esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Road Traffic Accidents Reduction Strategy Based on Intelligent Transportation Systems (TARS)
## 5 Results and Discussion
### Confidence Interval in Terms of Message Delivery Ratio  
In order to calculate the confidence interval, the highway scenario is evaluated and analyzed. The geographical (highway) trace file was retrieved from SUMO in order to achieve the real world scenario. Simulation was conducted in NS-2, neighboring vehicles with 40-200 vehicles travelling random routes with the help of a random mobility model. For each configuration, 50 trials were executed for 500 s and the simulation has been conducted with different random seeds; as a result, MDR results were achieved in the transmission range of 30 m-2100 m that are assumed to be normally distributed.  
The MDR showed the average probability of receiving a single message with the confidence interval. Figure 14 shows the 95% confidence interval in the highway scenario. The MDR shows the coverage of message dissemination, when the source vehicle transmits the message and the simulation computes the increase of message coverage with time. If the message coverage greatly increases in a short interval, then it means the proposed protocol achieves high message dissemination performance. TARS shows a 95% confidence interval as shown in Figure 14. In the highway scenario, the distance between vehicles is larger as compared to the dense areas. Therefore, by using a predefined threshold  
Figure 13: Message Delivery Ratio w.r.t. the Varied Vehicle Density in the highway scenario.  
value, the MDR requires that 6, 8, and 9 out 10 messages per second are received and are calculated to have an approximately 75%, 88%, and 95% confidence interval, respectively, in a case when the distances between the vehicles are smaller [45]. However, when the number of vehicles is increased then the number of messages also increases as well as the communication becoming difficult in cases when the distance between the vehicles is greater. So, there are a lot of chances that the single message does not reach the specified destination and the rate of message loss becomes greater when the number of vehicles is increasing [45].
# Sensor Micro and Nanoparticles for Microfluidic Application
## 2 Construction Principles of Sensor Particles
### Preparation of Sensor Particles in Microfluidic Systems
The average diameter and distribution half width size were 20 and 3.8 nm for Au/Ag core/shell as well as 45.6 and 7.4 nm for Au/Ag/Au multishell nanoparticles. It was demonstrated that plasmonic resonance shifted effectively due to the formation of core/shell structures. Therefore, prepared core/shell particles could be used for sensing and labelling applications. [PERSON] et al. [13] presented droplet-based polydimethylsiloxane (PDMS) microfluidic chip for synthesis of silica nanoparticles, whose diameter was controlled via reaction time and reagent concentration. Silica nanoparticles were synthesized by the Stober method and functionalized with fluorescein-isothiocyanate (FITC) fluorescent dye. When compared to a bulk process, it was shown that the growth speed of nanoparticle was higher in the droplet-based synthesis. For that reason, it was possible to control the size of the nanoparticles over a wider range and much faster. As a result, improvement of nanoparticle size uniformity was seen due to 3% relative standard deviation for 350 nm silica nanoparticles. Moreover, photobleaching of functionalized nanoparticles with FITC was slower in comparison to the photobleaching of pure fluorescein isothiamate in water. Despite high controllability of nanoparticle size, particle production rate was relatively low. [PERSON] et al. [9] demonstrated a promising approach to synthesize silver nanocrystals with controlled size and shape in droplet-based microreactors. During a seed-mediated synthesis, the nanocrystals were formed from seeds in the way that every seed evolved into the nanocrystals. A gas-liquid droplet microreactor was used for droplet generation (Figure 2). The device consisted of three syringes, which were filled with air, seed solution (Ag seeds and polyvinylpyrrolidone (PVP) in ethylene glycol (EG) solution) and precursor solution (AgNO3 and PVP in EG). The air segments created by using a droplet microreactor were important not only for separating the reaction solution but also for providing O2, generating reducing agent. Due to this, homogeneous nucleation and unwanted merging of the single seeds were prevented. It was demonstrated that by varying the amount of seeds or the time of the reaction, or AgNO3 concentration in the segments, edge lengths of Ag nanocubes could be tuned from 30 to 100 nm. Additionally, Ag octahedras were synthesized by reducing the concentration of PVP in the segments. However, the limitation of the presented method was associated with limited syringe volume because continuous flow must be interrupted for reagent reloading.
