esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Objective Electromagnetic Environmental Effects Assessment
### Adaptive Evaluation Method
Third, the proposed models are based on basic neural network methods, which makes them difficult to apply well to other strong nonlinear evaluation methods. In addition to the above assessment methods, there are also system dynamic methods [45], exponential methods [46], game theory methods [47], and others in the quantitative assessment of objective electromagnetic environmental effects. Generally, regardless of the methods, the above all need to classify the electromagnetic environment into four levels: slightly, mildly, moderately, and severely. Besides this, it also comprehensively determines the complexity of the electromagnetic environment in four domains. In practice, more evaluation indicators are involved, and each indicator needs to be weighted to evaluate the comprehensive electromagnetic environmental effects. In these studies, it is crucial to obtain more accurate weighting coefficients. In some studies, each level can be refined and divided into several internal levels to make the evaluation more accurate. Although the evaluation criteria at each level are similar, there is no uniform and strict definition of this value. Moreover, these studies mainly quantify the evaluation effects of an electromagnetic environment and pay less attention to the adaptability of the research of the equipment in the electromagnetic environment. However, the electromagnetic environmental effects assessment for the specific equipment is more meaningful in practice [32].
###### Abstract  
Electrochemical machining (ECM) is a preferred advanced machining process for machining Monel 400 alloys. During the machining, the toxic nickel hydroxides in the sludge are formed. Therefore, it becomes necessary to determine the optimum ECM process parameters that minimize the nickel presence (NP) emission in the sludge while maximizing the material removal rate (MRR). In this investigation, the predominant ECM process parameters, such as the applied voltage, flow rate, and electrolyte concentration, were controlled to study their effect on the performance measures (i.e., MRR and NP). A meta-heuristic algorithm, the grey wolf optimizer (GWO), was used for the multi-objective optimization of the process parameters for ECM, and its results were compared with the moth-flame optimization (MFO) and particle swarm optimization (PSO) algorithms. It was observed from the surface, main, and interaction plots of this experimentation that all the process variables influenced the objectives significantly. The TOPSIS algorithm was employed to convert multiple objectives into a single objective used in meta-heuristic algorithms. In the convergence plot for the MRR model, the PSO algorithm converged very quickly in 10 iterations, while GWO and MFO took 14 and 64 iterations, respectively. In the case of the NP model, the PSO tool took only 6 iterations to converge, whereas MFO and GWO took 48 and 88 iterations, respectively. However, both MFO and GWO obtained the same solutions of EC = 132.014 g/L, V = 2406 V, and FR = 2.8455 L/min with the best conflicting performances (i.e., MRR = 0.242 g/min and NP = 57.7202 PPM). Hence, it is confirmed that these metaheuristic algorithms of MFO and GWO are more suitable for finding the optimum process parameters for machining Monel 400 alloys with ECM. This work explores a greater scope for the ECM process with better machining performance.  
electrochemical machining (ECM); material removal rate (MRR); nickel presence (NP); grey wolf optimizer (GWO); moth-flame optimization algorithm (MFO); Monel 400 alloys
# Hypergraph-Driven High-Order Knowledge Tracing with a Dual-Gated Dynamic Mechanism
## 5 Experiments
### Experiment Results
#### 5.4.2 Ablation Study  
To validate the capability of hypergraph neural networks in capturing high-order relationships among knowledge concepts and examine the limitations of traditional binary Q-matrices(defining exercise-knowledge concept relationships), this study compares the performance between knowledge tracing models using hypergraph-enhanced embedding methods and those relying on static Q-matrix. Across three datasets, we evaluate the HGKT model and its two constructed variants against the LPKT baseline.  
* HGKT: Uses Q-matrix only for related learning gain;
* HGKT-LG: Replaces related learning gain with direct learning gain (no Q-matrix usage;)
* HGKT-Q: Entirely eliminates Q-matrix dependency;
* LPKT: Uses Q-matrix for related learning gain and knowledge states update.  
