esa-sceva/satcom-chunk-collection huggingface dataset:
# Concentration in Saliva and Antibacterial Effect of Xylitol Chewing Gum: In Vivo and In Vitro Study
## 2 Materials and Methods  
### Study Design  
This study took place in Sassari (Italy) and lasted from February 2018 to May 2018. The study was approved by the Ethics Committee of the University of Sassari (SS-15884-GC 23/ 09/2017) and registered at [[http://www.clinicaltrial.gov](http://www.clinicaltrial.gov)]([http://www.clinicaltrial.gov](http://www.clinicaltrial.gov)) (Protocol Registration Receipt NCT03971981) and conducted in accordance with the Declaration of Helsinki ([[http://www.wma.net](http://www.wma.net)]([http://www.wma.net](http://www.wma.net))).  
The research protocol was conducted in two phases (Figure 1). In the first, an in vivo double-blind cross-over study was performed to measure the salivary concentration of xylitol released from two types of chewing gum containing different amounts of the polyol. The study had a cross-over design: the first half of the sample used the chewing gum with xylitol as the only sweetener (100% xylitol chewing gum) and the other half used the chewing gum with xylitol among the sweeteners (22% xylitol chewing gum); after a 7-day wash-out period, the two sub-groups reversed the types of chewing gum. Subjects were instructed to consume the products as they normally do. Throughout the study the subjects were not allowed to use mouthwashes, other xylitol products, antimicrobial medications, etc. In the second phase of the designed protocol, the antibacterial efficacy of the two types of chewing gum was tested in vitro against nine different strains of oral bacteria.  
All chewing gum used in this investigation were produced and supplied by Perfetti Van Melle SpA (Lainate, Italy) and are commercially available in Italy. Except for the sweeteners, the two chewing gums were identical in weight (1.4 g), shape, color, and packaging. Their composition is displayed in Table 1.  
The xylitol amount was entirely contained in the hard coating for the 22% chewing gum, while the 100% xylitol gum contained the same amount in the hard coating and the remaining part in the gum pellet.
## 3 Optimized Distributed Raman Amplification Design over Multiple Fiber Spans
Thus, the accumulated noise from the Raman amplified link is more dominant compared with transceiver noise (fundamentally limited by higher-order parametric noise), and therefore the benefit of the Q\({}^{2}\) factor improvement can be revealed for long-haul transmission systems with an optimized mid-link OPC [21; 55]. Therefore, using the symmetrical EDF-assisted Raman link in long-haul transmission systems can improve the fiber nonlinearity compensation efficiency and the transmission performance in a mid-link OPC system.
# A Multi-Domain Model for Variable Gap Iron-Cored Wireless Power Transmission System
## 2 Mutual Induction Model: The Static Aspects
### The Governing Equations  
The inductive coupling between \(n\) ferrite core windings can be modeled starting from the description of the flux linkage \(\lambda_{ij}\), which is defined as the product between the number of turns of the transmitting coil \(N_{i}\) times the total magnetic flux \(\Phi_{ij}\) of the loop formed by both cores of the two coils; this is equal to the product between the magnetic flux density \(B_{j}\) generated by the coil \(i\) and measured through coil \(j\) times the average cross section of the core \(i\)\(A_{i}\), supposing that all the magnetic flux lines crossing the winding loop are completely contained inside the ferrite core.  
\[\lambda_{ij}=N_{i}\Phi_{ij} \tag{1}\]  
\[\Phi_{ij}=\oiint B_{j}dA_{i}=A_{i}B_{j} \tag{2}\]  
\[\mathrm{L}_{ij}=\frac{\lambda_{ij}}{I_{j}} \tag{3}\]  
Merging all the possible combinations of the coefficients \(i\) and \(j\) from \(1\) to \(n\) through the ratio between the flux linkage and the circulating current \(I_{j}\) reported in (3), it is possible to reconstruct the inductive coupling square matrix reported in (4).  
