esa-sceva/satcom-chunk-collection huggingface dataset:
# A Forwarding Latency Optimization Method for Software Data Plane Based on Spin-Polling
## 6 Evaluation
### Comparison with Other Schemes  
In this section, we evaluate the performance of each scheme in the scenario of a single Rx queue. The original data plane, as well as Smart Batching [19], is used to compare with our design. In the original data plane, we set \(\theta_{Rx}\) to 64 and \(\theta_{Tx}\) to 64, which is the most general configuration. We use the parameters recommended in their paper for the Smart Batching scheme and set \(\theta_{Tx}\) to 64. In our scheme, \(\theta_{Rx}\) is set to 64, \(T_{observed}\) to 1s, and \(\eta\) to 60%. The evaluation environment uses the topology of a single Rx queue, shown in Figure 3b. We measured the forwarding latency metrics for each scheme when the total input rate is increased from 1 Gbps to 15 Gbps. Note that according to the maximum throughput rates measured in Section 6.1, the data plane typically does not generate packet loss when the input traffic rate is in the [0, 15] Gbps range, so we do not discuss packet loss rates here. Figure 12a shows the performance of each scheme in terms of average forwarding latency. The forwarding latency of the Smart Batching scheme is almost the same as the original data plane. The Smart Batching scheme optimizes the forwarding latency based on A. However, the mechanism of A has been changed by the current IO frameworks such as DPDK. It is already difficult for the Smart Batching scheme to optimize the forwarding latency by dynamically adjusting A. The difference between the forwarding latency of our scheme and the original data plane and Smart Batching scheme at input rates below 8 Gbps is not significant but still lower than the latter two. In the [0, 8] Gbps interval, our scheme's maximum reduction in forwarding latency is about 3.56% relative to the original data plane (at an input rate of 5 Gbps). However, starting from 8 Gbps, the advantage of our scheme starts to emerge as the input rate keeps increasing. Compared to the original data plane, the forwarding latency of our scheme is reduced by up to about 45% (at an input rate of 12 Gbps).  
We also measured the CPU utilization for each scheme, as shown in Figure 12b. Relative to the original data plane, the CPU utilization of our scheme is slightly improved by one or two percentage points. The original data plane suffers from the much-wasted perfor  
Figure 12: The performance of each scheme on single queue scenario. (**a**) The average latency of each scheme under different input rates. (**b**) The CPU utilization of each scheme under different input rates.  
\begin{table}
\begin{tabular}{c c} \hline \hline \(\mathbf{\theta_{Tx}}\) & **CPU Utilization** \\ \hline
4 & 82.04\% \\
8 & 64.2\% \\
16 & 63.11\% \\
32 & 63.54\% \\
64 & 63.62\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The CPU utilization of the data plane at 10 Gbps for different \(\theta_{Tx}\).  
mance at low input rates. Our scheme utilizes a small fraction of the wasted performance to optimize the forwarding latency.
