esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulations and Discussions  
A comparative simulation is performed between the proposed systems and the conventional system composed of ESS and WF operated by MPPT control in order to validate the proposed systems composed of ESS and WF with KE control (the MA KE or the optimal KE control) based on the cost. We investigate two types of ESS: Li-ion and NaS battery. The real wind speed data (Five typical scenarios are selected for the analysis.) was measured at a WF in Hokkaido, Japan. These data were sampled every 3 s and their average and standard deviation (SD) are listed in Table 2. One Scenario is shown Figure 8. The costs and profits can be estimated accurately based on the simulation results that were obtained through enough wind data measured in four seasons. However, we do not have the data. As an alternative, we conduct one hour simulation based on five typical scenarios. The analysis condition, the parameters of ESSs and the controller parameters of WG and ESS are listed in Tables 3-5. Currently, the wind power price is 16 JPY/kWh in Japan [31]. However, we calculate the cost of the system based on the wind power price of 8 JPY/kWh, because the price will be cheaper in the near future. The price of batteries and the PCS are slightly different makers. The price was determined based on [10; 26; 32; 33]. In addition, the efficiency of the li-ion and NaS battery is within 85% to 95%, respectively. Therefore, it is assumed that these batteries have the same efficiency in this analysis. We set the number of steps for MA as 10 times larger than a 1-min. window in the technical requirement (\(1200\cdot T_{\text{s}}=600\) s). \(K_{\text{q}}\) and \(\alpha\) are determined using Figure 6, so that \(q\) becomes large when \(\Delta P_{\text{WF\_pu}}\geq 0.1\). In the simulation, \(K_{\text{p}}\) is determined by scenario simulations in order to ensure the technical requirement. Note that \(K_{\text{p}}\) of the conventional system is the highest among the systems, because the conventional system does not have the KE control. The parameters for \(a_{0}\) and \(a_{1}\) are the same as [14].  
The simulation was performed on MATLAB/Simulink 2019b and the simulation period was 3600 s. The time responses for scenarios 2 to 5 are omitted.  
Figure 7: Life cycles vs. depth of discharge.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Scenario 1**} & \multicolumn{2}{c}{**Scenario 2**} & \multicolumn{2}{c}{**Scenario 3**} & \multicolumn{2}{c}{**Scenario 4**} & \multicolumn{2}{c}{**Scenario 5**} \\ \hline  & **Avg.** & **SD** & **Avg.** & **SD** & **Avg.** & **SD** & **Avg.** & **SD** & **Avg.** & **SD** \\ \hline \(V_{1}\) & 7.30 & 1.36 & 8.45 & 1.61 & 8.34 & 1.72 & 10.4 & 2.15 & 10.4 & 2.15 \\ \(V_{2}
# Influencing Factors Analysis and Simulation Calibration of Restitution Coefficient of Rice Grain
## 2 Materials and Methods
### Test Principle
The restitution coefficient \(e\) of rice grain with the inclined collision platform is decomposed into the normal restitution coefficient \(e_{n}\) and the tangential restitution coefficient \(e_{t}\). Then:  
\[\left\{ \begin{array}{l}e_{n}=\frac{\left|v_{x^{\prime}}\right|}{v_{0^{ \prime}}}=\frac{2t_{m}\left|v_{z}\sin\alpha_{i}-v_{y}\cos\alpha_{i}\right|}{ \left|(z_{m}-z_{0})+gt_{m}^{2}\right|\sin\alpha_{i}}\\ \\ e_{t}=\frac{\sqrt{v_{x}^{2}+v_{y}^{2}}}{\left|v_{0^{\prime}}\right|}=\frac{2t _{m}\sqrt{v_{x}^{2}+(v_{z}\cos\alpha_{i}+y\sin\alpha_{i})^{2}}}{\left|(z_{m}- z_{0})+gt_{m}^{2}\right|\cos\alpha_{i}}\end{array} \right. \tag{8}\]  
