esa-sceva/satcom-chunk-collection huggingface dataset:
# Point-Sim: A Lightweight Network for 3D Point Cloud Classification
## 1 Introduction
And because of its feature magnitude change in vector space during feature extraction, this will affect the stability of the model and have an impact in the final naive feature matching stage. Inspired by the above work, we propose a nonparametric network model for point cloud classification task, which is composed of nonparametric modules, and uses the nonparametric attention block ResSimAM(Residual Simple Attention Module) to derive the attention weights, as well as the features during the feature extraction process, in order to enhance the weights of features with higher energy. In the feature extraction stage, a nonlinear feature transformation is achieved by using the Squash operation to squeeze the input features to a certain range without changing the direction in the vector space. The Squash operation helps to preserve the directional information of the feature vectors while eliminating the effect of magnitude, thereby allowing the network to better learn the structure and patterns in the data and better preserving the relationships between the feature vectors, which helps reduce numerical instability due to vector length variations for subsequent naive similarity matching. The key contributions of our contributions can be summarized as follows:  
1. Aiming at the problem that there is some blindness in Point-NN when using trigonometric functions to encode features for mapping features into high dimensional space, we calculate the energy of each feature by utilizing an energy function and then add weights for each feature according to its energy, which improves the model's ability to extract features without adding any trainable parameters to the original model. 2. In order to alleviate the influence of feature magnitude in the final naive feature matching, we use the Squash operation in the stage of feature extraction so that the features are squeezed to a certain range without changing the direction in the vector space, thereby eliminating the instability brought by the feature magnitude. This enables the network to better learn the structure and patterns in the data and improve the model classification ability. 3. We extend a lightweight parametric model by adding a small number of MLP layers to the nonparametric model feature extraction stage and applying the MLP to the final global features to obtain the final classification results, and we validate the performance of the model in the absence of other state-of-the-art operators. The remainder of the paper is structured as follows. Section 2 gives related work. Section 3 describes the nonparameter network Point-Sim and the lightweight network Point-Sim methods in detail. We evaluate our methods in Section 4. Section 5 concludes the paper.
## 3 Results
### Validation of the Atmospheric Correction Using Measurements on Non-Fluorescent Matter Canopy
For the O\({}_{2}\)-A band, the data for the SIF from the tower-based observations after atmospheric correction are all close to the 1:1 line, the root-mean-square error (RMSE) between the tower-based and ground-based observations decreases from 0.221 to 0.078 mW/m\({}^{2}\)/nm/sr, and the coefficient of determination (R\({}^{2}\)) increases from 0.78 to 0.84. These results indicate that the performance of the atmospheric correction method proposed in this study is satisfactory for the O\({}_{2}\)-A band.
# TFBN: A Cost Effective High Performance Hierarchical Interconnection Network
## 4 Static Network Performance Evaluation of a TFBN
### Diameter and Average Distance  
Even though hop distance is not the actual distance between a node to node, it is a crucial parameter to evaluate the performance of any network in its graph-theoretic model. The maximum hop distance among all pairs of nodes in a network using the shortest-path algorithm is called the diameter of that network. The average hop distance by considering all the distinct pairs of nodes in a network using the shortest-path algorithm is known as average distance. The diameter signifies the upper bound of the latency or indicates the latency during the saturation throughput. The average distance signifies the latency at no load and the network throughput. The lower the diameter and average distance, the better the network in terms of dynamic performance communication. Therefore, the nature of the dynamic performance communication (low latency and high throughput) can be estimated well prior to the actual evaluation of it. If it is not acceptably good, then we can change some connection and evaluate these two parameters again.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Node Degree** & **Diameter** & **Average Distance** & **Arc Connectivity** & **Bisection Width** \\ \hline \multicolumn{6}{c}{256 Node} \\ \hline
2D-Mesh & 4 & 30 & 10.67 & 2 & 16 \\
2D-Torus & 4 & 16 & 8 & 4 & 32 \\ TESH & 4 & 21 & 10.47 & 2 & 8 \\ TTN & 6 & 15 & 7.44 & 4 & 8 \\ TFBN & 8 & 10 & 5.75 & 4 & 8 \\ \hline \multicolumn{6}{c}{4096 Node} \\ \hline
2D-Mesh & 4 & 126 & 42.67 & 2 & 64 \\
2D-Torus & 4 & 64 & 32.00 & 4 & 128 \\ TESH & 4 & 32 & 17.80 & 2 & 8 \\ TTN & 6 & 24 & 12.60 & 4 & 8 \\ TFBN & 8 & 19 & 10.61 & 4 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of static network performance of various networks.  
