esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Several experimental works on graphene have been published, and all of them seem to indicate the presence of a population of hot carriers under strong optical excitation [3; 4; 5; 6; 7; 8; 10; 11; 387; 388; 39; 40; 41; 42]. In this work, we formulate a two-step relaxation model designed to help in understanding the dynamic characteristics of out-of-equilibrium carriers. While previous models targeting this goal have been published [3; 10; 36; 37; 43], ours differs from them in several features, (such as the distinction between non-thermalized and hot carriers and spatial diffusion of the latter) that enable us to explore the temporal evolution of hot carriers and their responses to various experimental parameters in this non-equilibrium state. We emphasize that the relaxation of the excited electrons occurs in two steps: (i) fast thermalization and (ii) slower cooling. Both steps are taken into consideration explicitly in our model via corresponding balance equations (while, e.g., Ref. [37] considers only one balance equation for the whole concentration of all non-equilibrium electrons and the model of Ref. [10] includes only energy balance equations for the cooling stage). In order to test the reliability of our model, we compare it to experimental results obtained in pump-probe experiments by [PERSON] et al. [7]. Our goal extends beyond the development of a hot electron dynamics model itself; we also present expressions for observables relevant in the framework of pump-probe spectroscopy. This not only elucidates the fundamental role that these electrons play but also facilitates a comprehensive understanding of pump-probe experiments and the operation of devices such as graphene-based photodetectors. The article is organized as follows. In Section 2.1, we provide an overview of the dynamics that occur in graphene upon ultrafast optical excitation. This includes the generation of a hot carrier gas and the subsequent relaxation through various decay channels. In Section 2.2, we present a detailed description of our model, which enables us to calculate the transient evolution of both non-thermalized and thermalized carriers, and we delve into its impact on graphene's optical conductivity and the distribution function of its electrons. In Section 3, we employ our hot electron model in order to reproduce and understand experimental differential reflectivity signals obtained by [PERSON] et al. [7]. The final section is dedicated to a summary and concluding remarks.
# Experimental Investigation on the Mechanical Properties of Curved Metallic Plate Dampers
## 2 Performance Parameters of Curved Plate Damper
### Elastic Stiffness Calculation Formula  
A single semi-circular arc steel plate was taken as the research object, which can be regarded as a curved beam with two fixed ends [44], as shown in Figure 2a. Figure 2b shows the deformation sketch and internal force analysis diagram of the central axis of the semi-circular arc steel plate when displacement (\(\Delta=1\)) occurs at the support.  
According to the elastic center method, using the symmetry of the structure, \(M\) and \(Q\) are unknown symmetric forces, \(F\) is an unknown anti-symmetric force, and the force method equation can be simplified as follows:  
\[\left.\begin{array}{c}\delta_{11}M+\delta_{12}Q+\Delta_{1C}=0\\ \delta_{21}M+\delta_{22}Q+\Delta_{2C}=0\\ \delta_{33}F+\Delta_{3C}=0\end{array}\right\} \tag{1}\]  
Using the rigid arm, the sum of \(\delta_{12}\) and \(\delta_{21}\) equals zero. Therefore, the simplified form of the equation is expressed as follows:  
\[\left.\begin{array}{c}\delta_{11}M+\Delta_{1C}=0\\ \delta_{22}Q+\Delta_{2C}=0\\ \delta_{33}F+\Delta_{3C}=0\end{array}\right\} \tag{2}\]  
where \(\Delta_{1C}\) and \(\Delta_{2C}\) are zero due to the horizontal displacement of the support. In addition, the bending moment (\(M\)) and vertical force (\(Q\)) at the elastic center are both zero. Thus, the above simplified equation can be written as follows:  
\[\delta_{33}F+\Delta_{3C}=0 \tag{3}\]  
where \(\delta_{33}\) and \(\Delta_{3C}\) can be written as  
\[\begin{array}{ll}\delta_{33}&=\int\frac{\overline{M}_{3}^{2}}{E\overline{I }}d_{s}+\int\frac{\overline{F}_{3}^{2}}{E\overline{A}}d_{s}+\int\frac{ \overline{Q}_{33}^{2}}{\overline{A}}d_{s}\\ &=\int\frac{\overline{F}_{3}^{2}}{\overline{A}}\frac{\overline{E}^{2}\sin^{2 }\alpha}{E\overline{I}}Rd\alpha+\int\frac{\overline{F}_{3}^{2}}{2}\frac{\sin^ {2}\alpha}{E\overline{A}}Rd\alpha+\int\frac{\overline{F}_{3}^{2}}{2}\frac{ \cos^{2}\alpha}{\overline{A}}Rd\alpha\\ &=\frac{R\pi}{2}\Big{(}\frac{\overline{R}^{2}}{E\overline{I}}+\frac{1}{E \overline{A}}+\frac{1}{\overline{A}\overline{A}}\Big{)}\end{array} \tag{4}\]  
\[\Delta_{3C}=-1 \tag{5}\]  
Introducing Equations (4) and (5) into (3), the force can be obtained as  
\[F=-\frac{\Delta_{3C}}{\delta_{33}}=\frac{2}{R\pi\Big{(}\frac{\overline{R}^{2} }{E\overline{I}}+\frac{1}{E\overline{A}}+\frac{1}{\overline{A}\overline{A}} \Big{)}} \tag{6}\]  
Figure 2: Diagram of a single semi-circular arc steel plate (**a**) and computing model (**b**).  
