esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Procedures
#### 2.3.3. Vertical Jump Performance Measurement  
Vertical jump performance, measured as jump height (cm), was evaluated from the CMJ and SJ using an OptoJump infrared timing system (Microgate, Bolzano, Italy). The participants first performed three trials of CMJs followed by three trials of SJs [15]. One minute of rest was given between two trials. Prior to performing both jumps, participants were instructed about the jumping technique and later performed at least two submaximal familiarization repetitions of CMJs and SJs to learn proper jumping techniques [4]. The CMJ was performed by flexing the knee to a squat position (approximately 90\({}^{\circ}\) of knee flexion) from an upright position and then immediately extending the hips and the knee into a vertical jump, whereas the SJ was performed by jumping to vertical from squat position (90\({}^{\circ}\) of knee flexion) [20]. When approaching the landing position, participants were advised to land with extended knees to avoid any measurement error resulting from prolonged flight time. Both jumps were performed with hands placed on hips and with legs straightened during the flight. The jump height was calculated from the recorded flight time (height = [gravitational acceleration (9.81 m/s\({}^{2}\)) \(\times\) flight time2] \(\times\) 8 - 1) [4], and the highest jump was used in the final analysis.
## 5 A Real Case Study
Then, according to Equations (1)-(8), the data for a3, b1-b4, d2, d3 were estimated. Because the Equations (1)-(8) are just for rough estimate, these data were not highly accurate. Considering the uncertainty and fuzziness of the data, Equation (25) was used to turn the numerical value into an IVNN value. The performance scores of the quantitative subcriteria are shown in Table 3. On the other hand, for the qualitative subcriteria, the experts group devoted their efforts to investigate the alternative plans and evaluate the performance score for the subcriteria a1, a2, d1, d4, e1-e3, f1-f4. The performance scores of the qualitative subcriteria were shown in Table 4. In addition, the subcriteria were divided into positive and negative. The score of positive criteria higher and negative criteria lower means the alternative better. In this paper, subcriteria b1, d4, f1, f3, f4 are negative and the others are positive. Figure 4: The weights of subcriteria. Figure 5: Influential network relationship map within systems. Thirdly, based on the weight of the sub-criteria and the IVNN scores of each alternative on each subcriteria, the final composite scores were calculated by improved ELECTRIC III method. After being told that \(q_{j}\) is the indifference threshold, \(p_{j}\) is the preference threshold and \(v_{j}\) is the veto threshold, the experts group suggested that \(\sum s(a_{i},a_{k})-\sum s(a_{k},a_{i})\)\((k=1,2\ldots n)\), respectively. The concordance index \(c(X_{i},X_{k})\) and the partial concordance index \(c_{\bar{1}}(X_{i},X_{k})\) were calculated by Equations (26) and (27), as shown in Table 5. The discordance index \(d_{j}(X_{i},X_{k})\) was achieved by Equation (28), as shown in Table 2.0 Then, overall concordance and partial discordance indices was obtained by Equation (29) as shown in Table 2.1 Finally, the degree of credibility of the outranking relation was calculated by Equation (30), and the rankings of alternative X1, X2, X3, X4 was shown in Table 2.2  
From Table 8, the SMPV plan X1 of the Golden Resources shopping mall in Beijing is the optimal selection. The alternative X1 is particularly superior to other alternative plans in terms of the economy, photovoltaic array, and contribution criteria, while these three criteria weighed more than a half of the entire criteria weights, so there is no doubt that plan X1 obtained the best position. However, plan X1 performs badly in the risk and architectural elements criteria. Respectively, Plan X2 have strength on the economy, but are weak on photovoltaic criteria. Yet, plan X2 is much better than plan X3 and X4, so that it can be the stand-by choice.
