esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Association Rules among Multiple Elements  
#### 4.2.1 Association Rule Mining  
The 4959 geochemical sampling points were each taken as an event in the Apriori algorithm. Then, we reorganized the geochemical concentration data into the original dataset, D, for association rule mining according to clustering by local univariate Moran's I, as shown in Table 7. Table 8 shows the statistics for the LMIC analysis. Some items frequently appear in events, whereas some items are very sparse. If the support threshold in the Apriori algorithm is set too low, the efficiency of the mining algorithm is low, and a large number of meaningless rules may be extracted. If the support threshold is set too high, the efficiency of the mining algorithm is high, but it may filter out some sparse items. For this study, we set the support threshold a \(\mathrm{S_{min}}\) = 0.05 and the confidence threshold at \(\mathrm{C_{min}}\) = 0.7.  
\begin{table}
\begin{tabular}{c c} \hline \hline \(\mathbf{I_{ab}}\) & **Negative Correlation** \\ \hline \(I_{ab}\)\(=-0.13\) & La-Co \\ \(I_{ab}\)\(=-0.15\) & La-Cu \\ \hline \hline \end{tabular}
\end{table}
Table 6: Bivariate global Moran’s I for elements with negative correlations.  
Figure 11: Clustering map of bivariate local Moran’s I with mainly negative cross correlation.  
We used the Apriori algorithm to mine out dozens of association rules, of which 15 rules were selected for interpretation (Table 9). The supports for Au and Pb in the Apriori algorithm are lower than the threshold, so no relevant association rules were mined. Meanwhile, the relevance of these association rules was judged according to the coexistence of elements and the geological environment in the study area.
## 2 Materials and Methods
### The Proposed PSO-SVM-RFE-MSSA-PNN Model  
The PNN structure diagram reveals that the smoothing factor affects the classification performance of PNN straightforwardly. If it is too large or too little, the convergence of the network will be too quick or too slow, preventing the optimal solution from being identified. As a result, the diagnostic accuracy and classification performance of the PNN will be drastically diminished. Since MSSA has significant benefits over other optimization algorithms in terms of global search and population diversity, this research employs MSSA to improve the classification performance of PNN by locating an appropriate one. To save storage capacity and improve the accuracy of diagnosis, we use PSO-SVM-RFE for feature selection. It eliminates redundant features and minimizes the input's feature dimension, thus obtaining a highly representative feature volume for various fault types. The design process of PSO-SVM-RFE-MSSA-PNN is shown in Figure 3, and the specific steps are as follows:  
1. The data samples were entered into PSO-SVM-RFE and ranked in order of importance for each feature.
2. The output layer takes the type corresponding to the maximum output weight obtained by the summing layer as the output type, and the result is as follows:  
\[\mathrm{Type}(v_{i})=\arg\max(v_{i}) \tag{20}\]  
Figure 2: The structure of PNN.  
* Select the specified number of features to construct feature subsets based on the ranking results, and obtain the simplified sample dataset based on the feature subsets.
* The simplified data samples are preprocessed and then randomly input to PNN.
* The initial parameters of MSSA are set as follows: the number of populations N, dimension d, and the maximum number of iterations \(l_{max}\). In addition, the population positions of MSSA are initialized by Equation (12).
* The fitness of salp individuals in the initial population is calculated and ranked. The fitness function in this paper is set as the mean square error function, as shown in Equation (20): \[f(x)=\sqrt{\frac{1}{n}\sum_{i=1}^{N}(Y_{i}-O_{i})^{2}}\] (20) where \(Y_{i}\) and \(O_{i}\) are the training accuracy and testing accuracy under the effect of a particular smoothing factor, respectively.
* The salp individual location with the best fitness was considered to be the current food location. Of the remaining N-1 salp individuals, the most adaptable salp is considered the leader, and the rest are considered followers.  
Figure 3: The proposed PSO-SVM-RFE-MSSA-PNN method for fault diagnosis.  
* Update \(c_{1}\) according to Equation (14). Step 8: Update the leader position by Equation (13) and the follower position by Equation (15). Step 9: The following process is continued if the maximum number of iterations is reached or the preset conditions are met. If not, return to step 6. Step 10: At the end of the training process, the optimized smoothing factor \(\sigma\) is input into PNN to obtain a PNN with global optimization performance. Then, input the test sample data into PNN to obtain the final diagnosis result.
# Qualification of Hydraulic Analysis Models for Optimal Design of Water Distribution Systems
## 3 Application and Results
### Description of Applied Water Distribution Systems  
As mentioned in the previous section, the three WDSs selected for the application of our model are shown in Figure 1.  
