esa-sceva/satcom-chunk-collection huggingface dataset:
# Systematic Evaluation of Different Coating Chemistries Used in Thin-Film Microextraction
## 2 Results and Discussion  
### Optimization of Desorption Solvent for Coating Evaluation  
The first part of this study comprised the optimization of desorption conditions for all the investigated extraction phases. Minimum carryover and complete desorption of the testing probes was essential to ensure reliable data to characterize the studied coatings. In this case, four different desorption solvents including methanol/acetonitrile/water (25/25/50, _v/v/v_, solvent 1), methanol/acetonitrile/water (40/40/20, _v/v/v_, solvent 2), methanol/acetonitrile (80/20, _v/v_, solvent 3), and acetonitrile/water (80/20, _v/v_, solvent 4) acidified with formic acid (0.1%, _v/v_) were evaluated. Figure 1 shows results corresponding to absolute recoveries and carryover values found for the PS-DVB coating (data corresponding to other coating chemistries is listed in Table S1). As can be seen in Figure 1, among the tested solvents, methanol/acetonitrile/water (40/40/20, _v/v/v_ solvent 2) showed the best desorption efficiency, the lowest carryover values for the majority of the tested compounds, and a good compromise in terms of chromatographic separation. Therefore, methanol/acetonitrile/water (40/40/20, _v/v/v_) with formic acid (0.1%, _v/v_) was employed as desorption solvent for the following coating evaluation experiments.
# Research on Interval Optimal Scheduling Strategy of Virtual Power Plants with Electric Vehicles
## 3 Optimized Scheduling Model  
The interval multi-objective optimization problem can be defined as follows:  
\[\left\{\begin{array}{l}\min\limits_{x\in\Omega}F(x)=({f_{1}}^{1}(x,c),{f_{1} }^{2}(x,c),\cdots,{f_{M}}^{1}(x,c))\\ s.t.\quad g_{j}(x,c)\geq a_{j}=[\underline{a_{j}},\overline{a_{j}}],\quad j=1,2,3,\ldots,p\\ \quad h_{k}(x,c)=b_{k}=[\underline{b_{k}},\overline{b_{k}}],\quad k=1,2,3, \cdots,q\\ \quad x\in\Omega\end{array}\right. \tag{12}\]  
Figure 4: Number of electric vehicles connected to the grid.  
Figure 5: Dispatching potential of electric vehicle cluster.  
where \(a=[\underline{a},\overline{a}]\) is the so-called interval number, \(w(a)=\overline{a}-\underline{a}\) is the width of \(a\),\(m(a)=(\underline{a}+\overline{a})/2\) is the midpoint of \(a\), \(x\) is the decision variable, \(\Omega\) is the decision space, \(c\) is the interval vector with \(c\) = (\(c_{1}\), \(c_{2}\),..., \(c_{l}\))\({}^{T}\), for which the \(i\)-th component \(c_{i}\), there are \(c_{i}=[c_{i},\overline{c}_{i}]\), \(g_{j}\) (\(x\), \(c)\geq a_{i}\) is the interval inequality constraint, \(h_{k}\) (\(x\), \(c\)) = \(b_{k}\) is the interval equation constraint [37], and \(f_{i}^{l}(x,c)\) denotes the \(i\)-th objective function as follows:  
\[f_{i}^{l}(x,c)=[\underline{f_{i}^{l}(x,c)},\overline{f_{i}^{l}(x,c)}] \tag{13}\]  
\[\underline{f_{i}^{l}(x,c)}=\min_{y\in c}f_{i}(x,y) \tag{14}\]  
\[\overline{f_{i}^{l}(x,c)}=\max_{y\in c}f_{i}(x,y) \tag{15}\]  
where \(x\) and \(y\) are the decision variables of the outer/inner level optimization problem, respectively. It is known that the interval multi-objective optimization problem is essentially a two-level optimization problem.  
In this paper, an optimal dispatching model incorporating electric vehicles is developed, in which uncertainties such as photovoltaic power, wind power, electrical load, and gas load are described using interval numbers as follows:  
\[[P_{PV,t}]=[\underline{P_{PV,t}},\overline{P_{PV,t}}] \tag{16}\]  
\[[P_{WPP,t}]=[\underline{P_{WPP,t}},\overline{P_{WPP,t}}] \tag{17}\]  
\[[P_{load,t}]=[\underline{P_{load,t}},\overline{P_{load,t}}] \tag{18}\]  
\[[V_{gas,t}]=[\underline{V_{gas,t}},\overline{V_{gas,t}}] \tag{19}\]  
The decision variables of the scheduling model are the power dispatch power of each device in the virtual power plant, as shown in Table 2.
