esa-sceva/satcom-chunk-collection huggingface dataset:
_Applied_  
_sciences_  
_Article_  
**Reliability-Based and Cost-Oriented Product Optimization Integrating Fuzzy Reasoning Petri Nets, Interval Expert Evaluation and Cultural-Based DMOPSO Using Crowding Distance Sorting**  
**[PERSON] 1,2, [PERSON] 1,2,* \(\copyright\), [PERSON] 3, [PERSON] 4,* and [PERSON] 1,2**  
1 State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China; [EMAIL_ADDRESS] (Z.H.); [EMAIL_ADDRESS] (J.T.)  
2 Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, Zhejiang University, Hangzhou 310027, China  
3 School of Mechatronics Engineering, University of Mining and Technology, Xuzhou 221116, China; [EMAIL_ADDRESS]  
4 Transportation College, Jilin University, Changchun 130020, China  
*Correspondence: [EMAIL_ADDRESS] (Y.F.); [EMAIL_ADDRESS] (G.T.); Tel.: +86-186-6821-1935 (Y.F.); +86-137-7785-6791 (G.T.)  
Received: 4 July 2017; Accepted: 31 July 2017; Published: 4 August 2017  
**Featured Application: By integrating fuzzy reasoning Petri Nets, interval expert evaluation and cultural-based dynamic multi-objective particle swarm optimization (DMOPSO) using crowding distance sorting, the proposed reliability-based and cost-oriented product optimization method is effective to conduct the early product design. A reasonable reliability optimization design scheme can be output, and it is of important guiding significance for the product detailed design. The superiority of the proposed method lies in its outstanding ability to deal with the uncertainties information and dynamic product failure propagation.**  
**Abstract: In reliability-based and cost-oriented product optimization, the target product reliability is apportioned to subsystems or components to achieve the maximum reliability and minimum cost. Main challenges to conducting such optimization design lie in how to simultaneously consider subsystem division, uncertain evaluation provided by experts for essential factors, and dynamic propagation of product failure. To overcome these problems, a reliability-based and cost-oriented product optimization method integrating fuzzy reasoning Petri net (FRPN), interval expert evaluation and cultural-based dynamic multi-objective particle swarm optimization (DMOPSO) using crowding distance sorting is proposed in this paper. Subsystem division is performed based on failure decoupling, and then subsystem weights are calculated with FRPN reflecting dynamic and uncertain failure propagation, as well as interval expert evaluation considering six essential factors. A mathematical model of reliability-based and cost-oriented product optimization is established, and the cultural-based DMOPSO with crowding distance sorting is utilized to obtain the optimized design scheme. The efficiency and effectiveness of the proposed method are demonstrated by the numerical example of the optimization design for a computer numerically controlled (CNC) machine tool.**  
**Keywords:** FRPN; reliability optimization design; DMOPSO; cultural algorithm; uncertainty; interval expert evaluation
Therefore, based on the reduced search space, PODESCA is reimplemented on the OPSC problem. The obtained results by the proposed algorithm without and with reducing the search space by the proposed SBDMT are then compared to validate their effectiveness. For further verification of the proposed methods, three RDSs; 69-bus, 85-bus, and large-scale 118-bus are used and then the results are compared with different previous methods in the literature. The main contributions of this paper can be highlighted as follows:  
* The proposed PODESCA is evolved by an improved hybridization of each of DE, SCA, and QOT, where their mathematical formulas are utilized in order to present a new, robust, and effective method. * The developed SBDMT reduces the OPSC problem's search space by utilizing four SBIs to be inputs of TOPSIS, which rearranges the system's buses so that the most potential locations for SC placement are only kept in the search space. * A comprehensive and detailed OF is employed to solve the OPSC problem in three different-scale RDSs, including one large-scale system, where various comparisons are presented. * The OPSC is solved by PODESCA without and with reducing the search space by the SBDMT, and the results are then compared. * The proposed methods are effective and robust especially for large-scale RDSs. The rest of the paper is organized as follows: the mathematical formulation of the OPSC problem is addressed in Section 2. The main research steps and methodology of this paper are explained in Section 3. Then, the details of the proposed PODESCA and SBDMT with their implementation procedures on the OPSC problem are presented in Section 4. In Section 5, PODESCA's performance analysis is performed by solving several benchmark functions and comparing the results with those from other novel algorithms. In Section 6, the results and comparisons are demonstrated and discussed. At last, the conclusions are pointed out in Section 7.
