esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Time-Scale Modification of Music Signals +
## 5 TSM Based on the Phase Vocoder (PV-TSM)
### The Short-Time Fourier Transform  
The most important tool of PV-TSM is the short-time Fourier transform (STFT), which applies the Fourier transform to every analysis frame of a given input signal. The STFT \(X\) of a signal \(x\) is given by  
\[X(m,k)=\sum_{r=-N/2}^{N/2-1}x_{m}(r)\ w(r)\ \exp(-2\pi ikr/N)\, \tag{12}\]  
where \(m\in\mathbb{Z}\) is the frame index, \(k\in[0:N-1]\) is the frequency index, \(N\) is the frame length, \(x_{m}\) is the \(mth\) analysis frame of \(x\)  
given in seconds, and the frequency index \(k\) corresponds to the physical frequency  
\[F_{\text{coef}}(k)=\frac{k\;F_{\text{s}}}{N} \tag{14}\]  
given in Hertz. The complex number \(X(m,k)\), also called a _time-frequency bin_, denotes the \(kth\) Fourier coefficient for the \(mth\) analysis frame. It can be represented by a magnitude \(|X(m,k)|\in\mathbb{R}^{+}\) and a phase \(\varphi(m,k)\in[0,1)\) as  
\[X(m,k)=|X(m,k)|\;\;\exp(2\pi i\;\varphi(m,k))\;. \tag{15}\]  
The magnitude of an STFT \(X\) is also called a _spectrogram_ which is denoted by \(Y\):  
\[Y=|X|\;. \tag{16}\]  
In the context of PV-TSM, it is necessary to reconstruct the output signal \(y\) from a modified STFT \(X^{\text{Mod}}\). Note that a modified STFT is in general not invertible [15]. In other words, there might be no signal \(y\) that has \(X^{\text{Mod}}\) as its STFT. However, there exist methods that aim to reconstruct a signal \(y\) from \(X^{\text{Mod}}\) whose STFT is close to \(X^{\text{Mod}}\) with respect to some distance measure. Following the procedure described in [16], we first compute time-domain frames \(x_{m}^{\text{Mod}}\) by using the inverse Fourier transform.  
\[x_{m}^{\text{Mod}}(r)=\frac{1}{N}\sum_{k=0}^{N-1}X^{\text{Mod}}(m,k)\;\;\exp(2 \pi ikr/N)\;. \tag{17}\]  
From these frames, we then derive synthesis frames  
\[y_{m}(r)=\frac{w(r)\;x_{m}^{\text{Mod}}(r)}{\sum\limits_{n\in\mathcal{Z}}w(r- nH_{\text{s}})^{2}} \tag{18}\]  
and reconstruct the output signal \(y\) by Equation (2). It can be shown that, when computing the synthesis frames by Equation (18), the STFT of \(y\) (when choosing \(H_{\text{a}}=H_{\text{s}}\)) minimizes a squared error distance measure defined in [16].
## 3 Results
### Proximal Tibial Stress Analysis  
As shown in Figure 5a, the peak proximal tibial stresses in all osteotomy models consistently occurred at the posterior aspect of the lateral hinge. Among the three porous structural configurations, tibial osteotomy models implanted with composite pore structures exhibited the lowest proximal stresses, a trend which was also observed across implants with different porosity levels. As the implant porosity increased, the tibial maximum stress demonstrated a consistent increasing trend. However, a notable exception was observed at 83% porosity, where circular-porous implants induced higher tibial maximum stress compared to square-porous implants. At other porosity levels, the maximum stress adhered to the following hierarchy: square-porous > circular-porous > hybrid-porous implants. Within the 53% to 83% porosity range, compared to composite pore implants, circular pore implants increased the maximum proximal stresses in osteotomy models by 5.79%, 19.37%, 18.10%, and 6.39%, respectively, while square pore implants elevated these stresses by 16.23%, 22.58%, 24.96%, and 1.58%. The maximum stress values of osteotomy models implanted with different porous structures are graphically presented in Figure 5b, with numerical data systematically compiled in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Structure**} & \multicolumn{4}{c}{**Porosity**} \\ \cline{2-5}  & **53\%** & **63\%** & **73\%** & **83\%** \\ \hline Hybrid & 22.12 & 23.34 & 24.20 & 31.01 \\ Square & 25.71 & 28.61 & 30.24 & 31.50 \\ Circular & 23.40 & 27.86 & 28.58 & 32.99 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Maximum stress value on the tibia after implantation of tibial implants with different porosities (unit: MPa).  
