esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Network Model  
In this paper we consider a series of WSN applications, which collect information periodically and simultaneously, and transport the data as packets to a sink node. Certainly there is no sink node number limitation, but we adopt only one sink node in this paper. Each node in the system is equipped with an omni-antenna. The data transmission and receiving are two separate processes and cannot work simultaneously. A node can work in one of four basic states: Receive (R), Transmit (T), Idle (I) and Sleep (S). The work cycle of a node is the time period from the last wake-up time to the next wake-up time. The work process is illustrated in Figure 1.When a node wakes up, it will be in the Active state. It can receive data, send data to its next-hop or stay in an Idle state. When it enters Sleep state, it does not respond to the environment until the next wake-up time. When it wakes up, it will start a new work cycle. In this paper, we set the same wake-up time for all nodes at a fixed time interval. Given its heavy work load or the busy channel, a node may not fall to sleep in a cycle. Each node simultaneously produces a fixed-size packet at a regular interval. And after a node receives the data from itself or from other nodes, it will store them in a first-in-first-out (FIFO) queue before it sends them out.  
The communication channel is a shared single channel. It is assumed that the some contention-based MAC protocol is applied, probably the CSMA/CA protocol [26]. We consider a stationary WSN and adopt a static route strategy for it. A node has only one next-hop node in its routing table. For any route in whichever follows, upstream refers to the direction which data packets are transmitted to the sink node. And in the downstream, the data packets are transmitted from the sink node. We call node A parent node of node B when node A is the next-hop in upstream of node B. And node B is the child node of node A. The static route is formed at the routing discovery (RD) stage at each experiment, as is shown in Figure 2.  
A network-wide time synchronization protocol is assumed to ensure that all of nodes will have the same wake-up time. However, the time accuracy of our CDSAs is not as strict as those of TDMA based approaches because the work cycle is much longer than a time slot.
## 5 Discussion
### Disadvantages of the Algorithm  
* It is not applicable (or difficult to apply) to real-time tasks.
* In order to obtain a detailed and correct map for robots with more degrees of freedom or for complex obstacles in the workspace area, it is necessary to increase the number of checked points and algorithm cycles.
* The algorithm is only suitable for robots with a serial structure. It is not applicable to parallel robots and closed structure robots.
* The even distribution of survey points on circles requires multiple reachability checks for points that are sometimes clearly unreachable. We believe that this process can be optimized in the future.  
Currently, the algorithm seeks the solutions to IK by going through every possible configuration of the robot. However, it could be determined in advance that some configurations are clearly unfeasible, which could reduce the total calculations and optimize the algorithm. In addition, if the obstacles in the environment do not change, the configuration space map for a particular robot is generated once and can be used repeatedly to determine different trajectories, even executed in real time. Often in robot control, it is not necessary to map the types of solutions. In such cases, the algorithm could be simplified to seek singular solutions by testing only a couple of configurations (which could be randomly chosen). This could improve the speed of the algorithm.
