esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results and Analysis
### Description of the Designed C-MHD Ars  
In the previous simulation and experiment on the radial velocity generation by MHD pump in middle channel, it has been confirmed that the thickness and the height of the central channel should be compromised for the velocity rate and steady. Besides, the inhomogeneity of the magnetic field in the sensing channel can bring out the measurement error at the peaks and valleys of an angular vibration. Based on these design experiences concerning the fluid and electromagnetic field, the C-MHD ARS in this paper is designed as shown in Figure 4. The head cover (1), the shell (6), and the bottom shell (8) with high permeability material form a closed magnetic circuit for magnetic shielding. A pair of permanent magnets (10) imposes the magnetic field for the MHD pump in the middle channel. The magnetic field in the bottom sensing channel along the vertical direction is generated by the permanent magnets (14). In the entire structural design, the independence of the magnetic field for MHD pump and the sensing part is critical. Therefore, the magnetic conductive gaskets (11) and the insulating spacers (5) are designed to make the enclosed magnetics lines along the shortest paths plotted. Furthermore, the position of the magnets (10) is above the center of the middle fluid channel to make the magnetic lines of the MHD pump far from the ones for sensing. The imposed magnetic  
Figure 3: The computation domains with the two interaction solving methods. (**a**) fluid domain using the second electric approach (**b**) fluid domain using the first magnetic approach.  
field distribution in the fluid channel is plotted in Figure 5. The average of the magnetic field \(B_{z}\) along axis \(z\) in the sensing channel is about 0.12 T and the inhomogeneity of the magnetic field is about 52.4%. The parts (12 and 13) acting as the electrodes of the sensing channels are electric-conductive, but non-magneto-conductive, and ensure that the direction of the magnetic field in the fluid is along the vertical axis. Meanwhile, the insulating parts (2 and 7) form the walls of the fluid channel 9 for electric isolation.  
As plotted in Figure 4, the fluid wall is composed of different materials at the top, external and bottom channels, which brings out a great challenge for the seal work. Therefore, Galinstan is chosen as the conductive fluid in the C-MHD ARS instead of Hg, for its less volatilization and nontoxicity, to explore the appropriate sealing technology. The physical parameters of Galinstan are listed in Table 1. Clearly, the material physical characteristics can satisfy the sensor environment temperature in the application, but it may be inferior to Hg in lower density and higher viscosity. Besides, the main mechanical structure parameters of the fluid channel are adjusted repeatedly in the simulation according to the experience in the previous paper [33; 34] and then given in Table 1.  
Figure 4: Schematic diagram of the designed C-MHD ARS in simulation and experiment.  
Figure 5: Contour and vector graph of the imposed magnetic field in the fluid channel.
# Research on the Posture Control Method of Hexapod Robot for Rugged Terrain
## 2 Adaptive Foot Trajectory Planning for Rugged Terrain
### Autonomous Terrain Judgment Strategy Based on Layer Identification of Tracking  
The purpose of distinguishing terrain is to use different pose control strategies to help the robot cross the terrain. The adaptive trajectory planning strategy for rugged terrain divides the swing phase trajectory of the foot into an upper guidance layer, a lower guidance layer, an upper adjustment layer, and a lower adjustment layer. According to the robot structure parameters and walking needs, the height of each layer of the trajectory is specified to be 40 mm. The purpose of layer identification of tracking (LIT) is to determine the coordinate range of which layer of the trajectory is located in Z-axis coordinate \(P_{tzi}\) of the leg coordinate system, \(\sum_{O^{\prime}m0}\) of foot end point \(P_{ti}\), and the interval of each layer is represented by \(S_{tg^{\prime}}\)\(S_{bg^{\prime}}\)\(S_{ta^{\prime}}\) and \(S_{ba}\). In execution, the position of each foot end can be  
Figure 5: Projection of foot-end trajectory model in the XOZ plane of the trunk coordinate system.  
