esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The devastating effects of rain-induced attenuation on communication links operating above 10 GHz during rainy events can significantly degrade signal quality, leading to interruptions in service and reduced data throughput. Understanding the spatial and seasonal distribution of rain heights is crucial for predicting these attenuation effects and for network performance optimization. This study utilized ten years of atmospheric temperature and geopotential height data at seven pressure levels (1000, 850, 700, 500, 300, 200, and 100 hPa) obtained from the Copernicus Climate Data Store (CDS) to deduce rain heights across nine stations in South Africa. The kernel density estimation (KDE) method was applied to estimate the temporal variation of rain height. A comparison of the measured and estimated rain heights shows a correlation coefficient of 0.997 with a maximum percentage difference of 5.3%. The results show that rain height ranges from a minimum of 3.5 km during winter in Cape Town to a maximum of about 5.27 km during the summer in Polokwane. The spatial variation shows a location-dependent seasonal trend, with peak rain heights prevailing at the low-latitude stations. The seasonal variability indicates that higher rain heights dominate in the regions (Polokwane, Pretoria, Nelspruit, Mahikeng) where there is frequent occurrence of rainfall during the winter season and vice versa. Contour maps of rain heights over the four seasons (autumn, spring, winter, and summer) were also developed for South Africa. The estimated seasonal rain heights show that rain-induced attenuations were grossly underestimated by the International Telecommunication Union (ITU) recommended rain heights at most of the stations during autumn, spring, and summer but fairly overestimated during winter. Durban had a peak attenuation of 15.9 dB during the summer, while Upington recorded the smallest attenuation of about 7.7 dB during winter at a 0.01% time exceedance. Future system planning and adjustments of existing infrastructure in the study stations could be improved by integrating these localized, seasonal radio propagation data in link budget design.  
network performance optimization; rain height; rain-induced attenuation; KDE; ITU; radio propagation; link budget design 2024 15 1354. [[https://doi.org/10.3390/atmos15111354](https://doi.org/10.3390/atmos15111354)]([https://doi.org/10.3390/atmos15111354](https://doi.org/10.3390/atmos15111354)) [[https://www.mdpi.com/journal/atmosphere](https://www.mdpi.com/journal/atmosphere)]([https://www.mdpi.com/journal/atmosphere](https://www.mdpi.com/journal/atmosphere))
## 2 Synchrophasor Sequence Modal Models and Extraction of Modal Parameters
### Synchrophasor Sequence Modal Models with Sub- or Super-Synchronous Components
In Equation (5), the angular frequency of each component is introduced, and its expression is  
\[\omega_{0}=2\pi\frac{f_{N}-f_{0}}{f_{\rm s}}\ ;\ \omega_{\rm sub}=2\pi\frac{f_{N}-f_{ \rm sub}}{f_{\rm s}}\ ;\ \omega_{\rm sup}=2\pi\frac{f_{\rm sup}-f_{N}}{f_{\rm s}} \tag{6}\]  