where \(F\) is the force at the end of the damper with unit displacement, which is the elastic stiffness of the damper with a semi-curved steel plate, and \(G\) is the shear modulus of elasticity (\(G=0.4E\)). The elastic stiffness of the damper can be simplified as follows:  
\[K=\frac{Ebt^{3}}{R\pi(6R^{2}+7t^{2}/4)} \tag{7}\]  
where \(E\) is the elastic modulus of the material, \(b\) is the width of the curved plate damper, and \(t\) is the thickness of the curved plate damper.
###### Abstract  
Accurately predicting the permeability of coarse-grained soils is crucial for ensuring geotechnical safety and performance. In this study, 64 coarse-grained soil (CGS) samples were designed using a negative exponential gradation equation (NEGE), and computational fluid dynamics-discrete element method (CFD-DEM) coupled seepage simulations were conducted to generate a permeability coefficient (k) dataset comprising 256 entries under varying porosity and gradation conditions. Three machine learning models--a neural network model (BPNN), a regression model (GPR), and a tree-based model (RF)--were employed to predict \(k\), with hyperparameters optimized via particle swarm optimization (PSO) and four-fold cross-validation applied to improve generalization. Gray relational analysis (GRA) revealed that all input parameters (\(\alpha\), \(\beta\), \(d_{\text{max}}\), n) significantly influence \(k\) (R > 0.6). The interquartile range (IQR) method confirmed data suitability for modeling. Among the models, BPNN achieved the best performance (R\({}^{2}\) = 0.99, MAE = 1.5, RMSE = 2.9, U\({}_{95}\) = 0.4), effectively capturing the complex nonlinear relationship between gradation and permeability. GPR (R\({}^{2}\) = 0.92) was hindered by kernel selection and noise sensitivity, while RF (R\({}^{2}\) = 0.97) was limited by its discrete regression nature. Compared to a traditional empirical model (R\({}^{2}\) = 0.9031), BPNN improved prediction accuracy by 10.13%, demonstrating the advantage of data-driven methods for evaluating CGS permeability.  
coarse-grained soil; permeability coefficient; CFD-DEM simulation; machine learning; particle swarm optimization; data-driven approach +
Footnote †: journal: Applied sciences
# Prediction of Weights during Growth Stages of Onion Using Agricultural Data Analysis Method
## 3 Dataset and Method  
### Dataset  
Here we are going to consider how to grow onions, which are popular among Koreans and used in various dishes, to improve onion growth. Generally, onions are sown in mid-August to mid-September, planted quite early in October to early November, and harvested between May and June of the following year. Figure 1 shows the onion growing step by step [11].  
In this study, we used datasets of the various environmental factors and the weights of onion during growth stages collected from farmers in several regions of Korea from March 2019 to June 2019. We used six factors such as mean wind speed, mean temperature, mean ground temperature, mean humidity, daily sunshine, and daily rainfall as the explanatory variables of the model, and the onion weight growth stage was used as a response variable. A description of each variable is given in Table 1 below.  