obtained by kinematic calculation based on the joint angular position sensing data in real time. The LIT autonomous judgment strategy is expressed as follows:  
\[\begin{array}{ll}if&P_{tzi}\in S_{tg}\;then\;LT\_State=0,\;HT\_State=1;\\ else\;if&P_{tzi}\in S_{bg}\;then\;LT\_State=1,\;HT\_State=0;\\ else\;if&P_{tzi}\in S_{ta}\;then\;LT\_State=1,\;HT\_State=0;\\ else\;if&P_{tzi}\in S_{ba}\;then\;LT\_State=0,\;HT\_State=1;\\ else\;\;\;\;\;LT\_State=0,\;HT\_State=0;\end{array}\]  
_LT_State_ is the flag for lightly rugged terrain, _HT_State_ is the flag for severely rugged terrain, and the status flag is 1 to stimulate the corresponding mode. When _LT_State_ = 0 and _HT_State_ = 0, it is judged as no foothold. As shown in Figure 6, in case (a), when the end of the foot swings from the reference ground to the terrain with a height range of [0,40] mm, ordinate \(P_{tzi}\) of end point \(P_{ti}\) in the leg coordinate system \(\sum_{O^{\prime}=0}\) is located in the lower guide layer, and the terrain is judged to be slightly rugged by leg i. In case (b), when the end of the foot swings from the reference ground to the terrain with a height range of [40,80] mm, ordinate \(P_{tzi}\) of end point \(P_{ti}\) in the leg coordinate system \(\sum_{O^{\prime}=0}\) is located in the upper guide layer, and the terrain is judged to be severely rugged by leg i. When the foothold is in the adjustment layer, the judgment is the same as that in the navigation layer. If any leg determines that the terrain is severely rugged terrain, the pose adjustment mode corresponding to the severely rugged terrain is stimulated.  
Figure 7 shows the control scheme used by the hexapod robot to walk on rough terrain.  
Figure 6: Two diagrams of autonomous terrain identification strategy: (**a**) Identification of light rough terrain and (**b**) identification of high rough terrain.
## 4 The Proposed Detection and Restoration Framework
### Detector Design  
The encoder designed in this paper for detection is a hybrid AE that combines both CNN and Long Short-Term Memory (LSTM) networks. The CNN component is primarily employed to process input data and extract their spatial features. Once the input data are processed by the convolutional encoder, the original input is fed into an LSTM layer. After both the convolutional encoder and LSTM layers have processed the data, their outputs are concatenated along the channel dimension. The concatenated data are then passed to the decoder, which consists of several transposed convolutional layers. These transposed convolutional layers can be viewed as the inverse operations of the convolutional layers  
Figure 2: Flowchart of the proposed adversarial example detection and restoration framework for automatic modulation classification.  
in the encoder. They gradually increase the spatial dimensions of the data and decrease the number of channels to restore the shape of the original input data. The AE structure designed for the detector is illustrated in Figure 3.  
As evident from Figure 3, the encoder segment comprises both a convolutional encoder and an LSTM layer. The convolutional encoder is responsible for extracting spatial features from the input data, while the LSTM layer handles the sequential nature of the input data. The outputs of these two components are then concatenated, forming a richer representation that encompasses both the spatial and sequential features of the input data. This design holds potential positive implications for adversarial example detection, particularly in terms of its capacity to learn complex patterns. The CNN and LSTM structures in the encoder part can learn features from both spatial and temporal dimensions, enabling the AE to capture more intricate patterns, thus enhancing its capability to detect adversarial examples.
# Minimum Variance Distortionless Response--Hanbury Brown and Twiss Sound Source Localization
## 4 The Sound Source Detection Experiment
### Distance to Sound Source Target  
To investigate the impact of distance between sound sources on the localization performance, experiments were conducted to verify different target sound source positions with coordinates of (2.5, 2), (5, 3), (5, 5), and (6, 8). The experimental results obtained using MVDR-HBT and HBT for the sound source coordinates of (2.5, 2) are shown in Figure 9 and compared with the localization results of HBT, summarized in Table 5. Figure 9 and Table 5 reveal that MVDR-HBT could accurately identify the direction of the sound source at (2.5, 2), and the bright stripes were more concentrated compared to those from HBT. As the distance gradually increased, the angle deviation of both algorithms also increased, and the angle deviation of MVDR-HBT was smaller than that of HBT. When the sound source coordinates were (2.5, 2), the angle deviation of MVDR-HBT was only 0.77\({}^{\circ}\), while that of HBT was 1.48\({}^{\circ}\). When the sound source coordinates were (6, 8), which was the farthest distance from the microphone array, the angle deviations of both MVDR-HBT and HBT were the largest. This experiment mainly aimed to locate far-field target sound sources, and the angular deviation of MVDR-HBT was 3.17\({}^{\circ}\) at this time, which was smaller than that of HBT.  
Figure 10: Experimental Results of MVDR-HBT for Single-Frequency to Multi-Frequency: (**a**) 600 Hz; (**b**) 200–2000 Hz.  
