esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel SINS/SRS/CNS Multi-Information Fusion Global Autonomous Navigation Method
## 5 Experiment and Discussion  
### Simulation and Analysis  
To validate the performance of the SINS/SRS/CNS multi-information fusion global autonomous navigation method, a simulation and an analysis were performed, combined with the system equations established in Section 3. The parameters of the navigation sensors for the simulation are shown in Table 1.  
The latitude of the original location was 87.5\({}^{\circ}\). The UAV flew at uniform velocity and arrived at latitude 88\({}^{\circ}\) at 212 s. The navigation coordinate frame was switched from the \(g\) frame to the \(G\) frame. The proposed SINS/SRS/CNS multi-information fusion autonomous navigation method with filter parameter conversion was compared with that without filter parameter conversion. Figure 4a-c demonstrate the position error, velocity error, and attitude error generated by these two methods, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Gyro constant bias (deg/h) & 0.01 \\ Accelerometer constant bias (ug) & 100 \\ Gyro angular random walk (deg/sqrt(h)) & 0.001 \\ Accelerometer velocity random walk & 10 \\ (ug/sqrt(Hz)) & 20 \\ CNS position error (m) & 10 \\ SBS velocity error (m/s) & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sensor rate settings.  
Figure 4: _Cont_.  
It can be seen from Figure 4 that the errors of multi-information fusion autonomous navigation without filter parameter conversion oscillate when entering the polar regions. In the attitude errors, the yaw error without the filter parameter conversion was the largest, and the oscillation amplitude reached up to 3'. Due to the low observability of the yaw during a uniform flight, the change in filter structure was prone to oscillation. The oscillation amplitude of the velocity error was 0.3 m/s. The oscillations in the velocity error and position error were smaller than that of the attitude error and could converge quickly. That is because the multi-information fusion autonomous navigation filter took the velocity provided by SRS and the position provided by CNS as the measurements. Therefore, the velocity and position could be corrected directly. During the conversion of the navigation coordinate frame, the errors of the multi-information fusion autonomous navigation method with the filter parameter conversion were lower, and there were no significant oscillations.  
The RMSEs (root-mean-square errors) of the SINS/SRS/CNS multi-information fusion autonomous navigation with and without filter parameter conversion are shown in Figure 5.  
Figure 4: Error curves of the two methods: (**a**) position error curves; (**b**) velocity error curves; and (**c**) attitude error curves.  
Figure 5: _Cont._  
Based on the results demonstrated in Figures 4 and 5, the navigation performance of the multi-information fusion autonomous navigation method with filter parameter conversion was better than that without filter parameter conversion. It could realize smooth switching between different coordinate frames and improve the navigation accuracy.
# A Frequency-Domain Estimation Scheme for Frequency Offset with Large Range in OFDM Systems
## 4 Simulation Systems and Results
### Simulation Results
#### 4.2.2 The MSE Performance with Different Numbers of Configured RBs  
Figure 6 shows the MSE of the normalized frequency offset with different \(N_{rb}\). The Doppler shift in the simulations is \(\epsilon=-0.3\). It is observed that the MSE of \(N_{rb}=40\) is smaller than that of \(N_{rb}=20\) at the same SNR and \(M\). When the number of configured RBs is large, the pilot sequence becomes longer and enables accurate estimations for the candidate frequency offsets. In addition, the longer the pilot sequence, the stronger the autocorrelation and the more accurate the selection from the candidate frequency offsets.
## 4 Numerical Examples  
To validate the proposed hybrid boundary-type meshless method for modeling stationary heat conduction problems, several test problems were investigated during the simulation of heat conduction problems in composite materials and compared with the analytical solutions. In order to evaluate the accuracy of the numerical solution, we used the absolute and relative error as shown in the following equations.  
\[\text{Absolute error}=u_{exact}(x_{i})-u_{nu}(x_{i}) \tag{24}\]  
\[\text{Relative error}=\sqrt{\frac{\left(u_{exact}(x_{i})-u_{nu}(x_{i}) \right)^{2}}{\left(u_{exact}(x_{i})\right)^{2}}} \tag{25}\]  
where \(u_{exact}(x_{i})\) and \(u_{nu}(x_{i})\) are the exact and numerical solutions for the inner points \(x_{i}\), respectively.
