esa-sceva/satcom-chunk-collection huggingface dataset:
# A Long-Distance WiFi Network as a Tool to Promote Social Inclusion in Southern Vercarruz, Mexico
## 2 Theoretical Framework
### Connectivity  
Wireless Sensors Networks (WSNs) typically communicate by unguided means via electromagnetic waves. Transmission and reception are carried out via antennas. The transmitter may have several antennas, and there are systems that use two, three, or even four antennas. Some antennas are used for transmission, others for reception, and the same antenna can operate in both modes. Designs can also be made with intermediate antennas (reaching distances of a few meters) or repeaters (reaching tens of kilometers) [30]. Wireless Local Area Networks (WLANs) are usually located in the same building, with a coverage between 10 m and 450 m. Among the best-known WLANs is Wireless Fidelity (WiFi), IEEE 802.11 standard. Currently, there are cards and interface devices that allow connectivity up to 450 m, as long as weather conditions are favorable and there is no interference or obstacles [31].  
Environmental surveillance using cameras plays a critical role in monitoring animal behaviors and promoting environmental awareness, aligning with sustainability goals. However, utilizing vehicle-mounted cameras for data collection can impact the uplink data rate, affecting sustainability efforts. The emergence of 5G networks brings improvements in high data rate services, including Internet of Things (IoT), but the heterogeneous cell coverage presents challenges for sustainable handover processes. Overcoming weaknesses in handover parameters and network destination selection is crucial for sustainable network operations [32].  
This paper [33] discusses the initiatives for Smart Cities and the various aspects involved, such as governance, economy, management, infrastructure, technology, and people. As cities grow, the communication needs of Smart Cities become crucial. Wireless technologies such as WiFi, ZigBee, Bluetooth, WiMax, 4G, and LTE have emerged as potential solutions for communication in Smart City projects. However, since many of these technologies operate in unlicensed frequency bands, issues related to interference and coexistence have become more prominent. This paper presents a review of wireless technologies available for IoT in Smart Cities, comparing them and highlighting the challenges associated with their coexistence. The suitability and characteristics of each technology in different domains are considered, along with the problems arising from congested unlicensed spectrum and difficulties in coexistence.  
In recent years, WiFi has evolved and has achieved universal acceptance, since it works in the 2.4 GHz band, which is almost universally available. However, it can still experience interference caused by ovens, microwaves, cell phones, and other devices. Another disadvantage of this technology is the lack of robust security in data transmission, as well as high power consumption.
# Truncated Newton Kernel Ridge Regression for Prediction of Porosity in Additive Manufactured SS316L
## 4 Results and Discussion
### Analysis of Regression Results  
To check the ability and adequacy of the established models related to the real SLM system, Figure 6 compares the relative density data determined experimentally with the values predicted using (a) \(RR\), (b) \(SVR\), and (c) \(KRR\). Ideally, if the predicted values are equal to the actual data, the points should lie closer to the diagonal line on the graph.  
The \(RR\) model results presented in Figure 6a show the lowest accuracy compared to the other models. Furthermore, the plot of the \(SVR\) presented in Figure 6b shows better agreement between the predicted and actual values, indicating a higher accuracy. Finally, Figure 6c demonstrates the predicted vs. actual plot of the \(KRR\) model. This model gives the highest accuracy, as the values were distributed closer to the diagonal line than other models. The \(KRR\) model may be adequate for predicting the relative density and strong enough to be used in future applications.  
Table 4 presents the optimal tuning parameters for each model and their corresponding accuracy represented by the \(R^{2}\) and \(MSE\). An \(R^{2}\) of 0.701 and an \(MSE\) of 0.299 were obtained  
Figure 6: Predicted vs. actual relative density for (**a**) \(RR\), (**b**) \(SVR\), and (**c**) \(KRR\) using 10-fold cross-validation.  
using \(RR\) with a tuning parameter (\(\lambda\)) of 0.006. Moreover, \(SVR\) showed a better performance with \(R^{2}\) of 0.83 and \(MSE\) of 0.17, along with tuning parameters \(\gamma\) and \(\varepsilon\) equal to 0.4 and 0.05, respectively. Finally, the \(KRR\) resulted in the best accuracy among the three models with an \(R^{2}\) of 0.853 and \(MSE\) of 0.1607, with tuning parameters \(\lambda\) and \(\sigma\) equal to 0.01 and 2.9, respectively. \(KRR\) has shown the best performance as it captures the nonlinearities and complex interactions of the SLM process. It may be good to note that the studied machine learning methods have employed a grid search approach to find the optimal tunning parameters. However, there is still room to find the optimal tunning parameters effectively.  
