esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effects of Plasma-Activated Water on Heavy Metals Accumulation in Water Spinach
## 2 Materials and Methods
### The Plasma Device and Parameters  
The non-thermal atmospheric plasma generator was designed and produced by the Aerothermal and Plasma Physics Lab (APPL), Department of Mechanical Engineering, National Chiao Tung University (Hsinchu, Taiwan). This system included a high-voltage power supply, an air pump, and an atmospheric pressure plasma jet (APPJ) (patent US10,121,638B1) generator [17]. This system was specifically designed to generate plasma beneath the water surface. Air was pumped into the electrode by the air pump under a regular atmosphere at a flow rate of 10 standard liters per minute (slm). The input air was ionized by the high-voltage electrode to generate plasma, which was then delivered through a quartz capillary into the water. Reverse osmotic (RO) water was used as the water source. The input power and frequency for the power supply were AV 110 V and 60 Hz, respectively. The output voltage, frequency, and power were set at 3.0 kV, 16 kHz, and 60 Watts, respectively.
# Effects of Cement-Mineral Filler on Asphalt Mixture Performance under Different Aging Procedures
## 3 Results and Discussion
### Mechanical Property  
Asphalt pavement structures are subjected to driving load, while asphalt mixtures experience the alternating action of pressure and tension. In an engineer's calculation of a pavement structure, the tensile fatigue failure of the asphalt surface layer is an ultimate failure state. Therefore, analysis of the mechanical properties of asphalt mixtures must be based on their tensile properties. In this study, the effects of different cement-mineral filler systems on the mechanical properties of asphalt mixtures were analyzed using two indices: bending stiffness modulus in the low-temperature bending test and indirect tensile strength in the freeze-thaw splitting test.  
Figure 5(c) shows that the proportional increase of cement content with the aging level will increase the bending stiffness modulus of the mixture. For the long-term aging test group, when the ratio of M\({}_{\text{PC}}\)/M\({}_{\text{MP}}\) is higher than 2:2, the bending stiffness modulus of the specimen will increase sharply and  
Figure 6: Results of the freeze–thaw splitting test of asphalt mixture under different aging conditions.  
directly enter the brittle failure state. Figure 6c shows that the splitting tensile strength of the specimens before and after freezing-thawing treatment reaches the maximum within the range of \(\mathrm{M_{PC}}\)/\(\mathrm{M_{MP}}\) from 1:3 to 2:2, and the best resistance is obtained. Thus, for the asphalt mixture with AC-20 gradation and SBS modified asphalt, or another asphalt mixture with similar components and composition, it is suggested that the content of cement not exceed 50% when substituted for mineral powder.
## 2 Proposed Methodology
### Feature Extraction Module Based on the Gated Linear Unit  
In general, spectral data are characterized by high dimensionality, complexity, correlation, and redundancy. Therefore, mapping different spectral data into their respective nonlinear feature-embedding subspaces and adjusting them to the same dimensionality not only improves the modelling ability of the model for non-aligned sequences, but also learns the intra-modal abstract features in the spectral data to remove the noise and redundancies in the original data and retain the information useful for the task, and standardizes the inputs to the feature fusion module. The specific design of the feature extraction module is shown in Figure 1.  
The model receives input from three different types of spectral data: the spectral reflectance data of the stained veneer surfaces \(S_{r}\in R^{dr}\), the spectral characteristic parameters of the stained veneer surfaces \(X_{p}\in R^{dp}\), and the spectral similarity metric parameters between stained and unstained veneers \(X_{m}\in R^{dm}\), where \(dr\) denotes the number of dimensions of each \(X_{r}\) data type. The feature extraction is performed via a gated linear unit, as described in Equation (5). The method employs a discard technique, a gating mechanism, residual connectivity, and a four-layer fully connected neural network, which enables the model to more accurately capture key features in the data and, to some extent, alleviate the gradient vanishing problem, ensuring the initial training phase's rapid convergence and stability.  
