esa-sceva/satcom-chunk-collection huggingface dataset:
# Vehicle Pose Estimation Method Based on Maximum Correntropy Square Root Unscented Kalman Filter
## 5 Results
### Simulation Results and Analysis
#### 5.1.3 The Running Time Comparison of Different Algorithms  
In order to investigate the computational efficiency of different SLAM algorithms, the running time of different SLAM algorithms was compared in Gaussian mixture noise and colored noise. The experimental results are shown in Table 4. In this experiment, the processor model of the laptop is 11 th Gen Intel (R) Core (TM) i7-1165G7 @ 2.80 GHz, the memory is 16G, and the software used in the experiment is MATLAB 2018a.  
Tables 6 and 7 demonstrate that the computational time of identical SLAM algorithms varies slightly between Gaussian mixture noise and colored noise environments. This variation primarily results from noise-induced differences in matrix operation complexity, though the observed time differences remain negligible relative to the total running time. The experimental results obtained in both non-Gaussian noise environments exhibit remarkable consistency. Comparative analysis reveals that the EKF-SLAM algorithm achieves the shortest execution time. SRUKF-SLAM and UKF-SLAM exhibit similar performance in terms of computational speed. The proposed MCSRUKF-SLAM algorithm achieves comparable computational efficiency to MCUKF-SLAM. While the square-root formulation of MCSRUKF-SLAM provides marginal improvements in numerical stability, statistical analysis confirms this advantage does not reach significance. Experimental results show that while MCSRUKF-SLAM requires marginally longer runtime, its superior system performance and robustness fully compensate for this minor drawback.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **Total Running Time (s)** & **Single Step Time (s)** \\ \hline EKF-SLAM [13] & 223.201 & 0.197 \\ UKF-SLAM [20] & 244.718 & 0.216 \\ SRUKF-SLAM [23] & 235.664 & 0.208 \\ MCUKF-SLAM [39] & 319.506 & 0.282 \\ The proposed MCSRUKF-SLAM & **311.575** & **0.275** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Running time comparison of five algorithms under Gaussian mixture noise conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & **Divergence Count per** & **Divergence Count per** & **Divergence Count per** \\  & **100 Runs (8 m/s)** & **100 Runs (15 m/s)** & **100 Runs (30 m/s)** \\ \hline EKF-SLAM [13] & 3 & 13 & 25 \\ UKF-SLAM [20] & 0 & 6 & 13 \\ SRUKF-SLAM [23] & 0 & 0 & 0 \\ MCUKF-SLAM [39] & 0 & 4 & 8 \\ The proposed MCSRUKF-SLAM & **0** & **0** & **0** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of divergence count per 100 runs for various algorithms at different speeds.
# Construction of Three-Dimensional Semantic Maps of Unstructured Lawn Scenes Based on Deep Learning
## 3 Materials and Methods
### Dataset Descriptions
#### 4.1.2 Lawn Environment  
We collected and established a dataset containing 7500 images of various lawn scenes. The lawn images used in the experiments were obtained on various lawns on the campus from mid-March to mid-September 2023. To validate the effectiveness of our semantic segmentation architecture, we typically used lawn environments such as the one shown in Figure 10. This image is presented from a bird's-eye view and depicts our envisioned lawn working scenario, which is not a completely flat area without obstacles. Instead, it often includes a variety of obstacles that hinder the operation of gardening pruning robots. It is precisely because of the presence of various obstacles that there is a need to establish 3D semantic maps to assist gardening pruning robots in working more efficiently.  
Figure 10: A schematic diagram of the experimental lawn, which often contains a variety of obstacles.  
Figure 9: Lawn images and their corresponding visualization labels.  
We captured some images with a resolution of 1280 \(\times\) 1920 using an Azure Kinect DK and some with a resolution of 640 \(\times\) 480 using an Intel Realsense D455 camera. High-resolution images were primarily used to aid with learning image information, while low-resolution photos were used for SLAM mapping. Generally, the shooting time was from 5:00 a.m. to 7:00 p.m. to ensure the diversity of the lawn environment, capturing lawns with different lighting conditions, locations, and obstacle situations.  
