esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Simulation Method of Riser Vortex Shock Vibration
### Calculation Model Verification  
#### 2.5.1 Grid Convergence Test  
A numerical model of vortex-solid coupling was constructed to calculate the vortex-induced vibration of a riser at a flow rate of 0.15 m/s, with a Reynolds number of 3000. By analyzing and comparing the numerical simulation results, it is found that the change rate of the root mean square of lift coefficient C\({}_{\text{L}_{\text{RMS}}}\) is less than 1%, and the change rate of the mean resistance coefficient C\({}_{\text{D}_{\text{mean}}}\) is also less than 1%. Specific data are shown in Table 2. From the above analysis, we could tell that the root mean square value of the lift coefficient and the average value of the drag coefficient tend to be stable, with the influence of the grid number on the numerical simulation having converged.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Total Number** & **Outermost** & **Near-Wall** & **Background Grid** & \multirow{2}{*}{**C\({}_{\text{L}_{\text{RMS}}}\)**} & \multirow{2}{*}{**C\({}_{\text{D}_{\text{mean}}}\)**} \\  & **of Foreground** & **Thickness of the** & **Foreground Grid** & **Thickness** & **Total Number of Units** & & \\ \hline Grid1 & 30,820 & 0.01D & 0.001D & 251,835 & 1.581 & 1.039 \\ \hline Grid2 & 43,152 & 0.008D & 0.001D & 359,676 & 1.584 & 1.042 \\ \hline Grid3 & 58,310 & 0.006D & 0.001D & 486,822 & 1.577 & 1.046 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Grid convergence test (Re = 3000).  
Figure 9: Comparison of vibration frequency of three sets of grid cross-flow (**left**) and forward-flow (**right**).  
It can be seen from Figure 9 that the difference among the cross-current vibration master frequency values is very small for different grid numbers and the master frequency variation of the downstream direction is within 5%. According to the above analysis, the number of grids at this time has little influence on the numerical simulation results.  
Therefore, the second set of grids with a median number of grids has been selected as the grids used in subsequent studies in this paper.
## 3 Applications of Computational Intelligence in Civil Engineering Research Domains
### Relevant Research Studies
#### 3.1.7 Hydraulics and Water Resources Engineering  
The domain of hydraulics and water resources engineering covers the disciplines of fluid mechanics, experimental and computational hydraulics, environmental hydraulics, marine engineering and port engineering, river hydraulics, hydrology and water resources management, hydraulic and hydrological engineering, engineering water supply and sanitation, sanitary engineering, water and urban wastewater treatment facilities, ecology and aquatic ecosystems.  
In the field in question, there has been a lot of research for several years related to hydraulic devices [66; 67], water resource management [68; 69] and environmental hydraulics [70; 71]. Despite all this, significant progress has recently been made in more specialized research fields. Cavitation, entrained air and foaming are all processes in which the deformation of air bubbles in a fluid flow field is of interest. This problem cannot be solved theoretically in complicated conditions, and a solution based on the precision of computational fluid dynamics is generally not acceptable. In [72], the authors suggest and describe a novel method for addressing this issue based on a hybrid sketch method for collecting experimental data and a comparison of machine learning algorithms for developing prediction models. The equivalent diameter and aspect ratio of air bubbles flowing near a sinking jet were predicted using three different models. The variables used by each model were unique. After constructing five various iterations of the additive regression of decision stump, bagging, K-star, random forest and support vector regression algorithms by adjusting their hyperparameters, the authors found that all five of them converged steadily.  
Two models produced accurate estimates of comparable diameter using four distinct measures. Every configuration of the third model was offered at a discount from the second. Differences in the input variables to the prediction models exhibit a more substantial effect on the precision of the findings when trying to forecast the bubble aspect ratio. The suggested method has promise for tackling complex issues in investigating multiphase flows.  
A typical example of the application of artificial intelligence in coastal engineering concerns methods such as artificial neural networks combined with fuzzy models, which are used to improve the prediction efficiency and reduce the time and cost spent on the experimental work of applying empirical formulas on the stability of breakwaters. Specifically, in [73], to predict the stability number of breakwaters, the least squares version of support vector machines (LSSVM) method was used, which takes seven independent variables (breakwater permeability, damage level, wave rate, slope angle, water depth, wave height and wave peak period) as inputs, and managed to predict the stability of breakwaters with an accuracy rate of 0.997.