# AE-CGAN Model based High Performance Network Intrusion Detection System
###### Abstract  
In this paper, a high-performance network intrusion detection system based on deep learning is proposed for situations in which there are significant imbalances between normal and abnormal traffic. Based on the unsupervised learning models autoencoder (AE) and the generative adversarial networks (GAN) model during deep learning, the study aim is to solve the imbalance of data and intrusion detection of high performance. The AE-CGAN (autoencoder-conditional GAN) model is proposed to improve the performance of intrusion detection. This model oversamples rare classes based on the GAN model in order to solve the performance degradation caused by data imbalance after processing the characteristics of the data to a lower level using the autoencoder model. To measure the performance of the AE-CGAN model, data is classified using random forest (RF), a typical machine learning classification algorithm. In this experiment, we used the canadian institute for cybersecurity intrusion detection system (CICIDS)2017 dataset, the latest public dataset of network intrusion detection system (NIDS), and compared the three models to confirm efficacy of the proposed model. We compared the performance of three types of models. These included single-RF, a classification model using only a classification algorithm, AE-RF which is processed by classifying data features, and the AE-CGAN model which is classified after solving the data feature processing and data imbalance. Experimental results showed that the performance of the AE-CGAN model proposed in this paper was the highest. In particular, when the data were unbalanced, the performances of recall and F1 score, which are more accurate performance indicators, were 93.29% and 95.38%, respectively. The AE-CGAN model showed much better performance.  
Keywords:NIDS; GAN; autoencoder; imbalanced data; deep learning; oversampling +
Footnote †: journal: Applied Sciences
# Multi-Objective Production Scheduling of Perishable Products in Agri-Food Industry
## 2 Scheduling of Agri-Food Industry
### Scheduling and Optimization Problems
#### 2.2.1 Exact Methods  
The scheduling problems are solved using optimization methods considering the problem data as constraints to be satisfied and by proposing an optimal and permissible solution. The optimality of the decision variables is measured against the criteria and objectives established by the higher hierarchical decision level. We distinguish between the methods \"Separation and Progressive Evaluation\" or \"Branch and Bound\", linear programming, and dynamic programming. These exact methods, in an implicit way, examine the totality research space and produce, in principle, an optimal solution [30]. When the computation time necessary to reach this solution is excessive, the approximate methods can provide a quasi-optimal solution after a reasonable calculation time.
# Dynamic Modeling and Full-Process Simulation of the Core Engine Test Rig Main Test System
## 5 Results and Discussion
### Normal-Temperature Operating Condition
The relative opening of F3 changes slightly while the relative openings of F9 and F10 change suddenly at 578 s, 758 s, 937 s, and 1060 s, and the simulation curve has obvious peaks at these four moments while the peak of the test curve is less obvious, which, together with the inaccuracy of the test results in the first 60 s, indicates that the flow-rate result measured by the total static pressure method is not necessarily accurate. As shown in Figure 9c, the variation trend of the simulation curves of temperature, total temperature, and wall temperature at GP22 outlet grid is consistent with that of the total temperature test curve at the engine inlet, and with the opening of F9 at 60 s, these curves keep moving down until the end of this operating condition at 1400 s due to the deflation effect, which can also be seen from the decrease of the GP1 temperature curve in Figure 9a. The maximum error between the simulation result and the test result of GP22 total temperature is less than 3%. As shown in Figure 9d, the variation trend of the total pressure simulation curves of GP23 initial and middle grids are consistent with that of the total pressure test curve in the front section of the exhaust bypass, and they are only different locally. As F10 is fully open in the first 60 s, the total pressures in the exhaust-bypass front section gradually increase from the initial value close to the atmosphere pressure with gradual opening of F3 during 16-35 s, reach the maximum value around 35 s, and then remain at around 0.18 MPa until 60 s or so. Afterwards, the total pressure curves vary with the change of F10, F9, and F3 opening curves and the pressure reduction of high-pressure gas cylinders. The maximum error between simulation and test is less than 12%. As shown in Figure 9e, the variation trend of the flow-rate simulation curve at GP23 initial grid is the same as that of the flow-rate test curve of the exhaust bypass. Because of the problem of flow-rate measurement method and the modeling accuracy of F3, F9, and F10 valves at some openings under normal-temperature condition, the simulation curve is mostly higher than the test curve. Due to the limited test data and technical specifications, the modeling accuracy of F3, F9, and F10 valves cannot be further improved. However, the mean error between simulation and test is less than 19%. As shown in Figure 9f, in the 0-1400 s dynamic change process, the variation trend of the total temperature simulation curves in the front section of the exhaust bypass is consistent with that of the test curve, except that the simulation curves have a peak around 16 s when F3 opens, and the maximum error in the rest of the time period is less than 2%.