\[L=\begin{bmatrix}L_{11}&L_{12}&\dots&L_{1n}\\ L_{21}&L_{22}&\dots&L_{2n}\\ \vdots&\vdots&\ddots&L_{(n-1)n}\\ L_{n1}&L_{n2}&L_{n(n-1)}&L_{nn}\end{bmatrix} \tag{4}\]  
where the diagonal terms define the auto-inductance parameters, and the non-diagonal terms define the mutual-inductance one. Defining the equality between the same combinations of \(i\) and \(j\) of the extra diagonal terms is possible to obtain the coupling coefficient \(K_{ij}\) through Equation (5).  
\[L_{ij}=L_{ji}=K_{ij}\sqrt{L_{ii}L_{jj}} \tag{5}\]
## 4 Discussion
### Biogenic Amines Content  
Fermented and protein-rich foods are prone to forming toxic nitrogenous bases such as biogenic amines. These low-molecular-weight compounds are generated due to the microbial decarboxylation of amino acids through substrate-specific decarboxylase enzymes [50; 51]. Some authors reported that tyramine, putrescine, and cadaverine are the predominant biogenic amines in fermented foods [51; 52]. As we could see, these compounds were those with the highest concentration in experimental sausages ranging from 0.094-0.223 mg g\({}^{-1}\) (tyramine), 0.048-0.147 mg g\({}^{-1}\) (putrescine), and 0.099-0.148 mg g\({}^{-1}\) (cadaverine). Among these biogenic amines, tyramine has attracted special consideration due to its potential health risks at high levels. It is synthetized by the fermentative microbial population of lactic acid bacteria [53]. For this reason, sausage samples had a high level of tyramine since the growth of lactic acid bacteria increased considerably during the ripening process. Our results are well-correlated with the microbial of lactic acid bacteria since the control (SK) had both the lowest concentration of tyramine and the lowest growth of lactic acid bacteria. LAB and _Enterobacteriaceae_ are capable of forming biogenic amines [54], though the LAB are the main producers [55]. Therefore, samples with a higher bacterial population are expected to contain more amines. However, the aliphatic diamines cadaverine and putrescine could potentiate the adverse effects of tyramine [56]. These substances are formed during food processing and storage by the action of bacteria. As BAs are indicators of the microbiological quality of food, their identification and quantification are important [57]. Agmatine can be metabolized to putrescine, the spermine and spermidine precursor [58]. The aminogenesis is strongly influenced by many factors, though the most relevant is the microbiological quality of raw material (decarboxylase activity is mainly attributed to enterobacteriaceae, pseudomonadaceae, microococcae, lactic acid bacteria, etc.), which can vary in each production batch. Furthermore, the characteristics of the raw material (composition, ion strength, pH, etc.), ingredients, additives, and the processing and storage conditions are also crucial factors that produce variations in the content of the different biogenic amines. Perhaps, due to the raw meat employed, the biogenic content in all sausages was quite higher compared with other studies [59]. Although they are considered a food hazard, there is no threshold for biogenic amines in European legislation, except for histamine in fish [60]. Biogenic amines are thermostable; therefore, after their formation, their elimination is quite difficult. Hence, it is critical to avoid their presence by employing appropriate materials and conditions to ensure food quality [52; 61; 62]. In addition, the fast acidification caused by negative amino decarboxylase cultures reduces the development of biogenic amines in sausages [48].
## 5 Discussion
### Paths for Future Work  
Our results encourage several paths for future work. In particular, the observation that viewing volume size does not appear to impact presence is interesting, as it may encourage focusing on rendering higher-quality views (fewer artifacts, more realistic effects) within small viewing volumes rather than necessarily augmenting the size of the volume itself.  
Additional studies could also be led to evaluate image-based rendering methods on a wider array of scenes and with larger sample sizes, in order to better understand the conditions under which view-dependent effects are appreciated, preferred over global texture maps, and perceived as being realistically rendered.  
As for the capture process, further testing could be led to evaluate the extent to which supplementing the rotations with additional captures in translation could help 3D reconstruction by consolidating parallax. Several different angle layouts (both cylindrical and spherical) could also be tested, and the resulting reconstructions compared. Furthermore, coverage at the zenith could be improved by adding cameras facing upwards. Other paths for improvement also include using a battery instead of the current power supply, and switching to higher-quality cameras.  
