esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Demonstration of MmWave Vehicle-to-Vehicle Communications Using IEEE 802.11 ad
## 1 Introduction
Furthermore, indirect communication using a relay node is possible when a link blockage occurs; a sending station chooses a relay node that has a line-of-sight (LOS) link to a receiver. In this study, we investigated the applicability of the IEEE 802.11 ad standard to a gigabit V2V communication (GiV2V) system in a vehicular testbed, for which we utilized off-the-shelf device modules based on the IEEE 802.11 ad standard and installed them on the roof of the vehicles. A LOS direct connection satisfying the IEEE 802.11 ad requirements between two vehicles was configured and the throughput and connectivity were measured while driving on the roads. This measurement was performed on low-speed campus roads and high-speed city roads, respectively, to evaluate the effect of vehicle mobility. To the best of our knowledge, this study was the first attempt at the measurement of V2V communications using commercial IEEE 802.11 ad modules. The highlights of our contributions in this study are as follows:  
* We conducted mmWave V2V communications using commercial IEEE 802.11 ad modules. * We analyzed inter-vehicle connectivity by mmWave of short-range radio. * We compared the mmWave V2V communications in different driving environments. According to our experimental results, the IEEE 802.11 ad modules perform only in a short radio range of approximately 10-20 m at boresight due to significant pathloss, which causes frequent disconnections between vehicles, especially during high mobility. However, the average disconnection time is approximately 1 s, irrespective of the vehicle speed, even though the deviation of disconnection differs based on the speed; thus, high-speed vehicles demonstrate a larger deviation of disconnection time than low-speed vehicles. Regardless of the frequent disconnections, the experimental results demonstrate that the mmWave connectivity can transmit a large amount of data within a few seconds in the future smart cars. The remainder of the paper is organized as follows. In Section 2, we introduce the background and related works on mmWave and vehicular communication technology. We describe the GiV2V system in Section 3. Section 4 describes our experimental configuration, followed by the results in Section 5. Finally, we discuss and conclude our study in Section 6.
# Development and Analysis of an Intensified Batch-Fed Wine Fermentation Process
## 3 Results  
### Flask Fermentations  
Flask fermentations followed a typical batch kinetic profile: early growth of biomass in the first few days, followed by the consumption of hexoses until the end of fermentation. As expected, higher initial YAN resulted in higher biomass content and faster fermentations; higher initial biomass resulted in faster YAN consumption and faster fermentations, as seen in Table 1. The specific growth rate, \(\mu\_\max\), is in line with observations by [PERSON] and [PERSON]. As expected, higher YAN results in higher \(\mu\_\max\), as YAN is the growth-limiting nutrient in the fermentations. The fastest growth rate is observed at High YAN and Low Biomass, giving the most YAN per unit biomass and perforce the highest specific growth rate.  
The fermentation data were normalized for Brix consumption instead of fermentation time to account for varying fermentation temperatures. Hotter fermentations are faster, while cooler fermentations are slower. By plotting the YAN consumption rate per unit Brix drop, normalized (divided) by current optical density and current YAN (\(Y\)-axis), as a function of current fermentation Brix (\(X\)-axis), Figure 1 is derived. Although all four flask fermentations had vastly different conditions, the normalized YAN uptake rates changed together as a function of fermentation progression. This figure shows how over the course of a wine fermentation, the yeast biomass consumes less YAN on a per cell basis, indicating  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fermentation** & **Max Rate, \({}^{\circ}\)Brix/h** & \(\mu\_\max\), 1/h** & **Fermentation Time, h** \\ \hline Control & 0.41 & 0.12 & 132 \\ \hline High YAN, Low Biomass & 0.43 & 0.15 & 120 \\ \hline Low YAN, High Biomass & 0.78 & 0.10 & 84 \\ \hline High YAN, High Biomass & 0.89 & 0.10 & 84 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Flask fermentation kinetics.  
the switch from primary to secondary metabolism, as well as the deactivation of yeast cells due to the presence of ethanol.  
