esa-sceva/satcom-chunk-collection huggingface dataset:
# A Pseudo-3D Model for Electromagnetic Acoustic Transducers (EMATs)
## 3 Horizontal Surface Plane Modelling--Directivity Analysis of Rayleigh Waves
### Analysis of the Beam Directivity of Rayleigh Waves  
Figure 13 shows the calculated Rayleigh waves' radiation pattern at \(z=0\), which is symmetrical with the center of the coil. Rayleigh waves are mainly generated along the y direction (main lobe) and some undeniable directions (side lobe). The characteristics of Rayleigh waves are quantitatively investigated by means of beam directivity, as shown in the red arc in Figure 13 (\(r=250\) mm, \(\theta_{1}=-70^{\circ}\), and \(\theta_{2}=70^{\circ}\)).  
The beam directivity of Rayleigh waves at \(z=0\) is shown in Figure 14. A main lobe contains a larger magnitude compared to the side lobes, which contain a smaller magnitude. The main lobe is centered at \(0^{\circ}\), and the side lobes are roughly centered at \(-25.5^{\circ}\), \(-18^{\circ}\), \(-10^{\circ}\), \(10^{\circ}\), \(18^{\circ}\), and \(25.5^{\circ}\), respectively. The largest magnitude of the side lobes is 25.87% that of the main lobe. In most applications, side lobes are usually undesirable.  
The beam directivity of the Rayleigh waves at various depths is shown in Figure 15. The magnitude of the beam directivity is normalised. From this image, the magnitude of the Rayleigh waves decays with the depth, especially for the depth larger than one Rayleigh wavelength. At a depth equalling to one Rayleigh wave's wavelength, \(z=6\) mm, the magnitude of the Rayleigh waves is 34.9% of that at \(z=0\). At a depth of 7 mm, the magnitude of the Rayleigh waves decays to 22.8% of that at the surface of the test piece (\(z=0\)). This observation confirms that Rayleigh waves mainly distribute within a depth equal to one Rayleigh wavelength.  
Figure 14: The simulated beam directivity of Rayleigh waves at \(z=0\). Reproduced with permission from [15], IEEE, 2016.  
Figure 13: The radiation pattern of Rayleigh waves generated by the meander-line-coil EMAT. Reproduced with permission from [18], Elsevier, 2017.
## 2 Materials and Methods
### Methods of Analysis of Selected Microorganisms  
_E. coli_ enumeration was performed following the standard method, in ISO 16649-2:2001 [32], as follows: after initial suspension preparation in buffered peptone water, selected dilutions were prepared, and 1 mL of each dilution was transferred into a Petri dish and mixed with Tryptone Bile X-glucuronide (TBX) agar (Oxoid, UK). Then the plates were incubated at 44 \(\pm\) 1 \({}^{\circ}\)C for 18-24 h. Typical colonies of \(\beta\)-glucuronidase-positive _E. coli_ were counted, after the pass of the determined incubation time.  
_Salmonella_ spp. enumeration was performed according to the modified standard method, in ISO 6579-1:2017 [33], and modification was as follows: after initial suspension preparation in the buffered peptone water, selected dilutions were prepared. Next, 1 mL of each dilution was transferred and spread onto Xylose Lysine Deoxycholate (XLD) agar (Oxoid, UK), and then the plates were incubated at 37 \(\pm\) 1 \({}^{\circ}\)C for 24 \(\pm\) 3 h. Typical _Salmonella_ spp. colonies were confirmed by the application of appropriate biochemical tests, after the incubation time.  
_L. monocytogenes_ enumeration was performed according to the standard method, in ISO 11290-2:2017 [34], as follows: after initial suspension preparation in buffered peptone water, selected dilutions were prepared. Next, 1 mL of each dilution was moved and spread onto Agar Listeria Ottaviani & Agosti (ALOA) (Oxoid, UK). Then the plates were incubated at 37 \(\pm\) 1 \({}^{\circ}\)C for 24 \(\pm\) 2 h and for an additional incubation for 24 \(\pm\) 2 h. Presumptive _L. monocytogenes_ colonies were confirmed by applying appropriate morphological and biochemical tests, after the pass of incubation.  
