esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mechanisms of Action of Lipases  
Lipases, triacylglycerol ester hydrolases (EC 3.1.1.3), are serine hydrolases with an active site containing an amino-acid triad of serine, histidine and aspartate [32]. Lipases are obtained from a variety of sources such as animal and plant tissues and microorganisms. Lipases show a wide range of pH and temperature for activity and vary from strain to strain regarding specificity and hydrolysis rate [33] Lipases exhibit good stability in non-aqueous mediums and exhibit neutral pH range; such stability is increased when the enzyme is immobilized.  
Lipases can catalyze esterification, inter-esterification, and trans-esterification reactions in non-aqueous environments. Lipases catalyze the hydrolysis of triacylglycerols at the aqueous-non aqueous interface but these enzymes can also catalyze the synthesis of esters from alcohols and long chain fatty acids in low moisture environment [33]. Lipases follow a two-step mechanism for the generation of fatty acid methyl esters in transesterification reactions, usually through the Ping-Pong Bi Bi mechanism [34].  
Most triacylglycerol lipases are regiospecific because they can only hydrolyze primary ester bonds at the sn-1 and sn-3 positions, external positions within the triacylglycerol, and can generate either one free fatty acid and diacylglycerol, or two free fatty acids and 2-monoacylglyceroll that remain unhydrolyzed. The full process from triacylglycerols into biodiesel and glycerol as end products is shown in Figure 1. Regiospecificity is characteristic of extracellular bacterial lipases from _Bacillus_ sp. [35, 36].  
Monoacylglycerolglycerol lipases (EC 3.1.1.23) catalyze the hydrolysis at the specific sn-2 position of 2-monoacylglycerolglycerol into free fatty acid and glycerol. Such lipases may be present in the enzyme extract and masked when measuring activity with standard activity methods like those based on triolein hydrolysis measurement. Monoacylglycerolglycerol lipases have been the object of few studies [37], although they might be present in some microbial enzyme preparations [38]. Other lipases are nonspecific and can act on any of the ester bonds of the triacylglycerolglycerol and therefore break down the triacylglycerolcerol to release free fatty acids and glycerol as the final products. This is the case of lipases from _Staphylococcus aureus_ and _hyticus_[39], _Geotrichum candidum_, _Corynebacterium acnes_, _Penicillium cyclopium_[24] and _Chromobacterium viscosou_[40]. Another alternative for the hydrolysis of monoacylglycerolglycerols is the acyl migration in the glycerol backbone from the sn-2 position to sn-1 or sn-3 positions [41].  
The specificity of lipases depends on the length of fatty acids, presence of double bonds, branched groups and, consequently, reaction rates may have important variations depending on the composition of triacylglycerolglycerols present in the fat waste. Lipases are especially active against medium to long chain fatty acids, which are those more usual in animal fat waste [17].
## 2 Materials and Methods  
### Study Area  
Altai Territory is located in the south of Western Siberia (Figure 1a) and covers an area of about 168,000 km\({}^{2}\) with a varied relief, including plains (steppe and forest-steppe zones), foothills, and mountains. The region is characterized by a continental climate due to frequent changes in air masses coming from the Atlantic, Arctic, and Central Asia, which determine a large contrast in weather conditions [47; 48]. The long-term average annual temperatures range in the Altai Territory is from 1.18 to 3.76 \({}^{\circ}\)C [49]. In the summer months, maximum temperatures reach 42 \({}^{\circ}\)C. At the same time, rather low temperatures are formed in winter with an absolute minimum down to \(-\)52 \({}^{\circ}\)C [50]. During the cold period of the year (from October to March), the prevailing wind directions are southern and southwestern [51]. Precipitation in the Altai Territory is not uniform. On the flat part of the territory, the amount of precipitation varies from 240-250 mm in dry-steppe regions to 550-570 mm in forest-steppe regions and increases from 650 to 900 mm in the foothills from west to east [52]. Seasonal snow cover is established in the second decade of November and is destroyed in the first decade of April. The height of the snowpack in the Altai Territory averages 40-60 cm; in the western regions, it decreases to 20-30 cm [48]. The contribution of snow melting to the water runoff of the large Ob River, which originates in the Altai Territory and flows into the Kara Sea, is about 70-80% [53].  
