esa-sceva/satcom-chunk-collection huggingface dataset:
# Fractional Order Viscoelastic Model for Stress Relaxation of Polyvinyl Chloride Geomembranes
###### Abstract  
Stress relaxation properties have a significant impact on the performance of polyvinyl chloride (PVC) geomembranes (GMBs) at the peripheral joints of the membrane faced rockfill dam (MFRD). This paper presents a fractional order viscoelastic model (FOVM) to measure the relaxation stress as a function of time. Model parameters were obtained by best fit to results from wide-width strip tensile tests conducted at three tensile rates and three initial strains for 48 h. The results of a 90 d stress relaxation test demonstrate the applicability of the model to describe the stress relaxation behavior of PVC GMBs. The tensile rate and initial strain marginally influenced the relaxation modulus rate, while having no effects on the fractional derivative order. Residual stress could account for the difference in relaxation stress between the longitudinal and transverse specimens. Finally, the FOVM could be used for predicting the service cycle under specifying failure stress criteria. Furthermore, it has great potential for applications in predicting the long-term deformation of PVC GMBs at the peripheral joints of MFRD. Furthermore, it has great potential for applications in predicting the long-term deformation of PVC GMBs at the peripheral joints of MFRD.  
geosynthetics; PVC geomembrane; stress relaxation; fractional order viscoelastic model Article  
Factional Order Viscoelastic Model for Stress Relaxation  
of Polyvinyl Chloride Geomembranes
## 2 Materials and Methods
### DNA Extraction and Quantification of ARGs  
Genomic DNA was extracted using the DNEasy PowerSoil Pro Kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions, with slight modifications to improve yield. Briefly, samples were subjected to vacuum filtration through 0.45 \(\upmu\)m membrane filters (Merck Millipore, Burlington, MA, USA); 200 mL of each sample was filtered to ensure sufficient microbial biomass retention. Each membrane filter was cut into small pieces, placed in the kit's bead tube, and subjected to bead-beating for efficient cell lysis. After the binding and washing steps, DNA was eluted in 50 uL of the elution buffer provided with the kit. The DNA concentration was then measured using a Qubit 4 Fluorometer and a Qubit High Sensitivity dsDNA Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA). The resulting DNA extracts were stored at \(-20\)\({}^{\circ}\)C for subsequent qPCR analysis.  
The quantitative analysis of the abundance of the 8 ARGs was conducted using quantitative Polymerase Chain Reaction (qPCR) with specific primers targeting genes encoding resistance to tetracycline (_tet_B, _tet_M), sulfonamide (_sull_, _sull_), macrolide (_erm_B), quinolone (_qnr_A, _qnr_S), and florfenicol (_flo_R). The qPCR was performed on a CFX96 system (Bio-Rad, Hercules, CA, USA) using SYBR green (Toyobo, Osaka, Japan). The qPCR conditions for the targeted genes were as follows: initial denaturation at 95 \({}^{\circ}\)C for 3 min, 39 cycles at 95 \({}^{\circ}\)C for 30 s, annealing for 30 s, and extension at 72 \({}^{\circ}\)C for 10 s. Standard curves were generated using 10-fold serial dilutions of the plasmid DNA of ARGs. Detailed qPCR conditions and primer information are summarized in Table S3 [40; 41; 42].
