esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The size of the publicly available datasets might range from hundreds of frames when a precise manually segmented binary mask is provided, to thousands of video frames if an approximated binary mask is created. The dataset CVC-EndosceneStill [23] provides manually segmented binary masks for polyp, background, lumen and specular lights classes. In all, 912 images are available. It also splits the dataset into training, validation and test sets and indicates the recommended metrics, so providing a common dataset which facilitates the proper comparison of methods. Kvasir-SEG [24] includes 1000 polyp images for which a polygonal binary mask and a bounding box are provided. On the other hand, CVC-VideoClinicDB [21,25] providesan elliptical approximation for over 30,000 frames. Nevertheless, these datasets lack clinical metadata, such as the polyp size or histological diagnosis and only provide the Paris classification [26; 27] in the best of the cases. Just recently, HyperKvasir [20] has been released. It includes labelled data for supervised training, but also provides unlabelled data. Regarding polyps, besides the images and binary masks of the Kvasir-SEG dataset, it also includes 1028 images and 73 videos labelled with the \"polyp\" class and 99,417 unlabelled images. All these datasets include white light (WL) images, but not NBI images. For purposes of polyp classification, only one publicly available dataset is available [28]. This dataset includes 76 colonoscopy videos, using both WL and NBI imaging, of 15 serrated adenomas, 21 hyperplastic lesions and 40 adenomas. The objective of this paper is to present the PICCOLO dataset with its associated clinical metadata and compare the performance results of different deep learning models trained with it and other publicly available datasets, as well as analyse the influence of the polyp morphology in the results. This paper is organized as follows: Section 2 details the acquisition and annotation protocols to obtain the PICCOLO dataset, as well as the public datasets and networks used in this study. In Section 3, we present and discuss the results of the experiments. Lastly, conclusions of this work are included in Section 4.
###### Abstract  
Although representing a paramount mechanism against nitrogen excess in agricultural landscapes, soil denitrification is still a largely unknown term in nitrogen balances at the watershed scale. In the present work, a comprehensive investigation of nitrogen sources and sinks in agricultural soils and waters was performed with the aim of gaining insights into the relevance of soil denitrification in a highly farmed sub-basin of the Po River delta (Northern Italy). Agricultural statistics, water quality datasets, and results of laboratory experiments targeting nitrogen fluxes in soils were combined to set up a detailed nitrogen budget along the terrestrial-freshwater continuum. The soil nitrogen budget was not closed, with inputs exceeding outputs by 72 kg N-ha\({}^{-1}\)-year\({}^{-1}\), highlighting a potential high risk of nitrate contamination. However, extensive monitoring showed a general scarcity of mineral nitrogen forms in both shallow aquifers and soils. The present study confirmed the importance of denitrification, representing ~37% of the total nitrogen inputs, as the leading process of nitrate removal in heavily fertilized fine-texture soils prone to waterlogged conditions.  
Keywords:agricultural soils; nitrogen budget; watershed; nitrate pollution; soil denitrification +
Footnote †: journal: Applied Sciences
## 3 Methodology and Data  
### DCC Data Sampling from VIIRS DNB Pixels  
DCC are typically identified based on their brightness temperature at 11-12 \(\mu\)m (\(<\) 205K) on a pixel by pixel basis [9; 10]. In this study, the VIIRS DNB DCC pixels are selected by identifying pixels in the 12 \(\mu\)m band (M15) with brightness temperatures corresponding to DCC and finding the collocated DNB pixel at night. Samples are collected globally within \(\pm\)25 degrees latitude which is in the Inter Tropical Convergence Zone (ITCZ). Since the lunar radiance depends on the time, location, lunar phase angle, lunar zenith angle, and DCC bidirectional reflection, the samples at pixel level are individually kept in the first step when processing each \"granule,\" which is the smallest unit in data processing, or data with 48 VIIRS scan-lines acquired in 86 s. Then, the following filtering criteria are applied to each granule: (1) Only near-nadir pixels (scan angle \(\pm\) 17 degrees from nadir) are kept and used in this study. This corresponds to view zenith angles less than 20 degrees (Figure 3), according to Equation (1).  
\[\alpha=\arcsin[R_{e}\sin(180-\theta)/(R_{e}+H_{\rm s})] \tag{1}\]  
where: \(\alpha\) is the scan angle; \(R_{e}\) is the Earth radius (6378 km); \(H_{\rm s}\) is the satellite altitude (829 km); and \(\theta\) is the view zenith angle.  
