esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Limitations of the Study  
All LUE-GPP models yielded robust _GPP_ estimates, and model performance could be improved by optimizing the model structure or constructing \(K_{\text{t}}\)-based dynamic _LUIE_. The LUE-GPP models can be easily applied due to their simplicity and less need for parameter correction; thus, combined with satellite remote sensing data, they are widely used for calculating regional or global _GPP_ estimates [1; 7; 8]. All LUE-GPP models calculated GPP under idealized conditions and adjusted the _LUIE_\({}_{\text{max}}\) to the actual value by incorporating eco-physiological constraints. However, the photosynthetic process is complex and is affected by other abiotic or biological constraints (leaf nutrients, leaf age, environmental CO\({}_{2}\) concentration, etc.). Thus, by considering the temperature and moisture factors only, eco-physiological constraints are not fully characterized, representing a limitation of the LUE-GPP model [56].  
In addition to model structure, quantification of environmental constraints, and calibration of model parameters, the accuracy of the LUE-GPP models may be affected by the biophysical variables and carbon flux measurements [48; 57]. For instance, the mismatched scale between MODIS image pixels and tower footprints could affect the evaluation of the LUE-GPP model, and the mismatch scale in the measurement period of LAI and environmental variables may also potentially contribute to the estimation uncertainties. Specifically, the \(GPP_{\text{EC}}\) uncertainties of up to 30% were reported due to random and systematic errors, partitioning of \(NEE\) into _GPP_ and \(R_{\text{e}}\), and the use of carbon flux gap-filling methods [58]. Moreover, MODIS products contain uncertainties, especially the calculation of _LSWI_ owing to persistent cloudy weather conditions in the study area. This may result in inaccurate quantification of \(W_{\text{s}}\) using the VPM and MVPM. However, this speculation requires further verification.  
Overall, this study introduced a novel method for model optimization of GPP estimates by incorporating \(K_{\text{t}}\) into the LUE-GPP model. The proposed method could improve GPP estimates even under various weather conditions; nevertheless, optimized accuracy still relies on the ecosystem type, canopy characteristics, and weather conditions. One drawback is that the application of the model on a large scale is flawed, and the improvements in daily scale GPP rely significantly on calculating daily \(K_{\text{t}}\). Remote sensing data on an 8-day time scale will neutralize the influence of different weather conditions on the PAR components and carbon flux during this period. For example, if there are cloudy and sunny days in an 8-day period (which is very common, especially in humid regions), the overestimation and underestimation in the performance of the traditional LUE-GPP model could be offset, resulting in an inaccurate model evaluation.
# Vibration Fatigue Analysis of a Simply Supported Cracked Beam Subjected to a Typical Load
## 2 Modal Analysis
### Modal Analysis  
As shown in Figure 1, the homogeneous slender straight beam can be regarded as the Euler-Bernoulli beam [21], and the vibration differential equation can be expressed as:  
\[\rho A\frac{\partial^{2}y(x,t)}{\partial t^{2}}+E^{*}I\frac{\partial^{4}y(x,t )}{\partial x^{4}}=0 \tag{3}\]  
where \(\rho\) is the material density; \(A\) is the cross-section area of the beam; and \(I\) is the cross-section moment of inertia.  
Equation (3) is a fourth order homogeneous differential equation with constant coefficients, and can be solved by the separation variable method [22]:  
\[y(x,t)=y(x)sin(\omega t) \tag{4}\]  
Substitute Equation (4) into Equation (3), and motion equations of two stage beams can be yielded:  
\[\left\{\begin{array}{l}y_{1}(x)=c_{1}\sin(\lambda x)+c_{2}\cos(\lambda x)+ c_{3}\mathrm{sinh}(\lambda x)+c_{4}\cosh(\lambda x)\\ y_{2}(x)=c_{5}\sin(\lambda x)+c_{6}\cos(\lambda x)+c_{7}\mathrm{sinh}(\lambda x )+c_{8}\cosh(\lambda x)\end{array}\right. \tag{5}\]  
where \(\lambda\) is the frequency constant, and \(\lambda^{4}=\omega^{2}\rho A/(E*I)\); \(c_{1},c_{2},\cdots,c_{8}\) are the undetermined coefficients, which can be determined by boundary conditions.  