In this work, we studied the snowpack and winter precipitation falling on the flat territory of the region, namely, within the catchment area of the Ob River from the city of Biysk to the city of Kamen-na-Obi (Figure 1b). According to the seasonal snow cover classification system [54], the snow cover of the studied area can belong to mixed of two phenomenological classes--the prairie and taiga. The studied area is characterized by a slight gradual decrease in atmospheric precipitation from the south-east to the northwest [55]. The largest city and capital of the Altai Territory, Barnaul, is located in the central part of the studied area. The average annual precipitation for Barnaul is 433 mm, of which \(\sim\)40% falls in the form of snow [56].
# Reduction of Pesticide Use in Fresh-Cut Salad Production through Artificial Intelligence
## 1 Introduction  
Due to the progressive increase of the world population, which, according to the Food and Agriculture Organization (FAO), will exceed 9 billion inhabitants in 2050 [1], the agricultural sector will have to satisfy an increasing demand for food. To ensure high productions, a crucial role will be played by pesticides.  
According to FAO, pesticides include any substance, or mixture of substances of chemical (natural or synthetic) or biological ingredients for repelling, destroying, or controlling any pest, or regulating plant growth [2]. Indeed, they can help to protect seeds and safeguard crops from unwanted plants, insects, bacteria, fungi and rodents. Pesticides include a wide range of herbicides, insecticides, fungicides, rodenticides, and nematicides.  
Usually, pesticide is a more general term that comprises plant protection products (PPPs) which are aimed to protect crops or desirable/useful plants. PPPs contain at least one active substance and have different functions such as to protect plants or plant productsagainst pests or diseases before or after harvest, to influence the life processes of the plant, to preserve plants products while destroying or to prevent the growth of undesired plants or parts of plants [3]. When pesticides are used irresponsibly, they might involve health risks, can have negative environmental impacts, on the soil and water, can reduce biodiversity, and, in some cases, decrease crop yield.  
The total pesticides used in agriculture remained stable in 2018 compared to 2017, with a light decrease from 4.15 Mt to 4.12 Mt, but in the last 30 years, from 1990 to 2018, the global use of pesticides in agriculture has increased from 1.80 to 2.66 kg/ha. The global application of pesticides increased in this period both for herbicides, fungicides, and insecticides [4].
## 2 A Brief Introduction to Laser-Scanning 3D Model Reconstruction Technology
### A Brief Description of the Laser-Scanning 3D Model Reconstruction Process
#### 2.2.3 Point Cloud Data Processing  
The substantial amount of raw point cloud data acquired from a 3D laser scanner inevitably contains noise, necessitating the denoising of the original point cloud data for nuclear facilities. Furthermore, the substantial amount of raw point cloud data introduces redundant information, resulting in an increased computational burden; therefore, it becomes imperative to simplify this voluminous point cloud data. Following the denoising and simplification processes, the point cloud data from various scan stations can be accurately registered into a unified coordinate system within the comprehensive spatial platform, which is a procedure commonly referred to as point cloud data registration. Most 3D laser scanners from different brands typically come with dedicated point cloud data processing software for data processing and management [32].  
