esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Proposed LCOE Methodology  
To determine the feasibility of each PV module technology, an LCOE model is proposed. The LCOE determines the price of each kWh produced by the PV system during its lifetime. The considered costs are initial investment, which is reduced by incentives (if granted by the government), yearly operation and maintenance costs and the residual value of PV modules. The initial capital investment cost (\(CI\)) [\(\mathbf{\xi}\)] is determined as the sum of the PV modules cost (\(C_{PV}\)) [\(\mathbf{\xi}\)], which differs for each PV technology and includes the value added tax and transportation costs, invertor cost (\(C_{inv}\)) [\(\mathbf{\xi}\)] and the installation and commissioning services cost (\(C_{s}\)) [\(\mathbf{\xi}\)], which may differ depending on the installed power of the PV system. Thus, the \(CI\) is determined using Equation (1):  
\[CI=C_{PV}+C_{inv}+C_{s} \tag{1}\]  
To reduce the value of \(CI\), government incentives (\(S_{PV}\)) [\(\mathbf{\xi}\)] may be accessed, if granted. Most European Union countries offer incentives through governmental programs to expand the capacity of renewable energy sources, including PV systems. These grants can be up to 90% of the total costs (up to 6027 \(\mathbf{\xi}\) for both PV system and battery system, at a currency conversion rate of 4.9774 RON for 1 EUR on 8 April 2025), the rest of the value must be covered by the prosumer [22]. The yearly operation and maintenance costs (\(OM\)) [\(\mathbf{\xi}\)/year] are independent of PV technology but correlated with the installed power of the PV system; the larger the system is, the highest the price is. Moreover, \(OM\) are determined considering the life span of the PV system (\(n\)) [years] and are discounted considering a discount rate (\(i\)) [\(\%\)]. The residual value of the PV system (\(V_{res}\)) [\(\mathbf{\xi}\)/kg] is the estimated value at the end of its lifetime. The recycling policy in European Union countries specifies that all producers or importers are required to implement waste management techniques or contract non-profit organizations or other companies' services to recover at least 75% (by weight) of the modules and to recycle at least 65% (by weight) of them [23]. Valuable materials may be recovered, such as silver, aluminium, silicon, copper and lead [24]. Some companies offer revenues for the PV system, which may generate additional revenue for the owner. All these costs are summed up and then divided by the cumulated energy produced by the system during its lifetime. The yearly energy output (\(W_{out}\)) [kWh] is determined by mathematical calculation [25] and then is discounted by rate \(i\), considering the life span of the PV system. First, the solar energy captured by the PV system (\(W_{s}\)) [kWh/year] is determined, considering the annual sum of global irradiation (\(G_{t}\)) [kWh/year/m\({}^{2}\)], specific to each location, the area of the PV module (\(S\)) [m\({}^{2}\)], presented in the technical sheet provided by the producer, and the number of PV modules (\(n_{PV}\)) [pcs] of the system, which depends on the installed power of the system.
## 4 Conclusions  
In this work, an iterative optimization algorithm based on a Laguerre-Gaussian mode and Zernike polynomial is proposed, targeting the actual problems of the existing DH-PSF positioning systems and, specifically, the low concentration of the main lobe energy and the system's uncorrected aberration. The proposed scheme improves the main lobe energy concentration without affecting the double-helix characteristics of the phase modulation function. The simulation results showed that the average efficiency of the spot main lobe after the optimization process could be improved by 45.51%, compared to its unoptimized counterpart. By building the experimental light-path and comparing the actual imaging effects of quantum dot labeled tumor cells, it was shown that the optimized image information entropy was reduced by 37.20% with respect to the unoptimized one. Thus, the image definition was significantly improved. The simulation experimental results strongly support that the proposed optimization process is of great importance for the improvement of the localization accuracy of fluorescent particles in cells and the in-depth study of tumor cell metabolic activities.  
Conceptualization, J.Y. and H.D.; methodology, J.Y. and H.D.; formal analysis, H.D.; investigation, H.D.; resources, J.Y., J.C., X.M., B.Q.L.; experimental operation, H.D., Z.C., L.Z.; data curation, H.D.; writing--original draft preparation, H.D.; writing--review and editing, J.Y. and H.D.; visualization, H.D.; funding acquisition, J.Y. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (No. 51975465), the Key Research and Development Program of Shaanxi (No. 2020 ZDLGY14-04), the China Postdoctoral Science Foundation (No. 2019M660251).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The author would like to thank the fellow apprentices in the research group for their help.  
