esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Wavefolding in the Digital Domain
### Evaluating the Lambert-W Function  
A particular challenge of using the Lambert-W function in VA modeling, where real-time operation is paramount, is that of computational efficiency. Optimizing the evaluation of \(W()\) is an active research topic (see, e.g., [64]). For the case of guitar distortion circuits, [PERSON] et al. proposed the use of a simplified iterative method which relies on a lookup table for its initial guess [57]. In this work, we propose approximating the value of \(W()\) directly using [PERSON]'s iteration, as suggested in [60]. In order to compute \(w_{m}\), an approximation to \(W(x)\), where \(x\in\mathbb{R}_{>0}\), we iterate over  
\[w_{m+1}=w_{m}(1+\varepsilon_{m}), \tag{41}\]  
where  
\[\varepsilon_{m} = \left(\frac{r_{m}}{1+w_{m}}\right)\left(\frac{q_{m}-r_{m}}{q_{m}- 2r_{m}}\right) \tag{42}\] \[r_{m} = \ln\left(\frac{x}{w_{m}}\right)-w_{m}\] (43) \[q_{m} = 2\left(1+w_{m}\right)\left(1+w_{m}+\frac{2}{3}r_{m}\right), \tag{44}\]  
and \(m=0,\,1,\,2,\,\ldots,\,M-1\). The value \(w_{0}\) is an initial guess and \(M\) is the number of iterations required for \(\varepsilon_{m}\) to approximate zero within machine-size floating point precision. The special case \(W(0)=0\) is defined separately.  
The efficiency of [PERSON]'s iteration will depend on the choice of initial guess. As explained in [60], an initial guess within \(10^{-4}\) of the solution will yield an approximation to \(W()\) accurate to within \(10^{-16}\) in just one iteration. Figure 10 shows the approximate times required to compute \(W(x)\) for a set of values of \(x\) between \(10^{-24}\) and \(10^{300}\) using [PERSON]'s iteration and the previously-proposed [PERSON]'s method [50]. This range was chosen as it covers all values of interest. For instance, when \(V_{\text{in}}=5\,\text{V}\), the argument of \(W()\) in the Serge wavefolder model will be approximately \(1.52\,\times\,10^{44}\). All times were computed by averaging the result of 30 iterations implemented under identical circumstances. A piecewise approximation was used to compute the initial guess, as described in [60]. From this plot, we can observe that [PERSON]'s iteration outperforms [PERSON]'s method by up to approximately 11 times. A MATLAB implementation of the Lambert-W function used to perform these measurements can be found in the accompanying website for this article.
# Linear Programming-Based Non-Probabilistic Reliability Bounds Method for Series Systems
## 4 Numerical Example
### Numerical Example 2  
In Figure 9, a gate-shaped frame problem [29] is modified and used as example 2. The length of each bar is \(L\), and the cross-section areas \(A_{i}\) (for \(i=1,\,2,\,3,\,4,\,5,\,6,\,7\)) are 1865 mm\({}^{2}\), 1635 mm\({}^{2}\), 1670 mm\({}^{2}\), 1680 mm\({}^{2}\), 2095 mm\({}^{2}\), 3290 mm\({}^{2}\), and 2685 mm\({}^{2}\), respectively. Three concentrated loads, \(F_{1}\), \(F_{2}\), and \(F_{3}\), are uncertain interval variables with the marginal intervals \(F_{1}{}^{I}=[270\) kN, 330 kN], \(F_{2}{}^{I}=[90\) kN, 110 kN], and \(F_{3}{}^{I}\ =\ [90\) kN, 110 kN], respectively.  
Without considering the buckling failure, the structure can be regarded as a seven-component series system. The corresponding seven performance functions can be expressed as follows:  
\[\begin{array}{l}g_{1}=S-\frac{\frac{F_{1}}{2\sqrt{3}}+\frac{3\pi}{4}+\frac{F _{1}}{4\sqrt{3}}}{A_{1}}\\ g_{2}=S-\frac{\frac{F_{1}}{2\sqrt{3}}+\frac  
After the above identification, 82 zero design variables have been removed. Table 6 and Figure 10 show the non-probabilistic reliability analysis results of the proposed method, NRBM, and MCS with \(10^{8}\) samples.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Lower Bounds** & **Upper Bounds** & **Maximum Relative Error** \\ \hline NRBM [26] & \(4.8693\times 10^{-2}\) & \(6.1556\times 10^{-2}\) & \(14.1762\%\) \\ LPNRBM & \(5.4235\times 10^{-2}\) & \(5.7595\times 10^{-2}\) & \(4.4081\%\) \\ MCS [21] & \(5.6736\times 10^{-2}\) & — \\ \hline \hline \end{tabular}
\end{table}
Table 6: Reliability analysis results for numerical example 2.  
