esa-sceva/satcom-chunk-collection huggingface dataset:
# E-GTN: Advanced Terrain Sensing Framework for Enhancing Intelligent Decision Making of Excavators
## 5 Results  
### Decision-Making Results  
The excavator's decision-making process is a real-time and multi-channel processing dynamic system, as shown in Figure 5. It involves three primary channels: the terrain  
Figure 4: Model training loss.  
reconstruction channel (Channel 1), the excavator control channel (Channel 2), and the DRL model operation channel (Channel 3).  
Channel 1: This channel employs two LIDAR scanners to capture point cloud data P(t), which are then used to create a 3D reconstruction of the terrain using the algorithm described in Section 4.2. The processing time for terrain reconstruction ranges from 240 ms to 280 ms.  
Channel 2: Operating at a frequency of 10 Hz, this channel executes every 100 ms. At each time step t, it receives the point cloud information P(t) from the terrain reconstruction channel and the current joint information of the excavator, combining them to form the state S(t), which encompasses the immediate conditions of both the environment and the excavator.  
Channel 3: The state S(t) is relayed to the DRL model operation channel where it first undergoes processing by the GridNet, taking 9 ms, followed by decision making through the DRL model, which takes 14 ms. Ultimately, the DRL model outputs an action A(t), a vector containing increments for four joint angles, to guide the excavator's subsequent movement.  
The complete operational results of the E-GTN are depicted in Figure 6. Within the simulated environment, the excavator's bucket has a capacity of 4.5 cubic meters. Under the implementation of our method, coupled with the GridNet's terrain feature extraction, the system achieved an average excavation volume of 3.7 cubic meters per scoop with a standard deviation of 0.27 cubic meters. In contrast, without the feature extraction, the average excavation volume dropped significantly to 0.45 cubic meters per scoop, with a standard deviation of 0.46 cubic meters. These findings underscore the efficacy of integrating GridNet in enhancing the precision of the excavation process, optimizing the excavator's operational capacity, and highlighting the importance of terrain feature extraction in the decision-making process.
###### Abstract
Despite its benefits, US imaging faces challenges such as variability in operator performance and image quality, as well as the need for extensive training to accurately interpret images due to artifacts, noise, and the lack of distinct global anatomical structures [31]. These challenges present opportunities for XAI methods to enhance US image interpretation by providing transparent, understandable insights that aid clinicians in making fast and accurate decisions during clinical procedures. By offering real-time, interpretable feedback in terms of additional information or by highlighting areas of concern on images, XAI may enhance diagnostic accuracy, clinician confidence, and patient trust in AI-driven assessments. While recent studies have started integrating XAI into AI-driven healthcare applications, a noticeable gap in research exists. Despite several reviews exploring XAI methods in general medical imaging applications [11; 14; 32], they lack a specific focus on oncological ultrasound imaging contexts. Moreover, these reviews fail to provide an extensive comparison of the advantages and disadvantages of various XAI methods for clinical use, as well as a comprehensive overview of the evaluation metrics employed. To the best of our knowledge, no review investigates the utilization and comparative effectiveness of XAI methods in oncological ultrasound imaging. This review aims to address this gap by investigating XAI methods for real-time ultrasound imaging data. This review will specifically analyze the role of XAI methods in segmentation, classification, and localization tasks. Our primary objective is to offer an overview of identified XAI methods and assess their qualitative and/or quantitative impact.
## 3 Calculation of Results and Discussion
### Detection of Short-Period Geomagnetic Disturbances, Minute-Resolution Data Processing
Figure 10 shows the processing results for the Paratunka observatory data for the same period, but the processing was sequential up to the storm beginning. The obtained results of processing (Figure 10, operations (8), (9) applied) agree with the results illustrated in Figure 9. That confirms the method efficiency and shows the possibilities of its application for fast detection of short-period geomagnetic disturbances preceding a magnetic storm beginning.