# Generalized Kinetic Monte Carlo Framework for Organic Electronics
## 3 kMC Framework for Organic Materials and Devices
### Modeling of the Organic Material
#### 3.1.3 Phosphorescent Dopants  
OSCs are one representative of the class of excitonic devices. In contrast to their inorganic counterparts, an absorbed photon does not directly excite a free electron-hole pair, but forms an exciton with binding energies exceeding \(500\,\mathrm{meV}\). A morphology that introduces a high exciton splitting efficiency is the bulk-heterojunction (BHJ) [3; 62]. The BHJ reduces the distance between the generation position of an exciton and the donor-acceptor interface. This distance should be as short as possible, as singlets have short diffusion lengths of 3-6 nm [63]. If the distance between origin and interface exceeds the diffusion length, singlet excitons probably decay before reaching an interface and cannot be converted into usable electrical energy.  
Figure 3: Chain morphologies on a two-dimensional \(20\times 20\) grid with an average polymer chain length of six monomers. Single material with Gaussian chains \(\eta=0\) (left), worm-like chains with preferred orientation \(\eta=0.9\) (middle) and chains in a BHJ where only the blue material is a polymer and the red material is small molecules (right).  
Another possibility of enhancing the splitting efficiency is to enhance the exciton lifetime and diffusion length. Triplet excitons exhibit an effective lifetime of 10 \(\mu\)s for P3 HT, which corresponds to a diffusion length of 100 nm [63]. The spin selection rules forbid the radiative decay of triplet excitons. However, due to the spin-orbit coupling triplets may radiatively decay, but this is very improbable for commonly-used organic materials. Triplet excitons cannot be generated by the photon absorption as it is forbidden by spin-selection rules [64]. In the presence of strong spin-orbit coupling, the spin-selection rules are lifted. Thus, singlet excitons can be converted into triplet excitons. This process is known as intersystem crossing (ISC) [64].  
Incorporating heavy metal atoms into the organic layer induces a strong spin-orbit coupling. Doping of a BHJ with nanoparticles can increase the effective exciton lifetime and thus improve the power conversion efficiency [24; 25; 65; 66]. Furthermore, in phosphorescent OLEDs, organometallic complexes are used to improve the efficiency. The electrically-generated triplet excitons decay radiatively and help thus to enhance the efficiency of the OLED [67; 68].  
Dopant sites are implemented to investigate the impact of doping with strong spin-orbit materials in OSCs or OLEDs. Dopant sites are assigned randomly to the lattice sites in the simulation box. The ratio of dopant to organic sites gives the volume concentration of the dopant. The spin-orbit coupling of an atom affects excitons only in its proximity. Thus, a radius is introduced, in which the spin-orbit coupling influences the excitons. As explained in Section 3.3, the intersystem crossing is activated within this radius.
Molecules2014194857-4879; doi:10.3390/molecules19044857  
_molecules_ ISSN **1420-3049**  
www.mdpi.com/journal/molecules  
Identification of Absorbed Constituents in the Rabbit Plasma and Cerebrospinal Fluid after Intranasal Administration of Asari Radix et Rhizoma by HS-SPME-GC-MS and HPLC-APCI-IT-TOF-MS\({}^{\text{n}}\)  
[PERSON]\({}^{\dagger}\), [PERSON]\({}^{\dagger}\), [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]\({}^{\ast}\)  
State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University Health Science Center, No.38, Xueyuan Road, Beijing 100191, China;  
E-Mails: clearfont(@163.com (C.L.); [EMAIL_ADDRESS] (F.X.); [EMAIL_ADDRESS] (D.-M.X.); [EMAIL_ADDRESS] (Y.J.); [EMAIL_ADDRESS] (M.-Y.S.); [EMAIL_ADDRESS] (G.-X.L.); [EMAIL_ADDRESS] (X.W.)  
\({}^{\dagger}\) These authors contributed equally to this work.  
\({}^{\ast}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel./Fax: +86-10-8280-1693.  
