esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental and Parametric Study on the Pull-Out Resistance of a Notched Perfobond Shear Connector
## 4 Finite Element Analysis
### Finite Element Type and Mesh  
As shown in Figure 8, the pull-out tests of perfobond shear connectors were meshed by using solid elements, rigid elements, and truss elements. The concrete slab, the steel beam, the perfobond rib, and the perforating rebar were modeled with eight-node reduced integration elements (C3D8R). The other reinforcing bars embedded in concrete were represented with three-dimensional two-node truss elements (T3D2). The jacking header and the base plate were meshed by using discrete rigid elements (R3D4). A global coarse mesh with an average size of 10 mm was applied to save the analysis time. A locally refined mesh with the smallest size of about 5 mm was applied near the hole of perfobond ribs to increase the accuracy.
## 4 Discussions  
In this research, the methods with linear regression, SVM, MLNN, and LSTM have been applied to deduct local solar irradiance. Weather information prediction is not easy and hard to get precise information. Even such a difficulty happened, it needs irradiance information to energy scheduling. Hence, the artificial intelligence approach is considered by training precious data such as temperature (\({}^{\circ}\)C), humidity ratio (g/kg), air pressure (mbar), wind velocity (m/s), and wind direction (0-360\({}^{\circ}\)) over eight years. Additionally, cloud cover, middle and low-layer cover were also tested with over one year of data. As a result we estimated long-term solar irradiance, which can be obtained without actual radiance sensors. From the results, MLNN and LSTM show better performance and higher accuracy. As mentioned before, LSTM model and LSTM were not always illustrated to be superior to other methods from an accuracy and impact point of view. From the MSE results, two ANN models showed good performance due to reliable information by NN training. From the impact point of view, this was how much each weather parameter impacted upon the local solar irradiance. Temperature and cloud cover (Tcc) significantly dominated. Then, the results with MLNN and LSTM models showed four main parameters affecting the actual solar irradiance. Additionally, it was shown in Figure 10 and Table 1 that the total cloud cover (Tcc) affects solar irradiance mostly. As a result, temperature and amount of Tcc significantly dominate local solar irradiance in the summer season. However, it is a reverse situation during the summer season because the humidity affects in reverse the solar irradiance. Specifically, the high humidity ratio in the summer is highly related to the total cloud cover and precipitation in Seoul, Korea, because of the climate. To be simplicity; data availability and small impact, wind velocity and direction, and air pressure could be neglected.  
The obtained results suggest the estimation of local solar irradiance by artificial neural networks based on weather reports. It helps to plan energy generation scheduling, but it still needs to improve from the limited information. Specially, more variable factors such as geomagnetic storm activity, ozone, aerosols, and pollutants affect solar irradiance; hence, future studies should consider additional environmental factors to increase the accuracy of prediction results.
# Dynamic Repositioning of Aerial Base Stations for Enhanced User Experience in 5G and Beyond
## 3 System Model  
Let \(U\) = {1,2,3, \(\ldots\), \(N_{ul}\)} be the set of ABSs and \(S\) = {1, 2,3, \(\ldots\),\(N_{s}\)} be the set of users. Let \(L\) = {1,2,3, \(\ldots\),\(N_{l}\)} be the set of \(N_{l}\) candidate locations for UAV placement. There are more candidate locations than the number of UAVs, i.e., \(N_{l}>N_{ul}\). All ABSs have the same transmission power \(P_{s}\). We assume that the main source of noise for a user is the interference caused by ABSs other than the one with which the user is associated and that the thermal noise is negligible.
# Effect of Fly Ash on the Properties of Ceramics Prepared from Steel Slag
###### Abstract  
In this study, SiO\({}_{2}\)-Al\({}_{2}\)O\({}_{3}\)-CaO-MgO steel slag ceramics containing 5 wt % MgO were used for the preparation of ceramic bodies, with the replacement of 5-20 wt % quartz and feldspar by fly ash. The effect of the addition of fly ash on the sintering shrinkage, water absorption, sintering range, and flexural strength of the steel slag ceramic was studied. Furthermore, the crystalline phase transitions and microstructures of the sintered samples were investigated by XRD, Fourier transform infrared (FTIR), and SEM. The results showed that the addition of fly ash affected the crystalline phases of the sintered ceramic samples. The main crystal phases of the base steel slag ceramic sample without fly ash were quartz, diopside, and augite. With increasing fly ash content, the quartz diffraction peak decreased gradually, while the diffraction peak intensity of anorthite became stronger. The mechanical properties of the samples decreased with the increasing amount of fly ash. The addition of fly ash (0-20 wt %) affected the optimum sintering temperature (1130-1160 \({}^{\circ}\)C) and widened the sintering range. The maximum addition amount of fly ash should be 15 wt %, for which the optimum sintering temperature was 1145 \({}^{\circ}\)C, water absorption was 0.03%, and flexural strength was 43.37 MPa higher than the Chinese national standard GBT 4100-2015 requirements.  
