esa-sceva/satcom-chunk-collection huggingface dataset:
# Ammonia Gas Adsorption in Fixed Bed and Fluidized Bed Using Bentonite Particles
## 1 Introduction
The fixed bed is formed at low gas velocities when the particles move separately, a certain vibration of them appears and the bed becomes expanded. The ammonia gas flows between particles, and the gas-particle surface contact is minimal. The fixed bed operation is carried out at \(U_{g}<U_{mf}\). In a fixed bed, variation of ammonia adsorption capacity depending on gas-particle contact is produced due to the mass transfer zone that moves from the inlet to the outlet of the adsorption column while the particle bed saturates [35]. In many fixed bed configurations, the adsorption process is limited by the transport of molecule gas into the interior of the particles [36]. In a fixed bed, the mass transfer is carried out by molecular diffusion, and in a fluidized bed, it occurs as an effect of the particle movement mainly by molecular diffusion and by convective diffusion [37]. The main objective of this paper on adsorption in a fluidized bed is to provide additional energy cost to overcome the inter-particle forces that are responsible for the cohesion of the small particles and other non-homogeneities during fluidization, like bubbling or slugging. Knowledge of dynamic conditions is essential for a good implementation of a fluidized bed in the retention of ammonia on bentonite particles [38; 39]. The aim of this study is to compare the ammonia adsorption kinetics in fixed and fluidized beds depending on the surface contact between the gas and bentonite particles. The low economic cost is given by the use of a low flow rate at the minimum fluidization velocity. An innovative objective in this research is testing, for the first time, new materials, such as as clay minerals, obtained by pillaring with aluminum polyhydroxocations during ammonia adsorption. In the literature, the adsorption of this pollutant in the liquid medium has been studied [40], and the novelty of this research is the adsorption of ammonia from the air by using fluidization as the gas pillared bentonite particle contact method. The research ends with the study of the regeneration cycles of the adsorbent particles by thermo-desorption. The effect of the temperature is very important if the ammonia desorption is by chemisorption [41; 42; 43] between the particles and gas molecules. In such cases, an increase in the temperature up to 300 \({}^{\circ}\)C can favor the desorption of ammonia gas from the bentonite particles [44]. One of the essential conditions for an efficient adsorbent is that it can be easily recovered and the thermo-desorption process should be as economical possible.
###### Abstract  
Plant-derived substances exhibit antioxidant and antibacterial activities and have been proven to have beneficial effects in wound healing and skin regeneration. Plant stem cells have recently received much attention as research materials in cosmetic development because they promote regeneration after damage. In this paper, we demonstrate for the first time that the plasma treatment of stem cells obtained from rice-seed embryos can be effective in enhancing antioxidant activity and in regenerating human skin. We investigated this potential utilizing micro-DBD (Dielectric Barrier Discharge) plasma as a pretreatment technique to enhance the vitality and functional activity of rice stem cells. The results of the cell culture experiments show that plasma-treated rice stem cell extracts (RSCE) have promising antioxidant and anti-skin aging activities. The results of quantitative real-time PCR (qRT-PCR) for major antioxidant enzymes and anti-aging genes confirm that the plasma technique used in the pretreatment of RSCE was able to enhance cell activities in skin regeneration, including cell survival, proliferation, and collagen enhancement for Human Fibroblast (HFB) degraded by oxidative stress. These results show that the relatively low energy of less than 300 W and an amount of NOx-based reactive nitrogen species (RNS) from plasma discharge of about 3 uL/L were the key factors and that RSCE, of which the antioxidant activity was enhanced by plasma treatment, appeared to be a major contributor to the protective effect of HFB against oxidative stress. Plasma-treated RSCE induced excellent anti-aging properties by stimulating HFB to promote collagen synthesis, thereby promoting skin regeneration. These properties can protect the skin from various oxidative stresses. This study demonstrates that plasma-treated extracts of stem cells derived from rice-seed embryos have an excellent regenerative effect on aging-treated HFB. Our results demonstrate the potential utility of plasma-treated RSCE as a skin anti-aging agent in cosmeceutical formulations for the first time.  
