esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result and Analysis
### Feature Engineering: Correlation-Based Feature Selection  
Feature engineering is a vital component of machine learning model development, as it involves selecting the most relevant variables, creating new features from existing ones, and transforming features to enhance model performance. One commonly used technique in feature engineering is correlation-based feature selection, which focuses on identifying and removing highly correlated features from the dataset. We applied a correlation-based feature selection approach to eliminate redundant features that exhibited strong correlations with one another.  
To begin, we computed the correlation matrix for the dataset, which measures the linear relationship between feature pairs, as shown in Figure 3. The matrix values range from \(-1\) to \(1\), where \(-1\) represents a perfect negative correlation, \(0\) indicates no correlation, and \(1\) indicates a perfect positive correlation. We set a threshold of \(0.85\) to identify feature pairs with high correlations. If the absolute correlation value between two features exceeded this threshold, we considered one of the features to be redundant and removed it from the dataset. By applying this technique, we aimed to reduce the dataset's dimensionality and address multicollinearity issues, which ultimately improved both the performance and interpretability of the resulting machine learning models. After applying correlation-based feature selection, the dataset's dimensionality was reduced, retaining only the most relevant and non-redundant features. This refined dataset was then used for training and evaluating the machine learning and deep learning models in the later stages of the study.
# Intelligent Diagnosis of Concrete Defects Based on Improved Mask R-CNN
## 7 Conclusions  
In this paper, an improved Mask-RCNN is proposed to conduct instance segmentation for defects in concrete. The main findings are presented as follows:  
1. The K-means clustering algorithm can improve the precision and recall rate of the Mask-RCNN network model for the target detection of multiscale concrete defects.
2. The improvement method proposed in this paper can reduce the number of model parameters and calculations, and improve the model calculation speed and inference speed. The improved Mask R-CNN model can more accurately locate and detect the defect, and the precision and recall rate are higher than the original model, and the missed detection and error mask operation are reduced.
3. Comparing the accuracy and inference time of the original Mask-RCNN model and the YOLOv5, Faster-RCNN for defect identification, the improved Mask-RCNN model has the highest overall accuracy (mAP = 92.5%) with a very small difference in inference time.
4. The improved Mask R-CNN proposed in this paper has higher detection accuracy for the images taken by the UAVs which is new and untrained; the overall accuracy, recall rate and mAP values reach 94.7%, 95.3%, and 90.6%, respectively, and it is suitable for actual engineering applications.  
Conceptualization, C.H. and Y.Z.; methodology, C.H.; resources, C.H. and Y.Z.; data curation, Y.Z. and X.X.; writing--original draft preparation, C.H.; writing--review and editing, C.H., Y.Z. and X.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 51708188.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare that they have no conflicts of interest to report regarding the present study.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Evaluation**} & \multicolumn{5}{c}{**Defect Type**} & \multirow{2}{*}{**Total**} \\  & & **Exposed** & & & & \\  & & **Bars** & & & & \\ \hline TP & 53 & 73 & 55 & 27 & 48 & 256 \\ FP & 4 & 2 & 2 & 1 & 4 & 13 \\ FN & 3 & 1 & 3 & 1 & 3 & 11 \\ Precision/\% & 92.9 & 97.3 & 96.4 & 96.4 & 92.3 & 94.7 \\ recall rate/\% & 94.6 & 98.6 & 94.8 & 96.4 & 94.1 & 95.3 \\ AP/\% & 86.3 & 95.9 & 91.2 & 92.7 & 87.1 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: UAVs defect identification results.
# State of Health Estimation for Lithium-Ion Batteries Based on TCN-RVM
## 3 Results and Discussion
### Quantifying the Uncertainty of the Predictions  
It can be observed from Figure 11 that the predicted values of the model are in good overall agreement with the actual values. Different subgraphs show that the width of the confidence intervals dynamically changes with data fluctuations. For instance, in sampling segments where the prediction results are relatively stable, such as the declining stages of  
Figure 10: SOH estimation and error analysis using different algorithms.  