Figure 9: Experimental results of MVDR-HBT and HBT at (2.5, 2): (**a**) HBT; (**b**) MVDR-HBT.
## 3 Results and Discussion
### Adsorption Performance
#### 3.2.5 Thermodynamic Analysis
Based on established industrial practices, the resin can be effectively regenerated using methanol, a non-polar solvent capable of desorbing hydrophobic compounds like m-cresol. This process typically involves soaking the exhausted resin in methanol under mild agitation, followed by rinsing and drying for reuse. The resin has demonstrated stable adsorption performance over multiple cycles, supporting its practical applicability for large-scale deployment. Furthermore, we emphasize that in cases where regeneration is not feasible, the spent resin--due to its adsorbed organic pollutants--should be treated in accordance with local hazardous waste management regulations to ensure safe and responsible disposal.
## 3 Proposed Reliable Routing Mechanism
### Routing Strategic Decision  
The nodes exchange routing information in the intra-cluster routing mechanism. A node broadcasts its table to its neighbors when its routing table changes. Each node maintains routing information of other nodes within its cluster, including destination, next hop, cluster id of destination node, metric, sequence number, and obtains the reliability of each node between the source and destination during route discovery. Moreover, the border node in each adjacent cluster is added to the local routing table, enabling routing to adjacent clusters. The routing information mentioned above is adopted for routing selection in intra-cluster routing. Each routing table of node focuses on the reliability and hop count. The reliability value is integrated with energy consumption and various surrounding conditions such as temperature, humidity, pressure and noise levels. Figure 13 shows an example of low-energy nodes decreasing the reliability.  
In the inter-cluster routing mechanism, the routing path, value of reliability and hop count appended to the RREQ packet. This routing information is adopted for routing selection in inter-cluster routing (as shown in Figure 14). The hop count can be adopted to minimize the length of the routing path while maintaining reasonable reliability.  
Figure 12: Inter-cluster routing.  
Figure 13: Energy consumption decrease the reliability.
## 5 Experiment and Result Analysis  
### Experimental Data and Evaluation Parameters  
All the environments are carried out on a computer with Intel(R) Core (TM)i5-9300H CPU @2.40 GHz and 16.00 GB RAM. We debugged the following parameters on MATLAB: \(\lambda=0.004\); \(\varepsilon_{0}=0.008\); \(\gamma=2\). The HR image is composed of 32 \(\times\) 32 blocks. A sparse dictionary, consisting of 3096 atoms of length 1024, is trained by the high-dimensional dictionary training method introduced in [26].  
We compared our method with the methods proposed by [PERSON] [30], [PERSON] [19], [PERSON] [11], and [PERSON] [21] and adopted the average gradient (AG) and information entropy (IE) as objective evaluation indicators.  
In order to verify the actual application effect of the infrared image reconstructed by our proposed algorithm, we used the image recognition algorithm proposed in literature [31] to design a comparative experiment. Through the recognition of infrared images reconstructed by different SR methods, the performance and practical application value of our method was verified. Since the basis of the image recognition algorithm lies in the effective extraction and correct matching of image feature points, the number of feature points obtained by image extraction and the correct matching rate of feature points were used as evaluation indicators. In order to more clearly reflect the difference between the performance of different algorithms, we randomly selected 80 sets of feature point matching results for visual display.  
In addition, we performed the same infrared image diagnosis process on the high-resolution images reconstructed by different SR algorithms. We sequentially performed super pixel segmentation, image recognition, super pixel merging, image segmentation, equipment area extraction, and equipment operating status diagnosis on the reconstructed infrared image. The super pixel segmentation and merging algorithm adopted the methods in the literature [32], and the image recognition still used the algorithm proposed in the literature [31]. The diagnostic process of infrared images is shown in Figure 4.  
Figure 4: Flow chart of infrared image fault diagnosis.
## 5 Conclusions  
A novel and efficient model, namely the MFFCESSC-SSD, is designed in this study to perform object detection in UAV images. A multi-scale feature fusion (MFF) scheme is initially applied to add semantic features to the shallow feature maps and suppress the impact of background noise using self-attention unit blocks. Notably, context mining is integrated into the SAUs to obtain global contextual information. The experiments showed that MFF successfully reduces noise and background interference. CESSC with CE-GN and SSC, which enhances contextual relationships while reducing module complexity, is designed. A dynamic mask ratio mechanism is proposed that can be automatically updated based on the information of the feature layer. Furthermore, CESSC introduces focal loss to address scale changes. As demonstrated in our experiments, our proposed approach yields the highest mAPC and FPS with both VisDrone and ARH2000 when compared to the most recently proposed state-of-the-art object detection algorithms. In conclusion, our approach opens up a new dimension for object detection, i.e., efficient target detection in 2D images using sparse convolution. We hope that this dimension will provide insights for object detection in UAV images for other applications.  