steel slag; fly ash; ceramics; crystalline phase; microstructures +
Footnote †: journal: Applied Physics Letters
## 5 Derivation of the CRB for Long Observation Intervals: CRB(\(\boldsymbol{\
In order to do so, we first define the vector \(\mathbf{\Psi}_{ext}\) of unknown parameters as:  
\[\mathbf{\Psi}_{ext}\triangleq\left[\mathbf{\Psi}_{0}^{T},...,\mathbf{\Psi}_{ L-1}^{T}\right]^{T}, \tag{46}\]  
where each subvector \(\mathbf{\Psi}_{l}\) corresponds to the \(l\)-th \(T_{coh}\) observation interval. We build now the matrix \(\mathbf{J}\left(\mathbf{\Psi}_{ext}\right)\) as:  
\[\mathbf{J}\left(\mathbf{\Psi}_{ext}\right) \triangleq\left[ \begin{array}{ccc}\mathbf{J}\left(\mathbf{\Psi} _{0}\right)&&\mathbf{0}\\ &\ddots&\\ \mathbf{0}&&\mathbf{J}\left(\mathbf{\Psi}_{L-1}\right)\end{array} \right] \tag{47}\] \[=\text{diag}\left\{\mathbf{J}\left(\mathbf{\Psi}_{0}\right), \ldots,\mathbf{J}\left(\mathbf{\Psi}_{L-1}\right)\right\}, \tag{48}\]  
where each \(\mathbf{J}(\mathbf{\Psi}_{l})\) is the FIM computed using Equations (20)-(34) for the \(l\)-th \(T_{coh}\) observation interval. The \(\mathbf{J}\left(\mathbf{\Psi}_{ext}\right)\) generated is equivalent to assuming all of the parameters in any \(\mathbf{\Psi}_{l}\) are independent from any of the parameters in a different subvector \(\mathbf{\Psi}_{k}\). This assumption is only used as a means to introduce the parameter dependencies between observation intervals by transforming \(\mathbf{J}\left(\mathbf{\Psi}_{ext}\right)\), once again, using a new Jacobian transformation matrix \(\mathbf{A}\in\mathbb{R}^{6L\times P}\), defined as:  
\[\mathbf{A}=\left\{\frac{\partial\mathbf{\Psi}_{ext_{j}}}{\partial\mathbf{\Psi }_{long_{j}}}\right\}, \tag{49}\]  
where \(i\) and \(j\) are the indexes used for the rows and columns, respectively. The number of columns in the matrix \(\mathbf{A}\), \(P\), is equal to the number of parameters in \(\mathbf{\Psi}_{long}\), i.e., \(3L+1+\) the number of elements in \(\mathbf{\mathfrak{s}}_{\Gamma}\). Since the parameter \(h\) was already in \(\mathbf{\Psi}\) and is considered constant for all of the \(L\) intervals, \(\partial h_{l}/\partial h=1\), for any \(l\). Finally, \(\mathbf{J}(\mathbf{\Psi}_{long})\) is obtained as:  
\[\mathbf{J}\left(\mathbf{\Psi}_{long}\right)=\mathbf{A}\,\mathbf{J}\left( \mathbf{\Psi}_{ext}\right)\mathbf{A}^{T}. \tag{50}\]  
A more detailed description of how the matrix \(\mathbf{A}\) is constructed is provided in Appendix A. The CRB can be obtained directly as \([\mathbf{J}^{-1}(\mathbf{\Psi}_{long})]_{ii}\). Furthermore, we shall point out that the previous derivation is independent of the model of \(\Gamma\) considered, as long as the gradients of \(|\Gamma|\) and \(\phi_{\Gamma}\) with respect to \(\mathbf{s}_{\Gamma}\) exist. No assumption was made regarding the number of parameters in \(\mathbf{s}_{\Gamma}\). Nonetheless, it is well known that the CRB can only increase with the number of unknown parameters considered, which intuitively makes sense, given the increased uncertainty.
