esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methodology
### Data  
Figure 1 illustrates the three manually annotated images (\(I_{\eta}\), \(I_{\zeta}\), and \(I_{\tau}\)) used for synthesizing computationally annotated datasets. Utilizing the methodology presented  
in our previous work [7], we computationally synthesize three datasets: \(\mathbb{D}_{\eta}\), a set of 8000 images derived from \(I_{\eta}\); \(\mathbb{D}_{\eta+\zeta}\), a set of 16,000 images derived from \(I_{\eta}\) and \(I_{\zeta}\) and \(\mathbb{D}_{\zeta+\tau}\), a set of 4000 images derived from \(I_{\zeta}\) and \(I_{\tau}\). Figure 2 illustrates examples of the synthesized images and their corresponding segmentation masks. In addition to these manually and computationally annotated images, for the training process, we utilize 10,592 unannotated image frames extracted from two video clips of wheat fields, with 5296 frames from each. Hereafter, we refer to these datasets as \(\mathbb{D}_{\rho_{1}}\) and \(\mathbb{D}_{\rho_{2}}\). We refer to the dataset resulting from combining \(\mathbb{D}_{\rho_{1}}\) and \(\mathbb{D}_{\rho_{2}}\) as \(\mathbb{D}_{\rho}\).  
We also use two sets of \(\Psi\) and \(\Gamma\) as the internal and external test sets, as introduced by [PERSON] et al. [7]. The set \(\Psi\) comprises 100 image frames, which were randomly selected from a video clip of a wheat field and have been manually annotated to serve as an internal test set. The set \(\Gamma\) represents a subset of 365 manually annotated images from the GWHD dataset [23], which includes images from five countries and spans 18 domains, covering various growth stages of wheat.
## 4 Beam Profile Measurements
### Measured Beam Profiles and Effects of the Mode Superposition
Figures 12a, 13a and 14a and Figures 12b, 13b and 14b show the wave phase on the radiation surface used in the beam profile calculations of Figures 12c, 13c and 14c and Figures 12d, 13d and 14d; the wave phase on the radiation surface was simulated using Equation (5), including the leaky attenuation coefficient term with adjustment of the phase delay between the superposed modes, and they were presented in case of reasonable quantitative matching with the measurement results as shown in Figure 15 (a representative example of the comparison result). Although the constructed RSI model is a continuous wave model using a single frequency (only the center frequency component of the excitation input is considered), its simulation result shows the good agreement with the experimental one in far-field characteristics and beam pattern. Figure 12: Simulation #1 of the leaky radiation beam profile based on the wave phase on the radiation surface. (**a**) Isotropic view of wave phase on radiation surface, (**b**) top view of wave phase on radiation surface, (**c**) vertical beam profile, and (**d**) lateral beam profile. Figure 13: Simulation #2 of the leaky radiation beam profile based on the wave phase on the radiation surface. (**a**) Isotropic view of wave phase on radiation surface, (**b**) top view of wave phase on radiation surface, (**c**) vertical beam profile, and (**d**) lateral beam profile. Consequently, simulation results of the beam profiles describe the fact that the A(0,2) mode affects leaky Lamb wave radiation with the A(0,0) mode in the waveguide plate with finite width, and the change of the wave behavior on the radiation surface caused by the mode superposition and the wave velocity difference results in the change of the radiation characteristics of the leaky radiation beam profile.