Figure 5: Comparison of detection results of different algorithms using VisDrone2019 dataset. The green boxes are the detected targets, and the MFFCESSC-SSD has the lowest leakage rate.  
**Author Contributions:** Methodology, Z.Y.; Data curation, J.S.; Writing--original draft, G.Q. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work has been funded by the National Natural Science Foundation of China (NSFC) under grant No. 52265035, Inner Mongolia Autonomous Region Science and Technology Program under grant No. 2021 GG0218, and the Natural Science Foundation of Inner Mongolia Autonomous Region under grant No. 2024 LHMS03036.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Acknowledgments:** The authors would like to thank the anonymous reviewers for their helpful remarks.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Simulation Results
### Circular Orbit Optimization  
The fixed sensors were installed at four locations at appropriate heights of 50 m in the east, west, south, and north area of the evaluation environment. The non-optimized UAV sensor draws a circular orbit with a radius of 75 m and an altitude of 100 m near the center of the target area. The optimized UAV sensor draws a circular orbit with the center coordinates and radius optimized.  
The placement of the fixed and non-optimized UAV and optimized UAV sensors and the localization error distribution are shown in Figures 8 and 9, respectively. It can be seen that when fixed sensors and non-optimized UAV are used a significant degradation of localization accuracy can be observed in zones with dense buildings, while relatively good accuracy is obtained overall when UAV sensors are used.  
Next, the cumulative distribution function (CDF) of localization accuracy is shown in Figure 10. Comparing the CDF 90% values, the value for the fixed sensor is 55.02 m and the value for the non-optimized UAV sensor is 77.96 m, whereas the value for the optimized UAV is 28.59 m, as shown in Table 4.  
In other words, our proposed system reduced 48% of localization error compared to the conventional scheme at our designed target of 90 th percentile CDF. This confirms that a single optimized UAV sensor provides better localization accuracy than four fixed base stations, which reveals the superiority of our proposed method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor** & **Average (m)** & **CDF 90\% (m)** \\ \hline Fixed & 19.69 & 55.02 \\ \hline Non-optimized & 28.18 & 77.96 \\ \hline Optimized & 13.09 & 28.59 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of localization error (circular orbit).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mathbf{u}_{i}(0),\mathbf{v}_{i}(0)\) & \(\mathbf{w}\) & \(\mathbf{c_{1},c_{2}}\) & **Number of Particles** & \(\mathbf{t_{MAX}}\) \\ \hline \(U(0,1)\) & 0.5 & \(U(0,0.14)\) & 100 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of PSO.
# A Dynamic Multiple-Query RRT Planning Algorithm for Manipulator Obstacle Avoidance
## 2 Environment and Notation
### Notation  
The path planning concretes on a geometric path in a configuration space. Considering the time constraints of the dynamic obstacle and trajectory planning, the state of obstacle avoidance problem is set in a configuration space with time constraint as \(S\subset R^{d}\), where \(d\in N,d\geq 2\)[16]. Therefore, \(O_{s},O_{d}(t)\subset S\). All the obstacles are configured as \(S_{obs}\subset(O_{c}\cup O_{d}(t))\). Thus, the free region of obstacles is set as \(S_{free}=S\backslash S_{obs}\) that the robot can reach. Set the extension tree \(T(N,V,E)\) embedded in \(S\), where \(N\), \(V\), and \(E\) are the nodes of the extension tree set, the vertices of the path set, and the edge set, respectively. Set the vertices of the path as \(V(t)\), where \(V(t):[t_{0},t_{end}]\to S\) is the discrete function from the start time \(t_{0}\) to the end time \(t_{end}\). An execution trajectory \(\tau(q_{start},q_{goal})\) is created by the robot's kinodynamic and other constraints, e.g., maximum acceleration. The start state \(q_{start}\) and the goal state \(q_{goal}\) both belong to \(V\). Set \(d(q_{i},q_{j})\) is a Euclidean distance function on \(S\), and all \(q_{i},q_{j}\in S,i\
eq j\).
