esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Overall Flowchart of Algorithmic Steps  
The schematic concept of the master-slave iterations can be illustrated as Figure 8. Overall algorithmic steps for the proposed method based on the master-slave iterations are described as follows. Figure 9 illustrates the flowchart of the proposed method. First, specify the elastic loads with their must-run durations and the 24 h inelastic loads; input generator parameters, which are the ramp rate, minimum up-time, minimum down-time and cost coefficients. In addition, the forecasted 24 h PV power generations in terms of means, standard deviations and skewness coefficients are given.  
In the proposed GA, both generator units and elastic demands are initialized to be feasible chromosomes. In the master-level calculation, \(P_{i}(t)\) and \(P_{s}(t)\) where \(t\) = 1, 2,..., \(T\), are determined using Step C1-C9 on the slave level. Please note that if any of the constraints (such as minimum up-time \(M_{i}^{on}\) and minimum down-time \(M_{i}^{off}\) and Equation (7) is violated for a chromosome, its corresponding penalty term is added to the fitness, Equation (1). In the proposed GA, the better chromosomes are selected using the roulette wheel.  
As described in the beginning of Section 3, \(G_{1}\) = 4, \(L\) = 5 and \(T\) = 24. In a day-ahead scheduling problem, \(T\) = 24 is always true. Moreover, the number (30 herein) of buses in a factory, which is addressed to consider both UC and DR, is actually large. Considering \(G_{1}\) = 4 and \(L\) = 5 is reasonable in a large factory power system. This studied problem is not the same as the traditional UC problem, which is defined in the transmission system and may include many buses and generators. Moreover, traditional DR is concerned in the distribution system or at home; however, the DR is emphasized herein in the end-user's factory power system. The proposed GA is very efficient to deal with the operational constraints, such as Equation (7), for the studied problem.  
Figure 8: The schematic master–slave iterations.
# FAS-Res2 net: An Improved Res2 net-Based Script Identification Method for Natural Scenes
## 5 Conclusions  
Aiming at the problems of complex background, variable text style, and shared characters of script images in natural scenes, this paper proposes a script multi-classification identification method based on convolutional neural network Res2 Net, namely FAS-Res2 Net. A convolutional neural network Res2 Net with improved residual blocks is used as the backbone of feature extraction, and improvements are mainly made in the feature extraction module and classification layer. For the problem of missing feature information in the deep network, the feature pyramid module was used to aggregate the deep and shallow semantic and geometric feature information of the image. An adaptive spatial feature fusion module was also integrated to calculate the weight of feature information of each layer, which solves the feature conflict between positive and negative samples and obtains the best adaptive local feature information. For the global feature of the image, two encoding blocks of the swin transformer were proposed to extract the image feature information and aggregated with the local information extracted by CNN. Finally, using the full convolution classifier instead of the traditional Linear classification reduces the network parameters and improves the training efficiency. In the future, the script identification model can be embedded into the text detection model. For script images in natural scenes, text detection directly performs script identification after determining the text position to realize the automation of script identification in natural scenes.  
Conceptualization, Z.Z.; methodology, Z.Z.; software, Z.Z.; investigation, Z.Z.; validation, Z.Z.; resources, H.M., X.X. and K.U.; writing--original draft preparation, Z.Z. and A.A.; writing--review and editing, Z.Z. and A.A.; visualization, Z.Z.; project administration, H.M., X.X. and K.U.; funding acquisition, A.A. and K.U. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Natural Science Foundation of China (No. 62266044, 61363064 and 61862061) and Natural Science Foundation of Science and Technology Department of Xinjiang Uygur Autonomous Region (No. 2021D01C119).  
Not applicable.  
We The datasets used in this paper are publicly available, including SIW-13 and CVSI-2015.  
The authors declare no conflict of interest.
