esa-sceva/satcom-chunk-collection huggingface dataset:
# Conserved Plastid Genomes of _Pourthiaea_ Trees: Comparative Analyses and Phylogenetic Relationship
## 3 Results
### SSRs and Long Repeats Analyses  
A total of 1345 SSRs were excavated in the 13 _Pourthiaae_ plastomes, with distributions ranging from 100 to 107 SSRs per genome (Figure 3A; Table S3). The greatest number of the SSRs were mononucleotide repeats (1016), followed by dinucleotide (234), tetranucleotide (80), pentanucleotide (11), trinucleotide (3) and hexanucleotide (1) repeats. The only hexanucleotide motif is AAATAT/ATTTT in _P. pustulata_ (Figure 3B). The greatest number of the mononucleotide SSRs were A/T (979, 96.36%) motifs, the dinucleotide SSRs were AT/AT (221, 94.44%) motifs, the trinucleotide SSRs were AAT/ATT (2, 66.67%) motifs, and all the tetranucleotide SSRs were AAAT/ATT (80) motifs (Figure 3B). The AAAAT/ATTT motif accounts for 9.09% (1) of the pentanucleotide SSRs (Figure 3B). Therefore, our results indicated that SSRs in plastomes usually consisted of polythymine (polyT) or polyadenine (polyA) repeats. Further analysis showed that 78.44% of the SSRs were located in the LSC region, 15.17% in the SSC region, and 6.39% in the IR region.  
Figure 1: Platosome map of three _Pourthiaae_ species. Genes located inside the circle are transcribed counterclockwise, and genes outside the circle are transcribed clockwise. The colored bars represent different functional groups of genes.  
For long repeats, the repeat number of each species ranged from 60 (_P. amphibaxa_, _P. arguta_, and _P. bliniu_) to 132 (_P. pilosicalyx_) (Figure 4A; Table S4). Forward repeats are the richest types (516), followed by palindromic (278), reverse (116), and complementary (82) repeats (Figure 4A). The long repeats were recorded mostly in sequence lengths of 30-40 bp (933) and 40-50 bp (61) (Figure 4B). _P. amphibaxa_ and _P. pustulata_ plastomes had one long repeat with a sequence length over 60 bp, while long repeats ranging from 50 to 60 bp were found in _P. beauverdiana_ (1), _P. parvifolia_ (1), _P. villosa_ (1), _P. amphibaxa_ (1), _P. hirsute_ (1), _P. impressivena_ (1), and _P. pilosicalyx_ (1) plastomes (Figure 4B).  
Figure 2: Comparison of IR boundaries among 13 _Pourthiaae_ plastomes.  
Figure 4: Long repeats in the 13 _Pourthiaea_ plastomes. (**A**) Number of different repeat types. (**B**) Number of different repeat lengths. Forward (F), palindromic (P), reverse (R), and complementary (C) repeats.  
Figure 3: Simple sequence repeats (SSRs) in the 13 _Pourthiaea_ plastomes. (**A**) Number of SSRs. (**B**) Number of SSR motifs.
# Removal of Nitrate Ions Using Thermally and Chemically Modified Bioadsorbents
## 2 Materials and Methods
### Methods
#### 2.2.2 Adsorption Tests  
For the evaluation of the adsorption capacity of yarn skin modified with NH4 Cl as an adsorbent of nitrate ions present in aqueous solution, a multilevel factorial experiment design developed in Statgraphics Centurion XVI.II was followed. Particle size (0.355, 0.5 and 1 mm) and pH (2, 4, 6, 8, 10, 12) were considered independent variables and adsorption capacity as the response for a total of 18 experiments. The effect of the pH of the solution, at the selected levels, was evaluated on the adsorption capacity of the synthesized carbon and a commercial carbon, resulting in 12 experiments.  
Adsorption tests were performed in an orbital shaker Lab-Shaker Incubator model IN-666, at 25 \({}^{\circ}\)C, shaking at 250 rpm for 24 h. For this purpose, 0.5 g of adsorbent was placed in contact with 50 mL of sodium nitrate solution with a concentration of 218 mg/L, adjusting the pH of the solutions to the values established in the design of the experiments [34]. The change in nitrate concentration was determined by UV-Vis spectroscopy at 543 nm [35]. The percentage of removal (RP) and the adsorption capacity (\(q_{e}\)) were calculated according to Equations (1) and (2), where \(C_{o}\) (mg L\({}^{-1}\)) is the initial concentration, \(C_{e}\) (mg L\({}^{-1}\)) is the final concentration, \(V\) (L) is the volume of the solution and \(w\) (g) is the mass of adsorbent [24].  
