esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Binary Competitive Swarm Optimizer for Power System Unit Commitment
## 2 Problem Formulation
### Constraints
#### 2.2.3 Minimum Up/Down-Time Limit Constraint  
As shown in the following Formula (6), the status shifting of different units are restrained by this constraint, which is related to the \(MUT_{j}\) and \(MDT_{j}\). In the unit system, the state only has two cases. If the state of unit is 1, it means the unit is on-line, and vice versa.  
\[u_{j,t}=\left\{\begin{array}{ll}1,&if\qquad 1\leq TON_{j,t-1}<MUT_{j}\\ 0,&if\qquad 1\leq TOFF_{j,t-1}<MDT_{j}\\ 0\quad or\quad 1,&otherwise\end{array}\right\} \tag{6}\]  
In the Formula (6), \(TON_{j,t-1}\) is denoted as the continuous opening time, and \(TOFF_{j,t-1}\) is the close time. If the running time of the unit \(j\)th is less than \(TON_{j,t-1}\), the unit should be turned on. Otherwise, it should be turned off. If the close time of the unit \(j\)th is less than \(TON_{j,t-1}\), the unit should be off-line. The running time or close time is also related to the start-up economic cost.
# BDS Satellite Clock Prediction Considering Periodic Variations
## 3 Results
### Accuracy Analysis of Clock Offset Fitting  
In order to evaluate the fitting accuracy of the improved model, 30-day (DOY 200 to 229, 2020) WHU and GFZ clock offset fitting are analyzed statistically. The root mean square (RMS) error is used as the evaluation criterion of the fitting accuracy. Figure 8 gives the RMS of the QPM, the SAM, and the TFAM fitting residuals after removing the anomalies, respectively.  
Figure 8 shows the mean fitting RMS of the WHU and GFZ clock offsets, respectively. It can be seen that both the SAM and the TFAM have better fitting accuracy than the QPM. For some satellites, the TFAM performs better than the SAM, and, for others, the fitting  
Figure 8: Mean RMS of the QPM, the SAM, and the TFAM fitting residuals.  
accuracy is basically the same. Adding a time-varying main periodic term to the clock prediction model indeed improves the fitting accuracy, and for the satellites with marked periodic variations, the TFAM can further improve the fitting accuracy. The BDS-3 MEO satellites have better fitting accuracy than BDS-2 MEO satellites, which implies that the BDS-3 MEO satellite clocks have better performance in modeling. This is mainly because the BDS-3 satellites are equipped with higher quality on-board atomic clocks than BDS-2. MEO satellites present better fitting performance than the GEO and IGSO satellites, which may be caused by different orbit model performance of MEO satellites and other satellites.  
In order to further evaluate the fitting accuracy, the improvement rate of the TFAM relative to the SAM is analyzed. The improvement is given in Figure 9.  
As shown in Figure 9, the improvement of fitting accuracy is presented by considering the time-varying periodic term. The improvement is not only in WHU clock offsets, but also in GFZ clock offsets. Nearly half of the satellites have clear improvements. Affected by inconsistent periodic variations of satellites, the improvement is different for different satellites. The maximum improvement of a single satellite for WHU clock offsets is 36.4%, while it is 50.0% for GFZ clock offsets. For WHU clock offsets, the fitting accuracy improves 10.5% and 12.2% for GFZ clock offsets. It is clear that the fitting residuals are further mitigated by considering the time-varying periodic term in the clock prediction model.
## 4 Results and Discussion  
We selected a 20/40 mesh LZ sand and 20/40 mesh XY ceramic, which are widely used in China, as experimental materials. The basic properties of LZ sand are similar to those of Lanzhou quartz sand in China, with an apparent density of 2.91 g/cm\({}^{3}\), a bulk density of 1.57 g/cm\({}^{3}\), a particle size range of 0.9-0.45 mm, and a crushing rate of 40.50% under a closing pressure of 52 MPa. The basic properties of XY ceramic are similar to those of Zhengzhou ceramic in China, with an apparent density of 3.11 g/cm\({}^{3}\), a bulk density of 1.71 g/cm\({}^{3}\), a particle size range of 0.9-0.45 mm, and a crushing rate of 3.75% under a closing pressure of 52 MPa. The crushing rate and fracture conductivity of the proppant were tested, according to the standard testing procedures recommended by SY/T 5108-2006 [33] and API RP56 [34].  
