esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation of Rock-Breaking Mechanism by Spherical Tooth Impact in Granite Formation
## 2 Model and Methodology
### Simulation Settings
#### 2.2.3 Simulation Parameter Setting  
In this simulation study, the simulation model primarily consists of specific physical parameters of rock, cohesive elements, and tungsten carbide buttons [33], including density (\(\rho\)), Young's modulus (\(E\)), Poisson's ratio (\(\gamma\)), damage displacement (\(\delta_{m}\)), nominal stress under normal mode (\(t_{n}^{0}\)), nominal stress under the first tangential mode (\(t_{n}^{0}\)), and nominal stress under the second tangential mode (\(t_{n}^{0}\)). The specific parameter values are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Parameter and Unit** & **Unit** & **Value** \\ \hline \multirow{6}{*}{Rock and Cohesion Unit} & Density (\(\rho\)) & kg:m\({}^{-3}\) & 2700 \\  & Elastic modulus (\(E\)) & MPa & 60,000 \\  & Poisson’s ratio (\(\gamma\)) & & 0.24 \\ \cline{1-1}  & Displacement at failure (\(\delta_{m}\)) & mm & 0.004 \\ \cline{1-1}  & Nominal stress of normal-only & MPa & 40 \\ \cline{1-1}  & Nominal stress in the first & MPa & 120 \\ \cline{1-1}  & direction (\(t_{n}^{0}\)) & MPa & 120 \\ \cline{1-1}  & Nominal stress in the second & MPa & 120 \\ \hline \multirow{3}{*}{Spherical Tooth} & Density (\(\rho\)) & kg:m\({}^{-3}\) & 15,000 \\ \cline{1-1}  & Elastic modulus (\(E\)) & MPa & 588,000 \\ \cline{1-1}  & Poisson’s ratio (\(\gamma\)) & & 0.22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameter setting table.  
Figure 3: Grid independence verification.
# Inter-UAV Routing Scheme Testbeds
## 2 Ad Hoc Routing
### Mobility Models
#### 2.2.2 Paparazzi  
Paparazzi is a path-planned stochastic mobility model. In order for the model to approximate all possible UAV maneuvers, it supports the following fundamental movements: stay-at, way-point, eight, scan and oval [54].
## 4 Results and Discussion
These two parameters are affected by the capability of the ammonia storage of the SCR catalyst, which greatly depends on the operational temperature. Figure 7 presents the effects of the exhaust gas temperature on the conversion efficiency. The results show that the conversion efficiency experiences three stages as the exhaust gas temperature decreases from 240 to 85 \({}^{\circ}\)C. When the exhaust temperature is higher than 216.6 \({}^{\circ}\)C, the conversion efficiency is high (above 98%) and stable. This stable, high-efficiency stage is exactly the SCR's normal operation state (state I). As the exhaust temperature drops below 216.6 \({}^{\circ}\)C, the conversion efficiency sharply decreases to a relatively low value (30-40%). This limited conversion efficiency is because the stored ammonia works and the SCR system has no urea injection during this stage with a temperature range of 216.6-88.1 \({}^{\circ}\)C, which is the ammonia storage state (state II). Once the exhaust temperature is lower than 88.1 \({}^{\circ}\)C, the conversion efficiency drops to its minimum. This near zero conversion efficiency shows that there is no catalytic reaction in this stage, and this stage is the SCR non-operation state (state III). The results prove that there are three states occurring for this real-road test method. Moreover, these three states of the SCR system are strongly related to the exhaust temperature.
## 4 Conclusions  
In most of the reported studies, beam-widening could only be realized in one or both of the E- and H-planes, and came at the cost of increased antenna size and complexity. These obviously limit the applications of wide-beam antennas. To address these problems, in this paper, a novel and simple method to widen the antenna beamwidth was put forward. By simply printing two quasi-intersecting rows of subwavelength cells on the dielectric substrate of a basic ALTSA, the beamwidth can be widened in all cut-planes that include the boresight direction with a tiny increase in antenna size and complexity. Additionally, the widening amplitudes at the center frequency are stable at around 200%, which produces a quasi-hemispherical pattern. Moreover, the main goal of this paper is to show the feasibility of this beam-broadening method. It is completely possible to use the basic ALSTA and subwavelength units with more complex structures and excellent performances to obtain better results. The designed antenna also has the characteristics of being low-cost and lightweight, easy to fabricate, and easy to integrate, making it a good candidate for satellite communication antennas.  