## 3 Results and Discussion
### Validation of Analytical Method
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Analyte** & \begin{tabular}{c} **Certified** \\ **Value** \\ **(mg/kg)** \\ \end{tabular} & \begin{tabular}{c} **Observed** \\ **Value** \\ **(mg/kg)** \\ \end{tabular} & \begin{tabular}{c} **Recovery** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Accuracy (\%)** \\ **\end{tabular} &
\begin{tabular}{c} **Precision (\%)** \\ **\end{tabular} \\ \hline CRM 7405-a & & & & & \\ Cd & 0.79 \(\pm\) 0.02 & 0.77 \(\pm\) 0.05 & 97.5 & \(-\)2.53 & 5.93 \\ Pb & 0.43 \(\pm\) 0.03 & 0.42 \(\pm\) 0.03 & 96.9 & \(-\)3.06 & 7.89 \\ As & 35.8 \(\pm\) 0.9 & 32.7 \(\pm\) 2.7 & 91.3 & \(-\)8.71 & 8.27 \\ As(V) & 10.1 \(\pm\) 0.5 & 9.7 \(\pm\) 0.2 & 95.6 & \(-\)4.41 & 1.85 \\ \hline SRM-3232 & & & & & \\ Cd & 0.426 \(\pm\) 0.008 & 0.397 \(\pm\) 0.025 & 93.2 & \(-\)6.81 & 6.21 \\ Pb & 1.032 \(\pm\) 0.039 & 1.087 \(\pm\) 0.066 & 105.4 & 5.36 & 6.05 \\ As & 38.3 \(\pm\) 1.3 & 34.9 \(\pm\) 2.8 & 91.0 & \(-\)8.99 & 8.16 \\ DMA & 0.479 \(\pm\) 0.077 & 0.409 \(\pm\) 0.019 & 85.4 & \(-\)14.64 & 4.6 \\ \hline SRM-1566b & & & & & \\ Cd & 2.48 \(\pm\) 0.08 & 2.35 \(\pm\) 0.18 & 94.9 & \(-\)5.11 & 7.67 \\ Pb & 0.308 \(\pm\) 0.009 & 0.301 \(\pm\) 0.017 & 97.6 & \(-\)2.37 & 5.67 \\ As & 7.65 \(\pm\) 0.65 & 7.56 \(\pm\) 0.56 & 98.8 & \(-\)1.18 & 7.42 \\ \hline CRM- & & & & & \\ DORM-4 & & & & & \\ Cd & 0.306 \(\pm\) 0.015 & 0.284 \(\pm\) 0.025 & 92.8 & \(-\)7.24 & 8.88 \\ Pb & 0.416 \(\pm\) 0.053 & 0.372 \(\pm\) 0.011 & 89.3 & \(-\)10.68 & 2.85 \\ As & 6.80 \(\pm\) 0.64 & 5.93 \(\pm\) 0.09 & 87.2 & \(-\)12.80 & 1.63 \\ \hline \end{tabular} \({}^{a}\) Accuracy (\%Bias) = (mean value of seven replicates for determination–certified concentrations) /(certified concentrations) \(\times\) 100%; \({}^{b}\) precision (\%Cv.) = (standard deviation among seven replicates for determination)/(mean value of seven replicates for determination) \(\times\) 100%.  
\end{table} 
Table 3: Quality parameters of analytical methods for the determination of heavy metals and As species by sample matrix. Figure 2: LC-ICP/MS chromatograms of 1, 5, 10, and 25 ug/kg standard solution of As species. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Scientific**} & \multicolumn{1}{c}{**Name**} & \multicolumn{1}{c}{**Cd**} & **Pb** & **As** & **AAV** & **AnFID** & **MMA** & **DMA** & **Aub** & **Acc** & **Unidentified** & **Sum of As** & **Extraction** \\ \multicolumn{1}{c}{**Name**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \\ \hline \hline \multicolumn{11}{c}{**Fah.
# Blockchain-Empowered Fair Computational Resource Sharing System in the D2D Network +
## 3 System Overview
### Supernode Coordination and Working Process  
In our system, the supernode will not only assign task assignments to devices in the D2D network, it will also work as a coordinator between a requester and worker pair by acknowledging their cooperation work. As shown in Figure 2, the step-by-step working process is as follows: (1) A requester notifies the supernode at the BS about the need for a cooperative task \(T\); (2) After calculation and analysis of the system conditions, the supernode will assign, say, 30% of cooperative task \(T\) to one helper and 70% of \(T\) to another helper around the requester. The supernode will notify the requester and each related helper about the cooperation assignment information. The requester then will send corresponding task portions to each assigned helper; (3) Each helper executes the task  
Figure 2: Task scheduling in the D2D network.  
portion on the device; (4) Upon completion, each helper will notify the supernode and send back the result to the requester; (5) When the requester gets back the computational result from a helper, she/he will notify the supernode about the successful reception of the result; (6) A transaction of the requester paying each related helper is confirmed by all three: the requester, the helper and the supernode. All three of them will store this transaction into their own transaction pool, waiting for a miner to put this transaction into the blockchain.