Using simple dimension order routing, we have estimated the diameter and average distance of TFBN, TTN, and TESH network by simulation studies for both the Level-2 and Level-3 networks. On the other hand, we have calculated those parameters by their respective formula for the mesh and torus networks. The results are tabulated in Table 1. It is depicted that both the diameter and average distance of the TFBN are substantially low as compared to those of TTN, TESH, torus, and mesh networks. Particularly, it is revealed that the average distance of the TFBN is getting lower as the hierarchical level of the TFBN is increasing from Level-2 to Level-3.
###### Abstract  
Life-cycle assessments (LCAs) were conducted to evaluate the replacement of sand with coal bottom ash (CBA) in concrete. CBA is a byproduct of coal-fueled electricity production. Sand was replaced with CBA at proportions of 0, 25, 50, 75, and 100 wt.%, and the resultant concretes were denoted as CBA0, CBA25, CBA50, CBA75, and CBA100, respectively. Two concrete mixture design methods (that resulted in different component qualities of concrete mixtures) were used: (i) Mixture with a fixed slump (MIX-fixed-SLUMP) and (ii) mixture with a fixed water/cement ratio (MIX-fixed-W/C). The ReCiPe2016 midpoint and single score (six methodological options) methods were followed to compare the environmental damage caused by the CBA-based concretes. The ReCiPe2016 results showed that replacing sand with CBA was environmentally (i) beneficial with the MIX-fixed-SLUMP design and (ii) harmful with the MIX-fixed-W/C design. Therefore, using CBA as a partial sand replacement in concrete production is a controversial issue as it highly depends on the concrete mixture design method.  
Keywords:coal bottom ash; sand replacement; concrete design method; LCA; ANOVA +
Footnote †: journal: Applied Sciences
# Ensemble Learning for Skeleton-Based Body Mass Index Classification
## 6 Results
### Results of Leave-One-Person-Out Cross-Validation
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Macro-Average**} & \multirow{2}{*}{**Accuracy**} \\ \cline{2-2} \cline{4-5}  & **TPR** & **PPV** & & \(F_{1}\) \\ \hline C-SVM + undersampling & 0.3397 & 0.3493 & 0.3057 & 0.3837 \\ C-SVM + SMOTE & 0.3981 & 0.3904 & 0.3926 & 0.5560 \\ \hline nu-SVM + undersampling & 0.3361 & 0.3474 & 0.3019 & 0.3766 \\ nu-SVM + SMOTE & 0.3620 & 0.3662 & 0.3633 & 0.5719 \\ \hline \hline \(k\)-NN + undersampling [28] & 0.3054 & 0.3162 & 0.2774 & 0.3410 \\ \(k\)-NN + SMOTE & 0.4852 & 0.4536 & 0.4627 & 0.5790 \\ \hline NB + undersampling & 0.2616 & 0.2949 & 0.2606 & 0.3393 \\ NB + SMOTE & 0.3404 & 0.3450 & 0.3422 & 0.5187 \\ \hline DT + undersampling & 0.3054 & 0.3162 & 0.2774 & 0.3410 \\ DT + SMOTE & 0.3387 & 0.3411 & 0.3394 & 0.5062 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Performance comparisons of the five benchmark methods over leave-one-person-out cross validation. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Macro-Average**} & \multirow{2}{*}{**Accuracy**} \\ \cline{2-2} \cline{4-5}  & **TPR** & **PPV** & & \(F_{1}\) \\ \hline Comparison model \#1 & 3 & 0.4889 & 0.4559 & 0.4646 & 0.5950 \\ Comparison model \#2 & 6 & 0.5300 & 0.4997 & 0.5030 & 0.6448 \\ Comparison model \#3 & 10 & 0.5825 & 0.5450 & 0.5579 & 0.6927 \\ Proposed ensemble model & 15 & 0.6422 & 0.5878 & 0.6058 & 0.7300 \\ Comparisons model \#4 & 21 & 0.5864 & 0.5564 & 0.5676 & 0.7069 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Leave-one-person-out cross-validation performance comparisons between ensemble models with different numbers of \(k\)-NN classifiers.
