esa-sceva/satcom-chunk-collection huggingface dataset:
# An Optimal Resonant Frequency Band Feature Extraction Method Based on Empirical Wavelet Transform
## 3 Optimal Resonant Frequency Band is Established Based on the EWT
### ASN Modified by Correlation Coefficient  
In practice, when the ASN was used as the evaluation index selected by the center, we found that the effect was not ideal. As described in [28], due to interaction and coupling effects between machine components, its high value will appear in multiple modes. In a large number of experiments, it was also found that when the detection signal is relatively pure, the ASN which relies more on cyclostationarity will have a larger error, so it should be adaptively corrected.  
The correlation coefficient is used as a statistical indicator to reflect the close relationship between the variables. The high correlation model can show more characteristics with the original vibration signal \(x(t)\). The correlation coefficient is calculated as follows:  
\[\rho_{X,Y}=\frac{\text{cov}(X,Y)}{\sigma_{x}\sigma_{y}}=\frac{E(XY)-E(X)E(Y)}{ \sqrt{E(X^{2})-E(X^{2})}\sqrt{E(Y^{2})-E(Y^{2})}} \tag{15}\]  
where \(X\) represents the original signal and \(Y\) represents each mode. The absolute value of \(\rho_{X,Y}\) is used to determine the correlation of each mode with the original signal. \(|\rho_{X,Y}|=1\) means there is a complete cross-correlation, and \(|\rho_{X,Y}|=0\) means it is completely irrelevant.  
When the SNR of the detection signal is relatively high, the noise interference in the signal is less, and the mode with a high correlation coefficient can reflect more fault information. When there is a signal with a low SNR, the noise interference in the signal is greater. The mode with a high ASN takes both the pulse characteristics and cyclostationarity characteristics into account to reflect more fault information. Therefore, it is reasonable to use the correlation coefficient as the adaptive factor to correct the value. The product of the two can play a complementary role with the modified adaptive average spectral negentropy (AASN) shown in the following formula:  
\[\Delta I_{c}(IMF_{i})=\rho_{X,Y_{i}}\times\Delta I_{1/2}(IMF_{i}) \tag{16}\]
# Oceania's 5G Multi-Tier Fixed Wireless Access Link's Long-Term Resilience and Feasibility Analysis
## 3 5G Base Coverage Network Layout
### 5G Radio Channel Signal Analysis at Sava City
To maximize control and flexibility with beamforming in an active array architecture, it is ideal to have separate weighting control over each antenna array component [64]. To complete this, each element needs its own transmit/receive module. This type of architecture is difficult to implement for array sizes that are typical of massive MIMO communication systems due to limitations on cost, space, and power. As a result, it is possible to consider the single scatterer scenario or the LOS channel example as a particular instance of precoding. When there is just one path available between the transmit and receive arrays, precoding turns into a beamforming technique. Array processing can enhance the quality of an MIMO wireless communication system. Depending on the characteristics of the channel, the arrays can be utilized to either raise the SNR by array gain, diversity gain, or increase the capacity through spatial multiplexing.
## 3 Simulation Results
### Annual PV Real Power Curtailment for Different Power Factor Control Abilities  
The annual PV real power curtailment, i.e., the total amount of PV power generation minus the actual injection in one year, is shown in Figure 8. If there is an energy storage system, we can take this curtailment as the annual amount for storage, but, in this paper, the energy storage system is not considered.  
From Figure 8, it can be found that, in order not to exceed 2.5% voltage variation, the excess real power is curtailed, and the real power curtailed by power factor of 1.0 is the largest, followed by the power factor of 0.95, and the third is the curtailment of 5321 kWh for power factor of 0.9. However, there is almost no curtailment for power factor of 0.9 or 0.85. Therefore, the PV inverter with a power factor of 0.9 or 0.85 can improve the utilization of solar energy and seems to not require the installation of an energy storage system for excess PV power generation.  
