esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Related Work on Correcting Typographical Errors
### Using Context and External Datasets  
The given candidates can be simply ranked by their pre-computed distances. On the other hand, some additional information, whether from nearby words or from additional corpa, can aid target word selection.  
The approach in [64] uses a Bayesian combination rule to rank the given candidates. First, the probabilities for substitutions, insertions, and other errors are collected from a corpus of millions of words of typewritten text. Then, given a misspelled word, its each inflection and the resulting word probabilites are combined to produce a probability estimate for each correction candidate.  
The \(n\)-gram language models [14] that are trained on a large external corpus can give a conditional probability of how likely a sequence of words is to be followed by a certain word. The \(n\)-gram model ranking for confusion sets is used in multiple works for spelling correction [54; 65; 66; 67; 68; 69]. The character-level \(n\)-gram also allows for the calculation of a distance measure (such as Hamming in [70]) by comparing the character \(n\)-grams between two strings [71]. Spelling correction systems using \(n\)-grams usually employ back-off techniques [65; 66; 68] or other [72; 73] smoothing techniques, and sometimes, due to its size, they even require a complex distributed setting [68; 74]. The extensions and problems with the \(n\)-gram models have already been discussed in Section 2.1.2.  
External datasets are especially well-exploited by the neural network approaches. The authors of [75; 76] used a FastText [77] shallow neural model to learn both known and unknown word vectors as a sum of character \(n\)-gram embeddings. Candidate words could then be scored with a cosine similarity to the context words vectors. The differences between these two works are text domains. In the study by [75], the model was trained in the Bangla language, while in the study by [76], the model was trained on English and Dutch clinical texts.  
The ability to learn from vast text resources eventually culminated in the state-of-the-art transformer models, discussed in Sections 3.5 and 4.2.
# HYDRUS-1D Simulation of Soil Water Dynamics for Sweet Corn under Tropical Rainfed Condition
## 3 Results and Discussions
### Water Contents  
The comparison between simulated and observed values of water contents in the soil profile within the root zone (0-60 cm) during two crop seasons shown in Figure 6. During the first and second season of field crop the simulated water content values at 60 cm depth ranged from 0.31 to 0.46 cm\({}^{3}\) cm\({}^{-3}\) and 0.31 to 0.49 cm\({}^{3}\) cm\({}^{-3}\) with average values of 0.41 and 0.43 cm\({}^{3}\) cm\({}^{-3}\) respectively. Whereas observed water content values for the first season and second season of field crop at 60 cm soil depth ranged from 0.32 to 0.45 cm\({}^{3}\) cm\({}^{-3}\) and 0.32 to 0.46 cm\({}^{3}\) cm\({}^{-3}\) with average values of 0.  
contents at different depths increased with rainfall events and then decreased until the next event due to evapotranspiration (ET). This variation of water content was larger in shallower depths as compared to deeper [30]. Simulated water content results at 140 cm soil depth were found constant for both seasons due to shallow water table, i.e., soil remained at field capacity (FC) or near saturation for all growing season. Whereas, a small fluctuation in observed water contents was recorded for 140 cm depth. The average simulated value at 140 cm depth for both seasons was 0.42 cm\({}^{3}\) cm\({}^{-3}\) whereas average observed values were 0.43 cm\({}^{3}\) cm\({}^{-3}\) and 0.44 cm\({}^{3}\) cm\({}^{-3}\) respectively. The simulated and observed water contents remained above the permanent wilting point (PWP, i.e., in the range of plant available water. The performance of HYDRUS-1D in predicting water content was found satisfactory.  
Figure 6: Simulated and observed soil water contents within the root zone (0–60 cm) during the **(a)** first season and **(b)** second season.
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 5 CPS-WSS Monitoring Application Challenges and Future Directions
### Future Directions
#### Advanced and Smart Data Transmission and Analytical Solutions  
The sensing devices in CPSs for various water supply system applications generate huge continuous and heterogeneous datasets, which must be processed and managed efficiently. Most of the current data processing methodologies are computationally complex and not power intensive. Advanced and robust data analytics solutions should further be investigated. The data analytical solutions should also consider advance lossless data compression methods. These compression methods will reduce the size of huge datasets before transmission and allow them to be retrieved without loss of information.
