esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Outcomes  
The following is a list of the principal outcomes postulated following a detailed examination of the various state-of-the-art predictive models presented in the previous sections:  
* The forecasting time, weather conditions, and system economy all have a significant impact on the accuracy of a given load forecasting model.
* To assess the reliability of a hybrid or single predictive model's reliability, the values of MAE, RMSE, and MAPE can be employed.
* A multitude of articles consider a plethora of climatic variables based on the specific application and location in question, rendering the use of standard variables irrelevant for LF, as the LF fails to account for them.  
* Geographical factors, such as temperature and humidity, render the STLF case sensitive, with location-specific variations impacting model performance and accuracy.
* Correlational analysis of the load-forecasting-related elements has a significant impact on the model's accuracy. Any alterations to the region will have a detrimental effect on the model's accuracy, given that these correlations are similarly dependent on the region in question.
* By incorporating econometric variables, loading data, or statistical processes, the values derived from STLF can be approximated to those of MTLF or LTLF.
* The ELM offers expedient training, the fuzzy C-means method offers a superior error analysis index, FRBS has the capacity to approximate all variables, and MLP and LSTM have the most precise classification of the single methods listed.
* From a literature perspective, the combination of various single predictive methods for load forecasting, such as ANN, SVM, or LSTM-based double predictive models, has demonstrated enhanced outcomes.
* In various studies, a variety of clustering techniques were employed, with \(K\)-means demonstrating superior performance.
* In load forecasting models, optimization strategies were employed with great frequency. Nevertheless, the GA and PSO techniques were predominantly applied due to their superior crossover and mutation performance and optimal fitness.
* The STLF plays a pivotal role in optimizing smart grid operations, particularly in the context of integrating advanced technologies such as smart meters, V2G, and second-life batteries.
* The use of smart meters provides valuable data for STLF, thereby enhancing forecasting accuracy.
* The integration of second-life batteries introduces a new level of adaptability, improving the stability and overall performance of the smart grid.  
In summary, the comprehensive analysis highlights the significance of each model's performance in a practical setting and its potential to contribute to the advancement of modern grid technology. It is of the utmost importance to continue refining and evaluating these models in order to achieve the ultimate goal of a fully intelligent power grid system.
## 4 Results on a Real Case: Proof-of-Concept Validation
### Results of the Mapping System  
First, given the current position of the robot (or team of robots), the maps and parcel masks are obtained. For our test, a GPS receiver with submeter precision was used. It is important to remark that the system must be inside the parcel to get the data accurately. From the initial position of the system, inside the parcel, the satellite maps of the parcel will be obtained using a satellite maps service via the Internet. In our case, two images were downloaded from the SIGPAC service, the photography of the parcel and the image with the boundaries of the parcel, as can be seen in Figure 3. Due to the characteristics of the latter image, some additional steps were included in Algorithm 3.  
In Algorithm 3, the image with the borders of the parcel (see _parcel image_ in Figure 3) is first processed to obtain a binary mask with pixels which belongs to the parcel and the pixels which are outside the parcel. For this reason, this image is first converted to black and white with a custom binarization algorithm (see Figure 4a). The purpose of the binarization is double: (1) to remove watermarks (red pixels in Figure 3) and (2) to highlight the borders (purple pixels in Figure 3).  
This early version of the binary mask is then dilated to perform a better connection between those pixels which correspond to the border (see Figure 4b). The dilation is performed three times with a 5 \(\times\) 5 mask. Then, the pixel corresponding to the position of the robots is flood-filled in white (see Figure 4c). Finally, the image is eroded to revert the dilated filtering previously performed and to remove the borders between parcels. For this purpose, the erosion algorithm is run 6 times with the same 5 \(\times\) 5 mask. After applying this last filter, the borders are removed and the parcel area is kept in the mask image. After obtaining the last binary mask, it is dilated again 3 times to better capture the real borders of the parcel (see Figure 4d).  
