esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Planning of Mobile Service Teams' Mission Subject to Orders Uncertainty Constraints
## 6 Computational Experiments
In addition to the above experiments, the effectiveness of the proposed approach was evaluated for the distribution networks of different sizes (different numbers of nodes and MSTs). The results are collected in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Nodes \(n\)** & **Number of MSTs K** & **Calculation Time (s)** \\ \hline
5 & 1 & \(<\)1 \\
5 & 2 & \(<\)1 \\
5 & 3 & \(<\)1 \\
5 & 4 & \(<\)1 \\ \hline
7 & 1 & \(<\)1 \\
7 & 2 & \(<\)1 \\
7 & 3 & 1 \\
7 & 4 & 5 \\ \hline
9 &  
To summarize, the experiments were carried out for networks containing 5-20 nodes in which services were made by sets consisting of 1-4 MSTs (the sizes of the instances considered correspond to the sizes of the networks encountered in practice [45]). The aim of the experiments was to estimate the time necessary to designate the routes to guarantee timely services in the case of disturbances _IS_ occurrence. In all instances considered, the synthesis of routes required considerable time expenditure. This means that the problems considered can be solved online mode when the size of the service distribution network does not exceed 15 nodes. In the case of larger networks, the effect of combinatorial explosion becomes of significant importance and limits the practical use of this method to the offline prototyping of possible variants of service mission scenarios.
# Comparison of Enhanced Noise Model Performance Based on Analysis of Civilian GPS Data
## 3 Analysis of Observations  
One may think of the collection of positions--emitted by the GPS chip--as a stochastic process, a sequence of random variables, \(X_{1},X_{2},\,\cdots,X_{t},\cdots\) labelled by time \(t=1,2,\,\ldots\) In our study, \(X_{t}\) may represent the latitude, longitude, or altitude returned by a GPS unit. We denote random variables with capital letters, e.g., \(X_{t}\), and its particular realisation with lower case letter, e.g., \(x_{t}\). Any estimator of a statistic, e.g., the expectation value \(\mu\), is denoted by the same letter but decorated with a symbol, e.g., \(\widehat{\mu}\).
# Research on Optimization of Multi-Objective Regional Public Transportation Scheduling
## 5 Example Analysis
** & \(D_{i}^{\star}\) & \(D_{i}^{-}\) & \(F_{i}\) \\ \hline
1 & 0.6423 & 0.3577 & 0.3577 & 26 & 0.2793 & 0.5673 & 0.6701 \\
2 & 0.3577 & 0.6423 & 0.6423 & 27 & 0.2452 & 0.4961 & 0.6693 \\
3 & 0.3179 & 0.6383 & 0.6675 & 28 & 0.4437 & 0.3625 & 0.4496 \\
4 & 0.4454 & 0.3691 & 0.4532 & 29 & 0.2465 & 0.4960 & 0.6680 \\
5 & 0.3555 & 0.6413 & 0.6433 & 30 & 0.5372 & 0.3449 & 0.3910 \\
6 & 0.5868 & 0.3471 & 0.3717 & 31 & 0.4181 & 0.3762 & 0.4736 \\
7 & 0.2944 & 0.6156 & 0.6765 & 32 & 0.3284 & 0.4164 & 0.5591 \\
8 & 0.3118 & 0.6289 & 0.6686 & 33 & 0.5686 & 0.3396 & 0.3739 \\
9 & 0.2552 & 0.4939 & 0.6593 & 34 & 0.3933 & 0.3829 & 0.4933 \\
10 & 0.5010 & 0.3452 & 0.4079 & 35 & 0.2509 & 0.5136 & 0.6718 \\
11 & 0.2769 & 0.4586 & 0.6235 & 36 & 0.6226 & 0.3521 & 0.3613 \\
12 & 0.2856 & 0.5803 & 0.6702 & 37 & 0.2894 & 0.5949 & 0.6728 \\
13 & 0.2888 & 0.4483 & 0.6083 & 38 & 0.2671 & 0.5418 & 0.6697 \\
14 & 0.2827 & 0.4526 & 0.6155 & 39 & 0.2766 & 0.5608 & 0.6697 \\
15 & 0.2563 & 0.4996 & 0.6610 & 40 & 0.2626 & 0.5377 & 0.6718 \\
16 & 0.3460 & 0.4083 & 0.5413 & 41 & 0.41 \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Departure Interval**} & \begin{tabular}{c} **The Total Waiting** \\ **Cost of Passengers** \\ **(Yuan)** \\ \end{tabular} & \
\begin{tabular}{c} **The Comprehensive** \\ **Service Ratio of** \\ **Buses** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Passengers Who Not** \\ **Achieved Easy** \\ **Transfer** \\ \end{tabular} \\ \hline Optimal departure & & & & \\ interval & 2641.18 & 1.3760 & 125 \\
3 min & 3578.25 & 1.1383 & 387 \\
4 min & 3610.00 & 1.2105 & 413 \\