_Received: 28 January 2014; in revised form: 18 March 2014 / Accepted: 14 April 2014 / Published: 17 April 2014_  
**Abstract:** Traditional Chinese Medicine (TCM) nasal therapy has been utilized to treat numerous diseases for over two millennia. It has many advantages compared with other routes. In this article, headspace-solid phase microextraction-gas chromatography-mass spectrometry and high performance liquid chromatography-atmospheric pressure chemical ionization-ion trap-time of flight-multistage mass spectrometry were applied for the first time to analyze the absorbed constituents in rabbit plasma and cerebrospinal fluid (CSF) after intranasal administration of Asari Radix et Rhiz (AR). In total, 47 absorbed AR constituents including 14 monoterpenes, 10 phenylpropanoids, four benzene derivatives, two alkanes, nine \(N\)-alkylamides and eight lignans were tentatively identified in the rabbit plasma and CSF. Thirty-three absorbed constituents are found to have different bioactivities related to the pharmacological actions of AR through bibliography data retrieval. These indicated that many types of constituents of TCM can be absorbed at the nasal cavity into both rabbit blood and CSF. This is the first study to explore the absorption of AR, and comprehensively analyze the absorbed constituents after intranasal administration of TCM.  
These findings extend our understanding of the effective substances of AR, and inspire us to make a hypothesis on the mechanism of additive effect of multiple constituents of TCMs, which is very worthy of further investigation.  
Keywords:nasal therapy; _Asarum heterotropoides_ var. _mandshuricum_; intranasal administration; effective substance; cerebrospinal fluid; absorption; SPME; GC-MS; LC-MS; additive effect
## 5 Coupling and Solution of Equations in EMS
### Causative Couplings
#### 5.2.1 EM and Mechanical  
We consider the case of an EMS, where besides EM, the mechanical domain is involved in forms of displacement [18; 19; 20; 21] or deformation [22].  
Let us consider the example of the classical old electromagnet given in Figure 2, which is a typical EMS involving electro-magneto-mechanical aspects illustrating the consideration of the different domains [23]. It consists of an electromagnet composed of a stationary part constituting non-conducting magnetic material (\(\mu\)) and a mobile armature constituting conducting magnetic material (\(\mu\), \(\sigma\)). A coil fed by a voltage source excites the stationary part, and the mobile armature is connected to a spring, a damper and an external force. The equations governing such a system are:  
\[\mathrm{m}.\ \mathrm{d}^{2}\ \mathrm{X}/\mathrm{d}t^{2}+\mathrm{c}.\ \mathrm{d}X/ \mathrm{d}t+\mathrm{k}\ \mathrm{X}=\mathrm{F}_{\mathrm{mag}}+\mathrm{F}_{\mathrm{ext}} \tag{9}\]  
\[\mathrm{d}\Psi/\mathrm{d}t+\mathrm{r}\,\mathrm{I}=\mathrm{U} \tag{10}\]In these equations, r is the resistance, \(\Psi\) is the magnetic linkage flux, U is the source voltage, and I is the current in the exciting coil. X is the mechanical displacement, Fmag and Fext are the magnetic and external forces, m, c and k are the mass of the moving object, the damping coefficient and the stiffness of the spring, respectively. It may be noted that (10) is a particular case of (8).  
We consider for example a step voltage on the source in the exciting coil. The unknown variables in these equations are the current I across the coil and the displacement X of the mobile armature. In such a system, the magnetic linkage flux \(\Psi\) and the magnetic force Fmag could generally be nonlinear functions of the magnetic saturation and the mechanical motion.  
To solve the problem, we have to consider the [PERSON]'s Equations (4)-(7) with the mechanical and circuit Equations (9) and (10). Generally, the coupling of the EM domain with the mechanical domain needs a simultaneous strong solution of the equations due to non-linearity of behaviors and closeness of the magnetic and mechanical time constants. The magnetic field solution could be performed using 2D or 3D discretized geometrical cells or elements with defined boundary conditions on the discretized domain limits. This can be achieved by, e.g., the finite element method (nodal, edge or facet). This obtains a local solution for nodes, edges or facets.
# Eco Valorization of _Eucalyptus globulus_ Bark and Branches through Liquefaction
## 2 Materials and Methods
### Chemical Composition  
Ash content determination was done in accordance with ISO 1762:2015 standard using the 40-60 mesh fraction. The sample was incenerated in a furnace at 525 \({}^{\circ}\)C for 3 h.  
Moisture content of bark and branches was determined gravimetrically. The method used consisted in the determination of mass loss of the sample of 40-60 mesh fraction, dried in an oven at 105 \(\pm\) 3 \({}^{\circ}\)C overnight.  
Extractives were determined according to Tappi T 204 om-07 method [58]. Extractions were done sequentially in a Soxhlet apparatus with solvents of increasing polarity: dichloromethane (6 h), ethanol (12 h) and hot water (12 h).  