By focusing on near-nadir samples, it greatly simplifies the problem and avoids complex effects (such as pixel size differences between DNB and M15 at high scan angles, atmospheric path length effect, and increased uncertainties in bi-directional reflection effect at high scan angles), while preserving enough samples. (2) Pixels with lunar zenith angles greater than 90 degrees are removed due to large uncertainties in radiances in those cases. (3) Granules with fewer than a threshold number of DCC pixels are excluded in the analysis to avoid effects of cloud fraction. There is a large variation in the number of DCC pixels in each granule (nominally between \(\sim\)10 and \(\sim\)250,000). This threshold is  
Figure 3: VIIRS DNB Observations of DCC Reflected Lunar Radiance at Night and Related Angles (not to scale).  
somewhat arbitrary and it is a tradeoff between number of samples and variability in the radiance values. In the long-term analysis in Section 4.3, a threshold of 400 pixels was used (each pixel covers an area of 750m \(\times\) 750m). The remaining pixels are aggregated for each granule, and the mode and mean radiance values of for each granule are computed. Since the lunar phase angle for each granule is practically constant as discussed earlier, the important variables of lunar phase and zenith angles vs. mode radiance are generated for each granule and then used in subsequent analysis. In the end of this sampling process, one \"near-nadir-sample\" is created for each granule satisfying the above criteria.
## 3 Realization of Adaptive Square Root Cubature Kalman Filter Algorithm in Vector Receiver
### Square Root Cubature Kalman Filter  
SRCKF contains two parts: time update and measurement update. The main difference from the CKF algorithm is that the QR decomposition is introduced during filtering, and the recursive update is directly performed in the form of the square root of the covariance matrix. The specific steps are as follows [11]:  
1. Time updating  
Assuming the state estimation and state estimation error covariance at time epoch \(k-1\) are \(\hat{X}_{k-1|k-1}\) and \(P_{k-1|k-1}\), then the posterior probability distribution is \(P(X_{k-1}|Z_{k-1})=N\Big{(}X_{k-1};\hat{X}_{k-1|k-1},P_{k-1|k-1}\Big{)}\), and from this we obtain by Cholesky decomposition that \(S_{k-1|k-1}=\textit{chol}\) (\(P_{k-1|k-1}\)),  
1. [label=(0)]
2. Calculating and propagating cubature points \[X_{i,k-1|k-1} =S_{k-1|k-1}\tilde{\varsigma}_{i}+\hat{x}_{k-1|k-1}\] (13) \[X_{i,\,k|k-1}^{*} =f(X_{i,\,k-1|k-1})\] (14) \[\tilde{\varsigma}_{i} =\sqrt{\frac{m}{2}}\left[1\right]_{i}\] (15) where \(m\) is the number of volume points.
3. Calculating the predicted value of states \[\hat{x}_{\,k|k-1}=\frac{1}{m}\sum_{i=1}^{m}X_{i,\,k|k-1}^{*}\] (16)
4. Calculating the square root of the covariance matrix of the prediction error \(\mathbf{W}_{k}\) \[S_{\,k|k-1}=\textbf{Tria}\left(\left[X_{k|k-1}^{*}\,S_{Q,k-1}\,\right]\right)\] (17) where \(S_{Q,k-1}\) represents the square root factor of \(Q_{k-1}\), that is, \(Q_{k-1}=S_{Q,k-1}S_{Q,k-1}^{\text{T}}\).
