esa-sceva/satcom-chunk-collection huggingface dataset:
# Path Planning of Obstacle-Crossing Robot Based on Golden Sine Grey Wolf Optimizer
## 3 Golden Sine Grey Wolf Optimizer
### Piecewise Linear Chaotic Map  
The GWO uses a random method to generate the initial population, which makes it difficult to guarantee the ergodicity and diversity of the initial population. Aiming at the problem of the single initial population of the algorithm, a piecewise linear chaotic map is introduced to generate the initial population. The piecewise linear chaotic map (PWLCM) can generate chaotic sequences with good randomness and ergodicity for the initial population by using a simple mathematical model. The piecewise linear chaotic sequence is defined as follows:  
\[x^{k}(n+1)=\left\{\begin{array}{l}\frac{x^{k}(n)}{p},0\leq x^{k}(n)<p\\ \frac{x^{k}(n)-p}{0.5-p},p\leq x^{k}(n)<0.5\\ \frac{1-p-x^{k}(n)}{0.5-p},0.5\leq x^{k}(n)<1-p\\ \frac{1-x^{k}(n)}{p},1-p\leq x^{k}(n)<1\end{array}\right., \tag{7}\]  
where \(x(n)\) is the \(n\)th item in the sequence, \(k\) is the number of individuals in the population and \(p\) is a constant between \([0,0.5]\).  
According to Equation (7), it is possible to generate a sequence of uniform distribution between \([0,1]\) of any length based on the randomly generated \(x(1)\). The effect is shown in Figure 1.  
After generating a sequence, the number in the sequence is converted into the value in the search area of the algorithm by Equation (8):  
\[X^{k}(0)=I_{min}+(I_{max}-I_{min})x^{k}, \tag{8}\]  
where \(X^{k}(0)\) is the characteristic of the individual with sequence number \(k\) in the initial population and \(I_{max}\) and \(I_{min}\) are the upper and lower bounds of the search region, respectively.
## 4 Advanced Strategies for eNO\({}_{3}\)R Toward Ammonia and Nitrogen
### Copper-Based Electrocatalyst for the eNO\({}_{3}\)R: A Case Study for Ammonia Production  
#### 4.1.1 Copper-Based Single Atom Catalyst (Cu-Based SACs)  
Cu-based SACs have demonstrated exceptional performance in the eNO\({}_{3}\)R owing to the intrinsic electronic configuration of Cu, which facilitates strong interaction with nitrogen oxyanions such as NO\({}_{3}^{-}\). For instance, Cu incorporated into 3,4,9,10-perylenetetracarboxylic dianhydride (Cu-PTCDA) exhibits high selectivity (FE = 85.9%) and a substantial production rate (436 \(\upmu\)g.h\({}^{-1}\).cm\({}^{-2}\)) for NH\({}_{3}\), surpassing other transition metals (Ag, Bi, Ir, Pt, Co, Fe, Ni). The Cu atom in Cu-PTCDA forms strong bonds with NO\({}_{3}^{-}\), promoted by the efficient orbital overlap between NO\({}_{3}^{-}\)'s 2p orbitals and Cu's d orbitals [159]. Cu incorporated into nitrogen-doped carbon (Cu-N-C) further enhances eNO\({}_{3}\)R activity, particularly Cu-N-C-800, which shows superior NH\({}_{4}\)\({}^{+}\)-N selectivity (80%) compared to bulk Cu. The Cu-N-C-800 catalyst features Cu-N\({}_{2}\) and Cu-N\({}_{4}\) moieties that exhibit high adsorption energies for NO\({}_{3}^{-}\) and NO\({}_{2}^{-}\), crucial for efficient eight-electron transfer in the eNO\({}_{3}\)R. This high adsorption capability enables Cu-N-C-800 to reduce NO\({}_{3}^{-}\) to NH\({}_{2}^{-}\) without releasing NO\({}_{2}^{-}\) into the electrolyte, highlighting its role in accelerating NH\({}_{3}\) production [160]. Contrasting Cu SACs with other transition metals, SACs reveal Cu's unique advantages in the eNO\({}_{3}\)R. While Fe SACs also show promising performance, particularly in terms of NH\({}_{3}\) FE and yield rate, Cu SACs exhibit superior thermodynamic properties and kinetics due to stronger NO\({}_{3}^{-}\) coordination with Cu-N\({}_{4}\) moieties [161]. Cu-based SACs represent a promising avenue for advancing electrocatalysts in the eNO\({}_{3}\)R, leveraging their unique electronic properties and strong interactions with nitrogen species to achieve high selectivity and efficiency in NH\({}_{3}\) production.
