esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
[10] obtained an IGSO and MEO satellite orbit precision of 10-20 cm and GEO satellite orbit precision of several meters, and satellite laser ranging (SLR) residuals were better than 10 cm for IGSO/MEO and several decimeters for GEO satellites. [PERSON] et al. [12] analyzed the impact of the distribution of ground tracking stations on the BeiDou POD by means of satellite position dilution of precision. The number and distribution of ground tracking stations are important factors influencing the precision of orbit determination. With the development of the BeiDou constellation, domestic and international organizations have accelerated the construction of BeiDou stations. In 2011, the MGEX project of IGS began researching several satellite systems, including the tracking and analysis of BeiDou signals [23]. At present, more than 100 stations in the MGEX project are able to receive BeiDou signals, and the distribution of stations has become more reasonable. In China, the National Administration of Surveying, Mapping and Geoinformation organized the construction of Chinese national continuous operating reference stations (CNCORS), and 360 stations have been evenly established throughout the country [24]. Of this number, 210 stations are able to receive data from four global navigation satellite systems, thereby providing continuous data for BeiDou orbit determination and positioning, as well as other related studies. This study aims to obtain the latest results of the BeiDou POD, and investigate the impact of the CNCORS on BeiDou precise orbits. Firstly, the BeiDou orbit determination method, the observation model, and the orbit dynamic model are introduced. Then, the accuracy of the BeiDou precise orbit based on different ground tracking stations is analyzed and compared, providing the results of the BeiDou precise orbits based on the current CNCORS and MGEX stations. We studied the influence of the CNCORS on BeiDou POD and the precision difference between the BDS single-system independent POD and the BDS/GPS joint POD. We also analyzed the orbit determination precision regarding the different tracking station distributions based on the orbit dilution of precision(ODOP). We compared the experimental results with the results of previous studies. Finally, the experimental results are discussed and summarized. The paper is organized as follows: data collection and processing methods are firstly described in Section 2. In Section 3, the results of the orbit precision comparison and satellite laser ranging (SLR) assessment for different strategies are derived. The results of experimental data are analyzed and discussed in Section 4. Conclusions are given in Section 5.
## 3 Materials and Methods
### Simulation Model Calibration
#### 3.4.1 Links Selection and Evaluation Metrics for Simulation Calibration  
As indicated in Section 3.3, the processed Type A data retains the actual route information of the vehicles, including the id of each link they enter and the corresponding entry time. The average speed of the vehicle on the link can be calculated as follows:  
\\[S_{m,i}=\\frac{l_{m}}{U_{m,j}^{+}-U_{m,j}^{-}} \\tag{2}\\]  
where \\(S_{m,i}\\) is the average speed of vehicle \\(i\\) on link \\(m\\), \\(l_{m}\\) is the length of link \\(m\\), \\(U_{m,j}^{-}\\) and \\(U_{m,i}^{+}\\) are the moment that vehicle \\(i\\) enters and exists link \\(m\\) respectively.  
Then, according to the 15 min time period length, the simulation time is divided into 8 periods, and the real average road speed in each period is calculated. The specific calculation method is shown as follows:  
\\[S_{m,t}=\\frac{l_{m}}{\\sum_{T_{l,i}\\in t}^{N_{m,t}}\\left(U_{m,i}^{+}-U_{m,i}^{- }\\right)/N_{m,t}} \\tag{3}\\]  
where \\(S_{m,t}\\) is the average speed of link \\(m\\) in time period \\(t\\), \\(l_{m}\\) is the length of link \\(m\\), \\(U_{m,i}^{-}\\) and \\(U_{m,i}^{+}\\) are the moment that vehicle \\(i\\) enters and exists link \\(m\\) in time period \\(t\\) respectively, and \\(N_{m,t}\\) is the number of vehicles using this link during the time period.  