# Structure-Enhanced Prompt Learning for Graph-Based Code Vulnerability Detection
## 2 Related Work  
### Learning-Based Vulnerability Detection  
In recent years, the application of deep learning techniques has significantly advanced the field of vulnerability detection. Pioneering research by [15] utilized Convolutional Neural Networks (CNNs) to interpret code as if it were a natural language, subsequently employing Random Forest classifiers for the actual detection task.  
[PERSON] [16] utilized BiLSTM models alongside code gadgets to identify vulnerabilities associated with library or API calls. This approach was later refined by SySeVR [17], which incorporated data and control dependencies for a more comprehensive detection framework. Furthermore, [PERSON] [18] applied centrality analysis to simplify program dependency graphs, enhancing the efficiency of CNN-based vulnerability detection.  
Graph-based methods have become prominent in vulnerability detection. [PERSON] utilizes Graph Neural Networks (GNNs) to model code dependencies effectively. In contrast, [PERSON] [8] analyzes the dependency graphs of the program by dividing them into subgraphs for GNN processing. To enhance detection accuracy, ReGVD incorporates residual connections within GNN layers. CPVD [19] represents code through code property graphs, employing Graph Attention Networks and Convolution Pooling Networks to extract feature vectors. Similarly, MAGNET [20] constructs a multi-granularity meta-pathgraph to capture the structural information of code snippets. GCL4 SVD [21] combines Code Graph Embedding to identify vulnerability patterns with Graph Confident Learning Denoising to reduce noise and improve accuracy. Furthermore, TrVD [22] and SCALE [23] apply tree structures to enhance detection performance.  
Other methods include VulCNN [24], which transforms code into image-like representations, and CSGVD, which uses pretrained language models for feature initialization.  
Previous methods typically simplified vulnerability detection into binary classification tasks, reducing labels to a 0/1 format. This simplification hindered the model's ability to capture rich, high-dimensional features and led to the loss of critical spatial and structural details. In contrast, our approach introduces prompts with task-specific structural information, which enhances the model's navigation within high-dimensional feature space. This allows the model to preserve complex semantic and spatial attributes, resulting in a more detailed and accurate vulnerability classification.
# A Cumulant-Based Method for Acquiring GNSS Signals
## 1 Introduction
Acquisition is a critical step in the GNSS receiver's positioning process, but issues such as false lock (e.g., code phase searching error) can occur. This can lead the GNSS receiver to incorrectly estimate the ranges between users and satellites, potentially resulting in positioning errors. Furthermore, the presence of multiple side-peaks in the BOC autocorrelation function makes Galileo satellite signal acquisition more challenging when using conventional methods. This study proposes a novel acquisition method in which we utilize the parallel FFT alongside the cumulant to suppress noise and reduce side-lobe effects during the acquisition of Galileo satellite signals. The remainder of this paper is organized as follows: Section 2 provides a brief introduction to the Galileo system and BOC signals. Section 3 describes the proposed acquisition method in detail. Simulation and experimental results are presented in Section 4. Finally, conclusions are drawn in Section 5.
# Open-Pit Map: An HD Map Data Model for Open-Pit Mines
## 5 Application
### Autonomous Driving System  
This autonomous driving software architecture is built on the foundation of vehicle-mounted computing units and primarily consists of scene application software, sensor inputs, a functional software platform, and execution layer software modules.  
