esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Discussion
### Scenario (3)  
In this scenario, load disruptions and wind turbine disruptions have been applied to the power system according to Figure 10. In this scenario, slight uncertainty related to the power system parameters is considered in the system inertia (\(H=-25\%\)). In Figure 12a-e, the frequency response of the power system to load disruptions, wind turbine disruptions and mild uncertainty related to the system parameters are shown. According to Figure 12a, the MFD and ST based on the proposed controller (optimized FOPD-FOPID controller using the DOSA) are equal to 0.00075 Hz and 3.76 s, respectively. The MFD and ST based on controller 1 are equal to 0.00163 Hz and 4.49 s, respectively; the MFD and ST based on controller 2 are equal to 0.0106 Hz and 24 s, respectively; the MFD and ST based on controller 3 are equal to 0.017 Hz and 42 s, respectively; and the MFD and ST based on controller 4 are equal to 0.0336 Hz and 48 s, respectively (Figure 12b-e). Based on the results obtained in this section, the proposed controller performs better in reducing the deviations of the power system compared to the other mentioned controllers and reduces the ST of the frequency deviations caused by the disruptions in the power system and is resistant to the mild uncertainty related to the system parameters.  
Figure 11: (**a**) The FR of the power system based on the proposed controller, Scenario (2). (**b**) The FR of the power system using controller 1, Scenario (2). (**c**) The FR of the power system using controller 2, Scenario (2), (**d**) The FR of the power system using controller 3, Scenario (2). (**e**) The FR of the power system using LFC based on controller 4, Scenario (2).
## 4 Towards Enhanced Global-Scale Local-Relevant Monitoring and Forecasting
### Anthropogenic Surface Modifications
#### 4.1.1 Urban Areas  
Although urban areas occupy 2.7% of the world's land excluding Antarctica (according to the Columbia University Socioeconomic Data and Applications Center Gridded Population of the World and the Global Rural-Urban Mapping Project (GRUMP)), they hosted 54% of the world's population in 2014 (according to United Nations report World Urbanization Prospects, 2014 revision) and forecasts for these areas are particularly crucial. The surface energy balance in urban areas is quite different from the one over vegetated surfaces, due to the complex three-dimensional building structure and construction materials, and therefore a special treatment for heating processes related to urban canyons is needed [301]. An urban model can be implemented as an additional tile or as a geo-located high-resolution subsurface. Inter-comparison studies involving several urban parameterisations [302; 303] with simplified approaches to urban modelling were shown to be well suited for NWP and permit to represent the heat-island effect that characterize the modified diurnal cycle in large cities [304]. This is another good example where continuous satellite observations are extremely useful to detect anthropogenic influence in an urban or human settlement ecosystem type. In places of their habitat, humans develop built-up areas. The main issue with built-up areas is that they do not release their heat as fast as the surrounding country side, which leads to increased daytime temperatures, reduced night-time cooling and higher air pollution levels--Urban Heat Island effect (UHI) [304]. For instance, over Berlin the mean annual difference in UHI between simulations with without the city is 0.5 \({}^{\circ}\)K, and in winter daily differences are as large as several degrees [305]. Significant advances in monitoring urban areas temperature with remote sensing have recently allowed to characterise the different fluxes including the urban anthropogenic heat flux [306].
## 3 Results and Discussion
### Adsorption Kinetics  
The adsorbent saturation of each adsorbent particle depends on the rate of diffusion of particles absorbed into the interior of the granule. Adsorption kinetics makes it possible to determine the dependence of the adsorption rate on the adsorbent properties and the conditions of the adsorption process itself. Adsorption kinetics therefore depends on the type of adsorbent, the method of its modification and the type of compound being removed. The achieved adsorption equilibrium is expressed by the relationship describing the change in the amount of adsorbed substance over time.  
The dependence of the final adsorbate concentration for WDA and WD (HCI) on adsorption is described in Figure 3.  
Figure 2: Influence of the carbon type on Cr(VI) adsorption capacity.  
From the obtained test results, it can be concluded that the adsorption time had a significant effect on the chromium (VI) removal efficiency of a particular carbon. The maximum reduction of chromate ions with respect to WDA occurred after 4.5 h. The rate of chromium removal on WD (HCl) carbon was higher and the adsorption equilibrium time occurred after 2.5 h. Then stabilization occurred and for further tests (determination of isotherms), retention times of 2.5 (WD (HCl)) and 4.5 (WDA) hours were assumed. The results of many authors indicate that the contact time for different carbons usually does not exceed 6 h [22; 24; 29].