Integrating the resultant exponential fit results in Equation (1), which estimates the maximum YAN that the current biomass concentration can consume before the end of fermentation with only 30 mg/L YAN residual:  
\[N_{Now}=e^{[3.4-\frac{\text{OD}(0.40002)}{\text{(2.778)}^{2}}[1-e^{(0.2278)^{ 2}\cdot Brx_{now}}]]} \tag{1}\]  
where OD = optical density, \(N_{Now}\) = maximum YAN the biomass can consume at current point in fermentation, and \(Brix_{now}\) = current Brix. This equation can be applied to determine the maximum amount of YAN that the current biomass concentration can consume by the end of fermentation. By subtracting the current YAN level from \(N_{Now}\), the maximum useful addition of YAN can be determined--any higher addition risks going unconsumed.
# Effect of Fibers on Mixture Design of Stone Matrix Asphalt
## 3 Results and Discussion
### Effect of Fiber on Bulk Specific Gravity, VV, and VFA
Figure 4 showed the percentage of voids in mineral aggregate (VFA) that are filled with asphalt in SMA mixtures with different fibers and fiber contents. The data from Figure 4 indicated that fiber types and contents had an obvious impact on VFA, which decreased as fiber content increased. All of the VFA values were between 73% and 82%. The percentage of voids in mineral aggregate that are filled with asphalt in SMA mixtures with blended fiber, flocculent lignin fiber, and polyester fiber decreased with the fiber content increasing from 0.1% to 0.4%, and then it maintained a slight decrease with fiber content from 0.4% to 0.5%. When the additive was mineral fiber, it had similar variety, with the only difference being the relatively smaller reduction in the content from 0.1% to 0.2% and 0.5% to 0.6%. The content change of mineral fiber had a negligible impact on the VFA values due to its large density and low oil absorption rate when the content was less than 0.2%. Then VFA showed a significant decrease after the content was more than 0.2%. However, when the mineral fiber content was up to 0.4%, it began to show its absorptive action and the mixture's adsorption, which made the VFA much smaller than that with polyester fiber. The three other fibers had obvious impacts on the VFA value, and the SMA mixture with polyester fiber had a much smaller VFA than that with blended fiber and flocculent lignin fiber. The results indicated that the mineral fiber and polyester fiber had better asphalt absorption and adsorption in the SMA mixture. To obtain the same VFA value, lower content is needed for mineral fiber and polyester fiber. Therefore, the fiber selection should comprehensively consider both absorptive action and the mixture's adsorption. Higher contents of mineral fiber and polyester fiber were not the best choice. Fiber with higher adsorption, but lower absorption, will improve the SMA mixture volume index.
# A Novel Method for Ground-Based Cloud Image Classification Using Transformer
## 4 Experimental Results and Discussion
### Comparison with Other Methods  
To verify the superiority of the proposed method, we compared it with other advanced methods, including CNN-based methods proposed for GCI classification such as DeepCloud [23], CloudNet [27], and CGAT [29], as well as classical CNN models such as GoogLeNet [58], VGG16 [59], ResNet34 [31], ResNet50 [31], DenseNet [60], MobileNet [61], EfficientNet-B0 [47], and EfficientNet-B5 [47]. In addition, we compared it with other Transformer-based classification models, such as ViT-B [39], ViT-L [39], and Swin-T [52]. The training and testing samples of each dataset were kept constant in the experiments, and the final testing results of each dataset are shown in Table 8.  
It can be seen from the experimental results that EfficientNet-B0 achieves the highest accuracy among the CNN-based methods, with 91.47%, 89.97%, and 90.48% in the ASGC, CCSN, and GCD datasets, respectively. The classification accuracy of the Transformer-based method is higher than that of the CNN-based method, which proves that Transformer is more capable of extracting image features. The accuracy of the original Swin-T in each dataset reaches 92.86%, 91.06%, and 92.38%. The proposed method combines CNN with Transformer and optimizes the loss function to compensate for the lack of local features and enhances the learning of supervised features, thus improving the classification accuracy. Using the proposed method, the accuracy is improved by 1.38%, 1.67%, and 1.19% compared with the original Swin-T.  
Figure 13: Grad-CAM visualization results in the ASGC dataset. Each input image is shown on the second line, and attention maps are shown on the first line: (**a**) Ac; (**b**) Cb; (**c**) Ci; (**d**) Cu.
