esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dynamic Compressive Mechanical Properties of Sandstone after Corrosion by Strong Alkali Solution
### Specimen Compression Damage Pattern  
As can be seen in Figure 10a, the specimen was in the splitting tensile damage when the stress was small, and the specimens were relatively intact as a whole; compared with Figure 10a, after 1 day of corrosion, the specimens were slightly less intact as a whole, and the number of fragments increased from one to four-five; with the extension of corrosion time, in Figure 10c-e, the fragmentation of the specimens increased, the number of fracture surfaces and fragments increased, and more small-sized fragments were created; comparing Figure 10b-e, it can be observed that with the prolongation of corrosion time, the sandstone specimen as a whole showed a trend of spalling-type damage from the outside to the inside, while in Figure 10f, the specimen was more obviously crushed by the impact crushing effect, and the damage pattern of the specimen shows that there are basically only small-sized fragments and granular debris.  
In terms of the specific consideration of the impact of corrosion, we believe that the reason is that with the extension of corrosion time, quartz, the main component of sandstone, constantly reacts with OH\({}^{-}\) ions in the strong alkaline solution [19]:  
\[\text{SiO}_{2}\ (\text{quartz})+2\text{OH}^{-}\rightarrow\text{H}_{2}\text{SiO }_{4}{}^{2-} \tag{6}\]  
In addition, other mineral components present in the sandstone in small amounts constantly react chemically or hydrolytically with OH\({}^{-}\) ions to produce new substances, e.g., sanidine reacts readily with water and OH\({}^{-}\) ions [31]:  
\[\text{KAlSi}_{3}\text{O}_{8}\ \ (\text{sanidine})+2\text{H}_{2}\text{O}+6\text{OH} ^{-}\rightarrow\text{Al}(\text{OH})_{4}{}^{-}+3\text{H}_{2}\text{SiO}_{4}{}^ {2-}+\text{K}^{+} \tag{7}\]  
With the corrosion time extended, the continuous water chemistry dissolves the sandstone gradually, the original dense structure becomes loose, the sandstone's internal secondary pores increase, and the original cracks continue to expand, causing the internal structural deterioration of the sandstone (which is consistent with the results from Refs. [19; 22; 31]), resulting in the dynamic compressive strength of the specimen also being reduced, so that the degree of damage to the specimen continues to increase, and the longer the corrosion time, the more obvious the increase, which corresponds to the law that their dynamic compressive strength decreases with increasing corrosion time.
## 4 Discussion
In Figure 7, a comparison of the BS compliance boundary (CB) or exclusion zone delimiting the area outside of which the EMF exposure is below the ICNIRP limits, is given for the theoretical maximum EIRP and for the actual maximum EIRP. The CB is determined based on the envelope of all available beams for the 8 CSI-RS ports beamforming and for a maximum instantaneous transmit power of 200 W. For the actual maximum, the CB assessment is done by scaling down the power by 8.8 dB (i.e., for a power level of about 26.4 W). The figure shows that the front compliance distance is more than halved when considering the effect of beamforming and traffic variation on the EMF exposure levels (for a maximum \(EIRP_{\text{act}}\) of \(-15\) dB the compliance distance in the front would be only 2.5 m, i.e., more than 5 times smaller).
## 4 Analysis of the Test Results
### Track System Strain Analysis  
The initial state of the specimen is regarded as the equilibrium state, where the strain is set to zero. To obtain the strain-time history law of each structural layer under the action of a high daily temperature load and a non-direct sunlight environment in summer, the cooperative working mechanism of each track system layer is further investigated. The mid-span section and both end sections of the tested beam are selected, and the track system layer strain is analyzed, as shown in Figure 7.  
The comparative analysis of the three sets of curves in Figure 7 shows that along the vertical depth direction of the structure at the mid-span section, the occurrence of the peak strain of the track system layers is gradually delayed. This is consistent with the specimen temperature change law. The strain difference of the structural layers of the track system gradually decreases from the peak strain until the CA mortar layer strain, and that of the track slab almost coincides at the sliding end section of the tested beam. The total strain of the track system layer is larger at the sliding end section and smaller at the mid-span section. The ambient temperature at the end of the test is higher than that at the beginning of the test; thus, the final temperature at the different sections of the track system is also higher than the initial temperature at the corresponding section. This means that the strain of the track layers does not return to its initial state. At the end of the test, the strain rates of the structural layers tend to zero at the fixed end section. At the sliding end section, the strain rates of the structural layers decrease with an absolute value greater than zero.  