According to the range of the particle size distribution of the LZ sand and the range of the particle size distribution of the XY ceramic [30; 31], the LZ sand and XY ceramic particles were subjected to computer simulations and mechanical analyses, respectively. The crushing rate and the fracture conductivity were obtained by numerical calculations.
# FEA-Based Ultrasonic Focusing Method in Anisotropic Media for Phased Array Systems
## 6 Conclusions  
A new FEA-based ultrasonic imaging methodology for austenitic welds is proposed in this paper. The methodology is composed of four steps: (1) measurement or prediction of grain orientation distribution and the anisotropic elastic constants; (2) simulation of wave propagation behavior; (3) construction of a wave propagation time database; and (4) computation of TFM intensity and TFM imaging. In this study, the grain orientation distribution was measured by a macrograph of the DMW specimen, and a new measurement method of the elastic constants was proposed using the measured grain orientation information and an optimization technique. The ultrasonic wave propagation behavior was calculated through FEA using the grain orientation and elastic constant information, after which the ultrasonic wave propagation time database was extracted. Finally, an FMC matrix in the phased array system was calculated through a series of finite element analyses for the simulated model embedded in a defect, and a TFM image was generated. The proposed TFM imaging results have a significant improvement in the performance of defect localization accuracy compared with basic TFM imaging results, and the usefulness of the proposed TFM imaging methodology in this study is validated.  
Conceptualization, S.M.; methodology, S.M.; software, H.S.; validation, S.M. and T.K.; formal analysis, S.M. and M.K.; investigation, S.M., T.K., H.-H.J. and S.H.; resources, S.M.; data curation, S.M.; writing--original draft preparation, S.M.; writing--review and editing, S.M., S.-W.K. and K.-M.K.; visualization, S.M.; supervision, S.M.; project administration, S.-W.K. and K.-M.K.; funding acquisition, S.-W.K. and K.-M.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (2021M2E4A1037979 and 2017M2A8A4015158).  
The authors declare no conflict of interest.
# The State-of-the-Art of Sensors and Environmental Monitoring Technologies in Buildings
## 3 Communication Technologies and Protocols
### Zigbee  
Zigbee (IEEE 802.15.4) is a global networking standard designed to use low-power digital radio signals for wireless personal area networks (WPAN). The technology operates in the 2.4 GHz and 900 MHz bands, supports star, tree and mesh topologies (enabling enhanced scalability, high reliability and a potentially broader coverage range), and is typically used to create communication networks that require a low data transfer rate (maximum of 250 kbps), and low power networking over a short distance (10-100 m) [92; 93]. A Zigbee network consists of three types of nodes, i.e., Zigbee Coordinator (Full-Function Device (FFD)) responsible for initializing a session, selecting frequency, and managing security), Zigbee Router (FFD responsible for relaying messages to other nodes) and Zigbee End Device (Reduced-Function Device (RFD)) able to send and receive messages but perform no other special tasks. Despite limitations, such as low data rate and short range, Zigbee finds its applications in key areas within buildings such as security systems, building automation systems (BAS), and for smart meter and sensor readings, which is attributed to its high reliability, security, low power consumption and very low cost in comparison to other technologies. Moreover, Zigbee Alliance, an association of companies offering wireless communication solutions worldwide, have developed a wide range of standards, such as Zigbee Building Automation, Zigbee Remote Control, Zigbee Smart Energy, Zigbee Smart Energy Profile 2 and Zigbee Home Automation, to cater for different user requirements with regards to building applications [94; 95].