The authors declare no conflict of interest.  
Figure 9: DH-PSF images of quantum dots in tumor cells: (**a**) before optimization; (**b**) after optimization.
## 2 Materials and Methods
### Characterization
#### 2.3.3 Mechanical Properties  
Room-temperature uniaxial tensile tests were performed on a universal testing machine Instron 5969 (Instron, Pianezza, Torino, Italy) equipped with a load cell of 10 kN (load accuracy: \(\pm 0.5\%\) of the reading) and operating at a crosshead speed of 500 mm/min. The strain was measured with a resistance extensometer Instron 2603 (Instron, Pianezza, Torino, Italy; extension accuracy (\(\mu\)m)--0.75% of extension, extension resolution (\(\mu\)m)--100) with a gauge length of 25 mm. The tests were carried out on ASTM D412 specimens [27]. From the tests, the stress corresponding to strain levels of 200 and 400 was determined (\(\sigma_{200}\), \(\sigma_{400}\)), together with the stress at break (\(\sigma_{\rm b}\)) and the corresponding strain (\(\epsilon_{\rm b}\)). The Young's modulus (E) was measured as the initial slope of the stress-strain curve. Five specimens were tested for each composition.  
To ensure that the mechanical properties of NR samples remain constant under long-term use, it is necessary to account for and evaluate the Mullins effect. The Mullins effect is commonly observed in elastomers subjected to cyclic deformations. It manifests as a softening phenomenon that leads to an appreciable change in the rubber's mechanical properties resulting from the first extension. In particular, the stress during unloading or second loading is considerably lower than the first loading stress [28; 29; 30]. Understanding after how many cycles the mechanical response of the material stabilizes is crucial from an application standpoint. Once the mechanical response is stabilized, the material must be used immediately after since, with time, the Mullins effect is recovered. To investigate the effect of crosslinking density on the Mullins effect, NR samples were subjected to uniaxial cyclic tensile tests ten times using an Instron 5969 testing machine (Instron, Pianezza, Torino, Italy) equipped with a load cell of 10 kN and operating at a crosshead speed of 125 mm/min. The strain was measured with a resistance extensometer Instron 2603 ((Instron, Pianezza, Torino, Italy. Gauge length of 25 mm). Specimens having dimensions of 25 \(\times\) 10 \(\times\) 3 mm\({}^{3}\) were used. For each NR formulation, the samples were cyclically stretched up to 200%, 300% and 400%. One specimen was tested for each degree of crosslinking and strain level. The energy dissipated and the stress corresponding to the maximum strain applied (\(\sigma_{\rm M200}\), \(\sigma_{\rm M300}\), \(\sigma_{\rm M400}\)) were determined for each cycle. To accurately assess the residual deformation of the samples, two lines spaced 1 cm apart were marked on the surfaces of the specimens. Following the completion of 10 cycles, the distance between the lines was measured immediately after the sample was dismounted from the instrument, and the residual deformation was then calculated.
## 2 Materials and Methods
### Used Model Quality Criteria and Their Interpretation  
Three different model scores describe the model quality in this work. The mean absolute error (MAE) is easily interpreted since it contains no non-linear transformation. It scores how well a model predicts the target feature on average. Being a linear calculation, it does not punish outliers in the prediction as the average levels them. Though always calculated with scaled values, the MAE is always shown rescaled in its corresponding unit in the Section 3). The root mean squared error (RMSE) is a double non-linear model score. Its unit is the same as the target feature, but the scale shifts due to the non-linear calculation. It loses interpretability, but generally, a low RMSE represents a more robust model than a  
Figure 8: Sequence of decoring and measurements of decoring progress.  
Figure 6: Sequence of experiments. Adapted with permission from Ref. [2]. 2021, Elsevier.  