## 5 Conclusions  
In this study, we synthesized 18F-Florbetaben A\(\beta\) PET images using GAN and visually and quantitatively evaluated the real and generated images. The similarity of the images that could statistically augment A\(\beta\) images was quantitatively measured for A\(\beta\)-negative (MMD:0.2284, FID:6.8253, 1-NN LOO accuracy:0.8562) and positive images (MMD:0.01865, FID:5.8919, 1-NN LOO accuracy:0.6233). We enhanced SVM/NN-based classifier using A\(\beta\) images generated by GAN (median-SVM, 0.943-0.956, median-NN, 0.946-0.963). The experimental results demonstrated that quantitative measurements were able to detect the similarity between the two distributions and to observe mode collapse better than the Visual Turing test and t-SNE.  
**Supplementary Materials:** The following are available online at [[https://github.com/kang2000h/GAN_evaluation](https://github.com/kang2000h/GAN_evaluation)]([https://github.com/kang2000h/GAN_evaluation](https://github.com/kang2000h/GAN_evaluation)), source code, model structure, weights, and figures used in this study and paper.  
**Author Contributions:** Conceptualization, H.K., J.-S.P., K.C. and D.-Y.K.; methodology, H.K.; software, H.K.; validation, H.K. and D.-Y.K.; formal analysis, H.K.; investigation, H.K.; resources, D.-Y.K.; data curation, H.K.; writing--original draft preparation, H.K.; writing--review and editing, H.K., J.-S.P., K.C. and D.-Y.K.; visualization, H.K.; supervision, D.-Y.K.; project administration, D.-Y.K.; funding acquisition, K.C., and D.-Y.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the National Research Foundation (NRF) of Korea funded by the Ministry of Science, ICT & Future Planning (NRF-2018 R1A2B2008178).  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Materials and Methods
### Contamination Influencing the Resistivity of a Soil  
It is always a challenge to establish a relationship for soil parameters versus electrical resistivity. The common assumption is that the soil skeleton plays the role of an isolator (in sandy soils), while the electric current flows through the soil voids filled with water. The results of the resistivity test could be significantly affected due to the mineralisation of water (or leachate from a landfill, in this case) [15; 17]. If the water content is constant, the resistivity of the soil becomes a function of salts dissolved in a soil water solution. The principal empirical equations concerning the relationships between soil resistivity and its physical parameters, have been presented by [PERSON] [25] and others [26; 27; 28; 29; 30].  
There is a direct correlation between the electrical conductivity (EC) and the amount of elements dissolved in water, and a similar correlation can be seen for the content of other elements and the water quality (chlorides, sulphates, and petroleum). Practically, even small, dissolved elemental changes in water affect the conductivity [31; 32]. Thus, it can be used as a way of recording the changes in water composition, which is also influenced by contamination of the groundwater. This feature, when properly calibrated and based on analytical assessment, could be used as valuable information for a soil-water environment quality assessment [24; 33].  
Water is not an efficient electrical conductor; it's electrical conductivity is 5 \(\times\) 10\({}^{-8}\) uS/cm (at temperature 25 \({}^{\circ}\)C). In dissolved suspensions, the EC is proportional to the concentration. The higher the concentration of a suspension is, the better the electrical conductivity will be. This is because there is a better dissociation of the elements into ions, in dissolved suspensions. In particular ranges, the relationships between the conductivity and electrolyte concentration are linear functions. Figure 1 presents the collection of data concerning the relationship of electrical conductivity and selected chemical solutions. During a number of investigations, it was also noticed that the conductivity of the electrolytes increases with a temperature rise of about 2%/1 \({}^{\circ}\)C. It is mainly caused by a decrease in the suspension viscosity and an increase in the mobility at higher temperatures. The EC of natural water falls in a range of 50 to 1000 uS/cm, but leachate conductivity can exceed 10,000 uS/cm. It is assumed that, for shallow groundwater, potentially affected by anthropogenic activity, values of EC above 1000 uS/cm indicate water contamination.  
The organic compounds present in water either dissociate insignificantly or not at all. That is why the conductivity of water influenced by organic pollutants is usually very low.
