esa-sceva/satcom-chunk-collection huggingface dataset:
# Securely Computing the Manhattan Distance under the Malicious Model and Its Applications
## 3 Preparatory Knowledge
### Encoding
#### 3.4.2 Calculation Principle  
The universal set \(U=\{u_{1},\cdots,u_{n}\}\), [PERSON] holds \(P(x_{1},\ y_{1})\), [PERSON] holds \(Q(x_{2},\ y_{2})\), and \(\{x_{1},\ x_{2},\ y_{1},\ y_{2}\}\in U\).  
\(P\) is coded as \(A=\{a_{11},\ \cdots,\ a_{1n},\ a_{21},\ \cdots,\ a_{2n}\}\); \(Q\) is coded as \(B=(b_{11},\ \cdots,\ b_{1n},\ b_{21},\ \cdots,\ b_{2n})\).  
Then, the Manhattan distance between points \(P\) and \(Q\) can be calculated according to the following formula:  
\[d(P,Q)=|x_{1}-x_{2}|+|y_{1}-y_{2}|=A\oplus B=\sum_{i=1}^{2}\sum_{j=1}^{n}(a_{ ij}\oplus b_{ij}) \tag{1}\]
## 5 Experimental Results and Discussion
### Test Results and Discussion  
The prototype is tested by the experimental platform mentioned in Section 2.1 as illustrated in Figure 8, and some of the corresponding results are shown in Figure 9.  
The absolute value of correlation coefficient difference in all cases of normal operation keeps far from the threshold \(r_{th}\) of 0.1, although there is a little ripple in some scenarios such as Figure 9a,b. The arc is random, which is manifested in that the combustion degree of the arc is not fixed (especially in the arc starting stage), and the distortion details of the signal have no fixed repetition law. The similarity of the PSD curves of the arc data of a window is used as the characteristic quantity for the judgment of the arc fault. Due to the randomness of the arc fault, the arc data in each sampling window is not completely consistent, so the correlation coefficients of the PSD curves in the adjacent windows are different.  
This result shows that the inverter noises are relatively stable and comply with the theoretical analysis in Section 2.2. However, the corresponding value of \(|r_{i}-r_{i-1}|\) will be rising dramatically in the event of an arc fault, which can reach as high as five or six times the threshold \(r_{th}\). Therefore, an arc fault can be detected in real-time without any false positive. Test results also show that the fault clear times are between 60 ms and 80 ms, which is faster than the standard requirement of a minimum of 200 ms.  
Figure 8: The prototype and the corresponding test scenario.  
Figure 7: The functional block diagram of the proposed prototype.  
As for the impact of MPPT, the corresponding data is collected as per the UL1699B standard requirement. These tests are generally conducted by varying solar irradiation or making the inverter startup. In those cases, the MPPT will be triggered and tune the operating point to a new state of maximum power output. Figure 10a is the current during the inverter starting, which includes the MPPT control process. A power spectral density (PSD) analysis using a 10 ms time window for a data partition of 50 ms in the startup process is performed, as shown in Figure 10c. It can be seen that there is almost no difference in the PSD for different time windows, reflecting that they follow a uniform randomness characteristic. Therefore, the proposed method can effectively distinguish the influence of the MPPT control from a real arc fault.  
In general, the above test results verify that the proposed method can fulfill the standard requirements for PV arc fault detection under different PV inverter conditions. Furthermore, it can be embedded in an MCU-based hardware circuit for real applications as well. However, the method is only validated under standard test conditions, and more application scenarios will be considered for generalization evaluation in future work.  
Figure 9: Test results of the designed prototype. (**a**) PV inverter \(\#A\); (**b**) PV inverter \(\#B\); (**c**) PV inverter \(\#C\).
