esa-sceva/satcom-chunk-collection huggingface dataset:
# RDTIDS: Rules and Decision Tree-Based Intrusion Detection System for Internet-of-Things Networks
## 4 Experimentation
### Comparative Study  
To evaluate RDTIDS system, we compare it with some well known classifiers and some recent ones namely J48, Jrip, Naive Bayes, MLP, REP Tree, Random Forest, FURIA [32], LIBSVM [31], J48 Consolidated [30], Forest PA [29], WISARD [28]. In this comparative study we use the different metrics detailed in Section 4.2, in addition to training and testing time. Table 5 summarizes the performance of RDTIDS system compared to the other classifiers for different attacks and Benign traffic. It shows that RDTIDS system gives the highest true negative rate (TNR) with 98.855% and the higest detection rate (DR) for six attacks type namely DDoS with 99.879%, DoS slowloris with 97.758%, DoS Slowhttptest with 93.841%, DoS GoldenEye with 67.571%, Heartbleed 100%, PortScan 99.881%, Infiltration 100%. Moreover, the RDTIDS system is very close to the highest detection rate for two types of attacks namely FTP Patator with 99.636% and SSH Patator with 99.909%. For the rest of the attacks types, the RDTIDS system gives an average performance compared to the other models. Overall, RDTIDS system is the model that performs best for most of the different attack types and it never has the lowest performance for any type of attack. Figures 3-6 present the overall performance of RDTIDS and other classifiers in terms of false alarm rate, global detection rate, accuracy, training and test time, respectively. In terms of detection rate, RDTIDS gives the highest overall detection rate (DR Overall) with 94.475% and 95.175% in CICIDS 2017 dataset and Bot-IoT dataset, respectively. In terms of accuracy, RDTIDS gives the highest accuracy with 96.665% and 96.995% in CICIDS 2017 dataset and Bot-IoT dataset, respectively. In terms of false alarm rate, the test time is 10000 seconds. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Predicted Class** \\ \cline{3-4}  & & **Negative Class** & **Positive** \\ \hline Actual class & Negative Class & True negative (TN) & False positive (FP) \\  & Positive Class & False negative (FN) & True positive (TP) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion Matrix. alarm rate (FAR), RDTIDS gives the lowest false alarm rate with 1.145% and 1.120% in CICIDS 2017 dataset and Bot-IoT dataset, respectively. The training time of RDTIDS is 195.5 s and the test time is 2.27 s, which represents an acceptable training and test time for a hybrid hierarchical model especially when compared to simple models such as MLP and SVM. Figure 4: Overall Performance of RDTIDS and other classifiers in terms of global detection rate. Figure 3: Overall Performance of RDTIDS and other classifiers in terms of false alarm rate.
## 2 Materials and Methods
### Loads Priority Model
#### 2.4.3 Penalty of Interruption  
The penalty of interruption is determined based on the value of lost load (VOLL). That is, the load with a higher value, in the event of an interruption, entails a higher penalty value in the objective function. The penalty associated with interruption is as follows:  
\[W^{pen}_{j,t}=pen_{j,t}(t,load\ type) \tag{10}\]  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Load Type** & **Demand (kW)** & **Connected Bus** & **Priority** & \(W^{crit}_{j,t}\) \\ \hline
**ECS/Pressurization** & 320 & 270 VDC & High & 2 \\
**Hydraulics** & 40 & 270 VDC & High & 2 \\
**Equip. Cooling** & 40 & 270 VDC & High & 2 \\
**ECS Fans** & 32 & 270 VDC & High & 2 \\
**ICS** & 40 & 115 VAC & High & 2 \\
**Flight Control** & 14 & 28 VDC & High & 2 \\
**Fuel Pumps** & 32 & 230 VAC & High & 2 \\
**Various AC Loads** & 140 & 115 VAC & Medium & 1 \\
**Ice Protection** & 60 & 230 VAC & Medium & 1 \\
**Forward Cargo AC** & 60 & 230 VAC & Low & 0 \\
**Galleys** & 120 & 230 VAC & Low & 0 \\
**Various DC Loads** & 20 & 28 VDC & Low & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Boeing 787 loads with their priorities.  
where \(W_{j,t}^{pen}\) is the penalty of interruption for load \(j\). Also, \(pen_{j,t}(t,load\)\(type)\) is the considered penalty of interruption for load type \(j\) at time \(t\).  