5 min & 3703.40 & 1.2802 & 443 \\
6 min & 3889.94 & 1.3432 & 462 \\
7 min & 3834.57 & 1.4177 & 510 \\
8 min & 3717.22 & 1.4981 & 533 \\
9 min & 4041.29 & 1.5253 & 548 \\
10 min & 4433.40 & 1.5511 & 594 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison and analysis of schedules. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Line No.**} & \multirow{2}{*}{\begin{tabular}{c} **Departure** \\ **Time** \\ \end{tabular} } & \begin{tabular}{c} **Waiting Time for** \\ **Transfer** \\ **Passengers (min)** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Passengers Who** \\ **Not Achieved** \\ **Easy Transfer** \\ \end{tabular} & \multirow{2}{*}{\
\begin{tabular}{c} **Full Load** \\ **Rate** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **R** \\ **R** \\ \end{tabular} } \\ \hline
1 & 7:04 7:12 7:21 & 238.0 & 46 & 0.867 & 0.697 \\
2 & 7:07 7:14 7:22 & 139.0 & 39 & 0.620 & 0.714 \\
3 & 7:10 7:19 7:25 & 215.0 & 29 & 0.683 & 0.684 \\
4 & 7:08 7:13 7:17 & 117.4 & 11 & 0.493 & 0.712 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The most satisfactory operation schedule. Figure 7: Comparison of calculation results for two algorithms.
# Research on the Efficient Space Debris Observation Method Based on Optical Satellite Constellations
## 5 Numerical Simulation Experiment
### Object Detectable Analysis  
#### 5.2.1 Detection Quantity Analysis  
As shown in Figure 9, the observation performance of space objects varies greatly among the different constellation configurations, and the observation efficiency of the constellations 12_3, 12_4, and 12_6 that were composed of 12 satellites was relatively poor. Among them, the 12_3 constellation configuration meant that 12 satellites were evenly distributed on three orbital planes, and the right ascension of each orbital plane was 120 degrees different. The meaning of other constellation configurations can be deduced in the same manner.  
Secondly, the constellations composed of 15, 16, and 18 satellites had good monitoring performances, of which the observation performances of the 15_3 and 18_3 satellites were relatively good. In addition, the constellations composed of 20, 21,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Constellation** & **Meaning** \\ \hline Constellation 1 & 12\_3P, 12 satellites were evenly distributed on three orbital planes \\ Constellation 2 & 12\_4P, 12 satellites were evenly distributed on four orbital planes \\ Constellation 3 & 12\_6P, 12 satellites were evenly distributed on six orbital planes \\ Constellation 4 & 15\_3P, 15 satellites were evenly distributed on three orbital planes \\ Constellation 5 & 15\_5P, 15 satellites were evenly distributed on five orbital planes \\ Constellation 6 & 16\_4P, 16 satellites were evenly distributed on four orbital planes \\ Constellation 7 & 18\_3P, 18 satellites were evenly distributed on three orbital planes \\ Constellation 8 & 18\_6P, 18 satellites were evenly distributed on six orbital planes \\ Constellation 9 & 20\_5P, 20 satellites were evenly distributed on five orbital planes \\ Constellation 10 & 21\_3P, 21 satellites were evenly distributed on three orbital planes \\ Constellation 11 & 24\_3P, 24 satellites were evenly distributed on three orbital planes \\ Constellation 12 & 24\_4P, 24 satellites were evenly distributed on four orbital planes \\ Constellation 13 & 24\_6P, 24 satellites were evenly distributed on six orbital planes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Constellation number table.  
Figure 9: The proportion of space objects within the detectable range of the different constellation configurations to the total.  
best performances and the largest numbers of observed targets. However, the larger the constellation size, the greater the cost. Therefore, the specific constellation to be selected for the space object observation should be comprehensively considered according to the demand and the cost.