Due to the simply supported beam, boundary conditions can be written as follows:  
\[\left\{\begin{array}{l}y_{1}(0)=0;y^{*}_{1}(0)=0\\ y_{2}(L)=0;y^{*}_{2}(L)=0\\ y_{1}(x_{c})=y_{2}(x_{c})\\ M_{1}(x_{c})=M_{2}(x_{c})\\ F_{1}(x_{c})=F_{2}(x_{c})\\ -E*\frac{\partial^{2}y_{1}(x_{c})}{\partial x^{2}}=K_{T}\Big{[}\frac{ \partial y_{1}(x_{c})}{\partial x}-\frac{\partial y_{2}(x_{c})}{\partial x} \Big{]}\end{array}\right. \tag{6}\]  
Figure 1: Model of the beam with a unilateral crack.  
Substitute Equation (5) into Equation (6), algebraic equations with undetermined coefficients \(c_{1},c_{2},\cdots,c_{8}\) can be obtained, which can be transformed into the matrix form:  
\[H[c_{1}\quad c_{2}\quad c_{3}\quad c_{4}\quad c_{5}\quad c_{6}\quad c_{7}\quad c _{8}]^{{}^{\prime}}=0 \tag{7}\]  
where \(H\) is the coefficient determinant matrix.  
Through the condition that the algebraic equations have nonzero solutions, the value of the coefficient determinant is zero, then natural frequencies of the simply supported cracked beam can be obtained:  
\[\det(H)=0 \tag{8}\]
# Influence of Applied Stress on the Ferroelectricity of Thin Zr-Doped HfO\({}_{2}\) Films
## 1 Introduction  
Doped ferroelectric HfO\({}_{2}\) materials have been widely studied alongside low power logic devices and non-volatile memory due to their scalability and compatibility with traditional CMOS processes [1; 2; 3; 4]. Meanwhile, they are widely used in micro- and nanofluidic devices [5; 6]; however, there are still problems regarding the fatigue or poor ferroelectricity in ferroelectric materials that prevent full application, so the optimization of modification for ferroelectric materials has been an important challenge for ferroelectric materials [7], e.g., with baseline technologies the write cycles of SRAM or DRAM is more than \(10^{16}\), whereas the endurance of HfO\({}_{2}\)-based FeRAM is about \(10^{5}\) to \(10^{10}\)[8; 9; 10] and Hf-based ferroelectric devices have so far been unable to achieve an endurance comparable to that of the conventional memory devices [11]. The endurance characteristics of ferroelectric FETs originate from two main sources. One is the fatigue of ferroelectric materials, caused by internal material stress, space charge, and electric domain pinning [12; 13]. On the other hand, it is also caused by the degradation of the reliability of the gate stack, where the high electric field will lead to an increase in the defect density in the interface layer and result in deterioration of the quality of the interface layer [14]. The current research on the optimization of ferroelectricity mainly includes doping [15; 16; 17; 18; 19; 20; 21; 22], the annealing temperature [23; 24], changing electrode materials [25; 26], the optimization of material growth methods [27] and so on. According to existing studies [28; 29], stress may change the generation of crystallinephases in ferroelectric materials and thus affect the formation of ferroelectric domains. Consequently, stress may have an effect on the ferroelectricity and fatigue characteristics of ferroelectric materials; however, little research has been reported on the effect of stress on the ferroelectricity of Hf-based ferroelectric materials.  
In this paper, we systematically investigate the effect of stress on the ferroelectricity of HZO ferroelectric materials by characterizing three aspects: remnant polarization, the coercive electric field, and endurance characteristics and obtain results for the regulation of stress regarding ferroelectricity for ferroelectric materials by comparing the properties of ferroelectric materials under different annealing temperatures. A 10-nm HZO capacitor structure with stress conditions at an operating voltage of 3 V with 2P\({}_{\rm r}\) between 30-45 \(\upmu\)C/cm\({}^{2}\) endurance characteristics operating in 10\({}^{9}\) cycles without breakdown is demonstrated. The optimized effect of stress on endurance greatly increases the potential of HZO ferroelectric materials for future applications in high performance low power devices.
