esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this paper, we present a new multi-user chaos-based communication system using Faster-than-Nyquist sampling to achieve higher data rates and lower energy consumption. The newly designed system, designated Multi-user Faster Than Nyquist Differential Chaos Shift Keying (MU-FTN-DCSK), uses the traditional structure of Differential Chaos Shift Keying (DCSK) communication systems in combination with a filtering system that goes below the Nyquist limit for data sampling. The system is designed to simultaneously enable transmissions from multiple users through multiple sampling rates resulting in semi-orthogonal transmissions. The design, performance analysis, and experimental results of the MU-FTN-DCSK system are presented to demonstrate the utility of the newly proposed system in enabling multi-user communications and enhancing the spectral efficiency of the basic DCSK design without the addition of new blocks. The MU-FTN-DCSK system presented in this paper demonstrates spectral gains for one user of up to 23% and a combined gain of 25% for four (\(U=4\)) users. In this paper, we present a proof of concept demonstrating a new degree of freedom in the design of Chaos-based communication systems and their improvement in providing wireless transmissions without complicated signal processing tools or advanced hardware designs.  
chaos-based communication systems; differential chaos shift keying; Faster-Than-Nyquist; interference; multi-user; sampling rate 20222227837  
2022227837 [[https://doi.org/10.3390/s22207837](https://doi.org/10.3390/s22207837)]([https://doi.org/10.3390/s22207837](https://doi.org/10.3390/s22207837))  
20222227837 [[https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors)]([https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors))
# Segmented Hybrid Impedance Control for Hyper-Redundant Space Manipulators
## 4 System Control
### Segmented Impedance Motion/Force Control  
In the HRSM's first segment operation task, if it has not yet made contact with external objects, there is no need to introduce impedance control. Instead, segmented force/position hybrid control is used to bring the first segment of the robotic arm closer to the target. Without collision, the segmented force/position hybrid control method is used, and there is no impedance in the controller. The control signal for the segmented manipulator arm is the same as the following equation:  
Figure 5: Block diagram of the robotic arm hybrid impedance controller.  
\[\begin{split}\Gamma_{\text{sec}1}=\text{P}(\text{q})(\overset{ \sim}{\text{M}}_{\text{sec}1}(\text{q})(\ddot{\text{q}}_{\text{d}}(\text{t})+ \text{K}_{\text{p}1}\text{q}_{\text{e}1}+\text{K}_{\text{d}1}\dot{\text{q}}_{ \text{e}1}))+(\text{I}-\text{P}(\text{q}))(\text{J}^{\text{T}}_{\text{sec}1}( \text{q})\\ (\text{F}_{\text{d}}+\text{K}_{\text{fp}}\text{F}_{\text{e}}+\text{K}_{ \text{fi}}\int\text{F}_{\text{e}}(\text{t})\text{dt}-\text{K}_{\text{damp}}v)), \end{split} \tag{35}\]  
When the HRSM link makes its first contact with the target object, the controller for the first segment of the robotic arm switches to the hybrid impedance control mode. This mode involves modifying the desired trajectory and continuing to detect and track the contact force. This allows smooth control of the robotic arm's motion without changing the structure of the hybrid force/position controller (inner loop) shown in Figure 5.  
After the arm collides with an external object, the desired corrections for angular velocity and angular acceleration are obtained based on the feedback of the contact force measurement and the designed segmented hybrid impedance control. At this point, the control signal for the first segment of the manipulator arm in joint space is given by:  
\[\begin{split}\Gamma_{\text{sec}1}=\Gamma_{\text{sec}1}=\text{P}( \text{q})(\overset{\sim}{\text{M}}_{\text{sec}1}(\text{q})(\ddot{\text{q}}_{ \text{H}}(\text{t})+\text{K}_{\text{p}1}(\text{q}_{\text{d}}-\text{q})+\text{ K}_{\text{d}1}(\dot{\text{q}}_{\text{H}}-\dot{\text{q}})))\\ +(\text{I}-\text{P}(\text{q}))(\text{J}^{\text{T}}_{\text{sec}1}( \text{q})(\text{F}_{\text{d}}+\text{K}_{\text{fp}}\text{F}_{\text{e}}+\text{K }_{\text{fi}}\int\text{F}_{\text{e}}(\text{t})\text{dt}-\text{K}_{\text{damp}}v )),\end{split} \tag{36}\]  
where \(\ddot{\text{q}}_{\text{H}}\) and \(\dot{\text{q}}_{\text{H}}\) are the expected angular acceleration and angular velocity of the joint after correction by the impedance model. Due to the decoupling of the dynamics of the two controllers through orthogonal projections, the controller adopts the error dynamics and stability analyses of the individual force and motion controllers within their respective subspaces. Consequently, the stability of the hybrid controller in closed-loop operation can be established.
