esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Sensitivity Investigations and Numerical Simulations  
This section is devoted to exploring the dynamical behavior of the controlled Jeffcott-rotor system. Solving the non-linear Equations (44) to (47) numerically using the predictor-corrector newton-Raphson method (See [44]) in terms of the system and control parameters \((\mu,\ \lambda,\ \delta_{1},\ \delta_{2})\) utilizing \(\sigma\) or \(E\) as a bifurcation parameter, one can obtain the different response curves as shown in Figures 4 and 5. In addition, the solution stability can be investigated via obtaining the eigenvalues of the linear system (49) to (52), where the stable solution is illustrated as a solid line and the unstable solution is shown as a dotted line. Moreover, to demonstrate the accuracy of the derived periodic solution given by Equations (42) to (47), the temporal normalized Equations (20) and (21) have been solved numerically using the ODE45 MATLAB solver, where the numerical results are plotted as a small circle and big dot against the analytical solutions given by Equations (44) to (47). The parameters' values \(E=0.03,\ \mu=0.015,\ \lambda=0.05,\ \delta_{1}=0.83,\ \delta_{2}=0.002,\ \beta=45 \lx@math@degree,\ \sigma=0,\) and \(\Omega=1+\sigma\) have been adopted to obtain the following response curves and bifurcation diagrams [12, 13, 26]. Before proceeding further, it should be remembered that \(u=\frac{x}{s_{0}}=a_{1}\cos(\Omega t-\varphi_{1})\) and \(v=\frac{y}{s_{0}}=a_{2}\cos(\Omega t-\varphi_{2})\). Accordingly, \(a_{1}\) denotes the vibration amplitude of the rotor in \(X\) direction with respect to the nominal air-gap size \(s_{0}\), while \(a_{2}\) represents the vibration amplitude of the rotor in \(Y\) direction with respect to the nominal air-gap size \(s_{0}\). So, if \(a_{1}\geq 1\) and/or \(a_{2}\geq 1\) this means that a rub and/or impact force between the rotor and the eight-pole actuator occurs. Therefore, the main target of this article is to control the Jeffcott rotor vibration amplitudes \(a_{1}\) and \(a_{2}\) below unity (i.e., \(a_{1}<1\) and \(a_{2}<1\)) regardless of the angular speed (\(\Omega=1+\sigma\)) and rotor eccentricity \(E\).  
Figure 4: \(\sigma\)—response curve of the uncontrolled Jeffcott rotor at various values of the shaft eccentricity \(E\): (**a**) oscillation amplitude in \(X\)—direction (\(a_{1}\)) versus \(\sigma\), and (**b**) oscillation amplitude in \(Y\)—direction (\(a_{2}\)) versus \(\sigma\).
# Machine Failure Prediction Using Survival Analysis
## 4 Materials and Methods  
### The Proposed Model  
The proposed prediction model is based on survival analysis for processing and extracting conclusions in a human decentralized context, focused on non-biological entities. Survival analysis concerns predicting the time until an event occurs. In this specific study, our interest is focused on using failure data to estimate the machine's life span under specific usage and stress conditions. The strategy followed for creating the model is:  
1. Examination of the significance of the functional characteristics of the machines;
2. Selection of the characteristics that are of statistical and practical importance for the prediction model;
3. Feeding these data into the random survival forest model for training and the ability to predict with the best possible accuracy.  
Model Workflow Description  
As presented in Figure 1, the initial step is to extract the helpful features from the dataset, using the RadViz, Rank2D, COX, and feature ranking of RSF. Each of the methods is used to achieve a different part of understanding of the data. RadViz visualization technique is used for the identification of clusters of highly correlated features with the survival of the machine. Rank2D, on the other hand, enables the identification of correlations among the features. The COX model is a suitable method for determining the features that exert the greatest impact on the survival time of the machine. Meanwhile, the feature ranking of RSF can model non-linear relationships between the predictors and the outcome, making it a valuable tool for feature selection in this domain.  
Figure 1: Model workflow diagram.  
Following the feature analysis, an RFE model is employed to identify the optimal set of features that have a significant impact on machine failure prediction. This step is crucial to ensure that the RSF model is trained with the most relevant and informative features, leading to improved predictive performance. By selecting the best subset of features using the RFE approach, the RSF model can effectively capture the complex relationships between the predictors and the survival outcome, leading to accurate and reliable predictions.  
