movement characteristics.  
The CSI amplitude has a stable characteristic when there is no movement of the environment. Its value basically remained stable. However, the amplitude of CSI will exhibit periodic fluctuations as the device moves. Figure 8 shows that the amplitude of the physical layer varies differently for different activities. Because the waveform moves in the person or device, the reflection path will be produced in different ways. As a result, the peaks and valleys will add up or down in the same direction over a period of time. Both clients and attackers may exhibit some fluctuations in the real environment. In addition, each client moves at a different frequency, which will only respond to a particular change in amplitude waveform. Attackers will present extremely similar waveforms. As Sybil attacks fake nodes from the same physical node, the forged packet information and forged packet structure will not affect CSI amplitude changes.
## 5 Results and Discussions
### Comparative Analysis  
The comparative analysis of the proposed ATS with ECC encryption algorithm is compared with various algorithms such as the modified grasshopper optimization algorithm (MGOA) [26], cuckoo search (CS) [27] and ant-bee colony (ABC) [31]. The proposed algorithm is compared with the other algorithms for the measures such as uploading time, downloading time, encryption time and decryption time. Figure 10 describes the comparative analysis of the encryption time for the proposed method with other methods such as MGOA, CS and ABC. The proposed method consumes only less encryption time when compared with other methods of secure m-learning.  
Figure 11 describes the comparative analysis of the decryption time for the proposed method with other methods such as MGOA, CS and ABC. The proposed method consumes only less decryption time when compared with other methods of secure m-learning.  
Figure 8: Duration of the student interaction and the responses.  
Figure 10: Comparative analysis for the encryption time.  
Figure 9: Reputation level and trust level of the students.  
Figure 12 describes the comparative analysis of the uploading time for the proposed method with other methods such as MGOA, CS and ABC. The proposed method outperforms other methods. The proposed method consumes only less uploading time when compared with other methods of secure m-learning.  
Figure 13 describes the comparative analysis of the downloading time for the proposed method with other methods such as MGOA, CS and ABC. The proposed method outperforms other methods. The proposed method consumes only less downloading time when compared with other methods of secure m-learning.  
Table 4 provides the comparative tabulation of the proposed ATS algorithm with various other optimization algorithms such as MGOA, CS and ABC. Various parameters like computation time, success rate and convergence rate are evaluated for various methods. From the tabulation, the results demonstrate that the proposed approach provides better performance when compared with other optimization algorithms.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Approaches** & **Computation Time** & **Success Rate** & **Convergence Rate** \\ \hline ATS (Proposed) & 0.35 s & 96.7\% & \(5\times 10^{3}\) \\ \hline MGOA & 0.78 s & 93\% & \(3.7\times 10^{3}\) \\ \hline CS & 1.2 s & 87\% & \(2.8\times 10^{3}\) \\ \hline ABC & 1.18 s & 85\% & \(2.6\times 10^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative analysis for various parameters.  
Figure 11: Comparative analysis for the decryption time.  
Figure 12: Comparative analysis for the uploading time.  
Figure 13: Comparative analysis for the downloading time.
## 2 FSW Experiments Considering Productivity
### Inherent Strain Method
As a result of the welding deformation during each stage of assembly, the structure differs from the initial design due to multiple assembly tolerances. The welding deformation calculation using thermal elasto-plastic analysis requires an excessively long analysis time; the inherent strain method was developed to solve this issue. The equivalent loading method based on inherent strain was initially developed; this method calculates the shrinkage force and the moment, which causes the deformation of the structure, considering the area of the heat affected zone, and then replaces them with equivalent loads to calculate the deformation of the structure [16; 17; 18; 19]. However, the problem of the equivalent loading method is that compressive residual stress occurs on the welds because the shrinkage force is applied as load. When heat is applied, actual welds are subject to shrinkage because thermal expansion is suppressed by the surrounding structures and constraints. On the contrary, when the welds are cooled to room temperature after welding, they are subject to tensile force because thermal shrinkage is suppressed by the surrounding structures and constraints. Due to this mechanism, tensile residual stress ultimately remains in the weld. The equivalent strain method based on inherent strain has been proposed to implement such a mechanism and has been used by various researchers to study welding characteristics [20; 21].
