esa-sceva/satcom-chunk-collection huggingface dataset:
# Product Model Derivation from Feature Model and Formal Specification
## 3 Product Derivation Based on Feature Model and Specification Approach
### Product Model Derivation Approach
#### 3.3.1 Integration Ordering  
The main goal of integration ordering is obtaining the sequence of pairs to be integrated. It includes the configuration of the feature model and the ordering of the selected features. Features are selected according to the user's needs, and the integration ordering is generated based on the product configuration.  
In the feature model, basic features are composed by the superimposition of their tree structures. The main idea of ordering is that the more closely related features are integrated first. The bottom-level features of the model are treated as dispersed independent leaf nodes, and the closeness of the leaf node relationships is determined by the hierarchical structure of the model after product configuration. The feature model is deeply traversed to find all non-leaf nodes, and the leaf nodes are merged under the same parent node in pairs. The result is a merged leaf node. Then, the nearest neighbor search algorithm is used to find the nearest related leaf nodes, and the nearest related leaf nodes are merged until there is only one merged leaf node at the end.  
Algorithm 1 shows the pseudo-code of the above process. In the algorithm, the input variable configuration is the user's operation for product configuration, which is unknown. The user selects features through configuration. The _juniorNodeSets_ is a collection of all features in the feature model. For example, the leaf nodes A1, A2, B1, C1, and D in Figure 4 have a common root node, all the non-leaf nodes A, B, C, and P are found, and, when judging whether their children are leaf nodes or not, if they are, then merging all the leaf nodes under the same node, such as A1 and A2, the leaf nodes under B, C remain unchanged because there is only one leaf node. Then, the nearest nearby leaf nodes are found by the nearest neighbor search algorithm, merging them in turn, recursively, to obtain the final result as [A1, A2, D, B1, C1]. An arbitrary ordering sequence indicates the operation of integrating the result of every two features with the next feature. Each feature corresponds to a module in SOFL, so in this example, modules A1 and A2 are first integrated as a feature pair, denoted as \(m_{f_{A1}}\cdot m_{f_{A2}}\), then, we merge D with the results of A1 and A2 integration, and obtain \(m_{f_{A1}}\cdot m_{f_{A2}}\cdot m_{f_{D}}\); finally, the ordering is \(m_{f_{A1}}\cdot m_{f_{A2}}\cdot m_{f_{D}}\cdot m_{f_{B1}}\cdot m_{f_{C1}}\).
# New Method for Simultaneous Arsenic and Selenium Speciation Analysis in Seafood and Onion Samples
## 3 Materials and Methods  
### Reagents and Standards  
Ultrapure water (>18.2 M\(\Omega\)\(\cdot\)cm) obtained from a Milli-Q Direct 8 purification unit (Millipore, Burlington, MA, USA, Merck), was used to prepare all the solutions. Arsenic salts (sodium meta-arsenite (As[III], Sigma-Aldrich, St. Louis, MI, USA), sodium hydroxy arsenate heptahydrate (As[V], Sigma-Aldrich, St. Louis, MI, USA), sodium cacodylate trihydrate (DMA, Sigma-Aldrich, St. Louis, MI, USA), sodium methylarsenate (MMA, Supelco, Bellefonte, PA, USA), arsenobetaine (Sigma-Aldrich, St. Louis, MI, USA)), antimony salts (potassium antimony tartrate hydrate (Sb[III], Sigma-Aldrich, St. Louis, MI, USA), potassium hexahydracy antimonate(V) (Sb[V], Sigma-Aldrich, St. Louis, MI, USA)), chromium salts (potassium chromate (Cr[III] Sigma-Aldrich, St. Louis, MI, USA), ammonium dichromate (Cr[VI] Sigma-Aldrich, St. Louis, MI, USA)), and selenium salts (sodium selenite (Se[IV], Sigma-Aldrich), sodium selenate (Se[VI], Sigma-Aldrich, St. Louis, MI, USA), seleno-l-cystine (Sigma-Aldrich, St. Louis, MI, USA), and seleno-l-methionine (Sigma-Aldrich, St. Louis, MI, USA)) were used to prepare standard solutions. Ammonium nitrate (NH4 NO3), ammonium carbonate ((NH4)2 CO3), ammonium decarbonate (NH4 HCO3) (Sigma-Aldrich, St. Louis, MI, USA), and methanol (Merck, USA) were used to prepare the mobile phases. Ammonia or nitric acid (Sigma-Aldrich, St. Louis, MI, USA) was used to adjust the pH of the mobile phases. To avoid contamination, all glassware and storage bottles were kept in 10% (\(v/v\)) nitric acid for at least 48 h, rinsed three times with ultrapure water, and kept dry until use.
