esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Synbiotics: A New Perspective in Caries Management  
As previously mentioned, the term'synbiotic' regards products that consist of both probiotics and prebiotics. [PERSON] and [PERSON] first proposed prebiotics in 1995 to promote symbiosis in gut microbiota [19]. Today, there is clear evidence that prebiotics enhances host's immune function [125, 126, 127, 128], selectively favoring health-promoting bacteria, such as _Lactobacillus_ and _Bifidobacteria_[129, 130, 131, 132, 133], employing potential adhesion sites of pathogenic strains, thereby exerting anti-adhesive properties and repressing the virulence of human pathogens per se [130, 132, 134].  
In dentistry, as in general medicine and the food industry [93], prebiotics are combined with probiotics to enhance the latter's ability to \"outgrow\" pathogens. Until today, only five combinations have been tested; all of them are at pre-clinical stages [135, 136, 137, 138, 139]. Glucomannan hydrolyses (GMH) or 3% galactooligosaccharides (GOS) and 1% fructoligosaccharides (FOS), when combined with _L. acidophilus_, suppress _S. mutans_ growth [135, 136, 137, 138]. In 2015, [PERSON] et al. proposed specific probiotic and prebiotic candidates that could be combined to serve as synbiotics [136]. The corresponding probiotics were specific strainsfrom the species _L. fermentum_, _L. plantarum_, and _L. paracasei_. The potential prebiotics were xylose, xylitol, and arabinose, which were the only saccharides tested to simultaneously inhibit _S. mutans_ growth and promote the survival of _Lactobacillus_.  
Another strategy of synbiotics evolving in the past few years is incorporating probiotics-specific prebiotics known for their ability to maintain the oral environment's pH at high levels when a cariogenic challenge occurs. These prebiotics are mainly urea and arginine [140]. Although urea benefits specific oral microorganisms [141; 142; 143], its anti-cariogenic effect is notable [144; 145], no study about its potential use in synbiotics has been conducted. Arginine, an amino acid strongly positively correlated with caries absence in adults [146], is also well-documented [147]. In contrast to urea, arginine has found one application in synbiotics. This was accomplished by [PERSON] and his partners [139]. According to this study, arginine concentration is directly correlated with LGG's viability, inhibition of _S. mutans_ per se, biofilm, in general, and lactic acid production, thereby preserving plaque pH after the treatment application. Nonetheless, clinical trials must be carried out soon to verify their application in the complex oral environment.  
Undoubtedly, the concept of synbiotics in caries management has high promise. For the moment, it is still in its infancy. Further clinical trials, which will investigate the in vivo effect of these formulations on the oral microenvironment, especially on dental plaque and its pH, are necessary to clarify whether synbiotics can facilitate our attempts to decrease caries incidence.
## 3 Results and Discussion
For all samples, the greatest increase in solid content during osmotic dehydration in a 50% sucrose solution was observed at the end of the process, which occurred after 2 h. The highest solid gain after the process was observed for PEF-pretreated materials, followed by US-pretreated materials, and the lowest solid gain was observed for strawberries subjected to osmotic dehydration alone. Similarly, [PERSON] et al. [46] found that solid gain after 120 min of osmotic dehydration of apples in 30% of sucrose solution increased when the