_KRR_ was not used in [PERSON] et al. (2021), and \(SVM\) was one of the best regression algorithms in their study. This implies that \(KRR\) may have better prediction capability for the relative density of the SS316L parts than other regression techniques. The present paper complements the recent study in [16] with the doubled data size and consideration of an additional better regression algorithm (i.e., \(KRR\)).
# An Improved Galerkin Framework for Solving Unsteady High-Reynolds Navier-Stokes Equations
###### Abstract  
The numerical simulation of unsteady, high-Reynolds-number incompressible flows governed by the Navier-Stokes (NS) equations presents significant challenges in computational fluid dynamics, primarily concerning numerical stability and computational efficiency. Standard Galerkin finite element methods often suffer from non-physical oscillations in convection-dominated regimes, while the multiscale nature of these flows demands prohibitively high computational resources for uniformly refined meshes. This paper proposes an improved Galerkin framework that synergistically integrates a Variational Multiscale Stabilization (VMS) method with an adaptive mesh refinement (AMR) strategy to overcome these dual challenges. Based on the Ritz-Galerkin formulation with the stable Taylor-Hood (\(P_{2}-P_{1}\)) element, a VMS term is introduced, derived from a generalized \(\theta\)-scheme. This explicitly constructs a subgrid-scale model to effectively suppress numerical oscillations without introducing excessive artificial diffusion. To enhance computational efficiency, a novel a posteriori error estimator is developed based on dual residuals. This estimator provides the robust and accurate localization of numerical errors by dynamically weighting the momentum and continuity residuals within each element, as well as the flux jumps across element boundaries. This error indicator guides an AMR algorithm that combines longest-edge bisection with local Delaunay re-triangulation, ensuring optimal mesh adaptation to complex flow features such as boundary layers and vortices. Furthermore, the stability of the Taylor-Hood element, essential for stable velocity-pressure coupling, is preserved within this integrated framework. Numerical experiments are presented to verify the effectiveness of the proposed method, demonstrating its ability to achieve stable, high-fidelity solutions on adaptively refined grids with a substantial reduction in computational cost.  
N  
Navier-Stokes equations; inf-sup condition; Variational Multiscale Stabilization; adaptive mesh; Galerkin finite element
## 2 Materials and Methods
### Machine Learning Models
From Figure 2 below, it is evident that hyperparameter tuning improved the accuracy and reduced variability across models, as evidenced by the boxplots below, which show higher median accuracy and narrower interquartile ranges from cross-validation results for CatBoost, XGBoost, and random forests. Hyperparameter tuning revealed optimal performance with n_estimators = 179 (number of trees) and max_features = 'log2' (feature subset size), balancing model complexity  
Figure 2: Model accuracy before and after hyperparameter tuning across different models. and generalization. CatBoost achieved optimal performance with learning_rate = 0.0367 and max_depth = 14, capturing complex patterns while maintaining stability during training. XGBoost performed best with a max_depth of 10, balancing predictive power and overfitting prevention. We implemented a stacking ensemble approach that stemmed from the critical need for robust, generalizable predictions in healthcare applications, where accuracy directly impacts outcomes. While base models like random forest, CatBoost, and XGBoost each demonstrated strengths--such as reducing overfitting, handling categorical data, and optimizing gradient boosting with regularization--relying on a single model risked overlooking nuanced patterns or inheriting algorithm-specific biases. A weighted average ensemble was designed to address these limitations, dynamically integrating predictions from all three models. The weights assigned to each model were determined by their classification accuracy, ensuring that more reliable models contributed more significantly to the final prediction. Specifically, random forest's weight reflected its stability across training folds, CatBoost's weight was adjusted for its categorical feature handling, and XGBoost's weight accounted for its efficiency in gradient boosting and regularization. This weighted integration allowed the ensemble to dynamically combine model-specific strengths while mitigating errors from less reliable predictions. The framework achieved the reliability required for high-stakes medical predictions by mitigating model-specific errors and leveraging collective intelligence. Figure 3 below illustrates the architecture of the ensemble model, highlighting the integration of multiple base models and the weighted averaging mechanism employed to enhance predictive performance.