Figure 8: Annual PV real power curtailment for control abilities \(pf=1.0\), 0.95, 0.9, and 0.85.  
Figure 7: Annual PV real power injection for control abilities \(pf=1.0\), 0.95, 0.9, and 0.85.  
From Figures 9 and 10, it can be seen that (1) the highest NPV is around NT$ 7.5 million with PV capacity of 1500 kWp, and the highest BCR is 1.095 with PV capacity of 1000 kWp; (2) when PV capacity is larger than 2200 kWp, the NPVs and BCRs for smart inverters are higher than that of normal inverter without reactive power control ability; (3) when PV capacity exceeds 3500 kWp, the NPVs and BCRs for those with control abilities _pf_ = 0.9 and 0.85 are greater than that with _pf_ = 0.95; (4) as the actual injection amounts for _pf_ = 0.9 and 0.85 are almost the same, the NPVs are very close and so are the BCRs; (5) since the reactive power compensation ability for _pf_ = 0.9 and 0.85 are very strong, even when PV capacity is high as 5000 kWp, the NPVs are still of no difference and so are the BCRs; hence, to analyze the difference between those with 0.9 and 0.85 power factors, the PV capacity must be larger than 5000 kWp, but it is out of the scope of Taiwan distribution system. In addition, for those with positive NPVs or with BCRs greater than 1, the maximal capacities of PV systems are around 2200 kWp for _pf_ = 1.0, 3500 kWp for _pf_ = 0.95, and above 5000 kWp for _pf_ = 0.9 and 0.85, respectively.
## 3 LPWAN-Based Agricultural Vehicle Tracking
### IoV through NB-IoT Connectivity  
NB-IoT is another LPWAN technology standardized by 3 GPP (with Release 13, LTE Cat NB1). It is a simplified version of the LTE specification, designed for high-speed communications through widely deployed cellular infrastructure. NB-IoT uses licensed frequency bands assigned to mobile operators. There are three frequency bands available for application in the European continent: B3 (1800 MHz), B8 (900 MHz), and B20 (800 MHz). These are integrated into LTE infrastructure with a single narrow band of 200 kHz bandwidth. Due to the narrow bandwidth, the data rate peaks around 127 kbps in downlink and 159 kbps in uplink (with Release 14, LTE Cat-NB2). It should be highlighted that the NB-IoT was designed to enhance the coverage of IoT applications, especially for  
Figure 2: LPWAN-based agricultural vehicle tracking with LoRa connectivity.  
indoor environments and hard-to-reach geographical areas. Therefore, NB-IoT has an advantage of 20 dB coverage improvement on the link budget compared to the legacy LTE system. This means the maximum coupling loss (MCL) can be up to 164 dB [36]. On this basis, NB-IoT can allow a wider coverage of up to 40 km in lower-density rural areas with a much higher payload length of up to 1600 bytes [37].  
The end devices are directly connected to the BS of the existing cellular network infrastructure for NB-IoT connectivity. Since this feature eliminates the dependency of an additional GWs installation, vehicle tracking can be realized with lower setup costs and higher flexibility on the farmlands. Beyond that, its robustness makes NB-IoT connectivity an ideal solution for many IoT applications [38]. In NB-IoT-based IoV network architecture, the agricultural vehicles are equipped with VTU that contains an NB-IoT transceiver. Each vehicle is connected to the nearest BS, which acts as a GW to relay the data between the agricultural vehicles and the cloud server. Compared with LoRa, NB-IoT handles two-way data traffic more simply, securely, and reliably. Also, it has the advantage of low latency. Although the burden of network subscription costs, NB-IoT offers high connection density (up to 100,000 end devices). It is noteworthy that NB-IoT connectivity depends on LTE coverage, which is only sometimes guaranteed in rural and remote suburban areas. Therefore, employment of NB-IoT technology in agricultural applications may only be valid in LTE-covered areas [18; 32]. Figure 3 shows the IoV network architectures with NB-IoT connectivity to realize the agricultural vehicle tracking application.