## 2 The Principle of the Drift-Insensitive Self-Comparison Method and the Experimental Setup
### The Principle of the Drift-Insensitive Self-Comparison Method
The DISC method will not complicate the existing devices or consume extra time. Meanwhile, in terms of the cancellation of the SCME, the DISC method, which updates the frequency drift rate every four clock detection cycles, is expected to work better than the traditional way described in reference [27], which typically needs forty clock detection cycles to update the frequency drift rate. It's worth noting that the DISC method cannot be used when the drift rate is very high (for example, the total frequency drift is larger than the FWHM of the spectrum in one clock feedback cycle) due to bad locking.
## 4 IPTMS Power Management Optimisation
The optimisation aims to minimise the fuel consumption by controlling the power split (ratio of battery power to total propulsion power) and the cooling ratio (ratio of TMS power required to engine power), while the constraints are the battery temperature and state of charge. The results show that the computational time is reduced from 4 h to 2 min with PMP. However, the fuel consumptions are increased by about 2%. Both military and civil aircraft need power management optimisation for IPTMS to obtain the optimal battery power supply strategy. When the battery provides required power for TMS and for other power consumers, such as avionics, motors, and anti-icing systems, there could be an optimal solution to manage power split between the battery and the engine-driven generators to minimise the energy consumption. The types of optimisation strategies depend on the requirements, such as computational speed, capability, and stability.
# Spinel Magnetic Iron Oxide Nanoparticles: Properties, Synthesis and Washing Methods
## 2 Crystal Structure and Magnetic Properties of Spinel Iron Oxide
### Crystal Structures  
#### 2.1.1 Magnetic Crystal Structure  
The Fe\({}_{3}\)O\({}_{4}\) magnetite crystallizes in a spinel type structure according to the space group Fd\(\overline{3}\)m and presents a lattice parameter close to 0.8396 nm at room temperature (JCPDS card 04-015-8200).  
The spinel structure was described for the first time by [PERSON] and [PERSON] in 1915 [3] and owes its name to the mineral MgAl\({}_{2}\)O\({}_{4}\). Spinels present the general chemical formula AB\({}_{2\times 4}\) with A and B as the metal cations Fe\({}^{2+}\) and Fe\({}^{3+}\) and X anions O\({}^{2-}\) for the magnetite.  
Several types of spinel structures can be described according to the distribution of cations A and B in the interstitial sites: \"direct\", \"reverse\" spinel structure or an intermediate structure. Magnetic adopts a spinel structure called \"reverse\". Considering magnetite, the crystal lattice is constituted of 32 anions O\({}^{2-}\) forming a face-centered cubic network. One-eighth of the 64 tetrahedral sites (denoted A) generated by this anionic arrangement are occupied by the trivalent cations Fe\({}^{3+}\) and half of the 32 octahedral sites (denoted B) are occupied by divalent cations Fe\({}^{2+}\) and trivalent cations Fe\({}^{3+}\) (Figure 1). The magnetite formula can be written in the form (Fe\({}^{3+}\))tetra[Fe\({}^{2+}\)Fe\({}^{3+}\)]octa(O\({}^{2-}\))\({}_{4}\) to better represent the position of the ions Fe\({}^{2+}\) and Fe\({}^{3+}\) in the different sites of the structure.  
The magnetite can be partially oxidized. It will be named \"sub stoichiometric magnetite\" with chemical formula Fe\({}_{3-\delta}\)O\({}_{4}\) where \(\delta\) represents the deviation from stoichiometric of the magnetite. Totally oxidized magnetite is called maghemite.  
Figure 1: Representation of mesh of the crystallographic structure of magnetite. The octahedra and tetrahedra materialize on of the planes (111) of the mesh.
