esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Audio Analysis Method
#### 3.4.1 Features
The work in [62] also briefly discussed the use of the DWT for swarming detection and compared it with the spectrogram; improved results were attributed to the increased frequency resolution provided by the DWT. Moreover, they showed that CWT had a good signal representation with a higher computational cost. Linear predictive coding (LPC), motivated by the speech/audio compression field [114], has also been used as a feature extraction method for queen presence detection and swarming detection [56; 64]. The work in [64], for example, compared LPC features with MFCCs and showed that MFCCs were slightly better for swarming detection (82.2% vs. 80.4% accuracy). Power spectral density (PSD) was also explored in [20] to look for spectral power changes in certain frequency ranges a few days prior to swarming; experiments showed that increases in spectral power around the 200 Hz region were an indication of swarming. Alternately, other studies have explored changes in audio signal duration and amplitude, as well as changes in spectral characteristics. While the work in [90], for example, explored changes in buzz duration and amplitude due to solar eclipse, the work in [91] showed changes in duration, amplitude, and fundamental frequency during floral sonication vibration (a behaviour in which certain bees generate vibrations by contracting their flight muscles to extract collected pollen). In turn, the authors in [28] showed that peak frequency and relative peak amplitude could be used to detect Varroa mite infestation, whereas in [99], these measures were used to discriminate between pollination from defence buzzes in bumblebees. Computational auditory scene analysis (CASA) tools (see, e.g., [115]) were also used in [46; 88] to detect the buzzing of bees from environmental recordings and to measure pollination efficacy by detecting bee buzzes in the environment, respectively. Lastly, the work in [67] proposed new sound indices, such as the acoustic complexity index, acoustic diversity index, acoustic evenness index, and the bioacoustic index to characterize changes in beehive acoustics when exposed to certain chemicals.
## 2 Materials and Methods
### 0.34 THz SHM Development  
Based on the anti-parallel Schottky diode chip for mixers, whose parameters are listed in Table 2, a 0.34 THz SHM using GaAs monolithic technology was proposed. A further look at the 3D diode chip model and diode port configuration in HFSS is given in Figure 6. The integration line had to be assigned in the port definition to specify the current polarity of the diode to ensure that the subsequent construction of the co-simulation diagram was correct. The proposed circuit configuration of the monolithic integrated SHM is shown in Figure 7. As noted in the figure, it mainly consisted of three parts, among which the diode chip had already been designed and determined. The remaining task was to complete the design of RF matching and LO/IF (local oscillator or intermediate frequency) matching circuits distributed on the left and right ends of the diode chip, thereby improving conversion efficiency. After each part was optimized, such as LO/IF CMRC filters and E-plane transition probes, all the sub-circuits were combined with the diode chip for 3D electromagnetic simulation. The S-parameter file with five ports of the whole circuit, as shown in Figure 7, was then exported from HFSS to ADS and schematically reconstructed. Harmonic balance simulation was then carried out for the mixer circuit to predict performance. After several iterations of co-simulation, the SHM circuit was finalized.  
A photograph of the fabricated THz GaAs monolithic integrated SHM circuit and assembly diagram in the split block is shown in Figure 8. When the mixer was assembled, the entire GaAs mixer circuit was aligned, along with a suspended microstrip slot milled by a high-precision computerized numerical control (CNC) process and fixed in the metal block through adhesive silver glue. The IF output port was finally connected to a K connector through a 50 \(\Omega\) transmission line realized on Rogers RT/duroid 5880 substrates. The simulation results showed that the double sideband (DSB) noise temperature and DSB conversion loss of SHM were lower than 1500 K and 8 dB, in the range of 321\(\sim\)354 GHz, respectively, and reached the lowest values of 697 K and 6.1 dB at 339 GHz.  
Figure 6: 3D diode chip model and diode port configuration in HFSS.  
Figure 7: Circuit configuration of the proposed monolithic integrated subharmonic mixer in HFSS.