## 2 Related Works  
### Virtual Reality (VR) Shopping  
As previously mentioned in Section 1, the utilization of VR (a part of the Metaverse) in the fashion industry can be categorized into experiential, entertainment, and informational types [1]. An experiential type, such as a virtual fitting system, involves content created by companies that consumers can directly experience or manipulate during the purchasing process, which enhances convenience and interest [6]. This type often employs extended reality (XR) and augmented reality (AR) technologies more so than VR. The entertainment type focuses more on amusement rather than inducing purchasing desires. Lastly, the informational type aims to provide product information. Although VR shopping integrates all three types, this study primarily focuses on the informational aspect. VR shopping is particularly strong in the \"information search\" and \"alternative evaluation\" stages of the consumer decision process (CDP), which are the most time-consuming and critically influence the final decision-making process [4; 13]. This strength comes from VR shopping's ability to combine the benefits of offline shopping--allowing products to be rotated 360\({}^{\circ}\) and viewed in three dimensions--with the advantages of online shopping, which include easy product comparison and access regardless of store hours [14].  
Therefore, VR shopping provides a better shopping experience than traditional online shopping (e-commerce) because it makes it easier for consumers to understand product appearance information in shopping environments where direct product experience is not possible [3; 15]. Moreover, various global companies (e.g., eBay, Alibaba, inVRsion, Hyundai, etc.) and fashion industry enterprises (e.g., Lurckychouette, Esquire, etc.) aim to provide consumers with better shopping experiences and environments through VR shopping platforms and VR-based product information provision. However, some of these companies' initiatives have not been commercialized, and VR shopping technology is still in its early stages with some deficiencies. Consequently, numerous studies are being conducted to advance VR shopping [16; 17].  
Previous studies have primarily focused on recreating existing malls and stores within VR, enabling consumers to immerse themselves and shop within this environment. However, there is a lack of research on how VR shopping's information provision method impacts existing consumers. Moreover, for fashion products, where the product's emotional appeal and appearance information significantly influence purchase decisions more than other product categories, empirical research on the effects of these information provision methods on existing consumers is needed. Therefore, this study specifically focuses on fashion products (shoes) and compares the impact of 3D graphic-based VR shopping--facilitated through head-mounted displays (HMDa)--on perceived product emotion and ease of product recognition, against the existing 2D image-based online shopping (e-commerce) method.
## 2 System Model  
Consider a WCS shown in Fig. 1, where, besides an interfering loop, there are altogether \(N\) independent control loops. Each control loop consists of a smart sensor (S), a smart actuator (A), a controller (C) and a physical process (P) to be controlled. To facilitate time synchronization, assume that the sensor and the actuator run on top of the same clock platform. The nodes communicate using the IEEE 802.11b protocol. The computation times of all control tasks on the controllers are assumed to be negligible relative to communication delays. The total delay within a control loop is consequently equal to the sum of the communication delay of sampled data from the sensor to the controller and the communication delay of control command from the controller to the actuator, including both waiting delays and transmission delays.  
In the context of wireless control, there are basically two classes of deadline misses. The first class is that the sample data or the control command is truly lost during the course of transmission due to bit errors, noise interference, low received signal strengths, etc. In contrast, in the second class of deadline misses, the control command is actually received by the actuator, but the communication delay exceeds the deadline, which equals the sampling period.
# A Clustering WSN Routing Protocol Based on k-d Tree Algorithm
## 1 Introduction
For example, a heterogeneous cluster formation causes data traffic within the clusters to become heterogeneous, that is, having varying dataflow responses in the majority of network nodes. This flow variability causes the network to show different delay and jitter values, limiting the usefulness of resource-intensive applications. Given the problems found in the cluster formation and CH node selection mechanisms, our goal is to propose a WSN communications protocol that uses a hierarchical routing schema called H-kddree. Its routing algorithm is based on the k-d tree algorithm, which allows creating partitions in an area with the mean of the data of one of its dimensions. Additionally, the H-kddree protocol proposes a low-variability CH node generation mechanism, with a positive impact on delay, jitter, and throughput, compared with the low-energy adaptive clustering hierarchy protocol (LEACH) and low-energy adaptive clustering hierarchy-centralized (LEACH-C). As a comparison, we have selected the LEACH and LEACH-C protocols, on account of being the most widely used hierarchical clustering protocol and also the most discussed in the literature. Clustering protocols work through one or several metrics that provide the necessary ability to manage network traffic efficiently and to improve the experience of a user or machine in different network environments. These improvements are usually in one or several network metrics such as load balancing, energy consumption, scalability, latency reduction, data traffic maximization, errors and data loss minimization. Quality of Service (QoS) is the improvement in one or several of these network metrics. This article analyzes the performance of the LEACH, LEACH-C and the proposed H-kddree protocol, by measuring the following metrics: delay, jitter, throughput, packet drop rate (PDR), and average network energy. This article is structured as follows: Section 2 includes a review of literature related to cluster formation and CH node selection. The fundamental basis for LEACH, LEACH-C and k-d tree is described in Section 3. Protocol considerations and a description of the configuration and data transmission phases are discussed in Section 4. Section 5 includes parameters, metrics, and results of the simulation of the proposed protocol. Finally, Section 6 presents the conclusions.
