esa-sceva/satcom-chunk-collection huggingface dataset:
# On-Line Tendency Control of Dissolved Oxygen Concentration during Aerobic Fed-Batch Fermentations
###### Abstract  
In this paper, an on-line control strategy that aims to guarantee the maintenance of a minimum dissolved oxygen (DO) concentration during aerobic fed-batch fermentations is proposed. It is a difficult task to maintain the DO concentration, especially during fed-batch fermentation, due to strongly nonlinear, variable conditions and probe dynamics. The algorithm uses information contained in the slope of the profile of the DO, as this evolves in a timely way to adapt to process variations. Moving window technology was used to track the DO tendency variation. This method was tested in _Corynebacterium glutamicum_ and _Pichia pastoris_ fermentations. The performance of tendency control was compared with that of manual control. The experimental results clearly show that the on-line tendency control of DO is effective and can also reduce the frequency of activity of the controller as well as the manpower burden.  
fed-batch fermentation; dissolved oxygen control; tendency variation; moving window +
Footnote †: journal: Applied Sciences
# Two-Way Multi-Antenna Relaying with Simultaneous Wireless Information and Power Transfer
## 2 System Model  
Consider a two-way relay network as shown in Figure 1, where end nodes T\({}_{1}\) and T\({}_{2}\), each of which is equipped with one antenna, exchange information through an energy-constrained intermediate relay node, R, possessing \(M\) antennas. This relay node will harvest energy from the two end nodes and use the harvested energy for forwarding their information. The relay node's antennas are spatially spaced in such a way that the received/transmitted signals undergo statistically independent fading. Throughout this paper, perfect timing and synchronization among T\({}_{1}\), T\({}_{2}\), and R are assumed, and BPSK modulation is used at T\({}_{1}\) and T\({}_{2}\). Let \(h_{1,m}\sim CN\big{(}0,\sigma_{1}^{2}\big{)}\) (or \(h_{2,m}\sim CN\big{(}0,\sigma_{2}^{2}\big{)}\)) denote the channel gain between the antenna of T\({}_{1}\) (or T\({}_{2}\)) and the \(m\)-th antenna of R, where \(\sigma_{1}^{2}=d_{1}^{-\
u}\) (or \(\sigma_{2}^{2}=d_{2}^{-\
u}\)) is due to the path loss with power path loss exponent \(v\) and distance \(d_{1}\) (or \(d_{2}\)) of the T\({}_{1}-\)R link (or the T\({}_{2}-\)R link), and \(m=1,2,...,M\). We presume that all the channels are static over an interval of \(2N\), which denotes the total block time in which a certain block of information is exchanged between T\({}_{1}\) and T\({}_{2}\) (see Figures 2a and 3a), and ignore the direct link between the end nodes owing to the larger distance compared with the T\({}_{1}-\)R and T\({}_{2}-\)R links [16]. For analytical simplicity, we assume that the relay node is located halfway between the end nodes, and thus \(\sigma_{1}^{2}=\sigma_{2}^{2}=\sigma^{2}\). As mentioned in Section 1, we consider two half-duplex relaying protocols for separate energy harvesting and information processing at the relay node, i.e., PSR and TSR.
## 5 Computational Analysis
### Grid Independence Study  
In the grid independence study section, three types of meshes were generated. Coarse mesh has a mesh size of 0.4 million cells, medium mesh with 1.2 million cells, and the fine mesh has 2.0 million cells using a perforated distributor plate. For each grid, simulations were carried out for fixed initial bed height, same particle size, and superficial velocity equal to 1.5*Umf. The pressure drop across the bed was compared with the experimental data. It was found that the computational pressure drop was not in good agreement with coarse mesh compared to medium and fine mesh (Table 6).  
Moreover, the medium and fine mesh results were quite close. Hence medium mesh was selected as an optimum mesh for the rest of the simulations. Moreover, previous researchers also exposed that as a rule of thumb for solid-gas mixing phenomena, 10 times particle size grid size is recommended for appropriate CFD results [40; 41]. This study's grid size was well under this rule to capture good computational results.  
