esa-sceva/satcom-chunk-collection huggingface dataset:
# Damage Detection in Beam Bridges Using Quasi-static Displacement Influence Lines
## 2 Damage Detection Using the Quasi-static Displacement Influence Lines of Bridges
### Damage Detection Using the Null Space Method Based on Quasi-static Displacement Influence Lines
#### 2.2.1 Hankel Matrix of the Damage Feature Established with Quasi-static Displacement ILs
\begin{array}{cccc}\beta_{1}&\beta_{2}&\cdots&\beta_{q}\\ \beta_{2}&\beta_{3}&\cdots&\beta_{q+1}\\ \vdots&\vdots&\ddots&\vdots\\ \beta_{p}&\beta_{p+1}&\cdots&\beta_{p+q-1}\end{array} \right] \tag{6}\]  
where \(\mathbf{H}\) is the Hankel matrix of damage feature \(\beta\) and \(p\) and \(q\) are the number of matrix rows and the number of matrix columns, respectively, that satisfy the relationship [25]\(s=p+q-1\), \((p<q)\). Based on the feature design of the Hankel matrix, each column of matrix \(\mathbf{H}\) is highly correlated. Each column of the Hankel matrix is called a delay vector \(\boldsymbol{\varphi}_{b}\), which is described according to the following equation:  
\[\boldsymbol{\varphi}_{b}=\left\{\beta_{b},\beta_{b+1},\cdots,\beta_{b+p+1} \right\}_{p\times 1}^{\text{T}} \tag{7}\]  
For a certain Hankel matrix, if \(p\) (the length of delay vector \(\boldsymbol{\varphi}_{b}\)) decreases in size, \(q\) (the number of delay vectors \(\boldsymbol{\varphi}_{b}\)) increases in size, thereby reducing the degree of correlation between the two delay vectors. If \(p\) is equal to one, then the Hankel matrix \(\mathbf{H}\) changes into the vector \(\beta\). In this situation, as only one \(\beta_{i}\) is varied, only \(1/s\) delay vectors \(\boldsymbol{\varphi}_{b}\) in the vector \(\beta\) will change and thus, it is difficult to detect the variation in the vector \(\beta\). With the help of the Hankel matrix, if only one \(\beta_{i}\) is varied, \(p/(s-p+1)\) delay vectors \(\boldsymbol{\varphi}_{b}\) of matrix \(\mathbf{H}\) will change. In particular, when \(p\) is close to \(s/2\), almost all delay vectors in the Hankel matrix will change, which makes it easier to identify the variation in \(\beta\). Therefore, it is advisable to treat the damage feature \(\beta\) as a Hankel matrix. The method used to construct the Hankel matrix in Reference [25] cannot be directly adopted for the quasi-static displacement ILs used in this study. If we take the form of the Hankel matrix defined in Reference [25], due to the limited number of displacement ILs, the size of the Hankel matrix will be so small that the damage feature will not be sensitive to the damage in the bridge. To address this issue, we propose to reconstruct the Hankel matrix of the damage feature, as described in Equation (6). With this new form of the Hankel matrix, a reasonable Hankel matrix of the damage feature is established even though only a few quasi-static displacement ILs are obtained for one load test of the bridge.
## 3 Materials and Methods
### Analysis of Volatile Compounds
#### 3.11.2 GC-MS Analysis of Volatile Compounds  
GC-MS analysis was conducted on Varian CP-3800/Saturn 2000 (Varian, Wallnut Creek, CA, USA) using a ZB-5 MS capillary column (30 m \(\times\) 0.25 mm inner diameter (i.d.) \(\times\) 0.25 \(\upmu\)m film thickness). The GC oven temperature was programmed at 50 \({}^{\circ}\)C, increased to 130 \({}^{\circ}\)C at a rate of 4 \({}^{\circ}\)C min\({}^{-1}\)subsequently raised to 180 \({}^{\circ}\)C at 10 \({}^{\circ}\)C min\({}^{-1}\) and finally to 280 \({}^{\circ}\)C at a rate of 2 \({}^{\circ}\)C min\({}^{-1}\). Helium was used as a carrier gas at a flow rate of 1.0 mL min\({}^{-1}\). The samples were injected at a 1:10 split mode. Mass spectra were obtained in an electronic ionisation (EI) mode of 70 eV with a scan range of _m/z_ 35-550. Identification of all volatile compounds was based on comparing the compound mass spectra obtained experimentally, with the mass spectra available in the NIST14 database. The experimentally determined retention index (RI) by [PERSON] was also compared with the RI in NIST WebBook and literature data [43]. The quantification analysis of identified volatile compounds was calculated by comparing the peak area of compounds with the peak area of the internal standard (_p_-methoxyphenol) with a concentration of 1 mg/mL.
