esa-sceva/satcom-chunk-collection huggingface dataset:
# EHANet: An Effective Hierarchical Aggregation Network for Face Parsing
## 5 Results and Discussion
### Results on CelebAMask-HQ
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Methods** & **Background** & **Skin** & **Nose** & **Eye-Glass** & **Left-Eye** & **Right-Eye** & **Left-Brow** & **Right-Brow** & **Left-Ear** & **Right-Ear** & **Mouth** \\ \hline DFANet [23] & 88.12 & 89.69 & 83.96 & _69.29_ & _69.19_ & _69.12_ & _66.04_ & _66.05_ & _66.52_ & _65.85_ & _70.23_ \\ DABNet [24] & 90.36 & 91.27 & 86.04 & 72.62 & 73.69 & 74.38 & 69.78 & 69.25 & 72.28 & 70.83 & 78.41 \\ UNet [5] & 89.36 & 92.25 & 87.83 & 77.64 & 79.45 & 79.75 & 74.17 & 73.98 & 77.28 & 76.00 & 83.47 \\ DANet [33] & **93.04** & 93.15 & 88.92 & 84.04 & 80.65 & 80.84 & 75.63 & 75.37 & **78.93** & **78.29** & _85.45_ \\ CE2P [35] & 92.78 & 93.17 & 88.54 & 84.44 & **81.95** & **82.03** & 75.55 & 75.51 & 78.35 & 77.72 & 85.65 \\ \hline
**EHANet(Ours)** & 91.80 & 92.90 & 88.57 & 84.00 & 81.58 & 81.92 & 75.02 & 74.93 & 78.25 & 77.90 & 85.55 \\
**EHANet(Ours)** + BA & 92.76 & 93.16 & 88.73 & 84.62 & 81.70 & 81.96 & 75.54 & 75.48 & 78.58 & 77.55 & 85.62 \\
**EHANet(Ours)** + BA \(\dagger\) & 92.98 & **93.26** & **89.02** & **84.91** & 81.85 & 82.02 & **75.66** & **75.52** & 78.76 & 78.00 & **85.69** \\ \hline
**Methods** & **Upper-Lip** & **Lower-Lip** & **Hair** & **Hat** & **Earring** & **Necklace** & **Neck** & **Cloth** & **FPS** & **mIoU (\%)** & **Pixel Acc. (\%)** \\ \hline DFANet [23] & 66.17 & 70.26 & 86.25 & 61.02 & 25.68 & 0.00 & 76.38 & 60.21 & 72 & 65.79 & 91.85 \\ DABNet [24] & 73.91 & 78.37 & 88.52 & 63.56 & 36.23 & 0.01 & 78.74 & 67.27 & **99** & 70.36 & 93.24 \\ UNet [5] & 79.32 & 82.00 & 88.16 & 36.62 & 42.58 & 0.00 & 79.92 & 61.88 & 81 & 71.46 & 93.00 \\ DANet [33] & 80.41 & 83.52 & **91.31** & 75.87 & **52.78** & 9.46 & 83.55 & **78.07** & 13 & 77.33 & 95.09 \\ CE2P [35] & 80.89 & 83.57 & 91.18 & 76.13 & 52.47 & 19.73 & 83.53 & 76.30 & 30 & 77.87 & 95.00 \\ \hline
**EHANet(Ours)** & 80.98 & 83.25 & 90.31 & 74.26 & 48.11 & 13.16 & 81.38 & 73.30 & 89 & 76.69 & 94.51 \\
**EHANet(Ours)** + BA & 80.96 & **83.60** & 91.08 & 76.30 & 51.82 & 17.61 & 83.37 & 76.10 & 83 & 77.71 & 94.96 \\
**EHANet(Ours)** + BA \(\dagger\) & **81.04** & 83.47 & 91.26 & **77.05** & 51.84 & **20.19** & **84.15** & 77.93 & 71 & **78.19** & **95.28** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Category-wise comparison (IoU) on the CelebMMask-HQ test set. The best performance for each individual class is marked with bold-face number. “BA” denotes boundary-aware branch, “+” denotes EHANet with ResNet34 backbone, “FPS” denotes frames per second. Resolution is unified to \(512\times 512\).