# Spontaneous Brillouin Scattering Spectrum and Coherent Brillouin Gain in Optical Fibers
## 2 Results
### Spatial Noise Filtering
#### 2.4.1 Discrete Spectrum of Guided Phonons
\begin{array}{ll}\langle g(t)g^{*}(t^{\prime})\rangle&=\int_{\Omega} \mathrm{d}\mathbf{r}\int_{\Omega}\mathrm{d}\mathbf{r^{\prime}}\langle f_{i}(t,\mathbf{r})u_{0i}(\mathbf{r})f_{j}^{*}(t^{\prime},\mathbf{r^{\prime}})u_{0j} ^{*}(\mathbf{r^{\prime}})\rangle\\ &=\int_{\Omega}\mathrm{d}\mathbf{r}\int_{\Omega}\mathrm{d}\mathbf{r^{\prime}} \langle f_{i}(t,\mathbf{r})f_{j}^{*}(t^{\prime},\mathbf{r^{\prime}})\rangle u _{0i}(\mathbf{r})u_{0j}^{*}(\mathbf{r^{\prime}})\\ &=\int_{\Omega}\mathrm{d}\mathbf{r}\partial_{ij}u_{0i}(\mathbf{r})u_{0j}^{*}( \mathbf{r})\delta(t-t^{\prime}).\end{array}  \tag{21}\]  
Hence  
\[\langle g(t)g^{*}(t^{\prime})\rangle=Q^{\prime}\delta(t-t^{\prime}),\ \ Q^{\prime}=\int_{\Omega}\mathrm{d}\mathbf{r}Q_{ij}u_{0i}(\mathbf{r})u_{0j}^{ *}(\mathbf{r}). \tag{22}\]  
Finally the symmetric Lorentzian line shape is again arrived at  
\[S_{a}(\omega)=\frac{Q^{\prime}\omega_{0}^{2}}{4E_{0}^{2}}\left|i(\omega-\omega _{0})+\frac{1}{2}\Gamma\right|^{-2}. \tag{23}\]  
It is seen that the hypothesis of spatially uncorrelated noise leads to a power spectral density that is independent of position. The quantity \(Q^{\prime}\) appears as an average of the Gaussian variance \(Q\) over the modal distribution \(\mathbf{u}_{0}(\mathbf{r})\).
## 4 Results and Analysis
### Cross-Validation
#### 4.2.2 Temporal Consistency  
Compared to the time series data of the FY-FPAR, MODIS-FPAR, GEOV1-FPAR, and GLASS-FPAR of various vegetation types in 2014 (Figure 7), FY-FPAR and the other three products all showed good consistency over their time series.  
Figure 6: The difference frequency distribution between FY-FPAR and FPAR products (2014). (**a–c**) are the difference frequency between FY-FPAR and MODIS-FPAR; FY-FPAR and GEOV1-FPAR; and FY-FPAR and GLASS-FPAR, respectively.  
Figure 7: _Cont._  
Overall, the annual FY-FPAR of the grassland, cropland, shrub, broadleaf forest, and needleleaf forest maintained a high consistency with the corresponding MODIS-FPAR, GEOV1-FPAR, and GLASS-FPAR in the time-series (GLASS-FPAR only included the data from June to November). The FPAR time-series curves of the five vegetation types remained smooth and continuous. Among these curves, the FY-FPARs of the grassland, shrub, and needleleaf forest, with the other three FPAR products, showed a general increase in their trends at the beginning, which dropped off after reaching its maximum around July and August. This change can be clearly explained in terms of the seasonal growth regulation of vegetation, which was consistent with the characteristics of a higher FY-FPAR in summer and a lower FY-FPAR in winter. The change of the broadleaf forest FY-FPAR alongside the other three FPAR products in the time-series was not clear and remained relatively stable. The FY-FPAR of the cropland was more special in the time-series, showing a trend of increasing first and then falling, before going up and then falling, including two peaks in April and July and three troughs in February, June, and October, which are associated with the seasonal growth regulation of double seasonal crops and highlight the information on crop rotation. In addition, the FPAR time-series curve of the shrub area was significantly different from that of other vegetation types, and its FY-FPAR was generally higher than its GEOV1-FPAR and GLASS-FPAR, which were closer to the MODIS-FPAR. It was inferred that the pixels of the reflectance data before inversion contained mixed pixels of shrubs and croplands and shrubs and grasses, which gave the FY-FPAR a certain degree of variation.