Figure 10: Reliability analysis results by NRBM, MCS, and LPNRBM for Example 2.  
It can be seen from Table 6 and Figure 10 that the LPNRBM can also provide a greater lower bound and a smaller upper bound than the NRBM. For this example, it should be pointed out that even if the NRBM considers all possible \(7!=5040\) component-sorting alternatives, its maximum relative error still reaches \(14.18\%\). For practical engineering problems, this error cannot be ignored. In other words, the NRBM may no longer apply to this engineering problem due to its accuracy. However, the bounds given by the LPNRBM for the condition of existing information always maintain a low error level.
# Simulation of Fidelity in Entanglement-Based Networks with Repeater Chains
## 3 Entanglement Generation and Distribution  
At a fundamental level, a quantum network can be conceptualized as a system for the efficient distribution of high-quality entangled states over long distances and for an arbitrary group of participants. This section briefly reviews the mechanisms necessary for generating entanglement over multiple links in a network and for enhancing or preserving the qubits as close as possible to a state with maximal entanglement.
## 3 Experimental Setup  
The proposed method explores the effect of a robust classifier on deep CNN architecture. Most common CNN architectures, such as GoogLeNet and ResNet, are used as a backbone [29]. The important features of each architecture were initially learned, followed by training with the three sets of GI endoscopic datasets. In the feature extraction procedure, the original architectures were not changed. The primary attention-based model was used for baseline network training. Stochastic gradient descent (SGD) was implemented to optimize the network with a 0.001 initial training rate. Total training epochs and batch size were 120 and 32, respectively. Input was resized to 128 \(\times\) 128 pixels to crop the ROI. The programming language implemented in python 3.6.12 and Pytorch 1.7.1 DL library ([[https://github.com/pytorch/pytorch](https://github.com/pytorch/pytorch)]([https://github.com/pytorch/pytorch](https://github.com/pytorch/pytorch)) (accessed on 11 February 2021)) was used for the experiments. Experiments were implemented on a server-based Ubuntu 16.04.6 LTS. The system was equipped with four graphic processing units (GPUs) of NVIDIA GeForce RTX 2080 Ti with 11 GB memory each.  
We used several evaluation metrics to validate the classification performance of the proposed method, including accuracy (_Acc_), precision (_Pre_), recall (_Rec_), sensitivity (Sen), specificity (Spe), and F1-score (\(F1\)). \(F1\) measures a test accuracy by calculating the harmonic mean of _Pre_ and _Rec_[2]. _Acc_ specifies the ratio of GI-disease patients to non-diseased patients.  
