esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Scale Adaptive Aggregate Graph Convolutional Network for Skeleton-Based Action Recognition
## 4 Experiments
### Comparison with State-of-the-Art Methods  
We compared our final model with state-of-the-art skeleton-based action recognition methods on the three datasets of NTU-RGB-D 60, NTU-RGB-D 120 and Kinetics. These methods for comparison include RNN-based methods [7; 11; 17], CNN-based methods [26; 28; 45; 48] and GCN-based methods [13; 14; 15; 36; 37; 49; 50; 51; 52; 53; 54; 55].  
In the NTU-RGB-D 60 and NTU-RGB-D 120 datasets, we obtained the top-1 recognition accuracies on the two recommended benchmarks (X-sub, X-view) and (X-sub120, X-set120), which are shown in Tables 5 and 6, respectively. Additionally, in the Kinetics dataset, the human joints data was obtained by an OpenPose toolbox, which is not as standardized as in NTU-RGB-D dataset, which can help verify the effectiveness of the model when the data is inaccurate. The recognition task is more challenging due to the variety of actions; we report the top-1 and top-5 recognition accuracies, as shown in Table 7. Our model achieved great results on both of the datasets, which verifies the superiority of our proposed model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Input** & **X-Sub (\%)** \\ \hline \multirow{3}{*}{Single-modality stream} & Joint only & 88.3 \\  & Bone only & 88.5 \\  & Motion only & 87.2 \\ \hline \multirow{3}{*}{Two-modality streams} & Joint and Bone & 89.7 \\  & Joint and Motion & 88.6 \\  & Bone and Motion & 88.9 \\ \hline Three-modality streams & Joint and Bone and Motion & 90.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparisons of the recognition accuracy with a different input stream on the NTU RGB+D X-sub benchmark.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **X-Sub (\%)** & **X-View (\%)** \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} HBRNN [7] \\ ST-LSTM [11] \\ HCN [26] \\ VA-fusion [28] \\ \end{tabular} } & 59.1 & 64.0 \\  & 69.2 & 77.7 \\  & 86.5 & 91.1 \\  & 89.4 & 95.0 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} ST-GCN [13] \\ DPRL [53] \\ SR-TSL [55] \\ Motif-GCN [14] \\ AS-GCN [16] \\ 2s-AGCN [15] \\ SGN [49] \\ NAS-GCN [36] \\ DGNN [54] \\ Shift-GCN [37] \\ DC-GCN+ADG [50] \\ PA-ResGCN-B19 [51] \\ \end{tabular} } & 81.5 & 88.3 \\  & 83.5 & 89.8 \\  & 84.2 & 90.2 \\  & 86.8 & 94.2 \\  & 88.5 & 95.1 \\  & 89.0 & 94.5 \\  & 89.4 & 95.7 \\  & 89.9 & 96.1 \\  & 89.7 & 96.5 \\  & 90.8 & 96.6 \\  & 90.9 & 96.0 \\ \hline \multicolumn{2}{c}{MSAAGCN} & 90.9 & 95.9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparisons of the Top-1 recognition accuracy with the state-of-the-art methods on the NTU RGB+D dataset.
# Marine Internet for Internetworking in Oceans: A Tutorial
## 3 Environments for Communication in Oceans
### Communication Medium  
This section introduces the main communication technologies used above the water surface and underwater in ocean environments.
