esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Determination of Volatile Compounds  
A Gas Chromatography-Mass Spectrometry (GC-MS) system (TRACE 1310-ISO, Thermo Fisher Scientific, San Jose, CA, USA) equipped with a DB-WAX UI capillary column (60 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m film thickness, Agilent Technologies) was used for the determination of volatile compounds according to the methods described by other authors [44; 45; 41] with slight modifications. Volatile aroma compounds from wines and dealcohlized fractions were extracted by solid-phase microextraction (SPME). Five milliliters of each sample (wines and dealcohlized fractions), 10 \(\upmu\)L of 2-octanol (with concentration 88.2 mg/L), and 1.5 g of sodium chloride (NaCl) were put in a 15 mL glass headspace vial and immediately sealed with a polytetrafluoroethylene silicone septum. Each vial was equilibrated with stirring on a magnetic stirrer (300 rpm) at 40 \({}^{\circ}\)C for 30 min. Then, an SPME fiber coated with divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) sorbent (50/30 \(\upmu\)m, fiber length 1 cm, Supelco, Bellefonte, PA, USA), held in a manual holder was used for extraction (30 min) of the volatile compounds in the headspace of the vial under the above conditions. The SPME fiber was then transferred to the injector port of the GC-MS system, where the volatiles were desorbed at 250 \({}^{\circ}\)C for 10 min in splitless mode. The GC oven temperature was started at 40 \({}^{\circ}\)C for 5 min, then increased to 200 \({}^{\circ}\)C at 4 \({}^{\circ}\)C/min and maintained for 20 min with helium (purity 99.9%) as the carrier gas at a constant flow rate of 1.0 mL/min. The mass spectrometer used electron ionization at 70 eV with an ion source temperature of 250 \({}^{\circ}\)C and a transfer temperature of 200 \({}^{\circ}\)C and scanned in a mass range of 50 to 350 \(m\)/z. The volatile compounds (obtained as peaks and mass spectra) were identified by comparing their mass spectra with those in the National Institute for Standards and Technology library (NIST 14, search version 2.0). The retention index for each compound was calculated using C\({}_{6}\)-C\({}_{24}\) n-alkane series (Supelco, Bellefonte, PA, USA) and compared the values reported in the literature and the NIST database ([[http://webbook.nist.gov/chemistry/cas-ser.html](http://webbook.nist.gov/chemistry/cas-ser.html)]([http://webbook.nist.gov/chemistry/cas-ser.html](http://webbook.nist.gov/chemistry/cas-ser.html)) (accessed on 15 August 2021)). Identified volatile compounds were then quantified against the respective labelled internal standards (reported in Section 2.1). Concentrations of other volatile compounds were calculated using 2-octanol and expressed as \(\upmu\)g/L.
###### Abstract  
A reliability analysis can become intricate when addressing issues related to nonlinear implicit models of complex structures. To improve the accuracy and efficiency of such reliability analyses, this paper presents a surrogate model based on an adaptive AdaBoost algorithm. This model employs an adaptive method to determine the optimal training sample set, ensuring it is as evenly distributed as possible on both sides of the failure curve and fully contains the information it represents. Subsequently, with the integration and iterative characteristics of the AdaBoost algorithm, a simple binary classifier is iteratively applied to build a high-precision alternative model for complex structural fault diagnosis to cope with multiple failure modes. Then, the Monte Carlo simulation technique is employed to meticulously assess the failure probability. The accuracy and stability of the proposed method's iterative convergence process are validated through three numerical examples. The findings of the study illuminate that the proposed method is not only remarkably precise but also exceptionally efficient, capable of addressing the challenges related to the reliability evaluation of complex structures under multi-failure mode. The method proposed in this paper enhances the application of mechanical structures and facilitates the utilization of complex mechanical designs.  