PEF pretreatment (250 and 400 V/cm, 20 \({}^{\circ}\)C, 1:1 product-to-water ratio) was applied in relation to the osmo-dehydrated sample without pretreatment. Furthermore, in the research of [PERSON] et al. [50], a PEF pretreatment applied before osmotic dehydration of blueberries considerably increased the rate of solid gain and caused the reduction in the dehydration time from 130 h to 48 h compared to the control samples of osmo-dehydrated without pretreatment (the process was stopped when samples reached to 3.5 g/g initial dry mass of water loss). The application of PEF treatments resulted in the perforation of the cell membrane, which, in turn, facilitated greater water and solute diffusivity. Consequently,  
Figure 1: Mass loss \(\Delta M_{t}^{g}\)**(a)**, water loss \(\Delta M_{t}^{g}\)**(b)**, and solid gain \(\Delta M_{t}^{ST}\)**(c)** during osmotic dehydration of strawberries in 50% sucrose solution at 30 \({}^{\circ}\)C using US and PEF treatments; SA: osmotic dehydrated strawberries in 50% sucrose solution; SA_PEF1 and SA_PEF2: pulsed electric field-treated strawberries with the energy of 1 or 2.5 kJ/kg and subjected to osmotic dehydration; SA_US1 and SA_US2: ultrasound-treated strawberries for 30 and 90 s and subjected to osmotic dehydration. the PEF-pretreated samples exhibited significantly higher rates of water loss and solid gain compared to the non-PEF-treated samples. The extract content of the fresh strawberries was 8.9 \(\pm\) 0.2 \({}^{\circ}\)Brix (Figure 2). Along with the duration of the osmotic dehydration process, an increase in the extract content of the strawberry tissue was noted for all samples. This is due to the penetration of osmoactive substances from the solution into the plant tissue. Such operations make it possible to enrich the dehydrated materials with beneficial components for the human's health and to shape the food's taste profile [2]. The use of pretreatments also affected changes in the extract content. Materials treated with PEF or US were characterized by a gradual increase in the extract content with the process time. It was observed that the PEF pretreatment caused the highest increase in the extract content among the analyzed materials, whereas the US pretreatment also affected the higher extract content in the strawberries compared to the material given to the osmotic dehydration alone. Statistical analysis showed after 2 h of the process, a significant difference between the strawberries subjected to osmotic dehydration alone and the strawberries pretreated with PEF (1 or 2.5 kJ/kg) and US (90 s) (\(p\) < 0.05).
# Multi-Patch Time Series Transformer for Robust Bearing Fault Detection with Varying Noise
## 5 Experiments
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Noise Type** & **Model** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline \multirow{8}{*}{Electrical interference noise} & LSTM & 0.9884 & 0.9914 & 0.9899 & 0.9907 \\ \cline{2-6}  & GRU & 0.988 & 0.992 & 0.9891 & 0.9905 \\ \cline{2-6}  & FCN & 0.9878 & 0.9928 & 0.989 & 0.9909 \\ \cline{2-6}  & ResNet & 0.9883 & 0.9925 & 0.9894 & 0.9909 \\ \cline{2-6}  & InceptionTime & 0.989 & 0.993 & 0.9905 & 0.9918 \\ \cline{2-6}  & XceptionTime & 0.9878 & 0.9926 & 0.9896 & 0.9911 \\ \cline{2-6}  & XCM & 0.9882 & 0.993 & 0.9897 & 0.9913 \\ \cline{2-6}  & LSTMAttention & 0.9864 & 0.991 & 0.9879 & 0.9894 \\ \cline{2-6}  & GRUAttention & 0.9894 & 0.993 & 0.9901 & 0.9916 \\ \cline{2-6}  & TSSequencer & 0.9866 & 0.9918 & 0.988 & 0.9899 \\ \cline{2-6}  & PatchTST & 0.9874 & 0.9923 & 0.9885 & 0.9904 \\ \cline{2-6}  & **MultiPatchTST** & **0.9907** & **0.993** & **0.9918** & **0.9927** \\ \hline \multirow{8}{*}{Harmonic noise} & LSTM & 0.984 & 0.9903 & 0.9858 & 0.988 \\ \cline{2-6}  & GRU & 0.9838 & 0.9912 & 0.9849 & 0.988 \\ \cline{2-6}  & FCN & 0.9831 & 0.9906 & 0.9847 & 0.9877 \\ \cline{2-6}  & ResNet & 0.9845 & 0.9909 & 0.9864 & 0.9886 \\ \cline{2-6}  & InceptionTime & 0.9818 & 0.989 & 0.9839 & 0.9864 \\ \cline{2-6}  & XceptionTime & 0.9829 & 0.9894 & 0.9848 & 0.987 \\ \cline{2-6}  & XCM & 0.9865 & 0.9914 & 0.9878 & 0.9898 \\ \cline{2-6}  & LSTMAttention & 0.9842 & 0.9917 & 0.9857 & 0.9887 \\ \cline{2-6}  & GRUAttention & 0.9817 & 0.9912 & 0.9838 & 0.9875 \\ \cline{2-6}  & TSSequencer & 0.9818 & 0.9894 & 0.9835 & 0.9864 \\ \cline{2-6}  & PatchTST & 0.9842 & 0.9918 & 0.9859 & 0.9888 \\ \cline{2-6}  & **MultiPatchTST** & **0.9868** & **0.992** & **0.9883** & **0.99** \\ \hline \multirow{8}{*}{Random shock noise} & LSTM & 0.9545 & 0.968 & 0.9598 & 0.9638 \\ \cline{2-6}  & GRU & 0.9534 & 0.9668 & 0.9584 & 0.9625 \\ \cline{2-6}  & FCN & 0.9521 & 0.9667 & 0.9581 & 0.9623 \\ \cline{2-6}  & ResNet & 0.9534 & 0.9667 & 0.9597 & 0.9631 \\ \cline{2-6}  & InceptionTime & 0.9526 & 0.9661 & 0.9586 & 0.9623 \\ \cline{2-6}  & XceptionTime & 0.9536 & 0.9676 & 0.9592 & 0.9634 \\ \cline{2-6}  & XCM & 0.9541 & 0.968 & 0.9583 & 0.9631 \\ \cline{2-6}  & LSTMAttention & 0.9479 & 0.962 & 0.9553 & 0.9586 \\ \cline{2-6}  & GRUAttention & 0.9546 & 0.9685 & 0.9601 & 0.9643 \\ \cline{2-6}  & TSSequencer & 0.9522 & 0.9653 & 0.9582 & 0.9617 \\ \cline{2-6}  & PatchTST & 0.9538 & 0.9677 & 0.9602 & 0.9639 \\ \hline \multirow{2-6}{*}{**MultiPatchTST**} & **0.9549** & **0.9685** & **0.9602** & **0.9644** \\ \cline{2-6}  & & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._
# Analysis of the Water Quality Improvement in Urban Stream Using MIKE 21 FM
## 2 Theoretical Background  
### Overview of Ozone Treatment and Stream Water Quality Factors  
#### 2.1.1 Stream Water Quality Factors  
Although several factors determine the water quality of the stream, those corresponding to the stream's habitability include dissolved oxygen (DO), biological oxygen demand (BOD), total phosphorus (TP), and total nitrogen (TN) [2; 4; 5].  
BOD is the biochemical oxygen demand and is an important indicator used in relation to water treatment and river water quality. BOD concentration is also used to judge the water treatment methods and operation efficiency. The decomposition speed of organic matter and consumption speed of oxygen can be estimated by measuring the BOD; high BOD indicates that the organic contamination is high [19; 20]. Therefore, the BOD can be used to verify the current status of river water quality and the effect of water quality improvement.  
Total nitrogen (TN) is the total amount of nitrogenous compounds included underwater, including organic nitrogen, ammonium nitrogen, and nitrite and nitrate nitrogen. It increases with the artificial inflow of domestic sewage, industrial wastewater, and livestock wastewater; however, it is included in natural water during the nitrogen circulation process of nature and hence not easy to control [20].  
Phosphorus is a nutrient important for plant growth. In most lakes, phosphorus is the limiting nutrient, which means that everything that plants and algae need to grow is available in excess (sunlight, warmth, water, nitrogen, etc.) except phosphorus [21]. Phosphorus acts as a limiting substrate of the eutrophication of closed water bodies, such as lakes and swamps, along with nitrogen. A large amount of phosphorus is included in synthetic detergents. A very high phosphorus concentration is found in livestock and agricultural wastewater [22].  