At this point, all weights associated with each load in the system have been defined: these are \(W_{j,t}^{criti}\), \(W_{j,t}^{ENS}\) and \(W_{j,t}^{pen}\). The final weights associated for each load can be calculated as follows:  
\[W_{j,t}^{L}=W_{j,t}^{criti}+w_{ENS}\times W_{j,t}^{ENS}+w_{pen}\times W_{j,t}^{pen} \tag{11}\]  
\[w_{ENS}+w_{pen}=1 \tag{12}\]  
where \(W_{j,t}^{L}\) is the total priority weight of load \(j\) at time \(t\). \(w_{ENS}\) and \(w_{pen}\) are the EENS and penalty importance weights, respectively. These weights can be assigned by the load management unit when a fault occurs. A schematic overview summarizing the proposed prioritization model is shown in Figure 4.
# Signals Intelligence System with Software-Defined Radio
## 2 Materials and Methods
### System Definition and Testing
#### 2.3.2 Network Training  
Automated ML functionality from the \"Microsoft Azure AutoML\" service was used to define, train, test, and validate the intelligent network. After the definition of the new model has been started, the database that is used for training and validation is chosen. The next step is to analyze the data to identify potential problems. As possible problems, the following can be mentioned: missing data in certain columns, not all input data having the same format, or for some input data, we do not have output data.  
The training is configured to be performed using \"Signal Type\" as the target column. This column describes the type of signal that is characterized by the input data. As hardware support, a virtual machine is used with a 6-core CPU, 28 GB RAM (random-access memory), and 56 GB (gigabyte) storage memory.  
To configure the model training, three options are available. From the three options, the model that performs data classification based on the input data is chosen. The \"Deep Learning\" option is also enabled to ensure a better extraction of the model's features and to obtain a more efficient trained model. The first additional setting configured is the metric used. Accuracy is chosen because it best characterizes the model to be implemented. By accuracy, what is meant is the number of correctly predicted input data out of the total amount of data. It is chosen to use all possible algorithms to see exactly which one is the most efficient. The running time is set to the maximum possible of 24 h, which is the maximum training time offered for a model where the \"Deep Learning\" option is activated. No step is set for the validation metric, trying to obtain a result as close to perfection as possible. After all configuration work is complete, the training process is started.  
The training process begins by checking several elements. The first thing that is checked is whether the input data is divided in an approximately equal way for each signal type. The second checked element is the lack of samples in any of the input vectors. The last element checked is if one or multiple samples from the input vectors have a value too high that may bias the detection result in a wrong way. By a much too high value is understood the fact that this value has an order of magnitude two or more times higher than the other samples from the rest of the data vectors. This value can spoil the detection because the model resulting from training with this data will always expect certain samples from the input data to have such high values, which will not happen if the data are correct.
## 4 Results and Discussion
### Assessment of the POCS Pressure Drop
#### 4.1.1 3D CFD Configurations  
The investigation into the fluid pressure drop induced by the POCS structure has been carried out for two different configurations, namely straight and curved. The achieved results are presented in Figure 8. In particular, the pressure difference was analyzed for different flow inlet velocities. Overall, the straight configuration is responsible for lower pressure drops, while the curved version leads to higher values. Such differences are present due to the different length of the considered structures.  
As described in the previous section, the POCS characteristic module is axially repeated four times for the straight version. Considering the fine mesh (label C-Straight), the obtained pressure drops show a maximum value that is approximately 10 mbar for an inlet velocity of 50.0 m/s, while a 3 mbar pressure difference is registered for 25.0 m/s. Given the subsequent electrical heating application, the POCS geometry for the curved configuration was accordingly designed: the axial module is repeated six times, with the aim of maximizing the wetted surface area. The obtained pressure drops increase with respect to the straight configurations, leading to approximately 5 and 20 mbar for the 25.0 and 50.0 m/s inlet velocities. As shown in Figure 8, the different employed computational grids result in a similar estimation of the pressure drops. The percentage difference between the reference and fine meshes, namely B-Curved and C-Curved, is at maximum approximately the 3%. Hence, the reference mesh was selected for the cold start assessment. A qualitative estimation of the differences induced by the utilization of different computational grids can be carried out through Figure 9, where a visualization of the fluid pressure field is presented for the A-Straight and C-Straight meshes. In addition, the POCS boundary walls have been superimposed, so that the grid resolution corresponding to the structure under investigation can be appreciated.  