The two-loop network [24] is a small-size network that allows us to verify the validity of our results. This network is represented in Figure 1 and consists of 6 demand nodes, 1 reservoir, and 8 pipes. The Hanoi network [25] is a famous system located in the city of Hanoi, Vietnam, and has been frequently used by researchers. It consists of 31 demand nodes, 1 single reservoir, and 34 pipes. The third water distribution system is a large-scale network called the Balerma network [26]. It is located in Italy and consists of 443 demand nodes, 4 reservoirs, and 454 pipes.
# Applications of Nanomaterial Coatings in Solid-Phase Microextraction (SPME)
## 4 Advances in the Adsorbent Type Used in the Extraction Phase of the SPME Fiber
### SPME Based on Nanoparticles
#### 4.1.2 Metal Oxide Nanoparticles in SPME  
Metal oxide nanoparticles (MONPs) are important in physics, chemistry, and materials research because of their various structural shapes and electrical characteristics, ranging from being insulators to being conductors. MONPs such as iron oxide, aluminum oxide, titanium oxide, and others are gaining popularity as efficient and cost-effective sorbents. Their nanoscale size results in a vast surface area and strong reactivity, making them excellent for removing heavy metals and dyes from aquatic environments. Furthermore, their functional properties--which are influenced by factors including crystal structure, composition, and morphology--support a variety of pollution control applications, especially as ecologically friendly adsorbents in wastewater treatment [66; 67; 68; 69; 70; 71; 72]. Metal oxides are becoming more and more popular in chemical sensors and extraction materials, particularly for SPME, because of their excellent stability, adsorption capacity, and affordability. Environmental applications can benefit from the high surface-to-volume ratio and superior thermal stability of metal oxides such as cobalt oxide, titanium dioxide, lead oxide, zirconium oxide, and zinc oxide. Their special qualities, which change depending on their structure and content, make them very efficient sorbents.  
TiO\({}_{2}\), Fe\({}_{3}\)O\({}_{4}\), MnO\({}_{2}\), ZnO, and CuO are examples of metal oxide nanoparticles (MONPs) that are frequently employed in wastewater treatment to eliminate heavy metals and dyes. Their expanding relevance in pollution management and green chemistry solutions is highlighted by their usefulness for environmental remediation due to their photocatalysis, adsorption, and antibacterial action properties [45; 66; 73; 74]. Subsequently, we will review some of these MONPs in detail.
## 4 Fault Protection Strategy
### Voltage Protection  
\(\bullet\) Bus over-voltage protection  
When the bus-voltage exceeds \(U_{r}\), an over-voltage signal is generated by a comparator. If the signal lasts longer than \(t_{r}\), switch \(T7\) will be turned ON and the pumping voltage will discharge through the brake resistance \(R\), as shown in Figure 6.  
Switch \(T7\) should keep the conducted state for \(t_{r}\) after the over-voltage signal disappears. The purpose of the conducted delay \(t_{r}\) is to prevent the power device from switching frequently, which helps to reduce losses. However, the delay should not be too long otherwise the brake resistance will be too hot, which may lead to a low efficiency. In this paper, \(t_{r}\) is taken to be 5 ms, which is programable according to the clock period. In order to eliminate the effect of signal jamming on system stability, a signal is recognized as a spike signal when the over-voltage period is less than \(t_{s}\), and can be ignored. Here, \(t_{s}\) is taken to be 1 ms.  
\(\bullet\) Bus under-voltage protection  
Figure 7 shows bus under-voltage protection control logic, and /_UV_ is the protection signal. It is set to \"1\" when the system is under a normal state and \"0\" if the DC-link voltage is lower than a certain value. In order to eliminate the effect of a jamming signal, it is recognized as an interference signal when /_UV_ lasts less than \(t_{r}\). Here, \(t_{r}\) is taken to be 1 ms. If the /_UV_ signal lasts longer than \(t_{s}\), MOSFET \(T8\) will be switched OFF and the drive system will be disconnected from the boost converter to prevent a second over-current fault. When the /_UV_ signal disappears, \(T8\) keeps the cutoff state for \(t_{s}\) before it is back to power. It is noted that the boost converter will take over to regulate the voltage back to normal during \(t_{s}\). In this paper, \(t_{s}\) is taken to be 50 ms.  
Figure 6: Bus over-voltage protection logic.