# Development of a Model Material for Dynamic Geotechnical Model Tests
## 4 Experimental Results and Sensitivity Analysis
### Sensitivity Analysis
#### 4.2.1 Sensitivity Analysis of Static Physico-Mechanical Parameters  
Based on the orthogonal test results, a range analysis was conducted on the five static parameters, and a range analysis diagram was drawn, as shown in Figure 2. In the figure, the broken-line graph represents the average value of the test index, and the bar graph represents the range. The results for sensitivity to the four influencing groups, cementing material/solid material (A), gypsum/cementing material (B), quartz sand/aggregate (C) and ferric concentrate powder/(barite powder + ferric powder) (D), were C \(>\) A \(>\) B \(>\) D for density, A \(>\) B \(>\) C \(>\) D for pressure strength, A \(>\) B \(>\) D \(>\) C for tensile strength, B \(>\) A \(>\) C \(>\) D for cohesive force and A \(>\) B \(>\) C \(>\) D for internal friction angle.  
The sensitivity of the static physico-mechanical parameters to the proportions of the components in the model materials was further analysed. Density was the most sensitive to the proportion of quartz sand, and its sensitivity decreased with the increase in the proportion of quartz sand and was positively correlated with the proportions of barite powder and ferric powder. The compressive strength was the most sensitive to the proportion of cementing materials in the solid materials, and its sensitivity increased with the increase in proportion of cementing materials; it was mainly affected by the proportion of gypsum. The tensile strength was the most sensitive to the proportion of cementing materials in the solid materials, and its sensitivity increased with the increase in cementing materials but was less affected by the proportion of other materials. The cohesion was the most sensitive to the proportion of gypsum in the cementing material, and its sensitivity increased with the increase in the proportion of the cementing material. Gypsum had major role in cohesion and promoted an increase in cohesion. The internal friction angle was the most sensitive to the proportion of cementing material in the solid material, and its sensitivity decreased with the increase in the proportion of cementing material and increased with the increase in the proportion of quartz sand in the aggregate.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Group** & \(\rho\)**/g\(\cdot\)cm\({}^{-3}\) & \(\sigma_{H}\)/MPa** & \(\sigma_{e}\)/MPa** & **c/MPa** & \(\varphi\)\({}^{o}\) & **G\({}_{d}\)**/GPa** & **E\({}_{d}\)/GPa** \\ \hline
1 & 2.54 & 0.0132 & 0.19 & 0.15 & 36.63 & 0.0243 & 0.1220 \\
2 & 2.50 & 0.0114 & 0.20 &
## 2 An Optimization Model for Wind Farms with an ESS
### Operational Constraints of an ESS  
The ESS can charge and discharge power within its state of charge (SOC) limit. Hence, the SOC of the ESS, \(SOC_{t}\), is limited by the minimum and maximum SOC levels:  
\[SOC^{\min}\leq SOC_{t}\leq SOC^{\max}\ \forall t. \tag{6}\]  
The charging-discharging operations determine the SOC and the initial SOC is assumed to be half of the storage capacity:  
\[SOC_{t}=\begin{cases}Cap^{ESS}/2+P_{t}^{ch}\times\eta^{ESS,eff}-P_{t}^{\textit{ dch}}/\eta^{ESS,eff},&if\ t=1\\ SOC_{t-1}+P_{t}^{ch}\times\eta^{ESS,eff}-P_{t}^{\textit{dch}}/\eta^{ESS,eff},& otherwise\end{cases}. \tag{7}\]  
The ESS can only operate in charging-discharging mode at a single time-step as follows:  
\[\delta_{t}^{ch}+\delta_{t}^{\textit{dch}}\leq 1\ \forall t. \tag{8}\]  
The amount of discharging or charging power is limited by the capacity and the ESS charging-discharging efficiency. These characteristics are given by  
\[0\leq P_{t}^{ch}\leq\eta^{ch}\times\delta_{t}^{ch}\times Cap^{ESS}/5\ \forall t\ \text{and} \tag{9}\]  
\[0\leq P_{t}^{\textit{dch}}\leq\eta^{\textit{dch}}\times\delta_{t}^{\textit{ dch}}\times Cap^{ESS}/5\ \forall t. \tag{10}\]  
The capacity multiplications and the ESS charging-discharging efficiency are divided by 5 since we assume that the ESS can be fully discharged or charged in 25 min [24].  