As revealed in Figure 5, the HGKT-Q framework's implementation of hypergraph neural networks achieves significantly better performance than traditional Q-matrix-driven approaches LPKT, demonstrating the effectiveness of high-order relationship modeling in educational data analytics. However, the performance superiority of HGKT over HGKT-LG and HGKT-Q indicates the Q-matrix serves as a static knowledge concept-exercise mapping, its synergistic combination with learning gain through related computation enables precise exercise-specific knowledge enhancement quantification. The performance superiority of HGKT-LG over HGKT-Q indicates Q-matrix's implicit structural benefits even without explicit multiplicative operations, whereas HGKT's optimal performance stems from its Q-matrix-constrained explicit operations that focus learning gains on exercise-relevant concepts. These findings collectively highlight the necessity of integrating static Q-matrix with hypergraph neural networks for effective knowledge tracing.  
Figure 4: The performance comparison of HGKT vs. baseline models.
# Comparative Analysis of Derivatization Reagents for Catecholamines and Amino Acids
###### Abstract  
We compared four derivatization reagents to analyze catecholamines and amino acids by matrix-assisted laser desorption/ionization (MALDI) mass spectrometry, 2,4,6-trimethylpyryylium tetrafluoroborate (TMPy), 2,4-diphenyl-pyranylium tetrafluoroborate (DPP-TFB), 4-(anthracen-9-yl)-2-fluoro-1-methylpyridin-1-1-amin iodide (FMP-10), and triphenyl pyrillium (TPP) were used as derivatization reagents that can specifically modify primary amines or hydroxy groups in target molecules. Three derivatization reagents, not including TPP, reacted with all target molecules. The derived catecholamines dopamine and L-DOPA, and the amino acids GABA and glycine, were efficiently ionized in comparison with non-derivatized targets. Comparative analysis indicated that IMPy and FMP-10 produced general increases in signal-to-noise ratios (S/N), whereas DPP and TPP produced specific increases in the S/N of GABA and DA. Notably, TMPy is a small molecule that efficiently reacts with target molecules due to the absence of high bulk and steric hinderance.  
derivatization; mass spectrometry; dopamine; L-DOPA; GABA; 2,4,6-trimethylpyryylium tetrafluoroborate
# A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification
## 4 Discussion  
In this work, a novel HR robust method for short-term ECG biometric identification was developed. Raw ECG signals were filtered with wavelet denoising and segmented into heartbeats by taking the detected T peak points as delimiters. Then the heartbeat was processed by the proposed QRS-centered resampling strategy and standardized to 400 sampling points. The QRS-centered strategy is inspired and based on the prior ECG identification works: Firstly, to our knowledge, all the existing literature about ECG identification has taken QRS complex or its related form as features and QRS complex is very important for identifying a person [9; 10; 12; 18; 40; 41; 42; 43; 44; 45; 46]. To preserve all potential identity information of the QRS complex, we use a length-fixed window to keep the QRS complex original. Secondly, it is also found that mapping the heartbeat into a regular interval of segments does help to deal with the HR variability problem [5; 20]. Thus, we segment the heartbeat into three parts, which are the first, the QRS and the third part respectively, and unify them. In this way, the extracted heartbeat can correct T wave shift without using traditional QT correction formula, and contain sufficient information of QRS such as QRS negative area, amplitude, and so on at the same time. As a result, HR variability is removed and more potential information is provided for the subsequent steps, which is beneficial for the identification accuracy. After that, PCANet was implemented to learn discrimination among heartbeats from different persons by taking the principal eigenvectors as filter banks. PCANet can be analyzed by comparing it with the Convolutional Neural Network (CNN): Like CNN, the PCANet also has convolution filter bank in each stage; the binary quantization of the PCANet at the output stage performs similar function like the nonlinear layer in CNN; the pooling layer of the PCANet is set to be the block-wise histograms of binary codes. In fact, PCANet can be essentially considered as a CNN model, which has strong capability of feature extraction [26; 47; 48]. Moreover, the PCANet seems to be more likely to achieve better performance than traditional CNN in ECG biometric identification, especially for ECG data without outlier correction. As we all know, ECG is not a strict periodic signal, and local distortions and variation on heartbeats may appear even without HR variability. Traditional CNN may be influenced by these distortions because its convolutional filter bank is learned in a data-adapting way. Compared with traditional CNN, the filter bank of the PCANet is prefixed by analyzing the main difference between subjects based on the combination of patch matrices of the training data. This learning way gives more holistic observations of the original ECG data, and the learned intra-invariance can essentially capture more identification
