esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Root Mean Square Deviation Analysis  
The root mean square deviation (RMSD) measures the alteration in the displacement of atoms for a particular frame with respect to a reference frame for all the trajectories. Figure 9A-D shows the RMSD of the odorant-binding protein 7 in complex with the lead phytochemicals. The red line (left Y-axis) in the plots shows the RMSD evolution of the protein after all protein frames are first aligned on the reference frame backbone. The ligand RMSD (right Y-axis) shows how stable the lead phytochemicals are with respect to the protein. The protein-ligand complex was first aligned on the reference protein backbone. Figure 9A shows the plots of the RMSD of the \(\beta\)-selinene complex system.  
Figure 7: A stacked bar interaction plots of the residues of the odorant-binding protein 7 with the lead phytochemicals (**A–D**) in the selected trajectory across the MD simulation period of 100 ns.  
The protein RMSD was equilibrated before the first 10 ns, while the ligand RMSD was equilibrated after the first 20 ns. Both systems were stabilized with minimal fluctuations and converged at 100 ns. Both the protein and ligand RMSD for the sulactone complexed system were equilibrated before 10 ns of the simulation with minimal fluctuation (less than 2 A) after equilibration. The protein RMSD of the \(\beta\)-caryophelene bound system was equilibrated after 20 ns with a sharp peak after equilibration, while the ligand RMSD was equilibrated at the beginning of the simulation. Both systems experienced minimal fluctuations that were within the acceptable range of <3 A, and convergence was also reached toward 100 ns. The protein RMSD plots for the \(\alpha\)-ylangene bound system followed a different pattern from the ligand RMSD. Both systems experienced minimal fluctuations that were within the acceptable range of <3 A, and convergence was also reached toward 100 ns. The protein RMSD plots for the \(\alpha\)-ylangene bound system followed a different pattern from the ligand RMSD. Both systems were equilibrated before 20 ns but experienced several deep and peak fluctuations throughout the simulation. The deviance in the RMSD plots further explains the reduction in the secondary structure elements (alpha-helices  
Figure 8: A timeline representation of interactions and contacts (H-bonds, hydrophobic ionic, water bridges) between the residues of the odorant-binding protein 7 and the lead phytochemicals (**A–D**) in the selected trajectory across the MD simulation period of 100 ns.  
and beta-strands) (Figure 5D). The binding of \(\alpha\)-ylangene reduced the stability of the protein [53]. In general, the odorant protein complexed with the lead phytochemicals except \(\alpha\)-ylangene demonstrated a high degree of structural stability and compactness [54]; hence, the systems can be adapted for other experimental models.
## 3 Results  
Fourteen patients (Table 2) consisting of six males and eight females were included in this study. The mean age was 27.7 years old (ranging from 21 to 37 years old). All patients had skeletal type III malocclusion and were classified into two groups based on the procedures they underwent. Patients in the one-jaw group were given BSSO alone (\(n=5\)) and those in the two-jaw group underwent Le Fort I osteectomies combined with mandibular setbacks using BSSO (\(n=9\)). Genioplasty was performed on a few of the individuals in both groups. On average, the T\({}_{1}\) data were collected 6.5 \(\pm\) 2.5 months (range of 4 to 13 months) after surgery. The mean distance of the sagittal mandibular setback in the one-jaw group was 6.5 \(\pm\) 3.3 mm (range of 1.0 to 13.1 mm) while the average surgical movements in the sagittal plane of the two-jaw group were as follows: 1.8 \(\pm\) 1.3 mm for maxilla advancement (range of 0.7 to 4.1 mm) and 4.5 \(\pm\) 3.1 mm for mandibular setback (range of 1.5 to 8.7 mm). The intraexaminer agreement was high, with an intraclass correlation coefficient greater than 0.9.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Point** & **Definition** \\ \hline Pronasale (P) & Most anterior midpoint of the nasal tip \\ Subnasale (Sn) & The midpoint on the nasolabial soft tissue contour between the columella crest and the upper lip \\ Soft tissue point A (A’) & Point of greatest concavity on the contour of the upper lip \\ Stomion superior (Stou) & Midpoint of the lower border of the upper lip \\ Stomion inferior (Stou) & Midpoint of the upper border of the lower lip \\ Soft tissue point B (B’) & Point of greatest concavity on the contour of the lower lip \\ Pogonion (Pg) & The most anterior midpoint of the chin \\ \hline \hline \end{tabular}
\end{table}
Table 1: Soft tissue landmarks.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Sex**} & \multirow{2}{*}{**Age**} & \multirow{2}{*}{**Procedure**} & \multicolumn{2}{c}{**Surgical Movement (Sagittal)**} \\ \cline{5-6}  & & & & & **Maxilla (mm)** & **Mandible (mm)** \\ \hline \multicolumn{6}{l}{One-jaw group} \\ \hline
1 & M & 24 & BSSO & N/A & \(-\)6.55 \\
2 & M & 27 & BSSO & N/A & \(-\)13.11 \\
3 & M & 29 & BSSO & N/A & \(-\)5.6 \\
4 & F & 37 & BSSO & N/A & \(-\)6.14 \\
5 & F & 30 & BSSO + genioplasty & N/A & \(-\)0.95 \\ Two-jaw group & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The study sample’s characteristics.