## 3 Results
### Presence of Pests  
After nspection, no damage was found to the packaging in which bars B1, B2 and B3 were placed. The tested products did not contain live pests, parts of their bodies or other traces of their activity. During storage, no traces of the presence of pests were found in the tested products. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Mycotoxin (\(\mu\)g/kg)**} & \multicolumn{3}{c}{**Products at Day 0**} & \multicolumn{3}{c}{**Products after Storage at 4 \({}^{\circ}\)C**} & \multicolumn{3}{c}{**Products after Storage at 22 \({}^{\circ}\)C**} \\ \cline{2-11}  & **B1** & **B2** & **B3** & **B1/4** & **B2/4** & **B3/4** & **B1/22** & **B2/22** & **B3/22** \\ \hline Aflatoxin B1 & \textless{}0.10 & \textless{}0.10 & \textless{}0.10 & \textless{}0.10 & \textless{}0.10 & \textless{}0.10 & \textless{}0.10 & \textless{}0.10 & \textless{}0.10 \\ Aflatoxin B2 & \textless{}0.05 & \textless{}0.05 & \textless{}0.05 & \textless{}0.05 & \textless{}0.05 & \textless{}0.05 & \textless{}0.05 & \textless{}0.05 & \textless{}0.05 & \textless{}0.05 \\ Aflatoxin G1 & \textless{}0.10 & \textless{}0.10 & \textless{}0.10 & \textless{}0.10 & \textless{}0.10 & \textless{}0.10 & \textless{}0.10 & \textless{}0.10 & \textless{}0.10 \\ Aflatoxin G2 & \textless{}0.05 & \textless{}0.05 & \textless{}0.05 & \textless{}0.05 & \textless{}0.05 & \textless{}0.05 & \textless{}0.05 & \textless{}0.05 & \textless{}0.05 \\ Sum of aflatoxin B1, B2, G1, G2 & \textless{}0.10 & \textless{}0.10 & \textless{}0.10 & \textless{}0.10 & \textless{}0.10 & \textless{}0.10 & \textless{}0.10 & \textless{}0.10 & \textless{}0.10 \\ Ochratoxin A & \textless{}0.25 & \textless{}0.25 & \textless{}0.25 & \textless{}0.25 & \textless{}0.25 & \textless{}0.25 & \textless{}0.25 & \textless{}0.25 & \textless{}0.25 \\ Zearalenone & \textless{}10 & \textless{}10 & \textless{}10 & \textless{}10 & \textless{}10 & \textless{}10 & \textless{}10 & \textless{}10 & \textless{}10 \\ Deoxynivalenol & \textless{}100 & \textless{}100 & \textless{}100 & \textless{}100 & \textless{}100 & \textless{}100 & \textless{}100 & \textless{}100 & \textless{}100 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Presence of selected mycotoxins in HPB bars immediately after production (time 0) and after 3 months of storage at 4 \({}^{\circ}\)C and 22 \({}^{\circ}\)C (\(n=3\)).
# The XTRA Option at the NEUTRA Facility--More Than
## 1 Introduction-Method and Instrumentation
### Standard Configuration of the NEUTRA Facility  
NEUTRA [1] is the thermal neutron imaging instrument at the spallation neutron source SINQ [6] of the Paul Scherrer Institute (PSI) in Switzerland. It provides neutron radiography and tomography options of medium to large size objects (a few mm up to 35 cm) using a thermal neutron spectrum. The instrument has been operational since the late 1990s and was the first dedicated neutron imaging instrument at PSI, established together with the spallation source (Figure 2a). It has seen more than 20 years of international scientific and industrial user service. For nearly 15 years, the special feature of a 320 kV X-ray tube has been available at NEUTRA, allowing bi-modal imaging with identical imaging geometry, enabling straight forward registration of X-ray and neutron datasets.  
