esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Optimal Trajectory Planning for Woodworking Manipulators Using an Improved PSO Algorithm
## 2 Materials and Methods
### The Traditional PSO Algorithm  
PSO is an intelligent optimization algorithm originating from research into the behavior of birds foraging in forests. In PSO, bird swarms are abstracted into particle swarms with each bird representing a particle. The particles iteratively solving the extremum of the fitness function in the search space simulate the process of birds sharing information with each other to search for the largest food in the forest, as shown in Figure 4.  
The process of particle iterative solution is achieved by continuously updating the particles' position and velocity information. Figure 5 shows the process of updating particle position and velocity. In the \(D\) dimension search space, we can assume that the particle number is \(N\), \(X_{i}\) = [\(X_{i1}\), \(X_{i2}\),..., \(X_{iD}\)], which represents the position of the \(i\)th particle, \(V_{i}\) = [\(V_{i1}\), \(V_{i2}\),..., \(V_{iD}\)], which represents the velocity of the \(i\)th particle. \(P_{i}\) = [\(P_{i1}\), \(P_{i2}\),..., \(P_{iD}\)] represents the current best position of \(i\)th particle and can be updated by the following equation:  
\[P_{i}(\mathit{iter}+1) = \left\{\begin{array}{ll}P_{i}(\mathit{iter}),&\mathit{fitness}(X _{i}(\mathit{iter}+1)\ >X_{i}(\mathit{iter}))\\ X_{i}(\mathit{iter}+1),&\mathit{fitness}(X_{i}(\mathit{iter}+1)\ <X_{i}(\mathit{iter}))\end{array} \right., \tag{19}\]  
where \(\mathit{iter}\) represents iterations; \(\mathit{fitness}\) (\(0\)) is fitness function; \(X_{i}(\mathit{iter})\) and \(P_{i}(\mathit{iter})\) are the position and current best position of the \(i\)th particle when the iteration number is \(\mathit{iter}\), respectively.  
\(P_{\mathit{gbest}}\) = [\(P_{\mathit{gbest1}}\), \(P_{\mathit{gbest2}}\),... \(P_{\mathit{gbestD}}\)] is the current global best position. Then, the formula for updating the position and velocity of particles is as follows:  
\[V\overset{iter+1}{ij}=\omega V\overset{iter}{ij}\ +\ c_{1}r_{1}\Big{(}P_{ij}-X \overset{iter}{ij}\Big{)}+c_{2}r_{2}\Big{(}P_{\mathit{gbestj}}-X\overset{iter}{ ij}\Big{)}, \tag{20}\]  
\[X\overset{iter+1}{ij}=X\overset{iter}{ij}+V\overset{iter+1}{ij}, \tag{21}\]  
where \(\omega\) is inertia weight; \(c_{1}\) and \(c_{2}\) are learning factors; \(r_{1}\) and \(r_{2}\) are the random numbers in the range (0,1); \(V\overset{iter}{ij}\) and \(X\overset{iter}{ij}\) are the velocity and position of the \(j\)th dimensional component of the \(i\)th particle when the number of iterations is \(\mathit{iter}\), respectively. It is called basic PSO [34] when inertia weight \(\omega\) = 1.  
Figure 3: Joint 1 trajectory planning results by 3-5-3 polynomial interpolation.
## 4 Proposed Blowfish Hybridized Weight Attribute-Based Encryption
### System Level Process  
System level processes in the proposed system are described as:  
1. **System Setup**: The challenger runs the global setup algorithm to obtain the global public parameters. The data holder selects a security parameter, subsequently sends a request to  
Figure 2: Single blowfish round.  
the algorithm phase interface setup, as a consequence it yields the secret key \(S_{K}\). The data holder then ciphers each \(S_{K}\) component and sends the encrypted components along with the signature to the central authority (CA). However, the CA authenticates the holder's signature. Further, if it is correct, the CA will utilize the system public and master key to create a public and secret key for a new user. Further, the WAA defines the weight for the attributes in the organization domain.
2. **Key Generation**: When a new user wants to connect to the system, the CA will allocate a unique user ID to the consumer. However, the consumer then cyphers its attribute set, and sends it along with its signature to WAA. The attribute authority authenticates the consumer's signature. If it is right, WAA creates the equivalent attribute secret keys and weight for the new consumer. Subsequently, the CA and WAA, individually, convey the consumer's system secret key and attribute secret key to the new consumer. The challenger obtains the corresponding keys by running the authority setup and central authority setup algorithms and gives the public keys to the attackers.
