esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization and Modelling of Potential Seaborne Disasters, in the ANA Region
## 3 Literature Review
### Bowtie Modelling  
The concept of bowtie modelling methodology is about risk of failures and how they can be prevented and mitigated through safety barriers. It can be considered as a further advancement to the Swiss Cheese Model (SCM) that originated by the work of [21], where safety barriers are represented as the slices of the cheese, and the holes in the cheese slices represent areas of weaknesses, or breaches, in each safety barrier. Then in a perfect storm situation, a hazardous situation occurs when these holes are aligned in such a way that one can imagine a ray of light that manages to poke through despite the existence of safety barriers (the slices). While the more barriers we have, the harder the system is to penetrate, the downfall to this is that the system becomes more opaque, complex, and costly to be managed. The main drawback of the SCM is that the relationships between different holes and between slices are not captured. The benefits of bowtie modelling are that there is a concise communication of risks, their impact, and the causal factors, as well as modelling the need for, or existence of, safety barriers.  
Bowtie Modelling has been applied to model resilience and assess risk, including those related to pandemics [4]. Briefly, the Bowtie Modelling process is named after its shape as a bowtie, where the knot constitutes the hazard, and the left side deals with causes of the threat and means of prevention, whereas the right side is related to consequences of the incident and means of mitigation against its impact, as will be described in detail later in Section 4.2.2.
## 2 Field Sampling Analysis of Roadway Rock Bolts
### Analysis of Deformation Characteristics of Roof Rock Bolts in 41072 Roadway of Changing Mine  
In order to analyze the deformation characteristics of the rock bolts on the roadway roof, six rock bolts were selected on site to analyze their deformation characteristics. The bolts in Figure 5 are directly removed during the process of roof brushing in the severely deformed area of the roadway. The deformation characteristics of roof bolt of roadway 41072 are shown in Figure 6. The 1\({}^{\#}\) and 6\({}^{\#}\) rock bolts are taken from the top corner positions. Obvious shear deformation occurs at the interface between the roof and the coal seam. The rock bolts in the fine sandstone section of the roof only bend within a range of 5 cm near the bedding plane. The rock bolts in the coal seam above the entire gangue are obviously bent, and the shear dislocation direction is towards the middle of the roadway. The lateral shear dislocation of the 1\({}^{\#}\) rock bolt reaches 16.2 cm, and the lateral shear dislocation of the 6\({}^{\#}\) rock bolt is 15.6 cm. No obvious deformation occurs in the rock bolt body below the gangue surface.  
Figure 4: Subsidence of Roadway Roof.  
Figure 5: Rock Bolts’ Removal Position.  
The 2\({}^{\#}\) and 5\({}^{\#}\) rock bolts are taken from the west two and east two positions (Figure 2). Obvious shear deformation occurs at the interface between the roof and the coal seam. The deformation form is similar to that of the top corner rock bolts. The lateral shear dislocations of the 2\({}^{\#}\) and 5\({}^{\#}\) rock bolts are 21.5 cm and 13.4 cm, respectively. Continuous bending deformation occurs in the rock bolt body below the gangue surface, and the bending angles are 10\({}^{\circ}\) and 7\({}^{\circ}\), respectively.  
The 3\({}^{\#}\) and 4\({}^{\#}\) rock bolts are taken from the middle of the roadway. Compared with the deformation of the rock bolts at other positions, the bending and shear deformation of the rock bolts in the middle is relatively small. Slight shear deformation occurs at the interface between the roof and the coal seam. The lateral dislocations are 3.7 cm and 3.3 cm, respectively. Bending deformation also occurs in the lower part of the rock bolt body below the gangue surface.
