esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Commercial and Environmental Compatibility
### Environmental Viability  
[PERSON] et al. [50] used probabilistic material flow modelling to study the possible environmental risks of NS in the environment. For the EU zone, they showed that the predicted environmental concentration (PEC) of NS in surface water ranges from 0.053-3.3 \(\upmu\)g/kg, and as high as 60 \(\upmu\)g/kg in shallow soils. For this estimation, they assumed that 10% of the total NS consumption--that is a modest 18,000 tonne/year--is released to the environment. From a toxicological perspective, dissolved silica is only toxic at concentrations well-above the solubility limit, in the hundreds of mg/L range [53]. From an ecotoxicological perspective, exposure of micro-organisms (e.g., _P. subcapitata_, _D. magna_,... ) to PECs above 0.4 g/L can lead to a nearly 50% mortality rate. For 5% NS concentration in typical saturated sandy soil, the PEC of nano-silica in soil totals around 76 g/L. As such, compared to traditional calcium-based proppants, NS is relatively less harmful and should [PERSON] no direct environmental risks to humans for controlled and contained groundwork applications; although the likely impacts on microorganisms need to be further established. [PERSON] [47] showed that synthetic amorphous silica (NS being a peculiar form of which) in granular soils appears in two different forms of dispersed coating units and sub-rounded flocs (precipitation conditions dependent). He showed that NS flocs with rounded globular (as seen in the present work-Figure 2b) or short tangled laminar shape are less likely to be dislocated by groundwater flow from the particle-level sites into the groundwater, thereby posing little environmental risk at strictly local levels (also see [54]).
# Solanesol Biosynthesis in Plants
## 5 Conclusions and Perspectives  
Solanesol is a non-cyclic terpene alcohol that consists of nine isoprene units and mainly accumulates in solanaceous plants such as tobacco, potato, and tomato. Solanesol serves an important role in the interactions between plants and their environment, and it is a key intermediate for the pharmaceutical synthesis of ubiquinone-based supplements and drugs. Notably, although solanesol and its derivatives are highly valuable from a pharmaceutical perspective, solanesol as a C45 compound may act as a tumorigenic precursor in tobacco smoke [56,57]. While in recent years, studies on the identification of key enzymatic genes in solanesol biosynthesis and gene function have achieved significant progress, a number of questions on the regulatory mechanisms of solanesol synthesis remain unanswered. Genome sequencing of solanaceous plants such as tobacco [58], potato [59], and tomato [60] has paved the way for deeper studies on the metabolic regulation of solanesol biosynthesis. Transcriptomics and metabolomics studies may aid in resolving the metabolic flux distribution of solanesol and the mechanisms through which it interacts with other metabolic pathways. From the metabolomics point of view, it would be interesting to analyse which metabolites are secondarily produced after disruption of some of the solanesol biosynthetic genes. We have generated _N1 SPS1_-overexpressing to tobacco plants in our laboratory, which will allow us to evaluate the effects of _SPS1_ overexpression on solanesol and related metabolites, photosynthesis, and the expression levels of key solanesol biosynthetic and related genes in tobacco. Moreover, overexpression of key enzymatic genes will allow tobacco plants with high solanesol content to be obtained, with significant importance for medical applications. Microbial heterologous expression of key tobacco enzymatic genes may be used to identify their function and to generate solanesol derivatives of medicinal value.  
We acknowledge financial support from Central Public-interest Scientific Institution Basal Research Fund (1610232016002 and 1610232016016) and the Agricultural Science and Technology Innovation Program (ASTIP-TRIC05). [PERSON] initiated the writing of this review and designed the structure of this review, interpreted results, and drafted the manuscript. [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] compiled information and contributed to the revision of the manuscript.
# Effect of the Silanization of Aerosil OX50 in the Properties of Light-Cured Dental Composites
## 3 Results
### Characterization of the Filler  
FTIR spectra, mass loss (TGA), and the first derivative (DTGA) of the silanized and untreated aerosil OX50 are shown in Figure 2a,b. The FTIR spectrum of non-silanized aerosil OX50 showed two typical bands of silicon oxide at 1100 and 801 cm\({}^{-1}\). In the case of silanized aerosil OX50, an almost imperceptible third band can be observed around 1707 cm\({}^{-1}\). In the TGA of the untreated aerosil OX50 no mass loss was observed, which can be explained by the high stability of SiO\({}_{2}\). In contrast, silanized aerosil OX50 showed a decomposition temperature at 437 \({}^{\circ}\)C with a mass loss of approximately 2%.  