## 2 Materials and Methods
### Data
#### 2.3.1 Flood Inventory Data  
The flood inventory data of an area present crucial information about past flood events, such as the date of occurrence, locations, and their impacts on the area. Today, geographic information systems are very useful tools to generate these maps [73]. However, other sources such as in situ data collection, related past reports and maps, and satellite spatial images can be used. In this study, the flood inventory map was generated based on past flood events' data gathered from various authorities. A total of 139 points were considered from past flood events for the generation and testing of the flood susceptibility map. Out of these 139 points, 98 were randomly selected for training, while the test of the 41 points was used for the validation of the generated flood susceptibility map. Non-flooded points were mainly chosen randomly by using ArcGIS 10.8.2 and generally taking into consideration the weak possibility of the point being flooded. Figure 1 shows randomly selected flooded (\(n=139\)) and non-flooded (\(n=139\)) points extracted for training and testing of the flood susceptibility map of the sub-basin.
# DeepGOMIMO: Deep Learning-Aided Generalized Optical MIMO with CSI-Free Detection
## 4 Simulation Results
### Simulation Setup  
In our simulations, we consider a 4 \(\times\) 4 (\(N_{r}=N_{t}=4\)) GOMIMO system configured in a typical 5 m \(\times\) 5 m \(\times\) 3 m room. The 2 \(\times\) 2 square LED array is placed at the center of the ceiling and the spacing between two adjacent LEDs is 2 m. The height of the receiving plane is 0.85 m, and two receiver locations over the receiving plane, i.e., the center (2.5 m, 2.5 m, 0.85 m) and the corner (0 m, 0 m, 0.85 m), are considered for performance evaluation. The receiver consists of a 2 \(\times\) 2 square PD array, where the spacing between two adjacent PDs is 10 cm. For both GOSM and GOSMP mappings, two out of four LEDs are activated for signal transmission, i.e., \(N_{a}=2\). Moreover, unipolar nonzero 4-PAM modulation is adopted in the GOMIMO system, and hence the corresponding spectral efficiencies for GOSM and GOSMP mappings are 4 and 6 bits/s/Hz, respectively. In addition, we adopt transmitted SNR as the measure to evaluate the BER performance of the GOIMO system, which is defined as the ratio of the transmitted electrical signal power to the additive noise power [8; 15]. The other simulation parameters of the GOMIMO system can be found in Table 1.  
The detailed parameters of the CSI-free DNN detectors for GOSM and GOSMP are given in Table 2. For GOSM, the number of neurons of four hidden layers is 128, 64, 32, and 16, respectively. The learning rate is 0.01 when the receiver is located at the center of the receiving plane, and it is reduced to 0.001 when the receiver is moved to the corner. Moreover, the scaling factors are set to \(1\times 10^{5}\) and \(2\times 10^{5}\) when the receiver is located at the center and the corner, respectively. For GOSMP, every hidden layer contains 64 neurons, and the learning rates are 0.01 and 0.005 when the receiver is located at the center and the corner of the receiving plane, respectively. In addition, the scaling factors of \(1\times 10^{5}\) and \(1\times 10^{6}\) are used for center and corner received locations, respectively. For both GOSM and GOSMP, the lengths of training set and validation set are assumed to be 150,000 and 50,000, respectively. In order to accelerate the convergence speed, we use the minibatch technique in training, and each minibatch contains 100 transmitted signal vectors.
## 4 Proposed Data Collection Algorithms
### Probabilistic Neighborhood Covering Set-Based Greedy Heuristic Algorithm (PNCS-GHA)
#### 4.2.1 Probabilistic Neighborhood Covering Set Construction Phase  
In our PNCS-GHA, all the locations of nodes are assumed to be pre-known by the control center. On the basis of the location information and the given \(p\), a minimum probabilistic neighborhood covering set (PNCS) can be constructed by control center to shorten the path length of data collection tour. Algorithm 1 presents the main procedure of PNCS construction.  
Figure 5: TDMA-based multiple access control protocol.  
```
0: The candidate nodes set \(N=[1,2,...,n]\); the locations of nodes in \(N\); the parameter \(p\);
0: The minimum probabilistic neighborhood covering set, \(S\);
1: calculate the probabilistic neighborhood contour \(d\_p\) according to the given \(p\) and equations in Section 3;
2:loop
3: each node in \(N\) calculates its neighbor nodes set \(Nei(i)\) (also belong to \(N\)) within contour \(d\_p\), and its neighbor weight \(num(i)\), respectively;
4:\(s_{j}\) = min {arg max \(\{num(i)|i\in N\}\)};
5: add \(s_{j}\) into \(S\);
6: remove \(s_{j}\) and \(Nei(s_{j})\) from candidate set \(N\);
7:if\(N\
eq\Phi\)then
8: repeat loop;
9:endif
10:endloop
```  
**Algorithm 1** Probabilistic neighborhood covering set construction  
We illustrate an example for our minimum covering set construction mechanism in Figure 6. First, every node in the 3D network is joined into the candidate set \(N\). Each candidate node calculates its neighbor node set and neighbor weight. Then the node with the maximum neighbor weight will be inserted into the covering set. As shown in Figure 6a, the node \(a\) has the biggest neighbor weight,  
Figure 6: An example of PNCS construction (**a**) The first PNCS node selection; (**b**) The second PNCS node selection; (**c**) The third PNCS node selection; (**d**) The final PNCS construction.  
