esa-sceva/satcom-chunk-collection huggingface dataset:
# The Analysis of WJ Distribution as an Extended Gaussian Function: Case Study
### Some Initial Considerations of the WJ Distribution  
The WJ distribution is a double exponential function [13]  
\[f(x,\alpha,\beta,\
u)=\frac{\beta}{\Gamma(\alpha/\beta)}e^{-\alpha(x-\
u)-e^{- \beta(\
u-\
u)}} \tag{2}\]  
or equivalently  
\[f(x,\alpha,\beta,\mu_{WJ})=\frac{\beta}{\Gamma(\alpha/\beta)}e^{-\alpha[x-\mu _{WJ}-\frac{1}{\beta}\Psi(\frac{\alpha}{\beta})]-e^{-\beta[\
u-\mu_{WJ}-\frac{ 1}{\beta}\Psi(\frac{\alpha}{\beta})]}} \tag{3}\]  
with \(\
u=\mu_{WJ}+\Psi(\alpha/\beta)/\beta\), defined over the domain of \((-\infty,+\infty)\). In terms of Equations (2) or (3), the WJ function has three parameters \(\alpha\), \(\beta\) and \(\mu_{WJ}\): The parameter \(\mu_{WJ}\) shifts the location of the curve but does not affect the shape of the distribution (we may set it to 0 just for the sake of discussion). The parameters \(\alpha\) and \(\beta\) taking positive values jointly determine the shape of the curve and make a contribution of \(\Psi(\alpha/\beta)/\beta\) to the horizontal position of the curve.  
In contrast to the symmetric shape of the Gaussian curve about the line \(x=\mu_{GS}\), the characteristics of the WJ distribution are more complex and assorted with changes in the parameters \(\alpha\) and \(\beta\) (as deliberated later in the text, such curves can behave similarly to the Gaussian for specific sets of \(\alpha\) and \(\beta\)). A more direct intuition of the behavior of the WJ distribution is unquestionably beneficial from 2D plotting by fixing the ratio of \(\alpha\) to \(\beta\) in the scaled form of \(f_{WJ}(x,\alpha,\beta,\mu_{WJ})\Gamma(\alpha/\beta)/\beta\) vs. \((x-\
u)\), as shown in Figure 1. Figure 1 exhibits the cases of \(\alpha/\beta=1\) and \(\alpha\in(0.5,1,2,3,4)\). The various kinds of curves demonstrate an asymmetrical bell-like shape and tighten the distribution as the \(\alpha\) value increases (and the \(\beta\) value increases simultaneously), with the peaks roughly located at the same position. Figure 1b sums up the results of setting \(\alpha/\beta=2\) and \(\beta\in(0.5,1,2,3,4)\), in which the curves are characterized by tightening the shapes and a right-shifting of the maxima with an increasing value of \(\beta\). Comparing Figure 1a with Figure 1b, the curves look more symmetrical when \(\alpha/\beta=2\). Purportedly, the WJ distribution with proper choices of \(\alpha\) and \(\beta\) can be highly symmetrical like a Gaussian function, sharing some characteristics in common.
## 4 Results  
### Literature Analysis Results  
#### 4.1.1 Status by Country  
The analysis of GAI research related to security was conducted by examining the distribution of publications across various countries. Figure 5 presents the distribution of research publications by country or region, offering a global perspective on research activities in this field.  
As shown in the figure, the United States leads the research output, with over 250 publications, reflecting its significant focus and investment in GAI and security. China follows closely with nearly 200 publications, demonstrating its substantial contribution to the field. India ranks third, with more than 100 documents, indicating a growing presence in GAI-related research. The United Kingdom, with a notable number of publications, ranks fourth globally.  
Other countries, including Australia, Germany, Canada, and Italy, contribute moderately to the research landscape, with publication numbers ranging between 50 and 100 each. South Korea and Saudi Arabia, albeit with lower publication volumes, also participate in the global research efforts on GAI and security. This distribution underscores the international nature of GAI research, with significant contributions from both Western and Asian countries. It also highlights the widespread recognition of GAI's importance in addressing security challenges in various regions.
