esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Plastid Genome, rrDNA, and Nuclear SNPs Assembly and Annotation  
We assembled the complete plastid genome sequences and the nrDNA sequences following the methods of [PERSON] et al. [90]. Map function of Geneious Prime v.2020 [91] and reference sequences (MG675223.1 and MH710901.1) were used to filter out the plastid and the nrDNA reads. Then, the De Novo Assemble function of Geneious Prime v.2020 [91] with a low sensitivity setting was applied to assemble the complete plastome and the nrDNA sequences. Then, we bridged gaps using 20 replicates of Fine Tuning in Geneious Prime v.2020 [91] when necessary. The assembled plastome sequences were annotated using Plastid Genome Annotator [92] and then manually checked the annotations by Geneious Prime v.2020 [91]. Illustrations of the new _Clematis_ plastome sequences were drawn by the Organellar Genome DRAW tool [93]. The nrDNA sequences were annotated using Geneious Prime v.2020 [91].  
In this study, SNPs sequences were also obtained from the genome skimming data. Detailed justification of the SNP data and its assembling pipelines have been presented in our recently published paper, which mainly discussed the genome-partitioning strategies and the methodological issues for the phylogenetic analysis of _Clematis_[60]. Firstly, we used one high-depth genome data of C. _breviaudata_ and the GATB process [94] using GATB-Minia ([[https://github.com/GATB/gatb-minia-pipeline](https://github.com/GATB/gatb-minia-pipeline)]([https://github.com/GATB/gatb-minia-pipeline](https://github.com/GATB/gatb-minia-pipeline)), accessed on 1 June 2022) to assemble a draft genome. Then, we used RepeatMasker v.4.0.9 [95] to remove repetitive and low-coverage regions from this draft genome. Then, we mapped five genome skimming data from _Clematis_ species (_C. leschenaultiana_, _C. repens_, _C. songorica_, _C. ribetana_, _C. viridis_) to the draft genome using Geneious Prime v.2020 [91], used a script \"low_seq_del.py\" ([[https://github.com/Jhe1004/low_seq_del](https://github.com/Jhe1004/low_seq_del)]([https://github.com/Jhe1004/low_seq_del](https://github.com/Jhe1004/low_seq_del)), accessed on 1 June 2022) to remove the regions that none of the five samples matched, and obtained a _Clematis_ reference genome. Finally, all the genome skimming data were mapped to this reference genome to acquire the SNPs dataset. [PERSON] et al. [60] tested two pipelines (GATK and Geneious pipelines) for assembling the SNP sequences and showed that the Geneious pipeline generated a larger dataset and more robust phylogeny for _Clematis_ than the GATK pipeline. In this study, both methods were also tested, and the same results were obtained. So, we only discussed the results from the Geneious pipeline with a missing data threshold of 5% (Geneious-0.05 MS, percentage of gaps per alignment column) [96], which generated the most robust phylogeny in this paper.
## 4 Simulation and Analysis of Results
### Comparision with Other Algorithms  
Based on some sets that have large points in TSPLIB, we can obtain the following by experiment. Table 5 is the results of each heuristic and bionic algorithm under different data sets. From the experimental results in Figure 14, the TPSMA results are obviously better than GA and PSO, and similar to ACO. Since the initial pheromone distribution of ACO is unpredictable, the reasonable distribution of pheromones needs to be gradually formed by iteration. Therefore, the convergence speed is very slow. Although TPSMA optimization results were slightly worse than ACO, TPSMA optimization speed was significantly better than ACO. Compared with the current best optimization result, which is from LKH, the result of TPSMA is not as good as LKH; however, it is closer to the optimal result than other algorithms. Moreover, LKH is in virtue of the \(5-opt\) principle which is based on the \(\lambda-opt\) algorithm. The more task points there are, the more iteration time it will cost, and the convergence performance will be poor.  
Figure 13: Analysis results of SMA and TPSMA under the four sets of data.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Name** & & **PSO** & **GA** & **ACO** & **LKH** & **TPSMA** \\ \hline \multirow{4}{*}{**Results of path length**} & **eil51** & 1257 & 519 & 453 & **426** & 464 \\  & **eil76** & 2040 & 727 & 583 & **538** & 620 \\  & **lin105** & 96,429 & 30,167 & 15,303 & **14,379** & 16,424 \\  & **bier127** & 542,558 & 196,276 & 128,147 & **118,282** & 129,390 \\  & **kroA200** & 290,368 & 87,786 & 33,471 & **29,368** & 34,972 \\  & **gil262** & 23,780 & 7769 & 2779 & **2378** & 2881 \\ \hline
**Algorithm Time Complexity** & \(N^{3}\) & \(N^{3}\) & \(N^{4}\) & \(N^{5}\) & \(N^{3}\) \\ \hline
**Theoretical difficulty** & Complexity & Simple & Complexity Complexity & **Simple** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparisons of optimization results and algorithm features.  