# Hydrogen from Depleted/Depleting Hydrocarbon Reservoirs: A Reservoir Engineering Perspective
## 5 H\({}_{2}\) Generation in DGRs
### Reservoir Engineering Aspects
#### 5.2.3 Associated Risks  
The thermal effects of hot water/steam injection should be considered in the designing of new wells and recompletion of existing wells. Thermal cycling can result in low-cycle fatigue failure in the casing string, either in the pipe body or at a connection. Therefore, it is important to consider the strain capacity of the casing and connections used in related wells [69, 70]. The effect of the thermally induced stresses can also be relevant on the geomechanical stability of the reservoir. This is of particular importance considering the confinement issue of the generated gases, e.g., CO\({}_{2}\) and H\({}_{2}\)[71]. On the other hand, if H\({}_{2}\) is stored or produced at the top of the reservoir, as claimed, it becomes more difficult to ensure containment requirements. This is due to the considerably higher mobility of H\({}_{2}\), which has a lower viscosity (approximately two times lower than CO\({}_{2}\) at the relevant pressure and temperature) and higher diffusivity.  
There are some other discussions on the challenges of in situ H\({}_{2}\) production in DGRs. One of these is related to the extreme chemical inertness of the CH\({}_{4}\) molecule (due to strong C-H bonds and the lack of polarity), which impedes the CH\({}_{4}\) decomposition process. As a result, it is affirmed that carrying out reaction (4) at a reasonable rate would require an energy input, either in the form of much higher temperatures (>1000 \({}^{\circ}\)C) or as electrical discharge (plasma). The use of catalysts as in the above-given example may reduce the maximum temperature of the process but at the cost of added complexity (e.g., difficulty of separating the carbon product from the catalyst) [72].  
The key question in the engineering process is \"how and why should the in situ H\({}_{2}\) generation in a DGR be competitive to SMR at surface plants including GCS?\".
## 3 Results
### Isotherm Behaviors
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Unit**} & \multicolumn{3}{c}{**Temperature**} \\  & & **20 \({}^{\circ}\)C** & **35 \({}^{\circ}\)C** & **50 \({}^{\circ}\)C** \\ \hline Langmuir & & & & \\ Q\({}^{\circ}\)max & mg/g & 183.6 & 198.0 & 243.6 \\ K\({}_{\rm L}\) & L/mg & 0.0047 & 0.0045 & 0.0041 \\ R\({}^{2}\) & — & 0.994 & 0.995 & 0.995 \\ \(\chi^{2}\) & — & 6.73 & 7.02 & 11.23 \\ Freundlich & & & & \\ K\({}_{\rm F}\) & (mg/g)/(mg/L)\({}^{-1/n}\) & 2.58 & 2.80 & 2.77 \\ n\({}_{\rm F}\) & — & 1.507 & 1.502 & 1.441 \\ R\({}^{2}\) & — & 0.992 & 0.989 & 0.986 \\ \(\chi^{2}\) & — & 7.93 & 16.02 & 29.20 \\ Redlich– & & & & \\ Peterson & & & & \\ K\({}_{\rm RP}\) & L/g & 1.37 & 0.99 & 1.00 \\ \(a_{RP}\) & (mg/L)\({}^{-}\)g & 0.098 & 1.006 & 0.004 \\ g & — & 0.583 & 0.00 & 1.00 \\ R\({}^{2}\) & — & 0.991 & 0.901 & 0.994 \\ \(\chi^{2}\) & — & 8.23 & 169.62 & 12.84 \\ [PERSON] & & & & \\ Q\({}_{\rm max}\) & mg/g & 299.9 & 215.4 & 211.6 \\ K\({}_{\rm g}\) & L/mg & 0.002 & 0.004 & 0.005 \\ n\({}_{\rm L}\) & — & 0.823 & 0.955 & 1.088 \\ R\({}^{2}\) & — & 0.996 & 0.995 & 0.995 \\ \(\chi^{2}\) & — & 4.96 & 7.84 & 12.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameters of adsorption isotherms of CV dye onto DSD adsorbent.