CS2_35-36 and CS2_35-37, the 50% and 75% confidence intervals are relatively narrow. This indicates that the model exhibits stable prediction performance with low uncertainty when processing such data. When there are obvious fluctuations in the data, such as the local mutation points in CS2_35-37 and CS2_35-38, the confidence intervals widen accordingly. This reflects that when dealing with fluctuating data, the model can quantify uncertainty by adjusting the intervals, demonstrating its adaptability to data with different characteristics.  
This feature of adaptive interval adjustment indicates that the TCN-RVM model incorporates an uncertainty quantification mechanism, which can dynamically adjust the prediction intervals according to the data characteristics, thereby enhancing the robustness and credibility of the model. From the perspective of the 75% confidence interval in Figure 11, most of the actual SOH values fall within the prediction interval, indicating that the model still has good coverage capability at a high confidence level.  
This is of great significance for battery health management: at the high confidence level of 75%, the model can be used for battery life early warning to ensure that the predicted SOH interval contains the true value, thereby reducing the risk of replacing the battery too early or too late. At the lower confidence level of 50%, the prediction interval is narrower, which is suitable for daily battery maintenance work, as it can provide more accurate SOH estimations to further optimize the charge-discharge strategy.
## 5 Conclusions  
Knowledge about enhancing phenylpropanoid biosynthesis in _A. rugosa_ is continuously being researched because it has excellent therapeutic properties for health, especially in eliminating free radicals that cause certain human diseases. Our results indicate that _ZmLC_ gene overexpression and light treatment induce phenylpropanoid accumulation in _A. rugosa_ hairy roots. This accumulation is related to the control of the following genes: _PAL_, _C4H_, _4 CL_, _CHS_, _CHI_, _TAT_, _HPPR_, and _RAS_. The DPPH and ABTS free radical scavenging capacity of phenylpropanoids tested at 6 concentrations (from 31.25 to 1000 mg/L) showed a gradual increase as the concentration increased, regardless of the sample, with an IC\({}_{50}\) for DPPH of 0.53 mg/mL after 30 min of incubation in the dark and IC\({}_{50}\) for ABTS of 0.48 mg/mL after 16 h of incubation in the dark. This is the first report on the enhancement of phenylpropanoid production and antioxidant activity in _A. rugosa_ transgenic hairy root cultures by overexpressing the maize LC transcription factor. Further research is needed on exploring the effects of other transcription factors or environmental stressors in _A. rugosa_ hairy roots.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14209617/s1](https://www.mdpi.com/article/10.3390/app14209617/s1)]([https://www.mdpi.com/article/10.3390/app14209617/s1](https://www.mdpi.com/article/10.3390/app14209617/s1)), Table S1: Primers used for qRT-PCR analysis. Table S2: HPLC conditions for phenylpropanoid compounds detected in this study.  
Conceptualization, D.H.C. and S.U.P.; methodology, B.V.N., J.K.K., J.L., K.K. and R.S.; software, B.V.N., J.K.K., J.L., K.K. and R.S.; validation, B.V.N., J.K.K., J.L., K.K. and R.S.; formal analysis, B.V.N., J.K.K., J.L., K.K. and R.S.; investigation, J.K.K., D.H.C. and S.U.P.; resources, J.K.K., D.H.C. and S.U.P.; data curation, B.V.N., J.K.K., J.L., K.K. and R.S.; writing--B.V.N. and R.S.; writing--review and editing, R.S., D.H.C. and S.U.P.; visualization, J.K.K., D.H.C. and S.U.P.; acquisition, J.K.K., D.H.C. and S.U.P.; project administration, J.K.K., D.H.C. and S.U.P.; funding acquisition, D.H.C. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the research grant of Kangwon National University in 2022.  
This material was supported by the research grant of Kangwon National University in 2022.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article/Supplementary Materials, further inquiries can be directed to the corresponding authors.  
**Conflicts of Interest:** Author [PERSON] was employed by the company EuHerb Inc. 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.