\[Acc=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
\[Pre=\frac{TP}{TP+FP} \tag{2}\]  
\[Rec=\frac{TP}{TP+FP} \tag{3}\]  
\[F1=2\times\frac{Pre\times Rec}{Pre+Rec} \tag{4}\]
## 2 Materials and Methods
### Calculation Method of Inflow Volume  
The theoretical inflow volume of N\({}_{2}\), O\({}_{2}\), and CO\({}_{2}\) passing through the MFC can be derived using the following method. For a single gas in the circulation part, the calculation  
Figure 2: Mixers with different length–diameter ratios (L/D). involves adding the volume of the original gas to the volume of the newly introduced gas and then subtracting the volume of the gas discharged from the relief valve. This resulting volume should be equal to the product of the total volume of the circulation part and the target concentration. Therefore, the calculation for the inflow volume of CO\({}_{2}\) or O\({}_{2}\) passing through the MFC can be represented as:  
(Volume of original gas + Volume of new gas \(-\) Volume of discharged gas) = (Total volume of circulation part) \(\times\) (Target concentration). Consequently, the inflow volume of CO\({}_{2}\), O\({}_{2}\) and N\({}_{2}\) satisfies the following formulas, respectively:  
\[C_{c}V+X-(X+Y+Z)C_{c}=C_{cs}V \tag{1}\]  
\[C_{o}V+Y-(X+Y+Z)C_{o}=C_{os}V \tag{2}\]  
When \(X\), \(Y\), and \(Z\) are all positive, it is feasible to achieve the target concentrations of CO\({}_{2}\) and O\({}_{2}\) by introducing the corresponding volume. However, the two formulas mentioned have three unknowns and cannot be directly solved. In practical applications, a quantitative step-by-step adjustment control strategy is adopted, as illustrated in Figure 3. The following agreements are made:  
1. Prioritize ensuring the CO\({}_{2}\) concentration through a step-by-step adjustment strategy;
2. When the CO\({}_{2}\) concentration is normal, the subsequent gas introduced always contains the target proportion of CO\({}_{2}\) in order to reach the target O\({}_{2}\) concentration while keeping the CO\({}_{2}\) concentration constant;
3. If the CO\({}_{2}\) concentration is higher, N\({}_{2}\) is used to dilute its concentration to the target level instead of O\({}_{2}\). The specific control process, outlined in the figure, is divided into different scenarios. 2. If the original CO\({}_{2}\) concentration is higher than the target concentration, i.e., C\({}_{c}>C_{cs}\):  
In this case, N\({}_{2}\) is introduced to dilute the gas, and the inflow volume of N\({}_{2}\) (\(Z_{1}\)) must satisfy the following formula:  
\[C_{c}V-C_{c}Z_{1}=C_{cs}V \tag{3}\]  
The inflow volume of N\({}_{2}\) (\(Z_{1}\)) is determined by the following formula:  
\[Z_{1}=\frac{(C_{c}-C_{cs})V}{C_{c}} \tag{4}\]  
Then, the theoretical O\({}_{2}\) concentration C\({}_{o1}\) after mixing is:  
\[C_{o1}=C_{o}\left(1-\frac{Z_{1}}{V}\right)=\frac{C_{o}C_{cs}}{C_{c}} \tag{5}\]  
At this point, the intermediate concentration of O\({}_{2}\) (\(C_{o1}\)) is compared with the target concentration of O\({}_{2}\). 1.
## Appendix B  
The terms in (2) and (3) are defined as:  
\[F_{s} = M_{1}^{-1}(C_{11}\dot{q}_{s}+C_{12}\dot{q}_{b}+g_{1}),\] \[F_{b} = M_{2}^{-1}(C_{21}\dot{q}_{s}+C_{22}\dot{q}_{b}+g_{2}),\] \[\Delta F_{s} = M_{1}^{-1}f_{1},\;\Delta F_{b}=M_{2}^{-1}f_{2},\] \[G_{s} = M_{1}^{-1}B_{1},\;G_{b}=M_{2}^{-1}B_{2},\] \[M_{1} = M_{sb}^{-1}M_{ss}^{-1}-M_{bb}^{-1}M_{bs}^{-1},\] \[M_{2} = M_{ss}^{-1}M_{sb}^{-1}-M_{bs}^{-1}M_{bb}^{-1},\] \[C_{11} = M_{bb}^{-1}C_{bs}-M_{sb}^{-1}C_{ss},\;C_{12}=M_{bb}^{-1}C_{bb}-M_ {sb}^{-1}C_{sb},\] \[C_{21} = M_{bs}^{-1}C_{bs}-M_{ss}^{-1}C_{ss},\;C_{22}=M_{bs}^{-1}C_{bb}-M_ {ss}^{-1}C_{sb},\] \[f_{1} = M_{bb}^{-1}f_{b}-M_{sb}^{-1}f_{s^{\prime}},\;f_{2}=M_{bs}^{-1}f_ {b}-M_{ss}^{-1}f_{s^{\prime}}\] \[g_{1} = M_{bb}^{-1}g_{b^{\prime}}g_{2}=M_{bs}^{-1}g_{b^{\prime}},\] \[B_{1} = M_{sb}^{-1}B_{s}-M_{bb}^{-1}B_{b^{\prime}},\;B_{2}=M_{ss}^{-1}B_ {s}-M_{bs}^{-1}B_{b},\] \[M_{ss} = \left[\begin{array}{cc}m_{1}&m_{2}\\ m_{2}&m_{5}\end{array}\right],\;M_{bb}=\left[\begin{array}{cc}m_{8}&0\\ 0&m_{9}\end{array}\right],\] \[M_{sb} = M_{bs}^{T}=\left[\begin{array}{cc}m_{3}&m_{4}\\ m_{6}&m_{7}\end{array}\right],\] \[C_{ss} = \left[\begin{array}{cc}c_{11}&c_{12}\\ c_{21}&c_{22}\end{array}\right],\;C_{sb}=\left[\begin{array}{cc}c_{13}&c_{14 }\\ c_{23}&c_{24}\end{array}\right],\] \[C_{bs} = \left[\begin{array}{cc}c_{31}&c_{32}\\ c_{41}&c_{42}\end{array}\right],\;C_{bb}=\left[\begin{array}{cc}c_{33}&c_{34 }\\ 0&c_{44}\end{array}\right],\] \[f_{s} = \left[\begin{array}{cc}\zeta_{x}\dot{x}&\zeta_{y}\dot{y}\end{array} \right]^{T},\;f_{b}=\left[\begin{array}{cc}\zeta_{\theta}\dot{\theta}&\zeta_{ \varphi}\dot{\varphi}\end{array}\right]^{T},\;g_{b}=\left[\begin{array}{cc}g_{ 3}&g_{4}\end{array}\right]^{T},\] \[B_{b} = \frac{1}{r_{w}}\left[\begin{array}{cc}0&-C_{\varphi}\\ C_{\theta}&S_{\varphi}S_{\theta}\end{array}\right],\;B_{s}=\frac{r_{s}}{r_{w}} \left[\begin{array}{cc}0&1\\ 1&0\end{array}\right].