The subsynchronous and supersynchronous components constitute a paired set of frequency-coupled oscillation elements exhibiting interdependent dynamic oscillations. The frequency \(f_{0}\) of the fundamental component is close to the rated frequency \(f_{N}\) of the power system, and the relationship between their frequencies is \(f_{\rm sub}+f_{\rm sup}=2f_{N}\). Accounting to Equation (6), \(\omega_{\rm sub}=\omega_{\rm sup}^{*}\) and \(\omega_{\rm sub}^{*}=\omega_{\rm sup}\) can be deduced. To simplify Equation (5), let \(\omega_{\rm s}=\omega_{\rm sub}=\omega_{\rm sup}^{*}\). \(\dot{X}_{\rm sub}(k)\) and \(\dot{X}_{\rm sup}(k)\) can be combined into an oscillation component \(\dot{X}_{\rm s}(k)\) to simplify the synchrophasor sequence \(\dot{X}(k)\), and thus we have  
\[\dot{X}(k)=\dot{X}_{0}^{+}(k)+\dot{X}_{0}^{-}(k)+\dot{X}_{\rm s}^{+}(k)+\dot{ X}_{\rm s}^{-}(k)=\dot{X}_{0}(k)+\dot{X}_{\rm s}(k) \tag{7}\]  
\[ \begin{cases}\dot{X}_{\rm s}^{+}(k)=\dot{X}_{\rm sub}^{+}(k)+\dot{X}_{\rm sup }^{+}(k)\\ \dot{X}_{\rm s}^{-}(k)=\dot{X}_{\rm sub}^{-}(k)+\dot{X}_{\rm sup}^{-}(k)\end{cases}  \tag{8}\]  
Since the ERA can be synonymously reformulated as a technique for extracting modal parameters, we study the modal model of synchrophasors. According to [15], the synchrophasors can be expressed as the sum of four modes, as shown in the following formula:  
\[\dot{X}(k)=\sum_{m=1}^{4}R_{m}e^{\omega_{m}k} \tag{9}\]  
\[ \begin{cases}R_{1}=G^{*}(f_{0},+1)x_{0}e^{-\mathrm{i}\phi_{0}}\\ R_{2}=G(f_{0},-1)x_{0}e^{\mathrm{i}\phi_{0}}\\ R_{3}=G^{*}(f_{\rm sub},+1)x_{\rm sub}e^{-\mathrm{i}\psi_{\rm sub}}+G(f_{\rm sup },-1)x_{\rm sup}e^{\mathrm{i}\phi_{\rm sup}}\\ R_{4}=G(f_{\rm sub},-1)x_{\rm sub}e^{\mathrm{i}\phi_{\rm sub}}+G^{*}(f_{\rm sup },+1)x_{\rm sup}e^{-\mathrm{i}\phi_{\rm sup}}\end{cases}  \tag{10}\]  
\[\omega_{1}=\mathrm{i}\omega_{0},\omega_{2}=-\mathrm{i}\omega_{0},\omega_{3}= \mathrm{i}\omega_{s},\omega_{4}=-\mathrm{i}\omega_{s} \tag{11}\]  
where \(\omega_{m}\) is the angular frequencies and \(R_{m}\) is the constant parameters, while \(k\) denotes the temporal variation characteristics of the angular frequency.
# MVACLNet: A Multimodal Virtual Augmentation Contrastive Learning Network for Rumor Detection
## 3 Methodology  
### Problem Definition  
The multimodal rumor detection task is defined as a binary classification task, which aims to train a classifier to judge a given multimodal post as a rumor or a non-rumor. Formally, let \(P=\{p_{1},p_{2},...,p_{n}\}\) be a set of posts on social media for detection, where \(p_{i}\) is the \(i\)-th post and \(n\) is the number of posts. Each post \(p_{i}=\{T_{i},V_{i},y_{i}\}\in P\) consists of the text content \(T_{i}\), the attached image \(V_{i}\), and the ground-truth label \(y_{i}\in\{0,1\}\) (i.e., non-rumor or rumor).
## 4 Tracking
### Experiment and Analysis
#### 4.3.1 Case One  
In this case, the CV, CA, Singer model, current model, IMM and the adaptive model were compared. The tracking covariance of each model with UKF estimation method is shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Estimation Method** & **Linear** & **Nonlinear** & \
\begin{tabular}{c} **Non-Gaussian** \\ **Process/Measurement Noise** \\ \end{tabular} &
\begin{tabular}{c} **Computational Complexity** \\ \end{tabular} \\ \hline Kalman Filter & Yes & No & No & low \\ EKF & / & Yes & No & medium \\ UKF & / & Yes & No & medium \\ CKF & / & Yes & No & medium \\ PL & / & Yes & Yes & high \\ \hline \hline \end{tabular}
\end{table}
Table 2: The difference of state estimation method.  