## 3 Methodology
### Detection Approach  
The overall technical framework and process of this study are divided into three parts, as illustrated in Figure 8. The image processing section involves collecting image data using devices, enhancing images through image processing, such as rotation, flipping, blurring, and low-light enhancement (using the EnlightenGAN [39] algorithm), and using the improved Cycle GAN to expand the dataset. Subsequently, Labeling is utilized to annotate the image data to obtain labels for the data. In the model training section, the obtained data and labels are divided into trainset, and testset in an 8:1:1 ratio. The allocated datasets are then trained using YOLOv5-BiFC to obtain a trained model, which is subsequently tested using a camera and the testset. The results section validates the dataset and analyzes the results.
## 3 Results and Discussion
### Ultimate Loading Capacity
The steel fibers also assisted in preventing the early spalling of the concrete  
Figure 5: The ultimate load-carrying capacities of columns. cover that helped the core concrete to resist the higher loads [39]. The specimens GC-0.75F-0E and GCD-0.75F-0E had maximum loads of 998 kN and 950 kN in groups GC and GCD, respectively, indicating a 17% and 20% increase in load-carrying capacity than their without-fiber counterparts, i.e., GC-0F-0E (860 kN) and GCD-0F-0E (790 kN). The fiber-reinforced specimens of group GC, GC-0.75F-15E, GC-0.75F-35E, and GC-0.75F-50E, attained maximum loads of 729 kN, 460 kN, and 340 kN, respectively, indicating a 27%, 35%, and 36% increase in load-carrying capacity as compared to their without-fiber counterparts, viz., GC-0F-15E (570 kN), GC-0F-35E (335 kN), and GC-0F-50E (250 kN). The load-carrying capacities of GCD columns, i.e., GCD-0.75F-15E (655 kN), GCD-0.75F-35E (390 kN), and GCD-0.75F-50E (265 kN), have been increased by 27%, 33%, and 33%, respectively, than their without-fiber counterparts, viz., GCD-0F-15E (515 kN), GCD-0F-35E (292 kN), and GCD-0F-50E (201 kN). The trend of load enhancements in fiber-reinforced specimens of group GC was almost identical to those of group GCD specimens.
## 3 Experimental Results
### Landslide Dam Formation Process  
The formation process of the landslide dam is shown in Figure 5 by taking B2-4 as an example. The landslide mass begins to emerge from the trapdoor at \(t\) = 0 s. After 1.2 s, the landslide material accelerates toward the exit of the chute under the influence of gravity and enters the flume at a high speed. Subsequently, the landslides flow into the river at high speed to initiate deposition. At \(t\) = 1.26 s, the head of the landslide mass has entered the flume, but a large volume of the landslide material remains in motion. The landslide material plunges into the river and initiates severe turbulent flow due to the interaction between the landslide mass and water flow. The landslide material flows to and ascends the opposite bank from \(t\) = 1.4 s to \(t\) = 1.6 s. The material then descends after it reaches the maximum runup height. Material is deposited up and downstream within the flume during the deposition process. Landslide movement ends at \(t\) = 5.5 s, and this deposition blocks the river completely. The resulting blockage raises the water level upstream. The kinetic energy of the landslide head is greatest when it enters the flume, and the rest of the landslide mass enters the flume at a slower speed, which significantly dampens water surface perturbations and surge effects.  
The total movement time \(T\) of the landslide is divided into the landslide time and the dam formation time. As shown in Figure 6, the change in the total time \(T\) is mainly influenced by the dam formation time. Increasing the slide angle will significantly decrease the time of landslide movement and dam formation. As the landslide volume varies, the times of landslide movement and dam formation change more significantly for the cases with a slide angle of 30\({}^{\circ}\) than for the other two cases. The total movement time \(T\) increases with decreasing particle size. Compared with large particles, small particles in the landslide debris have a larger equivalent friction coefficient [35]. Therefore, larger particle sizes decrease the time of the landslide and deposition processes.  
Figure 5: Sequences of the landslide dam formation at different times. (**a**) Front view; (**b**) left side view.