We collected a total of 178 observations from 28 farms and used them in the experiment. During the observation period, environmental factor data were used from the meteorological office data observed by region, and the onion weight was obtained from the measurements by the staff of the RDA (Rural Development Administration). Of the data collected from 28 farms, a total of 63 observations, repeated 9 times in 7 farms with relatively good measurements were used in the experiment. Here, each environmental variable was measured for 7 days and repeated for 9 weeks. Onion weight was measured 9 times from 7 farms during the same period. Finally, the data of environmental factors consisted of data obtained from measuring six factors through nine repeated measurements each week for 7 days for 7 objects. Therefore, the environmental factors are given in the form of a \(64\times 35\) matrix.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Role** & **Variable Name** & **Unit of Measure** \\ \hline Response variable & Onion weight & g/one unit \\  & Mean wind speed & mps \\  & Mean temperature & \({}^{\circ}\)C \\ Six environmental variables & Mean ground temperature & \({}^{\circ}\)C \\  & Mean humidity & \% \\  & Daily sunshine & J/cm\({}^{2}\) \\  & Daily rainfall & mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: The response variable and environmental variables used in data set.  
Figure 1: Onion growing states in the cultivation process.  
In addition, data on onion weights were obtained from nine replicate measurements of 7 farms. Thus, onion weight data is given in a matrix of \(9\times 7\). Tables 2 and 3 below show some part of the data on average wind speed among the six environmental factors as well as onion weight observed during the growth period.
# Geo-Electrical Detection of Impermeable Membranes in the Subsurface
## 6 Determining the Position of the Membrane  
In the previous section, the on-the-fly calculation of the kernel and the misfit using simulated membrane positions was described. The misfit plots in those examples were made by using a single Mode 2 array with a 80 cm size of the tool. As mentioned, the distance between the electrodes (i.e., the tool size) has a positive effect in the looking ahead distance. In this section, we investigate this looking ahead capability with respect to different sizes of the tool (or different distances between the electrodes. Figure 11 presents the looking ahead capabilities of detecting the membrane for tool sizes varying from 10 cm to 80 cm. The x-axis represents the various locations of the simulated membrane and the misfit is colour-coded. The bottom row of the plot (80 cm size) is a summary of Figures 8-10. The other rows correspond to different electrode spacings in the Mode 2 array. If there is a global minimum in the misfit for a given configuration, then the corresponding shows a single pixel colour-coded as black and all other pixels within the row with various colours. If the minimum in the misfit is broad, i.e., there are several black pixels within the same row, then the position of the membrane is not defined very well and no unique solution could be obtained. In this case, it is an indication that is the distance between the membrane and the tool is too large to determine the membrane's position.  
Each panel in Figure 11 shows the results for a specified distance between the tool and the membrane when the tool is approaching it. For larger distances (1.3 m to 0.9 m) most of the arrays cannot detect the position of the membrane, as indicated by the wide band of black pixels. However, is it still clear that the membrane is not present within the first several decimetres ahead of the tool. For 0.9 m to 0.7 m, the largest tools start detecting the membrane accurately. The black misfit becomes narrower and is presented as a single pixel. At 0.7 m distance, the tool with sizes between 60 and 80 cm provide a good estimate of the membrane position. At closer distances, the smaller tools sizes also pinpoint the membrane at the right location. The blue spike indicates the most probable location, when all measurements are combined. Similar results were found also in the homogeneous cases, with the tank filled with either water or sand (not shown). Overall we conclude that  
* a tool with a size of 45 cm was able to detect the membrane 10 to 20 cm from the true position at a distance of 0.6 m.
* a tool of size 0.65 m and larger could do the same from a distance of 0.8 m.
* closer to the membrane its location could be pinpointed at the true position or at 0.1 m.