## 4 Results
### Concentration Analysis
The S-ratio (Figure 8a), defined as \(\text{IRM}_{\text{0-3T}}/\text{IRM}_{\text{1T}}\), is still the most sensitive parameter for estimating the predominance of antiferromagnetic mineralogy--values near to zero--or ferromagnetic minerals--values near the unity. The entire profile is dominated by magnetite, except in the middle part of the TD9 unit where the presence of a hematite/geothite (high coercivity) mixture is evident. It is important to recall, though, that the presence of high coercivity component could be revealed only if it represents at least 95% of the magnetic fraction, otherwise the magnetite covers it up [47]. The ARM/IRM\({}_{\text{1T}}\) ratio (Figure 8b) presents a decrease in TD7 and in the middle TD9, while it seems quite constant through the rest of the profile. A key ratio for magnetic grain size is the \(\text{IRM}_{\text{1T}}/\chi_{\text{1f}}\) (Figure 8c), as high values correspond to small size particles, whereas low values correspond to large grain size. The profile variations reveal maximum values in the middle part of unit TD9, indicating the minimum particle grain size. We will contrast this distribution with the King plot. Figure 7: Remanent magnetization profiles: (**a**) saturation magnetization (\(\text{M}_{\text{s}}\))—taken from the hysteresis loops—(**b**) antihysteretic remanent magnetization (ARM), (**c**) isothermal remanent magnetization at 1 T (\(\text{IRM}_{\text{1T}}\)). Simplified stratigraphic log to the left. In order to discriminate the relative concentration of each magnetic component, we carried out a particular study based on the contrasting coercivity values and Curie points. We initially produced a saturation magnetization (SIRM) at 5 T followed by alternating field demagnetization at 100 mT to remove the contribution of any \"soft\" ferrimagnetic minerals (e.g., magnetite). Next, we performed one step of thermal demagnetization at 130 \({}^{\circ}\)C to remove the goethite contribution. Therefore, after these two steps, hematite should be the only magnetic mineral contributing to the total magnetization [37]. We plotted the relative contribution of magnetite versus goethite and hematite, called M/GH (Figure 9a), and goethite versus hematite, G/H (Figure 9b), obtained, respectively, by:  
\[\frac{\mathrm{M}}{\mathrm{GH}}=\frac{\mathrm{SIRM}_{\mathrm{5T}}-\mathrm{SIRM }_{\mathrm{100 mT}}}{\mathrm{SIRM}_{\mathrm{5T}}} \tag{2}\]  
and  
\[\frac{\mathrm{G}}{\mathrm{H}}=\frac{\mathrm{SIRM}_{\mathrm{100 mT}}-\mathrm{ SIRM}_{\mathrm{130}\,^{\circ}\mathrm{C}}}{\mathrm{SIRM}_{\mathrm{100 mT}}} \tag{3}\]The M/GH ratio (Figure 9a) shows a slight decrease of the magnetite concentration in TD7 unit. In TD8.9, there is an increase in this mineral, whereas in the middle part of TD9, we find the minimum values of magnetite, as also revealed from previous parameters. Finally, in all subunits of TD10, this content is more or less constant. On the other hand, the G/H ratio variation (Figure 9b) is not conclusive at all. Although, a lower goethite concentration in the TD8.9 unit and a higher abundance in the middle-upper part of TD9 are appreciable.
# AI-Assisted Security Alert Data Analysis with Imbalanced Learning Methods
## 2 Related Work
### Limitations  
Random undersampling involves discarding the majority class data, and these may contain important information essential for constructing rule-based classifiers, potentially leading to failure or decreased performance. However, naive oversampling by replicating minority class samples can lead to overfitting and, thus, less effective models [6]. Other oversampling techniques, such as SMOTE for nominal (SMOTEN), assume that the data to be oversampled have only categorical features. We discovered that, when used for security alert data oversampling, SMOTEN produced poor results. In addition, some oversampling techniques do not consider neighboring examples from other classes, leading to increased class overlap and additional noise. Moreover, they have limitations when used in extremely imbalanced data scenarios. Cost-sensitive learning techniques are likely to fail if positive instances are sparse, whereas clustering algorithms are prone to overfitting.  