\]
## 3 Methodology
### Data Collection  
The dataset used in this study was structured as follows: outdoor temperature data were sourced from the open weather data portal of the Korea Meteorological Administrationfor Cheonan City, South Korea, in 2023, while indoor temperature data were generated by simulating average indoor temperature conditions representative of the four seasons. Each dataset consists of 8761 hourly temperature measurements collected over a one-year period, with both time and temperature values recorded to a precision of one decimal place. Based on these datasets, the LSTM model was subsequently developed.  
Figure 1a presents the outdoor temperature data sourced from the Korea Meteorological Administration's open weather data portal for Cheonan, South Korea, whereas Figure 1b presents the simulated average indoor temperature data representative of a typical South Korean household. For clarity, the outdoor temperature data shown correspond to the 24 h measurements recorded on 1 January 2023, whereas the indoor temperature data represent simulated 24 h temperature values for 31 December 2023.  
The rationale for not utilizing more than one year of data to develop the model stems from the observation that graphing outdoor temperature data from the three years prior to 2023 revealed no significant variations that would meaningfully alter the control parameters. Consequently, only the measured and simulated temperature data from 2023 were employed. The LSTM model was trained to predict indoor temperature at time \(t+1\), using input features including timestamp encoding, outdoor temperature from KMA data, and a simulated indoor temperature profile based on a 25 \({}^{\circ}\)C comfort setting. All data were sampled hourly.  
Figure 1: Cont.
## 2 Materials and Methods  
CIGS:Te thin films were deposited on 2 cm \(\times\) 2 cm Corning glass using an RF magnetron co-sputtering equipment (IDT Engineering Co., Gyeonggi, Republic of Korea) [21], with 5.08-cm-diameter CIGS (RNDKorea Corp., Gyeonggi, Republic of Korea, Cu\({}_{0.9}\)In\({}_{0.7}\)Ga\({}_{0.3}\)Se\({}_{2}\), 99.99%-purity) and Te (RNDKorea Corp., 99.99%-purity) targets at fixed powers of 35 W and 10 W, respectively. All samples were fabricated under the following fixed conditions: pre-sputtering for 3 min at each run, base pressure of 133.3224 \(\times\) 10\({}^{-6}\) Pa, Ar gas flow rate of 50 sccm, frequency of 13.56 MHz, and working pressure of 999.9178 \(\times\) 10\({}^{-3}\) Pa with a substrate-to-target distance of 5.0 cm during sputtering at room temperature. The deposition time for the Te target was varied from 0 to 1140 s to obtain a Te thickness of 0 to 88 nm at a constant thickness of approximately 400 nm, while the total deposition time was adjusted from 3600 to 2916 s for the proper comparison of optical properties. The deposition rates of CIGS and Te were 6.67 and 4.00 nm/min at the process condition, respectively. The estimated or expected thicknesses of Te were (1) 0, (2) 18, (3) 34, (4) 50, (5) 63, and (6) 76 nm, deposited at various co-sputtering times for Te target of (1) 0, (2) 270, (3) 510, (4) 750, (5) 945, and (6) 1140 s, respectively, keeping the total thickness of the CIGS:Te thin films at 400 nm (Figure S1). The measured total thickness of the CIGS:Te thin films was 397-417 nm (Figure S2), which is not significantly different from the target thickness. After the co-sputtering deposition of the non-equilibrium CIGS:Te thin films, the samples were subjected to RTA (GRT-100, GD-Tech Co., Gyeongsangbuk, Republic of Korea) at 400 \({}^{\circ}\)C for 20 min under a N\({}_{2}\) gas atmosphere (Figure S1).  