Figure 4: Filtering the parcel images to detect the parcel or grove where the robots are located. (**a**) after applying the custom binarization, (**b**) after dilating, (**c**) after filling the current parcel, (**d**) after erosion.  
Figure 3: Obtaining the satellite maps from the current position.  
Then, it is only necessary to determine the bounding box surrounding the parcel in the parcel mask (see Figure 4d) to crop both images. This procedure is visually depicted on the top side of Figure 5.  
The first mask, _Path_, contains the path where the robot can navigate (see Figure 5b). The second mask, _Trees_, contains the area assigned to orange trees (see Figure 5c). The last mask, _Obstacles_, contains those pixels which are considered obstacles; this mask corresponds to the inverse of the path (see Figure 5d).
# Hybrid Fuzzy-DDPG Approach for Efficient MPPT in Partially Shaded Photovoltaic Panels
## 2 Materials and Methods
### Mathematical Model of a Photovoltaic Panel  
To comprehensively characterize the nonlinear behavior of the photovoltaic panel, the following mathematical model is presented. By employing the single-diode equivalent circuit, this formulation encapsulates the interactions among photocurrent generation, diode conduction dynamics, and resistive losses, thereby providing a robust foundation for developing advanced MPPT strategies. This model is essential for deriving control laws that guarantee convergence to the global maximum power point under varying irradiance and temperature conditions, including partial shading.  
As shown in Figure 1, the equivalent circuit of a photovoltaic (PV) cell consists of a current source representing the photocurrent (\(I_{ph}\)), a diode (\(D\)) modeling the p-n junction behavior, a series resistance (\(R_{s}\)), and a parallel (shunt) resistance (\(R_{sh}\)) that accounts for leakage currents.  
The output current \(I_{pv}\) of the PV cell is given by Kirchhoff's law as follows [33]:  
\[I_{pv}=I_{ph}-I_{d}-I_{sh} \tag{1}\]  
where:  
* \(I_{ph}\) is the light-generated current, proportional to the solar irradiance.
* \(I_{d}\) is the diode current.
* \(I_{sh}\) is the shunt leakage current. The shunt current is modeled as follows: \[I_{sh}=\frac{V+I_{pv}R_{s}}{R_{p}}\] (2)  
Figure 1: Single-diode equivalent circuit model of a photovoltaic (PV) cell, including series resistance \(R_{s}\) and shunt resistance \(R_{sh}\). The photocurrent \(I_{ph}\) is generated due to incident solar irradiation, while the diode \(D\) represents the intrinsic p-n junction behavior.  
where:  
* \(R_{s}\) is the series resistance.
* \(R_{p}\) is the parallel resistance.  
The diode current \(I_{d}\) follows the Shockley diode equation:  
\[I_{d}=I_{0}\bigg{(}\exp\bigg{(}\frac{qV_{d}}{Akt_{c}}\bigg{)}-1\bigg{)} \tag{3}\]  
where:  
* \(I_{0}\) is the reverse saturation current of the diode.
* \(q=1.6\times 10^{-19}\) C is the electronic charge.
* \(k=1.38\times 10^{-23}\) J/K is the Boltzmann constant.
* \(A\) is the diode ideality factor.
* \(T_{c}\) is the cell temperature in Kelvin.
* \(V_{d}\) is the voltage across the diode:  
\[V_{d}=V+I_{pv}R_{s} \tag{4}\]  
The light-generated current \(I_{ph}\) is affected by solar irradiance and temperature:  
\[I_{ph}=(I_{sc}+K_{I}(T_{c}-T_{r}))\times\frac{G}{G_{STC}} \tag{5}\]  
where:  
* \(I_{sc}\) is the short-circuit current at standard test conditions (STC) (\(T=25\,^{\circ}\)C, \(G_{STC}=1000\) W/m\({}^{2}\)).
* \(K_{I}\) is the short-circuit current temperature coefficient.
* \(T_{r}\) is the reference temperature.
* \(G\) is the actual solar irradiance.  