## 3 Results and Discussion  
### Experimental Results  
The vibration tests were performed at different temperatures with different cushioning materials, according to the test method described in Section 2. Frequency ranges, vibration transmissibility, and damping ratios were determined. Frequency ranges and vibration transmissibility were measured using vibration tests directly. Three frequency values were stated at each temperature and static load. The first value was \(T_{r}\cong 1.1\). The transmissibility plot left the direct coupling zone and reached the amplification zone stable, according to our observation. The strict border between direct coupling and amplification zone occurs when Tr > 1, but the observed measured value showed that the transmissibility was not 1 strictly in the direct coupling zone, but varied between 0.9 and 1.1. However, once the transmissibility reached 1.1, the plot did not return to the direct coupling zone. The second frequency  
Figure 8: Vibration transmissibility and frequency curves for (**a**) PE30 at a static load of 3.488 kPa, (**b**) PE30 at a static load of 4.651 kPa, and (**c**) PE30 at a static load of 6.976 kPa (frequency is in logarithmic scale). Figure 9: Vibration transmissibility and frequency curves for (**a**) XPE at a static load of 3.488 kPa, (**b**) XPE at a static load of 4.651 kPa, and (**c**) XPE at a static load of 6.976 kPa (frequency is in logarithmic scale). The damping ratios were estimated by applying linear vibration theory with single degree-of-freedom system with viscous damping based on Equations (5) and (6). Equation (5) was used when \(f_{f}\cong\,f_{n}\), and Equation (6) was used when \(T_{r}\,\gg 1\) (Tables 2-4). Each table separately provides the experimental results of PE25, PE30, and XPE cushioning materials at different temperatures and at different static loads.
## 3 Results and Discussion
### Optimization of Purge and Trap (P&T) Preparation
#### 3.3.2 Effect of Desorption Time  
The desorption time of P&T preparation is also a fatal operating condition parameter, influencing the purging efficiency and detection sensitivity. The influence of desorption time on the signal intensities  
Figure 1: Effect of injection port temperature on MS/MS detection signal intensities of the twelve analytes (20 \(\upmu\)g/L). The peak area at the injection port temperature of 250 \({}^{\circ}\)C is used as a reference (100%). (**a**) the peak area ratio of TBA, MTBE, DIPE, ETBE, TBF, and TAME; (**b**) the peak area ratio of benzene, toluene, ethylbenzene, m+p–xylene, and o–xylene.  
Figure 2: Effect of sample temperature on signal intensities of the twelve analytes (20 \(\upmu\)g/L). The peak area at the sample temperature of 25 \({}^{\circ}\)C is used as a reference (100%). (**a**) the peak area ratio of TBA, MTBE, DIPE, ETBE, TBF, and TAME; (**b**) the peak area ratio of benzene, toluene, ethylbenzene, m+p–xylene, and o–xylene.  
of selected analytes were examined, with individual concentrations of 20 ug/L at five different desorption times (1, 2, 3, 4 and 5 min). As shown in Figure 3a,b, the ratio values increased significantly when the desorption time increased from 1 to 2 min. However, when we further increased desorption time from 3 to 5 min, the ratio values stayed pretty the same. This phenomenon indicated that 2 min was enough for these selected analytes to dissociate during detection process. Therefore, 2 min desorption time was designated for this detection method.