Figure 11 selects some of the pictures from our dataset, displaying the annotated labels overlaid on the original images. Regarding the overall lawn environment, we have demonstrated in Figure 10(a),b that the pictures contain all the elements we mentioned previously, including lawns, people, obstacles, bushes, tree trunks, and boundaries. However, capturing only pictures of types (a) and (b) would result in a large area of lawn in the pictures, making it difficult for other types of labels to be learned by the semantic segmentation network. Therefore, for other types of objects, we used pictures in which they appear extensively, such as in Figure 10(c), where our picture focuses on an Edge, and similarly, in Figure 10(d), which focuses on Bushes. For the labels People, Obstacles, and Trunk, we also obtained many pictures in which these objects appear extensively to aid in the learning of our network.
## 5 Simulation Results
Figure 7 illustrates the secrecy rate versus the number of Eves. We set \(P_{max}\) as 10 dBm. It is observable that the rate of decline is more rapid with a smaller number of Eves, and the decline will slow down when the number of Eves is large. The fundamental reason behind this phenomenon is that, when the number of Eves is small, the spatial DoF of the holographic surface plays a greater role, but as the number of Eves increases, there is a lack of sufficient spatial DoF, and artificial noise plays a major role. At first, there is a reduction in spatial DoF, and then there is a reduction only in the allocated power for each eavesdropper suppression. Figure 8 illustrates the secrecy rate of the RHS with different sizes versus the power of the BS. The observation can be made that a greater number of radiation elements on the holographic surface will result in a higher secrecy rate. This is expected since the DoF of the RHS will be higher with more radiation elements on the RHS; thus, the suppression against eavesdroppers will be higher. Note that, when the power is low, the secrecy rate gap between different size RHSs is different from when the power is high. This can be attributed to the fact that, at low power levels, the primary aim of beamforming is to mitigate the eavesdropper's eavesdropping rate. When the number of radiation elements is limited to four (resulting in a low DoF on the RHS), the effectiveness of eavesdropper suppression is limited, so it is obviously lower than the secrecy rate when the size is larger. When the  
Figure 5: The rates of UE and each Eve versus the power of the RHS. Figure 6: The secrecy rate versus the power of the RHS. power is higher, the main goal is to increase the communication rate of the receiver. The drawback of low DoF at smaller sizes is alleviated by power distribution.
# Design and Optimization of Piezoelectric-Powered Portable UV-LED Water Disinfection System
## 2 Operation of the Disinfection System  
A general system overview of the piezoelectric-powered portable UV-LED water disinfection system is already presented in [18]. Apart from this, here, operation of the system is presented briefly. The proposed system, basically, is a portable water storage system (just like a canteen) that can be used for disinfecting drinking water without any battery or external power unit as well as any chemical component (eco-friendly design). The system comprises three main components, body, moving block and drinking glass. The body consists of two interwoven hollow cylinders. The outer cylinder functions as the shell of the system while the inner cylinder is simply a placeholder for the drinking glass. Moreover, there are two piezoelectric blocks for energy harvesting--one on the inner sole of the shell and the other on the outer sole of the inner cylinder. The moving block is free-running and is embedded tightly in between the hollow cylinders. Finally, the drinking glass is placed inside the inner cylinder for storing the drinking water. A technical drawing illustrating the main components of the system is shown in Figure 1. The system starts operation by shaking, i.e., shaking up and down by the user. Meanwhile, the bottom of the moving part hits the piezoelectric blocks with which mechanical energy is simply converted into electrical energy. The generated electrical energy is continuously stored in a supercapacitor which is an integral part of the piezoelectric energy harvester. The voltage level of the supercapacitor is displayed to the user so that they can stop shaking at a pre-defined voltage charging level (max) of the supercapacitor. Once the voltage level is reached, the required energy is ready for use, and the user can press the button to ignite UV-LED radiation for the disinfection process. The disinfection continues until the voltage level of the supercapacitor reaches the pre-defined level. Shown in Figure 2 is a simple diagram of the electronic circuit of the system. The following section describes the physical-mathematical model of this flow, and concludes with the optimal design parameters.  