# Elastoplastic Constitutive Model for Energy Dissipation and Crack Evolution in Rocks
## 5 Validation of the Theoretical Model
### Analysis of Strength Parameters  
Based on the yield function \(f\) in Equation (3), the strength parameters (\(c,\varphi\)) were inversely determined by processing the stress-strain relationships in Figure 3c. As illustrated in Figure 4a, with \(\overline{E}=6.51\) GPa and \(\overline{\mu}=0.244\), the plastic shear strain \(\gamma_{p}\) was determined using Equation (8). For \(\gamma_{p}\) ranging from 0 to 2.0% at intervals of 0.1%, the subsequent yield strengths \((\sigma_{1}-\sigma_{3})\) were inversely determined. As shown in Figure 4b, five Mohr circles were drawn at the same \(\gamma_{p}\). Tangents between adjacent circles provided eight tangent points, which were then used to fit the tangent line and derive the strength parameters (\(c,\varphi\)). The specific findings are illustrated in Figure 4c. The cohesion \(c\) and internal friction angle \(\varphi\) can be fitted as follows [3]:  
\[c(\gamma_{p})=\alpha_{1}+\alpha_{2}/\left(\gamma_{p}+\alpha_{3}\right)-\alpha _{4}\text{exp}\big{(}-\alpha_{5}\gamma_{p}\big{)} \tag{44}\]  
\[\varphi\big{(}\gamma_{p}\big{)}=\beta_{1}+\big{(}\gamma_{p}+\beta_{2}\big{)} \text{exp}\big{[}(\gamma_{p}-\beta_{3})^{2}/\beta_{4}\big{]} \tag{45}\]  
where \(\alpha_{1}\sim\alpha_{5}\) and \(\beta_{1}\sim\beta_{4}\) are the fitting parameters, and the fitting results are shown in Figure 4c.  
Figure 4: _Cont._
# Dynamic SAFFRON: Disease Control Over Time via Group Testing
## 3 Proposed Algorithms and Analysis
### Dynamic Individual Testing Algorithm  
In the dynamic individual testing algorithm, we consider randomized, individual testing of \(T\) individuals at each time instance \(t\geq 1\), where \(T\) individuals to be tested are uniformly randomly selected from the whole population, independent across both individuals and time. In [38], we proved that the dynamic individual testing algorithm (weak dynamic individual testing algorithm in that paper) is a symmetric and converging dynamic testing algorithm. Furthermore, we derived \(E[\alpha(t)]\) results for the disease control time. Here, we derive \(\epsilon\)-disease control time performance results of the dynamic individual testing algorithm.  
In the following theorem, we present our result for the \(\epsilon\)-disease control time performance metric when the dynamic individual testing algorithm is used in our proposed dynamic system.  
**Theorem 2**.: _When the dynamic individual testing algorithm is used, we have_  
\[\bar{t}_{\epsilon}=\frac{\ln(\epsilon/np+o(1))}{\ln\Big{(}(1-\frac{T}{\bar{n }})(1+nq(1-p))\Big{)}} \tag{6}\]  
Proof.: We start with the mean of non-isolated infections  
\[E[\lambda(t)] =nP(U_{i}(t)=1) \tag{7}\] \[=np((1+nq(1-p)))^{t}\prod_{j=1}^{t}p^{\prime}(j)+o(n) \tag{8}\]where (7) follows from the definition of \(\lambda(t)\) as the total number of non-isolated infected individuals and (8) follows from Lemma 2. Then, we have  
\[\epsilon =np((1+nq(1-p)))^{\tilde{t}_{\epsilon}}\prod_{j=1}^{\tilde{t}_{ \epsilon}}p^{\prime}(j)+o(n) \tag{9}\] \[=np\bigg{(}(1-\frac{T}{n})(1+nq(1-p))\bigg{)}^{\tilde{t}_{ \epsilon}}+o(n) \tag{10}\]  
where (9) follows from the definition of \(\epsilon\)-disease control time and (10) follows from the fact that \(p^{\prime}(t)=(1-T/n)\) for all \(t\geq 1\) when the dynamic individual testing algorithm is used. Finally, we obtain  
\[\tilde{t}_{\epsilon}=\frac{\ln(\epsilon/np+o(1))}{\ln\Big{(}(1-\frac{T}{n})(1 +nq(1-p))\Big{)}} \tag{11}\]  
by arranging the terms.  
In Theorem 2, we characterize the \(\epsilon\)-disease control time metric for dynamic individual testing algorithm. Since it is defined as the time instance when there are \(\epsilon\) non-isolated infected individuals in the mean sense, \(\epsilon\)-disease control time metric presents a characterization of how fast a given algorithm can control the disease spread.
