esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Traditional Machine-Learning-Based Methods  
Since 2006, traditional machine-learning-based methods have been gradually applied in the field of EM detection, such as in [67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79]. The main idea of this method is to determine the EMs category according to the acquired feature information and the corresponding network structure. In [67], a back propagation neural network was employed for bacteria detection. After several preprocessing steps such as threshold-based segmentation and denoising, morphological features of bacteria are extracted and then sent to a back propagation neural network for detection. In [68], a genetic algorithm-neural network method was presented for tubercle bacillus detection. By applying a color filter, moving \(k\)-means clustering, and region growing, a suitable segmented image was obtained, which is then sent to the color filter, moving \(k\)-means clustering and region growing for the final detection. In [69], a probabilistic neural network is applied for pathogens detection. First, the original image is processed by background correction and object isolation. Then, regions that may contain pathogens were selected. At last, a probabilistic neural network is built for pathogen detection.  
Based on the research on traditional machine learning methods in this field, we find that the most widely used classification model is the support vector machine (SVM) classifier, mentioned in [70; 71; 72; 73; 74; 75; 76; 77; 78; 79]. SVM can construct an optimal separation hyperplane in the feature space of the data to maximize the gap between positive and negative samples in the training set [80], which makes SVM an efficient classifier for binary classification tasks. Furthermore, SVM can efficiently use smaller training samples. This enables SVM in achieving higher classification accuracies on a smaller training set. Therefore, after the development of related technologies of SVM, it has been gradually applied to the detection of EMs. In [71], an SVM classifier is proposed for P. minimum species detection. In addition, to improve the accuracy of the detection results, the SVM classifier is combined with a random forest classifier. In [74], a multi-class SVM was proposed for EM detection. First, the Sobel edge detector is applied for image segmentation. After that, shape features, Fourier descriptors, and some other features were extracted from processed images and then sent to a multi-class SVM for detecting EMs. In [78], an SVM classifier is applied for planktonic organisms detection. The preprocessing step includes threshold segmentation, robust refocusing criterion, and re-segmentation. After that, the processed image is detected by an SVM classifier.
## 9 Evaluation Methods and Results
It can be observed that the LR algorithm produced optimal accuracy of 82% and an f1-score of 79% over cyberbullying class among ML techniques, whereas, from ensemble techniques, the Bagging ensemble algorithm provided better performance with an accuracy score of 78% and F1-score of 72%. To extract more significant conclusions, we plotted the classifier results when training and testing over the BoW approach. The summary of model comparison over various evaluation metrics is presented in Figure 14.
# Metabolite Profiling of _Christia__vevertilionis_ Leaf Metabolome via Molecular Network Approach
## 3 Results and Discussion
### C-glycosflavone Derivatives
#### 3.1.9 Carotenoids  
The inspection of MS/MS spectra of the nodes in Cluster L (see Figure S17) revealed that they exhibited characteristic carotenoid fragment ions at \(m/z\) 119 and 145, which originate from the fragmentation of the polyene backbone chain via C9-C10 and C13\({}^{\prime}\)-C14\({}^{\prime}\) cleavages and C6-C7 and C15-C15\({}^{\prime}\) cleavages, respectively.  
The nodes with precursor ions [M + H]\({}^{+}\) at \(m/z\) 601.424 and 585.430 were characterized as epoxy carotenoids based on the characteristic fragment ions observed at \(m/z\) 221, resulting from the C10-C11 cleavage of the polyene chain. The node with a precursor ion [M + H]\({}^{+}\) at \(m/z\) 601.425 yielded fragment ions at \(m/z\) 583 for [(M+H)-18]\({}^{+}\) and 521 for [(M+H)-80]\({}^{+}\) due to loss of water molecules at one of the hydroxyl group positions and loss of two 5,6-epoxy units, respectively. The absence of a key fragment ion at \(m/z\) 393.000 (due to the cleavage of the double bond allylic to the allelic carbon) that would have been observed in neoxanthin [35] led to the conclusion that this carotenoid was, in fact, violaxanthin (**54**), the structural isomer of neoxanthin. The identity of the node with precursor ion at \(m/z\) 585.430 [M + H]\({}^{+}\) was characterized as antheraxanthin (**58**). Additional fragment ions observed at \(m/z\) 567 for [(M+H)-18]\({}^{+}\) and 549 for [(M+H)-18-18]\({}^{+}\) supported the presence of two hydroxyl groups in compound **58**[36].  