The charging power can only be supplied from the wind farm when the following conditions are met:  
\[0\leq P_{t}^{ch}\leq P_{t}^{Wind}\ \forall t. \tag{11}\]
# Study on the Anti-Interference Performance of Substrate-Free PEDOT:PSS ECG Electrodes
## 2 Materials and Methods
### Experimental Design  
The static influence factors include the contact state, electrode size, and electrode shape. The substrate-free PEDOT:PSS electrodes that go through water transfer printing can adjust their bending energy and the interface contact energy by gelation and redrying, thereby achieving conformal contact with the skin [7]. The PEDOT:PSS electrodes that do not go through the water transfer printing mainly rely on the van der Waals force to form partial contact with skin. Using this feature, PEDOT:PSS electrodes forming conformal contact and partial contact to skin were fabricated. To study the electrode size effect, three different sizes of circular PEDOT:PSS electrodes with diameters of 10 mm, 20 mm, and 30 mm were prepared for skin-electrode impedance measurements and ECG detection analyses. The thickness of the electrodes used for these studies is 10 \(\upmu\)m.  
The dynamic influencing factors include friction between clothing on the electrodes and breathing frequency. For the study of dynamic influencing factors, the impedance measurements were carried out at frequencies of 1, 10, and 30 Hz and the number of scanning points was 30. To simulate the impact of clothing on the electrodes in daily life, insulating fabric was utilized to replicate typical friction during everyday activities. This includes applying light back-and-forth friction at a frequency of 2 Hz and an intensity of approximately 0.05 N to the electrodes during the ECG detection process. The electrodes' positions were marked to enable accurate assessment of any potential movement induced by the friction. The breathing frequency of adults at rest is usually around 12 to 20 breaths per minute. During exercise, sometimes breathing frequency was more than 40 beats per minute. To study the breathing frequency effect, two different breathing rates, slow breathing of about 20 times/min and fast breathing of about 40 times/min, were studied.  
Figure 4: Schematic diagram of an equivalent circuit model for the skin–electrode impedance.
# Predicting Player Churn of a Free-to-Play Mobile Video Game Using Supervised Machine Learning
## 5 Results  
### Initial Churn Prediction  
The first machine learning model was trained with a feature extraction period of 10 days after installation. Using features calculated from the first 10 days of play, the model attempted to predict whether the user would churn within the following 7 days (days 11 to 17). The goal was to evaluate the initial assumptions for gameplay and relevant features for prediction on \"stable\" users which survived the first 10 days. For this approach, user profiles consisted of the following features: session count, average session duration, average event count per session, and average money spent per day, with each of the parameters being calculated for the first 1, 3, 7, and 10 days of play, totaling 16 features.  
The values were adjusted using Scikit-learn's MinMaxScaler class on an interval of (0, 1) and fed to the RF algorithm. This model was experimentally determined to yield the best results when compared with models utilizing other available scaling solutions in combination with popular classification algorithms (linear regression, naive Bayes, K-nearest neighbors, decision tree, and linear discriminant analysis), using 30-fold cross-validation. The model's performance was deemed subpar, as it only achieved a classification precision of 56%, which, however, is high enough to warrant additional optimization. The initial model was only a proof of concept on a large dataset. It also indicated which features should be redefined--features showcasing player activity during the whole observed period, as well as further gameplay-specific features. In addition, the feedback from the game developer was to focus on shorter time periods to include the players who are active in the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Sampling Period** & **Size (Amount of Users)** & **Use Case** \\ \hline D1 & 26 October 2020–22 November 2020 & 819,643 & Initial churn analysis focusing on player in-game choices. \\ \hline D2 & 27 January 2018–27 May2018 & 243,003 & First part of the research focusing on player behavior. \\ \hline D3 & 27 April 2020–27 May 2020 & 427,138 & Final part of the research focusing on a more in-depth look at player behavior. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset D1, D2, and D3 details.  
game over a shorter time period. Recognizing those players early would enable possible actions which would increase the chances of retaining those players.
