esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The quantity and quality of cropland are the key to ensuring the sustainable development of national agriculture. Remote sensing technology can accurately and timely detect the surface information, and objectively reflect the state and changes of the ground objects. Using high-resolution remote sensing images to accurately extract cropland is the basic task of precision agriculture. The traditional model of cropland semantic segmentation based on the deep learning network is to down-sample high-resolution feature maps to low resolution, and then restore from low-resolution feature maps to high-resolution ideas; that is, obtain low-resolution feature maps through a network, and then recover to high resolution by up-sampling or deconvolution. This will bring about the loss of features, and the segmented image will be more fragmented, without very clear and smooth boundaries. A new methodology for the effective and accurate semantic segmentation cropland of high spatial resolution remote sensing images is presented in this paper. First, a multi-temporal sub-meter cropland sample dataset is automatically constructed based on the prior result data. Then, a fully convolutional neural network combined with contextual feature representation (HRNet-CFR) is improved to complete the extraction of cropland. Finally, the initial semantic segmentation results are optimized by the morphological post-processing approach, and the broken spots are ablated to obtain the internal homogeneous cropland. The proposed method has been validated on the Jilin-1 data and Gaofen Image Dataset (GID) public datasets, and the experimental results demonstrate that it outperforms the state-of-the-art method in cropland extraction accuracy. We selected the comparison of Deeplabv3+ and UPerNet methods in GID. The overall accuracy of our approach is 92.03%, which is 3.4% higher than Deeplabv3+ and 5.12% higher than UperNet.  
high-resolution remote sensing image; contextual features; fully convolutional neural network; cropland extraction; morphological post-processing
###### Abstract  
Biomass of microalgae and the components contained in their cells can be used for the production of heat, electricity, and biofuels. The aim of the presented study was to determine the optimal conditions that will be the most favorable for the production of large amounts of microalgae biomass intended for energy purposes. The study analyzed the effect of the type of lighting, the time of lighting culture, and the pH of the culture medium on the growth of _Chlorella vulgaris_ biomass. The experiment was carried out in vertical tube photobioreactors in three photoperiods: 12/12, 18/6, and 24/0 h (light/dark). Two types of lighting were used in the work: high-pressure sodium light and light-emitting diode. The increase in biomass was determined by the gravimetric method, by the spectrophotometric method on the basis of chlorophyll a contained in the microalgae cells. The number of microalgae cells was also determined with the use of a hemocytometer. The optimal conditions for the production of biomass were recorded at a neutral pH, illuminating the cultures for 18 h a day. The obtained results were 546 \(\pm\) 7.88 mg\(\cdot\)L\({}^{-1}\) dry weight under sodium lighting and 543 \(\pm\) 1.92 mg\(\cdot\)L\({}^{-1}\) dry weight under light-emitting diode, with maximum biomass productivity of 27.08 \(\pm\) 7.80 and 25.00 \(\pm\) 5.1 mg\(\cdot\)L\({}^{-1}\).d\({}^{-1}\), respectively. The maximum content of chlorophyll a in cells was determined in the 12/12 h cycle and pH 6 (136 \(\pm\) 14.13 mg\(\cdot\)m\({}^{-3}\)) under light-emitting diode and 18/6 h, pH 7 (135 \(\pm\) 6.17 mg\(\cdot\)m\({}^{-3}\)) under sodium light, with maximum productivity of 26.34 \(\pm\) 2.01 mg\(\cdot\)m\({}^{-3}\).d\({}^{-1}\) (light-emitting diode) and 24.21 \(\pm\) 8.89 mg\(\cdot\)m\({}^{-3}\).d\({}^{-1}\) (sodium light). The largest number of microalgae cells (2.1 \(\times\) 10\({}^{6}\)) was obtained at pH 7 and photoperiod of 18/6 h under sodium light, and 12/12 h under light-emitting diode. Based on the results, it can be concluded that the determination of the optimal parameters for the growth and development of microalgae determines the production of their biomass, and such research should be carried out before starting the large-scale production process. In quantifying the biomass during cultivation, it is advantageous to use direct measurement methods.  