# Will the Use of Pharmacogenetics Improve Treatment Efficiency in COVID-19?
## 4 Azithromycin  
Azithromycin is an azalide, macrolide antimicrobial agent with anti-inflammatory properties which is used to treat patients with viral infections [2]. It prevents severe respiratory tract infections since it hinders bacterial protein synthesis via binding to the 50S component of the 70S ribosomal subunit [54]. The combination of azithromycin and hydroxychloroquine (HCQ) has been suggested to exert a synergistic effect on COVID-19 disease [55]. The effectiveness of these two drugs in the treatment of COVID has been suggested not to be associated with the prevention of infection, but with their lysosomesomotropic properties which buffer the acidic environment (pH 4-5) of the endolysosomal lumen where SARS-CoV-2 moves after ACE-2 receptor-mediated endocytosis [56]. The increase of intravesicular pH up to near 7.0 is associated with the impairment of lysosomal functions and activities responsible for virus entry and replication cycle [56; 57]. In general, macrolide agents trigger many drug-drug interactions, however, azithromycin is not an important substrate of CYP3A4, SLCO1B1, or SLCO1B3, and therefore its administration is associated with fewer interactions compared to other macrolides [58]. Azithromycin does not interact with cytochrome P450 enzymes, therefore genetic variations within this group of enzymes cannot affect treatment with azithromycin. However, its pharmacokinetics can be influenced by the activity of the P-glycoprotein (P-gp) transporter encoded by ABCB1 [3; 59]. [PERSON] [57] suggested that ABCB1 might act as an enhancer by extensively confining azithromycin when the trapping is solely dependent on the passive diffusion. The interaction of azithromycin with P-gp was suggested to be the reason for its efficacy in the COVID-19 treatment as well as its synergistic actions when co-administrated with hydroxychloroquine [55; 56]. Polymorphisms within the ABCB1 were demonstrated to alter peak azithromycin concentrations in healthy volunteers after a single dose (500 mg) [60]. Marked differences in Cmax and Tmax of azithromycin were associated with the presence of the ABCB1 rs2032582/rs1045642 genotype. Increased systemic exposure to azithromycin, especially when it is combined with hydroxychloroquine or chloroquine, may be even life-threatening as a result of the additive effects of both drugs on QT prolongation which may cause fatal arrhythmias [40].
## 4 Experimental Verification and Discussion
### Profile Consistency of Gratings  
Ten sample points of two experimental gratings were respectively measured to examine the profile consistency. A confocal microscope (OLS 4100, Olympus, Japan) with the objective lens MPLAPON100 XLTEX was used to observe and measure the geometric parameters. The local profiles of the two gratings are shown in Figure 8 and the measurement  
Figure 7: Wavefront deviations caused by errors in the duty cycle (**a–c**) or etching depth (**d–f**) from the geometric reconstruction of 2, 4, and 6 \(\upmu\)m gratings. The duty cycle error was less than 10% and the depth error was less than 100 nm. The grating model had an etching depth of 400 nm and a duty cycle of 0.5.  
data are listed in Table 1. Meanwhile, the average (AVG) and standard deviation (STD) of measurement data were calculated. For linear gratings, the duty cycle \(D\) and etching depth \(h\) were the main sources of fabrication errors. The linewidth d was measured using the midpoint of the ridge as the reference.  
According to the measurement data in Table 1, the etching depth is the main source of fabrication errors, and the depth errors of two gratings are all approximately equal to 10 nm. As for the duty cycle errors, the consistency of the 4 \(\upmu\)m grating is better than that of the 2 \(\upmu\)m grating. However, the duty cycle errors can be ignored in scalar approximation. Therefore, the evaluation results of the two grating wavefronts are almost the same based on scalar approximation.