Keywords:stem cells; rice-seed embryos; micro-DBD plasma; antioxidant; RSCE; HFB +
Footnote †: journal: Applied Sciences
# A Methodology Based on Deep Learning for Contact Detection in Radar Images
## 3 Methodology
### Phase 1: Preprocessing and Enhancement
#### 3.1.1 Image Cropping  
First, the raw image is cropped to a size of 1024 \(\times\) 2048. This decision to crop the image is because, for this study, we only consider analyzing a distance of 37.5 km, which is equivalent to 2048 samples of the 8192 provided by the radar. After all, the vessel requires only the analysis up to this distance to be used, and thus, the computational overhead is also avoided. Likewise, the decision to take only 1024 azimuths is made since, in the middle zone of the image, the highest probability of observing a contact is established, so in the clipping process, only the average azimuths of the entire radar image are chosen. Therefore, if we analyze that there are 2048 azimuths and we number them from 1 to 2048 when clipping, the samples from azimuth 513 to azimuth 1536 are selected. So, azimuth 1 to 512 are eliminated, and azimuth 1537 to 2048 are eliminated. In this process, we obtain the _bin1_ file.
# Optimization of Inventory Management to Prevent Drug Shortages in the Hospital Supply Chain
## 2 Related Work
### Minimization of Drug Shortages in Hospital Supply Chains
According to [35], when a hospital's personnel is entrusted with the task of manually monitoring, logging, and restocking the inventory, the results are either imprecise, as human errors are bound to happen, or insufficient whenever the proper processes were not followed to the letter. Hence, as mentioned by [36], the use of Machine Learning (ML), which is a type of algorithmic training (or Artificial Intelligence (AI)), where the algorithms are constantly processed as additional data enables them to be more predictive, is gaining momentum in HSC management. [PERSON] showed how ML can help HSC management to become more refined while making it less cumbersome [18]. By swiftly processing enormous volumes of data in order to discern patterns and uncover insights that may be too complex for or hidden from human perception (even those with considerable experience), ML has the capacity to enable health care providers to steadily provide the right provisions, at the right cost, place, and time [37]. Drug shortages can also be caused by increased demand or decreased supply [38]. Increased demand, particularly where the parties in the pharmaceutical supply chain employ just-in-time inventory control [38], has been found to be the cause of shortages close to 13% of the time [39]. That same study found that drug shortages caused by decreased supply due to manufacturing problems, at about 23%, surpasses drug shortages caused by increased demand [39]. Not only are genuine real-time computerized systems utilized to streamline the performance of tasks for medication inventory management, they also present a historical reporting that is accurate, complete, and real-time [36]. This is achieved through RL. This method is considered to be a subset of ML consisting of adopting appropriate actions so as to maximize the rewards in a certain condition [32]. However, Reference [37] illustrated that when an instruction dataset lacks information, it is bound to ascertain the missing information from its past experience. Pharmacy staff and purchasing managers utilize this information in order to optimize inventory so as to prevent drug shortages, as well as to give priority to patient safety while cutting unnecessary costs [33]. The mathematical programming model can be used in the determination of automated refilling decisions. For example, a mathematical model considers the various types of drugs, ordering sizes, shortages, and holding costs. Therefore, a hospital can use the model to compute these aspects, as well as determine the specific refilling schedule of drugs in supply. If there is a drug shortage, the costs caused by the shortage can be automatically anticipated to determine the necessary amount of drug refillings. To the best of our knowledge, the studies presented to date do not have a learning model to deal with the shortage issue. Therefore, in this work, we propose a deep learning model to formulate and develop a dynamic inventory model for drugs in a hospital setting.