\[Z_{n}=Gated\ Linear\ Unit(X_{n}),n\in\{s,p,m\} \tag{5}\]
## 4 Methodology  
### Project Workflow  
The overall workflow for the production of the VR experience of the Kaiserdom is schematically represented in Figure 2. Special focus was given to achieving a realistic 1:1 representation of the cathedral, including the integration of panoramic photos in the VR experience (see Section 4.6). The project was divided into five major phases of development (Figure 2): (1a) data acquisition by terrestrial laser scanning with one Riegl VZ-400 scanner (outside) and two Zoller + Frohlich IMAGER 5006 scanners (inside), (1b) registration and geo-referencing of scans using RiScan Pro and LaserControl, (1c) segmentation of point clouds into object tiles, (2a) 3D solid modelling with AutoCAD using segmented point clouds, (2b) generation of panoramic images using PTGui, (3) texture mapping of polygon models using Autodesk Maya and Substance Painter, (4a) placement of meshes and building the scene within the UE4 game engine, (4b) integration of motion control and interactions in UE4, (4c) integration of 360\({}^{\circ}\) panoramic imagery, and (5) immersive and interactive visualisation of the cathedral in the VR system HTC Vive Pro using Steam VR 2.0 as an interface between the game engine and the Head Mounted Display (HMD).  
Figure 1: Panoramic view of the Imperial Cathedral in Königslutter, Germany (**top**), the hunting frieze on the external wall of the main apsis of the cathedral with story-telling figures (**centre**), and a panoramic view of the interior of the cathedral (**bottom**).
# Experimental Study on the Pre-Peak Mechanical and Seepage Characteristics of Granite
## 3 Analysis of Test Results
### Analysis of Seepage Evolution
By observing Figure 9, it can be concluded that when the progressive failure process of the rock samples was in stage I, with the increase in the axial pressure, the permeability of the granite as a whole showed a decreasing trend. The cracks gradually closed at this stage, and the seepage channel slowly shrank. When the axial stress crossed the crack closure threshold (\(\sigma_{cc}\)) and entered the linear elasticity stage (II), the closure of the internal pore cracks in the rock was complete, and the seepage channels were barren, leading to further attenuation of the permeability of the rock samples. It can be observed in Figure 9 that when the axial stress crossed the crack initiation threshold (\(\sigma_{ci}\)) and entered into the stable crack expansion stage (III) and the unstable crack expansion stage (IV), the permeability of the granite rose. This indicates that secondary crack expansion was occurring within the sample and that the width of the seepage channels was expanding and increasing in number, resulting in the gradual recovery of the permeability of the granite. According to Figure 9, it can be found that the limitation of the permeability of rock samples by the pressure was quite critical. [PERSON] et al. [38] suggested that this is because the confining pressure acts to consolidate the pore and fissure pressures within the rock sample.
# DAG Hierarchical Schedulability Analysis for Avionics Hypervisor in Multicore Processors
## 4 Schedulability Analysis
### Scheduling
#### 4.1.2 The CP Priority Execution (CPPE)  
The CP is regarded as a series of capability parents. It can be safely said that every full path could be regarded as the parents, which provides the time interval of its path length for other joints to run in parallel. However, since the CP offers the maximum capacity, the maximum total parallel workload can be achieved. It offers the basis for minimizing the disturbing workload on the whole CP.  
For a schedule \(S\) with CPPE and a schedule \(S^{\prime}\) that prioritizes a random complete path over the CP, the total parallel workload of parents in \(S\) is always equivalent to or higher than that of \(S^{\prime}\), \(\alpha\geq\alpha^{\prime}\).  