## 3 Results and Discussion
### Physical Properties of Mandarin Juice Powders
#### 3.1.1 Colour Differences among Powder Samples
\begin{table}
\begin{tabular}{c c c c c} \hline  & **L* & **a* & **b* & **Chroma** & **Hue Angle** \\ \hline
**A1 powder** & 88.30 \(\pm\) 0.56 a & 8.04 \(\pm\) 0.65 b & 30.40 \(\pm\) 0.59 f & 29.54 \(\pm\) 2.53 a & 74.94 \(\pm\) 2.71 e \\
**A2 powder** & 86.04 \(\pm\) 0.29 ad & 5.69 \(\pm\) 0.45 d & 50.26 \(\pm\) 0.40 c & 48.08 \(\pm\) 2.77 b & 83.28 \(\pm\) 0.81 c \\
**A3 powder** & 87.10 \(\pm\) 0.39 b & 3.39 ad * & 52.57 \(\pm\) 0.70 b & 52.71 \(\pm\) 0.79 & 86.50 \(\pm\) 0.28 a \\
**RS1 powder** & 85.11 \(\pm\) 1.43 d & 9.26 \(\pm\) 0.10 a & 37.09 \(\pm\) 0.37 e & 37.95 \(\pm\) 13.2 ad & 73.69 \(\pm\) 2.53 a \\
**RS2 powder** & 85.22 \(\pm\) 0.50 d & 8.97 \(\pm\) 0.85 a & 42.85 \(\pm\) 0.80 ad & 43.54 \(\pm\) 0.85 c & 78.67 \(\pm\) 0.75 d \\
**RS3 powder** & 86.33 \(\pm\) 0.73 bc & 6.79 \(\pm\) 0.11 c & 54.68 \(\pm\) 0.24 a & 54.28 \(\pm\) 1.23 a & 84.69 \(\pm\) 1.51 bc \\ \hline \end{tabular}  
* A1—peel-removed mandarin fruit from Afourer cultivar; A2—flavedo-removed mandarin fruit from Afourer cultivar; A3—whole Afourer mandarin fruit; RS1—peel-removed mandarin fruit from Richard Special cultivar; RS2—flavedo-removed mandarin fruit from Richard Special cultivar; L* represents lightness; b* indicates the blue-yellow component; a* indicates the green-red component; Values are expressed as mean \(\pm\) standard deviation; Different lower-case letters (a–f) indicate statistical significance (_p_ < 0.05) among samples in the same column as analysed by [PERSON]’s test.  
\end{table} 
Table 1: Color attributes of mandarin powders derived from Afourer and Richard Special cultivars. (300 \(\upmu\)g/g FW vs. 40 \(\upmu\)g/g FW) [1]. Therefore, the removal of peel or flavedo during sample preparation could greatly reduce the yellowness in the mandarin juice powders.
## 3 Experimental and Computational Methods
### Computational Setup
#### 3.2.2 Boundary Conditions  
The lower boundary is set as a symmetry boundary condition (BC). The upper boundary is defined as an isothermal wall with a specified temperature taken equal to (\(T_{t}\)), as given in Table 1. More precisely, a standard wall function is used with no-slip boundary condition. Because the simulations carried out in this work are intended to solve the transient period, including the acceleration of the flow in the convergent up to the critical flow rate, then, for the shock wave initiation and movement towards the nozzle outlet at the right side of the grid, a transonic pressure outlet BC is used with a transition from subsonic to supersonic with a Mach number in the range (0.9-1.1). A Neumann BC is used when the Mach number is greater than 1.1, while a pressure is specified at the outlet when the Mach number is smaller than 0.9. This outlet pressure is obtained by linearly relaxing the pressure from its initial value to a relatively low pressure (\(P_{exit}\)), in order to gradually accelerate the flow through the nozzle to its steady state. The analytical calculations performed give us the values of the pressure \(P_{exit}\), as listed in the last column of Table 1. The relaxation time is estimated at a tenth of a second.At the inlet, we use the total pressure and temperature, given in Table 1, as BC. In addition, the inlet mass fraction of nitrogen N\({}_{2}\) is specified, in addition to the CO\({}_{2}\), in order to investigate the effects of the impurities on the CO\({}_{2}\) flow.
