esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Method
### Data
#### 2.2.3 TRMM Microwave Imager (TMI) Level 2 Hydrometeor Profile Product  
In this study, the latest version (version 7) of the TRMM Microwave Imager (TMI) Level 2 Hydrometeor Profile Product (termed TRMM 2A12, hereafter) was introduced as a comparison for the new rain area delineation method proposed by this study. TRMM 2A12 contains the vertical profiles of hydrometeors, as well as precipitation derived from the TRMM 1B11 brightness temperature using the Goddard profiling algorithm (termed GPROF), which estimates the precipitation information at various vertical heights by blending the TMI observations with dynamical cloud models using the Bayesian method. The vertical spatial resolutions of TRMM 2A12 are 0.5 km (from the surface to 4 km), 1 km (from 4 to 6 km), 2 km (from 6 to 10 km) and 4 km (from 10 to 18 km). The horizontal footprint spatial resolution is about 5 km, which is in accordance with the spatial resolution of TRMM 1B11.
## 3 Methodology
### Acquisition and Adjustment of the Surface Triangular Mesh
#### 3.3.1 Acquisition of the Surface Triangular Mesh  
(1) Features of the tetrahedral mesh  
The tetrahedral mesh in Figure 5c can be divided into two parts. One part is the internal tetrahedral mesh, of which the surface triangular mesh must be shared by two tetrahedral meshes. The other part is the superficial tetrahedral mesh. The tetrahedral mesh in this part has at least one nonshared surface. In these superficial meshes, if any one of the surfaces of a tetrahedral mesh appears only once, the surface can be used as part of the surface triangular mesh.  
(2) Special case  
One problem that is introduced by the superficial tetrahedral mesh is that for the surface of the triangular mesh, some edges are shared by more than two triangles, as shown in Figure 5i. The edges are shared by four triangles. Therefore, it is necessary to ensure that any edge of the surface triangular mesh is shared by only two triangles. Otherwise, the tetrahedral mesh that does not satisfy the conditions is classified into the meshes of Figure 5b, and this rule is taken as a judgment condition.
## 3 Results and Discussion
### Prediction the Optimal Conditions by ANN-GA  
The optimum fitness value was obtained when the iterations of GA stopped after 57 generations as shown in Figure 16. The predicted removal efficiency of MG under the ANN-GA optimized condition was 94.12% when initial pH, temperature, contact time and initial concentration were 5.70, 27.19 \({}^{\circ}\)C, 110.62 min and 607.03 mg/L, respectively, whereas the confirmatory removal efficiency (90.72%) was achieved under the conditions of 5.70 for initial pH, 27.20 \({}^{\circ}\)C for temperature, 111.00 min for contact time and 600.00 mg/L for initial concentration. A small absolute error (3.40%) between the confirmatory and predicted results reveals a good prediction by using the ANN-GA model.
## 2 Related Works
### Domain Adaptation Approaches  
Domain adaptation methods can generally be categorized into supervised and unsupervised approaches. Supervised DA utilizes target-domain label information, either partially or entirely, to reduce discrepancies between databases. For instance, [PERSON] et al. [24] proposed a three-stage domain adaptation framework that includes pretraining, computing cluster centroids, and adaptation stages, explicitly utilizing target-domain labels. [PERSON] et al. [25] developed a dynamic transfer network designed to address cross-database domain shifts through adaptive fine-tuning, using a partial set of target labels.  
In contrast, unsupervised domain adaptation methods aim to bridge the gap between domains without relying on target labels, typically by aligning distributions or through adversarial learning. [PERSON] et al. [26] introduced a multi-source adversarial learning framework capturing domain-invariant and domain-specific ECG features simultaneously. [PERSON] et al. [27] proposed a dual-channel network employing consistency-based adversarial training to minimize feature discrepancies between ECG databases. [PERSON] et al. [28] utilized pseudo-labeling combined with personalized ECG representations, derived from information-theoretic approaches, for patient-specific adaptation.
