esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Analysis
### Experiment 2  
In this case study, the BER performance of the IM-MIMO-OFDM system is compared against the conventional MIMO-OFDM system using MMSE detectors. The proposed system's performance using an OSIC detector is also compared. The tests were conducted using \(N_{t}=2\), \(N_{r}=2\) for all systems, a sub-block size of \(N_{\rm C}=2\) for the MIMO-OFDM-IM system, and 4-QAM modulation for data in the conventional MIMO-OFDM system, while maintaining a spectral efficiency of 1.2 for both systems. The simulation parameters are shown in Table 3.  
The results in Figure 4 show that for the given channel, the conventional MIMO-OFDM system has inferior performance compared to the MIMO-OFDM-IM system. The BER achieved at low \(E_{b}/N_{0}\) is similar for the MIMO-OFDM-IM receivers with MMSE detection and the proposed OSIC detection, which is significant considering that the OSIC detector has the advantage of significantly reduced computational complexity. The conventional MIMO-OFDM system experiences a loss of approximately 3 dB for the medium-to-high \(E_{b}/N_{0}\) range. The main reason for this is that for the frequency-selective channel, the IM technique is more suitable for mitigating high Doppler dispersion, primarily due to the utilization of null subcarriers in transmission. Finally, it can be observed that the OSIC nonlinear detector does not exhibit an error floor at high \(E_{b}/N_{0}\) levels.
# Optimal Design and Real Time Implementation of Autonomous Microgrid Including Active Load
## 4 Results and Discussion
### Time Domain Simulation
Fourthly, the microgrid performance has been examined when the CPL voltage stepped down to 0.5p.u. The DGs output power and active load DC voltage responses are provided in Figure 10a,b. Figure 8: Microgrid response when a three-phase fault occurred at line 2. Figure 7: Microgrid response when a three-phase fault occurred at CPL bus. Figure 9: Microgrid response when the microgrid loses DG1. It can be observed from the given results that the system performance has a good overshoot and settling time performance. Furthermore, the results depict impressively enhanced damping characteristics. Figure 10 shows reasonable damping characteristics of the proposed controller. It is worth mentioning that if the microgrid has a protection system, the protection system should respond very quickly for such a big disturbance. However, the main goal of this study is to check the effectiveness of the proposed controller for such disturbance like this. Fifthly, to guarantee the controller capability, another disturbance has been applied at dc side of the active load. Through the time domain simulation, the proposed controller has been explored when the DC reference voltage stepped up by 5%. Figure 11a,b show the DGs output power response and the DC voltage response of the active load respectively. To demonstrate its superiority, the proposed optimal controller is finally compared with the controller presented in [12]. The DC voltage response of the active load in both cases when a three-phase fault occurred at CPL bus is shown in Figure 12. The optimal parameters of are used to show the priority of the proposed case. From the controller results shown, the proposed controller has improved dynamic performance after getting disturbed.
## 4 Study on MS Events in the Surrounding Rock during Excavation and Unloading
### MS Activity Controlled by Adverse Geological Structures  
The concentration zone is now specifically examined for further study after the temporal and spatial dispersion characteristics of microseisms were examined in Section 4.2.  
The research period from 15 September to 11 October 2020, was selected. During this period, blasting and excavation at the working chamber (stakes 0+340 m to 0+450 m) were carried out in the spillway tunnel. The engineering geological drawing (as shown in Figure 10) shows that this area intersected with faults f27 and f31.  
Figure 11 shows the distribution and density cloud diagram of MS events that occurred when the excavation of the spillway tunnel reached the area controlled by adverse geological structures. A total of 65 MS events were induced during the excavation and unloading process. In this period, due to the influence of excavation disturbance and adverse structural planes, stress concentration tended to occur at the intersection of multiple faults, which led to the concentration of MS events with greater magnitudes within the control range of the structural plane.  
Figure 10: Geological section of the spillway tunnel.  
Figure 9: Distribution of the ES/EP value of MS events.  