# Current State of the Gold Mining Waste from the Ores of the Ursk Deposit (Western Siberia, Russia)
## 1 Introduction
The Novo-Urskoye barite-polymeretallic deposit was discovered in 1932 as part of the Urskoye ore field, which unites the Novo-Urskoye, Beloklyuchevskoye, and Samoilovskoye deposits and a number of ore occurrences. Following the discovery, the exploitation of the Novo-Urskoe deposit oxidation zone for gold began. From the surface, it was represented by a typical iron hat with a high content of barite and gold. Oxidized ore bodies were mined up to a depth of 40-50 m. Hypogene ore almost entirely consisted of sulfides: pyrite, sphalerite, chalcopyrite, fahl ore, galena, arsenopyrite, bornite, covellite, and chalocite, as well as a gangue mineralogy comprising quartz, sericite, barite, calcite, and gypsum. Only the upper oxidized part of the ore bodies was mined, including quartz-barite loose (the most oxidized part of the ore bodies) and quartz-pyrite loose (less oxidized), from which free gold was extracted by cyanidation. Wastes from processing were stored side by side in the floodplain of the stream in two bulk dumps without any protective technical structures or dams. As a result, piles with a height of 10-12 m were formed over time [6]. The quartz-pyrite loose dump has been largely reprocessed in the last decade for the secondary recovery of barite. The second pile originated from the waste of the oxidation zone and remained unchanged ever since. The authors of [26] identified the migration of gold with accumulation in a natural geochemical barrier peat in the drainage valley. The geophysical and geochemical study of the Ursk mine tailings area, which was performed in 2015, made it possible to determine two directions of migration of acid drainage. Surface runoff flows along the natural slope of the valley. In the vertical direction along the natural fault (Figure 1), drainage flows penetrate into ground and underground waters to a depth of more than 20 m [25]. The significance of this investigation is associated to the necessity of the actual information regarding the degree of acid weathering of Ursk mine tailings area and adjacent territory. The aim of this study is to reveal the vertical and lateral zoning of the Ursk mine tailings area in terms of electrophysical characteristics (electrical resistivity, magnetic susceptibility, and temperature) and chemical composition of surface streams: Ur River and Ursk reservoir, which are located 7.7 km from the dumps. The novelty of this investigation is related to implementation of remote sensing and modern nondestructive geophysical and classical hydrochemical methods for complex evaluation of the mine tailings area. Figure 2: Photos of the Ursk dump, the drainage valley, and the “manmade waterfall”.
# The Effects of Topology on Throughput Capacity of Large Scale Wireless Networks
## 5 A Lower Bound on Capacity  
For the three-dimensional heterogeneous wireless ad hoc networks where the nodes are distributed according to the SNCP, we can get that the achievable throughput capacity is :  
\[R(n)=\Omega\left(n^{\frac{2\gamma}{3}-1}\underline{\Phi}^{\frac{2}{3}}\right). \tag{32}\]  
Comparing the transmission of intra-cluster and inter-cluster, we can find the bottleneck of the throughput capacity is in the space with minimum node density. Thus, for the entire network, the achievable throughput capacity is \(\Omega\left(n^{\frac{2\gamma}{3}-1}\underline{\Phi}^{\frac{2}{3}}\right)\).  
Furthermore, compared with the results in [19], we have obtained a tighter lower bound of throughput capacity in the 3D heterogeneous wireless networks. In particular, when the nodes are distributed uniformly, i.e., \(\underline{\Phi}=\Theta(\overline{\Phi})=\Theta(n^{1-\gamma})\), then the per-node throughput can achieve \(\Omega(\frac{1}{\sqrt[3]{n}})\), which is the throughput capacity of three-dimensional homogeneous wireless networks.
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 5 Simulation Results Analysis
### Simulation Parameters
#### 5.1.1 PHY Layer Simulation Parameters  
Table 1 shows the main configuration parameters of PHY layer used in all simulation scenarios. We selected the PHY II operating mode, intended for both indoor and outdoor environments, using MCS-ID number 16, since support for the minimum clock and data rates for a given PHY is mandatory. Optical transmission power of devices is of 30 mW, in accordance with the limitations of power consumption in battery-powered mobile terminals. According to the optical channel model used, transmitters' directivity is characterized by its half power angle, \(\theta_{1/2}\), while receivers' directivity is defined by its FOV. According to [20], both parameters are assigned a value of 60\({}^{\circ}\), to ensure validity of the implemented channel model, since the calculation of received optical power takes into account only the direct component of the signal. In order to simplify the calculation process of the model, thevalues used for the concentrator gain \((G)\) and the transmission coefficient of the optical filter \((T)\) are considered to be constants, so they do not depend on the angle of incidence \((\psi)\).  
The values selected to define the rest of characteristics of optical transmitters and receivers are commonly used values to characterize VLC transmitters and receivers, similar to those used in [37; 38; 39]. Lastly, the contribution of noise in the channel (thermal and shot noise) has been considered as negligible in all simulations. Thus, the implementation of the simulation model is simplified, ensuring also that failures in frame transmissions are only caused by collisions (simultaneous transmissions on the network). In this way, noise level or transmission errors do not affect the simulation results.