# A Hybrid Deep Learning Approach for Improved Detection and Prediction of Brain Stroke
## 2 Review Methodology  
A systematic technique was used to construct the search strategy for the paper titled \"A Hybrid Deep Learning Approach for Improved Detection and Prediction of Brain Stroke\". Extensive searches were conducted across significant academic databases such as PubMed, Web of Science, and Scopus to collect pertinent research papers, reviews, and reports published between 2008 and 2024. The search used specific terms like \"brain stroke\", \"deep learning\", \"stroke prediction\", \"artificial intelligence\", and \"machine learning\". The inclusion criteria required that the selected literature be published in English and focus on the use of AI and deep learning techniques in stroke detection, prediction, and management. Articles were reviewed for relevance using predetermined inclusion and exclusion criteria, resulting in the inclusion of high-quality and relevant sources. The data from the selected research were thoroughly evaluated and synthesized to offer a comprehensive knowledge of how hybrid deep learning models might improve brain stroke prediction. This organized search technique enabled a thorough study of AI's potential for improving stroke diagnosis and patient outcomes (Figure 1).  
Figure 1: PRISMA flow diagram for search strategy and selection criteria (Adapted from the preferred reporting systemic reviews and meta-analyses (PRISMA) guidelines).
## 4 Engineering Example Application
### External Factors Affecting Slope Displacement
#### 4.2.2 The Effect of Water Level on Landslide  
During a rise in river level, water infiltrates the slope from the slope surface, causing the water level inside the slope to gradually increase. However, due to soil infiltration, the river level outside the slope rises faster than the water level inside. As a result, hydrostatic pressure is generated inside the slope; meanwhile, dynamic water pressure acts outward from the slope surface. This dynamic water pressure, pointing away from the slope, promotes slope stability.  
When the river water level in front of the slope decreases, water continues to seep out from the inside of the slope. The rate of water level decline inside the slope is lower than the rate outside, resulting in dynamic water pressure pointing out of the slope. This, along with a decrease in hydrostatic pressure inside the slope, has a negative impact on slope stability.  
Soil permeability also influences slope stability. When the permeability coefficient of the slope soil is low, the groundwater level response in the slope is slower, creating a larger hydraulic gradient. This leads to a larger dynamic water pressure, which adversely affects slope stability. Generally, the influence of dynamic water pressure on slope stability is greater than that of hydrostatic pressure. However, when the water level stabilizes at a normal level, the hydrostatic pressure increases the sliding force of the potential sliding body of the slope. Therefore, when studying slope stability, it is necessary to consider soil permeability, particularly when there are fluctuations in the river water level. Attention should be given to the influence of dynamic water pressure on slope stability.
## 5 Critical Analysis
### Segmentation
The high computational speed and leading accuracy make this method an effective solution for practical applications requiring rapid pavement condition assessment. In [PERSON] et al. [99], to make pixel-level crack detection more accurate, an UperNet with an attention module acts as a decoder, and a transformer-based semantic segmentation network acts as an encoder, effectively improving the crack feature acquisition capability. After comparative tests with ANN, FCN, PSPNet, UNet and STU models on three datasets--CFD, Crack 500, and CrackSC--the proposed model shows the highest performance in terms of F1 score and recall. The network demonstrates superior detection of fine and complex pavement cracks under noisy conditions. [PERSON] et al. [100]. proposed using the PSA-Net deep learning architecture for road crack detection. The network effectively integrates spatial and contextual information by using a feature pyramid and an attention mechanism. This enables accurate detection in complex road environments. Tested on a self-constructed dataset and compared with five methods such as HED, RCF, PCN, etc., the results show that PSA-Net is superior to existing methods. [PERSON] et al. [101]., based on the SegNet model, combine a densely connected architecture with a gating attention mechanism, while adding an atrous convolutional dense connection module (AD-block), which enables the algorithm to effectively recognize different crack features. A gating attention unit (GAU) is introduced to improve the accuracy of crack localization. Tested on the DeepCrack, CFD, and Crack500 datasets, the proposed method achieves an MIoU of 87.53%, a precision of 87.67%, a recall of 85.41%, and an F1 score of 86.52% on the DeepCrack dataset. The results show that the method provides an effective solution for road crack identification. Table 16 shows all the publicly available datasets mentioned above, with links to download each one. Table 17 shows the links to the codes of the 305 papers screened for bibliometric analysis where the algorithm is publicly available.