# An Integrated DQN and RF Packet Routing Framework for the V2X Network
## 3 Design of the IDRF System
### Offline Phase
#### 3.1.2 Adding the Vehicle Arrival Time at the Intersection into Vehicle Trajectory Data  
When a vehicle passes through an intersection, the timestamp of its arrival is not always recorded precisely. However, we can use the actual vehicle trajectory information to infer when the vehicle arrived at the intersection. After calculating the vehicle's arrival time at the intersection, timestamps are added to the original vehicle trajectory information (Figure 9). Assuming that vehicle \(v_{1}\) passes the trajectory point \(T_{T}^{1}\) of a road segment at time \(T\), its coordinates at that moment are \((T_{x}^{1},T_{Ty}^{1})\) and it belongs to road segment \(i\). As vehicle \(v_{1}\) passes the trajectory point \(T_{T}^{1}\) of the subsequent road segment at time \(T\)', its coordinates at that moment become \(\left(T_{T_{x}^{\prime}}^{1},T_{Ty}^{1}\right)\) and it belongs to road segment \(j\). Assuming that intersection \(R\) is the shared junction for road segments \(i\) and \(j\) and that none of its trajectory information is available, we calculate the time at which vehicle \(v_{1}\) passes intersection \(R\). Assuming the coordinates of intersection \(R\) are \((I_{x},I_{y})\) the total distance traveled between two trajectory points, \(T_{T}^{1}\) and \(T_{T}^{1}\), is first calculated by \(fd\) using Equation (2). The total travel time for vehicle \(v_{1}\) is equal to \(ft=T^{\prime}-T\). The time \(tr\) needed for vehicle \(v_{1}\) to arrive at intersection \(R\) from the trajectory point at time \(T\) is proportional to the distance between this trajectory point and intersection \(R\) divided by the distance between the two trajectory points (Equation (3)). Therefore, the time taken for vehicle \(v_{1}\) to reach the intersection is calculated by \(T+ft\times tr\). After correcting, we insert the arrival time into the original trajectory data (Figure 11).  
\[fd =\sqrt{\left(T_{T_{x}}^{1}-I_{x}\right)^{2}+\left(T_{Ty}^{1}-I_{y} \right)^{2}}\ +\sqrt{\left(T_{T_{x}^{1}}^{1}-I_{x}\right)^{2}+\left(T_{Ty}^{1}-I_{y} \right)^{2}} \tag{2}\] \[tr =\frac{\sqrt{\left(T_{T_{x}}^{1}-I_{x}\right)^{2}+\left(T_{Ty}^{ 1}-I_{y}\right)^{2}}}{fd} \tag{3}\]  
Figure 11: Insertion of the vehicle’s arrival time at the intersection (the red frame is the arrival time inserted to the current dataset).  
Figure 10: The road segment correction is based on the vehicle trajectory continuity algorithm (the red dots are intersections and dotted lines are perpendicular distances to two road segments).
## 3 Results and Discussion
### Safety Criteria for Foodstuffs  
Both fresh and dehydrated rosmary and sage samples underwent microbiological analysis to assess their suitability for human consumption. This assessment is essential before using aromatic herbs and spices in food applications, as they serve as carriers for foodborne pathogens [79; 80]. The investigation specifically targeted the main bacterial pathogens, including members of the Enterobacteriaceae family, _E. coli_, CPS, _Salmonella_ spp., and _L. monocytogenes_. These pathogens are well-known causative agents of global foodborne illness outbreaks and pose significant public health risks [81; 82]. None of the samples analyzed revealed detectable levels of these pathogens.  
These results are undoubtedly due to the high-quality standards followed throughout the entire process, from harvesting to processing [83].  
Figure 6 shows the viable counts of TMM and molds present on rosmary and sage before and after the dehydration process. Fresh aromatic herb samples hosted levels of TMM and molds between 10\({}^{3}\) and 10\({}^{4}\) Colony Forming Units (CFU)/g. The presence of these microorganisms likely originates from environmental contamination [84; 85].  
Statistically significant differences (\(p<0.001\)) were detected among fresh and dried rosmary and sage. Therefore, the drying process completely eradicated TMM and molds from rosmary and sage, rendering their levels undetectable (reported as \"2\" in Figure 6). From a microbiological standpoint, the dried herbs produced in this study met the specifications set by the International Commission for Spices, Herbs, and Dried Vegetable Seasonings, which establishes a limit of 10\({}^{4}\) CFU/g total bacteria at 30 \({}^{\circ}\)C [86; 87].