3. **Encryption**: Before uploading a data file to the cloud, the data holder initially logs in with a unique ID, and then randomly chooses a symmetric data file encryption key to encode the data. Furthermore, the data holder defines a \"weighted threshold access structure\" (W) for the corresponding data users and the data files and then encrypts the data with W as shown in Figure 3.
4. **Decryption**: The data consumer initially downloads the data from the cloud to the local, and then requests the decryption algorithm to decrypt the data. If the data consumer's attributed secret key is authorized, then the system assigns dissimilar weights to different attributes as per their importance. Subsequently, the user can decrypt the corresponding data file with respect to W. Once a user is invalidated, then the user will not be able to decrypt the data file as shown in Figure 4.  
Figure 3: Data owner side (encryption process).
###### Abstract
In the past decades, increasing consumers' awareness about the numerous complex interactions between health and diet shifted the food market paradigm from the \"food for nourish population\" to the \"food for health preservation and promotion\" [12; 13; 14]. This shift forced food science to incorporate many modern methods and concepts from biochemistry, pharmacology, medicine and biotechnology [15; 16]. Currently, many foodstuffs that were known and widely consumed for centuries are undergoing global scientific reevaluation and critical examination, and the study of traditional fermented foods form one of the most researched topics in this respect [17]. Among many potentially health-promoting properties of fermented food, the most relevant from the point of national dietary recommendations are their antioxidant, antihypertensive, anti-atherogenic and anti-thrombogenic properties [18]. Additionally, investigations of the volatile organic compounds (VOC) responsible for the characteristic aromas and flavors of fermented food form an important area of research, since products' VOC profile has a strong influence on consumers' preference and level of consumption [19; 20]. Moreover, recent development of a concept (though controversial) regarding food (or flavor) pairing, which states \"that ingredients sharing flavor compounds are more likely to taste well together than ingredients that do not\", promotes VOC analysis in as many products as possible to incorporate them into flavor networks [21; 22]. Therefore, this study was undertaken to analytically characterize commercialized fermented beverages widely consumed in Russia (kefir and ryazhenka) and South Africa (amasi and mhaewu) and to establish their potential health-promoting properties. For each product, the antioxidant capacity and ability to inhibit the angiotensin I converting enzyme (ACE) were assessed; for dairy beverages (kefir, ryazhenka and amasi), the profiles of fatty acids (FA) were measured and health-related FA nutritional indices were calculated. Additionally, the VOC profile of each product was determined. While for kefir several studies describing its antioxidant and antihypertensive properties, as well as FA profile and volatile organic compounds, have been previously published, to the best of our knowledge, currently, there are no such published data regarding ryazhenka, amasi and mhaewu.
# Lamb Wave-Based FDM-PPM Method Data Transmission Scheme in Plate Structures
## 2 Lamb Wave Communication Method Based on FDM-PPM  
### Construction of FDM-PPM Signals  
In designing the signals, this study thoroughly considered Lamb waves' dispersion characteristics and multimode properties. Based on the frequency sweep curve of the piezoelectric patches in the experiments, the direct S\({}_{0}\) mode Lamb wave signal was selected as the carrier for information transmission. In analyzing the signal duration, the direct S\({}_{0}\) mode signal is ensured not to overlap with other mode signals in the time domain, thereby avoiding inter-symbol interference.  
Within this limited time, rational encoding communication signals are the key to improving the communication rate. In sensor networks, the finite frequency bandwidth of the waveguide is typically divided and allocated to different sensor nodes. Each sensor node is assigned a specific set of carrier frequencies to distinguish between individual sensor nodes. This paper proposes an FDM-PPM modulation method for Lamb wave data transmission systems with several subcarriers. The core idea of this method is first to modulate the information to be transmitted using FDM and then perform secondary modulation on the phase and position of the signal pulse to obtain the information-carrying Lamb wave transmission signal.
# Object-Independent Grasping in Heavy Clutter
## 2 Dbp
### Grasp Candidate Generator  
The grasp candidate generator provides grasp poses that are likely to lead to a successful object grasping. In this, three processes are involved: depth filtering, region clustering, and grasp candidate generation.  