At last, a turbulent simulation of the curved configuration was carried out with the aim of verifying the impact of the assumption of laminar simulations on the achieved pressure drops. The selected operating condition refers to the inlet velocity equal to 50.0 m/s, for which the highest variability in terms of pressure drop was registered. Furthermore, as shown in Table 5, it is one of the operating points characterized by a turbulent flow regime (Reynolds number \(>\) 2900). The reported Reynolds number is computed through the results of the fine mesh (label C-Curved). The \(y^{+}\) requirements for the selected turbulence  
Figure 8: Assessment of the POCS pressure drop: straight and curved POCS configurations.  
model, that is the \(k\)-\(\omega\)-\(SST\), can be met according to the fine mesh only. The computed pressure drop is 17.98 mbar, which needs to be compared with the 18.05 mbar of the laminar simulation. As expected, the obtained difference is negligible and the assumption of laminar simulations does not introduce significant errors into the performed numerical analysis.
## 4 Microstructure of SFRC Exposed to High Temperature  
It has been well established that high temperatures will severely damage the chemical composition and microstructure of concrete but adding steel fibers can improve the microstructure of concrete to some extent. Through SEM observations in Figure 12, [PERSON] et al. [100] reported that the diameter of steel fiber changes and the oxygen content in steel fibers increases with an increase in temperature. This may be due to the small pore near the steel fiber preventing the evaporation of water, and a large amount of water vapor accumulated in the matrix, which caused the steel to rust and oxidation products to deposit on the fiber surface. [PERSON] et al. [18] showed that the density of microcracks in the ordinary concrete is much less than that in fiber-reinforced concrete at 400 \({}^{\circ}\)C. The microcracks in fiber-reinforced concrete are mainly formed between and around the sand and aggregate skeleton and have a thickness of approximately 1 mm, while the microcracks in ordinary concrete usually occur between large aggregates, with a thickness of about 10 mm. [PERSON] and [PERSON] [33] showed that the porosity of concrete increased with the maximum heating temperature, but the porosity of SFRC was lower and the average pore diameter was smaller than that of concrete without fiber.  
[PERSON] et al. [101] investigated the microstructure of the matrix-fiber interface of steel-fiber-reinforced high-performance concrete exposed to high temperatures. As is shown in Figure 13, their results indicated that the matrix-fiber bond strength degraded gradually with increasing exposure time. Moreover, after a period of high-temperature exposure of SFRC, the matrix-fiber bond ruptured progressively and hence thus gradually reduced the activation efficiency  
Figure 12: Morphologies of fibers in SFRC exposed to different high temperatures of 200 \({}^{\circ}\)C (**a**) and 400 \({}^{\circ}\)C (**b**) [100].  
of steel fiber, which may be due to the contraction arising from the discharge of capillary water and the long-term decomposition of some hydrates at high temperatures. However, it can only reduce the contribution of steel fiber to the high-temperature performance of concrete, rather than eliminate the effect. A few studies [11; 13; 41] have concluded that steel fiber improved the microstructure of RPC after an elevated temperature. In addition, the investigation of [PERSON] et al. [13; 42] indicated that steel fibers positively affected the microstructure of RAC after a high-temperature exposure.  
In this section, the effect of steel fibers on the microstructure of heated concrete was described. Compared with the ordinary concrete, SFRC had lower porosity and better microstructure. However, as the exposure time increased and the exposure temperature increased, the steel fibers began to rust and oxidized gradually, reducing their activation efficiency, and eventually leading to a decrease in the contribution to the high temperature performance of the concrete.