# Blind Image Quality Assessment Based on Multi-Window Method and HSV Color Space
## 3 Experiments and Results
### Impact of Varying the Number of NFMs  
As shown in Table 5, we examined how the number of the NFMs used in BIQA-SC would be affected the performance. We denote BIQA-SC using \(n(n\in 1,2,\ldots,5)\) NFMs as M\(n\). It can be seen that better performance in terms of RMSE, SROCC, and PLCC is likely to be achieved if multiple NFMs are used. However, the prediction performance does not improve too much when the number of the NFMs is larger than 3 on most of the databases. To better analyze the difference between the BIQA-SC using different numbers of the NFMs, we calculated the statistical significance between each two of the M\(n\) algorithms by conducting a \(t\)-test at 95% significance level between SROCC values of these algorithms across the 1000 train-test iterations. The \(t\)-test results are shown in Table 6. The symbol 1 (\(-1\)) indicates that the algorithm in the row is statistically superior (inferior) to the algorithm in the column, and 0 indicates that the two compared algorithms are statistically indistinguishable. Clearly, M3, M4, and M5 were superior to M1 and M2 on nearly all databases, while M3, M4, and M5 achieved better evaluation performance on different database. Considering the balance of performance and computational cost, we use three NFMs in the proposed BIQA-SC. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**LIVE**} & \multicolumn{4}{c}{**TID2013**} & \multicolumn{4}{c}{**CSIQ**} \\ \cline{2-13}
**Metric** & **M1** & **M2** & **M3** & **M4** & **M5** & **M1** & **M2** & **M3** & **M4** & **M5** & **M1** & **M2** & **M3** & **M4** & **M5** \\ \hline M1 & 0 & \(-1\) & \(-1\) & \(-1\) & \(-1\) & 0 & \(-1\) & \(-1\) & \(-1\) & 0 & \(-1\) & 0 & 0 & \(-1\) \\ M2 & 1 & 0 & \(-1\) & \(-1\) & 0 & 1 & 0 & \(-1\) & 0 & \(-1\) & 1 & 0 & 1 & 1 & 0 \\ M3 & 1 & 1 & 0 & 1 & 1 & 1 & 0 & 1 & 0 & 0 & \(-1\) & 0 & 0 & \(-1\) \\ M4 & 1 & 1 & \(-1\) & 0 & 1 & 1 & 0 & \(-1\) & 0 & \(-1\) & 0 & \(-1\) & 0 & 0 & \(-1\) \\ M5 & 1 & 0 & \(-1\) & \(-1\) & 0 & 1 & 1 & 0 & 1 & 0 & 1 & 1 & 0 \\ \hline \multicolumn{13}{c}{**MD1**} & \multicolumn{4}{c}{**MD2**} & \multicolumn{4}{c}{**WIQCD**} \\ \cline{2-13}
**Metric** & **M1** & **M2** & **M3** & **M4** & **M5** & **M1** & **M2** & **M3** & **M4** & **M5** & **M1** & **M2** & **M3** & **M4** & **M5** \\ \hline M1 & 0 & \(-1\) & \(-1\) & \(-1\) & \(-1\) & 0 & \(-1\) & \(-1\) & \(-1\) & 0 & \(-1\) & \(-1\) & \(-1\) & \(-1\) \\ M2 & 1 & 0 & \(-1\) & \(-1\) & \(-1\) & 1 & 0 & \(-1\) & \(-1\) & \(-1\) & 1 & 0 & \(-1\) & 0 & 0 \\ M3 & 1 & 1 & 0 & 1 & 0 & 1 & 1 & 0 & \(-1\) & \(-1\) & 1 & 1 & 0 & 0 & 0 \\ M4 & 1 & 1 & \(-1\) & 0 & \(-1\) & 1 & 1 & 1 & 0 & 1 & 1 & 0 & 0 & 0 \\ M5 & 1 & 1 & 0 & 1 & 0 & 1 & 1 & 1 & \(-1\) & 0 & 1 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistical significance comparison of M1, M2, M3, M4, and M5 with \(t\)-test between SROCC values of M\(n\) algorithms on six databases, where BIQA-SC using \(n(n\in 1,2,\ldots,5)\) NFMs is as M\(n\).
# The Trends and Content of Research Related to the Sustainable Development Goals: A Systemic Review
## 2 Materials and Methods  
### Research Article Selection  
As mentioned in the previous section, we are interested in understanding the overview of the academic articles published after the ideas of Sustainable Development Goals were released. We conducted a systematic literature review as the guiding principle of this bibliometric study. The first step would be determining the research questions, which can then decide the databases and searching strategies. Primary research questions include:  
RQ1: What countries published the most SDG-related papers and were cited most?  
RQ2: What academic institutes published most SDG-related papers and were cited most?  
RQ3: What is the distribution of academic fields of these papers?  
RQ4: What are the characteristics and themes of the leading paper clusters?  
RQ5: What are the critical papers in each of the leading clusters?  
RQ6: What are the major patterns of the SDG-related review papers?  
RQ7: What SDGs were examined in the SDG-related review papers?  