Figure 4: Tibial surface and cross-section stress field distribution by FEA simulation under different physiological activities: (**a**) standing; (**b**) walking; (**c**) jogging; (**d**) running. Tibial surface and cross-section strain field distribution by FEA simulation under different physiological activities: (**e**) standing; (**f**) walking; (**g**) jogging; (**h**) running. (**i**) Maximum equivalent stress values in the intact tibia under different physiological activities. (**j**) Maximum equivalent strain values in the intact tibia under different physiological activities.
# A Digital Twin-Based Approach for Detecting Cyber-Physical Attacks in ICS Using Knowledge Discovery
## 5 Methodology
### Process Discovery  
As discussed in Section 2, a fundamental process mining task is process discovery; given a log \(L\), the aim is to construct a process model that supports its traces. Computing a process model which satisfies precedence constraints defined by the expert is a problem that has been shown to be intractable in general.  
**Theorem 1** (cf. [42]).: _If \(\
ot\prec\
otin S\), then computing a dependency graph for a log \(L\) that satisfies the constraints in \(S\) is in \(\mathbb{P}\); otherwise, the problem is \(\mathbb{NP}\)-hard._  
Figure 5 illustrates the a priori known process model for our case study using Business Process Modeling Notation (BPMN). This model is constructed by leveraging available a priori domain knowledge on interactivity dependencies.  
The process begins when the ingredients are added to the mixing tank, causing the level register value (IW0) to rise. This increase triggers an adjustment in the stirring blade  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Timestamp** & **Trace ID** & **Activity** \\ \hline
18:30:30 & 1 & IW0\_Ascending \\
18:30:30 & 1 & IW1\_Ascending \\
18:30:30 & 1 & IW2\_Descending \\
18:30:30 & 1 & MW0\_Ascending \\
18:30:30 & 1 & QX00\_True\(\rightarrow\)False \\
18:30:30 & 1 & QX01\_True\(\rightarrow\)False \\ \hline \hline \end{tabular}
\end{table}
Table 4: Excerpt of the final event log derived from the initial raw data.  
Figure 5: TheBPMN model representing the a priori known process of the mixer PLC in our case study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Timestamp** & **Register** & **Previous Value** & **Current Value** \\ \hline
18:30:30 & IW0 & 0.0 & 5.0 \\
18:30:30 & IW1 & 0.0 & 10.0 \\
18:30:30 & IW2 & 20.0 & 15.0 \\
18:30:30 & MW0 & 0.0 & 3.0 \\
18:30:30 & QX00 & True & False \\
18:30:30 & QX01 & True & False \\ \hline \hline \end{tabular}
\end{table}
Table 3: A portion of the initial raw device log.  
speed (IW1) and activates the heating element (QX00), resulting in a rise in the temperature (IW2). Subsequently, the valve actuator (QX00) is engaged to drain the tank into the bottling bay. As the process progresses, the system transitions to a descending phase, characterized by a reduction in both temperature and stirring speed.  
In contrast, Figure 6 depicts the process model generated by Heuristics Miner algorithm [43] directly from the data generated in our case study.  
Although the model can derive a (conceptual) process model representing how the data \"flow\" over the registers, it fails to capture a comprehensive representation of certain events, such as the change in the QX00 actuator. Additionally, the model includes wrong edges, i.e., connecting states that should not be linked, which further complicates the accurate depiction of the process  
We finish this section by pointing out that a properly refined process model describing the expected behavior is crucial in specific application contexts, including anomaly detection.
## 4 4-Chain Based Routing
### Protocol Operations
#### 4.2.3 Formation of Interconnection between Chains  
In order to find a global optimum path, every node \(i\) compares its distance from its parent node \(p_{i}\) with the distance from the nearest neighbor in the other chains. If the neighbor in the other chain is closer than \(p_{i}\), the node \(i\) transmits data to neighbor instead of sending it to the \(p_{i}\). It also updates its parent node on the basis of this comparison. We also formulate this selection of interconnecting nodes between the chains as:  
\[N_{i}^{R}=\left\{k_{i}\in\aleph\mid d(i,k_{i})\leq R\right\} \tag{36}\]  
In the Equation (36), \(N_{i}^{R}\) is the set of neighbors of node \(i\), present in its transmission range, \(R\).  