# Effect of Methane Adsorption on Mechanical Performance of Coal
## 3 Results
Therefore, methane adsorption causes an expansion in coal volume, an increase in coal toughness, and a decrease in elastic modulus. As shown in Table 1 and Figure 4, at a given adsorption equilibrium pressure and with the increase in configuring pressure, the compression degree of the coal sample increased, as well as the longitudinal wave speed. On the contrary, at a constant confining pressure, the increase in adsorption equilibrium pressure resulted in an increase in the amount of methane adsorbed on coal. In addition, the coal volume expanded, which led to the continuous decrease in the longitudinal wave speed, which showed a variation of 10%. Figure 3: Gas coal triaxial compression stress-strain curve. (**a**) Stress-strain curve of Gas coal when confining pressure is 0 MPa. (**b**) Stress-strain curve of Gas coal when confining pressure is 3 MPa. (**c**) Stress-strain curve of Gas coal when confining pressure is 6 MPa. (**d**) Stress-strain curve of Gas coal when confining pressure is 9 MPa. Figure 4: Longitudinal wave speed. (**a**) Meager coal. (**b**) Gas coal. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Coal Quality** & **Adsorption Equilibrium** & **Confining Pressure** & **Compressive** & **Modulus of** & **Longitudinal Wave** \\
**Pressure _p_/MPa** & _p_/MPa & **Strength \(\sigma_{c}\)/MPa** & **Elasticity E/GPa** & **Velocity/m.s\({}^{-1}\)** \\ \hline \hline \multirow{8}{*}{Meager coal} & & 0 & 9.17 & 1.98 & 2238.943 \\  & & 3 & 14.08 & 2.59 & 2338.796 \\  & & 6 & 22.38 & 3.87 & 2342.136 \\  & & 9 & 26.21 & 4.86 & 2350.827 \\ \cline{2-6}  & & 0 & 8.89 & 1.69 & 2056.944 \\  & & 3 & 13.87 & 2.41 & 2151.145 \\  & & 6 & 22.19 & 3.73 & 2154.296 \\  & & 9 & 26.13 & 4.79 & 2190.796 \\ \cline{2-6}  & & 0 & 8.68 & 1.43 & 1858.014 \\  & & 3 & 13.61 & 2.29 & 1933.194 \\  & & 6 & 22.01 & 3.59 & 2090.706 \\  & & 9 & 25.97 & 4.67 & 2210.848 \\ \hline \multirow{8}{*}{Gas coal} & & 0 & 8.36 & 1.69 & 2100.566 \\  & & 3 & 12.65 & 2.18 & 2210.477 \\  & & 6 & 19.62 & 3.78 & 2223.801 \\  & & 9 & 24.85 & 4.39 & 2243.496 \\ \cline{2-6}  & & 0 & 8.03 & 1.33 & 1919.577 \\  & & 3 & 12.41 & 1.97 & 2021.629 \\  & & 6 & 19.50 & 3.58 & 2043.039 \\  & & 9 & 24.71 & 4.26 & 2089.539 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental results.
# The Impact of Gravity on Different Height Systems: A Case Study on Mt. Medvednica
## 2 Height Systems
### Heights Defined Without Assumptions About Mass Distribution  
#### 2.1.1. Geopotential Numbers  
One of the height systems that fall into this group is the so-called geopotential numbers. To explain the need for their introduction, let us examine Figure 1, which illustrates the principle of geometric leveling in the real gravitational field where the sum of the leveled height differences between points \(A\) and \(B\) will not be equal to the difference between heights \(H_{A}\) and \(H_{B}\).  
This is because the leveling increment \(\delta n\) differs from the corresponding increment \(\delta H_{B}\) of the \(H_{B}\) height. As seen in Figure 1, it is caused by the non-parallelism of the level surfaces, which arises from the different distribution of mass density within the Earth's interior. Therefore, in geometric leveling, gravity must also be measured at benchmarks, and the transition to geopotential heights must be made. If the potential difference between two points is denoted as \(\delta W\), it can be expressed as follows [4]:  
\[\delta W=-\mathrm{g}\delta\mathrm{n}, \tag{1}\]  
from which the next expression can be obtained:  
\[W_{B}-W_{A}=-\sum_{A}^{B}\mathrm{g}\delta\mathrm{n}. \tag{2}\]  
The sum can be replaced with an integral, so Equation (2) becomes the following:  
\[W_{B}-W_{A}=-\int_{A}^{B}\mathrm{g}\delta\mathrm{n}, \tag{3}\]  
where the integral is independent of the integration path, so different leveling lines from \(A\) to \(B\) should give the same result. Point \(O\) is located at sea level (on the geoid) and an arbitrary point \(A\) on some different level surface is connected to point \(O\) by geometric leveling in the real gravitational field (Figure 2).  
According to Equation (3), the potential difference between points \(O\) and \(A\) is given by the following [4]:  
\[W_{O}-W_{A}=\int_{O}^{A}\mathrm{g}\delta\mathrm{n}=C, \tag{4}\]  
where \(C\) represents the geopotential number of a given point and can be described as the difference between the Earth's gravity potential at the observed point and potential on the reference geopotential surface. However, the use of geopotential numbers in practice is not very convenient because they do not have the dimension of height but are expressed  
Figure 1: Geometric leveling (according to [PERSON] and [PERSON] [4]).  
in m\({}^{2}\)s\({}^{-2}\). For this reason, geopotential numbers are divided by different values of gravity for practical application, resulting in various height systems [2].