# Energy-Efficient Opportunistic Routing Algorithm for Post-Disaster Mine Internet of Things Networks
## 5 Performance Evaluation
### Simulation Settings  
In this paper, we consider the post-disaster data transmission of MIoT in the planar accident area rescue scenarios, such as a fully mechanized coal face [44; 45]. Thus, we deploy all nodes randomly in a sensing area of 300 m \(\times\) 50 m in the simulation. This deployment approach ensures that the simulation results in different scenarios are independent of each other. Suppose a sink node with unlimited energy is fixed at the edge of the sensing area and acts as a gateway for wireless communication with sensor nodes. Similar to most wireless network routing protocols, we use the Carrier Sense Multiple Access (CSMA) protocol as the underlying MAC protocol. If the channel is idle, the forwarding node can broadcast the data packet. Otherwise, it backs off, and the packet will be dropped after backing off for the maximum retransmission times. The maximum retransmission times of each node is set to 4, and the maximum error ratio of each link is set to 0.2. We assume that the network generates a data packet from a random sensor node to the sink every  
Figure 7: Flowchart of the proposed DEOR algorithm.  
0.1 s. The size of the data packet is set to 1024 bits. The parameter settings required for the simulation are shown in Table 3.  
The following routing metrics are used to evaluate the performance of routing algorithms: (1) energy consumption (EC): EC is defined as the total energy consumption required to deliver data packets from source nodes to the sink. (2) Average hop count (AHC): AHC is defined as the average hop count on the routing path from the source to the sink for forwarding data packets. (3) Packet delivery ratio (PDR): PDR is defined as the ratio of the number of packets successfully received at the sink to the number of data packets generated by source nodes. (4) Network lifetime (NL): NL is defined as the time (in seconds) from the simulation starting moment to the moment that the first node completely depletes its energy.
## 4 Simulation Study
### Comparison of the Nominal Performance  
The nominal response of the three controllers to the tracking signal is given in Figure 6. As shown in Figure 6a, the reference signal of \(\alpha_{c}\) was designed as a \"square\" with an amplitude of 5\({}^{\circ}\) and a width of 20 s. Each of the controller tracks the reference command well with a rise time of 1.0 s and an overshoot of 2%. As presented in Figure 6b, the reference signal of \(\mu_{c}\) was a \"doublet\" with an amplitude of 5\({}^{\circ}\) and a width of 5 s. The rise time of the three controllers was around 0.8 s. However, there was an overshoot of 4% for the NDI-PI controller, while the other two controllers experienced no overshoot. In Figure 6c, it is observed that \(\beta\) tracking error was always smaller than 0.6\({}^{\circ}\), which was the coupling response to the bank maneuvers. Moreover, the estimated lumped disturbances are shown in Figure 6d. Though there were notable estimation mismatches between the NDI-DO and the NDI-AW, the closed-loop performance under the two schemes were close to each other.