The protein content was determined in the extracted sample with a 1% solution of pepsin in 0.1 M HCl (liquid-to-wood ratio 50). The resulting solution was left in the oven at 37 \({}^{\circ}\)C for 4 h and then filtered with warm water until neutralization. The remaining residue was dried at 60 \({}^{\circ}\)C until constant weight.  
Tannin content was determined in a sample without extractives and proteins with a solution of NaOH at 0.3% (v/v). The resulting mixture was kept under a nitrogen atmosphere reflux during 1 h. The extracted material was filtered off, washed with hot water until neutralization and dried until constant weight at 60 \({}^{\circ}\)C.  
Lignin content was determined from extractives-free samples such as a Klason lignin, according to Tappi T22 om-02 standard procedure [59]. This method consists in the direct determination of lignin and is based on isolation and determination of acid-insoluble lignin by two-step hydrolysis: first with concentrated sulfuric acid (72%), followed by hydrolysis with diluted sulfuric acid (3%).  
The cellulose content was determined by the Kurscher and Hoffer method, which consists in the treatment of samples with nitric acid and ethanol (1:4, v/v). This method involves the sample free from extractives with a 50 mL solution of nitric acid and ethanol (1:4, v/v) for 3 h in reflux. At the end of each hour, the supernatant was removed by decanting and more 50 mL of ethanol and nitric acid solution were added (1:4, v/v). The insoluble residue obtained after 3 h is filtered off and washed with warm water until neutral reaction. The hemicellulose content was determined indirectly by the difference between the total polysaccharides content and cellulose.
## 2 Materials and Methods
### Constitutive Model  
The representation of the constitutive model is based on the strain rate tensor \"\(\underline{\dot{\gamma}}\)\" and viscous stress tensor \"\(\underline{\underline{\tau}}\)\" which is expressed by the following equation,  
\[\underline{\dot{\gamma}}=\
abla\overrightarrow{V}+\left(\
abla\overrightarrow {V}\right)^{\top} \tag{22}\]  
The significance of \"\(\underline{\dot{\gamma}}\)\" and \"\(\underline{\tau}\)\" are expressed by \(\dot{\gamma}\) and \(\tau\) respectively as given in Equations (23)-(25),  
\[\dot{\gamma}=\sqrt{\frac{1}{2}\Pi_{\gamma}}=\sqrt{\frac{1}{2}\underline{\dot{ \gamma}}\vdots\underline{\dot{\gamma}}} \tag{23}\]  
\[\tau=\sqrt{\frac{1}{2}\Pi_{\tau}}=\sqrt{\frac{1}{2}\underline{\tau}\vdots \underline{\tau}} \tag{24}\]  
where, \(\
abla\overrightarrow{V}\), \(\overrightarrow{V}\) and \(\Pi_{\tau}\) are the velocity-gradient tensor, velocity vector, and stress tensor of the second invariant, whereas superscript \"\(T\)\" represents its transpose. For the viscous fluid, constitutive behavior is given according to the following equation,  
\[\tau=\mu\dot{\underline{\dot{\gamma}}} \tag{25}\]  
where, \"\(\mu\)\" represents the dynamic viscosity of blood. It may be noted that Equations (4)-(25) have been adopted from literature [14]. In the present study, blood was assumed to be two separates fluids, and RBC behaves as a non-Newtonian Carreau model. The detailed rheological properties of blood are given in Table 1. Blood viscosity \(\mu\) (kg/m-s) as a function of shear rate \(\dot{\gamma}\) (in s\({}^{-1}\)), was considered by the following equation,  
\[\mu=\mu_{\infty}+(\mu_{0}-\mu_{\infty})\Big{[}1+(\lambda\gamma\,^{2})^{2} \Big{]}^{(\frac{(n-1)}{2})} \tag{26}\]
# A Choreography-Based and Collaborative Road Mobility System for L'Aquila City
## 4 Case Study
### The CHOReVOLUTION Synthesis Method at Work on the CRMS Use Case  
This section describes how the CHOReVOLUTION synthesis method can be exploited to realize the CRMS use case in the context of L'Aquila city. The following is a description of the services that play the roles of the participants: Roads Status, Weather, Air Quality, Congestion and Accidents.  