## 2 Materials and Methods
### Experimental Procedures and Measurement of \(R_{\rm s}\) and the Related Environmental Factors
Each pixel contained the best possible observation coverage, low view angle, absence of clouds or cloud shadow and aerosol loading [5; 23]. The land surface temperature (MOD11A2, 1 km) was derived by applying the generalized split-window algorithm [5; 27]. The nighttime land surface temperature observed by MODIS onboard the Terra satellite is hereafter referred to as LST. It is known that the most widely used normalized difference vegetation index (NDVI) is affected by soil reflectance in sparsely vegetated areas and saturates in cases of dense and multi-layered canopies [28]. The eight-day leaf area index (LAI) product (MOD15A2, 1 km) derived from surface characteristics [5] such as reflectance, land cover type, and other ancillary information, was mostly less than three in the geographic unit of study site, demonstrating that the most widely used NDVI could be a more suitable indicator of vegetation biophysical parameters than the enhanced vegetation index (EVI). The value of the normalized vegetation index (NDVI) was calculated from the two wavelength bands of surface reflectance using the following formula [29]:  
\[\mathrm{NDVI}=\frac{\rho\mathrm{Nir}-\rho Red}{\rho\mathrm{Nir}+\rho Red} \tag{1}\]  
where \(\rho_{\mathrm{Nir}}\) and \(\rho_{\mathrm{Red}}\) represent the reflectance for the near-infrared (MODIS band 2) and red (MODIS band 1) wavelength bands, respectively.
## 4 Path Model Estimation
### Improved Particle Swarm Optimization  
A number of particles which fly around in the search space to find the best solution are used in PSO [35; 36; 37; 38]. Each particle denotes a candidate solution for optimization problems, which has two characteristics: position and velocity. Each position corresponds to a fitness value, calculated by the objective function. Each particle searches for the optimal solution in the solution space according to the fitness value. Moreover, it memorizes the current optimal solution at each step when the algorithm is iterated. The memory of each particle is updated by tracking two extrema. One is the best solution found by itself, called the individual extremum \(\mathbf{P_{t}}\). The other is the best solution that the swarm has obtained so far, called the global extremum \(\mathbf{P_{g}}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image** & **Number of Background Pixels (Black)** & **Number of Path Pixels (White)** & **Number of Center Pixels (Blue)** \\ \hline Figure 7a & 294852 & 12268 & 80 \\ Figure 7b & 292697 & 14503 & 83 \\ Figure 7c & 299500 & 14420 & 50 \\ Figure 7d & 298011 & 9094 & 95 \\ Figure 7e & 295200 & 11896 & 104 \\ Figure 7f & 298149 & 8972 & 79 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pixel classification of path images.  
Figure 7: The center pixels of the path images. (**a**) A straight path, (**b**) a straight path with highlights, (**c**) a straight path with coverings, (**d**) a curve path, (**e**) a curve path with highlights and coverings, and (**f**) a curve path with coverings.  
In the process of parameter estimation, the key operations of the genetic algorithm [39] include selection, crossover, and mutation, while PSO uses a real number coding method, without selection, crossover, and mutation, thereby having a simpler structure and a higher efficiency of calculation. However, conventional PSO is prone to falling into the local optimum and missing the global extremum [40; 41]. In fact, the inertia weight \(\omega\) of the algorithm reflects the ability of the particle to inherit the previous velocity at the current step. [PERSON] [42] indicated that a large inertia weight is beneficial to the global search, while a smaller inertia weight is more favorable for the local search. In this sense, it is more advisable to update the inertia weight \(\omega\) and acceleration factors \(c_{1}\) and \(c_{2}\) adaptively at each step, in order to take a tradeoff between the local search performance and the global search one. Hence, an IPSO algorithm is proposed here to avoid being trapped into the local optimum position.