# Fiber Orientation Reconstruction from SEM Images of Fiber-Reinforced Composites
## 3 Results and Discussion  
### Single-Fiber Deflection Angle Measurement Results  
The SEM images (Figure 4) of the specimen fracture reveal two forms of fiber orientation information: pull-out cavity and protruding fiber. During the destruction of the composite material, these fibers are extracted from the matrix, leaving only the pull-out cavity on the matrix. The orientation direction of the cavity is identical to the orientation direction of the fiber. The protruding fibers are exposed in whole or partly outside the matrix, and the ends and sides of the fibers have varying degrees of brightness due to the sensitivity of secondary electrons to the tilt angle of the sample surface.
# Design of OCC Indoor Positioning System Based on Flat Panel Light and Angle Sensor Assistance
## 3 Fuzzy Positioning  
The receiver side of LP-VLP system mainly collects images from the cell phone camera and performs a series of processing on the images, and finally decodes the graphic code to obtain the location information to achieve positioning. The workflow of the receiver side in this paper mainly consists of four parts. Figure 4 shows the flow chart of the whole fuzzy localization process.  
The acquired images may be overexposed, skewed, and noisy, so digital image processing is performed to enhance the images in order to extract the LED-ID information from the images.
# 3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors
## 3 The Proposed Method
### The Improved 3D Gray-Level Co-Occurrence Matrix (IGLCM_3D)
#### 3.2.3 Computation of the Three Gradient-Based Matrices and the Corresponding Feature Vectors
The azimuth-based feature vector is computed from the AACM matrix by considering the entropy indicator:  
\[\text{Entropy\_1}\ =\ -\sum_{\text{i}=1}^{\text{Nq}}\sum_{\text{j}=1}^{\text{Nq}} \text{AACM}_{\text{n}}(\text{i},\text{j})\times\log(\text{AACM}_{\text{n}}( \text{i},\text{j})),\text{ for AACM}_{\text{n}}(\text{i},\text{j})\ \
eq\ 0 \tag{20}\]  
The elevation-based feature vector is computed from the EACM matrix. The same indicator is used, the entropy:  
\[\text{Entropy\_2}\ =\ -\sum_{\text{i}=1}^{\text{Nq}}\sum_{\text{j}=1}^{ \text{Nq}}\text{AACM}_{\text{n}}(\text{i},\text{j})\times\log(\text{EACM}_{ \text{n}}(\text{i},\text{j})),\text{ for EACM}_{\text{n}}(\text{i},\text{j}) \ \
eq\ 0 \tag{21}\]  
A high value for the entropy indicator is obtained for heterogeneous input textures and a low value for coherent textured images since the entropy describes the degree of randomness. In this context, the entropy is used to depict differences regarding the local anisotropy. For building the two orientation-based feature vectors, this entropy indicator is computed for all AACM and EACM matrices and the same fusion strategy as for GLCM is used. The final azimuth-based feature vector contains four characteristics and the same is valid for the elevation-based feature vector.
# Determination of a Complete Conversion Model for Gasification of Lignite Char
## 4 Results and Discussion
### Confirmation of Reaction Model  
The kinetic model for the gasification of the char has to describe sufficiently the change in reaction rate with ongoing carbon conversion. In Figure 2, the Arrhenius plot is shown for the CO\({}_{2}\) gasification in configurations No. 2 and 5 as well as for the steam gasification in configuration No. 3 and 8 for the volumetric model.  
It can be seen that the gradient of the Arrhenius graph (i.e., activation energy) increases with increasing temperature. Generally, two possible explanations exist for the change in reactivity. Firstly, two separate reactions with different activation energies and frequency factors could determine the gasification of the char at different temperatures, e.g., a catalyzed and a non-catalyzed reaction. In this case, a suitable reaction model must be selected. Secondly, the reactivity of the char increases more with ongoing gasification than the volumetric model predicts. In this case, another conversion model  
Figure 1: Frequency factor over sample mass for all four conversion models.  
should be used, like the GM, RPM or JM. Only additional runs with different heating rates can make the distinction between those two possible explanations, as stated by [PERSON] et al. [23].  
The test configuration No. 1 of the CO\({}_{2}\) gasification was repeated with heating rates of 20 K/min and 40 K/min in another oven of the TGA. In Figure 3, one representative run for each heating rate is plotted. The solid lines represent the reaction rate according to the Arrhenius model for both runs. The dashed lines show the reaction rate according to the Arrhenius model, when only inverse temperatures of more than \(8.2\times 10^{-4}\) K\({}^{-1}\) are considered. It is apparent that the change in reaction rate happens at different temperatures.  
