esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling of Characteristics of Complex Microstructure and Heterogeneity at the Core Scale
## 3 Results
### Pore Structure Parameter Analysis
The range of the horizontal axis shows that the aspect ratio ranges of the microcracks in each sample are different, as shown in Figure 11. Notably, in samples 24, 25 and 132, the cumulative microcrack density varies significantly when \(P\) = 0 and \(P\) = 5 (approximately 0.06, 0.04 and 0.03), indicating that with an increase in pressure to 5 MPa, many microcracks close, and the proportion of microcracks with a small aspect ratio is the largest. As the pressure increases to 10 MPa, the decrease in the cumulative microcrack density gradually decreases, indicating that in these two samples, microcracks with smaller aspect ratios account for a larger proportion, and microcracks with smaller aspect ratios have an absolute advantage. Figure 11f shows that for samples 24, 132 and 25, the cumulative crack density decreases rapidly in the small aspect ratio interval with a similar trend, suggesting that the microcracks with small aspect ratios account for a similar percentage of the total density, but the density decreases sequentially due to the difference in the total density of the microcracks. On the other hand, for samples 30 and 132, the curves are flatter, indicating a more even distribution of microcracks that have a similar range of aspect ratios but slightly different densities. Figure 12 shows the curves of porosity \(\phi\) versus aspect ratio \(\alpha\) for the microfractures in the five samples at the different effective pressures. As shown in Figure 12, the distribution interval of the aspect ratio becomes narrower and moves towards a small aspect ratio as the pressure increases. A comparison of the aspect ratio, porosity and range of the dominant microcracks in each sample is shown in Figure 12f. As shown in Figure 12, samples 24,  
Figure 11: Cumulative crack density distribution as a function of the aspect ratio in samples (**a**) 24, (**b**) 25, (**c**) 26, (**d**) 30, and (**e**) 132 and (**f**) comprehensive comparison at 0 MPa  
132, and 25 have a rapid decrease in porosity at low pressures, and at high pressures, the microporosity is almost completely closed. Figure 12f shows that not only does the porosity of these three samples decrease gradually but also the range of the aspect ratio distribution becomes narrower and the centre moves in the direction of a small aspect ratio. The decrease in porosity of samples 26 and 30 is more uniform with decreasing pressure, and the microcracks are not completely closed at 40 MPa. Figure 12f shows that the distributions of the aspect ratios of these two samples are almost the same, and the porosity in the centre decreases.
# Hybrid Early Warning System for Rock-Fall Risks Reduction
## 5 Results and Discussion
### Detection Model Results
When traceability had a value between zero and one, and field of view height represented the image frame height measured in pixels, the results showed that the average traceability in the morning at high light conditions was 0.91, while its average values during the low light conditions were 0.77. When comparing the results in Table 3 with their counterparts in Table 4, we observed that tracking rocks during the morning was better than during the evening. It was confirmed that the system's ability to detect and track rocks during the high light is better than it is during low light conditions.
## 5 Research Results
### Publications That Provide Guidelines and Strategy Considerations  
Figure 6 depicts the 143 final publications published since 2015, with the key search terms defined in PICOC. The \"total\" bar represents the total number of publications that have the term in the keywords, abstract, or title.  
Figure 4: Number of publications by journal title. Source: own development (2023).  
Figure 5: Number of publications by keyword used. Source: own development (2023).  
The terms \"business continuity plan\" or \"disaster recovery plan\" are referenced in 138 publications. Individually they are referred to in 60 abstracts, 33 titles, and 74 keywords. The five publications missing of the 143 total publications were manually added to the SLR.  
The terms \"guideline\", \"guidance\", or \"strategy\" are referenced in 67 publications. Individually they are referred to in 60 abstracts, six titles and 11 keywords. The amount rises to 72 publications when considering \"streamline\" and all its synonyms in the search terms. Individually they are referred to in 69 abstracts, nine titles, and four keywords.  
Under the term \"framework\" and its synonyms, they are identified in 96 publications. Individually they are referred to in 95 abstracts, 28 titles, and 13 keywords.  
It should be noted that five of the six standards (ITIL, COBIT, CMMI, ISO 22301, and NFPA 1600) studied in this work have been updated since 2018 and, thus, potentially are considered in the 63 publications resulting from the SLR.  
