esa-sceva/satcom-chunk-collection huggingface dataset:
# Antimicrobials in Dentistry
## 2 Antimicrobials Used in Dentistry
### Chitosan  
Chitin is a natural biopolymer sourced from crustacean shells, which on deacetylation yields chitosan. The monomeric units of chitosan contain a primary amine and two hydroxyl groups and in acidic medium the protonation of the amine groups leads to a charged polymer. Chitosan is a cationic polysaccharide and the degree of deacetylation governs both solubility and the availability of the free amino groups [110]. It possesses broad-spectrum antimicrobial activity and has an effective killing rate against Gram-positive and Gram-negative bacteria; however, the efficacy depends on the properties of the chitosan  
Figure 5: Schematic illustration of pendent eugenyl residues from the polymeric surface of the EgMA-containing resin composites (Mod.CUB) and microbial biofilms grown on cured resin discs tested with live/dead staining. The pie chart shows the effect of EgMA incorporation on the mean percentages of dead (red), live (green) and unknown (black) biovolumes. Adapted from [PERSON] et al., 2017, with permission from Elsevier.  
used such as the physical state (solid or water soluble form), molecular weight, charge density, concentration, hydrophilicity, chelating capacity, pH of the medium, temperature, interaction time and factors that relate to the microbial species [111; 112; 113; 114]. The favourable biocompatibility, biodegradability, hydrophilicity, haematostatic and antiantigenic properties of chitosan have thus led to its use in a variety of biomedical applications [115; 116]. Chitosan has been used in different aspects of clinical dentistry, whilst many experimental research studies highlight the potential of application in other dental applications [117]. It has been used in dentifrices, toothpastes and chewing gums that confer antimicrobial activity and reportedly limit enamel demineralisation by forming a protective layer on enamel surfaces in the presence of mucin present in saliva [118; 119]. This polysaccharide has also been used as a carrier for the delivery of drugs and compounds such as naringin, chlorhexidine, statins, metronidazoleazole, doxycycline and other antibiotics for the treatment of periodontal disease, endodontic therapy and preventive dentistry. The incorporation of chitosan in glass ionomer cements was reported to not only enhance the antibacterial activity, but also to improve flexural and compressive strength of the cement. A methacrylate derivative of chitosan [120] has been reported as a potential component in dental adhesives that is able to reduce microbial ingress, which is beneficial towards the longevity of the restoration. Similarly, chitosan-coated dental implants have been reported to assist in bone regeneration whilst maintaining their antibacterial properties [121].  
Chitosan has been used in gels and oral formulations for periodontal treatment. Furthermore, its haemostatic properties have been exploited to form sponges, films and membranes for use in oral wound dressings and to curb excessive bleeding such as Axiostat(r), Celox(tm) and Hemcon(r) for clinical use post tooth extraction [122; 123].
# Experimental Evaluation of Thermal Performance and Durability of Thermally-Enhanced Concretes
## 1 Introduction
Hydration products, Ca(OH)\({}_{2}\), reacts with CO\({}_{2}\) and it forms CaCO\({}_{3}\). It results in reduction of pH of the concrete. In this study, performance of thermally enhanced normalweight (NWAC) and lightweight concretes (LWAC) mixed with various insulating materials (diatomic powder, hollow micro-sphere and micro-foam agent) were evaluated through a series of tests. The quick thermal conductivity meter (QTM), the guarded hot plate (GHP) and the calibrated hot box (CHB) tests were performed to investigate thermal performance of the thermally-enhanced concrete with various insulating materials. Furthermore, accelerated carbonation and cyclic freeze-thaw tests were conducted to evaluate the durability performance of the thermally-enhanced concretes.