_Applied_  
_sciences_  
_Article_  
Thermal Lattice Boltzmann Simulation of  
Evaporating Thin Liquid Film for Vapor Generation  
[PERSON] 1,t\({}^{\text{\textregistered}}\), [PERSON] 2 and [PERSON] 1,*  
1 Institute of Automation, School of IoT Engineering, Jiangnan University, Wuxi 214122, China; [EMAIL_ADDRESS]  
2 Department of Modern Mechanics, University of Science and Technology of China, Hefei 230000, China; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-0510-8591-0633  
+ Current address: School of IoT Engineering, Jiangnan University, Lihu Rd. 1800, Wuxi, Jiangsu, China  
Received: 28 April 2018; Accepted: 14 May 2018; Published: 16 May 2018  
**Featurered Application: This work can potentially be used in applications where thin film evaporation is involved, for example, cooling and vapor generation. The approach introduced in this paper may facilitate the phenomenon-level understanding of heat and mass transfer, as well as the device-level design.**  
Thin film evaporation (TFE) plays an important role in many industrial applications, such as power generation, cooling, and thermal management. Effective evaporation takes place in the thin liquid film region with relatively low film thickness and low intermolecular forces. In this paper, a numerical approach based on the thermal lattice Boltzmann method (TLBM) is employed to investigate the heat and mass transfer phenomena in TFE. The TLBM approach is validated by simulating some benchmark problems, and is then used to study a vapor generation problem where TFE is involved. Specifically, vapor is generated from evaporating pores, the solid walls of which are hydrophilic. Factors that affect the overall vapor generation efficiency are investigated via the numerical approach. Methods that can improve the overall efficiency are further proposed. Simulations reveal that distributed scenarios (using distributed small pores instead of a big one) and hydrophobic pore ends render more efficient vapor generation.  
thin film evaporation; thermal lattice Boltzmann method; double distribution; vapor generation
## 9 Non-Symmetric Section
### Trans-Moments
By the parallel axis theorems, Equation (30b,c), the separation (see Figure 20a,b) between (i) the horizontal axes \(x\) (through G) and \(x_{E}\) (through E) is  
\[E_{y}=Y^{\prime}-e_{y}=5d/12-9d/87=0.3132d\] (32a) and (ii) the separation (see Figure 11b) between vertical axes \[y\] (through G) and \[y_{E}\] (through E) is \[E_{x}=X^{\prime}+e_{x}=5d/36+21d/153=0.2761d \tag{32b}\]  
Applying Equation (30b,c),  
\[I_{Ex}=I_{x}+A{E_{y}}^{2}=17d^{3}t/72+2 dt(0.3132d)^{2}=0.4323d^{3}t\]  
\[I_{Ey}=I_{y}+A{E_{x}}^{2}=87d^{3}t/1944+(2 dt)(0.2761)^{2}=0.1972d^{3}t\]  
Separating the two trans-moment terms in Equation (30a), their summation at the fixing (\(z=0\)) is \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Point** & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline \(x/d\) & 7/36 & 0 & \(-5/36\) & \(-5/36\) & \(-5/36\) & 0 & 19/36 \\ \(y/d\) & 7/12 & 7/12 & 7/12 & 0 & \(-5/12\) & \(-5/12\) & \(-5/12\) \\ Equation (1) & 5.834 & 2.528 & 0.166 & \(-2.362\) & \(-4.167\) & \(-1.805\) & 7.169 \\ \(\sigma_{z}/\)MPa & 216.05 & 93.61 & 6.15 & \(-87.47\) & \(-154\) & \(-66.84\) & 265.5 \\ Equation (2) & 2.723 & 1.88 & 1.278 & \(-0.6019\) & \(-1.95\) & \(-1.343\) & 0.9445 \\ \(\sigma_{z}/\)MPa & 100.83 & 69.62 & 47.32 & \(-22.29\) & \(-72.02\) & \(-49.73\) & 34.97 \\ Equation (3) & 8.557 & 4.408 & 1.444 & \(-2.964\) & \(-6.112\) & \(-3.148\) & 8.114 \\ \(\sigma_{z}/\)MPa & 316.9 & 163.2 & 53.5 & \(-109.8\) & \(-226.3\) & \(-116.6\) & 300.5 \\ Equation (4) & \(-3.111\) & \(-2.464\) & 1.112 & 1.76 & 2.222 & 0.4625 & \(-6.225\) \\ \(\sigma_{z}/\)MPa & \(-115.2\) & \(-91.2\) & 43.2 & 65.2 & 82.3 & 17.1 & \(-230.5\) \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Asymmetric bending stress calculations for median line points 1, 2, 3... 7 by Equations (1)–(4). \[\sigma_{z} =(M_{x}\ E_{y})/I_{Ex}+(M_{y}\ E_{x})/I_{Ey}\] (33a) \[=(F_{y}\ L)E_{y}/I_{Ex}+(F_{x}\ L)E_{x/I\!/\!E_{y}}\] We substitute for this section's properties as given above \[\sigma_{z} =F_{y}L(0.3132d)/(0.4323d^{3}t)+F_{x}L(0.2761d)/(0.1972d^{3}t)\] (33b) \[=(0.7245F_{y}+1.40F_{x})L/(d^{3}t)\] which reveals that \[\sigma_{z}\] is uniform for a given force combination. For example, taking case (3) above ( \[F_{x}=F_{y}=F\] ), Equation ( 33a ) leads to \[\sigma_{z} =2.1245 FL/(d^{3}t)\] which, for \[F=100\ \mathrm{N}\], \[\sigma_{z} =20.98\ \mathrm{MPa}\], which is to be distributed to each stress level calculated under (3) in \[\sigma_{z} =(M_{x}\ E_{y})/I_{Ex}+(M_{y}\ E_{x})/I_{Ey}\]0 This distribution is demonstrated for the simpler case of a symmetric channel section that follows.