# Causality Analysis with Different Probabilistic Distributions Using Transfer Entropy
## 1 Introduction
It has been used in simple simulation (theoretical) applications in its basic version [17] and a few applications of TE in the domain of causal analysis for industrial processes [18]. TE has been extensively applied to real-world objects in other domains, e.g., climate research, finance, neuroscience, and more. A distinction can be drawn between two separate approaches: the assumed Probabilistic Density Function (PDF) may be explicitly provided; alternatively, a non-parametric model of the distribution-like histogram or more advanced estimators relying on fixed binning with ranking, kernel density estimation, or the Darbellay-Vajda approach [19] can be used. Previous papers have presented estimators differing from the classic approach [20; 21] as well as statistical tests implemented in modern toolboxes [22; 23]. The possibility of using alternative estimators is a separate task, and is not developed in this study. Preliminary analysis for this subject has already been addressed [24], and it is obvious that this subject requires further attention. The topic shows the enormous potential of the chosen method in this case. Combined with the multithreading associated with data analysis [25], the conducted research sheds light on its hitherto unconsidered possibilities. The considered research addresses the Gaussian probability density function. Few studies have been carried out on other distributions or on the impact of the consequent change in the quality of the obtained results [26]. This study uses data from a simulation model to determine the relationship between the variables, using the TE method with variant probability distributions. The rest of this paper is organized as follows. Section 2 presents the Transfer Entropy approach (Section 2.1) and discusses the basic properties of the distributions selected for calculations (Section 2.2). Section 3 describes the simulation system used in this paper. Section 4 contains the calculation results for the generated simulation data, based on the selected distributions after evaluating the quality of fit for each of them. The results for the Gaussian distribution method are referenced in the same section. Finally, Section 5 contains the formulated suggestions and conclusions on the design of causality analysis with Transfer Entropy to obtain reliable information relationships.
## 2 Materials and Methods
### Data Preprocessing  
If there are values in the dataset that are missing, invalid, or difficult to process, the algorithm may end up with overfitted, less accurate, or even misleading results. So, before a model can be trained, data preparation procedures such as data transformation, statistical testing, and more must be performed. The missing data were filled in using the moving average method [28].  
The other preprocessing in this work involves the removal of collinear parameters and normalization. The collinear parameters contain the same information about the variables and can lead to redundancies in the calculations. For each parameter \(x\) and \(y\), the collinear coefficient is calculated by Equation (1) [29]:  
\[\rho_{x,y}=\frac{cov(x,y)}{\sigma_{x}\sigma_{y}} \tag{1}\]  
where \(cov(x,y)\), \(\sigma_{x}\), and \(\sigma_{y}\) are the covariance of \(x\) and \(y\), the variance of \(x\), and the variance of \(y\), respectively. The values \(1\) and \(-1\) of the correlation coefficient indicate the linear and inverse linear correlation, respectively, and the value \(0\) means no correlation. In this work, if \(\rho_{x,y}\) exceeded a threshold, then one of the parameters was removed from the dataset. In the end, the parameters temperature (\(T_{min}\), \(T_{max}\)), average humidity (\(HR_{Avg}\)), average wind speed (\(WS_{Avg}\)) and solar radiation (\(SR_{Avg}\)), precipitation (_Prec_), ETo, and SWC were used as inputs to the models. Tables 15 and 16 in the Appendix A show the collinear coefficients between variables in the different datasets.  
Figure 2: ETo and SWC at different levels for each site.  
The goal of normalization is to bring the values of the dataset to a common scale without distorting the differences in the ranges of values. The standardization formula given by Equation (2) was used to normalize the data [30]:  
\[x_{new}=\frac{x_{old}-\bar{x}}{\sigma} \tag{2}\]  
where \(\bar{x}\) is the mean value of the data.
# Towards Efficient Sink Mobility in Underwater Wireless Sensor Networks
## 3 Motivation  
As discussed in [11; 16], SEEC is a localization free protocol. In SEEC, the network field is divided into ten regions, each of which are subdivided into five left regions and five right regions. Two mobile sinks (MS1 and MS2) are used in sparse regions. MS1 changes its position from the topmost sparse region to the less sparse region in every round except the region of MS2. MS2 remains in the topmost sparse region until the death of all sensor nodes in that region. Both mobile sinks remain in the midpoint of the region. This movement of mobile sink in SEEC is not efficient because the deployment of nodes is random and the case may appear that the two of the most sparse regions are adjacent to each other either on the left side or on the right side. In this scenario, both mobile sinks will be in the same transmission range area. This movement leads to packet drop on the other side of sparse regions, which decrease the throughput of the network.  