# On-Board Wind Scatterometry
## 3 Wind Scatterometry Processing Chain and On-Board Processing Modifications
### The On-Board Pre-Processing
Key outputs from X-factor determination procedures are the locations and NRCS of the slices. In Figure 3b, in addition to the LUT used for Doppler effect correction employed in the traditional observing chain, two LUTs are proposed in this research to complete this procedure, i.e., the geolocation LUT and the X-factor LUT. They are calculated in advance according to the rectangle-lined region in Figure 3a, considering all possible observing position, altitude, and antenna orientation parameters in discrete steps. Applications of these two LUTs can accelerate processing speed for RT applications. As shown in Figure 3b with satellite position, speed, and antenna observing orientation, the position of a slice relative to the satellite can be found from a geolocation LUT. With the same inputs, the X-factor can be found. Inputs for both LUTs are the latitude, the ascending/descending label, and the antenna azimuth angle. The steps for the construction of them are 0.085\({}^{\circ}\) and 0.3\({}^{\circ}\) of latitude and antenna azimuth angle, respectively. Considerations in balancing the calculating ability of on-board hardware and step sampling density for LUTs are provided after the experiment section.
# Distributed Attitude Synchronization for Spacecraft Formation Flying via Event-Triggered Control
## 5 Conclusions  
In this paper, an event-driven attitude coordinated control method has been established for multiple spacecraft systems subject to limited energy and computational resources under directed communication topologies. The uniformly ultimately bounded stabilization of the formation system has been guaranteed by Lyapunov theory and it has proved that the control system is able to achieve attitude synchronization under the proposed control strategy. In addition, an event-triggered strategy that does not require continuous state information of its neighbors has been designed to reduce the computational burden and the consumption of energy. Additionally, the inter-event time between two serial events has been proved to have a strictly positive lower bound, implying that the control system is Zeno-free under the developed event-based mechanism. Finally, numerical simulations are carried out to verify the proposed theoretical results. Compared with the time-driven method, the designed event-driven control scheme can make the closed-loop system save more than 90% computation resources and keep high-precision convergence of \(\|\omega_{\vec{e}}\|_{\infty}<3\times 10^{-4}\) and \(\|\omega_{\vec{e}}\|_{\infty}<2\times 10^{-4}\) by selecting suitable design parameters. In the future, we will focus on the attitude consensus for multiple spacecraft systems subject to unknown disturbances and time-varying communication delays.  
[PERSON] and [PERSON] conceived of the presented idea; [PERSON] and [PERSON] designed the control algorithm and performed the simulation experiment. All authors wrote the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Major Program of National Natural Science Foundation of China (Grant Nos. 61690210, 61690213).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
# An Approach to Industrial Automation Based on Low-Cost Embedded Platforms and Open Software
## 2 System Description  
The process selected for the demonstration of the methodology proposed in this article corresponds to a multi-tank system. Figure 1 shows the P&ID diagram of this system. The plant consists of three tanks: two reserve tanks and another one used for pumping purposes. The description of the measuring and control devices is as follows:  
* The check valve (FV-100) protects the pump from water hammer.
* The pressure relief valve (FV-108) protects the pump and the pipe from pressure increases caused by the closing of the flow control valve (FCV-101).
* The control valves FCV-101, FCV-103 and FCV-106 regulate the levels of the reserve tanks.
* The level transmitters (LT-104 and LT-105) record the levels of tanks 1 and 2 and transmit them to the main controller (FC-201).  
* The multi-tank system input and output flows are monitored by flow sensors (FT-102 and FT-107).
* The electrical signals from the level and flow sensors are calibrated by the nodes (LY-200 and FY-200). These data are sent to the node that performs control and supervision functions (FC-201), which then sends information to a data logger server (UR-300).
* The process is locally monitored by a human to machine interface (HMI) (UG-301), which communicates directly with the controller node, while a SCADA system (UG-302) exchanges real-time data with the UR-300.
# Toward Explainable AutoEncoder-Based Diagnosis of Dynamical Systems
## 5 Approach  
This section describes our approach: we outline our input data, and how we transform those data for diagnostic purposes.  
We assume that we measure data \(\xi\), where each \(\xi\) is a \(q\)-vector in a space \(\mathcal{X}\subseteq\mathbb{R}^{q}\). A discrete-step time-series signal is denoted by a time set \(Z^{+}\) and signal \(\xi\subseteq\mathbb{R}^{Z+}\).  