Figure 1: Illustration of the main components [18].
# A Method for Identifying the Wear State of Grinding Wheels Based on VMD Denoising and AO-CNN-LSTM
## 2 Grinding Wheel Wear State Recognition Based on VMD Denoising and AO-CNN-LSTM  
In Section 2.1, this paper first introduces the principle of the VMD algorithm and performs denoising on the AE dataset in experiments, successfully removing noise while retaining useful information. In Section 2.2, this paper introduces the AO-optimized CNN-LSTM model for recognizing the wear status of grinding wheels. It elaborates on the principle and superior performance of the Eagle Optimization algorithm, as well as the advantages of CNN and LSTM, and the combined predictive model CNN-LSTM used in this paper. Finally, a neural network model named AO-CNN-LSTM is proposed, demonstrating the flowchart of optimizing neural network parameters using the AO algorithm and the flowchart of the AO-CNN-LSTM neural network model.
## 3 Results and Discussion
### Debonding Damage Identification
Hence, the identified results by the proposed model for 134 of the training data were almost identical to the reference value. Figure 9 shows that despite being informed by the largest amount of training data (605 data), the identification accuracy of Type 3 (single damage at each of two joints) damage is 88%, which is the lowest of the three types of damage composition. Here, the overall accuracy of the ANN-based damage identification model presented in this study is governed by the damage configuration Type 3, where damage variables are the most complicated (single damage at each of two joints) and the number of training data is the largest (605 data). From the above results, it could be said that the damage identification model proposed in this paper could identify the debonding damage occurring in the composite blade spar cap-shear web joint with high accuracy. Additionally, to improve the accuracy of the debonding damage identification model, it is necessary to select proper damage variables that could express the debonding damage that may occur in the actual blade and to obtain appropriate training data for each variable. Figure 8: Overall damage identification accuracy.
## 5 Discussion  
### Processing Time and Disk Usage  
For most existing InSAR time series analysis software packages (e.g., StaMPS or GIAnT), users generally need to produce the interferograms over the area of interest themselves. If we start by downloading a stack of Sentinel-1 SLC data and then produce a whole set of the interferograms, the process takes a large amount of time and disk space. For example, to produce \(\sim\)100 images and \(\sim\)300 interferograms used in the case study, \(\sim\)1 TB of Sentinel-1 SLC products must be downloaded, and \(\sim\)600 GB of coregistered SLC data must be created, along with \(\sim\)300 GB of interferograms and other intermediate files, corresponding to \(\sim\)2 TB in total. In addition to the computational resource requirements, extensive knowledge of InSAR processing and Sentinel-1 data is also needed to prepare the data for time series analysis.  
One of the advantages of LiCSBAS is that users do not need to produce interferograms if the LiCSAR products are available in their area of interest, saving significant amounts of time and disk space. The size of GeoTIFF files for \(\sim\)300 interferograms is only \(\sim\)5 GB (Table 2). The size of the files  
Figure 14: (**a**) Amplitude of the annual displacement in the Echigo Plain. (**b**) Timing of the lower peak of the annual displacement.  
produced by LiCSBAS for the Japan test frame is only \(\sim\)30 GB. LiCSBAS processing times are also reasonable, even for an entire frame (\(\sim\)5 hours; Table 2). The estimates presented here are based on an Intel Xeon E5-2690 CPU (8 cores, 2.90 GHz), \(\sim\)5 GB maximum of used RAM, and a download speed of \(\sim\)30 MB/sec. Further, if the area of interest is a specific part within the frame, clipping at Step 0-5 greatly saves processing time and disk space but also could improve the result because of reducing the impact of potential unwrapping errors (Section 4).  
Downsampling at Step 0-2 makes further processing much faster and file sizes much smaller at the cost of resolution (Table 2), whilst broadly, the same result can be derived (Figure S12). If the full resolution (\(\sim\)100 m) is not required (e.g., for large-scale strain estimation), downsampling is worth incorporating. Even when full resolution is preferred as the final result, quick processing with the downsampled dataset is useful to check if a good result can be obtained and to adjust parameters/thresholds prior to processing at full or higher resolution.