Cluster L was also constituted by carotenoids containing both \(\alpha\) and \(\beta\)-ionone rings as the end groups (\(m/z\) 568.428, [M]\({}^{++}\)), as well as carbonyl-containing end groups (\(m/z\) 367.263, [M + H]\({}^{+}\)). The node with a precursor ion [M + H]\({}^{+}\) at \(m/z\) 367.263 was assigned as \(\beta\)-apo-12\({}^{\prime}\)-luteinal (**57**) based on the formation of the characteristic fragment ions at \(m/z\) 349 for [(M+H)-18]\({}^{+}\), due to loss of water molecule at the hydroxyl group position, and at \(m/z\) 161, corresponding to a hydroxylated terminal \(\beta\)-ring, resulting from a C10-C11 cleavage. The major product ions observed in the MS/MS spectrum of **57** are presented in Figure S20. Meanwhile, the node with radical cation [M]\({}^{++}\) at \(m/z\) 568.428, which exhibited a fragment ion at \(m/z\) 135, resulting from C7-C8 cleavage, was consistent with the characteristic fragment ion of carotenoids containing a hydroxylated \(\beta\)-ionone ring. On the basis of this information, this node was subsequently identified as lutein (**59**) and was further distinguished from its structural isomer, zeaxanthin, based on the characteristic fragment ion at \(m/z\) 430, generated via elimination of the terminal \(\alpha\)-ionone ring (138 Da) [37].
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## 4 The Proposed Clustering Protocol
### Settling Phase
#### 4.4.2 Optimal Number of CH Selection
When the BS gets the \(O_{CHs}\), then it ensures that the already selected \(R_{CHs}\) are near to the optimal number or not. If the network is uniformly divided into clusters on the basis of selected \(R_{CHs}\), then the BS allows the system to move on the next phase. Otherwise, it picks a few nodes with higher residual energy and lesser communication distance according to the calculated optimal number through the  
Figure 6: The optimal number of CH selection, and optimal number of cluster formation in the network. Markov model. In the first round, the BS selects the CHs on the basis of communication distance from the entire network. However, in the other rounds the sensor nodes, including CHs send their residual energy information with the data packets. As the rounds proceed, each node consumes energy in sensing, transmission, and reception. At the end of each round the \(C_{MNs}\) and CHs calculate their remaining energy and send this information to the BS with the data packets. Now, the BS is well aware of the remaining energies of all the \(C_{MNs}\) and the CHs at the end of every round. So, the BS uses this information for selecting the \(O_{CHs}\) for the next round. As a result, after each round the BS updates the residual energy information of each node in the network. Finally, the \(O_{CHs}\) are selected on the basis of remaining energy and communication distance. If sometimes the nodes selected through this criterion are less than an optimal number, then the CHs from the \(R_{CHs}\) list and the \(W_{CHs}\) list with higher residual energy are preferred to be selected as \(O_{CHs}\). The optimal CH selection, and cluster formation procedures are shown in Figure 7.