Figure 7: The measurement space of RFID readers and the reference trajectory. Note: The unit of each axes is meter.  
It is noted that the covariances of the IMM and adaptive model are relatively small comparing with other models, and the CV model get the worst covariance. It is because that the trajectory generated by the simulated RFID tracking system has higher-order dynamic feature and complexity.  
On the other hand, the covariances of the IMM and adaptive model obtained via the simulated system are much smaller than that in the practical system. This is because, on the one hand, the practical tracking case is more complex than the simulation, and, on the other hand, these models are based on given structures. If the structure is not in good accordance with the reality, the model will be impossible to grasp the characteristics of the actual movement, or correctly determine the relationship between the external environment and its own movement. This is the biggest obstacle to robot's mobile intelligence.
# Power Transformer Fault Diagnosis Based on Dissolved Gas Analysis by Correlation Coefficient-DBSCAN
## 4 Example Analysis and Results
### CCDBSCAN Algorithm Steps  
The fault diagnosis method of CCDBSCAN is divided into the following steps:  
1. Data initialization: calculate the content percentage data from the DGA gas content data to be collected. The equation used for data initialization is \[x_{i}=\frac{x_{i}}{x_{1}+x_{2}+x_{3}+x_{4}+x_{5}}(i=1,2,\ldots,5)\] (15) where \(x_{1}\) is the hydrogen content, in uL/L, \(x_{2}\) is the methane content, in uL/L, \(x_{3}\) is the ethane content, in uL/L, \(x_{4}\) is the ethylene content, in uL/L, \(x_{5}\) is the acetylene content, in uL/L.
2. Using chaotic sequence to get its amplification coefficient.
3. Pattern classification with CCDBSCAN method.
4. Fault diagnosis of fault transformer data.
# NextGen ADS-B Software-Defined Reception with Enhanced Techniques
## 3 Methodology and Solutions  
The system structure and related MATLAB simulation processing blocks are displayed in Figure 1. It composes of the Demodulator at 1090 MHz, Preamble Pulse Detection, Enhanced Bit Detection CRC Error Detection and Correction, and the ADS-B Decoding blocks on the receiving side. Details related to each block are provided in the following sections.  
Before the design of the ADS-B receiver, it is crucial to consider the following major expectations:  
* Digital domain environment: All design has been done in the discrete-time domain since the aim is to use SDR devices and run the avionic function as an App.
* Processing Efficiency: The design should be performed in a way that all information is done in real-time without any missing information.
* ADS-B Receiving Device Output: Need to be in a proper format that can be utilized by other systems to be displayed or saved in the database.
## 2 Materials and Methods
### Training Data Generation
#### 2.5.1 Open Water Training Data Mask  
Open water training data was defined using Sentinel-1 C-band SAR image composites. The comparatively shorter wavelength of the C-band system provides better discrimination of 'pure' open water targets as it is more sensitive to 'contamination' from emergent vegetation or the edges/banks of water bodies compared to L-band imagery. Equally, C-band imagery is better able to discriminate between open water and other targets that exhibit low backscatter. For instance, in L-band imagery, flat grassland can appear similar to open water, but in C-band imagery, grassland has a higher backscatter than open water.  
Monthly Sentinel-1 composites (10 m) were generated and downloaded from the Microsoft Planetary Computer [39]. Monthly median composites were used, rather than single observations, to reduce the influence of rain or rough water caused by wind that can affect the C-Band VV backscatter signal. Sentinel-1 monthly median composites were resampled to 50 m to match the resolution and raster grid of the ALOS-2 PALSAR-2 data using nearest neighbour resampling. Split-based image thresholding [24] was then applied to the VV image band to generate a low backscatter image mask. This mask is then applied to ALOS-2 PALSAR-2 ScanSAR imagery based on the closest matching date to define training data for open water. This enabled training data to be generated for a range of periods, representing a range of different hydrological conditions.