## 1 Introduction
This model was later extensively researched through different points of view such as the semi-inverse variation principle [41], Lie symmetry [44], conservation rules [45], and the system of undetermined coefficients [37]. Consider the nonlinear Schrodinger and resonant nonlinear Schrodinger equations in the appearance of 3 OD and 4 OD without both GVD and disturbance. The equations are as follows:  
\[iu_{t}+i\alpha u_{xxx}+\beta u_{xxxx}+cF\left(\left|u\right|^{2}\right)u=0, \tag{1}\]  
\[iu_{t}+i\alpha u_{xxx}+\beta u_{xxxx}+cF\left(\left|u\right|^{2}\right)u+c_{3} \left(\frac{\left|u\right|_{xx}}{\left|u\right|}\right)u=0. \tag{2}\]  
In Equations (1) and (2), \(u\left(x,t\right)\) is the complex valued wave function and \(x\) (space) and \(t\) (time) are independent variables. The coefficients \(\alpha\) and \(\beta\) are real constants, while \(c_{3}\) is the Bohm potential that occurs in Madelung fluids. The Bohm potential term of disturbance generates quantum behavior, so that quantum characteristics are closely related to their special characteristics. Therefore, we have the chirped NLSE's disturbance expression giving us the introduction of the theory of hidden variables. Therefore, it will be more crucial to retrieve accurate solutions for the development of quantum mechanics from disturbed chiral (resonant) NLSE [46]. Furthermore, the functional \(F\) is a real valued algebraic function that represents the source of nonlinearity and \(F\left(\left|u\right|^{2}\right)u:C\to C\). In more detail, the function \(F\left(\left|u\right|^{2}\right)u\) is \(p\)-times continuously differentiable, so that:\[F\left(|u|^{2}\right)u\in\bigcup_{m,\,n=1}^{\infty}C^{p}\left((-n,n)\times(-m,m):R ^{2}\right).\]  
Suppose that \(F\left(u\right)=\ c_{1}u+c_{2}u^{2}\), so Equations (1) and (2) can be rewritten as:  
\[iu_{t}+i\alpha u_{xxx}+\beta u_{xxxx}+\left(c_{1}|u|^{2}+c_{2}|u|^{4}\right)u =0, \tag{3}\]  
\[iu_{t}+i\alpha u_{xxx}+\beta u_{xxxx}+\left(c_{1}|u|^{2}+c_{2}|u|^{4}\right)u +c_{3}\left(\frac{|u|_{xx}}{|u|}\right)u=0. \tag{4}\]  
Equation (3) was investigated by making \(c_{2}=0\) in [47] via the Kudryashov approach. The conservation laws to obtain the conserved densities for Schrodinger's nonlinear cubic-quarter equation have been analyzed in Kerr and power-law media [45]. The undetermined coefficients method has been employed to construct bright soliton and singular soliton solutions of Equation (1), when nonlinearity has been taken into consideration in the instances of the Kerr law and power law [37]. In this study, we use two methods to investigate soliton solutions of the cubic-quartic nonlinear Schrodinger equation and cubic-quartic resonant nonlinear Schrodinger equation with the parabolic law, namely Equations (3) and (4).
## 2 Materials and Methods
### Data Processing  
#### 2.6.1 GPR  
The noise in the electromagnetic signals recorded using the GPR in the present study was removed for analysis. The data were also edited, with spatial resampling and interpolation being applied to the traces. The data processing involved the following steps (e.g., [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64]): static correction of zero-time, gain, time filtering (1D), and spatial filtering (2D), that is, background removal and moving average trace subtraction. The hyperbole overlap method was used for the time-depth conversion, by obtaining a propagation velocity of 0.092 m/ns.
# Responses to the Impact of Drought on Carbon and Water Use Efficiency in Inner Mongolia
## 2 Study Area and Methods
### Data Sources and Preprocessing  
2.2.1. GPP, NPP, NDVI, and ET Data  
GPP (MOD17A2 Hv006), NPP (MOD17A3 HGFv-006), NDVI (MOD13A2 Hv006), and ET (MOD16A2v006) data from the MODIS products utilized in this study were retrieved from the National Aeronautics and Space Administration (NASA) website ([[http://modis.gsfc.nasa.gov/data](http://modis.gsfc.nasa.gov/data)]([http://modis.gsfc.nasa.gov/data](http://modis.gsfc.nasa.gov/data)), accessed on 1 January 2023). The time series spans 20 years, from 2001 to 2020. The MODISET, PET, and GPP products have a spatial and temporal resolution of 500 m (8 d)\({}^{-1}\); the NPP product has a spatial and temporal resolution of 500 m -year\({}^{-1}\); the NDVI product has a spatial and temporal resolution of 1000 m (16 d)\({}^{-1}\); and the data format  
Figure 1: The research area’s geographic dispersion. Digital elevation model (**a**); various types of vegetation (**b**).  
is HDF. For computation of the vegetation CUE, WUE, and DSI indices, the data from the research region for 20 years were batch stitched, projection converted, and formatted by the MRT tool and resampled to 1000 m.