## 2 Basic Concept
### Fundamental Principles of the Utilized Cryptographic Framework
#### 2.1.1 Theoretical Foundations of Elliptic Curve Cryptography  
In the realm of contemporary public-key cryptographic methods, Elliptic Curve Cryptography (ECC) stands out as a sophisticated approach derived from the principles of elliptic curve theory. This form of asymmetric encryption is renowned for its capacity to generate keys that are not only more compact and faster but are also superior in efficiency. Within the ECC framework, a mathematical operation is employed to amalgamate a pair of distinct keys, which subsequently serve the purpose of encoding and decoding information. The foundational element of this cryptographic system is an elliptical curve denoted as E, accompanied by a collection of coordinate points on the curve, expressed as (x, y). The representation of these points adheres to specific mathematical equations associated with the graph of the elliptical curve. Within the framework of the ECC asymmetric cryptographic setup, an elliptic curve, E, is used. This curve includes points with coordinates(x, y) characterized by \(\mathrm{P_{f}}\in\mathrm{*}\mathrm{P_{f}}\left(\mathrm{x,y}\right)\). The coordinates of the points on the graph can be determined using the following equation.  
Equation (a) \(\mathrm{Y^{2}}=\mathrm{x^{3}}+\mathrm{ax}+\mathrm{b}\)  
Two values, a and b, can be opted for from the fp field as long as they meet condition \(4\mathrm{a^{3}}+27\mathrm{b^{2}}\
eq 0\). The Elliptic Curve Encryption (ECC) system can be described based on two basic operations: dot addition and scalar multiplication. These two operations are the main functions in this encryption system. The process of scalar multiplication can be described as the calculation result of multiplying a point called P on a curve with a scalar value, K (The scalar multiplication of an elliptic curve is an operation that adds a point, P, on the curve k times.). To illustrate the correct understanding of this concept, consider the following process that represents a scalar multiplication of a point, P, by a scalar number, K (with K times P).  
Equation (b) \(\mathrm{KP}=\mathrm{P}+\mathrm{P}+\mathrm{P}+\mathrm{P}\ldots+\mathrm{P}\)
###### Abstract
In summary, IoT technology faces challenges in security and resource limitations. Optimising CP-ABE is crucial to address the constraints of IoT devices. Researchers are actively developing lightweight solutions to improve IoT security and operational efficiency while minimising the strain on device resources, ensuring that they meet the practical demands of IoT applications. The key contributions of this work are as follows:  
* The proposed scheme eliminates reliance on a central certificate authority (CA), enabling fully decentralised key management. This enhances flexibility, reduces failure points, lowers costs, and suits multi-scenario IoT applications like smart cities and industrial automation. * To improve security and efficiency in resource-constrained IoT environments, the scheme introduces a proxy key mechanism that allows partial decryption outsourcing without exposing the original attribute keys. This approach mitigates the risk of key leakage and misuse, ensuring that IoT devices can securely delegate computations while maintaining strict access control policies. * The scheme is formally proven to be secure against chosen-plaintext attacks (CPAs) under the decisional bilinear Diffie--Hellman (DBDH) assumption. This ensures that an adversary lacking the required attributes cannot distinguish an encrypted ciphertext from a random element, thereby preserving strong data confidentiality and preventing unauthorised access. This paper is structured as follows: Section 2 reviews related work, while Section 3 introduces the necessary preliminaries. Section 4 presents the system model, scheme definition, and notation. Section 5 provides a detailed description of the proposed scheme. Sections 6 and 7 present the security analysis and performance evaluation, respectively. Finally, Section 8 concludes this paper.
## 2 Water Resources Monitoring in Semi-Arid Regions: The WARM-SAR Project
### Digital Elevation Models (DEM) Estimation and Related Products  
In [23], a reliable DEM was extracted by means of an interferometric processing of two images acquired at the end of the dry season, when the interferometric coherence is expected to be sufficiently high. The resolution of the DEM (9 m, obtained by the 3-multilook of the 3 m input SAR data) is significantly higher with respect to those previously available: SRTM (resolution 90 m) and ASTER (resolution 30 m). The availability of such a high-resolution DEM allows the estimation of the bathymetry of the small reservoirs that dry up completely at the end of the arid season [15] and the derivation of an analytical relation between reservoirs' surface area and retained volume.  
The estimate of the reservoirs bathymetry from remotely sensed imagery provides valuable information about reservoir capacity. In fact, in Sahel, where sedimentation of the reservoirs due to strong soil erosion is extremely fast [24] and quickly changes the topography, there is a lack of updated topographic information. The average sedimentation rates for six reservoirs of the study area were estimated, concluding that, in about 20-30 years, most of the studied reservoirs lost more than 70% of their original capacity [25].
