esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods and Materials
### Statistical Machine Learning Models and SHAP Method
#### 2.4.3 Neural Networks  
Neural networks (NNs) have become a cornerstone of modern machine learning, capturing widespread interest in recent years. Despite their current prominence, their conceptual foundations were laid decades ago, inspired by biological processes observed in the human brain. The earliest formalisation of a neural model is credited to [PERSON] and [PERSON] (2003), who introduced a simplified abstraction of a neuron aimed at mimicking its basic decision-making behaviour.  
Their model defines a function \(f:\mathbb{R}^{d}\rightarrow\{0,1\}\) given by  
\[f(x_{1},\ldots,x_{d})=\mathbb{I}_{\mathbb{R}^{+}}\left(\sum_{i=1}^{d}w_{i}x_{i} -\theta\right),\]  
where \(w_{i}\) and \(\theta\) are real-valued parameters, \(d\in\mathbb{N}\), and \(\mathbb{I}_{\mathbb{R}^{+}}\) denotes the indicator function, defined as  
\[\mathbb{I}_{\mathbb{R}^{+}}(x)=\begin{cases}0,&\text{if }x<0,\\ 1,&\text{if }x\geq 0.\end{cases}\]  
In the context of neural networks, this step function is referred to as the _activation function_, which determines whether the artificial neuron becomes active based on a weighted combination of its inputs. The former guides us to state that NNs can possess the remarkable property of being universal function approximators. This means they are capable of approximating a wide class of functions to the desired degree of accuracy, provided sufficient depth, width, and appropriate parameters. In particular, even relatively simple architectures, such as feedforward networks with a single hidden layer and a non-linear activation function like the sigmoid, can approximate any continuous function on compact subsets of \(\mathbb{R}^{n}\). The following formal statement, knowns as the Universal Approximation Statement [44], illustrates this idea for Lipschitz functions: Let \(f:[-1,1]^{n}\to[-1,1]\) be a \(\rho\)-Lipschitz function. Fix some \(\varepsilon>0\). Then, there exists a neural network  
\[N:[-1,1]^{n}\to[-1,1],\]  
with the sigmoid activation function, such that for every \(x\in[-1,1]^{n}\),  
\[|f(x)-N(x)|\leq\varepsilon.\]  
This property can be generalised, and it highlights the expressive power of neural networks [44].
# Design of SWB Antenna with Triple Band Notch Characteristics for Multipurpose Wireless Applications
### Contributions  
In this work, design and analysis of an SWB antenna with triple band notch characteristics for multiple wireless applications are presented. The designed antenna is capable of removing three interfering bands at advanced wireless services (AWS), and C and X band systems.  
* To attain the notched frequency band for AWS (1.8-2.2 GHz), an E-shaped stub is loaded on the top of the radiator.
* A split elliptical slot at the base of the radiating patch is used to achieve a band notch at the C band (4-7.2 GHz).
* By placing a C-shaped resonator near the feed, a band notch at the X band (9.8-10.4 GHz) is acquired.  
The main prominence of the designed radiator is its very wide bandwidth and large bandwidth dimension ratio (BDR), which is obtained by modifying the electrical length of the radiator. The presented antenna structure attains a frequency range of 1.67-47.5 GHz (VSWR < 2) with a bandwidth ratio of 28.44:1 and a fractional bandwidth of 186%. The results are captured by simulating the model in HFSS 19.0 software.  
This paper is organized into different sections as follows: The first section presents an introduction to the band-notched SWB antenna. The design steps and characteristics of the proposed antenna are investigated in Section 2. Significant parameters that control the bandwidth and impedance matching are illustrated in Section 3. Then the simulated and experimental results viz. current distribution, gain, and radiation patterns are illustrated in Section 4. Section 5 focuses on modelling of equivalent circuit for the proposed antenna. Time-domain characteristics, namely system fidelity factor (SFF), group delay, and transfer function, are presented in Section 6. Section 7 highlights the main features of the proposed SWB antenna by including a comparative analysis. Finally, the paper is concluded in Section 8.
# A Novel Magnetic Equivalent Circuit Model for the Corner of Inductor Core
## 2 Modeling and Analysis of Corner
### Modeling of Corner  
The flux density distribution of the diagonal line of a corner, \(La\) as shown in Figure 3, is analyzed and presented in Figure 4. We can see that the flux density \(B\) in the corner has nonlinear distribution. Specifically, the flux is concentrated around the inner corner because the flux selects a minimum reluctance path, where the reluctance is equal to the ratio of length and the product of permeability and cross-section area [19]. With the current rising, the flux density increases and the distribution changes because of the nonlinear permeability of the core material.  