# A B5G Non-Terrestrial-Network (NTN) and Hybrid Constellation Based Data Collection System (DCS)
## 2 Relate Work
Those IoT systems are applied in various frequency bands, from the VHF band (140 MHz) to the C band (5 GHz). For traditional DCS devices, it is necessary to independently set up a data receiving channel for different signal modes. This will greatly increase the operational burden on satellites. Fortunately, reconfigurable receiving equipment can solve this problem. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristics** & **StarLink** & **OneWeb** & **LightSpeed** & **O3b** \\ \hline Downlink data rate & 100 Mbps & 200 Mbps & 50 Mbps & 400 Mbps \\ \hline Uplink data rate & 14 Mbps & 40 Mbps & 10 Mbps & 150 Mbps \\ \hline Number of satellites & 1584 & 358 & 289 & 34 \\ \hline Latency & 35 ms & 70 ms & 40 ms & 100 ms \\ \hline Orbit altitude & 560 km & 1200 km & 1015 km & 3000 km \\ \hline Coverage diameter & 650 km & 1400 km & 1200 km & 3500 km \\ \hline Frequency bands & Ku, Ka & Ku, Ka & C, Ka, Ku & Ka \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Current and Planned non-GSO systems. \begin{table}
\begin{tabular}{c c c} \hline \hline
**KPI** & **Terrestrial 5G** & **NTN B5G** \\ \hline Peak data rate & 20 Gbps & 1 Tbps \\ Experienced date rate & 0.1 Gbps & 1 Gbps \\ Peak spectral efficiency & 30 bps/Hz & 60 bps/Hz \\ Experienced spectral efficiency & 0.3 bps/Hz & 3 bps/Hz \\ Maximum bandwidth & 1 GHz & 100 GHz \\ Area traffic capacity & 10 Mbps/m\({}^{2}\) & 1 Gbps/m\({}^{2}\) \\ Connection density & 106 devices/km\({}^{2}\) & 107 devices/km\({}^{2}\) \\ Energy efficiency & Not specified & 1 Tb/J \\ Latency & 1 ms & 1 us \\ Bit error rate & \(10^{-5}\) & \(10^{-9}\) \\ Jitter & Not specified & 1 us \\ Mobility & 500 km/h & 1000 km/h \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Key performance indicators. An SDR-based reconfigurable multi-modal transceiver for CubeSats is proposed [26]. It can support various standard transmission formats. The main software architecture depends on the Fast-Fourier Transform (FFT) processing block. With the idea of modularization, it is possible to design systems with different functions on a unified platform  
There are five upcoming NTN system [27]. The performance of these communications is simulated by AGI's Systems Tool Kit (STK). A LoRa module based on a Software Defined Radio (SDR) is proposed [28]. Its main function is to receive information from remote oil fields. However, these studies cannot meet the requirements of low cost, global coverage, and multi-modal compatibility at the same time. Therefore, a CubeSat constellation-based NTN-mMTC system is necessary. The DCS equipment is an important component of the NTN-mMTC system. In order to achieve reconfigurability, SDR-based DCS equipment needs to be studied.
# Development of a Hardware-in-the-Loop Platform for a Teleoperation Flexibility Robotic System
## 4 Simulations
### Verification Mapping Method  
To control the telerobotic system, the Jacobian method must ensure stability of the inverse kinematics when the CDHR robot receives the setpoint from the haptic device. In the case study, two points, A and B, are considered. The home of the CDHR robot is point A, and its coordinates are \([1027.4;\,-5.3,\,5.3]\) mm. The desired position is point B. The results of verification of the Jacobian method are presented in Table 2.  
The results presented in Table 2 indicate the effectiveness of the Jacobian method. Through five setpoints B, the position error of the CDHR robot is rather small, and its amplitude is within the interval \([-10^{-5};10^{-5}]\) mm. Therefore, the proposed method for determining the inverse kinematics of the redundant robot is highly precise.  
After application of the Jacobian method, the following case study of the telerobotic system was implemented. Position mapping was applied to the system with the parameters  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **B** & **Error Px** & **Error Py** & **Error Pz** \\  & **(mm)** & **(10\({}^{-5}\) mm)** & **(10\({}^{-5}\) mm)** & **(10\({}^{-5}\) mm)** \\ \hline
1 & [900; 0] & 0.9130 & 0.5629 & \(-\)0.7353 \\ \hline
2 & [900; 10; \(-10\)] & 0.9136 & 0.5788 & \(-\)0.7519 \\ \hline
3 & [900; \(-10\); 10] & 0.9133 & 0.5474 & \(-\)0.7189 \\ \hline
4 & [900; 20; 10] & 0.9170 & 0.6014 & \(-\)0.7176 \\ \hline
5 & [900; 10; 40] & 0.9191 & 0.5911 & \(-\)0.6683 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results obtained using Jacobian method.  
Figure 9: Symbolic model.  
After receiving the desired planning from the haptic device, the CDHR robot executes the inverse kinematic method to obtain the joint variables, which are also used as inputs to the Simscape model. The position of the CDHR robot's end-effector, as determined using the Simscape model, is shown in Figure 10. The solid blue line denotes the setpoint, which is received from the haptic device after position mapping, and the red line denotes the position of the CDHR robot in the Simscape model. The CDHR robot tracks the reference of the desired planned path. The errors performance of the robot is shown in Figure 10b, and the amplitude of this error is within the interval \([-0.01;0.01]\) mm. Therefore, the position mapping method is highly accurate when applied to the teleoperation system.