5. Measurement update 1. [label=(0)]
6. Calculating and propagating volume points \[X_{i,\,k|k-1} =S_{\,k|k-1}\tilde{\varsigma}_{i}+\hat{x}_{k|k-1}\] (18) \[Z_{i,\,k|k-1} =h(X_{i,\,k|k-1})\] (19)
7. Calculating the measured predicted value \[\hat{z}_{\,k|k-1} =\frac{1}{m}\sum_{i=1}^{2m}Z_{i,\,k|k-1}\] (20)
8. Calculating the square root of the innovation covariance matrix \[S_{zz,k|k-1} =Tria\Big{(}\Big{[}\gamma_{k|k-1}S_{R,k}\Big{]}\Big{)}\] (21) where \(S_{R,k}=\textit{chol}(R_{k})\), which means that \(S_{R,k}\) is the square root factor of the measurement error covariance matrix \(R_{k}\) \[\eqref{  
\[\chi_{k|k-1}=\frac{1}{\sqrt{m}}\Big{[}X_{1,k|k-1}-\hat{x}_{k|k-1}\;\;X_{2,k|k-1}- \hat{x}_{k|k-1}\cdots X_{m,k|k-1}-\hat{x}_{k|k-1}\Big{]} \tag{22}\]  
\[P_{xz,k|k-1}=\chi_{k|k-1}\gamma_{k|k-1}^{T} \tag{23}\]  
(5) Estimating filter gain  
\[K_{k}=\Big{(}P_{xz,k|k-1}/S_{zz,k|k-1}^{T}\Big{)}/S_{zz,k|k-1} \tag{24}\]  
(6) Status update  
\[\hat{x}_{k|k}=\hat{x}_{k|k-1}+K_{k}\Big{(}z_{k}-\hat{z}_{k|k-1}\Big{)} \tag{25}\]  
(7) Calculating the square root factor of the state estimation error covariance matrix  
\[S_{k|k}=Tria\Big{(}\Big{[}X_{k|k-1}-K_{k}\gamma_{k|k-1}\;\;K_{k}S_{R,k}\Big{]} \Big{)} \tag{26}\]
## 3 Modeling Framework
### Aerodynamic Corrections via CFD  
The physical model described in Section 3.2 assumes steady-state aerodynamic conditions during UAV flight. However, in dense urban environments, UAVs are subjected to significant airflow perturbations due to the presence of nearby obstacles, such as buildings, road infrastructure, or even the buses themselves. These perturbations affect the aerodynamic efficiency of the UAV and induce **variations in thrust**, which directly impact energy consumption.  
To quantify these effects, computational fluid dynamics (CFD) simulations were conducted using ANSYS 2022. The simulations aimed to characterize the thrust variation experienced by a quadcopter-type UAV (specifically a DJI Phantom 3 (from DJI company, France)) in different flight conditions relevant to the delivery mission profile described in Figure 2.
## 2 Material Growth Procedure
That is related to the non-intentional period and Sb composition distribution, which was also confirmed by the T2 SLs' high-order peak shift. Samples were grown in different periods of time (all within three months of one another), with proper reproducibility. The 66.05\({}^{\circ}\) (in average) peak represents GaAs substrate, as presented in Figure 2a-d. It must be stressed that the FWHM of the \(\sim\)5 \(\upmu\)m GaSb buffer layer grown on GaAs substrate changed from 120 arcsec to 130 arcsec, respectively. In our case, with the 1.2 \(\upmu\)m GaSb buffer, the FWHM of the GaSb layer grown on GaAs substrate read \(\sim\)200 arcsec [13]. It must be underlined that even if the intensity of T2 SL InAs/InAsSb was much higher than from GaSb, the FWHM of the 0 th peak was dominated by the GaSb buffer layer. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{\(T_{\text{growth}}\)[\({}^{\circ}\)C]} & \multirow{2}{*}{**NoBe**} & \multirow{2}{*}{\(P\) [nm]} & \multirow{2}{*}{\(x_{\text{Sb}}\)} & \multirow{2}{*}{\(I\) [\(\upmu\)m]} & \multicolumn{2}{c}{\(\lambda_{50\%\textit{cut-off}}\) [nm]} & \multirow{2}{*}{**FWHM 0\({}^{\text{th}}\)/GaSb [arcsec]**} & \multirow{2}{*}{\(\Delta a\)/\(a\) GaAs-0 th/GaSb [\%]} \\ \cline{5-10}  & & & & & **210 K** & & & \\ \hline \multicolumn{10}{c}{**Set I**} \\ \hline
450 & 1\({}^{\circ}\) & 9.5 & 0.4 & 3.01 & 10 & 10.3 & 216 & 7.294 \\ \hline
425 & 2\({}^{\circ}\) & 9.4 & 0.39 & 2.98 & 11 & 11.4 & 215 & 7.255 \\ \hline
400 & 3\({}^{\circ}\) & 9.46 & 0.38 & 3.0 & 10.6 & 11.1 & 196 & 7.267 \\ \hline