Through this process, we obtain the actual average speeds of links with trajectory data passing through them during each time period. Subsequently, we select those roads that have trajectory data passing through them during all time periods as the target links for simulation calibration. Fifty target roads are chosen as the calibration objects in this study, and their distribution details are shown in Figure 4. The selection of these target roads aims to ensure that the calibration results can cover and represent the actual conditions of the entire transportation network.  
Similarly, the simulation output from MATSim directly provides the entry and exit times for each vehicle on links. The simulated average link speed within can be obtained using the abovementioned methods.  
In this study, Root Mean Square Error (RMSE) and Normalized Root Mean Square Error (NRMSE) are selected as evaluation metrics for simulation calibration. These metrics are primarily used to measure the magnitude of the error between the predicted average  
Figure 4: Distribution of links to be calibrated (green) in the multi-model network.  
link speeds from the model and the actual observed link speeds. We count the RMSE and NRMSE between real-world and simulated speeds as follows:  
\\[RMSE_{t}=\\frac{\\sum_{i=1}^{M}\\bigl{(}S_{m,t}-S^{\\prime}_{m,t}\\bigr{)}^{2}}{M} \\tag{4}\\]  
\\[NRMSE_{t}=\\frac{\\sum_{i=1}^{M}\\bigl{(}S_{m,t}-S^{\\prime}_{m,t}\\bigr{)}^{2}}{ \\sum_{m=1}^{M}S_{m,t}^{2}} \\tag{5}\\]  
where \\(RMSE_{t}\\) and \\(NRMSE_{t}\\) are the RMSE and NRMSE values of time period \\(t\\) respectively, \\(S^{\\prime}_{m,t}\\) is the simulated average speed of link \\(m\\) in time period \\(t\\), and \\(M\\) is the number of target links selected for calibration.
# An Internet of Things-Based Low-Power Integrated Beekeeping Safety and Conditions Monitoring System
## 4 Proposed IBSMC System Experimentation
### Scenario I(a), RF BSDP Protocol Experimentation  
The experimental system infrastructure for Scenario I includes a beehive array consisting of four beehive cells equipped with RF12B transponders placed circularly to the central beehive in fixed distances of 10 m, 25 m, 35 m, 45 m and 55 m accordingly. For experimental purposes, the BSDP data transmission has been uniformly set to a random idle interval between 30-60 sec for each beehive cell and each cell transmits a total number of 1000 acknowledgement bit set frames. The expected acknowledgment frames are received by all nodes. That is, each node is set in reception mode for a fixed maximum RTO time interval of 25 s after each frame transmission.  
If a frame is lost or no ACK received, it is retransmitted at least six times using the same BSDP sequence number field value. Then the mean beehive array RTT time of frames-ACKs over distance as well as total number of frames lost and retransmitted are calculated. The results are presented in Figure 4a,b accordingly.  
Since acknowledgements have to be sent to each beehive in case of an event, each beehive cell has its own unique BSDP beehive ID. That is, a maximum number of 254 beehive cells can be assigned to each central hive. The Jeelabs group ID [50] is unique for an entire array consisted of many central hives and the node ID has fixed hex values of 0x1F for the central hive and 0x15 for all beehive cells for ACK reception purposes. The BSDP protocol payload size is of 25 Bytes including the RF12B header [50] and the ACK frame size of 13 Bytes. The expected maximum packet loss is set by the author to 5% for the protocol to operate successfully in contenting environments of 254 beehive cells transmitting data hourly and asynchronously. The purpose of this experiment is to determine the maximum beehive distance where the system will maintain less than 5% frame loss (including RTR frames) as well as to calculate the RTO value for the beehive transponders to remain in a receiving ACK state prior to retransmission timeout. According to Figure 4b experimental results, the system maintains a 3% packet loss for up to 55 m of distance. However, if the retransmitted frames are taken into account as part of the system's extra energy expenditure, the 5% packet loss (PLOS) limit is reached at 40-45 m of distance (5.6% PLOS at 45 m). Based on the functional limit of 45 m, the RTO value of the system can be calculated as 4 \(\times\) max (RTT), set to 5.5-5.8 s.
