esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Calculation of the System Reliability of Dam Slope Stability Considering Multiple Slip Pathways  
After obtaining the reliability index of each slip pathway of the dam slope \(\beta_{E}^{i}(i=1,\) 2, \(\ldots\), \(m)\), the system failure probability of the dam slope can be calculated based on a series system. In this paper, the Ditlevsen narrow bounds method [23] is used to determine the upper and lower bounds of the system failure probability \(P_{f^{\prime}}^{s}\) and its expression is as follows:  
\[P_{f}^{1}+\sum_{i=2}^{m}\max\left(P_{f}^{i}-\sum_{j=1}^{i-1}P_{f}^{i,j},0 \right)\leq P_{f}^{s}\leq\sum_{i=1}^{m}P_{f}^{i}-\sum_{i=2}^{m}\max\left(P_{ f}^{i,j}\right) \tag{23}\]  
where \(P_{f}^{i}\) is the failure probability of the i-th slip pathway, \(P_{f}^{i}=\Phi\left(-\beta^{i}\right)\) is the joint failure probabilities of the \(i\)-th and \(j\)-th slip pathways, and \(P_{f}^{i,j}=\Phi\left(-\beta_{E}^{i},-\beta_{E}^{j},\rho_{ij}\right)\), where \(\rho_{ij}\) is the correlation coefficient between the \(i\)-th and \(j\)-th slip pathways, which can be determined by the following formula:  
\[\rho_{ij}=\sum_{k=1}^{n}\alpha_{Ek}^{i}\alpha_{Ek}^{j} \tag{24}\]  
where \(\alpha_{Ek}^{i}\) and \(\alpha_{Ek}^{j}\) are the coefficients of the equivalent linear limit state equations for the slip pathway \(i\) and slip pathway \(j\).  
The reliability index of the dam slope stability system corresponding to the system failure probability \(P_{f}^{s}\) is as follows:  
\[\beta^{s}=-\Phi^{-1}\left(P_{f}^{s}\right) \tag{25}\]
## 3 Results and Discussion
### Build up Daughter Nuclides  
To estimate the amount of radioactivity vaporizing in the burning of the mantles, gamma emissions from the mantles were measured before and after burning, with results recorded in Figure 4; zero-hour represents the unburned mantle while the 1, 4, 168, 240, and 720 h temporal points represent the time delays between initial and post-burn measurements. Subsequent to the initial ignition and termination period, daughter nuclides fractional evaporation is observed, detected at the first-hour temporal point, the initial concentrations of \({}^{212}\)Pb (t\({}_{1/2}\) 10.6 h), \({}^{212}\)Bi (t\({}_{1/2}\) 1.01 h), and \({}^{208}\)Tl (t\({}_{1/2}\) 3.05 m) decreasing by 30%, 37% and 43%, respectively. At 168 h, the build-up of the daughter nuclides restores the initial concentrations, the residual ash retaining the greater thorium fraction. The result accord with previous studies of [PERSON] and [PERSON] (2016) and [PERSON] et al. (2011) and [PERSON] et al. (2017), each concluding that thorium is subject to minimal evaporation during burning.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Th-232 (Bq/Mantle)** & **Reference** \\ \hline
1 & 0.56–4.8 & [4]—Iran \\
2 & 2411 & [5]—US \\
3 & 247 & [6]—Netherland \\
4 & 750–1800 & [10]—Italy \\
5 & 248–893 & [12]—Kingdom of Saudi Arabia \\
6 & 483–2025 & [11]—Kingdom of Saudi Arabia \\
7 & 350–4560 & [17]—Kingdom of Saudi Arabia \\
8 & 1410 & [7]—Japan \\
9 & 1000 & [13]—Norway \\
10 & 500–4000 & [13]—Germany \\
11 & 795–1054 & [14]—Austria \\
12 & 1386–1963 & [16]—Spain \\
13 & **5.1–12,517** & **Present study—Malaysia** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Activity of radionuclide Th-232 in the gas mantle. Literature data and data from this study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{3}{c}{**Activity \(\pm\) 1 Sigma (Bq/Mantle)**} \\ \cline{2-4}  & **Ra-228** & **Th-228** & **Th-232 * \\ \hline \multicolumn{4}{c}{Pristine mantle} \\  & 13,252 \(\pm\) 1385 & 11,325 \(\pm\) 875 & 12,288 \(\pm\) 1130 \\  & 13,534 \(\pm\) 1411 & 11,959 \(\pm\) 1021 & 12,746 \(\pm\) 1216 \\ \multicolumn{4}{c}{Average} & 13,393 \(\pm\) 1398 & 11,642 \(\pm\) 948 & 12,517 \(\pm\) 1173 \\ \multicolumn{4}{c}{Mantle dissolved in 0.5 M HNO\({}_{3}\)} \\  & 11,853 \(\pm\) 751 & 10,646 \(\pm\) 506 & 11,249 \(\pm\) 62 \\  & 11,432 \(\pm\) 822 & 10,734 \(\pm\) 511 & 11,083 \(\pm\) 667 \\ \multicolumn{4}{c}{Average} & 11,642 \(\pm\) 787 & 10,690 \(\pm\) 499 & 11,166 \(\pm\) 643 \\ \hline \hline \end{tabular}  
* Activity of Th-232, averaged from Ra-228 and Th-228; Name of manufacturer and model not available on the mantle package.  