For a PV module with \(N_{s}\) series-connected cells, the overall output current is as follows:  
\[I_{pv}=I_{ph}-I_{0}\bigg{(}\exp\bigg{(}\frac{q(V+I_{pv}R_{s})}{Akt_{c}N_{s}} \bigg{)}-1\bigg{)} \tag{6}\]
## 3 Results and Discussion
### Degradation Pathways  
Figure 6 presents the possible transformation pathways of Et-PABA based on the six observed TPs. On one hand, SO4\({}^{\bullet-}\) and \({}^{\bullet}\)OH can directly attack the carbon on the benzene ring, resulting in hydroxylation of Et-PABA. Through this degradation pathway, TP1 was produced. This product was further hydroxylated to produce a product of TP2. On the other hand, TP3 was a product of Et-PABA formed after attacks by ROS caused the C-O bond between the carbonyl group and the ethoxy group to be broken. Under further actions of ROS, due to the oxidation or removal of the amino group, TP3  
Figure 4: Effects of different anions (**A**) and humic acid (HA) concentrations (**B**) on the removal efficiency of Et-PABA. Conditions: [Et-PABA] = 0.05 mM, [anion] = 5 mM, [HA] = 5–20 mg/L, [PDS] = 2.0 mM, and without pH adjustment.  
was converted into TP4 and TP6, respectively. Similarly, hydroxylation of TP4 produced TP5. Finally, these intermediates of Et-PABA can be further degraded to lower organic acids.
## 2 Materials and Methods  
A first step is to determine lens sensitivity to uniform astigmatism \(W_{UA}\), uniform coma \(W_{UC}\), linear astigmatism \(W_{LA}\), and field tilt \(W_{FT}\). These aberrations are calculated as a single surface in the lens is tilted a small amount such as 0.1\({}^{\circ}\). Equations (1)-(4) are used to calculate the aberrations using an on-axis real ray, and two real rays, +1 and \(-1\), at full aperture in the plane of symmetry of the perturbed system. Then the lens sensitivity to the aberrations is calculated as the RSS (root sum square) of the aberrations contributed by each tilted surface.  
\[W_{UC}=\frac{{{OPL}(+1)-{OPL}(-1)}}{2} \tag{1}\]  
\[W_{UA}=8\frac{{{(SAGT(0)-SAGS(0))}}}{{{F}^{2}}} \tag{2}\]  
\[W_{LA}=8\frac{{{(SAGT(+1)-SAGS(+1))-(SAGT(-1)-SAGS(-1))}}}{{{2F}^{2}}} \tag{3}\]  
\[W_{FT}=8\frac{{{SAGS(+1)-SAGS(-1)}}}{{{2F}^{2}}} \tag{4}\]  
In Equations (1)-(4), OPL stands for the optical path length, SAGT stands for the sag of the tangential field curve, SAGS stands for the sag of the sagittal field curve, and F stands for the f-number of the system so that the aberration is given as a wavefront error.  
Keeping in mind that tilting a single surface reduces the system symmetry to plane symmetry, the calculation of uniform coma requires the OPL of two rays in the plane of symmetry. Since lens design software can provide directly the sag of the astigmatic field curves in the plane of symmetry, then the calculation of uniform astigmatism, linear astigmatism, and field tilt is simplified. A discussion of these aberrations is found in reference [4].  
The choice of 0.1\({}^{\circ}\) surface tilt is because this amount of surface tilt often separates what is easy to manufacture from what is difficult to manufacture. The sensitivity analysis could be done with a larger surface tilt and the results would be similar because uniform coma, linear astigmatism, and field tilt are proportional to surface tilt.  
Depending on how the data is processed Zernike polynomials or other type of polynomials can be used. However, here for clarity we chose a simple aberration representation.  
The analyses discussed below were done with Zemax and OpticStudio lens design software.