Some intrusion detection and prioritization methods encompass manual time-consuming alert analysis procedures, where a report is sent to security operators with alerts listed in ascending order of importance. Deep learning approaches are characterized by complex data models, making them extremely expensive to train, and the lack of feature extraction in some of these approaches leads to a long training time. In addition, most of these approaches concentrate on evaluation metrics that are not well suited for highly imbalanced datasets, such as accuracy and recall, because most benchmark datasets are not highly imbalanced. In the proposed approach, an ensemble resampling technique is implemented to tackle the highly skewed data issue often observed in security alert data and to improve positive alert detection while keeping the FPR low.
# ADPSCAN: Structural Graph Clustering with Adaptive Density Peak Selection and Noise Re-Clustering
## 5 Experiments  
In this section, we evaluate our proposed algorithm, ADPSCAN, on synthetic and real networks to demonstrate its advantages.  
**Selection of Comparative Methods.** To assess the performance of ADPSCAN, we compare it against state-of-the-art structural graph clustering algorithms. Additionally, to thoroughly validate the effectiveness of our method, we also compare it with five other representative graph clustering algorithms.  
**DistanceSCAN**[24] is the state-of-the-art graph clustering algorithm based on distance, which utilizes structural similarities and distance information between nodes to identify core nodes and their community structures within a graph.  
**GS*-Query**[17] is a recently proposed graph clustering approach, which is an index-based approach.  
**DPSCAN**[25] is a density peak-based structural graph clustering method that identifies core nodes using density and similarity metrics.  
**FludC**[15] is also a recently proposed graph clustering approach, which is based on the idea of fluids interacting in an environment.  
**Girvan-Newman**[26] (GN) is a method used in network analysis to detect community structure in networks. It identifies communities within networks by iteratively removing edges with high betweenness centrality, revealing clusters of densely interconnected nodes.  
**SCAN**[16] is a method that detects communities in networks by clustering nodes based on structural similarity and hop-reachability, distinguishing hubs and outliers effectively.  
**Evaluation Measures.** To conduct a comprehensive comparison of the performance of various graph clustering algorithms, we employ two prevalent metrics: NormalizedMutual Information (NMI) and Adjusted Rand Index (ARI), both of which are commonly used to gauge the effectiveness of clustering outcomes.
# UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review
## 4 UAV-Enabled MEC and Assisted MEC Based on AI
In [136], the authors have used the semi-Markov process and DRL-based algorithms to address the UAV-enabled MEC networks' offloading decisions and resource management policies. The authors of [137] have developed a platform known as a flying UAV MEC, in which UAVs are outfitted with computing resources and provide task-offloading services to users. The suggested architecture's primary purpose was to maximize UAV aerial trajectory, user association, and resource allocation. The trajectory control method has been used to address the optimization issue, assisted by an RL-based technique. The results of this approach outperformed the results of the equivalent benchmark methods. Moreover, the authors of [138] formulated an ML-based novel framework to optimize UAV trajectories by estimating the user's mobility data to find optimal initial UAV placement locations. The joint problem, consisting of UAVs trajectories and power control to improve the sum rate and maintain the data rate requirements of mobile users, was also addressed in the employment of a multi-agent Q-learning-based technique. A smart offloading based on the deep Q-learning approach was proposed by the authors of [139] to optimize performance in terms of the delay perceived by ground devices connected to the FANET, to enable MEC in the 5G field.
