esa-sceva/satcom-chunk-collection huggingface dataset:
# An Untraceable Data Sharing Scheme in Wireless Sensor Networks
## 5 Secure Data Sharing between the Source Node and Sink Node  
To securely transmit the data to the sink node and defend against the smart adversary, we propose an untraceable data sharing scheme in wireless sensor networks. The flowchart of our scheme is presented in Figure 3. Initially, the source node monitors the target and generates message \(X\) that contains all the necessary information about the pandas. Then, we divide message \(X\) into a set of sub-messages \(x_{1},\ x_{2},\cdots,x_{n}\) and map them to a set of message shares \(s_{1},\ s_{2},\cdots,s_{n}\). An anonymous cloud is constructed based on the approach proposed in [4]. At the border of the cloud, the fake source nodes are responsible for sending these shares to the sink node independently. In the package delivery process, we employ a random routing algorithm to diverse the routing paths. Finally, the sink node recovers message \(X\) based on the received shares and, the data sharing process between the source node and the sink node is completed. The detailed process of delivering a package from the source node to the sink node is given in the following.
###### Abstract  
A simple, rapid and efficient methodology was developed and validated for the analysis of four _Alternaria_ toxins in jujube: Tenuazonic acid, alternirol, alternirol monomethyl ether, and tentoxin. Under the optimized extraction procedure, chromatographic conditions, and instrumental parameters, the four toxins were effectively extracted via a quick, easy, cheap, effective, rugged, and safe (QuEChERS) method, and quantified by ultra-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS). Matrix-matched calibrations ranging from 0.01 to 0.5 \(\upmu\)g mL\({}^{-1}\) were conducted for the quantification due to the matrix effect. A blank jujube sample was spiked at 40, 80 and 160 \(\upmu\)g kg\({}^{-1}\), obtaining recoveries in the range of 83.5-109.6%. Limits of detection and limits of quantification were in the range of 0.14-0.26 and 0.47-0.87 \(\upmu\)g kg\({}^{-1}\), respectively. Finally, the developed method was applied for the quantification of the four toxins in 14 jujube samples, including black spot-infected and uninfected samples. Results showed that the predominant toxin detected in all the samples was tenuazonic acid, the content of which was associated with the infection level; alternirol, alternirol monomethyl ether, and tentoxin were detected in all the infected samples and some of the uninfected samples with rather low contents.  
Alternaria toxins; jujube; QuEChERS; UPLC-MS/MS +
Footnote †: journal: Accepted: 19 September 2018
# A Hybrid Data-Fusion System by Integrating CFD and PNN for Structural Damage Identification
## 1 Introduction
At the same time, the traditional neural network technology has the advantages of short training time and high recognition accuracy as long as the feature extraction is effective. In order to make full use of the massive nonlinear characteristic data and integrate FD, this paper focuses on the integration of correlation fractal dimension and PNN with the data fusion technique to detect structural damage, presenting a novel hybrid damage detection system which contains three intelligent models and is exactly powerful for large-scale and complex structures. The three models are a single PNN classifier model (without data fusion), an eigen-level data fusion model and a decision-level data fusion model. It has excellent performance of damage detection, especially for a complex structure with measurement data that is enormous and has uncertainties. Practice has shown that the proposed system can obtain three kinds of reasonable damage detection results under the case of noises, and it also further proved the anti-noise capabilities and robustness of the system. Finally, this paper is organized as follows. The hybrid data-fusion system for damage detection is described in Section 2. A numerical example and a laboratorial seven-storey steel frame of both single- and multi-damage patterns are validated with the proposed system in Sections 3 and 4. Section 5 presents the conclusion.