# Improving the Performance of Turbo-Coded Systems under Suzuki Fading Channels
## 3 Noise Distribution
### Suzuki Distribution  
Suzuki distribution is a combination of Rayleigh and log-normal distributions, such that [14; 17]:  
\[p(h)=\int_{0}^{\infty}\frac{h}{\sqrt{2\pi}\sigma_{h}\alpha^{3}}\exp(-\frac{h^{ 2}}{2\alpha^{2}})\exp(-\frac{(\ln(\alpha)-\mu_{h})^{2}}{2\sigma_{h}^{2}})d\alpha, \tag{4}\]  
where \(\sigma_{h}\) and \(\mu_{h}\) represent the channel variance and mean. There is no analytic solution to Equation (4). Instead, numerical methods are widely used to find an approximate value of this integration. In our analysis, we utilize this integral to derive noise distribution after the ZFE.
## 4 Results and Discussion
### Fold Angle
The \(C_{Fy}\) increased in the windward half and decreased in the leeward half with increasing fold angles, which means that the blade folding movement in the positive direction strengthened the lateral force away from the main shaft. Additionally, the occurrence of a stall also resulted in a slight reduction of the \(C_{Fy}\). Variations of the \(C_{Fz}\) showed that the fold angle intensified the fluctuations of \(C_{Fz}\). With increasing positive fold angles, the blade experienced larger aerodynamic forces upward in the fore half and downward in the aft half, which was reversed with negative fold angles. Moreover, the effect of the fold angle on the \(C_{Fz}\) was much stronger in the aft half than that in the fore half. Figure 6: Instantaneous variables of a blade element on the equator with different fold angles: (a) angle of attack (AoA); (b) \(C_{Fi}\) and (c) \(C_{Fi}\). Figure 7: Instantaneous aerodynamic forces of a blade with different fold angles: (a) \(C_{Fx}\); (b) \(C_{Fy}\) and (c) \(C_{Fz}\). As was mentioned above, the folding movement of the blades led to changes in both the pitch angle and the swept area. Variations of the swept area of the blades could also contribute to the variations in overall performance of the VAWT. However, as can be seen in Figure 8a, the variations of the swept area for fold angles from \(-2^{\circ}\) to \(8.5^{\circ}\) were less than 0.3%, which could be neglected, suggesting that the variations of the overall performance of the VAWT were mainly owed to pitch movement. Variations of the \(C_{P}\) versus the fold angle are shown in Figure 8b. As can be seen, the maximum \(C_{P}\) was obtained at a fold angle of \(4^{\circ}\), and it decreased gradually when the blades were folded in two directions. It is shown that the sensitivity of the \(C_{P}\) to the folding movement in the negative direction was higher than that in the positive direction. However, the occurrence of a stall, caused by large positive fold angles (\(8.5^{\circ}\) in this case), resulted in a sharp drop of the \(C_{P}\). The findings coincided with previous experimental results [4]. As for the average thrust of a blade, the \(C_{Thurst}\) grew when the blades were folded in the positive direction (shown in Figure 9). In addition, it diminished with the occurrence of a stall. The \(C_{Lateral}\) was approximately proportional to the fold angle. As a result, the fold angle strengthened the asymmetry of the lateral force of a blade. Fold angles above \(2^{\circ}\) led to lateral forces pointing to the windward half, and those lower than \(2^{\circ}\) led to forces pointing to the leeward half. The vertical forces kept downward for most of the fold angles, except for minor positive fold angles (\(2^{\circ}\) in this case). In addition, the \(C_{Vertical}\) increased when the blades were folded in both directions.