## 3 Proposed Algorithmic Procedure
### Dimension Measurement Principle
Figure 7: **(a)** Visual sensor calibration; **(b)** Calibration image; **(c)** Extracting the calibrate point on the laser stripes; **(d)** Laser plane fitting. For the improved application in engineering projects, a robust approach has been developed by iteratively dropping data parameters with excessive errors. The internal and external parameters are assumed to obey normal distribution, and expressed as N(m,\(\sigma^{2}\)). Then, 99.7% of the data should lie inside the range [m - 3\(\sigma\),m + 3\(\sigma\)]. When data lie outside the range, they can be culled given the large error in data compared with their true value. Therefore, the internal and extrinsic parameters of the visual sensor are reestimated in this work by using the remaining parameter sets that meet the \(\pm\)3\(\sigma\) standards. The proposed algorithm of the detail processing steps is as follows:  
1. Acquire N calibration pattern images with laser stripes in different positions, and feature points and calibration points can be detected successfully from these images. 2. Select M images from the N calibration pattern images randomly in Step (1) to form a new image set, and there would be form CN M subsets image total, where CN M is a binomial coefficient. 3. The internal and external parameters of the visual sensor by these subsets image in Step (2) are calculated respectively with the method described above. A total of CN M sets of parameter data are generated to form a matrix of R\({}^{\text{n}\times 12}\), where each row in the matrix corresponds to the parameters (\(\alpha\),\(\beta\),u\({}_{0}\),v\({}_{0}\),k\({}_{1}\),k\({}_{2}\),a\({}_{1}\),b\({}_{1}\),c\({}_{1}\),a\({}_{2}\),b\({}_{2}\),c\({}_{2}\)) of the device calculated by each subset of images. 4. For each column parameter of the R\({}^{\text{n}\times 12}\) matrix in Step (3), calculate the mean mp and standard deviation \(\sigma_{\text{p}}\) via the method of maximum likelihood estimation of a normal distribution. 5. From the matrix parameters in Step (4), remove the row in which value of at least one parameter data (\(\alpha\),\(\beta\),u\({}_{0}\),v\({}_{0}\),k\({}_{1}\),k\({}_{2}\),a\({}_{1}\),b\({}_{1}\),c\({}_{1}\),a\({}_{2}\),b\({}_{2}\),c\({}_{2}\)) not lie inside the range of [m - 3\(\sigma\),m + 3\(\sigma\)] to form a new matrix and repeat the operation of Step (4) for the new matrix until no data were removed from the new matrix. 6. The nature mean of each column of the final matrix in Step (5) is used as the final internal and external parameters of the visual sensor.
## 5 Discussion
### Performance Analysis for Different Subjects  
To analyze performances across different subjects, we conducted an additional experiment using the CSI data from the CSI-HAR dataset, which includes detailed information about the subjects involved in data collection. Specifically, the CSI-HAR dataset contains records of each activity performed 20 times by 3 voluntary subjects of varying ages, ranging from 25 to 70 years old. The subjects represent a diverse group, consisting of an adult, a middle-aged person, and an elderly person.  
Table 8 presents an analysis of the performance of the proposed CNN-GRU-AttNet model on individual subjects. Notably, the F1-scores of subject 2 (a middle-aged person) show consistently high values, exceeding 95% for all activities. On the other hand, when using the CSI data of subject 1 (an adult), the F1-scores for some activities (lie down, bend, sit down, stand up, and walk) are found to be lower than 95%. These findings strongly suggest that there are notable differences in the CSI data captured from different subjects.
## 5 Restoration Plan Strategy after a Blackout in a Wide Area or a Country
### Generator Start-Up and Tie Lines Sequence Decision Model Based on the A* Algorithm
#### 5.3.2 Implementation of the A* Algorithm in the Restoration Process  
The decision-making process for the selection of the start-up generator and the tie-line succession based on the A* Algorithm is depicted in a flowchart in Figure 8. Using this algorithm, the best generator (either BS unit or NBS unit) is selected for each phase of the restoration process. In the subsequent step, the objective function value of the remaining generators will be updated, and the highest-valued generator will then start. This procedure is finished when all generators have been recovered.  
The procedure is the following:  
1. First, the grid's status data from lines, substations, circuit breakers, and loads must be collected.
2. Fau  
Figure 8: Flowchart of the optimal start-up generator and the tie line sequence model.  
* Using the A* Algorithm, the shortest path for each generator that has not been recovered is found.
* The objective function for each generator that has not been recovered is calculated.
* The generator with the maximum value restarts.
* If the constraints are met, the system operation outcome using the specific generator is verified. The current node is updated if the conditions are satisfied. If not, the subsequent generator in the sequence is chosen and the current generator is skipped.
* From the present node, the shortest paths for each remaining generator are once again searched and steps e, f, and g are repeated until all generators have been recovered.
# Framework for Fast Experimental Testing of Autonomous Navigation Algorithms
## 6 Experiments
### GNSS/SLAM Fusion Framework Experiments
#### 6.4.1 Localization
In this case, we can observe a behavior similar to that of the path \(R2\) where the fusion adapts to the GNSS system in the off-map area (\(x>75\) m). In the case of the path made using the RTK system, we see that the low variances makes the fusion to closely follow the observations. For this reason, we see how the red line (AMCL) is superimposed to the rest of the subsystems in Figure 16 right. Figure 17 shows the result of performing manual driving through the path \(R4\). In this case, the trajectories run off the map in the whole area defined by \(x>75\) m and \(y<-90\) m. We see that the  
Figure 14: Trajectories through the \(R1\) path expressed by localization using the fusion described in Section 5. (**left**) Using GNSS standalone. (**right**) Using GNSS floating point. In both cases, the fusion gives more weight to AMCL, because the robot is inside the map during the whole trajectory. Figure 15: Trajectories through the \(R2\) path expressed by localization using the fusion described in Section 5. (**left**) Using GNSS standalone. (**right**) Using GNSS floating point. In both cases, the loop closure of the right quadrant is performed off-map. We see that thanks to the fusion of GNSS, we can maintain the trajectory to re-enter the map area. behavior is similar to the cases described for the paths \(R2\) and \(R3\) with the difference that in this case most of the journey is off-map and verifying that the re-entry can be done successfully even after a long journey without mapped references.