# Clustering Analysis of Wind Turbine Alarm Sequences Based on Domain Knowledge-Fused Word2 vec
## 5 Results and Discussion
### Experiment Settings
#### 5.1.3 Evaluation Metrics
After obtaining the optimal parameters for DK-Word2 vec, we proceed to cluster all the alarm sequences and evaluate them using internal evaluation metrics. The internal evaluation metric used in the paper is the silhouette coefficient (SC). The SC is a clustering evaluation metric used to assess the quality of clustering results. It provides a measure of how well the data points within a cluster are separated from each other compared to the neighboring clusters. It is defined as follows:  
\[\text{SC}=\frac{D_{in}-D_{out}}{\text{max}(D_{in},D_{out})}, \tag{14}\]  
where \(D_{in}\) represents the average distance between a sample and all other samples within the identical cluster, and \(D_{out}\) signifies the average distance between a sample and all other points within the adjacent nearest cluster. The range of SC is \([-1,1]\). A higher SC indicates better-defined clusters, while a lower value suggests that the clusters are not well-separated or might be incorrectly formed.
## 4 Discussion  
### Influence of Cutting Parameters on Output Parameters  
Observing the slope of the S/N plots in Figure 6, it can be concluded that the feed rate has the highest influence on the thrust force. Analysis of variance analysis, conducted by [PERSON] et al. (2018), also concluded that the feed rate has the highest influence on thrust forces during the drilling of Hoganas PM steel foam. These findings are in agreement with the grey relation analysis, which showed that feed rate has the highest influence on the tested output parameters with 31%. Higher thrust forces caused by increases in feed rates can be attributed to higher chip loading of the tool. Increasing cutting speeds led to higher thrust forces as well but, as the slope of the curve is flatter, the influence is not as pronounced when compared to the influence of the feed rate. Grey relation analysis also showed a far lower influence of cutting speed on output parameters with just 5%. Increases in thrust force at higher cutting speeds could be caused by higher amounts of built-up edge formation, which is in accordance with the BUE length, \(L_{BUE}\), and height, \(H_{BUE}\), trends shown in the S/N plots.  
The brittleness of the porous structure allows the tool to follow the path of least resistance, leading to deviations between the inlet and outlet of the drilled hole. However, the lowest deviations were noted while machining at a higher cutting speed. From the S/N plots, it can be concluded that cutting speed has the lowest influence on hole deviation. However, increasing the feed rate greatly increases hole deviation. This is most likely caused by increased thrust forces that force the tool through the workpiece material at higher rates ([PERSON] et al., 2019).
## 3 Results and Discussion
### Characterization of Laminated CNCs-Coated PET Films (P-CNC-P)  
#### 3.3.1 Delamination Test  
In light of the data from a peeling test of laminated plastic films (Table 3) collected according to ASTM-D882-09, stiffness, breaking strength and elastic modulus were all acceptable. Different values of the delamination test proved that the strength of the adhesion strongly depends on the surface chemistry of the polymers and cellulose nanocrystals. Furthermore, it can be indicated that the laminated plastic films based on esterified cellulose nanocrystals displayed the best performance in terms of mechanical properties. These values of a peeling test can be further improved if the lamination is set on an industrial scale. Finally, it can be confirmed that the corona-treatment subjected to coated polymers prior the lamination definitely led to a reinforcement of the adhesion between the cellulose nanocrystals and the adhesive.  
Figure 9: (**a**) Transparency (blu) and (**b**) haze (orange) of uncoated and coated polymers with the three types of CNCs calculated from the mean \(\pm\) standard deviation (\(n=4\)).
## 4 Analysis of Factors Affecting the Impact Factor
### Comparison of Impact Factor in This Study with International Standards  
The definitions of impact factor vary across international standards [30; 49] and can be grouped into three main types: those related to span length, those based on bridge natural frequency, and others. This study compares standards tied to bridge span length, including the Japanese standard [50], German standard DIN1072 [51], and European standard [52]; the Chinese standard (JTG D60-2015) [46], which is related to bridge natural frequency; and the American standard AASHTO LRFD (2012) [53], which is based on component location, and the Canadian standard OHBDC (1991) [54], which considers the number of vehicle axles. The maximum and average impact factors of the corrugated steel web box girder under grade A, B, and C pavement conditions were calculated and compared with the values specified by various international standards, as shown in Figure 17. The European standard CEN 2003 specifies impact factors separately for shear and bending moment, which are 0.2 and 0.4 for the bridge model used in this study. The impact factors calculated according to the standards of the United States, Canada, and Germany tend to be close to 0.3. The impact factors obtained from the Chinese and Japanese standards are relatively similar, at 0.26 and 0.25, respectively.  
