esa-sceva/satcom-chunk-collection huggingface dataset:
# Interplay Between Wear and Thermal Expansion in 6082 Aluminum: A Simulation and Experimental Study
## 3 Results and Discussion
### Wear Evolution  
Figure 8 shows the evolution of wear distribution on the disc, highlighting significant differences between cases with and without thermal effects. Without thermal effects, wear primarily concentrates at the nominal contact center (20 mm from the disc center) between the disc and pin. From 0 to 0.4 min, wear depth develops rapidly. This occurs due to the small actual contact area from poor geometrical conformity during the running-in phase, concentrating the load on a few points and causing higher stress and wear. From 0.4 to 2 min, wear depth develops more slowly as the contact area enlarges, with the wear region gradually expanding outward from the nominal contact areas. However, the maximum wear depth remains at the nominal contact center. As the rotational speed increases from 100 to 200 and 300 RPM, wear depth slightly increases.  
In contrast, thermal effects result in deeper wear. At 100 RPM, wear mainly develops at the nominal contact center, but at 200 RPM, an additional wear center appears outside this area. At 300 RPM, the depth of the newly formed wear center surpasses the original wear center. This shift in wear center location is attributed to changes in contact pressure, as shown in Figure 9. The outer region of nominal contact, with higher sliding speed, experiences greater thermal expansion. This increases pin-to-disc contact pressure, causing the original contact center to wear down quickly, shifting the wear center outward. This phenomenon appears only at higher loads (above 10 N) and speeds (200 RPM or more), indicating that sufficient energy is needed for the material to expand thermally enough to alter contact behavior.  
Figure 8: Wear evolution under (**a**) 15 N and 100 RPM with the thermal effect, (**b**) 15 N and 200 RPM with the thermal effect, (**c**) 15 N and 300 RPM with the thermal effect, (**d**) 15 N and 100 RPM without the thermal effect, (**e**) 15N and 200 RPM without the thermal effect, and (**f**) 15 N and 300 RPM without the thermal effect (the results are taken from the FEM simulation).
## 2 Methods and Modeling
### Conservation of Charge
#### 2.3.3 Charge Transfer  
At the interface between the solid MIEC and electrolyte material the O\({}^{2-}\) are exchanged. The local charge transfer voltage (overpotential) is the driving force for this process [17]. The current density is modeled by the Butler-Volmer equation:  
\[i_{\text{a/c}}=\left\{\begin{array}{ll}i_{0,\text{a}}\cdot\left[\exp\!\! \left(\frac{\alpha_{\text{f}}^{2}\cdot F\cdot(V_{\text{s}}-V_{\text{el}}-V_{ \text{0}})}{R\cdot T}\right)-\exp\left(\frac{\alpha_{\text{b}}^{2}\cdot F\cdot (V_{\text{s}}-  
used), \(V_{\rm{s}}\) is solid potential, \(V_{\rm{el}}\) is electrolyte potential, and \(V_{0,\rm{a/c}}\) is anode/cathode potential that can be obtained from Nernst equation, respectively.  
\[V_{0,\rm{a}}=0 \tag{16}\]  
\[\begin{split}& V_{0,\rm{c}}=\frac{-\Delta G_{\rm{H_{2}}}-k_{\rm{th }}\cdot T-R\cdot T\cdot\rm{ln}\left(\frac{P_{\rm{H_{2}}}\sqrt{\Omega_{\rm{O_{2} }}}}{P_{\rm{H_{2}O}}}\right)}{2\cdot F}=\\ &=\frac{-\Delta G_{\rm{H_{2}}}-k_{\rm{th}}\cdot T-R\cdot T\cdot \rm{ln}\left(\frac{P_{\rm{H_{2}}}M_{\rm{H_{2}O}}}{(1-P_{\rm{H_{2}}})M_{\rm{H_{ 2}O}}}\cdot\frac{\frac{\Psi_{\rm{O_{2}}}M_{\rm{N_{2}}}}{\Psi_{\rm{O_{2}}}M_{ \rm{N_{2}}}+(1-\Psi_{\rm{O_{2}}})^{\rm{-M_{O_{2}}}}M_{\rm{O_{2}}}}}\right)}{2 \cdot F}\end{split} \tag{17}\]  
Partial pressures of oxygen, hydrogen and water are denoted with \(p_{\rm{O_{2}}}\), \(P_{\rm{H_{2}}}\), and \(p_{\rm{H_{2}O}}\), respectively, and the \(\Delta G_{\rm{H_{2}}}\) denotes Gibbs free energy for hydrogen. The temperature coefficient \(k_{\rm{th}}\) is calculated from Equation (17), considering open circuit voltage of measured SOFC (OCV = \(V_{0,\rm{c}}-V_{0,\rm{a}}\)), operating temperature, and partial pressures (or mass fractions) of gas species.