# DMFO-CD: A Discrete Meth-Flame Optimization Algorithm for Community Detection
## 5 Experimental Evaluation
### Statistical Analysis
#### 5.5.2 Wilcoxon Signed-Rank Test  
In order to verify the significant difference between DMFO-CD and other comparative algorithms, the Wilcoxon signed-rank test was employed [78]. This test is a non-parametric statistical test that requires two sets of random observations and two hypotheses. In this paper, the two sets of observations represent the obtained modularity values from each algorithm, i.e., the DMFO-CD algorithm and the compared one, over 30 runs on karate, dolphins, football, polbooks, all four webkb, adjnoun and 10 runs on email-Eu and dblp datasets. The null hypothesis (H\({}_{0}\)) was assumed that there is no significant difference between the mean values of the two observation sets (modularity values). While the alternative hypothesis (H\({}_{1}\)) is that there is a significant difference in the average values of the two sets. Table 6 presents the results of this test at significance level \(\alpha=0.05\). The  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Network** & **DPSO-PDM** & **GA-Net** & **GACD** & **EGACD** & **DECS** & **DMFO-CD** \\ \hline Karate & 2.60 & 1.97 & 5.00 & 5.00 & 1.43 & 5.00 \\ \hline Dolphins & 4.78 & 2.70 & 1.42 & 1.88 & 4.22 & 6.00 \\ \hline Football & 5.15 & 3.00 & 1.57 & 1.43 & 4.83 & 5.02 \\ \hline Polbooks & 5.00 & 1.00 & 2.60 & 3.40 & 3.00 & 6.00 \\ \hline WebKB-Cornell & 3.03 & 1.00 & 5.07 & 4.13 & 2.00 & 5.77 \\ \hline WebKB-Texas & 1.03 & 2.97 & 5.80 & 4.70 & 2.00 & 4.50 \\ \hline WebKB-Washington & 1.10 & 2.87 & 5.57 & 4.10 & 2.03 & 5.33 \\ \hline WebKB-Wisconsin & 2.10 & 1.13 & 5.07 & 4.07 & 2.77 & 5.87 \\ \hline AdjNoun & 2.83 & 2.00 & 4.97 & 4.23 & 1.17 & 5.80 \\ \hline
**Avg. Rank** & 3.07 & 2.07 & 4.12 & 3.66 & 2.61 & 5.48 \\ \hline
**Overall Rank** & 4 & 6 & 2 & 3 & 5 & 1 \\ \hline Email-Eu & 6.00 & 1.10 & 5.00 & 4.00 & 2.30 & 2.60 \\ \hline Dblp & 6.00 & 1.00 & 4.00 & 3.00 & 2.00 & 5.00 \\ \hline
**Avg. Rank** & 6 & 1.05 & 4.5 & 3.5 & 2.15 & 3.8 \\ \hline
**Overall Rank** & 1 & 6 & 2 & 4 & 5 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The Friedman test on modularity.  
\(p\) value refers to the significant difference between each pair of algorithms (DMFO-CD and one other algorithm). The considerable difference exists only if \(p\) value \(<\alpha\). Therefore, the results prove that the null hypothesis is rejected on most of the networks.
## 2 Related Works
### Matrix Factorization  
In image classification, machine learning methods commonly include sparse coding, low rank learning, and linear and nonlinear dimensionality reduction techniques. The optimization model in these methods can be considered a constrained matrix factorization problem, aiming to learn the corresponding coefficient matrix or projection matrix through matrix factorization. Our approach seeks to learn a low-dimensional feature representation as the input of multi-label learning, effectively overcoming the negative impact of high-dimensional data and noise on traditional multi-label classification methods. Given a data matrix \(X\in\mathbb{R}^{d\times n}\), where \(d\) is the feature dimension and \(n\) is the number of data samples, we perform the matrix factorization on \(X\). Formally, we derive that  
\[\min\Bigl{|}\Bigl{|}X-VU^{T}\Bigr{|}\Bigr{|}_{F^{\prime}}^{2},\ \ s.t.\ \ V^{T}V=I, \tag{1}\]  
where \(V\in\mathbb{R}^{d\times r}\) is the basis matrix and \(U\in\mathbb{R}^{n\times r}\) is the coefficient matrix, and it can be considered as the new representation of \(X\). The constraint \(V^{T}V=I\) can eliminate redundancy and irrelevant information.  