AdaBoost algorithm; adaptive iteration; complex structure; multiple-failure mode; failure probability +
Footnote †: journal: applied sciences
## 3 Automated Diagnosis of COVID-19
### Image-Level Diagnosis: Classification-Based Models
#### 3.1.3 Feature Extraction
Compared to conventional 2D methods that predict the infection according to a single CT slice, 3D methods can make the diagnosis more accurate. In addition, some researchers also proposed some methods for the post-processing of the extracted features. For example, [151] used PCA to find the most influential features, and [PERSON] et al [154] used the Relieff algorithm to rank the extracted features. In practice, radiologists also need to consider information such as epidemiology and clinical manifestations for diagnosis. Therefore, some methods also combine auxiliary external information with visual features to improve the model. [PERSON] et al. [161] combine clinical features, including age, sex and co-morbidity, with CNN features. Similarly, since the infected area usually lies near the edge of the lung, [PERSON] et al. additionally provided the distance-from-edge information [107] of the local patch to the network. [PERSON] et al. [162] and \begin{table}
\begin{tabular}{l l} \hline
**CNN Structure** & **Paper** & **Total** \\ \hline ResNet [37] & [40,42,43,49,51,53,55–57,62,63,67,70,75,77,83,84,87,92,94–96,98,107–110,110–135] & 54 \\ GoogLeNet [100] & [1,42,48,57,109,111,112,121,124–127,136–138] & 18 \\ DenseNet [101] & [43,49,51,52,55,65,67,70,73,86,95,96,96,96,108,111,119,120,124–126,132–134,137,139–143] & 29 \\ VGG [102] & [55,62,75,82–84,94,95,108,109,111,118,119,122,126,129,131,1334,136,137,144–149] & 28 \\ MobileNet [103] & [51,55,74,83,84,111,119,125,134,136,137,139,151] & 13 \\ SqueezeNet [104] & [13,49,50,57,83,84,109] & 7 \\ AlexNet [105] & [43,57,83–85,94,109,127,148,151–154] & 13 \\ Capsule [106] & [44,58,73] & 3 \\ \hline \end{tabular}
\end{table} 
Table 4: CNNs used By COVID-19 diagnosis models. [PERSON] et al. [163] calculated human-designed features, including using the volume, infection lesion number, histogram distribution, surface area and radionics information.
# Analytic Time Reentry Cooperative Guidance for Multi-Hypersonic Glide Vehicles
## 6 Conclusions  
1. A cooperative guidance algorithm for HGV reentry is proposed, which combines swarm intelligence optimization and analytical method. The guidance command can be obtained quickly, and the reentry flight can accurately satisfy the time cooperative constraint, where the average time error does not exceed 0.5 s.
2. Reentry guidance mission simulation experiments show that the algorithm proposed can better solve the problem of time cooperative guidance for the same reentry point of sub-wave launching aircraft and launching aircraft in different locations. It also has a certain adaptability to the time-cooperative guidance of moving targets and has a good engineering application value.
3. Monte Carlo simulation results show that the proposed analytical time reentry guidance algorithm for HGVs has a good anti-interference performance, where the average landing error is 17.73 km.  
In this paper, the cooperative consistency constraint of attack angle was not studied. In the next work, the proposed algorithm will be applied to the cooperative task of attack angle consistency constraint for dynamic targets.  
Conceptualization, H.X. and G.C.; methodology, H.X.; software, X.L.; validation, H.X., X.L. and H.W.; writing--original draft preparation, H.X.; writing-review and editing, Y.F. and Y.W.; supervision, G.C.; funding acquisition, G.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 61773387).  
Not applicable.  
All data generated or analyzed during this study are included in this published article.  
The authors declare no conflict of interest.
# AE-CGAN Model based High Performance Network Intrusion Detection System
## 6 Experiment and Results
### Performance Comparison of Single-RF, AE-RF and AE-CGAN-RF  
To verify the high performance of the AE-CGAN model proposed in this paper, the performance of the single-RF model that is classified only as RF without oversampling and the AE-RF model that classifies the low-dimension feature extracted based on AE is compared.  
As shown in Table 4, AE-RF model showed an improvement in performance compared to single-RF model. These results show that important features were well compressed based on AE. In addition, the performance of AE-CGAN-RF with sparse class oversampled based on low-level compressed features has been further enhanced.  