# URANS Analysis of a Launch Vehicle Aero-Acoustic Environment
###### Abstract  
Predicting and mitigating acoustic levels become critical because of the harsh acoustic environment during space vehicle lift-off. This paper aimed to study the aero-acoustic environment during a rocket lift-off. The sound propagation within a launch event was studied using dedicated computational fluid dynamics (CFD). The resolution of all the phenomena that occur is unfeasible. We discuss the turbulence simplification and propose a feasible simulation through an unsteady Reynolds-averaged Navier-Stokes (URANS) model. The results were validated with experimental data showing a good correlation near the fairing surface and an improvable accuracy in the far field. To assess noise generation, the main shock waves were identified, and the evolution of the generated sound pressure was assessed. Moreover, vertical directivity was revealed by data analysis of the pressure field surrounding the fairing.  
CFD; acoustics; rocket launch +
Footnote †: journal: Physics Letters A
## 3 Results
### Determination of the \(\boldsymbol{\mathrm{R}}\boldsymbol{\mathrm{E}}\)V Size  
Figure 8 elucidates the variation characteristics of pore parameters versus cube sizes, with the \(x\)-axis denoting the side length of cubes and the \(y\)-axis representing the 3D pore parameters. Notably, erratic fluctuations manifest in the 3D parameters when the cube size is small (e.g., 200 \(\upmu\)m). The fluctuations weaken with the increase in cube size and the pore parameters tend to reach a stable value. The minimum size in the x-coordinate in the stable state is defined at the RVE size.  
Figure 8: Variation of the pore parameters with the increase in cube side length. (**a**–**h**) represents different pore parameters, and the start point represents different sub-volume selection schemes. (**a**) total porosity \(P_{o}\); (**b**) connected porosity \(P_{c}\); (**c**) isolated porosity \(P_{i}\); (**d**) average coordination number \(C_{\rm N}\); (**e**) the median of pore volume \(M_{\rm V}\); (**f**) the median of pore area \(M_{\rm A}\); (**g**) the median of pore equivalent radius \(M_{\rm R}\); (**h**) the median of throat channel length \(M_{\rm L}\).  
Analysis of Figure 8 reveals the contributions of different pore parameters to distinct RVE sizes. For the Q\({}_{2}\) specimen, the geometrical RVE size is determined as 400, 200, 500, 500, 500, 300, 300, and 500 \(\upmu\)m, respectively based on the pore parameters of \(P_{o}\), \(P_{c}\), \(P_{i}\), \(C_{\text{N}}\), \(M_{\text{V}}\), \(M_{\text{A}}\), \(M_{\text{R}}\), and \(M_{\text{L}}\). For Q\({}_{3}\) loses, the corresponding RVE size is 600, 600, 600, 500, 600, 400, 600, and 600 \(\upmu\)m, respectively. Relying on a single pore parameter, such as total porosity or connected porosity, may lead to errors, primarily an underestimation of the RVE size.  
Figure 9 shows all the results of the geometrical REV sizes based on different pore parameters. It can be observed that the REV size varies with both the pore parameters and the starting point of the extracted cubes. Generally, the prevalence of smaller RVEs (e.g., light blue in Figure 9) is pronounced in Q\({}_{2}\), signifying a more uniform microstructure. In contrast, the frequency of small RVEs or light blue elements amplifies for the specimens of Q\({}_{3}\), C1, and F1, indicating a more uniform structure of intact loess than the remolded ones. The RVE size for Q\({}_{2}\) specimen is 500 \(\upmu\)m, whereas for other specimens, it is 600 \(\upmu\)m.  
To validate the reliability of the REV size, we compared the pore size distribution (PSD) curves of the ROI cube and the initial 3D cylinder with those of the geometric RVE cubes. As shown in Figure 10, the PSD curves for the different geometric RVE cubes align closely with those of the ROI cubes and the 3D cylinder model. Thereby, the current geometric RVE sizes can be considered reliable.  
Figure 9: REV sizes based on different pore parameters of the specimens. (**a**) the Q\({}_{3}\) specimen; (**b**) the Q\({}_{2}\) specimen; (**c**) the F1 specimen; (**d**) the C1 specimen; A-D\({}_{0}\) represents the start point of the selected cube.
