esa-sceva/satcom-chunk-collection huggingface dataset:
# A Singular Perturbation Approach-Based Non-Cascade Sliding Mode Control for Surface-Mounted PMSMs
## 5 Conclusions  
This paper has investigated the problem of the non-cascade SMC strategy for the surface-mounted PMSMs speed regulation systems with disturbances. A singular perturbation decomposition approach is firstly presented to construct the composite sliding surface, while the fixed-point recursive algorithm is employed to calculate the solutions of transformation matrices. By incorporating the TD and a nonlinear function into the designed SMC law, the eventual TD-based SMC strategy has been presented in details. The simulation results demonstrate that the TD can arrange the favorable transition dynamic, while the effective differential signal can be provided, simultaneously. In addition, the closed-loop PMSM regulation system performances are characterized by small overshoot and steady-state error. Our future work will concentrate on the design and analysis of disturbance observer (DO), and we will devote ourselves to conducting the DO-based SMC, thus promoting the tracking performances in the presence of the parametric uncertainties and external disturbances.  
This is a joint work and the authors were in charge of their expertise and capability: Z.C. for investigation and analysis; H.Y. for validation and revision; S.M. for methodology; M.A. for writing and revision; A.B. for manuscript revision; Y.B. for data analysis. All authors have read and agreed to the published version of the manuscript.  
This work were supported by the National Natural Science Foundation of China under Grant 41576096, 42176211, Fundamental Research Funds for the Central Universities under Grant 3216002104D, and Postgraduate Research & Practice Innovation Program of Jiangsu Province under Grant SJCX20_0013.  
The authors would like to express their gratitude to all those who helped them during the writing of this paper. And the authors would like to thank the reviewers for their valuable comments and suggestions.  
The authors declare no conflict of interest.  
Figure 7: The characteristic curves of the regulation errors.
# A Telepresence System for Therapist-in-the-Loop Training for Elbow Joint Rehabilitation
## 2 System Overview
### Model for Therapist-Haptic Device Interaction  
During the therapy, the therapist tunes his/her reaction to generate a desirable therapeutic response based on the patient's need via the visually shared model. The behaviour can result in either assisting or resisting the motion to patients. An impedance control where the robot is rendered as having mass, spring, and damper properties was used for the haptic device. The control formula is given by Equation (9)  
\[f_{T}=m(\ddot{x}_{d}-\ddot{x}_{T})+b(\dot{x}_{d}-\dot{x}_{T})+k(x_{d}-x_{T}) \tag{9}\]  
where \(\ddot{x}_{d}\), \(\dot{x}_{d}\), \(x_{d}\) are the desired acceleration, velocity and position; \(\ddot{x}_{T}\), \(\dot{x}_{T}\), \(x_{T}\) are actual acceleration, velocity and position, respectively.  
The device is operated in a two-dimensional space which is consistent with the visually shared training model. Therefore, the torque for two active joints can be calculated by Equation (10).  
\[\left[\begin{array}{c}\tau_{1}\\ \tau_{2}\end{array}\right]=J_{polar}(q)^{\top}\left[\begin{array}{c}k_{\rho}e _{\rho}+b_{\rho}\dot{e}_{\rho}\\ k_{\theta}e_{\theta}+b_{\theta}\dot{e}_{\theta}\end{array}\right] \tag{10}\]  
where, \(e_{\rho}\), \(\dot{e}_{\rho}\), \(e_{\theta}\) and \(\dot{e}_{\theta}\) are the radial position error, radial velocity error, angular position error, and angular velocity error between the actual trajectory with the designed trajectory. \(J_{polar}(q)\) is the Jacobian from the haptic device's motor part to the handle, where \(q_{1}\) and \(q_{2}\) are the two generalized coordinates as shown in Figure 4.  
Figure 4: Schematic of the haptic device with virtual stiffness and damping.
## 2 Materials and Methods
### Laboratory Equipment  
Vicon(r) Nexus Motion Capture system (Vicon Motion System Ltd., Oxford, UK) was used to capture reflective marker data. The marker data was calculated using a Plug-in-Gait model to produce gait parameters, e.g., walking speed and cadence, and kinematic and kinetic joint parameters, e.g., joint angles, forces, moments, and powers, etc. A total of 15 infrared digital cameras with a strobe head unit, an optical filter, and a distinct video camera, including cables and lenses, in each are the main components of the Vicon(r) Nexus Motion Capture system. All cameras relate to Nexus software version 2.12.0 and are directed or focused on the capture volume area to be able to capture images at 200 Hz.  