# MT-CMVAD: A Multi-Modal Transformer Framework for Cross-Modal Video Anomaly Detection
## 3 Supposed Method  
Existing approaches for video anomaly detection exhibit three principal limitations that our Transformer-based methodology addresses. First, conventional temporal modeling techniques (e.g., RNNs, 3D CNNs) struggle to capture long-range dependencies due to their sequential processing nature and local receptive fields. While methods like GAN-based prediction [15] and memory networks [18] partially alleviate these issues, they introduce computational overhead and remain sensitive to appearance variations. Second, cross-modal frameworks [9; 21] often suffer from semantic misalignment between modalities and high computational complexity, particularly when processing heterogeneous data streams. Third, existing Transformer adaptations [19; 23] either require extensive training data or depend on auxiliary modalities for guidance--for instance, leveraging natural language descriptions to supervise the learning of discriminative visual representations--which limits their applicability in real-world scenarios with limited supervision.  
To systematically address these limitations, our framework employs a hierarchical Transformer architecture with three specialized modules, whose technical details will be elaborated in the following subsections.
## 4 Simulation Experiment of IABCBAS Algorithm Path Planning Application
### Path Modeling  
The path obtained by the search algorithm from the start point to the end point in the 2D grid map can be regarded as a set \(T\) of path points during motion planning. Path planning requires the vehicle to travel from the start point to the goal point, avoiding all obstacles and finding the shortest path, as shown in  
\[T=\{S,U_{1},U_{2},\ldots U_{M-1},G\}, \tag{20}\]  
\[\text{Length}T=\|U_{1}-S\|+\sum_{i=1}^{M-1}\|U_{i+1}-U_{i}\|+\|U_{M-1}-G\|, \tag{21}\]  
\[\left\{\begin{array}{l}T_{\text{min}}=\text{argmin}(\text{Length}_{v})\\ P(\text{Length}_{v})=0\end{array}\right., \tag{22}\]  
where \(S\) is the start point (x\({}_{0}\),y\({}_{0}\)); U\({}_{1}\), U\({}_{2}\),..., U\({}_{\text{M-1}}\) is the via path node; \(G\) is the goal point (x\({}_{\text{M}}\),y\({}_{\text{M}}\)); \(T_{min}\) is the minimum value of the search path; \(Length_{v}\) is the shortest planned path \(v\); and \(P((Length_{v})\) is the probability of collision between the path and the obstacle.  
The path points are also treated as a series of discrete points in the 3D environment. The next movable path point is generated once the movement direction DIR is determined, as shown in Equation (23). The optimal path point that avoids obstacles is identified, as shown in Equation (24), and added to the shortest path \(T_{min}Length\).  
\[\left\{\begin{array}{l}x_{i+1}=x_{i}+\text{dir}_{x}\times\text{rand}q_{1 xy} \\ y_{i+1}=y_{i}+\text{dir}_{y}\times\text{rand}q_{1 xy}\\ z_{i+1}=z_{i}+\text{dir}_{z}\times\text{rand}q_{1 xy}\end{array}\right., \tag{23}\]  
\[\left\{\begin{array}{l}U_{\text{min}}=\sqrt{(U_{1}+U_{2})\times\text{pri}} \\ U_{1}=\sqrt{\left(x_{i+1}-x_{i}\right)^{2}+\left(y_{i+1}-y_{i}\right)^{2}+ \left(z_{i+1}-z_{i}\right)^{2}}\\ U_{2}=\sqrt{\left(x_{i}-x_{i-1}\right)^{2}+\left(y_{i}-y_{i-1}\right)^{2}+ \left(z_{i}-z_{i-1}\right)^{2}}\\ T_{\text{min}}\text{Length}=\sum\limits_{i=0}^{B}u_{i}\end{array}\right., \tag{24}\]  
where \(U_{1}\) and \(U_{2}\) are the Euclidean distances between the current and the next path point and endpoint, respectively; and Pri is analogous to \(P(Length_{v})\), which avoids the searched path points colliding with obstacles in the map.