Table 5 above compares CFD and experimental results using various resolutions of grids. It can be concluded from the above table that the pressure drops well matches medium and fine mesh. In contrast, the maximum solid volume fraction at a specified bed height (L/D = 0.4) does not change well for medium and heavy mesh. Therefore medium mesh as an optimum mesh was selected for the computational purpose.  
The maximum solid volume fraction along the radius of the gasifier shown in Figure 15 depicts that for coarse mesh, the maximum solid volume fraction is around 0.38. In contrast, it is found to be 0.51 and 0.58 for medium and fine mesh, respectively. Moreover, medium and fine mesh volume fraction trend trends are pretty close. Hence medium mesh based on lower computational cost is preferred for simulations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mesh Generated**} & \multirow{2}{*}{**Grid Size (Number of Cells)**} & \multicolumn{2}{c}{**Pressure Drop (CFD)**} & \multirow{2}{*}{**Pressure Drop (Experiment)**} & \multirow{2}{*}{**Maximum Solid Volume Fraction**} \\  & & **(Pa)@ Superficial Velocity** & & & \\  & & **(m/s) [1.5 Umf]** & & & \\ \hline Light & 4.0 \(\times\) 10\({}^{5}\) & 902 & & 0.38 \\ \hline Medium & 1.2 \(\times\) 10\({}^{6}\) & 1052 & & 0.51 \\ \hline Heavy & 2.0 \(\times\) 10\({}^{6}\) & 1071 & & 0.58 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Grid Independence study results from a comparison of different mesh generated.  
Figure 14: Mesh of fluidization column along with air distributor plates.
## 2 Materials and Methods
### MVG Parameters and Flow Configuration  
The same setup as [PERSON] and [PERSON]'s second case serves as the base configuration for this study ([PERSON] and [PERSON], 2019). The flow parameters are defined by a free-stream velocity of \(U_{\infty}=6\,\mathrm{m/s}\) and a kinematic viscosity of \(\
u=1.4607\times 10^{-5}\,\mathrm{m^{2}/s}\), making comparison possible between the experimental results from the base case and the simulations in this work. The flow direction is along the x-axis, while y and z denote the wall-normal and spanwise directions, respectively.  
The spanwise inner distance between the centers of two MVGs in a pair is denoted by \(d\), and the spanwise distance between two pairs of MVGs is represented by \(\Lambda\). Each MVG has a width (\(w\)), length (\(L\)), and height (\(h\)) and forms an angle (\(\theta\)) with respect to the free-stream velocity. To extend the current parametric study, the height, inner distance, and spanwise periodicity are varied. The parameters include \(h\) ranging from [0.360:0.0554:0.526] \(\delta_{99,MVG}\) and \(\Lambda\) covering [1.949:0.900:7.34] \(\delta_{99,MVG}\), where the bracketed values denote the initial point, spacing, and endpoint of the parameter grid. The ratio \(d/\lambda\) varies between [0.2, 0.35, 0.5, 0.65, and 0.8]. This configuration results in a total of 140 cases. A schematic overview of all parameters is provided in Figure 2. Table 1 lists the values for these different parameters for the base case.  
Figure 1: Overview of the computational domain, with the green plane showing the inlet and orange showing outlets. The wall region with a single MVG is gray. A symmetry condition is applied on the side walls.
# Numerical Insight into the Kelvin-Helmholtz Instability Appearance in Cavitating Flow
## 3 Numerical Procedure
### Physics and Solver Settings  
It is important to set proper simulation settings, as are the initial conditions from which to start the simulation, so we first performed simulations as single-phase simulations for each mass flow under stationary conditions. The outcomes were then considered as the initial conditions for further transient simulations. Numerical calculations were carried out using time-dependent Reynolds-averaged Navier-Stokes equations. Homogeneous water and water vapor mixture was considered and a Schnerr-Sauer cavitation model [24] with an evaporation pressure of 2340 Pa was used. A modified SST K-\(\omega\) model, using the turbulent viscosity correction suggested by [PERSON] et al. [16] and [PERSON] et al. [17], mentioned above, was used for the turbulent model. For pressure and velocity coupling, the PISO algorithm [25] was used. We used the second-order upwind scheme [26] for spatial numerical discretization of solving hyperbolic partial differential equations for all but the pressure and volume fraction, providing more precise results with marginally higher computer resource usage compared to the first-order upwind scheme. The PRESTO interpolation scheme was used to discretize the pressure [27], and the first-order upwind scheme [26] was used for the discretization of the volume fraction. Time integration was modeled for transient solutions using the implicit transient formulation of bounded second-order.  