On the other hand, most of the flux line shape is approximately a circle and the flux density is concentrated in the inner corner. A quarter-circle area is named Part A, in which the radius is the limb width. The energy in Part A and energy in the corner are compared, as shown in Figure 5.  
It can be seen that the ratio of the energy in Part A and the corner is higher than 93% in the range of 20 A to 1000 A, and it slightly decreases as the current increases. Therefore, we chose Part A to replace the corner and use the circle path to study the corner with a negligible error.  
Considering the non-uniform flux density \(B\) at the corner, we selected \(n\) parallel reluctance branches to replace the traditional single corner reluctance. Here we divide Part A into \(n\) equal divisions along line \(Lab\) and make \(n\) quarter circles by the inner corner and divided points, which divides Part A into \(n\)-\(1\) quarter rings and \(1\) quarter circle, as shown in Figure 6a.  
Figure 4: The flux density distribution of line \(La\).  
Figure 3: The divided parts of a corner.  
The area of each branch \(A_{c,i}\) is expressed in Equation (1).  
\[A_{c,i}=\frac{w_{i}\cdot l_{c}}{n}, \tag{1}\]  
Taking the middle line of geometry as the length of the magnetic path, the length of the \(i\)th magnetic path \(l_{c,i}\) is expressed in Equation (2).  
\[l_{c,i}=\frac{w_{i}\cdot\pi}{4n}(2i-1), \tag{2}\]  
Thus the \(i\)th reluctance \(R_{c,i}\) is calculated in Equation (3).  
\[R_{c,i}=\frac{\pi\cdot(2i-1)}{4l_{c}\cdot\mu_{i}(B)}, \tag{3}\]  
where \(u_{i}(B)\) is the \(i\)th branch permeability.  
In the corner, the flux density distribution can be accurately reflected with more parallel \(R_{c,i}\) branches. However, too many parallel flux paths can cause the magnetic equivalent circuit of the corner to be too complicated and affect the convergence of the calculation, especially when the branch is deeply saturated. In this paper, we chose three parallel branches, the inner section, middle section, and the outer section of the corner, which can balance the accuracy of the solution and keep the model simple, as shown in Figure 6b.
## 3 Results  
### UAV Imagery and YOLOv3 Model Accuracy  
We trained the YOLOv3 object detection model for high-throughput measurement of soybean plant numbers from the UAV imagery data. The effect of various confidence thresholds on the accuracy of the YOLOv3 model was examined to establish the most accurate model detecting the soybean seedling in the test data set images. The established YOLOv3 model with a 0.50 confidence threshold scored the highest accuracy with an \(r^{2}\) value of 0.88 and RMSE of 0.96 plants m\({}^{-2}\) as shown in Figure 5. The results from the other models with different confidence threshold values are listed in Table S1. The YOLOv3, as the best model using the confidence threshold of 0.50, was hereafter applied for the UAV imagery of all observation fields to measure the actual plant density value, which was further used as the response variable of the PLS and RF regression models.
# Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022
## 2 Materials and Methods  
### Study Area  
The Valencian Community (Figure 1), situated along the eastern coast of Spain, holds significant economic and cultural prominence. Positioned at approximately 39\({}^{\circ}\) north latitude and 0\({}^{\circ}\) west longitude, overlooking the Mediterranean coast, this region has experienced substantial transformations in land cover, primarily due to urban expansion and agricultural development. This characteristic renders it an ideal subject for our study, which seeks to explore the dynamic shifts in land cover, elucidating the evolving urbanisation patterns and their interaction with the natural environment.
# Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications
## 7 Conclusions  
In this paper, the efficiency of the ANN method was shown for the estimation and the forecasting of solar irradiation.  
Successively, several ANN models were developed:  
* An ANN model to determine the 5-min GHI from more available meteorological data (a maximum of seven measured meteorological data). The nRMSE of the optimized 6-inputs model was 19.35%.
* An ANN model to compute the 5-min GTI from GHI with an accuracy around 8% for the optimal configuration.
