esa-sceva/satcom-chunk-collection huggingface dataset:
# Decentralized Robust Control of a Network of Inverter-Based Distributed Generation Systems
## 2 Methodology  
### Digraph of a Network of Inverter-Interfaced Systems  
In this section, the concept of a directed graph (digraph) for a network of IBDGs including \(n\) inverters, which is used for the formulation of the state space model of the network, is introduced. The IBDGs are connected together through interconnection impedances.  
A digraph \(\mathcal{D}\) by definition comprises a non-empty finite set of vertices, represented by \(\mathcal{V}(\mathcal{D})\) (vertex set), and a finite set of edges, represented by \(\mathcal{E}(\mathcal{D})\), in which each edge is an ordered pair of two vertices. The vertices \(v_{i}\) and \(v_{j}\) are called adjacent (or neighbors) if an edge exists between them. The set of adjacent vertices of a vertex \(v_{i}\) is represented by \(\mathcal{N}(v_{i})\), and its number of elements determines the degree of the vertex \(v_{i}\), which is denoted by \(deg(v_{i})\). The adjacency matrix of a digraph is defined by an \(n\times n\) matrix \(P=[a_{ij}]\), in which \(a_{ij}\) is the number of edges from \(v_{i}\) to \(v_{j}\)[30].  
In a digraph of a network of IBDGs, each inverter is represented by a vertex. The direction of each edge, which appears as arrows in the digraph, is determined based on the choice of the reference positive direction of the current between two vertices. Therefore, the arrow from \(v_{i}\) to \(v_{j}\) means that the reference positive direction of current is from inverter #\(i\) to inverter #\(j\). Moreover, the quasi-adjacency matrix \(S=[s_{ij}]\) is defined as follows:  
\[s_{ij}=\begin{cases}1,&\text{if the edge direction is from $v_{i}$ to $v_{j}$}\\ -1,&\text{if the edge direction is from $v_{j}$ to $v_{i}$}\\ 0,&\text{if no edge exists between $v_{i}$ and $v_{j}$}\end{cases} \tag{1}\]  
It can be shown that the matrix \(S\) has the following relationship with the matrix \(M\):  
\[S=P-P^{T} \tag{2}\]  
As an example, consider the digraph of a network of four IBDGs connected together, as illustrated in Figure 1.  
Based on the above-mentioned definitions, the matrices \(M\) and \(S\) for this digraph are calculated as follows:  
\[P=\left[\begin{array}{cccc}0&1&0&0\\ 0&0&1&0\\ 1&0&0&0\\ 0&0&1&0\end{array}\right],\ S=\left[\begin{array}{cccc}0&1&-1&0\\ -1&0&1&0\\ 1&-1&0&-1\\ 0&0&1&0\end{array}\right]\]  
Figure 1: The digraph representation of a network of four IBDGs.  
The basic concepts of digraphs presented in this subsection are used in the next section to formulate the dynamical equations of a network of IBDGs in an islanded NG.
## 4 Experiment and Analysis
### SemanticKITTI  
Table 3 reproduces the experimental results of several classic algorithms for semantic segmentation of 3D point clouds in SemanticKITTI dataset, including PointNet [24], PointNet++ [35], SPG [33], SqueezeSegV2 [34], RangeNet++ [19], and RandLA-Net [5]. PointNet and PointNet++ are not suitable for 3D point cloud semantic segmentation in large scenes, so the accuracy is very poor. SqueezeSegV2 proposes a spatially adaptive convolution to better capture symbiotic relationships between objects in LiDAR images, and also uses KNN for post-processing. RandLA-Net is a 3D point cloud semantic segmentation for large scenes, which leads to its better effect, and it is used as a representative to compare with our method. RandLA-Net has a module for local feature extraction, so its semantic segmentation accuracy of some objects is relatively high. In contrast, our method has a global feature extraction module and two kinds of local feature extraction modules. Therefore, our method (LDE-Net) is higher than RandLA-Net in semantic segmentation accuracy for some objects and overall. Figure 6 shows the results of semantic segmentation between our method (LDE-Net) and RandLA-Net on the SemanticKITTI dataset. It can be seen from the results that road and sidewalk are similar in some aspects. RandLA-Net does not distinguish well between road and sidewalk. Since LDE-Net has two kinds of local feature extraction modules and global feature extraction modules, it can better reference the neighborhood information so that these classes of semantic segmentation accuracy is higher. Table 3 shows the accuracy of LDE-Net on the semanticKITTI dataset. Due to the addition of the global feature extraction modules and two local feature extraction modules, LDE-Net is 1% higher than RandLA-Net on the mIoU. