esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Water Spray Removal
\[I_{O}(x,y,\lambda)=\frac{I_{T}(x,y,\lambda)-I_{A}(x,y,\lambda)}{t(x,y,\lambda )}=\frac{I_{T}(x,y,\lambda)-I_{A}(x,y,\lambda)}{1-\big{(}I_{A}(x,y,\lambda)/I_ {A}^{\infty}(x,y,\lambda)\big{)}}\;. \tag{7}\]  
Two key parameters, \(I_{A}(x,y,\lambda)\) and \(I_{A}{}^{\infty}(x,y,\lambda)\), should be considered to find \(I_{O}(x,y,\lambda)\). The \(I_{A}(x,y,\lambda)\) can be expressed as \(I_{A}(x,y,\lambda)=(S_{0}(x,y,\lambda)-p_{c})/{p_{c}}^{\infty}\), where \({p_{c}}^{\infty}\) is the DoCP corresponding to an object at infinity. Therefore, the core of this open problem is to calculate the intensity of airlight by utilizing the CP characteristics of scattered light. In combination with Equations (5) and (7), the final way to calculate the object radiance in a clear-day image \(I_{O}(x,y,\lambda)\) is expressed by Equation (8). \[I_{O}(x,y,\lambda)=\frac{p_{c}\cdot I_{T}(x,y,\lambda)-\Delta I(x,y,\lambda) \cdot(1-t(x,y,\lambda))}{p_{c}\cdot t(x,y,\lambda)}\;, \tag{8}\]  
where \(\Delta I(x,y,\lambda)=(I_{B}(x,y,\theta_{P})-I_{W}(x,y,\theta^{\prime}p))\). Although the wavelet decomposition enables the image recovery in the low-frequency components, the absolute values of the wavelet coefficients become larger, which would result in a decrease in image clarity in high-spatial frequency regions [10]. Hence, a dual-threshold process algorithm is taken to adjust the high-frequency coefficients, as shown in Equation (9). This can remove the coefficients within a certain threshold range and enhance those outside the threshold range. \[W_{out}=\left\{ \begin{array}{ll}W_{in}+(T_{2}\cdot(G-1))-(T_{1}-G)&W_{in}>T_ {2}\\ G\cdot(W_{in}-T_{1})&T_{1}<W_{in}<T_{2}\\ 0&-T_{1}\leq W_{in}<T_{1}\\ G\cdot(W_{in}+T_{1})&-T_{2}\leq W_{in}<-T_{1}\\ W_{in}-(T_{2}\cdot(G-1))+(T_{1}\cdot G)&W_{in}<-T_{2}\end{array} \right., \tag{9}\]  
where \(W_{in}\) and \(W_{out}\) are the wavelet coefficients before and after enhancement transfer, respectively; \(G\) is the gain, which is empirically selected within a range in a scene and weather condition; \(T_{1}\) and \(T_{2}\) are two thresholds with \(T_{1}<T_{2}\), and \(T_{1}\) is determined by the image size \(n\) and the variance of the image \(\sigma\)[10]. The coefficients transferred by the function shown in Equation (9) will be adjusted by an appropriate gain to improve the clarity of the images. The appropriate gain is obtained by evaluating the corresponding clear image quality through iterative gain in the range. Therefore, with the separate processing of the low-spatial frequency and high-spatial frequency components, both the clarity and visibility of rainy-day images can be improved.
## 3 Results and Discussion
### Steel Strain  
The strain curves of the four measuring points of the specimens at different characteristic points are shown in Figure 12.  
Figure 12 shows that the strain values of measuring points H1, H5, Z9, and G1 of the three specimens at the crack point are small, and the difference is not obvious. The reinforcements bear less load under lateral cyclic loading in the early stage of loading. After each specimen reaches the yield point, due to the plastic development of reinforcement, the strain value changes irregularly, so the strain values at different measuring points begin to appear different, and the strain value difference gradually becomes obvious. For instance, according to the strain values of the four selected characteristic positions, the strain value of H1 at the bottom of the reinforcement at the outermost side of the hidden column is the largest. The strain value of G1 at the bottom stirrup of the hidden column and the strain value of H5 at the bottom of the vertical distribution reinforcement at the outermost inner side of the hidden column is in the middle. The strain value of Z9 at the top of the outermost stressed reinforcement of the hidden column is the smallest, and the steel bar does not yield when the specimen reaches the limit state. The above examples show that the bottom of the longitudinal reinforcement of the hidden column first yields under the lateral cyclic loading, followed by the stirrup at the bottom of the hidden column and the bottom of the vertical distribution of the reinforcement of the specimen, while the strain of the reinforcement at the top of the longitudinal reinforcement is small.  