We can get the degree of difficulty about algorithm principle and the algorithm time complexity in Table 5. It can be seen that the TPSMA algorithm is simple in principle and easy to analyze. At the same time, the algorithm time complexity of TPSMA (\(N^{3}\)) is less \(1/N\) times than that of ACO (\(N^{4}\)), and less \(1/N^{2}\) times than that of LKH (\(N^{5}\)). TPSMA has fast convergence speed and short optimization time due to low algorithm time complexity, especially in large data points.  
According to the above analysis, it can be concluded from Figure 15 that TPSMA has good searching ability by a unique way, and fast optimization speed by low time complexity. At the same time, TPSMA is simple in theory and easy to research, therefore it is better to study than the others. Furthermore, the research time of TPSMA is short and the algorithm is not mature enough--there are many performances and potential to be developed due to its unique optimization method and effectiveness.  
Figure 14: Comparison of optimization results under TSPLIB data sets.  
Figure 15: Comparison and analysis of algorithm characteristics.
# An Analysis of Rock Bolt Dynamic Responses to Evaluate the Anchoring Degree of Fixation
## 2 Materials and Methods
### The Physics of the Problem  
Structural analyses can predict how components will behave under loading, vibration, and other physical effects. This helps with the design of robust mechanical components by validating designs using simulations and reducing the need for physical testing. The structural model adopted herein allowed us to perform a linear static analysis, a transient analysis, and modal and frequency response analyses. A typical programmatic workflow for solving a structural problem includes the following steps:  
* Assign the structural properties of the material, including Young's modulus, Poisson's ratio, and the mass density.
* Specify a damping model and its values for a dynamic problem.
* Specify boundary loads and constraints.
* Specify the initial displacement and input for a dynamic problem.
* Solve the problem and plot results such as the displacement, velocity, acceleration, stress, strain, von Mises stress, principal stress and strain, and frequency response.  
The structural properties of the material were assigned according to the following typical values listed in the literature:  
_Steel rod:_  
* Young's modulus: 200 GPa;
* Poisson's ratio: 0.3;
* Density: 7850 kg/m\({}^{3}\);
* Damping factor: 0.1.  
_Resin (good quality)_:  
* Young's modulus: 5 GPa;
* Poisson's ratio: 0.3;
* Density: 2000 kg/m\({}^{3}\);
* Damping factor: 0.03.
## 2 Materials and Methods
### Preparation of Foamed Hydrophobic Magnesium oxychloride Cement Test Block  
In this study, the initial raw material ratio of magnesium oxychloride cement was determined to be MgO:MgCl\({}_{2}\):H\({}_{2}\)O = 6.5:1:13 (molar ratio) [20], and MgO was calculated as \(\alpha\)-MgO. Firstly, we pre-hydrolyzed the organosilane in magnesium chloride solution, mixed magnesium chloride hexahydrate and distilled water at a molar ratio of MgCl\({}_{2}\):H\({}_{2}\)O = 1:13, then added H\({}_{2}\)O\({}_{2}\), FAS, and TEOS; the amount of H\({}_{2}\)O\({}_{2}\) added was 4% to 12% of the mass of the magnesium oxide powder. Then, FAS and TEOS were added, and the amounts of FAS were 0.5%, 1%, 1.5%, 2%, and 2.5% of the mass of magnesium  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Components** & **MgO** & **SiO\({}_{2}\)** & **CaO** & **Fe\({}_{2}\)O\({}_{3}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Loss on Ignition** \\ \hline Mass fraction: \% & 84.27 & 6.25 & 1.68 & 0.28 & 0.57 & 6.95 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The chemical compositions of the magnesia powder.  
oxide powder. TEOS was added according to the molar ratio of FAS and TEOS with n: m (n = 0.5, 1, 1.5, 2, 2.5, m = 6, 8, 10, 12, 14). The mixed solution was stirred at 60 \({}^{\circ}\)C for 8 h to obtain the treated MgCl\({}_{2}\) solution.  