NEUTRA consists of evacuated neutron flight-tubes downstream of a 20 mm diameter pinhole and is viewed in SINQ's D\({}_{2}\)O moderator tank. NEUTRA features three measurement positions to trade flux versus resolution. These positions are at distances of 3.8 m (position 1), 7.3 m (position 2), and 10.5 m (position 3) from the collimating pinhole. They provide collimation ratios from 200 to 550, with about an order of magnitude difference in available flux and maximum beam cross sections from 10 \(\times\) 10 to 35 \(\times\) 35 cm\({}^{2}\). Position 1 is tightly enclosed by shielding walls and can be assessed only from the top. In this position (position 1) the optional 320 kV X-ray source has been installed and can be moved remotely into the operating position in the neutron beam axis (Figure 2b). The standard detectors utilized at NEUTRA consist of a scintillator screen, light optics, and a digital camera system [7]. Scintillator screens and cameras, as well as the detector systems are easily exchangeable and enable straightforward adaptations to each individual use case.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **150 keV X-ray (cm\({}^{-1}\))** & **Thermal Neutrons (cm\({}^{-1}\))** \\ \hline Wood & 0.1 & 1.1 \\ Iron & 1.5 & 1.2 \\ Tin & 4.8 & 0.21 \\ Lead & 22.81 & 0.38 \\ Boron & 0.28 & 102 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Linear radiation attenuation coefficients of selected materials for X-rays (at 150 kV) and thermal neutrons (averaged Maxwellian spectrum around 25 meV) [2; 3].  
Figure 1: Demonstration of the different attenuation behavior of thermal neutrons (**a**) and X-rays at 150 keV (**b**) for the case of a hard-disk drive (data taken at PSI’s facilities).
## Abbreviations  
The following abbreviation are used in this manuscript:  
LCC  
Line commutated converter  
AFC  
Auxiliary frequency control  
AGC  
Automatic generation control  
VFF-RLS  
Variable forgetting factor recursive least squares  
DQN  
Deep Q-network  
VSC  
Voltage source converter  
ADC  
Adaptive DC voltage droop control  
HVDC  
High-voltage direct current  
MTDC  
Multi-terminal direct current  
DL  
Deep learning  
RL  
Reinforcement learning  
ACE  
Area control error  
PI  
Proportional integral  
ITAE  
Integrated time absolute error
# DNA Barcoding as a Plant Identification Method
## 3 Nuclear Genome DNA Barcodes
### _Its2_  
_ITS2_ can be used as an alternative DNA barcode for taxa [38; 39]; 92.7% of species have been correctly identified in more than 6500 samples from 4800 species [40; 41]. Notably, its secondary structure is also informative, enabling species identification [42]. _ITS2_ may also be used as a complementary locus for _COI_ to identify animal species [3; 31; 40; 41]. _ITS2_ is easy to amplify; conserved, so it is easy to design universal primers; shorter, therefore easier to sequence; and, due to its high diversity, can be used to distinguish even closely related species [40; 41]. Although _ITS2_ has many strengths, it is not ideal for identifying every plant due to the presence of multiple copies with high levels of intraspecific variation as well as heterogeneity as a result of concerted evolution [43].
## 2 Materials and Methods
### Chemicals  
Absolute ethanol (99.9%), anhydrous sodium sulfate (99.8%), sodium chloride (99.8%), hydrochloric acid (36.0-38.0%) and dichloromethane (HPLC grade, 99.99%) were obtained from Aladdin (Shanghai, China). Ethyl acetate (99.5%), isoamyl acetate (99%), hexyl acetate (99%), ethyl butyrate (99%), ethyl hexanoate (99%), ethyl lactate (99%), ethyl octanoate (99%), ethyl decanoate (99%), ethyl dodecanoate (99%), ethyl tetradecanoate (98%), ethyl oleate (97%), isobutyl acetate (99.5%), ethyl isobutyrate (99%), ethyl isovalerate (99%), 1-butanol (99%), 3-methyl-1-butanol (99%), 1-octanol (99%), leaf alcohol (99%), 3-hydroxy  
Figure 1: A process flow of DS and PS production.  