## 2 RNAi in the Western Corn Rootworm  
RNAi is an inherent post-transcriptional gene silencing mechanism utilizing various non-coding RNAs (ncRNA) as substrates [34]. RNAi is thought to have evolved as a defense mechanism against invading nucleic acids such as viruses and transposable elements or anomalous endogenous transcription products [35,36]. RNAi was first described in _Caenorhabditis elegans_ in 1998 and was subsequently shown to be conserved in most eukaryotic organisms [37,38].  
Three RNAi pathways are present within eukaryotic organisms, each triggered by distinct, short, duplexed, non-coding nucleic acids: microRNA (miRNA), piw-interacting RNA (piRNA), and short interfering RNA (siRNA) [39] (reviewed by [PERSON] and [PERSON] [40]). Each ncRNA embeds within an Argonautte protein and acts as a guide for the direct targeting of a single-stranded RNA molecule within the cell [41]. Despite overlap within the core machinery, each pathway differs in functionality. The RNAi pathway triggered by miRNA is involved in miRNA processing [42], regulation of nucleic acids through translation repression [43,44], and target degradation [45, 46, 47, 48]. The piRNA pathway is involved in the silencing of transposable elements [49] and germline development [50]. Finally, the siRNA pathway responds to endogenous siRNAs [51] and long exogenous dsRNAs present during viral infections [52], which are then sliced into siRNAs within the cytoplasm. The siRNA pathway can be triggered exogenously through the application of synthetic dsRNA for specific silencing of inherent messenger RNA (mRNA) [34]. This pathway is commonly used for gene function studies and, more recently, for pest management [53,54]. Thus, this review focuses on the siRNA RNAi pathway in WCR.  
In 2007, [PERSON] et al. [55] demonstrated that oral exposure to _DvV-ATPase A_ dsRNA elicited a silencing response in WCR larvae, leading to mortality (LC50 < 0.52 ng/cm\({}^{2}\)). _In planta_ expression conferred maize root protection (mean node injury score of 0.25), exhibiting the possibility of utilizing the natural RNAi response for pest management.  
Another early study used RNAi in WCR to assess the function of WCR orthologues to _laccase 2 (lac2)_ and _chitin synthase 2 (chs2)_ by injecting dsRNA into second and third instars [56]. Injection of 200 ng/\(\upmu\)L _DvLac2_-specific dsRNA resulted in a 95.8% reduction in _lac2_ expression and the prevention of post-moltticular tanning, while injection of 200 ng/\(\upmu\)L _DvCHS2_-specific dsRNA reduced chitin levels in midgut tissues by 78%. Both studies demonstrated that WCR has a robust RNAi response and systemic gene knockdown could be generated in WCR through both injection and feeding.  
Silencing via the siRNA pathway involves three steps: dsRNA uptake, gene silencing, and systemic spread. Understanding the RNAi pathway in WCR is imperative to characterize potential resistance mechanisms. Much of the mode of action in WCR is understood and will be described in further detail in this review. However, knowledge gaps still exist and further examination in WCR is warranted.