## 4 Design of Decoupled Detection Head for Elliptical Center Sampling
### Loss Function Design  
The losses used in this paper include classification, regression, and intersection over union (IoU) loss. The classification loss \(L_{cls}\) employs a cross-entropy loss function, while the regression loss \(L_{reg}\) uses the Kullback-Leibler divergence (KLD) loss function [33], defined by Equation (15). The KLD loss leverages the ellipse center sampling approach from Section 4.3 to convert a rotated rectangle \((C_{x},C_{y},w,h,\theta)\) into a distance representation of a two-dimensional elliptical Gaussian distribution \((\mu,e)\). This method addresses the inconsistency between model evaluation and loss, making it more suitable for measuring the distribution in rotated detection. Although the KLD loss is parametrically semi-coupled, the introduction of \(\epsilon_{t}^{-1}\) enables better optimization of the center point coordinate regression, leading to more accurate target detection coordinates.  
\[L_{reg}=\frac{1}{2}(\mu_{p}-\mu_{t})^{T}e_{p}^{-1}(\mu_{p}-\mu_{t})+\frac{1}{ 2}T_{r}(\epsilon_{t}^{-1}\epsilon_{p})+\frac{1}{2}ln\frac{|\epsilon_{t}|}{| \epsilon_{p}|}-1 \tag{15}\]  
In the formulas, the subscripts \(p\) and \(t\) represent the model predictions and labels, respectively. Figure 12 demonstrates that the KLD loss function outperforms the smooth L1 loss function.
# Enhancing Vibration Analysis in Hydraulic Presses: A Case Study Evaluation
###### Abstract  
Vibration monitoring is essential for maintaining the optimal performance and reliability of industrial machinery, which experiences dynamic forces and vibrations during operation. This study delved into a comprehensive analysis of vibration monitoring in hydraulic presses, utilizing advanced measurement systems equipped with accelerometers. The proposed system included a three-axis accelerometer, data acquisition unit, and dedicated measurement software, facilitating the precise monitoring and analysis of vibrations. The influence of the sensor placement and sampling frequency on the measurement results was examined. A time and frequency analysis of the recorded measurements was performed. The results demonstrated the correlation between vibration levels and various production parameters, such as the number of parts simultaneously produced and press pressure. These findings underscore the potential for vibration measurement as a pivotal component in controlling production parameter settings.  
vibration monitoring; hydraulic presses; measurement system; proactive maintenance +
Footnote †: journal: Applied Sciences
# Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning
## 4 Clinical Dataset
### Missing Data  
Missing data are a common challenge in medical research, especially in cardiovascular health studies, where participant withdrawal can reduce sample sizes. Missingness refers to absent information, which can introduce bias and reduce analysis efficiency. Understanding the reasons behind missing data, known as missingness mechanisms, is crucial for accurate analyses. There are three main mechanisms: MCAR (missing completely at random), MAR (missing at random), and MNAR (missing not at random) [31; 32].  
To address missing data, various methods are available, each with its advantages and limitations. These include Complete Case Analysis, Mean or Median Imputation, Regression Imputation, Multiple Imputation (MI), and others. MI is favored for its ability to account for uncertainty by creating multiple datasets with varied imputed values.  
In this research, Multiple Imputation by Chained Equations (MICE) was chosen due to its robustness and ability to provide reliable results in the presence of missing data. The imputation principle involves estimating or replacing missing values based on statistical techniques to minimize bias and preserve the dataset's statistical properties. Multiple Imputation by Chained Equations (MICE) is an iterative method where missing values are imputed by modeling each incomplete variable conditional on the observed values of other variables. This approach generates multiple imputed datasets, which are analyzed separately and then combined to account for imputation uncertainty [33]. The choice of imputation method depends on factors like data distribution and relationships. For categorical variables with no discernible relationships, MICE is suitable. It imputes missing values by modeling each variable with missing data as a function of other variables, iteratively updating imputed values until convergence is reached.