# PACER: Platform for Android Malware Classification, Performance Evaluation and Threat Reporting +
## 2 Background
### Machine-Learning-Based Android Malware Detection
#### 2.1.2 Dynamic Features-Based Detection  
Dynamic features are extracted from the Android application package, i.e., _apk_ after installing or executing them in a controlled environment (emulator, virtual machine, etc.). The dynamic analysis focuses mainly on running time and interactive features of apps, therefore it is more complex and slower than the static analysis. The usability of dynamic analysis, in fact, lies in the ability to detect \"obfuscated\" and \"polymorphic\" malware which mostly escapes under static analysis [4]. Finding optimum running time for dynamic analysis is also difficult, as the start of malicious activities can vary from sample to sample [32]. The dynamic features are extracted through system calls, network activities, user interaction, Central Processing Unit (CPU) consumption, and other phone activities [4]. Table 3 lists and summarizes some of the popular works related to dynamic analysis using dynamic features and ML-based Android Malware detection (Input/Output (I/O), Read/Write (R/W)).  
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline \multirow{2}{*}{**Authors**} & \multirow{2}{*}{**Features**} & \multicolumn{3}{c}{**Performance Metrics**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Best Model**} \\ \cline{3-4} \cline{6-7}  & & **Accuracy** & & & & \\ \hline [PERSON] et al. [21] & \begin{tabular}{c} Permission and \\ Metadata \\ \end{tabular} & NA & NA & NA & Not Available & Apriori \\ \hline [PERSON] et al. [22] & Permission & 86.41 & 0.19 & 0.91 & Not Available & Random forest \\ \hline [PERSON] et at. [31] & Manifest & 94.83 & 0.05 & 0.94 & Not Available & Random Forest \\ \hline [PERSON] et el. [23] & Permission & 0.65 & 0.65 & 0.65 & Not Available & \begin{tabular}{c} Neural \\ Network \\ \end{tabular} \\ \hline [PERSON] et al. [24] & Permission & 92.1 & 0.06 & 0.90 & Available & Bayesian \\ \hline [PERSON] et al. [29] & \
\begin{tabular}{c} Permission \\ and API \\ \end{tabular} & 93.60 & 0.92 & 0.88 & Available & Bagging \\ \hline [PERSON] et al. [30] &
\begin{tabular}{c} Manifest and \\ Source code \\ \end{tabular} & 93.90 & NA & NA & Available & SVM \\ \hline [PERSON] et al. [1] & Permission & 94.84 & 0.95 & 0.95 & Available & Random Forest \\ \hline [PERSON] et al. [27] & Permission & 89 & NA & NA & Available & Ensemble \\ \hline [PERSON] et al. [27] & Source Code & 95.1 & NA & NA & Available & Ensemble \\ \hline [PERSON] et al. [28] & Permission & 93.62 & NA & NA & Not available & SVM \\ \hline \hline \end{tabular}
\end{table}
Table 2: Android Malware Detection Using Static Features.
## 4 Conclusions  
In this study, we investigated how the layer-wise locally adaptive activation function affects the performance of PINN for solving 1D consolidation problems, and for this purpose, two 1D consolidation problems are solved by using L-LAAF-PINN with the layer-wise locally adaptive activation function in comparison with baseline PINN. Based on the results obtained, the following conclusions can be drawn:  
* For solving 1D nonlinear consolidation problems, the baseline PINN is seriously affected by the loss term weight settings and random states, and the accuracy of the baseline PINN solution decreases as the weights of the training loss terms decrease, and the random states also lead to the instability of the performance of the baseline PINN.
* Regardless of the loss term weight settings, the L-LAAF-PINN model with the layer-wise locally adaptive activation function solves the 1D consolidation problem with significantly better convergence speed and prediction accuracy than the benchmark PINN model.