## 3 Structure and Operation of DL-Based Index Estimator
### Proposed DL-Based Index Estimator
#### 3.1.1 Lfc-Dnn  
The LFC-DNN takes the instantaneous received signal at the PDs (\(\mathbf{y}_{i}\)) as the input. The predicted instantaneous active LED index vector for the corresponding input is obtained as a vector of length \(n/2\) at the output. For a \(4\times 4\) system, the received signal vector of length four (\(\mathbf{y}_{i}\)) is taken as the DNN network input. Subsequently, the LFC-DNN outputs a vector of length two as the estimated LED index value, for example, \(\mathbf{\bar{I}}_{i}=\left[\bar{I}_{i}(1)\ \bar{I}_{i}(2)\right]^{T}\) with \(\bar{I}_{1}(i)\) as the estimated active index for the \(\mathbf{T}(i,1)\) signal. The estimated active index \(\mathbf{\hat{I}}_{i}\) can be derived with hard decision decoding as  
\[\hat{I}_{i}(j)=\begin{cases}1,&\bar{I}_{i}(j)\geq 0.5\\ 0,&\bar{I}_{i}(j)<0.5,\end{cases} \tag{4}\]  
If \(\hat{I}_{i}(1)=1\), the signal \(\mathbf{T}(i,1)\) has a positive value and was transmitted by the first LED. If \(\hat{I}_{i}(1)=0\), the signal \(\mathbf{T}(i,1)\) has a negative value and was transmitted by the second LED. For \(i=1,\ldots,4N/n\), the estimated active index vectors \(\mathbf{\hat{I}}_{i}\) are concatenated to determine the estimated vector \(\mathbf{\hat{I}}=\left[\mathbf{\hat{I}}_{1}\ldots,\mathbf{\hat{I}}_{i}\ldots \mathbf{\hat{I}}_{4N/n}\right]\). Finally, with the estimated active LED indexes, the  
Figure 2: IE-DNN-assisted active LED IE structures for GLIM-MIMO system: (**a**) LFC-DNN, (**b**) SFC-DNN, (**c**) CNN.  
demodulation process is generally conducted with FFT and QAM demodulation steps to obtain the original binary information sequence, similar to the previous demodulator [18]. All four sub-blocks are composed of fully connected layers (FCLs) and activation functions \(f(0),...,f(3)\). The numbers of neurons in the FCLs are 32, 16, 16, and 2, respectively. Moreover, the activation functions for the first three sub-blocks \(f(0),f(1),f(2)\) are \"ReLU\" activation functions, while the activation function for the last sub-block \(f(3)\) is a \"Sigmoid\" activation function. The \"ReLU\" function is as following form  
\[\text{ReLU}(x)=\begin{cases}x,&x\geq 0\\ 0,&x<0\end{cases} \tag{5}\]  
while the \"Sigmoid\" function has the following form  
\[\text{S}(x)=\frac{1}{1+e^{-x}}. \tag{6}\]
# Attempts to Obtain Material Based on Polyvinyl Alcohol with Barrier Properties against Water Vapor
## 3 Results and Discussion
### PVA Film Modified with 0.75% CA, 0.2 g BW, CBS and Gly 1.5% (PVA II)  
The PVA II film is homogeneous, transparent, with a whitish tinge and is easy to peel off from the Petri dish, being 0.33 mm thick. The film can be seen in Table 2.  
The film was subjected to heat treatment for curing and the WVTR was recorded, which is shown in Figure 3, compared to that of the PE and untreated PVA film.  
The use of heat treatment was effective and improved the water barrier of PVA II films. The best results were achieved with the film treated at 125 \({}^{\circ}\)C which had a 0.73% lower WVTR on the first day than the untreated PVA II film. The difference increased to 2.29% on the 8 th day and reached 1.1% on the 34 th day. When compared to pure PVA film, the PVA II film treated at 125 \({}^{\circ}\)C exhibited a decrease in WVTR of 5.78% on day 5, which increased to 6.83% on day 34. Conversely, the pure PVA film showed a 5.73% higher WVTR than the untreated PVA II film. Furthermore, compared to PE film, a PVA II film cured at 125 \({}^{\circ}\)C demonstrated a 4.22% higher WVTR on the 13 th day, with the difference increasing to 5.93% on the 34 th day.  
Figure 2: WVTR of untreated PE, PVA, PVA I films and PVA I film heat treated at 125 \({}^{\circ}\)C and 150 \({}^{\circ}\)C (the results were obtained in triplicate. The average of the three determinations was reported, with a maximum error of 5%).  
These results can be attributed to the hydrophobic character of BW in the film matrix, which reduces water vapor transmission through the matrix, thereby enhancing the film's water barrier properties. BW was found to impart higher hydrophobicity to the film compared to other lipid sources [37,38].  
Statistically significant differences of 95% (_p_ < 0.05) were recorded between the PVA II film treated at 125 \({}^{\circ}\)C and the films PVA II and PVA II treated at 150 \({}^{\circ}\)C, respectively, which shows that the film treated at 125 \({}^{\circ}\)C has better barrier properties against water vapor. All three films recorded statistically significant differences in proportion of 99% (_p_ < 0.01) compared to the PVA film, which means that all films improved the WVP of the PVA film according to One-Way Anova, the Tukey model.