# GA-StackingMD: Android Malware Detection Method Based on Genetic Algorithm Optimized Stacking
## 4 Experiments
### Experiment Steps
#### 4.2.3 The Optimized Combination of Base Classifier Selection  
The first step of GA-StackingMD is to select the best combination of the base classifiers. The five algorithms have 31 combinations. Set fixed parameters to compare the performances of different combinations. Taking CIC-AndMal2017 as an example, the results are shown in Table 3.  
The 31 groups are sorted according to accuracy, and the combination of \"KNN+LGBM\" achieves the best accuracy of 94.67% and has the best performance in F1-score, precision,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifiers** & **Accuracy** & **F1-Score** & **Precision** & **Recall** \\ \hline SVM & 94.83\% & 87.55\% & 91.34\% & 84.06\% \\ KNN & 91.69\% & 78.71\% & 88.29\% & 71.01\% \\ LGBM & 96.08\% & 90.57\% & 94.49\% & 86.96\% \\ CatBoost & 95.30\% & 88.28\% & 95.76\% & 81.88\% \\ RandomForest & 94.04\% & 84.92\% & 93.86\% & 77.54\% \\ Stacking & 96.55\% & 91.60\% & 96.77\% & 86.96\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: CIC-AndMal2017 detection results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifiers** & **Accuracy** & **F1-Score** & **Precision** & **Recall** \\ \hline SVM & 98.24\% & 98.82\% & 98.78\% & 98.86\% \\ KNN & 97.85\% & 98.57\% & 98.44\% & 98.69\% \\ LGBM & 98.34\% & 98.89\% & 98.75\% & 99.03\% \\ CatBoost & 98.11\% & 98.74\% & 98.50\% & 98.98\% \\ RandomForest & 97.75\% & 98.49\% & 98.41\% & 98.58\% \\ Stacking & 98.56\% & 99.03\% & 98.98\% & 99.09\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: CICMalDroid2020 detection results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Accuracy** & **F1-Scores** & **Precision** & **Recall** \\ \hline [’RF’, ’CATTREE’] & 89.34\% & 68.22\% & 79.35\% & 59.84\% \\ [’RF’] & 89.5\% & 68.84\% & 79.57\% & 60.66\% \\ [’CATTREE’] & 89.81\% & 73.03\% & 73.95\% & 72.13\% \\..  
and recall. It should be noted that some of the combinations have the same accuracy. In this step, eight groups of combinations get the same highest accuracy. Considering all of the combinations including the two algorithms, KNN and LGBM, we choose the KNN and LGBM combination as the best one. In the same way, the selected combination of CICMalDroid2020 is \"RF+KNN+LGBM\", with 94.30% accuracy and 96.18% F1-score.
# Creep Behavior of Rocks and Its Application to the Long-Term Stability of Deep Rock Tunnels
## 5 Potential Factors Influencing Rock Creep  
### Hydraulic Pressure, Stress Level, Water Content, and Temperature  
In addition to the internal factors mainly related to the composition and structure of rocks, the creep behavior is also influenced by several external potential factors that cannot be overlooked. It is very difficult to present in great detail all these factors in a single article, but the most relevant are reported herein. The most pronounced external factors are hydraulic pressure [82; 83], stress level, water content, and temperature [84; 85; 23; 86; 84]. Hydraulic pressure itself can strongly affect the creep properties of rocks. In fact, the evolution of cracks and damage may facilitate rock failure under water seepage in existing rock discontinuities [83]. More broadly, hydraulic pressure can accelerate the evolution of cracks in rocks and thus affect the duration of creep phases. The increase of hydraulic confining pressure leads to the increase in creep deformation and also the increase in creep strain rate [83]. For its part, stress level is an obvious key factor influencing rock creep behavior. Generally, it governs the resistance of rocks. Stress level can justify the extent of creep phases over time and can affect both axial and shear creep of rocks. For instance, the creep strain and steady-state creep strain rate increase with the increase of the shear stress for the same elapsed time [87]. Moreover, the creep strain rate generally increases with the increase in deviatoric stress [86]. Rock creep is usually defined from a stress level. When the latter is modified, the creep parameters are also modified. Therefore, for fairer conclusions on any engineering applicability of rock creep behavior, it is strongly suggested to study rock creep under different stress levels. Regarding the water content, its influence on the creep behavior of rocks is extremely significant. Generally speaking, water strongly weakens the mechanical properties of rocks. It dramatically affects the creep deformation of rocks. For instance, dry mudstones have their total cumulative volumetric strain 503% less than that of saturated mudstones [88]. This is one of the remarkable effects of water on the properties of rocks. Specifically, the creep resistance of rocks is generally reduced by the presence of water. Aggressive water has more adverse impacts on rocks due to its corrosive effects. Moreover, under prolonged exposure to water, soluble and carbonate rocks (such as limestone, dolomite, and gypsum) can even be karstified. Tunnel ruptures under the effects of water are frequent. In fact, the water content can easily be increased in the rocks surrounding the deep tunnels as a result of groundwater pressures and flows [89]. These are very common at great depth. Table 1 summarizes the relevant effects of water on creep behavior of different rock types.