## 2 Materials and Methods
### Objectives
Edge gateways aggregate local sensor data and translate heterogeneous protocols into standard formats suitable for the 5G core. This approach not only accommodates mixed vendor environments but also simplifies long-term system evolution. A recent 5G-enabled waste management pilot at Bovias Campus of Politecnico di Milano demonstrates real-time fill-level monitoring via connected trash bins and improved responsiveness in maintenance schedules [26]. Similarly, any campus service generating continuous data, such as parking usage or laboratory equipment status, benefits from 5G's high throughput and reliability. Overall, this layered, interoperable design addresses the challenges of integrating legacy systems and open-access educational environments, thus establishing a foundation for advanced, AI-enabled decision support. \begin{table}
\begin{tabular}{c p{142.3 pt} p{142.3 pt}} \hline
**Objective** & **Domain** & **Description** \\ \hline
1 & Predictive Maintenance & Employ AI analytics on IoT data for proactive maintenance scheduling, reducing downtime and operational disruptions. \\ \hline
2 & Dynamic Scheduling & Implement AI-driven scheduling algorithms to adaptively manage and optimise timetables in real-time based on changing conditions and constraints. \\ \hline
3 & Real-time Resource Allocation & Facilitate optimal use of campus facilities, digital resources, and utilities by dynamically matching resource availability with actual demand patterns. \\ \hline
4 & Asset Tracking & Enhance asset visibility, reduce losses, and streamline inventory management through continuous monitoring using IoT technologies, Radio Frequency Identification (RFID, Global Positioning System (GPS). \\ \hline
5 & Security and Privacy & Embed strong cybersecurity measures and ensure privacy compliance by integrating end-to-end encryption, authentication, and AI-based threat detection. \\ \hline
6 & Scalability and Integration & Ensure the modular, interoperable, and scalable adoption of AI and IoT modules, enabling incremental and seamless integration into existing educational systems. \\ \hline \end{tabular}
\end{table} 
Table 3: Core objectives of the AI wireless educational management framework.
# Experimental Study on the Permeability of Ecological Slopes under Rainfall Infiltration Conditions
## 3 Test Results and Analysis
### Determination of Matrix Section of Test Clay  
A pressure plate apparatus manufactured by GCTS (Tempe, AZ, USA) was used for the indoor tests to measure the soil-water characteristic curve of the test clay as shown in Figure 6. Then, the VG model [28] was used to fit the measured data points, and the expression of the VG model is shown in Equation (1):  
\[\theta_{w}=\theta_{res}+\frac{\theta_{sat}-\theta_{res}}{\left[1+\left(\frac{ \psi}{a}\right)^{n}\right]^{m}} \tag{1}\]  
where \(\theta_{\omega}\) denotes the volumetric water content; \(\theta_{sat}\) and \(\theta_{res}\) are the saturated water content and residual water content, respectively; \(\psi\) represents the matrix suction (kPa); \(a\), \(m\), and \(n\) are the fitting parameters, and \(m=1-1/n\). The fitting parameters of the soil-water characteristic curve of the specific test clay are given in Table 3, and the soil-water characteristic curve of the test clay is depicted in Figure 7.  
The curve correlation coefficient \(R^{2}\) = 0.982 indicates a good fit. The relationship between soil-water content and matrix suction in the slope model can be known from the VG model. Therefore, the corresponding relationship between matrix suction at the slope surface monitoring point and time can be calculated based on the relationship between volumetric water content at the slope surface monitoring point and time with the help of the VG model.  
Figure 8 illustrates the relationship of matrix suction with time at the surface monitoring points of the four overburden slopes. After 4 h of continuous rainfall and 7 h of natural evaporation, the matric suction of the shallow soil of the four overburden slopes changed significantly. In general, the matric suction shows a trend of first unchanged, then decreased, then increased, and finally unchanged, but there are differences in the response  
Figure 6: Pressure plate instrument.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Type** & \(a\) **(cm)** & \(n\) & \(m\) & \(R^{2}\) \\ \hline Van Genuchten & 5.462 & 1.249 & 0.199 & 0.982 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters for fitting the soil–water characteristic curve of the test clay.  
Figure 7: Soil–water characteristic curve of the test clay.  
time when the matrix suction of the shallow soil of the different vegetation overburden slopes begins to decrease. From the VG model, it can be found that there is an inverse relationship between water content and matrix suction. Therefore, the variation in matrix suction on the slope surface over time is exactly opposite to the trend of water content on the slope surface over time, but the initial response time is consistent.