photobioreactor; microalgal biomass; algae cultivation conditions; optimization Article
# Heart Failure Prediction Based on Bootstrap Sampling and Weighted Fusion LightGBM Model
## 5 Experimental Results and Analysis
### Comparative Analysis of Model Effects
#### 5.2.3 Experiment and Comparison of Fusion Model
In addition, the explanatory ability of the model has also decreased. Compared with some simple basic models, such as decision trees, the structure and decision-making process of the fusion model are more complicated, and it is difficult to directly explain the prediction results. In some fields that require a high explanatory ability of the model, such as medical decision making, financial risk assessment, etc., this may affect the credibility and application acceptance of the model. It is also very difficult to adjust parameters. The fusion model involves the parameters of several basic models as well as the weight parameters during the fusion process, and its optimization procedure is relatively intricate. A greater amount of time and computational resources are required to identify the optimal parameter combination so as to attain the best possible performance. The performance of the fusion model depends largely on the basic model selected and its performance. If there is a big deviation or variance in the basic model itself, it will be difficult for the fusion model to achieve the desired effect. The model of this paper is constructed by a complex fusion strategy, which combines the advantages of multiple sub-models and different parameter configurations of the same model. This fusion method can reduce the fluctuation of model performance to a certain extent. The fusion model can maintain a relatively stable performance as far as possible under different data distribution and sample conditions. This makes it reliable in the actual heart disease diagnosis scene, and its performance will not be greatly reduced due to a slight fluctuation in data. Because of the integration of sub-models and various parameter configurations, the model in this paper often has better generalization ability when facing new data. It will not rely too much on a specific model structure or parameter setting, and it can better adapt to new and unprecedented data of heart patients. In practical applications, when dealing with new case data, its data distribution may deviate slightly from that of the training data. However, the model presented in this paper is more likely to make accurate classification predictions, unlike some single models that tend to experience a decline in performance when applied to new data. Overall, the fusion model demonstrates remarkable advantages in performance metrics, particularly in terms of recall and the F1 score. However, it also has some overfitting issues and confronts challenges like high computational complexity, weak interpretability, and the difficulty of parameter adjustment. In practical applications, it is necessary to weigh the advantages and disadvantages of the fusion model according to specific needs and scenarios and then decide whether to adopt it.
# Active Optics and Aberration Correction Technology for Sparse Aperture Segmented Mirrors
###### Abstract  
Active optics and aberration correction techniques for sparse aperture segmented mirrors are studied. A finite element model of the sparse aperture segmented mirror was established, and a multi-aperture aberration polynomial was derived. According to the hard spot theorem, a co-phase maintenance method based on the change of the edge sensor position in the conventional mode is derived. And a co-phase maintenance method based on the change of the aberration of the segmented mirror surface without the participation of the edge sensor is proposed. The method can correct aberrations of the segmented mirror surface, which are caused by the rigid body displacement along the horizontal direction of the segments. This method can reduce the RMS of the segmented mirror surface to 2.2 nm. The correction principle of the Warping Harness (WI) technique is derived. For the problems of tedious steps and a small number of target aberrations, the correction method is proposed to directly target the aberrations of the segmented mirrors, which is simple and has a wider range of target aberrations. Using this method, the amplitude of each aberration of the stitched mirror is corrected to below \(10^{-4}\)nm. It is also verified that combining the generalized ridge estimation method and the differential evolution algorithm can effectively solve the correction quantity. Finally, it is verified that the SiC material can effectively improve the adaptability of the segmented mirror to gravity load by reducing the mirror's weight.  
segmented telescope; active optical co-phase maintenance; warping harness aberration correction technology 20231340633
# Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
## 4 Materials and Methods  
### Modeling Choices  
#### 4.1.1 Maze Size and Complexity  
As stated above, we model a collection of mazes with different layouts, sizes and complexities. To simplify the computation and implementation of the system, the mazes were discretized using a grid method similar to the one in [72]. The maze is decomposed into a grid, where the unit grid size is the same as the agent size, thereby making the step size equal to a unit grid. This discretization helps visualize the system as a matrix, which makes computation of signal reception, agent localization and move generation easier. Naturally, future work would address the situations in which the size of the agent and the size of the grid unit are not equal. Figure 11 shows one sample of each of the five complexities (listed in Table 3) of the mazes used in the tests.  