First, suppose the length of parent \(\delta_{i}^{*}\) is shortened by \(\Delta\) after the change from \(S\) to \(S^{\prime}\). The same reduction applies on its finish time, i.e., \(h^{\prime}(\delta_{i}^{*})=h(\delta_{i}^{*})-\Delta\). Because nodes in \(\delta_{i}^{*}\) are shortened, the finish time \(h(v_{j})\) of a child node \(v_{j}\in H(\delta_{i}^{*})\cup G(\delta_{i}^{*})\) can also be reduced by a value from \(\Delta/m\) (i.e., a reduction on \(v_{j}\)'s interference, if all the shortened nodes in \(\delta_{i}^{*}\) belong to \(C(v_{j})\)) to \(\Delta\) (if all such nodes belong to \(\mathit{predecessor}(v_{j})\)). By Definition 1, a child \(v_{j}\in H(\delta_{i}^{*})\cup G(\delta_{i}^{*})\) can contribute to the \(\alpha_{i}\) if \(h(v_{j})\leq h(\delta_{i}^{*})\) or \(h(v_{j})-C_{j}\leq h(\delta_{i}^{*})\). Therefore, \(\alpha_{i}\) cannot increase in \(S^{\prime}\), as the reduction on \(h(\delta_{i}^{*})\) (i.e., \(\Delta\)) is always equal or higher than that of \(h(v_{j})\) (i.e., \(\Delta/m\) or \(\Delta\)).  
Second, let \(L\) and \(L^{\prime}\) denote the length of the parent path under \(S\) and \(S^{\prime}\) (with \(L\geq L^{\prime}\)), respectively. The time for non-critical nodes to execute in parallel with the parent path is \(L^{\prime}\) on each of \(m-1\) cores under \(S^{\prime}\). Thus, a child path with its length increased from \(L^{\prime}\) to \(L\) directly leads to an increase of \((L-L^{\prime})\) in the interfering workload, as at most \(L^{\prime}\) in the child can execute in parallel with the parent.  
Therefore, both effects cannot increase the parallel workload after the change from \(S\) to \(S^{\prime}\), and hence, \(\alpha\geq\alpha^{\prime}\).  
\(\forall v_{j}\in\Phi^{*},\forall v_{k}\in\Phi\to q_{j}<q_{k}\).
## 2 Materials and Methods
### Ros  
Human PBMC were isolated as previously described and cultured in tissue culture treated 24 well plates. After 24 h, the non-adherent cell fraction (lymphocytes) was dis-carded by the cell culture medium removal, and the adherent cell fraction (human macrophages) was kept. Murine macrophages were isolated from mouse peritoneal cavity. Briefly, after cervical dislocation, 5 mL of ice cold DMEM-F12 culture media was injected into the peritoneal cavity. After this, the peritoneal liquid was aspirated with a 5 mL sterile syringe. The recovered liquid was centrifuged at 400\(\times\)\(g\) for 10 min. Peritoneal cells present in the pellet were counted and their viability was evaluated by the Trypan blue exclusion technique. Then, human macrophages and murine peritoneal macrophages were grown in 24-well plates (1 \(\times\) 10\({}^{5}\) cells/well) for 24 h in DMEM F-12 supplemented with 10% FBS. After this, the cell culture medium was replaced with serum-free DMEM F-12, and the cells were incubated for 6 h. Then, cells were treated simultaneously with the wet or dry bDLE and LPS, 100 ng/mL for human macrophages, and 2 mg/mL for murine peritoneal macrophages, and incubated for 6 h. Next, cells were stained with 100 uL of ROS/Superoxide detection reagent for 1 h (ROS/superoxide detection kit ENZ-51010, Enzo Life Sciences, Lausen, Switzerland). Fluorescence was measured in a multi-detection reader (Bio-Tek Instruments Inc., Winooski, VT, USA) at the excitation and emission wavelengths of 488 nm and 520 nm, respectively, according to manufacturer instructions. For the assay, negative control cells were treated with the antioxidant N-acetylcysteine (NAC) at 5 mM for 60 min; positive control cells were treated with pyocyanin at 400 uM for 20 min.