Particularly when oversampling the number of rare attack classes such as Bot, Infiltration, and Heartbleed, you can see that not only has the performance of rare classes improved, but also the performance of regular classes. In addition, the F1 score, which is used as an indicator of performance when data is unbalanced, has significantly improved performance compared to other metrics. As such, it was confirmed that performance was improved when class was balanced and classified using oversampling.
## 4 Experimental Verification
### Experimental Results Analysis  
Figure 4 shows the time-domain diagram of the measured vibration signals, which indicates that the outer race fault feature is hardly detected by time-domain characteristic and it is necessary to analyze using the advanced analysis method. Additionally, the noisy component has an impact on the identification of bearing fault characteristic frequency. In order to show the detailed information on the signal with noise, the time scale is selected from zero to 0.25 s.  
In order to compare the effect of different denoising methods for identifying mechanical failure characteristic frequency, the wavelet analysis and the proposed second-order TV methods with non-convex penalty function were used to analyze the measured bearing fault signal. After using the regularization parameter selection method, we can obtain \(\lambda=1.1\). The results are shown in Figures 5 and 6, respectively. The denoising result by the proposed method in time-domain was shown in Figure 5a. Meanwhile, directly frequency spectrum analysis was carried out and plotted in Figure 5b. From Figure 5b, it can be seen that the proposed method can clearly detect outer race fault frequency 87 Hz (\(f_{o}\)), second harmonic frequency 175 Hz (\(2f_{o}\)), the third harmonic frequency 262 Hz (\(3f_{o}\)), and quadruplicated frequency 349 Hz (\(4f_{o}\)). Additionally, the rotation frequency \(f_{r}\) and its second harmonic frequency \(2f_{r}\) can be inspected. The conclusion can be made that there is an outer race fault on the bearing test rig, which is consistent with the actual situation. The typical wavelet denoising method with previous parameter settings was also used to analyze the data and the results are shown in Figure 6. Similarly, the time-domain graph and frequency-domain graph were plotted in Figure 6a,b, respectively. Since the amplitude of fault frequency of the outer race is smaller than the others, it can be hardly identified by the wavelet denoising method. Thus, the result demonstrated that the proposed improved total variation method has greater superiority in signal denoising.  
Figure 4: The measured vibration signal of bearing test rig in time-domain.
# Model-Based Virtual Components in Event-Based Controls: Linking the FMI and IEC 61499
## 4 Experimental Evaluation
### Experiment Description
#### 4.1.3 Software PLC Controller  
Polling external hardware introduced significant delay. To study the effects of the event mapping algorithms in more detail, a second event-based controller that was solely implemented in a software PLC was derived from the reference model. In contrast to the hardware controller, output events could be directly sent, and protocol translation was avoided. Since both controller implementations received measurements and status signals from the virtual component and sent control outputs back, a closed-loop simulation was performed. The virtual OLTC started its operation as soon as a control signal was received, and the controller continued its operation as soon as the OLTC finished its last switching command. Due to the partially asynchronous behavior, synchronization and communication delays were directly reflected in the experiment outcome.
## 3 Methodology
### Feature Extraction  
* For classical ML models, the term frequency/inverse document frequency with N-gram is used to build the feature matrix.
* N-grams are commonly employed in text mining and natural language processing. They are essentially a group of co-occurring words within a particular frame, and computing the n-grams normally moves one word forward, while in more complex cases, it can move N-words forward. N-grams are utilized to keep the context of newly acquired words. It employs a collection of sequentially ordered words based on the value of the N variable. If N = 1, it might be a unigram, and a bigram if N = 2 [27].
* TF-IDF is a statistical measure used to weigh the importance of each word in the corpus. It is a feature extraction method used for classification and recommendation in NLP. The TF-IDF implementation process is divided into two parts. Begin by counting how many times each term appears in the document or tweet. The frequency of each word occurrence (IDF) was then calculated over all papers or tweets. The less important the term, the lower the TF-IDF value. The bigger TF-IDF values, on the other hand, indicate that there are fewer common words in the corpus and therefore are significant [28, 29].