# Joint Delay-Doppler Estimation Performance in a Dual Source Context
## 2 Signal Model
### Narrowband Signal Model  
A band-limited signal \(s(t)\), with bandwidth \(B\), transmitted over a carrier frequency \(f_{c}\) (\(\lambda_{c}=c/f_{c}\)) is considered in this study. This signal can be expressed as  
\[s(t)=\sum_{n=N_{1}^{\prime}}^{N_{2}^{\prime}}s\left(\frac{n}{F_{s}}\right) \text{sinc}\left(\pi F_{s}\left(t-\frac{n}{F_{s}}\right)\right)\rightleftarrows S (f)=\frac{1}{F_{s}}\sum_{n=N_{1}^{\prime}}^{N_{2}^{\prime}}s\left(\frac{n}{F _{s}}\right)e^{-j2\pi n\frac{f}{F_{s}}},\ -\frac{F_{s}}{2}\leq f\leq\frac{F_{s}}{2}, \tag{2}\]  
where \(\text{sinc}(\cdot)\) is the sine cardinal function, f is the frequency, \(F_{s}\geq B\) is the sampling frequency, \(N_{1}^{\prime},N_{2}^{\prime}\in\mathbb{Z}^{2}\), \(N_{1}^{\prime}<N_{2}^{\prime}\) and \(\rightleftarrows\) refers to the time-frequency pair that will be helpful for the bounds derivation. Using (1), the dual source complex analytic signal at the output of the receiver's antenna can be expressed as  
\[x_{R}(t) =d_{R}(t;\boldsymbol{\eta}_{0},\rho_{0},\phi_{R,0})+d_{R}(t; \boldsymbol{\eta}_{1},\rho_{1},\phi_{R,1})+w_{R}(t),\] \[d_{R}(t;\boldsymbol{\eta}_{i},\rho_{i},\phi_{R,i}) =\rho_{i}e^{i\phi_{R,i}}s((1-b_{i})(t-\tau_{i}))e^{i\omega_{c}(1- b_{i})t}e^{-j\omega_{c}\tau_{i}},\]  
where \(w_{R}(t)\) is a zero-mean white complex circular Gaussian noise, \(\omega_{c}=2\pi f_{c}\), and for \(i\in\{0,1\}\), \(\boldsymbol{\eta}_{i}^{T}=[\tau_{i},b_{i}],\rho_{i}\in]0,+\infty[\) and \(\phi_{R,i}\) the amplitude and phase of the complex coefficients induced by the propagation characteristics (fading, reflection, etc.), the polarization mismatches and the antenna gains.  
Under the narrowband signal hypothesis, i.e., the time-frequency product \(B\cdot T_{c}\) (with \(B\) the signal bandwidth and \(T_{c}\) the coherent integration time) is smaller than the inverse Doppler term \(c/v\), the Doppler effect on the band-limited baseband signal \(s(t)\) is usually neglected so that: \(s((1-b)(t-\tau))\approx s(t-\tau)\) (ch.9 [1]). Therefore, the baseband output of the receiver's Hilbert filter with bandwidth \(F_{s}\) containing a direct signal and a single specular reflection can be approximated by  
\[x(t) \triangleq x_{R}(t)e^{-j\omega_{c}t}=d(t;\boldsymbol{\theta}_{0})+d(t; \boldsymbol{\theta}_{1})+w(t)\, \tag{3}\] \[d(t;\boldsymbol{\theta}_{i}) \triangleq\rho_{i}e^{i\phi_{R}}s(t-\tau_{i})e^{-j\omega_{c}b_{i}( t-\tau_{i})}\, \tag{4}\]  
where for \(i\in\{0,1\}\), \(\boldsymbol{\theta}_{1}^{T}=[\boldsymbol{\eta}_{1}^{T},\rho_{i},\rho_{i},\phi_ {i}]\), \(\phi_{i}=\phi_{R,i}-\omega_{c}(1+b_
# Payment Maintenance Decision Making Based on Optimization Models
## 5 Discussion
### Analysis of Model Suitability  
The theoretical suitability of the proposed models was analyzed as follows. A linear optimization model must satisfy the following implicit assumptions.  