## 3 Results
### Logistic Regression Model
#### 3.1.3 Variables That Passed the Test in the Mode  
The results of the logistic regression model showed that the screened influencing factors (\(p<0.05\)) were age (\(OR=1.061\)), gender (\(OR=0.257\)), alcohol consumption (\(OR=1.473\)), the presence of infectious diseases (0.597), fibrinogen (\(OR=0.709\)), the international normalized ratio of prothrombinogen (\(OR=0.261\)), albumin (\(OR=0.980\)), erythrocyte-specific volume (\(OR=0.791\)), platelet distribution width (\(OR=0.743\)), mean hemoglobin content (\(OR=0.817\)), percent neutrophils (\(OR=1.019\)), neutrophil count (\(OR=0.878\)), hemoglobin (\(OR=1.087\)), and percent large platelets (\(OR=1.020\)), as shown in Table 1.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Parameters** & **SE (Partial)** & **Biased Coefficient** & **Wald \(\chi^{2}\)** & \(p\)-Value** & \(OR\) **(95\% CI)** \\ \hline General Indicators & & & & & \\ (a person’s) age & 0.004 & 0.057 & 252.332 & \(<\)0.01 & 1.061 (1.051–1.066) \\ Distinguishing between & 0.102 & \(-\)0.538 & 28.039 & \(<\)0.01 & 0.257 (0.155–0.389) \\ the sexes & 0.108 & 0.314 & 8.357 & \(<\)0.05 & 1.473 (1.106–1.692) \\ Drinking wine & 0.151 & \(-\)0.451 & 9.239 & \(<\)0.05 & 0.597 (0.477–0.852) \\ \hline Coagulation Test & & & & & \\ Fbg & 0.049 & \(-\)0.342 & 48.311 & \(<\)0.01 & 0.709 (0.645–0.782) \\ PT-INR & 0.227 & \(-\)1.403 & 35.856 & \(<\)0.01 & 0.261 (0.155–0.389) \\ \hline Liver and Kidney & & & & & \\ Function & & & & & \\ ALB (X9) & 0.012 & \(-\)0.025 & 3.922 & \(<\)0.05 & 0.980 (0.952–1.000) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Logistic regression analysis of factors affecting chronic atrophic gastritis.
## 3 Proposed Scheme  
### Initialization Phase  
#### 3.1.1 Initializing Pairwise Number \(k\) and Session Key  
For every billing period, the CC generates randomly the pairwise number for every node in one neighborhood denoted as \(k\), and broadcasts it to all SMs.  
We generate session keys between every node with the computational DH key exchange protocol as the initial key in RC4 to generate the keystream between pairwise nodes. Once one node joins a neighborhood size of \(n\), it generates itself one DH public key \(g^{a}\) (mod \(M\)) and remains the secret key \(a\), \(M\) are DH parameters, and then broadcasts the public key. By this Computational Diffie-Hellman CDH exchange key, any two pairwise nodes can identify their session key formed as \(g^{ab}\).
# Multi-Backup Beams for Instantaneous Link Recovery in mmWave Communications
## 3 System Model
### Channel Model  
The geometric channel model, \(\mathbf{H}\), is adopted here due to the scattering nature of mmWave propagation. This is highly attributed to the large obstacle dimensions, as compared to the propagating wavelength at these bands. Consequently, this yields in reduced scattering profile, and hence results in poor scattering signal profile of a low number of rays, i.e., Poisson distribution. In turn, this results in high dependence on the geometry of the objects in the propagation link. This model is expressed as [17]  
\[\mathbf{H}=\sqrt{\frac{N_{\text{BS}}N_{\text{MS}}}{\Gamma_{bl}}}\sum_{k=1}^{K} \sum_{l=1}^{L}h_{l}V_{\text{BS}}\mathbf{U}_{\text{MS}}^{\text{H}}, \tag{8}\]  
where the variables \(\Gamma_{bl}\), and \(h_{l}\) represent the blockage path loss model, and the gain of the \(l\)-th path. The signal profile here, is composed of L total number of paths that are observed in \(K\) total number of clusters, i.e., \(L\in K\). These paths follow Rician-distribution that accounts for the LoS-to-NLoS plink  
Figure 2: Digital beamformer at the BS.  
transition caused by blockage effects. Namely, the path gain is modeled as \(h_{l}\sim R\ (0,\ \zeta)\), where \(\zeta\) is the power ratio between the dominant and other paths. Moreover, the beamforming and combining matrices, \(\mathbf{V_{BS}}\) and \(\mathbf{U_{MS}}\) (which also represent the response vectors), are evaluated using their far-field array factors (AF), as presented in the beamforming models.
## 7 Conclusions  
In this paper, an energy market management for a DN and an MMG based on a dynamic game is proposed, and an electricity price incentive mechanism is studied. As a leader, the DNO sets an electricity selling and buying price with the minimum daily operating cost while satisfying the voltage and power constraints of the DN. As a follower, an MGO optimizes the transferable load and energy storage according to the electricity price. The differential evolution algorithm and mixed integer programming method are applied to obtain the equilibrium solution for the game strategy. Numerical simulations of the modified IEEE-33 bus system validate the effectiveness of the proposed strategy. Compared with the grid electricity price, the optimized electricity price can effectively improve energy sharing levels between MGs, reduce the operating cost of the system and ensure the reliability and stability of the system operation. Future work will consider the influence of microgrids with different characteristics and seasonal variations on the energy management of the distribution network using the proposed strategy.  