## 3 Validation
### Numerical Model  
#### 3.2.1 Surface Pressure on the HST  
[PERSON] et al. [24] carried out a 1:20 scaled model experiment of a moving train at the Key Laboratory of Railway Safety at Central South University to investigate the dynamic changes in pressure when a HST passes through noise barriers. The experimental setup by [PERSON] is illustrated in Figure 6a. An aerodynamic model with the same dimensions as described in Ref. [24] was constructed, as shown in Figure 6b. The numerical simulation results were compared with [PERSON]'s experimental results to validate the accuracy of the train surface pressure obtained in this study. The computational domain had dimensions of 12 m \(\times\) 5 m \(\times\) 60 m, with the HST and noise barrier models positioned at the center of the computational domain.  
Figure 5: Analysis of grid independence: (**a**) three mesh models; (**b**) head car \(C_{D}\) with different mesh models.  
Figure 6: Validation of train surface pressure: (**a**) 1:20 scale moving model [24], (**b**) computational domain for CFD, and (**c**) comparison of pressure time history curves at the T2 point.  
Figure 6c shows a comparison of the aerodynamic pressures at measurement point T2 on the train surface, obtained through numerical simulations and the experimental measurements by [PERSON]. The results indicate that, unlike the moving model experiment, the pressure curve obtained through numerical simulation exhibits smoother fluctuations. Moreover, there is a high degree of consistency between the two sets of results, which strongly validates the reliability of the surface pressure on the HST obtained by numerical simulation.
## 4 Results
### Correlation Analyses
#### 4.2.2 Self-Reported OSIVQ Spatial Score and Spatial Relations Identification Task  
As in the previous case, spatial visualizers are expected to perform better at estimating distances in a spatial layout. However, the correlation showed a negative, non-significant trend (Figure 8b, Table 2). A non-significant trend was observed in participants who reported themselves as spatially-oriented, performing worse than on specific spatial identification tasks.  
In regard to correlating the aforementioned with other continuous variables (Figure 9), only one with any relevance was found: the self-reported spatial score from the OSIVQ correlated positively with exploration speed (\(r=0.48\), \(p=0.03\)), i.e., the spatially-oriented participants had a mild tendency to walk through the virtual environment more quickly. A positive correlation between exploration distance and exploration time, and a negative correlation between exploration time and exploration speed are both easily explained, as these variables were heavily dependent on each other. No other correlation approached a statistical significance.  
Figure 8: Correlation between self-reported object preferences in the OSIVQ and the Object Visual Recognition Test (**a**). Correlation between self-reported spatial preferences in the OSIVQ and the Spatial Relations Identification Task (**b**).
# Comparison of Machine Learning Methods to Up-Scale Gross Primary Production
## 5 Conclusions  
Up-scaling methods in remote sensing provide a new observational approach to extend the scale of GPP estimates. In this paper, GPP from field eddy covariance systems were up-scaled to the regional scale using five machine learning methods (Cubist, RF, SVM, ANN, DBN). Then, footprints of the GPP at flux tower sites were obtained by using an FSAM model. At last, the up-scaled GPP were validated at the field scale, footprint scale and regional scale. The results of this paper demonstrated the applicability and reliability of up-scaling GPP at flux tower sites, with machine learning methods, and up-scaled GPP using RF could obtain the highest accuracy.  
Generally, the spatial distributions of up-scaled GPP using five machine learning methods were consistent. Direct validation with GPP at flux tower sites demonstrated that, although up-scaled GPP using machine learning methods (Cubist, RF, SVM, ANN, DBN) could obtain a high accuracy (R\({}^{2}\) ranged from 0.79 to 0.86, and RMSE ranged from 0.99 g C m\({}^{-2}\) d\({}^{-1}\) to 1.45 g C m\({}^{-2}\) d\({}^{-1}\)), up-scaled GPP using RF obtained the highest accuracy (R\({}^{2}\) = 0.86, RMSE = 0.99 g C m\({}^{-2}\) d\({}^{-1}\)). Second, compared with validating with GPP at flux tower sites data, better linear relationships were obtained when validating the up-scaled GPP at the footprint scale (R\({}^{2}\) ranged from 0.80 to 0.88, and RMSE ranged from 0.89 g C m\({}^{-2}\) d\({}^{-1}\) to 1.24 g C m\({}^{-2}\) d\({}^{-1}\)). Moreover, the precision of up-scaled GPP using RF was the highest (R\({}^{2}\) = 0.88, RMSE = 0.89 g C m\({}^{-2}\) d\({}^{-1}\)). Third, cross comparison with MODIS GPP products demonstrated that a good linear relationship existed between up-scaled GPP and MODIS GPP (R\({}^{2}\) ranged from 0.81 to 0.84, and RMSE ranged from 0.68 g C m\({}^{-2}\) d\({}^{-1}\) to 0.89 g C m\({}^{-2}\) d\({}^{-1}\)). However, up-scaled GPP from machine learning methods were higher than MODIS GPP in most areas.  