Figure 7: Strain-time history curve for the track system. (**a**) 0 L; (**b**) 1/2 L; (**c**) L.
## 3 Research Results and Discussion
### Wear Volume Measurement  
Quantification of wear data is represented by specific wear rate data [34]. Figure 12 presents shaft wear intensity after successive fatigue cycles. Macroscopic observations demonstrated the presence of wear traces on either side of the shaft axle seat, which is why the wear intensity measurement was conducted separately for each shaft side. Shaft axle seat wear intensity on the left side is considerably lower than on the right side, which was also noted during macroscopic observations. The left side of the sample corresponds to the side from which the joint assembly started. In the case of the left side of the axle seat, the increase in wear stability is steady, and it grows evenly with the number of  
Figure 11: Macroscopic image of the shaft surface after the third fatigue cycle (1,000,000 cycles).  
fatigue cycles. The situation on the right side of the sample is different. In the first two fatigue cycles, wear increases steadily, but a sudden increase in wear intensity is noted in the third fatigue cycle. The magnitude of wear is influenced by both joint assembly processes and fatigue loads. Therefore, that situation should be explained in terms of the mechanism of fretting wear development in forced-in joints. In the case of that type of joint, roughness is particularly important in terms of the actual contact between the surfaces and, consequently, for initiating the development of fretting wear. Joining the shaft with the sleeve--whose bondable surfaces have a similar roughness profile--will cause a situation where, as a result of the operation of the tangent force, first of all, the mutual shearing of the micro irregularities on both surfaces will occur. In view of the gap shape, wear products on the right side of the shaft axle seat will be removed more easily, and therefore, a greater contact area between the first bodies will come into being. The right side of the joint is wider than the left one, and this can be seen in Figure 13.  
The direct contact between the first bodies and the occurrence of microvibrations at the same time will be conducive to the creation of adhesive bonds, which will then become broken, thus creating microprojections, micropullouts, and material build-ups. In turn, relative slips, with the highest amplitude in this area, will initiate plastic deformation processes. This is the way in which greater wear intensity at the right side of the shaft axle seat should be explained when compared to the left side of the axle seat.
## 4 Design and Functional Verification
### Performance Comparison Analysis  
To ensure the synchronized deployment of the three sets of folding wings, the mechanism must enable the rotation of three cranks--each connected to a folding wing--by a specific angle using a single driving source. This requires calculating the mechanism's dimensions to meet motion requirements and analyzing its transmission efficiency. The initial folded and deployed states, along with the vector models of the selected three configurations, are detailed in Table 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Configuration** & **Folding State** & **Deployable State** & **Vector Model** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Folding and unfolding states and vector models of the three configurations.  
The transmission efficiency for each configuration under various dimensional parameters is presented in Figure 12. The results indicate that the transmission efficiency of configuration (o) consistently exceeds that of configurations (c) and (e).  
Configuration (o) corresponds to mechanism O1, which exhibits the maximum transmission efficiency, while O10 demonstrates the minimum efficiency. For configuration (c), mechanism C1 achieves the highest transmission efficiency, whereas C7 shows the lowest transmission efficiency.  
Figure 11: Scale information of each configuration: (**a**) dimensional parameters of configuration (o); (**b**) dimensional parameters of configuration (c); (**c**) dimensional parameters of configuration (e).  
Figure 12: Comparison of transmission efficiency across different configurations and dimensions.  
In configuration (e), mechanism E1 has the maximum transmission efficiency, and E10 has the minimum. Detailed parameters are provided in Table 7.  