(1) Assumption of linearization: a function is considered linear [26] when the following equation is satisfied:  
\[f(x_{i})=\sum_{i=1}^{n}c_{i}x_{i}+K \tag{9}\]  
In the equation, \(K\) and \(c_{i}\) are constants. The objective functions of the two proposed models can be converted to the form shown in Equation (9). Therefore, these objective functions can be considered as linear functions.  
(2) Assumptions of proportionality: A change in the decision-making variable should cause the objective function to change proportionally. Because the relationship between the decision-making variable and objective function can be expressed in the form given in Equation (13) for both models, the assumption of proportionality is satisfied for the decision-making variable and the objective function.  
(3) Assumption of additivity: The value of the objective function should equal the sum of the individual contributions from each decision-making variable to the objective function. This assumption is satisfied for all models, as is evident from their equations.  
(4) Assumption of the fixed parameter: All the parameters are fixed constants, and no random factor is introduced into the analysis. The PCI used in all models is also a fixed parameter. Therefore, this assumption is also satisfied.  
Figure 3: Pareto front of the MBMMQ model solved by genetic algorithms.
## 1 Introduction
Intrinsic cognitive load is related to the complexity of the task or content, which depends on the connections between elements and the learner's prior knowledge or expertise [9; 10]. Memory is the cognitive process of encoding, storing, and retrieving information, which is fundamental to learning and behavior [11]. According to the multi-store memory model, memory processing occurs in three stages: sensory memory, short-term memory, and long-term memory. Information briefly resides in sensory memory, is transferred to short-term memory, and, through rehearsal, moves into long-term storage [12]. [PERSON] forgetting curve highlights that memory decay is most rapid shortly after learning: approximately 58% of information is retained after 19 min; only 21% remains after a month [13]. Based on these insights, this study aimed to explore effective TBM training methods for construction workers, focusing on cognitive load and the effects of varying visual complexity and the number of training items. To do so, three versions of training materials were developed: A-type included 13 visually dense items, B-type presented the same items with simplified visuals and aligned verbal cues, and C-type featured 8 essential items derived from the B-type set (Table 2). Therefore, the aim of this study is to examine how the complexity of visual information and the number of educational items used in Tool Box Meeting (TBM) materials affect the cognitive load and memory retention of construction workers. Based on this analysis, the study seeks to suggest strategies to enhance the effectiveness of on-site safety training. To address this aim, recall experiments were conducted to investigate the effects of visual complexity and the number of training items on learning performance and cognitive load. Accordingly, the study is guided by the following research questions:  
**RQ1. **: _Does the visual complexity of TBM training materials affect the recall performance of construction workers?_
**RQ2. **: _Does reducing the number of TBM training items improve workers' memory retention?_  
The following hypotheses were developed: \begin{table}
\begin{tabular}{c c c c} \hline
**Group** & **Number of Items** & **Visual Complexity** & **Explanation Style** \\ \hline A & 13 & High & Visual-focused materials with dense information \\ B & 13 & Medium (Simplified from A) & Simplified with verbal guidance \\ C & 8 & Medium (Same as B) & Reduced number of items for focused delivery \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of instructional styles and material characteristics for Groups A, B, and C.  
* _TBM materials with high visual complexity will result in lower recall performance than simplified materials;_
* _A reduced number of TBM training items will increase the recall rate of construction workers._
## 5 Discussion
### Participation Factor Analysis  
To quantify the level of participation of the \(i\)-th state variable in the \(m\)-th mode, the participation factors (\(p_{i,m}\)) were computed, as defined in [26]. Results depicted in Figure 9 show that the first mode, which is nonoscillatory, is impacted only by angular speeds of generators, i.e., \(\Delta\omega_{1}\), \(\Delta\omega_{2}\) and \(\Delta\omega_{3}\). Two oscillatory modes (2, 3 and 4, 5) are additionally impacted by the generator angles \(\Delta\delta_{21}\) and \(\Delta\delta_{31}\). Mode 2, 3 is mainly impacted by the angle \(\Delta\delta_{21}\), and speed \(\Delta\omega_{2}\), and 4, 5 is mainly impacted by the angle \(\Delta\delta_{31}\), and speed \(\Delta\omega_{3}\).  