All publications use \"business continuity plan\" or \"disaster recovery plan\", and it is relevant to highlight that 20% of all publications suggest a framework, considering the title. Almost half of the publications, in their abstract, refer to the terms \"guideline\" or \"streamline\", possibly suggesting that business continuity planning is an area that needs intervention to reduce development or implementation constraints.
# Workflow Trace Profiling and Execution Time Analysis in Quantitative Verification
## 5 Time Analysis
### Moments to Quantiles  
Moments are important statistical parameters for probability distributions. We can report the moments of FPT \(Y\) as the final time metrics of the trace components. In particular, the first FPT moment \(E[Y]\) is the mean execution time. The variance of \(Y\), which measures the deviation of the execution time from its mean, equals \(E[Y^{2}]-E[Y]^{2}\). Moreover, skewness (a measure of the asymmetry of the probability distribution) and kurtosis (a measure of the \"tailedness\" of the probability distribution) are also defined by moments.  
It is also possible to infer more intuitive metrics, such as quantiles, from moments. If the probability distribution of \(Y\) has a specific form, say, a PDF \(f(y|\theta)\) where the parameters \(\theta\) are unknown, by the method of moments ([21], Chapter 10.2), we can usually construct an equation system, whose coefficients are moments of \(Y\) (up to some order) and whose variables are \(\theta\). By solving this equation system, we can obtain the values of \(\theta\) and thus infer the quantiles of \(f(y|\theta)\). For example, if \(Y\) has a normal distribution \(\mathcal{N}(\mu,\sigma^{2})\), we have \(\mu=E[Y]\) and \(\delta^{2}=E[Y^{2}]-E[Y]^{2}\) immediately; if \(Y\) has a Gamma distribution \(\Gamma(\alpha,\beta)\), where \(\alpha\) is the shape parameter and \(\beta\) is the rate parameter, we can derive the following two equations: \(\alpha/\beta=E[Y]\) and \(\alpha/\beta^{2}=E[Y^{2}]-E[Y]^{2}\). This method can be applied to almost all common forms of probability distributions.  
In practice, we can use the historical data to estimate the form of the distribution function for \(Y\). However, if the form of the probability distribution of \(Y\) is unknown, there are other existing methods to estimate or approximate the quantiles of \(Y\). For example, one method is to estimate the PDF of \(Y\) with the maximum-entropy distribution (i.e., a distribution with \"least information\" according to information theory) [25]. Another method is to use semidefinite programming to compute the bounds of the quantiles of \(Y\)[29]. Hence, it is possible to extend our approach to include those methods in future work.
###### Abstract  
Atmospheric turbulence is a typical risk that threatens the flight safety of civil aviation aircraft. A method of estimating aircraft's vertical acceleration in turbulence is proposed. Based on the combination of wing and horizontal tail, the continuous change of aerodynamic force in turbulent flight is obtained by unsteady vortex ring method. Vortex rings are assigned on the mean camber surface to further improve the computing accuracy. The incremental aerodynamic derivatives of lift and pitching moment are developed, which can describe the turbulence effects on aircraft. Furthermore, a new acceleration-based eddy dissipation rate (EDR) algorithm was developed to estimate the turbulence severity. Compared with wind tunnel test data, the aerodynamic performance of the lifting surface was computed accurately. A further test on wing-tail combination showed that the computed pitching moment change due to control-surface deflections approaches the aircraft-modeling data. The continuous change of vertical acceleration at any longitudinal locations of aircraft is obtained in turbulent flight. Compared with traditional transfer function-based EDR algorithms, the proposed algorithm shows higher accuracy and stability. Furthermore, the adverse influence of aircraft maneuvering on EDR estimation is eliminated.  
