esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Determination of the Antimicrobial Activity  
The antimicrobial activity against bacteria and fungi that cause food spoilage was evaluated by using the agar diffusion method described by [PERSON] et al. [PERSON] et al. (2016), with slight modifications. The bacteria _Staphylococcus aureus_, _Bacillus subtilis_, _Bacillus cereus_, _Escherichia coli_, and _Salmonella typhimurium_ were obtained from the collection of the Department of Food Science and Technology (Kaunas University of Technology). They were grown on Plate Count Agar (Biolife, Milan, Italy) in an incubator at 37 \({}^{\circ}\)C for 24 h. Bacteria were collected from agar slants after growing to prepare inocula containing \(\sim\)10\({}^{8}\) cells/mL of bacteria using a McFarland 0.5 standard. In each Petri dish, 133 \(\upmu\)L of the indicator strain suspension and 20 mL of soft Plate Count Agar were mixed carefully. The fungi _Apergillus versicolor_, _Penicillium chrysogenum_, _Penicillium cyclopium_, _Apergillus terreus_, and _Penicillium_ sp. were obtained from the collection of the Institute of Botany of Nature Research Centre (Lithuania). They were grown on Potato Dextrose Agar (Biolife) at 25 \({}^{\circ}\)C for 3 days.  
The antimicrobial activity of the hemp seed protein fractions (with and without enzymatic hydrolysis) against bacteria that cause food spoilage was also evaluated. In brief, 70 \(\upmu\)L of each protein fraction (10 mg/mL in distilled water) were added to each well (with a diameter of 6 mm) that had been cut in the agar plates and incubated at 30 \({}^{\circ}\)C for 24 h. The antimicrobial activity of the protein fractions was evaluated by measuring the clear zone of inhibition in millimetres. Antimicrobial activity (the inhibition of bacteria growth around the well) was evaluated using the following scale: (\(-\)) no clear/growth inhibition zone around the well (no inhibition); (+/\(-\)) up to 1 mm clear/growth inhibition zone; (+) 1-3 mm clear/growth inhibition zone; (++) 3-5 mm clear/growth inhibition zone; and (++) \(>\)5 mm clear/growth inhibition zone.  
To evaluate the antimicrobial activity of fermented hemp seeds against bacteria and fungi that cause food spoilage, medium (20 mL) with a suspension of microorganisms (133 \(\upmu\)L of the indicator strain suspension, as described above) was poured onto the fermented seeds, which were placed in the middle of a Petri dish. The plates were incubated at 30 \({}^{\circ}\)C for 24 h (for bacteria) or at 25 \({}^{\circ}\)C for 48 h (for fungi).
# Numerical Analysis of Fluid Stream Division to Supply Multiple Hydraulic Receivers
## 4 Discrete Model and CFD Analysis
### Determination of Boundary Conditions and Turbulence Model  
The boundary condition at the inlet was the average fluid velocity (velocity specification method: Magnitude, Normal to Boundary). Due to the specificity of the valve operation in the tested system, a fixed flow rate value \(Q_{in}=25.0\)\(\text{ dm}^{3}\text{ min}^{-1}\) was assigned. The static pressure (outlet condition: Gauge Pressure) with the value of \(p_{out}=0.1\) MPawas assumed as the boundary condition at the outlet. The overview of physical parameters used in CFD studies and then in the Simulink model is shown in Table 1.  
The estimated value of the Reynolds Number (see Table 1) indicates transient or turbulent flow, depending on the valve spool position. Based on the review of the literature related to CFD studies on hydraulic pumps ([19]) and valves ( [20; 21; 22; 23]), the \(k-\varepsilon\) turbulence model was chosen. Initial tests were performed to select one of the available variants of the \(k-\varepsilon\) model: Standard, Realizable and RNG. As the differences in results obtained with individual variants are negligible, the Standard \(k-\varepsilon\) variant was finally chosen since it required the shortest computation time. In this case, the calculations were approximately 10% and 11% faster than using the Realizable and RNG variants. The chosen turbulence model is based on determining factors such as kinetic energy of the turbulence \(k\), kinetic energy dissipation \(\varepsilon\) and turbulent viscosity \(\mu_{t}\) using the transport equations [24; 25; 26; 27]. The required factors are calculated using the specific model parameters as intensity \(I\), length scale \(\ell\) and constants: \(s_{k}\), \(s_{\varepsilon}\), \(C_{1\varepsilon}\), \(C_{2\varepsilon}\) and \(C_{\mu}\). Table 2 summarizes the parameter values assigned for the flow control valve model taking into account the aforementioned publications and general ANSYS recommendations [28].