## 4 Evaluation  
Before presenting the evaluation results, we first show the benefits of considering link correlation in flooding and opportunistic routing, using a simple example with one sender and two receivers. The packet reception rates on both links are 0.5. (1) For flooding, the sender tends to deliver a number of packets (say, 10 packets) to the two receivers. After the first round of transmission, both links have lost five packets. With positively correlated links, the five lost packets on each link will be the same, and the sender needs to retransmit only five packets. However, with negatively correlated links, the five lost packets on each link will be different, and the sender needs to retransmit 10 packets (five packets for each receiver). We can see that, if we select the senders with positive correlation for flooding, the transmissions will be saved [17]; (2) For opportunistic routing, the sender tends to deliver a packet to at least one node of its receivers. With one packet transmission, if the links are positively correlated, there is a 50% probability that both links lose the packet. However, if the links are negatively correlated, _i.e._, when a receiver loses a packet, the other receives the packet, we can infer that at least one node can receive the packet. Thus, if we select the senders with negative correlated links for opportunistic routing, the performance will be improved [15].  
We can infer that if link correlation is inaccurately measured, the above benefits will degrade. Therefore, by improving the estimation accuracy, LACE could be used for further improving the protocol performance of flooding and opportunistic routing.  
To study the effect of LACE on protocol performance, we implement a stand-alone interface of LACE in TinyOS on TelosB motes. We first test the accuracy of link correlation estimation in TOSSIMsimulation with a 10 \(\times\) 10 network (the \"Meyer-heavy\" noise trace is used and the gain of each link is randomly set in range of [\(-\)95,\(-\)70] dbm). Next we incorporate LACE into existing network layer protocols (a link correlation-aware opportunistic routing [15] and a reliable multi-message flooding (dissemination) protocol (CoCo) [17]) to study the performance improvements. Our 4 \(\times\) 10 testbed is used for real motes' evaluation.
## 3 Results and Discussion
### Magnetic Analysis  
Figure 4a,b shows the room-temperature magnetic hysteresis loops of the CuFe\({}_{2}\)O\({}_{4}\) and ZnFe\({}_{2}\)O\({}_{4}\) nanoparticles prepared by the sol-gel self-combustion technique. The Magnetic hysteresis (M-H) curve gives the information about the magnetic parameters such as saturation magnetization (Ms) and coercivity (Hc). Normally, the magnetization in spinel ferrites arises due to the difference in the magnetic moments of cations which are distributed in the octahedral and tetrahedral sites. From the literature review data, when the particle diameter is 50 nm or less, the particles appear to have superparamagnetic character. However, in our case, particle size is found to be 56 nm for CuFe\({}_{2}\)O\({}_{4}\) and 68 nm ZnFe\({}_{2}\)O\({}_{4}\), respectively. Both copper ferrite and zinc ferrite samples exhibit ferromagnetic behavior (see inset Figure 1a,b) at room temperature with saturation magnetization for ZnFe\({}_{2}\)O\({}_{4}\) (28.8 Am\({}^{2}\)/Kg) and CuFe\({}_{2}\)O\({}_{4}\) (50.63 Am\({}^{2}\)/Kg), respectively. The low value of Ms in both samples is due to the presence of very small sized particles. Generally, magnetic properties varied by varying the particle size, shape and crystallinity, etc. The observed value of saturation magnetization for ZnFe\({}_{2}\)O\({}_{4}\) (28.8 Am\({}^{2}\)/Kg) was significantly lower than the reported bulk MgFe\({}_{2}\)O\({}_{4}\), CuFe\({}_{2}\)O\({}_{4}\) and NiFe\({}_{2}\)O\({}_{4}\) spinel ferrites (41.7, 45.7 and 49.1 emu/g, respectively) [47, 48] but higher than CuFe\({}_{2}\)O\({}_{4}\) (50.63 Am\({}^{2}\)/Kg). These values are generally expected for nanosized materials. From the hysteresis, small size zinc ferrite nanoparticles exhibited low saturation magnetization compares to copper ferrite nanoparticles. Saturation magnetization (Ms) is an intrinsic factor of magnetic nanoparticles; it is influenced by the crystalline structure, chemical composition and method of preparation. The difference in saturation magnetization between CuFe\({}_{2}\)O\({}_{4}\) and ZnFe\({}_{2}\)O\({}_{4}\) may be due to the structural morphology, particle size and crystallinity of the corresponding nanoparticles. It has been reported that the magnetic properties are dependent on the method of synthesis and heating treatment. The exhibited coercivity of CuFe\({}_{2}\)O\({}_{4}\) and ZnFe\({}_{2}\)O\({}_{4}\) nanoparticles is 215.11 Gauss and 58.97 Gauss, respectively, and the values are strongly dependent on size.  