The features that are deemed important through the various layers of feature analysis and selection are utilized in the training of the RSF model. By incorporating only the top-performing features, the RSF model can achieve a higher level of accuracy and reliability in predicting machine failure. The trained model can then be used to make predictions on new datasets and aid in proactive maintenance, ultimately reducing downtime and costs associated with machine failure.
# Meta-Learning Approach for Adaptive Anomaly Detection from Multi-Scenario Video Surveillance
## 5 Conclusions  
In this study, a comprehensive approach to video anomaly detection was presented by leveraging a multi-scenario anomaly detection dataset containing diverse normal and anomalous video sequences. Frames were extracted from these videos, resized to 224 \(\times\) 224 pixels with three color channels, and normalized to ensure consistency and optimal input formatting for the model. The proposed method demonstrated the effectiveness of the swin transformer in capturing detailed spatial dependencies within video frames, while a detector model utilizing a 10-shot one-query MAML approach was employed to classify frames as normal or anomalous. This combination of swin transformer for feature extraction and MAML for meta-learning resulted in significant improvements in anomaly detection accuracy and demonstrated superior generalization to novel and complex datasets.  
However, several limitations remain: the current approach does not fully exploit temporal dynamics over long video sequences, which could hinder detection of anomalies evolving over time; the computational complexity of the swin transformer may limit real-time deployment, especially on resource-constrained devices; and despite the benefits of meta-learning, the model's performance may degrade on entirely unseen anomaly types or low-quality data.  
Future work could address these issues by integrating spatiotemporal models such as 3D swin transformers or temporal networks like BiLSTM to better capture sequential information, applying model compression techniques for efficiency, exploring unsupervised or self-supervised meta-learning paradigms to reduce reliance on labeled data, and validating the approach on additional diverse datasets to improve cross-domain robustness.  
The findings of this research have important implications for both academic and practical fields, offering advancements for automated surveillance and public safety systems, smart city infrastructure monitoring, and anomaly detection in retail and industrial environments. Moreover, this work contributes a strong baseline for future studies investigating meta-learning and transformer-based architectures in video anomaly detection.  
Conceptualization, D.K.S., D.R.P. and G.G.; methodology, B.S., D.K.S. and D.R.P.; software, D.K.S.; validation, D.R.P., G.G. and B.S.; formal analysis, D.K.S.; investigation, D.K.S.; resources, D.K.S.; data curation, D.K.S.; writing--original draft preparation, D.K.S. and G.G.; writing--review and editing, D.K.S., G.G. and B.S.; visualization, D.K.S. and D.R.P.; supervision, D.R.P.; project administration, B.S., D.R.P. and G.G.; funding acquisition, G.G. and B.S. All authors have read and agreed to the published version of the manuscript.  
The present Research has been conducted by the Research Grant of Kwangwoon University in 2025.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 4 Experiments and Results
### Evaluation Metrics  
Most of the state-of-the-art systems on sentiment analysis use four measuring parameters for performance evaluation: _Accuracy, F1 score, Precision,_ and _Recall_[27]. However, as we have a fine-grained sentiment analysis task (multi-label classification), and to handle imbalanced data, we will consider measuring metrics that deal with multi-label problems. Therefore, we will evaluate the proposed models using six multi-label performance measures. These metrics are Multi-label accuracy, Jaccard accuracy, Macro-averaged F1 score, Micro-averaged F1 score, Label ranking average precision score, and Hamming loss. These metrics treat the data as a collection of classes and extend a binary metric (e.g., Accuracy, F1 score, and Precision) to a multi-class by averaging binary metric calculations across the set of classes [65]. These performance measures are computed as follows:  
* Multi-label accuracy (_Multi-Acc._): \[Multi-Acc.=\frac{1}{D*m}\sum_{i=1}^{D}\sum_{j=1}^{m}\alpha(y_{ij}==y_{ij})\] (5) where \(D\) is the number of testing samples, \(m\) is the number of labels, \(y_{ij}\) is the ground truth label, and \(\hat{y_{ij}}\) is the predicted label.
* Jaccard accuracy (_Jac-Acc._): \[Jac-Acc.=\frac{1}{|D|}\sum_{i=1}^{D}\frac{Y_{i}\bigcap\hat{Y}_{i}}{Y_{i}\bigcup \hat{Y}_{i}}\] (6) where \(D\) is the number of testing samples, \(Y_{i}\) is the ground truth label, and \(\hat{Y}_{i}\) is the predicted label.
