esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
With climate change and the intensification of human activity, drought event frequency has increased, affecting the Gross Primary Production (GPP) of terrestrial ecosystems. Accurate estimation of the GPP and in-depth exploration of its response mechanisms to drought are essential for understanding ecosystem stability and developing strategies for climate change adaptation. Combining remote sensing technology and machine learning is currently the mainstream method for estimating the GPP in terrestrial ecosystems, which can eliminate the uncertainty of model parameters and errors in input data. This study employed extreme gradient boosting, random forest (RF), and light use efficiency models. Additionally, we integrated solar-induced chlorophyll fluorescence (SIF), near-infrared reflectance of vegetation, and the leaf area index (LAI) to construct various GPP estimation models. The standardised precipitation evapotranspiration index (SPED) was utilised at various timescales to analyse the relationship between the GPP and SPEI during dry years. Moreover, the potential pathways and coefficients of environmental factors that influence GPP were explored using structural equation modelling. Our key findings include the following: (1) the model combining the SIF and RF algorithms exhibits higher accuracy and applicability in estimating vegetation GPP in the arid zone of Xinjiang, with an overall accuracy (MODIS R\({}^{2}\)) of 0.775; (2) the vegetation in Xinjiang had different response characteristics to different timescales of drought, in which the optimal timescale for GPP to respond to drought was 9 months, with a mean correlation coefficient of 0.244 between grass land GPP and SPEI09, indicating high sensitivity; (3) using structural equation modelling, we found that temperature and precipitation can affect GPP both directly and indirectly through LAI. This study provides a reliable tool for estimating the GPP in Xinjiang, and its methodology and conclusions are important references for similar environments. In addition, this study bridges the research gap in drought response to GPP at different timescales, and the potential influence mechanism of natural factors on GPP provides a scientific basis for early warning of drought and ecosystem management. Further validation using a longer time series is required to confirm the robustness of the model.  
SIF; GPP; machine learning; SPEI; structural equation modelling; drought; Xinjiang arid zone +
Footnote †: journal:  
_Acticle_  
Solar-Induced Chlorophyll Fluorescence-Based GPP Estimation and Analysis of Influencing Factors and Xinjiang of Influencing Factors for Xinjiang Vegetation
# MAG Welding Tests of Modern High Strength Steels with Minimum Yield Strength of 700 MPa
## 3 Results and Discussion
### Impact Toughness Tests  
The Charpy-V impact toughness results for the base materials are presented in Figure 10. Figure 11 represents the test results of the welded specimens of all three materials. The S700 MC-2 has the highest impact toughness energies, when considering the impact toughness at the weld metal. Since the weld metal and welding procedure is the same for all of the welds, the compositional changes in weld metal chemistry by dilution have influence on weld metal impact toughness. Typical dilution in MIG/MAG welding process in butt weld is 20-40% depending on welding parameters and procedures [29]. However, all measured values at the weld metal exceeds the 31 J limit. The limit of 31 J can be considered as a requirement for the impact toughness energies for 5 mm specimens, based on requirements for ultra-high strength steel grade NV x690 according to DNV offshore standard, where x is a capital letter corresponding to a specified impact toughness test temperature, e.g., NV F690 at \(-60\)\({}^{\circ}\)C [30]. The most significant differences between the materials occur by the notch locations of FL + 1 mm. Generally, at the FL + 1 mm, the material S700 MC-2 has the highest absorbed energies, even though the energies are evidently sensitive to the heat input. At the FL + 1 mm, the impact toughness of the Q&T S690 QL is comparable to the TMCP steels only with the lowest heat input and with higher heat inputs, the impact energies of S690 QL drop severely compared to the other steels. It is notable that, at location FL + 3 mm, the impact toughness energies of TMCP steels are at a good level in every measurement, whereas the impact toughness of quenched and tempered S690 QL is reduced when higher heat inputs are used. This is significant in results at \(-60\)\({}^{\circ}\)C because, with higher heat inputs, the impact energy of S690 QL does not fulfil the requirement of 31 J. With lower heat inputs, the energies of S690 QL are higher than in the case of MC steels. At FL + 5 mm, the energies of TMCP steels are similar to those measured at FL + 3 mm. However, with high heat inputs, some reduction in impact toughness of S700 MC-1 can be observed compared to the results at FL + 3 mm, whereas S690 QL with higher heat inputs has a reduced impact toughness at the location of FL + 3 mm; with higher heat inputs at FL + 5 mm, the impact toughness is significantly improved.  
Figure 10: Charpy-V impact toughness results for the base materials. The error bars illustrate the standard deviation of the three individual tests.  
Figure 9: Average displacement-load/width curves for each heat input: (**a**) face side bending; (**b**) root side bending; (**c**) formation of the gap during testing induces the change in slope of the curve.