Furthermore, no particular differences can be found between the participation factors for the system with initial parameters and parameters obtained for different optimal solutions. The only exception is a case for the optimal parameters obtained using the controllability metric \(\text{EV}_{\text{max}}\). This case shows that participation factors for the first mode and speeds \(\Delta\omega_{1}\) and \(\Delta\omega_{2}\) are equal, while the angle \(\Delta\delta_{21}\) impacts only the mode 2, 3, and the angle \(\Delta\delta_{31}\) impacts only the mode 4, 5.
## 5 Numerical Results
### Discussion, Limitations and Applications  
The presented results illustrate that out of all considered deployment parameters, street width and building block length are the ones impacting the UAV LoS blockage probability the most. The impact of BS and UAV heights as well as the mean building block height is also noticeable. These parameters all need to be accounted for when estimating the required density of BSs to support UAVs in mmWave 5G systems. Note that in real deployments, these parameters are not independent as specified in [20]. Thus, in general, in city centers, where the mean building heights and width are larger, much higher BS deployment density will be required for the same target UAV LoS blockage probability as compared to the suburbs.  
We specifically emphasize the importance of rooftop-mounted BS. As we have observed, qualitatively, the density of ground-mounted BS deployment has to be extremely high, especially in city center deployment conditions. Here, to support the uninterrupted connectivity, it is much more economically sustainable for network operators to deploy dedicated BSs having almost unobstructed coverage for UAV. Our results demonstrate that one rooftop-mounted BS is equivalent to six to twelve ground-mounted ones in terms of UAV LoS blockage probability.  
Although the proposed model by design can capture the specifics of different deployments, it also has its limitations. Specifically, as the model assumes that visibility areas are all convex, the visibility areas created by BSs deployed on the crossroads need to be treated as two independent rectangular visibility areas. This implies that the accuracy of the model increases as the size of the analyzed regions with homogeneous building deployments increases. Furthermore, the independence of all visibility areas also implies that the BS locations should be close to the Poisson point process (PPP, [32]). Note that due to restrictions of BS locations in the city center and also due to the need for high densification to satisfy the growing customer needs, BS deployment locations are far from regular cellular structures. Specifically, many studies assume PPP as the deployment process for 4G/5G systems.  
The proposed model is especially usable in system-level simulations of mmWave NR deployments supporting UAVs. As noticed in [33], the handling of dynamic blockage events is one of the most time-consuming operations. Associating UAVs with the blockage process having the fraction of time in blockage coinciding with the UAV LoS blockage probability may efficiently address this challenge. Furthermore, the proposed model can be utilized by network operators at the network deployment phase to assess the density of mmWave BSs providing the required level of UAV coverage.
## 5 Frequency Hopping Techniques
### Channel Aware, Probabilistic-Channel-Usage Techniques
#### 5.3.2 Utility Based Adaptive Frequency Hopping  
The Utility Based Adaptive Frequency Hopping (UBAFH) technique extends the performance of the WRFH by introducing a parameter, \(\alpha\), which is called temperature [31]:  
\[P_{k}=\frac{Q_{k}^{\alpha}}{\sum_{i=1}^{K}Q_{i}^{\alpha}} \tag{15}\]  
The parameter \(\alpha\) can be adjusted to achieve different behaviours. Low temperatures will result in almost even channel usage, while for high temperature values, only the best channels will be used. When \(\alpha=1\), the probability distribution happens to be the same as for the WRFH technique. Additionally, lower and upper bounds on the usage probability can be introduced so that  
\[P_{min}\leq P_{k}\leq P_{min} \tag{16}\]  
\(P_{min}\) is introduced to ensure a minimum degree of frequency diversity, while \(P_{max}\) prevents the algorithm from converging to scenarios where only a few channels are used.