# State of the Art and Trends Review of _Smart Metering_ in Electricity Grids
## 4 Development of _Smart Metering_ Worldwide
### Europe
However, in 2013 the government announced that the _SM_ rollout phase would be delayed until 2015 since the communication infrastructure needed further trial and testing [46]. France started a pilot smart meter program for a widespread deployment of 35 million _SM_s by 2020 [54]. The \"_Linky_\" project, led by ERDF, which employs around 250,000 _SM_s and 4600 _DCs_, aimed at improving knowledge of residential consumption through the combined effects of an appropriate customer panel and a modeling method adapted to more frequent reading of consumer indices [58]. Spain has also participated in the wide deployment of _SM_s: almost 2 million _SM_s by 2013 and a rollout of 100% by 2018, in compliance with a Royal Decree in 2007. A group of Spanish utility companies formed the \"Spanish Utility Consortium\" to establish the foundation of Spain's _SM_s rollout project in 2009 [59]. Two main technologies are present in the rollout: _PRIME_ and _Meters and More_. While _PRIME_ only can perform over power lines, _Meters and More_ is able to work on power lines, public communication networks, and local optical links. Although in Germany the installation of _SM_s has been mandatory after major renovations and in new buildings since 2010, there has not yet been an explicit commitment made to a national rollout. In fact, the rollout to existing homes (up to 500,000 _SM_s by mid-2012) has remained in a pilot phase. After a report of the Federal Ministry of Economics indicating the lack of economic benefits of a full rollout of _SM_s for German consumers, Germany has delayed it until at least 2020 [60]. To date, much of the focus has been on pilot projects and small-scale trials, of which five major projects were still ongoing in 2014 [54]. By 2014 Finnish utilities had completed their _SM_ rollouts, covering 98% of all consumers, who now have access to their hourly consumption data through utility online information. In addition, utilities and other market players have introduced further new _Smart Metering_-based services and products, such as in-home displays, real-time feedback systems, demand response, and smart home products [54].
## 3 Computer-Aided Analytical Modeling of Class-E Series-Tuned RFPAs
### Minimum Value of \(Q_{s}\)  
The HB simulation was conducted for all three topologies. The efficiency versus \(Q_{s}\) plots shown in Figure 4 demonstrate exponential behavior, which can be modeled by Equation (30).  
\[y=a+be^{-x} \tag{30}\]  
where \(y\) denotes the efficiency and \(x\) represents \(Q_{s}\).  
The values of \(a\) and \(b\) were determined for the three topologies using the curve_fit tool from the scipy library in Python [26]. Optimal parameter values and their estimated covariances were returned by the function. The curve-fitting model was found to exhibit behavior almost identical to that of the simulation data, as shown in Figure 4.  
Both Topology 1 and Topology 2.1 exhibit similar behavior, while Topology 2.2 has a different \(b\) value, as revealed by the data modeling. Nevertheless, for all three cases (Topologies 1, 2.1, and 2.2), the value of \(a\) is approximately 99.  
The term \(Q_{min}\) is now defined as the value of \(Q_{s}\), where a stable efficiency of at least 99% is achieved, assuming a 1% numerical error due to the HB simulation [27]. According to the requirements, this efficiency can be adjusted but must be no less than 99%. To determine \(Q_{min}\), the curve-fitting model is used to solve Equation (31) for each topology respectively.  
\[99=a+be^{-cQ_{min}} \tag{31}\]
# Comprehensive Investigations on Fluid Flow and Cavitation Characteristics in Rotating Disk System
## 3 Simulation Analysis
### Results of Simulation Analysis
#### 3.3.2 Effect of Cavitation on Velocity  
The oil film with a texture rate of 6.41% was chosen to analyze the flow and cavitation vapor movement in hexahedral dimple. The local area at the upper left corner of the oil film is enlarged in Figure 21, where (a1), (a2), and (a3) are the oil velocity vector diagrams at different times, and we can see that the oil flows smoothly in the non-texture region and induces turbulence when it passes through the texture boundary. The vapor vector diagrams are represented by (b1), (b2), and (b3), respectively. Because the arrows of the cavitation vapor and oil remain essentially the same, we can assume that the vapor originated from the oil's phase change.  
The fluid velocity streamlines on the internal cross section of a single texture dimple are shown in Figure 22. Where (a1), (a2), (a3) are velocity streamlines of oil at different times, correspondingly, (b1), (b2), (b3) are velocity streamlines of vapor. In the non-texture region, oil flows relatively smoothly, and the hexahedral structure has a more pronounced effect on oil retention. When flowing into the hexahedral dimple, two large vortices are generated, and the lowest pressure often appears in the vortex center, so it is where cavitation occurs initially. The relationship between cavitation and pressure will be investigated further.  
Figure 21: Vector diagram of local oil flow velocity in oil films with different times ((**a1**) is velocity vectors of oil when \(t=0.003\) s; (**a2**) is velocity vectors of oil when \(t=0.014\) s; (**a3**) is velocity vectors of oil when \(t=0.1\) s; (**b1**) is velocity vectors of vapor when \(t=0.003\) s; (**b2**) is velocity vectors of vapor when \(t=0.014\) s; (**b3**) is velocity vectors of vapor when \(t=0.1\) s).  