# The Modified Void Nucleation and Growth Model (MNAG) for Damage Evolution in BCC [PERSON]
## 1 Introduction
Another study on void evolution in single-crystal and nanocrystalline Al under triaxial tension suggested, while the grain size could alter the number of voids, it did not alter the overall void volume fraction. In general, the void nucleation and growth threshold stresses obtained from the data fit the NAG model for nanocrystalline Al, which were lower than in single-crystal Al [54]. However, as the above models did not incorporate the dynamics of void coalescence, which is crucial to define the later stage damage accumulation under high strain-rate loading; the applicability of these models to the available experimental as well as MD dynamic loading data was rather limited. The objectives of this work were:  
1. To propose a modified NAG model, which we term as the MNAG model, to better describe void evolution at later times through the addition of a term to describe void coalescence. This robustness of this new analytical form was tested against the other commonly used damage models by using void evolution data generated from MD simulations. 2. To evaluate and compare several widely utilized semi-analytical damage models in terms of how well they could describe the void evolution behavior, as extracted from MD shock loading simulations, using single-crystal and nanocrystalline Ta as model systems. 3. To utilize the extract parameters associated with void nucleation, growth, and coalescence rates from specific simulations and to provide insights into the role of crystal orientation and grain size in dictating damage, which remained a challenge under dynamic loading. This paper is organized in the following manner: The computational methods are presented in Section 2. Description of the MNAG model, its application to Ta, comparison of MNAG model to other damage models, as well as the size effects are presented in Section 3. Finally, a brief conclusion is presented in Section 4.
# A High-Resolution, Wide-Swath SAR Imaging System Based on Tandem SAR Satellites
## 2 Imaging System Based on Tandem SAR Satellites
### Comparison of the Two Scanning Methods  
The scanning method adopted in Sat-2 can keep the entire aperture time of targets, which is different from the traditional method. As shown in Figures 4 and 5, each point represents an azimuth sample in the echo of a ground target and corresponds to a transmitted pulse. The traditional scanning method assigns a continuous pulse train to one subswath, thus truncating the target's aperture time. The proposed scanning method randomly chooses the pulses assigned to one subswath; it can retain the entire aperture time, although with downsampling.  
Figure 3: Timing diagram of the subswath’s pulse transmitting and receiving, where the vertical line indicates the pulse emission time, and the long red line indicates that the current pulse is assigned to this subswath. The short gray line suggests that the current pulse is assigned to other subswaths. S\({}_{1}\), S\({}_{2}\), and S\({}_{5}\) represent the reflected echoes of pulses 1, 2, and 5, respectively.  
Figure 2: Pulse assignment diagram of the new scanning method, where each pulse is randomly assigned to one of the subswaths on the ground and different subswaths have different squint angles.  
An example system is designed in Section 4. After the echoes are received, the coarse observations obtained by Sat-1 are used as prior knowledge to achieve high-resolution imaging from the received Sat-2 data.
# Runway Pavement Structural Analysis Using Remote Laser Doppler Vibrometers
## 1 Introduction
While the incorporation of intelligent sensors mitigates the shortcomings of traditional testing methods, such as regular inspections and runway occupancy, the predominantly used embedded sensors present difficulties for intelligent upgrades at operating airports [17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. The advancement of remote sensing technology has brought forth fresh prospects for intelligent assessment of runway structural performance [28; 29]. The Remote Laser Doppler Vibrometer (RLDV), an innovative optical instrument, leverages the Doppler effect and the heterodyne interference principle to measure the surface vibrations of objects [30]. Presently, this device finds wide-ranging applications in fields such as evaluating rock block stability [31], detecting internal flaws in concrete structures [32], supervising the condition of hydropower dams [33], and analyzing the vibration patterns of aircraft blades [34]. These applications have achieved precise, non-contact monitoring of vibrations in civil engineering structures. This study introduces a remote method for the measurement and analysis of runway pavement structural deflection induced by transiting aircraft with a Remote Laser Doppler Vibrometer (RLDV). A design framework was proposed for a vibration monitoring system for rigid pavements that incorporates RLDV technology. Subsequently, an appropriate data correction method was developed and validated to address challenges associated with minor angle measurements and vibrations from fixed-end beams. Thereafter, the reliability of RLDV in measuring pavement deflection was established by analyzing the correlation between RLDV and HWD deflection data. Conclusively, an assessment technique for pavement structural performance was established based on deflection test data gathered using RLDV.