There are many image classification algorithms based on matrix factorization. For example, in [25], sparse coding was applied to natural image classification, and a spatial pyramid matching algorithm using sparse representation was proposed. The classification accuracy on Caltech-101 and Caltech-256 datasets shows that the spatial pyramid matching model based on sparse coding achieves good results. Ref. [26] expands the sparse representation theory and applies it to face recognition. Using training samples as a dictionary, test samples can be represented as linear combinations of base vectors in the dictionary, and further characterized by the degree of sparsity of the combination coefficients. That is, the linear combination coefficients of base vectors consistent with the test sample category tend to be non-zero, while the coefficients of base vectors belonging to other categories are zero. This method obtains sparse coefficients by minimizing the \(\ell_{1}\) norm, and uses the minimum reconstruction error to classify face data. However, Ref. [26] does not study dictionaries, and when training and testing samples are simultaneously contaminated with noise, the implementation effect of this method will deteriorate. [PERSON] [27] proposed an improved ELM-AE architecture that exploited low-rank matrix decomposition to learn optimal features. In this article, we introduce manifold structure regularization based on matrix factorization. By introducing manifold learning to maintain the manifold relationship between low order samples, we solve the problem of matrix factorization ignoring local structural features between samples, allowing us to obtain the most robust low rank representation. This robust low order representation could be utilized and applied to ultimately obtain better classifiers.
# Antibiotic Resistance in Seawater Samples from East Coast of Spain
## 2 Materials and Methods
### Detection of ARG  
The analysis for the presence of ARG was carried out both in direct water samples and in bacteria isolated from these waters. For the DNA extraction from both sample types, the GenE-luteTM Bacterial Genomic DNA Kit (Sigma-Aldrich, Madrid, Spain) ref. NA2110) was used according to the manufacturer's instructions. For the water samples, a volume of 250 mL was filtered through a 0.45 \(\upmu\)m pore size membrane (Millipore) to retain the bacterial cells. The membranes were aseptically cut and suspended in 500 \(\upmu\)L of lysis buffer included in the kit. Next, 80 mg of glass beads was added, and the mixture was shaken for 20 min at 3000 rpm (Disruptor Gene, USA Scientific, Ocala, FL, USA). For ARG detection from _E. coli_ and enterococci isolates, bacterial cells from an overnight culture were harvested and suspended in 500 \(\upmu\)L of TE buffer (10 mM Tris-HCl and 1 mM EDTA, pH 8), and then, DNA was extracted.  
The ARGs chosen for this study were _bla_TEM, _erm_B, _qnr_S, _sul_I, and _tetW_, which confer resistance to \(\beta\)-lactam antibiotics, macrolides, quinolones, sulfonamides, and tetracyclines, respectively. These genes are involved in a variety of resistance mechanisms. These include antibiotic efflux, antibiotic inactivation, antibiotic target replacement, antibiotic target modification and antibiotic target protection.  
The PCR mixture was prepared in a total volume of 50 \(\upmu\)L containing 5 \(\upmu\)L of the DNA template and 45 \(\upmu\)L of the reaction mixture including 1X PCR buffer (Sigma-Aldrich), 1.5 mM of MgCl\({}_{2}\) (Sigma, Aldrich), 0.2 mM of dNTPs, 0.25 \(\upmu\)M of each primer (TIB MOLBIOL, Madrid, Spain), and 2.5 U of Taq polymerase (Sigma, Aldrich). The primer sequences and cycling conditions for five PCRs are shown in Table 1. MiliQ water was used as a negative control; as positive controls for each gene, _E. coli_ isolates from our own collection tested in previous work were used. The PCR products were analyzed using gel electrophoresis on 1.2% agarose gel in 1 xTAE buffer (40 mM of Tris, 20 mM of acetic acid, 1 mM of EDTA) plus 5 \(\upmu\)L of RedSAfe (iNtRON Biotechnology, Ecogen, Madrid, Spain) per 100 mL. Ten \(\upmu\)L of PCR product, previously mixed with 6x loading buffer, was used. Two molecular weight markers were included in each gel (GeneRuler 100 bp DNA Ladder, 0.5 \(\upmu\)g/\(\upmu\)L, Thermo Fischer Scientific, Madrid, Spain). Finally, the fragments were visualized in a transilluminator under UV light.