## 2 Study Area and Method
### Model Parameters and Input Data
#### 2.3.2 Forest Distribution Data of Zhejiang Province  
Classification of forest types for Zhejiang Province was based on the Landsat TM imagery from 1984 to 2014. After radiation correction and geometric correction, the maximum likelihood classification method was used to obtain the forest distribution information of coniferous forests, broadleaf forests, and bamboo forests in Zhejiang Province [41]. To keep the same spatial resolution with other data, the local average method [42] was used to push the scale of the Zhejiang forest distribution data from 30 m resolution to 1 km resolution abundance data.
# Biopurification of Oligosaccharides by Immobilized _Kluyveromyces Lactis_
## 3 Results
### Lactose Removal on Defined Salt Media and Distilled Water  
In order to simplify the purification media, the lactose removal activity was evaluated using the distilled water without the addition of any media components. The _K. lactis_ could not grow well in distilled water compared to YNB without amino acids (Figure 1A). The lactose consumption and ethanol production, on the other hand, showed comparable patterns even though a lag period (15 h) existed when the cells were grown in distilled water (Figure 1B,C). From this, it is concluded that the distilled water instead of YNB media could be used for lactose removal media once the low cell growth was overcome.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Media**} & \multirow{2}{*}{**Substrate**} & \multirow{2}{*}{**Maximum**} & \multirow{2}{*}{**\(\mu_{\text{max}}\)**} & \multirow{2}{*}{**Substrate**} & \multirow{2}{*}{**Ethanol**} \\  & & **OD\({}_{\text{600}}\)** (AU)** & & (h\({}^{-1}\)) & & **Consumption 1** (g/L) \\ \hline \multirow{3}{*}{YPD} & Glucose & 7.9 & 0.33 & 20.0 g/L & 10.0 g/L \\ \cline{2-6}  & Galactose & 5.7 & 0.20 & 11.0 g/L & 4.2 g/L \\ \cline{2-6}  & Lactose & 6.9 & 0.31 & 20.0 g/L & 12.0 g/L \\ \hline YNB & Glucose & 5.4 & 0.31 & 17.0 g/L & 7.3 g/L \\ \cline{2-6} (with amino acids) & Galactose & 3.5 & 0.29 & 6.8 g/L & 3.5 g/L \\ \cline{2-6}  & Lactose & 5.1 & 0.31 & 19.0 g/L & 9.3 g/L \\ \hline YNB (without amino acids) & Lactose & 5.0 & 0.27 & 14.0 g/L & 9.5 g/L \\ \hline \hline \end{tabular}  
* Substrate consumption and ethanol production were determined at 15 h fermentation.  
\end{table}
Table 1: Growth characteristics of _Kluyveromyces lactis_ in different media.  
Figure 1: Lactose assimilation activity by _Kluyveromyces lactis_ in YNB media without amino acid (open circle) and distilled water (filled circle). Cell growth, lactose, and ethanol production in suspension culture are shown in (**A**), (**B**), and (**C**). Lactose consumption and ethanol production by immobilized cells are shown in (**D**) and (**E**), respectively. The efficiency of biopurification is presented in (**F**).
# A Novel Hybrid Model for Cantonese Rumor Detection on Twitter
## 4 Experiments and Evaluation
### Ablation Study  
There are two kinds of features used in our model for rumor detection, i.e., semantic features and sentiment features. In addition, the attention mechanism and word embeddings play an important role in the model. To evaluate the contributions of these significant components to the model, we take turns to exclude them from the model:  
* XGA-SF-1: Only the semantic features are used.
* XGA-SF-2: Only the sentiment features are used.
* XG: The attention mechanism is removed.
* XGA-CE: The word embeddings are removed and only the character embeddings are used.
* XGA: Full model.  