\[N_{i}^{j}=\left\{k_{i}^{j}\in N_{i}^{R}\wedge k_{i}^{j}\in G_{j}\right\} \tag{37}\]  
Here, \(N_{i}^{j}\) is the set of neighbors of node \(i\), which are present in its chain and \(\bar{N_{i}^{j}}\) is the set of neighbors not present in the chain of node \(i\). \(G_{j}\) is the group of nodes present in _jth_ chain.  
\[\bar{N_{i}^{j}}=\left\{\bar{k_{i}^{j}}\in N_{i}^{R}\wedge\bar{k_{i}^{j}}\
otin G _{j}\right\} \tag{38}\]  
Any node \(i\) selects a parent node \(p_{i}\) from the other chain, if the nearest node \(k_{i}^{j}\) in its chain is farther than the node \(\bar{k_{i}^{j}}\) in the other chain.  
\[p_{i}=\begin{cases}k_{i}^{j}&\text{if }d_{i}^{j}<\bar{d_{i}^{j}}\\ \bar{k_{i}^{j}}&\text{otherwise}\end{cases} \tag{39}\]
## 3 Geomagnetic Activity and Ionospheric Weather
### Geomagnetic Indices and Solar Activity
This period was not included in the NOAA Space Weather Prediction Center list of solar proton events affecting the Earth environment [48]. Nevertheless, any additional ionization contributes to increased electrical conductivity at certain altitudes. In the last three panels (Figure 3c-e), the hourly values of the solar wind (SW) dynamic pressure Pd (Pd = n \(\cdot\) V\({}^{2}\), where V and n is the SW speed and density, respectively), the interplanetary magnetic field (IMF) Bz component, and the Dst and a\({}_{\text{p}}\) indices are presented. The data were obtained from the NASA OMNI database, which provides interplanetary parameters referred to as the Earth's orbit (1 AU). At 18 UT, 22 January, an abrupt increase in Pd from 1 hPa up to 4.5 nPa was observed. A steep front and a gradual trailing edge are typical features of an interplanetary magnetic cloud. At the same time, the IMF Bz turned south for several hours but reached only \(-\)5 nT. The compression of the magnetopause resulted in a positive excursion of Dst up to 15 nT. The geomagnetic response to the arrival of the SWPd pulse is global and covers a wide range of latitudes [49]. This effect is manifested in the growth of the a\({}_{\text{p}}\) index up to 18 nT at the end of 22 January, when it approached the upper limit of the quiet activity level. The SW driver was too weak to trigger even a minor magnetic storm (according to NOAA criteria, a G1 minor storm occurs  
Figure 3: (**a**) the X-ray flux (the horizontal line shows the M-flare level), (**b**) the solar proton \(>\)10 MeV flux, (**c**) the SW Pd, (**d**) the IMF Bz component, (**e**) the Dst and a\({}_{\text{p}}\) indices on 21–23 January 2024. The EQ time is marked by vertical red line. when \(\rm a_{P}>48\) nT). At the same time, increased fluxes of X-rays and EUV, as well as solar protons, do produce additional ionization throughout the ionosphere and even the middle atmosphere. The flare at 23 UT on 22 January seemed to be the most geoeffective because it was accompanied by (while minor) a magnetic cloud and solar proton flux enhancement. Even under geomagnetic conditions that are formally considered quiet, the ionosphere may become irregular or disturbed, making it difficult to distinguish between the growth of Ne due to seismicity and the natural variability caused by space weather effects. Examination of TEC is important in establishing the background ionospheric plasma density over the area of occurrence of a possible ionospheric anomaly associated with the coseismic effect.
# Coastal Zone Classification Based on U-Net and Remote Sensing
## 4 Discussion  
### Advantages of U-Net Deep Learning Models  
In this research, we conducted a comprehensive comparison of the U-Net model for coastal zone LUCC against other state-of-the-art deep learning models, such as SegNet and DeepLab v3+, as well as traditional machine learning models like SVM and RF. The experimental results demonstrated that U-Net was a powerful tool for LUCC tasks due to its unique architecture. The U-Net design includes a contracting path to capture contextual information and a symmetric expanding path for precise localization, which are particularly beneficial for tasks requiring detailed boundary preservation [52]. Unlike traditional machine learning models such as SVM and RF, which require extensive feature engineering to perform well, deep learning models like U-Net can automatically learn relevant features from raw data. This capability makes U-Net more flexible and less dependent on the quality of manually engineered features [75; 76]. Our experimental results show that the three selected deep learning algorithms, including U-Net, achieved an OA greater than 85%, outperforming the two traditional statistical learning models. This finding aligns with [PERSON]'s research conclusions in the Shenzhen area [77]. Although DeepLab v3+ offers high accuracy, its complexity and computational intensity are greater than those of U-Net [58]. U-Net excels in accuracy and detailed segmentation, especially when dealing with complex boundaries and limited training data. While SegNet provides computational efficiency, it may compromise some details. DeepLab v3+ delivers state-of-the-art performance but at the cost of increased complexity and computational demands. In contrast, SVM and Random Forest, although robust traditional methods, require extensive feature engineering and may struggle with high-dimensional data scenarios where the automatic feature extraction capabilities of deep learning provide a significant advantage.