In South Korea, the Ministry of Environment mandates the stream water quality standards for protecting the aquatic ecosystem from water pollution and preserving the water quality suitable for use. In the Ministry of Environment standards, the degree of goodness of the water quality is displayed by classifying the water quality factors, such as pH, BOD, TOC, SS, DO, and TP into 1-7 grades [23].
## 2 Material and Methods
### Microbiological Analysis  
Samples were homogenized (1/10 dilution) with tryptic soy broth with 0.6% yeast extract (TSBYE; DB, NJ, USA) in a Masticator Classic (IUL S.A., Barcelona, Spain) for 1 min and subsequently 10-fold diluted in 0.1% Bacto Peptone (Difco Laboratories, Detroit, MI, USA) with 0.85% NaCl (Merck, Darmstadt, Germany). For the periodical enumeration of _Salmonella_, homogenates were plated on Brilliant Green Agar (BGA; Difco Laboratories, Detroit, MI, USA) and incubated at 37 \({}^{\circ}\)C for 24-48 h. For _L monocytogenes_, homogenates were plated on the selective and differential medium Chromogenic Listeria Agar (CLA; Oxoid Basingstoke, UK) and incubated at 37 \({}^{\circ}\)C for 48 h. For expected counts below the limit of quantification, i.e., 4 cfu/g (resulting from platting 4 mL of homogenate in a 14 cm diameter plate), the presence of both pathogens in 15 g test samples was determined by enrichment of the homogenates at 37 \({}^{\circ}\)C for 48 h. The enriched homogenates were streaked on selective media (BGA for _Salmonella_ and CLA for _L. monocytogenes_) and incubated at 37 \({}^{\circ}\)C for 24-48 h. Presumptive colonies were confirmed by PCR [20].  
Statistical Analysis and Mathematical Modeling of the Pathogen Behavior during the Storage of the DCH after the HPP  
The statistical analysis and mathematical modeling were conducted on data transformed into log change (log N/N\({}_{0}\)), i.e., the decimal logarithm of the pathogen concentration at each sampling point minus the concentration of the pathogen at the beginning of the storage (immediately after the HPP). Values of the log change > 0.5 log units were considered growth behavior, log changes <\(-\)0.5 log were considered inactivation behavior, and log changes between \(-\)0.5 and 0.5 were considered not microbiologically relevant changes [18] and are termed \"survival\" in this article.  
Principal component analysis (PCA) was conducted to provide a general overview of the dynamics along the storage of the pathogen in terms of changes in the concentration (log change) after HPP for the different combination of conditions. To perform the PCA, the _estim_ncpPCA_ and _imputePCA_ functions from the _missMDA_ package of R software [21] were used to deal with the missing values, i.e., when the pathogen was not detected. The PCA with the confidence ellipses around the categoric variables (\(a_{w}\), fat, and pressure level) at a confidence level of 0.95 was obtained with the _PCA_ and _plotllipses_ functions from the _FactoMinerR_ package of R software [21]. Statistical differences in microbial log change (log \(N/N_{0}\)) along the storage time between trials were assessed through an ANOVA test followed by a Tukey's honestly significant difference test. For this, the _aov_ function from the _stats_ package and the _TukeyHSD_ function from the _agricolae_ package of the R software [21] were used.
## 2 Materials and Methods
### Trend Analysis  
A nonparametric test called the Mann-Kendall (M-K) [48; 49] method is often used to determine if the time series of the variable under study has a monotonic trend. The alternative hypothesis (H\({}_{1}\)) in the M-K test denotes the existence of a monotonic trend in the data, which may be positive or negative, whereas the null hypothesis (H\({}_{0}\)) indicates that there is no monotonic trend in the series. Positive standardized test statistic \(Z_{\text{MK}}\) values denote a rising trend within the time series data, while negative \(Z_{\text{MK}}\) values imply a declining trend. This study used the M-K trend analysis to look at the trend of flash droughts and the NDVI in a selected time period and the significance was tested at 95% confidence interval. The M-K trend test was performed and the trendline equation and R\({}^{2}\) value were added using Excel.  