\[RP(\%)100\frac{C_{o}-C_{e}}{C_{o}} \tag{1}\]  
\[q_{e}\left(\frac{mg}{g}\right)=\frac{(C_{o}-C_{e})V}{w} \tag{2}\]
## 2 Numerical Analysis  
### Governing Equation  
To resolve the incompressible flow, we must estimate the flow structure for a wide range of scales. The DNS method can resolve a wide range of scales, from large ones to the Kolmogorov, which is the smallest scale. However, until now, its computational cost has been too high for it to be used as a design tool. LESs resolve only a large scale of motion, and the effect of smaller scales is modeled by the sub-grid scale (SGS) modeling.  
In LESs, the governing equations for a large eddy are applied after a spatial filtering operation. The spatial filtering operation is given by:  
\[\overline{f}(x)=\int G(x-x^{\prime})f(x^{\prime})dx^{\prime} \tag{1}\]  
where \(f\) is a turbulent field and \(G\) is a spatial filter.  
The continuity and momentum equations for incompressible flows are obtained by filtering the Navier-Stokes equations. The filtered Navier-Stokes equations are given by:  
\[\frac{\partial\overline{u}_{i}}{\partial x_{i}}=0 \tag{2}\]  
\[\frac{\partial\overline{u}_{i}}{\partial t}+\overline{u}_{j}\frac{\partial \overline{u}_{i}}{\partial x_{j}}=-\frac{1}{\rho}\frac{\partial\overline{p}}{ \partial x_{i}}+2\
u\frac{\partial}{\partial x_{j}}\overline{S}_{ij}-\frac{ \partial\tau_{ij}^{r}}{\partial x_{j}} \tag{3}\]  
where \(\tau_{ij}^{r}\) is the residual stress tensor. \(\tau_{ij}^{r}\) is given by:  
\[\tau_{ij}^{r}=\overline{u_{i}u_{j}}-\overline{u}_{i}\overline{u}_{j} \tag{4}\]  
In Equation (4), the residual stress tensor is unknown and should be modeled. The eddy viscosity Boussinesq hypothesis is used for modeling the residual stress tensor as follows:  
\[\tau_{ij}^{r}=-2\mu_{i}\overline{S}_{ij}+\frac{1}{3}\tau_{kk}\delta_{ij} \tag{5}\]  
where \(\mu_{i}\) is the turbulent viscosity of the sub-grid scale. The isotropic part of \(\delta_{ij}\) is added to the filtered static pressure term without being modeled. In Equation (5), \(\overline{S}_{ij}\) is the filtered rate of the strain tensor and is defined by:  
\[\overline{S}_{ij}=\frac{1}{2}\left(\frac{\partial\overline{u}_{i}}{\partial x _{j}}+\frac{\partial\overline{u}_{j}}{\partial x_{i}}\right) \tag{6}\]  
The Smagorinsky-Lilly model was used as a subgrid-scale model. In this study, the Smagorinsky constant \(C_{s}\) was set to 0.065 ([PERSON] and [PERSON] [35]).
# Resilience Analysis of Traffic Network under Emergencies: A Case Study of Bus Transit Network
## 3 Methodology
### Transport Network Resilience Analysis Model Based on Complex Network
\begin{table}
\begin{tabular}{c c} \hline \hline
**Mathematical Symbols** & **Meaning** \\ \hline \(G=\{V,E\}\) & Scaleless weighted network of public transport network \\ \(V\) & Collection of nodes \\ \(E\) & Set of edges \\ \(n\) & Number of nodes \\ \(m\) & Number of edges \\ \(v_{i}\)\(i=1\), \(2\), \(\ldots\), \(n\) & Node \(i\) \\ \(e_{l}\)\(l=1\), \(2\), \(\ldots\), \(m\) & Edge \(l\) \\ \(k_{i}\) & Node degree \\ \(e_{ij}\in E\) & Edge of \(v_{i}\) and \(v_{j}\) \\ \(w\big{(}e_{ij}\big{)}\) & Weight of \(e_{ij,}\) is the normalized line resilience value \\ \(d_{ij}\) & Distance of \(v_{i}\) and \(v_{j}\) \\ \(N_{i}\) & The set of adjacent points of \(v_{i}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mathematical symbol definition. Figure 10: Public transit network resilience analysis model. According to this study's definition of bus network resilience based on network recovery capacity and the characteristics of weighted scale-free networks, the resilience of a bus network is defined as follows:  
\[R_{total}=(\frac{\sum_{i=1}^{n}C(v_{i})}{n}+\frac{\sum_{l=1}^{m}B(e_{l})}{m})/2 \tag{22}\]  
where \(C(v_{i})\) is the normalized value of the weighted value \({C_{C}}^{\prime}(v_{i})\) based on the closeness centrality of the \(v_{i}\) node, which can be considered as the resilience value of the node. The normalization formula is:  
\[C(v_{i})=\frac{{C_{C}}^{{}^{\prime}}(v_{i})-{C_{C}}^{{}^{\prime}}(v_{i})_{min}} {{C_{C}}^{{}^{\prime}}(v_{i})_{max}-{C_{C}}^{{}^{\prime}}(v_{i})_{min}} \tag{23}\]  