# A Dynamic Assessment Methodology for Accident Occurrence Probabilities of Gas Distribution Station
## 2 Methodology
### Dynamic Bayesian Network Analysis
#### 2.3.1 Bayesian Updating Estimation of RE Probabilities  
In scenarios where failure data are inadequate, the aforementioned methods can be utilized to derive the initial, or prior, probability of REs. Nevertheless, it is widely acknowledged that generic failure data do not accurately reflect the operational conditions and environments specific to GDS facilities. Furthermore, the relatively short operational history of GDSs in China contributes to the scarcity of plant-specific data, which, in turn, influences experts' linguistic assessments of RE occurrence probabilities. Consequently, the direct use of initial probabilities inevitably introduces uncertainties into the PRA results for the GDS. However, upon the acquisition of new specific failure data pertaining to the GDS, the prior probabilities of REs can be updated through Bayesian estimation techniques.  
For continuous variables, the equation of Bayesian theorem is as follows [32]:  
\[f(\lambda|E)=\frac{f(\lambda)\cdot L(E|\lambda)}{\int_{0}^{\infty}f(\lambda) \cdot L(E|\lambda)d\lambda}\propto f(\lambda)\cdot L(E|\lambda) \tag{14}\]  
where \(f(\lambda\mid E)\) is posterior probability density function (PDF) of the failure rate (\(\lambda\)); \(f(\lambda)\) is the prior PDF of \(\lambda\); and \(L(E\mid\lambda)\) is likelihood function of \(\lambda\) based on plant-specific data (\(E\)).  
For discrete variables, the Bayesian equation is as follows [32]:  
\[f(\lambda_{i}|E)=\frac{f(\lambda_{i})\cdot L(E|\lambda_{i})}{\sum_{j=1}^{m}f( \lambda_{i})\cdot L(E|\lambda_{i})}\propto f(\lambda_{i})\cdot L(E|\lambda_{i}) \tag{15}\]  
where \(m\) denotes the number of variables. \(\lambda_{i}\) denotes the \(i\)th variable.  
The schematic illustration of the Bayesian updating estimation for the occurrence probability of REs is presented in Figure 4. The detailed methodology of this updating approach can be found in our previous research [7]. As the volume of specific failure data collected increases, the uncertainty associated with the estimated results will decrease correspondingly.  
Figure 4: The schematic of Bayesian updating estimation.
# Traffic Light and Arrow Signal Recognition Based on a Unified Network
## 3 Approach
### Traffic Light Detection and Recognition  
Computation cost is a concern in traffic light detection. YOLOv3 [22] balances between accuracy and processing speed. Therefore, we integrated YOLOv3 into our approach for traffic light detection. The network framework of YOLOv3 can be divided into three parts. Figure 3 shows the network structure of YOLOv3. First, Darknet-53 is used to extract feature maps from the input images. Next, the feature pyramid network (FPN) integrates low-level and high-level features to produce feature maps of three scales. Finally, the prediction layer predicts objects of varying sizes in the feature maps. Figure 4 shows the input and output of the detection network.  
Figure 3: Network structure of YOLOv3.  
Figure 2: Results of traffic light detection using a combination of image and LiDAR data. (**a**) Input image, (**b**) processed result, and (**c**) the segmented image.  
For traffic light recognition, we introduced a new technique combining object detection and classification. We used YOLOv3-tiny [22] to detect and classify light states. Having a framework similar to that of YOLOv3, YOLOv3-tiny also comprises three parts, with the main difference being the feature-extraction network. Specifically, YOLOv3-tiny has fewer convolution layers and pooling layers, and its FPN produces feature maps of only two scales, in which the prediction layer predicts objects of different sizes. The smaller number of convolution layers contributes to its higher speed but reduces its accuracy.  