## 4 Properties
### Biodegradation Properties
#### 4.2.2 Enzymatic Degradation  
The products of enzymatic degradation and hydrolysis are the same, but the two pathways differ in the catalyst involved. The cleavage of ester bonds of APs is mediated by lipases, cutinases, serine proteases, PHB depolymerase, PCL depolymerase, elastase esterase, proteinase K, and trypsin [92]. Enzymatic degradation is therefore a heterogeneous process, which occurs via two steps of adsorption and hydrolysis. First, the hydrophobic sites of enzymes adhere to solid substrates by hydrophobic interactions, then through their catalytic sites favor the polymer hydrolysis. Contrarily to hydrolysis, enzymatic degradation is a surface eroding process, which only proceeds on the outer layer of the substrate, since the enzyme is not capable of penetrating the polymer matrix  
Figure 3: Cartoon representing hydrolysis and enzymatic degradation process as a function of the incubation time for aliphatic polyesters.  
(Figure 3). Thus, through enzymatic degradation the polymer weight decreases, while molecular weight and polydispersity do not significantly vary [91]. The low molecular weight degradation products are solubilized in the surrounding aqueous medium. To monitor the evolution of the enzymatic degradation, especially during the initial stages, simple yet effective techniques can be used. In particular, the surface crystallinity increase can be measured by infrared spectroscopy in attenuated reflection mode (ATR-FTIR), and the increase of surface roughness and opacity can be evaluated by a semiquantitative film opacity assay [93].  
Two degradation mechanisms exist, since bond cleavage can occur either at random points along the macromolecular backbone (endo-type degradation) or at the ends of the polymer chain (exo-type degradation). Lipase or PHA depolymerase action is based on endo-type bond cleavage, therefore it does not depend on molar mass and polydispersity index.  
Recently, mild enzymatic degradation of PLLA has been exploited with the aim of introducing hydroxyl and carboxylic groups on the polymer surface by employing _Humicola insolens_ cutinase [94; 95; 96]. The studies demonstrated the suitability of cutinase-mediated processes to functionalize PLA surface in a controlled fashion, since no effect on the bulk characteristics of the polymer were recorded. This technique, which led to a substantial water contact angle decrease, may thus open up new possibilities in the biomedical field, such as for controlled drug release applications or further post-modification of APs [94; 95; 96].
## 5 Data Processing and Artificial Intelligence Methods
### Secondary-Level Data Processing  
[PERSON] et al. [5] aimed to determine lameness based on locomotor characteristics. The measured data and the preprocessing are described in Section 5.2. During the preprocessing, after the application of a special normalization methodology, the lame and non-lame movements were better separated. However, based on the measured data, linear separability was not assumed. The SVM method was used for the separation, but data were transformed into a linearly separable format using the radial base function (RBF) neural network. The classical elimination technique resulted in an accuracy of only 65% even after parameter optimization, while the SVM method increased the accuracy to 76%.
# Litholytic Activities of Natural Bioactive Compounds and Their Mechanism Insights
## 1 Introduction  
Urolithiasis (urinary stone disease) is one of the painful disorders of the urinary tract that is caused by the formation of calcite stones in the kidneys, bladder, and urethra [1]. There are numerous types of kidney stones that differ in chemical composition, with more than 80% of them being composed of calcium oxalate (CaOx), with calcium oxalate monohydrate (COM) as the major form [2].  
Epidemiological studies have demonstrated that the incidence and prevalence of kidney stone formation has been increasing in the last forty years, is more predominant in men (12%) than in women (6%), and is frequent among 20-40 year olds for both men and women [3]. This prevalence may be due to several environmental, genetic, and nutritional factors, such as low urinary volume, dietary habits (diets rich in animal protein), and global warming, as well as metabolic alterations [4].  