###### Abstract  
The study was to evaluate the effects of chitosan-based coating combined with rosmarinic acid (RA) with different concentrations of \(\varepsilon\)-pollylysine (\(\varepsilon\)-PL) on flavor retention and sensorial properties of half-smooth tongue sole fillets during storage at 4 \({}^{\circ}\)C. Results showed that chitosan-based coatings combined with \(\varepsilon\)-PL and RA contributed to the reduction of off-flavor compounds, such as total volatile base nitrogen (TVB-N), trimethylamine (TMA), and ATP-related compounds, and accumulation of free amino acids (FAAs). Nineteen volatile organic compounds were analyzed by gas chromatography-mass spectrometer (GC/MS) during storage, including seven alcohols, six aldehydes, and six ketones. The coating treated fresh half-smooth tongue sole (HTS) fillets significantly reduced the relative content of off-odor volatiles, such as 1-octen-3-ol, propanal, hexanal, and octanal. According to sensory evaluation results, chitosan-based coating combined with \(\varepsilon\)-PL and RA was an effective way to maintain quality of HTS fillets during refrigerated storage.  
Keywords:half-smooth tongue sole; refrigerated storage; edible coating; \(\varepsilon\)-pollylysine; shelf life +
Footnote †: journal: Coatings
## 6 Complexity Analysis of Recurrence-Analysis-Based Detector
### Algorithmic Complexity of Recurrence-Analysis-Based Detector  
To make decisions, the RAD uses a statistic test defined in Equation (18). The RAD detection process is summarized in Figure 17.  
The algorithmic complexity of the RAD essentially concerns the distance matrix computation and the calculation of average values of distance matrix coefficients. From the observed signal \(y(n)\) containing \(N\) samples, we obtain \(K=N-(m-1)\tau\) state vectors. Each state vector contains \(m\) coordinates. The distance \(d_{ij}=\left\|\mathbf{x}-\mathbf{x}_{j}\right\|\) from the state vector \(\mathbf{x}_{i}\) to other state vectors \(\mathbf{x}_{j}\) requires \(mK\) addition operations. Because we exploit only the upper triangular part of the distance matrix with the main diagonal coefficients, we use \(\frac{m}{2}K(K-2)\) addition operations. The computation of the average value of the first upper diagonal requires \(K\) elementary operations, and the average value of other coefficients  
Figure 16: Different steps of the CFD detection process.  
Figure 17: Different steps of the RAD detection process.  
requires \(\frac{(K-2)^{2}}{2}\) elementary operations. The statistic test of the RAD requires one addition operation. Consequently, the computation complexity \(C_{RAD}\) of the RAD becomes  
\[C_{RAD} = 2N^{2}\,+\,4N(\tau\,-\,m\tau\,-\,1)\,+\,\left[2(m\tau)^{2}+2\tau^{ 2}-4m\tau^{2}-4\tau+2m\tau+6\right] \tag{56}\]  
From Equations (52), (55), and (56), in Figure 18, we generated the curves of the evolution of the algorithmic complexity of the ED, CFD, and RAD based on the number of samples contained in the observed signal \(y(n)\). The results in Figure 18 show that ED is the simplest algorithm, whereas CFD is the most complicated one. We also notice that the RAD algorithm is less complicated than the CFD algorithm.
## 5 Experimental Case Planning, Results, and Analysis
### Experimental Case
#### 5.2.2 Case Study 2--AI Model with Limited Data
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Across Loading**} & \multirow{2}{*}{**Accuracy Index**} & \multirow{2}{*}{\begin{tabular}{c} **Residual VGG-Based** \\ **Slamese Network** \\ **Five-Shot Five-Way** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Residual VGG-Based** \\ **Siamese Network** \\ **One-Shot Five-Way** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Fine-Tuned** \\ **ResNet50** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **DCGAN and CNN** \\ **[23]** \\ \end{tabular} } \\ \cline{2-2} \cline{6-6}  & & Average accuracy & 99.91 (\%) & 99.89 (\%) & 94.06 (\%) & 79.41 (\%) \\ \cline{2-6}  & & STD & 0.1315 & 0.1409 & 6.273 & 7.837 \\ \hline
100\% to 50\% load & Average accuracy & 98.55 (\%) & 98.48 (\%) & 96.83 (\%) & 79.14 (\%) \\ \cline{2-6}  & & STD & 0.8771 & 0.9488 & 1.284 & 7.483 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Imbalance ratio of 10:1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Across Loading**} & \multirow{2}{*}{\begin{tabular}{c} **Accuracy Index** \\ **Model** \\ **Accuracy Index** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Residual VGG-Based** \\ **Siamese Network** \\ **Five-Shot Five-Way** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Residual VGG-Based** \\ **Siamese Network** \\ **One-Shot Five-Way** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Fine-Tuned** \\ **ResNet50** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **DCGAN and CNN** \\ **[23]** \\ \end{tabular} } \\ \cline{2-2} \cline{6-6}  & Average accuracy & 99.77 (\%) & 99.72 (\%) & 91.11 (\%) & 71.39 (\%) \\ \cline{2-6}  & STD & 0.2683 & 0.2736 & 6.621 & 8.024 \\ \hline
100\% to 50\% load & Average accuracy & 98.34 (\%) & 98.15 (\%) & 95.21 (\%) & 69.03 (\%) \\ \cline{2-6}  & STD & 1.178 & 1.182 & 1.801 & 7.748 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Imbalance ratio of 20:1.  