* At last, a forecasting ANN tool was developed to predict hourly DNI and GHI i for a time horizon from h+1 to h+6. The forecasting of hourly solar irradiation from h+1 to h+6 using ANN was realized with an nRMSE from 22.57% for h+1 to 34.85% for h+6 for GHI and an nRMSE from 38.23% for h+1 to 61.88% for h+6 for DNI.  
ANN methods are very promising even if new artificial intelligence methods are in development, such as regression trees and random forest.  
Software: A.F. and J.L.D.; Methodology: C.V., G.N. and M.L.N.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 12: Comparison of forecasting models for GHI and DNI in terms of nRMSE (Odeillo site).
## 1 Introduction  
A laser of around 3.5 \(\upmu\)m is of particular interest in defence and security, material processing, greenhouse gas monitoring, and spectroscopy [1; 2; 3; 4]. Since the introduction of the dual-wavelength pump (DWP) [5], using the \({}^{4}\)F\({}_{9/2}\)\(\rightarrow\)\({}^{4}\)I\({}_{9/2}\) transition in an erbium-doped fluoride (Er\({}^{3+}\):ZrF\({}_{4}\)) fiber has become an attractive method for generating a 3.5 \(\upmu\)m laser. In 2015, [PERSON] et al. reported a watt-level continuous wave (CW) 3.44 \(\upmu\)m fluoride fiber laser [6], and the power record increased to 5.6 W [7] and 14.9 W [8] successively.  
The DWP scheme requires a 976 nm pump (\(P_{976}\)) and a 1976 nm pump (\(P_{1976}\)) as shown in Appendix A, Figure 11. Now, the \(P_{1976}\) in DWP schemes must be core-coupled to the active fiber to have a sufficient pump absorption rate. Moreover, a high beam quality laser at 1976 nm is essential for core pumping, which is provided by the Tm\({}^{3+}\)-doped silica fiber. However, a 790 nm diode-pumped 1976 nm fiber laser increases the complexity of the system and adds an additional energy conversion process which reduces the electro-optical (EO) conversion efficiency, and the core pumping setup is hard for coupling and bad for stability. With the development of the GaSb-based \(\sim\)2 \(\upmu\)m diode [9; 10; 11; 12], a single emitter up to 1.4 W and a 19-emitter-bar up to 16 W is available. It is attractive to use a 976 nm and 2 \(\upmu\)m laser diode to pump a 3.5 \(\upmu\)m fiber laser directly and to operate a clad pump scheme simultaneously, due to its advantages in high integration, accessibility, and stability. With no literature report, the feasibility of using a clad pump scheme to generate a 3.5 \(\upmu\)m laser remains to be studied.
## Materials and Methods
### Methodology
#### 2.2.4 Simulation Study  
We conducted a simulation study to verify the model efficiency, the numerical convergence and the limitations in the estimation procedure. We simulated a set of 800 extremes artificially censored from a GEV model with a parametric setting given by Equation (5) and 40 percent random censorship.  
We chose the \(x_{1}\) and \(x_{2}\) covariates, corresponding to the longitude and latitude, uniformly from the interval \([-91,-107]\) and \([14,20]\), respectively.  
\[\begin{split}\mu_{t}&=8e^{\left[\frac{1}{2}(x_{1}-17)^ {2}+(x_{2}+98.5)^{2}\right]}\\ \sigma_{t}&=\sigma=1\\ \kappa_{t}&=\kappa=-0.6\end{split} \tag{5}\]  
To determine how the result is affected by the number of nodes in Equation (1), we adjusted two models, the first setting \(p_{2}=20\) and the second with \(p_{2}=80\) in Equation (1). The estimates of the shape parameter were \(-0.59\) and \(-0.69\), respectively. The estimates of the \(\sigma\) parameter were \(1.38\) and \(1.05\), respectively. The functions used to generate the trend of the maxima and defined by the scale parameter of the GEV distribution, are shown in Figure 2a. Because the function corresponding to \(\mu\) involves the product of the exponentials to the square of the covariates \(x_{1}\) and \(x_{2}\), it cannot be represented as the sum of additive functions of these two covariates. For simplicity, functions for the scale and shape parameter are constant.  