Equation (8) is simplified to obtain the restitution coefficient between rice grain and the inclined collision platform after the coordinate transformation. \[e=\sqrt{e_{n}^{2}+e_{t}^{2}}=\frac{2t_{m}\sqrt{v_{z}^{2}+\left[v_{z}+v_{y}( \tan\alpha_{i}-\frac{1}{\tan\alpha_{i}})\right]^{2}+2v_{y}^{2}+\frac{v_{y}^{2} }{\cos^{2}\alpha_{i}}}}{2(z_{m}-z_{0})+gt_{m}^{2}} \tag{9}\]It can be seen from Equations (6) and (9) that when the initial falling velocity of rice grain and the collision angle \(\alpha_{i}\) are fixed, the restitution coefficient between rice grain and various contact materials can be obtained by measuring the displacement change of rice grain before and after collision with the high-speed camera. The motion of rice grains after collision is decomposed into horizontal uniform motion and vertical uniform deceleration motion. The instant of rice grain bounce-off from the collision interface is sampled and recorded as the initial point. The coordinates of this point in the spatial reference wall are \((y_{0},z_{0})\), and in the mirror are \((x_{0},z_{0})\). When the rice grain bounced a certain height and moved \(n\) frames, the movement time is \(t_{n}\). The coordinate value in the spatial reference wall is \((y_{n},z_{n})\), and in the reflecting mirror is \((x_{n},z_{n})\). Then, the instantaneous moving speed in the horizontal direction is obtained. \[v_{x}=\frac{x_{n}-x_{0}}{t_{n}} \tag{10}\]  
\[v_{y}=\frac{y_{n}-y_{0}}{t_{n}} \tag{11}\]  
The rice grain moved at a uniform deceleration in the vertical direction, and the instantaneous velocity in the middle is equal to the average velocity in this period. \[\overline{v}_{n}=\frac{z_{n}-z_{0}}{t_{n}} \tag{12}\]  
\[v_{z}=\overline{v}_{n}+\frac{1}{2}gt_{n} \tag{13}\]  
Equation (10), (11), and (13) are combined into Equation (9), and the relationship between the restitution coefficient and displacement variation is obtained. \[e=\frac{2t_{m}\sqrt{\frac{\left(x_{n}-x_{0}\right)^{2}+\left(y_{n}-y_{0} \right)^{2}+\left(z_{n}-z_{0}\right)^{2}}{t_{n}^{2}}+g(z_{n}-z_{0})+\frac{g^{2 }t_{n}^{2}}{4}}}{2(z_{m}-z_{0})+gt_{m}^{2}} \tag{14}\]  
In the process of the high-speed camera test, the restitution coefficient between rice grain and various contact materials can be calculated by collecting the coordinate values of the rice grain centroid between two pictures and substituting Equation (14).
# Smart Grid Theft Detection Based on Hybrid Multi-Time Scale Neural Network
## 3 Proposed Method
### Time GAN Generation of Data  
From the analysis in Section 3.1, it was evident that the ratio of normal electricity consumers to electricity theft consumers in the SGCC dataset was close to 10:1. Thus, the model constructed from such a dataset may be biased toward normal electricity customers, which may easily result in a lower accuracy rate of electricity theft customers [28]. Methods commonly used to solve such problems include random under sampling (RUS) and random over sampling (ROS) to reduce the quantitative differences between the two types of data [29]. The RUS technique randomly discards majority class data by random under sampling, which reduces the dimensionality of the dataset, but the random removal of data may discard potentially important information. The ROS technique increases the dataset size by replicating a few classes of data, and although this method does not discard any information, the developed model can suffer from overfitting problems because it is a simplistic replication of the data. In this paper, data were generated using the Time GAN algorithm, which is a generative adversarial network that can generate new samples from existing electricity theft data, thus expanding the scale of electricity theft data and making the ratio of electricity theft data and normal data relatively balanced. By increasing the samples of electricity theft data, it can improve the detection ability of the model for electricity theft users and improve the classification accuracy of the model. At the same time, the new samples generated by Time GAN also have high realism and can be used for training and testing of the model. This paper evaluated the effectiveness of Time GAN in addressing the data imbalance issue. To this end, different methods were compared in a series of experiments to determine the performance of the models. The results demonstrated the efficacy of Time GAN in solving the data imbalance problem.