The convergence criteria were established by evaluating the progression of various flow parameters (absolute pressure at the inlet and outlet, and velocities at the microchannel inlet, outlet, and throat). Following the sum of the imbalance of transport equations between iterations for all cells in the computational domain, the observed flow parameters were converged when falling below \(10^{-5}\) of the iterative numerical solution of the individual equations at each time step of the simulation. The iteration error was estimated to be less than 0.02%. By assessing its effect against the average pressure difference and the cavity length, the size of the time step was obtained. There was little difference in these parameters if the time step was shorter than 5 \(\upmu\)s, but a shorter one--1 \(\upmu\)s was ultimately chosen for observation of the Kelvin-Helmholtz instability. We conducted 50 ms of computational modeling for each case, where the last 30 ms was applicable for further analysis.
## 2 Materials and Methods
### Outcome Measures  
The Fugl-Meyer assessment (FMA) score was used as the main outcome in this study. It evaluates motor function, sensory function, balance, range of motion, and joint pain and is divided into 66 points for the upper extremity. The FMA method was chosen because of its high reliability, reproducibility, and validity [22; 30; 31]. It comprises four subscales: Part A (Shoulder/Elbow/Forearm) assesses movements involving the shoulder, elbow, and forearm. Part B (Wrist) evaluates wrist movements, including flexion, extension, and circumduction. Part C (Hand) assesses hand movements, grip ability, and finger coordination. Part D (Coordination/Speed) evaluates rapid alternating and coordinated upper extremity movements. Each item is scored on a 3-point scale (0-2), where higher scores indicate better motor recovery.  
The severity of motor function impairment according to the FMA score is as follows: \(\leq\)20, severe; 21-45, moderate; and 46-66, mild paralysis [32]. Secondary outcomes included the modified Ashworth scale (MAS), which was used to assess the degree of spasticity [24]; range of motion (ROM), using active and passive joint movement; and subjective chronic pain, using the visual analog scale (VAS) [33].
# Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
## 2 Related Works
### Preretinal Space Segmentation  
Despite the plethora of available retina layer segmentation methods, preretinal space segmentation from OCT images is not widely researched. To the best of our knowledge, deep learning methods have not yet been used for this task, and only a handful of reports of other approaches can be found in the literature. For instance, [PERSON] et al. [46] showed that it might be possible to measure the volume of the preretinal space after re-calibrating the native OCT device segmentation algorithm to search for the preretinal space instead of the retina. This approach, however, does not allow to perform any further numerical analysis concerning the retina morphology, is not fully automatic, requires re-calibration of the device for each scan, and most of all, is not device-independent, thus cannot be employed in worldwide research. The first graph search-based approach that segmented both the retina borders (e.g., inner limiting membrane (ILM), and retinal pigment epithelium (RPE)) and the posterior vitreous cortex was published in 2014 [47]. However, as was reported, the graph search requires significant preprocessing (e.g., denoising, removing low quality areas of the image). Even with such preparations, this method is prone to errors due to the fact that the PCV line is frequently on the level of noise in the image or its reflectivity is too low. The main disadvantage of the graph search approach is the assumption that the PCV line
# Detection of Smoking in Indoor Environment Using Machine Learning
## Materials and Methods  
### Material  
#### 2.1.1 Training Set  
In order to increase the accuracy of predictions for various environments in real situations, it is essential to obtain a limited experimental feature model [25]. To create feature models of smoking for supervised machine learning, as shown in Figure 1, we used BORGWALDT's Smoking machine RM 200A2 (from Germany) to test cigarette smoke generation. RM200A2 is a fully automatic 20 port rotary smoking machine, which meets the requirements of ISO 3308. It is not only suitable for routine smoking according to ISO 4387 and ISO 8454 but also allows smoking of Fine Cut Smoking Articles (FCSAs) according to ISO 15592-3 [26].  