* Macro-averaged F1 score (_F1-Macro_): \(F1\)-Macro_ = F1 score averaging on each label.
* Micro-averaged F1 score (_F1-Micro_): \(F1\)-Micro = F1 score averaging on the prediction matrix (global calculation).
* Label ranking average precision score (_LRAP_): \(LRAP\) = Average over each ground truth label assigned to each sample, of the ratio of true versus total labels with lower score. The goal of this metric is to assign better rank to the labels associated to each sample, and then compute whether the percentage of the higher-ranked labels were true labels.
* Hamming loss (_H-Loss_):
# Process-Aware Enactment of Clinical Guidelines through Multimodal Interfaces
## 2 Background
### Case Study: Chest Pain  
For a better comprehension of the TESTMED project as a whole, in this section, we provide details about a standard CG enacted for patients suffering from chest pain. Chest pain is defined as a pain or discomfort that is felt anywhere along the front of the body between the neck and upper abdomen. It can be an indicator of a possible heart attack, but it may also be a symptom of another disease. Chest Pain is considered as one of the most common reasons for the admission in the emergency room (5% of all visits) with high mortality in case of diagnosis failure and improper dismissal (2-4%) [33]. When a patient suffering from chest pain reaches the emergency room, typically s/he is visited by a doctor, who investigates the patient history and the risk factors. Furthermore, a _chest pain score_ is calculated, which enables the doctor to classify patients into low and high-risk subsets for cardiac events.  
Figure 3 shows the chest pain score adopted by DEA. The score is derived from a set of four clinical characteristics: _(ii)_ the _localization_ of the pain; _(ii)_ the _character_ of the pain; _(iii)_ the _radiation_ of the pain and the _(iv) associated symptoms_. A partial score is associated with each characteristic, and the sum of these values produces a final score that predicts the angina probability. A chest pain score  
Figure 2: Transforming CGs into patient-specific care pathways.  
lower than four identifies a low-risk probability of coronary disease, whereas a score greater or equal than four can be classified as an intermediate-high probability of coronary risk, i.e., it is required to admit the patient for clinical observation. Different values of the rate correspond to different clinical treatments to be followed by the patient.
## 3 Results
### Adsorption of Isopropyl Alcohol  
Solution pH is an important factor as it affects both adsorbent and adsorbate. The effects of initial solution pH on IPA adsorption by activated carbon was investigated in this study, as revealed in Figure 4. The pKa of IPA is reported to be 16.5 which means that, at the tested range, the IPA is present mainly in the protonated form [26]. The optimal IPA adsorption capacity of 32 mg/g was found for the experiments conducted in an aqueous solution at pH 7, closest to the point of zero charge of fresh activated carbon used in this study. In this range, the hydrogen-bonding between the IPA molecule and the oxygen-containing functional groups on the surface of activated carbon is the greatest [27]. [PERSON] and [PERSON] tested adsorption of IPA by granulated activated carbon and observed an adsorption capacity of 90 mg/g of activated carbon at an IPA concentration of 1500 mg/L. This higher adsorption capacity, however, was achieved in a continuous system with evaporated acetone in air [28]. One study showed that the IPA concentration and the equilibrium capacity is not necessarily proportional, with their IPA concentrations of 13.4, 22.1 and 31.2 mg/L leading to equilibrium capacities of 0.1737, 0.1581 and 0.1469 mg/g, respectively [29].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type of Activated** & \multicolumn{5}{c}{**Element Content (wt. \%)**} \\ \cline{2-7}
**Carbon** & **C** & **O** & **Mg** & **P** & **Si** & **K** & **Cl** \\ \hline
**Original** & 89.60 \(\pm\) & 9.26 \(\pm\) & 0.18 \(\pm\) & 0.24 \(\pm\) & 0.24 \(\pm\) & 0.34 \(\pm\) & 0.14 \(\pm\) \\
**Activated Carbon** & 1.66 & 2.23 & 0.17 & 0.25 & 0.24 & 0.22 & 0.12 \\ \hline
**Fresh Activated** & 92.80 \(\pm\) & 6.40 \(\pm\) & -a & -a & 0.08 \(\pm\) & 0.56 \(\pm\) & 0.08 \(\pm\) \\
**Carbon** & 1.15 & 0.90 & -a & -a & 0.14 & 0.50 & 0.14 \\ \hline \hline \end{tabular}  
*: Not detectable.  