The results of the simulation showed that the proposed model correctly estimates the actual function used for the trend (Figure 2a). Although the magnitude of the estimates differs from the original, the difference decreases as the number of knots increases, from \(p=20\) to \(p=80\) (Figure 2b,c). Similarly, a comparison of estimators of \(\kappa\) for both \(p=20\) and \(p=80\) reveals that estimates of the shape parameter was more accurate in the \(p=20\) case, with an error less than \(0.01\), compared with the estimate obtained with \(p=80\), for which the deviation was around \(0.1\). In contrast, the estimators of the scale parameter are slightly skewed to the left due to the effect of the a priori distributions on the parameters.  
An independent testing set of \(1000\) observations was simulated to validate the fitted model. The correlation between the observed and the estimates was \(0.86\) and \(0.94\) with \(p=20\) and \(p=80\), respectively. The parameters estimated by the spline function can barely be interpreted individually because they constitute a large set of parameters, which have the main objective of approximating the nonlinear characteristics of an arbitrary function. However, because the variables have been standardized, it is possible to evaluate the effect of the change of each coefficient on the change in the parameters of the GEV distribution.  
Figure 2: (**a**) Real functions and (**b,c**) functions obtained by fitting the parameters of a non-stationary GEV model with \(P_{2}=20\) and \(P_{2}=80\) knots, respectively, to simulated data with a sample size of \(n=500\).
# Cost Analysis and Optimization of Modern Power System Operations
## 8 Conclusions
#### Key Findings  
* Integration of renewable energy resources leads to a significant reduction in fossil fuel-based power generation and overall operating cost, promoting both economic and environmental benefits.
* Energy Storage Systems (ESS) play a crucial role in enhancing power system flexibility. Their ability to shift energy temporally allows better integration of variable renewable generation and supports grid stability.
* From both the generation and demand perspective, ESS becomes increasingly important with higher levels of renewable penetration. This reduces the fuel consumption of thermal units, contributing to lower operational costs.
* The use of ESS and renewables also aids in mitigating the environmental impact of conventional generation and enhances energy security by reducing dependency on fossil fuels.
* The study of demand-side flexibility (Case 4) reveals that even modest levels of flexibility (e.g., 10%) lead to notable reductions in operating costs, highlighting the value of smart appliances and consumer responsiveness in modern grid operations.  
A.P.: original draft preparation; P.P.S.: writing--review and editing; LP.: supervision, writing--review and editing; S.S.: supervision, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets generated during and/or analyzed during the current study are available from the corresponding author, on reasonable request.  
The authors declare that they have no conflicts of interest.
# Efficacy and Tolerability of a Microneedling Device Plus Exosomes for Treating Melasma
###### Abstract  
Melasma is a challenging skin condition which involves both structural and functional skin alterations. Despite the availability of various treatment options, the management remains complex. This is the first study to investigate topical application of _Rosa damascena_ stem cell exosomes when used concomitantly with microneedling in women and men with facial melasma. We recruited 20 subjects with Fitzpatrick skin types I-III, exhibiting melasma of varying severity. The modified Melasma Area and Severity Index (mMASI) and Global Aesthetic Improvement Scale (GAIS) were utilized to evaluate treatment response. The treatment protocol involved microneedling followed by exosome application over four or five sessions, at 4-week intervals. Ninety percent of subjects demonstrated a significant improvement in mMASI scores, while only 10% showed no change. GAIS assessment further supports overall improvement, with just 10% categorized as \"not changed\". Tolerability was favorable, with mild, transient side effects. Our findings suggest promising outcomes with this combined therapy, underscoring its potential as a safe and effective approach for treating melasma, particularly in severe and moderate cases. However, further research with larger sample sizes and control arms is warranted to validate these findings and explore long-term efficacy.  
m pacs: 1
## 2 Materials and Methods
### Remote Sensing Data
#### 2.4.5 Validation  
Previous studies have used either gross primary production (GPP) flux data from FLUXNET [29; 65] or Phenocam [66] or both [67] to validate their satellite-derived phenological metrics. Unfortunately, these datasets were not available in the study area as there is no flux tower station or Phenocam in Indonesia. The nearest flux tower station is in Malaysia, some 660 km away from our study area. Therefore, the validation of satellite-based phenological metrics in this study cannot be made with phenological metrics derived from ground data. While most remote sensing studies have used higher spatial resolution images for accuracy or validation purposes, fewer studies used them to validate phenological metrics. Two recent studies have compared phenological parameters derived from higher spatial resolution satellite data [68; 69].  