2-butanone (95%), ethyl 2-furoate (99%), ethyl benzoate (99%), benzyl acetate (98%), phenethyl acetate (99%), phenylethyl alcohol (99%), benzaldehyde (98%), (E,E)-farnesol (96%), linaloid (98%), acetic acid (99.7%), 2-methylpropanoic acid (99%), 3-methylbutanoic acid (99%), hexanoic acid (99%), heptanoic acid (98%), decanoic acid (99%), 2-ethylbutyric acid (IS3) (99%), \(\gamma\)-hexalactone (98%), \(\gamma\)-decalactone (98%), \(\gamma\)-octalactone (98%), amyl acetate (99%), 3-methylthiopropanol (98%), hexanal (90%) and benzyl alcohol (99.5%) were obtained from J&K Scientific Ltd. (Beijing, China). Isoamyl lactate (98%), isoamyl decanoate (97%), 3-methylbutyl dodecanoate (97%), ethyl pentadecanoate (97%), ethyl hexadecanoate (95%), isobutanol (99%), 1,1-diethyloxyethane (96%), decanal (95%), furfural (98%), \(\gamma\)-nonalactone (97%), 4-heptanol (IS2) (98%), 2-ethyl-1-hexanol (99%), 3-methyl-3-butten-1-ol (98%), citronello1 (92%), octanoic acid (98%), and nonanoic acid (98%) were obtained from Tokyo Chemical Industry Co., Ltd. (Tokyo, Japan). 1-hexanol (98%), (E)-\(\beta\)-Damascenone (98%), trans-\(\beta\)-ionone (97%), and p-toluenesulfonic acid n-butyl ester (97%) were purchased from Macklin Biochemical Co., Ltd. (Shanghai, China). trans-nerolidol (95%), and diethyl succinate (99%) were bought from Yuanye Bio-Technology Co., Ltd. (Shanghai, China). In addition, 1-heptanol (99%) was from Aldrich Chemical Co., Inc. (St Louis, MO, USA). 3-Hexenol (97%) was obtained from Alfa Aesar Co., Ltd. (Beijing, China). And \(\gamma\)-butyrolactone (98%) was obtained from Tanmo Quality Assurance Technology Co., Ltd. (Beijing, China). 1-Propanol (99.9%), butyric acid (99%) was bought from Dr. Ehrenstorfer GmbH Co., Ltd. (Augsburg, Germany). Mixtures of n-alkanes C7-C25 (Sigma-Aldrich, Shanghai, China) were used to determine the linear retention indices of the compounds.
## 2 Methods: Modeling and Numerical Calculation Method
### Numerical Calculation Strategy  
#### 2.2.1 Numerical Calculation Method  
Ansys Fluent 18.0 was used to perform steady numerical simulations of the computational domain of the mining pump. In the computational process, it is assumed that there is no energy and mass exchange between the particle phase and the fluid phase, and that gravity is taken into account. The computation of the fluid domain adopts the SST \(k\)-\(\omega\) turbulence model, the motion of the particle phase in the computational domain adopts the DPM model based on Euler-Lagrange coordinates, there is momentum exchange in this model, the discretization of the Navier-Stokes equation adopts the second-order upwind style, and the computational convergence precision is set as \(10^{-4}\) in the numerical simulation.  