# Multi-Path Data Distribution Mechanism Based on RPL for Energy Consumption and Time Delay
## 4 Analysis of the Performance
### Analysis of the Network Performance
#### 4.2.2 Network-Lifetime  
We validated the longest lifetime for Algorithms 2 and 4 using different network sizes. Figure 9 shows that our algorithm can achieve a longer lifetime. This advantage is more obvious especially when the network size is not large. Algorithm 2 can provide the longest lifetime, and Algorithm 4 is a bit weaker than Algorithm 2. This is mainly because when the energy balance reaches a certain threshold, the node will choose the multi-path forwarding according to the cache utilization ratio, and a new energy imbalance will occur. However, it will certainly repeat the energy balance algorithm to achieve energy balance. However, it should be noted that with the increase in the number of network nodes, the network survival time suddenly dropped. The nodes around the sink node have to use a lot of energy to transmit the data. By this moment, none of these three algorithms can greatly reduce energy consumption.  
Figure 8: Residual energy distribution (cache size 50). (**a**) For original RPL; (**b**) For Algorithm 2; (**c**) For Algorithm 4.  
Figure 7: Residual energy distribution (cache size 30). (**a**) For original RPL; (**b**) For Algorithm 2; (**c**) For Algorithm 4.  
Figure 9: Network lifetime in different size.
## 2 Background and Related Research
### Deep Learning Model
#### 2.1.4 Attention Mechanism  
The RNN-based seq2 seq model has two major problems. Since all the information cannot be compressed in one fixed-size vector, there can be instances of information loss, and a vanishing gradient problem [18]. Hence the accuracy deteriorates with the increased length of the input. As an alternative, an attention mechanism has been proposed as an emerging technique for increasing the prediction accuracy, although this decreases when the input sequence becomes long [19]. That is, the decoder predicts the output word in every time step, and the entire input sentence from the encoder is referenced once again. However, not all the input sentences are referenced equally; the part of the input word that is related to the word to be predicted at that time receives more attention [20]. The attention mechanism gives initial weights to each input, and these weights are updated during training according to the correlation between each input and the final prediction.
# Blind Detection for Serial Relays in Free Space Optical Communication Systems
## 1 Introduction
Section 3 presents our derived closed-form error probability for blind OOK detection. Our numerical results and discussions are presented in Section 4. Section 5 is devoted to the main conclusions. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Pros** & **Cons** \\
**Number** & & \\ \hline
[7] & Novel data detection methodology & No mathematical model \\ \hline
[13] & Two decision steps & Monte Carlo simulations is missing \\ \hline
[15] & Gamma-gamma channel which covers moderate to strong range of atmospheric turbulence & Missing of weak turbulence regime \\ \hline
[16] & Proposed data efficient channel estimation method & Single iteration method \\ \hline
[18] & Two decision steps & No closed form equation \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Pros and cons of Free-space optical (FSO) blind detection papers in the literature.
# Smart Low-Cost Control System for Fish Farm Facilities
## 5 Smart Control Algorithm for Aquaculture
### Included Algorithms in the Complete System
#### 5.1.3 Fault-Tolerance Algorithms  
Those algorithms are the ones that ensure that the system is robust and can operate even when parts of the system are disconnected, or there are failures in the data sending. Several algorithms are included. The main ones are related to failures in the network or in the DB and are described below.  
* The algorithms relative to the failure in the network include using 3G/4G in the gateway of sea cages to ensure connectivity with the DB in case of failure of the LoRa network. If the gateway node does not receive any response from the DB when data are sent it supposes that the connectivity through LoRa is lost and the backup system based on 3G/4G starts to operate. Then, data are sent again with the new communication technology.
* If connectivity with DB is not restored after changing the wireless technology, the gateway assumes that the failure is in the DB itself. Therefore, a message is sent to all the sensor and actuator nodes in order to run the predefined control algorithm and save all the gathered information.
* Another way in which a failure in the connectivity with the DB can be prevented from affecting the regular operation of the actuators is when a message from the DB is expected and not received. In that case, the gateway body assumes that the connectivity is lost and alerts the local nodes to use the predefined control algorithm and save all the gathered information.
* If a node does not receive the response when data are forwarded to the gateway, it automatically assumes that the connectivity with the gateway is lost and attempts to connect with the backup gateway. The redundancy of critical elements such as the gateway buoy is another example of fault tolerance mechanisms.