# Central Non-Linear Model-Based Predictive Vehicle Dynamics Control
## 3 Central Predictive Control
### Prediction
#### 3.1.3 Pitch Behavior  
In order to obtain the prediction model for the pitch behavior, the vehicle body is cut free in the \(x-z\) plane. Subsequently, the principle of angular momentum is set up around the vehicle's pitch center. The transformation of the resulting equation to the pitch acceleration \(\ddot{\theta}(k)\) yields:  
\[\begin{array}{l}\frac{1}{l_{yy}}lbreakh_{GP}m\alpha_{x}\cos\theta(k)+h_{GP} mg\sin\theta(k)-2\left(l_{S,f}^{2}c_{S,f}+l_{S,r}^{2}c_{S,r}\right)\sin\theta(k)\\ -u_{1}(k)\frac{l_{S,f}}{s_{\text{St},f}}-u_{2}(k)\frac{l_{S,r}}{s_{\text{St},r}} \\ -\Big{(}(u_{3}(k)+u_{4}(k))l_{D,f}^{2}\Big{)}\dot{\theta}(k)\cos\theta(k)\\ -\Big{(}(u_{5}(k)+u_{6}(k))l_{D,r}^{2}\Big{)}\dot{\theta}(k)\cos\theta(k)]= \ddot{\theta}(k).\end{array} \tag{17}\]  
\(J_{yy}\) denotes the moment of inertia about the lateral axis of the vehicle. The distance between the center of gravity and the pitch center is defined by \(h_{\text{GP}}\). The parameters \(l_{\text{S},i}\), \(\text{D}_{i,j}\) and \(l_{\text{St},i}\) represent the distances between the center of gravity plane and the force application points of the springs, dampers and stabilizers, respectively. Based on the pitch acceleration \(\ddot{\theta}(k)\) at time \(k\), the pitch rate \(\dot{\theta}(k+1)\) and the pitch angle \(\theta(k+1)\) for the time \(k+1\) are likewise determined using the scheme of the semi-implicit Euler method:  
\[\dot{\theta}(k+1)=\dot{\theta}(k)+\ddot{\theta}(k)t_{\text{S}}, \tag{18}\]  
\[\theta(k+1)=\theta(k)+\dot{\theta}(k+1)t_{\text{S}}. \tag{19}\]  
This procedure likewise allows the pitch behavior to be predicted as a function of the manipulated variables.
## 3 Test Description
### Test Method  
Figure 4 shows the experimental method. The servo motor was controlled by a computer and rotated at a specified speed. A transmission shaft drove the sliding block to perform simple harmonic oscillations along the linear guideway. Figure 4b shows the slide motion analysis diagram. The displacement equation of the slider can be expressed as  
\[s=R+L-R\cdot\cos\alpha-L\cdot\cos\beta. \tag{6}\]  
The design value of \(L\) was 300 mm. When \(R\) was 3, 5, 10, 20, and 30 mm, the \(\lambda\) values, where \(\lambda=R/L\), were \(\frac{1}{100}\), \(\frac{1}{\delta 0}\), \(\frac{1}{30}\), \(\frac{1}{15}\), and \(\frac{1}{10}\), respectively. Furthermore, expanding \(\cos\beta=\sqrt{1-\lambda^{2}\cdot\sin^{2}\alpha}\) as a Newtonian binomial, since the \(\lambda\) values were very small and the higher order terms were ignored, the approximate equation of the slider displacement could be expressed as  
\[s=R\bigg{[}(1-\cos\alpha)+\frac{\lambda}{4}(1-\cos 2\alpha)\bigg{]}. \tag{7}\]  
As \(\frac{\lambda}{4}\) is very small, in the following calculation, we only take \(s=R(1-\cos\alpha)\). Before the experiment, each driving wheel was tested to ensure the accuracy of \(R\). Each data segment was measured in the radius order (3, 5, 10, 20, and 30 mm) and frequency order (0.5, 1,..., 10 Hz).  
Several experiments (>50) were carried out in the 20 m \(\times\) 20 m space of the State Key Laboratory of Precision Measuring Technology and Instruments in Tianjin University from March to June 2017. Four transmitting stations were located in front of the testbed to ensure that each receiver could accept signals from every transmitting station. The test apparatus was placed on the marble ground surface to ensure that the oscillation table generated no displacements during the tests. The oscillation frequency was set to 0.5-10 Hz, and the amplitude range was set to 3-30 mm. These measurements were chosen because the natural frequency of the strong rigid structure exceeds 10 Hz. The oscillation frequency at a 30-mm amplitude of oscillation is relatively low.  
The minimum duration of the experiment was set to two minutes, exceeding the duration of events that caused the dynamic displacements, such as earthquakes. However, the low-frequency (\(\leq\)0.5 Hz) segment of the experiment lasted several minutes to obtain the necessary data to achieve significant results.  
Figure 3: Hardware used in the experiments containing the experimental device and the wMPS system. A receiver was installed on top of the sliding block of the oscillation device. The wMPS stations were set in nearby positions.