Several micrographs of aerosil OX50 were taken before and after silanization and representative ones are shown in Figure 3. In general, aerosil OX50 particles are spherical in shape and easily agglomerate, which is typical of nanoparticles. The agglomerates in the untreated aerosil OX50 (Figure 3a) are round and 150 nm in size. In the case of the silanized aerosil OX50 (Figure 3b), there is an increase in the cohesion between the agglomerated particles.  
Figure 2: Characterization of the filler: (**a**) FTIR spectrum; (**b**) TGA thermograms of untreated and silanized aerosil OX50.  
The particle size distribution of aerosil OX50 (Figure 4), obtained from micrograph measurements, shows a normal distribution. Most of the particles are in the 40-50 nm range in agreement with the manufacturer's description. Furthermore, no polydispersity is observed, and the average particle size was 44 \(\pm\) 1 nm.
# FPGA-Based Manchester Decoder for IEEE 802.15.7 Visible Light Communications
## 4 FPGA Implementation
### Bit Decision, Synchronization, and Managing  
The \(\upmu\)C reads the phase. From its value, it decides the value of the bit (see Table 3) and calculates the offset correction \(l\) according to (8) to maintain the synchronization. The \(\upmu\)C finally writes the offset \(l\) in the circular buffer triggering the starting of the evaluation of the next bit. The detect bit is moved in memory and is available for further processing or directly passed to the final application. The \(\upmu\)C can use the remaining time (see Section 5.1) for other tasks, like searching for data preambles, data unpackaging, CRC checks, etc.
# Resilience in the Context of Cyber Security: A Review of the Fundamental Concepts and Relevance
## 3 Cyber Resilience: Fundamental Concepts
### The Relevance of Cyber Resilience in Various Segments
#### 3.2.1 Civil and Military Aviation  
Cyber security and cyber resilience are emerging and urgent issues in next-generation air traffic surveillance systems, where critical air navigation systems in civil aviation can negatively affect the economic and political sectors, to the point that the International Civil Aviation Organization--ICAO considers cyber security and cyber resilience as emerging issues in the field of aviation worldwide [35]. In military aviation, cyber and electromagnetic activities have become prevalent on today's battlefields and the armed forces are witnessing the introduction of increasingly complex and interconnected weapons systems, to the point where US military doctrine classifies cyberspace as the fifth dimension of the battlefield [36; 37].
# Routing Protocols for Low Power and Lossy Networks in Internet of Things Applications
## 4 Enhanced Routing Solutions for Internet of Things Networks
### RPL Objective Functions
Thus, although PAOF considers two routing metrics, the primary decision is based on the ETX, the second metric being used just in case of a significant difference among ETX values of candidates nodes. The main features of the OFs studied during this subsection are shown in Table 6. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Proposal** & **Base Protocol** & **Objectives** & **Description** & **Strengths** & **Weaknesses** & **Target Applications** \\ \hline EAOF [90] & RPL & - Provides energy efficiency and balance & - Lexical composite OF based on ETA and remaining energy & - Lexical composite OF based on ETA and remaining energy & - Inclauses the network lifetime & - Packet delivery rate is determined & - Biomedical WSN applications \\ \hline L\({}^{2}\)AM [97] & RPL & - Performing energy consumption balancing and improves reliability & - Additive composite OF based on ETX & - Competitive with default RPL & - Packet delivery ratio is - Easy implementation & - Packet delivery ratio is not studied & - Not specified \\ \hline OF-FL [98] & RPL & - Provides a configurable routing decision with a fuzzy system & - OF based on fuzzy systems that mix and end-to-end delay, ETX, and node energy, and hop count & - Routing decisions are made considering different network aspects & - Implementation of a fuzzy system can extend the memory usage & - Implementation of fuzzy parameters is not trivial and can strongly affect the network performance & - Not specified \\ \hline FUZZY OF [99] & RPL & - Improves the QoS of RPL using fuzzy systems & - OF based on fuzzy systems that mix energy delay, and ETX & - Reduces the packet loss ratio - Reduces the end-to-end delay & - Implementation of a fuzzy system can extend the memory usage & - Not specified \\ \hline DQCA-OF [100] & RPL & - Attends to the routing requirements of various IoT applications & - Set of OrS based on ETX, number of hops, and energy consumption combined using fuzzy systems & - Can change its features in running time to attend to the routing requirements & - Implementation of a fuzzy system can extend the memory usage & - Not specified \\ \hline SCAOF [101] & RPL & - Enhances the applicability of RPL in agricultural LIN, providing QoS & - Additive and lexical composite OF based on energy, link color, and ETAX & - Performance evaluation realized in a real testbed & - Complex approach requires an extended version of RFL & - Agricultural LINs \\ \hline PAOF [102] & RPL & - Provides load balancing & - Lexical composite OF based on parent number and ETX & - Distribution of work load of nodes & - Does not consider node energy information & - Not specified \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison among studied RFL objective functions. L\({}^{2}\)AM, Lifetime and Latency Aggregatable Metric.