turbulence; aircraft acceleration; eddy dissipation rate; unsteady vortex lattice model; vortex ring +
Footnote †: journal: Applied
## 3 Results and Discussion
### Characterization of Camelina Cake and Diets
#### 3.1.2 Fatty Acid Composition  
The fatty acid content of camelina cake and diets is shown in Tables 1 and 3. Regarding the three different diets, the effect of the increasing camelina cake is remarkable already in the CAM10  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Experimental Diets**} \\ \hline
**Parameter** & **Control (C)** & **CAM10** & **CAM20** \\ \hline DPPH, \% & 28.2 \(\pm\) 0.30 & 70.2 \(\pm\) 0.20 & 74.6 \(\pm\) 0.10 \\ TPC, mgCAE/g & 8.5 \(\pm\) 0.10 & 10.9 \(\pm\) 0.10 & 12.3 \(\pm\) 0.20 \\ Flavonoids, g/kg & 2.24 \(\pm\) 0.03 & 4.44 \(\pm\) 0.06 & 7.92 \(\pm\) 0.10 \\ Tannins, g/kg & 3.38 \(\pm\) 0.05 & 4.18 \(\pm\) 0.04 & 4.52 \(\pm\) 0.05 \\ Tocopherols, mg/kg & 321 \(\pm\) 20 & 464 \(\pm\) 12 & 537 \(\pm\) 10 \\ SFA, g/kg & 5.6 \(\pm\) 0.20 & 7.3 \(\pm\) 0.05 & 8.0 \(\pm\) 0.08 \\ MUFA, g/kg & 7.5 \(\pm\) 0.26 & 11.6 \(\pm\) 0.00 & 12.0 \(\pm\) 0.04 \\ n-6 PUFA, g/kg & 15.2 \(\pm\) 0.62 & 21.9 \(\pm\) 0.27 & 22.5 \(\pm\) 0.05 \\ n-3 PUFA, g/kg & 1.0 \(\pm\) 0.04 & 8.7 \(\pm\) 0.22 & 14.3 \(\pm\) 0.02 \\ n-6/n-3 & 15.83 & 253 & 1.57 \\ Glucosinales, mmol/kg & 0 & 1.66 \(\pm\) 0.08 & 3.37 \(\pm\) 0.02 \\ Sinapine, g/kg & 0 & 0.12 \(\pm\) 0.01 & 0.46 \(\pm\) 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Antioxidant capacity (DPPH), total phenolic compounds (TPC), flavonoids, tannins,tocopherol content, fatty acid composition, and antinutritional compounds (mean value \(\pm\) SE) of the experimental diets. Control = corn-soybean meal basal diet; CAM10 = basal diet containing camelina cake at 10%; CAM20 = basal diet containing camelina cake at 20%.  
diet. The corresponding ratio n-6/n-3 decreases from 15.8 (C) to 2.53 and 1.57 in CAM10 and CAM20 respectively. Consequently, an important improvement in the nutritional quality of the eggs was achieved ([PERSON] et al., in preparation).
## 1 Introduction
For the edge-based extraction method, the feature edges of the model are extracted by estimating the differential quantity of the model. A common means includes calculating the dihedral angle of the triangle edge. After the feature edges are extracted, the model feature lines are obtained by setting a certain threshold to connect the edges that meet the recognition conditions to form a closed loop. The method is simple and fast but poorly targeted, and the accuracy of the extraction results is low. A series of subsequent processes are also required to filter the extracted results [22]. In view of the serious defects and shortcomings of the above-mentioned existing methods in the extraction of feature lines of oblique photogrammetry 3D models with dense data, complex structures, and obvious noise, we first proposed a fast and intelligent feature line detection method of 3D models based on AABB (axis-aligned bounding box) collision detection. The existing edge line restoration methods for 3D models are mainly as follows. (1) Feature points are selected using an interactive method, and the selected feature points are fitted to the target line to complete the restoration. This method is a semi-automatic method that completes the edge line restoration with less interaction. However, the human interaction of this method is complex, and it takes a long time when there are many triangles and cutting surfaces. (2) Automatic identification and selection of feature vertices by adding limit lines or edges, and completing the edge restoration of the 3D model by moving the selected vertices to the corresponding limit lines or edges. This method is highly automated. However, due to the complex and noisy 3D model of oblique photogrammetry, the automatic selection of vertices to be modified is often not ideal, which makes the restoration results far from expected [23; 24]. (3) Based on the topological relationship of triangulation, the shortest path method is used to search for the edge feature line, and the auxiliary surface is used to reduce the noise to improve the restoration effect. The edge restorations of a 3D model based on continuous triangulation can be achieved [25]. However, this method of extracting edge features lines requires a lot of time and computer performance, and the accuracy of the extracted feature line needs to be improved. (4) Using the separability of triangles, a simple method based on cutting triangular patches is proposed. By analyzing triangles with different crossing modes and putting forward corresponding cutting schemes, the simplex of a 3D model based on continuous triangulation cutting can be achieved [26]. Based on the above methods and innovations, we propose a method by which to restore the edge line by cutting and fitting the triangle of the edge line. This method can eliminate the time costs of constructing the topology and can also avoid the triangle self-intersection problem caused by straightening the edge line directly.