# Game Difficulty Prediction Based on Facial Cues and Game Performance
## 4 Experimental Results
### Comparison of Performance on the Three-Class Classification Task  
Figure 7 reports the performance of these methods on the three-class classification task. We observe that MLP achieves the highest results on all four evaluation metrics, and the values of these metrics were close to 0.8, while those of other methods were below 0.7. The results indicate that MLP is more suitable for predicting game difficulty than the other methods in the three-class task. In addition, surprisingly, KNN, which achieved satisfactory performance on the two-class classification task, does not perform well on the three-class task. Additionally, AB, which achieved the worst performance on the two-class classification task, is ranked second on the three-class task. These results imply that the same methodology could perform differently on different tasks. Figure 8 presents the confusion matrices for the top three methods: MLP, AB, and GB. We observe that the accuracy of all three methods in predicting the \"simple\" class is inferior to that of the remaining two classes. This disparity could be due to the small sample size of that class in the dataset. An imbalanced dataset significantly restricts the estimation performance of the model [39]. In addition, MLP achieves high prediction accuracies (exceeding 80%) on both the normal and hard categories. Although its accuracy in the \"simple\" class is only 0.714, it  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline SVM & 0.833 & 0.830 & 0.831 & 0.833 \\ KNN & 0.861 & 0.851 & 0.854 & **0.857** \\ Bayes & 0.714 & 0.716 & 0.714 & 0.714 \\ DT & 0.693 & 0.695 & 0.690 & 0.690 \\ RF & 0.811 & 0.803 & 0.806 & 0.810 \\ GB & 0.833 & 0.830 & 0.831 & 0.833 \\ AB & 0.688 & 0.685 & 0.686 & 0.690 \\ MLP & **0.865** & **0.865** & **0.857** & **0.857** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison on the two-class classification task. Bold represents the best.  
Figure 6: Confusion matrices of MLP, KNN, and GB on the two-class classification task.  
also outperforms AB and GB by a large margin. The results reveal the advantage of MLP in handling complex tasks.
# A Multi-Physics Modeling-Based Vibration Prediction Method for Switched Reluctance Motors
###### Abstract  
Currently, vibration has been one crucial factor hindering the application of switched reluctance motor (SRM). Hence, it is of crucial importance to predict and suppress this undesirable vibration. This paper proposes a multi-physics analysis-based vibration prediction approach for SRM. It consists of three modules: digital controller and drive circuit module, electromagnetic field module, and mechanical module. In the mechanical module, it not only includes the influence of the stator, but also fully considers the influence of rotor, end cover, bearing, and other components of the motor on the system modal. Moreover, the vibration data under different control strategies are obtained in real time, and data dynamic interaction between the three segments can also be achieved. By combining the electromagnetic forces and the system structure modal, the vibration of SRM can be predicted. Finally, the effectiveness of the proposed method was verified on 12/8 poles, 1.5 KW SRM drive system test bench. The results demonstrate that the modal simulation method based on static pre-calculation achieves high accuracy, and the vibration spectrums predicted by the proposed method shows good agreement with the experimental results.  