Comparing the calculated impact factors in this study with those specified by various international standards reveals the following: For the average impact factor, the mean values across all pavement grades, except for the shear impact factor specified by the European standard, are less than or equal to the calculated values of all standards. Regarding the maximum impact factor, the peak value for the grade A road is only lower than those specified by the American and European standards. For grades B and C, the maximum values are only lower than the bending moment impact factor specified by the European standard. Furthermore, it can be observed that the mean impact factor values across all pavement grades are lower than the values calculated by the Chinese standard. However, the maximum impact factor values for all road grades exceed those calculated by the Chinese standard. Therefore, it can be seen that the impact factor calculation methods in most national standards seem to favor scenarios with good pavement conditions. When pavement conditions deteriorate, some standards may underestimate the impact response of corrugated steel web bridges. For bridges with poor pavement roughness, it is more  
Figure 16: Mid-span vertical displacement of steel bottom plate and concrete bottom plate box girder: (a) 20 km/h; (b) 60 km/h; (c) 120 km/h.  
appropriate to analyze their dynamic performance through finite element vehicle-bridge coupling vibration simulation.
## 1 Introduction
### Literature Review
#### 1.1.3 Genetic Algorithm-Based Automated Assembly Planning  
The GA is a widely applied construction methodology that aims to optimize costs and time [28; 29; 30]. It showcases efficiency in swiftly attaining optimal solutions by utilizing objective functions for straightforward problem-solving [31]. Moreover, the versatilityof the GA enables the exploration of diverse solutions and addresses multi-objective construction scheduling problems through various search strategies [32; 33]. Derived from natural selection principles, the GA involves stages of selection, crossover, and mutation, driving the iterative evolution of initially random genes towards a form best suited for the defined objective function [17; 24]. The selection process typically employs the roulette-wheel method, favoring individuals with higher fitness as potential parents [9]. The essence of the GA lies in explicitly defining the objective function of the problem, allowing for the gradual evolution of genes to align with the intended optimization objective.  
As stated in Section 1.1.2, GA emerged prominently within metaheuristic methods for optimizing schedules, particularly in determining optimal assembly sequences for components. [PERSON] et al. (2014) [17] based their methodology on BIM, extracted information from the model, and defined a Matrix of Structurability Constraints. They employed a GA to automatically generate sequences, ensuring that the prefabricated components were installed in a structurally stable order. [PERSON] et al. (2018) [7] investigated this domain by combining BIM with a novel technique called the Improved Genetic Algorithm, which was specifically designed to improve Assembly Sequence Planning. They envisioned each precast concrete component as a gene in the algorithm, focusing on precast concrete components. They generated ideal assembly sequences by methodically analyzing geometric properties such as weight, spatial occupancy, and inter-component interference, and then presented visual results via the BIM model. Similarly, [PERSON] et al. (2022) [34] explored optimization strategies by employing a Multilevel Elitist Genetic Algorithm. Their focus extended beyond rectifying geometric characteristics to resolve the interference in paths related to precast components. Their method aimed to minimize assembly disruptions by intricately refining pathways and geometric attributes using sophisticated genetic algorithmic techniques. Acknowledging the inherent limitations of the GA in converging toward suboptimal solutions in specific scenarios, [PERSON] et al. (2023) [9] introduced an annealing algorithm, which is a strategic enhancement for refining assembly sequences. This augmentation aims to mitigate the convergence issues observed in GA applications, thereby further optimizing the assembly sequence.