# How Useful Are Strain Rates for Estimating the Long-Term Spatial Distribution of Earthquakes?
## Appendix A Appendix A.1 Strain Rate Covariates  
[PERSON] and [PERSON] [30] utilized GNSS data from 9 geodetic networks across NZ, comprising 362 GNSS stations. They calculated continuous horizontal velocity and associated strain rate fields at the Earth's surface in NZ between 1991 and 1998. They adopted the method proposed by [PERSON] et al. [26], which enhanced NZ's known deformations and discovered new features in both the North and South Islands.  
[PERSON] (1950-2012) directed most of the GNSS data acquisition in NZ [14]. His last shear strain rate model of NZ was based on survey-mode GNSS data collected in NZ between January 1996 and February 2011. This model was based on the method of [PERSON] and [PERSON] [30] and was submitted to Land Information New Zealand (LINZ) to be used in updating the geodetic and cadastral systems following the 4 September 2010 Darfield earthquake. This was the shear strain rate (BSS) used by [PERSON] et al. [11].  
The Global Strain Rate Model (GSRM v.2.1) of [PERSON] et al. [13] is an improved version of the GSRM-1 Kreemer et al. model in [2] with a larger input dataset and improved spatial resolution. The maximum shear strain rate from GSRM v.2.1 is referred to here as the GSS model. Like the BSS model, the GSS model was obtained using the Haines and Holt method [25; 27; 30] to model the strain rate field. GSS is derived from worldwide GNSS data between 1 January 1996 and 31 December 2013, including velocities from 233 published and unpublished regionally focused studies. Table A1 lists the NZ-focused studies used in the GNSS compilation of the GSS model [13]. Apart from in the southeast South Island region, the GSS model uses less up-to-date data than the BSS model.  
The NZ strain rate model of Haines and Wallace [14] was produced from GNSS interseismic velocities at 918 stations between 1995 and 2013 [31]. This model was based on the novel method of [PERSON] et al. [32]. They used a physics-based approach to invert the GNSS displacement fields for Vertical Derivatives of Horizontal Stress (VDoHS) rates. This method uses constraints from linear elasticity in place of artificial smoothing. This is to ensure physical plausibility, higher resolution, and an improved signal-to-noise ratio compared to the commonly used methods of [PERSON] [25] and [PERSON] and Haines [30]. The one modification made to the published Haines and Wallace model of [14] is one implemented by the NSHM Geodesy Working Group: a sill model of rapid magmatic contraction in the Taupo Volcanic Zone (TVZ) was removed from the GNSS velocities, and then the velocities were re-inverted using the [PERSON] et al. [32] method [31]. The maximum shear strain rate from this modified model is the scalar quantity that we use in our hybrid modelling and is henceforth referred to as HWS [33].
# An Ensemble Tree-Based Model for Intrusion Detection in Industrial Internet of Things Networks
## 3 Materials and Methods
### Data Preprocessing  
Data preprocessing is a serious first step in streamlining the training of ML models. For the purpose of research, all datasets are openly accessible for download. To minimize storage space requirements and to prevent redundancy, duplicate samples (flows) are eliminated. The flow identifiers, IP addresses, ports, and timestamps are eliminated to eliminate forecast bias against the attackers within end network nodes. Then, using a categorical encoding approach, numerical values are assigned to the strings and non-numeric characteristics. The features in these datasets include protocols and services, which have been compiled as their native string values, as well as ensemble classifiers. However, these are built to function effectively with numerical data.  
Hot encoding and label encoding are the two primary methods for encoding the features. The former adds X features to a feature to convert it into X categories, utilizing 0 to indicate that a category is not present and 1 to indicate it is. Nevertheless, this enhances the dataset's dimensionality, which could impact the ML models' effectiveness and performance. Hence, each category is converted to an integer using the label encoding technique.  