## 4 Case Study
### Determination of the Optimal Solution Based on the Coalitional Game Model
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Game Utility**} & \multicolumn{6}{c}{**Conceptual Scheme**} \\ \cline{2-7}  & **CS\({}_{1}\)** & **CS\({}_{2}\)** & **CS\({}_{3}\)** & **CS\({}_{4}\)** & **CS\({}_{5}\)** & **CS\({}_{6}\)** \\ \hline \(U_{P}(S_{P},\,S_{\text{E}})\) & 0.946 & 0.901 & 1.027 & 0.946 & 1.070 & 1.110 \\ \(U_{E}(S_{P},\,S_{E})\) & 1.004 & 1.043 & 0.988 & 0.959 & 1.029 & 0.977 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Game utility matrix of warehouse logistics system under each strategy combination. Figure 3: Impact relationship between evaluation objectives. The results in Figure 4 indicate that the CV and economic utilities of each strategy are related to each other by mutualistic and antagonistic relationships. Therefore, it is impossible to optimize the overall utility of a solution by solely focusing on CV or economic objectives, as it is important balance the interests of both players. By adopting the Shapley value to obtain the ratio of importance between CV and economic objectives in the design of a warehouse logistics system, it becomes possible to ensure that the utility of the grand coalition is always greater than that of any single coalition (i.e., \(\varphi_{P}(v)>v([P])\) and \(\varphi_{E}(v)>v([E])\)). This also circumvents the limitation of decreasing system performance by excessively considering economic objectives. The optimal solution, CS\({}_{1}\), can provide the required level of system performance (\(\varphi_{P}(v)=0.946\)) without significantly compromising the economic value (\(\varphi_{E}(v)=0.982\)). Although CS\({}_{2}\) is better in terms of economic value (\(\varphi_{E}(v)=1.043\)), it will decrease CV (\(\varphi_{P}(v)=0.901\)), and therefore fail to satisfy the customers' design expectations.
## 2 Materials and Methods
### UPLC-qTOF MS Analysis  
The UPLC-qTOF MS allows an analysis of the complete compound profile and its derivatives, which are not detected by HPLC-DAD. Before analysis, samples were filtered with 0.2 \(\upmu\)M PTFE filters and placed in 2 mL vials.  
The phenolic and carotenoid analysis was performed on an Ultimate 3000 Dionex UHPLC coupled to an ultrahigh resolution Qq-time of flight (UHR-QqTOF) mass spectrometer (Impact II, Bruker Daltonics, Germany), according to the method described by [13], with slight modifications. Separation of metabolites was performed using a reversed-phase column ACQUITY UPLC(r) BEH 130A C18, 1.7 \(\upmu\)m, 2.1 \(\times\) 100 mm (Waters, Milford, MA, USA). For polyphenols, mobile phases were set as follows: A (water 0.1% formic acid); B (acetonitrile 0.1% formic acid); flow, 0.250 mL/min; 0-10 min, 100 to 79% A; 10-14 min, 79 to 73% A; 14-18.3 min, 73 to 42% A; 18.3-24 min, 42 to 0% A; 24-26 min, 0 to 100% A. For carotenoids, mobile phases were set as follow: A (water 0.1% formic acid); B (acetonitrile/methanol (70/30) 0.1% formic acid); flow, 0.250 mL/min; 0-2 min, 15% A; 2-11.6 min, 15 to 0% A; 11.6-14 min, 0 to 15% A; 14-15 min, 15% A [25]. For polyphenols, parameters for MS analysis were set using electrospray ionization (ESI) in negative ionization mode with spectra acquired over a range from \(m\)/\(\gtrsim\) 20 to 1000 in an Auto MS scan mode. For carotenoids, parameters for MS analysis were set using electrospray ionization (ESI) in positive ionization mode with spectra acquired over a range from \(m\)/\(\gtrsim\) 50 to 2000 in an Auto MS scan mode [25]. The selected parameters were as follows: capillary voltage, 2.5 kV (negative mode, polyphenols) and 4.5 kV (positive mode, carotenoids); drying gas temperature, 200 \({}^{\circ}\)C; drying gas flow, 8.0 L/min; nebulizing gas pressure, 2 bar; collision radio frequency (RF), 300 Vpp; transfer time, 120 \(\upmu\)s; and prepulse storage, 8 \(\upmu\)s. Postacquisition internal mass calibration used sodium formate clusters, with sodium formate delivered by a syringe pump at the start of each chromatographic analysis.  
The carotenoids data are expressed by the recovery index.
## 4 Three-Dimensional Modelling by the SfM-MVS Approach
### SfM and MVS Pipeline  
The main steps of a typical SfM pipeline can be summarized as follows: (i) feature extraction for an individual image, (ii) feature matching for each image pair and geometric verification, and (iii) reconstruction initialization [82].  
