esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Selecting Optimal Measurement Poses Based on Observability Index
### Introduction to Observability Indices
However, \(O_{1}\) has some shortcomings: it cannot be guaranteed that the \(\mathrm{d}\mathbf{q}_{\mathrm{B}}\) vector will be inevitably large in a large hyperellipsoid. There is a possibility that the minimum semi-axes of a large hyperellipsoid is very small, and it is theoretically possible to make \(\mathrm{d}\mathbf{q}_{\mathrm{B}}\) as small as the minimum semi-axes [22]. \[O_{2}=\frac{\sigma_{L}}{\sigma_{1}} \tag{35}\]  
\(O_{2}\) is a measure of the eccentricity of the \(\mathrm{d}\mathbf{q}_{\mathrm{B}}\) hyperellipsoid [20]. The larger the \(O_{2}\), the closer the hyperellipsoid is to the hypersphere, which will result in the uniformity of singular values. This measure does not consider the volume of the hyperellipsoid, but rather considers its eccentricity. \[O_{3}=\sigma_{L} \tag{36}\]  
Maximizing \(O_{3}\) requires a large minimum semi-axes of the hyperellipsoid [21]. Maximizing \(O_{3}\) will increase the minimum semi-axes and volume of the hyperellipsoid, making the selected measurement poses more sensitive to the errors of kinematic parameters. \[O_{4}=\frac{{\sigma_{L}}^{2}}{\sigma_{1}} \tag{37}\]  
\(O_{4}\) is the square of the minimum singular value of the identification Jacobian matrix divided by the maximum singular value, and is the product of \(O_{2}\) and \(O_{3}\). Maximizing  
Figure 3: Geometric significance of singular values. will make the minimum semi-axes of the hyperellipsoid larger, and make the maximum semi-axes smaller. In addition, it results in a smaller eccentricity [22]. \[O_{5}=\frac{1}{\left(\frac{1}{\sigma_{1}}+\frac{1}{\sigma_{2}}+\dots+\frac{1}{ \sigma_{L}}\right)} \tag{38}\]  
\(O_{5}\) is the reciprocal sum of all singular values, and then takes the reciprocal. It is the harmonic mean divided by the number of parameters \(L\)[23]. In general, the larger the value of observability index, the more sensitive the pose error \(\mathrm{d}\mathbf{q}_{\mathrm{B}}\) is to the variation of the kinematic parameter error \(\mathrm{d}\mathbf{C}\), and the smaller the impact of measurement noise. Therefore, a group of poses with larger observability index are more likely to achieve higher calibration accuracy. However, there is currently no consensus on the performance of the five observability indices. In order to determine which index is most advantageous for the kinematic calibration of the six-hardpoint positioning mechanism, this paper has conducted many numerical simulations of calibration and compared their performance. Finally, we will use the best performance observability index for the calibration method.
# Adaptive Emergency Call Service for Disaster Management
## 3 System Model
### Outband D2D Communication  
When the sender is out of coverage, the emergency call source or relay sends a control message via the physical sidelink control channel (PSCCH). The PSCCH serves,implicitly, to synchronize the sender with the potential receivers. It is used by ProSe-enabled UEs to send the sidelink control information (SCI) that informs the receivers about the data transmission parameters used during the next sidelink period: subframes and radio resource blocks [39]. More precisely, the PSCCH indicates the index of the used subframes (time), the used radio resource blocks (frequencies), the modulation and coding scheme, and the D2D group destination ID. Each UE listens continuously to the PSCCH channel to detect if another UE is transmitting in the current sidelink period. Once the PSCCH message is received, the relay node tunes to the corresponding resources in the physical sidelink shared channel (PSSCH) to receive the emergency data.  
In summary, an outband D2D multi-hop model based on 4G/5G sidelink communication is considered for emergency call forwarding. This model, called the 5G-SOS protocol, is described in the following section.
# Perturbations During Gait on a Split-Belt Treadmill: A Scoping Review
## 4 Discussion
### Limitations of the Reviewed Studies and Suggestions for Future Research  
A crucial limitation of included studies is the controlled laboratory setting, which does not replicate the complexity of walking in the real world. Thus, the observed adaptive responses may differ from those in everyday situations. Another limitation is the ability to anticipate change due to the recurrent nature of perturbations. Anticipating perturbations may cause individuals to adopt a cautious gait or prepare in advance [34], skewing results and not accurately reflecting natural, spontaneous responses.  
Participant demographics in the included studies also present limitations. Most studies focused on young, healthy adults, with fewer studies examining older adults or individuals with neurological impairments. Moreover, the varied methodologies of the included studies further complicate synthesizing the results. Additionally, the relatively small sample sizes in many studies may limit the statistical power and robustness of the findings.  