## 3 Information Centric Networking (ICN)
### Caching  
Content caching is one of the fundamental architectural building blocks of ICN networks, enabling fast, reliable, and scalable content delivery. Each NDN node in the network has the capability to cache named content and respond to requests for it. Reacting to a content request, an NDN node either responds directly if it has the content in its local cache, or sends a request message to other nodes. Content is cached once the request is fulfilled. ICN caching democratizes content delivery since caching is implemented by all network routers involved instead of a few specialized cache nodes. Several caching schemes [52] have been proposed for optimal caching and efficient resource utilization.
## 2 Materials and Methods
### Training and Validation Sample Data  
Two thousand four hundred homogeneously distributed sample points were collected to train the RF classifier and validate the LULC classification results (Table 1). About 850 of these points were obtained from the Brazil Data Cube, a geospatial platform developed by the National Institute for Space Research (INPE), Brazil [44]. Considering the size of the study area and the research objectives, this initial dataset was integrated into QGIS with additional sample points, obtaining LULC information from Bing maps, Google maps, and the PL data (visible and infrared composite orthoimages) [45]. This approach is largely used in the literature [16; 46]. The visual comparison of the available base images and the classification of sampling points was performed within the interface of the AcATaMa QGIS plug-in [47]. According to the already available ground truth dataset, seven classes were used for the LULC classification (Table 1).
## Materials and Methods
### Estimating Moisture (VCI) and Temperature (TCI) Contributions to VHI  
The procedure to estimate parameter \(\alpha\) in Equation (1) follows the one described in Ref. [13]. Here, VHI is first estimated with the three different LST datasets by assuming the traditional \(\alpha=0.5\), i.e., equal contributions of the moisture (VCI) and temperature (TCI) conditions to vegetation health. For each pixel, the month of climatological maximum NDVI is chosen, and time series of VHI of that month are correlated with time series of SPEI from the previous month at time-scales of 1-, 3-, 6-, 9-, and 12-months. The month of maximum NDVI is chosen since it is a good indicator to assess the influence of droughs measured with SPEI in the growing season to the vegetation health of the peak vegetation productivity period. The choice of this month also simplifies the analysis in terms of the amount of information to process. Then, the SPEI time-scale that presents the maximum correlation with VHI is chosen [62] and a \(p\)-value of 0.1 for the two-tailed t-test is used to reject the hypothesis of no correlation.  
For pixels where the obtained correlation is significant, an iterative maximization of the correlation between VHI and SPEI is performed, using values of \(\alpha\) ranging from 0.025 to 0.975 in steps of 0.05. This may be regarded as an adjustment of the contributions of VCI and TCI for pixels where the traditional definition of VHI is positively correlated with SPEI. Figure 1 shows the resulting maps of \(\alpha\) and respective SPEI time-scale.
# Gaussian Process Model-Based Performance Uncertainty Quantification of a Typical Turboshaft Engine
## 1 Introduction
Two objectives of this study are to quantitatively describe the characterization and propagation of the component performance uncertainty in the engine, and additionally determine the principal contributors to the performance uncertainty of the engine. To this end, a model is firstly built to simulate the turboshaft engine performance based on thermodynamic cycle. Six important component performance parameters are chosen as the uncertain parameters to be investigated. Each uncertain parameter is characterized with a suitable probability density function. Moreover, three performance metrics (PMs) of the engine are also determined considering the general acceptance requirements of customers. To reduce the computational cost, three independent GPMs associated with the PMs are built to substitute the expensive simulation model. The accuracies of the models are examined considering the effects of run size and sampling error. Based on the GPMs with better prediction performance, uncertainty analysis is performed by applying LHS to propagate the component performance uncertainty to the engine performance. The accuracy of the uncertainty analysis result is analyzed considering both approximation and sampling errors. A detailed comparison between the GPM-based LHS and the simulation-based MCS is performed to validate the accuracy, robustness, and convergence rate of the proposed method. Two different scenarios are investigated and compared, where six uncertain parameters are considered to be mutually independent and partially correlated, respectively. Eventually, the key uncertain parameters are determined by implementing the variance-based sensitivity analysis. Similarly, the effects of approximation error and sampling error are explained in the sensitivity analysis. The paper is organized as follows. Firstly, the configuration, operating principle, and simulation model of the turboshaft engine under investigation are described. Next, the methodologies employed in this study are introduced. After that, the uncertainty quantification of the engine performance is reported in detail. Finally, concluding remarks are given.