# Misalignment-Resilient Propagation Model for Underwater Optical Wireless Links +
## 2 Underwater Photon Propagation Model
### Monte Carlo Algorithm
#### 2.1.1 Coordinate System and Initial Conditions  
In the algorithm geometry, a Cartesian coordinate system is considered where the receiver plane is situated on a fixed point in the \(x/y\) plane. The emitter is pointed in the direction \(z\), which is referred to here as the optical axis. To start propagating photons through the medium, they must be given a position and a vector of propagation that properly simulates a laser beam. Photons start to propagate in the plane \(z=0\), and their initial positions \((x_{0},y_{0},z_{0})\) are determined from a sampled Gaussian distribution, which is more realistic than a flat distribution, with a standard deviation equal to the laser beam's waist radius \(w_{0}\)[22]:  
\[\rho(r)=\exp\biggl{(}\frac{-r^{2}}{2w_{0}}\biggr{)}. \tag{3}\]  
Given a sampled radius, the \((x,y)\) positions are easily calculated as follows:  
\[x  
divergence \(\psi_{div}\) (represented in Figure 2) and on the laser beam waist radius \(w_{0}\), and it is given by  
\[f_{l}=\frac{w_{0}}{\psi_{div}}. \tag{5}\]  
The initial polar angle of each photon is obtained through the ray transfer matrix analysis using the paraxial approximation. The optical ray coordinates are defined as \((h,\beta)\), where \(h\) is the height and \(\beta\) is the slope angle relative to the optical axis. The effect of the lens on the initial ray coordinates is obtained by multiplication with the optical element matrix of the lens, given by [4]  
\[\begin{bmatrix}h_{1}\\ \beta_{1}\end{bmatrix}=\begin{bmatrix}1&0\\ \dfrac{-1}{f_{l}}&1\end{bmatrix}\begin{bmatrix}h_{0}\\ \beta_{0}\end{bmatrix}. \tag{6}\]  
For each photon, the initial azimuthal angle \(\phi_{0}\) is taken randomly from \([0,2\pi]\) since the beam is radially symmetric, which is accomplished by taking a random number \(q\) uniformly distributed between \([0,1]\) and multiplying it by \(2\pi\). Given a certain point \(P\) in space with an origin \(O\), the azimuthal angle is defined as the angle between the positive \(x\) axis and the projection of the line segment \(\overline{OP}\) on the \(x/y\) plane, as illustrated in Figure 3.  
The photon direction vector is divided into its projections on the three axes in order to avoid complex and time-consuming running trigonometric functions. These projections \(\mu_{x}\), \(\mu_{y}\) and \(\mu_{z}\) are called the direction cosines, being defined by  
\[\begin{split}\mu_{x}&=\cos(\theta_{x}),\\ \mu_{y}&=\cos(\theta_{y}),\\ \mu_{z}&=\cos(\theta_{z}),\end{split} \tag{7}\]  
where \(\left(\theta_{x},\theta_{y},\theta_{z}\right)\) are the angles between the propagation vector and the Cartesian axes, which must comply with the normalization \(\mu_{x}^{2}+\mu_{y}^{2}+\mu_{z}^{2}=1\).  
Figure 2: Scheme of the opening of the optical beam passing through a lens.
# Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection
###### Abstract  
Effective feature selection can help improve the classification performance in bearing fault diagnosis. This paper proposes a novel feature selection method based on bearing fault diagnosis called Feature-to-Feature and Feature-to-Category- Maximum Information Coefficient (FF-FC-MIC), which considers the relevance among features and relevance between features and fault categories by exploiting the nonlinearity capturing capability of maximum information coefficient. In this method, a weak correlation feature subset obtained by a Feature-to-Feature-Maximum Information Coefficient (FF-MIC) matrix and a strong correlation feature subset obtained by a Feature-to-Category-Maximum Information Coefficient (FC-MIC) matrix are merged into a final diagnostic feature set by an intersection operation. To evaluate the proposed FF-FC-MIC method, vibration data collected from two bearing fault experiment platforms (CWRU dataset and CUT-2 dataset) were employed. Experimental results showed that accuracy of FF-FC-MIC can achieve 97.50%, and 98.75% on the CWRU dataset at the motor speeds of 1750 rpm, and 1772 rpm, respectively, and reach 91.75%, 94.69%, and 99.07% on CUT-2 dataset at the motor speeds of 2000 rpm, 2500 rpm, 3000 rpm, respectively. A significant improvement of FF-FC-MIC has been confirmed, since the \(p\)-values between FF-FC-MIC and the other methods are \(1.166\times 10^{-3}\), \(2.509\times 10^{-5}\), and \(3.576\times 10^{-2}\), respectively. Through comparison with other methods, FF-FC-MIC not only exceeds each of the baseline feature selection method in diagnosis accuracy, but also reduces the number of features.  