## 5 Discussion
### Usefulness of the Identified Critical Scenario Components
#### 5.1.2 Change in Traffic Lights  
In an autonomous-type system, an oncoming vehicle approaching the intersection and the status of the traffic lights can be detected through the sensors mounted on the ego vehicle. The system is expected to plan a right turn at an intersection based on the traffic rules and detection results, which change with time. For example, in a situation where the traffic light for the oncoming vehicle's lane is red and the ego vehicle's lane is green, the ego vehicle is expected to turn right because the oncoming vehicle should not enter the intersection according to traffic rules. However, suppose that an oncoming vehicle ignores the red light and enters the intersection. In this case, a collision may occur because the ego vehicle's planner does not consider the decision error of the oncoming vehicle. The autonomous-type system can detect an oncoming vehicle and predict the possibility that the oncoming vehicle will enter the intersection, even with a red light, and avoid a collision with it. However, suppose that the ego vehicle waits for the oncoming vehicle to pass. In this case, the phase of the traffic lights might change, and the ego vehicle might be left inside the intersection, possibly blocking the traffic in the intersecting lane. In a cooperative-type system, unlike an autonomous-type system, it is possible to obtain information about the oncoming vehicle and the timing of light changes from others. However, although the information used in the system is different, planning under the traffic rules while adapting to the situation changing with time is the same as in the autonomous-type system, and the cooperative-type system will face the same issue. As described earlier, this scenario with a change in traffic lights is critical for ADSs because it requires planning that adapts to situations where multiple factors (e.g., approaching oncoming vehicle and traffic light status) change with time.  
The examinations conducted show that each identified scenario component presents challenges for the ADS, and it may end up in a collision if it cannot overcome these challenges. Therefore, the usefulness of the identified critical scenario components was confirmed. Furthermore, the proposed method used to identify these scenario components based on driver error analyses is valid.
# Predicting Decompensation Risk in Intensive Care Unit Patients Using Machine Learning
## 2 Materials and Methods
### Decompensation Score  
We propose a patient's decompensation score, which is defined as the area under the predicted sequence of decompensation risk within the forecast window, and is formulated as follows:  
\[\textit{decompensation score}=\int_{t=0}^{t_{m}}P(t)dt \tag{1}\]  
where \(P(t)\) is the predicted decompensation risk, \(t\) is a time point within a \(t_{m}\)-hour forecast window (e.g., 24 h), and \(\{P(0),P(1),\ldots,P(t),\ldots,P(t_{m})\}_{t=0}^{t_{m}}\), the sequence of decompensation risks. Since the risk \(P(t)\) could take a value between 0 and 1, the proposed decompensation score could range from zero (lowest) to \(t_{m}\) (highest). A patient with a low decompensation score value suggests they are less likely to decompensate within the considered forecast window. In this study we used \(t_{m}=24\), although the length of the forecast window could be altered if the available data allows.  
To assess the proposed decompensation score, it was compared against the National Early Warning Score (NEWS, [16]). NEWS is widely used in many healthcare settings worldwide, primarily in the UK, as the standard score for detecting deterioration in acutely ill patients. NEWS values could range from 0 to 20, and it is generally recognised that a value between 0 and 5 indicates a low risk of deterioration, while values above 10 represent a high risk of deterioration.
# Numerical Analysis of Vibroacoustic Response of Timber Floor Panels Damped with Porous Materials
## 4 Dynamic Analyses and Parametric Study  
The finite element formulation presented in this article is implemented in Matlab and will be utilized to investigate the vibroacoustic response of the system. For modeling the wooden panel (joists, beams and wooden floor), 8-node solid elements are employed, resulting in a mesh with 16,608 elements. The porous layer is modeled using voluminal elements of the hexahedral type, featuring 8 nodes per element, and a total of 824 elements. A compatible mesh is ensured at the interface between the panel and the porous layer.
