esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of the Effects of Locally Applied Resveratrol and Cigarette Smoking on Bone Healing
## 3 Results
### New Bone Area  
H&E-stained preparations were examined microscopically and scored according to the amount of new bone formation. The difference between the groups in new bone formation levels was found to be statistically significant (\(p<0.05\)). The difference between the groups was examined by pairwise comparison tests with Bonferroni correction. It was determined that the level at which new bone formation was not observed (Score 0) was significantly higher in the control and CS groups (60%) than in the RES group (0%). Low (Score 1) bone formation was found to be significantly higher in the CS + RES group (50%) than in the RES group (0%). Although it was not statistically significant, it was observed that the level of new bone formation was highest in the RES group at medium, medium-high, and high levels. While new bone formation was not observed at medium, medium-high, and high levels in the CS group, new bone formation was observed at medium and medium-high levels in the CS + RES group. In the control group, medium-high and high levels of new bone formation were not observed, while in the RES group, medium-high and high levels of bone formation were observed (Figure 1, Table 1).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **New Bone** & **Control** & **RES** & **CS + RES** & **CS** \\  & **Area Scores** & **(_n_, \%)** & **(_n_, \%)** & **(_n_, \%)** & **(_n_, \%)** & **(_n_, \%)** \\ \hline \multirow{4}{*}{**New Bone Area**} & Score 0 & 6 (60.0) a ** & 0 (0.0) b  & 1 (10.0) a,b  & 6 (60.0) a  \\ \cline{2-6}  & Score 1 & 3 (30.0) a,b  & 0 (0.0) b  & 5 (50.0) a  & 4 (40.0) a,b  \\ \cline{2-6}  & Score 2 & 1 (10.0) a  & 4 (36.4) a  & 2 (20.0) a  & 0 (0.0) a  \\ \cline{2-6}  & Score 3 & 0 (0.0) a  & 3 (27.3) a  & 2 (20.0) a  & 0 (0.0) a  \\ \cline{2-6}  & Score 4 & 0 (0.0) a  & 4 (36.4) a  & 0 (0.0) a  \\ \hline \hline \end{tabular}  
* Fisher–Freeman–Halton Exact Test: 30.58. RES: resveratrol; CS. cigarette smoking. Different letters on the same line indicate a significant difference. Statistical differences are indicated by letters such as a, b, and ab. There is a statistically significant difference between groups indicated with different letters (\({}^{\circ}p<0.05\)). There is no statistically significant difference in groups with the same letters.  
\end{table}
Table 1: Comparison of new bone formation levels.
# Numerical and Experimental Analysis of Pressure Pulsation Attenuator Based on Helmholtz Resonator
## 2 Working Principle of Vibration Elimination  
The HTPA is a Helmholtz fluid resonator, which consists of a neckline and a closed-end chamber connected in series, as shown in Figure 2. The fluid in the neckline is equivalent to the vibrating mass \(m\), and the fluid in the chamber is used as the spring element, both of which constitute an MSD system. The fluid pressure pulsation \(P\) in the main line is external excitation, and the fluid pressure pulsation acts on the liquid column in the neckline, which is forced to vibrate. When the frequency of the pressure pulsation in the main oil circuit is the same as the inherent frequency of the HTPA, the fluid in the neckline vibrates at the fastest rate and most of the pressure pulsation energy is absorbed. Thus, the pressure pulsation is reduced.
## 2 Data and Methodology
### Modeling of Power Curve  
In order to estimate the power output, and thus the economic viability of any wind turbine using the predicted wind speed and direction, a power curve is needed. A theoretical approximation can be developed based on the theory of drag machines in order to obtain an estimated power curve for our turbine. The general equation for the the power coefficient (\(C_{p}\)) in function of tip-speed ratio (\(\lambda\)) is the following one for a drag machine, with \(C_{D}\) being the corresponding drag coefficient [30]:  
\[C_{P}=\frac{1}{2}C_{D}\lambda(1-\lambda)^{2}. \tag{1}\]  
The derivative of \(C_{P}\) shows that its maximum value is given for \(\lambda=\frac{1}{3}\), with a \(C_{p,max}\) of 0.15. For that, an inferior limit \(C_{D}\approx 2\) is considered, a typical drag value for the frontal incidence in a cylindrical profile.  