## 1 Introduction  
Historically, zeolites have been defined as crystalline porous aluminosilicates, consisting of tetrahedra in which Si\({}^{4+}\) or Al\({}^{3+}\) cations are found inside and O\({}^{2-}\) anions are placed at their vertices [1, 2]. Owing to the increasing knowledge of the zeolitic structure, the current definition has evolved to be, crystalline porous materials for which the structures are formed by tetrahedra enclosing a variety of cations in the interior and with O\({}^{2-}\) anions at the tetrahedral vertices [3].  
HPM-1 is a pure Si chiral STW zeolite with helical pores [4]. Its tridimensional channel system can be decomposed into double 4-ring (D4R) units and [4\({}^{6}\)5\({}^{8}\)8\({}^{2}\)10\({}^{2}\)] cavities, forming 10-ring helical channels connected by straight 8-ring pores [5, 6]. This structure is shown in Figure 1. The zeolite was obtained from the organic structure of the directing agent 2E134 TIM (2-ethyl-1,3,4-trimethylimidazolium). These chiral zeolites have become a focus of study for the last few years [7, 8]. The importance of synthesizing these structures arises from their application in the petroleum industry and from their use in obtaining chirally pure drugs.  
Recently, the incorporation of aluminum (Si/Al = 107) in its non-calcined structure was reported, and the obtained structure was named high silica HPM-1 [5]. This material already shows catalytic properties in the isobutene isomerization, which demonstrates its high selectivity. The germanoaluminosilicate was also synthesized by [PERSON] et al. [11] who achieved enantiomeric enrichment. However, a literature search revealed that the calcined aluminosilicate has not been previously described, as only the characterizations of the as-synthesized samples were presented. However, there is interest for understanding its application in catalysis.  
Ethanol dehydration is used as a model reaction to confirm the existence of acidic sites [12]. At low reaction temperatures, the main products are ethylene and diethyl ether [13]. The generation of the former product is due to an endothermic reaction (Equation (1)), which is favored thermodynamically at moderate temperatures [14; 15].  
\[\mathrm{C_{2}H_{5}OH}\rightarrow\mathrm{C_{2}H_{4}}+\mathrm{H_{2}O}\quad \Delta H(298\;\mathrm{K})=+44.9\;\mathrm{kJ/mol} \tag{1}\]  
The second reaction (Equation (2)) is an exothermic reaction resulting in competition between the two products.  
\[2\mathrm{C_{2}H_{5}OH}\rightarrow\mathrm{C_{2}H_{5}OC_{2}H_{5}}+\mathrm{H_{2}O }\qquad\Delta H(298\;\mathrm{K})=-25.1\;\mathrm{kJ/mol} \tag{2}\]  
As previously mentioned, HPM-1 was obtained as a pure aluminosilicate by the traditional methods of heteroatom introduction in addition to the incorporation of a low quantity of aluminum prior to calcination. Therefore, further study is necessary to understand the behavior of this structure to facilitate the incorporation of different heteroatoms to optimize the synthesis process. For this purpose, the present work aims to study an alternative strategy of Al incorporation and is based on [PERSON] et al.'s published work on the addition of Al into the magadite framework [16].
## 4 Case Study
### Analysis
#### 4.2.1 Matrices Constructed Prior to _Step 7_  
Matrix HH captures the cause-and-effect linkages among the identified hazards. For instance, PCS inverter failure may lead to battery overcharging which would subsequently cause the battery's temperature to rise beyond the safe range; then, if thermal fuses fails, then thermal runaway might happen and therefore leads to battery ignition. Nonetheless, this matrix can also be converted into fault trees if preferred.  