The results of the ablation study are shown in Table 2. We can see that both semantic and sentiment features improve the performance of the model. Moreover, semantic features are more effective than sentiment features in the detection. This is because the sentiment polarity of some tweets is unclear. In addition, since the attention mechanism can give greater weights to typical rumor vocabulary, XGA performs better than XG. What's more, we compare XGA-CE with XGA and find that performing joint learning of character and word embeddings is useful because it can obtain both the words' external contexts and internal information. In conclusion, XGA outperforms all the other models, and all of these components make great contributions to the detection.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Embedding** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline BERT & 0.9119 & 0.9099 & 0.9102 & 0.9101 \\ GPT & 0.9099 & 0.9092 & 0.9063 & 0.9076 \\ ELMo & 0.9048 & 0.9048 & 0.9046 & 0.9047 \\ fastText & 0.9030 & 0.9031 & 0.9028 & 0.9029 \\ Word2 vec & 0.9079 & 0.9056 & 0.9063 & 0.9060 \\ XLNet & 0.9200 & 0.9224 & 0.9142 & 0.9176 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The performance of models with different embeddings.
# Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches
## 5 Conclusions  
In this study, several machine-learning-based icing detection models were proposed and compared to the existing physical-theory-based models using COMS and Himawari-8 satellite data. Two machine learning approaches--RF and MLL--were used to develop the icing detection models. Both machine learning-based models resulted in better performance (POD of 68-82% and POFD of 16-18%) than those of the existing physical-theory-based models (POD of 12-36% and POFD of 7-27%) when COMS data were used. Overall, the RF models produced better performance than the MLL models according to the five-fold cross validation. However, the RF models resulted in higher standard errors in the performance metrics than the MLL models, which implies a tendency for overfitting. The variable importance identified by the machine learning models generally followed the physical characteristics of icing, e.g., resulting in high contributions by cloud top height.  
Icing occurs in a variety of meteorological conditions, and thus a number of reference data collected from such conditions are required to develop more robust icing detection models. Although a small amount of qualified PIREP data, which might only cover a subset of those conditions, were used to develop the machine-learning-based icing detection models in this study, the results are promising. Therefore, the models deserve further research with more qualified reference data in order for governmental agencies to adopt such an approach from an operational perspective. The proposed approaches will be refined when more quality-controlled PIREP data become available. They will also be evaluated and optimized for the Geo-Kompsat-2 Advanced Meteorological Imager, which will be launched later in 2018 by the Korean government. In addition, the synergistic use of empirical machinelearning and physical-theory-based models will be investigated to improve the detection of potential icing areas in the future.  
This work was supported by the Development of Geostationary Meteorological Satellite Ground Segment (NMSC-2014-01) program, funded by the National Meteorological Satellite Centre (NMSC) of the Korea Meteorological Administration (KMA).  
[PERSON] led manuscript writing and contributed to the data analysis and research design. [PERSON] supervised this study, contributed to the research design and manuscript writing, and served as the corresponding author. [PERSON] and [PERSON] contributed to data processing and analysis. [PERSON] and [PERSON] contributed to the discussion of the results and manuscript writing.  
The authors declare no conflict of interest.
## 4 Working Mechanism of ESRVR
### Data Transmission and Void-Hole Detection and Mitigation
#### 4.3.1 Data Transmission  
The ID of the sink node is known to every node. Therefore, information from a node to the in-range sink node is automatically transferred in the form of data packets. However, a source node having data packets ready for transmission and far away from surface sink first checks its list of neighbors to choose a suitable neighbor. The suitable neighbor is the one that must satisfy two conditions. Firstly, it bears the minimum water pressure, ensuring that it is nearest to the water surface. Secondly, it had at least one neighbor further in the second hop with a lower depth (closer to the water surface) so that it could forward the data further (this second hop neighbor was in addition to the original source node from which the suitable neighbor would get packets). In this way, the chain of data forwarding continues, and the packets reach the sink, and hence the water surface. If multiple forwarder nodes qualify for information forwarding, the freedom of choice exists in such a circumstance, and any node could be selected. However, if a suitable neighbor has no neighbor node at all further in the second hop for continuing data routing or when the original source was neighborless, such a condition indicates the presence of a void-hole. This is because the absence of a neighbor means that if a neighborless node is selected, the data routing process will halt. As a result, packets will not be destined to the water surface. The void-hole condition is avoided with the strategy explained in the next section.
