esa-sceva/satcom-chunk-collection huggingface dataset:
# Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning
## 6 Results  
In this study, following the roadmap outlined in Figure 2, the dataset underwent partitioning into three sets: a training set and a combined validation and test set. Two splitting formats were applied: an 80-10-10 split and a 70-20-10 split. These splits allocate proportions of the dataset for training, validation, and testing purposes, facilitating robust model development and evaluation. In the 80-10-10 split, 80% of the data constituted the training set, while the remaining 20% were further divided into 10% for validation and 10% for testing. Similarly, in the 70-20-10 split, 70% of the data were allocated to the training set, and the remaining 20% were split into 10% for validation and 10% for testing. Additionally, Stratified K-Fold Cross-Validation was employed in this study, specifically utilizing a 10-fold Stratified Cross-Validation approach. This methodology involved partitioning the dataset into ten distinct subsets or \"folds\". The model underwent training ten times, with each iteration using nine folds for training and reserving the remaining fold for testing. Stratification ensured that each fold accurately represented the complete dataset, maintaining consistent proportions of class labels. This approach provided a reliable assessment of the model's generalization ability while preserving a balanced distribution of classes across each fold. In the dataset, there were 129 positive cases of AAS and 148,578 non-AAS cases. After handling missing and imbalanced data, and applying machine learning methods, the Relief feature method combined with random forest on an 80:10:10 splitting strategy consistently outperformed other combinations, demonstrating superior accuracy, sensitivity, and specificity (99.3%, 99.5%, and 99.3%, respectively). The identified top ten important features through Relief feature extraction, including age, troponin levels, and temperature, offer crucial insights into AAS prediction, enhancing diagnostic accuracy and improving patient outcomes in emergency departments. The following top results in Table 9 were observed in analyzing various classifiers and feature selection methods, considering accuracy, sensitivity, and specificity as key criteria. The table below presents the best results among all the methods applied. Based on the analysis, both the CFS and Relief feature methods demonstrate slightly better performance compared to PCA, as indicated by their highest values for accuracy, sensitivity, and specificity across various classifiers and scenarios. However, the Relief feature method combined with random forest on the SR 80:10:10 splitting strategy emerges as the most consistently high-performing combination, with an accuracy of 0.993, sensitivity of 0.995, specificity of 0.993, and AUC of 1.000.
# Assessment of Lightweight Concrete Thermal Properties at Elevated Temperatures
## 2 Materials and Methods
### Methodology
#### 2.3.1 LFA Method  
The LFA was developed by [PERSON] in 1961 [29]. This method is commonly used to measure the thermal diffusivity (\(\alpha\)), thermal conductivity (\(k\)), and specific heat (\(c_{p}\)) of a wide range of solid materials at different temperatures. In the LFA, the bottom surface of a sample is heated by an energy pulse with a certain power and duration. Both the intensity and the duration of the pulse can be adjusted in accordance with the measured sample. The resulting temperature change at the top surface of the sample is then measured with an infrared detector, as shown in Figure 2. The thermal diffusivity of the sample is the relation between the increase in temperature as a function of time, as shown in Equation (2) [30].  
\[\alpha=0.1388\frac{d^{2}}{t_{\frac{1}{2}}} \tag{2}\]  
where \(\alpha\) is the thermal diffusivity in cm\({}^{2}\)/s, \(d\) is the thickness of the sample in cm, and \(t_{\frac{1}{2}}\) is the time for the below surface to reach half of the maximum temperature rise in s (see Figure 2b).  
After thermal diffusivity, density (\(\rho\)) and specific heat were calculated. These values are used to determine the thermal conductivity of the sample at different temperatures using Equation (3):  
\[k(T)=\alpha(T)\times c_{p}(T)\times\rho(T) \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Bulk Density (kg/m\({}^{3}\))** & **True Density (kg/m\({}^{3}\))** & **Porosity (\%)** \\ \hline LWC-1 & 1740 & 2430 & 28.2 \\ LWC-2 & 1819 & 2485 & 26.6 \\ LWC-3 & 1906 & 2502 & 24 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Density and porosity of LWCs.  