First, depth filtering removes the image, maintaining only the data of the lowest \(p\%\), using the height. This is because objects located higher are generally easier to grasp. The lowest \(p\%\) is selected because high objects appear closer to the camera of the robot.  
Then, region clustering divides the filtered depth image into several object regions. Here, the depth image is clustered using a density-based spatial clustering of applications with noise (DBSCAN) algorithm on the filtered \(p\%\) data. Figure 2 shows an example of DBSCAN. If a circle of radius \(\varepsilon\) is drawn around points A and F, a minimum of five points falls in the circle. Because points A and F are in the same circle, they belong to the same cluster and are called the core points. When points B, C, D, E, and G are centered, instead, less than five points are in the circle; thus, these are called border points. Point H is never included; thus, it is called a noise point. Although DBSCAN does not need to set the number of clusters in advance, it can detect clusters with geometric shapes and outliers. In this study, \(\varepsilon\) was set to 10 pixels, and the minimum number of samples for one cluster was set to 5. However, these parameters can be changed depending on the environment, e.g., the number and shape of the target objects and the resolution of the depth sensor.  
Finally, grasp candidates are generated for each cluster estimated by DBSCAN. Figure 3 shows an example of the whole operation. In the rightmost figure, each cluster has 10 grasp candidates. Grasp candidates consist of the locations, grasp angles, and width of the gripper. The locations are determined from the centroids of the clusters. For example, in Figure 2, the centroid of the cluster is point M. The grasp angles are simply multiples of (180/\(n\)). The gripper width is equal to the smallest dimension of the width and height of a rectangle surrounding the cluster. In Figure 3, for example, the gripper width is \(h_{0}\).  
Figure 3: Operation of the grasp candidate generator.  
Figure 2: Example of DBSCAN (density-based spatial clustering of applications with noise).
## 3 Overvoltage Simulation Model Including Variable Capacitance
### Calculation of Arc Resistance and Variable Capacitance
Figure 4a is the actual pantograph structure of the HX\({}_{\text{D}}\)1 electric locomotive, and Figure 4b is the simulated pantograph with equal proportions. Since the pantograph dropping process is a process of two contacts being separated, the electric field change between them can be approximately regarded as the electric field change of the plate capacitor, and the gap capacitance decreases with the increase in the pantograph-catenary distance. However, with regard to the process of pantograph dropping, the distance between the pantograph and the catenary begins at 0 m, but it cannot be used as a denominator in the variable capacitance formula. Therefore, in order to ensure the numerical stability of the model, it is necessary to set the limit of the pantograph-catenary capacitance. After setting the grid and boundary conditions in ANSYS, the equivalent capacitance of 250 pF was simulated with a pantograph-catenary distance of 1 \(\upmu\)m. When the pantograph-catenary distance is less than or equal to 1 \(\upmu\)m, the gap  
\[\frac{dg}{dt}=\frac{dQ}{dt}\cdot\frac{dg}{dQ}=(P-P_{0})\cdot\frac{dg}{dQ} \tag{4}\]1 Actual structure and equal-proportion simulated structure of HX\({}_{\text{D}}\)1 electric locomotive pantograph. (**a**) Actual pantograph model. (**b**) Simulated pantograph model. capacitance is fixed at 250 pF. With the increase in pantograph-catenary distance, the capacitance between pantograph and catenary becomes smaller. The variable capacitance expression for the pantograph-catenary is as follows:  
\[C=\left\{ \begin{array}{ll}\frac{\epsilon\mathbb{S}}{L}=\frac{250}{L}&\left(L>1 \ \upmu\right)\\ 250&\left(L\leq 1\ \upmu\right)\end{array} \right. \tag{14}\]  
The equivalent circuit of the pantograph-catenary gap is shown in Figure 5, where \(R\) and \(C\) represent the arc resistance and variable capacitance between the pantograph and catenary (because there is no variable conductance module in the simulation software(ANSYS 2022-R1), there is only a variable resistance module, so variable resistance was used instead of variable conductance in the simulation model).