## 2 Results and Discussion
### Expression of Three TPSs in Different Tissues and Their Response to MeJA  
From the correlation network, five _TPSs_ (_TPS1-TPS5_), showed a positively correlated expression with \(\beta\)-farnesene content in _C. sinensis. TPS1_ has been functionally verified and proven to act as a linalool synthase/nerolidol synthase [26]. In previous studies, methyl jasmonate (MeJA) has been applied as an elicitor in order to increase the volatiles, including farnesene, in strawberry [27], mango [28] and grapes [29]. Here, to detect the response of the five TPS to MeJA, 1 mM MeJA aqueous solution in 0.05% DMSO was sprayed to _C. sinensis_ shoots, and expression level of these five TPSs were measured. As shown in Figure 5, _TPS2_, _TPS3_ and _TPS5_ strongly respond to MeJA (Figure 5a). Furthermore, the tissue-specific expression level of _TPS2_, _TPS3_ and _TPS5_ were also detected (Figure 5b). _TPS2_ and _TPS5_ are highly expressed in bud and FL, consistent with the accumulation of farnesene in _C. sinensis._ From the response of these 5 TPSs to MeJA and tissue-specific expression analysis, suggesting that _TPS2_ and _TPS5_ are good candidates related to farnesene biosynthesis.  
Figure 4: Correlation networks generated by the integration of expression levels of genes and terpenoids accumulation. The networks were generated by the integration of the expression levels (reads per kilobase per million (RPKM) value) of 75 (of 82) genes (green hexagons) and the accumulation of 41 terpenoids (orange circulars) using Pearson partial correlation analysis (r > 0.8 or r \(<\)\(-\)0.8, \(p\) \(<\) 0.05). Red hexagons, genes related to farnesene, named _TPS1-5_. Red links, positive correlation (r > 0.8): green links, negative links (r \(<\)\(-\)0.8).
## 4 Prediction of the Orphan Wells Locations
Figure 6: The predicted location of the orphan and abandoned wells based on the Random Forest regressor. Each dot on the map represents a well location, with color variations indicating the depth of each well, as shown by the color bar on the right. Depths range from approximately 5000 ft to 30,000 ft, with deeper wells represented in darker blue shades and shallower wells in lighter green shades. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Application** & **Advantages** & **Disadvantages** & **References** \\ \hline \multirow{2}{*}{Historical Records Review} & Ownership, production, and & Provides valuable context for & Often incomplete or difficult & [29] \\  & land use data & well locations & to access & [29] \\ \hline Conversations with & Supplementary information & Can provide & & \\ Local Residents & for well locations & additional insights & May lack reliability & [29] \\ \hline \multirow{2}{*}{Ground-Based Geophysical Methods} & Ground-penetrating radar, & Enables deep subsurface & Requires specialized & [29] \\  & electrical resistivity, seismic refraction, magnetometry & investigations & equipment and expertise & [29] \\ \hline \multirow{2}{*}{Aerial Surveys} & Drone-based magnetometry, & Effective for identifying & & \\  & LiDAR scanning, methane & surface anomalies and & Costly and requires & \\  & detection, satellite data & gas emissions & professional operation & [30] \\ \hline \multirow{2}{*}{Visual Methods and Observational Studies} & Aerial photographic & Aids in identifying & Depends on the presence of & \\  & interpretation, soil and & well locations & discernible surface indicators & [29] \\  & bedrock studies & & & \\ \hline \multirow{2}{*}{Methane Detection} & Identifying gas leaks at & Useful for detecting methane & Dependent on favorable & [29] \\  & well sites & emissions & surface conditions & \\ \hline \multirow{2}{*}{Excavation} & Direct verification of & Provides definitive well & High financial cost and may & [29] \\  & well locations & location confirmation & not always yield results & [29] \\ \hline \multirow{2}{*}{Electromagnetic Surveys} & \multirow{2}{*}{Subsurface investigations} & Adds to well location efforts & Limited applicability and & [29] \\  & & under specific conditions & requires interpretation & [29] \\ \hline \multirow{2}{*}{Pressure Transient Analysis} & Analyzing pressure changes to & Helps locate wells under & Requires specific conditions to & [29] \\  & locate wells & specific conditions & be effective & [29] \\ \hline \multirow{2}{*}{Site Disturbance Surveys} & Identifying site disturbances & Provides additional data for & Requires fieldwork and & [29] \\  & related to well activity & well identification & professional analysis & [29] \\ \hline \multirow{4}{*}{Unmanned Aerial Vehicles (UAVs)} & UAVs equipped with & & & \\  & advanced sensors (e.g., iPhone & & \\  & 12 mini (Apple, Cupertino, CA, & & \\  & USA) on DI Plantan 4 (DI, & & \\  & Shenzhen, China)) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Methods to locate orphan wells.