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 5 Utilization of AI Analysis in the IoMT
### Model Selection and Training
#### 5.4.2 Role of Segmentation Models  
Segmentation plays a pivotal role in understanding complex environments. The process of segmentation can be broadly categorized into semantic segmentation, instance segmentation, and panoptic segmentation [123]. Semantic segmentation enables systems to recognize various elements within a scene, such as people, furniture, or vehicles, facilitating context-aware decision-making. In other words, semantic segmentation categorizes each contents of an image into 'things' or'stuff'. Instance segmentation enhances this by allowing systems to identify and track specific objects or individuals, that is, they highlight individual instances of 'things'. Thus, instance segmentation enables personalized interactions and tailored responses. Panoptic segmentation further enriches the understanding by combining both semantic and instance-level information, empowering IoMT systems to interpret scenes comprehensively and adaptively respond to dynamic environments. This segment provides a brief discussion about the application of semantic segmentation algorithms (Table 10), instance segmentation algorithms (Table 11), and panoptic segmentation algorithms (Table 12) in the healthcare context. Semantic segmentation models such as FCN [123], SegNet [123], UNet [123], and DeconvNet [123] have significantly advanced segmentation capabilities through innovative architectures and learning strategies. FCN, a pioneering architecture, demonstrated end-to-end learning for pixel-wise segmentation, utilizing both downsampling and upsampling paths to capture contextual information and recover spatial semantics. UNet, widely used in medical imaging, employs an encoder-decoder structure to extract features and derive semantically meaningful information. SegNet and DeconvNet also follow this architecture, emphasizing feature extraction and upsampling for effective segmentation. FusionNet [123], a variant of UNet, incorporates \"summation-based skip connections\" to create a deeper network, resulting in enhanced data abstraction capabilities. FuseNet [123], on the other hand, introduces cross-modal fusion to capture enhanced local and global characteristics from images, which is particularly beneficial in complex scenarios. E-Net [123] utilizes filter factorization to reduce network complexity while maintaining performance. These architectural innovations contribute to improved semantic segmentation performance across various applications.
# Intelligent Communication in Wireless Sensor Networks
## 3 The Proposed Strategy
### Detecting Source Nodes  
After the base station receives a new task requested by an application, first, the base station broadcasts interest packets to detect the SNs that perform the task, according to the direct diffusion routing protocol. As a result, it generates a message (interest), and then sends it to all its neighboring nodes in a single hop. These neighbors send this message to their neighbors, and so on. The purpose of this message is to discover all the SNs that perform the task, as well as to know and identify all the sensors of our sensor network.
# A Predefined-Time Control for the Laser Acquisition in Space Gravitational Wave Detection Mission
## 3 Mathematical Model  
### Reference Coordinate Systems  
Earth-Centered Inertial frame \(\sum_{I}(Ex_{i}y_{i}z_{i})\): the equatorial plane at epoch J2000 is used as a base plane, the origin is located at the mass center of earth, the \(x_{i}\)-axis pointing to the mean vernal-equinox at epoch J2000, the \(z_{i}\)-axis normal to the mean equator at epoch J2000 and the \(y_{i}\)-axis completing a right-handed orthonormal reference system.  
Orbital coordinate frame \(\sum_{O}(Ox_{3}y_{o}z_{o})\): the origin is located at the mass center of the satellite, the \(z_{o}\)-axis is along the direction that from the satellite's mass center to the Earth's mass center, the \(x_{o}\)-axis in the orbital plane is perpendicular to \(z_{o}\)-axis and pointing to the motion direction and the \(y_{o}\)-axis is perpendicular to the orbital plane and form a right-handed triad.  
Spacecraft body coordinate frame \(\sum_{0}(Ox_{3}y_{5}z_{b})\): The origin of this coordinate system is located at the mass center of the satellite and its axes are fixed with the satellite body. The \(z_{b}\)-axis is located on the angular bisector between the two telescopes, the \(y_{b}\)-axis pointing toward the bottom panel and the \(x_{b}\)-axis forming a right-handed triad.  
Telescope swing mechanism coordinate frame \(\sum_{1}\) and \(\sum_{2}(Ox_{1}y_{1}z_{1}\) and \(Ox_{3}y_{5}z_{3})\): The origin is located at the telescope's rotation center axis, and the \(x_{j}\)-axis (\(j\) = 1,3) is along the telescope's nominal line of sight, the \(z_{j}\)-axis (\(j\) = 1,3) is along the axis of rotation and the \(y_{j}\)-axis (\(j\) = 1,3) forms a right-handed triad. In addition, \(j\) = 1 for the left one and \(j\) = 3 for the right one.  
Telescope body coordinate frame \(\sum_{2}\) and \(\sum_{4}(Ox_{2}y_{2}z_{2}\) and \(Ox_{4}y_{4}z_{4})\): This coordinate frame is fixedly connected on the telescope. The \(x_{j}\)-axis (\(j\) = 2,4) is along the telescope's line of sight, and the \(z_{j}\)-axis (\(j\) = 2,4) is along the axis of rotation, then \(y_{j}\)-axis (\(j\) = 2,4) can be determined by the right-handed rule. Additionally, \(j\) = 2 for the left one and \(j\) = 4 for the right one.  
The detailed coordinate systems are shown in Figure 7.  
Figure 7: Reference coordinate systems. (**a**) Satellite coordinate systems; (**b**) telescope coordinate systems.