# Gaussian Process Model-Based Performance Uncertainty Quantification of a Typical Turboshaft Engine
## 3 Methodologies
### Variance-Based Sensitivity Analysis
Thus, the formula of \(S_{X,N}\) can be rewritten as:  
\[\widetilde{S}_{X,N}=\frac{\frac{1}{N}\sum_{i=1}^{N}\widetilde{Y}_{i}\widetilde {Y}_{i}^{X}-\left(\frac{1}{N}\sum_{i=1}^{N}\widetilde{Y}_{i}\right)\left(\frac {1}{N}\sum_{i=1}^{N}\widetilde{Y}_{i}^{X}\right)}{\frac{1}{N}\sum_{i=1}^{N} \widetilde{Y}_{i}^{2}-\left(\frac{1}{N}\sum_{i=1}^{N}\widetilde{Y}_{i}\right) ^{2}}, \tag{34}\]  
where \(\widetilde{Y}_{i}\) follows a conditional GP, i.e., \(\widetilde{Y}_{i}\sim GP\big{(}\mu\big{(}\cdot\big{|}\mathbf{Y}^{n},\mathbf{\beta},\sigma_{z}^{2},\mathbf{\theta}\big{)},cov\big{(}\cdot,\cdot\big{|}\mathbf{Y}^{n},\mathbf{\beta},\sigma_{z}^{2},\mathbf{\theta}\big{)}\big{)}\). After substituting \(\widetilde{Y}_{i}\) for \(Y_{i}\), both the mean and variance of \(\widetilde{Y}_{i}\) can be taken into account in Equation (34). Therefore, the sensitivity index \(\widetilde{S}_{X,N}\) can be viewed as a random variable. Its expectation is the regular sensitivity index, implying the estimation of \(S_{X,N}\). Meanwhile, its variance can be used to evaluate the estimation accuracy of \(S_{X,N}\). Considering a sample set of \(\widetilde{S}_{X,N}\), denoted by \(\left\{\widetilde{S}_{X,N}^{(i)},i=1,2,\ldots,q\right\}\), the unbiased estimates of the mean and variance of \(\widetilde{S}_{X,N}\) can be derived to be  
\[\hat{\mu}_{\widetilde{S}_{X,N}}=\frac{1}{q}\sum_{i=1}^{q}\widetilde{S}_{X,N}^{ (i)}, \tag{35}\]  
and  
\[\hat{\sigma}_{\widetilde{S}_{X,N}}=\frac{1}{q-1}\sum_{i=1}^{q}\left(\widetilde {S}_{X,N}^{(i)}-\hat{\mu}_{\widetilde{S}_{X,N}}\right)^{2}, \tag{36}\]  
where \(\hat{\mu}_{\widetilde{S}_{X,N}}\) denotes the expectation of \(\widetilde{S}_{X,N}\), and \(\hat{\sigma}_{\widetilde{S}_{X,N}}\) denotes the variance of \(\widetilde{S}_{X,N}\). It should be noted that the quantity \(\hat{\sigma}_{\widetilde{S}_{X,N}}\) also represents the uncertainty of sensitivity index arising from the GPM. Thus, the uncertainty of estimating sensitivity indices by the GPM can be quantified. Moreover, the sampling error can also be investigated by considering the impact of sample size \(n\) on the sensitivity indices in this framework. It should be noted that the quantity \(n\) is associated with the sampling error due to the feature of the pick-freeze method, i.e., estimating the indices with a finite set of samples.
## 2 Signal Division Method of Bearing Health State  
Bearing operation usually goes through a stable period, which means it is in the healthy stage, and after fault occurrence time (FOT) the degradation level becomes severe and the component is in the fault stage. Health state division divides the bearing's whole life-cycle signal into different phases, which can usually be divided into the health phase and the fault phase. The result of the division expands the sample dataset for fault diagnosis, and the corresponding classification study of bearing fault diagnosis can be carried out.