# Predicting and Modeling Wildfire Propagation Areas with BAT and Maximum-State PageRank
## 2 Overview of Wildfire Propagation, Scale-Free Network, Barabasi-Albert Model, and BAT
### Wildfire Propagation  
The landscape is mapped into an undirected binary-state network \(G(V,E)\), where \(V\) is the set of nodes representing towns, cities, forests, etc., and \(E\) is the set of undirected links connecting the nodes in \(V\), such that two neighboring nodes are connected by an undirected link. Each node exhibits two states: with or without wildfire, and each link is assumed to be always perfect without any possible failure.  
For example, Figure 1 illustrates a landscape network representing eight areas labeled 0-7 with 15 links connecting these nodes, e.g., nodes 3 and 7 are neighboring areas as there is a link between the two nodes.  
The scale-free network grows independent of the number of nodes, i.e., the size of the network and underlying structure remains the same. The propagation of wildfire satisfies this property, and is thus considered as a scale-free model in this study. In this subsection, the model of the wildfire propagation is further modeled as a scale-free model with three prospects: ignition and spread of wildfire, probability of burning, and fire head intensity.  
The ignition and spread of wildfire exhibit a high correlation with heat diffusion, which is a general observed natural phenomenon [59] and can be studied as a scale-free network. Heat diffusion is observed from high-temperature regions to lower temperature regions and this phenomenon is observed in the propagation of wildfire. Therefore, recently, heat diffusion has been implemented to model numerous networks whose information or flow is shared and propagated on online social networks.  
From the MedSpread model, the probability of burning is defined in [31] as  
\[P=(1-e^{-r})p \tag{1}\]  
where \(r\) is the spread rate and \(p\) is the fire intensity, representing the relation between the fire spread and the probability of burning. Equation (1) is similar to the power functions.  
The fire head intensity \(q\) (kW/m) is the rate of energy release per unit length of the fire head (kW/m). Regardless of the depth of the fire and fuel characteristics, \(q\) is calculated as follows [36]:  
\[q=260\ L^{2.174} \tag{2}\]  
where \(L\) is the flame length (m). Equation (2) is a power function.  
Any model that follows the power law can be considered as a scale-free model, as reported in [44]. From the aforementioned heat diffusion and Equations (1) and (2), the wildfire propagation can be  
Figure 1: Example network.  
modeled as a scale-fee network. Hence, we adopt the scale-free model to estimate the possible affected areas during wildfire propagation.
## 4 Results and Experiments
### Dataset Description and Preprocessing Overview
#### 4.1.2 NF-BoT-IoT-v2 Dataset  
An IoT NetFlow-based dataset was developed by extending the NF-BoT-IoT dataset, featuring data meticulously extracted from publicly available pcap files and flows categorized based on their corresponding attack types. This dataset comprises 37,763,497 data flows, of which 37,628,460 (99.64%) are identified as attack instances, while 135,037 (0.36%) represent benign traffic [13]. It encompasses five distinct classes, including one benign category and four unique attack types. Data preprocessing is a critical component of machine learning workflows, transforming raw data into a structured and analysis-ready format to optimize model performance. For the NF-BoT-IoT-v2 dataset, this process began with handling missing values by removing incomplete records, followed by eliminating duplicate entries to prevent redundancy. Outliers were identified and addressed using a combination of Z-score and LOF methods, ensuring a more robust detection of anomalies and improving data integrity. Feature selection based on correlation analysis reduced dimensionality by retaining only the most significant features, while MinMaxScaler standardized the numerical data for consistent representation. Principal component analysis (PCA) was employed in the binary classification to minimize redundancy and retain essential features. The dataset was initially split into training and testing subsets then recombined for the application of the ADASYN method, generating synthetic samples to address class imbalances. After this augmentation, the dataset was re-split, keeping the test set intact. The ENN method was applied for undersampling, removing noisy or borderline samples, and class weights were adjusted during training to further address imbalances. This comprehensive approach ensured the model could effective learn from the data, enhancing its ability to generalize and deliver accurate predictions for detecting anomalies and classifying network traffic.