network not only increased accuracy but also lowered STD values in the diagnosis results, indicating the stability of the model's training performance, unaffected by imbalanced or insufficient training data, for instance, by issues such as overfitting or lack of convergence. (3) Compared to DCGAN and CNN models [23], the method proposed in this research has higher accuracy and does not require the adjustment of complex parameters like in GAN models, making it less difficult to train.
# Numerical Analysis of the Installation Process of Screw Piles Based on the FEM-SPH Coupling Method
## 1 Introduction
In addition, many researchers have also investigated the pile-soil interaction and load transfer mechanisms of screw piles under different conditions at a microscopic level based on the numerical simulation method [2; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. From these studies, it can be seen that the numerical simulations enable a range of factors to be considered, broadening the avenues of research. It is worth noting that most of these numerical studies on pile-soil interaction focus on compression, uplift and lateral loading processes, and little attention is paid to the installation process of screw piles. Therefore, it is necessary to investigate the pile-soil interaction during the installation process of a screw pile. The installation of screw piles is a dynamic and large deformation problem, which means that it is difficult to solve the deformation of the meshes by traditional finite element methods. Considering the limitations of traditional finite element methods in dealing with large deformations problems, many researchers [28; 29; 30; 31; 32] have gradually adopted the Smoothed Particle Hydrodynamic (SPH) method to deal with large deformation problems in geotechnical engineering. Their studies [28; 29; 30; 31; 32] all demonstrate the suitability of the SPH method for dealing with large deformation problems. In this paper, a FEM-SPH coupling method is introduced to investigate the installation process of screw piles. The screw pile was simulated using the finite element method (FEM), and the soil model was constructed using the SPH method. Additionally, a numerical model was established for the analysis of the installation of a screw pile in LS-DYNA. To verify the accuracy and reliability of the established model, the simulation results were compared with field test data and the traditional finite element method to demonstrate the applicability of the FEM-SPH numerical model in dealing with large soil deformations caused by screw pile installation. Then, the analysis of the screw pile installation process is presented from the aspect of the movements of surrounding soil particles at different depths. Finally, the installation simulations of different screw piles were carried out to understand the differences in pile structure parameters on the installation process.
## 1 Introduction
### Previous Studies--Pockmark Field Activity
#### 1.2.3 Gas Monitoring Module (GMM)  
The emissions of pockmark P4 were investigated further in 2003-2004. Then, a cabled observatory, the Gas Monitoring Module (GMM), measured the gas and CH\({}_{4}\) seepage above the crater for 201 days, providing--for the first time--a data set of such extent from a gas-bearing pockmark [42; 55]. Within the period of GMM deployment, more than 60 pulses of CH\({}_{4}\) increase were recorded. Increases in the dissolved methane concentration create a series of events that usually begin and end with changes in the physical properties of seawater. The most intense escapes are characterised by a sharp drop in seawater temperature (T) and pressure (P). These CH\({}_{4}\) peaks are also associated with H\({}_{2}\)S peaks. This seepage fluctuation, which is associated with T-P drops, can be attributed to an active process driven by gas pressure built up in the pockmark sediments and/or as a passive effect driven by oceanographic (e.g., upwelling) and morphological factors (e.g., T-P drops caused by turbulence and Bernoulli-type effects induced by sharp depressions) [42]. During the periods of operation of the GMM, in the wider area, a number of small seismic events occurred (M \(<\) 4, except for one event). A comparison of the observatory data with the seismological data was performed for earthquakes \(>\) 3 R that occurred within a radius of 30 km from the point location. Five events out of fifteen were slightly correlated with gas seepages, as indicated by the slight increase in dissolved methane concentration [41].