esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive PI + VPI Harmonic Current Compensation Strategy under Weak Grid Conditions
###### Abstract  
With the increase of the penetration rate of renewable energy power generation in the power system, the power grid gradually tends to be weak. Under the scenario of nonlinear load connecting to weak power grid, the grid-side voltage will produce unbalance, distortion and frequency deviation due to the influence of large internal impedance, insufficient inertia, three-phase current unbalance and harmonics of weak power grid. The operation of active power filters (APF) under above conditions can lead to significant degradation in harmonic compensation performance. An adaptive proportional integral (PI) + vector PI (VPI) harmonic compensation strategy for a three-phase APF is proposed for use in complex conditions of weak grid. The strategy consists of an adaptive notch filter based synchronous phase-locked loop (ANF-PLL) and an adaptive PI + VPI current control method. In this strategy, a series of adaptive lattice notch filters are introduced to improve the accuracy of grid phase and frequency estimation under the condition of voltage unbalance, distortion and a large range of frequency deviation. Then the phase and frequency information are used in the Park transformation of harmonic detection and current compensation control and the update of PI + VPI current control law parameters. A VPI current tracking controller with adaptive resonant frequency is constructed, which can improve the gain of the resonant frequency point and ensure that the APF can maintain the best compensation performance under the above weak grid conditions. Finally, the simulation and experimental results indicate that the ANF-PLL shows a better phase and frequency estimation performance under complex conditions of weak grid than the conventional and notch filter based methods, and the harmonic compensation strategy presented in this paper achieves a significant performance improvement under complex conditions of weak grid compared with Fixed VPI/SRF-PLL strategy and adaptive VPI/NF-PLL strategy.  
active power filter (APF); lattice notch filters; frequency deviation; gradient adaptive algorithm; vector proportional integral +
Footnote †: journal: Applied Sciences
## 5 Calibration Experiment and Algorithm Comparison
### Data Solving and Analysis
(**b**) Convergence of the three data sets calculated using the DIRECT algorithm. (**c**) Convergence of the three data sets calculated using the SA algorithm. (**d**) Convergence of the three data sets calculated using the LM-D algorithm. enhance efficiency, the final iteration results may lack global convergence. Conversely, prioritizing global convergence significantly elevates computational costs. During the analysis of data 3, it is notable that the runtime of the LM-D algorithm experienced a significant increase. This can be attributed to the LM-D algorithm engaging in two rounds of DIRECT iterations to escape from local optima. In both iterations, the DIRECT algorithm adeptly identified solutions smaller than the prevailing local optimum, underscoring the robust capacity of the DIRECT algorithm to evade local optima. The imposition of an initial search interval confined within hyperrectangles with a side length of less than 100 mm markedly reduced the search range compared to the direct application of the DIRECT method. This reduction contributed to shorter overall iteration times. From the above data sets (data 1, 2, and 3), it is clear that there is a significant difference in the convergence speed of the LM-D algorithm. In data 1, since the local optimum is also the global optimum, the LM-D algorithm converges to the global optimum using only the L-M part of the algorithm. In this case, the LM-D algorithm degenerates into the L-M algorithm, resulting in a fast solution. In data 2 and data 3, where multiple local optima exist, the LM-D algorithm interacts through iterations between the L-M algorithm and the DIRECT algorithm. Therefore, the algorithm takes longer to run. The DIRECT algorithm in the LM-D algorithm consumes more search time, which is the computational cost paid to seek the global optimum. It can be observed that the LM-D algorithm converges to the global optimum in all three data sets. This is the reason for the difference in convergence speed observed in data 1, 2, and 3. The outcomes derived from the analysis of the three sets of data suggest that the LM-D algorithm possesses distinct advantages, demonstrating robust global convergence and enhanced computational efficiency in comparison to other algorithms.
## 2 Theory of EMD and EEMD
### EEMD Method  
The results obtained by the EMD method have modal aliasing. In these results, different time scales are decomposed into one IMF component, or the same time scales component appears in different IMFs. In order to decrease the modal aliasing of EMD, we added white noise to the original signal, and then modal aliasing decreased effectively. This is called Ensemble Empirical Mode Decomposition.  