Enterobacteriaceae enumeration was performed according to the standard method, _in_ ISO 21528-2:2017 [35], as follows: after initial suspension preparation in the buffered peptone water, selected dilutions were prepared. Next, 1 mL of each dilution was transferred into a Petri dish and mixed with Violet Red Bile Glucose (VRBG) agar (Oxoid, UK). Then the plates were incubated at 37 \(\pm\) 1 \({}^{\circ}\)C for 24 \(\pm\) 2 h. Via tests for the fermentation of glucose and the presence of oxidase, typical colonies were confirmed, after incubation.  
Total plate count (TPC) enumeration was conducted in accordance with the standard method, in ISO 4833-1:2014 [36], as follows: after initial suspension preparation in the buffered peptone water, selected dilutions were prepared, and 1 mL of each dilution was transferred into a Petri dish and mixed with Plate Count (PCA) agar (Oxoid, UK). The plates were incubated at 30 \({}^{\circ}\)C for 72 h. Colonies of TPC were counted, after incubation time.
## 2 Oil Shale Properties
### Porosity/Permeability and Saturation Evolution
At temperatures below 300 \({}^{\circ}\)C, oil shale mainly consists of large pores, small pores, and micropores. The volume of mesopores remains relatively low and is primarily concentrated in the temperature range of 20-300 \({}^{\circ}\)C. As the temperature rises from 300 to 600 \({}^{\circ}\)C, the volume of large pores significantly decreases, while the volume of mesopores increases significantly. The volume of small pores decreases, and the volume of micropores experiences a sharp decline. Particularly in the temperature range of 400-500 \({}^{\circ}\)C, extensive pyrolysis of organic matter leads to the aggregation and connection of micropores, resulting in an increase in mesopores and a decrease in micropores [19]. The permeability of oil shale gradually increases with temperature. At 600 \({}^{\circ}\)C, the permeability reaches 3.0 \(\times\) 10\({}^{-8}\) m\({}^{2}\), which is nearly 600 times higher than the permeability at the initial stage of pyrolysis. As the temperature increases, the generation of new pores and the connection of existing pores in oil shale contribute to its transformation from a tight rock into a porous material with high permeability, facilitating the seepage of oil and gas. Therefore, it has been proved experimentally that raising the temperature of oil shale induces changes in pore size distribution, leading to an increase in permeability. This transformation allows for enhanced oil and gas flow through the rock.
# Rain Monitoring with Polarimetric GNSS Signals: Ground-Based Experimental Research
###### Abstract  
In recent years, there has been a preliminary research on monitoring rainfall information based on polarimetric Global Navigation Satellite System (GNSS) signals, which is a quite novel concept. After previous theoretical research on monitoring rain based on polarimetric phase shift of GNSS signals, the paper aims to detect rain using polarimetric GNSS signals from a ground-based experiment. Firstly, a conical horn antenna specially designed for receiving dual-polarized (H, horizontal, and V, vertical) GNSS signals was developed, and an experimental system for polarimetric GNSS rain detection was built. Then, taking Global Positioning System (GPS) satellites as signal source, a ground-based experiment was carried out at a mountain in Nanjing, where heavy rain tends to occur frequently in rainy season. Additionally, a data processing algorithm mainly following [PERSON] et al. (2016) to solve the problems of quality control, unlocking, hardware effect, phase ambiguity, multipath effect was applied independently to this ground-based data from the polarimetric GNSS rain detection system. Also, the multi-source data from nearby weather radar and weather stations was used for verification. Results from 14 GPS satellites show that the obtained phase shift is zero in all no-rain days while it is not zero during rainy days, which is in accordance with the actual situation. Compared with weather radar and rain gauges' data, the results verify that the phase shift is caused by rain. Besides, when individual cases are examined, many show that their tendencies of accumulated phase shift are quite similar to that of a weather station's rainfall data, even some correlation coefficients are up to 0.99. These demonstrate the reliability of our experimental system and the feasibility of the data processing algorithm. This study will provide technical support for future spaceborne experiment, which has promising applications in global rain monitoring.  