Figure 22: Velocity streamlines of the section inside the hexahedral texture dimple ((**a1**) is velocity streamlines of oil when \(t=0.003\) s; (**a2**) is velocity streamlines of oil when \(t=0.014\) s; (**a3**) is velocity streamlines of oil when \(t=0.1\) s; (**b1**) is velocity streamlines of vapor when \(t=0.003\) s; (**b2**) is velocity streamlines of vapor when \(t=0.014\) s; (**b3**) is velocity streamlines of vapor when \(t=0.1\) s).
## 3 Results
### Liver-Protection Effectiveness Evaluation Parameter Result
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**TG**} & \multicolumn{6}{c}{**Baseline (Visit 2)**} & \multicolumn{6}{c}{**Visit 4**} & \multicolumn{6}{c}{**Change from Baseline**} & \multicolumn{1}{c}{**Within**} & \multicolumn{1}{c}{**Between**} \\ \hline
**Group** & **N*** & **Mean** & **SD** & **Median** & **(Min, Max)** & **Mean** & **SD** & **Median** & **(Min, Max)** & **Mean** & **SD** & **Median** & **(Min, Max)** & **Group*** & **Group*** & **Group*** \\ \hline CA- & 40 & 173.1 & 82.1 & 153 & \((70,496)\) & 154.9 & 72.6 & 136 & \((60,399)\) & \(-18.3\) & 31.5 & \(-6\) & \((-108,52)\) & 0.0007 & 0.0004 & 0.0008 \\ Placebo & 39 & 169.2 & 78.0 & 146 & \((86,452)\) & 174.9 & 84.7 & 150 & \((93,503)\) & 5.7 & 29.7 & 2 & \((-56,90)\) & 0.2632 & 0.3221 & \\ \hline \multicolumn{2}{c}{**TC**} & \multicolumn{6}{c}{**Baseline (Visit 2)**} & \multicolumn{6}{c}{**Visit 4**} & \multicolumn{6}{c}{**Change from Baseline**} & \multicolumn{1}{c}{**Within**} & \multicolumn{1}{c}{**Within**} & \multicolumn{1}{c}{**Between**} \\ \hline
**Group** & **N*** & **Mean** & **SD** & **Median** & **(Min, Max)** & **Mean** & **SD** & **Median** & **(Min, Max)** & **Mean** & **SD** & **Median** & **(Min, Max)** & **group*** & **group*** & **group*** \\ \hline CA- & 40 & 169.4 & 22.7 & 168 & \((106,225)\) & 160.5 & 29.6 & 164 & \((104,226)\) & \(-8.9\) & 19.2 & \(-6\) & \((-62,29)\) & 0.0055 & 0.0028 & \(<\)0.0001 & \\ Placebo & 39 & 165.7 & 20.9 & 170 & \((123,204)\) & 182.1 & 29.8 & 177 & \((30,251)\) & 16.3 & 25.5 & 9 & \((-40,97)\) & 0.0003 & 0.0001 & \\ \hline
**LDL** & \multicolumn{6}{c}{**Baseline (Visit 2)**} & \multicolumn{6}{c}{**Visit 4**} & \multicolumn{6}{c}{**Change from Baseline**} & \multicolumn{1}{c}{**Within**} & \multicolumn{1}{c}{**Within**} & \multicolumn{1}{c}{**Between**} \\ \hline
**Group** & **N*** & **Mean** & **SD** & **Median** & **(Min, Max)** & **Mean** & **SD** & **Median** & **(Min, Max)** & **Mean** & **SD** & **Median** & **(Min, Max)** & **group*** & **group*** & **Group*** \\ \hline CA- & 40 & 88.6 & 23.9 & 96 & \((20,119)\) & 92.2 & 26.3 & 96 & \((15,145)\) & 3.6 & 25.1 & \(-3\) & \((-44,75)\) & 0.3738 & 0.5692 & 0.0100 \\ Placebo & 39 & 87.1 & 23.7 & 91 & \((29,126)\) & 106.8 & 25.8 & 105 & \((65,180)\) & 19.7 &
# Energy Efficient Multicast Communication in Cognitive Radio Wireless Mesh Network
## 1 Introduction
A multicast routing algorithm is proposed to choose the multicast path from source nodes to destination nodes while minimizing the number of relaying nodes and channels required. 3. The simulation results for the proposed algorithm are presented in terms of the number of relaying nodes needed, the number of channels used, and the total amount of energy used. The remaining paper is organized as follows. Section 2 discusses the previous work related to multicasting in CR-WMNs. Section 3 presents the network model and the minimum energy multicast tree construction problem for CR-WMNs. Section 4 presents the proposed cross-layer approach to the construction of an energy efficient multicast tree for CR-WMNs. Section 5 presents simulation results in order to demonstrate the performance of the proposed technique. Finally, Section 6 concludes the paper.