## 3 Results
### Optical Observation and SEM Analyses  
In Figure 6, some fracture zones of the sandwich specimens are shown. There is an evident delamination zone in fracture case of HHZ samples due to a low interfacial adhesion between the layers of damping material and composite.  
In Figures 7 and 8 are reported the pictures of a cross section of two sandwiches damped with Kraibon SUT and Kraibon HHZ, both untreated and untested. For each image at 110\(\times\) there is another magnitude in which the more interesting details are represented. The interface of Kraibon SUT appears continuous and well adherent with the faced layers, proving an excellent wettability. This feature could be one of the key reasons for the greater energy absorption of the composite damped with SUT. As a result of aging there are no substantial variations in the structure, which maintains excellent adhesion. In contrast, HHZ shows discontinuity and porosity within the damping layer. These defects have been found in the untreated sample and are therefore not attributed to treatment in temperature and humidity. These porosities are considered as preferential channels for water absorption.  
Figure 6: Breaking zones of sandwic with damping material after the impact test: Kraibon SUT (**a**) and Kraibon HHZ (**b**).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Impact Test—90 J** & **T300/EP** & **Sandwich** & **Sandwich** \\  & **ASTM D7136** & & **T300/EP + SUT** & **T300/EP + HHZ** \\ \hline Damping Layers & \(\Delta\)Peak force (6 m/s) [\%] & - & +125 & +118.8 \\ \cline{2-5} effect & \(\Delta\)Energy (6 m/s) [\%] & - & +10.9 & \(-\)25.1 \\ \hline Aging effect & \(\Delta\)Peak force (6 m/s) [\%] & +6.3 & \(-\)66.7 & \(-\)60.0 \\ \cline{2-5}  & \(\Delta\)Energy (6 m/s) [\%] & \(-\)56.3 & \(-\)52.9 & \(-\)55.5 \\ \hline Velocity effect & \(\mathrm{E_{6}/E_{1.5}}\) (Untreated) [\%] & - & \(-\)9.6 & \(-\)31.4 \\ \cline{2-5}  & \(\mathrm{E_{6}/E_{1.5}}\) (Aged) [\%] & - & \(-\)33.2 & \(-\)46.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Relative variation of the impact performance, considering the effects of: damping, aging, and velocity.
# Reducing Dynamic Power Consumption in Mixed-Critical Real-Time Systems
## 7 Discussion and Conclusions  
### Discussion  
An issue of the proposed work is practicality in terms of the probability of high-criticality mode. Recent work [35; 36] have considered the probability of execution times of tasks for mixed-criticality systems. In [37], they introduced the probabilistic confidence of a task and a system and provided statistical scheduling algorithm. In [35; 36], probabilistic scheduling algorithms are analyzed for mixed-criticality real-time systems with a consideration of mode-switch probabilities.  
As shown in Figure 6a, the proposed work shows the similar performance in low-\(P_{HI}\) systems. When the probability of high-criticality mode is extremely low (e.g., \(10^{-8}\)), the effect of power reduction in high-criticality mode is negligible. However, the proposed work is still useful in terms of followings.  