The four force platforms (Advanced Mechanical Technology, Inc., Watertown, MA, USA, AMTI, BP 600 mm \(\times\) 400 mm) were arranged in mixed and used to collect the ground reaction force in all three directions at the same time with a frequency of 1000 Hz so that the kinetic parameters in the lower limbs could be obtained using inverse dynamics. Before the data collection starts, the calibration of both Vicon and AMTI systems was carried out using both manual and automatic ways to achieve good quality capturing, verify or check the vertical and horizontal forces, and avoid a higher image error. All force plates were checked in all directions by comparing the weight converted to Newton with the calculated force. The changes should not exceed increases or decreases of 10 N. Otherwise, the calibration must be repeated with the lab technicians helping.
# Effect of Temperature on Channel Compensation in Optical Camera Communication
## 5 Methodology  
The main objective of this paper is to evaluate the effect of compensating an RGB OCC channel by using obsolete CSI in terms of temperature. As was aforementioned, the emission spectrum of an LED is highly dependent on temperature, usually displacing the peak wavelength to lower energies, decreasing the coherence (spectrum broadening) and lowering the light source's efficiency. These effects cause significant variations between the CSI of an LED at ambient temperature with respect to the same LED at its stationary temperature. In a real OCC link in which the transmitter was subject to significant temperature variations due to the environment (e.g., extreme weather conditions or industrial environments), the channel compensation procedure would result in an SINR penalty due to the difference between the reference CSI and the instantaneous CSI.
## 3 Results and Discussion
### Bacterial Community of ML Silage  
#### 3.5.1 The Dynamic Variation and Alpha Diversity of the Bacterial Community  
The dynamic variation of the bacterial community was illustrated by principal component analysis (Figure 2). The results of the principal coordinate analysis was based on UniFrac (unweighted) distances. In the CK, the bacterial community showed the clearest distinctions when the ensiling time was prolonged. Moreover, after 7 days of ensiling, it was obvious that the silages treated with CA were separate from the CK, which suggested that the microbial community changed during the ensiling process. The distinctiveness of the bacterial communities among all treatments might account for the better fermentation that occurred in the ML silages treated with CA.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Item** & **True Protein** &
\begin{tabular}{c} **Non-Protein** \\ **Nitrogen** \\ \end{tabular} & **Ammonia-N** & **Carboxypeptidase** & **Aminopeptidase** & **Acid Protease** \\ \hline True protein & 1 & & & & \\ Non-protein & \(-1.00\) & \(1\) & & & \\ nitrogen & \(0.00\) & \(1\) & & & \\ Ammonia-N & \(-0.65\) & \(0.65\) & \(1\) & & \\  & \(0.06\) & \(0.06\) & \(1\) & & \\ Carboxypeptidase & \(-0.81\) & \(0.81\) & \(0.80\) & \(1\) & \\  & \(<\)0.01 & \(<\)0.01 & \(<\)0.01 & \(1\) & \\ Aminopeptidase & \(-0.51\) & \(0.51\) & \(0.56\) & \(0.62\) & \(1\) \\  & \(0.16\) & \(0.16\) & \(0.11\) & \(0.08\) & \(1\) \\ Acid protease & \(0.56\) & \(-0.56\) & \(-0.74\) & \(-0.53\) & \(0.06\) \\  & \(0.11\) & \(0.11\) & \(0.02\) & \(0.14\) & \(0.87\) & \(1\) \\ \hline \end{tabular} Values at the top for each category denote the correlation coefficients; values at the bottom denote the \(p\)-value of the correlation.  
\end{table}
Table 4: [PERSON]’s correlations between protease activities and the protein fractions of mulberry leaf silages.  
Figure 2: Principal component analysis of the bacterial community for mulberry leaf silages (M, pre-ensiled material; CK, the control; CA1, 1% citric acid; CA2, 2% citric acid; D3, 3 days of ensiling; D7, 7 days of ensiling; D14, 14 days of ensiling; D30, 30 days of ensiling).  