In addition, with the change in the coal gangue replacement rate, the strain values of the three specimens are also different. The strain values of the four measuring points at the limit point are negatively correlated with the replacement rate of coal gangue. Compared with RFSW-1, the strain values of measuring point H1 in the ultimate state of specimens CGSW-2 and CGSW-3 decrease by 9.0% and 17.7%, respectively.
# An Energy-Efficient and Secure Routing Protocol for Intrusion Avoidance in IoT-Based WSN
## 5 Network Model and Discussion
### Average End-to-End Delay  
Under varying nodes density topologies in Figure 9, the performance of ESR protocol was evaluated against other solutions with respect to average end-to-end delay. In fact, due to high data traffic, congestion and an increasing number of malicious nodes, the value of end-to-end delay also increased. However, it was seen that the proposed ESR protocol had a lower end-to-end delay in the comparison of other solutions, particularly in large sized and high network load. The numerical results illustrated that ESR protocol achieved a 24% average decrease of end-to-end delay in the comparison of other solutions. Actually, the proposed ESR protocol formed the clusters in an adaptive manner and updates the status of cluster heads within a particular boundary of a cluster. In addition, ESR protocol gave more consistent and robust data forwarding nodes based on a link quality RSSI values that led to routing performance. Unlike other existing solutions, ESR protocol performed the measurement of network analysis to revolve the position of cluster heads in the sensor field, which reduced the chances of routing hole and minimized the data latency ratio.
# Deep Representation of a Normal Map for Screen-Space Fluid Rendering
## 3 Deep Normal Map Representation with cGANs  
Our method comprises two steps: training the deep learning model and performing fluid rendering based on the trained model (as shown in Figure 2).  
In the first step, we train two networks: a generator \(G\) and a discriminator \(D\). The cGAN learns a mapping from observed data \(y\) (including a screen-space normal map, a screen-space depth map, and a blurred screen-space depth map) and a typical random noise vector \(z\) to the target data, \(x\) (the normal map generated by the 3D reconstructed surface). Whereas the generator is trained to mimic the \"real\" normal map to mislead the discriminator, the discriminator is trained to judge the normal map generated by the generator as \"fake\" and the normal map generated by the 3D reconstructed surface as \"real\". Because we want the generator to create a normal map based on the input data condition, the generator and discriminator both observe the screen-space normal map.  
In the second step, we first create a depth map and a blurred depth map in accordance with the existing screen-space methods. Then, we create a normal map using the blurred depth map. The created normal map, a depth map, and a blurred depth map are given as the input data to the trained generator to create a new refined normal map. Finally, the refined normal map is used to calculate the lighting, reflection, and refraction for the final fluid rendering.
## 3 Results  
We compared the results between the elemarkTM and reference devices (Table 2). Linear regression analysis showed that TC, TG, and HDL had high correlations between the two devices (TC: correlation coefficient (R) = 0.97, coefficient of determination (R\({}^{2}\)) = 0.94, \(p\)-value = \(2.25\times 10^{-73}\); TG: R = 0.99, R\({}^{2}\) = 0.98, \(p\)-value = \(1.34\times 10^{-92}\); HDL: R = 0.97, R\({}^{2}\) = 0.93, \(p\)-value = \(1.67\times 10^{-69}\)) (Figure 3).  