Point cloud registration can rely on features points, features surfaces, and targets. To enhance the accuracy and efficiency of point cloud data processing, registration is commonly achieved through the alignment of target spheres [33; 34]. The registration process of point cloud data based on target spheres is shown in Figure 5, and the fundamental concept of point cloud registration using target spheres entails positioning three or more target spheres in the overlapping area of two scan stations during scanning, and matching the coordinates of the corresponding target spheres subsequently. The coordinate transformation relationship for two corresponding target spheres is represented by Equation (2). When the rotation matrix \(R\) (\(\lambda\), \(\omega\), \(\kappa\)) and translation matrix \(T\) (\(\Delta X\), \(\Delta Y\), \(\Delta Z\)) are calculated, this enables coordinate transformation and achieves the registration of point clouds obtained from adjacent scan stations [17].  
\[\left|\begin{array}{c}X\\ Y\\ Z\end{array}\right|=R\left|\begin{array}{c}x\\ y\\ z\end{array}\right|+T \tag{2}\]  
where (\(X\), \(Y\), \(Z\)) and (\(x\), \(y\), \(z\)) denote the coordinates of corresponding target spheres in two coordinate systems.  
The point cloud registration process typically begins with the initial step of conducting local registration of the point cloud data, which facilitates easy verification, adjustment, and modification until the accuracy requirements are satisfied. Subsequently, after completing local point cloud registration, it becomes possible to directly perform overall point cloud registration across numerous localized regions, thereby obtaining a comprehensive dataset of the nuclear facility point cloud.  
Figure 4: Layout diagram of target points and scanning stations.
# Chemical and Sensory Evaluation of Silicone and
###### Abstract
Bentonite fining does not remove MPs, and although fining with charcoal is moderately effective, it lacks the necessary selectivity [11]. Oak chips can partially mask ladybug taint without affecting MP concentrations [11], but are not suitable for many wine styles. [PERSON] et al. [17] reported that wines kept in contact with synthetic wine closures had lower levels of MPs compared to control wines. Synthetic closures are typically comprised of a combination of plastic polymers, and the MP reduction observed was putatively attributed to the sorptive properties of these polymers. Similarly, [PERSON] et al. [18] reported a decrease in MPs in wines produced from juices treated with silicone tubing, likely due to the sorptive properties of silicone. Therefore, sorption onto select, food-grade polymers may represent a general and promising strategy for managing juices and wines with high MP concentrations; however such an approach may also potentially lead to a reduction in beneficial volatile aroma compounds (VOCs). [PERSON] and [PERSON] [19] evaluated the ability of thirteen plastic polymers to reduce MP concentrations in wine, and found in these initial trials that silicone- and polylactic acid-based polymers showed the most promise, with reported reductions of IPMP and IBMP of up to 96% and 100% for silicone, and 53% and 36% for the polylactic acid-based polymer. However, the effects of these treatments on sensory quality and non-target volatile compounds were not reported. The current study seeks to assess the efficacy of silicone and polylactic acid polymers in remediating MP levels in red wine, with a focus on determining optimum surface areas. Importantly, we also measure the impact of these treatments on VOCs and the sensory properties of treated wines. The findings will establish the selectivity of these polymers, and assist in defining optimum conditions for their use in remediating wines with elevated MP concentrations.
## 2 Results and Discussion
### Limitations and Future Perspectives  
In the last part of this article, we would like to highlight some limitations of our research. The results of molecular docking on the inhibition of SpoT from \(H\). _pylori_ by selected phytochemical compounds have been confirmed in experiments with indirect methods. The inclusion of these methods was related to the supervisory function of SpoT in the survivability of \(H\). _pylori_, i.e., biofilm formation, morphological transformation, and antibiotic resistance. The capacity of dioscin to interfere with the above processes was demonstrated by reducing the amount of biofilm under stationary conditions (a crystal violet-based staining) and flow conditions (a microfluidic Biofilm system), low stimulation of coccoid formation (light and electron microscopy), and enhancement of the antibiotics' activity (checkerboard assays). To directly confirm the ability of dioscin to inhibit SpoT of \(H\). _pylori_, we should finally extend our research to the purified protein and isothermal titration calorimetry assays, which we intend to do in the future. In our opinion, this article may be an important voice in raising the scientific community's awareness to the potential of SpoT in promoting \(H\). _pylori_ survival. The first reports on SpoT of \(H\). _pylori_ were published relatively long ago--2006 [30; 31], but the continuation of research on this issue was averted for over a decade, and just in recent years has slowly reappeared [32; 33; 34]. We hope that the results presented in the current article, although preliminary, may help in the search for alternative therapies to combat the virulence of this pathogen.