* Although the probability of mode-switch of an individual task is low, the probability of the system mode-switch can be increased for a larger number of tasks. Let us assume that \(f_{i}\) is defined by the probability of the task's \(\tau_{i}\) mode-switch. Then, the probability of the system mode-switch of the task set \(T\) is derived by \(1-\Pi_{\tau_{i}\in T}(1-f_{i})\)[35]. Figure 11 shows the probability of the system mode-switch in terms of individual task's probability and the number of tasks (\(N\)). Let us note that the x-axis in Figure 11 is log-scale. In case of \(N=50\), the proposed work may affect the performance from the probability of task mode-switch of 0.002 because the proposed work shows performance gain where \(P_{HI}\geq 0.1\). On the contrary, when the number of tasks is higher (e.g., \(N=200\)), the probability of system mode-switch will become higher from lower task mode-switch probability (e.g., \(f_{i}=0.001\)). Thus, the proposed work will be useful depending on the number of tasks and task's mode-switch probability;  
Figure 11: The probability of mode switch w.r.t. task mode switch probability and the number of tasks.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Previous** & **Proposed** \\ \hline Frequency in HI-mode & Maximum & Optimized \\ Performance at low \(P_{HI}\) & Near optimal & Optimal \\ Performance at high \(P_{HI}\) & Good & Optimal \\ Frequency switch overhead & Moderate & High \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison summary.  
* The system mode-switch policy also affects the probability. In mixed-criticality systems, it is still an open issue on how long the system remains in high-criticality mode after the mode-switch occurrence. The proposed work performance is useful in mixed-criticality systems where the system should remain for a certain period after the mode-switch;
* Finally, the problem formulation with consideration of high-criticality mode is one contribution. Since the probability of mode-switch can be adjusted according to the system safety requirement, the proposed work will be useful when the system optimization is required in mixed-criticality systems.
## 5 Analysis of HFT Results
### Stress Shadow Influenced by Fracture Propagating Direction  
This time, the characteristics of the HFs generated according to the drilling direction of the horizontal well were examined. As shown in Figure 7, in the horizontal well drilled toward \(\sigma_{\rm{hmin}}\), HFs grew toward \(\sigma_{\rm{Hmax}}\); thus, fractures placed transverse (T-dir) to the well were created. When a fracture grows and the width increases, a SS is generated in the adjacent rock. These stresses locally increase the total stress and, as they are aggregated to \(\sigma_{\rm{hmin}}\), the change of \(\sigma_{\rm{Hmax}}/\sigma_{\rm{hmin}}\) anisotropy may induce the rotation of the fracture propagating direction. In this model, the SS, which was less than 0.3 MPa generated by T-dir fractures, was weighted on the minimum horizontal principal stress. However, the maximum principal stress in the formation was on average 13.8 MPa higher than the minimum, so it did not affect the propagating direction of the fractures. As a result, the facing distance between HFs remained the same as the distance between the initially designed fracture stages. On the other hand, the HFs of the horizontal well drilled toward \(\sigma_{\rm{Hmax}}\) in Figure 9 grew almost longitudinally as the fracture deviated obliquely from the horizontal well. Accordingly, the initially designed spacing between the stages was 76.5 m to 213.4 m, but the distance that the actual HFs faced decreased to 26%, resulting in closer spacing of 19.8 m to 55.2 m (Table 4). As shown in the right side of Figure 9, the 2 nd stage HF region, which locally faces the maximum fracture width (\(\sim\)2.2 cm) of the 1 st stage HF, was stressed by a maximum of 6.4 MPa due to the SS. Consequently, growth of the 2 nd stage HF was biased toward the non-SS area as shown in Figure 10, resulting in a relatively narrow proppant distribution in the fracture. Moreover, the width of the 2 nd stage fracture facing the 1 st stage fracture of yellow circle of SS area in Figure 10 was 0.03 cm or less, and the conductivity converged to 0 m\({}^{3}\), such that the weighted SS eventually caused the HFs to be generated only on one side of the horizontal well.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Unit** & **Actual Well Pattern** & **Proposed Well Pattern** & **Difference (\%)** \\ \hline HF area per unit amount of injected & m\({}^{-1}\) & 86.0 & 84.3 & 2.0 \\ fracturing fluid & cm & 1.26 & 1.30 & 3.2 \\ \hline Average conductivity & m\({}^{3}\) & \(2.283\times 10^{-13}\) & \(2.285\times 10^{-13}\) & 0.0 \\ \hline Amount of proppant per unit area of & kg/m\({}^{2}\) & 2.56 & 2.71 & 5.9 \\ the fracture & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of hydraulic fracture (HFs) generation.  