As a reference, a transferal area of 1 m\({}^{2}\) will be adopted. If there is simple control and regulation system to ensure an angular velocity that fulfills the optimum condition of \(\lambda=\frac{1}{3}\) for a given wind speed U, the generated power between the cut-in and rated wind speed would be  
\[P=C_{p,max}\frac{1}{2}\rho AU^{3}=0.091U^{3}(W), \tag{2}\]  
having an standard air density with \(\rho=1.225\) kg/m\({}^{3}\).  
Given the low wind speeds measured and calibrated in the building, a low rated wind speed \(U_{rated}\) should be adopted, which establishes also the value of the rated power and the main characteristic of the consequent generator. For \(U_{rated}=14\) m/s, the rated power is around 250 W for the mentioned area of 1 m\({}^{2}\). These approximate values are adopted for this study given its high similarity with the maximum power limit of a photovoltaic solar panel of the same dimension [31].  
This obviously allows the hybridization of solar-wind energy in buildings harnessing the same system of energy storage by a DC generator. In the previous work of the authors, the corresponding DC generator of 250 W is described in their laboratory wind tunnel [7; 32]. As mentioned above, this rated power implies an exact \(U_{rated}\) of 14 m/s, which will be used for the construction of the power curve with a low cut-in speed (2 m/s, usual for drag machines) and a cut-off of 25 m/s (the typical security value for wind turbines). For a generator of 200 W, \(U_{rated}=13\) m/s; for 150 W, 11.8 m/s; and, for 100 W, 10.3 m/s.  
All of them can be considered to estimate a final optimum annual energy production (\(AEP\)) for the different calibrated and non-calibrated wind speed time series. However, the main reference power curve will be given by the 250 W rated power, as shown in Figure 9. The expression \(P=0.091U^{3}\) can be corrected in a simple cubic polynomial fitting to fulfill the condition of \(P=0\) at 2 m/s and 250 at 14 m/s with two unknown parameters: \(P=aU^{3}+b\). The correction gives a very similar equation conserving the parameter \(a\): \(P=0.091U^{3}-0.728\).
# Semi-Supervised Speech Recognition Acoustic Model Training Using Policy Gradient
###### Abstract  
In this paper, we propose a policy gradient-based semi-supervised speech recognition acoustic model training. In practice, self-training and teacher/student learning are one of the widely used semi-supervised training methods due to their scalability and effectiveness. These methods are based on generating pseudo labels for unlabeled samples using a pre-trained model and selecting reliable samples using confidence measure. However, there are some considerations in this approach. The generated pseudo labels can be biased depending on which pre-trained model is used, and the training process can be complicated because the confidence measure is usually carried out in post-processing using external knowledge. Therefore, to address these issues, we propose a policy gradient method-based approach. Policy gradient is a reinforcement learning algorithm to find an optimal behavior strategy for an agent to obtain optimal rewards. The policy gradient-based approach provides a framework for exploring unlabeled data as well as exploiting labeled data, and it also provides a way to incorporate external knowledge in the same training cycle. The proposed approach was evaluated on an in-house non-native Korean recognition domain. The experimental results show that the method is effective in semi-supervised acoustic model training.  
speech recognition; semi-supervised training; reinforcement learning; policy gradient +
Footnote †: journal: Applied Sciences
## 4 Methodology of Technical-Economic Analysis
### Internal Rate of Return  
The internal rate of return (IRR) of an investment is the discount rate (\(a_{0}\)) for which the net present value is cancelled, respectively:  
\[NPV=\sum_{i=1}^{n}\frac{IN_{i}-C_{i}-I_{i}}{\left(1+a_{0}\right)^{i}}=0 \tag{2}\]  
\[IRR=a_{0} \tag{3}\]  
The solution of Equation (2) results from attempts of an iterative calculation, due to the fact that the equation cannot be solved analytically.  
The IRR value was interpreted as the percentage interest that could be accepted for both the investment and the working capital so that the proposed investment project does not produce losses. It is worth noting the following:  
* The internal rate of return (IRR) was used to estimate the economic efficiency of each solution proposed and analysed individually. The IRR cannot be used to compare several solutions as it may lead to false conclusions.
* The profitability of a project (solutions) was interpreted in relation to the IRR value, as follows: if the IRR has a unique value (in this analysis, only this situation is taken into account), the project is profitable if \(a\geq IRR\) (the comparison was made with values of the discount rate in the range of 5-10%).