The feature extraction task allows us to identify local features on each image, the so-called key points; the most common algorithm used in SfM software is the scale-invariant feature transform (SIFT) feature detector [83].  
The second step, the correspondence search task, can be divided into the following stages:  
* Feature matching: determination of images that show common parts of the scene through the analysis of key points obtained in the feature extraction phase.
* Geometric verification: using a robust estimation technique such as RANSAC or RANdom sample consensus, the image pairs that are potentially overlapping are verified [84].
* Regarding reconstruction initialization, the main steps are [85]:
* Initialization: once a geometrically verified image pair has been selected, the points in common between the two images are used as input for the construction of the point cloud.
* Image registration: provided there is a three-dimensional set of points and the corresponding two-dimensional projections in the image, it is necessary to estimate the pose of a calibrated camera (Perspective-n-Point (PnP)) in order to register new images to the current model [84].
* Triangulation: this task determines the 3D locations of the matched points using triangulate, which is based on the direct linear transformation (DLT) algorithm [81];
* Bundle adjustment: optimisation of 3D structures and camera motions of each view using the Levenberg-Marquardt (LM) algorithm [86; 87].  
Multi-view stereo algorithms allow us to increase the point cloud density previously generated during the SfM process. These algorithms take a possibly very large set of images and construct a 3D plausible geometry that explains the images under some reasonable assumptions, the most important being scene rigidity [88]. There are several MVS algorithms [89] in the literature, such as volumetric representations [90], [PERSON]'s PMVS implementation [91], and [PERSON]'s multi-view stereo for community photo collections approach [92]. A very efficient algorithm able to handle numerous images and take high accuracy was proposed by ref. [93], which is based on four distinguishable steps: (i) stereo pair selection, (ii) depth-map computation, and (iii) depth-map refinement and depth-map merging.
## 2 Results and Discussion
### Candidate Genes for Cytochromes P450 of the CYP71 Superfamily  
Each sequence of a minimal dataset of plant cytochromes P450, containing one member of each subfamily known from plants [34], was used to perform a BLAST search against the Trinity _T. laciniata_ transcriptome assembly. In this way, 67 contigs were annotated and assigned a putative CYP number based on homology to a single member of the minimal plant P450 collection (see Table S3). Almost all of the sequences appeared to be full length, containing nucleotides corresponding to the start and stop codons, with only 4 lacking some additional sequence information. A number of biosynthetic genes from general metabolism, including orthologous of the sterol 14\(\alpha\)-demethylase enzyme CYP51G1, ent-kaurene oxidase CYP701A, and steroid 23-alpha-hydroxylase CYP90A were found in the list of annotated _T. laciniata_ cytochromes P450. Of the 67 cytochrome P450 contigs, 12 could be assigned to the CYP71 family, 4 in the CYP76 family and 2 in the CYP83 family. These families are part of the CYP71 clade and thus of special interest, since members of this clade have been attributed to specialized terpenoid metabolism [16,27,35]. The sequences assigned the CYP numbers CYP71D183 (contig 1645) and CYP71D319 (contig 15,003) show close homology to CYP71 AV1 and CYP71 BL1+2 that are known to be involved in the oxidation of the sesquiterpenes amorphadiene and germacrene A, respectively [16,27]. These sequences are therefore of high interest in relation to guaianolide biosynthesis since numerous hydroxylations of the guaiene-type backbones are needed for the complete biosynthesis of the highly modified guaianolides found in _T. laciniata_. Any of the 18 genes in the 71, 76 and 83 families could potentially be involved in the biosynthesis of sesquiterpene lactones in _Thapsia_.  
In a recent study, a transcriptome dataset for _B. chinense_ was obtained using 454 sequencing, with saponin biosynthesis as the target enzymes (a triterpene) [25]. In this study, 44 cytochromes P450 were found, with 4 being in the CYP71 clade, thus the number found in _Thapsia_ is significantly higher, most likely due to the increased depth of the sequencing. With the CYP71 clan by far being the largest of the 11 land plant cytochrome P450 clans, encompassing more than half of all the plant cytochromes P450, extensive blooming of subfamilies within this clan appears to have taken place in a species-specific manner [36]. While giving rise to many interesting genes with diverse functions, this also makes cross-species comparison much more difficult with regard to functional annotation based on sequence similarity.  
With more than 240 cytochromes P450 in _Arabidopsis_[35], it is highly likely that not all cytochromes P450 from _T. laciniata_ were discovered in our root-specific sequencing due to the different expression profiles of the various enzymes.