* The L-LAAF-PINN model with the locally layered adaptive activation functions is more stable than the baseline PINN model in solving 1D consolidation problems for random states, especially in two nonlinear consolidation problems. However, the effect of loss term weight settings on PINN is hardly improved.
* In the inverse modeling of two 1D nonlinear consolidation problems, the convergence speed of L-LAAF-PINN is faster, indicating that the layer-wise locally adaptive activation functions still accelerate the parameter inversion speed in 1D nonlinear consolidation problems.  
Conceptualization, D.S.; methodology, J.Z.; software, J.Z.; validation, J.Z., D.S., and Y.C.; formal analysis, J.Z. and Y.C.; investigation, J.Z.; resources, J.Z.; writing--original draft preparation, J.Z.; writing--review and editing, Y.C.; visualization, J.Z.; supervision, Y.C. and  
Figure 15: 1D nonlinear consolidation: \(\alpha\) and \(\beta\) evolution over epochs for the PINN and L-LAAF-PINN models. (**a**) \(\alpha\); (**b**) \(\beta\).  
D.S.; funding acquisition, D.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (grant no. 52378354 and no. 52238007).  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 5 Conclusions
#### Limitations and Future Work  
In comparison to the state-of-the-art SVM-ANN [14], the proposed RFMLP model shows slightly low performance for the 'Prob' class from the NSL-KDD dataset. RF bootstrap subset cannot extract the significant features from data occasionally due to data scarcity. This factor, and the low number of samples, can lead to poor performance. In the future, we intend to use SMOTE and ADASYN to further investigate this aspect. In addition, we are planning to deploy appropriate feature reduction techniques in combination with the ensemble model.  
Conceptualization, M.N. and R.M.; Formal analysis, R.M.; Funding acquisition, N.R.; Investigation, M.N. and S.S.; Methodology, R.M., S.S. and M.U.; Project administration,  
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Ref.** & **Model** & **Dataset** & **DoS** & **Prob** & **R2L** & **U2R** & **Avg. Accuracy** \\ \hline Proposed & RFMLP & & 100.0 & 99.99 & 99.99 & 99.99 & 99.99 \\ [19] & PCA+MCA & & 99.99 & 98.18 & 97.06 & 81.82 & 94.20 \\ [16] & DNN & & - & - & - & 92.49 \\ [28] & DT-RFE & & 99.76 & 99.41 & 97.92 & 99.74 & 99.21 \\ \hline Proposed & RFMLP & & 100.0 & 99.98 & 99.97 & 100.0 & 99.98 \\ [14] & SVM-ANN & & 100.0 & 99.99 & 77.40 & 88.60 & 91.48 \\ [17] & Deep hierarchical & & 96.21 & 68.56 & 60.45 & 61.32 & 83.58 \\ [28] & DT-RFE & & 99.74 & 99.20 & 98.21 & 99.77 & 99.23 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Accuracy comparison of classifiers on KDD-CUP and NSL-KDD datasets.  
I.A. and N.R.; Resources, F.J.; Software, S.S. and M.U.; Supervision, N.R.; Validation, F.J. and I.A.; Visualization, F.J.; Writing--original draft, M.N. and M.U.; Writing--review & editing, I.A. All authors have read and agreed to the published version of the manuscript.
## 3 GROW Solution Overview  
GROW is a pioneering solution that aims to overcome the limitations of current underwater communications technologies. It enables long-range, broadband underwater wireless communications between an underwater Survey Unit (SU) and a Central Station Unit (CSU) at the surface through the use of one or more Data Mule Units (DMUs) [6]. In this section we present an overview of the GROW solution, including its communications and AUV localization components.
## 3 Modelling and Simulation  
The simulation was performed in Matlab/Simulink environment. The'moving-average function' and'moving-RMS function' has been used to provide a sliding window for instantaneously AVE and RMS value calculations.  