# Advances, Progress, and Future Directions of Renewable Wind Energy in Brazil (2000-2025-2050)
## 3 Wind Farm Projects: Experiences in Brazil
### Onshore Wind Energy Projects--In Operation (2000-2025)
#### 3.1.5. Campo Largo Wind Farm  
Bahia State  
Located in Bahia in northeastern Brazil, Campo Largo has an installed capacity of 687.9 MW and consists of 207 wind turbines. Panoramic views are shown in Figure 13, and Table 6 includes technical details [32].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Units** \\ \hline Location & Bahia State & - \\ Concession Owner & Engie Brazil & - \\ Type of Wind Farm & Onshore & - \\ Number of Turbines & 207 & - \\ Turbines Manufacturer & Vestas & - \\ Diameter of Turbines & 122 & m \\ Hub Height & 100–120 & m \\ Installed Capacity per Turbine & 3.3 & MW \\ Total Installed Capacity & 687.9 & MW \\ Capacity Factor & 0.41 & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Campo Largo wind farm main specifications.  
Figure 12: Panoramic view of wind turbines on Chui wind farm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Units** \\ \hline Location & Rio Grande do Sul & - \\ Concession Owner & CGT Eletrosul and Omega & - \\  & Geração & - \\ Type of Wind Farm & Onshore & - \\ Number of Turbines & 302 & - \\ Turbines Manufacturer & GE Energy & - \\ Diameter of Turbines & 100 & m \\ Hub Height & 80–120 & m \\ Installed Capacity per Turbine & 2.0 & MW \\ Total Installed Capacity & 582.8 & MW \\ Capacity Factor & 0.35 & - \\ \hline \end{tabular}
\end{table}
Table 5: Chui wind farm main specifications.  
As seen in Figure 13, the site is free from surrounding human settlements.  
Per Table 6, the wind farm features 207 Vestas turbines, each with a 122 m sweep diameter. The towers range in height from 100 to 120 m. Each unit generates 3.3 MW, totaling 687.9 MW, with an estimated capacity factor of 0.41.
# Parameter Optimization for an Accurate Swept-Sine Identification Procedure of Nonlinear Systems
## 1 Introduction
To this end, the entire identification procedure is revisited in order to verify the adequacy of the frequency band of each of the components that contribute to the identification procedure. A possible cause of criticality is identified in the choice of the parameters characterizing both the swept-sine signal used as excitation and the corresponding matched filter; the consequent criteria for choosing these parameters to optimize the response of the model even in the time domain are hypothesized. The proposed solution is verified through experimental tests in simulation; the tests are defined by making use of a simulated nonlinear system so that the expected ideal response is known, and this allows the effectiveness of the proposed solution to be verified. The results of these experiments fully confirm the hypothesis made; consequently, in this paper, we provide clear guidance for choosing the signal parameters to be used in applying the identification procedure. This paper is organized as follows: In Section 2.1, the [PERSON]'s model of nonlinear systems is briefly described and its identification procedure based on the use of exponential swept-sine signals as input is presented. In Section 2.2, we analyze the identification procedure based on swept-sine signals in the frequency domain and we highlight the features of the identification procedure that have the potential to lead to frequency limitations in the functions describing the responses of the individual filters of the [PERSON] model. We then identify the possible causes of the above limitations and propose a possible solution. In Section 3, we define an experiment to test the reliability of the assumption made about the causes of the limitations in the identification procedure and to identify the characteristics that the exponential swept-sine input signal is required to possess in order to enable accurate characterizations, in both the time and frequency domains of the linear filters present in the [PERSON] model to be identified. In Section 4, we discuss the results obtained. In Section 5, we draw conclusions and indicate possible evolution of the work.
# An Improved MOEA/D with Optimal DE Schemes for Many-Objective Optimization Problems
###### Abstract  
MOEA/D is a promising multi-objective evolutionary algorithm based on decomposition, and it has been used to solve many multi-objective optimization problems very well. However, there is a class of multi-objective problems, called many-objective optimization problems, but the original MOEA/D cannot solve them well. In this paper, an improved MOEA/D with optimal differential evolution (oDE) schemes is proposed, called MOEA/D-oDE, aiming to solve many-objective optimization problems. Compared with MOEA/D, MOEA/D-oDE has two distinguishing points. On the one hand, MOEA/D-oDE adopts a newly-introduced decomposition approach to decompose the many-objective optimization problems, which combines the advantages of the weighted sum approach and the Tchebycheff approach. On the other hand, a kind of combination mechanism for DE operators is designed for finding the best child solution so as to do the a posteriori computing. In our experimental study, six continuous test instances with 4-6 objectives comparing NSGA-II (nondominated sorting genetic algorithm II) and MOEA/D as accompanying experiments are applied. Additionally, the final results indicate that MOEA/D-oDE outperforms NSGA-II and MOEA/D in almost all cases, particularly in those problems that have complicated Pareto shapes and higher dimensional objectives, where its advantages are more obvious.  