The test statistic \(Z_{\text{MK}}\) for a given time series (\(x_{1}\), \(\ldots\), \(\ldots\), xn), was calculated as follows:  
\[S=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}sign\big{(}x_{j}-x_{i}\big{)} \tag{1}\]  
\[sign\big{(}x_{j}-x_{i}\ \ \big{)}=\left\{\begin{array}{l}+1,\ x_{j}>x_{i}\\ 0,\ x_{j}=x_{i}\\ -1,x_{j}<x_{i}\end{array}\right. \tag{2}\]  
\[Var(S)=\frac{1}{18}[n(n-1)(2n+5)-\sum_{p}t_{p}\ (t_{p}-1)\big{(}2t_{p}+5)] \tag{3}\]  
\[Z_{MK}=\left\{\begin{array}{l}\frac{(S-1)}{\sqrt{\text{nar}(S)}}\,\ S>0\\ 0\The flowchart of the overall research route carried out in this study is shown in (Figure 2). The flash drought calculation was carried out using SAS version 9.4, the maps were produced using ArcGIS 10.3 and the other analyses were carried out using R 4.3.1 and MS Excel version 2019.
## 4 Results and Discussion
### The Influence of Droplet Properties on Equivalent Substrates  
To better understand the equivalent substrate phenomenon, this study further examined the effects of contact angles (5-90\({}^{\circ}\)) (see Figure 7). It is acknowledged that the contact angle significantly impacts the droplet evaporation rate [41]. An increased contact angle lengthens the heat transfer path from the solid-liquid interface to the liquid-gas interface, thereby reducing the efficiency of heat transfer from the substrate to the liquid-gas interface. Moreover, for droplets with the same contact line radius but different contact angles, the surface area of the liquid-gas interface also varies. However, in this model, droplets with different contact angles all meet the equivalent substrate condition. This is because within a set of equivalent substrates, there is a balance between the substrate thermal conductivity and substrate thickness, resulting in nearly identical heat transfer from the substrate lower surface to the solid-liquid interface.  
The droplets with different evaporative cooling coefficients (\(Ec\) = 0.01-5) on droplet evaporation on a set of equivalent substrates are also numerically studied (see Figure 8). \(Ec\) is determined by four factors: the latent heat of vaporization, the diffusion coefficient in air, the temperature coefficient of saturated vapor concentration in air, and thermal conductivity. Thus, the variation in the evaporation cooling coefficient \(Ec\) can represent both the changes in liquid material properties and ambient pressure. While these parameters significantly influence droplet evaporation, the heat transfer from the substrate's lower surface to the solid-liquid interface remains consistent across equivalent substrates. Consequently, the evaporation rate of the same droplet on a given set of equivalent substrates is essentially uniform, indicating that the properties of the droplet have negligible effects on the equivalent substrate phenomenon.  
Figure 7: The variation in the evaporation rates of droplets on a set of equivalent substrates with different contact angles as the relative thermal conductivity of the substrate increases, where \(Ec\) equals to 1. (**a**) The contact angle is 5\({}^{\circ}\); (**b**) the contact angle is 10\({}^{\circ}\); (**c**) the contact angle is 30\({}^{\circ}\); (**d**) the contact angle is 50\({}^{\circ}\); (**e**) the contact angle is 70\({}^{\circ}\); and (**f**) the contact angle is 90\({}^{\circ}\).