###### Abstract  
It is important to accurately estimate the capacity of the battery in order to extend the service life of the battery and ensure the reliable operation of the battery energy storage system. As entropy can quantify the regularity of a dataset, it can serve as a feature to estimate the capacity of batteries. In order to analyze the effect of voltage dataset selection on the accuracy of entropy-based estimation methods, six voltage datasets were collected, considering the current direction (i.e., charging or discharging) and the state of charge level. Furthermore, three kinds of entropies (approximate entropy, sample entropy, and multiscale entropy) were introduced, and the relationship between the entropies and the battery capacity was established by using first-order polynomial fitting. Finally, the interaction between the test conditions, entropy features, and estimation accuracy was analyzed. Moreover, the results can be used to select the correct voltage dataset and improve the estimation accuracy.  
Keywords:lithium-ion battery; capacity estimation; entropy; current pulse +
Footnote †: journal: Applied Sciences
# Biological Properties of Sandalwood Oil and Microbial Synthesis of Its Major Sesquietrepenoids
## 2 The Source of Sandalwood Essential Oil, Its Main Components, and Its Applications
### Antioxidant Properties of Sandalwood Oil  
Oxidative stress is known to impact the onset of various diseases significantly. Throughout history, the effectiveness of sandalwood oil's antioxidant properties in counteracting damage caused by oxidative stress has been widely acknowledged. Numerous studies have demonstrated that Indian sandalwood oil possesses noteworthy antioxidant capabilities, effectively neutralizing reactive oxygen species triggered by external stressors such as urban dust, exposure to blue light, and inhalation of cigarette smoke [36; 37]. Sandalwood oil has been shown to reduce levels of squalene monohydroproexoxide, an indicator of oxidative stress, in a dose-dependent manner. Concentrations ranging from 1% to 10% have demonstrated protective properties against oxidative stress caused by exposure to urban dust and blue light [36]. Furthermore, the antioxidative efficacy of sandalwood oil has been validated through a variety of in vitro studies, demonstrating its ability to counteract free radicals and prevent the escalation of collagenase levels caused by pollutants, highlighting its potential as a protective and anti-aging component in the fields of cosmetics and dermatology [37]. In in vivo oxidative stress-induced models, \(\alpha\)-santalol and sandalwood oil would modulate parameters such as serum aminotransferase, alkaline phosphatase, bilirubin, superoxide dismutase, catalase, free sulfhydryl, protein carbonyl, nitric oxide, and liver lipid peroxide contents, which directly showed their antioxidant properties [38]. Subsequent research has revealed that sandalwood essence has phytoconstituents with potent antioxidant properties, making it a suitable candidate for use in the pharmaceutical and food industries [39].
## 4 Result and Discussion
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset** & **Deep Learning Models** & **RMSE** & **MAE** \\ \hline \multirow{4}{*}{DUTCHBANGALABANK} & CNN & 11.5777 & 11.4175 \\  & LSTM & 15.2332 & 15.1668 \\  & BiLSTM & 10.1262 & 10.0926 \\  & Proposed Stacking Ensemble of Neural Network & 1.0317 & 0.9021 \\ \hline \multirow{4}{*}{EXIMBANK} & CNN & 0.8822 & 0.7254 \\  & LSTM & 1.04872 & 0.8899 \\  & BiLSTM & 0.4895 & 0.3822 \\  & Proposed Stacking Ensemble of Neural Network & 0.4631 & 0.4093 \\ \hline \multirow{4}{*}{FUWANGFOOD} & CNN & 3.6536 & 2.7259 \\  & LSTM & 4.4977 & 3.5727 \\  & BiLSTM & 2.2982 & 1.8985 \\  & Proposed Stacking Ensemble of Neural Network & 2.5696 & 2.444 \\ \hline \multirow{4}{*}{GP} & CNN & 1.6824 & 1.4521 \\  & LSTM & 0.9660 & 0.7848 \\  & BiLSTM & 1.0760 & 0.9343 \\  & Proposed Stacking Ensemble of Neural Network & 0.5970 & 0.4611 \\ \hline \multirow{4}{*}{IBNSINA} & CNN & 1.4132 & 1.2915 \\  & LSTM & 1.6709 & 1.5288 \\  & BiLSTM & 1.0107 & 0.8399 \\  & Stacking Neural Network Ensemble & 0.9153 & 0.9659 \\ \hline \multirow{4}{*}{IFIC} & CNN & 1.1520 & 0.9446 \\  & LSTM & 1.81234 & 1.5902 \\  & BiLSTM & 1.8714 & 1.8041 \\  & Proposed Stacking Ensemble of Neural Network & 0.3626 & 0.3682 \\ \hline \multirow{4}{*}{JAMUNABANNK} & CNN & 1.0167 & 0.8089 \\  & LSTM & 0.4649 & 0.3752 \\  & BiLSTM & 0.7879 & 0.6579 \\  & Proposed Stacking Ensemble of Neural Network & 0.5614 & 0.5526 \\ \hline \multirow{4}{*}{KEYACOSMET} & CNN & 6.8706 & 6.6129 \\  & LSTM & 5.8391 & 5.7513 \\  & BiLSTM & 8.4249 & 8.3693 \\  & Proposed Stacking Ensemble of Neural Network & 1.0614 & 0.9095 \\ \hline \multirow{4}{*}{UTTARABANK} & CNN & 1.3121 & 1.0543 \\  & LSTM & 0.9504 & 0.7705 \\  & BiLSTM & 0.6997 & 0.5853 \\ \cline{1-1}  & Proposed Stacking Ensemble of Neural Network & 0.6741 & 0.6611 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: \(Cont.