## 4 Mathematical Model
### Material Properties  
The storage wall used in the composite Trombe wall was produced with cement mortar (CM) made from a mix of sand (round silica sand 0-4 mm), cement (EMC I 52.5 N, according to EN 196-1), water and micro-encapsulated PCM (Micronal(r) PCM DS 5001 X) at a proportion of mixed cement mortar in 17 % of the total mass of dry materials (sand, cement, PCM). The mix proportions are listed in Table 1.  
The mixture was poured into a mold and dried for more than two months in our laboratory. One storage element was ultimately built with the following dimensions: 4 cm thickness (mortar incorporating PCM), 59 cm width, and 71 cm height (65 cm from lower to upper vent). The thermal properties were characterized by applying the enthalpy method described and improved in previous articles [63; 64; 65; 66]. The thermophysical properties of the materials constituting the composite Trombe wall are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cement-Sand Mass Ratio** & **Water to Cement Ratio** & **PCM/(Cement + Sand) Mass Ratio** \\ \hline
1/2.6 & 1/1.1 & 1/4.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mix proportion of cement mortar integrating PCM Micronal®.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **Symbol** & **Explanation** & **Unit** & **Value** \\ \hline \multirow{8}{*}{Glazing} & \(\rho_{g}\) & density & kg/m\({}^{3}\) & 2500 \\  & \(c_{g}\) & specific heat capacity & J/(kg.K) & 830 \\  & \(\lambda_{g}\) & thermal conductivity & W/(m.K) & 1.47 \\  & \(\alpha_{g}\) & absorptivity & - & 0.84 \\  & \(\tau_{g}\) & transmissivity & - & 0.76 \\  & \(e_{g}\) & emissivity & - & 0.84 \\ \hline \multirow{8}{*}{Mortar + PCM} & \(\rho_{m\_{pcm}}\) & density & kg/m\({}^{3}\) & 1329 \\  & \(c_{solid}\) & specific heat at solid state & J/(kg.K) & 1178 \\  & \(c_{liquid}\) & specific heat at liquid state & J/(kg.K) & 1150 \\  & \(L_{A}\) & latent heat & J/kg & 17100 \\  & \(T_{A}\) & pure substance temperature & \({}^{\circ}\)C & 27.37 \\  & \(T_{M}\) & melting temperature & \({}^{\circ}\)C & 25.83 \\  & \(\lambda_{m\_{pcm}}\) & thermal conductivity & W/(m.K) & 0.62 \\  & \(\alpha_{m\_{pcm}}\) & absorptivity & - & 0.9 \\  & \(\varepsilon_{m\_{pcm}}\) & emissivity & - & 0.9 \\ \hline \multirow{8}{*}{Insulating wall} & \(\rho_{ins}\) & density & kg/m\({}^{3}\) & 30 \\  & \(c_{ins}\) & specific heat capacity & J/(kg.K) & 880 \\ \cline{1-1}  & \(\lambda_{ins}\) & thermal conductivity & W/(m.K) & 0.041 \\ \cline{1-1}  & \(\alpha_{ins}\) & absorptivity & - & 0.9 \\ \cline{1-1}  & \(\varepsilon_{ins}\) & emissivity & - & 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Thermophysical properties of the materials employed.