These limitations highlight several key areas where further research is needed. First, there is a notable lack of studies focusing on older adults and individuals with neurological impairments, i.e., populations that are particularly vulnerable to gait instability and falls. Only a few studies included these groups, limiting the generalizability of findings.  
Second, the methods used to induce perturbations vary significantly across studies. Differences in treadmill settings, perturbation timing, and intensity make it difficult to compare outcomes. Standardizing these parameters would facilitate more robust comparisons and help identify the most effective approaches for improving gait stability.  
Additionally, while motion capture and electromyography are commonly used to analyze gait, emerging technologies such as virtual reality (VR) have been underutilized. Integrating new technologies with split-belt treadmill perturbations could provide a more comprehensive understanding of real-world gait challenges.  
Finally, in most studies, follow-up duration was limited or absent. Future research should prioritize longitudinal studies to examine the long-term effects of perturbation-based training. Comparative studies are also needed to explore how different perturbations influence gait adaptation and fall prevention. These efforts will advance the field by providing clearer guidance for rehabilitation protocols and assistive device development.
## 3 Results
### Non-Conservative Force Modeling Effect of BDS
As shown in Figures 8 and 9, out of the three types of MEO satellites, only the slope of the SECM satellites (i.e., C29 and C30) was negative except for the ESA center, whereas the slopes of the other two types of satellites are positive. For BDS-3 SECM satellites, surface X has an area of 1.3 m\({}^{2}\), while surface Z has a larger area of 2.6 m\({}^{2}\). On the contrary, the X surface area of BDS-2 MEO and BDS-3 CAST satellites is larger than the area of the Z surface, with values of 3.8 and 3.4 m\({}^{2}\), and 2.9 and 2.2 m\({}^{2}\), respectively [38]. Compared with previous research, it was found that the slopes of SECM and Galileo (X area: 1.3 m\({}^{2}\), Z area: 3.0 m\({}^{2}\)) satellites are negative [17], whereas the slopes of BDS-2 MEO, CAST, and GLONASS (X area: 4.2 m\({}^{2}\), Z area: 1.7 m\({}^{2}\)) satellites are positive [39]. Thus, this phenomenon can be explained by the different shapes of satellites. Figure 7: RMS and mean offset of SLR residuals of long-termed tracked BDS satellites (eight satellites except C01 in Table 1) observed by high-performing stations for four ACs. The slope of the ESA BDS-3 CAST orbit was similar to that of SECM satellites, both of which had a negative value that was close to 0. By comparing the strategies of ACs, we found that ESA and WU utilized a PBW model in their solutions, which differs from the approach used by other ACs. It was found that a proper PBW model can enhance the stability of SLR residuals. For instance, when the estimated PBW model was applied in ESA, the RMS of the SLR residuals of \({}^{\mathrm{TM}}\)CAST and SECM satellites was 36 mm and 50 mm, respectively. The slopes of the CAST and SECM satellites were both \(-\)0.04 mm/deg, which is smaller than the slope of the WU CAST and SECM orbits of about \(-\)0.4 and 0.5 mm/deg. This phenomenon may be explained by the fact that ESA added a more accurate PBW model to the modeling of SRP, which is reflected in ESA's BDS-3 satellite SLR residuals having the best statistical results. Figure 8: SLR residuals as a function of the elongation angle and the sun elevation for four BDS-2 satellites (C08, C10, C11 and C13): (**top**): WU; (**bottom**): ESA. Figure 10: BDS satellites orientation in orbits at different sun elevations. The X, Y, and Z vectors indicate the axes of the SCF. Figure 9: SLR residuals as a function of the elongation angle and the sun elevation for four BDS-3 satellites (C20, C21, C29 and C30): (**top**): WU; (**bottom**): ESA.
Following their study, numerous researchers have conducted studies on the cavitation performance within pumps using various experimental methods, such as vibration signal monitoring [29; 30; 31], high-speed shooting techniques [32; 33; 34; 35], and Particle Image Velocimetry (PIV) technology [36; 37]. In general, research on pump cavitation performance has mainly focused on cavitating turbulent flows and cavitation characteristics under room temperature conditions, typically using water as the working fluid. However, there are certain differences in the thermal physical properties between refrigerants and water. Studies concerning the internal flow characteristics and cavitation behavior of refrigerant centrifugal pumps under different operating conditions have been very few. This paper focuses on analyzing the distributions of pressure, temperature, relative velocity, and bubble volume in a fluid self-lubricating centrifugal pump with R134a refrigerant under different operating conditions. The objective is to obtain insights into the changing patterns of flow characteristics and the cavitation process in refrigerant centrifugal pumps. The feasibility and stability of the prototype were validated, and the research results can provide guidance for the future design of refrigerant pumps.