## 5 Results and Discussion
### End of Discharge (EOD) Prediction  
The EOD prediction was done with the equations given in earlier sections. Similarly to the EOL prediction, 400 particles were used for the particle filter to get a good accuracy and maintain a reasonable computer workload. These particles were initialized using random numbers from a normal distribution, with mean 0.6, 900, \(2.2\times 10^{-5}\), \(3.8\times 10^{-3}\), \(1\times 10^{-7}\), and 4 for \(\alpha_{1,0},\alpha_{2,0},\alpha_{3,0},\alpha_{4,0},\alpha_{5,0}\), and \(E_{0}\), respectively (the six components of the particle filter state vector in Equations (20)-(25) where \(\alpha=10.8\)). The algorithms track the state of the system by adjusting these parameters so the predicted voltage approaches the real measurement. The state parameters evolve with time by means of the noise added in the state transition model. Several tests are carried out for the EOD prediction framework.
# Robust Control Examples Applied to a Wind Turbine Simulated Model
## 3 Data-Driven and Model-Based Control Designs
### Data-Driven Fuzzy Control Strategy
Therefore, the state of the fuzzy controller \(\mathbf{x}_{k}^{(c)}\) is updated using the process model state \(\mathbf{x}_{k}^{(m)}\) and the reference input \(r_{k}\). These computations are performed using standard matrix operations, thus making the algorithm suitable for real-time implementations [30]. Note that, with reference to the data-driven fuzzy approach, the stability issue is analyzed here both during the estimation stage and in simulation. On the one hand, the derivation of the fuzzy regulator is achieved in practice by means of a dynamic inversion of the process model of Equation (30). In fact, by using the fuzzy approach considered here, this dynamic inversion of the process model of Equation (31) is achieved by means of the computation of the TS fuzzy prototype describing the controller model of Equation (33). In more detail, the derivation of the fuzzy controller relies on two steps. In the first step, the fuzzy model of the controlled process of Equation (31) is obtained. This model is used to estimated the prototype of the fuzzy regulator of Equation (33). In fact, the fuzzy model of the process is exploited to generate the state \(\mathbf{x}_{k}^{(m)}\) and the other signals \(y_{k}\) that are required for the identification of the fuzzy model of the controller. Note also that in this iterative procedure, the simulations are performed using only one-step-ahead predictions provided by the relations of Equations (31) and (33). In this way, the practical implementation of this algorithm does not suffer from convergence problems. Moreover, the minimization of the difference between the reference and the desired output \(|r_{k+1}-f\left(\mathbf{x}_{k}^{(m)},\,u_{k}\right)|\) with proper accuracy guarantees the derivation of the optimal fuzzy controller. The analytical proof of the stability of the overall system can rely on Lyapunov theory, as described, e.g., in [31]. On the other hand, the stability of the overall system is verified in simulation, when the proposed fuzzy regulator is applied to the wind turbine benchmark and the Monte Carlo tool is considered. These simulation results will be summarized in Section 4. As already remarked, the effects of the model uncertainty and disturbance lead to a different behavior of the model with respect to controlled process, thus resulting in a mismatch between the process outputs \(y_{k}\) and their references \(r_{k}\). This mismatch can be compensated by means of the on-line mechanism described by the expressions of Equations (31) and (33). These issues motivate the model-based strategy relying on the adaptive algorithm proposed in Section 3.2. Note finally that the fuzzy controller proposed in this section and depicted in Figure 5 will replace the baseline wind turbine regulator of Section 2 and reported in Figure 4.
# Beam Training for Millimeter-Wave Communication Based on Tabu Table Enhanced Rosenbrock Algorithm
## 6 Experimental and Result Evaluations
### Boundary Processing  
In order to ensure that the beam search is fast and efficient, the boundary problem described in detail in Section 5 is solved by the wrapping around method, including the neighborhood boundary and the probe boundary. The wrap around at the neighborhood boundary is applied to ensure the continuity of the beam index, and the wrap around at the beam index is applied to ensure the continuity of the search direction. In Figure 4, we plot the results of 100 independent implementations in both classical technique (i.e., without wrapping around) and wrapping around. From the performance  
Figure 3: Performance of the Rosenbrock search in mm Wave beam training. (**a**) Classical Rosenbrock algorithm; (**b**) \(RA+SA\); (**c**) \(RA+SA+TS\) algorithm.  
analysis method above, it can be seen that applying the wrap around method can greatly improve the performance of the algorithm, so it is necessary to apply the wrap around method to solve the problem of the boundary.