Using the \(x\)-axis direction as an example, the equation for particle motion in the computational domain can be expressed as follows:  
\[\frac{du_{p}}{dt}=F_{D}\big{(}u-u_{p}\big{)}+\frac{g_{x}(\rho_{p}-\rho)}{\rho_{ p}}+F_{V}+F_{P}+F_{x} \tag{1}\]  
where \(F_{D}\big{(}u-u_{p}\big{)}\) is the drag force on a unit mass of solid-phase particles, as follows:  
\[F_{D}=\frac{18\mu}{\rho_{p}d_{p}^{2}}\frac{C_{D}R_{e}}{24} \tag{2}\]  
where, \(u\) is the fluid velocity (m/s), \(u_{p}\) is the particle velocity (m/s), \(\mu\) is the dynamic viscosity (N\(\cdot\)s/m\({}^{2}\)), \(\rho\) is the fluid density (kg/m\({}^{3}\)), \(\rho_{p}\) is the particle density (kg/m\({}^{3}\)), \(d_{p}\) is the particle diameter (mm), and \(g_{x}\) is the acceleration of gravity in the \(x\)-axis direction (m/s\({}^{2}\)).
# Numerical Investigation of a Novel Grinding Device
## 3 Results
### Detailed Distribution of Collisions  
Figure 5 shows the joint probability density function (PDF, with color map and contours) for the impact angle and the logarithm of the impact kinetic energy to further characterize the collisions and identify the optimal operating conditions. The marginal probability density functions and corresponding quartiles (the same as in Figure 3a,b) are also reported on the edges of each map. The joint distribution is built with a binning size of \(2^{\circ}\) for \(\theta_{\text{impact}}\) and \(0.1\) for \(\log_{10}(ke_{\text{r}})\). Thus, the probability of finding a collision with its parameters in a specific 2D bin (i.e., a 'pixel' of the figure) is the value of the bin multiplied by its area, i.e., \(0.2\). By further multiplying by the impact frequency and the operation time, the total number of collisions with a specific set of parameters is determined. Based on the optimization constraints discussed earlier, the in-depth analysis is limited to the cases of experimental interest with \(\Omega\) spanning from 300 to 900 rpm.  
The joint distributions in Figure 5 are all unimodal, and the most likely collisions have slightly higher kinetic energy and lower impact angle than the corresponding medians (as evident in panel a). At variance with the IWM that presented a negative correlation between \(\theta_{\text{impact}}\) and \(ke_{\text{r}}\), these variables are almost uncorrelated at 300 rpm and have a weak positive correlation at higher \(\Omega\). In addition, the marginal distribution of \(\theta_{\text{impact}}\) has an evident positive skewness (with a long tail at high angles). Thus, although most impacts are mainly tangential, some normal ones do occur.  
By increasing \(\Omega\) (from top to bottom in figure) the joint distribution shifts to the right (i.e., the impact kinetic energy increases) and becomes more compact and uniform with respect to the impact angle, as seen in Figure 3.  
As concerns the collisions of Ag (right column in Figure 5), the distributions are more scattered due to the lower number of data, marginally more spread for both variables, and slightly shifted at lower \(ke_{\text{r}}\).  
It is possible to determine the optimal operating conditions experimentally (e.g., \(\Omega\)) to maximize the quality and quantity of NPs. Combining this information with the distributions shown here, the optimization can be related to specific impact conditions (angle and energy) that can then be applied to any given bead mill system.