# Multi-Resolution SPH Simulation of a Laser Powder Bed Fusion Additive Manufacturing Process
## 2 Computational Framework  
Smoothed Particle Hydrodynamics (SPH) is a Lagrangian mesh-free method used here to discretize the governing partial differential equations in space. In principle, the derivation of SPH equations starts by taking the integral form of an arbitrary function:  
\[f(\underline{r})=\int_{\Omega}f(\underline{r}^{\prime})\,\delta(|\underline{r}- \underline{r}^{\prime}|)\,dV^{\prime} \tag{1}\]  
and replacing the Dirac delta function with a smoothing kernel function \(W(|\underline{r}-\underline{r}^{\prime}|,h)\) as:  
\[f(\underline{r})\approx\langle f(\underline{r})\rangle=\int_{\Omega_{s}}f( \underline{r}^{\prime})\,W(|\underline{r}-\underline{r}^{\prime}|,h)\,dV^{\prime} \tag{2}\]  
where \(dV^{\prime}\) is the weight of integration and \(h\) the smoothing length parameter to determine the size of a finite smoothing domain \(\Omega_{s}\) (i.e., support domain). According to the first SPH publications by [19; 20], the approximation in Equation (2) is valid if the kernel \(W\) is: (1) normalized, meaning \(\int W(|\underline{r}-\underline{r}^{\prime}|,h)\,dV^{\prime}=1\); (2) convergent to \(\delta\) as \(h\) goes to zero; and (3) differentiable at least more than once. In addition to these three essential conditions, [PERSON] [34] suggests that the kernel function should also have a compact support for efficiency reasons. The Wendland quintic [35] and quintic spline [36] functions meet all these conditions, and are chosen for the numerical simulations of this work. To discretize the kernel approximation of SPH, Equation (2) is transformed into a summation over neighboring particles. This can be achieved using, for instance, a Riemann sum over a set of \(N\) neighbors within \(\Omega_{s}\) to evaluate \(f\) at position \(\underline{r}\) via:  
\[\langle f(\underline{r})\rangle\approx\sum_{j=1}^{N}f(\underline{r}_{j})\,W(| \underline{r}-\underline{r}_{j}|,h)\,V_{j} \tag{3}\]  
or rewriting Equation (3) for another particle \(i\) at \(\underline{r}=\underline{r}_{i}\) instead of an arbitrary point:  
\[\langle f_{i}\rangle\approx\sum_{j=1}^{N}f_{j}\,W_{ij}\,V_{j} \tag{4}\]  
where \(V_{j}=m_{j}/\rho_{j}\) is an estimate of the volume assigned to particle \(j\) and the term \(W(|\underline{r}_{ij}|,h)=W(r_{ij},h)\) is abbreviated to \(W_{ij}\) for simplicity. Now, the spatial derivatives of \(f\) can be approximated through the spatial derivatives of the smoothing kernel. A useful formulation of the SPH gradient for multi-phase flows (see in [37]) is given as:  
\[\langle\
abla f_{i}\rangle\approx\rho_{i}\,\sum_{j}\left[\frac{f_{i}}{\rho_{i }^{2}}+\frac{f_{j}}{\rho_{j}^{2}}\right]\,\
abla W_{ij}\,m_{j} \tag{5}\]  
For simulating a powder bed fusion AM process, the computational model needs to account for the thermal, mechanical and material effects. In what follows, an overview of these thermal-mechanical-material equations, as well as their SPH discretizations, is outlined.
## 5 Experimental Results and Discussion
### Deep Urban Canyon
In Figure 15, about 16.7% of the estimated positions are located on the wrong side of the road before using the proposed RAIM. With the adding of RAIM, it is reduced to only 8.3%. Note that the most of the 8.4% reduction of the point with larger error are corrected to about 4 and 5 m. Note that the RAIM method can improve the proposed 3D map only in the case of receiving the multiple NLOS reflections or the abnormal measurements. As a result, positioning solutions with less than 3 m positioning error both before and after using RAIM are very similar, which account for 53.8% and 58.4% of solutions, respectively. In the case of the deep urban canyon, almost half of the solutions estimated by the 3D map method are on the wrong side of the street, as shown in Figure 16. This can be reduced to 13.7% if the RAIM satellite selection is applied. The positioning solutions within a level of 3 m lateral positioning error both before and after using RAIM are 10.2% and 61.5%, respectively. This result shows the RAIM FDE is essential for the proposed 3D map positioning method, especially in the deep urban canyon environment.