The fracture mechanism of MS events induced during this period was analyzed. Figure 12 shows the ratio of S-wave energy to P-wave energy (ES/EP) of MS events controlled by adverse geological structures. It can be seen from Figure 12 that the ES/EP value was less than 3 for only 12.1% of the MS events, greater than 3 for 87.9%, and greater than 10 for 31.8%. Therefore, the failure mode of MS events controlled by the adverse geological structure of the spillway tunnel was mainly shear failure, with traces of tensile failure. Analysis of the reasons indicated that the low mechanical strength of the fault and the nearby rock mass made it simple for cracks to develop along the direction of the fault plane and led to shear failure during the excavation and unloading and stress adjustment of the spillway tunnel.  
A bolt stress meter was installed on the side wall at stake 0+457 m to ensure the safety of site construction, and it started operation on September 22. Figure 13 shows the monitoring results. The readings on the bolt stress meter changed slowly from September 22 to October 6, but drastically after that period. It was believed that the excavation and unloading of the spillway tunnel caused cracks to develop along the direction of the fault plane and caused shear failure to predominate in a large area. According to field surveys, the block fall occurred at 0+443 m in the control area of the structural plane, as shown in Figure 14, where a smooth structural plane can be seen. It was considered that the block fall was caused by the growth and penetration of cracks in the direction of the structural plane.  
Figure 11: Distribution of MS events controlled by adverse geological structures.  
Figure 12: Distribution of the ES/EP value of MS events controlled by adverse geological structures.
## 4 Discussion
A small time period of variability in the breathing frequency will indicate the need for an increased effort in maintaining respiratory perfusion during measurement, indicating a rather low threshold of pressure-volume workload and a more viscous tissue. In contrast, a lack of variability will suggest a more elastic property of the lung tissue, with a longer maintenance period of constant effort. It is visible from Figure 13 that the model parameters are strongly correlated with the ECG and HR, as higher effort in breathing function is paired with a higher cardiac effort with an increased frequency. Spectrograms are ideal for detecting and capturing these variability patterns, with AI analysis being able to be used for diagnosis. Several limitations are present in this work. One limitation of this study is the exclusion of measurements taken in different body postures and during simulated apnea. The position of the heart within the chest can vary with changes in posture, potentially impacting respiratory impedance measurements. Testing participants in positions such as lying on their side with the arm bent under the head, as is often carried out in clinical procedures like ultrasounds, could provide further insights into how body positioning affects respiratory mechanics. Additionally, the absence of simulated apnea tests during peak inhalation and exhalation may have limited our ability to observe impedance changes, especially in conditions where fluid accumulation or respiratory issues are present, such as during severe colds or in patients with lung diseases. While these experiments were outside the scope of the current study, future research should consider incorporating these factors to provide a more comprehensive understanding of respiratory mechanics in varying physiological conditions. Another limitation of this study is its small sample size of only six volunteers. The primary objective of this work was to demonstrate a proof of concept by integrating the Forced Oscillation Technique (FOT) with continuous Equivital sensor monitoring for an assessment of non-invasive respiratory mechanics. While the data collected from this limited sample provided valuable initial insights and supported the feasibility of the approach, we recognize that a larger cohort would be necessary to draw more statistically robust and generalizable conclusions. Future studies should involve a greater number of participants, including individuals with diverse demographic characteristics and various respiratory conditions, to further validate the findings and to better assess the variability and reliability of the proposed methods.
## 5 Conclusions  
An extreme learning machine (ELM) is investigated in this paper for better flight-parameter-based flight load prediction, which determines the connection weights based on the least-squares criterion instead of using the iterative gradient descent method. Moreover, a genetic algorithm is proposed to optimize the randomly generated weights between the input and hidden layers, which improves the prediction accuracy of the ELM method. In addition, a mean impact value method is adopted to select the flight parameters, and the neuron number of the hidden layer is effectively determined. Some other major conclusions are as follows:  
1. The ELM method is an effective tool to establish a flight-parameter-based flight load prediction model even when some weights are randomly generated.
2. A genetic algorithm is an effective global optimization algorithm, which can be used to optimize the parameters of the neural networks.
3. The mean impact value method can effectively select the highly correlated flight parameters for accurate flight load prediction, and it is applicable to all neural networks.
4. The neuron number in the hidden layer should not be too small and is suggested to be a little large as long as the computation cost is affordable.  