## 3 Proposed Methodology  
To analyze the CKD dataset, a sequence of experiments has been conducted utilizing ML methods such as SVM, KNN, DT, RF, LR, and XGBoost classifier. Figure 2 depicts the general pattern of CKD classification.  
The mean approach was used to calculate missing values that are numeric, while the mode has been utilized to calculate missing values that are nominal during preprocessing. RFE, which is a Recursive Feature Elimination approach, has been utilized to select the aspects of relevance related to the attributes of high importance for the diagnosis of CKD. For disease diagnosis, these chosen features were given as inputs to the classifiers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Study and Year** & **ML Techniques** & **Performance** \\ \hline [PERSON] et al. (2019) [28] & Random forest and J48 algorithms & Random forest accuracy—78.25\% and J48 accuracy—85\% \\ \hline [PERSON] et al. (2020) [29] & Deep Learning Algorithm (DLA) & DLA accuracy—95\% \\ \hline [PERSON] et al. (2022) [30] & LR model + chi-square feature selection (K > 14), where K is number of features & Accuracy—97.5\% \\ \hline [PERSON] et al. (2019) [31] & Ant Colony-based Optimization (D-ACO) algorithm & D-ACO accuracy—95\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of recent related research on the detection of CKD.
###### Abstract  
The aim was to evaluate the effect of different whitening toothpastes on the enamel surface roughness and color variation. Twenty-four molars were sectioned and divided into eight groups (\(n=3\)) considering the following two factors under study: toothpaste type (Colgate(r) Total Original, Oral B(r) 3D White Luxe Perfection, Curaprox(r) Black is White, and Signal(r) White Now) and brushing protocol (short- and long-term). Surface roughness was examined by atomic force microscopy (AFM), and color change (\(\Delta\)E) was measured using the CIE L*a*b* system. Data were statistically analyzed using comparative parametric tests at a 5% significance level. In the short-term protocol, only the Signal(r) White Now toothpaste increased surface roughness (\(p=0.038\)) compared to the Colgate(r) Total Original group. No significant differences (\(p>0.05\)) were observed in surface roughness in the long-term protocol. Regarding color variation, no statistically significant differences (\(p>0.05\)) were observed in either protocol. Overall, the whitening toothpastes did not affect enamel surface roughness or color, except for Signal(r) White Now, which caused increased roughness in the short-term protocol. However, all toothpastes induced a visual change in color.  
roughness; color; enamel; whitening; toothpaste; brushing; aesthetics
# Pedestrian Trajectory Prediction Based on Dual Social Graph Attention Network
## 4 Experiment and Results
### Implementation Details  
In our approach, we compute two adjacency matrices after processing the data information through two information extraction functions. Following the normalization of these adjacency matrices, they are, respectively, fed into the STWGAT layer and the STGCN layer. Next, we utilize six TPCNNS layers for trajectory prediction. In the model, all activation functions employ PReLU, and we introduce Dropout regularization to prevent overfitting, with the Dropout rate is set at 0.1. The batch size is set to 128. All the training and testing for this study are conducted on an NVIDIA RTX 4060 GPU and an Intel i5-13600 KF CPU. The model is trained for a total of 300 epochs, using the Stochastic Gradient Descent (SGD) optimizer for model training. The learning rate is set to 0.01 for the first 150 epochs, and subsequently, the learning rate is reduced by a factor of 0.2 every 150 epochs thereafter.
# An Optimal Resonant Frequency Band Feature Extraction Method Based on Empirical Wavelet Transform
## 5 Application to Roller Bearings Testing
### Case 2: Analysis of NASA Outer Ring Fault Vibration Signal
#### 5.2.1 Original Fault Vibration Signal Analysis  
Next, we apply the proposed method to the original fault signal of the outer ring to verify the performance of the actual mechanical fault vibration signal when it is contaminated by weak noise.  