## 5 Conclusions  
A hybrid segmentation along with the feature fusion-based sign word recognition system was presented in this paper. For this purpose, a model was proposed that was able to translate sign gestures into text. The proposed model included image preprocessing, feature extraction and fusion, and gesture classification. To detect the hand gestures, we preprocessed input images using YCbCr and SkinMask segmentation. Cr component analysis was considered in the YCbCr segmentation, and SkinMask identified the pixels that corresponded to the skin color of the hand. Therefore, we used the proposed model to extract features from segmented images, where YCbCr and SkinMask segmented images were the CNN's two-channel inputs. The feature fusion was accomplished in the fully-connected layer. At the level of classification, the multiclass SVM classifier was compiled by the hand gesture dataset created by the authors. The results indicated that in the real-time environment, approximately 97.28% accuracy was achieved using trained features and the SVM classifier, and it led to better results than the state-of-the-art systems.  
Conceptualization, M.A.R., M.R.I. and J.S.; methodology, M.A.R.; software, M.A.R. and M.R.I.; validation, M.A.R., M.R.I. and J.S.; formal analysis, M.A.R. and J.S.; investigation, M.A.R. and J.S.; resources, J.S.; data curation, M.A.R. and M.R.I.; writing-original draft preparation, M.A.R; writing-review and editing, M.A.R, M.R.I and J.S.; visualization, M.A.R.; supervision, J.S.; project administration, J.S.; funding acquisition, J.S. This paper was prepared the contributions of all authors. All authors have read and approved the final manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Numerical Model
### Permeability Parameters of Soil  
The dissipation of excess pore pressure around the piezocone, a critical factor in assessing the geotechnical properties of soil, is influenced by a multitude of factors, including soil properties such as permeability and compressibility coefficients. Permeability, which describes the ability of soil to transmit fluids, is influenced by various factors, including the soil's void ratio, which is the ratio of the volume of voids to the volume of solids in the soil. As the void ratio increases, the number and size of the voids in the soil increase, and this leads to a higher permeability. In turn, a higher permeability allows for a more rapid \begin{table}
\begin{tabular}{c c c} \hline \hline
**Properties** & **Kaolin Silty Clay** &
\begin{tabular}{c} **Yellow River Delta** \\ **Silty Clay** \\ \end{tabular} \\ \hline Clay content (\%) & - & 30 \\ \hline \(D_{50}\) (mm) & - & 0.029 \\ \hline Liquid limit, \(LL\) (\%) & 80 & 35.2 \\ \hline Plasticity Index, \(Pl\) (\%) & 45 & 11.9 \\ \hline USCS classification & MH & CL \\ \hline Angle of internal friction, \(\varphi^{\prime}\) (\({}^{\circ}\)) & 23 & 31.2 \\ \hline Void ratio at \(p^{\prime}=1\) kPa on virgin consolidated line, \(e_{\mathrm{N}}\) & 2.35 & 1.17 \\ \hline Slope of normal consolidation line, \(\lambda\) & 0.244 & 0.096 \\ \hline Slope of swelling line, \(\kappa\) & 0.053 & 0.02 \\ \hline Plastic compression ratio, \(\Lambda=1-\kappa/\lambda\) & 0.783 & 0.792 \\ \hline Poisson ratio, \(\
u\) & 0.3 & 0.3 \\ \hline Submerged density, \(\rho^{\prime}\) (g/cm\({}^{3}\)) & 0.63 & 0.86 \\ \hline Coefficient of consolidation at 30 kPa (OCR = 1), \(c_{\mathrm{v}}\) (m\({}^{2}\)/s) & \(7.33\times 10^{-7}\) & \(7.04\times 10^{-7}\) \\ \hline Coefficient of permeability at 30 kPa (OCR = 1), \(k\) (m/s) & \(2.35\times 10^{-8}\) & \(1.11\times 10^{-8}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: MCC model parameters of the kaolin silty clay [25] and the Yellow River silty clay [1]. dissipation of excess pore pressure around the piezocone during penetration testing. This relationship between void ratio and permeability is particularly relevant in soils with low initial void ratios, such as clays, where small changes in void ratio can lead to significant changes in permeability and, consequently, the dissipation rate of excess pore pressure.