Parameters of the phase voltages in \(\alpha,\ \beta\)-system  
\[U_{a,rms}=230\ V\ \gg\ U_{a,max}\cong 325\ V\ \gg\ u_{a}(t)=U_{a,max}\sin( \omega t), \tag{27a}\] \[U_{\beta,rms}=230\ V\ \gg\ U_{\beta,max}\cong 325\ V\ \gg\ u_{\beta}(t)=-U_{\beta,max}\cos( \omega t). \tag{27b}\]  
Considering load power factor \(\cos(\varphi)=0.8\) and impedance 23 \(\Omega\), the parameters of the load will be \(R=18.4\ \Omega\), \(\omega L=13.8\ \Omega\ \gg\ L=43.93\ \mathrm{mH}\) for \(f=50\ \mathrm{Hz}\), and the phase currents \(l_{a,rms}=10\ \mathrm{A},\ I_{\beta,rms}=10\ \mathrm{A}\).  
Under harmonic supply voltage, the phase current waveforms are:  
\[i_{a}(t)=l_{a,max}\sin(\omega t-\varphi),i_{\beta}(t)=-l_{\beta,max}\cos( \omega t-\varphi) \tag{28}\]where \(\varphi=\text{atan}(\omega\tau)\), \(\tau=L/R\).  
In the case of harmonic waveforms of the voltage and current (under linear load):  
\[PF=\frac{P_{1}}{S_{1}}=\frac{U_{1,rms}I_{1,rms}\cos\varphi_{1}}{U_{1,rms}\cdot I _{1,rms}}=\cos\varphi_{1}. \tag{29}\]  
This means that the power factor is equal to the cosine of the phase shift--the fundamental harmonic factor.  
In the case of a harmonic voltage profile and a non-harmonic current profile, the power factor will be:  
\[PF=\frac{P_{1,av}}{S_{av}}=\frac{U_{1,rms}\sqrt{\sum_{v=1}^{\infty}\left(I_{v, ef}\right)^{2}\cos\varphi_{v}}}{U_{1,rms}\cdot I_{rms}}=\frac{I_{1,rms}}{I_{ rms}}\cos\varphi_{1} \tag{30}\]  
since all products of the higher harmonic components of the current and \(U_{1,rms}\) are zero.  
Analogically, the total harmonic distortion of the input current can be expressed as  
\[THD_{i[\%]}=\frac{I_{2,rms}}{I_{rms}}\frac{U_{rms}}{U_{rms}}100\%=\frac{D_{ av}}{S_{av}}100\% \tag{31}\]
# Cornering Stiffness Prediction Based on Geometric Method
## 2 Method Description
### Cornering Stiffness  
For small wheel loads, the cornering stiffness is generally close to linear with respect to wheel load, and thus the effect from the load shift due to small camber angles is expected to be negligible. For higher wheel loads, the cornering stiffness is non-linear and the camber angle influence should be noticeable already at small angles [23]. For large camber angles, most of the load will be transferred to one of the sides leading to a saturation of the effect on cornering stiffness in this model.  
From pure side slip measurements, we can parameterize the function between the cornering stiffness and the load:  
\[K_{y}=f(F_{z},p_{1},p_{2}\ldots p_{i}) \tag{24}\]  
where \(f\) represents the function. \(K_{y}\) is the cornering stiffness and \(F_{z}\) is the load. \(p_{1},p_{2}\ldots p_{i}\) are model parameters. With the assumption of symmetry of the tire, each part shares the total load equally. Meanwhile, the corresponding cornering stiffness is also half of the whole value. For each half-tire, we have:  
\[\frac{K_{y}}{2}=f(\frac{F_{z}}{2},p_{1}^{\prime},p_{2}^{\prime}\ldots p_{i}) \tag{25}\]  
We can obtain the cornering stiffness dependence with the load of the two parts from (24) and (25). This is inspired by [PERSON]'s similarity method described in [9]. We will now illustrate this on two typical tire models: the MF and the UniTire.