so node \(a\) is first added into \(S\), i.e., \(S=\{a\}\). Then the node \(a\) and its neighbor nodes set are removed from the candidate set \(N\). As the candidate set \(N\) is not empty, the nodes in current \(N\) recalculate their neighbor nodes sets and neighbors weights. Then as shown in Figure 5(b), the node \(b\) is added into \(S\), i.e., \(S=\{a,b\}\). The loop will be repeated until \(N=\phi\), the final probabilistic neighborhood covering set \(S\) as shown in Figure 5(d) is \(S=\{a,b,c,d,e,f,g\}\).
## 5 Numerical Simulations
### Simulation Results
#### 5.2.4 Network Topology and Robustness  
Generally, the robustness of the ADN against the CCF was affected by the different topological structure. In Figure 9, the left side shows the node degree distribution comparison diagram when the PN and CN adopted the ER network topological structure, while the right side shows the degree comparison diagram when the BA network topological structure was adopted. We observed from Figure 9 that the degree distribution of the ER network was more uniform than the BA network. The degree distribution of the BA network followed the power law. The average degree of the ER and BA network was 5 and 5 respectively for comparison.  
The relationship between the final expected proportions \(R_{\text{PF}}(\infty)\) of remaining normal operational nodes in the PN and network topology is shown in Figures 10 and 11. In Figure 10, the interval of the proportion of distributed generators was from 0.0 to 0.8. Of the nodes, 54.57% survived in the PN when the proportion of distributed generators was 0.6 with unintentional random initial failure rate \(\Theta_{P}\) = 0.002 under the ER topological structure and 16.87% of nodes survived in the PN when the proportion of distributed generators was 0.6 with unintentional random initial failure rate \(\Theta_{P}\) = 0.002 under the BA topological structure. Of the nodes, 54.57% survived in the CN when the proportion of distributed generators was 0.6 with unintentional random initial failure rate \(\Theta_{P}\) = 0.002 under the ER topological structure and 16.87% of nodes survived in the CN when the proportion of distributed generators was 0.6 with unintentional random initial failure rate \(\Theta_{P}\) = 0.002 under the BA topological structure. In Figure 11, the interval of unintentional random initial failure rate \(\Theta_{P}\) is from 0.002 to 0.008. Of the nodes, 27.27% survived in the PN for \(\Theta_{P}\) = 0.004 when the proportion of distributed generators was 0.8 under the BA topological structure. Of the nodes, 27.27% survived in the CN for \(\Theta_{P}\) = 0.004 when the proportion of distributed generators was 0.8 under the BA topological structure. Of the nodes, 27.27% survived in the CN for \(\Theta_{P}\) = 0.004 when the proportion of distributed generators was 0.8 under the BA topological structure.  
Figure 9: Comparison of node degree distribution in the CN and PN.  
As shown in Figures 10 and 11, the \(R_{\text{PF}}(\infty)\) of the ER network intersected with that of BA network due to the uncertainty of the CCF, and the \(R_{\text{PF}}(\infty)\) and \(R_{\text{IF}}(\infty)\) of the ER network were greater than that of BA network as a whole. Therefore, this agreed with our fourth conclusion. That is, the robustness of the ADN with ER network topological structure was greater than that with BA network topological structure under the same average node degree.