# Hybrid Trust Model for Node-Centric Misbehavior Detection in Dynamic Behavior-Homogeneous Clusters
## 6 Hybrid Trust Value Based on Weighted Vehicle Behavior  
In vehicular networks, vehicle state information falls under the category of basic safety-related information, such as Cooperative Awareness Messages (CAMs) and Basic Safety Messages (BSMs). These messages are time-triggered [26], exchanged at a fixed frequency (1-10 Hz) using a single-hop broadcast distribution mechanism as illustrated in Figure 5, which depicts the distribution mechanism of basic safety-related information in vehicular networks. They include identity information, specific data like driving speed, geographical location, and driving direction over a certain period, and static vehicle behavior information. This information encompasses aspects like direction, speed, acceleration, curvature, and the confidence level of yaw rate, etc. To ensure the integrity and authenticity of this information, basic safety messages also contain a digital signature and a public key for signature verification [27].
## 3 Analysis of Active Earth Pressure in Circular Shafts
### Circumferential Arching Effect  
When the soil behind the shaft wall reaches the active limit state, it is typically accompanied by centripetal displacement, generating a circumferential compression effect that increases the circumferential stress and enhances the stability of the soil. This phenomenon is referred to as the circumferential arching effect. The effect is unevenly distributed within the same horizontal soil layer and gradually weakens with an increase in radial distance, causing the circumferential stress to decrease progressively along the radial direction.  
For the horizontal differential element \(ABB^{\prime}A^{\prime}\), the average circumferential stress \(\sigma_{\rm{a}\theta}^{\prime}\) can be obtained using Equation (6) as follows:  
\[\sigma_{\rm{a}\theta}^{\prime}=\overline{\lambda}\sigma_{1}^{\prime}=\bigg{(}1 -\frac{1}{2}\sin\varphi\bigg{)}\sigma_{1}^{\prime} \tag{19}\]  
Furthermore, by combining Equations (15) and (19), the average circumferential stress coefficient \(K_{\theta}\) can be obtained as follows:  
\[K_{\theta}=\frac{\sigma_{\rm{a}\theta}}{\overline{\sigma}_{\rm{v}}}=\frac{ \sigma_{\rm{a}\theta}^{\prime}-\sigma_{0}}{\overline{\sigma}_{\rm{v}}^{\prime }-\sigma_{0}}=\frac{(1+\sin\varphi)(2-\sin\varphi)/2-K_{\sigma_{0}}}{1+\sin \varphi\big{[}1-\frac{2}{3}(\cos^{2}\theta_{A}+\cos\theta_{A}\cos\theta_{B}+ \cos^{2}\theta_{B})\big{]}-K_{\sigma_{0}}} \tag{20}\]  
In summary, considering the influence of the spatial arching effect, the relationships among the horizontal active earth pressure \(\sigma_{\rm{ahw}}^{\prime}\), the average shear stress \(\overline{\tau}^{\prime}\) between horizontal soil layers, the average circumferential stress \(\sigma_{\rm{a}\theta}^{\prime}\), and the average vertical stress \(\overline{\sigma}_{\rm{v}}^{\prime}\) on the horizontal differential soil element \(ABB^{\prime}A^{\prime}\) have been established.