## 5 Monitoring and Analysis of Surface Subsidence  
In accordance with surface conditions corresponding to the backfill panel and general trend in the mining-induced surface subsidence, two measuring lines for surface subsidence were arranged above the mining area in the test, separately along south-north direction and the direction of advance of the panel. The south-north measuring line with a length of 887 m was arranged to extend along the South Jianshe Road above the backfill panel, along which equidistant measuring points N\({}_{1}\) to N\({}_{22}\) were established. The measuring line along the direction of advance of the backfill panel was 218 m long and arranged above and along the backfill panel, along which equidistant points W\({}_{1}\) to W\({}_{7}\) were established. The layout of the measuring points is shown in Figure 14.  
The surface deformation above the backfill panel was monitored on more than 10 occasions, and the measurement accuracy met the requirements outlined in the Code of Measurement Procedures in Coal Mines. The surface monitoring data were processed,  
Figure 14: Layout of measuring lines for surface subsidence.  
Figure 13: Changes in surface deformation indices: (**a**) surface subsidence, (**b**) horizontal movement, (**c**) inclination, (**d**) curvature, (**e**) horizontal deformation.  
thus attaining curves of accumulated surface subsidence along the two measuring lines, as illustrated in Figure 15.  
Analysis of Figure 15 shows after mining of the backfill panel, overlying strata continue to move and subside due to the compressive creep deformation of backfill materials so the surface is also deformed to some extent; because the panel does not meet the requirement of full subsidence, the surface subsidence curves do not have a flat bottom. After the surface subsidence tends to stabilize, the maximum surface subsidence reaches 163.4 mm, which satisfies the fortification criterion of surface buildings above the backfill panel; surface buildings and structures are not influenced to any significant extent (Figure 16).
# FMS Scheduling under Availability Constraint with Supervisor Based on Timed Petri Nets
## 5 Modeling FMS with the Decomposed TPN to the TMG Set
### Illustrative Example  
In order to illustrate the proposed TPN decomposition methodology, we consider an FMS composed of two machines \(j=1,2\) operating two different jobs \(i=1,2\). The processing time of jobs (\(h\big{(}t_{i,j}\big{)}\)) and the preventive maintenance periods (\(\big{[}B_{j},E_{j}\big{]}\)) are respectively illustrated by Tables 1 and 2.  
According to Figure 3, the initial timed Petri net is composed by two timed marked graphs, TMG\({}^{1}\) and TMG\({}^{2}\), designing respectively the free-dynamics of jobs \(i_{1}\) and \(i_{2}\). The initial buffer \(j_{0}\) and the final buffer \(j_{3}\) are considered as virtual machines. \(i_{1}\) and \(i_{2}\) are initially in \(j_{0}\) corresponding with places \(p_{1,0}\) and \(p_{2,0}\), which are marked. Besides, the resource places \(p_{r1}\) and \(p_{r2}\) are designed to satisfy the production capacity constraints of \(j_{1}\) and \(j_{2}\). This first modeling step (Algorithm 1), satisfying Equations (12) and (13), represents the partial FMS scheduling with respect to the preventive maintenance and processing sequence.  
