esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Validation of Surrogated MIMO-NARX Model  
To explore the uncertainty propagation in a nonlinear dynamic system with MDOF, a total of 300 validation Monte Carlo simulations with different ground motions and structural properties are performed in this section by traditional dynamic history analysis using the CR algorithm [38]. The MIMO-NARX models obtained from the previous section are adopted to capture the nonlinear dynamic behavior while the corresponding coefficients are calibrated by the meta-models for each set of uncertainty variable values. Finally, structural responses are compared between the MIMO-NARX model prediction and MCS to evaluate its effectiveness.  
Figure 10: Meta-models to surrogate MIMO-NARX coefficients.  
An error evaluation criterion is introduced to measure the fitness between the predicted and original value and expressed as:  
\[\text{Fitness}=\left(1-\sqrt{\frac{\sum(x(i)-x^{s}(i))^{2}}{\sum(x(i)-\overline{x })^{2}}}\right)\times 100\% \tag{35}\]  
where \(x(i)\) represent the original response from MCS with mean \(\overline{x}\); and \(x^{s}(i)\) represent the predicted response by the surrogated MIMO-NARX model.
# Inverse Kinematics of a Serial Manipulator with a Free Joint for Aerial Manipulation
## 4 Simulation Setup
### Load-Picking Task  
The second scenario simulates the UAM picking a mass of 0.25 kg from the ground. The task was divided into the following stages: first, the end-effector moves along a line with a displacement of 10 cm in the horizontal direction and 10 cm in the vertical direction in 6 s; then, the end-effector maintains its position for 2 s and the load is grasped at \(t=t_{grasp}=8\) s; from \(t=8\) s to \(t=22\) s, the end-effector returns to its initial position; and finally, the end-effector is required to maintain its position for 3 s. The first part of the backward motion (8 s < \(t\) < 14 s) is slower to avoid a fast variation of the load force, which would cause a large altitude drop of the UAV. As in the trajectory tracking task, the desired trajectory was described through an acceleration profile with jerk continuity. The trajectory and the desired acceleration profiles of the end-effector are shown in Figure 5.  
The load was considered as a generalized force applied to the end-effector (\(\textbf{F}_{h}\)) in the dynamics and in the inverse kinematics equations.  
The first three terms of \(\textbf{F}_{h}\) were the sum of three terms: weight force, inertial force, and contact force between load and ground; friction of the contact was neglected. The contact between the load and the ground was modeled through a vertical spring of stiffness \(k_{s}\) with unloaded height of the spring \(z_{s}\). No torque was exerted on the end-effector; thus, the last three therms of \(\textbf{F}_{h}\) were null:  
\[\textbf{F}_{h}=\theta(t-t_{grasp})\left(\begin{bmatrix}-\ddot{x}_{h}\\ 0\\ g-\ddot{z}_{h}\\ 0\\ 0\\ 0\end{bmatrix}m_{h}+\begin{bmatrix}0\\ 0\\ max(0,k_{s}(z_{s}-z_{h}))\\ 0\\ 0\\ 0\end{bmatrix}\right) \tag{23}\]  
Here \(\theta\) is the Heaviside function; \(g\) is the acceleration of gravity; \(\ddot{x}_{h}\) and \(\ddot{z}_{h}\) are the horizontal and vertical acceleration of the end-effector, respectively; and \(z_{h}\) is the height of the end-effector. The stiffness of the contact (\(k_{s}\)) was set to 500 N/m.  
Typically, the damping of real joints is not negligible; thus, the damping of the non-actuated joint was set to 0.1 Ns/rad in this task.  
\begin{table}
\begin{tabular}{c c c c} \hline \(\boldsymbol{q_{1_{0}}}\) **(rad)** & \(\boldsymbol{q_{2_{0}}}\) **(rad)** & \(\boldsymbol{q_{3_{0}}}\) **(rad)** & \(\boldsymbol{q_{4_{0}}}\) **(rad)** \\ \hline \(-0.42\) & \(1.60\) & \(-1.80\) & / \\ \(1.67\) & \(0.95\) & \(-0.47\) & \(-1.9\) \\ \hline \end{tabular}
\end{table}
Table 2: Joint angles for the initial configuration of the UAM.  
Figure 5: **(left)** Trajectory of the end-effector for the load picking task and initial configuration of the 3-DOF manipulator. **(right)** Desired end-effector accelerations for the load picking task.