The LFA 447 NanoFlash(r) equipment works according to international standards [31; 32], and it was used to perform the experimental campaign. The tested samples were ground concrete particles; consequently, a special sample holder was used to measure thermal conductivity. This sample holder compresses the concrete dust between two layers of aluminum, applying pressure. The thermal conductivity of LWC was measured at temperatures of 100, 220, and 300 \({}^{\circ}\)C and pressures of 100, 240, and 290 N/cm\({}^{2}\) at each temperature. In order to be as close as possible to the porosity conditions of the LWC samples, a series of tests was performed while varying the applied sample pressure.
# On IRS-Assisted Covert Communication with a Friendly UAV
## 4 Optimization Algorithm
### Power Optimization  
For a given \(C\), the optimization problem formulated in (24) can be rewritten as  
\[\max_{P,\hat{P}_{u}}\frac{1}{T}\sum_{t=1}^{T}R_{b}[t], \tag{35}\] \[s.t.\] \[\xi^{*}[t]\geq 1-\epsilon,\] (36) \[0\leq\hat{P}_{u}[t]\leq\hat{P}_{u,\max}[t],\] (37) \[0\leq P[t]\leq P_{\max}[t], \tag{38}\]  
where (35) and (36) are non-convex functions and are not easily solvable. To solve the problem, \(R_{b}\) in (35) can be re-expressed as  
\[\gamma_{b}=\frac{P_{a}[t]\left|h_{IB}^{H}[t]\Theta[t]h_{AI}[t]\right|^{2}}{(1- P_{out}^{*}[t])\hat{P}_{u}[t]\left|h_{UB}[t]\right|^{2}+\sigma_{b}^{2}}. \tag{39}\]  
According to [PERSON]'s theory, (39) can be transformed into a linear function with an equation factor \(\eta\) as follows:  
\[\frac{P_{a}[t]\left|h_{IB}^{H}[t]\Theta[t]h_{AI}[t]\right|^{2}}{(1-P_{out}^{*}[ t])\hat{P}_{u}[t]\left|h_{UB}[t]\right|^{2}+\sigma_{b}^{2}}=\eta, \tag{40}\]  
where the initial value of \(\eta\) is obtained by choosing the small maximum values for \(P_{a}[t]\) and \(\hat{P}_{u}[t]\).  
According to (21) and (37), this can be equivalently expressed as  
\[P_{a}[t]\left|h_{IN}^{H}[t]\Theta[t]h_{AI}[t]\right|^{2}-\epsilon\hat{P}_{u}[t ]\left|h_{UHW}[t]\right|^{2}\leq 0. \tag{41}\]According to (40) and (41), (35) can be rewritten as  
\[\max_{P,\hat{P}_{u}}\frac{1}{T}\sum_{t=1}^{T}\left(P_{a}[t]\Big{|}h _{IB}^{H}[t]\Theta[t]h_{AI}[t]\Big{|}^{2}-\eta\hat{P}_{u}[t](1-P_{out}^{s}[t])|h _{LIB}[t]|^{2}-\eta c_{b}^{2}\right), \tag{42}\] \[s.t.\] \[P_{a}[t]\Big{|}h_{IW}^{H}[t]\Theta[t]h_{AI}[t]\Big{|}^{2}-\varepsilon \hat{P}_{u}[t]|h_{UW}[t]|^{2}\leq 0,\] (43) \[0\leq\hat{P}_{u}[t]\leq\hat{P}_{u,\max}[t],\] (44) \[0\leq\hat{P}_{u}[t]\leq\hat{P}_{u,\max}[t],\] (45) \[0\leq P[t]\leq P_{\max}[t]. \tag{46}\]  
Because (42) and (43) are convex functions, the optimal \(P,\hat{P}_{u}\) can be obtained by using the CVX solver, where the values of \(\eta\) can be updated with (40).