However, the Moore-Penrose inverse can be computationally expensive when dealing with very large datasets, and the optimized solutions of the genetic algorithm are not guaranteed to be globally optimal.  
In the future, other efficient optimization algorithms can be explored to avoid the high computation cost of a genetic algorithm. Moreover, how to consider the time dependency property of a training dataset in the extreme learning machine is a promising research direction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Neural Networks** & **Mean Relative Errors** & \begin{tabular}{c} **Maximum Relative** \\ **Errors** \\ \end{tabular} & \
\begin{tabular}{c} **Mean Squared Errors** \\ \end{tabular} &
\begin{tabular}{c} **Training Time (s)** \\ \end{tabular} \\ \hline BP & 2.7909\% & 15.5331\% & 13.7988 & 7.2818 \\ RBF & 2.3006\% & 11.093\% & 13.769 & 3.6208 \\ SVM [51] & 2.2698\% & 9.3018\% & 13.3625 & 0.3033 \\ LSTM [52] & 1.3601\% & 23.2184\% & 4.6971 & 58.5525 \\ GRU [53] & 1.2323\% & 5.4111\% & 3.4670 & 58.5631 \\ ELM & 1.0546\% & 6.3417\% & 2.9702 & 0.1237 \\ GA-enhanced ELM & 0.8376\% & 4.8627\% & 2.0044 & 164.3014 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Prediction accuracy of different neural networks.  
**Author Contributions:** Conceptualization, Y.Z. and S.C.; methodology, Y.Z.; validation, B.W. and S.C.; writing--original draft preparation, Y.Z.; writing--review and editing, S.C.; visualization, B.W.; supervision, Z.Y.; project administration, Z.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 12102346.  
Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 2 Six-Hardpoint Positioning Mechanism and Its Mathematical Model
### Error Model of Six-Hardpoint Positioning Mechanism
The target pose of the primary mirror used in the calibration is called the measurement pose. Set the number of measurement poses as \(n\) (where \(n\geq 7\)), and combine \(n\) error equations to obtain:  
\[d\textbf{q}_{B}=\textbf{J}_{B}d\textbf{C} \tag{27}\]where:  
\[dq_{B}=\left[dq_{1}\quad dq_{2}\quad\cdots\quad dq_{n}\right]^{T} \tag{28}\]  
\[J_{B}=\left[J_{b1}\quad J_{b2}\quad\cdots\quad J_{bn}\right]^{T} \tag{29}\]  
where \(\mathrm{d}q_{B}\) is a \(6n\times 1\) vector, and \(J_{\mathrm{B}}\) is a \(6n\times 42\) matrix. \(J_{\mathrm{B}}\) is called identification Jacobian matrix. Equation (27) is the final error model of the six-hardpoint positioning mechanism. The kinematic calibration of a six-hardpoint positioning mechanism can be carried out based on the error model. The \(n\) (\(n\geq 7\)) poses of the primary mirror are measured and the positional errors are calculated, then the least square method is used to solve Equation (27) to obtain the kinematic parameter error \(\mathrm{d}C\). Although kinematic calibration can achieve the solution and compensation of kinematic parameter errors, the accuracy of calibration is affected by measurement noise. Equipment such as a length gauge, a laser tracker, an LVDT, and a measuring arm can be used to measure the pose of the primary mirror, all of which have certain errors. In addition, the process of pose resolving can also cause errors [28; 29; 30; 31]. The above factors cause a certain amount of measurement noise in the pose error \(\mathrm{d}q_{B}\), which reduces the compensation accuracy of the kinematic parameter error \(\mathrm{d}C\), and thus affects the accuracy of calibration.