## 5 Conclusions
### Computational Scalability of DRLCC  
In addition to its scheduling effectiveness, we profiled how the computational cost of our PPO-based scheduler grows with fleet size and workload. Because our state vector scales as O(N) (three features per AMR plus eight queue stats), the neural network input dimension--and thus per-decision cost--grows linearly with the number of robots. On an NVIDIA RTX 3080, we observed the following:  
* Inference latency: Mean time per decision stayed below 0.5 ms (\(\sigma\) < 0.1 ms across seeds) for fleets up to 6 AMRs, ensuring real-time applicability even in our largest scenarios.
* End-to-end training time: Running 500 episodes took approximately 35 min to 45 min for the smallest config (4 AMRs, 80 jobs) and about 1.5 h for the largest (6 AMRs, 120 jobs).
* Policy-update cost: Each PPO update epoch (batch size 64, 6 passes) required on average 8-12 s with 4 AMRs, rising to 12-16 s with 6 AMRs.  
These results indicate that both inference and learning times increase roughly linearly with problem size and remain within practical bounds for typical smart-manufacturing cells. For still larger deployments, one could explore vectorized environments or distributed training to further accelerate convergence.  
Conceptualization, J.-W.K., R.A. and W.-T.K.; methodology, R.A. and J.-W.K.; software, R.A.; validation, R.A. and W.-T.K.; investigation, J.-W.K. and R.A.; writing--original draft preparation, R.A.; writing--review and editing, J.-W.K., R.A. and W.-T.K.; visualization, J.-W.K.; supervision, W.-T.K.; project administration, W.-T.K.; funding acquisition, W.-T.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Technology Innovation Program (Development of SDF-Based AI Autonomous Manufacturing Core Technology to Advance the Automobile Industry) funded by the Ministry of Trade, Industry and Energy (MOTIE), South Korea, under Grant RS-2024-00507388.  
Not Applicable.  
Data Availability Statement: The simulation data (SimPy event logs and metrics) generated and analyzed during this study are not deposited in a public repository but are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.
# Comparing Application-Level Hardening Techniques for Neural Networks on GPUs
## 3 Proposed Evaluation Methodology
### Data Analysis
#### 3.2.1 HTs Implementation Impact Evaluation
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{_Score (Out of 5)_} & \multicolumn{2}{c}{_Effort of Implementation (Complexity + Training Adaptations)_} \\ \cline{2-3}  & _Complexity_ & _Training Adaptations_ \\ \hline
**0** & No architecture modifications & No need of a training step \\ \hline
**1** & \# of replaced layers \(\leq\)3 &
\begin{tabular}{c} Need fine-tuning with \\ new \# epochs \(\ll\) original \# epochs \\ \end{tabular} \\ \hline
**2** & 3 \(<\) \# of replaced layers \(\leq\) 10 &
\begin{tabular}{c} Need fine-tuning with \\ new \# epochs \(\approx\) original \# epochs \\ \end{tabular} \\ \hline
**3** & \# of replaced layers \(\geq\) 10 &
\begin{tabular}{c} Need re-training \\ original setup = new setup \\ \end{tabular} \\ \hline
**4** & \
\begin{tabular}{c} The NN topology is modified \\ (e.g., changed layer ordering) \\ \end{tabular} &
\begin{tabular}{c} Need re-training \\ with the same setup but \\ new \# epochs \(>\) original \# epoch \\ \end{tabular} \\ \hline
**5** & \
\begin{tabular}{c} Additional operations are \\ introduced in normal \\ NN functioning \\ \end{tabular} &
\begin{tabular}{c} Need re-training with \\ a different setup \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Scoring system (out of 5) based on the effort required for implementation, considering both the complexity of NN modifications and the necessary adjustments to the training process. while \(w_{comp}\), \(w_{add}\), \(w_{sub}\), \(w_{mul}\), \(w_{div}\) represent the arbitrary weights corresponding to each operation. EoI and ITO are crucial to determine the feasibility of implementation of a given HT during the design stages of an NN architecture, even before it can be deployed on an edge device. In addition, our overhead estimation can give hints on the power consumption trend because the higher the ITO, the higher the required computational effort, thus, the higher the power consumption.
## 3 The Time Source Techniques
### Wwvb  
WWVB is a time signal radio station located near Fort Collins, Colorado, operated by the National Institute of Standards and Technology (NIST). It continuously transmits digital time codes on a 60 kHz carrier frequency, providing a stable frequency reference traceable to the national standard maintained by NIST [70]. These time signals are broadcast every minute without interruption.  