By adding white noise to the original signal, the signals of the different time scales are automatically separated into appropriate reference scales. Combining all IMFs together and calculating their mean value can eliminate the white noise that was added. This method can effectively decrease modal aliasing, improve decomposition accuracy, and retain the characteristics of the original signal.  
The steps of the EEMD method are as follows:  
Add white noise to the original signal and produce a new signal;  
Use the EMD method to decompose new signal to obtain IMF components;  
Repeat the above steps, adding new white noise each time;  
Add these IMFs belonging to the same orders together and calculate their mean value, then obtain the desired IMF.  
The EEMD method is shown in Figure 2:  
Figure 2: Vibration signal analysis process of EEMD.
## 2 Materials and Methods
### Engine Characteristics and Modeling
Therefore, the thermal solver was set to transient mode. The \"Simplified 1D\" model was derived from the detailed 1D model. The difference with the detailed model consists of considering the intake (I) and exhaust (Ex) plenum as a unique element, as highlighted in Figure 5. This approach permits a lower computational time at the expense of reducing the accuracy in the definition of the possible differences of in-cylinder intake and exhaust characteristics. The flow and combustion models are the same as the detailed model previously introduced. The third level of engine model, in terms of \"depth of details\", considered in this work is the \"map-based model\". This simple approach consists of a \"black box\" based on performance maps and characteristics to be interpolated to describe engine behavior across the speed-load range. The performance maps considered are a 2D characteristic  
Figure 5: Simplified 1D scheme of HD Diesel Engine. map of the Brake Mean Effective Pressure (BMEP), related to engine speed and load, and 2D characteristic maps of fuel and airflow rates, both related to engine speed and BMEP. As secondary maps, the 2D maps of NO\({}_{\mathsf{X}}\), CO, CO\({}_{2}\) and HC emissions were considered. These maps relate brake-specific emissions to engine speed and BMEP and allow for the calculation of instantaneous emission rates during simulations. All of the maps previously introduced are strictly associated with and generally provided by the OEMs, since they are obtained experimentally. The testing phase is both time-consuming and expensive. For this study, since the physical engine at the test bench was not available, the performance maps were obtained via a simulation approach, starting from the experimentally validated \"detailed 1D model\" previously introduced. Through a specific procedure, available in [28], and using a Full Factorial DoE, the engine performance maps were obtained.
# VANET Secure Reputation Evaluation & Management Model Based on Double Layer Blockchain
## 4 Design of the DBREMM
### Trust Fusion and Reputation Update  
Once \(v_{j}\) obtains \(T_{ji}^{di}\), the direct trust obtained through its own observation of the event \(W_{i}\) reported by \(v_{i}\) and \(T_{ji}^{in}\), the indirect trust obtained by fusing the opinions of other nodes, the node trust \(T_{ji}\) of \(v_{j}\) to \(v_{i}\) can be obtained by calculating (11)  
\[T_{ji}=\delta_{di}\times T_{ji}^{di}+\delta_{in}\times T_{ji}^{in} \tag{11}\]  
where \(\delta_{di}\) and \(\delta_{in}\) are direct/indirect weight factors and add up to 1. The assignment of the two values can change freely according to the actual application scenarios. Here, we simply set them each as 0.5.  
Upon obtaining \(T_{ji}\), \(v_{j}\) uploads the collaboration certification report \(O_{ji}\) as a form of \(\{S(v_{i}),S(v_{j}),W_{i},T_{ji},\{v_{k},...,v_{kn}\}\}\) to the RSU. The RSU stores all of the cooperative authentication reports of \(W_{i}\) returned within the specified time into event status list \(C_{e}vent\) for the subsequent record in the event chain. In addition, the overall trust \(T_{i}\) of \(v_{i}\) by reporting \(W_{i}\) is calculated by:  
\[T_{i}=\frac{\sum_{j=1}^{n}T_{ji}}{N} \tag{12}\]  
where \(T_{ji}\) is the node trust of \(v_{j}\) to \(v_{i}\) and N is the total number of nodes.  
When using overall trust for reputation updating, we specify two additional prerequisites:  
(i) _Quantities_: There must be a sufficient number of collaborative certification reports, that is,  
\[N\geqslant N_{thre} \tag{13}\]  
The RSU can update the reputation of the nodes only if it receives the validation reports that exceed the preset quantity threshold.  