## 2 Understanding the Key Extracellular Matrix Proteins
### Elastin  
The mechanism of elastin synthesis is much better understood compared with collagen. The elastic and resilient properties of the ECM are provided by elastin and microfibrils [42]. Elastin itself is the second most abundant macromolecule in the ECM and serves an important role in the regulation of the biomechanical properties of cells and tissues [36] such as blood vessels, lungs and skin [17]. Elastin is an insoluble molecule with a finite turnover in healthy tissue. The appearance of elastin was first reported in 1958, but the interest in understanding elastin biochemistry further sparked in 1963 when the relationship between elastin structure and function was first reported [17]. The most abundant amino acids present in elastin are glycine followed by alanine, valine and a low level of hydroxproline [43]. It has been shown that elastin and collagen are the only animal proteins that contain hydroxproline, with an abundance of 2-4% and 13%, respectively [44]. Elastin, just like collagen, is an insoluble protein synthesised from intracellularly soluble monomers called tropoelastin (TE) that are assembled extracellularly by crosslinking (Figure 4). After the tropoelastin mRNA is translated in the RER, the protein migrates to the Golgi apparatus where it binds to the elastin-binding protein (EBP) to inhibit self-aggregation and avoid early degradation [36]. The TE-EBP complex  
Figure 3: Collagen Synthesis pathway and demonstration of LOX-medicated crosslinks: A. Triple stranded helix is formed in the endoplasmic reticulum (ER) followed by post-translational modification in the Golgi apparatus. B. Procollagen is processed and assembled intracellularly before being secreted to the extracellular space. C. The N and C terminus are removed to form tropocollagen. D. Lysyl Oxidase (LOX) catalyses the deamination of the lysine. E. Conversion of a lysine residue into a reactive aldehyde. F. Aldol condensation reaction between neighbouring aldehyde groups. G. Crosslinks both within and between triple-helical molecules [where X is C=C(CHO)].  
is then exocytosed and the EBP releases the tropoelastin to the ECM environment. Extracellularly, the tropoelastin monomers align and interact with the microfibrillar scaffold and acquire the correct orientation to form the polymeric elastic fibres [36]. Tropoelastin has an intrinsic ability to undergo coaervation which is an endothermic process driven by entropy [45]. The process is believed to be largely due to the hydrophobic domains of tropoelastin [45]. With increasing temperature to physiological conditions, the tropoelastin molecules are correctly aligned for the subsequent enzymatic crosslinking of the molecules [45]. Lysyl oxidase (LOX) catalyses this reaction [36] in a similar way as for collagen. The structure of elastin is reported to be highly elastic and amorphous when compared to inextensible collagen [17].
## 3 Experimental Set-Up
### Experimental Matrix and Pre-Test Analysis
#### 3.1.2 Experimental Set-Up RELAP5-3D Model and Transient (LOFA)
As valve V945 closed, the mass flow rate in channels 1 to 5, which is measured by flowmeter 1, fell to 0 (Figure 11). As a consequence, the flow rate in the other channels (6 to 10, reported by flowmeter 2) tended to increase, but the controller immediately reacted by opening up the bypass valve V944, thus maintaining the flow through flowmeter 2 at the specified level. This transient \begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Value** \\ \hline Total mass flow through the test section (P174) & 466.6 g/s \\ Test section inlet temperature & 305.5 \({}^{\circ}\)C \\ Test section inlet pressure & 8 MPa \\ Mass flow through channels 1 to 5—Flow meter 1 (V946) & 218.7 g/s \\ Mass flow through channels 6 to 10—Flow meter 2 (V947) & 247.9 g/s \\ \hline \end{tabular}
\end{table} 
Table 1: Mock-up testing parameters. Figure 9: Helium temperature along each channel. also perturbs the inlet manifold mass flow rate, so valve V901 slightly closes to adjust the desired inlet flow. The transient does not significantly affect the pressure drops. As far as the FWMU cooling capabilities are concerned, the LOFA might have caused a significant temperature increase (Figure 12) in channels 3 and 4. This quick increase might be overestimated by RELAP because the heat conduction between neighboring structures is not considered, as discussed in [15]. Since the heat is redistributed across the different channels, a more realistic situation would see lower temperatures for channels 3 and 4, but slight temperature increases in the other channels. In any case, the results suggest that the maximum temperature should not exceed 550 \({}^{\circ}\)C in the first 30 s of the transient. In this regard, the figure obtained from the Ansys CFX analysis suggests a much quicker increase (in about 12 s the temperature reached 550 \({}^{\circ}\)C). Because of the discordant results, the Ansys CFX figure was taken as a reference for the experiments being the most limiting one. Figure 11: Mass flow rate through the inlet manifold, the bypass, and the flowmeters. Figure 10: Surface mock-up plate temperature for each channel.