## 3 Results and Discussion  
### Mechanical Properties of Asphalt Mixtures Modified by Diatomite and Crumb Rubber Particles  
#### 3.1.1 High-Temperature Stability  
Figure 5(a) shows the DS of the eight groups of asphalt mixtures. It can be found that SBS modified asphalt mixtures have higher DS compared with the base asphalt mixtures, which implies that the high-temperature resistance to permanent deformation of compound modified asphalt mixture can be improved by using the SBS modified asphalt as the binder.  
Moreover, it can be seen from Figure 5(a) that the DS of the framework dense asphalt mixtures is greater than that of suspension dense asphalt mixtures when matrix asphalt or SBS modified asphalt is used as a binder. However, this conclusion will be opposite after adding diatomic and rubber particles, indicating that the compound modified asphalt mixture with suspended dense gradation has better high-temperature performance, which is mainly because the addition of rubber particles with larger particle size and softer texture has an adverse effect on the framework embedding effect of framework dense asphalt mixtures.  
Figure 6: Test results of the high-temperature performance of asphalt mixtures: (**a**) DS results and (**b**) cross section of DRSMA.  
In addition, comparing the base asphalt mixture, the composite modified asphalt mixture, and the SBS modified asphalt mixture, it can be seen that the DS of DRAC (diatomite and rubber particles modified asphalt mixtures with suspended dense gradation) is 33.84% higher than that of AC (base asphalt mixtures with suspended dense gradation), and the DS of DRSMA (diatomite and rubber particles modified asphalt mixtures with framework dense gradation) is 19.58% higher than that of SMA (base asphalt mixtures with framework dense gradation). Although adding diatomite and rubber particles can improve the high-temperature performance of the asphalt mixture to a certain extent, there is still a certain gap compared with the modification effect of SBS. The addition of rubber particles and diatomite can absorb a large amount of free asphalt in the asphalt mixture and increase the relative proportion of structural asphalt. The rubber particles, as a relatively soft elastomer, are filled into the asphalt mixture. Some local weak areas are introduced in the material (as shown in Figure 6b), so its advantage in improving the high-temperature performance of the asphalt mixture is not obvious.
# Coordinated Control Method of Bus Signal Priority and Speed Adjustment Based on Stop-Skipping
## 5 Case Analysis
### Analysis of Results
#### 5.2.4 Comparative Analysis  
After the cooperative control of stop-skipping and signal priority, the objective function value is calculated (see situation II in Table 9), and the iteration is as shown in Figure 11a. After the cooperative control of stop-skipping, signal priority and speed adjustment, the objective function value is calculated (see situation III in Table 9), and the iteration is as shown in Figure 11b. Combined with the assumptions in Section 3.2, the collaborative optimization method proposed in this study is calculated and demonstrated.  
The total objective function of these three schemes is the lowest total cost (Equation (14)), but the time of vehicles arriving at the station and intersection (Formulas (5) and (12)) in their calculation process will have substantial changes due to different methods. Therefore, the genetic algorithm was used to calculate the solution separately, and three different results were obtained. A comparative analysis of three cases: I. Stations all parked and signals not prioritized.II. Stop-skipping and signal priority. III. Stop-skipping, signal priority and speed adjustment. See Table 9.  
It can be seen from the comparative results of Table 9 that the total target value was optimized for 11,174.14 s by using the methods of stop-skipping and signal priority. After adopting the coordinated control of stop-skipping, signal priority and speed adjustment, the total target value was optimized for 17,454.86 s. It can be seen that the methods of  
Figure 11: Schematic diagram of target value iteration. (**a**) Stop-skipping and signal priority; (**b**) stop-skipping, signal priority and speed adjustment.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Situations** & \begin{tabular}{c} **The Time Cost** \\ **of Passengers** \\ **(s)** \\ \end{tabular} & \begin{tabular}{c} **Company** \\ **Stopping Costs** \\ **(s)** \\ \end{tabular} & \
\begin{tabular}{c} **Target Value (s)** \\ **(s)** \\ \end{tabular} &
\begin{tabular}{c} **Optimization** \\ **Results (s)** \\ \end{tabular} \\ \hline I & 119,360.26 & 35,000 & 154,360.26 & — \\ II & 116,186.12 & 27,000 & 143,186.12 & 11,174.14 \\ III & 112,905.4 & 24,000 & 136,905.4 & 17,454.86 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparative analysis before and after optimization.  
signal priority and speed coordinated control in the stop-skipping mode could greatly reduce travel delays and operating costs and improve the efficiency of passenger travel. The comparison of quantitative results shows that the optimization method proposed in this study is feasible.