Web of Science (WOS) was selected as the target journal database. It employed many high-quality, interdisciplinary, and international journals with representative indexes as follows [12]:  
* SCI-EXPANDED: Science Citation Index Expanded (since 1945)
* SSCI: Social Sciences Citation Index (since 1956)
* A&HCI: Arts and Humanities Citation Index (since 1975)
* ESCI: Emerging Sources Citation Index (since 2015)  
The algorithm with the Boolean operators was hired to discover the precise and accurate set of papers that we needed. As the target papers were those with SustainableDevelopment Goals, which could be shown in the title, abstract, or keywords, the first trial was:  
TI = (SDG* OR sustainable development goal*)  
A total of 4208 papers were selected from 2013 till early June 2022 through conducting this algorithm. However, we found that a few articles had \"SDG\" in the titles, but those were the abbreviations of other scientific terms instead of the Sustainable Development Goals. Thus, we added one extra rule to make sure all SDGs in the papers were Sustainable Development Goals, making the new algorithm as  
TI = (SDG* OR sustainable development goal*) AND AB = sustainable development goal  
This excluded 1394 papers, and the resulting set of papers included only those associated with Sustainable Development Goals, being a total of 2814 papers.  
The other set of papers to be examined were those review papers with SDGs explicitly in the title. The following algorithm was used to generate the dataset:  
TI = (SDG* OR sustainable development goal*) AND TI = review AND AB = sustainable development goal  
There were 92 papers identified and collected from 2013 till now in the dataset for further analysis.
## 4 Finite Element Simulation Results
### Study on the Influence of Elevation
When the horizontal spacing is 200 m (uneven distribution), the collapse-resisting wind speeds of the observed members further decrease. During the design process, extreme uneven spacing distributions should be avoided to enhance the stability of the transmission tower. 2. The increase in the target tower height reduces the stability of the transmission tower-line system. Compared to the condition with a tower base height of 0 m, the collapse-resisting wind speeds of the observed members are significantly reduced when the tower base height is 50 m. During the design process, excessively high target towers should be avoided. 3. As the temperature increases, the adverse effects of uneven horizontal spacing distribution and increased target tower height on the transmission tower become more significant. With rising temperatures, the structural disaster resistance gradually weakens. It is recommended to choose steel materials with higher fire resistance or apply fire-resistant coatings to existing steel to enhance the bearing capacity of the transmission tower in high-temperature environments. 4. This paper investigates the disaster mechanism of transmission towers under the combined action of fire and wind loads, providing a reference for enhancing their disaster resistance and optimizing design schemes. Future research could further explore the impact of other disaster factors, such as ice and snow loads, seismic loads, etc., on transmission towers, offering insights for the design and safety assessment of transmission towers in complex environments. Methodology, L.Z. ; Software, S.Z. ; Writing--original draft, H.M.; Writing--review & editing, W.W.; Project administration, X.H. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China grant number 51908340 and The APC was funded by [PERSON]. Not applicable. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. Authors [PERSON] and [PERSON] were employed by the company Shandong Electric Power Engineering Consulting Institute Corporation Limited. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Parameter Sensitivity Analysis
### The Impact of Saturated Soil Permeability Coefficient on Frost Heave and Thaw Settlement  
By varying the permeability coefficient of the saturated soil surrounding the pipeline, the effects on frost heave and thaw settlement were observed, as shown in Figure 19. The results indicate that changes in the permeability coefficient have a minimal effect on frost heave and thaw settlement. This suggests that during the freeze-thaw cycle, the migration of moisture within the soil is primarily driven by the temperature gradient rather than by permeability. During the freezing stage, the temperature gradient plays a dominant role, causing directional moisture migration. In conditions with abundant water content and rapid freezing rates, moisture becomes largely stagnant, further diminishing the effect of permeability. The thaw settlement process is mainly influenced by volume changes due to the phase transition between ice and water and the subsequent re-adjustment of the soil structure, with the permeability coefficient having negligible impact on the final volume recovery and soil settlement.