Keywords:many-objective optimization; multi-objective evolutionary optimization based on decomposition (MOEA/D); differential evolutionary schemes +
Footnote †: journal:
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 5 Simulation Results Analysis
### Performance of Uplink Communications with Hidden Nodes
#### 5.4.1 Goodput and Message Loss Rate  
Figure 11 shows goodput results as a function of network load. The maximum goodput does not reach 20% of the transmission rate in any of the scenarios, which is well below the maximum goodput achieved when there are not hidden nodes in the network (between 65% and 72%). Furthermore, Figure 11 shows how the goodput drops abruptly when network load is higher than 30%, because of the increasing number of collisions.  
Goodput results are validated with message loss ratio values presented in Figure 12. This Figure shows a rapid increase in message loss ratio with network load, compared to the results obtained when there are no hidden nodes in the network. Comparing the graphs of goodput in Figure 11 and message loss rate in Figure 12, it can be seen that goodput is practically zero and message loss rate is almost 100% for network load values greater than 150%.  
Figure 10: Average current consumption per device, without hidden nodes.
# Robust Phase Estimation of Gaussian States in the Presence of Outlier Quantum States
## 5 Phase Estimation of Noisy Coherent State  
To illustrate advantages of the M-estimator in noisy quantum gaussian systems, we consider a phase estimation problem, which has many important applications for quantum information processing protocols [28; 29; 30; 31; 32; 33; 34; 35]. Suppose an unknown coherent state \(|\alpha\rangle\) is given. Importantly, the ideal state is parametrized by two real parameters \((\alpha_{R},\alpha_{I})\in\mathbb{R}^{2}\) (\(\alpha_{R}=\text{Re}\,\alpha\) and \(\alpha_{I}=\text{Im}\,\alpha\)). Our primary interest is to estimate the phase \(\theta=\arctan\left(\frac{\alpha_{I}}{\alpha_{R}}\right)\) of an unknown coherent state \(|\alpha\rangle\). In our setting, the phase \(\theta\) is the parameter of interest and the other parameter, the amplitude of the state, \(r=\sqrt{\alpha_{R}^{2}+\alpha_{I}^{2}}\) is the nuisance parameter of the model [26; 27]. The optimal estimation strategy for this problem is known. However, this optimal measurement depends on the unknown phase \(\phi\) and cannot be implemented. In the following, we consider a random mixture of two homodyne measurements at \(\phi=0,\pi/2\). As an estimator, we first apply M-estimators \((\hat{\alpha}_{I},\hat{\alpha}_{R})\) to infer the value \(\alpha\). We then convert it to phase by:  
\[\hat{\theta}=\arctan\left(\frac{\hat{\alpha}_{I}}{\hat{\alpha}_{R}}\right). \tag{37}\]  
A schematic diagram of our setting is given in Figure 1.
