esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Water Immersion Response in Pulse-Echo Mode
#### 3.2.1 Frequency Domain Response  
\(\bullet\) Response of transducers made with different FE materials  
The measured _SNSs_ in pulse-echo mode in water immersion for flat HS03 and HS06 transducers without any coating (apart from the layer of sputtered Au) are shown in Figure 6. The response of the HS03-transducers is clearly better than that of the HS06-transducer. The sensitivity of the HS03 transducer is about 15 dB higher. The bandwidth of the flat transducers made with the HS06 film is narrower, more resonant and centered at a lower frequency (about 0.6 MHz). In addition, for frequencies over 1.2 MHz the signal to noise ratio is very poor and peaks observed for HS06 between 2 and 3 MHz are due to the noise. On the contrary, the HS03 transducer presents a wider frequency band: rather flat between 0.4 and 1.3 MHz. Between 1.5 and 4.0 MHz it is also flat but 6 dB below. Beyond 4.0 MHz, the signal to noise ratio becomes very low, in part due to the lower energy output of the pulser at those frequencies. The main drawback of these transducers is the reduced peak sensitivity that is about \(-117\) and \(-130\) dB for HS03 and HS06, respectively.  
Due to the worse performance of HS06-transducers the rest of the study (performance of focused transducers and effect of surface coating) is limited to HS03-transducers.  
\(\bullet\) Response of focused transducers.  
Figure 7 shows the measured sensitivity (_SNS_) for the flat and the spherically focused transducers with focal distance of 22 and 35 mm. The obtained response is pretty similar, so it can be concluded  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Coating**} & \multicolumn{2}{c}{**\(\lambda\)/4 Resonance**} & **Relative Variation** & **Estimated Coating Thickness** \\  & **(kHz)** & **(\%)** & **(\(\mu\)m)** \\ \cline{2-5}  & **Before Coating** & **After Coating** & & \\ \hline PMMA & 285.6 & 283.2 & 0.85 & 0.3 \\ \hline PTFE & 290.5 & 288.0 & 0.84 & 0.15 \\ \hline Varnish & 280.8 & 200.2 & 28.7 & 25 \\ \hline Lacquer & 285.0 & 200.0 & 29.8 & 26 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Frequency shift due to the coating and estimated coating thickness.  
Figure 6: Pulse-echo sensitivity vs. frequency.  
that the effect of adapting the FE film to the spherical surface of the backing block has a limited effect on the transducer performance.  
* Response of transducers with different surface protection coatings.  
Figure 8 shows the measured \(SNS\) vs. frequency for the HS03 flat transducers with different coatings. The response below 1.5 MHz is similar, but above 1.5 MHz the signal to noise ratio SNR is poorer in this case compared with the uncoated transducers.
# Q-Learning Empowered Highly Dynamic and Latency-Aware Routing Algorithm for Flying Ad-Hoc Networks
## 4 QEMLR Routing Protocol
### Routing Establishment
that the current neighbor set of this node does not change frequently, thus implying that the neighbor set of nodes \(i\) is relatively fixed. In Equation (4), \(delay\_onehop_{i,j}\) represents the one-hop delay from node \(i\) to node \(j\), which can be calculated as shown in Figure 7.  
The reward function in Equation (5) can be derived by:  
\[reward(i,j)=\left\{ \begin{aligned} & r_{\max},j=\text{des or dest}\in N_{j}\\ &\delta\times NER_{j}+(1-\delta)\times PDR_{i},otherwise\end{aligned} \right. \tag{5}\]  
In this protocol, the reward value is set to \(r_{\max}\) if the destination node of the data packet exists in the neighbor set of the next state after the Hello packet is forwarded, or if the next hop is the destination node of the packet. \(\delta\) is a constant and \(NER_{j}\) is the number of new neighbor nodes that node \(j\) encounters per unit of time. The routing metric in the reward is weighted by the contrasting values of the total number of packets lost by node \(i\) and the total number of packets sent by node \(i\), denoted as \(PDR_{i}\).
# Affinity-Point Graph Convolutional Network for 3D Point Cloud Analysis
## 4 Experiments
### Classification on the ModelNet40 Dataset  
The ModelNet40 classification benchmark consists of 9843 train shapes and 2468 test shapes in 40 categories. Here, the point cloud data provided by [6] are used for classification, 1024 points are uniformly sampled from each shape, and all sampled points are normalized to a unit sphere. Similar to [33], during training, a random anisotropic scaling with a range of \([-0.66,1.5]\) and a translation with a range of \([-0.2,0.2]\) were used for data augmentation.  
The Adam optimizer with a momentum value of 0.9 was used for training. A dropout rate of 50% was set at each fully connected layer. Furthermore, the initial learning rate was set to 0.001, and the decay rate was set to 0.7 in all experiments During training, an early stop strategy was introduced based on the validation accuracy. The update was stopped when the accuracy did not improve after 10 epochs or the number of training epochs was more than 100. In addition, overall accuracy (OA) was used to evaluate the methods.  