The purpose of this step is to increase the difficulty of the reputation update, so as to prevent the possibility that malicious vehicle groups can quickly accumulate a reputation value through mutual praise in remote and other areas with sparse nodes through collusion;  
(ii) _Trust Convergence_: The fluctuation threshold is set to prove the reliability of the node trust and prevent malicious vehicles from deliberately giving low or high scores. Once condition (i) is satisfied, the node trust scores need to meet the requirement that more than 1/3 of all values fall within the following interval:  
\[E(T_{ji})\pm\varepsilon_{flu} \tag{14}\]  
where \(E(T_{ji})\) represents the expectation of the node trust scores received by all cooperating nodes, \(\varepsilon\) is the fluctuation threshold factor, its value can be quantified according to the event type, the required response speed and other factors. This step is to resist malicious events, such as on-off attacks or collusive attacks, and the inaccurate trust evaluation on the target node. Since the malicious nodes in the network are assumed to be no more than 1/3 (we use MPoR consensus, which is based on PBFT, that can guarantee security when malicious nodes are less than 1/3). As long as at least more than 1/3 of node trust values are in the acceptable fluctuation range, the trust evaluation of the target node is reliable.
# Enhancing Healthcare Security: A Unified RBAC and ABAC Risk-Aware Access Control Approach
## 3 Risk-Aware RBAC and ABAC Model: Proposed Approach
Subsequently, RiBAC evaluates the potential risks associated with the request, considering factors like environmental factors (e.g., time, location), resource factors (e.g., data sensitivity) and subject factors (e.g., user history) before forwarding it to the ABAC layer for further evaluation. In the final stage, ABAC dynamically evaluates access requests based on an array of collected attributes, including user privileges, environmental conditions, and risk levels, allowing for fine-grained control. Integrating these methods significantly enhances access control effectiveness, with continuous refinement based on feedback and adaptive adjustments to evolving security threats and user roles. This design  
Figure 2: The structure of the proposed Risk-Aware RBAC and ABAC model. enables the proposed model to exhibit enhanced adaptability in responding to diverse emergency scenarios, thereby mitigating the inherent instability of healthcare systems during accidents while concurrently maintaining an exceptionally high level of security. To contextualize the proposed model within existing research, it is important to highlight its novel integration of RBAC, ABAC, and RiBAC models. While previous models discussed in the literature have implemented individual or partial combinations of these approaches, the proposed model uniquely blends them to provide a layered, dynamic, and context-aware access control solution tailored to the complex and evolving requirements of healthcare systems. This integration enables enhanced features such as dynamic risk assessment, simplified administration, and scalability--characteristics that are not addressed in existing models.
# Development of the User Requirements for the Canadian WildFireSat Satellite Mission
## 3 Definition of the User Requirements  
The FMFs were considered in order to define EO User Requirements during the CWFMS feasibility study [49] taking into account space system capabilities and limitations, including payload technology capabilities, but without targeting a specific mission scope. With the initiation of the WFS mission, the CWFMS User Requirements were further refined to correspond to a specific mission scope and matching level of funding to produce the Mission Requirements specific for WFS [50]. A simple approach to defining these requirements would be to guarantee success by over prescribing the needs of the mission. However, this is not a programmatically feasible approach as it inflates the mission cost and complexity. In this study, we acknowledge that programmatic constraints will necessitate trade-off analysis during the implementation of the mission. As such, many of the User Requirements are stated as both \"goal\" (_SHOULID_) and \"threshold\" (_SHALL_) requirements to reflect optimal and minimum required performances. In this Section the critical WFS User Requirements required to fulfill each of the six FMFs are described in terms of Observation, Measurement, and Precision Requirements for the WFS mission.
## 3 Results and Discussion
### Biomass Growth Estimation  
Figure 3 shows the biomass growth of the cultures during the cultivation, using their NIR response at 1062.6 nm [48]. All measurements are normalized for direct comparison reasons, and the measurements of the Control culture are also presented for comparative reasons. The Control culture follows an exponential growth curve up to Day 5, followed by a decline phase, which agrees with the literature [29; 53; 55]. All Cd mono-MT cultures (Figure 3a) seem to be the lesser affected ones by the presence of the metals as they closely follow the same growth pattern with that of the Control culture [12]; it should be noted that the 10 ppm one presents a steeper decline phase. Zn mono-MT cultures (Figure 3b) also show similar characteristics with those of the Control culture. In this case, it should be noted that the 5 and 10 ppm Zn-treated cultures present a delayed exponential phase, never reaching a stationary plateau. The 10 ppm Pb mono-MT culture also presents a delayed exponential phase, while 1 and 5 ppm Pb mono-MT cultures follow the same characteristics with those of the Control culture, but with reduced spectral response/microalga biomass (Figure 3c) [19].  