For each of the five types of mazes, 10 different layouts were randomly generated. Table 3 summarizes these maze sizes and provides a comparison of the respective complexities. Here, we rely on a simple technique of counting the number of 90 degree turns an agents can make at the corners in the maze as a measure of the maze's complexity.  
Figure 11: (**a**–**e**) show the complexities of the 5 sample mazes (henceforth: M1-M5) referenced in this paper, cf. Table 3 for a numerical comparison. These are provided as examples. In total, 10 different layouts were generated for each complexity listed in Table 3 to further gauge the affect of randomness on the results. Maze (**b**) was previously used in Figure 4. The last panel, (**f**), illustrates how we calculate maze complexity, using the example of maze (**a**): the complexity is the number of 90 degree turns an agent can make in the maze, cf. (**f**).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Dimensions** & **Complexity** \\ \hline Maze 1 (M1) & 27 \(\times\) 27 & 22 \\ Maze 2 (M2) & 39 \(\times\) 39 & 47 \\ Maze 3 (M3) & 51 \(\times\) 51 & 81 \\ Maze 4 (M4) & 63 \(\times\) 63 & 129 \\ Maze 5 (M5) & 75 \(\times\) 75 & 210 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dimensions and average complexities of the 5 maze types used in simulations; see Figure 11.
## 4 Marker Tracking
### Marker Tracking with Improved Kalman Filter
#### 4.2.1 Kinematics Description of Fish  
The general model of freshwater fish is in accordance with the push model of the carangidae. It is Illustrated by the example of bream, the body structure of which is shown in Figure 6. Along the longitudinal axis of body, the bream can be divided into three parts: the head, the trunk and the tail. When swimming, the head and trunk swing is small, and the propulsion movement is completed mainly by the tail.  
According to [PERSON]'s experiment [23], the propulsion movement consists of two parts: the body's fluctuation and the caudal fin translational swing. The movement of caudal fin is shown in Figure 7 [24].  
Figure 6: Description of bream body structure and shape.  
Figure 7: Caudal fins trajectories of carangidae.  
Thus, the kinetic model can be established. If we consider the longitudinal direction of fish body as the \(X\)-axis and the lateral direction of body as the \(Y\)-axis, the body fluctuations can be described as:  
\[y_{b}(x,t)=\frac{A}{2}\sin(\omega t-kx)=\left(c_{0}+c_{1}x+c_{2}x^{2}\right)\sin (\omega t-kx) \tag{22}\]  
where \(x\) is the longitudinal displacement of the body, \(A\) is the amplitude of the caudal fin movement, \(y_{b}(x,t)\) is the lateral displacement of body fluctuation of position \(x\) at time \(t\), and \(\omega\), \(k\) are the wave angular frequency and wave number of body, respectively.  
The swing movement of caudal fins can be described as:  
\[\left\{\begin{array}{l}y_{cf}(t)=H\sin(\omega t-kL_{b})\\ \theta(t)=\frac{[\tan^{-1}(H\omega/u)-a_{mm}]}{\sin\varphi}\sin(\omega t-kL_{b }-\varphi)\end{array}\right. \tag{23}\]  
where \(L_{b}\), \(\alpha\) and \(\varphi\) are, respectively, the length of the fish, the strike angle, that is, the included angle between the swing axis and the center line of caudal fin, and the translational swing phase.