The alpha diversity of the bacterial community in ML silage is shown in Table 5. The coverage values of all samples were approximately 0.99, which indicated that most of the bacterial community was captured by the adequately large sequencing data. The Sobs, Ace and Chao1 indices decreased, but the Simpson index increased with a longer ensiling time, which indicated that the bacterial richness of ML silage decreased and dominant bacterial community richness increased. Compared with the CK, the silage treated with CA generally showed a lower Shannon index at the same stage of ensiling, which indicated that CA decreased the diversity of the bacterial community of ML silage.
# Train-Induced Unsteady Airflow in a Metro Tunnel with a Ventilation Shaft
## 4 Conclusions  
The Bernoulli equation of the unsteady flow was used to mathematically model the piston effect in a metro tunnel with and without a ventilation shaft. According to the established theoretical model, the influence of the shaft dimension and location on the piston wind and ventilation rate was analyzed. The following conclusions were obtained:  
1. The ventilation shaft has a significant impact on the piston wind. The maximum piston wind speed in a tunnel with a ventilation shaft is greater than the value in a tunnel without ventilation shaft. Similarly, the presence of a shaft increases the ventilation rate.
2. The maximum stable piston wind speed, \(v_{u-max}\), decreases with the increase in \(\varphi\), and \(v_{d-max}\) increases with the increase in \(\varphi\). Changes in the \(v_{u-max}\) and \(v_{d-max}\) versus \(\varphi\) can be well represented by power functions. Meanwhile, as \(\beta\) increases, the maximum piston wind speed (\(v_{u-max}\) and \(v_{d-max}\)) increases following the power function law. However, the changes in the \(v_{u-max}\) and \(v_{d-max}\) are negligible with the increase in \(H_{s}\).
3. The airflow in the shaft is affected by the shaft location and the shaft cross-section, but shaft height has no obvious effect on it. Both the \(v_{s-max}\) and \(v_{s-min}\) linearly decrease with the increase in \(\varphi\). \(v_{s-max}\) decreases with \(\beta\) in power function, while the \(v_{s-min}\) increases with \(\beta\) following the power function.
4. The location of the shaft and its cross-section have an obvious influence on the ventilation rate, but the influence of the height of the shaft is very small. The ventilation rate first increases and  
Figure 11: Effect on the ventilation rate under different influencing factors.  
then decreases with the increase in \(\varphi\). The varying trend in ventilation rates versus \(\varphi\) can be well represented by a quadratic function. Meanwhile, the attenuation of ventilation rates varies in the power function when \(\beta\) increases.  
Conceptualization, F.W. and M.W.; Methodology, F.W. and S.Z.; Software, F.W. and X.H.; Validation, X.H.; Investigation, S.Z.; Writing--original draft, F.W. and X.H.; Writing--review & editing, L.X., S.Z. and M.W.; Supervision, L.X. and M.W. All authors have read and agreed to the published version of the manuscript.  
Not applicable. Not applicable.  
Data Availability Statement: Data are contained within this article.  
The authors thank the reviewers for their great help with the article during the review process.  
The authors declare no conflicts of interest.
# Line Structure Extraction from LiDAR Point Cloud Based on the Persistence of Tensor Feature
## 4 Experiments and Discussions
### Line Structure Extraction in the Complex Area
Then, the line structure is extracted from the \(LCS\)' based on the discrete Morse theory, using different persistence thresholds \(\delta\), and the result is compared with the ground truth line structure, along with the 3D point cloud segmentation-based line extraction method, as depicted in Figure 10.  