\[{C_{C}}^{{}^{\prime}}(v_{i}):\]  
\[{C_{C}}^{{}^{\prime}}(v_{i})=\sum_{i=1,j\
eq i}^{n}\frac{S_{i}(n-1)}{d_{ij} \times k_{i}} \tag{24}\]  
\[S_{i}=\sum_{j\in N_{i}}w(e_{ij}) \tag{25}\]  
The weight used in \({C_{C}}^{\prime}(v_{i})\) is the unit weight of the node, i.e., \(S_{i}/k_{i}\), where the unit weight of a node is the sum of the weights of edges connected to the node divided by its degree, which reflects the weight of edges around the node. Referring to the concept of closeness centrality, the average distance between each node and other nodes is calculated, and for a given node, the higher the closeness centrality, the more central it is in the network.
## 5 Simulation Model and Results
### Performance at Varying Bit Error Rate (BER)
#### 5.5.1 C-HARQ _versus_ BER of Individual Links  
Figure 9 shows the impact of BER variations of individual links on the throughput efficiency and energy efficiency. The BER of a link is individually varied by varying the value of \(\alpha\) over the range shown in figure, while the others are kept constant at their original BER values. Additionally, the impact of an overall BER variation in the network is also obtained by multiplying \(\alpha\) to all the links simultaneously.  
Figure 8: Network settings for BER analysis.  
Figure 9: (**a**) Throughput efficiency and (**b**) Energy efficiency dependence on BER of different links, for C-HARQ, for data payload size = 6,000 bits, \(Nmx=2\) and \(nCN=5\).  
The results reveal that the performance of C-HARQ depends mainly on the link conditions of S-D and S-CN channels. A decrease in _BER_S-D_ causes a rapid performance improvement, however an increase in BER does not degrade the performance rapidly because of the error correction gains provided by convolution coding. The degradation in performance is mainly caused by the increase in _BER_S-CN_ as these channels determine the availability of _CNodes_ which can provide the required error correction information. Lastly, the CN-D link does not have any major impact because it is involved in only transferring the smaller FEC packets. Hence, it can be easily inferred that the performance of C-HARQ mainly depends on the links that are involved in transferring the data packet.
# Practical 3-D Beam Pattern Based Channel Modeling for Multi-Polarized Massive MIMO Systems +
## 4 Received Spatial Correlation
\right) \tag{53}\]  
\[h_{lk,Pk^{\prime},n}^{V/H}(t+\Delta t,\tau)=\left(\begin{array}{l}\delta(n-1) \sqrt{\frac{\mathbb{C}}{\mathbb{C}+\uparrow}}D_{DOS_{i},lk}^{BS}(t+\Delta t) \times\bar{a}(\phi,\theta)_{lk}\times \begin{bmatrix}\exp(j\Phi_{ LOS}^{p,D})&0\\ 0&\exp(j\Phi_{LOS}^{h,h})\end{bmatrix} \times D_{DOS,i,Pk^{\prime}}^{UE}(t+ \Delta t)\\ \times\exp\left(j\mathfrak{s}_{Pk^{\prime}}(l^{\prime}k^{\prime}-1).A_{V/H}^{UE }\parallel v\parallel\sin(\theta)_{AAA}\right)+\sqrt{\frac{A_{v}}{\mathbb{C}+ \uparrow}}\sum_{n=1}^{N}D_{n,i,k}^{BS}(t+\Delta t)\times\bar{a}(\phi,\theta)_{lk} \times\\ \begin{bmatrix}\exp(j\Phi_{n}^{(v,D)})&\sqrt{\lambda^{k}}\exp(j\Phi_{n}^{(v,h)} )\\ \sqrt{\lambda^{k}}\exp(j\Phi
# Re-Understanding the Sedimentary Environment of the Wufeng-Longmaxi Shales in the Sichuan Basin
## 3 The Sedimentary Environment of the WLS in the Sichuan Basin
### Relationship Between Shale Development and Surrounding Ancient Continents  
The development of marine shales in the Wufeng-Longmaxi Formation of Middle and Upper Yangtze is closely associated with ancient landmasses. From the Ordovician to the Silurian periods, the middle and upper Yangtze region experienced a gradual transition characterized by shallow waters and a diminishing sea area. During the Early to Middle Ordovician, this region was situated at a considerable distance from the ancient Songpan land to the northwest and the ancient Kangdian land to the west, resulting in a minimal terrigenous source supply. Consequently, a carbonate platform sedimentary structure developed in this area. Its lithology is characterized by interbedded deposits of calcareous sandstone, calcareous siltstone, sandy mudstone, thin limestone, and mudstone, as noted by [PERSON] et al. (2015) [63].  