## 5 Discussion
### Shear Strength Tests  
The shear strength tested in the laboratory for the 5% bentonite mixture, compacted at the optimum moisture content and subjected to 50, 100, and 200 kPa of vertical normal stresses, yielded an angle of shearing resistance of 33.5 degrees and a cohesion of 15.2 kPa. The friction angle increased to 38.6 and 36.4 degrees for the dry of optimum and the wet of optimum conditions, respectively. The cohesion was reported as 2.2 kPa and 6.5 kPa for the dry of optimum and wet of optimum conditions, respectively.  
For the 10% bentonite content mixture compacted at the optimum moisture content and the maximum dry density, the angle of shearing resistance was measured as 33.5 degrees and the cohesion as 30 kPa. The friction angle did not change significantly and was measured at 33.4\({}^{\circ}\) and 34.0\({}^{\circ}\) for the dry of optimum and the wet of optimum conditions, respectively. The cohesion was reported as 22.5 kPa and 18 kPa for the dry of optimum and wet of optimum conditions, respectively.  
The above parameters are useful when computing the allowable bearing capacity of the clay-sand layer. This may be needed if the layer is to support light structures.  
Higher shear strength is associated with higher bentonite content. This is also reflected in the penetration of the dynamic cone in this particular study. The shear strength may be reduced if the bentonite content is very high, as the clay may dominate the behavior of the mixture [34]. Stress partitioning may be the governing factor, and closer contact between particles can be achieved by applying more compaction energy. The shape of the sand, whether rounded or angular, and the interlocking between the particles can have a significant influence on the shearing strength.  
While shearing off the samples, vertical and horizontal displacements were observed. In examining the horizontal displacement, we can see that increasing the shear stress beyond the shear stress corresponding to 2 mm of horizontal displacement will result in excessive horizontal displacement for the two mixtures tested (5% and 10% bentonite content). The vertical displacement was high for the 200 kPa stress. Dilation did not occur at this stress for the 5% bentonite.
## 4 Material and Methods
### Abiotic Stress Induction  
To investigate the impact of abiotic stress on AsA biosynthesis, 27-day-old seedlings were transferred to hydroponics using a 50% Hoagland nutrient solution [69], where they remained for 72 h in a B.O.D. chamber under the same photoperiod and temperature conditions as under in vitro cultivation acclimatization. Subsequently, the seedlings were subjected to stress treatments. The acclimatized seedlings were then transferred again to hydroponics, now containing 100% Hoagland nutrient solution, in 3 L pots and divided into three groups. Then, each condition (control, saline stress, and water stress) was represented by three different pots with 10 seedlings each, where a pot represents one biological replicate. At each time point (24 and 48 h), three seedlings were collected from each biological replicate (one pot); in total, there were nine seedlings per condition (control, salt, and drought stress). The control group received only 100% Hoagland solution, the saline stress group received 100% Hoagland solution with 100 mM NaCl, and the water stress group received 100% Hoagland solution with 200 g\(\cdot\)L\({}^{-1}\) PEG 6000 (polyethylene glycol). Leaves and roots were collected at 24 and 48 h after initial treatment. After collection, the samples (three biological replicates and three technical replicates) were frozen in liquid nitrogen and stored at \(-80\)\({}^{\circ}\)C until the preparation of extracts for ascorbate quantification.