## 1 Introduction  
Harvest index (HI), i.e., co-efficient of economics, is the ratio of economic yield to the total biomass production above the ground. For grain-producing crops, the \"sink\" capacity of the grains is the basis for the transfer and storage of assimilation products. The efficiency of the transformation and distribution of the assimilation product \"source\" to the seed represents the unobstructed \"flow\" [1, 2]. Harvest index is always used for evaluating the \"flow\" in the \"source-sink-translocation\" theory.  
Rapeseed (_Brassica napus_ L, AACC, 2n = 38) is one of the most important oilseed crops worldwide. With the improvement of varieties and cultivation techniques, the crop harvest index continues to increase. Despite this, the HI of rapeseed is generally between 10% and 30%, which is much lower than that of tobacco (over 60%), rice (50-60%), peanut (50-60%), maize (40-50%), and wheat (40-50%) [3, 4, 5]. The increase in the output of rice, wheat, and barley mainly benefits from the increased harvest index after the Green Revolution [6], so the yield of rapeseed can be enhanced by increasing the HI as well. Although the complexity of HI can be affected by plant structures, various environmental factors, and nutrient distribution [3, 7], we can increase HI by adjusting related factors.  
In recent years, an increasing number of reports of _B. napus_ have been published on the major agronomic traits, such as plant height (PH), siliqua length (SL), thousand-seedweight (STW), branch angle (BA), and so on [8; 9; 10]. Nevertheless, the studies about the harvest index or the relationship with the agronomic traits were denumerable. Nine SNPs were identified to be significantly associated with HI, explaining 3.42% of the phenotypic variance [11]. [PERSON] et al. identified 294 single nucleotide polymorphisms (SNPs) significantly associated with HI and four HI-related traits by GWAS using 520 _B. napus_ accessions, and 33 functional candidate genes were predicted in the intervals [12]. A segregation DH population containing 348 lines from the cross between \"KenC-8\" and \"N53-2\" was used to detect quantitative trait loci (QTL) for HI, seed yield, biomass yield, and PH, and 160 QTL, 163 epistatic loci pairs for the studied traits [2]. [PERSON] et al. used a segregating population of _B. napus_, identified 1904 consensus QTL accounting for 22 traits, and formed a network containing developmental traits, seed quality traits, seed yield, and seed-yield components [13]. These results not only show us the feasibility of GWAS to locate quantitative trait loci (QTL) for complex agronomic traits, but improve our knowledge of interactions among complex traits and their related traits.  
In this study, we focused on 104 core breeding inbred lines of _B. napus_, sequenced by the Bnapus50K array, and cultivated them across three cropping seasons. HI and 13 HI-related traits were measured for GWAS to associate significant SNPs. A trait-SNP network was constructed, and promising candidate genes were forecast. Our study will provide an important basement for the genetic mechanism improving the harvest index of rapeseed.
## 4 Future Prospects for China's Weather Routing Services
### Development of a Weather Routing Service Based on Large-Scale AI Models  
The rapid advancement of artificial intelligence (AI) technology is poised to drive the intelligent transformation of weather routing services. By integrating domain-specific knowledge from ship meteorological navigation services, large-scale AI models (e.g., foundation models adapted for spatio-temporal forecasting, deep learning techniques like Graph Neural Networks or Transformers) can significantly enhance the intelligence level of weather routing systems [50; 51]. The AI-based weather routing service model synthesizes meteorological and oceanographic observations, global numerical weather forecasts, real-time ship AIS (Automatic Identification System) dynamics, and historical voyage databases to construct a spatiotemporal feature fusion framework. This framework establishes a multi-dimensional situational awareness system that integrates global hydmeteorological conditions, ship route monitoring, and historical navigation patterns.  
Furthermore, advanced weather routing systems can incorporate a shipping accident knowledge graph and a natural language processing (NLP) module capable of real-time parsing of maritime notices (e.g., NAVTEX, Notices to Mariners) issued by nations worldwide [52]. By correlating this information with ship navigation data, the model achieves dynamic risk perception and voyage risk early warning, enabling proactive adjustments to ensure maritime safety and operational efficiency. This integrated approach not only optimizes route planning under complex hydmeteorological conditions but also provides a scalable, cost-effective solution for advancing intelligent maritime navigation in the era of AI-driven innovation.  