**- Roads Status** provides real-time information about the roads status by combining several kinds of informations including data gained from liquid presence sensors (by 5G technologies) dislocated along the main streets of the city, and on the main communication roads that connect the city with the periphery.  
**- Weather** provides weather information by using data obtained from a local source, i.e., the Center of Excellence Telesensing of Environment and Model Prediction of Severe events (CETEMPS) ([[http://cetemps.aquila.infn.it](http://cetemps.aquila.infn.it)]([http://cetemps.aquila.infn.it](http://cetemps.aquila.infn.it))). The CETEMPS manages a considerable network of instrumentation and data from remote sensing (ground and satellite data): a primary station for receiving second generation Meteosat data ([[https://www.eumetsat.int/website/home/index.html](https://www.eumetsat.int/website/home/index.html)]([https://www.eumetsat.int/website/home/index.html](https://www.eumetsat.int/website/home/index.html))), a meteorological radar, a wind profiling radar, an acoustic radar, optical radars, a station for radio survey and ozone survey, weather stations, induced fluorescence detectors, radiometers and photometers.  
**- Air Quality** provides information on air quality, which is assessed by monitoring concentrations of pollutants by sensors deployed at specific points in the city. These analyses are accompanied by the study of meteorological parameters that affect the dispersion of pollutants (wind speed and  
Figure 6: Smart Data Collection choreography.  
direction, humidity, radiation, etc.). This service can be used to trace low-polluting routes or, in the case of multi-modal transport, which involves the use of alternative transportation vehicles such as electric bicycles, electric scooters, buses, cars, etc., to trace routes that allow for avoid areas with high pollution. It uses data gained from the Regional Environmental Protection Agency (ARTA) ([[https://www.artaabruzzo.it](https://www.artaabruzzo.it)]([https://www.artaabruzzo.it](https://www.artaabruzzo.it))) that manages the regional network (\"Regione Abruzzo\") for monitoring air quality.
# A Palm-Jacobaeus Loss Formula for Multi-Service Systems with Separated Resources
## 4 Results and Discussion
### Simulator  
In order to determine the accuracy of the analytical method for determining the probability of the non-availability of strictly determined resources for calls of particular traffic classes, the results of the analytical calculations have been compared with the simulation data. The simulator was developed for the sole purpose of this article and was based on the process interaction approach, in line with the detailed and specific assumptions concerning modeling of multi-serve traffic streams presented in [54]. The simulator was implemented in C++. The input data for the simulator are  
* the number \(k\) of resources,
* the capacity \(f\) of a single resource,
* the number \(M\) of traffic stream classes,
* the number \(t_{i}\) of demanded allocation units for a stream of class \(i\) (\(1\leq i\leq M\)),
* the intensity \(\mu_{i}\) of a service stream of class \(i\),
* the proportions of offered traffic \(A_{1}t_{1}:A_{2}t_{2}:\ldots:A_{M}t_{M}=x:y:\ldots:z\), i.e., the values of the parameters \(x,y,\ldots,z\),* the average value \(a\) of traffic offered to a single allocation unit in the system, where: \[a=\frac{\sum_{i=1}^{M}A_{i}t_{i}}{kf}.\] (8)  
On the basis of the parameters \(x,y,\ldots,z\), which define the proportions of offered traffic \(A_{1}t_{1}:A_{2}t_{\cdot}\ldots:A_{M}t_{M}\), the simulator determines the values of the traffic intensity \(A_{i}\) for each of the offered traffic classes (\(1\leq i\leq M\)). Then, on the basis of the definition of traffic intensity,  
\[A_{i}=\frac{\lambda_{i}}{\mu_{i}}, \tag{9}\]  
the simulator determines the value of the parameter \(\lambda_{i}\), which defines the intensity of generating traffic streams of class \(i\). The parameters \(\lambda_{i}\) and \(\mu_{i}\) are given to exponential distribution generators used in the program to determine the time intervals between the arrival of a next traffic stream of class \(i\) and the service time of a given call of class \(i\).  
To determine the probability of the non-availability of strictly determined resources for calls of particular traffic classes, the condition for the termination of a simulation experiment is the amount of elapsed time (duration time) for individual series necessary to generate a predefined number of calls of the class that is least active (most frequently, this is the class with the biggest number of demanded allocation units). The average result is calculated on the basis of 10 series. In practice, to obtain confidence intervals that are not greater than 5% of the average value of the simulation, it is necessary to generate about 100,000,000 calls of the least active class.