# Opposition-Based Adaptive Fireworks Algorithm
## 4 Simulation Studies and Discussions
### Comparison with FWA-Based Algorithms
#### 4.1.1 Comparison with AFWA  
To compare the performances of OAFWA and AFWA, both algorithms were tested on twelve benchmark functions.  
\begin{table}
\begin{tabular}{l c c} \hline
**Function** & **Dimension** & **Range** \\ \hline F1 \((x)=\sum_{i=1}^{x}x_{i}^{2}\) & 40 & \([-10,10]\) \\ F2 \((x)=\sum_{i=1}^{x}|x_{i}+\prod_{i=1}^{n}|x_{i}|\) & 40 & \([-10,10]\) \\ F3 \((x)=\sum_{i=1}^{x}\left(\sum_{i=1}^{x}x_{i}\right)\) & 40 & \([-10,10]\) \\ F4 \((x)=\sum_{i=1}^{x}\left(x_{i}^{2}\right)\) & 40 & \([-30,30]\) \\ F5 \((x)=\max\left|x_{i}\right|,1\leqslant x_{i}\leqslant n\) & 40 & \([-100,100]\) \\ F6 \((x)=\sum_{i=1}^{n}x_{i}^{2}+\sum_{i=1}^{n}x_{i}^{2}\) & 40 & \([-100,100]\) \\ F7 \((x)=\sum_{i=1}^{n}\left(x_{i}^{2}-10\cos\left(2\pi x_{i}\right)+10\right)\) & 40 & \([-1,1]\) \\ F8 \((x)=\frac{1}{100}\sum_{i=1}^{n}x_{i}^{2}-\prod_{i=1}^{n}\cos\left(\frac{x_{i}} {x_{i}}\right)+1\) & 40 & \([-100,100]\) \\ F9 \((x)=-20\exp\left(-0.2\sqrt{\sum_{i=1}^{n}x_{i}^{2}}\right)-\exp\left(\frac{1} {2}\sum_{i=1}^{n}\cos\left(2\pi x_{i}\right)\right)+20+e\) & 40 & \([-10,10]\) \\ F10 \((x)=\sum_{i=1}^{n/4}\left(\left(x_{i-3}+10x_{i-2}\right)^{2}+5\left(x_{i-1}-x_{ i}\right)^{2}+\left(x_{i-2}-2x_{i-1}\right)^{4}+10\left(x_{i-3}-x_{i}\right)^{4}\right)\) & 40 & \([-10,10]\) \\ F11 \((x)=\sum_{i=1}^{n}\left|x_{i}\sin\left(x_{i}\right)+0.1x_{i}\right|\) & 40 & \([-10,10]\) \\ F12 \((x)=1-\cos\left(2\pi\sqrt{\sum_{i=1}^{n}x_{i}^{2}}\right)+0.1\sqrt{\sum_{i=1}^{n} x_{i}^{2}}\) & 40 & \([-10,10]\) \\ \hline \end{tabular}
\end{table}
Table 1: Twelve benchmark functions.  
To evaluate whether the OAFWA results were significantly different from those of the AFWA, the OAFWA mean results during iteration for each benchmark function were compared with those of the AFWA. The Wilcoxon signed-rank test, which is a safe and robust, non-parametric test for pairwise statistical comparisons [28], was utilized at the 5% level to detect significant differences between these pairwise samples for each benchmark function.  
The _signrank_ function in Matlab 7.0 was used to run the Wilcoxon signed-rank test, as shown in Table 5.  
Here P is the p-value under test and H is the result of the hypothesis test. A value of H = 1 indicates the rejection of the null hypothesis at the 5% significance level.  
Table 5 indicates that OAFWA showed a large improvement over AFWA.
## 7 Future Trends in Next-Generation Smart Grids (SGs) After the Integration of EV Market
### Load Shaping and Peak Shifting via Time-of-Use (ToU) Pricing to Meet Fluctuating EV Loads  
Time-of-use (ToU) pricing is adopted in various regions to incentivize load shaping and peak shifting. Seventeen European countries, in addition to the USA and India, have adopted different forms of ToU tariffs. These forms include:  
* Static ToU pricing applies to all low-voltage consumers in Italy who do not select a supplier from the liberalized market, with different rates for day and night usage.
* Real-time pricing, which includes different forms such as dynamic real-time pricing (e.g., hourly pricing), has been adopted in countries like Spain, the UK, and Estonia. Other dynamic pricing forms based on monthly averages are utilized in countries like Denmark, Norway, and Sweden.  