Many methods have been used to treat and curb kidney stones, such as the combination of dietary procedures and surgical as well as drug treatments. However, their serious adverse side effects and recurrence remain as problems to overcome [5]. Furthermore, interest in alternative treatment options has increased. These options include herbal medicines as old as human history that could be useful as an alternative or complementary therapy in the management of kidney stones. In this regard, many plants have been shown to possess anti-lithiasis action [6]. Indeed, several plants currently remain the basis of a large part of the medications used for the prevention and treatment of many diseases due to their content in secondary metabolites [7]. Indeed, in recent years, several studies have provided reliable results on the effects of numerous secondary metabolites such as polyphenolic compounds, flavonoids, stilbenes, tannins, and terpenoids on many diseases, such as kidney stones [8]. These compounds exhibit litholytic effects via their different mechanisms of action. Therefore, the aim of this review was the exploration and highlight of studies on the anti-lithiasis effect of secondary metabolites and their mechanisms of action.
## 2 Materials and Methods
### Determination of Flocculation Activity  
The flocculation activity was determined using an artificial colloidal suspension made of kaolin clay. In terms of surface charge behavior, the kaolin clay mimics wastewater sludge (zeta potential = \(-\)32 mV) [25] and provides better reproducible results than the sludge itself in terms of suspension stability. The flocculation activity was determined following a method described earlier [26] with minor modifications. Kaolin clay suspension (5 g L\({}^{-1}\), pH = 7) in 1% w/v aqueous CaCl\({}_{2}\) with EPS as the test biofloculant was thoroughly vortexed for 1 min, and was kept at room temperature for 5 min. The EPS concentrations were varied in the range of 2-20 mg L\({}^{-1}\). The entire mixture was prepared to a final volume of 20 mL in test tubes. Finally, the optical density of the aqueous phase was measured with a spectrophotometer (UV-1800 UV-Vis Shimadzu, Kyoto, Japan) at 550 nm wavelength. A control treatment was set up in a similar way where extracted EPS was replaced with distilled water. Flocculation activity was determined by the following equation  
\[\text{Flocculation activity}(\%)=\frac{\text{B}-\text{A}}{\text{B}}\times 100 \tag{1}\]  
where, A is optical density of samples containing EPS, and B is the optical density of the control [9].
## 5 Results and Discussion  
### Hyperparameters Tuning Results  
In this section, three ML models including GB, ANN and SVM were developed to predict the USC of the cement-stabilized soil. The hyper-parameters range of those ML models was also given in Table 2. To prepare the data for the hyper-parameters tuning process, the initial data set was random divided into two sets, including the training set (80%) and testing set (20%). To avoid data leakage, ML models were evaluated based on data from the 5 fold CV technique, which mean testing data was hidden in this step.  
In the process of hyper-parameter tuning, the model with the best R performance indicator was selected as the final model and the model's hyper-parameters were considered the optimum hyper-parameters. A summary of the optimal hyper-parameters of each model was presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Gradient Boosting**} & \multicolumn{2}{c}{**Artificial Neural Network**} & \multicolumn{2}{c}{**Support Vector Machine**} \\ \hline Learning rate & 0.01–0.3 & \begin{tabular}{c} Number of \\ neurons \\ \end{tabular} & 2–50 & \
\begin{tabular}{c} Regularization \\ parameter (C) \\ Kernel coefficient \\ Gamma (\(\gamma\)) \\ \end{tabular} & 0.001–1 \\ Min samples split & 2–20 &
\begin{tabular}{c} Activation \\ function (**) \\ \end{tabular} & 1, 2, 3 & Kernel type (***) & 1, 2, 3 \\ Min samples leaf & 2–20 & Max iteration & 1000–4000 & \\ Max depth & 0–20 & Learning rate & 0.001–0.2 & \\ Data used & 5 Fold CV & Data used & 5 Fold CV & Data used & 5 Fold CV \\ Performance index & R & Performance index & R \\ \hline \hline \end{tabular} (*): 1—Quasi-Newton method; 2—Stochastic Gradient Descent; 3—Adam; (**): 1—Logistic; 2—Tanh; 3—Relu; (***): 1—Polynomial; 2—Radial basis function; 3—Sigmoid.  
\end{table}
Table 2: Hyper-parameters space of machine learning (ML) models.  
It can be seen that all three models showed good performance after hyper-parameter optimization when the R criterion was above 0.87. The hyper-parameters combined quite complexly to create the best model. In the GB model, the higher learning rate seemed to bring better performance when in the ANN model, Adam was the best training algorithm for this data set. Furthermore, the SVM model with the kernel type of the radial basis function gives better performance than the sigmoid function. Besides, the lower the gamma on SVM model, the lower the performance. Out of the three models, ANN and GB showed outstanding performance compared to the SVM model. To be more specific, the best R criteria of the ANN and GB model was 0.93 and 9.29 respectively compared to 0.871 of the SVM model.