## 2 Governing Equations
### v2f Turbulence Model  
This turbulence model was developed in more recent times and it is particularly used for jet impingement problems. [PERSON] [34] has performed numerous researches on jet impingement, showing the excellent results from this turbulence model in 2D axisymmetric cases. The peculiarity of this model is the use of the term \(v^{2}\), which replaces the turbulence kinetic energy \(k\) for the eddy viscosity calculation. The quantity \(v^{2}\) can be interpreted as the fluctuation of velocity perpendicular to the current lines. This term allows the right magnitude to be kept regarding the damping of turbulence transport near the wall. The transport equations for \(k\), \(\varepsilon\), \(v^{2}\) and \(f\) (elliptic relaxation function) are reported:  
\[\frac{\partial}{\partial t}(\rho k)+\frac{\partial}{\partial x_{i}}(\rho ku_ {i})=P-\rho\varepsilon+\frac{\partial}{\partial x_{j}}\left[\left(\mu+\frac{ \mu_{t}}{\sigma_{k}}\right)\frac{\partial k}{\partial x_{j}}\right]+S_{k} \tag{16}\]  
\[\frac{\partial}{\partial t}(\rho\varepsilon)+\frac{\partial}{\partial x_{i}} (\rho\varepsilon u_{i})=\frac{C_{\epsilon 1}^{\prime}P-C_{\epsilon 2}\rho \varepsilon}{T}+\frac{\partial}{\partial x_{j}}\left[\left(\mu+\frac{\mu_{t}} {\sigma_{\varepsilon}}\right)\frac{\partial\varepsilon}{\partial x_{j}} \right]+S_{\varepsilon} \tag{17}\]  
\[\frac{\partial}{\partial t}\left(\rho v^{2}\right)+\frac{\partial}{\partial x _{i}}\left(\rho v^{2}u_{i}\right)=\rho kf-6\rho v^{2}\frac{\varepsilon}{k}+ \frac{\partial}{\partial x_{j}}\left[\left(\mu+\frac{\mu_{t}}{\sigma_{k}} \right)\frac{\partial v^{2}}{\partial x_{j}}\right]+S_{v^{2}} \tag{18}\]  
\[f-L^{2}\frac{\partial^{2}f}{\partial x_{j}^{2}}=(C_{1}-1)\frac{\frac{2}{- \frac{v^{2}}{\varepsilon}}}{T}+C_{2}\frac{P}{\rho k}+\frac{5\frac{v^{2}}{ \varepsilon}}{T}+S_{f} \tag{19}\]  
where:  
\[P=2\mu_{t}S^{2},\ \ \ S^{2}\equiv S_{ij}S_{ij},\ \ \ S_{ij}=\frac{1}{2} \left(\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u_{j}}{\partial x _{i}}\right) \tag{20}\]  
The turbulent length and time scales are defined as follows:  
\[T^{\prime}=max\left[\frac{k}{\varepsilon},6\sqrt{\frac{v}{\varepsilon}}\right] \tag{21}\]  
\[T=min\left[T^{\prime},\frac{\alpha}{\sqrt{3}}\frac{k}{v^{2}C_{\mu}\sqrt{2S^{2} }}\right] \tag{22}\]  
\[L^{\prime}=min\left[\frac{k^{\frac{3}{2}}}{\varepsilon},\frac{1}{\sqrt{3}} \frac{k^{\frac{3}{2}}}{v^{2}C_{\mu}\sqrt{2S^{2}}}\right] \tag{23}\]  
\[L=C_{t}max\left[L^{\prime},C_{\eta}\left(\frac{v^{3}}{\varepsilon}\right)^{ \frac{1}{5}}\right] \tag{24}\]  
The values of the constants and turbulent Prandtl numbers are: \(\alpha=0.6\), \(C_{1}=1.5\), \(C_{2}=0.3\), \(C_{\epsilon 1}=1.5\), \(C_{\epsilon 2}=1.9\), \(C_{\eta}=70\), \(C_{\mu}=0.22\), \(C_{L}=0.23\), \(\sigma_{k}=1\) and \(\sigma_{\varepsilon}=1.3\). Finally, the eddy viscosity and the term \(C_{\epsilon 1}\) are defined as:  
\[\mu_{t}=\rho C_{\mu}v^{2}T \tag{25}\]\[C^{\prime}_{\epsilon 1}=C_{\epsilon 1}\left(1+0.055\sqrt{\frac{k}{v^{2}}}\right) \tag{26}\]
# Investigation of Mixed-Mode I/II Fracture under Impact Loading Using Split-Hopkinson Pressure Bar
## 1 Introduction
Therefore, in this paper, a variable angle single cleavage semi-circle (VASCSC) specimen was proposed to investigate the mixed-mode I/II crack propagation behavior in the whole propagation process. The Split-Hopkinson pressure bar device was employed to conduct impact tests cooperated by a synchronized crack velocity measuring system. The crack extending velocity data were detected accurately and the fracture phenomena were analyzed. Moreover, by using a finite element method, a determination of the dynamic initiation fracture toughness of mixed-mode crack was demonstrated. Additionally, an AUTODYN code was applied to realize the numerical prediction of mixed-mode crack. With the combination of the experimental approach and numerical method, the present literature illustrates a comprehensive method to investigate the crack propagation behavior of mixed-mode I/II crack, which can be applied in evaluating dynamic crack propagation behaviour, calculating dynamic initiation fracture toughness, and predicting crack trajectory. More critically, some crucial conclusions in this paper can be potentially applied in civil engineering for design structures and hazard prediction for reference.