# A Deep Reinforcement Learning Approach for Active SLAM
## 2 Problem Formulation
### Neural Network Approach  
In Deep Q-learning formulation, the analytical computation of the action-value function (Q-function) is approximated by a DNN which coefficients \(\theta\) are iteratively updated by using the well-known back-propagation algorithm [36]. Let the loss function of this new optimization problem be the quadratic loss,  
\[\mathcal{L}(\theta)=\frac{1}{n}\sum_{i=1}^{n}\left(y^{(i)}-\hat{y}^{(i)} \right)^{2} \tag{9}\]  
where \(n\) is the batch size, \(\hat{y}=Q(s,a|\theta)\) the estimated Q-value by the DNN, and \(y\) the target Q-value, that can be computed using an auxiliary identical neural network, following [13], with parameters \(\theta^{*}\), as:  
\[y=\mathcal{R}(s,a)+\gamma Q(s^{\prime},\arg\max_{a}Q(s^{\prime},a|\theta)| \theta^{*}) \tag{10}\]Therefore, just by feeding reward function from Equation (8) in the previous equation, the neural network is recursively taught to visit those states that contributes to an uncertainty reduction. For the sake of understanding of the approach, Algorithm 2 shows the proposed learning process, using a dueling double DQN with PER embedded in Gazebo simulation environment.  
```
Parameters: \(\alpha\in(0,1]\), \(\gamma\in(0,1]\), \(U\), \(U_{e}\), \(\varepsilon_{0}\), \(\varepsilon_{f}\), \(\varepsilon\leftarrow\varepsilon_{0}\)  Initialize memory \(\mathcal{M}\), \(\delta\approx 0\) and \(\alpha^{\prime}\in(0,1]\)  Initialize DNN with parameters \(\theta\) and \(\theta^{*}\leftarrow\theta\)  Initialize ROS master, Gazebo and SLAM algorithm for episode \(\gets 1\) to max episodes do \(\blacktriangleright\) Unpause simulation  Reset robot pose, map and SLAM algorithm  Perceive \(s_{t}\) \(\blacktriangleright\) Pause simulation while\(s_{t}\) not terminal do  Select \(a_{t}\leftarrow\pi(s_{t}|\varepsilon)\) \(\blacktriangleright\) Unpause simulation  Execute \(a_{t}\) during \(t_{a}\) seconds  Recover \(\Sigma\), compute desired \(f(\Sigma)\) and \(r_{t}\)  Perceive \(s_{t+1}\) \(\blacktriangleright\) Pause simulation if\(s_{t+1}\) is terminal then\(e_{t}=1\) else\(e_{t}=0\)  Store tuple \((s_{t},a_{t},r_{t},s_{t+1},e_{t})\) in \(\mathcal{M}\) with \(\max(\text{p})\)  Sample minibatch from \(\mathcal{M}\) for each tuple in minibatch do if\(s_{i+1}\) is terminal then\(Q_{target}\gets r_{i}\) else\(Q_{target}\leftarrow\) \(r_{i}+\gamma Q(s_{i+1},\operatorname*{arg\,max}_{a}Q(s_{i+1},a|\theta)|\theta^{*})\) endif \(\Omega_{i}\gets Q_{target}-Q(s_{i},a_{i}|\theta)\) \(\blacktriangleright_{i}\leftarrow(\Omega_{i}+\delta)^{\alpha}\) endfor  Perform optimization on \(\Omega^{2}\) w.r.t. \(\theta\)  Every \(U\) steps set \(\theta^{*}\leftarrow\theta\) \(s_{t}\gets s_{t+1}\) \(\varepsilon\leftarrow\min\Big{(}\varepsilon_{0},\varepsilon-(\varepsilon_{0}- \varepsilon_{f})/U_{e}\Big{)}\) endwhile endfor
```  
**Algorithm 2** Dueling Double Deep Q-learning with PER in simulation environment.
###### Abstract  
Effective and accurate diagnosis of engine health is key to ensuring the safe operation of engines. Inlet distortion is due to the flow or the pressure variations. In the paper, an acoustic emission (AE) online monitoring technique, which has a faster response time compared with the ordinary vibration monitoring technique, is used to study the inlet distortion of an engine. The results show that with the deterioration of the inlet distortion, the characteristic parameters of AE signals clearly evolve in three stages. Stage I: when the inlet distortion J \(\leq\) 30%, the characteristic parameters of the AE signal increase as J increases and the amplitude saturates at J = 23%, faster than the other three parameters (the strength, the root mean square (RMS), and the average signal level (ASL)). Stage II: when the inlet distortion 30% \(<\) J \(\leq\) 43.64%, all the parameters saturate with only slight fluctuations as J increases and the engine works in an unstable statue. Stage III: when the inlet distortion J \(>\) 43.64%, the engine is prone to surge. Furthermore, an intelligent recognition method of the engine inlet distortion based on a unit parameter entropy and the back propagation (BP) neural network is constructed. The recognition accuracy is as high as 97.5%, and this method provides a new approach for engine health management.  