Configuration (o) achieves a maximum transmission efficiency of 0.866 across ten different scale parameters, with a minimum of 0.858. In comparison, configuration (c) reaches a maximum efficiency of 0.846, while configuration (e) achieves a maximum of 0.819. Notably, the highest transmission efficiency for configuration (o) is 2.4% greater than that of configuration (c) and 5.7% higher than that of configuration (e). These results demonstrate that the optimized configuration meets practical requirements. Moreover, the performance indicators proposed in this study can be generalized and applied to the optimization of other mechanism designs.
## 2 Materials and Methods
### DisCOptER Algorithm  
Due to their superior angular resolution, arcs of length \(l\) will occur in the optimal discrete path, while the length of flight path segments in the collocation solution is around \(\delta\tau T\phi\). Hence, we expect the accuracy of continuous and discrete optimization approaches to be comparable if \(l=\delta\tau T\phi\). Obviously, for increasing accuracy \(l\to 0\), the collocation effort of order \(\mathcal{O}(\delta\tau^{-1}\log\delta\tau/\log(\omega\|\chi_{0}-\chi_{*}\|))\) grows much slower than the \(A^{*}\) effort of complexity \(\mathcal{O}(l^{-6})\). On the other hand, the collocation approach converges only locally.  
We therefore propose a hybrid algorithm that combines the strengths of discrete optimization and optimal control, see Algorithm 1. First, a discrete shortest path of low accuracy is calculated using the \(A^{*}\) algorithm as described in Section 2.3. This is used as initial iterate for solving the KKT system (5) to the desired final accuracy using the ordinary [PERSON] method. Employing linesearch or trust region globalization would enlarge the convergence domain of [PERSON]'s method and allow for coarser graphs to be used, but at the cost of less robust and efficient convergence. In favour of robustness and simplicity, we restrict the attention to the ordinary [PERSON] method.  
```
Input :\(x_{O},x_{D}\in\mathbb{R}^{2}\), \(w\in C^{2}(\mathbb{R}^{2})^{2}\), \(\overline{\sigma}>0\), \(\text{TOL}>0\) Output :approximate solution \((T,x,v)\in\mathbb{R}\times C^{0}([0,1])^{2}\times L^{2}([0,1])^{2}\) of (1) with error of order \(\text{TOL}^{2}\), \(\text{TOL}\), \(\text{TOL}\), respectively
1 Choose \(\delta\tau=\mathcal{O}(\sqrt{\text{TOL}})\)
2 Define \((h,l)\)-dense graph \(G\) with \(l>\delta\tau\|x_{D}-x_{O}\|\), but sufficiently small, and \(h=\mathcal{O}(l^{2})\)
3 Calculate shortest path on \(G\) using \(A^{*}\) algorithm (see Section 2.3)
4 Interpolate path onto collocation discretization (see Section 2.4.1)
5 Calculate a continuous solution by solving the nonlinear problem (5), via direct collocation and [PERSON]'s method (see Section 2.2)
```  
**Algorithm 1**DisCOptER algorithm  
Algorithms that combine methods from discrete and continuous optimization have been proposed before. A reverse deterministic combination going from continuous to discrete has been proposed in [41] based on dynamic programming principles. Other combinations involve a stochastic discrete stage, like rapidly-exploring random trees (RRT, RRT*) (see, e.g., [42]) or Probabilistic Roadmaps (PRMs) [43] in combination with a second NLP stage. Another approach was taken in [44], where the authors use a combination of \(A^{*}\) and RRT* to find an optimal trajectory. These algorithms reveal remarkable performance when it comes to obstacle avoidance. Since our goal is, however, to develop an algorithm with a priori error estimates and bounded runtime, we do not make use of any stochastic approaches in the DisCOptER algorithm.
## 3 Results  
### Oil in Water Emulsion  
The oil in water emulsions were characterized by DLS to identify the best conditions to prepare oil droplet sizes for loading into the HNT lumen. Figure 1 shows that all the hydrodynamic radii of the prepared suspensions were in the range of 4-22 nm, suggesting the possibility of an efficient entrapment of the oil droplets into the nanotubes. The hydrodynamic volume (Figure 1) was calculated, assuming spherical-like micelles, and was reported as a function of \(\mathrm{R_{oil/SDS}}\). We observed a linear increase in volume as the oil content increased, underlining the crucial role of oil in droplet size.  