## 2 Materials and Methods
### Data Source and Its Pre-Processing  
Landsat images were obtained from the USGS [35], with time periods of 27 August 2010 and 7 September 2020, respectively. The selected time period has low cloud cover, and the image quality meets the research requirements. The remote sensing images were preprocessed using ENVI5.3 software, which took into account radiometric calibration, atmospheric correction, and geometric refinement correction, while keeping the error to less than one image element. Finally, the remote sensing images were stitched and cropped based on the vector boundaries of the Urat front banner, and relevant parameters were inverted on the preprocessed remote sensing images [36; 37]. The normalized vegetation index (NDVI) is a biophysical parameter representing the surface vegetation status, whereas surface albedo reflects the physical characteristics of the surface. By combining these two indicators to extract desertification information together and avoiding the use of a single spectral information classification, quantitative dynamic monitoring of desertification can be made more convenient and effective.  
The MODIS/Terra MOD17A3H data products [38] provide accurate measurements of terrestrial vegetation growth, including global gross primary productivity (GPP) and annual net primary productivity (NPP). The NPP data were synthesized from 45 periods (8-day synthetic data) at a spatial resolution of 500 m in gC/m\({}^{2}\), and were processed using projection change, mosaicking, and cropping to investigate the spatial and temporal variation of NPP in the two years of 2010 and 2020 in the Urat front banner, as well as to conduct the driving factor analysis for this study [39; 40]. Negative values, fire point instability values, and background noise are not removed from the monthly products of NPP, but the annual data for 2010 and 2020 provide cloud-free mean lights and exclude transient lights, so the use of MOD17A3 data for these two years to validate only the NPP estimated by the CASA model is sufficient for the experiment.
# Robustness of Cyber-Physical Supply Networks in Cascading Failures
## 5 Numerical Simulation
### The Cascading Process under Cyber Node Failure  
#### 5.1.1 The Cascading Process of One Node Failure  
We first investigated the relationship between \(R\) and \(\varphi\) with different values of \(\delta\), and the results are presented in Figure 5. \(R\) ascends with the increase in \(\varphi\). For the cyber supply network, nodes' subsequent failures are provoked by loads exceeding the upper bound. The higher the upper-bound value, the smaller the cascading size [16]. \(R\) also ascends with the increase in \(\delta\). As a critical parameter for adjusting the cyber node capacity, the rise of \(\delta\) can improve the network's cascading robustness. For example, in the case of \(\varphi=1.1\) and \(\delta=0.1\), \(R\) is 0.61. When \(\varphi\) stays the same, \(\delta\) rises to 0.9, and \(R\) is 0.73, the cascading failures  
Figure 4: Flowchart of the numerical simulation.  
are reduced. Under the same set of other parameters, the increase of \(\varphi\) and \(\delta\) can improve \(R\), indicating that the damage of cascading failures to the network will decline.  
We also observed that there is a critical threshold \(\varphi^{*}\). When \(\varphi\) is less than or equal to \(\varphi^{*}\), the cascading failures can be triggered. When \(\varphi\) is greater than \(\varphi^{*}\), the network will not have cascading failures. This is evident in the case of \(\varphi^{*}=1.35\) and \(\delta\)= 0.9, and \(\varphi<\varphi^{*}\) will lead to the cascading failures of the network.  
Figure 6 presents the heatmaps of \(R\) within the parameter space \([\varphi,\delta]\). We observed that the critical thresholds \(\varphi^{*}\) and \(\delta^{*}\) divide the heatmaps into two zones: safety zone and sensitive zone. In the safety zone, the value of \(R\) is relatively large, and \(R\) increases slightly with the increase in \(\delta\) when \(\varphi>1.2\). In the sensitive zone, the value of \(R\) is relatively small, and \(R\) increases significantly with the increase in \(\delta\) when \(\varphi<1.2\).