The quantitative comparisons with the state-of-the-art methods are summarized in Table 1. It can be seen from Table 1 that the proposed method performs better than most other methods. Even using only xyz information as the input, the proposed method achieved a comparative result (92.1%) to most of the existing models. In the same case, AP-GCN achieved results with only 0.001% lower recognition than DGCNN [19], which achieved the best recognition result. Additionally, AP-GCN achieved 1.5% higher than the classical PointNet++ [7] and 0.4% higher than the classical PointCNN [10].  
The computational complexity of each method with the same input (1k points from ModelNet40) is shown in Table 1. Except PointNet, the time consumption of the proposed  
Figure 2: The architecture of AP-GCN.  
method is minimal compared with the other methods. It can be seen that the proposed also achieved the lowest computational complexity.
# An Innovative Low-Cost Equipment for Electro-Concentration of Microalgal Biomass
###### Abstract  
Microalgal harvesting is one of the most challenging processes in the development of algal research and development. Several methods, such as centrifugation, flocculation and filtration, are available at the laboratory scale. However, the requirement for expensive pieces of equipment and the possibility of biomass contamination are recurring gaps that hinder the development of microalgae R&D (research and development) in different parts of the world. Recently, electroflotation has been proved to be a suitable method for the harvesting of different species of microalgae and cyanobacteria. To this day, there are no companies that sell laboratory-scale electroflotation equipment; this is mainly due to the gap in the knowledge of which factors (time, mixing rate, number of electrodes and others) will affect the efficiency of concentration without reducing the biomass quality. This paper aims to build an innovative, low-cost electroflotation system for under 300 USD (United States dollar) with cheap and resistant materials. To achieve our goal, we tested the interaction of three variables (time, mixing rate and amount of electrodes). Results showed that an efficiency closer to 100% could be achieved in under 20 min using \(>\) 10 electrodes and 150 rpm (round per minute). We hope this innovative approach can be used by different researchers to improve our knowledge of the concentration and harvesting of algae and cyanobacteria.  
dewatering; response surface methodology; Arduino; aluminum electrodes; microalgae harvesting +
Footnote †: journal: Applied Soft. 2020, 10, 4841; doi:10.3390/app10144841
# HI-Sky: Hash Index-Based Skyline Query Processing
## 4 Performance Evaluation
### Result of Experiments
#### 4.2.5 Comparisons Using Real-World Dataset  
The last experiment demonstrated the real world application of HI-Sky using a real world dataset called Gas Sensors for home activity monitoring [34]. The dataset is freely available at UCI Machine Learning Repository. In addition, Z-SKY was excluded from the experiment because the data is real valued data, and normalization into an integer type causes a large number of skylines to disappear.  
Figure 12 shows the results of performance for each method. In this case, the real world dataset has correlations between specific dimensions and has clustered data distribution. Therefore, BBS-R and BBS-Q show better performance in skyline computation time than SFS in this experiment. However, in case of the dominance test calls, the comparison between the nodes occur more than the SFS as in the previous experiments. However, in all cases, HI-Sky performed better than other methods because the distribution of the real world dataset greatly influenced by the pruning of data space and the elimination of unnecessary dominance test between the GLADs.
# Near-Space Wide-Area and High-Resolution Imaging System Design and Implementation
## 2 Wide-Area and High-Resolution System Design
### High-Resolution Camera Design
#### 2.1.4 Tolerance Analysis  
For the optical system that has been designed, a tolerance analysis is conducted to fully consider the impact of processing and assembly on the imaging quality of the system. The optical lens group adopts the centering machining process, and the tolerances are selected based on the experience values of medium- and high-precision objective lenses.  
In order to predict the comprehensive effect of all tolerances during the overall assembly process of the system, a Monte Carlo method was used to analyze the influence of tolerances based on the design software. The MTF value of the optical system was used as the image quality evaluation index, and the spatial frequency was selected based on the optical angular resolution of the short and long focal ends.  
As is shown in the tolerance analysis result in Figure 10, according to the selected component processing and assembly tolerances, the lens can be processed and adjusted, with a 60% probability of achieving a full field of view range with a selected spatial frequency MTF greater than 0.15, and a high one-time pass rate, which can meet production requirements.