From the flow chart of SEEC [11; 16], it is observed that the searching of sparse and dense region is done only once after the deployment of network. By the passage of time, due to ocean currents, sparse regions can be increased [5]. Two mobile sinks are moved on the basis of sparse stack, which was not updated periodically. This factor also results in less throughput in SEEC [11; 16].  
The above discussion motivated to design a scheme that should utilize mobile sinks efficiently, which should result in better throughput and network lifetime.
# Improving Freight Traffic Efficiency at Urban Intersections Using Heavy Vehicle Platooning
## 3 Methodology
### Base Model Development
#### 3.5.1 Simulation Configuration  
Each simulation scenario was run for a total duration of 3600 s, with a simulation step of 1 s to ensure accurate vehicle-movement representation. The total intersection demand was 5200 veh/hr, distributed across all approaches based on observed turning movement counts, with a heavy vehicle composition of 28%. The Wiedemann 99 car-following model was used, with parameter adjustments to reflect platoon behavior and heavy vehicle acceleration constraints. A total of 20 replications were conducted to ensure statistical significance and account for variability in stochastic traffic conditions. For each scenario, results were averaged across 20 replications, and 95% confidence intervals were calculated to validate statistical significance. To ensure reproducibility, the key simulation parameters are summarized in Table 1.
# Method and System for Heart Rate Estimation Using Linear Prediction Filtering +
###### Abstract
A high-precision classifier for two classes of low-cost remote monitoring was proposed in [12]. The researchers used the versatile ESP32 for data acquisition, and a 1D-CNN implemented in MATLAB v24.1 was trained and reached a precision of 92.7%. In the work of [11], the researchers also used the ESP32 system for ECG data acquisition, followed by cloud computing training and classification. As one more example, a 1D-CNN was implemented with Tensorflow libraries for cardiac frequency estimation in [10]. Another relevant theme in ECG processing is compressed sensing techniques [13; 14; 15]. Compressing the ECG signals leads to a sparse representation by downsampling, thereby saving memory space as well as internet capacity, with a minimum loss of information. Heart rate is one of the most relevant measures that can be estimated via ECG signals, and its unit is beats per minute (bpm); its measure has a diversity of applications, e.g., see [16]. Interpreting this measure is more significant if it can be detected in small windows as compared to a single computation of a mean in larger time intervals. The variability of heart rhythm in short time intervals [17] can be a rich signature to help classifier systems in automated diagnosis. Several works in the specialized literature approach the estimation of cardiac frequency with different sensors, systems, or methods to estimate this significant quantity for the purpose of developing diagnosis tools [18; 19; 20; 21; 22; 23; 24; 25]. The contribution of this work is twofold. First, the concept of an LPC-based automated heart rhythm estimator using the reconstruction and error of ECG signals with low-order linear prediction coefficients is proved in an experimental test bed. The method was previously described in [26], wherein a theoretical and simulated test and a comparison with other methods using canonical datasets were carried out. Second, the low-cost prototype system composed of an ESP32 microprocessor module and an AD8232 ECG signal conditioning module is detailed, and the experimental tests conducted using a synthetic ECG wave produced with a Tektronix AFG1022 function generator are presented in the Section 3. The last section reports some conclusions and perspectives for future works. In the following sections, the following notation is used in general. A time domain signal is denoted as lowercase letters with discrete time in brackets, e.g., \(x[n]\), \(\hat{x}[n]\), \(e[n]\); lowercase or uppercase letters are for parameters, e.g., \(p\) is a positive integer as the order of LPC, \(a_{k}\) are the scalars coefficients of the LPC filter, \(r_{k}\) is the autocorrelation coefficients; \(sup_{n}\{\bullet[n]\}\) if for the supremum value of the sequence \(\bullet[n]\); \(\mu_{\frac{1}{2}}\{\bullet[n]\}\) is the median value of the sequence \(\bullet[n]\).