The H\({}_{3}\)PO\({}_{4}\) was then mixed with the treated MgCl\({}_{2}\) solution. The amount of H\({}_{3}\)PO\({}_{4}\) added was 1% of the total MgO. This was mixed with magnesium oxide powder and stirred well to form magnesium oxychloride cement slurry. The slurry was cast in steel mold of \(20\times 20\times 20\) mm and \(40\times 40\times 160\) mm, vibrated, compacted, and cured at room temperature for 1 d. It was then demolded and air-cured. After curing for 1-28 d, the hydrophobic foamed magnesium oxychloride cement represented by F-MOC was finally obtained. N-MOC means that the blank control foamed magnesium oxychloride cement was added with H\({}_{2}\)O\({}_{2}\), but FAS and TEOS were not added.
## 2 Related Work
### Context Information Extraction  
In view of the semantic segmentation task, exploring the context information can make semantic segmentation results more accurate. ASPP [9] utilized the convolutions of different receptive fields to capture context information. DenseASPP [10] is further improved on the basis of the ASPP network, which introduced dense connections into ASPP to generate multi-scale features to retain more semantic information. [PERSON] et al. [11] proposed a PSPNet network that integrated multi-scale features with a pyramid pooling module to realize the capture of global context information. ParseNet [19] used global pooling to compute global features and enhanced the feature representation of each pixel. [PERSON] et al. [20] proposed the Adaptive Pyramid Context network (APCNet) for semantic segmentation, which is composed of multiple ACM blocks; each ACM calculates the context vector of each region by using the local affinity of global guidance. OCRNet [21] transforms the pixel classification problem into an object area classification problem, mainly extracting rich semantic context information by enhancing object information. Despite their success, a limitation of these networks is that they do not perform well in learning the global context and long-range spatial dependencies, which may lead to inconsistent segmentation inside large objects or inferior results for small categories. Thus, in this paper, we propose the GTM to obtain global context information via the self-attention mechanism.
The remainder of this article is organized as follows: Section 2 depicts the foundations and background of this work, namely the physical layer of the IEEE 802.11p amendment, details of the vehicular channel model used in this work, and the ELM neural network subject to supervised and SS learning. Section 3 carefully presents the novel and improved SS-ELM algorithm used to estimate and equalize the communication channel of an IEEE 802.11p-based V2V system. Section 4 depicts the evaluation scenario, describes the optimization process of the SS-ELM parameters, and compares the BER and execution time of the proposed scheme with other state-of-the art and traditional IEEE 802.11 channel estimation techniques. Finally, Section 5 contains concluding remarks.
## 4 Experiments  
We collected adverse-weather-affected traffic data from Weibo, preprocessed it as discussed in Section 3, and finally obtained a total of 128,815 tweets from 1 January to 1 August 2017 as the dataset.  
Figure 3: Warning level distribution of 3200 samples.  
The dataset was separated into training data, validation data, and test data. The details are shown in Table 4. For the city selection, we selected the 31 capitals of each province, autonomous region, and municipality in mainland China as monitoring cities. Furthermore, to verify whether the traffic incidents in social media reflected the real-world traffic situation, we also collected 15,130 news items from 1 January to 1 August 2017, which is discussed below.  
In the following experiments, we first tried six types of regression models to verify whether there are relationships between adverse weather and traffic incidents in social media; we also selected a model for city-level warning according to the model evaluation results. Second, we mapped and visualized the warning levels of 200 traffic incidents. Finally, we verified the relationship of traffic incidents in social media to real-world traffic incidents to further prove that our proposed city-level traffic situation awareness and alerting model can be deployed in both cyberspace and physical space.
# Building Information Modelling Facility Management (BIM-FM)
## 6 Barriers Affecting the Implementation of BIM-FM
### The FM Practitioner View  
As evidenced by [PERSON] et al. (2018), FM practitioners generally agree that the lack of FM involvement at early stages of BIM planning and a lack of interoperability between stages negatively affect BIM adoption. This statement is echoed by [PERSON] and [PERSON] (2018), who initially claimed that the problems of BIM-FM (in their case specifically, HBIM) can be overcome with \"well-trained BIM knowledge as well as concept and processes\". This sentiment was subsequently found to be an oversimplification when [PERSON] and [PERSON] (2018) later interviewed a range of (established) conservators and facilities' managers and identified additional barriers including, but not limited to the following:  
* Difficulties collecting data for older buildings;
* Time related to creating a BIM;
* Expenses related to servers and licensing.  
Whilst [PERSON] and [PERSON] (2018) only interviewed five experts, four of the five experts had over ten years' experience in the construction industry indicating a high level of reliability to the data. [PERSON] and [PERSON] (2018) suggest that the standards (COBie) and processes are well-developed, however there are systemic issues associated with how the industry actually operates (referring mainly to new build construction), which prevent the application of BIM-FM. Specifically, there is a disparity between the client and industry's understanding of their management needs ([PERSON] et al., 2018).  
