esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
A brief overview of the experimental campaigns carried out over three years by the BILLI system will be presented and discussed here.  
Figure 5: (**a**) Lidar return at 12\({}^{\circ}\) of elevation: two wide and jagged peaks from the volcanic plume are clearly visible; the CO\({}_{2}\) profiles inside the volcanic plume are shown in the two red boxes. The uncertainty was equal, respectively, to 6% for the left-side plume peak and 7.5% for the right-side plume peak. (**b**) Vertical scan (fixed azimuth: 230\({}^{\circ}\)) of the volcanic plume (CO\({}_{2}\) excess) acquired on 31 July 2016 (12:47 p.m.–1:03 p.m., local civil time). With that fixed azimuth and in the elevation range 7–9\({}^{\circ}\), rockfaces reflect the laser pulse, corresponding to peaks that are not linked to the plume (rockfaces are disentangled from plume because of the narrowness of peaks). (**c**) Carbon dioxide flux measured at the northeast crater on 31 July 2016 (12:22 p.m.–6:08 p.m., local civil time). The bars show the error in carbon dioxide flux (33%). Please note that the previous figures were extrapolated and rearranged from Figure 2a, Figure 4c, Figure 5, respectively, reported in [42].  
The first experimental campaign was carried out during October 2014. The system was placed \(\approx\)100 m far from the fumaroles of Pozzuoli Solfatara (Pisciarelli, Phlegraen Fields). The system performed a horizontal scan at different elevations, retrieving, for the first time, CO\({}_{2}\) concentration dispersion maps and time-series of lidar-derived CO\({}_{2}\) fluxes [21; 37; 38].  
About ten months later (June, 2015), other tests were carried out at Stromboli Volcano Island, where BILLI operated nearly 24 h for almost a week, performing horizontal and vertical scans. CO\({}_{2}\) excess of a few tens of ppm was measured in some minutes in the volcanic plume up to a distance of nearly 3 km far from the system. In addition, in this case, daily cycles of CO\({}_{2}\) flux were successfully retrieved [39; 40; 41].  
Finally, the potential of the BILLI DIAL system was confirmed by the last field campaign at Mt. Etna at the end of July 2016, where, for the first time, in-plume CO\({}_{2}\) concentration peaks were retrieved at a distance exceeding 4 km [42].  
It should be noted that during all field campaigns, eye safety regulations were followed and weather did not alter the BILLI operation, except for a moderate influence of the wind, as is reported in Section 3.2 [21; 37; 38; 39; 40; 41; 42].
# Optical Flow Odometry with Panoramic Image Based on Spherical Congruence Projection
## 4 Experiments and Discussion
### Runtime Evaluation  
Table 10 summarizes the average processing time (in seconds) for three major components of our system, spherical pixel mapping, feature extraction, and optical flow tracking, compared with two representative baselines--LF-VIO and VINS-MONO. The results show that our method achieves comparable performance in both feature extraction and optical flow tracking stages. This demonstrates that working within a spherical structure does not introduce significant computational penalties to the tracking pipeline itself. In addition, feature extraction is not necessary for each frame as long as sufficient feature points remain after optical flow tracking.  
Figure 14: The error pattern of optical flow vectors in different local zones.The green optical flow lines represent the estimated optical flow, while the purple optical flow lines represent the ground truth.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Keypoints Number**} & \multicolumn{2}{c}{**RPEt (\%)**} & \multicolumn{2}{c}{**RPEr (deg/m)**} & \multicolumn{2}{c}{**ATE (m)**} \\  & **RMSE** & **Std** & **RMSE** & **Std** & **RMSE** & **Std** \\ \hline
100 & 1.946 & 1.080 & 1.071 & 0.579 & 1.079 & 0.414 \\
200 & 1.922 & 1.045 & **1.052** & **0.566** & 0.886 & 0.318 \\
400 & 1.828 & **0.957** & 1.104 & 0.590 & **0.838** & **0.307** \\
600 & **1.820** & 0.983 & 1.067 & 0.582 & 0.949 & 0.357 \\
800 & 1.854 & 1.004 & 1.062 & 0.589 & 0.926 & 0.350 \\ \hline \hline \end{tabular} The bold numbers represent the best results in the same scenario.  