# Real-Time Semantic Segmentation of Point Clouds Based on an Attention Mechanism and a Sparse Tensor
## 2 Related Works  
### Point Cloud Semantic Segmentation  
The semantic labeling of preprocessed [12] disorganized point cloud data acquired by sensors [13] is the goal of semantic segmentation of point clouds. In general, we can classify the existing methods into four categories, namely, raw point-based, projection-based, graph model-based, and voxelization-based methods.  
Raw point-based methods are usually performed directly on irregular point clouds. A representative work is PointNet [4], which uses MLPs to extract features of points and global features through symmetric functions. [PERSON] et al. [5] proposed kernel point convolution (KPConv) to directly process point clouds, where the convolution kernel of KPConv consists of a series of kernel points with weights, each with an influence distance. [PERSON] et al. [14] proposed a spatially transformed point convolution (STPC) to achieve the anisotropic convolutional filtering of point clouds and introduced a spatial direction dictionary to capture the geometric structure of the data.  
Projection-based methods typically project 3D data onto 2D images and then perform subsequent segmentation using image processing methods [15; 16; 17; 18]. However, this method cannot take full advantage of the structural information of the 3D data. To solve the problem of the uneven distribution of LiDAR points in space, [PERSON] et al. [6] performed end-to-end learning of 2D semantic segmentation models via polarized bird's-eye-view representations. To solve the problem of large differences in the distribution of features at different image locations, [PERSON] et al. [7] proposed a spatially adaptive convolution (SAC).  
Regarding graph model-based methods, [PERSON] et al. [19] used point convolution and point pooling methods to learn the high-level features of point clouds and then combined point convolution, point pooling, and a graph structure to propose a point cloud feature-learning network for the feature learning of point clouds. To solve the hyperspectral image problem, [PERSON] [20] et al. proposed the feature fusion network to learn the spectral features of nodes and cluster the hyperspectral images.  
Due to the sparse nature of the point cloud data, the method of voxelizing the point cloud into a dense grid is less efficient. For this reason, [PERSON] et al. [8] proposed Minkowski and generalized sparse convolution algorithms for processing 3D videos. For better recognition of small elements, such as pedestrians and bicycles, [PERSON] et al. [9] proposed sparse point voxel convolution and 3D-NAS for 3D scene understanding. [PERSON] et al. [21] proposed a LiDAR segmentation framework consisting of cylindrical coordinate voxel partitioning and the asymmetric 3D convolutional network, Cylinder3D.
## 5 Conclusions  
This paper introduces two theoretical methods to estimate the power losses caused by windage and churning behavior of various rotating components (disc, spur gear, straight level gear, and orthogonal face gear). Experiments conducted using a specific lubrication test rig verify the theoretical methods.  
The initial immersion depth and rotating speeds have significant effects on churning power losses. The no-load losses increase as the depth and rotation speed increase. The no-load power losses of various rotating components evaluated by the dimensional analysis method and the fluid flow method correlate well with the experimental results. Additionally, different types of gears exhibit varying windage effects. The windage losses of spatial crossed gears (bevel and face gears) are significantly higher than those of spur cylindrical gears.  
Regardless of rotating speeds and oil depths, the power losses generated from churning behavior contribute to over 80% of the total no-load losses. At high speeds, windage losses can account for more than 10% of the total, in contrast to the insignificant losses at low speeds. Moreover, the bevel gear experiences greater windage power loss compared to the spur gear or face gear. The primary factor that might enhance the windage behavior is the pitch angle of the bevel gear.  
Conceptualization, Y.D. and C.Y.; methodology, H.L.; software, C.Y.; validation, H.L., Y.D. and X.Z.; formal analysis, H.L.; investigation, C.Y.; resources, Y.D.; data curation, C.Y.; writing--original draft preparation, X.Z.; writing--review and editing, C.Y.; visualization, X.Z.; supervision, X.Z. and Y.D.; project administration, Y.D.; funding acquisition, Y.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 52305081 and 52475078), the Natural Science Foundation of Changsha City of China (Grant No. kq2208275), and the National Defense Pre-Research Foundation of China (Grant No. KY-1044-2023-0451).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors are deeply grateful for the help of [PERSON] from AECC South Industry Company Limited, Zhuzhou.  
The authors declare no conflicts of interest.