Additionally, future works could reproduce the study with high-dynamic-range (HDR) data, by capturing multiple exposures and rendering the data on an adapted display or using a tone-mapping operator. Indeed, using HDR data may be significant, because photographs with a limited dynamic range may cause areas with highlights to be oversaturated and may thus be the source of noticeable artifacts during rendering. In this way, it could be interesting to lead a user study investigating the extent to which viewers perceive fewer artifacts when using HDR data as input. Future research could also focus on the choice of a tone-mapping operator, which could be selected either using objective criteria or based on user study results (to obtain a perceptive-driven metric).
## 1 Introduction
In a platooning system, critical conditions to be examined and ensured are string stability and stringent latency in the broadcasting of messages. The stability of a string of vehicles is defined as, for any set of bounded initial disturbances to all the vehicles, the position fluctuations of all the vehicles remain bounded and these fluctuations approach zero as time t \(\rightarrow\infty\)[9]. The other condition involves the broadcasting of messages like the acceleration, velocity, location, and position of vehicles, which comprise the Cooperative Awareness Messages (CAM) in ETSI or Basic Safety Message (BSM) in SAE standards that are frequently established by the CV to organize the platoon [10]. Furthermore, a vast amount of data are extracted by autonomous vehicles, and the computing ability required to manage these extracted data is lacking. To tackle the aforesaid problem, state-of-the-art data processing technology cited as Cloud Computing (CC) technology is concerned. Nonetheless, in conventional cloud computing, information dissemination between the far-placed vehicles and the remote cloud leads to an extreme delay, thereupon causing incompatible mobility scenarios. By placing an MEC server at the LTE Base Station (BS), storage and computing resources can be provided, which in turn enhances the network capability. Hence, Multi-access Edge Computing (MEC) is appraised as a promising solution to facilitate computational resources for Automated Vehicles (AVs) to minimize latency. Meanwhile, the conventional way of optimizing no longer serves the significant growth of AVs owing to the uncertain constraints analogous to heightened complexities and a weak convergence rate. Such identified challenges demand efficient optimization techniques to address the intricate constraints effectively. Reinforcement learning counts as one of the dominant machine-learning tools wherein MADDPG or Multi-Agent Deep Deterministic Policy Gradient is latterly adopted. MADDPG, an expansion of DDPG, comprises the Multi-Agent Policy Gradient, which has been regarded as a potential solution for optimizing radio resource management in AV platooning scenarios. However, the Q-function employed by MADDPG for discovering the optimum actions by the agent adversely leads to function approximation issues. Consequently, the errors contribute to the overestimated values and, in turn, link to suboptimal policies. Overestimation bias remains prevalent in the actor-critic environment. Hence, novel approaches are necessitated to mitigate the impact on both environments. Therefore, a cascaded approach for optimizing radio resource management in the platooning scenarios by mitigating the estimation bias issues and minimizing the latency is proposed.
# Artificial Neural Networks in Coordinated Control of Multiple Hovercrafts with Unmodeled Terms
## 2 Problem Formulation
### Radial Basis Function Neural Networks  
Radial Basis Function Neural Networks (RBFNNs) can be used to approximate the unmodeled nonlinear dynamic terms due to their universal approximation capability [23]. For any unknown smooth function \(f(\mathbf{x}):\mathbb{R}^{n}\rightarrow\mathbb{R}^{m}\) can be approximated by RBFNNs in the following form, given by  
\[\hat{f}(\mathbf{x})=\mathbf{W}^{T}\boldsymbol{\sigma}(\mathbf{x}) \tag{7}\]  
Figure 1: Simple Model of The Vehicle.  