Figure 10a shows the ground truth line structure, and it is manually drawn from the original dataset. Figure 10b shows the result computed by the point cloud segmentation-based method [23] (with parameter [Max angle: \(10^{\circ}\), Max relative distance: 2.0 m, Max distance: 0.1 m, Min points per facet: 10, Max edge length 0.07 m)), and the line structure is extracted using the rim of segmented geometric objects. In Figure 10c-e, line segments \(\{p_{critical},l_{connection}\}\) are extracted from the \(LCS\)' with different persistence threshold values \(\delta\), ranging from 0.01 to 10, and related line structures \(\{\iota\}\) are computed. A low or high threshold \(\delta\) will cause the overconnection or misconnection problems in the extracted line structure, as depicted in Figure 10c-e. Here, the optimal threshold \(\delta=0.1\) is interactively set through a trivial operation, as depicted in Figure 10d. Figure 9: The \(LCS\) and \(LCS\)’ with different grid sizes \(\kappa\) in the complex area. The related statistics of extracted line structure in the complex area are depicted in Table 4. The total length and effective length of extracted line structure are compared with the ground truth line structure, and only the line segment within buffer area (0.05 m) of the ground truth line segment is considered to be the effective line structure. The length of the ground truth line structure in Figure (a)a is 53.98 m, and the total length of line extraction results in Figure (b)b-e is 289.58 m, 67.92 m, 67.36 m, and 58.28 m, respectively. However, the effective length of each result is 156.26 m, 56.99 m, 57.77 m, and 43.80 m. Although it has a longer length than others in Figure (b)b, there are too many misconnections. With a low threshold value \(\delta\), there can be over-connections in Figure (c)c, while there can be misconnections in Figure (e)e using a high threshold value \(\delta\). Among conducted experiments, the line structure extracted by the proposed method (in Figure (d)d) outperforms the others. Hence, with 85.76% of line segments effectively extracted, the experiment result in Figure (d)d holds the best performance, and it turns out that the proposed method can extract the line structure from the dataset with proper geometric and topological features. In addition, the rate of effectively extracted line segments among experimented datasets remains steady (87.33% for the scene of parterre, 85.76% for the scene of indoor stair), and it turns out to be an effective method to extract line structures in different scenarios.
# Wireless-Channel Key Distribution Based on Laser Synchronization
## 3 Experimental Results
### Laser Synchronization Induced by a Common Wireless Signal
To maintain laser synchronization with cross-correlation exceeding 0.9, the discrepancy ranges for injection power, bias current, and center wavelength are as follows: \(-\)0.010-0.004 nm, \(-\)7.7-3.4%, and \(-\)10-23%, respectively. These tolerated mismatches demonstrate that laser synchronization is less sensitive to external operations and imply synchronization robustness. Nevertheless, a high sensitivity of chaos synchronization to mismatches in laser inner parameters is required to ensure security, which poses difficulties for [PERSON] in obtaining a parameter-matched laser. It has been shown that synchronization is only achievable within a maximum tolerance of \(\pm\)1%  
Figure 3: Temporal waveforms of **(a-1)** the drive signal, **(a-2)** signals received by antennas \(\mathrm{Rx_{A}}\) and \(\mathrm{Rx_{B}}\), and **(a-3)** the DFB laser outputs. **(b)** Correlation plots for the \(\mathrm{DFB_{A}}\) and \(\mathrm{DFB_{B}}\) outputs. **(c)** Correlation plots for the drive signal and \(\mathrm{DFB_{B}}\) output. for laser inner parameters, such as active region length, linear gain coefficient, transparency carrier density, and so on [26]. This means that achieving synchronization necessitates the use of lasers manufactured on the same wafer, which is typically unattainable for Eve [33]. It is worth noting that the cross-correlation between two lasers with mismatched internal parameters is significantly lower than the cross-correlation between their outputs and the drive signal. Therefore, extracting information about the key from lasers with mismatched internal parameters is no more effective than extracting it directly from the drive signal.
## 3 Preliminaries
### Notations and Definitions  
**Emotion Vector**: Emotion vector is designed to quantify an individual's emotion. In this study, six basic emotion categories [11] are used to express compound emotions. An individual's emotional state is represented by using a 6-dimensional vector as follows:  
\[S=\{s_{happy},s_{sadness},S_{sager},s_{surprise},S_{fear},s_{disgust}\}. \tag{1}\]  
where \(s_{d}\) denotes the probability of the \(d\)-th basic emotion category and it is intermediate value within the range \([0,1]\). For example, \(s_{1}\) denotes the happiness score of the emotion vector.  
**Social Network**: A social network represented as an undirected graph \(G=\{V,E\}\), where \(V=\{v_{1},v_{2},\ldots,v_{n}\}\) is a collection of users, and \(E\subseteq V\times V\) is the set of edges whose element \(e_{ij}=(v_{i},v_{j})\) indicates that user \(v_{i}\) and user \(v_{j}\) are friends in the social network.  