Matrix CH captures which components are the direct/immediate causes of the hazards. For instance, C3.2 (PCS inverter) is directly responsible for H3 (Battery overcharged because it is the component that realizes (or actuates) charging/discharging even though in reality it is EMU who inappropriately commands PCS controller (which then commands PCS inverter) to continue charging the battery. Additionally, C4.2 (BMS Battery Pack) is immediately responsible for H1, H2, and H3 simply because it is the component where these hazards manifest themselves. In other words, this matrix indicates which component should be investigated first in case of hazard.  
Matrix CC captures all necessary interactions between the system components. It can be useful to point out: firstly, which components are immediately affected in case of component malfunction/compromised/removal; for instance, in case of C3.2 malfunction/removal, directly affected components are C3.3 and C4.2 as their inputs are provided by C3.2; and secondly, which components could be directly causing a component to malfunction; for example, in case of C4.1 malfunction, immediate suspects would be C2 and 4.2 as they provide inputs to C4.1.  
Matrix SC captures which components are the intended providers, receivers, or transmission medium of the necessary signals. In case of observing a signal being inappropriate, we can find out which components are its receivers, sources, or transmission mediums, and thereafter conduct checks on them if necessary. Depending on the system architecture, there could be multiple providers, receivers, or transmission mediums for the same signal.  
Matrix RC captures which requirements might be violated in case of component malfunction/compromised. The purpose of this matrix is identical to that of [23]. The criticality of a component may be determined according to the number/criticality of requirements that may be violated due to its malfunction. The criticality of the components/requirements is a major factor in the mitigation strategy.
## 3 Finite Element Modeling
### Material Model and Dimension of the Model  
The foam concrete was simulated using the CDP model available in ABAQUS. The uniaxial stress-strain relationship obtained from the tests was input into the CDP model in ABAQUS. It was validated through the test results that the CDP model can efficiently model the cracking behaviour of the foam concrete and it is easy to converge under moth monotonic and cyclical loads. In the tension zone of the concrete, the strength of the concrete is taken as 1/15, presuming that the concrete stops working after cracking. The cracking strain of the concrete is therefore taken as the total strain minus the elastic strain, which is used for the strain after the cracking of the foam concrete. The stress-strain relationship curve of the foam concrete [14; 15] and the steel components used in simulation are shown as follows:  
1. Stress-strain relationship curve of the foam concrete:  
\[y=\left\{\begin{array}{cc}-1.041x^{3}+1.019x^{2}+1.028x-0.017&(0\leq x\leq 1 )\\ 204x^{3}-674x^{2}+737.1x-266.1&(1\leq x\leq 1.2)\\ -0.005x^{3}+0.052x^{2}-0.211x+0.559&(x\geq 1.2)\end{array}\right.\]  
where  
\(x\)--is the ratio of the strain to the maximum strain;  
\(y\)--is the ratio of the stress to the compressive strength of foam concrete.  
2. Stress-strain relationship curve of the steel  
A perfect elastic-plastic stress-strain relationship, using [PERSON] yield criterion, is shown as follows:  
\[\sigma=\left\{\begin{array}{cc}E_{\mathrm{s}}\varepsilon&(-\varepsilon_{y} \leq\varepsilon\leq\varepsilon_{y})\\ f_{y}&(\varepsilon>\varepsilon_{y})\\ -f_{y}&(\varepsilon<-\varepsilon_{y})\end{array}\right.\]  
where  
\(\sigma\)--stress of the steel;  
\(\varepsilon\)--strain of the steel;  
\(E_{\mathrm{s}}\)--Young's modulus;  
\(f_{y}\)--yield stress;  
\(\varepsilon_{y}\)--yield strain is the limit value of strain.  
Taking slab1 as an example, the size of the composite panels is 3000 \(\times\) 1000 \(\times\) 150 mm, the size of the cold-formed thin-walled C steel channel is 100 \(\times\) 50 \(\times\) 20 mm, with wall thickness is 2 mm, and the spacing of cold-formed thin-walled C steel channels is 180 mm. The density of the foam concrete is 1200 kg/m\({}^{3}\) and the steel is Q235B. The concrete parameters of foam concrete are shown in Table 2.  