\end{table}
Table 3: Activity of radionuclides from the Th-232 series in mantle M5.
# Implementation Aspects in Regularized Structural Equation Models
## 3 Research Questions
### RQ2: Exact Optimization or Differentiable Approximation?  
In the second research question, RQ2, we compare exact optimization and approximate optimization approaches based on differentiable approximations for regularized SEMs. Previous work argued that the exact approach should be generally preferred. We thoroughly investigate whether this preference is justified. Notably, approximate optimization with differentiable optimization functions is easier to implement because general-purpose optimizers are widely available and provide reliable convergence guarantees if adequate starting values are used in the estimation.
# Recent Advances in Black Silicon Surface Modification for Enhanced Light Trapping in Photodetectors
## 2 Wet Methods for Black Silicon Fabrication
### Metal-Assisted Chemical Etching (MACE)  
The MACE is a highly inexpensive and efficient technique, utilizing various metal elements such as silver (Ag), copper (Cu), nickel (Ni), and aluminum (Al). The process involves submerging a meticulously cleaned and deoxidized sample in an etching solution, leading to the creation of metal nanoparticles (NPs) on the Si surface. This drives an improvement in the light-trapping performance and influences the sample's reflectivity. It is worth noting that each element has its distinct characteristics that impact the resulted BSi surface and structure. For instance, Ag ions affect the porosity of BSi, Cu offers better optical absorption properties, Ni can fabricate BSi surface in less process steps, while Al enables precise control over the morphology of the nanopores. The following paragraphs will discuss assisted chemical etching based upon Ag, Cu, Ni, and Al, with keen interest in their corresponding etching impact on the BSi surface and resultant optical response.
## 2 Materials and Methods
### Fibers and Data Acquisition
#### 2.1.3 Category 3: Pure Silica Fiber (RAD)  
The telecom industry has pursued single-mode fiber development for the purpose of low-loss signal communication in high-radiation environments. A readily available fiber design, described as radiation-resilient or radiation-hardened, employs a pure silica core (i.e., there are no dopants in the core). Category 3 FBGs employ the same Type II inscription method as Category 2 FBGs and with the pure silica fiber design provide a specimen made of a single material with no dopants and no photosensitivity requirements.