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**mIou**} & \multirow{2}{*}{**OA**} & \multicolumn{8}{c}{**Class Accuracy**} \\ \cline{3-14}  & & & **Man-Made** & **Natural** & **HighVeg** & **Low Veg** & **Buildings** & **Hard Scep** & **Scanning** & **Cars** \\ \hline SnapNet & 59.1 & 88.6 & 82.0 & 77.3 & 79.7 & 22.9 & 91.1 & 18.4 & 37.3 & 64.4 \\ SEGCloud & 61.3 & 88.1 & 83.9 & 66.0 & 86.0 & 40.5 & 91.1 & 30.9 & 27.5 & 64.3 \\ RF\_MSSF & 62.7 & 90.3 & 87.6 & 80.3 & 81.8 & 36.4 & 92.2 & 24.1 & 42.6 & 56.6 \\ ShellNet & 69.3 & 93.2 & 96.3 & 90.4 & 83.9 & 41.0 & 94.2 & 34.7 & 43.9 & 70.2 \\ GAENet & 70.8 & 91.9 & 86.4 & 77.7 & 88.5 & 60.6 & 94.2 & 37.3 & 43.5 & 77.8 \\ SPG & 73.2 & 94.0 & 97.4 & 92.6 & 87.9 & 44.0 & 84.2 & 31.0 & 63.5 & 76.2 \\ KPConv & 74.6 & 92.9 & 90.9 & 82.2 & 84.2 & 47.9 & 94.9 & 40.0 & 77.3 & 79.7 \\ RandLA-Net & 77.4 & 94.8 & 95.6 & 91.4 & 86.6 & 51.5 & 95.7 & 51.5 & 69.8 & 76.8 \\ Our & 77.6 & 95.0 & 96.8 & 90.5 & 85.1 & 52.3 & 97.5 & 54.3 & 71.2 & 79.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of different methods in Semantic3D.
## 4 Results and Discussion
### Summer Maize Recognition in Huanghuaihai Plain
#### 4.3.3 Validation of the Extracted Summer Maize Area in Huanghuaihai Plain
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Province** & \begin{tabular}{c} **Statistical** \\ **Area/10\({}^{3}\) ha** \\ \end{tabular} & \
\begin{tabular}{c} **Extracted** \\ **Area/10\({}^{3}\) ha** \\ \end{tabular} &
\begin{tabular}{c} **High (+) or Low** \\ **(-)/\%** \\ \end{tabular} & **Accuracy/\%** \\ \hline Beijing & 133.0 & 129.7 & \(-\)2.51 & 97.49 \\ Tianjin & 179.0 & 175.4 & \(-\)1.96 & 98.04 \\ Hebei & 2684.3 & 2995.4 & 11.59 & 88.41 \\ Henan & 3100.0 & 3322.7 & 7.18 & 92.81 \\ Shandong & 3010.0 & 3428.2 & 13.59 & 86.41 \\ \hline \hline \end{tabular} Note: High (+) or low (\(-\))/% = (statistical area-extracted area)/statistical area \(\times\) 100, and accuracy (%) is 100-Abs (high or low).  
\end{table} 
Table 5: Comparison of extracted area and statistical data of summer maize for each province in 2012.  
the gap was largest in Lushan county, with 90.7% and 62.5%, respectively; the gap was smallest in Changyuan, 67.7% and 68.6%, respectively (Table 6). The mean proportion of the public part area to the statistical data was 82.3%, which meets the request for precision. These suggest that the method proposed in this paper is reliable.
# LSTM Recurrent Neural Network for Hand Gesture Recognition Using EMG Signals
## 2 Methodology
### Model Optimization  
Once the model is defined, the next step was to select a cost function in order to optimize it through training and obtain the desired results in the classifier. In general, when  
Figure 6: LSTM Cell structure.  
using an output layer with a SOFTMAX activation function, a categorical cross entropy loss function \(\mathcal{L}(\hat{y},y)\) is selected, which allows optimizing the cost function \(\mathcal{J}(W,b)\) that sums the loss over the training examples. The categorical cross entropy loss allows a correct penalization of incorrect predictions and improve the performance in multi-class classification problems compared with other loss functions like the mean squared error (_MSE_). The categorical cross entropy loss and the cost function are shown in Equations (11) and (12).  
\[\mathcal{L}(g,y)=-\sum_{j=1}^{C}y_{j}log(\hat{y}_{j}) \tag{11}\]  
\[\mathcal{J}(W,b)=\frac{1}{m}\sum_{i=1}^{m}\mathcal{L}(\hat{y},y) \tag{12}\]  
where:  
\(\hat{y}_{j}\) correspond to the models predicted probability for class \(j\).  