where \(\mathbf{x}\in\Omega\subset\mathbb{R}^{n}\), \(\Omega\) is a compact set. The adjustable weight matrix with \(n\) neurons is denoted by \(\mathbf{W}\in\mathbb{R}^{n\times m}\) under the assumption that it is a bounded matrix, that is  
\[\mathbf{W}\leq\mathbf{W}_{\max}. \tag{8}\]  
It is important to point out that here when we say matrix \(\mathbf{x}\in\mathbb{R}^{m\times n}\) is smaller than or equal to \(\mathbf{x}_{\max}\in\mathbb{R}^{m\times n}\), we mean all elements of \(\mathbf{x}\) are smaller than or equal to their corresponding elements of \(\mathbf{x}_{\max}\in\mathbb{R}^{m\times n}\). Moreover, \(\sigma(\mathbf{x})\) is the basis function vector and \(\sigma_{i}(x_{i})=\exp(-(x_{i}-\mu_{i})^{T}(x_{i}-\mu_{i})/c_{i}^{2})\), \(i=1,2,\ldots,n\) denotes its component, \(\mu_{i}\) is the center of the receptive field, and \(c_{i}\) represents the width of the Gaussian function. Moreover, it is relevant to point out that, in order to achieve better approximate results, we should make the neuron number \(n\) large enough and choose the parameters properly. Going back to the smooth function \(f(\mathbf{x})\) mentioned above, there is an ideal weight \(\mathbf{W}_{d}\) such that  
\[f(\mathbf{x})=\mathbf{W}_{d}^{T}\sigma(\mathbf{x})+\epsilon(\mathbf{x}) \tag{9}\]  
where \(\epsilon(\mathbf{x})\) denotes the approximation error and satisfies \(||\epsilon(\mathbf{x})||\leq\epsilon_{\max}\), where \(\epsilon_{\max}\) is a positive number. It is noted that \(\mathbf{W}_{d}\) is an \"artificial\" quantity for the purpose of mathematic analysis, in the process of controller design, we need to estimate it [24]. A simple RBFNNs is given by Figure 2.
# Effects of Freeze-thaw Cycles on the Mechanical Properties and Microstructure of a Dispersed Soil
## 3 Result and Discussion
### Particle Size Analysis Test  
The soil particle size distribution curves under varying freeze-thaw cycles were obtained by analyzing the test results and plotting the data, as illustrated in Figure 12. The arrows indicate the curves' trend with increasing freeze-thaw cycles. The alteration in the particle size distribution of the soil samples encompasses a reduction in the proportion of large particles and an augmentation in the proportion of small particles.  
To better analyze the changes in the particle size composition of the soil samples, the particles in each sample were categorized into three groups: clay particles (<0.005 mm), silt particles (0.005-0.075 mm), and sand particles (0.075-2 mm). The change curves depicting the proportion of each particle group with the progression of freeze-thaw cycles were graphed, as depicted in Figure 13. The findings illustrate that the granular composition of the soil samples undergoes alterations as the number of freeze-thaw cycles increases. Specifically, the content of sand particles shows a significant decrease as the number of freeze-thaw cycles increases. Conversely, there is a gradual overall increase in the contents of clay and silt particles. Thus, the freeze-thaw process exerts a dual effect on soil particles.  
On the one hand, the freeze-thaw process causes larger particles in the soil to fragment into smaller particles. On the other hand, it leads to the aggregation of smaller particles into larger ones. Consequently, an increase in the number of freeze-thaw cycles results in the breakdown of sand particles into finer clay and silt particles. Additionally, some  
Figure 12: The particle size distribution curves of the soil samples’ particles after different numbers of freeze–thaw cycles. (The arrows indicate the curves’ trend with increasing freeze–thaw cycles).  
Figure 13: Variations in particle size composition of the soil samples after different numbers of freeze–thaw cycles.  
-clay particles may transform into silt particles due to aggregation. During the test, the fragmentation of sand grains was more pronounced, resulting in a decline in sand content and a rise in the proportions of silt and clay particles. Notably, the increment in clay content was relatively higher than that of silt particles. Overall, the content of finer particles in the soil samples increased after undergoing freeze-thaw cycles compared to samples that did not experience such processes.
# The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development
## 3 Results  
### Uncertainty Analysis of In Situ Measurements  
An overall high agreement existed between the in situ non-destructive measurements and destructive sample analysis (Figure 2). The R\({}^{2}\) was 0.80 for a linear fit between the SunScan and destructive sample LAI measurements. We further corrected the bias in the SunScan measurements based on the destructive data, which reduced the NRMSE from 50% to 17%. A quadratic fit demonstrated a high correlation (R\({}^{2}\) = 0.75) between the LCC measurements and leaf N content derived from the destructive sample analysis. The variance of the measured LCC was, however, relatively high when compared to that of the LAI, with the standard deviation of SPAD measurements ranging from +/- 3 to +/- 16.