Figure 11: Mesh of the model. (**a**) Mesh of the foam concrete slab; (**b**) Mesh of cold-formed thin-walled C steel channels.
## 3 Methods  
### Multifractal Inverse Distance Weighted (MIDW)  
Kriging and inverse distance weighting (IDW) are two frequently used methods for constructing contours and surfaces to determine spatial variations and anomalies. However, one limitation of Kriging is that it tends to smooth the data due to its reliance on variograms and moving weighted averages [29]. As a result, it may not effectively capture outliers during interpolation. A common drawback of moving average interpolation techniques lies in their failure to consider the local properties of the data. In contrast, the Modified Inverse  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**No.** & **Elements** & **Detection Limit** & **No.** & **Elements** & **Detection Limit** & **No.** & **Elements** & **Detection Limit** \\ \hline
1 & Ag & 0.02 & 14 & La & 30 & 27 & U & 0.5 \\
2 & As & 1 & 15 & Li & 5 & 28 & V & 20 \\
3 & Au & 0.0003 & 16 & Mn & 30 & 29 & W & 0.5 \\
4 & B & 5 & 17 & Mo & 0.4 & 30 & Y & 5 \\
5 & Ba & 50 & 18 & Nb & 5 & 31 & Zn & 10 \\
6 & Be & 0.5 & 19 & Ni & 2 & 32 & Zr & 10 \\
7 & Bi & 0.1 & 20 & P & 100 & 33 & SiO\({}_{2}\) & 0.10\% \\
8 & Cd & 0.05 & 21 & Pb & 2 & 34 & Al\({}_{2}\)O\({}_{3}\) & 0.10\% \\
9 & Co & 1 & 22 & Sb & 0.1 & 35 & TFe\({}_{2}\)O\({}_{3}\) & 0.05\% \\
10 & Cr & 15 & 23 & Sn & 1 & 36 & MgO & 0.05\% \\
11 & Cu & 1 & 24 & Sr & 5 & 37 & CaO & 0.05\% \\
12 & F & 100 & 25 & Th & 4 & 38 & Na\({}_{2}\)O & 0.05\% \\
13 & Hg & 0.0005 & 26 & Ti & 100 & 39 & K\({}_{2}\)O & 0.05\% \\ \hline \end{tabular}
\end{table}
Table 1: The detection limits of 39 elements [28].  
Figure 2: Stream sediment sample location map of the western Gangdese.  
Distance Weighting (MIDW) method incorporates a local singularity into the basic model of moving average interpolation, considering spatial correlations such as Kriging [30]. Compared to the traditional interpolation methods, the MIDW method has two significant advantages: (1) it improves the accuracy of interpolation results, and (2) it retains the local structure of the interpolation surface. The MIDW method has been proven to be effective in both exploration and environmental geochemistry [27; 30; 31].
# Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning
## 2 Materials and Methods
### Construction of Spatialization Model for Yield  
By constructing a yield spatialization model, we aimed to depict productivity visually at the pixel level and reveal spatial variations in winter wheat yield across Henan Province. The model utilized the county-level averages of each feature variable as a bridge, linking the feature variables at the pixel level to the county-level predicted yields. This process allowed for the back-calculation of county-level winter wheat yield data to the pixel level. The yield spatialization model is represented by Equation (4):  
\[y_{pixel}=\frac{y_{pre}\times\sum_{j=1}^{N}\frac{Feature_{i}}{Feature_{mean}}}{N}, \tag{4}\]  
where \(N\) represents the number of modeling driver types, which is 8 in this study; \(Feature_{i}\) corresponds to the pixel value for each class of modeling driver factors; \(Feature_{mean}\) denotes the pixel average value for each class of modeling driver factors; \(y_{pre}\) is the predicted yield; and \(y_{pixel}\) is the yield for each pixel.