## 2 Design Methodology
### Dynamic Analysis
\begin{array}{c}V=\sum\limits_{i=1}^{4}\frac{1}{2}K_{iy}\Delta y_{i}^{2}+ \sum\limits_{j=1}^{2}\frac{1}{2}K_{ji}\dot{\theta}_{j}^{2}\\ =K_{1y}(y_{1}-y_{in})^{2}+K_{1z}\theta_{1}^{2}+K_{2z}\theta_{1}^{2}+K_{3z}\theta _{2}^{2}+K_{3y}(y_{2x}-y_{3})^{2}+2K_{4 xy}y_{4}^{2}+2K_{5x}(y_{3x}-y_{5})^{2} \\ =K_{1y}\big{(}y_{2}+\frac{L_{1}(y_{3}-y_{2})}{L_{1}+L_{2}}-y_{in}\big{)}^{2}+ K_{1z}\left(\frac{y_{3}-y_{2}}{L_{1}+L_{2}}\right)^{2}+K_{2z}\left(\frac{y_{3}-y_{ 4}}{L_{1}+L_{2}}\right)^{2}\\ +K_{3z}\left(\frac{y_{3}-y_{4}}{L_{3}+L_{4}}\right)^{2}+K_{2x}y_{2}^{2}+K_{3y} \left(\frac{y_{3}-y_{3}}{2}\right)^{2}+2K_{4 xy}y_{4}^{2}+K_{5y}\left(\frac{y_{ 4}-y_{5}}{2}\right)^{2}\end{array}  \tag{29}\]  
where, \(\left(y_{j},y_{j}\right)\) are the displacement and velocity of the links, respectively. Their relations are illustrated in the system of Equations (30a) and (30b). Figure 6: The mathematical model of the CFDM. \[\left\{ \begin{array}{l}y_{1}=y_{2}+L_{1}\theta_{1}\\ y_{2c}=y_{2}+\frac{(L_{1}+L_{2})}{2}\theta_{1}\\ y_{3}=y_{2}+(L_{1}+L_{2})\theta_{1}\\ y_{3}=y_{4}+L_{3}\theta_{2}\\ y_{3c}=y_{4}+\frac{(L_{3}+L_{4})}{2}\theta_{2}\\ y_{5}=y_{4}+(L_{3}+L_{4})\theta_{2}\\ \end{array} \right. \tag{30a}\] \[\left\{ \begin{array}{l}\dot{y}_{1}=\dot{y}_{2}+L_{1}\dot{\theta}_{1}\\ \dot{y}_{2c}=\dot{y}_{2}+\frac{(L_{1}+L_{2})}{2}\dot{\theta}_{1}\\ \dot{y}_{3}=\dot{y}_{2}+(L_{1}+L_{2})\dot{\theta}_{1}\\ \dot{y}_{3}=\dot{y}_{4}+L_{3}\dot{\theta}_{2}\\ \dot{y}_{3c}=\dot{y}_{4}+\frac{(L_{3}+L_{4})}{2}\dot{\theta}_{2}\\ \dot{y}_{5}=\dot{y}_{4}+(L_{3}+L_{4})\dot{\theta}_{2}\\ \end{array} \right. \tag{30b}\]  
Using the Lagrangian method with \(\Gamma=T_{0}-V\),  
\[\frac{d}{dt}\left(\frac{\partial\Gamma}{\partial\dot{y}_{i}}\right)-\frac{ \partial\Gamma}{\partial y_{i}}=Q_{i}\hskip 28.452756 pti=1,\;2,\;\ldots,\;4 \tag{31}\]  
Replacing Equations (28)-(30) into Equation (31), the general movement equation of the mechanism is:  
\[\mathbf{M}\dot{y}+\mathbf{K}y=0 \tag{32}\]  
where, \(\mathbf{M}\) and \(\mathbf{K}\) are the mass and stiffness matrices, respectively. The elements of the matrices \(\mathbf{M}\) and \(\mathbf{K}\) are determined as follows:  
\[m_{ij}=\frac{\partial^{2}T_{0}}{\partial y_{i}\partial y_{j}};i,\;j=1,\;2,\; \ldots,\;n \tag{33a}\] \[k_{ij}=\frac{\partial^{2}V}{\partial y_{i}\partial y_{j}};i,\;j=1,\;2,\; \ldots,\;n \tag{33b}\]  
The natural frequency \(\omega_{0}\) of the mechanism can be easily found by solving the characterstic determinant in Equation (34) [39]:  
\[Det\left(\mathbf{K}-\omega^{2}\mathbf{M}\right)=0 \tag{34}\]
## 3 Results and Discussion
### Degree of Crosslinking  
According to TNBS assay, percentages of free amino groups in electrospun membranes crosslinked for different time were measured. As illustrated in Figure 2, a sustained increase in the degree of crosslinking was observed with the prolonging of time. To relate in detail, 30.8% of the available sites were crosslinked in the 6 h crosslinked sample. For the samples crosslinked for 12, 18 and 24 h, the average degree of crosslinking increased to 48.2%, 60.6% and 62.2%, respectively. However, there was no obvious increase in the degree of crosslinking after crosslinking for 18 h. In fact, there