\(y_{j}\) represents the correct class (label) of the training example.  
\(C\) is the total number of classes and \(m\) is the number of training examples in the mini batch.  
It is important to note that the model was optimized with a mini batch gradient descent strategy. The batch size was set during the hyper-parameter tuning process along with the number of training epochs (Training iterations). The optimization algorithm will be explained in greater detail in the next paragraph.  
In order to minimize the cost function \(\mathcal{J}(W,b)\), it is necessary to update the weights \(W\) and biases \(b\) of each layer. This must be done by an optimization algorithm such as gradient descent. In this research, the Adaptive Moment Estimation ADAM [67] algorithm was selected; that is, a combination of Gradient Descent With Momentum and Root Mean Square Propagation (RMSProp). The ADAM optimization algorithm is considered to be one of the best optimization algorithms to train neural networks [68; 69; 70].
# A Green Computing Business Aggregation Strategy for Low Earth Orbit Satellite Networks
## 2 System Model
### Communication Model  
The uplink channel capacity between a ground user and \(S_{i}\) is:  
\[C_{GS_{i}}=B_{GS_{i}}\log_{2}\biggl{(}1+\frac{P_{tx}G_{tx}H_{GS_{i}}}{N_{GS_{i}}} \biggr{)} \tag{3}\]  
where \(B_{GS_{i}}\) is the channel bandwidth, \(P_{tx}\) and \(G_{tx}\) are the transmit power and antenna gain of the user equipment, \(H_{GS_{i}}\) is the channel gain, and \(N_{GS_{i}}\) is the noise power [8].  
The transmission delay for business \(\mathbb{B}_{ij}\) from the user to \(S_{i}\) is given as follows:  
\[\tau_{ij}=\frac{B_{ij}}{C_{GS_{i}}} \tag{4}\]  
The channel capacity between \(S_{i}\) and \(S_{k}\) is:  
\[C_{S_{i}S_{k}}=B_{S_{i}S_{k}}\log_{2}\biggl{(}1+\frac{P_{TX}G_{TX}H_{S_{i}S_{k }}}{N_{S_{i}S_{k}}}\biggr{)} \tag{5}\]  
where \(B_{S_{i}S_{k}}\) is the bandwidth, \(P_{TX}\) is the transmission power, \(G_{TX}\) is the antenna gain of \(S_{i}\), \(H_{S_{i}S_{k}}\) is the channel gain between \(S_{i}\) and \(S_{k}\), and \(N_{S_{i}S_{k}}\) is the noise power.  
The delay required for transmitting \(\mathbb{B}_{ij}\) from \(S_{i}\) to \(S_{k}\) is as follows:  
\[\tau_{ijk}=\frac{B_{ij}}{C_{S_{i}S_{k}}} \tag{6}\]  
Since the computational results to be sent back to the ground are relatively small, the energy consumption and delay for the return transmission are negligible.
## 5 Conclusions  
This work summarized some recent applications of RS technologies in major coastal and river delta regions of the world, including major river deltas in China and Eastern Asia, the Po River Delta, the Venice Lagoon in Italy, the St. Petersburg Bay in Russia, the Nile Delta in Egypt, the Mississippi Delta River in the U.S., and the Amazon River in Brazil. Advanced RS techniques can evaluate the impacts of climate change and anthropogenic activities on the coasts and large river delta regions. RS methods can also study the interactions between the ocean and the coast and evaluate their implications in response to projected climate change. RS technologies can also measure and map the long-term evolution of coastal and deltaic environments, providing updated information on environmental variations, assessing hazards and risks, and understanding the relevant mechanisms. Many new RS satellite programs are being scheduled and deployed, and many new methods are being developed in concert. One such example is the integration of many similar satellite sensors, such as Sentinel-2 and Landsat, to establish a virtual constellation that allows frequent observations of highly dynamic environments. Combining optical and microwave RS images will make it possible to frequently and efficiently observe coastal environment evolution in cloudy and rainy areas, including the extraction of coastline information, deriving long-term land use and land cover change, and retrieving nearshore bathymetry, mapping coastal wetlands, and detecting coastal erosion.  
Not applicable.  