# P System with Fractional Reduction
## 3 Principles and Algorithms of Fraction Simplification
### Division Algorithm
#### 3.2.2 Algorithm  
Suppose we want to approximate the fraction \(\frac{x}{y}\). According to the principle of Section 3.2.1, the process to achieve fractional simplification by the division algorithm is described as follows:  
1. Input \(x,y(0<y<x)\);
2. Calculate \(\{a_{i}\},\{b_{i}\},\{r_{i}\}\), where \(i=2\), \(3,...,t\), _and_\(a_{1}=x,b_{1}=y,r_{1}=m\%n\);
3. Output \(k,l\).  
In this algorithm, the complexity of the algorithm is \(O(1)\) when m is a multiple of n. Generally, for the sequence \(\{r_{i}\}\), we know that \(r_{t+1}=0\) if the algorithm performs t coset operations. Comparing the sequence \(\{r_{t+1}\}\) with the Fibonacci sequence \(\{F_{i}\}\), we have \(F_{0}=1\leq r_{t}\) and \(F_{1}=1\leq r_{t-1}\). \(r_{k}\geq r_{k+1}+r_{k+2}\) can be obtained by \(r_{k}\)_mod_\(r_{k+1}=r_{k+2}(0\leq k\leq t-1)\). Therefore, \(r_{k}\geq F_{t-k}\) can be concluded by mathematical induction. In addition, we can obtain \(m=n_{0}\geq F_{t}\) and \(n=n_{1}\geq F_{t-1}\). That is, if our algorithm performs the remainder operation t times, then n must not be less than \(F_{t-1}\) and vice versa. According to the characteristics of the Fibonacci sequence, we have \(F_{t-1}\geq(1.618)^{t}/\sqrt{5}\), which gives us \(n\geq(1.618)^{t}/\sqrt{5}\) and \(t\leq log_{1.618}\Big{(}\sqrt{5}n\Big{)}\). Therefore, in the worst case, the complexity of the algorithm is \(O(logn)\).
Accumulating evidence additionally suggests that _L. monocytogenes_ is significantly susceptible to licorice root extracts and fractions [18; 19; 20]. Furthermore, the aqueous extract of licorice root demonstrates synergistic effects on the growth of _L. monocytogenes_ with aminoglycos such as gentamicin, with the presence of licorice extract reducing the minimum inhibitory concentration of gentamicin by 32-fold [21]. Previous works mainly focused on the anti-Listeria potency of glabridin, a prenylated isoflavian with well-known inhibitory effects against _L. monocytogenes_, with its bactericidal activity defined at concentrations above 25 ug mL\({}^{-1}\)[22; 23]. Considering the growing interest in replacing synthetic food antimicrobials with natural ones, mainly because of the public's conviction that natural antimicrobials are safer than synthetic analogs, the food industry is strongly inspired to discover safe and low-cost antimicrobial agents of natural origin [24]. Initial work has shown that licorice root extract could inhibit the growth of a range of Gram-positive bacteria, including _L. monocytogenes_[25]. Therefore, the objective of the present work was to explore for the first time the potential of licorice root extract (LRE) obtained by supercritical fluid extraction to control the growth of different _Listeria_ species, since outstanding differences in susceptibility among the Listeria species were found for natural and synthetic substances. Subsequently, the major and minor phytochemicals in LRE were elucidated with the employment of LC-DAD-qTOF-MS in order to explain its antimicrobial effects. Finally, the AntiBac-Pred, a web application for predicting the antibacterial activity of chemical compounds, was used to pinpoint the active phytoconstituents of LRE.