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 6 UAV Applications Areas
### Search and Rescue (SAR)  
UAVs are considered to be of pivotal importance in critical scenarios such as disaster management, rescue operations and public safety. UAVs can save a lot of manpower, resources and time by offering real-time imagery data of intended locations. Consequently, a SAR team can timely detect and decide where accurately the assistance is urgently required. UAVs can speed up SAR operations in disastrous situations like missing persons, avalanches, wildfires and poisonous gas infiltration. For instance, drones can be used to track mountaineers who are lost during any mission or protect human lives lost in any remote desert or forest. Thus, drones can assist in tracing unfortunate victims or any challenging terrains or hard atmospheric conditions. Drones can provide necessary medical supplies before the arrival of any ambulance or medical team. Drones can be equipped with medical kits, life-saving jackets and food supplies to disaster-stricken areas and remote location. Such drones can provide cloth, water and necessary items to stranded human beings in inaccessible areas before the arrival of rescue crews.
# Wire-Grid and Sparse MoM Antennas: Past Evolution, Present Implementation, and Future Possibilities
## 5 Wire-Grid Sparse Antennas
### OCGA Method: Explanation
For example, this step might be performed without taking into account the current in the excitation source, or the current matrix elements might be normalized with respect to the average current value. Then, the numbers of segments in which the current module is less than the given value of the threshold parameter are determined. The value of this parameter is set by the user or according to the manufacturer requirements or the desired accuracy). Later it will be called the grid element elimination tolerance (GEET). After that, the SLAE columns and rows that correspond to these numbers are cut out, and the remaining ones are shifted, which reduces its order. Finally, if necessary, a new \"sparse\" antenna, without the deleted elements that correspond to the eliminated SLAE columns and rows, is displayed.
## 3 Results and Discussion
### Chemical Characterization of Feedstock and Processed Solids
#### 3.2.1 Sugar Release
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & **Xylotriose (g/L)** & **Xylotetraose (g/L)** & **Total XOS (g/L)** \\ \hline \multirow{4}{*}{L140} & HTP (A) & ND & ND & ND \\ \cline{2-6}  & Alkaline (NaOH) (B) & & ND & ND & ND \\ \cline{2-6}  & Enzymatic & Xylanase (C) & ND & ND & ND \\ \cline{2-6}  & & CCTec2 (E) & 0.57 \(\pm\) 0.03 ** & 0.02 \(\pm\) 0.001 ** & 0.59 \(\pm\) 0.03 ** \\ \cline{2-6}  & Alkaline and enzymatic & Xylanase (D) & 1.32 \(\pm\) 0.1 ** & 0.13 \(\pm\) 0.01 ** & 1.48 \(\pm\) 0.2 ** \\ \cline{2-6}  & & CCTec2 (F) & 1.07 \(\pm\) 0.05 ** & 0.04 \(\pm\) 0.002 NS (0.016) & 1.11 \(\pm\) 0.05 ** \\ \hline \multirow{4}{*}{L160} & HTP (A) & & ND & ND & ND \\ \cline{2-6}  & Alkaline (NaOH) (B) & & ND & ND & ND \\ \cline{2-6}  & Enzymatic & Xylanase (C) & 1.12 \(\pm\) 0.08 ** & 0.08 \(\pm\) 0.01 & 1.23 \(\pm\) 0.12 ** \\ \cline{2-6}  & & CCTec2 (E) & 0.52 \(\pm\) 0.02 ** & 0.07 \(\pm\) 0.00 & 0.59 \(\pm\) 0.03 ** \\ \cline{2-6}  & Alkaline and enzymatic & Xylanase (D) & ND & ND & ND \\ \cline{2-6}  & & CCTec2 (F) & 1.14 \(\pm\) 0.06 NS (0.899) & 0.08 \(\pm\) 0.00 & 1.23 \(\pm\) 0.05 NS (0.899) \\ \hline \multirow{4}{*}{L180} & HTP (A) & & ND & ND & 0.01 \(\pm\) 0.00 ** \\ \cline{2-6}  & Alkaline (NaOH) (B) & & ND & ND & ND \\ \cline{2-6}  & Exymatic & Xylanase (C) & ND & ND & ND \\ \cline{2-6}  & & CCTec2 (E) & 0.76 \(\pm\) 0.04 ** & 0.01 \(\pm\) 0.00 ** & 0.78 \(\pm\) 0.03 ** \\ \cline{2-6}  & Alkaline and enzymatic & Xylanase (D) & 1.25 \(\pm\) 0.09 NS (0.754) & 0.04 \(\pm\) 0.00 NS (0.797) & 1.33 \(\pm\) 0.13 NS (0.721) \\ \cline{2-6}  & & CCTec2 (F) & 1.21 \(\pm\) 0.06 ** & 0.04 \(\pm\) 0.00 ** & 1.26 \(\pm\) 0.06 ** \\ \hline \hline \end{tabular}  
* NS: not statistically significant; ND: not determined; ** Correlation is significant at the 0.01 level (2-tailed).  