Categorical features were converted to numerical values for straightforward ML technique application. For instance, the categorical values \"open\" and \"closed\" for the door state feature from the GarageDoor dataset were converted into \"0\" and \"1\". Furthermore, duplicate, incompatible, and missing values were effectively handled. Additionally, this process permits the equal weighting of all features because network traffic properties are complex, and there are higher numbers than others. This could cause the ensemble model to weigh them more heavily, so it will pay attention to them. The min-max scaler uses Equation (1) to calculate all values for each feature, where \(X^{*}\) is a new feature value between 0 and 1, and X represents the unique feature value, where the feature maximum and minimum values are \(X_{max}\)_and_\(X_{min}\), respectively. Segments for training and testing are separated from the dataset, and these components are categorized according to the label features, which are crucial given the class imbalances of the datasets.  
\[\widetilde{x_{i}}=\frac{x_{i}-min(x)}{max(x)-min(x)}, \tag{1}\]
## 2 p-Refined Multilevel Quasi-Monte Carlo
### Mesh Hierarchies  
Multilevel methods rely on a hierarchy of levels to achieve variance reduction and, by doing so, reduce the total computational cost. In common practice, this hierarchy of levels is defined as a hierarchy of successively refined nested grids or meshes with a discretization based on the Finite Element Galerkin approximation. For a bounded domain \(\Omega\) in \(\mathbb{R}^{2}\), a mesh \(\mathcal{T}\) on \(\Omega\) is a partition into a number \(M\left(\mathcal{T}\right)\) of open disjoint subdomains \(\mathcal{T}=\left\{T_{j}\right\}_{j}^{M\left(\mathcal{T}\right)}\) with a mesh width \(h\). We follow [PERSON]'s definition of a finite element defined as triples \(\left(T,\mathcal{P},\mathcal{N}\right)\), see [25].  
**Definition 1**.: _A finite element is defined as \(\left(T,\mathcal{P},\mathcal{N}\right)\) with_  
1. \(T\subseteq\mathbb{R}\) _a closed subset with nonempty interior and piecewise smooth boundary, i.e., the element domain,_
2. \(\mathcal{P}\) _a finite-dimensional space of functions on_ \(T\)_, i.e., the space of shape functions, and_
3. \(\mathcal{N}=\left(N_{1},N_{2},...,N_{k}\right)\) _a basis for_ \(\mathcal{P}^{\prime}\)_, i.e., the set of nodal variables._  
Classically, the mesh hierarchy used for MLMC and MLQMC is based on an h-refinement scheme, i.e., a succession of meshes \(\mathcal{T}_{0},\mathcal{T}_{\ell},...,\mathcal{T}_{L}\) such that \(h_{0}>h_{\ell}>...>h_{L}\) and \(M\left(\mathcal{T}_{0}\right)<M\left(\mathcal{T}_{\ell}\right)<...<M\left( \mathcal{T}_{L}\right)\). Figures 1 and 2 show a hierarchy of nested uniform h-refined meshes, for the numerical examples considered in Sections 3.1 and 3.3, where the nodal points are at the intersections of the line segments.  
Figure 1: h-refined hierarchy of meshes used for model problem 1.  
Figure 2: h-refined hierarchy of meshes used for model problem 2.  
In the present paper, we propose to use a hierarchy of meshes based on a p-refinement scheme i.e., increasing the polynomial order of the interpolating shape functions, \((T,\mathcal{P}_{1},\mathcal{N}_{1})\in\mathcal{T}_{0}<(T,\mathcal{P}_{\ell+1}, \mathcal{N}_{\ell+1})\in\mathcal{T}_{\ell}<...<(T,\mathcal{P}_{L+1},\mathcal{N} _{L+1})\in\mathcal{T}_{L}\), such that \(\dim\mathcal{P}_{1}<\dim\mathcal{P}_{\ell+1}<...<\dim\mathcal{P}_{L+1}\). Figures 3 and 4 show a hierarchy of nested p-refined meshes, where the nodal points are represented as red dots.  