# Strategies for Scaleable Communication and Coordination in Multi-Agent (UAV) Systems
## 2 Reinforcement Learning
### Overview  
The Markov Decision Process (MDP) consists of three components: states, actions, and rewards. The system state is a snapshot of all the parameters of interest in a given time-instant. These parameters are application-specific but usually include data such as the agents' positions, velocities, altitudes and/or any other items relevant to the system operation. Actions cause the system to change from one state to another (i.e., actions such as \"right\", \"left\", \"up\", and \"down\" move individual agents in a two-dimensional grid to adjacent cells). Rewards assign value to desirable states and/or penalize undesirable states. For instance, states in which agents and interest-points overlap might receive positive reward as depicted in Figure 1c, whereas states in which two agents occupy the same cell (i.e., have collided) receive negative reward. This acts as a signal from environment to agent, encouraging certain behavior. The agent-environment feedback loop is depicted in Figure 2a. A policy determines the action of an agent in every state. The objective in RL is to compute the optimal policy; the policy that achieves the maximum reward over the operating lifetime.  
The Markov property limits the way states can evolve over time, namely, that future states are conditionally independent of past states given the current state; the future depends only on the past through the present. Another concept related to the Markov property is \"stationarity\". The system is stationary if the \"dynamics\" (i.e., the probabilities of transitioning from one state to another) do not change over time. Stationary dynamics are the default assumption in RL. However, strategies that reduce complexity (i.e., shorten training times) sometimes induce time-varying dynamics as an unintended side-effect.
# Damage Diagnosis for Offshore Wind Turbine Foundations Based on the Fractal Dimension
## 3 Damage Diagnose Strategy
### Normalization and Classification Tools  
Although matrix \(\mathbf{Z}\) in Equation (10) is a matrix of elements generally between 1 and 2, the data is normalized by using column-wise scaling. This way, each column, and consequently each sensor, will have the same influence on the posterior analysis. Otherwise, the sensors closest to the source of the excitation and furthest from the structural damage could have a superior influence and make it difficult to detect the damage. Column-wise scaling is performed by subtracting the mean of each column to the elements on that column and dividing the same elements by the standard deviation of the column.  
Figure 3: The diameter of a time series \(d\) is given by the distance between the first point and the point that provides the maximum distance.  
In this work, different classifiers have been used for the classification: \(k\) nearest neighbors (\(k\)NN) and support vector machines (SVM) with different kernels. In Sections 3.3.1 and 3.3.2 these methods will be briefly reviewed. Finally, it is important to note that 5-fold cross validation has been used to evaluate the classifier models.
# Condition Monitoring of Wind Turbine Blades Using Active and Passive Thermography
## 5 Results and Discussion  
The results of both the passive and active thermography are presented in this section. All temperatures were measured using the IR camera. The different image processing algorithms, employed to increase the quality of the thermal images, are described and their results are discussed in detail.
###### Abstract  
As a strategic replacement area for hydrocarbon exploration in the Tarim Basin, the Kuqa Depression has been the subject of relatively limited research on the sedimentary characteristics of the Triassic strata within its western Wushi Sag, which constrains exploration deployment in this region. This study focuses on the Wushi Sag, systematically analyzing the sedimentary facies types, the evolution of sedimentary systems, and the distribution patterns of the Triassic Kelamayi and Huangshanjie formations. This analysis integrates field outcrops, drilling cores, wireline logs, and 2D seismic data, employing methodologies grounded in foreland basin theory and clastic sedimentary petrology. The paleo-geomorphology preceding sedimentation was reconstructed through balanced section restoration to investigate the controlling influence of foreland tectonic movements on the distribution of sedimentary systems. By interpreting key seismic profiles and analyzing vertical facies successions, the study classifies and evaluates the petroleum accumulation elements and favorable source-reservoir-seal assemblages, culminating in the prediction of prospective exploration areas. The research shows that: (1) The Triassic in the Wushi Sag mainly develops fan-delta, braided-river-delta, and lacustrine-shallow lacustrine sedimentary systems, with strong planar distribution regularity. The exposed strata in the northern part are predominantly fan-delta and lacustrine systems, while the southern part is dominated by braided-river-delta and lacustrine systems. (2) The spatial distribution of sedimentary systems was demonstrably influenced by tectonic activity. Paleogeomorphological reconstructions indicate that fan-delta and braided-river-delta sedimentary bodies preferentially developed within zones encompassing fault-superposition belts, fault-transfer zones, and paleovalleys. Furthermore, Triassic foreland tectonic movements during its deposition significantly altered basin configuration, thereby driving lacustrine expansion. (3) The Wushi Sag exhibits favorable hydrocarbon accumulation configurations, featuring two principal source-reservoir assemblages: self-sourced structural-lithologic gas reservoirs with vertical migration pathways, and lower-source-upper-reservoir structural-lithologic gas reservoirs with lateral migration. This demonstrates substantial petroleum exploration potential. The results provide insights for identifying favorable exploration targets within the Triassic sequences of the Wushi Sag and western Kuqa Depression.  