# Bioassays with _Allium cepa_ for the Monitoring of Toxicity in the Groundwater of Yucatan, Mexico
## 2 Methods
### Biosassay  
For _A. cepa_ bioassay, 1 L of water was collected in amber glass bottles rinsed with deionized water and acetone. Root growth was initiated on 60 onions of similar sizes, and no leaf growth. To achieve this, the lower brown part of the bulb was removed to expose the root primordium, and it was put in contact with distilled water for 48 h until a root length of approximately 1 cm was observed. The best onions (five per treatment) with similar growth were selected, and roots were measured before exposure to evaluate growth. Roots were exposed to the water samples for 72 h. After 24 h, five root tips (approximately 5 mm long) were collected for microscopic study and preserved in 70% ethanol until analysis. After 72 h of exposure, roots were measured to estimate growth and were cut and frozen at \(-80^{\circ}\) until analysis of biomarkers of oxidative stress and B-esterase activities. Distilled water was used as a control [22; 23].
## 3 Theoretical Basis
Actually, the real floor stiffness is described by a symmetric 12 \(\times\) 12 matrix, \(\mathbf{\Delta}\mathbf{K}^{(BI)}\), characterized by 78 components, while the equivalent homogeneous orthotropic membrane is described by only 4 independent terms depending on 5 parameters. These terms are \(h\cdot E_{1}\), \(h\cdot E_{2}\), \(h\cdot G_{12}\), and \(\
u_{12}\), where \(E_{1}\) and \(E_{2}\) are the Young's moduli parallel and orthogonal to the RC joists' direction, respectively (Figure 7), \(G_{12}\) is the in-plane shear modulus, and \(\
u_{12}\) is the Poisson ratio. Since equality (27) cannot be exactly satisfied, the best thing we can do is to minimize the difference \(\mathbf{\Delta}\mathbf{F}^{(BI)}-\mathbf{\Delta}\mathbf{F}^{(BM)}\) between the forces at vertex nodes for some suitable displacement modes of the floor cell; that is, to minimize the quantity  
\[\varepsilon=\left\|\mathbf{\Delta}\mathbf{F}^{(BI)}-\mathbf{\Delta}\mathbf{F}^{(BM)} \right\|^{2}=\left[\mathbf{\Delta}\mathbf{K}^{(BI)}-\mathbf{\Delta}\mathbf{K}^{(BM)} \right]\mathbf{D}_{\mathbf{V}}\cdot\mathbf{D}_{\mathbf{V}}=\mathbf{E}\mathbf{D}_{\mathbf{V}}\cdot\mathbf{D}_{ \mathbf{V}} \tag{31}\]  
with respect to the free parameters \(h\cdot E_{1}\), \(h\cdot E_{2}\), \(h\cdot G_{12}\), and \(\
u_{12}\). With the influence of \(\
u_{12}\) on the in-plane behavior of the floor being initially neglected, the deformation patterns chosen to evaluate the equivalent membrane properties are those shown in Figure 9.  
Figure 9: Deformation patterns. The first deformation pattern is a pure extension along the joists' direction, the second one is a pure extension in the orthogonal direction, and the third one is a pure shear deformation. Then, \(\varepsilon\) can be quantified by the following sum  
\[\widetilde{\varepsilon}=\mathbf{E}\,\mathbf{D}^{(1)}.\mathbf{D}^{(1)}+\mathbf{E}\,\mathbf{D}^{(2)}. \mathbf{D}^{(2)}+\mathbf{E}\,\mathbf{D}^{(3)}.\mathbf{D}^{(3)} \tag{32}\]  
where \(\mathbf{D}^{(1)},\mathbf{D}^{(2)}\), and \(\mathbf{D}^{(3)}\) are the vectors of the generalized displacements of vertex nodes for the three deformation patterns shown in Figure 9.  