## 3 Results and Discussions
### Simulation Optimization of BTS Using Genetic Algorithm
Table 8 lists the pressure changes for the four cases of the vertical section of the BTS. The four cases are listed in Table 8, and they represent the installation positions of the air boosters. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Population size & 2000 \\ Number of generations & 400 \\ Goodness of convergence conditions & Proportion \\ Selection & Roulette \\ Reproduction (crossover ratio) & 0.85 \\ Probability of mutation & 0.01 \\ Crossover & One-point crossover \\ Function tolerance & \(1.0\times 10^{-6}\) \\ Constraint tolerance & \(1.0\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Genetic algorithm parameters. Figure 13: Results of genetic algorithm: (**a**) fitness value; (**b**) current best individual. Figure 14 shows the pressure change in the vertical pipe section for Case 1-4. The four cases show the optimization of the distance of the air boosters. Case 1 represents a pressure change in the simulation model, whereas Case 2 represents a pressure change in the experimental test. Case 3 represents a pressure change when installing the air boosters at equal intervals, whereas Case 4 represents a pressure change when applying the genetic algorithm to the air boosters. In Case 1, the final pressure is 2.87 bar, which is a linear decrease of 0.93 bar from 3.80 bar. In Case 2, the final pressure is 2.90 bar, which nonlinearly drops from 3.80 bar. When the air booster is installed at equal intervals (Case 3), the outlet pressure is 3.33 bar. When the air booster installation positions are adjusted by applying genetic algorithm optimization (Case 4), the outlet pressure is 3.53 bar, which is compensated as one moves away from the installation position point (\(y_{i}\)).
# Experimental and Theoretical Study of the Influence of Saline Soils on Frozen Wall Formation
## 3 Numerical Simulation of Heat and Mass Transfer on the FW Formation
### Discussion
The first factor favorably affects the increase in the FW's thickness; the second, on the contrary, reduces the FW's thickness. However, with a further increase in the initial salinity, the second factor begins to prevail, and the FW's thickness begins to decrease again. When considering sand (Figure 15), it can be observed that, in the inner frozen zone, the FW's thickness increases with increasing initial salinity. This is a consequence of the influence of the flow of porewater, due to which the temperature of the sand decreases faster to the left of the heat source than its freezing temperature decreases. However, in the outer frozen zone, where the effect of the flow of porewater is not so great, a similar effect can be observed, as in the case of chalk: the FW's thickness at the initial salinity of 3.46 g/100 g and 6.9 g/100 g coincides, and with a further increase in salinity, the FW's thickness decreases. Thus, it can be concluded that the occurrence of the effect of the coincidence of the FW's thicknesses at different initial salinities is determined not only by the thermophysical properties of the soil and porewater, but also by the presence of the flow of pore water. It should be noted that in the future, it is planned to test this model in the field when monitoring the AFG of potash mine shafts under construction. It is assumed that the obtained dependencies will allow a much better explanation of the observed features of temperature changes in the control wells. At the same time, experimental data on the real process of freezing of soils with known salinity will help to carry out a full-fledged validation of the model proposed by us. Figure 14: The dependence of the FW’s thickness on time for clay (**a**) and chalk (**b**).
## 1 Introduction
Substantially increased yields (4.4-fold higher) of transglycosylation products (allyl) manufactosides and allyl galactosides) were observed when the GH27 \(\alpha\)-galactosidase from the guar plant (Aga27A) was added to incubations of the GH5 \(\beta\)-mannanase from _Trichoderma reseei_, with galactomannan and allyl alcohol [25]. In this study, we further examine the transglycosylation capacity of Aga27A and compare it to a GH36 \(\alpha\)-galactosidases from _Bacteroides octaus_ (_Bo_Gal36A) [5]. The two enzymes were chosen because they belong to different families and were both shown to efficiently cleave galactosyl side groups from galactomannan [5; 26], but their transglycosylation capacity has not previously been thoroughly investigated. Furthermore, the activity towards internally attached galactosyls in galactomannan observed for _Bo_Gal36A stands out as an unusual property for a GH36 enzyme [5]. In order to expand the repertoire of enzymes that may be used with complex natural glycan donors in transglycosylation, _Bo_Gal36A and Aga27A were screened for their ability to utilize three different glycosyl donors, pNP-Gal, raffinose and galactomannan (locust bean gum, LBG), combined with a range of acceptors. The first screening was carried out using mass-spectrometry which was followed by screening using HPLC. We evaluated the stability of the studied enzymes under the screening conditions and quantified their transglycosylation products with raffinose and LBG as donors to evaluate their transglycosylation capacity. The presented screening procedure represents a quick and effective way to discover enzymes with the potential to utilize renewable polysaccharides for the production of well-defined glycosides. The study thus explores the utilization of polysaccharides as donor substrates in transglycosylation reactions for \(\alpha\)-galactosidases, a subject which so far is relatively unexplored.