## 2 Proposed Workflow
### Image Classification  
In the final step of the proposed workflow, part images are acquired and classified using the previously trained neural network. Based on the classification results, the parts are recognized, and the result is communicated to the user. The part recognition is implemented using a physical low-cost setup, which is shown in Figure 5. It comprises multiple laser-cut parts encasing a ground plate, which is referred to as the sensing area (37 cm \(\times\) 40 cm). The sensing area's background is adaptable to ensure maximum contrast for the AM parts placed on the backplate. This configuration also integrates an LED panel to realize consistent lighting of the sensing area, a camera (Arducam Autofocus, 5 Megapixel (Arducam Technology Co., Hong Kong, China) to capture images of the parts placed in the sensing area and the inferencing instance (Raspberry Pi 4 8 GB model B (Raspberry Pi Foundation, Cambridge, UK) with a Coral Edge Tensor Processing Unit (TPU, GoogleLLC, Mountain View, CA, USA))), which applies the trained neural network. Note that the components, excluding a display device, can be acquired for approximately USD 200. The required resolution and accuracy of the camera and the computational resources of the Raspberry Pi and TPU are comparatively low and scale with the price. As these are the main technical components, the setup could be integrated into existing machines or unpacking stations, as they would only need to be extended by a camera, a Raspberry Pi or comparable computing unit and a lighting unit. Some manufacturers already include these components in their machines.  
As shown in Figure 6, a graphical user interface (GUI) can be used to select a previously transferred image classification network on the inferencing instance and begin the part recognition process. Here, the AM parts to be recognized are placed manually in the sensing area. Then, the top-three class predictions for the given part are displayed in real time on the GUI with a refresh rate of 3-4 frames per second. The classification results are accompanied by a live view of the sensing area and images of the top three predicted parts. The user can evaluate the part information from the GUI by comparing the actual part to the classification results. Thus, the final recognition step is performed by the user; however, the selection is limited to only three different part geometries, drastically accelerating the recognition process. This allows for more reliable part recognition without the requirement of 100% top-one classification accuracy, which would be very difficult to achieve because printable part geometries are very diverse. Ensuring 100% accuracy by testing all possible part geometries would be highly infeasible in this application scenario.  
Figure 5: Low-cost setup used for the automated recognition of AM parts, comprising the sensing area, a camera to capture images of the part in the sensing area, an LED panel for consistent lighting conditions and an inferencing instance running the image classification network.
## 3 Results and Discussion
### Analysis of Coating Performance Test
#### 3.2.2 Coating Abrasive Wear Test  
The results of the abrasive wear tests are shown in Table 4, which shows that the introduction of the coating significantly improves the wear resistance of the material. Compared with the base material, the relative wear resistance (\(\varepsilon\)) of the ceramic coatings is increased, of which: the coating prepared at a temperature increase rate of 5 \({}^{\circ}\)C/min increases the relative wear resistance by a factor of 1.36, and the coating prepared at a temperature increase rate of 2 \({}^{\circ}\)C/min increases the relative wear resistance by a factor of 1.89.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Wear Weight Loss Per Unit Area (g/m\({}^{-2}\))** & **Relative Wear Resistance \(\varepsilon\)** \\ \hline Q235 & 168 & 1.00 \\
5 \({}^{\circ}\)C/min ceramic coating & 123 & 1.36 \\
2 \({}^{\circ}\)C/min ceramic coating & 89 & 1.89 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Wear performance test results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sample** & **Compactness** \\ \hline
2 \({}^{\circ}\)C/min ceramic coating & 20 min diffusion \\
5 \({}^{\circ}\)C/min ceramic coating & 8 min diffusion \\ \hline \hline \end{tabular}
\end{table}
Table 3: Coating Densification test results.  
At the same time, a lower heating rate (2 \({}^{\circ}\)C/min) helps to further improve the wear resistance of the coating, indicating that a reasonable control of the heating rate plays an important role in optimizing the wear resistance of ceramic coatings. In the process of preparing ceramic coatings with a heating rate of 2 \({}^{\circ}\)C/min, the free and bound water molecules inside the coating can be released in a slow and orderly manner, which promotes the densification of the coating and enhances the toughness and strength of its organizational structure at the same time. This optimization process improves the wear resistance of the coating, which is one of the fundamental reasons for the significant increase in its relative wear resistance.  
In addition, nanoparticles themselves have excellent properties such as high plasticity, high hardness, high-temperature resistance, and wear resistance. In the coating system, the design of nanoparticles as the main part and micron particles as the secondary part helps to inhibit the growth of micron-sized matrix grains, thus realizing the homogenization of the coating organization, improving its densification and reducing the formation of holes. Nanoparticles not only play a filling role in the coating, effectively improving the densification degree of the coating, but also play a \"pinning\" and \"bridging\" effect in the wear process [56; 57].  
This mechanism can inhibit crack expansion and effectively reduce the exfoliation of large particles during the wear process, thus improving the wear resistance of the coating.