Figure 9: Measured and simulated gain results of the proposed antenna in different cut\(-\)planes of (**a**) phi = 0\({}^{\circ}\), (**b**) phi = 45\({}^{\circ}\), (**c**) phi = 90\({}^{\circ}\), and (**d**) phi = 135\({}^{\circ}\).  
**Author Contributions:** Conceptualization, R.W.; investigation, R.W. and D.L.; methodology, R.W. and F.Y.; validation, R.W. and F.Y.; software, R.W. and D.L.; fabrication, R.W. and D.L.; data curation, R.W. and D.L.; writing--original draft, R.W.; supervision, F.Y.; funding acquisition, D.L. and F.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded in part by the National Natural Science Foundation of China under Grant 61871101, Grant 61721001, and Grant 61631006, in part by the Joint Fund of Equipment Pre-Research of Aerospace Science and Technology under Grant 6141B061008, and in part by the National Key Research and Development Program of China under Grant 2016 YFC0303501.  
**Data Availability Statement:** Not available.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Theory and Modeling
### Determining Scattering Angle: Lookup Table Method
The blue arrows show how drawing a value of \(P_{\Psi}(\psi)=0.694\) leads to a scattering angle of \(\psi\) about 10 degree, and drawing a value of \(P_{\Psi}(\psi)=0.827\) leads to a scattering angle of \(\psi\) about 20 degrees. \[P_{\Psi}(\psi)=\left\{ \begin{array}{cc}P_{\Psi}(\psi_{\min}),\text{if}( \Re\leq P_{\Psi}(\psi_{\min}))\\ P_{\Psi}(\psi_{i+1}),\text{if}(P_{\Psi}(\psi_{i})<\Re<=P_{\Psi}(\psi_{i+1})) \\ P_{\Psi}(\psi_{\max}),\text{if}(\Re>P_{\Psi}(\psi_{\max}))\end{array} \right. \tag{6}\]  
where \(P_{\Psi}(\psi_{\min})\) and \(P_{\Psi}(\psi_{\max})\) are cdf values at the minimum and maximum angles, respectively. \(P_{\Psi}(\psi_{i})\) is the cdf of the angle at row \(i\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SPFs** & **Angle Limit** & **Number of Samples** & **R\({}^{2}\)** & **Root Mean Square Error (RMSE)** \\ \hline \multirow{4}{*}{H-G} & all & 55 & 0.3766 & 323.9334 \\  & [20\({}^{\circ}\),140\({}^{\circ}\)] & 25 & 0.9950 & 0.0115 \\  & \textless{}20\({}^{\circ}\) & 22 & 0.2662 & 457.5366 \\  & \textgreater{}140\({}^{\circ}\) & 8 & 0.8217 & 4.2527 \(\times\) 10\({}^{-4}\) \\ \cline{2-5}  & all & 55 & 0.9996 & 14.7719 \\  & [20\({}^{\circ}\),140\({}^{\circ}\)] & 25 & 0.9992 & 0.0088 \\  & \textless{}20\({}^{\circ}\) & 22 & 0.9996 & 22.1625 \\  & \textgreater{}140\({}^{\circ}\) & 8 & 0.9260 & 2.1983 \(\times\) 10\({}^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The curve-fitting statistics of the two spfs compared with [PERSON]’s measurements.
# Enhanced Knowledge Graph Embedding by Jointly Learning Soft Rules and Facts
## 4 Our Method
### Embedding Learning
#### 4.3.1 Learning Resources  
Given a KG \(\mathcal{K}=\{\mathcal{E},\mathcal{R},\mathcal{T}\}\) with a set of triples \(\mathcal{T}=\{(e_{i},r_{k},e_{j})\}\), where head entity \(e_{i}\), tail entity \(e_{j}\in\mathcal{E}\), and relation \(r_{k}\in\mathcal{R}\). We obtain our learning resources as follows.  