GNSS; rain; polarimetric phase shift; experimental research; statistical analysis 2720191112293; doi:10.3390/rs11192293
## 4 Method to Represent an Arbitrary Observation Geometry
Two parameters, corresponding to the target and the optical sensor, can represent arbitrary relative positional relationships when \(i\) is determined. To represent the location in space more clearly, the right ascension and declination of the target and the optical sensor at the centre of the Earth are used to represent the corresponding locations. The declination of the target in the new coordinate system is \(0\). In this case, the observation geometry in any scenario can be represented by the right ascension of the target \(\alpha_{\text{sensor}}\) and the optical sensor \(\alpha_{\text{Target}}\) at the centre of the Earth in the new coordinate system, if \(i\) is determined. Therefore, when \(i\) is different, simulating the GDOP corresponding to \(0<\alpha_{\text{sensor}}<2\pi\) and \(0<\alpha_{\text{Target}}<2\pi\) as the starting observation time can help to analyse the effect of an arbitrary observation geometry on the initial orbit determination.
# Effect of Organic and Conventional Production on the Quality of Lemon \"Fino 49\"
## 3 Results and Discussion
### Sensory Analysis
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Compound** & **RT** & **KI (Exp)** & **KI (Lit)** & **ANOVA** & **Conventional** & **Organic** \\ \hline \hline \multicolumn{1}{l}{Linalool} & 8.293 & 1107 & 1110 & ** & 133.20 b & 146.07 a \\ \multicolumn{1}{l}{Nonanal} & 8.485 & 1113 & 1102 & NS & 84.47 & 79.20 \\ \multicolumn{1}{l}{Citronellal} & 9.942 & 1157 & 1165 & * & 52.86 a & 35.59 b \\ \multicolumn{1}{l}{Camphor} & 10.033 & 1159 & 1143 & NS & 8.57 & 10.52 \\ \multicolumn{1}{l}{1-Nonanol} & 10.274 & 1166 & 1173 & NS & 12.92 & 14.34 \\ \multicolumn{1}{l}{Borneol} & 10.928 & 1186 & 1177 & NS & 22.42 & 23.84 \\ \multicolumn{1}{l}{Terpinen-4-ololol} & 11.141 & 1192 & 1184 & * & 67.10 b & 80.13 a \\ \multicolumn{1}{l}{\(\alpha\)-Terpineol} & 11.681 & 1207 & 1200 & * & 210.76 b & 252.17 a \\ \multicolumn{1}{l}{Decanal} & 11.843 & 1211 & 1207 & * & 11.93 & 10.06 \\ \multicolumn{1}{l}{Nerol} & 12.714 & 1234 & 1228 & NS & 128.86 & 139.72 \\ \multicolumn{1}{l}{Neral} & 13.221 & 1247 & 1239 & ** & 754.04 b & 778.20 a \\ \multicolumn{1}{l}{Geraniol} & 13.689 & 1259 & 1255 & NS & 90.40 & 96.35 \\ \multicolumn{1}{l}{Geraniol} & 14.378 & 1277 & 1277 & NS & 1024 & 1064 \\ \multicolumn{1}{l}{Neryl acetate} & 17.890 & 1363 & 1366 & NS & 109.52 & 119.32 \\ \multicolumn{1}{l}{Geranyl acetate} & 18.706 & 1383 & 1381 & NS & 42.73 & 42.03 \\ \multicolumn{1}{l}{trans-Caryophyllene} & 20.547 & 1428 & 1418 & NS & 26.29 & 32.26 \\ \multicolumn{1}{l}{trans-\(\alpha\)-Bergamotene} & 20.988 & 1439 & 1435 & NS & 61.91 & 64.79 \\ \multicolumn{1}{l}{\(\alpha\)-Farnesene} & 23.740 & 1506 & 1509 & NS & 6.41 & 12.63 \\ \multicolumn{1}{l}{\(\beta\)-Bisabolene} & 24.051 & 1514 & 1509 & NS & 84.91 & 93.36 \\ \hline \multicolumn{1}{l}{Total} & & ** & 25,945 a & 25,585 b \\ \hline \hline \multicolumn{1}{l}{NS: not significant at \(p>0.05\); * and **: significant at \(p<0.05\) and \(0.01\), respectively. Values followed by different letters, within the same compound, were significantly different (\(p<0.05\)). RT = retention time, KI (Exp.) = experimental Kovats index, KI (Lit.) = literature Kovats index.} \\ \end{tabular}
\end{table} 
Table 6: _Cont._
## 2 Preliminary Theory and Related Work
### Constructing a Forced Linear Model  
[PERSON] and his team found that within the above analysis framework, including the \(r\)-th column in the linear model results in poor approximation. However, constructing the model with only the first \(r-1\) columns of the matrix \(V\) yields a more accurate linear model. They also observed that the statistical distribution of \(v_{r}(t)\) is Gaussian, and the appearance of \(v_{r}(t)\) near the onset of bursting oscillations is closely related to the occurrence of intermittent linear behavior in the system [19]. Inspired by the sparse identification of nonlinear dynamics (SINDy) algorithm, they developed a forced linear model:  
\[\widetilde{\boldsymbol{V}}_{1}^{r-1}=\boldsymbol{A}\boldsymbol{V}_{1}^{r-1}+ \boldsymbol{B}\boldsymbol{v}_{r}, \tag{21}\]  
where \(\boldsymbol{v}_{r}\) is considered as the driven input from \(\boldsymbol{V}_{1}^{r-1}\) constructing the linear model. The nonlinear model is reformulated into a forced linear model. A detailed explanation of the model is as follows:  
\[\begin{split}\widetilde{A}&=\begin{bmatrix}A_{(r-1) \times(r-1)}&B_{(r-1)\times 1}\\ \dots&a_{rr}\end{bmatrix}_{r\times r},\\ \underbrace{\begin{bmatrix}\widetilde{v}_{1}^{T}\\ \widetilde{v}_{2}^{T}\\ \vdots\\ \widetilde{v}_{r-1}^{T}\end{bmatrix}}_{(r-1)\times m}&=\underbrace{\begin{bmatrix} \boldsymbol{A}\\ (r-1)\times(r-1)\end{bmatrix}}_{(r-1)\times(r-1)}+\underbrace{\begin{bmatrix} \boldsymbol{v}_{1}^{T}\\ \boldsymbol{v}_{2}^{T}\\ \vdots\\ \underline{v}_{r-1}^{T}\end{bmatrix}}_{(r-1)\times m}+\underbrace{\begin{bmatrix} \boldsymbol{B}\\ (r-1)\times 1}\underbrace{\begin{bmatrix}\boldsymbol{v}_{r}^{T}\\ \boldsymbol{v}_{r}^{T}\end{bmatrix}}_{(r-1)\times 1}.\end{split} \tag{22}\]  
After obtaining the output matrix of the forced linear model, we can plot the reconstructed single-variable time series from the first column of the output matrix (the matrix on the left side of Equation (22)). The first two columns and the first three columns can be used to reconstruct the two-dimensional and three-dimensional flow fields in the delay embedding space, respectively.  