# The Impact of the S-Adenosylmethionine Analogue Sinefungin on Viral Life Cycles
## 3 Results
### Antiviral Activity
Sinefungin significantly reduced the plaque formation in a dose-dependent manner for both viruses, with greater efficacy observed against HSV-1 (\(\mathrm{IC}_{50}\) = 49.5 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) compared to that for SARS-CoV-2 (\(\mathrm{IC}_{50}\) = 100.1 \(\mathrm{\SIUnitSymbolMicro g}\)/mL). The data represent the mean \(\pm\) standard deviation (SD) of three independent experiments. For HSV-1, ****\(p\)-value \(\leq\) 0.0001; ****\(p\)-value = 0.0010. For SARS-CoV-2, ****\(p\)-value \(\leq\) 0.0001; ****\(p\)-value = 0.0010; ****\(p\)-value = 0.0349. Figure 4: Antiviral activity of sinefungin against SARS-CoV-2 in a viral yield reduction assay. VERO-76 cells infected with HSV-1 (MOI 0.01) were treated with sinefungin at concentrations ranging from 25 to 200 \(\upmu\)g/mL. The viral titers were quantified at 6 (**A**), 12 (**B**), and 24 (**C**) hours post-infection. Sinefungin induced a dose- and time-dependent decrease in the viral yield, with the most pronounced effect observed at 200 \(\upmu\)g/mL, resulting in a log\({}_{10}\) reduction of 2.17 at 6 h (**A**), 4.28 at 12 h (**B**), and 6.18 at 24 h (**C**). The data represent the mean \(\pm\) standard deviation (SD) from three independent experiments. ***\(p\)-value \(\leq\) 0.0001; ***\(p\)-value = 0.0007; ***\(p\)-value = 0.0060; ns \(p\)-value \(\geq\) 0.1866. Figure 5: The viral gene expression of HSV-1 (UL27) and SARS-CoV-2 (S gene) following treatment with sinefungin. A RT-qPCR analysis was performed on infected cells co-treated with sinefungin at concentrations of 200 \(\upmu\)M, 100 \(\upmu\)M, and 50 \(\upmu\)M. Gene expression levels are shown as values relative to those for the untreated virus control. A dose-dependent reduction in the gene expression was observed for both viruses, with a more pronounced effect in SARS-CoV-2. The data are presented as mean \(\pm\) standard deviation. The data represent the mean \(\pm\) standard deviation (SD) of three independent experiments, ***\(p\)-value \(\leq\) 0.0001; ***\(p\)-value \(\geq\) 0.0007; ns \(p\)-value \(\geq\) 0.1167.
# Expansion Lemma--Variations and Applications to Polynomial-Time Preprocessing
## Box 10  
\(r\)-Edge-Tree Packing Problem.  
**Input:**_An undirected graph \(G\) and an integer \(k\)._  
**Parameter:**_k_  
**Question:**_Is there a set of at least \(k\) pairwise disjointed collections of trees with \(r\) edges in \(G\)?_  
Let us focus on an \(r\)-tree crown decomposition \((C,H,X)\) in \(G\). The witness functions \(\pi_{1},\ldots,\pi_{r}\) from \(H\) to \(C\) correspond to a mapping \(M\) from \(H\) to some of the trees in \(B\); in particular \(v\in H\) is mapped to the tree \(\{v,\pi_{1}(v),\ldots,\pi_{r}(v)\}\). From the definition of \(\pi_{i},i\in[r]\), one can conclude that \(M\) is a matching and \(M(H)\) is a packing. Thus, \((C^{\prime},H,X^{\prime})\) is a generic crown decomposition in \(G^{\prime}\) where \(C^{\prime}=C\uplus M(H)\) and \(X^{\prime}=V(G^{\prime})\setminus(H\cup C^{\prime})\). Since \((G^{\prime},k)\) is a YES instance of \(r\)-red/blue distance-3 packing (see Box 9) if and only if \((G^{\prime}-(H\cup C^{\prime}),k-|H|)\) is a YES instance of \(r\)-red/blue distance-3 packing, one can conclude that \((G,k)\) is a YES instance of \(r\)-edge-tree-packing if and only if \((G-(H\cup C),k-|H|)\) is a YES instance of \(r\)-edge-tree-packing.  
Using the above reduction, the authors provided a kernel with at most \((r^{2}+1)(r+1)k\) vertices for \(r\)-edge-tree-packing. The authors also devised a methodology to transfer a kernelization result from a packing problem to the corresponding covering problem with a similar size (see [19] for more details). Thus, a kernel with at most \((r^{2}+1)(r+1)k\) vertices for \(r\)-edge-tree-covering (see Box 11) also follows.  
**Box 11**.: \(r\)-Edge-Tree-Covering Problem.  
\(r\)**-Edge-Tree-Covering**  
**Input:**_An undirected graph \(G\) and an integer \(k\)._  
**Parameter:**_\(k\)_  
**Question:**_Is there a set \(S\) of, at most, \(k\) vertices, such that \(G-S\) has no tree with \(r\) edges as a subgraph?_