The atmosphere monitoring sensors are installed in the vaping chamber while the machine generates smoked vapor from 20 cigarettes per one experiment. During the experiment, gases generated by cigarettes are discharged through the ventilated system at the top of the chamber. After each smoking, the chamber was ventilated in 5 min to initialize the atmosphere between the experiment. As shown in Figure 1, this machine contents separated vaping chamber so that it may minimize external environment changes. Tabacos used in this experiment are described in Table 1 as below. The experiment was performed as a type of cigarette set, and repeated 10 times per a set. During the smoking, TVOC, CO\({}_{2}\), PM 2.5 and 10, temperature and humidity were measured by 10 sets of the sensors (SVM30 and SPS 30) in the smoking chamber. The changes in temperature and humidity during the experiment were at temperature of 26 to 34 \({}^{\circ}\)C and humidity of 33 to 51% as shown in Figure 2.
## 3 Results and Discussion
### Volatile Compounds
#### 3.4.2. Alcohols  
Alcohols are considered to have a significant impact on the aroma of cider. Higher concentrations of alcohols can result in strong pungent odors and flavors, while moderate amounts contribute to the characteristic fruit aroma [31]. In this study, a total of 16 alcohol compounds were detected, with higher levels of 3-methyl-1-butanol, phenethyl alcohol, 2-methyl-1-propanol, and 1-hexanol. Studies have shown that 3-methyl-1-butanol has aromas of cheese, phenethyl alcohol has a rose aroma, and 1-hexanol has flavors of grass and toast. The presence of these compounds in Huaniu cider may contribute to a more complex and layered aroma. Higher alcohols typically make up 0.1-0.7% of ethanol production, and  
\begin{table}
\begin{tabular}{c c c c} \hline
**Compound** & **Content (\#g/L)** & **Compound** & **Content (\#g/L)** \\ \hline Butyl acetate & 4.57 \(\pm\) 0.36 & Caproic acid & 124.21 \(\pm\) 6.88 \\ Ethyl acetate & 4.49 \(\pm\) 0.06 & Octanoic acid & 52.73 \(\pm\) 2.49 \\ \hline Ethyl propionate & 4.22 \(\pm\) 0.19 & 2-Hydroxy-tetradanoic acid & 17.48 \(\pm\) 0.32 \\ \hline
2-Hydroxy-4-methyl-ethyl valerate & 4.15 \(\pm\) 0.11 & Isobutyric acid & 14.68 \(\pm\) 0.13 \\ \hline Ethyl dodecanoate & 4.09 \(\pm\) 0.21 & Orthodecanoic acid & 4.44 \(\pm\) 0.23 \\ \hline
2-Methylpropyl phenylacetate & 3.91 \(\pm\) 0.07 & Others & \\ \hline Ethyl heptanoate & 3.87 \(\pm\) 0.04 & Eugenol & 8.26 \(\pm\) 0.16 \\ \hline Ethyl & 3.25 \(\pm\) 0.05 & \(\grave{\rm a}\)-Farmesene & 7.82 \(\pm\) 0.25 \\
3-hydroxy-4-(benzyl)butyrate & 2.96 \(\pm\) 0.03 & Acetoin & 3.98 \(\pm\) 0.11 \\ \hline Methyl acetate & 2.45 \(\pm\) 0.04 & 2-Methyldecane & 2.87 \(\pm\) 0.03 \\ \hline Ethyl (Z)-4-decenoate & 2.43 \(\pm\) 0.02 & 3-(Hethoxy)-1-octene & 2.85 \(\pm\) 0.08 \\ \hline Propyl octanoate & 2.28 \(\pm\) 0.03 & Hexadecane & 2.82 \(\pm\) 0.13 \\ \hline
4-Chloro-3-hydroxy-3-methylbutyl & 2.22 \(\pm\) 0.01 & 6-Methyl-hexadecane & 1.12 \(\pm\) 0.09 \\ ester & 2.15 \(\pm\) 0.11 & 3-Octanone & 0.85 \(\pm\) 0.02 \\ \hline Ethyl 3-hydroxyoctanoate & 2.06 \(\pm\) 0.09 & (Z)-3-Methyl-2-decene, & 0.69 \(\pm\) 0.02 \\ \hline Methyl & \multirow{2}{*}{2.6\(\pm\) 0.09} & \multirow{2}{*}{(Z)-3-Methyl-2-decene,} & \multirow{2}{*}{0.69} \(\pm\) 0.02 \\ (E)-10-octadecan-8-methacrylate & & & \\ \hline
3-Methylbutyl methoxyacetate & 1.94 \(\pm\) 0.01 & & \\ \hline
2,6-Octadiene acid, & \multirow{2}{*}{1.26 \(\pm\) 0.05} & \multirow{2}{*}{excessive amounts can lead to undesirable odors and flavors. Appropriate concentrations of higher alcohols are crucial for providing the characteristic flavor of the fruit [32; 33]. These alcohols are formed through the decarboxylation and deamination of corresponding amino acids, such as leucine, isoleucine, and valine, which influence the flavor of cider and contribute to its overall quality [34].