In the proposed approach, there are four classes of light states: RedCircle, YellowCircle, GreenCircle, and Arrow. The light states of Arrow are then further classified into the LeftArrow, StraightArrow, and RightArrow classes using LeNet [23]. For example, when a light state of Red-Left-Right is established, YOLOv3-tiny detects one RedCircle and two Arrows. Moreover, the two Arrows will be recognized as LeftArrow and RightArrow classes by LeNet. Then, we can obtain the final traffic light state by merging the results obtained from the two networks.
# MILP-Based Approach for High-Altitude Region Pavement Maintenance Decision Optimization
## 4 Model Transformation  
It can be observed that the optimization model (the initial model, for short), composed of objective functions (15) to (19) and constraints (1) to (14), is a multi-objective nonlinear programming problem with both continuous variables (\(s_{i,u}\) and \(r_{i,u}\)) and integer variables (\(x_{i,j}\) and \(y_{i,u,m}\)), where it is difficult to obtain an exact solution via common methods. If we replace its nonlinear terms with linear terms, then it is transformed into a mixed-integerlinear programming (MILP) problem. MILP problems are popular because of their mature solving techniques. Therefore, this paper further reconstructs it into a MILP problem by transforming the nonlinear terms.
## 4 Results and Discussion
### Discussion of Collected Results  
Our analysis revealed that, across all tested network topologies, the camera feed exhibited consistent processing latency of approximately 100 ms. The network latency for  
Figure 15: Box plot comparison of the client’s request duration across different network scenarios.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Results (ms)** & **Wired** & **Wi-Fi** & **Hot-Spot** & **Overseas** \\ \hline Mean & 6.617 & 58.459 & 115.381 & 240.769 \\ \hline Median & 5.667 & 57.455 & 107.515 & 240.317 \\ \hline Standard Dev. & 1.357 & 3.629 & 30.577 & 1.714 \\ \hline Confidence Int. & 0.397 & 0.223 & 2.960 & 0.857 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results for latency across different network scenarios.  
the camera feed varied depending on the topology, with values of 120 ms for the virtual network, 140 ms for the wired network, and 200 ms for the local Wi-Fi network.  
In examining the robot control latency, we assessed several key components: the IK Solver, Move Execution, and client-to-host round-trip time. Notably, both the IK Solver and Move Request latency were found to be independent of the network topology, likely due to the static nature of the robot control system. In contrast, the client-to-host round-trip time varied significantly across different network configurations, with values of 6.617 ms for the wired local network, 58.459 ms for the Wi-Fi connection over VPN, 115.381 ms for the 4G cellular hotspot, and 240.769 ms for the overseas network connection.
# Industrial Dust Explosions. A Brief Review
## 1 Brief Review of Dust Explosion Basics
### Combustion of Dust Clouds in Air  
#### 1.4.1 Explosible Concentration Ranges of Dust Clouds in Air  
The explosive combustion of dust clouds, as illustrated in Figure 1c, cannot take place unless the mass of dust per unit volume of dust clouds is within certain limits. Figure 2 illustrates the explosive range of a typical natural organic material (maize starch) in air at ambient conditions.  
Figure 1: Illustration of the increase of the combustion rate of a given mass of combustible solid with increasing sub-division. From [PERSON] [1; 2; 3]. Copyright (2019), with permission from Elsevier.  
The explosibility limits differ for various dust materials. For example, zinc powder has a minimum explosive concentration in air of approximately 500 g/m\({}^{3}\).  
The unpleasant dust concentrations that sometimes occur in general working atmospheres in factories and call the attention of industrial hygiene authorities are orders of magnitude lower than the minimum explosive concentrations, as also illustrated in Figure 2.  
Therefore, sufficiently dense dust clouds in which dust explosions can be initiated are practically always located inside process equipment, such as mills, mixers, screens, dryers, cyclones, filters, bucket elevators, hoppers, silos, aspiration ducts, and pipes for pneumatic transport of powders. Such explosions are called primary explosions.  
However, as discussed in Section 1.6, below, primary dust explosions can give rise to much more severe secondary dust explosions and flash fires outside process equipment.