In this paper we will only consider two-dimensional uniform, Lagrange type elements. In order to construct both mesh refinement hierarchies, we use a combination of the open source mesh generator Gmsh [26] and Matlab[27].
## 2 Numerical Methodology  
### Model Simplifications  
Although differences between an actual case and a numerical model cannot be avoided, a simulation can more intuitively show the influences of some factors, such as electric field distributions inside samples and the process of coal cracking. In order to reduce computational time and ensure model convergence, it was necessary to simply the numerical model in a reasonable way. Therefore, five assumptions were made, as follows:  
**Assumption 1.**_The coal in question was dried and free of moisture._  
**Assumption 2.**_Coal is a linear elastic material at the microscale, and its deformations obey the small-deformation hypothesis._  
**Assumption 3.**_The coal matrix is a heterogeneous material, and its mechanical parameters follow the Weibull distribution._  
**Assumption 4.**_Coal mechanics obey the continuum damage mechanics, and its destruction follows the maximum tensile and Mohr-Coulomb criterion._  
**Assumption 5.**_The dielectric constants of both the coal matrix and pyrite remain constant._
## 4 Methodology
### Performance Evaluation  
Our study employed a confusion matrix to evaluate a binary classification problem. Such problems often involve classes of interest, including a minority class and its opposite class, commonly called positives and negatives. In this context, several key performance metrics are used [52; 53; 54]:  
**True Positive (TP):** these correctly anticipated positive results suggest that the actual and forecasted class values are accurate;  
**True Negative (TN):** the fact that neither the actual class value nor the expected class value is positive is one of the precisely predicted negative values;  
**False Positive (FP):** the situation where the expected class is valid but the actual class is false;  
**False Negative (FN):** a situation where the actual class is positive but the projected class is negative;  
**Accuracy:** the percentage of related samples among the retrieved samples is known as accuracy, another name for a positive value:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{17}\]  
**Precision:** precision is the ratio of accurately expected observations to all anticipated positive observations concerning positive observations:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{TN}} \tag{18}\]  
**Recall (Sensitivity):** the sensitivity is calculated quantitatively by dividing the total number of true positives by the sum of the true and false negatives:  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{TN}} \tag{19}\]  
**F-measure (F1 score):** The F-measure functions as a sort of average between the parameters \(P\) (Precision) and \(R\) (Recall). \(P\) represents the system's accuracy when compared to the predicted data. We express the number of predicted data to all expected data for prediction as a ratio or \(r\):  
\[\text{F1 Score}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{Precision }+\text{Recall}} \tag{20}\]
###### Abstract  
Using graphs as a data structure is a simple way to represent relationships between objects. Consequently, it has raised the need for algorithms to process, analyze, and extract meaningful information from graphs. Therefore, frequent subgraph mining (FSM) algorithms have been reported in the literature to discover interesting, unexpected, and useful patterns in graph databases. Frequent subgraph mining involves discovering subgraphs that appear no less than a user-specified threshold; this can be performed exactly or approximately. Although several algorithms for mining frequent approximate subgraphs exist, mining this type of subgraph in graph collections has scarcely been addressed. Thus, we propose AGCM-SLV, an algorithm for mining frequent approximate subgraphs within a graph collection that allows structural and label variations. Unlike other FSM approaches, our proposed algorithm tracks subgraph occurrences and their structural dissimilarities, allowing user-defined partial similarities between node and edge labels, and captures frequent approximate subgraphs (patterns) that would otherwise be overlooked. Experiments on real-world datasets demonstrate that our algorithm identifies more patterns than the most similar state-of-the-art algorithm with a shorter runtime. We also present experiments in which we add white noise to the graph collection at different levels, revealing that over 99% of the patterns extracted without noise are preserved under noisy conditions, making the proposed algorithm noise-tolerant.  
graph mining; frequent approximate subgraphs; pattern mining; structural variations; label variations Article