Figure 7: Decoring setup. Adapted with permission from Ref. [2]. 2021, Elsevier.  
low MAE since outliners are heavily punished. It is the main score for comparing models in the presented work. In this work, a lower RMSE means a better model. All models described as the best model of a series of calculations have the lowest RMSE for the test data of these models. The third model score is the coefficient of determination (R\({}^{2}\)). It measures how well a model fits the variation of the data. If the data are predicted perfectly, R\({}^{2}\) equals 1. If the resulting error of the prediction is the same as when using the mean of the data, R\({}^{2}\) equals 0. If the resulting error is worse than simply using the mean of the data, R\({}^{2}\) can become negative. Formulas 1 to 3 show the calculation of the scores, in which \(y_{i}\) represents the true value of a datapoint, \(\hat{y}_{i}\) the corresponding predicted value, and \(n\) the number of datapoints:  
\[\text{MAE}=\frac{\sum_{i}^{n}\lvert y_{i}-\hat{y}_{i}\rvert}{n} \tag{1}\]  
\[\text{RMSE}=\sqrt{\frac{\sum_{i}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}}{n}} \tag{2}\]  
\[\text{R}^{2}=1-\frac{\sum_{i}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}}{\sum_{i} ^{n}\left(y_{i}-\frac{1}{n}\sum_{i}^{n}y_{i}\right)^{2}} \tag{3}\]  
In the case of multiple randomly initialized calculation runs, the metrics are mean values over all runs. Therefore, mean MAE and mean RMSE refer to these metrics averaged over all runs. If a metric relates only to a single run, an additional marker is added, for example, best RMSE.  
Although the MAE is calculated with scaled parameters, it can be rescaled to its original unit. Particularly meaningful is the relative MAE, which puts the MAE in relation to the mean target value, in this case, the decored mass. Its formal calculation is depicted in Formula 4. The results will show a mean, relative MAE, which is the MAE averaged over all ten randomized runs of the calculation relative to the mean target value:  
\[\text{relative MAE}=\frac{\text{MAE}}{\frac{1}{n}\sum_{i}^{n}y_{i}} \tag{4}\]
## 4 Agent Training with Markov Decision Process (MDP)
### Design of the MDP Model
#### 4.1.1 Definitions of Each Element in the MDP Model
At timestep \(t\), the weight action \(A(t)\) (corresponding to \(w_{n}\) in Equation (15)) is determined by the agent after observing state \(S(t)\) through epoch \(E_{n}\), with a discrete action space defined as \(A(t)\in\{0.1,0.2,\ldots,2\}\) containing 20 possible weight values. The agent evaluates each component of the current state \(S(t)\) to assess epoch quality, quantizes this evaluation into a discrete weight value, and subsequently applies the selected weight to the weighted RLS algorithm to perform an iterative positioning update incorporating the current epoch's information. The reward function \(R(t)\) is designed to evaluate the agent's weighting decision \(A(t)\) during the state transition from \(S(t)\) to \(S^{\prime}(t)\), which can be formulated as:  
\[R(t)=k_{1}\cdot(e(t-1)-e(t))+k_{2}\cdot\frac{e(t-1)-e(t)}{e(t-1)+\sigma} \tag{23}\]  
where \(e(t)\) denotes the positioning error obtained by comparing the AWR-PF output with the true position at timestep \(t\), and \(e(0)\) denotes the initial positioning error. The minimum offset term \(\sigma\) is utilized to prevent the problem of an infinitely large reward due to \(e(t-1)\) becoming exactly zero or near zero. The reward coefficients \(k_{1}\) and \(k_{2}\) serve distinct purposes: \(k_{1}\) focuses on absolute error reduction to encourage a rapid initial error decrease, while \(k_{2}\) emphasizes relative error reduction (percentage change) to promote stable convergence during later positioning stages, collectively guiding the agent's learning toward both immediate and long-term positioning accuracy improvements during training. Our research aims to determine optimal epoch weights to minimize positioning errors. The optimization objective is defined as maximizing the expected discounted cumulative reward:  
\[V=\text{max}_{\theta}\mathbb{E}\left[\sum_{t=1}^{T}\gamma^{t-1}R(t)|\theta\right] \tag{24}\]  
where \(\theta\) denotes the parameters of the agent's neural network, and \(\gamma\in(0,1]\) denotes the discount factor that balances the importance between immediate and long-term rewards. This formulation effectively models the trade-off between rapid error reduction in early iterations and sustained accuracy improvement throughout the positioning process.