Since bursting oscillations are induced by the multi-scale behavior of dynamical systems, inspired by the HAVOK analysis framework, we hypothesize that the mechanism behind the generation of bursting oscillations can be described from a data-driven perspective as follows: The low-frequency periodic behavior on high-energy Koopman eigenfunctions is influenced by high-frequency intermittent bursts on low-energy Koopman eigenfunctions, leading to alternating periodic oscillations, which manifest as bursting oscillations. Therefore, by combining high-energy dominant eigenmodes with forced eigenmodes, we can quickly gain insights into the system's dynamics and predict the occurrence of bursting oscillations by identifying periodic intermittent bursts on the forced eigenmodes.
# Fatigue Performance and Model of Polyacrylonitrile Fiber Reinforced Asphalt Mixture
## 5 The Fatigue Model of Fiber Reinforced Asphalt Mixture  
According to the trial results, it was found that the strain and fiber content have a significant impact on the fatigue properties of the asphalt mixture by using statistical analysis of variance (_ANOVA_). Besides, the volume parameter, _VFA_, can also influence the fatigue properties of the asphalt mixture. However, the common fatigue models of asphalt mixture do not reflect the influence of fibers on the fatigue life. There exists an optimum fiber content (_OFC_) in the asphalt mixture from the above analysis. Therefore, a variable, \((1+2 OFC\cdot FC-FC^{2})\), which can reflect the influence of the fiber on the fatigue life, is introduced. Based on Equation (5), a new fiber reinforced asphalt mixture fatigue equation is developed and characterized by Equation (7). The new model reflected the impact of the strain of loading, initial flexural stiffness of mixture, volumetric parameters, _VFA_, and the fiber content on the fatigue life. By fitting all the test data in this study by multiple nonlinear regressions, the regression coefficients, \(R^{2}\), are showed in Table 6. According to the test data, the results of Equations (3) and (5) after fitting are shown in Table 6.  
\[N_{f}=k_{1}\bigg{(}\frac{1}{\varepsilon_{t}}\bigg{)}^{k_{2}}\bigg{(}\frac{1}{S _{0}}\bigg{)}^{k_{3}}VFA^{k_{4}}\bigg{(}1+2 OFC\cdot FC-FC^{2}\bigg{)}^{k_{5}}, \tag{7}\]  
where, \(N_{f}\) is the fatigue life; \(\varepsilon_{t}\) is the applied tensile strain; \(S_{0}\) is the mixture initial flexural stiffness; _VFA_ is voids filled with asphalt; \(FC\) is the fiber content; \(OFC\) is the optimum fiber content; and \(k_{1}\), \(k_{2}\), \(k_{3}\), \(k_{4}\), and \(k_{5}\) are experimental coefficients.  
By comparing the correlation coefficient, \(R^{2}\), of each regression fatigue equation, it can be concluded that after introducing fiber content into the fatigue equation, the correlation coefficient, \(R^{2}\)  
Figure 8: Distribution pattern of the fiber in the asphalt mixture. (**a**) 0.15% fiber content; (**b**) 0.3% fiber content; and (**c**) 0.45% fiber content.  
increases from about 81.6% to 95.4%. Therefore, the accuracy of the model (Equation (7)) is significantly improved, which can also be seen from the F statistics in Table 6.  
From the fatigue models above, it is shown that fatigue life decreases as the strain level and the initial stiffness increase. The fatigue life increases with VFA increasing. It means that increasing the asphalt content and/or reducing air voids would increase the fatigue life. For the fiber content, when the content is less than the optimum fiber content, increasing the fiber content will also lead to an increase of the fatigue life. However, when the fiber content is above the optimum content level, the fatigue life will decline as the fiber content increases.