feature selection; Maximum Information Coefficient (MIC); FF-MIC; FC-MIC; bearing fault diagnosis +
Footnote †: journal: Applied Sciences
# A Service-Caching Strategy Assisted by Double DQN in LEO Satellite Networks
## 2 System Model  
### Network Model  
As depicted in Figure 1, our work considers a network architecture composed of three tiers: cloud, fog, and edge. The cloud layer comprises service centers deployed on the ground or at medium and high orbits, containing all content and services. The middle layer consists of LEO satellites, which store a portion of content and services and connect to both the cloud service center and edge users through wireless networks. Edge users, such as vehicles, aircraft, and other terminal devices, have demands for content and services. Due to the fast movement of satellites, edge users are assumed to be stationary in the network model, while fog satellite nodes are in a state of high-speed motion. Each edge user locally stores historical data and content preference records. Since fog satellite nodes only cache a portion of content and services, when the cache of a fog satellite fails to match the demands of edge users, it needs to retrieve the corresponding content from the cloud server. Specifically, the following aspects are involved:  
1. Fog satellite nodes: Fog satellite nodes directly connect with edge users and provide network and content services. When the requested content is available at a fog satellite node, it interacts directly with the edge user in the form of content or services. If the requested content is not pre-cached, the fog node requests it from adjacent nodes or the cloud service center and caches the relevant content.
2. Adjacent satellites: If adjacent satellites cache the requested content, they collaborate to provide content or services to the satellite directly connected to the edge user and then continue to serve the edge user.
3. Cloud service center: The cloud service center is connected to the backbone internet. We assume it contains all content and services. Thus, when fog nodes are unable to provide content services, they initiate content retrieval requests to the cloud center, cache the content locally, and continue to provide services.
## 4 Results and Discussion
### Objective Results  
Figure 3 depicts the distributions of the Itakura-Saito spectral distances of GSSN and VTN with respect to GSSE and VTE, respectively. When analysing unstressed and stressed vowels (first two boxplots), it can be observed that GSS differences are higher for happy than for aggressive, while the opposite occurs with the VT cues. Moreover, stressed vowels present higher differences than the unstressed ones.  
If we consider the vowels, higher GSS differences are observed on [i] + ['], while [a] + ['a] present the lower ones. In between we find [u] + ['u], whose differences are halfway through the other vowels for aggressive speech, and closer to [a] + ['a] for happy. VT differences are higher for [u] + ['u], then for [i] + ['i], and finally for [a] + ['a].  
Regarding the effect of the stress, GSS distances are higher for stressed vowels than for the unstressed ones in both expressive configurations. Similarly, VT distances for ['a] are higher than [a]. Conversely, VT distances for ['i] and ['u] are lower than [i] and [u],  
Figure 3: Boxplots of the Itakura–Saito distances from GSSN to GSSE (first column) and from VTN to VTE (second column), for happy (first row) and aggressive (second row) vowels. Dotted lines represent the mean of the distributions, and whiskers are set to 5 th and 95 th percentiles.  
respectively. The stress increases the VT differences for [\(\mathrm{a}\)], while [\(\mathrm{i}\)] and [\(\mathrm{u}\)] present lower differences with respect to their unstressed counterparts.  
Figure 4 depicts the distributions of the spectral Kullback-Leibler distances from the vowels synthesised with the different configurations to the target \(\mathrm{GSS_{E}VT_{E}}\). According to the Wilcoxon test results, all the differences between configurations are statistically significant except for the \(\mathrm{GSS_{N}VT_{N}}\)-\(\mathrm{GSS_{E}VT_{N}}\) pair on happy [\(\mathrm{i}\)] vowels and \(\mathrm{GSS_{E}VT_{N}}\)-\(\mathrm{GSS_{N}VT_{E}}\) on aggressive [\(\mathrm{u}\)] vowels. Therefore, it can be observed that both GSS and VT cues make a relevant contribution to the generation of happy and aggressive vowels except in the aforementioned cases.