## 3 Experimental Results  
In this section, simulation results are presented and compared in terms of PDR and average E2E latency. In particular, Section 3.1 describes the results obtained for the fixed infrastructure density scenario. Moreover, Section 3.2 deals with the varying infrastructure density scenario.  
In both cases, 4G-based and 5G-based C-V2X are benchmarked for the different traffic intensity values from a perspective of the communication perimeter and message generation frequency. Finally, the obtained results are going to be put into the context of the communication requirements of the most popular V2I C-ITS services.
# Real-Time Communication Support for Underwater Acoustic Sensor Networks +
###### Abstract  
Underwater sensor networks represent an important and promising field of research due to the large diversity of underwater ubiquitous applications that can be supported by these networks, e.g., systems that deliver tsunami and oil spill warnings, or monitor submarine ecosystems. Most of these monitoring and warning systems require real-time communication in wide area networks that have a low density of nodes. The underwater communication medium involved in these networks is very harsh and imposes strong restrictions to the communication process. In this scenario, the real-time transmission of information is done mainly using acoustic signals, since the network nodes are not physically close. The features of the communication scenario and the requirements of the communication process represent major challenges for designers of both, communication protocols and monitoring and warning systems. The lack of models to represent these networks is the main stumbling block for the proliferation of underwater ubiquitous systems. This paper presents a real-time communication model for underwater acoustic sensor networks (UW-ASN) that are designed to cover wide areas with a low density of nodes, using any-to-any communication. This model is analytic, considers two solution approaches for scheduling the real-time messages, and provides a time-constraint analysis for the network performance. Using this model, the designers of protocols and underwater ubiquitous systems can quickly prototype and evaluate their solutions in an evolving way, in order to determine the best solution to the problem being addressed. The suitability of the proposal is illustrated with a case study that shows the performance of a UW-ASN under several initial conditions. This is the first analytic model for representing real-time communication in this type of network, and therefore, it opens the door for the development of underwater ubiquitous systems for several application scenarios.  
Keywords:underwater sensor networks; underwater monitoring; MAC protocol; acoustic transmission; submarine ubiquitous applications +
Footnote †: 15 May 2017; Accepted: 6 July 2017; Published: 14 July 2017
## 2 Materials and Methods  
### Materials  
Recycled glass fibers derive from shredded GFRPs from wind turbine blades after their dismantling (Siemens Games Renewable Energy S.A., Zamudio, Spain, and Consiglio Nazionale di Ricerca--Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato--Stiima CNR, Milano, Italy). The rGFRP powders contain 70% wt. of GF and have a nominal granulolometry < 80 \(\upmu\)m, 200 \(\upmu\)m and 1 mm [19; 20; 21]. Recycled carbon fibers come from expired pre-impregnated CFs (prepreg), which underwent pyrolysis, sizing, and shredding processes (Aernnova Aerospace S.A., Minano Mayor, Spain--Tecnalia Research & Innovation Center, San Sebastiano, Spain--Stiima CNR). The rCF powders have a nominal granulolometry of 63 \(\upmu\)m and 200 \(\upmu\)m [21; 22]. The different recycled fibers are visible in Figure 1.  