acoustic emission; inlet distortion; BP neural network; unit parameter entropy +
Footnote †: journal: Applied Sciences
## 5 Experimental Results
### Correlation Discovery: Dynamic Time Warping
#### ARIMA  
There are two commonly-used linear time series models in the literature, i.e., Autoregressive (AR) and Moving Average (MA) models. Combining these two, the Autoregressive Integrated Moving Average (ARIMA) model has been proposed in the literature. In a similar way to regression, ARIMA uses independent variables to predict a dependent variable (the series variable). The name autoregressive implies that the series values from the past are used to predict the current series value. In other words, the autoregressive component of an ARIMA model uses the lagged values of the series variable, that is values from previous time points, as predictors of the current value of the series variable.  
LR and GLM  
LR can be used to fit a forecasting model to an observed dataset, consisting of values of the response and explanatory variables. Upon learning of such a model, often fitted using the least squares approach, if additional values of the explanatory variables are collected without the accompanying  
\begin{table}
\begin{tabular}{l l} \hline Root Mean Squared Error (RMSE) & \(RMSE=\sqrt{\frac{\sum_{i=1}^{N}\ell_{i}^{2}}{N}}\) \\ \hline Mean Absolute Error (MAE) & \(MSE=\sqrt{\frac{\sum_{i=1}^{N}\ell_{i}}{N}}\) \\ \hline \multirow{2}{*}{
\begin{tabular}{l} Theil’s \(U2\) Decomposition \({}_{1}\) \\ \end{tabular} } & \multirow{2}{*}{\(U2=\frac{\sqrt{\frac{\sum_{i=1}^{N}\left(\frac{\ell_{i}-1}{N}\ell_{i}}{N} \right)^{2}}{N}}{\sqrt{\frac{\sum_{i=1}^{N}\left(\frac{\ell_{i}  
response value, the fitted model can be used to make a prediction of the response. GLM is a flexible generalization of ordinary LR that allows for response variables to have error distributions other than the normal (Gaussian) distribution.
# ReSQoV: A Scalable Resource Allocation Model for QoS-Satisfied Cloud Services
## 2 Related Work
The primary information for the predictor is resource information and recipient load. The neural network predicts potential workload and resource requirements from past data and system resource details. It lets the VM allocator correctly select the appropriate virtual machine. Using Ant Colony Optimization, the resource-efficient distribution, use, and management are described [24]. It meets the criteria of employing a suggested algorithm that predicts existing resources and forecasts future resource requirements. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
The work in Reference [28] showed resource management and scalability as possible remedial measures for QoS violations. One of the most essential modern cloud computing research objectives is managing and allocating resources effectively to prevent Quality of Service (QoS) violations. Consequently, several researchers have suggested strategies in the cloud setting that discuss the question of QoS breach. Many of these studies describe scalability in terms of workload and machine resource allocation. Scalability is critically relevant to the system's increased workload and cloud resource allocation. Scalability may be defined by available resources and how applications or services handle data flow, according to Ref. [28]. Improper data flow management can result in resources under-provisioning, leading to long response times or low throughput, or resource over-provisioning, resulting in high expenses and low resource utilization. There are two types of scalability. Horizontal scaling controls computational nodes to the system that includes replication of virtual machines. In contrast, vertical scaling manages to add more computing resources to an existing virtual machine. Several studies have focused on resource management based on user load or instruction workload estimates. The relation between both workload and resource is non-linear, however, if several concurrent users use cloud services, the serialization issue and the delay in accessing data will occur. The system then predicts the actual resource requirements for various operations and transactions. As a result, research focuses on the required resources while considering factors that influence the workload ratio of the resources.