# Digital Workflow to Fabricate Complete Dentures for
## 3 Discussion  
Since the 1980s, fabrication of dental prostheses using CAD-CAM technology has become faster and more convenient than conventional methods, with the development of various scanning devices, software, and manufacturing machines [3]. Recently, an increasing number of studies have focused on reporting the methodologies and clinical cases for the fabrication of complete dentures using intraoral scanners and CAD-CAM systems. Presently, the dedentulous area can be scanned using intraoral scanners without the aid of artificial indicators. Nevertheless, it was impossible to obtain the interarch relationship solely using intraoral scanners owing to the absence of distinct characteristics on the buccal side of the dedentulous gingivae. By using this technique, a 4-step completely digital workflow for complete denture fabrication in dedentulous patients was established: direct intrao  
Figure 2: Computer-aided designing and additive manufacturing procedures to fabricate complete dentures. (**A**) Reversed maxillary data derived from the united assembly. (**B**) Reversed mandibular data derived from the united assembly. (**C**) Establishment of the interarch relationship for original standard tessellation language (STL) files. Translucency of the united assembly adjusted to visualize the interarch relationship. (**D**) Artificial tooth arrangement using STL files of the transformed dedentulous maxillary and mandibular scan data and the united assembly data. Translucency of the united assembly was adjusted. (**E**) Additively manufactured try-in dentures. (**F**) Finalized digital complete dentures.  
ral scanning, interact relationship registration by the RAS technique, computer-aided designing, and additive manufacturing.  
[PERSON] et al. proposed a digital occlusal registration method, which overlaps the data obtained from facial scans with those obtained from occlusal rim scans using conventional methods [7]. However, errors may exist when using this method as the occlusal scan data are superimposed on the narrow, exposed surfaces when the patient smiles. The method proposed in the present study, i.e., the RAS technique, may be another effective option since it uses intaglio surfaces of each trial denture base for superimposition.  
The present workflow is advantageous for the following reasons: First, a fully digital workflow is established, eliminating the need for making impressions that may cause discomfort to elderly dedentulous patients, and the number of patient visits is reduced. Second, the technique can be expanded to the fabrication of new complete dentures using old complete dentures that exhibit decreased vertical dimension of occlusion (VDO). Old complete dentures can be refined to re-establish proper VDO, and the RAS technique can be applied by reversing the old complete dentures. This eliminates the step of remaking impressions of dedentulous arches.
## 3 Results
### Feature Wavelength Selection
#### 3.2.3 Joint-Feature Variable Screening Method
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variable Selection**} & \multirow{2}{*}{**Number of**} & \multirow{2}{*}{**SVR Modeling Parameters**} & \multicolumn{2}{c}{**Training Set**} & \multicolumn{2}{c}{**Test Set**} \\ \cline{3-6}  & & & **c** & **g** & **RMSEC/(\%)** & **R\({}^{2}\)c** & **RMSEP/(\%)** & **R\({}^{2}\)p** \\ \hline There is no & 2074 & 128 & 0.0009765 & 27.88 & 0.6595 & 26.63 & 0.4196 \\ SiPLS & 395 & 1024 & 0.0027621 & 13.20 & 0.8568 & 21.05 & 0.7763 \\ SiPLS–RF & 50 & 32,768 & 0.0013811 & 13.76 & 0.8559 & 17.04 & 0.8386 \\ SiPLS–IRIV & 16 & 8192 & 0.03125 & 18.57 & 0.7155 & 25.98 & 0.6344 \\ SiPLS–VCPA & 10 & 32,768 & 0.0110485 & 17.74 & 0.7418 & 23.94 & 0.6956 \\ SiPLS–CARS & 68 & 23,170.475 & 0.0019531 & 13.97 & 0.8392 & 19.51 & 0.8067 \\ CARS & 80 & 1024 & 0.0055242 & 14.57 & 0.8266 & 19.10 & 0.8149 \\ CARS-RF & 50 & 16,384 & 0.0009765 & 15.36 & 0.8073 & 21.99 & 0.7564 \\ CARS-VCPA & 10 & 256 & 0.125 & 18.27 & 0.7310 & 25.39 & 0.6703 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of SVR modeling results using different variable selection methods. Figure 11: Detection results of SVR modeling with optimized feature variables via SiPLS–RF algorithm. (**a**) Training set. (**b**) Testing set.