\end{table}
Table 1: Surface elemental characteristics of original and fresh activated carbons.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Activated Carbon** & **Micropore** & **BET Specific** & **Total Pore Volume** & **Mean Pore** \\
**Volume (cm3/g)** & **Surface Area** & **(m2/g)** & **(cm3/g)** & **Diameter (nm)** \\ \hline
**Original Activated** & 243.06 \(\pm\) 0.36 & 1057.9 \(\pm\) 1.59 & 0.423 \(\pm\) 0.001 & 1.6 \(\pm\) 0.002 \\
**Carbon** & 250.57 \(\pm\) 0.38 & 1090.6 \(\pm\) 1.64 & 0.435 \(\pm\) 0.001 & 1.6 \(\pm\) 0.002 \\ \hline
**Activated Carbon** & & & & \\
**after First** & 227.65 \(\pm\) 0.34 & 990.9 \(\pm\) 1.49 & 0.395 \(\pm\) 0.001 & 1.6 \(\pm\) 0.002 \\
**Cycle** & & & & \\ \hline
**Activated Carbon** & & & & \\
**after Second** & 227.88 \(\pm\) 0.34 & 991.9 \(\pm\) 1.49 & 0.394 \(\pm\) 0.001 & 1.6 \(\pm\) 0.002 \\
**Cycle** & & & & \\ \hline
**Activated Carbon** & & & & \\
**after Fourth** & 217.93 \(\pm\) 0.32 & 948.5 \(\pm\) 1.42 & 0.377 \(\pm\) 0.001 & 1.6 \(\pm\) 0.002 \\
**Cycle** & & & & \\ \hline \hline \end{tabular}  
* \end{table}
Table 2: Pore structure characteristics of original, fresh and ultrasound-regenerated activated carbon.
# Improvement of Wheat Grain Yield Prediction Model Performance Based on Stacking Technique
## 2 Materials and Methods
### Data Collection and Processing  
#### 2.2.1 ASD Canopy Hyperspectral Data  
The canopy spectral reflectance of winter wheat measurements was performed using an ASD FieldSpec spectroradiometer. The spectral range of the ground object spectrometer was 350-2500 nm, the spectral sampling interval was 1.4 nm in the range of 350-1000 nm, the spectral sampling interval was 2 nm in the range of 1000-2500 nm, the resampling interval was 1 nm, and the field of view of the spectrometer was 25\({}^{\circ}\)[45]. To obtain a higher-precision spectral reflectance, the data were collected between 10:00 and 14:00 Beijing time under clear and windless weather conditions. Before obtaining the spectral reflectance, a BaSO4 calibration panel was used for the spectral calibration to eliminate the influence of sky light change on the spectral measurement [46]. The whiteboard was calibrated once in about 15 min in sunny and windless weather. When collecting the spectral reflectance, the probe should be kept vertically downward, and the height should be stable at approximately 0.6-1.0 m above the canopy. Each plot randomly selected four sample points that represent the overall growth of the wheat for spectral data collection. The parameter setting was that each sample point automatically collected 10 spectral curves, so that 40 spectral curves could be obtained from each plot. After collecting the spectral data, the ViewSpec Pro software was used to process the spectral reflectance data. The average value of 10 spectral curves was taken as the reflectance of a sample point. After processing, the reflectance was derived, and the average reflectance values of the four sample points in each cell were obtained. The average reflectance values of the four sample points in each cell were then averaged again; finally, the canopy spectral reflectance data of each experimental plot were obtained. In this study, the average spectral reflectance of the winter wheat canopy was obtained at the jointing stage, flagging stage, anthesis stage, and grain filling stage.  
Figure 2: Technology roadmap.
## 2 Experimental and Finite Element Modal Analysis
### Experimental Modal Analysis  
Modal-analysis tests usually require an excitation system, a measurement system, and a data-analysis system. In this study, the experimental modal analysis was performed by means of a sinusoidal sweep, and a schematic diagram of the entire experimental modal-analysis system is illustrated in Figure 1. In the test, a vibration exciter (YMC L-800 type) was used to generate the exciting force for the plate, which was fixed to the exciter with a bolt (Figure 2). An eight-channel acquisition analyzer (m + p VibPilot) and a personal computer were used to perform the data analysis.  