## 3 Results and Discussion
### Scenario
In this scenario, \(\text{LUE}_{\text{E,max}}\) was the largest at a 30-cm lighting distance with VAP-HR, and both PPFD\({}_{\text{opt}}\) and \(\text{LUE}_{\text{E,max}}\) were relatively low at a 20-cm lighting distance; thus, the maximum \(\text{LUE}_{\text{E,max}}\) was approximately 20.3% larger than the minimum. In all cases, a larger \(\text{LUE}_{\text{E,max}}\) was achievable at a low PPFD\({}_{\text{opt}}\) for HR when comparing NR and HR. Figure 8: Light interception and coefficient for variance of light interception (\(\text{CV}_{\text{LI}}\)) under different light source arrangements and lighting distances. Light sources were arranged vertically above (VAP) the plant or between (BP) the plants. Figure 9: Difference in light interception when floor surface is changed from non-reflective to high-reflective material at different light arrangements and lighting distances. Light sources were arranged vertically above the plant (VAP) or between the plants (BP). Figure 11: Optimal photosynthetic photon flux density (PPFD) setup (PPFD\({}_{\text{opt}}\)) for achieving the maximal light use efficiency (LUE\({}_{\text{z,max}}\)) for different light arrangements, lighting distances, and floor reflectances. Light sources were arranged vertically above the plant (VAP) or between the plants (BP). Floor surface was set at non-reflective (NR) or highly-reflective (HR) material. Figure 10: Light interception (**A**), estimated net photosynthetic rate (**B**), and light use efficiency for intercepted light (LUE\({}_{\text{I}}\)) (**C**) at different light arrangements, lighting distances, and floor reflectances. Light sources were arranged vertically above the plant (VAP) or between the plants (BP). Floor surface was set at non-reflective (NR) or high-reflective (HR) material. When light intensities were increased, the change pattern of LUE was similar to quadratic functions that increase to a certain light intensity point and decrease afterwards. Between the scenario variables, the high level of light interception and low level of CVLI were connected to the high LUE\({}_{\text{E,max}}\), indicating that the high light-intercepting efficiency (LIe) is related to the potentially achievable LUE\({}_{\text{E,max}}\). In particular, in the case of a lighting distance of 20 cm, which showed a low LLe, the PPFD\({}_{\text{opt}}\) was relatively low, at approximately 310 umol m\({}^{-2}\) s\({}^{-1}\), due to their heterogeneity light distribution on leaves. Additionally, the overall increment in light interception by reflective material induced the higher LUE\({}_{\text{E,max}}\) at a relatively low level of PPFD. It is difficult to directly compare this result with others because there were no cases analyzing LUE in PFELs with the simulation. When compared with normal LUE in greenhouse cases, the LUE\({}_{\text{E,max}}\) in this study is approximately 1.8 times higher [15]. Additionally, compared with generally adopted PPFD levels for research or cultivation with less than 200 umol m\({}^{-2}\) s\({}^{-1}\)[9; 10; 33; 34], this result suggests that the larger range of PPFD can be efficient for improving LUE.
## 3 Results and Discussion  
### Study Selection  
Twenty-seven manuscripts published between 2011 and 2021 were included in the review. A full-text assessment was conducted on 209 manuscripts, excluding 182 studies following the exclusion criteria, as shown in Figure 2. Figure 3 summarizes the risk of bias.  
The frequency of publications has been rising, reflecting the increased attention on this subject due to their broad applicability. Results showed that most studies were conducted in Japan, followed by Turkey, Switzerland, Brazil, China, Egypt, USA, Canada, Chile, Germany, and Jordan.  
The outcomes were categorized according to study designs, level of evidence of the studies, compositional classification, manufacturer, commercial brand, and mechanical, optical, biological, surface, adaptability, and bond strength properties investigated.
## 2 Mechanical Performance of a Unilateral Combined Interface under Inclined Deformation
### Stress State of the Unilateral Contact Interface of a Composite Timber Column
#### 2.1.2 Vertical Stress Distribution at Any Point  
Each cross section along the column's height still remains in a planar state when the composite column is inclined. It is assumed that the vertical stress on the cross section is linearly distributed in the y direction and uniformly distributed along the width direction. The force analysis diagram of a composite timber column in an inclined state is shown in Figure 2b. \(P\left(y,x\right)\) is any point in the composite timber column. The values \(h_{A}\) and \(h_{B}\) are the distance from the central axis of column A and column B to the edge of the column, respectively, and these meet the requirement of \(h=h_{A}+h_{B}\). The following relations can be obtained from the geometric relationship: \(\tan\theta=y_{c}/x\), \(y_{O}=y_{c}+h\).  