## 1 Introduction
### State of the Art of Day-Ahead Load Forecasting
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Forecast Horizon** & **Data from** & **Tools** & **Features** \\ \hline \hline \multirow{9}{*}{[PERSON] et al., 2021 [22]} & \multirow{9}{*}{24 h, 6 h, 30 min} & Electric Reliability Council of Texas & Multilayer perceptron neural network & Electricity load, along with temperature, humidity, wind speed, solar irradiance, and pressure, is used to train the NN with different hidden layers, achieving excellent results with a mean square error (MSE) in the order of \(1\times 10^{-8}\). \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \multirow{9}{*}{[PERSON] et al., 2021 [23]} & \multirow{9}{*}{One day ahead and intra-day ahead} & \multirow{9}{*}{Several sites} & Artificial intelligence, neural networks, fuzzy logic, data mining & This review presents the current day-ahead and intra-day power forecasts in the residential sector, showing that an accuracy of up to 98\% can be achieved. \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \multirow{9}{*}{[PERSON] et al., 2022 [24]} & \multirow{9}{*}{One hour ahead} & \multirow{9}{*}{CENACE, Mexico} & \multirow{9}{*}{Random forest} & The load forecast values using different technologies yield MAPE values ranging from 2\% to 3\%. Neural basis expansion analysis (N-BEATS) provides load demand forecasts with a lower MAPE. \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \multirow{9}{*}{[PERSON] et al., 2023 [25]} & \multirow{9}{*}{One day ahead} & \multirow{9}{*}{Greece} & Recurrent, LSTM, and GRU neural networks & Short-term forecasts can be successfully made by capturing the underlying patterns, resulting in a root mean square error of 0.033. \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  &
# Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
## 3 Common Machine Learning Algorithms in IDS
### Machine Learning Models
#### 3.1.2 Deep Learning Models
Figure 3: The structure of an autoencoder. the parameters are learned first using unlabeled data, which is an unsupervised feature learning stage; then, the network is tuned through the labeled data, which is a supervised learning stage. The astonishing achievements of DNNs are mainly due to the unsupervised feature learning stage. **Convolutional Neural Network (CNN)**. CNNs are designed to mimic the human visual system (HVS); consequently, CNNs have made great achievements in the computer vision field [23; 24; 25]. A CNN is stacked with alternate convolutional and pooling layers, as shown in Figure 7. The convolutional layers are used to extract features, and the pooling layers are used to enhance the feature generalizability. CNNs work on 2-dimensional (2D) data, so the input data must be translated into matrices for attack detection. **Recurrent Neural Network (RNN)**. RNNs are networks designed for sequential data and are widely used in natural language processing (NLP) [26; 27; 28]. The characteristics of sequential data are contextual; analyzing isolated data from the sequence makes no sense. To obtain contextual information, each unit in an RNN receives not only the current state but also previous states. The structure of an RNN is shown in Figure 8. Where all the \(W\) items in Figure 8 are the same. This characteristic causes RNNs to often suffer from vanishing or exploding gradients. In reality, standard RNNs deal with only limited-length sequences. To solve the long-term dependence problem, many RNN variants have been proposed, such as long short-term memory (LSTM) [29], gated recurrent unit (GRU) [30], and bi-RNN [31]. Figure 6: The structure of the DNN. Figure 4.0 The structure of a CNN. The LSTM model was proposed by [PERSON] and [PERSON] in 1997 [29]. Each LSTM unit contains three gates: a forget gate, an input gate, and an output gate. The forget gate eliminates outdated memory, the input gate receives new data, and the output gate combines short-term memory with long-term memory to generate the current memory state. The GRU was proposed by [PERSON] et al. in 2014 [30]. The GRU model merges the forget gate and the input gate into a single update gate, which is simpler than the LSTM. **Generative Adversarial Network (GAN)**. A GAN model includes two subnetworks, i.e., a generator and a discriminator. The generator aims to generate synthetic data similar to the real data, and the discriminator intends to distinguish synthetic data from real data. Thus, the generator and the discriminator improve each other. GANs are currently a hot research topic used to augment data in attack detection, which partly ease the problem of IDS dataset shortages. Meanwhile, GANs belong to adversarial learning approaches which can raise the detection accuracy of models by adding adversarial samples to the training set.