The Architecture, Engineering and Construction (AEC), FM and BIM sectors have fairly well-established working practices, however with minimal commonality. This itself is a barrier, evidenced by [PERSON] et al. (2018), who found that information storage and management techniques during construction do not translate to the operation stage and thus result in incomplete data sets. This disparity may, arguably, have resulted from the lack of FM involvement at the planning stage, as discussed by [PERSON] et al. (2018), in addition to an overall industry resistance to change ([PERSON] et al., 2018).  
To avoid another over-simplification of the issues affecting BIM-FM, the subsequent sections will elaborate on some of the key barriers the authors believe most accurately limit BIM-FM implementation:  
* A lack of data exchange formats;
* A lack of alignment with FM;
* A lack of standardisation in BIM-FM;
* A lack of data retention.
# Support Vector Machine-Based EMG Signal Classification Techniques: A Review
## Appendix A Common Features
### Frequency Domain
* Frequency Median Density (FMD) FMD splits PSD into two equal parts and is defined as \[\sum_{l=1}^{\text{MDF}}{P_{l}}=\sum_{l=\text{MDF}}^{M}{P_{l}}=\frac{1}{2}\sum _{l=1}^{M}{P_{l}},\]3 (102)
* Frequency Mean Density (FMN) FMN is the frequency mean of the PSD and is described by \[\sum_{l=1}^{\text{MDF}}{P_{l}}=\sum_{l=\text{MDF}}^{M}{P_{l}}=\frac{1}{2}\sum _{l=1}^{M}{P_{l}},\]4 (103)
* Cepstrum Coefficients (CC) CC is the inverse of Fourier transform of the logarithmic power spectrum of a signal. Low-order coefficients are often used in speech recognition classification [35]. If \(F_{l}^{k}(p)\) for \((k=0,\ldots,N-1)\) is defined as Fourier transform, when the sample measured \(n\)th from the signal \(x_{l}(n)\), from the source \(l\)th, the Fourier transform is \[\sum_{l=1}^{\text{MDF}}{P_{l}}=\sum_{l=\text{MDF}}^{M}{P_{l}}=\frac{1}{2}\sum _{l=1}^{M}{P_{l}},\]5 (104) Then, W, being a CC characteristic parameter, is defined as \[\sum_{l=1}^{\text{MDF}}{P_{l}}=\sum_{l=\text{MDF}}^{M}{P_{l}}=\frac{1}{2}\sum _{l=1}^{M}{P_{l}},\]6 where \(n=0,1,\ldots,W-1\). **Appendix A.1.2**: Time-Frequency Domain (TFD)  
* Short Time Fourier Transform (STFT)STFT corresponds to the fast Fourier transform, which mathematically can be expressed as follows: \[\sum_{l=1}^{\text{MDF}}{P_{l}}=\sum_{l=\text{MDF}}^{M}{P_{l}}=\frac{1}{2}\sum _{l=1}^{M}{P_{l}},\]7 (10) with \(g\) as window function, \(k\) as time sample and \(m\) as frequency bins. * Continuous Wavelet Transform (CWT) CWT is the wavelet transform of a continuous signal. \[\sum_{l=1}^{\text{MDF}}{P_{l}}=\sum_{l=\text{MDF}}^{M}{P_{l}}=\frac{1}{2}\sum _{l=1}^{M}{P_{l}},\]8 (11) with \(t\) as translation parameter, \(a\) as scale parameter and \(\psi\) as mother wavelet function. * Discrete Wavelet Transform (DWT) DWT divides the signal into an approximation and detail coefficient by passing through low and high as complementary filters. The approximation coefficients are divided into second-level approximation detail coefficients. By repeating the process, a signal is divided into many components of lower resolution. * Wavelet Packet Transform (WPT) WPT is a DWT generalized version that applies to both low-pass results (approximations) and high-pass results (details). * Stationary Wavelet Transform (SWT) SWT does not decimate the signal in each stage, avoiding non-linear distortion problem of DWT and WPT. [PERSON] et al. [58] pointed out that Wavelet transforms allow focusing on specific frequency bands because of their use of subsets of coefficients, which could provide better robustness to the system, as in comparison with TD and DF features. Additionally, the TFD features generate a high dimension feature vector; therefore, dimensionality reduction is usually necessary so that, when using this vector, the speed can be increased.