## 3 Results
### Manufacturing and Assembly  
As already mentioned, the reactor body was manufactured by SLM from stainless steel 1.4404. After receipt, a thorough cleaning was carried out to remove powder residues from the production process. For test rig integration, a variety of fittings and the flange construction for the cooling unit needed to be attached to the reactor body. Figure 7 displays the assembled reactor. It stands on a platform for an easy mounting to the test rig and good accessibility. A high-temperature insulation (5 mm calcium silicate) between the reactor and the platform prevents excessive heat dissipation. Suitable piping combines the three gas inlets and three outlets of the reactor into one connection for reactants and product gas, respectively. Within these pipes, the measuring points for the reactant and product gas temperature as well as for the differential pressure of the reactor are provided. The fittings for the filling and monitoring of the heat pipes are located near the platform. For the flange construction, the reactor was welded into a precisely fitting cut-out of a blind flange. The counterpart is made of standard parts. The cooling unit formed in this way offers a compressed air inlet, a nozzle for water injection, an outlet for air/steam, a feed-through for the thermowell and other fittings for measurements.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Material** & **Form of Strain** & \(T_{\textit{designf}}\)\({}^{\circ}\)C & \(p_{\textit{designf}}\)**/bar** & \(Rp_{\textbf{0.2}}\)**/N/mm\({}^{2}\)** & **Safety Factor** \\ \hline
**Reaction channel** & 1.4404 (SLM) & Internal pressure & 425 & 10 & 446 \({}^{\text{a}}\) & 49.4 \\
**Heat pipes** & 1.4404 (SLM) & Internal pressure & 350 & 165 & 472 \({}^{\text{b}}\) & 8.3 \\
**Outer reactor wall (without lattice structure)** & 1.4404 (SLM) & Internal pressure & 300 & 10 & 489 \({}^{\text{c}}\) & 1.5 \\
**Outer reactor wall** & & & & & & \\
**(with lattice structure; weakest point: contact points with lattice)** & 1.4404 (SLM) & Tension due to internal pressure & 300 & 10 & 489 \({}^{\text{c}}\) & 5.1 \\ \hline \end{tabular}  
* own tensile test at 425 \({}^{\text{\circ}}\)C (see Table 1); \({}^{\text{b}}\) own tensile tests (see Table 1) interpolated at 350 \({}^{\text{\circ}}\)C; \({}^{\text{c}}\) own tensile tests at 300 \({}^{\text{\circ}}\)C (see Table 1).  
\end{table}
Table 2: Strength calculations for the individual structures of ADDmeth1.  
Figure 6: Metal print sample (1.4404) with fine structures.
# Mathematical Modelling of the Effects of Plasma Treatment on the Diffusivity of Biofilm
## 1 Introduction
There are some previous works explaining the antimicrobial resistance of the biofilm caused by diffusion limitations. [PERSON] et al. investigated the Ciprofloxacin penetration into _pseudomonas aeruginosa_ biofilms. They found that the transport of the antibiotic to the biofilm-substratum interface was found to be impeded by the biofilm [31]. [PERSON] et al. reported that the tobramycin diffusion across biofilms was the reason for dispersal of cells from the biofilm [2]. [PERSON] et al. hypothesized that polysaccharides played a major role in antibiotic diffusion, so they developed a physical model of microcolonies and biofilms to predict antibiotic penetration times. The authors also described diffusion of antibiotics into aggregates of _P. aeruginosa_[32]. These dispersed cells were more susceptible to tobramycin than the cells present within the biofilm. These studies were based on studying th-e reason behind antimicrobial resistance in biofilm in terms of diffusion. However, no studies have been done to investigate the reason for better penetration of biocide such as CHX after non-thermal plasma treatment of a biofilm. This study explains how the diffusion of CHX is enhanced after the plasma treatment in _P. aeruginosa_ biofilm with mathematical modeling. A mathematical model of CHX diffusion was developed to explain the enhanced killing of _P. aeruginosa_ biofilm after plasma treatment. This model further reports the impact of plasma treatment in combination with the biocide CHX for biofilm removal and can be assessed in future experimental work. Moreover, this model will provide knowledge on plasma decontamination and sterilization for challenging biomedical applications.