However, realizing this potential requires overcoming barriers such as ensuring sufficient high-quality training data, addressing model interpretability and reliability concerns, and integrating complex AI models with existing operational systems.
## 1 Introduction and Related Work  
### General Context  
This work concerns the optimization of functions defined over clouds (or sets) of points. The decision variables are sets of points, \(\{\mathbf{x}_{1},\ldots,\mathbf{x}_{n}\}\), where each point is a \(d\)-dimensional vector of continuous numbers, \(\forall\ i=1,\ldots n\), \(\mathbf{x}_{i}\in\mathbb{R}^{d}\). An important property of such sets is that they are invariant with respect to a change in numbering of the points.  
Many functions of practical interest are black-box functions over clouds of points. The example that initiated this work and that will be used as a test problem is the optimization of the layout of a wind farm. The longitude and latitude of the wind turbines is a point; the decision variable is the number and placement of the set of wind turbines. A typical objective is to maximize the average power produced by the wind farm while minimizing the total incurred cost. Such a problem has received a lot of attention lately. For instance, a code to simulate the wind farm and optimize it can be found in [1]. The operations research community has seized the problem of wind-farm layout design [2; 3; 4], installation [5] and operation [6]. There are other problems related to wind-farm design such as cable routing [7]. Here, we focus on the optimization of the positions, which boil down to optimizing sets of points.  
Numerous other problems involve sets of points, such as the positioning of sensors and actuators in a given domain [8], the optimization of an experimental design [9] and optimal uncertainty quantification [10], where critical densities can be seen as set of points, and mixture modeling [11].  
The associated optimization problem is formulated in Equation (1). For each variable \(\{\mathbf{x}_{1},\ldots,\mathbf{x}_{n}\}\) and for each \(i\in\{1,...,n\}\), \(\mathbf{x}_{i}\) varies in a continuous compact domain, \(\mathbf{D}\), of \(\mathbb{R}^{d}\):  
\[\max_{X=\{\mathbf{x}_{1},\ldots,\mathbf{x}_{n}\}}\quad\quad F(X)\;, \tag{1}\] \[n\in\{n_{\min},\ldots,n_{\max}\}\;,\] \[\forall i,\mathbf{x}_{i}\in\mathbf{D}\subset\mathbb{R}^{d}\;.\]  
The number of points is an integer bounded by \(n_{\min}\) and \(n_{\max}\). Two examples of clouds of points are given in Figure 1. They have different sizes and patterns so that there is no obvious method to define a topology over these sets. The nature of these design variables makes the function \(F\) invariant under a permutation of the points in the cloud \(X\). The space of clouds of points is continuous in the sense that each point can move continuously within the domain, but the number of points is an integer, thus a cloud cannot be directly represented as a continuous vector of fixed dimension. In addition, function \(F\) is seen as a black-box in the sense that no information related to its convexity and/or smoothness is assumed. These features of the problem make it hard to rely on standard optimization methods, such as gradient-based algorithms. In this article, we propose and investigate an ad hoc evolutionary algorithm [12; 13].
# Image Interpretability of nSight-1 Nanosatellite Imagery for Remote Sensing Applications
## 2 Datasets and Study Sites  
The nSight-1 images used in this study are comprised of the red, blue, and green spectral bands. With a 64 km swath, nSight-1 images are captured at a ground sampling distance of 32 m [14]. The sensor features a Bayer-pattern filter, which captures colour images in the visible region of the electromagnetic spectrum. Real-time onboard computer processing as shown in Figure 1 includes Bayer pattern demosaicing, RGB colour space conversion, and chroma subsampling and thumbnail downsampling [16]. Further colour optimization is applied to the images. The spectral response for the red, blue, and green bands are shown in Figure 2. Wavelengths longer than 720 nm are not taken due to the NIR-blocking filter onboard the nSight-1 imager.  
The compression imaging workflow for nSight-1 is shown below.  