## Material and Methods
### Unbalance Vibration Modeling of Grading Chain Drive Double Drums
\[\dot{\phi}_{1}=\omega_{1}+\frac{\dot{q}_{3}}{r_{1}};\,\ddot{\phi}_{1}=\frac{ \ddot{q}_{3}}{r_{1}};\,\delta_{\phi_{1}}=\frac{\delta_{q_{3}}}{r_{1}} \tag{6}\]  
\[\dot{\phi}_{2}=\frac{r_{1}}{r_{2}}\omega_{1}+\frac{\dot{q}_{2}}{r_{2}};\,\ddot {\phi}_{2}=\frac{\ddot{q}_{2}}{r_{2}};\,\delta_{\phi_{2}}=\frac{\delta_{q_{2}}} {r_{2}} \tag{7}\]  
According to the principle of virtual power and Lagrange Equation, the differential Equation of system vibration can be obtained as followed. \[\mathbf{M\ddot{q}}+\mathbf{C\dot{q}}+\mathbf{Kq}=\mathbf{F} \tag{8}\]  
where, the \(\mathbf{q}\), \(\dot{\mathbf{q}}\), and \(\ddot{\mathbf{q}}\), was displacement vector, velocity vector, and acceleration vector, respectively; the \(\mathbf{M}\), \(\mathbf{C}\), \(\mathbf{K}\), and \(\mathbf{F}\) were the mass matrix, damping matrix, stiffness matrix, and external force matrix (incentive matrix) of the system, respectively. Obviously, these were all symmetric matrices. Their expressions were as follows:  
\[\mathbf{M}=\left[ \begin{array}{ccc}m&0&0\\ 0&J_{2}/r_{2}^{2}&0\\ 0&0&J_{1}/r_{1}^{2}\end{array} \right]\]  
\[\mathbf{C}=\left[ \begin{array}{ccc}2c\cos^{2}\alpha&0&0\\ 0&2c&-2c\\ 0&-2c&2c\end{array} \right]\]  
\[\mathbf{K}=\left[ \begin{array}{ccc}(k_{1}+k_{2})\cos^{2}\alpha&-(k_{2}-k_{1}) \cos\alpha&(k_{2}-k_{1})\cos\alpha\\ -(k_{2}-k_{1})\cos\alpha&k_{1}+k_{2}&-(k_{1}+k_{2})\\ (k_{2}-k_{1})\cos\alpha&-(k_{1}+k_{2})&k_{1}+k_{2}\end{array} \right]\]  
\[\mathbf{F}=\left[ \begin{array}{c}me\omega_{2}^{2}\sin\phi_{2}\\ 0\\ N/r_{1}\end{array} \right]\]  
In order to simplify the model, the torsional vibration generated by the driving wheel was not considered. That was \(\phi_{1}=\omega_{1}t\); \(\dot{\phi}_{1}=\omega_{1}\); \(\ddot{\phi}_{1}=0\), then \(q_{1}=0\); \(\dot{q}_{1}=0\); \(\ddot{q}_{1}=0\). Because of \(\phi_{2}\approx\omega_{2}t\), the lateral differential vibration model system of the chain drive model could be simplified. The differential Equation of motion of the two-degree-of-freedom vibration system was as followed. \[\mathbf{M^{\prime}\ddot{q}}+\mathbf{C^{\prime}\dot{q}}+\mathbf{K^{\prime}q}=\mathbf{F^{\prime}} \tag{9}\]  
where \(\mathbf{M^{\prime}}=\left[ \begin{array}{cc}m&0\\ 0&J_{2}/r_{2}^{2}\end{array} \right]\), \(\mathbf{C^{\prime}}=\left[ \begin{array}{cc}2c\cos^{2}\alpha&0\\ 0&2c\end{array} \right]\), \(\mathbf{K^{\prime}}=\left[ \begin{array}{cc}(k_{1}+k_{2})\cos^{2}\alpha&-(k_{2}-k _{1})\cos\alpha\\ -(k_{2}-k_{1})\cos\alpha&k_{1}+k_{2}\end{array} \right]\), and \(\mathbf{F^{\prime}}=\left[ \begin{array}{c}me\omega_{2}^{2}\sin\phi_{2}\\ 0\end{array} \right]\). The natural frequency of chain drive lateral vibration model system was one of the inherent properties of the transmission system. It was usually determined by the structure, material, shape and other parameters of the system. It was less affected by damping and external excitation force.