## 1 Introduction
Furthermore, the study evaluates the optimization performance using SAR data acquired at three frequency bands (L-, C-, and X-band) to better demonstrate the effectiveness of the contrast enhancement method. These three bands are commonly used for vessel detection [2; 15; 25], each offering distinct advantages and limitations. The L-band (1-2 GHz) is well suited for detecting large vessels, as it provides better penetration through clouds and rain, making it effective in adverse weather conditions and rough seas. However, spatial resolution is normally lower compared to the other bands. The C-band (4-8 GHz) offers a balance between resolution and sensitivity to the ocean surface, making it ideal for detecting medium to large vessels in various sea states. It is widely used in SAR systems like Sentinel-1. The X-band (8-12 GHz) provides the highest resolution, enabling the detection of smaller vessels and fine details; however, it is more sensitive to sea surface roughness and weather conditions, which can limit its effectiveness in rough seas. It is very important to note that the goal of this work is to demonstrate how this contrast enhancement technique can be utilized with dual- and quad-pol data acquired at different bands to eventually improve the performance of ship detectors on SAR images. Therefore, we do not propose a new ship detection method but instead focus on a pre-processing algorithm based on polarimetry for enhancing the performance of any ship detector. The contributions of this study are therefore focused on three aspects not treated in [31] when the original quad-pol formulation was proposed, namely, the following:Evaluation of the contrast optimization using polarimetric SAR images acquired by multiple sensors operating at different frequency bands: L-, C-, and X-band. * Proposal of a formulation for the dual-polarimetric case (which is not detailed in the original formulation [31]), which will allow us to compare the optimization using both dual- and quad-polarimetric images. * Comparison of the performance provided by different combinations of polarimetric channels in the dual-pol case.
# Fast Fractional-Order Terminal Sliding Mode Control for Seven-Axis Robot Manipulator
## 4 Simulation and Experiment
### Experiment
#### 4.2.2 Experimental Results  
The trajectory tracking errors of the fourth link in joint space and the end effector tracking errors on the Z-axis in the Cartesian coordinate are plotted in Figure 6. The right side of Figure 6 shows the magnified figures to clarify the tracking error results. The results of SMCSPO are not plotted because they have the largest errors and can affect the readability of the figure. The errors of other links (except for the fourth link) and the end effector errors in X- and Y-axes in the Cartesian coordinate are shown in Figure 5 in Appendix C.  
The right side of Figure 6 shows that FFOTSMCSPO has the fewest tracking errors among the four controllers during the whole experiment interval. The controller of FOTSMCSPO using the fractional-order sliding surface showed fewer errors than TSMCSPO. This confirms that the fractional-order derivative improves control performance. The effect of TSMC can also be found by comparing the error result of FOTSMCSPO with FOSMCSPO. That is, it confirms that the FOTSMCSPO reduced more errors than FOSMCSPO.  
The results in Figure 6 also show lesser chattering in all four controllers. This demonstrates that the approach of SPO in controllers is useful for reducing chattering and ensures the proposed controller will be applied in practice.  
The numerical analysis of the experiment results is analyzed by comparing the mean absolute error (MAE). The MAE is calculated as  
\[\frac{\sum\limits_{k=1}^{n}\text{abs}(e(k))}{n},n=18750 \tag{37}\]  
Figure 5: Desired trajectory of 7 links.  
Table 2 shows the MAE results. In all seven links, the controller using fractional derivatives performed better with less MAE than the integer ones, FOTSMCSPO shows less MAE than TSMCSPO, and the results of FOSMCSPO are less than those using SMCSPO. Additionally, controllers using TSM show less errors than the conventional LSM ones, which is obtained from comparing FOTSMCSPO with FOSMCSPO, and TSMCSPO with SMCSPO. In the results of all five controllers, the new proposed controller FFOSMCSPO, using the TSM and applying the fractional-order derivative in both the sliding surface design and the reaching law design, has the least MAE. It exhibits the fastest convergence and demonstrates the improvement. Comparing the results in FOSMCSPO with TSMCSPO, FOSMCSPO exhibits less MAE. This means that TSMCSPO is more efficient than FOSMCSPO.
# A U-Shaped Architecture Based on Hybrid CNN and Mamba for Medical Image Segmentation
## 4 Experimental Results and Analysis  
### Dataset  
To evaluate HCMUNet's segmentation performance, we employ three benchmark datasets: ISIC 2018, Synapse, and ACDC. These datasets encompass diverse imaging modalities, including dermoscopy, computed tomography (CT), and magnetic resonance imaging (MRI). This variability enables a comprehensive evaluation of the model's segmentation accuracy, its generalizability across anatomical structures, and its robustness under varying imaging conditions and clinical scenarios.  
ISIC 2018 Dataset: Released by the International Skin Imaging Collaboration, the ISIC 2018 dataset is a widely used benchmark for skin lesion segmentation. It consists of 2694 dermoscopic images with corresponding segmentation masks, divided into 1886 training and 808 testing samples.  