\) \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset** & **Machine Learning Models** & **RMSE** & **MAE** \\ \hline \multirow{4}{*}{ABBANK} & ML ensemble method (RandomForest) & 4.3384 & 4.2509 \\  & ML ensemble method (AdaBoost) & 6.1229 & 6.0434 \\  & ML ensemble method (GradientBoosting) & 6.9083 & 6.8461 \\  & ML Stacking Ensemble Learning & 5.1710 & 4.8450 \\ \hline \multirow{4}{*}{ACIBANK} & ML ensemble method (RandomForest) & 2.3326 & 1.6824 \\  & ML ensemble method (AdaBoost) & 2.7053 & 2.0491 \\ \cline{1-1}  & ML ensemble method (GradientBoosting) & 2.4149 & 1.8078 \\ \cline{1-1}  & ML Stacking Ensemble Learning & 1.9230 & 1.6032 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experiment results obtained from 20 different companies’ stock price datasets of a frontier market using machine learning models such as RandomForest, AdaBoost, GradientBoosting and ML stacking Ensemble Learning.
## 3 Toward Next-Generation Public Safety Networks
### Public-Safety-Targeted Technologies
Although the standardization efforts on certain public safety technologies were frozen, 3 GPP continues to introduce further enhancements for better support of public safety services over cellular networks. We outline, in Table 2, the main improvements beyond Rel-12 of public safety technologies. On top of the introduction of new discovery and direct communication features in the Evolved Packet System (EPS) [27], a major enhancement to ProSe is the support of UE-to-network relaying for IoT and wearables in 3 GPP Rel-14 [28]. Supporting D2D connectivity in 5G networks, New Radio (NR) sidelink targets mainly Vehicle-to-Everything (V2X) services with solutions ratified in 3 GPP Rel-16. However, Rel-17 NR sidelink enhancements for public safety include the support of ProSe communications and UE-to-network-relaying over 5G networks [29]. Another set of improvements of public-safety-targeted cellular solutions is related to MCX services. Table 2 summarizes the enhancements that are common to the three types of MCX applications (i.e., MCPTT, MCData, and MCVideo). They include mission-critical security-related functionalities in Rel-14 [30] and support of multimedia broadcast multicast services (MBMS) in Rel-15 [31]. Beyond Rel-16, improvements focus on enhancing the functional MCX architectures [32] and identifying further requirements of MCX services. Examples of further requirements are related to the support of multiple devices, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Technology** & **3 GPP Release** & **Brief Description** \\ \hline GCSE & Rel-12 & Mechanisms enabling both unicast and multicast transmissions for group communications \\ \hline ProSe & Rel-12 & Architecture and radio interface for the support of D2D communications \\ \hline IOPS & Rel-13 & Isolated mode of operation that ensures communications between users via base stations without backhaul links \\ \hline MCPTT & Rel-13 & Delivering voice services for public safety users \\ \hline MCData & Rel-14 & Delivering non-real-time data services for public safety users \\ \hline MCVideo & Rel-14 & Delivering real-time video services for public safety users \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main cellular solutions for public safety. location, security, and media quality [33]. Enhanced handling of MCPTT emergency alerts, support of simultaneous video sessions, and off-network file distribution are among the enhancements that target each type of MCX application separately [31]. In addition to the above-discussed solutions, next-generation public safety networks can make use of other cellular features that are not specifically ratified for public safety operations. Examples of these additional enabling technologies are examined in the following section.