## 2 Design of Spectrometer  
The basic design of the dispersive X-ray spectrometer in this study consists of an elliptical mirror, a flat analyzer crystal, and a position sensitive detector [8]. First, the spectral resolution \(dE/E\) is described by  
\[\frac{dE}{E}=\frac{\Psi}{\tan\theta_{\mathrm{B}}}. \tag{1}\]  
where \(E\) and \(\theta_{\mathrm{B}}\) are the photon energy and the Bragg angle, respectively. \(\Psi\) is determined by parameters of the X-ray source and optics \(\Psi_{1}\) and the angular resolution of the detector \(\Psi_{2}\), as given by  
\[\Psi=\sqrt{\Psi_{1}^{2}+\Psi_{2}^{2}}, \tag{2}\]  
\[\Psi_{1}=\frac{\sqrt{\sigma^{2}+L^{2}\omega^{2}}}{L}, \tag{3}\]  
and  
\[\Psi_{2}=\frac{p}{L}. \tag{4}\]  
Here, \(\sigma\), \(\omega\), \(L\), and \(p\) are the X-ray source size, the Darwin width of the Bragg reflection, the distance from the X-ray source to the detector, and the pixel size of the detector, respectively.  
Second, the spectral range \(\Delta E\) is described by  
\[\frac{\Delta E}{E}=\frac{\Delta\theta}{\tan\theta_{\mathrm{B}}}. \tag{5}\]  
Here, \(\Delta\theta\) is the angular divergence of the X-ray beam on the analyzer crystal. Equation (5) shows that a larger divergence is required for expanding the covered spectral range. Although an elliptical mirror with total reflection was utilized in our previous experiment [10], the mirror cannot provide a sufficient divergence angle for measuring the entire spectrum in one shot due to its small reflection angle of a few mrad.  
To overcome the limitation, we used platinum/carbon multi-layer mirrors [12], which were installed as the last optics of the two-stage focusing system at SACLA [13], to produce a large divergence \(\Delta\theta\) of 22 mrad with a central photon energy of 9070 eV and a central incident angle of 14 mrad. We note that the reflectivity of the single mirror with 30 layers is \(\sim\)70% at a photon energy range between 8900 and 9300 eV. To obtain a high spectral resolution in the dispersive spectrometer, a large Bragg angle is required from Equation (1). For the distance \(L\) of 1.5 m and a pixel size \(p\) of 50 \(\upmu\)m for a Multi-Port Charge Coupled Device (MPCCD) detector [14] in the present geometry, we derive both the spectral range of 55 eV and the resolution of 80 meV at around 9070 eV, simultaneously, with a Bragg angle of 75.2\({}^{\circ}\) using a silicon (553) analyzer crystal.
## 2 3D Thermal Model
### Heat Transfer Coefficient Boundary Conditions
#### 2.2.1 The Effective Thermal Conductivity of Air Gap  
Rotor rotation induces outside air into the air-gap between the rotor and the stator from the end side of the motor, performing in convection between the rotor and the stator. The thermal field is connected to the fluid field by the heat transfer of the rotor and the stator through the air-gap. This heat transfer is further influenced by the slotting and the surface roughness of the rotor and the stator. All these make calculating the temperature field even more difficult. Recognizing that, the effective thermal conductivity \(\lambda_{ef}\) has been investigated [34; 35; 36]. The Reynolds number \(Re_{g}\) in the air gap and critical Reynolds number \(Re_{c}\) are:  
\[Re_{g}=\frac{\omega_{r}\delta}{v}=\frac{2\pi\delta n(R_{si}-\delta)}{60v}, \tag{5}\]  
\[Re_{c}=41.2\sqrt{\frac{R_{si}i}{\delta}}, \tag{6}\]  
where \(\omega_{r}\) is the peripheral speed of the rotor, 4.387 m/s; \(n\) is the motor speed, 105 rpm; \(\delta\) is the air-gap length, 0.001 m; \(v\) is the kinematic viscosity of air, 1.5 \(E^{-5}\) m\({}^{2}\)/s, and \(R_{si}\) is the inner radius of the stator 0.4 m. Then, we calculated \(Re_{g}\) and \(Re_{c}\) as 292.4 and 824 respectively, for our model. The air flow is laminar then the heat transfer mode is mainly that of conduction where \(\lambda_{ef}\) can be treated as being equal to that of air \(\lambda_{air}\). This leads to heat transfer of the stator to the rotor as conduction. However, we propose using the structure of the shaft with a spoke (duct) to cool the rotor.