# Flow-Induced Noise Characteristics Analysis of a Pipeline Structure in a Cabin Rigid Corner
## 3 The Acoustic Radiation Equation of the Pipeline Structure in a Rigid Corner
### Pipeline Acoustic Radiation Analysis  
A finite element model of the pipeline structure was established. In pipeline structure dynamic numerical analysis, both ends of the pipeline structure were simply supported. The dynamic response of the pipeline structure was analyzed using the excitation force of the pulsating pressure on the inner wall surface of the pipeline structure. The finite element method (FEM) was employed to verify the dynamic response of the pipeline structure. Considering that the business software MSC.Patran/Nastran 2012.2 is a proven and widely used numerical tool for structural dynamic response analysis, the FEM predictions were calculated using MSC/Nastran 2012. The pipe material is steel with the following mechanical properties: density \(\rho_{S}\) = 7800 kg/m\({}^{3}\); modulus of elasticity \(E\) = 210 GPa; and Poisson's ratio \(\mu\) = 0.3.  
The normal vibration velocity of the pipeline structure was defined as the acoustic boundary condition, the outer surface was defined as the acoustic radiation surface and the boundary element method (BEM) was applied to deal with the exterior acoustic radiation problem. In addition, the influence of the internal flow noise was neglected in the numerical calculations. In the numerical analysis for acoustic radiation, the acoustic parameters of the acoustic medium were as follows: density \(\rho\) = 1.225 kg/m\({}^{3}\) and acoustic velocity \(c\) = 340 m/s. The reference value of acoustic power was \(W_{0}\) = 1 \(\times\) 10\({}^{-12}\) W.  
In the numerical calculations, the calculated frequency range was between 1 Hz and 1000 Hz. The step size is 1 Hz in the 1-200 Hz frequency band and 2 Hz in the 200-1000 Hz frequency band.  
Firstly, the influences of flow velocity on acoustic radiation power in the pipeline structure are discussed, in which the pipeline structure was defined as the free-space field. Figure 7 indicates the influence of the flow velocity, showing that the acoustic radiation  
Figure 6: Comparison of fluctuating pressure. ((**a**) shows the turbulent pulse pressure patterns in Point A, (**b**) shows the turbulent pulse pressure patterns in Point B, and (**c**) shows the turbulent pulse pressure patterns in Point C).  
power of the pipeline structure increased significantly when the flow velocity increased in the pipe.
## 1 Introduction  
Harmful algal blooms (HABs) are naturally-occurring phenomena in fresh and marine water systems, often causing severe damage to the fishery industry worldwide, through massive kills of commercially cultured fishes and toxin-contamination of shellfish leading to closures of commercial shellfish harvests, and, in some cases, consumption of contaminated shellfish results in human illness and even death [1; 2; 3]. Lately, the acceleration of geographic expansion and intensity of HABs on a global scale associated with climate change have been reported [4; 5; 6; 7]. Hence, developments of effective countermeasures to control the HAB events are even more urgent and crucial concerns. Mechanical, chemical, genetic, biological, and environmental measures to prevent HAB have been proposed [4], and clay dispersion is the only practical measure confirmed to control HABs formed by _Cochlodiniumpolykrikoides_ in Korean coastal waters [8]. However, the negative impacts of clay on non-target organisms living in the seafloor and water column are also pointed out [9; 10; 11; 12]. The control of HABs directly intervenes with surroundings in dynamic ocean environments, demanding an establishment of measures as environmentally friendly as possible. Among all proposed strategies to mitigate HABs, biological controls using naturally living organisms may have the least impact on ecosystems in the present circumstances. Numbers of biological controls using microbes to bivalves have been proposed and tested extensively for several decades [13; 14; 15; 16; 17; 18; 19; 20]. In particular, bacteria that kill and/or inhibit the growth of HAB species often associated with bloom termination have received worldwide attention as possible biological control of HABs [21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31].  
Although the use of natural pathogenicity could be environmentally friendly and cost-effective to control HABs, introducing potentially invasive bacteria may require a careful manner [32; 33]. Recently, a biofilm of seagrass and macroalgae were found to be densely populated by algal growth-limiting bacteria (GLB), including both algicidal and growth-inhibiting bacteria [34; 35; 36; 37], indicating their function as an enormous source of GLB for the adjacent seawater [31; 38]. The latest microcosm study demonstrated that seawater in the _Zostera marina_ bed successfully suppressed artificial _Clattonella_ blooms [31], indicating that protection and restoration of seagrass and macroalgal beds prevent the outbreak of HABs through providing the specific microbial communities. However, these holistic approaches may sometimes not be flexible in actual field applications in that they often require specific habitat characteristics such as light, sediment or substrate types, currents, bioturbation, etc. [39; 40].  
In this study, _Ulva pertusa_ beds using mobile floating cages were artificially created to study the distribution of algal growth-limiting bacteria (GLB), seeking flexible and widely applicable technologies to control HABs. A natural macroalgal bed was also studied for comparison.