# Heterogeneous Distributed Big Data Clustering on Sparse Grids
## 1 Introduction
Sparse grids are a method for spatial discretization that has been applied to higher-dimensional settings with up to 166 dimensions and moderate intrinsic dimensionality [22]. Therefore, in contrast to many spatial partitioning approaches, the underlying sparse grid density estimation mitigates the issue of dimensionality. The sparse grid clustering method does not rely on assumptions about the distribution of data; it can successfully suppress noise and it can detect clusters of non-convex shape. Compared to \(k\)-means, sparse grid clustering does not require the number of clusters as a parameter of the algorithm. In this paper, we use the Euclidean norm as the measure for the similarity of data points. Methods based on sparse grids have been used for regression and classification tasks [22; 23]. In prior work, we have shown the applicability of these methods in heterogeneous computing and high-performance computing settings [24; 25; 26]. However, to our knowledge this work presents the first high-performance results for sparse grid clustering. Our algorithm was designed with a focus on high performance and performance portability. On the node-level we use OpenCL, as it offers basic portability across a wide range of hardware platforms. We not only support GPUs and processors of different vendors, our approach achieves a major fraction of the peak performance of all devices used. To map our method to clusters and supercomputers, we implemented a distributed manager-worker scheme. Due to the underlying method and the high-performance distributed approach, we show that sparse grid clustering is well-suited for large datasets. We provide results for 10-D datasets with up to 100 million data points and 100 clusters. The remainder of this paper is structured as follows. In Section 2, we give an overview of the sparse grid clustering algorithm. Then, in Section 3, we introduce the sparse grid density estimation as our core component. The other components of the algorithm are introduced in Section 4. Section 5 describes the parallel and distributed implementation and discusses features of the algorithms from a high-performance perspective. We show results for three node-level hardware platforms and two supercomputers in Section 6. Finally, in Section 7, we remark on implications of the presented algorithm and discuss future work.
# Evaluation of Clumping Effects on the Estimation of Global Terrestrial Evapotranspiration
## 2 Modeling Methodology
### ET Modeling
Leaf net photosynthesis rate (\(A\) in \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)) is calculated as [18]:  
\[A=\min(W_{c},W_{d})-R_{d} \tag{5}\]  
where \(R_{d}\) is leaf dark respiration (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)); \(W_{c}\) and \(W_{j}\) are Rubisco-limited and light-limited gross photosynthesis rates (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)), respectively. They are calculated as [43]:  
\[W_{c}=V_{cmax}\frac{C_{i}-\Gamma}{C_{i}+\text{K}} \tag{6}\]  
\[W_{j}=J\frac{C_{i}-\Gamma}{4.5C_{i}+10.5\Gamma} \tag{7}\]  
where \(V_{cmax}\) is leaf maximum carboxylation rate (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)); \(J\) is the electron transport rate (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)); \(\text{C}_{i}\) is the intercellular CO\({}_{2}\) concentration; \(\Gamma\) is the CO\({}_{2}\) compensation point without dark respiration; \(K\) is a function of enzyme kinetics. The values of leaf maximum rate of carboxylation at top of canopy normalized to 25 \({}^{\circ}\)C (\(V_{cmax0}^{25}\)) for various PFTs are taken from [PERSON] et al. [34], who produced global V\({}_{cmax}\) maps (2007-2017) based on GOME-2 SIF observation [44]. The default \(V_{cmax0}^{25}\) is used for the others [23], which is not available in [PERSON] et al. [34]. The surface resistance to the soil evaporation (\(r_{soil}\)) was calculated as [45]:  
\[r_{soil}=\text{e}^{8.206-4.255\times\frac{\theta_{1}}{\text{m}b}} \tag{8}\]  
where \(\theta_{1}\) and \(\theta_{s}\) are soil water content (m\({}^{3}\) m\({}^{-3}\)) and porosity (m\({}^{3}\) m\({}^{-3}\)) of the 1 st soil layer.
## 3 Bridge Vibration Response Based on Vehicle-Bridge Coupling
### Finite Element Modelling  
#### 3.1.1 Vehicle Modelling  
The four-axle vehicle used in this paper is the China National Heavy-Duty Truck HOWO-T6G four-axle dump truck; the vehicle model can be simulated in Ansys APDL using a moving mass model, with the body mass expressed in Mass21 units and the suspension system expressed in Combin14 with damping parameters and elastic mode parameters. The standard methods used for coupling the vehicle to the bridge in the model are the displacement coupling method, the birth-death cell method and the displacement contact method. The displacement coupling method is used more frequently, and in this paper, the displacement coupling method is used to realize the coupling between the vehicle and the bridge; after the finite element meshing is completed, contact pairs are established between the wheels and the bridge. The vehicle model in Ansys APDL is shown in Figure 2.