This work was also performed within the Dragon 5 ESA project ID 58351, entitled \"Global Climate Change, Sea Level Rise, Extreme Events and Local Ground Subsidence Effects in Coastal and River Delta Regions through Novel and Integrated Remote Sensing Approaches (GREENISH)\". Sentinel and Landsat datasets were downloaded from the ESA Sentinel Hub and USGS Earth Explorer sites, respectively.  
The authors declare no conflict of interest.
# Evaluation of Non-_Saccharomyces_ Yeast for Low-Alcohol Beer Production
## 3 Results and Discussion
### Limitations and Insights of a Study  
The studied non-_Saccharomyces_ yeasts may offer several benefits for the production of low-alcohol beers. Due to low ethanol production, the studied yeast strains may be used as primary fermenters in beer production, which would simplify the production of such beverages. Physical dealcoholization processes inherently remove volatile compounds from a beer, which negatively impacts its flavor. Directly obtaining a beer with a lower alcohol content might allow brewers to retain more of the flavor compounds in a finished beer. Some of the studied strains (such as TD1 or KL1) are characterized by higher enzymatic activities regarding the biotransformation. If such a yeast would allow more of the flavor precursors to be liberated in the fermentation conditions, they might allow brewers to obtain a beer with richer flavor. Additionally, the strains with high bioflavoring potential might be used in co-fermentation with brewing strains of _S. cerevisiae_ to obtain higher-alcohol beers with richer aromas.  
The main limitation of this study is the limited physicochemical analysis of the obtained beers. The study focused mainly on the biological analysis of the yeast strains. To evaluate the potential of the yeasts studied, a broader analysis of beers, such as sensory analysis, the composition of volatile compounds, acids, and sugars is required. However, such approach was by design. In a continuation of this research, a broader analysis focusing only on the physiochemical parameters and sensory characteristics of the obtained beers is planned. Additionally, if any of the studied strains were to be used in the production of beer, significant process parameters (fermentation temperature and time, pitching rate, etc.) would be required.
## 4 Results and Discussion
### The Gradient Modulus Analysis from the SASW Method  
Since the SASW method has the ability to provide gradient modulus varying with depth and determine the pavement thicknesses, Figures 8-10 select three sets of modulus profiles varying with depth and field cores at locations Y1/Y2/Y4 in Section I from among all the measurement locations. It is worth noting that the extracted core samples, as shown in Figures 8a, 9a and 10a, are placed upside down, whose depth distribution is opposite to the actual depth.  
In the analysis of the gradient modulus profile acquired through the SASW method, it can be discerned that the trend of modulus variation across the three measurement locations exhibits coherence (Figures 8a, 9a and 10a). Within the depth extending from 150 mm to 200 mm, there is an obvious decrease in modulus. Following this zone, a stabilization of the modulus is observed after a depth of more than 200 mm, indicating a homogenous material characteristic in this specific depth interval. This gradient modulus trend is consistent with the actual situation, as mentioned in the previous Section 3.3. Although the original structural design of the concrete pavement showed a 200-mm-thick concrete layer over cement-stabilized crushed stone and subgrade soil, the heights of the retrieved core samples were found to be under 200 mm due to the inherent damages within the observed concrete slabs. These inherent distresses, such as cracking and delamination within the depth of about 150 mm to 200 mm, affect the integrity and performance of core samples and are then reflected by the modulus value. After the depth of 200 mm, the modulus base layer has few variations, which may indicate there are no obvious damages within the base layer.  
It is reasonable that the gradient modulus profile measured by the SASW method could make inherent distress recognition possible. The modulus distribution has the capability to determine the location of the inherent distress based on the region where the modulus suddenly decreases. After obtaining a comprehensive evaluation of concrete pavement condition, the design of overlay asphalt thickness and material would be more targeted and suitable for each different concrete pavement.  
Figure 8: The modulus profile and field core at measurement location Y1. (**a**) SASW modulus profile; (**b**) field core sample.  
Figure 10: The modulus profile and field core at measurement location Y4. (**a**) SASW modulus profile; (**b**) field core sample.  
Figure 9: The modulus profile and field core at measurement location Y2. (**a**) SASW modulus profile; (**b**) field core sample.