# Group Authentication Scheme for Neighbourhood Area Networks (NANs) in Smart Grids
## 2 Group Authentication
### Security Requirements
The features required by the security scheme for a scenario that we consider are indicated in the last column in Table 1. The scheme is expected to provide for an authentication mechanism for the nodes on the network with the primary goals of the authentication are to tag a node as part of the network and active, to distribute shared keys once the node's identity is established as valid, and to distribute a shared secret within the group that is used for validated forwarding in intermediate nodes. By scalability, we mean the ability to add a large number of end devices and sufficient number of gateways for the number of groups formed. Scalability impacts performance (in terms of end-to-end delays), storage requirements (number of shared keys) on the intermediate devices including gateways, processing (delays due to forwarding traffic), and the delays in redistributing keys when a new node joins or an existing one leaves. Scalability is a desirable feature of the scheme. In the next section, we discuss the smart grid network model and consider the requirements for its secure operation. We also highlight the potential security threats we consider as a case study for testing the proposed solution. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Features**} & \multicolumn{6}{c}{**Security Schemes**} \\ \cline{2-9}  & **[PERSON] _et al._[9] & **[PERSON] [10]** & **[PERSON] _et al._[15] & **[PERSON] _et al._[14] & **[PERSON] _et al._[17] & **[PERSON] _et al._[19] & **Our Goal** \\ \hline Topology S/M/CT & M & N/A & N/A & S & M & N/A & S, M, CT \\ Multi-hop paths & Yes & Yes & Yes & No & Yes & N/A & Required \\ Valuated Forwarding at & & Yes & Yes & Yes & No & Yes & No & Required \\ intermediate nodes & & & & & & & & \\ Symmetric Crypto & Yes & No & No & No & No & EAS & Required \\ Resilient No Attack & No & No & No & Yes & No & Yes & Required \\ Resilient to replay attack & No & Yes & Yes & Yes & Yes & Yes & Required \\ Resilient to Sybil attack & Yes & Yes & Yes & Yes & Yes & Yes & Required \\ Centralized Authentication & No & No & No & No & Yes & Yes & Required \\ Specifically designed for NAN & No & No & No & No & Yes & Yes & Required \\ Nodes are not time synchronized & No & Yes & Yes & No & Yes & No & Required \\ Scalability & N/A & N/A & N/A & N/A & N/A & N/A & Required \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A comparison of the availability of features in security schemes.
## 3 Results  
Of the 599 samples, 80% were randomly selected for training (479 samples) and 20% for validation (120 samples). Figure 4a shows the performance of the neural network during training and validation. It is evident that the best performance, measured by the mean squared error (MSE), was achieved after 14 iterations (epochs) for training, with an error of 9.68 \(\times\) 10\({}^{-4}\), while for validation, the error was 1.023 \(\times\) 10\({}^{-3}\) in the 14 th iteration. The total training time was 3 s for the 14 iterations, as indicated in Table 1. Figure 4b displays the error histogram, represented by the difference between the desired output and the obtained output (\(\mathbf{Y_{des}}-\mathbf{Y_{ob}}\) 1) relative to zero error. It is notable that the error remained around zero for the analyzed data.  
Figure 5 illustrates the correlation between the desired output (\(x\)-axis) and the obtained output (\(y\)-axis). It is observed that, for training the network in Figure 5a with 80% of the samples, that is, 479 samples, the value of the coefficient of determination (R\({}^{2}\)) was 0.9994, indicating a strong correlation between the outputs obtained by the ANN and those desired by the CPF. To validate the model in Figure 5b, with 20% of the samples that were not used in training (120 samples), the R\({}^{2}\) value was 0.9993, demonstrating a strong correlation between the outputs (similarity between the outputs). A similar result was observed in Figure 5c for 100% of the samples (599 samples), with an R\({}^{2}\) of 0.9994. Based on these results, we can infer that the ANN was able to predict the total power losses (real and reactive) with a low error (MSE), both during training and, especially, during model validation, which are samples that were not part of the training.