Labeled triples: As we have mentioned in Section 2.1, we take the observed triples in \(\mathcal{T}\) as positive examples. For each positive triple \((e_{i},r_{k},e_{j})\), we generate negative triple \((e^{\prime}_{i},r_{k},e_{j})\) or \((e_{i},r_{k},e^{\prime}_{j})\) by randomly corrupting the head entity \(e_{i}\) or the tail entity \(e_{j}\), where \(e^{\prime}_{i}\in\mathcal{E}\setminus\{e_{i}\}\) and \(e^{\prime}_{j}\in\mathcal{E}\setminus\{e_{j}\}\). Then, we assign a label \(y_{i}\) to each triple \(x_{l}\), \(y_{l}=1\) if \(x_{l}\) is positive and \(y_{l}=-1\) otherwise. A labeled triple is denoted as \((x_{l},y_{l})\). Let \(\mathcal{L}=\{(x_{l},y_{l})\}\) represent the set of these labeled triples.  
Soft rules. We denote a set of Horn clause rules extracted from the rule mining module as \(\mathcal{F}=\{(f_{p},c_{p})\}_{p=1}^{P}\), where \(f_{p}\) is the \(p^{\text{th}}\) logical rule defined over \(\mathcal{K}\), \(c_{p}\in(0,1]\) is the confidence value of \(f_{p}\), and \(P\) is the number of all soft rules. For each logical rule \(f_{p}\), we denote its groundings generated by the grounding generation stage as \(\mathcal{G}_{p}=\{g_{pq}\}_{q=1}^{Q_{p}}\), where \(Q_{p}\) is the number of groundings and \(g_{pq}\) is the \(q^{\text{th}}\) grounding in \(\mathcal{G}_{p}\), e.g., \((e_{u},r_{m},e_{v})\Rightarrow(e_{v},r_{n},e_{u})\) given the form of \(f_{p}\) as \(\forall x,y:(x,r_{m},y)\Rightarrow(y,r_{n},x)\). Let \(\mathcal{G}=\cup_{p=1}^{p}\mathcal{G}_{p}\) denote the set of all these groundings.
###### Abstract  
Multi-access edge computing (MEC) enhances service quality for users and reduces computational overhead by migrating workloads and application data to the network edge. However, current solutions for task offloading and cache replacement in edge scenarios are constrained by factors such as communication bandwidth, wireless network coverage, and limited storage capacity of edge devices, making it challenging to achieve high cache reuse and lower system energy consumption. To address these issues, a framework leveraging cooperative edge servers deployed in wireless access networks across different geographical regions is designed. Specifically, we propose the Distributed Edge Service Caching and Offloading (DESCO) network architecture and design a decentralized resource-sharing algorithm based on consistent hashing, named Cache Chord. Subsequently, based on DESCO and aiming to minimize overall user energy consumption while maintaining user latency constraints, we introduce the real-time computation offloading (RCO) problem and transform RCO into a multi-player static game, prove the existence of Nash equilibrium solutions, and solve it using a multi-dimensional particle swarm optimization algorithm. Finally, simulation results demonstrate that the proposed solution reduces the average energy consumption by over 27% in the DESCO network compared to existing algorithms.  
multi-access edge computing: computation offloading; consistent hashing: P2P overlay network; multi-dimensional discrete PSO +
Footnote †: journal:
## 4 Independent Set
### Lower Bound  
Similarly to the lower bound presented in Section 2.4. we present a construction which enables us to give a lower bound for the approximation factor of local algorithms for independent set with a locality distance of \(k\) hops.  