We validated the NDVI model using high spatial resolution data. Initially, data from three satellites (Sentinel, Landsat, and Planet), which offer better spatial resolution than MODIS, were selected for this purpose. After the pre-processing steps, only the Sentinel-2 had sufficient data for further processing in TIMESAT. Planet data shows the lowest percentage in terms of data availability and area coverage in the study area, while Landsat only offers a few images during the wintering period and is prone to the impacts of cloud cover.  
Therefore, the Sentinel-2 data (Sentinel-2A and Sentinel-2B) were chosen to validate the MODIS NDVI phenology model in this study. These data provide 10 m spatial resolution with 5-day temporal resolutions. Three years of data between 2017 and 2019 were used for validation because Sentinel-2B was launched in early 2017. Data were downloaded from USGS Earth Explorer platform. It is a level 1C processing product and is geometrically refined. The pixels in this product provide Top-Of-Atmosphere (TOA) reflectance. We first applied atmospheric corrections before removing the clouds and resampled it to 250 m. During the analysis in TIMESAT, a similar process of time-series data smoothing and setting was applied. Although three full-year data were used, only two complete growing seasons were detected and used for comparison. This is because the rubber season in the study area started and ended around middle of the year, and TIMESAT is programmed to only extract the phenological metrics of a complete growing season.
## 3 Results
### Reliability Analysis  
#### 3.2.1 Inter-Device Reliability  
The Intraclass Correlation Coefficients of CIELAB values of MC-DSG, SC-DSG, and VITA Easyshade V are presented in Table 2. All three digital shade-matching devices showed excellent reliability with ICC > 0.9 for L*, a*, and b* values. However, when comparing the reliability among the three devices, the MC-DSG showed significantly lower reliability than the spectrophotometer in a* (_p_ = 0.024) and b* (_p_ = 0.002) values. Additionally, SC-DSG also had significantly lower reliability in b* value (_p_ = 0.004) as compared to VITA Easyshade V.
## 3 Applications
### Environmental Monitoring
In addition, it is also equally important to design an intelligent security system for ensuring normal data transmission between underwater devices. Due to the prevailing complexities in IoT systems, which consist of different networks, the involvement of billions of underwater intelligent objects is inevitable. Additionally, it is essential to guarantee the security and privacy of users in the IoUT. Any IoUT layer may face a threat from malicious attacks from outside, which have the potential to collapse the entire network. The blockchain security features such as the distributed ledger and consensus-based approval can be used to protect smart ocean communication. The smart ocean systems are largely applicable to ocean activities that are closely related to human beings. These activities include ocean pollution monitoring, ocean deep range monitoring, underwater navigation, resource exploration, underwater tourism, disaster management, and various others. These activities help people to understand the ocean to the fullest, utilize the ocean's resources, and secure the ocean more efficiently, enabling enhanced services to be rendered to mankind. To ensure secure transmission of data across multiple networks in an underwater environment, blockchain-based solutions can be used.
# Potential of Halophytes-Associated Microbes for the Phytoremediation of Metal-Polluted Saline Soils
## 3 Selection of Microbes for the Phytoremediation of Metal Contaminated Saline Soils
### Metal Tolerance and Metal Inactivation Capacities
#### 3.2.3 Arbuscular Mycorrhizal Fungi  
Fungi also influence enzymatic systems under various stress. It is particularly the case of arbuscular mycorrhizal fungi (AMF). As reported by [PERSON] and [PERSON] [138], AMF are relatively sensitive to salinity, but with variations depending on strains. [PERSON] et al. [139] showed that plants inoculated with _Glomus moseae_ under Cd and salt stress had a lower proline content and SOD and CAT activities than in the control. Moreover, the inoculation increased shoot height and roots biomass, and according to the authors, this was linked to the ability of the fungi to reduce oxidative stress in the plant. Through other mechanisms, mycorrhizal colonization can facilitate metals' immobilization in the rhizosphere or inside roots. The AMF _Glomus geosporum_ isolated from the halophyte _Aster tripolium_, whether sampled on polluted or non-polluted salt marshes, increased the accumulation of Cd and Cu in the roots, compared to the non-inoculated plants, but without enhancing plant growth [140]. Moreover, metal concentration can positively influence the AMF colonization. For instance, [PERSON] et al. [141] showed that AMF colonization of the halophyte _Spartina patens_ was higher in Ni-amended treatment than in the control.