**Author Contributions:** Formal analysis, C.H., W.S., C.Z. and B.W.; Funding acquisition, C.H.; Methodology, X.T. and T.R.; Writing--original draft, X.T.  
**Funding:** This work was supported by the National Key R&D Program of China (Nos. 2016 YFB0900400).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Nomenclature**  
**Abbreviations**  
DN & The distribution network  
DNO & The distribution network operator  
MG & The microgrid  
MMG & The multi-microgrid  
MOO & The microgrid operator  
PV & The photovoltaic  
**Symbols**  
\(P_{S}^{l}\) & The selling price of the DNO at time t  
\(P_{i}^{l}\) & The buying price of the DNO at time t  
\(P_{S}^{l}\) & The selling price of the DN at time t  
\(P_{i}^{l}\) & The buying price of the DN at time t  
\(S_{i}^{b}\) & The operation cost of the \(i\)-th MGO  
\(C_{\text{MGO},i}\) & Total net power purchase load  
\(nI_{s,i}^{l}\) & Total net load of electricity sold  
\(ess_{i}\) & The energy storage of the \(i\)-th MGO  
\(l_{i}^{l}\) & The transferable value of load in the \(i\)-th MG  
\(C_{\text{DNO}}\) & The operating cost of the DNO  
\(C_{\text{DN}}^{t}\) & The transaction costs with the DN  
\(C_{\text{MG}}^{t}\) & The transaction costs with the MGO  
\(\Pi\) & The penalty function  
\(v_{i}^{t}\) & The voltage of node \(i\)
# An Untraceable Data Sharing Scheme in Wireless Sensor Networks
## 5 Secure Data Sharing between the Source Node and Sink Node
### Message Splitting  
Initially, the source node generates message \(X\) and then we map \(X\) to a set of message segments through interleaving encoder. In this paper, we attempt to design a \((t,n)\)-secret sharing scheme and hence the original message \(X\) needs to be split to exactly \(t\) sub-messages with equal lengths. For convenience sake, we assume that \(X\) is denoted as a binary number and the size, \(|X|\), where \(X\) is defined as the number of bits. If the size of \(X\) cannot be divided by \(t\), we fill \(X\) by a set of specific symbols at the tail until it can be divided into \(t\) equal-length sub-messages. Then, we split \(X\) to a set of sub-messages through an interleaving encoder. As an example, in Figure 4, we split \(X\) to four sub-messages, \(x_{1},x_{2},x_{3},x_{4}\), and each sub-message contains a part of message \(X\). Moreover, each sub-message is composed of four message fragments which are discontinuously extracted from \(X\). In this way, each sub-message is of no clear semantic information. In other words, even if the plaintext of a sub-message is captured by the adversaries, they cannot get valuable information about message \(X\). However, the sink node can recover \(X\) by an interleaving decoder once it can get \(x_{1},x_{2},x_{3},x_{4}\).  
Figure 4: Message splitting based on interleaving encoder.  
Figure 3: Process of data sharing between the source node and sink node.