## 4 Dyversify Architecture
### Event/Anomaly Detection
#### 4.6.3 Event Detection: MP-Events  
This component assumes that repeating patterns represent repeating system or consumer behavior and uses the Matrix Profile to search for new occurrences of relevant patterns. Relevant patterns are provided by the user interacting with the dashboard; any labeled event will be tracked.  
This and the previous component differ in how they use the Matrix Profile. The MP-events component compares a _query pattern_ sequence against the incoming measurements, while the previous component performs a so-called _self-join_[37] to look for unique patterns in the incoming measurements. The processing flow and parameters are similar as the previous component, except here a local minimum is used, as we are interested in the best possible match for each query pattern.  
The query patterns originate from two sources, as shown in Figure 1. The first source is the Kafka event topic, where all detection events are submitted, but because we filter on labeled events, effectively only those of the dashboard are used. Still, by reading from the Kafka topic, rather than creating a direct link to the dashboard, we have a loose coupling that would allow transparent changes in the future. The second source of query patterns is the Stardog database, which is queried using a SPARQL HTTP API. Where Kafka acts as the live event feed, Stardog is the historical repository. Retrieval of historical events is needed in cases where an MP-events instance does not start processing a stream from the start. This can occur due to recovery of a crashed instance or due to reassignment after horizontal rescaling. Horizontal scalability is achieved in the same way as the MP-outliers component. Reading from two Kafka topics did pose some challenges; these are discussed in Section 6.  
As a Matrix Profile instance can only process a single measurement stream, MP-events creates one Matrix Profile per pattern. As MP-events consumes measurements, it forwards each measurement to all Matrix Profile instances tracking that specific stream. This does mean this approach will have scalability issues if the number of patterns would keep growing, though this was not a problem for Dyversify. Evaluations showed that incoming measurements for a single pattern were processed in 15 ms.
## 4 Results and Discussion
### Reliability Analysis  
Evaluating the questionnaire and its individual questions is essential to ensure the consistency of each question group supporting the respective variables and the relevance of the selected questions [44]. Reliability testing was conducted using Cronbach's Alpha statistic [45], and construct validity was assessed through the reliabilities of the individual factors, measured using Cronbach's Alpha coefficient and Exploratory Factor Analysis (EFA).  
The reliability testing yielded an overall Cronbach's Alpha value of 0.821, which is considered acceptable as it surpasses the minimum acceptable threshold of 0.7 [46; 47]. The Cronbach's Alpha coefficients for all five factors on the questionnaire were satisfactory, as they exceeded the recommended value of 0.7. Furthermore, the results of construct validity  
Figure 1: Respondents’ affiliation within the Silicon Mountain technology ecosystem.  
Figure 2: Respondents’ field of specialization.  
from EFA indicated no cross-loading constructs, leading to the identification of seven main barriers for IoT adoption. According to [PERSON] and [PERSON] [48], factor analysis typically requires a sample size of 100 or more, but it can be performed with a minimum of 50 samples. This study's sample size exceeded that threshold.
## 4 Experimental Results and Analysis
### Crack Propagation and Failure Modes
As seen in Figure 11, no loosening of bolts or deformation of steel plates in the RSL system was observed at the termination of the experiment, suggesting that the system functioned effectively throughout the experiment. Since no deformation occurred in the steel plates after the experiment, the buckled and damaged steel links could be replaced simply by releasing the high-tensile bolts attached to the slot, affirming the RSL system's effectiveness as a structural fuse. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Drift Ratio (\%)**} \\ \cline{2-6}
**Specimen** & **Initial Crack** & **Shear Crack** & **Steel Link Buckled** & **Concrete Spalling** & **Terminate Experiment** \\ \hline NE-10 & 0.75 & 2.2 & 2.75 & 4.5 & 4.5 \\ \hline E130-10 & 0.75 & 1 & 2.75 & 3.5 & 4.5 \\ \hline NE-5 & 0.75 & 2.2 & 2.2 & 4.5 & 4.5 \\ \hline E65-5 & 0.75 & 2.2 & 2.2 & 4.5 & 4.5 \\ \hline E130-5 & 0.75 & 1.75 & 2.2 & 3.5 & 4.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Initiation of crack occurrence. Figure 10: (**a**) A steel link that came into contact with the end of the sliding slot and (**b**) the buckling of the steel link.