## 3 Experiments and Results
### Experimental Results
#### 3.3.2 Experiment 2 Using the Yaan Dataset  
For the second image pair, the proposed method also obtained 29 matched tie points, which is significantly larger than those of the Patch-SIFT (\(N_{cp}=9\)) and SIFT (\(N_{cp}=4\)) methods. Since the numbers of tie points extracted by the Patch-SIFT method were less than the minimum number required for the cubic polynomial model, only the quadratic polynomial model was used for the Patch-SIFT method. For a similar reason, only the linear polynomial model was used for the SIFT method. Using the cubic polynomial model, the proposed method yielded a running time of 1.56 s, an \(RMSE_{M}\) value of 1.81 pixels, and an \(RMSE_{T}\) value of 1.75 pixels. The running time is only 34.29% of the SIFT (4.55 s) and 48.30% of the Patch-SIFT (3.23 s). The \(RMSE_{M}\) and \(RMSE_{T}\) values are also significantly lower than those of the other two methods. Similarly to the first image pair, the running time, \(RMSE_{M}\), and \(RMSE_{T}\) of the proposed method obtained using the quadratic polynomial model were very close  
Figure 12: Registered images of the Wenchuan dataset. (**a**) The original image; (**b**) registered image using the proposed method; (**c**) registered image using Patch-SIFT; and (**d**) registered image using SIFT.  
to those obtained using the cubic polynomial model. Similarly, the distribution of the matched tie points extracted by the proposed method (Figure 13a) is also more even than those of the Patch-SIFT (Figure 13b) and the SIFT (Figure 13c) methods.  
The registration results of the Yaan dataset are shown in Figure 14. Although there was a slight displacement for the road along the river in the registered image produced by the proposed method, the proposed method yielded better performance than the other two methods. In contrast, the registration results of Patch-SIFT (Figure 14c) and SIFT (Figure 14d) show even more noticeable displacements for the roads and constructions than the original image (shown in Figure 14a).
# HinPhish: An Effective Phishing Detection Approach Based on Heterogeneous Information Networks
## 3 Preliminaries
### Machine Learning Classification  
Machine learning classification is a supervised learning method in which a computer program learns from a training set and makes predictions on new data based on the learned \"knowledge\". The basis of machine learning classification is the classification algorithm, which determines the efficiency and effect of classification. In this section, we review several classic classification algorithms that are often used for phishing detection.  
**Naive Bayes (NB)** classification is a probabilistic machine learning method. It is not only straightforward, but also powerful. It has been considered preferable in many applications, such as for the classification of text, detection of spam emails, and prediction of disease. It is based on [PERSON]'s theorem, which states the assumption of independence among predictors. Equation (1) demonstrates how to calculate the conditional probability:  
\[P(C_{i}|x_{1},x_{2}\ldots,x_{n})=\frac{P(x_{1},x_{2}\ldots,x_{n}|C_{i})*P(C_{i} )}{P(x_{1},x_{2}\ldots,x_{n})},(1<i<k) \tag{1}\]  
where \(P(C_{i})\) is the prior probability, which represents the probability that samples belong to the \(i\)th category and \(P(x_{1},x_{2}\ldots,x_{n})\) is named the probability of the evidence. \(P(x_{1},x_{2}\ldots,x_{n}|C_{i})\) is the probability of the evidence, given that the samples belong to \(C_{i}\). \(P(C_{i}|x_{1},x_{2}\ldots,x_{n})\) is the posterior probability, which is the probability that samples belong to \(C_{i}\), according to the given evidence.  
**Support Vector Machine (SVM)** is a classifier that represents the training data as points in space, separated into different categories by a gap which is as wide as possible. New points can then be added to the space, predicting which category they fall into and which space they will belong to. It uses a subset of training points in the decision function, which makes it memory-efficient and highly effective in high-dimensional spaces.  