## 2 Whole Slide Imaging  
The introduction of digital pathology technologies and, in particular, whole slide imaging has significantly improved the efficiency of modern clinical pathology departments by facilitating the storage, viewing, processing, and sharing of digital scans of tissue glass slides. The application of deep learning feature extraction methods for whole slide imaging (WSI) may improve, in the near future, the productivity, accuracy, and reproducibility of pathological diagnoses [19; 20; 21; 22; 23; 24; 25]. Whole slide imaging represents the digital equivalent of histological specimens scanned using digital scanners. High-resolution images are generated by digital scanners that can apply multiple magnifications and focal planes at different resolution levels. The workflow of WSI systems requires good histological technology to overcome practical challenges such as poor staining and tissue folds, which may negatively affect the quality of the scanned slides. In order to avoid these challenges, preparing glass slides for scanning requires a standardized approach to tissue sample fixation, embedding, sectioning, and staining [26]. Moreover, digital scanners may differ according to the methodology used to focus high-quality scanners on a range of depths across each tile, which is a time-consuming process [27]. From a practical point of view, surgical pathology slides require scanning at X 20 magnification, whereas cytology slides require scanning at X 40 magnification, which is necessary to ensure higher cytological diagnostic accuracy [28]. WSI-based analysis may be subdivided into four sequential steps as follows: (i) image acquisition, (ii) image storage, (iii) image processing, and (iv) image visualization.
## 3 Results and Discussion
### Summary of SARs of the Antimicrobial Activity of Nitrostyrenes  
a. The fluorination of the methylenedioxy substituent of **A-1** and Compound **A** was counterproductive in improving the bioactivity of **A-1** against all organisms.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Strain** & **N0\({}_{2}\)** & **N0\({}_{2}\)** \\ \hline _Staphylococcus aureus_ & 128 & 2 \\ _Bacillus subtilis_ & 256 & 2 \\ _Enterococcus faecalis_ & 64 & 5.5 \\ _Escherichia coli_ & 256 & 27 \\ _Candida albicans_ & 32 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative MIC (\(\mu\)g/mL) values of 4-F-nitrostyrene and Compound **I**.  
b. The highest antimicrobial activity was found with Compound **D** (4-bromo-\(\beta\)-methyl-\(\beta\)-nitrostyrene), which gave good protection against most strains, except _E. coli_; however, it performed better than **A-1**, which was poor against _E. coli_. Compound **D** gave good protection against _S. aureus_, _B. subtilis_ and _C. albicans_, but only marginal protection against _E. faecalis_.  
c. Furthermore, Compound **D** gave identical results to **I** (4-fluoro-\(\beta\)-methyl-\(\beta\)-nitrostyrene), supporting previous data that **I** is superior to **A-1**, although still not being very effective against _E. coli_.  
d. The relative effects of halogen substitution at the 4-position (Compounds **D**, **F** and **I**) indicate small differences from substitution by Br-, Cl- and F- for protection against the microorganisms tested.  
e. The substitution of Br- for CH\({}_{3}\) at the \(\beta\)-carbon side chain (Compounds **G** and **H**) gave almost identical results for all strains, except for _C. albicans_. Good protection was provided by Compound **H** against this fungus.  
f. The parent compound, \(\beta\)-nitrostyrene (**E**), was found to offer little antimicrobial protection against all strains, justifying the testing of \(\beta\)-nitrostyrene derivatives, and it was the least effective antibacterial of all compounds tested.
# A Visualization Tool for Flood Dynamics Monitoring Using a Graph-Based Approach
## 5 Discussion  
This method was adapted to be operational on a larger scale: a different water delineation approach was applied (Otsu tiled thresholding), the minimal mapping unit was ensured in a different way (by application of morphological operators) and code efficiency was improved. This results in an innovative framework to visualize floods that is not only straightforward but also efficient. Depending on the flood type (disperseness), calculation times differ between 42 min and 6 h24 min.  
While the Mozambique flood occurs in a coastal zone and the India flood takes places inland, their global variation maps (Figures 4a and 5b) show similarities. The majority of the high dynamic zones are located in or near the riverbeds (of the Buzi river in Mozambique and of the Ganges in India). This is not surprising because these areas are prone to flooding if the water level in the river rises. However the high dynamic zones are not restricted to the riverbeds only. These zones can point out interesting areas in the context of damage assessment and data assimilation.  