## 2 Navigation System
### Imu  
IMU stands for inertial measurement unit and is one of the most important parts of the drone for navigation since it is able to manage translation and vertical and rotational movements. These latter movements are called pitch, yaw, and roll; specifically, the pitch is the upward or downward movement of the nose about an axis from wing to wing, yaw is the leftward or rightward movement of the nose about an axis that goes up and down, and finally, the roll is the rotation about an axis from the nose to the tail [43]. While the identification of these axes and their rotation is simple in the case of fixed-wing UAVs (Figure 3a,c), it is somewhat more complex in the case of multicopters due to their geometry (Figure 3b,c). In addition to measuring trim (roll, pitch, and yaw), the IMU is able to measure velocity, changes in altitude, and gravitational forces acting on a UAV. Indeed, the IMU typically contains three orthogonal rate-gyroscopes and three orthogonal accelerometers measuring angular velocity and linear acceleration [44]. Recent advances in the construction of MEMS (microelectromechanical systems) devices have made it possible to produce inertial navigation systems that are small and lightweight and, consequently, can be efficiently adapted to UAV platforms [45].  
Figure 3: Identification of the three axes and three rotations on a fixed-wing UAV (**a**), on a quadcopter (**b**), and schematisation of axes and rotations (**c**).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Strategy** & **Needs Base?** & **Uses Carrier-Phase?** & **Orbits and Clocks** & **Accuracy** \\ \hline SPP & no & no & broadcast & \textless{}5 m \\ PPP & no & yes & precise & cm (multi-freq), dm (single-freq) \\ RTK & yes & yes & broadcast & cm (multi-freq), dm (single-freq) \\ PPK & yes & yes & broadcast & cm (multi-freq), dm (single-freq) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Features of several GNSS strategies [42].
# Chaves Thermal Spring Water Impact on Skin Health: Potential Cosmetic Application
## 2 Materials and Methods
### Skin Enzyme Inhibition
#### 2.3.2 Collagen Inhibition Assay  
The colorimetric metalloproteinases 1 (MMP1) inhibitor screening kit (abcam, Cambridge, UK) was performed according to the manufacturer's instructions. Briefly, the 0.75 U/L MMP1 enzyme was introduced to a flat-bottom 96-well microplate (Thermo Scientific, MA, USA), followed by the addition of Chaves thermal spring water samples, vitamin C (benchmark), N-Isobutyl-N-(4-methoxyphenylylsulfonyl)gly
# The Refined Simulation Methods of Fluctuating Wind and Aerodynamic Forces Relative to Moving Trains
###### Abstract  
This study proposes a framework to accurately simulate the fluctuating wind and aerodynamic forces relative to moving trains. It provides a novel method for calculating the power spectra of the fluctuating wind relative to moving trains and the time-frequency transform for admittance function to simulate unsteady aerodynamic forces. The proposed framework demonstrates broader applicability than existing methods, particularly in complex wind environments characterized by inhomogeneity and non-stationarity. Moreover, it presents a novel method to consider the phase of admittance function in simulating aerodynamic forces relative to moving trains. Considering the wind characteristics of inhomogeneity and non-stationarity, fluctuating wind and unsteady aerodynamic forces relative to moving trains are simulated for different wind environments. The results show that fluctuating wind and aerodynamic forces relative to moving trains are more prone to be affected by inhomogeneous characteristics in a short time. The admittance function has a 'filtering' effect, which can filter out the high-frequency components and decrease the peak values of the aerodynamic forces. Additionally, the phase of the admittance function presents the 'delay' between different aerodynamic forces. Under different wind environments, the fluctuating wind and unsteady aerodynamic forces relative to moving trains are significantly different. To accurately analyze the operational safety of trains under strong wind environments, it is essential to simulate the fluctuating wind and aerodynamic forces relative to moving trains accurately.  