Additionally, the samples were acid washed according to ISO 602:2015 to remove the mineral content. The acid washed samples were used in CO\({}_{2}\) gasification experiments (Table 2) of the configurations No. 1, 3, 5, and 7. Compared to the non-acid washed samples, the Arrhenius plots have the same shape, but exhibit a shift to lower frequency factors by about 0.15-0.4 log(min\({}^{-1}\)). This indicates the general catalytic effect of the mineral matter in the ash but without any temperature dependency.  
Therefore, it can be concluded that the change in char reactivity has to be explained by a suitable conversion model.  
Figure 3: Arrhenius plot for CO\({}_{2}\) gasification in configuration No. 1 with 20 K/min and 40 K/min heating rate.  
Figure 2: Arrhenius plot for (**a**) CO\({}_{2}\) gasification (configurations No. 2, 5) and (**b**) steam gasification (configuration No. 3, 8) (VM).
# S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices
## 3 SBAUPT-Based Pedestrian Dead Reckoning (S-PDR)
### Step Detection
#### 3.1.1 Adaptive Cut-Off Frequency Filter  
The acceleration measurements represent the device motion. The measurements are poised with high noise introduced by the sensor and the motion noise of the user--for example shaking hands. The developed step detection algorithm utilizes an adaptive cut-off frequency filter, where the cut-off frequency is frequently updated based on the estimated speed of motion.
# Design and Optimization of Lattice Structures: A Review
## 3 Design and Optimization of Uniform Lattice Structures
### Unit Cell Design Based on Topology Optimization  
Academic literatures show that topology optimization is one of the methods to obtain uniform lattice structures [86; 108; 140; 151; 176; 179; 180]. By optimizing unit-cell struts' sizes (i.e., thickness, length and diameter, etc.) and geometries, topology optimization effectively obtains lattice structures  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Condition** & **Interpretation** \\ \hline \(f(x,y,z)=0\) & On surface \\ \(f(x,y,z)<0\) & Inside \\ \(f(x,y,z)>0\) & Outside \\ \hline \hline \end{tabular}
\end{table}
Table 3: Implicit surface in/out convention.  
Figure 15: Two types of unit cells [175]: (**a**) Schoen Gyroid; (**b**) Schwartz Diamond.  
with specific performance, that is under condition of the specific constraints, the unit cells can be optimized to obtain specific performance requirements, and then unit cells can be arranged periodically to get the uniform lattice structures. [PERSON] et al. [180] created a new kind of programmable uniform lattice structure material with Poisson's ratio between \(-0.8\) and \(0.8\) by optimizing the unit cells. In large deformation up to \(20\%\) even more, the Poisson's ratio of lattice structure was almost constant.  
Homogenization method is one of the methods of topology optimization, and refers to a way to replace the composite with a kind of equivalent material model. Using homogenization to optimize lattice structures can be divided into two steps [23]. First, the local problem based on a unit cell is solved to get the effective material properties. Second, periodic materials are used to replace homogeneous materials with equivalent properties to solve the overall problem. [PERSON] [181] used homogenization method to optimize the topology structures of unit cells, and obtained uniform lattice structure. [PERSON] applied constraint load to the structure within the design domain, and optimized the topology structure of unit cells under the constraint conditions. Finally, the uniform lattice structure was formed, as shown in Figure 16. Although there were load constraints in different directions in the design domain, the uniform lattice structure had almost the same isotropic equivalent behavior as expected.  
[PERSON] and [PERSON] have made valuable research on topology optimization of unit cells [182; 183]. The team carried out a detailed experimental study on the lattice structure with uniform negative Poisson's ratio. Through the tensile test, the unit cell structure with negative Poisson's ratio could be obtained by topology optimization. Figure 9 was one of topology structures of unit cells with negative Poisson ratio (Figure 17a), and the uniform lattice structure formed by arranging the unit cell (Figure 17b).  
Figure 16: Design and optimal material [181]: (**a**) optimal unit cell; (**b**) lattice structure.