# Two-Dimensional Human Pose Estimation with Deep Learning: A Review
## 3 Evaluation Metrics and Benchmark Datasets
### Benchmark Datasets  
#### 3.2.1 Max Planck Institute for Informatics (MPII)  
The MPII dataset [46] is a widely recognized benchmark in human pose estimation and is extensively used to evaluate the performance of state-of-the-art algorithms. It consists of approximately 25,000 images covering over 40,000 human instances, each annotated with precise keypoint locations. All images were systematically collected based on a predefined taxonomy of daily human activities, encompassing 410 distinct activity categories. Each image is labeled with an activity category and extracted from YouTube videos, with adjacent unlabeled frames included to support research on temporal modeling and contextual understanding. The test set provides richer annotations, including body part occlusion states and 3D orientation information for the torso and head, thereby enabling advanced tasks beyond 2D pose estimation. Figure 5 displays representative examples from the MPII Human Pose dataset.
## 2 Methodology
### Scenarios  
The type of environment, vehicle speed, and road traffic density determine the propagation characteristics of the vehicle channel. Traffic density and vehicle speed are usually higher in suburban environments. These conditions allowed the selection of the most suitable areas for the measurement campaign to characterize the path loss propagation. A series of avenues and ring roads were found, forming a concentric ring around the city of Valencia, which, according to its characteristics, was defined as a suburban environment and is referred to as scenario 3. Figure 4 illustrates the routes taken in this scenario with a yellow line. Moving inland in the concentric rings, a clearly urban environment with a high road traffic density is presented and referred to as scenario 1. The routes taken are shown with a red line in Figure 4. Within this urban environment, some areas with particular and interesting characteristics were selected. Thus, we analyzed the area of the old town of the city, where narrow streets without a defined shape meet small squares, abrupt intersections, and cobbled pedestrian streets with a large absence of pedestrians. We also utilized a special area within this old town, such as the Plaza del Ayuntamiento. Figure 4 shows in green the routes taken in these urban environments with low road traffic density, which are referred to as scenario 2.  
Scenario 1: This is an urban environment with high traffic density, with an average of 44,200 vehicles/24 h. Two avenues in the city of Valencia were chosen for the analysis, namely Avenida del Puerto (one-way traffic) and Avenida Blasco Ibanez (two-way traffic), with four and five lanes in each direction, respectively. Figure 5 shows a view of the measurement scenario.  
Figure 3: Tx/Rx vehicles together with on-board equipment at 700 MHz and 5.9 GHz.  
Scenario 2: This is the urban center of the city of Valencia, the old town, with low traffic density at an average of 7500 vehicles/24 h and with narrow streets and one-way traffic. The streets are approximately 10 meters wide, including, in some cases, parking lots and sidewalks on both sides of the street. Figure 4(b) shows a view of the measurement scenario.  
Scenario 3: This scenario is the Ronda Norte in the city of Valencia, Spain, with an average intensity of 71,200 vehicles/24 h, with three and four lanes of traffic in each direction, with wide open spaces on both sides alternating with buildings close to the roadway and medium-height trees along several sections of the avenue. Figure 4(c) shows a view of the measurement scenario.  
It is relevant to note that the measurements were carried out at 700 MHz and 5.9 GHz (DSRC band), with the Tx and Rx vehicles traveling in the same direction several times over the measurement scenario on weekdays between 10:00 and 13:00. Measurements were also carried out under normal driving conditions, with alternations of LOS and NLOS between the Tx vehicle and the Rx vehicle.
# Monitoring Forest Phenology in a Changing World
## 3 Emerging Technological Advancements
### LiDAR  
Light Detection And Ranging (LiDAR) is beginning to be applied in phenology studies [202], along with its more widespread uses in mapping above ground biomass [203] and forest carbon stocks [204]. LiDAR data can estimate leaf area index (LAI) [205], thus repeated measures could detect phenological events like leaf flush and senescence at the scale of individual tree crowns. This method has potential to provide detailed information about the intensity of phenological events that is unavailable through other methods. For example, LiDAR could potentially be used to quantify the total biomass of new (or old) leaves that have been flushed (or lost) per unit time.  