In practice, to minimize \(\widetilde{\varepsilon}\), the method based on the search of the equality between the vertex nodes' displacements obtained by the 2D and 3D models is adopted. In particular, when the vertex nodes' displacements under a deformation pattern are similar, within a tolerance, for the two models, the value of the equivalent membrane parameter associated with the considered deformation pattern accurately describes the behavior of the real floor under that pattern. The method described above is based on the classic homogenization approach of a microscopically inhomogeneous material.
## Appendix A Appendix A Appendix A.1 Data from the Coating Experiment [49]
 \tag{11}\]  
Delete \({x_{1}}^{2}\) and recalculate the equation. \[ \begin{split} 5.\ \mathrm{y_{Ra}}=-2.231-0.00125x_{1}+22.228x_{2}+0. 00395x_{3}+0.0590{x_{3}}^{2}+1.372x_{1}x_{3}\\ (0.181)\ (0.001)\ (0.979)\ (0.004)\ (0.020)\end{split}  \tag{12}\]  
Although the linear term \(x_{3}\) is insignificant (\(p>0.05\)), the quadratic term \({x_{3}}^{2}\) and interaction term \(x_{1}x_{3}\) are significant in the model. The linear term \(x_{3}\) is hierarchically retained in the equation. Delete \(x_{1}\) and recalculate the equation. \[ \begin{split} 6.\ \mathrm{y_{Ra}}=-2.714+22.288x_{2}+0.000289x_{3}+0. 0583{x_{3}}^{2}+1.372x_{1}x_{2}\\ (<\)0.001) (0.0199) (0.005) (0.003)\\ R^{2}=0.958,\ \mathrm{R^{2}}_{\mathrm{adj}}=0.942,\ \mathrm{s}=0.195,\ \mathrm{PRES}=1.013.\end{split}  \tag{13}\]  
The normality and constant variance tests are passed. One suspicious data point is found, point 7 (\(\mathrm{t_{i}}=2.350\), \(\mathrm{DFFITS_{i}}=2.058\)). Evaluation of the RSM Models for \(y_{\mathrm{Rc}}\), Tangent Force [28]  
\[ \begin{split} 1.\ \mathrm{y_{Rc}}=264.240-0.294x_{1}+199.40x_{2}-7.0 94x_{3}+0.000473{x_{1}}^{2}+3750.468{x_{2}}^{2}\\ (0.725)\ (0.858)\ (0.481)\ (0.736)\ (0.143)\\ +2.668{x_{3}}^{2}-0.0070x_{1}x_{2}+0.00244x_{1}x_{3}+62.058x_{2}x_{ 3}\\ (0.029)\ (0.943)\ (0.244)\ (0.051)\\ R^{2}=0.994,\ \mathrm{R^{2}}_{\mathrm{adj}}=0.985,\ \mathrm{s}=9.039, \ \mathrm{PRES}=3864.088\end{split}  \tag{14}\]  
Delete \(x_{1}x_{2}\) and recalculate the equation. \[ \begin{split} 2.\ \mathrm{y_{Rc}}=269.233-0.311x_{1}+177.735x_{2}-7.0 92x_{3}+0.000473{x_{1}}^{2}+3750.468{x_{2}}^{2}\\ (0.675)\ (0.857)\ (0.444)\ (0.715)\ (0.112)\\ +2.668{x_{3}}^{2}+0.0277x_{1}x_{3}+62.050x_{2}x_{3}\\ (0.018)\ (0.205)\ (0.034)\end{split}  \tag{15}\]  
Delete \({x_{1}}^{2}\) and recalculate the equation. \[ \begin{split} 3.\ \mathrm{y_{Rc}}=241.953-0.0410x_{1}+62.922x_{2}-6. 563x_{3}+4000.060{x_{2}}^{2}+2.722{x_{3}}^{2}\\ (0.490)\ (0.943)\ (0.445)\ (0.062)\ (0.007)\\ +0.0243{x_{1}}x_{3}+62.050x_{2}x_{3}\\ (0.178)\ (0.024)\end{split}  \tag{16}\]Delete \(x_{1}x_{3}\) and recalculate the equation. \[ \begin{split} 4.\ y_{\text{Rc}}=261.851-0.104x_{1}+58.033x_{2}+0.842x_{3 }+4010.659{x_{1}}^{2}+2.764{x_{3}}^{2}\\ (0.027)(0.950)(0.905)(0.072)(0.008)\\ +62.050x_{2}x_{3}\\ (0.027)\end{split}  \tag{10}\]  