Figure 7: (**a**) V2G and G2V charging scenarios for EVs where bi-directional energy trade can happen between the SG and smart home, (**b**) static and dynamic wireless charging of EVs, and also V2V wireless charging between EVs.  
Variable peak pricing, which is a hybrid of static and dynamic prices by means of pre-defined pricing, except at peak demand during the day, when real-time prices apply.
* Critical peak pricing, which is a hybrid of static and dynamic pricing by means of pre-defined pricing all year, except for a maximum of 22 days where the highest wholesale prices occur, and is adopted in countries like Lithuania, Romania, and France.  
Figure 8 presents graphical representations of the different forms of ToU tariffs [128]. How pricing strategies could be integrated with blockchain-based SG architecture without causing disputes is a current challenge that will be resolved in the future. Such tariff rates would be necessitated for the stable operation of blockchain-based SG electricity trading operations in the presence of a highly dynamic EV consumer market.
## 3 Results and Discussion
### Filtration Performance Analysis
After three and five cycles of in situ growth of the metal skeleton, the filtration efficiency was not significantly improved, which may be due to the increase in the diameter of the composite fibers after the growth of the metal skeleton; although they have a higher specific surface area, the adsorption and filtration efficiency of particles did not exhibit a large improvement. When the growth cycle increased to seven cycles, the filtration efficiency curve decreased significantly; the reason for this phenomenon may be that the metal skeleton size was too large, resulting in the filtration process of the fiber surface CuBTC crystals falling off, which was recorded by the laser particle counter, resulting in the experimental results showing a decline in filtration efficiency. Figure 13: N\({}_{2}\) adsorption–desorption isotherms for different samples. Figure 14c shows the relationship between the filtration efficiency and filtration resistance of 3 \(\upmu\)m NaCl particles at different stages of fiber membrane treatment. The reason for choosing 3 \(\upmu\)m for separate analysis is that the filtration efficiency of 3 \(\upmu\)m particles is the main test for aluminum masks in various countries, and according to the related research, it shows the relationship between NaCl aerosol filtration efficiency and typical bacterial and viral filtration efficiency [10; 46]. After five cycles, CuBTC@PET filtration efficiency is 95.3%, and the pressure drop is only 40 Pa; bacterial and viral filtration efficiencies are also standardized.
# Investigating the Applications of Machine Learning Techniques to Predict the Rock Brittleness Index
## 3 Modelling Process and Results  
The present study developed five ML models to predict _BI_ of the rock material. To develop the models, a database contained 110 datasets was used. These data were split into the train and test with the ratio of train to test being 70%: 30%. Thus, 77 samples for training and 33 samples were used for testing. As pointed out earlier, five ML models including, RF, CHAID, SVM, KNN and ANN were developed to estimate _BI_ of the rock. Each of these models were evaluated using a simple ranking system and a gains chart. The three best models are discussed in more detail.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Symbol** & **Unit** & **Category** & **Min** & **Max** & **Mean** \\ \hline P-wave velocity & \(V_{p}\) & m/s & Input & 2870 & 7702 & 5491.6 \\ Density & \(D\) & g/cm\({}^{3}\) & Input & 2.37 & 2.79 & 2.59 \\ Schmidt hammer & \(R_{n}\) & - & Input & 20 & 61 & 40.5 \\ rebound number & \(Is_{50}\) & MPa & Input & 0.89 & 7.1 & 3.6 \\ Point load strength & \(BI\) & - & Output & 8.90 & 24.01 & 15.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Range, Mean, Unit, Category and Symbol of Inputs and Output Parameters in Predicting _BI_ of the Rock Samples.  
Figure 4: Geological map of tunnel location and its route.
# Experiment and Analytical Model for Pore Structure of Early-Age Composite Cement Pastes by LF-NMR
## 4 Conclusions  
Based on the cement materials, supplementary cementitious materials, and measurement techniques studied in this paper, the main conclusions are as follows.  
1. For all the cement pastes examined in the study, the most likely pore diameter reduces progressively with increasing curing age. Fly ash-blended cement pastes show a larger likely pore diameter than neat pastes up to 14 days, but it becomes smaller by 28 days. The pore size of slag-blended pastes increases initially and then decreases at 3 days. Moreover, the pore diameter of silica fume paste remains consistently smaller than that of neat paste from 3 to 28 days.