For data curation, Q.Z.; writing--original draft preparation, T.Y.; writing--review and editing, R.S.; visualization, T.Y. and Q.Z.; funding acquisition, R.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2016 YFB0501502, 2017 YFA0603002) and the National Natural Science Foundation of China (41531174), and the Fundamental Research Funds of CAF (CAFYBBB2021 SY009).  
MODIS products data used in this study are available at [[https://modis.gsfc.nasa.gov/data/](https://modis.gsfc.nasa.gov/data/)]([https://modis.gsfc.nasa.gov/data/](https://modis.gsfc.nasa.gov/data/)) (accessed on 20 May 2021), [[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)) (accessed on 20 May 2021). Meteorological data used in this study are available at www.heihedata.org/data (accessed on 20 May 2021).  
Thanks for the reviewers' insightful suggestions and comments to revise this paper.  
The authors declare no conflict of interest.
# An Overview of Cooperative Driving in the European Union: Policies and Practices
## Abbreviations  
* A2/M2 CVC & A2/M2 Connected Vehicle Corridor
* ADAS & Advanced Driver Assistance Systems
* AG & Amsterdam Group
* APZ & Automotive Proving ground Zala
* BASt & Federal Highway Research Institute
* BUTE & Budapest University of Technology and Economics
* C2C-CC & Car2 Car Communication Consortium
* CACC & Cooperative Adaptive Cruise Control
* CAM & Cooperative Awareness Message
* CAV & Concessioni Autostradali Venete
* CCAM & Cooperative Connected and Automated Mobility
* CCN & Connected and Cooperative Navigation
* CCRW & Cooperative Collision Risk Warning
* CELC & Cooperative Emergency Lane Change
* C-ITS & Cooperative-Intelligent Transportation Systems
* CTAG & Centro Tecnologico de Automocion de Galicia
* DENM & Decentralized Environmental Notification Message
* Df & Department for Transport
* DSRC & Dedicated Short-Range Communication
* DT & Deployment and Tests
* EBL & Emergency electronic Brake Light
* EC & European Commission
* ECo-AT & European Corridor Austrian Testbed
* ETSI & European Committee for Standardisation
* EU & European Union
* EVA & Emergency Vehicle Approaching
* FMI & Finnish Meteorological Institute
* FTA & Finnish Transport Agency
* GLOSA & Green Light Optimal Speed Advisory
* GUI & Graphical User Interface
* HE & Highways England
* HGV & Heavy Goods Vehicle
* HLG & High Level Group
* HLN & Hazardous Location Notifications
* HMI & Human Machine Interface
* I2V & Infrastructure-to-Vehicle
* ITRL & Integrated Transport Research Lab
* IVI & In-Vehicle Information
* KCC & Kent County Council
* LZM & Loading Zone Management
* MAC & Medium Access Control
* MAP & Map Data
* MCA & Motorcycle Approaching indication
* MCTO & Multimodal Cargo Transport Optimization
* MND & Ministry of National Development
* MPC & Model Predictive Controller
* NR & New Radio
* OBU & On-Board Unit
* OEMs & Original Equipment Manufacturers
* PACE & Parking Autonomously in Cooperative Environments
* PHY & Physical
* PVD & Probe Vehicle Data
* RSD & Road and Motorway Directorate
* RSU & Road-Side Unit
## 4 Discussion
### Energy Performance  
One of the major issues in the industry that is extensively studied relates to the high energy demand of the residential sector, which accounts for 40% of global consumption and is primarily associated with heating and cooling systems [123; 124]. Generally, residential energy demand is met using fossil fuels, emitting greenhouse gases (GHGs) that contribute to global warming [125]. Due to rising global temperatures, an increase in energy demand for cooling systems is expected, from 28% by 2030 to 59% by 2070, creating a vicious cycle [126; 127].  