Delete \({x_{2}}^{2}\) and recalculate the equation. \[ \begin{split} 5.\ y_{\text{Rc}}=52.737-0.103x_{1}+1902.950x_{2}+4.737x _{3}+3.543{x_{3}}^{2}+62.505x_{2}x_{3}\\ (0.046)(<0.001)(0.544)(0.002)(0.046)\\ R^{2}=0.988,\ R^{2}_{\text{adj}}=0.982,\text{s}=9.654,\text{ PRESS}=2279.002\end{split}  \tag{11}\]  
The normality and constant variance tests are passed. Two suspicious data points are found, the 12 th (t\({}_{1}\) = 2.322) 13 th (t\({}_{1}\) = \(-2.326\)).
## 3 Traditional ToF-Based Method
For disjoint ellipsoids, as shown in Figure 5a, there was no curve intersected by the ellipsoids that were drawn based on errors contained in \(T_{i}\) and \(T_{j}\). For disjoint curves, as shown in Figure 5b, zooming in from another angle, the two curves do not cross. The lack of intersections affected the construction of curves and intersection points. In a word, ToF measurement errors caused two problems in 3-D target localization, pseudo-targets, and disjoints. First, it was impossible to differentiate between pseudo-targets to obtain the target using traditional methods. Some scattered signals from the target are not employed to construct curves or intersection points, so part of the target information is lost. The traditional ToF-based method cannot solve these problems.
## 3 Results and Discussion
### Rock Mechanical Properties  
Based on the uniaxial/triaxial compression tests, the _UCS_, \(C\), _\(\varphi\)_, _E\({}_{\rm s}\)_, and _v\({}_{\rm s}\)_ can be determined, as shown in Figure 6. We can observe that the _UCS_ of the samples was between 48.40 and 147.86 MPa, with an average of 98.48 MPa (Figure 6a); the _E\({}_{\rm s}\)_ of the samples ranged from 3.02 to 20.63 GPa with a mean value of 11.64 GPa (Figure 6b); the _v\({}_{\rm s}\)_ of the samples ranged from 0.13 to 0.36 with a mean value of 0.22 (Figure 6c); the \(C\) of the samples was between 14.65 and 34.60 MPa, with a mean value of 23.97 MPa (Figure 6d); and the _\(\varphi\)_ of the samples was between 27.61 and 46.94\({}^{\circ}\), with an average of 34.89 \({}^{\circ}\) (Figure 6e). According to previous works [17; 18; 19; 20; 21; 22; 23; 24; 29; 30; 31; 32; 33; 34; 35], our results fall within acceptable ranges. Additionally, there is a wide range of variation in the strength parameters and static elastic parameters of different samples, indicating that the rock samples from the Permian Lucaogou Formation in the Junggar Basin have strong heterogeneity.
## 5 Application Scenario: State and Disturbance Estimation for an Underactuated Hovercraft  
As a more complex benchmark application, consider the motion of an underactuated hovercraft in its body-fixed coordinate frame [57; 58]. In these coordinates, it is desired to employ the ellipsoid techniques summarized in the previous sections to compute guaranteed outer state enclosures in the presence of bounded noise and disturbances. Moreover,  
Figure 8: Visualization of the thick ellipsoid state estimation procedure for the example system in (82).  
the thick ellipsoid state estimation algorithm of Section 4 is employed to reconstruct bounds on external disturbances.