**Random Forest (RF)** is an ensemble learning method for classification, regression, and similar processes. It operates by constructing a multitude of decision trees during the training stage and outputs the class as the mode of the classes, classification, or mean prediction (regression) of the individual trees. A random forest is a meta-estimator that fits a number of trees on various subsamples of data sets and then uses an average to improve the accuracy in the model's predictive nature. Due to its forest structure, the instabilities of the individual decision trees can be mitigated.
## 3 Results and Discussion
### Economic Evaluation Analysis  
In this study, a simple economic evaluation analysis (Table 3) for the three neutralization methods was performed using the Engineering Economics Analysis method [21]. The price of pretreatment reagents, water, wastewater treatment and methane were achieved according to current market price, the average industrial water price, the average industrial wastewater treatment cost and the average liquefied natural gas (LNG) price in October 2021 in China, respectively (Lime is 944.44 RMB/t, LDN is 0.00 RMB/t, CN is 0.00 RMB/t, industrial water is 4.1 RMB/t, industrial wastewater treatment is 2.00 RMB/t and the methane price is 4.88 RMB/m\({}^{3}\)). The profit value was calculated based on the RM, so there was no additional calculation of cost consumption which were the same (e.g., labor costs, energy costs, CS costs, depreciation costs of the instruments and equipment, costs of use-and-throw materials). The net profit was defined as the energy revenue from the methane production minus the cost of starting material.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **R\({}_{\text{m}}\) (mL\({}_{\text{N}}\cdot\)d\({}^{-1}\))** & **\(\lambda\) (d)** & **P\({}_{\text{m}}\) (mL\({}_{\text{N}}\))** & **R\({}^{2}\)** \\ \hline LDN-B a & 364.8 & 0.1 & 1065.6 & 0.9897 \\ CN-B a & 385.0 & 0.0 & 1210.7 & 0.9827 \\ WW-B a & 445.1 & 0.1 & 1283.2 & 0.9870 \\ RM-B a & 286.2 & 0.0 & 1045.8 & 0.9842 \\ LDN-L b & 721.0 & 1.1 & 3108.2 & 0.9942 \\ CN-L b & 453.2 & 1.8 & 4398.6 & 0.9946 \\ WW-L b & 431.8 & 0.6 & 4010.2 & 0.9955 \\ RM-L b & 432.3 & 0.8 & 3513.9 & 0.9832 \\ LDN-SS c & 163.9 & 1.6 & 1744.2 & 0.9962 \\ CN-SS c & 143.9 & 3.9 & 2128.1 & 0.9975 \\ WW-SS c & 58.9 & \(-\)0.2 & 205.1 & 0.9632 \\ RM-SS c & 172.8 & 2.5 & 1708.5 & 0.9962 \\ \hline \hline \end{tabular}  
* BMP test with 5 g of substrate (VS basis). b Liquid anaerobic digestion with 9 g of substrate (VS basis). c Solid-state anaerobic digestion with 20 g of substrate (VS basis).  
\end{table}
Table 2: Kinetic parameters of biomethane production with the modified Gompertz model.  
Figure 5: Curve fitting of the Gompertz equation to methane production data for (**a**) BMP, (**b**) L-AD and (**c**) SS-AD tests. Dots denote the means of data and lines denote the Gompertz curves.  
For all neutralization methods, the cost of the lime pretreatment reagent and water in the first step was the same (87.41 RMB/t TS). The total cost of LDN and CN was 0.00 RMB/t TS, which was collected for free. The total cost of WW was 122.00 RMB/t TS, of which 82.00 RMB/t TS was used for neutralization and 40.00 RMB/t TS for wastewater treatment. LDN and CN were more advantageous in terms of cost compared to WW. However, among the three neutralization methods, the net profit of CN was the only positive one with 100.89 and 98.74 RMB/t TS in L-AD and SS-AD, respectively. Therefore, CN was more economically attractive for practical applications.