# Collision Avoidance Behavior Mining Model Considering Encounter Scenarios
## 1 Introduction  
### Background  
In recent years, with the continuous advancement of shipping technology, intelligent ships, as a frontier technology in the field of waterborne transportation, have made the realization of autonomous navigation capabilities increasingly imperative. Global maritime accident statistics indicate that ship-to-ship collisions are the most common type of accidents worldwide ([PERSON] and [PERSON] [1]; [PERSON] et al. [2]). In certain local navigation segments or port waters, high-density ship traffic significantly increases the probability of collisions. As a result, collision avoidance strategies under various scenarios have become a central issue in autonomous navigation research. Accurate collision avoidance information in encounter scenarios is crucial for effective ship behavior analysis and collision avoidance decision making. Therefore, research on collision avoidance behavior mining is both necessary and urgent.  
Meanwhile, with the rapid development of maritime monitoring technology, particularly the widespread application of the AIS, the spatiotemporal data of ships have seen significant growth. Over the past few decades, international scholars have focused on the analysis of maritime traffic data based on AIS, as well as the development of ship navigation models and accident probability prediction tools ([PERSON] et al. [3], [PERSON] et al. [4], [PERSON] et al. [5], [PERSON] et al. [6], [PERSON] et al. [7]). AIS provides a wealth of ship navigation information, including the ship's name, detailed information, position, speed, and course, with broad coverage and high timeliness. The information provided by AIS helps to perceive ship encounter scenarios, especially ship-to-ship collision avoidance studies. However, regarding the determination of encounter scenarios with collision risks, Rule 7 of the COLREGs only states that \"if the bearing of one ship relative to another approaching ship remains almost constant, a collision risk is assumed.\" In other words, the judgment of whether a ship is at collision risk depends on the perception of the observer. In AIS-based ship collision avoidance studies, there is an urgent need for an accurate and objective method for identifying and mining avoidance behaviors in near-collision scenarios.
## 4 Mobile Localization Algorithms
### Distributed Localization Algorithms
#### 4.2.5 Multi-Stage AUV-Aided Localization (MS-AUV) Scheme  
To shorten localization time and improve localization accuracy of AUV-Aided localization technique, MS-AUV is proposed in [52]. MS-AUV combines the flexibility of the AUV-aided localization and the energy efficiency of \"Silent Localization\", which has been studied in [26], where unknown nodes passively listen localization message. Simulation results show that the whole localization process can be completed in less than 10 minutes and can cover more than 95% of the whole network. However, similar to other multi-stage algorithms, e.g., MS-DNR, accumulated error is inevitable.
# Quantifying Uncertainties in OC-SMART Ocean Color Retrievals: A Bayesian Inversion Algorithm
## 3 Methodology for Quantifying Uncertainties
### Bayesian Inversion
By taking the square root of the diagonal elements of \(\mathbf{\hat{S}}(\mathbf{x})\), we arrive at the standard uncertainty estimate of the posterior:  
\[\sigma=\sqrt{\text{diag}(\mathbf{\hat{S}}(\mathbf{x}))}. \tag{8}\]  
Equation (6) implies that the state vector \(\mathbf{x}\) is changed iteratively until convergence is reached. This optimal estimation process can be time consuming and computationally taxing, and may not converge if the initial guess of \(\mathbf{x}\) is too far from the true value. As explained in Section 3.1.1 below, the OC-SMART approach uses a multi-layer neural network (MLNN) to solve the inverse problem. The benefit of this approach is that once the neural network is properly trained it should give a result that is as good as that obtained by the Gauss-Newton method, but in a significantly shorter computational time. The MLNN approach solves the inverse problem (rather than iteratively as in the Gauss-Newton method) by a training procedure that typically involves the solution of numerous non-linear inversion problems using the gradient descent method to optimize the weights and biases in the neural network (see Equation (10) below). For our purposes, sensor L1B calibrated radiances and sun-satellite geometry data are processed by OC-SMART to yield the desired retrieval parameters. These L2 retrieval parameters are inserted into the forward model and used to compute simulated Rayleigh-corrected TOA radiances, as explained in some detail in Section 3.1.1 below. The difference between the simulated and measured radiance is calculated for all wavelengths and if the mean of these differences is lower than an acceptable threshold of 10% we consider the retrieval parameters for this pixel to be converged, as illustrated in Figure 2. The application of this convergence check indicates that pixels for which the retrieval values have not converged are usually found in optically complex areas, such as bays and inlets, as well as in the proximity of cloud-contaminated pixels.