Furthermore, a first evaluation of the temporal stability of the prepared quaternary systems was carried out repeating the DLS measurement after 24 hours of storage at 25 \({}^{\circ}\)C. Retention of 99% of the initial size was obtained, showing the colloidal stability of the mixture.  
The \(\zeta\) potential for the microemulsions provided a negative value of \(\sim\!-\!12\) mV. These findings suggest the presence of a negative charge at the micelle/water interface, which guarantees the selective electrostatic interaction with HNT positive inner surface.  
Figure 1: (**a**) Hydrodynamic radius; (**b**) Hydrodynamic volume of prepared microemulsions.
# A Charging and Discharging Data Privacy Protection Scheme for V2G Networks Based on Cloud-Fog-End
## 6 Conclusions  
The V2G network faces serious security issues and formidable privacy protection challenges. To ensure the security and efficiency of large-scale charging and discharging data transmission in the V2G network, a cloud-fog-based V2G network architecture is designed, and a charging and discharging data privacy protection scheme is proposed. The proposed scheme achieves anonymity and the traceability of EV users' identities through a pseudonym mechanism. The designed CLASC algorithm guarantees the security of uploading charging and discharging privacy data.  
The security analysis determined that the proposed scheme not only meets the required security features, including conditional anonymity, confidentiality, and unforgeability, but can also resist common attacks such as impersonation, replay, and distributed denial of service (DDoS). The experimental analysis indicated that the proposed scheme has high efficiency in both computation and communication, making it suitable for V2G network environments with limited resources. Further research can address the challenge of balancing security and efficiency and achieving rapid identity authentication between EVs and FNs before charging and discharging data transmission.  
Conceptualization, B.W. and Z.S.; methodology, S.Z.; simulation and analysis, Z.S.; writing--original draft preparation, Z.S.; supervision, B.W. and S.Z.; project administration, B.W.; funding acquisition, S.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Fundamental Research Funds for the Central Universities (2018 ZD06).  
Not applicable.  
The original contributions presented in the study are included in the article material and further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 7 Route Maintenance  
Route maintenance is the procedure by which a source node is able to detect the faults of the forwarding path towards the destination node. These faults can be due to some malicious attack pattern, with link broken event, and even below defined threshold trust level. In this scenario, the source node needs to reroute the information with an alternative route. In case of link broken event, i.e., the topology has changed, or the route trust does not meet the criteria so that it will trigger new trust judgment. Another thing Route maintenance assures the packet delivery within a specific time interval after a route cache crosses the maximum valid time, the packet will expire automatically.
## 2 System Architecture  
### Transmitter  
The block diagram of the proposed TDOM is shown in Figure 1. At the transmitter, according to the amplitude mapping rule shown in Table 1, the serial binary data bits \(b\)= [\(b_{1}\), \(b_{2}\), \(\ldots\)] are firstly converted into modulation amplitude \(a_{n}\) (\(n\) = 0, \(\ldots\), \(N\), where (\(N\) + 1) is the total number of carriers). Then, multiple carriers (sinusoidal waves, as an example) are modulated using \(a_{n}\). After modulation, each carrier is sequentially delayed and directly superimposed in the time domain, as shown in Figure 2. The delay time \(\tau\) can be set arbitrarily. In this paper, all the delays \(\tau\) are made the same, i.e., \(\tau_{1}\) = \(\tau_{2}\) = \(\ldots\) = \(T/N\), where \(T\) is the duration of one carrier, and the duration of one TDOM symbol is set to be 2\(T\). The communication symbol \(S(t)\) is shown in Equation (1), where \(a_{n}\) represents the \(n\)th modulation amplitude,\(f_{n}\) represents the frequency of the \(n\)th carrier, and \(\tau_{n}\) represents the delay between carriers. When \(n\) = 0, \(\tau_{0}\) = 0.  