* For DL models, word embedding is used to present the word matrix. Word embedding is a method for transforming words in textual input into vectors. It is superior to traditional bag-of-words encoding techniques, which use enormous sparse vectors to encode a whole vocabulary by scoring each word in a vector. The basic idea behind word embedding is that words similar to each other will be adjacent in space. The word's \"embedding\" refers to its location in the learned vector space [30]. Word  
Figure 1: The main phases of prediction Arabic sentiment analysis.  
embedding can also be taught as part of a deep learning model, which takes longer but can be customized to a specific training dataset. Each word is represented as a multidimensional unique feature vector in the vector space of a selected dimension in a word embedding. The fundamental idea is to put feature vectors for frequently occurring words in close proximity in space [31]. We used the AraVec word embedding, which is a Python-based open-source project that seeks to provide robust and free word embedding models to the Arabic NLP research community through the usage of the pre-trained distributed word representation. Words are represented in a continuous space as vectors, with numerous syntactic and semantic links encoded between them [32; 33]. We used two approaches of AraVec, which are the CBOW and the skip-gram models with 300 dimensions. The CBOW model learns embeddings by predicting the middle word in a sequence based on the words in that sequence, regardless of their order in the sentence. The skip-gram Model seeks to predict the surrounding contextual words given the core word.
# Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection
## 5 Experimental Results and Discussion  
To demonstrate the efficiency of our algorithm and its individual components, we performed several experiments on simulated and real data. The variables that are tested with these experiments include: (i) the performance of our sampling scheme; (ii) the accuracy of our adaptive thresholding method for inlier classification; (iii) the effect of GA population size on the performance of the algorithm and (iv) the performance of the overall algorithm under different levels of noise, outliers and degeneracy. Table 1 summarizes the criteria used to assess these variables.  
The experimental results obtained from the proposed technique are compared with those of the following state-of-the-art techniques: RANSAC, MSAC, MLESAC, LO-RANSAC ([PERSON] et al., 2012), StaRSAC, Cov-RANSAC, Multi-GS-RANSAC and least median of squares (LMedS). Note that for implementing these techniques, the programs were prepared by the authors of this manuscript except for the following ones. The MATLAB built-in computer-vision toolbox was used for LMedS. For measuring the uncertainty of the fundamental matrix in Cov-RANSAC, the code was provided by the original authors [54]. The code for Multi-GS sampling was also provided by the authors [27].
###### Abstract  
The construction industry faces persistent challenges, including labor shortages and safety hazards, while traditional construction methods are increasingly strained by the complexity and sustainability demands of modern projects. The integration of robotics shows significant potential for mitigating labor shortages and enhancing safety on construction sites. The current adoption of robotics technologies is driven by both the maturity of robotics technology and the potential for cost reduction compared with manual labor. This review synthesizes recent advancements and trends in construction robotics through a bibliometric analysis of 212 publications indexed in Web of Science from 2002 to 2024. Key findings indicate a 320% increase in research output from 2015 to 2022, with dominant clusters focusing on autonomous navigation, human-robot collaboration, and sustainability-driven automation. Geographically, China and the United States lead in number of publications, with 67 and 65 articles, respectively; however, cross-border collaborations remain sparse, constituting fewer than 5% of co-authored papers. Keyword co-occurrence analysis reveals evolving priorities, including artificial intelligence (AI)-driven adaptive control, modular prefabrication, and the ethical implications of automation. Despite technological advancements, critical gaps remain in terms of interoperability, workforce retraining, and regulatory frameworks. This study emphasizes the need for interdisciplinary integration, standardized protocols, and policy alignment to bridge the divide between academic innovation and industry adoption, ultimately facilitating the global transition toward Construction 4.0.  
construction robotics; bibliometric analysis; sustainable automation; human-robot collaboration +
Footnote †: journal: Applied sciences
## 4 Evaluation and Discussion
### Performance Evaluation
The pattern of the proposed algorithm is more balanced than the two referenced algorithms. For the referenced maximum service time algorithm, the first satellite is idle and the fourth satellite is associated with seven UAVs. In Figure 4(b), the number of UAVs associated with the four satellites using the proposed algorithm is 3, 3, 3, and 2, and it is 4, 4, 0, and 3 and 6, 3, 1, and 1 for the two referenced algorithms, respectively. The third satellite is idle when using the maximum capacity algorithm. Meanwhile, the third and fourth satellites are associated with only one UAV when using the maximum service time algorithm. Such an unbalanced pattern will cause the waste of channel resources and the overload of the satellite. Figure 4: Quality of the system based on the adjustable performance indicator.