## 3 Results and Comparative Experiments
### Experimental Results and Comparative Experiments

Table 1: This table provides a detailed display of the CASLOM model’s performance across eight datasets, with key performance indicators including effective coverage area, coverage rate, total length of survey lines, and weighted average overlap rate. Figure 10: This figure displays the survey line results of our model on different datasets (only showing the second, fourth, and sixth datasets). Each point connected in the figure represents the optimal survey lines determined by the model. (**a**) The survey line results for the second dataset. (**b**) The survey line results for the fourth dataset. (**c**) The survey line results for the sixth dataset. 3.18%, significantly lower than other models, indicating a notable advantage in avoiding measurement overlap. Overall, the experimental results shown in Table 2 indicate that, when comparing our model's performance with other multibeam echosounder models in unknown and complex marine environments, our model demonstrates significant advantages in several key performance metrics, showcasing its efficiency and reliability in marine surveys.
## 4 Integrated Fixed Time Sliding Mode Control for Motion and Vibration
### Base and Joint Flexible Vibration Suppression Control  
In the fast subsystem, the state equation of variable \(\mathbf{q}_{\rm f}\) representing flexible vibration under fast time scale \(t_{\rm f}\) is shown in Equation (19). Performance indicator functions are constructed as follows:  
\[\mathbf{J}_{\rm df}=\frac{1}{2}\int_{0}^{\infty}\left(\mathbf{q}_{\rm f}^{\rm T}\mathbf{Q }_{\rm f}\mathbf{q}_{\rm f}+\tau_{\rm nf}^{\rm T}\mathbf{R}_{\rm f}\tau_{\rm nf}\right) \mathrm{d}t_{\rm f}, \tag{43}\]  
where symmetric weight matrix \(\mathbf{Q}_{\rm f}\geq\mathbf{0}\), \(\mathbf{R}_{\rm f}>\mathbf{0}\).  
For any matrix \(\wp\), satisfying \(\wp\wp^{\rm T}=\mathbf{Q}_{\rm f}\), if the solution \(\mathbf{P}\) of Riccati Equation (44) is symmetric and positive definite, then the matrix pair \(\{A_{\rm f},\wp\}\) is completely observable.  
\[\mathbf{P}\mathbf{A}_{\rm f}+\mathbf{A}_{\rm f}^{\rm T}\mathbf{P}-\mathbf{P}\mathbf{B}_{\rm f}\mathbf{R}_ {\rm f}^{\rm-1}\mathbf{B}_{\rm f}\mathbf{P}+\mathbf{Q}_{\rm f}=\mathbf{0}. \tag{44}\]  
Because the array pair \(\{A_{\rm f},\mathbf{B}_{\rm f}\}\) is completely controllable, there is an optimal control in Equation (19) as follows:  
\[\tau_{\rm nf}=-\mathbf{R}_{\rm f}^{\rm-1}\mathbf{B}_{\rm f}^{\rm T}\mathbf{P}\mathbf{q}_{\rm f}. \tag{45}\]  
The optimal performance index function is as follows:  
\[J_{\rm df}^{*}=\frac{1}{2}\mathbf{q}_{\rm f}^{\rm T}(0)\mathbf{P}\mathbf{q}_{\rm f}(0), \tag{46}\]  
where \(\mathbf{P}\) is the unique solution of Equation (44).  
According to the above analysis, we have that the integrated fixed time sliding mode control for motion and vibration (IFSM) proposed in this paper is mainly composed of fixed time sliding mode controller based on hybrid trajectory (37), (38), virtual force (42), fast controller (45), controller to be designed (13) and motor general controller (12).