## 3 Coded Cooperation with Hard Information Relaying and Relay Selection
### Outage Probability Analyses of the CC-HIR-RS Scheme  
Each relaying channel is a cascade of the two channels \(S-R_{r}\) and \(R_{r}-D\) with capacities \(C_{SR_{r}}\) and \(C_{R,D}\), respectively. On each channel, reliable communication at information rate \(R\) is possible only if \(R\) is less than the corresponding capacity. The capacity of the en-to-end channel \(S-R_{r}-D\) is then given by the  
\[C_{SR_{r}D}=\min\{C_{SR_{r}},C_{R,D}\}, \tag{6}\]  
where  
\[C_{T_{x}R_{x}}=\frac{1}{2}\log_{2}(1+\Gamma_{T_{x}R_{x}}),T_{x}R_{ x}=SR_{r},R_{r}D. \tag{7}\]  
Hence, end-to-end reliable communication is possible only if \(R<C_{SR_{r}D}\). In the following subsections, we will discuss and derive the closed-form outage probability of the the CC-HIR-RS scheme with constant PSR in all relays (hence, it is denoted as CC-HIR-RS-CPS) and of the CC-HIR-RS scheme with optimal power allocation in which the optimal PSR in each relay is determined to maximize the capacity of the end-to-end channel (hence, it is denoted as CC-HIR-RS-OPS).
# Reliability Improved Cooperative Communication over Wireless Sensor Networks
###### Abstract  
With the development of smart devices and connection technologies, Wireless Sensor Networks (WSNs) are becoming increasingly intelligent. New or special functions can be obtained by receiving new versions of program codes to upgrade their software systems, forming the so-called smart Internet of Things (IoT). Due to the lossy property of wireless channels, data collection in WSNs still suffers from a long delay, high energy consumption, and many retransmissions. Thanks to wireless software-defined networks (WSDNs), software in sensors can now be updated to help them transmit data cooperatively, thereby achieving more reliable communication. In this paper, a Reliability Improved Cooperative Communication (RICC) data collection scheme is proposed to improve the reliability of random-network-coding-based cooperative communications in multi-hop relay WSNs without reducing the network lifetime. In WSNs, sensors in different positions can have different numbers of packets to handle, resulting in the unbalanced energy consumption of the network. In particular, nodes in non-hotspot areas have up to 90% of their original energy remaining when the network dies. To efficiently use the residual energy, in RICC, high data transmission power is adopted in non-hotspot areas to achieve a higher reliability at the cost of large energy consumption, and relatively low transmission power is adopted in hotspot areas to maintain the long network lifetime. Therefore, high reliability and a long network lifetime can be obtained simultaneously. The simulation results show that compared with other scheme, RICC can reduce the end-to-end Message Fail delivering Ratio (MFR) by 59.4%-62.8% under the same lifetime with a more balanced energy utilization.  
Keywords:wireless sensor networks; cooperative communications; adaptive transmitting power; message fail delivering ratio (MFR); network lifetime +
Footnote †: journal:  
_Article_
# Mechanical Integrity Analysis of a Printed Circuit Heat exchanger with Channel Misalignment
## 3 Results and Discussion
### The Utilization Factor Increases as the Misalignment Increases  
The linearization method stress intensity values along the SCLs are still acceptable compared to the requirements of ASME III design conditions. However, deeper analysis should be done to observe the real effects of this misalignment condition. One of important factors in design and manufacturing is the utilization factor. Utilization factor is defined as the ratio of the worst condition to the acceptable condition. In this case, the ratio of the highest stress intensity resulting from the linearization method along SCLs and the \(S_{o}\) value are considered. The utilization factor is the ratio of the stress intensity resulting from linearization method along SCLs to the \(S_{o}\) as the minimum acceptable conditions.  
Figure 8: Stress classification line for stress linearization method.  
Figure 7: Primary membrane stress for various misalignment conditions: (**a**) sodium to sodium channel; (**b**) steam to steam channel; (**c**) between steam and sodium channels; And combined membrane and bending stress: (**d**) between sodium channel; (**e**) between steam channel; (**f**) between steam and sodium channels.  