was no statistical differences in the degree of crosslinking between 18 h and 24 h. It has been reported that the coupling reaction in the initial crosslinking approach is mainly concentrated on the outer surface of collagen fibers due to the slow penetration of crosslinking agents into dense fibers [37]. In our study, for 6 and 12 h crosslinked samples, the crosslinking reaction mainly occurred on the outer surface of electrospun fibers, followed by a sharp increase in the degree of crosslinking. The number of available sites for crosslinking was gradually decreased on the outer surface, which limits the effect of increasing crosslinking time. When the time exceeded 18 h, there was almost a small amount of available sites for GA on the outer surface, followed by a tiny increase.  
Figure 1: Circular dichroism spectra (baseline subtracted) of raw collagen, electrospun collagen and denatured collagen.  
Figure 2: Analysis of degree of crosslinking introduced into collagen nanofibrous membranes as a function of the crosslinking time (Significant differences were marked by *** for \(p<0.001\) compared to the 18 h and 24 h crosslinking).
## 2 Problem Definition  
### Imaging Mission Description  
In this study, the concept of an \"imaging mission\" is expanded to include a comprehensive series of processes in Earth observation satellite operations. These encompass not only the remote sensing tasks where satellites equipped with sensors capture images of designated terrestrial regions but also the entire sequence of steps from initial user request to the final delivery of processed images. This intricate process is initiated with the user's requirements, which detail the desired area for observation, level of importance, resolution, and other specifications. These requirements are then compiled and conveyed to the ground station. At the ground station, an initial imaging acquisition plan is formulated, taking into account the user's needs. Following this, a comprehensive review of the satellite's orbit and status information is conducted to establish an optimized imaging mission plan. As depicted in Figure 2, the process flow is represented by various colored lines: the blue line denotes the flow of user requirements, while the green line indicates the flow of information about the satellite, such as its orbit and status. These elements are integrated in the mission scheduling subsystem to develop an optimized imaging mission plan, which is then communicated to the satellite via S-band telemetry. Once the imaging plan is received, a cluster of satellites executes the mission as per the instructions and transmits the raw data back to the ground station using X-band communication, illustrated by the red line in Figure 2. These data undergo a series of corrections and post-processing steps before being rendered as a calibrated image product, ready for delivery to the user. The focus of this paper is primarily on the integration of user requirements with satellite information to establish an optimal imaging mission plan for satellite constellation, ensuring that the entire imaging mission is conducted efficiently, meeting the specific needs of the users.  
Figure 2: Schematic illustration of imaging mission flow.