## 3 Methodology
### Spectral Water Index Methods  
We employed two spectral water index methods, [PERSON]'s NDWI and [PERSON]'s NDWI, to find which NDWI model of the ALI data is the best indicator of LSW mapping. The foundation of the spectral water index method is that a water body has a strong absorbability and low radiation from visible wavelengths to infrared wavelengths. The division of operations of the spectral water index method not only enhances the spectral signals by contrasting the reflectance between different wavelengths but also cancels out a large portion of the noise components that are common in different wavelength regions [38]. [PERSON]'s NDWI [32] is defined as  
\[NDWI=\frac{\rho_{\textit{Green}}-\rho_{\textit{NIR}}}{\rho_{\textit{Green}}+ \rho_{\textit{NIR}}} \tag{3}\]  
where \(\rho_{\textit{Green}}\) and \(\rho_{\textit{NIR}}\) are the reflectance of the green and NIR bands, respectively. [PERSON]'s NDWI is designed to (1) maximize the reflectance of a water body by using green wavelengths, (2) minimize the low reflectance in NIR of water bodies, and (3) take advantage of the high reflectance in NIR of vegetation and soil features [42]. As a result, the water body information will be enhanced and the background (vegetation and soil features) information will be restricted in [PERSON]'s NDWI images. This means that the water bodies can be identified by applying a threshold to [PERSON]'s NDWI images. [PERSON]'s NDWI can enhance information about water bodies and restrict information about vegetation and soil features, but it cannot completely distinguish built-up features from water bodies. [PERSON]'s NDWI [34] was developed to address this problem; it is defined as  
\[MNDWI=\frac{\rho_{\textit{Green}}-\rho_{\textit{SWIR}}}{\rho_{\textit{Green}}+ \rho_{\textit{SWIR}}} \tag{4}\]  
where \(\rho_{\textit{SWIR}}\) is the reflectance in the SWIR band. [PERSON]'s NDWI can enhance open-water features while efficiently suppressing and even removing built-up features as well as vegetation and soil features. In this study, Bands 4, 6, and 7 of the ALI sensor were selected to form two NDWI models, namely, NDWI\({}_{A4,6}\) and NDWI\({}_{A4,7}\), based on [PERSON]'s NDWI (Equation (3)). Bands 2 and 4 of the TM and ETM+ sensors were used to form two NDWI models, namely, NDWI\({}_{T2,4}\) and NDWI\({}_{E2,4}\), using [PERSON]'s NDWI. For [PERSON]'s NDWI (Equation (4)), Bands 4, 8, 9, and 10 of the ALI sensor were
## 5 Performance Evaluation
### Simulation results
LEACH. In LEACH, all the nodes should take turns to be a cluster head to communicate with the sink node, and since the distance between each node and the sink node is different, the energy consumption for each node is different. As a result, some node with higher energy consumption will die soon. As Figure 11 shows, the network lifetime of EAP and HEED is over 200 rounds longer than that of LEACH.
## 3 Fuzzy Logic Q-Learning Routing Scheme with Asymmetric Link Aware
### Neighbors Evaluation Criteria
#### 3.3.3 Asymmetric Link  
There are two different models for considering the extra wireless transmission capabilities of heterogeneous nodes in MANETs routing [34]. One is trying to use a long-range asymmetric link, and the other avoids it. The former exploits the long-range wireless link to transmit data. There have been many attempts to accomplish this [35; 36; 37]. There are several benefits to considering transmission over long-range links, such as reducing the number of hops on the path and increasing the lifetime of network nodes with limited energy. This reduces latency and improves network connectivity and routing efficiency, which makes the path more robust. The latter model tries to avoid the use of a remote connection to transmit data (see, e.g., [38; 39]). These models consider that the use of long-range transport may increase energy consumption and reduce overall network throughput.  
In conventional homogeneous wireless ad hoc networks, a network node can only communicate with neighbor nodes in its transmission range. In contrast, in heterogeneous wireless ad hoc networks,some network nodes can reach other nodes within a broader transmission range. Therefore, the network topology and routing mechanisms are quite different from those in homogeneous networks. As an example, a homogeneous network topology without asymmetric links is depicted in Figure 3a, in which all wireless links are symmetric and labeled with transmission costs in both directions.  
Node A has high transmission power and has the ability to reach much farther area in the network, thus a wireless asymmetric link is added in Figure 3b. We label asymmetric links from node A to its neighbors with cost 0 to represent the high transmission power of node A [40]. Consequently, a forwarding node should forward the packet to a candidate \(C_{i}\) that has higher transmission power, and avoid relying on candidate nodes with short transmission, if possible (Figure 4). For these purposes, it is desirable to allow a candidate with high transmission capability to relay the forwarded packet to cover a larger transmission area, which can statistically reduce the number of intermediate nodes involved in packet forwarding.  
Figure 4: An example of the candidate selection process.  
Figure 3: The network topology in homogeneous and heterogeneous wireless communication: (**a**) topology in homogeneous scenario; and (**b**) topology in heterogeneous scenario.