Figure 16: Illustration of the bearing experiment platform. (**a**) Bearing test rig; (**b**) Sensor placement illustration.  
Figure 17: (**a**) Time-domain waveform of the original signal; (**b**) Time-domain waveform of SNR = 10 dB mixing signal.  
As a comparison, the EMD and FK methods are used to analyze the same signals to verify the superiority of the proposed method in the case of weak noise. By comparing and observing each IMF, we choose the mode with the most abundant fault information for the envelope spectrum analysis. As shown in Figure 22, it can be observed that although the EMD shows a better decomposition ability in a weaker noise environment, the characteristic frequency and the harmonic component amplitude it extracts are all weaker than the proposed method. Similarly, as shown by the dotted circle in Figure 23, the center of the demodulation band displayed by the kurtogram is 1667 Hz, and the bandwidth is 3333 Hz. It is determined that the position and size of the filter are quite similar to the proposed method, which shows that when the central frequency can be precisely determined, the merging criterion utilized in the construction of the filter size is effective and reasonable.  
Figure 21: Hilbert envelope spectrum of the optimal merging mode.  
Figure 20: The Hilbert envelope spectrum of IMF2.  
Figure 22: The Hilbert envelope spectrum analysis results by the EMD method.
# Enhanced Performance of nZVI/MXene@CNTs for Rapid As(III) Removal from Aqueous Solutions
## 2 Materials and Methods
### Batch Removal Experiments  
The As(III) removal capacity of nZVI/MXene@CNTs was investigated by batch experiments. In a 150 mL conical flask, 50 mL of different concentrations of As(III) aqueous solution were mixed with 0.4 g/L nZVI/MXene@CNTs and put on a constant temperature shaker running at 150 rpm. The effect of different factors, such as the pH and dosage of materials on the removal of As(III) by nZVI/MXene@CNTs, was investigated and the initial pH value was controlled, using 0.1 M HCl/NaOH diluent. The influence of the interfering ions on the removal ability of nZVI/MXene@CNTs was investigated by employing aqueous As(III) solutions, containing 0.01 mol/L Mg\({}^{2+}\), Ca\({}^{2+}\), Cl\({}^{-}\) and SO\({}_{4}\)\({}^{2-}\). The leaching experiment with 0.1 M NaOH solution was used to test the regeneration and recoverable function of nZVI/MXene@CNTs. The thermodynamics, kinetics and isotherms were also studied. The isotherm data were evaluated by Langmuir and Freundlich isotherm models. To fit the adsorption data, pseudo-first-order and pseudo-second-order kinetic models were employed. The residual As(III) and As(V) concentrations after filtering were measured using inductively coupled plasma mass spectrometry (ICP-MS; PerkinElmer NexlON 300X, Waltham, MA, USA). The adsorption amount \(q_{e}\) was calculated (Equation (1)). The adsorption kinetics, isotherms and thermodynamic experiments and calculations are available in the Supplementary Materials. All of the removal experiments were repeated three times, with the average results used to assess the performance:  
\[q_{e}=\frac{V(C_{0}-C_{e})}{m} \tag{1}\]  
where, \(q_{e}\) is the adsorption capacity, mg/g; \(V\) is the volume of solution, L; \(C_{0}\) is the initial concentration of As(III), mg/L; \(C_{e}\) is the residual concentration of As(III), mg/L; \(m\) is the adsorbent dosage, g.