## 3 Health Benefits
### Neuroprotective Effect  
Neurodegenerative disorders are characterized by a progressive loss of selectively vulnerable populations of neurons [160]. Conditions such as Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis and frontotemporal lobar dementia are some of the most challenging problems in developed societies with ageing populations [161].  
Research in recent years has suggested that the inflammatory response of the central nervous system (CNS) may have an impact in the development of neurodegenerative diseases. In response to neuronal damage, astrocytes and microglial brain cells can release proinflammatory cytokines, such as IL-1, TNF-\(\alpha\) and IL-6, into the cerebrospinal fluid [162]. On the other hand, it has been shown that compounds present in artichoke may play an important role in reducing neuroinflammatory. A recent study tested the effects of _C. scolymus_ L. on [PERSON]'s disease in rats induced with rotenone [123], observing that the compounds present in artichoke leaves can significantly reduce the inflammatory markers IL-1\(\beta\) and IL-6 as well as the apoptotic markers caspase-3 and cytochrome-c. [PERSON] et al. [121] evaluated the neuroprotective effects against aflatoxin in male rats, observing a reduction in free radical production and an increase in antioxidant enzyme activity and also reporting a reduction in markers related to inflammation as AChE and NO [163]. Many polyphenols have been shown to inhibit platelet aggregation, preventing thrombus formation and reducing the risk of cerebral ischemia [164]. In addition, some of these molecules can also reduce hypertension, an important risk factor for cerebral hemorrhage. Polyphenols may reduce inflammation through other pathways such as the control of gene expression regulatory proteins like histones and the transcriptional factor NF-kB [165]. It has also been shown that phenolic compounds can improve serotonin levels in the brain and stimulate the production of brain-derived neurotrophic factor (BDNF), reducing inflammation [166].  
Figure 2: Beneficial effects of _Cynara scolymus_ L. \(\uparrow\) Increase; \(\downarrow\) decrease.  
One study evaluated the effect of cynarin on 4-aminopyridine-induced glutamate release in rat synaptosomes [167]. This study concludes that cynarin, through the suppression of P/Q-type Ca\({}^{2+}\) channels, inhibits PKA activation, thereby decreasing the availability of synaptic vesicles and contributing to the inhibition of glutamate release in the cerebral cortex terminals. This compound present in artichoke could therefore be beneficial against neurodegenerative diseases, as a lot of evidence suggests that glutamatergic dysregulation is an important contributor to the pathology of these diseases [168].