## 3 Results
To further highlight and compare the behavior of the four studied oils, the calculated slope of the linearity regions previously discussed were plotted as a function of the UV irradiation time. As depicted in Figure 7, AKO and JO samples displayed a similar behavior characterized by a slope increase proportional to the UV irradiation time. The trend for the EVOO sample showed, instead, an exponential behavior. While, finally, the SAO behavior was not curve fitted with any simple equation as the slope just doubled following UV irradiation, but it was independent of the exposure time. To summarize, by considering all the information acquired through EPR spectroscopy, the following considerations can be made regarding the four tested vegetable oils:  
* JO possesses the highest antioxidant properties as, in the absence of UV irradiation treatment, it has the highest calculated IT (50 min). Additionally, it also possesses the greatest oxidative stability, as following the maximum tested UV irradiation time (120 min) the IT value is reduced to only a third of the initial value (from 50 to 15 min) while the same value is 4-, 5-, and 5.7-fold reduced for the AKO, SAO, and EVOO samples, respectively; \begin{table}
\begin{tabular}{c c c c} \hline
**JO** & **Best-Fit Line Equation** & **R2** & **Linearity Time Range (min)** \\ \hline No UV & y = 1535x \(-\) 6234 & 0.9949 & 50–180 \\
30 min UV & y = 1715x \(-\) 38,985 & 0.9992 & 35–180 \\
60 min UV & y = 2227x \(-\) 87,839 & 0.9983 & 25–180 \\
120 min UV & y = 2940x + 28,732 & 0.9970 & 20–100 \\ \hline \end{tabular}
\end{table} 
Table 6: Best-fit line (R \(>\) 0.99) equation for the linear portion for the \(\rm H_{p/p}\) intensity against incubation time at 70 \({}^{\circ}\)C graphs obtained for SAO samples both not irradiated and UV irradiated for 30, 60, and 120 min. Figure 6: \(\rm H_{p/p}\) intensity against incubation time at 70 \({}^{\circ}\)C for JO samples. Means (n = 6) \(\pm\) SD. * AKO starts as less powerful but seems to be quite stable to oxidation overtime. However, it should be noted that following 120 min of UV irradiation, the thermal treatment caused the alteration of the lipid matrix thus compromising the physical and chemical stability of this oil and consequently of the hypothetical cosmetic product in which it should be inserted;
* EVOO possesses high antioxidant properties but low oxidative stability, as highlighted from the exponential behavior depicted in Figure 7;
* SAO is the least stable tested oil with respect to oxidation, as after just 30 min of UV irradiation the H\({}_{\text{p/p}}\) values determined are close the maximum obtained (120 min UV irradiation).
# Specific Modeling Issues on an Adaptive Winglet Skeleton
## 1 Introduction
### The Reference FE Model  
For the purpose of this research, focus is given to the passive structure of the winglet, shown in Figure 2, sized to withstand the worst-case loading conditions that it is expected to experience in normal operation. The structural layout, consisting of upper and lower CFRP panels, internal CFRP spans and ribs, is overall modeled by 10,593 2D elements, defining a reference finite element (FE) model [23] considered in this study to investigate specific modeling issues.  
Both the upper and lower skin panels consist of the same CFRP lay-up sequence represented by CQUAD4 and CTRIA3 elements in relation with laminate property entries (PCOMP) on the middle surface. All laminates are symmetric and are mainly composed of fabric layers. UD-plies in x direction are used for reinforcement. The material orientation is defined on the respective CQUAD4/CTIA3 entry and is aligned along the span. All the elements used in the FE model are linear. The thickness of the laminates of the skins, spars and ribs is between 2.7 and 3.6 mm. The final weight of the complete structure is below the preliminary design target of 50 kg.  
For the analysis of the laminates, a maximum/minimum strain criterion in 11- and 22-direction is used. Additionally, stress is also evaluated in 11-, 22- and 12-directions. Theply stresses and strains are read from the FEM results. The failure indexes for each ply were then calculated. The enveloped results were then elaborated to allow for computation of the minimum failure index.  
Figure 1: Morphing winglet concept with upper and lower control surfaces [24].
## 4 Discussion
### Melt Extrusion-Printed Products  
The experimental data in Figure 10A (factor level B3) showed that the PLA parts printed with the highest fill density exhibit the greatest tensile strengths. The fill density defines the amount of plastic used on the inside of the print. A higher fill density indicates that there is more plastic on the inside of the print, thus leading to a stronger object.  
The experimental data in Figure 10A (factor level E3) suggests that one can obtain 3D-printed PLAs of the greatest strength by utilizing the highest print speed. To gain mechanical strengths, the molecular chain entanglement among various strips is important. During the printing process, the polymeric melt begins to cool as soon as it is extruded from the nozzle. Increasing the print speed keeps the polymeric strips hot for a longer time. It is easier for a molecular chain to entangle at the interfaces at a higher temperature and gain in strength. Thus, the printed parts expressed higher mechanical strengths.  
The empirical data in Figure 10A suggest that one should use the lowest oven temperature (25 \({}^{\circ}\)C, factor level C1) to print parts of the greatest mechanical strengths. Keeping the oven temperature at a lower level provides a fast cooling environment for potential material quenching. Printed parts might thus exhibit higher tensile properties.  
The experimental data suggested the adoption of the highest nozzle temperature to print parts with the greatest mechanical strengths (factor level B3 in Figure 10A). A higher nozzle temperature keeps the materials hot for a longer time. It is easier for the material strips to merge during the printing process. The printed parts thus exhibited greater strength. Finally, again, due to the irregular fractures (Figure 11A), the effect of print orientation on melt extrusion-printed parts is thus calculated to be relatively insignificant.  