# Fast 50 Hz Updated Static Infrared Positioning System Based on Triangulation Method
## 2 Materials and Methods
### Angle Determination Method
#### 2.2.3 Approximation Method vs. Weighted Mean of Angles Method  
The presented methods for determining the angle \(\varphi_{i}\) give the same results for ideal conditions when the IR intensity measurements are undisturbed. In real-world conditions, however, measurements are always distorted by noise, which can have many sources. Typical sources of this noise are disturbances in the power supply voltage (which affects the reference voltage), temperature changes in the measurement system, electromagnetic induction (EMI) noise or the analogue-to-digital conversion process itself. To test the effect of measurement noise on the results, a simulation experiment was carried out. This consisted of generating an ideal measurement spectrum \(I_{D_{j}}\) according to the mathematical model of the measurement photodiode shown in Figure 3, and then introducing noise into these measurements. The generated measurement noise was a random signal with a normal distribution and a preset standard deviation \(\sigma\). Twenty values of standard deviation ranging from 0 to 0.004 with a step of 0.0002 were examined. For each value of \(\sigma\), 1000 spectrum measurements were generated, for which the angle \(\varphi_{i}\) was determined using both the Approximation method and the Weighted Mean of Angles method. Finally, the root mean square error (RMSE) of \(\varphi_{i}\) from the 1000 spectrums for each \(\sigma\) was determined. The results of the simulation experiment are shown in Figure 4.  
As can be seen in Figure 4, the RMSE error increases linearly with increasing dispersion of the measurements, but for the Approximation method with the fitting function \(I_{1}(\theta)\) described by Equation (2), it increases about five times or even more than in the other two cases. The Approximation method with fitting function \(I_{2}(\theta)\) gives the best results, while the results for the Weighted Mean of Angles method are slightly worse than Approximation \(I_{2}(\theta)\) (less than 0.1 degrees).  
If the analog measurement is performed by an STM32 family microcontroller with a resolution of 12 bits, the dispersion of the measurement ranges from 10 to 20 LSB (for normalized measurement: \(LSB=1/2^{12}=0.00024\)), which gives a \(\sigma\) in the range of 0.0024-0.0048 [51]. As can be seen in Figure 4, for such a \(\sigma\) dispersion range, the value of the \(\varphi_{i}\) angle determined by the Approximation \(I_{2}(\theta)\) method or the Weighted Mean of Angles method is much more accurate than using the Approximation method with the fitting function \(I_{1}(\theta)\).  
Due to the fact that the post-processing calculations take place directly on the receiver's on-board microcontroller, it was decided to choose the Weighted Mean of Angles method, which, despite its simplicity, gives acceptable uncertainties, not much higher than the much more computationally complex (and requiring prior data preparation, i.e., removal and rearrangement) Approximation \(I_{2}(\theta)\) method.
# Obstacle Avoidance Path Planning for the Dual-Arm Robot Based on an Improved RRT Algorithm
###### Abstract  
In the future of automated production processes, the manipulator must be more efficient to complete certain tasks. Compared to single-arm robots, dual-arm robots have a larger workspace and stronger load capacity. Coordinated motion planning of multi-arm robots is a problem that must be solved in the process of robot development. This paper proposes an obstacle avoidance path planning method for the dual-arm robot based on the goal probability bias and cost function in a rapidly-exploring random tree algorithm (GA_RRT). The random tree grows to the goal point with a certain probability. At the same time, the cost function is calculated when the random state is generated. The point with the lowest cost is selected as the child node. This reduces the randomness and blindness of the RRT algorithm in the expansion process. The detection algorithm of the bounding sphere is used in the process of collision detection of two arms. The main arm conducts obstacle avoidance path planning for static obstacles. The slave arm not only considers static obstacles, but also takes on the role of the main arm at each moment as a dynamic obstacle for path planning. Finally, MATLAB is used for algorithm simulation, which proves the effectiveness of the algorithm for obstacle avoidance path planning problems for the dual-arm robot.  