moving trains; fluctuating wind; aerodynamic forces; admittance function; inhomogeneous; non-stationary Article
## 5 Experiments  
To provide a comprehensive evaluation of the algorithm performance, we conducted a series of simulation experiments that were divided into two main parts. The image classification models utilized encompass baseline ones like AlexNet [13] and ResNet50 [14], and also comprised the latest MobileNet [16], TFA-SCNN [24], STFT-CDSC [28] and VViT [29]. Meanwhile, the IQ input baseline model was CNN-IQ [7]. Additionally, we took into account the latest IQ-based model AWN [20]. The first part offers insights into the key numerical metrics of the various deep learning models, covering the sample processing time, number of parameters, and memory footprint of the models. At the same time, ablation experiments were performed to verify the validity of the modules of the CDRNN model. Figure 4 and Table 2 detail the results of the first part of the simulation, mainly in terms of module validity as well as modelling efficiency and resource usage. The second section is to compare the performance difference between different image classification models and our proposed deep learning model with the IQ input model. Figures 5-10 and Table 3 present the simulation results, focusing on the performance of the models in terms of STFT-transformed window length effect, classification accuracy and adaptability to the electromagnetic environment. We define classification accuracy as the ratio of the number of correct predictions to the total number of input samples. The last section compares the recognition performance of the models for different channel conditions.  
Through these exhaustive experiments and data presentations, we were able to comprehensively evaluate and demonstrate the comprehensive performance of the proposed algorithm. All experiments were simulated on Intel(R) Xeon(R) Silver 4210R and NVIDIA GeForce RTX 3080 Ti using Pytorch.
## 2 Materials and Methods
### Dataset and Data Processing
#### Land Cover Data  
A 1-km spatial resolution land cover product, MOD12Q1, was applied in this study to analyze NPP and its variation across vegetation types. The MOD12Q1 land cover dataset which was extensively applied to monitor land use and land cover change [44], is mainly based on the International Geosphere-Biosphere program (IGBP) classification system that obtains a classification algorithm of the decision tree and artificial neural network [45]. The MOD12Q1 land cover classes were further reclassified into seven major land cover classes (Figure S1) in the present study: evergreen needle-leaf forest (ENF), evergreen broadleaf forest (EBF), deciduous needle-leaf forest (DNF), deciduous broadleaf forest (DBF), farmland grassland and meadow (GM), and shrubs. However, the data only reflected the land cover classifications and could not consider changes in land cover that occurred over our study period. It was estimated that vegetation changes might be relatively stable over a short time period of approximately 10 years at a regional or global scale [42].
# Research on Double-Arc Cutting Tool Design and Cutting Performance
## 3 Fabrications and Detection of DACT
### Experimental Results and Conclusions
#### 4.2.2 Milling of the Ruled Surface  
The surface textures of the machined ruled surface are shown in Figure 24. The surface texture varies with the selected position of the ruled surface.  
The surface textures of the ruled surface machined by the DACT and BEM when selecting the position with an inclination angle of approximately 10\({}^{\circ}\) are shown in Figures 24a and 24b, respectively.  
The surface textures of the ruled surface machined by the DACT and BEM when selecting the position with an inclination angle of 0\({}^{\circ}\) are shown in Figures 24c and 24d, respectively.  
The surface textures of the ruled surface machined by the DACT and BEM when selecting the position with an inclination angle of approximately 20\({}^{\circ}\) are shown in Figures 24e and 24f, respectively.  
Figure 24: Comparison of workpiece surface between DACT and BEM: (**a**) surface machined by DACT with inclination angle of approximately 10\({}^{\circ}\); (**b**) surface machined by BEM with inclination angle of approximately 10\({}^{\circ}\); (**c**) surface machined by DACT with inclination angle of 0\({}^{\circ}\); (**d**) surface machined by BEM with inclination angle of 0\({}^{\circ}\); (**e**) surface machined by DACT with inclination angle of approximately 20\({}^{\circ}\); (**f**) surface machined by BEM with inclination angle of approximately 20\({}^{\circ}\).  
Through numerical analysis, the variance, the standard deviation, and the range of the cross-section profile curve data taken from the ruled surface machined by the DACT is 58.0884, 7.6216, and 29.1406, respectively. Meanwhile, the variance, the standard deviation, and the range of the cross-section profile curve data taken from the ruled surface machined by the BEM is 92.7146, 9.6288, and 31.9336, respectively.  
It is obvious that the variance, the standard deviation, and the range of the cross-section profile curve data taken from the ruled surface machined by the DACT are always smaller than for the BEM; thus, better processing quality can be obtained when machining with the DACT.