Flood duration is a parameter often used for damage assessment in an agricultural [2; 3] or urban context [4], for emergency relief or insurance claims. Because flood duration fails to incorporate spatio-temporal dynamics, we propose to combine both flood duration and (global) variation. If the flood duration parameter is not available, (global) variation values could be used as an operational parameter since they reflect the flood duration. In this way, variation values can be combined with land-use maps, road networks and population density maps in the context of emergency relief.  
Calculated variation values could also be linked to the sensitivity of observation locations in a data assimilation framework, thereby improving flood predictions [5; 6; 7]. Observation targeting is a process in which supplementary observations are assimilated to improve the analysis in selected regions, and thereby reduce the uncertainty of a (flood) model [21]. When remotely sensed observations are used for data assimilation, thinning strategies are often applied to eliminate redundant information before the data assimilation [8]. Several authors determine their observation locations (whether or not for data assimilation purposes) on the basis of variances, be it model variance or observation variance [5; 21; 22]. In this way, observed (global) variation, calculated by the current framework, could function as an estimation for model variation in data scarce regions.  
Finally, global variation results can be combined with the maximum flood extent in order to increase the understanding of the flood dynamics. It might even be interesting to add both maps, combining maximum flood extent (binary) and the global variation map in one unified flood extent/variation map. In this way, the area component can be combined with a dynamics component. The result of this approach is displayed in Figure 8 for the India flood.  
Figure 8: Unified flood map India.
# Ground-Based Cloud Image Segmentation Method Based on Improved U-Net
## 3 Experimentation and Results
### Example Analysis
#### 3.3.2 Comparison of Different Model Indicators
Only the improved U-Net proposed in this paper performs best, with the lowest ER index of 0.105, and the highest ER index of the FCN is 0.152. Combined with the accuracy index, the FCN has the lowest segmentation accuracy and the highest error rate. Similarly, taking the TCDD dataset as an example, SegNet's accuracy, precision, recall, and F1-score all remain at a low level. In comparisons of accuracy, F1-score, MIoU, and ER, U-Net and the FCN perform similarly, with differences fluctuating between 0 and 0.002. CloudSegNet shows a slight improvement on TCDD. The six evaluation metrics for the proposed improved U-Net are 0.943, 0.955, 0.970, 0.944, 0.893, and 0.507, indicating that its performance on the TCDD dataset is also the best, with the most stable improvement. Combining the above six metrics, five of the six evaluation metrics of the improved U-Net are higher for the SWINySEG dataset than for the other models, and the recall value is just 0.012 lower than the best-performing CloudSegNet. For the TCDD dataset, the improved U-Net evaluation metrics are superior to those of other models. The experimental comparison of different metrics proves the superior performance and best segmentation results of the improved U-Net proposed in this paper in the cloud image segmentation task.
## 2 Optimization Problem and the Method of Finding the Optimal Solution
### Particle Swarm Optimization Algorithm  
Particle Swarm Optimization Algorithm (PSOA) [25] is an algorithm inspired by two phenomena--swarm motion particles as well fish nebula. It describes the movement of swarm in the direction of the best individual. Despite targeted movement, the algorithm assumes randomness to increase the ability to change the best individual across the population. In order to model these phenomena, certain assumptions are introduced  
* In each iteration, the number of individuals is constant,
* Only the best ones are transferred to the next iteration and the rest are randomly selected.  