The crystalline structures of the films were analyzed using X-ray diffraction (XRD, PANalytical B.V., Almelo, The Netherlands, X'pert-PRO-MRD, Cu K\(\alpha\) = 0.15405 nm, 40 kV, 30 mA) over a 2\(\theta\) range of 10\({}^{\circ}\)-90\({}^{\circ}\) with a step size of 0.026\({}^{\circ}\) and scanning speed of 8.5\({}^{\circ}\)/min. Field emission scanning electron microscopy (FESEM, JEOL, Tokyo, Japan, JSM-7500F) was used to reveal the morphological characteristics of the CIGST thin films. An energy dispersive X-ray spectroscope (EDX, Oxford Instruments, Abingdon, Oxfordshire, UK, INCA) attached to the FESEM was used to characterize the chemical composition of the CIGS:Te thin films. The optical and electrical properties of the CIGST thin films were measured using an ultraviolet-visible (UV) spectrophotometer (Varian Techtron, Mulgrave, Australia, Cary500 scan) over a range of 200-2000 nm and a Hall-effect measurement system (Accent Optical Technologies, Bend, OR, USA, HL5500 PC) at room temperature, respectively.
## Results
### Foot Strike Detection Methods  
Differences between FS detection methods (M1 to M4) and the reference method, force plate FS detection, are shown in Tables 2-5. M1 was characterized by a great offset reaching from \(-\)11.47 \(\pm\) 4.21 ms (PU500-RFS) to \(-\)15.28 \(\pm\) 3.94 ms (PU100-FFS). Using M1, FS was detected later than with the force plate (vertical ground reaction force >10 N) for all footwear conditions, and for both foot strike patterns. M2 showed a high accuracy for PU100 (\(-\)1.42 \(\pm\) 13.53 ms) and PU500 (1.08 \(\pm\) 10.65 ms) when running with RFS pattern. However, the FS detection method was characterized by a great variability (95% CI: \(-\)4.53 to 2.35 ms and \(-\)1.67 to 3.84 ms) and a low repeatability (RC: 37.68 and 27.30) for both of these footwear conditions. Additionally, for PU1000 (RFS) and all footwear conditions when running FFS pattern, low accuracy, high variability, and high RCs were found for M2. The greatest accuracy (lowest mean differences), compared to the reference, was found for M3 for RFS and FFS when running in all footwear conditions. Interestingly, M3 detected the FS on average before the force plate for the RFS pattern (between 0.48 \(\pm\) 0.26 ms and 0.70 \(\pm\) 0.26 ms) and later than the force plate for the FFS pattern (between \(-\)0.95 \(\pm\) 2.05 ms and \(-\)2.37 \(\pm\) 1.93 ms). In comparison, a contrary trend was found for M4. Negative differences were observed for the RFS pattern and positive differences were observed for the FFS pattern.  