To measure the natural frequencies and mode shapes of the plate, a novel measurement system was designed. More specifically, two accelerometers, one for control and the other for measurement, were used to acquire the frequency response data. Compared to the measurement of the natural frequency, it is relatively difficult to measure the mode shape of the structure, since the mode-shape data are different for different locations. To this end, there are some useful techniques based on high-precision laser instruments, which can obtain almost complete mode shapes in a contactless way [22; 23]. Nevertheless, most of these techniques are expensive and inoperable. In order to measure full-field mode shapes in a simple and affordable way, a fine-sand vibration method was employed in this study. During test operation, fine colored sand was uniformly spread on the top of the plate (Figure 3), and when the plate resonated, the corresponding mode shape could be observed on the plate.  
Figure 1: Schematic diagram of the experimental modal-analysis system.  
Figure 2: Experimental configuration: The plate and the vibration exciter.  
The test conditions used in the experimental modal analysis are listed in Table 1, and the test procedure comprised the following steps:  
_Step 1_. Perform a broadband frequency sweep test to obtain the frequency response data of the plate in the specified frequency range.  
_Step 2_. Use the data analysis system to obtain the natural frequencies from the frequency response data acquired in _Step 1_.  
_Step 3_. Perform a narrow-band slow frequency sweep test near the natural frequency, allowing the fine sand on the plate to concentrate in areas with relatively small amplitude and visualize the mode shapes.  
The frequency response data acquired from the test were drawn in the semi-logarithmic coordinate system (Figure 4), and
# Asphalt Pavement Transverse Cracking Detection Based on Vehicle Dynamic Response
## 4 Optimized Short-Time Fourier Transform (STFT) Algorithm  
### Basic Principle  
Traditional Fourier transforms can only reveal a signal's overall frequency component, and cannot indicate the time of each component. Therefore, two signals with significant temporal domain differences might have the same spectrum map. It is challenging to record the Fourier transform for signals that change, such as vehicle vibration signal mutations caused by minute transverse cracks. Such mutations are frequently crucial in highway pavement damage investigation.  
The short-time Fourier transform (STFT) is a joint time-frequency analysis technique for time-varying and non-stationary signals. It intercepts the time domain signal using a fixed-length window function. Then, the STFT uses the intercepted signal to conduct a Fourier transform. This results in the local spectrum across a brief time window. The final transformation generates a two-dimensional function about time and frequency domains [23].  
\[STFT(\omega,\;t)=\int_{-\infty}^{+\infty}F(t)\;G(t-\tau)e^{-i\omega t}dt \tag{18}\]  
Figure 4: Testing road. (**a**) Pavement structure; (**b**) divided sections.  
As is shown in Equation (18), STFT is a Fourier transform made by multiplying the signal \(F\)(t) by a \(\tau\)-centered window function \(G\)(\(t-\tau\)).
## 3 Methodology
### Filter Feature Subset Selection Method  
Feature Subset Selection (FSS), just like the FFR, assesses, ranks and select features based on some properties. However, in the case of FSS, the major focus is the search method which is used to generate a subset of features that collectively have good prediction potentials. It considers the existence of better predictive performance when a feature is combined with other features other [32]. Two FSS methods were considered in this study. As mentioned earlier, FSS is based on various search methods. Therefore, these search methods traverse the feature space to generate a subset with high predictive potentials. Consequently, the performance of FSS varies per the search methods [29]. Table 2 presents a detailed description of the FSS methods used in this study, and Table 3 shows the various search methods with their parameters used in the FSS methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Filter Feature Rank Method** & **Search Method** & **Characteristics** & **Reference** \\ \hline Information Gain Attribute & & & \\ Evaluator (IG) & Ranker Search & Probability-based & [18; 32] \\ \hline Relief Feature Attribute Evaluator & & & \\ (RFA) & Ranker Search & Instance-based & [29; 32] \\ \hline Gain Ratio Attribute Evaluator (GR) & Ranker Search & Probability-based & [29; 32] \\ \hline Clustering Variation Attribute & & & \\ Evaluator (CV) & Ranker Search & Statistics-based & [18; 29] \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of Filter Feature Ranking (FFR) Methods.