During the Wufeng-Longmaxi period, the middle and upper Yangtze region was characterized by numerous ancient lands, shoals, islands, and underwater low rises, forming a restricted sea area [15; 64; 65; 66]. As illustrated in Figure 5, these features are represented by the brown, yellow, and light green blocks. The Jiangjin-Chongqing-Huaying Mountain block, depicted in Figure 5, extends from Dongshen 1 to Zao 3 to Huaying Mountain and is primarily characterized by an internal gentle slope during the deposition period from the Cross Pu Formation to the Linxiang Formation, during which thick carbonate rocks developed. Following the onset of the Wufeng stage, the deposition thickness decreased somewhat. Throughout the Long period, the block continued to exhibit characteristics of an underwater low rise, resulting in the deposition of relatively thin graptolite-bearing shales [66]. This form of underwater low rise enabled the Weiyuan-Changning area in Southwest China to maintain an effective closure. An analysis of the discovered and operational shale gas fields, represented by the red areas in this figure, indicates that their distribution is limited. These fields do not correspond to shallow-deep water shelves but rather to more restricted shallow-water environments, such as lagoons and relatively closed bays. The peach star in Figure 5 denotes the salinity sampling point, and the sampling data further confirm that the entire Sichuan Basin represents a desalinated sea environment.
## 1 Introduction  
Fluoropolymers are the polymer materials containing fluorine atoms in their chemical structures. From general organic polymer concepts, there are two types of fluoropolymer materials, _i.e._ perfluoropolymers and partially fluorinated polymers. In the former case, all the hydrogen atoms in the analogous hydrocarbon polymer structures were replaced by fluorine atoms. In the latter case, there are both hydrogen and fluorine atoms in the polymer structures. The fluoropolymer industry discussed here is mainly concerned with the perfluoropolymers, although in some cases the partially fluorinated polymers are included. In the latter case, there are both hydrogen and fluorine atoms in the polymer structures, along with additional elements in selected cases, such as polyvinylidenefluoride (PVDF) and polychlorotrifluoroethylene (PCTFE).  
Fluoropolymers possess excellent properties such as outstanding chemical resistance, weather stability, low surface energy, low coefficient of friction, and low dielectric constant. These properties come from the special electronic structure of the fluorine atom, the stable carbon-fluorine covalent bonding, and the unique intramolecular and intermolecular interactions between the fluorinated polymer segments and the main chains.  
Due to their special chemical and physical properties, the fluoropolymers are widely applied in the chemical, electrical/electronic, construction, architectural, and automotive industries. The world consumption of fluoropolymers is growing tremendously. Worldwide sales of fluoropolymers in 2000 exceeded $2.0 billion compared with $1.5 billion in 1994. Even though the fluoropolymer industry was affected in the same way as other industries because of the economic downturn after 2008, there will be a turnaround along with the recovery of the world economy for fluoropolymer markets, especially in the motor vehicles, wire and cable, advanced batteries, fuel cells and photovoltaic modules.  
This review discusses the development of the most popular industrial fluoropolymers along with their chemical structures, basic properties, and general applications. For additional details, previously published books and journals would be helpful for monomer activities, polymerization methods, processing methods, polymer properties, and the applications [1; 2; 3; 4; 5; 6; 7; 8].