Based on the actual mining production and the scope of vehicle transport activities, the open-pit mining site is divided into four major basic scenarios: ore transportation, mining, unloading, and parking (fueling, maintenance). Each basic scenario includes sub-scenarios such as automatic tracking, automatic following, obstacle avoidance, dynamic planning, automatic parking, automatic start and stop, vehicle infrastructure cooperation, and more. It also includes safety management modules for cooperative vehicle infrastructure mode management, scheduling management, functional safety, fault diagnosis, etc. This forms autonomous driving control strategies for different scenarios within the mining area, ensuring the safe and stable operation of vehicles.
# Adaptive Multi-Channel Residual Shrinkage Networks for the Diagnosis of Multi-Fault Gearbox
## 5 Conclusions  
In this paper, fault diagnosis under multi-component faults in gearboxes is investigated and a new intelligent fault diagnosis method, namely AMC-RSN, is proposed. The effectiveness of AMC-RSN is verified by three different types of datasets. Compared with other residual networks, the experimental results show that AMC-RSN achieves the best diagnosis under different types of multi-fault classification and noise interference. The summary is as follows: (1) The maximum pooling channel can effectively extract the fault target features; (2) the introduction of the Meta-ACON activation function can effectively improve the classification accuracy; (3) through cross-comparison, it can be seen that the proposed method can extract richer features of the gearbox with multiple faults. The study is for the diagnosis of target faults under multiple faults, which can be interpreted as the diagnosis of important part faults under multiple fault conditions. It was also tested that AMC-RSN can have good diagnosis under mixed faults, but the limitation is that it has not yet distinguished the specific faults of mixed faults. We will continue to follow up with research on how to identify the specific faults of each component under multiple faults.  
Conceptualization, X.L.; methodology, W.C. and K.S.; software, W.C.; validation, W.C. and K.S.; formal analysis, X.L.; investigation, Y.X. and J.X.; data curation, H.M.; writing--original draft preparation, W.C.; writing--review and editing, W.C.; visualization, W.C.; supervision, X.L.; funding acquisition, H.M. All authors have read and agreed to the published version of the manuscript.  
Figure 12: AMC-RSN with visualization of confusion matrix. (**a**) Case I. (**b**) Case. II. (**c**) Case III.  
**Funding:** This research is funded by the National Natural Science Foundation of China (51365006), National Natural Science Foundation of China (62073090), Natural Science Foundation of Guangxi Province (2018 GXNSFAA180206) and Natural Science Foundation of Guangxi Province (2018 GXNSFAA 281312).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results and Discussions
### Sensorless Speed Control When There Are Changes in the Switching Angle  
The estimated and actual speeds responses are shown in Figure 22a,b, with the changes of the switching angles to 11 degrees and 13 degrees, respectively, at 0.02 s. The observer estimated speeds almost approach the reference speeds, even though the change of the switching angles, but the actual speed, deviates quietly. The speed-tracking error graphs for both cases are also shown in Figure 23a,b.  
Figure 23: (**a**) Speed-tracking error when switching angle is changed to 11 degrees (**b**) Speed-tracking error when switching angle is changed to 13 degrees.  
Figure 22: (**a**) Estimated and actual speeds when switching angle is changed to 11 degrees and (**b**) Estimated and actual speeds when switching angle is changed to 13 degrees.  
From all the above conditions, the DSMC-based sliding mode observer offers fast and accurate measurements of the rotor position and speed at the time of starting, as compared with the DSMC-based SMO. The good thing observed in the proposed observer is that it offers a robust performance and less chattering, even under a sudden change in the electrical or mechanical parameters.
# BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets
## 4 Experimental Analysis
### Different Scenario  
As seen in Table 1, the only set that provided data from different scenarios is S2. As a consequence, S2 was the only set of data required in the following experiments.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Dataset**} & \multicolumn{2}{c}{**S1**} & \multicolumn{2}{c}{**S2**} \\ \hline  & **Visit** & \multicolumn{2}{c}{**D1V1**} & \multicolumn{2}{c}{**D1V2**} & \multicolumn{2}{c}{**D1V1**} \\ \hline  & **dMetric** & **Accuracy (\%)** & **EER (\%)** & **Accuracy (\%)** & **EER (\%)** & **Accuracy (\%)** & **EER (\%)** \\ \hline  & **0.3** & 92.81 & 0 & 93.25 & 0 & 96.30 & 0 \\ One-day enrollment. & **0.5** & **98.19** & **0** & **97.96** & **0** & **95.86** & **0** \\  & **0.7** & 96.9 & 0 & 96.42 & 0 & 95.8 & 0 \\  & **0.9** & 96.37 & 0 & 97.12 & 0 & 74.58 & 1.58 \\ \hline  & **0.3** & 94.42 & 0.04 & 94.09 & 0.04 & 97.24 & 0 \\ Two-days enrollment. & **0.5** & 90.17 & 0.45 & 89.58 & 0.57 & 91.64 & 0.03 \\  & **0.7** & 93.09 & 0.08 & 93.12 & 0.08 & 97.62 & 0 \\  & **0.9** & **94.55** & **0.04** & **95.2** & **0.04** & **96.39** & **0** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Identification and verification results for same day in S1 and S2 for scenario R. The best considered options for one-day and two-days enrollment are in bold font.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Dataset**} & \multicolumn{2}{c}{**S1**} & \multicolumn{2}{c}{**S2**} \\ \hline  & **Visit** & \multicolumn{2}{c}{**D2V1**} & \multicolumn{2}{c}{**D2V2**} & \multicolumn{2}{c}{**D2V1**} \\ \hline  & **dMetric** & **Accuracy (\%)** & **EER (\%)** & **Accuracy (\%)** & **EER (\%)** & **Accuracy (\%)** & **EER (\%)** \\ \hline  & **0.3** & 66.43 & 4 & 67.54 & 4 & 67.99 & 6.3 \\ One-day enrollment & **0.5** & **76.86** & **2** & **76.50** & **2.53** & **66.14** & **4.59** \\  & **0.7** & 71.58 & 3.71 & 71.62 & 3.06 & 67.78 & 5.45 \\  & **0.9** & 70.39 & 4 & 71.53 & 4 & 48.72 & 6.54 \\ \hline  & **0.3** & 92.54 & 0.04 & 92.27 & 0.04 & 98.81 & 0 \\ Two-days enrollment & **0.5** & 87.81 & 0.24 & 88.31 & 0.24 & 93.75 & 0.17 \\  & **0.7** & 94.83 & 0.12 & 94.95 & 0.12 & 98.91 & 0 \\  & **0.9** & **97.68** & **0** & **97.17** & **0** & **98.12** & **0** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Identification and verification results for different days in S1 and S2 for scenario R. The best considered options for one-day and two-days enrollment are in bold font.
## 3 Results
### Multi-Spectral Data  
For Mississippi (Figure 5a, 512 \(\times\) 512, 8 bit [25]) TM multi-spectral images, the sixth band heavily affected by the atmosphere is crossed out. The other 6 bands are expanded to 39: original data, 1-6; second-order auto-correlated bands, 7-12; second-order cross-correlated bands, 13-27; square root function, 28-33; and logarithmic function, 34-39.  
It is supposed that \(T_{start}\) = 0.95 during the discussion of the parameters' influence on Mississippi's clustering results. If only the original six bands are processed by two-scale wavelet decomposition, only four classes are created because the features need to be stronger. As the first iteration absorbs too many classes, the intra-class adjustment costs most of the time. The experiment also shows that second-order correlation expanded bands  
Figure 4: Flow chart of WFCRMCA.  
(7 th-27 th) provide more class information, but nonlinear correlation developed bands (28 th-39 th) make the clustering results stable.  
The expanding spectrum method increases data processing complexity; however, if there are only several classes, the clustering speed is low because the big class has to spend more time calculating the center. Therefore, this method will maintain the clustering at a stable speed for multi-spectral data. Table 1 shows that this method could identify the potential specific classes, leading to higher clustering accuracy.  
Figure 6 is the clustering result of the parameters in Table 2. It can be seen that class 1 is low land or meadow (Figure 6a), class 2 is beach (Figure 6b), class 3 is river channel (Figure 6c), class 6 is dyke (Figure 6f), and class 9 is the slope on the bank (Figure 6i). The clustering results maintain significant signals and efficiently embody the minor signs. If the data are divided into 18 classes by the K-means algorithm, one iteration, on average, uses 60 s, so this clustering method, according to the features on the spectral curves of remotely sensed objects, is more flexible on parameter choice and has a quicker clustering speed than the standard clustering algorithm (such as K-means).