WWVB utilizes its own clock system, comprising a group of cesium beam clocks, which are steered to align with UTC (NIST) in Boulder. Although the UTC-synchronization accuracy at the WWVB time source can reach up to 35 ns, receivers located at various distances from the WWVB transmitter in Fort Collins, Colorado, experience differing levels of path delays. These delays, determined by the receiver's distance, limit WWVB synchronization accuracy to between 1 and 15 ms [71]. This limitation arises because the system operates in a one-way mode without compensating for path delay, resulting in signal delays of up to 15 ms at some locations within the continental United States [72]. However, if path delays are corrected, WWVB can deliver UTC with an uncertainty of approximately 100 us [70].  
Similar to GPS, due to its ease of application, the cost of adopting WWVB timing is relatively low.  
The WWVB radio time service operates at a short wavelength, allowing its signals to penetrate indoors [73]. However, the 60 kHz radio time signal is highly susceptible to interference from sources such as screens, fluorescent lamps, nearby power lines, and transformers. The signal coverage area can be different in the day and night affected by noise ratio.  
As a result, in terms of time accuracy, the uncertainty level of WWVB time dissemination is insufficient for applications requiring high precision, such as determining phasor angle separation. WWVB is more suitable for remote devices with lower precision and reliability requirements, such as SCADA monitoring or timestamp non critical information.
## 1 Introduction  
The pathological background for stroke may either be ischemic or hemorrhagic disturbances of the cerebral blood circulation. In Caucasian populations and Asia countries approximately 60-80% of all strokes are ischemic. There are limited data sources on type of stroke from other parts of the world because of small sample sizes and being hospital-based [1, 2]. The pathological mechanism of cell and tissue damage caused by cerebral ischemia is complex, involving energy depletion, calcium overload, production of inflammatory mediators, oxidative stress, and other pathogenic factors [3, 4]. Cerebral ischemia could trigger a series of inflammatory responses; however, the energy metabolism pathway and related key enzymes, such as adenosine monophosphate-activated protein kinase (AMPK), perisosome proliferation-activated receptor alpha (PPAR\(\alpha\)), and peroxisome proliferator-activated receptor \(\gamma\)coactivator-1 alpha (PGC-1\(\alpha\)), among others, have protective effects against damage caused by ischemia/reperfusion [5]. An increase in hypoxia inducible factor-1\(\alpha\) (HIF-1\(\alpha\)) was found to be closely related to ischemic neuron damage [6]. The relationship between hypoxia-inducible factor-1\(\alpha\) (HIF-1\(\alpha\)) expression and energy metabolism or inflammatory responses has not yet been clarified, and the regulation of HIF-1\(\alpha\) expression is a potential question of neuroprotection research. Middle cerebral artery occlusion (MCAO) is an acknowledged animal stroke model of cerebral ischemia and can be also use to assess drug neuroprotection in vivo. Compared with other animal models, the MCAO model in rats has the advantages of a consistent location and range of cerebral infarction, good repeatability, and accurate simulation of pathogenesis in humans [7]. Additionally, a simple and effective oxygen glucose deprivation/reoxygenation (OGD/R) neural cells known as ischemia model in vitro can be successfully applied to simulate cerebral ischemia model in vivo.  
Betulinic acid (BA) is a pentacyclic triterpenoid derived chemically from botulin that exerts biological actions, including anti-viral, anti-malarial, anti-microbial, and anti-cancer properties [8; 9]. BA was found to exhibit protective effects on cardiovascular and renal ischemia/reperfusion damage [10; 11]. However, the protection of BA against cerebral ischemia still remains unclear. In the present study, the protective action of BA on cerebral ischemia/reperfusion was investigated in order to elucidate its signal pathways. Clopidogrel, a platelet aggregation inhibitor, functions via inhibiting platelets and is widely acknowledged as a beneficial treatment in shock [12; 13]. Based on these properties, we employed clopidogrel as comparative drug to obtain a comprehensive understanding of BA.