## 2 Governing Equations and Methods  
### Isotropic Elasticity and Plane-Strain Approximation  
The plane-strain approximation pertaining to in-plane elastic displacement \(\mathbf{u}=(u_{1},u_{2})^{\mathrm{T}}\) results in the following partial differential equation (Lame system [31])  
\[\mu\
abla^{2}\mathbf{u}+(\lambda+\mu)\
abla\
abla\cdot\mathbf{u}+\mathbf{F}= \rho\frac{\partial^{2}}{\partial t^{2}}\mathbf{u}, \tag{1}\]where \(\mu\) and \(\lambda\) are the Lame parameters, \(\mathbf{F}\) is an external in-plane body force, \(\
abla\) is the gradient in Cartesian coordinates in two-dimensional (2D) space, \(\rho\) is the density, \(\mathbf{a}\cdot\mathbf{b}\) is the scalar product of two vectors \(\mathbf{a},\mathbf{b}\in\mathbb{R}^{2}\), and \(t\) represents time.  
By introducing \(\mathbf{u}=\mathbf{u}_{0}\exp(\mathbf{i}\kappa\cdot\mathbf{x}-\mathrm{i} \omega t))\) in Equation (1), and in the absence of body forces (\(\mathbf{F}=\mathbf{0}\)), a characteristic equation for the amplitudes \(\mathbf{u}_{0}\) can be derived. The notations \(\kappa\) and \(\omega\) are used to represent the wave vector, and radian frequency, respectively. Such characteristic eigenvalue problems have nontrivial solutions if and only if  
\[\omega/\kappa=c_{\mathrm{s,p}}, \tag{2}\]  
where \(c_{s}=\sqrt{\mu/\rho}\) and \(c_{\mathrm{p}}=\sqrt{(\lambda+2\mu)/\rho}\), are shear and compression wave speed, respectively, and \(\kappa=|\kappa|\).
## 6 Conclusions and Future Works  
This paper has comprehensively addressed the joint dynamic task offloading and resource scheduling problem in LEO (LEO) satellite edge computing networks. Our findings underscore the importance of leveraging both LEO and GEO satellites in future network designs, as LEO satellites can provide both computational resources and efficient data transmission, enhancing overall network performance. The proposed system model incorporates both data service transmission and computational task offloading, framed as a long-term cost function minimization problem with constraints. Key contributions include the development of a priority-based policy adjustment algorithm to handle transmission scheduling conflicts and a DQN-based algorithm for dynamic task offloading and computation scheduling. These methods are integrated into a joint scheduling strategy that optimizes overall system performance. Simulation results demonstrate significant improvements in average system cost, queue length, energy consumption, and task completion rate compared to baseline strategies, highlighting the strategy's effectiveness and efficiency. Future work will extend the framework to more complex network scenarios and explore the integration of machine learning with traditional optimization methods to further enhance performance.  
In future research, we intend to investigate more advanced machine learning methods that can be combined with traditional optimization frameworks to further enhance resource allocation and interference management in heterogeneous satellite networks. For example, integrating deep reinforcement learning (DRL) techniques with our ADMM-based solution can help the network adapt to rapidly changing channel conditions and user demands by continuously learning optimal actions from environmental feedback. Similarly, multi-agent RL can be employed to coordinate decisions across multiple network entities (e.g., LEO, GEO satellites, and edge nodes), potentially accelerating convergence and improving overall system performance. Beyond RL, leveraging supervised or unsupervised learning methods for traffic prediction, node clustering, or link reliability assessment could also complement our optimization models, providing richer insights into network dynamics. These hybrid approaches--blending the reliability of mathematical optimization with the adaptability of machine learning--present promising avenues for future enhancement of resource management strategies in dual-layer satellite networks.  
Conceptualization, J.L. and R.C.; methodology, J.L. and T.Z.; software, T.Z.; formal analysis, J.L.; writing--original draft preparation, J.L.; writing--review and editing, R.C. and C.L.; project administration, C.L.; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Innovation Key R&D Program of Chongqing grant number CSTB2023 TIAD-STX0025.  
The data that support the findings of this study are available upon reasonable request from the authors.  
The authors declare no conflicts of interest.