## 1 Introduction
Subsequently, we [8] combined the abovementioned model and the empirical equation of the cooling capacity in the absorption subsystem to develop the SASCHCS model. Because such models depend on the fixed heat transfer temperature difference, it is difficult to derive the exact off-design performance of the system. Therefore, the traditional off-design modeling of the refrigeration system is done by the lumped parameter describing each component according to the size and heat transfer characteristics. Although the abovementioned method is used widely in the off-design modeling of a normal absorption chiller and vapor compression chiller [18; 19; 20], it is unfeasible for the SASCHCS. First, the number of iterations for model solving is largely owed to the increased components (approximately seven heat exchangers), i.e., the number of iterations is up to 107 even when the iteration number of each component is only 10, thus leading to the long solving time. Next, the convergence becomes extremely difficult as the operating condition deviates severely from the design condition owing to the complicated iteration. Consequently, it is essential to simplify the iteration to develop the off-design model of the hybrid system. Because the off-design modeling of the SASCHCS relies primarily on the coupling of the cooling output in the absorption subsystem and the thermodynamic characteristics of the compression subsystem component, the iteration can be simplified if the absorption subsystem is treated as a black box and is described by a simple equation that avoids complicated calculations, i.e., the characteristic equation [21] or the empirical expression [22]. Inspired by this idea, we [23] modeled and simulatedthe hybrid system with some simplifications, in which the absorption subsystem is hypothesized to be a subcooler with fixed subcooling power, and the entire system is equivalent to a vapor compression chiller with a subcooler. It was demonstrated that the solving time reduces significantly and the model converged easily in the overall conditions. We found that the abovementioned methodology is effective for off-design modeling of the SASCHCS. Therefore, we attempted to build the off-design model of the hybrid system in the previous foundation. The system is described by coupling the characteristic equation of the absorption subsystem and the lumped parameter model of the compression subsystem. Subsequently, a prototype and its corresponding experimental system are developed to verify the model. Moreover, the thermodynamic performance for different conditions is simulated and analyzed in detail. The novelty of our present study is the development of an exact and easily solved off-design model for the SASCHCS. It is mentioned that the off-design model is not dynamic but quasi-static because the model is mainly employed to analyze the performance for different conditions instead of variation processes. This study can serve as a foundation for the operation analysis of the SASCHCS as well as promote its development.
# Flow Sensing-Based Congestion Detection for D2D Streaming on a 5G gNB
## 2 Related Work
proposedan optimal video streaming solution in a dense 5G network with D2D communications [12]. This solution focused on an optimal rate allocation and description distribution for high-performance video streaming, particularly achieving a high QoE for energy efficiency while restricting co-channel interference over D2D communications in 5G networks. This algorithm optimizes the video segments by characterizing channel state information of the D2D link. [PERSON] et al. proposed a solution to mitigate the expected downloading time; this solution was an algorithm for video streaming by applying the skewed behavior of users and the cooperation between the base station and small cells [26]. However, this solution does not satisfy the characteristics of wireless channels and encoding techniques of D2D streaming. [PERSON] et al. proposed a solution to solve the problem of balancing the benefit among the cooperators, who seek to join a cooperative video streaming session [27]. This solution avoids unfairness since the cooperators with better downlinks, i.e., higher download rates, expend more cellular traffic and incur higher costs. To improve the reliability of D2D communications and measure the streaming rate in D2D links, [PERSON] et al. proposed a cluster-based interference management algorithm [28; 29]. This solution is that a cluster is considered as a frequency reuse for its own D2D pairs. [PERSON] et al. proposed a clustering algorithm for real-time video streaming between DUEs and CUEs on LTE networks [30]. This solution detects the requested videos with consumed energy power and reconstructed distortion. The objective of this is to manage more media flow by taking into account the collaboration between the BS and DUEs. Generally, DUEs and CUEs under an excellent D2D communication environment may enjoy a high-quality media streaming service, while users with poor network service may not. The existing flow control-based solutions does not satisfy QoS for users because the traffic bit rate is unequal to the bandwidth for flow streaming objects. Recently, various solutions have been proposed to emphasize the importance of accessing multimedia objects with minimal latency, such as text, audio, graphics, images, animation, video, and interactive content [2; 14; 12]. An efficient method of solving the latency of the D2D streaming in the 5G-based wireless mobile network is to apply D2D communication technology for multimedia delivery. Therefore, this paper proposes a solution to optimize the conditions of multimedia delivery to satisfy the requirements of D2D streaming in the 5G-based wireless mobile network.
# The 6G Ecosystem as Support for IoE and Private Networks: Vision, Requirements, and Challenges
## 5 Use Cases and Applications
### High-Performance Precision Agriculture  
The synergy between environmental data collection and agriculture finds realization within an IoT-driven ecosystem. In precision farming, IoT devices and sensors are strategically positioned on plants, such as within intelligent greenhouses, to facilitate data acquisition. The paramount requirements for these devices include compact size, energy efficiency to enable ultra-dense deployment, and prolonged operational longevity [91].  
Existing communication technologies for wireless sensor networks, for example, IEEE 802.15.4-based protocols, are energy-intensive and large, so they can be combined with nano UAVs or UGVs to support real-time and high-performance precision farming applications for online crop or soil monitoring tasks. Creating this communications scenario, focused on symbiotic communications and ubiquitous wireless access, between traditional WSNs, cellular and UAVs, or UGVs can improve active radios' energy efficiency and operating time, countering the usual coverage gaps in rural areas [66; 97].