Wushi Sag; foreland basin; Huangshanjie formation; Kelamayi formation; paleogeography; source-reservoir configuration
# Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey
## 5 Analysis of Data-Driven Models for WSNs
### Event-Driven Model
[85] propose an entropy-driven data aggregation with gradient distribution (EDAGD) deployment strategy for maximizing WSNs lifetime. The EDAGD strategy contains three algorithms. A multi-hop tree-based data aggregation (MTDA) framework is proposed for minimizing path distance, which decreases data transmission distance in the transmission process by limiting the number of hops required. An entropy-driven aggregation tree-based routing algorithm (ETA) is suggested for the Choquet integral and entropy, which uses data aggregation to monitor abnormal events. During the data transmission process in abnormal areas, sensor data are set to function, and the remaining nodes are set to sleep to save energy. For reducing the energy hop problem, a gradient deployment algorithm (GDA) is used.
## 2 Real-Time Measurement System
### Hardware Composition  
The hardware composition of the system for the vacuum experiment is shown in Figure 2. The system consists of an optical platform, an ion thruster with ion optics, a light source, probes, cooperative targets, a telemicroscope with charge-coupled device (CCD), and a four-axis positioning platform.  
The ion optics was fixed on the ion thruster, and the ion thruster was fixed on the test bench and kept perpendicular to the ground. The probes and cooperative targets were mounted in the center of the grids. The LED focusing video light provided a light source that illuminated the probes area. The color temperature range of the LED was 3200-5600 K, and the light angle adjustment range was 15-55\({}^{\circ}\). The telemicroscope lens uses the NAVITAR 12x Zoom Lens System; the CCD telemicroscope image resolution is 1600 pixel \(\times\) 1200 pixel which can capture 12 frames per second; the sensor chip size is 1/2.5\({}^{\circ}\); and the chip cell size is 2.8 \(\upmu\)m \(\times\) 2.8 \(\upmu\)m. The telemicroscope was mounted on a high-precision four-axis positioning platform (three translational degrees of freedom and one rotational degree of freedom), and the position and angle were adjusted so that the telemicroscope's optical axis and the longitudinal axis of the ion optics were perpendicular (the position accuracy of the linear table was 5 \(\upmu\)m and that of the rotation table was 0.1\({}^{\circ}\)). The working distance of the telemicroscope lens from the probe was adjusted to 300 mm in the experiment of this paper. Equipment such as video lights and the telemicroscope were installed outside the plasma flow of the grid, with little effect from the plasma beam and high temperature.
# Automotive OEM Demand Forecasting: A Comparative Study of Forecasting Algorithms and Strategies
## 3 Methodology
### Modeling
#### 3.4.1 Feature Analysis and Prediction Techniques
We considered the naive forecast (last observed value as prediction) as the baseline method. We train twelve different batch ML models: on top of MLR, support vector regressor (SVR) [56], and multilayer perceptron regressor (MLPR) [57], which we found were used in automotive demand forecasting literature, we also evaluate Ridge [58]; Lasso [59]; Elastic Net [60]; K-nearest-neighbor regressor (KNNR) [61]; tree-based regressors (decision tree regressor (DTR), random forest regressor (RFR), and GBRT); a voting ensemble created using the most promising and diverse algorithms (KNNR, SVR, and RFR); as well as a stacked regression [62] considering KNNR, SVR, and RidgeCV as underlying estimators; and a GBRT model as final regressor. We also take into account four streaming ML algorithms: Adaptive Random Forest Regressor (ARFR) [63], Hoeffding Tree Regressor (HTR) [64], and Hoeffding Adaptive Tree Regressor (HATR) [65]. Additionally, we also consider forecasts obtained as the average demand for the last three months (MA(3)) and the ones obtained from statistical forecasting methods (exponential smoothing, random walk, ARIMA(1,1,0), and ARIMA(2,1,0)). We did not create deep learning models since we consider that not enough data was available to train them. When training the models, we used MSE as the loss function where possible. We choose MSE because it has the desired property of penalizing higher errors more, thus reducing substantial discrepancies in predicted values.