Each particle moves according to  
\[\overline{x_{i}^{t+1}}=\lfloor\overline{x_{i}^{t}}+\mathbf{v_{i}^{t}}\rfloor, \tag{12}\]  
where \(\mathbf{v_{i}^{t}}\) is the velocity of the \(i\)-th molecule in the \(t\)-iteration. The velocity is calculated on the basis of various factors such as the position of the best individuals in current iteration \(t\) and labeled as \(\overline{x_{best}^{t}}\), which allows them to move in that direction. It is described as  
\[\mathbf{v_{i}^{t+1}}=\mathbf{v_{i}^{t}}\cdot\phi_{p}\cdot\alpha\cdot\left( \overline{x_{best}^{t}}-\overline{x_{i}^{t}}\right)+\phi_{s}\cdot\beta\cdot \left(\overline{x_{best}^{t}}-\overline{x_{i}^{t}}\right), \tag{13}\]  
where \(\alpha,\beta\in\langle 0,1\rangle\) are the values chosen in random way and \(\phi_{p}\), \(\phi_{s}\) are swarm controlling factors. If \(\phi_{s}>\phi_{p}\), all particles move in the direction of the best one. In the case when \(\phi_{s}\leq\phi_{p}\), all individuals move in random way. At the end of the iteration, only the best particles are transferred to the next iteration. The missing particles are added to population at random. The complete algorithm is presented in Algorithm 3.  
```
1:Start,
2:Define \(\phi_{p},\phi_{s}\), \(best\_ratio\), number of iteration \(T\) and n,
3:Define fitness function \(f(\cdot)\),
4:Create an initial population,
5:\(t:=0\),
6:while\(t<T\)do
7: Calculate velocity using Equation (13),
8: Move each individual according to Equation (12),
9: Sort population according to \(f(\cdot)\),
10: Take \(best\_ratio\) of population to next iteration,
11: Complete the remainder of the population randomly,
12:\(t++\),
13:endwhile
14: Return the best particle,
15: Stop.
```  
**Algorithm 3**Particle Swarm Optimization Algorithm
## 5 Experiments and Analysis of Results
### Optimal Hyperparameter Experiments  
Experiments are carried out here on the values of the hyperparameter \(\lambda\) in Equation (2), i.e., to explore the optimal ratio that each model needs to introduce for the information generated by the EdaCSE algorithm.  
Table 15 shows that for the \"bert-base\" model, when a small proportion (less than 0.4) of the EdaCSE method is introduced, it does not have a positive impact on the model, while when the proportion is greater than or equal to 0.4, the performance of the model is significantly improved, and 0.6 is the optimal introduction proportion. For the \"roberta-base\" model, the best results were obtained by introducing the EdaCSE method at a scale of only 0.3, and for any value of the introduced scale, the model's results were improved compared with the original model (37.89). For the \"roberta-large\" model, it can be seen that the best results are obtained when we introduce the EdaCSE method at a large scale (0.6) to the model. As in the case of the \"roberta-base\" model, the introduction of the method at any scale has a positive effect on the original model to varying degrees.
## 4 Simulation Tests and Results
### Case Study 2  
The aim in the second case study was to maximize the coverage of a two-dimensional, squared area of \(20\times 20\). The number of the available sensors was equal to 32 while their sensing range varied between 0.8 and 2.  
More specifically, five sensors had a sensing range equal to 0.8, 20 sensors had a sensing range equal to 1.5, and the last seven had a sensing range equal to 2. The size of the population in the GA algorithm was selected to be equal to 200 while the swarm size in the PSO algorithm was adjusted to be equal to 200.  
The results of the simulations performed in case study 2 are synoptically presented in Table 4.  
Additionally, the optimal positions of the network nodes calculated with the use of the GA algorithm are depicted in Figure 7 while the corresponding best and mean fitness values for each generation are shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **GA** & **PSO** \\ \hline Mean Value & 59.37 & 58.83 \\ Standard Deviation & 0.18 & 0.38 \\ Average Time (sec) & 1.43 & 2.18 \\ Mean Function Evaluations & 16,000 & 27,200 \\ Standard Deviation Function Evaluations & 693 & 3020 \\ Best Fitness & 59.69 & 59.32 \\ p-value & 0.00 \\ Ideal Area Coverage & & 59.85 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation results of case study 2.  
Figure 6: Best fitness value for each iteration of the PSO algorithm in case study 1.  