Laser scanning equipment can be placed on-board aircraft [206], UAVs [207; 208] or conducted from the ground [202], but the cost of LiDAR equipment (~US$100,000) currently leaves it out of reach for most studies as a method for monitoring. We expect, instead, that it will be most usefully deployed in environments such as tropical forests where phenological events vary in intensity at the scale of individual tree crowns.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 9 Research Gaps and Opportunities
### Opportunities for Future Research
#### 9.2.5 Addressing Dynamic Environments  
Dynamic underwater environments, with moving objects such as marine life or floating debris, pose significant challenges for SLAM systems. Future research should focus on the following aspects:* **Dynamic Object Filtering:** developing deep learning-based object tracking methods, such as attention-based transformers, to detect and filter dynamic objects from SLAM computations, preventing map corruption and maintaining accuracy [168; 182];
* **Environmental Awareness:** enhancing SLAM systems with environmental awareness capabilities to adapt to real-time changes in conditions such as water clarity and light levels;
* **Predictive Models:** integrating predictive models of underwater environments that can anticipate and compensate for dynamic changes, improving overall robustness.
# Development and Simulation of Motion Control System for Small Satellites Formation
## 3 Problem Statement  
The following problem is posed. Let the master satellite move freely along its trajectory around the Earth, and let the slave satellite move along a given trajectory in relative coordinates \(x-y-z\). Let the trajectory be given by the functions:  
\[\mathbf{x}_{d}(t)=\left[\begin{array}{c}x_{d}(t)\\ y_{d}(t)\\ z_{d}(t)\end{array}\right]\quad\dot{\mathbf{x}}(\mathbf{t})_{d}=\left[ \begin{array}{c}\dot{x}_{d}(t)\\ \dot{y}_{d}(t)\\ \dot{z}_{d}(t)\end{array}\right]\quad\ddot{\mathbf{x}}_{d}(t)=\left[ \begin{array}{c}\dot{x}_{d}(t)\\ \dot{y}_{d}(t)\\ \dot{z}_{d}(t)\end{array}\right]. \tag{10}\]  
Let us introduce the following error vector:  
\[\mathbf{e}\triangleq\mathbf{x}-\mathbf{x}_{d}. \tag{11}\] \[\mathbf{e}=\left[\begin{array}{cccccc}e_{x}&e_{y}&e_{z}&\dot{e }_{x}&\dot{e}_{y}&\dot{e}_{z}\end{array}\right]^{\mathrm{T}}.\]  
The following control aim is taken:  
\[\|\mathbf{e}\|\to 0\;\;\mathrm{as}\;\;t\rightarrow\infty \tag{12}\]  
where \(\|\cdot\|\) corresponds to the standard \(L^{2}\) norm. Let \(\mathbf{x}_{\mathrm{d}}(t)\) be a stable trajectory for (9). Using (11), let us make the variable substitution in (9) [31; 32], which has the structure described by (4). This leads to the following expression  
\[\dot{\mathbf{e}}=\mathbf{A}(\mathbf{e}+\mathbf{x}_{\mathrm{d}},t)\mathbf{e}+ \mathbf{B}(\mathbf{u}-\ddot{\mathbf{x}}_{\mathrm{d}}\,) \tag{13}\]  
Let us make the changes: \(\ddot{\mathbf{A}}(\mathbf{e},t)=\mathbf{A}(\mathbf{e}+\mathbf{x}_{\mathrm{d}},t)\) and \(\ddot{\mathbf{u}}=\mathbf{u}-\ddot{\mathbf{x}}_{d}\). Note that the structure of the matrix \(\mathbf{A}\) in (9) remains the same. Then system (13) takes the form:  
\[\dot{\mathbf{e}}=\ddot{\mathbf{A}}(\mathbf{e},t)\mathbf{e}+\mathbf{B}\ddot{ \mathbf{u}} \tag{14}\]
# Error Recovery in the Time-Triggered Paradigm with FTT-CAN
## 5 Resource Optimization Process  
This section presents methods to compute the worst-case response time (WCRT) of messages, taking into consideration the interference of the server and an algorithm for minimizing the size of the synchronous window, complementing the analysis presented in the previous section.