We assume that the data-generator (LTI system, such as a pump or motor) generates data with multiple different distributions; we call each of these a distinguished _mode_. In diagnostic terms, the modes correspond to nominal or faulty conditions of the motor. We define the set of modes as \(\Gamma=\{\gamma_{1},\ldots,\gamma_{J}\}\).  
Given a data set \(\mathcal{D}\), we want to train an autoencoder (AE) \(\mathcal{A}_{\theta}\) with parameters \(\theta\) in parameter set \(\Theta\) to distinguish the data according to their mode distribution. Assume that we have a loss function that scores correctly diagnosing faults in inputs \(\xi\) in a test set \(\mathcal{D}_{test}\)  
**Task 1** (Autoencoder Learning).: _Our learning task is to generate the autoencoder parameters \(\theta^{*}\) that minimize diagnostics loss over a test set while maximizing mode distance, given true fault label \(\hat{y}_{i}\) for test instance \(i\)._  
\[\theta^{*}=\arg\min_{\theta\in\Theta}\max_{\gamma_{j}\in\Gamma}\sum_{i\in \mathcal{D}_{test}}\mathcal{L}(\hat{y}_{i},A_{\theta}(\xi_{i}))\,, \tag{10}\]  
_where \(\mathcal{L}(\hat{y}_{i},A_{\theta}(\xi_{i}))\) measures the loss between the true fault label \(\hat{y}_{i}\) and the autoencoder output \(A_{\theta}(\xi_{i})\)._  
We outline our approach in Figure 1. We train an AE and use a loss function \(\mathcal{L}\) that aims to optimize diagnostic accuracy. Specifically, we encode a loss function \(\mathcal{L}_{SPA}\) that generates a code based on the subspace principal angle (SPA). Here, we encode the input in a specific form (Hankel matrix), and perform AE-based diagnosis by defining the code as a representation that aims to maximize  
We use the architecture to encode the time-series model. A fully connected architecture is the simplest approach, providing a clear method for the model, although it has not a specific structure for encoding temporal representations.  
The LSTM is designed to encode temporal features, and hence should be better than a fully connected architecture for time-series models.
# A Conceptual Blueprint for Making Neuromsculoskeletal Melically Useful
## 5 Opportunities for Enhancement
### Enhanced Model Fidelity  
Opportunities for enhancement exist for generating higher fidelity neuromusculoskeletal models that capture short- and long-term tissue-level behavior. For some clinical problems where functional outcome depends on tissue-level stresses and strains, rigid body skeletal models controlled by lumped-parameter Hill-type muscle-tendon models will not be sufficient. To capture tissue-level effects in muscles, ligaments, bones, and/or articular cartilage, finite element (FE) models with deformable tissue properties are the logical choice [102, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263]. In muscles, for example, non-uniform tissue-level behavior could be important for predicting muscle hypertrophy or injury in response to exercise, the effects of aging and disuse, the progression of muscular dystrophy, and muscle function in microgravity. In ligaments, it could be important for predicting failure mechanisms and the best way to perform reconstructions. In bones, it could be important for predicting the risk of fracture due to unfavorable loading conditions, osteoporosis, or joint replacement. In articular cartilage, it could be important for predicting conditions that initiate, accelerate, or slow the development of osteoarthritis.  
For more complex clinical problems where functional outcome depends on how muscle, bone, and/or cartilage tissue changes over time, FE models can be combined with either empirical or agent-based adaptation models. As noted earlier, bone adaptation to an altered loading environment is the classic historical example of adaptive simulations. However, recent work has extended adaptive FE simulation methods to muscle and articular cartilage as well. For example, 3D FE models of muscle have been combined with agent-based adaptation models to simulate disease-related changes in muscle function caused by muscular dystrophy [255] and to propose improved tissue regeneration approaches to treat severe muscle injuries [102]. For articular cartilage, a specimen-specific 3D FE model of a patelltofemoral joint has been coupled with an empirical adaptation model to predict articular cartilage wear over time during in vitro testing [264]. The challenge with adaptive simulations is that a new FE analysis must be performed each time the adaptation model updates the FE model's tissue properties. Furthermore, since it is unrealistic to simulate thousands or millions of motion cycles, the number of cycles for which the most recent FE simulation results can be extrapolated must be determined (see [265, 266] for further details).