## 3 Results
### Powder Characterization
#### 3.1.2 Morphology  
The gas atomized powders had a generally spherical morphology with some small satellite features as shown in Figures 3 and 4 below. Typically, the commercial vendor powders visually showed a slightly higher satellite content than the Ames produced powders and automated size distribution analysis results (not shown) revealed the Ames powders had a higher fine powder proportion present in the 45-106 \(\upmu\)m size cut.
# Numerical and Parametric Analysis for Enhancing Performances of Water Photovoltaic/Thermal System
## 3 Results and Discussion
### Model Validation  
The validation process included the model of the PV unit, mainly formed by the glass, PV cells, and Tedlar (Equations (15)-(17)). This model was tested under different operating conditions, e.g., solar irradiance and outdoor temperature. Furthermore, the results were compared with the data for Nominal Operating Cell Temperature conditions (NOCT: \(E_{sun}\) = 800 W/m\({}^{2}\), \(T_{a}\) = 20 \({}^{\circ}\)C and \(w\) = 1 m/s). In Figure 3, the PV cells temperature is evaluated for different solar irradiances of 600 W/m\({}^{2}\), 800 W/m\({}^{2}\), and 1000 W/m\({}^{2}\) with an ambient temperature of 20 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, and 35 \({}^{\circ}\)C, respectively. From this figure, the effect of environmental conditions on a PV temperature is clearly shown (a minimum PV temperature was noted at about 39 \({}^{\circ}\)C when solar radiation and ambient temperature were lower, about 600 W/m\({}^{2}\) and 20 \({}^{\circ}\)C). It can be also observed that the difference between PV temperature values and NOCT is quite negligible. According to the commercial PV datasheet characteristic summarized in Table 1, the NOCT value was 45 \(\pm\) 2 \({}^{\circ}\)C, while for the three layers model, the PV temperature was 43.96 \({}^{\circ}\)C, which proves the effectiveness of PV developed model. For the rest of the PV/T system (absorber 1, fluid and absorber 2), the developed Equations (18)-(20) were written based on the PV/T model validated experimentally in [24].  
Figure 3: Comparison of the developed PV model and NOCT model.
## 5 System Architecture for the Generation and Utilization of the 3D Indoor Routing Service  
The system architecture of the web application is divided into the server side and the client side (depicted in Figure 2). On the server side, a PostgreSQL database with PostGIS extension serves as a data container for the _IndoorOSM_ data. Therefore, the raw OSM data, which can be obtained from either the OSM web page or on several OSM data pages like Geofabrik [38], is imported by using the Open Source command line tool _OSMOSIS_[39]. Furthermore, the server side consists of the C\(++\)_pyrouting_ libraries. Pyrouting provides shortest route computation capabilities, whereas the computation is based on the wide-known Dijkstra algorithm [16]. Additionally, the system architecture consists of  
Figure 1: The basic hierarchical principle of a complete building (**a**) and the detailed floorplan information for an exemplary building floor/level (**b**) in _IndoorOSM_.  
some other algorithms for the automated creation of a XML3D/HTML webpage which visualizes the interior rooms of the desired buildings and additionally allows for the control of the visibility of different floors as well as the computation and visualization of routes. Those algorithms are based on Java. The different routes inside the desired building are pre-computed and stored as individual route tiles on the web server. On the client side, the web application can be visualized in any XML3D-capable web browsers. By using JavaScript and AJAX technology, it is possible to load and visualize the pre-computed route tiles. The server side of the developed application and the required algorithms are described in great detail in the following sub-sections, whereas the client application (from a user's perspective) is elaborated in Section 6.