switched reluctance motor; vibration prediction; multi-physics modelling Article  
A Multi-Physics Modeling-Based Vibration Prediction Method for Switched Reluctance Motors  
[PERSON], [PERSON] *, [PERSON], [PERSON] and [PERSON]
# Soft Decision Decoding with Cyclic Information Set and the Decoder Architecture for Cyclic Codes
## 3 Soft Decision Decoding Algorithm Based on Cyclic Information Set
### Efficient Iterative Generating Method for Adjacent Candidate Sequences
In the extending process, the correlation discrepancy metric of the extended sequences may become larger and larger, and it is impossible to be a candidate sequence. Thus, discarding these sequences does not affect the results of the candidate information sequence generation. The discarding process significantly reduces the complexity of generating the candidate information sequences, because the number of calculations and comparisons of the correlation discrepancy is reduced, especially for cyclic codes of long code length. ```
Input: the set \(\varphi_{d-1}\) of the \((d-1)\) iteration, the absolute value \(|m_{d-1}|\) of the \(d\)-th component of the information sequence, and the hard decision result \(u_{d-1}\). Output: the set \(\varphi_{d}\). 1. Extend the set \(\varphi_{d-1}\) to \(\varphi_{d}^{0}\) and \(\varphi_{d}^{1}\). 1. Add \(u_{d-1}\) to the binary sequence of each element in \(\varphi_{d-1}\) and obtain the set \(\varphi_{d}^{0}\). 1.2 Add the negative of \(u_{d-1}\), i.e., \(u_{d-1}\) to the binary sequence of each element in \(\varphi_{d-1}\). Update the correlation discrepancy of each element to the result of adding \(|m_{d-1}|\) and obtain the set \(\varphi_{d}^{1}\). 2. Construct the set \(\varphi_{d}\). 2. Combine \(\varphi_{d}^{0}\) and \(\varphi_{d}^{1}\), i.e., \(\varphi_{d}=\varphi_{d}^{0}\cup\varphi_{d}^{1}\). 2.2 Compare the number of elements in \(\varphi_{d}\) with required number \(C\), if \(|\varphi_{d}|\leq C\), all the elements form \(\varphi_{d}\), otherwise, goto step 2.3. 2.3 Compare the elements of the two sets, \(\varphi_{d}^{0}\) and \(\varphi_{d}^{1}\), and output \(C\) elements with the smallest correlation discrepancy, obtaining a new set \(\varphi_{d}\). ```  
**Algorithm 2** The \(d\)-th iteration of the candidate sequence generating algorithm
# Adaptive PI Control Strategy for Optimal Microgrid Autonomous Operation
## 6 Description and Outcomes of the Simulations
### Incident 1 (Off-Grid Made)  
In the initial Incident, the MGD usually operates and is linked to the grid. At 2 s, the MGD is forcefully split from the grid (off-grid mode). Table 5 shows the optimal PI gains for the DGRs with EBSABA, LMSRE, PSO, and SFO techniques. Figure 4a-c show the DGR's voltages with EBSABA and the other methods. Figure 5a-c display the complex power in the DGRs with EBSABA, LMSRE, PSO, and SFO techniques. It is essential to keep in mind that the MPUST for the off-grid mode of the given approach is less than 8.5% in Figure 4a. Furthermore, the suggested controller's \(\mathrm{T}_{\mathrm{st}}\) is lowered to 37 ms according to the 2% criteria, and the \(\mathrm{E}_{\mathrm{st}}\) is 0.2%. As a result, the introduced optimizer provides the least overshoots, the fastest damping, and the most appropriate Est. It is worth noting that the EBSABA outperforms the LMSRE, PSO, and SFO approaches in MPUST, MPOST, Tst, and Est, demonstrating the rigor, validity, and practicality of the proposed EBSABA over the LMSRE, PSO, and SFO techniques.
# Fast 50 Hz Updated Static Infrared Positioning System Based on Triangulation Method
###### Abstract  
One of the important issues being explored in Industry 4.0 is collaborative mobile robots. This collaboration requires precise navigation systems, especially indoor navigation systems where GNSS (Global Navigation Satellite System) cannot be used. To enable the precise localization of robots, different variations of navigation systems are being developed, mainly based on trilateration and triangulation methods. Triangulation systems are distinguished by the fact that they allow for the precise determination of an object's orientation, which is important for mobile robots. An important feature of positioning systems is the frequency of position updates based on measurements. For most systems, it is 10-20 Hz. In our work, we propose a high-speed 50 Hz positioning system based on the triangulation method with infrared transmitters and receivers. In addition, our system is completely static, i.e., it has no moving/rotating measurement sensors, which makes it more resistant to disturbances (caused by vibrations, wear and tear of components, etc.). In this paper, we describe the principle of the system as well as its design. Finally, we present tests of the built system, which show a beacon bearing accuracy of \(\Delta\varphi=0.51^{\circ}\), which corresponds to a positioning accuracy of \(\Delta R=6.55\) cm, with a position update frequency of \(I_{update}=50\) Hz.  
positioning system; triangulation; autonomous navigation; IR sensors Article  
Faster 50 Hz Updated Static Infrared Positioning System