2. The test results indicate that when the contents of fly ash, slag, and silica fume are below 30%, 30%, and 10%, respectively, the mineral admixture content has minimal impact on the most probable pore diameter. But as the admixture content increases, the proportion of gel pores increases.
3. A bimodal model, which combines the Shimomura and Maekawa model with the Weibull distribution function, was developed to account for the PSD of both the left and right peaks in cement paste with and without the addition of fly ash, slag, and silica fume. The outcomes of this model align well with the experimental data.  
Conceptualization, [PERSON], [PERSON], B.H. and X.L.; methodology, [PERSON], G.Y. and B.H.; validation, [PERSON] and B.H.; formal analysis, B.H. and X.L.; investigation, [PERSON]; resources, B.H.; data curation, X.L.; writing--original draft preparation, [PERSON]; writing--review and editing, G.Y. and B.H.; supervision, [PERSON]; project administration, G.Y. and B.H.; funding acquisition, [PERSON] and X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Research on Key Technologies for Improving the Quality and Safety Assurance of the Huai River to Sea Waterway Phase II Project (RHSD2/FW-2024-03), Guangxi Science and Technology Program (grant Guike AA23062034), China Postdoctoral Science Foundation (2024M750729), Fundamental Research Funds for the Central Universities (B240201179), Natural Science Foundation of Jiangsu Province (BK20241532), and Technology Project of State Grid Jiangsu Electric Power Co., Ltd. (J2023094).  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company China Railway Shanghai Design Institute Group Corporation Limited. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Design of RBMM
### Buffer Partitioning Based on Improved Dynamic Threshold Scheme
Therefore, using \(N_{rt}\) to represent the number of real-time flows, we further expand the parameter \(\lambda\) as follows:  
\[\lambda=\alpha\cdot\frac{N}{1+N_{PHRD}}\cdot\left(\frac{N_{rt}}{N-N_{rt}+1} \right)^{1/2} \tag{5}\]  
Since traffic characteristics in the buffer vary over time, the reserved buffer threshold should be periodically updated using an exponentially weighted moving average to ensure the method's effectiveness, as shown in Equation (6). \[\theta_{t}=\gamma\cdot\theta_{t-\Delta t}+(1-\gamma)\cdot\frac{B}{\lambda+1} \tag{6}\]  
The complete buffer partitioning strategy is presented in Algorithm 1. Table 1 lists the main symbols used in Algorithm 1 along with their descriptions. \begin{table}
\begin{tabular}{l c} \hline \hline
**Notation** & **Description** \\ \hline \(B\) & Buffer capacity \\ \(N\) & Number of flows in the reordering buffer \\ \(\gamma\) & Exponentially weighted moving average (EWMA) parameter \\ \(\Delta t\) & Interval for updating threshold values \\ \(T\) & PHRD flow confirmation time: flows marked for longer than \(T\) are regarded as \\ \(\alpha\) & PHRD flows \\ \(\theta_{t}\) & Control parameter in DT scheme \\ \(\lambda\) & Buffer partition coefficient \\ \(N_{PHRD}\) & Number of PHRD flows in the buffer \\ \(N_{rt}\) & Number of real-time flows in the buffer \\ \(q_{k}\) & Boolean variable, \(q_{k}=1\) if the flow with \(Eid_{k}\) is a real-time flow; \(q_{k}=0\) otherwise \\ \(p_{\text{size\_thresh}}\) & Moving average of packet size (flow category classification threshold) at time \(t\) \\ \(s_{k}(t)\) & Flow size of \(Eid_{k}\) at time \(t\) \\ \(m_{k}(t)\) & Boolean variable, \(m_{k}(t)=1\) if the flow with \(Eid_{k}\) is marked at time \(t\); \(m_{k}(t)=0\) \\ \(p_{k}(t)\) & Boolean variable, \(p_{k}(t)=1\) if the flow with \(Eid_{k}\) is a PHRD flow at time \(t\); \(p_{k}(t)=0\) otherwise \\ \(r_{k}\) & Time when the flow with \(Eid_{k}\) is marked \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of notations in Algorithm 1.