# An Accelerated Slicing Algorithm for Frep Models
## 5 Discussion and Conclusions  
Loop operations are often required for the 3D FRep modelling of complex forms using the unit cell replication. K-d trees and R-trees can be effectively used as acceleration data structures in slicing processes for 3D printing of such FRep models. The accelerated slicing algorithm was proposed, which is based on a novel compound adaptive criterion and a novel acceleration criterion. It reduces the number of processed cells during adaptive contouring and replaces the loop operation by a spatial search.  
Computational tests of the new slicing algorithm were conducted for two examples of FRep models with complex geometries. It shows 30% reduction in the number of processed cells for the filter model; 100 times reduction in the contouring time for the filter model and 2 times reduction in the contouring time for the free-form model accordingly. The median threshold strategy of a space division was used for the K-d tree construction. The quadratic algorithm was employed for the R-tree construction. The R-tree application to the filter model shows a bit higher performance compared to K-d tree construction due to use of the spatial query for the search of intersections between the bounding boxes of the unit cells. Generally, the experimental results show equal performance for R-tree and K-d tree. The computation complexity of the contouring process using the novel accelerated slicing algorithm becomes O (\(\log n\)) in contrast to O(\(n\)). The accelerated slicing algorithm was incorporated into the system of additive manufacturing for FRep modelling (FRepCAM). This system uses the direct manufacturing approach, which allows for printing FRep models directly without creating an intermediate STL file. The slicing algorithm significantly reduces the time needed to contour FRep models during 3D printing preparation. Both models (Figures 7 and 12) were sliced with FRepCAM and fabricated. An Ultimaker S5 FDM 3D printer was used to construct the filter model (Figure 14a, radius 25 mm, height 20 mm). A Wanhao Duplicator 7 DLP 3D printer was used to construct the free-form FRep model (Figure 14b, length 100 mm, width 50 mm, height 20 mm).  
Figure 13: Plot of the experimental results with the free-form model.
## 1 Introduction  
### Motivation  
The more-electric aircraft (MEA) uses the electric power to supply the aircraft's non-propulsive systems like hydraulic and pneumatic which have been replaced by the electrical systems. Currently,most of the electrical power required by an MEA is provided by the engine mounted generators which have the maximum efficiency of 40% in flight. This efficiency decreases to 20% on the ground using the emergency power unit and also with noise pollution and gaseous emissions [1]. Now in the era of artificial intelligence, deep learning has achieved great success [2; 3; 4], because it requires very little engineering by hand, so it can easily use the increase of available computation and data. In order to monitor and track [5; 6; 7] the surrounding objects in real time, the intelligent algorithm used in aircraft will consume a lot of power. Intelligent aircraft needs stable and reliable electric supply, and the electric consumption is increasing. Although battery can replenish from environment [8], the size of these generators should be increased due to increasing electric demand in the advanced MEAs, which lead to the occupation of larger available space and also increase the system's weight.  
The hybrid fuel cell/battery system is a suitable power resource in transportation systems [9]. Nowadays, fuel cells are being used by airplane manufacturers, especially Boeing and Airbus, because of their high power efficiency, low noise, and near-zero pollution. Hybridizing the fuel cells with supercapacitors or batteries as the energy storage systems (ESS) can improve their performance and efficiency [10]. The feasibility of using fuel cells as the emergency power unit of aircraft has been considered in many research studies [1], and [11; 12; 13; 14; 15]. Therefore, the replacement of the emergency power unit based on the conventional generation system like ram air turbine by the hybrid fuel cell system is a proper solution to get closer to the advancement of electric aircraft [16]. Also, this makes the emergency power unit more efficient at low aircraft's altitude and speed.  
The energy management, including management of generation, conversion, and distribution plays an important role in an MEA. The energy management system should control different units, including generators, storage devices and loads. Generally, the electrical system in an aircraft may operate in three modes: normal, abnormal or emergency where main generators in these modes work ideally, isolated or with faults, respectively [16]. In all modes, the energy management system is responsible for controlling various units. Load shedding is one way of controlling systems in abnormal or emergency conditions.