The following procedures are done using real-time onboard processing: Bayer pattern demosaicing, RGB colour space conversion, chroma subsampling, and thumbnail downsampling while the JPEG compression process is done on the onboard computer [15]. One of the advantages of nSight-1 nanosatellite is that it uses a tasking image acquisition strategy. This entails that the nanosatellite only captures images over targeted areas that have been pre-programmed and does not have a fixed  
Landsat 8 OLI images consist of nine VNIR and SWIR spectral bands at 30 m spatial resolution. The Landsat 8 OLI blue, green, and red spectral bands are strategically designated at 0.452-0.512, 0.533-0.590, and 0.636-0.673 micrometres of the electromagnetic spectrum respectively to capture land cover features such as water, vegetation, soil, and geology. Images used in this study are collected over sites in South Africa, USA, Canada, Botswana, Egypt, Bolivia, and Saudi Arabia. The study sites are characterized by diverse land use and land cover types discernible from nSight-1 nanosatellite images.
## 3 The Proposed Method
### Path Finding from an ESWG and Enhancing Obtained Path Quality  
Once an ESWG is modeled, a path searching algorithm is employed to search a \(P\) from it. In this paper, we used \(A^{*}\) algorithm for computing a \(P\) between \(s\) and \(t\) from an ESWG. The A* is reliable algorithms for extracting a \(P\) of low-cost. The evaluation function utilized by this algorithm is expressed in Equation (25):  
\[f(n)=g(n)+h(n) \tag{25}\]  
In Equation (25), the \(f(n)\) denotes the estimated path cost in total between \(s\) and \(t\) via a node \(n\), the \(g(n)\) denotes the actual distance to reach node \(n\), and the \(h(n)\) is a heuristic function that computes the distance from node \(n\) to \(t\). This algorithm was selected to make \(P\)'s computing process fast. By exploring an ESWG using this algorithm, an abstract \(P\) is found. We consider both length and time of the obtained \(P\) for evaluating its quality. Meanwhile, in some cases, the \(P\) cannot be of minimal length, which needs post-processing to shorten it. We present the working of the \(A^{*}\) algorithm while finding a path between \(s\) and \(t\) in Figure 7.  
In many UAV practical applications, the length of the \(P\) is paramount, and to preserve UAV resources, it should be minimum. To address this issue, we shorten \(P\)'s length by including more nodes in close proximity of it, and refine the sharp turns. The path quality improvements is mainly carried out by determining the adjacent \(P\)'s neighbor nodes, find the proximity between the adjacent neighbor nodes and \(P\) nodes, and in the close proximity of the \(P\), we introduce new nodes with relatively denser resolution and add smooth edges. The reason to add more nodes closely is to retain visibility to improve \(P\) quality. After injecting additional nodes, a \(P\) of good quality is obtained by jointly using the newly added and the \(P\) nodes. This path-refining method has the potential to improve path quality significantly with reduced computing cost.
# Risk-Aware Wireless Positioning in
## 3 Problem Formulation
### Limited Power Budget  
As we consider the mobile user in the marine environment, the user's battery power budget should also be considered. The user's positioning signal frequency should be carefully controlled considering the user's battery power budget. We denote by \(W\) the power consumption of the user's one time transmission of the positioning signal. Then, when the user is in cell \(i\), the user's power consumption becomes \(W\times f_{i}\) per second. Thus, the user's average power consumption per second is equal to the sum of the product of the power consumption per second in cell \(i\) and the probability of belonging to cell \(i\) for all cells is described as follows.  
\[\sum_{i=1}^{N}(p_{i}\times W\times f_{i}). \tag{10}\]  
When \(L\) is the expected operating time in seconds, the product of the average power consumption per second and the desired operating time should not exceed the total energy of the user, denoted by \(E\), that is,  
\[\sum_{i=1}^{N}(p_{i}\times W\times f_{i}\times L)\leq E. \tag{11}\]
# Deep Learning-Based Human Action Recognition with Key-Frames Sampling Using Ranking Methods
## 3 Proposed Methodology
### Architecture of the Proposed Method  
The overall architecture of the proposed system includes two modules, as shown in Figure 2. They are: (1) key-frames sampling using the ranking method and (2) recognition of action using the 3D-CNN model. Initially, the rank values (\(\psi_{1}\), \(\psi_{2}\),..., \(\psi_{n-1}\)) are calculated between the neighboring frames from the raw depth sequence having \(n\) frames using rank metrics (SSIM and CCM). The rank values are then sorted in ascending order and selected \(k\)-ranked frames from \(n\) raw frames. Finally, the sampled frames are sent to a 3D-CNN model to produce class labels as outputs.