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 4 Applications and Target Industry
### Social Media  
Social media in FRSs allows users to interact with each other and share content, such as comments, photos, videos, and links [135; 146]. This is a platform that allows users to create networks, post content, and engage with one another in various ways. Social media can include sites such as Facebook, Twitter, LinkedIn, Instagram, Pinterest, and YouTube [147]. Studies have found that recommender systems can help to increase the accuracy and relevancy of recommendations by leveraging the vast amount of user data from social media platforms [148; 149; 150; 151; 152]. Therefore, using an FRS can enable the personalization of recommendations by leveraging user preferences and interests from social media profiles and can eliminate bias by incorporating the collective wisdom of users from different social media platforms. FRSs can benefit social media in a number of ways, i.e., they can provide more accurate and relevant recommendations, which can increase user engagement and satisfaction. They can also help reduce bias and personalize recommendations, which can improve the user experience [153]. Moreover, they can provide better insights into user preferences and interests, which can be used to inform marketing campaigns and product development.
# Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals
## 3 Materials and Methods
### Optimization Deep Features  
For optimizing the features, the LSTM has been used. LSTM is an extension of a recurrent neural network (RNN). An RNN is a neural network with recurrent outputs and inputs. In LSTMs, the exploding and vanishing gradient problems are overcome [57]. The features optimized by the RNN-LSTM model were derived from the intermediate representations extracted using the VGG16 model, which was used as part of the ensemble deep learning approach in the cardiac biometrics system.  
The ensemble feature representation was then fed to a boosting machine learning classifier (e.g., AdaBoost or Gradient Boosting) for human identification. The classifier learned to differentiate between individuals based on the combined information from the VGG16 and LSTM models. The optimization process using the RNN-LSTM model aimed to leverage the complementary strengths of the VGG16 model (spatial representation) and LSTM model (temporal representation). By combining the two approaches, the system could potentially achieve enhanced accuracy and robustness in recognizing and identifying individuals based on their unique cardiac biometric data captured using ECG signals.  
An LSTM is composed of three gates: input, forget, and output. LSTM's activation functions involve sigmoid and hyperbolic tangents [58]. Figure 6 shows the architecture of an LSTM cell. Here, the input is the features that have been extracted by the VGG16 pretrained model. The model architecture comprises an LSTM layer with 32 units and an input shape of (1024,1) for each feature from the preceding step. Subsequently, a dense layer with 90 units corresponding to the classes of patients is included. The model was compiled using specific hyperparameters, including a Softmax activation function in the output layer, categorical cross entropy as the loss function, and the Adam optimizer for optimization. These hyperparameters yielded optimal performance outcomes. The pseudocode for this step is shown in Algorithm 3.
## 3 Power Management Scheme of MG with EV Charging/Discharging Scheduling Algorithm
### Multi-Objective Optimization for Power Management of Interdependent MG and EV Fleets
#### 3.2.2 Objective Functions
\[\min\left[\sum_{i=1}^{L}T_{re}^{i}(t)\times O_{ch}^{i}(t)-\sum_{i=1}^{L}T_{re}^{ i}(t)\times O_{ch}^{i}(t)\right] \tag{22}\]  
\[\min\left[-\left(SOC_{max}^{i}-SOC_{EV}^{i}(t)\right)^{2}\times O_{ch}^{i}(t) +\left(SOC_{max}^{i}-SOC_{EV}^{i}(t)\right)^{2}\times O_{ch}^{i}(t)\right] \tag{23}\]  
\[\min\left[-\left(\sum_{i=1}^{L}PW_{ch}^{i}\times O_{ch}^{i}(t)+\sum_{i=1}^{L} PW_{ch}^{i}\times O_{ch}^{i}(t)\right)\right] \tag{24}\]  
where \(T_{re}^{i}(t)\) represents the remaining parking time of the \(i\)-th EV before the scheduled departure from the charging station at time \(t\). The priority of charging/discharging EV can be provided using \(T_{re}^{i}(t)\) and \(SOC_{max}^{i}-SOC_{EV}^{i}(t)\) in (22) and (23). Depending on these equations, if an EV has less remaining parking time and a wide gap between the current SOC and the target SOC, the probability that the EV is assigned to charging operation among EV charging candidates gets higher. On the contrary, if an EV has larger remaining parking time and a narrow gap between the current SOC and the target SOC, the probability that the EV is assigned to discharging operation among EV discharging candidates gets higher. The charging/discharging of parked EVs can be activated and utilized to the power management of the MG according to (24). With these objective functions and constraints mentioned above, the multi-objective optimization is performed by the genetic algorithm (GA) that has advantage of global search ability with its random nature and flexibility [67]. In the multi-objective optimization, the parameters of GA take crossover probability 0.8, mutation probability 0.01, the maximum number of generations 100, and population size 500.