The system accuracy results showed that 99.1% of the TC concentration values at 100 mg/dL or above were within \(\pm\)15% of the reference results, 96.7% of the TG concentration values at 100 mg/dL or above were within \(\pm\)15% of the reference results, and 100% of the HDL concentration values less than 100 mg/dL were within \(\pm\)10 mg/dL of the reference results (Table 2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**TC Concentration (mg/dL)** & **N** & **Total** & **\%** \\ \hline Below 200 mg/dL (Normal) & 86 & 116 & 74.1 \\
200–239 mg/dL (Borderline High) & 24 & 116 & 20.7 \\ Above 240 mg/dL (High) & 6 & 116 & 5.2 \\ \hline
**TG concentration (mg/dL)** & **N** & **Total** & **\%** \\ \hline Below 150 mg/dL (Normal) & 71 & 116 & 61.2 \\
150–199 mg/dL (Borderline High) & 22 & 116 & 19.0 \\
200–499 mg/dL (High) & 22 & 116 & 19.0 \\ Above 500 mg/dL (Very high) & 1 & 116 & 0.9 \\ \hline
**HDL concentration (mg/dL)** & **N** & **Total** & **\%** \\ \hline Below 40 mg/dL (Low) & 22 & 116 & 19.0 \\
40–59 mg/dL (Normal) & 60 & 116 & 51.7 \\ Above 60 mg/dL (High) & 34 & 116 & 29.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample distribution of total cholesterol (TC), triglyceride (TG), and high-density lipoprotein (HDL) concentrations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**<100 mg/dL**} & \multicolumn{3}{c}{\(\geq\) 100 mg/dL**} \\  & **Within** & **Within** & **Within** & **Within** & **Within** & **Within** \\  & \(\pm\)**5 mg/dL** & \(\pm\)**10 mg/dL** & \(\pm\)**15 mg/dL** & \(\pm\)**5\%** & \(\pm\)**10\%** & \(\pm\)**15\%** \\ \hline
**TC** & N/A (Device range: 100–450 mg/dL) & 87/116 & 112/116 & 115/116 & 116/116 \\  & 8/24 & 17/24 & 23/24 & 42/92 & 71/92 & 89/92 & 92/92 \\
**TG** & 33.30\% & 70.80\% & 95.80\% & 45.70\% & 77.20\% & 96.70\% & 100\% \\
**HDL** & 98/116 & 116/116 & 116/116 & N/A (device range: 25–95 mg/dL) \\  & 84.50\% & 100\% & 100\% & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: System accuracy.
# Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning
## 4 Experiments and Evaluation
### Hardware Acceleration Platform Comparison  
In order to verify the superiority of the proposed deployment scheme, the mainstream deep learning general-purpose processor CPU and GPU were selected for experimental comparison of inference performance. The CPU selected is the AMD R7-4800H and the GPU is NVIDIA RTX 2060 (6G), and the table shows the performance test data comparison of FPGA, GPU, and CPU. As can be seen from the data in Table 3, the FPGA deployment scheme proposed by us reduces power consumption by 81.91% and increases FPS by 317.88% compared with CPU AMD R7-4800H. Compared to the GPU NVIDIA RTX 2060, power consumption is reduced by 91.41% and FPS is increased by 8.50%. Our design solution has a detection speed much higher than the normal human eye persistence time of 24 frames per second, which meets the real-time requirements of object detection, and the power consumption is only 7.2 W, which can meet the low power consumption requirements of airborne and spaceborne aircraft.  
The improved model deployed to Xilinx KV 260 after quantization has an mAP of 82.61%, which is 0.42% lower than that of the model before quantization, which is due to the slight accuracy degradation of floating-point numbers due to quantization. During the quantization process of Vitis AI, precision loss occurs due to the conversion of original floating-point parameters into fixed-point representation. In floating-point representation, parameters can have higher precision and can represent numbers with multiple decimal places, while fixed-point representation can only represent numbers with a fixed number of decimal places or integers. In the quantization process, floating-point parameters are rounded or truncated into fixed-point parameters to accommodate limited computational resources of hardware platforms. This can cause information loss and rounding errors, as the fine details and decimal places of the original model are constrained to fewer bits. Precision loss in the quantization process can potentially impact the performance and accuracy of the model. As the parameters become coarser, the model may fail to capture subtle features and complex relationships present in the original model. As shown in Figure 12, it is evident that despite the accuracy loss resulting from the quantization process, overall, there is minimal impact on the target detection effect. This implies that the quantization process does not hinder the practical application of the model in real-world projects.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Hardware** & **Device** & **Frequency** & **mAP (\%)** & **Power (W)** & **FPS** \\
**Platform** & AMD & & & & \\  & R7-4800H & 2900 & 83.03 & 39.8 & 11.52 \\  & NVIDIA RTX & & & & \\  & 2060 & 1365 & 83.03 & 88.39 & 44.37 \\
**FPGA** & **XilinxKV260** & 200 & **82.61** & **7.2** & **48.14** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hardware platform comparison.
## 6 Conclusions  
The SI black box method was used in this paper to develop the FBBA mathematical model. The SI approach was used in MATLAB software, and the model structure ARX331 was chosen as the final mathematical model for the actual plant, with a best fit value of 94.34%. To validate the model estimated from the SI black box method, the PID controller was used in both the simulation and experimental setup. The auto-tuned and GA methods were used to tune the PID parameters. The GA tuning method outperforms the other methods in simulation, with a rise time of 1.26 s, a settling time of 2.29 s, and an overshoot percentage of 0%. The actual experiment results show that PID-GA follows the simulation pattern, with better controller performance based on the rise time and settling time. In conclusion, the mathematical model representing the FBBA system was successfully developed, and the FBBA position control using PID was implemented in a real-time setup.  