## 4 Results and Discussion
### Surface Finish
#### 4.2.2 Post-Processing Influence  
Post-processing further improved surface quality for both technologies. Light sanding or acetone vapor smoothing reduced FDM roughness by up to 50%, achieving \(R_{a}\approx 6\,\mathrm{\SIUnitSymbolMicro m}\). SLA parts, after post-curing and a brief surface polish, achieved \(R_{a}\approx 1.4\,\mathrm{\SIUnitSymbolMicro m}\). While  
Figure 1: Representative specimens printed via FDM (ABS) and SLA resin, illustrating typical surface finish differences in as-printed condition.  
Figure 2: Average surface roughness (\(R_{a}\)) for FDM (PLA, ABS) and SLA specimens, with one standard deviation as error bars.  
post-processing narrows the gap, SLA retains a clear advantage for applications requiring ultra-smooth surfaces or low friction.
# Spatio-Temporal Variations of Ecosystem Water Use Efficiency and Its Drivers in Southwest China
## 2 Data and Methods
### Data Analysis Methods
#### 2.3.3 Random Forest (RF) Model  
In this study, the influence of temperature, precipitation, and EVI on changes to ecosystem WUE were ranked on a pixel scale using the RF model, which is a powerful nonparametric machine learning algorithm with a clear structure that is easy to interpret and stable with less susceptibility to overfitting [28]. The RF model is commonly used for clustering and regression analysis and to assess the importance of influencing factors [29; 30; 31]. In this study, RF regression analysis was conducted with the \"randomForest\" function of R software ([[https://www.r-project.org/](https://www.r-project.org/)]([https://www.r-project.org/](https://www.r-project.org/)) (accessed on 1 December 2022)) with WUE as the dependent variable and temperature, precipitation, and EVI as the independent variables. After adjustments, the parameters \"mtry\" and \"ntree\" were set at 3 and 500, respectively, while all other parameters were set at default values. The mean decrease accuracy (%IncMSE) was calculated to quantify the relative importance of the independent variables. The RF model defines %IncMSE as the extent that model accuracy is reduced by excluding a variable. Therefore, for a given variable, the larger the %IncMSE, the greater the importance.
## 3 Results and Discussion
### Basic Parameters for the RCCI-SHV Operation  
#### 3.1.1 State of Charge Selection  
As a preliminary study, the effect of the initial SOC on the final fuel consumption was evaluated. Four values were chosen (0.84, 0.64, 0.44 and 0.24) as initial SOC values. Its charging limits were scaled by the initial value maintaining the upper and lower limits for all the cases. Therefore, the results are function of the electrical characteristics of the vehicle, such as the charging and discharging voltage, in addition to the resistance, both functions of the actual SOC. Figure 8 shows the results obtained for the SOC sweep regarding its behavior along the driving cycle (left) and the final fuel consumption (right). As can be seen, the SOC variation is very small for the initial phases of the cycle. In these conditions, there are a considerable number of decelerations, where the brake energy recovery can take place. In addition, once the engine starts, the battery is recharged fast, because the power demand is not as high. Nonetheless, as the cycle advances towards the extra high phase, the energy demand increases, thus decreasing the SOC value. In this case, the ICE plays a key role in driving the SOC to its original level. The impact of these changes for the different initial SOCs on the final fuel consumption can be verified in Figure 8b. An absolute variation lower than 2% when comparing the two extreme values is verified. Therefore, it can be stated that, despite the variations, using initial SOC values above 0.24 returns satisfactory results.  
Figure 7: Time-vehicle speed profiles of the real driving emissions (RDE) (a) and worldwide harmonized light vehicles test cycle (WLTC) (b) cycles.  
With the aim of verifying the influence of the driving cycle characteristics on the parametric study results, the same analysis was performed for the RDE driving cycle, as shown in Figure 9. The SOC variation along the cycle presents similar behavior to that found in the WLTC cycle, with the highest decrease in the SOC values occurring during the highway phase. Regarding the final fuel consumption, its variations for the different SOCs are close to 1%, smaller than those found with the WLTC. Again, the initial SOC of 0.24 was the one that presented the highest fuel consumption. The optimum operating range was found between 0.44 and 0.84. Therefore, an intermediate value of 0.64 was chosen as the boundary condition for the comparison against the combustion modes.