## 3 Experimental Results and Analysis  
### Image Enhancement Comparison Experiment  
To verify the effectiveness of the proposed improved algorithm, the illumination inhomogeneous corridor image and the real image of an underground coal mine are selected for enhancement analysis experiments. Simulation experiments are conducted by MATLAB, and the proposed algorithms are compared with other classical image enhancement algorithms, including Multisim Scale Retinex (MSR), Contrast Limited Adaptive Histogram Equalization (CLAHE), Homomorphic Filter (HF) and the algorithm based on common PSO.  
The image of the corridor with poor lighting conditions is selected for image analysis of different algorithms, and the image enhancement effects of each comparison algorithm and the proposed algorithm are shown in Figure 8 below.  
Figure 8: Comparison corridor image enhancement effect.  
The image of the conveyor belt during coal mine production is selected for image analysis by different algorithms, and the image enhancement effects of each comparison algorithm and the proposed algorithm are shown in Figure 9 below.  
The original images, Figures 8a and 9a, have low brightness resulting in insufficient image detail information. The contrast and brightness of images in Figures 8b and 9b are improved by processing with MSR, but the phenomenon of over-enhancement appears, and the brighter areas of the original images are not better suppressed. The overall brightness and sharpness of images in Figures 8c and 9c are improved after processing with CLAHE, but the phenomenon of halo appears in the brighter areas of the original images. The contrast of images Figures 8c and 9c is improved more after HF enhancement, but the overall brightness of the images is not improved enough compared with the original images, and some detailed information in the images is not reflected enough. After using the algorithm based on common PSO to process the images, the brightness and contrast of images in Figures 8e and 9e are better balanced, but the images still have detailed noise interference. The brightness of the image processed by the proposed algorithm is enhanced appropriately, and the original color and detail information of the image is retained better. Moreover, the noise is suppressed better while enhancing the image, which has a better visual effect compared with other image enhancement algorithms.
# Experimental & FEM Analysis of Orthodontic Mini-Inplant Design on Primary Stability
## 2 Materials and Methods
### Pull-Out Test  
The pull-out test represents one of the biomechanical in vitro experiments, and it can be conducted on cadaveric bone, animal bone or artificial material. The objective of this analysis, which is conducted on swine ribs, is to determine the bearing capacity of the implant-bone system, i.e., the value of the pull-out force that will lead to the extraction of the mini-implant from the bone specimen ([PERSON] and [PERSON], 2015).  
Before experimental analysis of primary stability is performed, it is necessary to adequately prepare the specimen. When the bone scan is finished, the bone is placed on the steel implement specially designed for the needs of the experiment. The implement contains two L-profiles that demand very precise production. The bottom part of the implement has four holes, where the bottom surface of the swine rib is attached with four screws. In the production process, special care is taken on hole locations, which need to provide sufficient distance between the screws and the inserted mini-implant in order to preserve the bone's structure. Self-drilling mini-implants are hand tightened in a perpendicular direction on the bone surface using a handheld screwdriver, as shown in Figure 4a. Therefore, the construction of the implement with necessary distances of holes provides a pure pull-out force without additional bending moments.  
Experimental research is performed with a universal digital machine for material testing, Zwick 143501 model (Zwick GmbH & Co., Ulm, Germany), with a force transducer U2A by the HBM (Hottinger Baldwin Mestechnik GmbH, Darmstadt, Germany) manufacturer. Specimens are placed into the testing machine via the implement. The bottom part of the implement, for which the swine rib is fixated, is placed in the bottom clamp of the testing machine. The upper part of the implement, via the upper clamp, is fixated to the movable part of the testing machine, where the force transducer is placed (Figure 4b).  
Figure 5 gives a comparative representation of the experimental pull-out tests carried out on the 20 samples of both the Tomas(r)-pin and Perfect Anchor mini-implants, where the pull-out force-displacement results are shown.  
Figure 4: The insertion of the mini-implant into bone, with a 90\({}^{\circ}\) angle, through the hole in the implement (**a**), and the implement attached with fixed and movable parts of the testing machine (**b**).  
It is noticeable that the Tomas(r)-pin mini-implant can handle greater pull-out forces with greater displacements before the implant is extracted from the bone.  
Variances of pull-out force for subsamples do not have a significant difference (\(p<0.515\)). A statistically significant difference of mean for pulling forces between implants Perfect Anchor and Tomas(r)-pin is obtained (\(p<0.028\)) [3].