## 4 Aerodynamic Performance Analysis
In order to analyze the influence of the airfoil with a blunt trailing edge on vorticity, the vorticity diagram of the airfoil trailing edge was analyzed, as shown in Figure 8. It can be seen from the figure that the value of \(K\) affects the size of vortices and the position of vortex generation/shedding. When \(\alpha=8^{\circ}\), the airfoil with a blunt trailing edge (\(K=0.6\)) generates smaller vortices, and the vortices on the suction surface and pressure surface are located at the rear of the trailing edge (Figure 8b-\(\lx@sectionsign\)). The vortices generated by the trailing edge of the original airfoil (\(K=0\)) are larger and are all near the suction surface (Figure 8a-\(\lx@sectionsign\)). The shedding/generation of vortices will have a greater impact on the aerodynamic performance of the airfoil. When \(\alpha=12^{\circ}\), this phenomenon changed. The vortices generated by the \(K=0\) and \(K=0.6\) airfoils are near the suction surface, and the vortices generated by the \(K=0.6\) airfoil pressure surface are closer to the suction surface (Figure 8c-\(\lx@sectionsign\)),-d-\(\lx@sectionsign\)). When the position of the vortices generated by the suction surface is the same (Figure 8c-\(\lx@sectionsign\)1),-d-\(\lx@sectionsign\)), the lift coefficientis the same (Figure 7c-(1),d-(2)). It was also found that the closer the vortices generated by the pressure surface are to the suction surface, the smaller the lift coefficient is. The lift coefficient increases with the clockwise vortex fall-off. As shown in Figures 7 and 8, when \(\alpha=8^{\circ}\), the shedding position of the wake vortex with \(K=0.6\) is farther from the airfoil. With the increase in the angle of attack to \(\alpha=12^{\circ}\), the shedding position of the wake vortex with \(K=0\) is farther from the airfoil. Under the same \(K\)-value, the shedding distance of the wake vortex decreases with the increase in the angle of attack. Under the same Reynolds number, the farther the shedding position is, the greater the shedding frequency is, which corresponds to the \(S_{t}\) number in Figure 6f. Figure 6: Aerodynamic performance of airfoil. (**a**) Time domain diagram of lift coefficient. (**b**) Time domain diagram of drag coefficient. (**c**) Lift coefficient at different angles of attack. (**d**) Drag coefficient at different angles of attack. (**e**) Lift–drag ratio at different angles of attack. (**f**) Strouhal number at different angles of attack. Figure 8: Vortex structure at trailing edge. (**a**) \(K=0\), \(\alpha=8^{\circ}\). (**b**) \(K=0.6\), \(\alpha=8^{\circ}\). (**c**) \(K=0\), \(\alpha=12^{\circ}\). (**d**) \(K=0.6\), \(\alpha=12^{\circ}\).
# Inductance Estimation Based on Wavelet-GMDH for Sensorless Control of PMSM
## 4 Implementation of Wavelet-GMDH Based Compensation Control
To further minimize errors, the number of regression layers can be increased to seven or more, but it leads to an increase in the processor load due to the complex computation. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{**Input Variables**} & \multirow{2}{*}{**No. of Layers**} & \multirow{2}{*}{**RMSE (\%)**} & \multirow{2}{*}{**MSE (\%)**} & \multirow{2}{*}{**MAE (\%)**} & \multicolumn{2}{c}{**Linear Regression Index**} \\ \cline{5-8}  & & & & & & **R** & \({}^{2}\) \\ \hline
1 & \(l_{u},l_{b},l_{c}\), \(V_{\text{\tiny diff}}\), \(V_{\text{\tiny diff}}\) & 5 layers & 1.8077 & 3.2678 & 1.4462 & 0.9865 & 0.9732 \\ \hline
2 & & 7 layers & 1.76658 & 3.1208 & 1.4124 & 0.9872 & 0.9746 \\ \hline
3 & \(l_{c},l_{b},l_{c}\), \(V_{\text{\tiny diff}}\), \(V_{\text{\tiny diff}}\), \(l_{a},l_{b}\) & 5 layers & 1.3414 & 1.7994 & 1.0672 & 0.9926 & 0.9853 \\ \hline
4 & & 7 layers & 1.2722 & 1.6185 & 1.0195 & 0.9934 & 0.9868 \\ \hline
5 & \(l_{u},b,l_{c}\), \(V_{\text{\tiny diff}}\), \(V_{\text{\tiny diff}}\), \(l_{a},l_{b}\), \(V_{\text{\tiny diff}}\) & 7 layers & 0.6105 & 0.3727 & 0.4877 & 0.9985 & 0.9970 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of training results for \(q\)-axis inductance. Figure 4: Comparison of three-phase current signals. (**a**) Raw signals without a wavelet filter; (**b**) denoised signals with a wavelet filter. Figure 6 presents the linear regression results, histogram of the estimation errors, and estimation errors of the wavelet-GMDH simulation for case 5 comprising eight input variables with five layers. As shown in Figure 6a, the slope of the fitting line in the linear regression is close to 1. In Figure 6b, analysis of the error histogram indicates that the error values exhibit characteristics of a normal distribution based on a zero-mean, indicating the robustness of the trained model across the dataset. Furthermore, the estimation errors, directly related to the RMSE, MSE, and MAE, appear relatively uniform, with the mean value remaining below 1%, as shown in Figure 6c.