# Research on Out-of-Plane Bending Test of PVB Laminated Glass Plate with Different Number of Layers
## 3 Results and Discussion
### In-Plane Distribution of Deflection and Maximum Principal Strain  
Traditional out-of-plane bending tests on glass require the use of displacement meters to measure displacement. However, displacement meters can only be placed at a few strategic points, making it challenging to capture a continuous displacement distribution. In this study, we employ DIC binocular vision technology and high-resolution cameras to photograph the speckles in the monitoring area at high frequencies. Utilizing the principle of binocular vision, the displacement field is determined by analyzing changes in the speckle patterns. Figure 9 depicts the displacement distribution changes of samples 3L-a to 3L-c during the loading sequence, with Pu1 representing the ultimate load. Spatially, the displacement contours are nearly circular at the centroid and transition to a rounded square shape near the edges to conform to the boundary support conditions. Over time, the  
Figure 8: Failure forms of LG after two successive failures.  
Figure 7: Failure forms of LG before and after the first failure event.  
magnitude of central deflection increases, with the lines of equal displacement gradually expanding outward, while the spacing between them decreases as the loading progresses.  
Figure 10 presents the distribution of the maximum principal strain for sample 3L-a, measured immediately before failure. The figure indicates that the maximum principal strain values are higher closer to the plate's center, with the strain direction radiating outward from the center.  
From the analysis of displacement and strain distributions, both exhibit similar patterns, with the maximum values occurring near the plate's center. Consequently, in engineering design calculations for four-sided simply supported PVB laminated glass under symmetric lateral loads, displacement and strain verification should focus on the plate's center. According to classical thin plate bending theory, the maximum compressive or tensile stress in anti-symmetric bending of thin plates is found on the plate's top and bottom surfaces, with equal magnitudes. Furthermore, like concrete, silicate glass displays signifi  
Figure 9: Load–lateral displacement curve of three-layer laminated glass center.  
cantly higher compressive strength compared to tensile strength. Therefore, verifying stress and strain on the tensile surface becomes more critical. In summary, for PVB laminated glass under anti-symmetric bending, design calculations for deflection, stress, and strain need only consider the tensile surface at the plate's center.
# Product Demand Prediction with Spatial Graph Neural Networks
## 1 Introduction
Through an in-depth examination of the challenges inherent in online sales, a comprehensive review of existing methodologies, and the introduction of an innovative GNN-based approach, we endeavor to equip sellers with a robust tool to navigate the complexities of the online marketplace. In our investigation, we propose the following research questions (RQs) to evaluate the efficacy of our spatial GNN model in the context of geographic product demand prediction: RQ1: How does the performance of the SGNN model in predicting product demand compare to a variety of established baseline models? RQ2: To what extent does each component within the SGNN model contribute to the improvement of product demand prediction accuracy? RQ3: How stable is the SGNN model's predictive accuracy across a range of hyperparameter settings? Our findings validate the effectiveness of our approach and offer actionable insights for optimizing online sales strategies. In essence, this paper enriches the academic discourse on demand prediction in e-commerce and addresses a critical gap with its pioneering GNN-based methodology. By doing so, it lays the groundwork for future research and practical applications poised to transform the online sales landscape.
## 4 Discussion
### Validation of Models  
The performance of our models was rigorously assessed using both qualitative and quantitative validation approaches. This included the analysis of independent data sources such as local and national news reports, which documented real-world events that impacted transportation infrastructure, including landslides, floods, and other mass movement phenomena.  
Validation Methodology:  
Temporal and Spatial Correlation: We systematically compared the high-risk zones identified by our models with the geographic and temporal data from news reports. By doing so, we ensured that the model predictions aligned closely with documented real-world mass movement events. Each event's timestamp and precise location were essential to validating the model's ability to forecast hazards effectively.  
Visual Evidence: A qualitative assessment was performed by overlaying images from news sources onto the spatial predictions of our models. This visual comparison provided additional insights into the model's accuracy. Furthermore, we processed radar satellite imagery, including Sentinel-1 Single Look Complex (SLC) and Ground Range Detected (GRD) formats, to refine our spatial analysis and validate the delineation of risk zones.  
Quantitative Metrics:  
Precision: We measured the percentage of accurately predicted high-risk zones compared to the total number of predictions.  