# Optimized Accelerated Over-Relaxation Method for Robust Signal Detection: A Metaheuristic Approach
## 1 Introduction  
The rapid advancements in wireless communication are enabling better connectivity and faster data transfers [1]. As we move from 4G/5G to future-generation networks such as beyond 5G and 6G, there is a growing demand for higher data rates, especially due to the large number of devices in the Internet of Things (IoT) [2] and sensor networks [3, 4]. This evolution also requires reduced latency and enhanced spectrum efficiency, which are becoming increasingly significant [5, 6, 7, 8]. Thus, massive MIMO systems are essential for meeting the growing demands of higher data rates. Massive MIMO effectively accomplishes these growing demands because of hundreds to thousands of antennas at the BS [9]. This type ofarrangement and other benefits provide improved spatial multiplexing, beamforming, and interference reduction [10]. However, the development and deployment of large systems like massive MIMO is challenging, especially in signal detection [11], due to the complex characteristics of received signals. High-performance and efficient detection algorithms are needed to maximize their full capabilities while minimizing computational costs [12]. The challenge of efficient signal detection in large-scale antenna arrays has garnered significant attention in recent research [13]. The performance of massive MIMO systems is strongly influenced by the complexity of the detection algorithms employed, as these algorithms often require extensive computational resources. Although the maximum-likelihood (ML) detector is recognized for its optimal performance [14], its computational demands grow exponentially with the number of transmitters [15], as it involves evaluating all possible transmitted symbols. To address this, simple linear detectors like zero-forcing (ZF) and linear minimum mean square error (MMSE) are commonly used. These detectors offer a good balance between computational efficiency and performance [16]. In the massive MIMO regime, linear MMSE is particularly appropriate due to its favorable trade-off between complexity and performance [17]. As the number of antennas increases, it approaches optimal estimation performance due to channel hardening effects, while maintaining relatively low computational complexity [18]. It also improves signal quality by mitigating the impact of noise and interference due to the large number of antennas [19]. This makes linear MMSE an excellent choice for practical massive MIMO implementations, offering near-optimal performance. So it makes linear MMSE very easy to use but has limited effectiveness in computational complexity, particularly in situations with heavy system loads [20]. The reason behind high complexity is the complicated operation of extensive matrix inversion, and this brings great challenges in execution.
## 5 Designs of TDMA Protocol-Based Distributed Coalition Formation Games
### Network Configuration Protocol  
The state transitions of the FSM depicted in Figure 3 are controlled by the Network Configuration Protocol. The state-transition conditions are described as follows:  
1. Initiation: When a UAV node initially starts up, it enters the quasi-member state.
2. Discovering network header: When the quasi-member and quasi-network header UAV node receive the invite-to-join (ITJ) message from the network header again, they transform to the member state.
3. Losing contact with the network header temporally: When the member UAV node cannot receive the schedule assignment broadcast periodically from the network header over the limit time, the state of the UAV node changes from member to quasi-member.
4. Losing contact with network header completely: When the quasi-member UAV node cannot receive the schedule assignment ITJ message two consecutive times, the UAV node enters the quasi-network header state. The network header will delete this  
Figure 3: FSM of our proposed scheme.  
UAV node from the member list if it cannot receive packets from this UAV node three consecutive times.
5. Electing network header: When acting as a quasi-network header over a limited time, this UAV node elects itself as a network header and broadcasts the ITJ advertisement message periodically. The other quasi-network header UAV node joins its network and forwards the ITJ advertisement message when it receives the ITJ advertisement message.
6. Meeting another network header: When a network header receives another valid ITJ advertisement message, it enters the quasi-network header state and compares the node identification with another network header. The UAV network header with smaller identification then becomes a new network header.
7. Merging by another network: When a network header receives another valid ITJ advertisement message, the UAV network header with smaller identification merges another network. The UAV network header with larger identification joins the new network and changes to the member state.