Figure 14: FBBA in real-time experiment multi-step response.  
Figure 13: FBBA in real-time experiment step response.  
**Author Contributions:** Conceptualization, A.A.M.F. and M.N.M.; methodology, M.N.M.N., A.A.M.F., M.N.M., M.A.M.Y. and S.M.; software, M.N.M.N., M.N.M. and M.A.M.Y.; validation, I.N.A.M.N.; formal analysis, A.A.M.F.; investigation M.N.M.N.; resources, I.N.A.M.N.; data curation, M.N.M.N.; writing--original draft preparation, M.N.M.N.; writing--review and editing, I.N.A.M.N., A.A.M.F. and M.N.N.M.; visualization, A.A.M.F.; supervision, I.N.A.M.N. and A.A.M.F.; project administration, I.N.A.M.N.; funding acquisition, S.M., A.A.M.F. and I.N.A.M.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors would like to acknowledge the spons provided by Ministry of Higher Education Malaysia (MOHE) through support under Fundamental Research Grant Scheme (FRGS/1/2019/TK04/UTM/02/41) and FRGS RACER (RACER/1/2019/TK04/UTHM//8). The authors would also like to express appreciation to Universiti Teknologi Malaysia, vote no (5F137) and Universiti Tun Hussein Onn Malaysia which make this research viable and effective.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Animals and Farms
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Flock No.** & **Additional Space** & \begin{tabular}{c} **Laving** \\ **Period** \\ \end{tabular} & **LV1** & **LV2** & **LV3** & **Hybrid Line(s)** & \begin{tabular}{c} **Flock Size** \\ \end{tabular} & \
\begin{tabular}{c} **Daylight/Artificial Light Type/Color** \\ \end{tabular} \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{None} & 1 & 31 & 46 & 66 & BB (15\%) DW (85\%) & 3589 & No/HFN/white \\ \cline{3-8}  & & 2 & 33 & 48 & 67 & BB (20\%) DW (80\%) & 2890 & No/HFN/white \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{None} & 1 & 31 & 48 & 68 & LB & 4250 & Yes/HFN, LED/white \\ \cline{3-8}  & & 2 & 33 & 46 & 65 & LB & 4018 & LED/white \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{None} & 1 & 33 & 45 & 65 & LB & 5453 & Yes/HFN, lightbulbs/white \\ \cline{3-8}  & & 2 & 32 & 45 & 65 & LB & 5336 & lightbulbs/white \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{Free range + winter garden} & 1 & 31 & 46 & 65 & LSL & 5897 & Yes/HFN, LED, lightbulbs/white \\ \cline{3-8}  & & 2 & 32 & 46 & 67 & LB (50\%) LSL (50\%) & 5912 & lightbulbs/white \\ \hline \multirow{2}{*}{5} & \multirow{2}{*}{Free range + winter garden} & 1 & 30 & 46 & 65 & LB & 5044 & Yes/HFN, LED, lightbulbs/white \\ \cline{3-8}  & & 2 & 30 & 45 & 66 & LB (50\%) LSL (50\%) & 5065 & lightbulbs/white \\ \hline \multirow{2}{*}{6} & \multirow{2}{*}{Free range + winter garden} & 1 & 31 & 45 & 66 & LB & 4194 & Yes/HFN, LED, lightbulbs/white \\ \cline{3-8}  & & 2 & 30 & 45 & 67 & LB & 4234 & lightbulbs/white \\ \hline \multirow{2}{*}{7} & \multirow{2}{*}{Free range} & 1 & 33 & 44 & 65 & LB (50\%) DW (50\%) & 1450 & Yes/LED, lightbulbs/white, yellow \\ \cline{3-8}  & & 2 & 33 & 46 & 53 & BB (50\%) DW (50\%) & 1450 &
\begin{tabular}{c} Yes/LED, lightbulbs/white \\ \end{tabular} \\ \hline \multirow{2}{*}{8} & \multirow{2}{*}{Free range} & 1 & 32 & 48 & 66 & LB (50\%) LSL (50\%) & 2004 & Yes/lightbulbs/white \\ \cline{3-8}  & & 2 & 31 & 47 & 68 & LB (50\%) LSL (50\%) & 2100 & Yes/lightbulbs/white \\ \hline \multirow{2}{*}{9} & \multirow{2}{*}{Free range + winter garden} & 1 & 30 & 46 & 68 & LB (75\%) LSL (25\%) & 2000 & Yes/HFN, lightbulbs/white \\ \cline{3-8}  & & 2 & 32 & 44 & 67 & LB (63\%) LSL (37\%) & 1999 & lightbulbs/white \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of the layer flocks. All flocks were housed in aviaries. Flocks 11 and 16 were only studied in the first laying period. Abbreviations: DW = Dekalb White; BB = Bovans Brown; LB = Lohmann Brown or Lohmann Brown Extra; LSL = Lohmann Selected Leghorn; HFN = high-frequency neon tube; LED = light-emitting diode; LV = laying visit (week of life).