\[S(t)=\sum_{n=0}^{N}a_{n}sin[2\pi f_{n}(t-\tau_{n})] \tag{1}\]  
It is worth mentioning that the TDOM does not have any restriction on the carrier frequency \(f_{n}\). Firstly, orthogonality between all frequencies is no longer required. This means that the frequency interval between each carrier can be narrower and the system can therefore achieve higher spectral efficiency. In addition, the carrier frequency can be set very flexibly. All carrier frequencies can be different, i.e., \(f_{0}\
eq f_{1}\
eq\ldots\
eq f_{N}\), or all frequencies can be the same, i.e., \(f_{0}\) = \(f_{1}\) = \(\ldots\) = \(f_{N}\). When all carriers have the same frequency, the TDOM system evolves into a single-carrier communication system, and it can carry multiple information bits in only one single-frequency subchannel, providing the highest spectral efficiency.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**B** & **a** \\ \hline
[0,0] & \(-1.05\) \\
[0,1] & \(-0.35\) \\
[1,1] & 0.35 \\
[1,0] & 1.05 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Magnitude mapping rules.  
Figure 1: Block diagram of the time-delay overlapping modulation.
# Bistatic Sea Clutter Suppression Method Based on Compressed Sensing Optimization
###### Abstract  
In order to reduce the sea clutter interference in the detection of sea surface targets, we propose a bistatic sea clutter suppression method based on compressed sensing optimization in this paper. The proposed method mitigates the interference effect by reconstructing and cancelling out the sea clutter. Since the fixed sparse base is not always completely applicable for the sparse representation of sea clutter, we propose a sparse base optimization algorithm based on transfer learning to convert the fixed sparse base into an adaptive one. Moreover, we introduce a sensing matrix optimization algorithm to decrease the cross-correlation coefficient between the measurement matrix and the sparse base matrix, which can enhance the signal reconstruction quality. Finally, we use the orthogonal matching pursuit algorithm to reconstruct the sea clutter and employ the reconstructed results to cancel and suppress the sea clutter. The simulation results demonstrate that the proposed method outperforms the traditional methods such as the root time-domain cancellation method and the singular value decomposition method (SVD). Therefore, the proposed method has great practical significance for the detection of bistatic sea surface targets.  
bistatic sea clutter; detection of sea surface targets; compressed sensing; transfer learning +
Footnote †: journal: Applied  
0
Footnote 0: 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Multi-View Synthesis of Sparse Projection of Absorption Spectra Based on Joint GRU and U-Net
## 1 Introduction
Compared to traditional reconstruction methods, deep learning offers excellent advantages in terms of reconstruction efficiency and accuracy [17; 18; 19]. To reconstruct nonlinear tomographic absorption spectra with limited data, [PERSON], [PERSON] et al. used a convolutional neural network. The reconstruction process was accelerated, but satisfactory results required at least six projection views [20]. [PERSON], [PERSON] et al. used a U-shaped residual network for super-resolution reconstruction of the temperature field [21]. Obtaining sufficient probe data can be challenging due to harsh test conditions in practical engineering applications. Although deep learning-based methods can produce laminar temperature field images with a limited number of angles, the quality of the reconstruction is relatively low. In addition, these methods do not fully exploit the sequential properties among the probe projection data to enhance the projection data and improve the temperature field reconstruction. It is a pressing issue in combustion diagnostics to ensure the quality of temperature field reconstruction with fewer projection views [22]. This paper proposes a neural network-based projection data enhancement algorithm named GMResUNet to address the problem of low accuracy in TDLAS detection reconstruction with limited detection projection angles. The network model utilizes the gate recurrent unit (GRU) and multilevel residual learning based on U-Net to achieve the multi-view synthesis of sparse projection data. The U-shaped deep learning network's cross-connection structure ensures shallow correlation structuring while preserving the deep feature information of the integral absorbance data [23]. The multilevel residuals can learn the residual part more efficiently, reducing the number of parameters that need to be learned and improving the network's efficiency and generalization ability [24]. The GRU can learn the sequence properties among the projection angles [25]. Compared to traditional interpolation methods and UNet, the GMResUNet proposed in this study exploitsthe sequence characteristics of the projection data from adjacent viewpoints to achieve multi-view synthesis of sparse projection data. The synthesized projection data significantly improve the reconstruction accuracy of the combustion temperature field, especially when there are only a small number of probe viewpoints.