# Compression Index Regression of Fine-Grained Soils with Machine Learning Algorithms
## 2 Dataset and Methods
### Performance Metrics of Regression  
The performance metrics of regression are used to measure a model's predictive ability. The most common error metrics include the mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and R-squared (R\({}^{2}\)). In Equation (2), MAE is defined as the mean of the differences between the predicted values and the actual values. The MSE, as shown in Equation (3), is the mean of the squared errors, while the RMSE, as shown in Equation (4), measures the magnitude of the prediction errors by taking the square root of the MSE. Lower MAE, MSE, and RMSE values indicate a better model performance. R-squared, also known as the coefficient of determination, shows how much of the data's variation the model can explain; its value ranges from 0 to 1, with a higher value indicating a better model. These metrics are critical for assessing the accuracy of the model's predictions and understanding how closely they align with the actual values, as shown in Equation (5).  
\[\mathrm{MAE}=\frac{1}{n}\sum_{i=1}^{n}|Actual_{i}-Pred_{i}| \tag{2}\]\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}(Actual_{i}-Pred_{i})^{2} \tag{3}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(Actual_{i}-Pred_{i})^{2}} \tag{4}\]  
\[\text{R}^{2}=1-\frac{SSR}{SST}=1-\frac{\sum_{i=1}^{n}(Actual_{i}-Pred_{i})^{2} }{\sum_{i=1}^{n}(Actual_{i}-Mean(Actual_{i}))^{2}} \tag{5}\]
## 4 Results and Discussion
### On Different Scenarios for Dealing with the Unbalanced Data Set
Figure 10: Boxplots exhibiting the relative distributions of accuracy obtained with the different scenarios considered for fault classification. See text for details. Applying Friedman to the results in Figure 10 yielded p\({}_{\text{\tiny Friedman}}\)= 1.865 \(\times\) 10\({}^{-19}\) < 0.05, meaning there is a statistically significant difference between the six scenarios. Table 3 shows the subsequent _posthoc_ results. From these, one should conclude that there is no statistically significant difference between scenarios RF-GAN and RF-GAN1 and that these outperform all the others. This is an interesting observation as both RF-GAN and RF-GAN1 use the technique described in Section 2.2 to select the best current generator and that the only difference between these scenarios is that in RF-GAN all the faulty state data are synthetic while in RF-GAN1 only 13,860 faulty examples are synthetic while the remaining 140 are the original faulty states presented in the training data set. This further endorses the quality of the obtained generators. The average accuracy of RF-i was 87.88% while, with an undersampling balanced dataset, RF-b2 reached 94.5%. RF-GAN had an average accuracy of 97.06%, RF-GAN1 97.75%, and RF-GAN2 95.38%. SMOTE had an averaged accuracy of 95.17%. We observe that the GAN based average accuracies are all higher than the unbalanced (RF-i), undersampling (RF-b2), and oversampling (SMOTE) scenarios. Within the GAN based scenarios, RF-GAN has shown a difference of 1.68% relatively to RF-GAN2 in terms of average accuracy. Curiously enough, we observed no advantage on the application of (24). For the moment, we keep in mind that (26) is not the only possibility to used the Vapnik-loss in a GAN and that other forms are currently being studied.
## 3 Results and Discussion
### PdM Assessment of Case Study
#### 3.1.3 Step 3: Failure Mechanisms and Related Symptoms  
After critical failure modes are identified, the associated symptoms that enable detection, tracking, and prediction of the underlying failure mechanisms (that if allowed to develop, result in the failure mode) must be determined. This includes identifying the relevant descriptors measuring the related symptom [75]. Such descriptors can be unprocessed raw data or processed data in terms of the mean or (exponential) moving average, etc. [9]. ISO 13379-1 [8] highlights the importance of selecting a good descriptor for detection purposes.  
To give an example, this means the looseness (failure mode) of a rotor caused by a mechanical failure (failure mechanism) that increases the system's vibration (measurable symptom) can be visualized through the average vibration level in the time domain (descriptor).