\end{table}
Table 9: Experimental results on the influence of keypoints number on SCP-VIO accuracy.  
However, unlike these baselines, our method includes a spherical pixel mapping step, which introduces an additional average overhead of approximately 0.2266 s per frame. This overhead is expected, as all projection-based panoramic methods--including cube mapping, equirectangular projection, and geodesic grid-based systems--require a similar transformation from raw images to a geometrically consistent representation. Our spherical congruence projection is no exception.
## 3 Simulation
### Continuous Sinusoidal Distribution  
In the last case study, we studied the possibility of determining a continuous dielectric constant distribution using the proposed method, because in most of the existing volume holographic gratings, the dielectric constants are considered sinusoidally distributed. The distribution function is defined as  
\[\omega=\omega_{0}+\omega_{A}\sin(x)\quad(0<x<\Lambda), \tag{26}\]  
where \(\omega_{0}\) and \(\omega_{A}\) represent the average value and variation amplitude of nanoparticle concentration in one pitch, respectively.  
In this case, the parameters \(\omega_{0}\) and \(\omega_{A}\) are measurands we need to determine. \(H\), \(\theta\), and \(\varphi\) are reconstructed as well. The measured and fitted Mueller matrix spectra are shown in Figure 4, and the corresponding parameters are listed in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\mathbf{H}\) (\(\mathbf{\mu}\)m) & & \(\mathbf{\omega_{b}}\) & \(\mathbf{\omega_{\mathrm{k}}}\) & \(\mathbf{H}\) & \(\mathbf{\theta}\) & \(\mathbf{\varphi}\) \\ \hline \multirow{3}{*}{3} & Initial value & 0.18 & 1.1 & 2.8 & 23.5 & 18 \\  & Measured value & 0.20 & 1.02 & 2.85 & 23.56 & 18.97 \\  & True values & 0.2 & 1 & 3 & 25 & 20 \\ \hline \multirow{3}{*}{5} & Initial value & 0.18 & 1.1 & 4.8 & 23.5 & 18 \\  & Measured value & 0.21 & 1.00 & 4.98 & 23.75 & 20.01 \\ \cline{1-1}  & True values & 0.2 & 1 & 5 & 25 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation results of case 2.  
Figure 3: Measured Mueller matrix spectra (blue) and fitted Mueller matrix spectra (red) for the volume gratings whose dielectric constant is a periodic linear distribution, with thicknesses of (**a**) 3 \(\upmu\)m and (**b**) 5 \(\upmu\)m, respectively.  
Since the nanoparticle distribution in one pitch is symmetric again, the achieved anisotropy in the spectrum is not as significant as in the second case, where the values of the off-diagonal elements shown in Figure 4 are at the similar level as in Figure 2. If further attention is paid to the results list in Table 3, a significant improvement on the accuracy can be observed comparing to the precious two cases. It may be attributed to the less singular points for the case of a continuous distribution, which decreases the numerical errors.  
The above simulations demonstrate the feasibility, effectiveness, robustness of the proposed method. It is worth to point out, more complex distribution function with more parameters need to be determined will not noticeably degrade the performance of the proposed method, since the number of wavelength points in the spectrum usually is enough for obtaining the solution. If we compare the Mueller matrix spectra reported in Figures 3-5, significant differences can be distinguished, although the important parameters such as thickness, pitch, angle of incidence and azimuth, even similar interval of the refractive index changes are shared. Such high sensitivity can be used to verify the distribution function type selection, i.e., an inappropriate selection of function type is highly possible to result in the failure of regression.