Differences between M3 and the reference method were significantly lower (large effects of \(d\) \(\geq\) 0.8) than for the three other FS detection methods regarding all footwear conditions and both running styles, except M2 for PU100--RFS (trivial effect: \(d\) = 0.19) and PU500--RFS (non-significant). Missing effects between M2 and M3 could be the result of the high variability (95% CI) and low precision (SD)  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Footwear Condition**} & \multicolumn{2}{c}{**Rearfoot Stiffness (N/mm)**} & \multicolumn{2}{c}{**Forefoot Stiffness (N/mm)**} \\ \cline{2-5}  & **Mean** & **SD** & **Mean** & **SD** \\ \hline PU100 & 302.9 & 0.1 & 403.0 & 0.0 \\ PU500 & 264.3 & 0.1 & 324.0 & 0.0 \\ PU1000 & 143.3 & 0.1 & 279.9 & 0.0 \\ \hline \end{tabular}
\end{table}
Table 1: Group mean \(\pm\) standard deviation (SD) for mechanical testing of the three footwear conditions (PUMA Speed 100 (PU100); PUMA Speed 500 (PU500); PUMA Speed 1000 (PU1000)) for rearfoot and forefoot stiffness; statistical significance (_p_ < 0.05), and large effects (_d_ \(\geq\) 0.8) were found between all footwear conditions for both foot areas.
# A Robust Mutual Authentication with a Key Agreement Scheme for Session Initiation Protocol
## The Proposed Scheme
### Stolen Memory Device Attack  
Stolen memory device attacks happen when an attacker \(A\) steals a memory device, extracts the data stored in it, and logs in to the server as a legal client [19; 20]. In our proposed scheme, with the data \(\{B_{i},C_{i},m,b,K_{s}\}\) stored in the memory devices, to log in to the server, \(A\) has to construct a legal REQUEST message \(\{H_{i}^{\prime},R_{c}^{\prime}\}\). However, without the client's true identity \(ID_{i}\) and the server's private key \(k_{s}\), \(A\) cannot recreate \(A_{i}=h(ID_{i}||k_{s})\), which is essential in \(H_{i}\) and \(R_{c}\). Therefore, our proposed scheme resists stolen memory device attacks.
## 1 Introduction
In this paper, we focused on the application of an EnKF to examine the reliability of Lagrangian DA performance using cost-effective drifters to improve velocity and direction of residual current estimates in Currimumudi Lake. Our aim was to: (i) investigate the performance of Lagrangian DA, for improving the accuracy of model predictions; (ii) reduce the programming effort required for assimilation of Lagrangian data into hydrodynamic models; (iii) examine the effect of Eulerian and Eulerian-Lagrangian assimilation on DA performance; and (iv) investigate the spatial variation of residual currents in the domain through identifying five model scenarios increasing the modelling time window from 21 h to 8 days. Delft3D flexible mesh in 2D mode was used for hydrodynamic simulations for five different time windows (i.e., 21 h, one day, two days, four days, and eight days). We then calculated Eulerian velocities from Lagrangian positions using a pseudo-Lagrangian approach and assimilated Lagrangian velocities using the EnKF method, embodied in OpenDA. In the next step, we conducted an extensive study to quantify the efficiency of EnKF-Lagrangian data assimilation as a function of the number of ensembles, the frequency of assimilation, and the number of drifters, taking into account the uncertainties associated with the boundary forcing of the model and observations. The performance of the Lagrangian assimilation was also compared with Eulerian data assimilation (i.e., fixed-point velocities), along with assimilating both Lagrangian and Eulerian velocities. To examine the effect of DA on the residual velocity direction in the lake, we quantified the variation of residual velocity direction in a situation when the model time window increased while a constant assimilation window was adopted. The paper is organised as follows: in Section 2, we describe the materials and methods; in Section 3, we provide a brief introduction to the DA environment (OpenDA) and its EnKF module and DA set up, along with describing a post-processing method to remove fluctuations from drifter data. The DA experiments and monitoring network used to investigate the DA performance are presented in Section 4, followed by results and discussion in Section 5. Finally, we present a detailed summary of the study and the conclusions from the simulation results in Section 6.
## 3 Experiment and Results  
### Experiment  
#### 3.1.1 Training Details  
The network training was executed using the PyTorch toolkit, with a total of 500 iterations for the training process. The dataset, acquired via numerical simulation, includes 1900 sets of data. The dimension of the projection data is \(704\times 320\) and the size of the reconstructed CT images is \(512\times 512\). The batch size was configured to 1 for the training. During the training,validation was performed after every 100 iterations to assess the model's performance. The experiments were conducted in an environment equipped with an NVIDIA GeForce RTX 3060 GPU and a 12 th Gen Intel(r) Core(tm) i7-10700F CPU, running at 2.10 GHz.