# Bioaugmentation Treatment of a PAH-Polluted Soil in a Slurry Bioreactor
## 3 Results and Discussion
### Evolution of Bacterial Quantity and Diversity during the Bioslurry Treatments
_Pseudomonas_ and _Rhodococcus_ have also been described as integrating bacterial consortia for PAH degradation [59]. Notably, most of the isolated bacteria appeared in at least two of the three experimental conditions, thereby suggesting that the associated environmental changes, if any, do not significantly affect bacterial populations. Interestingly, _R. erythropolis_, which was added to Treatment B, was found only in this reactor, while _P. stutzeri_, added to Treatment C, was present in all three experiments. Therefore, it appears that the persistence of _R. erythropolis_ under the experimental conditions used was lower than that of _P. stutzeri_. This remark is consistent with the distinct degrading capacities of the two bacteria. In this regard, while _R. erythropolis_ showed a greater capacity to reduce lighter PAHs at 15 days (Table 3), _P. stutzeri_ showed much greater efficiency at shorter times and a greater reduction of all PAHs at 15 days (Table 3 and Figure 3). In turn, the DGGE fingerprint analysis (Figure 6) of the microbial community during the treatments confirmed the persistence of many bands (that is, the maintenance of bacterial populations, as previously discussed). However, variations in the intensity of the bands were also appreciated. The analysis of the isolated _R. erythropolis_ and _P. stutzeri_ strains used for bioaugmentation supports the persistence of the latter in the three experimental conditions tested, as also shown by the culture experiments. Detailed analysis of the microbial activities using omic techniques, similar to those already used in the full-scale biopile treatment performed at the same site [25], will provide additional useful information for the rational selection of the most suitable bacteria for bioaugmentation purposes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Phylogenetic** & **Similarity** & **Identified Bacteria** & \multicolumn{2}{c}{**Experiments**} \\ \cline{2-5}
**Affiliation** & **\%** & & **A** & **B** & **C** \\ \hline Actinobacteria & 98 & _Microbacterium_ & & & \\ Sphingobacteria & 100 & _Olivibacter soli_ & & & \\ Gammaproteobacteriaobacteria & 98 & _Pseudomonas chlororaphis_ & & & \\ Betaproteobacteria & 98 & _Diaphorobacter_ & & & \\ Gammaproteobacteria & 99 & _Pseudoxanthomonas_ sp. & & \\ Gammaproteobacteriaobacteria & 99 & _Pseudomonas_ sp. & & \\ Gammaproteobacteria & 99 & _Pseudomonas stutzeri_ & & \\ Actinobacteria & 98 & _Rhodococcus erythropolis_ & & \\ Alphaproteobacteria & 99 & _Rhizobium_ sp. & & \\ Gammaproteobacteria & 99 & _Pseudomonas alcaligenes_ & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Identification and phylogenetic assignation (RFLP, Restriction Fragment Lenght Polymorfism) of culturable bacteria isolated from the three bioslurry experiments. Gray fills indicate the presence of the bacteria in the corresponding experiment.
## 2 Mechanism of Low-Frequency Pressure Fluctuation  
The origin of low-frequency pressure fluctuations in open-jet wind tunnels is not yet fully understood. The low-frequency pressure fluctuations involve several factors, including the structural modes of the wind tunnel, instability of the jet shear layer, fluid-structure interaction, vortex-acoustic interaction, and acoustic propagation. The phenomenon of low-frequency pressure fluctuations in different wind tunnels often involves different excitation mechanisms, which poses challenges to the theoretical prediction and design of control methods [5; 8; 16; 26; 28; 29; 30; 31; 32].  
The excitation source and the resonance mechanism are the two primary factors responsible for the generation of significant low-frequency pressure fluctuations in open-jet wind tunnels. The excitation source derives energy from the wind tunnel airflow and generates the pulsations, while the excitation mechanism amplifies certain pulsation modes into significant low-frequency pressure fluctuations.
## 5 Verification and Application
### Discussion
Among the methods that identify the satisfaction level as high, the discounted PHFET method performs the best, achieving the highest belief of 0.8705 and demonstrating superior convergence performance by quickly converging to 1. Additionally, compared to the PHFET method without discounting, which assigned the belief mass of \(v_{1}\) to 0.7028, and only uses one of the credibility and reliability discounting factors, which results in belief masses of 0.7103 and 0.7105, respectively, the PHFET method with both discounting factors allocates a higher belief degree to the target concentration. Thus, the effectiveness and superiority of the uncertainty and conflict-based discounting strategy of PHFET is demonstrated.