## 2 Results
### VOCs Released from M. perisacae Infested and Uninfested Plant
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**Compound Name**} & \multirow{2}{*}{**RT 1**} & \
\begin{tabular}{c} **Uninfested Plant** \\ **Area \(\pm\) SD 2** \\ \end{tabular} &
\begin{tabular}{c} **Infested Plant** \\ **Area \(\pm\) SD** \\ \end{tabular} & \multirow{2}{*}{**LSD 4**} & \multirow{2}{*}{**_p_-Value**} \\ \hline
1 & Propane, 2-methoxy & 3.12 & 23.10 \(\pm\) 3.13 & 7.84 \(\pm\) 2.70 & 11.45 & 0.020 * \\
2 & _n_-Hexane & 3.28 & 15.38 \(\pm\) 4.21 & 8.40 \(\pm\) 3.83 & 15.8 & 0.199 \\
3 & Benzene & 3.61 & 72.20 \(\pm\) 1.55 & 601.75 \(\pm\) 28.09 & 78 & 0.305 \\
4 & 3-Hexen-1-ol, (E) & 6.38 & ND 3 & 28.83 \(\pm\) 1.51 & 4.197 & 0.223 \\
5 & 4,6-Heptadiyn-3-one & 9.33 & 90.28 \(\pm\) 2.26 & 601.75 \(\pm\) 28.09 & 78.2 & 1.211 \\
6 & Toluene & 11.02 & 12.50 \(\pm\) 3.48 & 1.65 \(\pm\) 0.31 & 9.7 & 0.653 \\
7 & Oxime-, methoxy-phenyl & 12.43 & 757.69 \(\pm\) 322.83 & 680.68 \(\pm\) 300.96 & 1223.9 & 0.200 \\
8 & 2-Pentenal, (E)- & 12.49 & 16.86 \(\pm\) 0.82 & 23.36 \(\pm\) 0.76 & 3.105 & 0.136 \\
9 & Alpha-Pinene & 13.32 & 24.44 \(\pm\) 4.96 & 131.41 \(\pm\) 16.53 & 47.87 & 0.003 * \\
10 & Sabinee & 13.47 & 72.54 \(\pm\) 34.72 & 137.59 \(\pm\) 37.07 & 140.8 & 0.377 \\
11 & Myrcene & 15.22 & 20.15 \(\pm\) 7.96 & 68.45 \(\pm\) 30.99 & 88.7 & 0.046 * \\
12 & beta-Pinene & 16.25 & ND & 55.75 \(\pm\) 17.03 & 47.24 & 0.930 \\
13 & 1-Hexanone, & 16.81 & 21.05 \(\pm\) 3.78 & 35.38 \(\pm\) 7.44 & 23.14 & 0.004 * \\
14 & _p_-Cymene & 17.28 & 422.85 \(\pm\) 144.03 & 564.67 \(\pm\) 82.08 & 459.7 & 0.339 \\
15 & 3-Hexen-1-ol, acetate, (Z) & 17.48 & 394.93 \(\pm\) 152.39 & 245.99 \(\pm\) 62.11 & 456.3 & 0.277 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Volatile compounds detected in the headspace of infested and uninfested cabbage with _M. persicae_ by using solid phase microextraction (SPME).
# Ontology-Based Production Simulation with OntologySim
## 2 Foundation
### Simulation  
The term simulation refers to mapping a system to analyze its dynamic processes in a simplified replica (of the target system) and to obtain transferable knowledge [29]. When modeling the simulation, different approaches can be used either individually or in combination. There is a distinction between Discrete Event Simulation (DES), which has event-based step sizes, System Dynamics, which has a top-down approach over system changes per time, and Agent-based Simulation, which executes decision processes based on  
Figure 2: Summary of the OntologySim foundation, based on [8; 10].  
agents. Agent-based simulation can be further divided into multi-agent-based and single agent-based systems [30]. For the application in complex systems, multi-agent-based can be advantageous because of the decentralized and robust structure [31; 32; 33]. The OntologySim is a discrete event-based, multi-agent system. The exact classification of the simulation is discussed in Section 4.  
Besides the modeling type, a simulation consists of the following components according to the VDI3633 [29]: a simulation kernel, data management (see Figure 3), user interface, and interfaces to external databases. The simulation kernel contains the model world and necessary elements, particularly events to ensure automatic execution [29]. The last level, the interfaces to external databases, is not included in this simulation because the OntologySim attempts to represent as many applications as possible, and this interlocking with a particular external database conflicts with the many individual external databases prevailing in real production systems. Hence, a config file or owl file specifying the required information to be extracted before or while execution and API interfaces serve as external interfaces. For more detail, the reader is referred to Section 7.