## 2 Materials and Methods
### Extraction of EOs
#### 2.1.3 Experimental Procedure
PEF was applied, according to Table 2. The mixture was then transferred to a 1 L volumetric flask, where 130 mL of tap water was introduced. The volumetric flask was then connected to the Clevenger apparatus and the same procedure as described above was followed. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \(L\) & \(d\) (cm) & \(h\) (cm) & \(U\) (kV) & \(t_{on}\) (\(\upmu\)s) & \# & \(\rho\) & \(I\) (A) & \(E\) (kV/cm) & \(W_{p}\) (j) & \(t_{PEF}\) (\(\upmu\)s) & \(W_{t}\) (kJ) (kJ/kg) & \(\Delta T\) (\({}^{\circ}\)C) \\ \hline
10 & 5 & 6 & 10 & 10 & 312 & 0.8 & 96 & 2 & 9.6 & 3120 & 3 & 10 & 2.4 \\ \hline \end{tabular}
\end{table} 
Table 1: PEF parameters calculation example for one eucalyptus experiment, for, f = 5 Hz. Experimental Parameters Justification  
In this paper, we show the influence of PEF pre-treatment in the optimization of hydrodistillation for EO extraction, using parameters that would make sense to the industry, for technology acceptance:  
(a) Distillations times,  
For a quality oil, the extraction time should not be too long. So, the time of distillation by HD, without PEF, should be chosen in order to extract almost the totality of the essential oil of the aromatic plant. Beyond this duration, only a small quantity of EO should be extracted, but with lower quality and with great losses of energy. Considering the latter, 60 min is a common time used by the industry and used by other authors [19,21]. As mentioned in [8]: \"Prolonged distillation produces only a small amount of essential oil but does add unwanted high boiling point compounds and oxidation products.\". The introduction of a PEF pre-treatment has the objective of further reducing the distillation time, e.g., 30 min, by promoting extraction rate and saving energy at the same time. (b) PEF protocol,  
In relation to the PEF protocol used, for mass extraction in plants the parameters are normally: for the electric field \(<\)5 kV/cm, and for specific energy \(<\)20 kJ/kg. In principle the higher the electric field, kV/cm, the better is the extraction yield, but there is the possibility to extract some unwanted components that could impact the quality of the oil. In addition, other factors should be taken into consideration, such as the scalability of the equipment and the cost associated, namely relative to the increase in the voltage of the equipment. Increasing the electric field two-fold means that the Joule heating increases four times, and the cost of the equipment is higher, as the electrical current increases two-fold also. Higher specific energies, such as 20 kJ/kg, can lead to oil absorption during HD, as described in [7], and in some cases with higher fields, e.g., 3 kV/cm, worst yield results were reported, as described by [21]. Considering the latter and the results of other authors [19,21], 2 kV/cm was chosen and 10 kJ/kg.
# Length-Bounded Hybrid CPU/GPU Pattern Matching Algorithm for Deep Packet Inspection
## 3 Length-Bounded Hybrid CPU/GPU Pattern Matching Algorithm  
### Overall Architecture and Procedure  
Figure 2 illustrates the architecture of LHPMA. As for the cooperation, the CPU is responsible for pre-filtering the packets, and the GPU is responsible for inspecting the packets by full pattern matching. Here, an additional process, the \"length-bounded separation algorithm\" (LBSA), is executed by the CPU. Different from the previous work, the incoming packets pass the LBSA before they are sent to the prefilter buffer in the CPU host memory, in which those packets to be processed by the prefilter are stored. The task of LBSA is checking the payload lengths and determining the allocation of those packets. The LBSA is described in the next subsection. After the LBSA process, some packets are stored in the prefilter buffer and thus delivered to the CPU prefiltering (dashed-line arrow); on the other hand, the other packets are directly sent to the full-matching buffer in the CPU host memory (dotted-line arrow), which is used for storing the packets to be processed by the GPU for full pattern matching. In addition, when the prefilter buffer becomes full, the prefiltering is executed; prefiltered packets are also sent to the full-matching buffer (dashed arrow).  
Once the full matching buffer is full, the buffered packets are transferred to the GPU device memory and accessed by the full pattern matching algorithm. Here, the full pattern matching is implemented using the AC algorithm, which requires the lookup tables (state transition table, accept states table and failure states table) being copied to the GPU texture memory to accelerate the inspection speed because of the much lower access latency. Finally, the matched result from the full pattern matching algorithm is sent back to the CPU. Figure 3 illustrates the overall procedure of LHPMA.  
Figure 1: Hybrid CPU/GPU pattern matching algorithms.  
Figure 3: LHPMA overall procedure.  
Figure 2: LHPMA architecture.