## 2 Background
The Construction domain includes software design, development, and testing. The Verification domain covers testing and analysis to ensure the software is secure. The Deployment domain includes release management, operations, and incident management. SAMM also includes a measurement model, which assesses an organization's maturity level for each domain. The model evaluates the organization's practices, policies, and procedures against the SAMM framework. The results provide a roadmap for improvement. SAMM is a flexible and adaptable framework that allows organizations to tailor the model to their needs. It provides guidance and best practices to improve software security and helps organizations to mature their software security processes over time [26]. * [PERSON]'s Secure Software Development Life Cycle Process, in his article \"Security Software Building Security in Seven Touchpoints for Software Security,\" [PERSON]'s proposal focuses on integrating security into the software development life cycle (SDLC) through seven touchpoints [27]. The seven touchpoints include (i) Requirements: Define the software security requirements and establish the basis for the rest of the development process. (ii) Design: Design a secure software architecture considering the previously established security requirements. (iii) Implementation: Write lines of code and apply secure coding practices. (iv) Testing: Perform security tests to ensure compliance with previously established security requirements. (v) Integration: Ensure proper software integration with other systems and maintain security. (vi) Deployment: Implement the software securely and configure it properly. (vii) Maintenance: Implement safe maintenance practices to ensure the continued software security and quickly address security issues. This approach focuses on integrating security into all stages of the software development lifecycle to ensure the resulting software is secure and reliable [27]. We can identify several differences between the Secure Software Development Life Cycle (S-SDLC), Security Assurance Maturity Model (SAMM), and McGraw's Secure Software Development Life Cycle Process: (i) Approach: The S-SDLC and McGraw's SDLC process focus on integrating security into the software development life cycle. In contrast, SAMM focuses on improving the organization's overall security program. (ii) Framework Structure: The S-SDLC and McGraw's SDLC process provide a structured approach to software development, while SAMM provides a framework for assessing an organization's overall security maturity. (iii) Domains and Maturity Levels: The S-SDLC has six phases, McGraw's SDLC process has seven touchpoints, and SAMM has four domains, each with three maturity levels. (iv) Emphasis: The S-SDLC and McGraw's SDLC process emphasize the software development process, while SAMM emphasizes the security program. (v) Assessment: The S-SDLC and McGraw's SDLC process do not include an assessment model, while SAMM includes a measurement model that assesses an organization's security maturity level.
###### Abstract  
An ecosystem model serves as an important tool to understand the carbon cycle in the forest ecosystem. However, the sensitivities of parameters and uncertainties of the model outputs are not clearly understood. Parameter sensitivity analysis (SA) and uncertainty analysis (UA) play a crucial role in the improvement of forest gross primary productivity GPP simulation. This study presents a global SA based on an extended Fourier amplitude sensitivity test (EFAST) method to quantify the sensitivities of 16 parameters in the Flux-based ecosystem model (FBEM). To systematically evaluate the parameters' sensitivities, various parameter ranges, different model outputs, temporal variations of parameters sensitivity index (SI) were comprehensively explored via three experiments. Based on the numerical experiments of SA, the UA experiments were designed and performed for parameter estimation based on a Markov chain Monte Carlo (MCMC) method. The ratio of internal CO\({}_{2}\) to air CO\({}_{2}\) (\(f_{Ci}\)), canopy quantum efficiency of photon conversion (\(\alpha_{q}\)), maximum carboxylation rate at 25 \({}^{\circ}\)C (\(V_{m}^{25}\)) were the most sensitive parameters for the GPP. It was also indicated that \(\alpha_{q},\ E_{Vm}\) and \(Q_{10}\) were influenced by temperature throughout the entire growth stage. The result of parameter estimation of only using four sensitive parameters (RMSE = 1.657) is very close to that using all the parameters (RMSE = 1.496). The results of SA suggest that sensitive parameters, such as \(f_{ci},\alpha_{q},\ E_{Vm},V_{m}^{25}\) strongly influence on the forest GPP simulation, and the temporal characteristics of the parameters' SI on GPP and NEE were changed in different growth. The sensitive parameters were a major source of uncertainty and parameter estimation based on the parameter SA could lead to desirable results without introducing too great uncertainties.  
sensitivity analysis; flux-based ecosystem model; extended Fourier amplitude sensitivity test (EFAST); Howland forest; Markov chain Monte Carlo 2020; 12584; doi:10.3390/su12072584
## 2 Materials and Methods
### Spectral Analysis of Energy-Containing Materials
#### 2.2.2 Band Selection for Two-Color Temperature Measurements  
Potassium (K) is commonly found in biomass fuels [37] and various energy-containing materials [38]. Consequently, characteristic spectra of the element K can be emitted during the combustion of these materials or the detonation of munitions.  