## 4 Improved DeepSORT Algorithm
### DeepSORT Tracking Algorithm
#### 4.1.1 Kalman Filter (KF) Algorithm  
The KF algorithm is a classical algorithm of state estimation, which is famous for its high efficiency and stability in state estimation and prediction. The basic principle of the KF algorithm is to predict the future state of the system and optimize the state estimation by estimating the current state of the system, combining a dynamic model of the system and the observed data. KF can obtain the three values of the state quantity, which are the true value of the state \(x_{k}\), the predicted value of the state \(\widetilde{x}_{k}^{-}\) and the optimal estimate \(\widetilde{x}_{k}\). The predicted value of the state \(\widetilde{x}_{k}^{-}\) can be obtained via the state prediction equation:  
\[\widetilde{x}_{k}^{-}=\mathbf{A}\times\widetilde{x}_{k-1}+\mathbf{B}\times u _{k} \tag{4}\]  
where \(\mathbf{A}\) is the state transition matrix, \(\mathbf{B}\) is the control input matrix, and \(u_{k}\) is the control input quantity. The optimal state estimate \(\widetilde{x}_{k}\) can be obtained from the state update equation:  
\[\widetilde{x}_{k}=\widetilde{x}_{k}^{-}+\mathbf{K}(z_{k}-\mathbf{H}\times \widetilde{x}_{k}^{-}) \tag{5}\]  
where \(z_{k}\) is the observed measurement of the true value of the actual state, \(\mathbf{H}\) is the state observation matrix, and \(\mathbf{K}\) is the Kalman gain matrix. The Kalman gain actually represents the ratio of model prediction error to measurement error in the estimation process of the optimal state. \(\mathbf{Q}\) is the process noise matrix, \(\mathbf{R}\) is the measurement noise matrix, \(\mathbf{P}_{k}\) is the covariance matrix between the true value and the best estimate, \(\mathbf{P}_{k}^{-}\) is the covariance matrix  
Figure 8: Workflow of theDeepSORTtracking algorithm.  
between the true value and the predicted value, and the \(\mathbf{K}\) under the optimal estimation condition is as follows:  
\[\mathbf{K}=\mathbf{P}_{k}^{-}\mathbf{H}^{T}(\mathbf{H}\mathbf{P}_{k}^{-} \mathbf{H}^{T}+\mathbf{R})^{-1} \tag{6}\]  
The estimation principle of KF is to minimize the covariance of the optimal state estimation and make it closer to the true value. The estimation error covariance matrix is as follows:  
\[\mathbf{P}_{k}=(\mathbf{I}-\mathbf{K}\mathbf{H})\times\mathbf{P}_{k}^{-} \tag{7}\]  
The prediction covariance matrix is as follows:  
\[\mathbf{P}_{k+1}^{-}=\mathbf{A}\mathbf{P}_{k}\mathbf{A}^{T}+\mathbf{Q} \tag{8}\]  
Formulas (4)-(8) are the core of [PERSON] filtering algorithm, which dynamically adjusts state estimation and prediction to minimize the error between the system state estimation and the actual state.
## 3 Results and Discussion
### FTIR, XRD, and XPS Characterization
Figure 2: XPS spectra of (**a**) GO/P(AA-co-AM) and (**b**) P(AA-co-AM). Figure 3 shows the FTIR spectra of the GO/P(AA-co-AM) hydrogels after the adsorption of Cu(II), Pb(II), Zn(II), and Cd(II) ions. It can be seen from the figure that the -OH absorption band located around \(\sim\)3400 cm\({}^{-1}\) on GO/P(AA-co-AM) before adsorption of metal ions has shifted to the right after adsorption, and the strong stretching vibration peak before adsorption has become smoother, which indicates that the adsorption of the GO/P(AA-co-AM) hydrogel is directly related to -OH. The absorption peak at 1668 cm\({}^{-1}\) is the absorption peak of the carbonyl C=O stretching vibration in the amide group (-CONH\({}_{2}\)) in acrylamide (AM) when acrylic acid (AA) and acrylamide (AM) are cross-linked to form a hydrogel. The absorption peak at 1535 cm\({}^{-1}\) is the carbonyl C=O stretching vibration peak in the carboxylate anion (-COO-) in acrylic acid (AA), or the carbonyl C=O stretching vibration peak in the carboxylate group (-COOH) formed after the oxidation of graphite, or the stretching vibration peak of the olefin group (C=C). The vibrational peak at 1050 cm\({}^{-1}\) is the result of the rightward shift of the olefin group (=C-H-) after monosubstitution before adsorption. The changes in the stretching and vibrational peaks of the functional groups on the surface of the GO/P(AA-co-AM) hydrogels after the adsorption of metal ions indicate that the oxygen-containing functional groups on the surface of the GO/P(AA-co-AM) hydrogels play an important role in the adsorption mechanism of the four metal ions. The oxygen-containing functional groups on the surface of the hydrogel play a role.