**Theorem 9**.: _Let \(\mathcal{A}\) be a local algorithm for independent set in unit line graphs with a locality distance of \(k\) hops in the setting of location aware nodes. The best performance ratio which can be achieved by \(\mathcal{A}\) is \(\frac{k}{k+1}\)._  
Proof.: Consider the unit line graph \(G_{1}=(V,E)\) consisting of \(6+2k\) vertices (see Figure 12). Let \(I_{1}\) be the independent set which is computed for \(G_{1}\) by \(\mathcal{A}\). Note that an optimal independent set for \(G_{1}\) has \(k+3\) vertices. Let \(V_{1}\) be the set of vertices in \(G_{1}\) which are at most \(k-1\) hops away from the very right  
**Figure 12**. A local algorithm \(\mathcal{A}\) with locality distance \(k\) has to take the same decisions for the vertices \(u\) and \(v\) in \(G_{1}\), \(G_{u}\) and \(G_{v}\). The black vertices show a possible independent set computed by \(\mathcal{A}\). In order to simplify this figure the rectangular boxes represent \(k\) vertices on the line.  
\[\begin{array}{l}G\\ G_{u}\\ G_{v}\end{array}\]  
or the very left vertex (in Figure 12 these are the vertices in the box with the label \"\(k\) vertices\"). If \(k=0\) we define \(V_{1}:=\emptyset\). Let \(V_{2}:=V\setminus V_{1}\) (thus \(|V_{2}|=6\)). We distinguish three cases:  
Case 1: There is no vertex \(u\in V_{2}\) such that \(u\in I_{1}\). If \(k=0\) then \(|I_{1}|=0\) and thus the performance ratio of \(\mathcal{A}\) is \(0\). If \(k\geq 1\) then \(|I_{1}|\leq k+1\) and so the performance ratio of \(\mathcal{A}\) is at most \(\frac{k+1}{k+3}\leq\frac{k}{k+1}\).  
Case 2: There is no vertex \(v\in V_{2}\) such that \(v\
otin I_{1}\). Then \(I_{1}\) is not an independent set which is a contradiction.  
Case 3: There is a vertex \(u\in V_{2}\) with \(u\in I_{1}\) and a vertex \(v\in V_{2}\) with \(v\
otin I_{1}\). Now consider the graphs \(G_{u}\) consisting of \(u\) and \(v\) respectively and \(k\) nodes each to their left and their right (see Figure 12). Let \(I_{u}\) and \(I_{v}\) be the independent sets computed for \(G_{u}\) and \(G_{v}\) by \(\mathcal{A}\). As the locality distance of \(\mathcal{A}\) is \(k\) and \(u\in I_{1}\) and \(v\
otin I_{1}\), it holds that \(u\in I_{u}\) and \(v\
otin I_{v}\). If \(k\) is even then \(|I_{v}|\leq k\) whereas the optimal independent set for \(I_{v}\) has \(k+1\) vertices. So the performance ratio of \(\mathcal{A}\) is at most \(\frac{k}{k+1}\). If \(k\) is odd then \(|I_{u}|\leq k\) whereas the optimal independent set for \(I_{u}\) has \(k+1\) vertices. So the performance ratio of \(\mathcal{A}\) is at most \(\frac{k}{k+1}\).  
**Corollary 4**.: _Let \(\mathcal{A}\) be a local algorithm for independent set in unit disk graphs (UDGs) with a locality distance of \(k\) hops in the setting of location aware nodes. The best performance ratio which \(\mathcal{A}\) can achieve is \(\frac{k}{k+1}\)._
# Enhancement of Underwater Images with Retinex Transmission Map and Adaptive Color Correction
## 3 Proposed Underwater Image Enhancement Method
### Adaptive Color Correction  
In order to deal with possible low contrast and color shifts in some cases, we propose an adaptive color correction step to alleviate these problems and improve the enhancement result.  