## 4 Results and Discussion  
### Data Acquisition  
With reference to the DL/T 1540-2016 electric power industry standard of the People's Republic of China, we built a transformer core vibration-signal acquisition platform to collect vibration signal data. The main equipment includes transformers, voltage regulators, vibration sensors, etc. Among them, the transformer adopts a three-phase dry-type transformer. The sensor adopts a B&K 4533-B-004 piezoelectric vibration acceleration sensor with a measurement frequency range of 0.2 Hz-12.8 kHz, sensitivity of (\(\pm\)10%) 100 mV/g, and accuracy of 0.001 m/s\({}^{2}\). During the experiment, sensors were used to measure the vibration data of the A-phase overlap area on the transformer iron core along the stacking direction, which was transmitted to the computer for storage via a data cable. The experimental platform is shown in Figure 5.  
This experiment simulated six different locations of iron core looseness, namely, iron core looseness at the upper yoke of phase A, iron core looseness at the upper yoke of phase C, iron core looseness at the lower yoke of phase A, iron core looseness at the lower yoke of phase B, and iron core looseness at the lower yoke of phase C. By loosening the bolts at the corresponding points, the clamping force of the clamp on the iron core is reduced, and the loosening displacement is set to 0.5 cm to simulate the loosening of the iron core. During the experiment, we performed 100 replicates of the experiment for each fault type, with a sampling time of 0.1 s and a sampling frequency of 2 KHz. The types of faults are shown in Table 1.
## 4 Results
### Simulations
#### 4.1.2 Simulated Cyst Targets  
The second simulated phantom contains anechoic cysts and a homogeneous speckle background. Figure 5 presents the simulated cyst target images reconstructed by various methods. Anechoic cysts in the CPWC+NSVD images of Figure 5d-e have less noise than the CPWC and CPWC+SVD images of Figure 5a-c. Compared to Figure 5f-h, the corresponding Figure 5i-k and Figure 5l-n show reduced dark-region artifacts in the background speckle and higher background intensity. Figure 6 displays the lateral profiles of two cyst targets centered at depths of 18 mm and 42 mm and distributed at the middle line (\(x=0\) mm) in Figure 5. The CPWC+NSVD shows reduced noise inside the anechoic cysts as compared to CPWC and CPWC+SVD. Similarly, when combined with the NSVD filter, the adaptive weighting methods also show better noise suppression.  
In Figure 5a, we indicate the marked regions used to calculate CR, CNR, gCNR, and sSNR, according to Equations (19)-(22). Being within the red circle means inside the cyst, while being within the two green circles means inside the background speckle. Figure 7 gives the barplot of the quantified values for each method. CPWC+NSVD has improved CR and CNR values relative to CPWC. Compared to CPWC, CPWC+NSVD (\(\beta=3\)) can achieve CR improvements of 6.93 dB/4.52 dB (26.4%/34.5%) and CNR improvements of 0.05/0.16 (3%/12.3%). The adaptive weighting methods based on NSVD all have improved CR, CNR, and sSNR. CF+NSVD (\(\beta=3\)) has improvements of 14.53 dB/7.28 dB (29%/26.1%) in CR, 0.09/0.25 (7.4%/27.5%) in CNR, and 0.08/0.23 (6.5%/24.2%) in sSNR. The CR, CNR, and sSNR improvements of GCF+NSVD (\(\beta=3\)) are 12.76 dB/6.99 dB (29.8%/31.6%), 0.11/0.32 (6.9%/28.8%), and 0.11/0.27 (6.8%/22.3%). The corresponding improvements of SLSC-NSVD (\(\beta=3\)) are 4.66 dB/6.99 dB (10.3%/23.7%), 0.16/0.42 (10.5%/43.3%), and 0.16/0.42 (10.4%/42%), respectively. In the near region, gCNR values of different methods are similar. For the deep region, the NSVD leads to a gCNR improvement by about 0.14 for adaptive weighting methods. It can be observed that \(\beta\) has an effect on CR, CNR, gCNR and sSNR. For adaptive weighting methods, NSVD (\(\beta=2\)) leads to improvements of CR, CNR, and sSNR by about 2 dB, 0.1, and 0.1 compared with NSVD (\(\beta=3\)), respectively.  