## 2 Materials and Methods
### DNA Extraction and Quantitative Detection of E. coli Strains by qPCR  
According to the manufacturer's instructions for bacteria, genomic DNA from the bacterial cultures and samples (1 mL) was extracted using the Quick-DNA Miniprep Plus kit (Zymo Research, Irvine, USA). All quantitative polymerase chain reactions (qPCR) were performed using the Rotor-Gene Q (Qiagen, West Sussex, UK) instrument and software to generate the standard curve and microbial quantification. The final volume after all reactions was 20 \(\upmu\)L, among them 1 \(\upmu\)L of template DNA, 4 \(\upmu\)L of 5\(\times\) Hot FirePol EvaGreen qPCR Mix Plus (ROX) (Solis BioDyne, Tartu, Estonia), and 0.5 \(\upmu\)L of each universal primer: forward primer 27F (5'-AGAGTTTGATCCTGGCTC-3') and reverse primer 939R (5'-CTTGGCGGCCCCGCCTAATTC-3') were used for the amplification of the 16S rDNA gene fragment. The cycling parameters were: 12 min at 95 \({}^{\circ}\)C, followed by 40 cycles for 30 s at 95 \({}^{\circ}\)C, 40 s at 60 \({}^{\circ}\)C, and 60 s at 72 \({}^{\circ}\)C. Reactions were carried out in triplicate. In all cases, negative control of amplification was included using 1 \(\upmu\)L of water instead of a DNA template. A fluorescence threshold was chosen to determine the first cycle (CT) where the signals were above the threshold value. Standard curves were generated using 10-fold serial dilutions of DNA extracted from _E. coli_ ATCC 10536 and strain 317, covering a range from 10\({}^{-4}\) to 10\({}^{-10}\). The coefficient of correlation (R2) obtained for the standard curve of _E. coli_ ATCC 10536 and _E. coli_ 317 was 0.9986 and 0.9909, respectively, with the efficiency of the slope E = \(-\)3.32 \(\pm\) 0.11 [22; 23; 24].  
Evaluation of antibiotic susceptibility using antibiotics prescribed for treatment of recurrent urinary infections was performed by disk diffusion method on Muller-Hinton agar [25].  
Figure 1: Schematic representation of the in vitro catheterization model.
# Detecting Jamming in Smart Grid Communications via Deep Learning
## 4 Background  
In this section, we introduce background concepts about (a) digital modulation (Section 4.1), (b) Convolutional Neural Networks (CNNs) (Section 4.2), and (c) autoencoders (Section 4.3), which are preparatory for the subsequent analysis.
# Photocatalytic Activity of Monosized AuZnO Composite Nanoparticles
## 2 Experimental
### Measurement of Photocatalytic Activity  
For photocatalytic studies, a 36 W UV lamp, fired mainly at 365 nm (Philips, Amsterdam, Holland), and a 50 W HSX-F/UV visible output of 390-770 nm (NBeT) was used as light source, and the distance to the reaction beaker was found to be 10 cm. RhB at a concentration of 5 mg/L was prepared by dissolving the dye. A total of 10 mg of the catalyst was added to 30 mL of 5 mg/L RhB in water to carry out the reaction. The mixtures were stirred under dark conditions for 30 min before irradiation, to disperse the catalysts and reach the adsorption equilibrium. Then, under ultraviolet or visible radiation, approximately 4 mL samples were withdrawn every 15 min and separated by centrifuging. The residual concentration of RhB was determined by taking the UV-Vis absorption spectra (TU-1900, Beijing, China), to measure the absorption of RhB in the range of 200-800 nm. The \(y\)-axis of degradation is called as \(C/C_{0}\), where \(C\) is the RhB concentration at each irradiation time interval determined at \(\lambda\)max, and \(C_{0}\) is the initial concentration when the adsorption-desorption equilibrium was achieved. JCPDS (joint committee on powder diffraction standards).
## Appendix B Proof of Theorem 2  
For proving Theorem 2, the following lemma is given.  