The graphs shown in Figure 9 below indicate that the utilization factor increased with the increase of misalignment of sodium channel conditions. The maximum utilization factor is shown with 0.85 mm misalignment with both negative and positive shifting. The conditions still maintain the utilization factor at 30% of the allowable value, which is the safe condition for design load application.  
Since this proposed design is newly considered, the elevated temperature for other purposes should be considered. The utilization factor under elevated temperature (those exceeding 500 \({}^{\circ}\)C) is obtained, as shown in Figure 10. The figure shows that the condition of misalignment will be acceptable up to 725 \({}^{\circ}\)C. This means the application conditions above 725 \({}^{\circ}\)C cannot comply with the misalignment condition of 0.85 mm. This suggests that this proposed design can comply with the requirements of the ASME code for system applications under 725 \({}^{\circ}\)C.
## 2 Finite Element Model and Research Method  
### Calculation Model  
The calculation model of a 500 kV power transformer is shown in Figure 1 and the values of the initial parameters are listed in Table 1.  
The boundary value problem of the electrostatic field can be expressed by Equation (1).  
\[\left\{\begin{array}{ll}-\
abla\cdot\varepsilon\
abla\varphi=\rho,&\varphi\in \Omega;\\ \varphi\big{|}_{\Gamma_{1}}=5\times 10^{5},&\varphi\big{|}_{\Gamma_{2}}=2 \times 10^{5};\\ \varphi\big{|}_{\Gamma_{3}}=0;\\ \frac{\partial\varphi}{\partial n}\big{|}_{\Gamma_{4,5}}=0.&\end{array}\right. \tag{1}\]  
where \(\varepsilon\) is the dielectric constant; \(\phi\) is the electric scalar potential of the electrostatic field; \(\Omega\) is the whole computational domain; \(\Gamma_{1}\) is the high voltage winding boundary; \(\Gamma_{2}\) is the low voltage winding  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Physical Meanings** & **Initial Values** \\ \hline \(R_{1}\) & Upper arc radius of the electrostatic ring & 18 mm \\ \(R_{2}\) & Lower arc radius of the electrostatic ring & 8 mm \\ \(T\) & Thickness of the electrostatic ring & 26 mm \\ \(H\) & Distance from the upper surface of the electrostatic ring to the iron yoke & 274 mm \\ \(S\) & Thickness of the insulating layer & 4 mm \\ \(W\) & Starting position of the first arc & 48 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initial parameter values.  
Figure 1: Calculation model of 500 kV power transformer insulation structure. (**a**) Integral structure; and (**b**) partial structure.  
boundary; \(\Gamma_{3}\) is the regulating winding, the upper, and the right boundary; and \(\Gamma_{4,5}\) are the left and lower boundary, respectively.  
The APDL is applied to realize automatic pre-processing as follows: (1) The relative dielectric constants of oil and pressboard were 2.2 and 4.5, respectively; (2) the voltage applied on the high voltage winding was 500 kV (\(\Gamma_{1}\)), the low voltage winding was 200 kV (\(\Gamma_{2}\)), and the voltage regulating winding and the upper and the right boundary were 0 kV (\(\Gamma_{3}\)). These are the first kind of boundary conditions and (3) the left and lower boundary are the second kind of homogeneous boundary conditions (\(\Gamma_{4,5}\)). In addition, the boundary conditions of Figure 1b were interpolated based on the results of Figure 1a.  
The computer processor we used had an Intel(R) Core(TM) i5-7500 with 8.00 GB of memory. After modeling, meshing, applying boundary conditions, and solving by APDL, the electric field intensity nephogram of the initial structure is shown in Figure 2. By analyzing the initial insulation structure of the power transformer, it can be found that the maximum electric field intensity appeared at the upper right corner of the electrostatic ring. Therefore, we try to reduce the maximum electric field intensity by changing the structure parameters related to the electrostatic ring.