Figure 14: Differential scanning calorimetry (DSC) curves of PLA as purchased, after melt extrusion printing, and after solution extrusion printing.
# Tor Anonymous Traffic Identification Based on Parallelizing Dilated Convolutional Network
## 2 Related Work  
### Tor Website Fingerprinting Attack Architecture  
The user communicates with the target web page in two stages: the user installs the Tor browser client, accesses the directory server to obtain a list of available relay nodes, selects the three best relay nodes, establishes a link to the target server via these three Tor relay nodes and finally sends and receives packets over the communication link established between the user and the target web page. In theory, two approaches can be used to capture Tor traffic data during the above process. In the first approach, the attacker acts as a Tor ingress node, as only all Tor nodes are transmitting encrypted data and the node closest to the sender is able to read the original unencrypted data. In the second approach, the attacker acts as, for example, an ISP (Internet Service Provider) or an AS (Autonomous System) to capture the traffic between the sender and the Tor entry node. In practical terms, the first approach is infeasible because the Tor protocol randomly selects the Tor nodes to be assigned and it is difficult for a particular sender to connect directly to the attacker's node. The second approach is feasible, which fits our real-world scenario, and Figure 1 represents this way of the traffic capture approach.  
In this attack architecture, we assume that the attacker is passive, i.e., he can only access the communication link between the user and the Tor network entry node, intercepting the packet information between the client and the Tor network but cannot forward, modify or discard it. At the same time, there is no background traffic in the web page stream, excluding the effect of noisy traffic. The attacker can obtain the IP address of the client through the TLS traffic information on the client and server side, but not the URL of the sensitive web page directly. In this way, the attacker aims to identify the Tor anonymous traffic through the intercepted packet information. Furthermore, to simplify the scenario, we assume that the Tor user only views one web page at a time and does not make any changes to the content.
## 2 Composition of Resource Scheduling Module
### Task Planning Module  
Task planning module focuses on the \"task requests\" generated via the phased array radar. Task requests specify the dwell type--such as search, confirmation, or tracking--and include relevant execution parameters, such as the direction of the transmitted beam, transmission power, expected execution time, and time window, etc. When planning dwell tasks, it is necessary to consider the working requirements of the radar, the working environment, and the filtering results of the data processing outputs.
## 3 Results and Discussion
### Pore Size Distribution: Porosity Curves and Cumulative Pore-Volume Curves  
Two types of curves can be used to evaluate the pore size distribution, namely the porosity curve and the cumulative pore-volume curve.  
The first one represents, for each pore length, the volume of pores with a length equal to or less than it. The value of the pore volume can be expressed either in absolute terms (i.e., mm\({}^{3}\) of pores) or in relative terms, by dividing the total volume by the volume of the specimen. This last option is more interesting, as it allows comparison of porosities between specimens of different sizes.  
The second one represents, for each pore length, the percentage of the total pore volume that has a length equal to or less than this one.  
In both cases, as a previous step to the graphical representation of the data, we need to collect all the pores and order them from the shortest to the longest length.  
Figure 16: Neighborhood test. Comparison between real and theoretical histograms. From (**a–f**) specimens 1 to 6.  
Figure 17 illustrates that the specimens show different porosity values. Some specimens (such as CUB-06) show particularly high overall porosity values while others (such as CUB-02) have clearly lower values. In addition, the curve reveals that some specimens (such as CUB-01, CUB-04 and CUB-06) show some very large pores (more than 10 mm in the case of CUB-06).  
Figure 18 reveals that the specimens are divided into two groups. On the one hand, specimens CUB-02, CUB-03 and CUB-05 show a much steeper curve, which indicates that their porosity is smaller; that is, the percentage of small pores is clearly higher. On the other hand, the other three specimens show a smoother curve, which means that they have a higher percentage of larger pores.  
From the data of the cumulative pore-volume curve, it is possible to define the parameter \"maximum pore size\" which, by analogy with the maximum aggregate size widely  
Figure 17: Porosity curves.  
Figure 18: Cumulative pore-volume curves.  
used in concrete, is defined as that pore length related to 90% of the total pore volume (Table 3).  
It is interesting to note that specimens showing a lower maximum pore size also show a smaller pore concentration factor, i.e., they have more evenly distributed pores.  
The analysis of pore size distribution allows us to have an overall idea of the porosity of a specimen. This is especially interesting when we want to analyze the influence of components (fibers, fillers, etc.) or additives (air entraining agents, superplasticizers, etc.) on the concrete matrix. It should be noted that in many cases it is convenient to discard the larger pores (>approx. 5 mm), since in many cases they represent a high percentage of the porosity volume, but do not have a significant macroscopic influence.