## 2 Volterra Model and FSI Method
### Volterra Kernel Identification Based on Impulse Function  
The nonlinear aerodynamic force of the bridge girder section is approximately a time-invariant system. It has been proven that the establishment of a truncated low-order nonlinear model, namely a first-order truncated Volterra series, can adequately describe the nonlinear effects ([PERSON] et al. [24]; [PERSON] et al. [25]). For a first-order truncated Volterra series, Equation (1) can be rewritten as Equation (2), and the first-order kernel can be directly expressed as the output response under the action of a unit impulse equation, that is, \(h_{1}(t)=y_{1}(t)\). Such a first-order kernel is entirely linear. Therefore, Equation (3) is utilized to identify the first-order Volterra kernel. It can be seen that the first-order Volterra kernel obtained by Equation (3) considers the nonlinear effect of different amplitudes, which is different from the general linear kernel.  
\[y(t)=h_{0}+\int\limits_{0}^{t}h_{1}(\tau_{1})u(t-\tau_{1})d\tau_{1} \tag{2}\]  
\[h_{1}(t)=\frac{1}{\beta^{2}-\beta}\{\beta^{2}[y_{1}(t)-h_{0}]-[y_{\beta}(t)-h_ {0}]\} \tag{3}\]  
where \(y_{1}(t)\) indicates the system output under the unit-impulse response; \(y_{\beta}(t)\) indicates the system output under \(\beta\) times the unit-impulse response.  
The smoothed-ramped function is usually utilized as the impulse equation for the Volterra kernel identification. There are three commonly used smoothed-ramped functions, namely the sinusoidal function, the polynomial function, and the exponential function. The expressions of the three smoothed-ramped functions are shown in Equations (4a)-(4c)  
\[V_{sin}=\left\{\begin{array}{cc}\frac{\pi}{2}\frac{A}{T}\sin(\frac{\pi}{2}t )&0\leq t<T\\ 0&t\geq T\end{array}\right. \tag{4a}\]  
\[V_{poly}=\left\{\begin{array}{cc}\frac{A}{T^{3}}\left(30t^{2}-\frac{60}{T}t ^{3}+\frac{30}{T^{2}}t^{4}\right)&0\leq t<T\\ 0&t\geq T\end{array}\right. \tag{4b}\]  
\[V_{exp}=\left\{\begin{array}{cc}A\exp\biggl{\{}-\biggl{[}b(t-\frac{T}{2}) \biggr{]}^{2}\biggr{\}}&0\leq t<T\\ 0&t\geq T\end{array}\right. \tag{4c}\]  
where \(A\) and \(T\) denote the amplitude and duration of the impulse function, respectively.  
It should be noted that there is a discontinuity in acceleration in the case of a sinusoidal function, and the displacement, velocity, and acceleration of the smoothed-ramped function based on the exponential function can only be approximately close to 0.0 at the beginning and end time. Therefore, the smoothed-ramped function based on the polynomial function is applied to identify the kernels in this study. Taking the heaving motion as an example, Figure 1 shows the structural displacement, velocity, and acceleration under a vertical pulse designed by Equation (4b).
## 4 Results and Discussion  
Indicative results of the two models are presented, along with the respective values of the modeling error metrics for 2002. All metrics are computed on the test datasets. In addition to these metrics, plots of the error distribution as a function of the hour of the day and day of the week are generated to spot specific weak points in the forecasting. The various plots comparing the day-ahead hourly actual and forecasted load for typical months for the year 2022 are generated and optically checked for specific instances of prediction failure.
## 2 Materials and Methods
### Data Collection and Preprocessing
#### 2.2.2 Digital Elevation Models
Although Copernicus DEM is a rather recent dataset and its accuracy has not been extensively studied, some researchers have demonstrated that its vertical accuracy ranges between 2 and 6 m [61; 62]. Several studies have tried to assess and compare the accuracy between freely available DEMs using either GPS-measured ground control points or finer-scale DEMs produced through photogrammetric processes [63; 64; 65; 66; 67; 68; 69]. The vertical accuracy of the world DEM files depends on the geomorphology of the ground and the terrain of each specific region, but, generally speaking, Copernicus GLO-30 and AW3D30 appear to outperform NASADEM, SRTM, and ASTERGDEM [67; 70]. As a result, the Copernicus GLO-30 DEM was selected as the primary source of elevation information in the present research. The dataset's tiles were converted into a mosaic raster file, projected from WGS84 to ETRS89, and clipped to the area of interest's extent. The elevation in the area of interest varies from \(-\)23 to 1175 m, with the more mountainous locations found in Greece and the Mediterranean complex of islands, while mainland France, Spain, and the Adriatic Sea present lower elevation combined with shallow water locations and major river deltas.