## 2 Results and Discussion
### Esters  
There were also significant differences in the type and amount of esters present in the three wines. In general, the number and proportion of esters in Chardonnay wine (34.2%) were higher than those of Cabernet Gernischet (31.0%) and Cabernet Sauvignon (26.3%), but the total ester contents in the three wines exhibited the opposite order. Although their amounts differed in the three wines, ethyl acetate, isoamyl acetate, ethyl lactate and ethyl octanoate were the major esters found in the aroma compounds. Phenethyl acetate and heptyl acetate were absent in Cabernet Sauvignon and Cabernet Gernischet wines, respectively, while isobutyl acetate was unique to Chardonnay wine. Acetate esters are the result of the reaction of acetyl-CoA with higher alcohols formed from degradation of amino acids or carbohydrates [23]. On the other hand, fatty acid ethyl esters are produced enzymatically during yeast fermentation and from ethanolysis of acyl-CoA formed during fatty acid synthesis or degradation. Their concentration depends on several factors: yeast strain, fermentation temperature, aeration degree and sugar content [23]. Ethyl lactate, a product of malolactic fermentation during wine vinicification [24], was lower in Chardonnay wine than in Cabernet Sauvignon and Cabernet Gernischet wines.
# Classifying Microscopic Images of Reactive Lymphocytosis Using Two-Step Tandem AI Models
## 4 Results
### Clinical Assessments of the AI Model
#### 4.2.3 Analysis of Reactive Lymphocytosis Cases  
Table 11  
and average F-measure of 0.857, 0.889, 0.793, and 0.803, respectively. By contrast, the two-step AI model showed values of 0.863, 0.913, 0.841, and 0.869, respectively. All indicators of total accuracy (+0.006), average recall (+0.024), average precision (+0.048), and average F-measure (+0.066) showed improvement in the two-step AI model. Figure 6 shows the recall, precision, and F-measure distribution of the A-lymph category in the clinical assessments. The one- and two-step models showed 0.594 \(\pm\) 0.192 (mean \(\pm\) standard deviation [SD]) and 0.680 \(\pm\) 0.174 for recall, 0.813 \(\pm\) 0.124 and 0.759 \(\pm\) 0.173 for precision, and 0.672 \(\pm\) 0.169 and 0.712 \(\pm\) 0.164 for the F-measure, respectively. A significant improvement in recall was found (\(p\) < 0.01). Figure 7 shows representative images classified into the lymph or A-lymph category by experts and the two-step AI model. A-lymph with strong cytoplasmic basophilia or with moderate cytoplasmic basophilia and intense atypical nuclear changes (appearance of many nucleoli or agglutinated nuclei) were classified into the A-lymph category by both experts and the AI model. Lymph from small to medium size was also classified into the Lymph category by both experts and the AI model. By contrast, large Lymph with mild cytoplasmic basophilia but without atypical nuclear changes was misclassified into A-lymph, and A-lymph with mild cytoplasmic basophilia and atypical nuclear changes was misclassified into Lymph by the AI model. In addition, part of large Lymph or monocyte-like A-lymph (Type I) was misclassified into the Mono category by the AI model. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Experts/AI** & \begin{tabular}{c} **Neutrophil** \\ **(Band/Segment)** \\ \end{tabular} & **Eosino** & **Baso** & **Mono** & **Lymph** & **A-Lymph** & **EB** & **Recall** \\ \hline \begin{tabular}{c} **Neutrophil** \\ **(Band/Segment)** \\ \end{tabular} & 985 & 1 & 46 & 0 & 0 & 0 & 0 & 0.954 \\ \hline \begin{tabular}{c} **Eosino** \\ **Baso** \\ \end{tabular} & 0 & 44 & 0 & 0 & 0 & 0 & 0 & 1.000 \\ \hline \begin{tabular}{c} **Mono** \\ **Lymph** \\ \end{tabular} & 0 & 0 & 18 & 0 & 0 & 0 & 0 & 1.000 \\ \hline \begin{tabular}{c} **Mono** \\ **Lymph** \\ \end{tabular} & 1 & 2 & 0 & 585 & 101 & 41 & 0 & 0.801 \\ \hline \begin{tabular}{c} **Lymph** \\ **A-lymph** \\ \end{tabular} & 1 & 0 & 1 & 49 & 2010 & 77 & 11 & 0.935 \\ \hline \begin{tabular}{c} **A-lymph** \\ **EB** \\ \end{tabular} & 0 & 0 & 0 & 37 & 361 & 720 & 0 & 0.644 \\ \hline \
\begin{tabular}{c} **Precision** \\ **F-measure** \\ \end{tabular} & 0.998 & 0.936 & 0.277 & 0.872 & 0.813 & 0.859 & - \\ \hline
\begin{tabular}{c} **F-measure** \\ \end{tabular} & 0.976 & 0.967 & 0.434 & 0.835 & 0.870 & 0.736 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Clinical Assessment of the one-step AI model for the reactive lymphocytosis cases.