Figure 3: (**a**) Transmission electron microscope (TEM) image of calcined copper ferrite powder and the corresponding particle size histogram; (**b**) TEM image of calcined zinc ferrite powder and the corresponding particle size histogram.
# Optimization Method of Square Hole Measurement Based on Generalized Point Photogrammetry
###### Abstract  
The theory of generalized point photogrammetry extends the traditional point-based photogrammetry to line-based photogrammetry, expanding the application scope of photogrammetry in engineering. In order to solve the problem of accurate positioning between the square rod and the square hole in the current project, the position of the square hole should be accurately measured first. For this purpose, an optimization method of square hole measurement based on generalized point photogrammetry is proposed. This method first uses the traditional photogrammetric method to calculate the initial coordinates of the four points of the square hole and extract the four line segments on the image. The error equation based on generalized photogrammetry is constructed by the constraint conditions between the four spatial points, and the iterative calculation is carried out until the error is less than the threshold or the iteration number is reached. The reliability of the method is verified by numerical simulation experiments and engineering experiments. The experimental results show that the method can effectively improve the measurement accuracy and can rapidly converge. The method has high engineering application value.  
generalized point; photogrammetry; square hole; constraints; location 2023
# A Full-Coverage Path-Planning Algorithm for a Glass-Curtain-Wall-Cleaning Robot Driven by Ropes
## 1 Introduction
To further improve the cleaning coverage rate, a full-coverage path-planning algorithm based on an improved priority heuristic was developed, which does not ignore the inset area of U-shaped obstacles. By introducing two sets of priority criteria with which to judge the forward direction, the robot can switch directions to cover the whole area when encountering U-shaped obstacles. Furthermore, by planning a return route taking the least amount of time possible when entering a dead zone, an escape strategy was designed to prevent the robot from being unable to choose a direction. Aiming at problems in terms of the structure design, wall modeling, and path planning of cleaning robots, we propose a five-rope 3D driving configuration, a grid modeling method, and a full-coverage path-planning algorithm including a dead zone escape strategy to achieve the efficient global cleaning of a curved wall with complex obstacles. The rest of this paper is arranged as follows. Section 2 introduces the rope-driven cleaning robot structure. The wall model gridding and region decomposition are shown in Section 3. Section 4 introduces the path-planning algorithm based on the priority heuristic. Section 5 describes the experimental procedure and results of the analysis. Section 6 provides several conclusions from this study.
# Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks
## 4 Discussion
### Limitations and Future Works  
Although the automatic detection of epileptic EEG signals using the micro-capsule network framework has achieved promising results, the proposed method still has limitations. First, deep learning relies heavily on large quantities of epileptic samples. In practical applications, collecting such samples poses significant risks to patients and imposes substantial burdens on clinicians during data acquisition. Second, existing public epileptic EEG datasets are fragmented and inconsistent with the requirements of real-time seizure-type detection, necessitating refinement of the network framework to support real-time analysis. Third, the proposed framework simplifies the model architecture to optimize computing resources, enable deployment on portable devices, and achieve a more practical design. Fourth, training the micro-capsule network framework for epilepsy EEG signals remains computationally intensive, necessitating further optimization of the architecture and hyperparameters. Additionally, the next step is to develop an independent epilepsy detection application based on capsule neural networks. This application prioritizes a user-friendly interface, enabling clinical doctors to readily access EEG signal analysis results and seizure risk assessments, thereby enhancing interaction with the system.