**Emotion Time Series**: Emotion time series depicts the process of an individual's emotion changes in a period of time. Given a user \(v_{i}\), their emotion time series are defined as \(X=\{x_{1},x_{2},\ldots,x_{T}\}\in\mathbb{R}^{T\times[S]}\), where \(|S|\) is the dimension of the emotion vector. For each time stamp \(t\in\{1,2,\ldots,T\}\), \(x_{t}\) represents the emotion vector of user \(v_{i}\) at time stamp \(t\).  
**Masking Series**: Masking series marks valid data in emotion time series. A user \(v_{i}\)'s masking series is represented as \(M=\{m_{1},m_{2},\ldots,m_{T}\}\), where \(m_{t}=0\) if the value of \(x_{t}\) is missing; otherwise, \(m_{t}=1\).  
**Observed Series**: Observed series is composed of the valid values in the emotion time series. For each user \(v_{i}\), the valid values are extracted from the emotion time series and concatenated to construct the observed series \(X^{{}^{\prime}}=\{x^{{}^{\prime}}_{1},x^{{}^{\prime}}_{2},\ldots,x^{{}^{ \prime}}_{u}\}\), where \(u\) denotes the number of valid emotion vectors in the emotion time series.  
**Time Interval Series**: Time interval series represents the elapsed time between adjacent validues in the emotion time series. For each user \(v_{i}\), the time interval series is defined as \(\Delta T=\{\Delta t_{1},\Delta t_{2},\ldots,\Delta t_{u}\}\), where \(u\) is the number of valid emotion vectors in the emotion time series. The time interval \(\Delta t_{u}\) is calculated as follows:  
\[\Delta t_{u}=\left\{\begin{array}{ll}1,&u=1\\ t_{u}-t_{u-1},&u\
eq 1\end{array}\right. \tag{2}\]  
where \(t_{u}\) represents the timestamp of \(u\)-th valid emotion vector.  
An example of the emotion time series \(X\), observed series \(X^{{}^{\prime}}\), masking series \(M\), and time interval series \(\Delta T\) is shown in Figure 2.  
Figure 2: Example of the emotion time series \(X\), observed series \(X^{{}^{\prime}}\), masking series \(M\), time interval series \(\Delta T\).
# Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring
## 2 Materials and Methods
### Online Learning Direct-to-Satellite Packet Scheduling
#### 2.4.2 Modified Monte Carlo Learning  
Monte Carlo learning methods approximate a value function in a simple and interpretable way by taking the value of a state to be the average return at the end of a training episode [18; 19]. In the direct-to-satellite packet scheduling, the episodes are of length one, i.e., there is no sequential decision-making, simplifying the problem. If the reward is taken to be 1 for a successful transmission and 0 for an unsuccessful transmission, then the value function can be taken to be the average rate of successful transmission from a given state. If for a given state of satellite pass characteristics and RF noise, transmission is successful 50% of the time, then the value function is 0.5.  
However, Monte Carlo learning requires a discrete state space, whereas the state space for this problem is continuous, so we discretize the state space. Using the Swarm pass checker, it is known that all satellite passes shown are between 15 and 90 degrees and almost all between 10 and 60 min. Additionally, while RF noise is technically continuous, the modems only report whole numbers, e.g., \(-95\) dBm. If only integers within the range \(-93\) dBm (the highest noise Swarm reports success transmitting with) to \(-106\) dBm (the lowest noise measured in this project) are considered, this is naturally discretized. Table 4 shows how the state space has been chosen to be discretized. With 5 buckets for each state variable, some simple combinatorics gives 125 unique combinations, where the total set of 125 combinations represents the discretized state space.  
The remaining question is that of the policy \(\pi\). Clearly, once a good approximation of the true value function is made, the policy \(\pi\) should exploit that knowledge to select the most promising satellite passes. At the beginning, the system will not know about a good satellite pass, and it will thus have to explore with passes of different characteristics. This is an example of the _exploration-exploitation_ problem in reinforcement learning [18,19]. A common common approach is to explore early on and gradually exploit more with time.