Referring to the NIST (National Institute of Standards and Technology) spectral database [39], we can identify the wavelengths corresponding to the prominent spectral peaks of K (404.4 nm, 691.1 nm, 693.8 nm, 766.3 nm, and 769.8 nm) in the visible range. Notably, the spectral lines at 691.1 nm and 693.8 nm of potassium are consistently observed in a variety of compounds found in different energy-containing materials [40].  
The wavelength interval in the two-color method is a critical parameter for calculating temperature. To ensure the accuracy of the calculated temperature, the wavelength interval should be at least 30 nm [41]. Consequently, we selected two spectral lines, 694 nm and 768 nm, for temperature testing based on our previous research [42; 43; 44].  
The intervals between the wavelengths were set to 10 nm, 20 nm, 30 nm, 40 nm, 50 nm, and 60 nm to investigate the effect of wavelength intervals on the accuracy of temperature measurements at 800 \({}^{\circ}\)C, as shown in Table 1.  
When the wavelength interval is 10 nm, the temperature distribution fluctuates significantly with the wavelength, resulting in a temperature measurement error of 3.8%. For wavelength intervals greater than 30 nm, the temperature measurement error decreases as the wavelength interval increases, falling below 1% at 40 nm and 60 nm, both measuring 0.8%. This indicates that there is no significant difference in temperature measurements at intervals greater than 40 nm. Therefore, to ensure the accuracy of the calculations, the wavelength interval should be at least 30 nm or greater.
## 2 DC Microgrid System Based on Distributed PV Technology
### Survey of DC Appliances  
In order to clarify the current residential building electricity consumption and DC electrical type, the project investigated the electricity consumption of 100 households in Qingdao, and the results showed that the average daily electricity consumption was 7.14 kWh. Also, the types of household DC appliances, the average number of devices per household, and the average running time were investigated, as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \begin{tabular}{c} **Residential** \\ **Lighting** \\ \end{tabular} & \begin{tabular}{c} **Cell** \\ **Phone** \\ \end{tabular} & \
\begin{tabular}{c} **Microwave** \\ **Oven** \\ \end{tabular} &
\begin{tabular}{c} **Laptop** \\ \end{tabular} \\ \hline Average power consumption (W) & 25 & 80 & 1500 & 65 \\ Average number of devices per & 8 & 3 & 1 & 1 \\ household & 3 & 2.5 & 0.017 & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of domestic DC appliances.  
Figure 1: Schematic diagram of PV microgrid system.  
The daily power consumption of household DC appliances is shown in Figure 2, where \"other\" includes a small amount of household dry battery charging as well as the total power consumption of the DC line itself.  
According to the survey results, the commonly used DC appliances in residential households include laptops, lightings, mobile phones, etc. Every type of electrical appliance requires the corresponding different voltage. At present, the commonly used voltages for DC electrical appliances are 5 V, 12 V, 14 V, and 24 V.
# Causation Correlation Analysis of Aviation Accidents: A Knowledge Graph-Based Approach
## 3 Aviation Accident Case Research
### Analysis of ACAKG Model Results
#### 3.4.4 Correlation Analysis between Aviation Accident Causation Types and Flight Phase  
The correlation between accident causation type and flight phase is also categorized into direct and indirect correlations (DCTP and ICTP), which indicate the degree of correlation between the current accident causal factor type and the flight phase in which future accidents will occur as a direct or indirect result. The direct and indirect correlations between causation type and time can be calculated using Equations (14) and (15).  
Figure 8 shows the statistical results of the direct and indirect correlations between the causal type and the different flight phases. It can be seen from the figure that the indirect correlation between each causal type and the flight phase is higher than the direct correlation in different phases. Specifically, the indirect correlation of the H-type causal factors is significantly higher than their direct correlation in all phases; the direct correlation of the H-type causal factors is higher than the direct correlation of the other causal factor types; the comparative results of the direct correlation of the causal factor types in different phases are H-type \(>\) E-type \(>\) M-type \(>\) A-type, with the indirect correlations being H-type \(>\) A-type \(>\) E-type \(>\) M-type; and the correlations of different causal factors in the take-off/landing and leveling phases are higher than those in the ground and maneuvering phases.