## 4 Mechanism Discussion
### Formation of Gypsum  
A sulfate attack can lead to significant mechanical damage (cracking), related mainly to differential internal expansions, caused, according to the literature [35; 36], primarily by ettringite formation and secondly by gypsum. As sulfate ions continue to intrude, ettringite is accompanied by gypsum precipitation. Gypsum accumulation causes volumetric expansion, leading to the formation of microcracks under expansion stress (Figure 8e), which facilitates the rate of ions in the solution into the material, increasing the contact area between the ions and the material and allowing more erosion products to be produced [37].  
Figure 8: SEM micrograph of CGB under chemical erosion.(**a**–**c**) represent the hydration products; (**d**) represents the decomposition of hydration products; (**e**) represents gypsum precipitation; (**f**) represents microcrack expansion.  
Additionally, the production of gypsum depletes Ca(OH)2. It reduces the alkalinity inside the specimen, leading to the decomposition of hydration products (Figure 8d), resulting in a loss of strength and the durability of the CGB. This process further exacerbates the destruction of the CGB. The reaction equation is as follows:  
\[\text{Ca}^{2+}+\text{SQ}_{4}^{2-}\rightarrow\text{CaSO}_{4} \tag{4}\]  
\[\text{CaSO}_{4}+2\text{H}_{2}\text{O}\rightarrow\text{CaSO}_{4}\cdot 2\text{H}_{2} \text{O} \tag{5}\]
## 3 Methodology
### Phase 1: Comprehensive Ablation Study Framework
#### 3.1.3 Modular Implementation Strategy  
Each transformer model was implemented with a modular architecture design that enables the selective removal of components without affecting other architectural elements. The implementation utilizes PyTorch's modular framework to create independent component classes that can be dynamically enabled or disabled during model instantiation. It ensures that ablation studies maintain architectural integrity while isolating the specific contribution of individual components. This modular design also enables a consistent comparison between different models by standardizing the ablation process regardless of the underlying transformer variant.
## 3 Q2: What Are the Challenges of the Current UIoT System
### Security Challenges of UIoT
#### 3.3.3 Network and Device Management  
The dynamic behavior of nodes and changes in topology as discussed earlier in Section 3.2.5 and other issues such as the limited battery, limited memory, routing, etc., can impact the management of networks and devices underwater. Therefore, as shown in Figure 4, it is difficult to manage the underwater network management system functionalities such as fault, configuration, accounting, performance, security and constrained (FCAPSC) management in the UIoT environment. Therefore, the attacker can target FCAPSC functionalities [21].
###### Abstract  
To investigate the effect of _Botrytis cinerea_ infection severity on the flavor characteristics of Petit Manseng noble rot wine, this study analyzed wines produced from Petit Manseng grapes grown in the eastern foothills of Helan Mountain, Ningxia, China. The grapes were categorized into three groups based on infection status: uninfected, mildly infected, and severely infected with _Botrytis cinerea_. Headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS) and an electronic nose were employed to detect and analyze the aroma components of wines under the three infection conditions. Additionally, trained sensory panelists conducted sensory evaluations of the wine aromas. The results revealed that wines made from severely infected grapes exhibited the richest and most complex aroma profiles. A total of 70 volatile compounds were identified, comprising 32 esters, 17 alcohols, 5 acids, 8 aldehydes and ketones, 4 terpen, and 4 other compounds. Among these, esters and alcohols accounted for the highest contents. Key aroma-active compounds included isoamyl acetate, ethyl decanoate, phenethyl acetate, ethyl laurate, hexanoic acid, linalool, decanoic acid, citronellol, ethyl hexanoate, and methyl octanoate. Sensory evaluation indicated that the \"floral aroma\", \"pineapple/banana aroma\", \"honey aroma\", and \"overall aroma intensity\" were most pronounced in the severely infected group. These findings provide theoretical support for the harvesting of severely _Botrytis cinerea_-infected Petit Manseng grapes and the production of high-quality noble rot wine in this region.  