# Modeling and Optimization of NO2 Stations in the Smart City of Barcelona
## 4 Methodology  
The methodology employed in this study to achieve the proposed objectives is described in this section. Various tools are used to conduct the descriptive analysis, predict NO\({}_{2}\) behavior, and optimize the distribution of stations using an optimization algorithm. In particular, the descriptive analysis is performed using Python 3.10. The libraries NumPy [31], Pandas [32], Seaborn [33], and Matplotlib [34] are used for data analysis and visualization. The Folium library [35] is used to work with the map of Barcelona and generate heat maps. The Geocoder library is used to obtain coordinates for points of interest in Barcelona. The Scikit-learn library [36] is used for clustering analysis concentrations at the stations. This same library is necessary to apply ML models to predict concentrations at each station. As for the optimization algorithm, it is implemented using Python 3.10 with support of Numpy and Pandas libraries to perform array manipulations and manage the input datasets, respectively.
# Target Identification via Multi-View Multi-Task Joint Sparse Representation
## 4 Multiple Feature Extraction Methods for Target Identification  
In this paper, we introduce four typical feature extraction methods to make the advantages of complementary features, including the scale-invariant feature transformation (SIFT), the histogram of oriented gradient (HOG) [9], the local binary pattern (LBP) [47], and the Hu invariant matrix. Specifically, HOG is edge distributions of objects captured based on gradient features. LBP has a powerful function of representing object texture. Hu moment invariants are fast and can better describe larger objects in the image. In addition, to ensure the quality of extracted features, a simple and effective illumination normalization method is adopted before feature extraction [19]. The unit norm normalization is applied to extract feature vectors for each particle's viewing angle [46]. To clearly show the feature extraction process, we adopt four feature extraction methods to extract features from the same image, which are elaborated as follows. Since the ship has significant texture features and edge features, we choose the ship image as an example, and the original image of the ship is presented in Figure 2.
# Research on a Point Cloud Registration Method Based on Dynamic Neighborhood Features
## 2 Methods  
The main process of the proposed point cloud data registration method based on dynamic neighborhood features is illustrated in Figure 1. After acquiring the source and target point cloud data, statistical outlier removal is first performed to filter out noise. Subsequently, the point cloud is divided into small, equally sized voxels, and the centroid of the points within each voxel is selected as a feature point. A dynamic neighborhood is determined for each feature point based on its local average curvature, point cloud density, and detail coefficients from a curvature-based wavelet transform. The local feature descriptor for each feature point is then constructed as a cumulative histogram of the projected distances from the remaining points within its dynamic neighborhood onto the feature point along the direction of maximum principal curvature. Feature point pairing is achieved by calculating the chi-squared distance between the cumulative histograms of the projected distances for feature points in the two point clouds. The pair with the minimum chi-squared distance is considered the best match and retained. Matching pairs with chi-squared distances exceeding a predefined threshold are discarded to improve matching accuracy. Finally, the transformation matrix between the source and target point clouds is computed using the retained best matching pairs. This transformation matrix is then applied to register the source point cloud to the target point cloud [15,16].
# Optimal Pattern Synthesis of Linear Array Antennas Using the Nonlinear Chaotic Grey Wolf Algorithm
###### Abstract  
The grey wolf optimization (GWO) algorithm is a new nature-inspired meta-heuristic algorithm inspired by the social hierarchy and hunting behavior of grey wolves. In this paper, the GWO algorithm is improved to overcome previous shortcomings of being easily trapped in local optima and having a low convergence rate. The proposed enhancement of the GWO algorithm utilizes logistic-tent double mapping to generate initialized populations, which enhances its global search capability and convergence rate. This improvement is called the nonlinear chaotic grey wolf optimization (NCGWO) algorithm. The performance of the NCGWO algorithm was evaluated with four representative benchmark functions. Then, the NCGWO algorithm was applied to perform an optimal pattern synthesis of linear array antennas (LAAs) using two distinct approaches: optimizing the amplitudes of the antenna currents while preserving uniform spacing and optimizing the positions of the antennas while assuming uniform excitation. To validate the effectiveness of the proposed approach, the results obtained by the NCGWO algorithm were compared with those obtained by other intelligent algorithms. Additionally, the NCGWO algorithm was applied to a more complex planar antenna array to further validate its performance. Our results demonstrate that the NCGWO algorithm exhibits superior performance regarding electromagnetic optimization problems compared to widely recognized algorithms.  
n _et al._ Article  
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onlinear chaotic grey wolf optimization (NCGWO) algorithm; logistic-tent double mapping; array antennas; pattern synthesis