## 2 Materials and Methods  
### Project Overview and Data Acquisition  
Phase II of a water supply project in Xinjiang, China (KS Tunnel) is 283.41 km long and is being constructed with a cluster of 11 TBMs. KS Tunnel is the longest among all water conveyance tunnels under construction and completed worldwide. This tunnel has an average and maximum burial depth of 428 m and 774 m, respectively, and is a typical deep burial ultra-long water conveyance tunnel. According to the geological survey, the strata lithology exposed along KS Tunnel is mainly characterized as Paleozoic Silurian, Devonian, Carboniferous, and Variscan Orogen intrusive rocks. The surrounding rocks of the tunnel are mainly Type II and III hard rock, accounting for 42.65% and 43.91%, respectively. Most KS Tunnel sections are in fresh rock with underdeveloped fractures. Their surrounding rocks are blocky and thickly laminated. Groundwater mainly comprises bedrock fracture water with a low flow rate.  
In this paper, the surrounding rock data obtained from KS Tunnel contained sedimentary and igneous rocks. In order to establish reliable SRE classification criteria, metamorphic rock data were also needed. Therefore, the metamorphic rock data from the water conveyance tunnel project in Lanzhou, China, was used [29]. Figure 1 shows the geological profiles of the two projects. The geological condition information and main TBM parameters are shown in Table 1. The surrounding rock parameters of KS Tunnel were obtained based on field sampling, laboratory testing and preliminary engineering investigations. Figure 2a-c show the processes of acquiring parameters related to the uniaxial compressive strength of rock. For the sample test, we used a rock pressure tester. We set the loading rate as 0.2-1.0 MPa/s according to \"Section 3.2.5\" of the \"Code for Rock Tests in Water and Hydropower Projects\" (SL264-2020). TBM tunneling parameters were obtained in real time using a computer. In addition, stake numbers were used to correspond geological data to TBM tunneling parameters. In addition, KS Tunnel was constructed using a cluster of 11 TBMs due to the overall hard and intact geological conditions; thus, an open-type TBM was used. The geological conditions of the Lanzhou water conveyance tunnel were not suitable for an open-type TBM compared to KS Tunnel, with a relatively large average burial depth. Therefore, a double-shield-type TBM was used.
## 3 Results and Discussion
### Effect of Coexisting Anions  
In general, some coexisting anions in water would strongly compete with NO3\({}^{-}\)-N to occupy active sites via electrostatic interaction. Thus, the selective absorption of resin is of vital importance in evaluating the practical application, as well as in considering the absorption performance. A520E is known for its good selectivity toward NO3\({}^{-}\)-N, and its adsorption capacities are still relatively high in the present of Cl\({}^{-}\) or SO4\({}^{2-}\)[20]. As displayed in Figure 4, a slightly higher adsorption amount of AEE-3 was detected compared to A520E with an identical addition of anions. The phenomena may be rationalized by the fact that the long alkyl chain on AEE-3 is conducive to the adsorption selectivity for NO3\({}^{-}\)-N with lower hydration energy than Cl\({}^{-}\) or SO4\({}^{2-}\).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Resin**} & \multirow{2}{*}{\(\begin{array}{c}\mathbf{Q_{e,\,exp}}\\ \mathbf{(mg/g)}\end{array}\)} & \multicolumn{4}{c}{**Pseudo-First-Order**} & \multicolumn{4}{c}{**Pseudo-Second-Order**} \\ \cline{2-9}  & & \(\begin{array}{c}\mathbf{Q_{e}}\\ \mathbf{(mg/g)}\end{array}\) & \(\begin{array}{c}\mathbf{\mathit{k_{1}}}\\ \mathbf{(1/min)}\end{array}\) & \(\begin{array}{c}\mathbf{\mathit{R^{2}}}\\ \mathbf{(mg/g)}\end{array}\) & \(\begin{array}{c}\mathbf{\mathit{Q_{e}}}\\ \mathbf{(mg/g)}\end{array}\) & \(\begin{array}{c}\mathbf{\mathit{10^{3}k_{2}}}\\ \mathbf{(g/(mg\cdot min))}\end{array}\) & \(\begin{array}{c}\mathbf{\mathit{R^{2}}}\\ \mathbf{(mg/(g\cdot min))}\end{array}\) \\ \hline AEE-3 & 32.75 & 30.85 & 0.327 & 0.9371 & 33.66 & 6.06 & 0.9819 & 7.13 \\ A520E & 30.22 & 29.19 & 0.100 & 0.9897 & 30.14 & 1.45 & 0.9987 & 1.16 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Kinetic parameters of NO3\({}^{-}\)-N adsorption by two resins.  