# Precise Channel Estimation Approach for a mmWave MIMO System
## 7 Simulation Results and Discussion
### Results and Discussions
#### 7.4.2 Comparison of NMSE  
The performance of OMP, TSSR, VAMP, ADMM and the proposed Ex-ADMM algorithm in terms of NMSE, at different SNR points with respect to different training symbol lengths T, is depicted in Figure 3a-c. The following relation is used to calculate NMSE:  
\[\text{NMSE}\triangleq\text{E}\left(10\log_{10}\frac{\left\|\hat{\text{H}}- \text{H}\right\|_{\text{F}}^{2}}{\left\|\text{H}\right\|_{\text{F}}^{2}}\right)\]  
where \(\hat{\text{H}}\) is the estimated channel matrix and H is the true channel matrix. \(\hat{\text{H}}\) is determined by \(\text{H}^{(\text{I}_{\text{max}})}\). The performance of OMP at different SNR points is almost constant for all training symbol lengths, and, due to its huge dictionary matrix, does not change for any increment of T. Thus, OMP may suffer from a discretization problem. One can therefore say that the OMP is not capable enough to recover small training symbols.  
Figure 3: \(Cont.\)On the other side, it was found that VAMP exhibited poor performance at T \(<\) 800, but, as depicted in Figure 3b,c, the performance of VAMP increases as the training symbol length increases. VAMP is used to calculate statical information on sparse signals, which is impossible in case of small training symbol lengths, but, at low-to-mid SNR points, as the training symbol length increases to T \(>\) 800, it exhibits a significant improvement in NMSE performance compared to other algorithms. TSSR is used for two-stage estimation. In the simulation results, TSSR is not capable of recovering the estimated values for low and high training lengths. TSSR exploits the low rank and sparsity of any channel matrix individually. In contrast, ADMM has the capacity to exploits low rank and sparsity together, with the channel matrix for any training symbol length.  
Estimation, in the proposed Ex-ADMM algorithm, is done by SVT implemented on H, which exploits the low rank property of H and l\({}_{1}\) - norm, enforced on the submatrix to ensure the sparsity. Estimation of H\({}_{\Psi}\) becomes noisier, as the proposed Ex-ADMM algorithm and ADMM lack array gain, but the noise is not severe enough to cause a major effect on the estimated values. The proposed Ex-ADMM algorithm performed better than OMP, TSSR, VAMP and ADMM in terms of NMSE at different SNR points with respect to different training symbol lengths.
## 2 Materials and Methods
### Anthropometric Assessment  
Anthropometric assessment was performed in accordance with the recommendations of the International Society for the Advancement of Kinanthropometry (ISAK) [27] by a level II ISAK-accredited practitioner, accompanied by a level I ISAK-accredited practitioner recording the measurements. An intra-observer technical error of the measurement (TEM) of 5% for skinfolds and 1% for breadths and girths was considered for each measurement. The instruments used for anthropometric measurements consisted of a Holtain(r) wall stadiometer (precision \(\pm\) 1 mm), a Seca(r) weight scale (precision \(\pm\) 0.1 kg), a Holtain(r) bone breadth caliper (range from 50 to 570 mm), a Holtain(r) skinfold caliper (constant precision \(\pm\) 10 gr-mm\({}^{-2}\), interpolation precision \(\pm\) 0.2 mm), a Holtain(r) anthropometric tape (precision \(\pm\) 1 mm) and complementary materials (e.g., a dermograph pencil to mark the subject). Measurements were taken in a room maintained a constant temperature of 24 \({}^{\circ}\)C, with participants wearing shorts. Two measurements were taken for height, body mass and girths (relaxed arm, flexed and contracted arm, mid-thigh and calf girths) and bone breadths (biepicondylar humerus, bistyloid and biepicondylar femur), and three for skinfolds (triceps, subscapular, iliac crest, abdominal, front thigh and medial calf). All measurements were taken in the morning (after at 8:30 am) and on the right side of the body, regardless of which side dominant [27]. For the present study we selected height, weight, body mass index (BMI), all the skinfolds assessed and the sum of 6 skinfolds. In addition, we estimated % body fat using [PERSON]'s equation [28] and muscular mass using [PERSON]'s equation [29]. To calculate the somatotype, we used the three-component system (mesomorphy, endomorphy and ectomorphy) proposed by [PERSON] [30], thereby establishing the mean somatotype and position in the somatochart for male and female athletes.