## 2 Methods
### Data Augmentation Using AAFT and Signal Processing  
In contrast to mechanical or electrical signal measurement, it is difficult to measure a large number of consistent sEMG signals for a repetitive task due to muscle fatigue. To prevent either under- or over-fitting and increase the generalization ability of a neural network, therefore, a proper data augmentation technique must be applied. The measured both sEMG and joint angle data are augmented by adopting the amplitude adjusted Fourier transform (AAFT) (Python code for AAFT can be found at [[https://github.com/manu-mannattil/nolitsa](https://github.com/manu-mannattil/nolitsa)]([https://github.com/manu-mannattil/nolitsa](https://github.com/manu-mannattil/nolitsa)), accessed on 2 February 2021 [50]). We here recapitulate AAFT by the following [51]:  
1. Generate a Gaussian sequence, say \(y(n)\), using pseudo-random generator;
2. Reorder \(y(n)\) according to the rank of the measured original data \(x(n)\), say this reordered sequence \(z(n)\);
3. Perform Fourier transform to \(z(n)\): \[Y(k)=\sum_{n=0}^{N-1}z(n)e^{-j2\pi nk/N}\]
4. Randomize phase: \(Y^{\prime}(k)=Y(k)e^{i\phi}\), where \[\text{When data are even: }\left\{\begin{array}{l}\phi(f_{0})=0\\ \phi(f_{i})=-\phi(f_{k}),\quad i=2-\frac{N}{2},\,k=N-\frac{N}{2}+1\\ \phi(f_{N/2})=0\end{array}\right.\] When data are odd: \[\left\{\begin{array}{l}\phi(f_{0})=0\\ \phi(f_{i})=-\phi(f_{k}),\quad i=2-\frac{N+1}{2},\,k=N-\frac{N+1}{2}+1\end{array}\right.\]
5. Perform inverse Fourier transform: \[y^{\prime}(n)=\frac{1}{N}\sum_{k=1}^{N-1}Y^{\prime}(k)e^{j2\pi nk/N}\]
6. According to the rank of \(y^{\prime}(n)\), reorder the measured original data \(x(n)\); this yields the surrogate data \(x^{\prime}(n)\).  
The AAFT generates the surrogates of the original data in terms of temporal correlation, amplitude distribution, power spectral density [51; 52]. For the sEMG measurement, since it is rather difficult to obtain a large amount of consistent data due to muscle fatigue, the AAFT is frequently used for data augmentation. In this study, the AAFT was employed with the same purpose and similar application can be found in [53]. We note that a ratio between the number of data after being augmented and that of the original data will be referred to as the level of data augmentation. For instance, if the total number of data was tripled, then the level of data augmentation was 3 and will be denoted by AAFT(3).  
After data augmentation by AAFT, moving root mean square (RMS) filtering was applied to both sEMG and joint angle data (original and surrogates) with a sliding window length 10. Recall that each sEMG channel as well as the elbow joint angle data contained 80 time series data samples. These data were integrated over every five samples, which yielded \(8\times 16\) for the 8-channel sEMG data and \(1\times 16\) for the elbow joint angle data, respectively. The \(8\times 16\) sEMG data were intrachannel normalized and the \(1\times 16\) elbow joint angle data were interchannel normalized.
# Cell-Based Target Localization and Tracking with an Active Camera
## 4 Experimental Methods  
### Experimental Platform and Scene  
The proposed method was tested on 50 cm \(*\) 250 cm horizontal and vertical channels. The experimental equipment used in the experiment is shown in Figure 10. It mainly includes a PTZ camera. This active camera is produced by Hikvision Company. Its model is DS-2 DF8231 IW-A, the focal length range of this camera is 6 mm to 186 mm, it has two degrees of freedom of rotation, and the pan and tilt angles are between 0-90 degrees and 0-360 degrees, as shown in Figure 10. In this experiment, the resolution of the captured  
Figure 9: Coordinate transformation model.  
image is set to 1920 \(\times\) 1080 (pixels). Another experimental device used in this experiment is a wireless remote-control car, as shown in Figure 10b. The length, width and height of the car are 22.5 cm, 14 cm and 13.5 cm, respectively.  