## 8 Seismic Performance Evaluation
### Seismic Displacement Demand Estimation  
The estimated maximum seismic displacement demand at the deck and arch crown of a model bridge subjected to the scaled ground motions listed in Tables 2 and 3 are presented in Tables 4 and 5, respectively. For near-field ground motion (Table 4), the maximum vertical and transverse displacements for the deck and arch crown were from Earthquakes 3 and 4, respectively, while the largest displacements in the longitudinal direction were obtained under Earthquakes 7 and 1. For far-field earthquakes (Table 5), the dynamic analysis revealed that bridges subjected to ground motion from Earthquake 2 exhibited the greatest displacement in the vertical and transverse directions of the bridge superstructure, while the largest displacement values were obtained in Earthquakes 5, 4 and 8 for the longitudinal, transverse and vertical directions, respectively.  
The average values of the maximum displacement are also shown in Tables 4 and 5. Interestingly, the listed values for the maximum displacement of the deck do not occur at the same locations. Figure 11 indicates the maximum displacement for the deck and arch crown histories of a bridge subjected to Imperial Valley-06 ground motion recorded at the Array #7 station. A significant relative difference between the deck and arch displacement can be observed in longitudinal direction, while the deck and arch displacement in the other directions show almost identical results for the time history analysis. This indicates that no significant residual deck displacement remains at the end of the analysis; hence, no damage has occurred in either the piers or the arch, which play the greatest role in supporting the internal forces of the deck. Although the maximum vertical displacement of the deck and arch is 0.178 m, by the end of the time-history analysis only a residual displacement of 0.061 remains, which is effectively negligible.
# Sentiment Analysis on Algerian Dialect with Transformers
## 4 Classification of Algerian YouTube Comments Using Transformers
### Evaluation Metrics  
The primary metric we used during evaluation and experimentation is the macro-averaged F1-score across the three classes (negative, neutral, and positive), given by the equation [33]:  
\[AvgF1-Macro=\frac{1}{3}(F1^{Neg}+F1^{Neu}+F1^{Pos})\]  
where \(F1^{Neg}\), \(F1^{Neu}\), and \(F1^{Pos}\) are the F1-scores for negative, neutral, and positive classes, respectively, which are the harmonic mean of precision and recall metrics:  
\[F1^{Neg}=2\cdot\frac{P^{Neg}\cdot R^{Neg}}{P^{Neg}+R^{Neg}}\]  
\[F1^{Neu}=2\cdot\frac{P^{Neu}\cdot R^{Neu}}{P^{Neu}+R^{Neu}}\]  
\[F1^{Pos}=2\cdot\frac{P^{Pos}\cdot R^{Pos}}{P^{Pos}+R^{Pos}}\]  
\(P^{Neg}\), \(P^{Neu}\), and \(P^{Pos}\) are the precision scores for negative, neutral and positive classes, respectively, and \(R^{Neg}\), \(R^{Neu}\), and \(R^{Pos}\) are the recall scores for negative, neutral, and positive classes, respectively.  
Recall and precision scores are given by the following equations:  
\[P=\frac{TP}{TP+FP}\]  
\[R=\frac{TP}{TP+FN}\]  
where \(TP\) is the number of true positives of that particular class, \(FP\) is the number of false positives, and \(FN\) is the number of false negatives.  
After further simplifications, the macro-average F1-score can also be written as the following:  
\[AvgF1-Macro=\frac{TP}{TP+\frac{1}{2}\cdot(FP+FN)}\]You will also see accuracy in this work; it is quite simple and is given by:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\]  
where \(TN\) is the total number of true negatives. It can also be written as:  
\[Accuracy=\frac{TP+TN}{Total\ number\ of\ samples}\]  
Since our dataset is not balanced, as shown in the data analysis section, we will take the F1-score metric as our primary metric. We also offer accuracy scores occasionally, but we will use the macro-averaged F1-score to make decisions.