450 & 4\({}^{\circ}\) & 9.38 & 0.39 & 2.97 & 8 & 8.3 & 236 & 7.279 \\ \hline
425 & 5\({}^{\circ}\) & 9.4 & 0.39 & 2.98 & 10.4 & 10.8 & 200 & 7.270 \\ \hline
400 & 6\({}^{\circ}\) & 9.4 & 0.39 & 2.98 & 11.1 & 11.6 & 180 & 7.294 \\ \hline \multicolumn{10}{c}{**Set II**} \\ \hline
450 & 7\({}^{\circ}\) & 10.46 & 0.39 & 3.01 & 10.1 & 10.3 & 205 & 7.266 \\ \hline
425 & 8\({}^{\circ}\) & 10.47 & 0.39 & 3.02 & 12.6 & 13.3 & 185 & 7.273 \\ \hline
400 & 9\({}^{\circ}\) & 10.53 & 0.405 & 3.03 & 17 & 18 & 270T2 SLs InAs/InAsSb period and Sb composition values were simulated by PANalytical software, and their data are presented in Table 1. For the simulation, sharp interfaces between InAs/InAsSb were assumed. In addition, the magnified 0 th/GaSb HRXRD spectra for samples 4, 5, 6 (FWHM 236, 200, 180 arcsec) are presented in Figure 3. It must be stressed that an average (for samples 4, 5, 6) \(\Delta a/a\) GaAs-0 th/GaSb was 7.271%. Figure 3: The magnified 0 th/GaSb HRXRD spectra for samples 4, 5, and 6 (FWHM 236, 200, 180 arcsec). Figure 2: HRXRD spectra: (**a**) samples 1, 2, 3; (**b**) samples 4, 5, 6; (**c**) samples 7, 8, 9; (**d**) samples 10, 11, 12.
_Appl. Sci._**2014**, \(4\), 57-65; doi:10.3390/app4010057  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Diffraction Focal Position and Vector Diffraction Theory for Micro Holographic Storage Systems**  
**[PERSON] 1, [PERSON] 2, [PERSON] 2, [PERSON] 1,*, [PERSON] 2 and [PERSON] 1**  
1 School of Mechanical Engineering, Yonsei University, 262 Seoungsanno, Seodaemun-gu, Seoul 120-749, Korea; E-Mails: [EMAIL_ADDRESS] (C.-K.M.); [EMAIL_ADDRESS] (Y.-P.P.)  
2 Center for Information Storage Device, Yonsei University, 262 Seoungsanno, Seodaemun-gu, Seoul 120-749, Korea; E-Mails: [EMAIL_ADDRESS] (H.M.); [EMAIL_ADDRESS] (D.-H.K.); [EMAIL_ADDRESS] (K.-S.P.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +82-2-2123-4530; Fax: +82-2-365-8460.  
_Received: 18 November 2013; in revised form: 12 February 2014 / Accepted: 18 February 2014 / Published: 13 March 2014_  
**Abstract:** In this study, we proposed a method to determine the optimal focal position for micro-holographic storage systems, using vector diffraction theory; the theory provides exact solutions when the numerical aperture (NA) exceeds 0.6. The best diffraction focus was determined by the position and wavelength corresponding to minimal spherical aberration. The calculated refractive index modulation, polarization illumination, and boundary conditions at the interface of different media were analyzed. From the results of our analysis, we could confirm the size of micrograting as a function of NA and wavelength, based on vector diffraction theory, compared with scalar diffraction theory which defines the micrograting by \(\lambda/(2\)NA). To demonstrate our analysis, we adapted an optical alignment method using a Twyman-Green interferometer, and could obtain good agreement between analysis and experimental results.  
**Keywords:** microholographic storage system; diffraction focus; scalar diffraction theory; vector diffraction theory; polarization; refractive index modulation; interferogram; diffraction efficiency
## 6 Discussion
### Effects of Different Algorithms on Identification Results  
The RFECV-Optuna-XGBoost model, when applied to the data prediction and analysis of the KS Tunnel, demonstrated superior performance on the test set, with an accuracy of 0.9833, a precision of 0.9803, a recall of 0.9813, and an F1 score of 0.9807. The main reason for the different performances of the different recognition algorithms is their different capabilities.
# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
## 6 Method Comparison and Analysis
### Method Comparison  
As discussed earlier, while deep learning methods achieve superior predictive performance, this does not imply that other approaches lack merit. Table 3 provides a systematic comparison of the advantages and disadvantages of various traffic flow prediction methods, offering a comprehensive overview of their core characteristics. By outlining key aspects such as the data requirements, computational complexity, and interpretability, this table facilitates a clearer understanding of the distinctions among different methodologies. Moreover, it serves as a valuable reference for researchers in identifying the most suitable approach for specific application scenarios, thereby enhancing the methodological selection process in traffic flow prediction research.
## Appendix B Derivation of First-Partial Derivative  
Equation (A1) shows the derivation of the first-partial derivative of the mean squared error (MSE) with respect to \(h_{j}\).  
\[\begin{array}{c}L\left(h_{j}\right)=\frac{1}{N}\sum\limits_{i=1}^{N}\left[Y_{ fit}\left(s_{i},h_{j}\right)-Y_{obs}(s_{i})\right]^{2}\\ \frac{\partial}{\partial h_{j}}L\left(h_{j}\right)=\frac{\partial}{\partial h _{j}}\left[\frac{1}{N}\sum\limits_{i=1}^{N}\left[Y_{fit}\left(s_{i},h_{j} \right)-Y_{obs}(s_{i})\right]^{2}\right]\\ \frac{\partial}{\partial h_{j}}L\left(h_{j}\right)=\frac{1}{N}\cdot\frac{ \partial}{\partial h_{j}}\left[\sum\limits_{i=1}^{N}\left[Y_{fit}\left(s_{i}, h_{j}\right)-Y_{obs}(s_{i})\right]^{2}\right]\\ \frac{\partial}{\partial h_{j}}L\left(h_{j}\right)=\frac{2}{N}\cdot\sum\limits _{i=1}^{N}\left[Y_{fit}\left(s_{i},h_{j}\right)-Y_{obs}(s_{i})\right]\cdot \frac{\partial}{\partial h_{j}}\left[Y_{fit}\left(s_{i},h_{j}\right)-Y_{obs} (s_{i})\right]\\ \frac{\partial}{\partial h_{j}}L\left(h_{j}\right)=\frac{2}{N}\cdot\sum\limits _{i=1}^{N}\left[Y_{fit}\left(s_{i},h_{j}\right)-Y_{obs}(s_{i})\right]\cdot \frac{\partial}{\partial h_{j}}Y_{fit}\left(s_{i},h_{j}\right)\\ L^{\prime}\left(h_{j}\right)=\frac{2}{N}\left(\sum\limits_{i=1}^{N}\left[Y_{ fit}\left(s_{i},h_{j}\right)-Y_{obs}(s_{i})\right]\right)\cdot Y^{\prime} _{fit}\left(s_{i},h_{j}\right)\end{array}\] (A1)  
Equation (A2) shows the derivation of the derivative of the squared exponential (SE) covariance function with respect to the testing point, \(x_{*}\).  
\[\begin{array}{c}k(x,x_{*})=\sigma_{f}^{2}\left[exp\left(-\frac{\left|x-x_{* }\right|^{2}}{2\ell^{2}}\right)\right]\\ \frac{\partial}{\partial x_{*}}k(x,x_{*})=\frac{\partial}{\partial x_{*}} \left[\sigma_{f}^{2}\left[exp\left(-\frac{\left|x-x_{*}\right|^{2}}{2\ell^{2} }\right)\right]\right]\\ \frac{\partial}{\partial x_{*}}k(x,x_{*})=\sigma_{f}^{2}\cdot\frac{\partial}{ \partial x_{*}}\left[exp\left(-\frac{\left|x-x_{*}\right|^{2}}{2\ell^{2}} \right)\right]\\ \frac{\partial}{\partial x_{*}}k(x,x_{*})=\sigma_{f}^{2}\cdot exp\left(-\frac {\left|x-x_{*}\right|^{2}}{2\ell^{2}}\right)\cdot\frac{\partial}{\partial x_{* }}\left[-\frac{\left|x-x_{*}\right|^{2}}{2\ell^{2}}\right]\\ \frac{\partial}{\partial x_{*}}k(x,x_{*})=\sigma_{f}^{2}\cdot exp\left(-\frac {\left|x-x_{*}\right|^{2}}{2\ell^{2}}\right)\cdot\left(-\frac{\left|x-x_{*} \right|^{2}}{2\ell^{2}}\right)\\ \frac{\partial}{\partial x_{*}}k(x,x_{*})=\sigma_{f}^{2}\cdot exp\left(-\frac {\left|x-x_{*}\right|^{2}}{2\ell^{2}}\right)\cdot\left(-\frac{\left|x-x_{*} \right|^{2}}{2\ell^{2}}\right)\\ k^{\prime}(x,x_{*})=\sigma_{f}^{2}\cdot\left(\frac{\left|x-x_{*}\right|}{\ell^ {2}}\right)\cdot exp\left(-\frac{\left|x-x_{*}\right|^{2}}{2\ell^{2}}\right) \end{array}\] (A2)