The decomposed TPN is determined as shown in Figure 4. It includes the direct arcs that schedule the possible processing sequences (Step 3). For instance, the timed direct arc \((t_{2,1},t_{1,0})\) ensures the processing of \(i_{2}\) before \(i_{1}\) through the value of \(\theta_{(2,1)}\). Besides, to avoid that the jobs conflict, a waiting time (Step 4) is added to corresponding transitions. For example, \(\varphi\big{(}Z\big{(}t_{1,c_{1}}\big{)}\big{)}\) represents the time delay of \(i_{1}\) at machine \(j_{1}\) whether \(j_{2}\) is unavailable for maintenance.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Machine 1** & **Machine 2** \\ \hline
**Part 1** & \(3\) \(t.s\) & \(5\) \(t.s\) \\
**Part 2** & \(4\) \(t.s\) & \(2\) \(t.s\) \\ \hline \end{tabular}
\end{table}
Table 1: The processing time.  
\begin{table}
\begin{tabular}{c c c} \hline  & \(B_{j}\) & \(E_{j}\) \\ \hline Machine 1 & \(3\) & \(6\) \\ Machine 2 & \(4\) & \(8\) \\ \hline \end{tabular}
\end{table}
Table 2: The preventive maintenance periods.  
Figure 3: The initial TPN model.  
The final scheduling through the decomposed TPN is determined by computing the supervisor parameters, which are the subject of the next sections.
## 3 Results and Discussion
### HS-SPME-GC-MS Volatile Fingerprinting
Therefore, it is suggested that these flavour compounds interacted with molecules present in the different chickpea-flour-containing soups, lowering their presence in the headspace of the respective soups. The chickpea flours contained fat, protein, and a different type of starch compared to PS. Proteins are able to bind aroma molecules, using reversible hydrophobic interactions or irreversible covalent binding [25; 26]. Starch is able to bind aroma molecules by adsorption involving hydrogen bonds. Different starch types are reported to show different aroma retentions [27]. In foods, flavours are distributed between the lipid and the water phase. When the lipid content of a food is changed, the headspace volatile profile can significantly change, which could lead to an different overall perception of the flavour [26]. Terpenoids, such as \(\alpha\)-curcumene, alloaromadendrene, \(\gamma\)-muurolene, and trans-calamenene, are important flavour compounds which could give rise to herbal aromas [53; 54; 55; 56]. Possibly, the reference soup could therefore be perceived as more herbal compared to the chickpea-flour-containing soups. However, the aroma and flavour of a product are dependent on the odour thresholds, concentration, and interactions of the volatile compounds present [22; 23; 24]. The NG-O containing soup possessed higher concentrations of hydrocarbons, alcohols, aldehydes, terpenoids, and sulphur compounds. Of the identified compounds, 8 of the 15 were hydrocarbons, which were not considered to be very important for the aroma of the soup [50; 51]. Of the other discriminant compounds, only benzothiazole, heptanal, and 1-octanol were present in the NG-O flour. Benzothiazole was the only volatile that was also present in higher concentration in the NG-O flour compared to the other chickpea flours and PS. The heptanal and 1-octanol could have resulted from (enzymatic) lipid breakdown during the flour production or soup production. Benzothiazole has been previously reported in pea flour [23]. The sulphur compounds diallyl sulphide and ally-1-(_E_)-propenyl-disulphide originated from the other soup ingredients but appeared to be more stable in the NG-O containing soup, which could significantly influence the sensory profile of this soup [26]. The soups with the pre-gelatinised chickpea flours had a lower number of characteristic volatile compounds (discriminant compounds with positive VID). The PG-O soup contained higher concentrations of ethanol, methyl-1-butanol, 3-methylbutanal, and 2-methyl butanal. All these compounds were present in the PG-O flour and both alcohols were present in higher abundance in the PG-O flour compared to the other chickpea flours and PS.