## 2 Background of WUE Estimation by Remote Sensing
### Vegetation Growth Dominates the Seasonal Variability of the Ecosystem WUE  
Vegetation is both the main body of carbon sequestration and the largest source of evapotranspiration in ecosystems and plays a key role in the seasonal variability of WUE [91]. As the main controlling factor of WUE, the CO\({}_{2}\) has a direct effect on WUE, which decreases by about 20% due to the change in LAI for various types of vegetation [1]. In the analytical WUE model, the input structural and physiological parameters of vegetation are also important to evaluate the coupled water and carbon cycles and variations in WUE. At the same time, the LAI is a negative exponential response to WUE, and it explains about 43% of the variability in annual T/ET at the global ecosystem scale [103]. Vegetation indices (i.e., NDVI, EVI) show a strong correlation with WUE and can capture the seasonal variability of WUE at an 8-day scale across several ecosystems [96; 97]. Correct measurement and modeling of WUE for various time scales, especially for shorter time scales at the ecosystem level, needs more understanding of ecosystem characteristics and its responses to environmental forcing [102]. Fortunately, a framework of this model provides an opportunity for coupling remote sensing algorithms. At the same time, a strong correlation between ecosystem WUE and vegetation indices also needs to couple environmental factors to improve WUE estimation in the peak growth period.
## 3 Results and Discussion
### UV-Visible Measurements  
Figure 7 illustrates the absorption spectra of the ZnEuNa NCs. Using these data, the band gap energy of direct transition is determined. As shown in Table 3, the values of Eg varied in the range 3.23-3.28 eV, and are comparable with those obtained for ZnO:Eu nanoparticles by [PERSON] et al. [34]. The incorporation of the Eu\({}^{3+}\) ion does not allow a significant change in the E\({}_{\text{g}}\) value. However, E\({}_{\text{g}}\) decreases following codoping by Na\({}^{+}\) ions. In fact, the simple doping with Eu\({}^{3+}\) ions decreases the density of defects since these ions occupy the vacant sites of Zn. Therefore, the crystallinity of ZnO is improved by simple doping at a low level. However, the addition of Na\({}^{+}\) ions with larger ionic radii in respect of Zn, can create various defects in the crystal lattice. For lower concentration, the Na\({}^{+}\) ion substitutes the Zn\({}^{2+}\) which migrate to interstitial sites and act as deep donor levels positioned close to the conduction band. Likewise, the zinc vacancies (V\({}_{\text{Zn}}\)) act as acceptor defects located near the valence band. Thus, most transitions occur between the energy levels located at the edges of the bands related to the deep defects (zinc vacancy V\({}_{\text{Zn}}\) and Zinc interstitial Zn\({}_{\text{i}}\)) [35]. The more Na\({}^{+}\) ions are introduced, the more the concentration of defects and the densities of energy levels at the edges of the bands are high, thus reducing the energies of the transitions. Similar shifts in gap energies have been reported [36; 37].  
Figure 7: Absorption spectra of undoped and Eu, Na codoped ZnO NCs.  
The UV-visible curves exhibit a band tail (inset Figure 7) corresponding to the well-known Urbach energy \(E_{u}\). The absorption coefficient \(\alpha\) in this zone is depicted as [38]:  
\[\alpha=\alpha_{0}\mathrm{exp}\big{(}\left(h\mathrm{v}-E_{g}\ \right)/E_{u}\big{)}\]  
The decrease in the Urbach energy after Eu\({}^{3+}\) doping (Table 3) confirmed the reduction in the defects density, as previously mentioned. It confirms the improvement in crystalline quality of the samples. However, the value of \(E_{u}\) increases with codoping rate up to 3% Na\({}^{+}\) concentration, which shows the creation of additional defects by the incorporation of this element whose ionic radius is larger than that of Zn\({}^{2+}\). Beyond 3% in Na\({}^{+}\) ion, Urbach's energy gradually decreases because sodium limits the formation of ZnO NCs and it will be located on the surface.  
In conclusion, the variations of both Eg and Eu with the added Na\({}^{+}\) concentration show the successful dopant insertion in the lattice with a tendency to affect the ZnO crystallinity [23].