# Processing of EMG Signals with High Impact of Power Line and Cardiac Interferences
## 2 Material and Methods
### Third Order High-Pass IIR Filters  
To reduce the low-frequency spectral range from the recorded signals, high-pass filters were chosen and implemented using the MATLAB environment [29]. To achieve the compromise between quality of filtering and numerical complexity, the 3 rd order IIR Butterworth filtering was used.  
Figure 8: Diagram of the proposed EMG signal processing method with low computing power demand.  
Figure 7: Block diagram of acquisition device.  
The transfer function of such filters is presented by Equation (1)  
\[H(z)=\frac{\sum_{i=0}^{3}b_{i}z^{-i}}{\sum_{i=0}^{3}a_{i}z^{-i}}. \tag{1}\]  
where: \(a_{i}\), \(b_{i}\) are the filter coefficients, and z is the Z-transform variable, corresponding to the unit delay of the signal samples.  
The time-domain formula implemented in a DSP processor can be expressed as follows  
\[y(n)=\sum_{i=0}^{3}b(i)x(n-i)-\sum_{i=1}^{3}a(i)y(n-i). \tag{2}\]  
where \(x\) and \(y\) denote the samples of input and output signals of the filter, respectively.  
The examples of raw and filtered signals are presented in Figures 9 and 10. Table 1 contains the filters' coefficients for different cutoff frequencies. In order to ensure a low level of rounding errors, 24-bit input data should be processed using at least 32-bit fixed-point arithmetic.  
Figure 10: Zoom spectrum of EMG signal for right abdominal muscle after HP filtering using 3 rd order Butterworth HP filter, (**a**) for \(f_{c}=2\) Hz, (**b**) for \(f_{c}=10\) Hz.  
Figure 9: The example of the EMG signals measured on the right abdominal muscle, (**a**) raw signal, (**b**) after HP filtering using 3 rd order Butterworth HP filter, \(f_{c}=2\) Hz.
## 1 Introduction
The CH-SIMS dataset encompasses not only IMAs for each clip but also IUAs for each modality [13], which is consistent with our stance and endeavors to train the unimodal model with IUAs, but does not delve deeply enough into the two aforementioned problems: unimodal representation learning and multimodal fusion strategy. Problem Statement: In most existing multimodal datasets with IMAs:  
\[D_{1}=\{X_{\sigma},Y_{M}\}, \tag{1}\]  
where \(\sigma\) represents different modalities, \(X\) is the data, and \(Y_{M}\) is the IMAs. MEC models based on these datasets take \(X_{\sigma}\) as input and \(Y_{M}\) as output in the whole training stage. This process can be expressed as  
\[X_{\sigma}\overset{f}{\Rightarrow}Y_{M}, \tag{2}\]  
where \(f\) is responsible for both unimodal representation and multimodal fusion. We define CH-SIMS as  
\[D_{2}=\{X_{\sigma},Y_{\sigma},Y_{M}\}, \tag{3}\]  
where \(Y_{\sigma}\) represents IUAs and \(Y_{M}\) represents IMAs. We express the training process based on CH-SIMS as  
\[X_{\sigma}\overset{f_{1}}{\Rightarrow}Y_{\sigma}\overset{f_{2}}{\Rightarrow}Y _{M}, \tag{4}\]  
where \(f_{1}\) is responsible for unimodal representation and \(f_{2}\) for multimodal fusion. To find optimal \(f_{1}\) and \(f_{2}\), we propose a Conformer-based MEC model (Uni2 Mul), which integrates a sub-task to predict IUAs and IMAs. We argue that to achieve a satisfactory unimodal representation, it is necessary to not only design a suitable feature extraction network, but also train it with the correct IUAs rather than IMAs. Moreover, a satisfactory fusion strategy can better leverage the effectiveness of the unimodal representation under a multi-task framework. The contributions of this work can be summarized as follows:  
The key contributions are using IUAs for better unimodal learning (see Figure 2b) and the two-stage approach to account for unimodal-multimodal inconsistencies during fusion (see Figure 2d). Uni2 Mul advances MEC by improving unimodal representation learning and fusion. The remainder of the paper is organized as follows: Section 2 describes the related work, Section 3 discusses the proposed methods and overall architecture of the Uni2 Mul model, Section 4 explains the experimental setup and results, Section 5 presents the discussion, and Section 6 summarizes our work.