# Near-Space Wide-Area and High-Resolution Imaging System Design and Implementation
###### Abstract  
The near-space atmosphere is thin, and the atmospheric refraction and scattering on optical observation is very small, making it very suitable for wide-area and high-resolution surveillance using high-altitude balloon platforms. This paper adopts a \(9344\times 7000\) CMOS sensor to obtain high-resolution images, generating large-field-of-view imaging through the swing scanning of the photoelectric sphere and image stitching. In addition, a zoom lens is designed to achieve flexible applications for different scenarios, such as large-field-of-view and high-resolution imaging. The optical design results show that the camera system has good imaging quality within the focal length range of 320 mm-106.7 mm, and the relative distortion values at different focal lengths are less than 2%. The flight results indicate that the system can achieve seamless image stitching at a resolution of 0.2 m@20 km and the imaging field of view angle exceeds \(33^{\circ}\). This system will perform other near-space flight experiments to verify its ultra-wide (field of view exceeding \(100^{\circ}\)) high-resolution imaging application.  
near space; high resolution; optical design; imaging system 2023236454. [[https://doi.org/10.3390/s23146454](https://doi.org/10.3390/s23146454)]([https://doi.org/10.3390/s23146454](https://doi.org/10.3390/s23146454))
# A Hybrid DNN Multilayered LSTM Model for Energy Consumption Prediction
## 2 Related Studies
### Deep Learning Models  
Several deep learning models have been developed for energy consumption prediction. [PERSON] et al. used an LSTM model to predict energy consumption [25]. [PERSON] et al. developed a bidirectional LSTM model linked to a rolling update and attention algorithm to make energy consumption forecasts [26]. [PERSON] et al. developed a pooling deep RNN to forecast household energy consumption. In this study, overfitting was an issue in the algorithm [13]. Feature extraction techniques involve various regular patterns, noise, and spectral analysis. However, the algorithms decrease energy consumption accuracy, while the methods decrease accuracy because of the nonlinearity and irregular trends of the electrical energy consumption datasets. The LSTM [25] and CNN [27] algorithms excel in pattern and sequence learning. CNN models often struggle with learning temporal features in energy consumption data. [PERSON] et al. proposed a GRU model to predict future energy consumption for short intervals of time, decreasing error rates [28]. To deal with the shortcomings of single algorithms, hybrid models have been developed to effectively predict energy consumption. [PERSON] et al. [29] used a hybrid bidirectional LSTM and CNN. [PERSON] et al. [30] used a hybrid CNN and LSTM to predict energy consumption. [PERSON] et al. proposed a WT multiple LSTM model to improve power consumption prediction performance [31]. [PERSON] et al. proposed the CNN-BiGRU model to reduce errors in individual household electricity consumption prediction [32]. Moreover, [PERSON] et al. proposed the CNN-ESN model, which generated high prediction accuracy for power consumption forecasting [33]. [PERSON] et al. developed a deep R-CNN with ML-LSTM model for short-term electricity load forecasting and conducted an experiment for daily and hourly power consumption forecasts [34].  
Therefore, based on previous studies, a model that could perform better with complex data and excel in sequence learning together, which initiates the idea of building a hybrid DNN multilayered LSTM model, should be developed. Such a model could improve predictive performance, which could be used in energy management. Table 1 presents a summary of the related studies.  
ing prediction results. LSTM has been preferred over the RNN for energy consumption prediction, especially for time series problems. The transformer is one of the newest and most commonly employed techniques for sequence prediction and, therefore, requires further investigation in power consumption prediction studies. Furthermore, most of the introduced models have been used for short-term power consumption prediction, which typically covers predictions up to several days. However, medium- and long-term horizons should be considered for developing generalized models.