# A Survey on Big IoT Data Indexing: Potential Solutions, Recent Advancements, and Open Issues
## 1 Introduction  
Widespread utilization of Internet of Things (IoT) systems and applications has resulted in the massive data expansion promising greater benefits for businesses and individuals, but also introducing significant challenges for big data analytics. Such an expansion also plays an important role in the dynamics of large data. In particular, large data can be classified according to their volume, variety and velocity (\"3V's\" for short). These categories were first introduced by Gartner, Inc. to highlight some elements of the challenges associated with large volume data [1, 2, 3, 4, 5]. Afterwards, veracity and value have been incorporated as two additional categories (\"5V's\") [6, 7]. Others have also expanded this big data category to 6V's and 7V's [8]. The capacity to process and use large amounts of IoT data, e.g., smart-city, smart-grid, e-health, Internet of Vehicles (IoV), Internet of Video Things (IoV), agriculture, etc., is a key factor in the success of a project [9, 10, 11]. The process of indexing large amounts of IoT data comprises many phases where a range of IoT data is analyzed to highlight changes.  
Throughout the last decade, privacy violations have increased dramatically. Although private data is emerging as an extremely important resource for business development, the activities of collecting, processing and personal trading data are leading to increasing privacy disclosure risks, and numerous privacy disclosure incidents [12, 13].  
The use of data indexing and IoT in large datasets is extremely resource-intensive, and IoT may be an exceptional alternative. The process of merging technologies increasesthe possibilities of deploying IoT in more effective domains that attempt to extend the ideas of social interconnectedness to IoT; consider the most common ones, namely, the Social Internet of Things (SIoT) [14; 15], the Multiple IoT Environment (MIE) [15; 16], and the Multiple Internets of Things (MIoT) [15; 17; 18]. Figure 1 represents the process of discovering and searching large IoT indexing data from different IoT devices under a three-tier fog computing architecture. Implementing large-scale IoT data integration solutions in a fog computing architecture [19; 20; 21] can help overcome data and indexing issues.  
In addition, it can also contribute to improving collaboration and communication between different objects in a smart city. Hence, the management and indexing of large-scale data has been the subject of several large-scale data reviews. In this study, the authors focus, however, on large IoT data in the context of indexing a massive amount of data.
# Multibeam Wideband Transmit Beamforming Using 2D Sparse FIR Trapezoidal Filters
### Circuit Complexity vs. Flexibility  
The overall circuit complexity and cost of such a solution is not scalable for even modest values of \(K\). Fully digital approaches allow maximum flexibility on the number of independent beams, beam angles, beam sizes, and location of nulls. The PAs are directly driven by dedicated DACs at each antenna in the aperture, implying there is no need for RF-analog phase-shifters or true time-delay units to be used. Nevertheless, we note that fully digital approaches require up-link chains with the additional dynamic range due to the fact that \(K\) independent signals are multiplexed into the same \(N\) channels. Further, DAC resolution needs to increase by \(\log_{2}K\) bits compared with the reference case where phasing and combining occur at the RF stage.  
Figure 1: Conventional fully analog phasing matrices for achieving \(K\) beams: an \(N\)-element wideband antenna array is fed through \(N\) power amplifiers (PAs) using a digital intermediate frequency (IF) or baseband signal processor having \(N\) digital to analog converters (DACs).
## 3 Results and Discussion
### Shading of Low Energy  
The shading of the low-energy image of sample 1# is shown in Figure 8a, and the shading of the high-energy image is shown in Figure 8b, both of which are grayscale images processed with shading (0-1) and converted into 8-bit depth. From Figure 8, it can be seen that the image of shading of low-energy \(S_{L}\) was clear and smooth, close to real material mass thickness and almost free from cross-talk noise; while the image of shading of high-energy \(S_{H}\) was different. In this paper, low-energy shading \(S_{L}\) was selected as the parameter of material identification, mainly for the following reasons: (i) the random quantization error was small caused by low-energy X-ray without absorption of the intermediate copper sheet and hardening. (ii) The positive correlation crossing origin  
Figure 7: (a) Raw low-energy image; (b) raw high-energy image; (c) low-energy image with filtering; (d) high-energy image with filtering.  
between \(S_{L}\) and mass thickness was convenient for the calculation of thickness, mass, and center-of-gravity coordinates, which was clearer than negative correlation between \(S_{L}\) and measurement value of low-energy integral of transmission signal after attenuation \(T_{L}\) and less affected by X-ray source fluctuations. (iii) As the ratio value of shading degree, \(S_{L}\) directly reflected material attenuation characteristics. Compared with the decreased value of energy integral of transmission signal \(T_{d}\), it reduced the fan-shaped effect to a certain extent. In short, the distortion out of the fan-shaped effect of the X-ray source and the cross-talk noise could be reduced to a certain extent by the shading of low-energy \(S_{L}\).