Figure 5: B-mode images of simulated phantom with cyst targets formed by different methods. (**a**) CPWC, (**b**) CPWC+SVD (\(\beta=2\)), (**c**) CPWC+SVD (\(\beta=3\)), (**d**) CPWC+NSVD (\(\beta=2\)), (**e**) CPWC+NSVD (\(\beta=3\)), (**f**) CF, (**g**) GCF, (**h**) SLSC, (**i**) CF+NSVD (\(\beta=2\)), (**j**) GCF+NSVD (\(\beta=2\)), (**k**) SLSC+NSVD (\(\beta=2\)), (**l**) CF+NSVD (\(\beta=3\)), (**m**) GCF+NSVD (\(\beta=3\)), (**n**) SLSC+NSVD (\(\beta=3\)). The area in the red circle and between the two green circles indicates the inside and outside the cyst, respectively, used for calculating contrast ratio (CR), contrast-to-noise ratio (CNR), generalized CNR (gCNR), and speckle signal-to-noise ratio (sSNR).
# A Hybrid Domain Color Image Watermarking Scheme Based on Hyperchaotic Mapping
## 2 Background
### Bhattacharyya Distance  
Bhattacharyya distance [25] is a method to evaluate the similarity between two probability distributions. It calculates the distance between two distributions based on comparing the similarity between them. For two discrete probability distributions p and q, the distance provides a quantitative metric, which is defined as follows:  
\[D_{b}(p,q)=-\ln(BC(p,q)) \tag{6}\]  
where \(BC(p,q)\) represents the Bhattacharyya coefficient. In the case of discrete distribution and continuous distribution, the Bhattacharyya coefficient is defined as  
\[BC(p,q)=\sum\sqrt{p(x)q(x)} \tag{7}\]  
\[BC(p,q)=\int\sqrt{p(x)q(x)}dx \tag{8}\]  
For two normal distributions \(p_{i}=N(\mu_{i},\sigma_{i}^{2})\), where \(\mu_{i}\) and \(\sigma_{i}\) denote the expectation and standard deviation of the distribution, respectively, the Bhattacharyya distance is defined as follows [25]:  
\[D_{b}=\frac{1}{8}(\mu_{1}-\mu_{2})^{T}\sigma^{-1}(\mu_{1}-\mu_{2})+\frac{1}{2} \ln\left(\frac{|\sigma|}{\sqrt{|\sigma_{1}||\sigma_{2}|}}\right) \tag{9}\]  
where \(\sigma=\frac{(\sigma_{1}+\sigma_{2})}{2}\).
# A Compact Numerical Scheme for the Heat Transfer of Mixed Convection Flow in Quantum Calculus
## 4 Convergence of Numerical Scheme
Similarly, subtracting Equation (43) from Equation (41) yields  
\[\frac{\mathbf{e}_{i+1}^{n+1}-\mathbf{e}_{i+1}^{n}}{\Delta t(b+2)}+\frac{\mathbf{b}(\mathbf{e}_ {i+1}^{n+1}-\mathbf{e}_{i}^{n})}{\Delta t(b+2)}+\frac{\mathbf{e}_{i-1}^{n+1}-\mathbf{e}_{ i-1}^{n}}{\Delta t(b+2)}=\frac{A}{2(\Delta x)^{2}}(1+q)\Big{(}\overline{\mathbf{e}}_{i+1}^{n+1}- \overline{\mathbf{e}}_{i}^{n+1}+\overline{\mathbf{e}}_{i-1}^{n+1}\Big{)}+\frac{B}{(b +2)}\Big{(}\overline{\mathbf{e}}_{i+1}^{n+1}+b\overline{\mathbf{e}}_{i}^{n+1}+ \overline{\mathbf{e}}_{i-1}^{n+1}\Big{)} \tag{50}\]  
Applying \(\|.\|_{\infty}\) on both sides of Equation (50) yields  
\[ \begin{array}{c}\mathbf{e}^{n+1}\leq\mathbf{e}^{n}+\frac{1}{|b|}\big{(}\mathbf{e}^{n+1}+ \mathbf{e}^{n}+\mathbf{e}^{n+1}+\mathbf{e}^{n}\big{)}+\frac{\|A\|_{\infty}\Delta t(1+q)(b+ 2)}{(\Delta x)^{2}}\Big{(}2\overline{\mathbf{e}}^{n+1}\Big{)}+\Delta t\|\ B\ \|_{\infty}(|b|+2)\overline{\mathbf{e}}^{n+1}\\ +M\Big{(}O((\Delta t)^{2},(\Delta x)^{4q})\Big{)}\end{array}  \tag{51}\]  
Re-writing Equation (51) gives  
\[ \begin{array}{c}\mathbf{e}^{n+1}\leq\frac{|b|+2}{|b|-2}\mathbf{e}^{n}+\frac{|b||\ A\ \|_{\infty}\Delta t(1+q)(b+2)}{(|b|-2)(\Delta x)^{2}}\Big{(}2\overline{\mathbf{e}}^{n +1}\Big{)}+\frac{\Delta t|b||\ B\ \|_{\infty}(|b|+2)}{(|b|-2)}\overline{\mathbf{e}}^{n+1}+M\Big{(}O((\Delta t)^{2},( \Delta x)^{4q})\Big{)}\end{array}  \tag{52}\]  
Inequality (52) can be expressed as  
\[\mathbf{e}^{n+1}\leq\delta\mathbf{e}^{n}+M\Big{(}O((\Delta t)^{2},(\Delta x)^{4q})\Big{)} \tag{53}\]  