Synapse Multi-Organ Segmentation Dataset: Provided as part of the MICCAI 2015 multi-atlas abdominal CT segmentation challenge, this dataset is widely used in medicalimage segmentation. It includes 30 contrast-enhanced abdominal CT scans with a total of 3779 axial slices, annotated with labels for eight organs: aorta, gallbladder, left and right kidneys, liver, pancreas, spleen, and stomach. In our experiments, we use 21 cases for training and nine for testing.  
ACDC Dataset: The Automatic Cardiac Diagnosis Challenge (ACDC) dataset is a widely used benchmark for cardiac MRI segmentation. It comprises cardiac MRI scans of 100 patients, with manual annotations provided for the left ventricle, right ventricle, and myocardium. In our experiments, we use 70 cases for training and 30 for testing.
# Robust Backstepping Control Applied to UAVs for Pest Recognition in Maize Crops
## 5 Experimental Results
### Experimental Results Applying Synthesized Controllers
To compare the performances of both controllers, the Integral Absolute Error (IAE) is computed for the robust backstepping (BS) and PD, and these values are reported in Table 2.  
Table 2 shows that the robust backstepping controller outperforms the PD controller in the trajectory-tracking phase of the quadrotor in the presence of disturbances; additionally, the standard deviation is calculated to analyze the error dispersion, and the mean value of \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{3}{c}{**Trajectory Tracking**} \\ \hline
**Performance Index** & **BS** & **PD** & \(\sigma_{BS}\) & \(\sigma_{PD}\) \\ \hline IAEx & 674.0325 & 732.8948 & 0.3195 & 0.6095 \\ IAEy & 646.48 & 849.0646 & 0.5691 & 0.6949 \\ IAEz & 59.96 & 85.4140 & 0.06747 & 0.0678 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative analysis of the performance IAE for the trajectory tracking phase. Figure 11: Error signals (PD controllers). Figure 12: Control signals (PD controllers). Figure 10: Trajectory tracking (PD controllers). the 10 conducted experimental tests is calculated and compared with the results obtained using the PD control. Additionally, Table 3 shows the mean of the integral of the absolute value of the signal control, defined as  
\[J_{control}=\sum_{i=0}^{N}\lvert control(i)\rvert,\]  
where \(N\) is the number of samples. The index \(J_{control}\) is related with the energy consumption of the actuators. According to the results showed on Table 3, the energy consumption required by the Robust Backstepping Controller (RBC) is lower than the PD controller. It implies that the control signal of the RBC is smaller and softer than the control signal of PD, which allows the captured images to be less blurred and distorted.
## 4 Simulation Results
### Channel and Feature Selection  
There are 8 and 6 channels, which are selected by the GA using KNN as the fitness function for valence and arousal scenarios, respectively as shown in Figure 3. Firstly, the TIF extracted from the 4 signal bands using DWT is used for the OCH selection. Then, the SCFs, which include 18 and 14 features for the classification of high/low valence and arousal, are addressed by the GA using the SCAF. The names and times selected by the GA of the chosen channels and features are shown in Table 2.  
Figure 3: Selected channels for valence and arousal scenarios.
## 8 Conclusions  
For dealing with the difficult identification of effective microseismic signals in a strong-noise environment, a method based on VMD and GA-SVM is proposed. The GA-SVM adaptively identifies effective IMFs generated by the VMD processing of microseismic signals, and, thus, recognizes the effectiveness of signals.  
By using the method for the identification of effective microseismic signals proposed in this paper, effective microseismic signals with artificially added noise were successfully recognized. The accurate reconstruction of the signals suppresses the influence of noise and improves the signal-to-noise ratio, which proves that this method can not only recognize effective signals but also effectively deal with strong noise environments in practical situations.  
In the context of the strong noise collected in the test field and actual engineering projects, we judged the effectiveness of microseismic signals and successfully distinguished between effective and ineffective components of decomposed signals. In other words, we separated effective signals from ineffective ones and accomplished the identification of the effectiveness of microseismic signals. This would help to discover effective microseismic signals with a high signal-to-noise ratio for the subsequent localization and interpretation of microseismic events.  
Conceptualization, B.W.; Investigation, S.C., L.X. and Z.S.; Data curation, S.S.; Writing--original draft, S.S.; Supervision, L.Z. and L.H.; Funding acquisition, B.W. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Foundation of China (No. 42174165) and Natural Science Foundation of Jiangsu Province (No. BK20230197).  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.