The basic formula of color correction is by  
\[J_{CS}^{c}=k^{c}\left(J^{c}-J_{low}^{c}\right)=\frac{J^{c}-J_{low}^{c}}{J_{ip}- \bar{I}_{low}}, \tag{11}\]  
where \(J_{up}^{c}\) and \(J_{low}^{c}\) are the upper and lower stretching parameters of \(c\) channel, and \(k^{c}=1/(\bar{I}_{up}-\bar{I}_{low}^{c})\) is the stretch coefficient. There are two popular choices for stretching parameters \(\bar{I}_{up}^{c}\) and \(J_{low}^{c}\). The first one is to directly use the maximum and minimum channel intensity \(max(J^{c}(x))\) and \(min(J^{c}(x))\), while the other one considers second-order statistics of the channel intensity [26]:  
\[J_{up}^{c}=\bar{J}^{c}+\mu\cdot std(J^{c}) \tag{12}\]  
\[J_{low}^{c}=\bar{J}^{c}-\mu\cdot std(J^{c}) \tag{13}\]  
where \(\bar{J}^{c}\) is the mean value of the \(c\) channel, \(std(J^{c})\) is the standard deviation of the \(c\) channel, and \(\mu\) is an adjustment hyper-parameter, usually set among 2\(\sim\)3.  
We have investigated both these two choices. Empirically, we found that neither of them can produce good results for all situations. They both produce unwanted artifacts in the corrected results. However, they have the potential to compensate for each other. Specifically speaking, artifacts are prone to occur if one channel is over-enhanced compared to the other two channels, regardless of which choice of stretching parameters is selected. From the angle of Equation (11), when the stretch coefficient \(k^{c}\) of one channel is too large compared with those of the other two channels, artifacts are prone to occur.  
Therefore, we proposed an adaptive color correction strategy which uses variance of the three-channel stretching coefficients \(\{k^{c}\}\) to indicate the degree of channel stretching  
Figure 2: Example of unsuccessful preliminary recovery result and the final adaptive color correction result. (**a**) the original image; (**b**) preliminary recovery result; (**c**) adaptive color correction result produced by the proposed method.  
imbalance. By comparing the variances of \(\{K^{c}\}\) of the aforementioned two choices, the strategy selects the one with less variance, as detailed in Algorithm 1.  
```
0: Preliminary recovery result \(I^{c}(x)\)
0: Adaptive color correction result \(J^{c}_{\mathrm{CC}}(x)\)
1: calculate \(k^{c}_{1}=\frac{1}{max(I^{c}(x))-min(I^{c}(x))}\) and \(k^{c}_{2}=\frac{1}{2\mu\cdot std(J^{c})}\), \(c\in\{r,g,b\}\)
2:if\(std(k^{c}_{1})<std(k^{c}_{2})\)then
3:\(I^{c}_{up}=max(J^{c}(x))\)
4:\(I^{c}_{low}=min(I^{c}(x))\)
5:else
6:\(I^{c}_{up}=\int^{c}+\mu\cdot std(J^{c})\)
7:\(I^{c}_{low}=\int^{c}-\mu\cdot std(J^{c})\)
8:\(I^{c}_{\mathrm{CC}}=\frac{1}{I_{up}-I_{low}}\)
9:return\(I^{c}_{\mathrm{CC}}\)
```  
**Algorithm 1** Adaptive color correction
## 3 Results and Discussions  
### Color Evaluation of the Epicarp, Pulp, and Whole Fruit of \(S\). melongenia  
The color evaluation in the CIE \(L^{*}\)\(a^{*}\)\(b^{*}\) color space was performed on fresh and lyophilized eggplant epicarp and the results are shown in Table 2. The luminosity scale, represented by the parameter \(L^{*}\), varies between 0 and 100, and the parameters \(a^{*}\) (green to red) and \(b^{*}\) (blue to yellow) vary between \(-\)120 and 120 [33]. In the color evaluation of the fresh epicarp, for the parameters \(L^{*}\), \(a^{*}\), and \(b^{*}\), were obtained the mean values of 26.2 \(\pm\) 0.5; 5.1 \(\pm\) 0.2 and 1.5 \(\pm\) 0.1, respectively. On the other hand, in the lyophilized eggplant epicarp the parameter \(L^{*}\) showed a value of 34 \(\pm\) 2, and the values in parameters \(a^{*}\) and \(b^{*}\) were 2.8 \(\pm\) 0.1 and \(-\)0.17 \(\pm\) 0.01, respectively.  