The experimental scene was modeled, and the world coordinate system was constructed (Figure 11a). We divided the operating channel of the WRC-Jeep into a horizontal channel (Figure 11c) and a vertical channel (Figure 11b) and divided each channel into five equally sized cells. The size of each cell was 50 cm \(*\) 50 cm, and the vertices of each cell were calibrated. In addition, we calibrated the world coordinates of each vertex.  
Figure 11: Experimental scene. (**a**) Experimental scene modeling. (**b**) Vertical channel. (**c**) Horizontal channel.  
Figure 10: Experimental equipment. (**a**) Active camera with pan and tilt functions. (**b**) Wireless remote-control Jeep car.  
Before the experiment, the camera needed to be calibrated. The results of camera calibration are as follows:  
\[M=\left[\begin{array}{ccc}2807.668433&0&963.0151539\\ 0&2810.606781&557.4054265\\ 0&0&1\end{array}\right] \tag{21}\]  
From the internal parameter matrix, the coordinates of the principal point of the camera can be obtained as: \(O^{\prime}\) = (963,557).
## 3 Compliance Model of _n_-4R Compliant Parallel Pointing Mechanism
### Compliance of the Right-Circular Flexure Hinge  
Compliance is an important performance indicator of the compliant mechanism, and the compliance of the flexure hinge is the basis for the compliance modeling of the mechanism. Each branch of the _n_-4R compliant parallel pointing mechanism contains four similar right-circular flexure hinges denoted by \(R_{1}\), \(R_{2}\), \(R_{3}\), and \(R_{4}\). The parameters defining the right-circular flexure hinges are shown in Figure 3, where \(r\) is the radius of the flexure hinge, \(w\) is the width of the flexure hinge, and \(t_{0}\) is the minimum thickness of the flexure hinge along the _x_-axis.  
Since the deformation of the right-circular flexure hinge is mainly concentrated in the circular part of the hinge, deformation outside the circular part of the hinge can be ignored. Based on the assumption of small elastic deformation, the deformation of the flexure hinge in all directions should satisfy the principle of linear superposition. Assuming that the force \(\mathbf{f}=[f_{x},f_{y},f_{z}]^{\mathrm{T}}\) and the moment \(\mathbf{m}=[m_{x},m_{y},\,m_{z}]^{\mathrm{T}}\) act at the end of the flexure hinge, the resulting linear displacement and angular displacement at the end of the hinge are \(\delta=[\delta_{x},\,\delta_{y},\,\delta_{z}]^{\mathrm{T}}\) and \(\mathbf{\theta}=[\theta_{x},\,\theta_{y},\,\theta_{z}]^{\mathrm{T}}\), respectively. Then the relationship between the displacement and the input force at the end of the flexure hinge is defined [25]:  
\[\left[\begin{array}{c}\theta_{x}\\ \theta_{y}\\ \theta_{z}\\ \delta_{x}\\ \delta_{y}\\ \delta_{z}\end{array}\right]=\left[\begin{array}{cccccc}C_{\theta_{x},m_{x}} &0&0&0&0&0\\ 0&C_{\theta_{y},m_{y}}&0&0&0&C_{\theta_{y},f_{z}}\\ 0&0&C_{\theta_{z},m_{z}}&0&C_{\theta_{z},f_{y}}&0\\ 0&0&0&C_{\theta_{z},f_{x}}&0&0\\ 0&0&C_{\delta_{y},m_{z}}&0&C_{\delta_{y},f_{y}}&0\\ 0&C_{\delta_{z},m_{y}}&0&0&0&C_{\delta_{z},f_{z}}\end{array}\right]\left[ \begin{array}{c}m_{x}\\ m_{y}\\ m_{z}\\ f_{x}\\ f_{y}\\ f_{z}\end{array}\right] \tag{1}\]  
where the \(6\,\times\,6\) matrix is compliance matrix \(\mathbf{C}\) of the right-circular flexure hinges and is computed in Appendix A.1.
# Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication
## 3 Analysis of Simulation Results
### Performance Analysis of the System after Preprocessing in Shallow Water Channels  
In the preprocessing stage, firstly, the CIR is reconstructed by the OMP algorithm, then the channel equalization is realized by VTRM technology, and finally, the processed signal data are input into the neural network model to complete the signal demodulation. Through simulation analysis, it was found that the performance of the preprocessed DL-based system is better than that of the preprocessed conventional system.  