## 3 Results  
### Salt Stress and Compost Reduced AMF Root Colonization  
The mycorrhizal frequency (F) of the vitroplant root system was negatively affected by salinity (\(p<0.001\), F = 561.06). The application of native AMF consortium (AMF\({}_{1}\)) and exotic AMF strain (AMF\({}_{2}\)) allowed higher mycorrhizal frequencies, reaching 86% and 90% respectively, under non-stressed conditions (Figure 1). Under non-stressed conditions, the highest values were recorded in plants inoculated with PGPR+AMF2 reaching a frequency of 94%, followed by the plants inoculated with PGPR+AMF1 and AMF\({}_{2}\) with frequencies of 92% and 90% respectively. Nevertheless, the application of compost with whatever AMF reduced mycorrhizal frequency. Under stressed conditions (120 and 240 mM), the same findings were observed. The combined application of PGPR and AMF resulted in the highest records reaching 82% by the PGPR+AMF2 treatment under 120 mM conditions and 81% by the PGPR+AMF1 under 240 mM conditions.  
As for the mycorrhizal frequency, the mycorrhizal intensity was significantly affected by salinity (\(p<0.001\), F = 760.41), especially under 240 mM of NaCl. However, the combined use of PGPR and AMF\({}_{2}\) improved mycorrhizal intensity under both salinity levels. Under non-stressed conditions, the PGPR+AMF2 treatment resulted in the highest mycorrhizal intensity reaching 64%, followed by PGPR+AMF1 (62%), AMF\({}_{2}\) (61%) and AMF\({}_{1}\) (60%) treatments. Under stressed conditions, the PGPR+AMF2 treatment also gave the highest mycorrhizal intensity compared to the other treatments. Based on these results, we can point out the negative effect of salinity and compost on AMF colonization.
prompt intervention, ABE can progress to kernicterus, a severe neurological condition causing permanent brain damage, lifelong disabilities such as cerebral palsy, hearing loss, and developmental problems, or even death [6; 7]. Therefore, the early identification and accurate diagnosis of ABE are crucial in neonatal care to prevent these severe outcomes.  
Traditional diagnostic methodologies for ABE predominantly rely on clinical assessments and serum bilirubin measurements. Clinicians typically initiate the diagnosis of Neonatal Acute Bilirubin Encephalopathy (NABE) by noting signs of jaundice, such as the yellowing of skin and eyes. These clinical observations are commonly verified through blood tests that measure the total serum bilirubin (TSB) or transcutaneous bilirubin (TcB) levels. However, the reliance on these traditional diagnostic methods presents significant limitations [8]. The visibility of jaundice, crucial for initial assessment, can be markedly less apparent in darker-skinned infants, potentially leading to underdiagnosis or delayed intervention. Additionally, the variability in serum bilirubin, affected by age, health status, and blood test timing, can introduce diagnostic uncertainties, further complicating the effective identification and management of NABE.  
The Bilirubin-Induced Neurologic Dysfunction (BIND) scoring system is commonly used in conjunction with traditional methods to enhance accuracy in assessing neurological impairments. The BIND score provides an objective tool by combining clinical features, abnormalities in brain Magnetic Resonance Imaging (MRI), and aberrant auditory brainstem responses [9]. This system categorizes impairment severity into mild (1-3 points), moderate (4-6 points), and severe (7-9 points) levels based on parameters like mental status, muscle tone, and crying characteristics [10]. A score of 0 indicates normal neurological function in newborns. Despite its significance in evaluating NABE and identifying bilirubin deposition within brain nuclei, the BIND scoring system has limitations. Neurological assessments in suspected ABE cases often focus on symptoms of central nervous system involvement, such as lethargy, hypotonia, irritability, high-pitched crying, poor feeding, and, in severe cases, seizures. However, these symptoms are not exclusive to ABE and may overlap with other neonatal diseases, making differential diagnosis challenging without more definitive tests. Moreover, there is a lack of sensitivity in detecting early brain changes caused by bilirubin, often leading to severe damage before clinical symptoms manifest. Therefore, the need for more advanced and sensitive diagnostic tools is highlighted, especially in scenarios where direct observation of bilirubin deposition in the brain is challenging.