According to the relative magnitude of the compressive stiffness of the two columns, \(N_{A}\) and \(N_{B}\) can be obtained by distributing the vertically concentrated load \(N\) to the centers of the tops of column A and column B.  
\[N_{A}=\frac{EA_{A}}{EA_{A}+EA_{B}}N \tag{1}\]  
\[N_{B}=\frac{EA_{B}}{EA_{A}+EA_{B}}N \tag{2}\]  
where \(E\) represents the modulus of the elasticity of the wood and \(A_{A}\) and \(A_{B}\) are the sectional areas of column A and column B, respectively.  
According to the basic assumptions, the vertical stress of the plane at \(P\) satisfies the linear distribution in the y direction, so the expression of the vertical stress at \(P\) can be assumed as:  
\[\sigma_{x}(y)=ky+c \tag{3}\]  
The force analysis of the body above \(P\) can then be carried out and the following equation can be obtained from the static equilibrium equation \(\sum F_{x}=0\):  
\[b\int_{y_{c}}^{y_{c}+2h}\left(ky+c\right)dy-\left(N_{A}+N_{B}\right)=0 \tag{4}\]  
where \(b\) represents the width of the column.  
Equation (5) can be obtained from the moment equilibrium equation \(\sum M_{\left(\text{O}\right)}=0\). Note that \(N_{A}+N_{B}=N\) and \(N_{B}h_{A}-N_{A}h_{B}=M^{\prime}\) are specified in advance.  
\[-N_{A}(y_{c}-L\tan\theta+h_{B})+N_{B}(L\tan\theta+h_{A}-y_{c})+b\int_{y_{c}}^ {y_{c}+2h}\left(ky+c\right)(y_{c}+h-y)dy\,=0 \tag{5}\]  
According to Equations (4) and (5), the coefficients \(k\) and \(c\) can be expressed as:  
\[k=\frac{3[N(L\tan\theta-y_{c})+M^{\prime}]}{2 bh^{3}} \tag{6}\]  
\[c=\frac{N}{2 bh}-\frac{3(y_{c}+h)[N(L\tan\theta-y_{c})+M^{\prime}]}{2 bh^{3}} \tag{7}\]  
By substituting Equations (6) and (7) into Equation (3), the vertical stress at \(P\) can be calculated as:  
\[\sigma_{x}(y)=\frac{3[N(L-x)\tan\theta+M^{\prime})]}{2 bh^{3}}y+\frac{N}{2 bh} -\frac{3(x\tan\theta+h)[N(L-x)\tan\theta+M^{\prime}]}{2 bh^{3}} \tag{8}\]
## 2 Literature Review
in [20] proposed IDS considers the correlation between the attributes and uses a feature selection method, the CFS-BA, to reduce the data's dimensionality. The system was then implemented by combining C4.5 and RF algorithms through voting. The results showed that the system achieved 99.8% accuracy on the NSL-KDD dataset with 10 selected features. Even though the results showed adequate accuracy, the system was tested using only 10 validations, which is not a guarantee if the entire test dataset is used. Even though there have been a number of studies [21; 22] in the field of IDS, a number of problems still need to be solved. An enhanced Long Short-Term Memory (LSTM) network was proposed by [PERSON] et al. in [23] to distinguish between benign and malicious traffic, identify the attack category, and define the type of sub-attacks. ToN-IoT and InSDN datasets were used to assess the proposed system. The authors of [24] proposed a Transformer-based IoT NIDS method to determine the characteristics of attacks and their effects based on the types of data generated within the heterogeneous IoT environments. Using self-attention mechanisms, this method learns contextual embedding for input network features. It is capable of handling continuous and categorical features. The method uses network traffic data and telemetry information from IoT sensors to detect intrusions. To tackle the same, a more diverse model was formed in the current work, where several classifiers that were not used in the prior research were combined to optimize the model performance. As such, a more robust model was obtained that could be effectively used for intrusion detection.