Similarly, the optimal positions of the network nodes calculated by using PSO algorithm are illustrated in Figure 9 while the corresponding best fitness values for each iteration are graphically presented in Figure 10.  
Figure 8: Best and mean fitness values for each generation of the GA algorithm in case study 2.  
Figure 7: Optimal node positions with the use of the GA algorithm in case study 2.
## 4 Results and Discussion
### Activity Concentrations of the Radioisotopes
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Country Name**} & \multicolumn{3}{c}{**Activity Concentration (Bq/kg)**} & \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Literature ID**} \\ \cline{2-2} \cline{5-5}  & \({}^{226}\)Ra & \({}^{232}\)Th & \({}^{40}\)K & \\ \hline Egypt (ElSibai-Abua EITyur granite) & (16.89–72) & (21.97–70) & (689.23–1037.73) & Current \\  & 87.3 & 38.23 & 860.71 & work \\ Egypt (Gebel Mueiha granites) & (44.8–175.4) & (29.4–111.4) & (525.2–1045.2) & [9] & L1 \\ Egypt (Gebel El-Missikat granites) & (104.2–4536.9) & (71–163.1) & (17.5–1039.7) & [7] & L2 \\ Egypt (Gebel El-Qattar granules) & 572.3 & 114.5 & 764.1 & [7] & L2 \\ Egypt (Gebel El-Qattar granules) & (381–459.4) & (28–122.7) & (779.9–963.1) & [8] & L3 \\ Egypt (Saint Katherine granites) & 104.4 & 78.8 & 892.9 & [8] & L3 \\ Egypt (Saint Katherine granites) & (17–28.59) & (26.1–121.1) & (940.6–1322.9) & [12] & L4 \\ Egypt (commercial Egyptian granites) & 46.4 & 65.7 & 1186.5 & [12] & L4 \\ Egypt (commercial Egyptian granites) & (25–356) & (5–161) & (100–1796) & [23] & L5 \\ Egypt (commercial Egyptian granites) & 138 & 82 & 1081 & [23] & L5 \\ Sudan (Niuba mountains granites) & (12.8–28.7) & (21.1–39.8) & (111.6–443.3) & [24] & L6 \\  & (20.6 & 30.5 & 295.2 & [24] & L6 \\ Palestine (imported granite) & (18.6–151.2) & (28.8–211) & (400–1256.8) & [25] & L7 \\  & 71 & 82 & 780.8 & [25] & L7 \\ KSA (local and imported granite) & (1.53–77.16) & (0.5–189.82) & (19.4–7632.37) & [26] & L8 \\ Jordan (local granite used as building materials) & 41.5 & 58.4 & 897 & [27] & L9 \\ Serbia (imported granite used in the (23–280) & (7–426) & (550–2240) & [28] & L10 \\ construction industry) & 107 & 132 & 1276 & [28] & L10 \\ Greece (granites used as building materials) & (1–170) & (1–354) & (49–1592) & [29] & L11 \\ India (granites used in Indian dwellings) & 64 & 81 & 1104 & [29] & L11 \\ Turkey (granite used as construction material) & 82 & 112 & 1908 & [30] & L12 \\  & (10–187) & (16–354) & (104–1630) & [31] & L13 \\ Turkey (granite used as construction material) & 80 & 101 & 974 & [31] & L13 \\ Spain (Extremadura granite) & (14–22) & (8–122) & (1098–1633) & [32] & L14 \\  & 101 & 48 & 1293 & [33] & L15 \\ Italy (commercial ornamental stones) & (12–390) & (20–490) & (240–2000) & [33] & L15 \\  & 112 & 107 & 1063 & [34] & L16 \\ Cyprus (imported granites) & (1–588) & (1–906) & (50–1606) & [35] & L17 \\  & 77 & 143 & 1215 & [36] & L18 \\ Brazil (commercial granites) & (5.2–169) & (4.5–448.5) & (190–2028) & [37] & L19 \\ USA (commercial granites) & 31 & 73 & 1648 & [34] & L20 \\ SE part of Nigeria & 129 & 131 & 882 & [35] & L20 \\ Pakistan (natural granite) & (46–6120) & (92–3214) & (899–1927) & [38] & L20 \\  & 659 & 598 & 1218 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The activity concentration (current work) compared with previous studies.