gray mold; noble rot character; Petit Manseng; sweet white wines Article
# Wind Speed Measurement via Visual Recognition of Wind-Induced Waving Light Stick Target
## 2 Theoretical Modeling
### Imaging Formulation
#### 2.1.4 Coupled View  
In practice, the movement of the target in pure bottom, side, or front views is seldom the case shown. It is more likely that it swings in a combination of these views. The two components of side and front views are involved in coupled view, as in Figure 9. In side view where the yaw angle and the initial stick position are respectively denoted as \(a_{\rm CV}\) and \({}^{\prime}\)IP-CV\({}^{\prime}\), the stick rotation has a component normal to the imaging plane as in front view. The yaw angle \(a_{\rm CV}\) is calculated by decomposing the stick waving into two steps, as in Figure 9a:  
(1) Front rotation in the plane parallel to the imaging plane. During this step, the yaw angle \(a_{\rm CV-FV}\) is calculated by the imaging angular model in front view as in Equation (24).  
(2) Side rotation in the plane perpendicular to the imaging plane. In this step, the yaw angle \(a_{\rm CV-SV}\) is calculated by the imaging model as in Equations (14) and (18), respectively, for centered and deviated side views.  
The two steps can be mathematically geometrized in a hexahedron model as in Figure 9b.  
Figure 8: Schematic of the front view model in deviated imaging. (**a**) Schematic model; (**b**) geometry model.  
In the three right triangles \(\triangle\)OAC, \(\triangle\)OCD and \(\triangle\)OAD, geometry relations yield:  
\[\begin{array}{l}\cos\alpha_{\text{CV-FV}}=\left|\,\text{OA}\,\right|\,/\,|\, \text{OC}\,\right|\\ \cos\alpha_{\text{CV-SV}}=\left|\,\text{OC}\,\right|\,/\,|\,\text{OD}\,\right|\\ \cos\alpha_{\text{CV}}=\left|\,\text{OA}\,\right|\,/\,|\,\text{OD}\,\right| \end{array} \tag{26}\]  
Equation (26) is also given as:  
\[\alpha_{\text{CV}}=\cos^{-1}(\cos\alpha_{\text{CV-SV}}\text{-}\cos\alpha_{ \text{CV-FV}}) \tag{27}\]  
where \(\alpha_{\text{CV}}\), \(\alpha_{\text{CV-SV}}\), and \(\alpha_{\text{CV-FV}}\) are the yaw angles in coupled, side, and front views.  
The wind direction \(\alpha_{\text{WD}}\) is defined between the optical centerline and the plane that places the initial and waved stick, that is, the angle between CD and AD, which is given as:  
\[\alpha_{\text{WD}}=\cos^{-1}\!\left(\frac{\left|\text{CD}\right|}{\left|\text {AD}\right|}\right)=\cos^{-1}\!\left(\frac{\left|\text{CD}\right|/\left|\text {OD}\right|}{\left|\text{AD}\right|/\left|\text{OD}\right|}\right)=\cos^{-1 }\!\left(\frac{\sin\alpha_{\text{CV-SV}}}{\sin\alpha_{\text{CV}}}\right) \tag{28}\]  
where \(\alpha_{\text{CV}}\) is the coupled view angle from Equation (26), \(\alpha_{\text{CV-SV}}\) is the yaw angle component in pure side view from Equation (18), where the function g(X\({}_{\text{P}}\)) is defined as in Equation (19).  
From above, the imaging formulae of stick yaw angle as well as the wind direction are given in Equations (27) and (28) for coupled side-front view.