## 6 Conclusions and Future Works  
In this paper an application-specific survey for WBANs wa presented. Currently, rescue and critical missions, mobile workforce safety and management-based applications are not dealt with through wearable technology. However, due to tremendous capabilities and emergence of smart and tiny wearable technologies in near future it will be possible to have effective measure in harsh and disaster environments. In this context, it is necessary to understand the capabilities and limitations of the existing approaches and standards.  
This survey highlights the important features and requirements of the new potential applications for WBANs. It emphasizes on the utilization of the new features introduced in key enabling WBAN-specific standard, _i.e._, IEEE 802.15.6, for the given applications. A comprehensive and comparative study of protocol stack design for WBANs is carried out. It is evident from the current research that existing algorithms and protocols for physical, MAC and routing cannot completely satisfy the specific requirements emerged from the new applications paradigm discussed in this paper.  
Several limitations of the existing research projects are highlighted and further it is mentioned that the lack of IEEE 802.15.6-based radio transceivers and devices are limiting the penetration for the significant and realistic research and applications development. Finally, several open research issues and challenges are discussed and explored as possible topics for future work.
# Research Progress on Machine Learning Prediction of Compressive Strength of Nano-Modified Concrete
## 4 The Application of Machine Learning in Nano-Modified Concrete
### Support Vector Machine (SVM) Algorithm  
The advantage of SVM is that it is very effective in solving classification and regression problems with high-dimensional features, and still has good results when the feature dimension is larger than the number of samples, without having to rely on all the data; when the number of samples is not a huge amount of data, the classification accuracy is high, and the generalization ability is strong.  
SVMs were utilized to develop a predictive model for CS of nano-silica concrete. The input variables include cement content, aggregate, nano-silica properties, water-to-cement ratio (W/B), and prediction duration. The results showed that SVM performed better in CS prediction [24]. For nanomaterials in concrete and cementitious composites, a single ML algorithm (e.g., SVM) is commonly used to construct a prediction model. The boosting models of the SVM algorithm exhibit a strong and resilient relationship with the target values, as indicated by their high R\({}^{2}\) value of 0.88. The error distribution plot further confirms that the SVM boosting models have a small maximum error of approximately 5.77 MPa and a minimum error of only 0.05 MPa. Finally, a graphical user interface was also created [31]. [PERSON] et al. [57] utilized experimental studies and ML techniques to evaluate the CS and resistivity of graphene nanosheet concrete. Three ML methods, Gene Expression Programming (GEP), Random Forest Regression (RFR), and SVM were developed using eight input variables. Five different AI models, namely multivariate adaptive regression spline curve (MARS), M5P model tree (M5P-MT), least squares support vector machine (LS-SVM), multilayer perceptron neural network (MLP-NN), and multivariate linear regression (MLR), were used to predict the CS of fiber-reinforced concrete (FRC) containing nano-silica, among which LS-SVM performed better [31]. The effectiveness of the model was evaluated by employing various AI methods to establish accurate correlations between the input and output variables of the concrete mix, a sample dataset of 1030 concrete compressive strength tests conducted at the University of California, Irvine. Simulation results show that SVM can build reliable predictive models that reduce expensive and time-consuming laboratory tests [58; 59].
# A Filter Feature Selection Algorithm Based on Mutual Information for Intrusion Detection
###### Abstract  
For a large number of network attacks, feature selection is used to improve intrusion detection efficiency. A new mutual information algorithm of the redundant penalty between features (RPFMI) algorithm with the ability to select optimal features is proposed in this paper. Three factors are considered in this new algorithm: the redundancy between features, the impact between selected features and classes and the relationship between candidate features and classes. An experiment is conducted using the proposed algorithm for intrusion detection on the KDD Cup 99 intrusion dataset and the Kyoto 2006+ dataset. Compared with other algorithms, the proposed algorithm has a much higher accuracy rate (i.e., 99.772%) on the DOS data and can achieve better performance on remote-to-login (R2L) data and user-to-root (U2R) data. For the Kyoto 2006+ dataset, the proposed algorithm possesses the highest accuracy rate (i.e., 97.749%) among the other algorithms. The experiment results demonstrate that the proposed algorithm is a highly effective feature selection method in the intrusion detection.  
feature selection; mutual information; intrusion detection; classification +
Footnote †: journal: Applied Sciences