## 2 Investigated Compressor  
The compressor studied in this paper was a 2.5-stage highly loaded axial compressor. It was designed and tested by in the Laboratory of Light-duty Gas-turbine, Institute of Engineering Thermophysics, as shown in Figure 1. It is mainly used in small turbomachinery. The compressor was designed to produce a total pressure ratio of 2.7 at the designed mass flow rate of 4.6 kg/s with an adiabatic efficiency of 86.5%. The key aerodynamic and geometrical parameters of the axial compressor are presented in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Value** \\ \hline Design total pressure ratio & \(\pi_{td}\) & 2.7 \\ Design mass flow rate (kg/sec) & \(m_{d}\) & 4.6 \\ Design adiabatic efficiency & \(\eta_{d}\) & 0.865 \\ Design rotational speed (rpm) & \(n_{d}\) & 25,000 \\ Design rotor tip speed (m/s) & \(V_{cd}\) & 361 \\ Casing diameter (mm) & \(D_{c}\) & 276 \\ Rotor tip gap (mm) & \(G_{r}\) & 0.2 \\ Stator hub gap(mm) & \(G_{s}\) & 0.15 \\ Inlet hub-tip radius ratio & \(R_{h}/R_{c}\) & 0.72 \\ Blade numbers & \(N\) & 31,36,59,45,69 \\ Tip stage loading coefficient & \(\psi_{c}\) & 0.46,0.37 \\ Power(kw) & \(p\) & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key aerodynamic and geometric parameters of the 2.5-stage highly loaded compressor.  
In the preceding literature, various compressor design theories were briefly highlighted. The 2.5-stage highly loaded compressor is engineering designed based on a commercial software Concept AxCent for three-dimensional blade design. The simplified schematic diagram of the design process is shown in Figure 2. To achieve the design goal, several engineering design philosophies were used. Multiple circular arc rotor profiles and bow stators with large turning angle were used to avoid the scale range flow separation. Among all, the three-dimensional CFD calculation is the core of the design process. More design details can be found in the literature [22]. Below, the numerical simulation is introduced in detail.
# An FPGA-Based Performance Analysis of Hardware Caching Techniques for Blockchain Key-Value Database
## 2 Related Work
### Overview of the Caching System on FPGA NIC for Blockchain  
The conventional key-value stores (KVS) hardware cache system is shown in Figure 1. The dashed arrows indicate the cache miss behavior whereas the bold arrow indicates the cache hit behavior. The cache system is designed on Reduced Latency Dynamic Random-Access Memory (RLDRAM) and is based on a direct-mapped method to mitigate cache complexity [22].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Device** & **Hash Rate Mhash/Sec** &
\begin{tabular}{c} **Efficiency Rating** \\ **Hash/Sec/Dollar** \\ \end{tabular} \\ \hline
**Intel CPU** & 10.13 & 0.09 \\ \hline
**AMD GPU** & 363.94 & 1.33 \\ \hline
**FPGA** & 2533 & 0.709 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hash rate and efficiency rating comparison.  
At first, the client requests the host to obtain data as shown in Figure 1. The DRAM controller receives the request and extracts the key. It first searches the key on DRAM; if the data are present in the DRAM, it will be a cache hit and FPGA NIC will reply with the result to the client. If the data are not present in DRAM, then it will be a cache miss. The DRAM controller forwards the request from the client to the server in case of a cache miss. The server checks for the key, replies to the client, and sends the result to FPG NIC. The RLDRAM in FPGA NIC saves the data sent by the server for the future.