## 4 Experiments
### Comparison with the State-of-the-Art
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Datasets** & **DCP** & **MSCR** & **ACE** & **TBNN** & **DehazeC** & **FSAD** & **Our** \\  & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline RESIDE & 17.6384/0.8544 & 16.3526/0.8096 & 16.9427/0.7925 & 36.6923/0.9819 & 30.5733/0.9736 & 27.3653/0.9573 & 37.3959/0.9912 \\ O-Haze & 13.1259/0.4843 & 12.9329/0.4893 & 13.1228/0.5102 & 25.3586/0.7883 & 19.5411/0.6828 & 18.3945/0.5730 & 27.8472/0.8265 \\ Dense-Haze & 11.3957/0.4691 & 10.7468/0.4135 & 9.6284/0.4409 & 16.2190/0.5722 & 12.5832/0.4782 & 12.2284/0.4937 & 17.2514/0.6342 \\ NTIRE 2020 & 12.4926/0.4463 & 12.1688/0.4274 & 12.1039/0.4392 & 20.9754/0.7164 & 17.4927/0.6055 & 16.4856/0.8583 & 21.2485/0.7182 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative comparison of haze images. Figure 4: Qualitative comparisons with SOTA methods on two real sand dust images. (**a**) Input; (**b**) DCP; (**c**) MSRCR; (**d**) RGHS; (**e**) Park; (**f**) TBNN; (**g**) USDR-Net; (**h**) Ours. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Datasets** & **RCP** & **Park** & **FUnIEGAN** & **Uwnet** & **NU’2 NET** & **PUIE-NET** & **Our** \\  & **UIQM/UCIQE** & **UIQM/UCIQE** & **UIQM/UCIQE** & **UIQM/UCIQE** & **UIQM/UCIQE** & **UIQM/UCIQE** & **UIQM/UCIQE** \\ \hline SUIM & 4.1746/0.3526 & 4.4736/0.3380 & 4.9362/0.3865 & 4.5729/0.3702 & 4.8352/0.4117 & 4.8794/0.4125 & 4.9746/0.
# Topology Optimization of Functionally Graded Structure for Thermal Management of Cooling Plate
## 1 Introduction
[22] presented a novel iterative topology optimization method for a three-dimensional cooling plate design to achieve good performance regarding the temperature difference of lithium-ion batteries. The topology optimization method can effectively optimize the structure for heat dissipation. However, the current study mainly focused on the balance of multi-objectives, wherein the nonuniform distribution of thermal load may lead to unsatisfying results of the structure design by the conventional SIMP-based topology optimization, which lacks attention to deal with most traditional cooling plate designs. Therefore, to decease the maximum temperature and improve the thermal homogeneity of the battery, an appropriate method for structural design to improve the temperature distribution of the battery should be further studied. To further contribute to this research topic, a density-based topology optimization pipeline is proposed for a cooling plate design with a functionally graded structure to decrease the maximum temperature and improve the thermal homogeneity of the battery. We first employ a Helmholtz-type Partial Differential Equation (PDE)-based filter and smoothed Heaviside projection operator to produce the discrete solution, and another variable-radius Helmholtz PDE filter with a local volume constraint is utilized for the functionally graded structure design. Several examples are conducted to demonstrate the development of the proposed method for cooling plate optimization. The rest of this paper is organized as follows: Section 2 presents the theory of topology optimization of the heat-fluid coupling problem and formulates the optimization objective of a cooling plate with functionally graded structure for thermal load management. Then, numerical examples are carried out to demonstrate the effectiveness of the proposed method in Section 3. Finally, conclusion and future works are summarized in Section 4.