## 4 Results and Discussion
### Hydraulic Radial Force on All Blades  
The impeller of the research object in this study is an open impeller composed of six blades and a hub. By excluding the hub and only counting the hydraulic radial force acting on all the blades together, time varying curves are obtained, as shown in Figure 12(a). Through a comparison with Figure 12, it can be found that the hydraulic radial force on all the blades and that on the entire impeller have almost the same variation curves. The hydraulic radial force on all the blades is divided by the that on the entire impeller to obtain the ratio of them, and the variation of this ratio is shown in Figure 12(b). Figure 12(b) indicates that the ratio of them is concentrated between 1 and 1.2, which shows that the hydraulic radial force on all the blades is very close in magnitude to that acting on the entire impeller.  
Furthermore, the angles of the hydraulic radial force on all the blades at each condition are calculated and plotted in Figure 14. Similarly, it can be found that Figures 12 and 14 have a high degree of similarity. This shows that when studying the hydraulic radial force acting on the entire impeller, other parts of the impeller such as the hub can be ignored, and only the forces all the blades need to be considered. Doing so results in relatively small errors and brings great convenience to the subsequent research on the formation mechanism of the hydraulic radial force of the impeller.  
The hydraulic radial force on all blades is also decomposed into components in the X and Y directions, as shown in Figure 15. Similarly, Figures 11 and 15 also have a high degree of similarity, which further confirms that the hydraulic radial force on all blades can be used to approximately replace the that on the impeller.  
Figure 13: Time-varying curves of hydraulic radial force on all blades: (**a**) force magnitude; (**b**) ratio to force on the impeller.
# Cross-Correlation Wavelet-Domain-Based Particle Swarm Optimization for Lightning Mapping
## 3 Results and Discussions
### Optimal Selection of PSO Parameters Using Simulated Data
The generated incident angles (_Az_ and _El_) from the handcrafted lightning signals were assumed to be real positive and negative values. Figure 5 shows the simulated constructed lightning map using the PSO optimization algorithm, which occurred at 300 iterations. Figure 5a shows the constructed map of a lightning source location (_Az_ and _El_ angles). Meanwhile, Figure 5b shows the expanded view of the lightning maps. The point source was positioned at azimuth angles in the range of 40\({}^{\circ}\) to 50\({}^{\circ}\) and elevation angles of between 18\({}^{\circ}\) and 40\({}^{\circ}\). Using the PSO algorithm, the elevation shape and the height of the channel of the simulated lightning map, shown in black, presented certain consistency with the handcrafted lightning map, shown in red. Furthermore, the regression coefficient (_R_) was used as an indicator of the predictive model's training process performance of the elevation and azimuth angles. The estimated lightning map is displayed in black circle, while the blue line represents the reference value  
Figure 4: PSO performance for estimating lightning maps. (**a**) The PSO-convergence curves as a function of number of iterations and fitness value. (**b**) An expanded view of (**a**). Figure 5: PSO algorithm: (**a**) simulated lightning map plotted in elevation vs. azimuth; (**b**) expanded view of the simulated lightning maps of (**a**). (actual target). The regression coefficient results of both elevation and azimuth angles are very close to unity, which validates the accuracy of estimated constructed lightning maps based on the proposed PSO algorithm. Figure 6 illustrates the regression values of the elevation and azimuth angles at 300 iterations are limited to \(R_{El}=0.99484\) and \(R_{Az}=0.99348\). Through the regression classifications, the accuracy of estimating lightning maps using a PSO algorithm provides a measure of the variable through computing importance scores as a training model. Moreover, Figure 7 shows that the elevation and azimuth angle at 200 iterations having more regression coefficients of \(R_{El}=0.9948\) and \(R_{Az}=0.9938\), respectively. Figure 6: Regression performance for estimating lightning map at 100 iterations: (**a**) elevation regression line; (**b**) azimuth regression line. Figure 7: Lightning mapping regression performance at 200 iterations: (**a**) elevation regression performance; (**b**) azimuth regression performance.