In turn, the application of _Pseudomonas putida_ VM1450 for the clean-up of soil polluted with 2,4-dichlorophenoxyacetic acid resulted in the increased removal of this herbicide from soil and its reduced accumulation in plant tissues [13]. [PERSON] et al. [14] demonstrated that treating barley seeds with strains of the _Pseudomonas_ genus increased germination and root growth in soil contaminated with petroleum oil. Apart from the capability of organic xenobiotic utilization and plant growth promotion, some rhizospheric _Pseudomonas_ show resistance to heavy metals and possess features that can alter metal bioavailability, which makes them a useful tool in the remediation of heavy-metal-polluted environments [15; 16]. These bacteria contribute to the mobilization and uptake of heavy metals by plants and improve plant growth under heavy metal exposure. For example, [PERSON] et al. [17] noted an increase in Zn and Cd accumulation in the shoot tissues of _Silene vulgaris_ by 43.8% and 112.6%, respectively, in plants inoculated with _Pseudomonas helmanticensis_ H16 compared to control plants treated with bacterial necromass. It is worth emphasizing that _Pseudomonas_ species can adapt to various environmental conditions due to abundant genetic diversity [18]. However, knowledge about genome plasticity remains limited. The application of high-throughput sequencing techniques and functional genomics provides a new direction to achieve a deeper understanding of the genetic basis for versatile metabolic potential and environmental adaptation [19]. In this study, a new _Pseudomonas qingdaonensis_ ZCR6 strain isolated from the rhizosphere of _Zea mays_ growing in soil co-contaminated with hydrocarbons and heavy metals was investigated, and its plant-growth-promoting traits along with its ability to degrade hydrocarbons and resist heavy metals were investigated. To the best of our knowledge, there is no previous report on rhizosphere-associated _P. qingdaonensis_ with the characteristics of hydrocarbon degradation, heavy metal resistance, biosurfactant production, and plant growth promotion.
# Pharmacokinetics of Pullulan-Dexamethasone Conjugates in Retinal Drug Delivery
## 4 Discussion
The close interactions that Muller cells have with other cells are crucial in the actions of antioxidants, neurotrophic factors and growth factors in the retina [80]. In this respect, Muller cells are an important cell target in retinal drug development and targeted localization of pullulan conjugates to the Muller cells opens possibilities to deliver drugs to these cells in a targeted manner. Furthermore, biocompatibility of the materials was confirmed in more detail in ex vivo studies with mouse organotypic retinal explants. _Pullululan conjugates as potential intravitreal drug delivery systems._ The preclinical results showed that pullulan is a safe polymeric backbone for intravitreal drug delivery and capable of extending dexamethasone retention in the eye. Previously, polymeric drug conjugates have been widely investigated as drug delivery systems for cancer treatment [84; 85; 86]. For ocular treatments, polymeric drug conjugates have been only sparsely studied [66; 67; 59]. In principle, polymeric conjugates provide major advantages: (1) large molecular size prevents elimination across blood-ocular barriers and slows diffusion to the anterior chamber, thereby prolonging drug residence in the eye; (2) covalent conjugation of drugs to the polymer backbone enables controlled drug release from soluble free polymer conjugates and self-assembled nanoparticles. Control of drug release is particularly significant because non-covalent drug loading to nanosystems results often in relatively fast drug release. Furthermore, covalent links allow generation of site-specific drug release in retina [10] or even in sub-cellular compartments [87]. Furthermore, OCT and fundus imaging showed no opacity in the vitreous after intravitreal pullulan conjugate injection, representing another advantage compared to implants, suspensions and microspheres [88]. Dexamethasone could be delivered as a water solution of polymeric conjugate without any visual disturbance. Further, the conjugated drug would not permeate to the lens, thereby decreasing the risk of corticosteroid cataract. Long-term exposure of dexamethasone may also cause higher intraocular pressure through increasing the stiffness of the trabecular meshwork [89]. Low dexamethasone dose (50 \(\upmu\)g) and only partial release (<2.5 \(\upmu\)g) from pullulan-DEX (50 \(\upmu\)g) within the eye should reduce free drug exposure to the lens and trabecular meshwork to negligible levels as compared to the exposure after intravitreal administration of Ozurdex implant (700 \(\upmu\)g) and dexamethasone suspension (50 \(\upmu\)g). This factor may reduce the incidence of ocular adverse effects. The small needle size is another potential advantage of intravitreal polymeric conjugates. Compared to the 22 G Ozurdex needle the required needle size (31-32 G) for polymer conjugate is minimal thereby reducing the invasiveness of the treatment.