## 1 Introduction
### What Programming Language and Operation System Are Used?  
Without a doubt, the \"C\" programming language is the number one choice and the de-facto market standard for embedded systems and thus CubeSat's software development [24]. The following factors contribute to the popularity of \"C\":  
* It has minimalistic overhead, which is a must for the low power systems;
* It allows one to use both COTS and a truly proprietary hardware (processor) platform;
* Typical CubeSat FSW requires a lot of near-hardware programming, where \"C\" is very powerful and effective;
* It requires less time to start development (in comparison to object-oriented languages such as C++ or Rust).  
Therefore, in this article, the authors concentrate on the use of the \"C\" language for the experiment. With the experimental work, the authors will later compare the performance of the pure \"C\" code and \"C\"-written containers for the WASM3 containers engine. The term \"Native C\", is introduced later in the article, means the C-language written software that is compiled by a dedicated and specialized to the specific hardware platform compiler. In other words, it is the most effectively compiled source code for a given hardware platform. Looking into the operation systems (OS) used in the CubeSat industry, it is pretty clear that it is an OS called FreeRTOS that is leading the development team's choice [25]. Therefore, FreeRTOS is selected to be used for all the following experiments described in this article.
# MA_W-Net-Based Dual-Output Method for Microseismic Localization in Strong Noise Environments
## 3 Numerical Example  
### Data Organization  
For microseismic source localization, we typically need a good understanding of the background velocity model of the study area. In this study, we assume that we could obtain a good velocity model of the study area using many velocity inversion methods. We selected the Marmousi [32] model as the background velocity model, which can simulate complex geological conditions. We assumed that the true velocity model is known. We assessed the model's predictive performance across various SNRs. Then, a comparison was made with the single-task localization model.  
We use the finite difference method to generate simulated microseismic records. Figure 9 shows the velocity model, which has an extent of 640 m \(\times\) 1280 m and a grid spacing of 10 m: A total of 64 geophones were positioned at every other grid point on the velocity model's surface. We use a Ricker wavelet with a sampling rate of 1 ms as a function of the source time, and the source location and excitation moment are randomly generated. Data were generated using one seismic source at a time, 4000 training data and labels were prepared, and the simulated data were divided into training and test sets in a 9:1 ratio.  
Our computing device is NVIDIA Quadro RTX 4000, our neural network framework is TensorFlow-GPU 2.7.0, with 8 GB of video memory. We use ReLU as the activation function of the network, with the Sigmoid function applied in the final layer. We trained the network using [PERSON]'s algorithm [33]. The entire training process was carried out for 600 iterations, the learning rate was set to 0.0001, and the number of training data in each batch was 32. This setting provides a basis for subsequent model evaluation and comparison.
# Probing Collisional Plasmas with MCRS: Opportunities and Challenges
## 3 Results and Discussion
### Contributors to Changes in the Resonant Behavior
#### 3.2.2 Influence of the Cavity Walls' Temperature
Although this effect is measurable, it is significantly smaller than the correction term of several tens of kHz/K due to thermal expansion of the cavity walls. Hence, the complex relationship between the change in the width of the peak and quality factor is not taken into account. The complexity of correcting for this effect lies in the fact that the correction term of the probed frequency depends on the spectral distance to the resonance frequency. Due to the frequency-resolved measurement approach used in the experiments at atmospheric pressure [18; 19; 20], it is very difficult to determine the spectral distance because the data in a spectral response for a single time step is obtained with variations in the temperature of the cavity. The other mechanism in which the conductance \(\sigma\) of the wall influences the response of a cavity is via the skin depth of the resonant field into the metal walls, which makes the cavity appear to be a bit bigger for the microwaves than the physical dimensions of the cavity. The magnitude of this mechanism is estimated for the TM\({}_{\rm 010}\) resonant mode and the setup used in reference [18]. The skin depth \(\delta\) is expressed as [51],  
\[\delta=\sqrt{\frac{\rho}{\pi f_{\rm res}\mu_{0}\mu_{\rm r}}}, \tag{22}\]  
where \(\rho\) is the resistivity (\(\rho=1/\sigma\)) and \(\mu_{\rm r}\) the relative permeability, which is 1.000022 for aluminium [52]. The resistivity \(\rho\) of aluminium [50] is used and interpolated in order to calculate the skin depth for 300 K and 301 K. The difference in \(\delta\) between the two temperatures is \(\sim\)3 nm, which translates into a \(\frac{\Delta f_{\rm res}}{\Delta T_{\rm cav}}\) of 0.3 kHz/K assuming the microwaves penetrate a single skin depth into the opposing walls on both sides. Hence, also the change in resonant behavior due to the alteration of skin depth is much smaller than the effect that thermal expansion induces on the resonant behavior. However, this effect is also well within the measurement range with the renewed resolution discussed in Section 3.1. To recapitulate, a change in the temperature of the metal structure surrounding the cavity affects the resonant behavior in three ways: thermal expansion of the cavity, widening of the peak due to Ohmic losses, and a change of the skin depth making the cavity 'virtually' larger. Thermal expansion of the cavity's walls appeared to be the dominant process and is, therefore, the only mechanism that is taken into account in the temperature-corrected apparent frequencies. Please be aware that for different wall materials and dimensions of the cavity itself, the extent of each of these contributions should be reconsidered.
# Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation
## 3 Situation-Based Contextual Model: Definitions and Formalizations  
Situations are increasing in unprecedented ways in different areas of pervasive environments. Users can be found in different situations surrounded by various mobile devices, such as personal mobile phones to accomplish their daily tasks. In practice, the specification of a huge number of situations for each user is quite difficult. However, previous studies [27; 28] reveal that the recommendation of the right situation rule in the right context is also a real problem for users, even beyond the users' experiences. Therefore, it is necessary to recommend useful rules through a recommendation process according to the available devices with current context to automatically enrich user's agenda. In this context, three approaches can be used, which are content-based filtering, collaborative filtering and hybrid filtering [28]. In this work of recommendation of relevant situation rules, ontology and hybrid filtering with Bayesian-classifier can play a crucial role to implement context-aware rules classification that enhances relevancy of suggested rules. Realizing ontology-based probabilistic system for context-aware situations enrichment and adaptation in smart environments gives two main benefits for both semantic and relevance recommendation. The first benefit is achieved through the generic and extendable ontology model Multi-OCSM (Multi-layered Ontology-based Composite Situation Model) [28] to unify the representation of situation rules as well as the sharing and classification of context rules. Multi-OCSM is a formal context and situation-based ontology that can play a vital role in facilitating reasoning by formally representing and reusing smart domain knowledge. We used Multi-OCSM to classify user's situation rules (e.g., role-based, localization-based and rule-based on localization and role) before the recommendation process to achieve a high quality of recommendation considering three context categories (user preference, situation context and device capability). We aim to achieve a modular and extensible ontology, additional fields should be easily incorporated according to the characteristics of the examined smart domain. The second benefit is attained through the combination of user explicit and implicit preference, situation context, and device capability with the ultimate goal of covering a multidimensional recommendation space and performing the recommendation system. We detail in the next section the multidimensional recommendation space of the proposed recommendation approach.
# A Novel Wavelet Selection Method for Seismic Signal Intelligent Processing
## 3 Results  
To verify the feasibility of our method, we used it in an automated classification study of mining-induced microseismic data and a P arrival picking research of seismic data.
## 3 Hardware Description  
### The Network  
The system goal is to create an identification network which is able to localize mammals in open environments. The network architecture proposed is made up of three different elements: primary nodes, secondary nodes and base stations (see Figure 1). Primary and secondary nodes are mounted on animals and therefore are mobile nodes, while base stations are static nodes. Secondary nodes are the simplest elements in the network. They take kinetic energy from the animal movements which produce just enough power to create and transmit a unique identification (ID) to the environment without confirmation of its reception. If a primary node is within the range of transmission, it receives and stores the transmitted ID. Moreover, primary nodes are able to obtain their global position thanks to a Global Positioning System (GPS) which can be switched on and off depending on the final application needs. Therefore, a primary node, which receives a transmission from a secondary node, approximates the secondary node position through its own location. Moreover, while the primary node is moving in the environment, it creates a table with the different secondary nodes IDs received, their approximated position and the time when the transmission was produced. When a primary node enters within the base station communication range, it downloads all the information acquired from the secondary nodes along with its own trajectory information. A base station is a static battery-powered node which has access to the Internet and is able to offer the data to a final monitoring system. Therefore, once the base station receives the data dumped by the primary node, it will send it to the monitoring system. This monitoring system will receive information from different base stations, hence it will merge all the information and supply it to the final user.  
Following these principles, the final system has information about position estimates of primary and secondary nodes. Therefore, it will be able to reconstruct the movements carried out by the different animals. Notice, that this reconstruction is an approximation of the real trajectories followed by the individuals, because of the stochastic transmission of secondary nodes, the probabilistic reception of the primary nodes and the discretization of the GPS readings depending on the final application. As observed, the system provides a network structure to monitor animals in open environments. However,for its correct working in a specific and real application, some parameters such as the time the primary nodes are listening to the secondary nodes, the times a GPS connects to obtain its position, the ratio between primary and secondary nodes, or the density of base stations among others, must be defined for each specific application. Because secondary nodes are non-battery-powered nodes, simpler and cheaper than primary nodes, the designers' goal should be to try to reduce the number of primary nodes and base stations in favor of secondary nodes.  
For the correct understanding of the network, the forthcoming subsections detail the different hardware nodes implemented.