# A Hybrid Routing Protocol Based on Naive Bayes and Improved Particle Swarm Optimization Algorithms
###### Abstract  
Clustering of sensor nodes is a prominent method applied to wireless sensor networks (WSNs). In a cluster-based WSN scenario, the sensor nodes are assembled to generate clusters. The sensor nodes also have limited battery power. Therefore, energy efficiency in WSNs is crucial. The load on the sensor node and its distance from the base station (BS) are the significant factors of energy consumption. Therefore, load balancing according to the transmission distance is necessary for WSNs. In this paper, we propose a hybrid routing algorithm based on Naive Bayes and improved particle swarm optimization algorithms (HRA-NP). The cluster heads (CHs) are selected according to the CH conditional probability, which is estimated by the Naive Bayes classifier. After the selection of the CHs, the multi-hop routing algorithm is applied to the CHs. The best routing path from each CH to the \(BS\) is obtained from an improved particle swarm optimization (PSO) algorithm. Simulations were conducted on evaluation factors such as energy consumption, active sensor nodes per round, the sustainability of the network, and the standard deviation of a load on the sensor node. It was observed that HRA-NP outperforms comparable algorithms, namely DUCF, ECRRS, and FC-RBAT, based on the evaluation factors.  
wireless sensor network; routing protocol; clustering; energy consumption optimization; channel model; naive Bayes +
Footnote †: journal: Electronics  
0
Footnote 0: Copyright** © 2022 by the authors. Liconsee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 3 Coherence Detection and Inland Water Body Monitoring
### Inland Water Body Detection
At the same time, spaceborne GNSS-R technology will play an increasingly important role in inland water monitoring, providing strong support for achieving the sustainable utilization of water resources and environmental protection.
# Performance Analysis of RIS-Assisted SatComs Based on a ZFBF and Co-Phasing Scheme
## 2 System Model
### RIS-Assisted SatCom Channel Model
In (1), \(\mathbf{D}_{k}\) is a rain fading matrix, and \(\mathbf{B}_{k}\in\mathbb{C}^{N\times K}\) is the coefficient matrix whose entries represent the gains of the beam radiation pattern with path loss between the satellite and RIS \(k\), respectively, given as [18]:  
\[\mathbf{D}_{k} =|c_{k}|\exp(j\mathbf{\psi}_{k})\mathbf{I}_{N}, \tag{6}\] \[\mathbf{B}_{k} =\sqrt{\xi_{k}}\mathbf{a}_{k}^{\text{T}}\otimes\mathbf{1}_{N\times 1}. \tag{7}\]  
In (7), \(\xi_{k}\) and the entries of \(\mathbf{a}_{k}\in\mathbb{R}^{K\times 1}\) are the coefficients representing, respectively, the effects of the path loss and beam pattern as given by:  
\[\xi_{k}=\bigg{(}\frac{\lambda}{4\pi d_{k}}\bigg{)}^{2}, \tag{8}\]  
Figure 1:0  
where \(a_{k,m}\) is the \(m\)th element of \(\mathbf{a}_{k}\), \(d_{k}\) is the distance from the satellite to RIS \(k\), \(G_{\text{max}}\) is the maximum gain of the satellite beam, \(J_{n}(\cdot)\) is the first-kind Bessel function of order \(n\), and \(u_{k,m}=2.07123\frac{\sin\phi_{k,m}}{\sin\phi_{3}\text{dB}}\). Also, \(\phi_{3\text{dB}}\) and \(\phi_{k,m}\) are the 3 dB angle of the beam and the angle between the center of beam \(m\) and RIS \(k\), respectively. In (6), \(|c_{k}|\) is the rain fading channel gain that follows a double-log-normal distribution such as \(|c_{k}|\sim\mathcal{L}^{2}\mathcal{N}(\eta_{k},\sigma_{k}^{2})\), and \(\psi_{k}\) is the phase of the fading channel at RIS \(k\) that is distributed uniformly over \(\left[0,2\pi\right)\), as in [18]. Hence, the elements in \(\mathbf{a}_{k}\) and \(\mathbf{B}_{k}\) are deterministic values depending on the locations of the satellite and UEs.
# Impact of Heart Rate Fragmentation on the Assessment of Heart Rate Variability +
###### Abstract  
Heart rate fragmentation (HRF) is a type of sinoatrial instability characterized by frequent (often every beat) appearance of inflection in the R-R interval time series, despite the electrocardiograms appearing to be sinus rhythm. Because the assessment of parasympathetic function by heart rate variability (HRV) analysis depends on the assumption that the high-frequency component (HF, 0.15-0.4 Hz) of HRV is mediated solely by the cardiac parasympathetic nerve, HRF that is measured as a part of HF power confounds the parasympathetic functional assessment by HRV. In this study, we analyzed HRF in a 24-h electrocardiogram big data and investigated the changes in HRF with age and sex and its influence on the assessment of HRV. We observed that HRF is often observed during childhoods (0-20 year) and increased after 75 year, but it has a large impact on individual differences in HF power at ages 60-90.  
electrocardiogram; heart rate; heart rate variability; heart rate fragmentation; autonomic nervous function; bio-signal processing Article