In this context, both traditional and prefabricated modular solutions, studied and developed, meet thermal demands, including the most stringent worldwide. Lightweight wood or steel partitions, concrete and masonry structures, and prefabricated elements meet the criteria using thermal insulation materials, mainly glass wool, expanded polystyrene, or polyurethane [128]. Simulations have shown that the energy demand for air conditioning decreases with construction systems that minimize energy losses through conduction [129]. To achieve this, construction systems must employ thermal insulation materials with low thermal conductivity, thereby reducing the thermal transmittance of the element. Measures aimed at reducing this value, from a physical standpoint, mainly decrease thermal energy losses through conduction (primarily in winter) but do not necessarily guarantee a lower overall annual energy demand [130]. Recent studies suggest that thermal inertia reduces energy consumption for both heating and cooling. Therefore, new industrialized construction solutions aiming to leverage the positive effects of thermal inertia and reduce cooling demand should use high-density materials with high specific heat capacity [131]. Additionally, factors such as climate conditions, temperature requirements, material density, and arrangement within construction systems must be considered. It is important to note that thermal inertia has only been theoretically addressed, lacking empirical demonstrations in real-world environments [132; 133].  
Over time, companies have aimed to use low-cost materials while developing elements ensuring thermal comfort to reduce cooling demand during the operational stages of residences [134]. While some construction solutions incorporate new insulation materials addressing sustainability aspects, they are not cost competitive. Hence, industrialized construction solutions continue to use traditional insulation materials that reduce energy losses through conduction without significantly altering project costs but negatively impacting the environment. This poses an additional challenge: developing (and certifying) low-carbon footprint thermal insulation materials competitive in costs compared to traditional materials.
# Reversible Data Hiding Scheme in Homomorphic Encrypted Image Based on EC-EG
## 4 Simulation Experiment and Theoretical Analysis  
### Analysis of Embedded Capacity and PSNR  
The 8-bit gray-scale images of \(512\times 512\) size were selected in the experiment conducted by Java8 and MATLAB R2015b. Peak signal-to-noise ratio (PSNR) was calculated by Equation (22), which is the most commonly used criterion for evaluating image quality after embedding data.  
\[PSNR=10\times\log_{10}\left[\frac{255^{2}}{MSE}\right] \tag{22}\]MSE is the mean square error between the cover image and the embedded image.  
\[MSE=\frac{1}{M\times N}\sum_{i=0}^{M-1}\sum_{j=0}^{N-1}\left[p(i,j)-c(i,j)\right]^ {2} \tag{23}\]  
where \(M\times N\) denotes the size of the image, \(p(i,j)\) denotes pixel value of the cover image and \(c(i,j)\) denotes pixel value of the embedded image. Through the size of PSNR value, we can clearly know whether the image with hidden data will be observed by the naked eye. Table 1 shows the embedding capacity and PSNR values of the Lena image under different n. Under the same embedding capacity, the PSNR value increases correspondingly with the increase of n. However, when n = 4, the PSNR value of the image is instead decreased because the distortion introduced by constructing the MCC becomes large. Table 2 shows the PSNR values and embedding capacities of the four images with the optimal parameter \(n=3\). Moreover, the maximum size of images' \(L\_map\) is shown in Table 3. Different stages of Lena, F-16, Man and Hill images using the proposed scheme are shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Embedding Capacity (bits)**} & \multicolumn{5}{c}{**PSNR (dB)**} \\ \cline{3-6}  & & _n =_ 1 & _n =_ 2 & _n =_ 3 & _n =_ 4 \\ \hline \multirow{6}{*}{Lena} & 4095 & 51.681 & 53.448 & 54.719 & 45.353 \\ \cline{2-6}  & 8190 & 48.765 & 51.13 & 51.786 & 43.472 \\ \cline{2-6}  & 12,285 & 46.993 & 49.022 & 49.618 & 41.04 \\ \cline{2-6}  & 16,380 & 45.658 & 48.012 & 48.32 & 40.132 \\ \cline{2-6}  & 24,570 & 43.034 & 45.684 & 45.885 & 37.97 \\ \cline{2-6}  & 32,760 & 40.82 & 44.301 & 44.456 & 36.384 \\ \cline{2-6}  & 36,855 & 39.983 & 43.634 & 43.92 & 35.609 \\ \cline{2-6}  & 49,140 & 38.219 & 42.09 & 42.667 & 34.353 \\ \cline{2-6}  & 65,520 & 35.829 & 40.03 & 41.146 & 32.611 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Embedding capacity and PSNR values under different \(n\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Embedding Capacity (bits)**} & \multicolumn{5}{c}{**PSNR (dB)**} \\ \cline{2-6}  & **Lena** & **F-16** & **Man** & **Hill** \\ \hline
4095 & 54.719 & 44.913 & 47.637 & 54.526 \\ \hline
8190 & 51.786 & 43.715 & 44.158 & 50.866 \\ \hline
12,285 & 49.618 & 42.896 & 42.486 & 48.695 \\ \hline
16,380 & 48.32 & 42.12 & 41.422 & 46.843 \\ \hline