# Measuring the Effect of Circular Public Procurement on Government's Environmental Impact
## 3 Results
### Effect Estimation Based on Interviews
Figure 4: Different indicators for CPP implementation provided different scores. The first bar is the percent of tenders with criteria directly relating to circularity (e.g., on recycling and longer lifetime). The second bar is the percent of tenders with criteria that trigger circularity (including, for example, criteria that stimulate reduced carbon footprints). The third bar is the percent of tenders with CPP criteria that are more ambitious than the market standard. The fourth bar is the percent of tenders with CPP criteria that are both more ambitious than the market standard and also operationalized. The light gray bars in the latter two bars reflect the tenders that included criteria for which it remained unclear if the CPP criteria were more ambitious than the market standard or if they were operationalized. The results show that the score of CPP implementation depended on the definition of what the CPP criteria were. Procurement criteria can trigger circular strategies without explicitly referencing circular strategies such as reuse or recycling. For example, criteria on reducing the carbon footprint of products can and do trigger the development and sale of products with more recycled content, longer life spans and more reuse opportunities. In the sample, three tenders that asked for a CO\({}_{2}\) performance ladder certificate [36] led to the supply of products with longer lifetimes and higher fractions of recycled content. Hence, when analyzing CPP implementation and its effects, it is important to realize that a CE is a means to reach environmental and socioeconomic goals, and criteria that focus on these goals can in fact trigger the purchase of more circular products and hence could be considered CPP. For monitoring the implementation of CPP, this means a choice has to be made for including these criteria or not. In particular, the monitoring of CPP implementation based on the results of search engines in the gray and peer-reviewed literature, such as [37], is likely to underestimate CPP application in practice when search terms solely focus on typical circularity vocabulary. Here, 38% of the tenders under consideration contained criteria with direct references to circularity (recycled content, longer life span, etc.). When including criteria that can trigger circular strategies, 43% of the tenders had CPP criteria. The ranges in Figure 4 illustrate the difference of the outcomes with or without the tenders for which ambition or operationalization remained unclear. The results show that applications of current CPP criteria are not an indicator for the implementation success of CPP. Up to two thirds of the tenders with CPP criteria were shown to be ineffective in triggering contracting the buying of the CPP-targeted products.
## 2 Inverse Analysis Containing to LFWD Test
### Result of Inverse Analysis
#### 2.3.2 Results  
The analysis indicates that the FE model can be calibrated for the LFWD deflections at the test site by using the appropriate constitutive equations, given as Equation (1), together with the base modulus and exponent values presented in Table 1. In addition, the ballast and ground are considered as linear elastic materials, and the corresponding parameters adopted in previous research [6] are summarized in Table 1. The constitutive equations can be used to represent the behavior of the subgrade layer, and the LFWD deflections determined from the FE model are shown in Figure 4. The deflection fitting error obtained using Equation (4) is 1.84%, which is less than the considered tolerance of 3% [14].  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & Young’s Modulus, \(E\) (GPa) & 30 \\ Sleeper & Poisson’s ratio & 0.18 \\  & Density (kg/m\({}^{3}\)) & 2500 \\  & Length \(\times\) Width \(\times\) Height (m) & 2.6 \(\times\) 0.25 \(\times\) 0.2 \\ \hline  & Young’s Modulus, \(E\) (MPa) & 180 \\ Ballast & Poisson’s ratio & 0.25 \\  & Density (kg/m\({}^{3}\)) & 1700 \\ \hline  & \({}^{1}\) Base modulus \(K_{1}\) (MPa) & 275 \\ Top layer & \({}^{1}\) Exponent \(K_{2}\) & 0.25 \\  & Poisson’s ratio & 0.3 \\  & Density (kg/m\({}^{3}\)) & 1950 \\ \hline  & \({}^{1}\) Base modulus \(K_{1}\) (MPa) & 196.6 \\ Bottom layer & \({}^{1}\) Exponent \(K_{2}\) & 0.2 \\  & Poisson’s ratio & 0.25 \\  & Density (kg/m\({}^{3}\)) & 2000 \\ \hline  & \({}^{1}\) Base modulus \(K_{1}\) (MPa) & 186.1 \\  & \({}^{1}\) Exponent \(K_{2}\) & 0.2 \\  & Poisson’s ratio & 0.25 \\  & Density (kg/m\({}^{3}\)) & 2000 \\ \hline  & Young’s Modulus, \(E\) (MPa) & 120 \\ Foundation soil & Poisson’s ratio & 0.27 \\  & Density (kg/m\({}^{3}\)) & 2100 \\ \hline \hline \end{tabular} \({}^{\dagger}\) Back-analyzed value.  
\end{table}
Table 1: Properties of track component materials.  
Figure 4: Comparison of measured and computed peak deflection values at different test points.