where \(\delta=\frac{|b|+2}{|b|-2}\mathbf{e}^{n}+\Big{(}\frac{2|b||\ A\|_{\infty}\Delta t(1+ q)(b+2)}{(|b|-2)(\Delta x)^{2}}+\frac{\Delta t|b|(|b|+2)\|B\|_{\infty}}{(|b|-2)}\Big{)} \delta_{1}\). Substituting \(n=0\) in (53) yields  
\[e\leq\delta e^{0}+M\Big{(}O((\Delta t)^{2},(\Delta x)^{4q})\Big{)} \tag{54}\]  
Since an initial condition is exact, so \(e^{0}=0\); therefore, inequality (54) becomes  
\[e\leq M\Big{(}O\Big{(}(\Delta t)^{2},(\Delta x)^{4q}\Big{)}\Big{)} \tag{55}\]  
For \(n=1\), inequality (53) becomes  
\[e^{2}\leq\delta e^{1}+M\Big{(}O((\Delta t)^{2},(\Delta x)^{4q})\Big{)}\leq(1+ \delta)M\Big{(}O((\Delta t)^{2},(\Delta x)^{4q})\Big{)} \tag{56}\]  
If this is continued, then for any finite number \(n\), the following inequality can be constructed  
\[e^{n}\leq\Big{(}\delta^{n-1}+\delta^{n-2}+\ldots+\delta+1\Big{)}M\Big{(}O\Big{(} (\Delta t)^{2},(\Delta x)^{4q}\Big{)}\Big{)}=\frac{1-\delta^{n}}{1-\delta}M \Big{(}O\Big{(}(\Delta t)^{2},(\Delta x)^{4q}\Big{)}\Big{)}. \tag{57}\]  
when \(n\to\infty\) in (57), the series \(\ldots+\delta^{n-1}+\ldots+\delta+1\) becomes an infinite geometric series that will converge if \(|\delta|<1\). \(\square\)
## 4 Results and Analysis
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Project Details** & **Project Location** & **Product Category** & **Product Specifications** & **Diene Cost** & **Indirect Cost** & **Total Life Cycle Cost** & **Indirect Cost** & **Indirect Cost** & **a** ** **a** ** **Precentage of the Total ICC** \\ \hline Project 1: Superping-learning Model & Municipality of Polymers & Indoor lighting & Green Products & 25,282.58 & 5189.91 & 33,428.19 & 24.50\% \\ (2017-23179) & & & & & & & & \\ \hline Project 2: Superping-learning Model & Municipality of Lateness & Indoor lighting & Green Products & 22,748.37 & 9679.63 & 37,229.33 & 28\% \\ (2017-23179) & & & & & & & \\ \hline Project 3: Superping-learning Model & Municipality of Lateness & Indoor lighting & Green Products & 12,041.12 & 13,024.32 & 13,024.33 & 76\% \\ (2017-23179) & & & & & & & \\ \hline Project 5: Superping-learning Model & Municipality of Format & Indoor lighting & Green Products & 64,964.16 & 16,224.30 & 81,731.32 & 20\% \\ \hline Project 4: Superping-learning Model & Municipality of Format & Indoor lighting & Green Products & 6772.32 & 580.40 & 5500.36 & 11\% \\ \hline Project 6: Superping-learning Model & Municipality of Format & Indoor lighting & Green Products & 2738.28 & 1271.25 & 3997.63 & 31.85\% \\ (2017-23179) & & & & & & & \\ \hline Project 5: GPMCount/Inferring Envelope & University of Patras & Indoor lighting & Green Products & 23,431.30 & 2374.93 & 3056.69 & 6.8\% \\ (2017-23179) & & & & & & & \\ \hline Project 6: Crosscut of Mays project & Municipality of Patras & Indoor lighting & Green Products & 31,758.96 & 12,697.25 & 44,449.21 & 28.85\% \\ \hline Project 6: Crosscut of Mays project & Municipality of Pat  
Moreover, in 7 out of 15 projects, although the investment cost of green products is higher than that of conventional products, their LCC is lower, and this is an indication of efficient investment. In the remaining 8 projects both investment cost and LCC are higher; hence the investment appears to be unprofitable. In these cases, the corresponding cost difference is not proportional. An illustrative example is the case of project 5, where the green products cost is 2.3 times higher, but the corresponding difference for LCC is only 1%. In this case, if the award was based on the lowest price, the non-green products would have been selected; by leveraging product life cycle assessment the selection would have been different. This shows that there is potential to make GPP equally profitable investments.