**Lemma A2**.: _If \(M(k)\) satisfies that for a \(\mathcal{K}_{\infty}\) class function \(f(x)=\big{(}(D_{G}x)\tilde{c}^{-D_{G}x}\big{)}^{2}\) and the constant \(\tilde{c}\), there exist \(K_{3}\in Z\bigcap[0,\infty)\) such that \(M(k)+1<f^{-1}(k)\) for all \(k\in Z\bigcap[K_{3},\infty)\), then for the constant \(\tilde{c}\) and any \(\epsilon\in(\hat{h},1)\), there exists an integer \(K_{2}\geq K_{3}\) such that_  
\[\Big{(}1-\tilde{c}^{D_{G}(M(k)+1)}\Big{)}^{\frac{k}{D_{G}(M(k)+1)}}<e^{\sqrt{ k}}\] (A3)  
_for all \(k\in Z\bigcap[K_{2},\infty)\), where \(\hat{h}^{\prime}=\hat{h}^{\sqrt{\tilde{\delta}}}\), and \(\hat{h}\in[0,\frac{1}{\tilde{\epsilon}}]\) is shown by Lemma 2 in [27]._  
Proof of Lemma A2.: Firstly, according to \(M(k)+1<f^{-1}(k)\) for all \(k\in Z\bigcap[K_{3},\infty)\), we get that \(\frac{k}{f(M(k)+1)}>1\) for all \(k\in Z\bigcap[K_{3},\infty)\). Then, we have  
\[\frac{k}{D_{G}(M(k)+1)}>\frac{\sqrt{kf(M(k))}}{D_{G}(M(k)+1)}\geq\sqrt{k} \tilde{c}^{-D_{G}(M(k)+1)}\]  
for all \(k\in Z\bigcap[K_{3},\infty)\).  
Secondly, since \(M(k)\to\infty\) as \(k\to\infty\), we can get from Lemma 2 in [27] that for any \(\epsilon\in(\hat{h},1)\), there exists an integer \(K_{3}^{\prime}>0\) such that \((1-\tilde{c}^{D_{G}(M(k)+1)})^{\tilde{c}^{-D_{G}(M(k)+1)}}<\epsilon\) for all \(k\in Z\bigcap[K_{3}^{\prime},\infty)\), where \(K_{3}^{\prime}\) is related to \(\epsilon\) and \(\tilde{c}\).  
Hence, for any \(\epsilon\in(\hat{h},1)\), there exists \(K_{2}=\max\{K_{3},K_{3}^{\prime}\}>0\) such that  
\[\Big{(}1-\tilde{c}^{D_{G}(M(k)+1)}\Big{)}^{\frac{k}{D_{G}(M(k)+1)}}<\epsilon^ {\sqrt{k}}\]  
for all \(k\in Z\bigcap[K_{2},\infty)\).  
Now, the proof of Theorem 2 is given.  
Proof of Theorem 2.: For the \(\mathcal{K}_{\infty}\) class function \(f(x)=\big{(}(D_{G}x)\tilde{c}^{-D_{G}x}\big{)}^{2}\), since \(a\in(0,\tilde{c})\), we have  
\[f(M(k)+1) \leq\Big{(}(D_{G}(-\log_{a}k^{b}+M+1))\tilde{c}^{-D_{G}(-\log_{a}k^{b }+M+1)}\Big{)}^{2}\] \[<\Big{(}(D_{G}(-\log_{a}k^{b}+M+1))\tilde{c}^{-D_{G}(M+1)}\Big{)} ^{2}k^{2 bD_{G}}.\]  
Furthermore, since \(b\in(0,\frac{1}{zD_{G}})\), we have that \(2 bD_{G}<1\). Hence, we have that there exists an integer \(K_{3}>0\) such that \(f(M(k)+1)<k\) for all integers \(k\geq K_{3}\). Since \(f(x)\) is the \(\mathcal{K}_{\infty}\) class function, for all integers \(k\geq K_{3}\), we have that \(M(k)+1<f^{-1}(k)\). Then, using Lemma A2, we have that there exists a \(K_{2}\geq K_{3}\) such that the unbounded maximum time delay \(M(k)\leq-\log_{a}k^{b}+M\) satisfies (A3) for all \(k\in Z\bigcap[K_{2},\infty)\).