An ethoxylate bisphenol A diacrylate resin (SR349) was used as a matrix in the ink formulations for the UV-DIW process (Arkema, Colombes, France) together with ethyl phenyl (2,4,6-trimethyl benzoyl) phosphonate (TPO-L) as photo-initiator (Lambson Limited, Wetherby, UK), and dicumyl peroxide as a thermal initiator (Sigma-Aldrich Corporation, St. Louis, MO, USA). In addition, 3% wt. of TPO-L and 0.3% wt. of dicumyl peroxide were added to SR349 to initiate photo-polymerization and increase crosslinking during thermal post-curing. An epoxy-based thermosetting resin made of bisphenol-A-based oligomer and 1,4-butanediol di-glycidyl ether (Araldite BY158) was used as a matrix, in order to pour for indirect FFF 3D printing (Huntsman Corporation, The Woodlands, TX, USA), together with a curing agent (Aradur 21) in the weight ratio of 100/28 (Huntsman Corporation).  
Figure 1: Mechanically recycled glass fibers and mechanically and thermally carbon fibers used in this work (from left to right): rGFRPs (1 mm, 200 \(\upmu\)m, <80 \(\upmu\)m) and rCFs (63 \(\upmu\)m, 200 \(\upmu\)m).  
Polyethylene terephthalate glycol (PETG) and polylactic acid (PLA) filaments were used to fabricate the rigid molds and silicone counter molds of the indirect FFF 3D printing workflow (Prusa Research, Prague, Czech Republic). A poly-addition casting silicone rubber with a hardness of 30 ShoreA (Gls-PRO 30) was poured into the rigid molds and silicone counter molds to obtain the molds for the final parts (Prochima Srl, Colli al Metauro, Italy). This silicone is made of two reagents, to be mixed at a volume ratio of 1:1.
## 3 Proposed Model and Problem Formulation
### System Model
C2 identifies the constraint that transmission power of AV and any transmitting vehicle (\(P_{V}\)) must be lower than the maximum transmission power (\(P_{Vmax}\)). \(C3\) denotes the constraints imposed by the maximum required signal-interference-plus-noise (\(SINR_{th}\)). Based on the proposed idea, it is assumed that the inter-vehicles are located at a position between the AV2X (\(d_{AVX}\)). Thus, the distance between the AV and the relay-vehicle (\(d_{AVY1}\)) and the distance between the relay-vehicle and the destination (\(d_{V1X}\)) can be expressed as:  
\[d_{AVV1}=(1-q)d_{AVX} \tag{5}\]  
\[d_{V1X}=qd_{AVX} \tag{6}\]  
The connectivity between AV2V and AV2X is identified in Figure 2, including the direct communication between AV2V and the communication between AV2X through inter-vehicular communication. The proposed model attest system efficiency and reliability under various operating conditions using standard benchmarks as (i) throughput; (ii) packet delivery ratio; (iii) packet loss rate; and (iv) average delivery latency. Figure 2: Vehicle communication through (**a**) AV2V communication, and (**b**) AV2X communication. The transmission links between AV2V and AV2X are subjected to Rayleigh fading with additive white Gaussian noise (AWGN) and propagation path-loss [29]. The channel fades for different links are considered to be statistically mutually independent. Additionally, as a wireless standard for vehicular communications, the dedicated short range communications (DSRC) is considered as the most promising protocol to be used in automotive system [5]. DSRC technology involves one-way or two-way short range wireless communication channels specifically designed for automotive use. The US Federal Communications Commission (FCC) has allocated 75 MHz spectrum in the 5.9 GHz band for short-range communications to be used in ITS including V2I and V2V. On the other hand, the European Telecommunications Standards Institute (ETSI) has allocated 30 MHz of spectrum in the 5.9 GHz band for ITS [30]. The physical and MAC layer of the DSRC layered model employs the IEEE 802.11p standard for vehicular communication. The IEEE 802.11p standard supports eight different data rates based on the orthogonal frequency division multiplexing (OFDM) method [6].
# Blind Interleaver Parameters Estimation Using Kolmogorov-Smirnov Test
## 3 Proposed Algorithm
### Kolmogorov-Smirnov Test  
Figure 3 shows an example of the K-S statistics. The larger the K-S value, the greater is the difference between the two distributions. This motivated us to consider the K-S test as a measure for estimating interleaver parameters in our proposed algorithm. Compared to KLD, the K-S test has a relatively lower complexity. Because it does not require a logarithmic operation and needs only the CDFs of the two distributions, it can calculate the degree of difference between two distributions with low complexity.