Figure 9: HFT results of horizontal wells drilled toward \(\sigma_{\rm{Hmax}}\)-dir.
## 3 Results
### Effects of CIAR on Th2 Cytokine Production in OVA Induced Mouse Nasal Fluid  
Inhibition of Th-2 cytokine levels was further investigated in mice nasal fluid (Figure 3). Exposure to OVA caused an elevation in the levels of nasal Th-2 signaling molecules. However, the administration of CIAR in incremental doses significantly reduced the production of nasal Th-2 cytokine-related signaling molecules. In concordance with the results obtained in serum analysis, the CIAR-M and CIAR-H groups demonstrated a higher degree of suppression of the inflammatory effects induced by OVA (\(p<0.05\)).  
Figure 2: Effects of Th-2 related signal level of CIAR on blood of mice with ovalbumin-induced allergic rhinitis. Effect on (**A**) IL-4, (**B**) IgE, (**C**) TSLP, (**D**) IL-33, (**E**) IL-25, (**F**) TNF, and (**G**) IL-5 levels in serum samples. Data are shown as the mean \(\pm\) SE. Significant differences are indicated as \({}^{*}\)\(p<0.05\) and \({}^{**}\)\(p<0.01\), compared to the OVA group. NT—normal mice; OVA—OVA-exposed mice; DEX—dexamethasone-treated OVA group (1 mg/kg/day); CIAR-L—low-dose-administered OVA group (150 mg/kg/day); CIAR-M—middle-dose-administered OVA group (300 mg/kg/day); CIAR-H—high-dose-administered OVA group (450 mg/kg/day).  
CIAR Ameliorates Nasal Respiratory Epithelial Thickness Damaged by OVA Inhalation in Mice with Allergic Rhinitis  
Nasal epithelial tissue thickness was assessed by histological examination. Representative immunohistochemical images of mouse nasal respiratory epithelium are shown in Figure 4 and Table 1. The increase in respiratory epithelium thickness from 19.65 \(\pm\) 3.65 to 53.25 \(\pm\) 1.96 \(\upmu\)m was observed post exposure to inhaled OVA (_p_ \(<\) 0.05). Administration of CIAR at doses of 150 mg/kg, 300 mg/kg, and 450 mg/kg resulted in a reduction of the respiratory epithelial thickness to 36.77 \(\pm\) 2.25, 26.28 \(\pm\) 3.09, and 22.72 \(\pm\) 1.23 \(\upmu\)m, respectively (_p_ \(<\) 0.05).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Group** & **NT** & **OVA** & **Dex** & **CIAL-L** & **CIAL-M** & **CIAR-H** \\ \hline Epithelium & 19.64 \(\pm\) & 53.26 \(\pm\) & 25.17 \(\pm\) & 36.77 \(\pm\) & 26.28 \(\pm\) & 22.72 \(\pm\) \\ thickness & 3.65 & 1.96 & 2.99 ** & 2.25 * & 3.09 ** & 1.23 ** \\ \hline \end{tabular} The data are presented as the mean \(\pm\) SEM. * \(p\) \(<\) 0.05, **_p_ \(<\) 0.01 versus OVA group.  
\end{table}
Table 1: The values for the thickness of the respiratory epithelium in OVA-induced mice.  
Figure 3: Effects of Th-2 related signal level of CIAR on nasal fluid of mice with ovalbumin-induced allergic rhinitis. Effect on (**A**) IL-4, (**B**) IgE, (**C**) TSLP, (**D**) IL-33, (**E**) IL-25, and (**F**) TNF levels in nasal fluid samples. Data are shown as the mean \(\pm\) SE. Significant differences are indicated as * \(p\) \(<\) 0.05 and **_p_ \(<\) 0.01, compared to the OVA group. NT—normal mice; OVA—OVA-exposed mice; DEX—dexamethasone-treated OVA group (1 mg/kg/day); CIAR-L—low-dose-administered OVA group (150 mg/kg/day); CIAR-M—middle-dose-administered OVA group (300 mg/kg/day); CIAR-H—high-dose-administered OVA group (450 mg/kg/day).