# Detection of Malware by Deep Learning as CNN-LSTM Machine Learning Techniques in Real Time
## 5 Conclusions  
In this research study, through the utilization of the CNN-LSTM to overcome major malware detection deficiencies, including the inefficiency of human feature building and the limitations of existing learning algorithms, we built a novel deep neural network ensemble by stacking CNN and LSTM techniques. The proposed CNN-LSTM method is used for the detection of advance malware without any feature engineering. Table 1, provided above, illustrates that the CNN-LSTM has the highest detection accuracy, which is 99%, among the other malware detection methods. For the rest of the classifiers, accuracy is 98% for DT and 95% for SVM. The accuracy of the LSTM model is 99%, precision of the CNN-LSTM is 99%, recall accuracy is 99%, and F1 score is 1. With the proposed CNN-LSTM model, malware detection accuracy is improved during training to around 1, with testing accuracy extremely close to training accuracy. All of this is possible thanks the interplay between the skills of a CNN-LSTM to extract spatially local correlations and the ability of a CNN-LSTM to represent sequences and learn from long-term dependencies.  
[leftmargin=*] **Author Contributions:** [PERSON] and T.F. contributed equally to the study's conception. All authors have read and agreed to the published version of the manuscript.  
[leftmargin=*] **Funding:** This work is supported by the National Natural Science Foundation of China (No. 62162039) and (61762060), and the Key Research and Development Program of Gansu Province (No. 20 YF3 GA016).  
[leftmargin=*] **Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.  
[leftmargin=*] **Conflicts of Interest:** The authors declare no conflict of interest.  
[leftmargin=*] **Abbreviations**  
The following abbreviations are used in this research study.  
CNNConvolutional Neural network  
LSTMLong short-term memory  
IoTInternet of things  
DBNDeep Belief Networks  
RNNRecurrent Neural Network  
FPRFalse Positive Rate  
RBMRestricted Boltzmann Machine  
DTDecision Tree  
SVMSupport Vector Machine  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **TPR** & **FPR** & **Detection Accuracy** \\ \hline CNN-LSTM & 1 & 0.0031 & 99\% \\ \hline DT & 0.99 & 0.0039 & 98\% \\ \hline SVM & 0.97 & 0.0043 & 95\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: CNN-LSTM method comparison.
# Influence of Incident Shock on Fuel Mixing in Scramjet
## 2 Numerical Method  
### Governing Equations  
The equations for the conservation of mass and momentum are as follows [26]:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial\big{(}\rho u_{j}\big{)}}{\partial x _{j}}=0 \tag{1}\]  
\[\frac{\partial\big{(}\rho u_{i}\big{)}}{\partial t}+\frac{\partial\big{(}\rho u _{i}u_{j}+p\delta_{ij}-\tau_{ij}\big{)}}{\partial x_{j}}=0 \tag{2}\]  
The vectors \(u_{i}\) is the component of velocity, \(x_{j}\) is the position, \(\rho\) is the density, \(p\) is the pressure, and \(\tau_{ij}\) is the viscous stress tensor defined as follows:  
\[\tau_{ij}=2\mu\bigg{(}S_{ij}-\frac{1}{3}\frac{\partial u_{k}}{\partial x_{k}} \delta_{ij}\bigg{)} \tag{3}\]  
where \(\mu\) is molecular viscosity, \(\delta_{ij}\) is the Kronecker delta, and \(S_{ij}\) is the strain-rate tensor given by:  
\[S_{ij}=\frac{1}{2}(\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u_{j }}{\partial x_{i}}) \tag{4}\]  
Since this work has focused on fuel mixing, the species transport equation is considered as follows:  
\[\frac{\partial\big{(}\rho Y_{n}\big{)}}{\partial t}+\frac{\partial\big{[}\rho Y _{n}(u_{i}+V_{i,n})\big{]}}{\partial x_{j}}=\dot{\omega}_{n} \tag{5}\]  
\[V_{i,n}=-\frac{D_{n}}{Y_{n}}\frac{\partial Y_{n}}{\partial x_{i}} \tag{6}\]  
where \(Y_{n}\) is the mass fraction of species \(n\) and \(n\) is the number of chemical species, \(\dot{\omega}_{n}\) is the reaction rate for species \(n\), and \(D_{n}\) is the species diffusion coefficient. The energy equation is given by:  
\[\frac{\partial\big{(}\rho E\big{)}}{\partial t}+\frac{\partial\big{[}\big{(} \rho E+p\big{)}u_{i}+q_{i}-u_{j}\tau_{ij}\big{]}}{\partial x_{i}}=0 \tag{7}\]  
\[E=e+\frac{1}{2}u_{k}u_{k} \tag{8}\]  
\[e=\sum_{n=1}^{N}Y_{n}h_{n}-\frac{p}{\rho} \tag{9}\]  
where \(q_{i}\) is the heat flux vector, and \(h_{n}\) is the specific enthalpy of species \(n\).