# Performance Evaluation of GPS Auto-Surveying Techniques
## 2 Materials and Methods
### Position Determination
#### 2.3.2 Observations Model  
In the EKF, the non-linear navigation equations are linearized in order to construct the observations model. The observations equation is given by  
\[\mathbf{z_{k}}=\mathbf{h}[\mathbf{x}(t_{k})]+\mathbf{v}_{k} \tag{17}\]  
where \(\mathbf{z_{k}}\) is the measured pseudorange vector with \(n\geq 4\) observations, \(\mathbf{v}_{k}\) the observation noise and \(\mathbf{h}[\mathbf{x}(t_{k})]\) is the navigation equation vector composed of \(n\) instances of Equation (4). The navigation equations can be linearized by obtaining the Jacobian matrix of \(\mathbf{h}[\mathbf{x}(t_{k})]\), which yields the observation matrix, \(\mathbf{H}\). For the case of \(n\) observations, this matrix yields  
\[\mathbf{H}_{k}=\begin{bmatrix}\frac{x^{1}-\delta}{R^{1}}&\frac{y^{1}-\delta}{R ^{1}}&\frac{z^{1}-\delta}{R^{1}}&1&0\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ \frac{x^{n}-\delta}{R^{n}}&\frac{y^{n}-\delta}{R^{n}}&\frac{z^{n}-\delta}{R^{n} }&1&0\end{bmatrix} \tag{18}\]where \(\hat{R}^{i}\) is the satellite-receiver range, taken at the estimated position, given by Equation (2).  
The last parameter of the observations model is the observation noise covariance matrix, \(\mathbf{R}_{k}\). In this paper, since the measurements are considered uncorrelated, the noise covariance matrix is reduced to a diagonal matrix, given by  
\[\mathbf{R}_{k}=\begin{bmatrix}\sigma_{1}^{2}&&\mathbf{0}\\ &\sigma_{2}^{2}&&\\ &&\ddots&\\ &\mathbf{0}&&\sigma_{n}^{2}\end{bmatrix} \tag{19}\]
# Seismic Wave Amplification Characteristics in Slope Sections of Various Inclined Model Grounds
## 4 Comparative Analysis of 1 g Shaking Table Experiment Results and Numerical Simulation Results
### Spectral Acceleration(SA) Analysis  
The numerical analysis data obtained using the Abaqus program were converted to Spectral Acceleration (SA) to see if they were appropriately depicted in the frequency domain, as shown in Figure 9. The data from three accelerometers (AS11-13), measured in the slope section (lower, middle, and upper parts), were compared to check the alignment in the frequency domain. The AS11-13 results are represented in black, red, and blue, respectively. The experimental results are depicted with solid lines, while the numerical analysis results are shown with dotted lines. The comparison results showed that the shaking table experiment data and the numerical analysis data matched well in the frequency domain. In the upper part of the slope, the experimental results generally showed higher SA. This is believed to be due to physical factors such as amplification due to the natural frequency of the model ground and the presence of cracks, which can cause some amplification, unlike numerical calculations. Case 1 showed an average difference of about 3.22%, Case 2 about 3.39%, Case 3 about 2.56%, and Case 4 about 2.24%. Overall, the results showed a difference of about 2.85%, confirming that the results were very similar. The good match between the numerical analysis and experimental results suggests that the seismic testing using the 1 g shaking table is validated.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & Relative Density 75\% Ground Property Conditions & & \\ Properties & Value & Properties & Value \\ \hline Mass density [kg/m\({}^{3}\)] & 1833 & [PERSON]’s modulus [kPa] & 17,160 \\ Friction Angle [\({}^{\circ}\)] & 23 & Cohesion [kg/cm\({}^{2}\)] & 0.32 \\ \hline \hline  & Relative Density 85\% Ground Property Conditions & & \\ Properties & Value & Properties & Value \\ \hline Mass density [kg/m\({}^{3}\)] & 1916 & [PERSON]’s modulus [kPa] & 24,418 \\ Friction Angle [\({}^{\circ}\)] & 25 & Cohesion [kg/cm\({}^{2}\)] & 0.21 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Input parameters.  
Figure 8: Shear wave velocity.