# Secure and Time-Aware Communication of Wireless Sensors Monitoring Overhead Transmission Lines
## 4 Transmission Grid Model in Quality of Protection Modeling Language
### General Information  
The structures used in the QoP-ML represent high level of abstraction which allows concentrating on the quality of protection analysis. The QoP-ML consists of processes, functions, message channels, variables, and Quality of Protection (QoP) metrics. Processes are global objects grouped into the main process, which represents the single computer (host). The process specifies behavior, functions represent a single operation or a group of operations, channels outline the environment in which the process is executed. The QoP metrics define the influence of functions and channels on the quality of protection. In [17] the syntax, semantics and algorithms of the QoP-ML are presented in greater detail.
## 2 Experimental Program and Procedure
### Experimental Testing
#### 2.2.3 Bonding Test  
A slant shear test is adopted to determine the bond strength between fly ash-based geopolymer paste and OPC concrete. This test method was performed to evaluate the bond strength between old and new overlay concrete as described in ASTM C882 standard [22]. To prepare specimens for the slant shear test, a 50 mm \(\times\) 50 mm \(\times\) 125 mm prism mould was used, in which the first half part was filled by OPC concrete as a substrate, whereas the second half part was filled by fly ash-based geopolymer paste as a repair material as shown in Figure 2.  
The slant shear test samples were prepared using an OPC concrete with the mix proportion illustrated in Table 2. The OPC concrete mix was firstly prepared and poured into the prism moulds until half was filled (Figure 2). The samples were wrapped using a plastic film, then cured for 28 days at room temperature of \(24\pm 2\)\({}^{\circ}\)C and relative humidity of 75-85%. After 28 days of curing, the pre-prepared half part OPC concrete samples were used as a substrate for fly ash-based geopolymer paste, where the second half was filled by using FA-based geopolymer paste. The final samples were wrapped using a plastic film and, then cured at room temperature of \(24\pm 2\)\({}^{\circ}\)C and relative humidity of 75%-85% for 24 h. Then they were demoulded and wrapped with plastic film to prevent any moisture loss and left  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Components**} & \multirow{2}{*}{**OPC (kg m\({}^{-3}\))**} & \multicolumn{2}{c}{**Aggregates (kg m\({}^{-3}\))**} & \multirow{2}{*}{**Water (kg m\({}^{-3}\))**} & \multirow{2}{*}{**Properties of OPC**} \\ \cline{3-3} \cline{5-5}  & & **Fine** & & **Coarse** & \\ \hline Proportions & 410 & 648 & 1152 & 205 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mix proportion and strength of OPC.  
Figure 2: Schematic diagram presents; (a) slant shear test features, (b) prepared sample.  
at ambient temperature for 14 days before being tested. The purpose of this test was to evaluate the bonding strength between old OPC concrete substrate and FA-based geopolymer paste, including both the compressive strengths of the OPC substrate and the new overlay geopolymer paste.
## 3 Source Rocks Geochemical Characteristics and Distribution  
As mentioned above, the natural gas generation potential of the Permian source rocks is the key to achieving breakthrough in natural gas exploration. The Permian source rock mainly consists of the Jiamuhe Formation, the Fengcheng Formation, and the Lower Wuerhe Formation [10]. Since the distribution of hydrocarbon source rocks of the Jiamuhe Formation are relatively limited and the quality of source rocks of the Lower Wuerhe Formation are poor, only the geochemical characteristics of source rocks in the Fengcheng Formation are discussed. The geochemical features of source rocks include organic matter abundance, type, and maturity.