Figure 2: Representative optical microscopy images collected with \(\times\)60 objective lens on Day 7 of cultivation: (**a**) Control, (**b**) Mix 5 ppm, (**c**) Cu 10 ppm, and (**d**) Ni 5 ppm.  
Cu and Ni mono-MT cultures (Figures 3d and 3e, respectively) are the most affected by the presence of metals [19]. In the case of Cu, the 1 ppm culture seems to be affected after Day 1 by the metal, but it follows the Control culture after Day 5 of cultivation. On the other hand, the 10 ppm culture seems to immediately decline, while the 5 ppm one starts to present an exponential phase after a decline period. Similarly, the 1 ppm Ni culture declines immediately, and it follows an exponential phase after Day 1 of cultivation, while the 5 and 10 ppm cultures start to decline after Day 1, with an exponential phase on the last day of cultivation. It should be noted that the 1 ppm Ni-treated culture biomass has an increasing tendency and slightly surpasses the Control culture. This behavior for the 1 ppm Ni-treated culture was reported in the literature [41].  
All results indicate that microalga biomass growth is affected not only by the kind of metal but also by its concentration [15]. In the case of the multi-MT cultures, the 1 ppm culture presents an exponential phase, followed by a decline; the reduced reflectance values correspond to the reduced microalga biomass. Finally, the 5 and 10 ppm cultures quickly enter a decline phase. In this case, the 5 ppm culture begins to enter a growth phase after Day 5 of cultivation.
## 3 Background and Preliminaries
### Anomaly Detection in Wireless Sensor Networks
#### 3.3.3 Characteristics of Sensor Data  
Sensing data are collected in the form of data streams which may be large volumes of real observations collected from the environment [43]. Some WSNs are designed only to collect one type of data such as temperature, light, humidity. This kind of data is called univariate data. Recent WSNs are designed to collect multiple types of data from the field simultaneously, which are called multivariate data. The nodes in these networks are usually equipped by more than one sensor to collect different types of data at the same time. In the multivariate data, each type of data is called an attribute or feature. A sensing data measurement is said to be anomalous if one or more of its attributes are anomalous [44]. With univariate data, the anomaly detection can be easily achieved by observing that the single data attribute is anomalous compared with the attributes of other data instances. However, anomaly detection in multivariate WSNs is challenging because the individual attributes may not show anomalous behavior but when are taken together they may display anomalous behavior [45]. Though analysis of multivariate data is computationally expensive, anomaly detection on multivariate data gives high accuracy if the relations between different attributes are carefully exploited [46; 47]. Sensing data also has spatial and temporal correlations between sensor readings. Temporal correlation means that the readings collected at one time period are related to the readings collected at the previous time period. Spatial correlations means that the readings of nodes that are geographically close to each other are expected to be correlated [48]. According to [9], the spatial and temporal correlation between sensing data attributes help to specify the source of anomaly.