\end{table} 
Table 3: Xylo-oligosaccharides yields in the hydrolysates after HTP, alkaline and enzymatic treatments of the solid residues. proved the efficiency of enzymatic hydrolysis of wheat straw using a cellulase produced by _P. waksmaniti_ and obtained a maximal concentration of reducing sugar (343.95 mg/g substrate) [60]. Moreover, [PERSON] et al. [61] applied an enzymatic hydrolysis to pretreated residues of WS by HTP (at 210 and 230 \({}^{\circ}\)C) and observed that the conditions applied for the enzymatic hydrolysis allowed a higher recovery of glucose (30.6 g/L glucose representing around 90% yield) from the residues pretreated at 230 \({}^{\circ}\)C compared to those at 210 \({}^{\circ}\)C (78.8%). They concluded that enzymatic hydrolysis allowed a high digestibility of cellulose fibers due to the physical degradation of lignocellulosic, while diminishing the hemicellulose content in the residues and their inhibition towards cellulolytic enzymes [61]. When xylanase was used in the hydrolysis, the highest yield of glucose was detected in the hydrolysate at 180 \({}^{\circ}\)C, 6.67 \(\pm\) 0.50 g/L, after enzymatic and alkaline treatment of the residues.
## 4 Experimental Results and Analysis
### Road Data Extraction
#### 4.2.3 Traffic Signs Detection
\begin{table}
\begin{tabular}{c c c} \hline
**Group** & **Class** & **Number of Detections** \\ \hline \hline \multirow{6}{*}{Traffic Signs} & Speed Limit & 757 \\ \cline{2-3}  & Stop Sign & 206 \\ \cline{2-3}  & Animal Crossing & 95 \\ \cline{2-3}  & Bicycle Crossing & 209 \\ \cline{2-3}  & Zebra Crossing & 4846 \\ \cline{2-3}  & Yield Sign & 982 \\ \cline{2-3}  & Roundabout Sign & 664 \\ \cline{2-3}  & School Zone & 21 \\ \hline Temporary Signs & Road work & 192 \\ \hline Traffic Lights & Red and Green & 1089 \\ \hline \multirow{2}{*}{Traffic Density} & Ongoing & 34,891 \\ \cline{2-3}  & Incoming & 21,058 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of detected objects. zones. These categories assume paramount significance due to their potential to significantly influence road conditions and vehicular emissions. Their selection as distinctive features stems from their capacity to tangibly impact the dynamics of the road environment and the emission levels of vehicles navigating through them. A notable emphasis is placed on our methodology for speed limit detection. By sequentially processing the image stream, we maintain the continuity of the detected speed limit value across subsequent images. When a speed limit is identified, this numerical value persists until a different speed limit figure is detected. For instance, as shown in Figure 9, the model has successfully identified a speed limit of 70 km/h. This speed limit value is then attributed to subsequent images until an alternative speed limit detection is encountered. For the remaining detection categories, a straightforward approach is adopted. We assign a value of 1 to indicate presence and 0 to signify absence in each respective image. This streamlined approach uniformly encapsulates the detection outcomes within the dataset.
# Enhancing Image Dehazing with a Multi-DCP Approach with Adaptive Airlight and Gamma Correction
## 4 Proposed Method
### Image Dehazing  
Throughout the haze removal process, the hazy image \(I(x)\) undergoes traversal across its color dimensions alongside the estimated airlight \(AR\) and \(t^{eh}(x)\). A dehazed image \(I_{dz(x)}\) can be obtained via the following:  
\[I_{dz(x)}=\frac{I(x)-AR}{\max(t^{eh}(x),t_{0})}+AR \tag{11}\]  
where \(t_{0}\) is the threshold to preserve the transmission map and is set to 0.1 in this paper. The dehazing process mitigates the influence of airlight from each channel, restoring the lost color and brightness of the hazy image.