# Automatic System for the Detection of Defects on Olive Fruits in an Oil Mill
## 2 Materials and Methods
### Image Processing  
Figure 3 shows the general block diagram designed for processing the infrared images acquired after the cleaning of the fruit. As can be observed, the process is structured in two main blocks: olive detection and defect detection. The average computation time of the complete algorithm was around 100 s for each image.  
Figure 3: Image processing block diagram.  
Figure 2: Images acquired on the conveyor after the cleaning process. (**a**) Infrared camera (Nano M2590-NIR). (**b**) Visible band camera (Nano C242).  
The olive detection block is composed of a preprocessing stage and two segmentation steps. From a global point of view, this block is based on the iterative application of the active contours technique [24] assisted with mathematical morphology operations. The type of images to be analysed render classical segmentation techniques inapplicable. This way, the applied techniques should be robust to the presence of noise and other spurious elements, and should be able to extract objects even when the boundary between them and the background is not clear.  
The active contour technique allows us to segment objects based on models that use a priori information about the shape of these objects. If the model is adequate, the presence of false positives or negatives is expected to be low. These models are the result of the preprocessing stage, where two type of masks are obtained: global and adaptative. The active contour segmentation is carried out in two steps, using a global mask for the first, and an adaptative masks for the latter. The mathematical morphologic operations are a required step to adjust the models between steps.  
The detection of the defects is performed individually, evaluating each of the objects or olive contours found in the previous stage. The intersection of the results obtained using the global and adaptative segmentation techniques allows to detect the defects of the olives, minimizing the false positives produced mainly by glows, changes in the intensity level of the olive borders or occlusions with other olives. The extracted regions of each olive are evaluated to decide in the classification stage whether they are considered as defective or not. The classification is performed using a decision tree that evaluates descriptors of the regions such as circularity, elongation and area.
## 2 Materials and Methods  
### Specim Design  
The specimens used during the experiments consist of two parts. The first part is a block of pure aluminum EN AW-1050A (BIKAR-Metalle GmbH, Bad Berleburg, Germany) with the dimensions \(60\times 40\times 10\) mm\({}^{3}\), which acts as the base material for the bead-on-plate welding during the experimental process. This size is chosen as it is big enough to diminish any boundary effects on the temperature propagation on the timescale of the performed measurements. Therefore, on the timescale of interest, the specimen can be treated as a semi-infinite body. Regarding the aluminum and the process laser system used, the intensity threshold for initializing a deep penetration welding process is \(I_{\text{key}}=5\times 10^{6}\) W/cm\({}^{2}\) according to [12]. The second part of the specimen comprises the refractory probe, which consists of a high-purity tantalum tube (Goodfellow GmbH, Hamburg, Germany) sealed at the front end with 20-\(\upmu\)m tantalum foil (Goodfellow GmbH, Hamburg, Germany) using micro deep-drawing, as depicted in Figure 2. With an outer diameter of \(d_{\text{out}}=1.05\) mm and an inner diameter of \(d_{\text{in}}=0.6\) mm, the size of the tube is chosen in order to minimize the disturbance to the melt pool system as much as possible while leaving enough space for the measuring spots of the pyrometers to fit inside completely. The foil is force-fit onto the tantalum tube, establishing only a mechanical connection between the two parts. A drill hole with a diameter slightly bigger than the outer diameter of the probe and with a depth corresponding to the length of the tube \(l_{\text{probe}}=8\) mm is drilled into the front side of the aluminum block and equipped with the tantalum tube with the sealed end facing the drill hole, as shown in Figure 3.  
This way, a refractory measuring channel is established, maintaining the accessibility of the tantalum foil by the pyrometer throughout the ongoing welding process while shielding it from the prerunning melt. Another important requirement is that the probe maintains its solid state at least during the relevant experimental timespan. The tantalum foil at the end of the probe acts as a temperature screen capable of displaying the temperature values that occur during the welding process in the regions of interest. The temperature of the weld material is projected onto the solid foil, which, according to the adapted temperature, radiates in the manner of a grey body. By maintaining its solid phase, it can be ensured that the emissivity of the tantalum foil does not change abruptly.  
Figure 1: Schematic of the process observation: (**a**) total view of the sample showing pyrometry and high-speed imaging methods during the welding process, and (**b**) close-up on the melt pool probing.