## 2 Results and Discussion
### Analysis of \"in Vivo\" VOCs
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Control**} & \multicolumn{3}{c}{_O. sphegodes_ subsp. _sphgodes_} & _O. bertoluni_ subsp. _benacensis_ & _N. tridentata_ \\ \hline
**Compounds** & **RT*** & **Mean (\(n=3\))*** & **SD*** & **Mean (\(n=3\))*** & **SD*** & **Mean (\(n=3\))*** & **SD*** & **Mean (\(n=3\))*** & **SD*** \\ \hline
3-decen-1-ol & 29.59 & ND & - & 1.49 & 0.00 & ND & - & ND & - \\
1-heptadecanol & 30.46 & ND & - & ND & - & 0.42 & 0.23 & 0.31 & 0.08 \\
1-dodecanol & 32.23 & ND & - & 0.49 & 0.04 & 0.14 & 0.13 & ND & - \\ isothymol & 34.70 & ND & - & 0.19 & 0.00 & ND & - & ND & - \\
1.4-benzenedi & 34.86 & 1.08 & 0.77 & 0.11 & 0.00 & 0.69 & 0.18 & ND & - \\ \hline
**Total** & & **8.05** & & **6.35** & & **7.76** & & **6.27** & \\ \hline _Furans_ & & & & & & & & & \\
2-methylfuran & 3.19 & ND & - & 0.09 & 0.06 & ND & - & ND & - \\
4-ethylfuran & 4.21 & 0.21 & 0.00 & ND & - & 0.04 & 0.06 & 0.24 & 0.03 \\
2-butylfuran & 11.62 & 0.21 & 0.00 & ND & - & ND & - & 0.13 & 0.01 \\ \hline
**Total** & & **0.42** & & **0.09** & & **0.04** & & **0.37** & \\ \hline _Terpenes_ & & & & & & & & & \\ \(\alpha\)-pinene & 6.03 & ND & - & 4.65\({}^{\circ}\) & 0.72 & 1.05 & 0.00 & 0.34 & 0.11 \\ thjuene & 6.38 & 0.23 & 1.01 & 0.35 & 0.00 & ND & - & 0.44 & 0.03 \\ \(\beta\)-pinene & 9.61 & 0.08 & 0.00 & 2.19 & 0.00 & 0.17 & 0.25 & 4.59\({}^{\circ}\) & 1.01 \\ sajuene & 10.55 & ND & - & 1.31 & 0.00 & ND & - & ND & - \\
8.3.carene & 12.00 & 0.22 & 0.00 & 0.81 & 0.51 & 0.09 & 0.07 & 0.28 & 0.01 \\
9-mycrene & 13.33 & ND & - & 1.22 & 0.30 & ND & - & ND & - \\ d-limonene & 14.69 & 0.04 & 0.27 & 22.13\({}^{\ddagger}\) & 1.68 & 0.15 & 0.31 & ND & - \\ sabinene & 14.74 & 0.04 & 0.02 & ND & - & ND & - & 0.09 & 0.00 \\ terpene & 16.33 & 0.92 & 0.00 & ND & - & ND & - & 0.47 & 0.03 \\ \(\gamma\)-terpinene & 16.45 & ND & - & 1.09 & 0.97 & ND & - & 0.31 & 0.90 \\ \(\alpha\)-cymene & 16.91 & ND & - & 1.41 & 0.24 & 0.05 & 0.01 & 0.10 & 0.09 \\ \(p\)-cymene & 17.36 & 0.33 & 0.06 & 0.23 & 0.18 & 0.20 & 0.29 & 0.32 & 0.07 \\ \(\alpha\)-terpionelene & 17.71 & 0.76 & 1.58 & 0.12 & 0.14 & ND & - & 0.57 & 0.08 \\ \(\alpha\)-cyclocitral & 18.56 & ND & - & 0.62 & 0.00 & 1.82 & 2.50 & ND & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._