Keywords:dual-arm robot; improved RRT algorithm; path planning; autonomous obstacle avoidance +
Footnote †: journal: Applied Physics
# Self-Organizing Control of Mega Constellations for Continuous Earth Observation
## 2 Materials and Methods
### Stability Proof of Satellite Control
#### 2.3.2 Stability Proof of Relative Motion-Bound Control
\begin{split}&\frac{\mathrm{d}\phi_{i}}{\mathrm{d}t}=\frac{ \partial\phi_{i}}{\partial\mathbf{X}_{i}^{\mathrm{T}}}\frac{\mathrm{d} \mathbf{X}_{i}}{\mathrm{d}t}+\frac{\partial\phi_{i}}{\partial\mathbf{X}_{i}^{ \mathrm{T}}}\frac{\mathrm{d}\mathbf{X}_{\mathrm{t}}}{\mathrm{d}t}=\frac{ \partial\phi_{i}}{\partial\mathbf{X}_{i}^{\mathrm{T}}}\Bigg{(}\frac{\partial \mathbf{X}_{i}}{\partial\mathbf{V}_{i}^{\mathrm{T}}}\mathbf{u}_{i}+\frac{ \partial\mathbf{X}_{i}}{\partial\mathbf{V}_{i+1}^{\mathrm{T}}}\mathbf{u}_{i+1} \Bigg{)}\\ &=\left\{\begin{array}{l l l l l l}-\frac{\partial\phi_{i}}{ \partial\mathbf{X}_{i}^{\mathrm{T}}}\frac{\partial\mathbf{X}_{i}}{\partial \mathbf{V}_{i}^{\mathrm{T}}}\frac{\partial\mathbf{X}_{i}^{\mathrm{T}}}{ \partial\mathbf{X}_{i}}\frac{\partial\phi_{i}}{\partial\mathbf{X}_{i}}-\frac{ \partial\phi_{i}}{\partial\mathbf{X}_{i}^{\mathrm{T}}}\frac{\partial\mathbf{X}_{ i}}{\partial\mathbf{V}_{i+1}^{\mathrm{T}}}\frac{\partial\mathbf{X}_{i}^{ \mathrm{T}}}{\partial\mathbf{V}_{i+1}}\frac{\partial\phi_{i}}{\partial \mathbf{X}_{i}},&if\widehat{d}_{i,i+1}>d_{\mathrm{max}}and\widehat{d}_{i+1,i+2 }\leq d_{\mathrm{max}}\\ &-\frac{\partial\phi_{i}}{\partial\mathbf{X}_{i}^{\mathrm{T}}}\frac{ \partial\mathbf{X}_{i}}{\partial\mathbf{V}_{i}^{\mathrm{T}}}\frac{\partial\mathbf{X}_{ i}^{\mathrm{T}}}{\partial\mathbf{X}_{i}}\frac{\partial\phi_{i}}{\partial \mathbf{X}_{i}},&if\widehat{d}_{i,i+1}>d_{\mathrm{max}}and\widehat{d}_{i+1,i+2 }>d_{\mathrm{max}}\\ &=\left\{\begin{array}{l l l l l l}-4k_{\mathrm{bound}}^{2} \Big{(}\widehat{d}_{i,i+1}-d_{\mathrm{max}}\Big{)}^{2}\frac{\partial\widehat{d}_ {i,i+1}}{\partial\mathbf{V}_{i}^{\mathrm{T}}}\frac{\partial\widehat{d}_{i,i+1}}{ \partial\mathbf{V}_{i}}-4k_{\mathrm{bound}}^{2}\Big{(}\widehat{d}_{i,i+1}-d_{ \mathrm{max}}\Big{)}^{2}\frac{\partial\widehat{d}_{i,i+1}}{\partial\mathbf{V}_{ i+1}^{\mathrm{T}}}\frac{\partial\widehat{d}_{i,i+1}}{\partial\mathbf{V}_{i+1}},\\ &if\widehat{d}_{i,i+1}>d_{\mathrm{max}}and\widehat{d}_{i+1,i+2}\leq d_{\mathrm{ max}}\\ &-4k_{\mathrm{bound}}^{2}\Big{(}\widehat{d}_{i,i+1}-d_{\mathrm{max}}\Big{)}^{2} \frac{\partial\widehat{d}_{i,i+1}}{\partial\mathbf{V}_{i}^{\mathrm{T}}}\frac{ \partial\widehat{d}_{i,j+1}}{\partial\mathbf{V}_{i}},&if\widehat{d}_{i,i+1}>d_{ \mathrm{max}}and\widehat{d}_{i+1,i+2}>d_{\mathrm{max}}\end{array}\right.\end{split}  \tag{56}\]  
Since \(k_{\mathrm{bound}}>0\), the time derivative of \(\phi_{i}\) is negative definite. To that end, the stability of the relative motion-bound control under self-organizing rules is proven.