# Photopolymerization of Ceramic Resins by Stereolithography Process: A Review
## 6 Effects of Post-Processing on Geometrical, Physical and Mechanical Properties
### Dimensional Accuracy for Ceramic Resins  
One of the most important aspects from a technological point of view is the dimensional accuracy of the complete process. SLA process gives usually good dimensional accuracy, but post-processing causes the loss of the polymeric binder, and consequently, volumetric shrinkage leads to an uncertainty in the final dimension of printed parts. Different aspects can influence the proportion of volumetric loss. Debinding and sintering phases give rise to volumetric shrinkage but each for different physical reasons. In debinding treatment, shrinkage is the consequence of the mass loss due to decomposition of polymeric binder, so it can be expected a range of dimensional value for the final parts through an evaluation of powder fraction. In the sintering phase, other aspects influence the volumetric shrinkage occurring, such as the medium size of powder particles or final temperature, and anisotropies are often expected [76]. Correlation between particle size and maximum sintering temperature is important for the physical behavior of diffusion phenomena triggered in thermal treatment [77]. [PERSON] et al. [78] showed that fine particles and sintering additives combined in the resin mixture cause an increase of volumetric shrinkage. Table 2 reports the values of volumetric shrinkage for different materials used for SLA applications.  
Figure 9: Schematic representation of warping due to volumetric shrinkage. Adapted from [73].  
In Zirconia resins high solid loadings are usually heavy to obtain, not only for the optical compatibility with the monomeric mixture but also for rheological reasons. Consequently, dimensional shrinkage in the range of 18-36% is expected for relatively high solid loadings and particle sizes typical for stabilized powders. Alumina resins allow a wider range of solid loadings, thanks to a better rheological and optical compatibility with the organic phase, but usually high solid loadings are preferred in order to reduce probability in crack formation during post-treatment.
## 2 Materials and Methods
### RTMN 2.0--Extension of RTMN
#### 2.2.2 The RTMN 2.0 Elements  
RTMN 2.0, in comparison to the first version of RTMN, has the following extensions:  
* Adding HRC modeling elements, including safety in combination with collaboration modes and task types of humans and robots. This has significantly enriched the former RTMN model and enabled it to be applied to HRC application areas.
* Adding requirements, with KPI as a basic element.
* Adding the link from requirements/KPI to robot control.
* Adding decision-making elements.  
RTMN 2.0 was designed especially for modeling human-robot collaboration processes. Together with version 1.0, it is capable of modeling light-out automated robotic processes as well as human-robot collaboration processes. The model contains two sets of elements: the RTMN elements and the extended elements. A complete overview is presented in Figures 1-3. It consists of all the elements of RTMN 2.0, with the newest improvements and extensions.