## 2 Literature Review  
### Smart Manufacturing System (SMS)  
In the 1980s, the basis of smart manufacturing was flexibility, computer-integration, and intelligent manufacturing. Japan established the intelligent manufacturing system (IMS) program in 1995 and has since led the world in this domain. In the US, non-profit ventures have conducted research on IMSs and related activities under the NGMSs program [3]. In the EU, studies on intelligent manufacturing were conducted under IMS program expansion [3; 17]. NIST defines SMSs as a fully integrated collaborative manufacturing system that responds in real-time to satisfy the changing demands and conditions in factories, supply networks, and customer needs [2; 3; 18]. Different definitions exist for SMSs depending on the perspective: engineering, interconnection and communication, and predictive analysis and decision-making perspectives; this is illustrated in Table 1.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Perspective of Definition** & **Description** \\ \hline Engineering view & Enable rapid manufacturing of products, dynamic response to demands, and real-time optimization [19]. \\ \hline Interconnection and communication (IoT and CPS) view & Increase in production rate and decrease in production waste using sensors and communication technologies to capture data at all manufacturing stages [20]. \\ \hline Predictive analysis and decision-making view & Optimize planning and control of manufacturing operations, predictive manufacturing, fault diagnosis, etc. [21]. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Other definitions of the smart manufacturing system (modified from [2]).  
Various studies have focused on the SMS architecture; representative architectures for SMSs include the Industry 4.0 architecture by IBM, service-oriented smart manufacturing system by NIST, and RAMI 4.0 by Platform Industrie 4.0, as listed in Table 2[22]. Industry 4.0 by IBM includes two layers: a platform/hybrid cloud layer and an equipment/device layer. The platform/hybrid cloud layer performs plant-wide data-processing and analytics, whereas the equipment/device layer acts as a middleman between the smart devices/tools and plant/enterprise. The service-oriented SMS by NIST is similar to Industry 4.0 by IBM[23]. The service-oriented SMS utilizes a service bus to connect various types of services, including those pertaining to the operational technology domain, information technology, and virtual domain. Furthermore, the service bus connects the enterprise to external collaborators. RAMI 4.0 consists of six layers: the business, functional, communication, integration, and asset layers. Each layer performs functions that define rules, integrate services, enable preprocessing with data analysis, provide event-generation with connectivity, and improve the physical performance of each component [24].
# Research on Dual-Emotion Feature Fusion and Performance Improvement in Rumor Detection
## 4 Experiments and Results
### Dataset and Model Parameter Settings  
The Weibo-16 dataset has become the benchmark dataset for Chinese rumor detection; each entry is labeled as a rumor or non-rumor [25]. The Weibo-16 dataset contains actual rumor and non-rumor information collected from the Weibo platform. After screening and labeling, these data have high authenticity and can reflect the actual situation of rumors spread on social media. As one of the largest social media platforms in China, Weibo has a wide user base, covering people of different ages, occupations, and regions. Therefore, the Weibo-16 dataset is highly representative in the field of rumor detection.  
In the original dataset, the subset of rumors has a higher repetition rate. Considering that redundant data affect the learning and evaluation effect of the trainer, this paper used a text similarity clustering algorithm to eliminate repeated rumors and comments. When splitting the training set and the test set, there is a need to take into account that content duplication can increase the risk of data breaches and make the model tend to learn the characteristics of a particular event (because they may be repeated multiple times during training), which limits the generalization of the model. Therefore, in this experiment, highly similar rumors and comments were filtered out, and a version of Weibo-16 (t) with the elimination of duplicate data was made. Table 2 divides the preprocessed dataset into the training set and test set in a ratio of 3:2.  
The Weibo-16 rumor text contains 1355 rumors and 2351 non-rumors. Among the 1,874,678 comments, real comments account for 43.1%, and fake comments account for 56.9%. The training set is used to train the model classifier, so the number of texts in the training sets is relatively large. After the model is trained, the test set is used to verify the accuracy of the model.  
The Chinese-specific affective resource corpus defines the dimension value of each affective feature, and the sub-feature dimensions of the dual affective features are determined from the corresponding affective dictionary, such as \(d_{f}\), \(d_{e}\), \(d_{s}\), and \(d_{a}\). Based on the existing experimental parameters used by Dr. [PERSON] (2018), the relevant data parameters are integrated into the experimental model and are listed in Table 3. \(d_{f}\) is the output of the pre-trained emotion classifier, with a value of 8; \(d_{e}\) is the size of the Chinese emotion category dimension: 21; the emotion score \(d_{s}\) is determined by How Net and has only 1 dimension; \(d_{a}\) represents the number dimension of features: 15; and the full dimension \(d\) is obtained by Equation (12) and has a dimension of 66. When extracting comment emotions, the extraction length is set to the first 100 comments; the output dimension of the multi-layer perceptron (MLP) is set to 32 when calculating the accuracy of the rumor.