# GIS-Based Solar Radiation Mapping, Site Evaluation, and Potential Assessment: A Review
## 3 Solar Site Evaluation Using GISs
### Overview
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**References** & **Methods** & **Scale** & **Location** & **Solar Map** & **Result** & **Energy Type** \\ \hline Fluri [24] & Boolean overlay & National & South Africa & Institution & Suitable area & CSP \\ \hline [PERSON] et al. [25] & Boolean overlay & Regional & Eastern Morocco & Analyst tool & Suitable area & CSP \\ \hline [PERSON] et al. [26] & Boolean overlay & Regional & Eastern Morocco & Interpolation & Suitable area & CSP \\ \hline [PERSON] et al. [27] & Boolean overlay & National & Tibet, China & Satellite image & Suitable area & PV \\ \hline [PERSON] et al. [28] & Boolean overlay & Regional & Wyoming, US & Institution & Suitable area & PV, CSP \\ \hline [PERSON] et al. [29] & Boolean overlay & Continental & Middle-East & Interpolation & Suitable area & Solar, Wind \\ \hline Anwarzai and Nagasaka [30] & Boolean overlay & National & Afghanistan & Institution & Suitable area & PV, CSP, Wind \\ \hline [PERSON] and Ghedira [31] & Weighted sum & National & United Arab Emirates & Satellite image & Suitability map & PV, CSP \\ \hline [PERSON] et al. [32] & Weighted sum & Continental & Europe & Institution & Suitability map & PV \\ \hline [PERSON] [33] & Weighted sum & Regional & Colorado & Institution & Suitability map & Solar, Wind \\ \hline [PERSON] and [PERSON] [34] & Weighted sum & National & Ecuador & Institution & Suitability map & PV, CSP, Wind \\ \hline [PERSON] et al. [35] & Weighted sum, Fuzzy & Regional & Western Turkey & Institution & Suitability map & PV \\ \hline [PERSON] et al. [36] & Weighted sum, PVMapper & Regional & Southwestern US & Institution & Suitability map & PV \\ \hline [PERSON] et al. [37] & SWARA 1, WASP 2 & National & Iran & Institution & Suitability for cities & PV \\ \hline [PERSON] et al. [38] & AHP 3 & National & Zimbabwe & Interpolation & Suitability map & CSP \\ \hline [PERSON] and [PERSON] [39] & AHP & National & Saudi Arabia & Analyst tool & Suitability map & PV \\ \hline [PERSON] [40] & AHP & Regional & Karapinar, Turkey & Institution & Suitability map & Solar \\ \hline [PERSON] et al. [41] & AHP & Continental & West Africa & Institution & Suitability map & PV, CSP \\ \hline [PERSON] et al. [42] & AHP & Regional & Eastern Morocco & Institution & Suitability map & PV \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of previous solar site evaluation studies using GISs.
## 6 Energy-Efficient, Priority-Based Forwarding (EPF)
### Node Side Architecture  
Like the traditional NDN architecture, Node side architecture contains CS that keeps the record of the content. The PIT keeps the information of unsatisfied packets. The additional part of EPF is its Local Information Table (LIT) and priority forwarding table. The LIT stores information of controller, neighbor IDs, residual energy and content names. Figure 7 shows the node side architecture.
## 3 System Model
### Training  
As in SAES [35], we build the training dataset by sampling image pairs from the SPED_900 dataset [46]. The RGB images are resized to 240 \(\times\) 320 \(\times\) 3 before being fed into the network, and the ground truth is processed into a binary classification with a label space of 0 and 1. Our method is implemented by the PyTorch framework [47] on an Intel Xeon CPU E5-2678 v3 (2.50 GHz) and an NVIDIA Geforce Titan XP GPU. To speed up the training process, the parameters of AlexNet, pre-trained on the ImageNet dataset [48], are adopted to initialize our feature extraction module. Adam [49], with \(\beta_{1}\) = 0.9, \(\beta_{2}\) = 0.99, is utilized as the optimizer to train the network for up to 70 epochs, and the batch size is 64. The initial learning rate is set to 0.001 and reduced by 0.1 times every 30 epochs. Early stopping techniques are introduced to prevent the model from overfitting.  
The LCD task is to determine whether an image pair is collected from the same location, resulting in either \"loop closure\" or \"non-loop closure\" outcomes. The model needs to ensure that the prediction for loop closure pairs is as close to 1 as possible and for non-loop closure pairs as close to 0 as possible. Therefore, this can be treated as a discrete binary classification problem, with the output value representing the probability of loop closure. The Binary Cross Entropy (BCE) is adopted as the loss function to train the proposed method, which can be defined as:  
\[Loss=-\frac{1}{n}\sum_{i}[y_{i}\log S_{i}+(1-y_{i})\log(1-S_{i})] \tag{16}\]  
where \(S_{i}\) and \(y_{i}\) represent the predicted similarity score and the label of the \(i\)-th image pair, respectively. \(y_{i}=1\) means that the image pair is taken from the same location. Then the error function can be simplified as \(Loss=-\frac{1}{n}\sum_{i}\log S_{i}\), and the loss will be large for a small \(S_{i}\). Conversely, the \(y_{i}\) will be 0 when encountering a negative pair collected from different places. The error calculated from the simplified function \(Loss=-\frac{1}{n}\sum_{i}\log(1-S_{i})\) will be significant if \(S_{i}\) is large. BCE effectively evaluates how well the model's predictions match the ground truth.