## 3 Numerical Simulation Models and Methods
### Mesh Generation  
A self-developed scripting program was used to generate the constructed randomly packed particles and geometric models. The reconstructed spherical particles will have point contacts or narrow gaps between them, which is not suitable for mesh generation. When meshing directly, the grid cells near the contact points will have large skewness, making the simulation hard to converge and affecting the calculation accuracy, so the contact between the particles needs to be processed. The main existing treatment methods include decreased size, increased size, bridges, and caps [38]. The decreased size and increased size methods change the radius of the sphere, which changes the porosity of the packed bed. The bridges and caps methods only treat the contact points locally, ensuring the accuracy of the geometric model of the packed bed. In this study, we used the bridges method for contact treatments to simulate the airflow process more realistically, setting the diameter of the cylindrical bridge to 0.1 \(d_{p}\) and increasing the diameter of the geometric by 1% [39]. Due to the complexity of the packed bed, the model was meshed using polyhedral in ANSYS Fluent Meshing and the maximum mesh size was set to \(1/15\,d_{p}\)[40]. The particle surfaces and geometric cylindrical surfaces were set up with three boundary layers using the smooth transition method, and the boundary layer growth rate was 0.27. The mesh division and contact treatment are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Symbol** & **Value** \\ \hline Packed bed dimension & \(D\times H\), m & 0.1 \(\times\) 0.075 \\ Particle diameter & \(d_{p}\), m & 0.00724 \\ Particle density & \(\rho_{p}\), kg/m\({}^{3}\) & 1257 \\ Poisson ratio & \(v\) & 0.4 \\ Modulus of elasticity & \(E\), Pa & 7.6 \(\times\) 10\({}^{8}\) \\ static friction coefficient & \(\mu_{s}\) & 0.2 \\ rolling friction Coefficient & \(\mu_{r}\) & 0.02 \\ Coefficient of restitution & \(e\) & 0.627 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Soybean particle parameters used in the DEM simulation.  
Figure 3: Schematic diagram of grid and contact treatment.
## 3 System Model
### Computation Model
#### 3.2.2 Remote Execution  
For the remote execution method, the computation task of mobile device \(j\), which is connected with UAV \(i\), will be transmitted and executed on one of the UAVs or the cloud server, and the total execution time can be expressed as:  
\[T_{i,j}^{e}=T_{i,j}^{off}+T_{i,j}^{e,exec} \tag{4}\]  
\[T_{i,j}^{c}=T_{i,j}^{off}+\delta+T_{i,j}^{e,exec}, \tag{5}\]  
where \(\delta\) is a constant referring to the delay of propagation for transmitting the task between the UAV and the cloud. \(T_{i,j}^{off}\), \(T_{i,j}^{e,exec}\), and \(T_{i,j}^{e,exec}\) indicate the transmitting and processing time for executing the task of mobile device \(j\) at the UAV and cloud server, respectively, which can be calculated as follows:  
\[T_{i,j}^{off}=\frac{b_{i,j}}{r_{i,j}} \tag{6}\]  
\[T_{i,j}^{e,exec}=\frac{c_{i,j}}{f_{i,j}^{e}} \tag{7}\]  
\[T_{i,j}^{c,exec}=\frac{c_{i,j}}{f_{i,j}^{c}}, \tag{8}\]  
where \(f_{i,j}^{e}\) and \(f_{i,j}^{c}\) denote the capability of computation for the UAV and the cloud server that is allocated to mobile device \(j\). As a consequence, the consumption of energy for transmitting and processing the task of mobile device \(j\) remotely at the UAV and cloud server can be calculated as follows:  
\[E_{i,j}^{r}=p_{i,j}T_{i,j}^{off}, \tag{9}\]  
In view of the above models, the energy and time overhead for processing the computation task of mobile device \(j\) can be expressed as, respectively:  
\[E_{i,j}=\Big{[}\varphi_{i,j,0}E_{i,j}^{l}+\sum_{a=1}^{M+1}\varphi_{i,j,a}E_{i,j}^{r}\Big{]}, \tag{10}\]  
\[T_{i,j}=\Big{[}\varphi_{i,j,0}T_{i,j}^{l}+\varphi_{i,j,M+1}T_{i}^{c}+\sum_{a= 1}^{M}\varphi_{i,j,a}T_{i,j}^{e}\Big{]} \tag{11}\]
## 6 Experiment Result
### Target Platform Configuration  
In the experiment, our target platform, Xilinx XC7Z020, was an FPGA with DPR capability. As described in Section 3, the reconfiguration data of the FPGA regions were transmitted by ICAP which was 32-bit and had a clock of 100 MHz. According to the configuration frame number and size, we evaluated the time overhead of reconfiguring single unit CLB, DSP and BRAM as 73.8, 287, 307.5 clock cycles respectively. The reconfiguration time was proportional to the amount of resources required by the reconfigurable region in which the new hardware accelerator had to be allocated [34]. To reduce the complexity of solving the ILP formulation, in the experiment, the execution time and reconfiguration overhead of different resources were both divided by 10. So, the reconfiguration overhead of one CLB, DSP and BRAM was 7, 28, 30 respectively.