# A Capacity Enhancement Method for Frequency-Hopping Anti-Jamming Communication Systems
## 1 Introduction
### Novelty and Main Contributions  
* Firstly, the capacity enhancement method of FH anti-jamming communication systems is proposed, the capacity enhancement model of FH anti-jamming communication systems is established, the expression of FH capacity enhancement signals is given, and the spectral characteristics of the signal are deduced;
* Secondly, the FH capacity-boosting signal demodulation scheme is designed;
* Finally, the effect of the new method on improving capacity, BER performance, and anti-jamming performance is analyzed.
## 3 Results  
### S1 S1 SAR RVI & S2 GP VWC Temporal Profiles  
In an attempt to analyze multi-seasonal VWC time series over the averaged ROI-1 presented in Table 1, the temporal profiles of S2 GP VWC and S1 RVI orbits 68 and 141 over the irrigated winter wheat sites 2020 are displayed in Figure 3. It is worth highlighting that the typical crop rotation at the study site is: maize-sunflower-wheat [53].  
Figure 3: Temporal profiles (mean value and standard deviation) of averaged selected ROI-1 over a winter wheat parcel belonging to the BVCR 2020 crop campaign. S2 GP VWC time series from December 2018 to the end of January 2021 (**a**). S1 RVI orbit 141 smoothed (dashed blue line) and original (dashed black line) time series (**b**). S1 RVI orbit 68 smoothed (dashed red line) and original (dashed black line) time series (**c**). S1 RVI Orbit 68 (red line) and S1 orbit 141 (blue line) RVI time series (**d**).  
The temporal profiles for S2 GP VWC and S1 RVI for orbits 68 and 141 for the winter wheat 2021 averaged ROI-2 are shown in Figure 4. Regarding the winter wheat crop for the 2021 campaign, it can be seen that the summer predecessors' crops were sunflower for hybrid seed in 2021 and maize in 2020. An appreciable difference can be distinguished when the S1 RVI for orbits 68 and 141 are plotted jointly (see Figures 3d and 4d). Note that there is a time asynchrony between S1 acquisitions as both satellites S1-A and S1-B share descendent orbit 68. Moreover, the RVI amplitude offset between orbits 68 and 141 is due to the different SAR sensor's acquisition geometry [25]. The mean local incidence angle over the study site for S1 orbit 141 is approximately 33\({}^{\circ}\), whereas for orbit 68 it is around 43\({}^{\circ}\).
## 4 Experiments
### Screening Algorithm Experiment  
After expanding the screening area obtained by fitting the circle, texture features, shape features, and a combination of texture and shape features are used for screening to determine the spike area. Several screening algorithms are compared to verify their effectiveness. Among them, the texture features are described using second-order moments, the shape features are described using cosine values and SED, and the combined features are described using second-order moments combined with cosine values and SED. During the robot's operation, the surface condition of the spiral rail spikes exhibits randomness. Therefore, in the screening experiments for four types of samples, the template rail spike used for solving the SED remains consistent. Examples of successful and unsuccessful screening are shown in Figure 12, and the success rates of different algorithms for locating spikes are shown in Figure 13.  
Figure 12: Spike area screening results: (**a**) successful filtering results; (**b**) unsuccessful filtering results.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & Normal Category & Water Stain Category & Rust Category & Oil Stain Category \\ \hline Traditional Canny & 98\% & 89\% & 93\% & 80\% \\ algorithm & 99\% & 99\% & 99\% & 98\% \\ \hline \end{tabular}
\end{table}
Table 5: Circle detection accuracy.  
Experimental results of different screening algorithms are analyzed. Under the shape feature comparison, the success rate of SED screening is higher than 97%, which is the best screening algorithm because SED effectively reduces the interference of data noise on the screening process and is the most direct and stable in characterizing the vector relationship. In contrast, the cosine value screening only uses the angle between feature vectors for similarity measurement, ignoring the absolute magnitude information of the vectors, with the highest screening success rate being only 60%. Regarding texture feature comparison, the interference area is similar in texture to the road stud area, and using the second moment as a screening criterion is highly prone to misjudgment, with a screening success rate below 75%. By combining shape feature and texture feature comparisons integrating the second moment with HOG feature similarity through mathematical operations for screening, the results are poor, with a screening success rate not exceeding 77%. The analysis indicates that the low screening rate of texture features in the fusion algorithm directly affects the overall results, and the need to adjust the weight of texture features and shape features under different conditions leads to poor robustness.