# MMNET: A Multi-Modal Network Architecture for Underwater Networking
#### 1.1.1 Difficult Communication  
In the past few years, people have focused on acoustic communication for UWSNs. However, although the transmission distance of acoustic communication in underwater communication is longer than that of electromagnetic wave, acoustic communication has the characteristics of long propagation delay, high bit error rate and low transmission bandwidth. These limitations of acoustic communication have seriously affected the performance requirements of UWSNs [6, 7]. In recent years, new underwater communication methods such as magnetic induction (MI) and light wave have been used to expand UWSNs to meet the needs of UWSNs. Although MI and optical solve the characteristics of acoustic communication, such as long propagation delay and low transmission bandwidth, MI is seriously attenuated in water, and the communication distance is only tens of meters. Optical transmission distance is larger than MI, which can reach hundreds of meters. However, it is interfered by light scattering and ambient light, and the transmission distance is far less than acoustic communication. The existing underwater communication methods cannot meet the needs of underwater networking (Table 1). However, the combination of different communication modes can better meet the communication requirements of underwater networks. Therefore, an UWSN framework supporting heterogeneous communication of various hardware is needed. Most of the existing underwater multi-mode communication frameworks are based on the adaptation layer, and need a special routing algorithm to support, but cannot achieve the versatility, re-programmability and transparency of multi-mode communication [7, 8, 9, 10, 11, 12, 13, 14].
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 3 CU Split Likelihood Modelling Using SVMs
### CU Split Likelihood Modelling
#### 3.1.1 Support Vector Machines (SVMs)  
SVM is a supervised machine learning method that is used in classification and regression problems [41, 42]. It is often used as a non-probabilistic binary classifier that predicts the class for a given input vector. For example, let \(D\) be a training dataset of size \(n\), where  
\[D=\{(x_{i},y_{i})\quad|\quad x_{i}\in\mathbb{R}^{p},\quad y_{i}\in\{-1,1\}\}_{ i=1}^{n}. \tag{3}\]  
Here, \(x_{i}\) is a \(p\)-dimensional input vector and \(y_{i}\) is the corresponding class to which each \(x_{i}\) belongs. During the training phase, SVM constructs a hyperplane that represents the largest separation of the dataset \(D\) (known as the margin) in a higher-dimensional space, solving the primal optimisation function defined as  
\[\begin{split}\min_{w,b,\xi}&\frac{1}{2}w^{t}w+C\sum_{i =1}^{N}\xi_{i}\\ \text{s.t.}& y_{i}(w\phi(x_{i}+b))+\xi_{i}-1\\ &\xi\geq 0,\quad i=1,...,n,\end{split} \tag{4}\]  
where \(w\), \(\xi\), and \(C>0\) are the normal vector to the hyperplane, slack variable, and regularisation parameter, respectively. Here, \(\phi(x_{i})\) acts as the Kernel function that maps \(x_{i}\) into a higher-dimensional space in the case of nonlinear classification. Once the hyperplane is determined, the classifications for the new samples are obtained using the decision function given by,  
\[\text{sgn}(w^{T}\phi(x)+b). \tag{5}\]  
A typical separation of a dataset and a hyperplane (line in blue colour) that achieves the maximum possible hyperplane margin \(M1\), where \(M1>M2\), is illustrated in the Figure 3.
## 4 Development Stages of the Proposed Approach
### Accuracy Assessment  
To judge the accuracy of the forecasting approaches, several benchmarking indicators such as mean absolute deviation (_MAD_) or mean absolute error (_MAE_), mean squared error (_MSE_), mean absolute percent error (_MAPE_), root mean square error (_RMSE_) are quite common and adopted in [58; 59; 60; 61]. However, for assessing the strength of our proposed approach, the _MAD_, the _MSE_ and the coefficient of determination were determined and compared with those obtained through the conventional Holt-Winters method. The equations for the three metrics are as follows [57; 58; 60; 62; 63]:  
\[MAD=\sum\lvert error\rvert/n \tag{10}\]  
\[MSE=\sum\lvert error\rvert^{2}/n \tag{11}\]  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}(y_{i}-\hat{y}_{i})}{\sum\limits_{i=1}^{n} (y_{i}-\overline{y}_{i})} \tag{12}\]  
Figure 2: Structure of the level prediction model. Adapted from [44].