# A Comparative Analysis of the TDCGAN Model for Data Balancing and Intrusion Detection
## 1 Introduction
This layer aggregates the outputs from the three discriminators and applies an election process to select the optimal result, choosing the one with the highest classification accuracy. This method is similar to an ensemble approach, where combining multiple inputs leads to a more accurate outcome.. The ensemble method in TDCGAN combines different neural network architectures and tuning strategies to achieve a superior result. Specifically, TDCGAN integrates multiple network architectures with distinct roles (e.g., generator and discriminator), each contributing to different aspects of the learning process. This integration is akin to an ensemble approach where multiple models are combined to improve overall performance. TDCGAN tackles the challenge of high-class imbalance through an analysis of the UGR'16 dataset. To assess the effectiveness of the proposed GAN model, TDCGAN, across various datasets, this paper introduces an evaluation framework. This framework involves applying the TDCGAN model to different Intrusion Detection System (IDS) datasets for comprehensive performance evaluation. These datasets are CIC-IDS2017, CSE-CIC-IDS2018, KDD-cup 99, and BOT-IOT datasets. Following that, four machine learning techniques were employed to conduct a comparative analysis of intrusion detection classification tasks on the four datasets. The methods are: Decision Tree, Random Forest, Multi-Layer Perceptron (MLP), and Naive Bayes. This analysis was performed both before and after applying data balancing techniques. The results showed a substantial improvement in machine learning performance after data balancing, which highlights the importance of addressing class imbalance in machine learning models, even when the initial performance appears satisfactory. By balancing the dataset, the machine learning model's ability to generalize and accurately classify instances from both classes improved significantly. This is crucial in scenarios where correctly identifying instances from the minority class is equally important as the majority class. The key contributions of this paper are outlined as follows:  
1. Utilize the TDCGAN model to address a data imbalance in four benchmark datasets for an intrusion detection system. 2. Employ different machine learning techniques to test intrusion detection classification across four benchmark datasets before and after data balancing. 3. Perform a comparative analysis of the models utilized. The remainder of this paper is organized as follows. Section 2 gives an overview about IDS datasets, machine learning methods, and TDCGAN. Section 3 presents the experiment design and setup. The results are given in Section 4. Finally, Section 5 gives the conclusions.
## 4 Conclusions  
Understanding the dynamics and effectivity of bioretention systems is an essential step in creating effective measures to control stormwater runoff pollution. In this study, saturated zone and plants were introduced into the conventional bioretention system to determine the influence of rainfall intensity, concentration, and antecedent dry periods on stormwater runoff treatment performance, in terms of effluent quality, adsorption, and complexation. The results show that nitrogen and phosphorus removal could significantly be improved by introducing saturated zones and plants for different rainfall intensities, concentrations, and wet-dry cycles. In the early stages of rainfall, the quality of effluent water from bioretention systems can reach water quality standards of Grade III and IV for surface water in China, but the effluent quality deteriorated over time. The results indicate that the combined use of saturated areas, media, plants, and microorganisms in the bioretention system is an effective measure for ecological purification. According to the SEM-EDS analysis, phosphate precipitates were found to have formed on the media surface after the adsorption. The XRD analysis shows that the original crystal structure of the media does not change during rainwater infiltration. Rainfall intensity can significantly influence the removal of pollutants in the bioretention systems. The results suggest longer rainfall recurrence intervals would result in reduced treatment capacity of bioretention systems in normal events. In terms of removal efficiency, the dry period was found to have a negative correlation for TP and a positive correlation for TN; however, it showed little effect on the removal efficiency for NH\({}_{4}\)+-N. Further research on road surface runoff management can focus on the microbial mechanisms towards long-term purification and the transformation mechanism of nitrogen.  
Conceptualization, H.L. and Z.J.; methodology, X.C.; software, B.H.; validation, M.T.; formal analysis, L.G.; investigation, Y.W.; resources, S.Z.; data curation, L.G., X.C. and Z.Z.; writing--original draft preparation, H.L.; writing--review and editing, Z.J.; visualization, B.H., Z.Z. and X.C.; supervision, Z.J.; project administration, Z.J.; funding acquisition, Z.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science and Technology Support Program, grant number 2015 BAL02B04, and the Technology Project of China Housing and Urban-Rural Development Ministry, grant number 2015-K7-012; a Project Funded by the National First-class Disciplines (PNFD), and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).  
The authors would like the anonymous reviewers for their insightful suggestions. Thanks also to [PERSON] and [PERSON] for their help in the laboratory experiments.  
The authors declare no conflict 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.