## 5 Results
### Performance
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**No** & **Title** & **Author** & **Features** & **Classifier** & **Accuracy Results** \\ \hline
7 & Epileptic seizure classification using statistical features of EEG signal (2017) & [PERSON] et al. [53] & mean, median, maximum, range, standard deviation, median absolute deviation, mean absolute deviation, max norm, & & \\ \hline
8 & Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection (2007) & [PERSON] and [PERSON] [72] & standard deviation, correlation dimension, largest Lyapunov exponent & & \\ \hline
9 & Seizure detection using wavelet transform and a new statistical feature (2011) & [PERSON] et al. [39] & [PERSON] et al. [39] & fourth moment divided by second moment, difference between maximum and minimum, zero-crossing of the wavelet coefficients & K-nearest neighbors (KNN), Bayesian & 96.83\% 98.17\% \\ \hline
10 & Seizure detection in EEG using time-frequency analysis and SVM (2011) & [PERSON] and [PERSON] [40] & variance, energy, power spectral density (PSD) & Support-vector machine (SVM) & 98.75\% \\ \hline
11 & Classification of EEG signal using wavelet transform and support vector machine for epileptic seizure detection (2010) & [PERSON] et al. [41] & energy, entropy, standard deviation & Support-vector machine (SVM) & 91.2\% \\ \hline
12 & Automatic seizure detection using wavelet transform and SVM in long-term intracranial EEG (2012) & [PERSON] [42] & relative energy, relative amplitude, coefficient of variation, fluctuation index & Support-vector machine (SVM) & 95.33\% \\ \hline
13 & Lyapunov features based EEG signal classification by multi-class SVM (2011) & [PERSON] et al. [43] & maximum, minimum, mean, standard deviation & Multi-class Support-vector machine (SVM), Radial Basis Function Neural Network & \\ \hline
14 & Seizure detection in EEG signals using support vector machines (2012) & [PERSON] et al. [46] & mean, variance, dominant frequency, mean power spectrum & Support-vector machine (SVM) & 98\% \\ \hline
15 & Detection of epilepsy during seizure-free periods (2013) & [PERSON] et al. [48] & maximum, minimum, range, standard deviation & Support-vector machine (SVM) & 98\% \\ \hline
16 & Feature extraction and classification of EEG for automatic seizure detection (2011) & [PERSON] [49] & inter-quartile range (IQR), median absolute deviation (MAD), energy of variation, coefficient of variation & Linear Discriminate Analysis (LAD) & 96.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: _Cont._
# A Novel Framework for Online Remaining Useful Life Prediction of an Industrial Slurry Pump
## 2 Methodology
### Development and Design of the Hybrid Deep LSTM Model  
To utilize the selected data points of HDTs for _RUL_ prediction, a hybrid deep neural network model with LSTM and classic neural network layers was constructed in this study. The developed model was taking advantage of LSTM for its ability to model sequential data, while the classic fully connected layers were working for their outstanding capability of modeling non-linearity in data [2]. After each LSTM layer, a fully connected layer  
Figure 5: Increasing trends of slope points (**a**,**c**) and selected data points of HDTs (**b**,**d**) for channel 2 and channel 4, respectively.  
was added for creating a hybrid building block. In order to avoid overfitting during the network training process, dropout layers were triggered on the fully connected layers. The architecture of the hybrid deep LSTM model is illustrated in Figure 6. The number of LSTM layers/units and the initial learning rates for channel 2 and channel 4 datasets were determined by the Bayesian Optimization (BO) method. The number of hybrid building blocks was kept equal to the number of LSTM layers. Similarly, the number of units in fully connected layers were also kept equal to the number of units in the LSTM layers. The number of LSTM layers, units, and initial learning rate for channel 2 datasets were found to be 2, 244, 0.018, while for channel 4 datasets, they were observed as 3, 258, and 0.024, respectively.  
Working Mechanism of the Developed Model  
As discussed in Section 1 of the paper, both the datasets were modeled using the unsupervised learning technique of neural networks. Therefore, the developed model estimated the online _RUL_ by predicting one prediction point, i.e., \(\hat{y}_{p+1}\), after every single iteration of the given datasets. The input data was in the form of \(y_{p+1}\), \(y_{p+2}\)\(\ldots\)\(y_{p+m}\), which produced the output \(\hat{y}_{p+1}\) at the time-step \(t_{1}\). The predicted value \(\hat{y}_{p+1}\) became the part of the input data for the next iteration in the form of \(y_{p+1}\), \(y_{p+2}\)\(\ldots\)\(y_{p+m}\), \(\hat{y}_{p+1}\) and produced the result \(\hat{y}_{p+2}\) at time-step \(t_{2}\). Similarly, the output \(\hat{y}_{p+2}\) became the last part of the input data in the form of \(y_{p+1}\), \(y_{p+2}\)\(\ldots\)\(y_{p+m}\), \(\hat{y}_{p+1}\), \(\hat{y}_{p+2}\)\(\ldots\), \(\hat{y}_{p+m}\), and so on. This process remained in progress until the resulting prediction point did not strike the threshold line.