# Forecasting the Number of Road Accidents in Polish Provinces Using Trend Models
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data/Model** & **Annual Data** & **Monthly Data** \\ \hline Exponential & y = \(5751.5\mathrm{e}^{-0.048\mathrm{x}}\) & y = \(3818.4\mathrm{e}^{-8E-06\mathrm{x}}\) \\ \cline{2-3}  & R\({}^{2}\) = \(0.7969\) & R\({}^{2}\) = \(0.0093\) \\ \hline Linear & y = \(-125.74\mathrm{x}+5453.3\) & y = \(-0.0187\mathrm{x}+3570.8\) \\ \cline{2-3}  & R\({}^{2}\) = \(0.8736\) & R\({}^{2}\) = \(0.0077\) \\ \hline Logarithmic & y = \(-856.4\mathrm{ln}(x)+5920.7\) & y = \(-816\mathrm{ln}(x)+11472\) \\ \cline{2-3}  & R\({}^{2}\) = \(0.7035\) & R\({}^{2}\) = \(0.0085\) \\ \hline Polynomial of 2 nd degree & y = \(-4.2552\mathrm{x}^{2}-32.128\mathrm{x}+5094.5\) & y = \(4E\cdot 05\mathrm{x}^{2}-3.0715\mathrm{x}+67295\) \\ \cline{2-3}  & R\({}^{2}\) = \(0.9027\) & R\({}^{2}\) = \(0.0669\) \\ \hline Polynomial of 3 rd degree & y = \(-0.9622\mathrm{x}^{3}+27.499\mathrm{x}^{2}-318.11\mathrm{x}+5678.7\) & y = \(-4E\cdot 09\mathrm{x}^{3}+0.0005\mathrm{x}^{2}-24.164\mathrm{x}+360727\) \\ \cline{2-3}  & R\({}^{2}\) = \(0.9441\) & R\({}^{2}\) = \(0.0683\) \\ \hline Polynomial of 4 th degree & y = \(-0.0701\mathrm{x}^{4}+2.1228\mathrm{x}^{3}-16.844\mathrm{x}^{2}-89.152\mathrm{x }+5374.6\) & y = \(-3E\cdot 11\mathrm{x}^{4}+5E\cdot 06\mathrm{x}^{3}-0.3065\mathrm{x}^{2}+8523.8\mathrm{x}-9E+07\) \\ \cline{2-3}  & R\({}^{2}\) = \(0.9501\) & R\({}^{2}\) = \(0.2084\) \\ \hline Polynomial of 5 th degree & y = \(-0.0057\mathrm{x}^{5}+0.2409\mathrm{x}^{4}-4.0384\mathrm{x}^{3}+35.93\mathrm{x }^{2}-\) & y = \(2E\cdot 14\mathrm{x}^{5}-4E\cdot 09\mathrm{x}^{4}+0.0003\mathrm{x}^{3}-12.543\mathrm{x}^{2}+ 263934\mathrm{x}\) \\ \cline{2-3}  & 271.46\(\mathrm{x}+5551.8\) & \(-2E+09\) \\ \cline{2-3}  & R\({}^{2}\) = \(0.9511\) & R\({}^{2}\) = \(0.2952\) \\ \hline Polynomial of 6 th degree & y = \(0.0014\mathrm{x}^{6}-0.1005\mathrm{x}^{5}+2.6393\mathrm{x}^{4}-33.023\mathrm{x }^{3}+\) & y = \(1E\cdot 17\mathrm{x}^{6}-3E\cdot 12\mathrm{x}^{5}+3E\cdot 07\mathrm{x}^{4}-0.0176\mathrm{x}^{3}+ 547.22\mathrm{x}^{2}\) \\ \cline{2-3}  & 205.98\(\mathrm{x}^{2}-703.03\mathrm{x}+5883.4\) & \(-9E+06\mathrm{x}+6E+10\) \\ \hline Potentiometric & y = \(6386.4\mathrm{x}^{-0.219}\) & y = \(96700\mathrm{x}^{-0.334}\) \\ \cline{2-3}  & R\({}^{2}\) = \(0.5906\) & R\({}^{2}\) = \(0.0101\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Trend models for the Lesser Poland province.