The simulation results of the preprocessed DL-based CSK-SS communication system in shallow water channels are shown in Figure 12. When each symbol carries \(4\) bit, \(3\) bit, and \(2\) bit information, and the magnitude of the BER is \(10^{-3}\), the SNR of the LSTM neural network model is about \(9.5\) dB, \(10\) dB, and \(10.5\) dB lower than that of the conventional system, respectively. In addition, when each symbol carries \(4\) bit and \(3\) bit information, the SNR required by the BiLSTM neural network model to achieve the same system performance is reduced by about \(1\) dB and \(0.5\) dB, respectively, compared with the LSTM neural network model.  
When each symbol carries \(2\) bit information, the performance of the two models is similar, but the BiLSTM neural network model still has a slight advantage at \(-8\) dB. The whys and wherefores are that after preprocessing, the performance of the DL-based system  
Figure 12: BER curve of DL-based system and conventional system after preprocessing.  
is better than that of the conventional system and the BiLSTM neural network model has more advantages in performance.  
As shown in Figure 13, compared with the DL-based CSK-SS UWA communication system without VTRM technology, for the BiLSTM neural network model, the required SNR is reduced by about 7 dB, 3.5 dB, and 2 dB, respectively. VTRM technology suppresses the ISI caused by multipath expansion of the UWA channel so that the multipath signal energy is superimposed to obtain the focusing gain. In addition, VTRM can make the signal components coherently superimposed and the noise components incoherently superimposed to increase the SNR of the signal. Therefore, after the preprocessing of the communication signal is completed by using this technology, the performance of the system will be further improved when demodulated by the neural network model.
###### Abstract  
With the popularity of High Dynamic Range (HDR) display technology, consumer demand for HDR images is increasing. Since HDR cameras are expensive, reconstructing High Dynamic Range (HDR) images from traditional Low Dynamic Range (LDR) images is crucial. However, existing HDR image reconstruction algorithms often fail to recover fine details and do not adequately address the fundamental principles of the LDR imaging pipeline. To overcome these limitations, the **Reversing Lossy**UNet (_RLUNet_) has been proposed, aiming to effectively balance dynamic range expansion and recover overexposed areas through a deeper understanding of LDR image pipeline principles. The RLUNet model comprises the Reverse Lossy Network, which is designed according to the LDR-HDR framework and focuses on reconstructing HDR images by recovering overexposed regions, dequantizing, linearizing the mapping, and suppressing compression artifacts. This framework, grounded in the principles of the LDR imaging pipeline, is designed to reverse the operations involved in lossy image operations. Furthermore, the integration of the Texture Filling Module (TFM) block with the Recovery of Overexed Regions (ROR) module in the RLUNet model enhances the visual performance and detail texture of the overexposed areas in the reconstructed HDR image. The experiments demonstrate that the proposed RLUNet model outperforms various state-of-the-art methods on different testsets.  
HDR image reconstruction; reversing lossy UNet; lossy image operation; texture filling module
# Multi-Wavelength Quantum Key Distribution Emulation with Physical Unclonable Function
## 3 Bitwise Transform
### Octal Transform  
When expanding the QKD system by adding more wavelengths, higher input will be required. Understanding quaternary transform, and operating under the same assumption--that the mask is configured to remove 50% of the stream--you will find that octal transform takes a very similar form, with only a minor alteration in the process.  
The octal transform sequence is as follows: mask the stream to remove the fuzzy bits:  
\[\begin{split} S&:01010101\ 01010101\\ \circled{M}&:00110011\ 10011001\\ K&:01011010\end{split} \tag{15}\]split the mask into two chunks:  
\[M:\;00110011\;10011001\quad\longmapsto\quad\begin{array}{l}M_{1}:00110011\\ M_{2}:10011001\end{array} \tag{16}\]  
and apply those two chunks through the octal transform operator:  
\[K:01011010 \tag{17}\] \[M_{1}:00110011\] \[\mathbb{O}_{8}M_{2}:10011001\] \[K_{8}:14275063\]  
\[\begin{array}{|c|c|c|c|}\hline S&M_{1}&M_{2}&S\oplus_{8}M_{1}\oplus_{8}M_{2 }\\ \hline 0&0&0&0\\ \hline 0&0&1&1\\ \hline 0&1&0&2\\ \hline 0&1&1&3\\ \hline 1&0&0&4\\ \hline 1&0&1&5\\ \hline 1&1&0&6\\ \hline 1&1&1&7\\ \hline\end{array} \tag{18}\]  
Instead of XORing the mask after splitting, as in quaternary transform, we use both halves of the mask to attain the three needed inputs of octal transform. Of course, this is not the only way; see Section 3.6 for more options.  
Remaining consistent, the output takes the form of the binary interpretation, where order of the inputs matter.  
The eight states can be mapped as desired, and per application. The next section will explore how to utilize the lesser states that come with the octal base.