Figure 3: Adsorption kinetics of NO3\({}^{-}\)-N onto two resins at 298 K.
###### Abstract  
In free-space optical (FSO) communication systems, on-off keying (OOK) is a widely used modulation format. Coherent and non-coherent OOK receivers with sensitivities of \(-54.60\) dBm and \(-51.25\) dBm, respectively, were built with a communication rate of \(1\) Gbit/s and a bit error rate of \(10^{-3}\). In an FSO communication system, the parameters must be designed to ensure a sufficient link margin. In contrast to optical fiber systems, FSO systems have ambient light (AL) noise such as sunlight. The efficiency of sunlight coupling in the single-mode fiber (SMF) of the receivers was calculated in this study. For a signal light with AL, the change in the main components of noise and the sensitivity deterioration were theoretically analyzed and experimentally verified in conditions of coherent reception and non-coherent reception with a preamplifier. For coherent reception, the theoretical sensitivity deterioration results are consistent with the experimental results which indicate that coherent reception exhibits better anti-AL noise performance than non-coherent reception when the power spectral density of the AL is the same. Coherent and non-coherent receivers coupled with SMF can work in direct sunlight. When the receiver lens diameter is greater than \(4.88\times 10^{-4}\) m, the anti-AL noise performance of the receiver can be improved by increasing the receiver lens diameter.  
OOK modulation; ambient light noise; coherent reception; non-coherent reception; noise power spectral density; sunlight coupling efficiency
# Evaluation of Grid-Based Rainfall Products and Water Balances over the Mekong River Basin
## 4 Results and Discussion
### Evaluation of Precipitation Products' Hydrological Performance using SWAT Model  
This section explores the potential of the selected precipitation products for hydrological simulation over the MRB using the SWAT model. The model was calibrated against the daily discharge collected from six hydrological gauges within the MRB. Sensitive analysis and model calibration were performed using the SUFI-2 algorithm integrated into the SWAT-CUP tool. The analysis was conducted over 8 years (1998-2006) with different precipitation data at daily time scales, while the other meteorological information remained unchanged.
## 3 Results
### Simulation of Dust Storm on 14 February 2018
#### 3.3.2 Spatial Pattern of Vertical Dust Flux Using GOCART Wind Erosion Model  
The GOCART scheme calculated the spatial pattern of vertical dust flux over a 3-h interval using the GFS analysis/forecast as large scale forcing on 14 February 2018, as shown in Figure 8.  
Figure 7: Average of Storm Dust Flux (\(\upmu\)g/m\({}^{2}\)s) Pattern 14 February 2018 with AFWA scheme.  
Figure 8: Spatial pattern of dust flux (\(\mu\)g/m\({}^{2}\)s) with 3 h interval by Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) scheme.  
The average spatial pattern of vertical dust flux (VDF) simulated with the GOCART model indicates that the Jazmourian Wetland Basin can be a source of dust in the southeast of Iran (Figure 9). Additionally, it indicates that parts of the Loot Desert in Kerman, Sistan, and Baluchestan provinces can be internal dust sources. In fact, the simulated vertical flux of dust by the GOCART scheme reaches 7536 \(\upmu\)g/(m\({}^{2}\)s) in the area.  
Overall, the VDF results using GOCART suggest that central, eastern and south-eastern parts of Iran have the highest potential for dust emission and these areas may be considered as internal Sources.  
According to the results of two dust schemes, the vertical flux values simulated by AFWA are lower than those simulated by the GOCART scheme. The number of dust sources shown by the GOCART scheme in the study area is also higher than for the AFWA scheme. Therefore, as a final step, in order to verify and compare the schemes, the correlation between the results of the surface dust concentration simulated by the AFWA and GOCART schemes with the horizontal view of the synoptic stations will be investigated.