Recall: The model's ability to detect and identify events reported in media was evaluated.  
F1 Score: This is a harmonic mean of precision and recall, providing a balanced assessment of the model's performance.  
Kappa Statistics: Although field validation was not directly feasible due to the challenges of the remote and extensive study area, the reliability of model predictions was enhanced by statistical analysis using Kappa metrics, where applicable.  
Validation with Specific Events:  
The validation approach involved cross-referencing the model-detected events with specific dates from media reports. For rockfall events, we focused on incidents documented on 11 January, 22 March, 31 August, 16 September, 3 October, 28 November, and 2 October 2022. Similarly, for flood events, we used reports from 20 January 2023, 5 January 2021, 26 June 2020, 17 September 2019, 15 November 2017, and 18 July 2016. By aligning our predictions with these events, we confirmed the spatial and temporal accuracy of our models.  
Justification for Validation Approach:  
While we acknowledge the importance of post-classification field validation, practical constraints such as the remoteness of certain sites and the broad temporal scope of our dataset made extensive fieldwork unfeasible. To compensate, we employed independent news sources and remote sensing data, establishing a robust validation framework that could be replicated in other regions. The alignment between our model predictions and documented incidents, supported by the processing of Sentinel-1 radar imagery and AI-based traffic detection, substantiates the model's effectiveness.
# Human Activity Signatures Captured Under Different Directions Using SISO and MIMO Radar Systems
## 2 Radar Signal Preprocessing
The TV mean Doppler shift \(B_{ij}^{(1)}(t)\) can be obtained from the micro-Doppler signature \(S_{ij}(f,t)\) according to the relation [50]  
\[s_{i}(t,t^{\prime})=\sum_{n=0}^{\infty}c_{i}(t^{\prime})\delta(t-t_{n,i}) \tag{2}\]1As the Doppler frequency \(f\) can be mapped on the radial velocity \(v\) according to \(v=c_{0}f/(2f_{0})\), we can obtain the TV radial velocity profile \(V_{ij}(v,t)\) from the TV micro-Doppler signature \(S_{ij}(f,t)\) as  
\[s_{i}(t,t^{\prime})=\sum_{n=0}^{\infty}c_{i}(t^{\prime})\delta(t-t_{n,i}) \tag{2}\]2  
The TV micro-Doppler signature \(S_{ij}(f,t)\) in (11) is computed from the composite beat signal \(s_{b,ij}(t,t^{\prime})\), and thus it contains the micro-Doppler information of all \(\mathcal{L}\) scatterers with their respective strengths. Analogously, the radial velocity profile \(V_{ij}(v,t)\) in (13) contains the radial velocity information of each scatterer with respect to the slow time \(t\). The strength or power of the \(l\)th scatterer in the radial velocity profile \(V_{ij}(v,t)\) depends on the path gain \(a_{ij}^{(l)}\). From the TV radial velocity profile \(V_{ij}(v,t)\), we can compute the TV radial velocity distribution \(p_{ij}(v,t)\) by  
\[s_{i}(t,t^{\prime})=\sum_{n=0}^{\infty}c_{i}(t^{\prime})\delta(t-t_{n,i}) \tag{2}\]3  
from which we can obtain the mean radial velocity \(\bar{v}_{ij}(t)\) using the relation  
\[s_{i}(t,t^{\prime})=\sum_{n=0}^{\infty}c_{i}(t^{\prime})\delta(t-t_{n,i}) \tag{2}\]4  
As good descriptive statistics that quantitatively summarize all the main features of the TV micro-Doppler signature \(S_{ij}(f,t)\) or radial velocity distribution \(p_{ij}(v,t)\), we can utilize the mean Doppler shift \(B_{ij}^{(1)}(t)\) or the mean radial velocity \(\bar{v}_{ij}(t)\), respectively. The TV mean Doppler shift \(B_{ij}^{(1)}(t)\) in (12) is basically a weighted arithmetic mean of the TV micro-Doppler signature \(S_{ij}(f,t)\). Similarly, the mean radial velocity \(\bar{v}_{ij}(t)\) in (15) is the weighted average of the velocity components of all \(\mathcal{L}\) human body segments computed for each time instance \(t\). The TV radial velocity distribution \(p_{ij}(v,t)\) and TV mean radial velocity \(\bar{v}_{ij}(t)\) will play an important role in analyzing real-world measurement data in Sections 3 and 4.