## 2 Modal Based Damage Detection: Development & Challenges
### Development of Modal-Based Damage Sensitive Features
#### 2.1.1 Natural Frequencies  
The original attempt to discern damage in structures was proposed by [PERSON] et al. [31] in the late 1970s, and was later expanded by [PERSON] & [PERSON] [32], where frequency shifts were used to detect damage. [PERSON] [33] also stated that the natural frequency approach is potentially useful for the routine integrity assessment of structures, given its ease of application and the global nature of the identified frequencies. Generally, an assumption of linear-stationary structural response is made to  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Level** & **Label** & **Description** \\ \hline
1 & Detection & Detection of damage in structure \\
2 & Localization & Localization of detected damage \\
3 & Assessment & Quantification of damage severity \\
4 & Prediction & Estimation of remaining service life \\ \hline \hline \end{tabular}
\end{table}
Table 1: Damage identification levels.  
allow the use of a Fourier-type transform to obtain the frequency content of measured vibration data, prior to employing peak-peaking to discern modal frequencies. The global nature of modal frequencies may not always lend itself well to damage localisation in complex structures [34; 35; 36], although they can be utilised to do so for more regular geometries when given the undamaged and damaged states, in addition to a sufficient number of frequencies depending on geometry complexity, for instance; two for a beam and three for an arch [37; 38].
# A Novel Generalized Group-Sparse Mixture Adaptive Filtering Algorithm
## 1 Introduction
In this article, we put our effort to propose a mixed error criterion (MEC) algorithm based on squared error and \(p\)-order sign errors for identifying sparse systems in impulsive noise, and a family of generalized group-sparse mixture adaptive filtering (GGS-MAF) algorithms are realized by integrating proportionate group-update approach into the MEC algorithm to utilize the prior group-sparse information. Simulation results reveal that the devised GGS-MAF algorithms converge faster and has lower normalized misalignment (NM) for identifying group-sparse systems in impulsive noises (IN).
# AI-Based Analysis of Policies and Images for Privacy-Conscious Content Sharing
## 6 Ongoing Work
### Overview  
Figure 1 shows an overview of the system prototype. The user communicates in natural language (NL) with a conversational agent, or _chatbot_. The chatbot can be executed on multiple personal devices, including workstations, smartphones, or virtual assistants. The goal of the chatbot is to provide the user with advice about the most appropriate decisions to take regarding their privacy policies and multimedia sharing practices.  
Each time the user installs a new app, or plugs a new smart device, the chatbot contacts the AI-powered privacy agent for acquiring a NL description of the app/device privacy policies. The agent queries the NL policy description database to obtain the requested descriptions. Those descriptions are obtained applying natural language processing (NLP) methods to raw privacy policies retrieved from the Web using the Google Custom Search APIs. The agent makes use of machine learning (ML) algorithms to understand which paragraphs of the raw privacy policy contain practice descriptions.  
The agent uses an annotated dataset of privacy policies to train the ML models. In particular, it trains a different classifier for each data type. A feature engineering process is used for generating a feature vector for each paragraph, removing unnecessary data such as stopwords. Once the structured information is extracted from paragraphs, the chatbot generates a synthetic description of the privacy policy of the app/device and communicates it in NL to the user. The chatbot may also answer a user's questions regarding specific aspects of the policy, such as the intended use of certain kinds of personal information, or the sharing policies about particular data.  
Moreover, when the user wants to publish a new personal image on a social network, they may ask the chatbot about the suggested privacy level of the image. The chatbot queries the AI powered privacy agent, which forwards the image to the image privacy reasoner. The latter is in charge of extracting a set of features regarding the image, including face detection features, hue-saturation data, scale-invariant features, as well as tags describing the image content. Tags and face-detection features are extracted using computer vision techniques. The feature vector is provided to the ML image reasoner, which classifies the image as either public or private. The ML algorithm uses a model trained on a dataset of labeled images. The predicted label is communicated to the chatbot, which suggests the most appropriate privacy level to the user.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Private Images F\({}_{1}\)** & **Public Images F\({}_{1}\)** & **F\({}_{1}\)** & **Accuracy** \\ \hline
0.628 & 0.620 & 0.624 & 0.624 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results of image privacy level prediction using the weighted graph method.