## 3 Results and Discussion
### Identification of Aroma Compounds in Peach Spirits
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & & & & \multicolumn{3}{c}{**FD Factor**} \\
**No.** & **Compounds** & **CAS** & **Odor Descriptor** & **Identification** & **RI b** & \multicolumn{3}{c}{**DS**} & \multicolumn{3}{c}{**PS**} \\ \cline{5-10}  & & & & & & **LLE c** & **SPME c** & **LLE c** & **SPME c** \\ \hline
53 & \(\gamma\)-Nonalactone & 104-61-0 & Milky notes & MS, RI, aroma, S & 1957 & - & - & /243 & - \\
54 & \(\gamma\)-Decalactone & 706-14-9 & Apricot and peach & MS, RI, aroma, S & 2158 & - & - & /729 & - \\
**Aromatic compound** & & & & & & & & \\
55 & Benzaldehyde & 100-52-7 & Almond, woody & MS, RI, aroma, S & 1485 & -/243 & - & -/9 & 9 \\
56 & Ethyl benzoaate & 93-89-0 & Floral, fruits & MS, RI, aroma, S & 1639 & -/81 & 3 & -/1 & 3 \\
57 & Benzyl acetate & 140-11-4 & Fiscal, sweet & MS, RI, aroma, S & 1695 & -/243 & - & -/1 & 3 \\
58 & Phenethyl acetate & 103-45-7 & Honey & MS, RI, aroma, S & 1787 & 1/2187 & 9 & -/2187 & 3 \\
59 & Benzyl alcohol & 100-51-6 & Fruit & MS, RI, aroma, S & 1843 & 3/- & - & 81/81 & 27 \\
60 & Phenylethyl alcohol & 60-12-8 & Dried & MS, RI, aroma, S & 1879 & 1/6561 & 3 & 81/6561 & 81 \\
**Terpene** & & & & & & & & \\
61 & Linalool & 78-70-6 & Floral & MS, RI, aroma, S & 1521 & -/9 & - & -/9 & 1 \\
62 & Citronellol & 106-22-9 & Rose-like & MS, RI, aroma, S & 1740 & -/81 & - & 9/- & - \\
63 & (E)-\(\beta\)- & 2369-685- & Cooked & MS, RI, aroma, S & 1820 & -/2187 & 3 & -/81 & 81 \\
64 & Daimscenone & 7 & apple & aroma, S & & & & \\
64 & Dihydro-\(\beta\)-ionol & 3293-47-8 & Woody, MS, RI, & 1950 & -/243 & 3 & -/3 & 27 \\
65 & trans-Nerolidol & 40716-66- & 3 & Floral, tea & MS, RI, aroma, S & 2017 & -/729 & - & - & - \\
66 & trans-\(\beta\)-ionone & 79-77-6 & Sweet, floral & MS, RI, aroma, S & 2065 & -/27 & - & - & - \\
67 & Eugenol & 97-53-0 & Sweet & MS, RI, aroma, S & 2130 & - & -/9 & - \\
68 & (E,E)-Farnesol & 106-28-5 & Sweet, floral & MS, RI, aroma, S & 2326 & -/27 & - & - & - \\
**Acids** & & & & & & & & \\
69 & Acetic acid & 64-19-7 & Acidic & MS, RI, aroma, S & 1405 & 3/- & - & 1/- & 3 \\
70 & Methylpropanoic acid & 79-31-2 & Acidic & MS, RI, aroma, S & 1526 & 3/- & - & 9/- & 1 \\
71 & Butyric acid & 107-92-6 & Acidic & MS, RI, aroma, S & 1598 & - & - & 9/- & - \\
72 & Methylbutanoic acid & 503-74-2 & Sweat & MS, RI, aroma, S & 1629 & 81/- & - & 243/- & 27 \\
73 & Hexanoic acid & 142-62-1 & Acidic, cheese & MS, RI, aroma, S & 1804 & 3/- & - & 27/- & - \\
74 & Heptanoic acid & 111-14-8 & Acidic & MS, RI, aroma, S & 1913 & 1/- & - & - & - \\
75 & Octanoic acid & 124-07-2 & Vegetable, fatty & MS, RI, aroma, S & 2021 & 9/- & 1 & - & 9 \\
76 & Nonanoic acid & 112-05-0 & Coffee & MS, RI, aroma, S & 2130 & 81/- & - & 9/- & 27 \\
77 & Decanoic acid & 334-48-5 & Fatty & MS, RI, aroma, S & 2235 & 3/- & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)