## 1 Introduction  
Chronic gastric-inflammatory diseases are mainly related to lifestyle, such as excessive consumption of strong acids, alkaline agents, the presence of the bacterium _Helicobacter pylori_, abuse in the consumption of fatty foods, and the irresponsible use of non-steroidal anti-inflammatory drugs (NSAIDs) [1]; the last two factors also cause liver damage [2]. Ureases have been shown to catalyze the hydrolysis of urea to form ammonia and carbon dioxide. Ammonia allows the growth of _H. pylori_ bacteria in the presence of extra-cellular gastric acid, responsible for the negative effects [3]. In previous studies where oregano (_Origanum vulgare_) and blueberry (_Vaccinium corymbosum_) extracts were applied, the enzymatic activity of ureases was inhibited by 40 and 9%, respectively. The authors established that this inhibitory activity could be related to the content of polyphenols, which can chelate the active site of enzymes [4]. Elastase is a proteolytic enzyme related to inflammatory diseases of epithelial tissue [5]. Neutrophils and macrophages (blood cells) secrete elastase in the presence of toxic agents and pathogens such as fungi, bacteria, and viruses, with the ability to degrade components of the extracellular matrix that amplify inflammation [6; 7]. B-glucuronidase is associated with liver damage and occurs with elevated levels of this enzyme in the blood [8]. This enzyme catalyzes the hydrolysis of \(\beta\)-D-glucuronic acid [9], which, at abnormal levels, limits the process of xenobiotic biotransformation of the conjugation of toxins with glucuronic acid; thus, it could reduce the capacity of the system to deactivate toxic compounds and eliminate them from the body [10].  
_Opuntia robusta_ (nopal taponochtle) and _Opuntia streptacantha_ (nopal cardon) prickly pears contain a wide variety of functional compounds that promote liver health [11; 12]. The leaves of _Tithonia tubaeformis_ and _Sylbum marinum_ have an anti-inflammatory effect attributed to their bioactive compounds such as phenols [13; 14].  
The fruits of _Opuntia oligacantha_ c.f. forst (xoconostle) contains bioactive compounds such as phenols and flavonoids that have attributed the ability to inhibit biological enzymes such as \(\alpha\)-amylase and \(\alpha\)-glucosidase [15]. Furthermore, it has been attributed antioxidant and antimicrobial activity [16]. It has been observed that during stomach digestion there is absorption of some phenols [17], having a bioavailability close to 50% [18] and an absorption of 5-10% of the total intake thanks to different transporters present in the epithelial cells of the small intestine [10]. The exposure of phenolic compounds to different pH conditions and inhibitors or enzymes of the gastrointestinal tract represent an influential factor in the use of their anti-oxidant properties [17]. The objective of the present study was to evaluate the inhibition of the enzymes urease, elastase, and \(\beta\)-glucuronidase related to gastroprotective, hepatotoxicity, and anti-inflammatory effect, respectively, by different structures of acid prickly pear by applying in vitro tests and gastrointestinal simulated conditions.
## 3 Modal Parameter Identification
### Improved Stochastic Subspace Identification Algorithm  
As the signal length affects the recognition accuracy and operation efficiency of SSI, it is important to control the signal length. In this work, an improved method of modal parameter identification is presented by adding a sliding window in the calculation of modal parameters. Generally, the window size determines the accuracy of recognition. If the window is too small, modals do not appear. On the contrary, if the window is large, the calculation process becomes complex and more error modals appear in the stability diagram. Therefore, both the window size and the step size are set through sliding windows to realize modal parameter identification with high efficiency (Figure 3).  
Generally, the characteristics of the tested structure, the sampling frequency, and the noise level for the chosen size of the calculated window should be comprehensively considered. Here, specific analysis steps are applied to contain these factors based on the order determination of the stability diagram:  
1. Assume that the initial window time is \(t_{1}\), the sampling frequency is _sf_, and the initial window length is \(L_{1}=sf\times t_{1}\).
2. Let the lag time, \(\Delta t\), between previous and later windows and the data length correspond to the \(i\)th window, as \(L_{i}=[t_{1}+(i-1)\times\Delta t]\times sf\).  
3. Identify the corresponding stability diagrams of each window by the SSI algorithm. If the system order is \(N\), the maximum number of stable points will be the same corresponding to each mode. The related number of stable points for the first three modes are \(f_{1}\), \(f_{2}\), and \(f_{3}\), and the percentages, \(GF_{i}=f_{i}/N\), of these modes can be obtained.
4. Change the value of \(GF_{i}\) with the signal length. When the percentage exceeds 90%, the corresponding window time is supposed to be the appropriate one, otherwise the window time will be increased.