Through the statistical analysis of the results, it was verified that the process of dehydration (by lyophilization) of the eggplant epicarp had significant influence (\(p\) < 0.05) in all the evaluated color parameters. The \(L^{*}\) parameter had lower values in the fresh epicarp, indicating a lighter hue when compared to the lyophilized sample. In contrast, the parameter \(a^{*}\) showed higher values in the fresh epicarp, showing a color nearest to the red hue. The same decrease, after the lyophilization process, was verified in parameter \(b^{*}\), which revealed that the lyophilized epicarp presents a close hue to the blue color.  
According to several results available in the literature, it is possible to verify that different dehydration processes can modify the physicochemical properties of the fruits ([PERSON], 2017). In the present study, it was evident that the lyophilization process led to a decrease in the purple color of the epicarp. This may be associated with the degradation of anthocyanins present in the sample, mainly verified by the decrease in the value presented in parameter \(a^{*}\)([PERSON], 2017). Other authors ([PERSON], 2017; [PERSON], 2017) also studied this process and demonstrated that the color change after dehydration processes is associated with the degradation of anthocyanin pigments.  
However, it is important to mention that the applied dehydration process must be chosen considering the degree of degradation of anthocyanins, in order to preserve the original physical and chemical characteristics of the samples as much as possible. According to literature, one of the methodologies that shows less associated degradation is the lyophilization process ([PERSON], 2017). In fact, [PERSON] et al. (2018) studied the effect of dehydration by lyophilization and hot-air drying on the content of anthocyanins in raspberries and the results showed that the lyophilization process was more efficient in preserving the anthocyanin compounds in the samples.
# Application of Single-Electron Transistor to Biomolecule and Ion Sensors
## 5 Experimental Demonstrations of Biomolecule Sensing Based on a Single-Electron Transistor
### Detection of PSA  
The immunodetection of PSA has been achieved using an SET sensor [22]. The surface modification of the gate insulator was carried out as follows (Figure 11). After silicon nitride surface was amino-silanized, the surface was aldehyde-functionalized. Then, the surface was coupled to the antibody of PSA (anti-PSA). The immunodetection of PSA can be carried out using the specific binding of the antigen and antibody.  
For the binding event of 4 ng/mL of PSA to anti-PSA, a positive \(\Delta V_{g}\) of 30 mV was observed (Figure 12). The positive \(\Delta V_{g}\) is consistent with the negative charge of PSA in solution at pH 7.5 due to the isoelectric point (6.9) of PSA. It should be noted that 4 ng/mL is the required detection level for practical use because this is the cut-off value for the precise inspection of prostate cancer.  
Figure 11: Surface modification of the gate insulator for the detection of prostate specific antigen (PSA).  
Figure 10: Brain current (\(I_{d}\))-gate voltage (\(V_{g}\)) characteristics of an SET sensor for the detection of streptavidin. Reproduced with permission from [22], Copyright 2013, AIP Publishing LLC.
# Key Technologies of Photonic Artificial Intelligence Chip Structure and Algorithm
## 2 Hardware Structures for Linear Operation
### Comparison of three Linear ONNs  
The above is the analysis of the structural characteristics of the three neural networks.  
From the analysis, it can be seen that PFNN is the simplest of the three networks. It is the easiest structure to implement. However, because of its simple structure, the computation of the matrix is limited.  
The PCNN can complete convolution operation on account of its complex hidden layers, which is the core of convolution. However, it also causes the complexity of the structure. Furthermore, because the hidden layers participate in the operation, the operation time will be longer than PFNN.  
PFNN and PCNN mostly use MZI and MRR to realize matrix operation. For an MZI, its system is relatively simple and has stronger versatility. However, due to excessive loss during cascading, it is not suitable for large-scale integration. For MRR, its size is relatively small and large-scale integration is easier. However, it is more sensitive to temperature. There are great challenges in achieving precise control.  
Different from PFNN and PCNN, PRNN has the structural characteristics of a recurrent network. Its application is to enrich or compress data dimensions. It can also be used to deal with tasks related to time series. Its structure uses a reservoir. For the reservoir, the internal principle utilizes random projection to transform the dimensionality of the data. Therefore, there is no need for complicated control, and it has strong fault tolerance for integrated technology. However, due to this special principle, it is difficult to apply to most occasions.